networkx.utils.decorators.argmap

class argmap(func, *args, try_finally=False)[source]

A decorator to apply a map to arguments before calling the function

This class provides a decorator that maps (transforms) arguments of the function before the function is called. Thus for example, we have similar code in many functions to determine whether an argument is the number of nodes to be created, or a list of nodes to be handled. The decorator provides the code to accept either – transforming the indicated argument into a list of nodes before the actual function is called.

This decorator class allows us to process single or multiple arguments. The arguments to be processed can be specified by string, naming the argument, or by index, specifying the item in the args list.

Parameters
funccallable

The function to apply to arguments

*argsiterable of (int, str or tuple)

A list of parameters, specified either as strings (their names), ints (numerical indices) or tuples, which may contain ints, strings, and (recursively) tuples. Each indicates which parameters the decorator should map. Tuples indicate that the map function takes (and returns) multiple parameters in the same order and nested structure as indicated here.

try_finallybool (default: False)

When True, wrap the function call in a try-finally block with code for the finally block created by func. This is used when the map function constructs an object (like a file handle) that requires post-processing (like closing).

See also

not_implemented_for
open_file
nodes_or_number
random_state
py_random_state
networkx.community.quality.require_partition
require_partition

Notes

An object of this class is callable and intended to be used when defining a decorator. Generally, a decorator takes a function as input and constructs a function as output. Specifically, an argmap object returns the input function decorated/wrapped so that specified arguments are mapped (transformed) to new values before the decorated function is called.

As an overview, the argmap object returns a new function with all the dunder values of the original function (like __doc__, __name__, etc). Code for this decorated function is built based on the original function’s signature. It starts by mapping the input arguments to potentially new values. Then it calls the decorated function with these new values in place of the indicated arguments that have been mapped. The return value of the original function is then returned. This new function is the function that is actually called by the user.

Three additional features are provided.

1) The code is lazily compiled. That is, the new function is returned as an object without the code compiled, but with all information needed so it can be compiled upon it’s first invocation. This saves time on import at the cost of additional time on the first call of the function. Subsequent calls are then just as fast as normal.

2) If the “try_finally” keyword-only argument is True, a try block follows each mapped argument, matched on the other side of the wrapped call, by a finally block closing that mapping. We expect func to return a 2-tuple: the mapped value and a function to be called in the finally clause. This feature was included so the open_file decorator could provide a file handle to the decorated function and close the file handle after the function call. It even keeps track of whether to close the file handle or not based on whether it had to open the file or the input was already open. So, the decorated function does not need to include any code to open or close files.

3) The maps applied can process multiple arguments. For example, you could swap two arguments using a mapping, or transform them to their sum and their difference. This was included to allow a decorator in the quality.py module that checks that an input partition is a valid partition of the nodes of the input graph G. In this example, the map has inputs (G, partition). After checking for a valid partition, the map either raises an exception or leaves the inputs unchanged. Thus many functions that make this check can use the decorator rather than copy the checking code into each function. More complicated nested argument structures are described below.

The remaining notes describe the code structure and methods for this class in broad terms to aid in understanding how to use it.

Instantiating an argmap object simply stores the mapping function and the input identifiers of which arguments to map. The resulting decorator is ready to use this map to decorate any function. Calling that object (argmap.__call__, but usually done via @my_decorator) a lazily compiled thin wrapper of the decorated function is constructed, wrapped with the necessary function dunder attributes like __doc__ and __name__. That thinly wrapped function is returned as the decorated function. When that decorated function is called, the thin wrapper of code calls argmap._lazy_compile which compiles the decorated function (using argmap.compile) and replaces the code of the thin wrapper with the newly compiled code. This saves the compilation step every import of networkx, at the cost of compiling upon the first call to the decorated function.

When the decorated function is compiled, the code is recursively assembled using the argmap.assemble method. The recursive nature is needed in case of nested decorators. The result of the assembly is a number of useful objects.

sigthe function signature of the original decorated function as

constructed by argmap.signature(). This is constructed using inspect.signature but enhanced with attribute strings sig_def and sig_call, and other information specific to mapping arguments of this function. This information is used to construct a string of code defining the new decorated function.

wrapped_namea unique internally used name constructed by argmap

for the decorated function.

functionsa dict of the functions used inside the code of this

decorated function, to be used as globals in exec. This dict is recursively updated to allow for nested decorating.

mapblockcode (as a list of strings) to map the incoming argument

values to their mapped values.

finallyscode (as a list of strings) to provide the possibly nested

set of finally clauses if needed.

mutable_argsa bool indicating whether the sig.args tuple should be

converted to a list so mutation can occur.

After this recursive assembly process, the argmap.compile method constructs code (as strings) to convert the tuple sig.args to a list if needed. It joins the defining code with appropriate indents and compiles the result. Finally, this code is evaluated and the original wrapper’s implementation is replaced with the compiled version (see argmap._lazy_compile for more details).

Other argmap methods include _name and _count which allow internally generated names to be unique within a python session. The methods _flatten and _indent process the nested lists of strings into properly indented python code ready to be compiled.

More complicated nested tuples of arguments also allowed though usually not used. For the simple 2 argument case, the argmap input (“a”, “b”) implies the mapping function will take 2 arguments and return a 2-tuple of mapped values. A more complicated example with argmap input ("a", ("b", "c")) requires the mapping function take 2 inputs, with the second being a 2-tuple. It then must output the 3 mapped values in the same nested structure (newa, (newb, newc)). This level of generality is not often needed, but was convenient to implement when handling the multiple arguments.

Examples

Most of these examples use @argmap(...) to apply the decorator to the function defined on the next line. In the NetworkX codebase however, argmap is used within a function to construct a decorator. That is, the decorator defines a mapping function and then uses argmap to build and return a decorated function. A simple example is a decorator that specifies which currency to report money. The decorator (named convert_to) would be used like:

@convert_to("US_Dollars", "income")
def show_me_the_money(name, income):
    print(f"{name} : {income}")

And the code to create the decorator might be:

def convert_to(currency, which_arg):
    def _convert(amount):
        if amount.currency != currency:
            amount = amount.to_currency(currency)
        return amount
    return argmap(_convert, which_arg)

Despite this common idiom for argmap, most of the following examples use the @argmap(...) idiom to save space.

Here’s an example use of argmap to sum the elements of two of the functions arguments. The decorated function:

@argmap(sum, "xlist", "zlist")
def foo(xlist, y, zlist):
    return xlist - y + zlist

is syntactic sugar for:

def foo(xlist, y, zlist):
    x = sum(xlist)
    z = sum(zlist)
    return x - y + z

and is equivalent to (using argument indexes):

@argmap(sum, "xlist", 2)
def foo(xlist, y, zlist):
    return xlist - y + zlist

or:

@argmap(sum, "zlist", 0)
def foo(xlist, y, zlist):
    return xlist - y + zlist

Transforming functions can be applied to multiple arguments, such as:

def swap(x, y):
    return y, x

# the 2-tuple tells argmap that the map `swap` has 2 inputs/outputs.
@argmap(swap, ("a", "b")):
def foo(a, b, c):
    return a / b * c

is equivalent to:

def foo(a, b, c):
    a, b = swap(a, b)
    return a / b * c

More generally, the applied arguments can be nested tuples of strings or ints. The syntax @argmap(some_func, ("a", ("b", "c"))) would expect some_func to accept 2 inputs with the second expected to be a 2-tuple. It should then return 2 outputs with the second a 2-tuple. The returns values would replace input “a” “b” and “c” respectively. Similarly for @argmap(some_func, (0, ("b", 2))).

Also, note that an index larger than the number of named parameters is allowed for variadic functions. For example:

def double(a):
    return 2 * a

@argmap(double, 3)
def overflow(a, *args):
    return a, args

print(overflow(1, 2, 3, 4, 5, 6))  # output is 1, (2, 3, 8, 5, 6)

Try Finally

Additionally, this argmap class can be used to create a decorator that initiates a try…finally block. The decorator must be written to return both the transformed argument and a closing function. This feature was included to enable the open_file decorator which might need to close the file or not depending on whether it had to open that file. This feature uses the keyword-only try_finally argument to @argmap.

For example this map opens a file and then makes sure it is closed:

def open_file(fn):
    f = open(fn)
    return f, lambda: f.close()

The decorator applies that to the function foo:

@argmap(open_file, "file", try_finally=True)
def foo(file):
    print(file.read())

is syntactic sugar for:

def foo(file):
    file, close_file = open_file(file)
    try:
        print(file.read())
    finally:
        close_file()

and is equivalent to (using indexes):

@argmap(open_file, 0, try_finally=True)
def foo(file):
    print(file.read())

Here’s an example of the try_finally feature used to create a decorator:

def my_closing_decorator(which_arg):
    def _opener(path):
        if path is None:
            path = open(path)
            fclose = path.close
        else:
            # assume `path` handles the closing
            fclose = lambda: None
        return path, fclose
    return argmap(_opener, which_arg, try_finally=True)

which can then be used as:

@my_closing_decorator("file")
def fancy_reader(file=None):
    # this code doesn't need to worry about closing the file
    print(file.read())
__init__(func, *args, try_finally=False)[source]

Methods

__init__(func, *args[, try_finally])

assemble(f)

Collects components of the source for the decorated function wrapping f.

compile(f)

Compile the decorated function.

signature(f)

Construct a Signature object describing f