The big package is a grab-bag of cool code for use in your programs.

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# big

Copyright 2022-2026 by Larry Hastings

big is a Python package of small functions and classes that aren't big enough to get a package of their own. It's zillions of useful little bits of Python code I always want to have handy.

For years, I've copied-and-pasted all my little helper functions between projects--we've all done it. But now I've finally taken the time to consolidate all those useful little functions into one big package--no more copy-and-paste, I just install one package and I'm ready to go. And, since it's a public package, you can use 'em too!

Not only that, but I've taken my time and re-thought and retooled a lot of this code. All the difficult-to-use, overspecialized, cheap hacks I've lived with for years have been upgraded with elegant, intuitive APIs and dazzling functionality. big is chock full of the sort of little functions and classes we've all hacked together a million times--only with all the API gotchas fixed, and thoroughly tested with 100% coverage. It's the missing batteries Python never shippet. It's the code you would have written... if only you had the time. And every API is a pleasure to use!

big requires Python 3.6 or newer. It has no required dependencies to run. (big's test suite havs a few external dependencies, but big itself will run fine without them.) big is 100% pure Python code--no C extension needed, no compilation step.

The current version is 0.13.1.

Think big!

Why use big?

It's true that much of the code in big is short, and one might reasonably have the reaction "that's so short, it's easier to write it from scratch every time I need it than remember where it is and how to call it". I still see value in these short functions in big because:

everything in big is tested,
every interface in big has been thoughtfully considered and designed.

For example, consider Log(*destinations, **options). It's easy to write a quick little disposable log function. I should know; I've done it myself, many times. But big's Log class is feature-rich, thoroughly debugged, and lightning fast. Rather than waste your time hacking together something cheap, just use big!

Using big

To use big, just install the big package (and its dependencies) from PyPI using your favorite Python package manager.

Once big is installed, you can simply import it. However, the top-level big package doesn't contain anything but a version number. Internally big is broken up into submodules, aggregated together loosely by problem domain, and you can selectively import just the functions you want. For example, if you only want to use the text functions, just import the text submodule:

import big.text

If you'd prefer to import everything all at once, simply import the big.all module. This one module imports all the other modules, and imports all their symbols too. So, one convenient way to work with big is this:

import big.all as big

That will make every symbol defined in big accessible from the big object. For example, if you want to use multisplit, you can access it with just big.multisplit.

You can also use big.all with import *:

from big.all import *

but that's up to you. Me, I generally use import big.all as big .

big is licensed using the MIT license. You're free to use it and even ship it in your own programs, as long as you leave my copyright notice on the source code.

The best of big

Although big is crammed full of fabulous code, a few of its subsystems rise above the rest. If you're curious what big might do for you, here are the six things in big I'm proudest of:

linked_list
string
Bound inner classes
The multi- family of string functions
big.state
Enhanced TopologicalSorter

And here are six little functions/classes I use all the time:

eval_template_string
iterator_context
Log
ModuleManager
pushd
re_partition

Index

API Reference, By Module

`big.all`

This submodule doesn't define any of its own symbols. Instead, it imports every other submodule in big, and uses import * to import every symbol from every other submodule, too. Every public symbol in big is available in big.all.

When I'm using big in my own projects, I tend to import it as

import big.all as big

That way, all big's symbols are available as one big flat namespace.

`big.boundinnerclass`

Class decorators that implement bound inner classes. See the Bound inner classes tutorial for more information.

`BoundInnerClass(cls)`

Class decorator for an inner class. When accessing the inner class through an instance of the outer class, "binds" the inner class to the instance. This changes the signature of the inner class's __init__ from

def __init__(self, *args, **kwargs):`

def __init__(self, outer, *args, **kwargs):

where outer is the instance of the outer class.

Compare this to functions:

If you put a function inside a class, and access it through an instance I of that class, the function becomes a method. When you call the method, I is automatically passed in as the first argument.
If you put a class inside a class, and access it through an instance of that class, the class becomes a bound inner class. When you call the bound inner class, I is automatically passed in as the second argument to __init__, after self.

Note that this has an implication for all subclasses. If class B is decorated with BoundInnerClass, and class S is a subclass of B, such that issubclass(S, B), class S must be decorated with either BoundInnerClass or UnboundInnerClass.

`UnboundInnerClass(cls)`

Class decorator for an inner class that prevents binding the inner class to an instance of the outer class.

If class B is decorated with BoundInnerClass, and class S is a subclass of B, such that issubclass(S, B) returns True, class S must be decorated with either BoundInnerClass or UnboundInnerClass.

`bound_inner_base(cls)`

Simple wrapper for Python 3.6 compatibility for bound inner classes.

Returns the base class for declaring a subclass of a bound inner class while still in the outer class scope. Only needed for Python 3.6 compatibility; unnecessary in Python 3.7+, or when the child class is defined after exiting the outer class scope.

See the Bound inner classes tutorial for more information.

`bound_to(cls)`

Returns the outer instance that cls is bound to, or None.

If cls is a bindable inner class that was bound to an outer instance, returns that outer instance. If cls is any other variety of type object, returns None. Raises TypeError if cls is not a class object.

BoundInnerClass doesn't keep strong references to outer instances. If cls was bound to an object that has since been destroyed, bound_to will return None.

See the Bound inner classes tutorial for more information.

`BOUNDINNERCLASS_OUTER_ATTR`

A string constant containing the attribute name that BoundInnerClass uses to store its per-instance cache on the outer instance. If your outer class uses __slots__, you must include this attribute in your slots definition.

However, rather than using this attribute directly, we suggest you use BOUNDINNERCLASS_OUTER_SLOTS to add the necessary attribute to your __slots__ tuple.

See the Bound inner classes tutorial for more information.

`BOUNDINNERCLASS_OUTER_SLOTS`

A tuple containing BOUNDINNERCLASS_OUTER_ATTR. If your outer class uses __slots__, you can add this to your slots definition to ensure BoundInnerClass works correctly.

Example:

class Foo:
    __slots__ = ('x', 'y', 'z') + BOUNDINNERCLASS_SLOTS

    @BoundInnerClass
    class Bar:
        ...

See the Bound inner classes tutorial for more information.

`is_bound(cls)`

Returns True if cls is a bound inner class that has been bound to a specific outer instance. Said another way, is_bound(cls) returns True if bound_to(cls) would return a non-None value.

Returns False for unbound inner classes and non-participating classes. Raises TypeError if cls is not a class object.

See the Bound inner classes tutorial for more information.

`is_boundinnerclass(cls)`

Returns True if cls was decorated with @BoundInnerClass, or is a bound wrapper class created from one.

Returns False for @UnboundInnerClass classes and regular classes. Raises TypeError if cls is not a class object.

See the Bound inner classes tutorial for more information.

`is_unboundinnerclass(cls)`

Returns True if cls was decorated with @UnboundInnerClass, or is a wrapper class created from one.

Returns False for @BoundInnerClass classes and regular classes. Raises TypeError if cls is not a class object.

See the Bound inner classes tutorial for more information.

`type_bound_to(instance)`

Returns the outer instance that type(instance) is bound to, or None.

This is a convenience function equivalent to calling bound_to(type(instance)).

BoundInnerClass doesn't keep strong references to outer instances. If type(instance) was bound to an object that has since been destroyed, type_bound_to will return None.

See the Bound inner classes tutorial for more information.

`unbound(cls)`

Returns the unbound version of a bound class.

If cls is a bound inner class, returns the original unbound class. If cls is already unbound (or not a bindable inner class), returns cls.

Raises ValueError if cls inherits directly from a bound class (e.g. class Child(o.Inner)), since such classes have no unbound version. Raises TypeError if cls is not a class object.

See the Bound inner classes tutorial for more information.

`big.builtin`

Fundamental functions and types that don't fit neatly into any other submodule. (Named builtin to avoid a name collision with Python's builtins module.)

`ClassRegistry()`

A dict subclass with attribute-style access, useful as a class decorator for registering base classes.

BoundInnerClass encourages heavily-nested classes, but Python's scoping rules make it clumsy to reference base classes defined in a different class scope. ClassRegistry solves this by giving you a place to store references to base classes you can access later.

To use, create a ClassRegistry instance, then use it as a decorator to register classes. Access registered classes as attributes on the ClassRegistry. By default the class's __name__ is used as the attribute name; pass a string argument to use a custom name instead.

When using with BoundInnerClass, put @base() above @BoundInnerClass.

`get_float(o, default=_sentinel)`

Returns float(o), unless that conversion fails, in which case returns the default value. If you don't pass in an explicit default value, the default value is o.

`get_int(o, default=_sentinel)`

Returns int(o), unless that conversion fails, in which case returns the default value. If you don't pass in an explicit default value, the default value is o.

`get_int_or_float(o, default=_sentinel)`

Converts o into a number, preferring an int to a float.

If o is already an int or float, returns o unchanged. Otherwise, tries int(o). If that conversion succeeds, returns the result. Otherwise, tries float(o). If that conversion succeeds, returns the result. Otherwise returns the default value. If you don't pass in an explicit default value, the default value is o.

`ModuleManager()`

A class that manages your module's namespace, including __all__.

ModuleManager makes it easy to populate __all__ and clean up temporary symbols. Instantiate a ModuleManager at module scope, use its methods to declare exports and deletions, then call the instance at the end of your module to finalize.

ModuleManager provides two methods, both of which can be used as decorators or called with string arguments:

mm.export(*args) adds symbols to __all__. When used as a decorator, adds the decorated function or class by name. When called with strings, adds those strings to __all__.

mm.delete(*args) marks symbols for deletion. When used as a decorator, marks the decorated function or class for deletion. When called with strings, marks those names for deletion.

When the ModuleManager instance is called, it deletes all symbols on the deletions list from the module namespace. It also automatically deletes itself, and any module-level references to its export and delete methods.

`pure_virtual()`

A decorator for class methods. When you have a method in a base class that's "pure virtual"--that must not be called, but must be overridden in child classes--decorate it with @pure_virtual(). Calling that method will throw a NotImplementedError.

Note that the body of any function decorated with @pure_virtual() is ignored. By convention the body of these methods should contain only a single ellipsis, literally like this:

class BaseClass:
    @big.pure_virtual()
    def on_reset(self):
        ...

`try_float(o)`

Returns True if o can be converted into a float, and False if it can't.

`try_int(o)`

Returns True if o can be converted into an int, and False if it can't.

`big.deprecated`

Old versions of functions (and classes) from big. These versions are deprecated, either because the name was changed, or the semantics were changed, or both.

Unlike the other modules, the contents of big.deprecated aren't automatically imported into big.all. (big.all does import the deprecated submodule, it just doesn't from deprected import * all the symbols.)

`big.file`

Functions for working with files, directories, and I/O.

`fgrep(path, text, *, encoding=None, enumerate=False, case_insensitive=False)`

Find the lines of a file that match some text, like the UNIX fgrep utility program.

path should be an object representing a path to an existing file, one of:

a string,
a bytes object, or
a pathlib.Path object.

text should be either string or bytes.

encoding is used as the file encoding when opening the file.

If text is a str, the file is opened in text mode.
If text is a bytes object, the file is opened in binary mode. encoding must be None when the file is opened in binary mode.

If case_insensitive is true, perform the search in a case-insensitive manner.

Returns a list of lines in the file containing text. The lines are either strings or bytes objects, depending on the type of pattern. The lines have their newlines stripped but preserve all other whitespace.

If enumerate is true, returns a list of tuples of (line_number, line). The first line of the file is line number 1.

For simplicity of implementation, the entire file is read in to memory at one time. If case_insensitive is true, fgrep also makes a lowercased copy.

`file_mtime(path)`

Returns the modification time of path, in seconds since the epoch. Note that seconds is a float, indicating the sub-second with some precision.

`file_mtime_ns(path)`

Returns the modification time of path, in nanoseconds since the epoch.

`file_size(path)`

Returns the size of the file at path, as an integer representing the number of bytes.

`grep(path, pattern, *, encoding=None, enumerate=False, flags=0)`

Look for matches to a regular expression pattern in the lines of a file, similarly to the UNIX grep utility program.

path should be an object representing a path to an existing file, one of:

a string,
a bytes object, or
a pathlib.Path object.

pattern should be an object containing a regular expression, one of:

a string,
a bytes object, or
an re.Pattern, initialized with either str or bytes.

encoding is used as the file encoding when opening the file.

If pattern uses a str, the file is opened in text mode. If pattern uses a bytes object, the file is opened in binary mode. encoding must be None when the file is opened in binary mode.

flags is passed in as the flags argument to re.compile if pattern is a string or bytes. (It's ignored if pattern is an re.Pattern object.)

Returns a list of lines in the file matching the pattern. The lines are either strings or bytes objects, depending on the type of text. The lines have their newlines stripped but preserve all other whitespace.

If enumerate is true, returns a list of tuples of (line_number, line). The first line of the file is line number 1.

For simplicity of implementation, the entire file is read in to memory at one time.

Tip: to perform a case-insensitive pattern match, pass in the re.IGNORECASE flag into flags for this function (if pattern is a string or bytes) or when creating your regular expression object (if pattern is an re.Pattern object.

(In older versions of Python, re.Pattern was a private type called re._pattern_type.)

`pushd(directory)`

A context manager that temporarily changes the directory. Example:

with big.pushd('x'):
    pass

This would change into the 'x' subdirectory before executing the nested block, then change back to the original directory after the nested block.

You can change directories in the nested block; this won't affect pushd restoring the original current working directory upon exiting the nested block.

You can safely nest with pushd blocks.

`read_python_file(path, *, newline=None, use_bom=True, use_source_code_encoding=True)`

Opens, reads, and correctly decodes a Python script from a file.

path should specify the filesystem path to the file; it can be any object accepted by builtins.open (a "path-like object").

Returns a str containing the decoded Python script.

Opens the file using builtins.open.

Decodes the script using big's decode_python_script function. The newline, use_bom and use_source_code_encoding parameters are passed through to that function.

`safe_mkdir(path)`

Ensures that a directory exists at path. If this function returns and doesn't raise, it guarantees that a directory exists at path.

If a directory already exists at path, safe_mkdir does nothing.

If a file exists at path, safe_mkdir unlinks path then creates the directory.

If the parent directory doesn't exist, safe_mkdir creates that directory, then creates path.

This function can still fail:

path could be on a read-only filesystem.
You might lack the permissions to create path.
You could ask to create the directory x/y and x is a file (not a directory).

`safe_unlink(path)`

Unlinks path, if path exists and is a file.

`search_path(paths, extensions=('',), *, case_sensitive=None, preserve_extension=True, want_directories=False, want_files=True)`

Search a list of directories for a file. Given a sequence of directories, an optional list of file extensions, and a filename, searches those directories for a file with that name and possibly one of those file extensions.

search_path accepts the paths and extensions as parameters and returns a search function. The search function accepts one filename parameter and performs the search, returning either the path to the file it found (as a pathlib.Path object) or None. You can reuse the search function to perform as many searches as you like.

paths should be an iterable of str or pathlib.Path objects representing directories. These may be relative or absolute paths; relative paths will be relative to the current directory at the time the search function is run. Specifying a directory that doesn't exist is not an error.

extensions should be an iterable of str objects representing extensions. Every non-empty extension specified should start with a period ('.') character (technically os.extsep). You may specify at most one empty string in extensions, which represents testing the filename without an additional extension. By default extensions is the tuple `('',)``. Extension strings may contain additional period characters after the initial one.

Shell-style "globbing" isn't supported for any parameter. Both the filename and the extension strings may contain filesystem globbing characters, but they will only match those literal characters themselves. ('*' won't match any character, it'll only match a literal '*' in the filename or extension.)

case_sensitive works like the parameter to pathlib.Path.glob. If case_sensitive is true, files found while searching must match the filename and extension exactly. If case_sensitive is false, the comparison is done in a case-insensitive manner. If case_sensitive is None (the default), case sensitivity obeys the platform default (as per os.path.normcase). In practice, only Windows platforms are case-insensitive by convention; all other platforms that support Python are case-sensitive by convention.

If preserve_extension is true (the default), the search function checks the filename to see if it already ends with one of the extensions. If it does, the search is restricted to only files with that extension--the other extensions are ignored. This check obeys the case_sensitive flag; if case_sensitive is None, this comparison is case-insensitive only on Windows.

want_files and want_directories are boolean values; the search function will only return that type of file if the corresponding want_ parameter is true. You can request files, directories, or both. (want_files and want_directories can't both be false.) By default, want_files is true and want_directories is false.

paths and extensions are both tried in order, and the search function returns the first match it finds. All extensions are tried in a path entry before considering the next path.

Returns a function:

    search(filename)

which returns either a pathlib.Path object on success or None on failure.

`touch(path)`

Ensures that path exists, and its modification time is the current time.

If path does not exist, creates an empty file.

If path exists, updates its modification time to the current time.

`translate_filename_to_exfat(s)`

Ensures that all characters in s are legal for a FAT filesystem.

Returns a copy of s where every character not allowed in a FAT filesystem filename has been replaced with a character (or characters) that are permitted.

`translate_filename_to_unix(s)`

Ensures that all characters in s are legal for a UNIX filesystem.

Returns a copy of s where every character not allowed in a UNIX filesystem filename has been replaced with a character (or characters) that are permitted.

`big.graph`

A drop-in replacement for Python's graphlib.TopologicalSorter with an enhanced API. This version of TopologicalSorter allows modifying the graph at any time, and supports multiple simultaneous views, allowing iteration over the graph more than once.

See the Enhanced TopologicalSorter tutorial for more information.

`CycleError()`

Exception thrown by TopologicalSorter when it detects a cycle.

`TopologicalSorter(graph=None)`

An object representing a directed graph of nodes. See Python's graphlib.TopologicalSorter for concepts and the basic API.

New methods on TopologicalSorter:

`TopologicalSorter.copy()`

Returns a shallow copy of the graph. The copy also duplicates the state of get_ready and done.

`TopologicalSorter.cycle()`

Checks the graph for cycles. If no cycles exist, returns None. If at least one cycle exists, returns a tuple containing nodes that constitute a cycle.

`TopologicalSorter.print(print=print)`

Prints the internal state of the graph. Used for debugging.

print is the function used for printing; it should behave identically to the builtin print function.

`TopologicalSorter.remove(node)`

Removes node from the graph.

If any node P depends on a node N, and N is removed, this dependency is also removed, but P is not removed from the graph.

Note that, while remove() works, it's slow. (It's O(N).) TopologicalSorter is optimized for fast adds and fast views.

`TopologicalSorter.reset()`

Resets get_ready and done to their initial state.

`TopologicalSorter.view()`

Returns a new View object on this graph.

`TopologicalSorter.View`

A view on a TopologicalSorter graph object. Allows iterating over the nodes of the graph in dependency order.

Methods on a View object:

`TopologicalSorter.View.bool()`

Returns True if more work can be done in the view--if there are nodes waiting to be yielded by get_ready, or waiting to be returned by done.

Aliased to TopologicalSorter.is_active for compatibility with graphlib.

`TopologicalSorter.View.close()`

Closes the view. A closed view can no longer be used.

`TopologicalSorter.View.copy()`

Returns a shallow copy of the view, duplicating its current state.

`TopologicalSorter.View.done(*nodes)`

Marks nodes returned by ready as "done", possibly allowing additional nodes to be available from ready.

`TopologicalSorter.View.print(print=print)`

Prints the internal state of the view, and its graph. Used for debugging.

print is the function used for printing; it should behave identically to the builtin print function.

`TopologicalSorter.View.ready()`

Returns a tuple of "ready" nodes--nodes with no predecessors, or nodes whose predecessors have all been marked "done".

Aliased to TopologicalSorter.get_ready for compatibility with graphlib.

`TopologicalSorter.View.reset()`

Resets the view to its initial state, forgetting all "ready" and "done" state.

`big.heap`

Functions for working with heap objects. Well, just one heap object really.

`Heap(i=None)`

An object-oriented wrapper around the heapq library, designed to be easy to use--and easy to remember how to use. The heapq library implements a binary heap, a data structure used for sorting; you add objects to the heap, and you can then remove objects in sorted order. Heaps are useful because they have are efficient both in space and in time; they're also inflexible, in that iterating over the sorted items is destructive.

The Heap API in big mimics the list and collections.deque objects; this way, all you need to remember is "it works kinda like a list object". You append new items to the heap, then popleft them off in sorted order.

By default Heap creates an empty heap. If you pass in an iterable i to the constructor, this is equivalent to calling the extend(i) on the freshly-constructed Heap.

In addition to the below methods, Heap objects support iteration, len, the in operator, and use as a boolean expression. You can also index or slice into a Heap object, which behaves as if the heap is a list of objects in sorted order. Getting the first item (Heap[0], aka peek) is cheap, the other operations can get very expensive.

Methods on a Heap object:

`Heap.append(o)`

Adds object o to the heap.

`Heap.clear()`

Removes all objects from the heap, resetting it to empty.

`Heap.copy()`

Returns a shallow copy of the heap. Only duplicates the heap data structures itself; does not duplicate the objects in the heap.

`Heap.extend(i)`

Adds all the objects from the iterable i to the heap.

`Heap.remove(o)`

If object o is in the heap, removes it. If o is not in the heap, raises ValueError.

`Heap.popleft()`

If the heap is not empty, returns the first item in the heap in sorted order. If the heap is empty, raises IndexError.

`Heap.append_and_popleft(o)`

Equivalent to calling Heap.append(o) immediately followed by Heap.popleft(). If o is smaller than any other object in the heap at the time it's added, this will return o.

`Heap.popleft_and_append(o)`

Equivalent to calling Heap.popleft() immediately followed by Heap.append(o). This method will never return o, unless o was already in the heap before the method was called.

`Heap.queue`

Not a method, a property. Returns a copy of the contents of the heap, in sorted order.

`big.itertools`

Functions and classes for working with iteration.

`iterator_context(iterator, start=0)`

Iterates over iterable. Yields (ctx, o) where o is each value yielded by iterable, and ctx is a "context" variable of type IteratorContext containing metadata about the iteration.

ctx supports the following attributes:

`ctx.countdown`

contains the "opposite" value of `ctx.index`. The values yielded by `ctx.countdown` are the same as `ctx.index`, but in reversed order. (If `start` is 0, and the iterator yields four items, `ctx.index` will be `0`, `1`, `2`, and `3` in that order, and `ctx.countdown` will be `3`, `2`, `1`, and `0` in that order.) `ctx.countdown` requires the iterator to support `__len__`; if it doesn't, `ctx.countdown` will be undefined.

`ctx.current`

contains the current value yielded by the iterator (`o` as described above).

`ctx.index`

contains the index of this value. The first time the iterator yields a value, this will be `start`; the second time, it will be `start + 1`, etc.

`ctx.is_first`: is true only for the first value yielded, and false otherwise.
`ctx.is_last`: is true only for the last value yielded, and false otherwise. (If the iterator only yields one value, `is_first` and `is_last` will both be true.)
`ctx.length`: contain the total number of items that will be yielded. `ctx.length` requires the iterator to support `__len__`; if it doesn't, `ctx.length` will be undefined.
`ctx.next`: contains the next value to be yielded by this iterator if there is one. (If `o` is the last value yielded by the iterator, `ctx.previous` will be an `undefined` value.)
`ctx.previous`: contains the previous value yielded if this is the second or subsequent time this iterator has yielded a value. (If this is the first time the iterator has yielded, `ctx.previous` will be an `undefined` value.)

`iterator_filter(iterator, *, stop_at_value=undefined, stop_at_in=None, stop_at_predicate=None, stop_at_count=None, reject_value=undefined, reject_in=None, reject_predicate=None, only_value=undefined, only_in=None, only_predicate=None)`

Wraps any iterator, filtering the values it yields based on rules you specify as keyword-only parameters.

There are three categories of rules, examined in order:

"stop_at" rules cause the iterator to become exhausted. If a value passes a "stop_at" rule, the iterator immediately becomes exhausted without yielding that value.

"reject" rules act as a blacklist. If a value passes any "reject" rule, it's discarded and iteration continues.

"only" rules act as a whitelist. If a value doesn't pass all "only" rules, it's discarded and iteration continues.

Each category supports three suffix variants that define the test:

A rule ending in _value passes if the yielded value == the argument.

A rule ending in _in passes if the yielded value is in the argument (which must support the in operator).

A rule ending in _predicate takes a callable as its argument; it passes if calling the argument with the yielded value returns a true value.

There is one additional rule: stop_at_count, an integer. The iterator becomes exhausted after yielding stop_at_count items. If stop_at_count is initially <= 0, the iterator is initialized in an exhausted state.

`PushbackIterator(iterable=None)`

Wraps any iterator, letting you push items to be yielded first.

The PushbackIterator constructor accepts one argument, an iterable. When you iterate over the PushbackIterator instance, it yields values from that iterable. You may also pass in None, in which case the PushbackIterator is created in an "exhausted" state.

PushbackIterator also supports a `push(o)`` method, which "pushes" that object onto the iterator. If any objects have been pushed onto the iterator, they're yielded first, before attempting to yield from the wrapped iterator. Pushed values are yielded in first-in-first-out order, like a stack.

When the wrapped iterable is exhausted, you can still call push to add new items, at which point the PushbackIterator can be iterated over again.

`PushbackIterator.next(default=None)`

Equivalent to next(PushbackIterator), but won't raise StopIteration. If the iterator is exhausted, returns the default argument.

`PushbackIterator.push(o)`

Pushes a value into the iterator's internal stack. When a PushbackIterator is iterated over, and there are any pushed values, the top value on the stack will be popped and yielded. PushbackIterator only yields from the iterator it wraps when this internal stack is empty.

Example: you have a pushback iterator J, and you call J.push(3) followed by J.push('x'). The next two times you iterate over J, it will yield 'x', followed by 3.

It's explicitly supported to push values that were never yielded by the wrapped iterator. If you create J = PushbackIterator(range(1, 20)), you may still call J.push(33), or J.push('xyz'), or J.push(None), etc.

`big.log`

A lightweight, high-performance text-oriented thread-safe logging module, intended for debug-print-style use. Not a full-fledged application logger like Python's logging module.

Log is flexible in where output is written (stdout, files, lists, arbitrary callables, or custom Destination objects) and when (by default it runs in a background thread for minimal overhead).

See the The big Log tutorial for an introduction and examples.

`default_clock()`

The default clock function used by Log. Returns the current time, expressed as integer nanoseconds (>= 0) since some earlier event.

In Python 3.7+, this is time.monotonic_ns. In Python 3.6 this is a compatibility function that calls time.monotonic and converts the result to integer nanoseconds.

`Log(*destinations, **options)`

A lightweight, high-performance text-oriented thread-safe log object intended for debug-print-style use. To use, create a Log instance, then call it to log messages:

    j = Log()
    j("Hello, world!")

Calling the Log instance is equivalent to calling Log.print.

Log has three states: initial (just created or reset), logging (actively accepting messages), and closed (ignoring all writes). The log transitions from initial to logging automatically the first time a message is logged.

See the The big Log for an in-depth tutorial on how to use Log, including examples.

Destinations

Positional arguments to the Log constructor define destinations for log messages. The log sends every formatted message to every destination.

If you construct a Log and don't specify any positional arguments, it will send logged messages to builtins.print.

Destinations can be any of the following:

print (or builtins.print) — Log messages are printed using print(s, end=''). Equivalent to Log.Print().

bytes, str or pathlib.Path — Log messages are buffered locally and written to the named file. Equivalent to Log.File(path).

list — Log messages are appended to the list. Equivalent to Log.List(list).

io.TextIOBase — Log messages are written to the file-like object. Equivalent to Log.FileHandle(handle).

callable — The callable is called with every formatted log message. Equivalent to Log.Callable(callable).

TMPFILE — A special sentinel value. Log messages are written to a timestamped temporary file.

Log.Destination — Used directly as a destination.

You can also call Log.map_destination to manually convert any of the above types to the appropriate Destination object.

Keyword-only options

Keyword-only parameters for the Log constructor specify configuration for the log. Log accepts the following keyword-only parameters:

name — The name for this log. Default is 'Log'.

threading — If true (the default), log messages are sent to a background thread for formatting and writing, reducing overhead in the calling thread. If false, messages are formatted and written immediately, using a lock for thread safety. Log is always thread-safe regardless of this setting.

indent — Number of spaces to indent per nesting level when using Log.enter. Default is 4.

width — Width in characters used for formatting separator lines. Default is 79.

clock — A function returning nanoseconds since some arbitrary past event, expressed as an integer. Default is default_clock.

timestamp_clock — A function returning seconds since the UNIX epoch, expressed as a float. Default is time.time.

timestamp_format — A function that formats values returned by timestamp_clock into a human-readable string. Default is big.time.timestamp_human.

prefix — A format string used to format text inserted at the beginning of every log message. Default is prefix_format(3, 10, 12).

formats — A dict mapping format names to format dicts. A format dict has a "template" key (a template string with {}-style placeholders) and an optional "line" key (a fill character for separator lines). Log has six built-in formats: "print", "box", "enter", "exit", "start", and "end". User-defined formats are automatically added as methods on the Log instance. To suppress the start or end log banners, set "start" or "end" to None in the formats dict; this also works for the "enter" and "exit" formats to suppress the enter and exit banners.

All keyword-only options are also available as read-only properties on the Log instance.

Read-only properties

In addition to the constructor options above, Log exposes these read-only properties:

dirty — True if any formatted text has been written to the log since it was started or last flushed.

closed — True if the log is in the "closed" state.

start_time_ns — The time reported by Log.clock when the log was started, in nanoseconds.

start_time_epoch — The wall-clock time reported by Log.timestamp_clock when the log was started, as seconds since the UNIX epoch.

end_time_epoch — The wall-clock time when the log was closed, as seconds since the UNIX epoch.

`Log.box(s)`

Logs s to the log, formatted with a three-sided box around it to call attention to the message.

See the The big Log tutorial for more.

`Log.close(block=True)`

Closes the log. This ensures the log is in the "closed" state, in which all writes are silently ignored. To write to the log again after closing, call Log.reset().

If the log is in "logging" state, it gets closed in an orderly fashion. This includes logging an "end" banner, if an "end" format is defined.
If the log is in "initial" state, it goes directly to closed state, and no "end" banner will be logged.
If the log is already "closed", this is a no-op.

If block is true (the default), close won't return until after the log is fully closed. If false, the log may be closed asynchronously.

See the The big Log tutorial for more.

`Log.enter(message)`

Logs message to the log formatted with a box, then indents subsequent log output. Call Log.exit() to outdent. Nesting may be arbitrarily deep.

Log.enter returns a context manager; if used with a with statement, Log.exit() will be called automatically upon exiting the block.

See the The big Log tutorial for more.

`Log.exit()`

Outdents the log from the most recent Log.enter() call.

See the The big Log tutorial for more.

`Log.flush(block=True)`

Flushes the log, if it is in "logging" state and is dirty. A log is "dirty" if any formatted text has been written since the log was started or last flushed.

If block is true (the default), flush won't return until after the log is flushed. If false, the log may be flushed asynchronously.

See the The big Log tutorial for more.

`Log.map_destination(o)`

Class method. Maps o to the appropriate Destination object using the same conversion rules as the Log constructor's positional arguments (e.g. builtins.print becomes Log.Print(), a str becomes Log.File(path), etc.).

Returns the Destination object, or raises TypeError if o is not a recognized type.

`Log.print(*args, end='\n', sep=' ', flush=False, format='print')`

Logs a message, with an interface similar to builtins.print.

The arguments are formatted into a string as sep.join(str(a) for a in args) + end, then logged using the specified format (default "print").

Calling the Log instance directly (e.g. log("message")) is equivalent to calling this method.

If flush is true, the log is flushed immediately after logging this message.

See the The big Log tutorial for more.

`Log.reset()`

Resets the log to its initial state.

If the log is in "initial" state, this is a no-op.
If the log is in "logging" state, the log is closed, then reset.
If the log is in "closed" state, the log is reset.

Log.reset() is the only way to reopen a closed log.

See the The big Log tutorial for more.

`Log.write(formatted)`

Writes a pre-formatted string directly to the log with no further formatting or modification.

See the The big Log tutorial for more.

`Log.Destination`

Base class for objects that perform the actual logging for a Log. A Destination is owned by a Log, and the Log sends it events by calling named methods.

All Destination subclasses must implement the write method:

    write(elapsed, thread, formatted)

Subclasses may also optionally override these seven methods:

    flush()
    reset()
    start(start_time_ns, start_time_epoch)
    end(elapsed)
    log(elapsed, thread, format, message, formatted)
    enter(elapsed, thread, message, formatted)
    exit(elapsed, thread, message, formatted)

The default implementations of log, enter, and exit all call self.write(elapsed, thread, formatted). Subclasses need not call the base class method for any of these seven methods.

Subclasses may also override register(owner), but must call the base class implementation via super().register(owner). Subclasses must also call the base class __init__ without arguments.

The meaning of the arguments to the various Destination methods:

elapsed is the elapsed time since the log was started/reset, in nanoseconds.
thread is the threading.Thread handle for the thread that logged the message. This can be None for messages that aren't logged from any particular thread; currently this includes the "start banner" (using format "start") and the "end banner" (using format "end").
formatted is the formatted log message. This is always a str.
format is the name of the format applied to message to produce formatted.
message is the original message passed in to a Log method. This can be an empty string if no text was actually logged. (The "start banner" and "end banner" also specify empty strings for their message arguments.)
owner is the Log object that owns this Destination.

Log guarantees events are sent in this order:

    register → start → [write | log | enter | exit | flush]* → [flush] → end

register is always the first event sent; it's sent exactly once, when the destination is added to the Log, in the constructor.
start is lazily sent immediately before the first message is logged (one of write, log, or enter). If no message is ever logged, start is never sent.
flush is only sent if the log is dirty. The log is only considered "dirty" if any non-empty formatted strings were sent to the log.
When the log is closed, if start was ever sent, it sends an optional flush (if the log is dirty, followed by end.
If the log is reset, if start was ever sent, the log is immediately closed--possibly sending flush, always sending end--followed by a reset event. reset should reset the destination to its initial state.
If the log is closed but start was never sent, Log doesn't bother sending any messages to the destinations. No flush, and no end. And if a log in this state is reset, no reset event is sent. (Nothing has happened, so there's no point in notifying the destinations of meaningless non-events.)

See the The big Log tutorial for more.

`Log.Destination.Buffer(destination=None)`

A Destination that buffers log messages before sending them to another Destination.

Every formatted log message is stored in an internal buffer. When the log is flushed, Buffer concatenates all buffered messages into one string, writes that string to the underlying Destination, and flushes it.

If destination is None (the default), Buffer wraps a Log.Print() destination. Otherwise, destination is mapped using Log.map_destination.

See the The big Log tutorial for more.

`Log.Callable(callable)`

A Destination wrapping a callable.

Calls callable(formatted) for every formatted log message.

See the The big Log tutorial for more.

`Log.File(path, initial_mode="at", *, flush=False)`

A Destination that writes to a file in the filesystem. path may be a bytes, str or pathlib.Path object. (bytes objects are decoded to str using os.fsdecode.)

If flush is false (the default), formatted log messages are buffered internally. When the log is flushed, File concatenates all buffered messages, opens the file, writes them with one write call, and closes it.

If flush is true, the file is opened and kept open. Every formatted log message is written and flushed immediately. On close, the file is closed; on reset, it is reopened.

The first time the file is opened, it uses initial_mode (default "at"). After the first time, File always uses mode "at".

See the The big Log tutorial for more.

`Log.FileHandle(handle, *, flush=False)`

A Destination wrapping an already-open Python file handle (an io.TextIOBase instance).

Every formatted log message is written to the file handle immediately. FileHandle will never close the handle; it only writes to it and flushes it.

If flush is true, the file handle is flushed after every write. By default flush is false.

See the The big Log tutorial for more.

`Log.List(list)`

A Destination wrapping a Python list.

Appends every formatted log message to list.

See the The big Log tutorial for more.

`Log.Print()`

A Destination that writes to stdout using builtins.print.

Calls builtins.print(formatted, end='', flush=True) for every formatted log message.

This is the default destination when no destinations are passed to the Log constructor.

See the The big Log tutorial for more.

`Log.Sink()`

A Destination that retains all log events in order as SinkEvent objects.

You may iterate over a Sink to yield all events logged so far. Sink also has a print method that prints the events so far with some simple formatting.

See the The big Log tutorial for more.

`Log.TmpFile(*, flush=False)`

A Destination subclass of Log.File that writes to a timestamped temporary file.

The filename is computed approximately as:

tempfile.gettempdir() / "{Log.name}.{start timestamp}.{pid}.txt"

The filename is recomputed on register and reset events, so resetting the Log closes the old file and opens a new one.

The sentinel value TMPFILE is a pre-created Log.TmpFile() instance.

See the The big Log tutorial for more.

`OldDestination()`

A Log.Destination subclass providing backwards compatibility with the old big.log.Log interface. Deprecated; will be removed no earlier than March 2027.

See the The big Log tutorial for more.

`OldLog(clock=None)`

A drop-in replacement for the old big.log.Log class, reimplemented on top of the new Log. Provides interface compatibility with the old Log to ease the transition to the new one. Deprecated; will be removed no earlier than March 2027.

See the The big Log tutorial for more.

`prefix_format(time_seconds_width, time_fractional_width, thread_name_width=12)`

Returns a prefix format string for use with the prefix option of the Log constructor.

The returned format string produces a prefix of the form:

    [{elapsed} {thread.name}]

formatted with the specified widths. For example, the default Log prefix is prefix_format(3, 10, 12), which produces output like:

    [003.0706368860   MainThread]

See the The big Log tutorial for more.

`SinkEvent`

Base class for log events stored by Log.Sink. Each subclass represents a different type of event.

All SinkEvent objects have these read-only properties:

type — A string identifying the event type (e.g. "start", "end", "write", "log", "enter", "exit").

number — The log's sequence number (incremented on each reset).

elapsed — Elapsed time in nanoseconds since the log was started.

duration — Time in nanoseconds since the previous event with a message.

thread — The threading.Thread that logged the event, or None.

formatted — The formatted log message string.

message — The original unformatted message, or None.

format — The format name used, or None.

depth — The nesting depth at the time of the event.

The subclasses are:

SinkStartEvent,
SinkEndEvent,
SinkWriteEvent,
SinkLogEvent,
SinkEnterEvent,
and SinkExitEvent.

See the The big Log tutorial for more.

`SinkStartEvent`

A SinkEvent representing the start of a log. Has a configuration property containing a dict of the Log's configuration at start time.

`SinkEndEvent`

A SinkEvent representing the end of a log.

`SinkWriteEvent`

A SinkEvent representing a Log.write call.

`SinkLogEvent`

A SinkEvent representing a Log.print call (or any format-based log call).

`SinkEnterEvent`

A SinkEvent representing a Log.enter call.

`SinkExitEvent`

A SinkEvent representing a Log.exit call.

`TMPFILE`

A pre-created Log.TmpFile() sentinel value. Pass this as a destination to the Log constructor to log to a timestamped temporary file.

See the The big Log tutorial for more.

`big.metadata`

Contains metadata about big itself.

`metadata.version`

A Version object representing the current version of big.

`big.scheduler`

A replacement for Python's sched.scheduler object, adding full threading support and a modern Python interface.

Python's sched.scheduler object was added way back in 1991, and it was full of clever ideas. It abstracted away the concept of time from its interface, allowing it to be adapted to new schemes of measuring time--including mock time, making testing easy and repeatable. Very nice!

Unfortunately, sched.scheduler predates multithreading becoming common, much less multicore computers. It certainly predates threading support in Python. And its API isn't flexible enough to correctly handle some common scenarios in multithreaded programs:

If one thread is blocking on sched.scheduler.run, and the next scheduled event will occur at time T, and a second thread schedules a new event which occurs at a time < T, sched.scheduler.run won't return any events to the first thread until time T.
If one thread is blocking on sched.scheduler.run, and the next scheduled event will occur at time T, and a second thread cancels all events, sched.scheduler.run won't exit until time T.

big's Scheduler object fixes both these problems.

Also, sched.scheduler is thirty years behind the times in Python API design--its design predates many common modern Python conventions. Its events are callbacks, which it calls directly. Scheduler fixes this: its events are objects, and you iterate over the Scheduler object to see events as they occur.

Scheduler also benefits from thirty years of experience with sched.scheduler. In particular, big reimplements the relevant parts of the sched.scheduler test suite, ensuring Scheduler will never trip over the problems discovered by sched.scheduler over its lifetime.

`Event(scheduler, event, time, priority, sequence)`

An object representing a scheduled event in a Scheduler. You shouldn't need to create them manually; Event objects are created automatically when you add events to a Scheduler.

Supports one method:

`Event.cancel()`

Cancels this event. If this event has already been canceled, raises ValueError.

`Regulator()`

An abstract base class for Scheduler regulators.

A "regulator" handles all the details about time for a Scheduler. Scheduler objects don't actually understand time; it's all abstracted away by the Regulator.

You can implement your own Regulator and use it with Scheduler. Your Regulator subclass must implement three methods: now, sleep, and wake. It must also provide a lock attribute.

Normally a Regulator represents time using a floating-point number, representing a fractional number of seconds since some epoch. But this isn't strictly necessary. Any Python object that fulfills these requirements will work:

The time class must implement __le__, __eq__, __add__, and __sub__, and these operations must be consistent in the same way they are for number objects.
If a and b are instances of the time class, and a.__le__(b) is true, then a must either be an earlier time, or a smaller interval of time.
The time class must also implement rich comparison with numbers (integers and floats), and 0 must represent both the earliest time and a zero-length interval of time.

`Regulator.lock`

A lock object. The Scheduler uses this lock to protect its internal data structures.

Must support the "context manager" protocol (__enter__ and __exit__). Entering the object must acquire the lock; exiting must release the lock.

This lock does not need to be recursive.

`Regulator.now()`

Returns the current time in local units. Must be monotonically increasing; for any two calls to now during the course of the program, the later call must never have a lower value than the earlier call.

A Scheduler will only call this method while holding this regulator's lock.

`Regulator.sleep(t)`

Sleeps for some amount of time, in local units. Must support an interval of 0, which should represent not sleeping. (Though it's preferable that an interval of 0 yields the rest of the current thread's remaining time slice back to the operating system.)

If wake is called on this Regulator object while a different thread has called this function to sleep, sleep must abandon the rest of the sleep interval and return immediately.

A Scheduler will only call this method while not holding this regulator's lock.

`Regulator.wake()`

Aborts all current calls to sleep on this Regulator, across all threads.

A Scheduler will only call this method while holding this regulator's lock.

`Scheduler(regulator=default_regulator)`

Implements a scheduler. The only argument is the "regulator" object to use; the regulator abstracts away all time-related details for the scheduler. By default Scheduler uses an instance of SingleThreadedRegulator, which is not thread-safe.

(If you need the scheduler to be thread-safe, pass in an instance of a thread-safe Regulator class like ThreadSafeRegulator.)

In addition to the below methods, Scheduler objects support being evaluated in a boolean context (they are true if they contain any events), and they support being iterated over. Iterating over a Scheduler object blocks until the next event comes due, at which point the Scheduler yields that event. An empty Scheduler that is iterated over raises StopIteration. You can reuse Scheduler objects, iterating over them until empty, then adding more objects and iterating over them again.

`Scheduler.schedule(o, time, *, absolute=False, priority=DEFAULT_PRIORITY)`

Schedules an object o to be yielded as an event by this schedule object at some time in the future.

By default the time value is a relative time value, and is added to the current time; using a time value of 0 should schedule this event to be yielded immediately.

If absolute is true, time is regarded as an absolute time value.

If multiple events are scheduled for the same time, they will be yielded by order of priority. Lowever values of priority represent higher priorities. The default value is Scheduler.DEFAULT_PRIORITY, which is 100. If two events are scheduled for the same time, and have the same priority, Scheduler will yield the events in the order they were added.

Returns an Event object, which can be used to cancel the event.

`Scheduler.cancel(event)`

Cancels a scheduled event. event must be an object returned by this Scheduler object. If event is not currently scheduled in this Scheduler object, raises ValueError.

`Scheduler.queue`

A list of the currently scheduled Event objects, in the order they will be yielded.

`Scheduler.non_blocking()`

Returns an iterator for the events in the Scheduler that only yields the events that are currently due. Never blocks; if the next event is not due yet, raises StopIteration.

`SingleThreadedRegulator()`

An implementation of Regulator designed for use in single-threaded programs. It doesn't support multiple threads, and in particular is not thread-safe. But it's much higher performance than thread-safe Regulator implementations.

`ThreadSafeRegulator()`

A thread-safe implementation of Regulator designed for use in multithreaded programs.

`big.state`

Code that makes it easy to write simple state machines.

There are lots of popular Python libraries for implementing state machines. But they all seem to be designed for large-scale state machines. These libraries are sophisticated and data-driven, with expansive APIs. And, as a rule, they require the state to be a passive object (e.g. an Enum), and require you to explicitly describe every possible state transition.

That approach is great for massive, super-complex state machines--you need the features of a sophisticated library to manage all that complexity. It also enables clever features like automatically generating diagrams of your state machine, which is great!

But most of the time this level of sophistication is unnecessary. There are lots of use cases for small scale, simple state machines, where the sophisticated data-driven approach and expansive, complex API only gets in the way. I prefer writing my state machines with active objects--where states are implemented as classes, events are implemented as method calls on those classes, and you transition to a new state by simply overwriting a state attribute with a different state instance.

big.state makes it easy to write this style of state machine. It has a deliberately minimal, simple interface--the constructor for the main StateManager class only has four parameters, and it only exposes three attributes. The module also has two decorators to make your life easier. And that's it! But even this small API surface area makes it effortless to write some pretty big state machines.

(Of course, you can also use big.state to write tiny data-driven state machines too. Although big.state makes state machines with active states easy to write, it's agnostic about how you actually implement your state machine. Really, big.state makes it easy to write any kind of state machine you like!)

big.state provides features like:

method calls that get called when entering and exiting a state,
"observers", callables that get called each time you transition to a new state, and
safety mechanisms to catch bugs and prevent design mistakes.

Recommended best practices

The main class in big.state is StateManager. This class maintains the current "state" of your state machine, and manages transitions to new states. The constructor takes one required parameter, the initial state.

Here are my recommendations for best practices when working with StateManager for medium-sized and larger state machines:

Your state machine should be implemented as a class.
- You should store StateManager as an attribute of that class, preferably called state_manager. (Your state machine should have a "has-a" relationship with StateManager, not an "is-a" relationship where it inherits from StateManager.)
- You should decorate your state machine class with the accessor decorator--this will save you a lot of boilerplate. If your state machine is stored in o, decorating with accessor lets you can access the current state using o.state instead of o.state_manager.state.
Every state should be implemented as a class.
- You should have a base class for your state classes, containing whatever functionality they have in common.
- You're encouraged to define these state classes inside your state machine class, and use BoundInnerClass so they automatically get references to the state machine they're a part of.
Events should be method calls made on your state machine object.
- Your state base class should have a method for every event, decorated with `pure_virtual'.
- As a rule, events should be dispatched from the state machine to a method call on the current state with the same name.
- If all the code to handle a particular event lives in the states, use the dispatch decorator to save you more boilerplate when calling the event method. Similarly to accessor, this creates a new method for you that calls the equivalent method on the current state, passing in all the arguments it received.

Example code

Here's a simple example demonstrating all this functionality. It's a state machine with two states, On and Off, and one event method toggle. Calling toggle transitions the state machine from the Off state to the On state, and vice-versa.

from big.all import accessor, BoundInnerClass, dispatch, pure_virtual, StateManager

@accessor()
class StateMachine:
    def __init__(self):
        self.state_manager = StateManager(self.Off())

    @dispatch()
    def toggle(self):
        ...

    @BoundInnerClass
    class State:
        def __init__(self, state_machine):
            self.state_machine = state_machine

        def __repr__(self):
            return f"<{type(self).__name__}>"

        @pure_virtual()
        def toggle(self):
            ...

    @BoundInnerClass
    class Off(State):
        def on_enter(self):
            print("off!")

        def toggle(self):
            sm = self.state_machine
            sm.state = sm.On() # sm.state is the accessor

    @BoundInnerClass
    class On(State):
        def on_enter(self):
            print("on!")

        def toggle(self):
            sm = self.state_machine
            sm.state = sm.Off()

sm = StateMachine()
print(sm.state)
for _ in range(3):
    sm.toggle()
    print(sm.state)

This code demonstrates both accessor and dispatch. accessor lets us reference the current state with sm.state instead of sm.state_manager.state, and dispatch lets us call sm.toggle() instead of sm.state_manager.state.toggle().

For a more complete example of working with StateManager, see the test_vending_machine test code in tests/test_state.py in the big source tree.

`accessor(attribute='state', state_manager='state_manager')`

Class decorator. Adds a convenient state accessor attribute to your class.

When you have a state machine class containing a StateManager object, it can be wordy and inconvenient to access the state through the state machine attribute:

    class StateMachine:
        def __init__(self):
            self.state_manager = StateManager(self.InitialState)
        ...
    sm = StateMachine()
    # vvvvvvvvvvvvvvvvvvvv that's a lot!
    sm.state_manager.state = NextState()

The accessor class decorator creates a property for you--a shortcut that directly accesses the state attribute of your state manager. Just decorate your state machine class with @accessor():

    @accessor()
    class StateMachine:
        def __init__(self):
            self.state_manager = StateManager(self.InitialState)
        ...
    sm = StateMachine()
    # vvvvvv that's a lot shorter!
    sm.state = NextState()

The state attribute evaluates to the same value:

    sm.state == sm.state_manager.state

And setting it sets the state on your StateManager instance. These two statements now do the same thing:

    sm.state_manager.state = new_state
    sm.state = new_state

By default, this decorator assumes your StateManager instance is in the state_manager attribute, and you want to name the new accessor attribute state. You can override these defaults; the decorator's first parameter, attribute, should be the string used for the new accessor attribute, and the second parameter, state_manager, should be the name of the attribute where your StateManager instance is stored.

For example, if your state manager is stored in an attribute called sm, and you want the short-cut to be called st, you'd decorate your state machine class with

@accessor(attribute='st', state_manager='sm')

`dispatch(state_manager='state_manager', *, prefix='', suffix='')`

Decorator for state machine event methods, dispatching the event from the state machine object to its current state.

dispatch helps with the following scenario:

You have your own state machine class which contains a StateManager object.
You want your state machine class to have methods representing events.
Rather than handle those events in your state machine object itself, you want to dispatch them to the current state.

Simply create a method in your state machine class with the correct name and parameters but a no-op body, and decorate it with @dispatch. The dispatch decorator will rewrite your method so it calls the equivalent method on the current state, passing through all the arguments.

For example, instead of writing this:

    class StateMachine:
        def __init__(self):
            self.state_manager = StateManager(self.InitialState)

        def on_sunrise(self, time, *, verbose=False):
            return self.state_manager.state.on_sunrise(time, verbose=verbose)

you can literally write this, which does the same thing:

    class StateMachine:
        def __init__(self):
            self.state_manager = StateManager(self.InitialState)

        @dispatch()
        def on_sunrise(self, time, *, verbose=False):
            ...

Here, the on_sunrise function you wrote is actually thrown away. (That's why the body is simply one "..." statement.) Your function is replaced with a function that gets the state_manager attribute from self, then gets the state attribute from that StateManager instance, then calls a method with the same name as the decorated function, passing in using *args and **kwargs.

Note that, as a stylistic convention, you're encouraged to literally use a single ellipsis as the body of these functions, as in the example above. This is a visual cue to readers that the body of the function doesn't matter. (In fact, the original on_sunrise method above is thrown away inside the decorator, and replaced with a customized method dispatch function.)

The state_manager argument to the decorator should be the name of the attribute where the StateManager instance is stored in self. The default is 'state_manager', but you can specify a different string if you've stored your StateManager in another attribute. For example, if your state manager is in the attribute smedley, you'd decorate with:

    @dispatch('smedley')

The prefix and suffix arguments are strings added to the beginning and end of the method call we call on the current state. For example, if you want the method you call to have an active verb form (e.g. reset), but you want it to directly call an event handler that starts with on_ by convention (e.g. on_reset), you could do this:

    @dispatch(prefix='on_')
    def reset(self):
        ...

This is equivalent to:

    def reset(self):
        return self.state_manager.state.on_reset()

If you have more than one event method, instead of decorating every event method with the same copy-and-pasted dispatch call, it's better to call dispatch once, cache the function it returns, and decorate with that. Like so:

    my_dispatch = dispatch('smedley', prefix='on_')

    @my_dispatch
    def reset(self):
        ...

    @my_dispatch
    def sunrise(self):
        ...

`State()`

Base class for state machine state implementation classes. Use of this base class is optional; states can be instances of any type except types.NoneType.

`StateManager(state, *, on_enter='on_enter', on_exit='on_exit', state_class=None)`

Simple, Pythonic state machine manager.

Has three public attributes:

state

The current state. You transition from one state to another by assigning to this attribute.

next

The state the StateManager is transitioning to, if it's currently in the process of transitioning to a new state. If the StateManager isn't currently transitioning to a new state, its next attribute is None. And if the StateManager is currently transitioning to a new state, its next attribute will not be None.

During the time the manager is currently transitioning to a new state, it's illegal to start a second transition. (In other words: you can't assign to state while next is not None.)

observers

A list of callables that get called during every state transition. It's initially empty; you may add and remove observers to the list as needed.

The callables will be called with one positional argument, the state manager object.
Since observers are called during the state transition, they aren't permitted to initiate state transitions.
You're permitted to modify the list of observers at any time--even from inside an observer callback. Note that this won't modify the list of observers called until the next state transition. (Upon every state transition, StateManager locally caches the list of observers before calling any of them.)
If an observer raises an exception, StateManager remembers the first exception, continues calling the remaining observers, completes the state transition, and then re-raises that first exception. (If more than one observer raises an exception, only the first exception is retained and re-raised.)

The constructor takes the following parameters:

state

The initial state. It can be any valid state object; by default, any Python value can be a state except None. (But also see the state_class parameter below.)

on_enter

on_enter represents a method call on states called when entering that state. The value itself is a string used to look up an attribute on state objects; by default on_enter is the string 'on_enter', but it can be any legal Python identifier string, or any false value.

If on_enter is a valid identifier string, and this StateManager object transitions to a state object O, and O has an attribute defined with this name, StateManager will call that attribute (with no arguments) immediately after transitioning to that state. Passing in a false value for on_enter to the StateManager constructor disables this behavior.

on_enter is called immediately after the transition is complete, which means you're expressly permitted to make a state transition inside an on_enter call.

If defined, on_enter will be called on the initial state object, from inside the StateManager constructor.

on_exit

on_exit is similar to on_enter, except the attribute is called when transitioning away from a state object. Its default value is 'on_exit'.

on_exit is called during the state transition, which means you're expressly forbidden from making a state transition inside an on_exit call.

If the on_exit method raises an exception, the state transition is aborted, and the state machine stays in the current state.

state_class

state_class is used to enforce that this StateManager only ever transitions to valid state objects. It should be either None or a class. If it's a class, the StateManager object will require every value assigned to its state attribute to be an instance of that class. If it's None, states can be any object (except None).

State transitions

To transition to a new state, simply assign to the state attribute.

If state_class is None, you may use any value as a state except None.
It's illegal to assign to state while currently transitioning to a new state. (Or, in other words, at any time self.next is not None.)
If the current state object has an on_exit method, it will be called (with zero arguments) during the transition to the next state. This means it's illegal to initiate a state transition inside an on_exit call. If the on_exit method raises an exception, the state transition is aborted, and the StateManager stays in the current state.
If you assign an object to state that has an on_enter attribute, that method will be called (with zero arguments) immediately after we have transitioned to that state. This means it's permitted to initiate a state transition inside an on_enter call.
If the current state object's on_exit raises an exception, the transition is aborted, state remains unchanged, and next is restored to None.
If an observer raises an exception, the transition still completes, next is restored to None, and StateManager re-raises the first observer exception after completing the transition.
It's illegal to attempt to transition to the current state. If state_manager.state is already foo, state_manager.state = foo will raise an exception. (However, it's legal to transition to an object bar even if foo == bar is true. Equivalent objects are fine; you just can't transition to literally the same object.)

Sequence of events during a state transition

If you have an StateManager instance called state_manager, and you transition it to new_state:

    state_manager.state = new_state

StateManager will execute the following sequence of events:

Set state_manager.next to new_state.
- At of this moment state_manager is "transitioning" to the new state.
If state_manager.state has an on_exit attribute, call state_manager.state.on_exit().
For every object o in a snapshot of the state_manager.observers list, call o(self).
- If an observer raises an exception, StateManager remembers the first exception, then keeps calling the remaining observers.
Set state_manager.state to new_state.
- As of this moment, the transition is complete, and state_manager is now "in" the new state.
Set state_manager.next to None.
If state_manager.state has an on_enter attribute, call state_manager.state.on_enter().
If an observer raised an exception, re-raise the first observer exception now.

`TransitionError()`

Exception raised when attempting to execute an illegal state transition.

There are only two types of illegal state transitions:

An attempted state transition while we're in the process of transitioning to another state. In other words, if state_manager is your StateManager object, you can't set state_manager.state when state_manager.next is not None.
An attempt to transition to the current state. This is illegal:

  state_manager = StateManager()
  state_manager.state = foo
  state_manager.state = foo # <-- this statement raises TransitionError

Note that transitioning to a different but identical object is expressly permitted.

`big.template`

Functions for parsing strings containing a simple template syntax, patterned after Django Templates and Jinja. Similar in spirit to Python 3.14+ t-strings.

`Formatter(template, map=None, *, stretch=True, width=79, **kwargs)`

A sophisticated template formatter, similar to str.format.

The Formatter constructor takes the following arguments: * template, a string. Calling the Formatter object is like calling the str.format method on that string. * map, a dict or None, default None. If a dict, pre-initializes values used at interpolation time. * width, an integer, default 79, the target width of lines when computing "starred interpolations". * stretch, a boolean, default True, also used in conjunction with "starred interpolations".

Also, additional **kwargs are used as additional pre-initialized map values, and take precedence over the "map" parameter.

Returns a Formatter object. Calling this object formats the template string using str.format_map and returns the result. Substitutions in the template use str.format_map syntax. The signature of this callable is:

   fn(message=''', **kwargs)

The **kwargs passed in here are also used as values for the interpolation, and take precedence over any value passed in to the constructor.

Formatter has two additional features:

* Special support for an interpolation named `"{message}"`,
  which are formatted in conjunction with the "message" parameter.
  If your template contains one or more lines containing `"{message}"`,
  these "message lines" are formatted using the lines of the `message`
  argument.  The "message" argument is split by the newline character
  (`'\n'`) and these are zipped together with the "message lines";
  the first "message line" will be formatted with the first line
  of the "message" parameter, the second with the second, etc.

    * If there are more "message lines" in the template than lines
      in the "message" parameter, the additional "message lines"
      are discarded.  Example: if there are three "message lines"
      in the template, but only two lines in the "message" parameter,
      the third "message line" won't appear in the output.

    * If there are more lines in the "message" parameter than
      "message lines" in the template, the last template
      "message line" will be repeated.  Example: if there are
      three lines in the "message" parameter, but only two
      "message lines" in the template, the last "message line"
      will be repeated, used to format the last two lines of
      the "message" parameter.

* Values whose keys end with `'*'` (e.g. `"{line*}"`) are special:
  they are "starred interpolations".  Their value is repeated
  zero or more times then truncated until the line is at least
  "width" characters.  Starred interpolations must not use:

    * dotted expressions (`"{line.foo*}"`)
    * indexing (`"{line[3]*}"`)
    * a conversion (`"{line*!r}"`)
    * or a format spec (`"{line*:5}"`)

If `stretch` is true, `Formatter` calculates the width of the
longest formatted line (assuming all starred interpolations
are length 0), then recomputes width as

    width = max(longest_line, width)

This means the starred interpolations will "stretch" to fit the longest line of the output.

Example:

    fmt = Formatter('{line*}\\n{name} start\\n>> {message}\\n<< {message}\\n{double*}{line*}',
      {'line*': '-', 'double*': '=', 'name': 'Log'},
      width=20)
    print(fmt("hello\\nthere\\nworld!"))

This prints:

    --------------------
    Log start
    >> hello
    << there
    << world!
    ==========----------

`eval_template_string(s, globals, locals=None, *, parse_expressions=True, parse_comments=False, parse_whitespace_eater=False)`

Parses and evaluates a template string, returning the rendered result.

s is parsed using parse_template_string, then each Interpolation is evaluated using Python's built-in eval() with the provided globals and locals dicts. Filters are applied in order. The rendered string is returned with all interpolations replaced by their values.

parse_comments and parse_whitespace_eater may be enabled optionally; they are disabled by default. Statement parsing is not supported by eval_template_string.

`Interpolation(expression, *filters, debug='')`

Represents a {{ }} expression interpolation from a parsed template.

expression contains the text of the expression. filters is a tuple containing the text of each filter expression, if any. If the expression ended with =, debug contains the text of the expression along with the = and all whitespace; otherwise it is an empty string.

See parse_template_string.

`parse_template_string(s, *, parse_expressions=True, parse_comments=False, parse_statements=False, parse_whitespace_eater=False, quotes=('"', "'"), multiline_quotes=(), escape='\\')`

Parses a string containing simple template markup, yielding its components.

Returns a generator yielding str objects (literal text), Interpolation objects (parsed expressions), and Statement objects (parsed statements).

The supported delimiters are:

{{ ... }} — An expression. Parsed into an Interpolation object. Expressions may include filters separated by |.

{% ... %} — A statement. Parsed into a Statement object. Quoted strings inside statements are preserved and respected when looking for the close delimiter.

{# ... #} — A comment. The delimiters and all text between them are discarded.

{>} — The whitespace eater. These three characters and all subsequent whitespace are discarded.

Each delimiter type can be individually enabled or disabled via its corresponding boolean keyword-only parameter. By default only parse_expressions is true.

`Statement(statement)`

Represents a {% %} statement from a parsed template.

statement contains the text of the statement, including all leading and trailing whitespace.

See parse_template_string.

`big.text`

Functions for working with text strings. There are several families of functions inside the text module; for a higher-level view of those families, read the following tutorials:

The multi- family of string functions
Word wrapping and formatting

All the functions in big.text will work with either str or bytes objects, except the three Word wrapping and formatting functions. When working with bytes, by default the functions will only work with ASCII characters.

Support for bytes and str

The big text functions all support both str and bytes. The functions all automatically detect whether you passed in str or bytes using an intentionally simple and predictable process, as follows:

At the start of each function, it'll test its first "string" argument to see if it's a bytes object.

is_bytes = isinstance(<argument>, bytes)

If isinstance returns True, the function assumes all arguments are bytes objects. Otherwise the function assumes all arguments are str objects.

As a rule, no further testing, casting, or catching exceptions is done.

Functions that take multiple string-like parameters require all such arguments to be the same type. These functions will check that all such arguments are of the same type.

Subclasses of str and bytes will also work; anywhere you should pass in a str, you can also pass in a subclass of str, and likewise for bytes.

`ascii_linebreaks`

A tuple of str objects, representing every line-breaking whitespace character defined by ASCII.

Useful as a separator argument for big functions that accept one, e.g. the big "multi-" family of functions.

Also contains '\r\n'. If you don't want to include this string, use ascii_linebreaks_without_crlf instead. See the tutorial section on The Unix, Mac, and DOS linebreak conventions for more.

For more information, please see the Whitespace and line-breaking characters in Python and big tutorial.

`ascii_linebreaks_without_crlf`

Equivalent to ascii_linebreaks without '\r\n'.

`ascii_whitespace`

A tuple of str objects, representing every whitespace character defined by ASCII.

Useful as a separator argument for big functions that accept one, e.g. the big "multi-" family of functions.

Also contains '\r\n'. If you don't want to include this string, use ascii_whitespace_without_crlf instead. See the tutorial section on The Unix, Mac, and DOS linebreak conventions for more.

For more information, please see the Whitespace and line-breaking characters in Python and big tutorial.

`ascii_whitespace_without_crlf`

Equivalent to ascii_whitespace without '\r\n'.

`bytes_linebreaks`

A tuple of bytes objects, representing every line-breaking whitespace character recognized by the Python bytes object.

Useful as a separator argument for big functions that accept one, e.g. the big "multi-" family of functions.

Also contains b'\r\n'. If you don't want to include this string, use bytes_linebreaks_without_crlf instead. See the tutorial section on The Unix, Mac, and DOS linebreak conventions for more.

For more information, please see the Whitespace and line-breaking characters in Python and big tutorial.

`bytes_linebreaks_without_crlf`

Equivalent to bytes_linebreaks with '\r\n' removed.

`bytes_whitespace`

A tuple of bytes objects, representing every line-breaking whitespace character recognized by the Python bytes object. (bytes.isspace, bytes.split, etc will tell you which characters are considered whitespace...)

Useful as a separator argument for big functions that accept one, e.g. the big "multi-" family of functions.

Also contains b'\r\n'. If you don't want to include this string, use bytes_whitespace_without_crlf instead. See the tutorial section on The Unix, Mac, and DOS linebreak conventions for more.

For more information, please see the Whitespace and line-breaking characters in Python and big tutorial.

`bytes_whitespace_without_crlf`

Equivalent to bytes_whitespace without '\r\n'.

`combine_splits(s, *split_arrays)`

Takes a string s, and one or more "split arrays", and applies all the splits to s. Returns an iterator of the resulting string segments.

A "split array" is an array containing the original string, but split into multiple pieces. For example, the string "a b c d e" could be split into the split array ["a ", "b ", "c ", "d ", "e"].

For example,

    combine_splits('abcde', ['abcd', 'e'], ['a', 'bcde'])

returns ['a', 'bcd', 'e'].

Note that the split arrays must contain all the characters from s. ''.join(split_array) must recreate s. combine_splits only examines the lengths of the strings in the split arrays, and makes no attempt to infer stripped characters. (So, don't use the string's .split method if you want to use combine_splits. Instead, consider big's multisplit with keep=True or keep=ALTERNATING.)

`decode_python_script(script, *, newline=None, use_bom=True, use_source_code_encoding=True)`

Correctly decodes a Python script from a bytes string.

script should be a bytes object containing an encoded Python script.

Returns a str containing the decoded Python script.

By default, Python 3 scripts must be encoded using UTF-8. (This was established by PEP 3120.) Python scripts are allowed to use other encodings, but when they do so they must explicitly specify what encoding they used. Python defines two methods for scripts to specify their encoding; decode_python_script supports both.

The first method uses a "byte order mark", aka "BOM". This is a sequence of bytes at the beginning of the file that indicate the file's encoding.

If use_bom is true (the default), decode_python_script will recognize a BOM if present, and decode the file using the encoding specified by the BOM. Note that decode_python_script removes the BOM when it decodes the file.

The second method is called a "source code encoding", and it was defined in PEP 263. This is a "magic comment" that must be one of the first two lines of the file.

If use_source_code_encoding is true (the default), decode_python_script will recognize a source code encoding magic comment, and use that to decode the file. (decode_python_script leaves the magic comment in place.)

If both these "use_" keyword-only parameters are true (the default), decode_python_script can handle either, both, or neither. In this case, if script contains both a BOM and a source code encoding magic comment, the script will be decoded using the encoding specified by the BOM, and the source code encoding must agree with the BOM.

The newline parameter supports Python's "universal newlines" convention. This behaves identically to the newline parameter for Python's open() function.

`Delimiter(close, *, escape='', multiline=True, quoting=False)`

Class representing a delimiter for split_delimiters.

close is the closing delimiter character. It must be a valid string or bytes object, and cannot be a backslash ('"\"' or b"\\").

If escape is true, it should be a string; when inside this delimiter, you can escape the trailing delimiter with this string. If escape is false, there is no escape string for this delimiter.

quoting is a boolean: does this set of delimiters "quote" the text inside? When an open delimiter enables quoting, split_delimiters will ignore all other delimiters in the text until it encounters the matching close delimiter. (Single- and double-quotes set this to True.)

If escape is true, quoting must also be true.

If multiline is true, the closing delimiter may be on the current line or any subsequent line. If multiline is false, the closing delimiter must appear on the current line.

`encode_strings(o, *, encoding='ascii')`

Converts an object o from str to bytes. If o is a container, recursively converts all objects and containers inside.

o and all objects inside o must be either bytes, str, dict, set, list, tuple, or a subclass of one of those.

Encodes every string inside using the encoding specified in the encoding parameter, default is 'ascii'.

Handles nested containers.

If o is of, or contains, a type not listed above, raises TypeError.

`format_map(s, mapping)`

An implementation of `str.format_map` supporting *nested replacements.*

Unlike str.format_map, big's format_map allows you to perform string replacements inside of other string replacements:

  big.format_map("{{extension} size}",
      {'extension': 'mp3', 'mp3 size': 8555})

returns the string '8555'.

Another difference between str.format_map and big's format_map is how you escape curly braces. To produce a '{' or '}' in the output string, add '\{' or '\}' respectively. (To produce a backslash, '\\', you must put four backslashes, '\\\\'.)

See the documentation for str.format_map for more.

`gently_title(s, *, apostrophes=None, double_quotes=None)`

Uppercases the first character of every word in s, leaving the other letters alone. s should be str or bytes.

(For the purposes of this algorithm, words are any contiguous run of non-whitespace characters.)

This function will also capitalize the letter after an apostrophe if the apostrophe:

is immediately after whitespace, or
is immediately after a left parenthesis character ('('), or
is the first letter of the string, or
is immediately after a letter O or D, when that O or D
- is after whitespace, or
- is the first letter of the string.

In this last case, the O or D will also be capitalized.

Finally, this function will capitalize the letter after a quote mark if the quote mark:

is after whitespace, or
is the first letter of a string.

(A run of consecutive apostrophes and/or quote marks is considered one quote mark for the purposes of capitalization.)

All these rules mean gently_title correctly handles internally quoted strings:

    He Said 'No I Did Not'

and contractions that start with an apostrophe:

    'Twas The Night Before Christmas

as well as certain Irish, French, and Italian names:

    Peter O'Toole
    Lord D'Arcy

If specified, apostrophes should be a str or bytes object containing characters that should be considered apostrophes. If apostrophes is false, and s is bytes, apostrophes is set to a bytes object containing the only ASCII apostrophe character:

If apostrophes is false and s is str, apostrophes is set to a string containing these Unicode apostrophe code points:

    '‘’‚‛

Note that neither of these strings contains the "back-tick" character:

This is a diacritical used for modifying letters, and isn't used as an apostrophe.

If specified, double_quotes should be a str or bytes object containing characters that should be considered double-quote characters. If double_quotes is false, and s is bytes, double_quotes is set to a bytes object containing the only ASCII double-quote character:

If double_quotes is false and s is str, double_quotes is set to a string containing these Unicode double-quote code points:

    "“”„‟«»‹›

`int_to_words(i, *, flowery=True, ordinal=False)`

Converts an integer into the equivalent English string.

int_to_words(2) -> "two"
int_to_words(35) -> "thirty-five"

If the keyword-only parameter flowery is true (the default), you also get commas and the word and where you'd expect them. (When flowery is true, int_to_words(i) produces identical output to inflect.engine().number_to_words(i), except for negative numbers: inflect starts negative numbers with "minus", big starts them with "negative".)

If the keyword-only parameter ordinal is true, the string produced describes that ordinal number (instead of that cardinal number). Ordinal numbers describe position, e.g. where a competitor placed in a competition. In other words, int_to_words(1) returns the string 'one', but int_to_words(1, ordinal=True) returns the string 'first'.

Numbers >= 10**66 (one thousand vigintillion) are only converted using str(i). Sorry!

`linebreaks`

A tuple of str objects, representing every line-breaking whitespace character recognized by the Python str object. Identical to str_linebreaks.

Useful as a separator argument for big functions that accept one, e.g. the big "multi-" family of functions.

Also contains '\r\n'. See the tutorial section on The Unix, Mac, and DOS linebreak conventions for more.

For more information, please see the Whitespace and line-breaking characters in Python and big tutorial.

`linebreaks_without_crlf`

Equivalent to linebreaks without '\r\n'.

`merge_columns(*columns, column_separator=" ", overflow_response=OverflowResponse.RAISE, overflow_before=0, overflow_after=0)`

Merge an arbitrary number of separate text strings into columns. Returns a single formatted string.

columns should be an iterable of "column tuples". Each column tuple should contain three items:

(text, min_width, max_width)

text should be a single string, either str or bytes, with newline characters separating lines. min_width and max_width are the minimum and maximum permissible widths for that column, not including the column separator (if any).

Note that this function does not text-wrap the text of the columns. The text in the columns should already be broken into lines and separated by newline characters. (Lines in that are longer than that column tuple's max_width are handled with the overflow_strategy, described below.)

column_separator is printed between every column.

overflow_strategy tells merge_columns how to handle a column with one or more lines that are wider than that column's max_width. The supported values are:

OverflowStrategy.RAISE: Raise an OverflowError. The default.
OverflowStrategy.INTRUDE_ALL: Intrude into all subsequent columns on all lines where the overflowed column is wider than its max_width.
OverflowStrategy.DELAY_ALL: Delay all columns after the overflowed column, not beginning any until after the last overflowed line in the overflowed column.

When overflow_strategy is INTRUDE_ALL or DELAY_ALL, and either overflow_before or overflow_after is nonzero, these specify the number of extra lines before or after the overflowed lines in a column.

For more information, see the tutorial on Word wrapping and formatting.

`multipartition(s, separators, count=1, *, reverse=False, separate=True)`

Like str.partition, but supports partitioning based on multiple separator strings, and can partition more than once.

s can be either str or bytes.

separators should be an iterable of objects of the same type as s.

By default, if any of the strings in separators are found in s, returns a tuple of three strings: the portion of s leading up to the earliest separator, the separator, and the portion of s after that separator. Example:

>>> multipartition('aXbYz', ('X', 'Y'))
('a', 'X', 'bYz')

If none of the separators are found in the string, returns a tuple containing s unchanged followed by two empty strings.

Returns a tuple of slices of s—including zero-length boundary slices when needed—so concatenating the returned values reconstitutes the original s.

multipartition is greedy: if two or more separators appear at the leftmost location in s, multipartition partitions using the longest matching separator. For example:

>>> multipartition('wxabcyz', ('a', 'abc'))
('wx', 'abc', 'yz')

Passing in an explicit count lets you control how many times multipartition partitions the string. multipartition will always return a tuple containing (2*count)+1 elements. Passing in a count of 0 will always return a tuple containing s.

If separate is false, multiple adjacent separator strings get joined together, behaving like one big separator. If separate is true, they're kept separate. Example:

>>> multipartition('aXYbYXc', ('X', 'Y',),          separate=False)
('a', 'XY', 'b', 'YX', 'c')
>>> multipartition('aXYbYXc', ('X', 'Y',),          separate=True )
('a', 'X', '', 'Y', 'b', 'Y', '', 'X', 'c')
>>> multipartition('aXYbYXc', ('X', 'Y',), count=2, separate=True )
('a', 'X', '', 'Y', 'bYXc')

If reverse is true, multipartition behaves like str.rpartition. It partitions starting on the right, scanning backwards through s looking for separators.

For more information, see the tutorial on The multi- family of string functions.

`multisplit(s, separators=None, *, keep=False, maxsplit=-1, reverse=False, separate=False, strip=False)`

Splits strings like str.split, but with multiple separators and options.

s can be str or bytes.

separators should either be None (the default), or an iterable of str or bytes, matching s.

If separators is None and s is str, multisplit will use big.whitespace as separators. If separators is None and s is bytes, multisplit will use big.ascii_whitespace as separators.

Returns an iterator yielding values split from s. The values yielded are slices of the original object, or in some cases adjacent slices joined with +. All slices are yielded in left-to-right order; this even includes zero-length strings, which are sliced from the contextually correct spot.

If keep is true (or ALTERNATING), and strip is false, joining these strings together will recreate s.

multisplit is greedy: if two or more separators start at the same location in s, multisplit splits using the longest matching separator. For example:

big.multisplit('wxabcyz', ('a', 'abc'))

yields 'wx' then 'yz'.

keep indicates whether or not multisplit should preserve the separator strings in the strings it yields. It supports four values:

false (the default): Discard the separators.
true (apart from ALTERNATING and AS_PAIRS): Append the separators to the end of the split strings. You can recreate the original string by using "".join to join the strings yielded by multisplit.
ALTERNATING: Yield alternating strings in the output: strings consisting of separators, alternating with strings consisting of non-separators. If "separate" is true, separator strings will contain exactly one separator, and non-separator strings may be empty; if "separate" is false, separator strings will contain one or more separators, and non-separator strings will never be empty, unless "s" was empty. You can recreate the original string by using "".join to join the strings yielded by multisplit.
AS_PAIRS: Yield 2-tuples containing a non-separator string and its subsequent separator string. Either string may be empty; the separator string in the last 2-tuple will always be empty, and if "s" ends with a separator string, both strings in the final 2-tuple will be empty.

separate indicates whether multisplit should consider adjacent separator strings in s as one separator or as multiple separators each separated by a zero-length string. It supports two values:

false (the default): Group separators together. Multiple adjacent separators behave as if they're one big separator.
true: Don't group separators together. Each separator should split the string individually, even if there are no characters between two separators. (multisplit will behave as if there's a zero-character-wide string between adjacent separators.)

strip indicates whether multisplit should strip separators from the beginning and/or end of s. It supports five values:

false (the default): Don't strip separators from the beginning or end of "s".
true (apart from LEFT, RIGHT, and PROGRESSIVE): Strip separators from the beginning and end of "s" (similarly to `str.strip`).
LEFT: Strip separators only from the beginning of "s" (similarly to `str.lstrip`).
RIGHT: Strip separators only from the end of "s" (similarly to `str.rstrip`).
PROGRESSIVE: Strip from the beginning and end of "s", unless "maxsplit" is nonzero and the entire string is not split. If splitting stops due to "maxsplit" before the entire string is split, and "reverse" is false, don't strip the end of the string. If splitting stops due to "maxsplit" before the entire string is split, and "reverse" is true, don't strip the beginning of the string. (This is how `str.strip` and `str.rstrip` behave when you pass in `sep=None`.)

maxsplit should be either an integer or None. If maxsplit is an integer greater than -1, multisplit will split text no more than maxsplit times.

reverse changes where multisplit starts splitting the string, and what direction it moves through the string when parsing.

false (the default): Start splitting from the beginning of the string and parse moving right (towards the end).
true: Start splitting from the end of the string and parse moving left (towards the beginning).

Splitting starting from the end of the string and parsing moving left has two effects. First, if maxsplit is a number greater than 0, the splits will start at the end of the string rather than the beginning. Second, if there are overlapping instances of separators in the string, multisplit will prefer the rightmost separator rather than the leftmost. Consider this example, where reverse is false:

multisplit("A x x Z", (" x ",), keep=big.ALTERNATING) => "A", " x ", "x Z"

If you pass in a true value for reverse, multisplit will prefer the rightmost overlapping separator:

multisplit("A x x Z", (" x ",), keep=big.ALTERNATING, reverse=True) => "A x", " x ", "Z"

For more information, see the tutorial on The multi- family of string functions.

`multistrip(s, separators, left=True, right=True)`

Like str.strip, but supports stripping multiple substrings from s.

Strips from the string s all leading and trailing instances of strings found in separators.

s should be str or bytes.

separators should be an iterable of either str or bytes objects matching the type of s.

If left is a true value, strips all leading separators from s.

If right is a true value, strips all trailing separators from s.

Processing always stops at the first character that doesn't match one of the separators.

Returns s unchanged, or a slice of s, with the leading and/or trailing separators stripped.

For more information, see the tutorial on The multi- family of string functions.

`normalize_whitespace(s, separators=None, replacement=None)`

Returns s, but with every run of consecutive separator characters turned into a replacement string. By default turns all runs of consecutive whitespace characters into a single space character.

s may be str or bytes.

separators should be an iterable of either str or bytes objects, matching s.

replacement should be either a str or bytes object, also matching s, or None (the default). If replacement is None, normalize_whitespace will use a replacement string consisting of a single space character.

Leading or trailing runs of separator characters will be replaced with the replacement string, e.g.:

normalize_whitespace("   a    b   c") == " a b c"

`Pattern(s, flags=0)`

A drop-in replacement for re.Pattern that preserves str subclasses.

Python's re module converts str subclasses to plain str when returning matched strings. Pattern preserves the subclass: if you search or match against a big.string, the strings returned in the Match object will be big.string slices, retaining their line number and column number information.

Pattern supports the same interface as re.Pattern. See the Python documentation for re.Pattern for the full API.

`python_delimiters`

A delimiters mapping suitable for use as the delimiters argument for split_delimiters. python_delimiters defines all the delimiters for Python, and is able to correctly split any modern Python text at its delimiter boundaries.

python_delimiters changes the rules a little bit for split_delimiters:

When you use split_delimiters with python_delimiters, it yields four values, not three. The fourth value is change. See split_delimiters for more information.
If you make a copy of python_delimiters and modify it, you will break its semantics. Internally python_delimiters is really just a symbolic token, and split_delimiters uses a secret, internal-only, manually modified set of delimiters. This was necessary because the Delimiters object isn't sophisticated enough (yet) to express all the semantics needed for python_delimiters.
When you call split_delimiters and pass in python_delimiters, you must include the linebreak characters in the text string(s) you pass in. This is necessary to support the comment delimiter correctly, and to enforce the no-linebreaks-inside-single-quoted-strings rule. If you're using big.lines to pre-process a script before passing it in to split_delimiters, consider calling it with clip_linebreaks=False.

Here's a list of all the delimiters recognized by python_delimiters:

(), {}, and [].
All four string delimiters: ', ", ''', and """.
All possible string prefixes, including all valid combinations of b, f, r, and u, in both lower and upper case.
Inside f-strings:
- The quoting markers {{ and }} are passed through in text unmodified.
- The converter (!) and format spec (:) inside the curly braces inside an f-string. These two delimiters are the only two that use the new change value yielded by split_delimiters.
Line comments, which "open" with # and "close" with either a linebreak (\n) or a carriage return (\r). (Python's "universal newlines" support should mean you won't normally see carriage returns here... unless you specifically permit them.) If the text being split ends with a comment without a newline, you'll see yield where open is '#', followed by a slightly-strange final yield: text will be the body of the comment, and the open, close, and change fields will all be an empty string.

`python_delimiters_version`

A dictionary mapping strings containing a Python major and minor version to python_delimiters objects.

By default, python_delimiters parses the version of the Python language matching the version it's being run under. If you run Python 3.12, and call big.split_delimiters and pass in python_delimiters, it will split delimiters based on Python 3.12. If you instead wanted to parse using the semantics from Python 3.8, you would instead pass in python_delimiters_version['3.8'] as the delimiters argument to split_delimiters.

There are entries in python_split_delimiters for every version of Python supported by big (currently 3.6 to 3.13).

`re_partition(text, pattern, count=1, *, flags=0, reverse=False)`

Like str.partition, but pattern is matched as a regular expression.

text can be a string or a bytes object.

pattern can be a string, bytes, or re.Pattern object.

text and pattern (or pattern.pattern) must be the same type.

If pattern is found in text, returns a tuple

(before, match, after)

where before is the text before the matched text, match is the re.Match object resulting from the match, and after is the text after the matched text.

If pattern appears in text multiple times, re_partition will match against the first (leftmost) appearance.

If pattern is not found in text, returns a tuple

(text, None, '')

where the empty string is str or bytes as appropriate.

Passing in an explicit count lets you control how many times re_partition partitions the string. re_partition will always return a tuple containing (2*count)+1 elements, and odd-numbered elements will be either re.Match objects or None. Passing in a count of 0 will always return a tuple containing s.

If pattern is a string or bytes object, flags is passed in as the flags argument to re.compile.

If reverse is true, partitions starting at the right, like re_rpartition.

Note: re_partition supports partitioning on subclasses of str or bytes, and the before and after objects in the tuple returned will be slices of the text object. However, the match object doesn't honor this this; the objects it returns from e.g. match.group will always be of the base type, either str or bytes. This isn't fixable, as you can't create re.Match objects in Python, nor can you subclass it.

(In older versions of Python, re.Pattern was a private type called re._pattern_type.)

`re_rpartition(text, pattern, count=1, *, flags=0)`

Like str.rpartition, but pattern is matched as a regular expression.

text can be a str or bytes object.

pattern can be a str, bytes, or re.Pattern object.

text and pattern (or pattern.pattern) must be the same type.

If pattern is found in text, returns a tuple

(before, match, after)

where before is the text before the matched text, match is the re.Match object resulting from the match, and after is the text after the matched text.

If pattern appears in text multiple times, re_partition will match against the last (rightmost) appearance.

If pattern is not found in text, returns a tuple

('', None, text)

where the empty string is str or bytes as appropriate.

Passing in an explicit count lets you control how many times re_rpartition partitions the string. re_rpartition will always return a tuple containing (2*count)+1 elements, and odd-numbered elements will be either re.Match objects or None. Passing in a count of 0 will always return a tuple containing s.

If pattern is a string, flags is passed in as the flags argument to re.compile.

Note: re_rpartition supports partitioning on subclasses of str or bytes, and the before and after objects in the tuple returned will be slices of the text object. However, the match object doesn't honor this this; the objects it returns from e.g. match.group will always be of the base type, either str or bytes. This isn't fixable, as you can't create re.Match objects in Python, nor can you subclass it.

(In older versions of Python, re.Pattern was a private type called re._pattern_type.)

`reversed_re_finditer(pattern, string, flags=0)`

An iterator. Behaves almost identically to the Python standard library function re.finditer, yielding non-overlapping matches of pattern in string. The difference is, reversed_re_finditer searches string from right to left.

pattern can be str, bytes, or a precompiled re.Pattern object. If it's str or bytes, it'll be compiled with re.compile using the flags you passed in.

string should be the same type as pattern (or pattern.pattern).

`split_delimiters(s, delimiters={...}, *, state=(), yields=None)`

Splits a string s at delimiter substrings.

s may be str or bytes.

delimiters may be either None or a mapping of open delimiter strings to Delimiter objects. The open delimiter strings, close delimiter strings, and escape strings must match the type of s (either str or bytes).

If delimiters is None, split_delimiters uses a default value matching these pairs of delimiters:

    () [] {} "" ''

The first three delimiters allow multiline, disable quoting, and have no escape string. The last two (the quote mark delimiters) enable quoting, disallow multiline, and specify their escape string as a single backslash. (This default value automatically supports both str and bytes.)

state specifies the initial state of parsing. It's an iterable of open delimiter strings specifying the initial nested state of the parser, with the innermost nesting level on the right. If you wanted split_delimiters to behave as if it'd already seen a '(' and a '[', in that order, pass in ['(', '['] to state.

(Tip: Use a list as a stack to track the state of split_delimiters. Push open delimiters with .append, and pop them off using .pop whenever you see a close delimiter. Since split_delimiters ensures that open and close delimiters match, you don't need to check them yourself!)

Yields a object of type SplitDelimitersValue. This object contains five fields:

text: A string, the text before the next opening, closing, or changing delimiter.
open: A string, the trailing opening delimiter.
close: A string, the trailing closing delimiter.
change: A string, the trailing change delimiter.
yields: An integer, either 3 or 4.

At least one of the four strings will always be non-empty. (Only one of open, close, and change will ever be non-empty in a single SplitDelimitersValue object.) If s doesn't end with an opening or closing delimiter, the final value yielded will have empty strings for open, close, and change.

The yields parameter to split_delimiters affects iteration over a SplitDelimitersValue object. yields may be None, 3, or 4:

If yields is 3, when iterating over a SplitDelimitersValue object, it will yield text, open, and close in that order.
If yields is 4, when iterating over a SplitDelimitersValue object, it will yield text, open, close, and change in that order.
If yields is None (the default), split_delimiters will use a value of 4 if its delimiters argument is python_delimiters, and a value of 3 otherwise.

(The yields parameter exists because previously split_delimiters always yielded an tuple containing three string values. python_delimiters required adding the fourth string value, change. Eventually split_delimiters will always yield an object yielding four values, but big is allowing for a transition period to minimize code breakage. See the release notes for big version 0.12.5 for more information.)

You may not specify backslash ('\\') as an open delimiter.

Multiple Delimiter objects specified in delimiters may use the same close delimiter string.

split_delimiters doesn't react if the string ends with unterminated delimiters.

See the Delimiter object for how delimiters are defined, and how you can define your own delimiters.

`split_quoted_strings(s, quotes=('"', "'"), *, escape='\\', multiline_quotes=(), state='')`

Splits s into quoted and unquoted segments.

Returns an iterator yielding 3-tuples:

    (leading_quote, segment, trailing_quote)

where leading_quote and trailing_quote are either empty strings or quote delimiters from quotes (or multiline_quotes), and segment is a substring of s. Joining together all strings yielded recreates s.

s can be either str or bytes.

quotes is an iterable of unique quote delimiters. Quote delimiters may be any non-empty string. They must be the same type as s, either str or bytes. By default, quotes is ('"', "'"). (If s is bytes, quotes defaults to (b'"', b"'").) If a newline character appears inside a quoted string, split_quoted_strings will raise SyntaxError.

multiline_quotes is like quotes, except quoted strings using multiline quotes are permitted to contain newlines. By default split_quoted_strings doesn't define any multiline quote marks.

escape is a string of any length. If escape is not an empty string, the string will "escape" (quote) quote delimiters inside a quoted string, like the backslash ('\') character inside strings in Python. By default, escape is '\\'. (If s is bytes, escape defaults to b'\\'.)

state is a string. It sets the initial state of the function. The default is an empty string (str or bytes, matching s); this means the parser starts parsing the string in an unquoted state. If you want parsing to start as if it had already encountered a quote delimiter--for example, if you were parsing multiple lines individually, and you wanted to begin a new line continuing the state from the previous line-- pass in the appropriate quote delimiter from quotes into state. Note that when a non-empty string is passed in to state, the leading_quote in the first 3-tuple yielded by split_quoted_strings will be an empty string:

    list(split_quoted_strings("a b c'", state="'"))

evaluates to

    [('', 'a b c', "'")]

Note:

split_quoted_strings is agnostic about the length of quoted strings. If you're using split_quoted_strings to parse a C-like language, and you want to enforce C's requirement that single-quoted strings only contain one character, you'll have to do that yourself.
split_quoted_strings doesn't raise an error if s ends with an unterminated quoted string. In that case, the last tuple yielded will have a non-empty leading_quote and an empty trailing_quote. (If you consider this an error, you'll need to raise SyntaxError in your own code.)
split_quoted_strings only supports the opening and closing markers for a string being the same string. If you need the opening and closing markers to be different strings, use split_delimiters.

`split_text_with_code(s, *, tab_width=8, allow_code=True, code_indent=4, convert_tabs_to_spaces=True)`

Splits s into individual words, suitable for feeding into wrap_words.

s may be either str or bytes.

Paragraphs indented by less than code_indent will be broken up into individual words.

If allow_code is true, paragraphs indented by at least code_indent spaces will preserve their whitespace: internal whitespace is preserved, and the newline is preserved. (This will preserve the formatting of code examples when these words are rejoined into lines by wrap_words.)

For more information, see the tutorial on Word wrapping and formatting.

`split_title_case(s, *, split_allcaps=True)`

Splits s into words, assuming that upper-case characters start new words. Returns an iterator yielding the split words.

Example:

    list(split_title_case('ThisIsATitleCaseString'))

is equal to

    ['This', 'Is', 'A', 'Title', 'Case', 'String']

If split_allcaps is a true value (the default), runs of multiple uppercase characters will also be split before the last character. This is needed to handle splitting single-letter words. Consider:

    list(split_title_case('WhenIWasATeapot', split_allcaps=True))

returns

    ['When', 'I', 'Was', 'A', 'Teapot']

but

    list(split_title_case('WhenIWasATeapot', split_allcaps=False))

returns

    ['When', 'IWas', 'ATeapot']

Note: uses the isupper and islower methods to determine what are upper- and lower-case characters. This means it only recognizes the ASCII upper- and lower-case letters for bytes strings.

`str_linebreaks`

A tuple of str objects, representing every line-breaking whitespace character recognized by the Python str object. Identical to linebreaks.

Useful as a separator argument for big functions that accept one, e.g. the big "multi-" family of functions.

Also contains '\r\n'. See the tutorial section on The Unix, Mac, and DOS linebreak conventions for more.

For more information, please see the Whitespace and line-breaking characters in Python and big tutorial.

`str_linebreaks_without_crlf`

Equivalent to str_linebreaks without '\r\n'.

`str_whitespace`

A tuple of str objects, representing every whitespace character recognized by the Python str object. Identical to whitespace.

Useful as a separator argument for big functions that accept one, e.g. the big "multi-" family of functions.

Also contains '\r\n'. See the tutorial section on The Unix, Mac, and DOS linebreak conventions for more.

For more information, please see the Whitespace and line-breaking characters in Python and big tutorial.

`str_whitespace_without_crlf`

Equivalent to str_whitespace without '\r\n'.

`strip_indents(lines, *, tab_width=8, linebreaks=linebreaks)`

Takes an iterable of lines, with or without linebreaks; strips the leading whitespace from each line and tracks the indent level. Yields 2-tuples of (depth, lstripped_line).

depth is an integer, the ordinal number of times the lines were indented to reach the current indent. Text at the leftmost column is at depth 0; if the line was indented three times, depth will be 3.

Uses an intentionally simple algorithm. Only understands tab and space characters as indent characters. Internally converts tabs to spaces for consistency, using the tab_width passed in.

Text can only dedent out to a previous indent. Raises IndentationError if there's an illegal dedent.

Blank lines and empty lines have the indent level of the next non-blank line, or 0 if there are no subsequent non-blank lines. If the line contains only whitespace, any trailing characters found in linebreaks will be preserved. Pass in None or an empty sequence for linebreaks to suppress this.

`strip_line_comments(lines, line_comment_markers, *, escape='\\', quotes=(), multiline_quotes=(), linebreaks=linebreaks)`

Strips line comments from an iterable of lines.

Line comments are substrings beginning with a special marker that mean the rest of the line should be ignored. strip_line_comments truncates each line at the beginning of the leftmost line comment marker and yields the result. If the line doesn't contain any unquoted comment markers, it's yielded unchanged.

line_comment_markers should be an iterable of strings denoting line comment markers (e.g. ['#'] or ['//']).

If quotes is specified, it must be an iterable of quote marker strings. strip_line_comments will parse the line using split_quoted_strings and ignore comment characters inside quoted strings. Quoted strings may not span lines; if a line ends with an unterminated quoted string, strip_line_comments will raise a SyntaxError.

If multiline_quotes is specified, it must be an iterable of quote marker strings. Quoted strings enclosed in multiline quotes may span multiple lines. There must be no quote markers in common between quotes and multiline_quotes.

escape is a string used to escape quote markers inside quoted strings, as per backslash inside strings in Python. The default is '\\'.

If lines end with linebreak characters, they will be preserved even when a comment is stripped.

`unicode_linebreaks`

A tuple of str objects, representing every line-breaking whitespace character defined by Unicode.

Useful as a separator argument for big functions that accept one, e.g. the big "multi-" family of functions.

Also contains '\r\n'. See the tutorial section on The Unix, Mac, and DOS linebreak conventions for more.

For more information, please see the Whitespace and line-breaking characters in Python and big tutorial.

`unicode_linebreaks_without_crlf`

Equivalent to unicode_linebreaks without '\r\n'.

`unicode_whitespace`

A tuple of str objects, representing every whitespace character defined by Unicode.

Useful as a separator argument for big functions that accept one, e.g. the big "multi-" family of functions.

Also contains '\r\n'. See the tutorial section on The Unix, Mac, and DOS linebreak conventions for more.

For more information, please see the Whitespace and line-breaking characters in Python and big tutorial.

`unicode_whitespace_without_crlf`

Equivalent to unicode_whitespace without '\r\n'.

`whitespace`

A tuple of str objects, representing every whitespace character recognized by the Python str object. Identical to str_whitespace.

Useful as a separator argument for big functions that accept one, e.g. the big "multi-" family of functions.

Also contains '\r\n'. See the tutorial section on The Unix, Mac, and DOS linebreak conventions for more.

For more information, please see the Whitespace and line-breaking characters in Python and big tutorial.

`whitespace_without_crlf`

Equivalent to whitespace without '\r\n'.

`wrap_words(words, margin=79, *, two_spaces=True)`

Combines words into lines and returns the result as a string. Similar to textwrap.wrap.

words should be an iterator yielding str or bytes strings, and these strings should already be split at word boundaries. Here's an example of a valid argument for words:

"this is an example of text split at word boundaries".split()

A single '\n' indicates a line break. If you want a paragraph break, embed two '\n' characters in a row.

margin specifies the maximum length of each line. The length of every line will be less than or equal to margin, unless the length of an individual element inside words is greater than margin.

If two_spaces is true, elements from words that end in sentence-ending punctuation ('.', '?', and '!') will be followed by two spaces, not one.

Elements in words are not modified; any leading or trailing whitespace will be preserved. You can use this to preserve whitespace where necessary, like in code examples.

For more information, see the tutorial on Word wrapping and formatting.

`big.tokens`

Functions and constants for working with Python's tokenizer.

Token constants

big.tokens defines a TOKEN_<n> constant for every token that could exist in any supported version of Python. If a token isn't defined in the current version, its value is set to -1, an invalid token value that won't match any tokens.

This lets you write version-independent code like:

if token.type == big.tokens.TOKEN_FSTRING_START:
   ...

In Python versions where FSTRING_START doesn't exist, TOKEN_FSTRING_START is -1 and the condition will never be true.

`generate_tokens(s)`

A convenient wrapper around tokenize.generate_tokens.

This function takes a str (or big.string) and handles the readline interface required by tokenize.generate_tokens internally.

If the argument is a big.string, the string values in the yielded TokenInfo objects will be big.string slices from the original string, preserving line and column information.

`big.time`

Functions for working with time. Currently deals specifically with timestamps. The time functions in big are designed to make it easy to use best practices.

`date_ensure_timezone(d, timezone)`

Ensures that a datetime.date object has a timezone set.

If d has a timezone set, returns d. Otherwise, returns a new datetime.date object equivalent to d with its tzinfo set to timezone.

`date_set_timezone(d, timezone)`

Returns a new datetime.date object identical to d but with its tzinfo set to timezone.

`datetime_ensure_timezone(d, timezone)`

Ensures that a datetime.datetime object has a timezone set.

If d has a timezone set, returns d. Otherwise, creates a new datetime.datetime object equivalent to d with its tzinfo set to timezone.

`datetime_set_timezone(d, timezone)`

Returns a new datetime.datetime object identical to d but with its tzinfo set to timezone.

`parse_timestamp_3339Z(s, *, timezone=None)`

Parses a timestamp string returned by timestamp_3339Z. Returns a datetime.datetime object.

timezone is an optional default timezone, and should be a datetime.tzinfo object (or None). If provided, and the time represented in the string doesn't specify a timezone, the tzinfo attribute of the returned object will be explicitly set to timezone.

parse_timestamp_3339Z depends on the python-dateutil package. If python-dateutil is unavailable, parse_timestamp_3339Z will also be unavailable.

`timestamp_3339Z(t=None, want_microseconds=None)`

Return a timestamp string in RFC 3339 format, in the UTC time zone. This format is intended for computer-parsable timestamps; for human-readable timestamps, use timestamp_human().

Example timestamp: '2021-05-25T06:46:35.425327Z'

t may be one of several types:

If t is None, timestamp_3339Z uses the current time in UTC.
If t is an int or a float, it's interpreted as seconds since the epoch in the UTC time zone.
If t is a time.struct_time object or datetime.datetime object, and it's not in UTC, it's converted to UTC. (Technically, time.struct_time objects are converted to GMT, using time.gmtime. Sorry, pedants!)

If want_microseconds is true, the timestamp ends with microseconds, represented as a period and six digits between the seconds and the 'Z'. If want_microseconds is false, the timestamp will not include this text. If want_microseconds is None (the default), the timestamp ends with microseconds if the type of t can represent fractional seconds: a float, a datetime object, or the value None.

`timestamp_human(t=None, want_microseconds=None, *, tzinfo=None)`

Return a timestamp string formatted in a pleasing way for the local timezone (by default). This format is intended for human readability; for computer-parsable time, use timestamp_3339Z().

Example timestamp: "2021/05/24 23:42:49.099437"

t can be one of several types:

If t is None, timestamp_human uses the current local time.
If t is an int or float, it's interpreted as seconds since the epoch.
If t is a time.struct_time, it's converted to a datetime.datetime object.
If t is a datetime.datetime object, it's used directly

If want_microseconds is true, the timestamp will end with the microseconds, represented as ".######". If want_microseconds is false, the timestamp will not include the microseconds.

If tzinfo is None (the default), the time is converted to the local timezone. If tzinfo is a datetime.timezone object, the time is converted to this timezone. The timezone is printed at the end of the string.

0.13 update: Added tzinfo parameter, and added the timezone to the end of the string.

`big.types`

New types for big. Currently contains string and linked_list.

`string`

string is a subclass of str that knows its own line number, column number, and source. Every operation that returns a substring returns a big.string that preserves this information.

See the The big string tutorial for an introduction and examples.

`string(s='', *, source=None, line_number=1, column_number=1, first_column_number=1, tab_width=8)`

A subclass of str that maintains line, column, and offset information.

string is a drop-in replacement for Python's str. It implements every str method; every operation that returns a substring returns a big.string that knows its own line and column information. For documentation of the standard str methods, see the Python documentation for str.

Keyword-only parameters to the constructor:

source — A human-readable string describing where this string came from (e.g. a filename). Included in where.
line_number — The line number of the first character. Default is 1.
column_number — The column number of the first character. Default is 1.
first_column_number — The column number to reset to after a linebreak. Default is 1.
tab_width — The distance between tab columns, used when computing column numbers. Default is 8.

Read-only properties:

line_number — The line number of this string.
column_number — The column number of this string.
source — The source string passed to the constructor.
origin — The original big.string this string was sliced from.
offset — The index of the first character of this string within origin.
first_column_number — The column number reset to after linebreaks.
tab_width — The tab width used for column calculations.
where — A human-readable location string for error messages, in the format "<source> line <n> column <n>" (or without the source if none was specified).

If you pass a big.string into Python modules implemented in C, the returned substrings will be plain str objects. big provides wrappers for two of these, drop-in replacements where the returned substrings will be big.string slices of the original string:

string.generate_tokens, a wrapper for tokenize.generate_tokens, and
string.compile, a wrapper for re.compile.

See the The big string tutorial for more.

`string.bisect(index)`

Splits the string at index. Returns a tuple of two strings: (string[:index], string[index:]).

`string.cat(*strings)`

Class method. Concatenates the str or big.string objects passed in. Roughly equivalent to big.string('').join(). Always returns a big.string.

`string.compile(flags=0)`

Returns a Pattern compiled from this string. Equivalent to re.compile(self, flags). All methods on the Pattern, and method calls on objects it returns, return big.string slices of the original string as appropriate.

`string.generate_tokens()`

Wraps tokenize.generate_tokens, preserving big.string slices in the yielded TokenInfo objects. Equivalent to calling big.tokens.generate_tokens with this string.

`string.partition(sep, , count=1)` and `string.rpartition(sep, , count=1)`

Behaves like str.partition and str.rpartition, but adds one feature: a count= parameter that specifies how many times to split the string. count must be an "index" with a value 0 or higher. The returned tuple will be length (count * 2) + 1.

`string_context`

The value returned by the string.context property.

str(s.context) evaluates to a string that represents the "context" of s--where s was sliced from in the larger string object. For example:

    line = "elif attempt(blast):"
    s = line.partition('(')[2][:5] # s is 'blast'
    print(s.context)

would print:

    elif attempt(blast):
                 ^^^^^

Note that str(s.context) only shows one line of context; if s is a multi-line string, this will only show the first line. If you want to show all lines, use s.context.all instead, see below.

string_context supports the following attributes:

parts is a tuple of "context line" tuples representing the lines of str(context). Each "context line" tuple matches (before, span, after, linebreak), and has named accessors for these values. These values are strings; joining all the strings of all the tuples produces str(context).
all is like str(context), but contains context lines for all lines, in case context contains linebreaks.
all_parts is like parts, but contains the parts for all the lines of all.

In addition, it provides many of the string properties, like where, origin, line_number, etc. These are the same as the string object the context was taken from.

`linked_list`

linked_list is a doubly-linked list with an interface that's a superset of both list and collections.deque. It also supports extracting and merging ranges of nodes with cut and splice, and its iterators behave like database cursors.

See the The big linked_list tutorial for an introduction and examples.

`linked_list(iterable=(), *, lock=None)`

A doubly-linked list.

iterable provides initial values. If lock is True, the list uses an internal threading.Lock for thread safety. If lock is a lock object, that lock is used (but the list cannot be pickled). If lock is False or None, no locking is used.

linked_list has explicit "head" and "tail" sentinel nodes. Iterating yields values between head and tail. linked_list supports len, indexing, slicing, in, ==, bool, pickling, and reversed.

See the The big linked_list tutorial for more.

`linked_list.append(object)`

Appends object to the end of the linked list.

`linked_list.clear()`

Removes all values from the linked list.

`linked_list.copy(*, lock=None)`

Returns a shallow copy of the linked list. lock is passed to the new list's constructor.

`linked_list.count(value)`

Returns the number of occurrences of value in the linked list.

`linked_list.cut(start=None, stop=None, *, lock=None)`

Cuts nodes from this list and returns them in a new linked_list.

start and stop, if specified, must be iterators over this list. If start is None, it defaults to the first node after head. (If the list is empty, this will be tail.) If stop is None, it defaults to tail. The range of nodes cut includes start but excludes stop. start must not point to a node after stop.

lock is passed to the new list's constructor; if None, the new list reuses this list's lock parameter.

If any nodes are cut, the start and stop iterators will still point at the same nodes--which means start will have been moved to the new list.

Raises SpecialNodeError if start points to head, because you can't cut the head of the list.

start and stop may be reverse iterators; however, the linked list resulting from a cut will have the elements in forward order. If either start or stop is a reverse iterator, then they must both be reverse iterators (or None), and:

start defaults to the last node before tail,
stop defaults to head,
start must not point to a node after stop, and
raises SpecialNodeError if start points to head.

See the The big linked_list tutorial for more.

`linked_list.extend(iterable)`

Extends the linked list by appending elements from iterable.

`linked_list.extendleft(iterable)`

Prepends the elements from iterable to the linked list, in reverse order. Provided for collections.deque compatibility.

`linked_list.find(value)`

Returns an iterator pointing at the first occurrence of value, or None if value does not appear.

`linked_list.index(value, start=0, stop=sys.maxsize)`

Returns the first index of value. Raises ValueError if value is not present. start and stop limit the search to a subsequence.

`linked_list.insert(index, object)`

Inserts object before index.

`linked_list.match(predicate)`

Returns an iterator pointing at the first value for which predicate(value) returns a true value, or None if no such value exists.

`linked_list.move(where, start=None, stop=None)`

Moves a range of nodes to after where.

start and stop, if specified, must be iterators over this list. If start is None, it defaults to the first node after head. (If the list is empty, this will be tail.) If stop is None, it defaults to tail. The range of nodes moved includes start but excludes stop. start must not point to a node after stop. where must be an iterator over this list. where must not point to a node being moved, or tail.

Raises SpecialNodeError if start points to head, because you can't move the head of the list.

start and stop may be reverse iterators. If either start or stop is a reverse iterator, then they must both be reverse iterators (or None), and:

start defaults to the last node before tail,
stop defaults to head,
start must not point to a node after stop, and
raises SpecialNodeError if start points to head.

`linked_list.pop(index=-1)`

Removes and returns the value at index (default last).

`linked_list.prepend(object)`

Prepends object to the beginning of the linked list.

`linked_list.rcount(value)`

Returns the number of occurrences of value in the linked list. Equivalent to linked_list.count but searches in reverse order.

`linked_list.rcut(start=None, stop=None, *, lock=None)`

Like linked_list.cut, except all directions are reversed: start must not point to a node before stop, and the cut range is from stop forwards to start. The returned list is still in forwards order.

`linked_list.remove(value, default=undefined)`

Removes and returns the first occurrence of value. If value does not appear, returns default if specified, otherwise raises ValueError.

`linked_list.reverse()`

Reverses all nodes in the linked list, including special nodes.

`linked_list.rextend(iterable)`

Extends the linked list by prepending elements from iterable, in forwards order.

`linked_list.rfind(value)`

Returns an iterator pointing at the last occurrence of value, or None if value does not appear.

`linked_list.rmatch(predicate)`

Returns an iterator pointing at the last value for which predicate(value) returns a true value, or None if no such value exists.

`linked_list.rmove(where, start=None, stop=None)`

Like linked_list.move, but start must come after stop, the nodes are inserted before where, and where cannot be head. All other behaviors are unchanged (e.g. start is inclusive, stop is exclusive).

`linked_list.rpop(index=0)`

Removes and returns the value at index (default first).

`linked_list.rotate(n)`

Rotates the linked list n steps to the right. If n is negative, rotates left. Provided for collections.deque compatibility.

`linked_list.rremove(value, default=undefined)`

Removes and returns the last occurrence of value. If value does not appear, returns default if specified, otherwise raises ValueError.

`linked_list.rsplice(other, *, where=None)`

Like linked_list.splice, except: if where is None, the nodes are prepended to the list. If where is not None, the nodes are inserted before (rather than after) the node pointed to by where. Raises SpecialNodeError if where is head, because you can't insert nodes before head.

`linked_list.sort(key=None, reverse=False)`

Sorts the linked list in ascending order. Arguments are the same as list.sort. linked_list.sort moves nodes rather than swapping values, so iterators continue to point at the same nodes.

`linked_list.splice(other, *, where=None)`

Moves all nodes from other into this list. other must be a linked_list; after a successful splice, other will be empty.

where must be an iterator over this list, or None. If where is an iterator, the nodes are inserted after the node pointed to by where. If where is None, the nodes are appended. Raises SpecialNodeError if where is tail, because you can't insert nodes after tail.

See the The big linked_list tutorial for more.

`linked_list.tail()`

Returns a forwards iterator pointing at the linked list's tail sentinel node.

`SpecialNodeError`

A LookupError subclass raised when an operation is attempted on a special (sentinel) node that doesn't support it.

`UndefinedIndexError`

An IndexError subclass raised when accessing an undefined index in a linked_list (before head or after tail).

`linked_list_iterator`

Iterates over a linked_list, yielding values in order. Created by calling iter() on a linked_list or by calling linked_list.find() etc.

A linked_list_iterator behaves like a cursor: when it yields a value, it continues pointing at that node until explicitly advanced. Indexing and slicing are relative to the current node, and negative indices access previous nodes (not the end of the list).

linked_list explicitly supports removing nodes while iterating. If the current node is removed, the iterator points at a "special" placeholder node until advanced.

See the The big linked_list tutorial for more.

`linked_list_iterator.after(count=1)`

Returns a new iterator pointing at the node count steps after the current node.

`linked_list_iterator.append(value)`

Appends value immediately after the current node.

`linked_list_iterator.before(count=1)`

Returns a new iterator pointing at the node count steps before the current node.

`linked_list_iterator.copy()`

Returns a copy of the iterator, pointing at the same node in the same linked list.

`linked_list_iterator.count(value)`

Returns the number of occurrences of value between the current node and tail.

`linked_list_iterator.cut(stop=None, *, lock=None)`

Bisects the list at the current node. Cuts nodes starting at the current node up to (but not including) stop, and returns them as a new linked_list.

If stop is None, all subsequent nodes are cut (the original list gets a new tail). lock is passed to the new list's constructor.

See the The big linked_list tutorial for more.

`linked_list_iterator.exhaust()`

Advances the iterator to point to tail.

`linked_list_iterator.extend(iterable)`

Extends the list by appending elements from iterable after the current node.

`linked_list_iterator.find(value)`

Returns an iterator pointing at the nearest next occurrence of value, or None if not found before tail.

`linked_list_iterator.insert(index, object)`

Inserts object after the index'th node relative to the current position.

`linked_list_iterator.is_special()`

Returns True if the iterator is pointing at a special (sentinel) node, False otherwise.

`linked_list_iterator.linked_list`

Returns the linked_list this iterator belongs to.

`linked_list_iterator.match(predicate)`

Returns an iterator pointing at the nearest next value for which predicate(value) returns a true value, or None if no such value exists before tail.

`linked_list_iterator.move(where, stop=None)`

Moves nodes from the current node up to (but not including) stop to after where. If stop is None, the moved range extends to tail.

`linked_list_iterator.next(default=undefined, *, count=1)`

Advances the iterator by count steps and returns the value there. If the iterator is exhausted, returns default if specified, otherwise raises StopIteration.

`linked_list_iterator.pop(index=0)`

Removes and returns the value at index relative to the current position. If index is 0 (the default), removes the current node and the iterator advances backwards to the previous node.

`linked_list_iterator.prepend(value)`

Inserts value immediately before the current node.

`linked_list_iterator.previous(default=undefined, *, count=1)`

Advances the iterator backwards by count steps and returns the value there. If the iterator reaches head, returns default if specified, otherwise raises StopIteration.

`linked_list_iterator.rcount(value)`

Returns the number of occurrences of value between the current node and head.

`linked_list_iterator.rcut(stop=None, *, lock=None)`

Like linked_list_iterator.cut, except it cuts backwards: nodes from stop up to and including the current node. If stop is None, all preceding nodes are cut.

`linked_list_iterator.remove(value, default=undefined)`

Removes the nearest next occurrence of value. Returns default if specified and value is not found, otherwise raises ValueError.

`linked_list_iterator.reset()`

Resets the iterator to point to head.

`linked_list_iterator.rextend(iterable)`

Extends the list by prepending elements from iterable before the current node, preserving their order.

`linked_list_iterator.rfind(value)`

Returns an iterator pointing at the nearest previous occurrence of value, or None if not found before head.

`linked_list_iterator.rmatch(predicate)`

Returns an iterator pointing at the nearest previous value for which predicate(value) returns a true value, or None if no such value exists before head.

`linked_list_iterator.rmove(where, stop=None)`

Like linked_list_iterator.move, but moves the range to before where and walks the range in the reverse direction.

`linked_list_iterator.rpop(index=0)`

Removes and returns the value at index relative to the current position (default 0).

`linked_list_iterator.rremove(value, default=undefined)`

Removes the nearest previous occurrence of value. Returns default if specified and value is not found, otherwise raises ValueError.

`linked_list_iterator.rsplice(other)`

Removes all nodes from other and inserts them immediately before the current node.

`linked_list_iterator.rtruncate()`

Truncates the linked list at the current node, discarding the current node and all previous nodes. After this operation, the iterator points to head.

`linked_list_iterator.special()`

Returns the special attribute of the current node: None for normal nodes, or a string ('head', 'tail', or 'special') for sentinel nodes.

`linked_list_iterator.splice(other)`

Removes all nodes from other and inserts them immediately after the current node.

`linked_list_iterator.truncate()`

Truncates the linked list at the current node, discarding the current node and all subsequent nodes. After this operation, the iterator points to tail.

`linked_list_reverse_iterator`

Iterates over a linked_list in reverse order, yielding values from tail towards head. Created by calling reversed() on a linked_list or on a linked_list_iterator.

Provides the same interface as linked_list_iterator, but with all directions reversed: next advances towards head, previous advances towards tail, and so on.

See the The big linked_list tutorial for more.

`linked_list_reverse_iterator.after(count=1)`

Behaves like linked_list_iterator.before.

`linked_list_reverse_iterator.append(value)`

Behaves like linked_list_iterator.prepend.

`linked_list_reverse_iterator.before(count=1)`

Behaves like linked_list_iterator.after.

`linked_list_reverse_iterator.copy()`

Returns a copy of the reverse iterator, pointing at the same node.

`linked_list_reverse_iterator.count(value)`

Behaves like linked_list_iterator.rcount.

`linked_list_reverse_iterator.cut(stop=None, *, lock=None)`

Behaves like linked_list_iterator.rcut.

`linked_list_reverse_iterator.exhaust()`

Advances the reverse iterator to point to head.

`linked_list_reverse_iterator.extend(iterable)`

Behaves like linked_list_iterator.rextend.

`linked_list_reverse_iterator.find(value)`

Behaves like linked_list_iterator.rfind.

`linked_list_reverse_iterator.insert(index, object)`

Inserts object relative to the current position, with reversed index direction.

`linked_list_reverse_iterator.is_special()`

Behaves like linked_list_iterator.is_special.

`linked_list_reverse_iterator.linked_list`

Returns the linked_list this iterator belongs to.

`linked_list_reverse_iterator.match(predicate)`

Behaves like linked_list_iterator.rmatch.

`linked_list_reverse_iterator.move(where, stop=None)`

Behaves like linked_list_iterator.rmove.

`linked_list_reverse_iterator.next(default=undefined, *, count=1)`

Behaves like linked_list_iterator.previous.

`linked_list_reverse_iterator.pop(index=0)`

Behaves like linked_list_iterator.rpop.

`linked_list_reverse_iterator.prepend(value)`

Behaves like linked_list_iterator.append.

`linked_list_reverse_iterator.previous(default=undefined, *, count=1)`

Behaves like linked_list_iterator.next.

`linked_list_reverse_iterator.rcount(value)`

Behaves like linked_list_iterator.count.

`linked_list_reverse_iterator.rcut(stop=None, *, lock=None)`

Behaves like linked_list_iterator.cut.

`linked_list_reverse_iterator.remove(value, default=undefined)`

Behaves like linked_list_iterator.rremove.

`linked_list_reverse_iterator.reset()`

Resets the reverse iterator to point to tail.

`linked_list_reverse_iterator.rextend(iterable)`

Behaves like linked_list_iterator.extend.

`linked_list_reverse_iterator.rfind(value)`

Behaves like linked_list_iterator.find.

`linked_list_reverse_iterator.rmatch(predicate)`

Behaves like linked_list_iterator.match.

`linked_list_reverse_iterator.rmove(where, stop=None)`

Behaves like linked_list_iterator.move.

`linked_list_reverse_iterator.rpop(index=0)`

Behaves like linked_list_iterator.pop.

`linked_list_reverse_iterator.rremove(value, default=undefined)`

Behaves like linked_list_iterator.remove.

`linked_list_reverse_iterator.rsplice(other)`

Behaves like linked_list_iterator.splice.

`linked_list_reverse_iterator.rtruncate()`

Behaves like linked_list_iterator.truncate.

`linked_list_reverse_iterator.special()`

Behaves like linked_list_iterator.special.

`linked_list_reverse_iterator.splice(other)`

Behaves like linked_list_iterator.rsplice.

`linked_list_reverse_iterator.truncate()`

Behaves like linked_list_iterator.rtruncate.

`big.version`

Support for version metadata objects.

`Version(s=None, *, epoch=None, release=None, release_level=None, serial=None, post=None, dev=None, local=None)`

Constructs a Version object, which represents a version number.

You may define the version one of two ways:

by passing in a version string to the s positional parameter. Example: Version("1.3.24rc37")
by passing in keyword-only arguments setting the specific fields of the version. Example: Version(release=(1, 3, 24), release_level="rc", serial=37)

big's Version objects conform to the PEP 440 version scheme, parsing version strings using that PEP's official regular expression.

Version objects support the following features:

They're immutable once constructed.
They support the following read-only properties:
- epoch
- release
- major (release[0])
- minor (a safe version of release[1])
- micro (a safe version of release[2])
- release_level
- serial
- post
- dev
- local
Version objects are hashable.
Version objects support ordering and comparison; you can ask if two Version objects are equal, or if one is less than the other.
str() on a Version object returns a normalized version string for that version. repr() on a Version object returns a string that, if eval'd, reconstructs that object.
Version objects normalize themselves at initialization time:
- Leading zeroes on version numbers are stripped.
- Trailing zeroes in release (and trailing .0 strings in the equivalent part of a version string) are stripped.
- Abbreviations and alternate names for release_level are normalized.
Don't tell anybody, but, you can also pass a sys.version_info object or a packaging.Version object into the constructor instead of a version string. Shh!

When constructing a Version by passing in a string s, the string must conform to this scheme, where square brackets denote optional substrings and names in angle brackets represent parameterized substrings:

[<epoch>!]<major>(.<minor_etc>)*[<release_level>[<serial>]][.post<post>][.dev<dev>][+<local>]

All fields should be non-negative integers except for:

<major>(.<minor_etc>)* is meant to connote a conventional dotted version number, like 1.2 or 1.5.3.8. This section can contain only numeric digits and periods ('.'). You may have as few or as many periods as you prefer. Trailing .0 entries will be stripped.
<release_level> can only be be one of the following strings:
- a, meaning an alpha release,
- b, meaning a beta release, or
- rc, meaning a release candidate. For a final release, skip the release_level (and the serial).
<local> represents an arbitrary sequence of alphanumeric characters punctuated by periods.

Alternatively, you can construct a Version object by passing in these keyword-only arguments:

epoch

A non-negative int or None. Represents an "epoch" of version numbers. A version number with a higher "epoch" is always a later release, regardless of all other fields.

release

A tuple containing one or more non-negative integers. Represents the conventional part of the version number; the version string 1.3.8 would translate to Version(release=(1, 3, 8)).

release_level

A str or None. If it's a str, it must be one of the following strings:

a, meaning an alpha release,
b, meaning a beta release, or
rc, meaning a release candidate.

serial

A non-negative int or None. Represents how many releases there have been at this release_level. (The name is taken from Python's sys.version_info.)

post

A non-negative int or None. Represents "post-releases", extremely minor releases made after a release:

Version(release=(1, 3, 5)) < Version(release=(1, 3, 5), post=1)

dev

A non-negative int or None. Represents an under-development release. Higher dev numbers represent later releases, but any release where dev is not None comes before any release where dev is None. In other words:

Version(release=(1, 3, 5), dev=34) < Version(release=(1, 3, 5), dev=35)
Version(release=(1, 3, 5), dev=35) < Version(release=(1, 3, 5))

local

A tuple of one or more str objects containing only one or more alphanumeric characters or None. Represents a purely local version number, allowing for minor build and patch differences but with no API or ABI changes.

`Version.format(s)`

Returns a formatted version of s, substituting attributes from self into s using str.format_map.

For example,

    Version("1.3.5").format('{major}.{minor}')

returns the string '1.3'.

Tutorials

The big `string`

Python's tokenize and re (regular expression) modules both had to solve an API problem. In both cases, you submit a large string to them, and they split it up and return little substrings--tiny little slices of the big string. Often, the user needs to know where those little slices came from. How do you communicate that?

What tokenize and re did was add extra information accompanying the string. But they took different--and incompatible--approaches. They both represent "where" the little bitty strings came from differently:

tokenize.tokenize returns a TokenInfo object containing the line and column numbers of the string it contains).
search and match methods on a compiled regular expression return a Match object which tells you the index where the string started in the original string.

This is sufficient--barely. It's also fragile. What if you further subdivide the string? What if you join the text with the antecedent or subsequent text from the original? Now you have to clumsily track these offsets yourself. And if you want line and column information, the re module's Match object is of no help.

And what if you're parsing your text yourself, rather than using tokenize or re? If you split up a string into lines using the splitlines method on a string, you have to track the line numbers yourself. Worse yet, if you split by lines, then use re to subdivide the string, you have to mate your offset tracking with the re.Match object's tracking. What a pain!

big's string object solves all that. It's a drop-in replacement for Python's str object, and in fact is a subclass of str. What it gives you: any time you extract a substring of a string object, the substring knows its own offset, line number, and column number relative to the original string. You don't need to figure it out yourself, and you don't need to store the information separately using some fragile external representation. Any time you have a string object, you automatically know where it came from. (You can even specify a "source" for the text--the original filename or what have you--and the string object will retain that too.)

This makes producing syntax error messages effortless. If s is a string object, and represents a syntax error because it was an unexpected token in the middle of a text you're parsing, you can simply write this:

raise SyntaxError(f'{s.where}: unexpected token {s}')

where is a property, an automatically-formatted string containing the line and column information for the string. And if you specified a "source", it contains that too. For example, if you initialized the string with "source" set to /home/larry/myscript.py, and s was the token whule (whoops! mistyped while!), from line number 12, column number 15, the text of the exception would read:

"/home/larry/myscript.py" line 12 column 15: unexpected token 'whule'

Tomorrow's methods, today

big supports older versions of Python; as of this writing it supports all the way back to 3.6. (The Python core development team dropped support for 3.6 several years ago!)

The string object supports all the methods of the str object. At the moment there's a new str method as of version 3.7, isascii. Rather than only provide that in 3.7+, string makes that available in 3.6 too.

Naughty modules not honoring the subclass

It was important that string not only be a drop-in replacement for str. The only way for that to work: it had to literally be a subclass of str. There's a lot of code that says

if isinstance(obj, str):

and if string objects failed that test they'd break code.

This has an unfortunate side-effect. CPython ships with modules in its standard library written in C that check to see "is this object a str object?" And if the object passes that test, they use low-level C API calls on the str object to interact with it. The problem is, these low-level C API calls ignore the fact that this is a subclass of str, and they sidestep the overloaded behaviors of the string object. This means that, for example, when they extract a substring from the object, they don't get a string object preserving the offsets, they just get a plain old str object.

Fixing this in CPython would be worthwhile, but it'd be a lot of work and it would only benefit the future. We want to solve our problem today. So big provides workarounds for the two worst offenders: re and tokenize. big's string object has a compile method that is a drop-in replacement for re.compile, and all the methods you call on it will return string objects instead of str objects. The string object also has a method called generate_tokens that produces the same output as tokenize.generate_tokens, except (of course!) all the strings returned in its TokenInfo objects are string objects.

Unfortunately, there's one more wrinkle. The objects returned by CPython's re module don't let you instantitate them, nor subclass them. They deliberately set an internal flag that means "Python code is not permitted to subclass this class". This means it's impossible for String.compile to return objects that pass isinstance tests. String.compile returns a Pattern object, but it's not an instance of re.Pattern, and isinstance tests will fail. big was forced to reimplement these objects, and we ensure they behave identically to the originals, but CPython makes this facet of incompatibility unfixable.

The big `linked_list`

Background

A linked list is a fundamental data structure in computer science, second only perhaps to the array and the record. And yet Python has never officially shipped with a linked list!

There is a linked list hidden in the standard library of CPython; collections.deque is implemented internally using a linked list. So it's possible to use a deque where you'd want a real linked list--like, a use case where you frequently insert and remove values in the middle of the list. This would have better performance than doing it with, say, the classic Python list, where inserts and removals from the middle of the list are an O(n) operation. However, the deque API makes it inconvenient to use as a linked list.

In 2025, I wanted a linked list for a project. I surveyed the linked lists available for Python at the time, decided I didn't want to use any of them--so I wrote my own. Now you get to use it too!

Overview

big's linked_list itself behaves externally like a list or a deque; you insert/append/prepend values to the list, and it stores them in order and manages the storage.

Where big's linked_list shines is in its iterators. linked_list iterators are more like "database cursors"; they act like a moveable virtual head of the list, centered on any value you like.

Also, unlike Python's other data structures, linked_list explicitly supports modifying the list during iteration. You can have as many iterators iterating over a list as you like, and you can add or remove nodes anywhere to your heart's content.

In addition, linked_list supports thread-safety through automatic internal locking.

Implementation details

Internally a big linked_list is a traditional doubly-linked-list. The list is stored in a series of nodes; each node contains forwards and backwards references, to the next and previous nodes respectively, as well as a reference to your value. This classic design makes its performance predictable: inserting and removing elements anywhere in the list is O(1), whereas accessing elements by index is O(n).

There are acutally two types of node in a big linked_list: "data" nodes, which store a value, and "special" nodes, which don't store a value. Why are these "special" nodes needed? Several reasons. First, linked_list makes a design choice that's uncommon but not exactly rare for linked lists: the "head" and "tail" nodes are "special" nodes in the linked list. When you create a new linked_list object, it contains two nodes, not zero: the newly-created list already contains "head" and "tail" nodes. This makes for a nice implementation; every insert and delete simply updates four references, rather than needing lots of "if we're pointed at the head" special cases all over the place.

There's a third type of "special" node: a deleted node. If an iterator is pointing at a data node containing a value X, and you remove X from the linked list, the data is removed but the node stays in place. That node is demoted to a "special" node--again, "data" nodes store a reference to a value, "special" nodes don't. This change is harmless; the iterator can continue pointing to it indefinitely, or can iterate forward or backwards without difficulty. The fact that the node was demoted is invisible to the user of the iterator if all they're doing is conventional iteration. However, this implementation choice will have ramifications for the "iterators as database cursors" APIs, as we'll see shortly.

(In case you're wondering: once the last iterator departs a "special" node resulting from a deleted value, linked_list removes the node.)

`linked_list` methods

linked_list provides a superset of the union of the APIs of list and collections.deque. Every method call supported by both list and deque is supported by linked_list, and you can read the documentation for those types to see the basics.

However, there are also some important changes. First and foremost, for list and deque methods that return an index into the list, the linked_list equivalent returns an iterator. This is a superior API, due to the "database cursor" features of linked_list iterators. It's also better for performance, as this reduces accessing values by index, which is O[n] on linked_list.

In addition, linked_list contains many "reversed" versions of methods. These are named by taking the original method name and prepending it with r. For example:

extend is complemented with rextend, which inserts the values from the iterable in front of the head of the list in forwards order.
find is complemented by rfind, which searches for a value starting at the end of the list and searching backwards.

linked_list also supports many of Python's "magic methods":

__add__: t + x returns a new list containing the contents of t appended with x; x must be an iterable.
__bool__: bool(t) returns True if t contains any values, or False if t is empty.
__contains__: v in t evaluates to True if the value v is in t.
__copy__: copy.copy(t) returns a shallow copy of the list.
__delitem__: del t[3] will remove the fourth value in t. Also supports slices.
__deepcopy__: copy.deepcopy(t) returns a deep copy of the list.
__eq__ and the other five "rich comparison" methods: t == t2 is true if and only if t and t2 are of the same type and contain the same values in the same order.
__getitem__: t[3] evaluates to the fourth value in t. Also supports slices.
__iadd__: t += x appends the contents of iterable x to t.
__imul__: t *= n results in t containing n copies of its own contents.
__iter__: iter(t) returns a forward iterator over t.
__len__: len(t) returns the number of items in t. If t is empty, this is 0.
__mul__: t * n return a new list containing n copies of the contents of t.
__reversed__: reversed(t) returns a reverse iterator over t.
__repr__: repr(t) produces a custom repr showing the current contents of the list.
__setitem__: t[3] = v will overwrite the fourth value in t with v. Also supports slices.

Finally, linked_list supports methods that lets you move nodes directly from one list to another, rather than inserting new nodes. If you're moving lots of nodes, this can be a huge performance win. The relevant methods:

cut lets you specify a range of nodes to remove from a linked_list. You can specify the start and stop for the range of nodes to cut, as iterators. The nodes are removed from the linked list, and returned in their own new linked list.
splice lets you move all the nodes of one linked_list into another. After splicing linked list A into linked list B, A will be empty, and B will contain all of A's nodes, in order.
rcut and rsplice are "reversed" versions of cut and splice.

`linked_list` iterators

While developing linked_list, it occured to me: the usual use case for a linked list involves an arbitrarily-long sequence of data, which you iterate over and process. For example, compilers generally represent the program being compiled as a linked list of "basic blocks".

When using a linked list for these sorts of use cases, you generally operate on a pointer to the linked list node under current consideration. In Python parlance, you iterate over the list, getting a reference to each node in the list in turn. You perform your computation on that node, then iterate to the next one.

However, you often want to modify the list while you're doing this. You may want to remove the node, or insert new nodes, or both--replace the node with something else. But idiomatic Python iterators don't let you do anything like that. All they know how to do is "advance to the next value and yield it". This was a genius design choice for Python, but for our linked list it's simply not enough.

linked_list solves this by making its iterators far more powerful. One way of describing this is like a database cursors: a linked_list iterator points at a value (or "row"), and lets you modify the list (or "table") relative to that value. I think of it more like a moveable virtual list "head"; the iterator points at a value, and provides APIs that let it behave like a linked_list pointed at that value. linked_list iterators provide nearly the entire API that linked_list itself provides, though modified to make sense given the context of pointing at any arbitrary node in the list.

Indexing

Iterators support all operations you can perform by indexing into a linked list; you can get, set, and delete values.

However, the meaning of the index is slightly different for an iterator. Negative indices don't start at the end and work backwards; instead, they start at the current node and work backwards. If t is a linked list containing range(5), and it is an iterator pointing at value 2, indexing would look like this:

                            it
                            |
                            v
[head] <-> [0] <-> [1] <-> [2] <-> [3] <-> [4] <-> [tail]
        it[-2]  it[-1]   it[0]   it[1]   it[2]

If you advanced it once, so it pointed to the value 3, indexing would now look like this:

                                    it
                                    |
                                    v
[head] <-> [0] <-> [1] <-> [2] <-> [3] <-> [4] <-> [tail]
        it[-3]  it[-2]  it[-1]   it[0]   it[1]

Indexing into or past the "head" and "tail" nodes raises an IndexError.

You can also use slices, e.g. it[-3:5:2]. There are two important differences from slicing into list or deque objects:

First, negative indices in slices work like negative indices normally, retreating backwards into the list.
Second, slices into linked_list iterators don't clamp for you. Indexing into or past the "head" and "tail" nodes raises an IndexError, rather than silently clamping the indices to a legal range.

Method calls

You can also make method calls on the iterator, to operate on the list starting at the current node. These operations always operate relative to the current node, rather than relative to the beginning (or end) of the list. Also, as a rule, methods that operate on one or more nodes always operate on the current node.

Here are some examples. In these examples, it is always a forwards iterator:

it.pop pops and returns the value the iterator currently points at, then moves the iterator back one node.
it.rpop pops and returns the value the iterator currently points at, then moves the iterator forward one node.
it.append inserts a value after the current node.
it.prepend inserts a value before the current node.
it.find searches for a value, starting at the current node and continuing forwards.
it.rfind searches for a value, starting at the current node and continuing backwards.
it.truncate deletes all values at or after the current node. When it.truncate is done, it will be pointing at "tail", and it[-1] will be unchanged.

Magic methods

linked_list iterators also implements many of the magic methods supported by linked_list. For example, if it is a forward iterator pointing at an arbitrary node:

__bool__: bool(it) returns True if it is not pointed at "tail".
__contains__: v in it evaluates to True if the value v is found at or after it in the list.
__eq__: it == it2 is true if and only if it and it2 point to the same node. Iterators don't support relative comparison (less-than, etc).
__iter__: iter(it) returns a copy of it.
__len__: len(t) returns the number of items at or after it in the list. If it points to "tail", this returns 0.
__reversed__: reversed(t) returns a reverse iterator pointing at the same node as it.

Special nodes

There are two rules that apply to iterators when interacting with special nodes:

Special nodes never have a value.
When an iterator navigates through a linked_list, it automatically skips over special nodes.

Let's see specifically how iterators interact with special nodes. We'll create a new empty linked list, then create an iterator over that linked list, and call next on it twice:

t = LinkedList((1,))
it = iter(t)
value_a = next(it, None)
value_b = next(it, None)

When this is done, value_a will be 1, and value_b will be None. How does this work internally?

Our iterator it started out pointing at the "head" node. When you call next(it, None) the first time, it advances to 1 and returns it. The iterator is now pointing at the node for the value 1. Calling next(it, None) the second time advances to the "tail" node; this would normally raise StopIteration, but the second argument to next is a "default value" it will return instead of raising. So this second call to next(it, None) just returns None. After this second next call, it points to the "tail" node.

Deleted nodes

If an iterator is pointing at a value, and that value is deleted, the node is demoted from a "data" node to a "special" node. The iterator continues to point to it. Consider this example:

t = linked_list([1, 2, 3, 4, 5])
it = t.find(3)
del t[2]

The internal layout of the list and iterator now looks like this:

                               it
                               |
                               v
[head] <-> [1] <-> [2] <-> [special] <-> [4] <-> [5] <-> [tail]

Here it points to a "special" node, where the value 2 used to be. (To be clear: the value of the node isn't the string "special". As mentioned before, special nodes have no value. We just put the word "special" there to annotate that as a special node.)

If you now iterated over the linked list:

for i in t:
   print(i)

you'd see 1, 2, 4, and 5, like you'd expect. The special node is still there, but remember the rule: linked list iterators automatically skip over special nodes.

When you have an iterator pointed at a special node, you can do almost anything you can do with an iterator pointed at a normal node. You can:

navigate, using next or previous or find or rfind or match or rmatch
create new iterators using before or after
insert new values using append or prepend or extend or extendleft
attempt to remove values using remove or rremove

What can't you do when pointing at a special node? Any operation that attempts to interact with the value of the current node will raise SpecialNodeError (a subclass of LookupError). For example:

Evaluating it[0].
Evaluating it[-1:1].
Popping the current value using it.pop() or it.rpop().

If you're worried about whether your iterator is pointing at a special node, you can check the special property. That returns None for a normal node, "head" for the head node, "tail" for the tail node, and "special" for any other special node.

Reverse iterators

linked_list objects also support reverse iteration. You create a reverse iterator by calling reversed on the list. You can also create a reverse iterator by calling reversed on a forwards iterator; this returns a reverse iterator pointing at the same node.

Conceptually, a reverse iterator behaves identically to a forwards iterator, except the reverse iterator "sees" the list backwards. If you have a linked list that looks like this:

[head] <-> [1] <-> [2] <-> [special] <-> [3] <-> [4] <-> [5] <-> [tail]

a reverse iterator would see the list like this:

[tail] <-> [5] <-> [4] <-> [3] <-> [special] <-> [2] <-> [1] <-> [head]

Apart from this behavioral change, reverse iterators behave identically to forwards iterators. They support the exact same APIs with the same arguments.

This makes the behavior of a reverse iterator easy to predict. For example, if fi is a forwards iterator, and ri is a reverse iterator on the same list:

A newly-created reverse iterator points to the "tail" node, and ri.reset() resets ri so it points at the "tail" node again.
A reverse iterator becomes exhausted once it reaches the "head" node. ri.exhaust() moves ri so it points at the "head" node.
ri.append() inserts before the current node, ri.prepend() inserts after the current node. Remember, from the perspective of ri, it's inserting those nodes in the correct places!
ri[1] evaluates to the previous value in the list, and ri[-1] evaluates to the next value in the list.

One thing that doesn't change: when inserting multiple nodes (splice, extend, rextend), the nodes are always inserted in forwards order. Effectively, if fi and ri point to the same node, fi.extend(X) and ri.rextend(X) would do the same thing, and fi.rextend(X) and ri.extend(X) would also do the same thing

Invariants

An iterator pointing at a node will continue to point at that node until it takes action to move to a new node.
If you use an iterator to append a new value, and nobody deletes that value, and you subsequently advance that iterator with next() enough times, the iterator will yield that value.
- If you use an iterator to prepend a new value, and nobody deletes that value, and you subsequently advance that iterator with previous() enough times, the iterator will yield that value.
As a rule, actions on iterators that act on multiple nodes include the node they're pointing at.
iterator[0] always refers to the node the iterator is currently pointing at, even if it's a special node. If the index is non-zero, it skips over special nodes.
You can't ever insert a node before head. You can't ever insert a node after tail.

The big `Log`

tl;dr

Here's the "elevator pitch" for why you want to use Log.

Do you ever do print-style debugging? Of course you do. The big Log makes print-style debugging so much better!

Log automatically prepends each log message with the elapsed time so far, as well as the name of the thread that logged the message.
Want to write to a file instead? Maybe a temporary file with a dynamically-generated filename, so old logs don't get overwritten? Maybe you want to buffer all the output until the program exits, then print it all at once? Or maybe you're done with debugging and you just want to switch all logging off? Log makes it effortless to switch between all these options--or even to log to multiple places at the same time. You can easily write to a temporary file and print to the screen, or any combination of outputs. And the API is so easy, it's even easy to remember.
Log adds some nice formatting options; you can call attention to one message by calling the box method, which draws a box around the value in the output. You can also indent and dedent the log output using the enter and exit methods.
If your program is multithreaded, you've probably observed print statements interleaving. Python's print() doesn't format the entire message then write it to stdout all at once; it does it a bit at a time, including the ending newline. Log only writes complete messages.
By default Log uses threading, which means it spends a lot less time in each call than calling print would. So, less overhead for your program, not to mention less Heisenberg-uncertainty around synchronization due to the time spent in print. (Yes, print is written in C, so its code runs very fast. But logging using a Log in threaded mode does way less work. According to the wall clock, logging using Log consumes way less time in your thread than logging using print.)

Overview

big's Log object is a high-performance logging mechanism, suitable for debugging. It has a very convenient interface, and it's easy to get up and logging with very little configuration. It's thread-safe, ensuring logged messages are rendered atomically (unlike calling print from multiple threads, which can interleave messages.) And with default parameters, logging a message is very quick--perhaps 5x faster than calling print.

Although it's really designed for "print-style" debugging--where you print out all the state in your program that you need to diagnose a problem--it's usable for classic application logging, like Python's logging module. That said, it has a very different interface, and is missing a lot of features as compared to the logging module, so it's hardly a drop-in replacement.

tl;dr

The downside of feature-rich classes and functions is that it can be hard to remember how to use them. So here's all you need to remember to be productive with Log:

When you create your Log object, just pass in the objects where you want the log to go--print, a file path, an open file handle, a list, whatever. Then call that Log object to log messages; it behaves like print.

Getting started

Let's start by showing you a whole Python script that exercises many of the big Log features. We'll show you the script, then its output, and then we'll go over it line by line and discuss each of the features independently.

Here's the script:

import big.all as big

j = big.Log()
j.print("Hello, world!  2 + 2 =", 2 + 2)
j("Hello, world, round 2!  4 + 4 =", 4 + 4)
j.write("This was written without formatting!\nLine breaks work too!\n")
j.box(f"Today's important number is {6 * 7}!")
with j.enter("Newline subsystem"):
    j("Line one!\nAnd here's line two.\n  Leading spaces are preserved too!\nAnd here's the final line")

If you copy that to a new file, and run it (with big installed), you'll see this on the output:

===============================================================================
Log start at 2026/02/15 13:46:51.410751 PST
===============================================================================
[000.0004777570   MainThread] Hello, world!  2 + 2 = 4
[000.0004971740   MainThread] Hello, world, round 2!  4 + 4 = 8
This was written without formatting!
Line breaks work too!
[000.0005088170   MainThread] +------------------------------------------------
[000.0005088170   MainThread] | Today's important number is 42!
[000.0005088170   MainThread] +------------------------------------------------
[000.0005117220   MainThread] +-----+------------------------------------------
[000.0005117220   MainThread] |start| Newline subsystem
[000.0005117220   MainThread] +-----+------------------------------------------
[000.0005174830   MainThread]     Line one!
[000.0005174830   MainThread]     And here's line two.
[000.0005174830   MainThread]       Leading spaces are preserved too!
[000.0005174830   MainThread]     And here's the final line
[000.0005208190   MainThread] +-----+------------------------------------------
[000.0005208190   MainThread] | end | Newline subsystem
[000.0005208190   MainThread] +-----+------------------------------------------
===============================================================================
Log finish at 2026/02/15 13:46:51.411292 PST
===============================================================================

Let's break it down, line by line.

To use Log, simply instantiate a Log object:

j = big.Log()

With all default parameters, your Log will send the log to stdout via builtins.print. However, it'll use a separate thread to do the actual printing. Logging a message will lightly pre-format the message, then send it to the Log object's internal thread; that thread finishes the formatting and sends it to print.

The usual method call to log a message is the Log.print method:

j.print("Hello, world!  2 + 2 =", 2 + 2)

Log.print has a similar signature to builtins.print, although it doesn't support the file parameter.

This is so common, there's a shortcut: calling the Log instance itself behaves identically to calling the print method:

j("Hello, world, round 2!  4 + 4 =", 4 + 4)

The three lines we've examined so far produce these first five lines of the output:

===============================================================================
Log start at 2026/02/15 13:14:13.648816 PST
===============================================================================
[000.0004797210   MainThread] Hello, world!  2 + 2 = 4
[000.0005004200   MainThread] Hello, world, round 2!  4 + 4 = 8

As you can see, Log automatically adds a "start" banner showing the current local time that the log was started. (There's a symmetric "end" banner produced when the log is closed.) These banners are only emitted if some logging operation produces non-empty formatted output.

Also, every printed log message gets a "prefix", showing the elapsed time so far (since the start of the log) and the thread that logged the message. The default prefix is fixed width; it's the thing enclosed in '[...]' at the start of each line (after the start banner).

(Obviously, if you run this script, your times will be different. Yours will be in the future... unless you've borrowed Guido's time machine.)

Let's explore the other Log methods that append messages to the log. The simplest one is write, which only takes one str argument, and writes that string to the log without any further formatting whatsoever:

j.write("This was written without formatting!\nLine breaks work too!\n")

This is useful in case you've carefully formatted some text by hand and you want to dump it straight into the log. (Or you simply want to suppress the per-line prefix.) Passing an empty string to Log.write is a no-op; it does not start the log and it does not emit banners.

Log also supports a method called box that writes a message with a three-sided box drawn around it:

j.box(f"Today's important number is {6 * 7}!")

If you want to call attention to a logged message, use box instead of print. Note that the box method also only takes a single str object. (But formatting it is no problem--just use an f-string!)

The above two lines in the script produce these five lines in the output:

This was written without formatting!
Line breaks work too!
[000.0005261250   MainThread] +------------------------------------------------
[000.0005261250   MainThread] | Today's important number is 42!
[000.0005261250   MainThread] +------------------------------------------------

You can see, the message logged with Log.write doesn't get the "prefix". And it's easy to pick out the "boxed" message due to the lines Log draws around it.

Let's finish up, examining the final two lines of the script.

Log supports two intertwined methods, enter and exit. enter logs a message in a box, then adds an indent that applies to subsequent messages. exit outdents, then re-logs the message from the most recent enter in another box. This is a great way to add some structure to your log, showing visually how your program enters and exits conceptual subsystems (modules, functions, classes, algorithms, what have you).

For convenience, Log.enter also returns a "context manager". If you use a call to Log.enter as the argument to a with statement, it'll automatically call Log.exit when you exit the with statement. We use that feature in the example script.

The last line demonstrates one more feature of Log. All Log methods that write to the log support newline characters, not just Log.write. But when you log a message using the other methods--methods that write the "prefix"--the lines are split up before they're formatted for the log, and each line gets the prefix.

Here are the last two lines of the script:

with j.enter("Newline subsystem"):
    j("Line one!\nAnd here's line two.\n  Leading spaces are preserved too!\nAnd here's the final line")

Those two lines produce these thirteen lines in the output:

[000.0005117220   MainThread] +-----+------------------------------------------
[000.0005117220   MainThread] |enter| Newline subsystem
[000.0005117220   MainThread] +-----+------------------------------------------
[000.0005174830   MainThread]     Line one!
[000.0005174830   MainThread]     And here's line two.
[000.0005174830   MainThread]       Leading spaces are preserved too!
[000.0005174830   MainThread]     And here's the final line
[000.0005208190   MainThread] +-----+------------------------------------------
[000.0005208190   MainThread] |exit | Newline subsystem
[000.0005208190   MainThread] +-----+------------------------------------------
===============================================================================
Log finish at 2026/02/15 13:46:51.411292 PST
===============================================================================

Notice how the logged message inside the "enter" / "exit" section are indented by four spaces. If you nested another with j.enter inside the first one, text logged inside that would be indented by eight spaces.

Once the script exited, Log automatically closed the log for you, including writing the "end" banner with the end time of the log.

Other methods

Log has three more methods; none of these write messages to the log.

Log.flush(block=True) flushes the log. In some configurations, the log will buffer up messages, instead of sending them right away. A flush call will flush all buffered messages. If block is true (the default), the flush call won't exit until all messages have been flushed. If block is false, flush may start the flushing process but won't necessarily wait until it's complete.

Log.close(block=True) closes the log. If the log is dirty, it flushes first. When a log is closed, it ignores all attempts to write log messages to the log. No error is reported--the messages are simply ignored. The only way to re-open a closed log is to call its reset method. If a log is still in its initial state when you close it, it closes directly without emitting the start banner, end banner, or any destination events besides register. The block parameter works the same as it does for flush.

Log.reset() resets a log to its initial state. Resetting the log is the only way to re-open a closed log. After reset, the log behaves like a fresh log and won't emit the start banner until some operation produces non-empty formatted output.

Every Log also registers an "atexit" handler. The "atexit" handler closes the Log if it hasn't been closed already.

Destinations

All our logging so far has just gone to builtins.print, which means it shows up on stdout. But what if you want to send the log somewhere else?

All positional arguments to the Log constructor specify destinations. A destination is simply an object that the log sends messages to. You can specify as many destinations as you like when creating your Log instance; however you can't add or remove destinations later.

As we've already seen, if you don't specify any destinations when you construct the Log, the default is to log to builtins.print. You can explicitly configure your log to to this by passing in print as a positional argument:

j = Log(print)

But there are lots of other options! If you specify an list, Log will append all the logged message strings to that list:

a = []
j = Log(a)

If you specify a string, Log will assume that string represents a filename, and append the log to that file:

j = Log('/tmp/log.txt')

(You can also specify a pathlib.Path object--that works the same.)

When writing to a file, Log will buffer up messages until the log is flushed, then open the file, flush all messages with one big write, and close the file.

Some more options:

If you pass in an open file handle (like the result of calling open), Log will write log messages to the file handle.
If you pass in a callable, Log will call the callable once for every formatted message, with just one argument, the formatted log message.
There's a special sentinel value in the big.log module: big.log.TMPFILE. If you specify that as a destination, Log will send the log to a file with a dynamically generated name in your configured temporary directory. The filename starts with the name of the log; this is followed by the start time, then the process ID, and finally ends with .txt. Every time the log resets it switches to a new filename.
A destination of None doesn't log anywhere. If you want to create a Log object that doesn't actually write the log anywhere, construct it as big.Log(None).

If you pass in multiple destinations, Log will send all the log messages to all of them. You could log to TMPFILE, print, and an array, all at the same time!

Finally, if you like the "buffer-until-flush" behavior of files, but want to use that with another destination, wrap that destination with a call to Log.Buffer(). Without an argument, Log.Buffer() buffers all messages until flush, then writes them all to builtins.print. If you specify a destination (as a positional parameter), that destination is where the log messages are sent when the log is flushed. See the Custom Destinations section below for more information on this and similar advanced techniques.

Configuration via keyword-only parameters

You configure your Log instance by supplying keyword arguments to the Log constructor. Note that, like the list of destinations, these can only be specified as arguments to the constructor; you can't change any of these settings once the Log instance is initialized.

The most important setting is the threading parameter. This configures whether or not the Log instance uses a worker thread to do all the actual logging. By default threading is true, which means the log uses an external thread to format and write the log messages. Log messages and other operations are sent to that thread via a queue.Queue, which ensures log messages are sent to the log in a high-performance and thread-safe way.

If you pass in threading=False to the Log constructor, the log won't use an external thread. Instead, every logging call will write to the log immediately; it uses a threading.Lock to guarantee atomicity.

You can set the name of the log with the name parameter. This is shown in the start and end banners.

indent defaults to 4; this is how many spaces enter indents the log. width defaults to 79; this is the column that "line" formats are truncated at.

clock is the clock used to compute the time at which log messages were logged. It should return an integer, which is the number of nanoseconds since some previous event. By default it uses time.monotonic_ns. (Well, on Python 3.7+ anyway. On Python 3.6 it simulates time.monotonic_ns by calling time.monotonic and multiplying it by a billion.)

timestamp_clock is the clock used to produce timestamps used in the log. It should return a float, which is the number of seconds since the UNIX epoch. The default value is time.time. timestamp_format is a callable that should convert a timestamp_clock value into a pleasing human-readable format; the default value is big.time.timestamp_human.

prefix is the string that will be inserted in front of every line of a logged message (except Log.write). It's formatted using the Line formatting rules as described in the next section. The formats parameter establishes the formatting applied to log messages of various types; this is quite involved, so see the Formats section below for documentation on that.

Line formatting

Several configuration settings for Log use what's called "line formatting". This formatting method lets you substitute in dynamic values at runtime when writing to the log; the values are recomputed and substituted every time Log formats a message for the log. For example, the prefix is freshly reformatted for every log message.

The formatting is done using the format method on the string.

Here are the values you can substitute when formatting the prefix:

elapsed: The elapsed time since the log was started, as float-seconds.
format: The format being applied to this log message.
line: The string used for repeating horizontal lines. See next section, Formats.
name: The "name" of the log passed in to the Log constructor.
thread: A handle to the thread that logged this message.
time: The time of the log message, as float-seconds-since-epoch.
timestamp: The time value, formatted using the "timestamp_format" callable passed in to the Log constructor.

There are three more (line, message, and prefix) you can use when formatting a "format", see next section.

Formats and format dicts

The formatting of the various formatted log messages is specified using a "format". Log supports those six formats, and has default values for each of those formats; you can change how those messages are formatted, or add your own.

The formats parameter to the Log constructor should specify a dict. A key in this dict is the name of a format; a custom format name must be a valid Python identifier, and can't collide with an existing Log method name. The matching value should be a "format dict", which specifies the formatting to use for this message.

A "format dict" in turn supports only two fields, and one is optional. The required one is "template", which should be a string; this string is formatted with the "line formatting" rules specified in the previous section, with some additional values. The optional one is "line", and you'll see what that's used for in a moment.

The string value of "template" is allowed to be a multi-line string. Each line will be split up and formatted independently, using the "line formatting" approach above. But a template supports three additional values:

line: The value of the "line" value from the format dict. If {line} is the last thing on a template line (either immediately before a '\n', or immediately before the end of the template string), the "line" value will be repeatedly appended to the formatted string until the line is >= Log.width, and then the line will then be truncated at Log.width characters. This is how box and the other methods draw those horizontal lines.
message: The message that was logged. You can specify multiple lines containing {message}, however, all lines containing {message} must be contiguous. The message will be split by lines, and then the template lines containing {message} will be "zipped" together with the lines of the message. For example, the first template line containing {message} will get the first line of the message, the second template line containing {message} will get the second line of the message, etc. If there are more template lines than lines in the message, it skips the subsequent template lines; if there are more lines in the message than template lines, the last template line is repeated.
prefix: A string pre-formatted using the prefix format string passed in to the Log constructor.

Predefined and user-defined formats

Log comes with six pre-defined formats:

print: used (by default) by Log.print and Log.__call__
box: used by Log.box
enter: used by Log.enter
exit: used by Log.exit
start: used to produce the "start banner" whenever the log is started
end: used to produce the "end banner" whenever the log is closed

You can override the default formatting for these by passing in a new format dict for that value to the formats parameter to the Log constructor. For example, to remove the prefix for lines printed by Log.print and Log.__call__, construct your Log as follows:

j = big.Log(..., formats={"print": {"template": "{message}"}})

You can also suppress the start, end, enter, and exit banners by setting any of those formats to None in the formats dict you pass in. This log won't print the start and end banners:

j = big.Log(..., formats={"start": None, "end": None})

You can also add your own formats! Here's some sample code that defines a new format we'll call "critical":

j = big.Log(formats={"critical": {"template": "{prefix} =critical=start{line}\n{prefix} == {message}\n{prefix} =critical=end{line}", "line": "=-"}})

You'll notice, the template contains several newline characters, but doesn't end with a newline. Log automatically adds a trailing newline character for you.

How do you use it? Log.print and Log.__call__ both take a format keyword-only parameter, which specifies the format to apply to the message. So you can use it like this:

j("My hair is on fire!", format="critical")

However, Log also adds a method to the log instance for every user-defined format. You can just call j.peanut directly:

j.critical("We're almost out of coffee!")

This line, using the "critical" format specified above, produces the following output in the log:

[000.0002390010   MainThread]  =critical=start=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
[000.0002390010   MainThread]  == We're almost out of coffee!
[000.0002390010   MainThread]  =critical=end=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=

(And yes, you could use custom formats to simulate "log levels" with Log.)

Custom Destinations

Log supports sending the log to lots of different kinds of objects, called "destinations". The actual implementation wraps each of these diverse object types in a wrapper object that handles logging to that "destination" object. These wrapper objects are all subclasses of a base class called Log.Destination. You can write your own Destination subclasses, and Log will happily send the log to those too!

First, declare your subclass. You probably need to write an __init__; that's fine, but you must call the base class init.

class MyDestination(Log.Destination):
    def __init__(self, o):
        super().__init__()
        ...

The methods on Destination subclasses called by the Log object are referred to as "events". They generally map to method calls on the Log object, and represent the user calling that method on the Log.

Second, all Destination subclasses must implement a write method, like so:

    def write(elapsed, thread, formatted):
        ...

The elapsed parameter is the elapsed time since the log was started / reset, in nanoseconds. The thread parameter is the threading.Thread handle for the thread that wrote this message, or None if the message isn't associated with a particular thread (like the "start banner" and "end banner" messages). Finally, formatted is the formatted string to be written to the log. Destination.write is called directly to implement Log.write.

Next there are seven optional Destination methods representing higher-level events sent by the Log object:

    def log(self, elapsed, thread, format, message, formatted):
        ...

    def flush(self):
        ...

    def reset(self):
        ...

    def start(self, start_time_ns, start_time_epoch):
        ...

    def end(self, elapsed):
        ...

    def enter(self, elapsed, thread, message):
        ...

    def exit(self, elapsed, thread):
        ...

Destination.log is called for Log.print, Log.__call__, and the start, end, enter, and exit banners when those banners produce non-empty formatted text. Destination.enter and Destination.exit are separate events that bracket changes in indentation depth. Destination.start and Destination.end report lifecycle changes for the log itself. If you don't override Destination.log, the base class implementation calls self.write(elapsed, thread, formatted).

Destination also supports optional methods handling the two Log methods that don't send a log message:

    def reset(self):
        ...

    def flush(self):
        ...

Finally, Destination objects support a register method, which is called when they're passed in to the Log constructor. If you override this method, you must call the base class method, passing in the owner parameter, like so:

    def register(self, owner):
        super().register(owner)
        ...

Log objects obey a certain lifecycle. If the log ever produces non-empty formatted output, the Destination methods will be called in this order:

register
  |
  v
start
  |
  +<---------------------------------+
  |                                  |
  v                                  |
write | log | enter | exit | flush   |
  |                                  |
  |                                  |
  +----------------------------------+
  |
  v
[flush]
  |
  v
end

The final flush, before end, is optional; it's only called if the Log is dirty. If a log is created and then closed without ever producing non-empty formatted output, destinations only receive the initial register event.

Final notes on `Log`

There's no guarantee that log messages will be logged in strict chronological order. It usually happens that way, but it's not guaranteed. If two threads both send a message at the same time, they might arrive in any order--and it's possible the later of the two arrives first. This is a known behavior, and is unlikely to change in future versions.

Bound inner classes

Overview

One feature missing from Python pertains to "inner classes"--classes defined inside other classes.

Consider this Python code:

class Outer(object):
    def think(self):
        pass

o = Outer()
o.think()

We've defined a function think inside class Outer. When you call o.think, Python automatically passes in the o object as the first parameter (by convention called self). In object-oriented parlance, o is bound to think, and indeed Python calls the object o.think a bound method:

    >>> o.think
    <bound method Outer.think of <__main__.Outer object at 0x########>>

And if you refer to Outer.think--if you get your reference to the think function from the class instead of an instance of the class--you just get the normal function.

    >>> Outer.think
    <function Outer.think at 0x7b5f4f49f110>

But there's no similar mechanism for a class defined inside another class. Let's change our example and add a class inside Outer:

class Outer(object):
    def think(self):
        pass

    class Inner(object):
        def __init__(self):
            pass

o = Outer()
o.think()
i = o.Inner()

But classes defined inside classes don't behave the same as functions defined inside classes. No matter how you reference Inner, you get the same class object, whether you access it through the outer class (Outer.Inner) or through an instance of the outer class (o.Inner):

    >>> Outer.Inner
    <class '__main__.Outer.Inner'>
    >>> o.Inner
    <class '__main__.Outer.Inner'>

And if you call o.Inner(), Python won't automatically pass in o as an argument into the way it does for o.think(). If you want it passed in, you have to pass it in yourself, like so:

class Outer(object):
    def think(self):
        pass

    class Inner(object):
        def __init__(self, outer):
            self.outer = outer

o = Outer()
o.method()
i = o.Inner(o)

This seems redundant. You don't have to pass in o explicitly to method calls, why should you have to pass it in explicitly to inner classes?

Well--now you don't have to! You can just decorate the inner class with @big.BoundInnerClass, and the BoundInnerClass decorator takes care of the rest.

Using bound inner classes

Let's modify the above example to use our BoundInnerClass decorator:

from big import BoundInnerClass

class Outer(object):
    def think(self):
        pass

    @BoundInnerClass
    class Inner(object):
        def __init__(self, outer):
            self.outer = outer

o = Outer()
o.method()
i = o.Inner()

Notice that Inner.__init__ now takes an outer parameter. But you didn't have to pass it in yourself! When you call o.Inner(), o is automatically passed in as the outer argument to Inner.__init__. That's what the @BoundInnerClass decorator does for you.

Decorating an inner class like this always inserts a second positional parameter, after self. And, like self, you don't have to use the name outer; you can use any name you like. (But we'll always use the name outer in this documentation.)

Inheritance

Bound inner classes get slightly complicated when mixed with inheritance. It's not all that difficult, you merely need to obey some rules:

Rule 1: A bound inner class can inherit normally from any unbound class.

Rule 2: If your bound inner class calls super().__init__, and its parent class is also a bound inner class, don't pass in outer manually. When you instantiate a bound inner class, outer will be automatically passed in to all __init__ methods of every bound inner parent class.

Rule 3: A bound inner class can only inherit from a parent bound inner class if the parent is defined in the same outer class or a base of the outer class. If Child inherits from Parent, and Child and Parent are both decorated with @BoundInnerClass (or @UnboundInnerClass), both classes must be defined in the same outer class (e.g. Outer) or in a base class of Child's outer class. This is a type relation constraint; bound inner classes guarantee that "outer" is an instance of the outer class.

Rule 3a: A corollary of rule 3: A subclass of a bound inner class, whether bound or unbound, can only be defined in the same outer class or a subclass of the outer class.

Rule 4: Directly inheriting from a bound inner class is unsupported. If o is an instance of Outer, and Outer.Inner is an inner class decorated with @BoundInnerClass, don't write a class that directly inherits from o.Inner, for example class Mistake(o.Inner). You should always inherit from the unbound version, like this: class GotItRight(Outer.Inner)

Rule 5: An inner class that inherits from a bound inner class, and which also wants to be bound to the outer object, should be decorated with BoundInnerClass.

Rule 6: An inner class that inherits from a bound inner class, but doesn't want to be bound to the outer object, should be decorated with UnboundInnerClass.

Restating the last two rules: every class that descends from any class decorated with BoundInnerClass must itself be decorated with either BoundInnerClass or UnboundInnerClass. Which one you use depends on what behavior you want--whether or not you want your inner subclass to automatically get the outer instance passed in to its __init__.

Here's a simple example using inheritance with bound inner classes:

from big import BoundInnerClass, UnboundInnerClass

class Outer(object):

    @BoundInnerClass
    class Parent(object):
        def __init__(self, outer):
            self.outer = outer

    @UnboundInnerClass
    class Child(Parent):
        def __init__(self):
            super().__init__()

o = Outer()
child = o.Child()

We followed the rules:

Outer.Parent inherits from object; since object isn't a bound inner class, there are no special rules about inheritance Outer.Parent needs to obey.
Since Outer.Child inherits from a BoundInnerClass, it must be decorated with either BoundInnerClass or UnboundInnerClass. It doesn't want the outer object passed in, so it's decorated with UnboundInnerClass.
Child.__init__ calls super().__init__, but doesn't pass in outer.
Both Parent and Child are defined in the same class.

Note that, because Child is decorated with UnboundInnerClass, it doesn't take an outer parameter. Nor does it pass in an outer argument when it calls super().__init__. But when the constructor for Parent is called, the correct outer parameter is passed in--like magic!

If you wanted Child to also get the outer argument passed in to its __init__, just decorate it with BoundInnerClass instead of UnboundInnerClass, like so:

from big import BoundInnerClass

class Outer(object):

    @BoundInnerClass
    class Parent(object):
        def __init__(self, outer):
            self.outer = outer

    @BoundInnerClass
    class Child(Parent):
        def __init__(self, outer):
            super().__init__()
            assert self.outer == outer

o = Outer()
child = o.Child()

Again, Child.__init__ doesn't need to explicitly pass in outer when calling super.__init__, but the correct value for outer does get passed in to Parent.__init__.

You can see more complex examples of using inheritance with BoundInnerClass (and UnboundInnerClass) in the big test suite.

Miscellaneous notes

A bound inner class is a subclass of the original (unbound) class. o.Inner is a subclass of Outer.Inner.
Bound inner classes bound to different outer instances are different classes. This is symmetric with methods; if you have two objects a and b that are instances of the same class, a.BoundInnerClass != b.BoundInnerClass, just as a.method != b.method.
If you refer to a inner class directly from the outer class (like Outer.Inner) rather than an instance (like o.Inner) you get the original (unbound) class.
- You might be able call Outer.Inner directly, to construct an Inner object without using a bound version of the class. You'll have to pass in the outer parameter by hand--just like you'd have to pass in the self parameter by hand when calling a method via the class (Outer.method) rather than via an instance of the class (o.method). This won't work if Outer.Inner is a subclass of another bound inner class, and calls its super().__init__. The injection of the outer instance argument happens when the class is bound, and this handles injecting the argument for all the base classes too. Without this binding mechanism getting involved, the outer argument won't get supplied when calling the base class's __init__.
Bound inner classes are cached in the outer object, which both provides a small speedup and ensures that isinstance relationships are consistent. This is an explicit feature, and you're permitted to rely on it.
- If you use slots on your outer class, you must add a slot for BoundInnerClass to store its cache. Just add BOUNDINNERCLASS_OUTER_SLOTS to your slots tuple, like so:

__slots__ = ('x', 'y', 'z') + BOUNDINNERCLASS_OUTER_SLOTS

Binding only goes one level deep. If you had a bound inner class C define inside another bound inner class B, which in turn was defined inside a class A, the constructor for C would be called with the B object, but not the A object.
If you support Python 3.6, and you define bound inner child classes, you'll need to wrap all the bound inner base classes of those child classes with big.boundinnerclass.bound_inner_base. For example: class Child(bound_inner_base(Parent)): This is unnecessary in Python 3.7+. However, using bound_inner_base works fine in all versions of Python supported by big.
The rewrite of bound inner classes that shipped with big version 0.13 removed some old provisos:
- You may now rename your inner classes, even after decorating them with @BoundInnerClass (or @UnboundInnerClass). In previous versions this would break some internal mechanisms, but renaming your classes is now explicitly supported behavior.
- The race condition around creating and caching the bound version of an inner class from multiple threads has been prevented, by adding just a little internal locking. The implementation doesn't need to lock very often, so the performance cost is negligible.
- It's no longer required to call super().__init__ in bound inner subclasses! In fact it was probably never necessary. It's totally up to you whether or not you call super().__init__.

The `multi-` family of string functions

This family of string functions was inspired by Python's str.split, str.rsplit, and str.splitlines methods. These string splitting methods are well-designed and often do what you want. But they're surprisingly narrow and opinionated. What if your use case doesn't map neatly to one of these functions? str.split supports two very specific modes of operation--unless you want to split your string in exactly one of those two modes, you probably can't use str.split to solve your problem.

So what can you use? There's re.split, but that can be hard to use.¹ Regular expressions can be difficult to get right, and the semantics of re.split are subtly different from the usual string splitting functions. Not to mention, it doesn't support reverse!

Now there's a new answer: multisplit. The goal of multisplit is to be the be-all end-all string splitting function. It's designed to supercede every mode of operation provided by str.split, str.rsplit, and str.splitlines, and it can even replace str.partition and str.rpartition too. multisplit does it all!

The downside of multisplit's awesome flexibility is that it can be hard to use... after all, it takes five keyword-only parameters. However, these parameters and their defaults are designed to be easy to remember.

The best way to cope with multisplit's complexity is to use it as a building block for your own text splitting functions. For example, big uses multisplit to implement multipartition, normalize_whitespace, lines, and several other functions.

The values returned or yielded by these functions are slices of the original object, or in some cases adjacent slices joined with +. All slices are returned in left-to-right order; this even includes zero-length strings, which are sliced from the contextually correct spot.

Using `multisplit`

To use multisplit, pass in the string you want to split, the separators you want to split on, and tweak its behavior with its five keyword arguments. It returns an iterator that yields string segments from the original string in your preferred format. The separator list is optional; if you don't pass one in, it defaults to an iterable of whitespace separators (either big.whitespace or big.ascii_whitespace, as appropriate).

The cornerstone of multisplit is the separators argument. This is an iterable of strings, of the same type (str or bytes) as the string you want to split (s). multisplit will split the string at each non-overlapping instance of any string specified in separators.

multisplit lets you fine-tune its behavior via five keyword-only parameters:

keep lets you include the separator strings in the output, in a number of different formats.
separate lets you specify whether adjacent separator strings should be grouped together (like str.split operating on whitespace) or regarded as separate (like str.split when you pass in an explicit separator).
strip lets you strip separator strings from the beginning, end, or both ends of the string you're splitting. It also supports a special progressive mode that duplicates the behavior of str.split when you use None as the separator.
maxsplit lets you specify the maximum number of times to split the string, exactly like the maxsplit argument to str.split.
reverse makes multisplit behave like str.rsplit, starting at the end of the string and working backwards. (This only changes the behavior of multisplit if you use maxsplit, or if your string contains overlapping separators.)

To make it slightly easier to remember, all these keyword-only parameters default to a false value. (Well, technically, maxsplit defaults to the special value -1, for compatibility with str.split. But that's its special "don't do anything" magic value. All the other keyword-only parameters default to False.)

multisplit also inspired multistrip and multipartition, which also take this same separators arguments. There are also other big functions that take a separators argument, for example comment_markers for lines_filter_line_comment_lines.)

Demonstrations of each `multisplit` keyword-only parameter

To give you a sense of how the five keyword-only parameters changes the behavior of multisplit, here's a breakdown of each of these parameters with examples.

`maxsplit`

maxsplit specifies the maximum number of times the string should be split. It behaves the same as the maxsplit parameter to str.split.

The default value of -1 means "split as many times as you can". In our example here, the string can be split a maximum of three times. Therefore, specifying a maxsplit of -1 is equivalent to specifying a maxsplit of 2 or greater:

    >>> list(big.multisplit('apple^banana_cookie', ('_', '^'))) # "maxsplit" defaults to -1
    ['apple', 'banana', 'cookie']
    >>> list(big.multisplit('apple^banana_cookie', ('_', '^'), maxsplit=0))
    ['appleXbananaYcookie']
    >>> list(big.multisplit('apple^banana_cookie', ('_', '^'), maxsplit=1))
    ['apple', 'bananaYcookie']
    >>> list(big.multisplit('apple^banana_cookie', ('_', '^'), maxsplit=2))
    ['apple', 'banana', 'cookie']
    >>> list(big.multisplit('apple^banana_cookie', ('_', '^'), maxsplit=3))
    ['apple', 'banana', 'cookie']

maxsplit has interactions with reverse and strip. For more information, see the documentation regarding those parameters below.

`keep`

keep indicates whether or not multisplit should preserve the separator strings in the strings it yields. It supports four values: false, true, and the special values ALTERNATING and AS_PAIRS.

When keep is false, multisplit throws away the separator strings; they won't appear in the output.

    >>> list(big.multisplit('apple#banana-cookie', ('#', '-'))) # "keep" defaults to False
    ['apple', 'banana', 'cookie']
    >>> list(big.multisplit('apple-banana#cookie', ('#', '-'), keep=False))
    ['apple', 'banana', 'cookie']

When keep is true, multisplit keeps the separators, appending them to the end of the separated string:

    >>> list(big.multisplit('apple$banana~cookie', ('$', '~'), keep=True))
    ['appleX', 'bananaY', 'cookie']

When keep is ALTERNATING, multisplit keeps the separators as separate strings. The first string yielded is always a non-separator string, and from then on it always alternates between a separator string and a non-separator string. Put another way, if you store the output of multisplit in a list, entries with an even-numbered index (0, 2, 4, ...) are always non-separator strings, and entries with an odd-numbered index (1, 3, 5, ...) are always separator strings.

    >>> list(big.multisplit('appleXbananaYcookie', ('X', 'Y'), keep=big.ALTERNATING))
    ['apple', 'X', 'banana', 'Y', 'cookie']

Note that ALTERNATING always emits an odd number of strings, and the first and last strings yielded are always non-separator strings. Like str.split, if the string you're splitting starts or ends with a separator string, multisplit will emit an empty string at the beginning or end, to preserve the "always begin and end with non-separator string" invariant:

    >>> list(big.multisplit('1a1z1', ('1',), keep=big.ALTERNATING))
    ['', '1', 'a', '1', 'z', '1', '']

Finally, when keep is AS_PAIRS, multisplit keeps the separators as separate strings. But it doesn't yield bare strings; instead, it yields 2-tuples of strings. Every 2-tuple contains a non-separator string followed by a separator string.

If the original string starts with a separator, the first 2-tuple will contain an empty non-separator string and the separator:

    >>> list(big.multisplit('^apple-banana^cookie', ('-', '^'), keep=big.AS_PAIRS))
    [('', '^'), ('apple', '-'), ('banana', '^'), ('cookie', '')]

The last 2-tuple will always contain an empty separator string:

    >>> list(big.multisplit('apple*banana+cookie', ('*', '+'), keep=big.AS_PAIRS))
    [('apple', '*'), ('banana', '+'), ('cookie', '')]
    >>> list(big.multisplit('apple*banana+cookie***', ('*', '+'), keep=big.AS_PAIRS, strip=True))
    [('apple', '*'), ('banana', '+'), ('cookie', '')]

(This rule means that AS_PAIRS always emits an even number of strings. Contrast that with ALTERNATING, which always emits an odd number of strings, and the last string it emits is always a non-separator string. Put another way: if you ignore the tuples, the list of strings emitted by AS_PAIRS is the same as those emitted by ALTERNATING, except AS_PAIRS appends an empty string.)

Because of this rule, if the original string ends with a separator, and multisplit doesn't strip the right side, the final tuple emitted by AS_PAIRS will be a 2-tuple containing two empty strings:

    >>> list(big.multisplit('appleXbananaYcookieX', ('X', 'Y'), keep=big.AS_PAIRS))
    [('apple', 'X'), ('banana', 'Y'), ('cookie', 'X'), ('', '')]

This looks strange and unnecessary. But it is what you want. This odd-looking behavior is discussed at length in the section below, titled Why do you sometimes get empty strings when you split?

The behavior of keep can be affected by the value of separate. For more information, see the next section, on separate.

`separate`

separate indicates whether multisplit should consider adjacent separator strings in s as one separator or as multiple separators each separated by a zero-length string. It can be either false or true.

    >>> list(big.multisplit('apple=?banana?=?cookie', ('=', '?'))) # separate defaults to False
    ['apple', 'banana', 'cookie']
    >>> list(big.multisplit('apple=?banana?=?cookie', ('=', '?'), separate=False))
    ['apple', 'banana', 'cookie']
    >>> list(big.multisplit('apple=?banana?=?cookie', ('=', '?'), separate=True))
    ['apple', '', 'banana', '', '', 'cookie']

If separate and keep are both true values, and your string has multiple adjacent separators, multisplit will view s as having zero-length non-separator strings between the adjacent separators:

    >>> list(big.multisplit('appleXYbananaYXYcookie', ('X', 'Y'), separate=True, keep=True))
    ['appleX', 'Y', 'bananaY', 'X', 'Y', 'cookie']

    >>> list(big.multisplit('appleXYbananaYXYcookie', ('X', 'Y'), separate=True, keep=big.AS_PAIRS))
    [('apple', 'X'), ('', 'Y'), ('banana', 'Y'), ('', 'X'), ('', 'Y'), ('cookie', '')]

`strip`

strip indicates whether multisplit should strip separators from the beginning and/or end of s. It supports five values: false, true, big.LEFT, big.RIGHT, and big.PROGRESSIVE.

By default, strip is false, which means it doesn't strip any leading or trailing separators:

    >>> list(big.multisplit('%|apple%banana|cookie|%|', ('%', '|'))) # strip defaults to False
    ['', 'apple', 'banana', 'cookie', '']

Setting strip to true strips both leading and trailing separators:

    >>> list(big.multisplit('%|apple%banana|cookie|%|', ('%', '|'), strip=True))
    ['apple', 'banana', 'cookie']

big.LEFT and big.RIGHT tell multistrip to only strip on that side of the string:

    >>> list(big.multisplit('.?apple.banana?cookie.?.', ('.', '?'), strip=big.LEFT))
    ['apple', 'banana', 'cookie', '']
    >>> list(big.multisplit('.?apple.banana?cookie.?.', ('.', '?'), strip=big.RIGHT))
    ['', 'apple', 'banana', 'cookie']

big.PROGRESSIVE duplicates a specific behavior of str.split when using maxsplit. It always strips on the left, but it only strips on the right if the string is completely split. If maxsplit is reached before the entire string is split, and strip is big.PROGRESSIVE, multisplit won't strip the right side of the string. Note in this example how the trailing separator Y isn't stripped from the input string when maxsplit is less than 3.

    >>> list(big.multisplit('^apple^banana_cookie_', ('^', '_'), strip=big.PROGRESSIVE))
    ['apple', 'banana', 'cookie']
    >>> list(big.multisplit('^apple^banana_cookie_', ('^', '_'), maxsplit=0, strip=big.PROGRESSIVE))
    ['apple^banana_cookie_']
    >>> list(big.multisplit('^apple^banana_cookie_', ('^', '_'), maxsplit=1, strip=big.PROGRESSIVE))
    ['apple', 'banana_cookie_']
    >>> list(big.multisplit('^apple^banana_cookie_', ('^', '_'), maxsplit=2, strip=big.PROGRESSIVE))
    ['apple', 'banana', 'cookie_']
    >>> list(big.multisplit('^apple^banana_cookie_', ('^', '_'), maxsplit=3, strip=big.PROGRESSIVE))
    ['apple', 'banana', 'cookie']
    >>> list(big.multisplit('^apple^banana_cookie_', ('^', '_'), maxsplit=4, strip=big.PROGRESSIVE))
    ['apple', 'banana', 'cookie']

`reverse`

reverse specifies where multisplit starts parsing the string--from the beginning, or the end--and in what direction it moves when parsing the string--towards the end, or towards the beginning_ It only supports two values: when it's false, multisplit starts at the beginning of the string, and parses moving to the right (towards the end of the string). But when reverse is true, multisplit starts at the end of the string, and parses moving to the left (towards the beginning of the string).

This has two noticable effects on multisplit's output. First, this changes which splits are kept when maxsplit is less than the total number of splits in the string. When reverse is true, the splits are counted starting on the right and moving towards the left:

    >>> list(big.multisplit('apple-banana|cookie', ('-', '|'), reverse=True)) # maxsplit defaults to -1
    ['apple', 'banana', 'cookie']
    >>> list(big.multisplit('apple-banana|cookie', ('-', '|'), maxsplit=0, reverse=True))
    ['apple-banana|cookie']
    >>> list(big.multisplit('apple-banana|cookie', ('-', '|'), maxsplit=1, reverse=True))
    ['apple-banana', 'cookie']
    >>> list(big.multisplit('apple-banana|cookie', ('-', '|'), maxsplit=2, reverse=True))
    ['apple', 'banana', 'cookie']
    >>> list(big.multisplit('apple-banana|cookie', ('-', '|'), maxsplit=3, reverse=True))
    ['apple', 'banana', 'cookie']

The second effect is far more subtle. It's only relevant when splitting strings containing multiple overlapping separators. When reverse is false, and there are two (or more) overlapping separators, the string is split by the leftmost overlapping separator. When reverse is true, and there are two (or more) overlapping separators, the string is split by the rightmost overlapping separator.

Consider these two calls to multisplit. The only difference between them is the value of reverse. They produce different results, even though neither one uses maxsplit.

    >>> list(big.multisplit('appleXYZbananaXYZcookie', ('XY', 'YZ'))) # reverse defaults to False
    ['apple', 'Zbanana', 'Zcookie']
    >>> list(big.multisplit('appleXYZbananaXYZcookie', ('XY', 'YZ'), reverse=True))
    ['appleX', 'bananaX', 'cookie']

Reimplementing library functions using `multisplit`

Here are some examples of how you could use multisplit to replace some common Python string splitting methods. These exactly duplicate the behavior of the originals.

def _multisplit_to_split(s, sep, maxsplit, reverse):
    separate = sep != None
    if separate:
        strip = False
    else:
        sep = big.ascii_whitespace if isinstance(s, bytes) else big.whitespace
        strip = big.PROGRESSIVE
    result = list(big.multisplit(s, sep,
        maxsplit=maxsplit, reverse=reverse,
        separate=separate, strip=strip))
    if not separate:
        # ''.split() == '   '.split() == []
        if result and (not result[-1]):
            result.pop()
    return result

def str_split(s, sep=None, maxsplit=-1):
    return _multisplit_to_split(s, sep, maxsplit, False)

def str_rsplit(s, sep=None, maxsplit=-1):
    return _multisplit_to_split(s, sep, maxsplit, True)

def str_splitlines(s, keepends=False):
    linebreaks = big.ascii_linebreaks if isinstance(s, bytes) else big.linebreaks
    l = list(big.multisplit(s, linebreaks,
        keep=keepends, separate=True, strip=False))
    if l and not l[-1]:
    	# yes, ''.splitlines() returns an empty list
        l.pop()
    return l

def _partition_to_multisplit(s, sep, reverse):
    if not sep:
        raise ValueError("empty separator")
    l = tuple(big.multisplit(s, (sep,),
        keep=big.ALTERNATING, maxsplit=1, reverse=reverse, separate=True))
    if len(l) == 1:
        empty = b'' if isinstance(s, bytes) else ''
        if reverse:
            l = (empty, empty) + l
        else:
            l = l + (empty, empty)
    return l

def str_partition(s, sep):
    return _partition_to_multisplit(s, sep, False)

def str_rpartition(s, sep):
    return _partition_to_multisplit(s, sep, True)

You wouldn't want to use these, of course--Python's built-in functions are so much faster!

Why do you sometimes get empty strings when you split?

Sometimes when you split using multisplit, you'll get empty strings in the return value. This might be unexpected, violating the Principle Of Least Astonishment. But there are excellent reasons for this behavior.

Let's start by observing what str.split does. str.split really has two major modes of operation: when you don't pass in a separator (or pass in None for the separator), and when you pass in an explicit separator string. In this latter mode, the documentation says it regards every instance of a separator string as an individual separator splitting the string. What does that mean? Watch what happens when you have two adjacent separators in the string you're splitting:

    >>> '1,2,,3'.split(',')
    ['1', '2', '', '3']

What's that empty string doing between '2' and '3'? Here's how you should think about it: when you pass in an explicit separator, str.split splits at every occurance of that separator in the string. It always splits the string into two places, whenever there's a separator. And when there are two adjacent separators, conceptually, they have a zero-length string in between them:

    >>> '1,2,,3'[4:4]
    ''

The empty string in the output of str.split represents the fact that there were two adjacent separators. If str.split didn't add that empty string, the output would look like this:

    ['1', '2', '3']

But then it'd be indistinguishable from splitting the same string without two separators in a row:

    >>> '1,2,3'.split(',')
    ['1', '2', '3']

This difference is crucial when you want to reconstruct the original string from the split list. str.split with a separator should always be reversable using str.join, and with that empty string there it works correctly:

    >>> ','.join(['1', '2', '3'])
    '1,2,3'
    >>> ','.join(['1', '2', '', '3'])
    '1,2,,3'

Now take a look at what happens when the string you're splitting starts or ends with a separator:

    >>> ',1,2,3,'.split(',')
    ['', '1', '2', '3', '']

This might seem weird. But, just like with two adjacent separators, this behavior is important for consistency. Conceptually there's a zero-length string between the beginning of the string and the first comma. And str.join needs those empty strings in order to correctly recreate the original string.

    >>> ','.join(['', '1', '2', '3', ''])
    ',1,2,3,'

Naturally, multisplit lets you duplicate this behavior. When you want multisplit to behave just like str.split does with an explicit separator string, just pass in keep=False, separate=True, and strip=False. That is, if a and b are strings,

     big.multisplit(a, (b,), keep=False, separate=True, strip=False)

always produces the same output as

     a.split(b)

For example, here's multisplit splitting the strings we've been playing with, using these parameters:

    >>> list(big.multisplit('1,2,,3', (',',), keep=False, separate=True, strip=False))
    ['1', '2', '', '3']
    >>> list(big.multisplit(',1,2,3,', (',',), keep=False, separate=True, strip=False))
    ['', '1', '2', '3', '']

This "emit an empty string" behavior also has ramifications when keep isn't false. The behavior of keep=True is easy to predict; multisplit just appends the separators to the previous string segment:

    >>> list(big.multisplit('1,2,,3', (',',), keep=True, separate=True, strip=False))
    ['1,', '2,', ',', '3']
    >>> list(big.multisplit(',1,2,3,', (',',), keep=True, separate=True, strip=False))
    [',', '1,', '2,', '3,', '']

The principle here is that, when you use keep=True, you should be able to reconstitute the original string with ''.join:

    >>> ''.join(['1,', '2,', ',', '3'])
    '1,2,,3'
    >>> ''.join([',', '1,', '2,', '3,', ''])
    ',1,2,3,'

keep=big.ALTERNATING is much the same, except we insert the separators as their own segments, rather than appending each one to the previous segment:

    >>> list(big.multisplit('1,2,,3', (',',), keep=big.ALTERNATING, separate=True, strip=False))
    ['1', ',', '2', ',', '', ',', '3']
    >>> list(big.multisplit(',1,2,3,', (',',), keep=big.ALTERNATING, separate=True, strip=False))
    ['', ',', '1', ',', '2', ',', '3', ',', '']

Remember, ALTERNATING output always begins and ends with a non-separator string. If the string you're splitting begins or ends with a separator, the output from multisplit specifying keep=ALTERNATING will correspondingly begin or end with an empty string.

And, as with keep=True, you can also recreate the original string by passing these arrays in to ''.join:

    >>> ''.join(['1', ',', '2', ',', '', ',', '3'])
    '1,2,,3'
    >>> ''.join(['', ',', '1', ',', '2', ',', '3', ',', ''])
    ',1,2,3,'

Finally there's keep=big.AS_PAIRS. The behavior here seemed so strange, initially I thought it was wrong. But I've given it a lot of thought, and I've convinced myself that this is correct:

    >>> list(big.multisplit('1,2,,3', (',',), keep=big.AS_PAIRS, separate=True, strip=False))
    [('1', ','), ('2', ','), ('', ','), ('3', '')]
    >>> list(big.multisplit(',1,2,3,', (',',), keep=big.AS_PAIRS, separate=True, strip=False))
    [('', ','), ('1', ','), ('2', ','), ('3', ','), ('', '')]

That tuple at the end, just containing two empty strings:

    ('', '')

It's so strange. How can that be right?

In short, it's similar to the str.split situation. When called with keep=AS_PAIRS, multisplit guarantees that the final tuple will contain an empty separator string. If the string you're splitting ends with a separator, it must emit the empty non-separator string, followed by the empty separator string.

Think of it this way: with the tuple of empty strings there, you can easily convert one keep format into any another. (Provided that you know what the separators were--either the source keep format was not false, or you only used one separator string when calling multisplit). Without that tuple of empty strings at the end, you'd also have to have an if statement to add or remove empty stuff from the end.

I'll demonstrate this with a simple example. Here's the output of multisplit splitting the string '1a1z1' by the separator '1', in each of the four keep formats:

>>> list(big.multisplit('1a1z1', '1', keep=False))
['', 'a', 'z', '']
>>> list(big.multisplit('1a1z1', '1', keep=True))
['1', 'a1', 'z1', '']
>>> list(big.multisplit('1a1z1', '1', keep=big.ALTERNATING))
['', '1', 'a', '1', 'z', '1', '']
>>> list(big.multisplit('1a1z1', '1', keep=big.AS_PAIRS))
[('', '1'), ('a', '1'), ('z', '1'), ('', '')]

Because the AS_PAIRS output ends with that tuple of empty strings, we can mechanically convert it into any of the other formats, like so:

>>> result = list(big.multisplit('1a1z1', '1', keep=big.AS_PAIRS))
>>> result
[('', '1'), ('a', '1'), ('z', '1'), ('', '')]
>>> [s[0] for s in result] # convert to keep=False
['', 'a', 'z', '']
>>> [s[0]+s[1] for s in result] # convert to keep=True
['1', 'a1', 'z1', '']
>>> [s for t in result for s in t][:-1] # convert to keep=big.ALTERNATING
['', '1', 'a', '1', 'z', '1', '']

If the AS_PAIRS output didn't end with that tuple of empty strings, you'd need to add an if statement to restore the trailing empty strings as needed.

Other differences between multisplit and str.split

str.split returns an empty list when you split an empty string by whitespace:

>>> ''.split()
[]

But not when you split by an explicit separator:

>>> ''.split('x')
['']

multisplit is consistent here. If you split an empty string, it always returns an empty string, as long as the separators are valid:

>>> list(big.multisplit(''))
['']
>>> list(big.multisplit('', ('a', 'b', 'c')))
['']

Similarly, when splitting a string that only contains whitespace, str.split also returns an empty list:

>>> '     '.split()
[]

This is really the same as "splitting an empty string", because when str.split splits on whitespace, the first thing it does is strip leading whitespace.

If you multisplit a string that only contains whitespace, and you split on whitespace characters, it returns two empty strings:

>>> list(big.multisplit('     '))
['', '']

This is because the string conceptually starts with a zero-length string, then has a run of whitespace characters, then ends with another zero-length string. So those two empty strings are the leading and trailing zero-length strings, separated by whitespace. If you tell multisplit to also strip the string, you'll get back a single empty string:

>>> list(big.multisplit('     ', strip=True))
['']

And multisplit behaves consistently even when you use different separators:

>>> list(big.multisplit('ababa', 'ab'))
['', '']
>>> list(big.multisplit('ababa', 'ab', strip=True))
['']

And I should know--multisplit is implemented using re.split!

Whitespace and line-breaking characters in Python and big

Overview

Several functions in big take a separators argument, an iterable of separator strings. Examples of these functions include lines and multisplit. Although you can use any iterable of strings you like, most often you'll be separating on some form of whitespace. But what, exactly, is whitespace? There's more to this topic than you might suspect.

The good news is, you can almost certainly ignore all the complexity. These days the only whitespace characters you're likely to encounter are spaces, tabs, newlines, and maybe carriage returns. Python and big handle all those easily.

With respect to big and these separators arguments, big provides four values designed for use as separators. All four of these are tuples containing whitespace characters:

When working with str objects, you'll want to use either big.whitespace or big.linebreaks. big.whitespace contains all the whitespace characters, big.linebreaks contains just the line-breaking whitespace characters.
big also has equivalents for working with bytes objects: bytes_whitespace and bytes_linebreaks, respectively.

Apart from exceptionally rare occasions, these are all you'll ever need. And if that's all you need, you can stop reading this section now.

But what about those exceptionally rare occasions? You'll be pleased to know big handles them too. The rest of this section is a deep dive into these rare occasions.

Python

Here's the list of all characters recognized by Python str objects as whitespace characters:

# char    decimal   hex      name
##########################################
'\t'    , #     9 - 0x0009 - tab
'\n'    , #    10 - 0x000a - newline
'\v'    , #    11 - 0x000b - vertical tab
'\f'    , #    12 - 0x000c - form feed
'\r'    , #    13 - 0x000d - carriage return
'\x1c'  , #    28 - 0x001c - file separator
'\x1d'  , #    29 - 0x001d - group separator
'\x1e'  , #    30 - 0x001e - record separator
'\x1f'  , #    31 - 0x001f - unit separator
' '     , #    32 - 0x0020 - space
'\x85'  , #   133 - 0x0085 - next line
'\xa0'  , #   160 - 0x00a0 - non-breaking space
'\u1680', #  5760 - 0x1680 - ogham space mark
'\u2000', #  8192 - 0x2000 - en quad
'\u2001', #  8193 - 0x2001 - em quad
'\u2002', #  8194 - 0x2002 - en space
'\u2003', #  8195 - 0x2003 - em space
'\u2004', #  8196 - 0x2004 - three-per-em space
'\u2005', #  8197 - 0x2005 - four-per-em space
'\u2006', #  8198 - 0x2006 - six-per-em space
'\u2007', #  8199 - 0x2007 - figure space
'\u2008', #  8200 - 0x2008 - punctuation space
'\u2009', #  8201 - 0x2009 - thin space
'\u200a', #  8202 - 0x200a - hair space
'\u2028', #  8232 - 0x2028 - line separator
'\u2029', #  8233 - 0x2029 - paragraph separator
'\u202f', #  8239 - 0x202f - narrow no-break space
'\u205f', #  8287 - 0x205f - medium mathematical space
'\u3000', # 12288 - 0x3000 - ideographic space

This list was derived by iterating over every character defined in Unicode, and testing to see if the split() method on a Python str object splits at that character.

The first surprise: this isn't the same as the list of all characters defined by Unicode as whitespace. It's almost the same list, except Python adds four extra characters: '\x1c', '\x1d', '\x1e', and '\x1f', which respectively are called "file separator", "group separator", "record separator", and "unit separator". I'll refer to these as "the four ASCII separator characters".

These characters were defined as part of the original ASCII standard, way back in 1963. As their names suggest, they were intended to be used as separator characters for data, the same way Ctrl-Z was used to indicate end-of-file in the CPM and earliest FAT filesystems. But the four ASCII separator characters were rarely used even back in the day. Today they're practically unheard of.

As a rule, printing these characters to the screen generally doesn't do anything--they don't move the cursor, and the screen doesn't change. So their behavior is a bit mysterious. A lot of people (including early Python programmers it seems!) thought that meant they're whitespace. This seems like an odd conclusion to me. After all, all the other whitespace characters move the cursor, either right or down or both; these don't move the cursor at all.

The Unicode standard is unambiguous: these characters are not whitespace. And yet Python's "Unicode object" behaves as if they are. So I'd say this is a bug; Python's Unicode object should implement what the Unicode standard says.

It seems that the C library used by GCC and clang on my workstation agree. I wrote a quick C program to print out what characters are and aren't whitespace, according to the C function isspace(). It seems the C library agrees with Unicode: it doesn't consider the four ASCII separator characters to be whitespace.

Here's the program, in case you want to try it yourself.
 #include <stdio.h>
 #include <ctype.h>

 int main(int c, char *a[]) {
         int i;
         printf("\nisspace table.\nAdd the row and column numbers together (in hex).\n\n");
         printf("     | 0 1 2 3 4 5 6 7 8 9 a b c d e f\n");
         printf("-----+--------------------------------\n");
         for (i = 0 ; i < 256 ; i++) {
                 char *message = isspace(i) ? "Y" : "n";
                 if ((i % 16) == 0)
                         printf("0x%02x |", i);
                 printf(" %s", message);
                 if ((i % 16) == 15)
                         printf("\n");
         }
         return 0;
 }
Here's its output on my workstation:
isspace table.
Add the row and column numbers together (in hex).

     | 0 1 2 3 4 5 6 7 8 9 a b c d e f
-----+--------------------------------
0x00 | n n n n n n n n n Y Y Y Y Y n n
0x10 | n n n n n n n n n n n n n n n n
0x20 | Y n n n n n n n n n n n n n n n
0x30 | n n n n n n n n n n n n n n n n
0x40 | n n n n n n n n n n n n n n n n
0x50 | n n n n n n n n n n n n n n n n
0x60 | n n n n n n n n n n n n n n n n
0x70 | n n n n n n n n n n n n n n n n
0x80 | n n n n n n n n n n n n n n n n
0x90 | n n n n n n n n n n n n n n n n
0xa0 | n n n n n n n n n n n n n n n n
0xb0 | n n n n n n n n n n n n n n n n
0xc0 | n n n n n n n n n n n n n n n n
0xd0 | n n n n n n n n n n n n n n n n
0xe0 | n n n n n n n n n n n n n n n n
0xf0 | n n n n n n n n n n n n n n n n
0x1c through 0x1f are represented by the last four n characters on the second line, the 0x10 line. The fact that they're ns tells you that this C standard library doesn't consider those characters to be whitespace.

Like many bugs, this one has lingered for a long time. The behavior is present in Python 2, there's a ten-year-old issue on the Python issue tracker about this, and it's not making progress.

The second surprise has to do with bytes objects. Of course, bytes objects represent binary data, and don't necessarily represent characters. Even if they do, they don't have any encoding associated with them. However, for convenience--and backwards-compatibility with Python 2--Python's bytes objects support several method calls that treat the data as if it were "ASCII-compatible".

The surprise: These methods on Python bytes objects recognize a different set of whitespace characters. Here's the list of all bytes recognized by Python bytes objects as whitespace:

# char  decimal  hex    name
#######################################
'\t'    , #  9 - 0x09 - tab
'\n'    , # 10 - 0x0a - newline
'\v'    , # 11 - 0x0b - vertical tab
'\f'    , # 12 - 0x0c - form feed
'\r'    , # 13 - 0x0d - carriage return
' '     , # 32 - 0x20 - space

This list was derived by iterating over every possible byte value, and testing to see if the split() method on a Python bytes object splits at that byte.

The good news is, this list is the same as ASCII's list, and it agrees with Unicode. In fact this list is quite familiar to C programmers; it's the same whitespace characters recognized by the standard C function isspace() (in ctypes.h). Python has used this function to decide which characters are and aren't whitespace in 8-bit strings since its very beginning.

Notice that this list doesn't contain the four ASCII separator characters. That these two types in Python don't agree only enhances the mystery.

Line-breaking characters

The situation is slightly worse with line-breaking characters. Line-breaking characters (aka linebreaks) are a subset of whitespace characters; they're whitespace characters that always move the cursor down to the next line. And, as with whitespace generally, Python str objects don't agree with Unicode about what is and is not a line-breaking character, and Python bytes objects don't agree with either of those.

Here's the list of all Unicode characters recognized by Python str objects as line-breaking characters:

# char    decimal   hex      name
##########################################
'\n'    , #   10 0x000a - newline
'\v'    , #   11 0x000b - vertical tab
'\f'    , #   12 0x000c - form feed
'\r'    , #   13 0x000d - carriage return
'\x1c'  , #   28 0x001c - file separator
'\x1d'  , #   29 0x001d - group separator
'\x1e'  , #   30 0x001e - record separator
'\x85'  , #  133 0x0085 - next line
'\u2028', # 8232 0x2028 - line separator
'\u2029', # 8233 0x2029 - paragraph separator

This list was derived by iterating over every character defined in Unicode, and testing to see if the splitlines() method on a Python str object splits at that character.

Again, this is different from the list of characters defined as line-breaking whitespace in Unicode. And again it's because Python defines some of the four ASCII separator characters as line-breaking characters. In this case it's only the first three; Python doesn't consider the fourth, "unit separator", as a line-breaking character. (I don't know why Python draws this distinction... but then again, I don't know why it considers the first three to be line-breaking. It's all a mystery to me.)

Here's the list of all characters recognized by Python bytes objects as line-breaking characters:

# char  decimal hex      name
#######################################
'\n'    , #  10 0x000a - newline
'\r'    , #  13 0x000d - carriage return

This list was derived by iterating over every possible byte, and testing to see if the splitlines() method on a Python bytes object splits at that byte.

It's here we find our final unpleasant surprise: the methods on Python bytes objects don't consider '\v' (vertical tab) and '\f' (form feed) to be line-break characters. I assert this is also a bug. These are well understood to be line-breaking characters; "vertical tab" is like a "tab", except it moves the cursor down instead of to the right. And "form feed" moves the cursor to the top left of the next "page", which requires advancing at least one line.

How big handles this situation

To be crystal clear: the odds that any of this will cause a problem for you are extremely low. In order for it to make a difference:

you'd have to encounter text using one of these six characters where Python disagrees with Unicode and ASCII, and
you'd have to process the input based on some definition of whitespace, and
it would have to produce different results than you might have other wise expected, and
this difference in results would have to be important.

It seems extremely unlikely that all of these will be true for you.

In case this does affect you, big has a complete set of predefined whitespace tuples that will handle any of these situations. big defines a total of ten tuples, sorted into five categories.

In every category there are two values: one that contains whitespace, the other contains linebreaks. The whitespace tuple contains all the possible values of whitespace--characters that move the cursor either horizontally, or vertically, or both, but don't print anything visible to the screen. The linebreaks tuple contains the subset of whitespace characters that move the cursor vertically.

The most important two values start with str_: str_whitespace and str_linebreaks. These contain all the whitespace characters recognized by the Python str object.

Next are two values that start with unicode_: unicode_whitespace and unicode_linebreaks. These contain all the whitespace characters defined in the Unicode standard. They're the same as the str_ tuples except we remove the four ASCII separator characters.

Third, two values that start with ascii_: ascii_whitespace and ascii_linebreaks. These contain all the whitespace characters defined in ASCII. (Note that these contain str objects, not bytes objects.) They're the same as the unicode_ tuples, except we throw away all characters with a code point higher than 127.

Fourth, two values that start with bytes_: bytes_whitespace and bytes_linebreaks. These contain all the whitespace characters recognized by the Python bytes object. These tuples contain bytes objects, encoded using the ascii encoding. The list of characters is distinct from the other sets of tuples, and was derived as described above.

Finally we have the two tuples that lack a prefix: whitespace and linebreaks. These are the tuples you should use most of the time, and several big functions use them as default values. These are simply copies of str_whitespace and str_linebreaks respectively.

(big actually defines an additional ten tuples, as discussed in the very next section.)

The Unix, Mac, and DOS linebreak conventions

Historically, different platforms used different ASCII characters--or sequences of ASCII characters--to represent "go to the next line" in text files. Here are the most popular conventions:

\n    - UNIX, Amiga, macOS 10+
\r    - macOS 9 and earlier, many 8-bit computers
\r\n  - Windows, DOS

(There are a couple more conventions, and a lot more history, in the Wikipedia article on newlines.)

Handling these differing conventions was a real mess, for a long time--not just for computer programmers, but in the daily lives of many computer users. It was a continual problem for software developers back in the 90s, particularly those who frequently switched back and forth between the two platforms. And it took a long time before software development tooling figured out how to seamlessly handle all the newline conventions.

Python itself went through several iterations on how to handle this, eventually implementing "universal newlines" support, added way back in Python 2.3.

These days the world seems to have converged on the UNIX standard, '\n'; Windows supports it, and it's the default on every other modern platform. So in practice these days you probably don't have end-of-line conversion problems; as long as you're decoding files to Unicode, and you don't disable "universal newlines", it probably all works fine and you never even noticed.

However! big strives to behave identically to Python in every way. And even today, Python considers the DOS linebreak sequence to be one linebreak, not two.

The Python splitlines method on a string splits the string at linebreaks. And if the keepends positional parameter is True, it appends the linebreak character(s) at the end of each substring. A quick experiment with splitlines will show us what Python thinks is and isn't a linebreak. Sure enough, splitlines considers '\n\r' to be two linebreaks, but it treats \r\n as a single linebreak:

   ' a \n b \r c \r\n d \n\r e '.splitlines(True)

produces

   [' a \n', ' b \r', ' c \r\n', ' d \n', '\r', ' e ']

Naturally, if you use big to split by lines, you get the same result:

list(big.multisplit(' a \n b \r c \r\n d \n\r e ', big.linebreaks, separate=True, keep=True))

How do we achieve this? big has one more trick. All of the tuples defined in the previous section--from whitespace to ascii_linebreaks--also contain the DOS linebreak convention:

'\r\n'

(The equivalent bytes_ tuples contain the bytes equivalent, b'\r\n.)

Because of this inclusion, when you use one of these tuples with one of the big functions that take separators, it'll recognize \r\n as if it was one whitespace "character". (Just in case one happens to creep into your data.) And since functions like multisplit are "greedy", preferring the longest matching separator, if the string you're splitting contains '\r\n', it'll prefer matching '\r\n' to just '\r'.

If you don't want this behavior, just add the suffix _without_crlf to the end of any of the ten tuples, e.g. whitespace_without_crlf, bytes_linebreaks_without_crlf.

Whitespace and line-breaking characters for other platforms

What if you need to split text by whitespace, or by lines, but that text is in bytes format with an unusual encoding? big makes that easy too. If one of the builtin tuples won't work for you, you can can make your own tuple from scratch, or modify an existing tuple to meet your needs.

For example, let's say you need to split a document by whitespace, and the document is encoded in code page 850 or code page 437. (These two code pages are the most common code pages in English-speaking countries.)

Normally the easiest thing would be to decode it a str object using the 'cp850' or 'cp437' text codec as appropriate, then operate on it normally. But you might have reasons why you don't want to decode it--maybe the document is damaged and doesn't decode properly, and it's easier to work with the encoded bytes than to fix it. If you want to process the text with a big function that accepts a separator argument, you could make your own custom tuples of whitespace characters. These two codepages have the same whitespace characters as ASCII, but they both add one more: value 255, "non-breaking space", a space character that is not line-breaking. (The intention is, this character should behave like a space, except you shouldn't break a line at this character when word wrapping.)

It's easy to make the appropriate tuples yourself:

cp437_linebreaks = cp850_linebreaks = big.bytes_linebreaks
cp437_whitespace = cp850_whitespace = big.bytes_whitespace + (b'\xff',)

Those tuples would work fine as the separators argument for any big function that takes one.

What if you want to process a bytes object containing UTF-8? That's easy too. Just convert one of the existing tuples containing str objects using big.encode_strings. For example, to split a UTF-8 encoded bytes object b using the Unicode line-breaking characters, you could call:

multisplit(b, encode_strings(unicode_linebreaks, encoding='utf-8'))

Note that this technique probably won't work correctly for most other multibyte encodings, for example UTF-16. For these encodings, you should decode to str before processing.

Why? It's because multisplit could find matches in multibyte sequences straddling characters. Consider this example:

>>> haystack = '\u0101\u0102'
>>> needle = '\u0201'
>>> needle in haystack
False
>>>
>>> encoded_haystack = haystack.encode('utf-16-le')
>>> encoded_needle = needle.encode('utf-16-le')
>>> encoded_needle in encoded_haystack
True

The character '\u0201' doesn't appear in the original string, but the encoded version appears in the encoded string, as the second byte of the first character and the first byte of the second character:

>>> encoded_haystack
b'\x01\x01\x02\x01'
>>> encoded_needle
b'\x01\x02'

But you can avoid this problem if you know you're working in bytes on two-byte sequences. Split the bytes string into two-byte segments and operate on those.

Word wrapping and formatting

big contains three functions used to reflow and format text in a pleasing manner. In the order you should use them, they are split_text_with_code, wrap_words(),, and optionally merge_columns. This trio of functions gives you the following word-wrap superpowers:

Paragraphs of text representing embedded "code" don't get word-wrapped. Instead, their formatting is preserved.
Multiple texts can be merged together into multiple columns.

"text" vs "code"

The big word wrapping functions also distinguish between "text" and "code". The main distinction is, "text" lines can get word-wrapped, but "code" lines shouldn't. big considers any line starting with enough whitespace to be a "code" line; by default, this is four spaces. Any non-blank line that starting with four spaces is a "code" line, and any non-blank line that starts with less than four spaces is a "text" line.

In "text" mode:

words are separated by whitespace,
initial whitespace on the line is discarded,
the amount of whitespace between words is irrelevant,
individual newline characters are ignored, and
more than two newline characters are converted into exactly two newlines (aka a "paragraph break").

In "code" mode:

all whitespace is preserved, except for trailing whitespace on a line, and
all newline characters are preserved.

Also, whenever split_text_with_code switches between "text" and "code" mode, it emits a paragraph break.

Split text array

A split text array is an intermediary data structure used by big.text functions to represent text. It's literally just an array of strings, where the strings represent individual word-wrappable substrings.

split_text_with_code returns a split text array, and wrap_words() consumes a split text array.

You'll see four kinds of strings in a split text array:

Individual words, ready to be word-wrapped.
Entire lines of "code", preserving their formatting.
Line breaks, represented by a single newline: '\n'.
Paragraph breaks, represented by two newlines: '\n\n'.

Examples

This might be clearer with an example or two. The following text:

hello there!
this is text.


this is a second paragraph!

would be represented in a Python string as:

"hello there!\nthis is text.\n\n\nthis is a second paragraph!"

Note the three newlines between the second and third lines.

If you then passed this string in to split_text_with_code, it'd return this split text array:

[ 'hello', 'there!', 'this', 'is', 'text.', '\n\n',
  'this', 'is', 'a', 'second', 'paragraph!']

split_text_with_code merged the first two lines together into a single paragraph, and collapsed the three newlines separating the two paragraphs into a "paragraph break" marker (two newlines in one string).

Now let's add an example of text with some "code". This text:

What are the first four squared numbers?

    for i in range(1, 5):


        print(i**2)

Python is just that easy!

would be represented in a Python string as (broken up into multiple strings for clarity):

"What are the first four squared numbers?\n\n"
+
"    for i in range(1, 5):\n\n\n"
+
"        print(i**2)\n\nPython is just that easy!"

split_text_with_code considers the two lines with initial whitespace as "code" lines, and so the text is split into the following split text array:

['What', 'are', 'the', 'first', 'four', 'squared', 'numbers?', '\n\n',
  '    for i in range(1, 5):', '\n', '\n', '\n', '        print(i**2)', '\n\n',
  'Python', 'is', 'just', 'that', 'easy!']

Here we have a "text" paragraph, followed by a "code" paragraph, followed by a second "text" paragraph. The "code" paragraph preserves the internal newlines, though they are represented as individual "line break" markers (strings containing a single newline). Every paragraph is separated by a "paragraph marker".

Here's a simple algorithm for joining a split text array back into a single string:

prev = None
a = []
for word in split_text_array:
    if not (prev and prev.isspace() and word.isspace()):
        a.append(' ')
    a.append(word)
text = "".join(a)

Of course, this algorithm is too simple to do word wrapping. Nor does it handle adding two spaces after sentence-ending punctuation. In practice, you shouldn't do this by hand; you should use wrap_words.

Merging columns

merge_columns merges multiple strings into columns on the same line.

For example, it could merge these three Python strings:

[
"Here's the first\ncolumn of text.",
"More text over here!\nIt's the second\ncolumn!  How\nexciting!",
"And here's a\nthird column.",
]

into the following text:

Here's the first    More text over here!   And here's a
column of text.     It's the second        third column.
                    column!  How
                    exciting!

(Note that merge_columns doesn't do its own word-wrapping; instead, it's designed to consume the output of wrap_words.)

Each column is passed in to merge_columns as a "column tuple":

(s, min_width, max_width)

s is the string, min_width is the minimum width of the column, and max_width is the minimum width of the column.

As you saw above, s can contain newline characters, and merge_columns obeys those when formatting each column.

For each column, merge_columns measures the longest line of each column. The width of the column is determined as follows:

If the longest line is less than min_width characters long, the column will be min_width characters wide.
If the longest line is less than or equal to min_width characters long, and less than or equal to max_width characters long, the column will be as wide as the longest line.
If the longest line is greater than max_width characters long, the column will be max_width characters wide, and lines that are longer than max_width characters will "overflow".

Overflow

What is "overflow"? It's a condition merge_columns may encounter when the text in a column is wider than that column's max_width. merge_columns needs to consider both "overflow lines", lines that are longer than max_width, and "overflow columns", columns that contain one or more overflow lines.

What does merge_columns do when it encounters overflow? merge_columns supports three "strategies" to deal with this condition, and you can specify which one you want using its overflow_strategy parameter. The three strategies are:

OverflowStrategy.RAISE: Raise an OverflowError exception. The default.
OverflowStrategy.INTRUDE_ALL: Intrude into all subsequent columns on all lines where the overflowed column is wider than its max_width. The subsequent columns "make space" for the overflow text by not adding text on those overflowed lines; this is called "pausing" their output.
OverflowStrategy.DELAY_ALL: Delay all columns after the overflowed column, not beginning any until after the last overflowed line in the overflowed column. This is like the INTRUDE_ALL strategy, except that the columns "make space" by pausing their output until the last overflowed line.

Enhanced `TopologicalSorter`

Overview

big's TopologicalSorter is a drop-in replacement for graphlib.TopologicalSorter in the Python standard library (new in 3.9). However, the version in big has been greatly upgraded:

prepare is now optional, though it still performs a cycle check.
You can add nodes and edges to a graph at any time, even while iterating over the graph. Adding nodes and edges always succeeds.
You can remove nodes from graph g with the new method g.remove(node). Again, you can do this at any time, even while iterating over the graph. Removing a node from the graph always succeeds, assuming the node is in the graph.
The functionality for iterating over a graph now lives in its own object called a view. View objects implement the get_ready, done, and __bool__ methods. There's a default view built in to the graph object; the get_ready, done, and __bool__ methods on a graph just call into the graph's default view. You can create a new view at any time by calling the new view method.

Note that if you're using a view to iterate over the graph, and you modify the graph, and the view now represents a state that isn't coherent with the graph, attempting to use that view raises a RuntimeError. (I'll define what I mean by view "coherence" in the next subsection.)

This implementation also fixes some minor warts with the existing API:

In Python's implementation, static_order and get_ready/done are mutually exclusive. If you ever call get_ready on a graph, you can never call static_order, and vice-versa. The implementaiton in big doesn't have this restriction, because its implementation of static_order creates and uses a new view object every time it's called.
In Python's implementation, you can only iterate over the graph once, or call static_order once. The implementation in big solves this in several ways: it allows you to create as many views as you want, and you can call the new reset method on a view to reset it to its initial state.

View coherence

So what does it mean for a view to no longer be coherent with the graph? Consider the following code:

g = big.TopologicalSorter()
g.add('B', 'A')
g.add('C', 'A')
g.add('D', 'B', 'C')
g.add('B', 'A')
v = g.view()
g.ready() # returns ('A',)
g.add('A', 'Q')

First this creates a graph g with a classic "diamond" dependency pattern. Then it creates a new view v, and gets the currently "ready" nodes, which consists just of the node 'A'. Finally it adds a new dependency: 'A' depends on 'Q'.

At this moment, view v is no longer coherent. 'A' has been marked as "ready", but 'Q' has not. And yet 'A' depends on 'Q'. All those statements can't be true at the same time! So view v is no longer coherent, and any attempt to interact with v raises an exception.

To state it more precisely: if view v is a view on graph g, and you call g.add('Z', 'Y'), and neither of these statements is true in view v:

'Y' has been marked as done.
'Z' has not yet been yielded by get_ready.

then v is no longer "coherent".

(If 'Y' has been marked as done, then it's okay to make 'Z' dependent on 'Y' regardless of what state 'Z' is in. Likewise, if 'Z' hasn't been yielded by get_ready yet, then it's okay to make 'Z' dependent on 'Y' regardless of what state 'Y' is in.)

Note that you can restore a view to coherence. In this case, removing either Y or Z from g would resolve the incoherence between v and g, and v would start working again.

Also note that you can have multiple views, in various states of iteration, and by modifying the graph you may cause some to become incoherent but not others. Views are completely independent from each other.

Release history

0.13.1

2026/03/23

This is mostly a bugfix and polish release for 0.13, though I added one new helper class in big.template and a few small APIs.

linked_list got new APIs and a heap of bug fixes! It's more correct than ever!
- Added move() / rmove() to linked_list, linked_list_iterator, and linked_list_reverse_iterator. Moves nodes internally inside a linked list--like a cut followed by a splice, but cheaper.
- Breaking API change: splice used to allow you to pass in tail for where, and rsplice used to allow you to pass in head for where, and honestly its behavior was a little weird when you did. Those values are no longer allowed. The rule is: you can't ever add nodes before head or after tail; sadly, in 0.13, splice and rsplice got it wrong.
- reverse() and sort() now move nodes rather than swapping values; this means iterators continue to point to the same value. (What about special nodes? reverse reverses those too, just like data nodes; sort groups special nodes with their subsequent data node, or tail.)
- Fixed a number of iterator, locking, rotation, clearing, and cut/splice edge cases.
- The "head" and "tail" nodes are now instances of special classes that disallow writing to some attributes. This would have caught an obscure regression bug (which is also fixed) and should preclude similar bugs in the future.
string got one new feature and some str compatibility improvements:
- Added string.context: a property returning a string_context object. str(s.context) produces a "context string", showing the entire line s was sliced from, and adding a second line below it with a line of carets ("^^^") calling attention to s in context. This can make error messages even nicer! The full string_context object contains the individual components, as well as the full context string for multi-line strings. (str(s.context) only shows the first line of context for multi-line slices.)
- Lots of little bugfixes: reverse-slice edge cases, join([]), signed zfill(), removesuffix(''), partition('') and rpartition(''), and replace('', ...).
- Added broader __index__ support where string mirrors str APIs.
- Improved support for stateless subclasses of string. (If you want to subclass string and add new attributes, you'll probably have a rough time. File a bug and maybe we can improve the interfaces for you.)
Several quality-of-life improvements for the new Log class:
- The log object no longer logs the start banner or end banner unless some operation actually logs some (formatted) output. If you never log a message, you don't get spurious (and uninteresting) start and end banners.
- Mapping 'enter' or 'exit' to None in the formats dict you pass in to the constructor will suppress the enter and exit banners respectively.
- Log.write('') is ignored; you have to log some text for real to cause the start and end banners to happen.
- Note: I have a major, backwards-incompatible rewrite of Log under process. The Log interface will change some, Destination will change completely, and Sink will change a whole lot too. You're gonna love it! (In the meantime... don't get too comfortable!)
Added Formatter to big.template. Formatter is a reusable formatter for multi-line text templates with clever support for repeated / stretched line-fill fields via "starred interpolations".
StateManager fixes in big.state:
- If on_exit raises an exception, the transition is aborted; state remains unchanged, and next is reset to None.
- StateManager now handles observers raising an exception. If any observer raises an exception, StateManager remembers the first exception raised, continues calling the remaining observers, completes the transition, and then re-raises that first exception.
- Observer lists are no longer cached internally--they're now snapshotted at the start of every transition. This fixes an obscure edge case: if you replaced one observer A with another observer B, and A == B even though they're different objects, the StateMachine wouldn't refresh its cache and would continue calling A.
- Trimmed no-op StateMachine.on_enter and StateMachine.on_exit methods. They were useless in and of themselves, but I put them there on the theory that they'd help with autocomplete for these methods in subclasses when using advanced editors like PyCharm. But that's not a strong enough reason to keep 'em. Sorry, you'll just have to type def on_enter(self): by hand yourself, like some sort of caveman.
big.text multi-function fixes and polish:
- multistrip, multisplit, and multipartition/multirpartition now correctly accept one-shot iterables--like generators--for their separator argument.
- multistrip: fixed strip=PROGRESSIVE when maxsplit=None.
- Added __index__ support for maxsplit and count parameters.
- Documentation updates, reflecting these functions returning slices of the original object (rather than guaranteed str or bytes objects). This has been true for a while, but the documentation was stale.
Minor bugfixes in parse_template_string in big.template:
- Improved error message for an unterminated comment; it now shows where the comment started, not where it ended.
- Now catch tokenization errors and re-raise a nicer exception.

0.13

2026/02/17

It's been more than a year... and I've been busy!

Added three new modules:
- big.types, which contains core types,
- big.tokens, useful functions and values when working with Python's tokenizer, and
- big.template, functions that parse strings containing a simple template syntax.
Added linked_list to new module big.types. linked_list is a thoughtful implementation of a standard linked list data structure, with an API and UX modeled on Python's list and collections.deque objects. Unlike Python's builtins, you're permitted to add and remove values to a linked_list while iterating. linked_list also supports locking.
Added string to new module big.types. string is a subclass of str that tracks line number and column number offsets for you. Just initialize one big string containing an entire file, and every substring of that string will know its line number, column number, and offset in characters from the beginning.
big.lines and all the "lines modifier" functions are now deprecated; string replaces all of it (and it's a massive upgrade!). big.lines will move to the deprecated module no sooner than March 2026, and will be removed no sooner than November 2026.
Added strip_indents and strip_line_comments to big.text. These provide the same functionality as the old lines_strip_indent and lines_strip_line_comments line modifier functions, but now operate on iterables of strings instead of "lines" iterators.
Added Pattern to big.text. This is a wrapper around re.Pattern that preserves slices of str subclasses.
Added parse_template_string and eval_template_string to new module big.template.
- parse_template_string parses a string containing Jinja-like interpolations, and returns an iterator that yields strings and Interpolation objects. (This is similar to "t-strings" in Python 3.14+.)
- eval_template_string calls parse_template_string to parse a string, then evaluates the expressions (and filters) using eval. It returns the resulting string with all substitutions rendered.
Rewrote BoundInnerClass, and it's a huge improvement. The rewrite removes some old concerns:
- You no longer need the parent.cls hack! (Well, you do if you support Python 3.6, but it's no longer needed in Python 3.7+. Bound inner class adds a new function, bound_inner_base, to help with the transition.)
- The bound inner class implementation now relies on comparison by identity instead of by name, which means you may now add aliases and/or rename your inner classes to your heart's content.
- Bound inner classes no longer keep a strong reference to the outer instance; they use weakrefs. This reduces reference cycles, making it easier to reclaim abandoned bound inner class objects, albeit at the cost of adding a weakref "get ref" call every time a bound inner class is instantiated.
- Bound inner classes now have explicit support for slots!
- Bound inner classes now have accurate signatures, preserving the signature of the original class's __init__ but with the outer parameter removed.
- BoundInnerClass adds locking, to prevent a race condition when caching the same bound inner class created simultaneously in multiple threads. It's rarely used and should have no real impact on performance.
Added new functions to the big.boundinnerclass module:
- unbound returns the unbound base class of cls if cls is a bound inner class.
- is_boundinnerclass returns true if called on a class decorated with @BoundInnerClass, whether or not it has been bound to an instance.
- is_unboundinnerclass returns true if called on a class decorated with @UnboundInnerClass, whether or not it has been bound to an instance.
- is_bound returns true if called on a bound inner class that has been bound to an instance.
- bound_to returns the instance that cls has been bound to, if cls is a bound inner class bound to an instance.
- [type_bound_to](#type_bound_tocls) returns the instance that type(o) has been bound to, if type(o) is a bound inner class bound to an instance.
- bound_inner_base is only needed to use BoundInnerClass with Python 3.6. It's unnecessary in Python 3.7+.
Added generate_tokens to new module big.tokens. generate_tokens is a convenience wrapper around Python's tokenize.generate_tokens, which has an abstruse "readline"-based interface. tokens.generate_tokens instead lets you simply pass in a string object, and returns a generator yielding tokens. It also preserves slices of str subclasses--if the string you pass in is a big.string object, the string values it yields will be slices from that original big.string!
The big.tokens module also contains definitions for every token defined by any version of Python supported by big (3.6+). big's version always starts with TOKEN_, e.g. token.COMMA is big.tokens.TOKEN_COMMA. Tokens not defined in the currently running version of Python have a value of TOKEN_INVALID, which is -1.
Added iterator_context to big.itertools. iterator_context is like an extended version of Python's enumerate, directly inspired by Jinja's "loop special variables" and Mako's "loop context". It wraps an iterator and provides helpful metadata.
Added iterator_filter to big.itertools. iterator_filter is a pass-through iterator that filters values. You pass in an iterator, and rules for what values you want to see / don't want to see, and it returns an iterator that only yields the values you want.
Rewrote the entire big.log module. I'd stopped using the old Log class, yet on a couple recent projects I hacked up a quick-and-dirty log... clearly the old Log wasn't solving my problem anymore. The new Log is designed explicitly for lightweight logging, mostly for debugging. It's simple to use, feature-rich, high-performance, and by default runs in "threaded" mode where logging calls are 5x faster than calling print!
- I added a backwards-compatible OldLog to big.log in case anybody is using the old Log class. This provides the API and functionality of the old Log class, but is reimplemented on top of the new Log. Hopefully the way I did it will ease your transition to the obviously-superior new Log. The old Log has been relocated to the big.deprecated module. Both OldLog and the old Log are deprecated, and will be removed someday, no earlier than March 2027.
Added ModuleManager to big.builtin. ModuleManager helps you manage a module's namespace, making it easy to populate __all__ and clean up temporary symbols.
Added ClassRegistry to big.builtin. ClassRegistry helps you use inheritance with heavily nested class hierarchies, by giving you a place to store references to base classes you can access later. Very useful with BoundInnerClass!
The string returned by big.time.timestamp_human now includes the timezone, using the local timezone by default. If you want to override that and use a specific timezone, you can pass in a datetime.timezone object via the new tzinfo keyword-only parameter.
Added support for Python 3.14, mainly to support t-strings:
- python_delimiters now recognizes all the new string prefixes containing t (or T).
- big.tokens supports the new tokens associated with t-strings, although that's a new module anyway.
Sped up test/test_text.py. The tests confirm that big's list of whitespace characters is accurate. It used to test if a particular character c was whitespace by using len(f'a{c}b'.split()) == 2. D'oh! It's obviously much faster to simply ask it with c.isspace(). The resulting loop runs 3x faster... saving a whole 0.1 seconds on my workstation! Modifying the equivalent code for bytes instead of Unicode objects is also faster, but that optimization only saved 0.0000014 seconds. Hat tip to Eric V. Smith for his suggestions on how to make Big's test suite so much faster!
split_quoted_strings in big.text now obeys subclasses of str better. (It now works well with big.string for example.)
Removed a bunch of old deprecated stuff:
- Old names for sets of characters:
  - whitespace_without_dos
  - ascii_whitespace_without_dos
  - newlines
  - newlines_without_dos
  - ascii_newlines
  - ascii_newlines_without_dos
  - utf8_whitespace
  - utf8_whitespace_without_dos
  - utf8_newlines
  - utf8_newlines_without_dos
- Old functions / classes / aliases:
  - split_quoted_strings
  - lines_strip_comments
  - parse_delimiters and its associated stuff:
    - Delimiter (a class)
    - delimiter_parentheses
    - delimiter_square_brackets
    - delimiter_curly_braces
    - delimiter_angle_brackets
    - delimiter_single_quote
    - delimiter_double_quotes
    - parse_delimiters_default_delimiters
    - parse_delimiters_default_delimiters_bytes
  - The old alias lines_filter_comment_lines

0.12.8

2025/01/06

Added search_path to the big.file module. search_path implements "search path" functionality; given a list of directories, a filename, and optionally a list of file extensions to try, returns the first existing file that matches.
multisplit and split_delimiters now properly support subclasses of str. All strings yielded by these functions are now guaranteed to be slices of the original s parameter passed in, or otherwise produced by making method calls on the original s parameter that return strings.

0.12.7

2024/12/15

A teeny tiny new feature.

LineInfo now supports a copy method, which returns a copy of the LineInfo object in its current state.

0.12.6

2024/12/13

It's a big release tradition! Here's another small big release, less than a day after the last big big release.

New feature: decode_python_script now supports "universal newlines". It accepts a new newline parameter which behaves identically to the newline parameter for Python's built-in open function.
Bugfix: The universal newlines support for read_python_file was broken in 0.12.5; the newline parameter was simply ignored. It now works great--it passes newline to decode_python_script. (Sorry I missed this; I use Linux and don't need to convert newlines.)
Added Python 3.13 to the list of supported releases. It was already supported and tested, it just wasn't listed in the project metadata.

Note: Whoops! Forgot to ever release 0.12.6 as a package. Oh well.

0.12.5

2024/12/13

Added decode_python_script to the big.text module. decode_python_script scans a binary Python script and decodes it to Unicode--correctly. Python scripts can specify an explicit encoding in two diferent ways: a Unicode "byte order mark", or a PEP 263 "source file encoding" line. decode_python_script handles either, both, or neither.
Added read_python_file to the big.file module. read_python_file reads a binary Python file from the filesystem and decodes it using decode_python_script.
Added python_delimiters to the big.text module. This is a new predefined set of delimiters for use with split_delimeters, enabling it to correctly process Python scripts. python_delimiters defines all delimiters defined by Python, including all 100 possible string delimiters (no kidding!). If you want to parse the delimiters of Python code, and you don't want to use the Python tokenizer, you should use python_delimiters with split_delimiters.

Note that defining python_delimiters correctly was difficult, and big's Delimiters API isn't expressive enough to express all of Python's semantics. At this point the python_delimiters object doesn't itself actually define all its semantics; rather, at module load time it's compiled into a special internal runtime format which is cached, and then there's manually-written code that tweaks this compiled form so python_delimiters can correctly handle Python's special cases. So, you're encouraged to use python_delimiters, but if you modify it and use the modified version, the modified version won't inherit all those tweaks, and will lose the ability to handle many of Python's weirder semantics.

Important note: When you use python_delimiters, you must include the linebreak characters in the lines you split using split_delimiters. This is necessary to support the comment delimiter correctly, and to enforce the no-linebreaks-inside-single-quoted-strings rule.

There can be small differences in Python's syntax from one version to another. python_delimiters is therefore version-sensitive, using the semantics appropriate for the version of Python it's being run under. If you want to parse Python delimiters using the semantics of another version of the language, use instead python_delimiters_version[s] where s is a string containing the dotted Python major and minor version you want to use, for example python_delimiters_version["3.10"] to use Python 3.10 semantics. (At the moment there are no differences between versions; this is planned for future versions of big.)
Added python_delimiters_version to the big.text module. This maps simple Python version strings ("3.6", "3.13") to python_delimiters values implementing the semantics for that version. Currently all the values of this dict are identical, but that should change in the future.
A breaking API change to split_delimiters is coming.

split_delimiters now yields an object that can yield either three or four values. Previous to 0.12.5, the split_delimiters iterator always yielded a tuple of three values, called text, open, and close. But python_delimiters required adding a fourth value, change.

When change is true, we are changing from one delimiter to another, without entering a new nested delimiter. The canonical example of this is inside a Python f-string:
```
`f"{abc:35}"`
```
Here the colon (:) is a "change" delimiter. Inside the curly braces inside the f-string, before the colon, the hash character (#) acts as a line comment character. But after the colon it's just another character. We've changed semantics, but we haven't pushed a new delimiter pair. The only way to accurately convey this behavior was to add this new change field to the values yielded by split_delimiters.

The goal is to eventually transition to split_delimiters yielding all four of these values (text, open, close, and change). But this will be a gradual process; as of 0.12.5, existing split_delimiters calls will continue to work unchanged.

split_delimiters now yields a custom object, called SplitDelimitersValue. This object is configurable to yield either three or four values. The rules are:
- If you pass in yields=4 to split_delimiters, the object it yields will yield four values.
- If you pass in delimiters=python_delimiters to split_delimiters, the object it yields will yield four values. (python_delimiters is new, so any calls using it must be new code, therefore this change won't break existing calls.)
- Otherwise, the object yielded by split_delimiters will yield three values, as it did in versions prior to 0.12.5.
split_delimiters will eventually change to always yielding four values, but big won't publish this change until at least June 2025. Six months after that change--at least December 2025--big will remove the yields parameter to split_delimiters.
Minor semantic improvement: PushbackIterator no longer evaluates the iterator you pass in in a boolean context. (All we really needed to do was compare it to None, so now that's all we do.)
A minor change to the Delimiter object used with split_delimiters: previously, the quoting and escape values had to agree, either both being true or both being false. However, python_delimiters necessitated relaxing this restriction, as there are some delimiters (! inside curly braces in an f-string, : inside curly braces in an f-string) that are "quoting" but don't have an escape string. So now, the restriction is simply that if escape is true, quoting must also be true.

0.12.4

2024/11/15

New function in the text module: format_map. This works like Python's str.format_map method, except it allows nested curly-braces. Example: big.format_map("The {extension} file is {{extension} size} bytes.", {'extension': 'mp3', 'mp3 size': 8555})
New method: Version.format is like strftime but for Version objects. You pass in a format string with Version attributes in curly braces and it formats the string with values from that Version object.
The Version constructor now accepts a packaging.Version object as an initializer. Embrace and extend!
lines now takes two new arguments:
- clip_linebreaks, default is true. If true, it clips the linebreaks off the lines before yielding them, otherwise it doesn't. (Either way, the linebreaks are still stored in info.end.)
- source, default is an empty string. source should represent the source of the line in a meaninful way to the user. It's stored in the LinesInfo objects yielded by lines, and should be incorporated into error messages.
LineInfo.clip_leading and LineInfo.clip_trailing now automatically detect if you've clipped the entire line, and if so move all clipped text to info.trailing (and adjust the column_number accordingly).
LineInfo.clip_leading and LineInfo.clip_trailing: Minor performance upgrade. Previously, if the user passed in the string to clip, the two functions would throw it away then recreate it. Now they just use the passed-in string.
Changed the word "newline" to "linebreak" everywhere. They mean the same thing, but the Unicode standard consistently uses the word "linebreak"; I assume the boffins on the committee thought about this a lot and argued and finally settled on this word for good (if unpublished?) reasons.
Add explicit support (and CI coverage & testing) for Python 3.13. (big didn't need any changes, it was already 100% compatible with 3.13.)

p.s. 56

0.12.3

2024/09/17

Optimized split_delimiters. The new version uses a much more efficient internal representation of how to react to the various delimiters when processing the text. Perfunctory timeit experiments suggest this new split_delimiters is maybe 5-6% faster than it was in 12.2.

Minor breaking change: split_delimiters now consistently raises SyntaxError for mismatched delimiters. (Previously it would sometimes raise ValueError.)

0.12.2

2024/09/11

A minor semantic change to lines_strip_indent: when it encounters a whitespace-only line, it clips the line to trailing in the LineInfo object. It used to clip such lines to leading. But this changed LineInfo.column_number in a nonsensical way.

This behavior is policy going forward: if a lines modifer function ever clips the entire line, it must clip it to trailing rather than leading. It shouldn't matter one way or another, as whitespace-only lines arguably shouldn't have any explicit semantics. But it makes intuitive sense to me that their empty line should be at column number 1, rather than 9 or 13 or whatnot. (Especially considering that with lines_strip_indent their indent value is synthetic anyway, inferred by looking ahead.)
Major cleanup to the lines modifier test suites.

0.12.1

2024/09/07

In fine big tradition, here's an update published immediately after a big release.

Surprisingly, even though this is only a small update, it still adds two new packages to big: metadata and version.

There's sadly one breaking change.

`big.metadata`

New package. A package containing metadata about big itself. Currently only contains one thing: version.

`big.version`

New package. A package for working with version information.

`lines_strip_line_comments`

This API has breaking changes.

The default value for quotes has changed. Now it's what it should always have been: empty. No quote marks are defined by default, which means the default behavior of lines_strip_line_comments is now to simply truncate the line at the leftmost comment marker.

Processing quote marks by default was always too opinionated for this function. Consider: having ' active as a quote marker meant that single-quotes need to be balanced,

which means you can't process a line like this that only has one.

Wish I'd figured this out before the release yesterday! Hopefully this will only cause smiles, and no teeth-gnashing.

`metadata.version`

New value. A Version object representing the current version of big.

`Version`

New class. Version represents a version number. You can construct them from PEP 440-compliant version strings, or specify them using keyword-only parameters. Version objects are immutable, ordered, and hashable.

0.12

2024/09/06

Lots of changes this time! Most of 'em are in the big.text module, particularly the lines and lines modifier functions. But plenty of other modules got in on the fun too.

big even has a new module: deprecated. Deprecated functions and classes get moved into this module. Note that the contents of deprecated are not automatically imported into big.all.

The following functions and classes have breaking changes:

Delimiter

LineInfo

lines_strip_line_comments

split_delimiters

split_quoted_strings

These functions have been renamed:

lines_filter_comment_lines is now lines_filter_line_comment_lines

lines_strip_comments is now lines_strip_line_comments

parse_delimiters is now split_delimiters

big has five new functions:

combine_splits

encode_strings

LineInfo.clip_leading

LineInfo.clip_trailing

split_title_case

Finally, here's an in-depth description of all changes in big 0.12, sorted by API name.

`bytes_linebreaks` and `bytes_linebreaks_without_crlf`

Extremely minor change! Python's bytes and str objects don't agree on which ASCII characters represent line breaks. The str object obeys the Unicode standard, which means there are four:

\n \v \f \r

For some reason, Python's bytes object only supports two:

\n \r

I have no idea why this is. We might fix it. And if we do, big is ready. It now calculates bytes_linebreaks and bytes_linebreaks_without_crlf on the fly to agree with Python. If either (or both) work as newline characters for the splitlines method on a bytes object, they'll automatically be inserted into these iterables of bytes linebreaks.

`combine_splits`

New function. If you split a string two different ways, producing two arrays that sum to the original string, combine_splits will merge those splits together, producing a new array that splits in every place any of the two split arrays had a split.

Example:

>>> big.combine_splits("abcdefg", ['a', 'bcdef', 'g'], ['abc', 'd', 'efg'])
['a, 'bc', 'd', 'ef', 'g']

`Delimiter`

This API has breaking changes.

Delimiter is a simple data class, representing information about delimiters to split_delimiters (previously parse_delimiters). split_delimiters has changed, and some of those changes are reflected in the Delimiter object; also, some changes to Delimiter are simply better API choices.

The old Delimiter object is deprecated but still available, as big.deprecated.Delimiter. It should only be used with big.deprecated.parse_delimiters, which is also deprecated. big.deprecated.Delimiter will be removed when big.deprecated.parse_delimiters is removed, which will be no sooner than September 2025.

Changes:

The first argument to the old Delimiter object was open, and was stored as the open attribute. These have both been completely removed. Now, the "open delimiter" is specified as a key in a dictionary of delimiters, mapping open delimiters to Delimiter objects.
The old Delimiter object had a boolean backslash attribute; if it was True, that delimiter allows escaping using a backslash. Now Delimiter has an escape parameter and attribute, specifying the escape string you want to use inside that set of delimiters.
Delimiter also now has two new attributes, quoting and multiline. These default to False and True respectively; you can specify values for these with keyword-only arguments to the constructor.
The new Delimiter object is read-only after construction, and is hashable.

`encode_strings`

Slightly liberalized the types it accepts. It previously required o to be a collection; now o can be a bytes or str object. Also, it now explicitly supports set.

`get_int_or_float`

Minor behavior change. If the o you pass in is a float, or can be converted to float (but couldn't be converted directly to an int), get_int_or_float will experimentally convert that float to an int. If the resulting int compares equal to that float, it'll return the int, otherwise it'll return the float.

For example, get_int_or_float("13.5") still returns 13.5 (a float), but get_int_or_float("13.0") now returns 13 (an int). (Previously, get_int_or_float("13.0") would have returned 13.0.)

This better represents the stated aesthetic of the function--it prefers ints to floats. And since the int is exactly equal to the float, I assert this is completely backwards compatible.

`Heap`

Minor updates to the documentation and to the text of some exceptions.

`LineInfo`

This API has breaking changes.

Breaking change: the LineInfo constructor has a new lines positional parameter, added in front of the existing positional parameters. This new first argument should be the lines iterator that yielded this LineInfo object. It's stored in the lines attribute. (Why this change? The lines object contains information needed by the lines modifiers, for example tab_width.)

Minor optimization: LineInfo objects previously had many optional fields, which might or might not be added dynamically. Now all fields are pre-added. (This makes the CPython 3.13 runtime happier; it really wants you to set all your class's attributes in its __init__.)

Minor breaking change: the original string stored in the line attribute now includes the linebreak character, if any. This means concatenating all the info.line strings will reconstruct the original s passed in to lines.

New feature: while some methods used to update the leading attribute when they clipped leading text from the line, the "lines modifiers" are now very consistent about updating leading, and the new symmetrical attribute trailing.

New feature: LineInfo now has an end attribute, which contains the end-of-line character that ended this line.

These three attributes allow us to assert a new invariant: as long as you modify the contents of line (e.g. turning tabs into spaces),

info.leading + line + info.trailing + info.end == info.line

LineInfo objects now always have these attributes:

lines, which contains the base lines iterator.
line, which contains the original unmodified line.
line_number, which contains the line number of this line.
column_number, which contains the starting column number of the first character of this line.
indent, which contains the indent level of the line if computed, and None otherwise.
leading, which contains the string stripped from the beginning of the line. Initially this is the empty string.
trailing, which contains the string stripped from the end of the line. Initially this is the empty string.
end, which is the end-of-line character that ended the current line. For the last line yielded, info.end will always be the empty string. If the last character of the text split by lines was an end-of-line character, the last line yielded will be the empty string, and info.end will also be the empty string.
match, which contains a Match object if this line was matched with a regular expression, and None otherwise.

`LineInfo.clip_leading` and `LineInfo.clip_trailing`

LineInfo also has two new methods: LineInfo.clip_leading and LineInfo.clip_trailing(line, s). These methods clip a leading or trailing substring from the current line, and transfer it to the relevant field in LineInfo (either leading or trailing). clip_leading also updates the column_number attribute.

The name "clip" was chosen deliberately to be distinct from "strip". "strip" functions on strings remove substrings and throws them away; my "clip" functions on strings removes substrings and puts them somewhere else.

`lines_filter_comment_lines`

lines_filter_comment_lines has been renamed to lines_filter_line_comment_lines. For backwards compatibility, the function is also available under the old name; this old name will eventually be removed, but not before September 2025.

`lines_filter_line_comment_lines`

This API has breaking changes.

New name for lines_filter_comment_lines.

Correctness improvements: lines_filter_line_comment_lines now enforces that single-quoted strings can't span lines, and multi-quoted strings must be closed before the end of the last line.

Minor optimization: for every line, it used to lstrip a copy of the line, then use a regular expression to see if the line started with one of the comment characters. Now the regular expression itself skips past any leading whitespace.

`lines_grep`

New feature: lines_grep has always used re.search to examine the lines yielded. It now writes the result to info.match. (If you pass in invert=True to lines_grep, lines_grep still writes to the match attribute--but it always writes None.)

If you want to write the re.Match object to another attribute, pass in the name of that attribute to the keyword-only parameter match.

`lines_rstrip` and `lines_strip`

New feature: lines_rstrip and lines_strip now both accept a separators argument; this is an iterable of separators, like the argument to multisplit. The default value of None preserves the previous behavior, stripping whitespace.

`lines_sort`

New feature: lines_sort now accepts a key parameter, which is used as the key argument for list.sort. The value passed in to key is the (info, line) tuple yielded by the upstream iterator. The default value preserves the previous behavior, sorting by the line (ignoring the info).

`lines_strip_comments`

This function has been renamed lines_strip_line_comments and rewritten, see below. The old deprecated version will be available at big.deprecated.lines_strip_comments until at least September 2025.

Note that the old version of line_strip_comments still uses the current version of LineInfo, so use of this deprecated function is still exposed to those breaking changes. (For example, LineInfo.line now includes the linebreak character that terminated the current line, if any.)

`lines_strip_indent`

Bugfix: lines_strip_indent previously required whitespace-only lines to obey the indenting rules, which was a mistake. My intention was always for lines_strip_indent to behave like Python, and that includes not really caring about the intra-line-whitespace for whitespace-only lines. Now lines_strip_indent behaves more like Python: a whitespace-only line behaves as if it has the same indent as the previous line. (Not that the indent value of an empty line should matter--but this behavior is how you'd intuitively expect it to work.)

`lines_strip_line_comments`

This API has breaking changes.

lines_strip_line_comments is the new name for the old lines_strip_comments lines modifier function. It's also been completely rewritten.

Changes:

The old function required quote marks and the escape string to be single characters. The new function allows quote marks and the escape string to be of any length.
The old function had a slightly-smelly triple_quotes parameter to support multiline strings. The new version supports separate parameters for single-line quote marks (quotes) and multiline quote marks (multiline_quotes).
The backslash parameter has been renamed to escape.
The rstrip parameter has been removed. If you need to rstrip the line after stripping the comment, wrap your lines_strip_line_comments call with a lines_rstrip call.
The old function didn't enforce that strings shouldn't span lines--single-quoted and triple-quoted strings behaved identically. The new version raises SyntaxError if quoted strings using non-multiline quote marks contain newlines.

(lines_strip_line_comments has always been implemented using split_quoted_strings; this is why it now supports multicharacter quote marks and escape strings. It also benefits from the new optimizations in split_quoted_strings.)

`multisplit`

Minor optimizations. multisplit used to locally define a new generator function, then call it and return the generator. I promoted the generator function to module level, which means we no longer rebind it each time multisplit is called. As a very rough guess, this can be as much as a 10% speedup for multisplit run on very short workloads. (It's also never slower.)

I also applied this same small optimization to several other functions in the text module. In particular, merge_columns was binding functions inside a loop (!!). (Dumb, huh!) These local functions are still bound inside merge_columns, but now at least they're outside the loop.

Another minor speedup for multisplit: when reverse=True, it used to reverse the results three times! multisplit now explicitly observes and manages the reversed state of the result to avoid needless reversing.

`parse_delimiters`

This function has been renamed split_delimiters and rewritten, see below. The old version is still available, using the name big.deprecated.parse_delimiters module, and will be available until at least September 2025.

`Scheduler`

Code cleanups both in the implementation and the test suite, including one minor semantic change.

Cleaned up Scheduler._next, the internal method call that implements the heart of the scheduler. The only externally visible change: the previous version would call sleep(0) every time it yielded an event. On modern operating systems this should yields the rest of the current thread's current time slice back to the OS's scheduler. This can make multitasking smoother, particularly in Python programs. But this is too opinionated for library code--if you want a sleep(0) there, by golly, you can call that yourself when the Scheduler object yields to you. I've restructured the code and eliminated this extraneous sleep(0).

Also, rewrote big chunks of the test suite (tests/test_scheduler.py). The multithreaded tests are now much better synchronized, while also becoming easier to read. Although it seems intractable to purge all race conditions from the test suite, this change has removed most of them.

`split_delimiters`

This API has breaking changes.

split_delimiters is the new name for the old parse_delimiters function. The function has also been completely re-tooled and re-written.

Changes:

parse_delimiters took an iterable of Delimiters objects, or strings of length 2. split_delimiters takes a dictionary mapping open delimiter strings to Delimiter objects, and Delimiter objects no longer have an "open" attribute.
split_delimiters now accepts an state parameter, which specifies the initial state of nested delimiters.
split_delimiters no longer cares if there were unclosed open delimiters at the end of the string. (It used to raise ValueError.) This includes quote marks; if you don't want quoted strings to span multiple lines, it's up to you to detect it and react (e.g. raise an exception).
The internal implementation has changed completely. parse_delimiters manually parsed the input string character by character. split_delimiters uses multisplit, so it zips past the uninteresting characters and only examines the delimiters and escape characters. It's always faster, except for some trivial calls (which are fast enough anyway).
Another benefit of using multisplit: open delimiters, close delimiters, and the escape string may now all be any nonzero length. (In the face of ambiguity, split_delimiters will always choose the longer delimiter.)

`split_quoted_strings`

This API has breaking changes.

split_quoted_strings has been completely re-tooled and re-written. The new API is simpler, easier to understand, and conceptually clarified. It's a major upgrade!

Changes:

The value it yields is different:
- The old version yielded (is_quote, segment), where is_quote was a boolean value indicating whether or not segment was quoted. If segment was quoted, it began and ended with (single character) quote marks. To reassemble the original string, join together all the segment strings in order.
- The new version yields (leading_quote, segment, trailing_quote), where leading_quote and trailing_quote are either matching quote marks or empty. If they're true values, the segment string is inside the quotes. To reassemble the original string, join together all the yielded strings in order.
The backslash parameter has been replaced by a new parameter, escape. escape allows specifying the escape string, which defaults to '\' (backslash). If you specify a false value, there will be no escape character in strings.
By default quotes only contains ' (single-quote) and " (double-quote). The previous version also recognized """ and ''' as multiline quote marks by default; this is no longer true, as it's too opinionated and Python-specific.
The old version didn't actually distinguish between single-quoted strings and triple-quoted strings. It simply didn't care whether or not there were newlines inside quoted strings. The new version raises a SyntaxError if there's a newline character inside a string delimited with a quote marker from quotes.
The old version accepted a stinky triple_quotes parameter. That's been removed in favor of a new parameter, multiline_quotes. multiline_quotes is like quotes, except that newline characters are allowed inside their quoted strings.
split_quoted_string accepts another new parameter, state, which sets the initial state of quoting.
Thd old implementation of split_quoted_string used a hand-coded parser, manually analyzing each character in the input text. Now it uses multisplit, so it only bothers to examine the interesting substrings. multisplit has a large startup cost the first time you use a particular set of iterators, but this information is cached for subsequent calls. Bottom line, the new version is much faster for larger workloads. (It can be slower for trivial examples... where speed doesn't matter anyway.)
Another benefit of switching to multisplit: quotes now supports quote delimiters and an escape string of any nonzero length. In the case of ambiguity--if more than one quote delimiter matches at a time--split_quoted_string will always choose the longer delimiter.

`split_title_case`

New function. split_title_case splits a string at word boundaries, assuming the string is in "TitleCase".

`StateManager`

Small performance upgrade for StateManager. observers. StateManager always uses a copy of the observer list (specifically, a tuple) when calling the observers; this means it's safe to modify the observer list at any time. StateManager used to always make a fresh copy every time you called an event; now it uses a cached copy, and only recomputes the tuple when the observer list changes.

(Note that it's not thread-safe to modify the observer list from one thread while also dispatching events in another. Your program won't crash, but the list of observers called may be unpredictable based on which thread wins or loses the race. But this has always been true. As with many libraries, the StateManager API leaves locking up to you.)

p.s. I'm getting close to declaring big as being version 1.0. I don't want to do it until I'm done revising the APIs.

p.p.p.s. Yet again I thank Eric V. Smith for his willingness to humor me in my how-many-parameters-could-dance-on-the-head-of-a-pin API theological discussions.

0.11

released 2023/09/19

Breaking change: renamed almost all the old whitespace and newlines tuples. Worse yet, one symbol has the same name but a different value: ascii_whitespace! I've also changed the suffix _without_dos to the more accurate and intuitive _without_crlf, and similarly changed newlines to linebreaks. Sorry for all the confusion. This resulted from a lot of research into whitespace and newline characters, in Python, Unicode, and ASCII; please see the new tutorial Whitespace and line-breaking characters in Python and big to see what all the fuss is about. Here's a summary of all the changes to the whitespace tuples:

  RENAMED TUPLES (old name -> new name)
    ascii_newlines               -> bytes_linebreaks
    ascii_whitespace             -> bytes_whitespace
    newlines                     -> linebreaks

    ascii_newlines_without_dos   -> bytes_linebreaks_without_crlf
    ascii_whitespace_without_dos -> bytes_whitespace_without_crlf
    newlines_without_dos         -> linebreaks_without_crlf
    whitespace_without_dos       -> whitespace_without_crlf

  REMOVED TUPLES
    utf8_newlines
    utf8_whitespace

    utf8_newlines_without_dos
    utf8_whitespace_without_dos

  UNCHANGED TUPLES (same name, same meaning)
    whitespace

  NEW TUPLES
    ascii_linebreaks
    ascii_whitespace
    str_linebreaks
    str_whitespace
    unicode_linebreaks
    unicode_whitespace

    ascii_linebreaks_without_crlf
    ascii_whitespace_without_crlf
    str_linebreaks_without_crlf
    str_whitespace_without_crlf
    unicode_linebreaks_without_crlf
    unicode_whitespace_without_crlf

Changed split_text_with_code implementation to use StateManager. (No API or semantic changes, just an change to the internal implementation.)
New function in the big.text module: encode_strings, which takes a container object containing str objects and returns an equivalent object containing encoded versions of those strings as bytes.
When you call multisplit with a type mismatch between 's' and 'separators', the exception it raises now includes the values of 's' and 'separators'.
Added more tests for big.state to exercise all the string arguments of accessor and dispatch.
The exhaustive multisplit tester now lets you specify test cases as cohesive strings, rather than forcing you to split the string manually.
The exhaustive multisplit tester is better at internally verifying that it's doing the right thing. (There are some internal sanity checks, and those are more accurate now.)
Whoops! The name of the main class in big.state is StateManager. I accidentally wrote StateMachine instead in the docs... several times.
Originally the multisplit parameter 'separators' was required. I changed it to optional a while ago, with a default of None. (If you pass in None it uses big.str_whitespace or big.bytes_whitespace, depending on the type of s.) But the documentation didn't reflect this change until... now.
Improved the prose in The multi- family of string functions tutorial. Hopefully now it does a better job of selling multisplit to the reader.
The usual smattering of small doc fixes and improvements.

My thanks again to Eric V. Smith for his willingness to consider and discuss these issues. Eric is now officially a contributor to big, increasing the project's bus factor to two. Thanks, Eric!

0.10

released 2023/09/04

Added the new big.state module, with its exciting StateManager class!
int_to_words now supports the new ordinal keyword-only parameter, to produce ordinal strings instead of cardinal strings. (The number 1 as a cardinal string is 'one', but as an ordinal string is 'first').
Added the pure_virtual decorator to big.builtin.
The documentation is now much prettier! I finally discovered a syntax I can use to achieve a proper indent in Markdown, supported by both GitHub and PyPI. You simply nest the text you want indented inside an HTML description list as the description text, and skip the description item (<dl><dd>). Note that you need a blank line after the <dl><dd> line, or else Markdown will ignore the markup in the following paragraph. Thanks to Hugo van Kemenade for his help confirming this! Oh, and, Hugo also fixed the image markup so the big banner displays properly on PyPI. Thanks, Hugo!

0.9.2

released 2023/07/22

Extremely minor release. No new features or bug fixes.

Fixed coverage, now back to the usual 100%. (This just required changing the tests, which didn't find any new bugs.)
Made the tests for Log deterministic. They now use a fake clock that always returns the same values.
Added GitHub Actions integration. Tests and coverage are run in the cloud after every checkin. Thanks to Dan Pope for gently walking me through this!
Fixed metadata in the pyproject.toml file.
Added badges for testing, coverage, and supported Python versions.

0.9.1

released 2023/06/28

Added the new big.log module, with its new Log class! I wrote this for another project--but it turned out so nice I just had to add it to big!

0.9

released 2023/06/15

Bugfix! If an outer class Outer had an inner class Inner decorated with @BoundInnerClass, and o is an instance of Outer, and o evaluated to false in a boolean context, o.Inner would be the unbound version of Inner. Now it's the bound version, as is proper.
Modified tests/test_boundinnerclasses.py:
- Added regression test for the above bugfix (of course!).
- It now takes advantage of that newfangled "zero-argument super".
- Added testing of an unbound subclass of an unbound subclass.

0.8.3

released 2023/06/11

Added int_to_words.
All tests now insert the local big directory onto sys.path, so you can run the tests on your local copy without having to install. Especially convenient for testing with old versions of Python!

Note: tomorrow, big will be one year old!

0.8.2

released 2023/05/19

Convert all iterator functions to use my new approach: instead of checking arguments inside the iterator, the function you call checks arguments, then has a nested iterator function which it runs and returns the result. This means bad inputs raise their exceptions at the call site where the iterator is constructed, rather than when the first value is yielded by the iterator!

0.8.1

released 2023/05/19

Added parse_delimiters (ed: now split_delimiters) and Delimiter.

0.8

released 2023/05/18

Major retooling of str and bytes support in big.text.
- Functions in big.text now uniformly accept str or bytes or a subclass of either. See the Support for bytes and str section for how it works.
- Functions in big.text are now more consistent about raising TypeError vs ValueError. If you mix bytes and str objects together in one call, you'll get a TypeError, but if you pass in an empty iterable (of a correct type) where a non-empty iterable is required you'll get a ValueError. big.text generally tries to give the TypeError higher priority; if you pass in a value that fails both the type check and the value check, the big.text function will raise TypeError first.
Major rewrite of re_rpartition. I realized it had the same "reverse mode" problem that I fixed in multisplit back in version 0.6.10: the regular expression should really search the string in "reverse mode", from right to left. The difference is whether the regular expression potentially matches against overlapping strings. When in forwards mode, the regular expression should prefer the leftmost overlapping match, but in reverse mode it should prefer the rightmost overlapping match. Most of the time this produces the same list of matches as you'd find searching the string forwards--but sometimes the matches come out very different. This was way harder to fix with re_rpartition than with multisplit, because Python's re module only supports searching forwards. I have to emulate reverse-mode searching by manually checking for overlapping matches and figuring out which one(s) to keep--a lot of work! Fortunately it's only a minor speed hit if you don't have overlapping matches. (And if you do have overlapping matches, you're probably just happy re_rpartition now produces correct results--though I did my best to make it performant anyway.) In the future, big will probably add support for the PyPI package regex, which reimplements Python's re module but adds many features... including reverse mode!
New function: reversed_re_finditer. Behaves almost identically to the Python standard library function re.finditer, yielding non-overlapping matches of pattern in string. The difference is, reversed_re_finditer searches string from right to left. (Written as part of the re_rpartition rewrite mentioned above.)
Added apostrophes, double_quotes, ascii_apostrophes, ascii_double_quotes, utf8_apostrophes, and utf8_double_quotes to the big.text module. Previously the first four of these were hard-coded strings inside gently_title. (And the last two didn't exist!)
Code cleanup in split_text_with_code, removed redundant code. I think it has about the same number of if statements; if anything it might be slightly faster.
Retooled re_partition and re_rpartition slightly, should now be very-slightly faster. (Well, re_rpartition will be slower if your pattern finds overlapping matches. But at least now it's correct!)
Lots and lots of doc improvements, as usual.

0.7.1

released 2023/03/13

Tweaked the implementation of multisplit. Internally, it does the string splitting using re.split, which returns a list. It used to iterate over the list and yield each element. But that meant keeping the entire list around in memory until multisplit exited. Now, multisplit reverses the list, pops off the final element, and yields that. This means multisplit drops all references to the split strings as it iterates over the string, which may help in low-memory situations.
Minor doc fixes.

0.7

released 2023/03/11

Breaking changes to the Scheduler:
- It's no longer thread-safe by default, which means it's much faster for non-threaded workloads.
- The lock has been moved out of the Scheduler object and into the Regulator. Among other things, this means that the Scheduler constructor no longer takes a lock argument.
- Regulator is now an abstract base class. big.scheduler also provides two concrete implementations: SingleThreadedRegulator and ThreadSafeRegulator.
- Regulator and Event are now defined in the big.scheduler namespace. They were previously defined inside the Scheduler class.
- The arguments to the Event constructor were rearranged. (You shouldn't care, as you shouldn't be manually constructing Event objects anyway.)
- The Scheduler now guarantees that it will only call now and wake on a Regulator object while holding that Regulator's lock.
Minor doc fixes.

0.6.18

released 2023/03/09

Retooled multisplit and multistrip argument verification code. Both functions now consistently check all their inputs, and use consistent error messages when raising an exception.

0.6.17

released 2023/03/09

Fixed a minor crashing bug in multisplit: if you passed in a list of separators (or separators was of any non-hashable type), and reverse was true, multisplit would crash. It used separators as a key into a dict, which meant separators had to be hashable.
multisplit now verifies that the s passed in is either str or bytes.
Lots of doc fixes.

0.6.16

released 2023/02/26

Fixed Python 3.6 support! Some equals-signs-in-f-strings and some other anachronisms had crept in. 0.6.16 has been tested on all versions from 3.6 to 3.11 (as well as having 100% coverage).
Made the dateutils package an optional dependency. Only one function needs it, parse_timestamp_3339Z().
Minor cleanup in PushbackIterator(). It also uses slots now, which should make it a bit faster.

0.6.15

released 2023/01/07

Added the new functions datetime_ensure_timezone(d, timezone) and datetime_set_timezone(d, timezone). These allow you to ensure or explicitly set a timezone on a datetime.datetime object.
Added the timezone argument to parse_timestamp_3339Z().
gently_title() now capitalizes the first letter after a left parenthesis.
Changed the secret multirpartition function slightly. Its reverse parameter now means to un-reverse its reversing behavior. Stated another way, multipartition(reverse=X) and multirpartition(reverse=not X) now do the same thing.

0.6.14

released 2022/12/11

Improved the text of the RuntimeError raised by TopologicalSorter.View when the view is incoherent. Now it tells you exactly what nodes are conflicting.
Expanded the tutorial on multisplit.

0.6.13

released 2022/12/11

Changed translate_filename_to_exfat(s) behavior: when modifying a string with a colon (':') not followed by a space, it used to convert it to a dash ('-'). Now it converts the colon to a period ('.'), which looks a little more natural. A colon followed by a space is still converted to a dash followed by a space.

0.6.12

tagged 2022/12/04

Bugfix: When calling TopologicalSorter.print(), it sorts the list of nodes, for consistency's sakes and for ease of reading. But if the node objects don't support < or > comparison, that throws an exception. TopologicalSorter.print() now catches that exception and simply skips sorting. (It's only a presentation thing anyway.)
Added a secret (otherwise undocumented!) function: multirpartition, which is like multipartition but with reverse=True.
Added the list of conflicted nodes to the "node is incoherent" exception text.

Note: although version 0.6.12 was tagged, it was never packaged for release.

0.6.11

tagged 2022/11/13

Changed the import strategy. The top-level big module used to import all its child modules, and import * all the symbols from all those modules. But a friend (hi Mark Shannon!) talked me out of this. It's convenient, but if a user doesn't care about a particular module, why make them import it. So now the top-level big module contains nothing but a version number, and you can either import just the submodules you need, or you can import big.all to get all the symbols (like big itself used to do).

Note: although version 0.6.11 was tagged, it was never packaged for release.

0.6.10

released 2022/10/26

All code changes had to do with multisplit:
- Fixed a subtle bug. When splitting with a separator that can overlap itself, like ' x ', multisplit will prefer the leftmost instance. But when reverse=True, it must prefer the rightmost instance. Thanks to Eric V. Smith for suggesting the clever "reverse everything, call re.split, and un-reverse everything" approach. That let me fix this bug while still implementing on top of re.split!
- Implemented PROGRESSIVE mode for the strip keyword. This behaves like str.strip: when splitting, strip on the left, then start splitting. If we don't exhaust maxsplit, strip on the right; if we do exhaust maxsplit, don't strip on the right. (Similarly for str.rstrip when reverse=True.)
- Changed the default for strip to False. It used to be NOT_SEPARATE. But this was too surprising--I'd forget that it was the default, and turning on keep wouldn't return everything I thought I should get, and I'd head off to debug multisplit, when in fact it was behaving as specified. The Principle Of Least Surprise tells me that strip defaulting to False is less surprising. Also, maintaining the invariant that all the keyword-only parameters to multisplit default to False is a helpful mnemonic device in several ways.
- Removed NOT_SEPARATE (and the not-yet-implemented STR_STRIP) modes for strip. They're easy to implement yourself, and this removes some surface area from the already-too-big multisplit API.
Modernized pyproject.toml metadata to make flit happier. This was necessary to ensure that pip install big also installs its dependencies.

0.6.8

released 2022/10/16

Renamed two of the three freshly-added lines modifier functions: lines_filter_contains is now lines_containing, and lines_filter_grep is now lines_grep.

0.6.7

released 2022/10/16

Added three new lines modifier functions to the text module: lines_filter_contains, lines_filter_grep, and lines_sort.
gently_title now accepts str or bytes. Also added the apostrophes and double_quotes arguments.

0.6.6

released 2022/10/14

Fixed a bug in multisplit. I thought when using keep=AS_PAIRS that it shouldn't ever emit a 2-tuple containing just empty strings--but on further reflection I've realized that that's correct. This behavior is now tested and documented, along with the reasoning behind it.
Added the reverse flag to re_partition.
whitespace_without_dos and newlines_without_dos still had the DOS end-of-line sequence in them! Oops!
- Added a unit test to check that. The unit test also ensures that whitespace, newlines, and all the variants (utf8_, ascii_, and _with_dos) exactly match the set of characters Python considers whitespace and newline characters.
Lots more documentation and formatting fixes.

0.6.5

released 2022/10/13

Added the new itertools module, which so far only contains PushbackIterator.
Added lines_strip_comments [ed: now lines_strip_line_comments and split_quoted_strings to the text module.

0.6.1

released 2022/10/13

I realized that whitespace should contain the DOS end-of-line sequence ('\r\n'), as it should be considered a single separator when splitting etc. I added that, along with whitespace_no_dos, and naturally utf8_whitespace_no_dos and ascii_whitespace_no_dos too.
Minor doc fixes.

0.6

released 2022/10/13

A big upgrade!

Completely retooled and upgraded multisplit, and added multistrip and multipartition, collectively called The multi- family of string functions. (Thanks to Eric Smith for suggesting multipartition! Well, sort of.)
- [multisplit](#multisplits-separatorsnone--keepfalse-maxsplit-1-reversefalse-separatefalse-stripfalse) now supports five (!) keyword-only parameters, allowing the caller to tune its behavior to an amazing degree.
- Also, the original implementation of [multisplit](#multisplits-separatorsnone--keepfalse-maxsplit-1-reversefalse-separatefalse-stripfalse) got its semantics a bit wrong; it was inconsistent and maybe a little buggy.
- multistrip is like str.strip but accepts an iterable of separator strings. It can strip from the left, right, both, or neither (in which case it does nothing).
- multipartition is like str.partition, but accepts an iterable of separator strings. It can also partition more than once, and supports reverse=True which causes it to partition from the right (like str.rpartition).
- Also added useful predefined lists of separators for use with all the multi functions: whitespace and newlines, with ascii_ and utf8_ versions of each, and without_dos variants of all three newlines variants.
Added the Scheduler and Heap classes. Scheduler is a replacement for Python's sched.scheduler class, with a modernized interface and a major upgrade in functionality. Heap is an object-oriented interface to Python's heapq module, used by Scheduler. These are in their own modules, big.heap and big.scheduler.
Added lines and all the lines_ modifiers. These are great for writing little text parsers. For more information, please see the tutorial on lines and lines modifier functions.
Removedstripped_lines and rstripped_lines from the text module, as they're superceded by the far superior lines family.
Enhanced normalize_whitespace. Added the separators and replacement parameters, and added support for bytes objects.
Added the count parameter to re_partition and re_rpartition.

0.5.2

released 2022/09/12

Added stripped_lines and rstripped_lines to the text module.
Added support for len to the TopologicalSorter object.

0.5.1

released 2022/09/04

Added gently_title and normalize_whitespace to the text module.
Changed translate_filename_to_exfat to handle translating ':' in a special way. If the colon is followed by a space, then the colon is turned into ' -'. This yields a more natural translation when colons are used in text, e.g. 'xXx: The Return Of Xander Cage' is translated to 'xXx - The Return Of Xander Cage'. If the colon is not followed by a space, turns the colon into '-'. This is good for tiresome modern gobbledygook like 'Re:code', which will now be translated to 'Re-code'.

0.5

released 2022/06/12

Initial release.

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0.13.1

Mar 24, 2026

0.13

Feb 17, 2026

0.12.8

Jan 7, 2025

0.12.7

Dec 15, 2024

0.12.5

Dec 13, 2024

0.12.4

Nov 15, 2024

0.12.3

Sep 18, 2024

0.12.2

Sep 12, 2024

0.12.1

Sep 8, 2024

0.12

Sep 6, 2024

0.11

Sep 19, 2023

0.10

Sep 4, 2023

0.9.2

Jul 21, 2023

0.9.1

Jun 28, 2023

0.9

Jun 16, 2023

0.8.3

Jun 12, 2023

0.8.2

May 19, 2023

0.8.1

May 19, 2023

0.8

May 18, 2023

0.7.1

Mar 13, 2023

0.7

Mar 11, 2023

0.6.18

Mar 9, 2023

0.6.17

Mar 9, 2023

0.6.16

Feb 27, 2023

0.6.15

Jan 8, 2023

0.6.14

Dec 12, 2022

0.6.13

Dec 6, 2022

0.6.12

Dec 4, 2022

0.6.11

Nov 14, 2022

0.6.10

Oct 27, 2022

0.6.8

Oct 16, 2022

0.6.7

Oct 16, 2022

0.6.6

Oct 15, 2022

0.6.5

Oct 14, 2022

0.6.1

Oct 13, 2022

0.6

Oct 13, 2022

0.5.2

Sep 11, 2022

0.5.1

Sep 3, 2022

0.5

Jun 12, 2022

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big 0.13.1

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Copyright 2022-2026 by Larry Hastings

Why use big?

Using big

The best of big

Index

Modules

Functions, classes, and values

Tutorials

API Reference, By Module

big.all

big.boundinnerclass

BoundInnerClass(cls)

UnboundInnerClass(cls)

bound_inner_base(cls)

bound_to(cls)

BOUNDINNERCLASS_OUTER_ATTR

BOUNDINNERCLASS_OUTER_SLOTS

is_bound(cls)

is_boundinnerclass(cls)

is_unboundinnerclass(cls)

type_bound_to(instance)

unbound(cls)

big.builtin

ClassRegistry()

get_float(o, default=_sentinel)

get_int(o, default=_sentinel)

get_int_or_float(o, default=_sentinel)

ModuleManager()

pure_virtual()

try_float(o)

try_int(o)

big.deprecated

big.file

fgrep(path, text, *, encoding=None, enumerate=False, case_insensitive=False)

file_mtime(path)

file_mtime_ns(path)

file_size(path)

grep(path, pattern, *, encoding=None, enumerate=False, flags=0)

pushd(directory)

read_python_file(path, *, newline=None, use_bom=True, use_source_code_encoding=True)

safe_mkdir(path)

safe_unlink(path)

search_path(paths, extensions=('',), *, case_sensitive=None, preserve_extension=True, want_directories=False, want_files=True)

touch(path)

translate_filename_to_exfat(s)

translate_filename_to_unix(s)

big.graph

CycleError()

TopologicalSorter(graph=None)

TopologicalSorter.copy()

TopologicalSorter.cycle()

TopologicalSorter.print(print=print)

TopologicalSorter.remove(node)

TopologicalSorter.reset()

TopologicalSorter.view()

TopologicalSorter.View

TopologicalSorter.View.__bool__()

TopologicalSorter.View.close()

TopologicalSorter.View.copy()

TopologicalSorter.View.done(*nodes)

TopologicalSorter.View.print(print=print)

TopologicalSorter.View.ready()

TopologicalSorter.View.reset()

big.heap

Heap(i=None)

Heap.append(o)

Heap.clear()

Heap.copy()

Heap.extend(i)

Heap.remove(o)

Heap.popleft()

`big.all`

`big.boundinnerclass`

`BoundInnerClass(cls)`

`UnboundInnerClass(cls)`

`bound_inner_base(cls)`

`bound_to(cls)`

`BOUNDINNERCLASS_OUTER_ATTR`

`BOUNDINNERCLASS_OUTER_SLOTS`

`is_bound(cls)`

`is_boundinnerclass(cls)`

`is_unboundinnerclass(cls)`

`type_bound_to(instance)`

`unbound(cls)`

`big.builtin`

`ClassRegistry()`

`get_float(o, default=_sentinel)`

`get_int(o, default=_sentinel)`

`get_int_or_float(o, default=_sentinel)`

`ModuleManager()`

`pure_virtual()`

`try_float(o)`

`try_int(o)`

`big.deprecated`

`big.file`

`fgrep(path, text, *, encoding=None, enumerate=False, case_insensitive=False)`

`file_mtime(path)`

`file_mtime_ns(path)`

`file_size(path)`

`grep(path, pattern, *, encoding=None, enumerate=False, flags=0)`

`pushd(directory)`

`read_python_file(path, *, newline=None, use_bom=True, use_source_code_encoding=True)`

`safe_mkdir(path)`

`safe_unlink(path)`

`search_path(paths, extensions=('',), *, case_sensitive=None, preserve_extension=True, want_directories=False, want_files=True)`

`touch(path)`

`translate_filename_to_exfat(s)`

`translate_filename_to_unix(s)`

`big.graph`

`CycleError()`

`TopologicalSorter(graph=None)`

`TopologicalSorter.copy()`

`TopologicalSorter.cycle()`

`TopologicalSorter.print(print=print)`

`TopologicalSorter.remove(node)`

`TopologicalSorter.reset()`

`TopologicalSorter.view()`

`TopologicalSorter.View`

`TopologicalSorter.View.bool()`

`TopologicalSorter.View.close()`

`TopologicalSorter.View.copy()`

`TopologicalSorter.View.done(*nodes)`

`TopologicalSorter.View.print(print=print)`

`TopologicalSorter.View.ready()`

`TopologicalSorter.View.reset()`

`big.heap`

`Heap(i=None)`

`Heap.append(o)`

`Heap.clear()`

`Heap.copy()`

`Heap.extend(i)`

`Heap.remove(o)`

`Heap.popleft()`

`Heap.append_and_popleft(o)`

`Heap.popleft_and_append(o)`

`Heap.queue`

`big.itertools`

`iterator_context(iterator, start=0)`

`iterator_filter(iterator, *, stop_at_value=undefined, stop_at_in=None, stop_at_predicate=None, stop_at_count=None, reject_value=undefined, reject_in=None, reject_predicate=None, only_value=undefined, only_in=None, only_predicate=None)`

`PushbackIterator(iterable=None)`

`PushbackIterator.next(default=None)`

`PushbackIterator.push(o)`

`big.log`

`default_clock()`

`Log(*destinations, **options)`

`Log.box(s)`

`Log.close(block=True)`

`Log.enter(message)`

`Log.exit()`

`Log.flush(block=True)`

`Log.map_destination(o)`