Configs#

Configs provide library-level storage of configuration settings. These settings can be made in code or from environment variables.

config#

alias of NetworkXConfig(backend_priority=BackendPriorities(algos=[], generators=[]), backends=Config(parallel=ParallelConfig(active=False, backend=’loky’, n_jobs=None, verbose=0, temp_folder=None, max_nbytes=’1M’, mmap_mode=’r’, prefer=None, require=None, inner_max_num_threads=None, backend_params={}), cugraph=Config(use_compat_graphs=True), graphblas=Config()), cache_converted_graphs=True, fallback_to_nx=False, warnings_to_ignore=set())

class NetworkXConfig(**kwargs)[source]#

Configuration for NetworkX that controls behaviors such as how to use backends.

Attribute and bracket notation are supported for getting and setting configurations:

>>> nx.config.backend_priority == nx.config["backend_priority"]
True
Parameters:
backend_prioritylist of backend names or dict or BackendPriorities

Enable automatic conversion of graphs to backend graphs for functions implemented by the backend. Priority is given to backends listed earlier. This is a nested configuration with keys algos, generators, and, optionally, function names. Setting this value to a list of backend names will set nx.config.backend_priority.algos. For more information, see help(nx.config.backend_priority). Default is empty list.

backendsConfig mapping of backend names to backend Config

The keys of the Config mapping are names of all installed NetworkX backends, and the values are their configurations as Config mappings.

cache_converted_graphsbool

If True, then save converted graphs to the cache of the input graph. Graph conversion may occur when automatically using a backend from backend_priority or when using the backend= keyword argument to a function call. Caching can improve performance by avoiding repeated conversions, but it uses more memory. Care should be taken to not manually mutate a graph that has cached graphs; for example, G[u][v][k] = val changes the graph, but does not clear the cache. Using methods such as G.add_edge(u, v, weight=val) will clear the cache to keep it consistent. G.__networkx_cache__.clear() manually clears the cache. Default is True.

fallback_to_nxbool

If True, then “fall back” and run with the default “networkx” implementation for dispatchable functions not implemented by backends of input graphs. When a backend graph is passed to a dispatchable function, the default behavior is to use the implementation from that backend if possible and raise if not. Enabling fallback_to_nx makes the networkx implementation the fallback to use instead of raising, and will convert the backend graph to a networkx-compatible graph. Default is False.

warnings_to_ignoreset of strings

Control which warnings from NetworkX are not emitted. Valid elements:

  • "cache": when a cached value is used from G.__networkx_cache__.

Notes

Environment variables may be used to control some default configurations:

  • NETWORKX_BACKEND_PRIORITY: set backend_priority.algos from comma-separated names.

  • NETWORKX_CACHE_CONVERTED_GRAPHS: set cache_converted_graphs to True if nonempty.

  • NETWORKX_FALLBACK_TO_NX: set fallback_to_nx to True if nonempty.

  • NETWORKX_WARNINGS_TO_IGNORE: set warnings_to_ignore from comma-separated names.

and can be used for finer control of backend_priority such as:

  • NETWORKX_BACKEND_PRIORITY_ALGOS: same as NETWORKX_BACKEND_PRIORITY to set backend_priority.algos.

This is a global configuration. Use with caution when using from multiple threads.

class Config(**kwargs)[source]#

The base class for NetworkX configuration.

There are two ways to use this to create configurations. The recommended way is to subclass Config with docs and annotations.

>>> class MyConfig(Config):
...     '''Breakfast!'''
...
...     eggs: int
...     spam: int
...
...     def _on_setattr(self, key, value):
...         assert isinstance(value, int) and value >= 0
...         return value
>>> cfg = MyConfig(eggs=1, spam=5)

Another way is to simply pass the initial configuration as keyword arguments to the Config instance:

>>> cfg1 = Config(eggs=1, spam=5)
>>> cfg1
Config(eggs=1, spam=5)

Once defined, config items may be modified, but can’t be added or deleted by default. Config is a Mapping, and can get and set configs via attributes or brackets:

>>> cfg.eggs = 2
>>> cfg.eggs
2
>>> cfg["spam"] = 42
>>> cfg["spam"]
42

For convenience, it can also set configs within a context with the “with” statement:

>>> with cfg(spam=3):
...     print("spam (in context):", cfg.spam)
spam (in context): 3
>>> print("spam (after context):", cfg.spam)
spam (after context): 42

Subclasses may also define _on_setattr (as done in the example above) to ensure the value being assigned is valid:

>>> cfg.spam = -1
Traceback (most recent call last):
    ...
AssertionError

If a more flexible configuration object is needed that allows adding and deleting configurations, then pass strict=False when defining the subclass:

>>> class FlexibleConfig(Config, strict=False):
...     default_greeting: str = "Hello"
>>> flexcfg = FlexibleConfig()
>>> flexcfg.name = "Mr. Anderson"
>>> flexcfg
FlexibleConfig(default_greeting='Hello', name='Mr. Anderson')
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