# config¶

The configuration is loaded upon import from a YAML file in the directory where PyPhi is run: pyphi_config.yml. If no file is found, the default configuration is used.

The various options are listed here with their defaults

>>> import pyphi
>>> defaults = pyphi.config.DEFAULTS


It is also possible to manually load a YAML configuration file within your script:

>>> pyphi.config.load_config_file('pyphi_config.yml')


Or load a dictionary of configuration values:

>>> pyphi.config.load_config_dict({'SOME_CONFIG': 'value'})


## Theoretical approximations¶

This section with deals assumptions that speed up computation at the cost of theoretical accuracy.

• pyphi.config.ASSUME_CUTS_CANNOT_CREATE_NEW_CONCEPTS: In certain cases, making a cut can actually cause a previously reducible concept to become a proper, irreducible concept. Assuming this can never happen can increase performance significantly, however the obtained results are not strictly accurate.

>>> defaults['ASSUME_CUTS_CANNOT_CREATE_NEW_CONCEPTS']
False


## System resources¶

These settings control how much processing power and memory is available for PyPhi to use. The default values may not be appropriate for your use-case or machine, so please check these settings before running anything. Otherwise, there is a risk that simulations might crash (potentially after running for a long time!), resulting in data loss.

• pyphi.config.PARALLEL_CONCEPT_EVALUATION: Control whether concepts are evaluated in parallel when computing constellations.

>>> defaults['PARALLEL_CONCEPT_EVALUATION']
False

• pyphi.config.PARALLEL_CUT_EVALUATION: Control whether system cuts are evaluated in parallel, which requires more memory. If cuts are evaluated sequentially, only two BigMip instances need to be in memory at once.

>>> defaults['PARALLEL_CUT_EVALUATION']
True


Warning

PARALLEL_CONCEPT_EVALUATION and PARALLEL_CUT_EVALUATION should not both be set to True. Enabling both parallelization modes will slow down computations. If you are doing $$\Phi$$-computations (with big_mip, main_complex, etc.) PARALLEL_CUT_EVALUATION will be fastest. Use PARALLEL_CONCEPT_EVALUATION if you are only computing constellations.

• pyphi.config.NUMBER_OF_CORES: Control the number of CPU cores used to evaluate unidirectional cuts. Negative numbers count backwards from the total number of available cores, with -1 meaning “use all available cores.”

>>> defaults['NUMBER_OF_CORES']
-1

• pyphi.config.MAXIMUM_CACHE_MEMORY_PERCENTAGE: PyPhi employs several in-memory caches to speed up computation. However, these can quickly use a lot of memory for large networks or large numbers of them; to avoid thrashing, this options limits the percentage of a system’s RAM that the caches can collectively use.

>>> defaults['MAXIMUM_CACHE_MEMORY_PERCENTAGE']
50


## Caching¶

PyPhi is equipped with a transparent caching system for the BigMip and Concept objects, which stores them as they are computed to avoid having to recompute them later. This makes it easy to play around interactively with the program, or to accumulate results with minimal effort. For larger projects, however, it is recommended that you manage the results explicitly, rather than relying on the cache. For this reason it is disabled by default.

• pyphi.config.CACHE_BIGMIPS: Control whether BigMip objects are cached and automatically retreived.

>>> defaults['CACHE_BIGMIPS']
False


Note

Concept caching only has an effect when a database is used as the the caching backend.

• pyphi.config.NORMALIZE_TPMS: Control whether TPMs should be normalized as part of concept normalization. TPM normalization increases the chances that a precomputed concept can be used again, but is expensive.

>>> defaults['NORMALIZE_TPMS']
True

• pyphi.config.CACHING_BACKEND: Control whether precomputed results are stored and read from a database or from a local filesystem-based cache in the current directory. Set this to ‘fs’ for the filesystem, ‘db’ for the database. Caching results on the filesystem is the easiest to use but least robust caching system. Caching results in a database is more robust and allows for caching individual concepts, but requires installing MongoDB.

>>> defaults['CACHING_BACKEND']
'fs'

• pyphi.config.FS_CACHE_VERBOSITY: Control how much caching information is printed. Takes a value between 0 and 11. Note that printing during a loop iteration can slow down the loop considerably.

>>> defaults['FS_CACHE_VERBOSITY']
0

• pyphi.config.FS_CACHE_DIRECTORY: If the caching backend is set to use the filesystem, the cache will be stored in this directory. This directory can be copied and moved around if you want to reuse results _e.g._ on a another computer, but it must be in the same directory from which PyPhi is being run.

>>> defaults['FS_CACHE_DIRECTORY']
'__pyphi_cache__'

• pyphi.config.MONGODB_CONFIG: Set the configuration for the MongoDB database backend. This only has an effect if the caching backend is set to use the database.

>>> defaults['MONGODB_CONFIG']['host']
'localhost'
>>> defaults['MONGODB_CONFIG']['port']
27017
>>> defaults['MONGODB_CONFIG']['database_name']
'pyphi'
>>> defaults['MONGODB_CONFIG']['collection_name']
'cache'

• pyphi.config.REDIS_CACHE: Specifies whether to use Redis to cache Mice.

>>> defaults['REDIS_CACHE']
False

• pyphi.config.REDIS_CONFIG: Configure the Redis database backend. These

are the defaults in the provided redis.conf file.

>>> defaults['REDIS_CONFIG']['host']
'localhost'
>>> defaults['REDIS_CONFIG']['port']
6379


## Logging¶

These are the settings for PyPhi logging. You can control the format of the logs and the name of the log file. Logs can be written to standard output, a file, both, or none. See the documentation on Python’s logger for more information.

• pyphi.config.LOGGING_CONFIG['file']['enabled']: Control whether logs are written to a file.

>>> defaults['LOGGING_CONFIG']['file']['enabled']
True

• pyphi.config.LOGGING_CONFIG['file']['filename']: Control the name of the logfile.

>>> defaults['LOGGING_CONFIG']['file']['filename']
'pyphi.log'

• pyphi.config.LOGGING_CONFIG['file']['level']: Control the concern level of file logging. Can be one of 'DEBUG', 'INFO', 'WARNING', 'ERROR', or 'CRITICAL'.

>>> defaults['LOGGING_CONFIG']['file']['level']
'INFO'

• pyphi.config.LOGGING_CONFIG['stdout']['enabled']: Control whether logs are written to standard output.

>>> defaults['LOGGING_CONFIG']['stdout']['enabled']
True

• pyphi.config.LOGGING_CONFIG['stdout']['level']: Control the concern level of standard output logging. Same possible values as file logging.

>>> defaults['LOGGING_CONFIG']['stdout']['level']
'WARNING'

• pyphi.config.LOG_CONFIG_ON_IMPORT: Controls whether the current configuration is printed when PyPhi is imported.

>>> defaults['LOG_CONFIG_ON_IMPORT']
True


## Numerical precision¶

• pyphi.config.PRECISION: Computations in PyPhi rely on finding the Earth Mover’s Distance. This is done via an external C++ library that uses flow-optimization to find a good approximation of the EMD. Consequently, systems with zero $$\Phi$$ will sometimes be computed to have a small but non-zero amount. This setting controls the number of decimal places to which PyPhi will consider EMD calculations accurate. Values of $$\Phi$$ lower than 10e-PRECISION will be considered insignificant and treated as zero. The default value is about as accurate as the EMD computations get.

>>> defaults['PRECISION']
6


## Miscellaneous¶

• pyphi.config.VALIDATE_SUBSYSTEM_STATES: Control whether PyPhi checks if the subsystems’s state is possible (reachable from some past state), given the subsystem’s TPM (which is conditioned on background conditions). If this is turned off, then calculated $$\Phi$$ values may not be valid, since they may be associated with a subsystem that could never be in the given state.

>>> defaults['VALIDATE_SUBSYSTEM_STATES']
True

• pyphi.config.SINGLE_NODES_WITH_SELFLOOPS_HAVE_PHI: If set to True, this defines the Phi value of subsystems containing only a single node with a self-loop to be 0.5. If set to False, their $$\Phi$$ will be actually be computed (to be zero, in this implementation).

>>> defaults['SINGLE_NODES_WITH_SELFLOOPS_HAVE_PHI']
False

• pyphi.config.READABLE_REPRS: If set to True, this causes repr calls on PyPhi objects to return pretty-formatted and legible strings. Although this breaks the convention that __repr__ methods should return a representation which can reconstruct the object, readable representations are convenient since the Python REPL calls repr to represent all objects in the shell and PyPhi is often used interactively with the REPL. If set to False, repr returns more traditional object representations.

>>> defaults['READABLE_REPRS']
True


pyphi.config.load_config_dict(config)

Parameters: config (dict) – The dict of config to load.
pyphi.config.load_config_file(filename)

Load config from a YAML file.

pyphi.config.load_config_default()

pyphi.config.get_config_string()

Return a string representation of the currently loaded configuration.

pyphi.config.print_config()

Print the current configuration.

class pyphi.config.override(**new_conf)

Decorator and context manager to override config values.

The initial configuration values are reset after the decorated function returns or the context manager completes it block, even if the function or block raises an exception. This is intended to be used by testcases which require specific configuration values.

Example

>>> from pyphi import config
>>>
>>> @config.override(PRECISION=20000)
... def test_something():
...     assert config.PRECISION == 20000
...
>>> test_something()
>>> with config.override(PRECISION=100):
...     assert config.PRECISION == 100
...

__enter__()

Save original config values; override with new ones.

__exit__(*exc)

Reset config to initial values; reraise any exceptions.