Loading a configuration

Various aspects of PyPhi’s behavior can be configured.

When PyPhi is imported, it checks for a YAML file named pyphi_config.yml in the current directory and automatically loads it if it exists; otherwise the default configuration is used.

The various settings are listed here with their defaults.

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

It is also possible to manually load a configuration file:

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

Or load a dictionary of configuration values:

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

Many settings can also be changed on the fly by simply assigning them a new value:

>>> pyphi.config.PROGRESS_BARS = True

Approximations and theoretical options

These settings control the algorithms PyPhi uses.

  • 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.

  • CUT_ONE_APPROXIMATION: When determining the MIP for \(\Phi\), this restricts the set of system cuts that are considered to only those that cut the inputs or outputs of a single node. This restricted set of cuts scales linearly with the size of the system; the full set of all possible bipartitions scales exponentially. This approximation is more likely to give theoretically accurate results with modular, sparsely-connected, or homogeneous networks.

    >>> defaults['CUT_ONE_APPROXIMATION']
  • MEASURE: The measure to use when computing distances between repertoires and concepts. The default is 'EMD', the Earth Mover’s Distance. 'KLD' is the Kullback-Leibler Divergence. 'L1' is the \(L_1\) distance. 'ENTROPY_DIFFERENCE' is the absolute value of the difference in entropy of the two distributions, abs(entropy(a) - entropy(b)). Also included are 'PSQ2' and 'MP2Q'. 'KLD' and 'MP2Q' cannot be used as measures when performing \(\Phi\) computations because of their asymmetry.

    >>> defaults['MEASURE']
  • PARTITION_TYPE: Controls the type of partition used for \(\varphi\) computations.

    If set to 'BI', partitions will have two parts.

    If set to 'TRI', partitions will have three parts. In addition, computations will only consider partitions that strictly partition the mechanism the mechanism. That is, for the mechanism (A, B) and purview (B, C, D) the partition:

    A,B    ∅
    ─── ✕ ───
     B    C,D

    is not considered, but:

     A     B
    ─── ✕ ───
     B    C,D

    is. The following is also valid:

    A,B     ∅
    ─── ✕ ─────
     ∅    B,C,D

    In addition, this setting introduces “wedge” tripartitions of the form:

     A     B     ∅
    ─── ✕ ─── ✕ ───
     B     C     D

    where the mechanism in the third part is always empty.

    In addition, in the case of a \(\varphi\)-tie when computing MICE, The 'TRIPARTITION' setting choses the MIP with smallest purview instead of the largest (which is the default).

    Finally, if set to 'ALL', all possible partitions will be tested.

    >>> defaults['PARTITION_TYPE']
  • PICK_SMALLEST_PURVIEW: When computing MICE, it is possible for several MIPs to have the same \(\varphi\) value. If this setting is set to True the MIP with the smallest purview is chosen; otherwise, the one with largest purview is chosen.

    >>> defaults['PICK_SMALLEST_PURVIEW']
  • USE_SMALL_PHI_DIFFERENCE_FOR_CONSTELLATION_DISTANCE: If set to True, the distance between constellations (when computing a BigMip) is calculated using the difference between the sum of \(\varphi\) in the constellations instead of the extended EMD.

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.

  • PARALLEL_CONCEPT_EVALUATION: Controls whether concepts are evaluated in parallel when computing constellations.

  • PARALLEL_CUT_EVALUATION: Controls whether system cuts are evaluated in parallel, which is faster but requires more memory. If cuts are evaluated sequentially, only two BigMip instances need to be in memory at once.

    >>> defaults['PARALLEL_CUT_EVALUATION']
  • PARALLEL_COMPLEX_EVALUATION: Controls whether systems are evaluated in parallel when computing complexes.



    Only one of PARALLEL_CONCEPT_EVALUATION, PARALLEL_CUT_EVALUATION, and PARALLEL_COMPLEX_EVALUATION can be set to True at a time. For maximal efficiency, you should parallelize the highest level computations possible, e.g., parallelize complex evaluation instead of cut evaluation, but only if you are actually computing complexes. You should only parallelize concept evaluation if you are just computing constellations.

  • NUMBER_OF_CORES: Controls 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']
  • 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 setting limits the percentage of a system’s RAM that the caches can collectively use.



PyPhi is equipped with a transparent caching system for BigMip 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.

  • CACHE_BIGMIPS: Controls whether BigMip objects are cached and automatically retrieved.

    >>> defaults['CACHE_BIGMIPS']
  • CACHE_POTENTIAL_PURVIEWS: Controls whether the potential purviews of mechanisms of a network are cached. Caching speeds up computations by not recomputing expensive reducibility checks, but uses additional memory.

    >>> defaults['CACHE_POTENTIAL_PURVIEWS']
  • CACHING_BACKEND: Controls whether precomputed results are stored and read from a local filesystem-based cache in the current directory or from a database. Set this to 'fs' for the filesystem, 'db' for the database.

    >>> defaults['CACHING_BACKEND']
  • FS_CACHE_VERBOSITY: Controls how much caching information is printed if the filesystem cache is used. Takes a value between 0 and 11.

    >>> defaults['FS_CACHE_VERBOSITY']


    Printing during a loop iteration can slow down the loop considerably.

  • FS_CACHE_DIRECTORY: If the filesystem is used for caching, 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 Python is being run.

    >>> defaults['FS_CACHE_DIRECTORY']
  • MONGODB_CONFIG: Set the configuration for the MongoDB database backend (only has an effect if CACHING_BACKEND is 'db').

    >>> defaults['MONGODB_CONFIG']['host']
    >>> defaults['MONGODB_CONFIG']['port']
    >>> defaults['MONGODB_CONFIG']['database_name']
    >>> defaults['MONGODB_CONFIG']['collection_name']
  • REDIS_CACHE: Specifies whether to use Redis to cache Mice.

    >>> defaults['REDIS_CACHE']
  • REDIS_CONFIG: Configure the Redis database backend. These are the defaults in the provided redis.conf file.

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


These settings control how PyPhi handles log messages. Logs can be written to standard output, a file, both, or none. If these simple default controls are not flexible enough for you, you can override the entire logging configuration. See the documentation on Python’s logger for more information.


After PyPhi has been imported, changing these settings will have no effect unless you call configure_logging() afterwards.

  • LOG_STDOUT_LEVEL: Controls the level of log messages written to standard output. Can be one of 'DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL', or None. 'DEBUG' is the least restrictive level and will show the most log messages. 'CRITICAL' is the most restrictive level and will only display information about fatal errors. If set to None, logging to standard output will be disabled entirely.

    >>> defaults['LOG_STDOUT_LEVEL']
  • LOG_FILE_LEVEL: Controls the level of log messages written to the log file. This setting has the same possible values as LOG_STDOUT_LEVEL.

    >>> defaults['LOG_FILE_LEVEL']
  • LOG_FILE: Controls the name of the log file.

    >>> defaults['LOG_FILE']
  • LOG_CONFIG_ON_IMPORT: Controls whether the configuration is printed when PyPhi is imported.

    >>> defaults['LOG_CONFIG_ON_IMPORT']


    If this is enabled and LOG_FILE_LEVEL is INFO or higher, then the log file can serve as an automatic record of which configuration settings you used to obtain results.

  • PROGRESS_BARS: Controls whether to show progress bars on the console.

    >>> defaults['PROGRESS_BARS']


    If you are iterating over many systems rather than doing one long-running calculation, consider disabling this for speed.

Numerical precision

  • PRECISION: If MEASURE is EMD, then the Earth Mover’s Distance is calculated with an external C++ library that a numerical optimizer to find a good approximation. Consequently, systems with analytically zero \(\Phi\) will sometimes be numerically found 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']


  • VALIDATE_SUBSYSTEM_STATES: Controls whether PyPhi checks if the subsystems’s state is possible (reachable with nonzero probability 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.

  • VALIDATE_CONDITIONAL_INDEPENDENCE: Controls whether PyPhi checks if a system’s TPM is conditionally independent.

  • SINGLE_MICRO_NODES_WITH_SELFLOOPS_HAVE_PHI: If set to True, the Phi value of single micro-node subsystems is the difference between their unpartitioned constellation (a single concept) and the null concept. If set to False, their Phi is defined to be zero. Single macro-node subsystems may always be cut, regardless of circumstances.

  • REPR_VERBOSITY: Controls the verbosity of __repr__ methods on PyPhi objects. Can be set to 0, 1, or 2. If set to 1, calling repr on PyPhi objects will return pretty-formatted and legible strings, excluding repertoires. If set to 2, repr calls also include repertoires.

    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 0, repr returns more traditional object representations.

    >>> defaults['REPR_VERBOSITY']
  • PRINT_FRACTIONS: Controls whether numbers in a repr are printed as fractions. Numbers are still printed as decimals if the fraction’s denominator would be large. This only has an effect if REPR_VERBOSITY > 0.

    >>> defaults['PRINT_FRACTIONS']

The config API


Load configuration values.

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

Load config from a YAML file.


Load default config values.


Return a string representation of the currently loaded configuration.


Print the current configuration.


Reconfigure PyPhi logging based on the current configuration.

class pyphi.config.override(**new_conf)

Decorator and context manager to override configuration 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 tests which require specific configuration values.


>>> 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

Save original config values; override with new ones.


Reset config to initial values; reraise any exceptions.