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wmayner/pyphi | pyphi/tpm.py | marginalize_out | def marginalize_out(node_indices, tpm):
"""Marginalize out nodes from a TPM.
Args:
node_indices (list[int]): The indices of nodes to be marginalized out.
tpm (np.ndarray): The TPM to marginalize the node out of.
Returns:
np.ndarray: A TPM with the same number of dimensions, with th... | python | def marginalize_out(node_indices, tpm):
"""Marginalize out nodes from a TPM.
Args:
node_indices (list[int]): The indices of nodes to be marginalized out.
tpm (np.ndarray): The TPM to marginalize the node out of.
Returns:
np.ndarray: A TPM with the same number of dimensions, with th... | [
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wmayner/pyphi | pyphi/tpm.py | infer_edge | def infer_edge(tpm, a, b, contexts):
"""Infer the presence or absence of an edge from node A to node B.
Let |S| be the set of all nodes in a network. Let |A' = S - {A}|. We call
the state of |A'| the context |C| of |A|. There is an edge from |A| to |B|
if there exists any context |C(A)| such that |Pr(B... | python | def infer_edge(tpm, a, b, contexts):
"""Infer the presence or absence of an edge from node A to node B.
Let |S| be the set of all nodes in a network. Let |A' = S - {A}|. We call
the state of |A'| the context |C| of |A|. There is an edge from |A| to |B|
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wmayner/pyphi | pyphi/tpm.py | infer_cm | def infer_cm(tpm):
"""Infer the connectivity matrix associated with a state-by-node TPM in
multidimensional form.
"""
network_size = tpm.shape[-1]
all_contexts = tuple(all_states(network_size - 1))
cm = np.empty((network_size, network_size), dtype=int)
for a, b in np.ndindex(cm.shape):
... | python | def infer_cm(tpm):
"""Infer the connectivity matrix associated with a state-by-node TPM in
multidimensional form.
"""
network_size = tpm.shape[-1]
all_contexts = tuple(all_states(network_size - 1))
cm = np.empty((network_size, network_size), dtype=int)
for a, b in np.ndindex(cm.shape):
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wmayner/pyphi | pyphi/compute/parallel.py | get_num_processes | def get_num_processes():
"""Return the number of processes to use in parallel."""
cpu_count = multiprocessing.cpu_count()
if config.NUMBER_OF_CORES == 0:
raise ValueError(
'Invalid NUMBER_OF_CORES; value may not be 0.')
if config.NUMBER_OF_CORES > cpu_count:
log.info('Reque... | python | def get_num_processes():
"""Return the number of processes to use in parallel."""
cpu_count = multiprocessing.cpu_count()
if config.NUMBER_OF_CORES == 0:
raise ValueError(
'Invalid NUMBER_OF_CORES; value may not be 0.')
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wmayner/pyphi | pyphi/compute/parallel.py | MapReduce.init_progress_bar | def init_progress_bar(self):
"""Initialize and return a progress bar."""
# Forked worker processes can't show progress bars.
disable = MapReduce._forked or not config.PROGRESS_BARS
# Don't materialize iterable unless we have to: huge iterables
# (e.g. of `KCuts`) eat memory.
... | python | def init_progress_bar(self):
"""Initialize and return a progress bar."""
# Forked worker processes can't show progress bars.
disable = MapReduce._forked or not config.PROGRESS_BARS
# Don't materialize iterable unless we have to: huge iterables
# (e.g. of `KCuts`) eat memory.
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wmayner/pyphi | pyphi/compute/parallel.py | MapReduce.worker | def worker(compute, task_queue, result_queue, log_queue, complete,
*context):
"""A worker process, run by ``multiprocessing.Process``."""
try:
MapReduce._forked = True
log.debug('Worker process starting...')
configure_worker_logging(log_queue)
... | python | def worker(compute, task_queue, result_queue, log_queue, complete,
*context):
"""A worker process, run by ``multiprocessing.Process``."""
try:
MapReduce._forked = True
log.debug('Worker process starting...')
configure_worker_logging(log_queue)
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wmayner/pyphi | pyphi/compute/parallel.py | MapReduce.start_parallel | def start_parallel(self):
"""Initialize all queues and start the worker processes and the log
thread.
"""
self.num_processes = get_num_processes()
self.task_queue = multiprocessing.Queue(maxsize=Q_MAX_SIZE)
self.result_queue = multiprocessing.Queue()
self.log_que... | python | def start_parallel(self):
"""Initialize all queues and start the worker processes and the log
thread.
"""
self.num_processes = get_num_processes()
self.task_queue = multiprocessing.Queue(maxsize=Q_MAX_SIZE)
self.result_queue = multiprocessing.Queue()
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wmayner/pyphi | pyphi/compute/parallel.py | MapReduce.initialize_tasks | def initialize_tasks(self):
"""Load the input queue to capacity.
Overfilling causes a deadlock when `queue.put` blocks when
full, so further tasks are enqueued as results are returned.
"""
# Add a poison pill to shutdown each process.
self.tasks = chain(self.iterable, [P... | python | def initialize_tasks(self):
"""Load the input queue to capacity.
Overfilling causes a deadlock when `queue.put` blocks when
full, so further tasks are enqueued as results are returned.
"""
# Add a poison pill to shutdown each process.
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wmayner/pyphi | pyphi/compute/parallel.py | MapReduce.maybe_put_task | def maybe_put_task(self):
"""Enqueue the next task, if there are any waiting."""
try:
task = next(self.tasks)
except StopIteration:
pass
else:
log.debug('Putting %s on queue', task)
self.task_queue.put(task) | python | def maybe_put_task(self):
"""Enqueue the next task, if there are any waiting."""
try:
task = next(self.tasks)
except StopIteration:
pass
else:
log.debug('Putting %s on queue', task)
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wmayner/pyphi | pyphi/compute/parallel.py | MapReduce.run_parallel | def run_parallel(self):
"""Perform the computation in parallel, reading results from the output
queue and passing them to ``process_result``.
"""
try:
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result = self.empty_result(*self.context)
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... | python | def run_parallel(self):
"""Perform the computation in parallel, reading results from the output
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"""
try:
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result = self.empty_result(*self.context)
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wmayner/pyphi | pyphi/compute/parallel.py | MapReduce.finish_parallel | def finish_parallel(self):
"""Orderly shutdown of workers."""
for process in self.processes:
process.join()
# Shutdown the log thread
log.debug('Joining log thread')
self.log_queue.put(POISON_PILL)
self.log_thread.join()
self.log_queue.close()
... | python | def finish_parallel(self):
"""Orderly shutdown of workers."""
for process in self.processes:
process.join()
# Shutdown the log thread
log.debug('Joining log thread')
self.log_queue.put(POISON_PILL)
self.log_thread.join()
self.log_queue.close()
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wmayner/pyphi | pyphi/compute/parallel.py | MapReduce.run_sequential | def run_sequential(self):
"""Perform the computation sequentially, only holding two computed
objects in memory at a time.
"""
try:
result = self.empty_result(*self.context)
for obj in self.iterable:
r = self.compute(obj, *self.context)
... | python | def run_sequential(self):
"""Perform the computation sequentially, only holding two computed
objects in memory at a time.
"""
try:
result = self.empty_result(*self.context)
for obj in self.iterable:
r = self.compute(obj, *self.context)
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wmayner/pyphi | pyphi/conf.py | configure_logging | def configure_logging(conf):
"""Reconfigure PyPhi logging based on the current configuration."""
logging.config.dictConfig({
'version': 1,
'disable_existing_loggers': False,
'formatters': {
'standard': {
'format': '%(asctime)s [%(name)s] %(levelname)s '
... | python | def configure_logging(conf):
"""Reconfigure PyPhi logging based on the current configuration."""
logging.config.dictConfig({
'version': 1,
'disable_existing_loggers': False,
'formatters': {
'standard': {
'format': '%(asctime)s [%(name)s] %(levelname)s '
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wmayner/pyphi | pyphi/conf.py | Option._validate | def _validate(self, value):
"""Validate the new value."""
if self.values and value not in self.values:
raise ValueError(
'{} is not a valid value for {}'.format(value, self.name)) | python | def _validate(self, value):
"""Validate the new value."""
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wmayner/pyphi | pyphi/conf.py | Config.options | def options(cls):
"""Return a dictionary of the ``Option`` objects for this config."""
return {k: v for k, v in cls.__dict__.items() if isinstance(v, Option)} | python | def options(cls):
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wmayner/pyphi | pyphi/conf.py | Config.defaults | def defaults(self):
"""Return the default values of this configuration."""
return {k: v.default for k, v in self.options().items()} | python | def defaults(self):
"""Return the default values of this configuration."""
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wmayner/pyphi | pyphi/conf.py | Config.load_dict | def load_dict(self, dct):
"""Load a dictionary of configuration values."""
for k, v in dct.items():
setattr(self, k, v) | python | def load_dict(self, dct):
"""Load a dictionary of configuration values."""
for k, v in dct.items():
setattr(self, k, v) | [
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wmayner/pyphi | pyphi/conf.py | Config.load_file | def load_file(self, filename):
"""Load config from a YAML file."""
filename = os.path.abspath(filename)
with open(filename) as f:
self.load_dict(yaml.load(f))
self._loaded_files.append(filename) | python | def load_file(self, filename):
"""Load config from a YAML file."""
filename = os.path.abspath(filename)
with open(filename) as f:
self.load_dict(yaml.load(f))
self._loaded_files.append(filename) | [
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wmayner/pyphi | pyphi/conf.py | PyphiConfig.log | def log(self):
"""Log current settings."""
log.info('PyPhi v%s', __about__.__version__)
if self._loaded_files:
log.info('Loaded configuration from %s', self._loaded_files)
else:
log.info('Using default configuration (no configuration file '
'p... | python | def log(self):
"""Log current settings."""
log.info('PyPhi v%s', __about__.__version__)
if self._loaded_files:
log.info('Loaded configuration from %s', self._loaded_files)
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log.info('Using default configuration (no configuration file '
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wmayner/pyphi | pyphi/convert.py | be2le_state_by_state | def be2le_state_by_state(tpm):
"""Convert a state-by-state TPM from big-endian to little-endian or vice
versa.
Args:
tpm (np.ndarray): A state-by-state TPM.
Returns:
np.ndarray: The state-by-state TPM in the other indexing format.
Example:
>>> tpm = np.arange(16).reshape([... | python | def be2le_state_by_state(tpm):
"""Convert a state-by-state TPM from big-endian to little-endian or vice
versa.
Args:
tpm (np.ndarray): A state-by-state TPM.
Returns:
np.ndarray: The state-by-state TPM in the other indexing format.
Example:
>>> tpm = np.arange(16).reshape([... | [
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wmayner/pyphi | pyphi/convert.py | to_multidimensional | def to_multidimensional(tpm):
"""Reshape a state-by-node TPM to the multidimensional form.
See documentation for the |Network| object for more information on TPM
formats.
"""
# Cast to np.array.
tpm = np.array(tpm)
# Get the number of nodes.
N = tpm.shape[-1]
# Reshape. We use Fortr... | python | def to_multidimensional(tpm):
"""Reshape a state-by-node TPM to the multidimensional form.
See documentation for the |Network| object for more information on TPM
formats.
"""
# Cast to np.array.
tpm = np.array(tpm)
# Get the number of nodes.
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wmayner/pyphi | pyphi/convert.py | state_by_state2state_by_node | def state_by_state2state_by_node(tpm):
"""Convert a state-by-state TPM to a state-by-node TPM.
.. danger::
Many nondeterministic state-by-state TPMs can be represented by a
single a state-by-state TPM. However, the mapping can be made to be
one-to-one if we assume the state-by-state TPM... | python | def state_by_state2state_by_node(tpm):
"""Convert a state-by-state TPM to a state-by-node TPM.
.. danger::
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wmayner/pyphi | pyphi/convert.py | state_by_node2state_by_state | def state_by_node2state_by_state(tpm):
"""Convert a state-by-node TPM to a state-by-state TPM.
.. important::
A nondeterministic state-by-node TPM can have more than one
representation as a state-by-state TPM. However, the mapping can be
made to be one-to-one if we assume the TPMs to be... | python | def state_by_node2state_by_state(tpm):
"""Convert a state-by-node TPM to a state-by-state TPM.
.. important::
A nondeterministic state-by-node TPM can have more than one
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wmayner/pyphi | profiling/code_to_profile.py | load_json_network | def load_json_network(json_dict):
"""Load a network from a json file"""
network = pyphi.Network.from_json(json_dict['network'])
state = json_dict['state']
return (network, state) | python | def load_json_network(json_dict):
"""Load a network from a json file"""
network = pyphi.Network.from_json(json_dict['network'])
state = json_dict['state']
return (network, state) | [
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wmayner/pyphi | profiling/code_to_profile.py | all_network_files | def all_network_files():
"""All network files"""
# TODO: list explicitly since some are missing?
network_types = [
'AND-circle',
'MAJ-specialized',
'MAJ-complete',
'iit-3.0-modular'
]
network_sizes = range(5, 8)
network_files = []
for n in network_sizes:
... | python | def all_network_files():
"""All network files"""
# TODO: list explicitly since some are missing?
network_types = [
'AND-circle',
'MAJ-specialized',
'MAJ-complete',
'iit-3.0-modular'
]
network_sizes = range(5, 8)
network_files = []
for n in network_sizes:
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wmayner/pyphi | profiling/code_to_profile.py | profile_network | def profile_network(filename):
"""Profile a network.
Saves PyPhi results, pstats, and logs to respective directories.
"""
log = logging.getLogger(filename)
logfile = os.path.join(LOGS, filename + '.log')
os.makedirs(os.path.dirname(logfile), exist_ok=True)
handler = logging.FileHandler(logf... | python | def profile_network(filename):
"""Profile a network.
Saves PyPhi results, pstats, and logs to respective directories.
"""
log = logging.getLogger(filename)
logfile = os.path.join(LOGS, filename + '.log')
os.makedirs(os.path.dirname(logfile), exist_ok=True)
handler = logging.FileHandler(logf... | [
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wmayner/pyphi | pyphi/timescale.py | run_tpm | def run_tpm(tpm, time_scale):
"""Iterate a TPM by the specified number of time steps.
Args:
tpm (np.ndarray): A state-by-node tpm.
time_scale (int): The number of steps to run the tpm.
Returns:
np.ndarray
"""
sbs_tpm = convert.state_by_node2state_by_state(tpm)
if sparse... | python | def run_tpm(tpm, time_scale):
"""Iterate a TPM by the specified number of time steps.
Args:
tpm (np.ndarray): A state-by-node tpm.
time_scale (int): The number of steps to run the tpm.
Returns:
np.ndarray
"""
sbs_tpm = convert.state_by_node2state_by_state(tpm)
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wmayner/pyphi | pyphi/timescale.py | run_cm | def run_cm(cm, time_scale):
"""Iterate a connectivity matrix the specified number of steps.
Args:
cm (np.ndarray): A connectivity matrix.
time_scale (int): The number of steps to run.
Returns:
np.ndarray: The connectivity matrix at the new timescale.
"""
cm = np.linalg.matr... | python | def run_cm(cm, time_scale):
"""Iterate a connectivity matrix the specified number of steps.
Args:
cm (np.ndarray): A connectivity matrix.
time_scale (int): The number of steps to run.
Returns:
np.ndarray: The connectivity matrix at the new timescale.
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wmayner/pyphi | pyphi/compute/network.py | _reachable_subsystems | def _reachable_subsystems(network, indices, state):
"""A generator over all subsystems in a valid state."""
validate.is_network(network)
# Return subsystems largest to smallest to optimize parallel
# resource usage.
for subset in utils.powerset(indices, nonempty=True, reverse=True):
try:
... | python | def _reachable_subsystems(network, indices, state):
"""A generator over all subsystems in a valid state."""
validate.is_network(network)
# Return subsystems largest to smallest to optimize parallel
# resource usage.
for subset in utils.powerset(indices, nonempty=True, reverse=True):
try:
... | [
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wmayner/pyphi | pyphi/compute/network.py | all_complexes | def all_complexes(network, state):
"""Return a generator for all complexes of the network.
.. note::
Includes reducible, zero-|big_phi| complexes (which are not, strictly
speaking, complexes at all).
Args:
network (Network): The |Network| of interest.
state (tuple[int]): Th... | python | def all_complexes(network, state):
"""Return a generator for all complexes of the network.
.. note::
Includes reducible, zero-|big_phi| complexes (which are not, strictly
speaking, complexes at all).
Args:
network (Network): The |Network| of interest.
state (tuple[int]): Th... | [
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wmayner/pyphi | pyphi/compute/network.py | complexes | def complexes(network, state):
"""Return all irreducible complexes of the network.
Args:
network (Network): The |Network| of interest.
state (tuple[int]): The state of the network (a binary tuple).
Yields:
SystemIrreducibilityAnalysis: A |SIA| for each |Subsystem| of the
|N... | python | def complexes(network, state):
"""Return all irreducible complexes of the network.
Args:
network (Network): The |Network| of interest.
state (tuple[int]): The state of the network (a binary tuple).
Yields:
SystemIrreducibilityAnalysis: A |SIA| for each |Subsystem| of the
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wmayner/pyphi | pyphi/compute/network.py | major_complex | def major_complex(network, state):
"""Return the major complex of the network.
Args:
network (Network): The |Network| of interest.
state (tuple[int]): The state of the network (a binary tuple).
Returns:
SystemIrreducibilityAnalysis: The |SIA| for the |Subsystem| with
maxima... | python | def major_complex(network, state):
"""Return the major complex of the network.
Args:
network (Network): The |Network| of interest.
state (tuple[int]): The state of the network (a binary tuple).
Returns:
SystemIrreducibilityAnalysis: The |SIA| for the |Subsystem| with
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state (tuple[int]): The state of the network (a binary tuple).
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wmayner/pyphi | pyphi/compute/network.py | condensed | def condensed(network, state):
"""Return a list of maximal non-overlapping complexes.
Args:
network (Network): The |Network| of interest.
state (tuple[int]): The state of the network (a binary tuple).
Returns:
list[SystemIrreducibilityAnalysis]: A list of |SIA| for non-overlapping
... | python | def condensed(network, state):
"""Return a list of maximal non-overlapping complexes.
Args:
network (Network): The |Network| of interest.
state (tuple[int]): The state of the network (a binary tuple).
Returns:
list[SystemIrreducibilityAnalysis]: A list of |SIA| for non-overlapping
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wmayner/pyphi | pyphi/examples.py | basic_network | def basic_network(cm=False):
"""A 3-node network of logic gates.
Diagram::
+~~~~~~~~+
+~~~~>| A |<~~~~+
| | (OR) +~~~+ |
| +~~~~~~~~+ | |
| | |
| v |
+~+~~~~~~+ +~~~~~+~+
... | python | def basic_network(cm=False):
"""A 3-node network of logic gates.
Diagram::
+~~~~~~~~+
+~~~~>| A |<~~~~+
| | (OR) +~~~+ |
| +~~~~~~~~+ | |
| | |
| v |
+~+~~~~~~+ +~~~~~+~+
... | [
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wmayner/pyphi | pyphi/examples.py | basic_noisy_selfloop_network | def basic_noisy_selfloop_network():
"""Based on the basic_network, but with added selfloops and noisy edges.
Nodes perform deterministic functions of their inputs, but those inputs
may be flipped (i.e. what should be a 0 becomes a 1, and vice versa) with
probability epsilon (eps = 0.1 here).
Diagr... | python | def basic_noisy_selfloop_network():
"""Based on the basic_network, but with added selfloops and noisy edges.
Nodes perform deterministic functions of their inputs, but those inputs
may be flipped (i.e. what should be a 0 becomes a 1, and vice versa) with
probability epsilon (eps = 0.1 here).
Diagr... | [
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wmayner/pyphi | pyphi/examples.py | residue_network | def residue_network():
"""The network for the residue example.
Current and previous state are all nodes OFF.
Diagram::
+~~~~~~~+ +~~~~~~~+
| A | | B |
+~~>| (AND) | | (AND) |<~~+
| +~~~~~~~+ +~~~~~~~+ |
... | python | def residue_network():
"""The network for the residue example.
Current and previous state are all nodes OFF.
Diagram::
+~~~~~~~+ +~~~~~~~+
| A | | B |
+~~>| (AND) | | (AND) |<~~+
| +~~~~~~~+ +~~~~~~~+ |
... | [
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Diagram::
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| A | | B |
+~~>| (AND) | | (AND) |<~~+
| +~~~~~~~+ +~~~~~~~+ |
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wmayner/pyphi | pyphi/examples.py | propagation_delay_network | def propagation_delay_network():
"""A version of the primary example from the IIT 3.0 paper with
deterministic COPY gates on each connection. These copy gates essentially
function as propagation delays on the signal between OR, AND and XOR gates
from the original system.
The current and previous st... | python | def propagation_delay_network():
"""A version of the primary example from the IIT 3.0 paper with
deterministic COPY gates on each connection. These copy gates essentially
function as propagation delays on the signal between OR, AND and XOR gates
from the original system.
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wmayner/pyphi | pyphi/examples.py | macro_network | def macro_network():
"""A network of micro elements which has greater integrated information
after coarse graining to a macro scale.
"""
tpm = np.array([[0.3, 0.3, 0.3, 0.3],
[0.3, 0.3, 0.3, 0.3],
[0.3, 0.3, 0.3, 0.3],
[0.3, 0.3, 1.0, 1.0],
... | python | def macro_network():
"""A network of micro elements which has greater integrated information
after coarse graining to a macro scale.
"""
tpm = np.array([[0.3, 0.3, 0.3, 0.3],
[0.3, 0.3, 0.3, 0.3],
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[0.3, 0.3, 1.0, 1.0],
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wmayner/pyphi | pyphi/examples.py | blackbox_network | def blackbox_network():
"""A micro-network to demonstrate blackboxing.
Diagram::
+----------+
+-------------------->+ A (COPY) + <---------------+
| +----------+ |
| +----------+ ... | python | def blackbox_network():
"""A micro-network to demonstrate blackboxing.
Diagram::
+----------+
+-------------------->+ A (COPY) + <---------------+
| +----------+ |
| +----------+ ... | [
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wmayner/pyphi | pyphi/examples.py | actual_causation | def actual_causation():
"""The actual causation example network, consisting of an ``OR`` and
``AND`` gate with self-loops.
"""
tpm = np.array([
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cm = np.array([
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retu... | python | def actual_causation():
"""The actual causation example network, consisting of an ``OR`` and
``AND`` gate with self-loops.
"""
tpm = np.array([
[1, 0, 0, 0],
[0, 1, 0, 0],
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cm = np.array([
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wmayner/pyphi | pyphi/examples.py | prevention | def prevention():
"""The |Transition| for the prevention example from Actual Causation
Figure 5D.
"""
tpm = np.array([
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[0.5, 0.5, 0],
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[0.5, 0.5, 1],
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... | python | def prevention():
"""The |Transition| for the prevention example from Actual Causation
Figure 5D.
"""
tpm = np.array([
[0.5, 0.5, 1],
[0.5, 0.5, 0],
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[0.5, 0.5, 1],
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wmayner/pyphi | benchmarks/benchmarks/subsystem.py | clear_subsystem_caches | def clear_subsystem_caches(subsys):
"""Clear subsystem caches"""
try:
# New-style caches
subsys._repertoire_cache.clear()
subsys._mice_cache.clear()
except TypeError:
try:
# Pre cache.clear() implementation
subsys._repertoire_cache.cache = {}
... | python | def clear_subsystem_caches(subsys):
"""Clear subsystem caches"""
try:
# New-style caches
subsys._repertoire_cache.clear()
subsys._mice_cache.clear()
except TypeError:
try:
# Pre cache.clear() implementation
subsys._repertoire_cache.cache = {}
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wmayner/pyphi | pyphi/utils.py | all_states | def all_states(n, big_endian=False):
"""Return all binary states for a system.
Args:
n (int): The number of elements in the system.
big_endian (bool): Whether to return the states in big-endian order
instead of little-endian order.
Yields:
tuple[int]: The next state of ... | python | def all_states(n, big_endian=False):
"""Return all binary states for a system.
Args:
n (int): The number of elements in the system.
big_endian (bool): Whether to return the states in big-endian order
instead of little-endian order.
Yields:
tuple[int]: The next state of ... | [
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wmayner/pyphi | pyphi/utils.py | np_hash | def np_hash(a):
"""Return a hash of a NumPy array."""
if a is None:
return hash(None)
# Ensure that hashes are equal whatever the ordering in memory (C or
# Fortran)
a = np.ascontiguousarray(a)
# Compute the digest and return a decimal int
return int(hashlib.sha1(a.view(a.dtype)).hex... | python | def np_hash(a):
"""Return a hash of a NumPy array."""
if a is None:
return hash(None)
# Ensure that hashes are equal whatever the ordering in memory (C or
# Fortran)
a = np.ascontiguousarray(a)
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wmayner/pyphi | pyphi/utils.py | powerset | def powerset(iterable, nonempty=False, reverse=False):
"""Generate the power set of an iterable.
Args:
iterable (Iterable): The iterable from which to generate the power set.
Keyword Args:
nonempty (boolean): If True, don't include the empty set.
reverse (boolean): If True, reverse... | python | def powerset(iterable, nonempty=False, reverse=False):
"""Generate the power set of an iterable.
Args:
iterable (Iterable): The iterable from which to generate the power set.
Keyword Args:
nonempty (boolean): If True, don't include the empty set.
reverse (boolean): If True, reverse... | [
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wmayner/pyphi | pyphi/utils.py | load_data | def load_data(directory, num):
"""Load numpy data from the data directory.
The files should stored in ``../data/<dir>`` and named
``0.npy, 1.npy, ... <num - 1>.npy``.
Returns:
list: A list of loaded data, such that ``list[i]`` contains the the
contents of ``i.npy``.
"""
root = ... | python | def load_data(directory, num):
"""Load numpy data from the data directory.
The files should stored in ``../data/<dir>`` and named
``0.npy, 1.npy, ... <num - 1>.npy``.
Returns:
list: A list of loaded data, such that ``list[i]`` contains the the
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"""Annotate the decorated function or method with the total execution
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The result is annotated with a `time` attribute.
"""
start = time()
result = func(*args, **kwargs)
end = time()
result.time = round(end - start, config.PRECISION)
r... | python | def time_annotated(func, *args, **kwargs):
"""Annotate the decorated function or method with the total execution
time.
The result is annotated with a `time` attribute.
"""
start = time()
result = func(*args, **kwargs)
end = time()
result.time = round(end - start, config.PRECISION)
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wmayner/pyphi | pyphi/models/mechanism.py | _null_ria | def _null_ria(direction, mechanism, purview, repertoire=None, phi=0.0):
"""The irreducibility analysis for a reducible mechanism."""
# TODO Use properties here to infer mechanism and purview from
# partition yet access them with .mechanism and .partition
return RepertoireIrreducibilityAnalysis(
... | python | def _null_ria(direction, mechanism, purview, repertoire=None, phi=0.0):
"""The irreducibility analysis for a reducible mechanism."""
# TODO Use properties here to infer mechanism and purview from
# partition yet access them with .mechanism and .partition
return RepertoireIrreducibilityAnalysis(
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wmayner/pyphi | pyphi/models/mechanism.py | MaximallyIrreducibleCauseOrEffect.damaged_by_cut | def damaged_by_cut(self, subsystem):
"""Return ``True`` if this MICE is affected by the subsystem's cut.
The cut affects the MICE if it either splits the MICE's mechanism
or splits the connections between the purview and mechanism.
"""
return (subsystem.cut.splits_mechanism(self... | python | def damaged_by_cut(self, subsystem):
"""Return ``True`` if this MICE is affected by the subsystem's cut.
The cut affects the MICE if it either splits the MICE's mechanism
or splits the connections between the purview and mechanism.
"""
return (subsystem.cut.splits_mechanism(self... | [
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wmayner/pyphi | pyphi/models/mechanism.py | Concept.eq_repertoires | def eq_repertoires(self, other):
"""Return whether this concept has the same repertoires as another.
.. warning::
This only checks if the cause and effect repertoires are equal as
arrays; mechanisms, purviews, or even the nodes that the mechanism
and purview indices ... | python | def eq_repertoires(self, other):
"""Return whether this concept has the same repertoires as another.
.. warning::
This only checks if the cause and effect repertoires are equal as
arrays; mechanisms, purviews, or even the nodes that the mechanism
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wmayner/pyphi | pyphi/models/mechanism.py | Concept.emd_eq | def emd_eq(self, other):
"""Return whether this concept is equal to another in the context of
an EMD calculation.
"""
return (self.phi == other.phi and
self.mechanism == other.mechanism and
self.eq_repertoires(other)) | python | def emd_eq(self, other):
"""Return whether this concept is equal to another in the context of
an EMD calculation.
"""
return (self.phi == other.phi and
self.mechanism == other.mechanism and
self.eq_repertoires(other)) | [
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wmayner/pyphi | pyphi/actual.py | directed_account | def directed_account(transition, direction, mechanisms=False, purviews=False,
allow_neg=False):
"""Return the set of all |CausalLinks| of the specified direction."""
if mechanisms is False:
mechanisms = utils.powerset(transition.mechanism_indices(direction),
... | python | def directed_account(transition, direction, mechanisms=False, purviews=False,
allow_neg=False):
"""Return the set of all |CausalLinks| of the specified direction."""
if mechanisms is False:
mechanisms = utils.powerset(transition.mechanism_indices(direction),
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wmayner/pyphi | pyphi/actual.py | account | def account(transition, direction=Direction.BIDIRECTIONAL):
"""Return the set of all causal links for a |Transition|.
Args:
transition (Transition): The transition of interest.
Keyword Args:
direction (Direction): By default the account contains actual causes
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transition (Transition): The transition of interest.
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wmayner/pyphi | pyphi/actual.py | _evaluate_cut | def _evaluate_cut(transition, cut, unpartitioned_account,
direction=Direction.BIDIRECTIONAL):
"""Find the |AcSystemIrreducibilityAnalysis| for a given cut."""
cut_transition = transition.apply_cut(cut)
partitioned_account = account(cut_transition, direction)
log.debug("Finished evalua... | python | def _evaluate_cut(transition, cut, unpartitioned_account,
direction=Direction.BIDIRECTIONAL):
"""Find the |AcSystemIrreducibilityAnalysis| for a given cut."""
cut_transition = transition.apply_cut(cut)
partitioned_account = account(cut_transition, direction)
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wmayner/pyphi | pyphi/actual.py | _get_cuts | def _get_cuts(transition, direction):
"""A list of possible cuts to a transition."""
n = transition.network.size
if direction is Direction.BIDIRECTIONAL:
yielded = set()
for cut in chain(_get_cuts(transition, Direction.CAUSE),
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"""A list of possible cuts to a transition."""
n = transition.network.size
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yielded = set()
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wmayner/pyphi | pyphi/actual.py | sia | def sia(transition, direction=Direction.BIDIRECTIONAL):
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Args:
transition (Transition): The candidate system.
Returns:
AcSystemIrreducibilityAnalysis: A nested structure containing all the
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wmayner/pyphi | pyphi/actual.py | nexus | def nexus(network, before_state, after_state,
direction=Direction.BIDIRECTIONAL):
"""Return a tuple of all irreducible nexus of the network."""
validate.is_network(network)
sias = (sia(transition, direction) for transition in
transitions(network, before_state, after_state))
return... | python | def nexus(network, before_state, after_state,
direction=Direction.BIDIRECTIONAL):
"""Return a tuple of all irreducible nexus of the network."""
validate.is_network(network)
sias = (sia(transition, direction) for transition in
transitions(network, before_state, after_state))
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wmayner/pyphi | pyphi/actual.py | causal_nexus | def causal_nexus(network, before_state, after_state,
direction=Direction.BIDIRECTIONAL):
"""Return the causal nexus of the network."""
validate.is_network(network)
log.info("Calculating causal nexus...")
result = nexus(network, before_state, after_state, direction)
if result:
... | python | def causal_nexus(network, before_state, after_state,
direction=Direction.BIDIRECTIONAL):
"""Return the causal nexus of the network."""
validate.is_network(network)
log.info("Calculating causal nexus...")
result = nexus(network, before_state, after_state, direction)
if result:
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wmayner/pyphi | pyphi/actual.py | nice_true_ces | def nice_true_ces(tc):
"""Format a true |CauseEffectStructure|."""
cause_list = []
next_list = []
cause = '<--'
effect = '-->'
for event in tc:
if event.direction == Direction.CAUSE:
cause_list.append(["{0:.4f}".format(round(event.alpha, 4)),
ev... | python | def nice_true_ces(tc):
"""Format a true |CauseEffectStructure|."""
cause_list = []
next_list = []
cause = '<--'
effect = '-->'
for event in tc:
if event.direction == Direction.CAUSE:
cause_list.append(["{0:.4f}".format(round(event.alpha, 4)),
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wmayner/pyphi | pyphi/actual.py | true_ces | def true_ces(subsystem, previous_state, next_state):
"""Set of all sets of elements that have true causes and true effects.
.. note::
Since the true |CauseEffectStructure| is always about the full system,
the background conditions don't matter and the subsystem should be
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"""Set of all sets of elements that have true causes and true effects.
.. note::
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.. note::
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wmayner/pyphi | pyphi/actual.py | true_events | def true_events(network, previous_state, current_state, next_state,
indices=None, major_complex=None):
"""Return all mechanisms that have true causes and true effects within the
complex.
Args:
network (Network): The network to analyze.
previous_state (tuple[int]): The state ... | python | def true_events(network, previous_state, current_state, next_state,
indices=None, major_complex=None):
"""Return all mechanisms that have true causes and true effects within the
complex.
Args:
network (Network): The network to analyze.
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wmayner/pyphi | pyphi/actual.py | extrinsic_events | def extrinsic_events(network, previous_state, current_state, next_state,
indices=None, major_complex=None):
"""Set of all mechanisms that are in the major complex but which have true
causes and effects within the entire network.
Args:
network (Network): The network to analyze.
... | python | def extrinsic_events(network, previous_state, current_state, next_state,
indices=None, major_complex=None):
"""Set of all mechanisms that are in the major complex but which have true
causes and effects within the entire network.
Args:
network (Network): The network to analyze.
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wmayner/pyphi | pyphi/actual.py | Transition.apply_cut | def apply_cut(self, cut):
"""Return a cut version of this transition."""
return Transition(self.network, self.before_state, self.after_state,
self.cause_indices, self.effect_indices, cut) | python | def apply_cut(self, cut):
"""Return a cut version of this transition."""
return Transition(self.network, self.before_state, self.after_state,
self.cause_indices, self.effect_indices, cut) | [
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wmayner/pyphi | pyphi/actual.py | Transition.cause_repertoire | def cause_repertoire(self, mechanism, purview):
"""Return the cause repertoire."""
return self.repertoire(Direction.CAUSE, mechanism, purview) | python | def cause_repertoire(self, mechanism, purview):
"""Return the cause repertoire."""
return self.repertoire(Direction.CAUSE, mechanism, purview) | [
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wmayner/pyphi | pyphi/actual.py | Transition.effect_repertoire | def effect_repertoire(self, mechanism, purview):
"""Return the effect repertoire."""
return self.repertoire(Direction.EFFECT, mechanism, purview) | python | def effect_repertoire(self, mechanism, purview):
"""Return the effect repertoire."""
return self.repertoire(Direction.EFFECT, mechanism, purview) | [
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wmayner/pyphi | pyphi/actual.py | Transition.repertoire | def repertoire(self, direction, mechanism, purview):
"""Return the cause or effect repertoire function based on a direction.
Args:
direction (str): The temporal direction, specifiying the cause or
effect repertoire.
"""
system = self.system[direction]
... | python | def repertoire(self, direction, mechanism, purview):
"""Return the cause or effect repertoire function based on a direction.
Args:
direction (str): The temporal direction, specifiying the cause or
effect repertoire.
"""
system = self.system[direction]
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wmayner/pyphi | pyphi/actual.py | Transition.state_probability | def state_probability(self, direction, repertoire, purview,):
"""Compute the probability of the purview in its current state given
the repertoire.
Collapses the dimensions of the repertoire that correspond to the
purview nodes onto their state. All other dimension are already
si... | python | def state_probability(self, direction, repertoire, purview,):
"""Compute the probability of the purview in its current state given
the repertoire.
Collapses the dimensions of the repertoire that correspond to the
purview nodes onto their state. All other dimension are already
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wmayner/pyphi | pyphi/actual.py | Transition.probability | def probability(self, direction, mechanism, purview):
"""Probability that the purview is in it's current state given the
state of the mechanism.
"""
repertoire = self.repertoire(direction, mechanism, purview)
return self.state_probability(direction, repertoire, purview) | python | def probability(self, direction, mechanism, purview):
"""Probability that the purview is in it's current state given the
state of the mechanism.
"""
repertoire = self.repertoire(direction, mechanism, purview)
return self.state_probability(direction, repertoire, purview) | [
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wmayner/pyphi | pyphi/actual.py | Transition.purview_state | def purview_state(self, direction):
"""The state of the purview when we are computing coefficients in
``direction``.
For example, if we are computing the cause coefficient of a mechanism
in ``after_state``, the direction is``CAUSE`` and the ``purview_state``
is ``before_state``.... | python | def purview_state(self, direction):
"""The state of the purview when we are computing coefficients in
``direction``.
For example, if we are computing the cause coefficient of a mechanism
in ``after_state``, the direction is``CAUSE`` and the ``purview_state``
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wmayner/pyphi | pyphi/actual.py | Transition.mechanism_indices | def mechanism_indices(self, direction):
"""The indices of nodes in the mechanism system."""
return {
Direction.CAUSE: self.effect_indices,
Direction.EFFECT: self.cause_indices
}[direction] | python | def mechanism_indices(self, direction):
"""The indices of nodes in the mechanism system."""
return {
Direction.CAUSE: self.effect_indices,
Direction.EFFECT: self.cause_indices
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wmayner/pyphi | pyphi/actual.py | Transition.purview_indices | def purview_indices(self, direction):
"""The indices of nodes in the purview system."""
return {
Direction.CAUSE: self.cause_indices,
Direction.EFFECT: self.effect_indices
}[direction] | python | def purview_indices(self, direction):
"""The indices of nodes in the purview system."""
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Direction.CAUSE: self.cause_indices,
Direction.EFFECT: self.effect_indices
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wmayner/pyphi | pyphi/actual.py | Transition.cause_ratio | def cause_ratio(self, mechanism, purview):
"""The cause ratio of the ``purview`` given ``mechanism``."""
return self._ratio(Direction.CAUSE, mechanism, purview) | python | def cause_ratio(self, mechanism, purview):
"""The cause ratio of the ``purview`` given ``mechanism``."""
return self._ratio(Direction.CAUSE, mechanism, purview) | [
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wmayner/pyphi | pyphi/actual.py | Transition.effect_ratio | def effect_ratio(self, mechanism, purview):
"""The effect ratio of the ``purview`` given ``mechanism``."""
return self._ratio(Direction.EFFECT, mechanism, purview) | python | def effect_ratio(self, mechanism, purview):
"""The effect ratio of the ``purview`` given ``mechanism``."""
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wmayner/pyphi | pyphi/actual.py | Transition.partitioned_repertoire | def partitioned_repertoire(self, direction, partition):
"""Compute the repertoire over the partition in the given direction."""
system = self.system[direction]
return system.partitioned_repertoire(direction, partition) | python | def partitioned_repertoire(self, direction, partition):
"""Compute the repertoire over the partition in the given direction."""
system = self.system[direction]
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wmayner/pyphi | pyphi/actual.py | Transition.partitioned_probability | def partitioned_probability(self, direction, partition):
"""Compute the probability of the mechanism over the purview in
the partition.
"""
repertoire = self.partitioned_repertoire(direction, partition)
return self.state_probability(direction, repertoire, partition.purview) | python | def partitioned_probability(self, direction, partition):
"""Compute the probability of the mechanism over the purview in
the partition.
"""
repertoire = self.partitioned_repertoire(direction, partition)
return self.state_probability(direction, repertoire, partition.purview) | [
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wmayner/pyphi | pyphi/actual.py | Transition.find_mip | def find_mip(self, direction, mechanism, purview, allow_neg=False):
"""Find the ratio minimum information partition for a mechanism
over a purview.
Args:
direction (str): |CAUSE| or |EFFECT|
mechanism (tuple[int]): A mechanism.
purview (tuple[int]): A purview... | python | def find_mip(self, direction, mechanism, purview, allow_neg=False):
"""Find the ratio minimum information partition for a mechanism
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Args:
direction (str): |CAUSE| or |EFFECT|
mechanism (tuple[int]): A mechanism.
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wmayner/pyphi | pyphi/actual.py | Transition.find_causal_link | def find_causal_link(self, direction, mechanism, purviews=False,
allow_neg=False):
"""Return the maximally irreducible cause or effect ratio for a
mechanism.
Args:
direction (str): The temporal direction, specifying cause or
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... | python | def find_causal_link(self, direction, mechanism, purviews=False,
allow_neg=False):
"""Return the maximally irreducible cause or effect ratio for a
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Args:
direction (str): The temporal direction, specifying cause or
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wmayner/pyphi | pyphi/actual.py | Transition.find_actual_cause | def find_actual_cause(self, mechanism, purviews=False):
"""Return the actual cause of a mechanism."""
return self.find_causal_link(Direction.CAUSE, mechanism, purviews) | python | def find_actual_cause(self, mechanism, purviews=False):
"""Return the actual cause of a mechanism."""
return self.find_causal_link(Direction.CAUSE, mechanism, purviews) | [
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wmayner/pyphi | pyphi/actual.py | Transition.find_actual_effect | def find_actual_effect(self, mechanism, purviews=False):
"""Return the actual effect of a mechanism."""
return self.find_causal_link(Direction.EFFECT, mechanism, purviews) | python | def find_actual_effect(self, mechanism, purviews=False):
"""Return the actual effect of a mechanism."""
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wmayner/pyphi | pyphi/db.py | find | def find(key):
"""Return the value associated with a key.
If there is no value with the given key, returns ``None``.
"""
docs = list(collection.find({KEY_FIELD: key}))
# Return None if we didn't find anything.
if not docs:
return None
pickled_value = docs[0][VALUE_FIELD]
# Unpic... | python | def find(key):
"""Return the value associated with a key.
If there is no value with the given key, returns ``None``.
"""
docs = list(collection.find({KEY_FIELD: key}))
# Return None if we didn't find anything.
if not docs:
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wmayner/pyphi | pyphi/db.py | insert | def insert(key, value):
"""Store a value with a key.
If the key is already present in the database, this does nothing.
"""
# Pickle the value.
value = pickle.dumps(value, protocol=constants.PICKLE_PROTOCOL)
# Store the value as binary data in a document.
doc = {
KEY_FIELD: key,
... | python | def insert(key, value):
"""Store a value with a key.
If the key is already present in the database, this does nothing.
"""
# Pickle the value.
value = pickle.dumps(value, protocol=constants.PICKLE_PROTOCOL)
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wmayner/pyphi | pyphi/db.py | generate_key | def generate_key(filtered_args):
"""Get a key from some input.
This function should be used whenever a key is needed, to keep keys
consistent.
"""
# Convert the value to a (potentially singleton) tuple to be consistent
# with joblib.filtered_args.
if isinstance(filtered_args, Iterable):
... | python | def generate_key(filtered_args):
"""Get a key from some input.
This function should be used whenever a key is needed, to keep keys
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"""
# Convert the value to a (potentially singleton) tuple to be consistent
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wmayner/pyphi | pyphi/memory.py | cache | def cache(ignore=None):
"""Decorator for memoizing a function using either the filesystem or a
database.
"""
def decorator(func):
# Initialize both cached versions
joblib_cached = constants.joblib_memory.cache(func, ignore=ignore)
db_cached = DbMemoizedFunc(func, ignore)
... | python | def cache(ignore=None):
"""Decorator for memoizing a function using either the filesystem or a
database.
"""
def decorator(func):
# Initialize both cached versions
joblib_cached = constants.joblib_memory.cache(func, ignore=ignore)
db_cached = DbMemoizedFunc(func, ignore)
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wmayner/pyphi | pyphi/memory.py | DbMemoizedFunc.get_output_key | def get_output_key(self, args, kwargs):
"""Return the key that the output should be cached with, given
arguments, keyword arguments, and a list of arguments to ignore.
"""
# Get a dictionary mapping argument names to argument values where
# ignored arguments are omitted.
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# Get a dictionary mapping argument names to argument values where
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wmayner/pyphi | pyphi/memory.py | DbMemoizedFunc.load_output | def load_output(self, args, kwargs):
"""Return cached output."""
return db.find(self.get_output_key(args, kwargs)) | python | def load_output(self, args, kwargs):
"""Return cached output."""
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wmayner/pyphi | pyphi/subsystem.py | Subsystem.cache_info | def cache_info(self):
"""Report repertoire cache statistics."""
return {
'single_node_repertoire':
self._single_node_repertoire_cache.info(),
'repertoire': self._repertoire_cache.info(),
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} | python | def cache_info(self):
"""Report repertoire cache statistics."""
return {
'single_node_repertoire':
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wmayner/pyphi | pyphi/subsystem.py | Subsystem.clear_caches | def clear_caches(self):
"""Clear the mice and repertoire caches."""
self._single_node_repertoire_cache.clear()
self._repertoire_cache.clear()
self._mice_cache.clear() | python | def clear_caches(self):
"""Clear the mice and repertoire caches."""
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wmayner/pyphi | pyphi/subsystem.py | Subsystem.apply_cut | def apply_cut(self, cut):
"""Return a cut version of this |Subsystem|.
Args:
cut (Cut): The cut to apply to this |Subsystem|.
Returns:
Subsystem: The cut subsystem.
"""
return Subsystem(self.network, self.state, self.node_indices,
... | python | def apply_cut(self, cut):
"""Return a cut version of this |Subsystem|.
Args:
cut (Cut): The cut to apply to this |Subsystem|.
Returns:
Subsystem: The cut subsystem.
"""
return Subsystem(self.network, self.state, self.node_indices,
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wmayner/pyphi | pyphi/subsystem.py | Subsystem.indices2nodes | def indices2nodes(self, indices):
"""Return |Nodes| for these indices.
Args:
indices (tuple[int]): The indices in question.
Returns:
tuple[Node]: The |Node| objects corresponding to these indices.
Raises:
ValueError: If requested indices are not in ... | python | def indices2nodes(self, indices):
"""Return |Nodes| for these indices.
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indices (tuple[int]): The indices in question.
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wmayner/pyphi | pyphi/subsystem.py | Subsystem.cause_repertoire | def cause_repertoire(self, mechanism, purview):
"""Return the cause repertoire of a mechanism over a purview.
Args:
mechanism (tuple[int]): The mechanism for which to calculate the
cause repertoire.
purview (tuple[int]): The purview over which to calculate the
... | python | def cause_repertoire(self, mechanism, purview):
"""Return the cause repertoire of a mechanism over a purview.
Args:
mechanism (tuple[int]): The mechanism for which to calculate the
cause repertoire.
purview (tuple[int]): The purview over which to calculate the
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wmayner/pyphi | pyphi/subsystem.py | Subsystem.effect_repertoire | def effect_repertoire(self, mechanism, purview):
"""Return the effect repertoire of a mechanism over a purview.
Args:
mechanism (tuple[int]): The mechanism for which to calculate the
effect repertoire.
purview (tuple[int]): The purview over which to calculate the... | python | def effect_repertoire(self, mechanism, purview):
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mechanism (tuple[int]): The mechanism for which to calculate the
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wmayner/pyphi | pyphi/subsystem.py | Subsystem.repertoire | def repertoire(self, direction, mechanism, purview):
"""Return the cause or effect repertoire based on a direction.
Args:
direction (Direction): |CAUSE| or |EFFECT|.
mechanism (tuple[int]): The mechanism for which to calculate the
repertoire.
purview ... | python | def repertoire(self, direction, mechanism, purview):
"""Return the cause or effect repertoire based on a direction.
Args:
direction (Direction): |CAUSE| or |EFFECT|.
mechanism (tuple[int]): The mechanism for which to calculate the
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wmayner/pyphi | pyphi/subsystem.py | Subsystem.partitioned_repertoire | def partitioned_repertoire(self, direction, partition):
"""Compute the repertoire of a partitioned mechanism and purview."""
repertoires = [
self.repertoire(direction, part.mechanism, part.purview)
for part in partition
]
return functools.reduce(np.multiply, reper... | python | def partitioned_repertoire(self, direction, partition):
"""Compute the repertoire of a partitioned mechanism and purview."""
repertoires = [
self.repertoire(direction, part.mechanism, part.purview)
for part in partition
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] | deeca69a084d782a6fde7bf26f59e93b593c5d77 | https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/subsystem.py#L424-L430 | train |
wmayner/pyphi | pyphi/subsystem.py | Subsystem.expand_repertoire | def expand_repertoire(self, direction, repertoire, new_purview=None):
"""Distribute an effect repertoire over a larger purview.
Args:
direction (Direction): |CAUSE| or |EFFECT|.
repertoire (np.ndarray): The repertoire to expand.
Keyword Args:
new_purview (tu... | python | def expand_repertoire(self, direction, repertoire, new_purview=None):
"""Distribute an effect repertoire over a larger purview.
Args:
direction (Direction): |CAUSE| or |EFFECT|.
repertoire (np.ndarray): The repertoire to expand.
Keyword Args:
new_purview (tu... | [
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wmayner/pyphi | pyphi/subsystem.py | Subsystem.cause_info | def cause_info(self, mechanism, purview):
"""Return the cause information for a mechanism over a purview."""
return repertoire_distance(
Direction.CAUSE,
self.cause_repertoire(mechanism, purview),
self.unconstrained_cause_repertoire(purview)
) | python | def cause_info(self, mechanism, purview):
"""Return the cause information for a mechanism over a purview."""
return repertoire_distance(
Direction.CAUSE,
self.cause_repertoire(mechanism, purview),
self.unconstrained_cause_repertoire(purview)
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wmayner/pyphi | pyphi/subsystem.py | Subsystem.effect_info | def effect_info(self, mechanism, purview):
"""Return the effect information for a mechanism over a purview."""
return repertoire_distance(
Direction.EFFECT,
self.effect_repertoire(mechanism, purview),
self.unconstrained_effect_repertoire(purview)
) | python | def effect_info(self, mechanism, purview):
"""Return the effect information for a mechanism over a purview."""
return repertoire_distance(
Direction.EFFECT,
self.effect_repertoire(mechanism, purview),
self.unconstrained_effect_repertoire(purview)
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wmayner/pyphi | pyphi/subsystem.py | Subsystem.cause_effect_info | def cause_effect_info(self, mechanism, purview):
"""Return the cause-effect information for a mechanism over a purview.
This is the minimum of the cause and effect information.
"""
return min(self.cause_info(mechanism, purview),
self.effect_info(mechanism, purview)) | python | def cause_effect_info(self, mechanism, purview):
"""Return the cause-effect information for a mechanism over a purview.
This is the minimum of the cause and effect information.
"""
return min(self.cause_info(mechanism, purview),
self.effect_info(mechanism, purview)) | [
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wmayner/pyphi | pyphi/subsystem.py | Subsystem.evaluate_partition | def evaluate_partition(self, direction, mechanism, purview, partition,
repertoire=None):
"""Return the |small_phi| of a mechanism over a purview for the given
partition.
Args:
direction (Direction): |CAUSE| or |EFFECT|.
mechanism (tuple[int]): ... | python | def evaluate_partition(self, direction, mechanism, purview, partition,
repertoire=None):
"""Return the |small_phi| of a mechanism over a purview for the given
partition.
Args:
direction (Direction): |CAUSE| or |EFFECT|.
mechanism (tuple[int]): ... | [
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wmayner/pyphi | pyphi/subsystem.py | Subsystem.find_mip | def find_mip(self, direction, mechanism, purview):
"""Return the minimum information partition for a mechanism over a
purview.
Args:
direction (Direction): |CAUSE| or |EFFECT|.
mechanism (tuple[int]): The nodes in the mechanism.
purview (tuple[int]): The node... | python | def find_mip(self, direction, mechanism, purview):
"""Return the minimum information partition for a mechanism over a
purview.
Args:
direction (Direction): |CAUSE| or |EFFECT|.
mechanism (tuple[int]): The nodes in the mechanism.
purview (tuple[int]): The node... | [
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mechanism (tuple[int]): The nodes in the mechanism.
purview (tuple[int]): The nodes in the purview.
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wmayner/pyphi | pyphi/subsystem.py | Subsystem.cause_mip | def cause_mip(self, mechanism, purview):
"""Return the irreducibility analysis for the cause MIP.
Alias for |find_mip()| with ``direction`` set to |CAUSE|.
"""
return self.find_mip(Direction.CAUSE, mechanism, purview) | python | def cause_mip(self, mechanism, purview):
"""Return the irreducibility analysis for the cause MIP.
Alias for |find_mip()| with ``direction`` set to |CAUSE|.
"""
return self.find_mip(Direction.CAUSE, mechanism, purview) | [
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