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16,000 | wmayner/pyphi | pyphi/compute/subsystem.py | ComputeSystemIrreducibility.process_result | def process_result(self, new_sia, min_sia):
"""Check if the new SIA has smaller |big_phi| than the standing
result.
"""
if new_sia.phi == 0:
self.done = True # Short-circuit
return new_sia
elif new_sia < min_sia:
return new_sia
retur... | python | def process_result(self, new_sia, min_sia):
"""Check if the new SIA has smaller |big_phi| than the standing
result.
"""
if new_sia.phi == 0:
self.done = True # Short-circuit
return new_sia
elif new_sia < min_sia:
return new_sia
retur... | [
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16,001 | wmayner/pyphi | pyphi/compute/subsystem.py | ConceptStyleSystem.concept | def concept(self, mechanism, purviews=False, cause_purviews=False,
effect_purviews=False):
"""Compute a concept, using the appropriate system for each side of the
cut.
"""
cause = self.cause_system.mic(
mechanism, purviews=(cause_purviews or purviews))
... | python | def concept(self, mechanism, purviews=False, cause_purviews=False,
effect_purviews=False):
"""Compute a concept, using the appropriate system for each side of the
cut.
"""
cause = self.cause_system.mic(
mechanism, purviews=(cause_purviews or purviews))
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16,002 | wmayner/pyphi | pyphi/labels.py | NodeLabels.coerce_to_indices | def coerce_to_indices(self, nodes):
"""Return the nodes indices for nodes, where ``nodes`` is either
already integer indices or node labels.
"""
if nodes is None:
return self.node_indices
if all(isinstance(node, str) for node in nodes):
indices = self.lab... | python | def coerce_to_indices(self, nodes):
"""Return the nodes indices for nodes, where ``nodes`` is either
already integer indices or node labels.
"""
if nodes is None:
return self.node_indices
if all(isinstance(node, str) for node in nodes):
indices = self.lab... | [
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16,003 | wmayner/pyphi | pyphi/models/subsystem.py | _null_sia | def _null_sia(subsystem, phi=0.0):
"""Return a |SystemIrreducibilityAnalysis| with zero |big_phi| and empty
cause-effect structures.
This is the analysis result for a reducible subsystem.
"""
return SystemIrreducibilityAnalysis(subsystem=subsystem,
cut_subsys... | python | def _null_sia(subsystem, phi=0.0):
"""Return a |SystemIrreducibilityAnalysis| with zero |big_phi| and empty
cause-effect structures.
This is the analysis result for a reducible subsystem.
"""
return SystemIrreducibilityAnalysis(subsystem=subsystem,
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16,004 | wmayner/pyphi | pyphi/models/subsystem.py | CauseEffectStructure.labeled_mechanisms | def labeled_mechanisms(self):
"""The labeled mechanism of each concept."""
label = self.subsystem.node_labels.indices2labels
return tuple(list(label(mechanism)) for mechanism in self.mechanisms) | python | def labeled_mechanisms(self):
"""The labeled mechanism of each concept."""
label = self.subsystem.node_labels.indices2labels
return tuple(list(label(mechanism)) for mechanism in self.mechanisms) | [
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16,005 | wmayner/pyphi | pyphi/direction.py | Direction.order | def order(self, mechanism, purview):
"""Order the mechanism and purview in time.
If the direction is ``CAUSE``, then the purview is at |t-1| and the
mechanism is at time |t|. If the direction is ``EFFECT``, then the
mechanism is at time |t| and the purview is at |t+1|.
"""
... | python | def order(self, mechanism, purview):
"""Order the mechanism and purview in time.
If the direction is ``CAUSE``, then the purview is at |t-1| and the
mechanism is at time |t|. If the direction is ``EFFECT``, then the
mechanism is at time |t| and the purview is at |t+1|.
"""
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16,006 | wmayner/pyphi | pyphi/models/cmp.py | sametype | def sametype(func):
"""Method decorator to return ``NotImplemented`` if the args of the wrapped
method are of different types.
When wrapping a rich model comparison method this will delegate (reflect)
the comparison to the right-hand-side object, or fallback by passing it up
the inheritance tree.
... | python | def sametype(func):
"""Method decorator to return ``NotImplemented`` if the args of the wrapped
method are of different types.
When wrapping a rich model comparison method this will delegate (reflect)
the comparison to the right-hand-side object, or fallback by passing it up
the inheritance tree.
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16,007 | wmayner/pyphi | pyphi/models/cmp.py | general_eq | def general_eq(a, b, attributes):
"""Return whether two objects are equal up to the given attributes.
If an attribute is called ``'phi'``, it is compared up to |PRECISION|.
If an attribute is called ``'mechanism'`` or ``'purview'``, it is
compared using set equality. All other attributes are compared ... | python | def general_eq(a, b, attributes):
"""Return whether two objects are equal up to the given attributes.
If an attribute is called ``'phi'``, it is compared up to |PRECISION|.
If an attribute is called ``'mechanism'`` or ``'purview'``, it is
compared using set equality. All other attributes are compared ... | [
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16,008 | wmayner/pyphi | benchmarks/time_emd.py | time_emd | def time_emd(emd_type, data):
"""Time an EMD command with the given data as arguments"""
emd = {
'cause': _CAUSE_EMD,
'effect': pyphi.subsystem.effect_emd,
'hamming': pyphi.utils.hamming_emd
}[emd_type]
def statement():
for (d1, d2) in data:
emd(d1, d2)
... | python | def time_emd(emd_type, data):
"""Time an EMD command with the given data as arguments"""
emd = {
'cause': _CAUSE_EMD,
'effect': pyphi.subsystem.effect_emd,
'hamming': pyphi.utils.hamming_emd
}[emd_type]
def statement():
for (d1, d2) in data:
emd(d1, d2)
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16,009 | wmayner/pyphi | pyphi/distribution.py | marginal_zero | def marginal_zero(repertoire, node_index):
"""Return the marginal probability that the node is OFF."""
index = [slice(None)] * repertoire.ndim
index[node_index] = 0
return repertoire[tuple(index)].sum() | python | def marginal_zero(repertoire, node_index):
"""Return the marginal probability that the node is OFF."""
index = [slice(None)] * repertoire.ndim
index[node_index] = 0
return repertoire[tuple(index)].sum() | [
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16,010 | wmayner/pyphi | pyphi/distribution.py | marginal | def marginal(repertoire, node_index):
"""Get the marginal distribution for a node."""
index = tuple(i for i in range(repertoire.ndim) if i != node_index)
return repertoire.sum(index, keepdims=True) | python | def marginal(repertoire, node_index):
"""Get the marginal distribution for a node."""
index = tuple(i for i in range(repertoire.ndim) if i != node_index)
return repertoire.sum(index, keepdims=True) | [
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16,011 | wmayner/pyphi | pyphi/distribution.py | independent | def independent(repertoire):
"""Check whether the repertoire is independent."""
marginals = [marginal(repertoire, i) for i in range(repertoire.ndim)]
# TODO: is there a way to do without an explicit iteration?
joint = marginals[0]
for m in marginals[1:]:
joint = joint * m
# TODO: shoul... | python | def independent(repertoire):
"""Check whether the repertoire is independent."""
marginals = [marginal(repertoire, i) for i in range(repertoire.ndim)]
# TODO: is there a way to do without an explicit iteration?
joint = marginals[0]
for m in marginals[1:]:
joint = joint * m
# TODO: shoul... | [
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16,012 | wmayner/pyphi | pyphi/distribution.py | purview | def purview(repertoire):
"""The purview of the repertoire.
Args:
repertoire (np.ndarray): A repertoire
Returns:
tuple[int]: The purview that the repertoire was computed over.
"""
if repertoire is None:
return None
return tuple(i for i, dim in enumerate(repertoire.shape... | python | def purview(repertoire):
"""The purview of the repertoire.
Args:
repertoire (np.ndarray): A repertoire
Returns:
tuple[int]: The purview that the repertoire was computed over.
"""
if repertoire is None:
return None
return tuple(i for i, dim in enumerate(repertoire.shape... | [
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16,013 | wmayner/pyphi | pyphi/distribution.py | flatten | def flatten(repertoire, big_endian=False):
"""Flatten a repertoire, removing empty dimensions.
By default, the flattened repertoire is returned in little-endian order.
Args:
repertoire (np.ndarray or None): A repertoire.
Keyword Args:
big_endian (boolean): If ``True``, flatten the rep... | python | def flatten(repertoire, big_endian=False):
"""Flatten a repertoire, removing empty dimensions.
By default, the flattened repertoire is returned in little-endian order.
Args:
repertoire (np.ndarray or None): A repertoire.
Keyword Args:
big_endian (boolean): If ``True``, flatten the rep... | [
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16,014 | wmayner/pyphi | pyphi/distribution.py | max_entropy_distribution | def max_entropy_distribution(node_indices, number_of_nodes):
"""Return the maximum entropy distribution over a set of nodes.
This is different from the network's uniform distribution because nodes
outside ``node_indices`` are fixed and treated as if they have only 1
state.
Args:
node_indic... | python | def max_entropy_distribution(node_indices, number_of_nodes):
"""Return the maximum entropy distribution over a set of nodes.
This is different from the network's uniform distribution because nodes
outside ``node_indices`` are fixed and treated as if they have only 1
state.
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node_indic... | [
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16,015 | wmayner/pyphi | pyphi/macro.py | run_tpm | def run_tpm(system, steps, blackbox):
"""Iterate the TPM for the given number of timesteps.
Returns:
np.ndarray: tpm * (noise_tpm^(t-1))
"""
# Generate noised TPM
# Noise the connections from every output element to elements in other
# boxes.
node_tpms = []
for node in system.no... | python | def run_tpm(system, steps, blackbox):
"""Iterate the TPM for the given number of timesteps.
Returns:
np.ndarray: tpm * (noise_tpm^(t-1))
"""
# Generate noised TPM
# Noise the connections from every output element to elements in other
# boxes.
node_tpms = []
for node in system.no... | [
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16,016 | wmayner/pyphi | pyphi/macro.py | _partitions_list | def _partitions_list(N):
"""Return a list of partitions of the |N| binary nodes.
Args:
N (int): The number of nodes under consideration.
Returns:
list[list]: A list of lists, where each inner list is the set of
micro-elements corresponding to a macro-element.
Example:
... | python | def _partitions_list(N):
"""Return a list of partitions of the |N| binary nodes.
Args:
N (int): The number of nodes under consideration.
Returns:
list[list]: A list of lists, where each inner list is the set of
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16,017 | wmayner/pyphi | pyphi/macro.py | all_partitions | def all_partitions(indices):
"""Return a list of all possible coarse grains of a network.
Args:
indices (tuple[int]): The micro indices to partition.
Yields:
tuple[tuple]: A possible partition. Each element of the tuple
is a tuple of micro-elements which correspond to macro-element... | python | def all_partitions(indices):
"""Return a list of all possible coarse grains of a network.
Args:
indices (tuple[int]): The micro indices to partition.
Yields:
tuple[tuple]: A possible partition. Each element of the tuple
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16,018 | wmayner/pyphi | pyphi/macro.py | all_coarse_grains | def all_coarse_grains(indices):
"""Generator over all possible |CoarseGrains| of these indices.
Args:
indices (tuple[int]): Node indices to coarse grain.
Yields:
CoarseGrain: The next |CoarseGrain| for ``indices``.
"""
for partition in all_partitions(indices):
for grouping ... | python | def all_coarse_grains(indices):
"""Generator over all possible |CoarseGrains| of these indices.
Args:
indices (tuple[int]): Node indices to coarse grain.
Yields:
CoarseGrain: The next |CoarseGrain| for ``indices``.
"""
for partition in all_partitions(indices):
for grouping ... | [
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16,019 | wmayner/pyphi | pyphi/macro.py | all_coarse_grains_for_blackbox | def all_coarse_grains_for_blackbox(blackbox):
"""Generator over all |CoarseGrains| for the given blackbox.
If a box has multiple outputs, those outputs are partitioned into the same
coarse-grain macro-element.
"""
for partition in all_partitions(blackbox.output_indices):
for grouping in all... | python | def all_coarse_grains_for_blackbox(blackbox):
"""Generator over all |CoarseGrains| for the given blackbox.
If a box has multiple outputs, those outputs are partitioned into the same
coarse-grain macro-element.
"""
for partition in all_partitions(blackbox.output_indices):
for grouping in all... | [
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16,020 | wmayner/pyphi | pyphi/macro.py | all_blackboxes | def all_blackboxes(indices):
"""Generator over all possible blackboxings of these indices.
Args:
indices (tuple[int]): Nodes to blackbox.
Yields:
Blackbox: The next |Blackbox| of ``indices``.
"""
for partition in all_partitions(indices):
# TODO? don't consider the empty set... | python | def all_blackboxes(indices):
"""Generator over all possible blackboxings of these indices.
Args:
indices (tuple[int]): Nodes to blackbox.
Yields:
Blackbox: The next |Blackbox| of ``indices``.
"""
for partition in all_partitions(indices):
# TODO? don't consider the empty set... | [
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16,021 | wmayner/pyphi | pyphi/macro.py | coarse_graining | def coarse_graining(network, state, internal_indices):
"""Find the maximal coarse-graining of a micro-system.
Args:
network (Network): The network in question.
state (tuple[int]): The state of the network.
internal_indices (tuple[int]): Nodes in the micro-system.
Returns:
t... | python | def coarse_graining(network, state, internal_indices):
"""Find the maximal coarse-graining of a micro-system.
Args:
network (Network): The network in question.
state (tuple[int]): The state of the network.
internal_indices (tuple[int]): Nodes in the micro-system.
Returns:
t... | [
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16,022 | wmayner/pyphi | pyphi/macro.py | all_macro_systems | def all_macro_systems(network, state, do_blackbox=False, do_coarse_grain=False,
time_scales=None):
"""Generator over all possible macro-systems for the network."""
if time_scales is None:
time_scales = [1]
def blackboxes(system):
# Returns all blackboxes to evaluate
... | python | def all_macro_systems(network, state, do_blackbox=False, do_coarse_grain=False,
time_scales=None):
"""Generator over all possible macro-systems for the network."""
if time_scales is None:
time_scales = [1]
def blackboxes(system):
# Returns all blackboxes to evaluate
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16,023 | wmayner/pyphi | pyphi/macro.py | emergence | def emergence(network, state, do_blackbox=False, do_coarse_grain=True,
time_scales=None):
"""Check for the emergence of a micro-system into a macro-system.
Checks all possible blackboxings and coarse-grainings of a system to find
the spatial scale with maximum integrated information.
Use... | python | def emergence(network, state, do_blackbox=False, do_coarse_grain=True,
time_scales=None):
"""Check for the emergence of a micro-system into a macro-system.
Checks all possible blackboxings and coarse-grainings of a system to find
the spatial scale with maximum integrated information.
Use... | [
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16,024 | wmayner/pyphi | pyphi/macro.py | effective_info | def effective_info(network):
"""Return the effective information of the given network.
.. note::
For details, see:
Hoel, Erik P., Larissa Albantakis, and Giulio Tononi.
“Quantifying causal emergence shows that macro can beat micro.”
Proceedings of the
National Academy o... | python | def effective_info(network):
"""Return the effective information of the given network.
.. note::
For details, see:
Hoel, Erik P., Larissa Albantakis, and Giulio Tononi.
“Quantifying causal emergence shows that macro can beat micro.”
Proceedings of the
National Academy o... | [
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16,025 | wmayner/pyphi | pyphi/macro.py | SystemAttrs.node_labels | def node_labels(self):
"""Return the labels for macro nodes."""
assert list(self.node_indices)[0] == 0
labels = list("m{}".format(i) for i in self.node_indices)
return NodeLabels(labels, self.node_indices) | python | def node_labels(self):
"""Return the labels for macro nodes."""
assert list(self.node_indices)[0] == 0
labels = list("m{}".format(i) for i in self.node_indices)
return NodeLabels(labels, self.node_indices) | [
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16,026 | wmayner/pyphi | pyphi/macro.py | MacroSubsystem._squeeze | def _squeeze(system):
"""Squeeze out all singleton dimensions in the Subsystem.
Reindexes the subsystem so that the nodes are ``0..n`` where ``n`` is
the number of internal indices in the system.
"""
assert system.node_indices == tpm_indices(system.tpm)
internal_indices... | python | def _squeeze(system):
"""Squeeze out all singleton dimensions in the Subsystem.
Reindexes the subsystem so that the nodes are ``0..n`` where ``n`` is
the number of internal indices in the system.
"""
assert system.node_indices == tpm_indices(system.tpm)
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16,027 | wmayner/pyphi | pyphi/macro.py | MacroSubsystem._blackbox_partial_noise | def _blackbox_partial_noise(blackbox, system):
"""Noise connections from hidden elements to other boxes."""
# Noise inputs from non-output elements hidden in other boxes
node_tpms = []
for node in system.nodes:
node_tpm = node.tpm_on
for input_node in node.inputs:... | python | def _blackbox_partial_noise(blackbox, system):
"""Noise connections from hidden elements to other boxes."""
# Noise inputs from non-output elements hidden in other boxes
node_tpms = []
for node in system.nodes:
node_tpm = node.tpm_on
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16,028 | wmayner/pyphi | pyphi/macro.py | MacroSubsystem._blackbox_time | def _blackbox_time(time_scale, blackbox, system):
"""Black box the CM and TPM over the given time_scale."""
blackbox = blackbox.reindex()
tpm = run_tpm(system, time_scale, blackbox)
# Universal connectivity, for now.
n = len(system.node_indices)
cm = np.ones((n, n))
... | python | def _blackbox_time(time_scale, blackbox, system):
"""Black box the CM and TPM over the given time_scale."""
blackbox = blackbox.reindex()
tpm = run_tpm(system, time_scale, blackbox)
# Universal connectivity, for now.
n = len(system.node_indices)
cm = np.ones((n, n))
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16,029 | wmayner/pyphi | pyphi/macro.py | MacroSubsystem._blackbox_space | def _blackbox_space(self, blackbox, system):
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.. TODO: change this ^
This shrinks the size of the TPM by the number of hidden indices; now
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.. TODO: change this ^
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16,030 | wmayner/pyphi | pyphi/macro.py | MacroSubsystem._coarsegrain_space | def _coarsegrain_space(coarse_grain, is_cut, system):
"""Spatially coarse-grain the TPM and CM."""
tpm = coarse_grain.macro_tpm(
system.tpm, check_independence=(not is_cut))
node_indices = coarse_grain.macro_indices
state = coarse_grain.macro_state(system.state)
# U... | python | def _coarsegrain_space(coarse_grain, is_cut, system):
"""Spatially coarse-grain the TPM and CM."""
tpm = coarse_grain.macro_tpm(
system.tpm, check_independence=(not is_cut))
node_indices = coarse_grain.macro_indices
state = coarse_grain.macro_state(system.state)
# U... | [
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16,031 | wmayner/pyphi | pyphi/macro.py | MacroSubsystem.cut_mechanisms | def cut_mechanisms(self):
"""The mechanisms of this system that are currently cut.
Note that although ``cut_indices`` returns micro indices, this
returns macro mechanisms.
Yields:
tuple[int]
"""
for mechanism in utils.powerset(self.node_indices, nonempty=Tru... | python | def cut_mechanisms(self):
"""The mechanisms of this system that are currently cut.
Note that although ``cut_indices`` returns micro indices, this
returns macro mechanisms.
Yields:
tuple[int]
"""
for mechanism in utils.powerset(self.node_indices, nonempty=Tru... | [
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16,032 | wmayner/pyphi | pyphi/macro.py | MacroSubsystem.apply_cut | def apply_cut(self, cut):
"""Return a cut version of this |MacroSubsystem|.
Args:
cut (Cut): The cut to apply to this |MacroSubsystem|.
Returns:
MacroSubsystem: The cut version of this |MacroSubsystem|.
"""
# TODO: is the MICE cache reusable?
ret... | python | def apply_cut(self, cut):
"""Return a cut version of this |MacroSubsystem|.
Args:
cut (Cut): The cut to apply to this |MacroSubsystem|.
Returns:
MacroSubsystem: The cut version of this |MacroSubsystem|.
"""
# TODO: is the MICE cache reusable?
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16,033 | wmayner/pyphi | pyphi/macro.py | MacroSubsystem.potential_purviews | def potential_purviews(self, direction, mechanism, purviews=False):
"""Override Subsystem implementation using Network-level indices."""
all_purviews = utils.powerset(self.node_indices)
return irreducible_purviews(
self.cm, direction, mechanism, all_purviews) | python | def potential_purviews(self, direction, mechanism, purviews=False):
"""Override Subsystem implementation using Network-level indices."""
all_purviews = utils.powerset(self.node_indices)
return irreducible_purviews(
self.cm, direction, mechanism, all_purviews) | [
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16,034 | wmayner/pyphi | pyphi/macro.py | MacroSubsystem.macro2micro | def macro2micro(self, macro_indices):
"""Return all micro indices which compose the elements specified by
``macro_indices``.
"""
def from_partition(partition, macro_indices):
micro_indices = itertools.chain.from_iterable(
partition[i] for i in macro_indices)
... | python | def macro2micro(self, macro_indices):
"""Return all micro indices which compose the elements specified by
``macro_indices``.
"""
def from_partition(partition, macro_indices):
micro_indices = itertools.chain.from_iterable(
partition[i] for i in macro_indices)
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16,035 | wmayner/pyphi | pyphi/macro.py | MacroSubsystem.macro2blackbox_outputs | def macro2blackbox_outputs(self, macro_indices):
"""Given a set of macro elements, return the blackbox output elements
which compose these elements.
"""
if not self.blackbox:
raise ValueError('System is not blackboxed')
return tuple(sorted(set(
self.macro... | python | def macro2blackbox_outputs(self, macro_indices):
"""Given a set of macro elements, return the blackbox output elements
which compose these elements.
"""
if not self.blackbox:
raise ValueError('System is not blackboxed')
return tuple(sorted(set(
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16,036 | wmayner/pyphi | pyphi/macro.py | CoarseGrain.micro_indices | def micro_indices(self):
"""Indices of micro elements represented in this coarse-graining."""
return tuple(sorted(idx for part in self.partition for idx in part)) | python | def micro_indices(self):
"""Indices of micro elements represented in this coarse-graining."""
return tuple(sorted(idx for part in self.partition for idx in part)) | [
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16,037 | wmayner/pyphi | pyphi/macro.py | CoarseGrain.reindex | def reindex(self):
"""Re-index this coarse graining to use squeezed indices.
The output grouping is translated to use indices ``0..n``, where ``n``
is the number of micro indices in the coarse-graining. Re-indexing does
not effect the state grouping, which is already index-independent.
... | python | def reindex(self):
"""Re-index this coarse graining to use squeezed indices.
The output grouping is translated to use indices ``0..n``, where ``n``
is the number of micro indices in the coarse-graining. Re-indexing does
not effect the state grouping, which is already index-independent.
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16,038 | wmayner/pyphi | pyphi/macro.py | CoarseGrain.macro_state | def macro_state(self, micro_state):
"""Translate a micro state to a macro state
Args:
micro_state (tuple[int]): The state of the micro nodes in this
coarse-graining.
Returns:
tuple[int]: The state of the macro system, translated as specified
... | python | def macro_state(self, micro_state):
"""Translate a micro state to a macro state
Args:
micro_state (tuple[int]): The state of the micro nodes in this
coarse-graining.
Returns:
tuple[int]: The state of the macro system, translated as specified
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16,039 | wmayner/pyphi | pyphi/macro.py | CoarseGrain.make_mapping | def make_mapping(self):
"""Return a mapping from micro-state to the macro-states based on the
partition and state grouping of this coarse-grain.
Return:
(nd.ndarray): A mapping from micro-states to macro-states. The
|ith| entry in the mapping is the macro-state correspon... | python | def make_mapping(self):
"""Return a mapping from micro-state to the macro-states based on the
partition and state grouping of this coarse-grain.
Return:
(nd.ndarray): A mapping from micro-states to macro-states. The
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16,040 | wmayner/pyphi | pyphi/macro.py | CoarseGrain.macro_tpm_sbs | def macro_tpm_sbs(self, state_by_state_micro_tpm):
"""Create a state-by-state coarse-grained macro TPM.
Args:
micro_tpm (nd.array): The state-by-state TPM of the micro-system.
Returns:
np.ndarray: The state-by-state TPM of the macro-system.
"""
validate.... | python | def macro_tpm_sbs(self, state_by_state_micro_tpm):
"""Create a state-by-state coarse-grained macro TPM.
Args:
micro_tpm (nd.array): The state-by-state TPM of the micro-system.
Returns:
np.ndarray: The state-by-state TPM of the macro-system.
"""
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16,041 | wmayner/pyphi | pyphi/macro.py | CoarseGrain.macro_tpm | def macro_tpm(self, micro_tpm, check_independence=True):
"""Create a coarse-grained macro TPM.
Args:
micro_tpm (nd.array): The TPM of the micro-system.
check_independence (bool): Whether to check that the macro TPM is
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Raises:
... | python | def macro_tpm(self, micro_tpm, check_independence=True):
"""Create a coarse-grained macro TPM.
Args:
micro_tpm (nd.array): The TPM of the micro-system.
check_independence (bool): Whether to check that the macro TPM is
conditionally independent.
Raises:
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16,042 | wmayner/pyphi | pyphi/macro.py | Blackbox.hidden_indices | def hidden_indices(self):
"""All elements hidden inside the blackboxes."""
return tuple(sorted(set(self.micro_indices) -
set(self.output_indices))) | python | def hidden_indices(self):
"""All elements hidden inside the blackboxes."""
return tuple(sorted(set(self.micro_indices) -
set(self.output_indices))) | [
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16,043 | wmayner/pyphi | pyphi/macro.py | Blackbox.outputs_of | def outputs_of(self, partition_index):
"""The outputs of the partition at ``partition_index``.
Note that this returns a tuple of element indices, since coarse-
grained blackboxes may have multiple outputs.
"""
partition = self.partition[partition_index]
outputs = set(par... | python | def outputs_of(self, partition_index):
"""The outputs of the partition at ``partition_index``.
Note that this returns a tuple of element indices, since coarse-
grained blackboxes may have multiple outputs.
"""
partition = self.partition[partition_index]
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16,044 | wmayner/pyphi | pyphi/macro.py | Blackbox.reindex | def reindex(self):
"""Squeeze the indices of this blackboxing to ``0..n``.
Returns:
Blackbox: a new, reindexed |Blackbox|.
Example:
>>> partition = ((3,), (2, 4))
>>> output_indices = (2, 3)
>>> blackbox = Blackbox(partition, output_indices)
... | python | def reindex(self):
"""Squeeze the indices of this blackboxing to ``0..n``.
Returns:
Blackbox: a new, reindexed |Blackbox|.
Example:
>>> partition = ((3,), (2, 4))
>>> output_indices = (2, 3)
>>> blackbox = Blackbox(partition, output_indices)
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16,045 | wmayner/pyphi | pyphi/macro.py | Blackbox.macro_state | def macro_state(self, micro_state):
"""Compute the macro-state of this blackbox.
This is just the state of the blackbox's output indices.
Args:
micro_state (tuple[int]): The state of the micro-elements in the
blackbox.
Returns:
tuple[int]: The s... | python | def macro_state(self, micro_state):
"""Compute the macro-state of this blackbox.
This is just the state of the blackbox's output indices.
Args:
micro_state (tuple[int]): The state of the micro-elements in the
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16,046 | wmayner/pyphi | pyphi/macro.py | Blackbox.in_same_box | def in_same_box(self, a, b):
"""Return ``True`` if nodes ``a`` and ``b``` are in the same box."""
assert a in self.micro_indices
assert b in self.micro_indices
for part in self.partition:
if a in part and b in part:
return True
return False | python | def in_same_box(self, a, b):
"""Return ``True`` if nodes ``a`` and ``b``` are in the same box."""
assert a in self.micro_indices
assert b in self.micro_indices
for part in self.partition:
if a in part and b in part:
return True
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16,047 | wmayner/pyphi | pyphi/macro.py | Blackbox.hidden_from | def hidden_from(self, a, b):
"""Return True if ``a`` is hidden in a different box than ``b``."""
return a in self.hidden_indices and not self.in_same_box(a, b) | python | def hidden_from(self, a, b):
"""Return True if ``a`` is hidden in a different box than ``b``."""
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16,048 | wmayner/pyphi | pyphi/network.py | irreducible_purviews | def irreducible_purviews(cm, direction, mechanism, purviews):
"""Return all purviews which are irreducible for the mechanism.
Args:
cm (np.ndarray): An |N x N| connectivity matrix.
direction (Direction): |CAUSE| or |EFFECT|.
purviews (list[tuple[int]]): The purviews to check.
me... | python | def irreducible_purviews(cm, direction, mechanism, purviews):
"""Return all purviews which are irreducible for the mechanism.
Args:
cm (np.ndarray): An |N x N| connectivity matrix.
direction (Direction): |CAUSE| or |EFFECT|.
purviews (list[tuple[int]]): The purviews to check.
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16,049 | wmayner/pyphi | pyphi/network.py | Network._build_tpm | def _build_tpm(tpm):
"""Validate the TPM passed by the user and convert to multidimensional
form.
"""
tpm = np.array(tpm)
validate.tpm(tpm)
# Convert to multidimensional state-by-node form
if is_state_by_state(tpm):
tpm = convert.state_by_state2state... | python | def _build_tpm(tpm):
"""Validate the TPM passed by the user and convert to multidimensional
form.
"""
tpm = np.array(tpm)
validate.tpm(tpm)
# Convert to multidimensional state-by-node form
if is_state_by_state(tpm):
tpm = convert.state_by_state2state... | [
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16,050 | wmayner/pyphi | pyphi/network.py | Network._build_cm | def _build_cm(self, cm):
"""Convert the passed CM to the proper format, or construct the
unitary CM if none was provided.
"""
if cm is None:
# Assume all are connected.
cm = np.ones((self.size, self.size))
else:
cm = np.array(cm)
utils... | python | def _build_cm(self, cm):
"""Convert the passed CM to the proper format, or construct the
unitary CM if none was provided.
"""
if cm is None:
# Assume all are connected.
cm = np.ones((self.size, self.size))
else:
cm = np.array(cm)
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16,051 | wmayner/pyphi | pyphi/network.py | Network.potential_purviews | def potential_purviews(self, direction, mechanism):
"""All purviews which are not clearly reducible for mechanism.
Args:
direction (Direction): |CAUSE| or |EFFECT|.
mechanism (tuple[int]): The mechanism which all purviews are
checked for reducibility over.
... | python | def potential_purviews(self, direction, mechanism):
"""All purviews which are not clearly reducible for mechanism.
Args:
direction (Direction): |CAUSE| or |EFFECT|.
mechanism (tuple[int]): The mechanism which all purviews are
checked for reducibility over.
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16,052 | wmayner/pyphi | pyphi/jsonify.py | _loadable_models | def _loadable_models():
"""A dictionary of loadable PyPhi models.
These are stored in this function (instead of module scope) to resolve
circular import issues.
"""
classes = [
pyphi.Direction,
pyphi.Network,
pyphi.Subsystem,
pyphi.Transition,
pyphi.labels.No... | python | def _loadable_models():
"""A dictionary of loadable PyPhi models.
These are stored in this function (instead of module scope) to resolve
circular import issues.
"""
classes = [
pyphi.Direction,
pyphi.Network,
pyphi.Subsystem,
pyphi.Transition,
pyphi.labels.No... | [
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16,053 | wmayner/pyphi | pyphi/jsonify.py | jsonify | def jsonify(obj): # pylint: disable=too-many-return-statements
"""Return a JSON-encodable representation of an object, recursively using
any available ``to_json`` methods, converting NumPy arrays and datatypes to
native lists and types along the way.
"""
# Call the `to_json` method if available and... | python | def jsonify(obj): # pylint: disable=too-many-return-statements
"""Return a JSON-encodable representation of an object, recursively using
any available ``to_json`` methods, converting NumPy arrays and datatypes to
native lists and types along the way.
"""
# Call the `to_json` method if available and... | [
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16,054 | wmayner/pyphi | pyphi/jsonify.py | _check_version | def _check_version(version):
"""Check whether the JSON version matches the PyPhi version."""
if version != pyphi.__version__:
raise pyphi.exceptions.JSONVersionError(
'Cannot load JSON from a different version of PyPhi. '
'JSON version = {0}, current version = {1}.'.format(
... | python | def _check_version(version):
"""Check whether the JSON version matches the PyPhi version."""
if version != pyphi.__version__:
raise pyphi.exceptions.JSONVersionError(
'Cannot load JSON from a different version of PyPhi. '
'JSON version = {0}, current version = {1}.'.format(
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16,055 | wmayner/pyphi | pyphi/jsonify.py | PyPhiJSONDecoder._load_object | def _load_object(self, obj):
"""Recursively load a PyPhi object.
PyPhi models are recursively loaded, using the model metadata to
recreate the original object relations. Lists are cast to tuples
because most objects in PyPhi which are serialized to lists (eg.
mechanisms and purv... | python | def _load_object(self, obj):
"""Recursively load a PyPhi object.
PyPhi models are recursively loaded, using the model metadata to
recreate the original object relations. Lists are cast to tuples
because most objects in PyPhi which are serialized to lists (eg.
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16,056 | wmayner/pyphi | pyphi/jsonify.py | PyPhiJSONDecoder._load_model | def _load_model(self, dct):
"""Load a serialized PyPhi model.
The object is memoized for reuse elsewhere in the object graph.
"""
classname, version, _ = _pop_metadata(dct)
_check_version(version)
cls = self._models[classname]
# Use `from_json` if available
... | python | def _load_model(self, dct):
"""Load a serialized PyPhi model.
The object is memoized for reuse elsewhere in the object graph.
"""
classname, version, _ = _pop_metadata(dct)
_check_version(version)
cls = self._models[classname]
# Use `from_json` if available
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16,057 | wmayner/pyphi | pyphi/distance.py | _compute_hamming_matrix | def _compute_hamming_matrix(N):
"""Compute and store a Hamming matrix for |N| nodes.
Hamming matrices have the following sizes::
N MBs
== ===
9 2
10 8
11 32
12 128
13 512
Given these sizes and the fact that large matrices are needed infrequ... | python | def _compute_hamming_matrix(N):
"""Compute and store a Hamming matrix for |N| nodes.
Hamming matrices have the following sizes::
N MBs
== ===
9 2
10 8
11 32
12 128
13 512
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16,058 | wmayner/pyphi | pyphi/distance.py | effect_emd | def effect_emd(d1, d2):
"""Compute the EMD between two effect repertoires.
Because the nodes are independent, the EMD between effect repertoires is
equal to the sum of the EMDs between the marginal distributions of each
node, and the EMD between marginal distribution for a node is the absolute
diff... | python | def effect_emd(d1, d2):
"""Compute the EMD between two effect repertoires.
Because the nodes are independent, the EMD between effect repertoires is
equal to the sum of the EMDs between the marginal distributions of each
node, and the EMD between marginal distribution for a node is the absolute
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16,059 | wmayner/pyphi | pyphi/distance.py | entropy_difference | def entropy_difference(d1, d2):
"""Return the difference in entropy between two distributions."""
d1, d2 = flatten(d1), flatten(d2)
return abs(entropy(d1, base=2.0) - entropy(d2, base=2.0)) | python | def entropy_difference(d1, d2):
"""Return the difference in entropy between two distributions."""
d1, d2 = flatten(d1), flatten(d2)
return abs(entropy(d1, base=2.0) - entropy(d2, base=2.0)) | [
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16,060 | wmayner/pyphi | pyphi/distance.py | psq2 | def psq2(d1, d2):
"""Compute the PSQ2 measure.
Args:
d1 (np.ndarray): The first distribution.
d2 (np.ndarray): The second distribution.
"""
d1, d2 = flatten(d1), flatten(d2)
def f(p):
return sum((p ** 2) * np.nan_to_num(np.log(p * len(p))))
return abs(f(d1) - f(d2)) | python | def psq2(d1, d2):
"""Compute the PSQ2 measure.
Args:
d1 (np.ndarray): The first distribution.
d2 (np.ndarray): The second distribution.
"""
d1, d2 = flatten(d1), flatten(d2)
def f(p):
return sum((p ** 2) * np.nan_to_num(np.log(p * len(p))))
return abs(f(d1) - f(d2)) | [
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16,061 | wmayner/pyphi | pyphi/distance.py | mp2q | def mp2q(p, q):
"""Compute the MP2Q measure.
Args:
p (np.ndarray): The unpartitioned repertoire
q (np.ndarray): The partitioned repertoire
"""
p, q = flatten(p), flatten(q)
entropy_dist = 1 / len(p)
return sum(entropy_dist * np.nan_to_num((p ** 2) / q * np.log(p / q))) | python | def mp2q(p, q):
"""Compute the MP2Q measure.
Args:
p (np.ndarray): The unpartitioned repertoire
q (np.ndarray): The partitioned repertoire
"""
p, q = flatten(p), flatten(q)
entropy_dist = 1 / len(p)
return sum(entropy_dist * np.nan_to_num((p ** 2) / q * np.log(p / q))) | [
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16,062 | wmayner/pyphi | pyphi/distance.py | klm | def klm(p, q):
"""Compute the KLM divergence."""
p, q = flatten(p), flatten(q)
return max(abs(p * np.nan_to_num(np.log(p / q)))) | python | def klm(p, q):
"""Compute the KLM divergence."""
p, q = flatten(p), flatten(q)
return max(abs(p * np.nan_to_num(np.log(p / q)))) | [
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16,063 | wmayner/pyphi | pyphi/distance.py | directional_emd | def directional_emd(direction, d1, d2):
"""Compute the EMD between two repertoires for a given direction.
The full EMD computation is used for cause repertoires. A fast analytic
solution is used for effect repertoires.
Args:
direction (Direction): |CAUSE| or |EFFECT|.
d1 (np.ndarray): ... | python | def directional_emd(direction, d1, d2):
"""Compute the EMD between two repertoires for a given direction.
The full EMD computation is used for cause repertoires. A fast analytic
solution is used for effect repertoires.
Args:
direction (Direction): |CAUSE| or |EFFECT|.
d1 (np.ndarray): ... | [
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16,064 | wmayner/pyphi | pyphi/distance.py | repertoire_distance | def repertoire_distance(direction, r1, r2):
"""Compute the distance between two repertoires for the given direction.
Args:
direction (Direction): |CAUSE| or |EFFECT|.
r1 (np.ndarray): The first repertoire.
r2 (np.ndarray): The second repertoire.
Returns:
float: The distance... | python | def repertoire_distance(direction, r1, r2):
"""Compute the distance between two repertoires for the given direction.
Args:
direction (Direction): |CAUSE| or |EFFECT|.
r1 (np.ndarray): The first repertoire.
r2 (np.ndarray): The second repertoire.
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float: The distance... | [
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16,065 | wmayner/pyphi | pyphi/distance.py | system_repertoire_distance | def system_repertoire_distance(r1, r2):
"""Compute the distance between two repertoires of a system.
Args:
r1 (np.ndarray): The first repertoire.
r2 (np.ndarray): The second repertoire.
Returns:
float: The distance between ``r1`` and ``r2``.
"""
if config.MEASURE in measure... | python | def system_repertoire_distance(r1, r2):
"""Compute the distance between two repertoires of a system.
Args:
r1 (np.ndarray): The first repertoire.
r2 (np.ndarray): The second repertoire.
Returns:
float: The distance between ``r1`` and ``r2``.
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16,066 | wmayner/pyphi | pyphi/distance.py | MeasureRegistry.register | def register(self, name, asymmetric=False):
"""Decorator for registering a measure with PyPhi.
Args:
name (string): The name of the measure.
Keyword Args:
asymmetric (boolean): ``True`` if the measure is asymmetric.
"""
def register_func(func):
... | python | def register(self, name, asymmetric=False):
"""Decorator for registering a measure with PyPhi.
Args:
name (string): The name of the measure.
Keyword Args:
asymmetric (boolean): ``True`` if the measure is asymmetric.
"""
def register_func(func):
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16,067 | wmayner/pyphi | pyphi/partition.py | partitions | def partitions(collection):
"""Generate all set partitions of a collection.
Example:
>>> list(partitions(range(3))) # doctest: +NORMALIZE_WHITESPACE
[[[0, 1, 2]],
[[0], [1, 2]],
[[0, 1], [2]],
[[1], [0, 2]],
[[0], [1], [2]]]
"""
collection = list(col... | python | def partitions(collection):
"""Generate all set partitions of a collection.
Example:
>>> list(partitions(range(3))) # doctest: +NORMALIZE_WHITESPACE
[[[0, 1, 2]],
[[0], [1, 2]],
[[0, 1], [2]],
[[1], [0, 2]],
[[0], [1], [2]]]
"""
collection = list(col... | [
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16,068 | wmayner/pyphi | pyphi/partition.py | bipartition_indices | def bipartition_indices(N):
"""Return indices for undirected bipartitions of a sequence.
Args:
N (int): The length of the sequence.
Returns:
list: A list of tuples containing the indices for each of the two
parts.
Example:
>>> N = 3
>>> bipartition_indices(N)
... | python | def bipartition_indices(N):
"""Return indices for undirected bipartitions of a sequence.
Args:
N (int): The length of the sequence.
Returns:
list: A list of tuples containing the indices for each of the two
parts.
Example:
>>> N = 3
>>> bipartition_indices(N)
... | [
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16,069 | wmayner/pyphi | pyphi/partition.py | bipartition | def bipartition(seq):
"""Return a list of bipartitions for a sequence.
Args:
a (Iterable): The sequence to partition.
Returns:
list[tuple[tuple]]: A list of tuples containing each of the two
partitions.
Example:
>>> bipartition((1,2,3))
[((), (1, 2, 3)), ((1,),... | python | def bipartition(seq):
"""Return a list of bipartitions for a sequence.
Args:
a (Iterable): The sequence to partition.
Returns:
list[tuple[tuple]]: A list of tuples containing each of the two
partitions.
Example:
>>> bipartition((1,2,3))
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16,070 | wmayner/pyphi | pyphi/partition.py | directed_bipartition | def directed_bipartition(seq, nontrivial=False):
"""Return a list of directed bipartitions for a sequence.
Args:
seq (Iterable): The sequence to partition.
Returns:
list[tuple[tuple]]: A list of tuples containing each of the two
parts.
Example:
>>> directed_bipartition... | python | def directed_bipartition(seq, nontrivial=False):
"""Return a list of directed bipartitions for a sequence.
Args:
seq (Iterable): The sequence to partition.
Returns:
list[tuple[tuple]]: A list of tuples containing each of the two
parts.
Example:
>>> directed_bipartition... | [
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16,071 | wmayner/pyphi | pyphi/partition.py | bipartition_of_one | def bipartition_of_one(seq):
"""Generate bipartitions where one part is of length 1."""
seq = list(seq)
for i, elt in enumerate(seq):
yield ((elt,), tuple(seq[:i] + seq[(i + 1):])) | python | def bipartition_of_one(seq):
"""Generate bipartitions where one part is of length 1."""
seq = list(seq)
for i, elt in enumerate(seq):
yield ((elt,), tuple(seq[:i] + seq[(i + 1):])) | [
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16,072 | wmayner/pyphi | pyphi/partition.py | directed_bipartition_of_one | def directed_bipartition_of_one(seq):
"""Generate directed bipartitions where one part is of length 1.
Args:
seq (Iterable): The sequence to partition.
Returns:
list[tuple[tuple]]: A list of tuples containing each of the two
partitions.
Example:
>>> partitions = direct... | python | def directed_bipartition_of_one(seq):
"""Generate directed bipartitions where one part is of length 1.
Args:
seq (Iterable): The sequence to partition.
Returns:
list[tuple[tuple]]: A list of tuples containing each of the two
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Example:
>>> partitions = direct... | [
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16,073 | wmayner/pyphi | pyphi/partition.py | directed_tripartition_indices | def directed_tripartition_indices(N):
"""Return indices for directed tripartitions of a sequence.
Args:
N (int): The length of the sequence.
Returns:
list[tuple]: A list of tuples containing the indices for each
partition.
Example:
>>> N = 1
>>> directed_tripar... | python | def directed_tripartition_indices(N):
"""Return indices for directed tripartitions of a sequence.
Args:
N (int): The length of the sequence.
Returns:
list[tuple]: A list of tuples containing the indices for each
partition.
Example:
>>> N = 1
>>> directed_tripar... | [
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16,074 | wmayner/pyphi | pyphi/partition.py | directed_tripartition | def directed_tripartition(seq):
"""Generator over all directed tripartitions of a sequence.
Args:
seq (Iterable): a sequence.
Yields:
tuple[tuple]: A tripartition of ``seq``.
Example:
>>> seq = (2, 5)
>>> list(directed_tripartition(seq)) # doctest: +NORMALIZE_WHITESPA... | python | def directed_tripartition(seq):
"""Generator over all directed tripartitions of a sequence.
Args:
seq (Iterable): a sequence.
Yields:
tuple[tuple]: A tripartition of ``seq``.
Example:
>>> seq = (2, 5)
>>> list(directed_tripartition(seq)) # doctest: +NORMALIZE_WHITESPA... | [
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16,075 | wmayner/pyphi | pyphi/partition.py | k_partitions | def k_partitions(collection, k):
"""Generate all ``k``-partitions of a collection.
Example:
>>> list(k_partitions(range(3), 2))
[[[0, 1], [2]], [[0], [1, 2]], [[0, 2], [1]]]
"""
collection = list(collection)
n = len(collection)
# Special cases
if n == 0 or k < 1:
re... | python | def k_partitions(collection, k):
"""Generate all ``k``-partitions of a collection.
Example:
>>> list(k_partitions(range(3), 2))
[[[0, 1], [2]], [[0], [1, 2]], [[0, 2], [1]]]
"""
collection = list(collection)
n = len(collection)
# Special cases
if n == 0 or k < 1:
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16,076 | wmayner/pyphi | pyphi/partition.py | mip_partitions | def mip_partitions(mechanism, purview, node_labels=None):
"""Return a generator over all mechanism-purview partitions, based on the
current configuration.
"""
func = partition_types[config.PARTITION_TYPE]
return func(mechanism, purview, node_labels) | python | def mip_partitions(mechanism, purview, node_labels=None):
"""Return a generator over all mechanism-purview partitions, based on the
current configuration.
"""
func = partition_types[config.PARTITION_TYPE]
return func(mechanism, purview, node_labels) | [
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16,077 | wmayner/pyphi | pyphi/partition.py | mip_bipartitions | def mip_bipartitions(mechanism, purview, node_labels=None):
r"""Return an generator of all |small_phi| bipartitions of a mechanism over
a purview.
Excludes all bipartitions where one half is entirely empty, *e.g*::
A ∅
─── ✕ ───
B ∅
is not valid, but ::
A ... | python | def mip_bipartitions(mechanism, purview, node_labels=None):
r"""Return an generator of all |small_phi| bipartitions of a mechanism over
a purview.
Excludes all bipartitions where one half is entirely empty, *e.g*::
A ∅
─── ✕ ───
B ∅
is not valid, but ::
A ... | [
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16,078 | wmayner/pyphi | pyphi/partition.py | wedge_partitions | def wedge_partitions(mechanism, purview, node_labels=None):
"""Return an iterator over all wedge partitions.
These are partitions which strictly split the mechanism and allow a subset
of the purview to be split into a third partition, e.g.::
A B ∅
─── ✕ ─── ✕ ───
B C ... | python | def wedge_partitions(mechanism, purview, node_labels=None):
"""Return an iterator over all wedge partitions.
These are partitions which strictly split the mechanism and allow a subset
of the purview to be split into a third partition, e.g.::
A B ∅
─── ✕ ─── ✕ ───
B C ... | [
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16,079 | wmayner/pyphi | pyphi/partition.py | all_partitions | def all_partitions(mechanism, purview, node_labels=None):
"""Return all possible partitions of a mechanism and purview.
Partitions can consist of any number of parts.
Args:
mechanism (tuple[int]): A mechanism.
purview (tuple[int]): A purview.
Yields:
KPartition: A partition of... | python | def all_partitions(mechanism, purview, node_labels=None):
"""Return all possible partitions of a mechanism and purview.
Partitions can consist of any number of parts.
Args:
mechanism (tuple[int]): A mechanism.
purview (tuple[int]): A purview.
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16,080 | openstack/pyghmi | pyghmi/ipmi/oem/lenovo/imm.py | naturalize_string | def naturalize_string(key):
"""Analyzes string in a human way to enable natural sort
:param nodename: The node name to analyze
:returns: A structure that can be consumed by 'sorted'
"""
return [int(text) if text.isdigit() else text.lower()
for text in re.split(numregex, key)] | python | def naturalize_string(key):
"""Analyzes string in a human way to enable natural sort
:param nodename: The node name to analyze
:returns: A structure that can be consumed by 'sorted'
"""
return [int(text) if text.isdigit() else text.lower()
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16,081 | openstack/pyghmi | pyghmi/ipmi/events.py | EventHandler.fetch_sel | def fetch_sel(self, ipmicmd, clear=False):
"""Fetch SEL entries
Return an iterable of SEL entries. If clearing is requested,
the fetch and clear will be done as an atomic operation, assuring
no entries are dropped.
:param ipmicmd: The Command object to use to interrogate
:param clear:... | python | def fetch_sel(self, ipmicmd, clear=False):
"""Fetch SEL entries
Return an iterable of SEL entries. If clearing is requested,
the fetch and clear will be done as an atomic operation, assuring
no entries are dropped.
:param ipmicmd: The Command object to use to interrogate
:param clear:... | [
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16,082 | openstack/pyghmi | pyghmi/ipmi/command.py | Command.oem_init | def oem_init(self):
"""Initialize the command object for OEM capabilities
A number of capabilities are either totally OEM defined or
else augmented somehow by knowledge of the OEM. This
method does an interrogation to identify the OEM.
"""
if self._oemknown:
... | python | def oem_init(self):
"""Initialize the command object for OEM capabilities
A number of capabilities are either totally OEM defined or
else augmented somehow by knowledge of the OEM. This
method does an interrogation to identify the OEM.
"""
if self._oemknown:
... | [
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16,083 | openstack/pyghmi | pyghmi/ipmi/command.py | Command.reset_bmc | def reset_bmc(self):
"""Do a cold reset in BMC
"""
response = self.raw_command(netfn=6, command=2)
if 'error' in response:
raise exc.IpmiException(response['error']) | python | def reset_bmc(self):
"""Do a cold reset in BMC
"""
response = self.raw_command(netfn=6, command=2)
if 'error' in response:
raise exc.IpmiException(response['error']) | [
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16,084 | openstack/pyghmi | pyghmi/ipmi/command.py | Command.xraw_command | def xraw_command(self, netfn, command, bridge_request=(), data=(),
delay_xmit=None, retry=True, timeout=None):
"""Send raw ipmi command to BMC, raising exception on error
This is identical to raw_command, except it raises exceptions
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16,085 | openstack/pyghmi | pyghmi/ipmi/command.py | Command.raw_command | def raw_command(self, netfn, command, bridge_request=(), data=(),
delay_xmit=None, retry=True, timeout=None):
"""Send raw ipmi command to BMC
This allows arbitrary IPMI bytes to be issued. This is commonly used
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Example: ipmicmd... | python | def raw_command(self, netfn, command, bridge_request=(), data=(),
delay_xmit=None, retry=True, timeout=None):
"""Send raw ipmi command to BMC
This allows arbitrary IPMI bytes to be issued. This is commonly used
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16,086 | openstack/pyghmi | pyghmi/ipmi/command.py | Command.get_power | def get_power(self):
"""Get current power state of the managed system
The response, if successful, should contain 'powerstate' key and
either 'on' or 'off' to indicate current state.
:returns: dict -- {'powerstate': value}
"""
response = self.raw_command(netfn=0, comman... | python | def get_power(self):
"""Get current power state of the managed system
The response, if successful, should contain 'powerstate' key and
either 'on' or 'off' to indicate current state.
:returns: dict -- {'powerstate': value}
"""
response = self.raw_command(netfn=0, comman... | [
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16,087 | openstack/pyghmi | pyghmi/ipmi/command.py | Command.get_event_log | def get_event_log(self, clear=False):
"""Retrieve the log of events, optionally clearing
The contents of the SEL are returned as an iterable. Timestamps
are given as local time, ISO 8601 (whether the target has an accurate
clock or not). Timestamps may be omitted for events that canno... | python | def get_event_log(self, clear=False):
"""Retrieve the log of events, optionally clearing
The contents of the SEL are returned as an iterable. Timestamps
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16,088 | openstack/pyghmi | pyghmi/ipmi/command.py | Command.decode_pet | def decode_pet(self, specifictrap, petdata):
"""Decode PET to an event
In IPMI, the alert format are PET alerts. It is a particular set of
data put into an SNMPv1 trap and sent. It bears no small resemblence
to the SEL entries. This function takes data that would have been
rec... | python | def decode_pet(self, specifictrap, petdata):
"""Decode PET to an event
In IPMI, the alert format are PET alerts. It is a particular set of
data put into an SNMPv1 trap and sent. It bears no small resemblence
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] | f710b1d30a8eed19a9e86f01f9351c737666f3e5 | https://github.com/openstack/pyghmi/blob/f710b1d30a8eed19a9e86f01f9351c737666f3e5/pyghmi/ipmi/command.py#L577-L593 |
16,089 | openstack/pyghmi | pyghmi/ipmi/command.py | Command.get_inventory_descriptions | def get_inventory_descriptions(self):
"""Retrieve list of things that could be inventoried
This permits a caller to examine the available items
without actually causing the inventory data to be gathered. It
returns an iterable of string descriptions
"""
yield "System"
... | python | def get_inventory_descriptions(self):
"""Retrieve list of things that could be inventoried
This permits a caller to examine the available items
without actually causing the inventory data to be gathered. It
returns an iterable of string descriptions
"""
yield "System"
... | [
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16,090 | openstack/pyghmi | pyghmi/ipmi/command.py | Command.get_inventory_of_component | def get_inventory_of_component(self, component):
"""Retrieve inventory of a component
Retrieve detailed inventory information for only the requested
component.
"""
self.oem_init()
if component == 'System':
return self._get_zero_fru()
self.init_sdr()
... | python | def get_inventory_of_component(self, component):
"""Retrieve inventory of a component
Retrieve detailed inventory information for only the requested
component.
"""
self.oem_init()
if component == 'System':
return self._get_zero_fru()
self.init_sdr()
... | [
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16,091 | openstack/pyghmi | pyghmi/ipmi/command.py | Command.get_inventory | def get_inventory(self):
"""Retrieve inventory of system
Retrieve inventory of the targeted system. This frequently includes
serial numbers, sometimes hardware addresses, sometimes memory modules
This function will retrieve whatever the underlying platform provides
and apply so... | python | def get_inventory(self):
"""Retrieve inventory of system
Retrieve inventory of the targeted system. This frequently includes
serial numbers, sometimes hardware addresses, sometimes memory modules
This function will retrieve whatever the underlying platform provides
and apply so... | [
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16,092 | openstack/pyghmi | pyghmi/ipmi/command.py | Command.get_health | def get_health(self):
"""Summarize health of managed system
This provides a summary of the health of the managed system.
It additionally provides an iterable list of reasons for
warning, critical, or failed assessments.
"""
summary = {'badreadings': [], 'health': const.H... | python | def get_health(self):
"""Summarize health of managed system
This provides a summary of the health of the managed system.
It additionally provides an iterable list of reasons for
warning, critical, or failed assessments.
"""
summary = {'badreadings': [], 'health': const.H... | [
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This provides a summary of the health of the managed system.
It additionally provides an iterable list of reasons for
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16,093 | openstack/pyghmi | pyghmi/ipmi/command.py | Command.get_sensor_reading | def get_sensor_reading(self, sensorname):
"""Get a sensor reading by name
Returns a single decoded sensor reading per the name
passed in
:param sensorname: Name of the desired sensor
:returns: sdr.SensorReading object
"""
self.init_sdr()
for sensor in s... | python | def get_sensor_reading(self, sensorname):
"""Get a sensor reading by name
Returns a single decoded sensor reading per the name
passed in
:param sensorname: Name of the desired sensor
:returns: sdr.SensorReading object
"""
self.init_sdr()
for sensor in s... | [
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:param sensorname: Name of the desired sensor
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16,094 | openstack/pyghmi | pyghmi/ipmi/command.py | Command._fetch_lancfg_param | def _fetch_lancfg_param(self, channel, param, prefixlen=False):
"""Internal helper for fetching lan cfg parameters
If the parameter revison != 0x11, bail. Further, if 4 bytes, return
string with ipv4. If 6 bytes, colon delimited hex (mac address). If
one byte, return the int value
... | python | def _fetch_lancfg_param(self, channel, param, prefixlen=False):
"""Internal helper for fetching lan cfg parameters
If the parameter revison != 0x11, bail. Further, if 4 bytes, return
string with ipv4. If 6 bytes, colon delimited hex (mac address). If
one byte, return the int value
... | [
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If the parameter revison != 0x11, bail. Further, if 4 bytes, return
string with ipv4. If 6 bytes, colon delimited hex (mac address). If
one byte, return the int value | [
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] | f710b1d30a8eed19a9e86f01f9351c737666f3e5 | https://github.com/openstack/pyghmi/blob/f710b1d30a8eed19a9e86f01f9351c737666f3e5/pyghmi/ipmi/command.py#L740-L769 |
16,095 | openstack/pyghmi | pyghmi/ipmi/command.py | Command.set_net_configuration | def set_net_configuration(self, ipv4_address=None, ipv4_configuration=None,
ipv4_gateway=None, channel=None):
"""Set network configuration data.
Apply desired network configuration data, leaving unspecified
parameters alone.
:param ipv4_address: CIDR nota... | python | def set_net_configuration(self, ipv4_address=None, ipv4_configuration=None,
ipv4_gateway=None, channel=None):
"""Set network configuration data.
Apply desired network configuration data, leaving unspecified
parameters alone.
:param ipv4_address: CIDR nota... | [
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Apply desired network configuration data, leaving unspecified
parameters alone.
:param ipv4_address: CIDR notation for IP address and netmask
Example: '192.168.0.10/16'
:param ipv4_configuration: Method to use to configure the ... | [
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] | f710b1d30a8eed19a9e86f01f9351c737666f3e5 | https://github.com/openstack/pyghmi/blob/f710b1d30a8eed19a9e86f01f9351c737666f3e5/pyghmi/ipmi/command.py#L787-L825 |
16,096 | openstack/pyghmi | pyghmi/ipmi/command.py | Command.get_net_configuration | def get_net_configuration(self, channel=None, gateway_macs=True):
"""Get network configuration data
Retrieve network configuration from the target
:param channel: Channel to configure, defaults to None for 'autodetect'
:param gateway_macs: Whether to retrieve mac addresses for gateways... | python | def get_net_configuration(self, channel=None, gateway_macs=True):
"""Get network configuration data
Retrieve network configuration from the target
:param channel: Channel to configure, defaults to None for 'autodetect'
:param gateway_macs: Whether to retrieve mac addresses for gateways... | [
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Retrieve network configuration from the target
:param channel: Channel to configure, defaults to None for 'autodetect'
:param gateway_macs: Whether to retrieve mac addresses for gateways
:returns: A dictionary of network configuration data | [
"Get",
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] | f710b1d30a8eed19a9e86f01f9351c737666f3e5 | https://github.com/openstack/pyghmi/blob/f710b1d30a8eed19a9e86f01f9351c737666f3e5/pyghmi/ipmi/command.py#L880-L916 |
16,097 | openstack/pyghmi | pyghmi/ipmi/command.py | Command.get_sensor_data | def get_sensor_data(self):
"""Get sensor reading objects
Iterates sensor reading objects pertaining to the currently
managed BMC.
:returns: Iterator of sdr.SensorReading objects
"""
self.init_sdr()
for sensor in self._sdr.get_sensor_numbers():
rsp = ... | python | def get_sensor_data(self):
"""Get sensor reading objects
Iterates sensor reading objects pertaining to the currently
managed BMC.
:returns: Iterator of sdr.SensorReading objects
"""
self.init_sdr()
for sensor in self._sdr.get_sensor_numbers():
rsp = ... | [
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Iterates sensor reading objects pertaining to the currently
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:returns: Iterator of sdr.SensorReading objects | [
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16,098 | openstack/pyghmi | pyghmi/ipmi/command.py | Command.get_sensor_descriptions | def get_sensor_descriptions(self):
"""Get available sensor names
Iterates over the available sensor descriptions
:returns: Iterator of dicts describing each sensor
"""
self.init_sdr()
for sensor in self._sdr.get_sensor_numbers():
yield {'name': self._sdr.sen... | python | def get_sensor_descriptions(self):
"""Get available sensor names
Iterates over the available sensor descriptions
:returns: Iterator of dicts describing each sensor
"""
self.init_sdr()
for sensor in self._sdr.get_sensor_numbers():
yield {'name': self._sdr.sen... | [
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:returns: Iterator of dicts describing each sensor | [
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16,099 | openstack/pyghmi | pyghmi/ipmi/command.py | Command.get_network_channel | def get_network_channel(self):
"""Get a reasonable 'default' network channel.
When configuring/examining network configuration, it's desirable to
find the correct channel. Here we run with the 'real' number of the
current channel if it is a LAN channel, otherwise it evaluates
a... | python | def get_network_channel(self):
"""Get a reasonable 'default' network channel.
When configuring/examining network configuration, it's desirable to
find the correct channel. Here we run with the 'real' number of the
current channel if it is a LAN channel, otherwise it evaluates
a... | [
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