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wmayner/pyphi | pyphi/subsystem.py | Subsystem.effect_mip | def effect_mip(self, mechanism, purview):
"""Return the irreducibility analysis for the effect MIP.
Alias for |find_mip()| with ``direction`` set to |EFFECT|.
"""
return self.find_mip(Direction.EFFECT, mechanism, purview) | python | def effect_mip(self, mechanism, purview):
"""Return the irreducibility analysis for the effect MIP.
Alias for |find_mip()| with ``direction`` set to |EFFECT|.
"""
return self.find_mip(Direction.EFFECT, mechanism, purview) | [
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wmayner/pyphi | pyphi/subsystem.py | Subsystem.phi_cause_mip | def phi_cause_mip(self, mechanism, purview):
"""Return the |small_phi| of the cause MIP.
This is the distance between the unpartitioned cause repertoire and the
MIP cause repertoire.
"""
mip = self.cause_mip(mechanism, purview)
return mip.phi if mip else 0 | python | def phi_cause_mip(self, mechanism, purview):
"""Return the |small_phi| of the cause MIP.
This is the distance between the unpartitioned cause repertoire and the
MIP cause repertoire.
"""
mip = self.cause_mip(mechanism, purview)
return mip.phi if mip else 0 | [
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wmayner/pyphi | pyphi/subsystem.py | Subsystem.phi_effect_mip | def phi_effect_mip(self, mechanism, purview):
"""Return the |small_phi| of the effect MIP.
This is the distance between the unpartitioned effect repertoire and
the MIP cause repertoire.
"""
mip = self.effect_mip(mechanism, purview)
return mip.phi if mip else 0 | python | def phi_effect_mip(self, mechanism, purview):
"""Return the |small_phi| of the effect MIP.
This is the distance between the unpartitioned effect repertoire and
the MIP cause repertoire.
"""
mip = self.effect_mip(mechanism, purview)
return mip.phi if mip else 0 | [
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wmayner/pyphi | pyphi/subsystem.py | Subsystem.phi | def phi(self, mechanism, purview):
"""Return the |small_phi| of a mechanism over a purview."""
return min(self.phi_cause_mip(mechanism, purview),
self.phi_effect_mip(mechanism, purview)) | python | def phi(self, mechanism, purview):
"""Return the |small_phi| of a mechanism over a purview."""
return min(self.phi_cause_mip(mechanism, purview),
self.phi_effect_mip(mechanism, purview)) | [
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wmayner/pyphi | pyphi/subsystem.py | Subsystem.find_mice | def find_mice(self, direction, mechanism, purviews=False):
"""Return the |MIC| or |MIE| for a mechanism.
Args:
direction (Direction): :|CAUSE| or |EFFECT|.
mechanism (tuple[int]): The mechanism to be tested for
irreducibility.
Keyword Args:
purviews (tuple[int]): Optionally restrict the possible purviews
to a subset of the subsystem. This may be useful for _e.g._
finding only concepts that are "about" a certain subset of
nodes.
Returns:
MaximallyIrreducibleCauseOrEffect: The |MIC| or |MIE|.
"""
purviews = self.potential_purviews(direction, mechanism, purviews)
if not purviews:
max_mip = _null_ria(direction, mechanism, ())
else:
max_mip = max(self.find_mip(direction, mechanism, purview)
for purview in purviews)
if direction == Direction.CAUSE:
return MaximallyIrreducibleCause(max_mip)
elif direction == Direction.EFFECT:
return MaximallyIrreducibleEffect(max_mip)
return validate.direction(direction) | python | def find_mice(self, direction, mechanism, purviews=False):
"""Return the |MIC| or |MIE| for a mechanism.
Args:
direction (Direction): :|CAUSE| or |EFFECT|.
mechanism (tuple[int]): The mechanism to be tested for
irreducibility.
Keyword Args:
purviews (tuple[int]): Optionally restrict the possible purviews
to a subset of the subsystem. This may be useful for _e.g._
finding only concepts that are "about" a certain subset of
nodes.
Returns:
MaximallyIrreducibleCauseOrEffect: The |MIC| or |MIE|.
"""
purviews = self.potential_purviews(direction, mechanism, purviews)
if not purviews:
max_mip = _null_ria(direction, mechanism, ())
else:
max_mip = max(self.find_mip(direction, mechanism, purview)
for purview in purviews)
if direction == Direction.CAUSE:
return MaximallyIrreducibleCause(max_mip)
elif direction == Direction.EFFECT:
return MaximallyIrreducibleEffect(max_mip)
return validate.direction(direction) | [
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wmayner/pyphi | pyphi/subsystem.py | Subsystem.phi_max | def phi_max(self, mechanism):
"""Return the |small_phi_max| of a mechanism.
This is the maximum of |small_phi| taken over all possible purviews.
"""
return min(self.mic(mechanism).phi, self.mie(mechanism).phi) | python | def phi_max(self, mechanism):
"""Return the |small_phi_max| of a mechanism.
This is the maximum of |small_phi| taken over all possible purviews.
"""
return min(self.mic(mechanism).phi, self.mie(mechanism).phi) | [
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wmayner/pyphi | pyphi/subsystem.py | Subsystem.null_concept | def null_concept(self):
"""Return the null concept of this subsystem.
The null concept is a point in concept space identified with
the unconstrained cause and effect repertoire of this subsystem.
"""
# Unconstrained cause repertoire.
cause_repertoire = self.cause_repertoire((), ())
# Unconstrained effect repertoire.
effect_repertoire = self.effect_repertoire((), ())
# Null cause.
cause = MaximallyIrreducibleCause(
_null_ria(Direction.CAUSE, (), (), cause_repertoire))
# Null effect.
effect = MaximallyIrreducibleEffect(
_null_ria(Direction.EFFECT, (), (), effect_repertoire))
# All together now...
return Concept(mechanism=(),
cause=cause,
effect=effect,
subsystem=self) | python | def null_concept(self):
"""Return the null concept of this subsystem.
The null concept is a point in concept space identified with
the unconstrained cause and effect repertoire of this subsystem.
"""
# Unconstrained cause repertoire.
cause_repertoire = self.cause_repertoire((), ())
# Unconstrained effect repertoire.
effect_repertoire = self.effect_repertoire((), ())
# Null cause.
cause = MaximallyIrreducibleCause(
_null_ria(Direction.CAUSE, (), (), cause_repertoire))
# Null effect.
effect = MaximallyIrreducibleEffect(
_null_ria(Direction.EFFECT, (), (), effect_repertoire))
# All together now...
return Concept(mechanism=(),
cause=cause,
effect=effect,
subsystem=self) | [
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wmayner/pyphi | pyphi/subsystem.py | Subsystem.concept | def concept(self, mechanism, purviews=False, cause_purviews=False,
effect_purviews=False):
"""Return the concept specified by a mechanism within this subsytem.
Args:
mechanism (tuple[int]): The candidate set of nodes.
Keyword Args:
purviews (tuple[tuple[int]]): Restrict the possible purviews to
those in this list.
cause_purviews (tuple[tuple[int]]): Restrict the possible cause
purviews to those in this list. Takes precedence over
``purviews``.
effect_purviews (tuple[tuple[int]]): Restrict the possible effect
purviews to those in this list. Takes precedence over
``purviews``.
Returns:
Concept: The pair of maximally irreducible cause/effect repertoires
that constitute the concept specified by the given mechanism.
"""
log.debug('Computing concept %s...', mechanism)
# If the mechanism is empty, there is no concept.
if not mechanism:
log.debug('Empty concept; returning null concept')
return self.null_concept
# Calculate the maximally irreducible cause repertoire.
cause = self.mic(mechanism, purviews=(cause_purviews or purviews))
# Calculate the maximally irreducible effect repertoire.
effect = self.mie(mechanism, purviews=(effect_purviews or purviews))
log.debug('Found concept %s', mechanism)
# NOTE: Make sure to expand the repertoires to the size of the
# subsystem when calculating concept distance. For now, they must
# remain un-expanded so the concept doesn't depend on the subsystem.
return Concept(mechanism=mechanism, cause=cause, effect=effect,
subsystem=self) | python | def concept(self, mechanism, purviews=False, cause_purviews=False,
effect_purviews=False):
"""Return the concept specified by a mechanism within this subsytem.
Args:
mechanism (tuple[int]): The candidate set of nodes.
Keyword Args:
purviews (tuple[tuple[int]]): Restrict the possible purviews to
those in this list.
cause_purviews (tuple[tuple[int]]): Restrict the possible cause
purviews to those in this list. Takes precedence over
``purviews``.
effect_purviews (tuple[tuple[int]]): Restrict the possible effect
purviews to those in this list. Takes precedence over
``purviews``.
Returns:
Concept: The pair of maximally irreducible cause/effect repertoires
that constitute the concept specified by the given mechanism.
"""
log.debug('Computing concept %s...', mechanism)
# If the mechanism is empty, there is no concept.
if not mechanism:
log.debug('Empty concept; returning null concept')
return self.null_concept
# Calculate the maximally irreducible cause repertoire.
cause = self.mic(mechanism, purviews=(cause_purviews or purviews))
# Calculate the maximally irreducible effect repertoire.
effect = self.mie(mechanism, purviews=(effect_purviews or purviews))
log.debug('Found concept %s', mechanism)
# NOTE: Make sure to expand the repertoires to the size of the
# subsystem when calculating concept distance. For now, they must
# remain un-expanded so the concept doesn't depend on the subsystem.
return Concept(mechanism=mechanism, cause=cause, effect=effect,
subsystem=self) | [
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wmayner/pyphi | pyphi/models/actual_causation.py | _null_ac_sia | def _null_ac_sia(transition, direction, alpha=0.0):
"""Return an |AcSystemIrreducibilityAnalysis| with zero |big_alpha| and
empty accounts.
"""
return AcSystemIrreducibilityAnalysis(
transition=transition,
direction=direction,
alpha=alpha,
account=(),
partitioned_account=()
) | python | def _null_ac_sia(transition, direction, alpha=0.0):
"""Return an |AcSystemIrreducibilityAnalysis| with zero |big_alpha| and
empty accounts.
"""
return AcSystemIrreducibilityAnalysis(
transition=transition,
direction=direction,
alpha=alpha,
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wmayner/pyphi | pyphi/models/actual_causation.py | Event.mechanism | def mechanism(self):
"""The mechanism of the event."""
assert self.actual_cause.mechanism == self.actual_effect.mechanism
return self.actual_cause.mechanism | python | def mechanism(self):
"""The mechanism of the event."""
assert self.actual_cause.mechanism == self.actual_effect.mechanism
return self.actual_cause.mechanism | [
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wmayner/pyphi | pyphi/models/actual_causation.py | Account.irreducible_causes | def irreducible_causes(self):
"""The set of irreducible causes in this |Account|."""
return tuple(link for link in self
if link.direction is Direction.CAUSE) | python | def irreducible_causes(self):
"""The set of irreducible causes in this |Account|."""
return tuple(link for link in self
if link.direction is Direction.CAUSE) | [
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wmayner/pyphi | pyphi/models/actual_causation.py | Account.irreducible_effects | def irreducible_effects(self):
"""The set of irreducible effects in this |Account|."""
return tuple(link for link in self
if link.direction is Direction.EFFECT) | python | def irreducible_effects(self):
"""The set of irreducible effects in this |Account|."""
return tuple(link for link in self
if link.direction is Direction.EFFECT) | [
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wmayner/pyphi | pyphi/models/fmt.py | make_repr | def make_repr(self, attrs):
"""Construct a repr string.
If `config.REPR_VERBOSITY` is ``1`` or ``2``, this function calls the
object's __str__ method. Although this breaks the convention that __repr__
should return a string which can reconstruct the object, readable reprs are
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to have meaningful and useful representations.
Args:
self (obj): The object in question
attrs (Iterable[str]): Attributes to include in the repr
Returns:
str: the ``repr``esentation of the object
"""
# TODO: change this to a closure so we can do
# __repr__ = make_repr(attrs) ???
if config.REPR_VERBOSITY in [MEDIUM, HIGH]:
return self.__str__()
elif config.REPR_VERBOSITY is LOW:
return '{}({})'.format(
self.__class__.__name__,
', '.join(attr + '=' + repr(getattr(self, attr))
for attr in attrs))
raise ValueError('Invalid value for `config.REPR_VERBOSITY`') | python | def make_repr(self, attrs):
"""Construct a repr string.
If `config.REPR_VERBOSITY` is ``1`` or ``2``, this function calls the
object's __str__ method. Although this breaks the convention that __repr__
should return a string which can reconstruct the object, readable reprs are
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to have meaningful and useful representations.
Args:
self (obj): The object in question
attrs (Iterable[str]): Attributes to include in the repr
Returns:
str: the ``repr``esentation of the object
"""
# TODO: change this to a closure so we can do
# __repr__ = make_repr(attrs) ???
if config.REPR_VERBOSITY in [MEDIUM, HIGH]:
return self.__str__()
elif config.REPR_VERBOSITY is LOW:
return '{}({})'.format(
self.__class__.__name__,
', '.join(attr + '=' + repr(getattr(self, attr))
for attr in attrs))
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wmayner/pyphi | pyphi/models/fmt.py | indent | def indent(lines, amount=2, char=' '):
r"""Indent a string.
Prepends whitespace to every line in the passed string. (Lines are
separated by newline characters.)
Args:
lines (str): The string to indent.
Keyword Args:
amount (int): The number of columns to indent by.
char (str): The character to to use as the indentation.
Returns:
str: The indented string.
Example:
>>> print(indent('line1\nline2', char='*'))
**line1
**line2
"""
lines = str(lines)
padding = amount * char
return padding + ('\n' + padding).join(lines.split('\n')) | python | def indent(lines, amount=2, char=' '):
r"""Indent a string.
Prepends whitespace to every line in the passed string. (Lines are
separated by newline characters.)
Args:
lines (str): The string to indent.
Keyword Args:
amount (int): The number of columns to indent by.
char (str): The character to to use as the indentation.
Returns:
str: The indented string.
Example:
>>> print(indent('line1\nline2', char='*'))
**line1
**line2
"""
lines = str(lines)
padding = amount * char
return padding + ('\n' + padding).join(lines.split('\n')) | [
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wmayner/pyphi | pyphi/models/fmt.py | margin | def margin(text):
r"""Add a margin to both ends of each line in the string.
Example:
>>> margin('line1\nline2')
' line1 \n line2 '
"""
lines = str(text).split('\n')
return '\n'.join(' {} '.format(l) for l in lines) | python | def margin(text):
r"""Add a margin to both ends of each line in the string.
Example:
>>> margin('line1\nline2')
' line1 \n line2 '
"""
lines = str(text).split('\n')
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wmayner/pyphi | pyphi/models/fmt.py | box | def box(text):
r"""Wrap a chunk of text in a box.
Example:
>>> print(box('line1\nline2'))
┌───────┐
│ line1 │
│ line2 │
└───────┘
"""
lines = text.split('\n')
width = max(len(l) for l in lines)
top_bar = (TOP_LEFT_CORNER + HORIZONTAL_BAR * (2 + width) +
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bottom_bar = (BOTTOM_LEFT_CORNER + HORIZONTAL_BAR * (2 + width) +
BOTTOM_RIGHT_CORNER)
lines = [LINES_FORMAT_STR.format(line=line, width=width) for line in lines]
return top_bar + '\n' + '\n'.join(lines) + '\n' + bottom_bar | python | def box(text):
r"""Wrap a chunk of text in a box.
Example:
>>> print(box('line1\nline2'))
┌───────┐
│ line1 │
│ line2 │
└───────┘
"""
lines = text.split('\n')
width = max(len(l) for l in lines)
top_bar = (TOP_LEFT_CORNER + HORIZONTAL_BAR * (2 + width) +
TOP_RIGHT_CORNER)
bottom_bar = (BOTTOM_LEFT_CORNER + HORIZONTAL_BAR * (2 + width) +
BOTTOM_RIGHT_CORNER)
lines = [LINES_FORMAT_STR.format(line=line, width=width) for line in lines]
return top_bar + '\n' + '\n'.join(lines) + '\n' + bottom_bar | [
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wmayner/pyphi | pyphi/models/fmt.py | side_by_side | def side_by_side(left, right):
r"""Put two boxes next to each other.
Assumes that all lines in the boxes are the same width.
Example:
>>> left = 'A \nC '
>>> right = 'B\nD'
>>> print(side_by_side(left, right))
A B
C D
<BLANKLINE>
"""
left_lines = list(left.split('\n'))
right_lines = list(right.split('\n'))
# Pad the shorter column with whitespace
diff = abs(len(left_lines) - len(right_lines))
if len(left_lines) > len(right_lines):
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right_lines += [fill] * diff
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fill = ' ' * len(left_lines[0])
left_lines += [fill] * diff
return '\n'.join(a + b for a, b in zip(left_lines, right_lines)) + '\n' | python | def side_by_side(left, right):
r"""Put two boxes next to each other.
Assumes that all lines in the boxes are the same width.
Example:
>>> left = 'A \nC '
>>> right = 'B\nD'
>>> print(side_by_side(left, right))
A B
C D
<BLANKLINE>
"""
left_lines = list(left.split('\n'))
right_lines = list(right.split('\n'))
# Pad the shorter column with whitespace
diff = abs(len(left_lines) - len(right_lines))
if len(left_lines) > len(right_lines):
fill = ' ' * len(right_lines[0])
right_lines += [fill] * diff
elif len(right_lines) > len(left_lines):
fill = ' ' * len(left_lines[0])
left_lines += [fill] * diff
return '\n'.join(a + b for a, b in zip(left_lines, right_lines)) + '\n' | [
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wmayner/pyphi | pyphi/models/fmt.py | header | def header(head, text, over_char=None, under_char=None, center=True):
"""Center a head over a block of text.
The width of the text is the width of the longest line of the text.
"""
lines = list(text.split('\n'))
width = max(len(l) for l in lines)
# Center or left-justify
if center:
head = head.center(width) + '\n'
else:
head = head.ljust(width) + '\n'
# Underline head
if under_char:
head = head + under_char * width + '\n'
# 'Overline' head
if over_char:
head = over_char * width + '\n' + head
return head + text | python | def header(head, text, over_char=None, under_char=None, center=True):
"""Center a head over a block of text.
The width of the text is the width of the longest line of the text.
"""
lines = list(text.split('\n'))
width = max(len(l) for l in lines)
# Center or left-justify
if center:
head = head.center(width) + '\n'
else:
head = head.ljust(width) + '\n'
# Underline head
if under_char:
head = head + under_char * width + '\n'
# 'Overline' head
if over_char:
head = over_char * width + '\n' + head
return head + text | [
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wmayner/pyphi | pyphi/models/fmt.py | labels | def labels(indices, node_labels=None):
"""Get the labels for a tuple of mechanism indices."""
if node_labels is None:
return tuple(map(str, indices))
return node_labels.indices2labels(indices) | python | def labels(indices, node_labels=None):
"""Get the labels for a tuple of mechanism indices."""
if node_labels is None:
return tuple(map(str, indices))
return node_labels.indices2labels(indices) | [
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wmayner/pyphi | pyphi/models/fmt.py | fmt_number | def fmt_number(p):
"""Format a number.
It will be printed as a fraction if the denominator isn't too big and as a
decimal otherwise.
"""
formatted = '{:n}'.format(p)
if not config.PRINT_FRACTIONS:
return formatted
fraction = Fraction(p)
nice = fraction.limit_denominator(128)
return (
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else formatted
) | python | def fmt_number(p):
"""Format a number.
It will be printed as a fraction if the denominator isn't too big and as a
decimal otherwise.
"""
formatted = '{:n}'.format(p)
if not config.PRINT_FRACTIONS:
return formatted
fraction = Fraction(p)
nice = fraction.limit_denominator(128)
return (
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wmayner/pyphi | pyphi/models/fmt.py | fmt_part | def fmt_part(part, node_labels=None):
"""Format a |Part|.
The returned string looks like::
0,1
───
∅
"""
def nodes(x): # pylint: disable=missing-docstring
return ','.join(labels(x, node_labels)) if x else EMPTY_SET
numer = nodes(part.mechanism)
denom = nodes(part.purview)
width = max(3, len(numer), len(denom))
divider = HORIZONTAL_BAR * width
return (
'{numer:^{width}}\n'
'{divider}\n'
'{denom:^{width}}'
).format(numer=numer, divider=divider, denom=denom, width=width) | python | def fmt_part(part, node_labels=None):
"""Format a |Part|.
The returned string looks like::
0,1
───
∅
"""
def nodes(x): # pylint: disable=missing-docstring
return ','.join(labels(x, node_labels)) if x else EMPTY_SET
numer = nodes(part.mechanism)
denom = nodes(part.purview)
width = max(3, len(numer), len(denom))
divider = HORIZONTAL_BAR * width
return (
'{numer:^{width}}\n'
'{divider}\n'
'{denom:^{width}}'
).format(numer=numer, divider=divider, denom=denom, width=width) | [
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wmayner/pyphi | pyphi/models/fmt.py | fmt_partition | def fmt_partition(partition):
"""Format a |Bipartition|.
The returned string looks like::
0,1 ∅
─── ✕ ───
2 0,1
Args:
partition (Bipartition): The partition in question.
Returns:
str: A human-readable string representation of the partition.
"""
if not partition:
return ''
parts = [fmt_part(part, partition.node_labels).split('\n')
for part in partition]
times = (' ',
' {} '.format(MULTIPLY),
' ')
breaks = ('\n', '\n', '') # No newline at the end of string
between = [times] * (len(parts) - 1) + [breaks]
# Alternate [part, break, part, ..., end]
elements = chain.from_iterable(zip(parts, between))
# Transform vertical stacks into horizontal lines
return ''.join(chain.from_iterable(zip(*elements))) | python | def fmt_partition(partition):
"""Format a |Bipartition|.
The returned string looks like::
0,1 ∅
─── ✕ ───
2 0,1
Args:
partition (Bipartition): The partition in question.
Returns:
str: A human-readable string representation of the partition.
"""
if not partition:
return ''
parts = [fmt_part(part, partition.node_labels).split('\n')
for part in partition]
times = (' ',
' {} '.format(MULTIPLY),
' ')
breaks = ('\n', '\n', '') # No newline at the end of string
between = [times] * (len(parts) - 1) + [breaks]
# Alternate [part, break, part, ..., end]
elements = chain.from_iterable(zip(parts, between))
# Transform vertical stacks into horizontal lines
return ''.join(chain.from_iterable(zip(*elements))) | [
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wmayner/pyphi | pyphi/models/fmt.py | fmt_ces | def fmt_ces(c, title=None):
"""Format a |CauseEffectStructure|."""
if not c:
return '()\n'
if title is None:
title = 'Cause-effect structure'
concepts = '\n'.join(margin(x) for x in c) + '\n'
title = '{} ({} concept{})'.format(
title, len(c), '' if len(c) == 1 else 's')
return header(title, concepts, HEADER_BAR_1, HEADER_BAR_1) | python | def fmt_ces(c, title=None):
"""Format a |CauseEffectStructure|."""
if not c:
return '()\n'
if title is None:
title = 'Cause-effect structure'
concepts = '\n'.join(margin(x) for x in c) + '\n'
title = '{} ({} concept{})'.format(
title, len(c), '' if len(c) == 1 else 's')
return header(title, concepts, HEADER_BAR_1, HEADER_BAR_1) | [
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wmayner/pyphi | pyphi/models/fmt.py | fmt_concept | def fmt_concept(concept):
"""Format a |Concept|."""
def fmt_cause_or_effect(x): # pylint: disable=missing-docstring
return box(indent(fmt_ria(x.ria, verbose=False, mip=True), amount=1))
cause = header('MIC', fmt_cause_or_effect(concept.cause))
effect = header('MIE', fmt_cause_or_effect(concept.effect))
ce = side_by_side(cause, effect)
mechanism = fmt_mechanism(concept.mechanism, concept.node_labels)
title = 'Concept: Mechanism = {}, {} = {}'.format(
mechanism, SMALL_PHI, fmt_number(concept.phi))
# Only center headers for high-verbosity output
center = config.REPR_VERBOSITY is HIGH
return header(title, ce, HEADER_BAR_2, HEADER_BAR_2, center=center) | python | def fmt_concept(concept):
"""Format a |Concept|."""
def fmt_cause_or_effect(x): # pylint: disable=missing-docstring
return box(indent(fmt_ria(x.ria, verbose=False, mip=True), amount=1))
cause = header('MIC', fmt_cause_or_effect(concept.cause))
effect = header('MIE', fmt_cause_or_effect(concept.effect))
ce = side_by_side(cause, effect)
mechanism = fmt_mechanism(concept.mechanism, concept.node_labels)
title = 'Concept: Mechanism = {}, {} = {}'.format(
mechanism, SMALL_PHI, fmt_number(concept.phi))
# Only center headers for high-verbosity output
center = config.REPR_VERBOSITY is HIGH
return header(title, ce, HEADER_BAR_2, HEADER_BAR_2, center=center) | [
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wmayner/pyphi | pyphi/models/fmt.py | fmt_ria | def fmt_ria(ria, verbose=True, mip=False):
"""Format a |RepertoireIrreducibilityAnalysis|."""
if verbose:
mechanism = 'Mechanism: {}\n'.format(
fmt_mechanism(ria.mechanism, ria.node_labels))
direction = '\nDirection: {}'.format(ria.direction)
else:
mechanism = ''
direction = ''
if config.REPR_VERBOSITY is HIGH:
partition = '\n{}:\n{}'.format(
('MIP' if mip else 'Partition'),
indent(fmt_partition(ria.partition)))
repertoire = '\nRepertoire:\n{}'.format(
indent(fmt_repertoire(ria.repertoire)))
partitioned_repertoire = '\nPartitioned repertoire:\n{}'.format(
indent(fmt_repertoire(ria.partitioned_repertoire)))
else:
partition = ''
repertoire = ''
partitioned_repertoire = ''
# TODO? print the two repertoires side-by-side
return (
'{SMALL_PHI} = {phi}\n'
'{mechanism}'
'Purview = {purview}'
'{direction}'
'{partition}'
'{repertoire}'
'{partitioned_repertoire}').format(
SMALL_PHI=SMALL_PHI,
mechanism=mechanism,
purview=fmt_mechanism(ria.purview, ria.node_labels),
direction=direction,
phi=fmt_number(ria.phi),
partition=partition,
repertoire=repertoire,
partitioned_repertoire=partitioned_repertoire) | python | def fmt_ria(ria, verbose=True, mip=False):
"""Format a |RepertoireIrreducibilityAnalysis|."""
if verbose:
mechanism = 'Mechanism: {}\n'.format(
fmt_mechanism(ria.mechanism, ria.node_labels))
direction = '\nDirection: {}'.format(ria.direction)
else:
mechanism = ''
direction = ''
if config.REPR_VERBOSITY is HIGH:
partition = '\n{}:\n{}'.format(
('MIP' if mip else 'Partition'),
indent(fmt_partition(ria.partition)))
repertoire = '\nRepertoire:\n{}'.format(
indent(fmt_repertoire(ria.repertoire)))
partitioned_repertoire = '\nPartitioned repertoire:\n{}'.format(
indent(fmt_repertoire(ria.partitioned_repertoire)))
else:
partition = ''
repertoire = ''
partitioned_repertoire = ''
# TODO? print the two repertoires side-by-side
return (
'{SMALL_PHI} = {phi}\n'
'{mechanism}'
'Purview = {purview}'
'{direction}'
'{partition}'
'{repertoire}'
'{partitioned_repertoire}').format(
SMALL_PHI=SMALL_PHI,
mechanism=mechanism,
purview=fmt_mechanism(ria.purview, ria.node_labels),
direction=direction,
phi=fmt_number(ria.phi),
partition=partition,
repertoire=repertoire,
partitioned_repertoire=partitioned_repertoire) | [
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wmayner/pyphi | pyphi/models/fmt.py | fmt_cut | def fmt_cut(cut):
"""Format a |Cut|."""
return 'Cut {from_nodes} {symbol} {to_nodes}'.format(
from_nodes=fmt_mechanism(cut.from_nodes, cut.node_labels),
symbol=CUT_SYMBOL,
to_nodes=fmt_mechanism(cut.to_nodes, cut.node_labels)) | python | def fmt_cut(cut):
"""Format a |Cut|."""
return 'Cut {from_nodes} {symbol} {to_nodes}'.format(
from_nodes=fmt_mechanism(cut.from_nodes, cut.node_labels),
symbol=CUT_SYMBOL,
to_nodes=fmt_mechanism(cut.to_nodes, cut.node_labels)) | [
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wmayner/pyphi | pyphi/models/fmt.py | fmt_sia | def fmt_sia(sia, ces=True):
"""Format a |SystemIrreducibilityAnalysis|."""
if ces:
body = (
'{ces}'
'{partitioned_ces}'.format(
ces=fmt_ces(
sia.ces,
'Cause-effect structure'),
partitioned_ces=fmt_ces(
sia.partitioned_ces,
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center_header = True
else:
body = ''
center_header = False
title = 'System irreducibility analysis: {BIG_PHI} = {phi}'.format(
BIG_PHI=BIG_PHI, phi=fmt_number(sia.phi))
body = header(str(sia.subsystem), body, center=center_header)
body = header(str(sia.cut), body, center=center_header)
return box(header(title, body, center=center_header)) | python | def fmt_sia(sia, ces=True):
"""Format a |SystemIrreducibilityAnalysis|."""
if ces:
body = (
'{ces}'
'{partitioned_ces}'.format(
ces=fmt_ces(
sia.ces,
'Cause-effect structure'),
partitioned_ces=fmt_ces(
sia.partitioned_ces,
'Partitioned cause-effect structure')))
center_header = True
else:
body = ''
center_header = False
title = 'System irreducibility analysis: {BIG_PHI} = {phi}'.format(
BIG_PHI=BIG_PHI, phi=fmt_number(sia.phi))
body = header(str(sia.subsystem), body, center=center_header)
body = header(str(sia.cut), body, center=center_header)
return box(header(title, body, center=center_header)) | [
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wmayner/pyphi | pyphi/models/fmt.py | fmt_repertoire | def fmt_repertoire(r):
"""Format a repertoire."""
# TODO: will this get unwieldy with large repertoires?
if r is None:
return ''
r = r.squeeze()
lines = []
# Header: 'S P(S)'
space = ' ' * 4
head = '{S:^{s_width}}{space}Pr({S})'.format(
S='S', s_width=r.ndim, space=space)
lines.append(head)
# Lines: '001 .25'
for state in utils.all_states(r.ndim):
state_str = ''.join(str(i) for i in state)
lines.append('{0}{1}{2}'.format(state_str, space,
fmt_number(r[state])))
width = max(len(line) for line in lines)
lines.insert(1, DOTTED_HEADER * (width + 1))
return box('\n'.join(lines)) | python | def fmt_repertoire(r):
"""Format a repertoire."""
# TODO: will this get unwieldy with large repertoires?
if r is None:
return ''
r = r.squeeze()
lines = []
# Header: 'S P(S)'
space = ' ' * 4
head = '{S:^{s_width}}{space}Pr({S})'.format(
S='S', s_width=r.ndim, space=space)
lines.append(head)
# Lines: '001 .25'
for state in utils.all_states(r.ndim):
state_str = ''.join(str(i) for i in state)
lines.append('{0}{1}{2}'.format(state_str, space,
fmt_number(r[state])))
width = max(len(line) for line in lines)
lines.insert(1, DOTTED_HEADER * (width + 1))
return box('\n'.join(lines)) | [
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wmayner/pyphi | pyphi/models/fmt.py | fmt_ac_ria | def fmt_ac_ria(ria):
"""Format an AcRepertoireIrreducibilityAnalysis."""
causality = {
Direction.CAUSE: (fmt_mechanism(ria.purview, ria.node_labels),
ARROW_LEFT,
fmt_mechanism(ria.mechanism, ria.node_labels)),
Direction.EFFECT: (fmt_mechanism(ria.mechanism, ria.node_labels),
ARROW_RIGHT,
fmt_mechanism(ria.purview, ria.node_labels))
}[ria.direction]
causality = ' '.join(causality)
return '{ALPHA} = {alpha} {causality}'.format(
ALPHA=ALPHA,
alpha=round(ria.alpha, 4),
causality=causality) | python | def fmt_ac_ria(ria):
"""Format an AcRepertoireIrreducibilityAnalysis."""
causality = {
Direction.CAUSE: (fmt_mechanism(ria.purview, ria.node_labels),
ARROW_LEFT,
fmt_mechanism(ria.mechanism, ria.node_labels)),
Direction.EFFECT: (fmt_mechanism(ria.mechanism, ria.node_labels),
ARROW_RIGHT,
fmt_mechanism(ria.purview, ria.node_labels))
}[ria.direction]
causality = ' '.join(causality)
return '{ALPHA} = {alpha} {causality}'.format(
ALPHA=ALPHA,
alpha=round(ria.alpha, 4),
causality=causality) | [
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wmayner/pyphi | pyphi/models/fmt.py | fmt_account | def fmt_account(account, title=None):
"""Format an Account or a DirectedAccount."""
if title is None:
title = account.__class__.__name__ # `Account` or `DirectedAccount`
title = '{} ({} causal link{})'.format(
title, len(account), '' if len(account) == 1 else 's')
body = ''
body += 'Irreducible effects\n'
body += '\n'.join(fmt_ac_ria(m) for m in account.irreducible_effects)
body += '\nIrreducible causes\n'
body += '\n'.join(fmt_ac_ria(m) for m in account.irreducible_causes)
return '\n' + header(title, body, under_char='*') | python | def fmt_account(account, title=None):
"""Format an Account or a DirectedAccount."""
if title is None:
title = account.__class__.__name__ # `Account` or `DirectedAccount`
title = '{} ({} causal link{})'.format(
title, len(account), '' if len(account) == 1 else 's')
body = ''
body += 'Irreducible effects\n'
body += '\n'.join(fmt_ac_ria(m) for m in account.irreducible_effects)
body += '\nIrreducible causes\n'
body += '\n'.join(fmt_ac_ria(m) for m in account.irreducible_causes)
return '\n' + header(title, body, under_char='*') | [
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wmayner/pyphi | pyphi/models/fmt.py | fmt_ac_sia | def fmt_ac_sia(ac_sia):
"""Format a AcSystemIrreducibilityAnalysis."""
body = (
'{ALPHA} = {alpha}\n'
'direction: {ac_sia.direction}\n'
'transition: {ac_sia.transition}\n'
'before state: {ac_sia.before_state}\n'
'after state: {ac_sia.after_state}\n'
'cut:\n{ac_sia.cut}\n'
'{account}\n'
'{partitioned_account}'.format(
ALPHA=ALPHA,
alpha=round(ac_sia.alpha, 4),
ac_sia=ac_sia,
account=fmt_account(
ac_sia.account, 'Account'),
partitioned_account=fmt_account(
ac_sia.partitioned_account, 'Partitioned Account')))
return box(header('AcSystemIrreducibilityAnalysis',
body,
under_char=HORIZONTAL_BAR)) | python | def fmt_ac_sia(ac_sia):
"""Format a AcSystemIrreducibilityAnalysis."""
body = (
'{ALPHA} = {alpha}\n'
'direction: {ac_sia.direction}\n'
'transition: {ac_sia.transition}\n'
'before state: {ac_sia.before_state}\n'
'after state: {ac_sia.after_state}\n'
'cut:\n{ac_sia.cut}\n'
'{account}\n'
'{partitioned_account}'.format(
ALPHA=ALPHA,
alpha=round(ac_sia.alpha, 4),
ac_sia=ac_sia,
account=fmt_account(
ac_sia.account, 'Account'),
partitioned_account=fmt_account(
ac_sia.partitioned_account, 'Partitioned Account')))
return box(header('AcSystemIrreducibilityAnalysis',
body,
under_char=HORIZONTAL_BAR)) | [
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wmayner/pyphi | pyphi/models/fmt.py | fmt_transition | def fmt_transition(t):
"""Format a |Transition|."""
return "Transition({} {} {})".format(
fmt_mechanism(t.cause_indices, t.node_labels),
ARROW_RIGHT,
fmt_mechanism(t.effect_indices, t.node_labels)) | python | def fmt_transition(t):
"""Format a |Transition|."""
return "Transition({} {} {})".format(
fmt_mechanism(t.cause_indices, t.node_labels),
ARROW_RIGHT,
fmt_mechanism(t.effect_indices, t.node_labels)) | [
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wmayner/pyphi | pyphi/validate.py | direction | def direction(direction, allow_bi=False):
"""Validate that the given direction is one of the allowed constants.
If ``allow_bi`` is ``True`` then ``Direction.BIDIRECTIONAL`` is
acceptable.
"""
valid = [Direction.CAUSE, Direction.EFFECT]
if allow_bi:
valid.append(Direction.BIDIRECTIONAL)
if direction not in valid:
raise ValueError('`direction` must be one of {}'.format(valid))
return True | python | def direction(direction, allow_bi=False):
"""Validate that the given direction is one of the allowed constants.
If ``allow_bi`` is ``True`` then ``Direction.BIDIRECTIONAL`` is
acceptable.
"""
valid = [Direction.CAUSE, Direction.EFFECT]
if allow_bi:
valid.append(Direction.BIDIRECTIONAL)
if direction not in valid:
raise ValueError('`direction` must be one of {}'.format(valid))
return True | [
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wmayner/pyphi | pyphi/validate.py | tpm | def tpm(tpm, check_independence=True):
"""Validate a TPM.
The TPM can be in
* 2-dimensional state-by-state form,
* 2-dimensional state-by-node form, or
* multidimensional state-by-node form.
"""
see_tpm_docs = (
'See the documentation on TPM conventions and the `pyphi.Network` '
'object for more information on TPM forms.'
)
# Cast to np.array.
tpm = np.array(tpm)
# Get the number of nodes from the state-by-node TPM.
N = tpm.shape[-1]
if tpm.ndim == 2:
if not ((tpm.shape[0] == 2**N and tpm.shape[1] == N) or
(tpm.shape[0] == tpm.shape[1])):
raise ValueError(
'Invalid shape for 2-D TPM: {}\nFor a state-by-node TPM, '
'there must be ' '2^N rows and N columns, where N is the '
'number of nodes. State-by-state TPM must be square. '
'{}'.format(tpm.shape, see_tpm_docs))
if tpm.shape[0] == tpm.shape[1] and check_independence:
conditionally_independent(tpm)
elif tpm.ndim == (N + 1):
if tpm.shape != tuple([2] * N + [N]):
raise ValueError(
'Invalid shape for multidimensional state-by-node TPM: {}\n'
'The shape should be {} for {} nodes. {}'.format(
tpm.shape, ([2] * N) + [N], N, see_tpm_docs))
else:
raise ValueError(
'Invalid TPM: Must be either 2-dimensional or multidimensional. '
'{}'.format(see_tpm_docs))
return True | python | def tpm(tpm, check_independence=True):
"""Validate a TPM.
The TPM can be in
* 2-dimensional state-by-state form,
* 2-dimensional state-by-node form, or
* multidimensional state-by-node form.
"""
see_tpm_docs = (
'See the documentation on TPM conventions and the `pyphi.Network` '
'object for more information on TPM forms.'
)
# Cast to np.array.
tpm = np.array(tpm)
# Get the number of nodes from the state-by-node TPM.
N = tpm.shape[-1]
if tpm.ndim == 2:
if not ((tpm.shape[0] == 2**N and tpm.shape[1] == N) or
(tpm.shape[0] == tpm.shape[1])):
raise ValueError(
'Invalid shape for 2-D TPM: {}\nFor a state-by-node TPM, '
'there must be ' '2^N rows and N columns, where N is the '
'number of nodes. State-by-state TPM must be square. '
'{}'.format(tpm.shape, see_tpm_docs))
if tpm.shape[0] == tpm.shape[1] and check_independence:
conditionally_independent(tpm)
elif tpm.ndim == (N + 1):
if tpm.shape != tuple([2] * N + [N]):
raise ValueError(
'Invalid shape for multidimensional state-by-node TPM: {}\n'
'The shape should be {} for {} nodes. {}'.format(
tpm.shape, ([2] * N) + [N], N, see_tpm_docs))
else:
raise ValueError(
'Invalid TPM: Must be either 2-dimensional or multidimensional. '
'{}'.format(see_tpm_docs))
return True | [
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wmayner/pyphi | pyphi/validate.py | conditionally_independent | def conditionally_independent(tpm):
"""Validate that the TPM is conditionally independent."""
if not config.VALIDATE_CONDITIONAL_INDEPENDENCE:
return True
tpm = np.array(tpm)
if is_state_by_state(tpm):
there_and_back_again = convert.state_by_node2state_by_state(
convert.state_by_state2state_by_node(tpm))
else:
there_and_back_again = convert.state_by_state2state_by_node(
convert.state_by_node2state_by_state(tpm))
if np.any((tpm - there_and_back_again) >= EPSILON):
raise exceptions.ConditionallyDependentError(
'TPM is not conditionally independent.\n'
'See the conditional independence example in the documentation '
'for more info.')
return True | python | def conditionally_independent(tpm):
"""Validate that the TPM is conditionally independent."""
if not config.VALIDATE_CONDITIONAL_INDEPENDENCE:
return True
tpm = np.array(tpm)
if is_state_by_state(tpm):
there_and_back_again = convert.state_by_node2state_by_state(
convert.state_by_state2state_by_node(tpm))
else:
there_and_back_again = convert.state_by_state2state_by_node(
convert.state_by_node2state_by_state(tpm))
if np.any((tpm - there_and_back_again) >= EPSILON):
raise exceptions.ConditionallyDependentError(
'TPM is not conditionally independent.\n'
'See the conditional independence example in the documentation '
'for more info.')
return True | [
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wmayner/pyphi | pyphi/validate.py | connectivity_matrix | def connectivity_matrix(cm):
"""Validate the given connectivity matrix."""
# Special case for empty matrices.
if cm.size == 0:
return True
if cm.ndim != 2:
raise ValueError("Connectivity matrix must be 2-dimensional.")
if cm.shape[0] != cm.shape[1]:
raise ValueError("Connectivity matrix must be square.")
if not np.all(np.logical_or(cm == 1, cm == 0)):
raise ValueError("Connectivity matrix must contain only binary "
"values.")
return True | python | def connectivity_matrix(cm):
"""Validate the given connectivity matrix."""
# Special case for empty matrices.
if cm.size == 0:
return True
if cm.ndim != 2:
raise ValueError("Connectivity matrix must be 2-dimensional.")
if cm.shape[0] != cm.shape[1]:
raise ValueError("Connectivity matrix must be square.")
if not np.all(np.logical_or(cm == 1, cm == 0)):
raise ValueError("Connectivity matrix must contain only binary "
"values.")
return True | [
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wmayner/pyphi | pyphi/validate.py | node_labels | def node_labels(node_labels, node_indices):
"""Validate that there is a label for each node."""
if len(node_labels) != len(node_indices):
raise ValueError("Labels {0} must label every node {1}.".format(
node_labels, node_indices))
if len(node_labels) != len(set(node_labels)):
raise ValueError("Labels {0} must be unique.".format(node_labels)) | python | def node_labels(node_labels, node_indices):
"""Validate that there is a label for each node."""
if len(node_labels) != len(node_indices):
raise ValueError("Labels {0} must label every node {1}.".format(
node_labels, node_indices))
if len(node_labels) != len(set(node_labels)):
raise ValueError("Labels {0} must be unique.".format(node_labels)) | [
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wmayner/pyphi | pyphi/validate.py | network | def network(n):
"""Validate a |Network|.
Checks the TPM and connectivity matrix.
"""
tpm(n.tpm)
connectivity_matrix(n.cm)
if n.cm.shape[0] != n.size:
raise ValueError("Connectivity matrix must be NxN, where N is the "
"number of nodes in the network.")
return True | python | def network(n):
"""Validate a |Network|.
Checks the TPM and connectivity matrix.
"""
tpm(n.tpm)
connectivity_matrix(n.cm)
if n.cm.shape[0] != n.size:
raise ValueError("Connectivity matrix must be NxN, where N is the "
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return True | [
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wmayner/pyphi | pyphi/validate.py | state_length | def state_length(state, size):
"""Check that the state is the given size."""
if len(state) != size:
raise ValueError('Invalid state: there must be one entry per '
'node in the network; this state has {} entries, but '
'there are {} nodes.'.format(len(state), size))
return True | python | def state_length(state, size):
"""Check that the state is the given size."""
if len(state) != size:
raise ValueError('Invalid state: there must be one entry per '
'node in the network; this state has {} entries, but '
'there are {} nodes.'.format(len(state), size))
return True | [
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wmayner/pyphi | pyphi/validate.py | state_reachable | def state_reachable(subsystem):
"""Return whether a state can be reached according to the network's TPM."""
# If there is a row `r` in the TPM such that all entries of `r - state` are
# between -1 and 1, then the given state has a nonzero probability of being
# reached from some state.
# First we take the submatrix of the conditioned TPM that corresponds to
# the nodes that are actually in the subsystem...
tpm = subsystem.tpm[..., subsystem.node_indices]
# Then we do the subtraction and test.
test = tpm - np.array(subsystem.proper_state)
if not np.any(np.logical_and(-1 < test, test < 1).all(-1)):
raise exceptions.StateUnreachableError(subsystem.state) | python | def state_reachable(subsystem):
"""Return whether a state can be reached according to the network's TPM."""
# If there is a row `r` in the TPM such that all entries of `r - state` are
# between -1 and 1, then the given state has a nonzero probability of being
# reached from some state.
# First we take the submatrix of the conditioned TPM that corresponds to
# the nodes that are actually in the subsystem...
tpm = subsystem.tpm[..., subsystem.node_indices]
# Then we do the subtraction and test.
test = tpm - np.array(subsystem.proper_state)
if not np.any(np.logical_and(-1 < test, test < 1).all(-1)):
raise exceptions.StateUnreachableError(subsystem.state) | [
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wmayner/pyphi | pyphi/validate.py | cut | def cut(cut, node_indices):
"""Check that the cut is for only the given nodes."""
if cut.indices != node_indices:
raise ValueError('{} nodes are not equal to subsystem nodes '
'{}'.format(cut, node_indices)) | python | def cut(cut, node_indices):
"""Check that the cut is for only the given nodes."""
if cut.indices != node_indices:
raise ValueError('{} nodes are not equal to subsystem nodes '
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wmayner/pyphi | pyphi/validate.py | subsystem | def subsystem(s):
"""Validate a |Subsystem|.
Checks its state and cut.
"""
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cut(s.cut, s.cut_indices)
if config.VALIDATE_SUBSYSTEM_STATES:
state_reachable(s)
return True | python | def subsystem(s):
"""Validate a |Subsystem|.
Checks its state and cut.
"""
node_states(s.state)
cut(s.cut, s.cut_indices)
if config.VALIDATE_SUBSYSTEM_STATES:
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wmayner/pyphi | pyphi/validate.py | partition | def partition(partition):
"""Validate a partition - used by blackboxes and coarse grains."""
nodes = set()
for part in partition:
for node in part:
if node in nodes:
raise ValueError(
'Micro-element {} may not be partitioned into multiple '
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nodes.add(node) | python | def partition(partition):
"""Validate a partition - used by blackboxes and coarse grains."""
nodes = set()
for part in partition:
for node in part:
if node in nodes:
raise ValueError(
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wmayner/pyphi | pyphi/validate.py | coarse_grain | def coarse_grain(coarse_grain):
"""Validate a macro coarse-graining."""
partition(coarse_grain.partition)
if len(coarse_grain.partition) != len(coarse_grain.grouping):
raise ValueError('output and state groupings must be the same size')
for part, group in zip(coarse_grain.partition, coarse_grain.grouping):
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# Check that all elements in the partition are in one of the two
# state groupings
raise ValueError('elements in output grouping {0} do not match '
'elements in state grouping {1}'.format(
part, group)) | python | def coarse_grain(coarse_grain):
"""Validate a macro coarse-graining."""
partition(coarse_grain.partition)
if len(coarse_grain.partition) != len(coarse_grain.grouping):
raise ValueError('output and state groupings must be the same size')
for part, group in zip(coarse_grain.partition, coarse_grain.grouping):
if set(range(len(part) + 1)) != set(group[0] + group[1]):
# Check that all elements in the partition are in one of the two
# state groupings
raise ValueError('elements in output grouping {0} do not match '
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wmayner/pyphi | pyphi/validate.py | blackbox | def blackbox(blackbox):
"""Validate a macro blackboxing."""
if tuple(sorted(blackbox.output_indices)) != blackbox.output_indices:
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blackbox.output_indices))
partition(blackbox.partition)
for part in blackbox.partition:
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'Every blackbox must have an output - {} does not'.format(
part)) | python | def blackbox(blackbox):
"""Validate a macro blackboxing."""
if tuple(sorted(blackbox.output_indices)) != blackbox.output_indices:
raise ValueError('Output indices {} must be ordered'.format(
blackbox.output_indices))
partition(blackbox.partition)
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raise ValueError(
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wmayner/pyphi | pyphi/validate.py | blackbox_and_coarse_grain | def blackbox_and_coarse_grain(blackbox, coarse_grain):
"""Validate that a coarse-graining properly combines the outputs of a
blackboxing.
"""
if blackbox is None:
return
for box in blackbox.partition:
# Outputs of the box
outputs = set(box) & set(blackbox.output_indices)
if coarse_grain is None and len(outputs) > 1:
raise ValueError(
'A blackboxing with multiple outputs per box must be '
'coarse-grained.')
if (coarse_grain and not any(outputs.issubset(part)
for part in coarse_grain.partition)):
raise ValueError(
'Multiple outputs from a blackbox must be partitioned into '
'the same macro-element of the coarse-graining') | python | def blackbox_and_coarse_grain(blackbox, coarse_grain):
"""Validate that a coarse-graining properly combines the outputs of a
blackboxing.
"""
if blackbox is None:
return
for box in blackbox.partition:
# Outputs of the box
outputs = set(box) & set(blackbox.output_indices)
if coarse_grain is None and len(outputs) > 1:
raise ValueError(
'A blackboxing with multiple outputs per box must be '
'coarse-grained.')
if (coarse_grain and not any(outputs.issubset(part)
for part in coarse_grain.partition)):
raise ValueError(
'Multiple outputs from a blackbox must be partitioned into '
'the same macro-element of the coarse-graining') | [
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wmayner/pyphi | pyphi/registry.py | Registry.register | def register(self, name):
"""Decorator for registering a function with PyPhi.
Args:
name (string): The name of the function
"""
def register_func(func):
self.store[name] = func
return func
return register_func | python | def register(self, name):
"""Decorator for registering a function with PyPhi.
Args:
name (string): The name of the function
"""
def register_func(func):
self.store[name] = func
return func
return register_func | [
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wmayner/pyphi | pyphi/compute/subsystem.py | ces | def ces(subsystem, mechanisms=False, purviews=False, cause_purviews=False,
effect_purviews=False, parallel=False):
"""Return the conceptual structure of this subsystem, optionally restricted
to concepts with the mechanisms and purviews given in keyword arguments.
If you don't need the full |CauseEffectStructure|, restricting the possible
mechanisms and purviews can make this function much faster.
Args:
subsystem (Subsystem): The subsystem for which to determine the
|CauseEffectStructure|.
Keyword Args:
mechanisms (tuple[tuple[int]]): Restrict possible mechanisms to those
in this list.
purviews (tuple[tuple[int]]): Same as in |Subsystem.concept()|.
cause_purviews (tuple[tuple[int]]): Same as in |Subsystem.concept()|.
effect_purviews (tuple[tuple[int]]): Same as in |Subsystem.concept()|.
parallel (bool): Whether to compute concepts in parallel. If ``True``,
overrides :data:`config.PARALLEL_CONCEPT_EVALUATION`.
Returns:
CauseEffectStructure: A tuple of every |Concept| in the cause-effect
structure.
"""
if mechanisms is False:
mechanisms = utils.powerset(subsystem.node_indices, nonempty=True)
engine = ComputeCauseEffectStructure(mechanisms, subsystem, purviews,
cause_purviews, effect_purviews)
return CauseEffectStructure(engine.run(parallel or
config.PARALLEL_CONCEPT_EVALUATION),
subsystem=subsystem) | python | def ces(subsystem, mechanisms=False, purviews=False, cause_purviews=False,
effect_purviews=False, parallel=False):
"""Return the conceptual structure of this subsystem, optionally restricted
to concepts with the mechanisms and purviews given in keyword arguments.
If you don't need the full |CauseEffectStructure|, restricting the possible
mechanisms and purviews can make this function much faster.
Args:
subsystem (Subsystem): The subsystem for which to determine the
|CauseEffectStructure|.
Keyword Args:
mechanisms (tuple[tuple[int]]): Restrict possible mechanisms to those
in this list.
purviews (tuple[tuple[int]]): Same as in |Subsystem.concept()|.
cause_purviews (tuple[tuple[int]]): Same as in |Subsystem.concept()|.
effect_purviews (tuple[tuple[int]]): Same as in |Subsystem.concept()|.
parallel (bool): Whether to compute concepts in parallel. If ``True``,
overrides :data:`config.PARALLEL_CONCEPT_EVALUATION`.
Returns:
CauseEffectStructure: A tuple of every |Concept| in the cause-effect
structure.
"""
if mechanisms is False:
mechanisms = utils.powerset(subsystem.node_indices, nonempty=True)
engine = ComputeCauseEffectStructure(mechanisms, subsystem, purviews,
cause_purviews, effect_purviews)
return CauseEffectStructure(engine.run(parallel or
config.PARALLEL_CONCEPT_EVALUATION),
subsystem=subsystem) | [
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Args:
subsystem (Subsystem): The subsystem for which to determine the
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wmayner/pyphi | pyphi/compute/subsystem.py | conceptual_info | def conceptual_info(subsystem):
"""Return the conceptual information for a |Subsystem|.
This is the distance from the subsystem's |CauseEffectStructure| to the
null concept.
"""
ci = ces_distance(ces(subsystem),
CauseEffectStructure((), subsystem=subsystem))
return round(ci, config.PRECISION) | python | def conceptual_info(subsystem):
"""Return the conceptual information for a |Subsystem|.
This is the distance from the subsystem's |CauseEffectStructure| to the
null concept.
"""
ci = ces_distance(ces(subsystem),
CauseEffectStructure((), subsystem=subsystem))
return round(ci, config.PRECISION) | [
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wmayner/pyphi | pyphi/compute/subsystem.py | evaluate_cut | def evaluate_cut(uncut_subsystem, cut, unpartitioned_ces):
"""Compute the system irreducibility for a given cut.
Args:
uncut_subsystem (Subsystem): The subsystem without the cut applied.
cut (Cut): The cut to evaluate.
unpartitioned_ces (CauseEffectStructure): The cause-effect structure of
the uncut subsystem.
Returns:
SystemIrreducibilityAnalysis: The |SystemIrreducibilityAnalysis| for
that cut.
"""
log.debug('Evaluating %s...', cut)
cut_subsystem = uncut_subsystem.apply_cut(cut)
if config.ASSUME_CUTS_CANNOT_CREATE_NEW_CONCEPTS:
mechanisms = unpartitioned_ces.mechanisms
else:
# Mechanisms can only produce concepts if they were concepts in the
# original system, or the cut divides the mechanism.
mechanisms = set(
unpartitioned_ces.mechanisms +
list(cut_subsystem.cut_mechanisms))
partitioned_ces = ces(cut_subsystem, mechanisms)
log.debug('Finished evaluating %s.', cut)
phi_ = ces_distance(unpartitioned_ces, partitioned_ces)
return SystemIrreducibilityAnalysis(
phi=phi_,
ces=unpartitioned_ces,
partitioned_ces=partitioned_ces,
subsystem=uncut_subsystem,
cut_subsystem=cut_subsystem) | python | def evaluate_cut(uncut_subsystem, cut, unpartitioned_ces):
"""Compute the system irreducibility for a given cut.
Args:
uncut_subsystem (Subsystem): The subsystem without the cut applied.
cut (Cut): The cut to evaluate.
unpartitioned_ces (CauseEffectStructure): The cause-effect structure of
the uncut subsystem.
Returns:
SystemIrreducibilityAnalysis: The |SystemIrreducibilityAnalysis| for
that cut.
"""
log.debug('Evaluating %s...', cut)
cut_subsystem = uncut_subsystem.apply_cut(cut)
if config.ASSUME_CUTS_CANNOT_CREATE_NEW_CONCEPTS:
mechanisms = unpartitioned_ces.mechanisms
else:
# Mechanisms can only produce concepts if they were concepts in the
# original system, or the cut divides the mechanism.
mechanisms = set(
unpartitioned_ces.mechanisms +
list(cut_subsystem.cut_mechanisms))
partitioned_ces = ces(cut_subsystem, mechanisms)
log.debug('Finished evaluating %s.', cut)
phi_ = ces_distance(unpartitioned_ces, partitioned_ces)
return SystemIrreducibilityAnalysis(
phi=phi_,
ces=unpartitioned_ces,
partitioned_ces=partitioned_ces,
subsystem=uncut_subsystem,
cut_subsystem=cut_subsystem) | [
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wmayner/pyphi | pyphi/compute/subsystem.py | sia_bipartitions | def sia_bipartitions(nodes, node_labels=None):
"""Return all |big_phi| cuts for the given nodes.
This value changes based on :const:`config.CUT_ONE_APPROXIMATION`.
Args:
nodes (tuple[int]): The node indices to partition.
Returns:
list[Cut]: All unidirectional partitions.
"""
if config.CUT_ONE_APPROXIMATION:
bipartitions = directed_bipartition_of_one(nodes)
else:
# Don't consider trivial partitions where one part is empty
bipartitions = directed_bipartition(nodes, nontrivial=True)
return [Cut(bipartition[0], bipartition[1], node_labels)
for bipartition in bipartitions] | python | def sia_bipartitions(nodes, node_labels=None):
"""Return all |big_phi| cuts for the given nodes.
This value changes based on :const:`config.CUT_ONE_APPROXIMATION`.
Args:
nodes (tuple[int]): The node indices to partition.
Returns:
list[Cut]: All unidirectional partitions.
"""
if config.CUT_ONE_APPROXIMATION:
bipartitions = directed_bipartition_of_one(nodes)
else:
# Don't consider trivial partitions where one part is empty
bipartitions = directed_bipartition(nodes, nontrivial=True)
return [Cut(bipartition[0], bipartition[1], node_labels)
for bipartition in bipartitions] | [
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wmayner/pyphi | pyphi/compute/subsystem.py | _sia | def _sia(cache_key, subsystem):
"""Return the minimal information partition of a subsystem.
Args:
subsystem (Subsystem): The candidate set of nodes.
Returns:
SystemIrreducibilityAnalysis: A nested structure containing all the
data from the intermediate calculations. The top level contains the
basic irreducibility information for the given subsystem.
"""
# pylint: disable=unused-argument
log.info('Calculating big-phi data for %s...', subsystem)
# Check for degenerate cases
# =========================================================================
# Phi is necessarily zero if the subsystem is:
# - not strongly connected;
# - empty;
# - an elementary micro mechanism (i.e. no nontrivial bipartitions).
# So in those cases we immediately return a null SIA.
if not subsystem:
log.info('Subsystem %s is empty; returning null SIA '
'immediately.', subsystem)
return _null_sia(subsystem)
if not connectivity.is_strong(subsystem.cm, subsystem.node_indices):
log.info('%s is not strongly connected; returning null SIA '
'immediately.', subsystem)
return _null_sia(subsystem)
# Handle elementary micro mechanism cases.
# Single macro element systems have nontrivial bipartitions because their
# bipartitions are over their micro elements.
if len(subsystem.cut_indices) == 1:
# If the node lacks a self-loop, phi is trivially zero.
if not subsystem.cm[subsystem.node_indices][subsystem.node_indices]:
log.info('Single micro nodes %s without selfloops cannot have '
'phi; returning null SIA immediately.', subsystem)
return _null_sia(subsystem)
# Even if the node has a self-loop, we may still define phi to be zero.
elif not config.SINGLE_MICRO_NODES_WITH_SELFLOOPS_HAVE_PHI:
log.info('Single micro nodes %s with selfloops cannot have '
'phi; returning null SIA immediately.', subsystem)
return _null_sia(subsystem)
# =========================================================================
log.debug('Finding unpartitioned CauseEffectStructure...')
unpartitioned_ces = _ces(subsystem)
if not unpartitioned_ces:
log.info('Empty unpartitioned CauseEffectStructure; returning null '
'SIA immediately.')
# Short-circuit if there are no concepts in the unpartitioned CES.
return _null_sia(subsystem)
log.debug('Found unpartitioned CauseEffectStructure.')
# TODO: move this into sia_bipartitions?
# Only True if SINGLE_MICRO_NODES...=True, no?
if len(subsystem.cut_indices) == 1:
cuts = [Cut(subsystem.cut_indices, subsystem.cut_indices,
subsystem.cut_node_labels)]
else:
cuts = sia_bipartitions(subsystem.cut_indices,
subsystem.cut_node_labels)
engine = ComputeSystemIrreducibility(
cuts, subsystem, unpartitioned_ces)
result = engine.run(config.PARALLEL_CUT_EVALUATION)
if config.CLEAR_SUBSYSTEM_CACHES_AFTER_COMPUTING_SIA:
log.debug('Clearing subsystem caches.')
subsystem.clear_caches()
log.info('Finished calculating big-phi data for %s.', subsystem)
return result | python | def _sia(cache_key, subsystem):
"""Return the minimal information partition of a subsystem.
Args:
subsystem (Subsystem): The candidate set of nodes.
Returns:
SystemIrreducibilityAnalysis: A nested structure containing all the
data from the intermediate calculations. The top level contains the
basic irreducibility information for the given subsystem.
"""
# pylint: disable=unused-argument
log.info('Calculating big-phi data for %s...', subsystem)
# Check for degenerate cases
# =========================================================================
# Phi is necessarily zero if the subsystem is:
# - not strongly connected;
# - empty;
# - an elementary micro mechanism (i.e. no nontrivial bipartitions).
# So in those cases we immediately return a null SIA.
if not subsystem:
log.info('Subsystem %s is empty; returning null SIA '
'immediately.', subsystem)
return _null_sia(subsystem)
if not connectivity.is_strong(subsystem.cm, subsystem.node_indices):
log.info('%s is not strongly connected; returning null SIA '
'immediately.', subsystem)
return _null_sia(subsystem)
# Handle elementary micro mechanism cases.
# Single macro element systems have nontrivial bipartitions because their
# bipartitions are over their micro elements.
if len(subsystem.cut_indices) == 1:
# If the node lacks a self-loop, phi is trivially zero.
if not subsystem.cm[subsystem.node_indices][subsystem.node_indices]:
log.info('Single micro nodes %s without selfloops cannot have '
'phi; returning null SIA immediately.', subsystem)
return _null_sia(subsystem)
# Even if the node has a self-loop, we may still define phi to be zero.
elif not config.SINGLE_MICRO_NODES_WITH_SELFLOOPS_HAVE_PHI:
log.info('Single micro nodes %s with selfloops cannot have '
'phi; returning null SIA immediately.', subsystem)
return _null_sia(subsystem)
# =========================================================================
log.debug('Finding unpartitioned CauseEffectStructure...')
unpartitioned_ces = _ces(subsystem)
if not unpartitioned_ces:
log.info('Empty unpartitioned CauseEffectStructure; returning null '
'SIA immediately.')
# Short-circuit if there are no concepts in the unpartitioned CES.
return _null_sia(subsystem)
log.debug('Found unpartitioned CauseEffectStructure.')
# TODO: move this into sia_bipartitions?
# Only True if SINGLE_MICRO_NODES...=True, no?
if len(subsystem.cut_indices) == 1:
cuts = [Cut(subsystem.cut_indices, subsystem.cut_indices,
subsystem.cut_node_labels)]
else:
cuts = sia_bipartitions(subsystem.cut_indices,
subsystem.cut_node_labels)
engine = ComputeSystemIrreducibility(
cuts, subsystem, unpartitioned_ces)
result = engine.run(config.PARALLEL_CUT_EVALUATION)
if config.CLEAR_SUBSYSTEM_CACHES_AFTER_COMPUTING_SIA:
log.debug('Clearing subsystem caches.')
subsystem.clear_caches()
log.info('Finished calculating big-phi data for %s.', subsystem)
return result | [
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Args:
subsystem (Subsystem): The candidate set of nodes.
Returns:
SystemIrreducibilityAnalysis: A nested structure containing all the
data from the intermediate calculations. The top level contains the
basic irreducibility information for the given subsystem. | [
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wmayner/pyphi | pyphi/compute/subsystem.py | _sia_cache_key | def _sia_cache_key(subsystem):
"""The cache key of the subsystem.
This includes the native hash of the subsystem and all configuration values
which change the results of ``sia``.
"""
return (
hash(subsystem),
config.ASSUME_CUTS_CANNOT_CREATE_NEW_CONCEPTS,
config.CUT_ONE_APPROXIMATION,
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config.PRECISION,
config.VALIDATE_SUBSYSTEM_STATES,
config.SINGLE_MICRO_NODES_WITH_SELFLOOPS_HAVE_PHI,
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) | python | def _sia_cache_key(subsystem):
"""The cache key of the subsystem.
This includes the native hash of the subsystem and all configuration values
which change the results of ``sia``.
"""
return (
hash(subsystem),
config.ASSUME_CUTS_CANNOT_CREATE_NEW_CONCEPTS,
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config.PARTITION_TYPE,
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wmayner/pyphi | pyphi/compute/subsystem.py | concept_cuts | def concept_cuts(direction, node_indices, node_labels=None):
"""Generator over all concept-syle cuts for these nodes."""
for partition in mip_partitions(node_indices, node_indices):
yield KCut(direction, partition, node_labels) | python | def concept_cuts(direction, node_indices, node_labels=None):
"""Generator over all concept-syle cuts for these nodes."""
for partition in mip_partitions(node_indices, node_indices):
yield KCut(direction, partition, node_labels) | [
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wmayner/pyphi | pyphi/compute/subsystem.py | directional_sia | def directional_sia(subsystem, direction, unpartitioned_ces=None):
"""Calculate a concept-style SystemIrreducibilityAnalysisCause or
SystemIrreducibilityAnalysisEffect.
"""
if unpartitioned_ces is None:
unpartitioned_ces = _ces(subsystem)
c_system = ConceptStyleSystem(subsystem, direction)
cuts = concept_cuts(direction, c_system.cut_indices, subsystem.node_labels)
# Run the default SIA engine
# TODO: verify that short-cutting works correctly?
engine = ComputeSystemIrreducibility(
cuts, c_system, unpartitioned_ces)
return engine.run(config.PARALLEL_CUT_EVALUATION) | python | def directional_sia(subsystem, direction, unpartitioned_ces=None):
"""Calculate a concept-style SystemIrreducibilityAnalysisCause or
SystemIrreducibilityAnalysisEffect.
"""
if unpartitioned_ces is None:
unpartitioned_ces = _ces(subsystem)
c_system = ConceptStyleSystem(subsystem, direction)
cuts = concept_cuts(direction, c_system.cut_indices, subsystem.node_labels)
# Run the default SIA engine
# TODO: verify that short-cutting works correctly?
engine = ComputeSystemIrreducibility(
cuts, c_system, unpartitioned_ces)
return engine.run(config.PARALLEL_CUT_EVALUATION) | [
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wmayner/pyphi | pyphi/compute/subsystem.py | sia_concept_style | def sia_concept_style(subsystem):
"""Compute a concept-style SystemIrreducibilityAnalysis"""
unpartitioned_ces = _ces(subsystem)
sia_cause = directional_sia(subsystem, Direction.CAUSE,
unpartitioned_ces)
sia_effect = directional_sia(subsystem, Direction.EFFECT,
unpartitioned_ces)
return SystemIrreducibilityAnalysisConceptStyle(sia_cause, sia_effect) | python | def sia_concept_style(subsystem):
"""Compute a concept-style SystemIrreducibilityAnalysis"""
unpartitioned_ces = _ces(subsystem)
sia_cause = directional_sia(subsystem, Direction.CAUSE,
unpartitioned_ces)
sia_effect = directional_sia(subsystem, Direction.EFFECT,
unpartitioned_ces)
return SystemIrreducibilityAnalysisConceptStyle(sia_cause, sia_effect) | [
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wmayner/pyphi | pyphi/compute/subsystem.py | ComputeCauseEffectStructure.compute | def compute(mechanism, subsystem, purviews, cause_purviews,
effect_purviews):
"""Compute a |Concept| for a mechanism, in this |Subsystem| with the
provided purviews.
"""
concept = subsystem.concept(mechanism,
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cause_purviews=cause_purviews,
effect_purviews=effect_purviews)
# Don't serialize the subsystem.
# This is replaced on the other side of the queue, and ensures
# that all concepts in the CES reference the same subsystem.
concept.subsystem = None
return concept | python | def compute(mechanism, subsystem, purviews, cause_purviews,
effect_purviews):
"""Compute a |Concept| for a mechanism, in this |Subsystem| with the
provided purviews.
"""
concept = subsystem.concept(mechanism,
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# Don't serialize the subsystem.
# This is replaced on the other side of the queue, and ensures
# that all concepts in the CES reference the same subsystem.
concept.subsystem = None
return concept | [
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wmayner/pyphi | pyphi/compute/subsystem.py | ComputeCauseEffectStructure.process_result | def process_result(self, new_concept, concepts):
"""Save all concepts with non-zero |small_phi| to the
|CauseEffectStructure|.
"""
if new_concept.phi > 0:
# Replace the subsystem
new_concept.subsystem = self.subsystem
concepts.append(new_concept)
return concepts | python | def process_result(self, new_concept, concepts):
"""Save all concepts with non-zero |small_phi| to the
|CauseEffectStructure|.
"""
if new_concept.phi > 0:
# Replace the subsystem
new_concept.subsystem = self.subsystem
concepts.append(new_concept)
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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
return min_sia | 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
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return new_sia
return min_sia | [
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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(
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effect = self.effect_system.mie(
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return Concept(mechanism=mechanism, cause=cause, effect=effect,
subsystem=self) | 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))
effect = self.effect_system.mie(
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return Concept(mechanism=mechanism, cause=cause, effect=effect,
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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.labels2indices(nodes)
else:
indices = map(int, nodes)
return tuple(sorted(set(indices))) | 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.labels2indices(nodes)
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indices = map(int, nodes)
return tuple(sorted(set(indices))) | [
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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_subsystem=subsystem,
phi=phi,
ces=_null_ces(subsystem),
partitioned_ces=_null_ces(subsystem)) | 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,
cut_subsystem=subsystem,
phi=phi,
ces=_null_ces(subsystem),
partitioned_ces=_null_ces(subsystem)) | [
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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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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|.
"""
if self is Direction.CAUSE:
return purview, mechanism
elif self is Direction.EFFECT:
return mechanism, purview
from . import validate
return validate.direction(self) | 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|.
"""
if self is Direction.CAUSE:
return purview, mechanism
elif self is Direction.EFFECT:
return mechanism, purview
from . import validate
return validate.direction(self) | [
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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.
"""
@functools.wraps(func)
def wrapper(self, other): # pylint: disable=missing-docstring
if type(other) is not type(self):
return NotImplemented
return func(self, other)
return wrapper | 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.
"""
@functools.wraps(func)
def wrapper(self, other): # pylint: disable=missing-docstring
if type(other) is not type(self):
return NotImplemented
return func(self, other)
return wrapper | [
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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 with
:func:`numpy_aware_eq`.
"""
try:
for attr in attributes:
_a, _b = getattr(a, attr), getattr(b, attr)
if attr in ['phi', 'alpha']:
if not utils.eq(_a, _b):
return False
elif attr in ['mechanism', 'purview']:
if _a is None or _b is None:
if _a != _b:
return False
elif not set(_a) == set(_b):
return False
else:
if not numpy_aware_eq(_a, _b):
return False
return True
except AttributeError:
return False | 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 with
:func:`numpy_aware_eq`.
"""
try:
for attr in attributes:
_a, _b = getattr(a, attr), getattr(b, attr)
if attr in ['phi', 'alpha']:
if not utils.eq(_a, _b):
return False
elif attr in ['mechanism', 'purview']:
if _a is None or _b is None:
if _a != _b:
return False
elif not set(_a) == set(_b):
return False
else:
if not numpy_aware_eq(_a, _b):
return False
return True
except AttributeError:
return False | [
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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)
results = timeit.repeat(statement, number=NUMBER, repeat=REPEAT)
return min(results) | 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)
results = timeit.repeat(statement, number=NUMBER, repeat=REPEAT)
return min(results) | [
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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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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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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: should we round here?
# repertoire = repertoire.round(config.PRECISION)
# joint = joint.round(config.PRECISION)
return np.array_equal(repertoire, joint) | 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: should we round here?
# repertoire = repertoire.round(config.PRECISION)
# joint = joint.round(config.PRECISION)
return np.array_equal(repertoire, joint) | [
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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) if dim == 2) | 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) if dim == 2) | [
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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 repertoire in big-endian
order.
Returns:
np.ndarray: The flattened repertoire.
"""
if repertoire is None:
return None
order = 'C' if big_endian else 'F'
# For efficiency, use `ravel` (which returns a view of the array) instead
# of `np.flatten` (which copies the whole array).
return repertoire.squeeze().ravel(order=order) | 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 repertoire in big-endian
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Returns:
np.ndarray: The flattened repertoire.
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if repertoire is None:
return None
order = 'C' if big_endian else 'F'
# For efficiency, use `ravel` (which returns a view of the array) instead
# of `np.flatten` (which copies the whole array).
return repertoire.squeeze().ravel(order=order) | [
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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_indices (tuple[int]): The set of node indices over which to take
the distribution.
number_of_nodes (int): The total number of nodes in the network.
Returns:
np.ndarray: The maximum entropy distribution over the set of nodes.
"""
distribution = np.ones(repertoire_shape(node_indices, number_of_nodes))
return distribution / distribution.size | 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.
Args:
node_indices (tuple[int]): The set of node indices over which to take
the distribution.
number_of_nodes (int): The total number of nodes in the network.
Returns:
np.ndarray: The maximum entropy distribution over the set of nodes.
"""
distribution = np.ones(repertoire_shape(node_indices, number_of_nodes))
return distribution / distribution.size | [
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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.nodes:
node_tpm = node.tpm_on
for input_node in node.inputs:
if not blackbox.in_same_box(node.index, input_node):
if input_node in blackbox.output_indices:
node_tpm = marginalize_out([input_node], node_tpm)
node_tpms.append(node_tpm)
noised_tpm = rebuild_system_tpm(node_tpms)
noised_tpm = convert.state_by_node2state_by_state(noised_tpm)
tpm = convert.state_by_node2state_by_state(system.tpm)
# Muliply by noise
tpm = np.dot(tpm, np.linalg.matrix_power(noised_tpm, steps - 1))
return convert.state_by_state2state_by_node(tpm) | 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.nodes:
node_tpm = node.tpm_on
for input_node in node.inputs:
if not blackbox.in_same_box(node.index, input_node):
if input_node in blackbox.output_indices:
node_tpm = marginalize_out([input_node], node_tpm)
node_tpms.append(node_tpm)
noised_tpm = rebuild_system_tpm(node_tpms)
noised_tpm = convert.state_by_node2state_by_state(noised_tpm)
tpm = convert.state_by_node2state_by_state(system.tpm)
# Muliply by noise
tpm = np.dot(tpm, np.linalg.matrix_power(noised_tpm, steps - 1))
return convert.state_by_state2state_by_node(tpm) | [
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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:
>>> _partitions_list(3)
[[[0, 1], [2]], [[0, 2], [1]], [[0], [1, 2]], [[0], [1], [2]]]
"""
if N < (_NUM_PRECOMPUTED_PARTITION_LISTS):
return list(_partition_lists[N])
else:
raise ValueError(
'Partition lists not yet available for system with {} '
'nodes or more'.format(_NUM_PRECOMPUTED_PARTITION_LISTS)) | 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
micro-elements corresponding to a macro-element.
Example:
>>> _partitions_list(3)
[[[0, 1], [2]], [[0, 2], [1]], [[0], [1, 2]], [[0], [1], [2]]]
"""
if N < (_NUM_PRECOMPUTED_PARTITION_LISTS):
return list(_partition_lists[N])
else:
raise ValueError(
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'nodes or more'.format(_NUM_PRECOMPUTED_PARTITION_LISTS)) | [
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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-elements.
"""
n = len(indices)
partitions = _partitions_list(n)
if n > 0:
partitions[-1] = [list(range(n))]
for partition in partitions:
yield tuple(tuple(indices[i] for i in part)
for part in partition) | 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
is a tuple of micro-elements which correspond to macro-elements.
"""
n = len(indices)
partitions = _partitions_list(n)
if n > 0:
partitions[-1] = [list(range(n))]
for partition in partitions:
yield tuple(tuple(indices[i] for i in part)
for part in partition) | [
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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 in all_groupings(partition):
yield CoarseGrain(partition, 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 in all_groupings(partition):
yield CoarseGrain(partition, grouping) | [
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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_groupings(partition):
coarse_grain = CoarseGrain(partition, grouping)
try:
validate.blackbox_and_coarse_grain(blackbox, coarse_grain)
except ValueError:
continue
yield coarse_grain | 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_groupings(partition):
coarse_grain = CoarseGrain(partition, grouping)
try:
validate.blackbox_and_coarse_grain(blackbox, coarse_grain)
except ValueError:
continue
yield coarse_grain | [
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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 here
# (pass `nonempty=True` to `powerset`)
for output_indices in utils.powerset(indices):
blackbox = Blackbox(partition, output_indices)
try: # Ensure every box has at least one output
validate.blackbox(blackbox)
except ValueError:
continue
yield blackbox | 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 here
# (pass `nonempty=True` to `powerset`)
for output_indices in utils.powerset(indices):
blackbox = Blackbox(partition, output_indices)
try: # Ensure every box has at least one output
validate.blackbox(blackbox)
except ValueError:
continue
yield blackbox | [
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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:
tuple[int, CoarseGrain]: The phi-value of the maximal |CoarseGrain|.
"""
max_phi = float('-inf')
max_coarse_grain = CoarseGrain((), ())
for coarse_grain in all_coarse_grains(internal_indices):
try:
subsystem = MacroSubsystem(network, state, internal_indices,
coarse_grain=coarse_grain)
except ConditionallyDependentError:
continue
phi = compute.phi(subsystem)
if (phi - max_phi) > constants.EPSILON:
max_phi = phi
max_coarse_grain = coarse_grain
return (max_phi, max_coarse_grain) | 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:
tuple[int, CoarseGrain]: The phi-value of the maximal |CoarseGrain|.
"""
max_phi = float('-inf')
max_coarse_grain = CoarseGrain((), ())
for coarse_grain in all_coarse_grains(internal_indices):
try:
subsystem = MacroSubsystem(network, state, internal_indices,
coarse_grain=coarse_grain)
except ConditionallyDependentError:
continue
phi = compute.phi(subsystem)
if (phi - max_phi) > constants.EPSILON:
max_phi = phi
max_coarse_grain = coarse_grain
return (max_phi, max_coarse_grain) | [
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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
if not do_blackbox:
return [None]
return all_blackboxes(system)
def coarse_grains(blackbox, system):
# Returns all coarse-grains to test
if not do_coarse_grain:
return [None]
if blackbox is None:
return all_coarse_grains(system)
return all_coarse_grains_for_blackbox(blackbox)
# TODO? don't consider the empty set here
# (pass `nonempty=True` to `powerset`)
for system in utils.powerset(network.node_indices):
for time_scale in time_scales:
for blackbox in blackboxes(system):
for coarse_grain in coarse_grains(blackbox, system):
try:
yield MacroSubsystem(
network, state, system,
time_scale=time_scale,
blackbox=blackbox,
coarse_grain=coarse_grain)
except (StateUnreachableError,
ConditionallyDependentError):
continue | 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
if not do_blackbox:
return [None]
return all_blackboxes(system)
def coarse_grains(blackbox, system):
# Returns all coarse-grains to test
if not do_coarse_grain:
return [None]
if blackbox is None:
return all_coarse_grains(system)
return all_coarse_grains_for_blackbox(blackbox)
# TODO? don't consider the empty set here
# (pass `nonempty=True` to `powerset`)
for system in utils.powerset(network.node_indices):
for time_scale in time_scales:
for blackbox in blackboxes(system):
for coarse_grain in coarse_grains(blackbox, system):
try:
yield MacroSubsystem(
network, state, system,
time_scale=time_scale,
blackbox=blackbox,
coarse_grain=coarse_grain)
except (StateUnreachableError,
ConditionallyDependentError):
continue | [
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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 the ``do_blackbox`` and ``do_coarse_grain`` args to specifiy whether to
use blackboxing, coarse-graining, or both. The default is to just
coarse-grain the system.
Args:
network (Network): The network of the micro-system under investigation.
state (tuple[int]): The state of the network.
do_blackbox (bool): Set to ``True`` to enable blackboxing. Defaults to
``False``.
do_coarse_grain (bool): Set to ``True`` to enable coarse-graining.
Defaults to ``True``.
time_scales (list[int]): List of all time steps over which to check
for emergence.
Returns:
MacroNetwork: The maximal macro-system generated from the
micro-system.
"""
micro_phi = compute.major_complex(network, state).phi
max_phi = float('-inf')
max_network = None
for subsystem in all_macro_systems(network, state, do_blackbox=do_blackbox,
do_coarse_grain=do_coarse_grain,
time_scales=time_scales):
phi = compute.phi(subsystem)
if (phi - max_phi) > constants.EPSILON:
max_phi = phi
max_network = MacroNetwork(
network=network,
macro_phi=phi,
micro_phi=micro_phi,
system=subsystem.micro_node_indices,
time_scale=subsystem.time_scale,
blackbox=subsystem.blackbox,
coarse_grain=subsystem.coarse_grain)
return max_network | 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 the ``do_blackbox`` and ``do_coarse_grain`` args to specifiy whether to
use blackboxing, coarse-graining, or both. The default is to just
coarse-grain the system.
Args:
network (Network): The network of the micro-system under investigation.
state (tuple[int]): The state of the network.
do_blackbox (bool): Set to ``True`` to enable blackboxing. Defaults to
``False``.
do_coarse_grain (bool): Set to ``True`` to enable coarse-graining.
Defaults to ``True``.
time_scales (list[int]): List of all time steps over which to check
for emergence.
Returns:
MacroNetwork: The maximal macro-system generated from the
micro-system.
"""
micro_phi = compute.major_complex(network, state).phi
max_phi = float('-inf')
max_network = None
for subsystem in all_macro_systems(network, state, do_blackbox=do_blackbox,
do_coarse_grain=do_coarse_grain,
time_scales=time_scales):
phi = compute.phi(subsystem)
if (phi - max_phi) > constants.EPSILON:
max_phi = phi
max_network = MacroNetwork(
network=network,
macro_phi=phi,
micro_phi=micro_phi,
system=subsystem.micro_node_indices,
time_scale=subsystem.time_scale,
blackbox=subsystem.blackbox,
coarse_grain=subsystem.coarse_grain)
return max_network | [
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Use the ``do_blackbox`` and ``do_coarse_grain`` args to specifiy whether to
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coarse-grain the system.
Args:
network (Network): The network of the micro-system under investigation.
state (tuple[int]): The state of the network.
do_blackbox (bool): Set to ``True`` to enable blackboxing. Defaults to
``False``.
do_coarse_grain (bool): Set to ``True`` to enable coarse-graining.
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time_scales (list[int]): List of all time steps over which to check
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MacroNetwork: The maximal macro-system generated from the
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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 of Sciences 110.49 (2013): 19790-19795.
Available online: `doi: 10.1073/pnas.1314922110
<http://www.pnas.org/content/110/49/19790.abstract>`_.
"""
validate.is_network(network)
sbs_tpm = convert.state_by_node2state_by_state(network.tpm)
avg_repertoire = np.mean(sbs_tpm, 0)
return np.mean([entropy(repertoire, avg_repertoire, 2.0)
for repertoire in sbs_tpm]) | 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 of Sciences 110.49 (2013): 19790-19795.
Available online: `doi: 10.1073/pnas.1314922110
<http://www.pnas.org/content/110/49/19790.abstract>`_.
"""
validate.is_network(network)
sbs_tpm = convert.state_by_node2state_by_state(network.tpm)
avg_repertoire = np.mean(sbs_tpm, 0)
return np.mean([entropy(repertoire, avg_repertoire, 2.0)
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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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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 = tpm_indices(system.tpm)
tpm = remove_singleton_dimensions(system.tpm)
# The connectivity matrix is the network's connectivity matrix, with
# cut applied, with all connections to/from external nodes severed,
# shrunk to the size of the internal nodes.
cm = system.cm[np.ix_(internal_indices, internal_indices)]
state = utils.state_of(internal_indices, system.state)
# Re-index the subsystem nodes with the external nodes removed
node_indices = reindex(internal_indices)
nodes = generate_nodes(tpm, cm, state, node_indices)
# Re-calcuate the tpm based on the results of the cut
tpm = rebuild_system_tpm(node.tpm_on for node in nodes)
return SystemAttrs(tpm, cm, node_indices, state) | 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)
internal_indices = tpm_indices(system.tpm)
tpm = remove_singleton_dimensions(system.tpm)
# The connectivity matrix is the network's connectivity matrix, with
# cut applied, with all connections to/from external nodes severed,
# shrunk to the size of the internal nodes.
cm = system.cm[np.ix_(internal_indices, internal_indices)]
state = utils.state_of(internal_indices, system.state)
# Re-index the subsystem nodes with the external nodes removed
node_indices = reindex(internal_indices)
nodes = generate_nodes(tpm, cm, state, node_indices)
# Re-calcuate the tpm based on the results of the cut
tpm = rebuild_system_tpm(node.tpm_on for node in nodes)
return SystemAttrs(tpm, cm, node_indices, state) | [
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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:
if blackbox.hidden_from(input_node, node.index):
node_tpm = marginalize_out([input_node], node_tpm)
node_tpms.append(node_tpm)
tpm = rebuild_system_tpm(node_tpms)
return system._replace(tpm=tpm) | 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
for input_node in node.inputs:
if blackbox.hidden_from(input_node, node.index):
node_tpm = marginalize_out([input_node], node_tpm)
node_tpms.append(node_tpm)
tpm = rebuild_system_tpm(node_tpms)
return system._replace(tpm=tpm) | [
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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))
return SystemAttrs(tpm, cm, system.node_indices, system.state) | 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))
return SystemAttrs(tpm, cm, system.node_indices, system.state) | [
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wmayner/pyphi | pyphi/macro.py | MacroSubsystem._blackbox_space | def _blackbox_space(self, blackbox, system):
"""Blackbox the TPM and CM in space.
Conditions the TPM on the current value of the hidden nodes. The CM is
set to universal connectivity.
.. TODO: change this ^
This shrinks the size of the TPM by the number of hidden indices; now
there is only `len(output_indices)` dimensions in the TPM and in the
state of the subsystem.
"""
tpm = marginalize_out(blackbox.hidden_indices, system.tpm)
assert blackbox.output_indices == tpm_indices(tpm)
tpm = remove_singleton_dimensions(tpm)
n = len(blackbox)
cm = np.zeros((n, n))
for i, j in itertools.product(range(n), repeat=2):
# TODO: don't pull cm from self
outputs = self.blackbox.outputs_of(i)
to = self.blackbox.partition[j]
if self.cm[np.ix_(outputs, to)].sum() > 0:
cm[i, j] = 1
state = blackbox.macro_state(system.state)
node_indices = blackbox.macro_indices
return SystemAttrs(tpm, cm, node_indices, state) | python | def _blackbox_space(self, blackbox, system):
"""Blackbox the TPM and CM in space.
Conditions the TPM on the current value of the hidden nodes. The CM is
set to universal connectivity.
.. TODO: change this ^
This shrinks the size of the TPM by the number of hidden indices; now
there is only `len(output_indices)` dimensions in the TPM and in the
state of the subsystem.
"""
tpm = marginalize_out(blackbox.hidden_indices, system.tpm)
assert blackbox.output_indices == tpm_indices(tpm)
tpm = remove_singleton_dimensions(tpm)
n = len(blackbox)
cm = np.zeros((n, n))
for i, j in itertools.product(range(n), repeat=2):
# TODO: don't pull cm from self
outputs = self.blackbox.outputs_of(i)
to = self.blackbox.partition[j]
if self.cm[np.ix_(outputs, to)].sum() > 0:
cm[i, j] = 1
state = blackbox.macro_state(system.state)
node_indices = blackbox.macro_indices
return SystemAttrs(tpm, cm, node_indices, state) | [
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.. TODO: change this ^
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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)
# Universal connectivity, for now.
n = len(node_indices)
cm = np.ones((n, n))
return SystemAttrs(tpm, cm, node_indices, state) | 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)
# Universal connectivity, for now.
n = len(node_indices)
cm = np.ones((n, n))
return SystemAttrs(tpm, cm, node_indices, state) | [
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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=True):
micro_mechanism = self.macro2micro(mechanism)
if self.cut.splits_mechanism(micro_mechanism):
yield mechanism | 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=True):
micro_mechanism = self.macro2micro(mechanism)
if self.cut.splits_mechanism(micro_mechanism):
yield mechanism | [
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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?
return MacroSubsystem(
self.network,
self.network_state,
self.micro_node_indices,
cut=cut,
time_scale=self.time_scale,
blackbox=self.blackbox,
coarse_grain=self.coarse_grain) | 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?
return MacroSubsystem(
self.network,
self.network_state,
self.micro_node_indices,
cut=cut,
time_scale=self.time_scale,
blackbox=self.blackbox,
coarse_grain=self.coarse_grain) | [
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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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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)
return tuple(sorted(micro_indices))
if self.blackbox and self.coarse_grain:
cg_micro_indices = from_partition(self.coarse_grain.partition,
macro_indices)
return from_partition(self.blackbox.partition,
reindex(cg_micro_indices))
elif self.blackbox:
return from_partition(self.blackbox.partition, macro_indices)
elif self.coarse_grain:
return from_partition(self.coarse_grain.partition, macro_indices)
return 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)
return tuple(sorted(micro_indices))
if self.blackbox and self.coarse_grain:
cg_micro_indices = from_partition(self.coarse_grain.partition,
macro_indices)
return from_partition(self.blackbox.partition,
reindex(cg_micro_indices))
elif self.blackbox:
return from_partition(self.blackbox.partition, macro_indices)
elif self.coarse_grain:
return from_partition(self.coarse_grain.partition, macro_indices)
return macro_indices | [
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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.macro2micro(macro_indices)
).intersection(self.blackbox.output_indices))) | 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(
self.macro2micro(macro_indices)
).intersection(self.blackbox.output_indices))) | [
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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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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.
Returns:
CoarseGrain: A new |CoarseGrain| object, indexed from ``0..n``.
Example:
>>> partition = ((1, 2),)
>>> grouping = (((0,), (1, 2)),)
>>> coarse_grain = CoarseGrain(partition, grouping)
>>> coarse_grain.reindex()
CoarseGrain(partition=((0, 1),), grouping=(((0,), (1, 2)),))
"""
_map = dict(zip(self.micro_indices, reindex(self.micro_indices)))
partition = tuple(
tuple(_map[index] for index in group)
for group in self.partition
)
return CoarseGrain(partition, self.grouping) | 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.
Returns:
CoarseGrain: A new |CoarseGrain| object, indexed from ``0..n``.
Example:
>>> partition = ((1, 2),)
>>> grouping = (((0,), (1, 2)),)
>>> coarse_grain = CoarseGrain(partition, grouping)
>>> coarse_grain.reindex()
CoarseGrain(partition=((0, 1),), grouping=(((0,), (1, 2)),))
"""
_map = dict(zip(self.micro_indices, reindex(self.micro_indices)))
partition = tuple(
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return CoarseGrain(partition, self.grouping) | [
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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
by this coarse-graining.
Example:
>>> coarse_grain = CoarseGrain(((1, 2),), (((0,), (1, 2)),))
>>> coarse_grain.macro_state((0, 0))
(0,)
>>> coarse_grain.macro_state((1, 0))
(1,)
>>> coarse_grain.macro_state((1, 1))
(1,)
"""
assert len(micro_state) == len(self.micro_indices)
# TODO: only reindex if this coarse grain is not already from 0..n?
# make_mapping calls this in a tight loop so it might be more efficient
# to reindex conditionally.
reindexed = self.reindex()
micro_state = np.array(micro_state)
return tuple(0 if sum(micro_state[list(reindexed.partition[i])])
in self.grouping[i][0] else 1
for i in self.macro_indices) | 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
by this coarse-graining.
Example:
>>> coarse_grain = CoarseGrain(((1, 2),), (((0,), (1, 2)),))
>>> coarse_grain.macro_state((0, 0))
(0,)
>>> coarse_grain.macro_state((1, 0))
(1,)
>>> coarse_grain.macro_state((1, 1))
(1,)
"""
assert len(micro_state) == len(self.micro_indices)
# TODO: only reindex if this coarse grain is not already from 0..n?
# make_mapping calls this in a tight loop so it might be more efficient
# to reindex conditionally.
reindexed = self.reindex()
micro_state = np.array(micro_state)
return tuple(0 if sum(micro_state[list(reindexed.partition[i])])
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] | 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
by this coarse-graining.
Example:
>>> coarse_grain = CoarseGrain(((1, 2),), (((0,), (1, 2)),))
>>> coarse_grain.macro_state((0, 0))
(0,)
>>> coarse_grain.macro_state((1, 0))
(1,)
>>> coarse_grain.macro_state((1, 1))
(1,) | [
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] | deeca69a084d782a6fde7bf26f59e93b593c5d77 | https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L466-L496 | train |
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 corresponding to the
|ith| micro-state.
"""
micro_states = utils.all_states(len(self.micro_indices))
# Find the corresponding macro-state for each micro-state.
# The i-th entry in the mapping is the macro-state corresponding to the
# i-th micro-state.
mapping = [convert.state2le_index(self.macro_state(micro_state))
for micro_state in micro_states]
return np.array(mapping) | 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
|ith| entry in the mapping is the macro-state corresponding to the
|ith| micro-state.
"""
micro_states = utils.all_states(len(self.micro_indices))
# Find the corresponding macro-state for each micro-state.
# The i-th entry in the mapping is the macro-state corresponding to the
# i-th micro-state.
mapping = [convert.state2le_index(self.macro_state(micro_state))
for micro_state in micro_states]
return np.array(mapping) | [
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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.tpm(state_by_state_micro_tpm, check_independence=False)
mapping = self.make_mapping()
num_macro_states = 2 ** len(self.macro_indices)
macro_tpm = np.zeros((num_macro_states, num_macro_states))
micro_states = range(2 ** len(self.micro_indices))
micro_state_transitions = itertools.product(micro_states, repeat=2)
# For every possible micro-state transition, get the corresponding
# previous and next macro-state using the mapping and add that
# probability to the state-by-state macro TPM.
for previous_state, current_state in micro_state_transitions:
macro_tpm[mapping[previous_state], mapping[current_state]] += (
state_by_state_micro_tpm[previous_state, current_state])
# Re-normalize each row because we're going from larger to smaller TPM
return np.array([distribution.normalize(row) for row in macro_tpm]) | 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.
"""
validate.tpm(state_by_state_micro_tpm, check_independence=False)
mapping = self.make_mapping()
num_macro_states = 2 ** len(self.macro_indices)
macro_tpm = np.zeros((num_macro_states, num_macro_states))
micro_states = range(2 ** len(self.micro_indices))
micro_state_transitions = itertools.product(micro_states, repeat=2)
# For every possible micro-state transition, get the corresponding
# previous and next macro-state using the mapping and add that
# probability to the state-by-state macro TPM.
for previous_state, current_state in micro_state_transitions:
macro_tpm[mapping[previous_state], mapping[current_state]] += (
state_by_state_micro_tpm[previous_state, current_state])
# Re-normalize each row because we're going from larger to smaller TPM
return np.array([distribution.normalize(row) for row in macro_tpm]) | [
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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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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
conditionally independent.
Raises:
ConditionallyDependentError: If ``check_independence`` is ``True``
and the macro TPM is not conditionally independent.
Returns:
np.ndarray: The state-by-node TPM of the macro-system.
"""
if not is_state_by_state(micro_tpm):
micro_tpm = convert.state_by_node2state_by_state(micro_tpm)
macro_tpm = self.macro_tpm_sbs(micro_tpm)
if check_independence:
validate.conditionally_independent(macro_tpm)
return convert.state_by_state2state_by_node(macro_tpm) | 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:
ConditionallyDependentError: If ``check_independence`` is ``True``
and the macro TPM is not conditionally independent.
Returns:
np.ndarray: The state-by-node TPM of the macro-system.
"""
if not is_state_by_state(micro_tpm):
micro_tpm = convert.state_by_node2state_by_state(micro_tpm)
macro_tpm = self.macro_tpm_sbs(micro_tpm)
if check_independence:
validate.conditionally_independent(macro_tpm)
return convert.state_by_state2state_by_node(macro_tpm) | [
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Raises:
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Returns:
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Subsets and Splits