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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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Return the irreducibility analysis for the effect MIP. Alias for |find_mip()| with ``direction`` set to |EFFECT|.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/subsystem.py#L607-L612
train
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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Return the |small_phi| of the cause MIP. This is the distance between the unpartitioned cause repertoire and the MIP cause repertoire.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/subsystem.py#L614-L621
train
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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Return the |small_phi| of the effect MIP. This is the distance between the unpartitioned effect repertoire and the MIP cause repertoire.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/subsystem.py#L623-L630
train
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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Return the |small_phi| of a mechanism over a purview.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/subsystem.py#L632-L635
train
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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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|.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/subsystem.py#L664-L693
train
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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Return the |small_phi_max| of a mechanism. This is the maximum of |small_phi| taken over all possible purviews.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/subsystem.py#L709-L714
train
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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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.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/subsystem.py#L720-L742
train
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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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/subsystem.py#L745-L785
train
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, account=(), partitioned_account=() )
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Return an |AcSystemIrreducibilityAnalysis| with zero |big_alpha| and empty accounts.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/actual_causation.py#L354-L364
train
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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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/actual_causation.py#L213-L216
train
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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The set of irreducible causes in this |Account|.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/actual_causation.py#L248-L251
train
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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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/actual_causation.py#L254-L257
train
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 invaluable since the Python interpreter calls `repr` to represent all objects in the shell. Since PyPhi is often used in the interpreter we want 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 invaluable since the Python interpreter calls `repr` to represent all objects in the shell. Since PyPhi is often used in the interpreter we want 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`')
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/fmt.py#L47-L76
train
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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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
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/fmt.py#L79-L102
train
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') return '\n'.join(' {} '.format(l) for l in lines)
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r"""Add a margin to both ends of each line in the string. Example: >>> margin('line1\nline2') ' line1 \n line2 '
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/fmt.py#L105-L113
train
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) + 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
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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r"""Wrap a chunk of text in a box. Example: >>> print(box('line1\nline2')) ┌───────┐ │ line1 │ │ line2 │ └───────┘
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/fmt.py#L119-L139
train
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): 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'
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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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>
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/fmt.py#L142-L167
train
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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Center a head over a block of text. The width of the text is the width of the longest line of the text.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/fmt.py#L170-L192
train
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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Get the labels for a tuple of mechanism indices.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/fmt.py#L195-L199
train
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 ( str(nice) if (abs(fraction - nice) < constants.EPSILON and nice.denominator in NICE_DENOMINATORS) 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 ( str(nice) if (abs(fraction - nice) < constants.EPSILON and nice.denominator in NICE_DENOMINATORS) else formatted )
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Format a number. It will be printed as a fraction if the denominator isn't too big and as a decimal otherwise.
[ "Format", "a", "number", "." ]
deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/fmt.py#L202-L219
train
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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Format a |Part|. The returned string looks like:: 0,1 ─── ∅
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/fmt.py#L227-L249
train
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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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.
[ "Format", "a", "|Bipartition|", "." ]
deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/fmt.py#L252-L283
train
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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Format a |CauseEffectStructure|.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/fmt.py#L286-L298
train
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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Format a |Concept|.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/fmt.py#L301-L318
train
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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Format a |RepertoireIrreducibilityAnalysis|.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/fmt.py#L321-L360
train
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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Format a |Cut|.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/fmt.py#L363-L368
train
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, '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))
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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Format a |SystemIrreducibilityAnalysis|.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/fmt.py#L376-L398
train
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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Format a repertoire.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/fmt.py#L401-L426
train
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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Format an AcRepertoireIrreducibilityAnalysis.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/fmt.py#L429-L444
train
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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Format an Account or a DirectedAccount.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/fmt.py#L447-L461
train
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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Format a AcSystemIrreducibilityAnalysis.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/fmt.py#L464-L485
train
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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Format a |Transition|.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/fmt.py#L488-L493
train
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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Validate that the given direction is one of the allowed constants. If ``allow_bi`` is ``True`` then ``Direction.BIDIRECTIONAL`` is acceptable.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/validate.py#L18-L31
train
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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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.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/validate.py#L34-L71
train
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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Validate that the TPM is conditionally independent.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/validate.py#L74-L90
train
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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Validate the given connectivity matrix.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/validate.py#L93-L105
train
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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Validate that there is a label for each node.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/validate.py#L108-L115
train
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 " "number of nodes in the network.") return True
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Validate a |Network|. Checks the TPM and connectivity matrix.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/validate.py#L118-L128
train
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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Check that the state is the given size.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/validate.py#L147-L153
train
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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Return whether a state can be reached according to the network's TPM.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/validate.py#L156-L167
train
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 ' '{}'.format(cut, node_indices))
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Check that the cut is for only the given nodes.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/validate.py#L170-L174
train
wmayner/pyphi
pyphi/validate.py
subsystem
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: 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: state_reachable(s) return True
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Validate a |Subsystem|. Checks its state and cut.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/validate.py#L177-L186
train
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 ' 'macro-elements'.format(node)) 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( 'Micro-element {} may not be partitioned into multiple ' 'macro-elements'.format(node)) nodes.add(node)
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Validate a partition - used by blackboxes and coarse grains.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/validate.py#L195-L204
train
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): 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 ' '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 ' 'elements in state grouping {1}'.format( part, group))
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Validate a macro coarse-graining.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/validate.py#L207-L220
train
wmayner/pyphi
pyphi/validate.py
blackbox
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) for part in blackbox.partition: if not set(part) & set(blackbox.output_indices): raise ValueError( '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) for part in blackbox.partition: if not set(part) & set(blackbox.output_indices): raise ValueError( 'Every blackbox must have an output - {} does not'.format( part))
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Validate a macro blackboxing.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/validate.py#L223-L235
train
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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Validate that a coarse-graining properly combines the outputs of a blackboxing.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/validate.py#L238-L258
train
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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Decorator for registering a function with PyPhi. Args: name (string): The name of the function
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/registry.py#L23-L32
train
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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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.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/compute/subsystem.py#L66-L99
train
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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Return the conceptual information for a |Subsystem|. This is the distance from the subsystem's |CauseEffectStructure| to the null concept.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/compute/subsystem.py#L102-L110
train
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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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.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/compute/subsystem.py#L113-L150
train
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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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.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/compute/subsystem.py#L184-L201
train
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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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.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/compute/subsystem.py#L214-L292
train
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, config.MEASURE, config.PRECISION, config.VALIDATE_SUBSYSTEM_STATES, config.SINGLE_MICRO_NODES_WITH_SELFLOOPS_HAVE_PHI, config.PARTITION_TYPE, )
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, config.CUT_ONE_APPROXIMATION, config.MEASURE, config.PRECISION, config.VALIDATE_SUBSYSTEM_STATES, config.SINGLE_MICRO_NODES_WITH_SELFLOOPS_HAVE_PHI, config.PARTITION_TYPE, )
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The cache key of the subsystem. This includes the native hash of the subsystem and all configuration values which change the results of ``sia``.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/compute/subsystem.py#L297-L312
train
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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Generator over all concept-syle cuts for these nodes.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/compute/subsystem.py#L390-L393
train
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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Calculate a concept-style SystemIrreducibilityAnalysisCause or SystemIrreducibilityAnalysisEffect.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/compute/subsystem.py#L396-L410
train
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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Compute a concept-style SystemIrreducibilityAnalysis
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/compute/subsystem.py#L447-L456
train
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, purviews=purviews, 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, purviews=purviews, 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
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Compute a |Concept| for a mechanism, in this |Subsystem| with the provided purviews.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/compute/subsystem.py#L39-L52
train
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) return concepts
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Save all concepts with non-zero |small_phi| to the |CauseEffectStructure|.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/compute/subsystem.py#L54-L62
train
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 elif new_sia < min_sia: return new_sia return min_sia
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Check if the new SIA has smaller |big_phi| than the standing result.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/compute/subsystem.py#L170-L181
train
wmayner/pyphi
pyphi/compute/subsystem.py
ConceptStyleSystem.concept
def concept(self, mechanism, purviews=False, cause_purviews=False, effect_purviews=False): """Compute a concept, using the appropriate system for each side of the cut. """ cause = self.cause_system.mic( mechanism, purviews=(cause_purviews or purviews)) effect = self.effect_system.mie( mechanism, purviews=(effect_purviews or purviews)) 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( mechanism, purviews=(effect_purviews or purviews)) return Concept(mechanism=mechanism, cause=cause, effect=effect, subsystem=self)
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/compute/subsystem.py#L372-L384
train
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) else: indices = map(int, nodes) return tuple(sorted(set(indices)))
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Return the nodes indices for nodes, where ``nodes`` is either already integer indices or node labels.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/labels.py#L82-L93
train
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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Return a |SystemIrreducibilityAnalysis| with zero |big_phi| and empty cause-effect structures. This is the analysis result for a reducible subsystem.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/subsystem.py#L179-L189
train
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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The labeled mechanism of each concept.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/subsystem.py#L69-L72
train
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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Order the mechanism and purview in time. If the direction is ``CAUSE``, then the purview is at |t-1| and the mechanism is at time |t|. If the direction is ``EFFECT``, then the mechanism is at time |t| and the purview is at |t+1|.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/direction.py#L31-L44
train
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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Method decorator to return ``NotImplemented`` if the args of the wrapped method are of different types. When wrapping a rich model comparison method this will delegate (reflect) the comparison to the right-hand-side object, or fallback by passing it up the inheritance tree.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/cmp.py#L19-L32
train
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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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/models/cmp.py#L108-L133
train
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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Time an EMD command with the given data as arguments
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/benchmarks/time_emd.py#L320-L335
train
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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Return the marginal probability that the node is OFF.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/distribution.py#L50-L55
train
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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Get the marginal distribution for a node.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/distribution.py#L58-L62
train
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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Check whether the repertoire is independent.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/distribution.py#L65-L78
train
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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The purview of the repertoire. Args: repertoire (np.ndarray): A repertoire Returns: tuple[int]: The purview that the repertoire was computed over.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/distribution.py#L81-L93
train
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 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)
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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.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/distribution.py#L130-L151
train
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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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/distribution.py#L155-L172
train
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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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L61-L88
train
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( 'Partition lists not yet available for system with {} ' 'nodes or more'.format(_NUM_PRECOMPUTED_PARTITION_LISTS))
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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]]]
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L665-L684
train
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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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.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L687-L704
train
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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Generator over all possible |CoarseGrains| of these indices. Args: indices (tuple[int]): Node indices to coarse grain. Yields: CoarseGrain: The next |CoarseGrain| for ``indices``.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L734-L745
train
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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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.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L748-L761
train
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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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L764-L782
train
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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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|.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L827-L853
train
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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Generator over all possible macro-systems for the network.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L857-L891
train
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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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L894-L940
train
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) for repertoire in sbs_tpm])
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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>`_.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L969-L989
train
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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Return the labels for macro nodes.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L99-L103
train
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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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.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L199-L225
train
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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Noise connections from hidden elements to other boxes.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L228-L242
train
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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Black box the CM and TPM over the given time_scale.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L245-L255
train
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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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L257-L286
train
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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Spatially coarse-grain the TPM and CM.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L289-L301
train
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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The mechanisms of this system that are currently cut. Note that although ``cut_indices`` returns micro indices, this returns macro mechanisms. Yields: tuple[int]
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L313-L325
train
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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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|.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L335-L352
train
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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Override Subsystem implementation using Network-level indices.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L354-L358
train
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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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L360-L378
train
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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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L380-L389
train
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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Indices of micro elements represented in this coarse-graining.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L430-L432
train
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( tuple(_map[index] for index in group) for group in self.partition ) return CoarseGrain(partition, self.grouping)
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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)),))
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L442-L464
train
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])]) in self.grouping[i][0] else 1 for i in self.macro_indices)
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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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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L498-L514
train
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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Create a state-by-state coarse-grained macro TPM. Args: micro_tpm (nd.array): The state-by-state TPM of the micro-system. Returns: np.ndarray: The state-by-state TPM of the macro-system.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L516-L543
train
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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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.
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deeca69a084d782a6fde7bf26f59e93b593c5d77
https://github.com/wmayner/pyphi/blob/deeca69a084d782a6fde7bf26f59e93b593c5d77/pyphi/macro.py#L545-L568
train