neurolang.neurolang module¶

class neurolang.neurolang.ExplicitVBR(voxels, affine_matrix, image_dim=None, prebuild_tree=False)¶

Bases: VolumetricBrainRegion

Attributes:

aabb_tree
bounding_box
center
width

Methods

`to_ijk`(affine)	return ijk voxels coordinates corresponding to the affine matrix transform
`to_xyz`([affine])	return world coordinates of the region corresponding to the affine matrix transform

build_tree
from_spatial_image_label
generate_bounding_box
remove_empty_bounding_boxes
spatial_image
to_explicit_vbr
to_ijk_set
to_xyz_set

property aabb_tree¶

property bounding_box¶

build_tree()¶

generate_bounding_box(voxels_ijk)¶

spatial_image(out=None, value=1)¶

to_ijk(affine)¶: return ijk voxels coordinates corresponding to the affine matrix transform

to_xyz(affine=None)¶: return world coordinates of the region corresponding to the affine matrix transform

class neurolang.neurolang.ExplicitVBROverlay(voxels, affine_matrix, overlay, image_dim=None, prebuild_tree=False)¶

Bases: ExplicitVBR

Attributes:

aabb_tree
bounding_box
center
width

Methods

`to_ijk`(affine)	return ijk voxels coordinates corresponding to the affine matrix transform
`to_xyz`([affine])	return world coordinates of the region corresponding to the affine matrix transform

build_tree
from_spatial_image_label
generate_bounding_box
remove_empty_bounding_boxes
spatial_image
to_explicit_vbr
to_ijk_set
to_xyz_set

spatial_image(out=None, background_value=0)¶

class neurolang.neurolang.NeurolangDL(program_ir=None)¶

Bases: QueryBuilderDatalog

Deterministic Datalog Frontend for Neurolang. QueryBuilderDatalog with a RegionFrontendDatalogSolver program intermediate representation

Attributes:

current_program: Returns the list of expressions that have currently been
environment: Dynamic context that can be used to create symbols to write a Datalog program.
functions: Returns the list of symbols corresponding to callables
region_names: Returns the list of symbol names with Region type
region_set_names: Returns the list of symbol names with set_type
scope: Dynamic context that can be used to create symbols to write a Datalog program.
symbol_table: Projector to the program_ir’s symbol_table
symbols: Iterator through the symbol’s names
types: Returns a list of the types of the symbols currently

Methods

`add_atlas_set`(name, atlas_labels, spatial_image)	Creates an atlas set: 1- for each region specified by a label and name in atlas_labels, creates associated ExplicitVBR and symbols Tuple[region_name: str, Region] 2- groups regions in an AbstractSet[Tuple[str, Region]] symbol with specified name
`add_constraint`(antecedent, consequent)	Creates an right implication of the consequent by the antecedent and adds the rule to the current program: antecedent -> consequent
`add_region`(region[, name])	Adds region fe.Symbol to symbol_table
`add_region_set`(region_set[, name])	Creates an AbstractSet fe.Symbol containing the elements specified in the iterable with a List[Tuple[Region]] format
`add_symbol`(value[, name, type_])	Creates a symbol with given value and adds it to the current symbol_table.
`add_tuple_set`(iterable[, type_, name])	Creates an AbstractSet fe.Symbol containing the elements specified in the iterable with a List[Tuple[Any, ...]] format (see examples).
`all`(quantified_variable, body)	Universal predicate on the body.
`create_region`(spatial_image[, label, ...])	Creates an ExplicitVBR out of the voxels of a dense spatial_image with specified label
`del_symbol`(name)	Deletes the symbol with parameter name from the symbol_table
`execute_datalog_program`(code)	Execute a Datalog program in classical syntax.
`exists`(quantified_variable, body)	Existential predicate on the body.
`get_symbol`(symbol_name)	Retrieves symbol via its name, either providing a fe.Expression with the correct name or the name itself
`load_neurosynth_mni_peaks_reported`(data_dir)	Load the coordinates for the peaks reported by studies in the Neurosynth dataset.
`load_neurosynth_study_ids`(data_dir[, name, ...])	Load all study ids (PMIDs) that are part of the Neurosynth database.
`load_neurosynth_term_study_associations`(data_dir)	Load TF-IDF values for each (term, study) association within the Neurosynth database.
`make_implicit_regions_explicit`(affine, dim)	Raises NotImplementedError for now
`new_region_symbol`([name])	Returns symbol with type Region
`new_symbol`([type_, name])	Creates a symbol and associated expression, optionally specifying it's type and/or name
`predicate_parameter_names`(predicate_name)	Get the names of the parameters for the given predicate
`query`(*args)	Performs an inferential query on the database.
`reset_program`()	Clears current symbol table
`solve_all`()	Returns a dictionary of "predicate_name": "Content" for all elements in the solution of the Datalog program.
`sphere`(center, radius[, name])	Creates a Region symbol associated with the spherical volume described by its center and volume

magic_sets_rewrite_program

class neurolang.neurolang.NeurolangPDL(chase_class: ~typing.Type[~neurolang.datalog.chase.Chase] = <class 'neurolang.datalog.chase.Chase'>, probabilistic_solvers: ~typing.Tuple[~typing.Callable] = (<function solve_succ_query>, <function solve_succ_query>, <function solve_succ_query_sdd_direct>), probabilistic_marg_solvers: ~typing.Tuple[~typing.Callable] = (<function solve_marg_query>, <function solve_marg_query>, <function solve_marg_query>), check_qbased_pfact_tuple_unicity=False)¶

Bases: QueryBuilderDatalog

Complements QueryBuilderDatalog class with probabilistic capabilities 1- add extensional probabilistic facts and choices 2- sove probabilistic queries

Attributes:

current_program: Returns the list of Front End Expressions that have
environment: Dynamic context that can be used to create symbols to write a Datalog program.
functions: Returns the list of symbols corresponding to callables
region_names: Returns the list of symbol names with Region type
region_set_names: Returns the list of symbol names with set_type
scope: Dynamic context that can be used to create symbols to write a Datalog program.
symbol_table: Projector to the program_ir’s symbol_table
symbols: Iterator through the symbol’s names
types: Returns a list of the types of the symbols currently

Methods

`add_atlas_set`(name, atlas_labels, spatial_image)	Creates an atlas set: 1- for each region specified by a label and name in atlas_labels, creates associated ExplicitVBR and symbols Tuple[region_name: str, Region] 2- groups regions in an AbstractSet[Tuple[str, Region]] symbol with specified name
`add_constraint`(antecedent, consequent)	Creates an right implication of the consequent by the antecedent and adds the rule to the current program: antecedent -> consequent
`add_probabilistic_choice_from_tuples`(iterable)	Add probabilistic choice from tuples whose first element contains the probability label attached to that tuple.
`add_probabilistic_facts_from_tuples`(iterable)	Add probabilistic facts from tuples whose first element contains the probability label attached to that tuple.
`add_region`(region[, name])	Adds region fe.Symbol to symbol_table
`add_region_set`(region_set[, name])	Creates an AbstractSet fe.Symbol containing the elements specified in the iterable with a List[Tuple[Region]] format
`add_symbol`(value[, name, type_])	Creates a symbol with given value and adds it to the current symbol_table.
`add_tuple_set`(iterable[, type_, name])	Creates an AbstractSet fe.Symbol containing the elements specified in the iterable with a List[Tuple[Any, ...]] format (see examples).
`add_uniform_probabilistic_choice_over_set`(...)	Add uniform probabilistic choice over values in the iterable.
`all`(quantified_variable, body)	Universal predicate on the body.
`create_region`(spatial_image[, label, ...])	Creates an ExplicitVBR out of the voxels of a dense spatial_image with specified label
`del_symbol`(name)	Deletes the symbol with parameter name from the symbol_table
`execute_datalog_program`(code)	Execute a Datalog program in classical syntax.
`exists`(quantified_variable, body)	Existential predicate on the body.
`get_symbol`(symbol_name)	Retrieves symbol via its name, either providing a fe.Expression with the correct name or the name itself
`load_neurosynth_mni_peaks_reported`(data_dir)	Load the coordinates for the peaks reported by studies in the Neurosynth dataset.
`load_neurosynth_study_ids`(data_dir[, name, ...])	Load all study ids (PMIDs) that are part of the Neurosynth database.
`load_neurosynth_term_study_associations`(data_dir)	Load TF-IDF values for each (term, study) association within the Neurosynth database.
`load_ontology`(paths[, connector_symbol_name])	Loads and parses ontology stored at the specified paths, and store them into attributes
`make_implicit_regions_explicit`(affine, dim)	Raises NotImplementedError for now
`new_region_symbol`([name])	Returns symbol with type Region
`new_symbol`([type_, name])	Creates a symbol and associated expression, optionally specifying it's type and/or name
`predicate_parameter_names`(predicate_name)	Get the names of the parameters for the given predicate
`query`(*args)	Performs an inferential query on the database.
`reset_program`()	Clears current symbol table
`solve_all`()	Returns a dictionary of "predicate_name": "Content" for all elements in the solution of the Datalog program.
`sphere`(center, radius[, name])	Creates a Region symbol associated with the spherical volume described by its center and volume

magic_sets_rewrite_program

add_probabilistic_choice_from_tuples(iterable: ~typing.Iterable[~typing.Tuple[~typing.Any, ...]], type_: ~typing.Type = <class 'neurolang.type_system.Unknown'>, name: str | None = None) → Symbol¶

Add probabilistic choice from tuples whose first element contains the probability label attached to that tuple. In the tuple (p, a, b, …), p is the float probability of tuple (a, b, …) to be True and all remaining tuples to be False in any possible world.

Note that, contrary to a list of probabilistic facts, this represents a choice among possible values for the predicate, meaning that tuples in the set are mutually exclusive. See example for details.

Parameters:

iterableIterable[Tuple[Any, …]]: the first float number represents the probability of the tuple constituted of the remaining elements Note that the float probabilities must sum to 1.
type_Type, optional: type for resulting AbstractSet if None will be inferred from the data, by default ir.Unknown
nameOptional[str], optional: name for the resulting fe.Symbol, if None will be fresh, by default None

Returns:

fe.Symbol: see description

Raises:

DistributionDoesNotSumToOneError: if float probabilities do not sum to 1.

add_probabilistic_facts_from_tuples(iterable: ~typing.Iterable[~typing.Tuple[~typing.Any, ...]], type_: ~typing.Type = <class 'neurolang.type_system.Unknown'>, name: str | None = None) → Symbol¶

Add probabilistic facts from tuples whose first element contains the probability label attached to that tuple. In the tuple (p, a, b, …), p is the float probability of tuple (a, b, …) to be True in any possible world.

Note that each tuple from the iterable is independant from the others, meaning that multiple tuples can be True in the same possible world, contrary to a probabilistic choice. See example for details.

Parameters:

iterableIterable[Tuple[Any, …]]: the first float number represents the probability of the tuple constituted of the remaining elements
type_Type, optional: type for resulting AbstractSet if None will be inferred from the data, by default ir.Unknown
nameOptional[str], optional: name for the resulting fe.Symbol, if None will be fresh, by default None

Returns:

fe.Symbol: see description

add_uniform_probabilistic_choice_over_set(iterable: ~typing.Iterable[~typing.Tuple[~typing.Any, ...]], type_: ~typing.Type = <class 'neurolang.type_system.Unknown'>, name: str | None = None) → Symbol¶

Add uniform probabilistic choice over values in the iterable.

Every tuple in the iterable will be assigned the same probability to be True, with all remaning tuples False, in any possible world.

Note that, contrary to a list of probabilistic facts, this represents a choice among possible values for the predicate, meaning that tuples in the set are mutually exclusive.

See example for details.

Parameters:

iterableIterable[Tuple[Any, …]]: typically List[Tuple[Any, …]], other types of Iterable will be cast as lists
type_Type, optional: type for resulting AbstractSet if None will be inferred from the data, by default ir.Unknown
nameOptional[str], optional: name for the resulting fe.Symbol, if None will be fresh, by default None

Returns:

fe.Symbol: see description

property current_program: List[Expression]¶

Returns the list of Front End Expressions that have currently been declared in the program, or through the program’s constraints

Returns:

List[fe.Expression]: see description

load_ontology(paths: str | List[str], connector_symbol_name=None) → None¶

Loads and parses ontology stored at the specified paths, and store them into attributes

Parameters:

pathstyping.Union[str, List[str]]: where the ontology files are stored
connector_symbol_namestr: name to be used in the connector_symbol. if None, the name is randomly generated

Returns:

Expression: the new connector_symbol, to be used in the query program.

solve_all() → Dict[str, NamedRelationalAlgebraFrozenSet]¶

Returns a dictionary of “predicate_name”: “Content” for all elements in the solution of the Datalog program. Typically, probabilities are abstracted and processed similar to symbols, though of different nature (see examples)

Returns:

Dict[str, NamedRelationalAlgebraFrozenSet]: extensional and intentional facts that have been derived through the current program, optionally with probabilities