Welcome to PyOpenWorm’s documentation!

Our main README is available online on Github. [1] This documentation contains additional materials beyond what is covered there.

[1]	http://github.com/openworm/PyOpenWorm

Contents:

PyOpenWorm

PyOpenWorm package

PyOpenWorm

OpenWorm Unified Data Abstract Layer.

An introduction to PyOpenWorm can be found in the README on our Github page.

Most statements correspond to some action on the database. Some of these actions may be complex, but intuitively a.B(), the Query form, will query against the database for the value or values that are related to a through B; on the other hand, a.B(c), the Update form, will add a statement to the database that a relates to c through B. For the Update form, a Statement object describing the relationship stated is returned as a side-effect of the update.

The Update form can also be accessed through the set() method of a Property and the Query form through the get() method like:

a.B.set(c)

and:

a.B.get()

The get() method also allows for parameterizing the query in ways specific to the Property.

PyOpenWorm.loadConfig(f)

Load configuration for the module.

PyOpenWorm.loadData(conf, data='OpenWormData/WormData.n3', dataFormat='n3', skipIfNewer=False)

Load data into the underlying database of this library.

XXX: This is only guaranteed to work with the ZODB database.

Parameters:

• data – (Optional) Specify the file to load into the library
• dataFormat – (Optional) Specify the file format to load into the library. Currently n3 is supported
• skipIfNewer – (Optional) Skips loading of data if the database file is newer than the data to be loaded in. This is determined by the modified time on the main database file compared to the modified time on the data file.

PyOpenWorm.disconnect(c=False)

Close the database.

PyOpenWorm.connect(configFile=False, conf=None, do_logging=False, data=False, dataFormat='n3')

Load desired configuration and open the database

Parameters:

• configFile – (Optional) The configuration file for PyOpenWorm
• conf – (Optional) a configuration object for the connection. Takes precedence over configFile
• do_logging – (Optional) If true, turn on debug level logging
• data – (Optional) specify the file to load into the library
• dataFormat – (Optional) file format of data. Currently n3 is supported

PyOpenWorm.config(key=None)

Gets the main configuration for the whole PyOpenWorm library.

Returns:the instance of the Configure class currently operating.

Subpackages

PyOpenWorm.data_trans package

Data translators

Some DataSource and DataTranslator types. Some deal with generic file types (e.g., comma-separated values) while others are specific to the format of a kind of file housed in PyOpenWorm.

Submodules

PyOpenWorm.data_trans.bibtex module

class PyOpenWorm.data_trans.bibtex.BibTexDataSource(bibtex_file_name, **kwargs)

Bases: PyOpenWorm.data_trans.local_file_ds.LocalFileDataSource

File name : DatatypeProperty

Attribute: file_name

Torrent file name : DatatypeProperty

Attribute: torrent_file_name

MD5 hash : DatatypeProperty

Attribute: md5

SHA-256 hash : DatatypeProperty

Attribute: sha256

SHA-512 hash : DatatypeProperty

Attribute: sha512

Input source : ObjectProperty

Attribute: source

The data source that was translated into this one

Translation : ObjectProperty

Attribute: translation

Information about the translation process that created this object

Description : DatatypeProperty

Attribute: description

Free-text describing the data source

class PyOpenWorm.data_trans.bibtex.BibTexDataTranslator(**kwargs)

Bases: PyOpenWorm.datasource.DataTranslator

Input type(s): PyOpenWorm.data_trans.bibtex.BibTexDataSource

Output type(s): PyOpenWorm.data_trans.bibtex.EvidenceDataSource

URI: None

input_type

alias of BibTexDataSource

output_type

alias of EvidenceDataSource

translate()

Notionally, this method takes a data source, which is translated into some other data source. There doesn’t necessarily need to be an input data source.

class PyOpenWorm.data_trans.bibtex.EvidenceDataSource(*args, **kwargs)

Bases: PyOpenWorm.datasource.DataSource

Context : ObjectProperty

Attribute: evidence_context

The context

Input source : ObjectProperty

Attribute: source

The data source that was translated into this one

Translation : ObjectProperty

Attribute: translation

Information about the translation process that created this object

Description : DatatypeProperty

Attribute: description

Free-text describing the data source

PyOpenWorm.data_trans.common_data module

PyOpenWorm.data_trans.connections module

class PyOpenWorm.data_trans.connections.ConnectomeCSVDataSource(*args, **kwargs)

Bases: PyOpenWorm.data_trans.csv_ds.CSVDataSource

A CSV data source whose CSV file describes a neural connectome

Basically, this is just a marker type to indicate what’s described in the CSV – there’s no consistent schema

class PyOpenWorm.data_trans.connections.NeuronConnectomeCSVTranslation(**kwargs)

Bases: PyOpenWorm.datasource.GenericTranslation

class PyOpenWorm.data_trans.connections.NeuronConnectomeCSVTranslator(**kwargs)

Bases: PyOpenWorm.data_trans.csv_ds.CSVDataTranslator

Input type(s): PyOpenWorm.data_trans.connections.ConnectomeCSVDataSource, PyOpenWorm.data_trans.data_with_evidence_ds.DataWithEvidenceDataSource

Output type(s): PyOpenWorm.data_trans.data_with_evidence_ds.DataWithEvidenceDataSource

URI: http://openworm.org/entities/translators/NeuronConnectomeCSVTranslator

output_type

alias of PyOpenWorm.data_trans.data_with_evidence_ds.DataWithEvidenceDataSource

translation_type

alias of NeuronConnectomeCSVTranslation

make_translation(sources)

It’s intended that implementations of DataTranslator will override this method to make custom Translations according with how different arguments to Translate are (or are not) distinguished.

The actual properties of a Translation subclass must be defined within the ‘translate’ method

translate(data_source, neurons_source, muscles_source)

Notionally, this method takes a data source, which is translated into some other data source. There doesn’t necessarily need to be an input data source.

class PyOpenWorm.data_trans.connections.NeuronConnectomeSynapseClassTranslation(**kwargs)

Bases: PyOpenWorm.datasource.GenericTranslation

class PyOpenWorm.data_trans.connections.NeuronConnectomeSynapseClassTranslator(**kwargs)

Bases: PyOpenWorm.data_trans.csv_ds.CSVDataTranslator

Adds synapse classes to existing connections

output_type

alias of PyOpenWorm.data_trans.data_with_evidence_ds.DataWithEvidenceDataSource

translation_type

alias of NeuronConnectomeSynapseClassTranslation

make_translation(sources)

It’s intended that implementations of DataTranslator will override this method to make custom Translations according with how different arguments to Translate are (or are not) distinguished.

The actual properties of a Translation subclass must be defined within the ‘translate’ method

translate(data_source, neurotransmitter_source)

Notionally, this method takes a data source, which is translated into some other data source. There doesn’t necessarily need to be an input data source.

PyOpenWorm.data_trans.context_datasource module

class PyOpenWorm.data_trans.context_datasource.VariableIdentifierContext(maker=None, **kwargs)

Bases: PyOpenWorm.data_trans.context_datasource.VariableIdentifierMixin, PyOpenWorm.context.Context

A Context that gets its identifier and its configuration from its ‘maker’ passed in at initialization

class PyOpenWorm.data_trans.context_datasource.VariableIdentifierContextDataObject(maker=None, **kwargs)

Bases: PyOpenWorm.data_trans.context_datasource.VariableIdentifierMixin, PyOpenWorm.contextDataObject.ContextDataObject

A ContextDataObject that gets its identifier and its configuration from its ‘maker’ passed in at initialization

defined_augment()

This fuction must return False if identifier_augment() would raise an IdentifierMissingException. Override it when defining a non-standard identifier for subclasses of DataObjects.

PyOpenWorm.data_trans.context_merge module

class PyOpenWorm.data_trans.context_merge.ContextMergeDataTranslator(**kwargs)

Bases: PyOpenWorm.datasource.DataTranslator

Input type(s): PyOpenWorm.datasource.OneOrMore (PyOpenWorm.data_trans.data_with_evidence_ds.DataWithEvidenceDataSource)

Output type(s): PyOpenWorm.data_trans.data_with_evidence_ds.DataWithEvidenceDataSource

URI: http://openworm.org/entities/translators/ContextMergeDataTranslator

output_type

alias of PyOpenWorm.data_trans.data_with_evidence_ds.DataWithEvidenceDataSource

translate(*sources)

Notionally, this method takes a data source, which is translated into some other data source. There doesn’t necessarily need to be an input data source.

PyOpenWorm.data_trans.csv_ds module

class PyOpenWorm.data_trans.csv_ds.CSVDataSource(*args, **kwargs)

Bases: PyOpenWorm.data_trans.local_file_ds.LocalFileDataSource

CSV file name : DatatypeProperty

Attribute: csv_file_name

Header column names : DatatypeProperty

Attribute: csv_header

CSV field delimiter : DatatypeProperty

Attribute: csv_field_delimiter

File name : DatatypeProperty

Attribute: file_name

Torrent file name : DatatypeProperty

Attribute: torrent_file_name

MD5 hash : DatatypeProperty

Attribute: md5

SHA-256 hash : DatatypeProperty

Attribute: sha256

SHA-512 hash : DatatypeProperty

Attribute: sha512

Input source : ObjectProperty

Attribute: source

The data source that was translated into this one

Translation : ObjectProperty

Attribute: translation

Information about the translation process that created this object

Description : DatatypeProperty

Attribute: description

Free-text describing the data source

class PyOpenWorm.data_trans.csv_ds.CSVDataTranslator(**kwargs)

Bases: PyOpenWorm.datasource.DataTranslator

Input type(s): PyOpenWorm.datasource.DataSource

Output type(s): PyOpenWorm.datasource.DataSource

URI: None

class PyOpenWorm.data_trans.csv_ds.CSVHTTPFileDataSource(**kwargs)

Bases: PyOpenWorm.data_trans.http_ds.HTTPFileDataSource

Header column names : DatatypeProperty

Attribute: csv_header

CSV field delimiter : DatatypeProperty

Attribute: csv_field_delimiter

URL : DatatypeProperty

Attribute: url

MD5 hash : DatatypeProperty

Attribute: md5

SHA-256 hash : DatatypeProperty

Attribute: sha256

SHA-512 hash : DatatypeProperty

Attribute: sha512

Input source : ObjectProperty

Attribute: source

The data source that was translated into this one

Translation : ObjectProperty

Attribute: translation

Information about the translation process that created this object

Description : DatatypeProperty

Attribute: description

Free-text describing the data source

PyOpenWorm.data_trans.data_with_evidence_ds module

class PyOpenWorm.data_trans.data_with_evidence_ds.DataWithEvidenceDataSource(*args, **kwargs)

Bases: PyOpenWorm.datasource.DataSource

Evidence context : ObjectProperty

Attribute: evidence_context

The context in which evidence for the “Data context” is defined

Data context : ObjectProperty

Attribute: data_context

The context in which primary data for this data source is defined

Combined context : ObjectProperty

Attribute: combined_context

Context importing both the data and evidence contexts

Input source : ObjectProperty

Attribute: source

The data source that was translated into this one

Translation : ObjectProperty

Attribute: translation

Information about the translation process that created this object

Description : DatatypeProperty

Attribute: description

Free-text describing the data source

PyOpenWorm.data_trans.excel_ds module

class PyOpenWorm.data_trans.excel_ds.XLSXHTTPFileDataSource(**kwargs)

Bases: PyOpenWorm.data_trans.http_ds.HTTPFileDataSource

URL : DatatypeProperty

Attribute: url

MD5 hash : DatatypeProperty

Attribute: md5

SHA-256 hash : DatatypeProperty

Attribute: sha256

SHA-512 hash : DatatypeProperty

Attribute: sha512

Input source : ObjectProperty

Attribute: source

The data source that was translated into this one

Translation : ObjectProperty

Attribute: translation

Information about the translation process that created this object

Description : DatatypeProperty

Attribute: description

Free-text describing the data source

PyOpenWorm.data_trans.file_ds module

class PyOpenWorm.data_trans.file_ds.FileDataSource(**kwargs)

Bases: PyOpenWorm.datasource.DataSource

MD5 hash : DatatypeProperty

Attribute: md5

SHA-256 hash : DatatypeProperty

Attribute: sha256

SHA-512 hash : DatatypeProperty

Attribute: sha512

Input source : ObjectProperty

Attribute: source

The data source that was translated into this one

Translation : ObjectProperty

Attribute: translation

Information about the translation process that created this object

Description : DatatypeProperty

Attribute: description

Free-text describing the data source

PyOpenWorm.data_trans.http_ds module

class PyOpenWorm.data_trans.http_ds.HTTPFileDataSource(**kwargs)

Bases: PyOpenWorm.data_trans.file_ds.FileDataSource

URL : DatatypeProperty

Attribute: url

MD5 hash : DatatypeProperty

Attribute: md5

SHA-256 hash : DatatypeProperty

Attribute: sha256

SHA-512 hash : DatatypeProperty

Attribute: sha512

Input source : ObjectProperty

Attribute: source

The data source that was translated into this one

Translation : ObjectProperty

Attribute: translation

Information about the translation process that created this object

Description : DatatypeProperty

Attribute: description

Free-text describing the data source

PyOpenWorm.data_trans.local_file_ds module

class PyOpenWorm.data_trans.local_file_ds.LocalFileDataSource(*args, **kwargs)

Bases: PyOpenWorm.capability.Capable, PyOpenWorm.data_trans.file_ds.FileDataSource

File paths should be relative – in general, path names on a given machine are not portable

accept_capability_provider(cap, provider)

The Capable should replace any previously accepted provider with the one given.

PyOpenWorm.data_trans.neuron_data module

class PyOpenWorm.data_trans.neuron_data.NeuronCSVDataSource(*args, **kwargs)

Bases: PyOpenWorm.data_trans.csv_ds.CSVDataSource

BibTeX files : DatatypeProperty

Attribute: bibtex_files

List of BibTeX files that are referenced in the csv file by entry ID

CSV file name : DatatypeProperty

Attribute: csv_file_name

Header column names : DatatypeProperty

Attribute: csv_header

CSV field delimiter : DatatypeProperty

Attribute: csv_field_delimiter

File name : DatatypeProperty

Attribute: file_name

Torrent file name : DatatypeProperty

Attribute: torrent_file_name

MD5 hash : DatatypeProperty

Attribute: md5

SHA-256 hash : DatatypeProperty

Attribute: sha256

SHA-512 hash : DatatypeProperty

Attribute: sha512

Input source : ObjectProperty

Attribute: source

The data source that was translated into this one

Translation : ObjectProperty

Attribute: translation

Information about the translation process that created this object

Description : DatatypeProperty

Attribute: description

Free-text describing the data source

class PyOpenWorm.data_trans.neuron_data.NeuronCSVDataTranslator(*args, **kwargs)

Bases: PyOpenWorm.data_trans.csv_ds.CSVDataTranslator

Input type(s): PyOpenWorm.data_trans.neuron_data.NeuronCSVDataSource

Output type(s): PyOpenWorm.data_trans.data_with_evidence_ds.DataWithEvidenceDataSource

URI: http://openworm.org/entities/translators/NeuronCSVDataTranslator

input_type

alias of NeuronCSVDataSource

output_type

alias of PyOpenWorm.data_trans.data_with_evidence_ds.DataWithEvidenceDataSource

translate(data_source)

Notionally, this method takes a data source, which is translated into some other data source. There doesn’t necessarily need to be an input data source.

PyOpenWorm.data_trans.wormatlas module

class PyOpenWorm.data_trans.wormatlas.WormAtlasCellListDataSource(*args, **kwargs)

Bases: PyOpenWorm.data_trans.csv_ds.CSVDataSource

CSV file name : DatatypeProperty

Attribute: csv_file_name

Header column names : DatatypeProperty

Attribute: csv_header

CSV field delimiter : DatatypeProperty

Attribute: csv_field_delimiter

File name : DatatypeProperty

Attribute: file_name

Torrent file name : DatatypeProperty

Attribute: torrent_file_name

MD5 hash : DatatypeProperty

Attribute: md5

SHA-256 hash : DatatypeProperty

Attribute: sha256

SHA-512 hash : DatatypeProperty

Attribute: sha512

Input source : ObjectProperty

Attribute: source

The data source that was translated into this one

Translation : ObjectProperty

Attribute: translation

Information about the translation process that created this object

Description : DatatypeProperty

Attribute: description

Free-text describing the data source

class PyOpenWorm.data_trans.wormatlas.WormAtlasCellListDataTranslation(**kwargs)

Bases: PyOpenWorm.datasource.GenericTranslation

defined_augment()

This fuction must return False if identifier_augment() would raise an IdentifierMissingException. Override it when defining a non-standard identifier for subclasses of DataObjects.

identifier_augment()

Override this method to define an identifier in lieu of one explicity set.

One must also override defined_augment() to return True whenever this method could return a valid identifier. IdentifierMissingException should be raised if an identifier cannot be generated by this method.

Raises:

IdentifierMissingException

class PyOpenWorm.data_trans.wormatlas.WormAtlasCellListDataTranslator(**kwargs)

Bases: PyOpenWorm.data_trans.csv_ds.CSVDataTranslator

Input type(s): PyOpenWorm.data_trans.wormatlas.WormAtlasCellListDataSource, PyOpenWorm.data_trans.data_with_evidence_ds.DataWithEvidenceDataSource

Output type(s): PyOpenWorm.data_trans.data_with_evidence_ds.DataWithEvidenceDataSource

URI: http://openworm.org/entities/translators/WormAtlasCellListDataTranslator

output_type

alias of PyOpenWorm.data_trans.data_with_evidence_ds.DataWithEvidenceDataSource

translation_type

alias of WormAtlasCellListDataTranslation

make_translation(sources)

It’s intended that implementations of DataTranslator will override this method to make custom Translations according with how different arguments to Translate are (or are not) distinguished.

The actual properties of a Translation subclass must be defined within the ‘translate’ method

translate(data_source, neurons_source)

Notionally, this method takes a data source, which is translated into some other data source. There doesn’t necessarily need to be an input data source.

PyOpenWorm.data_trans.wormbase module

class PyOpenWorm.data_trans.wormbase.MuscleWormBaseCSVTranslator(**kwargs)

Bases: PyOpenWorm.data_trans.csv_ds.CSVDataTranslator

Input type(s): PyOpenWorm.data_trans.wormbase.WormBaseCSVDataSource

Output type(s): PyOpenWorm.data_trans.data_with_evidence_ds.DataWithEvidenceDataSource

URI: http://openworm.org/entities/translators/MuscleWormBaseCSVTranslator

input_type

alias of WormBaseCSVDataSource

output_type

alias of PyOpenWorm.data_trans.data_with_evidence_ds.DataWithEvidenceDataSource

translate(data_source)

Upload muscles and the neurons that connect to them

class PyOpenWorm.data_trans.wormbase.NeuronWormBaseCSVTranslator(**kwargs)

Bases: PyOpenWorm.data_trans.csv_ds.CSVDataTranslator

Input type(s): PyOpenWorm.data_trans.wormbase.WormBaseCSVDataSource

Output type(s): PyOpenWorm.data_trans.data_with_evidence_ds.DataWithEvidenceDataSource

URI: http://openworm.org/entities/translators/NeuronWormBaseCSVTranslator

input_type

alias of WormBaseCSVDataSource

output_type

alias of PyOpenWorm.data_trans.data_with_evidence_ds.DataWithEvidenceDataSource

translate(data_source)

Notionally, this method takes a data source, which is translated into some other data source. There doesn’t necessarily need to be an input data source.

class PyOpenWorm.data_trans.wormbase.WormBaseCSVDataSource(*args, **kwargs)

Bases: PyOpenWorm.data_trans.csv_ds.CSVDataSource

CSV file name : DatatypeProperty

Attribute: csv_file_name

Header column names : DatatypeProperty

Attribute: csv_header

CSV field delimiter : DatatypeProperty

Attribute: csv_field_delimiter

File name : DatatypeProperty

Attribute: file_name

Torrent file name : DatatypeProperty

Attribute: torrent_file_name

MD5 hash : DatatypeProperty

Attribute: md5

SHA-256 hash : DatatypeProperty

Attribute: sha256

SHA-512 hash : DatatypeProperty

Attribute: sha512

Input source : ObjectProperty

Attribute: source

The data source that was translated into this one

Translation : ObjectProperty

Attribute: translation

Information about the translation process that created this object

Description : DatatypeProperty

Attribute: description

Free-text describing the data source

class PyOpenWorm.data_trans.wormbase.WormbaseIonChannelCSVDataSource(*args, **kwargs)

Bases: PyOpenWorm.data_trans.csv_ds.CSVDataSource

CSV file name : DatatypeProperty

Attribute: csv_file_name

Header column names : DatatypeProperty

Attribute: csv_header

CSV field delimiter : DatatypeProperty

Attribute: csv_field_delimiter

File name : DatatypeProperty

Attribute: file_name

Torrent file name : DatatypeProperty

Attribute: torrent_file_name

MD5 hash : DatatypeProperty

Attribute: md5

SHA-256 hash : DatatypeProperty

Attribute: sha256

SHA-512 hash : DatatypeProperty

Attribute: sha512

Input source : ObjectProperty

Attribute: source

The data source that was translated into this one

Translation : ObjectProperty

Attribute: translation

Information about the translation process that created this object

Description : DatatypeProperty

Attribute: description

Free-text describing the data source

class PyOpenWorm.data_trans.wormbase.WormbaseIonChannelCSVTranslator(**kwargs)

Bases: PyOpenWorm.data_trans.csv_ds.CSVDataTranslator

Input type(s): PyOpenWorm.data_trans.wormbase.WormbaseIonChannelCSVDataSource

Output type(s): PyOpenWorm.data_trans.data_with_evidence_ds.DataWithEvidenceDataSource

URI: http://openworm.org/entities/translators/WormbaseIonChannelCSVTranslator

input_type

alias of WormbaseIonChannelCSVDataSource

output_type

alias of PyOpenWorm.data_trans.data_with_evidence_ds.DataWithEvidenceDataSource

translate(data_source)

Notionally, this method takes a data source, which is translated into some other data source. There doesn’t necessarily need to be an input data source.

class PyOpenWorm.data_trans.wormbase.WormbaseTextMatchCSVDataSource(*args, **kwargs)

Bases: PyOpenWorm.data_trans.csv_ds.CSVDataSource

initial_cell_column : DatatypeProperty

Attribute: initial_cell_column

The index of the first column with a cell name

cell_type : DatatypeProperty

Attribute: cell_type

The type of cell to be produced

CSV file name : DatatypeProperty

Attribute: csv_file_name

Header column names : DatatypeProperty

Attribute: csv_header

CSV field delimiter : DatatypeProperty

Attribute: csv_field_delimiter

File name : DatatypeProperty

Attribute: file_name

Torrent file name : DatatypeProperty

Attribute: torrent_file_name

MD5 hash : DatatypeProperty

Attribute: md5

SHA-256 hash : DatatypeProperty

Attribute: sha256

SHA-512 hash : DatatypeProperty

Attribute: sha512

Input source : ObjectProperty

Attribute: source

The data source that was translated into this one

Translation : ObjectProperty

Attribute: translation

Information about the translation process that created this object

Description : DatatypeProperty

Attribute: description

Free-text describing the data source

class PyOpenWorm.data_trans.wormbase.WormbaseTextMatchCSVTranslator(**kwargs)

Bases: PyOpenWorm.data_trans.csv_ds.CSVDataTranslator

Input type(s): PyOpenWorm.data_trans.wormbase.WormbaseTextMatchCSVDataSource

Output type(s): PyOpenWorm.data_trans.data_with_evidence_ds.DataWithEvidenceDataSource

URI: http://openworm.org/entities/translators/WormbaseTextMatchCSVTranslator

input_type

alias of WormbaseTextMatchCSVDataSource

output_type

alias of PyOpenWorm.data_trans.data_with_evidence_ds.DataWithEvidenceDataSource

translate(data_source)

Notionally, this method takes a data source, which is translated into some other data source. There doesn’t necessarily need to be an input data source.

Submodules

PyOpenWorm.bibtex module

PyOpenWorm.bibtex.bibtex_to_document(bibtex_entry, context=None)

Takes a single BibTeX entry and translates it into a Document object

PyOpenWorm.bibtex_customizations module

PyOpenWorm.bibtex_customizations.author(record)

Split author field by ‘and’ into a list of names.

Parameters:record (dict) – the record. Returns:dict – the modified record.

PyOpenWorm.bibtex_customizations.customizations(record)

Use some functions delivered by the library

Parameters:record – a record Returns:– customized record

PyOpenWorm.bibtex_customizations.doi(record)

Parameters:record (dict) – the record. Returns:dict – the modified record.

PyOpenWorm.biology module

class PyOpenWorm.biology.BiologyType(**kwargs)

Bases: PyOpenWorm.dataObject.DataObject

PyOpenWorm.bittorrent module

class PyOpenWorm.bittorrent.BitTorrentDataSourceDirLoader(base_directory=None)

Bases: PyOpenWorm.datasource_loader.DataSourceDirLoader

PyOpenWorm.capabilities module

class PyOpenWorm.capabilities.FilePathCapability

Bases: PyOpenWorm.capability.Capability

Provides a file path.

PyOpenWorm.capability module

Defines ‘capabilities’, pieces of functionality that an object needs which must be injected.

A given capability can be provided by more than one capability provider, but, for a given set of providers, only one will be bound at a time. Logically, each provider that provides the capability is asked, in a user-provided preference order, whether it can provide the capability for the specific object and the first one which can provide the capability is bound to the object.

The core idea is dependency injection: a capability does not modify the object: the object receives the provider and an identifier for the capability provided, but how the object uses the provider is up to the object. This is important because the user of the object should not condition its behavior on the particular capability provider used, although it may know about which capabilities the object has.

Note, that there may be some providers that lose their ability to provide a capability. This loss should be communicated with a ‘CannotProvideCapability’ exception when the relevant methods are called on the provider. This may allow certain operations to be retried with a provider lower on the capability order, but a provider that throws CannotProvide may validly be asked if it can provide the capability again – if it still cannot provide the capability, it should communicate that when asked.

Providers may keep state between calls to provide a capability, but their correctness must not depend on any ordering of method calls except that, of course, their __init__ is called first.

exception PyOpenWorm.capability.CannotProvideCapability(cap, provider)

Bases: Exception

Thrown by a provider when it cannot provide the capability during the object’s execution

exception PyOpenWorm.capability.NoProviderAvailable(cap, receiver=None)

Bases: Exception

exception PyOpenWorm.capability.NoProviderGiven(cap, receiver=None)

Bases: Exception

PyOpenWorm.cell module

class PyOpenWorm.cell.Cell(name=None, lineageName=None, **kwargs)

Bases: PyOpenWorm.biology.BiologyType

A biological cell.

All cells with the same name are considered to be the same object.

Parameters:

name : str

The name of the cell

lineageName : str

The lineageName of the cell

blast()

Return the blast name.

Example:

>>> c = Cell(name="ADAL")
>>> c.blast() # Returns "AB"

Note that this isn’t a Property. It returns the blast extracted from the ‘’first’’ lineageName saved.

defined_augment()

This fuction must return False if identifier_augment() would raise an IdentifierMissingException. Override it when defining a non-standard identifier for subclasses of DataObjects.

identifier_augment(*args, **kwargs)

Override this method to define an identifier in lieu of one explicity set.

One must also override defined_augment() to return True whenever this method could return a valid identifier. IdentifierMissingException should be raised if an identifier cannot be generated by this method.

Raises:

IdentifierMissingException

description

A description of the cell

divisionVolume

The volume of the cell at division

Example:

>>> v = Quantity("600","(um)^3")
>>> c = Cell(lineageName="AB plapaaaap")
>>> c.divisionVolume(v)

lineageName

The lineageName of the cell Example:

>>> c = Cell(name="ADAL")
>>> c.lineageName() # Returns ["AB plapaaaapp"]

name

The ‘adult’ name of the cell typically used by biologists when discussing C. elegans

PyOpenWorm.cell_common module

PyOpenWorm.channel module

class PyOpenWorm.channel.Channel(name=None, **kwargs)

Bases: PyOpenWorm.biology.BiologyType

A biological ion channel.

Attributes:

Models : Property

defined_augment()

This fuction must return False if identifier_augment() would raise an IdentifierMissingException. Override it when defining a non-standard identifier for subclasses of DataObjects.

identifier_augment()

Override this method to define an identifier in lieu of one explicity set.

One must also override defined_augment() to return True whenever this method could return a valid identifier. IdentifierMissingException should be raised if an identifier cannot be generated by this method.

Raises:

IdentifierMissingException

appearsIn

Cell types in which the ion channel has been expressed

description

A description of the ion channel

expression_pattern

A pattern of expression of this cell within an organism

gene_WB_ID

Wormbase ID of the encoding gene

gene_class

Classification of the encoding gene

gene_name

Name of the gene that codes for this ion channel

model

Get experimental models of this ion channel

name

Ion channel’s name

neuroml_file

A NeuroML describing a model of this ion channel

proteins

Proteins associated with this channel

subfamily

Ion channel’s subfamily

class PyOpenWorm.channel.ExpressionPattern(wormbaseid=None, **kwargs)

Bases: PyOpenWorm.biology.BiologyType

defined_augment()

This fuction must return False if identifier_augment() would raise an IdentifierMissingException. Override it when defining a non-standard identifier for subclasses of DataObjects.

identifier_augment()

Override this method to define an identifier in lieu of one explicity set.

One must also override defined_augment() to return True whenever this method could return a valid identifier. IdentifierMissingException should be raised if an identifier cannot be generated by this method.

Raises:

IdentifierMissingException

description

Natural language description of the expression pattern

wormbaseID

Alias to wormbaseid

wormbaseURL

The URL for the expression pattern in Wormbase

wormbaseid

The ID for the expression pattern in Wormbase

PyOpenWorm.channel_common module

PyOpenWorm.channel_common.CHANNEL_RDF_TYPE = rdflib.term.URIRef('http://openworm.org/entities/Channel')

Shared RDF type for channels

PyOpenWorm.channelworm module

class PyOpenWorm.channelworm.ChannelModel(modelType=None, *args, **kwargs)

Bases: PyOpenWorm.dataObject.DataObject

A model for an ion channel.

There may be multiple models for a single channel.

Example usage:

# Create a ChannelModel
>>> cm = P.ChannelModel()
# Create Evidence object
>>> ev = P.Evidence(author='White et al.', date='1986')
# Assert
>>> ev.asserts(cm)
>>> ev.save()

conductance

The conductance of this ion channel. This is the initial value, and should be entered as a Quantity object.

gating

The gating mechanism for this channel (“voltage” or name of ligand(s) )

ion

The type of ion this channel selects for

modelType

The type of model employed to describe a channel

class PyOpenWorm.channelworm.HomologyChannelModel(**kwargs)

Bases: PyOpenWorm.channelworm.ChannelModel

class PyOpenWorm.channelworm.PatchClampChannelModel(**kwargs)

Bases: PyOpenWorm.channelworm.ChannelModel

class PyOpenWorm.channelworm.PatchClampExperiment(**kwargs)

Bases: PyOpenWorm.experiment.Experiment

Store experimental conditions for a patch clamp experiment.

Ca_concentration

Calcium concentration

Cl_concentration

Chlorine concentration

blockers

Channel blockers used for this experiment

cell

The cell this experiment was performed on

cell_age

Age of the cell

initial_voltage

Starting voltage of the patch clamp

ion_channel

The ion channel being clamped

membrane_capacitance

Initial membrane capacitance

mutants

Type(s) of mutants being used in this experiment

patch_type

Type of patch clamp being used (‘voltage’ or ‘current’)

pipette_solution

Type of solution in the pipette

PyOpenWorm.cli module

PyOpenWorm.cli.additional_args(parser)

Add some additional options specific to CLI

PyOpenWorm.cli_command_wrapper module

exception PyOpenWorm.cli_command_wrapper.CLIUserError

Bases: Exception

class PyOpenWorm.cli_command_wrapper.CLIAppendAction(mapper, key, index=-1, *args, **kwargs)

Bases: PyOpenWorm.cli_command_wrapper.CLIStoreAction

class PyOpenWorm.cli_command_wrapper.CLIArgMapper

Bases: object

Stores mappings for arguments and maps them back to the part of the object they come from

runners = None

Mapping from subcommand names to functions which run for them

class PyOpenWorm.cli_command_wrapper.CLIStoreAction(mapper, key, index=-1, *args, **kwargs)

Bases: argparse.Action

Interacts with the CLIArgMapper

class PyOpenWorm.cli_command_wrapper.CLIStoreTrueAction(*args, **kwargs)

Bases: PyOpenWorm.cli_command_wrapper.CLIStoreAction

class PyOpenWorm.cli_command_wrapper.CLISubCommandAction(mapper, *args, **kwargs)

Bases: argparse._SubParsersAction

PyOpenWorm.cli_common module

PyOpenWorm.cli_hints module

PyOpenWorm.collections module

class PyOpenWorm.collections.Bag(**kwargs)

Bases: PyOpenWorm.dataObject.DataObject

A convenience class for working with a collection of objects

Example:

v = Bag('unc-13 neurons and muscles')
n = P.Neuron()
m = P.Muscle()
n.receptor('UNC-13')
m.receptor('UNC-13')
for x in n.load():
    v.value(x)
for x in m.load():
    v.value(x)
# Save the group for later use
v.save()
...
# get the list back
u = Bag('unc-13 neurons and muscles')
nm = list(u.value())

defined_augment()

This fuction must return False if identifier_augment() would raise an IdentifierMissingException. Override it when defining a non-standard identifier for subclasses of DataObjects.

identifier_augment()

Override this method to define an identifier in lieu of one explicity set.

One must also override defined_augment() to return True whenever this method could return a valid identifier. IdentifierMissingException should be raised if an identifier cannot be generated by this method.

Raises:

IdentifierMissingException

add

An alias for value

group_name

Alias for name

name

The name of the group of objects

value

An object in the group

PyOpenWorm.command module

exception PyOpenWorm.command.ConfigMissingException(key)

Bases: PyOpenWorm.command.GenericUserError

exception PyOpenWorm.command.GenericUserError

Bases: Exception

An error which should be reported to the user. Not necessarily an error that is the user’s fault

exception PyOpenWorm.command.InvalidGraphException

Bases: PyOpenWorm.command.GenericUserError

Thrown when a graph cannot be translated due to formatting errors

exception PyOpenWorm.command.NoConfigFileError

Bases: PyOpenWorm.command.GenericUserError

exception PyOpenWorm.command.POWDirMissingException

Bases: PyOpenWorm.command.GenericUserError

exception PyOpenWorm.command.StatementValidationError(statements)

Bases: PyOpenWorm.command.GenericUserError

exception PyOpenWorm.command.UnreadableGraphException

Bases: PyOpenWorm.command.GenericUserError

Thrown when a graph cannot be read due to it being missing, the active user lacking permissions, etc.

class PyOpenWorm.command.POW

Bases: object

High-level commands for working with PyOpenWorm data

add_graph(url=None, context=None, include_imports=True)

Fetch a graph and add it to the local store.

Parameters:

url : str

The URL of the graph to fetch

context : rdflib.term.URIRef

If provided, only this context and, optionally, its imported graphs will be added.

include_imports : bool

If True, imports of the named context will be included. Has no effect if context is None.

clone(url=None, update_existing_config=False)

Clone a data store

Parameters:

url : str

URL of the data store to clone

update_existing_config : bool

If True, updates the existing config file to point to the given file for the store configuration

commit(message)

Write the graph to the local repository

Parameters:

message : str

commit message

context(context=None, user=False)

Read or set current target context for the repository

Parameters:

context : str

The context to set

user : bool

If set, set the context only for the current user. Has no effect for retrieving the context

diff()

Show differences between what’s in the working context set and what’s in the serializations

fetch_graph(url)

Fetch a graph

Parameters:

url : str

URL for the graph

git(*args)

Runs git commmands in the .pow directory

Parameters:

*args

arguments to git

imports_context(context=None, user=False)

Read or set current target imports context for the repository

Parameters:

context : str

The context to set

user : bool

If set, set the context only for the current user. Has no effect for retrieving the context

init(update_existing_config=False)

Makes a new graph store.

The configuration file will be created if it does not exist. If it does exist, the location of the database store will, by default, not be changed in that file

Parameters:

update_existing_config : bool

If True, updates the existing config file to point to the given file for the store configuration

list_contexts()

List contexts

merge()

push()

reconstitute(data_source)

Recreate a data source by executing the chain of translators that went into making it.

Parameters:

data_source : str

Identifier for the data source to reconstitute

save(module, provider=None, context=None)

Save the data in the given context

Saves the “mapped” classes declared in a module and saves the objects declared by the “provider” (see the argument’s description)

Parameters:

module : str

Name of the module housing the provider

provider : str

Name of the provider, a callble that accepts a context object and adds statements to it. Can be a “dotted” name indicating attribute accesses

context : str

The target context

say(subject, property, object)

Make a statement

Parameters:

subject : str

The object which you want to say something about

property : str

The type of statement to make

object : str

The other object you want to say something about

serialize(context=None, destination=None, format='nquads', include_imports=False, whole_graph=False)

Serialize the current data context or the one provided

Parameters:

context : str

The context to save

destination : file or str

A file-like object to write the file to or a file name. If not provided, messages the result.

format : str

Serialization format (ex, ‘n3’, ‘nquads’)

include_imports : bool

If true, then include contexts imported by the provided context in the result. The default is not to include imported contexts.

whole_graph: bool

Serialize all contexts from all graphs (this probably isn’t what you want)

tag()

translate(translator, output_key=None, output_identifier=None, data_sources=(), named_data_sources=None)

Do a translation with the named translator and inputs

Parameters:

translator : str

Translator identifier

imports_context_ident : str

Identifier for the imports context. All imports go in here

output_key : str

Output key. Used for generating the output’s identifier. Exclusive with output_identifier

output_identifier : str

Output identifier. Exclusive with output_key

data_sources : list of str

Input data sources

named_data_sources : dict

Named input data sources

config_file

The config file name

log_level

Log level

powdir

The base directory for PyOpenWorm files. The repository provider’s files also go under here

store_name

The file name of the database store

class PyOpenWorm.command.POWDirDataSourceDirLoader(basedir=None)

Bases: PyOpenWorm.datasource_loader.DataSourceDirLoader

class PyOpenWorm.command.POWSource(parent)

Bases: object

Commands for working with DataSource objects

create(kind, key, *args, **kwargs)

Create the source and add it to the graph.

Arguments are determined by the type of the data source

Parameters:

kind : rdflib.term.URIRef

The kind of source to create

key : str

The key, a unique name for the source

list(context=None, kind=None, full=False)

List known sources

Parameters:

kind : str

Only list sources of this kind

context : str

The context to query for sources

full : bool

Whether to (attempt to) shorten the source URIs by using the namespace manager

list_kinds(full=False)

List kinds of sources

Parameters:

full : bool

Whether to (attempt to) shorten the source URIs by using the namespace manager

show(*data_source)

Parameters:

*data_source : str

The ID of the data source to show

data

Commands for saving and loading data for DataSources

class PyOpenWorm.command.POWSourceData(parent)

Bases: object

Commands for saving and loading data for DataSources

retrieve(source, archive='data.tar', archive_type=None)

Retrieves the data for the source

Parameters:

source : str

The source for data

archive : str

The file name of the archive. If this ends with an extension like ‘.zip’, and no archive_type argument is given, then an archive will be created of that type. The archive name will not have any extension appended in any case.

archive_type : str

The type of the archive to create.

class PyOpenWorm.command.SaveValidationFailureRecord(user_module, stack, validation_record)

Bases: PyOpenWorm.command.SaveValidationFailureRecord

class PyOpenWorm.command.UnimportedContextRecord(context, node_index, statement)

Bases: PyOpenWorm.command.UnimportedContextRecord

PyOpenWorm.command_util module

class PyOpenWorm.command_util.IVar(default_value=None, doc=None, value_type=<class 'str'>, name=None)

Bases: object

A descriptor for instance variables amended to provide some attributes like default values, value types, etc.

class PyOpenWorm.command_util.PropertyIVar(*args, **kwargs)

Bases: PyOpenWorm.command_util.IVar

PyOpenWorm.configure module

exception PyOpenWorm.configure.BadConf

Bases: Exception

Special exception subclass for alerting the user to a bad configuration

class PyOpenWorm.configure.ConfigValue

Bases: object

A value to be configured. Base class intended to be subclassed, as its only method is not implemented

class PyOpenWorm.configure.Configure(**initial_values)

Bases: object

A simple configuration object. Enables setting and getting key-value pairs

Unlike a dict, Configure objects will execute a function when retrieving values to enable deferred computation of seldom-used configuration values. In addition, entries in a Configure can be aliased to one another.

copy(other)

Copy this configuration into a different object.

Parameters:other – A different configuration object to copy the configuration from this object into Returns:

get(pname, default=NO_DEFAULT)

Get some parameter value out by asking for a key. Note that unlike dict, if you don’t specify a default, then a KeyError is raised

Parameters:

pname : str

they key of the value you want to return.

default : object

The default value to return if there’s no entry for pname

Returns:

The value corresponding to the key

link(*names)

Call link() with the names of configuration values that should always be the same to link them together

classmethod open(file_name)

Open a configuration file and read it to build the internal state.

Parameters:file_name – configuration file encoded as JSON Returns:a Configure object with the configuration taken from the JSON file

class PyOpenWorm.configure.Configureable(conf=None, **kwargs)

Bases: object

An object which can accept configuration. A base class intended to be subclassed.

get(pname, default=None)

Gets a config value from this Configureable’s conf

See also

Configure.get

class PyOpenWorm.configure.ImmutableConfigure(**initial_values)

Bases: PyOpenWorm.configure.Configure

PyOpenWorm.connection module

class PyOpenWorm.connection.Connection(pre_cell=None, post_cell=None, number=None, syntype=None, synclass=None, termination=None, **kwargs)

Bases: PyOpenWorm.biology.BiologyType

number

The weight of the connection

post_cell

The post-synaptic cell

pre_cell

The pre-synaptic cell

synclass

The kind of Neurotransmitter (if any) sent between pre_cell and post_cell

syntype

The kind of synaptic connection. ‘gapJunction’ indicates a gap junction and ‘send’ a chemical synapse

termination

Where the connection terminates. Inferred from type of post_cell at initialization

PyOpenWorm.context module

class PyOpenWorm.context.ClassContext(key=None, imported=(), ident=None, mapper=None, base_namespace=None, **kwargs)

Bases: PyOpenWorm.context.Context

class PyOpenWorm.context.ClassContextMeta

Bases: PyOpenWorm.context.ContextMeta

class PyOpenWorm.context.Context(key=None, imported=(), ident=None, mapper=None, base_namespace=None, **kwargs)

Bases: PyOpenWorm.import_contextualizer.ImportContextualizer, PyOpenWorm.context.ContextualizableDataUserMixin

A context. Analogous to an RDF context, with some special sauce

save(graph=None, inline_imports=False, autocommit=True, saved_contexts=None)

Alias to save_context

save_context(graph=None, inline_imports=False, autocommit=True, saved_contexts=None)

Save the context to a graph

class PyOpenWorm.context.ContextContextManager(ctx, to_import)

Bases: object

The context manager created when Context::__call__ is passed a dict

class PyOpenWorm.context.ContextMeta

Bases: PyOpenWorm.contextualize.ContextualizableClass

class PyOpenWorm.context.ContextualizableDataUserMixin(*args, **kwargs)

Bases: PyOpenWorm.contextualize.Contextualizable, PyOpenWorm.data.DataUser

class PyOpenWorm.context.QueryContext(graph, *args, **kwargs)

Bases: PyOpenWorm.context.Context

PyOpenWorm.contextDataObject module

class PyOpenWorm.contextDataObject.ContextDataObject(**kwargs)

Bases: PyOpenWorm.dataObject.DataObject

Represents a context

PyOpenWorm.context_common module

PyOpenWorm.context_store module

exception PyOpenWorm.context_store.ContextStoreException

Bases: Exception

PyOpenWorm.contextualize module

class PyOpenWorm.contextualize.Contextualizable(*args, **kwargs)

Bases: PyOpenWorm.contextualize.BaseContextualizable

A BaseContextualizable with the addition of a default behavior of setting the context from the class’s ‘context’ attribute. This generally requires that for the metaclass of the Contextualizable that a ‘context’ data property is defined. For example:

>>> class AMeta(ContextualizableClass):
>>>     @property
>>>     def context(self):
>>>         return self.__context

>>>     @context.setter
>>>     def context(self, ctx):
>>>         self.__context = ctx

>>> class A(six.with_metaclass(Contextualizable)):
>>>     pass

class PyOpenWorm.contextualize.ContextualizableClass

Bases: type

A super-type for contextualizable classes

PyOpenWorm.contextualize.contextualize_helper(context, obj, noneok=False)

Does some extra stuff to make access to the type of a ContextualizingProxy work more-or-less like access to the the wrapped object

PyOpenWorm.contextualize.decontextualize_helper(obj)

Removes contexts from a ContextualizingProxy

PyOpenWorm.data module

class PyOpenWorm.data.Data(conf=None, **kwargs)

Bases: PyOpenWorm.configure.Configure

Provides configuration for access to the database.

Usually doesn’t need to be accessed directly

closeDatabase()

Close a the configured database

init_database()

Open the configured database

classmethod load(file_name)

Load a file into a new Data instance storing configuration in a JSON format

classmethod open(file_name)

Load a file into a new Data instance storing configuration in a JSON format

classmethod process_config(config_dict, **kwargs)

Load a file into a new Data instance storing configuration in a JSON format

class PyOpenWorm.data.DataUser(*args, **kwargs)

Bases: PyOpenWorm.configure.Configureable

A convenience wrapper for users of the database

Classes which use the database should inherit from DataUser.

add_reference(g, reference_iri)

Add a citation to a set of statements in the database

Parameters:triples – A set of triples to annotate

add_statements(graph)

Add a set of statements to the database. Annotates the addition with uploader name, etc

Parameters:graph – An iterable of triples

infer()

Fire FuXi rule engine to infer triples

retract_statements(graph)

Remove a set of statements from the database.

Parameters:graph – An iterable of triples

class PyOpenWorm.data.RDFSource(**kwargs)

Bases: PyOpenWorm.configure.Configureable, PyOpenWorm.configure.ConfigValue

Base class for data sources.

Alternative sources should dervie from this class

get()

Gets a config value from this Configureable’s conf

See also

Configure.get

open()

Called on PyOpenWorm.connect() to set up and return the rdflib graph. Must be overridden by sub-classes.

class PyOpenWorm.data.SPARQLSource(**kwargs)

Bases: PyOpenWorm.data.RDFSource

Reads from and queries against a remote data store

"rdf.source" = "sparql_endpoint"

open()

Called on PyOpenWorm.connect() to set up and return the rdflib graph. Must be overridden by sub-classes.

class PyOpenWorm.data.SleepyCatSource(**kwargs)

Bases: PyOpenWorm.data.RDFSource

Reads from and queries against a local Sleepycat database

The database can be configured like:

"rdf.source" = "Sleepycat"
"rdf.store_conf" = <your database location here>

open()

Called on PyOpenWorm.connect() to set up and return the rdflib graph. Must be overridden by sub-classes.

class PyOpenWorm.data.DefaultSource(**kwargs)

Bases: PyOpenWorm.data.RDFSource

Reads from and queries against a configured database.

The default configuration.

The database store is configured with:

"rdf.source" = "default"
"rdf.store" = <your rdflib store name here>
"rdf.store_conf" = <your rdflib store configuration here>

Leaving unconfigured simply gives an in-memory data store.

open()

Called on PyOpenWorm.connect() to set up and return the rdflib graph. Must be overridden by sub-classes.

class PyOpenWorm.data.ZODBSource(*args, **kwargs)

Bases: PyOpenWorm.data.RDFSource

Reads from and queries against a configured Zope Object Database.

If the configured database does not exist, it is created.

The database store is configured with:

"rdf.source" = "ZODB"
"rdf.store_conf" = <location of your ZODB database>

Leaving unconfigured simply gives an in-memory data store.

open()

Called on PyOpenWorm.connect() to set up and return the rdflib graph. Must be overridden by sub-classes.

PyOpenWorm.dataObject module

class PyOpenWorm.dataObject.BaseDataObject(**kwargs)

Bases: PyOpenWorm.identifier_mixin._IdMixin, yarom.graphObject.GraphObject, PyOpenWorm.context.ContextualizableDataUserMixin

An object backed by the database

Attributes:

rdf_type : rdflib.term.URIRef

The RDF type URI for objects of this type

rdf_namespace : rdflib.namespace.Namespace

The rdflib namespace (prefix for URIs) for objects from this class

properties : list of Property

Properties belonging to this object

owner_properties : list of Property

Properties belonging to parents of this object

classmethod DatatypeProperty(*args, **kwargs)

Attach a, possibly new, property to this class that has a simple type (string,number,etc) for its values

Parameters:

linkName : string

The name of this property.

owner : PyOpenWorm.dataObject.BaseDataObject

The name of this property.

classmethod ObjectProperty(*args, **kwargs)

Attach a, possibly new, property to this class that has a complex BaseDataObject for its values

Parameters:

linkName : string

The name of this property.

owner : PyOpenWorm.dataObject.BaseDataObject

The name of this property.

value_type : type

The type of BaseDataObject for values of this property

classmethod UnionProperty(*args, **kwargs)

Attach a, possibly new, property to this class that has a simple type (string,number,etc) or BaseDataObject for its values

Parameters:

linkName : string

The name of this property.

owner : PyOpenWorm.dataObject.BaseDataObject

The name of this property.

clear_po_cache()

Clear the property-object cache for this object.

This cache is maintained by and shared by the properties of this object. It isn’t necessary to clear this cache manually unless you modify the RDFLib graph indirectly (e.g., through the store) at runtime.

contextualize_augment(context)

For MappedClass, rdf_type and rdf_namespace have special behavior where they can be auto-generated based on the class name and base_namespace. We have to pass through these values to our “proxy” to avoid this behavior

decontextualize()

Return the object with all contexts removed

defined_augment()

This fuction must return False if identifier_augment() would raise an IdentifierMissingException. Override it when defining a non-standard identifier for subclasses of DataObjects.

get_owners(property_class_name)

Return a generator of owners along a property pointing to this object

graph_pattern(shorten=False, show_namespaces=True, **kwargs)

Get the graph pattern for this object.

It should be as simple as converting the result of triples() into a BGP

Parameters:

shorten : bool

Indicates whether to shorten the URLs with the namespace manager attached to the self

id_is_variable()

Is the identifier a variable?

identifier_augment()

Override this method to define an identifier in lieu of one explicity set.

One must also override defined_augment() to return True whenever this method could return a valid identifier. IdentifierMissingException should be raised if an identifier cannot be generated by this method.

Raises:

IdentifierMissingException

make_identifier_from_properties(names)

Creates an identifier from properties

retract()

Remove this object from the data store.

save()

Write in-memory data to the database. Derived classes should call this to update the store.

variable()

Must return a Variable object that identifies this GraphObject in queries.

The variable can be randomly generated when the object is created and stored in the object.

po_cache = None

A cache of property URIs and values. Used by RealSimpleProperty

properties_are_init_args = True

If true, then properties defined in the class body can be passed as keyword arguments to __init__. For example:

>>> class A(DataObject):
...     p = DatatypeProperty()

>>> A(p=5)

If the arguments are written explicitly into the __init__ method definition, then no special processing is done.

class PyOpenWorm.dataObject.ContextMappedClass(name, bases, dct)

Bases: yarom.mappedClass.MappedClass, PyOpenWorm.contextualize.ContextualizableClass

after_mapper_module_load(mapper)

Called after the module has been loaded. See PyOpenWorm.mapper.Mapper

contextualize_class_augment(context)

For MappedClass, rdf_type and rdf_namespace have special behavior where they can be auto-generated based on the class name and base_namespace. We have to pass through these values to our “proxy” to avoid this behavior

definition_context

Unlike self.context, definition_context isn’t meant to be overriden

query

Stub. Eventually, creates a proxy that changes how some things behave for purposes of querying

class PyOpenWorm.dataObject.DataObject(**kwargs)

Bases: PyOpenWorm.dataObject.BaseDataObject

PyOpenWorm.datasource module

exception PyOpenWorm.datasource.DuplicateAlsoException

Bases: Exception

class PyOpenWorm.datasource.BaseDataTranslator(**kwargs)

Bases: PyOpenWorm.dataObject.DataObject

Translates from a data source to PyOpenWorm objects

input_type

alias of DataSource

output_type

alias of DataSource

translation_type

alias of Translation

make_translation(sources=())

It’s intended that implementations of DataTranslator will override this method to make custom Translations according with how different arguments to Translate are (or are not) distinguished.

The actual properties of a Translation subclass must be defined within the ‘translate’ method

translate(*args, **kwargs)

Notionally, this method takes a data source, which is translated into some other data source. There doesn’t necessarily need to be an input data source.

class PyOpenWorm.datasource.DataObjectContextDataSource(context, **kwargs)

Bases: PyOpenWorm.datasource.DataSource

Input source : ObjectProperty

Attribute: source

The data source that was translated into this one

Translation : ObjectProperty

Attribute: translation

Information about the translation process that created this object

Description : DatatypeProperty

Attribute: description

Free-text describing the data source

class PyOpenWorm.datasource.DataSource(**kwargs)

Bases: PyOpenWorm.dataObject.DataObject

A source for data that can get translated into PyOpenWorm objects.

The value for any field can be passed to __init__ by name. Additionally, if the sub-class definition of a DataSource assigns a value for that field like:

class A(DataSource):
    some_field = 3

that value will be used over the default value for the field, but not over any value provided to __init__.

commit()

Commit the data source locally

This includes staging files such as they would be available for a translation. In general, a sub-class should implement commit_augment() rather than this method, or at least call this method via super

For example, if the data source produces a file, that file should be in

defined_augment()

This fuction must return False if identifier_augment() would raise an IdentifierMissingException. Override it when defining a non-standard identifier for subclasses of DataObjects.

identifier_augment()

Override this method to define an identifier in lieu of one explicity set.

One must also override defined_augment() to return True whenever this method could return a valid identifier. IdentifierMissingException should be raised if an identifier cannot be generated by this method.

Raises:

IdentifierMissingException

class PyOpenWorm.datasource.DataSourceType(name, bases, dct)

Bases: PyOpenWorm.dataObject.ContextMappedClass

A type for DataSources

Sets up the graph with things needed for MappedClasses

class PyOpenWorm.datasource.DataTransatorType(name, bases, dct)

Bases: PyOpenWorm.dataObject.ContextMappedClass

class PyOpenWorm.datasource.DataTranslator(**kwargs)

Bases: PyOpenWorm.datasource.BaseDataTranslator

A specialization with the GenericTranslation translation type that adds sources for the translation automatically when a new output is made

translation_type

alias of GenericTranslation

make_translation(sources=())

It’s intended that implementations of DataTranslator will override this method to make custom Translations according with how different arguments to Translate are (or are not) distinguished.

The actual properties of a Translation subclass must be defined within the ‘translate’ method

class PyOpenWorm.datasource.GenericTranslation(**kwargs)

Bases: PyOpenWorm.datasource.Translation

A generic translation that just has sources in order

defined_augment()

This fuction must return False if identifier_augment() would raise an IdentifierMissingException. Override it when defining a non-standard identifier for subclasses of DataObjects.

identifier_augment()

Override this method to define an identifier in lieu of one explicity set.

One must also override defined_augment() to return True whenever this method could return a valid identifier. IdentifierMissingException should be raised if an identifier cannot be generated by this method.

Raises:

IdentifierMissingException

class PyOpenWorm.datasource.OneOrMore(source_type)

Bases: object

Wrapper for DataTranslator input DataSource types indicating that one or more of the wrapped type must be provided to the translator

class PyOpenWorm.datasource.PersonDataTranslator(**kwargs)

Bases: PyOpenWorm.datasource.BaseDataTranslator

A person who was responsible for carrying out the translation of a data source manually

person

A person responsible for carrying out the translation.

class PyOpenWorm.datasource.Translation(**kwargs)

Bases: PyOpenWorm.dataObject.DataObject

Representation of the method by which a DataSource was translated and the sources of that translation. Unlike the ‘source’ field attached to DataSources, the Translation may distinguish different kinds of input source to a translation.

defined_augment()

This fuction must return False if identifier_augment() would raise an IdentifierMissingException. Override it when defining a non-standard identifier for subclasses of DataObjects.

identifier_augment()

Override this method to define an identifier in lieu of one explicity set.

One must also override defined_augment() to return True whenever this method could return a valid identifier. IdentifierMissingException should be raised if an identifier cannot be generated by this method.

Raises:

IdentifierMissingException

PyOpenWorm.datasource_loader module

DataSourceLoaders take a data source identifier and retrieve the primary data (e.g., CSV files, electrode recordings) from some location (e.g., a file store, via a bittorrent tracker).

Each loader can treat the base_directory given as its own namespace and place directories in there however it wants.

exception PyOpenWorm.datasource_loader.LoadFailed(data_source, loader, *args)

Bases: Exception

class PyOpenWorm.datasource_loader.DataSourceDirLoader(base_directory=None)

Bases: object

Loads a data files for a DataSource

The loader is expected to organize files for each data source within the given base directory.

PyOpenWorm.document module

exception PyOpenWorm.document.PubmedRetrievalException

Bases: Exception

exception PyOpenWorm.document.WormbaseRetrievalException

Bases: Exception

class PyOpenWorm.document.BaseDocument(**kwargs)

Bases: PyOpenWorm.dataObject.DataObject

class PyOpenWorm.document.Document(bibtex=None, doi=None, pubmed=None, wormbase=None, **kwargs)

Bases: PyOpenWorm.document.BaseDocument

A representation of some document.

Possible keys include:

pmid, pubmed: a pubmed id or url (e.g., 24098140)
wbid, wormbase: a wormbase id or url (e.g., WBPaper00044287)
doi: a Digitial Object id or url (e.g., s00454-010-9273-0)
uri: a URI specific to the document, preferably usable for accessing
     the document

defined_augment()

This fuction must return False if identifier_augment() would raise an IdentifierMissingException. Override it when defining a non-standard identifier for subclasses of DataObjects.

identifier_augment()

Override this method to define an identifier in lieu of one explicity set.

One must also override defined_augment() to return True whenever this method could return a valid identifier. IdentifierMissingException should be raised if an identifier cannot be generated by this method.

Raises:

IdentifierMissingException

update_from_wormbase(replace_existing=False)

Queries wormbase for additional data to fill in the Document.

If replace_existing is set to True, then existing values will be cleared.

author

An author of the document

date

Alias to year

doi

A Digital Object Identifier (DOI), optional

pmid

A PubMed ID (PMID) that points to a paper

title

The title of the document

uri

A non-standard URI for the document

wbid

An ID from WormBase.org that points to a record, optional

wormbaseid

An alias to wbid

year

The year (e.g., publication year) of the document

PyOpenWorm.documentContext module

class PyOpenWorm.documentContext.DocumentContext(document)

Bases: PyOpenWorm.context.Context

A Context that corresponds to a document.

class PyOpenWorm.documentContext.DocumentContextMeta

Bases: PyOpenWorm.context.ContextMeta

PyOpenWorm.evidence module

exception PyOpenWorm.evidence.EvidenceError

Bases: Exception

class PyOpenWorm.evidence.Evidence(**kwargs)

Bases: PyOpenWorm.dataObject.DataObject

A representation which provides evidence, for a group of statements.

Attaching evidence to an set of statements is done like this:

>>> from PyOpenWorm.connection import Connection
>>> from PyOpenWorm.evidence import Evidence
>>> from PyOpenWorm.context import Context

Declare contexts:

>>> ACTX = Context(ident="http://example.org/data/some_statements")
>>> BCTX = Context(ident="http://example.org/data/some_other_statements")
>>> EVCTX = Context(ident="http://example.org/data/some_statements#evidence")

Make statements in ACTX and BCTX contexts:

>>> ACTX(Connection)(pre_cell="VA11", post_cell="VD12", number=3)
>>> BCTX(Connection)(pre_cell="VA11", post_cell="VD12", number=2)

In EVCTX, state that a that a certain document supports the set of statements in ACTX, but refutes the set of statements in BCTX:

>>> doc = EVCTX(Document)(author='White et al.', date='1986')
>>> EVCTX(Evidence)(reference=doc, supports=ACTX.rdf_object)
>>> EVCTX(Evidence)(reference=doc, refutes=BCTX.rdf_object)

Finally, save the contexts:

>>> ACTX.save_context()
>>> BCTX.save_context()
>>> EVCTX.save_context()

One note about the reference predicate: the reference should, ideally, be an unambiguous link to a peer-reviewed piece of scientific literature detailing methods and data analysis that supports the set of statements. However, in gather data from pre-existing sources, going to that level of specificity may be difficult due to deficient query capability at the data source. In such cases, a broader reference, such as a Website with information which guides readers to a peer-reviewed article supporting the statement is sufficient.

defined_augment()

This fuction must return False if identifier_augment() would raise an IdentifierMissingException. Override it when defining a non-standard identifier for subclasses of DataObjects.

identifier_augment()

Override this method to define an identifier in lieu of one explicity set.

One must also override defined_augment() to return True whenever this method could return a valid identifier. IdentifierMissingException should be raised if an identifier cannot be generated by this method.

Raises:

IdentifierMissingException

reference

The resource providing evidence supporting/refuting the attached context

refutes

A context naming a set of statements which are refuted by the attached reference

supports

A context naming a set of statements which are supported by the attached reference

PyOpenWorm.evidence.evidence_for(qctx, ctx, evctx=None)

Returns an iterable of Evidence

Parameters:

qctx : object

an object supported by evidence. If the object is a Context with no identifier, then the query considers statements ‘staged’ (rather than stored) in the context

ctx : Context

Context that bounds where we look for statements about qctx. The contexts for statements found in this context are the actual targets of Evidence.supports statements.

evctx : Context

if the Evidence.supports statements should be looked for somewhere other than ctx, that can be specified in evctx. optional

PyOpenWorm.evidence.query_context(graph, qctx)

graph : rdflib.graph.Graph

Graph where we can find the contexts for statements in qctx

qctx : PyOpenWorm.context.Context

Container for statements

PyOpenWorm.experiment module

class PyOpenWorm.experiment.Experiment(**kwargs)

Bases: PyOpenWorm.dataObject.DataObject

Generic class for storing information about experiments

Should be overridden by specific types of experiments (example: see PatchClampExperiment in channelworm.py).

Overriding classes should have a list called “conditions” that contains the names of experimental conditions for that particular type of experiment. Each of the items in “conditions” should also be either a DatatypeProperty or ObjectProperty for the experiment as well.

get_conditions()

Return conditions and their associated values in a dict.

reference

Supporting article for this experiment.

PyOpenWorm.git_repo module

PyOpenWorm.identifier_mixin module

PyOpenWorm.identifier_mixin.IdMixin(typ=<class 'object'>, hashfunc=None)

Mixin that provides common identifier logic

Parameters:

typ : type

The type of object to use as the hash function’s super class. Defaults to ‘object’

hashfunc : function

The function to use for encoding data provided to make_identifier. Should return an object can .encode() to a bytes (a.k.a. str in Python 2). Defaults to hashlib.sha224()

PyOpenWorm.import_contextualizer module

class PyOpenWorm.import_contextualizer.ImportContextualizer

Bases: object

Interface for classes that ‘contextualize’ an import.

Contextualizing an import means that if an object is defined with the name X in some context, and an import statement is written like this:

import X.a

X will be invoked like this:

a = X(__import__('a'))

On the other hand, for an import statement of this form:

from X.a import A

will cause X to be invoked as:

_temp = X(__import__('a', globals(), locals(), ('A',)), ('A',))
A = _temp.A

and the import statement:

from X.a import A as AA

will cause X to be invoked as:

_temp = X(__import__('a', globals(), locals(), ('A',)), ('A',))
AA = _temp.A

meaning that the contextualizer won’t know what the ‘as’ name is.

For the astute reader, you may notice parallels between the protocol for ImportContextualizer and the __import__ function itself. You may also be wondering why the contextualizer isn’t merely a proxy for __import__. The first reason is that I want the true import to always happen, regardless of what the contextualizer does, so that the semantics of the contextualizer are very clear. The second reason is that requiring a real proxy would require more complexity in the contextualizers to ensure that they are properly handling exceptions, module attributes and the return value of __import__, ensuring that the right __import__ is used, as well as handling the ‘fromlist’ correctly.

PyOpenWorm.import_override module

PyOpenWorm.inverse_property module

For declaring inverse properties of GraphObjects

exception PyOpenWorm.inverse_property.InversePropertyException

Bases: Exception

class PyOpenWorm.inverse_property.InversePropertyMixin

Bases: object

Mixin for inverse properties.

Augments RealSimpleProperty methods to update inverse properties as well

PyOpenWorm.mapper module

exception PyOpenWorm.mapper.UnmappedClassException

Bases: Exception

class PyOpenWorm.mapper.Mapper(base_class_names, base_namespace=None, imported=(), name=None, **kwargs)

Bases: PyOpenWorm.module_recorder.ModuleRecordListener, PyOpenWorm.configure.Configureable

Keeps track of relationships between classes, between modules, and between classes and modules

decorate_class(cls)

Extension point for subclasses of Mapper to apply an operation to all mapped classes

load_module(module_name)

Loads the module.

lookup_class(cname)

Gets the class corresponding to a fully-qualified class name

DecoratedMappedClasses = None

Maps RDF types to properties of the related class

MappedClasses = None

Maps classes to decorated versions of the class

base_modules = None

The base class for objects that will be mapped.

Defined once the module containing the class is loaded

base_namespace = None

Modules that have already been loaded

PyOpenWorm.module_recorder module

PyOpenWorm.muscle module

class PyOpenWorm.muscle.Muscle(name=None, lineageName=None, **kwargs)

Bases: PyOpenWorm.cell.Cell

A single muscle cell.

See what neurons innervate a muscle:

Example:

>>> mdr21 = Muscle('MDR21')
>>> innervates_mdr21 = mdr21.innervatedBy()
>>> len(innervates_mdr21)
4

innervatedBy

Neurons synapsing with this muscle

neurons

Alias to innervatedBy

receptor

Alias to receptors

receptors

Receptor types expressed by this type of muscle

PyOpenWorm.my_neuroml module

class PyOpenWorm.my_neuroml.NeuroML(*args, **kwargs)

Bases: PyOpenWorm.data.DataUser

classmethod generate(o, t=2)

Get a NeuroML object that represents the given object. The type determines what content is included in the NeuroML object:

Parameters:

• o – The object to generate neuroml from
• t – The what kind of content should be included in the document - 0=full morphology+biophysics - 1=cell body only+biophysics - 2=full morphology only

Returns:

A NeuroML object that represents the given object.

Return type:

NeuroMLDocument

classmethod write(o, n)

Write the given neuroml document object out to a file :param o: The NeuroMLDocument to write :param n: The name of the file to write to

PyOpenWorm.network module

class PyOpenWorm.network.Network(worm=None, **kwargs)

Bases: PyOpenWorm.biology.BiologyType

A network of neurons

aneuron(name)

Get a neuron by name.

Example:

# Grabs the representation of the neuronal network
>>> net = Worm().get_neuron_network()

# Grab a specific neuron
>>> aval = net.aneuron('AVAL')

>>> aval.type()
set([u'interneuron'])

Parameters:name – Name of a c. elegans neuron Returns:Neuron corresponding to the name given Return type:PyOpenWorm.neuron.Neuron

defined_augment()

This fuction must return False if identifier_augment() would raise an IdentifierMissingException. Override it when defining a non-standard identifier for subclasses of DataObjects.

identifier_augment()

Override this method to define an identifier in lieu of one explicity set.

One must also override defined_augment() to return True whenever this method could return a valid identifier. IdentifierMissingException should be raised if an identifier cannot be generated by this method.

Raises:

IdentifierMissingException

interneurons()

Get all interneurons

Returns:A iterable of all interneurons Return type:iter(Neuron)

motor()

Get all motor

Returns:A iterable of all motor neurons Return type:iter(Neuron)

neuron_names()

Gets the complete set of neurons’ names in this network.

Example:

# Grabs the representation of the neuronal network
>>> net = Worm().get_neuron_network()

#NOTE: This is a VERY slow operation right now
>>> len(set(net.neuron_names()))
302
>>> set(net.neuron_names())
set(['VB4', 'PDEL', 'HSNL', 'SIBDR', ... 'RIAL', 'MCR', 'LUAL'])

sensory()

Get all sensory neurons

Returns:A iterable of all sensory neurons Return type:iter(Neuron)

neuron

Returns a set of all Neuron objects in the network

neurons

Alias to neuron

synapse

Returns a set of all synapses in the network

synapses

Alias to synapse

worm

The worm connected to the network

PyOpenWorm.neuron module

class PyOpenWorm.neuron.ConnectionProperty(**kwargs)

Bases: PyOpenWorm.pProperty.Property

A representation of the connection between neurons. Either a gap junction or a chemical synapse

TODO: Add neurotransmitter type. TODO: Add connection strength

get(pre_post_or_either='pre', **kwargs)

Get a list of connections associated with the owning neuron.

Parameters:

pre_post_or_either: str

What kind of connection to look for. ‘pre’: Owner is the source of the connection ‘post’: Owner is the destination of the connection ‘either’: Owner is either the source or destination of the connection

Returns:

list of Connection

set(conn, **kwargs)

Add a connection associated with the owner Neuron

Parameters:

conn : PyOpenWorm.connection.Connection

connection associated with the owner neuron

Returns:

A PyOpenWorm.neuron.Connection

class PyOpenWorm.neuron.Neighbor(**kwargs)

Bases: PyOpenWorm.pProperty.Property

get(**kwargs)

Get a list of neighboring neurons.

Parameters:

See parameters for PyOpenWorm.connection.Connection

Returns:

list of Neuron

set(other, **kwargs)

Set the value of this property

Derived classes must override.

class PyOpenWorm.neuron.Neuron(name=False, **kwargs)

Bases: PyOpenWorm.cell.Cell

A neuron.

See what neurons express some neuropeptide

Example:

# Grabs the representation of the neuronal network
>>> net = P.Worm().get_neuron_network()

# Grab a specific neuron
>>> aval = net.aneuron('AVAL')

>>> aval.type()
set([u'interneuron'])

#show how many connections go out of AVAL
>>> aval.connection.count('pre')
77

>>> aval.name()
u'AVAL'

#list all known receptors
>>> sorted(aval.receptors())
[u'GGR-3', u'GLR-1', u'GLR-2', u'GLR-4', u'GLR-5', u'NMR-1', u'NMR-2', u'UNC-8']

#show how many chemical synapses go in and out of AVAL
>>> aval.Syn_degree()
90

Parameters:

name : string

The name of the neuron.

Attributes:

neighbor : Property

Get neurons connected to this neuron if called with no arguments, or with arguments, state that neuronName is a neighbor of this Neuron

connection : Property

Get a set of Connection objects describing chemical synapses or gap junctions between this neuron and others

GJ_degree()

Get the degree of this neuron for gap junction edges only

Returns:total number of incoming and outgoing gap junctions Return type:int

Syn_degree()

Get the degree of this neuron for chemical synapse edges only

Returns:total number of incoming and outgoing chemical synapses Return type:int

get_incidents(type=0)

Get neurons which synapse at this neuron

innexin

Innexin types associated with this neuron

neuropeptide

Name of the gene corresponding to the neuropeptide produced by this neuron

neurotransmitter

Neurotransmitters associated with this neuron

receptor

The receptor types associated with this neuron

receptors

Alias to py:attr:receptor

type

The neuron type (i.e., sensory, interneuron, motor)

PyOpenWorm.pProperty module

class PyOpenWorm.pProperty.Property(name=False, owner=False, **kwargs)

Bases: PyOpenWorm.contextualize.Contextualizable, PyOpenWorm.data.DataUser

Store a value associated with a DataObject

Properties can be be accessed like methods. A method call like:

a.P()

for a property P will return values appropriate to that property for a, the owner of the property.

Parameters:

owner : PyOpenWorm.dataObject.DataObject

The owner of this property

name : string

The name of this property. Can be accessed as an attribute like:

owner.name

get(*args)

Get the things which are on the other side of this property

The return value must be iterable. For a get that just returns a single value, an easy way to make an iterable is to wrap the value in a tuple like (value,).

Derived classes must override.

has_value()

Returns true if the Property has any values set on it.

This may be defined differently for each property

one()

Returns a single value for the Property whether or not it is multivalued.

set(*args, **kwargs)

Set the value of this property

Derived classes must override.

class PyOpenWorm.pProperty.PropertyMeta(name, bases, dct)

Bases: PyOpenWorm.contextualize.ContextualizableClass

PyOpenWorm.package_utils module

PyOpenWorm.plot module

class PyOpenWorm.plot.Plot(data=None, *args, **kwargs)

Bases: PyOpenWorm.dataObject.DataObject

Object for storing plot data in PyOpenWorm.

Parameters:

data : 2D list (list of lists)

List of XY coordinates for this Plot.

Example usage ::

>>> pl = Plot([[1, 2], [3, 4]])
>>> pl.get_data()
# [[1, 2], [3, 4]]

get_data()

Get the data stored for this plot.

set_data(data)

Set the data attribute, which is user-facing, as well as the serialized _data_string attribute, which is used for db storage.

PyOpenWorm.rdf_go_modifiers module

PyOpenWorm.rdf_query_util module

PyOpenWorm.rdf_query_util.get_most_specific_rdf_type(types, context=None, bases=())

Gets the most specific rdf_type.

Returns the URI corresponding to the lowest in the DataObject class hierarchy from among the given URIs.

PyOpenWorm.rdf_query_util.oid(identifier_or_rdf_type=None, rdf_type=None, context=None, base_type=None)

Create an object from its rdf type

Parameters:

identifier_or_rdf_type : str or rdflib.term.URIRef

If rdf_type is provided, then this value is used as the identifier for the newly created object. Otherwise, this value will be the rdf_type of the object used to determine the Python type and the object’s identifier will be randomly generated.

rdf_type : str, rdflib.term.URIRef, False

If provided, this will be the rdf_type of the newly created object.

Returns:

The newly created object

PyOpenWorm.simpleProperty module

class PyOpenWorm.simpleProperty.ContextMappedPropertyClass(*args, **kwargs)

Bases: yarom.mappedProperty.MappedPropertyClass, PyOpenWorm.contextualize.ContextualizableClass

class PyOpenWorm.simpleProperty.ContextualizedPropertyValue(value)

Bases: yarom.propertyValue.PropertyValue

class PyOpenWorm.simpleProperty.DatatypeProperty(resolver, **kwargs)

Bases: yarom.propertyMixins.DatatypePropertyMixin, PyOpenWorm.simpleProperty.PropertyCountMixin, PyOpenWorm.simpleProperty.RealSimpleProperty

get()

Gets a config value from this Configureable’s conf

See also

Configure.get

class PyOpenWorm.simpleProperty.ObjectProperty(resolver=None, *args, **kwargs)

Bases: PyOpenWorm.inverse_property.InversePropertyMixin, PyOpenWorm.simpleProperty._ContextualizingPropertySetMixin, PyOpenWorm.simpleProperty.PropertyCountMixin, PyOpenWorm.simpleProperty.RealSimpleProperty

get()

Gets a config value from this Configureable’s conf

See also

Configure.get

class PyOpenWorm.simpleProperty.POCache

Bases: tuple

The predicate-object cache object

class PyOpenWorm.simpleProperty.RealSimpleProperty(owner, **kwargs)

Bases: PyOpenWorm.data.DataUser, PyOpenWorm.contextualize.Contextualizable

clear()

Clears values set in all contexts

decontextualize()

Return the object with all contexts removed

get()

Gets a config value from this Configureable’s conf

See also

Configure.get

class PyOpenWorm.simpleProperty.UnionProperty(resolver, **kwargs)

Bases: PyOpenWorm.simpleProperty._ContextualizingPropertySetMixin, PyOpenWorm.inverse_property.InversePropertyMixin, yarom.propertyMixins.UnionPropertyMixin, PyOpenWorm.simpleProperty.PropertyCountMixin, PyOpenWorm.simpleProperty.RealSimpleProperty

A Property that can handle either DataObjects or basic types

get()

Gets a config value from this Configureable’s conf

See also

Configure.get

PyOpenWorm.statement module

class PyOpenWorm.statement.Statement(subject, property, object, context)

Bases: PyOpenWorm.statement.Statement

PyOpenWorm.text_util module

PyOpenWorm.utils module

Common utilities for translation, massaging data, etc., that don’t fit elsewhere in PyOpenWorm

PyOpenWorm.utils.grouper(iterable, n, fillvalue=None)

Collect data into fixed-length chunks or blocks

PyOpenWorm.website module

class PyOpenWorm.website.Website(title=None, **kwargs)

Bases: PyOpenWorm.document.BaseDocument

A representation of a website

defined_augment()

This fuction must return False if identifier_augment() would raise an IdentifierMissingException. Override it when defining a non-standard identifier for subclasses of DataObjects.

identifier_augment()

Override this method to define an identifier in lieu of one explicity set.

One must also override defined_augment() to return True whenever this method could return a valid identifier. IdentifierMissingException should be raised if an identifier cannot be generated by this method.

Raises:

IdentifierMissingException

title

The official name for the website

url

A URL for the website

PyOpenWorm.worm module

class PyOpenWorm.worm.Worm(scientific_name=False, **kwargs)

Bases: PyOpenWorm.biology.BiologyType

A representation of the whole worm

defined_augment()

True if the name is defined

get_neuron_network()

Return the neuron network of the worm.

Example:

# Grabs the representation of the neuronal network
>>> net = P.Worm().get_neuron_network()

# Grab a specific neuron
>>> aval = net.aneuron('AVAL')

>>> aval.type()
set([u'interneuron'])

#show how many connections go out of AVAL
>>> aval.connection.count('pre')
77

Returns:An object to work with the network of the worm Return type:PyOpenWorm.Network

get_semantic_net()

Get the underlying semantic network as an RDFLib Graph

Returns:A semantic network containing information about the worm Return type:rdflib.ConjunctiveGraph

identifier_augment(*args, **kwargs)

Result is derived from the name property

muscles()

Get all Muscle objects attached to the Worm.

Example:

>>> muscles = P.Worm().muscles()
>>> len(muscles)
96

Returns:A set of all muscles Return type:set

cell

A cell in the worm

muscle

A type of muscle which is in the worm

name

Alias to scientific_name

neuron_network

The neuron network of the worm

scientific_name

Scientific name for the organism

PyOpenWorm.worm_common module

For Users

PyOpenWorm Data Sources

The sources of data for PyOpenWorm are stored in the OpenWormData repository. A few DataTranslators translate these data into common PyOpenWorm data sources. You can list these by running:

pow source list

and you can show some of the properties of a data source by running:

pow source show $SOURCE_IDENTIFIER

For instance, you can run the following to see the top-level data source, try:

pow source show http://openworm.org/data

This will print out summary descriptions of the sources that contribute to the main data source.

A Note on PyOpenWorm Data

Below, each major element of the worm’s anatomy that PyOpenWorm stores data on is considered individually. The data being used is tagged by source in a superscript, and the decisions made during the curation process (if any) are described.

Neurons

• Neuron names [2]: Extracted from WormBase. Dynamic version on this google spreadsheet. Staged in this csv file. Parsed by this method.
• Neuron types [1]: Extracted from WormAtlas.org. Staged in this csv file. Parsed by this method.
• Cell descriptions [1]: Extracted from WormAtlas.org. Staged in this tsv file. Parsed by this method.
• Lineage names [1]: Extracted from WormAtlas.org. Dynamic version on this google spreadsheet. Staged in this tsv file. Parsed by this method.
• Neurotransmitters [1]: Extracted from WormAtlas.org. Dynamic version on this google spreadsheet. Staged in this csv file. Parsed by this method.
• Neuropeptides [1]: Extracted from WormAtlas.org. Dynamic version on this google spreadsheet. Staged in this csv file. Parsed by this method.
• Receptors [1]: Extracted from WormAtlas.org. Dynamic version on this google spreadsheet. Staged in this csv file. Parsed by this method.
• Innexins [1]: Extracted from WormAtlas.org. Dynamic version on this google spreadsheet. Staged in this csv file. Parsed by this method.

Gene expression data below, additional to that extracted from WormAtlas concerning receptors, neuropeptides, neurotransmitters and innexins are parsed by this method:

• Monoamine secretors and receptors, neuropeptide secretors and receptors [4]: Dynamic version on this google spreadsheet. Staged in this csv file.

Muscle cells

• Muscle names [2]: Extracted from WormBase. Dynamic version on this google spreadsheet. Staged in this csv file. Parsed by this method.
• Cell descriptions [1]: Extracted from WormAtlas.org. Dynamic version on this google spreadsheet. Staged in this tsv file. Parsed by this method.
• Lineage names [1]: Extracted from WormAtlas.org. Dynamic version on this google spreadsheet. Staged in this tsv file. Parsed by this method.
• Neurons that innervate each muscle [3]: Extracted from data personally communicated by S. Cook. Staged in this csv file. Parsed by this method.

Connectome

• Gap junctions between neurons [3]: Extracted from data personally communicated by S. Cook. Staged in this csv file. Parsed by this method.
• Synapses between neurons [3]: Extracted from data personally communicated by S. Cook. Staged in this csv file. Parsed by this method.

Curation note

There was another source of C. elegans connectome data that was created by members of the OpenWorm project that has since been retired. The history of this spreadsheet is mostly contained in this forum post We decided to use the Emmons data set [3] as the authoritative source for connectome data, as it is the very latest version and updated version of the C. elegans connectome that we are familiar with.

---

Data Source References

[1]	(1, 2, 3, 4, 5, 6, 7, 8, 9) Altun, Z.F., Herndon, L.A., Wolkow, C.A., Crocker, C., Lints, R. and Hall, D. H. (2015). WormAtlas. Retrieved from http://www.wormatlas.org - WormAtlas Complete Cell List

[2]

(1, 2) Harris, T. W., Antoshechkin, I., Bieri, T., Blasiar, D., Chan, J., Chen, W. J., … Sternberg, P. W. (2010). WormBase: a comprehensive resource for nematode research. Nucleic Acids Research, 38(Database issue), D463–7. http://doi.org/10.1093/nar/gkp952 Lee, R. Y. N., & Sternberg, P. W. (2003). Building a cell and anatomy ontology of Caenorhabditis elegans. Comparative and Functional Genomics, 4(1), 121–6. http://doi.org/10.1002/cfg.248

[3]	(1, 2, 3, 4) Emmons, S., Cook, S., Jarrell, T., Wang, Y., Yakolev, M., Nguyen, K., Hall, D. Whole-animal C. elegans connectomes. C. Elegans Meeting 2015 http://abstracts.genetics-gsa.org/cgi-bin/celegans15s/wsrch15.pl?author=emmons&sort=ptimes&sbutton=Detail&absno=155110844&sid=668862

[4]	Bentley B., Branicky R., Barnes C. L., Chew Y. L., Yemini E., Bullmore E. T., Vertes P. E., Schafer W. R. (2016) The Multilayer Connectome of Caenorhabditis elegans. PLoS Comput Biol 12(12): e1005283. http://doi.org/10.1371/journal.pcbi.1005283

Requirements for data storage in OpenWorm

Our OpenWorm database captures facts about C. elegans. The database stores data for generating model files and together with annotations describing the origins of the data. Below are a set of recommendations for implementation of the database organized around an RDF model.

Interface

Access is through a Python library which communicates with the database. This library serves the function of providing an object oriented view on the database that can be accessed through the Python scripts commonly used in the project. The api is described separately.

Data modelling

Biophysical and anatomical data are included in the database. A sketch of some features of the data model is below. Also included in our model are the relationships between these types. Given our choice of data types, we do not model the individual interactions between cells as entities in the database. Rather these are described by generic predicates in an RDF triple. For instance, neuron A synapsing with muscle cell B would give a statement (A, synapsesWith, B), but A synapsing with neuron C would also have (A, synapsesWith, C). Data which belong to the specific relationship between two nodes is attached to an rdf:Statement object which points to the statement. This choice is intended to easy querying and extension later on.

Nervous system

In the worm’s nervous system, we capture a few important data types (listed below). These correspond primarily to the anatomical structures and chemicals which are necessary for the worm to record external and internal stimuli and activate its body in response to those stimuli.

Data types

A non-exhaustive list of neurological data types in our C. elegans database:

• receptor types identified in the nerve cell
• neurons
• ion channels
• neurotransmitters
• muscle receptors

Development

Caenorhabditis elegans has very stable cell division patterns in the absence of mutations. This means that we can capture divisions in our database as static ‘daughter_of’ relationships. The theory of differentiation codes additionally gives an algorithmic description to the growth patterns of the worm which describes signals transmitted between developing cells. In order to test this theory we would like to leverage existing photographic data indicating the volume of cells at the time of their division as this relates to the differentiation code stored by the cell. Progress on this issue is documented on Github.

Aging

Concurrently with development, we would like to begin modeling the effects of aging on the worm. Aging typically manifests in physiological changes due to transcription errors or cell death. These physiological changes can be represented abstractly as parameters to the function of biological entities. See Github for further discussion.

Information assurance

Reasoning and Data integrity

To make full use of RDF storage it’s recommended to leverage reasoning over our stored data. Encoding rules for the worm requires a good knowledge of both C. elegans and the database schema. More research needs to be done on this going forward. Preliminarily, SPIN, a constraint notation system based on SPARQL looks like a good candidate for specifying rules, but an inference engine for enforcing the rules still needs to be found.

Input validation

Input validation is to be handled through the interface library referenced above. In general, incorrect entry of biological names will result in an error being reported identifying the offending entry and providing a acceptable entries where appropriate. No direct access to the underlying data store will be provided.

Provenance

Tracking the origins of facts stated in the database demands a method of annotating statements in our database. Providing citations for facts must be as simple as providing a global identifier (e.g., URI, DOI) or a local identifier (e.g., Bibtex identifier, Pubmed ID). A technique called RDF reification allows us to annotate arbitrary facts in our database with additional information. This technique allows for the addition of structured citation data to facts in the database as well as annotations for tracking responsibility for uploads to the database. Further details for the attachment of evidence using this technique are given in the api.

In line with current practices for communication through the source code management platform, Github, we would like to track responsibility for new uploads to the database. Two methods are proposed for tracking this information: RDF named graphs and RDF reification. Tracking information must include, at least, a time-stamp on the update and linking of the submitted data to the uploader’s unique identifier (e.g., email address). Named graphs have the advantage of wide support for the use of tracking uploads. The choice between these depends largely the support of the chosen data store for named graphs.

Access control

Write access to data in the project has been inconsistent between various data sources in the project. Going forward, write access to OpenWorm databases should be restricted to authenticated users to forestall the possibility of malicious tampering.

One way to accomplish this would be to leverage GitHub’s fork and pull model with the data as well as the code. This would require two things:

• Instead of remote hosting of data, data is local to each copy of the library within a local database
• A serialization method dumps a new copy of the data out to a flat file enabling all users of the library to contribute their modifications to the data back to the PyOpenWorm project via GitHub.

A follow on to #2 is that the serialization method would need to preserve the ordering of data elements and write in some plain text format so that a simple diff on GitHub would be able to illuminate changes that were made.

Miscellaneous

Versioning

Experimental methods are constantly improving in biological research. These improvements may require updating the data we reference or store internally. However, in making updates we must not immediately expunge older content, breaking links created by internal and external agents. Ideally we would have a means of deprecating old data and specifying replacements. On the level of single resources, this is a trivial mapping which may be done transparently to all readers. For a more significant change, altering the schema, human intervention may be required to update external readers.

Why RDF?

RDF offers advantages in resilience to schema additions and increased flexibility in integrating data from disparate sources. [1] These qualities can be valued by comparison to relational database systems. Typically, schema changes in a relational database require extensive work for applications using it. [2] In the author’s experience, RDF databases offer more freedom in restructuring. Also, for data integration, SPARQL, the standard language for querying over RDF has Federated queries which allow for nearly painless integration of external SPARQL endpoints with existing queries.

[1]	http://answers.semanticweb.com/questions/19183/advantages-of-rdf-over-relational-databases

[2]	http://research.microsoft.com/pubs/118211/andy%20maule%20-%20thesis.pdf

FuXi

FuXi is implemented as a semantic reasoning layer in PyOpenWorm. In other words, it will be used to automatically infer (and set) properties from other properties in the worm database. This means that redundant information (ex: explicitly stating that each object is of class “dataType”) and subclass relationships (ex: that every object of type “Neuron” is also of type “Cell”), as well as other relationships, can be generated by the firing of FuXi’s rule engine, without being hand-coded.

Aside from the time it saves in coding, FuXi may allow for a smaller footprint in the cloud, as many relationships within the database could be inferred after download.

A rule might be:

• { x is “Neuron” } => { x is “Cell” }

And a fact might be:

• { “ADLR” is “Neuron” }

Given the above rule and fact, FuXi could infer the new fact:

• { “ADLR” is “Cell” }

The advantage of local storage of the database that goes along with each copy of the library is that the data will have the version number of the library. This means that data can be ‘deprecated’ along with a deprecated version of the library. This also will prevent changes made to a volatile database that break downstream code that uses the library.

Adding Data to YOUR OpenWorm Database

So, you’ve got some biological data about the worm and you’d like to save it in PyOpenWorm, but you don’t know how it’s done?

You’ve come to the right place!

A few biological entities (e.g., Cell, Neuron, Muscle, Worm) are pre-coded into PyOpenWorm. The full list is available in the API. If these entities already cover your use-case, then all you need to do is add values for the appropriate fields and save them. If you have data already loaded into your database, then you can load objects from it:

>>> from PyOpenWorm.neuron import Neuron
>>> n = Neuron.query()
>>> n.receptor('UNC-13')
PyOpenWorm.statement.Statement(...obj=yarom.propertyValue.PropertyValue(rdflib.term.Literal(u'UNC-13')), context=None)
>>> for x in n.load():
...     do_something_with_unc13_neuron(n)  # doctest.SKIP

If you need additional entities it’s easy to create them. Documentation for this is provided here.

Typically, you’ll want to attach the data that you insert to entities already in the database. This allows you to recover objects in a hierarchical fashion from the database later. Worm, for instance, has a property, neuron_network, which points to the Network which should contain all neural cells and synaptic connections. To initialize the hierarchy you would do something like:

>>> from PyOpenWorm.context import Context
>>> from PyOpenWorm.worm import Worm
>>> from PyOpenWorm.network import Network
>>> ctx = Context(ident='http://example.org/c-briggsae')
>>> w = ctx(Worm)('C. briggsae') # The name is optional and currently defaults to 'C. elegans'
>>> nn = ctx(Network)()          # make a neuron network
>>> w.neuron_network(nn)         # attach to the worm the neuron network
PyOpenWorm.statement.Statement(...)
>>> n = ctx(Neuron)('NeuronX')   # make a neuron
>>> n.receptor('UNC-13')         # state that the neuron has a UNC-13 type receptor
PyOpenWorm.statement.Statement(...)
>>> nn.neuron(n)                 # attach to the neuron network
PyOpenWorm.statement.Statement(...)
>>> ctx.save_context()           # save all of the data attached to the worm

It is possible to create objects without attaching them to anything and they can still be referenced by calling load on an instance of the object’s class as in n.load() above. This also points out another fact: you don’t have to set up the hierarchy for each insert in order for the objects to be linked to existing entities. If you have previously set up connections to an entity (e.g., Worm('C. briggsae')), assuming you only have one such entity, you can refer to things attached to it without respecifying the hierarchy for each script. The database packaged with PyOpenWorm should have only one Worm and one Network.

Remember that once you’ve made all of the statements, you must save the context in which the statements are made.

Future capabilities:

• Adding propositional logic to support making statements about all entities matching some conditions without needing to load() and save() them from the database.

• Statements like:

  ctx = Context(ident='http://example.org/c-briggsae')
  w = ctx.stored(Worm)()
  w.neuron_network.neuron.receptor('UNC-13')
  l = list(w.load()) # Get a list of worms with neurons expressing 'UNC-13'
  

  currently, to do the equivalent, you must work backwards, finding all neurons with UNC-13 receptors, then getting all networks with those neurons, then getting all worms with those networks:

  worms = set()
  n = ctx.stored(Neuron)()
  n.receptor('UNC-13')
  for ns in n.load():
      nn = ctx.stored(Network)()
      nn.neuron(ns)
      for z in nn.load():
          w = ctx.stored(Worm)()
          w.neuron_network(z)
          worms.add(w)
  l = list(worms)
  

  It’s not difficult logic, but it’s 8 extra lines of code for a, conceptually, very simple query.

• Also, queries like:

  l = list(ctx.stored(Worm)('C. briggsae').neuron_network.neuron.receptor()) # get a list
  #of all receptors expressed in neurons of C. briggsae
  

  Again, not difficult to write out, but in this case it actually gives a much longer query time because additional values are queried in a load() call that are never returned.

  We’d also like operators for composing many such strings so:

  ctx.stored(Worm)('C. briggsae').neuron_network.neuron.get('receptor', 'innexin') # list
  #of (receptor, innexin) values for each neuron
  

  would be possible with one query and thus not requiring parsing and iterating over neurons twice–it’s all done in a single, simple query.

Contexts

Above, we used contexts without explaining them. In natural languages, our statements are made in a context that influences how they should be interpreted. In PyOpenWorm, that kind of context-sensitivity is modeled by using PyOpenWorm.context.Context objects. To see what this looks like, let’s start with an example.

Basics

I have data about widgets from BigDataWarehouse (BDW) that I want to translate into RDF using PyOpenWorm, but I don’t want put them with my other widget data since BDW data may conflict with mine. Also, if get more BDW data, I want to be able to relate these data to that. A good way to keep data which are made at distinct times or which come from different, possibly conflicting, sources is using contexts. The code below shows how to do that:

>>> from rdflib import ConjunctiveGraph
>>> from PyOpenWorm.context import Context
>>> # from mymod import Widget  # my own POW widget model
>>> # from bdw import Load # BigDataWarehouse API

>>> # Create a Context with an identifier appropriate to this BDW data import
>>> ctx = Context(ident='http://example.org/data/imports/BDW_Widgets_2017-2018')

>>> # Create a context manager using the default behavior of reading the
>>> # dictionary of current local variables
>>> with ctx(W=Widget) as c:
...     for record in Load(data_set='Widgets2017-2018'):
...         # declares Widgets in this context
...         c.W(part_number=record.pnum,
...             fullness=record.flns,
...             hardiness=record.hrds)
Widget(ident=rdflib.term.URIRef(...))


>>> # Create an RDFLib graph as the target for the data
>>> g = ConjunctiveGraph()

>>> # Save the data
>>> ctx.save_context(g)

>>> # Serialize the data in the nquads format so we can see that all of our
>>> # statements are in the proper context
>>> print(g.serialize(format='nquads').decode('UTF-8'))
<http://openworm.org/entities/Widget/12> <http...> <http://example.org/data/imports/BDW_Widgets_2017-2018> .
<http://openworm.org/entities/Widget/12> <...

If you’ve worked with lots of data before, this kind of pattern should be familiar. You can see how, with later imports, you would follow the naming scheme to create new contexts (e.g., http://example.org/data/imports/BDW_Widgets_2018-2019). These additional contexts could then have separate metadata attached to them or they could be compared:

>>> len(list(ctx(Widget)().load()))
1
>>> len(list(ctx18(Widget)().load()))  # 2018-2019 context
3

Context Metadata

Contexts, because they have identifiers just like any other objects, so we can make statements about them as well. An essential statement is imports: Contexts import other contexts, which means, if you follow PyOpenWorm semantics, that when you query objects from the importing context, that the imported contexts will also be available to query.

Making data objects

To make a new object type like Neuron or PyOpenWorm.worm.Worm, for the most part, you just need to make a Python class.

Say, for example, that I want to record some information about drug reactions in C. elegans. I make Drug and Experiment classes to describe C. elegans reactions:

>>> from PyOpenWorm.dataObject import (DataObject,
...                                    DatatypeProperty,
...                                    ObjectProperty,
...                                    Alias)
>>> from PyOpenWorm.worm import Worm
>>> from PyOpenWorm.evidence import Evidence
>>> from PyOpenWorm.document import Document
>>> from PyOpenWorm.context import Context
>>> from PyOpenWorm.mapper import Mapper
>>> from PyOpenWorm import connect, ModuleRecorder

>>> class Drug(DataObject):
...     name = DatatypeProperty()
...     drug_name = Alias(name)
...     def identifier_augment(self):
...         return self.make_identifier_direct(self.name.onedef())
...
...     def defined_augment(self):
...         return self.name.has_defined_value()

>>> class Experiment(DataObject):
...     drug = ObjectProperty(value_type=Drug)
...     subject = ObjectProperty(value_type=Worm)
...     route_of_entry = DatatypeProperty()
...     reaction = DatatypeProperty()

# Do some accounting stuff to register the classes. Usually happens behind
# the scenes.
>>> m = Mapper(('PyOpenWorm.dataObject.DataObject',))
>>> ModuleRecorder.add_listener(m)
>>> m.process_classes(Drug, Experiment)

So, we have created I can then make a Drug object for moon rocks and describe an experiment by Aperture Labs:

>>> ctx = Context(ident='http://example.org/experiments', mapper=m)
>>> d = ctx(Drug)(name='moon rocks')
>>> e = ctx(Experiment)(key='experiment001')
>>> w = ctx(Worm)('C. elegans')
>>> e.subject(w)
PyOpenWorm.statement.Statement(...Context(.../experiments"))

>>> e.drug(d)
PyOpenWorm.statement.Statement(...)

>>> e.route_of_entry('ingestion')
PyOpenWorm.statement.Statement(...)

>>> e.reaction('no reaction')
PyOpenWorm.statement.Statement(...)

>>> ev = Evidence(key='labresults', reference=Document(author="Aperture Labs"))
>>> ev.supports(ctx)
PyOpenWorm.statement.Statement(...)

and save those statements:

>>> ctx.save()

For simple objects, this is all we have to do.

You can also add properties to an object after it has been created by calling either ObjectProperty or DatatypeProperty on the class:

>>> d = ctx(Drug)(name='moon rocks')
>>> Drug.DatatypeProperty('granularity', owner=d)
__main__.Drug_granularity(owner=Drug(ident=rdflib.term.URIRef(u'http://openworm.org/entities/Drug/moon%20rocks')))

>>> d.granularity('ground up')
PyOpenWorm.statement.Statement(...Context(.../experiments"))

>>> do = Drug()

Properties added in this fashion will not propagate to any other objects:

>>> do.granularity
Traceback (most recent call last):
    ...
AttributeError: 'Drug' object has no attribute 'granularity'

They will, however, be saved along with the object they are attached to.

Sharing Data with other users

Sharing is key to PyOpenWorm. This document covers the appropriate way to share changes with other PyOpenWorm users.

The shared PyOpenWorm database is stored in a Git repository distinct from the PyOpenWorm source code. Currently the database is stored in a Github repository here .

When you create a database normally, it will be stored in a format which is opaque to humans. In order to share your database you have two options: You can share the scripts which are used to create your database or you can share a human-readable serialization of the database. The second option is better since it doesn’t require re-running your script to use the generated data, but it is best to share both.

For sharing the serialization, you should first clone the repository linked above, read the current serialization into your database (see below for an example of how you would do this), and then write out the serialization:

import PyOpenWorm as P
P.connect('path/to/your/config/file')
P.config()['rdf.graph'].serialize('out.n3', format='n3')
P.disconnect()

Commit, your changes to the git repository, push to a fork of the repository on Github and submit a pull request on the main repository. If for some reason your are unwilling or unable to create a Github account, post to the OpenWorm-discuss mailing list with a patch on the main repository with your changes and someone will have a look, possibly ask for adjustments or justification for your addition, and ultimately merge the changes for you.

To read the database back in you would do something like:

import PyOpenWorm as P
P.connect('path/to/your/config/file')
P.config()['rdf.graph'].parse('out.n3', format='n3')
P.disconnect()

Scripts are also added to the repository on Github to the scripts subdirectory.

Working with contexts

Contexts are introduced in Adding Data to YOUR OpenWorm Database. Here we provide a little more…context.

Background

Contexts were introduced to PyOpenWorm (POW) as a generic tool for grouping statements. We need to group statements to make statements about statements like “Who made these statements?” or “When were these statements made?”. That’s the main usage. Beyond that, we need a way to share statements. Contexts have identifiers by which we can naturally refer to contexts from other contexts.

POW need a way to represent contexts with the existing statement form. Other alternatives were considered, such as using Python’s context managers, but I (Mark) also wanted a way to put statements in a context that could also be carried with the subject of the statement. Using the wrapt package’s proxies allows to achieve this while keeping the interface of the wrapped object the same, which is useful since it doesn’t require a user of the object to know anything about contexts unless they need to change the context of a statement.

The remainder of this page will go into doing some useful things with contexts.

Classes and contexts

POW can load classes as well as instances from an RDF graph. The packages which define the classes must already be installed in the Python library path, and a few statements need to be in the graph you are loading from or in a graph imported (transitively) by that graph. The statements you need are these

:a_class_desc <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://openworm.org/entities/PythonClassDescription> .
:a_class_desc <http://openworm.org/entities/ClassDescription/module> :a_module .
:a_class_desc <http://openworm.org/entities/PythonClassDescription/name> "AClassName" .

:a_module <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://openworm.org/entities/PythonModule> .
:a_module <http://openworm.org/entities/PythonModule/name> "APackage.and.module.name" .

where :a_class_desc and :a_module are placeholders for objects which will typically be created by POW on the user’s behalf, and AClassName is the name of the class available at the top-level of the module APackage.and.module.name. These statements will be created in memory by POW when a module defining a DataObject-derived class is first processed by a Mapper which will happen after the module is imported.

pow Command Line

The pow command line provides a high-level interface for working with PyOpenWorm-managed data. The central object which pow works on is the repository, which contains the database – a set of files in binary format. The sub-commands act on important files inside the repository or with entities in the database.

To get usage information:

pow --help

To clone a repository:

pow clone $database_url

This will clone a repository into .pow in your current working directory. After a successful clone, a binary database usable as a PyOpenWorm store will have been created from the serialized graphs in the repository.

To save changes made to the database:

pow commit -m "I'm a commit message!"

To recreate the database from serialized graphs, including uncommited changes:

pow regendb

To make a new repository:

pow init

This will create a repository in .pow in your current working directory.

For Developers

Testing in PyOpenWorm

Preparing for tests

PyOpenWorm should be installed like:

python setup.py develop

The default database should be populated like:

pow clone https://github.com/openworm/OpenWormData.git

Running tests

Tests should be run via setup.py like:

python setup.py test

you can pass options to pytest like so:

python setup.py test --addopts '-k DataIntegrityTest'

Writing tests

Tests are written using Python’s unittest. In general, a collection of closely related tests should be in one file. For selecting different classes of tests, tests can also be tagged using pytest marks like:

@pytest.mark.tag
class TestClass(unittest.TestCase):
    ...

Currently, marks are used to distinguish between unit-level tests and others which have the inttest mark

Adding documentation

Documentation for PyOpenWorm is housed in two locations:

1. In the top-level project directory as INSTALL.md and README.md.
2. As a Sphinx project under the docs directory

By way of example, to add a page about useful facts concerning C. elegans to the documentation, include an entry in the list under toctree in docs/index.rst like:

worm-facts

and create the file worm-facts.rst under the docs directory and add a line:

.. _worm-facts:

to the top of your file, remembering to leave an empty line before adding all of your wonderful worm facts.

You can get a preview of what your documentation will look like when it is published by running sphinx-build on the docs directory:

sphinx-build -w sphinx-errors docs build_destination

The docs will be compiled to html which you can view by pointing your web browser at build_destination/index.html. If you want to view the documentation locally with the ReadTheDocs theme you’ll need to download and install it.

API Documentation

API documentation is generated by the Sphinx autodoc extension. The format should be easy to pick up on, but a reference is available here. Just add a docstring to your function/class/method and add an automodule line to PyOpenWorm/__init__.py and your class should appear among the other documented classes.

Substitutions

Project-wide substitutions can be (conservatively!) added to allow for easily changing a value over all of the documentation. Currently defined substitutions can be found in conf.py in the rst_epilog setting. More about substitutions

Conventions

If you’d like to add a convention, list it here and start using it. It can be reviewed as part of a pull request.

1. Narrative text should be wrapped at 80 characters.
2. Long links should be extracted from narrative text. Use your judgement on what ‘long’ is, but if it causes the line width to stray beyond 80 characters that’s a good indication.

RDF semantics for PyOpenWorm

In the context of PyOpenWorm, biological objects are classes of, for instance, anatomical features of a worm. That is to say, statements made about C. elegans are not about a specific worm, but are stated about the entire class of worms. The semantics of a property SimpleProperty/value value triple are that if any value is set, then without any additional statements being made, an instance of the object has been observed to have the value at some point in time, somewhere, under some set of conditions. In other words, the statement is an existential quantification over the associated object(class).

The purpose of the identifiers for Properties is to allow statements to be made about them directly. An example:

<http://openworm.org/entities/Entity/1> <http://openworm.org/entities/Entity/interactsWith> <http://openworm.org/entities/Entity_interactsWith/2> .
<http://openworm.org/entities/Entity_interactsWith/2> <http://openworm.org/entities/SimpleProperty/value> <http://openworm.org/entities/Entity/3> .

<http://openworm.org/entities/Entity/4> <http://openworm.org/entities/Entity/modulates> <http://openworm.org/entities/Entity_modulates/5> .
<http://openworm.org/entities/Entity_modulates/5> <http://openworm.org/entities/SimpleProperty/value> <http://openworm.org/entities/Entity_interactsWith/2>

RDF structure for PyOpenWorm

For most use cases, it is (hopefully) not necessary to write custom queries over the RDF graph in order to work with PyOpenWorm. However, if it does become necessary, it will be helpful to have an understanding of the structure of the RDF graph. Thus, a summary is given below.

For all DataObjects which are not Properties, there is an identifier of the form

<http://openworm.org/entities/Object_type/md5sum>

stored in the graph. This identifier will be associated with type data:

<http://openworm.org/entities/Object_type/md5sum> rdf:type <http://openworm.org/entities/Object_type> .
<http://openworm.org/entities/Object_type/md5sum> rdf:type <http://openworm.org/entities/parent_of_Object_type> .
<http://openworm.org/entities/Object_type/md5sum> rdf:type <http://openworm.org/entities/parent_of_parent_of_Object_type> .
...

Properties have a slightly different form. They also have an identifier, which for SimpleProperties will look like this:

<http://openworm.org/entities/OwnerType_propertyName/md5sum>

OwnerType is the type of the Property’s owner and propertyName is the name by which the property is accessed from an object of the owner’s type. Other Properties will not necessarily have this form, but all of the standard Properties are implemented in terms of SimpleProperties and have no direct representation in the graph. For other Properties it is necessary to refer to their documentation or to examine the triples released by the Property of interest.

A DataObject’s identifier is connected to a property in a triple like:

<http://openworm.org/entities/OwnerType/md5sum> <http://openworm.org/entities/OwnerType/propertyName> <http://openworm.org/entities/OwnerType_propertyName/md5sum>

and the property is connected to its values like:

<http://openworm.org/entities/OwnerType_propertyName/md5sum> <http://openworm.org/entities/SimpleProperty/value> "A literal value"

The following API calls do not yet exist, but would be excellent next functions to implement

Population()

A collection of cells. Constructor creates an empty population.

Population.filterCells(filters : ListOf(PairOf(unboundMethod, methodArgument))) : Population

Allows for groups of cells to be created based on shared properties including neurotransmitter, anatomical location or region, cell type.

Example:

p = Worm.cells()
p1 = p.filterCells([(Cell.lineageName, "AB")]) # A population of cells with AB as the blast cell

NeuroML()

A utility for generating NeuroML files from other objects. The semantics described above do not apply here.

NeuroML.generate(object : {Network, Neuron, IonChannel}, type : {0,1,2}) : neuroml.NeuroMLDocument

Get a NeuroML object that represents the given object. The type determines what content is included in the NeuroML object:

• 0=full morphology+biophysics
• 1=cell body only+biophysics
• 2=full morphology only

NeuroML.write(document : neuroml.NeuroMLDocument, filename : String)

Write out a NeuroMLDocument

PyOpenWorm coding standards

Pull requests are required to follow the PEP-8 Guidelines for contributions of Python code to PyOpenWorm, with some exceptions noted below. Compliance can be checked with the pep8 tool and these command line arguments:

--max-line-length=120 --ignore=E261,E266,E265,E402,E121,E123,E126,E226,E24,E704,E128

Refer to the pep8 documentation for the meanings of these error codes.

Lines of code should only be wrapped before 120 chars for readability. Comments and string literals, including docstrings, can be wrapped to a shorter length.

Some violations can be corrected with autopep8.

Querying for data objects

X.query form

Creates modified version of the DataObject subclass which is fit for using in queries. May do other additional things latter, but, principally, it overrides the identifier generation based on attributes.

Examples for querying for a Neuron object:

Neuron.query(name='AVAL')
ctx(Neuron).query(name='AVAL')
ctx.stored(Neuron).query(name='AVAL')
ctx.mixed(Neuron).query(name='AVAL')

this can be important for when a class generates identifiers based on some number of properties, but a subclass doesn’t use the superclass identifier scheme (Cell and Neuron are an example). The query form allows to query from the superclass as you normally would to get subclass instances.

Issues

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