pycldf.dataset

The core object of the API, bundling most access to CLDF data, is the pycldf.Dataset . In the following we’ll describe its attributes and methods, bundled into thematic groups.

Dataset initialization

class pycldf.dataset.Dataset(tablegroup)[source]

API to access a CLDF dataset.

Parameters:: tablegroup (csvw.metadata.TableGroup) –

__init__(tablegroup)[source]

A Dataset is initialized passing a TableGroup. For convenience methods to get such a TableGroup instance, see the factory methods

in_dir()
from_metadata()
from_data()

Parameters:: tablegroup (csvw.metadata.TableGroup) –

classmethod from_data(fname)[source]

Initialize a Dataset from a single CLDF data file.

See https://github.com/cldf/cldf#metadata-free-conformance

Return type:: Dataset
Parameters:: fname (typing.Union[str, pathlib.Path]) –

classmethod from_metadata(fname)[source]

Initialize a Dataset with the metadata found at fname.

Parameters:: fname (typing.Union[str, pathlib.Path]) – A URL (str) or a local path (str or pathlib.Path). If fname points to a directory, the default metadata for the respective module will be read.
Return type:: Dataset

classmethod in_dir(d, empty_tables=False)[source]

Create a Dataset in a (possibly empty or even non-existing) directory.

The dataset will be initialized with the default metadata for the respective module.

Return type:

Dataset

Parameters:

d (typing.Union[str, pathlib.Path]) –
empty_tables (bool) –

Accessing dataset metadata

class pycldf.Dataset(tablegroup)[source]

API to access a CLDF dataset.

Parameters:: tablegroup (csvw.metadata.TableGroup) –

property bibname: str

Returns:: Filename of the sources BibTeX file.

property bibpath: Union[str, Path]

Returns:: Location of the sources BibTeX file. Either a URL (str) or a local path (pathlib.Path).

property directory: Union[str, Path]

Returns:: The location of the metadata file. Either a local directory as pathlib.Path or a URL as str.

property module: str

Returns:: The name of the CLDF module of the dataset.

property properties: dict

Returns:: Common properties of the CSVW TableGroup of the dataset.

Accessing schema objects: components, tables, columns, etc.

Similar to capability checks in programming languages that use duck typing, it is often necessary to access a datasets schema, i.e. its tables and columns to figure out whether the dataset fits a certain purpose. This is supported via a dict-like interface provided by pycldf.Dataset, where the keys are table specifiers or pairs (table specifier, column specifier). A table specifier can be a table’s component name or its url, a column specifier can be a column name or its propertyUrl.

check existence with in:

if 'ValueTable' in dataset: pass
if ('ValueTable', 'Language_ID') in dataset: pass

retrieve a schema object with item access:

table = dataset['ValueTable']
column = dataset['ValueTable', 'Language_ID']

retrieve a schema object or a default with .get:

table_or_none = dataset.get('ValueTableX')
column_or_none = dataset.get(('ValueTable', 'Language_ID'))

remove a schema object with del:

del dataset['ValueTable', 'Language_ID']
del dataset['ValueTable']

Note: Adding schema objects is not supported via key assignment, but with a set of specialized methods described in Editing metadata and schema.

class pycldf.Dataset(tablegroup)[source]

API to access a CLDF dataset.

Parameters:: tablegroup (csvw.metadata.TableGroup) –

__contains__(item)[source]

Check whether a dataset specifies a table or column.

Parameters:: item – See __getitem__()
Return type:: bool

__getitem__(item)[source]

Access to tables and columns.

If a pair (table-spec, column-spec) is passed as item, a Column will be returned, otherwise item is assumed to be a table-spec.

A table-spec may be :rtype: typing.Union[csvw.metadata.Table, csvw.metadata.Column]

a CLDF ontology URI matching the dc:conformsTo property of a table
the local name of a CLDF ontology URI, where the complete URI matches the the dc:conformsTo property of a table
a filename matching the url property of a table

A column-spec may be

a CLDF ontology URI matching the propertyUrl of a column
the local name of a CLDF ontology URI, where the complete URI matches the propertyUrl of a column
the name of a column

Raises:: SchemaError – If no matching table or column is found.

property column_names: SimpleNamespace

In-direction layer, mapping ontology terms to local column names (or None).

Note that this property is computed each time it is accessed (because the dataset schema may have changed). So when accessing a dataset for reading only, calling code should use readonly_column_names.

Returns:: an types.SimpleNamespace object, with attributes <object>s for each component <Object>Table defined in the ontology. Each such attribute evaluates to None if the dataset does not contain the component. Otherwise, it’s an types.SimpleNamespace object mapping each property defined in the ontology to None - if no such column is specified in the component - and the local column name if it is.

property components: Dict[str, Table]

Returns:: Mapping of component name to table obejcts as defined in the dataset.

get(item, default=None)[source]

Acts like dict.get.

Parameters:: item – See __getitem__()
Return type:: typing.Union[csvw.metadata.Table, csvw.metadata.Column, None]

get_foreign_key_reference(table, column)[source]

Retrieve the reference of a foreign key constraint for the specified column.

Parameters:

table (typing.Union[str, csvw.metadata.Table]) – Source table, specified by filename, component name or as Table instance.
column (typing.Union[str, csvw.metadata.Column]) – Source column, specified by column name, CLDF term or as Column instance.

Return type:

typing.Optional[typing.Tuple[csvw.metadata.Table, csvw.metadata.Column]]

Returns:

A pair (Table, Column) specifying the reference column - or None.

readonly_column_names[source]

Returns:: types.SimpleNamespace with component names as attributes.

property tables: list

Returns:: All tables defined in the dataset.

Editing metadata and schema

In many cases, editing the metadata of a dataset is as simple as editing properties(), but for the somewhat complex formatting of provenance data, we provide the shortcut add_provenance().

Likewise, csvw.Table and csvw.Column objects in the dataset’s schema can be edited “in place”, by setting their attributes or adding to/editing their common_props dictionary. Thus, the methods listed below are concerned with adding and removing tables and columns.

class pycldf.Dataset(tablegroup)[source]

API to access a CLDF dataset.

Parameters:: tablegroup (csvw.metadata.TableGroup) –

add_columns(table, *cols)[source]

Add columns specified by cols to the table specified by table.

Parameters:: table (typing.Union[str, csvw.metadata.Table]) –
Return type:: None

add_component(component, *cols, **kw)[source]

Add a CLDF component to a dataset.

Adding data

The main method to persist data as CLDF dataset is write(), which accepts data for all CLDF data files as input. This does not include sources, though. These must be added using add_sources().

class pycldf.Dataset(tablegroup)[source]

API to access a CLDF dataset.

Parameters:: tablegroup (csvw.metadata.TableGroup) –

add_sources(*sources, **kw)[source]

Add sources to the dataset.

Parameters:: sources – Anything accepted by pycldf.sources.Sources.add().

Reading data

Reading rows from CLDF data files, honoring the datatypes specified in the schema, is already implemented by csvw. Thus, the simplest way to read data is iterating over the csvw.Table objects. However, this will ignore the semantic layer provided by CLDF. E.g. a CLDF languageReference linking a value to a language will be appear in the dict returned for a row under the local column name. Thus, we provide several more convenient methods to read data.

class pycldf.Dataset(tablegroup)[source]

API to access a CLDF dataset.

Parameters:: tablegroup (csvw.metadata.TableGroup) –

get_object(table, id_, cls=None, pk=False)[source]

Get a row of a component as pycldf.orm.Object instance.

Return type:: pycldf.orm.Object

get_row(table, id_)[source]

Retrieve a row specified by table and CLDF id.

Raises:: ValueError – If no matching row is found.
Parameters:: table (typing.Union[str, csvw.metadata.Table]) –
Return type:: dict

get_row_url(table, row)[source]

Get a URL associated with a row. Tables can specify associated row URLs by

listing one column with datatype anyURI or
specfying a valueUrl property for their ID column.

For rows representing objects in web applications, this may be the objects URL. For rows representing media files, it may be a URL locating the file on a media server.

Parameters:

table (typing.Union[str, csvw.metadata.Table]) – Table specified in a way that __getitem__ understands.
row – A row specified by ID or as dict as returned when iterating over a table.

Return type:

typing.Optional[str]

Returns:

a str representing a URL or None.

iter_rows(table, *cols)[source]

Iterate rows in a table, resolving CLDF property names to local column names.

Parameters:

table (typing.Union[str, csvw.metadata.Table]) – Table name.
cols – List of CLDF property terms which must be resolved in resulting dict s. I.e. the row dicts will be augmented with copies of the values keyed with CLDF property terms.

Return type:

typing.Iterator[dict]

objects(table, cls=None)[source]

Read data of a CLDF component as pycldf.orm.Object instances.

Parameters:

table (str) – table to read, specified as component name.
cls (typing.Optional[typing.Type]) – pycldf.orm.Object subclass to instantiate objects with.

Return type:

pycldf.util.DictTuple

Returns:

Writing (meta)data

class pycldf.Dataset(tablegroup)[source]

API to access a CLDF dataset.

Parameters:: tablegroup (csvw.metadata.TableGroup) –

write(fname=None, zipped=None, **table_items)[source]

Write metadata, sources and data. Metadata will be written to fname (as interpreted in pycldf.dataset.Dataset.write_metadata()); data files will be written to the file specified by csvw.Table.url of the corresponding table, interpreted as path relative to directory().

Parameters:

zipped (typing.Optional[typing.Iterable]) – Iterable listing keys of table_items for which the table file should be zipped.
table_items (typing.List[dict]) – Mapping of table specifications to lists of row dicts.
fname (typing.Optional[pathlib.Path]) –

Return type:

pathlib.Path

Returns:

Path of the CLDF metadata file as written to disk.

write_metadata(fname=None)[source]

Write the CLDF metadata to a JSON file.

Fname:: Path of a file to write to, or None to use the default name and write to directory().
Parameters:: fname (typing.Union[str, pathlib.Path, None]) –
Return type:: pathlib.Path

write_sources(zipped=False)[source]

Write the sources BibTeX file to bibpath()

Return type:: typing.Optional[pathlib.Path]
Returns:: None, if no BibTeX file was written (because no source items were added), pathlib.Path of the written BibTeX file otherwise. Note that this path does not need to exist, because the content may have been added to a zip archive.
Parameters:: zipped (bool) –

Reporting

class pycldf.Dataset(tablegroup)[source]

API to access a CLDF dataset.

Parameters:: tablegroup (csvw.metadata.TableGroup) –

stats(exact=False)[source]

Compute summary statistics for the dataset.

Return type:: typing.List[typing.Tuple[str, str, int]]
Returns:: List of triples (table, type, rowcount).

validate(log=None, validators=None, ontology_path=None)[source]

Validate schema and data of a Dataset:

Make sure the schema follows the CLDF specification and
make sure the data is consistent with the schema.

Parameters:

log (typing.Optional[logging.Logger]) – a logging.Logger to write ERRORs and WARNINGs to. If None, an exception will be raised at the first problem.
validators (typing.Optional[typing.List[typing.Tuple[str, str, callable]]]) – Custom validation rules, i.e. triples (tablespec, columnspec, attrs validator)

Raises:

ValueError – if a validation error is encountered (and log is None).

Return type:

bool

Returns:

Flag signaling whether schema and data are valid.

Dataset discovery

We provide two functions to make it easier to discover CLDF datasets in the file system. This is useful, e.g., when downloading archived datasets from Zenodo, where it may not be known in advance where in a zip archive the metadata file may reside.

pycldf.sniff(p)[source]

Determine whether a file contains CLDF metadata.

Parameters:: p (pathlib.Path) – pathlib.Path object for an existing file.
Return type:: bool
Returns:: True if the file contains CLDF metadata, False otherwise.

pycldf.iter_datasets(d)[source]

Discover CLDF datasets - by identifying metadata files - in a directory.

Parameters:: d (pathlib.Path) – directory
Return type:: typing.Iterator[pycldf.dataset.Dataset]
Returns:: generator of Dataset instances.

Sources

When constructing sources for a CLDF dataset in Python code, you may pass pycldf.Source instances into pycldf.Dataset.add_sources(), or use pycldf.Reference.__str__() to format a row’s source value properly.

Direct access to pycldf.dataset.Sources is rarely necessary (hence it is not available as import from pycldf directly), because each pycldf.Dataset provides access to an apprpriately initialized instance in its sources attribute.

class pycldf.Source(genre, id_, *args, _check_id=True, _lowercase=False, _strip_tex=None, **kw)[source]

A bibliograhical record, specifying a source for some data in a CLDF dataset.

Parameters:

genre (str) –
id_ (str) –
_check_id (bool) –
_lowercase (bool) –
_strip_tex (typing.Optional[typing.Iterable[str]]) –

classmethod from_entry(key, entry, **_kw)[source]

Create a cls instance from a pybtex entry object.

Parameters:

key – BibTeX citation key of the entry
entry – pybtex.database.Entry instance
_kw – Non-bib-metadata keywords to be passed for cls instantiation

Returns:

cls instance

class pycldf.Reference(source, desc)[source]

A reference connects a piece of data with a Source, typically adding some citation context often page numbers, or similar.

Parameters:

source (pycldf.sources.Source) –
desc (typing.Optional[str]) –

__str__()[source]: String representation of a reference according to the CLDF specification.

See also

https://github.com/cldf/cldf#sources

class pycldf.dataset.Sources[source]

A dict like container for all sources linked to data in a CLDF dataset.

add(*entries, **kw)[source]

Add a source, either specified as BibTeX string or as Source.

Parameters:: entries (typing.Union[str, pycldf.sources.Source]) –

expand_refs(refs, **kw)[source]

Turn a list of string references into proper Reference instances, looking up sources in self.

This can be used from a pycldf.Dataset as follows:

>>> for row in dataset.iter_rows('ValueTable', 'source'):
...     for ref in dataset.sources.expand_refs(row['source']):
...         print(ref.source)

Parameters:: refs (typing.Iterable[str]) –
Return type:: typing.Iterable[pycldf.sources.Reference]

static parse(ref)[source]

Parse the string representation of a reference into source ID and context.

Raises:: ValueError – if the reference does not match the expected format.
Parameters:: ref (str) –
Return type:: typing.Tuple[str, str]

Subclasses supporting specific CLDF modules

class pycldf.Generic(tablegroup)[source]

Generic datasets have no primary table.