Module tf.fabric

Fabric

The main class that works the core API is Fabric.

It is responsible for feature data loading and saving.

Tutorial

The tutorials for specific annotated corpora (see tf.about.corpora) put the TF API on show for vastly different corpora.

Generic API versus apps

This is the API of TF in general. TF has no baked in knowledge of particular corpora.

However, TF comes with several apps that make working with specific tf.about.corpora easier. Such an app may be as simple as a config.yaml file, even an empty one. The extra functions of those apps are available through the advanced API: A, see tf.app.

Fabric has built-in volume support: it can load volumes of a work and it can collect volumes into a new work.

Fabric is an extension of tf.core.fabric where volume support is added.

Classes

class Fabric (locations=None, modules=None, silent='auto', volume=None, collection=None, **kwargs)
Expand source code Browse git 
class Fabric(FabricCore):
    """Initialize the core API for a corpus.

    !!! note "Implementation"
        `Fabric` is implemented as a subclass of `tf.core.fabric.FabricCore`

    See `tf.core.fabric.FabricCore` for most of the functionality.
    Here we document the volume support only.

    Parameters
    ----------
    collection: string, optional None
        If the collection exists, it will be loaded instead of the whole corpus.
        If the collection does not exist an error will be generated.

    volume: string, optional None
        If the volume exists, it will be loaded instead of the whole corpus.
        If the volume does not exist an error will be generated.

    When determining whether the volume exists, only the first members of `locations`
    and `modules` will be used.
    There the volumes reside under a directory `_local`.
    You may want to add `_local` to your `.gitignore`, so that volumes generated
    in a back-end directory will not be pushed.

    !!! caution "Volumes and collections"
        It is an error to load a volume as a collection and vice-versa

        You get a warning if you pass both a volume and a collection.
        The collection takes precedence, and the volume is ignored in that case.
    """

    def __init__(
        self,
        locations=None,
        modules=None,
        silent=SILENT_D,
        volume=None,
        collection=None,
        **kwargs,
    ):

        if modules is None:
            module = [""]
        elif type(modules) is str:
            module = [normpath(x.strip()) for x in itemize(modules, "\n")]
        else:
            module = [normpath(str(x)) for x in modules]
        module = module[0] if module else ""
        module = module.strip("/")

        if locations is None:
            location = LOCATIONS if LOCATIONS else [""]
        elif type(locations) is str:
            location = [normpath(x.strip()) for x in itemize(locations, "\n")]
        else:
            location = [normpath(str(x)) for x in locations]
        location = location[0] if location else ""
        location = location.rstrip("/")

        setDir(self)
        location = expandDir(self, location)
        sep = "/" if location and module else ""

        location = f"{location}{sep}{module}"
        sep = "/" if location else ""
        volumeBase = f"{location}{sep}{LOCAL}"
        collectionBase = f"{location}{sep}{LOCAL}"

        TM = Timestamp(silent=silent)

        if collection:
            collectionLoc = f"{collectionBase}/{collection}"
            self.collectionLoc = collectionLoc
            locations = collectionLoc
            modules = [""]
            if not dirExists(locations):
                TM = Timestamp(silent=silent)
                TM.error(f"Collection {collection} not found under {ux(collectionLoc)}")
        elif volume:
            volumeLoc = f"{volumeBase}/{volume}"
            self.volumeLoc = volumeLoc
            locations = volumeLoc
            modules = [""]
            if not dirExists(locations):
                TM.error(f"Volume {volume} not found under {ux(volumeLoc)}")

        if collection and volume:
            TM.warning(
                f"Both collection={collection} and volume={volume} specified.", tm=False
            )
            TM.warning("Ignoring the volume", tm=False)

        super().__init__(locations=locations, modules=modules, silent=silent, **kwargs)
        self.volumeBase = volumeBase
        self.collectionBase = collectionBase
        self.collection = collection
        self.volume = None if collection else volume
        self.exportMQL = types.MethodType(exportMQL, self)

    def _makeApi(self):
        api = super()._makeApi()

        if self.collection:
            self.collectionInfo = self.features[OTYPE].metaData.get("collection", None)
            if self.collectionInfo is None:
                self.error("This is not a collection!")
                self.good = False
                return None

        elif self.volume:
            self.volumeInfo = self.features[OTYPE].metaData.get("volume", None)
            if self.volumeInfo is None:
                self.error("This is not a volume!")
                self.good = False
                return None
        return api

    def getVolumes(self):
        """Lists available volumes within the dataset.

        See `tf.volumes.extract.getVolumes`.
        """

        volumeBase = self.volumeBase
        return getVolumes(volumeBase)

    def extract(
        self, volumes=True, byTitle=True, silent=SILENT_D, overwrite=None, show=False
    ):
        """Extract volumes from the currently loaded work.

        This function is only provided if the dataset is a work,
        i.e. it is loaded as a whole.
        When a single volume of a work is loaded, there is no `extract` method.

        See `tf.volumes.extract` and note that parameters
        `workLocation`, `volumesLocation`, `api`
        will be supplied automatically.
        """

        silent = silentConvert(silent)
        volume = self.volume
        volumeBase = self.volumeBase
        api = self.api

        if volume:
            self.error("Cannot extract volumes from a single volume of a work")
            return

        return extract(
            None,
            volumeBase,
            volumes=volumes,
            byTitle=byTitle,
            silent=silent,
            api=api,
            overwrite=overwrite,
            checkOnly=False,
            show=show,
        )

    def collect(
        self,
        volumes,
        collection,
        volumeType=None,
        volumeFeature=None,
        mergeTypes=None,
        featureMeta=None,
        silent=SILENT_D,
        overwrite=None,
    ):
        """Creates a work out of a number of volumes.

        Parameters
        ----------

        volumes: tuple
            Just the names of the volumes that you want to collect.

        collection: string
            The name of the new collection

        See `tf.volumes.collect` for the other parameters and note that parameter
        `workLocation` will be supplied automatically from `collection`.
        """

        volumeBase = self.volumeBase

        return collect(
            tuple(f"{volumeBase}/{name}" for name in volumes),
            f"{volumeBase}/{collection}",
            volumeType=volumeType,
            volumeFeature=volumeFeature,
            mergeTypes=mergeTypes,
            featureMeta=featureMeta,
            silent=silent,
            overwrite=overwrite,
        )

Initialize the core API for a corpus.

Implementation

Fabric is implemented as a subclass of FabricCore

See FabricCore for most of the functionality. Here we document the volume support only.

Parameters

collection : string, optional None
If the collection exists, it will be loaded instead of the whole corpus. If the collection does not exist an error will be generated.
volume : string, optional None
If the volume exists, it will be loaded instead of the whole corpus. If the volume does not exist an error will be generated.

When determining whether the volume exists, only the first members of locations and modules will be used. There the volumes reside under a directory _local. You may want to add _local to your .gitignore, so that volumes generated in a back-end directory will not be pushed.

Volumes and collections

It is an error to load a volume as a collection and vice-versa

You get a warning if you pass both a volume and a collection. The collection takes precedence, and the volume is ignored in that case.

Ancestors

Methods

def collect(self,
volumes,
collection,
volumeType=None,
volumeFeature=None,
mergeTypes=None,
featureMeta=None,
silent='auto',
overwrite=None)
Expand source code Browse git 
def collect(
    self,
    volumes,
    collection,
    volumeType=None,
    volumeFeature=None,
    mergeTypes=None,
    featureMeta=None,
    silent=SILENT_D,
    overwrite=None,
):
    """Creates a work out of a number of volumes.

    Parameters
    ----------

    volumes: tuple
        Just the names of the volumes that you want to collect.

    collection: string
        The name of the new collection

    See `tf.volumes.collect` for the other parameters and note that parameter
    `workLocation` will be supplied automatically from `collection`.
    """

    volumeBase = self.volumeBase

    return collect(
        tuple(f"{volumeBase}/{name}" for name in volumes),
        f"{volumeBase}/{collection}",
        volumeType=volumeType,
        volumeFeature=volumeFeature,
        mergeTypes=mergeTypes,
        featureMeta=featureMeta,
        silent=silent,
        overwrite=overwrite,
    )

Creates a work out of a number of volumes.

Parameters

volumes : tuple
Just the names of the volumes that you want to collect.
collection : string
The name of the new collection

See tf.volumes.collect for the other parameters and note that parameter workLocation will be supplied automatically from collection.

def extract(self, volumes=True, byTitle=True, silent='auto', overwrite=None, show=False)
Expand source code Browse git 
def extract(
    self, volumes=True, byTitle=True, silent=SILENT_D, overwrite=None, show=False
):
    """Extract volumes from the currently loaded work.

    This function is only provided if the dataset is a work,
    i.e. it is loaded as a whole.
    When a single volume of a work is loaded, there is no `extract` method.

    See `tf.volumes.extract` and note that parameters
    `workLocation`, `volumesLocation`, `api`
    will be supplied automatically.
    """

    silent = silentConvert(silent)
    volume = self.volume
    volumeBase = self.volumeBase
    api = self.api

    if volume:
        self.error("Cannot extract volumes from a single volume of a work")
        return

    return extract(
        None,
        volumeBase,
        volumes=volumes,
        byTitle=byTitle,
        silent=silent,
        api=api,
        overwrite=overwrite,
        checkOnly=False,
        show=show,
    )

Extract volumes from the currently loaded work.

This function is only provided if the dataset is a work, i.e. it is loaded as a whole. When a single volume of a work is loaded, there is no extract method.

See tf.volumes.extract and note that parameters workLocation, volumesLocation, api will be supplied automatically.

def getVolumes(self)
Expand source code Browse git 
def getVolumes(self):
    """Lists available volumes within the dataset.

    See `tf.volumes.extract.getVolumes`.
    """

    volumeBase = self.volumeBase
    return getVolumes(volumeBase)

Lists available volumes within the dataset.

See getVolumes().

Inherited members