Module tf.convert.walker

Walker

You can convert a dataset to TF by writing a function that walks through it.

That function must trigger a sequence of actions when reading the data. These actions drive Text-Fabric to build a valid Text-Fabric dataset. Many checks will be performed.

to and from MQL

If your source is MQL, you are even better off: Text-Fabric has a module to import from MQL and to export to MQL. See FabricCore.importMQL() and FabricCore.exportMQL().

Set up

Here is a schematic set up of such a conversion program.

from tf.fabric import Fabric
from tf.convert.walker import CV

TF = Fabric(locations=OUT_DIR)
cv = CV(TF)

def director(cv):
  # your code to unwrap your source data and trigger
  # the generation of TF nodes, edges and features

slotType = 'word'  # or whatever you choose
otext = {  # dictionary of config data for sections and text formats
    ...
}
generic = {  # dictionary of metadata meant for all features
    ...
}
intFeatures = {  # set of integer valued feature names
    ...
}
featureMeta = {  # per feature dicts with metadata
   ...
}

good = cv.walk(
    director,
    slotType,
    otext=otext,
    generic=generic,
    intFeatures=intFeatures,
    featureMeta=featureMeta,
    warn=True,
    force=False,
    silent=False,
)

if good:
  ... load the new TF data ...

See CV.walk().

Walking

When you walk through the input data source, you'll encounter things that have to become slots, non-slot nodes, edges and features in the new data set.

You issue these things by means of an action method from cv, such as cv.slot() or cv.node(nodeType).

When your action creates slots or non slot nodes, Text-Fabric will return you a reference to that node, that you can use later for more actions related to that node.

curPara = cv.node('para')

To add features to nodes, use a cv.feature() action. It will apply to a node passed as argument.

To add features to edges, issue a cv.edge() action. It will require two node arguments: the from node and the to node.

There is always a set of current embedder nodes. When you create a slot node

curWord = cv.slot()

then TF will link all current embedder nodes to the resulting slot.

There are actions to add nodes to the set of embedder nodes, to remove them from it, and to add them again.

Dynamic Metadata

When the director runs, you have already specified all your feature metadata, including the value types.

But if some of that information is dependent on what you encounter in the data, you can do two things:

(A) Run a preliminary pass over the data and gather the required information, before running the director.

(B) Update the metadata later on by issuing cv.meta() actions from within your director, see below.

Action methods

The cv class contains methods that are responsible for particular actions that steer the graph building:

Example

Follow the conversion tutorial

Or study a more involved example: Old Babylonian

Expand source code Browse git
"""
# Walker

You can convert a dataset to TF by writing a function that walks through it.

That function must trigger a sequence of actions when reading the data.
These actions drive Text-Fabric to build a valid Text-Fabric dataset.
Many checks will be performed.

!!! hint "to and from MQL"
    If your source is MQL, you are even better off: Text-Fabric has a
    module to import from MQL and to export to MQL.
    See `tf.fabric.Fabric.importMQL` and `tf.fabric.Fabric.exportMQL`.

## Set up

Here is a schematic set up of such a conversion program.

```python
from tf.fabric import Fabric
from tf.convert.walker import CV

TF = Fabric(locations=OUT_DIR)
cv = CV(TF)

def director(cv):
  # your code to unwrap your source data and trigger
  # the generation of TF nodes, edges and features

slotType = 'word'  # or whatever you choose
otext = {  # dictionary of config data for sections and text formats
    ...
}
generic = {  # dictionary of metadata meant for all features
    ...
}
intFeatures = {  # set of integer valued feature names
    ...
}
featureMeta = {  # per feature dicts with metadata
   ...
}

good = cv.walk(
    director,
    slotType,
    otext=otext,
    generic=generic,
    intFeatures=intFeatures,
    featureMeta=featureMeta,
    warn=True,
    force=False,
    silent=False,
)

if good:
  ... load the new TF data ...
```

See `tf.convert.walker.CV.walk`.

## Walking

When you walk through the input data source, you'll encounter things
that have to become slots, non-slot nodes, edges and features in the new data set.

You issue these things by means of an *action method* from `cv`, such as
`cv.slot()` or `cv.node(nodeType)`.

When your action creates slots or non slot nodes,
Text-Fabric will return you a reference to that node,
that you can use later for more actions related to that node.

```python
curPara = cv.node('para')
```

To add features to nodes, use a `cv.feature()` action.
It will apply to a node passed as argument.

To add features to edges, issue a `cv.edge()` action.
It will require two node arguments: the *from* node and the *to* node.

There is always a set of current *embedder nodes*.
When you create a slot node

```python
curWord = cv.slot()
```

then TF will link all current embedder nodes to the resulting slot.

There are actions to add nodes to the set of embedder nodes,
to remove them from it,
and to add them again.

## Dynamic Metadata

When the director runs, you have already specified all your feature
metadata, including the value types.

But if some of that information is dependent on what you encounter in the
data, you can do two things:

(A) Run a preliminary pass over the data and gather the required information,
before running the director.

(B) Update the metadata later on
by issuing `cv.meta()` actions from within your director, see below.

## Action methods

The `cv` class contains methods that are responsible for particular *actions*
that steer the graph building:

*   `tf.convert.walker.CV.slot`
*   `tf.convert.walker.CV.node`
*   `tf.convert.walker.CV.terminate`
*   `tf.convert.walker.CV.resume`
*   `tf.convert.walker.CV.feature`
*   `tf.convert.walker.CV.edge`
*   `tf.convert.walker.CV.meta`
*   `tf.convert.walker.CV.occurs`
*   `tf.convert.walker.CV.linked`
*   `tf.convert.walker.CV.active`
*   `tf.convert.walker.CV.activeNodes`
*   `tf.convert.walker.CV.activeTypes`
*   `tf.convert.walker.CV.get` and `cv.get(feature, nf, nt)`
*   `tf.convert.walker.CV.stop`

!!! hint "Example"
    Follow the
    [conversion tutorial](https://nbviewer.jupyter.org/github/annotation/banks/blob/master/programs/convert.ipynb)

    Or study a more involved example:
    [Old Babylonian](https://github.com/Nino-cunei/oldbabylonian/blob/master/programs/tfFromATF.py)
"""

import collections
import functools
import re

from ..parameters import WARP, OTYPE, OSLOTS, OTEXT
from ..core.helpers import itemize, isInt


class CV(object):
    S = "slot"
    N = "node"
    T = "terminate"
    R = "resume"
    F = "feature"
    E = "edge"

    def __init__(self, TF, silent=False):
        self.TF = TF
        tmObj = TF.tmObj
        isSilent = tmObj.isSilent
        setSilent = tmObj.setSilent

        self.wasSilent = isSilent()
        setSilent(silent)

    def _showWarnings(self):
        tmObj = self.TF.tmObj
        error = tmObj.error
        info = tmObj.info
        indent = tmObj.indent

        warnings = self.warnings
        warn = self.warn

        if warn is None:
            if warnings:
                info("use `cv.walk(..., warn=False)` to make warnings visible")
                info("use `cv.walk(..., warn=True)` to stop on warnings")
        else:
            method = error if warn else info

            if warnings:
                for (kind, msgs) in sorted(warnings.items()):
                    method(f"WARNING {kind} ({len(msgs)} x):")
                    indent(level=2)
                    for msg in sorted(set(msgs))[0:20]:
                        if msg:
                            method(f"{msg}", tm=False)
                self.warnings = {}
                if warn:
                    info("use `cv.walk(..., warn=False)` to continue after warnings")
                    info("use `cv.walk(..., warn=None)` to suppress warnings")
                    self.good = False
                else:
                    info("use `cv.walk(..., warn=True)` to stop after warnings")
                    info("use `cv.walk(..., warn=None)` to suppress warnings")

    def _showErrors(self):
        tmObj = self.TF.tmObj
        error = tmObj.error
        info = tmObj.info
        indent = tmObj.indent
        forcedStop = self.forcedStop

        errors = self.errors

        if errors:
            for (kind, msgs) in sorted(errors.items()):
                error(f"ERROR {kind} ({len(msgs)} x):")
                indent(level=2)
                for msg in sorted(set(msgs))[0:20]:
                    if msg:
                        error(f"{msg}", tm=False)
            self.errors = {}
            self.good = False

        if forcedStop:
            error("STOPPED by the stop() instruction")
        elif not errors:
            if self.good:
                info("OK")
            else:
                error("STOPPED because of warnings")

    def walk(
        self,
        director,
        slotType,
        otext={},
        generic={},
        intFeatures=set(),
        featureMeta={},
        warn=True,
        generateTf=True,
        force=False,
    ):
        """Asks a director function to walk through source data and receives its actions.

        The `director` function should unravel the source.
        You have to program the `director`, which takes one argument: `cv`.
        From the `cv` you can use a few standard actions that instruct Text-Fabric
        to build a graph.

        This function will check whether the metadata makes sense and is minimally
        complete.

        During node creation the section structure will be watched,
        and you will be warned if irregularities occur.

        After the creation of the feature data, some extra checks will be performed
        to see whether the metadata matches the data and vice versa.

        The new feature data will be written to the output directory of the
        underlying TF object.  In fact, the rules are exactly the same as for
        `tf.fabric.Fabric.save`.

        Parameters
        ----------
        slotType: string
            The node type that acts as the type of the slots in the data set.

        oText: dict
            The configuration information to be stored in the `otext` feature
            (see `tf.core.text`):

            * section types
            * section features
            * structure types
            * structure features
            * text formats

        generic: dict
            Metadata that will be written into the header of all generated TF features.

            You can make changes to this later on, dynamically in your director.

        intFeatures: iterable
            The set of features that have integer values only.

            You can make changes to this later on, dynamically in your director.

        featureMeta: dict of dict
            For each node or edge feature descriptive metadata can be supplied.

            You can make changes to this later on, dynamically in your director.

        warn: boolean, optional `True`
            This regulates the response to warnings:

            `True` (default): stop after warnings (as if they are errors);

            `False` continue after warnings but do show them;

            `None` suppress all warnings.

        silent: boolean, optional `None`
            By this you can suppress informational messages: `silent=True`.

        force: boolean, optional `False`
            This forces the process to continue after errors.
            Your TF set might not be valid.
            Yet this can be useful during testing, when you know
            that not everything is OK, but you want to check some results.
            Especially when dealing with large datasets, you might want to test
            with little pieces. But then you get a kind of non-fatal errors that
            stand in the way of testing. For those cases: `force=True`.

        generateTf: boolean, optional `True`
            You can pass `False` here to suppress the actual writing of TF data.
            In that way you can dry-run the director to check for errors and warnings

        director: function
            An ordinary function that takes one argument, the `cv` object, and
            should not deliver anything.

            Writing this function is the main job to do when you want to convert a data source
            to TF.

            See `tf.convert.walker` for more details.

        Returns
        -------
        boolean
            Whether the operation was successful
        """

        tmObj = self.TF.tmObj
        info = tmObj.info
        indent = tmObj.indent
        setSilent = tmObj.setSilent

        indent(level=0, reset=True)
        info("Importing data from walking through the source ...")

        self.force = force
        self.good = True
        self.forcedStop = False
        self.errors = collections.defaultdict(list)
        self.warnings = collections.defaultdict(list)
        self.warn = warn
        self.slotType = slotType

        self.intFeatures = set(intFeatures)
        self.featureMeta = featureMeta
        self.metaData = {}
        self.nodeFeatures = {}
        self.edgeFeatures = {}

        indent(level=1, reset=True)
        self._prepareMeta(otext, generic)

        indent(level=1, reset=True)
        self._follow(director)

        indent(level=1, reset=True)
        self._removeUnlinked()

        indent(level=1, reset=True)
        self._checkGraph()

        indent(level=1, reset=True)
        self._checkFeatures()

        indent(level=1, reset=True)
        self._reorderNodes()

        indent(level=1, reset=True)
        self._reassignFeatures()

        if generateTf:

            indent(level=0)

            if self.good or self.force:
                self.good = self.TF.save(
                    metaData=self.metaData,
                    nodeFeatures=self.nodeFeatures,
                    edgeFeatures=self.edgeFeatures,
                )

        self._showWarnings()
        setSilent(self.wasSilent)

        return self.good

    def _prepareMeta(self, otext, generic):
        varRe = re.compile(r"\{([^}]+)\}")

        tmObj = self.TF.tmObj
        info = tmObj.info
        indent = tmObj.indent

        if not self.good and not self.force:
            return

        info("Preparing metadata... ")

        intFeatures = self.intFeatures
        featureMeta = self.featureMeta

        errors = self.errors

        self.metaData = {
            "": generic,
            OTYPE: {"valueType": "str"},
            OSLOTS: {"valueType": "str"},
            OTEXT: otext,
        }
        metaData = self.metaData

        self.intFeatures = intFeatures
        self.sectionTypes = []
        self.sectionFeatures = []
        self.sectionFromLevel = {}
        self.levelFromSection = {}
        self.structureTypes = []
        self.structureFeatures = []
        self.structureLevel = {}
        self.textFormats = {}
        self.textFeatures = set()

        if not generic:
            errors['Missing feature meta data in "generic"'].append(
                "Consider adding provenance metadata to all features"
            )
        if not otext:
            errors['Missing "otext" configuration'].append(
                "Consider adding configuration for text representation and section levels"
            )
        else:
            sectionInfo = {}
            for f in ("sectionTypes", "sectionFeatures"):
                if f not in otext:
                    errors['Incomplete section specs in "otext"'].append(
                        f'no key "{f}"'
                    )
                    sectionInfo[f] = []
                else:
                    sFields = itemize(otext[f], sep=",")
                    sectionInfo[f] = sFields
                    if f == "sectionTypes":
                        for (i, s) in enumerate(sFields):
                            self.levelFromSection[s] = i + 1
                            self.sectionFromLevel[i + 1] = s
            sLevels = {f: len(sectionInfo[f]) for f in sectionInfo}
            if min(sLevels.values()) != max(sLevels.values()):
                errors["Inconsistent section info"].append(
                    " but ".join(f'"{f}" has {sLevels[f]} levels' for f in sLevels)
                )
            self.sectionFeatures = sectionInfo["sectionFeatures"]
            self.sectionTypes = sectionInfo["sectionTypes"]

            structureInfo = {}
            for f in ("structureTypes", "structureFeatures"):
                if f not in otext:
                    structureInfo[f] = []
                    continue
                sFields = itemize(otext[f], sep=",")
                structureInfo[f] = sFields
            if not structureInfo:
                info("No structure definition found in otext")
            sLevels = {f: len(structureInfo[f]) for f in structureInfo}
            if min(sLevels.values()) != max(sLevels.values()):
                errors["Inconsistent structure info"].append(
                    " but ".join(f'"{f}" has {sLevels[f]} levels' for f in sLevels)
                )
                structureInfo = {}
            if not structureInfo or all(
                len(info) == 0 for (s, info) in structureInfo.items()
            ):
                info("No structure nodes will be set up")
                self.structureFeatures = []
                self.structureTypes = []
            self.structureFeatures = structureInfo["structureFeatures"]
            self.structureTypes = structureInfo["structureTypes"]
            self.featFromType = {
                typ: feat
                for (typ, feat) in zip(self.structureTypes, self.structureFeatures)
            }
            self.structureSet = set(self.structureTypes)

            textFormats = {}
            textFeatures = set()
            for (k, v) in sorted(otext.items()):
                if k.startswith("fmt:"):
                    featureSet = set()
                    features = varRe.findall(v)
                    for ff in features:
                        fr = ff.rsplit(":", maxsplit=1)[0]
                        for f in fr.split("/"):
                            featureSet.add(f)
                    textFormats[k[4:]] = featureSet
                    textFeatures |= featureSet
            if not textFormats:
                errors['No text formats in "otext"'].append('add "fmt:text-orig-full"')
            elif "text-orig-full" not in textFormats:
                errors["No default text format in otext"].append(
                    'add "fmt:text-orig-full"'
                )
            self.textFormats = textFormats
            self.textFeatures = textFeatures

        info(f'SECTION   TYPES:    {", ".join(self.sectionTypes)}', tm=False)
        info(f'SECTION   FEATURES: {", ".join(self.sectionFeatures)}', tm=False)
        info(f'STRUCTURE TYPES:    {", ".join(self.structureTypes)}', tm=False)
        info(f'STRUCTURE FEATURES: {", ".join(self.structureFeatures)}', tm=False)
        info("TEXT      FEATURES:", tm=False)
        indent(level=2)
        for (fmt, feats) in sorted(textFormats.items()):
            info(f'{fmt:<20} {", ".join(sorted(feats))}', tm=False)
        indent(level=1)

        for feat in WARP + ("",):
            if feat in intFeatures:
                if feat == "":
                    errors["intFeatures"].append(
                        'Do not declare the "valueType" for all features'
                    )
                else:
                    errors["intFeatures"].append(
                        f'Do not mark the "{feat}" feature as integer valued'
                    )
                self.good = False

        for (feat, featMeta) in sorted(featureMeta.items()):
            good = self._checkFeatMeta(
                feat,
                featMeta,
                checkRegular=True,
                valueTypeAllowed=False,
                showErrors=False,
            )
            if not good:
                self.good = False
            metaData.setdefault(feat, {}).update(featMeta)
            metaData[feat]["valueType"] = "int" if feat in intFeatures else "str"

        self._showErrors()

    def _checkFeatMeta(
        self,
        feat,
        featMeta,
        checkRegular=False,
        valueTypeAllowed=True,
        showErrors=True,
    ):
        errors = collections.defaultdict(list)
        good = True

        if checkRegular:
            if feat in WARP + ("",):
                if feat == "":
                    errors["featureMeta"].append(
                        'Specify the generic feature meta data in "generic"'
                    )
                    good = False
                elif feat == OTEXT:
                    errors["featureMeta"].append(
                        f'Specify the "{OTEXT}" feature in "otext"'
                    )
                    good = False
                else:
                    errors["featureMeta"].append(
                        f'Do not pass metaData for the "{feat}" feature in "featureMeta"'
                    )
                    good = False
        if "valueType" in featMeta:
            if not valueTypeAllowed:
                errors["featureMeta"].append(
                    f'Do not specify "valueType" for the "{feat}" feature in "featureMeta"'
                )
                good = False
            elif featMeta["valueType"] not in {"int", "str"}:
                errors["featureMeta"].append('valueType must be "int" or "str"')
                good = False

        for (e, eData) in errors.items():
            self.errors[e].extend(eData)
        if showErrors:
            self._showErrors
        return good

    def stop(self, msg):
        """Stops the director. No further input will be read.

            cv.stop(msg)

        The director will exit with a non-good status  and issue the message `msg`.
        If you have called `walk()` with `force=True`, indicating that the
        director must proceed after errors, then this stop command will cause termination
        nevertheless.

        Parameters
        ----------
        msg: string
            A message to display upon stopping.

        Returns
        -------
        None
        """

        tmObj = self.TF.tmObj
        error = tmObj.error

        error(f"Forced stop: {msg}")
        self.good = False
        self.force = False
        self.forcedStop = True

    def slot(self):
        """Make a slot node and return the handle to it in `n`.

            n = cv.slot()


        No further information is needed.
        Remember that you can add features to the node by later

            cv.feature(n, key=value, ...)

        calls.

        Parameters
        ----------
        None

        Returns
        -------
        node reference: tuple
            The node reference consists of a node type and a sequence number,
            but normally you do not have to dig these out.
            Just pass the tuple as a whole to actions that require a node argument.
        """

        curSeq = self.curSeq
        curEmbedders = self.curEmbedders
        oslots = self.oslots
        levelFromSection = self.levelFromSection
        warnings = self.warnings

        self.stats[self.S] += 1
        nType = self.slotType

        curSeq[nType] += 1
        seq = curSeq[nType]

        inSection = False
        for eNode in curEmbedders:
            if eNode[0] in levelFromSection:
                inSection = True
            oslots[eNode].add(seq)

        if levelFromSection and not inSection:
            warnings["slot outside sections"].append(f"{seq}")

        return (nType, seq)

    def node(self, nType, slots=None):
        """Make a non-slot node and return the handle to it in `n`.

            n = cv.node(nodeType)

        You have to pass its *node type*, i.e. a string.
        Think of `sentence`, `paragraph`, `phrase`, `word`, `sign`, whatever.

        There are two modes for this function:

        * Auto: (`slots=None`):
          Non slot nodes will be automatically added to the set of embedders.
        * Explicit: (`slots=iterable`):
          The slots in iterable will be assigned to this node and nothing else.
          The node will not be added to the set of embedders.
          Put otherwise: the node will be terminated after construction.
          However: you could resume it later to add other slots.

        Remember that you can add features to the node by later

            cv.feature(n, key=value, ...)

        calls.

        Parameters
        ----------
        nType: string
            A node type, not the slot type
        slots: iterable of int, optional `None`
            The slots to assign to this node.
            If left out, the node is left as an embedding node and
            subsequent slots will be added to it automatically.
            All slots in the iterable must have been generated before
            by means of the `cv.slot()` action.

        Returns
        -------
        node reference or None
            If an error occurred, `None` is returned.
            The node reference consists of a node type and a sequence number,
            but normally you do not have to dig these out.
            Just pass the tuple as a whole to actions that require a node argument.
        """

        slotType = self.slotType
        errors = self.errors

        if nType == slotType:
            errors[f'use `cv.slot()` instead of `cv.node("{nType}")`'].append(None)
            return

        curSeq = self.curSeq
        curEmbedders = self.curEmbedders

        self.stats[self.N] += 1

        curSeq[nType] += 1
        seq = curSeq[nType]
        node = (nType, seq)

        self._checkSecLevel(node, before=True)

        if slots:
            maxSlot = curSeq[slotType]

            for s in slots:
                if not 1 <= s <= maxSlot:
                    errors[f'slot out of range in `cv.node(({nType}, {seq}))`'].append(f"{s}")
                else:
                    oslots = self.oslots
                    oslots[node].add(s)

            self.stats[self.T] += 1
        else:
            curEmbedders.add(node)

        return node

    def terminate(self, node):
        """**terminate** a node.

            cv.terminate(n)

        The node `n` will be removed from the set of current embedders.
        This `n` must be the result of a previous `cv.slot()` or `cv.node()` action.

        Parameters
        ----------
        node: tuple
            A node reference, obtained by one of the actions `slot` or `node`.

        Returns
        -------
        None
        """

        self.stats[self.T] += 1
        if node is not None:
            self.curEmbedders.discard(node)
            self._checkSecLevel(node, before=False)

    def resume(self, node):
        """**resume** a node.

            cv.resume(n)

        If you resume a non-slot node, you add it again to the set of embedders.
        No new node will be created.

        If you resume a slot node, it will be added to the set of current embedders.
        No new slot will be created.

        Parameters
        ----------
        node: tuple
            A node reference, obtained by one of the actions `slot` or `node`.

        Returns
        -------
        None
        """

        curEmbedders = self.curEmbedders
        oslots = self.oslots

        self.stats[self.R] += 1

        (nType, seq) = node
        if nType == self.slotType:
            for eNode in curEmbedders:
                oslots[eNode].add(seq)
        else:
            self._checkSecLevel(node, before=None)
            curEmbedders.add(node)

    def feature(self, node, **features):
        """Add **node features**.

            cv.feature(n, name=value, ... , name=value)

        Parameters
        ----------
        node: tuple
            A node reference, obtained by one of the actions `slot` or `node`.
        **features: keyword arguments
            The names and values of features to assign to this node.

        Returns
        -------
        None

        !!! caution "None values"
            If a feature value is `None` it will not be added!
        """

        nodeFeatures = self.nodeFeatures

        self.stats[self.F] += 1

        for (k, v) in features.items():
            if v is None:
                continue
            # self._checkType(k, v, self.N)
            nodeFeatures[k][node] = v

    def edge(self, nodeFrom, nodeTo, **features):
        """Add **edge features**.

            cv.edge(nf, nt, name=value, ... , name=value)

        Parameters
        ----------
        nodeFrom, nodeTo: tuple
            Two node references, obtained by one of the actions `slot` or `node`.
        **features: keyword arguments
            The names and values of features to assign to this edge
            (i.e. pair of nodes).

        Returns
        -------
        None

        !!! note "None values"
            You may pass values that are `None`,
            and a corresponding edge will be created.
            If for all edges the value is `None`,
            an edge without values will be created.
            For every `nodeFrom`, such a feature
            essentially specifies a set of nodes `{ nodeTo }`.
        """

        edgeFeatures = self.edgeFeatures

        self.stats[self.E] += 1

        for (k, v) in features.items():
            # self._checkType(k, v, self.E)
            edgeFeatures[k][nodeFrom][nodeTo] = v

    def occurs(self, feat):
        """Whether the feature `featureName` occurs in the resulting data so far.

            occurs = cv.occurs(featureName)

        If you have assigned None values to a feature, that will count, i.e.
        that feature occurs in the data.

        If you add feature values conditionally, it might happen that some
        features will not be used at all.
        For example, if your conversion produces errors, you might
        add the error information to the result in the form of error features.

        Later on, when the errors have been weeded out, these features will
        not occur any more in the result, but then TF will complain that
        such is feature is declared but not used.
        At the end of your director you can remove unused features
        conditionally, using this function.

        Parameters
        ----------
        feat: string
            The name of a feature

        Returns
        -------
        boolean
        """

        nodeFeatures = self.nodeFeatures
        edgeFeatures = self.edgeFeatures
        if feat in nodeFeatures or feat in edgeFeatures:
            return True
        return False

    def meta(self, feat, **metadata):
        """Add, modify, delete metadata fields of features.

            cv.meta(feature, name=value, ... , name=value)

        Parameters
        ----------
        feat: string
            The name of a feature
        **metaData: pairs of name and value
            If a `value` is `None`, that `name` will be deleted from the
            metadata fields of the feature.
            A bare `cv.meta(feature)` will remove the all metadata from the feature.
            If you modify the field `valueType` of a feature, that feature will be
            added or removed from the set of `intFeatures`.
            It will be checked whether you specify either `int` or `str`.


        Returns
        -------
        None
        """

        errors = self.errors
        intFeatures = self.intFeatures
        metaData = self.metaData
        featMeta = metaData.get(feat, {})

        good = True

        if not metadata:
            if feat in metaData:
                del metaData[feat]
                intFeatures.discard(feat)

        for (field, text) in metadata.items():
            if text is None:
                if field == "valueType":
                    errors['did not delete metadata field "valueType"'].append(feat)
                    good = False
                else:
                    if feat in metaData and field in metaData[feat]:
                        del metaData[feat][field]
            else:
                metaData.setdefault(feat, {})[field] = text
                if field == "valueType":
                    if text == "int":
                        intFeatures.add(feat)
                    else:
                        intFeatures.discard(feat)

        self.good = self._checkFeatMeta(feat, featMeta) and good and self.good

    def linked(self, node):
        """Returns the slots `ss` to which a node is currently linked.

            ss = cv.linked(n)


        If you construct non-slot nodes without linking them to slots,
        they will be removed when TF validates the collective result
        of the action methods.

        If you want to prevent that, you can insert an extra slot, but in order
        to do so, you have to detect that a node is still unlinked.

        This action is precisely meant for that.

        Parameters
        ----------
        node: tuple
            A node reference, obtained by one of the actions `slot` or `node`.

        Returns
        -------
        boolean
        """

        oslots = self.oslots
        return tuple(oslots.get(node, []))

    def active(self, node):
        """Returns whether a node is currently active.

        Active nodes are the nodes in the set of current embedders.

            isActive = cv.active(n)

        If you construct your nodes in a very dynamic way, it might be
        hard to keep track for each node whether it has been created, terminated,
        or resumed, in other words, whether it is active or not.

        This action is provides a direct and precise way to know
        whether a node is active.

        Parameters
        ----------
        node: tuple
            A node reference, obtained by one of the actions `slot` or `node`.

        Returns
        -------
        boolean
        """

        return node in self.curEmbedders

    def activeNodes(self, nTypes=None):
        """The currently active nodes, i.e. the embedders.

            nodes = cv.activeTypes()
            nodes = cv.activeTypes(nTypes=("sentence", "clause"))

        Parameters
        ----------
        nType: iterable optional `None`
            If None, all active nodes are returned.
            Else the iterable lists a few node types,
            and only active nodes in these types are returned.

        Returns
        -------
        set
        """

        if nTypes is None:
            return set(self.curEmbedders)
        nTypes = set(nTypes)
        return {n for n in self.curEmbedders if n[0] in nTypes}

    def activeTypes(self):
        """The node types of the currently active nodes, i.e. the embedders.

            nTypes = cv.activeTypes()

        Parameters
        ----------
        None

        Returns
        -------
        set
        """

        return {node[0] for node in self.curEmbedders}

    def get(self, feature, *args):
        """Retrieve feature values.

            cv.get(feature, n) and cv.get(feature, nf, nt)

        `feature` is the name of the feature.

        For node features, `n` is the node which carries the value.

        For edge features, `nf, nt` is the pair of from-to nodes which carries the value.

        Parameters
        ----------
        feature: string
            The name of a feature
        node: tuple
            A node reference, obtained by one of the actions `slot` or `node`.
            The node in question when retrieving the value of a node feature.
        nodeFrom, nodeTo: tuple
            Two node references, obtained by one of the actions `slot` or `node`.
            The nodes in question when retrieving the value of an edge feature.

        Returns
        -------
        string or integer
        """

        errors = self.errors
        nodeFeatures = self.nodeFeatures
        edgeFeatures = self.edgeFeatures
        nArgs = len(args)
        if nArgs == 0 or nArgs > 2:
            errors["use `cv.get(ft, n)` or `cv.get(ft, nf, nt)`"].append(None)
            return None

        return (
            nodeFeatures.get(feature, {}).get(args[0], None)
            if len(args) == 1
            else edgeFeatures.get(feature, {}).get(args[0], {}).get(args[1], None)
        )

    def _checkSecLevel(self, node, before=True):
        levelFromSection = self.levelFromSection
        sectionFeatures = self.sectionFeatures
        nodeFeatures = self.nodeFeatures
        warnings = self.warnings
        curEmbedders = self.curEmbedders

        (nType, seq) = node

        msg = "starts" if before is True else "ends" if before is False else "resumes"

        if levelFromSection:
            level = levelFromSection.get(nType, None)
            if level is None:
                return

            headingFeature = sectionFeatures[level - 1]
            nHeading = nodeFeatures.get(headingFeature, {}).get(node, "??")

            for em in curEmbedders:
                eType = em[0]
                if eType in levelFromSection:
                    eLevel = levelFromSection.get(eType, None)
                    eHeadingFeature = sectionFeatures[eLevel - 1]
                    eHeading = nodeFeatures.get(eHeadingFeature, {}).get(em, "??")

                if eType == nType:
                    warnings[
                        f'section {nType} "{nHeading}" of level {level}'
                        f" enclosed in another {nType}: {eHeading}"
                    ].append(None)
                elif eType in levelFromSection:
                    eLevel = levelFromSection[eType]
                    if eLevel > level:
                        warnings[
                            f'section {nType} "{nHeading}" of level {level} {msg}'
                            f' inside a {eType} "{eHeading}" of level {eLevel}'
                        ].append(None)

    def _follow(self, director):

        # after node = yield ('N', nodeType) all slot nodes that are yielded
        # will be linked to node, until a ('T', node) is yielded.
        # If needed, you can resume this node again, after which new slot nodes
        # continue to be linked to this node.
        # If you resume a slot node, it all non slot nodes in the current context
        # will be linked to it.

        tmObj = self.TF.tmObj
        info = tmObj.info

        if not self.good:
            return

        info("Following director... ")

        slotType = self.slotType
        errors = self.errors

        self.oslots = collections.defaultdict(set)
        self.nodeFeatures = collections.defaultdict(dict)
        self.edgeFeatures = collections.defaultdict(
            lambda: collections.defaultdict(dict)
        )
        self.nodes = collections.defaultdict(set)
        nodes = self.nodes

        self.curSeq = collections.Counter()
        self.curEmbedders = set()
        curEmbedders = self.curEmbedders

        self.stats = {
            actionType: 0
            for actionType in (self.S, self.N, self.T, self.R, self.F, self.E)
        }

        director(self)

        if not self.stats:
            self.good = False
            return

        for (actionType, amount) in sorted(self.stats.items()):
            info(f'"{actionType}" actions: {amount}')

        totalNodes = 0

        for (nType, lastSeq) in sorted(self.curSeq.items()):
            for seq in range(1, lastSeq + 1):
                nodes[nType].add(seq)
            slotRep = " = slot type" if nType == slotType else ""
            info(f'{lastSeq:>8} x "{nType}" node {slotRep}', tm=False)
            totalNodes += lastSeq
        info(f"{totalNodes:>8} nodes of all types", tm=False)

        self.totalNodes = totalNodes

        if curEmbedders:
            embedCount = collections.Counter()
            for (nType, seq) in curEmbedders:
                embedCount[nType] += 1
            for (nType, amount) in sorted(
                embedCount.items(), key=lambda x: (-x[1], x[0]),
            ):
                errors["Unterminated nodes"].append(f"{nType}: {amount} x")

        self._showErrors()

    def _removeUnlinked(self):
        tmObj = self.TF.tmObj
        info = tmObj.info
        indent = tmObj.indent

        if not self.good and not self.force:
            return

        nodeTypes = self.curSeq
        nodes = self.nodes
        slotType = self.slotType
        oslots = self.oslots
        nodeFeatures = self.nodeFeatures
        edgeFeatures = self.edgeFeatures

        unlinked = {}

        for nType in nodeTypes:
            if nType == slotType:
                continue
            for seq in range(1, nodeTypes[nType] + 1):
                if (nType, seq) not in oslots:
                    unlinked.setdefault(nType, []).append(seq)

        if unlinked:
            info("Removing unlinked nodes ... ")
            indent(level=2)
            totalRemoved = 0
            for (nType, seqs) in unlinked.items():
                theseNodes = nodes[nType]
                lSeqs = len(seqs)
                totalRemoved += lSeqs
                rep = " ..." if lSeqs > 5 else ""
                pl = "" if lSeqs == 1 else "s"
                info(f'{lSeqs:>6} unlinked "{nType}" node{pl}: {seqs[0:5]}{rep}')
                for seq in seqs:
                    node = (nType, seq)
                    theseNodes.discard(seq)
                    for (f, fData) in nodeFeatures.items():
                        if node in fData:
                            del fData[node]
                    for (f, fData) in edgeFeatures.items():
                        if node in fData:
                            del fData[node]
                            for (fNode, toValues) in fData:
                                if node in toValues:
                                    del toValues[node]
            pl = "" if totalRemoved == 1 else "s"
            info(f"{totalRemoved:>6} unlinked node{pl}")
            self.totalNodes -= totalRemoved
            info(f"Leaving {self.totalNodes:>6} nodes")
            indent(level=1)

    def _checkGraph(self):
        tmObj = self.TF.tmObj
        info = tmObj.info

        if not self.good and not self.force:
            return

        info("checking for nodes and edges ... ")

        nodes = self.nodes
        errors = self.errors
        edgeFeatures = self.edgeFeatures

        # edges refer to nodes

        for (k, featureData) in edgeFeatures.items():
            for nFrom in featureData:
                (nType, seq) = nFrom
                if nType not in nodes or seq not in nodes[nType]:
                    errors["Edge feature: illegal node"].append(
                        f'"{k}": from-node  {nFrom} not in node set'
                    )
                    continue
                for nTo in featureData[nFrom]:
                    (nType, seq) = nTo
                    if nType not in nodes or seq not in nodes[nType]:
                        errors["Edge feature: illegal node"].append(
                            f'"{k}": to-node  {nTo} not in node set'
                        )

        self._showErrors()

    def _checkFeatures(self):
        tmObj = self.TF.tmObj
        info = tmObj.info

        if not self.good and not self.force:
            return

        info("checking features ... ")

        intFeatures = self.intFeatures
        metaData = self.metaData

        nodes = self.nodes
        nodeFeatures = self.nodeFeatures
        edgeFeatures = self.edgeFeatures

        errors = self.errors

        for feat in intFeatures:
            if (
                feat not in WARP
                and feat not in nodeFeatures
                and feat not in edgeFeatures
            ):
                errors["intFeatures"].append(
                    f'"{feat}" is declared as integer valued, but this feature does not occur'
                )
        for nType in self.sectionTypes:
            if nType not in nodes:
                errors["sections"].append(
                    f'node type "{nType}" is declared as a section type, but this node type does not occur'
                )
        for feat in self.sectionFeatures:
            if feat not in nodeFeatures:
                errors["sections"].append(
                    f'"{feat}" is declared as a section feature, but this node feature does not occur'
                )
        for nType in self.structureTypes:
            if nType not in nodes:
                errors["structure"].append(
                    f'node type "{nType}" is declared as a structure type,'
                    f" but this node type does not occur"
                )
        for feat in self.structureFeatures:
            if feat not in nodeFeatures:
                errors["structure"].append(
                    f'"{feat}" is declared as a structure feature, but this node feature does not occur'
                )
                nodeFeatures[feat] = {}

        structureSet = self.structureSet
        featFromType = self.featFromType
        for nType in nodes:
            if nType not in structureSet:
                continue
            feat = featFromType[nType]
            for seq in nodes[nType]:
                if (nType, seq) not in nodeFeatures[feat]:
                    errors["structure features"].append(
                        f'"structure element "{nType}" {seq} has no value for "{feat}"'
                    )

        for feat in self.textFeatures:
            if feat not in nodeFeatures:
                errors["text formats"].append(
                    f'"{feat}" is used in a text format, but this node feature does not occur'
                )

        for feat in WARP:
            if feat in nodeFeatures or feat in edgeFeatures:
                errors[feat].append(f'Do not construct the "{feat}" feature yourself')

        for feat in sorted(nodeFeatures) + sorted(edgeFeatures):
            if feat not in self.metaData:
                errors["feature metadata"].append(
                    f'node feature "{feat}" has no metadata'
                )

        for feat in sorted(metaData):
            if (
                feat
                and feat not in WARP
                and feat not in nodeFeatures
                and feat not in edgeFeatures
            ):
                errors["feature metadata"].append(
                    f'node feature "{feat}" has metadata but does not occur'
                )

        for (feat, featData) in sorted(nodeFeatures.items()):
            if None in featData:
                errors["feature values assigned to None"].append(
                    f'node feature "{feat}" has a node None'
                )
        for (feat, featData) in sorted(edgeFeatures.items()):
            if None in featData:
                errors["feature values assigned to None"].append(
                    f'edge feature "{feat}" has a from-node None'
                )
            for toValues in featData.values():
                if None in toValues:
                    errors["feature values assigned to None"].append(
                        f'edge feature "{feat}" has a to-node None'
                    )

        for (feat, featData) in sorted(edgeFeatures.items()):
            if feat in WARP:
                continue
            hasValues = False
            for (nodeTo, toValues) in featData.items():
                if any(v is not None for v in toValues.values()):
                    hasValues = True
                    break

            if not hasValues:
                edgeFeatures[feat] = {
                    nodeTo: set(toValues) for (nodeTo, toValues) in featData.items()
                }
            metaData.setdefault(feat, {})["edgeValues"] = hasValues

        for feat in intFeatures:
            if feat in WARP:
                continue
            if feat in nodeFeatures:
                featData = nodeFeatures[feat]
                for (k, v) in featData.items():
                    if not isInt(v):
                        (nType, node) = k
                        errors["Not a number"].append(
                            f'"node feature "{feat}": {nType} {node} => "{v}"'
                        )
            if feat in edgeFeatures and metaData[feat]["edgeValues"]:
                featData = edgeFeatures[feat]
                for (fromNode, toValues) in featData.items():
                    (fType, fNode) = fromNode
                    for (toNode, v) in toValues.items():
                        (tType, tNode) = toNode
                        if not isInt(v):
                            errors["Not a number"].append(
                                f'"edge feature "{feat}":'
                                f' {fType} {fNode} ="{v}"=> {tType} {tNode}'
                            )

        self._showErrors()

    def _reorderNodes(self):
        tmObj = self.TF.tmObj
        info = tmObj.info

        if not self.good and not self.force:
            return

        info("reordering nodes ...")

        nodeTypes = self.curSeq
        nodes = self.nodes
        slotType = self.slotType

        nTypes = (slotType,) + tuple(
            sorted(nType for nType in nodes if nType != slotType)
        )

        self.nodeMap = {}
        self.maxSlot = nodeTypes[slotType]

        nodeMap = self.nodeMap
        maxSlot = self.maxSlot

        n = 0

        for nType in nTypes:
            canonical = self._canonical(nType)
            if nType == slotType:
                sortedSeqs = range(1, maxSlot + 1)
            else:
                seqs = nodes[nType]
                info(f'Sorting {len(seqs)} nodes of type "{nType}"')
                sortedSeqs = sorted(seqs, key=canonical)
            for seq in sortedSeqs:
                n += 1
                nodeMap[(nType, seq)] = n

        self.maxNode = n
        info(f"Max node = {n}")

        self._showErrors()

    def _canonical(self, nType):
        oslots = self.oslots

        def before(nodeA, nodeB):
            slotsA = oslots[(nType, nodeA)]
            slotsB = oslots[(nType, nodeB)]
            if slotsA == slotsB:
                return 0

            aWithoutB = slotsA - slotsB
            if not aWithoutB:
                return 1

            bWithoutA = slotsB - slotsA
            if not bWithoutA:
                return -1

            aMin = min(aWithoutB)
            bMin = min(bWithoutA)
            return -1 if aMin < bMin else 1

        return functools.cmp_to_key(before)

    def _reassignFeatures(self):
        tmObj = self.TF.tmObj
        info = tmObj.info
        indent = tmObj.indent

        if not self.good and not self.force:
            return

        info("reassigning feature values ...")

        nodeMap = self.nodeMap
        oslots = self.oslots
        nodeFeatures = self.nodeFeatures
        edgeFeatures = self.edgeFeatures

        otype = {n: nType for ((nType, seq), n) in nodeMap.items()}
        oslots = {nodeMap[node]: slots for (node, slots) in oslots.items()}

        nodeFeaturesProto = self.nodeFeatures
        edgeFeaturesProto = self.edgeFeatures

        nodeFeatures = collections.defaultdict(dict)
        edgeFeatures = collections.defaultdict(lambda: collections.defaultdict(dict))

        indent(level=2)

        for k in sorted(nodeFeaturesProto):
            featureDataProto = nodeFeaturesProto[k]
            ln = len(featureDataProto)
            pl = "" if ln == 1 else "s"
            info(f'node feature "{k}" with {ln} node{pl}')
            featureData = {}
            for (node, value) in featureDataProto.items():
                featureData[nodeMap[node]] = value
            nodeFeatures[k] = featureData

        for k in sorted(edgeFeaturesProto):
            featureDataProto = edgeFeaturesProto[k]
            ln = len(featureDataProto)
            pl = "" if ln == 1 else "s"
            info(f'edge feature "{k}" with {ln} start node{pl}')
            featureData = {}
            for (nodeFrom, toValues) in featureDataProto.items():
                if type(toValues) is set:
                    toData = {nodeMap[nodeTo] for nodeTo in toValues}
                else:
                    toData = {}
                    for (nodeTo, value) in toValues.items():
                        toData[nodeMap[nodeTo]] = value
                featureData[nodeMap[nodeFrom]] = toData
            edgeFeatures[k] = featureData

        nodeFeatures["otype"] = otype
        edgeFeatures["oslots"] = oslots

        indent(level=1)

        self.oslots = None
        self.otype = None
        self.nodeFeatures = nodeFeatures
        self.edgeFeatures = edgeFeatures

        self._showErrors()

Classes

class CV (TF, silent=False)
Expand source code Browse git
class CV(object):
    S = "slot"
    N = "node"
    T = "terminate"
    R = "resume"
    F = "feature"
    E = "edge"

    def __init__(self, TF, silent=False):
        self.TF = TF
        tmObj = TF.tmObj
        isSilent = tmObj.isSilent
        setSilent = tmObj.setSilent

        self.wasSilent = isSilent()
        setSilent(silent)

    def _showWarnings(self):
        tmObj = self.TF.tmObj
        error = tmObj.error
        info = tmObj.info
        indent = tmObj.indent

        warnings = self.warnings
        warn = self.warn

        if warn is None:
            if warnings:
                info("use `cv.walk(..., warn=False)` to make warnings visible")
                info("use `cv.walk(..., warn=True)` to stop on warnings")
        else:
            method = error if warn else info

            if warnings:
                for (kind, msgs) in sorted(warnings.items()):
                    method(f"WARNING {kind} ({len(msgs)} x):")
                    indent(level=2)
                    for msg in sorted(set(msgs))[0:20]:
                        if msg:
                            method(f"{msg}", tm=False)
                self.warnings = {}
                if warn:
                    info("use `cv.walk(..., warn=False)` to continue after warnings")
                    info("use `cv.walk(..., warn=None)` to suppress warnings")
                    self.good = False
                else:
                    info("use `cv.walk(..., warn=True)` to stop after warnings")
                    info("use `cv.walk(..., warn=None)` to suppress warnings")

    def _showErrors(self):
        tmObj = self.TF.tmObj
        error = tmObj.error
        info = tmObj.info
        indent = tmObj.indent
        forcedStop = self.forcedStop

        errors = self.errors

        if errors:
            for (kind, msgs) in sorted(errors.items()):
                error(f"ERROR {kind} ({len(msgs)} x):")
                indent(level=2)
                for msg in sorted(set(msgs))[0:20]:
                    if msg:
                        error(f"{msg}", tm=False)
            self.errors = {}
            self.good = False

        if forcedStop:
            error("STOPPED by the stop() instruction")
        elif not errors:
            if self.good:
                info("OK")
            else:
                error("STOPPED because of warnings")

    def walk(
        self,
        director,
        slotType,
        otext={},
        generic={},
        intFeatures=set(),
        featureMeta={},
        warn=True,
        generateTf=True,
        force=False,
    ):
        """Asks a director function to walk through source data and receives its actions.

        The `director` function should unravel the source.
        You have to program the `director`, which takes one argument: `cv`.
        From the `cv` you can use a few standard actions that instruct Text-Fabric
        to build a graph.

        This function will check whether the metadata makes sense and is minimally
        complete.

        During node creation the section structure will be watched,
        and you will be warned if irregularities occur.

        After the creation of the feature data, some extra checks will be performed
        to see whether the metadata matches the data and vice versa.

        The new feature data will be written to the output directory of the
        underlying TF object.  In fact, the rules are exactly the same as for
        `tf.fabric.Fabric.save`.

        Parameters
        ----------
        slotType: string
            The node type that acts as the type of the slots in the data set.

        oText: dict
            The configuration information to be stored in the `otext` feature
            (see `tf.core.text`):

            * section types
            * section features
            * structure types
            * structure features
            * text formats

        generic: dict
            Metadata that will be written into the header of all generated TF features.

            You can make changes to this later on, dynamically in your director.

        intFeatures: iterable
            The set of features that have integer values only.

            You can make changes to this later on, dynamically in your director.

        featureMeta: dict of dict
            For each node or edge feature descriptive metadata can be supplied.

            You can make changes to this later on, dynamically in your director.

        warn: boolean, optional `True`
            This regulates the response to warnings:

            `True` (default): stop after warnings (as if they are errors);

            `False` continue after warnings but do show them;

            `None` suppress all warnings.

        silent: boolean, optional `None`
            By this you can suppress informational messages: `silent=True`.

        force: boolean, optional `False`
            This forces the process to continue after errors.
            Your TF set might not be valid.
            Yet this can be useful during testing, when you know
            that not everything is OK, but you want to check some results.
            Especially when dealing with large datasets, you might want to test
            with little pieces. But then you get a kind of non-fatal errors that
            stand in the way of testing. For those cases: `force=True`.

        generateTf: boolean, optional `True`
            You can pass `False` here to suppress the actual writing of TF data.
            In that way you can dry-run the director to check for errors and warnings

        director: function
            An ordinary function that takes one argument, the `cv` object, and
            should not deliver anything.

            Writing this function is the main job to do when you want to convert a data source
            to TF.

            See `tf.convert.walker` for more details.

        Returns
        -------
        boolean
            Whether the operation was successful
        """

        tmObj = self.TF.tmObj
        info = tmObj.info
        indent = tmObj.indent
        setSilent = tmObj.setSilent

        indent(level=0, reset=True)
        info("Importing data from walking through the source ...")

        self.force = force
        self.good = True
        self.forcedStop = False
        self.errors = collections.defaultdict(list)
        self.warnings = collections.defaultdict(list)
        self.warn = warn
        self.slotType = slotType

        self.intFeatures = set(intFeatures)
        self.featureMeta = featureMeta
        self.metaData = {}
        self.nodeFeatures = {}
        self.edgeFeatures = {}

        indent(level=1, reset=True)
        self._prepareMeta(otext, generic)

        indent(level=1, reset=True)
        self._follow(director)

        indent(level=1, reset=True)
        self._removeUnlinked()

        indent(level=1, reset=True)
        self._checkGraph()

        indent(level=1, reset=True)
        self._checkFeatures()

        indent(level=1, reset=True)
        self._reorderNodes()

        indent(level=1, reset=True)
        self._reassignFeatures()

        if generateTf:

            indent(level=0)

            if self.good or self.force:
                self.good = self.TF.save(
                    metaData=self.metaData,
                    nodeFeatures=self.nodeFeatures,
                    edgeFeatures=self.edgeFeatures,
                )

        self._showWarnings()
        setSilent(self.wasSilent)

        return self.good

    def _prepareMeta(self, otext, generic):
        varRe = re.compile(r"\{([^}]+)\}")

        tmObj = self.TF.tmObj
        info = tmObj.info
        indent = tmObj.indent

        if not self.good and not self.force:
            return

        info("Preparing metadata... ")

        intFeatures = self.intFeatures
        featureMeta = self.featureMeta

        errors = self.errors

        self.metaData = {
            "": generic,
            OTYPE: {"valueType": "str"},
            OSLOTS: {"valueType": "str"},
            OTEXT: otext,
        }
        metaData = self.metaData

        self.intFeatures = intFeatures
        self.sectionTypes = []
        self.sectionFeatures = []
        self.sectionFromLevel = {}
        self.levelFromSection = {}
        self.structureTypes = []
        self.structureFeatures = []
        self.structureLevel = {}
        self.textFormats = {}
        self.textFeatures = set()

        if not generic:
            errors['Missing feature meta data in "generic"'].append(
                "Consider adding provenance metadata to all features"
            )
        if not otext:
            errors['Missing "otext" configuration'].append(
                "Consider adding configuration for text representation and section levels"
            )
        else:
            sectionInfo = {}
            for f in ("sectionTypes", "sectionFeatures"):
                if f not in otext:
                    errors['Incomplete section specs in "otext"'].append(
                        f'no key "{f}"'
                    )
                    sectionInfo[f] = []
                else:
                    sFields = itemize(otext[f], sep=",")
                    sectionInfo[f] = sFields
                    if f == "sectionTypes":
                        for (i, s) in enumerate(sFields):
                            self.levelFromSection[s] = i + 1
                            self.sectionFromLevel[i + 1] = s
            sLevels = {f: len(sectionInfo[f]) for f in sectionInfo}
            if min(sLevels.values()) != max(sLevels.values()):
                errors["Inconsistent section info"].append(
                    " but ".join(f'"{f}" has {sLevels[f]} levels' for f in sLevels)
                )
            self.sectionFeatures = sectionInfo["sectionFeatures"]
            self.sectionTypes = sectionInfo["sectionTypes"]

            structureInfo = {}
            for f in ("structureTypes", "structureFeatures"):
                if f not in otext:
                    structureInfo[f] = []
                    continue
                sFields = itemize(otext[f], sep=",")
                structureInfo[f] = sFields
            if not structureInfo:
                info("No structure definition found in otext")
            sLevels = {f: len(structureInfo[f]) for f in structureInfo}
            if min(sLevels.values()) != max(sLevels.values()):
                errors["Inconsistent structure info"].append(
                    " but ".join(f'"{f}" has {sLevels[f]} levels' for f in sLevels)
                )
                structureInfo = {}
            if not structureInfo or all(
                len(info) == 0 for (s, info) in structureInfo.items()
            ):
                info("No structure nodes will be set up")
                self.structureFeatures = []
                self.structureTypes = []
            self.structureFeatures = structureInfo["structureFeatures"]
            self.structureTypes = structureInfo["structureTypes"]
            self.featFromType = {
                typ: feat
                for (typ, feat) in zip(self.structureTypes, self.structureFeatures)
            }
            self.structureSet = set(self.structureTypes)

            textFormats = {}
            textFeatures = set()
            for (k, v) in sorted(otext.items()):
                if k.startswith("fmt:"):
                    featureSet = set()
                    features = varRe.findall(v)
                    for ff in features:
                        fr = ff.rsplit(":", maxsplit=1)[0]
                        for f in fr.split("/"):
                            featureSet.add(f)
                    textFormats[k[4:]] = featureSet
                    textFeatures |= featureSet
            if not textFormats:
                errors['No text formats in "otext"'].append('add "fmt:text-orig-full"')
            elif "text-orig-full" not in textFormats:
                errors["No default text format in otext"].append(
                    'add "fmt:text-orig-full"'
                )
            self.textFormats = textFormats
            self.textFeatures = textFeatures

        info(f'SECTION   TYPES:    {", ".join(self.sectionTypes)}', tm=False)
        info(f'SECTION   FEATURES: {", ".join(self.sectionFeatures)}', tm=False)
        info(f'STRUCTURE TYPES:    {", ".join(self.structureTypes)}', tm=False)
        info(f'STRUCTURE FEATURES: {", ".join(self.structureFeatures)}', tm=False)
        info("TEXT      FEATURES:", tm=False)
        indent(level=2)
        for (fmt, feats) in sorted(textFormats.items()):
            info(f'{fmt:<20} {", ".join(sorted(feats))}', tm=False)
        indent(level=1)

        for feat in WARP + ("",):
            if feat in intFeatures:
                if feat == "":
                    errors["intFeatures"].append(
                        'Do not declare the "valueType" for all features'
                    )
                else:
                    errors["intFeatures"].append(
                        f'Do not mark the "{feat}" feature as integer valued'
                    )
                self.good = False

        for (feat, featMeta) in sorted(featureMeta.items()):
            good = self._checkFeatMeta(
                feat,
                featMeta,
                checkRegular=True,
                valueTypeAllowed=False,
                showErrors=False,
            )
            if not good:
                self.good = False
            metaData.setdefault(feat, {}).update(featMeta)
            metaData[feat]["valueType"] = "int" if feat in intFeatures else "str"

        self._showErrors()

    def _checkFeatMeta(
        self,
        feat,
        featMeta,
        checkRegular=False,
        valueTypeAllowed=True,
        showErrors=True,
    ):
        errors = collections.defaultdict(list)
        good = True

        if checkRegular:
            if feat in WARP + ("",):
                if feat == "":
                    errors["featureMeta"].append(
                        'Specify the generic feature meta data in "generic"'
                    )
                    good = False
                elif feat == OTEXT:
                    errors["featureMeta"].append(
                        f'Specify the "{OTEXT}" feature in "otext"'
                    )
                    good = False
                else:
                    errors["featureMeta"].append(
                        f'Do not pass metaData for the "{feat}" feature in "featureMeta"'
                    )
                    good = False
        if "valueType" in featMeta:
            if not valueTypeAllowed:
                errors["featureMeta"].append(
                    f'Do not specify "valueType" for the "{feat}" feature in "featureMeta"'
                )
                good = False
            elif featMeta["valueType"] not in {"int", "str"}:
                errors["featureMeta"].append('valueType must be "int" or "str"')
                good = False

        for (e, eData) in errors.items():
            self.errors[e].extend(eData)
        if showErrors:
            self._showErrors
        return good

    def stop(self, msg):
        """Stops the director. No further input will be read.

            cv.stop(msg)

        The director will exit with a non-good status  and issue the message `msg`.
        If you have called `walk()` with `force=True`, indicating that the
        director must proceed after errors, then this stop command will cause termination
        nevertheless.

        Parameters
        ----------
        msg: string
            A message to display upon stopping.

        Returns
        -------
        None
        """

        tmObj = self.TF.tmObj
        error = tmObj.error

        error(f"Forced stop: {msg}")
        self.good = False
        self.force = False
        self.forcedStop = True

    def slot(self):
        """Make a slot node and return the handle to it in `n`.

            n = cv.slot()


        No further information is needed.
        Remember that you can add features to the node by later

            cv.feature(n, key=value, ...)

        calls.

        Parameters
        ----------
        None

        Returns
        -------
        node reference: tuple
            The node reference consists of a node type and a sequence number,
            but normally you do not have to dig these out.
            Just pass the tuple as a whole to actions that require a node argument.
        """

        curSeq = self.curSeq
        curEmbedders = self.curEmbedders
        oslots = self.oslots
        levelFromSection = self.levelFromSection
        warnings = self.warnings

        self.stats[self.S] += 1
        nType = self.slotType

        curSeq[nType] += 1
        seq = curSeq[nType]

        inSection = False
        for eNode in curEmbedders:
            if eNode[0] in levelFromSection:
                inSection = True
            oslots[eNode].add(seq)

        if levelFromSection and not inSection:
            warnings["slot outside sections"].append(f"{seq}")

        return (nType, seq)

    def node(self, nType, slots=None):
        """Make a non-slot node and return the handle to it in `n`.

            n = cv.node(nodeType)

        You have to pass its *node type*, i.e. a string.
        Think of `sentence`, `paragraph`, `phrase`, `word`, `sign`, whatever.

        There are two modes for this function:

        * Auto: (`slots=None`):
          Non slot nodes will be automatically added to the set of embedders.
        * Explicit: (`slots=iterable`):
          The slots in iterable will be assigned to this node and nothing else.
          The node will not be added to the set of embedders.
          Put otherwise: the node will be terminated after construction.
          However: you could resume it later to add other slots.

        Remember that you can add features to the node by later

            cv.feature(n, key=value, ...)

        calls.

        Parameters
        ----------
        nType: string
            A node type, not the slot type
        slots: iterable of int, optional `None`
            The slots to assign to this node.
            If left out, the node is left as an embedding node and
            subsequent slots will be added to it automatically.
            All slots in the iterable must have been generated before
            by means of the `cv.slot()` action.

        Returns
        -------
        node reference or None
            If an error occurred, `None` is returned.
            The node reference consists of a node type and a sequence number,
            but normally you do not have to dig these out.
            Just pass the tuple as a whole to actions that require a node argument.
        """

        slotType = self.slotType
        errors = self.errors

        if nType == slotType:
            errors[f'use `cv.slot()` instead of `cv.node("{nType}")`'].append(None)
            return

        curSeq = self.curSeq
        curEmbedders = self.curEmbedders

        self.stats[self.N] += 1

        curSeq[nType] += 1
        seq = curSeq[nType]
        node = (nType, seq)

        self._checkSecLevel(node, before=True)

        if slots:
            maxSlot = curSeq[slotType]

            for s in slots:
                if not 1 <= s <= maxSlot:
                    errors[f'slot out of range in `cv.node(({nType}, {seq}))`'].append(f"{s}")
                else:
                    oslots = self.oslots
                    oslots[node].add(s)

            self.stats[self.T] += 1
        else:
            curEmbedders.add(node)

        return node

    def terminate(self, node):
        """**terminate** a node.

            cv.terminate(n)

        The node `n` will be removed from the set of current embedders.
        This `n` must be the result of a previous `cv.slot()` or `cv.node()` action.

        Parameters
        ----------
        node: tuple
            A node reference, obtained by one of the actions `slot` or `node`.

        Returns
        -------
        None
        """

        self.stats[self.T] += 1
        if node is not None:
            self.curEmbedders.discard(node)
            self._checkSecLevel(node, before=False)

    def resume(self, node):
        """**resume** a node.

            cv.resume(n)

        If you resume a non-slot node, you add it again to the set of embedders.
        No new node will be created.

        If you resume a slot node, it will be added to the set of current embedders.
        No new slot will be created.

        Parameters
        ----------
        node: tuple
            A node reference, obtained by one of the actions `slot` or `node`.

        Returns
        -------
        None
        """

        curEmbedders = self.curEmbedders
        oslots = self.oslots

        self.stats[self.R] += 1

        (nType, seq) = node
        if nType == self.slotType:
            for eNode in curEmbedders:
                oslots[eNode].add(seq)
        else:
            self._checkSecLevel(node, before=None)
            curEmbedders.add(node)

    def feature(self, node, **features):
        """Add **node features**.

            cv.feature(n, name=value, ... , name=value)

        Parameters
        ----------
        node: tuple
            A node reference, obtained by one of the actions `slot` or `node`.
        **features: keyword arguments
            The names and values of features to assign to this node.

        Returns
        -------
        None

        !!! caution "None values"
            If a feature value is `None` it will not be added!
        """

        nodeFeatures = self.nodeFeatures

        self.stats[self.F] += 1

        for (k, v) in features.items():
            if v is None:
                continue
            # self._checkType(k, v, self.N)
            nodeFeatures[k][node] = v

    def edge(self, nodeFrom, nodeTo, **features):
        """Add **edge features**.

            cv.edge(nf, nt, name=value, ... , name=value)

        Parameters
        ----------
        nodeFrom, nodeTo: tuple
            Two node references, obtained by one of the actions `slot` or `node`.
        **features: keyword arguments
            The names and values of features to assign to this edge
            (i.e. pair of nodes).

        Returns
        -------
        None

        !!! note "None values"
            You may pass values that are `None`,
            and a corresponding edge will be created.
            If for all edges the value is `None`,
            an edge without values will be created.
            For every `nodeFrom`, such a feature
            essentially specifies a set of nodes `{ nodeTo }`.
        """

        edgeFeatures = self.edgeFeatures

        self.stats[self.E] += 1

        for (k, v) in features.items():
            # self._checkType(k, v, self.E)
            edgeFeatures[k][nodeFrom][nodeTo] = v

    def occurs(self, feat):
        """Whether the feature `featureName` occurs in the resulting data so far.

            occurs = cv.occurs(featureName)

        If you have assigned None values to a feature, that will count, i.e.
        that feature occurs in the data.

        If you add feature values conditionally, it might happen that some
        features will not be used at all.
        For example, if your conversion produces errors, you might
        add the error information to the result in the form of error features.

        Later on, when the errors have been weeded out, these features will
        not occur any more in the result, but then TF will complain that
        such is feature is declared but not used.
        At the end of your director you can remove unused features
        conditionally, using this function.

        Parameters
        ----------
        feat: string
            The name of a feature

        Returns
        -------
        boolean
        """

        nodeFeatures = self.nodeFeatures
        edgeFeatures = self.edgeFeatures
        if feat in nodeFeatures or feat in edgeFeatures:
            return True
        return False

    def meta(self, feat, **metadata):
        """Add, modify, delete metadata fields of features.

            cv.meta(feature, name=value, ... , name=value)

        Parameters
        ----------
        feat: string
            The name of a feature
        **metaData: pairs of name and value
            If a `value` is `None`, that `name` will be deleted from the
            metadata fields of the feature.
            A bare `cv.meta(feature)` will remove the all metadata from the feature.
            If you modify the field `valueType` of a feature, that feature will be
            added or removed from the set of `intFeatures`.
            It will be checked whether you specify either `int` or `str`.


        Returns
        -------
        None
        """

        errors = self.errors
        intFeatures = self.intFeatures
        metaData = self.metaData
        featMeta = metaData.get(feat, {})

        good = True

        if not metadata:
            if feat in metaData:
                del metaData[feat]
                intFeatures.discard(feat)

        for (field, text) in metadata.items():
            if text is None:
                if field == "valueType":
                    errors['did not delete metadata field "valueType"'].append(feat)
                    good = False
                else:
                    if feat in metaData and field in metaData[feat]:
                        del metaData[feat][field]
            else:
                metaData.setdefault(feat, {})[field] = text
                if field == "valueType":
                    if text == "int":
                        intFeatures.add(feat)
                    else:
                        intFeatures.discard(feat)

        self.good = self._checkFeatMeta(feat, featMeta) and good and self.good

    def linked(self, node):
        """Returns the slots `ss` to which a node is currently linked.

            ss = cv.linked(n)


        If you construct non-slot nodes without linking them to slots,
        they will be removed when TF validates the collective result
        of the action methods.

        If you want to prevent that, you can insert an extra slot, but in order
        to do so, you have to detect that a node is still unlinked.

        This action is precisely meant for that.

        Parameters
        ----------
        node: tuple
            A node reference, obtained by one of the actions `slot` or `node`.

        Returns
        -------
        boolean
        """

        oslots = self.oslots
        return tuple(oslots.get(node, []))

    def active(self, node):
        """Returns whether a node is currently active.

        Active nodes are the nodes in the set of current embedders.

            isActive = cv.active(n)

        If you construct your nodes in a very dynamic way, it might be
        hard to keep track for each node whether it has been created, terminated,
        or resumed, in other words, whether it is active or not.

        This action is provides a direct and precise way to know
        whether a node is active.

        Parameters
        ----------
        node: tuple
            A node reference, obtained by one of the actions `slot` or `node`.

        Returns
        -------
        boolean
        """

        return node in self.curEmbedders

    def activeNodes(self, nTypes=None):
        """The currently active nodes, i.e. the embedders.

            nodes = cv.activeTypes()
            nodes = cv.activeTypes(nTypes=("sentence", "clause"))

        Parameters
        ----------
        nType: iterable optional `None`
            If None, all active nodes are returned.
            Else the iterable lists a few node types,
            and only active nodes in these types are returned.

        Returns
        -------
        set
        """

        if nTypes is None:
            return set(self.curEmbedders)
        nTypes = set(nTypes)
        return {n for n in self.curEmbedders if n[0] in nTypes}

    def activeTypes(self):
        """The node types of the currently active nodes, i.e. the embedders.

            nTypes = cv.activeTypes()

        Parameters
        ----------
        None

        Returns
        -------
        set
        """

        return {node[0] for node in self.curEmbedders}

    def get(self, feature, *args):
        """Retrieve feature values.

            cv.get(feature, n) and cv.get(feature, nf, nt)

        `feature` is the name of the feature.

        For node features, `n` is the node which carries the value.

        For edge features, `nf, nt` is the pair of from-to nodes which carries the value.

        Parameters
        ----------
        feature: string
            The name of a feature
        node: tuple
            A node reference, obtained by one of the actions `slot` or `node`.
            The node in question when retrieving the value of a node feature.
        nodeFrom, nodeTo: tuple
            Two node references, obtained by one of the actions `slot` or `node`.
            The nodes in question when retrieving the value of an edge feature.

        Returns
        -------
        string or integer
        """

        errors = self.errors
        nodeFeatures = self.nodeFeatures
        edgeFeatures = self.edgeFeatures
        nArgs = len(args)
        if nArgs == 0 or nArgs > 2:
            errors["use `cv.get(ft, n)` or `cv.get(ft, nf, nt)`"].append(None)
            return None

        return (
            nodeFeatures.get(feature, {}).get(args[0], None)
            if len(args) == 1
            else edgeFeatures.get(feature, {}).get(args[0], {}).get(args[1], None)
        )

    def _checkSecLevel(self, node, before=True):
        levelFromSection = self.levelFromSection
        sectionFeatures = self.sectionFeatures
        nodeFeatures = self.nodeFeatures
        warnings = self.warnings
        curEmbedders = self.curEmbedders

        (nType, seq) = node

        msg = "starts" if before is True else "ends" if before is False else "resumes"

        if levelFromSection:
            level = levelFromSection.get(nType, None)
            if level is None:
                return

            headingFeature = sectionFeatures[level - 1]
            nHeading = nodeFeatures.get(headingFeature, {}).get(node, "??")

            for em in curEmbedders:
                eType = em[0]
                if eType in levelFromSection:
                    eLevel = levelFromSection.get(eType, None)
                    eHeadingFeature = sectionFeatures[eLevel - 1]
                    eHeading = nodeFeatures.get(eHeadingFeature, {}).get(em, "??")

                if eType == nType:
                    warnings[
                        f'section {nType} "{nHeading}" of level {level}'
                        f" enclosed in another {nType}: {eHeading}"
                    ].append(None)
                elif eType in levelFromSection:
                    eLevel = levelFromSection[eType]
                    if eLevel > level:
                        warnings[
                            f'section {nType} "{nHeading}" of level {level} {msg}'
                            f' inside a {eType} "{eHeading}" of level {eLevel}'
                        ].append(None)

    def _follow(self, director):

        # after node = yield ('N', nodeType) all slot nodes that are yielded
        # will be linked to node, until a ('T', node) is yielded.
        # If needed, you can resume this node again, after which new slot nodes
        # continue to be linked to this node.
        # If you resume a slot node, it all non slot nodes in the current context
        # will be linked to it.

        tmObj = self.TF.tmObj
        info = tmObj.info

        if not self.good:
            return

        info("Following director... ")

        slotType = self.slotType
        errors = self.errors

        self.oslots = collections.defaultdict(set)
        self.nodeFeatures = collections.defaultdict(dict)
        self.edgeFeatures = collections.defaultdict(
            lambda: collections.defaultdict(dict)
        )
        self.nodes = collections.defaultdict(set)
        nodes = self.nodes

        self.curSeq = collections.Counter()
        self.curEmbedders = set()
        curEmbedders = self.curEmbedders

        self.stats = {
            actionType: 0
            for actionType in (self.S, self.N, self.T, self.R, self.F, self.E)
        }

        director(self)

        if not self.stats:
            self.good = False
            return

        for (actionType, amount) in sorted(self.stats.items()):
            info(f'"{actionType}" actions: {amount}')

        totalNodes = 0

        for (nType, lastSeq) in sorted(self.curSeq.items()):
            for seq in range(1, lastSeq + 1):
                nodes[nType].add(seq)
            slotRep = " = slot type" if nType == slotType else ""
            info(f'{lastSeq:>8} x "{nType}" node {slotRep}', tm=False)
            totalNodes += lastSeq
        info(f"{totalNodes:>8} nodes of all types", tm=False)

        self.totalNodes = totalNodes

        if curEmbedders:
            embedCount = collections.Counter()
            for (nType, seq) in curEmbedders:
                embedCount[nType] += 1
            for (nType, amount) in sorted(
                embedCount.items(), key=lambda x: (-x[1], x[0]),
            ):
                errors["Unterminated nodes"].append(f"{nType}: {amount} x")

        self._showErrors()

    def _removeUnlinked(self):
        tmObj = self.TF.tmObj
        info = tmObj.info
        indent = tmObj.indent

        if not self.good and not self.force:
            return

        nodeTypes = self.curSeq
        nodes = self.nodes
        slotType = self.slotType
        oslots = self.oslots
        nodeFeatures = self.nodeFeatures
        edgeFeatures = self.edgeFeatures

        unlinked = {}

        for nType in nodeTypes:
            if nType == slotType:
                continue
            for seq in range(1, nodeTypes[nType] + 1):
                if (nType, seq) not in oslots:
                    unlinked.setdefault(nType, []).append(seq)

        if unlinked:
            info("Removing unlinked nodes ... ")
            indent(level=2)
            totalRemoved = 0
            for (nType, seqs) in unlinked.items():
                theseNodes = nodes[nType]
                lSeqs = len(seqs)
                totalRemoved += lSeqs
                rep = " ..." if lSeqs > 5 else ""
                pl = "" if lSeqs == 1 else "s"
                info(f'{lSeqs:>6} unlinked "{nType}" node{pl}: {seqs[0:5]}{rep}')
                for seq in seqs:
                    node = (nType, seq)
                    theseNodes.discard(seq)
                    for (f, fData) in nodeFeatures.items():
                        if node in fData:
                            del fData[node]
                    for (f, fData) in edgeFeatures.items():
                        if node in fData:
                            del fData[node]
                            for (fNode, toValues) in fData:
                                if node in toValues:
                                    del toValues[node]
            pl = "" if totalRemoved == 1 else "s"
            info(f"{totalRemoved:>6} unlinked node{pl}")
            self.totalNodes -= totalRemoved
            info(f"Leaving {self.totalNodes:>6} nodes")
            indent(level=1)

    def _checkGraph(self):
        tmObj = self.TF.tmObj
        info = tmObj.info

        if not self.good and not self.force:
            return

        info("checking for nodes and edges ... ")

        nodes = self.nodes
        errors = self.errors
        edgeFeatures = self.edgeFeatures

        # edges refer to nodes

        for (k, featureData) in edgeFeatures.items():
            for nFrom in featureData:
                (nType, seq) = nFrom
                if nType not in nodes or seq not in nodes[nType]:
                    errors["Edge feature: illegal node"].append(
                        f'"{k}": from-node  {nFrom} not in node set'
                    )
                    continue
                for nTo in featureData[nFrom]:
                    (nType, seq) = nTo
                    if nType not in nodes or seq not in nodes[nType]:
                        errors["Edge feature: illegal node"].append(
                            f'"{k}": to-node  {nTo} not in node set'
                        )

        self._showErrors()

    def _checkFeatures(self):
        tmObj = self.TF.tmObj
        info = tmObj.info

        if not self.good and not self.force:
            return

        info("checking features ... ")

        intFeatures = self.intFeatures
        metaData = self.metaData

        nodes = self.nodes
        nodeFeatures = self.nodeFeatures
        edgeFeatures = self.edgeFeatures

        errors = self.errors

        for feat in intFeatures:
            if (
                feat not in WARP
                and feat not in nodeFeatures
                and feat not in edgeFeatures
            ):
                errors["intFeatures"].append(
                    f'"{feat}" is declared as integer valued, but this feature does not occur'
                )
        for nType in self.sectionTypes:
            if nType not in nodes:
                errors["sections"].append(
                    f'node type "{nType}" is declared as a section type, but this node type does not occur'
                )
        for feat in self.sectionFeatures:
            if feat not in nodeFeatures:
                errors["sections"].append(
                    f'"{feat}" is declared as a section feature, but this node feature does not occur'
                )
        for nType in self.structureTypes:
            if nType not in nodes:
                errors["structure"].append(
                    f'node type "{nType}" is declared as a structure type,'
                    f" but this node type does not occur"
                )
        for feat in self.structureFeatures:
            if feat not in nodeFeatures:
                errors["structure"].append(
                    f'"{feat}" is declared as a structure feature, but this node feature does not occur'
                )
                nodeFeatures[feat] = {}

        structureSet = self.structureSet
        featFromType = self.featFromType
        for nType in nodes:
            if nType not in structureSet:
                continue
            feat = featFromType[nType]
            for seq in nodes[nType]:
                if (nType, seq) not in nodeFeatures[feat]:
                    errors["structure features"].append(
                        f'"structure element "{nType}" {seq} has no value for "{feat}"'
                    )

        for feat in self.textFeatures:
            if feat not in nodeFeatures:
                errors["text formats"].append(
                    f'"{feat}" is used in a text format, but this node feature does not occur'
                )

        for feat in WARP:
            if feat in nodeFeatures or feat in edgeFeatures:
                errors[feat].append(f'Do not construct the "{feat}" feature yourself')

        for feat in sorted(nodeFeatures) + sorted(edgeFeatures):
            if feat not in self.metaData:
                errors["feature metadata"].append(
                    f'node feature "{feat}" has no metadata'
                )

        for feat in sorted(metaData):
            if (
                feat
                and feat not in WARP
                and feat not in nodeFeatures
                and feat not in edgeFeatures
            ):
                errors["feature metadata"].append(
                    f'node feature "{feat}" has metadata but does not occur'
                )

        for (feat, featData) in sorted(nodeFeatures.items()):
            if None in featData:
                errors["feature values assigned to None"].append(
                    f'node feature "{feat}" has a node None'
                )
        for (feat, featData) in sorted(edgeFeatures.items()):
            if None in featData:
                errors["feature values assigned to None"].append(
                    f'edge feature "{feat}" has a from-node None'
                )
            for toValues in featData.values():
                if None in toValues:
                    errors["feature values assigned to None"].append(
                        f'edge feature "{feat}" has a to-node None'
                    )

        for (feat, featData) in sorted(edgeFeatures.items()):
            if feat in WARP:
                continue
            hasValues = False
            for (nodeTo, toValues) in featData.items():
                if any(v is not None for v in toValues.values()):
                    hasValues = True
                    break

            if not hasValues:
                edgeFeatures[feat] = {
                    nodeTo: set(toValues) for (nodeTo, toValues) in featData.items()
                }
            metaData.setdefault(feat, {})["edgeValues"] = hasValues

        for feat in intFeatures:
            if feat in WARP:
                continue
            if feat in nodeFeatures:
                featData = nodeFeatures[feat]
                for (k, v) in featData.items():
                    if not isInt(v):
                        (nType, node) = k
                        errors["Not a number"].append(
                            f'"node feature "{feat}": {nType} {node} => "{v}"'
                        )
            if feat in edgeFeatures and metaData[feat]["edgeValues"]:
                featData = edgeFeatures[feat]
                for (fromNode, toValues) in featData.items():
                    (fType, fNode) = fromNode
                    for (toNode, v) in toValues.items():
                        (tType, tNode) = toNode
                        if not isInt(v):
                            errors["Not a number"].append(
                                f'"edge feature "{feat}":'
                                f' {fType} {fNode} ="{v}"=> {tType} {tNode}'
                            )

        self._showErrors()

    def _reorderNodes(self):
        tmObj = self.TF.tmObj
        info = tmObj.info

        if not self.good and not self.force:
            return

        info("reordering nodes ...")

        nodeTypes = self.curSeq
        nodes = self.nodes
        slotType = self.slotType

        nTypes = (slotType,) + tuple(
            sorted(nType for nType in nodes if nType != slotType)
        )

        self.nodeMap = {}
        self.maxSlot = nodeTypes[slotType]

        nodeMap = self.nodeMap
        maxSlot = self.maxSlot

        n = 0

        for nType in nTypes:
            canonical = self._canonical(nType)
            if nType == slotType:
                sortedSeqs = range(1, maxSlot + 1)
            else:
                seqs = nodes[nType]
                info(f'Sorting {len(seqs)} nodes of type "{nType}"')
                sortedSeqs = sorted(seqs, key=canonical)
            for seq in sortedSeqs:
                n += 1
                nodeMap[(nType, seq)] = n

        self.maxNode = n
        info(f"Max node = {n}")

        self._showErrors()

    def _canonical(self, nType):
        oslots = self.oslots

        def before(nodeA, nodeB):
            slotsA = oslots[(nType, nodeA)]
            slotsB = oslots[(nType, nodeB)]
            if slotsA == slotsB:
                return 0

            aWithoutB = slotsA - slotsB
            if not aWithoutB:
                return 1

            bWithoutA = slotsB - slotsA
            if not bWithoutA:
                return -1

            aMin = min(aWithoutB)
            bMin = min(bWithoutA)
            return -1 if aMin < bMin else 1

        return functools.cmp_to_key(before)

    def _reassignFeatures(self):
        tmObj = self.TF.tmObj
        info = tmObj.info
        indent = tmObj.indent

        if not self.good and not self.force:
            return

        info("reassigning feature values ...")

        nodeMap = self.nodeMap
        oslots = self.oslots
        nodeFeatures = self.nodeFeatures
        edgeFeatures = self.edgeFeatures

        otype = {n: nType for ((nType, seq), n) in nodeMap.items()}
        oslots = {nodeMap[node]: slots for (node, slots) in oslots.items()}

        nodeFeaturesProto = self.nodeFeatures
        edgeFeaturesProto = self.edgeFeatures

        nodeFeatures = collections.defaultdict(dict)
        edgeFeatures = collections.defaultdict(lambda: collections.defaultdict(dict))

        indent(level=2)

        for k in sorted(nodeFeaturesProto):
            featureDataProto = nodeFeaturesProto[k]
            ln = len(featureDataProto)
            pl = "" if ln == 1 else "s"
            info(f'node feature "{k}" with {ln} node{pl}')
            featureData = {}
            for (node, value) in featureDataProto.items():
                featureData[nodeMap[node]] = value
            nodeFeatures[k] = featureData

        for k in sorted(edgeFeaturesProto):
            featureDataProto = edgeFeaturesProto[k]
            ln = len(featureDataProto)
            pl = "" if ln == 1 else "s"
            info(f'edge feature "{k}" with {ln} start node{pl}')
            featureData = {}
            for (nodeFrom, toValues) in featureDataProto.items():
                if type(toValues) is set:
                    toData = {nodeMap[nodeTo] for nodeTo in toValues}
                else:
                    toData = {}
                    for (nodeTo, value) in toValues.items():
                        toData[nodeMap[nodeTo]] = value
                featureData[nodeMap[nodeFrom]] = toData
            edgeFeatures[k] = featureData

        nodeFeatures["otype"] = otype
        edgeFeatures["oslots"] = oslots

        indent(level=1)

        self.oslots = None
        self.otype = None
        self.nodeFeatures = nodeFeatures
        self.edgeFeatures = edgeFeatures

        self._showErrors()

Class variables

var E
var F
var N
var R
var S
var T

Methods

def active(self, node)

Returns whether a node is currently active.

Active nodes are the nodes in the set of current embedders.

isActive = cv.active(n)

If you construct your nodes in a very dynamic way, it might be hard to keep track for each node whether it has been created, terminated, or resumed, in other words, whether it is active or not.

This action is provides a direct and precise way to know whether a node is active.

Parameters

node : tuple
A node reference, obtained by one of the actions slot or node.

Returns

boolean
 
Expand source code Browse git
def active(self, node):
    """Returns whether a node is currently active.

    Active nodes are the nodes in the set of current embedders.

        isActive = cv.active(n)

    If you construct your nodes in a very dynamic way, it might be
    hard to keep track for each node whether it has been created, terminated,
    or resumed, in other words, whether it is active or not.

    This action is provides a direct and precise way to know
    whether a node is active.

    Parameters
    ----------
    node: tuple
        A node reference, obtained by one of the actions `slot` or `node`.

    Returns
    -------
    boolean
    """

    return node in self.curEmbedders
def activeNodes(self, nTypes=None)

The currently active nodes, i.e. the embedders.

nodes = cv.activeTypes()
nodes = cv.activeTypes(nTypes=("sentence", "clause"))

Parameters

nType : iterable optional None``
If None, all active nodes are returned. Else the iterable lists a few node types, and only active nodes in these types are returned.

Returns

set
 
Expand source code Browse git
def activeNodes(self, nTypes=None):
    """The currently active nodes, i.e. the embedders.

        nodes = cv.activeTypes()
        nodes = cv.activeTypes(nTypes=("sentence", "clause"))

    Parameters
    ----------
    nType: iterable optional `None`
        If None, all active nodes are returned.
        Else the iterable lists a few node types,
        and only active nodes in these types are returned.

    Returns
    -------
    set
    """

    if nTypes is None:
        return set(self.curEmbedders)
    nTypes = set(nTypes)
    return {n for n in self.curEmbedders if n[0] in nTypes}
def activeTypes(self)

The node types of the currently active nodes, i.e. the embedders.

nTypes = cv.activeTypes()

Parameters

None
 

Returns

set
 
Expand source code Browse git
def activeTypes(self):
    """The node types of the currently active nodes, i.e. the embedders.

        nTypes = cv.activeTypes()

    Parameters
    ----------
    None

    Returns
    -------
    set
    """

    return {node[0] for node in self.curEmbedders}
def edge(self, nodeFrom, nodeTo, **features)

Add edge features.

cv.edge(nf, nt, name=value, ... , name=value)

Parameters

nodeFrom, nodeTo : tuple
Two node references, obtained by one of the actions slot or node.
**features : keyword arguments
The names and values of features to assign to this edge (i.e. pair of nodes).

Returns

None
 

None values

You may pass values that are None, and a corresponding edge will be created. If for all edges the value is None, an edge without values will be created. For every nodeFrom, such a feature essentially specifies a set of nodes { nodeTo }.

Expand source code Browse git
def edge(self, nodeFrom, nodeTo, **features):
    """Add **edge features**.

        cv.edge(nf, nt, name=value, ... , name=value)

    Parameters
    ----------
    nodeFrom, nodeTo: tuple
        Two node references, obtained by one of the actions `slot` or `node`.
    **features: keyword arguments
        The names and values of features to assign to this edge
        (i.e. pair of nodes).

    Returns
    -------
    None

    !!! note "None values"
        You may pass values that are `None`,
        and a corresponding edge will be created.
        If for all edges the value is `None`,
        an edge without values will be created.
        For every `nodeFrom`, such a feature
        essentially specifies a set of nodes `{ nodeTo }`.
    """

    edgeFeatures = self.edgeFeatures

    self.stats[self.E] += 1

    for (k, v) in features.items():
        # self._checkType(k, v, self.E)
        edgeFeatures[k][nodeFrom][nodeTo] = v
def feature(self, node, **features)

Add node features.

cv.feature(n, name=value, ... , name=value)

Parameters

node : tuple
A node reference, obtained by one of the actions slot or node.
**features : keyword arguments
The names and values of features to assign to this node.

Returns

None
 

None values

If a feature value is None it will not be added!

Expand source code Browse git
def feature(self, node, **features):
    """Add **node features**.

        cv.feature(n, name=value, ... , name=value)

    Parameters
    ----------
    node: tuple
        A node reference, obtained by one of the actions `slot` or `node`.
    **features: keyword arguments
        The names and values of features to assign to this node.

    Returns
    -------
    None

    !!! caution "None values"
        If a feature value is `None` it will not be added!
    """

    nodeFeatures = self.nodeFeatures

    self.stats[self.F] += 1

    for (k, v) in features.items():
        if v is None:
            continue
        # self._checkType(k, v, self.N)
        nodeFeatures[k][node] = v
def get(self, feature, *args)

Retrieve feature values.

cv.get(feature, n) and cv.get(feature, nf, nt)

feature is the name of the feature.

For node features, n is the node which carries the value.

For edge features, nf, nt is the pair of from-to nodes which carries the value.

Parameters

feature : string
The name of a feature
node : tuple
A node reference, obtained by one of the actions slot or node. The node in question when retrieving the value of a node feature.
nodeFrom, nodeTo : tuple
Two node references, obtained by one of the actions slot or node. The nodes in question when retrieving the value of an edge feature.

Returns

string or integer
 
Expand source code Browse git
def get(self, feature, *args):
    """Retrieve feature values.

        cv.get(feature, n) and cv.get(feature, nf, nt)

    `feature` is the name of the feature.

    For node features, `n` is the node which carries the value.

    For edge features, `nf, nt` is the pair of from-to nodes which carries the value.

    Parameters
    ----------
    feature: string
        The name of a feature
    node: tuple
        A node reference, obtained by one of the actions `slot` or `node`.
        The node in question when retrieving the value of a node feature.
    nodeFrom, nodeTo: tuple
        Two node references, obtained by one of the actions `slot` or `node`.
        The nodes in question when retrieving the value of an edge feature.

    Returns
    -------
    string or integer
    """

    errors = self.errors
    nodeFeatures = self.nodeFeatures
    edgeFeatures = self.edgeFeatures
    nArgs = len(args)
    if nArgs == 0 or nArgs > 2:
        errors["use `cv.get(ft, n)` or `cv.get(ft, nf, nt)`"].append(None)
        return None

    return (
        nodeFeatures.get(feature, {}).get(args[0], None)
        if len(args) == 1
        else edgeFeatures.get(feature, {}).get(args[0], {}).get(args[1], None)
    )
def linked(self, node)

Returns the slots ss to which a node is currently linked.

ss = cv.linked(n)

If you construct non-slot nodes without linking them to slots, they will be removed when TF validates the collective result of the action methods.

If you want to prevent that, you can insert an extra slot, but in order to do so, you have to detect that a node is still unlinked.

This action is precisely meant for that.

Parameters

node : tuple
A node reference, obtained by one of the actions slot or node.

Returns

boolean
 
Expand source code Browse git
def linked(self, node):
    """Returns the slots `ss` to which a node is currently linked.

        ss = cv.linked(n)


    If you construct non-slot nodes without linking them to slots,
    they will be removed when TF validates the collective result
    of the action methods.

    If you want to prevent that, you can insert an extra slot, but in order
    to do so, you have to detect that a node is still unlinked.

    This action is precisely meant for that.

    Parameters
    ----------
    node: tuple
        A node reference, obtained by one of the actions `slot` or `node`.

    Returns
    -------
    boolean
    """

    oslots = self.oslots
    return tuple(oslots.get(node, []))
def meta(self, feat, **metadata)

Add, modify, delete metadata fields of features.

cv.meta(feature, name=value, ... , name=value)

Parameters

feat : string
The name of a feature
**metaData : pairs of name and value
If a value is None, that name will be deleted from the metadata fields of the feature. A bare cv.meta(feature) will remove the all metadata from the feature. If you modify the field valueType of a feature, that feature will be added or removed from the set of intFeatures. It will be checked whether you specify either int or str.

Returns

None
 
Expand source code Browse git
def meta(self, feat, **metadata):
    """Add, modify, delete metadata fields of features.

        cv.meta(feature, name=value, ... , name=value)

    Parameters
    ----------
    feat: string
        The name of a feature
    **metaData: pairs of name and value
        If a `value` is `None`, that `name` will be deleted from the
        metadata fields of the feature.
        A bare `cv.meta(feature)` will remove the all metadata from the feature.
        If you modify the field `valueType` of a feature, that feature will be
        added or removed from the set of `intFeatures`.
        It will be checked whether you specify either `int` or `str`.


    Returns
    -------
    None
    """

    errors = self.errors
    intFeatures = self.intFeatures
    metaData = self.metaData
    featMeta = metaData.get(feat, {})

    good = True

    if not metadata:
        if feat in metaData:
            del metaData[feat]
            intFeatures.discard(feat)

    for (field, text) in metadata.items():
        if text is None:
            if field == "valueType":
                errors['did not delete metadata field "valueType"'].append(feat)
                good = False
            else:
                if feat in metaData and field in metaData[feat]:
                    del metaData[feat][field]
        else:
            metaData.setdefault(feat, {})[field] = text
            if field == "valueType":
                if text == "int":
                    intFeatures.add(feat)
                else:
                    intFeatures.discard(feat)

    self.good = self._checkFeatMeta(feat, featMeta) and good and self.good
def node(self, nType, slots=None)

Make a non-slot node and return the handle to it in n.

n = cv.node(nodeType)

You have to pass its node type, i.e. a string. Think of sentence, paragraph, phrase, word, sign, whatever.

There are two modes for this function:

  • Auto: (slots=None): Non slot nodes will be automatically added to the set of embedders.
  • Explicit: (slots=iterable): The slots in iterable will be assigned to this node and nothing else. The node will not be added to the set of embedders. Put otherwise: the node will be terminated after construction. However: you could resume it later to add other slots.

Remember that you can add features to the node by later

cv.feature(n, key=value, ...)

calls.

Parameters

nType : string
A node type, not the slot type
slots : iterable of int, optional None
The slots to assign to this node. If left out, the node is left as an embedding node and subsequent slots will be added to it automatically. All slots in the iterable must have been generated before by means of the cv.slot() action.

Returns

node reference or None
If an error occurred, None is returned. The node reference consists of a node type and a sequence number, but normally you do not have to dig these out. Just pass the tuple as a whole to actions that require a node argument.
Expand source code Browse git
def node(self, nType, slots=None):
    """Make a non-slot node and return the handle to it in `n`.

        n = cv.node(nodeType)

    You have to pass its *node type*, i.e. a string.
    Think of `sentence`, `paragraph`, `phrase`, `word`, `sign`, whatever.

    There are two modes for this function:

    * Auto: (`slots=None`):
      Non slot nodes will be automatically added to the set of embedders.
    * Explicit: (`slots=iterable`):
      The slots in iterable will be assigned to this node and nothing else.
      The node will not be added to the set of embedders.
      Put otherwise: the node will be terminated after construction.
      However: you could resume it later to add other slots.

    Remember that you can add features to the node by later

        cv.feature(n, key=value, ...)

    calls.

    Parameters
    ----------
    nType: string
        A node type, not the slot type
    slots: iterable of int, optional `None`
        The slots to assign to this node.
        If left out, the node is left as an embedding node and
        subsequent slots will be added to it automatically.
        All slots in the iterable must have been generated before
        by means of the `cv.slot()` action.

    Returns
    -------
    node reference or None
        If an error occurred, `None` is returned.
        The node reference consists of a node type and a sequence number,
        but normally you do not have to dig these out.
        Just pass the tuple as a whole to actions that require a node argument.
    """

    slotType = self.slotType
    errors = self.errors

    if nType == slotType:
        errors[f'use `cv.slot()` instead of `cv.node("{nType}")`'].append(None)
        return

    curSeq = self.curSeq
    curEmbedders = self.curEmbedders

    self.stats[self.N] += 1

    curSeq[nType] += 1
    seq = curSeq[nType]
    node = (nType, seq)

    self._checkSecLevel(node, before=True)

    if slots:
        maxSlot = curSeq[slotType]

        for s in slots:
            if not 1 <= s <= maxSlot:
                errors[f'slot out of range in `cv.node(({nType}, {seq}))`'].append(f"{s}")
            else:
                oslots = self.oslots
                oslots[node].add(s)

        self.stats[self.T] += 1
    else:
        curEmbedders.add(node)

    return node
def occurs(self, feat)

Whether the feature featureName occurs in the resulting data so far.

occurs = cv.occurs(featureName)

If you have assigned None values to a feature, that will count, i.e. that feature occurs in the data.

If you add feature values conditionally, it might happen that some features will not be used at all. For example, if your conversion produces errors, you might add the error information to the result in the form of error features.

Later on, when the errors have been weeded out, these features will not occur any more in the result, but then TF will complain that such is feature is declared but not used. At the end of your director you can remove unused features conditionally, using this function.

Parameters

feat : string
The name of a feature

Returns

boolean
 
Expand source code Browse git
def occurs(self, feat):
    """Whether the feature `featureName` occurs in the resulting data so far.

        occurs = cv.occurs(featureName)

    If you have assigned None values to a feature, that will count, i.e.
    that feature occurs in the data.

    If you add feature values conditionally, it might happen that some
    features will not be used at all.
    For example, if your conversion produces errors, you might
    add the error information to the result in the form of error features.

    Later on, when the errors have been weeded out, these features will
    not occur any more in the result, but then TF will complain that
    such is feature is declared but not used.
    At the end of your director you can remove unused features
    conditionally, using this function.

    Parameters
    ----------
    feat: string
        The name of a feature

    Returns
    -------
    boolean
    """

    nodeFeatures = self.nodeFeatures
    edgeFeatures = self.edgeFeatures
    if feat in nodeFeatures or feat in edgeFeatures:
        return True
    return False
def resume(self, node)

resume a node.

cv.resume(n)

If you resume a non-slot node, you add it again to the set of embedders. No new node will be created.

If you resume a slot node, it will be added to the set of current embedders. No new slot will be created.

Parameters

node : tuple
A node reference, obtained by one of the actions slot or node.

Returns

None
 
Expand source code Browse git
def resume(self, node):
    """**resume** a node.

        cv.resume(n)

    If you resume a non-slot node, you add it again to the set of embedders.
    No new node will be created.

    If you resume a slot node, it will be added to the set of current embedders.
    No new slot will be created.

    Parameters
    ----------
    node: tuple
        A node reference, obtained by one of the actions `slot` or `node`.

    Returns
    -------
    None
    """

    curEmbedders = self.curEmbedders
    oslots = self.oslots

    self.stats[self.R] += 1

    (nType, seq) = node
    if nType == self.slotType:
        for eNode in curEmbedders:
            oslots[eNode].add(seq)
    else:
        self._checkSecLevel(node, before=None)
        curEmbedders.add(node)
def slot(self)

Make a slot node and return the handle to it in n.

n = cv.slot()

No further information is needed. Remember that you can add features to the node by later

cv.feature(n, key=value, ...)

calls.

Parameters

None
 

Returns

node reference: tuple
The node reference consists of a node type and a sequence number, but normally you do not have to dig these out. Just pass the tuple as a whole to actions that require a node argument.
Expand source code Browse git
def slot(self):
    """Make a slot node and return the handle to it in `n`.

        n = cv.slot()


    No further information is needed.
    Remember that you can add features to the node by later

        cv.feature(n, key=value, ...)

    calls.

    Parameters
    ----------
    None

    Returns
    -------
    node reference: tuple
        The node reference consists of a node type and a sequence number,
        but normally you do not have to dig these out.
        Just pass the tuple as a whole to actions that require a node argument.
    """

    curSeq = self.curSeq
    curEmbedders = self.curEmbedders
    oslots = self.oslots
    levelFromSection = self.levelFromSection
    warnings = self.warnings

    self.stats[self.S] += 1
    nType = self.slotType

    curSeq[nType] += 1
    seq = curSeq[nType]

    inSection = False
    for eNode in curEmbedders:
        if eNode[0] in levelFromSection:
            inSection = True
        oslots[eNode].add(seq)

    if levelFromSection and not inSection:
        warnings["slot outside sections"].append(f"{seq}")

    return (nType, seq)
def stop(self, msg)

Stops the director. No further input will be read.

cv.stop(msg)

The director will exit with a non-good status and issue the message msg. If you have called walk() with force=True, indicating that the director must proceed after errors, then this stop command will cause termination nevertheless.

Parameters

msg : string
A message to display upon stopping.

Returns

None
 
Expand source code Browse git
def stop(self, msg):
    """Stops the director. No further input will be read.

        cv.stop(msg)

    The director will exit with a non-good status  and issue the message `msg`.
    If you have called `walk()` with `force=True`, indicating that the
    director must proceed after errors, then this stop command will cause termination
    nevertheless.

    Parameters
    ----------
    msg: string
        A message to display upon stopping.

    Returns
    -------
    None
    """

    tmObj = self.TF.tmObj
    error = tmObj.error

    error(f"Forced stop: {msg}")
    self.good = False
    self.force = False
    self.forcedStop = True
def terminate(self, node)

terminate a node.

cv.terminate(n)

The node n will be removed from the set of current embedders. This n must be the result of a previous cv.slot() or cv.node() action.

Parameters

node : tuple
A node reference, obtained by one of the actions slot or node.

Returns

None
 
Expand source code Browse git
def terminate(self, node):
    """**terminate** a node.

        cv.terminate(n)

    The node `n` will be removed from the set of current embedders.
    This `n` must be the result of a previous `cv.slot()` or `cv.node()` action.

    Parameters
    ----------
    node: tuple
        A node reference, obtained by one of the actions `slot` or `node`.

    Returns
    -------
    None
    """

    self.stats[self.T] += 1
    if node is not None:
        self.curEmbedders.discard(node)
        self._checkSecLevel(node, before=False)
def walk(self, director, slotType, otext={}, generic={}, intFeatures=set(), featureMeta={}, warn=True, generateTf=True, force=False)

Asks a director function to walk through source data and receives its actions.

The director function should unravel the source. You have to program the director, which takes one argument: cv. From the cv you can use a few standard actions that instruct Text-Fabric to build a graph.

This function will check whether the metadata makes sense and is minimally complete.

During node creation the section structure will be watched, and you will be warned if irregularities occur.

After the creation of the feature data, some extra checks will be performed to see whether the metadata matches the data and vice versa.

The new feature data will be written to the output directory of the underlying TF object. In fact, the rules are exactly the same as for FabricCore.save().

Parameters

slotType : string
The node type that acts as the type of the slots in the data set.
oText : dict

The configuration information to be stored in the otext feature (see tf.core.text):

  • section types
  • section features
  • structure types
  • structure features
  • text formats
generic : dict

Metadata that will be written into the header of all generated TF features.

You can make changes to this later on, dynamically in your director.

intFeatures : iterable

The set of features that have integer values only.

You can make changes to this later on, dynamically in your director.

featureMeta : dict of dict

For each node or edge feature descriptive metadata can be supplied.

You can make changes to this later on, dynamically in your director.

warn : boolean, optional True

This regulates the response to warnings:

True (default): stop after warnings (as if they are errors);

False continue after warnings but do show them;

None suppress all warnings.

silent : boolean, optional None
By this you can suppress informational messages: silent=True.
force : boolean, optional False
This forces the process to continue after errors. Your TF set might not be valid. Yet this can be useful during testing, when you know that not everything is OK, but you want to check some results. Especially when dealing with large datasets, you might want to test with little pieces. But then you get a kind of non-fatal errors that stand in the way of testing. For those cases: force=True.
generateTf : boolean, optional True
You can pass False here to suppress the actual writing of TF data. In that way you can dry-run the director to check for errors and warnings
director : function

An ordinary function that takes one argument, the cv object, and should not deliver anything.

Writing this function is the main job to do when you want to convert a data source to TF.

See tf.convert.walker for more details.

Returns

boolean
Whether the operation was successful
Expand source code Browse git
def walk(
    self,
    director,
    slotType,
    otext={},
    generic={},
    intFeatures=set(),
    featureMeta={},
    warn=True,
    generateTf=True,
    force=False,
):
    """Asks a director function to walk through source data and receives its actions.

    The `director` function should unravel the source.
    You have to program the `director`, which takes one argument: `cv`.
    From the `cv` you can use a few standard actions that instruct Text-Fabric
    to build a graph.

    This function will check whether the metadata makes sense and is minimally
    complete.

    During node creation the section structure will be watched,
    and you will be warned if irregularities occur.

    After the creation of the feature data, some extra checks will be performed
    to see whether the metadata matches the data and vice versa.

    The new feature data will be written to the output directory of the
    underlying TF object.  In fact, the rules are exactly the same as for
    `tf.fabric.Fabric.save`.

    Parameters
    ----------
    slotType: string
        The node type that acts as the type of the slots in the data set.

    oText: dict
        The configuration information to be stored in the `otext` feature
        (see `tf.core.text`):

        * section types
        * section features
        * structure types
        * structure features
        * text formats

    generic: dict
        Metadata that will be written into the header of all generated TF features.

        You can make changes to this later on, dynamically in your director.

    intFeatures: iterable
        The set of features that have integer values only.

        You can make changes to this later on, dynamically in your director.

    featureMeta: dict of dict
        For each node or edge feature descriptive metadata can be supplied.

        You can make changes to this later on, dynamically in your director.

    warn: boolean, optional `True`
        This regulates the response to warnings:

        `True` (default): stop after warnings (as if they are errors);

        `False` continue after warnings but do show them;

        `None` suppress all warnings.

    silent: boolean, optional `None`
        By this you can suppress informational messages: `silent=True`.

    force: boolean, optional `False`
        This forces the process to continue after errors.
        Your TF set might not be valid.
        Yet this can be useful during testing, when you know
        that not everything is OK, but you want to check some results.
        Especially when dealing with large datasets, you might want to test
        with little pieces. But then you get a kind of non-fatal errors that
        stand in the way of testing. For those cases: `force=True`.

    generateTf: boolean, optional `True`
        You can pass `False` here to suppress the actual writing of TF data.
        In that way you can dry-run the director to check for errors and warnings

    director: function
        An ordinary function that takes one argument, the `cv` object, and
        should not deliver anything.

        Writing this function is the main job to do when you want to convert a data source
        to TF.

        See `tf.convert.walker` for more details.

    Returns
    -------
    boolean
        Whether the operation was successful
    """

    tmObj = self.TF.tmObj
    info = tmObj.info
    indent = tmObj.indent
    setSilent = tmObj.setSilent

    indent(level=0, reset=True)
    info("Importing data from walking through the source ...")

    self.force = force
    self.good = True
    self.forcedStop = False
    self.errors = collections.defaultdict(list)
    self.warnings = collections.defaultdict(list)
    self.warn = warn
    self.slotType = slotType

    self.intFeatures = set(intFeatures)
    self.featureMeta = featureMeta
    self.metaData = {}
    self.nodeFeatures = {}
    self.edgeFeatures = {}

    indent(level=1, reset=True)
    self._prepareMeta(otext, generic)

    indent(level=1, reset=True)
    self._follow(director)

    indent(level=1, reset=True)
    self._removeUnlinked()

    indent(level=1, reset=True)
    self._checkGraph()

    indent(level=1, reset=True)
    self._checkFeatures()

    indent(level=1, reset=True)
    self._reorderNodes()

    indent(level=1, reset=True)
    self._reassignFeatures()

    if generateTf:

        indent(level=0)

        if self.good or self.force:
            self.good = self.TF.save(
                metaData=self.metaData,
                nodeFeatures=self.nodeFeatures,
                edgeFeatures=self.edgeFeatures,
            )

    self._showWarnings()
    setSilent(self.wasSilent)

    return self.good