Module tf.convert.watm

Export to Web Annotation Text Model

The situation

This module can export a TF corpus to WATM (Web Annotation Text Model), which is the input format of the suite of systems developed by Team Text for serving text plus annotations over the web.

If we can convert TF corpora to WATM, then we have an avenue to the KNAW/HuC/DI/Team-Text web publishing machinery.

Given the fact that TF can already convert TEI and PageXML corpora, this completes a pipeline from source to publication.

We have done this for the following corpora:

All these corpora need distinct preprocessing steps before they are "canalized" into TF, see the illustration below.

confluence

At the same time, Maarten van Gompel is also making pipelines to the Team-Text publishing street. He uses his STAM software to build a pipeline from a corpus of letters by P.C. Hooft in Folia format to text segments and web annotations.

Excursion: STAM

stam

We now have two sytems, STAM and Text-Fabric that can untangle text and markup. They are implemented very differently, and have a different flavour, but at the same time they share the preference of separating the textual data from all the data around the text.

  • intent

    • STAM: make it easier for tools and infrastructure to handle texts with annotations.
    • TF: support researchers in analysing textual corpora.
  • implementation

  • organization

    • STAM: very neatly in a core with extensions.
    • TF: core data functionality in tf.core modules, search functionality in tf.search modules, lots of other functions are included in the code with varying degrees of integration and orderliness!
  • standards

    • STAM: actively seeks to interoperate with existing standards, but internally it uses its own way of organizing the data.
    • TF: also relies on a few simple conventions w.r.t. data organization and efficient serialization. These conventions are documented. It has several import and export functions, e.g. from TEI, PageXML, MQL, and to MQL, TSV. But it prefers to input and output data in minimalistic streams, without the often redundant strings that are attached to standard formats.
  • model

    • STAM: very generic w.r.t. annotations, annotations can target annotations and /or text segments.
    • TF: graph model where nodes stand for textual positions and subsets of them, nodes and edges can have features, which are the raw material of annotations, but annotations are not a TF concept.
  • query language

    • STAM: STAMQL, evolving as an SQL-like language, with user-friendly primitives for annotations.
    • TF: TF-Query, a noise-free language for graph templates with reasonable performance.
  • display

    • STAM: In development, see stam view in STAM tools.
    • TF: Powerful functions to display corpus fragments with highlighting in tf.advanced. The challenge is to build generic display functions that detect the peculiarities of the corpora.
  • API

    • STAM: in Rust and Python.
    • TF: Python.
  • GUI

    • STAM: not yet.
    • TF: locally served web interface for browsing and searching the corpus.

Both libraries can be used to manage corpus data in intricate ways for research and publishing purposes. How STAM and Text-Fabric will evolve in the dynamic landscape of corpora, analytical methods and AI, is something we cannot predict. For now, their different flavour and intent will define their appeal to the different categories of users.

The general idea

The idea of WATM is, like the idea of Text-Fabric, to untangle the text from its markup. Everything outside the text itself is coded in annotations.

Annotations look a lot like TF features, but they are a bit more general. Annotations can also annotate annotations, not only pieces of text.

We need this extra generality, because unlike TF, WATM does not have a concept of node. The only parallel are the slot nodes of TF, which corresponds to the tokens of the text in WATM.

Every node in TF is linked to a set of slot nodes. As such it can be mapped to an annotation to the corresponding tokens. Features of such nodes can be mapped to annotations on annotations.

TF also has edges. These can be mapped to WATM annotations whose targets are pairs: one for the thing the edge is from, and one for the thing the edge is to. These things are typical annotations that correspond to TF nodes, since TF edges are links between TF nodes.

If the TF dataset itself is the result of converting an XML file (e.g TEI or PageXML), then there is a further correspondence between the XML and the TF:

  • elements translate into nodes; element tags translate into node types;
  • attributes translate into features; values of attributes translate into values of features.

In our terminology below we assume that the TF data comes from XML files, but this is not essential. Whenever we talk about elements and tags, you may read nodes and node types if the TF dataset does not have an XML precursor. Likewise, for attributes you may read features.

The specifics

We generate tokens and annotations out of a TF dataset. Here is what we deliver and in what form. The files are either .tsv or .json, dependent on the configuration setting asTsv in the watm.yaml file in the project.

  • a bunch of files text-0.ext, text-1.ext: containing a list of tokenlike segments; Each file corresponds with a section in the TF dataset; the level of the sections that correspond with these files is given in the watm.yaml config file, under the key textRepoLevel. It can have the values 1 (top level), 2, and 3 (lower levels).
  • a bunch of files anno-1.ext, anno-2.ext, …: all generated annotations; We pack at most 400,000 annotations in one file, that keeps their size below 50MB, so that they still can live in a git directory without large file support. The numbering in the anno-i.*ext* files it independent of the numbering in thetext-`i.json files!
  • a pair of files anno2node.tsv and pos2node.tsv that map annotations resp. text positions to their corresponding TF nodes.

Format of the text files

A text-i.json is a JSON file with the following structure:

{
  "_ordered_segments": [
    "token1 ",
    "token2 ",
    ...
  ]
}

These tokens may contain newlines and tabs.

A text-i.tsv is a TSV file with the following structure:

token
token1
token2
...

The first line is a header line with fixed content: token.

Newlines and tabs must be escaped in TSV files. We do that by \n and \t.

  • each token1, token2, … corresponds to one token;
  • the item contains the text of the token plus the subsequent whitespace, if any;
  • if the corpus is converted from TEI, we skip all material inside the TEI-header.

Tokens

Tokens correspond to the slot nodes in the TF dataset. Depending on the original format of the corpus we have the following specifics.

TEI corpora

The base type is t, the atomic token. Atomic tokens are tokens as they come from some NLP processing, except when tokens contain element boundaries. In those cases tokens are split in fragments between the element boundaries.

It is guaranteed that a text segment that corresponds to a t does not contain element boundaries.

The original, unsplit tokens are also present in the annotations, they have type token.

Tokens have the attributes str and after, both may be empty.

PageXML corpora

The base type is token, it is available without NLP processing.

Tokens have the attributes str and after, both may be empty. They may also have the attributes rstr and rafter.

  • str is the logical string value of a token, after is empty or a space: what comes after the token before the next token.
  • rstr is the raw string value of a token, when it deviates from the logical value, otherwise no value. rafter analogously.

Example

token 1 2 3 4 5
rstr empty efflagitan ¬ do empty
str improbè efflagitando empty empty tandem

Format of the annotation files

The anno-1.json file is a JSON file with the following structure:

{
 "a000001": [
  "element",
  "tei",
  "p",
  "0:10-60"
 ],
 "a000002": [
  "element",
  "tei",
  "p",
  "0:60-70"
 ],
 ...
}

A anno-i.tsv is a TSV file with the following structure:

annoid  kind    namespace   body    target
a000001 element tei p   0:10-60
a000002 element tei p   0:60-70
...

The first line is a header line with fixed content: de field names separeted by tabs.

Newlines and tabs must be escaped in TSV files. We do that by \n and \t. It only has to be done for the body field.

When reading these lines, it is best to collect the information in a dict, keyed by the annoid, whose values are lists of the remaining fields, just as in the JSON.

You get a big dictionary, keyed by annotation ids and each value is the data of an annotation, divided in the following fields:

  • kind: the kind of annotation:

    • element: targets the text location where an element occurs, the body is the element name;
    • pi: targets the text location where a processing instruction occurs, the body is the target of the pi;
    • attribute: targets an annotation (an element or pi), the body has the shape name=value, the name and value of the attribute in question;
    • edge: targets two node annotations, the body has the shape *name* orname=value, where name is the name of the edge and value is the label of the edge if the edge has a label;
    • format: targets an individual token, the body is a formatting property for that token, all tokens in note elements get a format annotation with body note;
    • anno: targets an arbitrary annotation or text range, body has an arbitrary value; can be used for extra annotations, e.g. in the Mondriaan corpus to provide an URL to an artwork derived from an <rs> element.
  • namespace: the namespace of the annotation; an indicator where the information comes from. Possible values:

    • pagexml: annotation comes from the PageXML, possibly indirectly, e.g. h, w, x, y
    • tei: annotation comes literally from the TEI guidelines or the PageXML specification, or is processed straightforwardly from it;
    • tf: annotation is composed in a more intricate way from the original source or even added to it;
    • nlp: annotation is generated as a result of NLP processing;
    • tt: annotation is derived from other material in the source for the benefit of the Team Text infrastructure. Defined in the watm.yaml file next to this program. Currently used for annotations that derive from project specific requirements.
  • body: the body of an annotation (probably the kind and body fields together will make up the body of the resulting web annotation);

  • target: a string specifying the target of the annotation, of the following kinds:

    • single this is a target pointing to a single thing, either:

      • fn:bbb: a single token

      • fn:bbb-eee: a range of text segments in the _ordered_segments in the file text-fn.json; the token at position eee is not included. It is guaranteed that bbb <=eee`.

      • fn:bbb-fm:eee: a range of text segments starting at position bbb of the file text-fn.json and ending just before position eee in the file text-fm.json, including all tokens in all intermediate text-fi.json files for fn < fi < fm.

      • an annotation id

    • double this is a target pointing to two things:

      • fff->ttt where fff is a "from" target and ttt is a "to" target; both targets can vary independently between a range and an annotation id.

        N.B. It is allowed that fff and ttt target segments in distinct text-i.json files. In this case, it is not implied that the intermediate tokens are part of the target, because this target conveys the information that the body of the annotation is a property of the pair (fff, ttt).

        If fff and ttt target segments, than they must both contain a file specifier, even if both target a segment in the same token file.

Configuration

In the file config.yaml (in the directory where the program runs) certain parameters can be set:

  • textRepoLevel: the TF section level for which individual textRepo json files will be made. Default: 1: the top level. Other possible values: 2 and 3 (lower levels). Only the special TF section levels can be specified, not arbitrary node types. Because we must guarantee that all tokens in the corpus fall under one of the nodes belonging to this section level.

  • excludeElements: the names of elements for which no annotations will be generated. All node and edge features that target those elements will be filtered, so that there are no annotations that target non-existing annotations.

  • asTsv: the text and anno files are written as tsv instead of json.

    The text files consist of one token per line. The newline token is written as .

    The anno files are written as one anno per line. The tab separated fields are anno id, kind, namespace, body, target. Any tab or newline in the body must be written as resp. .

    The tsv files will have exactly one header line.

Caveat

The WATM representation of the corpus is a faithful and complete representation of the TF dataset and hence of the TEI/PageXML source from which the TF dataset has been converted.

Well, don't take this too literally, probably there are aspects where the different representations differ.

I am aware of the following:

  • If the TF has nodes whose slots are not an interval, the WATM will smooth that over: the target of those nodes will be the complete interval from its first slot to its last slot, including the gaps. The program will show warnings when this happens. Cases where this can happen are instances of text-critical elements in the TEI, where variant readings are given. When we construct sentences by means of NLP, we will exclude the non-chosen readings from the sentence, but these occupy slots between the start and the end of the sentence. Other cases occur where tokens, coming from the NLP, have been split because of intervening elements, which may leave an empty token. In such cases, the fragments of the original token are what ends up as tokens in the output, and they have the node type t, and not token.

  • The TEI to TF conversion has lost the exact embedding of elements in the following case:

    Suppose element A contains the same words as element B. Then the TF data does not know whether A is a child of B or the other way round.

    This is repairable by adding parenthood edges between nodes when constructing the TF data. We should then also convert these TF edges to WATM annotations, for which we need structured targets:

    If n is the parent of m, we must make an annotation with body "parent" and target [n, m].

    Something similar holds for the sibling relationship: if two nodes are adjacent in a TF dataset, we do not know whether they are siblings elements in the original XML. It is also possible to add sibling edges to the TF dataset.

    See tf.convert.tei under parentEdges and siblingEdges.

  • The TF to WATM conversion forgets the types of feature values: it does not make a distinction between the integer 1 and the string "1".

    This is repairable by creating annotations with structured bodies like {"att": value} instead of strings like att=value as we do now.

    In practice, the meaning of the features in TF are known, and hence the attributes in the WATM data, so this is not a blocking problem for now.

  • The excludeElements setting will prevent some TF information from reaching the WATM.

Expand source code Browse git
"""Export to Web Annotation Text Model

# The situation

This module can export a TF corpus to WATM (Web Annotation Text Model),
which is the input format of the suite of systems developed by Team Text for
serving text plus annotations over the web.

If we can convert TF corpora to WATM, then we have an avenue to the
[KNAW/HuC/DI/Team-Text](https://di.huc.knaw.nl/text-analysis-en.html)
web publishing machinery.

Given the fact that TF can already convert TEI and PageXML corpora, this
completes a pipeline from source to publication.

We have done this for the following corpora:

*   [`mondriaan/letters`](https://github.com/annotation/mondriaan)
*   [`translatin/corpus`](https://gitlab.huc.knaw.nl/translatin/corpus)
*   [`suriano/letters`](https://gitlab.huc.knaw.nl/suriano/letters)

All these corpora need distinct preprocessing steps before they are "canalized" into
TF, see the illustration below.

![confluence](../images/text-confluence.jpg)

At the same time,
[Maarten van Gompel](https://github.com/proycon)
is also making pipelines to the Team-Text publishing street. He uses his
[STAM](https://github.com/annotation/stam)
software to build a
[pipeline](https://github.com/knaw-huc/brieven-van-hooft-pipeline/blob/main/README.md)
from a corpus of letters by P.C. Hooft in Folia format to text segments and
web annotations.

# Excursion: STAM

![stam](../images/stam.png)

We now have two sytems,
[STAM](https://github.com/annotation/stam)
and Text-Fabric that can untangle text and markup.
They are implemented very differently, and have a different flavour, but at the
same time they share the preference of separating the textual data from all the data
around the text.

*   *intent*

    *   **STAM**: make it easier for tools and infrastructure to handle texts with
    annotations.
    *   **TF**: support researchers in analysing textual corpora.

*   *implementation*

    *   **STAM**: Rust + [Python bindings](https://github.com/annotation/stam-python).
    *   **TF**: Pure Python.

*   *organization*

    *   **STAM**: very neatly in a core with extensions.
    *   **TF**: core data functionality in `tf.core` modules, search functionality in
        `tf.search` modules, lots of other functions are included in the code with
        varying degrees of integration and orderliness!

*   *standards*

    *   **STAM**: actively seeks to interoperate with existing standards, but
        internally it uses its own way of organizing the data.
    *   **TF**: also relies on a few simple conventions w.r.t. data
        organization and efficient serialization. These conventions are
        documented. It has several import and export functions, e.g. from TEI,
        PageXML, MQL, and to MQL, TSV. But it prefers to input and output data
        in minimalistic streams, without the often redundant strings that are
        attached to standard formats.

*   *model*

    *   **STAM**: very generic w.r.t. annotations, annotations can target
        annotations and /or text segments.
    *   **TF**:
        [graph model](https://annotation.github.io/text-fabric/tf/about/datamodel.html)
        where nodes stand for textual positions and subsets of them, nodes and
        edges can have features, which are the raw material of annotations, but
        annotations are not a TF concept.

*   *query language*

    *   **STAM**:
        [STAMQL](https://github.com/annotation/stam/tree/master/extensions/stam-query),
        evolving as an SQL-like language, with user-friendly primitives for
        annotations.
    *   **TF**:
        [TF-Query](https://annotation.github.io/text-fabric/tf/about/searchusage.html),
        a noise-free language for graph templates with reasonable performance.

*   *display*

    *   **STAM**: In development, see `stam view` in
        [STAM tools](https://github.com/annotation/stam-tools).
    *   **TF**: Powerful functions to display corpus fragments with highlighting in
        `tf.advanced`. The challenge is to build generic display functions that detect
        the peculiarities of the corpora.

*   *API*

    *   **STAM**: in Rust and Python.
    *   **TF**: Python.

*   *GUI*

    *   **STAM**: not yet.
    *   **TF**: locally served web interface for browsing and searching the corpus.

Both libraries can be used to manage corpus data in intricate ways for research
and publishing purposes.
How STAM and Text-Fabric will evolve in the dynamic landscape of corpora, analytical
methods and AI, is something we cannot predict.
For now, their different flavour and intent will define their appeal to the different
categories of users.

# The general idea

The idea of WATM is, like the idea of Text-Fabric, to untangle the text from its
markup. Everything outside the text itself is coded in annotations.

Annotations look a lot like TF features, but they are a bit more general.
Annotations can also annotate annotations, not only pieces of text.

We need this extra generality, because unlike TF, WATM does not have a concept
of node. The only parallel are the slot nodes of TF, which corresponds to the
tokens of the text in WATM.

Every node in TF is linked to a set of slot nodes.
As such it can be mapped to an annotation to the corresponding tokens.
Features of such nodes can be mapped to annotations on annotations.

TF also has edges. These can be mapped to WATM annotations whose targets are
pairs: one for the thing the edge is *from*, and one for the thing the edge is *to*.
These things are typical annotations that correspond to TF nodes, since TF edges
are links between TF nodes.

If the TF dataset itself is the result of converting an XML file (e.g TEI or
PageXML), then there is a further correspondence between the XML and the TF:

*   elements translate into nodes; element tags translate into node types;
*   attributes translate into features; values of attributes translate into
    values of features.

In our terminology below we assume that the TF data comes from XML files,
but this is not essential. Whenever we talk about *elements* and *tags*,
you may read *nodes* and *node types* if the TF dataset does not have an XML
precursor. Likewise, for *attributes* you may read *features*.

# The specifics

We generate tokens and annotations out of a TF dataset. Here is what we deliver
and in what form. The files are either `.tsv` or `.json`, dependent on the
configuration setting `asTsv` in the `watm.yaml` file in the project.

*   a bunch of files `text-0.`*ext*, `text-1.`*ext*:
    containing a list of tokenlike segments;
    Each file corresponds with a section in the TF dataset; the level of the sections
    that correspond with these files is given in the `watm.yaml` config file,
    under the key `textRepoLevel`. It can have the values `1` (top level), `2`, and `3`
    (lower levels).
*   a bunch of files `anno-1.`*ext*, `anno-2.`*ext*, ...: all generated annotations;
    We pack at most 400,000 annotations in one file, that keeps their size below 50MB,
    so that they still can live in a git directory without large file support.
    The numbering in the `anno-`*i*`.*ext* files it independent of the numbering in
    the `text-`*i*`.json` files!
*   a pair of files `anno2node.tsv` and `pos2node.tsv` that map annotations resp. text
    positions to their corresponding TF nodes.

## Format of the text files

A `text-i.json` is a JSON file with the following structure:

```
{
  "_ordered_segments": [
    "token1 ",
    "token2 ",
    ...
  ]
}
```

These tokens may contain newlines and tabs.

A `text-i.tsv` is a TSV file with the following structure:

```
token
token1
token2
...
```

The first line is a header line with fixed content: `token`.

Newlines and tabs must be escaped in TSV files. We do that by `\\n` and `\\t`.

*   each `token1`, `token2`, ... corresponds to one token;
*   the item contains the text of the token plus the subsequent whitespace, if any;
*   if the corpus is converted from TEI, we skip all material inside the
    TEI-header.

### Tokens

Tokens correspond to the slot nodes in the TF dataset.
Depending on the original format of the corpus we have the following specifics.

#### TEI corpora

The base type is `t`, the *atomic* token.
Atomic tokens are tokens as they come from some NLP processing, except when tokens
contain element boundaries. In those cases tokens are split in fragments
between the element boundaries.

It is guaranteed that a text segment that corresponds to a `t` does not contain
element boundaries.

The original, unsplit tokens are also present in the annotations, they have
type `token`.

Tokens have the attributes `str` and `after`, both may be empty.

#### PageXML corpora

The base type is `token`, it is available without NLP processing.

Tokens have the attributes `str` and `after`, both may be empty.
They may also have the attributes `rstr` and `rafter`.

*   `str` is the *logical* string value of a token, `after` is empty or a space:
    what comes after the token before the next token.
*   `rstr` is the raw string value of a token, **when it deviates from the
    logical value**, otherwise no value. `rafter` analogously.

**Example**

token | 1 | 2 | 3 | 4 | 5
--- | --- | --- | --- | --- | ---
rstr | empty | `efflagitan` | `¬` | `do` | empty
str | `improbè` | `efflagitando` | empty | empty | `tandem`

## Format of the annotation files

The `anno-1.json` file is a JSON file with the following structure:

```
{
 "a000001": [
  "element",
  "tei",
  "p",
  "0:10-60"
 ],
 "a000002": [
  "element",
  "tei",
  "p",
  "0:60-70"
 ],
 ...
}
```

A `anno-i.tsv` is a TSV file with the following structure:

```
annoid  kind    namespace   body    target
a000001 element tei p   0:10-60
a000002 element tei p   0:60-70
...
```

The first line is a header line with fixed content: de field names separeted by tabs.

Newlines and tabs must be escaped in TSV files. We do that by `\\n` and `\\t`.
It only has to be done for the `body` field.

When reading these lines, it is best to collect the information in a dict,
keyed by the *annoid*, whose values are lists of the remaining fields, just as in
the JSON.

You get a big dictionary, keyed by annotation ids and each value is the data of
an annotation, divided in the following fields:

*   `kind`: the kind of annotation:
    *   `element`: targets the text location where an *element* occurs, the body
        is the element name;
    *   `pi`: targets the text location where a *processing instruction* occurs,
        the body is the  target of the *pi*;
    *   `attribute`: targets an annotation (an *element* or *pi*), the body has
        the shape *name*`=`*value*,
        the name and value of the attribute in question;
    *   `edge`: targets two node annotations, the body has the shape
        `*name* or `*name*`=`*value*,
        where *name* is the name of the edge and *value* is the label of the edge
        if the edge has a label;
    *   `format`: targets an individual token, the body is a formatting property
        for that token,
        all tokens in note elements get a `format` annotation with body `note`;
    *   `anno`: targets an arbitrary annotation or text range,
        body has an arbitrary value;
        can be used for extra annotations,
        e.g. in the Mondriaan corpus to provide an URL to an artwork derived
        from an `<rs>` element.

*   `namespace`: the namespace of the annotation; an indicator where the
    information comes from. Possible values:
    *   `pagexml`: annotation comes from the PageXML, possibly indirectly, e.g.
        `h`, `w`, `x`, `y`
    *   `tei`: annotation comes
        [literally](https://annotation.github.io/text-fabric/tf/convert/helpers.html#tf.convert.helpers.CM_LIT)
        from the TEI guidelines or the PageXML specification, or is
        [processed](https://annotation.github.io/text-fabric/tf/convert/helpers.html#tf.convert.helpers.CM_LITP)
        straightforwardly from it;
    *   `tf`: annotation is
        [composed](https://annotation.github.io/text-fabric/tf/convert/helpers.html#tf.convert.helpers.CM_LITC)
        in a more intricate way from the original source or even
        [added](https://annotation.github.io/text-fabric/tf/convert/helpers.html#tf.convert.helpers.CM_PROV)
        to it;
    *   `nlp`: annotation is generated as a result of
        [NLP processing](https://annotation.github.io/text-fabric/tf/convert/helpers.html#tf.convert.helpers.CM_NLP);
    *   `tt`: annotation is derived from other material in the source for the benefit
        of the Team Text infrastructure. Defined in the `watm.yaml` file next
        to this program.
        Currently used for annotations that derive from project specific
        requirements.

*   `body`: the body of an annotation (probably the *kind* and *body* fields
    together will make up the body of the resulting web annotation);

*   `target`: a string specifying the target of the annotation, of the
    following kinds:

    *   **single** this is a target pointing to a single thing, either:

        *   `fn:bbb`: a single token

        *   `fn:bbb-eee`: a range of text segments in the `_ordered_segments`
            in the file `text-fn.json`; the token at position `eee` is not included.
            It is guaranteed that `bbb <= `eee`.

        *   `fn:bbb-fm:eee`: a range of text segments starting at position `bbb` of the
            file `text-fn.json` and ending just before position `eee` in the file
            `text-fm.json`, including all tokens in all intermediate
            `text-fi.json` files for `fn < fi < fm`.

        *   an annotation id

    *   **double** this is a target pointing to two things:

        *   `fff->ttt` where `fff` is a "from" target and `ttt` is a "to" target;
            both targets can vary independently between a range and an annotation id.

            **N.B.** It is allowed that `fff` and `ttt` target segments in distinct
            `text-i.json` files. In this case, it is not implied that the intermediate
            tokens are part of the target, because this target conveys the information
            that the body of the annotation is a property of the pair `(fff, ttt)`.

            If `fff` and `ttt` target segments, than they must both contain a file
            specifier, even if both target a segment in the same token file.

# Configuration

In the file `config.yaml` (in the directory where the program runs) certain
parameters can be set:

*   `textRepoLevel`: the TF section level for which individual textRepo json files will
    be made. Default: `1`: the top level. Other possible values: `2` and `3` (lower
    levels). Only the special TF section levels can be specified, not arbitrary
    node types. Because we must guarantee that all tokens in the corpus fall under
    one of the nodes belonging to this section level.

*   `excludeElements`: the names of elements for which no annotations will be generated.
    All node and edge features that target those elements will be filtered, so that
    there are no annotations that target non-existing annotations.

*   `asTsv`: the text and anno files are written as tsv instead of json.

    The text files consist of one token per line.
    The newline token is written as `\n`.

    The anno files are written as one anno per line. The tab separated fields
    are *anno id*, *kind*, *namespace*, *body*, *target*.
    Any tab or newline in the body must be written as `\t` resp. `\n`.

    The tsv files will have exactly one header line.

# Caveat

The WATM representation of the corpus is a faithful and complete representation
of the TF dataset and hence of the TEI/PageXML source from which the TF dataset has been
converted.

Well, don't take this too literally, probably there are aspects where the
different representations differ.

I am aware of the following:

*   If the TF has nodes whose slots are not an interval, the WATM will smooth that
    over: the target of those nodes will be the complete interval from its first
    slot to its last slot, including the gaps.
    The program will show warnings when this happens.
    Cases where this can happen are instances of text-critical elements in the TEI,
    where variant readings are given. When we construct sentences by means of NLP,
    we will exclude the non-chosen readings from the sentence, but these occupy
    slots between the start and the end of the sentence.
    Other cases occur where tokens, coming from the NLP, have been split because of
    intervening elements, which may leave an empty token. In such cases, the fragments
    of the original token are what ends up as tokens in the output, and they have
    the node type `t`, and not `token`.

*   The TEI to TF conversion has lost the exact embedding of elements in the
    following case:

    Suppose element A contains the same words as element B. Then the TF data
    does not know whether A is a child of B or the other way round.

    This is repairable by adding parenthood edges between nodes when
    constructing the TF data. We should then also convert these TF edges to
    WATM annotations, for which we need structured targets:

    If `n` is the parent of `m`, we must make an annotation with body
    `"parent"` and target `[n, m]`.

    Something similar holds for the sibling relationship: if two nodes are adjacent
    in a TF dataset, we do not know whether they are siblings elements in the
    original XML. It is also possible to add sibling edges to the TF dataset.

    See `tf.convert.tei` under **parentEdges** and **siblingEdges**.

*   The TF to WATM conversion forgets the types of feature values: it does not
    make a distinction between the integer `1` and the string `"1"`.

    This is repairable by creating annotations with structured bodies like
    `{"att": value}` instead of strings like `att=value` as we do now.

    In practice, the meaning of the features in TF are known, and hence the attributes
    in the WATM data, so this is not a blocking problem for now.

*   The `excludeElements` setting will prevent some TF information from
    reaching the WATM.
"""

import collections
import json
import re

from tf.core.helpers import console
from tf.core.files import initTree, dirContents, expanduser as ex, readYaml
from tf.core.timestamp import DEEP
from tf.parameters import OTYPE, OSLOTS, URL_TF_DOCS
from tf.app import use


CONFIG_FILE = "watm.yaml"
NODEMAP_FILE = "anno2node.tsv"
SLOTMAP_FILE = "pos2node.tsv"

TT_NAME = "watm"

NS_TF = "tf"
NS_PAGEXML = "pagexml"
NS_TEI = "tei"
NS_NLP = "nlp"
NS_TT = "tt"
NS_NONE = "tf"

NS_FROM_OTYPE = dict(
    doc=NS_TF,
    page=NS_TF,
    file=NS_TF,
    folder=NS_TF,
    letter=NS_TF,
    chapter=NS_TF,
    chunk=NS_TF,
    word=NS_TF,
    char=NS_TF,
    token=NS_NLP,
    sentence=NS_NLP,
)
NS_FROM_FEAT = dict(
    otype=NS_TF,
    doc=NS_TF,
    page=NS_TF,
    line=NS_TF,
    after=NS_TF,
    rafter=NS_TF,
    str=NS_TF,
    rstr=NS_TF,
)

KIND_EDGE = "edge"
KIND_ELEM = "element"
KIND_PI = "pi"
KIND_ATTR = "attribute"
KIND_FMT = "format"
KIND_ANNO = "anno"

REL_RE = re.compile(r"""/tf\b""")

TR_SEP_LEVEL = 1


def rep(status):
    """Represent a boolean status for a message to the console.

    Parameters
    ----------
    status: boolean

    Returns
    -------
    string
    """
    return "OK" if status else "XX"


class WATM:
    """The export machinery is exposed as a class, wrapped around a TF dataset."""

    def __init__(self, app, nsOrig, skipMeta=False, extra={}, silent=False):
        """Wrap the WATM exporter around a TF dataset.

        Given an already loaded TF dataset, we make an inventory of all data
        we need to perform an export to WATM.

        Parameters
        ----------
        app: object
            A loaded TF dataset, as obtained by a call `use(...)`.
            See `tf.app.use`
        nsOrig: string
            A namespace corresponding to the format of the original, pre-Text-Fabric
            representation. For example `tei` for a TEI corpus, `pagexml` for a
            PageXML corpus. The namespace is not related to XML namespaces, it is
            merely a device to categorize the resulting annotations.
        skipMeta: boolean, optional False
            Only relevant for TEI corpora. If True, all material in the TEI Header
            will not be converted to tokens in the text.
            More precisely: all TF slots for which the feature `is_meta` has a true-ish
            value will be skipped. If there is no feature `is_meta` in the dataset,
            the setting of `skipMeta` will have no effect: nothing will be excluded.
        extra: dictionary, optional {}
            The data for extra annotations, which will be generated on the fly under the
            namespace `anno`. The keys are the names of features/attributes, the
            value for each key is a dictionary that maps nodes to values.
        silent: boolean, optional False
            Whether to suppress output to the console
        """
        self.app = app
        self.nsOrig = nsOrig
        self.extra = extra
        self.silent = silent
        api = app.api
        F = api.F
        E = api.E
        T = api.T
        sectionTypes = T.sectionTypes

        cfg = readYaml(asFile=CONFIG_FILE)
        self.cfg = cfg
        self.error = False

        textRepoLevel = cfg.textRepoLevel or 1

        if type(textRepoLevel) is not int or not 1 <= textRepoLevel <= 3:
            console(
                f"{CONFIG_FILE}: textRepoLevel must be an integer between 1 and 3",
                error=True,
            )
            self.error = True

        if len(sectionTypes) == 0:
            console(
                "No section types in corpus. "
                "We need at least one section level for tier-0",
                error=True,
            )
            self.error = True

        if self.error:
            return

        textRepoType = T.sectionTypes[textRepoLevel - 1]

        if not silent:
            console(f"textRepoLevel is section level '{textRepoType}'")
        self.textRepoType = textRepoType

        self.excludeElements = set(cfg.excludeElements or [])
        self.asTsv = cfg.asTsv

        self.L = api.L
        self.Es = api.Es
        self.F = F
        self.E = E
        self.Fs = api.Fs
        self.slotType = self.F.otype.slotType
        self.maxSlotPlus = self.F.otype.maxSlot + 1
        self.maxNodePlus = self.F.otype.maxNode + 1
        self.otypes = self.F.otype.all
        self.info = app.info
        self.repoLocation = app.repoLocation

        Fall = api.Fall
        Eall = api.Eall
        self.Fall = Fall
        self.Eall = Eall

        excludedFeatures = {OTYPE, OSLOTS, "after", "str"}
        self.nodeFeatures = [f for f in Fall() if f not in excludedFeatures]
        self.edgeFeatures = [f for f in Eall() if f not in excludedFeatures]

        FAllSet = set(Fall())

        self.fotypev = F.otype.v
        self.eoslots = E.oslots.s
        self.emptyv = F.empty.v if "empty" in FAllSet else None
        self.strv = F.str.v if "str" in FAllSet else None
        self.rstrv = F.rstr.v if "rstr" in FAllSet else None
        self.afterv = F.after.v if "after" in FAllSet else None
        self.rafterv = F.rafter.v if "rafter" in FAllSet else None
        is_metav = F.is_meta.v if "is_meta" in FAllSet else None
        self.is_metav = is_metav

        if not silent:
            app.dm(f"[WATM exporter documentation]({URL_TF_DOCS}/convert/watm.html)")

        if skipMeta and not is_metav:
            console(
                "skipMeta=True has no effect because feature is_meta is not defined.",
                error=True,
            )
            skipMeta = False

        self.skipMeta = skipMeta

    def makeText(self):
        """Creates the text data.

        The text is a list of tokens and will be stored in member `text` in this object.
        Additionally, the mapping from slot numbers in the TF data
        to indices in this list is stored in member `waFromTF`.
        """
        error = self.error
        silent = self.silent

        if error:
            if not silent:
                console("Cannot run because of an earlier error")
            return

        F = self.F
        L = self.L
        slotType = self.slotType
        textRepoType = self.textRepoType
        skipMeta = self.skipMeta

        emptyv = self.emptyv
        strv = self.strv
        rstrv = self.rstrv
        afterv = self.afterv
        rafterv = self.rafterv
        is_metav = self.is_metav

        texts = []
        waFromTF = {}

        self.texts = texts
        self.waFromTF = waFromTF

        for ti, sNode in enumerate(F.otype.s(textRepoType)):
            text = []
            texts.append(text)

            for s in L.d(sNode, otype=slotType):
                if skipMeta and is_metav(s):
                    continue

                after = rafterv(s) if rafterv else None

                if after is None:
                    after = afterv(s) if afterv else None

                if after is None:
                    after = ""

                if emptyv and emptyv(s):
                    value = after
                else:
                    string = rstrv(s) if rstrv else None

                    if string is None:
                        string = strv(s) if strv else None

                    if string is None:
                        string = ""

                    value = f"{string}{after}"

                text.append(value)
                t = len(text) - 1
                waFromTF[s] = (ti, t)

    def mkAnno(self, kind, ns, body, target):
        """Make a single annotation and return its id.

        Parameters
        ----------
        kind: string
            The kind of annotation.
        ns: string
            The namespace of the annotation.
        body: string
            The body of the annotation.
        target: string  or tuple of strings
            The target of the annotation.
        """
        annos = self.annos
        aId = f"a{len(annos):>08}"
        annos.append((kind, aId, ns, body, target))
        return aId

    def makeAnno(self):
        """Make all annotations.

        The annotations are stored in a big list, in member `anno` of this object.

        The mapping from slots to indices in the list of tokens is now extended
        with the mapping from nodes to corresponding node annotations.

        So member `waFromTF` is now a full mapping from all nodes in TF to
        tokens and/or annotations in WATM.
        """
        error = self.error
        silent = self.silent

        if error:
            if not silent:
                console("Cannot run because of an earlier error")
            return

        Es = self.Es
        F = self.F
        Fs = self.Fs
        fotypev = self.fotypev
        eoslots = self.eoslots
        nodeFeatures = self.nodeFeatures
        edgeFeatures = self.edgeFeatures
        slotType = self.slotType
        otypes = self.otypes
        nsOrig = self.nsOrig
        skipMeta = self.skipMeta
        extra = self.extra
        excludeElements = self.excludeElements

        waFromTF = self.waFromTF

        is_metav = self.is_metav

        isTei = nsOrig == NS_TEI

        annos = []
        texts = self.texts
        self.annos = annos

        invertedTargets = []
        farTargets = []
        discontinuousNodes = collections.defaultdict(list)

        def mkSingleTarget(n):
            ts = waFromTF[n]
            return f"{ts[0]}:{ts[1]}" if fotypev(n) == slotType else ts

        for otype in otypes:
            if otype == slotType or otype in excludeElements:
                continue

            for n in F.otype.s(otype):
                ws = eoslots(n)
                sb, se = (ws[0], ws[-1])
                if len(ws) != se - sb + 1:
                    discontinuousNodes[otype].append(n)

                if skipMeta and (is_metav(ws[0]) or is_metav(ws[-1])):
                    continue

                ti0, start = waFromTF[ws[0]]
                ti1, end = waFromTF[ws[-1]]

                if ti0 != ti1:
                    farTargets.append((otype, ti0, start, ti1, end))

                if ti0 == ti1 and end < start:
                    invertedTargets.append((otype, ti0, start, end))
                    start, end = (end, start)

                startPoint = f"{ti0}:{start}"
                endPoint = (
                    ("" if start == end else f"-{end + 1}")
                    if ti0 == ti1
                    else f"-{ti1}:{end + 1}"
                )
                target = f"{startPoint}{endPoint}"
                aId = (
                    self.mkAnno(KIND_PI, nsOrig, otype[1:], target)
                    if otype.startswith("?")
                    else self.mkAnno(
                        KIND_ELEM, NS_FROM_OTYPE.get(otype, nsOrig), otype, target
                    )
                )
                waFromTF[n] = aId

        for feat in nodeFeatures:
            ns = Fs(feat).meta.get("conversionCode", NS_FROM_FEAT.get(feat, nsOrig))

            if ns is None:
                console(
                    f"Node feature {feat} has no namespace, "
                    f"defaulting to {NS_NONE}",
                    error=True,
                )
                ns = NS_NONE

            isRend = False
            isNote = False

            if isTei:
                parts = feat.split("_", 2)
                isRend = len(parts) >= 2 and parts[0] == "rend"
                isNote = len(parts) == 2 and parts[0] == "is" and parts[1] == "note"

            if isRend or isNote:
                body = parts[1] if isRend else "note"

                for n, val in Fs(feat).items():
                    if n not in waFromTF or not val or skipMeta and is_metav(n):
                        continue

                    self.mkAnno(KIND_FMT, ns, body, mkSingleTarget(n))
            else:
                for n, val in Fs(feat).items():
                    if n not in waFromTF or val is None or skipMeta and is_metav(n):
                        continue

                    body = f"{feat}={val}"
                    self.mkAnno(KIND_ATTR, ns, body, mkSingleTarget(n))

        for feat in edgeFeatures:
            ns = Es(feat).meta.get("conversionCode", NS_FROM_FEAT.get(feat, nsOrig))

            if ns is None:
                console(
                    f"Edge feature {feat} has no conversion code, "
                    f"defaulting to {NS_NONE}",
                    error=True,
                )
                ns = NS_NONE

            for fromNode, toNodes in Es(feat).items():
                if fromNode not in waFromTF or skipMeta and is_metav(fromNode):
                    continue

                targetFrom = mkSingleTarget(fromNode)

                if type(toNodes) is dict:
                    for toNode, val in toNodes.items():
                        if toNode not in waFromTF or skipMeta and is_metav(toNode):
                            continue

                        body = f"{feat}={val}"
                        targetTo = mkSingleTarget(toNode)
                        target = f"{targetFrom}->{targetTo}"
                        self.mkAnno(KIND_EDGE, ns, body, target)
                else:
                    for toNode in toNodes:
                        if toNode not in waFromTF or skipMeta and is_metav(toNode):
                            continue

                        targetTo = mkSingleTarget(toNode)
                        target = f"{targetFrom}->{targetTo}"
                        self.mkAnno(KIND_EDGE, ns, feat, target)

        for feat, featData in extra.items():
            for n, value in featData.items():
                self.mkAnno(KIND_ANNO, NS_TT, f"{feat}={value}", mkSingleTarget(n))

        if len(invertedTargets):
            if not silent:
                console(f"WARNING: inverted targets, {len(invertedTargets)}x")
                for otype, ti0, start, end in invertedTargets:
                    text = texts[ti0]
                    sega = text[start]
                    segb = text[end - 1]
                    console(f"{otype:>20} {start:>6} `{sega}` > {end - 1} `{segb}`")

        if len(discontinuousNodes):
            nDis = sum(len(x) for x in discontinuousNodes.values())
            console(f"WARNING: {nDis} discontinuous nodes encountered")

            for otype, nodes in discontinuousNodes.items():
                nn = len(nodes)
                console(f"\t{nn} x of type {otype}")

                if not silent:
                    examples = ", ".join(str(n) for n in nodes[0:10])
                    console(f"\t\t{examples}")

        nFarTargets = len(farTargets)

        if nFarTargets:
            console(f"WARNING: targets across tier0 items, {nFarTargets}x")
            if not silent:
                for otype, ti0, start, ti1, end in farTargets[0:10]:
                    console(
                        f"{otype:>20} [{ti0:>4}:{start:>6}] - [{ti1:>4}:{end - 1:>6}]"
                    )
                if nFarTargets > 10:
                    console(f"... and {nFarTargets - 10} more.")

    def writeAll(self):
        """Write text and annotation data to disk.

        The data will be written as JSON files, or, is `asTsv` is in force, as TSV
        files.
        When the annotation data grows larger than a certain threshold, it will be
        divided over several files.

        The annotations are sorted by annotation id.
        """

        maxNodePlus = self.maxNodePlus
        maxSlotPlus = self.maxSlotPlus

        # text files

        error = self.error
        silent = self.silent

        if error:
            if not silent:
                console("Cannot run because of an earlier error")
            return

        app = self.app
        texts = self.texts
        annos = self.annos
        waFromTF = self.waFromTF
        asTsv = self.asTsv

        baseDir = self.repoLocation
        relative = app.context.relative
        version = app.version
        wRelative = REL_RE.sub(f"/{TT_NAME}/{version}/", relative, count=1)
        resultDir = f"{baseDir}{wRelative}"
        self.resultDir = resultDir

        initTree(resultDir, fresh=True)

        total = 0

        ext = "tsv" if asTsv else "json"

        for i, text in enumerate(texts):
            textFile = f"{resultDir}/text-{i}.{ext}"
            nText = len(text)
            total += nText

            with open(textFile, "w") as fh:
                if asTsv:
                    fh.write("token\n")
                    for t in text:
                        fh.write(t.replace("\t", "\\t").replace("\n", "\\n") + "\n")
                else:
                    json.dump(
                        dict(_ordered_segments=text), fh, ensure_ascii=False, indent=1
                    )

            if not silent:
                console(f"Text file {i:>4}: {nText:>8} segments to {textFile}")

        nTexts = len(texts)
        sep = "" if nTexts == 1 else "s"

        if not silent:
            console(f"Text files all: {total:>8} segments to {nTexts} file{sep}")

        # annotation files

        annoStore = {}

        for kind, aId, ns, body, target in annos:
            annoStore[aId] = (kind, ns, body, target)

        aIdSorted = sorted(annoStore.keys())

        thisAnnoStore = {}
        thisA = 1
        nAnnoFiles = 0

        LIMIT = 400000
        j = 0
        total = 0

        def writeThis():
            annoFile = f"{resultDir}/anno-{thisA:>01}.{ext}"

            with open(annoFile, "w") as fh:
                if asTsv:
                    fh.write("annoid\tkind\tnamespace\tbody\ttarget\n")
                    for aId, (kind, namespace, body, target) in thisAnnoStore.items():
                        body = body.replace("\t", "\\t").replace("\n", "\\n")
                        fh.write(f"{aId}\t{kind}\t{namespace}\t{body}\t{target}\n")
                else:
                    json.dump(thisAnnoStore, fh, ensure_ascii=False, indent=1)

            if not silent:
                console(
                    f"Anno file {thisA:>4}: {j:>8} annotations written to {annoFile}"
                )

        for aId in aIdSorted:
            if j >= LIMIT:
                writeThis()
                nAnnoFiles += 1
                thisA += 1
                thisAnnoStore = {}
                total += j
                j = 0

            thisAnnoStore[aId] = annoStore[aId]
            j += 1

        if len(thisAnnoStore):
            writeThis()
            nAnnoFiles += 1
            total += j

        if len(annoStore) != total:
            console(f"Sum of batches : {total:>8}", error=True)
            console(f"All annotations: {len(annoStore):>8}", error=True)
            console("Mismatch in number of annotations", error=True)

        sep = "" if nAnnoFiles == 1 else "s"

        if not silent:
            console(f"Anno files all: {total:>8} annotations to {nAnnoFiles} file{sep}")

        # node mapping files

        slotmapFile = f"{resultDir}/{SLOTMAP_FILE}"
        nodemapFile = f"{resultDir}/{NODEMAP_FILE}"

        with open(slotmapFile, "w") as fh:
            fh.write("position\tnode\n")
            for n in range(1, maxSlotPlus):
                (file, pos) = waFromTF[n]
                fh.write(f"{file}:{pos}\t{n}\n")

        with open(nodemapFile, "w") as fh:
            fh.write("annotation\tnode\n")
            for n in range(maxSlotPlus, maxNodePlus):
                aId = waFromTF.get(n, None)
                if aId is not None:
                    fh.write(f"{aId}\t{n}\n")

        if not silent:
            console(f"Slot mapping written to {slotmapFile}")
            console(f"Node mapping written to {nodemapFile}")

    @staticmethod
    def numEqual(nTF, nWA, silent):
        """Compare two numbers and report the outcome.

        Used for testing the WATM conversion.

        Parameters
        ----------
        nTF: integer
            The number as it is counted from the original TF dataset.
        nWA: integer
            The number as it is counted from the generated WATM dataset.

        Returns
        -------
        boolean
            Whether the two values are equal.
        """
        error = nTF != nWA

        if error or not silent:
            console(f"\tTF: {nTF:>6}\n\tWA: {nWA:>6}", error=error)

        return nTF == nWA

    @staticmethod
    def strEqual(tf, wa, silent):
        """Compare two strings and report the outcome.

        Used for testing the WATM conversion.

        Parameters
        ----------
        nTF: string
            The string as encountered in the original TF dataset.
        nWA: string
            The string as encountered in the generated WATM dataset.

        Returns
        -------
        boolean
            Whether the two values are equal.
        """
        different = False

        for i, cTF in enumerate(tf):
            if i >= len(wa):
                contextI = max((0, i - 10))
                console(f"\tWA {i}: {wa[contextI:i]} <END>", error=True)
                console(f"\tTF {i}: {tf[contextI:i]} <> {tf[i:i + 10]}", error=True)
                different = True
                break
            elif tf[i] != wa[i]:
                contextI = max((0, i - 10))
                console(
                    f"\tWA {i}: {wa[contextI:i]} <{wa[i]}> {wa[i + 1:i + 11]}",
                    error=True,
                )
                console(
                    f"\tTF {i}: {tf[contextI:i]} <{tf[i]}> {tf[i + 1:i + 11]}",
                    error=True,
                )
                different = True
                break

        if not different and len(wa) > len(tf):
            i = len(tf)
            contextI = max((0, i - 10))
            console(f"\tWA {i}: {wa[contextI:i]} <> {wa[i:i + 10]}", error=True)
            console(f"\tTF {i}: {tf[contextI:i]} <END>", error=True)
            different = True

        sampleWA = f"{wa[0:20]} ... {wa[-20:]}".replace("\n", " ")
        sampleTF = f"{tf[0:20]} ... {tf[-20:]}".replace("\n", " ")
        if not silent:
            console(f"\tTF: {sampleTF:>6}\n\tWA: {sampleWA:>6}")
        return not different

    def testAll(self, condensed=False):
        """Test all aspects of the WATM conversion.

        For all kinds of information, such as nodes, edges, features, tokens,
        annotations, we check whether the parts that should correspond between
        the TF dataset and the WATM annotations do so indeed.

        We present some statistics, and highlight the mismatches.

        Parameters
        ----------
        condensed: boolean, optional False
            If silent has been passed to the object, there is still some
            output for each corpus, namely whether all tests have passed.
            If `condensed` is True, we suppress this output.

        Returns
        -------
        boolean
            Whether all things that must agree do indeed agree.
        """
        error = self.error
        silent = self.silent

        if error:
            if not silent:
                console("Cannot run because of an earlier error")
            return

        self.testSetup()

        if self.error:
            console("WATM data is incomplete. Testing aborted")
            return

        good = True

        if not self.testText():
            good = False

        if not self.testElements():
            good = False

        if not self.testAttributes():
            good = False

        if not self.testExtra():
            good = False

        if not self.testEdges():
            good = False

        if not silent:
            console("Overall outcome ...")

        if not silent or not condensed:
            console(f"{rep(good)} - whether all tests passed", error=not good)

        if not good:
            self.error = True

    def testSetup(self):
        """Prepare the tests.

        We read the WATM dataset and store the tokens in member `testTokens`
        and the annotations in the member `testAnnotations`, and the node mapping
        in the member `nodeFromAid`.
        We unpack targets if they contain structured information.
        """

        # collect the files

        asTsv = self.asTsv
        resultDir = self.resultDir
        resultFiles = dirContents(resultDir)[0]

        ext = "tsv" if asTsv else "json"

        def fileSort(name):
            middle = name.split(".", 1)[0].split("-", 1)[1]
            return f"{middle:0>10}" if middle.isdecimal else middle

        textFiles = sorted(
            (f for f in resultFiles if f.startswith("text-") and f.endswith(f".{ext}")),
            key=fileSort,
        )
        annoFiles = sorted(
            (f for f in resultFiles if f.startswith("anno-") and f.endswith(f".{ext}")),
            key=fileSort,
        )
        mapFiles = [
            f
            for f in resultFiles
            if (f.startswith("anno") or f.startswith("pos")) and f.endswith("2node.tsv")
        ]

        if NODEMAP_FILE not in mapFiles:
            console(f"ERROR: Missing map file {NODEMAP_FILE}")
            self.error = True
        if SLOTMAP_FILE not in mapFiles:
            console(f"ERROR: Missing map file {SLOTMAP_FILE}")
            self.error = True

        if self.error:
            return

        # read the text files

        skipMeta = self.skipMeta
        is_metav = self.is_metav

        waSlotTF = {}
        tokenFiles = []

        slot = 1

        for tfl, textFile in enumerate(textFiles):
            with open(f"{resultDir}/{textFile}") as fh:
                if asTsv:
                    next(fh)
                    tokens = [t.rstrip("\n").replace("\\t", "\t").replace("\\n", "\n") for t in fh]
                else:
                    text = json.load(fh)
                    tokens = text["_ordered_segments"]

                tokenFiles.append(tokens)

                for offset in range(len(tokens)):
                    while skipMeta and is_metav(slot):
                        slot += 1

                    waSlotTF[slot] = (tfl, offset)
                    slot += 1

        self.testTokens = tokenFiles
        self.waSlotTF = waSlotTF

        # read the anno files

        annotations = []

        for annoFile in annoFiles:
            with open(f"{resultDir}/{annoFile}") as fh:
                if asTsv:
                    next(fh)
                    annos = {}
                    for line in fh:
                        (aId, kind, ns, body, target) = line.rstrip("\n").split("\t")
                        body = body.replace("\\t", "\t").replace("\\n", "\n")
                        annos[aId] = (kind, ns, body, target)
                else:
                    annos = json.load(fh)

                for aId, (kind, ns, body, target) in annos.items():
                    if "->" in target:
                        parts = target.split("->", 1)
                    else:
                        parts = [target]

                    newParts = []

                    for part in parts:
                        if ":" in part:
                            boundaries = part.split("-", 1)
                            fb, b = boundaries[0].split(":", 1)
                            fb = int(fb)
                            b = int(b)

                            if len(boundaries) == 1:
                                if kind == KIND_ELEM or kind == KIND_PI:
                                    part = (int(fb), int(b), int(fb), int(b) + 1)
                                else:
                                    part = (int(fb), int(b))
                            else:
                                eParts = boundaries[1].split(":", 1)

                                if len(eParts) == 1:
                                    fe, e = fb, int(eParts[0])
                                else:
                                    fe, e = eParts
                                    fe = int(fe)
                                    e = int(e)
                                part = (fb, b, fe, e)

                        newParts.append(part)

                    target = newParts[0] if len(newParts) == 1 else tuple(newParts)

                    annotations.append((aId, kind, body, target))

        annotations = sorted(annotations)
        self.testAnnotations = annotations

        # read the map files

        nodeFromAid = {}

        with open(f"{resultDir}/{SLOTMAP_FILE}") as fh:
            next(fh)
            for line in fh:
                (pos, slot) = line.rstrip("\n").split("\t")
                key = tuple(int(p) for p in pos.split(":"))
                nodeFromAid[key] = int(slot)

        with open(f"{resultDir}/{NODEMAP_FILE}") as fh:
            next(fh)
            for line in fh:
                (aId, node) = line.rstrip("\n").split("\t")
                nodeFromAid[aId] = int(node)

        self.nodeFromAid = nodeFromAid
        self.testNodes = set(nodeFromAid.values())

    def testText(self):
        """Test the text.

        We test the number of tokens and the equality of the resulting text:
        whether the TF and WATM datasets agree on it.

        Returns
        -------
        boolean
            Whether all these tests succeed.
        """
        maxSlotPlus = self.maxSlotPlus
        tokenFiles = self.testTokens
        texts = self.texts
        waSlotTF = self.waSlotTF
        silent = self.silent

        if not silent:
            console("Testing the text ...")

        nTokensTF = sum(1 if s in waSlotTF else 0 for s in range(1, maxSlotPlus))
        nTokensWA = sum(len(tokens) for tokens in tokenFiles)
        nGood = self.numEqual(nTokensTF, nTokensWA, silent)

        if not nGood or not silent:
            console(
                f"{rep(nGood)} - whether the amounts of tokens agree", error=not nGood
            )

        textWA = "".join("".join(tokens) for tokens in tokenFiles)
        textTF = "".join("".join(text) for text in texts)

        tGood = self.strEqual(textTF, textWA, silent)

        if not tGood or not silent:
            console(f"{rep(tGood)} - whether the text is the same", error=not tGood)

        return nGood and tGood

    def testElements(self):
        """Test the elements.

        We test the annotations representing elements/processing instructions
        and check whether they correspond 1-1 to the non-slot nodes in the TF
        dataset.

        Returns
        -------
        boolean
            Whether all these tests succeed.
        """
        maxSlotPlus = self.maxSlotPlus
        maxNodePlus = self.maxNodePlus
        fotypev = self.fotypev
        eoslots = self.eoslots
        waSlotTF = self.waSlotTF
        annotations = self.testAnnotations
        silent = self.silent
        excludeElements = self.excludeElements

        if not silent:
            console("Testing the elements ...")

        nElementsTF = 0
        nPisTF = 0

        for n in range(maxSlotPlus, maxNodePlus):
            nType = fotypev(n)
            isPi = nType.startswith("?")

            slots = eoslots(n)
            b = slots[0]
            e = slots[-1]

            if not (b in waSlotTF and e in waSlotTF):
                continue

            if isPi:
                nPisTF += 1
            else:
                if nType not in excludeElements:
                    nElementsTF += 1

        nElementsWA = 0
        nPisWA = 0

        nodeFromAid = self.nodeFromAid

        nElementsWA = sum(1 if a[1] == KIND_ELEM else 0 for a in annotations)
        nPisWA = sum(1 if a[1] == KIND_PI else 0 for a in annotations)

        eGood = self.numEqual(nElementsTF, nElementsWA, silent)

        if not eGood or not silent:
            console(
                f"{rep(eGood)} - whether the amounts of elements and nodes agree",
                error=not eGood,
            )

        if not silent:
            console("Testing the processing instructions ...")

        pGood = self.numEqual(nPisTF, nPisWA, silent)

        if not pGood or not silent:
            console(
                f"{rep(pGood)} - whether the amounts of processing instructions agree",
                error=not pGood,
            )

        if not silent:
            console("Testing the element/pi annotations ...")

        element = 0
        pi = 0
        other = 0
        goodName = 0
        wrongName = 0
        unmapped = 0

        if not silent:
            console(f"\t{len(nodeFromAid)} element/pi annotations")

        wrongTargets = []
        allTargets = 0
        goodTargets = 0

        for aId, kind, body, target in annotations:
            isElem = kind == KIND_ELEM
            isPi = kind == KIND_PI

            if not (isElem or isPi):
                other += 1
                continue

            if isElem:
                element += 1
            else:
                pi += 1

            tag = body
            node = nodeFromAid.get(aId, None)

            if node is None:
                unmapped += 1
                continue

            otype = fotypev(node)

            if isPi and tag == otype[1:] or not isPi and tag == otype:
                goodName += 1
            else:
                wrongName += 1

            if type(target) is not tuple or len(target) != 4:
                wrongTargets.append((aId, kind, body, target))
            else:
                node = nodeFromAid[aId]
                slots = eoslots(node)
                sb = slots[0]
                se = slots[-1]
                bTr = waSlotTF.get(sb, None)
                eTr = waSlotTF.get(se, None)

                if eTr is not None:
                    eTr = (eTr[0], eTr[1] + 1)

                bWA = (target[0], target[1])
                eWA = (target[2], target[3])

                bRep = f"{bWA}" if bTr == bWA else f"{bWA} XX {bTr}"
                eRep = f"{eWA}" if eTr == eWA else f"{eWA} XX {eTr}"

                if bTr is None or eTr is None or bTr != bWA or eTr != eWA:
                    wrongTargets.append((aId, kind, body, f"{bRep} - {eRep}"))
                else:
                    goodTargets += 1

            allTargets += 1

        if not silent:
            console(f"\tElement      : {element:>6} x")
            console(f"\tPi           : {pi:>6} x")
            console(f"\tOther        : {other:>6} x")
            console(f"\tGood name    : {goodName:>6} x")
            console(f"\tWrong name   : {wrongName:>6} x")
            console(f"\tGood target  : {goodTargets:>6} x")
            console(f"\tWrong target : {len(wrongTargets):>6} x")
            console(f"\tUnmapped     : {unmapped:>6} x")

        aGood = wrongName == 0 and unmapped == 0

        if not aGood or not silent:
            console(
                f"{rep(aGood)} - whether all element/pi annotations have good bodies",
                error=not aGood,
            )

        tGood = len(wrongTargets) == 0

        if not tGood or not silent:
            console(
                f"{rep(tGood)} - whether all element/pi annotations have good targets",
                error=not tGood,
            )
            if not tGood:
                tExamples = "\n\t\t".join(str(a) for a in wrongTargets[0:10])
                console(f"\t\t{tExamples}")

        return aGood and tGood and eGood and pGood

    def testAttributes(self):
        """Test the attributes.

        We test whether attributes and features correspond to each other.

        Some attributes in the original TEI are converted in a special way into
        TF features: this holds for the `rend` attribute.
        Basically, a value `rend="italic"` is translated into feature
        `is_italic=1`.
        In turn, these features have been translated into annotations of kind
        `format`. We test them separately.

        Returns
        -------
        boolean
            Whether all these tests succeed.
        """
        Fs = self.Fs
        Fall = self.Fall
        eoslots = self.eoslots
        waSlotTF = self.waSlotTF
        skipMeta = self.skipMeta
        annotations = self.testAnnotations
        nodeFromAid = self.nodeFromAid
        testNodes = self.testNodes
        nsOrig = self.nsOrig
        silent = self.silent

        isTei = nsOrig == NS_TEI

        if not silent:
            console("Testing the attributes ...")

        attWA = []

        for aId, kind, body, target in annotations:
            if kind != KIND_ATTR:
                continue
            if type(target) is tuple and len(target) == 4:
                target = (target[0], target[1])
            node = nodeFromAid[target]
            att, value = body.split("=", 1)
            attWA.append((node, att, value))

        attWA = sorted(attWA)

        if not silent:
            console(f"\t{len(attWA)} attribute values")

        good = 0
        wrong = []

        for node, att, valWA in attWA:
            val = Fs(att).v(node)
            valTF = None if val is None else str(val)

            if valWA == valTF:
                good += 1
            else:
                wrong.append((node, att, valWA, valTF))

        consistent = len(wrong) == 0

        if not silent:
            console(f"\tGood:     {good:>5} x")
        if not consistent or not silent:
            console(f"\tWrong:    {len(wrong):>5} x", error=not consistent)

            console(
                f"{rep(consistent)} - whether annotations are consistent with features",
                error=not consistent,
            )

        attTF = []

        for feat in Fall():
            if feat in {"otype", "str", "after"}:
                continue

            if skipMeta and feat == "is_meta":
                continue

            if isTei and (
                (feat != "is_meta" and feat.startswith("is_"))
                or feat.startswith("rend_")
            ):
                continue

            for node, valTF in Fs(feat).items():
                if node not in testNodes:
                    continue

                slots = eoslots(node)
                b = slots[0]
                e = slots[-1]

                if not (b in waSlotTF and e in waSlotTF):
                    continue

                attTF.append((node, feat, None if valTF is None else str(valTF)))

        attTF = sorted(attTF)

        if not silent:
            console(f"\tWA attributes: {len(attWA)}")
            console(f"\tTF attributes: {len(attTF)}")

        complete = attTF == attWA

        if not complete or not silent:
            console(
                f"{rep(complete)} - whether annotations are complete w.r.t. features",
                error=not complete,
            )

        if not silent:
            console("Testing the format attributes ...")

        fmtWA = []

        for aId, kind, body, target in annotations:
            if kind != KIND_FMT:
                continue
            if body == "note":
                continue
            if type(target) is tuple and len(target) == 4:
                target = (target[0], target[1])
            node = nodeFromAid[target]
            fmtWA.append((node, body))

        fmtWA = sorted(fmtWA)
        fmtFreqWA = collections.Counter()

        for node, body in fmtWA:
            fmtFreqWA[body] += 1

        if not silent:
            console(f"\t{len(fmtWA)} format values")
            console("\tformatting attributes: ")
            for fa, n in sorted(fmtFreqWA.items(), key=lambda x: (-x[1], x[0])):
                console(f"\t\t{n:>6} x {fa}")

        good = 0
        wrong = []

        for node, valWA in fmtWA:
            feat = f"rend_{valWA}"
            valTF = valWA if Fs(feat).v(node) else None

            if valWA == valTF:
                good += 1
            else:
                wrong.append((node, feat, valWA, valTF))

        fconsistent = len(wrong) == 0

        if not silent:
            console(f"\tGood:     {good:>5} x")

        if not fconsistent or not silent:
            console(f"\tWrong:    {len(wrong):>5} x")
            console(
                f"{rep(fconsistent)} - "
                f"whether format annotations are consistent with features",
                error=not fconsistent,
            )

        fmtTF = []

        for feat in Fall():
            if not feat.startswith("rend_"):
                continue

            value = feat.split("_", 2)[1]
            if value == "note":
                continue

            for node, valTF in Fs(feat).items():
                slots = eoslots(node)
                b = slots[0]
                e = slots[-1]

                if not (b in waSlotTF and e in waSlotTF):
                    continue

                fmtTF.append((node, value))

        fmtTF = sorted(fmtTF)

        if not silent:
            console(f"\tWA format attributes: {len(fmtWA)}")
            console(f"\tTF format attributes: {len(fmtTF)}")

        fcomplete = fmtTF == fmtWA

        if not fcomplete or not silent:
            console(
                f"{rep(complete)} - "
                f"whether format annotations are complete w.r.t. features",
                error=not fcomplete,
            )

        return consistent and complete and fconsistent and fcomplete

    def testExtra(self):
        """Test the extra data for on-the-fly annotations.

        Annotations that have been generated out of the data stored in the
        `extra` parameter with which the object has been initialized, all got
        the kind `anno`.

        Now we check these annotations against the data that went into it.

        Returns
        -------
        boolean
            Whether all these tests succeed.
        """
        annotations = self.testAnnotations
        nodeFromAid = self.nodeFromAid
        extra = self.extra
        silent = self.silent

        if not silent:
            console("Testing the extra annotations ...")

        attWA = []

        for aId, kind, body, target in annotations:
            if kind != KIND_ANNO:
                continue
            node = nodeFromAid[target]
            att, value = body.split("=", 1)
            attWA.append((node, att, value))

        attWA = sorted(attWA)

        attEX = []

        for feat, featData in extra.items():
            for n, value in featData.items():
                attEX.append((n, feat, value))

        attEX = sorted(attEX)

        if not silent:
            console(f"\t{len(attEX)} extra feature values")
            console(f"\t{len(attWA)} extra annotations")

        good = attWA == attEX

        def showData(tuples, isin, isout):
            data = {}

            for n, f, v in tuples:
                data.setdefault(f, {})[n] = v

            for f in sorted(data):
                fData = data[f]
                console(
                    f"\t{isin}: {f} misses {len(fData)} annotations in {isout}",
                    error=True,
                )
                for n in sorted(fData.keys())[0:3]:
                    console(f"\t\t\t{n:>7} = {fData[n]}", error=True)

        if not good:
            attWASet = set(attWA)
            attEXSet = set(attEX)

            onlyWA = attWASet - attEXSet
            onlyEX = attEXSet - attWASet

            if len(onlyWA):
                showData(onlyWA, "WA", "EX")
            else:
                if not silent:
                    console("\tWA: All extra annotations derive from the extra data")
            if len(onlyEX):
                showData(onlyEX, "EX", "WA")
            else:
                if not silent:
                    console("\tEX: All extra data ended up as annotations")

        if not good or not silent:
            console(
                f"{rep(good)} - whether the extra annotations agree", error=not good
            )

        return good

    def testEdges(self):
        """Test the edges.

        Edges in TF are links between nodes, and they translate into annotations of
        kind `edge` which target a pair of annotations: the `from` annotation,
        and the `to` annotation.

        Here we check whether the TF edges are faithfully and completely parallelled
        by annotations.

        Returns
        -------
        boolean
            Whether all these tests succeed.
        """
        Es = self.Es
        Eall = self.Eall
        annotations = self.testAnnotations
        silent = self.silent
        nodeFromAid = self.nodeFromAid
        testNodes = self.testNodes

        if not silent:
            console("Testing the edges ...")

        tfFromWAEdges = {}

        for aId, kind, body, target in annotations:
            if kind != KIND_EDGE:
                continue

            fro, to = target
            fromNode = nodeFromAid[fro]
            toNode = nodeFromAid[to]
            parts = body.split("=", 1)
            name, val = (body, None) if len(parts) == 1 else parts
            tfFromWAEdges.setdefault(name, {}).setdefault(fromNode, {})[toNode] = val

        if not silent:
            console(f"\tFound: {len(nodeFromAid)} nodes")

            for edge, edgeData in sorted(tfFromWAEdges.items()):
                console(f"\tFound edge {edge} with {len(edgeData)} starting nodes")

        allGood = True

        for edge in set(Eall()) | set(tfFromWAEdges):
            if edge == "oslots":
                continue

            if not silent:
                console(f"\tChecking edge {edge}")

            good = True

            x = f"edge {edge}: " if silent else "\t\t"

            if edge not in set(Eall()):
                console(f"{x}missing in TF data", error=True)
                good = False

            if edge not in tfFromWAEdges:
                console(f"{x}missing in annotation data", error=True)
                good = False

            if not good:
                continue

            dataTF = {}

            for f, ts in Es(edge).items():
                if f not in testNodes:
                    continue

                if type(ts) is dict:
                    for t, v in ts.items():
                        if t not in testNodes:
                            continue
                        dataTF.setdefault(f, {})[t] = v
                else:
                    for t in ts:
                        if t not in testNodes:
                            continue
                        dataTF.setdefault(f, {})[t] = None

            dataWA = tfFromWAEdges[edge]

            fromNodesTF = set(dataTF)
            fromNodesWA = set(dataWA)

            nFromTF = len(fromNodesTF)
            nFromWA = len(fromNodesWA)

            if fromNodesTF == fromNodesWA:
                if not silent:
                    console(f"\t\tsame {nFromTF} fromNodes")
            else:
                console(
                    f"{x}from nodes differ: {nFromTF} in TF, {nFromWA} in WA",
                    error=True,
                )
                good = False

            diffs = []

            nToChecked = 0

            for f, toNodeInfoTF in dataTF.items():
                toNodeInfoWA = dataWA[f]
                toNodeInfoTF = {
                    k: None if v is None else str(v) for (k, v) in toNodeInfoTF.items()
                }

                if toNodeInfoTF != toNodeInfoWA:
                    diffs.append((f, toNodeInfoTF, toNodeInfoWA))

                nToChecked += len(toNodeInfoTF)

            if len(diffs):
                good = False
                console(
                    f"{x}differences in toNodes for {len(diffs)} fromNodes", error=True
                )

                for f, toNodeInfoTF, toNodeInfoWA in sorted(diffs)[0:10]:
                    console(f"{x}\tfromNode {f}", error=True)

                    toNodesTF = set(toNodeInfoTF)
                    toNodesWA = set(toNodeInfoWA)

                    nToTF = len(toNodesTF)
                    nToWA = len(toNodesWA)

                    if toNodesTF == toNodesWA:
                        if not silent:
                            console(f"\t\t\tsame {nToTF} toNodes")
                    else:
                        console(
                            f"{x}\ttoNodes differ: {nToTF} in TF, {nToWA} in WA",
                            error=True,
                        )
                    for t in toNodesTF | toNodesWA:
                        doCompare = True
                        if t not in toNodesTF:
                            console(f"{x}\t\ttoNode {t} not in TF", error=True)
                            doCompare = False
                        else:
                            valTF = toNodeInfoTF[t]

                        if t not in toNodesWA:
                            console(f"{x}\t\ttoNode {t} not in WA", error=True)
                            doCompare = False
                        else:
                            valWA = toNodeInfoWA[t]

                        if doCompare:
                            if valTF == valWA:
                                if not silent:
                                    console(
                                        f"\t\t\t\ttoNode{t} values agree: {repr(valTF)}"
                                    )
                            else:
                                console(
                                    f"{x}\t\ttoNode{t} values differ: "
                                    f"TF: {repr(valTF)} WA: {repr(valWA)}",
                                    error=True,
                                )

            if not good or not silent:
                console(f"\t{rep(good)} - {nToChecked} toNodes checked", error=not good)

            if not good:
                allGood = False

        if not silent:
            console(f"{rep(allGood)} - whether all edges agree")

        return allGood


class WATMS:
    """Export corpora that are divided over multiple TF datasets.

    We set up and run WATM objects for each TF dataset, and generate results
    for them separately.

    We assume that all corpora have been generated by the same method and originate
    from the same original format.

    They must reside in the same repository, in adjacent directories under the `tf`
    top-level directory of the repo.
    """

    def __init__(
        self, org, repo, backend, nsOrig, skipMeta=False, extra={}, silent=False
    ):
        """Collect the parameters for the WATM machinery.

        We will initialize many `WATM` objects with mostly the same parameters.
        These are collected when we initialize this object.

        Parameters
        ----------
        org: string
            The organization of all TF datasets.
        repo: string
            The repo of all TF datasets.
        backend: string
            The backend of all TF datasets.
        nsOrig: string
            The original namespace of all TF datasets.
            See `tf.convert.watm.WATM`.
        skipMeta: boolean, optional False
            See `tf.convert.watm.WATM`.
        extra: dictionary, optional {}
            See `tf.convert.watm.WATM`.
        silent: boolean, optional False
            Whether to operate in silence.
        """
        self.org = org
        self.repo = repo
        self.backend = backend
        self.nsOrig = nsOrig
        self.skipMeta = skipMeta
        self.extra = extra
        self.silent = silent

        repoDir = ex(f"~/{backend}/{org}/{repo}")
        tfDir = f"{repoDir}/tf"
        docs = dirContents(tfDir)[1]
        if not silent:
            console(f"Found {len(docs)} docs in {tfDir}")
        self.docs = docs

    def produce(self, doc=None):
        """Convert all relevant TF datasets.

        Parameters
        ----------
        doc: string, optional None
            Subdirectory where one of the TF datasets resides.
            If passed, only this dataset will be converted.
            Otherwise all datasets will be converted.
        """
        org = self.org
        repo = self.repo
        backend = self.backend
        nsOrig = self.nsOrig
        skipMeta = self.skipMeta
        extra = self.extra
        docs = self.docs
        silent = self.silent

        chosenDoc = doc

        good = True

        for doc in sorted(docs, key=lambda x: (x[0], int(x[1:]))):
            if chosenDoc is not None and chosenDoc != doc:
                continue

            if not silent:
                console(f"{doc:>5} ... ", newline=False)
            A = use(
                f"{org}/{repo}:clone",
                relative=f"tf/{doc}",
                checkout="clone",
                backend=backend,
                silent=DEEP,
            )
            WA = WATM(A, nsOrig, skipMeta=skipMeta, extra=extra, silent=silent)
            WA.makeText()
            WA.makeAnno()
            WA.writeAll()
            WA.testAll(condensed=True)

            if WA.error:
                good = False

        console(f"WATM generation: {rep(good)}", error=not good)

Functions

def rep(status)

Represent a boolean status for a message to the console.

Parameters

status : boolean
 

Returns

string
 
Expand source code Browse git
def rep(status):
    """Represent a boolean status for a message to the console.

    Parameters
    ----------
    status: boolean

    Returns
    -------
    string
    """
    return "OK" if status else "XX"

Classes

class WATM (app, nsOrig, skipMeta=False, extra={}, silent=False)

The export machinery is exposed as a class, wrapped around a TF dataset.

Wrap the WATM exporter around a TF dataset.

Given an already loaded TF dataset, we make an inventory of all data we need to perform an export to WATM.

Parameters

app : object
A loaded TF dataset, as obtained by a call use(…). See use()
nsOrig : string
A namespace corresponding to the format of the original, pre-Text-Fabric representation. For example tei for a TEI corpus, pagexml for a PageXML corpus. The namespace is not related to XML namespaces, it is merely a device to categorize the resulting annotations.
skipMeta : boolean, optional False
Only relevant for TEI corpora. If True, all material in the TEI Header will not be converted to tokens in the text. More precisely: all TF slots for which the feature is_meta has a true-ish value will be skipped. If there is no feature is_meta in the dataset, the setting of skipMeta will have no effect: nothing will be excluded.
extra : dictionary, optional {}
The data for extra annotations, which will be generated on the fly under the namespace anno. The keys are the names of features/attributes, the value for each key is a dictionary that maps nodes to values.
silent : boolean, optional False
Whether to suppress output to the console
Expand source code Browse git
class WATM:
    """The export machinery is exposed as a class, wrapped around a TF dataset."""

    def __init__(self, app, nsOrig, skipMeta=False, extra={}, silent=False):
        """Wrap the WATM exporter around a TF dataset.

        Given an already loaded TF dataset, we make an inventory of all data
        we need to perform an export to WATM.

        Parameters
        ----------
        app: object
            A loaded TF dataset, as obtained by a call `use(...)`.
            See `tf.app.use`
        nsOrig: string
            A namespace corresponding to the format of the original, pre-Text-Fabric
            representation. For example `tei` for a TEI corpus, `pagexml` for a
            PageXML corpus. The namespace is not related to XML namespaces, it is
            merely a device to categorize the resulting annotations.
        skipMeta: boolean, optional False
            Only relevant for TEI corpora. If True, all material in the TEI Header
            will not be converted to tokens in the text.
            More precisely: all TF slots for which the feature `is_meta` has a true-ish
            value will be skipped. If there is no feature `is_meta` in the dataset,
            the setting of `skipMeta` will have no effect: nothing will be excluded.
        extra: dictionary, optional {}
            The data for extra annotations, which will be generated on the fly under the
            namespace `anno`. The keys are the names of features/attributes, the
            value for each key is a dictionary that maps nodes to values.
        silent: boolean, optional False
            Whether to suppress output to the console
        """
        self.app = app
        self.nsOrig = nsOrig
        self.extra = extra
        self.silent = silent
        api = app.api
        F = api.F
        E = api.E
        T = api.T
        sectionTypes = T.sectionTypes

        cfg = readYaml(asFile=CONFIG_FILE)
        self.cfg = cfg
        self.error = False

        textRepoLevel = cfg.textRepoLevel or 1

        if type(textRepoLevel) is not int or not 1 <= textRepoLevel <= 3:
            console(
                f"{CONFIG_FILE}: textRepoLevel must be an integer between 1 and 3",
                error=True,
            )
            self.error = True

        if len(sectionTypes) == 0:
            console(
                "No section types in corpus. "
                "We need at least one section level for tier-0",
                error=True,
            )
            self.error = True

        if self.error:
            return

        textRepoType = T.sectionTypes[textRepoLevel - 1]

        if not silent:
            console(f"textRepoLevel is section level '{textRepoType}'")
        self.textRepoType = textRepoType

        self.excludeElements = set(cfg.excludeElements or [])
        self.asTsv = cfg.asTsv

        self.L = api.L
        self.Es = api.Es
        self.F = F
        self.E = E
        self.Fs = api.Fs
        self.slotType = self.F.otype.slotType
        self.maxSlotPlus = self.F.otype.maxSlot + 1
        self.maxNodePlus = self.F.otype.maxNode + 1
        self.otypes = self.F.otype.all
        self.info = app.info
        self.repoLocation = app.repoLocation

        Fall = api.Fall
        Eall = api.Eall
        self.Fall = Fall
        self.Eall = Eall

        excludedFeatures = {OTYPE, OSLOTS, "after", "str"}
        self.nodeFeatures = [f for f in Fall() if f not in excludedFeatures]
        self.edgeFeatures = [f for f in Eall() if f not in excludedFeatures]

        FAllSet = set(Fall())

        self.fotypev = F.otype.v
        self.eoslots = E.oslots.s
        self.emptyv = F.empty.v if "empty" in FAllSet else None
        self.strv = F.str.v if "str" in FAllSet else None
        self.rstrv = F.rstr.v if "rstr" in FAllSet else None
        self.afterv = F.after.v if "after" in FAllSet else None
        self.rafterv = F.rafter.v if "rafter" in FAllSet else None
        is_metav = F.is_meta.v if "is_meta" in FAllSet else None
        self.is_metav = is_metav

        if not silent:
            app.dm(f"[WATM exporter documentation]({URL_TF_DOCS}/convert/watm.html)")

        if skipMeta and not is_metav:
            console(
                "skipMeta=True has no effect because feature is_meta is not defined.",
                error=True,
            )
            skipMeta = False

        self.skipMeta = skipMeta

    def makeText(self):
        """Creates the text data.

        The text is a list of tokens and will be stored in member `text` in this object.
        Additionally, the mapping from slot numbers in the TF data
        to indices in this list is stored in member `waFromTF`.
        """
        error = self.error
        silent = self.silent

        if error:
            if not silent:
                console("Cannot run because of an earlier error")
            return

        F = self.F
        L = self.L
        slotType = self.slotType
        textRepoType = self.textRepoType
        skipMeta = self.skipMeta

        emptyv = self.emptyv
        strv = self.strv
        rstrv = self.rstrv
        afterv = self.afterv
        rafterv = self.rafterv
        is_metav = self.is_metav

        texts = []
        waFromTF = {}

        self.texts = texts
        self.waFromTF = waFromTF

        for ti, sNode in enumerate(F.otype.s(textRepoType)):
            text = []
            texts.append(text)

            for s in L.d(sNode, otype=slotType):
                if skipMeta and is_metav(s):
                    continue

                after = rafterv(s) if rafterv else None

                if after is None:
                    after = afterv(s) if afterv else None

                if after is None:
                    after = ""

                if emptyv and emptyv(s):
                    value = after
                else:
                    string = rstrv(s) if rstrv else None

                    if string is None:
                        string = strv(s) if strv else None

                    if string is None:
                        string = ""

                    value = f"{string}{after}"

                text.append(value)
                t = len(text) - 1
                waFromTF[s] = (ti, t)

    def mkAnno(self, kind, ns, body, target):
        """Make a single annotation and return its id.

        Parameters
        ----------
        kind: string
            The kind of annotation.
        ns: string
            The namespace of the annotation.
        body: string
            The body of the annotation.
        target: string  or tuple of strings
            The target of the annotation.
        """
        annos = self.annos
        aId = f"a{len(annos):>08}"
        annos.append((kind, aId, ns, body, target))
        return aId

    def makeAnno(self):
        """Make all annotations.

        The annotations are stored in a big list, in member `anno` of this object.

        The mapping from slots to indices in the list of tokens is now extended
        with the mapping from nodes to corresponding node annotations.

        So member `waFromTF` is now a full mapping from all nodes in TF to
        tokens and/or annotations in WATM.
        """
        error = self.error
        silent = self.silent

        if error:
            if not silent:
                console("Cannot run because of an earlier error")
            return

        Es = self.Es
        F = self.F
        Fs = self.Fs
        fotypev = self.fotypev
        eoslots = self.eoslots
        nodeFeatures = self.nodeFeatures
        edgeFeatures = self.edgeFeatures
        slotType = self.slotType
        otypes = self.otypes
        nsOrig = self.nsOrig
        skipMeta = self.skipMeta
        extra = self.extra
        excludeElements = self.excludeElements

        waFromTF = self.waFromTF

        is_metav = self.is_metav

        isTei = nsOrig == NS_TEI

        annos = []
        texts = self.texts
        self.annos = annos

        invertedTargets = []
        farTargets = []
        discontinuousNodes = collections.defaultdict(list)

        def mkSingleTarget(n):
            ts = waFromTF[n]
            return f"{ts[0]}:{ts[1]}" if fotypev(n) == slotType else ts

        for otype in otypes:
            if otype == slotType or otype in excludeElements:
                continue

            for n in F.otype.s(otype):
                ws = eoslots(n)
                sb, se = (ws[0], ws[-1])
                if len(ws) != se - sb + 1:
                    discontinuousNodes[otype].append(n)

                if skipMeta and (is_metav(ws[0]) or is_metav(ws[-1])):
                    continue

                ti0, start = waFromTF[ws[0]]
                ti1, end = waFromTF[ws[-1]]

                if ti0 != ti1:
                    farTargets.append((otype, ti0, start, ti1, end))

                if ti0 == ti1 and end < start:
                    invertedTargets.append((otype, ti0, start, end))
                    start, end = (end, start)

                startPoint = f"{ti0}:{start}"
                endPoint = (
                    ("" if start == end else f"-{end + 1}")
                    if ti0 == ti1
                    else f"-{ti1}:{end + 1}"
                )
                target = f"{startPoint}{endPoint}"
                aId = (
                    self.mkAnno(KIND_PI, nsOrig, otype[1:], target)
                    if otype.startswith("?")
                    else self.mkAnno(
                        KIND_ELEM, NS_FROM_OTYPE.get(otype, nsOrig), otype, target
                    )
                )
                waFromTF[n] = aId

        for feat in nodeFeatures:
            ns = Fs(feat).meta.get("conversionCode", NS_FROM_FEAT.get(feat, nsOrig))

            if ns is None:
                console(
                    f"Node feature {feat} has no namespace, "
                    f"defaulting to {NS_NONE}",
                    error=True,
                )
                ns = NS_NONE

            isRend = False
            isNote = False

            if isTei:
                parts = feat.split("_", 2)
                isRend = len(parts) >= 2 and parts[0] == "rend"
                isNote = len(parts) == 2 and parts[0] == "is" and parts[1] == "note"

            if isRend or isNote:
                body = parts[1] if isRend else "note"

                for n, val in Fs(feat).items():
                    if n not in waFromTF or not val or skipMeta and is_metav(n):
                        continue

                    self.mkAnno(KIND_FMT, ns, body, mkSingleTarget(n))
            else:
                for n, val in Fs(feat).items():
                    if n not in waFromTF or val is None or skipMeta and is_metav(n):
                        continue

                    body = f"{feat}={val}"
                    self.mkAnno(KIND_ATTR, ns, body, mkSingleTarget(n))

        for feat in edgeFeatures:
            ns = Es(feat).meta.get("conversionCode", NS_FROM_FEAT.get(feat, nsOrig))

            if ns is None:
                console(
                    f"Edge feature {feat} has no conversion code, "
                    f"defaulting to {NS_NONE}",
                    error=True,
                )
                ns = NS_NONE

            for fromNode, toNodes in Es(feat).items():
                if fromNode not in waFromTF or skipMeta and is_metav(fromNode):
                    continue

                targetFrom = mkSingleTarget(fromNode)

                if type(toNodes) is dict:
                    for toNode, val in toNodes.items():
                        if toNode not in waFromTF or skipMeta and is_metav(toNode):
                            continue

                        body = f"{feat}={val}"
                        targetTo = mkSingleTarget(toNode)
                        target = f"{targetFrom}->{targetTo}"
                        self.mkAnno(KIND_EDGE, ns, body, target)
                else:
                    for toNode in toNodes:
                        if toNode not in waFromTF or skipMeta and is_metav(toNode):
                            continue

                        targetTo = mkSingleTarget(toNode)
                        target = f"{targetFrom}->{targetTo}"
                        self.mkAnno(KIND_EDGE, ns, feat, target)

        for feat, featData in extra.items():
            for n, value in featData.items():
                self.mkAnno(KIND_ANNO, NS_TT, f"{feat}={value}", mkSingleTarget(n))

        if len(invertedTargets):
            if not silent:
                console(f"WARNING: inverted targets, {len(invertedTargets)}x")
                for otype, ti0, start, end in invertedTargets:
                    text = texts[ti0]
                    sega = text[start]
                    segb = text[end - 1]
                    console(f"{otype:>20} {start:>6} `{sega}` > {end - 1} `{segb}`")

        if len(discontinuousNodes):
            nDis = sum(len(x) for x in discontinuousNodes.values())
            console(f"WARNING: {nDis} discontinuous nodes encountered")

            for otype, nodes in discontinuousNodes.items():
                nn = len(nodes)
                console(f"\t{nn} x of type {otype}")

                if not silent:
                    examples = ", ".join(str(n) for n in nodes[0:10])
                    console(f"\t\t{examples}")

        nFarTargets = len(farTargets)

        if nFarTargets:
            console(f"WARNING: targets across tier0 items, {nFarTargets}x")
            if not silent:
                for otype, ti0, start, ti1, end in farTargets[0:10]:
                    console(
                        f"{otype:>20} [{ti0:>4}:{start:>6}] - [{ti1:>4}:{end - 1:>6}]"
                    )
                if nFarTargets > 10:
                    console(f"... and {nFarTargets - 10} more.")

    def writeAll(self):
        """Write text and annotation data to disk.

        The data will be written as JSON files, or, is `asTsv` is in force, as TSV
        files.
        When the annotation data grows larger than a certain threshold, it will be
        divided over several files.

        The annotations are sorted by annotation id.
        """

        maxNodePlus = self.maxNodePlus
        maxSlotPlus = self.maxSlotPlus

        # text files

        error = self.error
        silent = self.silent

        if error:
            if not silent:
                console("Cannot run because of an earlier error")
            return

        app = self.app
        texts = self.texts
        annos = self.annos
        waFromTF = self.waFromTF
        asTsv = self.asTsv

        baseDir = self.repoLocation
        relative = app.context.relative
        version = app.version
        wRelative = REL_RE.sub(f"/{TT_NAME}/{version}/", relative, count=1)
        resultDir = f"{baseDir}{wRelative}"
        self.resultDir = resultDir

        initTree(resultDir, fresh=True)

        total = 0

        ext = "tsv" if asTsv else "json"

        for i, text in enumerate(texts):
            textFile = f"{resultDir}/text-{i}.{ext}"
            nText = len(text)
            total += nText

            with open(textFile, "w") as fh:
                if asTsv:
                    fh.write("token\n")
                    for t in text:
                        fh.write(t.replace("\t", "\\t").replace("\n", "\\n") + "\n")
                else:
                    json.dump(
                        dict(_ordered_segments=text), fh, ensure_ascii=False, indent=1
                    )

            if not silent:
                console(f"Text file {i:>4}: {nText:>8} segments to {textFile}")

        nTexts = len(texts)
        sep = "" if nTexts == 1 else "s"

        if not silent:
            console(f"Text files all: {total:>8} segments to {nTexts} file{sep}")

        # annotation files

        annoStore = {}

        for kind, aId, ns, body, target in annos:
            annoStore[aId] = (kind, ns, body, target)

        aIdSorted = sorted(annoStore.keys())

        thisAnnoStore = {}
        thisA = 1
        nAnnoFiles = 0

        LIMIT = 400000
        j = 0
        total = 0

        def writeThis():
            annoFile = f"{resultDir}/anno-{thisA:>01}.{ext}"

            with open(annoFile, "w") as fh:
                if asTsv:
                    fh.write("annoid\tkind\tnamespace\tbody\ttarget\n")
                    for aId, (kind, namespace, body, target) in thisAnnoStore.items():
                        body = body.replace("\t", "\\t").replace("\n", "\\n")
                        fh.write(f"{aId}\t{kind}\t{namespace}\t{body}\t{target}\n")
                else:
                    json.dump(thisAnnoStore, fh, ensure_ascii=False, indent=1)

            if not silent:
                console(
                    f"Anno file {thisA:>4}: {j:>8} annotations written to {annoFile}"
                )

        for aId in aIdSorted:
            if j >= LIMIT:
                writeThis()
                nAnnoFiles += 1
                thisA += 1
                thisAnnoStore = {}
                total += j
                j = 0

            thisAnnoStore[aId] = annoStore[aId]
            j += 1

        if len(thisAnnoStore):
            writeThis()
            nAnnoFiles += 1
            total += j

        if len(annoStore) != total:
            console(f"Sum of batches : {total:>8}", error=True)
            console(f"All annotations: {len(annoStore):>8}", error=True)
            console("Mismatch in number of annotations", error=True)

        sep = "" if nAnnoFiles == 1 else "s"

        if not silent:
            console(f"Anno files all: {total:>8} annotations to {nAnnoFiles} file{sep}")

        # node mapping files

        slotmapFile = f"{resultDir}/{SLOTMAP_FILE}"
        nodemapFile = f"{resultDir}/{NODEMAP_FILE}"

        with open(slotmapFile, "w") as fh:
            fh.write("position\tnode\n")
            for n in range(1, maxSlotPlus):
                (file, pos) = waFromTF[n]
                fh.write(f"{file}:{pos}\t{n}\n")

        with open(nodemapFile, "w") as fh:
            fh.write("annotation\tnode\n")
            for n in range(maxSlotPlus, maxNodePlus):
                aId = waFromTF.get(n, None)
                if aId is not None:
                    fh.write(f"{aId}\t{n}\n")

        if not silent:
            console(f"Slot mapping written to {slotmapFile}")
            console(f"Node mapping written to {nodemapFile}")

    @staticmethod
    def numEqual(nTF, nWA, silent):
        """Compare two numbers and report the outcome.

        Used for testing the WATM conversion.

        Parameters
        ----------
        nTF: integer
            The number as it is counted from the original TF dataset.
        nWA: integer
            The number as it is counted from the generated WATM dataset.

        Returns
        -------
        boolean
            Whether the two values are equal.
        """
        error = nTF != nWA

        if error or not silent:
            console(f"\tTF: {nTF:>6}\n\tWA: {nWA:>6}", error=error)

        return nTF == nWA

    @staticmethod
    def strEqual(tf, wa, silent):
        """Compare two strings and report the outcome.

        Used for testing the WATM conversion.

        Parameters
        ----------
        nTF: string
            The string as encountered in the original TF dataset.
        nWA: string
            The string as encountered in the generated WATM dataset.

        Returns
        -------
        boolean
            Whether the two values are equal.
        """
        different = False

        for i, cTF in enumerate(tf):
            if i >= len(wa):
                contextI = max((0, i - 10))
                console(f"\tWA {i}: {wa[contextI:i]} <END>", error=True)
                console(f"\tTF {i}: {tf[contextI:i]} <> {tf[i:i + 10]}", error=True)
                different = True
                break
            elif tf[i] != wa[i]:
                contextI = max((0, i - 10))
                console(
                    f"\tWA {i}: {wa[contextI:i]} <{wa[i]}> {wa[i + 1:i + 11]}",
                    error=True,
                )
                console(
                    f"\tTF {i}: {tf[contextI:i]} <{tf[i]}> {tf[i + 1:i + 11]}",
                    error=True,
                )
                different = True
                break

        if not different and len(wa) > len(tf):
            i = len(tf)
            contextI = max((0, i - 10))
            console(f"\tWA {i}: {wa[contextI:i]} <> {wa[i:i + 10]}", error=True)
            console(f"\tTF {i}: {tf[contextI:i]} <END>", error=True)
            different = True

        sampleWA = f"{wa[0:20]} ... {wa[-20:]}".replace("\n", " ")
        sampleTF = f"{tf[0:20]} ... {tf[-20:]}".replace("\n", " ")
        if not silent:
            console(f"\tTF: {sampleTF:>6}\n\tWA: {sampleWA:>6}")
        return not different

    def testAll(self, condensed=False):
        """Test all aspects of the WATM conversion.

        For all kinds of information, such as nodes, edges, features, tokens,
        annotations, we check whether the parts that should correspond between
        the TF dataset and the WATM annotations do so indeed.

        We present some statistics, and highlight the mismatches.

        Parameters
        ----------
        condensed: boolean, optional False
            If silent has been passed to the object, there is still some
            output for each corpus, namely whether all tests have passed.
            If `condensed` is True, we suppress this output.

        Returns
        -------
        boolean
            Whether all things that must agree do indeed agree.
        """
        error = self.error
        silent = self.silent

        if error:
            if not silent:
                console("Cannot run because of an earlier error")
            return

        self.testSetup()

        if self.error:
            console("WATM data is incomplete. Testing aborted")
            return

        good = True

        if not self.testText():
            good = False

        if not self.testElements():
            good = False

        if not self.testAttributes():
            good = False

        if not self.testExtra():
            good = False

        if not self.testEdges():
            good = False

        if not silent:
            console("Overall outcome ...")

        if not silent or not condensed:
            console(f"{rep(good)} - whether all tests passed", error=not good)

        if not good:
            self.error = True

    def testSetup(self):
        """Prepare the tests.

        We read the WATM dataset and store the tokens in member `testTokens`
        and the annotations in the member `testAnnotations`, and the node mapping
        in the member `nodeFromAid`.
        We unpack targets if they contain structured information.
        """

        # collect the files

        asTsv = self.asTsv
        resultDir = self.resultDir
        resultFiles = dirContents(resultDir)[0]

        ext = "tsv" if asTsv else "json"

        def fileSort(name):
            middle = name.split(".", 1)[0].split("-", 1)[1]
            return f"{middle:0>10}" if middle.isdecimal else middle

        textFiles = sorted(
            (f for f in resultFiles if f.startswith("text-") and f.endswith(f".{ext}")),
            key=fileSort,
        )
        annoFiles = sorted(
            (f for f in resultFiles if f.startswith("anno-") and f.endswith(f".{ext}")),
            key=fileSort,
        )
        mapFiles = [
            f
            for f in resultFiles
            if (f.startswith("anno") or f.startswith("pos")) and f.endswith("2node.tsv")
        ]

        if NODEMAP_FILE not in mapFiles:
            console(f"ERROR: Missing map file {NODEMAP_FILE}")
            self.error = True
        if SLOTMAP_FILE not in mapFiles:
            console(f"ERROR: Missing map file {SLOTMAP_FILE}")
            self.error = True

        if self.error:
            return

        # read the text files

        skipMeta = self.skipMeta
        is_metav = self.is_metav

        waSlotTF = {}
        tokenFiles = []

        slot = 1

        for tfl, textFile in enumerate(textFiles):
            with open(f"{resultDir}/{textFile}") as fh:
                if asTsv:
                    next(fh)
                    tokens = [t.rstrip("\n").replace("\\t", "\t").replace("\\n", "\n") for t in fh]
                else:
                    text = json.load(fh)
                    tokens = text["_ordered_segments"]

                tokenFiles.append(tokens)

                for offset in range(len(tokens)):
                    while skipMeta and is_metav(slot):
                        slot += 1

                    waSlotTF[slot] = (tfl, offset)
                    slot += 1

        self.testTokens = tokenFiles
        self.waSlotTF = waSlotTF

        # read the anno files

        annotations = []

        for annoFile in annoFiles:
            with open(f"{resultDir}/{annoFile}") as fh:
                if asTsv:
                    next(fh)
                    annos = {}
                    for line in fh:
                        (aId, kind, ns, body, target) = line.rstrip("\n").split("\t")
                        body = body.replace("\\t", "\t").replace("\\n", "\n")
                        annos[aId] = (kind, ns, body, target)
                else:
                    annos = json.load(fh)

                for aId, (kind, ns, body, target) in annos.items():
                    if "->" in target:
                        parts = target.split("->", 1)
                    else:
                        parts = [target]

                    newParts = []

                    for part in parts:
                        if ":" in part:
                            boundaries = part.split("-", 1)
                            fb, b = boundaries[0].split(":", 1)
                            fb = int(fb)
                            b = int(b)

                            if len(boundaries) == 1:
                                if kind == KIND_ELEM or kind == KIND_PI:
                                    part = (int(fb), int(b), int(fb), int(b) + 1)
                                else:
                                    part = (int(fb), int(b))
                            else:
                                eParts = boundaries[1].split(":", 1)

                                if len(eParts) == 1:
                                    fe, e = fb, int(eParts[0])
                                else:
                                    fe, e = eParts
                                    fe = int(fe)
                                    e = int(e)
                                part = (fb, b, fe, e)

                        newParts.append(part)

                    target = newParts[0] if len(newParts) == 1 else tuple(newParts)

                    annotations.append((aId, kind, body, target))

        annotations = sorted(annotations)
        self.testAnnotations = annotations

        # read the map files

        nodeFromAid = {}

        with open(f"{resultDir}/{SLOTMAP_FILE}") as fh:
            next(fh)
            for line in fh:
                (pos, slot) = line.rstrip("\n").split("\t")
                key = tuple(int(p) for p in pos.split(":"))
                nodeFromAid[key] = int(slot)

        with open(f"{resultDir}/{NODEMAP_FILE}") as fh:
            next(fh)
            for line in fh:
                (aId, node) = line.rstrip("\n").split("\t")
                nodeFromAid[aId] = int(node)

        self.nodeFromAid = nodeFromAid
        self.testNodes = set(nodeFromAid.values())

    def testText(self):
        """Test the text.

        We test the number of tokens and the equality of the resulting text:
        whether the TF and WATM datasets agree on it.

        Returns
        -------
        boolean
            Whether all these tests succeed.
        """
        maxSlotPlus = self.maxSlotPlus
        tokenFiles = self.testTokens
        texts = self.texts
        waSlotTF = self.waSlotTF
        silent = self.silent

        if not silent:
            console("Testing the text ...")

        nTokensTF = sum(1 if s in waSlotTF else 0 for s in range(1, maxSlotPlus))
        nTokensWA = sum(len(tokens) for tokens in tokenFiles)
        nGood = self.numEqual(nTokensTF, nTokensWA, silent)

        if not nGood or not silent:
            console(
                f"{rep(nGood)} - whether the amounts of tokens agree", error=not nGood
            )

        textWA = "".join("".join(tokens) for tokens in tokenFiles)
        textTF = "".join("".join(text) for text in texts)

        tGood = self.strEqual(textTF, textWA, silent)

        if not tGood or not silent:
            console(f"{rep(tGood)} - whether the text is the same", error=not tGood)

        return nGood and tGood

    def testElements(self):
        """Test the elements.

        We test the annotations representing elements/processing instructions
        and check whether they correspond 1-1 to the non-slot nodes in the TF
        dataset.

        Returns
        -------
        boolean
            Whether all these tests succeed.
        """
        maxSlotPlus = self.maxSlotPlus
        maxNodePlus = self.maxNodePlus
        fotypev = self.fotypev
        eoslots = self.eoslots
        waSlotTF = self.waSlotTF
        annotations = self.testAnnotations
        silent = self.silent
        excludeElements = self.excludeElements

        if not silent:
            console("Testing the elements ...")

        nElementsTF = 0
        nPisTF = 0

        for n in range(maxSlotPlus, maxNodePlus):
            nType = fotypev(n)
            isPi = nType.startswith("?")

            slots = eoslots(n)
            b = slots[0]
            e = slots[-1]

            if not (b in waSlotTF and e in waSlotTF):
                continue

            if isPi:
                nPisTF += 1
            else:
                if nType not in excludeElements:
                    nElementsTF += 1

        nElementsWA = 0
        nPisWA = 0

        nodeFromAid = self.nodeFromAid

        nElementsWA = sum(1 if a[1] == KIND_ELEM else 0 for a in annotations)
        nPisWA = sum(1 if a[1] == KIND_PI else 0 for a in annotations)

        eGood = self.numEqual(nElementsTF, nElementsWA, silent)

        if not eGood or not silent:
            console(
                f"{rep(eGood)} - whether the amounts of elements and nodes agree",
                error=not eGood,
            )

        if not silent:
            console("Testing the processing instructions ...")

        pGood = self.numEqual(nPisTF, nPisWA, silent)

        if not pGood or not silent:
            console(
                f"{rep(pGood)} - whether the amounts of processing instructions agree",
                error=not pGood,
            )

        if not silent:
            console("Testing the element/pi annotations ...")

        element = 0
        pi = 0
        other = 0
        goodName = 0
        wrongName = 0
        unmapped = 0

        if not silent:
            console(f"\t{len(nodeFromAid)} element/pi annotations")

        wrongTargets = []
        allTargets = 0
        goodTargets = 0

        for aId, kind, body, target in annotations:
            isElem = kind == KIND_ELEM
            isPi = kind == KIND_PI

            if not (isElem or isPi):
                other += 1
                continue

            if isElem:
                element += 1
            else:
                pi += 1

            tag = body
            node = nodeFromAid.get(aId, None)

            if node is None:
                unmapped += 1
                continue

            otype = fotypev(node)

            if isPi and tag == otype[1:] or not isPi and tag == otype:
                goodName += 1
            else:
                wrongName += 1

            if type(target) is not tuple or len(target) != 4:
                wrongTargets.append((aId, kind, body, target))
            else:
                node = nodeFromAid[aId]
                slots = eoslots(node)
                sb = slots[0]
                se = slots[-1]
                bTr = waSlotTF.get(sb, None)
                eTr = waSlotTF.get(se, None)

                if eTr is not None:
                    eTr = (eTr[0], eTr[1] + 1)

                bWA = (target[0], target[1])
                eWA = (target[2], target[3])

                bRep = f"{bWA}" if bTr == bWA else f"{bWA} XX {bTr}"
                eRep = f"{eWA}" if eTr == eWA else f"{eWA} XX {eTr}"

                if bTr is None or eTr is None or bTr != bWA or eTr != eWA:
                    wrongTargets.append((aId, kind, body, f"{bRep} - {eRep}"))
                else:
                    goodTargets += 1

            allTargets += 1

        if not silent:
            console(f"\tElement      : {element:>6} x")
            console(f"\tPi           : {pi:>6} x")
            console(f"\tOther        : {other:>6} x")
            console(f"\tGood name    : {goodName:>6} x")
            console(f"\tWrong name   : {wrongName:>6} x")
            console(f"\tGood target  : {goodTargets:>6} x")
            console(f"\tWrong target : {len(wrongTargets):>6} x")
            console(f"\tUnmapped     : {unmapped:>6} x")

        aGood = wrongName == 0 and unmapped == 0

        if not aGood or not silent:
            console(
                f"{rep(aGood)} - whether all element/pi annotations have good bodies",
                error=not aGood,
            )

        tGood = len(wrongTargets) == 0

        if not tGood or not silent:
            console(
                f"{rep(tGood)} - whether all element/pi annotations have good targets",
                error=not tGood,
            )
            if not tGood:
                tExamples = "\n\t\t".join(str(a) for a in wrongTargets[0:10])
                console(f"\t\t{tExamples}")

        return aGood and tGood and eGood and pGood

    def testAttributes(self):
        """Test the attributes.

        We test whether attributes and features correspond to each other.

        Some attributes in the original TEI are converted in a special way into
        TF features: this holds for the `rend` attribute.
        Basically, a value `rend="italic"` is translated into feature
        `is_italic=1`.
        In turn, these features have been translated into annotations of kind
        `format`. We test them separately.

        Returns
        -------
        boolean
            Whether all these tests succeed.
        """
        Fs = self.Fs
        Fall = self.Fall
        eoslots = self.eoslots
        waSlotTF = self.waSlotTF
        skipMeta = self.skipMeta
        annotations = self.testAnnotations
        nodeFromAid = self.nodeFromAid
        testNodes = self.testNodes
        nsOrig = self.nsOrig
        silent = self.silent

        isTei = nsOrig == NS_TEI

        if not silent:
            console("Testing the attributes ...")

        attWA = []

        for aId, kind, body, target in annotations:
            if kind != KIND_ATTR:
                continue
            if type(target) is tuple and len(target) == 4:
                target = (target[0], target[1])
            node = nodeFromAid[target]
            att, value = body.split("=", 1)
            attWA.append((node, att, value))

        attWA = sorted(attWA)

        if not silent:
            console(f"\t{len(attWA)} attribute values")

        good = 0
        wrong = []

        for node, att, valWA in attWA:
            val = Fs(att).v(node)
            valTF = None if val is None else str(val)

            if valWA == valTF:
                good += 1
            else:
                wrong.append((node, att, valWA, valTF))

        consistent = len(wrong) == 0

        if not silent:
            console(f"\tGood:     {good:>5} x")
        if not consistent or not silent:
            console(f"\tWrong:    {len(wrong):>5} x", error=not consistent)

            console(
                f"{rep(consistent)} - whether annotations are consistent with features",
                error=not consistent,
            )

        attTF = []

        for feat in Fall():
            if feat in {"otype", "str", "after"}:
                continue

            if skipMeta and feat == "is_meta":
                continue

            if isTei and (
                (feat != "is_meta" and feat.startswith("is_"))
                or feat.startswith("rend_")
            ):
                continue

            for node, valTF in Fs(feat).items():
                if node not in testNodes:
                    continue

                slots = eoslots(node)
                b = slots[0]
                e = slots[-1]

                if not (b in waSlotTF and e in waSlotTF):
                    continue

                attTF.append((node, feat, None if valTF is None else str(valTF)))

        attTF = sorted(attTF)

        if not silent:
            console(f"\tWA attributes: {len(attWA)}")
            console(f"\tTF attributes: {len(attTF)}")

        complete = attTF == attWA

        if not complete or not silent:
            console(
                f"{rep(complete)} - whether annotations are complete w.r.t. features",
                error=not complete,
            )

        if not silent:
            console("Testing the format attributes ...")

        fmtWA = []

        for aId, kind, body, target in annotations:
            if kind != KIND_FMT:
                continue
            if body == "note":
                continue
            if type(target) is tuple and len(target) == 4:
                target = (target[0], target[1])
            node = nodeFromAid[target]
            fmtWA.append((node, body))

        fmtWA = sorted(fmtWA)
        fmtFreqWA = collections.Counter()

        for node, body in fmtWA:
            fmtFreqWA[body] += 1

        if not silent:
            console(f"\t{len(fmtWA)} format values")
            console("\tformatting attributes: ")
            for fa, n in sorted(fmtFreqWA.items(), key=lambda x: (-x[1], x[0])):
                console(f"\t\t{n:>6} x {fa}")

        good = 0
        wrong = []

        for node, valWA in fmtWA:
            feat = f"rend_{valWA}"
            valTF = valWA if Fs(feat).v(node) else None

            if valWA == valTF:
                good += 1
            else:
                wrong.append((node, feat, valWA, valTF))

        fconsistent = len(wrong) == 0

        if not silent:
            console(f"\tGood:     {good:>5} x")

        if not fconsistent or not silent:
            console(f"\tWrong:    {len(wrong):>5} x")
            console(
                f"{rep(fconsistent)} - "
                f"whether format annotations are consistent with features",
                error=not fconsistent,
            )

        fmtTF = []

        for feat in Fall():
            if not feat.startswith("rend_"):
                continue

            value = feat.split("_", 2)[1]
            if value == "note":
                continue

            for node, valTF in Fs(feat).items():
                slots = eoslots(node)
                b = slots[0]
                e = slots[-1]

                if not (b in waSlotTF and e in waSlotTF):
                    continue

                fmtTF.append((node, value))

        fmtTF = sorted(fmtTF)

        if not silent:
            console(f"\tWA format attributes: {len(fmtWA)}")
            console(f"\tTF format attributes: {len(fmtTF)}")

        fcomplete = fmtTF == fmtWA

        if not fcomplete or not silent:
            console(
                f"{rep(complete)} - "
                f"whether format annotations are complete w.r.t. features",
                error=not fcomplete,
            )

        return consistent and complete and fconsistent and fcomplete

    def testExtra(self):
        """Test the extra data for on-the-fly annotations.

        Annotations that have been generated out of the data stored in the
        `extra` parameter with which the object has been initialized, all got
        the kind `anno`.

        Now we check these annotations against the data that went into it.

        Returns
        -------
        boolean
            Whether all these tests succeed.
        """
        annotations = self.testAnnotations
        nodeFromAid = self.nodeFromAid
        extra = self.extra
        silent = self.silent

        if not silent:
            console("Testing the extra annotations ...")

        attWA = []

        for aId, kind, body, target in annotations:
            if kind != KIND_ANNO:
                continue
            node = nodeFromAid[target]
            att, value = body.split("=", 1)
            attWA.append((node, att, value))

        attWA = sorted(attWA)

        attEX = []

        for feat, featData in extra.items():
            for n, value in featData.items():
                attEX.append((n, feat, value))

        attEX = sorted(attEX)

        if not silent:
            console(f"\t{len(attEX)} extra feature values")
            console(f"\t{len(attWA)} extra annotations")

        good = attWA == attEX

        def showData(tuples, isin, isout):
            data = {}

            for n, f, v in tuples:
                data.setdefault(f, {})[n] = v

            for f in sorted(data):
                fData = data[f]
                console(
                    f"\t{isin}: {f} misses {len(fData)} annotations in {isout}",
                    error=True,
                )
                for n in sorted(fData.keys())[0:3]:
                    console(f"\t\t\t{n:>7} = {fData[n]}", error=True)

        if not good:
            attWASet = set(attWA)
            attEXSet = set(attEX)

            onlyWA = attWASet - attEXSet
            onlyEX = attEXSet - attWASet

            if len(onlyWA):
                showData(onlyWA, "WA", "EX")
            else:
                if not silent:
                    console("\tWA: All extra annotations derive from the extra data")
            if len(onlyEX):
                showData(onlyEX, "EX", "WA")
            else:
                if not silent:
                    console("\tEX: All extra data ended up as annotations")

        if not good or not silent:
            console(
                f"{rep(good)} - whether the extra annotations agree", error=not good
            )

        return good

    def testEdges(self):
        """Test the edges.

        Edges in TF are links between nodes, and they translate into annotations of
        kind `edge` which target a pair of annotations: the `from` annotation,
        and the `to` annotation.

        Here we check whether the TF edges are faithfully and completely parallelled
        by annotations.

        Returns
        -------
        boolean
            Whether all these tests succeed.
        """
        Es = self.Es
        Eall = self.Eall
        annotations = self.testAnnotations
        silent = self.silent
        nodeFromAid = self.nodeFromAid
        testNodes = self.testNodes

        if not silent:
            console("Testing the edges ...")

        tfFromWAEdges = {}

        for aId, kind, body, target in annotations:
            if kind != KIND_EDGE:
                continue

            fro, to = target
            fromNode = nodeFromAid[fro]
            toNode = nodeFromAid[to]
            parts = body.split("=", 1)
            name, val = (body, None) if len(parts) == 1 else parts
            tfFromWAEdges.setdefault(name, {}).setdefault(fromNode, {})[toNode] = val

        if not silent:
            console(f"\tFound: {len(nodeFromAid)} nodes")

            for edge, edgeData in sorted(tfFromWAEdges.items()):
                console(f"\tFound edge {edge} with {len(edgeData)} starting nodes")

        allGood = True

        for edge in set(Eall()) | set(tfFromWAEdges):
            if edge == "oslots":
                continue

            if not silent:
                console(f"\tChecking edge {edge}")

            good = True

            x = f"edge {edge}: " if silent else "\t\t"

            if edge not in set(Eall()):
                console(f"{x}missing in TF data", error=True)
                good = False

            if edge not in tfFromWAEdges:
                console(f"{x}missing in annotation data", error=True)
                good = False

            if not good:
                continue

            dataTF = {}

            for f, ts in Es(edge).items():
                if f not in testNodes:
                    continue

                if type(ts) is dict:
                    for t, v in ts.items():
                        if t not in testNodes:
                            continue
                        dataTF.setdefault(f, {})[t] = v
                else:
                    for t in ts:
                        if t not in testNodes:
                            continue
                        dataTF.setdefault(f, {})[t] = None

            dataWA = tfFromWAEdges[edge]

            fromNodesTF = set(dataTF)
            fromNodesWA = set(dataWA)

            nFromTF = len(fromNodesTF)
            nFromWA = len(fromNodesWA)

            if fromNodesTF == fromNodesWA:
                if not silent:
                    console(f"\t\tsame {nFromTF} fromNodes")
            else:
                console(
                    f"{x}from nodes differ: {nFromTF} in TF, {nFromWA} in WA",
                    error=True,
                )
                good = False

            diffs = []

            nToChecked = 0

            for f, toNodeInfoTF in dataTF.items():
                toNodeInfoWA = dataWA[f]
                toNodeInfoTF = {
                    k: None if v is None else str(v) for (k, v) in toNodeInfoTF.items()
                }

                if toNodeInfoTF != toNodeInfoWA:
                    diffs.append((f, toNodeInfoTF, toNodeInfoWA))

                nToChecked += len(toNodeInfoTF)

            if len(diffs):
                good = False
                console(
                    f"{x}differences in toNodes for {len(diffs)} fromNodes", error=True
                )

                for f, toNodeInfoTF, toNodeInfoWA in sorted(diffs)[0:10]:
                    console(f"{x}\tfromNode {f}", error=True)

                    toNodesTF = set(toNodeInfoTF)
                    toNodesWA = set(toNodeInfoWA)

                    nToTF = len(toNodesTF)
                    nToWA = len(toNodesWA)

                    if toNodesTF == toNodesWA:
                        if not silent:
                            console(f"\t\t\tsame {nToTF} toNodes")
                    else:
                        console(
                            f"{x}\ttoNodes differ: {nToTF} in TF, {nToWA} in WA",
                            error=True,
                        )
                    for t in toNodesTF | toNodesWA:
                        doCompare = True
                        if t not in toNodesTF:
                            console(f"{x}\t\ttoNode {t} not in TF", error=True)
                            doCompare = False
                        else:
                            valTF = toNodeInfoTF[t]

                        if t not in toNodesWA:
                            console(f"{x}\t\ttoNode {t} not in WA", error=True)
                            doCompare = False
                        else:
                            valWA = toNodeInfoWA[t]

                        if doCompare:
                            if valTF == valWA:
                                if not silent:
                                    console(
                                        f"\t\t\t\ttoNode{t} values agree: {repr(valTF)}"
                                    )
                            else:
                                console(
                                    f"{x}\t\ttoNode{t} values differ: "
                                    f"TF: {repr(valTF)} WA: {repr(valWA)}",
                                    error=True,
                                )

            if not good or not silent:
                console(f"\t{rep(good)} - {nToChecked} toNodes checked", error=not good)

            if not good:
                allGood = False

        if not silent:
            console(f"{rep(allGood)} - whether all edges agree")

        return allGood

Static methods

def numEqual(nTF, nWA, silent)

Compare two numbers and report the outcome.

Used for testing the WATM conversion.

Parameters

nTF : integer
The number as it is counted from the original TF dataset.
nWA : integer
The number as it is counted from the generated WATM dataset.

Returns

boolean
Whether the two values are equal.
Expand source code Browse git
@staticmethod
def numEqual(nTF, nWA, silent):
    """Compare two numbers and report the outcome.

    Used for testing the WATM conversion.

    Parameters
    ----------
    nTF: integer
        The number as it is counted from the original TF dataset.
    nWA: integer
        The number as it is counted from the generated WATM dataset.

    Returns
    -------
    boolean
        Whether the two values are equal.
    """
    error = nTF != nWA

    if error or not silent:
        console(f"\tTF: {nTF:>6}\n\tWA: {nWA:>6}", error=error)

    return nTF == nWA
def strEqual(tf, wa, silent)

Compare two strings and report the outcome.

Used for testing the WATM conversion.

Parameters

nTF : string
The string as encountered in the original TF dataset.
nWA : string
The string as encountered in the generated WATM dataset.

Returns

boolean
Whether the two values are equal.
Expand source code Browse git
@staticmethod
def strEqual(tf, wa, silent):
    """Compare two strings and report the outcome.

    Used for testing the WATM conversion.

    Parameters
    ----------
    nTF: string
        The string as encountered in the original TF dataset.
    nWA: string
        The string as encountered in the generated WATM dataset.

    Returns
    -------
    boolean
        Whether the two values are equal.
    """
    different = False

    for i, cTF in enumerate(tf):
        if i >= len(wa):
            contextI = max((0, i - 10))
            console(f"\tWA {i}: {wa[contextI:i]} <END>", error=True)
            console(f"\tTF {i}: {tf[contextI:i]} <> {tf[i:i + 10]}", error=True)
            different = True
            break
        elif tf[i] != wa[i]:
            contextI = max((0, i - 10))
            console(
                f"\tWA {i}: {wa[contextI:i]} <{wa[i]}> {wa[i + 1:i + 11]}",
                error=True,
            )
            console(
                f"\tTF {i}: {tf[contextI:i]} <{tf[i]}> {tf[i + 1:i + 11]}",
                error=True,
            )
            different = True
            break

    if not different and len(wa) > len(tf):
        i = len(tf)
        contextI = max((0, i - 10))
        console(f"\tWA {i}: {wa[contextI:i]} <> {wa[i:i + 10]}", error=True)
        console(f"\tTF {i}: {tf[contextI:i]} <END>", error=True)
        different = True

    sampleWA = f"{wa[0:20]} ... {wa[-20:]}".replace("\n", " ")
    sampleTF = f"{tf[0:20]} ... {tf[-20:]}".replace("\n", " ")
    if not silent:
        console(f"\tTF: {sampleTF:>6}\n\tWA: {sampleWA:>6}")
    return not different

Methods

def makeAnno(self)

Make all annotations.

The annotations are stored in a big list, in member anno of this object.

The mapping from slots to indices in the list of tokens is now extended with the mapping from nodes to corresponding node annotations.

So member waFromTF is now a full mapping from all nodes in TF to tokens and/or annotations in WATM.

Expand source code Browse git
def makeAnno(self):
    """Make all annotations.

    The annotations are stored in a big list, in member `anno` of this object.

    The mapping from slots to indices in the list of tokens is now extended
    with the mapping from nodes to corresponding node annotations.

    So member `waFromTF` is now a full mapping from all nodes in TF to
    tokens and/or annotations in WATM.
    """
    error = self.error
    silent = self.silent

    if error:
        if not silent:
            console("Cannot run because of an earlier error")
        return

    Es = self.Es
    F = self.F
    Fs = self.Fs
    fotypev = self.fotypev
    eoslots = self.eoslots
    nodeFeatures = self.nodeFeatures
    edgeFeatures = self.edgeFeatures
    slotType = self.slotType
    otypes = self.otypes
    nsOrig = self.nsOrig
    skipMeta = self.skipMeta
    extra = self.extra
    excludeElements = self.excludeElements

    waFromTF = self.waFromTF

    is_metav = self.is_metav

    isTei = nsOrig == NS_TEI

    annos = []
    texts = self.texts
    self.annos = annos

    invertedTargets = []
    farTargets = []
    discontinuousNodes = collections.defaultdict(list)

    def mkSingleTarget(n):
        ts = waFromTF[n]
        return f"{ts[0]}:{ts[1]}" if fotypev(n) == slotType else ts

    for otype in otypes:
        if otype == slotType or otype in excludeElements:
            continue

        for n in F.otype.s(otype):
            ws = eoslots(n)
            sb, se = (ws[0], ws[-1])
            if len(ws) != se - sb + 1:
                discontinuousNodes[otype].append(n)

            if skipMeta and (is_metav(ws[0]) or is_metav(ws[-1])):
                continue

            ti0, start = waFromTF[ws[0]]
            ti1, end = waFromTF[ws[-1]]

            if ti0 != ti1:
                farTargets.append((otype, ti0, start, ti1, end))

            if ti0 == ti1 and end < start:
                invertedTargets.append((otype, ti0, start, end))
                start, end = (end, start)

            startPoint = f"{ti0}:{start}"
            endPoint = (
                ("" if start == end else f"-{end + 1}")
                if ti0 == ti1
                else f"-{ti1}:{end + 1}"
            )
            target = f"{startPoint}{endPoint}"
            aId = (
                self.mkAnno(KIND_PI, nsOrig, otype[1:], target)
                if otype.startswith("?")
                else self.mkAnno(
                    KIND_ELEM, NS_FROM_OTYPE.get(otype, nsOrig), otype, target
                )
            )
            waFromTF[n] = aId

    for feat in nodeFeatures:
        ns = Fs(feat).meta.get("conversionCode", NS_FROM_FEAT.get(feat, nsOrig))

        if ns is None:
            console(
                f"Node feature {feat} has no namespace, "
                f"defaulting to {NS_NONE}",
                error=True,
            )
            ns = NS_NONE

        isRend = False
        isNote = False

        if isTei:
            parts = feat.split("_", 2)
            isRend = len(parts) >= 2 and parts[0] == "rend"
            isNote = len(parts) == 2 and parts[0] == "is" and parts[1] == "note"

        if isRend or isNote:
            body = parts[1] if isRend else "note"

            for n, val in Fs(feat).items():
                if n not in waFromTF or not val or skipMeta and is_metav(n):
                    continue

                self.mkAnno(KIND_FMT, ns, body, mkSingleTarget(n))
        else:
            for n, val in Fs(feat).items():
                if n not in waFromTF or val is None or skipMeta and is_metav(n):
                    continue

                body = f"{feat}={val}"
                self.mkAnno(KIND_ATTR, ns, body, mkSingleTarget(n))

    for feat in edgeFeatures:
        ns = Es(feat).meta.get("conversionCode", NS_FROM_FEAT.get(feat, nsOrig))

        if ns is None:
            console(
                f"Edge feature {feat} has no conversion code, "
                f"defaulting to {NS_NONE}",
                error=True,
            )
            ns = NS_NONE

        for fromNode, toNodes in Es(feat).items():
            if fromNode not in waFromTF or skipMeta and is_metav(fromNode):
                continue

            targetFrom = mkSingleTarget(fromNode)

            if type(toNodes) is dict:
                for toNode, val in toNodes.items():
                    if toNode not in waFromTF or skipMeta and is_metav(toNode):
                        continue

                    body = f"{feat}={val}"
                    targetTo = mkSingleTarget(toNode)
                    target = f"{targetFrom}->{targetTo}"
                    self.mkAnno(KIND_EDGE, ns, body, target)
            else:
                for toNode in toNodes:
                    if toNode not in waFromTF or skipMeta and is_metav(toNode):
                        continue

                    targetTo = mkSingleTarget(toNode)
                    target = f"{targetFrom}->{targetTo}"
                    self.mkAnno(KIND_EDGE, ns, feat, target)

    for feat, featData in extra.items():
        for n, value in featData.items():
            self.mkAnno(KIND_ANNO, NS_TT, f"{feat}={value}", mkSingleTarget(n))

    if len(invertedTargets):
        if not silent:
            console(f"WARNING: inverted targets, {len(invertedTargets)}x")
            for otype, ti0, start, end in invertedTargets:
                text = texts[ti0]
                sega = text[start]
                segb = text[end - 1]
                console(f"{otype:>20} {start:>6} `{sega}` > {end - 1} `{segb}`")

    if len(discontinuousNodes):
        nDis = sum(len(x) for x in discontinuousNodes.values())
        console(f"WARNING: {nDis} discontinuous nodes encountered")

        for otype, nodes in discontinuousNodes.items():
            nn = len(nodes)
            console(f"\t{nn} x of type {otype}")

            if not silent:
                examples = ", ".join(str(n) for n in nodes[0:10])
                console(f"\t\t{examples}")

    nFarTargets = len(farTargets)

    if nFarTargets:
        console(f"WARNING: targets across tier0 items, {nFarTargets}x")
        if not silent:
            for otype, ti0, start, ti1, end in farTargets[0:10]:
                console(
                    f"{otype:>20} [{ti0:>4}:{start:>6}] - [{ti1:>4}:{end - 1:>6}]"
                )
            if nFarTargets > 10:
                console(f"... and {nFarTargets - 10} more.")
def makeText(self)

Creates the text data.

The text is a list of tokens and will be stored in member text in this object. Additionally, the mapping from slot numbers in the TF data to indices in this list is stored in member waFromTF.

Expand source code Browse git
def makeText(self):
    """Creates the text data.

    The text is a list of tokens and will be stored in member `text` in this object.
    Additionally, the mapping from slot numbers in the TF data
    to indices in this list is stored in member `waFromTF`.
    """
    error = self.error
    silent = self.silent

    if error:
        if not silent:
            console("Cannot run because of an earlier error")
        return

    F = self.F
    L = self.L
    slotType = self.slotType
    textRepoType = self.textRepoType
    skipMeta = self.skipMeta

    emptyv = self.emptyv
    strv = self.strv
    rstrv = self.rstrv
    afterv = self.afterv
    rafterv = self.rafterv
    is_metav = self.is_metav

    texts = []
    waFromTF = {}

    self.texts = texts
    self.waFromTF = waFromTF

    for ti, sNode in enumerate(F.otype.s(textRepoType)):
        text = []
        texts.append(text)

        for s in L.d(sNode, otype=slotType):
            if skipMeta and is_metav(s):
                continue

            after = rafterv(s) if rafterv else None

            if after is None:
                after = afterv(s) if afterv else None

            if after is None:
                after = ""

            if emptyv and emptyv(s):
                value = after
            else:
                string = rstrv(s) if rstrv else None

                if string is None:
                    string = strv(s) if strv else None

                if string is None:
                    string = ""

                value = f"{string}{after}"

            text.append(value)
            t = len(text) - 1
            waFromTF[s] = (ti, t)
def mkAnno(self, kind, ns, body, target)

Make a single annotation and return its id.

Parameters

kind : string
The kind of annotation.
ns : string
The namespace of the annotation.
body : string
The body of the annotation.
target : string or tuple of strings
The target of the annotation.
Expand source code Browse git
def mkAnno(self, kind, ns, body, target):
    """Make a single annotation and return its id.

    Parameters
    ----------
    kind: string
        The kind of annotation.
    ns: string
        The namespace of the annotation.
    body: string
        The body of the annotation.
    target: string  or tuple of strings
        The target of the annotation.
    """
    annos = self.annos
    aId = f"a{len(annos):>08}"
    annos.append((kind, aId, ns, body, target))
    return aId
def testAll(self, condensed=False)

Test all aspects of the WATM conversion.

For all kinds of information, such as nodes, edges, features, tokens, annotations, we check whether the parts that should correspond between the TF dataset and the WATM annotations do so indeed.

We present some statistics, and highlight the mismatches.

Parameters

condensed : boolean, optional False
If silent has been passed to the object, there is still some output for each corpus, namely whether all tests have passed. If condensed is True, we suppress this output.

Returns

boolean
Whether all things that must agree do indeed agree.
Expand source code Browse git
def testAll(self, condensed=False):
    """Test all aspects of the WATM conversion.

    For all kinds of information, such as nodes, edges, features, tokens,
    annotations, we check whether the parts that should correspond between
    the TF dataset and the WATM annotations do so indeed.

    We present some statistics, and highlight the mismatches.

    Parameters
    ----------
    condensed: boolean, optional False
        If silent has been passed to the object, there is still some
        output for each corpus, namely whether all tests have passed.
        If `condensed` is True, we suppress this output.

    Returns
    -------
    boolean
        Whether all things that must agree do indeed agree.
    """
    error = self.error
    silent = self.silent

    if error:
        if not silent:
            console("Cannot run because of an earlier error")
        return

    self.testSetup()

    if self.error:
        console("WATM data is incomplete. Testing aborted")
        return

    good = True

    if not self.testText():
        good = False

    if not self.testElements():
        good = False

    if not self.testAttributes():
        good = False

    if not self.testExtra():
        good = False

    if not self.testEdges():
        good = False

    if not silent:
        console("Overall outcome ...")

    if not silent or not condensed:
        console(f"{rep(good)} - whether all tests passed", error=not good)

    if not good:
        self.error = True
def testAttributes(self)

Test the attributes.

We test whether attributes and features correspond to each other.

Some attributes in the original TEI are converted in a special way into TF features: this holds for the rend attribute. Basically, a value rend="italic" is translated into feature is_italic=1. In turn, these features have been translated into annotations of kind format. We test them separately.

Returns

boolean
Whether all these tests succeed.
Expand source code Browse git
def testAttributes(self):
    """Test the attributes.

    We test whether attributes and features correspond to each other.

    Some attributes in the original TEI are converted in a special way into
    TF features: this holds for the `rend` attribute.
    Basically, a value `rend="italic"` is translated into feature
    `is_italic=1`.
    In turn, these features have been translated into annotations of kind
    `format`. We test them separately.

    Returns
    -------
    boolean
        Whether all these tests succeed.
    """
    Fs = self.Fs
    Fall = self.Fall
    eoslots = self.eoslots
    waSlotTF = self.waSlotTF
    skipMeta = self.skipMeta
    annotations = self.testAnnotations
    nodeFromAid = self.nodeFromAid
    testNodes = self.testNodes
    nsOrig = self.nsOrig
    silent = self.silent

    isTei = nsOrig == NS_TEI

    if not silent:
        console("Testing the attributes ...")

    attWA = []

    for aId, kind, body, target in annotations:
        if kind != KIND_ATTR:
            continue
        if type(target) is tuple and len(target) == 4:
            target = (target[0], target[1])
        node = nodeFromAid[target]
        att, value = body.split("=", 1)
        attWA.append((node, att, value))

    attWA = sorted(attWA)

    if not silent:
        console(f"\t{len(attWA)} attribute values")

    good = 0
    wrong = []

    for node, att, valWA in attWA:
        val = Fs(att).v(node)
        valTF = None if val is None else str(val)

        if valWA == valTF:
            good += 1
        else:
            wrong.append((node, att, valWA, valTF))

    consistent = len(wrong) == 0

    if not silent:
        console(f"\tGood:     {good:>5} x")
    if not consistent or not silent:
        console(f"\tWrong:    {len(wrong):>5} x", error=not consistent)

        console(
            f"{rep(consistent)} - whether annotations are consistent with features",
            error=not consistent,
        )

    attTF = []

    for feat in Fall():
        if feat in {"otype", "str", "after"}:
            continue

        if skipMeta and feat == "is_meta":
            continue

        if isTei and (
            (feat != "is_meta" and feat.startswith("is_"))
            or feat.startswith("rend_")
        ):
            continue

        for node, valTF in Fs(feat).items():
            if node not in testNodes:
                continue

            slots = eoslots(node)
            b = slots[0]
            e = slots[-1]

            if not (b in waSlotTF and e in waSlotTF):
                continue

            attTF.append((node, feat, None if valTF is None else str(valTF)))

    attTF = sorted(attTF)

    if not silent:
        console(f"\tWA attributes: {len(attWA)}")
        console(f"\tTF attributes: {len(attTF)}")

    complete = attTF == attWA

    if not complete or not silent:
        console(
            f"{rep(complete)} - whether annotations are complete w.r.t. features",
            error=not complete,
        )

    if not silent:
        console("Testing the format attributes ...")

    fmtWA = []

    for aId, kind, body, target in annotations:
        if kind != KIND_FMT:
            continue
        if body == "note":
            continue
        if type(target) is tuple and len(target) == 4:
            target = (target[0], target[1])
        node = nodeFromAid[target]
        fmtWA.append((node, body))

    fmtWA = sorted(fmtWA)
    fmtFreqWA = collections.Counter()

    for node, body in fmtWA:
        fmtFreqWA[body] += 1

    if not silent:
        console(f"\t{len(fmtWA)} format values")
        console("\tformatting attributes: ")
        for fa, n in sorted(fmtFreqWA.items(), key=lambda x: (-x[1], x[0])):
            console(f"\t\t{n:>6} x {fa}")

    good = 0
    wrong = []

    for node, valWA in fmtWA:
        feat = f"rend_{valWA}"
        valTF = valWA if Fs(feat).v(node) else None

        if valWA == valTF:
            good += 1
        else:
            wrong.append((node, feat, valWA, valTF))

    fconsistent = len(wrong) == 0

    if not silent:
        console(f"\tGood:     {good:>5} x")

    if not fconsistent or not silent:
        console(f"\tWrong:    {len(wrong):>5} x")
        console(
            f"{rep(fconsistent)} - "
            f"whether format annotations are consistent with features",
            error=not fconsistent,
        )

    fmtTF = []

    for feat in Fall():
        if not feat.startswith("rend_"):
            continue

        value = feat.split("_", 2)[1]
        if value == "note":
            continue

        for node, valTF in Fs(feat).items():
            slots = eoslots(node)
            b = slots[0]
            e = slots[-1]

            if not (b in waSlotTF and e in waSlotTF):
                continue

            fmtTF.append((node, value))

    fmtTF = sorted(fmtTF)

    if not silent:
        console(f"\tWA format attributes: {len(fmtWA)}")
        console(f"\tTF format attributes: {len(fmtTF)}")

    fcomplete = fmtTF == fmtWA

    if not fcomplete or not silent:
        console(
            f"{rep(complete)} - "
            f"whether format annotations are complete w.r.t. features",
            error=not fcomplete,
        )

    return consistent and complete and fconsistent and fcomplete
def testEdges(self)

Test the edges.

Edges in TF are links between nodes, and they translate into annotations of kind edge which target a pair of annotations: the from annotation, and the to annotation.

Here we check whether the TF edges are faithfully and completely parallelled by annotations.

Returns

boolean
Whether all these tests succeed.
Expand source code Browse git
def testEdges(self):
    """Test the edges.

    Edges in TF are links between nodes, and they translate into annotations of
    kind `edge` which target a pair of annotations: the `from` annotation,
    and the `to` annotation.

    Here we check whether the TF edges are faithfully and completely parallelled
    by annotations.

    Returns
    -------
    boolean
        Whether all these tests succeed.
    """
    Es = self.Es
    Eall = self.Eall
    annotations = self.testAnnotations
    silent = self.silent
    nodeFromAid = self.nodeFromAid
    testNodes = self.testNodes

    if not silent:
        console("Testing the edges ...")

    tfFromWAEdges = {}

    for aId, kind, body, target in annotations:
        if kind != KIND_EDGE:
            continue

        fro, to = target
        fromNode = nodeFromAid[fro]
        toNode = nodeFromAid[to]
        parts = body.split("=", 1)
        name, val = (body, None) if len(parts) == 1 else parts
        tfFromWAEdges.setdefault(name, {}).setdefault(fromNode, {})[toNode] = val

    if not silent:
        console(f"\tFound: {len(nodeFromAid)} nodes")

        for edge, edgeData in sorted(tfFromWAEdges.items()):
            console(f"\tFound edge {edge} with {len(edgeData)} starting nodes")

    allGood = True

    for edge in set(Eall()) | set(tfFromWAEdges):
        if edge == "oslots":
            continue

        if not silent:
            console(f"\tChecking edge {edge}")

        good = True

        x = f"edge {edge}: " if silent else "\t\t"

        if edge not in set(Eall()):
            console(f"{x}missing in TF data", error=True)
            good = False

        if edge not in tfFromWAEdges:
            console(f"{x}missing in annotation data", error=True)
            good = False

        if not good:
            continue

        dataTF = {}

        for f, ts in Es(edge).items():
            if f not in testNodes:
                continue

            if type(ts) is dict:
                for t, v in ts.items():
                    if t not in testNodes:
                        continue
                    dataTF.setdefault(f, {})[t] = v
            else:
                for t in ts:
                    if t not in testNodes:
                        continue
                    dataTF.setdefault(f, {})[t] = None

        dataWA = tfFromWAEdges[edge]

        fromNodesTF = set(dataTF)
        fromNodesWA = set(dataWA)

        nFromTF = len(fromNodesTF)
        nFromWA = len(fromNodesWA)

        if fromNodesTF == fromNodesWA:
            if not silent:
                console(f"\t\tsame {nFromTF} fromNodes")
        else:
            console(
                f"{x}from nodes differ: {nFromTF} in TF, {nFromWA} in WA",
                error=True,
            )
            good = False

        diffs = []

        nToChecked = 0

        for f, toNodeInfoTF in dataTF.items():
            toNodeInfoWA = dataWA[f]
            toNodeInfoTF = {
                k: None if v is None else str(v) for (k, v) in toNodeInfoTF.items()
            }

            if toNodeInfoTF != toNodeInfoWA:
                diffs.append((f, toNodeInfoTF, toNodeInfoWA))

            nToChecked += len(toNodeInfoTF)

        if len(diffs):
            good = False
            console(
                f"{x}differences in toNodes for {len(diffs)} fromNodes", error=True
            )

            for f, toNodeInfoTF, toNodeInfoWA in sorted(diffs)[0:10]:
                console(f"{x}\tfromNode {f}", error=True)

                toNodesTF = set(toNodeInfoTF)
                toNodesWA = set(toNodeInfoWA)

                nToTF = len(toNodesTF)
                nToWA = len(toNodesWA)

                if toNodesTF == toNodesWA:
                    if not silent:
                        console(f"\t\t\tsame {nToTF} toNodes")
                else:
                    console(
                        f"{x}\ttoNodes differ: {nToTF} in TF, {nToWA} in WA",
                        error=True,
                    )
                for t in toNodesTF | toNodesWA:
                    doCompare = True
                    if t not in toNodesTF:
                        console(f"{x}\t\ttoNode {t} not in TF", error=True)
                        doCompare = False
                    else:
                        valTF = toNodeInfoTF[t]

                    if t not in toNodesWA:
                        console(f"{x}\t\ttoNode {t} not in WA", error=True)
                        doCompare = False
                    else:
                        valWA = toNodeInfoWA[t]

                    if doCompare:
                        if valTF == valWA:
                            if not silent:
                                console(
                                    f"\t\t\t\ttoNode{t} values agree: {repr(valTF)}"
                                )
                        else:
                            console(
                                f"{x}\t\ttoNode{t} values differ: "
                                f"TF: {repr(valTF)} WA: {repr(valWA)}",
                                error=True,
                            )

        if not good or not silent:
            console(f"\t{rep(good)} - {nToChecked} toNodes checked", error=not good)

        if not good:
            allGood = False

    if not silent:
        console(f"{rep(allGood)} - whether all edges agree")

    return allGood
def testElements(self)

Test the elements.

We test the annotations representing elements/processing instructions and check whether they correspond 1-1 to the non-slot nodes in the TF dataset.

Returns

boolean
Whether all these tests succeed.
Expand source code Browse git
def testElements(self):
    """Test the elements.

    We test the annotations representing elements/processing instructions
    and check whether they correspond 1-1 to the non-slot nodes in the TF
    dataset.

    Returns
    -------
    boolean
        Whether all these tests succeed.
    """
    maxSlotPlus = self.maxSlotPlus
    maxNodePlus = self.maxNodePlus
    fotypev = self.fotypev
    eoslots = self.eoslots
    waSlotTF = self.waSlotTF
    annotations = self.testAnnotations
    silent = self.silent
    excludeElements = self.excludeElements

    if not silent:
        console("Testing the elements ...")

    nElementsTF = 0
    nPisTF = 0

    for n in range(maxSlotPlus, maxNodePlus):
        nType = fotypev(n)
        isPi = nType.startswith("?")

        slots = eoslots(n)
        b = slots[0]
        e = slots[-1]

        if not (b in waSlotTF and e in waSlotTF):
            continue

        if isPi:
            nPisTF += 1
        else:
            if nType not in excludeElements:
                nElementsTF += 1

    nElementsWA = 0
    nPisWA = 0

    nodeFromAid = self.nodeFromAid

    nElementsWA = sum(1 if a[1] == KIND_ELEM else 0 for a in annotations)
    nPisWA = sum(1 if a[1] == KIND_PI else 0 for a in annotations)

    eGood = self.numEqual(nElementsTF, nElementsWA, silent)

    if not eGood or not silent:
        console(
            f"{rep(eGood)} - whether the amounts of elements and nodes agree",
            error=not eGood,
        )

    if not silent:
        console("Testing the processing instructions ...")

    pGood = self.numEqual(nPisTF, nPisWA, silent)

    if not pGood or not silent:
        console(
            f"{rep(pGood)} - whether the amounts of processing instructions agree",
            error=not pGood,
        )

    if not silent:
        console("Testing the element/pi annotations ...")

    element = 0
    pi = 0
    other = 0
    goodName = 0
    wrongName = 0
    unmapped = 0

    if not silent:
        console(f"\t{len(nodeFromAid)} element/pi annotations")

    wrongTargets = []
    allTargets = 0
    goodTargets = 0

    for aId, kind, body, target in annotations:
        isElem = kind == KIND_ELEM
        isPi = kind == KIND_PI

        if not (isElem or isPi):
            other += 1
            continue

        if isElem:
            element += 1
        else:
            pi += 1

        tag = body
        node = nodeFromAid.get(aId, None)

        if node is None:
            unmapped += 1
            continue

        otype = fotypev(node)

        if isPi and tag == otype[1:] or not isPi and tag == otype:
            goodName += 1
        else:
            wrongName += 1

        if type(target) is not tuple or len(target) != 4:
            wrongTargets.append((aId, kind, body, target))
        else:
            node = nodeFromAid[aId]
            slots = eoslots(node)
            sb = slots[0]
            se = slots[-1]
            bTr = waSlotTF.get(sb, None)
            eTr = waSlotTF.get(se, None)

            if eTr is not None:
                eTr = (eTr[0], eTr[1] + 1)

            bWA = (target[0], target[1])
            eWA = (target[2], target[3])

            bRep = f"{bWA}" if bTr == bWA else f"{bWA} XX {bTr}"
            eRep = f"{eWA}" if eTr == eWA else f"{eWA} XX {eTr}"

            if bTr is None or eTr is None or bTr != bWA or eTr != eWA:
                wrongTargets.append((aId, kind, body, f"{bRep} - {eRep}"))
            else:
                goodTargets += 1

        allTargets += 1

    if not silent:
        console(f"\tElement      : {element:>6} x")
        console(f"\tPi           : {pi:>6} x")
        console(f"\tOther        : {other:>6} x")
        console(f"\tGood name    : {goodName:>6} x")
        console(f"\tWrong name   : {wrongName:>6} x")
        console(f"\tGood target  : {goodTargets:>6} x")
        console(f"\tWrong target : {len(wrongTargets):>6} x")
        console(f"\tUnmapped     : {unmapped:>6} x")

    aGood = wrongName == 0 and unmapped == 0

    if not aGood or not silent:
        console(
            f"{rep(aGood)} - whether all element/pi annotations have good bodies",
            error=not aGood,
        )

    tGood = len(wrongTargets) == 0

    if not tGood or not silent:
        console(
            f"{rep(tGood)} - whether all element/pi annotations have good targets",
            error=not tGood,
        )
        if not tGood:
            tExamples = "\n\t\t".join(str(a) for a in wrongTargets[0:10])
            console(f"\t\t{tExamples}")

    return aGood and tGood and eGood and pGood
def testExtra(self)

Test the extra data for on-the-fly annotations.

Annotations that have been generated out of the data stored in the extra parameter with which the object has been initialized, all got the kind anno.

Now we check these annotations against the data that went into it.

Returns

boolean
Whether all these tests succeed.
Expand source code Browse git
def testExtra(self):
    """Test the extra data for on-the-fly annotations.

    Annotations that have been generated out of the data stored in the
    `extra` parameter with which the object has been initialized, all got
    the kind `anno`.

    Now we check these annotations against the data that went into it.

    Returns
    -------
    boolean
        Whether all these tests succeed.
    """
    annotations = self.testAnnotations
    nodeFromAid = self.nodeFromAid
    extra = self.extra
    silent = self.silent

    if not silent:
        console("Testing the extra annotations ...")

    attWA = []

    for aId, kind, body, target in annotations:
        if kind != KIND_ANNO:
            continue
        node = nodeFromAid[target]
        att, value = body.split("=", 1)
        attWA.append((node, att, value))

    attWA = sorted(attWA)

    attEX = []

    for feat, featData in extra.items():
        for n, value in featData.items():
            attEX.append((n, feat, value))

    attEX = sorted(attEX)

    if not silent:
        console(f"\t{len(attEX)} extra feature values")
        console(f"\t{len(attWA)} extra annotations")

    good = attWA == attEX

    def showData(tuples, isin, isout):
        data = {}

        for n, f, v in tuples:
            data.setdefault(f, {})[n] = v

        for f in sorted(data):
            fData = data[f]
            console(
                f"\t{isin}: {f} misses {len(fData)} annotations in {isout}",
                error=True,
            )
            for n in sorted(fData.keys())[0:3]:
                console(f"\t\t\t{n:>7} = {fData[n]}", error=True)

    if not good:
        attWASet = set(attWA)
        attEXSet = set(attEX)

        onlyWA = attWASet - attEXSet
        onlyEX = attEXSet - attWASet

        if len(onlyWA):
            showData(onlyWA, "WA", "EX")
        else:
            if not silent:
                console("\tWA: All extra annotations derive from the extra data")
        if len(onlyEX):
            showData(onlyEX, "EX", "WA")
        else:
            if not silent:
                console("\tEX: All extra data ended up as annotations")

    if not good or not silent:
        console(
            f"{rep(good)} - whether the extra annotations agree", error=not good
        )

    return good
def testSetup(self)

Prepare the tests.

We read the WATM dataset and store the tokens in member testTokens and the annotations in the member testAnnotations, and the node mapping in the member nodeFromAid. We unpack targets if they contain structured information.

Expand source code Browse git
def testSetup(self):
    """Prepare the tests.

    We read the WATM dataset and store the tokens in member `testTokens`
    and the annotations in the member `testAnnotations`, and the node mapping
    in the member `nodeFromAid`.
    We unpack targets if they contain structured information.
    """

    # collect the files

    asTsv = self.asTsv
    resultDir = self.resultDir
    resultFiles = dirContents(resultDir)[0]

    ext = "tsv" if asTsv else "json"

    def fileSort(name):
        middle = name.split(".", 1)[0].split("-", 1)[1]
        return f"{middle:0>10}" if middle.isdecimal else middle

    textFiles = sorted(
        (f for f in resultFiles if f.startswith("text-") and f.endswith(f".{ext}")),
        key=fileSort,
    )
    annoFiles = sorted(
        (f for f in resultFiles if f.startswith("anno-") and f.endswith(f".{ext}")),
        key=fileSort,
    )
    mapFiles = [
        f
        for f in resultFiles
        if (f.startswith("anno") or f.startswith("pos")) and f.endswith("2node.tsv")
    ]

    if NODEMAP_FILE not in mapFiles:
        console(f"ERROR: Missing map file {NODEMAP_FILE}")
        self.error = True
    if SLOTMAP_FILE not in mapFiles:
        console(f"ERROR: Missing map file {SLOTMAP_FILE}")
        self.error = True

    if self.error:
        return

    # read the text files

    skipMeta = self.skipMeta
    is_metav = self.is_metav

    waSlotTF = {}
    tokenFiles = []

    slot = 1

    for tfl, textFile in enumerate(textFiles):
        with open(f"{resultDir}/{textFile}") as fh:
            if asTsv:
                next(fh)
                tokens = [t.rstrip("\n").replace("\\t", "\t").replace("\\n", "\n") for t in fh]
            else:
                text = json.load(fh)
                tokens = text["_ordered_segments"]

            tokenFiles.append(tokens)

            for offset in range(len(tokens)):
                while skipMeta and is_metav(slot):
                    slot += 1

                waSlotTF[slot] = (tfl, offset)
                slot += 1

    self.testTokens = tokenFiles
    self.waSlotTF = waSlotTF

    # read the anno files

    annotations = []

    for annoFile in annoFiles:
        with open(f"{resultDir}/{annoFile}") as fh:
            if asTsv:
                next(fh)
                annos = {}
                for line in fh:
                    (aId, kind, ns, body, target) = line.rstrip("\n").split("\t")
                    body = body.replace("\\t", "\t").replace("\\n", "\n")
                    annos[aId] = (kind, ns, body, target)
            else:
                annos = json.load(fh)

            for aId, (kind, ns, body, target) in annos.items():
                if "->" in target:
                    parts = target.split("->", 1)
                else:
                    parts = [target]

                newParts = []

                for part in parts:
                    if ":" in part:
                        boundaries = part.split("-", 1)
                        fb, b = boundaries[0].split(":", 1)
                        fb = int(fb)
                        b = int(b)

                        if len(boundaries) == 1:
                            if kind == KIND_ELEM or kind == KIND_PI:
                                part = (int(fb), int(b), int(fb), int(b) + 1)
                            else:
                                part = (int(fb), int(b))
                        else:
                            eParts = boundaries[1].split(":", 1)

                            if len(eParts) == 1:
                                fe, e = fb, int(eParts[0])
                            else:
                                fe, e = eParts
                                fe = int(fe)
                                e = int(e)
                            part = (fb, b, fe, e)

                    newParts.append(part)

                target = newParts[0] if len(newParts) == 1 else tuple(newParts)

                annotations.append((aId, kind, body, target))

    annotations = sorted(annotations)
    self.testAnnotations = annotations

    # read the map files

    nodeFromAid = {}

    with open(f"{resultDir}/{SLOTMAP_FILE}") as fh:
        next(fh)
        for line in fh:
            (pos, slot) = line.rstrip("\n").split("\t")
            key = tuple(int(p) for p in pos.split(":"))
            nodeFromAid[key] = int(slot)

    with open(f"{resultDir}/{NODEMAP_FILE}") as fh:
        next(fh)
        for line in fh:
            (aId, node) = line.rstrip("\n").split("\t")
            nodeFromAid[aId] = int(node)

    self.nodeFromAid = nodeFromAid
    self.testNodes = set(nodeFromAid.values())
def testText(self)

Test the text.

We test the number of tokens and the equality of the resulting text: whether the TF and WATM datasets agree on it.

Returns

boolean
Whether all these tests succeed.
Expand source code Browse git
def testText(self):
    """Test the text.

    We test the number of tokens and the equality of the resulting text:
    whether the TF and WATM datasets agree on it.

    Returns
    -------
    boolean
        Whether all these tests succeed.
    """
    maxSlotPlus = self.maxSlotPlus
    tokenFiles = self.testTokens
    texts = self.texts
    waSlotTF = self.waSlotTF
    silent = self.silent

    if not silent:
        console("Testing the text ...")

    nTokensTF = sum(1 if s in waSlotTF else 0 for s in range(1, maxSlotPlus))
    nTokensWA = sum(len(tokens) for tokens in tokenFiles)
    nGood = self.numEqual(nTokensTF, nTokensWA, silent)

    if not nGood or not silent:
        console(
            f"{rep(nGood)} - whether the amounts of tokens agree", error=not nGood
        )

    textWA = "".join("".join(tokens) for tokens in tokenFiles)
    textTF = "".join("".join(text) for text in texts)

    tGood = self.strEqual(textTF, textWA, silent)

    if not tGood or not silent:
        console(f"{rep(tGood)} - whether the text is the same", error=not tGood)

    return nGood and tGood
def writeAll(self)

Write text and annotation data to disk.

The data will be written as JSON files, or, is asTsv is in force, as TSV files. When the annotation data grows larger than a certain threshold, it will be divided over several files.

The annotations are sorted by annotation id.

Expand source code Browse git
def writeAll(self):
    """Write text and annotation data to disk.

    The data will be written as JSON files, or, is `asTsv` is in force, as TSV
    files.
    When the annotation data grows larger than a certain threshold, it will be
    divided over several files.

    The annotations are sorted by annotation id.
    """

    maxNodePlus = self.maxNodePlus
    maxSlotPlus = self.maxSlotPlus

    # text files

    error = self.error
    silent = self.silent

    if error:
        if not silent:
            console("Cannot run because of an earlier error")
        return

    app = self.app
    texts = self.texts
    annos = self.annos
    waFromTF = self.waFromTF
    asTsv = self.asTsv

    baseDir = self.repoLocation
    relative = app.context.relative
    version = app.version
    wRelative = REL_RE.sub(f"/{TT_NAME}/{version}/", relative, count=1)
    resultDir = f"{baseDir}{wRelative}"
    self.resultDir = resultDir

    initTree(resultDir, fresh=True)

    total = 0

    ext = "tsv" if asTsv else "json"

    for i, text in enumerate(texts):
        textFile = f"{resultDir}/text-{i}.{ext}"
        nText = len(text)
        total += nText

        with open(textFile, "w") as fh:
            if asTsv:
                fh.write("token\n")
                for t in text:
                    fh.write(t.replace("\t", "\\t").replace("\n", "\\n") + "\n")
            else:
                json.dump(
                    dict(_ordered_segments=text), fh, ensure_ascii=False, indent=1
                )

        if not silent:
            console(f"Text file {i:>4}: {nText:>8} segments to {textFile}")

    nTexts = len(texts)
    sep = "" if nTexts == 1 else "s"

    if not silent:
        console(f"Text files all: {total:>8} segments to {nTexts} file{sep}")

    # annotation files

    annoStore = {}

    for kind, aId, ns, body, target in annos:
        annoStore[aId] = (kind, ns, body, target)

    aIdSorted = sorted(annoStore.keys())

    thisAnnoStore = {}
    thisA = 1
    nAnnoFiles = 0

    LIMIT = 400000
    j = 0
    total = 0

    def writeThis():
        annoFile = f"{resultDir}/anno-{thisA:>01}.{ext}"

        with open(annoFile, "w") as fh:
            if asTsv:
                fh.write("annoid\tkind\tnamespace\tbody\ttarget\n")
                for aId, (kind, namespace, body, target) in thisAnnoStore.items():
                    body = body.replace("\t", "\\t").replace("\n", "\\n")
                    fh.write(f"{aId}\t{kind}\t{namespace}\t{body}\t{target}\n")
            else:
                json.dump(thisAnnoStore, fh, ensure_ascii=False, indent=1)

        if not silent:
            console(
                f"Anno file {thisA:>4}: {j:>8} annotations written to {annoFile}"
            )

    for aId in aIdSorted:
        if j >= LIMIT:
            writeThis()
            nAnnoFiles += 1
            thisA += 1
            thisAnnoStore = {}
            total += j
            j = 0

        thisAnnoStore[aId] = annoStore[aId]
        j += 1

    if len(thisAnnoStore):
        writeThis()
        nAnnoFiles += 1
        total += j

    if len(annoStore) != total:
        console(f"Sum of batches : {total:>8}", error=True)
        console(f"All annotations: {len(annoStore):>8}", error=True)
        console("Mismatch in number of annotations", error=True)

    sep = "" if nAnnoFiles == 1 else "s"

    if not silent:
        console(f"Anno files all: {total:>8} annotations to {nAnnoFiles} file{sep}")

    # node mapping files

    slotmapFile = f"{resultDir}/{SLOTMAP_FILE}"
    nodemapFile = f"{resultDir}/{NODEMAP_FILE}"

    with open(slotmapFile, "w") as fh:
        fh.write("position\tnode\n")
        for n in range(1, maxSlotPlus):
            (file, pos) = waFromTF[n]
            fh.write(f"{file}:{pos}\t{n}\n")

    with open(nodemapFile, "w") as fh:
        fh.write("annotation\tnode\n")
        for n in range(maxSlotPlus, maxNodePlus):
            aId = waFromTF.get(n, None)
            if aId is not None:
                fh.write(f"{aId}\t{n}\n")

    if not silent:
        console(f"Slot mapping written to {slotmapFile}")
        console(f"Node mapping written to {nodemapFile}")
class WATMS (org, repo, backend, nsOrig, skipMeta=False, extra={}, silent=False)

Export corpora that are divided over multiple TF datasets.

We set up and run WATM objects for each TF dataset, and generate results for them separately.

We assume that all corpora have been generated by the same method and originate from the same original format.

They must reside in the same repository, in adjacent directories under the tf top-level directory of the repo.

Collect the parameters for the WATM machinery.

We will initialize many WATM objects with mostly the same parameters. These are collected when we initialize this object.

Parameters

org : string
The organization of all TF datasets.
repo : string
The repo of all TF datasets.
backend : string
The backend of all TF datasets.
nsOrig : string
The original namespace of all TF datasets. See WATM.
skipMeta : boolean, optional False
See WATM.
extra : dictionary, optional {}
See WATM.
silent : boolean, optional False
Whether to operate in silence.
Expand source code Browse git
class WATMS:
    """Export corpora that are divided over multiple TF datasets.

    We set up and run WATM objects for each TF dataset, and generate results
    for them separately.

    We assume that all corpora have been generated by the same method and originate
    from the same original format.

    They must reside in the same repository, in adjacent directories under the `tf`
    top-level directory of the repo.
    """

    def __init__(
        self, org, repo, backend, nsOrig, skipMeta=False, extra={}, silent=False
    ):
        """Collect the parameters for the WATM machinery.

        We will initialize many `WATM` objects with mostly the same parameters.
        These are collected when we initialize this object.

        Parameters
        ----------
        org: string
            The organization of all TF datasets.
        repo: string
            The repo of all TF datasets.
        backend: string
            The backend of all TF datasets.
        nsOrig: string
            The original namespace of all TF datasets.
            See `tf.convert.watm.WATM`.
        skipMeta: boolean, optional False
            See `tf.convert.watm.WATM`.
        extra: dictionary, optional {}
            See `tf.convert.watm.WATM`.
        silent: boolean, optional False
            Whether to operate in silence.
        """
        self.org = org
        self.repo = repo
        self.backend = backend
        self.nsOrig = nsOrig
        self.skipMeta = skipMeta
        self.extra = extra
        self.silent = silent

        repoDir = ex(f"~/{backend}/{org}/{repo}")
        tfDir = f"{repoDir}/tf"
        docs = dirContents(tfDir)[1]
        if not silent:
            console(f"Found {len(docs)} docs in {tfDir}")
        self.docs = docs

    def produce(self, doc=None):
        """Convert all relevant TF datasets.

        Parameters
        ----------
        doc: string, optional None
            Subdirectory where one of the TF datasets resides.
            If passed, only this dataset will be converted.
            Otherwise all datasets will be converted.
        """
        org = self.org
        repo = self.repo
        backend = self.backend
        nsOrig = self.nsOrig
        skipMeta = self.skipMeta
        extra = self.extra
        docs = self.docs
        silent = self.silent

        chosenDoc = doc

        good = True

        for doc in sorted(docs, key=lambda x: (x[0], int(x[1:]))):
            if chosenDoc is not None and chosenDoc != doc:
                continue

            if not silent:
                console(f"{doc:>5} ... ", newline=False)
            A = use(
                f"{org}/{repo}:clone",
                relative=f"tf/{doc}",
                checkout="clone",
                backend=backend,
                silent=DEEP,
            )
            WA = WATM(A, nsOrig, skipMeta=skipMeta, extra=extra, silent=silent)
            WA.makeText()
            WA.makeAnno()
            WA.writeAll()
            WA.testAll(condensed=True)

            if WA.error:
                good = False

        console(f"WATM generation: {rep(good)}", error=not good)

Methods

def produce(self, doc=None)

Convert all relevant TF datasets.

Parameters

doc : string, optional None
Subdirectory where one of the TF datasets resides. If passed, only this dataset will be converted. Otherwise all datasets will be converted.
Expand source code Browse git
def produce(self, doc=None):
    """Convert all relevant TF datasets.

    Parameters
    ----------
    doc: string, optional None
        Subdirectory where one of the TF datasets resides.
        If passed, only this dataset will be converted.
        Otherwise all datasets will be converted.
    """
    org = self.org
    repo = self.repo
    backend = self.backend
    nsOrig = self.nsOrig
    skipMeta = self.skipMeta
    extra = self.extra
    docs = self.docs
    silent = self.silent

    chosenDoc = doc

    good = True

    for doc in sorted(docs, key=lambda x: (x[0], int(x[1:]))):
        if chosenDoc is not None and chosenDoc != doc:
            continue

        if not silent:
            console(f"{doc:>5} ... ", newline=False)
        A = use(
            f"{org}/{repo}:clone",
            relative=f"tf/{doc}",
            checkout="clone",
            backend=backend,
            silent=DEEP,
        )
        WA = WATM(A, nsOrig, skipMeta=skipMeta, extra=extra, silent=silent)
        WA.makeText()
        WA.makeAnno()
        WA.writeAll()
        WA.testAll(condensed=True)

        if WA.error:
            good = False

    console(f"WATM generation: {rep(good)}", error=not good)