# nzilbb-labbcat
Client library for communicating with [LaBB-CAT](https://labbcat.canterbury.ac.nz/)
servers using Python.
e.g.
```python
import labbcat
# Connect to the LaBB-CAT corpus
corpus = labbcat.LabbcatView("https://labbcat.canterbury.ac.nz/demo", "demo", "demo")
# Find all tokens of a word
matches = corpus.getMatches({"orthography":"quake"})
# Get the recording of that utterance
audio = corpus.getSoundFragments(matches)
# Get Praat TextGrids for the utterances
textgrids = corpus.getFragments(
matches, ["utterance", "word","segment"],
"text/praat-textgrid")
```
LaBB-CAT is a web-based linguistic annotation store that stores audio or video
recordings, text transcripts, and other annotations.
Annotations of various types can be automatically generated or manually added.
LaBB-CAT servers are usually password-protected linguistic corpora, and can be
accessed manually via a web browser, or programmatically using a client library like
this one.
The current version of this library requires LaBB-CAT version 20220307.1126.
## Documentation
Detailed documentation is available [here](https://nzilbb.github.io/labbcat-py/)
# Basic usage
*nzilbb-labbcat* is available in the Python Package Index
[here](https://pypi.org/project/nzilbb-labbcat/)
To install the module:
```
pip install nzilbb-labbcat
```
The following example shows how to:
1. upload a transcript to LaBB-CAT,
2. wait for the automatic annotation tasks to finish,
3. extract the annotation labels, and
4. delete the transcript from LaBB-CAT.
```python
import labbcat
# Connect to the LaBB-CAT corpus
corpus = labbcat.LabbcatEdit("http://localhost:8080/labbcat", "labbcat", "labbcat")
# List the corpora on the server
corpora = corpus.getCorpusIds()
# List the transcript types
transcript_type_layer = corpus.getLayer("transcript_type")
transcript_types = transcript_type_layer["validLabels"]
# Upload a transcript
corpus_id = corpora[0]
transcript_type = next(iter(transcript_types))
taskId = corpus.newTranscript(
"test/labbcat-py.test.txt", None, None, transcript_type, corpus_id, "test")
# wait for the annotation generation to finish
corpus.waitForTask(taskId)
corpus.releaseTask(taskId)
# get the "POS" layer annotations
annotations = corpus.getAnnotations("labbcat-py.test.txt", "pos")
labels = list(map(lambda annotation: annotation["label"], annotations))
# delete tha transcript from the corpus
corpus.deleteTranscript("labbcat-py.test.txt")
```
For batch uploading and other example code, see the *examples* subdirectory.
# Developers
To build, test, release, and document the module, the following prerequisites are required:
- `pip3 install twine`
- `pip3 install pathlib`
- `apt install python3-sphinx`
## Unit tests
```
python3 -m unittest
```
...or for specific tests:
```
python3 -m unittest test.TestLabbcatAdmin
```
## Documentation generation
```
cd docs
make clean
make
```
## Publishing
```
rm dist/*
python3 setup.py sdist bdist_wheel
twine check dist/*
twine upload dist/*
```
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