semvec/pypi-packages huggingface dataset:
moduledocs 1.0.1
Static documentation extraction tool for python language
Requires Python: >=3.5.*
Keywords: generate documentation markdown html static parser

# moduledocs

[![Build Status](https://travis-ci.org/machineandme/moduledocs.svg?branch=master)](https://travis-ci.org/machineandme/moduledocs)

Static documentation extraction tool for python language.

Supports markdown and html.
antlr-plsql 0.9.1
A procedural sql parser, written in Antlr4

# antlr-plsql

[![Build Status](https://travis-ci.org/datacamp/antlr-plsql.svg?branch=master)](https://travis-ci.org/datacamp/antlr-plsql)
[![PyPI version](https://badge.fury.io/py/antlr-plsql.svg)](https://badge.fury.io/py/antlr-plsql)

## Development

ANTLR requires Java, so we suggest you use Docker when building grammars. The `Makefile` contains directives to clean, build, test and deploy the ANTLR grammar. It does not run Docker itself, so run `make` inside Docker.

### Build the grammar

```bash
# Build the docker container
docker build -t antlr_plsql .

# Run the container to build the python grammar
# Write parser files to local file system through volume mounting
docker run -it -v ${PWD}:/usr/src/app antlr_plsql make build
```

### Set up the Python module

Now that the Python parsing files are available, you can install them with `pip`:

```bash
pip install -r requirements.txt
pip install -e .
```

And parse SQL code in Python:

```python
from antlr_plsql import ast
ast.parse("SELECT a from b")
```

### Using the AST viewer

If you're actively developing on the ANLTR grammar or the tree shaping, it's a good idea to set up the [AST viewer](https://github.com/datacamp/ast-viewer) locally so you can immediately see the impact of your changes in a visual way.

- Clone the ast-viewer repo and build the Docker image according to the instructions.
- Spin up a docker container that volume mounts the Python package, symlink-installs the package and runs the server on port 3000:

```bash
docker run -it \
  -u root \
  -v ~/workspace/antlr-plsql:/app/app/antlr-plsql \
  -p 3000:3000 \
  ast-viewer \
  /bin/bash -c "echo 'Install development requirements in development:' \
    && pip install --no-deps -e app/antlr-plsql \
    && python3 run.py"
```

When simultaneously developing other packages, volume mount and install those too:

```bash
docker run -it \
  -u root \
  -v ~/workspace/antlr-ast:/app/app/antlr-ast \
  -v ~/workspace/antlr-plsql:/app/app/antlr-plsql \
  -v ~/workspace/antlr-tsql:/app/app/antlr-tsql \
  -p 3000:3000 \
  ast-viewer \
  /bin/bash -c "echo 'Install development requirements in development:' \
    && pip install --no-deps -e app/antlr-ast \
    && pip install --no-deps -e app/antlr-plsql \
    && pip install --no-deps -e app/antlr-tsql \
    && python3 run.py"
```

- If you update the tree shaping logic in this repo, the app will auto-update.
- If you change the grammar, you will have to first rebuild the grammar (with the `antlr_plsql` docker image) and restart the `ast-viewer` container.

### Run tests

```bash
# Similar to building the grammar, but running tests
# and not saving the generated files
docker build -t antlr_plsql .
docker run -t antlr_plsql make build test
```

Or run the test locally, first build the grammar then run:

```python
pytest
```

## Travis deployment

- Builds the Docker image.
- Runs the Docker image to build the grammar and run the unit tests.
- Deploys the resulting python files to PyPi when a new release is made, so they can be installed easily.
dwhtools 0.0.2.1009
Dies ist eine kurze Beschreibung

# Entwicklung und testen
Test erfolgt Ã¼ber den Upload nach Pypi  
In Databricks kann dann mit folgendem Code getestet werden:  
<br>
import pkg_resources  
dbutils.library.installPyPI("dwhtools")  
dbutils.library.restartPython()  
pkg_resources.get_distribution("dwhtools").version