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d2s-0.3.3
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مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
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مشاهده بیشترتوضیحات
A Command Line Interface to orchestrate the integration of heterogenous data and the deployment of services consuming the integrated data. See https://d2s.semanticscience.org
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | d2s-0.3.3 |
| نام | d2s |
| نسخه کتابخانه | 0.3.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Vincent Emonet |
| ایمیل نویسنده | vincent.emonet@gmail.com |
| آدرس صفحه اصلی | https://github.com/MaastrichtU-IDS/d2s-cli |
| آدرس اینترنتی | https://pypi.org/project/d2s/ |
| مجوز | MIT License |
[](https://pypi.org/project/d2s) [](https://pypi.org/project/d2s)
[](https://github.com/MaastrichtU-IDS/d2s-cli/actions/workflows/test.yml) [](https://github.com/MaastrichtU-IDS/d2s-cli/actions/workflows/publish.yml)
A Command Line Interface to help orchestrate the integration of heterogenous data sources under a common [RDF Knowledge Graph](https://www.w3.org/RDF/) using Python, RML mappings, Bash, and GitHub Actions workflows (YAML).
You can find more informations about the Data2Services project on the [d2s documentation website 📖](https://d2s.semanticscience.org/docs/d2s-installation)
## Installation
Requirements:
* [Python 3.7+](https://d2s.semanticscience.org/docs/d2s-installation#install-pip)
* [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
* Optional: Java 11+ to use `d2s sparql upload`
* Optional: [`oc` command line tool](https://maastrichtu-ids.github.io/dsri-documentation/docs/openshift-install) for deploying to the [DSRI OpenShift cluster](https://maastrichtu-ids.github.io/dsri-documentation/) (for Maastricht University academics and students)
### Install from pypi
```bash
pip install d2s
```
> Use [pip](https://pypi.org/project/pip/), pip3 or [pipx](https://pipxproject.github.io/pipx/) depending on your preferences.
### Update
```bash
pip install --upgrade d2s
```
### Install from GitHub branch
You can also install it from the `master` branch, if you want the latest updates:
```bash
pip install git+https://github.com/MaastrichtU-IDS/d2s-cli.git@master
```
### Uninstall
```bash
pip uninstall d2s
```
## Use d2s
Display the default help command
```bash
d2s
```
### Generate metadata
Analyze a SPARQL endpoint metadata to generate [HCLS descriptive metadata](https://www.w3.org/TR/hcls-dataset/) for each graph:
```bash
d2s metadata analyze https://graphdb.dumontierlab.com/repositories/umids-kg -o metadata.ttl
```
Analyze a SPARQL endpoint metadata to generate metadata specific to Bio2RDF for each graph:
```bash
d2s metadata analyze https://bio2rdf.137.120.31.102.nip.io/sparql -o metadata.ttl -m bio2rdf
```
You can also generate detailed HCLS metadata for the dataset version and distribution by answering the questions after running this command:
```bash
d2s metadata create -o metadata.ttl
```
### Bootstrap a datasets conversion project
`d2s` can be used to help you converting datasets to RDF.
You will need to initialize the current folder, it is highly recommended to do this at the root of a Git repository where the conversion will be stored:
```bash
d2s init
```
This command will create a `datasets` folder to store the datasets conversions and a `.github/workflows` folder for the workflows, if it does not exist already.
> All `d2s` commands are designed to be run from the project folder
You can create a new dataset conversion:
```bash
d2s new dataset
```
You will be asked a few questions about the dataset via the terminal, then a folder will be generated with:
* Your dataset metadata
* Example YARRRML and RML mappings
* Example python preprocessing script
* Example bash script to download the data to convert
* A GitHub Action workflow to run the different steps of the processing
You can now edit the file generated in the `datasets` folder to implement your data conversion.
### Run the RML mapper
Requirements: Java installed
This feature is still experimental
`d2s` can be used to easily run the RML mapper:
```bash
d2s rml my-dataset
```
## Enable autocompletion
Enable commandline autocompletion in the terminal
> Recommended, it makes `d2s` much more user-friendly
* **ZSH**: add the import autocomplete line to the `~/.zshrc` file.
```bash
echo 'eval "$(_D2S_COMPLETE=source_zsh d2s)"' >> ~/.zshrc
```
> Set your terminal to use [ZSH](https://github.com/ohmyzsh/ohmyzsh/wiki/Installing-ZSH) by default:
>
> ```shell
> chsh -s /bin/zsh
> ```
> A [oh-my-zsh](https://ohmyz.sh/) theme can be easily chosen for a personalized experience. See [the zsh-theme-biradate](https://github.com/vemonet/zsh-theme-biradate) to easily install a simple theme and configure your terminal in a few minutes.
* **Bash**: add the import autocomplete line to the `~/.bashrc` file. Something like this probably:
```bash
echo 'eval "$(_D2S_COMPLETE=source d2s)"' >> ~/.bashrc
```
## Build and publish
### Install d2s for development
Install `d2s` as executable to run it from the terminal
Clone the repository:
```bash
git clone https://github.com/MaastrichtU-IDS/d2s-cli.git
cd d2s-cli
```
Install `d2s`:
```bash
pip install -e .
```
> `d2s` will be updated directly on change in the code.
#### Optional: isolate with a Virtual Environment
If you face conflicts with already installed packages, then you might want to use a [Virtual Environment](https://docs.python.org/3/tutorial/venv.html) to isolate the installation in the current folder before installing `d2s`:
```bash
# Create the virtual environment folder in your workspace
python3 -m venv .venv
# Activate it using a script in the created folder
source .venv/bin/activate
```
### Publish using Docker
To publish a new version on [pypi](https://pypi.org/project/d2s/):
* upgrade the version in [setup.py](https://github.com/MaastrichtU-IDS/d2s-cli/blob/master/setup.py#L6) (e.g. from `0.2.1` to `0.2.2`)
* use the following script to build and publish automatically using [Docker](https://docs.docker.com/install/):
```bash
./publish_pip.sh
```
> A test will be run using Docker before publishing to make sure `d2s init` works.
### Build locally
Building and publishing can be done locally:
```bash
# Build packages in dist/ folder
python3 setup.py sdist bdist_wheel
# Publish packages previously built in the dist/ folder
twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
```
Additional instructions to install twine locally (not needed)
```bash
pip install twine
```
> If you experience issues with Bash or ZSH because `d2s` is not defined when installing for dev. Then add `pip install --editable develop/d2s-cli` to `.zshrc`
You might need to install Python3.7 for dev (dev with python3.7 should work though)
```bash
sudo apt-get install python3.7 python3.7-venv python3.7-dev
# Set python3 to use 3.7
sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.7 1
sudo update-alternatives --config python3
```
> ```bash
>vim /usr/bin/gnome-terminal
>
> #!/usr/bin/python3.7
> ```
If you face issue uploading the package on pypi:
```bash
twine check dist/d2s-*-py3-none-any.whl
```
نیازمندی
| مقدار | نام |
|---|---|
| <8.0.0,>=7.0.0 | Click |
| >=6.1.1 | rdflib |
| - | SPARQLWrapper |
| - | python-dotenv |
| - | GitPython |
| - | requests |
| - | PyYAML |
| - | python-dateutil |
| - | pandas |
نحوه نصب
نصب پکیج whl d2s-0.3.3:
pip install d2s-0.3.3.whl
نصب پکیج tar.gz d2s-0.3.3:
pip install d2s-0.3.3.tar.gz