<commit_before>## QBer
**Author:**	<NAME>  
**Copyright:**	<NAME>, VU University Amsterdam  
**License:**	MIT License (see [license.txt](license.txt))  

QBer is a tool for automatically converting CSV or SAV files that contain statistical data (currently tailored for census data) to the [RDF Data Cube vocabulary of the W3C](http://www.w3.org/TR/vocab-data-cube/).

![Screenshot of QBer](screenshot.png)

### Installation

First, open a terminal or console and clone the QBer git repository to a directory of your choice:

`git clone https://github.com/CLARIAH-SDH/qber.git`  
(you can also download the source from Github)

Change directory to directory and then (to keep things nice and tidy) use [virtualenv](https://virtualenv.pypa.io/en/latest/installation.html) to create a virtual environment, and activate it:

`virtualenv .`

and

`source bin/activate` (on unix/linux-style systems)

Then install the required Python packages using [pip](https://pip.readthedocs.org):

`pip install -r requirements.txt`

Change directory to `src` and then:

`python run.py`

the QBer application will be running on <http://localhost:5000> (click this link, or copy it to your browser).

Make sure to always activate the virtualenv before running QBer again.<commit_msg>Added instruction for config_template.py and config.py<commit_after>## QBer
**Author:**	<NAME>  
**Copyright:**	<NAME>, VU University Amsterdam  
**License:**	MIT License (see [license.txt](license.txt))  

QBer is a tool for automatically converting CSV or SAV files that contain statistical data (currently tailored for census data) to the [RDF Data Cube vocabulary of the W3C](http://www.w3.org/TR/vocab-data-cube/).

![Screenshot of QBer](screenshot.png)

### Installation

First, open a terminal or console and clone the QBer git repository to a directory of your choice:

`git clone https://github.com/CLARIAH-SDH/qber.git`  
(you can also download the source from Github)

Change directory to directory and then (to keep things nice and tidy) use [virtualenv](https://virtualenv.pypa.io/en/latest/installation.html) to create a virtual environment, and activate it:

`virtualenv .`

and

`source bin/activate` (on unix/linux-style systems)

Then install the required Python packages using [pip](https://pip.readthedocs.org):

`pip install -r requirements.txt`

Copy the `config_template.py` file to `config.py` and make necessary changes (see documentation in the file). 
These settings are needed for posting the QBer generated RDF descriptions of your dataset to a triple store (we use Stardog).

Change directory to `src` and then:

`python run.py`

the QBer application will be running on <http://localhost:5000> (click this link, or copy it to your browser).

Make sure to always activate the virtualenv before running QBer again.
{'fraction_non_alphanumeric': 0.09839572192513368, 'fraction_numerical': 0.0042780748663101605, 'mean_word_length': 4.726530612244898, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 6, 'lorem ipsum': 0, 'www.': 2, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '5464846', 'n_tokens_mistral': 841, 'n_tokens_neox': 812, 'n_words': 346}