1. imports all data from a given directory into OpenRefine
2. transforms the data by applying OpenRefine transformation rules from all json files in another given directory and
3. finally exports the data in TSV (tab-separated values) format.
It orchestrates a [docker container for OpenRefine](https://hub.docker.com/r/felixlohmeier/openrefine/) (server) and a [docker container for a python client](https://hub.docker.com/r/felixlohmeier/openrefine-client/) that communicates with the OpenRefine API. By restarting the server after each process it reduces memory requirements to a minimum.
- **Step 1**: Do some experiments with your data (or parts of it) in the graphical user interface of OpenRefine. If you are fine with all transformation rules, [extract the json code](http://kb.refinepro.com/2012/06/google-refine-json-and-my-notepad-or.html) and save it as file (e.g. transform.json).
- **Step 2**: Put your data and the json file(s) in two different directories and execute the script. The script will automatically import all data files in OpenRefine projects, apply the transformation rules in the json files to each project and export all projects in TSV-files.
1. Install [Docker](https://docs.docker.com/engine/installation/#on-linux) and **a)** [configure Docker to start on boot](https://docs.docker.com/engine/installation/linux/linux-postinstall/#configure-docker-to-start-on-boot) or **b)** start Docker on demand each time you use the script: `sudo systemctl start docker`
2. Download the script and grant file permissions to execute: `wget https://github.com/felixlohmeier/openrefine-batch/raw/master/openrefine-batch.sh && chmod +x openrefine-batch.sh`
Why `sudo`? Non-root users can only access the Unix socket of the Docker daemon by using `sudo`. If you created a Docker group in [Post-installation steps for Linux](https://docs.docker.com/engine/installation/linux/linux-postinstall/) then you may call the script without `sudo`.
* any data that [OpenRefine supports](https://github.com/OpenRefine/OpenRefine/wiki/Importers). CSV, TSV and line-based files should work out of the box. XML, JSON, fixed-width, XSLX and ODS need one additional input parameter (see chapter [Options](https://github.com/felixlohmeier/openrefine-batch#options) below)
* path to directory where results and temporary data should be stored
* Transformed data will be stored in this directory in TSV (tab-separated values) format. Show results: `ls OUTPUT/*.tsv`
* OpenRefine stores data in directories like "1234567890123.project". You may have a look at the results by starting OpenRefine with this workspace. Delete the directories if you do not need them: `rm -r -f OUTPUT/*.project`
1. inputdir: path to directory with source files (multiple files may be imported into a single project [by providing a zip or tar.gz archive](https://github.com/OpenRefine/OpenRefine/wiki/Importers))
2. configdir: path to directory with [OpenRefine transformation rules (json files)](http://kb.refinepro.com/2012/06/google-refine-json-and-my-notepad-or.html)
3. outputdir: path to directory for exported files (and OpenRefine workspace)
4. crossdir: path to directory with additional OpenRefine projects (will be copied to workspace before transformation step to support the [cross function](https://github.com/OpenRefine/OpenRefine/wiki/GREL-Other-Functions#crosscell-c-string-projectname-string-columnname))
5. ram: maximum RAM for OpenRefine java heap space (default: 4G)
inputoptions (mandatory for xml, json, fixed-width, xslx, ods):
*`--recordPath=RECORDPATH` (xml, json): please provide path in multiple arguments without slashes, e.g. /collection/record/ should be entered like this: `--recordPath=collection --recordPath=record`
*`--columnWidths=COLUMNWIDTHS` (fixed-width): please provide widths separated by comma (e.g. 7,5)
*`--sheets=SHEETS` (xlsx, ods): please provide sheets separated by comma (e.g. 0,1), default: 0 (first sheet)
more inputoptions (optional, only together with inputformat):
