Dataset profiler

This repository contains a docker image for generating statistical dataset reportsin HTML and JSON using standardized input and outputs. This is based on the template for a generic containerized Python tool.

How standardized?

Tools using this template can be run by the toolbox-runner. That is only convenience, the tools implemented using this template are independent of any framework.

The main idea is to implement a common file structure inside container to load inputs and outputs of the tool. The template shares this structures with the R template and [Octave template](https://github.com/vforwater(tool_template_octave), but can be mimiced in any container.

Each container needs at least the following structure:

/
|- in/
|  |- parameters.json
|- out/
|  |- ...
|- src/
|  |- tool.yml
|  |- run.py

• parameters.json are parameters. Whichever framework runs the container, this is how parameters are passed.
• tool.yml is the tool specification. It contains metadata about the scope of the tool, the number of endpoints (functions) and their parameters
• run.py is the tool itself, or a Python script that handles the execution. It has to capture all outputs and either print them to console or create files in /out

How to build the image?

You can build the image from within the root of this repo by

docker build -t tbr_profile .

Use any tag you like. If you want to run and manage the container with toolbox-runner they should be prefixed by tbr_ to be recognized.

Alternatively, the contained .github/workflows/docker-image.yml will build the image for you on new releases on Github. You need to change the target repository in the aforementioned yaml and the repository needs a personal access token in the repository secrets in order to run properly.

How to run?

This template installs the json2args python package to parse the parameters in the /in/parameters.json. This assumes that the files are not renamed and not moved and there is actually only one tool in the container. For any other case, the environment variables PARAM_FILE can be used to specify a new location for the parameters.json and TOOL_RUN can be used to specify the tool to be executed. The run.py has to take care of that.

To invoke the docker container directly run something similar to:

docker run --rm -it -v /path/to/local/in:/in -v /path/to/local/out:/out -e TOOL_RUN=profile tbr_profile

Then, the output will be in your local out and based on your local input folder. Stdout and Stderr are also connected to the host.

With the toolbox runner, this is simplyfied:

import pandas as pd
from toolbox_runner import list_tools
tools = list_tools() # dict with tool names as keys

prof= tools.get('profile') 

# load any pdatas Dataframe
df = pd.read_csv('mydata.csv')
res = foobar.run(result_path='./', data=df)

# now, there is a tar.gz with all outputs
print(res)  # giving you the path

# but you can also export the JSON report
import json
json.loads(res.get_file('./out/report.json'),decode())

The example above will create a temporary file structure to be mounted into the container and then create a .tar.gz on termination of all inputs, outputs, specifications and some metadata, including the image sha256 used to create the output in the current working directory.

What's included

Currently, only variogram estimation using SciKit-GStat and kriging using GSTools is implemented. Simulations and parameter grid search are on the agenda.

You can learn about the available tools and their parameters directly from the container:

docker run --rm -it tbr_profile bash cat /srv/tool.yml

or using toolbox runner

from toolbox_runner import list_tools
tools = list_tools() # dict with tool names as keys

prof = tools.get('profile')
prof.title
prof.description
prof.parameters