yprov4dv 1.2.1

yProv4DV (Data Visualization) is a python utility which allows for packaging of code, inputs and outputs of data visualization scripts. Once integrated, it will produce a zip file which includes all information necessary for reproducibility of the current script, including a copy of the files used.

yProv4DV

A python utility for automatically packaging code, inputs and outputs of data visualization scripts.
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yProv4DV

yProv4DV (Data Visualization) is a python utility which allows for packaging of code, inputs and outputs of data visualization scripts. Once integrated, it will produce a zip file which includes all information necessary for reproducibility of the current script, including a copy of the files used. This library is part of the yProv framework, which means it can also produce W3C-prov compliant files useful for interpretability and reproducibility.

Installation

pip install yprov4dv

Current Compatibility

Currently, the yProv4DV library is able to track input files which are opened by the following libraries:

• pandas (read_csv, read_parquet, read_excel, read_json)
• xarray (open_dataset, open_mfdataset)
• geopandas (read_file)
• numpy (load)
• torch (load)
• rasterio (open)
• As well as the standard python calls (such as open())

Additionally, if data is plotted using:

• matplotlib (plot, bar, ...)
• seaborn (scatterplot, lineplot, barplot, histplot, boxplot) Then the subset of data used only for visualization can be saved in an isolated file (by setting the save_input_files_subset option to True).

Any type of output files generated during the execution of the program will also be logged, indipendently of file type.

Example

Inside the examples folder is contained an example of a simple data visualization script in python. It is already integrated with the yProv4DV library, and can be run with the prompt:

python ./examples/simple.py

This execution will create:

• The prov directory (which is customizable) and will hold all the information for the current execution, so inputs, outputs and source code (src), all in their respective folders. Additionally, in the same directory, the library creates a set of provenance files, containing a description of the current execution (in .json, dot and svg formats).
• prov.zip: containining all the aforementioned information in a zipped RO-Crate.

Parameters

To keep the number of yprov4dv calls to a minimum, the library exposes just three directives:

• def start_run(*args)
• def log_input(path_to_untracked_file)
• def log_output(path_to_untracked_file)

The behaviour of yProv4DV can be changed passing parameters to the start_run function. All possible fields are listed below:

• provenance_directory: (str) changes where the inputs, outputs and code directory are stored;
• prefix: (str) changes the prefix given to fields in the provenance document;
• run_name: (str) changes the run name inside the provenance file;
• create_json_file: (True or False) whether the json file is created or not;
• create_dot_file: (True or False) whether the dot file is created or not, cannot be True if YPROV4DV_CREATE_JSON_FILE is False;
• create_svg_file: (True or False) whether the svg file is created or not, cannot be True if YPROV4DV_CREATE_JSON_FILE or YPROV4DV_CREATE_DOT_FILE are False;
• create_rocrate: (True or False) whether the ro-crate zip is created or not;
• default_namespace: (str) changes the default namespace inside the provenance file
• save_input_files_full: (str) decides whether input files are saved in full
• save_input_files_subset: (str) decides whether inputs are saved as a subset (only the plotted data)
• skip_files_larger_than: (int) In Mb, files larger than the threshold will not be copied;
• verbose: (True or False),

For an example, run:

python ./examples/customized.py