science-data-structure 0.0.4

Structure for large data-sets in science

Science data structure

This library makes it straight forward to make a tree folder structure for large data-sets. For now it supports numpy arrays only, but I have plans to implement pandas, csv, tab-separated and excel soon.

The idea behind the library is to make a data-set browse-able with a normal file browser. The components can be rearranged with the use of Python, the terminal or a simple file-browser.

Install

Install through pip

pip install science-data-structure

Manual installation

python setup.py install

Command line tools

This library is bundled with command line tools to create a system wide author

science_data_structure global create author "<name>"

or

science_data_structure global create author

and you will be prompted for the name of the author. You only have to run the above commands a single time, the data is stored in a configuration file (the location is dependent of your OS). From the command line you can create a dataset:

science_data_structure create dataset "<name>" "<description>"

The author you have created for you system is added to this dataset. Go into the folder of the dataset and execute:

science_data_structure list author

to view all the authors in this dataset. Alternatively you can list the entire meta file

science_data_structure list meta

Examples

Simple data-set

In this simple example a data-set is created, with a single branch parabola. In this branch two "leafs" are added x and y. At the end of the example the data_set is written to disk.

Before we can create a dataset we need to create a meta file containing an author, you can do this with the earlier mentioned command line example above.

import science_data_structure.structures as structures
from pathlib import Path
import numpy

# initialize the empty data-set
dataset = structures.StructuredDataSet.create_dataset(Path("./."),
                                                      "test_set")


# add data to the data-set
data_set["parabola"]["x"] = numpy.linspace(-2, 2, 100)
data_set["parabola"]["y"] = data_set["parabola"]["x"].data ** 2

# write the data to disk
data_set.write()

Branch overriding

What will happen when a branch or a leaf is overwritten with another leaf or branch? This example extends the previous example

data_set["parabola"]["x"] = None

In this case the variable x stored in the branch parabola will be deleted upon the first write.