sinagot 0.2.0

Python library to batch scripts on a file-system workspace.

Sinagot

Sinagot is a Python library to batch multiple scripts on a file-system dataset with a simple API. Parallelization of data processing is made possible by Dask.distributed.

Installation

Sinagot is available on PyPi:

pip install sinagot

Full Documentation

https://sinagot.readthedocs.io

Concept

Sinagot main class is build around the sinagot.Workspace class. To create an instance, you must provide 3 pathes to :

• A configuration file in .toml format.
• A data folder.
• A scripts fodler.

Dataset is structured as a collection of records. A record is identified by an unique ID but many files can be generated for a single record. Those files are processed with scripts which generate other files as results.

Basic example

Harbor workspace

You can find in "example" folder of the git the harbor workspace that has a record per day of a harbor occupancy. In this example, a record is created each day to count the boats that occupy the harbor. The record ID include a timestamp for the day of recording.

In Unix environment, you can that type this to get the workspace :

wget -qO- https://github.com/YannBeauxis/sinagot/raw/master/example/harbor.tar.gz | tar xvz

To create the workspace instance :

>>> from sinagot import Workspace
>>> ws = Workspace('/path/to/harbor/workspace/folder')
>>> ws
<Workspace instance>

Explore records

You can list all records ids:

>>> list(ws.records.iter_ids())
['REC-20200602', 'REC-20200603', 'REC-20200601']

Create a Record instance. For a specific record:

>>> ws.records.get('REC-20200603')
<Record instance | id: REC-20200603>

Or the first record found:

>>> ws.records.first()
<Record instance | id: REC-20200602>

Records are not sort by their ids.

run scripts

You can run all scripts for each record of the dataset:

>>> ws.steps.run()
REC-20200602 | 2020-08-20 11:19:11,530 | count : Init run
REC-20200602 | 2020-08-20 11:19:11,531 | count : Processing run
REC-20200602 | 2020-08-20 11:19:11,556 | count : Run finished
...
REC-20200601 | 2020-08-20 11:19:11,625 | mean : Init run
REC-20200601 | 2020-08-20 11:19:11,626 | mean : Processing run
REC-20200601 | 2020-08-20 11:19:11,634 | mean : Run finished

Or for a single record:

>>> ws.records.get('REC-20200603').steps.run()
REC-20200603 | 2020-08-20 11:28:32,588 | count : Init run
REC-20200603 | 2020-08-20 11:28:32,590 | count : Processing run
REC-20200603 | 2020-08-20 11:28:32,616 | count : Run finished
REC-20200603 | 2020-08-20 11:28:32,619 | mean : Init run
REC-20200603 | 2020-08-20 11:28:32,621 | mean : Processing run
REC-20200603 | 2020-08-20 11:28:32,637 | mean : Run finished

More complex dataset

You can handle more complexity of dataset structure with task and modality concepts. During a recording session for a single record, data can be generate for differents task and each task can generate different kind of data called modality.

SoNeTAA usecase

The idea of Sinagot emerged for the data management of an EEG platform called SoNeTAA : https://research.pasteur.fr/en/project/sonetaa/ .

For documentation purpose SoNeTAA workspace structure will be used as example.

On SoNeTAA, a record with an ID with timestamp info in this format REC-[YYMMDD]-[A-Z], for example "REC-200331-A".

For a record, 3 tasks are performed:

• "RS" for Resting State
• "MMN" for MisMatch Negativity
• "HDC" for Human Dynamic Clamp.

3 modalities handle data depending of the tasks

• For each tasks, "EEG" modality create data from ElectroEncephalogram .
• A "behavior" modality create date only for HDC task.
• A "clinical" modality handle data used for every task.

Explore by task or modality

Each record collection or single record has subscopes corresponding to their tasks and modalities accessible as attribute.

For example to select only the task RS of the dataset:

>>> ws.RS
<RecordCollection instance | task: RS, modality: None>

A dataset subscope is a RecordCollection.

Or the EEG modality of a record:

>>> rec.EEG
<Record instance | id: REC-200331-A, task: None, modality: EEG>

You can select a specific couple of task and modality (called unit):

>>> ws.RS.EEG
<RecordCollection instance | task: RS, modality: EEG>
>>> ws.EEG.RS
<RecordCollection instance | task: RS, modality: EEG>