ingesture 0.1.0

Ingest gesturally-structured data into models with multiple export

ingesture

Ingest gesturally-structured data into models with multiple export

This package is not even close to usable, and is just a sketch at the moment. If for some reason you see it and would like to work on it with me, feel free to open an issue :)

Declare your data

Even the most disorganized data system has some structure. We want to be able to recover it without demanding that the entire acquisition process be reworked

To do that, we can use a family of specifiers to tell ingest where to get metadata

from datetime import datetime
from ingesture import Schema, spec
from pydantic import Field

class MyData(Schema):
    # parse metadata in a filename
    subject_id: str = Field(..., 
        description="The ID of a subject of course!",
        spec = spec.Path('electrophysiology_{subject_id}_*.csv')
    )
    # parse multiple values at once
    date: datetime
    experimenter: str
    date, experimenter = Field(...,
        spec = spec.Path('{date}_{experimenter}_optodata.h5')
    )
    
    
    # from inside a .mat file
    other_meta: int = Field(...
        spec = spec.Mat(
            path='**/notebook.mat', # 2 **s mean we can glob recursively
            field = ('nb', 1, 'user') # index recursively through the .mat
        )
    )
    # and so on

Then, parse your schema from a folder

data = MyData.make('/home/lab/my_data')

Or a bunch of them!

data = MyData.make('/home/lab/my_datas/*')

Multiple Strategies

todo

Hierarchical Modeling

Our data is rarely a single type, often there is a repeatable substructure that is paired with different macro-structures: eg. you have open-ephys data within a directory with behavioral data in one experiment and paired with optical data in another.

Make submodels and recombine them freely...

todo

Export Data

Once we have data in an abstract model, then we want to be able to export it to multiple formats! To do that we need an interface that describes the basic methods of interacting with that format (eg. .csv files are written differently than hdf5 files) and a mapping from our model fields to locations, attributes, and names in the target format.

Pydantic base export

json

From the Field specification

class MyData(Schema):
    subject_id: str = Field(
        spec = ...,
        nwb_field = "NWBFile:subject_id"
    )

From a Mapping object

class NWB_Map(Mapping):
    subject_id = 'NWBFile:subject_id'

class MyData(Schema):
    subject_id: str = Field(...)
    
    __mapping__ = NWB_Map