XeDocs - XENON Metadata management tool

XeDocs manages tracking versioned detector numbers, replacing CMT and ideally all hard-coded values. XeDocs both looks up data from its own online database, and uses straxen URL-style lookup to find other resources. To upload data to the XeDocs database, you must submit it as a PR to

What does Xedocs give you

Data reading

• Read data from multiple formats (e.g. mongodb, pandas) and locations with a simple unified interface.
• Custom logic implemented on the document class, e.g. creating a tensorflow model from the data etc.
• Multiple APIs for reading data, fun functional, ODM style, pandas and xarray.
• Read data as objects, dataframes, dicts, json.

Writing data

• Write data to multiple storage backends with the same interface
• Custom per-collection rules for data insertion, deletion and updating.
• Schema validation and type coercion so storage has uniform and consistent data.

Other

• Custom panel widgets for graphical representation of data, web client
• Auto-generated API server and client + openapi documentation
• CLI for viewing and downloading data

Basic Usage

Explore the available schemas

import xedocs

>>> xedocs.list_schemas()
>>> ['detector_numbers',
    'fax_configs',
    'plugin_lineages',
    'context_lineages',
    'pmt_area_to_pes',
    'global_versions',
    'electron_drift_velocities',
    ...]

>>> xedocs.help('pmt_area_to_pes')

>>>
        Schema name: pmt_area_to_pes
        Index fields: ['version', 'time', 'detector', 'pmt']
        Column fields: ['created_date', 'comments', 'value']

Read/write data from the shared development database, this database is writable from the default analysis username/password

import xedocs

db = xedocs.development_db()

docs = db.pmt_area_to_pes.find_docs(version='v1', pmt=[1,2,3,5], time='2021-01-01T00:00:00', detector='tpc')
to_pes = [doc.value for doc in docs]

# passing a run_id will attempt to fetch the center time of that run from the runs db
doc = db.pmt_area_to_pes.find_one(version='v1', pmt=1, run_id=25000, detector='tpc')
to_pe = doc.value

Read from the straxen processing database, this database is read-only for the default analysis username/password

import xedocs

db = xedocs.straxen_db()

...

Read from the the corrections gitub repository, this database is read-only

import xedocs

db = xedocs.corrections_repo(branch="master")

...

If you cloned the corrections gitub repo to a local folder, this database can be read too

import xedocs

db = xedocs.local_folder(PATH_TO_REPO_FOLDER)

...

Read data from alternative data sources specified by path, e.g csv files which will be loaded by pandas.

from xedocs.schemas import DetectorNumber

g1_doc = DetectorNumber.find_one(datasource='/path/to/file.csv', version='v1', field='g1')
g1_value = g1_doc.value
g1_error = g1_doc.uncertainty

The path can also be a github URL or any other URL supported by fsspec.

from xedocs.schemas import DetectorNumber

g1_doc = DetectorNumber.find_one(
                         datasource='github://org:repo@/path/to/file.csv', 
                         version='v1', 
                         field='g1')

Supported data sources

> - MongoDB collections > - TinyDB tables > - JSON files > - REST API clients

Please open an issue on rframe if you want support for an additional data format.

If you want a new datasource to be available from a schema class, you can register it to the class:

from xedocs.schemas import DetectorNumber

DetectorNumber.register_datasource('github://org:repo@/path/to/file.csv', name='github_repo')

# The source will now be available under the given name:

g1_doc = DetectorNumber.github_repo.find_one(version='v1', field='g1')

Documentation

Full documentation hosted by Readthedocs

Credits

This package was created with Cookiecutter and the briggySmalls/cookiecutter-pypackage project template.