eegdash.const

Configuration constants and mappings for EEGDash.

This module contains global configuration settings, allowed query fields, and mapping constants used throughout the EEGDash package. It defines the interface between EEGDash releases and OpenNeuro dataset identifiers, as well as validation rules for database queries.

Module Attributes

ALLOWED_QUERY_FIELDS	A set of field names that are permitted in database queries constructed via find() with keyword arguments.
DEFAULT_DESCRIPTION_FIELDS	BIDS-entity fields surfaced on every per-record description by EEGDashDataset when the constructor is called without an explicit description_fields= argument.
DATASET_SUMMARY_COLUMNS	Column layout for the DataFrame returned by search_datasets().
DATASET_FIELD_ALIASES	Per-canonical-column ordered alias paths fed to records_to_dataframe().
DATASET_QUERY_ALLOWED	Caller-friendly keyword filters accepted by search_datasets().
DATASET_QUERY_FIELD_SPEC	field_spec consumed by build_query_from_kwargs() for the search_datasets() keyword filters.
RELEASE_TO_OPENNEURO_DATASET_MAP	A mapping from Healthy Brain Network (HBN) release identifiers (e.g., "R11") to their corresponding OpenNeuro dataset identifiers (e.g., "ds005516").
SUBJECT_MINI_RELEASE_MAP	A mapping from HBN release identifiers to a list of subject IDs.
config	A global configuration dictionary for the EEGDash package.

eegdash.const.config = {'accepted_query_fields': ['data_name', 'dataset'], 'attributes': {'bidspath': 'str', 'data_name': 'str', 'dataset': 'str', 'modality': 'str', 'nchans': 'int', 'ntimes': 'int', 'run': 'str', 'sampling_frequency': 'float', 'session': 'str', 'subject': 'str', 'task': 'str'}, 'bids_dependencies_files': ['dataset_description.json', 'participants.tsv', 'events.tsv', 'events.json', 'eeg.json', 'electrodes.tsv', 'channels.tsv', 'coordsystem.json'], 'description_fields': ['subject', 'session', 'run', 'task', 'age', 'gender', 'sex'], 'required_fields': ['data_name']}

A global configuration dictionary for the EEGDash package.

eegdash.const.ALLOWED_QUERY_FIELDS = {'data_name', 'dataset', 'modality', 'nchans', 'ntimes', 'run', 'sampling_frequency', 'session', 'subject', 'task'}

A set of field names that are permitted in database queries constructed via find() with keyword arguments.

Type:

set

eegdash.const.DEFAULT_DESCRIPTION_FIELDS = ['subject', 'session', 'run', 'task', 'age', 'gender', 'sex']

BIDS-entity fields surfaced on every per-record description by EEGDashDataset when the constructor is called without an explicit description_fields= argument.

Type:

list

eegdash.const.DATASET_SUMMARY_COLUMNS = ('dataset_id', 'name', 'modality', 'task', 'n_subjects', 'source', 'license', 'dataset_doi')

Column layout for the DataFrame returned by search_datasets(). One row per matching dataset.

Type:

tuple

eegdash.const.DATASET_FIELD_ALIASES = {'dataset_id': ('dataset_id', 'dataset', '_id'), 'source': ('source', 'provider')}

Per-canonical-column ordered alias paths fed to records_to_dataframe(). Lets one summary column draw from several legacy/nested record fields, so the helper survives v1/v2 schema drift without per-endpoint glue.

Type:

dict

eegdash.const.DATASET_QUERY_ALLOWED = frozenset({'clinical_group', 'license', 'modality', 'n_subjects_min', 'source', 'task'})

Caller-friendly keyword filters accepted by search_datasets(). Distinct from ALLOWED_QUERY_FIELDS because the dataset-level documents have a different schema than per-recording records.

Type:

frozenset

eegdash.const.DATASET_QUERY_FIELD_SPEC = {'clinical_group': {'paths': ('clinical.group', 'clinical_group')}, 'modality': {'value_aliases': <function <lambda>>}, 'n_subjects_min': {'operator': '$gte', 'paths': ('n_subjects',), 'value_aliases': <function <lambda>>}, 'source': {'paths': ('source', 'provider')}}

field_spec consumed by build_query_from_kwargs() for the search_datasets() keyword filters. Encodes every legacy alias and range operator that endpoint needs.

Type:

dict

eegdash.const.RELEASE_TO_OPENNEURO_DATASET_MAP = {'R1': 'ds005505', 'R10': 'ds005515', 'R11': 'ds005516', 'R2': 'ds005506', 'R3': 'ds005507', 'R4': 'ds005508', 'R5': 'ds005509', 'R6': 'ds005510', 'R7': 'ds005511', 'R8': 'ds005512', 'R9': 'ds005514'}

A mapping from Healthy Brain Network (HBN) release identifiers (e.g., “R11”) to their corresponding OpenNeuro dataset identifiers (e.g., “ds005516”).

Type:

dict

eegdash.const.SUBJECT_MINI_RELEASE_MAP = {'R1': ['NDARAC904DMU', 'NDARAM704GKZ', 'NDARAP359UM6', 'NDARBD879MBX', 'NDARBH024NH2', 'NDARBK082PDD', 'NDARCA153NKE', 'NDARCE721YB5', 'NDARCJ594BWQ', 'NDARCN669XPR', 'NDARCW094JCG', 'NDARCZ947WU5', 'NDARDH670PXH', 'NDARDL511UND', 'NDARDU986RBM', 'NDAREM731BYM', 'NDAREN519BLJ', 'NDARFK610GY5', 'NDARFT581ZW5', 'NDARFW972KFQ'], 'R10': ['NDARAR935TGZ', 'NDARAV474ADJ', 'NDARCB869VM8', 'NDARCJ667UPL', 'NDARCM677TC1', 'NDARET671FTC', 'NDARKM061NHZ', 'NDARLD501HDK', 'NDARLL176DJR', 'NDARMT791WDH', 'NDARMW299ZAB', 'NDARNC405WJA', 'NDARNP962TJK', 'NDARPB967KU7', 'NDARRU560AGK', 'NDARTB173LY2', 'NDARUW377KAE', 'NDARVH565FX9', 'NDARVP799KGY', 'NDARVY962GB5'], 'R11': ['NDARAB678VYW', 'NDARAG788YV9', 'NDARAM946HJE', 'NDARAY977BZT', 'NDARAZ532KK0', 'NDARCE912ZXW', 'NDARCM214WFE', 'NDARDL033XRG', 'NDARDT889RT9', 'NDARDZ794ZVP', 'NDAREV869CPW', 'NDARFN221WW5', 'NDARFV289RKB', 'NDARFY623ZTE', 'NDARGA890MKA', 'NDARHN206XY3', 'NDARHP518FUR', 'NDARJL292RYV', 'NDARKM199DXW', 'NDARKW236TN7'], 'R2': ['NDARAB793GL3', 'NDARAM675UR8', 'NDARBM839WR5', 'NDARBU730PN8', 'NDARCT974NAJ', 'NDARCW933FD5', 'NDARCZ770BRG', 'NDARDW741HCF', 'NDARDZ058NZN', 'NDAREC377AU2', 'NDAREM500WWH', 'NDAREV527ZRF', 'NDAREV601CE7', 'NDARFF070XHV', 'NDARFR108JNB', 'NDARFT305CG1', 'NDARGA056TMW', 'NDARGH775KF5', 'NDARGJ878ZP4', 'NDARHA387FPM'], 'R3': ['NDARAA948VFH', 'NDARAD774HAZ', 'NDARAE828CML', 'NDARAG340ERT', 'NDARBA839HLG', 'NDARBE641DGZ', 'NDARBG574KF4', 'NDARBM642JFT', 'NDARCL016NHB', 'NDARCV944JA6', 'NDARCY178KJP', 'NDARDY150ZP9', 'NDAREC542MH3', 'NDAREK549XUQ', 'NDAREM887YY8', 'NDARFA815FXE', 'NDARFF644ZGD', 'NDARFV557XAA', 'NDARFV780ABD', 'NDARGB102NWJ'], 'R4': ['NDARAC350BZ0', 'NDARAD615WLJ', 'NDARAG584XLU', 'NDARAH503YG1', 'NDARAX272ZJL', 'NDARAY461TZZ', 'NDARBC734UVY', 'NDARBL444FBA', 'NDARBT640EBN', 'NDARBU098PJT', 'NDARBU928LV0', 'NDARBV059CGE', 'NDARCG037CX4', 'NDARCG947ZC0', 'NDARCH001CN2', 'NDARCU001ZN7', 'NDARCW497XW2', 'NDARCX053GU5', 'NDARDF568GL5', 'NDARDJ092YKH'], 'R5': ['NDARAH793FBF', 'NDARAJ689BVN', 'NDARAP785CTE', 'NDARAU708TL8', 'NDARBE091BGD', 'NDARBE103DHM', 'NDARBF851NH6', 'NDARBH228RDW', 'NDARBJ674TVU', 'NDARBM433VER', 'NDARCA740UC8', 'NDARCU633GCZ', 'NDARCU736GZ1', 'NDARCU744XWL', 'NDARDC843HHM', 'NDARDH086ZKK', 'NDARDL305BT8', 'NDARDU853XZ6', 'NDARDV245WJG', 'NDAREC480KFA'], 'R6': ['NDARAD224CRB', 'NDARAE301XTM', 'NDARAT680GJA', 'NDARCA578CEB', 'NDARDZ147ETZ', 'NDARFL793LDE', 'NDARFX710UZA', 'NDARGE994BMX', 'NDARGP191YHN', 'NDARGV436PFT', 'NDARHF545HFW', 'NDARHP039DBU', 'NDARHT774ZK1', 'NDARJA830BYV', 'NDARKB614KGY', 'NDARKM250ET5', 'NDARKZ085UKQ', 'NDARLB581AXF', 'NDARNJ899HW7', 'NDARRZ606EDP'], 'R7': ['NDARAY475AKD', 'NDARBW026UGE', 'NDARCK162REX', 'NDARCK481KRH', 'NDARCV378MMX', 'NDARCX462NVA', 'NDARDJ970ELG', 'NDARDU617ZW1', 'NDAREM609ZXW', 'NDAREW074ZM2', 'NDARFE555KXB', 'NDARFT176NJP', 'NDARGK442YHH', 'NDARGM439FZD', 'NDARGT634DUJ', 'NDARHE283KZN', 'NDARHG260BM9', 'NDARHL684WYU', 'NDARHN224TPA', 'NDARHP841RMR'], 'R8': ['NDARAB514MAJ', 'NDARAD571FLB', 'NDARAF003VCL', 'NDARAG191AE8', 'NDARAJ977PRJ', 'NDARAP912JK3', 'NDARAV454VF0', 'NDARAY298THW', 'NDARBJ375VP4', 'NDARBT436PMT', 'NDARBV630BK6', 'NDARCB627KDN', 'NDARCC059WTH', 'NDARCM953HKD', 'NDARCN681CXW', 'NDARCT889DMB', 'NDARDJ204EPU', 'NDARDJ544BU5', 'NDARDP292DVC', 'NDARDW178AC6'], 'R9': ['NDARAC589YMB', 'NDARAC853CR6', 'NDARAH239PGG', 'NDARAL897CYV', 'NDARAN160GUF', 'NDARAP049KXJ', 'NDARAP457WB5', 'NDARAW216PM7', 'NDARBA004KBT', 'NDARBD328NUQ', 'NDARBF042LDM', 'NDARBH019KPD', 'NDARBH728DFK', 'NDARBM370JCB', 'NDARBU183TDJ', 'NDARBW971DCW', 'NDARBZ444ZHK', 'NDARCC620ZFT', 'NDARCD182XT1', 'NDARCK113CJM']}

A mapping from HBN release identifiers to a list of subject IDs. This is used to select a small, representative subset of subjects for creating “mini” datasets for testing and demonstration purposes.

Type:

dict