bidsreader 0.2.1

Data loader and file reader for the OpenBIDS format

bidsreader

A Python library for reading and working with neuroimaging data stored in the BIDS (Brain Imaging Data Structure) format. Provides a structured, object-oriented interface for loading EEG and iEEG data, events, electrodes, and channel metadata, with built-in support for MNE-Python and PTSA.

Features

• Load BIDS-compliant EEG/iEEG datasets with minimal boilerplate
• Automatic detection of device type (EEG vs iEEG) and coordinate space
• Load events, electrodes, channels, raw data, and epochs through a unified reader API
• Filter trials by type across events DataFrames, MNE Raw, and MNE Epochs
• Convert between MNE and PTSA data formats
• Detect and convert EEG signal units (V, mV, uV, nV, etc.)
• Custom exception hierarchy for clear, actionable error messages

Installation

Prerequisites

• Python 3.10+
• Access to a BIDS-formatted dataset

Install from source

git clone <repository-url>
cd bidsreader
pip install -e .

Note: The project currently has no pyproject.toml or setup.py. To use it without one, add the project root to your Python path or install in development mode after creating a minimal pyproject.toml (see Development Setup).

Dependencies

Required:

Package	Purpose
mne	EEG data structures and I/O
mne-bids	BIDS path resolution and reading
pandas	Tabular data (events, channels)
numpy	Numeric operations

Optional:

Package	Purpose
ptsa	PTSA TimeSeries conversion (convert_unit, mne_*_to_ptsa)
pytest	Running the test suite

Install all dependencies:

pip install mne mne-bids pandas numpy
# Optional
pip install ptsa pytest

Quick Start

See a more robust tutorial in tutorials/

Basic usage with CMLBIDSReader

from bidsreader import CMLBIDSReader

# Initialize a reader (defaults to /data/LTP_BIDS for CML data)
reader = CMLBIDSReader(subject="R1001P", task="FR1", session=0)

# Load behavioral events
events = reader.load_events("beh")

# Load electrode locations
electrodes = reader.load_electrodes()

# Load channel metadata (intracranial requires acquisition type)
channels = reader.load_channels("monopolar")

# Load combined channel + electrode data
combined = reader.load_combined_channels("bipolar")

# Load raw EEG data (returns MNE Raw object)
raw = reader.load_raw(acquisition="monopolar")

# Load epochs around events
epochs = reader.load_epochs(tmin=-0.5, tmax=1.5, acquisition="monopolar")

Using a custom BIDS root

reader = CMLBIDSReader(
    root="/path/to/your/bids/dataset",
    subject="sub01",
    task="rest",
    session="01",
    device="eeg",
)

Querying dataset metadata

reader = CMLBIDSReader(root="/data/LTP_BIDS", subject="R1001P", task="FR1")

# List all subjects in the dataset
subjects = reader.get_dataset_subjects()

# List all tasks in the dataset
tasks = reader.get_dataset_tasks()

# List sessions for this subject
sessions = reader.get_subject_sessions()

# List tasks for this subject
subject_tasks = reader.get_subject_tasks()

# Get the highest session number across all subjects
max_session = reader.get_dataset_max_sessions(outlier_thresh=100)

Changing reader fields after creation

reader = CMLBIDSReader(subject="R1001P", task="FR1", session=0)

# Switch to a different session
reader.set_fields(session=1)

# Switch subject and task
reader.set_fields(subject="R1002P", task="catFR1")

Filtering events by trial type

from bidsreader import filter_events_df_by_trial_types, filter_by_trial_types

# Filter a DataFrame
events = reader.load_events("beh")
word_events, indices = filter_events_df_by_trial_types(events, ["WORD"])

# Filter across multiple data objects at once (with consistency checks)
filtered_df, filtered_raw_events, filtered_epochs, event_id, idx = filter_by_trial_types(
    ["WORD", "STIM"],
    events_df=events,
    epochs=epochs,
)

Unit detection and conversion

from bidsreader import detect_unit, get_scale_factor, convert_unit

# Detect the unit of an MNE object
unit = detect_unit(raw)  # e.g., "V"

# Get conversion factor
factor = get_scale_factor("V", "uV")  # 1_000_000.0

# Convert data to a target unit (returns a copy by default)
raw_uv = convert_unit(raw, "uV")

Converting MNE data to PTSA TimeSeries

from bidsreader import mne_epochs_to_ptsa, mne_raw_to_ptsa

# Convert epochs (requires events DataFrame with 'sample' column)
ts = mne_epochs_to_ptsa(epochs, events)

# Convert raw data (optionally select channels and time window)
ts = mne_raw_to_ptsa(raw, picks=["E1", "E2"], tmin=0.0, tmax=10.0)

Architecture

Class hierarchy

BaseReader              # Abstract base — BIDS path construction, metadata queries, field validation
└── CMLBIDSReader       # Concrete reader for the CML (Computational Memory Lab) dataset

Module overview

Module	Purpose
basereader.py	BaseReader class — shared BIDS logic and metadata queries
cmlbidsreader.py	CMLBIDSReader — CML-specific loading and auto-detection
filtering.py	Trial-type filtering for DataFrames, MNE Raw, and Epochs
convert.py	MNE to PTSA TimeSeries conversion
units.py	Unit detection, scaling, and conversion
helpers.py	Utility functions (validation, BIDS prefix handling, bipolar electrode merging)
exc.py	Custom exception hierarchy
_errorwrap.py	@public_api decorator for consistent exception wrapping

Exception hierarchy

All exceptions inherit from BIDSReaderError, so you can catch everything with a single handler:

BIDSReaderError
├── InvalidOptionError        # Invalid argument value
├── MissingRequiredFieldError # Required reader field not set
├── FileNotFoundBIDSError     # Expected BIDS file not found
├── AmbiguousMatchError       # Multiple files matched when one expected
├── DataParseError            # TSV/JSON parsing failure
├── DependencyError           # Optional dependency issue
└── ExternalLibraryError      # Unexpected error from MNE/pandas/etc.

from bidsreader.exc import BIDSReaderError, FileNotFoundBIDSError

try:
    events = reader.load_events()
except FileNotFoundBIDSError:
    print("Events file not found for this subject/session")
except BIDSReaderError as e:
    print(f"Something went wrong: {e}")

Creating a New Reader

To support a different BIDS dataset, subclass BaseReader and implement your dataset-specific logic. Here is a step-by-step guide.

Step 1: Create your reader class

Create a new file (e.g., bidsreader/myreader.py):

import pandas as pd
import mne
from pathlib import Path
from typing import Optional, Union
from .basereader import BaseReader
from ._errorwrap import public_api
from .helpers import validate_option
from .exc import FileNotFoundBIDSError


class MyDatasetReader(BaseReader):
    """Reader for the My Dataset BIDS archive."""

    # Valid options for constrained fields
    VALID_DEVICES = ("eeg", "meg")

    def __init__(
        self,
        root: Optional[Union[str, Path]] = "/data/my_dataset",
        subject: Optional[str] = None,
        task: Optional[str] = None,
        session: Optional[str | int] = None,
        space: Optional[str] = None,
        acquisition: Optional[str] = None,
        device: Optional[str] = None,
    ):
        # Validate device before passing to base
        device = validate_option("device", device, self.VALID_DEVICES)
        super().__init__(
            root=root,
            subject=subject,
            task=task,
            session=session,
            space=space,
            acquisition=acquisition,
            device=device,
        )

    # --- Override auto-detection hooks ---

    def _determine_device(self) -> Optional[str]:
        """Infer device type from subject ID or dataset structure.

        Return None if it cannot be determined.
        """
        if self.subject is None:
            return None
        # Example: subjects starting with "MEG" use MEG
        if self.subject.startswith("MEG"):
            return "meg"
        return "eeg"

    def _determine_space(self) -> Optional[str]:
        """Infer coordinate space from files on disk.

        Return None or raise FileNotFoundBIDSError / AmbiguousMatchError
        if it cannot be determined.
        """
        # Implement dataset-specific logic here
        return "MNI152NLin2009aSym"

    # --- Add your loading methods ---

    @public_api
    def load_events(self) -> pd.DataFrame:
        """Load behavioral events for the current subject/session/task."""
        self._require(("subject", "task", "session", "device"), context="load_events")

        bp = self._bp(datatype="beh", suffix="beh", extension=".tsv")
        matches = bp.match()
        if not matches:
            raise FileNotFoundBIDSError(f"No events file found for {bp}")

        return pd.read_csv(matches[0].fpath, sep="\t")

    @public_api
    def load_raw(self) -> mne.io.BaseRaw:
        """Load raw continuous data."""
        from mne_bids import read_raw_bids

        self._require(("subject", "task", "session", "device"), context="load_raw")
        bp = self._bp(datatype=self.device)
        return read_raw_bids(bp)

Step 2: Key patterns to follow

1. Validate constrained fields in __init__ using validate_option() before calling super().__init__().

2. Override _determine_device() and _determine_space() to enable automatic detection. These are called lazily the first time reader.device or reader.space is accessed. Return None if detection fails — the base class will emit a warning.

3. Use self._require(fields, context=...) at the start of each loading method to ensure the necessary fields are set before attempting file I/O.

4. Use self._bp(**kwargs) to construct BIDSPath objects for file matching. This handles BIDS-standard path construction using the reader's current field values.

5. Decorate all public methods with @public_api so that external exceptions (FileNotFoundError, JSONDecodeError, etc.) are automatically mapped to the BIDSReaderError hierarchy.

6. Use self._add_bids_prefix(field, value) when you need to manually construct BIDS-prefixed path segments (e.g., "sub-001", "ses-0").

Step 3: Export your reader

Add your reader to __init__.py:

from .myreader import MyDatasetReader

Step 4: Write tests

Follow the patterns in tests/conftest.py for fixtures and tests/test_cmlbidsreader.py for test structure. Key patterns:

• Use tmp_path fixtures to create temporary BIDS directory structures
• Use skip decorators for integration tests that require real data on disk
• Test both the happy path and error cases (missing fields, invalid options, missing files)

import pytest
from bidsreader import MyDatasetReader

@pytest.fixture
def my_reader(tmp_path):
    return MyDatasetReader(root=tmp_path, subject="EEG001", task="rest", session=1)

def test_device_detection(my_reader):
    assert my_reader.device == "eeg"

def test_missing_field_raises(tmp_path):
    reader = MyDatasetReader(root=tmp_path, subject="EEG001", task="rest")
    reader.session = None
    with pytest.raises(Exception):
        reader.load_events()

Development Setup

Running tests

# Run all tests
python -m pytest tests/

# Run a specific test file
python -m pytest tests/test_basereader.py -v

# Run with output
python -m pytest tests/ -v -s

Integration tests that depend on real data at /data/LTP_BIDS/ are skipped automatically when that data is not available.

Creating a pyproject.toml (recommended)

If you want proper pip install -e . support, create a pyproject.toml:

[build-system]
requires = ["setuptools>=64"]
build-backend = "setuptools.backends._legacy:_Backend"

[project]
name = "bidsreader"
version = "0.1.0"
description = "Data loader and file reader for the OpenBIDS format"
requires-python = ">=3.10"
dependencies = [
    "mne",
    "mne-bids",
    "pandas",
    "numpy",
]

[project.optional-dependencies]
ptsa = ["ptsa"]
dev = ["pytest"]

Then install with:

pip install -e ".[dev]"

API Reference

BaseReader

Method	Description
set_fields(**kwargs)	Set multiple reader fields at once (chainable)
get_dataset_subjects()	List all subjects in the dataset
get_dataset_tasks()	List all tasks in the dataset
get_subject_sessions()	List sessions for the current subject
get_subject_tasks()	List tasks for the current subject
get_dataset_max_sessions(outlier_thresh=None)	Get highest session number across all subjects

CMLBIDSReader

Inherits all BaseReader methods, plus:

Method	Description
is_intracranial()	Returns True if device is "ieeg"
load_events(event_type="beh")	Load events TSV ("beh" or device-type events)
load_electrodes()	Load electrode coordinates TSV
load_channels(acquisition=None)	Load channel metadata TSV (iEEG requires "monopolar" or "bipolar")
load_combined_channels(acquisition=None)	Merge channel + electrode data into one DataFrame
load_coordsystem_desc()	Load coordinate system JSON metadata
load_raw(acquisition=None)	Load raw continuous data (returns mne.io.BaseRaw)
load_epochs(tmin, tmax, events=None, baseline=None, acquisition=None, event_repeated="merge", channels=None, preload=False)	Create mne.Epochs from raw data and events

Standalone Functions

Function	Module	Description
filter_events_df_by_trial_types(events_df, trial_types)	filtering	Filter events DataFrame by trial type
filter_raw_events_by_trial_types(raw, trial_types)	filtering	Filter MNE Raw annotations by trial type
filter_epochs_by_trial_types(epochs, trial_types)	filtering	Filter MNE Epochs by trial type
filter_by_trial_types(trial_types, *, events_df, raw, epochs)	filtering	Filter multiple data objects with consistency checks
detect_unit(data, current_unit=None)	units	Detect or validate EEG data unit
get_scale_factor(from_unit, to_unit)	units	Get multiplicative conversion factor between units
convert_unit(data, target, *, current_unit=None, copy=True)	units	Convert EEG data to a target unit
mne_epochs_to_ptsa(epochs, events)	convert	Convert MNE Epochs to PTSA TimeSeries
mne_raw_to_ptsa(raw, picks=None, tmin=None, tmax=None)	convert	Convert MNE Raw to PTSA TimeSeries

License

TBD