bids.py

# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""Utilities to handle BIDS inputs."""
from __future__ import annotations

import json
import os
import sys
import typing as ty
from collections import defaultdict
from pathlib import Path

import yaml
from bids.layout import BIDSLayout

from aslprep import config
from aslprep.data import load as load_data


def collect_data(
    layout,
    participant_label,
    bids_filters=None,
):
    """Use pybids to retrieve the input data for a given participant."""
    queries = {
        "fmap": {"datatype": "fmap"},
        "flair": {"datatype": "anat", "suffix": "FLAIR"},
        "t2w": {"datatype": "anat", "suffix": "T2w"},
        "t1w": {"datatype": "anat", "suffix": "T1w"},
        "roi": {"datatype": "anat", "suffix": "roi"},
        "sbref": {"datatype": "perf", "suffix": "sbref"},
        "asl": {"datatype": "perf", "suffix": "asl"},
    }

    bids_filters = bids_filters or {}
    for acq, entities in bids_filters.items():
        queries[acq].update(entities)

    subj_data = {
        dtype: sorted(
            layout.get(
                return_type="file",
                subject=participant_label,
                extension=["nii", "nii.gz"],
                **query,
            )
        )
        for dtype, query in queries.items()
    }

    return subj_data


def collect_run_data(layout, asl_file):
    """Use pybids to retrieve the input data for a given participant."""
    queries = {
        "aslcontext": {"suffix": "aslcontext", "extension": ".tsv"},
        "sbref": {"suffix": "sbref", "extension": [".nii", ".nii.gz"]},
    }

    bids_file = layout.get_file(asl_file)

    run_data = {
        dtype: layout.get_nearest(
            bids_file.path,
            return_type="file",
            strict=False,  # aslcontext files aren't grabbed when strict=True, for some reason
            **query,
        )
        for dtype, query in queries.items()
    }

    if "sbref" in config.workflow.ignore:
        config.loggers.workflow.info("Single-band reference files ignored.")
        run_data["sbref"] = None

    # The aslcontext file is required
    if not run_data["aslcontext"]:
        raise FileNotFoundError(f"aslcontext file for {asl_file} not found.")

    # Now let's look for an m0scan
    m0scan_candidates = [
        f
        for f in bids_file.get_associations(kind="InformedBy")
        if f.entities["suffix"] == "m0scan"
    ]
    if m0scan_candidates:
        if len(m0scan_candidates) > 1:
            config.loggers.workflow.warning(
                f"More than one M0 file found for {asl_file}. "
                f"Using the first one ({m0scan_candidates[0].path})"
            )
        run_data["m0scan"] = m0scan_candidates[0].path
    else:
        run_data["m0scan"] = None

    m0scan_metadata = None
    asl_metadata = layout.get_metadata(asl_file)
    if (asl_metadata["M0Type"] == "Separate") and not run_data["m0scan"]:
        raise FileNotFoundError(f"M0 file for {asl_file} not found.")
    elif asl_metadata["M0Type"] == "Separate":
        m0scan_metadata = layout.get_file(run_data["m0scan"]).get_metadata()
        if not m0scan_metadata:
            raise Exception(f"No metadata for m0scan: {run_data['m0scan']}")
    elif run_data["m0scan"]:
        raise ValueError(
            f"M0Type is {run_data['asl_metadata']['M0Type']}, "
            f"but an M0 scan was found at {run_data['m0scan']}"
        )

    config.loggers.workflow.info(
        (
            f"Collected run data for {asl_file}:\n"
            f"{yaml.dump(run_data, default_flow_style=False, indent=4)}"
        ),
    )

    # Add metadata to dictionary now (we don't want to print these with the logger).
    run_data["asl_metadata"] = asl_metadata
    run_data["m0scan_metadata"] = m0scan_metadata

    return run_data


def collect_derivatives(
    derivatives_dir: Path,
    entities: dict,
    fieldmap_id: str | None,
    spec: dict | None = None,
    patterns: ty.List[str] | None = None,
):
    """Gather existing derivatives and compose a cache."""
    if spec is None or patterns is None:
        _spec, _patterns = tuple(
            json.loads(load_data.readable("io_spec.json").read_text()).values()
        )

        if spec is None:
            spec = _spec
        if patterns is None:
            patterns = _patterns

    derivs_cache = defaultdict(list, {})
    layout = BIDSLayout(derivatives_dir, config=["bids", "derivatives"], validate=False)
    derivatives_dir = Path(derivatives_dir)

    # search for both aslrefs
    for k, q in spec["baseline"].items():
        query = {**q, **entities}
        item = layout.get(return_type="filename", **query)
        if not item:
            continue
        derivs_cache[f"{k}_aslref"] = item[0] if len(item) == 1 else item

    for xfm, q in spec["transforms"].items():
        query = {**q, **entities}
        if xfm == "aslref2fmap":
            query["to"] = fieldmap_id
        item = layout.get(return_type="filename", **q)
        if not item:
            continue
        derivs_cache[xfm] = item[0] if len(item) == 1 else item
    return derivs_cache


def write_bidsignore(deriv_dir):
    """Write .bidsignore file."""
    bids_ignore = (
        "*.html",
        "logs/",
        "figures/",  # Reports
        "*_xfm.*",  # Unspecified transform files
        "*.surf.gii",  # Unspecified structural outputs
        # Unspecified functional outputs
        "*_aslref.nii.gz",
        "*_asl.func.gii",
        "*_mixing.tsv",
        "*_timeseries.tsv",
    )
    ignore_file = Path(deriv_dir) / ".bidsignore"

    ignore_file.write_text("\n".join(bids_ignore) + "\n")


def write_derivative_description(bids_dir, deriv_dir):
    """Write derivative dataset_description file."""
    from aslprep.__about__ import DOWNLOAD_URL, __url__, __version__

    bids_dir = Path(bids_dir)
    deriv_dir = Path(deriv_dir)
    desc = {
        "Name": "ASLPrep - ASL PREProcessing workflow",
        "BIDSVersion": "1.9.0",
        "PipelineDescription": {
            "Name": "ASLPrep",
            "Version": __version__,
            "CodeURL": DOWNLOAD_URL,
        },
        "CodeURL": __url__,
        "HowToAcknowledge": "Please cite our paper "
        "and include the generated citation boilerplate within the Methods "
        "section of the text.",
    }

    # Keys that can only be set by environment
    if "ASLPREP_DOCKER_TAG" in os.environ:
        desc["DockerHubContainerTag"] = os.environ["ASLPREP_DOCKER_TAG"]

    if "ASLPREP_SINGULARITY_URL" in os.environ:
        singularity_url = os.environ["ASLPREP_SINGULARITY_URL"]
        desc["SingularityContainerURL"] = singularity_url

        singularity_md5 = _get_shub_version(singularity_url)
        if singularity_md5 and singularity_md5 is not NotImplemented:
            desc["SingularityContainerMD5"] = _get_shub_version(singularity_url)

    # Keys deriving from source dataset
    orig_desc = {}
    fname = bids_dir / "dataset_description.json"
    if fname.exists():
        with fname.open() as fobj:
            orig_desc = json.load(fobj)

    if "DatasetDOI" in orig_desc:
        desc["SourceDatasetsURLs"] = [f"https://doi.org/{orig_desc['DatasetDOI']}"]

    if "License" in orig_desc:
        desc["License"] = orig_desc["License"]

    with (deriv_dir / "dataset_description.json").open("w") as fobj:
        json.dump(desc, fobj, indent=4)


def validate_input_dir(exec_env, bids_dir, participant_label):
    """Validate input directory.

    This function checks necessary validator requirements,
    but skips ones that should not influence the pipeline.
    """
    import subprocess
    import tempfile

    validator_config_dict = {
        "ignore": [
            "EVENTS_COLUMN_ONSET",
            "EVENTS_COLUMN_DURATION",
            "TSV_EQUAL_ROWS",
            "TSV_EMPTY_CELL",
            "TSV_IMPROPER_NA",
            "VOLUME_COUNT_MISMATCH",
            "BVAL_MULTIPLE_ROWS",
            "BVEC_NUMBER_ROWS",
            "DWI_MISSING_BVAL",
            "INCONSISTENT_SUBJECTS",
            "INCONSISTENT_PARAMETERS",
            "BVEC_ROW_LENGTH",
            "B_FILE",
            "PARTICIPANT_ID_COLUMN",
            "PARTICIPANT_ID_MISMATCH",
            "TASK_NAME_MUST_DEFINE",
            "PHENOTYPE_SUBJECTS_MISSING",
            "STIMULUS_FILE_MISSING",
            "DWI_MISSING_BVEC",
            "EVENTS_TSV_MISSING",
            "TSV_IMPROPER_NA",
            "ACQTIME_FMT",
            "Participants age 89 or higher",
            "DATASET_DESCRIPTION_JSON_MISSING",
            "FILENAME_COLUMN",
            "WRONG_NEW_LINE",
            "MISSING_TSV_COLUMN_CHANNELS",
            "MISSING_TSV_COLUMN_IEEG_CHANNELS",
            "MISSING_TSV_COLUMN_IEEG_ELECTRODES",
            "UNUSED_STIMULUS",
            "CHANNELS_COLUMN_SFREQ",
            "CHANNELS_COLUMN_LOWCUT",
            "CHANNELS_COLUMN_HIGHCUT",
            "CHANNELS_COLUMN_NOTCH",
            "CUSTOM_COLUMN_WITHOUT_DESCRIPTION",
            "ACQTIME_FMT",
            "SUSPICIOUSLY_LONG_EVENT_DESIGN",
            "SUSPICIOUSLY_SHORT_EVENT_DESIGN",
            "MALFORMED_BVEC",
            "MALFORMED_BVAL",
            "MISSING_TSV_COLUMN_EEG_ELECTRODES",
            "MISSING_SESSION",
        ],
        "error": ["NO_T1W"],
        "ignoredFiles": ["/dataset_description.json", "/participants.tsv"],
    }
    # Limit validation only to data from requested participants
    if participant_label:
        all_subs = {s.name[4:] for s in bids_dir.glob("sub-*")}
        selected_subs = {s[4:] if s.startswith("sub-") else s for s in participant_label}
        if bad_labels := selected_subs.difference(all_subs):
            error_msg = (
                "Data for requested participant(s) label(s) not found. "
                f"Could not find data for participant(s): {','.join(bad_labels)}. "
                "Please verify the requested participant labels."
            )
            if exec_env == "docker":
                error_msg += (
                    " This error can be caused by the input data not being "
                    "accessible inside the docker container. "
                    "Please make sure all volumes are mounted properly "
                    "(see https://docs.docker.com/engine/reference/commandline/"
                    "run/#mount-volume--v---read-only)"
                )

            elif exec_env == "singularity":
                error_msg += (
                    " This error can be caused by the input data not being "
                    "accessible inside the singularity container. "
                    "Please make sure all paths are mapped properly "
                    "(see https://www.sylabs.io/guides/3.0/user-guide/bind_paths_and_mounts.html)"
                )

            raise RuntimeError(error_msg)

        if ignored_subs := all_subs.difference(selected_subs):
            for sub in ignored_subs:
                validator_config_dict["ignoredFiles"].append(f"/sub-{sub}/**")

    with tempfile.NamedTemporaryFile("w+") as temp:
        temp.write(json.dumps(validator_config_dict))
        temp.flush()
        try:
            subprocess.check_call(["bids-validator", bids_dir, "-c", temp.name])
        except FileNotFoundError:
            print("bids-validator does not appear to be installed", file=sys.stderr)


def _get_shub_version(singularity_url):  # noqa: U100
    return NotImplemented


def find_atlas_entities(filename):
    """Extract atlas entities from filename."""
    import os

    fname = os.path.basename(filename)
    elements = fname.split("_")

    out = []
    for ent in ("tpl", "atlas", "res"):
        ent_parts = [el for el in elements if el.startswith(f"{ent}-")]
        ent_value = None
        if ent_parts:
            ent_value = ent_parts[0].split("-")[1]

        out.append(ent_value)

    suffix = elements[-1].split(".")[0]
    extension = "." + ".".join(elements[-1].split(".")[1:])
    out += [suffix, extension]

    return tuple(out)