config

tit.stats.config

Configuration dataclasses for cluster-based permutation testing.

Pure Python — no numpy, nibabel, or heavy dependencies. Mirrors the tit.opt.config / tit.sim.config pattern.

GroupComparisonConfig dataclass

GroupComparisonConfig(project_dir: str, analysis_name: str, subjects: list[Subject], test_type: TestType = UNPAIRED, alternative: Alternative = TWO_SIDED, cluster_threshold: float = 0.05, cluster_stat: ClusterStat = MASS, n_permutations: int = 1000, alpha: float = 0.05, n_jobs: int = -1, tissue_type: TissueType = GREY, nifti_file_pattern: str | None = None, group1_name: str = 'Responders', group2_name: str = 'Non-Responders', value_metric: str = 'Current Intensity', atlas_files: list[str] = list())

Configuration for group comparison permutation testing.

Subject dataclass

Subject(subject_id: str, simulation_name: str, response: int)

A single subject in a group comparison analysis.

load_subjects classmethod

load_subjects(csv_path: str) -> list[Subject]

Load group comparison subjects from a CSV file.

Expected columns: subject_id, simulation_name, response (0 or 1).

Source code in tit/stats/config.py

@classmethod
def load_subjects(cls, csv_path: str) -> list["GroupComparisonConfig.Subject"]:
    """Load group comparison subjects from a CSV file.

    Expected columns: subject_id, simulation_name, response (0 or 1).
    """
    import pandas as pd

    df = pd.read_csv(csv_path)
    required = {"subject_id", "simulation_name", "response"}
    missing = required - set(df.columns)
    if missing:
        raise ValueError(f"CSV missing required columns: {missing}")

    subjects = []
    for _, row in df.iterrows():
        sid = str(row["subject_id"]).replace("sub-", "")
        if sid.endswith(".0"):
            sid = sid[:-2]
        subjects.append(
            cls.Subject(
                subject_id=sid,
                simulation_name=str(row["simulation_name"]),
                response=int(row["response"]),
            )
        )
    return subjects

CorrelationConfig dataclass

CorrelationConfig(project_dir: str, analysis_name: str, subjects: list[Subject], correlation_type: CorrelationType = PEARSON, cluster_threshold: float = 0.05, cluster_stat: ClusterStat = MASS, n_permutations: int = 1000, alpha: float = 0.05, n_jobs: int = -1, use_weights: bool = True, tissue_type: TissueType = GREY, nifti_file_pattern: str | None = None, effect_metric: str = 'Effect Size', field_metric: str = 'Electric Field Magnitude', atlas_files: list[str] = list())

Configuration for correlation-based permutation testing.

Subject dataclass

Subject(subject_id: str, simulation_name: str, effect_size: float, weight: float = 1.0)

A single subject in a correlation analysis.

load_subjects classmethod

load_subjects(csv_path: str) -> list[Subject]

Load correlation subjects from a CSV file.

Expected columns: subject_id, simulation_name, effect_size. Optional column: weight.

Source code in tit/stats/config.py

@classmethod
def load_subjects(cls, csv_path: str) -> list["CorrelationConfig.Subject"]:
    """Load correlation subjects from a CSV file.

    Expected columns: subject_id, simulation_name, effect_size.
    Optional column: weight.
    """
    import pandas as pd

    df = pd.read_csv(csv_path)
    required = {"subject_id", "simulation_name", "effect_size"}
    missing = required - set(df.columns)
    if missing:
        raise ValueError(f"CSV missing required columns: {missing}")

    has_weights = "weight" in df.columns
    subjects = []
    for _, row in df.iterrows():
        if pd.isna(row["subject_id"]) or pd.isna(row["effect_size"]):
            continue

        sid = row["subject_id"]
        if isinstance(sid, float):
            sid = str(int(sid))
        else:
            sid = str(sid).replace("sub-", "")
            if sid.endswith(".0"):
                sid = sid[:-2]

        weight = (
            float(row["weight"])
            if has_weights and pd.notna(row.get("weight"))
            else 1.0
        )
        subjects.append(
            cls.Subject(
                subject_id=sid,
                simulation_name=str(row["simulation_name"]),
                effect_size=float(row["effect_size"]),
                weight=weight,
            )
        )

    if not subjects:
        raise ValueError("No valid subjects found in CSV")
    return subjects

GroupComparisonResult dataclass

GroupComparisonResult(success: bool, output_dir: str, n_responders: int, n_non_responders: int, n_significant_voxels: int, n_significant_clusters: int, cluster_threshold: float, analysis_time: float, clusters: list, log_file: str)

Result of a group comparison permutation test.

CorrelationResult dataclass

CorrelationResult(success: bool, output_dir: str, n_subjects: int, n_significant_voxels: int, n_significant_clusters: int, cluster_threshold: float, analysis_time: float, clusters: list, log_file: str)

Result of a correlation permutation test.