# -*- coding: utf-8 -*-
r"""
Consenrich core functions and classes.

"""

import logging
import os
from tempfile import NamedTemporaryFile
from typing import Callable, List, Optional, Tuple, DefaultDict, Any, NamedTuple

import numpy as np
import numpy.typing as npt
import pybedtools as bed
from scipy import signal, ndimage

from . import cconsenrich

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s - %(module)s.%(funcName)s -  %(levelname)s - %(message)s",
)
logging.basicConfig(
    level=logging.WARNING,
    format="%(asctime)s - %(module)s.%(funcName)s -  %(levelname)s - %(message)s",
)
logger = logging.getLogger(__name__)


def resolveExtendBP(extendBP, bamFiles: List[str]) -> List[int]:
    numFiles = len(bamFiles)

    if isinstance(extendBP, str):
        stringValue = extendBP.replace(" ", "")
        try:
            extendBP = (
                [int(x) for x in stringValue.split(",")] if stringValue else []
            )
        except ValueError:
            raise ValueError(
                "`extendBP` string must be comma-separated values (castable to integers)"
            )
    if extendBP is None:
        return [0] * numFiles
    elif isinstance(extendBP, list):
        valuesList = [int(x) for x in extendBP]
        valuesLen = len(valuesList)
        if valuesLen == 0:
            return [0] * numFiles
        if valuesLen == 1:
            return [valuesList[0]] * numFiles
        if valuesLen == numFiles:
            return valuesList
        raise ValueError(
            f"extendBP length {valuesLen} does not match number of bamFiles {numFiles}; "
            f"provide 0, 1, or {numFiles} values."
        )
    elif isinstance(extendBP, int) or isinstance(extendBP, float):
        return [int(extendBP)] * numFiles
    raise TypeError(
        f"Invalid extendBP type: {type(extendBP).__name__}. "
        "Expecting a single number (broadcast), a list of numbers matching `bamFiles`."
    )


class processParams(NamedTuple):
    r"""Parameters related to the process model of Consenrich.

    The process model governs the signal and variance propagation
    through the state transition :math:`\mathbf{F} \in \mathbb{R}^{2 \times 2}`
    and process noise covariance :math:`\mathbf{Q}_{[i]} \in \mathbb{R}^{2 \times 2}`
    matrices.

    :param deltaF: Scales the signal and variance propagation between adjacent genomic intervals.
    :param minQ: Minimum process noise level (diagonal in :math:`\mathbf{Q}_{[i]}`)
        for each state variable. Adjust relative to data quality (more reliable data --> lower minQ).
    :type minQ: float
    :param dStatAlpha: Threshold on the deviation between the data and estimated signal -- used to determine whether the process noise is scaled up.
    :type dStatAlpha: float
    :param dStatd: Constant :math:`d` in the scaling expression :math:`\sqrt{d|D_{[i]} - \alpha_D| + c}`
        that is used to up/down-scale the process noise covariance in the event of a model mismatch.
    :type dStatd: float
    :param dStatPC: Constant :math:`c` in the scaling expression :math:`\sqrt{d|D_{[i]} - \alpha_D| + c}`
        that is used to up/down-scale the process noise covariance in the event of a model mismatch.
    :type dStatPC: float
    """

    deltaF: float
    minQ: float
    maxQ: float
    offDiagQ: float
    dStatAlpha: float
    dStatd: float
    dStatPC: float


class observationParams(NamedTuple):
    r"""Parameters related to the observation model of Consenrich.
    The observation model is used to integrate sequence alignment count
    data from the multiple input samples and account for region-and-sample-specific
    noise processes corrupting data. The observation model matrix
    :math:`\mathbf{H} \in \mathbb{R}^{m \times 2}` maps from the state dimension (2)
    to the dimension of measurements/data (:math:`m`).

    :param minR: The minimum observation noise level for each sample
        :math:`j=1\ldots m` in the observation noise covariance
        matrix :math:`\mathbf{R}_{[i, (11:mm)]}`.
    :type minR: float
    :param numNearest: The number of nearest nearby sparse features to use for local
        variance calculation. Ignored if `useALV` is True.
    :type numNearest: int
    :param localWeight: The coefficient for the local noise level (based on the local surrounding window / `numNearest` features) used in the weighted sum measuring sample-specific noise level at the current interval.
    :type localWeight: float
    :param globalWeight: The coefficient for the global noise level (based on all genomic intervals :math:`i=1\ldots n`) used in the weighted sum measuring sample-specific noise level at the current interval.
    :type globalWeight: float
    :param approximationWindowLengthBP: The length of the local approximation window in base pairs (BP)
        for the local variance calculation.
    :type approximationWindowLengthBP: int
    :param sparseBedFile: The path to a BED file of 'sparse' regions for the local variance calculation.
    :type sparseBedFile: str, optional
    :param noGlobal: If True, only the 'local' variances are used to approximate observation noise
        covariance :math:`\mathbf{R}_{[:, (11:mm)]}`.
    :type noGlobal: bool
    :param useALV: Whether to use average local variance (ALV) to approximate observation noise
        covariances per-sample, per-interval. Recommended for estimating signals associated with
        repressive/heterochromatic elements.
    :type useALV: bool
    :param useConstantNoiseLevel: Whether to use a constant noise level in the observation model.
    :type useConstantNoiseLevel: bool
    :param lowPassFilterType: The type of low-pass filter to use (e.g., 'median', 'mean').
    :type lowPassFilterType: Optional[str]
    """

    minR: float
    maxR: float
    useALV: bool
    useConstantNoiseLevel: bool
    noGlobal: bool
    numNearest: int
    localWeight: float
    globalWeight: float
    approximationWindowLengthBP: int
    lowPassWindowLengthBP: int
    lowPassFilterType: Optional[str]
    returnCenter: bool


class stateParams(NamedTuple):
    r"""Parameters related to state and uncertainty bounds and initialization.

    :param stateInit: Initial value of the 'primary' state/signal at the first genomic interval: :math:`x_{[1]}`
    :type stateInit: float
    :param stateCovarInit: Initial state covariance (covariance) scale. Note, the *initial* state uncertainty :math:`\mathbf{P}_{[1]}` is a multiple of the identity matrix :math:`\mathbf{I}`
    :type stateCovarInit: float
    :param boundState: If True, the primary state estimate for :math:`x_{[i]}` is constrained within `stateLowerBound` and `stateUpperBound`.
    :type boundState: bool
    :param stateLowerBound: Lower bound for the state estimate.
    :type stateLowerBound: float
    :param stateUpperBound: Upper bound for the state estimate.
    :type stateUpperBound: float
    """

    stateInit: float
    stateCovarInit: float
    boundState: bool
    stateLowerBound: float
    stateUpperBound: float


class samParams(NamedTuple):
    r"""Parameters related to reading BAM files

    :param samThreads: The number of threads to use for reading BAM files.
    :type samThreads: int
    :param samFlagExclude: The SAM flag to exclude certain reads.
    :type samFlagExclude: int
    :param oneReadPerBin: If 1, only the interval with the greatest read overlap is incremented.
    :type oneReadPerBin: int
    :param chunkSize: maximum number of intervals' data to hold in memory before flushing to disk.
    :type chunkSize: int
    :param offsetStr: A string of two comma-separated integers -- first for the 5' shift on forward strand, second for the 5' shift on reverse strand.
    :type offsetStr: str
    :param extendBP: A list of integers specifying the number of base pairs to extend reads for each BAM file after shifting per `offsetStr`.
        If all BAM files share the same expected frag. length, can supply a single numeric value to be broadcasted. Ignored for PE reads.
    :type extendBP: List[int]
    :param maxInsertSize: Maximum frag length/insert for paired-end reads.
    :type maxInsertSize: int
    :param pairedEndMode: If > 0, only proper pairs are counted subject to `maxInsertSize`.
    :type pairedEndMode: int
    :param inferFragmentLength: Intended for single-end data: if > 0, the maximum correlation lag
       (avg.) between *strand-specific* read tracks is taken as the fragment length estimate and used to
       extend reads from 5'. Ignored if `pairedEndMode > 0` or `extendBP` set. This parameter is particularly
       important when targeting broader marks (e.g., ChIP-seq H3K27me3).
    :type inferFragmentLength: int

    .. tip::

        For an overview of SAM flags, see https://broadinstitute.github.io/picard/explain-flags.html

    """

    samThreads: int
    samFlagExclude: int
    oneReadPerBin: int
    chunkSize: int
    offsetStr: Optional[str] = "0,0"
    extendBP: Optional[List[int]] = []
    maxInsertSize: Optional[int] = 1000
    pairedEndMode: Optional[int] = 0
    inferFragmentLength: Optional[int] = 0


class detrendParams(NamedTuple):
    r"""Parameters related detrending and background-removal

    :param useOrderStatFilter: Whether to use a local/moving order statistic (percentile filter) to model and remove trends in the read density data.
    :type useOrderStatFilter: bool
    :param usePolyFilter: Whether to use a low-degree polynomial fit to model and remove trends in the read density data.
    :type usePolyFilter: bool
    :param detrendSavitzkyGolayDegree: The polynomial degree of the Savitzky-Golay filter to use for detrending
    :type detrendSavitzkyGolayDegree: int
    :param detrendTrackPercentile: The percentile to use for the local/moving order-statistic filter.
      Decrease for broad marks + sparse data if `useOrderStatFilter` is True.
    :type detrendTrackPercentile: float
    :param detrendWindowLengthBP: The length of the window in base pairs for detrending.
      Increase for broader marks + sparse data.
    :type detrendWindowLengthBP: int
    """

    useOrderStatFilter: bool
    usePolyFilter: bool
    detrendTrackPercentile: float
    detrendSavitzkyGolayDegree: int
    detrendWindowLengthBP: int


class inputParams(NamedTuple):
    r"""Parameters related to the input data for Consenrich.

    :param bamFiles: A list of paths to distinct coordinate-sorted and indexed BAM files.
    :type bamFiles: List[str]

    :param bamFilesControl: A list of paths to distinct coordinate-sorted and
        indexed control BAM files. e.g., IgG control inputs for ChIP-seq.

    :type bamFilesControl: List[str], optional

    """

    bamFiles: List[str]
    bamFilesControl: Optional[List[str]]


class genomeParams(NamedTuple):
    r"""Specify assembly-specific resources, parameters.

    :param genomeName: If supplied, default resources for the assembly (sizes file, blacklist, and 'sparse' regions) in `src/consenrich/data` are used.
      ``ce10, ce11, dm6, hg19, hg38, mm10, mm39`` have default resources available.
    :type genomeName: str
    :param chromSizesFile: A two-column TSV-like file with chromosome names and sizes (in base pairs).
    :type chromSizesFile: str
    :param blacklistFile: A BED file with regions to exclude.
    :type blacklistFile: str, optional
    :param sparseBedFile: A BED file with sparse regions used to estimate noise levels -- ignored if `observationParams.useALV` is True.
    :type sparseBedFile: str, optional
    :param chromosomes: A list of chromosome names to analyze. If None, all chromosomes in `chromSizesFile` are used.
    :type chromosomes: List[str]
    """

    genomeName: str
    chromSizesFile: str
    blacklistFile: Optional[str]
    sparseBedFile: Optional[str]
    chromosomes: List[str]
    excludeChroms: List[str]
    excludeForNorm: List[str]


class countingParams(NamedTuple):
    r"""Parameters related to counting reads in genomic intervals.

    :param stepSize: Step size (bp) for the genomic intervals (AKA bin size, interval length, width, etc.)
    :type stepSize: int
    :param scaleDown: If using paired treatment and control BAM files, whether to
        scale down the larger of the two before computing the difference/ratio
    :type scaleDown: bool, optional
    :param scaleFactors: Scale factors for the read counts.
    :type scaleFactors: List[float], optional
    :param scaleFactorsControl: Scale factors for the control read counts.
    :type scaleFactorsControl: List[float], optional
    :param numReads: Number of reads to sample.
    :type numReads: int
    :param applyAsinh: If true, :math:`\textsf{arsinh}(x)` applied to counts :math:`x` (log-like for large values and linear near the origin)
    :type applyAsinh: bool, optional
    :param applyLog: If true, :math:`\textsf{log}(x + 1)` applied to counts :math:`x`
    :type applyLog: bool, optional
    """

    stepSize: int
    scaleDown: Optional[bool]
    scaleFactors: Optional[List[float]]
    scaleFactorsControl: Optional[List[float]]
    numReads: int
    applyAsinh: Optional[bool]
    applyLog: Optional[bool]
    rescaleToTreatmentCoverage: Optional[bool] = False


class matchingParams(NamedTuple):
    r"""Parameters related to the (experimental) pattern matching routine packaged with this software.

    :param templateNames: A list of wavelet bases used for matching, e.g., `[haar, db2, sym4]`
    :type templateNames: List[str]
    :param cascadeLevels: A list of values -- the number of cascade iterations used for approximating the scaling/wavelet functions.
    :type cascadeLevels: List[int]
    :param iters: Number of random blocks in the cross correlation sequence to sample when building the null. Expected block length is equal to template length.
    :type iters: int
    :param alpha: Primary significance threshold on detected matches. Specifically, the
        :math:`1 - \alpha` quantile of an empirical null distribution. The empirical null
        distribution is built from cross-correlation values over randomly sampled blocks.
    :type alpha: float
    :param minSignalAtMaxima: Secondary significance threshold coupled with `alpha`.
        If None, the median non-zero signal estimate is used.
    :type minSignalAtMaxima: float
    :param merge: Whether to merge overlapping matches within `mergeGapBP` base pairs. A separate narrowPeak file will be created for the merged matches -- the original is preserved too.
    :type merge: bool
    :param mergeGapBP: If `merge` is True, this value sets the maximum bp-gap allowed between distinct matches (merged otherwise)
    :type mergeGapBP: int
    :param useScalingFunction: If True, use (only) the scaling function to build the matching template.
      If False, use (only) the wavelet function.
    :type useScalingFunction: bool
    :param excludeRegionsBedFile: A BED file with regions to exclude while building the empirical null distribution.

    See :func:`consenrich.matching.matchWavelet` for implementation.
    """

    templateNames: List[str]
    cascadeLevels: List[int]
    iters: int
    alpha: float
    minMatchLengthBP: Optional[int]
    maxNumMatches: Optional[int]
    minSignalAtMaxima: Optional[float]
    merge: bool = False
    mergeGapBP: int = 25
    useScalingFunction: bool = True
    excludeRegionsBedFile: Optional[str] = None


def _numIntervals(start: int, end: int, step: int) -> int:
    # helper for consistency
    length = max(0, end - start)
    return (length + step) // step


def getChromRanges(
    bamFile: str,
    chromosome: str,
    chromLength: int,
    samThreads: int,
    samFlagExclude: int,
) -> Tuple[int, int]:
    r"""Get the start and end positions of reads in a chromosome from a BAM file.

    :param bamFile: See :class:`inputParams`.
    :type bamFile: str
    :param chromosome: the chromosome to read in `bamFile`.
    :type chromosome: str
    :param chromLength: Base pair length of the chromosome.
    :type chromLength: int
    :param samThreads: See :class:`samParams`.
    :type samThreads: int
    :param samFlagExclude: See :class:`samParams`.
    :type samFlagExclude: int
    :return: Tuple of start and end positions (nucleotide coordinates) in the chromosome.
    :rtype: Tuple[int, int]

    :seealso: :func:`getChromRangesJoint`, :func:`cconsenrich.cgetFirstChromRead`, :func:`cconsenrich.cgetLastChromRead`
    """
    start: int = cconsenrich.cgetFirstChromRead(
        bamFile, chromosome, chromLength, samThreads, samFlagExclude
    )
    end: int = cconsenrich.cgetLastChromRead(
        bamFile, chromosome, chromLength, samThreads, samFlagExclude
    )
    return start, end


def getChromRangesJoint(
    bamFiles: List[str],
    chromosome: str,
    chromSize: int,
    samThreads: int,
    samFlagExclude: int,
) -> Tuple[int, int]:
    r"""For multiple BAM files, reconcile a single start and end position over which to count reads,
    where the start and end positions are defined by the first and last reads across all BAM files.

    :param bamFiles: List of BAM files to read.
    :type bamFiles: List[str]
    :param chromosome: Chromosome to read.
    :type chromosome: str
    :param chromSize: Size of the chromosome.
    :type chromSize: int
    :param samThreads: Number of threads to use for reading the BAM files.
    :type samThreads: int
    :param samFlagExclude: SAM flag to exclude certain reads.
    :type samFlagExclude: int
    :return: Tuple of start and end positions.
    :rtype: Tuple[int, int]

    :seealso: :func:`getChromRanges`, :func:`cconsenrich.cgetFirstChromRead`, :func:`cconsenrich.cgetLastChromRead`
    """
    starts = []
    ends = []
    for bam_ in bamFiles:
        start, end = getChromRanges(
            bam_,
            chromosome,
            chromLength=chromSize,
            samThreads=samThreads,
            samFlagExclude=samFlagExclude,
        )
        starts.append(start)
        ends.append(end)
    return min(starts), max(ends)


def getReadLength(
    bamFile: str,
    numReads: int,
    maxIterations: int,
    samThreads: int,
    samFlagExclude: int,
) -> int:
    r"""Infer read length from mapped reads in a BAM file.

    Samples at least `numReads` reads passing criteria given by `samFlagExclude`
    and returns the median read length.

    :param bamFile: See :class:`inputParams`.
    :type bamFile: str
    :param numReads: Number of reads to sample.
    :type numReads: int
    :param maxIterations: Maximum number of iterations to perform.
    :type maxIterations: int
    :param samThreads: See :class:`samParams`.
    :type samThreads: int
    :param samFlagExclude: See :class:`samParams`.
    :type samFlagExclude: int
    :return: The median read length.
    :rtype: int

    :raises ValueError: If the read length cannot be determined after scanning `maxIterations` reads.

    :seealso: :func:`cconsenrich.cgetReadLength`
    """
    init_rlen = cconsenrich.cgetReadLength(
        bamFile, numReads, samThreads, maxIterations, samFlagExclude
    )
    if init_rlen == 0:
        raise ValueError(
            f"Failed to determine read length in {bamFile}. Revise `numReads`, and/or `samFlagExclude` parameters?"
        )
    return init_rlen


def getReadLengths(
    bamFiles: List[str],
    numReads: int,
    maxIterations: int,
    samThreads: int,
    samFlagExclude: int,
) -> List[int]:
    r"""Get read lengths for a list of BAM files.

    :seealso: :func:`getReadLength`
    """
    return [
        getReadLength(
            bamFile,
            numReads=numReads,
            maxIterations=maxIterations,
            samThreads=samThreads,
            samFlagExclude=samFlagExclude,
        )
        for bamFile in bamFiles
    ]


def readBamSegments(
    bamFiles: List[str],
    chromosome: str,
    start: int,
    end: int,
    stepSize: int,
    readLengths: List[int],
    scaleFactors: List[float],
    oneReadPerBin: int,
    samThreads: int,
    samFlagExclude: int,
    offsetStr: Optional[str] = "0,0",
    applyAsinh: Optional[bool] = False,
    applyLog: Optional[bool] = False,
    extendBP: List[int] = [],
    maxInsertSize: Optional[int] = 1000,
    pairedEndMode: Optional[int] = 0,
    inferFragmentLength: Optional[int] = 0,
) -> npt.NDArray[np.float32]:
    r"""Calculate tracks of read counts (or a function thereof) for each BAM file.

    See :func:`cconsenrich.creadBamSegment` for the underlying implementation in Cython.

    :param bamFiles: See :class:`inputParams`.
    :type bamFiles: List[str]
    :param chromosome: Chromosome to read.
    :type chromosome: str
    :param start: Start position of the genomic segment.
    :type start: int
    :param end: End position of the genomic segment.
    :type end: int
    :param readLengths: List of read lengths for each BAM file.
    :type readLengths: List[int]
    :param scaleFactors: List of scale factors for each BAM file.
    :type scaleFactors: List[float]
    :param stepSize: See :class:`countingParams`.
    :type stepSize: int
    :param oneReadPerBin: See :class:`samParams`.
    :type oneReadPerBin: int
    :param samThreads: See :class:`samParams`.
    :type samThreads: int
    :param samFlagExclude: See :class:`samParams`.
    :type samFlagExclude: int
    :param offsetStr: See :class:`samParams`.
    :type offsetStr: str
    :param extendBP: See :class:`samParams`.
    :type extendBP: int
    :param maxInsertSize: See :class:`samParams`.
    :type maxInsertSize: int
    :param pairedEndMode: See :class:`samParams`.
    :type pairedEndMode: int
    :param inferFragmentLength: See :class:`samParams`.
    :type inferFragmentLength: int
    """

    if len(bamFiles) == 0:
        raise ValueError("bamFiles list is empty")

    if len(readLengths) != len(bamFiles) or len(scaleFactors) != len(bamFiles):
        raise ValueError(
            "readLengths and scaleFactors must match bamFiles length"
        )

    extendBP = resolveExtendBP(extendBP, bamFiles)
    offsetStr = ((str(offsetStr) or "0,0").replace(" ", "")).split(",")
    numIntervals = ((end - start) + stepSize - 1) // stepSize
    counts = np.empty((len(bamFiles), numIntervals), dtype=np.float32)
    for j, bam in enumerate(bamFiles):
        logger.info(f"Reading {chromosome}: {bam}")
        arr = cconsenrich.creadBamSegment(
            bam,
            chromosome,
            start,
            end,
            stepSize,
            readLengths[j],
            oneReadPerBin,
            samThreads,
            samFlagExclude,
            int(offsetStr[0]),
            int(offsetStr[1]),
            extendBP[j],
            maxInsertSize,
            pairedEndMode,
            inferFragmentLength,
        )
        # FFR: use ufuncs?
        counts[j, :] = arr
        counts[j, :] *= np.float32(scaleFactors[j])
        if applyAsinh:
            counts[j, :] = np.arcsinh(counts[j, :])
        elif applyLog:
            counts[j, :] = np.log1p(counts[j, :])
    return counts


def getAverageLocalVarianceTrack(
    values: np.ndarray,
    stepSize: int,
    approximationWindowLengthBP: int,
    lowPassWindowLengthBP: int,
    minR: float,
    maxR: float,
    lowPassFilterType: Optional[str] = "median",
) -> npt.NDArray[np.float32]:
    r"""Approximate local noise levels in a segment using an ALV approach.

    First, computes a segment-length simple moving average of `values` with a
    bp-length window `approximationWindowLengthBP`.

    Second, computes a segment-length simple moving average of squared `values`.

    Between these two averages, the difference between the latter and the square of the former
    approximates the local variance of the segment. These local variances
    are then combined with a median filter of length `lowPassWindowLengthBP`.

    :param values: An array of read-density-based values (typically from a single row in a sample-by-interval matrix)
    :type values: np.ndarray
    :param stepSize: See :class:`countingParams`.
    :type stepSize: int
    :param observationParams: See :class:`observationParams`
    :type observationParams: observationParams
    :param approximationWindowLengthBP: The length of the approximation window in base pairs (BP).
    :type approximationWindowLengthBP: int
    :param lowPassWindowLengthBP: The length of the low-pass filter window in base pairs (BP).
    :type lowPassWindowLengthBP: int
    :param lowPassFilterType: The type of low-pass filter to use (e.g., 'median', 'mean').
    :type lowPassFilterType: Optional[str]
    :seealso: :class:`observationParams`
    """
    values = np.asarray(values, dtype=np.float32)
    windowLength = int(approximationWindowLengthBP / stepSize)
    if windowLength % 2 == 0:
        windowLength += 1
    if len(values) < 3:
        constVar = np.var(values)
        if constVar < minR:
            return np.full_like(values, minR, dtype=np.float32)
        return np.full_like(values, constVar, dtype=np.float32)

    # first get a simple moving average of the values
    localMeanTrack: npt.NDArray[np.float32] = ndimage.uniform_filter(
        values, size=windowLength, mode="nearest"
    )

    #  ~ E[X_i^2] - E[X_i]^2 ~
    localVarTrack: npt.NDArray[np.float32] = (
        ndimage.uniform_filter(values**2, size=windowLength, mode="nearest")
        - localMeanTrack**2
    )

    # safe-guard: difference of convolutions returns negative values.
    # shouldn't actually happen, but just in case there are some
    # ...potential artifacts i'm unaware of edge effects, etc.
    localVarTrack = np.maximum(localVarTrack, 0.0)

    # low-pass filter on the local variance track: positional 'noise level' track
    lpassWindowLength = int(lowPassWindowLengthBP / stepSize)
    if lpassWindowLength % 2 == 0:
        lpassWindowLength += 1

    noiseLevel: npt.NDArray[np.float32] = np.zeros_like(
        localVarTrack, dtype=np.float32
    )
    if lowPassFilterType is None or (
        isinstance(lowPassFilterType, str)
        and lowPassFilterType.lower() == "median"
    ):
        noiseLevel = ndimage.median_filter(
            localVarTrack, size=lpassWindowLength
        )
    elif (
        isinstance(lowPassFilterType, str)
        and lowPassFilterType.lower() == "mean"
    ):
        noiseLevel = ndimage.uniform_filter(
            localVarTrack, size=lpassWindowLength
        )

    return np.clip(noiseLevel, minR, maxR).astype(np.float32)


def constructMatrixF(deltaF: float) -> npt.NDArray[np.float32]:
    r"""Build the state transition matrix for the process model

    :param deltaF: See :class:`processParams`.
    :type deltaF: float
    :return: The state transition matrix :math:`\mathbf{F}`
    :rtype: npt.NDArray[np.float32]

    :seealso: :class:`processParams`
    """
    initMatrixF: npt.NDArray[np.float32] = np.eye(2, dtype=np.float32)
    initMatrixF[0, 1] = np.float32(deltaF)
    return initMatrixF


def constructMatrixQ(
    minDiagQ: float, offDiagQ: float = 0.0
) -> npt.NDArray[np.float32]:
    r"""Build the initial process noise covariance matrix :math:`\mathbf{Q}_{[1]}`.

    :param minDiagQ: See :class:`processParams`.
    :type minDiagQ: float
    :param offDiagQ: See :class:`processParams`.
    :type offDiagQ: float
    :return: The initial process noise covariance matrix :math:`\mathbf{Q}_{[1]}`.
    :rtype: npt.NDArray[np.float32]

    :seealso: :class:`processParams`
    """
    minDiagQ = np.float32(minDiagQ)
    offDiagQ = np.float32(offDiagQ)
    initMatrixQ: npt.NDArray[np.float32] = np.zeros((2, 2), dtype=np.float32)
    initMatrixQ[0, 0] = minDiagQ
    initMatrixQ[1, 1] = minDiagQ
    initMatrixQ[0, 1] = offDiagQ
    initMatrixQ[1, 0] = offDiagQ
    return initMatrixQ


def constructMatrixH(
    m: int, coefficients: Optional[np.ndarray] = None
) -> npt.NDArray[np.float32]:
    r"""Build the observation model matrix :math:`\mathbf{H}`.

    :param m: Number of observations.
    :type m: int
    :param coefficients: Optional coefficients for the observation model,
        which can be used to weight the observations manually.
    :type coefficients: Optional[np.ndarray]
    :return: The observation model matrix :math:`\mathbf{H}`.
    :rtype: npt.NDArray[np.float32]

    :seealso: :class:`observationParams`, class:`inputParams`
    """
    if coefficients is None:
        coefficients = np.ones(m, dtype=np.float32)
    elif isinstance(coefficients, list):
        coefficients = np.array(coefficients, dtype=np.float32)
    initMatrixH = np.empty((m, 2), dtype=np.float32)
    initMatrixH[:, 0] = coefficients.astype(np.float32)
    initMatrixH[:, 1] = np.zeros(m, dtype=np.float32)
    return initMatrixH


def runConsenrich(
    matrixData: np.ndarray,
    matrixMunc: np.ndarray,
    deltaF: float,
    minQ: float,
    maxQ: float,
    offDiagQ: float,
    dStatAlpha: float,
    dStatd: float,
    dStatPC: float,
    stateInit: float,
    stateCovarInit: float,
    boundState: bool,
    stateLowerBound: float,
    stateUpperBound: float,
    chunkSize: int,
    progressIter: int,
    coefficientsH: Optional[np.ndarray] = None,
    residualCovarInversionFunc: Optional[Callable] = None,
    adjustProcessNoiseFunc: Optional[Callable] = None,
) -> Tuple[
    npt.NDArray[np.float32], npt.NDArray[np.float32], npt.NDArray[np.float32]
]:
    r"""Run consenrich on a contiguous segment (e.g. a chromosome) of read-density-based data.
    Completes the forward and backward passes given data and approximated observation noise
    covariance matrices :math:`\mathbf{R}_{[1:n, (11:mm)]}`.

    :param matrixData: Read density data for a single chromosome or general contiguous segment,
      possibly preprocessed. Two-dimensional array of shape :math:`m \times n` where :math:`m`
      is the number of samples/tracks and :math:`n` the number of genomic intervals.
    :type matrixData: np.ndarray
    :param matrixMunc: Uncertainty estimates for the read coverage data.
        Two-dimensional array of shape :math:`m \times n` where :math:`m` is the number of samples/tracks
        and :math:`n` the number of genomic intervals. See :func:`getMuncTrack`.
    :type matrixMunc: np.ndarray
    :param deltaF: See :class:`processParams`.
    :type deltaF: float
    :param minQ: See :class:`processParams`.
    :type minQ: float
    :param maxQ: See :class:`processParams`.
    :type maxQ: float
    :param offDiagQ: See :class:`processParams`.
    :type offDiagQ: float
    :param dStatAlpha: See :class:`processParams`.
    :type dStatAlpha: float
    :param dStatd: See :class:`processParams`.
    :type dStatd: float
    :param dStatPC: See :class:`processParams`.
    :type dStatPC: float
    :param stateInit: See :class:`stateParams`.
    :type stateInit: float
    :param stateCovarInit: See :class:`stateParams`.
    :type stateCovarInit: float
    :param chunkSize: Number of genomic intervals' data to keep in memory before flushing to disk.
    :type chunkSize: int
    :param progressIter: The number of iterations after which to log progress.
    :type progressIter: int
    :param coefficientsH: Optional coefficients for the observation model matrix :math:`\mathbf{H}`.
        If None, the coefficients are set to 1.0 for all samples.
    :type coefficientsH: Optional[np.ndarray]
    :param residualCovarInversionFunc: Callable function to invert the observation covariance matrix :math:`\mathbf{E}_{[i]}`.
        If None, defaults to :func:`cconsenrich.cinvertMatrixE`.
    :type residualCovarInversionFunc: Optional[Callable]
    :param adjustProcessNoiseFunc: Function to adjust the process noise covariance matrix :math:`\mathbf{Q}_{[i]}`.
        If None, defaults to :func:`cconsenrich.updateProcessNoiseCovariance`.
    :type adjustProcessNoiseFunc: Optional[Callable]
    :return: Tuple of three numpy arrays:
        - state estimates :math:`\widetilde{\mathbf{x}}_{[i]}` of shape :math:`n \times 2`
        - state covariance estimates :math:`\widetilde{\mathbf{P}}_{[i]}` of shape :math:`n \times 2 \times 2`
        - post-fit residuals :math:`\widetilde{\mathbf{y}}_{[i]}` of shape :math:`n \times m`
    :rtype: Tuple[np.ndarray, np.ndarray, np.ndarray]

    :raises ValueError: If the number of samples in `matrixData` is not equal to the number of samples in `matrixMunc`.
    """
    matrixData = np.ascontiguousarray(matrixData, dtype=np.float32)
    matrixMunc = np.ascontiguousarray(matrixMunc, dtype=np.float32)
    m: int = 1 if matrixData.ndim == 1 else matrixData.shape[0]
    n: int = 1 if matrixData.ndim == 1 else matrixData.shape[1]
    inflatedQ: bool = False
    dStat: float = np.float32(0.0)
    IKH: np.ndarray = np.zeros(shape=(2, 2), dtype=np.float32)
    matrixEInverse: np.ndarray = np.zeros(shape=(m, m), dtype=np.float32)

    matrixF: np.ndarray = constructMatrixF(deltaF)
    matrixQ: np.ndarray = constructMatrixQ(minQ, offDiagQ=offDiagQ)
    matrixQCopy: np.ndarray = matrixQ.copy()
    matrixP: np.ndarray = np.eye(2, dtype=np.float32) * np.float32(
        stateCovarInit
    )
    matrixH: np.ndarray = constructMatrixH(m, coefficients=coefficientsH)
    matrixK: np.ndarray = np.zeros((2, m), dtype=np.float32)
    vectorX: np.ndarray = np.array([stateInit, 0.0], dtype=np.float32)
    vectorY: np.ndarray = np.zeros(m, dtype=np.float32)
    matrixI2: np.ndarray = np.eye(2, dtype=np.float32)

    if residualCovarInversionFunc is None:
        residualCovarInversionFunc = cconsenrich.cinvertMatrixE
    if adjustProcessNoiseFunc is None:
        adjustProcessNoiseFunc = cconsenrich.updateProcessNoiseCovariance

    # ==========================
    # forward: 0,1,2,...,n-1
    # ==========================
    stateForward = np.memmap(
        NamedTemporaryFile(delete=True),
        dtype=np.float32,
        mode="w+",
        shape=(n, 2),
    )
    stateCovarForward = np.memmap(
        NamedTemporaryFile(delete=True),
        dtype=np.float32,
        mode="w+",
        shape=(n, 2, 2),
    )
    pNoiseForward = np.memmap(
        NamedTemporaryFile(delete=True),
        dtype=np.float32,
        mode="w+",
        shape=(n, 2, 2),
    )
    progressIter = max(1, progressIter)
    for i in range(n):
        if i % progressIter == 0:
            logger.info(f"Forward pass interval: {i + 1}/{n}")
        vectorZ = matrixData[:, i]
        vectorX = matrixF @ vectorX
        matrixP = matrixF @ matrixP @ matrixF.T + matrixQ
        vectorY = vectorZ - (matrixH @ vectorX)

        matrixEInverse = residualCovarInversionFunc(
            matrixMunc[:, i], np.float32(matrixP[0, 0])
        )
        Einv_diag = np.diag(matrixEInverse)
        dStat = np.median((vectorY**2) * Einv_diag)
        matrixQ, inflatedQ = adjustProcessNoiseFunc(
            matrixQ,
            matrixQCopy,
            dStat,
            dStatAlpha,
            dStatd,
            dStatPC,
            inflatedQ,
            maxQ,
            minQ,
        )
        matrixK = (matrixP @ matrixH.T) @ matrixEInverse
        IKH = matrixI2 - (matrixK @ matrixH)

        vectorX = vectorX + (matrixK @ vectorY)
        matrixP = (IKH) @ matrixP @ (IKH).T + (
            matrixK * matrixMunc[:, i]
        ) @ matrixK.T
        stateForward[i] = vectorX.astype(np.float32)
        stateCovarForward[i] = matrixP.astype(np.float32)
        pNoiseForward[i] = matrixQ.astype(np.float32)

        if i % chunkSize == 0 and i > 0:
            stateForward.flush()
            stateCovarForward.flush()
            pNoiseForward.flush()

    stateForward.flush()
    stateCovarForward.flush()
    pNoiseForward.flush()
    stateForwardArr = stateForward
    stateCovarForwardArr = stateCovarForward
    pNoiseForwardArr = pNoiseForward

    # ==========================
    # backward: n,n-1,n-2,...,0
    # ==========================
    stateSmoothed = np.memmap(
        NamedTemporaryFile(delete=True),
        dtype=np.float32,
        mode="w+",
        shape=(n, 2),
    )
    stateCovarSmoothed = np.memmap(
        NamedTemporaryFile(delete=True),
        dtype=np.float32,
        mode="w+",
        shape=(n, 2, 2),
    )
    postFitResiduals = np.memmap(
        NamedTemporaryFile(delete=True),
        dtype=np.float32,
        mode="w+",
        shape=(n, m),
    )

    stateSmoothed[-1] = np.float32(stateForwardArr[-1])
    stateCovarSmoothed[-1] = np.float32(stateCovarForwardArr[-1])
    postFitResiduals[-1] = np.float32(
        matrixData[:, -1] - (matrixH @ stateSmoothed[-1])
    )

    for k in range(n - 2, -1, -1):
        if k % progressIter == 0:
            logger.info(f"Backward pass interval: {k + 1}/{n}")
        forwardStatePosterior = stateForwardArr[k]
        forwardCovariancePosterior = stateCovarForwardArr[k]
        backwardInitialState = matrixF @ forwardStatePosterior
        backwardInitialCovariance = (
            matrixF @ forwardCovariancePosterior @ matrixF.T
            + pNoiseForwardArr[k + 1]
        )

        smootherGain = np.linalg.solve(
            backwardInitialCovariance.T,
            (forwardCovariancePosterior @ matrixF.T).T,
        ).T
        stateSmoothed[k] = (
            forwardStatePosterior
            + smootherGain @ (stateSmoothed[k + 1] - backwardInitialState)
        ).astype(np.float32)

        stateCovarSmoothed[k] = (
            forwardCovariancePosterior
            + smootherGain
            @ (stateCovarSmoothed[k + 1] - backwardInitialCovariance)
            @ smootherGain.T
        ).astype(np.float32)
        postFitResiduals[k] = np.float32(
            matrixData[:, k] - matrixH @ stateSmoothed[k]
        )

        if k % chunkSize == 0 and k > 0:
            stateSmoothed.flush()
            stateCovarSmoothed.flush()
            postFitResiduals.flush()

    stateSmoothed.flush()
    stateCovarSmoothed.flush()
    postFitResiduals.flush()
    if boundState:
        stateSmoothed[:, 0] = np.clip(
            stateSmoothed[:, 0], stateLowerBound, stateUpperBound
        ).astype(np.float32)

    return stateSmoothed[:], stateCovarSmoothed[:], postFitResiduals[:]


def getPrimaryState(
    stateVectors: np.ndarray, roundPrecision: int = 3
) -> npt.NDArray[np.float32]:
    r"""Get the primary state estimate from each vector after running Consenrich.

    :param stateVectors: State vectors from :func:`runConsenrich`.
    :type stateVectors: npt.NDArray[np.float32]
    :return: A one-dimensional numpy array of the primary state estimates.
    :rtype: npt.NDArray[np.float32]
    """
    out_ = np.ascontiguousarray(stateVectors[:,0], dtype=np.float32)
    np.round(out_, decimals=roundPrecision, out=out_)
    return out_


def getStateCovarTrace(
    stateCovarMatrices: np.ndarray, roundPrecision: int = 3
) -> npt.NDArray[np.float32]:
    r"""Get a one-dimensional array of state covariance traces after running Consenrich

    :param stateCovarMatrices: Estimated state covariance matrices :math:`\widetilde{\mathbf{P}}_{[i]}`
    :type stateCovarMatrices: np.ndarray
    :return: A one-dimensional numpy array of the traces of the state covariance matrices.
    :rtype: npt.NDArray[np.float32]
    """
    stateCovarMatrices = np.ascontiguousarray(
        stateCovarMatrices, dtype=np.float32
    )
    out_ = cconsenrich.cgetStateCovarTrace(stateCovarMatrices)
    np.round(out_, decimals=roundPrecision, out=out_)
    return out_


def getPrecisionWeightedResidual(
    postFitResiduals: np.ndarray,
    matrixMunc: np.ndarray,
    roundPrecision: int = 3,
    stateCovarSmoothed: Optional[np.ndarray] = None,
) -> npt.NDArray[np.float32]:
    r"""Get a one-dimensional precision-weighted array residuals after running Consenrich.

    Applies an inverse-variance weighting  of the post-fit residuals :math:`\widetilde{\mathbf{y}}_{[i]}` and
    returns a one-dimensional array of "precision-weighted residuals". The state-level uncertainty can also be
    incorporated given `stateCovarSmoothed`.

    :param postFitResiduals: Post-fit residuals :math:`\widetilde{\mathbf{y}}_{[i]}` from :func:`runConsenrich`.
    :type postFitResiduals: np.ndarray
    :param matrixMunc: An :math:`m \times n` sample-by-interval matrix -- At genomic intervals :math:`i = 1,2,\ldots,n`, the respective length-:math:`m` column is :math:`\mathbf{R}_{[i,11:mm]}`.
        That is, the observation noise levels for each sample :math:`j=1,2,\ldots,m` at interval :math:`i`. To keep memory usage minimal `matrixMunc` is not returned in full or computed in
        in :func:`runConsenrich`. If using Consenrich programmatically, run :func:`consenrich.core.getMuncTrack` for each sample's count data (rows in the matrix output of :func:`readBamSegments`).
    :type matrixMunc: np.ndarray
    :param stateCovarSmoothed: Smoothed state covariance matrices :math:`\widetilde{\mathbf{P}}_{[i]}` from :func:`runConsenrich`.
    :type stateCovarSmoothed: Optional[np.ndarray]
    :return: A one-dimensional array of "precision-weighted residuals"
    :rtype: npt.NDArray[np.float32]
    """

    n, m = postFitResiduals.shape
    if matrixMunc.shape != (m, n):
        raise ValueError(
            f"matrixMunc should be (m,n)=({m}, {n}): observed {matrixMunc.shape}"
        )
    if stateCovarSmoothed is not None and (
        stateCovarSmoothed.ndim < 3 or len(stateCovarSmoothed) != n
    ):
        raise ValueError(
            "stateCovarSmoothed must be shape (n) x (2,2) (if provided)"
        )

    postFitResiduals_CContig = np.ascontiguousarray(
        postFitResiduals, dtype=np.float32
    )

    needsCopy = (
        (stateCovarSmoothed is not None) and len(stateCovarSmoothed) == n) or (not matrixMunc.flags.writeable)

    matrixMunc_CContig = np.array(
        matrixMunc, dtype=np.float32, order="C", copy=needsCopy
    )

    if needsCopy:
        # adds the 'primary' state uncertainty to observation noise covariance :math:`\mathbf{R}_{[i,:]}`
        # primary state uncertainty (0,0) :math:`\mathbf{P}_{[i]} \in \mathbb{R}^{2 \times 2}`
        stateCovarArr00 = np.asarray(stateCovarSmoothed[:, 0, 0], dtype=np.float32)
        matrixMunc_CContig += stateCovarArr00

    np.maximum(matrixMunc_CContig, np.float32(1e-8), out=matrixMunc_CContig)
    out = cconsenrich.cgetPrecisionWeightedResidual(
        postFitResiduals_CContig, matrixMunc_CContig
    )
    np.round(out, decimals=roundPrecision, out=out)
    return out


def getMuncTrack(
    chromosome: str,
    intervals: np.ndarray,
    stepSize: int,
    rowValues: np.ndarray,
    minR: float,
    maxR: float,
    useALV: bool,
    useConstantNoiseLevel: bool,
    noGlobal: bool,
    localWeight: float,
    globalWeight: float,
    approximationWindowLengthBP: int,
    lowPassWindowLengthBP: int,
    returnCenter: bool,
    sparseMap: Optional[dict[int, int]] = None,
    lowPassFilterType: Optional[str] = "median",
) -> npt.NDArray[np.float32]:
    r"""Get observation noise variance :math:`R_{[:,jj]}` for the sample :math:`j`.

    :param chromosome: Tracks are approximated for this chromosome.
    :type chromosome: str
    :param intervals: Genomic intervals for which to compute the noise track.
    :param stepSize: See :class:`countingParams`.
    :type stepSize: int
    :param rowValues: Read-density-based values for the sample :math:`j` at the genomic intervals :math:`i=1,2,\ldots,n`.
    :type rowValues: np.ndarray
    :param minR: See :class:`observationParams`.
    :type minR: float
    :param maxR: See :class:`observationParams`.
    :type maxR: float
    :param useALV: See :class:`observationParams`.
    :type useALV: bool
    :param useConstantNoiseLevel: See :class:`observationParams`.
    :type useConstantNoiseLevel: bool
    :param noGlobal: See :class:`observationParams`.
    :type noGlobal: bool
    :param localWeight: See :class:`observationParams`.
    :type localWeight: float
    :param globalWeight: See :class:`observationParams`.
    :type globalWeight: float
    :param approximationWindowLengthBP: See :class:`observationParams` and/or :func:`getAverageLocalVarianceTrack`.
    :type approximationWindowLengthBP: int
    :param lowPassWindowLengthBP: See :class:`observationParams` and/or :func:`getAverageLocalVarianceTrack`.
    :type lowPassWindowLengthBP: int
    :param sparseMap: Optional mapping (dictionary) of interval indices to the nearest sparse regions. See :func:`getSparseMap`.
    :type sparseMap: Optional[dict[int, int]]
    :param lowPassFilterType: The type of low-pass filter to use in average local variance track (e.g., 'median', 'mean').
    :type lowPassFilterType: Optional[str]
    :return: A one-dimensional numpy array of the observation noise track for the sample :math:`j`.
    :rtype: npt.NDArray[np.float32]

    """
    trackALV = getAverageLocalVarianceTrack(
        rowValues,
        stepSize,
        approximationWindowLengthBP,
        lowPassWindowLengthBP,
        minR,
        maxR,
        lowPassFilterType,
    ).astype(np.float32)

    globalNoise: float = np.float32(np.mean(trackALV))
    if noGlobal or globalWeight == 0 or useALV:
        return np.clip(trackALV, minR, maxR).astype(np.float32)

    if useConstantNoiseLevel or localWeight == 0 and sparseMap is None:
        return np.clip(
            globalNoise * np.ones_like(rowValues), minR, maxR
        ).astype(np.float32)

    if sparseMap is not None:
        trackALV = cconsenrich.cSparseAvg(trackALV, sparseMap)

    return np.clip(
        trackALV * localWeight + np.mean(trackALV) * globalWeight, minR, maxR
    ).astype(np.float32)


def sparseIntersection(
    chromosome: str, intervals: np.ndarray, sparseBedFile: str
) -> npt.NDArray[np.int64]:
    r"""Returns intervals in the chromosome that overlap with the sparse features.

    Not relevant if `observationParams.useALV` is True.

    :param chromosome: The chromosome name.
    :type chromosome: str
    :param intervals: The genomic intervals to consider.
    :type intervals: np.ndarray
    :param sparseBedFile: Path to the sparse BED file.
    :type sparseBedFile: str
    :return: A numpy array of start positions of the sparse features that overlap with the intervals
    :rtype: np.ndarray[Tuple[Any], np.dtype[Any]]
    """

    stepSize: int = intervals[1] - intervals[0]
    chromFeatures: bed.BedTool = (
        bed.BedTool(sparseBedFile)
        .sort()
        .merge()
        .filter(
            lambda b: (
                b.chrom == chromosome
                and b.start > intervals[0]
                and b.end < intervals[-1]
                and (b.end - b.start) >= stepSize
            )
        )
    )
    centeredFeatures: bed.BedTool = chromFeatures.each(
        adjustFeatureBounds, stepSize=stepSize
    )

    start0: int = int(intervals[0])
    last: int = int(intervals[-1])
    chromFeatures: bed.BedTool = (
        bed.BedTool(sparseBedFile)
        .sort()
        .merge()
        .filter(
            lambda b: (
                b.chrom == chromosome
                and b.start > start0
                and b.end < last
                and (b.end - b.start) >= stepSize
            )
        )
    )
    centeredFeatures: bed.BedTool = chromFeatures.each(
        adjustFeatureBounds, stepSize=stepSize
    )
    centeredStarts = []
    for f in centeredFeatures:
        s = int(f.start)
        if start0 <= s <= last and (s - start0) % stepSize == 0:
            centeredStarts.append(s)
    return np.asarray(centeredStarts, dtype=np.int64)


def adjustFeatureBounds(feature: bed.Interval, stepSize: int) -> bed.Interval:
    r"""Adjust the start and end positions of a BED feature to be centered around a step."""
    feature.start = cconsenrich.stepAdjustment(
        (feature.start + feature.end) // 2, stepSize
    )
    feature.end = feature.start + stepSize
    return feature


def getSparseMap(
    chromosome: str,
    intervals: np.ndarray,
    numNearest: int,
    sparseBedFile: str,
) -> dict:
    r"""Build a map between each genomic interval and numNearest sparse features

    :param chromosome: The chromosome name. Note, this function only needs to be run once per chromosome.
    :type chromosome: str
    :param intervals: The genomic intervals to map.
    :type intervals: np.ndarray
    :param numNearest: The number of nearest sparse features to consider
    :type numNearest: int
    :param sparseBedFile: path to the sparse BED file.
    :type sparseBedFile: str
    :return: A dictionary mapping each interval index to the indices of the nearest sparse regions.
    :rtype: dict[int, np.ndarray]

    """
    numNearest = numNearest
    sparseStarts = sparseIntersection(chromosome, intervals, sparseBedFile)
    idxSparseInIntervals = np.searchsorted(intervals, sparseStarts, side="left")
    centers = np.searchsorted(sparseStarts, intervals, side="left")
    sparseMap: dict = {}
    for i, (interval, center) in enumerate(zip(intervals, centers)):
        left = max(0, center - numNearest)
        right = min(len(sparseStarts), center + numNearest)
        candidates = np.arange(left, right)
        dists = np.abs(sparseStarts[candidates] - interval)
        take = np.argsort(dists)[:numNearest]
        sparseMap[i] = idxSparseInIntervals[candidates[take]]
    return sparseMap


def getBedMask(
    chromosome: str,
    bedFile: str,
    intervals: np.ndarray,
) -> np.ndarray:
    r"""Return a 1/0 mask for intervals overlapping a sorted and merged BED file.

    This function is a wrapper for :func:`cconsenrich.cbedMask`.

    :param chromosome: The chromosome name.
    :type chromosome: str
    :param intervals: chromosome-specific, sorted, non-overlapping start positions of genomic intervals.
      Each interval is assumed `stepSize`.
    :type intervals: np.ndarray
    :param bedFile: Path to a sorted and merged BED file
    :type bedFile: str
    :return: An `intervals`-length mask s.t. True indicates the interval overlaps a feature in the BED file.
    :rtype: np.ndarray
    """
    if not os.path.exists(bedFile):
        raise ValueError(f"Could not find {bedFile}")
    if len(intervals) < 2:
        raise ValueError("intervals must contain at least two positions")
    bedFile_ = str(bedFile)

    # (possibly redundant) creation of uint32 version
    # + quick check for constant steps
    intervals_ = np.asarray(intervals, dtype=np.uint32)
    if (intervals_[1] - intervals_[0]) != (intervals_[-1] - intervals_[-2]):
        raise ValueError("Intervals are not fixed in size")

    stepSize_: int = intervals[1] - intervals[0]
    return cconsenrich.cbedMask(
        chromosome,
        bedFile_,
        intervals_,
        stepSize_,
    ).astype(np.bool_)
