__odrpack.pyi

from collections.abc import Callable
from typing import Annotated

from numpy.typing import ArrayLike


def loc_iwork(m: int, q: int, npar: int) -> dict[str, int]:
    """
    Get storage locations within the integer work space.

    Parameters
    ----------
    m : int
        Number of columns of data in the explanatory variable.
    q : int
        Number of responses per observation.
    npar : int
        Number of function parameters.

    Returns
    -------
    dict[str, int]
        A dictionary containing the 0-based indexes of the integer work array.
    """

def loc_rwork(n: int, m: int, q: int, npar: int, ldwe: int, ld2we: int, isodr: bool) -> dict[str, int]:
    """
    Get storage locations within the real work space.

    Parameters
    ----------
    n : int
        Number of observations.
    m : int
        Number of columns of data in the explanatory variable.
    q : int
        Number of responses per observation.
    npar : int
        Number of function parameters.
    ldwe : int
        Leading dimension of the `we` array.
    ld2we : int
        Second dimension of the `we` array.
    isodr : bool
        Indicates whether the solution is by ODR (True) or by OLS (False).

    Returns
    -------
    dict[str, int]
        A dictionary containing the 0-based indexes of the real work array.
    """

def odr(n: int, m: int, q: int, npar: int, ldwe: int, ld2we: int, ldwd: int, ld2wd: int, ldifx: int, ldstpd: int, ldscld: int, f: Callable, fjacb: Callable, fjacd: Callable, beta: Annotated[ArrayLike, dict(dtype='float64', order='C')], y: Annotated[ArrayLike, dict(dtype='float64', order='C', writable=False)], x: Annotated[ArrayLike, dict(dtype='float64', order='C', writable=False)], delta: Annotated[ArrayLike, dict(dtype='float64', order='C')], we: Annotated[ArrayLike, dict(dtype='float64', order='C', writable=False)] | None = None, wd: Annotated[ArrayLike, dict(dtype='float64', order='C', writable=False)] | None = None, ifixb: Annotated[ArrayLike, dict(dtype='int32', order='C', writable=False)] | None = None, ifixx: Annotated[ArrayLike, dict(dtype='int32', order='C', writable=False)] | None = None, stpb: Annotated[ArrayLike, dict(dtype='float64', order='C', writable=False)] | None = None, stpd: Annotated[ArrayLike, dict(dtype='float64', order='C', writable=False)] | None = None, sclb: Annotated[ArrayLike, dict(dtype='float64', order='C', writable=False)] | None = None, scld: Annotated[ArrayLike, dict(dtype='float64', order='C', writable=False)] | None = None, lower: Annotated[ArrayLike, dict(dtype='float64', order='C', writable=False)] | None = None, upper: Annotated[ArrayLike, dict(dtype='float64', order='C', writable=False)] | None = None, rwork: Annotated[ArrayLike, dict(dtype='float64', order='C')] | None = None, iwork: Annotated[ArrayLike, dict(dtype='int32', order='C')] | None = None, job: int | None = None, ndigit: int | None = None, taufac: float | None = None, sstol: float | None = None, partol: float | None = None, maxit: int | None = None, iprint: int | None = None, errfile: str | None = None, rptfile: str | None = None) -> int:
    """
    C++ wrapper for the Orthogonal Distance Regression (ODR) routine.

    Parameters
    ----------
    n : int
        Number of observations.
    m : int
        Number of columns in the independent variable data.
    q : int
        Number of responses per observation.
    npar : int
        Number of function parameters.
    ldwe : int
        Leading dimension of the `we` array, must be in `{1, n}`.
    ld2we : int
        Second dimension of the `we` array, must be in `{1, q}`.
    ldwd : int
        Leading dimension of the `wd` array, must be in `{1, n}`.
    ld2wd : int
        Second dimension of the `wd` array, must be in `{1, m}`.
    ldifx : int
        Leading dimension of the `ifixx` array, must be in `{1, n}`.
    ldstpd : int
        Leading dimension of the `stpd` array, must be in `{1, n}`.
    ldscld : int
        Leading dimension of the `scld` array, must be in `{1, n}`.
    f : Callable
        User-supplied function for evaluating the model, `f(x, beta)`.
    fjacb : Callable
        User-supplied function for evaluating the Jacobian w.r.t. `beta`,
        `fjacb(x, beta)`.
    fjacd : Callable
        User-supplied function for evaluating the Jacobian w.r.t. `delta`,
        `fjacd(x, beta)`.
    beta : np.ndarray[float64]
        Array of function parameters with shape `(npar)`.
    y : np.ndarray[float64]
        Dependent variables with shape `(q, n)`. Ignored for implicit models.
    x : np.ndarray[float64]
        Explanatory variables with shape `(m, n)`.
    delta : np.ndarray[float64]
        Initial errors in `x` data with shape `(m, n)`.
    we : np.ndarray[float64], optional
        Weights for `epsilon` with shape `(q, ld2we, ldwe)`. Default is None.
    wd : np.ndarray[float64], optional
        Weights for `delta` with shape `(m, ld2wd, ldwd)`. Default is None.
    ifixb : np.ndarray[int32], optional
        Indicates fixed elements of `beta`. Default is None.
    ifixx : np.ndarray[int32], optional
        Indicates fixed elements of `x`. Default is None.
    stpb : np.ndarray[float64], optional
        Relative steps for finite difference derivatives w.r.t. `beta`. Default is None.
    stpd : np.ndarray[float64], optional
        Relative steps for finite difference derivatives w.r.t. `delta`. Default is None.
    sclb : np.ndarray[float64], optional
        Scaling values for `beta`. Default is None.
    scld : np.ndarray[float64], optional
        Scaling values for `delta`. Default is None.
    lower : np.ndarray[float64], optional
        Lower bounds for `beta`. Default is None.
    upper : np.ndarray[float64], optional
        Upper bounds for `beta`. Default is None.
    rwork : np.ndarray[float64], optional
        Real work space. Default is None.
    iwork : np.ndarray[int32], optional
        Integer work space. Default is None.
    job : int, optional
        Controls initialization and computational method. Default is None.
    ndigit : int, optional
        Number of accurate digits in function results. Default is None.
    taufac : float, optional
        Factor for initial trust region diameter. Default is None.
    sstol : float, optional
        Sum-of-squares convergence tolerance. Default is None.
    partol : float, optional
        Parameter convergence tolerance. Default is None.
    maxit : int, optional
        Maximum number of iterations. Default is None.
    iprint : int, optional
        Print control variable. Default is None.
    errfile : str, optional
        Filename to use for error messages. Default is None.
    rptfile : str, optional
        Filename to use for computation reports. Default is None.

    Returns
    -------
    info : int
        Reason for stopping.

    Notes
    -----
    - Ensure all array dimensions and functions are consistent with the provided arguments.
    - Input arrays will automatically be made contiguous and cast to the correct type if necessary.
    """

def workspace_dimensions(n: int, m: int, q: int, npar: int, isodr: bool) -> tuple:
    """
    Calculate the dimensions of the workspace arrays.

    Parameters
    ----------
    n : int
        Number of observations.
    m : int
        Number of columns of data in the explanatory variable.
    q : int
        Number of responses per observation.
    npar : int
        Number of function parameters.
    isodr : bool
        Variable designating whether the solution is by ODR (`True`) or by OLS (`False`).

    Returns
    -------
    tuple[int, int]
        A tuple containing the lengths of the work arrays (`lrwork`, `liwork`).
    """