add ArrayLike types for input args

Author: HugoMVale

src/odrpack/odr_scipy.py

@@ -5,36 +5,36 @@
 from odrpack.__odrpack import loc_iwork, loc_rwork
 from odrpack.__odrpack import odr as _odr
 from odrpack.__odrpack import workspace_dimensions, stop_message
-from odrpack.result import BoolArray, F64Array, OdrResult
+from odrpack.result import BoolArrayLike, F64Array, F64ArrayLike, OdrResult
 
 __all__ = ['odr_fit']
 
 
 def odr_fit(f: Callable[[F64Array, F64Array], F64Array],
-            xdata: F64Array,
-            ydata: F64Array,
-            beta0: F64Array,
+            xdata: F64ArrayLike,
+            ydata: F64ArrayLike,
+            beta0: F64ArrayLike,
             *,
-            weight_x: float | F64Array | None = None,
-            weight_y: float | F64Array | None = None,
-            bounds: tuple[F64Array | None, F64Array | None] | None = None,
+            weight_x: float | F64ArrayLike | None = None,
+            weight_y: float | F64ArrayLike | None = None,
+            bounds: tuple[F64ArrayLike | None, F64ArrayLike | None] | None = None,
             task: Literal['explicit-ODR', 'implicit-ODR', 'OLS'] = 'explicit-ODR',
-            fix_beta: BoolArray | None = None,
-            fix_x: BoolArray | None = None,
+            fix_beta: BoolArrayLike | None = None,
+            fix_x: BoolArrayLike | None = None,
             jac_beta: Callable[[F64Array, F64Array], F64Array] | None = None,
             jac_x: Callable[[F64Array, F64Array], F64Array] | None = None,
-            delta0: F64Array | None = None,
+            delta0: F64ArrayLike | None = None,
             diff_scheme: Literal['forward', 'central'] = 'forward',
             report: Literal['none', 'short', 'long', 'iteration'] = 'none',
             maxit: int = 50,
             ndigit: int | None = None,
             taufac: float | None = None,
             sstol: float | None = None,
             partol: float | None = None,
-            step_beta: F64Array | None = None,
-            step_delta: F64Array | None = None,
-            scale_beta: F64Array | None = None,
-            scale_delta: F64Array | None = None,
+            step_beta: F64ArrayLike | None = None,
+            step_delta: F64ArrayLike | None = None,
+            scale_beta: F64ArrayLike | None = None,
+            scale_delta: F64ArrayLike | None = None,
             rptfile: str | None = None,
             errfile: str | None = None,
             ) -> OdrResult:
@@ -46,19 +46,19 @@ def odr_fit(f: Callable[[F64Array, F64Array], F64Array],
     f : Callable[[F64Array, F64Array], F64Array]
         Function to be fitted, with the signature `f(x, beta)`. It must return
         an array with the same shape as `y`.
-    xdata : F64Array
+    xdata : F64ArrayLike
         Array of shape `(n,)` or `(m, n)` containing the observed values of the
         explanatory variable(s).
-    ydata : F64Array
+    ydata : F64ArrayLike
         Array of shape `(n,)` or `(q, n)` containing the observed values of the
         response variable(s). When the model is explicit, `ydata` must contain
         a value for each observation. To ignore specific values (e.g., missing
         data), set the corresponding entry in `weight_y` to zero. When the model
         is implicit, `ydata` is not used (but must be defined).
-    beta0 : F64Array
+    beta0 : F64ArrayLike
         Array of shape `(npar,)` with the initial guesses of the model parameters,
         within the optional bounds specified by `bounds`.
-    weight_x : float | F64Array | None
+    weight_x : float | F64ArrayLike | None
         Scalar or array specifying how the errors on `xdata` are to be weighted.
         If `weight_x` is a scalar, then it is used for all data points. If
         `weight_x` is an array of shape `(n,)` and `m==1`, then `weight_x[i]`
@@ -73,7 +73,7 @@ def odr_fit(f: Callable[[F64Array, F64Array], F64Array],
         comprehensive description of the options, refer to page 26 of the
         ODRPACK95 guide. By default, `weight_x` is set to one for all `xdata`
         points.
-    weight_y : float | F64Array | None
+    weight_y : float | F64ArrayLike | None
         Scalar or array specifying how the errors on `ydata` are to be weighted.
         If `weight_y` is a scalar, then it is used for all data points. If
         `weight_y` is an array of shape `(n,)` and `q==1`, then `weight_y[i]`
@@ -88,7 +88,7 @@ def odr_fit(f: Callable[[F64Array, F64Array], F64Array],
         comprehensive description of the options, refer to page 25 of the
         ODRPACK95 guide. By default, `weight_y` is set to one for all `ydata`
         points.
-    bounds : tuple[F64Array | None, F64Array | None] | None
+    bounds : tuple[F64ArrayLike | None, F64ArrayLike | None] | None
         Tuple of arrays with the same shape as `beta0`, specifying the lower and
         upper bounds of the model parameters. The first array contains the lower
         bounds, and the second contains the upper bounds. By default, the bounds
@@ -99,11 +99,11 @@ def odr_fit(f: Callable[[F64Array, F64Array], F64Array],
         an orthogonal distance regression problem with an explicit model.
         `'implicit-ODR'` handles models defined implicitly. `'OLS'` performs
         ordinary least squares fitting.
-    fix_beta : BoolArray | None
+    fix_beta : BoolArrayLike | None
         Array with the same shape as `beta0`, specifying which elements of `beta`
         are to be held fixed. `True` means the parameter is fixed; `False` means
         it is adjustable. By default, all elements of `beta` are set to `False`.
-    fix_x : BoolArray | None
+    fix_x : BoolArrayLike | None
         Array with the same shape as `xdata`, specifying which elements of `xdata`
         are to be held fixed. Alternatively, it can be a rank-1 array of shape
         `(m,)` or `(n,)`, in which case it will be broadcast along the other
@@ -122,7 +122,7 @@ def odr_fit(f: Callable[[F64Array, F64Array], F64Array],
         signature `jac_x(x, beta)`. It must return an array with shape 
         `(q, m, n)` or a compatible shape. By default, the Jacobian is approximated
         numerically using the finite difference scheme specified by `diff_scheme`.
-    delta0 : F64Array | None
+    delta0 : F64ArrayLike | None
         Array with the same shape as `xdata`, containing the initial guesses of 
         the errors in the explanatory variable. By default, `delta0` is set to
         zero for all elements of `xdata`.
@@ -159,30 +159,30 @@ def odr_fit(f: Callable[[F64Array, F64Array], F64Array],
         explicit, the default value is `eps**(2/3)`, and when the model is
         implicit, the default value is `eps**(1/3)`, where `eps` is the machine
         precision in `float64`.
-    step_beta : F64Array | None
+    step_beta : F64ArrayLike | None
         Array with the same shape as `beta0` containing the _relative_ step
         sizes used to compute the finite difference derivatives with respect
         to the model parameters. By default, the step size is set internally 
         based on the value of `ndigit` and the type of finite differences
         specified by `diff_scheme`. For additional details, refer to pages 31
         and 78 of the ODRPACK95 guide.
-    step_delta : F64Array | None
+    step_delta : F64ArrayLike | None
         Array with the same shape as `xdata`, containing the _relative_ step 
         sizes used to compute the finite difference derivatives with respect to
         the errors in the explanatory variable. Alternatively, it can be a rank-1
         array of shape `(m,)` or `(n,)`, in which case it will be broadcast along
         the other axis. By default, step size is set internally based on the value
         of `ndigit` and the type of finite differences specified by `diff_scheme`.
         For additional details, refer to pages 31 and 78 of the ODRPACK95 guide.
-    scale_beta : F64Array | None
+    scale_beta : F64ArrayLike | None
         Array with the same shape as `beta0` containing the scale values of the
         model parameters. Scaling is used to improve the numerical stability
         of the regression, but does not affect the problem specification. Scaling
         should not be confused with the weighting matrices `weight_x` and 
         `weight_y`. By default, the scale is set internally based on the relative
         magnitudes of `beta`. For further details, refer to pages 32 and 84 of
         the ODRPACK95 guide.
-    scale_delta : F64Array | None
+    scale_delta : F64ArrayLike | None
         Array with the same shape as `xdata`, containing the scale values of the
         errors in the explanatory variable. Alternatively, it can be a rank-1
         array of shape `(m,)` or `(n,)`, in which case it will be broadcast along

src/odrpack/result.py

@@ -1,85 +1,87 @@
-from dataclasses import dataclass
-
-import numpy as np
-from numpy.typing import NDArray
-
-__all__ = ['OdrResult']
-
-F64Array = NDArray[np.float64]
-I32Array = NDArray[np.int32]
-BoolArray = NDArray[np.bool_]
-
-
-@dataclass(frozen=True)
-class OdrResult():
-    """
-    Results of an Orthogonal Distance Regression (ODR) computation.
-
-    Attributes
-    ----------
-    beta : F64Array
-        Estimated parameters of the model.
-    delta : F64Array
-        Differences between the observed and fitted `x` values.
-    eps : F64Array
-        Differences between the observed and fitted `y` values.
-    xplusd : F64Array
-        Adjusted `x` values after fitting, `x + delta`.
-    yest : F64Array
-        Estimated `y` values corresponding to the fitted model, `y + eps`.
-    sd_beta : F64Array
-        Standard deviations of the estimated parameters.
-    cov_beta : F64Array
-        Covariance matrix of the estimated parameters.
-    res_var : float
-        Residual variance, indicating the variance of the residuals.
-    nfev : int
-        Number of function evaluations during the fitting process.
-    njev : int
-        Number of Jacobian evaluations during the fitting process.
-    niter : int
-        Number of iterations performed in the optimization process.
-    irank : int
-        Rank of the Jacobian matrix at the solution.
-    inv_condnum : float
-        Inverse of the condition number of the Jacobian matrix.
-    info : int
-        Status code of the fitting process (e.g., success or failure).
-    stopreason : str
-        Message indicating the reason for stopping.
-    success : bool      
-        Whether the fitting process was successful.
-    sum_square : float
-        Sum of squared residuals (including both `delta` and `eps`).
-    sum_square_delta : float
-        Sum of squared differences between observed and fitted `x` values.
-    sum_square_eps : float
-        Sum of squared differences between observed and fitted `y` values.
-    iwork : I32Array
-        Integer workspace array used internally by `odrpack`. Typically for
-        advanced debugging.
-    rwork : F64Array
-        Floating-point workspace array used internally by `odrpack`. Typically
-        for advanced debugging.
-    """
-    beta: F64Array
-    delta: F64Array
-    eps: F64Array
-    xplusd: F64Array
-    yest: F64Array
-    sd_beta: F64Array
-    cov_beta: F64Array
-    res_var: float
-    nfev: int
-    njev: int
-    niter: int
-    irank: int
-    inv_condnum: float
-    info: int
-    stopreason: str
-    success: bool
-    sum_square: float
-    sum_square_delta: float
-    sum_square_eps: float
-    iwork: I32Array
-    rwork: F64Array
+from dataclasses import dataclass
+
+import numpy as np
+from numpy.typing import NDArray, ArrayLike
+
+__all__ = ['OdrResult']
+
+F64Array = NDArray[np.float64]
+F64ArrayLike = ArrayLike
+I32Array = NDArray[np.int32]
+BoolArray = NDArray[np.bool_]
+BoolArrayLike = ArrayLike
+
+
+@dataclass(frozen=True, slots=False)
+class OdrResult():
+    """
+    Results of an Orthogonal Distance Regression (ODR) computation.
+
+    Attributes
+    ----------
+    beta : F64Array
+        Estimated parameters of the model.
+    delta : F64Array
+        Differences between the observed and fitted `x` values.
+    eps : F64Array
+        Differences between the observed and fitted `y` values.
+    xplusd : F64Array
+        Adjusted `x` values after fitting, `x + delta`.
+    yest : F64Array
+        Estimated `y` values corresponding to the fitted model, `y + eps`.
+    sd_beta : F64Array
+        Standard deviations of the estimated parameters.
+    cov_beta : F64Array
+        Covariance matrix of the estimated parameters.
+    res_var : float
+        Residual variance, indicating the variance of the residuals.
+    nfev : int
+        Number of function evaluations during the fitting process.
+    njev : int
+        Number of Jacobian evaluations during the fitting process.
+    niter : int
+        Number of iterations performed in the optimization process.
+    irank : int
+        Rank of the Jacobian matrix at the solution.
+    inv_condnum : float
+        Inverse of the condition number of the Jacobian matrix.
+    info : int
+        Status code of the fitting process (e.g., success or failure).
+    stopreason : str
+        Message indicating the reason for stopping.
+    success : bool      
+        Whether the fitting process was successful.
+    sum_square : float
+        Sum of squared residuals (including both `delta` and `eps`).
+    sum_square_delta : float
+        Sum of squared differences between observed and fitted `x` values.
+    sum_square_eps : float
+        Sum of squared differences between observed and fitted `y` values.
+    iwork : I32Array
+        Integer workspace array used internally by `odrpack`. Typically for
+        advanced debugging.
+    rwork : F64Array
+        Floating-point workspace array used internally by `odrpack`. Typically
+        for advanced debugging.
+    """
+    beta: F64Array
+    delta: F64Array
+    eps: F64Array
+    xplusd: F64Array
+    yest: F64Array
+    sd_beta: F64Array
+    cov_beta: F64Array
+    res_var: float
+    nfev: int
+    njev: int
+    niter: int
+    irank: int
+    inv_condnum: float
+    info: int
+    stopreason: str
+    success: bool
+    sum_square: float
+    sum_square_delta: float
+    sum_square_eps: float
+    iwork: I32Array
+    rwork: F64Array