first working version

python/.gitignore

@@ -0,0 +1 @@
+__pycache__

python/meson.build

@@ -1,21 +1,19 @@
 project(
   'odrpack',
-  ['fortran', 'c', 'cpp'],
-  version : '0.1.0',
+  ['fortran', 'cpp'],
+  version : '0.0.1',
   meson_version: '>=1.5.0',
   default_options: [
     'buildtype=debugoptimized',
     'b_ndebug=if-release',
-    'c_std=c17',
     'cpp_std=c++17'
   ],
 )
 
-# meson_path = meson.current_build_dir()
-py = import('python').find_installation(pure: false)
-py_dep = dependency('python3', required: true)
-pybind11_dep = dependency('pybind11', required: true)
+python = import('python').find_installation(pure: false)
+python_dep = python.dependency()
 openblas_dep = dependency('openblas', required: true)
+# pybind11_dep = dependency('pybind11', required: true)
 
 # odrpack fortran
 odrpack_source = files(
@@ -28,19 +26,49 @@ odrpack_source = files(
   '../c/odrpack_capi.f90'
 )
 
-odrpack_lib = library(
+odrpack_lib = static_library(
   meson.project_name(),
   sources: odrpack_source,
   dependencies: [openblas_dep],
-  install: true
 )
 
-# Extension fortran module
-py.extension_module(
- '_odrpack',
-'_odrpack.py.cpp',
- dependencies : [py_dep, pybind11_dep],
+# extension module
+pybind_ext = python.extension_module(
+ '__odrpack',
+'./src/odrpack/__odrpack.py.cpp',
+ dependencies : [python_dep],
  link_with: odrpack_lib,
- include_directories: include_directories('../c/include/odrpack'),
+ include_directories: include_directories('../c/include/odrpack', 'extern/pybind11/include/'),
  install : true,
+ subdir: 'odrpack'
 )
+
+python.install_sources(
+  [
+  'src/odrpack/__init__.py',
+  'src/odrpack/driver.py',
+  'src/odrpack/exceptions.py',
+  'src/odrpack/result.py',
+  'src/odrpack/py.typed',
+  'src/odrpack/__odrpack.pyi',
+  ],
+  subdir: 'odrpack'
+)
+
+# Generate stubs
+# python -m pybind11_stubgen odrpack.__odrpack --output-dir "src"
+# stubgen_command = [
+#     python,
+#     '-m', 'pybind11_stubgen',
+#     'odrpack.__odrpack',
+#     '--output-dir', 'src'
+# ]
+
+# stubgen_target = custom_target(
+#     'generate-stubs',
+#     command: stubgen_command,
+#     input: [pybind_ext],
+#     output: 'stubs',
+#     depends: [pybind_ext],
+#     build_by_default: true
+# )

python/pyproject.toml

@@ -26,6 +26,9 @@ classifiers = [
 
 dependencies = ['numpy']
 
+[project.optional-dependencies]
+test = ["pytest"]
+
 [build-system]
 build-backend = "mesonpy"
 requires = ["meson-python>=0.15.0", "pybind11", "pybind11-stubgen"]

python/odrpack/__init__.py → python/src/odrpack/__init__.py

@@ -1,7 +1,6 @@
-
 """`odrpack` is a package for weighted orthogonal distance regression (ODR), also
 known as errors-in-variables regression. It is designed primarily for instances
-when both the explanatory and response variables have significant errors. 
+when both the explanatory and response variables have significant errors.
 The package implements a highly efficient algorithm for minimizing the sum of
 the squares of the weighted orthogonal distances between each data point and the
 curve described by the model equation, subject to parameter bounds. The nonlinear
@@ -11,3 +10,7 @@
 """
 
 __version__ = '0.0.1'
+
+from odrpack.driver import *
+from odrpack.exceptions import *
+from odrpack.result import *

python/src/odrpack/__odrpack.pyi

@@ -0,0 +1,177 @@
+from __future__ import annotations
+import numpy
+import typing
+__all__ = ['diwinf', 'dwinf', 'odr', 'workspace_dimensions']
+def diwinf(m: int, npar: int, nq: int) -> dict:
+    """
+    Get storage locations within the integer work space.
+    
+    Parameters
+    ----------
+    m : int
+        Number of columns of data in the explanatory variable.
+    npar : int
+        Number of function parameters.
+    nq : int
+        Number of responses per observation.
+    
+    Returns
+    -------
+    dict
+        A dictionary containing the 0-based indexes of the integer work array.
+    """
+def dwinf(n: int, m: int, npar: int, nq: int, ldwe: int, ld2we: int, isodr: bool) -> dict:
+    """
+    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.
+    npar : int
+        Number of function parameters.
+    nq : int
+        Number of responses per observation.
+    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
+        A dictionary containing the 0-based indexes of the real work array.
+    """
+def odr(n: int, m: int, npar: int, nq: int, ldwe: int, ld2we: int, ldwd: int, ld2wd: int, ldifx: int, ldstpd: int, ldscld: int, f: typing.Callable, fjacb: typing.Callable, fjacd: typing.Callable, beta: numpy.ndarray[numpy.float64], y: numpy.ndarray[numpy.float64], x: numpy.ndarray[numpy.float64], delta: numpy.ndarray[numpy.float64], we: numpy.ndarray[numpy.float64] | None = None, wd: numpy.ndarray[numpy.float64] | None = None, ifixb: numpy.ndarray[numpy.int32] | None = None, ifixx: numpy.ndarray[numpy.int32] | None = None, stpb: numpy.ndarray[numpy.float64] | None = None, stpd: numpy.ndarray[numpy.float64] | None = None, sclb: numpy.ndarray[numpy.float64] | None = None, scld: numpy.ndarray[numpy.float64] | None = None, lower: numpy.ndarray[numpy.float64] | None = None, upper: numpy.ndarray[numpy.float64] | None = None, work: numpy.ndarray[numpy.float64] | None = None, iwork: numpy.ndarray[numpy.int32] | 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.
+    npar : int
+        Number of function parameters.
+    nq : int
+        Number of responses per observation.
+    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, nq}`.
+    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(beta, x)`.
+    fjacb : Callable
+        User-supplied function for evaluating the Jacobian w.r.t. `beta`,
+        `fjacb(beta, x)`.
+    fjacd : Callable
+        User-supplied function for evaluating the Jacobian w.r.t. `delta`,
+        `fjacd(beta, x)`.
+    beta : np.ndarray[float64]
+        Array of function parameters with shape `(npar)`.
+    y : np.ndarray[float64]
+        Dependent variables with shape `(nq, 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 `(nq, 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.
+    work : 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
+    -------
+    result : dict
+        Dictionary with the following keys:
+        - beta : np.ndarray[float64]
+            Function parameters.
+        - delta : np.ndarray[float64]
+            Errors in `x` data.
+        - work : np.ndarray[float64]
+            Real work space.
+        - iwork : np.ndarray[int32]
+            Integer work space.
+        - 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, npar: int, nq: int, isodr: bool) -> tuple[int, int]:
+    """
+    Calculate the dimensions of the workspace arrays.
+    
+    Parameters
+    ----------
+    n : int
+        Number of observations.
+    m : int
+        Number of columns of data in the explanatory variable.
+    npar : int
+        Number of function parameters.
+    nq : int
+        Number of responses per observation.
+    isodr : bool
+        Variable designating whether the solution is by ODR (`True`) or by OLS (`False`).
+    
+    Returns
+    -------
+    tuple
+        A tuple containing the lengths of the work arrays (`lwork`, `liwork`).
+    """