Remove setup.cfg (#43)

- Remove setup.cfg
- Use ruff instead of flake8
- Fix propagate docstring
- Include examples in propagate docstring into doctest

.github/workflows/test.yml

@@ -18,15 +18,15 @@ jobs:
         include:
           # version number must be string, otherwise 3.10 becomes 3.1
           - os: windows-latest
-            python-version: "3.10"
+            python-version: "3.11"
           - os: macos-latest
-            python-version: "3.6"
+            python-version: "3.8"
           - os: ubuntu-latest
-            python-version: "pypy-3.7"
+            python-version: "pypy-3.8"
       fail-fast: false
     steps:
-    - uses: actions/checkout@v2
-    - uses: actions/setup-python@v2
+    - uses: actions/checkout@v3
+    - uses: actions/setup-python@v3
       with:
         python-version: ${{ matrix.python-version }}
     - run: python -m pip install --upgrade pip

.pre-commit-config.yaml

@@ -30,11 +30,12 @@ repos:
   - id: file-contents-sorter
   - id: trailing-whitespace
 
-# Python linter (Flake8)
-- repo: https://github.com/PyCQA/flake8
-  rev: 6.1.0
+# Ruff linter, replacement for flake8, isort, pydocstyle
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  rev: 'v0.0.280'
   hooks:
-  - id: flake8
+    - id: ruff
+      args: [--fix, --exit-non-zero-on-fix]
 
 # Python formatting
 - repo: https://github.com/psf/black
@@ -49,10 +50,3 @@ repos:
   - id: mypy
     args: [src]
     pass_filenames: false
-
-# doc string checking
-- repo: https://github.com/PyCQA/pydocstyle
-  rev: 6.3.0
-  hooks:
-  - id: pydocstyle
-    files: src/jacobi/.*\.py

pyproject.toml

@@ -1,14 +1,66 @@
 [build-system]
-requires = [
-    "setuptools>=42",
-    "setuptools_scm[toml]>=3.4",
-]
+requires = ["setuptools>=42", "setuptools_scm[toml]>=3.4"]
 build-backend = "setuptools.build_meta"
 
+[project]
+name = "jacobi"
+requires-python = ">=3.8"
+description = "Compute numerical derivatives"
+authors = [{ name = "Hans Dembinski" }, { email = "[email protected]" }]
+dynamic = ["version"]
+dependencies = ["numpy"]
+readme = "README.rst"
+classifiers = [
+    "Development Status :: 4 - Beta",
+    "License :: OSI Approved :: MIT License",
+    "Programming Language :: Python :: 3",
+    "Operating System :: OS Independent",
+    "Operating System :: POSIX :: Linux",
+    "Operating System :: MacOS",
+    "Operating System :: Microsoft :: Windows",
+    "Topic :: Scientific/Engineering",
+]
+license = { file = "LICENSE" }
+
+[project.urls]
+repository = "https://github.com/hdembinski/jacobi"
+documentation = "https://hdembinski.github.io/jacobi/"
+
+[project.optional-dependencies]
+test = ["pytest", "pytest-benchmark"]
+doc = ["sphinx", "sphinx-rtd-theme", "ipykernel"]
+plot = ["numdifftools", "matplotlib"]
+
+[tool.setuptools.packages.find]
+where = ["src"]
+
 [tool.setuptools_scm]
 write_to = "src/jacobi/_version.py"
 
 [tool.mypy]
 ignore_missing_imports = true
 allow_redefinition = true
 no_implicit_optional = false
+
+[tool.pytest.ini_options]
+minversion = "6.0"
+addopts = "--doctest-modules --strict-config --strict-markers -q -ra --ff"
+testpaths = ["src/jacobi", "tests"]
+filterwarnings = [
+    "error::numpy.VisibleDeprecationWarning",
+    "error::DeprecationWarning",
+]
+
+[tool.ruff]
+select = ["E", "F", "D"]
+extend-ignore = ["D203", "D212"]
+
+[tool.ruff.pydocstyle]
+convention = "numpy"
+
+[tool.ruff.per-file-ignores]
+"test_*.py" = ["B", "D"]
+"tests/bench.py" = ["D"]
+".ci/*.py" = ["D"]
+"bench/*.py" = ["D"]
+"doc/*.py" = ["D"]

src/jacobi/_jacobi.py

@@ -42,8 +42,8 @@ def jacobi(
         Whether to compute central (0), forward (1) or backward derivatives (-1).
         The default (None) uses auto-detection.
     mask : array or None, optional
-        If `x` is an array and `mask` is not None, compute the Jacobi matrix only for the
-        part of the array selected by the mask.
+        If `x` is an array and `mask` is not None, compute the Jacobi matrix only for
+        the part of the array selected by the mask.
     rtol : float, optional
         Relative tolerance for the derivative. The algorithm stops when this relative
         tolerance is reached. If 0 (the default), the algorithm iterates until the

src/jacobi/_propagate.py

@@ -24,11 +24,12 @@ def propagate(
     Parameters
     ----------
     fn: callable
-        Function with the signature `fn(x, *args)`, where `x` is a number or a sequence
-        of numbers and `*args` are optional auxiliary arguments. The function must
-        return a number or a sequence of numbers (ideally as a numpy array). The
-        length of `x` can differ from the output sequence. Error propagation is only
-        performed with respect to `x`, the auxiliary arguments are ignored.
+        Function with the signature `fn(x, [y, ...])`, where `x` is a number or a
+        sequence of numbers, likewise if other arguments are present they must have the
+        same format. The function must return a number or a sequence of numbers (ideally
+        as a numpy array). The length of `x` can differ from the output sequence. The
+        function should accept more than one argument only if there are no
+        correlations between these arguments. See example below for use cases.
     x: float or array-like with shape (N,)
         Input vector. An array-like is converted before passing it to the callable.
     cov: float or array-like with shape (N,) or shape(N, N)
@@ -53,63 +54,50 @@ def propagate(
     -----
     For callables `fn` which perform only element-wise computation, the jacobian is
     a diagonal matrix. This special case is detected and the computation optimised,
-    although can further speed up the computation by passing the argumet `diagonal=True`.
+    although can further speed up the computation by passing the argument
+    `diagonal=True`.
 
     In this special case, error propagation works correctly even if the output of `fn`
     is NaN for some inputs.
 
     Examples
     --------
-    General error propagation maps input vectors to output vectors::
-
-        def fn(x):
-            return x ** 2 + 1
-
-        x = [1, 2]
-        xcov = [[3, 1],
-                [1, 4]]
-
-        y, ycov = propagate(fn, x, xcov)
-
-    In the previous example, the function y = fn(x) treats all x values independently,
-    so the Jacobian computed from fn(x) has zero off-diagonal entries. In this case,
-    one can speed up the calculation significantly with a special keyword::
-
-        # same result as before, but faster and uses much less memory
-        y, ycov = propagate(fn, x, xcov, diagonal=True)
-
-    If the function accepts several arguments, their uncertainties are treated as
-    uncorrelated::
-
-        def fn(x, y):
-            return x + y
-
-        x = 1
-        y = 2
-        xcov = 2
-        ycov = 3
-
-        z, zcov = propagate(fn, x, xcov, y, ycov)
-
-    Functions that accept several correlated arguments must be wrapped::
-
-        def fn(x, y):
-            return x + y
-
-        x = 1
-        y = 2
-        sigma_x = 3
-        sigma_y = 4
-        rho_xy = 0.5
-
-        r = [x, y]
-        cov_xy = rho_xy * sigma_x * sigma_y
-        rcov = [[sigma_x ** 2, cov_xy], [cov_xy, sigma_y ** 2]]
-
-        def fn_wrapped(r):
-            return fn(r[0], r[1])
-
-        z, zcov = propagate(fn_wrapped, r, rcov)
+    General error propagation maps input vectors to output vectors.
+
+    >>> def fn(x):
+    ...     return x ** 2 + 1
+    >>> x = [1, 2]
+    >>> xcov = [[3, 1],
+    ...         [1, 4]]
+    >>> y, ycov = propagate(fn, x, xcov)
+
+    In the previous example, the function ``y = fn(x)`` treats all x values
+    independently and the Jacobian computed from ``fn(x)`` has zero off-diagonal
+    entries. In this case, one can speed up the calculation significantly with a special
+    keyword.
+
+    >>> y, ycov = propagate(fn, x, xcov, diagonal=True)
+
+    This produces the same result, but is faster and uses less memory. If the function
+    accepts several arguments, their uncertainties are treated as uncorrelated.
+
+    >>> def fn(x, y):
+    ...    return x + y
+    >>> x = 1
+    >>> y = 2
+    >>> xcov = 2
+    >>> ycov = 3
+    >>> z, zcov = propagate(fn, x, xcov, y, ycov)
+
+    Functions that accept several correlated arguments must be wrapped.
+
+    >>> def fn(x, y):
+    ...     return x + y
+    >>> rho_xy = 0.5
+    >>> cov_xy = rho_xy * (xcov * ycov) ** 0.5
+    >>> r = [x, y]
+    >>> rcov = [[xcov, cov_xy], [cov_xy, ycov]]
+    >>> z, zcov = propagate(lambda r: fn(r[0], r[1]), r, rcov)
 
     See Also
     --------