Merge branch 'main' into paddy/issue-275

.github/workflows/pull_request.yml

@@ -12,7 +12,8 @@ on:
 
 jobs:
   check-pr-title:
-    if: github.actor != 'pre-commit-ci[bot]'
+    if:
+      github.actor != 'pre-commit-ci[bot]' && github.actor != 'dependabot[bot]'
     runs-on: ubuntu-latest
     steps:
       - name: Enfore PR title format

.github/workflows/release.yml

@@ -2,14 +2,10 @@
 name: Release
 
 on:
-  workflow_dispatch:
-    inputs:
-      target:
-        default: testpypi
-        description: Deployment target. Can be pypi or testpypi.
   release:
     types:
       - published
+  workflow_dispatch:
 
 jobs:
   dist:
@@ -55,8 +51,16 @@ jobs:
     needs: dist
     runs-on: ubuntu-latest
     environment:
-      name: publish
-      url: https://pypi.org/p/glass
+      name: >-
+        ${{ (github.event_name == 'release' &&
+         github.event.action == 'published') &&
+         'publish' ||
+         'test-publish' }}
+      url: >-
+        ${{ (github.event_name == 'release' &&
+         github.event.action == 'published') &&
+         'https://pypi.org/project/glass' ||
+         'https://test.pypi.org/project/glass' }}
     permissions:
       id-token: write
     steps:
@@ -71,12 +75,11 @@ jobs:
 
       - name: Publish to PyPI
         if: >-
-          github.event.inputs.target == 'pypi' || (github.event_name ==
-          'release' && github.event.action == 'published')
+          github.event_name == 'release' && github.event.action == 'published'
         uses: pypa/gh-action-pypi-publish@release/v1
 
       - name: Publish to TestPyPI
-        if: github.event.inputs.target == 'testpypi'
+        if: github.event_name == 'workflow_dispatch'
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
           repository-url: https://test.pypi.org/legacy/

.pre-commit-config.yaml

@@ -33,7 +33,7 @@ repos:
         args:
           - --strict
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.7.4
+    rev: v0.8.0
     hooks:
       - id: ruff
       - id: ruff-format
@@ -44,15 +44,15 @@ repos:
         additional_dependencies:
           - black
   - repo: https://github.com/pappasam/toml-sort
-    rev: v0.24.1
+    rev: v0.24.2
     hooks:
       - id: toml-sort-fix
   - repo: https://github.com/rbubley/mirrors-prettier
     rev: v3.3.3
     hooks:
       - id: prettier
   - repo: https://github.com/igorshubovych/markdownlint-cli
-    rev: v0.42.0
+    rev: v0.43.0
     hooks:
       - id: markdownlint-fix
         args:

glass/__init__.py

@@ -1,6 +1,8 @@
 """GLASS package."""
 
 __all__ = [
+    "MultiPlaneConvergence",
+    "RadialWindow",
     "cls2cov",
     "combine",
     "cubic_windows",
@@ -32,11 +34,9 @@
     "multalm",
     "multi_plane_matrix",
     "multi_plane_weights",
-    "MultiPlaneConvergence",
     "partition",
     "position_weights",
     "positions_from_delta",
-    "RadialWindow",
     "redshift_grid",
     "redshifts",
     "redshifts_from_nz",

glass/lensing.py

@@ -45,6 +45,117 @@
     from glass.shells import RadialWindow
 
 
+@typing.overload
+def from_convergence(
+    kappa: npt.NDArray[np.float64],
+    lmax: int | None = None,
+    *,
+    potential: typing.Literal[True] = True,
+    deflection: typing.Literal[False] = False,
+    shear: typing.Literal[False] = False,
+    discretized: bool = True,
+) -> tuple[npt.NDArray[np.float64]]:
+    # returns psi
+    ...
+
+
+@typing.overload
+def from_convergence(
+    kappa: npt.NDArray[np.float64],
+    lmax: int | None = None,
+    *,
+    potential: typing.Literal[False] = False,
+    deflection: typing.Literal[True] = True,
+    shear: typing.Literal[False] = False,
+    discretized: bool = True,
+) -> tuple[npt.NDArray[np.complex128]]:
+    # returns alpha
+    ...
+
+
+@typing.overload
+def from_convergence(
+    kappa: npt.NDArray[np.float64],
+    lmax: int | None = None,
+    *,
+    potential: typing.Literal[False] = False,
+    deflection: typing.Literal[False] = False,
+    shear: typing.Literal[True] = True,
+    discretized: bool = True,
+) -> tuple[npt.NDArray[np.complex128]]:
+    # returns gamma
+    ...
+
+
+@typing.overload
+def from_convergence(
+    kappa: npt.NDArray[np.float64],
+    lmax: int | None = None,
+    *,
+    potential: typing.Literal[True] = True,
+    deflection: typing.Literal[True] = True,
+    shear: typing.Literal[False] = False,
+    discretized: bool = True,
+) -> tuple[
+    npt.NDArray[np.float64],
+    npt.NDArray[np.complex128],
+]:
+    # returns psi, alpha
+    ...
+
+
+@typing.overload
+def from_convergence(
+    kappa: npt.NDArray[np.float64],
+    lmax: int | None = None,
+    *,
+    potential: typing.Literal[True] = True,
+    deflection: typing.Literal[False] = False,
+    shear: typing.Literal[True] = True,
+    discretized: bool = True,
+) -> tuple[
+    npt.NDArray[np.float64],
+    npt.NDArray[np.complex128],
+]:
+    # returns psi, gamma
+    ...
+
+
+@typing.overload
+def from_convergence(
+    kappa: npt.NDArray[np.float64],
+    lmax: int | None = None,
+    *,
+    potential: typing.Literal[False] = False,
+    deflection: typing.Literal[True] = True,
+    shear: typing.Literal[True] = True,
+    discretized: bool = True,
+) -> tuple[
+    npt.NDArray[np.complex128],
+    npt.NDArray[np.complex128],
+]:
+    # returns alpha, gamma
+    ...
+
+
+@typing.overload
+def from_convergence(
+    kappa: npt.NDArray[np.float64],
+    lmax: int | None = None,
+    *,
+    potential: typing.Literal[True] = True,
+    deflection: typing.Literal[True] = True,
+    shear: typing.Literal[True] = True,
+    discretized: bool = True,
+) -> tuple[
+    npt.NDArray[np.float64],
+    npt.NDArray[np.complex128],
+    npt.NDArray[np.complex128],
+]:
+    # returns psi, alpha, gamma
+    ...
+
+
 def from_convergence(  # noqa: PLR0913
     kappa: npt.NDArray[np.float64],
     lmax: int | None = None,
@@ -53,7 +164,7 @@ def from_convergence(  # noqa: PLR0913
     deflection: bool = False,
     shear: bool = False,
     discretized: bool = True,
-) -> tuple[npt.NDArray[np.float64], ...]:
+) -> tuple[npt.NDArray[np.float64] | npt.NDArray[np.complex128], ...]:
     r"""
     Compute other weak lensing maps from the convergence.
 
@@ -175,7 +286,7 @@ def from_convergence(  # noqa: PLR0913
     ell = np.arange(lmax + 1)
 
     # this tuple will be returned
-    results: tuple[npt.NDArray[np.float64], ...] = ()
+    results: tuple[npt.NDArray[np.float64] | npt.NDArray[np.complex128], ...] = ()
 
     # convert convergence to potential
     fl = np.divide(-2, ell * (ell + 1), where=(ell > 0), out=np.zeros(lmax + 1))

glass/observations.py

@@ -89,7 +89,7 @@ def gaussian_nz(
     mean: float | npt.NDArray[np.float64],
     sigma: float | npt.NDArray[np.float64],
     *,
-    norm: npt.NDArray[np.float64] | None = None,
+    norm: float | npt.NDArray[np.float64] | None = None,
 ) -> npt.NDArray[np.float64]:
     """
     Gaussian redshift distribution.
@@ -130,11 +130,11 @@ def gaussian_nz(
 
 def smail_nz(
     z: npt.NDArray[np.float64],
-    z_mode: npt.NDArray[np.float64],
-    alpha: npt.NDArray[np.float64],
-    beta: npt.NDArray[np.float64],
+    z_mode: float | npt.NDArray[np.float64],
+    alpha: float | npt.NDArray[np.float64],
+    beta: float | npt.NDArray[np.float64],
     *,
-    norm: npt.NDArray[np.float64] | None = None,
+    norm: float | npt.NDArray[np.float64] | None = None,
 ) -> npt.NDArray[np.float64]:
     r"""
     Redshift distribution following Smail et al. (1994).

glass/shells.py

@@ -708,6 +708,45 @@ def partition_restrict(
     return part
 
 
+def _uniform_grid(
+    start: float,
+    stop: float,
+    *,
+    step: float | None = None,
+    num: int | None = None,
+) -> npt.NDArray[np.float64]:
+    """
+    Create a uniform grid.
+
+    Parameters
+    ----------
+    start
+        The minimum value.
+    stop
+        The maximum value.
+    step
+        The spacing.
+    num
+        The number of samples.
+
+    Returns
+    -------
+        The uniform grid.
+
+    Raises
+    ------
+    ValueError
+        If both ``step`` and ``num`` are given.
+
+    """
+    if step is not None and num is None:
+        return np.arange(start, np.nextafter(stop + step, stop), step)
+    if step is None and num is not None:
+        return np.linspace(start, stop, num + 1)
+    msg = "exactly one of grid step size or number of steps must be given"
+    raise ValueError(msg)
+
+
 def redshift_grid(
     zmin: float,
     zmax: float,
@@ -733,20 +772,8 @@ def redshift_grid(
     -------
         The redshift grid.
 
-    Raises
-    ------
-    ValueError
-        If both ``dz`` and ``num`` are given.
-
     """
-    if dz is not None and num is None:
-        z = np.arange(zmin, np.nextafter(zmax + dz, zmax), dz)
-    elif dz is None and num is not None:
-        z = np.linspace(zmin, zmax, num + 1)
-    else:
-        msg = 'exactly one of "dz" or "num" must be given'
-        raise ValueError(msg)
-    return z
+    return _uniform_grid(zmin, zmax, step=dz, num=num)
 
 
 def distance_grid(
@@ -777,20 +804,9 @@ def distance_grid(
     -------
         The redshift grid.
 
-    Raises
-    ------
-    ValueError
-        If both ``dx`` and ``num`` are given.
-
     """
     xmin, xmax = cosmo.dc(zmin), cosmo.dc(zmax)
-    if dx is not None and num is None:
-        x = np.arange(xmin, np.nextafter(xmax + dx, xmax), dx)
-    elif dx is None and num is not None:
-        x = np.linspace(xmin, xmax, num + 1)
-    else:
-        msg = 'exactly one of "dx" or "num" must be given'
-        raise ValueError(msg)
+    x = _uniform_grid(xmin, xmax, step=dx, num=num)
     return cosmo.dc_inv(x)  # type: ignore[no-any-return]