Bump ruff

.pre-commit-config.yaml

@@ -1,7 +1,7 @@
 ---
 repos:
   - repo: https://github.com/paddyroddy/.github
-    rev: v0.130.0
+    rev: v0.134.0
     hooks:
       - id: general-hooks
       - id: python-hooks

examples/arbitrary/_denoising_slepian_wavelet.py

@@ -11,7 +11,7 @@ def denoising_slepian_wavelet(
     slepian_wavelets: sleplet.functions.SlepianWavelets,
     snr_in: float,
     n_sigma: int,
-) -> npt.NDArray[np.complex_]:
+) -> npt.NDArray[np.complex128]:
     """Denoising demo using Slepian wavelets."""
     # compute wavelet coefficients
     w = sleplet.wavelet_methods.slepian_wavelet_forward(

examples/arbitrary/_slepian_wavelet_covariance.py

@@ -13,7 +13,7 @@ def compute_slepian_wavelet_covariance(
     slepian_wavelets: sleplet.functions.SlepianWavelets,
     *,
     var_signal: float,
-) -> npt.NDArray[np.float_]:
+) -> npt.NDArray[np.float64]:
     """Compute the theoretical covariance of the wavelet coefficients."""
     s_p = sleplet.slepian_methods.compute_s_p_omega(L, slepian_wavelets.slepian)
     wavelets_reshape = slepian_wavelets.wavelets[

examples/arbitrary/africa/denoising_slepian_africa.py

@@ -6,7 +6,7 @@
 
 sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))
 
-from _denoising_slepian_wavelet import denoising_slepian_wavelet  # noqa: E402
+from _denoising_slepian_wavelet import denoising_slepian_wavelet
 
 B = 3
 J_MIN = 2

examples/arbitrary/africa/progressive_wavelet_reconstruction_africa.py

@@ -21,7 +21,7 @@ def main() -> None:
     )
 
     # plot
-    f_p = np.zeros(swc.slepian.N, dtype=np.complex_)
+    f_p = np.zeros(swc.slepian.N, dtype=np.complex128)
     for p, coeff in enumerate(swc.wavelet_coefficients):
         print(f"plot reconstruction: {p}")
         f_p += sleplet.wavelet_methods.slepian_wavelet_inverse(

examples/arbitrary/africa/slepian_wavelet_covariance_africa.py

@@ -7,7 +7,7 @@
 
 sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))
 
-from _slepian_wavelet_covariance import compute_slepian_wavelet_covariance  # noqa: E402
+from _slepian_wavelet_covariance import compute_slepian_wavelet_covariance
 
 B = 3
 J_MIN = 2
@@ -32,7 +32,7 @@ def main() -> None:
 
     # initialise matrix
     covar_runs_shape = (RUNS, *covar_theory.shape)
-    covar_data_runs = np.zeros(covar_runs_shape, dtype=np.complex_)
+    covar_data_runs = np.zeros(covar_runs_shape, dtype=np.complex128)
 
     # set seed
     rng = np.random.default_rng(RANDOM_SEED)

examples/arbitrary/south_america/denoising_slepian_functions_south_america.py

@@ -19,7 +19,7 @@ def _denoising_slepian_function(
     noised_signal: sleplet.functions.SlepianSouthAmerica,
     snr_in: float,
     n_sigma: int,
-) -> npt.NDArray[np.complex_]:
+) -> npt.NDArray[np.complex128]:
     """Denoising demo using Slepian function."""
     # compute Slepian noise
     sigma_noise = sleplet.noise.compute_sigma_noise(

examples/arbitrary/south_america/denoising_slepian_south_america.py

@@ -6,7 +6,7 @@
 
 sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))
 
-from _denoising_slepian_wavelet import denoising_slepian_wavelet  # noqa: E402
+from _denoising_slepian_wavelet import denoising_slepian_wavelet
 
 B = 3
 J_MIN = 2

examples/arbitrary/south_america/progressive_wavelet_reconstruction_south_america.py

@@ -21,7 +21,7 @@ def main() -> None:
     )
 
     # plot
-    f_p = np.zeros(swc.slepian.N, dtype=np.complex_)
+    f_p = np.zeros(swc.slepian.N, dtype=np.complex128)
     for p, coeff in enumerate(swc.wavelet_coefficients):
         print(f"plot reconstruction: {p}")
         f_p += sleplet.wavelet_methods.slepian_wavelet_inverse(

examples/arbitrary/south_america/slepian_wavelet_covariance_south_america.py

@@ -7,7 +7,7 @@
 
 sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))
 
-from _slepian_wavelet_covariance import compute_slepian_wavelet_covariance  # noqa: E402
+from _slepian_wavelet_covariance import compute_slepian_wavelet_covariance
 
 B = 3
 J_MIN = 2
@@ -32,7 +32,7 @@ def main() -> None:
 
     # initialise matrix
     covar_runs_shape = (RUNS, *covar_theory.shape)
-    covar_data_runs = np.zeros(covar_runs_shape, dtype=np.complex_)
+    covar_data_runs = np.zeros(covar_runs_shape, dtype=np.complex128)
 
     # set seed
     rng = np.random.default_rng(RANDOM_SEED)

examples/mesh/denoising_slepian_mesh.py

@@ -26,7 +26,7 @@ def _denoising_mesh_slepian(
     mesh_slepian_wavelets: sleplet.meshes.MeshSlepianWavelets,
     snr_in: float,
     n_sigma: int,
-) -> npt.NDArray[np.complex_ | np.float_]:
+) -> npt.NDArray[np.complex128 | np.float64]:
     """Denoising demo using Slepian wavelets."""
     # compute wavelet coefficients
     w = sleplet.wavelet_methods.slepian_wavelet_forward(

examples/mesh/produce_table.py

@@ -3,7 +3,7 @@
 
 sys.path.append(str(pathlib.Path(__file__).resolve().parent))
 
-from denoising_slepian_mesh import main  # noqa: E402
+from denoising_slepian_mesh import main
 
 MESH_SNR_DICT = {
     "cheetah": -8.64,

examples/misc/_denoising_axisym.py

@@ -20,7 +20,7 @@ def denoising_axisym(  # noqa: PLR0913
     n_sigma: int,
     *,
     rotate_to_south_america: bool = False,
-) -> tuple[npt.NDArray[np.complex_], float | None, float]:
+) -> tuple[npt.NDArray[np.complex128], float | None, float]:
     """Reproduce the denoising demo from S2LET paper."""
     # compute wavelet coefficients
     w = sleplet.wavelet_methods.axisymmetric_wavelet_forward(

examples/misc/denoising_axisym_africa.py

@@ -5,7 +5,7 @@
 
 sys.path.append(str(pathlib.Path(__file__).resolve().parent))
 
-from _denoising_axisym import denoising_axisym  # noqa: E402
+from _denoising_axisym import denoising_axisym
 
 B = 2
 J_MIN = 0

examples/misc/denoising_axisym_earth.py

@@ -7,7 +7,7 @@
 
 sys.path.append(str(pathlib.Path(__file__).resolve().parent))
 
-from _denoising_axisym import denoising_axisym  # noqa: E402
+from _denoising_axisym import denoising_axisym
 
 B = 2
 J_MIN = 0

examples/misc/denoising_axisym_south_america.py

@@ -5,7 +5,7 @@
 
 sys.path.append(str(pathlib.Path(__file__).resolve().parent))
 
-from _denoising_axisym import denoising_axisym  # noqa: E402
+from _denoising_axisym import denoising_axisym
 
 B = 2
 J_MIN = 0

examples/misc/wavelet_transform.py

@@ -26,7 +26,7 @@ def main() -> None:
     plt.close()
 
 
-def _ricker(freq: float) -> npt.NDArray[np.float_]:
+def _ricker(freq: float) -> npt.NDArray[np.float64]:
     """Create a Ricker wavelet."""
     t = np.arange(-LENGTH / 2, (LENGTH - DELTA_T) / 2, DELTA_T)
     return (1.0 - 2.0 * (np.pi**2) * (freq**2) * (t**2)) * np.exp(

examples/polar_cap/eigenfunctions.py

@@ -53,7 +53,7 @@ def main() -> None:
 def _helper(
     ax: mpl.axes.Axes,
     slepian: sleplet.slepian.SlepianPolarCap,
-    x: npt.NDArray[np.float_],
+    x: npt.NDArray[np.float64],
     i: int,
     rank: int,
 ) -> None:

examples/polar_cap/simons_5_1.py

@@ -52,7 +52,7 @@ def _helper(  # noqa: PLR0913
     ax: npt.NDArray[typing.Any],
     slepian: sleplet.slepian.SlepianPolarCap,
     resolution: int,
-    x: npt.NDArray[np.float_],
+    x: npt.NDArray[np.float64],
     i: int,
     order: int,
     rank: int,

examples/polar_cap/slepian_coefficients.py

@@ -14,7 +14,7 @@
 def _earth_region_harmonic_coefficients(
     L: int,
     theta_max: int,
-) -> npt.NDArray[np.float_]:
+) -> npt.NDArray[np.float64]:
     """Harmonic coefficients of the Earth for the polar cap region."""
     region = sleplet.slepian.Region(theta_max=np.deg2rad(theta_max))
     earth = sleplet.functions.Earth(L, region=region)
@@ -26,7 +26,7 @@ def _earth_region_harmonic_coefficients(
 def _earth_region_slepian_coefficients(
     L: int,
     theta_max: int,
-) -> npt.NDArray[np.float_]:
+) -> npt.NDArray[np.float64]:
     """Compute the Slepian coefficients."""
     region = sleplet.slepian.Region(theta_max=np.deg2rad(theta_max))
     earth = sleplet.functions.Earth(L, region=region)

examples/polar_cap/slepian_error.py

@@ -52,9 +52,9 @@ def main() -> None:
 def _helper_sphere(
     L: int,
     region: sleplet.slepian.Region,
-    f: npt.NDArray[np.complex_],
-    flm: npt.NDArray[np.complex_ | np.float_],
-) -> npt.NDArray[np.float_]:
+    f: npt.NDArray[np.complex128],
+    flm: npt.NDArray[np.complex128 | np.float64],
+) -> npt.NDArray[np.float64]:
     """Calculate the difference in Slepian coefficients by integration of the sphere."""
     slepian = sleplet.slepian_methods.choose_slepian_method(L, region)
     output = np.abs(sleplet.slepian_methods.slepian_forward(L, slepian, f=f))
@@ -65,9 +65,9 @@ def _helper_sphere(
 def _helper_region(
     L: int,
     region: sleplet.slepian.Region,
-    f: npt.NDArray[np.complex_],
-    flm: npt.NDArray[np.complex_ | np.float_],
-) -> npt.NDArray[np.float_]:
+    f: npt.NDArray[np.complex128],
+    flm: npt.NDArray[np.complex128 | np.float64],
+) -> npt.NDArray[np.float64]:
     """Calculate the difference in Slepian coefficients by integration of the region."""
     slepian = sleplet.slepian_methods.choose_slepian_method(L, region)
     output = np.abs(

examples/wavelets/axisymmetric_wavelet_covariance.py

@@ -13,10 +13,10 @@
 
 
 def _compute_wavelet_covariance(
-    wavelets: npt.NDArray[np.complex_],
+    wavelets: npt.NDArray[np.complex128],
     *,
     var_signal: float,
-) -> npt.NDArray[np.float_]:
+) -> npt.NDArray[np.float64]:
     """Compute the theoretical covariance of the wavelet coefficients."""
     covar_theory = (np.abs(wavelets) ** 2).sum(axis=1)
     return covar_theory * var_signal
@@ -67,7 +67,7 @@ def axisymmetric_wavelet_covariance(
 
     # initialise matrix
     covar_runs_shape = (runs, *covar_theory.shape)
-    covar_data = np.zeros(covar_runs_shape, dtype=np.complex_)
+    covar_data = np.zeros(covar_runs_shape, dtype=np.complex128)
 
     # set seed
     rng = np.random.default_rng(RANDOM_SEED)

src/sleplet/_convolution_methods.py

@@ -3,11 +3,11 @@
 
 
 def sifting_convolution(
-    f_coefficient: npt.NDArray[np.complex_ | np.float_],
-    g_coefficient: npt.NDArray[np.complex_ | np.float_],
+    f_coefficient: npt.NDArray[np.complex128 | np.float64],
+    g_coefficient: npt.NDArray[np.complex128 | np.float64],
     *,
     shannon: int | None = None,
-) -> npt.NDArray[np.complex_ | np.float_]:
+) -> npt.NDArray[np.complex128 | np.float64]:
     """Compute the sifting convolution between two multipoles."""
     n = shannon if shannon is not None else np.newaxis
     # change shape if the sizes don't match

src/sleplet/_data/create_earth_flm.py

@@ -8,7 +8,7 @@
 import sleplet._smoothing
 
 
-def create_flm(L: int, *, smoothing: int | None = None) -> npt.NDArray[np.complex_]:
+def create_flm(L: int, *, smoothing: int | None = None) -> npt.NDArray[np.complex128]:
     """Create the flm for the whole Earth."""
     # load in data
     flm = _load_flm()
@@ -29,7 +29,7 @@ def create_flm(L: int, *, smoothing: int | None = None) -> npt.NDArray[np.comple
     return flm
 
 
-def _load_flm() -> npt.NDArray[np.complex_]:
+def _load_flm() -> npt.NDArray[np.complex128]:
     """Load coefficients from file."""
     mat_contents = sio.loadmat(
         sleplet._data.setup_pooch.find_on_pooch_then_local(

src/sleplet/_data/create_wmap_flm.py

@@ -8,7 +8,7 @@
 import sleplet._vars
 
 
-def create_flm(L: int) -> npt.NDArray[np.complex_]:
+def create_flm(L: int) -> npt.NDArray[np.complex128]:
     """Create the flm for the whole CMB."""
     # load in data
     cl = _load_cl()
@@ -17,7 +17,7 @@ def create_flm(L: int) -> npt.NDArray[np.complex_]:
     rng = np.random.default_rng(sleplet._vars.RANDOM_SEED)
 
     # Simulate CMB in harmonic space.
-    flm = np.zeros(L**2, dtype=np.complex_)
+    flm = np.zeros(L**2, dtype=np.complex128)
     for ell in range(2, L):
         sigma = np.sqrt(2 * np.pi / (ell * (ell + 1)) * cl[ell - 2])
         ind = ssht.elm2ind(ell, 0)
@@ -37,7 +37,7 @@ def create_flm(L: int) -> npt.NDArray[np.complex_]:
 def _load_cl(
     *,
     file_ending: str = "_lcdm_pl_model_wmap7baoh0",
-) -> npt.NDArray[np.float_]:
+) -> npt.NDArray[np.float64]:
     """
     Pick coefficients from file options are:
     * _lcdm_pl_model_yr1_v1.mat

src/sleplet/_integration_methods.py

@@ -9,7 +9,7 @@
 import sleplet._vars
 
 
-def calc_integration_weight(L: int) -> npt.NDArray[np.float_]:
+def calc_integration_weight(L: int) -> npt.NDArray[np.float64]:
     """Compute the spherical Jacobian for the integration."""
     thetas, phis = ssht.sample_positions(
         L,
@@ -22,28 +22,28 @@ def calc_integration_weight(L: int) -> npt.NDArray[np.float_]:
 
 
 def integrate_whole_sphere(
-    weight: npt.NDArray[np.float_],
-    *functions: npt.NDArray[np.complex_],
+    weight: npt.NDArray[np.float64],
+    *functions: npt.NDArray[np.complex128],
 ) -> complex:
     """Compute the integration for the whole sphere."""
     multiplied_inputs = _multiply_args(*functions)
     return (multiplied_inputs * weight).sum()
 
 
 def integrate_region_sphere(
-    mask: npt.NDArray[np.float_],
-    weight: npt.NDArray[np.float_],
-    *functions: npt.NDArray[np.complex_ | np.float_],
+    mask: npt.NDArray[np.float64],
+    weight: npt.NDArray[np.float64],
+    *functions: npt.NDArray[np.complex128 | np.float64],
 ) -> complex:
     """Compute the integration for a region of the sphere."""
     multiplied_inputs = _multiply_args(*functions)
     return (multiplied_inputs * weight * mask).sum()
 
 
 def integrate_whole_mesh(
-    vertices: npt.NDArray[np.float_],  # noqa: ARG001
+    vertices: npt.NDArray[np.float64],  # noqa: ARG001
     faces: npt.NDArray[np.int_],  # noqa: ARG001
-    *functions: npt.NDArray[np.complex_ | np.float_],
+    *functions: npt.NDArray[np.complex128 | np.float64],
 ) -> float:
     """Compute the integral of functions on the vertices."""
     multiplied_inputs = _multiply_args(*functions)
@@ -52,9 +52,9 @@ def integrate_whole_mesh(
 
 def integrate_region_mesh(
     mask: npt.NDArray[np.bool_],
-    vertices: npt.NDArray[np.float_],  # noqa: ARG001
+    vertices: npt.NDArray[np.float64],  # noqa: ARG001
     faces: npt.NDArray[np.int_],  # noqa: ARG001
-    *functions: npt.NDArray[np.complex_ | np.float_],
+    *functions: npt.NDArray[np.complex128 | np.float64],
 ) -> float:
     """Compute the integral of a region of functions on the vertices."""
     multiplied_inputs = _multiply_args(*functions)