Support Python 3.11–3.14 and NumPy 2

.github/workflows/ci.yml

@@ -14,17 +14,17 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        version: ["3.7", "3.8", "3.9", "3.10"]
+        version: ["3.11", "3.12", "3.13", "3.14"]
         os:
           - ubuntu-latest
           - macOS-latest
           - windows-latest
         arch: ["x64"]
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
       - name: Setup python ${{ matrix.version }}
-        uses: actions/setup-python@v1
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.version }}
           architecture: ${{ matrix.arch }}
@@ -33,14 +33,14 @@ jobs:
           pip install -U pip
           pip install .[test]
       - name: Cache Test Data
-        uses: actions/cache@v1
+        uses: actions/cache@v4
         with:
           path: tests/data
           key: ${{ runner.os }}-test-data
       - name: Test
         run: |
-          pytest --cov=Starfish --cov-report=xml --benchmark-skip --black
+          pytest --cov=Starfish --cov-report=xml --benchmark-skip
       - name: Coverage
-        uses: codecov/codecov-action@v1
+        uses: codecov/codecov-action@v4
         with:
           token: ${{ secrets.CODECOV_TOKEN }}

.github/workflows/deploy.yml

@@ -8,20 +8,20 @@ jobs:
   deploy:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v1
+      - uses: actions/checkout@v4
       - name: Set up Python
-        uses: actions/setup-python@v1
+        uses: actions/setup-python@v5
         with:
-          python-version: "3.9"
+          python-version: "3.12"
       - name: Install dependencies
         run: |
           python -m pip install -U pip
-          pip install -U pep517 twine
+          pip install -U build twine
       - name: Build
         run: |
-          python -m pep517.build --source --binary --out-dir dist/ .
+          python -m build --outdir dist/ .
       - name: Deploy
-        uses: pypa/gh-action-pypi-publish@master
+        uses: pypa/gh-action-pypi-publish@release/v1
         with:
           user: ${{ secrets.PYPI_USER }}
           password: ${{ secrets.PYPI_PASSWORD }}

.readthedocs.yml

@@ -1,13 +1,17 @@
 # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
 version: 2
 
+build:
+  os: ubuntu-24.04
+  tools:
+    python: "3.12"
+
 sphinx:
   configuration: docs/conf.py
 
 formats: all
 
 python:
-  version: 3.7
   install:
     - method: pip
       path: .

CHANGELOG.md

@@ -10,6 +10,13 @@
 
 ### Changed
 - Minor refactoring
+- Support Python 3.11 through 3.14; dropped support for Python 3.10 and earlier
+- Packaging metadata moved from `setup.py` to PEP 621 `pyproject.toml`
+- Removed the `nptyping` dependency in favor of `numpy.typing`, and the obsolete
+  `dataclasses` backport; relaxed upper version caps on `astropy`, `numpy`,
+  `scipy`, `scikit-learn`, `extinction`, `h5py`, `flatdict`, `toml`, and `tqdm`
+- Replaced `np.trapz` with `scipy.integrate.trapezoid` and `np.Inf` with
+  `np.inf` for NumPy 2 compatibility
 
 ### Fixed
 - A bug in how fix_c0 is toggled during Chebyshev polynomial optimization

Pipfile

@@ -1,22 +1,19 @@
 [requires]
-python_version = "3.7"
+python_version = "3.11"
 
 [packages]
 astropy = "*"
-dataclasses = "*"
 flatdict = "*"
 h5py = "*"
-nptyping = "*"
 numpy = "*"
 scikit-learn = "*"
 scipy = "*"
-toml = ">=0.10.1"
+toml = ">=0.10.2"
 tqdm = "*"
-extinction = ">=0.4.2"
+extinction = ">=0.4.7"
 
 [dev-packages]
 black = "*"
-coveralls = "*"
 flake8 = "*"
 ipython = "*"
 mypy = "*"
@@ -29,7 +26,6 @@ sphinx-bootstrap-theme = "*"
 pytest-benchmark = "*"
 sphinx-autodoc-typehints = "*"
 pre-commit = "*"
-pytest-black = "*"
 
 [pipenv]
 allow_prereleases = true

Starfish/__init__.py

@@ -7,7 +7,6 @@
     "emulator",
     "grid_tools",
     "models",
-    "samplers",
     "spectrum",
     "Spectrum",
     "transforms",

Starfish/emulator/emulator.py

@@ -5,7 +5,7 @@
 
 import h5py
 import numpy as np
-from nptyping import NDArray
+from numpy.typing import NDArray
 from scipy.interpolate import LinearNDInterpolator
 from scipy.linalg import cho_factor, cho_solve
 from scipy.optimize import minimize
@@ -68,18 +68,18 @@ class Emulator:
 
     def __init__(
         self,
-        grid_points: NDArray[float],
+        grid_points: NDArray[np.float64],
         param_names: Sequence[str],
-        wavelength: NDArray[float],
-        weights: NDArray[float],
-        eigenspectra: NDArray[float],
-        w_hat: NDArray[float],
-        flux_mean: NDArray[float],
-        flux_std: NDArray[float],
-        factors: NDArray[float],
+        wavelength: NDArray[np.float64],
+        weights: NDArray[np.float64],
+        eigenspectra: NDArray[np.float64],
+        w_hat: NDArray[np.float64],
+        flux_mean: NDArray[np.float64],
+        flux_std: NDArray[np.float64],
+        factors: NDArray[np.float64],
         lambda_xi: float = 1.0,
-        variances: Optional[NDArray[float]] = None,
-        lengthscales: Optional[NDArray[float]] = None,
+        variances: Optional[NDArray[np.float64]] = None,
+        lengthscales: Optional[NDArray[np.float64]] = None,
         name: Optional[str] = None,
     ):
         self.log = logging.getLogger(self.__class__.__name__)
@@ -144,7 +144,7 @@ def lambda_xi(self, value: float):
         self.hyperparams["log_lambda_xi"] = np.log(value)
 
     @property
-    def variances(self) -> NDArray[float]:
+    def variances(self) -> NDArray[np.float64]:
         """
         numpy.ndarray : The variances for each Gaussian process kernel.
 
@@ -158,12 +158,12 @@ def variances(self) -> NDArray[float]:
         return np.exp(values)
 
     @variances.setter
-    def variances(self, values: NDArray[float]):
+    def variances(self, values: NDArray[np.float64]):
         for i, value in enumerate(values):
             self.hyperparams[f"log_variance:{i}"] = np.log(value)
 
     @property
-    def lengthscales(self) -> NDArray[float]:
+    def lengthscales(self) -> NDArray[np.float64]:
         """
         numpy.ndarray : The lengthscales for each Gaussian process kernel.
 
@@ -177,7 +177,7 @@ def lengthscales(self) -> NDArray[float]:
         return np.exp(values).reshape(self.ncomps, -1)
 
     @lengthscales.setter
-    def lengthscales(self, values: NDArray[float]):
+    def lengthscales(self, values: NDArray[np.float64]):
         for i, value in enumerate(values):
             for j, ls in enumerate(value):
                 self.hyperparams[f"log_lengthscale:{i}:{j}"] = np.log(ls)
@@ -332,7 +332,7 @@ def __call__(
         params: Sequence[float],
         full_cov: bool = True,
         reinterpret_batch: bool = False,
-    ) -> Tuple[NDArray[float], NDArray[float]]:
+    ) -> Tuple[NDArray[np.float64], NDArray[np.float64]]:
         """
         Gets the mu and cov matrix for a given set of params
 
@@ -394,16 +394,16 @@ def __call__(
         return mu, cov
 
     @property
-    def bulk_fluxes(self) -> NDArray[float]:
+    def bulk_fluxes(self) -> NDArray[np.float64]:
         """
         numpy.ndarray: A vertically concatenated vector of the eigenspectra, flux_mean,
         and flux_std (in that order). Used for bulk processing with the emulator.
         """
         return np.vstack([self.eigenspectra, self.flux_mean, self.flux_std])
 
     def load_flux(
-        self, params: Union[Sequence[float], NDArray[float]], norm=False
-    ) -> NDArray[float]:
+        self, params: Union[Sequence[float], NDArray[np.float64]], norm=False
+    ) -> NDArray[np.float64]:
         """
         Interpolate a model given any parameters within the grid's parameter range
         using eigenspectrum reconstruction
@@ -426,7 +426,7 @@ def load_flux(
             flux *= self.norm_factor(params)[:, np.newaxis]
         return np.squeeze(flux)
 
-    def norm_factor(self, params: Union[Sequence[float], NDArray[float]]) -> float:
+    def norm_factor(self, params: Union[Sequence[float], NDArray[np.float64]]) -> float:
         """
         Return the scaling factor for the absolute flux units in flux-normalized spectra
 
@@ -570,7 +570,7 @@ def set_param_dict(self, params: dict):
             self.grid_points, self.grid_points, self.variances, self.lengthscales
         )
 
-    def get_param_vector(self) -> NDArray[float]:
+    def get_param_vector(self) -> NDArray[np.float64]:
         """
         Get a vector of the current trainable parameters of the emulator
 
@@ -581,7 +581,7 @@ def get_param_vector(self) -> NDArray[float]:
         values = list(self.get_param_dict().values())
         return np.array(values)
 
-    def set_param_vector(self, params: NDArray[float]):
+    def set_param_vector(self, params: NDArray[np.float64]):
         """
         Set the current trainable parameters given a vector. Must have the same form as
         :meth:`get_param_vector`

Starfish/emulator/plotting.py

@@ -4,7 +4,10 @@
 from matplotlib import gridspec
 import numpy as np
 
-plt.style.use("seaborn")
+try:
+    plt.style.use("seaborn-v0_8")
+except OSError:
+    pass
 
 
 def plot_reconstructed(emulator, grid, folder):
@@ -90,7 +93,7 @@ def plot_eigenspectra(emulator, params, filename=None):
     )
     ax = plt.subplot(gs[0])
     ax.plot(emulator.wl, reconstructed, lw=1)
-    ax.set_ylabel("$f_\lambda$ [erg/cm^2/s/A]")
+    ax.set_ylabel(r"$f_\lambda$ [erg/cm^2/s/A]")
     plt.setp(ax.get_xticklabels(), visible=False)
     for i in range(emulator.ncomps):
         ax = plt.subplot(gs[i + 1], sharex=ax)
@@ -149,6 +152,6 @@ def plot_emulator(emulator):
         axes[i].fill_between(Ttest, m - 2 * s, m + 2 * s, color="C1", alpha=0.4)
     axes[-1].set_xlabel("T (K)")
     plt.suptitle(
-        f"Weights for fixed $\log g={logg[0]:.2f}$, $[Fe/H]={Z[0]:.2f}$", fontsize=20
+        rf"Weights for fixed $\log g={logg[0]:.2f}$, $[Fe/H]={Z[0]:.2f}$", fontsize=20
     )
     plt.tight_layout(rect=[0, 0.03, 1, 0.95])

Starfish/grid_tools/interfaces.py

@@ -4,6 +4,7 @@
 
 import numpy as np
 from astropy.io import fits, ascii
+from scipy.integrate import trapezoid
 from scipy.interpolate import InterpolatedUnivariateSpline
 
 import Starfish.constants as C
@@ -138,7 +139,7 @@ def load_flux(self, parameters, header=False, norm=True):
         # If we want to normalize the spectra, we must do it now since later we won't have the full EM range
         if norm:
             flux *= 1e-8  # convert from erg/cm^2/s/cm to erg/cm^2/s/A
-            F_bol = np.trapz(flux, self.wl_full)
+            F_bol = trapezoid(flux, self.wl_full)
             # bolometric luminosity is always 1 L_sun
             flux *= C.F_sun / F_bol
 
@@ -393,7 +394,7 @@ def load_flux(self, parameters, norm=True):
         fl = fl[ind]
 
         if norm:
-            F_bol = np.trapz(fl, wl)
+            F_bol = trapezoid(fl, wl)
             fl = fl * (C.F_sun / F_bol)
             # the bolometric luminosity is always 1 L_sun
 
@@ -487,7 +488,7 @@ def load_flux(self, parameters, header=False, norm=True):
         fl = fl[ind]
 
         if norm:
-            F_bol = np.trapz(fl, wl)
+            F_bol = trapezoid(fl, wl)
             fl = fl * (C.F_sun / F_bol)
             # the bolometric luminosity is always 1 L_sun
 
@@ -538,7 +539,7 @@ def load_BTSettl(T, logg, Z, norm=False, trunc=False, air=False):
     fl = 10 ** (fl - 8.0)  # now in ergs/cm^2/s/A
 
     if norm:
-        F_bol = np.trapz(fl, wl)
+        F_bol = trapezoid(fl, wl)
         fl = fl * (C.F_sun / F_bol)
         # this also means that the bolometric luminosity is always 1 L_sun
 

Starfish/models/utils.py

@@ -1,7 +1,7 @@
 import logging
 
 import numpy as np
-from nptyping import NDArray
+from numpy.typing import NDArray
 from scipy.optimize import minimize
 import scipy.stats as st
 
@@ -105,7 +105,7 @@ def chi2(P, wave, resid, sigma):
         _amp = np.exp(log_amp)
         _sigma = np.exp(log_sigma)
         if _amp == 0 or _sigma == 0:
-            return np.Inf
+            return np.inf
         # Logistic prior for the widths out to 2sigma
         prior = st.uniform.logpdf(_sigma, 0, 2 * sigma0)
         # Put prior on widths and heights such that the integrated area should be less
@@ -151,7 +151,7 @@ def chi2(P, wave, resid, sigma):
 log = logging.getLogger(__name__)
 
 
-def covariance_debugger(cov: NDArray[float]):
+def covariance_debugger(cov: NDArray[np.float64]):
     """
     Special debugging information for the covariance matrix decomposition.
     """

Starfish/spectrum.py

@@ -1,7 +1,7 @@
 import h5py
 import numpy as np
 from dataclasses import dataclass
-from nptyping import NDArray
+from numpy.typing import NDArray
 from typing import Optional
 
 
@@ -26,10 +26,10 @@ class Order:
     name : str
     """
 
-    _wave: NDArray[float]
-    _flux: NDArray[float]
-    _sigma: Optional[NDArray[float]] = None
-    mask: Optional[NDArray[bool]] = None
+    _wave: NDArray[np.float64]
+    _flux: NDArray[np.float64]
+    _sigma: Optional[NDArray[np.float64]] = None
+    mask: Optional[NDArray[np.bool_]] = None
 
     def __post_init__(self):
         if self._sigma is None: