DM-41209: Make standalone, pip-installable lsst package

.gitignore

@@ -2,7 +2,14 @@
 build/
 dist/
 multiprofit.egg-info/
-*/__pycache__/
+__pycache__/
+.coverage
+.sconsign.dblite
+config.log
+version.py
+
+# Generated by pip install -e .
+python/lsst_multiprofit.egg-info/
 
 # IDE folders and files
 .cache/
@@ -11,6 +18,9 @@ multiprofit.egg-info/
 .vscode/
 compile_commands.json
 
+# test outputs
+tests/.tests
+
 # pytest plugins
 .hypothesis/
 prof/

SConstruct

@@ -0,0 +1,4 @@
+# -*- python -*-
+from lsst.sconsUtils import scripts
+# Python-only package
+scripts.BasicSConstruct("multiprofit", disableCc=True, noCfgFile=True)

examples/fithsc.py

@@ -18,9 +18,9 @@
 import gauss2d.fit as g2f
 import matplotlib as mpl
 import matplotlib.pyplot as plt
-from multiprofit.componentconfig import SersicConfig, SersicIndexConfig
-from multiprofit.fit_psf import CatalogExposurePsfABC, CatalogPsfFitterConfig, CatalogPsfFitter
-from multiprofit.fit_source import CatalogExposureSourcesABC, CatalogSourceFitterABC, CatalogSourceFitterConfig
+from lsst.multiprofit.componentconfig import SersicConfig, SersicIndexConfig
+from lsst.multiprofit.fit_psf import CatalogExposurePsfABC, CatalogPsfFitterConfig, CatalogPsfFitter
+from lsst.multiprofit.fit_source import CatalogExposureSourcesABC, CatalogSourceFitterABC, CatalogSourceFitterConfig
 import numpy as np
 from typing import Any, Iterable, Mapping
 

examples/test_gaussians.py

@@ -6,7 +6,7 @@
     nbenchmark=100, do_like=True, do_residual=True, do_grad=True, do_jac=True,
     do_meas_modelfit=False, nsub=4,
 )
-for x in test: 
+for x in test:
     print(f"re={x['reff']:.3f} q={x['axrat']:.2f}"
           f" ang={x['ang']:2.1f} { x['string']}")
 print(f'Test complete in {timer() - start:.2f}s')

examples/test_utils.py

@@ -22,7 +22,8 @@
 from importlib.util import find_spec
 import numpy as np
 import matplotlib.pyplot as plt
-from multiprofit.fitutils import get_model
+# TODO: Replace with e.g. fit_source.get_model?
+from lsst.multiprofit.fitutils import get_model
 import gauss2d as g2
 import timeit
 
@@ -181,7 +182,7 @@ def get_setup(xdim=15, ydim=15, r_major=1, axrat=1, angle=0, nsub=1,
         f'xdim={xdim}',
         f'ydim={ydim}',
         f'centroid = g2.Centroid({xdim}/2, {ydim}/2)',
-        f'kernel = g2.Gaussian(centroid=g2.Centroid(0, 0),'
+        'kernel = g2.Gaussian(centroid=g2.Centroid(0, 0),'
         'ellipse=g2.Ellipse(sigma_x=0., sigma_y=0))',
         'ellipse = g2.Ellipse(g2.EllipseMajor('
         f'r_major={r_major}*g2.M_SIGMA_HWHM, axrat={axrat}, angle={angle}, degrees=True))',
@@ -318,7 +319,7 @@ def gaussian_test(xdim=49, ydim=51, reffs=None, angs=None, axrats=None, nbenchma
                         ang_rad = np.deg2rad(ang)
                         for key in ("dev", "exp"):
                             times[f"mmf-{key}"] = np.min(timeit.repeat(
-                                f"msf.evaluate().addToImage(img)",
+                                "msf.evaluate().addToImage(img)",
                                 setup=(
                                     f"import numpy as np;"
                                     f"from lsst.shapelet import RadialProfile;"
@@ -360,7 +361,7 @@ def gradient_test(dimx=5, dimy=4, flux=1e4, reff=2, axrat=0.5, ang=0, bg=1e3,
                   reff_psf=0, axrat_psf=0.95, ang_psf=0, n_psfs=1, printout=False, plot=False):
 
     if n_psfs > 1:
-        raise ValueError(f"n_psfs>1 not yet supported")
+        raise ValueError("n_psfs>1 not yet supported")
 
     cen_x, cen_y = dimx/2., dimy/2.
     # Keep this in units of sigma, not re==FWHM/2

pyproject.toml

@@ -0,0 +1,106 @@
+[build-system]
+requires = ["setuptools", "lsst-versions >= 1.3.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "lsst-multiprofit"
+description = "Astronomical image and source model fitting code."
+license = {file = "LICENSE"}
+readme = "README.rst"
+authors = [
+    {name="Rubin Observatory Data Management", email="dm-admin@lists.lsst.org"},
+]
+classifiers = [
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
+    "Operating System :: OS Independent",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Topic :: Scientific/Engineering :: Astronomy",
+]
+keywords = [
+    "astronomy",
+    "astrophysics",
+    "fitting",
+    "lsst",
+    "models",
+    "modeling",
+]
+requires-python = ">=3.10.0"
+dependencies = [
+    "astropy",
+    "gauss2d",
+    "gauss2dfit",
+    "lsst-pex-config",
+    "lsst-utils",
+    "importlib_resources",
+    "matplotlib",
+    "numpy",
+    "pydantic",
+    "scipy",
+]
+dynamic = ["version"]
+
+[project.urls]
+"Homepage" = "https://github.com/lsst-dm/multiprofit"
+
+[project.optional-dependencies]
+galsim = ["galsim"]
+test = [
+    "pytest",
+]
+
+[tool.setuptools.packages.find]
+where = ["python"]
+
+[tool.setuptools.dynamic]
+version = { attr = "lsst_versions.get_lsst_version" }
+
+[tool.black]
+line-length = 110
+target-version = ["py311"]
+
+[tool.isort]
+profile = "black"
+line_length = 110
+
+[tool.ruff]
+exclude = [
+    "__init__.py",
+    "examples/fithsc.py",
+    "examples/test_utils.py",
+    "tests/*.py",
+]
+ignore = [
+    "N802",
+    "N803",
+    "N806",
+    "N812",
+    "N815",
+    "N816",
+    "N999",
+    "D107",
+    "D105",
+    "D102",
+    "D104",
+    "D100",
+    "D200",
+    "D205",
+    "D400",
+]
+line-length = 110
+select = [
+    "E",  # pycodestyle
+    "F",  # pycodestyle
+    "N",  # pep8-naming
+    "W",  # pycodestyle
+    "D",  # pydocstyle
+]
+target-version = "py311"
+
+[tool.ruff.pycodestyle]
+max-doc-length = 79
+
+[tool.ruff.pydocstyle]
+convention = "numpy"

python/lsst/__init__.py

@@ -0,0 +1,4 @@
+import pkgutil
+
+__path__ = pkgutil.extend_path(__path__, __name__)
+

python/lsst/multiprofit/__init__.py

@@ -0,0 +1,4 @@
+import pkgutil
+
+__path__ = pkgutil.extend_path(__path__, __name__)
+

multiprofit/asinhstretchsigned.py → python/lsst/multiprofit/asinhstretchsigned.py

@@ -31,9 +31,8 @@ def _prepare(values, clip=True, out=None):
     Prepare the data by optionally clipping and copying, and return the
     array that should be subsequently used for in-place calculations.
     """
-
     if clip:
-        return np.clip(values, 0., 1., out=out)
+        return np.clip(values, 0.0, 1.0, out=out)
     else:
         if out is None:
             return np.array(values, copy=True)
@@ -59,7 +58,7 @@ class AsinhStretchSigned(apvis.BaseStretch):
         to logarithmic behavior, expressed as a fraction of the
         normalized image.  Must be in the range between 0 and 1.
         Default is 0.1
-    """
+    """  # noqa: W505
 
     def __init__(self, a=0.1):
         super().__init__()
@@ -74,14 +73,14 @@ def __call__(self, values, clip=True, out=None):
         np.abs(values, out=values)
         np.true_divide(values, self.a, out=values)
         np.arcsinh(values, out=values)
-        np.true_divide(values, np.arcsinh(1. / self.a), out=values)
-        np.true_divide(1. + signs*values, 2., out=values)
+        np.true_divide(values, np.arcsinh(1.0 / self.a), out=values)
+        np.true_divide(1.0 + signs * values, 2.0, out=values)
         return values
 
     @property
     def inverse(self):
         """A stretch object that performs the inverse operation."""
-        return SinhStretchSigned(a=1. / np.arcsinh(1. / self.a))
+        return SinhStretchSigned(a=1.0 / np.arcsinh(1.0 / self.a))
 
 
 class SinhStretchSigned(apvis.BaseStretch):
@@ -99,24 +98,24 @@ class SinhStretchSigned(apvis.BaseStretch):
         The ``a`` parameter used in the above formula.  Default is 1/3.
     """
 
-    def __init__(self, a=1. / 3.):
+    def __init__(self, a=1.0 / 3.0):
         super().__init__()
         self.a = a
 
-#    [docs]
+    #    [docs]
 
     def __call__(self, values, clip=True, out=None):
         values = _prepare(values, clip=clip, out=out)
-        values *= 2.
-        values -= 1.
+        values *= 2.0
+        values -= 1.0
         np.true_divide(values, self.a, out=values)
         np.sinh(values, out=values)
-        np.true_divide(values, np.sinh(1. / self.a), out=values)
-        values += 1.
-        values /= 2.
+        np.true_divide(values, np.sinh(1.0 / self.a), out=values)
+        values += 1.0
+        values /= 2.0
         return values
 
     @property
     def inverse(self):
         """A stretch object that performs the inverse operation."""
-        return AsinhStretchSigned(a=1. / np.sinh(1. / self.a))
+        return AsinhStretchSigned(a=1.0 / np.sinh(1.0 / self.a))