style: Perform full linting and fixes to the whole codebase

.codespell-whitelist

@@ -0,0 +1,2 @@
+sie
+childs

.pre-commit-config.yaml

@@ -1,28 +1,26 @@
 exclude: |
   (?x)^(
-    tests/utils.py |
-    .devcontainer/
+    tests/utils.py
   )
 
 ci:
   autoupdate_commit_msg: "chore: update pre-commit hooks"
   autofix_commit_msg: "style: pre-commit fixes"
-  autofix_prs: false
 
 repos:
   - repo: https://github.com/psf/black
-    rev: "23.7.0"
+    rev: "23.11.0"
     hooks:
       - id: black-jupyter
 
   - repo: https://github.com/asottile/blacken-docs
-    rev: "1.15.0"
+    rev: "1.16.0"
     hooks:
       - id: blacken-docs
         additional_dependencies: [black==23.7.0]
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: "v4.4.0"
+    rev: "v4.5.0"
     hooks:
       - id: check-added-large-files
       - id: check-case-conflict
@@ -45,36 +43,33 @@ repos:
       - id: rst-inline-touching-normal
 
   - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: "v3.0.0"
+    rev: "v4.0.0-alpha.4"
     hooks:
       - id: prettier
         types_or: [yaml, markdown, html, css, scss, javascript, json]
         args: [--prose-wrap=always]
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.0.277"
+    rev: "v0.1.7"
     hooks:
       - id: ruff
         args: ["--fix", "--show-fixes"]
 
-  - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: "v1.4.1"
-    hooks:
-      - id: mypy
-        files: src|tests
-        args: []
-        additional_dependencies:
-          - pytest
+  # 2023-12-11: Not use mypy for now.
+  # - repo: https://github.com/pre-commit/mirrors-mypy
+  #   rev: "v1.7.1"
+  #   hooks:
+  #     - id: mypy
+  #       files: src|tests
+  #       args: []
+  #       additional_dependencies:
+  #         - pytest
 
   - repo: https://github.com/codespell-project/codespell
-    rev: "v2.2.5"
+    rev: "v2.2.6"
     hooks:
       - id: codespell
-
-  - repo: https://github.com/shellcheck-py/shellcheck-py
-    rev: "v0.9.0.5"
-    hooks:
-      - id: shellcheck
+        args: ["--write-changes", "--ignore-words", ".codespell-whitelist"]
 
   - repo: https://github.com/kynan/nbstripout
     rev: 0.6.1

docs/requirements.txt

@@ -1,5 +1,5 @@
+ipywidgets
 jupyter-book
+matplotlib
 sphinx
 sphinx_rtd_theme
-matplotlib
-ipywidgets

docs/source/tutorials/BasicIntroduction.ipynb

@@ -56,7 +56,7 @@
    "source": [
     "## Simulating an SIE lens\n",
     "\n",
-    "Here we will demo the very basics of lensing with a classic `SIE` lens model. We will see what it takes to make an `SIE` model, lens a backgorund `Sersic` source, and sample some examples in a simulator. Caustic simulators can generalize to very complex scenarios. In these cases there can be a lot of parameters moving through the simulator, and the order/number of parameters may change depending on what lens or source is being used. To streamline this process, caustic impliments a class called `Parametrized` which has some knowledge of the parameters moving through it, this way it can keep track of everything for you. For this to work, you must put the parameters into a `Packed` object which it can recognize, each sub function can then unpack the parameters it needs. Below we will show some examples of what this looks like."
+    "Here we will demo the very basics of lensing with a classic `SIE` lens model. We will see what it takes to make an `SIE` model, lens a background `Sersic` source, and sample some examples in a simulator. Caustic simulators can generalize to very complex scenarios. In these cases there can be a lot of parameters moving through the simulator, and the order/number of parameters may change depending on what lens or source is being used. To streamline this process, caustic implements a class called `Parametrized` which has some knowledge of the parameters moving through it, this way it can keep track of everything for you. For this to work, you must put the parameters into a `Packed` object which it can recognize, each sub function can then unpack the parameters it needs. Below we will show some examples of what this looks like."
    ]
   },
   {
@@ -155,7 +155,7 @@
    "source": [
     "## Where to go next?\n",
     "\n",
-    "The caustic tutorials are generally short and to the point, that way you can idenfity what you want and jump right to some useful code that demo's the particular problem you face. Below is a list of caustic tutorials and a quick description of what you will learn in each one::\n",
+    "The caustic tutorials are generally short and to the point, that way you can identify what you want and jump right to some useful code that demo's the particular problem you face. Below is a list of caustic tutorials and a quick description of what you will learn in each one::\n",
     "\n",
     "- `LensZoo`: here you can see all the built-in lens mass distributions in `caustic` and how they distort the same background Seric source.\n",
     "- `Playground`: here we demo the main visualizations of a lensing system (deflection angles, convergence, potential, time delay, magnification) in an interactive display so you can change the parameters by hand and see how the visuals change!\n",

docs/source/tutorials/InvertLensEquation.ipynb

@@ -102,7 +102,7 @@
     "paths = CS.collections[0].get_paths()\n",
     "caustic_paths = []\n",
     "for path in paths:\n",
-    "    # Collect the path into a descrete set of points\n",
+    "    # Collect the path into a discrete set of points\n",
     "    vertices = path.interpolated(5).vertices\n",
     "    x1 = torch.tensor(list(float(vs[0]) for vs in vertices))\n",
     "    x2 = torch.tensor(list(float(vs[1]) for vs in vertices))\n",

docs/source/tutorials/MultiplaneDemo.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Multiplane Lensing\n",
     "\n",
-    "The universe is three dimensional and filled with stuff. A light ray traveling to our telescope may encounter more than a single massive object on its way to our telescopes. This is handled by a multiplane lensing framework. Multiplane lensing involves tracing the path of a ray backwards from our telescope through each individual plane (which is treated similarly to typical single plane lensing, though extra factors account for the ray physically moving in 3D space) getting perturbed at each step until it finally lands on the source we'd like to image. For more mathmatical details see [Petkova et al. 2014](https://doi.org/10.1093/mnras/stu1860) for the formalism we use internally.\n",
+    "The universe is three dimensional and filled with stuff. A light ray traveling to our telescope may encounter more than a single massive object on its way to our telescopes. This is handled by a multiplane lensing framework. Multiplane lensing involves tracing the path of a ray backwards from our telescope through each individual plane (which is treated similarly to typical single plane lensing, though extra factors account for the ray physically moving in 3D space) getting perturbed at each step until it finally lands on the source we'd like to image. For more mathematical details see [Petkova et al. 2014](https://doi.org/10.1093/mnras/stu1860) for the formalism we use internally.\n",
     "\n",
     "The main concept to keep in mind is that a lot of quantities we are used to working with, such as \"reduced deflection angles\" don't really exist in multiplane lensing since these are normalized by the redshift of the source and lens, however there is no single \"lens redshift\" for multiplane! Instead we define everything with respect to results from full raytracing, once the raytracing is done we can define effective quantities (like effective reduced deflection angle) which behave similarly in intuition but are not quite the same in detail."
    ]
@@ -169,7 +169,7 @@
     "paths = CS.collections[0].get_paths()\n",
     "\n",
     "for path in paths:\n",
-    "    # Collect the path into a descrete set of points\n",
+    "    # Collect the path into a discrete set of points\n",
     "    vertices = path.interpolated(5).vertices\n",
     "    x1 = torch.tensor(list(float(vs[0]) for vs in vertices))\n",
     "    x2 = torch.tensor(list(float(vs[1]) for vs in vertices))\n",

docs/source/tutorials/VisualizeCaustics.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Visualize Caustics\n",
     "\n",
-    "Here we will demonstrate how to collect caustic lines using caustic! Since caustic (the code) uses autodiff and can get exact derivatives, it is actually very acurate at computing caustics. \n",
+    "Here we will demonstrate how to collect caustic lines using caustic! Since caustic (the code) uses autodiff and can get exact derivatives, it is actually very accurate at computing caustics. \n",
     "\n",
     "Conceptually a caustic occurs where the magnification of a lens diverges to infinity. A convenient way to measure the magnification in the image plane is by taking the determinant ($det$) of the jacobian of the lens equation ($A$), its reciprocal is the magnification. This means that anywhere that $det(A) = 0$ is a critical line in the image plane (magnification goes to infinity). If we take this line and raytrace it back to the source plane we can see the caustics which define boundaries for lensing phenomena."
    ]
@@ -125,7 +125,7 @@
     "paths = CS.collections[0].get_paths()\n",
     "\n",
     "for path in paths:\n",
-    "    # Collect the path into a descrete set of points\n",
+    "    # Collect the path into a discrete set of points\n",
     "    vertices = path.interpolated(5).vertices\n",
     "    x1 = torch.tensor(list(float(vs[0]) for vs in vertices))\n",
     "    x2 = torch.tensor(list(float(vs[1]) for vs in vertices))\n",

docs/source/tutorials/index.md

@@ -1,5 +1,5 @@
 # Tutorials
 
-Here you will find the jupyter notebook tutorials.
-It is recommended that you go through the tutorials yourself,
-but for quick reference a version of each tutorial is available here.
+Here you will find the jupyter notebook tutorials. It is recommended that you go
+through the tutorials yourself, but for quick reference a version of each
+tutorial is available here.

pyproject.toml

@@ -57,3 +57,7 @@ version-file = "src/caustics/_version.py"
 
 [tool.hatch.version.raw-options]
 local_scheme = "no-local-version"
+
+[tool.ruff]
+# Same as Black.
+line-length = 100

src/caustics/__init__.py

@@ -1,16 +1,23 @@
 from ._version import version as VERSION  # noqa
 
-from .constants import *
-from .lenses import *
-from .cosmology import *
-from .packed import *
-from .parametrized import *
-from .light import *
-from .utils import *
-from .sims import *
-from .tests import *
+from . import constants, lenses, cosmology, packed, parametrized, light, utils, sims
+from .tests import test
 
 # from .demo import *
 
 __version__ = VERSION
 __author__ = "Ciela"
+
+__all__ = [
+    # Modules
+    "constants",
+    "lenses",
+    "cosmology",
+    "packed",
+    "parametrized",
+    "light",
+    "utils",
+    "sims",
+    # Functions
+    "test",
+]

src/caustics/cosmology.py

@@ -10,6 +10,7 @@
 from .utils import interp1d
 from .constants import G_over_c2, c_Mpc_s, km_to_Mpc
 from .parametrized import Parametrized, unpack
+from .packed import Packed
 
 __all__ = (
     "h0_default",
@@ -276,7 +277,8 @@ def critical_surface_density(
 
 class FlatLambdaCDM(Cosmology):
     """
-    Subclass of Cosmology representing a Flat Lambda Cold Dark Matter (LCDM) cosmology with no radiation.
+    Subclass of Cosmology representing a Flat Lambda Cold Dark Matter (LCDM)
+    cosmology with no radiation.
     """
 
     def __init__(

src/caustics/data/__init__.py

@@ -1,3 +1,5 @@
-from .hdf5dataset import *
-from .illustris_kappa import *
-from .probes import *
+from .hdf5dataset import HDF5Dataset
+from .illustris_kappa import IllustrisKappaDataset
+from .probes import PROBESDataset
+
+__all__ = ["HDF5Dataset", "IllustrisKappaDataset", "PROBESDataset"]

src/caustics/lenses/__init__.py

@@ -1,13 +1,31 @@
-from .base import *
-from .epl import *
-from .external_shear import *
-from .pixelated_convergence import *
-from .multiplane import *
-from .nfw import *
-from .point import *
-from .pseudo_jaffe import *
-from .sie import *
-from .sis import *
-from .singleplane import *
-from .mass_sheet import *
-from .tnfw import *
+from .base import ThinLens, ThickLens
+from .epl import EPL
+from .external_shear import ExternalShear
+from .pixelated_convergence import PixelatedConvergence
+from .multiplane import Multiplane
+from .nfw import NFW
+from .point import Point
+from .pseudo_jaffe import PseudoJaffe
+from .sie import SIE
+from .sis import SIS
+from .singleplane import SinglePlane
+from .mass_sheet import MassSheet
+from .tnfw import TNFW
+
+
+__all__ = [
+    "ThinLens",
+    "ThickLens",
+    "EPL",
+    "ExternalShear",
+    "PixelatedConvergence",
+    "Multiplane",
+    "NFW",
+    "Point",
+    "PseudoJaffe",
+    "SIE",
+    "SIS",
+    "SinglePlane",
+    "MassSheet",
+    "TNFW",
+]