diff --git a/.github/workflows/draft_pdf.yml b/.github/workflows/draft_pdf.yml
new file mode 100644
index 00000000..82e1bf58
--- /dev/null
+++ b/.github/workflows/draft_pdf.yml
@@ -0,0 +1,28 @@
+name: Draft PDF
+
+on:
+ push:
+ branches: ["paper"] # run on main branch pushes
+ workflow_dispatch: # allow manual triggering
+
+jobs:
+ paper:
+ runs-on: ubuntu-latest
+ name: Paper Draft
+ steps:
+ - name: Checkout
+ uses: actions/checkout@v4
+ - name: Build draft PDF
+ uses: openjournals/openjournals-draft-action@master
+ with:
+ journal: joss
+ # This should be the path to the paper within your repo.
+ paper-path: paper/paper.md
+ - name: Upload
+ uses: actions/upload-artifact@v4
+ with:
+ name: paper
+ # This is the output path where Pandoc will write the compiled
+ # PDF. Note, this should be the same directory as the input
+ # paper.md
+ path: paper/paper.pdf
diff --git a/docs/index.html b/docs/index.html
index 39b2ca92..8d3d695f 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -1,3 +1,3 @@
-
+
diff --git a/minimal_example/example_cartesian_gradient.ipynb b/minimal_example/example_cartesian_gradient.ipynb
new file mode 100644
index 00000000..ee16dbda
--- /dev/null
+++ b/minimal_example/example_cartesian_gradient.ipynb
@@ -0,0 +1,184 @@
+{
+ "cells": [
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": [
+ "# Cartesian Gradient Example\n",
+ "\n",
+ "---\n",
+ "\n",
+ "This example provides a very simple demonstration of how to compute the gradient of a scalar field defined on a Cartesian grid using PyMetric. We'll first install\n",
+ "the PyMetric package and then create a field and take the gradient."
+ ],
+ "id": "2baca49099618917"
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-18T19:13:05.178616Z",
+ "start_time": "2025-10-18T19:13:00.988588Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "# Install the PyMetric package from the source\n",
+ "# code.\n",
+ "! pip install .."
+ ],
+ "id": "e84ed94947390571",
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Processing /Users/ediggins/Dev/pymetric\r\n",
+ " Installing build dependencies ... \u001b[?25ldone\r\n",
+ "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\r\n",
+ "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\r\n",
+ "\u001b[?25hRequirement already satisfied: numpy>=1.22 in /Users/ediggins/Dev/pymetric/.venv/lib/python3.12/site-packages (from pymetric-lib==0.0.2a2.dev26) (2.2.6)\r\n",
+ "Requirement already satisfied: h5py>=3.0 in /Users/ediggins/Dev/pymetric/.venv/lib/python3.12/site-packages (from pymetric-lib==0.0.2a2.dev26) (3.13.0)\r\n",
+ "Requirement already satisfied: scipy>=1.10 in /Users/ediggins/Dev/pymetric/.venv/lib/python3.12/site-packages (from pymetric-lib==0.0.2a2.dev26) (1.15.3)\r\n",
+ "Requirement already satisfied: sympy>=1.14.0 in /Users/ediggins/Dev/pymetric/.venv/lib/python3.12/site-packages (from pymetric-lib==0.0.2a2.dev26) (1.14.0)\r\n",
+ "Requirement already satisfied: matplotlib in /Users/ediggins/Dev/pymetric/.venv/lib/python3.12/site-packages (from pymetric-lib==0.0.2a2.dev26) (3.10.3)\r\n",
+ "Requirement already satisfied: tqdm in /Users/ediggins/Dev/pymetric/.venv/lib/python3.12/site-packages (from pymetric-lib==0.0.2a2.dev26) (4.67.1)\r\n",
+ "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /Users/ediggins/Dev/pymetric/.venv/lib/python3.12/site-packages (from sympy>=1.14.0->pymetric-lib==0.0.2a2.dev26) (1.3.0)\r\n",
+ "Requirement already satisfied: contourpy>=1.0.1 in /Users/ediggins/Dev/pymetric/.venv/lib/python3.12/site-packages (from matplotlib->pymetric-lib==0.0.2a2.dev26) (1.3.2)\r\n",
+ "Requirement already satisfied: cycler>=0.10 in /Users/ediggins/Dev/pymetric/.venv/lib/python3.12/site-packages (from matplotlib->pymetric-lib==0.0.2a2.dev26) (0.12.1)\r\n",
+ "Requirement already satisfied: fonttools>=4.22.0 in /Users/ediggins/Dev/pymetric/.venv/lib/python3.12/site-packages (from matplotlib->pymetric-lib==0.0.2a2.dev26) (4.58.0)\r\n",
+ "Requirement already satisfied: kiwisolver>=1.3.1 in /Users/ediggins/Dev/pymetric/.venv/lib/python3.12/site-packages (from matplotlib->pymetric-lib==0.0.2a2.dev26) (1.4.8)\r\n",
+ "Requirement already satisfied: packaging>=20.0 in /Users/ediggins/Dev/pymetric/.venv/lib/python3.12/site-packages (from matplotlib->pymetric-lib==0.0.2a2.dev26) (25.0)\r\n",
+ "Requirement already satisfied: pillow>=8 in /Users/ediggins/Dev/pymetric/.venv/lib/python3.12/site-packages (from matplotlib->pymetric-lib==0.0.2a2.dev26) (11.2.1)\r\n",
+ "Requirement already satisfied: pyparsing>=2.3.1 in /Users/ediggins/Dev/pymetric/.venv/lib/python3.12/site-packages (from matplotlib->pymetric-lib==0.0.2a2.dev26) (3.2.3)\r\n",
+ "Requirement already satisfied: python-dateutil>=2.7 in /Users/ediggins/Dev/pymetric/.venv/lib/python3.12/site-packages (from matplotlib->pymetric-lib==0.0.2a2.dev26) (2.9.0.post0)\r\n",
+ "Requirement already satisfied: six>=1.5 in /Users/ediggins/Dev/pymetric/.venv/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib->pymetric-lib==0.0.2a2.dev26) (1.17.0)\r\n",
+ "Building wheels for collected packages: pymetric-lib\r\n",
+ " Building wheel for pymetric-lib (pyproject.toml) ... \u001b[?25ldone\r\n",
+ "\u001b[?25h Created wheel for pymetric-lib: filename=pymetric_lib-0.0.2a2.dev26-py3-none-any.whl size=259935 sha256=25a8ab74cecc3a17193c28d3aa6675f536eff72b198bd9934afc762a7012129e\r\n",
+ " Stored in directory: /private/var/folders/wb/hm_py6x1025cwdg8jjs1rg_w0000gn/T/pip-ephem-wheel-cache-9mekzsk3/wheels/5c/a0/f2/adc1bf06554787ce9ad3de709272799b2f3e991ebe4c4ae46e\r\n",
+ "Successfully built pymetric-lib\r\n",
+ "Installing collected packages: pymetric-lib\r\n",
+ "Successfully installed pymetric-lib-0.0.2a2.dev26\r\n",
+ "\r\n",
+ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.1.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.2\u001b[0m\r\n",
+ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n"
+ ]
+ }
+ ],
+ "execution_count": 2
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-18T19:25:51.637294Z",
+ "start_time": "2025-10-18T19:25:51.576159Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "import numpy as np\n",
+ "from pymetric import DenseTensorField, CartesianCoordinateSystem2D, GenericGrid, pg_log\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Disable the logger\n",
+ "pg_log.setLevel(\"WARNING\")\n",
+ "\n",
+ "# Create the coordinate system and the grid.\n",
+ "cs = CartesianCoordinateSystem2D()\n",
+ "x, y = (np.linspace(-0.1, 1.1, 104), np.linspace(-0.1, 1.1, 104))\n",
+ "g = GenericGrid(cs, [x, y], ghost_zones=2)\n",
+ "\n",
+ "# Define a function of the coords.\n",
+ "func = lambda _x, _y: np.sin(10 * np.sqrt(_x**2 + _y**2)) * 5 * np.cos(3 * _x)\n",
+ "\n",
+ "# Create the dense field from the function.\n",
+ "f = DenseTensorField.from_function(func, g, [\"x\", \"y\"])\n",
+ "\n",
+ "fig, axes = plt.subplots(1, 1)\n",
+ "Q = axes.imshow(f[...].T, extent=g.bbox.ravel())\n",
+ "axes.set_xlabel(\"x\")\n",
+ "axes.set_ylabel(\"y\")\n",
+ "plt.colorbar(Q, ax=axes)\n",
+ "plt.show()"
+ ],
+ "id": "c806469c678a6c5c",
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "execution_count": 29
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-18T19:28:22.741360Z",
+ "start_time": "2025-10-18T19:28:22.597770Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "# Generate the gradient vector field for the scalar field.\n",
+ "grad_f = f.gradient()\n",
+ "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
+ "Q1 = axes[0].imshow(grad_f[..., 0].T, extent=g.gbbox.ravel())\n",
+ "axes[0].set_title(\"Gradient in x direction\")\n",
+ "axes[0].set_xlabel(\"x\")\n",
+ "axes[0].set_ylabel(\"y\")\n",
+ "axes[0].set_xlim(*g.bbox[0, :])\n",
+ "axes[0].set_ylim(*g.bbox[1, :])\n",
+ "plt.colorbar(Q1, ax=axes[0])\n",
+ "Q2 = axes[1].imshow(grad_f[..., 1].T, extent=g.gbbox.ravel())\n",
+ "axes[1].set_title(\"Gradient in y direction\")\n",
+ "axes[1].set_xlabel(\"x\")\n",
+ "axes[1].set_ylabel(\"y\")\n",
+ "axes[1].set_xlim(*g.bbox[0, :])\n",
+ "axes[1].set_ylim(*g.bbox[1, :])\n",
+ "plt.colorbar(Q2, ax=axes[1])\n",
+ "plt.show()"
+ ],
+ "id": "e963bc0b3c9f7e30",
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "execution_count": 33
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/minimal_example/readme.md b/minimal_example/readme.md
new file mode 100644
index 00000000..9fc9d8e3
--- /dev/null
+++ b/minimal_example/readme.md
@@ -0,0 +1,10 @@
+## Minimal Example
+
+---
+
+This directory contains a couple of Jupyter notebooks which demonstrate the
+basic capabilities of the code base. Each will attempt to install the package from
+the local source code and then run the example.
+
+For more detailed examples and tutorials, please refer to the
+[docs](https://pisces-project.github.io/PyMetric/dev/auto_examples/index.html).
diff --git a/paper/example.py b/paper/example.py
new file mode 100644
index 00000000..73facd12
--- /dev/null
+++ b/paper/example.py
@@ -0,0 +1,75 @@
+# noqa: D100
+import matplotlib.pyplot as plt
+import numpy as np
+
+import pymetric as pym
+
+# Define spherical coordinate system and grid
+cs = pym.coordinates.SphericalCoordinateSystem()
+grid = pym.grids.GenericGrid(
+ cs,
+ [
+ np.linspace(0.1, 4.9, 300), # r
+ np.linspace(0.01, np.pi - 0.01, 100), # θ
+ np.linspace(0.01, 2 * np.pi - 0.01, 100), # φ
+ ],
+ center="cell",
+ bbox=[(0, 5), (0, np.pi), (0, 2 * np.pi)],
+ ghost_zones=2,
+)
+
+# Define scalar field F(r, θ) = r * cos(θ)
+field = pym.DenseField.from_function(
+ lambda r, theta: r * np.cos(theta),
+ grid,
+ axes=["r", "theta"],
+)
+
+# Compute Laplacian and trim ghost zones
+F = field[2:-2, 2:-2]
+F_lap = field.element_wise_laplacian()[2:-2, 2:-2]
+R, Theta = grid.compute_domain_mesh(axes=["r", "theta"], origin="active")
+
+# Plot field and Laplacian
+fig, axes = plt.subplots(2, 1, figsize=(6, 6), sharex=True, gridspec_kw={"hspace": 0.0})
+vmin, vmax = -5, 5
+norm = plt.Normalize(vmin=vmin, vmax=vmax)
+cmap = "seismic"
+
+axes[0].pcolormesh(R, Theta, F, cmap=cmap, norm=norm)
+axes[0].set_ylabel(r"$\theta$")
+axes[0].text(
+ 0.5,
+ 0.9,
+ r"$F(r,\theta) = r\cos(\theta)$",
+ ha="center",
+ va="bottom",
+ transform=axes[0].transAxes,
+ fontsize=10,
+ bbox=dict(facecolor="white", alpha=0.7),
+)
+
+axes[1].pcolormesh(R, Theta, F_lap, cmap=cmap, norm=norm)
+axes[1].set_xlabel(r"$r$")
+axes[1].set_ylabel(r"$\theta$")
+axes[1].text(
+ 0.5,
+ 0.9,
+ r"$\nabla^2 F(r,\theta)$",
+ ha="center",
+ va="bottom",
+ transform=axes[1].transAxes,
+ fontsize=10,
+ bbox=dict(facecolor="white", alpha=0.7),
+)
+
+cbar = fig.colorbar(
+ plt.cm.ScalarMappable(norm=norm, cmap=cmap),
+ ax=axes,
+ orientation="vertical",
+ fraction=0.04,
+ pad=0.03,
+)
+cbar.set_label(r"$F(r,\theta)$ and $\nabla^2 F(r,\theta)$")
+
+plt.savefig("fig1.png", dpi=600)
diff --git a/paper/fig1.png b/paper/fig1.png
new file mode 100644
index 00000000..b6bf0aa4
Binary files /dev/null and b/paper/fig1.png differ
diff --git a/paper/paper.bib b/paper/paper.bib
new file mode 100644
index 00000000..9f4e1925
--- /dev/null
+++ b/paper/paper.bib
@@ -0,0 +1,65 @@
+@article{harris2020array,
+ title={Array programming with NumPy},
+ author={Harris, Charles R and Millman, K Jarrod and Van Der Walt, St{\'e}fan J and Gommers, Ralf and Virtanen, Pauli and Cournapeau, David and Wieser, Eric and Taylor, Julian and Berg, Sebastian and Smith, Nathaniel J and others},
+ journal={Nature},
+ volume={585},
+ number={7825},
+ pages={357--362},
+ year={2020},
+ publisher={Nature Publishing Group UK London},
+ doi={10.1038/s41586-020-2649-2}
+}
+@software{hdf5,
+author = {{The HDF Group}},
+title = {{Hierarchical Data Format, version 5}},
+url = {https://github.com/HDFGroup/hdf5}
+}
+@Manual{einsteinpy,
+title = {EinsteinPy: Python library for General Relativity},
+author = {{EinsteinPy Development Team}},
+year = {2024},
+url = {https://einsteinpy.org/},
+doi={10.48550/arXiv.2005.11288}
+}
+@article{perret2016dice,
+ title={DICE: Disk Initial Conditions Environment},
+ author={Perret, Valentin},
+ journal={Astrophysics Source Code Library},
+ pages={ascl--1607},
+ year={2016}
+}
+@article{yurin2014galic,
+ title={GALIC: Galaxy initial conditions construction},
+ author={Yurin, Denis and Springel, Volker},
+ journal={Astrophysics Source Code Library},
+ pages={ascl--1408},
+ year={2014},
+ doi={10.48550/arXiv.1402.1623}
+}
+@article{turk2010yt,
+ title={yt: A multi-code analysis toolkit for astrophysical simulation data},
+ author={Turk, Matthew J and Smith, Britton D and Oishi, Jeffrey S and Skory, Stephen and Skillman, Samuel W and Abel, Tom and Norman, Michael L},
+ journal={The Astrophysical Journal Supplement Series},
+ volume={192},
+ number={1},
+ pages={9},
+ year={2010},
+ publisher={IOP Publishing},
+ doi={10.1088/0067-0049/192/1/9}
+}
+@article{sympy,
+ title = {SymPy: symbolic computing in Python},
+ author = {Meurer, Aaron and Smith, Christopher P. and Paprocki, Mateusz and \v{C}ert\'{i}k, Ond\v{r}ej and Kirpichev, Sergey B. and Rocklin, Matthew and Kumar, AMiT and Ivanov, Sergiu and Moore, Jason K. and Singh, Sartaj and Rathnayake, Thilina and Vig, Sean and Granger, Brian E. and Muller, Richard P. and Bonazzi, Francesco and Gupta, Harsh and Vats, Shivam and Johansson, Fredrik and Pedregosa, Fabian and Curry, Matthew J. and Terrel, Andy R. and Rou\v{c}ka, \v{S}t\v{e}p\'{a}n and Saboo, Ashutosh and Fernando, Isuru and Kulal, Sumith and Cimrman, Robert and Scopatz, Anthony},
+ year = 2017,
+ month = jan,
+ keywords = {Python, Computer algebra system, Symbolics},
+ abstract = {
+ SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.
+ },
+ volume = 3,
+ pages = {e103},
+ journal = {PeerJ Computer Science},
+ issn = {2376-5992},
+ url = {https://doi.org/10.7717/peerj-cs.103},
+ doi = {10.7717/peerj-cs.103}
+}
diff --git a/paper/paper.md b/paper/paper.md
new file mode 100644
index 00000000..e7ae00e2
--- /dev/null
+++ b/paper/paper.md
@@ -0,0 +1,208 @@
+---
+title: 'PyMetric: A Geometry Informed Array Mathematics Package'
+tags:
+ - Python
+ - differential geometry
+ - modeling
+authors:
+ - name: Eliza C. Diggins
+ orcid: 0009-0005-9389-9098
+ corresponding: true # (This is how to denote the corresponding author)
+ affiliation: 1 # (Multiple affiliations must be quoted)
+ - name: Daniel R. Wik
+ orcid: 0000-0001-9110-2245
+ affiliation: 1
+affiliations:
+ - name: University of Utah Department of Physics and Astronomy, Salt Lake City, Utah, USA
+ index: 1
+date: 11 June 2025
+bibliography: paper.bib
+
+---
+
+# Summary
+
+PyMetric is a lightweight Python library designed to streamline differential geometry
+and vector calculus operations in user-defined coordinate systems, with a focus on applications
+in astrophysics and computational physics. The library was originally created to provide a
+geometric backend for the Pisces project, an in-development, general purpose astrophysical modeling and initial conditions
+library, but has since grown into an independent library due to its size and complexity. In many physical modeling tasks, it is both natural
+and advantageous to work in non-Cartesian coordinate systems that align with the inherent
+symmetries of the system. These coordinate systems can feature complex geometric structure which makes the
+explicit handling of differential operations cumbersome. This is particularly true for exotic coordinate
+systems (e.g. homoeoidal coordinate systems). PyMetric provides a unified abstraction
+that decouples the underlying coordinate representation from the operations themselves,
+allowing users to accurately compute gradients, divergences, Laplacians, and related geometric
+quantities through a consistent (and coordinate system agnostic) interface. This makes it easier to prototype and scale
+models in complex geometries without having to rewrite operations for each coordinate system.
+
+The core design of PyMetric relies on a hybrid symbolic-numeric model that balances efficiency,
+flexibility, and accuracy. Symbolic computation is used to derive key geometric quantities, such
+as metric tensors, Christoffel symbols, and Jacobians, from a minimal set of coordinate system properties.
+Once generated, these symbolic structures can be converted into efficient numerical routines that operate
+on array-backed data and are composed to perform higher-level operations.
+This approach allows PyMetric to support coordinate-aware computation with minimal overhead, avoiding the need for
+repeated symbolic manipulation during runtime, while maintaining high accuracy through analytically correct geometric
+expressions. The result is a powerful and extensible framework that enables NumPy-style [@harris2020array] workflows
+in complex coordinate systems without sacrificing physical fidelity.
+
+In addition to its symbolic-numeric foundation, PyMetric provides structured abstractions for grids
+and field data, supporting a range of coordinate systems and buffer backends. Because the PyMetric field abstraction
+is only minimally coupled to the underlying data storage, it can interface with a variety of array backends, including
+in-memory arrays and HDF5 [@hdf5] storage for lazy-loading and chunked computation. This design enables coordinate-aware
+operations to be applied efficiently to large, multidimensional datasets without compromising generality or performance.
+
+By automating core geometric operations across coordinate systems, PyMetric simplifies the development
+of physics-based modeling software that requires flexible geometric handling. Its design supports a broad spectrum
+of scientific computing applications, from simulating relativistic fluids to analyzing gravitational fields. In
+doing so, PyMetric establishes a modern and extensible foundation for geometry-aware computation in Python,
+enabling the creation of accurate, efficient, and scalable models in complex coordinate geometries.
+
+# Statement of need
+
+Modern astrophysical modeling requires a high degree of flexibility—both in physical assumptions
+and in computational infrastructure. The Pisces Project (of which PyMetric is a part)
+is a general-purpose model-building framework for astrophysics that aims to unify and extend
+existing tools for generating models and initial conditions (e.g., DICE [@perret2016dice], GALIC [@yurin2014galic])
+under a common, modular API.
+Its goal is to make it easier to construct complex, physically motivated models
+of systems such as galaxies, black holes, or relativistic fluids by exposing a simple and extensible
+interface for defining models, fields, and dynamics.
+
+A persistent challenge in building such extensible modeling tools is the limited and inconsistent
+support for coordinate systems found in most existing software. Codes like EinsteinPy [@einsteinpy],
+DICE, and yt [@turk2010yt] often
+hard-code assumptions about coordinate geometry, making them difficult to generalize to new physical
+contexts or non-Cartesian coordinate systems. This lack of abstraction limits reusability and complicates
+the construction of unified modeling workflows across domains such as general relativity,
+galactic dynamics, and fluid mechanics.
+
+To address this limitation, PyMetric was developed to be a lightweight library that standardizes
+coordinate-aware geometric computation. The library is designed to serve as the geometric backend
+for Pisces and similar modeling systems. It provides a consistent abstraction layer for defining
+coordinate systems, computing differential geometric quantities, and evaluating operators like gradients,
+divergences, and Laplacians; all without requiring the user to manage low-level
+details of tensor algebra or coordinate transformations.
+
+PyMetric emphasizes extensibility and modularity through four core interfaces:
+
+- **Coordinate System API** – Enables the definition and use of arbitrary coordinate systems with minimal required knowledge, while supporting symbolic derivation of metric-dependent quantities.
+- **Buffer API** – Provides a backend-agnostic interface for array storage, allowing seamless integration with systems like HDF5, XArray, Dask, and unit-aware arrays.
+- **Differential Geometry API** – Implements low-level, coordinate-independent formulations of core operations such as gradients, divergences, Laplacians, and volume elements.
+- **Grid and Field API** – Supports flexible discretization strategies and a variety of field types, including sparse and dense scalar, vector, and tensor fields.
+
+Together, these abstractions form a unified symbolic-numeric pipeline that allows high-level modeling code to operate naturally across diverse geometries and data representations. By standardizing geometric computation and decoupling it from specific coordinate assumptions or backend implementations, PyMetric addresses a longstanding gap in scientific Python infrastructure. This foundation enables Pisces to offer a powerful, composable, and user-friendly environment for building physically accurate models in astrophysics and beyond.
+
+# Methodology
+
+The core methodology behind PyMetric centers on a mathematically rigorous yet computationally practical
+framework for performing differential geometry operations in arbitrary coordinate systems. The library
+is designed to support seamless transitions between symbolic derivation and numerical evaluation, allowing
+for precise, efficient, and geometry-aware modeling.
+
+A coordinate system in PyMetric is defined minimally by:
+
+- A set of axes labels $(x^1, x^2, \ldots)$,
+- Forward and inverse transformations between these coordinates and Cartesian Space $T(x,y,z)$ and
+ $T^{-1}(x^1,x^2,x^3)$.
+- A symbolically defined metric tensor $g_{\mu\nu}$.
+
+From this core specification, PyMetric constructs key geometric quantities, such as the inverse metric
+$g^{\mu\nu}$, the metric density $\sqrt{g}$, and terms appearing in differential operations, such as
+$L^\nu = g^{-1/2} \partial_\mu (g^{1/2} g^{\mu\nu})$, which appears in the scalar Laplacian
+$\nabla^2 \phi = L^\nu \partial_\nu \phi + g^{\mu\nu} \partial^2_{\mu\nu} \phi$. These are represented both as symbolic
+expressions-using SymPy [@sympy]-and as NumPy-backed callables. These quantities are computed lazily: they are only derived when required for a specific operation,
+avoiding unnecessary overhead.
+
+Coordinate systems are categorized into types (e.g., orthogonal or curvilinear) that determine
+how symbolic properties are derived and which simplifications may apply. This abstraction enables users
+to model highly symmetric systems (e.g., spherical or ellipsoidal coordinates) as easily
+as more general curvilinear systems.
+
+## Field and Grid Operations
+
+Fields in PyMetric are array-backed data structures (typically NumPy or HDF5 buffers) that are explicitly
+associated with a coordinate system and grid. The grid handling supports flexible discretization strategies,
+including cell-centered and node-centered layouts, as well as ghost zones for finite-difference operations.
+Support is currently provided for single-grid configurations with arbitrary spacing; however, multigrid extension
+is planned for future releases.
+While fields behave like standard NumPy arrays,
+they also carry metadata about their geometric context, including coordinate labels, spacing,
+and metric-aware tensor properties.
+
+Operations on fields, such as computing covariant derivatives, applying Laplacians,
+or transforming between bases, are automatically dispatched to appropriate symbolic
+expressions and numerical kernels based on the field’s variance and the geometry of the underlying
+coordinate system.
+
+This design allows users to write high-level, reusable code that is agnostic to the specific geometry,
+while still benefiting from the mathematical correctness and efficiency of coordinate-aware computation.
+
+
+## Future Development
+
+The development roadmap for PyMetric is focused on deepening its mathematical capabilities and expanding
+its utility in advanced physical modeling contexts, particularly those involving curved and relativistic
+spacetimes. While the current implementation supports a robust suite of differential operators in orthogonal
+and curvilinear coordinate systems, several avenues for future growth are planned:
+
+1. Expanded Differential Operator Support:
+
+ PyMetric will be extended to support a broader range of tensor calculus operations, including:
+
+ - Covariant derivatives of higher-rank tensors, enabling modeling of tensor transport and geodesic deviation.
+ - Tensor contractions and curvature operations, including the Riemann, Ricci, and Einstein tensors,
+ to support simulations in general relativity and cosmology.
+
+These features will allow PyMetric to serve as a general-purpose differential geometry engine suitable
+for high-fidelity modeling in physics, engineering, and applied mathematics.
+
+2. Relativistic and Non-Flat Coordinate Systems
+
+ - A key area of expansion is support for relativistic geometries, where the metric tensor is no longer positive-definite and may depend dynamically on spacetime coordinates. Planned features include:
+ - General Lorentzian manifolds, including Schwarzschild, Kerr, and FLRW spacetimes, enabling direct modeling of astrophysical systems governed by Einstein’s field equations.
+
+PyMetric is explicitly intended as a modeling and analysis tool, not a time-domain simulation engine.
+It provides geometric infrastructure for constructing and analyzing equations defined on curved spacetimes,
+but does not aim to solve dynamical systems or perform numerical integration of time-evolving fields.
+
+# Usage Example
+
+To demonstrate the basic capabilities of the Pymetric library, we include a simple example of
+the typical workflow computing the Laplacian ($\nabla^2$) of a field in spherical coordinates.
+We use $F(r, \theta) = r \cos(\theta)$ as our test function, which has a known Laplacian of zero.
+A visualization of $F(r, \theta)$ and its Laplacian is shown in Figure 1, demonstrating
+the library’s ability to perform geometry-aware computations directly on array data.
+
+```python
+import pymetric as pym
+import numpy as np
+
+# Define spherical coordinate system and grid
+cs = pym.coordinates.SphericalCoordinateSystem()
+grid = pym.grids.GenericGrid(
+ cs,
+ [
+ np.linspace(0.1, 4.9, 300), # r
+ np.linspace(0.01, np.pi - 0.01, 100), # theta
+ np.linspace(0.01, 2 * np.pi - 0.01, 100), # phi
+ ],
+ center="cell",
+ bbox=[(0, 5), (0, np.pi), (0, 2 * np.pi)],
+ ghost_zones=2,
+)
+
+# Define scalar field F(r, theta) = r * cos(theta)
+# This is a good test case since Lap(F) = 0.
+field = pym.DenseField.from_function(
+ lambda r, theta: r * np.cos(theta),
+ grid,
+ axes=["r", "theta"],
+)
+
+# Compute Laplacian
+F_lap = field.element_wise_laplacian()
+```
+{ width=85% }
+
+# References
diff --git a/pymetric/_version.py b/pymetric/_version.py
index 6c7d66d0..a5076793 100644
--- a/pymetric/_version.py
+++ b/pymetric/_version.py
@@ -1,20 +1,33 @@
# file generated by setuptools-scm
# don't change, don't track in version control
-__all__ = ["__version__", "__version_tuple__", "version", "version_tuple"]
+__all__ = [
+ "__version__",
+ "__version_tuple__",
+ "version",
+ "version_tuple",
+ "__commit_id__",
+ "commit_id",
+]
TYPE_CHECKING = False
if TYPE_CHECKING:
from typing import Tuple, Union
VERSION_TUPLE = Tuple[Union[int, str], ...]
+ COMMIT_ID = Union[str, None]
else:
VERSION_TUPLE = object
+ COMMIT_ID = object
version: str
__version__: str
__version_tuple__: VERSION_TUPLE
version_tuple: VERSION_TUPLE
+commit_id: COMMIT_ID
+__commit_id__: COMMIT_ID
-__version__ = version = "0.1.dev22+g0f5941d"
-__version_tuple__ = version_tuple = (0, 1, "dev22", "g0f5941d")
+__version__ = version = "0.0.2a2.dev26"
+__version_tuple__ = version_tuple = (0, 0, 2, "a2", "dev26")
+
+__commit_id__ = commit_id = "g7a404da6c"