Add CLI

.gitignore

@@ -205,3 +205,9 @@ cython_debug/
 marimo/_static/
 marimo/_lsp/
 __marimo__/
+
+# Outputs
+outputs/
+
+# .DS_Store
+.DS_Store

README.md

@@ -17,16 +17,10 @@ curl -LsSf https://astral.sh/uv/install.sh | sh
 uv pip install -e .
 ```
 
-This installs `autosim` in editable mode along with its runtime dependencies:
-- `numpy>=1.24`
-- `scipy>=1.10`
-- `tqdm>=4.65`
-- `torch>=2.0`
-
 ### Install development dependencies (includes pytest)
 
 ```bash
-uv sync --group dev
+uv sync --extra dev
 ```
 
 ## Running tests
@@ -36,3 +30,91 @@ Once dev dependencies are installed:
 ```bash
 uv run pytest
 ```
+
+## Generate training data (Hydra CLI)
+
+Use the tiny CLI to generate train/valid/test splits from any simulator that
+inherits `SpatioTemporalSimulator`:
+
+```bash
+uv run autosim
+```
+
+List available simulator configs:
+
+```bash
+uv run autosim list
+```
+
+Simulator defaults now live in package configs under
+`src/autosim/configs/simulator` and can be selected via config groups.
+
+Available simulator config names:
+`advection_diffusion`, `advection_diffusion_multichannel`, `compressible_fluid_2d`,
+`conditioned_navier_stokes_2d`, `epidemic`, `flow_problem`, `gray_scott`,
+`gross_pitaevskii_equation_2d`, `hydrodynamics_2d`, `lattice_boltzmann`,
+`projectile`, `projectile_multioutput`, `reaction_diffusion`, `seir_simulator`,
+`shallow_water2d`.
+
+Override simulator and dataset settings from the command line via Hydra:
+
+```bash
+uv run autosim \
+	simulator=shallow_water2d \
+	simulator.nx=32 \
+	simulator.ny=32 \
+	simulator.T=10.0 \
+	dataset.n_train=50 dataset.n_valid=10 dataset.n_test=10 \
+	dataset.output_dir=examples/experimental/generated_datasets/shallow_water_small \
+	seed=123 overwrite=true
+```
+
+Use a faster built-in simulator config:
+
+```bash
+uv run autosim \
+	simulator=advection_diffusion \
+	simulator.n=16 simulator.T=0.2 simulator.dt=0.1 \
+	dataset.n_train=1 dataset.n_valid=1 dataset.n_test=1
+```
+
+Optionally save example rollout videos for selected batch indices after generation:
+
+```bash
+uv run autosim \
+	simulator=advection_diffusion_multichannel \
+	dataset.n_train=4 dataset.n_valid=1 dataset.n_test=1 \
+	visualize.enabled=true \
+	visualize.split=train \
+	visualize.batch_indices=[0,2] \
+	visualize.file_ext=gif
+```
+
+By default videos are written under
+`<dataset.output_dir>/examples/<split>/batch_<idx>.<ext>`.
+Use `visualize.file_ext=mp4` if ffmpeg is available.
+
+	Generate one combined dataset from ordered strata values (single sweep key):
+
+	```bash
+	uv run autosim \
+		simulator=gray_scott \
+		stratify.enabled=true \
+		stratify.key=simulator.pattern \
+		stratify.values=[gliders,bubbles,maze,worms,spirals,spots] \
+		dataset.n_train=240 dataset.n_valid=24 dataset.n_test=24 \
+		dataset.output_dir=outputs/gray_scott_combined
+	```
+
+	When stratification is enabled, each split size is divided equally across strata,
+	and results are concatenated in the exact order of `stratify.values`.
+	If a split size is not divisible by the number of strata, an error is raised.
+
+Bring your own simulator subclass (no registry needed):
+
+```bash
+uv run autosim \
+	simulator._target_=my_package.my_module.MySimulator \
+	++simulator.my_arg=42 \
+	simulator.log_level=warning
+```

examples/README.md

@@ -6,10 +6,12 @@ These simulation families are organized from foundational pattern dynamics to in
 
 ### Pattern-formation families
 
+- [Reaction-Diffusion](experimental/00_00_reaction_diffusion.ipynb): A spectral (FFT-based) two-species reaction-diffusion generator that produces diverse spatiotemporal patterns — spirals, spots, and labyrinthine textures — across reaction and diffusion parameter regimes.
 - [Gray-Scott](experimental/00_01_gray_scott.ipynb): A spectral ETDRK4 reaction-diffusion generator that spans diverse morphologies (spots, spirals, worms, and maze-like regimes) via feed/kill parameters.
 
 ### Weather-like and transport families
 
+- [Advection-Diffusion](experimental/01_00_advection_diffusion.ipynb): A 2D incompressible vorticity–streamfunction solver with spectral Poisson inversion that generates vorticity fields across a range of viscosities and forcing strengths.
 - [Shallow-Water 2D](experimental/01_01_shallow_water_equation.ipynb): A geophysical fluid model that evolves height and horizontal velocity fields to capture wave propagation, rotation effects, and balanced flow structure.
 
 ### Classical fluid dynamics families

examples/experimental/00_00_reaction_diffusion.ipynb

@@ -0,0 +1,133 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {},
+   "source": [
+    "# Reaction-Diffusion Simulator\n",
+    "\n",
+    "This notebook runs the `ReactionDiffusion` simulator — a coupled PDE system modelling two interacting species $u$ and $v$ with diffusion and nonlinear reaction kinetics.\n",
+    "\n",
+    "- **State variables**: `u` and `v` (species concentrations).\n",
+    "- **Conditioning variables**: `beta` (reaction rate strength), `d` (ratio of diffusion coefficients).\n",
+    "- **Dynamics**: spectral (FFT-based) spatial differentiation combined with `scipy` RK45 time integration.\n",
+    "- **Boundary conditions**: periodic in both spatial directions.\n",
+    "\n",
+    "### Why this is useful\n",
+    "\n",
+    "Reaction-diffusion systems produce a rich variety of spatiotemporal patterns — stripes, spots, spirals, and labyrinthine textures — depending on the parameter regime. This makes them a natural benchmark for learning PDEs that exhibit diverse structure from low-dimensional parameters.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "## Governing equations\n",
+    "\n",
+    "The two-species reaction-diffusion system takes the form:\n",
+    "\n",
+    "$$\n",
+    "\\frac{\\partial u}{\\partial t} = D_u \\nabla^2 u + R_u(u, v; \\beta),\n",
+    "\\qquad\n",
+    "\\frac{\\partial v}{\\partial t} = D_v \\nabla^2 v + R_v(u, v; \\beta),\n",
+    "$$\n",
+    "\n",
+    "where $D_v / D_u = d$ and $R_u$, $R_v$ are the nonlinear reaction terms parameterised by $\\beta$.\n",
+    "\n",
+    "### Boundary conditions\n",
+    "\n",
+    "Periodic in both spatial directions on the domain $[-L/2, L/2]^2$:\n",
+    "\n",
+    "$$\n",
+    "u(-L/2, y, t) = u(L/2, y, t), \\qquad u(x, -L/2, t) = u(x, L/2, t),\n",
+    "$$\n",
+    "\n",
+    "and likewise for $v$.\n",
+    "\n",
+    "### Parameters\n",
+    "\n",
+    "- **`beta`**: controls the strength of the reaction nonlinearity. Larger values push the system further from equilibrium.\n",
+    "- **`d`**: ratio of diffusion coefficients $D_v / D_u$. Small `d` means species $v$ diffuses much more slowly, favouring pattern formation.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "from autosim.experimental.simulations import ReactionDiffusion\n",
+    "from autosim.utils import plot_spatiotemporal_video"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim = ReactionDiffusion(\n",
+    "    return_timeseries=True,\n",
+    "    log_level=\"progress_bar\",\n",
+    "    n=64,\n",
+    "    L=20,\n",
+    "    T=32.2,\n",
+    "    dt=0.1,\n",
+    "    parameters_range={\n",
+    "        \"beta\": (1.0, 2.0),\n",
+    "        \"d\": (0.05, 0.3),\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "batch = sim.forward_samples_spatiotemporal(n=2, random_seed=42)\n",
+    "\n",
+    "print(\"data shape:\", batch[\"data\"].shape, \"[batch, time, x, y, channels={u, v}]\")\n",
+    "print(\"constant_scalars shape:\", batch[\"constant_scalars\"].shape)\n",
+    "print(\"sampled params (beta, d):\", batch[\"constant_scalars\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "anim = plot_spatiotemporal_video(\n",
+    "    batch[\"data\"],\n",
+    "    batch_idx=0,\n",
+    "    channel_names=[\"u\", \"v\"],\n",
+    "    preserve_aspect=True,\n",
+    ")\n",
+    "HTML(anim.to_jshtml())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "autosim (3.11.15)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/experimental/00_01_gray_scott.ipynb

@@ -104,7 +104,7 @@
     "        # T=300.0,\n",
     "        # dt=1.0,\n",
     "        # snapshot_dt=1.0,\n",
-    "        T=1280.0,\n",
+    "        T=1284.0,\n",
     "        dt=1.0,\n",
     "        snapshot_dt=4.0,\n",
     "        initial_condition=\"gaussians\",\n",
@@ -216,7 +216,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "autosim (3.11.14)",
+   "display_name": "autosim (3.11.15)",
    "language": "python",
    "name": "python3"
   },
@@ -230,7 +230,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.14"
+   "version": "3.11.15"
   }
  },
  "nbformat": 4,