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@adrn
Created March 13, 2026 14:00
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "3e04b65c",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import jax\n",
"import jax.numpy as jnp\n",
"from array_api_compat import array_namespace"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b40449d2",
"metadata": {},
"outputs": [],
"source": [
"# Here's some pretend \"library\" functions, i.e. functions in Astropy written with the\n",
"# array API. It doesn't have to know about JIT or jax\n",
"def normalize(x):\n",
" xp = array_namespace(x)\n",
" lo = xp.min(x)\n",
" hi = xp.max(x)\n",
" return (x - lo) / (hi - lo)\n",
"\n",
"def normalize_sum(x):\n",
" xp = array_namespace(x)\n",
" return xp.sum(normalize(x))"
]
},
{
"cell_type": "markdown",
"id": "4ad63284",
"metadata": {},
"source": [
"Works with numpy:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "3294ba6c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0. , 0.5, 1. ])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = np.array([1, 2, 3])\n",
"normalize(x)"
]
},
{
"cell_type": "markdown",
"id": "ebd07185",
"metadata": {},
"source": [
"Also JAX:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "7dba48c1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Array([0. , 0.5, 1. ], dtype=float32)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = jnp.array([1., 2, 3])\n",
"normalize(x)"
]
},
{
"cell_type": "markdown",
"id": "af19969d",
"metadata": {},
"source": [
"And we can JIT it:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "678556f0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Array([0. , 0.5, 1. ], dtype=float32)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"jax.jit(normalize)(x)"
]
},
{
"cell_type": "markdown",
"id": "a6facee4",
"metadata": {},
"source": [
"Or use grad:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "2db0a4cc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Array([-0.25, 0.5 , -0.25], dtype=float32)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"jax.grad(normalize_sum)(x)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "demo-notebooks (3.12.10)",
"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": "3.12.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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