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* use new mandatory build.os env variable for RTD * move examples to docs and make executable * shorten sphinx config and add myst_nb to decode notebooks * jupyter-book not needed for docs requirements * Minor README update * add NIR symbol pngs * add myst-nb as docs requirement * modify example titles * update primitives in docs * add sphinx autoapi * minor text edits in document titles * add sphinx-autoapi to docs requirements * fix autoapi upstream bug by pinning astroid dep * add html_title to docs * Change logo in sidebar * Corrected ruff github ci command --------- Co-authored-by: Jens E. Pedersen <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,8 +1,9 @@ | ||
| sphinx==5.0.2 | ||
| sphinx-book-theme | ||
| myst-parser | ||
| myst_nb | ||
| numpy | ||
| h5py | ||
| sphinx_external_toc | ||
| sphinxcontrib-mermaid | ||
| jupyter-book | ||
| astroid~=2.15 # https://github.com/readthedocs/sphinx-autoapi/issues/407 | ||
| sphinx-autoapi |
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| # Contributing to NIR | ||
| # Contributing | ||
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| ## Developer guide: Getting started | ||
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Lava\n", | ||
| "\n", | ||
| "... coming soon" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "language_info": { | ||
| "name": "python" | ||
| }, | ||
| "orig_nbformat": 4 | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Nengo\n", | ||
| "\n", | ||
| "[Nengo](nengo.ai) is a Python package for building, testing, and deploying neural networks.\n", | ||
| "The examples below shows how to import and export from Nengo to NIR and vice-versa.\n", | ||
| "\n", | ||
| "Examples:\n", | ||
| "* Lorentz oscillator: [nir-lorentz.py](https://github.com/neuromorphs/nir/tree/main/example/nengo/nir-lorentz.py)\n", | ||
| " * This script creates a Nengo model that simulates the Lorentz oscillator, maps it to NIR, and then back again into Nengo\n", | ||
| "* Leaky integrate-and-fire (LIF) tests [nir-test.py](https://github.com/neuromorphs/nir/tree/main/example/nengo/nir-test.py)\n", | ||
| " * Creates a NIR model for an Affine map and LIF population and map it into Nengo\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "vscode": { | ||
| "languageId": "plaintext" | ||
| } | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import nengo\n", | ||
| "import numpy as np\n", | ||
| "\n", | ||
| "import nir\n", | ||
| "\n", | ||
| "n = nir.NIRGraph(\n", | ||
| " nodes=[\n", | ||
| " nir.Input(shape=np.array([3])),\n", | ||
| " nir.Affine(weight=np.array([[8, 2, 10], [14, 3, 14]]).T, bias=np.array([1, 2])),\n", | ||
| " nir.LIF(\n", | ||
| " tau=np.array([1] * 2),\n", | ||
| " r=np.array([1] * 2),\n", | ||
| " v_leak=np.array([0] * 2),\n", | ||
| " v_threshold=np.array([1] * 2),\n", | ||
| " ),\n", | ||
| " nir.Output(shape=np.array([3])),\n", | ||
| " ],\n", | ||
| " edges=[(0, 1), (1, 2), (2, 3)],\n", | ||
| ")\n", | ||
| "\n", | ||
| "\n", | ||
| "def nir_to_nengo(n, swap_linear_order=False):\n", | ||
| " nengo_map = []\n", | ||
| "\n", | ||
| " model = nengo.Network()\n", | ||
| " with model:\n", | ||
| " filters = {}\n", | ||
| " for i, obj in enumerate(n.nodes):\n", | ||
| " if isinstance(obj, nir.Input):\n", | ||
| " node = nengo.Node(np.zeros(obj.shape), label=f\"Input {i} {obj.shape}\")\n", | ||
| " nengo_map.append(node)\n", | ||
| " elif isinstance(obj, nir.LIF):\n", | ||
| " N = obj.tau.flatten().shape[0]\n", | ||
| " ens = nengo.Ensemble(\n", | ||
| " n_neurons=N,\n", | ||
| " dimensions=1,\n", | ||
| " label=f\"LIF {i}\",\n", | ||
| " neuron_type=nengo.RegularSpiking(\n", | ||
| " nengo.LIFRate(tau_rc=obj.tau[0], tau_ref=0)\n", | ||
| " ),\n", | ||
| " # neuron_type=nengo.LIF(tau_rc=obj.tau[0], tau_ref=0),\n", | ||
| " gain=np.ones(N),\n", | ||
| " bias=np.zeros(N),\n", | ||
| " )\n", | ||
| " nengo_map.append(ens.neurons)\n", | ||
| " elif isinstance(obj, nir.LI):\n", | ||
| " filt = nengo.Node(\n", | ||
| " lambda t, x: x,\n", | ||
| " size_in=obj.tau.flatten().shape[0],\n", | ||
| " label=f\"LI {i} {obj.tau.shape}\",\n", | ||
| " )\n", | ||
| " filters[filt] = nengo.synapses.Lowpass(obj.tau[0])\n", | ||
| " nengo_map.append(filt)\n", | ||
| " elif isinstance(obj, nir.Affine):\n", | ||
| " weights = obj.weight\n", | ||
| " if swap_linear_order:\n", | ||
| " weights = weights.T\n", | ||
| " w = nengo.Node(\n", | ||
| " lambda t, x, obj=obj: weights @ x + obj.bias,\n", | ||
| " size_in=weights.shape[1],\n", | ||
| " size_out=weights.shape[0],\n", | ||
| " label=f\"({weights.shape[0]}x{weights.shape[1]})\",\n", | ||
| " )\n", | ||
| " nengo_map.append(w)\n", | ||
| " elif isinstance(obj, nir.Output):\n", | ||
| " nengo_map.append(\n", | ||
| " None\n", | ||
| " ) # because NIR spec doesn't tell me the size, I can't create this yet\n", | ||
| " else:\n", | ||
| " raise Exception(f\"Unknown NIR object: {obj}\")\n", | ||
| " for pre, post in n.edges:\n", | ||
| " if nengo_map[post] is None:\n", | ||
| " output = nengo.Node(\n", | ||
| " lambda t, x: x,\n", | ||
| " size_in=nengo_map[pre].size_out,\n", | ||
| " label=f\"Output {post}\",\n", | ||
| " )\n", | ||
| " nengo_map[post] = output\n", | ||
| " synapse = filters.get(nengo_map[post], None)\n", | ||
| "\n", | ||
| " if nengo_map[pre].size_out != nengo_map[post].size_in:\n", | ||
| " print(\"Error\")\n", | ||
| " print(\"pre\", nengo_map[pre])\n", | ||
| " print(\"post\", nengo_map[post])\n", | ||
| " 1 / 0\n", | ||
| "\n", | ||
| " else:\n", | ||
| " nengo.Connection(nengo_map[pre], nengo_map[post], synapse=synapse)\n", | ||
| "\n", | ||
| " return model\n", | ||
| "\n", | ||
| "\n", | ||
| "model = nir_to_nengo(n, swap_linear_order=True)\n" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "language_info": { | ||
| "name": "python" | ||
| }, | ||
| "orig_nbformat": 4 | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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