{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "qs-title",
   "metadata": {},
   "source": [
    "# Quickstart\n",
    "\n",
    "This is the 10-minute tour of `brainmass`. By the end you will have, using only the\n",
    "**high-level API**:\n",
    "\n",
    "1. picked a model and run it through the {class}`~brainmass.Simulator`,\n",
    "2. plotted the result with {mod}`brainmass.viz`,\n",
    "3. added noise in a single line,\n",
    "4. wired two regions into a delay-coupled {class}`~brainmass.Network` from a bundled connectome, and\n",
    "5. **fit a parameter by gradient descent** with the {class}`~brainmass.Fitter` against a bundled signal.\n",
    "\n",
    "No hand-written integration loops — the `Simulator`/`Network`/`Fitter` objects own the\n",
    "`brainstate.transform` machinery so you focus on the model.\n",
    "\n",
    ":::{note}\n",
    "The integration time step `dt` is a **unit-aware quantity** (`0.1 * u.ms`), passed to the\n",
    "`Simulator` — it is not a model argument. Quantities flow through brainmass via\n",
    "[`brainunit`](https://github.com/chaobrain/brainunit), so `u.ms`, `u.mm`, … keep your\n",
    "numbers physically meaningful.\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "qs-imports",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T06:26:59.242887Z",
     "iopub.status.busy": "2026-06-19T06:26:59.242646Z",
     "iopub.status.idle": "2026-06-19T06:27:04.191955Z",
     "shell.execute_reply": "2026-06-19T06:27:04.191148Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'0.0.6'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import brainmass\n",
    "import braintools\n",
    "import brainstate\n",
    "import brainunit as u\n",
    "import jax.numpy as jnp\n",
    "import numpy as np\n",
    "from brainstate.nn import Param\n",
    "\n",
    "# `dt` is a global, set once through the environment. The Simulator also takes an\n",
    "# explicit dt= below; setting it here lets delay-buffer sizing work when we build a\n",
    "# Network (its conduction delays are measured in dt-steps at construction time).\n",
    "brainstate.environ.set(dt=0.1 * u.ms)\n",
    "\n",
    "brainmass.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "qs-step1-md",
   "metadata": {},
   "source": [
    "## 1. Pick a model and run it\n",
    "\n",
    "A brainmass model is a `*Step` object — one update step of a neural-mass equation, sized\n",
    "for `in_size` regions. We pick the {class}`~brainmass.HopfStep` oscillator in its\n",
    "limit-cycle regime (`a > 0`) and hand it to a {class}`~brainmass.Simulator`. One\n",
    "`.run(duration, monitors=...)` call sets `dt`, initialises the state, steps the model,\n",
    "and returns the stacked trajectories as a plain dict (plus a `'ts'` time axis)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "qs-step1-code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T06:27:04.194922Z",
     "iopub.status.busy": "2026-06-19T06:27:04.194336Z",
     "iopub.status.idle": "2026-06-19T06:27:04.379839Z",
     "shell.execute_reply": "2026-06-19T06:27:04.379049Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "recorded keys: ['x', 'y', 'ts']\n",
      "x trajectory shape (steps, regions): (1800, 1)\n",
      "time axis: [20.1 20.2 20.3] ms ... 200. ms\n"
     ]
    }
   ],
   "source": [
    "node = brainmass.HopfStep(in_size=1, a=0.25, w=0.3)\n",
    "\n",
    "sim = brainmass.Simulator(node, dt=0.1 * u.ms)\n",
    "res = sim.run(200.0 * u.ms, monitors=[\"x\", \"y\"], transient=20.0 * u.ms)\n",
    "\n",
    "print(\"recorded keys:\", list(res))\n",
    "print(\"x trajectory shape (steps, regions):\", res[\"x\"].shape)\n",
    "print(\"time axis:\", res[\"ts\"][:3], \"...\", res[\"ts\"][-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "qs-step2-md",
   "metadata": {},
   "source": [
    "## 2. Plot it with `brainmass.viz`\n",
    "\n",
    "{mod}`brainmass.viz` collects thin, matplotlib-based helpers (install the `[viz]` extra).\n",
    "{func}`~brainmass.viz.plot_timeseries` takes the trajectory and the time axis directly;\n",
    "{func}`~brainmass.viz.plot_phase_portrait` plots one state variable against another, which\n",
    "for a Hopf node traces out its circular limit cycle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "qs-step2-code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T06:27:04.382096Z",
     "iopub.status.busy": "2026-06-19T06:27:04.381908Z",
     "iopub.status.idle": "2026-06-19T06:27:04.605046Z",
     "shell.execute_reply": "2026-06-19T06:27:04.604331Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 3.5))\n",
    "\n",
    "brainmass.viz.plot_timeseries(res[\"x\"], ts=res[\"ts\"], labels=[\"x\"], ax=ax1)\n",
    "ax1.set_title(\"Hopf limit cycle — time series\")\n",
    "\n",
    "brainmass.viz.plot_phase_portrait(res[\"x\"][:, 0], res[\"y\"][:, 0], ax=ax2)\n",
    "ax2.set_title(\"phase portrait (x vs y)\")\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "qs-step3-md",
   "metadata": {},
   "source": [
    "## 3. Add noise in one line\n",
    "\n",
    "Real activity fluctuates. Attach an Ornstein–Uhlenbeck process\n",
    "({class}`~brainmass.OUProcess`) to a state component when you build the model — the noise\n",
    "is added automatically inside `update()`, and the `Simulator` call is unchanged. Here we\n",
    "drop the node *below* the bifurcation (`a < 0`, a stable focus) so the dynamics are noise\n",
    "driven rather than a clean limit cycle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "qs-step3-code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T06:27:04.606772Z",
     "iopub.status.busy": "2026-06-19T06:27:04.606535Z",
     "iopub.status.idle": "2026-06-19T06:27:05.030280Z",
     "shell.execute_reply": "2026-06-19T06:27:05.029268Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "noisy = brainmass.HopfStep(\n",
    "    in_size=1, a=-0.05, w=0.3,\n",
    "    noise_x=brainmass.OUProcess(in_size=1, sigma=0.1, tau=20.0 * u.ms),  # <- one line\n",
    ")\n",
    "\n",
    "res_noisy = brainmass.Simulator(noisy, dt=0.1 * u.ms).run(200.0 * u.ms, monitors=[\"x\"])\n",
    "\n",
    "ax = brainmass.viz.plot_timeseries(res_noisy[\"x\"], ts=res_noisy[\"ts\"], labels=[\"x (noisy)\"])\n",
    "ax.set_title(\"Sub-critical Hopf node driven by OU noise\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "qs-step4-md",
   "metadata": {},
   "source": [
    "## 4. A two-region network (with a bundled connectome)\n",
    "\n",
    "{mod}`brainmass.datasets` ships small, license-clean example data so every notebook runs\n",
    "with **no download**. `load_dataset('example_connectome')` returns a typed\n",
    "{class}`~brainmass.datasets.Connectome` with structural `weights` and unit-aware\n",
    "`distances` (in `u.mm`).\n",
    "\n",
    "We take the first two regions and hand them to a {class}`~brainmass.Network`. The network\n",
    "zeros the self-connections, turns `distance / speed` into conduction **delays**, and feeds\n",
    "a diffusive coupling current back into the node — the same wiring you would otherwise write\n",
    "by hand. The `Simulator` then drives the whole network exactly as it drove the single node."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "qs-step4-code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T06:27:05.033906Z",
     "iopub.status.busy": "2026-06-19T06:27:05.033613Z",
     "iopub.status.idle": "2026-06-19T06:27:05.833239Z",
     "shell.execute_reply": "2026-06-19T06:27:05.832372Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "connectome: (8, 8) regions: [np.str_('R0'), np.str_('R1'), np.str_('R2'), np.str_('R3'), np.str_('R4'), np.str_('R5'), np.str_('R6'), np.str_('R7')]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "network output shape (steps, regions): (1800, 2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conn = brainmass.datasets.load_dataset(\"example_connectome\")\n",
    "print(\"connectome:\", conn.weights.shape, \"regions:\", list(conn.labels))\n",
    "\n",
    "# Use the first two regions as a minimal 2-node network.\n",
    "W = conn.weights[:2, :2]\n",
    "D = conn.distances[:2, :2]\n",
    "\n",
    "two_nodes = brainmass.HopfStep(in_size=2, a=0.2, w=0.3)\n",
    "net = brainmass.Network(\n",
    "    two_nodes,\n",
    "    conn=W,\n",
    "    distance=D,\n",
    "    speed=10.0 * u.mm / u.ms,   # delays = distance / speed\n",
    "    coupling=\"diffusive\",\n",
    "    coupled_var=\"x\",\n",
    "    k=0.5,                       # global coupling strength\n",
    ")\n",
    "\n",
    "res_net = brainmass.Simulator(net, dt=0.1 * u.ms).run(\n",
    "    200.0 * u.ms,\n",
    "    monitors=lambda m: m.node.x.value,   # read the coupled node state each step\n",
    "    transient=20.0 * u.ms,\n",
    ")\n",
    "print(\"network output shape (steps, regions):\", res_net[\"output\"].shape)\n",
    "\n",
    "ax = brainmass.viz.plot_timeseries(\n",
    "    res_net[\"output\"], ts=res_net[\"ts\"], labels=list(conn.labels[:2])\n",
    ")\n",
    "ax.set_title(\"Two coupled Hopf regions\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "qs-step5-md",
   "metadata": {},
   "source": [
    "## 5. Fit a parameter by gradient descent\n",
    "\n",
    "Here is brainmass's headline capability: because the whole simulation is differentiable,\n",
    "you can **backprop through the ODE solve** and fit parameters with gradients instead of\n",
    "grid or evolutionary search.\n",
    "\n",
    "We use the bundled `example_signal` dataset as the target. A {class}`~brainmass.HopfStep`\n",
    "in its limit cycle settles at an amplitude `~sqrt(a / beta)`, so the bifurcation parameter\n",
    "`a` controls how big the oscillation is. We mark `a` trainable with `Param(..., fit=True)`,\n",
    "start it too small, and let the {class}`~brainmass.Fitter` (gradient backend) drive the\n",
    "simulated oscillation's amplitude to match the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "qs-step5-code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T06:27:05.835356Z",
     "iopub.status.busy": "2026-06-19T06:27:05.835121Z",
     "iopub.status.idle": "2026-06-19T06:27:06.206513Z",
     "shell.execute_reply": "2026-06-19T06:27:06.205379Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target amplitude from example_signal: 0.7152\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FitResult(backend='grad', best_loss=3.10454e-07, n_steps=50, params=[a])\n",
      "a:        0.05  ->  1.0262\n",
      "loss:  0.3042  ->  0.00000\n",
      "amplitude matched: 0.7157  (target 0.7152)\n"
     ]
    }
   ],
   "source": [
    "# Target: the RMS amplitude of region 0 of the bundled example signal.\n",
    "sig = brainmass.datasets.load_dataset(\"example_signal\")\n",
    "target0 = sig.signal[:, 0]\n",
    "target_amp = jnp.asarray(float(np.sqrt(np.mean((target0 - target0.mean()) ** 2))))\n",
    "print(\"target amplitude from example_signal:\", round(float(target_amp), 4))\n",
    "\n",
    "# Model with ONE trainable parameter: the Hopf bifurcation parameter `a`.\n",
    "model = brainmass.HopfStep(\n",
    "    in_size=1,\n",
    "    a=Param(0.05, fit=True),   # <- the single knob the Fitter will tune\n",
    "    w=0.3, beta=1.0,\n",
    "    init_x=braintools.init.Constant(0.5),\n",
    ")\n",
    "\n",
    "\n",
    "def loss_fn(m):\n",
    "    \"\"\"Run the model and compare its settled amplitude to the target.\"\"\"\n",
    "    x = brainmass.Simulator(m, dt=0.1 * u.ms).run(\n",
    "        300.0 * u.ms, monitors=[\"x\"], transient=150.0 * u.ms\n",
    "    )[\"x\"][:, 0]\n",
    "    amp = jnp.sqrt(jnp.mean((x - jnp.mean(x)) ** 2))\n",
    "    return (amp - target_amp) ** 2, amp\n",
    "\n",
    "\n",
    "fitter = brainmass.Fitter(model, braintools.optim.Adam(lr=0.05), loss_fn=loss_fn)\n",
    "result = fitter.fit(n_steps=50)\n",
    "\n",
    "print(result)\n",
    "print(f\"a:        0.05  ->  {float(result.best_params['a']):.4f}\")\n",
    "print(f\"loss:  {result.history[0]:.4f}  ->  {result.best_loss:.5f}\")\n",
    "print(f\"amplitude matched: {float(result.prediction):.4f}  (target {float(target_amp):.4f})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "qs-step5-plot-md",
   "metadata": {},
   "source": [
    "The loss curve shows the gradient optimiser converging in a few dozen steps — no parameter\n",
    "sweep required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "qs-step5-plot-code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T06:27:06.208417Z",
     "iopub.status.busy": "2026-06-19T06:27:06.208274Z",
     "iopub.status.idle": "2026-06-19T06:27:06.296561Z",
     "shell.execute_reply": "2026-06-19T06:27:06.295757Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 3.5))\n",
    "ax.plot(result.history, marker=\".\")\n",
    "ax.set_xlabel(\"optimisation step\")\n",
    "ax.set_ylabel(\"loss\")\n",
    "ax.set_title(\"Gradient fit of the Hopf bifurcation parameter\")\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "qs-next",
   "metadata": {},
   "source": [
    "## Where to go next\n",
    "\n",
    "You have run, visualised, perturbed, coupled, and *fit* a model — all through the\n",
    "high-level API. To go deeper:\n",
    "\n",
    "- {doc}`/getting_started/key_concepts` — the mental model behind `*Step` models,\n",
    "  `Simulator`, `Network`, `Fitter` and units.\n",
    "- The fitting story is expanded across tutorials\n",
    "  {doc}`06 (gradients) </tutorials/06_fitting_with_gradients>`,\n",
    "  {doc}`07 (gradient-free) </tutorials/07_gradient_free_fitting>` and\n",
    "  {doc}`08 (training on tasks) </tutorials/08_training_on_tasks>`.\n",
    "- {doc}`/howto/choose_a_model` — browse every model with `brainmass.list_models()`.\n",
    "- {doc}`/tutorials/04_building_a_network` — full whole-brain networks.\n",
    "\n",
    "## See also\n",
    "\n",
    "- {doc}`/reference/orchestration` — `Simulator` / `Network` / `Fitter` API.\n",
    "- {doc}`/reference/datasets` and {doc}`/reference/viz` — the bundled data and plotting helpers.\n",
    "- {doc}`/reference/models` — the full model reference."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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