{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5f8516e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:35.932860Z",
     "iopub.status.busy": "2026-06-19T07:40:35.932650Z",
     "iopub.status.idle": "2026-06-19T07:40:41.173252Z",
     "shell.execute_reply": "2026-06-19T07:40:41.172498Z"
    },
    "tags": [
     "remove-cell"
    ]
   },
   "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"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import brainmass\n",
    "import brainstate\n",
    "import brainunit as u\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "import numpy as np\n",
    "brainstate.environ.set(dt=0.1 * u.ms)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2a91876",
   "metadata": {},
   "source": [
    "# Working with Units\n",
    "\n",
    "`brainmass` uses `brainunit` for unit-safe computations, preventing common errors in scientific computing.\n",
    "\n",
    "## Why Units Matter\n",
    "\n",
    "Without units, it's easy to make mistakes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e91814f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:41.176156Z",
     "iopub.status.busy": "2026-06-19T07:40:41.175452Z",
     "iopub.status.idle": "2026-06-19T07:40:41.179054Z",
     "shell.execute_reply": "2026-06-19T07:40:41.178385Z"
    }
   },
   "outputs": [],
   "source": [
    "# DANGEROUS: Are these in ms or s?\n",
    "tau = 10\n",
    "frequency = 0.5\n",
    "\n",
    "# What happens when you multiply them?\n",
    "result = tau * frequency  # Meaningless without units!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d9eb716",
   "metadata": {},
   "source": [
    "With units, errors are caught automatically:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3872884a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:41.181363Z",
     "iopub.status.busy": "2026-06-19T07:40:41.181145Z",
     "iopub.status.idle": "2026-06-19T07:40:41.184771Z",
     "shell.execute_reply": "2026-06-19T07:40:41.184150Z"
    }
   },
   "outputs": [],
   "source": [
    "import brainunit as u\n",
    "\n",
    "tau = 10 * u.ms\n",
    "frequency = 0.5 * u.Hz\n",
    "\n",
    "# Units are checked automatically\n",
    "result = tau * frequency  # ✓ Dimensionless (ms * Hz = ms * 1/s = 0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20c59a5f",
   "metadata": {},
   "source": [
    "## Basic Usage\n",
    "\n",
    "### Creating Quantities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "38e8e5bd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:41.186697Z",
     "iopub.status.busy": "2026-06-19T07:40:41.186510Z",
     "iopub.status.idle": "2026-06-19T07:40:41.206235Z",
     "shell.execute_reply": "2026-06-19T07:40:41.204999Z"
    }
   },
   "outputs": [],
   "source": [
    "import brainunit as u\n",
    "import jax.numpy as jnp\n",
    "\n",
    "# Scalar with units\n",
    "time_const = 10.0 * u.ms\n",
    "\n",
    "# Array with units\n",
    "rates = jnp.array([1.0, 2.0, 3.0]) * u.Hz\n",
    "\n",
    "# Common units\n",
    "voltage = -65 * u.mV\n",
    "current = 2.5 * u.nA\n",
    "capacitance = 100 * u.pF\n",
    "conductance = 10 * u.nS\n",
    "time = 1.5 * u.second"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69732c09",
   "metadata": {},
   "source": [
    "### Accessing Values and Units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e19469b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:41.209309Z",
     "iopub.status.busy": "2026-06-19T07:40:41.209117Z",
     "iopub.status.idle": "2026-06-19T07:40:41.212563Z",
     "shell.execute_reply": "2026-06-19T07:40:41.211699Z"
    }
   },
   "outputs": [],
   "source": [
    "tau = 10.0 * u.ms\n",
    "\n",
    "# Get magnitude (number)\n",
    "magnitude = tau.magnitude  # 10.0\n",
    "\n",
    "# Get unit\n",
    "unit = tau.unit  # millisecond\n",
    "\n",
    "# Convert to different unit\n",
    "tau_in_seconds = tau.to(u.second)  # 0.01 s\n",
    "tau_in_us = tau.to(u.us)  # 10000 μs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb866ecc",
   "metadata": {},
   "source": [
    "### Unit Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "adcc0a2d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:41.215048Z",
     "iopub.status.busy": "2026-06-19T07:40:41.214725Z",
     "iopub.status.idle": "2026-06-19T07:40:41.231860Z",
     "shell.execute_reply": "2026-06-19T07:40:41.231207Z"
    }
   },
   "outputs": [],
   "source": [
    "import brainunit as u\n",
    "\n",
    "# Addition/subtraction: units must match\n",
    "t1 = 10 * u.ms\n",
    "t2 = 5 * u.ms\n",
    "t_sum = t1 + t2  # 15 ms ✓\n",
    "\n",
    "t3 = 1 * u.second\n",
    "t_combined = t1 + t3  # 1.01 s (automatic conversion) ✓\n",
    "\n",
    "# Multiplication/division: units combine\n",
    "R = 10 * u.ohm\n",
    "I = 2 * u.amp\n",
    "V = R * I  # 20 volt ✓\n",
    "\n",
    "# Powers\n",
    "area = (5 * u.meter) ** 2  # 25 meter²"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8078810",
   "metadata": {},
   "source": [
    "## Common Units in Neuroscience\n",
    "\n",
    "### Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5ad3471b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:41.233995Z",
     "iopub.status.busy": "2026-06-19T07:40:41.233821Z",
     "iopub.status.idle": "2026-06-19T07:40:41.237752Z",
     "shell.execute_reply": "2026-06-19T07:40:41.236406Z"
    }
   },
   "outputs": [],
   "source": [
    "import brainunit as u\n",
    "\n",
    "# Time constants\n",
    "tau = 10 * u.ms  # milliseconds\n",
    "tau_slow = 0.5 * u.second  # seconds\n",
    "\n",
    "# Sampling intervals\n",
    "dt = 0.1 * u.ms\n",
    "TR = 2 * u.second  # fMRI repetition time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "247c0466",
   "metadata": {},
   "source": [
    "### Frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "26ebe7e0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:41.240460Z",
     "iopub.status.busy": "2026-06-19T07:40:41.240156Z",
     "iopub.status.idle": "2026-06-19T07:40:41.244912Z",
     "shell.execute_reply": "2026-06-19T07:40:41.243930Z"
    }
   },
   "outputs": [],
   "source": [
    "# Oscillation frequencies\n",
    "alpha = 10 * u.Hz  # 10 Hz\n",
    "theta = 5 * u.Hz\n",
    "gamma = 40 * u.Hz\n",
    "\n",
    "# Convert to angular frequency\n",
    "omega = 2 * jnp.pi * alpha  # rad/s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a605b488",
   "metadata": {},
   "source": [
    "### Voltage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2637cb2e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:41.246647Z",
     "iopub.status.busy": "2026-06-19T07:40:41.246473Z",
     "iopub.status.idle": "2026-06-19T07:40:41.249888Z",
     "shell.execute_reply": "2026-06-19T07:40:41.248897Z"
    }
   },
   "outputs": [],
   "source": [
    "# Membrane potentials\n",
    "V_rest = -70 * u.mV\n",
    "V_thresh = -55 * u.mV\n",
    "V_spike = 20 * u.mV\n",
    "\n",
    "# EEG potentials\n",
    "eeg_signal = 50 * u.uvolt  # microvolts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e18ff98",
   "metadata": {},
   "source": [
    "### Current"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "911a52f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:41.252063Z",
     "iopub.status.busy": "2026-06-19T07:40:41.251811Z",
     "iopub.status.idle": "2026-06-19T07:40:41.255761Z",
     "shell.execute_reply": "2026-06-19T07:40:41.254841Z"
    }
   },
   "outputs": [],
   "source": [
    "# Synaptic currents\n",
    "I_ext = 2.5 * u.pA  # picoamperes\n",
    "\n",
    "# Dipole moments (EEG/MEG)\n",
    "dipole = 10 * u.nA * u.meter  # nanoampere-meters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7acbb9e6",
   "metadata": {},
   "source": [
    "### Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9b344f44",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:41.257613Z",
     "iopub.status.busy": "2026-06-19T07:40:41.257445Z",
     "iopub.status.idle": "2026-06-19T07:40:41.262188Z",
     "shell.execute_reply": "2026-06-19T07:40:41.261114Z"
    }
   },
   "outputs": [],
   "source": [
    "# Firing rates\n",
    "rate_E = 10 * u.Hz\n",
    "rate_I = 15 * u.Hz\n",
    "\n",
    "# Rates can be added\n",
    "total_rate = rate_E + rate_I  # 25 Hz"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77605085",
   "metadata": {},
   "source": [
    "## Using Units with brainmass\n",
    "\n",
    "### Model Parameters\n",
    "\n",
    "Most brainmass models accept unit quantities:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d43d38b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:41.264761Z",
     "iopub.status.busy": "2026-06-19T07:40:41.264539Z",
     "iopub.status.idle": "2026-06-19T07:40:41.284852Z",
     "shell.execute_reply": "2026-06-19T07:40:41.283888Z"
    }
   },
   "outputs": [],
   "source": [
    "import brainmass\n",
    "import brainunit as u\n",
    "\n",
    "model = brainmass.WilsonCowanStep(\n",
    "    in_size=10,\n",
    "    tau_E=10. * u.ms,    # with units\n",
    "    tau_I=20. * u.ms,\n",
    "    # Recurrent coupling weights are dimensionless\n",
    "    wEE=12.0,  # E -> E coupling strength\n",
    "    wEI=13.0,  # I -> E coupling strength\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aeba99e",
   "metadata": {},
   "source": [
    "### State Variables\n",
    "\n",
    "Internal states maintain units:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f310ef40",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:41.286659Z",
     "iopub.status.busy": "2026-06-19T07:40:41.286507Z",
     "iopub.status.idle": "2026-06-19T07:40:42.020041Z",
     "shell.execute_reply": "2026-06-19T07:40:42.018848Z"
    }
   },
   "outputs": [],
   "source": [
    "model = brainmass.HopfStep(\n",
    "    in_size=5,\n",
    "    w=0.2,  # intrinsic angular frequency (dimensionless)\n",
    ")\n",
    "model.init_all_states()\n",
    "\n",
    "model.update()\n",
    "\n",
    "# In the normal-form Hopf model the state is dimensionless; models such as\n",
    "# JansenRitStep instead keep their internal states in physical units (e.g. mV).\n",
    "x = model.x.value\n",
    "y = model.y.value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2ef3326",
   "metadata": {},
   "source": [
    "### Noise with Units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "233e3213",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:42.022435Z",
     "iopub.status.busy": "2026-06-19T07:40:42.022238Z",
     "iopub.status.idle": "2026-06-19T07:40:42.027723Z",
     "shell.execute_reply": "2026-06-19T07:40:42.026694Z"
    }
   },
   "outputs": [],
   "source": [
    "# Noise in Hz (for firing rates)\n",
    "noise = brainmass.OUProcess(\n",
    "    in_size=10,\n",
    "    sigma=0.5 * u.Hz,\n",
    "    tau=20 * u.ms,\n",
    ")\n",
    "\n",
    "# Noise in mV (for voltages)\n",
    "noise_V = brainmass.OUProcess(\n",
    "    in_size=10,\n",
    "    sigma=5 * u.mV,\n",
    "    tau=10 * u.ms,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1966b09d",
   "metadata": {},
   "source": [
    "### Forward Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2e35e4d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:42.029816Z",
     "iopub.status.busy": "2026-06-19T07:40:42.029569Z",
     "iopub.status.idle": "2026-06-19T07:40:42.078140Z",
     "shell.execute_reply": "2026-06-19T07:40:42.077328Z"
    }
   },
   "outputs": [],
   "source": [
    "# BOLD (dimensionless or %)\n",
    "bold = brainmass.BOLDSignal(in_size=90)\n",
    "# Input: neural activity (Hz or dimensionless)\n",
    "# Output: BOLD % signal change\n",
    "\n",
    "# EEG lead-field: maps in_size sources -> out_size sensors.\n",
    "# Real lead-fields come from a head model; here we use a synthetic one.\n",
    "# Shape is (in_size, out_size) and it carries units of volt / (nA * meter).\n",
    "L_eeg = jnp.ones((68, 64)) * (u.volt / (u.nA * u.meter))\n",
    "\n",
    "eeg_model = brainmass.EEGLeadFieldModel(\n",
    "    in_size=68,\n",
    "    out_size=64,\n",
    "    L=L_eeg,  # with units\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10d6cc9a",
   "metadata": {},
   "source": [
    "## Unit Checking and Errors\n",
    "\n",
    "### Automatic Error Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3699c7e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:42.080638Z",
     "iopub.status.busy": "2026-06-19T07:40:42.080391Z",
     "iopub.status.idle": "2026-06-19T07:40:42.086602Z",
     "shell.execute_reply": "2026-06-19T07:40:42.085764Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error: Cannot calculate \n",
      "10 ms + 5 Hz, because units do not match: ms != Hz\n"
     ]
    }
   ],
   "source": [
    "import brainunit as u\n",
    "\n",
    "tau = 10 * u.ms\n",
    "rate = 5 * u.Hz\n",
    "\n",
    "# This will raise a unit-mismatch error:\n",
    "try:\n",
    "    result = tau + rate  # Can't add time and frequency!\n",
    "except u.UnitMismatchError as e:\n",
    "    print(f\"Error: {e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ced5af1f",
   "metadata": {},
   "source": [
    "### Common Unit Errors\n",
    "\n",
    "**Error 1: Mixing incompatible units**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "61390315",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:42.089068Z",
     "iopub.status.busy": "2026-06-19T07:40:42.088835Z",
     "iopub.status.idle": "2026-06-19T07:40:42.095338Z",
     "shell.execute_reply": "2026-06-19T07:40:42.094100Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Caught: Cannot calculate \n",
      "10 ms + 5 mV, because units do not match: ms != mV\n"
     ]
    }
   ],
   "source": [
    "# WRONG: adding incompatible quantities raises an error\n",
    "time = 10 * u.ms\n",
    "voltage = 5 * u.mV\n",
    "try:\n",
    "    result = time + voltage  # Error: can't add ms and mV\n",
    "except u.UnitMismatchError as e:\n",
    "    print(f\"Caught: {e}\")\n",
    "\n",
    "# CORRECT:\n",
    "# Don't add incompatible quantities"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bb09642",
   "metadata": {},
   "source": [
    "**Error 2: Forgetting units**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "47cd63c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:42.098559Z",
     "iopub.status.busy": "2026-06-19T07:40:42.098276Z",
     "iopub.status.idle": "2026-06-19T07:40:42.103157Z",
     "shell.execute_reply": "2026-06-19T07:40:42.102228Z"
    }
   },
   "outputs": [],
   "source": [
    "# WRONG:\n",
    "tau_E = 10 * u.ms\n",
    "tau_I = 20  # forgot units!\n",
    "ratio = tau_I / tau_E  # May work but wrong semantics\n",
    "\n",
    "# CORRECT:\n",
    "tau_E = 10 * u.ms\n",
    "tau_I = 20 * u.ms\n",
    "ratio = tau_I / tau_E  # 2.0 (dimensionless)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87d22e1e",
   "metadata": {},
   "source": [
    "**Error 3: Unit conversion mistakes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "272e120a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:42.105127Z",
     "iopub.status.busy": "2026-06-19T07:40:42.104948Z",
     "iopub.status.idle": "2026-06-19T07:40:42.108597Z",
     "shell.execute_reply": "2026-06-19T07:40:42.107721Z"
    }
   },
   "outputs": [],
   "source": [
    "# WRONG:\n",
    "tau_ms = 10  # assumed ms, but unclear\n",
    "tau_s = tau_ms / 1000  # manual conversion, error-prone\n",
    "\n",
    "# CORRECT:\n",
    "tau = 10 * u.ms\n",
    "tau_s = tau.to(u.second)  # 0.01 s, automatic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab98eb95",
   "metadata": {},
   "source": [
    "## Converting to Unitless\n",
    "\n",
    "Sometimes you need plain numbers (for plotting, external libraries):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bf008d8a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:42.112004Z",
     "iopub.status.busy": "2026-06-19T07:40:42.111694Z",
     "iopub.status.idle": "2026-06-19T07:40:42.368127Z",
     "shell.execute_reply": "2026-06-19T07:40:42.367395Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Signal')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import brainunit as u\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "time = jnp.arange(1000) * u.ms\n",
    "# 2 * pi * (10 Hz) * time is dimensionless, so jnp.sin returns a plain array\n",
    "signal = jnp.sin(2 * jnp.pi * 10 * u.Hz * time)\n",
    "\n",
    "# Convert to unitless for plotting (u.get_magnitude works for Quantity or array)\n",
    "time_ms = time.to(u.ms).magnitude  # array of numbers\n",
    "signal_unitless = u.get_magnitude(signal)\n",
    "\n",
    "plt.plot(time_ms, signal_unitless)\n",
    "plt.xlabel('Time (ms)')\n",
    "plt.ylabel('Signal')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "370cc17c",
   "metadata": {},
   "source": [
    "Or use brainunit's plotting support:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ad0993b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:42.370088Z",
     "iopub.status.busy": "2026-06-19T07:40:42.369823Z",
     "iopub.status.idle": "2026-06-19T07:40:42.459231Z",
     "shell.execute_reply": "2026-06-19T07:40:42.458458Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x73b8874ec690>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Some plotting functions may support units directly\n",
    "plt.plot(time, signal)  # may work with recent brainunit versions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a1cc456",
   "metadata": {},
   "source": [
    "## Best Practices\n",
    "\n",
    "1. **Always use units for physical quantities**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2bfcd201",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:42.461640Z",
     "iopub.status.busy": "2026-06-19T07:40:42.461426Z",
     "iopub.status.idle": "2026-06-19T07:40:42.464905Z",
     "shell.execute_reply": "2026-06-19T07:40:42.464165Z"
    }
   },
   "outputs": [],
   "source": [
    "# GOOD\n",
    "tau = 10 * u.ms\n",
    "frequency = 5 * u.Hz\n",
    "\n",
    "# BAD\n",
    "tau = 10  # unclear: ms? s?\n",
    "frequency = 5  # unclear: Hz? kHz?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d236bf10",
   "metadata": {},
   "source": [
    "2. **Use consistent units within modules**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0e726491",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:42.466842Z",
     "iopub.status.busy": "2026-06-19T07:40:42.466673Z",
     "iopub.status.idle": "2026-06-19T07:40:42.470270Z",
     "shell.execute_reply": "2026-06-19T07:40:42.469075Z"
    }
   },
   "outputs": [],
   "source": [
    "# Within a module, stick to one time unit\n",
    "tau_E = 10 * u.ms\n",
    "tau_I = 20 * u.ms  # not mixing with seconds\n",
    "dt = 0.1 * u.ms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "649eba4e",
   "metadata": {},
   "source": [
    "3. **Document units in comments when necessary**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "31181684",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:42.473030Z",
     "iopub.status.busy": "2026-06-19T07:40:42.472779Z",
     "iopub.status.idle": "2026-06-19T07:40:42.476537Z",
     "shell.execute_reply": "2026-06-19T07:40:42.475725Z"
    }
   },
   "outputs": [],
   "source": [
    "# Even with brainunit, document expected ranges\n",
    "tau = 10 * u.ms  # typical range: 5-50 ms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ed489b6",
   "metadata": {},
   "source": [
    "4. **Convert units explicitly when needed**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "578777b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:42.478735Z",
     "iopub.status.busy": "2026-06-19T07:40:42.478521Z",
     "iopub.status.idle": "2026-06-19T07:40:42.482563Z",
     "shell.execute_reply": "2026-06-19T07:40:42.481614Z"
    }
   },
   "outputs": [],
   "source": [
    "omega_Hz = 10 * u.Hz\n",
    "omega_rad = 2 * jnp.pi * omega_Hz  # rad/s\n",
    "\n",
    "# Convert time constant to frequency\n",
    "tau = 10 * u.ms\n",
    "f_cutoff = (1 / (2 * jnp.pi * tau)).to(u.Hz)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "948b815e",
   "metadata": {},
   "source": [
    "5. **Use dimensionless quantities appropriately**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fb93e65c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:42.485280Z",
     "iopub.status.busy": "2026-06-19T07:40:42.484939Z",
     "iopub.status.idle": "2026-06-19T07:40:42.488758Z",
     "shell.execute_reply": "2026-06-19T07:40:42.487646Z"
    }
   },
   "outputs": [],
   "source": [
    "# Ratios are naturally dimensionless\n",
    "tau_E = 10 * u.ms\n",
    "tau_I = 20 * u.ms\n",
    "ratio = tau_I / tau_E  # 2.0 (dimensionless)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad6fa855",
   "metadata": {},
   "source": [
    "## Troubleshooting\n",
    "\n",
    "**Issue: Dimension mismatch error**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e22be695",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:42.491549Z",
     "iopub.status.busy": "2026-06-19T07:40:42.491286Z",
     "iopub.status.idle": "2026-06-19T07:40:42.494408Z",
     "shell.execute_reply": "2026-06-19T07:40:42.493769Z"
    }
   },
   "outputs": [],
   "source": [
    "# Error: DimensionMismatch: cannot add Hz and mV\n",
    "# Solution: Check that you're not mixing incompatible quantities"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a637f0fd",
   "metadata": {},
   "source": [
    "**Issue: Lost units after JAX operations**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "19d27da6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:42.496481Z",
     "iopub.status.busy": "2026-06-19T07:40:42.496274Z",
     "iopub.status.idle": "2026-06-19T07:40:42.527165Z",
     "shell.execute_reply": "2026-06-19T07:40:42.526241Z"
    }
   },
   "outputs": [],
   "source": [
    "# Some JAX operations may strip units\n",
    "x = 10 * u.ms\n",
    "y = jnp.exp(x.magnitude)  # operate on the magnitude\n",
    "\n",
    "# Solution: reattach units when the result is dimensionless\n",
    "y = jnp.exp(x.magnitude) * u.UNITLESS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce2d3d77",
   "metadata": {},
   "source": [
    "**Issue: Slow performance with units**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6da210b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T07:40:42.529569Z",
     "iopub.status.busy": "2026-06-19T07:40:42.529257Z",
     "iopub.status.idle": "2026-06-19T07:40:42.533257Z",
     "shell.execute_reply": "2026-06-19T07:40:42.532369Z"
    }
   },
   "outputs": [],
   "source": [
    "# Units add overhead; for tight loops, consider converting to unitless\n",
    "tau_ms = tau.to(u.ms).magnitude  # convert once\n",
    "# ... use tau_ms in loop ..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe7fcdf3",
   "metadata": {},
   "source": [
    "## Next Steps\n",
    "\n",
    "- {doc}`/getting_started/quickstart` - See units in action\n",
    "- {doc}`/tutorials/04_building_a_network` - Units in network simulations\n",
    "- The `brainunit` documentation for advanced features\n",
    "\n",
    "## See Also\n",
    "\n",
    "- {doc}`/reference/models` - Model parameters and units\n",
    "- {doc}`/reference/forward` - Forward model units"
   ]
  }
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