{
 "cells": [
  {
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   "source": [
    "# Tutorial 5: Composing Connectors and Multi-Compartment Connectivity\n",
    "\n",
    "The previous tutorials generated one connectivity pattern at a time for point neurons. This tutorial covers two features that build on that:\n",
    "\n",
    "1. **Composing connectors** with operators: union (`+`), intersection (`*`), difference (`-`), and weight/delay scaling.\n",
    "2. **Multi-compartment connectivity**, where every edge also records *which compartment* it leaves from and *which compartment* it lands on: soma, basal dendrite, apical dendrite, or axon.\n",
    "\n",
    "Both features return the same `ConnectionResult` you already know, so the inspection tools from Tutorials 1-4 still apply.\n",
    "\n",
    "---\n",
    "\n",
    "## 1. Imports"
   ]
  },
  {
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     "shell.execute_reply": "2026-10-14T09:54:26.364370Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imports successful\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "import brainunit as u\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from braintools import conn, visualize as vis\n",
    "from braintools.init import Constant, ExponentialProfile, GaussianProfile, LinearProfile, Normal\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (12, 4)\n",
    "plt.rcParams[\"font.size\"] = 10\n",
    "\n",
    "print(\"Imports successful\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3d20687",
   "metadata": {},
   "source": [
    "## 2. Helper Functions\n",
    "\n",
    "`edge_set` turns a result into a Python set of `(pre, post)` pairs, which makes it easy to check the operators against ordinary set algebra. `as_plain` strips units before plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "61173e82",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T09:54:26.366373Z",
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     "shell.execute_reply": "2026-10-14T09:54:26.369146Z"
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   "outputs": [],
   "source": [
    "def as_plain(values):\n",
    "    \"\"\"Return a NumPy array of mantissas for arrays or brainunit quantities.\"\"\"\n",
    "    return np.asarray(u.get_mantissa(values))\n",
    "\n",
    "\n",
    "def edge_list(result):\n",
    "    \"\"\"List of (pre, post) neuron pairs, in edge order.\"\"\"\n",
    "    return list(zip(result.pre_indices.tolist(), result.post_indices.tolist()))\n",
    "\n",
    "\n",
    "def edge_set(result):\n",
    "    \"\"\"Set of (pre, post) neuron pairs in a ConnectionResult.\"\"\"\n",
    "    return set(edge_list(result))\n",
    "\n",
    "\n",
    "def unique_values(values, decimals=3):\n",
    "    \"\"\"Sorted distinct mantissas, for compact printing.\"\"\"\n",
    "    return sorted(set(np.round(as_plain(values), decimals).tolist()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b185271",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 3. Composing Connectors\n",
    "\n",
    "Every connector supports a small algebra. The operators do not generate anything immediately; they return a new connector that generates both operands and combines the results when it is called.\n",
    "\n",
    "| Expression | Result type | Edges kept |\n",
    "|---|---|---|\n",
    "| `a + b` | `CompositeConnectivity` | union: edges in `a` or `b` (duplicates removed) |\n",
    "| `a * b` | `CompositeConnectivity` | intersection: edges in both `a` and `b` |\n",
    "| `a - b` | `CompositeConnectivity` | difference: edges in `a` but not in `b` |\n",
    "| `a * 0.5`, `a.weight_scale(0.5)` | `ScaledConnectivity` | all edges of `a`, weights scaled |\n",
    "| `a.delay_scale(2.0)` | `ScaledConnectivity` | all edges of `a`, delays scaled |\n",
    "\n",
    "Both operands are called with the same `pre_size`, `post_size`, and positions, so they must describe the same pair of populations.\n",
    "\n",
    "The example combines a weighted random projection with a deterministic local ring. The ring has no weights of its own, so it acts as a *mask*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a93143eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T09:54:26.370154Z",
     "iopub.status.busy": "2026-10-14T09:54:26.370154Z",
     "iopub.status.idle": "2026-10-14T09:54:27.409131Z",
     "shell.execute_reply": "2026-10-14T09:54:27.409131Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "operator type: CompositeConnectivity\n",
      "random: 555 edges, ring: 720 edges\n",
      "\n",
      "union        a + b: 1167 edges, matches set algebra: True\n",
      "intersection a * b:  108 edges, matches set algebra: True\n",
      "difference   a - b:  447 edges, matches set algebra: True\n"
     ]
    }
   ],
   "source": [
    "n = 60\n",
    "\n",
    "random_proj = conn.Random(\n",
    "    prob=0.15,\n",
    "    weight=Normal(mean=1.0 * u.nS, std=0.2 * u.nS),\n",
    "    delay=Constant(2.0 * u.ms),\n",
    "    seed=42,\n",
    ")\n",
    "local_ring = conn.Ring(neighbors=6)  # unweighted: a pure topology mask\n",
    "\n",
    "random_result = random_proj(pre_size=n, post_size=n)\n",
    "ring_result = local_ring(pre_size=n, post_size=n)\n",
    "\n",
    "union = random_proj + local_ring\n",
    "intersection = random_proj * local_ring\n",
    "difference = random_proj - local_ring\n",
    "\n",
    "union_result = union(pre_size=n, post_size=n)\n",
    "intersection_result = intersection(pre_size=n, post_size=n)\n",
    "difference_result = difference(pre_size=n, post_size=n)\n",
    "\n",
    "R, L = edge_set(random_result), edge_set(ring_result)\n",
    "print(f\"operator type: {type(union).__name__}\")\n",
    "print(f\"random: {random_result.n_connections} edges, ring: {ring_result.n_connections} edges\")\n",
    "print()\n",
    "for name, result, expected in [\n",
    "    (\"union        a + b\", union_result, R | L),\n",
    "    (\"intersection a * b\", intersection_result, R & L),\n",
    "    (\"difference   a - b\", difference_result, R - L),\n",
    "]:\n",
    "    print(f\"{name}: {result.n_connections:4d} edges, matches set algebra: {edge_set(result) == expected}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ec306cfb",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2026-10-14T09:54:27.651694Z",
     "shell.execute_reply": "2026-10-14T09:54:27.651694Z"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x420 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 5, figsize=(20, 4.2))\n",
    "for ax, (title, result) in zip(axes, [\n",
    "    (\"Random (a)\", random_result),\n",
    "    (\"Ring mask (b)\", ring_result),\n",
    "    (\"a + b\", union_result),\n",
    "    (\"a * b\", intersection_result),\n",
    "    (\"a - b\", difference_result),\n",
    "]):\n",
    "    vis.connectivity_matrix(\n",
    "        (as_plain(result.weight2dense()) != 0).astype(float),\n",
    "        cmap=\"binary\",\n",
    "        center_zero=False,\n",
    "        show_colorbar=False,\n",
    "        ax=ax,\n",
    "        title=title,\n",
    "    )\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0efada36",
   "metadata": {},
   "source": [
    "Note that the union panel is built from non-zero *weights*, and it looks identical to the random panel: the ring-only edges are present in the result but carry weight `0`. The next section explains why.\n",
    "\n",
    "### How Weights and Delays Are Combined\n",
    "\n",
    "The edge sets follow set algebra exactly, but the per-edge values need rules:\n",
    "\n",
    "- **Union (`a + b`)**: an edge present in both operands keeps the values of the **left** operand. If only one operand has weights (or delays), edges contributed solely by the other operand get weight `0` (delay `0`). Give both operands weights when you need every edge to be effective.\n",
    "- **Intersection (`a * b`)**: weights are **multiplied** and delays are **averaged**. An operand without weights counts as `1` and an operand without delays is ignored, so intersecting with an unweighted mask keeps the weighted operand's values unchanged. If both operands carry weights the units multiply too (`nS * nS = nS^2`), which is rarely what you want.\n",
    "- **Difference (`a - b`)**: kept edges retain the left operand's weights and delays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "811b93c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T09:54:27.652699Z",
     "iopub.status.busy": "2026-10-14T09:54:27.652699Z",
     "iopub.status.idle": "2026-10-14T09:54:27.689614Z",
     "shell.execute_reply": "2026-10-14T09:54:27.689614Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a * mask\n",
      "  weight unit: nS\n",
      "  weights equal to the random projection's: True\n",
      "  delays: [2.0] ms\n",
      "\n",
      "a + mask\n",
      "  edges contributed only by the ring: 612\n",
      "  their weights: [0.0] nS\n",
      "\n",
      "a * weighted b\n",
      "  weight unit: nS^2\n"
     ]
    }
   ],
   "source": [
    "# Intersection with an unweighted mask keeps the original weights and delays.\n",
    "random_weights = dict(zip(edge_list(random_result), as_plain(random_result.weights).tolist()))\n",
    "same_weights = all(\n",
    "    np.isclose(random_weights[edge], w)\n",
    "    for edge, w in zip(edge_list(intersection_result), as_plain(intersection_result.weights))\n",
    ")\n",
    "print(\"a * mask\")\n",
    "print(f\"  weight unit: {u.get_unit(intersection_result.weights)}\")\n",
    "print(f\"  weights equal to the random projection's: {same_weights}\")\n",
    "print(f\"  delays: {unique_values(intersection_result.delays)} {u.get_unit(intersection_result.delays)}\")\n",
    "\n",
    "# Union: edges that only the unweighted ring contributed carry weight 0.\n",
    "ring_only = np.array([edge not in R for edge in edge_list(union_result)])\n",
    "print()\n",
    "print(\"a + mask\")\n",
    "print(f\"  edges contributed only by the ring: {int(ring_only.sum())}\")\n",
    "print(f\"  their weights: {unique_values(union_result.weights[ring_only])} {u.get_unit(union_result.weights)}\")\n",
    "\n",
    "# Two weighted operands: the units multiply.\n",
    "weighted_ring = conn.Ring(neighbors=6, weight=Constant(2.0 * u.nS))\n",
    "both_weighted = (random_proj * weighted_ring)(pre_size=n, post_size=n)\n",
    "print()\n",
    "print(\"a * weighted b\")\n",
    "print(f\"  weight unit: {u.get_unit(both_weighted.weights)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e080b75a",
   "metadata": {},
   "source": [
    "### Scaling Weights and Delays\n",
    "\n",
    "Multiplying a connector by a number scales its weights. The number must be on the **right** (`pattern * 0.5`); `0.5 * pattern` raises `TypeError`. `weight_scale()` and `delay_scale()` are the explicit forms. Scaling returns a new connector and never modifies the base connector's cached result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a2e79107",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T09:54:27.690617Z",
     "iopub.status.busy": "2026-10-14T09:54:27.690617Z",
     "iopub.status.idle": "2026-10-14T09:54:27.694887Z",
     "shell.execute_reply": "2026-10-14T09:54:27.694887Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scaled type: ScaledConnectivity\n",
      "mean weight  base: 0.994 nS  x0.5: 0.497 nS\n",
      "delays       base: [2.0] ms  x1.5: [3.0] ms\n",
      "same edges after scaling: True\n",
      "0.5 * pattern -> TypeError: unsupported operand type(s) for *: 'float' and 'Random'\n"
     ]
    }
   ],
   "source": [
    "half_weights = random_proj * 0.5\n",
    "slow = random_proj.delay_scale(1.5)\n",
    "\n",
    "half_result = half_weights(pre_size=n, post_size=n)\n",
    "slow_result = slow(pre_size=n, post_size=n)\n",
    "base_again = random_proj(pre_size=n, post_size=n)\n",
    "\n",
    "print(f\"scaled type: {type(half_weights).__name__}\")\n",
    "print(f\"mean weight  base: {u.math.mean(base_again.weights):.3f}  x0.5: {u.math.mean(half_result.weights):.3f}\")\n",
    "print(f\"delays       base: {unique_values(base_again.delays)} ms  x1.5: {unique_values(slow_result.delays)} ms\")\n",
    "print(f\"same edges after scaling: {edge_set(half_result) == edge_set(base_again)}\")\n",
    "\n",
    "try:\n",
    "    0.5 * random_proj\n",
    "except TypeError as exc:\n",
    "    print(f\"0.5 * pattern -> TypeError: {exc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6269702d",
   "metadata": {},
   "source": [
    "### Building a Projection from Parts\n",
    "\n",
    "Operators compose, so small building blocks can describe a structured projection. Here a dense local neighbourhood is joined with sparse long-range shortcuts, and the long-range part is weakened before the union. The `- local_mask` term keeps the shortcuts outside the neighbourhood, so every edge in the result comes from exactly one source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f0b1015b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T09:54:27.695891Z",
     "iopub.status.busy": "2026-10-14T09:54:27.695891Z",
     "iopub.status.idle": "2026-10-14T09:54:27.839489Z",
     "shell.execute_reply": "2026-10-14T09:54:27.839489Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "local edges:    399\n",
      "shortcut edges: 90\n",
      "composite:      489 edges\n",
      "weight values:  [0.3, 1.0] nS\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 650x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "local_mask = conn.Ring(neighbors=4)\n",
    "local = conn.Random(prob=0.8, weight=Constant(1.0 * u.nS), seed=1) * local_mask\n",
    "shortcuts = (conn.Random(prob=0.03, weight=Constant(1.0 * u.nS), seed=2) - local_mask) * 0.3\n",
    "\n",
    "composite = local + shortcuts\n",
    "composite_result = composite(pre_size=n, post_size=n)\n",
    "\n",
    "print(f\"local edges:    {local(pre_size=n, post_size=n).n_connections}\")\n",
    "print(f\"shortcut edges: {shortcuts(pre_size=n, post_size=n).n_connections}\")\n",
    "print(f\"composite:      {composite_result.n_connections} edges\")\n",
    "print(f\"weight values:  {unique_values(composite_result.weights)} nS\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6.5, 5.5))\n",
    "vis.connectivity_matrix(\n",
    "    as_plain(composite_result.weight2dense()),\n",
    "    cmap=\"viridis\",\n",
    "    center_zero=False,\n",
    "    show_colorbar=True,\n",
    "    ax=ax,\n",
    "    title=\"Local neighbourhood + weakened shortcuts\",\n",
    ")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc192f6d",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 4. Multi-Compartment Connectivity\n",
    "\n",
    "Multi-compartment connectors describe synapses between neurons with morphology. Compartments are identified by integer constants exported from `braintools.conn`:\n",
    "\n",
    "| Constant | Value | Name used in mappings |\n",
    "|---|---|---|\n",
    "| `conn.SOMA` | 0 | `'soma'` |\n",
    "| `conn.BASAL_DENDRITE` | 1 | `'basal_dendrite'` |\n",
    "| `conn.APICAL_DENDRITE` | 2 | `'apical_dendrite'` |\n",
    "| `conn.AXON` | 3 | `'axon'` |\n",
    "\n",
    "A multi-compartment result has `model_type == 'multi_compartment'` and two extra per-edge arrays, `pre_compartments` and `post_compartments`, aligned with `pre_indices` and `post_indices`. The neuron indices still refer to whole neurons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4b8e1a44",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T09:54:27.840793Z",
     "iopub.status.busy": "2026-10-14T09:54:27.840793Z",
     "iopub.status.idle": "2026-10-14T09:54:27.843859Z",
     "shell.execute_reply": "2026-10-14T09:54:27.843859Z"
    }
   },
   "outputs": [],
   "source": [
    "COMPARTMENT_NAMES = {\n",
    "    conn.SOMA: \"soma\",\n",
    "    conn.BASAL_DENDRITE: \"basal\",\n",
    "    conn.APICAL_DENDRITE: \"apical\",\n",
    "    conn.AXON: \"axon\",\n",
    "}\n",
    "\n",
    "\n",
    "def compartment_counts(result):\n",
    "    \"\"\"Number of edges for each (pre compartment, post compartment) pair.\"\"\"\n",
    "    pairs, counts = np.unique(\n",
    "        np.stack([result.pre_compartments, result.post_compartments], axis=1),\n",
    "        axis=0,\n",
    "        return_counts=True,\n",
    "    )\n",
    "    return {\n",
    "        f\"{COMPARTMENT_NAMES[int(pre)]} -> {COMPARTMENT_NAMES[int(post)]}\": int(count)\n",
    "        for (pre, post), count in zip(pairs, counts)\n",
    "    }\n",
    "\n",
    "\n",
    "def summarize_compartments(name, result):\n",
    "    print(name)\n",
    "    print(\"=\" * len(name))\n",
    "    print(f\"model_type: {result.model_type}\")\n",
    "    print(f\"shape: {result.shape}, connections: {result.n_connections}\")\n",
    "    for pair, count in compartment_counts(result).items():\n",
    "        print(f\"  {pair:<16s} {count}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8413197",
   "metadata": {},
   "source": [
    "## 5. `CompartmentSpecific`\n",
    "\n",
    "`CompartmentSpecific` is the general connector. `compartment_mapping` maps a source compartment to one target or a list of targets, using either the integer constants or the names above. `connection_prob` is a single probability for every mapped pair, or a dict keyed by `(source, target)`.\n",
    "\n",
    "Each mapped pair is sampled independently, so the same neuron pair can appear more than once with different target compartments, just as one axon can contact both the soma and a dendrite of the same cell. As with point connectors, self-connections are dropped by default when `pre_size == post_size`; pass `allow_self_connections=True` to keep them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c01f2e4d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T09:54:27.844863Z",
     "iopub.status.busy": "2026-10-14T09:54:27.844863Z",
     "iopub.status.idle": "2026-10-14T09:54:27.879983Z",
     "shell.execute_reply": "2026-10-14T09:54:27.879983Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CompartmentSpecific with per-pair probabilities\n",
      "===============================================\n",
      "model_type: multi_compartment\n",
      "shape: (100, 100), connections: 1803\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  axon -> soma     226\n",
      "  axon -> basal    1077\n",
      "  axon -> apical   500\n",
      "\n",
      "first edges (pre neuron/compartment -> post neuron/compartment):\n",
      "    0/axon   ->  34/soma\n",
      "    0/axon   ->  99/soma\n",
      "    1/axon   ->  26/soma\n",
      "    1/axon   ->  66/soma\n",
      "\n",
      "neuron pairs contacted on more than one compartment: 95\n"
     ]
    }
   ],
   "source": [
    "axon_targets = conn.CompartmentSpecific(\n",
    "    compartment_mapping={conn.AXON: [conn.SOMA, conn.BASAL_DENDRITE, conn.APICAL_DENDRITE]},\n",
    "    connection_prob={\n",
    "        (conn.AXON, conn.SOMA): 0.02,\n",
    "        (conn.AXON, conn.BASAL_DENDRITE): 0.10,\n",
    "        (conn.AXON, conn.APICAL_DENDRITE): 0.05,\n",
    "    },\n",
    "    weight=Normal(mean=1.0 * u.nS, std=0.1 * u.nS),\n",
    "    delay=Constant(1.0 * u.ms),\n",
    "    seed=0,\n",
    ")\n",
    "axon_result = axon_targets(pre_size=100, post_size=100)\n",
    "\n",
    "summarize_compartments(\"CompartmentSpecific with per-pair probabilities\", axon_result)\n",
    "print()\n",
    "print(\"first edges (pre neuron/compartment -> post neuron/compartment):\")\n",
    "for i in range(4):\n",
    "    print(\n",
    "        f\"  {axon_result.pre_indices[i]:>3d}/{COMPARTMENT_NAMES[int(axon_result.pre_compartments[i])]:<6s}\"\n",
    "        f\" -> {axon_result.post_indices[i]:>3d}/{COMPARTMENT_NAMES[int(axon_result.post_compartments[i])]}\"\n",
    "    )\n",
    "\n",
    "pairs = edge_list(axon_result)\n",
    "print()\n",
    "print(f\"neuron pairs contacted on more than one compartment: {len(pairs) - len(set(pairs))}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f4947bd",
   "metadata": {},
   "source": [
    "Names work as well as constants. When `connection_prob` is a dict, every mapped pair needs an entry: a missing pair is dropped with a `UserWarning` rather than silently ignored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a583f0eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T09:54:27.880547Z",
     "iopub.status.busy": "2026-10-14T09:54:27.880547Z",
     "iopub.status.idle": "2026-10-14T09:54:27.885375Z",
     "shell.execute_reply": "2026-10-14T09:54:27.885375Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Named mapping\n",
      "=============\n",
      "model_type: multi_compartment\n",
      "shape: (100, 100), connections: 1073\n",
      "  axon -> basal    519\n",
      "  axon -> apical   554\n",
      "\n",
      "warning: No connection probability provided for mapped pair (3, 1); this pair is dropped (probability defaults to 0.0).\n",
      "target compartments present: ['soma']\n"
     ]
    }
   ],
   "source": [
    "named = conn.CompartmentSpecific(\n",
    "    compartment_mapping={\"axon\": [\"basal_dendrite\", \"apical_dendrite\"]},\n",
    "    connection_prob=0.05,\n",
    "    seed=0,\n",
    ")\n",
    "summarize_compartments(\"Named mapping\", named(pre_size=100, post_size=100))\n",
    "\n",
    "with warnings.catch_warnings(record=True) as caught:\n",
    "    warnings.simplefilter(\"always\")\n",
    "    partial = conn.CompartmentSpecific(\n",
    "        compartment_mapping={conn.AXON: [conn.SOMA, conn.BASAL_DENDRITE]},\n",
    "        connection_prob={(conn.AXON, conn.SOMA): 0.05},  # (AXON, BASAL_DENDRITE) missing\n",
    "        seed=0,\n",
    "    )(pre_size=100, post_size=100)\n",
    "print()\n",
    "print(f\"warning: {caught[0].message}\")\n",
    "print(f\"target compartments present: {sorted(COMPARTMENT_NAMES[int(c)] for c in np.unique(partial.post_compartments))}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "882fbfac",
   "metadata": {},
   "source": [
    "## 6. Anatomical Shortcuts\n",
    "\n",
    "Common mappings have dedicated subclasses. They accept the same keyword arguments as `CompartmentSpecific` (`connection_prob`, `weight`, `delay`, `allow_self_connections`, `seed`, ...).\n",
    "\n",
    "| Connector | Mapping |\n",
    "|---|---|\n",
    "| `AxonToSoma` | axon -> soma |\n",
    "| `AxonToDendrite(target_dendrites=...)` | axon -> basal and apical dendrites (default) |\n",
    "| `DendriteToSoma(source_dendrites=...)` | basal and apical dendrites -> soma |\n",
    "| `SomaToDendrite(target_dendrites=..., prob_per_dendrite=...)` | soma -> dendrites, optionally with one probability per dendrite |\n",
    "| `DendriteToDendrite` | basal/apical -> basal/apical |\n",
    "| `BasalDendriteTargeting`, `ApicalDendriteTargeting` | axon -> one dendrite type |\n",
    "| `ProximalTargeting`, `DistalTargeting` | axon -> basal (proximal) or apical (distal) dendrites |\n",
    "| `AllToAllCompartments` | every compartment -> every compartment |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6e22d77a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T09:54:27.886379Z",
     "iopub.status.busy": "2026-10-14T09:54:27.886379Z",
     "iopub.status.idle": "2026-10-14T09:54:27.890937Z",
     "shell.execute_reply": "2026-10-14T09:54:27.890937Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxonToSoma\n",
      "==========\n",
      "model_type: multi_compartment\n",
      "shape: (80, 80), connections: 321\n",
      "  axon -> soma     321\n",
      "\n",
      "AxonToDendrite (basal only)\n",
      "===========================\n",
      "model_type: multi_compartment\n",
      "shape: (80, 80), connections: 321\n",
      "  axon -> basal    321\n",
      "\n",
      "DendriteToSoma\n",
      "==============\n",
      "model_type: multi_compartment\n",
      "shape: (80, 80), connections: 661\n",
      "  basal -> soma    321\n",
      "  apical -> soma   340\n",
      "\n",
      "SomaToDendrite (per-dendrite prob)\n",
      "==================================\n",
      "model_type: multi_compartment\n",
      "shape: (80, 80), connections: 640\n",
      "  soma -> basal    509\n",
      "  soma -> apical   131\n",
      "\n"
     ]
    }
   ],
   "source": [
    "shortcut_connectors = {\n",
    "    \"AxonToSoma\": conn.AxonToSoma(connection_prob=0.05, seed=0),\n",
    "    \"AxonToDendrite (basal only)\": conn.AxonToDendrite(\n",
    "        target_dendrites=[conn.BASAL_DENDRITE], connection_prob=0.05, seed=0\n",
    "    ),\n",
    "    \"DendriteToSoma\": conn.DendriteToSoma(connection_prob=0.05, seed=0),\n",
    "    \"SomaToDendrite (per-dendrite prob)\": conn.SomaToDendrite(\n",
    "        prob_per_dendrite={conn.BASAL_DENDRITE: 0.08, conn.APICAL_DENDRITE: 0.02}, seed=0\n",
    "    ),\n",
    "}\n",
    "\n",
    "for name, pattern in shortcut_connectors.items():\n",
    "    summarize_compartments(name, pattern(pre_size=80, post_size=80))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7f3fdcc",
   "metadata": {},
   "source": [
    "## 7. Inspecting Compartment Results\n",
    "\n",
    "`weight2dense()` and `delay2matrix()` work on neuron indices only. When one neuron pair is connected on several compartments, those edges share one matrix cell and only one value survives. To keep compartment information, build one matrix per target compartment yourself. `np.add.at` sums correctly when the same cell appears more than once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "66063e76",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T09:54:27.892132Z",
     "iopub.status.busy": "2026-10-14T09:54:27.892132Z",
     "iopub.status.idle": "2026-10-14T09:54:28.100003Z",
     "shell.execute_reply": "2026-10-14T09:54:28.100003Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x460 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum of per-compartment matrices equals total weight: True\n",
      "dense neuron-level matrix keeps 1708 of 1803 edges\n"
     ]
    }
   ],
   "source": [
    "def compartment_matrix(result, post_compartment):\n",
    "    \"\"\"Summed weights per neuron pair onto one target compartment.\"\"\"\n",
    "    keep = result.post_compartments == post_compartment\n",
    "    matrix = np.zeros(result.shape)\n",
    "    weights = np.ones(result.n_connections) if result.weights is None else as_plain(result.weights)\n",
    "    np.add.at(matrix, (result.pre_indices[keep], result.post_indices[keep]), weights[keep])\n",
    "    return matrix\n",
    "\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(16, 4.6))\n",
    "for ax, comp in zip(axes, [conn.SOMA, conn.BASAL_DENDRITE, conn.APICAL_DENDRITE]):\n",
    "    matrix = compartment_matrix(axon_result, comp)\n",
    "    vis.connectivity_matrix(\n",
    "        matrix,\n",
    "        cmap=\"viridis\",\n",
    "        center_zero=False,\n",
    "        show_colorbar=True,\n",
    "        ax=ax,\n",
    "        title=f\"axon -> {COMPARTMENT_NAMES[comp]} ({int((matrix != 0).sum())} pairs)\",\n",
    "    )\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "total_by_compartment = sum(compartment_matrix(axon_result, c).sum() for c in COMPARTMENT_NAMES)\n",
    "print(f\"sum of per-compartment matrices equals total weight: \"\n",
    "      f\"{np.isclose(total_by_compartment, as_plain(axon_result.weights).sum())}\")\n",
    "print(f\"dense neuron-level matrix keeps {int((as_plain(axon_result.weight2dense()) != 0).sum())} \"\n",
    "      f\"of {axon_result.n_connections} edges\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f709cef",
   "metadata": {},
   "source": [
    "## 8. Distance-Dependent Compartment Targeting\n",
    "\n",
    "`MorphologyDistance` makes the connection probability decay with the distance between the neurons' positions (`pre_positions` / `post_positions`, required). `decay_function` is a `braintools.init` distance profile (`GaussianProfile`, `ExponentialProfile`, `LinearProfile`, ...) that carries its own length scale; with no profile, `sigma` sets the width of the default Gaussian. (Passing the strings `'gaussian'`, `'exponential'` or `'linear'` is deprecated since 0.4.0.) `compartment_mapping` chooses the compartments (axon -> basal and apical dendrites by default); each mapped pair is sampled independently with the same distance profile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ce0c7c59",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T09:54:28.100003Z",
     "iopub.status.busy": "2026-10-14T09:54:28.100003Z",
     "iopub.status.idle": "2026-10-14T09:54:28.498832Z",
     "shell.execute_reply": "2026-10-14T09:54:28.498832Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x420 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gaussian     connections:  1933   max distance:  307.9 um   compartments: ['axon -> basal']\n",
      "exponential  connections:  1702   max distance:  607.1 um   compartments: ['axon -> basal']\n",
      "linear       connections:   372   max distance:   75.1 um   compartments: ['axon -> basal']\n"
     ]
    }
   ],
   "source": [
    "rng = np.random.default_rng(0)\n",
    "cell_positions = rng.uniform(0.0, 600.0, (150, 2)) * u.um\n",
    "\n",
    "morph_results = {}\n",
    "decay_profiles = {\n",
    "    \"gaussian\": GaussianProfile(80.0 * u.um),\n",
    "    \"exponential\": ExponentialProfile(80.0 * u.um),\n",
    "    \"linear\": LinearProfile(80.0 * u.um),\n",
    "}\n",
    "for decay, profile in decay_profiles.items():\n",
    "    morph_results[decay] = conn.MorphologyDistance(\n",
    "        decay_function=profile,\n",
    "        compartment_mapping={conn.AXON: conn.BASAL_DENDRITE},\n",
    "        weight=Constant(1.0 * u.nS),\n",
    "        seed=0,\n",
    "    )(\n",
    "        pre_size=150,\n",
    "        post_size=150,\n",
    "        pre_positions=cell_positions,\n",
    "        post_positions=cell_positions,\n",
    "    )\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(16, 4.2))\n",
    "for ax, (decay, result) in zip(axes, morph_results.items()):\n",
    "    vis.distribution_plot(\n",
    "        as_plain(result.get_distances()),\n",
    "        bins=30,\n",
    "        density=False,\n",
    "        alpha=0.7,\n",
    "        colors=[\"tab:purple\"],\n",
    "        edgecolor=\"black\",\n",
    "        ax=ax,\n",
    "        xlabel=\"connection distance (um)\",\n",
    "        ylabel=\"connections\",\n",
    "        title=f\"{decay} (length scale 80 um)\",\n",
    "    )\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "for decay, result in morph_results.items():\n",
    "    print(f\"{decay:<12s} connections: {result.n_connections:5d}   \"\n",
    "          f\"max distance: {as_plain(result.get_distances()).max():6.1f} um   \"\n",
    "          f\"compartments: {list(compartment_counts(result))}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "206e33c7",
   "metadata": {},
   "source": [
    "The linear profile reaches zero at `sigma`, so it never connects neurons further apart than 80 um, while the Gaussian and exponential profiles only become small.\n",
    "\n",
    "## 9. Dendritic Trees and Axonal Projections\n",
    "\n",
    "`DendriticTree` generates axon -> dendrite edges with two independent probabilities: `branch_targeting['proximal']` for basal dendrites and `branch_targeting['distal']` for apical dendrites. The names describe the usual anatomy; the connector does not model positions along a branch.\n",
    "\n",
    "`AxonalProjection` draws axon -> dendrite edges and assigns each one a random compartment from `target_compartments`. The probability of each neuron pair is built from up to two spatial terms, both of which need positions:\n",
    "\n",
    "- `topographic_map(pre_xy, post_xy)` replaces the uniform `connection_prob`. The map receives one row of each position array as plain numbers in the unit of `pre_positions`.\n",
    "- `arborization_sigma` multiplies the probability by `exp(-d**2 / (2 * sigma**2))`, confining each axon's arbor to a local field.\n",
    "\n",
    "Without either term every pair uses `connection_prob`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f7b1b7a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T09:54:28.499836Z",
     "iopub.status.busy": "2026-10-14T09:54:28.499836Z",
     "iopub.status.idle": "2026-10-14T09:54:28.592399Z",
     "shell.execute_reply": "2026-10-14T09:54:28.592399Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DendriticTree\n",
      "=============\n",
      "model_type: multi_compartment\n",
      "shape: (100, 100), connections: 2097\n",
      "  axon -> basal    1543\n",
      "  axon -> apical   554\n",
      "\n",
      "AxonalProjection with a topographic map\n",
      "=======================================\n",
      "model_type: multi_compartment\n",
      "shape: (80, 80), connections: 229\n",
      "  axon -> apical   229\n",
      "mean projection distance: 79.3 um\n",
      "\n",
      "arborization_sigma=None       connections: 3208   mean distance:  296.1 um\n",
      "arborization_sigma=150. um    connections:  916   mean distance:  161.6 um\n",
      "arborization_sigma=50. um     connections:  127   mean distance:   66.7 um\n"
     ]
    }
   ],
   "source": [
    "tree_result = conn.DendriticTree(\n",
    "    branch_targeting={\"proximal\": 0.15, \"distal\": 0.05},\n",
    "    weight=Constant(0.8 * u.nS),\n",
    "    seed=0,\n",
    ")(pre_size=100, post_size=100)\n",
    "summarize_compartments(\"DendriticTree\", tree_result)\n",
    "print()\n",
    "\n",
    "\n",
    "def retinotopic(pre_xy, post_xy, width=60.0):\n",
    "    \"\"\"Connection probability peaking where source and target share a position.\"\"\"\n",
    "    d2 = np.sum((pre_xy - post_xy) ** 2)\n",
    "    return 0.6 * np.exp(-d2 / (2 * width ** 2))\n",
    "\n",
    "\n",
    "source_positions = rng.uniform(0.0, 600.0, (80, 2)) * u.um\n",
    "target_positions = rng.uniform(0.0, 600.0, (80, 2)) * u.um\n",
    "\n",
    "projection_result = conn.AxonalProjection(\n",
    "    topographic_map=retinotopic,\n",
    "    target_compartments=[conn.APICAL_DENDRITE],\n",
    "    weight=Constant(1.0 * u.nS),\n",
    "    seed=0,\n",
    ")(\n",
    "    pre_size=80,\n",
    "    post_size=80,\n",
    "    pre_positions=source_positions,\n",
    "    post_positions=target_positions,\n",
    ")\n",
    "summarize_compartments(\"AxonalProjection with a topographic map\", projection_result)\n",
    "print(f\"mean projection distance: {as_plain(projection_result.get_distances()).mean():.1f} um\")\n",
    "print()\n",
    "\n",
    "for sigma in [None, 150.0 * u.um, 50.0 * u.um]:\n",
    "    arbor_result = conn.AxonalProjection(\n",
    "        connection_prob=0.5,\n",
    "        arborization_sigma=sigma,\n",
    "        seed=0,\n",
    "    )(\n",
    "        pre_size=80,\n",
    "        post_size=80,\n",
    "        pre_positions=source_positions,\n",
    "        post_positions=target_positions,\n",
    "    )\n",
    "    print(f\"arborization_sigma={str(sigma):<10s} connections: {arbor_result.n_connections:4d}   \"\n",
    "          f\"mean distance: {as_plain(arbor_result.get_distances()).mean():6.1f} um\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "402e2dc7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T09:54:28.593403Z",
     "iopub.status.busy": "2026-10-14T09:54:28.593403Z",
     "iopub.status.idle": "2026-10-14T09:54:28.736165Z",
     "shell.execute_reply": "2026-10-14T09:54:28.736165Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 700x650 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "src_xy = as_plain(source_positions)\n",
    "dst_xy = as_plain(target_positions)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6.5))\n",
    "ax.scatter(dst_xy[:, 0], dst_xy[:, 1], s=25, c=\"lightgray\", label=\"target cells\", zorder=1)\n",
    "ax.scatter(src_xy[:, 0], src_xy[:, 1], s=25, c=\"tab:red\", label=\"source cells\", zorder=2)\n",
    "for pre, post in edge_list(projection_result):\n",
    "    ax.plot([src_xy[pre, 0], dst_xy[post, 0]], [src_xy[pre, 1], dst_xy[post, 1]],\n",
    "            color=\"tab:blue\", alpha=0.25, linewidth=0.7, zorder=0)\n",
    "ax.set_xlabel(\"x (um)\")\n",
    "ax.set_ylabel(\"y (um)\")\n",
    "ax.set_title(\"Topographic axon -> apical dendrite projection\")\n",
    "ax.set_aspect(\"equal\")\n",
    "ax.legend(loc=\"upper right\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bb54e63",
   "metadata": {},
   "source": [
    "## 10. Synapse Placement\n",
    "\n",
    "`SynapticPlacement` separates *whether* two neurons connect from *where* the synapse lands. Each neuron pair forms one synapse with probability `connection_prob`, and each synapse is then placed on a compartment drawn in proportion to `compartment_preferences`. Placement rules are just different preference dicts: proximal-only input targets the basal dendrites, distal input the apical dendrites, and perisomatic input the soma.\n",
    "\n",
    "The three projections below share `connection_prob` and the seed, so they connect exactly the same neuron pairs; only the location of the synapses differs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "09eb0dda",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T09:54:28.737170Z",
     "iopub.status.busy": "2026-10-14T09:54:28.737170Z",
     "iopub.status.idle": "2026-10-14T09:54:28.798124Z",
     "shell.execute_reply": "2026-10-14T09:54:28.798124Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "proximal  synapses:  2321   soma=0.00  basal=1.00  apical=0.00\n",
      "distal    synapses:  2321   soma=0.00  basal=0.19  apical=0.81\n",
      "mixed     synapses:  2321   soma=0.21  basal=0.50  apical=0.29\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "placement_rules = {\n",
    "    \"proximal\": {conn.BASAL_DENDRITE: 1.0},\n",
    "    \"distal\": {conn.APICAL_DENDRITE: 0.8, conn.BASAL_DENDRITE: 0.2},\n",
    "    \"mixed\": {conn.SOMA: 0.2, conn.BASAL_DENDRITE: 0.5, conn.APICAL_DENDRITE: 0.3},\n",
    "}\n",
    "\n",
    "placement_fractions = {}\n",
    "for rule, preferences in placement_rules.items():\n",
    "    placed = conn.SynapticPlacement(\n",
    "        connection_prob=0.1,\n",
    "        compartment_preferences=preferences,\n",
    "        seed=0,\n",
    "    )(pre_size=150, post_size=150)\n",
    "    counts = np.bincount(placed.post_compartments, minlength=4)\n",
    "    placement_fractions[rule] = counts / counts.sum()\n",
    "    print(f\"{rule:<9s} synapses: {placed.n_connections:5d}   \" + \"  \".join(\n",
    "        f\"{COMPARTMENT_NAMES[c]}={placement_fractions[rule][c]:.2f}\" for c in (conn.SOMA, conn.BASAL_DENDRITE, conn.APICAL_DENDRITE)\n",
    "    ))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "targets = [conn.SOMA, conn.BASAL_DENDRITE, conn.APICAL_DENDRITE]\n",
    "x = np.arange(len(targets))\n",
    "for offset, (rule, fractions) in zip([-0.27, 0.0, 0.27], placement_fractions.items()):\n",
    "    ax.bar(x + offset, fractions[targets], width=0.27, label=rule, edgecolor=\"black\")\n",
    "ax.set_xticks(x, [COMPARTMENT_NAMES[c] for c in targets])\n",
    "ax.set_ylabel(\"fraction of synapses\")\n",
    "ax.set_title(\"SynapticPlacement: same connection rate, different placement\")\n",
    "ax.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "521fa4b0",
   "metadata": {},
   "source": [
    "## 11. Axonal Branching and Arborization\n",
    "\n",
    "Two connectors describe *how an axon spreads* rather than a per-pair probability:\n",
    "\n",
    "- `AxonalBranching` gives each axon a Poisson-distributed number of branches (mean `branches_per_axon`), each ending on a different target neuron. Without `branch_spread` the targets are chosen uniformly; with it (positions required) targets are chosen in proportion to `exp(-d**2 / (2 * branch_spread**2))`, so branches stay near the source.\n",
    "- `AxonalArborization` connects every pair within `arborization_radius` with probability `density`, and nothing beyond it.\n",
    "\n",
    "Both place synapses on basal or apical dendrites at random. As with the other spatial connectors, a plain-number length is interpreted in the unit of the positions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fb7dd95f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T09:54:28.799128Z",
     "iopub.status.busy": "2026-10-14T09:54:28.799128Z",
     "iopub.status.idle": "2026-10-14T09:54:29.008993Z",
     "shell.execute_reply": "2026-10-14T09:54:29.008993Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxonalBranching, uniform targets       edges:  1157   mean out-degree:  5.79   mean distance:  513.0 um\n",
      "AxonalBranching, branch_spread=60 um   edges:  1177   mean out-degree:  5.88   mean distance:   91.2 um\n",
      "AxonalArborization, radius=120 um      edges:   253   mean out-degree:  1.26   mean distance:   76.0 um\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x420 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "branch_positions = rng.uniform(0.0, 1000.0, (200, 2)) * u.um\n",
    "branch_call = dict(pre_size=200, post_size=200, pre_positions=branch_positions, post_positions=branch_positions)\n",
    "\n",
    "axon_patterns = {\n",
    "    \"AxonalBranching, uniform targets\": conn.AxonalBranching(branches_per_axon=6, seed=0),\n",
    "    \"AxonalBranching, branch_spread=60 um\": conn.AxonalBranching(\n",
    "        branches_per_axon=6, branch_spread=60.0 * u.um, seed=0\n",
    "    ),\n",
    "    \"AxonalArborization, radius=120 um\": conn.AxonalArborization(\n",
    "        arborization_radius=120.0 * u.um, density=0.15, seed=0\n",
    "    ),\n",
    "}\n",
    "axon_pattern_results = {name: pattern(**branch_call) for name, pattern in axon_patterns.items()}\n",
    "\n",
    "for name, result in axon_pattern_results.items():\n",
    "    out_degree = np.bincount(result.pre_indices, minlength=200)\n",
    "    print(f\"{name:<38s} edges: {result.n_connections:5d}   mean out-degree: {out_degree.mean():5.2f}   \"\n",
    "          f\"mean distance: {as_plain(result.get_distances()).mean():6.1f} um\")\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(16, 4.2))\n",
    "for ax, (name, result) in zip(axes, axon_pattern_results.items()):\n",
    "    vis.distribution_plot(\n",
    "        as_plain(result.get_distances()),\n",
    "        bins=30,\n",
    "        density=False,\n",
    "        alpha=0.7,\n",
    "        colors=[\"tab:green\"],\n",
    "        edgecolor=\"black\",\n",
    "        ax=ax,\n",
    "        xlabel=\"connection distance (um)\",\n",
    "        ylabel=\"connections\",\n",
    "        title=name,\n",
    "    )\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bbd9738",
   "metadata": {},
   "source": [
    "## 12. Custom Compartment Rules\n",
    "\n",
    "`CustomCompartment` wraps a function that receives the call arguments as keywords (`pre_size`, `post_size`, `pre_positions`, `post_positions`; positions are `None` when not given) and returns `(pre_indices, post_indices, pre_compartments, post_compartments)`. Optionally append `weights`, or `weights` and `delays`. Plain-number delays are interpreted as milliseconds.\n",
    "\n",
    "The rule below gives every neuron one input on its soma from its neighbour and one input on its apical dendrite from a neuron half-way around the population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3a951832",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T09:54:29.009997Z",
     "iopub.status.busy": "2026-10-14T09:54:29.009997Z",
     "iopub.status.idle": "2026-10-14T09:54:29.014059Z",
     "shell.execute_reply": "2026-10-14T09:54:29.014059Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CustomCompartment\n",
      "=================\n",
      "model_type: multi_compartment\n",
      "shape: (12, 12), connections: 24\n",
      "  axon -> soma     12\n",
      "  axon -> apical   12\n",
      "weights: [0.5, 2.0] nS\n",
      "delays:  [1.0, 3.0] ms\n"
     ]
    }
   ],
   "source": [
    "def neighbour_and_opposite(pre_size, post_size, **kwargs):\n",
    "    post = np.arange(post_size)\n",
    "    pre_neighbour = (post - 1) % pre_size\n",
    "    pre_opposite = (post + pre_size // 2) % pre_size\n",
    "\n",
    "    pre = np.concatenate([pre_neighbour, pre_opposite])\n",
    "    post_all = np.concatenate([post, post])\n",
    "    pre_comp = np.full(pre.size, conn.AXON)\n",
    "    post_comp = np.concatenate([\n",
    "        np.full(post_size, conn.SOMA),\n",
    "        np.full(post_size, conn.APICAL_DENDRITE),\n",
    "    ])\n",
    "    weights = np.concatenate([np.full(post_size, 2.0), np.full(post_size, 0.5)]) * u.nS\n",
    "    delays = np.concatenate([np.full(post_size, 1.0), np.full(post_size, 3.0)])  # ms\n",
    "    return pre, post_all, pre_comp, post_comp, weights, delays\n",
    "\n",
    "\n",
    "custom_result = conn.CustomCompartment(neighbour_and_opposite)(pre_size=12, post_size=12)\n",
    "summarize_compartments(\"CustomCompartment\", custom_result)\n",
    "print(f\"weights: {unique_values(custom_result.weights)} {u.get_unit(custom_result.weights)}\")\n",
    "print(f\"delays:  {unique_values(custom_result.delays)} {u.get_unit(custom_result.delays)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b86f2f2c",
   "metadata": {},
   "source": [
    "## 13. Composing Compartment Connectors\n",
    "\n",
    "The operators from Section 3 also work on multi-compartment connectors, and the compartment arrays are carried through. Edges are compared by neuron pair *and* compartment pair, so an axon -> soma edge and an axon -> dendrite edge between the same two neurons are different edges.\n",
    "\n",
    "A common use is to model one presynaptic population that targets different compartments with different strengths: strong, fast perisomatic synapses plus weaker, slower dendritic ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f5d6715f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-14T09:54:29.015063Z",
     "iopub.status.busy": "2026-10-14T09:54:29.015063Z",
     "iopub.status.idle": "2026-10-14T09:54:29.079861Z",
     "shell.execute_reply": "2026-10-14T09:54:29.079861Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxonToSoma + AxonToDendrite\n",
      "===========================\n",
      "model_type: multi_compartment\n",
      "shape: (100, 100), connections: 2562\n",
      "  axon -> soma     562\n",
      "  axon -> basal    997\n",
      "  axon -> apical   1003\n",
      "\n",
      "target    mean weight  mean delay\n",
      "soma          3.00 nS     0.80 ms\n",
      "basal         0.70 nS     2.00 ms\n",
      "apical        0.70 nS     2.00 ms\n",
      "\n",
      "(perisomatic + dendritic) - perisomatic has 2000 edges; dendritic alone has 2000\n"
     ]
    }
   ],
   "source": [
    "perisomatic = conn.AxonToSoma(\n",
    "    connection_prob=0.05,\n",
    "    weight=Constant(3.0 * u.nS),\n",
    "    delay=Constant(0.8 * u.ms),\n",
    "    seed=10,\n",
    ")\n",
    "dendritic = conn.AxonToDendrite(\n",
    "    connection_prob=0.10,\n",
    "    weight=Constant(0.7 * u.nS),\n",
    "    delay=Constant(2.0 * u.ms),\n",
    "    seed=11,\n",
    ")\n",
    "\n",
    "combined = perisomatic + dendritic\n",
    "combined_result = combined(pre_size=100, post_size=100)\n",
    "summarize_compartments(\"AxonToSoma + AxonToDendrite\", combined_result)\n",
    "\n",
    "print()\n",
    "print(f\"{'target':<8s} {'mean weight':>12s} {'mean delay':>11s}\")\n",
    "for comp in [conn.SOMA, conn.BASAL_DENDRITE, conn.APICAL_DENDRITE]:\n",
    "    keep = combined_result.post_compartments == comp\n",
    "    print(f\"{COMPARTMENT_NAMES[comp]:<8s} {as_plain(combined_result.weights)[keep].mean():>9.2f} nS \"\n",
    "          f\"{as_plain(combined_result.delays)[keep].mean():>8.2f} ms\")\n",
    "\n",
    "# Removing the perisomatic edges again leaves exactly the dendritic part.\n",
    "dendritic_only = (combined - perisomatic)(pre_size=100, post_size=100)\n",
    "print()\n",
    "print(f\"(perisomatic + dendritic) - perisomatic has {dendritic_only.n_connections} edges; \"\n",
    "      f\"dendritic alone has {dendritic(pre_size=100, post_size=100).n_connections}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e15d6478",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 14. Key Takeaways\n",
    "\n",
    "- `a + b`, `a * b`, and `a - b` combine connectors by union, intersection, and difference of their edges. The operands must describe the same pre/post populations.\n",
    "- Union keeps the left operand's values on shared edges and gives weight/delay `0` to edges from an operand without them. Intersection multiplies weights and averages delays, and an operand without weights or delays is neutral, so `pattern * unweighted_mask` is the idiomatic way to restrict a projection.\n",
    "- `pattern * factor`, `weight_scale()`, and `delay_scale()` scale values without touching the base connector; the scalar goes on the right.\n",
    "- Multi-compartment connectors return `model_type == 'multi_compartment'` results with per-edge `pre_compartments` and `post_compartments` using `SOMA`, `BASAL_DENDRITE`, `APICAL_DENDRITE`, and `AXON`.\n",
    "- Use `CompartmentSpecific` for arbitrary mappings, the anatomical shortcuts for common ones, `MorphologyDistance`, `AxonalProjection` (`topographic_map`, `arborization_sigma`), `AxonalBranching` (`branch_spread`), and `AxonalArborization` for spatial rules, `SynapticPlacement` to choose where synapses land independently of how many there are, and `CustomCompartment` for anything else.\n",
    "- `weight2dense()` is neuron-level; build per-compartment matrices when the same neuron pair is connected on several compartments.\n",
    "\n",
    "For the full list of multi-compartment connectors and their parameters, see the {doc}`API reference <../apis/conn>`."
   ]
  }
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