{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "afd2faf1-34c1-4081-bcfd-d17c8c9f43a3",
   "metadata": {},
   "source": [
    "# Electrode selecting\n",
    "\n",
    "This notebook demonstrates a simple version of an electrode-selecting method defined in `neurocarto.probe_npx.select_weaker:electrode_select`, which is a [Neuropixels probe](https://www.neuropixels.org/) electrode-selecting method with a weaker local density rule."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26224819-f0e8-454a-be28-f225362d0793",
   "metadata": {},
   "source": [
    "## Terms\n",
    "\n",
    "* `ChannelMap`: Neuropixels probe channel arrangement configuration (channel map).\n",
    "* `ProbeDesp`: an interface for communicating between the chanelmap `ChannelMap` and the GUI.\n",
    "* `ElectrodeDesp`: an interface for communicating between the electrode and the GUI, which contains category value.\n",
    "* **category**: indicates how an electrode is selected. In here, we have density-based categories.\n",
    "* **blueprint**: all electrode categories value (a collection). It decides how a channel map outcome.\n",
    "* **hardware restriction rule**: a predicate representing whether electrodes can be selected together. Defined in `NpxProbeDesp.probe_rule`\n",
    "* **local density rule**: a local density rule indicates how electrodes inside an area are selected to form a particular arrangement pattern by a given category.\n",
    "* **probability**: each electrode has a probability value depending on its category value. It decides the probability of being selected."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ad826aa-8730-4e94-853e-fe1c0c84f7a3",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f80a40c0-5ba9-4a23-86e6-a2896aafd1a0",
   "metadata": {},
   "source": [
    "predefined symbols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a41151c-29ff-438b-818a-c6d8cb61d5cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "M = ChannelMap\n",
    "K = tuple[int, int, int] # (shank, column, row)\n",
    "\n",
    "class E(ElectrodeDesp):\n",
    "    electrode: K\n",
    "    channel: int\n",
    "    category: str\n",
    "    prob: float # probability, but work as a priority here"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30c346d9-09d3-4cea-a582-fb6885dfce8f",
   "metadata": {},
   "source": [
    "### Hardware restriction rule\n",
    "\n",
    "The hardware restriction rule of the Neuropixels probes is that the channel identifies used in the channel map are unique, which means electrodes used in the channel map can not share the same channel identifier.\n",
    "\n",
    "The mapping between the electrode identification, and the channel identification is defined in `champ.probe_npx.npx:e2cb` and related functions. Users do not need to worry about how to use it, because `ElectrodeDesp` carried the channel information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d993414-0da1-4c18-bf61-176c55410e1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# class NpxProbeDesp\n",
    "def probe_rule(self, chmap: M | None, e1: E, e2: E) -> bool:\n",
    "    return e1.channel != e2.channel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "362cba98",
   "metadata": {},
   "source": [
    "Note: Due to performance issue, we usually inline above function, to skip method call overhead."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be10515f-0b29-46ee-b730-18456a440d2b",
   "metadata": {},
   "source": [
    "### Category\n",
    "\n",
    "Each electrode has an category value that indicates how electrodes in an area are selected. The local density rule uses it.\n",
    "\n",
    "We define basic categories in `ProbeDesp` and Neuropixels-specific categories in `NpxProbeDesp`:\n",
    "\n",
    "* `CATE_UNSET`: initial category value. It works like **low-priority** with lower priority.\n",
    "* `CATE_SET`: **pre-select** electrode are selected first.\n",
    "* `CATE_EXCLUDED`: **excluded** electrodes are never selected. \n",
    "* `CATE_LOW`: **low-priority** electrodes are selected last. No arrangement pattern. Pick randomly.\n",
    "* `CATE_FULL`: (Neuropixels-specific) **full-density** electrodes.\n",
    "* `CATE_HALF`: (Neuropixels-specific) **half-density** electrodes.\n",
    "* `CATE_QUARTER`: (Neuropixels-specific) **quarter-density** electrodes.\n",
    "\n",
    "We use the bolded string to represent each category's value to demonstrate the purpose."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d6c0bf7-38de-4217-b829-7c2a53dcc617",
   "metadata": {},
   "source": [
    "## Demostration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7116e39d",
   "metadata": {},
   "source": [
    "### Preparing\n",
    "\n",
    "If you want to provide your custom Neuropixels electrode selecting method, all of them should follow a particular function signature, which is defined in `neurocarto.probe_npx.select.ElectrodeSelector` protocol.\n",
    "\n",
    "Create a new file, and name it `my_selection.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c12d0d4-24b9-49e1-8d88-7980d36cc2ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "from neurocarto.probe_npx import *\n",
    "\n",
    "def electrode_select(desp: NpxProbeDesp, chmap: M, blueprint: list[E], **kwargs) -> ChannelMap:\n",
    "    \"\"\"\n",
    "    Selecting electrodes based on the electrode blueprint.\n",
    "\n",
    "    :param desp:\n",
    "    :param chmap: channelmap type. It is a reference.\n",
    "    :param blueprint: channelmap blueprint\n",
    "    :param kwargs: other parameters.\n",
    "    :return: generated channelmap\n",
    "    \"\"\"\n",
    "    step_1(...) # work as inlined function here\n",
    "    step_2(...) # work as inlined function here\n",
    "    step_loop(...) # work as inlined function here\n",
    "    return build_channelmap()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d08504f8",
   "metadata": {},
   "source": [
    "Check the function `my_selection` whether can be loaded by the function `load_selector()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f12ef15f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from neurocarto.probe_npx.select import load_select\n",
    "\n",
    "load_select('module.my_selection:my_selection') # if my_selection.py put under module 'module'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70b478d4",
   "metadata": {},
   "source": [
    "then you can use it in the command-line.\n",
    "\n",
    "    neurocarto --selector=module.my_selection:my_selection\n",
    "\n",
    "Currently providing two implementations:\n",
    "\n",
    "* `default` (`neurocarto.probe_npx.select_default:electrode_select`): default selection method.\n",
    "* `weaker` (`neurocarto.probe_npx.select_weaker:electrode_select`): same as default, but with a weaker local density rule.\n",
    "\n",
    "You can check the source code for actual runnable code."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eee7cf12-a102-48ed-a129-dc6898625ba4",
   "metadata": {},
   "source": [
    "### implement Idea\n",
    "\n",
    "* For all electrodes, we initialize their probability value depending on their category value.\n",
    "* We define a selected set `[e for e in blueprint if e.prob == 1]`, an excluded set `[e for e in blueprint if e.prob == 0]`, and a candidate set.\n",
    "* In each updating loop: we do\n",
    "  * Select one electrode from the candidate set, and set its probability to 1, and set the probability to 0 for corresponding electrodes.\n",
    "  * Update the neighbor candidate electrodes' probability by reducing their probability.\n",
    "* Use the selected set to build the final channel map."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d501044-f203-4c78-a521-f1e22d571f8e",
   "metadata": {},
   "source": [
    "### Step 1: Initialization\n",
    "\n",
    "Put all available electrodes into the candidate set `cand`. Initialize a probability value for all electrodes depending on their category value.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67741d3b-1da1-4274-8af9-2e0e4dc4dc80",
   "metadata": {},
   "outputs": [],
   "source": [
    "def step_1(desp: ProbeDesp[M, E], chmap: M, blueprint: list[E]):\n",
    "    # initialize a cancidate set\n",
    "    cand: dict[K, E] = {\n",
    "        it.electrode: E().copy(it, prob=0) for it in desp.all_electrodes(chmap)\n",
    "    }\n",
    "    # pass category value\n",
    "    for e in blueprint:\n",
    "        cand[e.electrode].category = e.category\n",
    "    # initialize probability value\n",
    "    for e in cand.values():\n",
    "        e.prob = category_mapping_probability(e.category)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16713c8a-d571-416b-9ba5-455ff9cc7eee",
   "metadata": {},
   "source": [
    "Here `category_mapping_probability` defined as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ba1199e1-ffe8-465c-9593-4ef74bf58399",
   "metadata": {},
   "outputs": [],
   "source": [
    "def category_mapping_probability(category: str) -> float:\n",
    "    match category:\n",
    "        case 'pre-selected':\n",
    "            return 1.0\n",
    "        case 'full-density':\n",
    "            return 0.9\n",
    "        case 'half-density':\n",
    "            return 0.8\n",
    "        case 'quarter-density':\n",
    "            return 0.7\n",
    "        case 'remainder':\n",
    "            return 0.6\n",
    "        case 'excluded':\n",
    "            return 0\n",
    "        case _:\n",
    "            return 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1842a523-bacd-46b5-aaf2-32c813cefdca",
   "metadata": {},
   "source": [
    "### Step 2: Pre-Selection\n",
    "\n",
    "Make the priority value of `pre-selected` electrodes to 1, and `excluded` electrodes to 0, as well as all electrodes that against the hardware restriction rule.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e30dfd3b-bd16-4982-9eaf-d269bcb603c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def step_2(desp: ProbeDesp[M, E], cand: dict[K, E]):\n",
    "    for e in cand.values():\n",
    "        if e.category == 'pre-selected':\n",
    "            _add(desp, cand, e)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4e65e7e-7921-4b54-bbd0-ca0988b9c659",
   "metadata": {},
   "source": [
    "where a helper function `_add` defined as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a062830-85f9-409d-997f-b6cf1ad0de16",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _add(desp: ProbeDesp[M, E], cand: dict[K, E], e: E):\n",
    "    e.prob = 1.0\n",
    "\n",
    "    for k in cand.values():\n",
    "        if e.electrode != k.electrode and not desp.probe_rule(None, e, k):\n",
    "            k.prob = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e935794f-f04c-4b58-b1a6-52a15f5ce794",
   "metadata": {},
   "source": [
    "### Step Loop: \n",
    "\n",
    "Start from this step, we enter a selecting loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea190faa-ded2-4fad-8e43-193a18b7f1df",
   "metadata": {},
   "outputs": [],
   "source": [
    "def step_loop(desp: ProbeDesp[M, E], chmap:M, cand: dict[K, E]):\n",
    "    while selected_electrode(cand) < chmap.n_channels:\n",
    "        if (e := pick_electrode(cand)) is not None:\n",
    "            # step 4\n",
    "            select_electrode(desp, cand, e)\n",
    "            # step 5\n",
    "            update_prob(desp, cand, e)\n",
    "        else:\n",
    "            break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a63528e-12e7-47ed-9164-b89f90b597e4",
   "metadata": {},
   "source": [
    "where `selected_electrode` used as a terminated condiction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed8f71fb-2245-42fb-a495-0c64fec33e50",
   "metadata": {},
   "outputs": [],
   "source": [
    "def selected_electrode(cond: dict[K, E]) -> int:\n",
    "    # selected electrode is defined as it.prob == 1\n",
    "    return len([it for it in cond.values() if it.prob == 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b55804d2-a405-4f4e-b917-8dcf121d8123",
   "metadata": {},
   "source": [
    "### Step 3: Select electrode\n",
    "\n",
    "Randomly select an electrode with the highest priority value from the candidate set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f66e7a0d-6bea-489f-9dd9-a852aecfdded",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "def pick_electrode(cand: dict[K, E]) -> E | None:\n",
    "    hp = max([it.prob for it in cand.values() if it.prob < 1])\n",
    "\n",
    "    if hp == 0: # all un-selected electrode are excluded\n",
    "        return None\n",
    "\n",
    "    sub = [it for it in cand.values() if hp <= it.prob < 1]\n",
    "    assert len(sub) > 0\n",
    "    return random.choice(sub)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1056c9cd-fa2d-4c45-9776-c7fbb8ee55fe",
   "metadata": {},
   "source": [
    "### step 4: Add to channel map & Apply Hardware Rule\n",
    "\n",
    "Set the priority of the selected electrode to 1, and set the priority of electrodes against the hardware restriction rule with the selected electrode to 0.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08bc9505-d375-41dd-a93c-1a100565bd1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_electrode(desp: ProbeDesp[M, E], cand: dict[K, E], e: E):\n",
    "    _add(desp, cand, e)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aac673d-5168-4f70-8c8c-e78c35f5910b",
   "metadata": {},
   "source": [
    "### Step 5: Apply local density rule\n",
    "\n",
    "Reduce the priority value of electrodes surrounded with the selected electrode with respect to its category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f607452a-5509-40d6-b0ee-169c9d3b5e14",
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_prob(desp: NpxProbeDesp, chmap: M, cand: dict[K, E], e: E):\n",
    "    for t in surr(cand, e):\n",
    "        if t is not None and t.prob < 1:\n",
    "            t.prob /= 2.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87f3bee4-0e3b-4a5a-8bb0-49cb60b2b20a",
   "metadata": {},
   "source": [
    "where"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7445830-c01e-427a-860a-5eac65598aca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def surr(cand: dict[K, E], e: E) -> Iterator[E | None]:\n",
    "    match e.category:\n",
    "        case 'half-density':\n",
    "            yield _get(cand, e, c=1,  r=0) # get an electrode move 1 column and 0 rows from the electrode e\n",
    "            yield _get(cand, e, c=-1, r=0)\n",
    "            yield _get(cand, e, c=0,  r=1)\n",
    "            yield _get(cand, e, c=0,  r=-1)\n",
    "        case 'quarter-density':\n",
    "            # only works for 2-column probe\n",
    "            yield _get(cand, e, c=-1, r=0)\n",
    "            yield _get(cand, e, c=1,  r=0)\n",
    "            yield _get(cand, e, c=-1, r=-1)\n",
    "            yield _get(cand, e, c=0,  r=-1)\n",
    "            yield _get(cand, e, c=1,  r=-1)\n",
    "            yield _get(cand, e, c=-1, r=1)\n",
    "            yield _get(cand, e, c=0,  r=1)\n",
    "            yield _get(cand, e, c=1,  r=1)\n",
    "            yield _get(cand, e, c=0,  r=2)\n",
    "            yield _get(cand, e, c=0,  r=-2)\n",
    "        case _:\n",
    "            return\n",
    "\n",
    "def _get(cand: dict[K, E], e: E, c: int, r: int) -> E | None:\n",
    "    eh, ec, er = e.electrode\n",
    "    return cand.get((eh, ec + c, er + r), None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09888da7",
   "metadata": {},
   "source": [
    "### Build the channelmap\n",
    "\n",
    "build a channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "624daa93",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_channelmap(desp: NpxProbeDesp, chmap: ChannelMap, cand: dict[K, E]) -> ChannelMap:\n",
    "    ret = desp.new_channelmap(chmap)\n",
    "\n",
    "    for e in cand.values():\n",
    "        if e.prob == 1:\n",
    "            desp.add_electrode(ret, e, overwrite=True)\n",
    "\n",
    "    return ret"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c2c6bea-8c7f-45e3-9c2f-279e7e9a4fc9",
   "metadata": {},
   "source": [
    "### control functions\n",
    "\n",
    "Here we use information entropy as a control function to measure each update iteration.\n",
    "Although the selection method will be terminated without the control function, we can still demonstrate how a control function works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af05c07f-4c5e-478e-a61d-6ba58708ef19",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "def information_entropy(cand: dict[K, E]) -> float:\n",
    "    return -sum([it.prob * math.log2(it.prob) for it in cand.values() if it.prob > 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62e49be7-d43f-4452-aa93-73fa0f59815d",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a9853693-bf13-423c-a359-2915df6990f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from neurocarto.probe_npx import *\n",
    "from neurocarto.probe_npx import plot\n",
    "from neurocarto.probe_npx.desp import NpxProbeDesp\n",
    "\n",
    "rc = matplotlib.rc_params_from_file('channelmap.matplotlibrc', fail_on_error=True, use_default_template=True)\n",
    "\n",
    "D = NpxProbeDesp()\n",
    "C = ChannelMap.from_imro('Fig3_example.imro')\n",
    "Q = D.load_blueprint('Fig3_example.blueprint.npy', C)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "29d794ff-fe56-4993-a36f-004d8e60522b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# blueprint\n",
    "\n",
    "# TODO label color\n",
    "with plt.rc_context(rc):\n",
    "    fg, ax = plt.subplots()\n",
    "\n",
    "    height = 5\n",
    "    plot.plot_category_area(ax, C.probe_type, Q, height=height, shank_width_scale=2)\n",
    "    plot.plot_probe_shape(ax, C.probe_type, height=height, color='gray', label_axis=True, shank_width_scale=2)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "135b1778-57ef-4ef5-b7ea-8b4d5b6381ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from neurocarto.probe_npx.select_weaker_debug import electrode_select\n",
    "# select_weaker_debug inject debugging function into origin selecting method.\n",
    "# so we can visualize the process\n",
    "\n",
    "R = electrode_select(D, C, Q)\n",
    "# shows measured value during the selection process\n",
    "plt.show() # TODO label units\n",
    "# N: number of selected electrodes / total channels\n",
    "# Q: selected electrode's initial priority value\n",
    "# P: selected electrode's actual priority value\n",
    "# H: normalized information entropy value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "53485d1f-60b3-48e7-8582-b7cc5d4025f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.rc_context(rc):\n",
    "    fg, ax = plt.subplots()\n",
    "    plot.plot_channelmap_block(ax, R, height=6, color='k', shank_width_scale=2)\n",
    "    plot.plot_probe_shape(ax, R, height=6, color='k', label_axis=True, shank_width_scale=2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12fb96e4-8a59-4849-95d4-7e6116db0b02",
   "metadata": {},
   "source": [
    "## Compare to the default selection\n",
    "\n",
    "1. The default selection uses a *stronger* local density rule that updates the probability value to 0 for neighbor electrodes of the selected electrode equivalently. The weaker rule just decreases the value.\n",
    "2. Those electrodes will never be selected in the future. Once it removes too many electrodes, the selections may produce an incomplete channel map. \n",
    "3. The weaker rule doesn't remove them from the candidate set, so they can be selected, which means the weaker rule always produces a complete channelmap (100% completed rate).\n",
    "4. The actual electrode density in the final channel map produced by the default selection method could only be lower than the category setting. For that produced by the weaker rule, except the conflicts area, the actual electrode density may be higher than the category setting."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}