{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "77b3226f",
   "metadata": {},
   "source": [
    "# Example of using library code\n",
    "\n",
    "So far, we only provide channelmap editing for the Neuropixels probe family, and only the 4-shank Neuropixels probe is tested, so the following example codes use the 4-shank Neuropixels probe as the targeting probe."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3f35f70",
   "metadata": {},
   "source": [
    "## Basic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52efba29",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from neurocarto.probe_npx import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17d2228a",
   "metadata": {},
   "source": [
    "### Create an empty channelmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65fa672d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ChannelMap[4,2,640,384,0]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = ChannelMap(24)\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c547cd9",
   "metadata": {},
   "source": [
    "The above output printed the information of channelmap `s`, in the format `ChannelMap[S,C,R,H,E]`, where `S` is the number of total shanks, `C` is the number of total columns in a shank, `R` is the number of total rows in a shank, `H` is the number of total channels, and `E` is the number of selected electrodes.\n",
    "\n",
    "For a valid channelmap, `H` and `E` should be equal."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54e08ea1",
   "metadata": {},
   "source": [
    "### Load/Save a channelmap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d20f22b2",
   "metadata": {},
   "source": [
    "`*.imro` (**im**ec **r**ead-**o**ut file) is the format of the Neuropixels channelmap used by \n",
    "[SpikeGLX](https://billkarsh.github.io/SpikeGLX/). \n",
    "[detail](https://billkarsh.github.io/SpikeGLX/help/imroTables/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ef54597",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load channelmap from file\n",
    "s = ChannelMap.from_imro('channelmap.imro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f9e1dcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save channelmap to file\n",
    "s.save_imro('channelmap.imro')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d15d8a2-5adc-48d7-ba06-f2d906601663",
   "metadata": {},
   "source": [
    "Note that if `s` is incompleted, which means the number of selected electrodes `E` is not equals to the total channel number `H`, `save_imro` will raise an error."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f6914d6",
   "metadata": {},
   "source": [
    "We support read channelmap from different file format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c49592c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = ChannelMap.from_meta('channelmap.meta') # from meta file from Spike outputs\n",
    "_ = ChannelMap.parse('(24,384)(...)') # from raw imro table format"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47b8da33",
   "metadata": {},
   "source": [
    "We support other output formats. The following code requires to install extra library (as optional dependency)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bf988e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Probe - IMEC - Neuropixels 2.0 - Four Shank - Prototype - 384ch - 1shanks"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#!pip install probeinterface\n",
    "p = s.to_probe() # type: Probe\n",
    "s = ChannelMap.from_probe(p)\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7019a395",
   "metadata": {},
   "source": [
    "Or other channelmap data output formats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63d3249c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>shank</th>\n",
       "      <th>column</th>\n",
       "      <th>row</th>\n",
       "      <th>in_used</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>channel</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>32</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>True</td>\n",
       "      <td>32</td>\n",
       "      <td>2115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>2130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>142</td>\n",
       "      <td>True</td>\n",
       "      <td>32</td>\n",
       "      <td>2130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>143</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>2145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>143</td>\n",
       "      <td>True</td>\n",
       "      <td>32</td>\n",
       "      <td>2145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>384 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         shank  column  row  in_used   x     y\n",
       "channel                                       \n",
       "0            0       0    0     True   0     0\n",
       "1            0       1    0     True  32     0\n",
       "2            0       0    1     True   0    15\n",
       "3            0       1    1     True  32    15\n",
       "4            0       0    2     True   0    30\n",
       "...        ...     ...  ...      ...  ..   ...\n",
       "379          0       1  141     True  32  2115\n",
       "380          0       0  142     True   0  2130\n",
       "381          0       1  142     True  32  2130\n",
       "382          0       0  143     True   0  2145\n",
       "383          0       1  143     True  32  2145\n",
       "\n",
       "[384 rows x 6 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#!pip install pandas\n",
    "s.to_pandas() # type: pd.Dataframe\n",
    "# empty channels use -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46840bc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr > th,\n",
       ".dataframe > tbody > tr > td {\n",
       "  text-align: right;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (384, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>channel</th><th>shank</th><th>column</th><th>row</th><th>in_used</th><th>x</th><th>y</th></tr><tr><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>bool</td><td>i64</td><td>i64</td></tr></thead><tbody><tr><td>0</td><td>0</td><td>0</td><td>0</td><td>true</td><td>0</td><td>0</td></tr><tr><td>1</td><td>0</td><td>1</td><td>0</td><td>true</td><td>32</td><td>0</td></tr><tr><td>2</td><td>0</td><td>0</td><td>1</td><td>true</td><td>0</td><td>15</td></tr><tr><td>3</td><td>0</td><td>1</td><td>1</td><td>true</td><td>32</td><td>15</td></tr><tr><td>4</td><td>0</td><td>0</td><td>2</td><td>true</td><td>0</td><td>30</td></tr><tr><td>5</td><td>0</td><td>1</td><td>2</td><td>true</td><td>32</td><td>30</td></tr><tr><td>6</td><td>0</td><td>0</td><td>3</td><td>true</td><td>0</td><td>45</td></tr><tr><td>7</td><td>0</td><td>1</td><td>3</td><td>true</td><td>32</td><td>45</td></tr><tr><td>8</td><td>0</td><td>0</td><td>4</td><td>true</td><td>0</td><td>60</td></tr><tr><td>9</td><td>0</td><td>1</td><td>4</td><td>true</td><td>32</td><td>60</td></tr><tr><td>10</td><td>0</td><td>0</td><td>5</td><td>true</td><td>0</td><td>75</td></tr><tr><td>11</td><td>0</td><td>1</td><td>5</td><td>true</td><td>32</td><td>75</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>372</td><td>0</td><td>0</td><td>138</td><td>true</td><td>0</td><td>2070</td></tr><tr><td>373</td><td>0</td><td>1</td><td>138</td><td>true</td><td>32</td><td>2070</td></tr><tr><td>374</td><td>0</td><td>0</td><td>139</td><td>true</td><td>0</td><td>2085</td></tr><tr><td>375</td><td>0</td><td>1</td><td>139</td><td>true</td><td>32</td><td>2085</td></tr><tr><td>376</td><td>0</td><td>0</td><td>140</td><td>true</td><td>0</td><td>2100</td></tr><tr><td>377</td><td>0</td><td>1</td><td>140</td><td>true</td><td>32</td><td>2100</td></tr><tr><td>378</td><td>0</td><td>0</td><td>141</td><td>true</td><td>0</td><td>2115</td></tr><tr><td>379</td><td>0</td><td>1</td><td>141</td><td>true</td><td>32</td><td>2115</td></tr><tr><td>380</td><td>0</td><td>0</td><td>142</td><td>true</td><td>0</td><td>2130</td></tr><tr><td>381</td><td>0</td><td>1</td><td>142</td><td>true</td><td>32</td><td>2130</td></tr><tr><td>382</td><td>0</td><td>0</td><td>143</td><td>true</td><td>0</td><td>2145</td></tr><tr><td>383</td><td>0</td><td>1</td><td>143</td><td>true</td><td>32</td><td>2145</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (384, 7)\n",
       "┌─────────┬───────┬────────┬─────┬─────────┬─────┬──────┐\n",
       "│ channel ┆ shank ┆ column ┆ row ┆ in_used ┆ x   ┆ y    │\n",
       "│ ---     ┆ ---   ┆ ---    ┆ --- ┆ ---     ┆ --- ┆ ---  │\n",
       "│ i64     ┆ i64   ┆ i64    ┆ i64 ┆ bool    ┆ i64 ┆ i64  │\n",
       "╞═════════╪═══════╪════════╪═════╪═════════╪═════╪══════╡\n",
       "│ 0       ┆ 0     ┆ 0      ┆ 0   ┆ true    ┆ 0   ┆ 0    │\n",
       "│ 1       ┆ 0     ┆ 1      ┆ 0   ┆ true    ┆ 32  ┆ 0    │\n",
       "│ 2       ┆ 0     ┆ 0      ┆ 1   ┆ true    ┆ 0   ┆ 15   │\n",
       "│ 3       ┆ 0     ┆ 1      ┆ 1   ┆ true    ┆ 32  ┆ 15   │\n",
       "│ …       ┆ …     ┆ …      ┆ …   ┆ …       ┆ …   ┆ …    │\n",
       "│ 380     ┆ 0     ┆ 0      ┆ 142 ┆ true    ┆ 0   ┆ 2130 │\n",
       "│ 381     ┆ 0     ┆ 1      ┆ 142 ┆ true    ┆ 32  ┆ 2130 │\n",
       "│ 382     ┆ 0     ┆ 0      ┆ 143 ┆ true    ┆ 0   ┆ 2145 │\n",
       "│ 383     ┆ 0     ┆ 1      ┆ 143 ┆ true    ┆ 32  ┆ 2145 │\n",
       "└─────────┴───────┴────────┴─────┴─────────┴─────┴──────┘"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#!pip install polars\n",
    "s.to_polars() # type: pl.DataFrame\n",
    "# empty channels use null"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b854b274",
   "metadata": {},
   "source": [
    "### Select electrodes manually"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e83c325",
   "metadata": {},
   "source": [
    "Select an electrode as a readout channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e534bea6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Electrode[0,0,0]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = ChannelMap(24)\n",
    "e = s.add_electrode((0, 0, 0)) # select an electrode at shank 0, column 0 and row 9\n",
    "e"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31781d3f",
   "metadata": {},
   "source": [
    "The above output printed the information of an electrode in format `Electrode[S,C,R]`, where `S` is shank index, `C` in column index, and `R` is row index."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd84208f",
   "metadata": {},
   "source": [
    "If a channel has been used, then you cannot select coorespond electrodes. A `ChannelHasUsedError` will be raised."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "feb1d8d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ChannelHasUsedError('Electrode[0,0,0]')\n",
      "Electrode[0,0,0]\n"
     ]
    }
   ],
   "source": [
    "s = ChannelMap.from_imro('channelmap.imro')\n",
    "\n",
    "try:\n",
    "    s.add_electrode((0, 0, 0))\n",
    "except ChannelHasUsedError as x:\n",
    "    print(repr(x))\n",
    "    print(x.electrode)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9bc803e",
   "metadata": {},
   "source": [
    "To ignore the selected electrode, you can use the `exist_ok` keyword in the `add_electrode` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5968bd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Electrode[0,0,0]\n"
     ]
    }
   ],
   "source": [
    "s = ChannelMap.from_imro('channelmap.imro')\n",
    "e = s.add_electrode((0, 0, 0), exist_ok=True)\n",
    "print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0301114",
   "metadata": {},
   "source": [
    "It could still raises a `ChannelHasUsedError` if the newly selected electrode will violate the hardware restriction. In the Neuropixels case, it means both electrodes are shared same channel. `ChannelMap` does not allow electrode overwriting, so you need to remove the previous one and re-select the new one. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97b3daa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ChannelHasUsedError('Electrode[0,0,144]')\n",
      "Electrode[1,0,0]\n"
     ]
    }
   ],
   "source": [
    "s = ChannelMap.from_imro('channelmap.imro')\n",
    "e = None\n",
    "try:\n",
    "    s.add_electrode((1, 0, 0))\n",
    "except ChannelHasUsedError as x:\n",
    "    print(repr(x))\n",
    "    s.del_electrode(x.electrode)\n",
    "    e = s.add_electrode((1, 0, 0))\n",
    "print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "800f590c",
   "metadata": {},
   "source": [
    "### Access electrodes from a channelmap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "263fec55",
   "metadata": {},
   "source": [
    "Access the selected electrode via its channel ID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "09ea135b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Electrode[0,0,0]\n",
      "[Electrode[0,0,0], Electrode[0,1,0], Electrode[0,0,1]]\n"
     ]
    }
   ],
   "source": [
    "s = ChannelMap.from_imro('channelmap.imro')\n",
    "print(s.channels[0])\n",
    "print(s.channels[0:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c6d0743",
   "metadata": {},
   "source": [
    "Access the selected electrodes via its position."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eecae6df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Electrode[0,0,0]\n",
      "[Electrode[0,0,0], Electrode[0,0,1], Electrode[0,0,2]]\n"
     ]
    }
   ],
   "source": [
    "print(s.electrodes[0, 0, 0]) # shank, column, row\n",
    "print(s.electrodes[0, 0, 0:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b284a9b6",
   "metadata": {},
   "source": [
    "Both properties `channels` and `electrodes` are iterable, which they can be put in the `for` loop. Both yeild electrode in the same order. The difference is `channels` may yield `None`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "31a843b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Electrode[0,0,0]; 1 None; 2 Electrode[0,0,1]; 3 Electrode[0,1,1]; \n",
      "0 Electrode[0,0,0]; 1 Electrode[0,0,1]; 2 Electrode[0,1,1]; 3 Electrode[0,0,2]; "
     ]
    }
   ],
   "source": [
    "del s.channels[1] # remove an electrode\n",
    "for c, e in enumerate(s.channels):\n",
    "    print(c, e, end='; ')\n",
    "    if c >= 3: print(); break;\n",
    "for i, e in enumerate(s.electrodes):\n",
    "    print(i, e, end='; ')\n",
    "    if i >= 3: break;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "777de13c",
   "metadata": {},
   "source": [
    "### Set electrode’s parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bc312be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True True\n",
      "False\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "s = ChannelMap.from_imro('channelmap.imro')\n",
    "print(s.channels[0].in_used, s.channels[1].in_used)\n",
    "\n",
    "# one by one\n",
    "s.channels[0].in_used = False\n",
    "print(s.channels[0].in_used)\n",
    "\n",
    "# by a reference\n",
    "e = Electrode(0, 0, 0)\n",
    "e.in_used = False\n",
    "s.channels[:] = e # apply to all selected electrodes\n",
    "print(s.channels[1].in_used)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9a12353",
   "metadata": {},
   "source": [
    "### Electrode positions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a256122",
   "metadata": {},
   "source": [
    "You can use properties `channel_pos_x`, `channel_pos_y`, and `channel_pos` to get electrodes position in um. The missing electrode will be filled with `nan`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "485fc746",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(384, 2)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0],\n",
       "       [32,  0],\n",
       "       [ 0, 15],\n",
       "       [32, 15],\n",
       "       [ 0, 30],\n",
       "       [32, 30],\n",
       "       [ 0, 45],\n",
       "       [32, 45],\n",
       "       [ 0, 60],\n",
       "       [32, 60]])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = ChannelMap.from_imro('channelmap.imro')\n",
    "p = s.channel_pos # a (C, 2) position (um) array\n",
    "print(p.shape)\n",
    "p[:10, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5c0bc9f",
   "metadata": {},
   "source": [
    "Besides above instance properties, we also provide two position functions for more options on return."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "74ae236a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array[int, E, (S, C, R)] (384, 3)\n",
      "[[ 0.  0.  0.]\n",
      " [nan nan nan]\n",
      " [ 0.  0.  1.]\n",
      " [ 0.  1.  1.]]\n",
      "Array[int, E, (X, Y)] (384, 2)\n",
      "[[ 0.  0.]\n",
      " [nan nan]\n",
      " [ 0. 15.]\n",
      " [32. 15.]]\n",
      "Array[int, E, (S, C, R)] (5120, 3)\n",
      "[[0 0 0]\n",
      " [0 1 0]\n",
      " [0 0 1]\n",
      " [0 1 1]]\n",
      "Array[int, E, (X, Y)] (5120, 2)\n",
      "[[ 0  0]\n",
      " [32  0]\n",
      " [ 0 15]\n",
      " [32 15]]\n"
     ]
    }
   ],
   "source": [
    "from neurocarto.probe_npx.npx import channel_coordinate, electrode_coordinate\n",
    "\n",
    "c = channel_coordinate(s)\n",
    "print('Array[int, E, (S, C, R)]', c.shape)\n",
    "print(c[:4])\n",
    "\n",
    "c = channel_coordinate(s, electrode_unit='xy')\n",
    "print('Array[int, E, (X, Y)]', c.shape)\n",
    "print(c[:4])\n",
    "\n",
    "c = electrode_coordinate(s)\n",
    "print('Array[int, E, (S, C, R)]', c.shape)\n",
    "print(c[:4])\n",
    "\n",
    "c = electrode_coordinate(s, electrode_unit='xy')\n",
    "print('Array[int, E, (X, Y)]', c.shape)\n",
    "print(c[:4])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9e7f290",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5a547d95",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from neurocarto.probe_npx import plot\n",
    "\n",
    "%matplotlib inline\n",
    "rc = matplotlib.rc_params_from_file('channelmap.matplotlibrc', fail_on_error=True, use_default_template=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db997d00",
   "metadata": {},
   "source": [
    "plot a channelmap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "37e5b750",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = ChannelMap.from_imro('channelmap.imro')\n",
    "\n",
    "with plt.rc_context(rc):\n",
    "    fg, ax = plt.subplots()\n",
    "    plot.plot_channelmap_block(ax, s, height=6, color='g', shank_width_scale=2)\n",
    "    plot.plot_probe_shape(ax, s, height=6, color='k', label_axis=True, shank_width_scale=2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df7af0d2",
   "metadata": {},
   "source": [
    "plot a channelmap in other style."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8c328bbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = ChannelMap.from_imro('channelmap.imro')\n",
    "\n",
    "with plt.rc_context(rc):\n",
    "    fg, ax = plt.subplots()\n",
    "\n",
    "    plot.plot_probe_shape(ax, s, height=6, color='gray', label_axis=True, shank_width_scale=2)\n",
    "    plot.plot_channelmap_grid(ax, s, height=6, color='g', lw=4, shank_width_scale=2)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2ace938",
   "metadata": {},
   "source": [
    "## Generate a Channelmap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e7ed14a",
   "metadata": {},
   "source": [
    "We provide some builtin channelmaps."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e9b760f",
   "metadata": {},
   "source": [
    "### Block-based channelmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "58e3524b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.rc_context(rc):\n",
    "    fg, ax = plt.subplots(1, 2)\n",
    "    kwargs = dict(height=3, shank_width_scale=2)\n",
    "    \n",
    "    s = npx24_single_shank(0)\n",
    "    plot.plot_channelmap_block(ax[0], s, color='g', **kwargs)\n",
    "    plot.plot_probe_shape(ax[0], s, color='k', label_axis=True, **kwargs)\n",
    "    \n",
    "    s = npx24_stripe(0)\n",
    "    plot.plot_channelmap_block(ax[1], s, color='g', **kwargs)\n",
    "    plot.plot_probe_shape(ax[1], s, color='k', label_axis=True, **kwargs)\n",
    "    ax[1].set_ylabel(None)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdc6c758",
   "metadata": {},
   "source": [
    "### Uniform channelmaps\n",
    "\n",
    "All selected electrodes are distributed uniformly in a particular density."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "81b8b8b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ss = [\n",
    "    # 1/2\n",
    "    npx24_half_density(shank=0),\n",
    "    npx24_half_density(shank=(0, 1)),\n",
    "    # 1/4\n",
    "    sq := npx24_quarter_density(shank=0),\n",
    "    npx24_quarter_density(shank=(0, 2)),\n",
    "    npx24_quarter_density(None),\n",
    "    # 1/8\n",
    "    npx24_one_eighth_density()\n",
    "]\n",
    "\n",
    "with plt.rc_context(rc):\n",
    "    fg, ax = plt.subplots(1, len(ss), figsize=(3 * len(ss), 10))\n",
    "    kwargs = dict(height=6, shank_width_scale=2)\n",
    "    \n",
    "    for i, s in enumerate(ss):\n",
    "        plot.plot_channelmap_block(ax[i], s, color='g', **kwargs)\n",
    "        plot.plot_probe_shape(ax[i], s, color='k', label_axis=True, **kwargs)\n",
    "        if i > 0: ax[i].set_ylabel(None)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7223d021",
   "metadata": {},
   "source": [
    "Notice the the channelmap `sq` (the third map) is an incompleted channelmap, because its number of selected electrodes do not reach the number of required channels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4997a071",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ChannelMap[4,2,640,384,320]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sq"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2594f46",
   "metadata": {},
   "source": [
    "## Generate a custom channelmap with a given blueprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "31907721",
   "metadata": {},
   "outputs": [],
   "source": [
    "from neurocarto.probe_npx.desp import NpxProbeDesp, NpxElectrodeDesp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "63d3a390",
   "metadata": {},
   "outputs": [],
   "source": [
    "D = NpxProbeDesp()\n",
    "C: ChannelMap = ChannelMap.from_imro('Fig3_example.imro')\n",
    "Q: list[NpxElectrodeDesp] = D.load_blueprint('Fig3_example.blueprint.npy', C)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f4d00d4",
   "metadata": {},
   "source": [
    "A blueprint `Q` is loaded from a file. It is not easy to make a new blueprint from zero. It is more easy to make a new one from the application."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80f6932e",
   "metadata": {},
   "source": [
    "### Electrode selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8c303bbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = D.select_electrodes(C, Q)\n",
    "\n",
    "with plt.rc_context(rc):\n",
    "    fg, ax = plt.subplots(1, 2)\n",
    "    kwargs = dict(height=6, shank_width_scale=2)\n",
    "    \n",
    "    plot.plot_category_area(ax[0], C.probe_type, Q, **kwargs)\n",
    "    plot.plot_probe_shape(ax[0], C, color='k', label_axis=True, **kwargs)\n",
    "    ax[0].set_title('Blueprint', fontdict=dict(fontsize=20))\n",
    "    \n",
    "    plot.plot_channelmap_block(ax[1], s, color='g', **kwargs)\n",
    "    plot.plot_probe_shape(ax[1], s, color='k', label_axis=True, **kwargs)\n",
    "    ax[1].set_ylabel(None)\n",
    "    ax[1].set_title('Channelmap', fontdict=dict(fontsize=20))\n",
    "    \n",
    "plt.show()"
   ]
  }
 ],
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