{
 "cells": [
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   "source": [
    "This example demonstrates advanced visualization techniques using HoloViews with the Bokeh plotting backend. You'll learn how to:\n",
    "\n",
    "1. Display multiple timeseries in a single plot using `subcoordinate_y`.\n",
    "2. Create and link a minimap to the main plot with `RangeToolLink`.\n",
    "\n",
    "Specifically, we'll simulate [Electroencephalography](https://en.wikipedia.org/wiki/Electroencephalography) (EEG) data, plot it, and then create a minimap based on the [z-score](https://en.wikipedia.org/wiki/Standard_score) of the data for easier navigation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8109537b-5fba-4f07-aba4-91a56f7e95c7",
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   "source": [
    "import numpy as np\n",
    "import holoviews as hv\n",
    "from bokeh.models import HoverTool\n",
    "from holoviews.plotting.links import RangeToolLink\n",
    "from scipy.stats import zscore\n",
    "\n",
    "hv.extension('bokeh')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c95f241-2314-42b0-b6cb-2c0baf332686",
   "metadata": {},
   "source": [
    "## Generating EEG data\n",
    "\n",
    "Let's start by simulating some EEG data. We'll create a timeseries for each channel using sine waves with varying frequencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f4a9dbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "N_CHANNELS = 10\n",
    "N_SECONDS = 5\n",
    "SAMPLING_RATE = 200\n",
    "INIT_FREQ = 2  # Initial frequency in Hz\n",
    "FREQ_INC = 5  # Frequency increment\n",
    "AMPLITUDE = 1\n",
    "\n",
    "# Generate time and channel labels\n",
    "total_samples = N_SECONDS * SAMPLING_RATE\n",
    "time = np.linspace(0, N_SECONDS, total_samples)\n",
    "channels = [f'EEG {i}' for i in range(N_CHANNELS)]\n",
    "\n",
    "# Generate sine wave data\n",
    "data = np.array([AMPLITUDE * np.sin(2 * np.pi * (INIT_FREQ + i * FREQ_INC) * time)\n",
    "                     for i in range(N_CHANNELS)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec9e71b8-a995-4c0f-bdbb-5d148d8fa138",
   "metadata": {},
   "source": [
    "## Visualizing EEG Data\n",
    "\n",
    "Next, let's dive into visualizing the EEG data. We construct each timeseries using a `Curve` element, assigning it a `label` and setting `subcoordinate_y=True`. All these curves are then aggregated into a list, which serves as the input for an `Overlay` element. Rendering this `Overlay` produces a plot where the timeseries are stacked vertically.\n",
    "\n",
    "Additionally, we'll enhance user interaction by implementing a custom hover tool. This will display key information—channel, time, and amplitude—when you hover over any of the curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9476769f-3935-4236-b010-1511d1a1e77f",
   "metadata": {},
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   "source": [
    "hover = HoverTool(tooltips=[\n",
    "    (\"Channel\", \"@channel\"),\n",
    "    (\"Time\", \"$x s\"),\n",
    "    (\"Amplitude\", \"$y µV\")\n",
    "])\n",
    "\n",
    "channel_curves = []\n",
    "for channel, channel_data in zip(channels, data):\n",
    "    ds = hv.Dataset((time, channel_data, channel), [\"Time\", \"Amplitude\", \"channel\"])\n",
    "    curve = hv.Curve(ds, \"Time\", [\"Amplitude\", \"channel\"], label=channel)\n",
    "    curve.opts(\n",
    "        subcoordinate_y=True, color=\"black\", line_width=1, tools=[hover],\n",
    "    )\n",
    "    channel_curves.append(curve)\n",
    "\n",
    "eeg = hv.Overlay(channel_curves, kdims=\"Channel\").opts(\n",
    "    xlabel=\"Time (s)\", ylabel=\"Channel\", show_legend=False, aspect=3, responsive=True,\n",
    ")\n",
    "eeg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4f603e2-039d-421a-ba9a-ed9e77efab99",
   "metadata": {},
   "source": [
    "## Creating the Minimap\n",
    "\n",
    "A minimap can provide a quick overview of the data and help you navigate through it. We'll compute the z-score for each channel and represent it as an image; the z-score will normalize the data and bring out the patterns more clearly. To enable linking in the next step between the EEG `Overlay` and the minimap `Image`, we ensure they share the same y-axis range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40fa2198-c3b5-41e1-944f-f8b812612168",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_positions = range(N_CHANNELS)\n",
    "yticks = [(i , ich) for i, ich in enumerate(channels)]\n",
    "\n",
    "z_data = zscore(data, axis=1)\n",
    "\n",
    "minimap = hv.Image((time, y_positions , z_data), [\"Time (s)\", \"Channel\"], \"Amplitude (uV)\")\n",
    "minimap = minimap.opts(\n",
    "    cmap=\"RdBu_r\", xlabel='Time (s)', alpha=.5, yticks=[yticks[0], yticks[-1]],\n",
    "    height=150, responsive=True, default_tools=[], clim=(-z_data.std(), z_data.std())\n",
    ")\n",
    "minimap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5b77970-342f-4428-bd1c-4dbef1e6a2b5",
   "metadata": {},
   "source": [
    "## Building the dashboard\n",
    "\n",
    "Finally, we use [`RangeToolLink`](../../../user_guide/Linking_Plots.ipynb) to connect the minimap `Image` and the EEG `Overlay`, setting bounds for the initial viewable area. Once the plots are linked and assembled into a unified dashboard, you can interact with it. Experiment by dragging the selection box on the minimap or resizing it by clicking and dragging its edges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "260489eb-2dbf-4c88-ba83-dd1cba0e547b",
   "metadata": {},
   "outputs": [],
   "source": [
    "RangeToolLink(\n",
    "    minimap, eeg, axes=[\"x\", \"y\"],\n",
    "    boundsx=(None, 2), boundsy=(None, 6.5)\n",
    ")\n",
    "\n",
    "dashboard = (eeg + minimap).opts(merge_tools=False).cols(1)\n",
    "dashboard"
   ]
  }
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