{
 "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 from different data groups in a single plot using `subcoordinate_y`.\n",
    "2. Normalize the timeseries per data group.\n",
    "3. 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) and position 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": "4d81eadd-95c7-4474-9f59-deb2c0cf3297",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import holoviews as hv\n",
    "from holoviews.operation.normalization import subcoordinate_group_ranges\n",
    "from holoviews.operation.datashader import rasterize\n",
    "from holoviews.plotting.links import RangeToolLink\n",
    "import colorcet as cc\n",
    "from scipy.stats import zscore\n",
    "hv.extension('bokeh')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c95f241-2314-42b0-b6cb-2c0baf332686",
   "metadata": {},
   "source": [
    "## Generating data\n",
    "\n",
    "Let's start by `EEG` and position (`POS`) data. We'll create a timeseries for each EEG channel using sine waves with varying frequencies, and random data for three position channels. We'll set these two data groups to have different amplitudes and units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ac3812a",
   "metadata": {},
   "outputs": [],
   "source": [
    "GROUP_EEG = 'EEG'\n",
    "GROUP_POS = 'Position'\n",
    "N_CHANNELS_EEG = 10\n",
    "N_CHANNELS_POS = 3\n",
    "N_SECONDS = 5\n",
    "SAMPLING_RATE_EEG = 200\n",
    "SAMPLING_RATE_POS = 25\n",
    "INIT_FREQ = 2  # Initial frequency in Hz\n",
    "FREQ_INC = 5  # Frequency increment\n",
    "AMPLITUDE_EEG = 1000  # EEG amplitude multiplier\n",
    "AMPLITUDE_POS = 2  # Position amplitude multiplier\n",
    "\n",
    "# Generate time for EEG and position data\n",
    "total_samples_eeg = N_SECONDS * SAMPLING_RATE_EEG\n",
    "total_samples_pos = N_SECONDS * SAMPLING_RATE_POS\n",
    "time_eeg = np.linspace(0, N_SECONDS, total_samples_eeg)\n",
    "time_pos = np.linspace(0, N_SECONDS, total_samples_pos)\n",
    "\n",
    "# Generate EEG timeseries data\n",
    "def generate_eeg_data(index):\n",
    "    return AMPLITUDE_EEG * np.sin(2 * np.pi * (INIT_FREQ + index * FREQ_INC) * time_eeg)\n",
    "\n",
    "eeg_channels = [str(i) for i in np.arange(N_CHANNELS_EEG)]\n",
    "eeg_data = np.array([generate_eeg_data(i) for i in np.arange(N_CHANNELS_EEG)])\n",
    "eeg_df = pd.DataFrame(eeg_data.T, index=time_eeg, columns=eeg_channels)\n",
    "eeg_df.index.name = 'Time'\n",
    "\n",
    "# Generate position data\n",
    "pos_channels = ['X', 'Y', 'Z'] # avoid lowercase 'x' and 'y' as channel/dimension names\n",
    "pos_data = AMPLITUDE_POS * np.random.randn(N_CHANNELS_POS, total_samples_pos).cumsum(axis=1)\n",
    "pos_df = pd.DataFrame(pos_data.T, index=time_pos, columns=pos_channels)\n",
    "pos_df.index.name = 'Time'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec9e71b8-a995-4c0f-bdbb-5d148d8fa138",
   "metadata": {},
   "source": [
    "## Visualizing EEG Data\n",
    "\n",
    "Next, let's dive into visualizing the data. We construct each timeseries using a `Curve` element, assigning it a `group`, a `label` and setting `subcoordinate_y=True`. All these curves are then aggregated into a list per data group, 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 about the group, channel, time, and amplitude value when you hover over any of the curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9476769f-3935-4236-b010-1511d1a1e77f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Curve per data series\n",
    "def df_to_curves(df, kdim, vdim, color='black', group='EEG', ):\n",
    "    curves = []\n",
    "    for i, (channel, channel_data) in enumerate(df.items()):\n",
    "        ds = hv.Dataset((channel_data.index, channel_data), [kdim, vdim])\n",
    "        curve = hv.Curve(ds, kdim, vdim, group=group, label=str(channel))\n",
    "        curve.opts(\n",
    "            subcoordinate_y=True, color=color if isinstance(color, str) else color[i], line_width=1, \n",
    "            hover_tooltips=hover_tooltips, tools=['xwheel_zoom'], line_alpha=.8,\n",
    "        )\n",
    "        curves.append(curve)\n",
    "    return curves\n",
    "\n",
    "hover_tooltips = [(\"Group\", \"$group\"), (\"Channel\", \"$label\"), (\"Time\"), (\"Value\")]\n",
    "\n",
    "vdim_EEG = hv.Dimension(\"Value\", unit=\"µV\")\n",
    "vdim_POS = hv.Dimension(\"Value\", unit=\"cm\")\n",
    "time_dim = hv.Dimension(\"Time\", unit=\"s\")\n",
    "\n",
    "eeg_curves = df_to_curves(eeg_df, time_dim, vdim_EEG, color='black', group='EEG')\n",
    "pos_curves = df_to_curves(pos_df, time_dim, vdim_POS, color=cc.glasbey_cool, group='POS')\n",
    "\n",
    "# Combine EEG and POS curves into an Overlay\n",
    "eeg_curves_overlay = hv.Overlay(eeg_curves, \"Channel\")\n",
    "pos_curves_overlay = hv.Overlay(pos_curves, \"Channel\")\n",
    "curves_overlay = (eeg_curves_overlay * pos_curves_overlay).opts(\n",
    "    xlabel=time_dim.pprint_label, ylabel=\"Channel\", show_legend=False, aspect=3, responsive=True,\n",
    ")\n",
    "curves_overlay"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "983e1f84-6006-4d64-9144-4aba0ad93946",
   "metadata": {},
   "source": [
    "Note that the overlay above has multiple wheel-zoom tools in the toolbar, you can hover over the icons in the toolbar to reveal each of the first two control the Y-axis zoom of their respective data group within each curve's subcoordinate range, and the third wheel zoom tool controls the X-axis scale of all the curves together.\n",
    "\n",
    "By default, all the curves, including across data groups, have the same y-axis range that is computed from the min and max across all channels. As a consequence, the position curves in blue, which have a much smaller amplitude than timeseries in the EEG data group, appear to be quite flat and are hard to inspect. To deal with this situation, we can transform the *Overlay* with the `subcoordinate_group_ranges` operation that will apply a min-max normalization of the timeseries per group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bb78a48-5c6a-4969-bf58-539fce784364",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply group-wise normalization\n",
    "normalized_overlay = subcoordinate_group_ranges(curves_overlay)\n",
    "normalized_overlay"
   ]
  },
  {
   "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 timeseries `Overlay` and the minimap `Image`, we ensure they share the same y-axis range. We will also leverage rasterization in case the full image resolution is too large to render on the screen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40fa2198-c3b5-41e1-944f-f8b812612168",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_positions = range(N_CHANNELS_EEG + N_CHANNELS_POS)\n",
    "\n",
    "# Reindex the lower frequency DataFrame to match the higher frequency index\n",
    "pos_df_interp = pos_df.reindex(eeg_df.index).interpolate(method='index')\n",
    "\n",
    "# concatenate the EEG and interpolated POS data and z-score the full data array\n",
    "z_data = zscore(np.concatenate((eeg_df.values, pos_df_interp.values), axis=1), axis=0).T\n",
    "\n",
    "minimap = rasterize(hv.Image((time_eeg, y_positions , z_data), [time_dim, \"Channel\"], \"Value\"))\n",
    "minimap = minimap.opts(\n",
    "    cmap=\"RdBu_r\", xlabel='', alpha=.7,\n",
    "    yticks=[(y_positions[0], f'EEG {eeg_channels[0]}'), (y_positions[-1], f'POS {pos_channels[-1]}')],\n",
    "    height=120, responsive=True, toolbar='disable', cnorm='eq_hist'\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 timeseries `Overlay`, setting bounds for the initially viewable area with `boundsx` and `boundsy`, and finally demonstrate setting an upper max zoom range of 3 seconds with `intervalsx`. 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, normalized_overlay, axes=[\"x\", \"y\"],\n",
    "    boundsx=(.5, 3), boundsy=(1.5, 12.5),\n",
    "    intervalsx=(None, 3),\n",
    ")\n",
    "\n",
    "dashboard = (normalized_overlay + minimap).cols(1).opts(shared_axes=False)\n",
    "dashboard"
   ]
  }
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