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holoviz · May 30, 2024 · 32d3526 · 32d3526
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commit 32d3526
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diff --git a/examples/gallery/demos/bokeh/eeg_viewer.ipynb b/examples/gallery/demos/bokeh/eeg_viewer.ipynb
@@ -41,6 +41,122 @@
     "Let's start by simulating some EEG data. We'll create a timeseries for each channel using sine waves with varying frequencies. We'll have two groups *A* and *B* of timeseries that have different amplitudes."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ac3812a",
+   "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.plotting.links import RangeToolLink\n",
+    "from scipy.stats import zscore\n",
+    "\n",
+    "hv.extension('bokeh')\n",
+    "\n",
+    "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 = 100  # EEG amplitude in µV\n",
+    "AMPLITUDE_POS = 10  # Position amplitude in cm\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 = np.arange(N_CHANNELS_EEG)\n",
+    "eeg_data = np.array([generate_eeg_data(i) for i in eeg_channels])\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']\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": "code",
+   "execution_count": null,
+   "id": "b9bae9af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define hover tool\n",
+    "hover = HoverTool(tooltips=[\n",
+    "    (\"Info\", \"@info\"),\n",
+    "    (\"Time\", \"$x s\"),\n",
+    "    (\"Value\", \"$y\")\n",
+    "])\n",
+    "\n",
+    "hover_tooltips = [\n",
+    "    (\"Type\", \"@info\"),\n",
+    "    (\"\")\n",
+    "    (\"Time\", \"$x s\"),\n",
+    "    (\"Value\", \"$y\")\n",
+    "]\n",
+    "\n",
+    "\n",
+    "amp_dim = hv.Dimension(\"Amplitude\", unit=\"µV\")\n",
+    "time_dim = hv.Dimension(\"Time\", unit=\"s\")\n",
+    "\n",
+    "# EEG data visualization\n",
+    "eeg_curves = []\n",
+    "for channel, channel_data in enumerate(eeg_data):\n",
+    "    info = f\"EEG {channel}\"\n",
+    "    ds = hv.Dataset((channel_data.index, channel_data, info), [time_dim, amp_dim, \"Type\", \"Channel\"])\n",
+    "    curve = hv.Curve(ds, time_dim, [amp_dim, \"info\"], group='EEG', label=info)\n",
+    "    curve.opts(\n",
+    "        subcoordinate_y=True, color=\"black\", line_width=1, hover_tooltips=hover_tooltips,\n",
+    "    )\n",
+    "    eeg_curves.append(curve)\n",
+    "\n",
+    "eeg_overlay = hv.Overlay(eeg_curves, kdims=\"Channel\").opts(\n",
+    "    xlabel=\"Time (s)\", ylabel=\"Channel\", show_legend=False, aspect=3, responsive=True,\n",
+    ")\n",
+    "\n",
+    "# Position data visualization\n",
+    "position_curves = []\n",
+    "for i, axis in enumerate(['x', 'y', 'z']):\n",
+    "    info = f\"Position {axis.upper()}\"\n",
+    "    ds = hv.Dataset((time_pos, pos_data[i], info), [\"Time\", \"Position\", \"info\"])\n",
+    "    curve = hv.Curve(ds, \"Time\", [\"Position\", \"info\"], group='Position', label=info)\n",
+    "    curve.opts(\n",
+    "        subcoordinate_y=True, color=\"blue\", line_width=1, tools=[hover],\n",
+    "    )\n",
+    "    position_curves.append(curve)\n",
+    "\n",
+    "position_overlay = hv.Overlay(position_curves, kdims=\"Axis\").opts(\n",
+    "    xlabel=\"Time (s)\", ylabel=\"Axis\", show_legend=True, aspect=3, responsive=True,\n",
+    ")\n",
+    "\n",
+    "# Combine EEG and position visualizations\n",
+    "combined_overlay = eeg_overlay * position_overlay\n",
+    "\n",
+    "# Apply group-wise normalization\n",
+    "normalized_overlay = subcoordinate_group_ranges(combined_overlay)\n",
+    "\n",
+    "# Display the dashboard\n",
+    "dashboard = normalized_overlay #opts(merge_tools=False)\n",
+    "dashboard"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,