Baseline stream timeseries dev workflow

workflows/stream_timeseries/dev/baseline_workflow_stream-timeseries.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1104765d-276b-488a-a44f-7aef27d7c520",
+   "metadata": {},
+   "source": [
+    "# Philipp's first demo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a4afd67-94aa-4a6a-b506-0edfc3639064",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import holoviews as hv\n",
+    "hv.extension('bokeh')\n",
+    "\n",
+    "buffer = hv.streams.Buffer(data=pd.DataFrame(np.random.randn(10, 10).cumsum(axis=1), columns=[chr(65+i) for i in range(10)]))\n",
+    "def out(data):\n",
+    "    return hv.NdOverlay(\n",
+    "        {i: hv.Curve(data, 'index', (chr(65+i), 'Amplitude')).opts(subcoordinate_y=True) for i in range(10)}\n",
+    "    )\n",
+    "\n",
+    "hv.DynamicMap(out, streams=[buffer]).opts(legend_position='right', width=800)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2a4a4534-8117-4d40-8a0b-325c004c31ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "for i in range(2, 1000):\n",
+    "    buffer.event(data=pd.DataFrame(np.random.randn(10, 10).cumsum(axis=1), columns=[chr(65+i) for i in range(10)], index=np.arange(i*10, i*10+10)))\n",
+    "    time.sleep(0.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "616eb087-d3d4-4d6a-9413-efed3d509679",
+   "metadata": {},
+   "source": [
+    "# Demetris' version, threaded stream, buttons, inspection plot\n",
+    "- Added an inspection plot (because I haven't figured out yet how to do a return to stream)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "caa75986-6d26-4bdc-8826-03396f3eabc5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import holoviews as hv\n",
+    "import panel as pn\n",
+    "from threading import Thread\n",
+    "\n",
+    "hv.extension('bokeh')\n",
+    "pn.extension()\n",
+    "\n",
+    "initial_data = pd.DataFrame(np.random.randn(10, 10).cumsum(axis=1), columns=[chr(65+i) for i in range(10)])\n",
+    "buffer = hv.streams.Buffer(data=initial_data, length=1000)\n",
+    "\n",
+    "accumulated_data = initial_data.copy()\n",
+    "\n",
+    "def create_stream_plot(data):\n",
+    "    return hv.NdOverlay(\n",
+    "        {i: hv.Curve(data, 'index', (chr(65+i), 'Amplitude')).opts(subcoordinate_y=True, tools=['hover']) for i in range(10)}, 'Channel'\n",
+    "    )\n",
+    "dmap_stream = hv.DynamicMap(create_stream_plot, streams=[buffer]).opts(width=800, axiswise=True, title='Streaming')\n",
+    "\n",
+    "def create_inspection_plot(data):\n",
+    "    return hv.NdOverlay(\n",
+    "        {i: hv.Curve(data, 'index', (chr(65+i), 'Amplitude')).opts(subcoordinate_y=True, tools=['hover']) for i in range(10)}, 'Channel'\n",
+    "    ).opts(width=800, axiswise=True, shared_axes=False, title='Inspection')\n",
+    "\n",
+    "inspection_plot = create_inspection_plot(initial_data)\n",
+    "\n",
+    "# globals to control streaming state\n",
+    "streaming = False\n",
+    "paused = False\n",
+    "counter = 2\n",
+    "\n",
+    "def stream_data():\n",
+    "    global streaming, counter, accumulated_data\n",
+    "    while streaming:\n",
+    "        if not paused:\n",
+    "            new_data = pd.DataFrame(np.random.randn(10, 10).cumsum(axis=1), columns=[chr(65+j) for j in range(10)], index=np.arange(counter*10, counter*10+10))\n",
+    "            new_data = new_data.reset_index()\n",
+    "            accumulated_data = pd.concat([accumulated_data, new_data])\n",
+    "            buffer.event(data=new_data)\n",
+    "            counter += 1\n",
+    "            dmap_stream.opts(xlim=(0, counter*10))\n",
+    "        time.sleep(0.07)\n",
+    "\n",
+    "\n",
+    "def start_stream(event):\n",
+    "    global streaming, paused, counter, accumulated_data\n",
+    "    if not streaming:\n",
+    "        buffer.clear()\n",
+    "        accumulated_data = pd.DataFrame(np.zeros((1, 10)), columns=[chr(65+i) for i in range(10)]).reset_index()\n",
+    "        buffer.event(data=accumulated_data)\n",
+    "        counter = 2  # Reset counter\n",
+    "        streaming = True\n",
+    "        paused = False\n",
+    "        thread = Thread(target=stream_data)\n",
+    "        thread.start()\n",
+    "    else:\n",
+    "        paused = False\n",
+    "\n",
+    "def pause_stream(event):\n",
+    "    global paused\n",
+    "    paused = True\n",
+    "\n",
+    "def stop_stream(event):\n",
+    "    global streaming, paused, accumulated_data\n",
+    "    streaming = False\n",
+    "    paused = False\n",
+    "    buffer.clear()  # Clear the buffer when stopping\n",
+    "    accumulated_data = pd.DataFrame(np.zeros((1, 10)), columns=[chr(65+i) for i in range(10)]).reset_index()\n",
+    "    buffer.event(data=accumulated_data)\n",
+    "\n",
+    "def inspect_plot(event):\n",
+    "    def update_inspection_plot():\n",
+    "        global inspection_plot\n",
+    "        inspection_data = accumulated_data.copy()\n",
+    "        inspection_plot = create_inspection_plot(inspection_data)\n",
+    "        right_plot_panel.object = inspection_plot\n",
+    "\n",
+    "    inspect_thread = Thread(target=update_inspection_plot)\n",
+    "    inspect_thread.start()\n",
+    "\n",
+    "# Buttons!\n",
+    "start_button = pn.widgets.Button(name='Start Streaming', button_type='primary')\n",
+    "pause_button = pn.widgets.Button(name='Pause Streaming', button_type='warning')\n",
+    "stop_button = pn.widgets.Button(name='Stop Streaming', button_type='danger')\n",
+    "inspect_button = pn.widgets.Button(name='Inspect', button_type='success')\n",
+    "\n",
+    "start_button.on_click(start_stream)\n",
+    "pause_button.on_click(pause_stream)\n",
+    "stop_button.on_click(stop_stream)\n",
+    "inspect_button.on_click(inspect_plot)\n",
+    "\n",
+    "right_plot_panel = pn.pane.HoloViews(inspection_plot)\n",
+    "layout = pn.Column(\n",
+    "    pn.Row(start_button, pause_button, stop_button, inspect_button),\n",
+    "    pn.Row(dmap_stream, right_plot_panel)\n",
+    ")\n",
+    "\n",
+    "layout.servable()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b6d4c50-37ae-418d-b296-c7b1a6df4590",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

workflows/stream_timeseries/environment.yml

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+name: neuro-stream-ts
+channels:
+  - conda-forge
+dependencies:
+  - python
+  - holoviews>=1.19.0
+  - bokeh>=3.3.1
+  - hvplot
+  - panel
+  - datashader
+  - numpy
+  - pandas
+  - xarray>=2024.5.0
+  - ipykernel
+  - jupyterlab
+  - jupyter_bokeh
+  - pip
+  - pip:
+    - holonote
+    - tsdownsample>=0.1.3