{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from awkward import Record as AwkwardRecord, Array as AwkwardArray\n",
    "import awkward as ak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "FILENAME = \"export-2024-11-12-16-59-20.parquet\"\n",
    "data = ak.from_parquet(FILENAME)[0]\n",
    "display(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Literal\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def to_scatter(\n",
    "    key: str, dev_history: AwkwardArray, plot_key: Literal[\"value\", \"rssi\"] = \"value\"\n",
    ") -> go.Scatter:\n",
    "    x = ak.to_numpy(dev_history[\"time\"])\n",
    "    if plot_key == \"rssi\":\n",
    "        y = ak.to_numpy(dev_history[\"rssi\"])\n",
    "    elif plot_key == \"value\":\n",
    "        y = ak.to_numpy(dev_history[\"value\"])\n",
    "    else:\n",
    "        raise ValueError(f\"Unknown plot_key: {plot_key}\")\n",
    "    return go.Scatter(x=x, y=y, mode=\"lines+markers\", name=key)\n",
    "\n",
    "\n",
    "scatters: list[go.Scatter] = []\n",
    "for k in data.fields:\n",
    "    val = data[k]\n",
    "    scatters.append(to_scatter(k, val, \"value\"))\n",
    "\n",
    "fig = go.Figure(scatters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "display(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "def dump_as_csv(mac:str, device_record: AwkwardRecord, filename:str):\n",
    "    time = ak.to_numpy(device_record[mac][\"time\"]) # type: ignore\n",
    "    value = ak.to_numpy(device_record[mac][\"value\"]) # type: ignore\n",
    "    rssi = ak.to_numpy(device_record[mac][\"rssi\"]) # type: ignore\n",
    "    with open(filename, \"w\") as f:\n",
    "        writer = csv.writer(f)\n",
    "        writer.writerow([\"time\", \"value\", \"rssi\"])\n",
    "        for t, v, r in zip(time, value, rssi):\n",
    "            writer.writerow([t, v, r])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dump_as_csv(\"A09E1AE4E710\", data, \"A09E1AE4E710.csv\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}