{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3fbb8cd1-679c-4f8a-8889-43ed516bfedd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Collecting aiofiles\n",
      "  Obtaining dependency information for aiofiles from https://files.pythonhosted.org/packages/c5/19/5af6804c4cc0fed83f47bff6e413a98a36618e7d40185cd36e69737f3b0e/aiofiles-23.2.1-py3-none-any.whl.metadata\n",
      "  Downloading aiofiles-23.2.1-py3-none-any.whl.metadata (9.7 kB)\n",
      "Collecting jsonlines\n",
      "  Obtaining dependency information for jsonlines from https://files.pythonhosted.org/packages/f8/62/d9ba6323b9202dd2fe166beab8a86d29465c41a0288cbe229fac60c1ab8d/jsonlines-4.0.0-py3-none-any.whl.metadata\n",
      "  Downloading jsonlines-4.0.0-py3-none-any.whl.metadata (1.6 kB)\n",
      "Requirement already satisfied: attrs>=19.2.0 in /home/appuser/.local/lib/python3.11/site-packages (from jsonlines) (23.1.0)\n",
      "Downloading aiofiles-23.2.1-py3-none-any.whl (15 kB)\n",
      "Downloading jsonlines-4.0.0-py3-none-any.whl (8.7 kB)\n",
      "Installing collected packages: jsonlines, aiofiles\n",
      "Successfully installed aiofiles-23.2.1 jsonlines-4.0.0\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install aiofiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5c72c2e7-d825-4a9f-a179-fbbe07c93000",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1684"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "from glob import glob\n",
    "import aiofiles\n",
    "\n",
    "output_dir = os.path.abspath(\"./output_tryagain\")\n",
    "\n",
    "jsonfiles = glob(os.path.join(output_dir, \"*.json\"))\n",
    "\n",
    "len(jsonfiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f6fa4495-d6a3-470f-9a79-3838f0e38794",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import json\n",
    "\n",
    "# decodeerrors = {}\n",
    "# deleted = []\n",
    "# nocontent = []\n",
    "# with_metadata = {}\n",
    "\n",
    "# async def layers_with_metadata():\n",
    "#     async def _check(f):\n",
    "#         delete = False\n",
    "#         async with aiofiles.open(f, 'r') as infile:\n",
    "#             content = await infile.read()\n",
    "#             if content:\n",
    "#                 data = json.loads(content)\n",
    "#                 if 'metadata' not in data:\n",
    "#                     delete = True\n",
    "#                 else:\n",
    "#                     with_metadata[f] = data\n",
    "#             else:\n",
    "#                 nocontent.append(f)\n",
    "#                 delete = True\n",
    "#         if delete:\n",
    "#             deleted.append(f)\n",
    "#             os.remove(f)\n",
    "#     return await asyncio.gather(*[_check(f) for f in jsonfiles])\n",
    "\n",
    "# import asyncio\n",
    "# import nest_asyncio\n",
    "\n",
    "# nest_asyncio.apply()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "34514f76-bf06-4793-8085-56dc266c6d04",
   "metadata": {},
   "outputs": [],
   "source": [
    "import asyncio\n",
    "import json\n",
    "\n",
    "async def read_data():\n",
    "    async def _read(f):\n",
    "        async with aiofiles.open(f, 'r') as infile:\n",
    "            content = await infile.read()\n",
    "            return json.loads(content)\n",
    "    tasks = [_read(f) for f in jsonfiles]\n",
    "    return await asyncio.gather(*tasks)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2879f4d6-dfc6-42f7-ac33-25457c62c204",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nest_asyncio\n",
    "\n",
    "nest_asyncio.apply()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "1b8c5363-774d-4cac-8cb5-a8746f2d113c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 41s, sys: 23 s, total: 3min 4s\n",
      "Wall time: 2min 50s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "data = asyncio.run(read_data())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "dee1ba14-ba44-435f-abc1-91f6cbee6153",
   "metadata": {},
   "outputs": [],
   "source": [
    "docs = {\n",
    "    \"servers\": [],\n",
    "    \"services\": [],\n",
    "    \"layers\": [],\n",
    "}\n",
    "\n",
    "for server in data:\n",
    "    server_md = server['metadata']\n",
    "    server_url = server_md['url']\n",
    "    server_hash = hash(server_md['url'])\n",
    "    server_md['hash'] = server_hash\n",
    "    for a in ('folders', 'services'):\n",
    "        if a in server_md:\n",
    "            del server_md[a]\n",
    "    docs[\"servers\"].append(server_md)\n",
    "\n",
    "    for service in server['services']:\n",
    "        service_md = service['metadata']\n",
    "        service_name = service_md['name'] = service['name']\n",
    "        service_url = service_md['url'] = service['url']\n",
    "        service_md['server_url'] = server_url\n",
    "        service_hash = hash(service_url)\n",
    "        service_md['hash'] = service_hash\n",
    "        service_md['server_hash'] = server_hash\n",
    "        layers = service_md['layers']\n",
    "        del service_md['layers']\n",
    "        docs['services'].append(service_md)\n",
    "\n",
    "        for layer_md in layers:\n",
    "            layer_md['server_url'] = server_url\n",
    "            layer_md['service_name'] = service_name\n",
    "            layer_md['service_url'] = service_url\n",
    "            layer_hash = hash(layer_md['url'])\n",
    "            layer_md['hash'] = layer_hash\n",
    "            layer_md['server_hash'] = server_hash\n",
    "            layer_md['service_hash'] = service_hash\n",
    "            \n",
    "            docs['layers'].append(layer_md)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "072b1b2e-1309-4e9e-96b0-30c665706e90",
   "metadata": {},
   "outputs": [],
   "source": [
    "layerdict = {}\n",
    "for l in docs['layers']:\n",
    "    if 'type' in l:\n",
    "        if l['type'] not in layerdict:\n",
    "            layerdict[l['type']] = []\n",
    "        layerdict[l['type']].append(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "5824e617-1211-4642-9714-3b5be74885d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Raster Layer', 'Group Layer', 'Feature Layer', 'Annotation Layer', 'Annotation SubLayer', 'Mosaic Layer', 'Raster Catalog Layer', 'Terrain Layer', 'Network Analysis Layer', 'Network Dataset Layer', 'Parcel Layer', 'Topology Layer', 'Mosiac Layer', 'Image Service Layer', 'Annotation Sublayer', 'Dimension Layer', 'Tin Layer', 'Table'])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layerdict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "30b154d9-5f90-41c6-a004-f19dcd00a132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 37 ms, sys: 1.28 ms, total: 38.3 ms\n",
      "Wall time: 51.8 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "server_df = pd.DataFrame(docs['servers']).astype(str).apply(lambda s: s.str.strip().replace('', None)).dropna(axis=1, how='all').astype(str)\n",
    "server_df.dropna(axis=1, how='all').to_parquet('servers.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "10cbce4a-8184-48ec-8bae-6bb3b8395ddf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 34.3 s, sys: 1.94 s, total: 36.3 s\n",
      "Wall time: 37.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "services_df = pd.DataFrame(docs['services']).astype(str).apply(lambda s: s.str.strip().replace('', None)).dropna(axis=1, how='all').astype(str)\n",
    "services_df.astype(str).to_parquet('services.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "44e8267c-b07d-4b0d-93f3-662fb6659776",
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_df = pd.DataFrame(docs['layers'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "dc247c57-3e17-4cb4-b69b-71ee052fa0ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 36s, sys: 25.7 s, total: 3min 1s\n",
      "Wall time: 3min 10s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "layer_df.astype(str).apply(lambda s: s.str.strip().replace('', None)).dropna(axis=1, how='all').astype(str).to_parquet('layers.parquet')"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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