{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2f01a012-002a-465c-9b09-681bdb3fc26d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class\n",
      "type\n",
      "stream\n",
      "expver\n",
      "dataset\n",
      "model\n",
      "repres\n",
      "obsgroup\n",
      "reportype\n",
      "levtype\n",
      "levelist\n",
      "param\n",
      "date\n",
      "year\n",
      "month\n",
      "hdate\n",
      "offsetdate\n",
      "fcmonth\n",
      "fcperiod\n",
      "time\n",
      "offsettime\n",
      "step\n",
      "anoffset\n",
      "reference\n",
      "number\n",
      "quantile\n",
      "domain\n",
      "frequency\n",
      "direction\n",
      "diagnostic\n",
      "iteration\n",
      "channel\n",
      "ident\n",
      "instrument\n",
      "method\n",
      "origin\n",
      "system\n",
      "activity\n",
      "experiment\n",
      "generation\n",
      "realization\n",
      "resolution\n"
     ]
    }
   ],
   "source": [
    "language_yaml = \"./language.yaml\"\n",
    "import yaml\n",
    "\n",
    "with open(language_yaml, \"r\") as f:\n",
    "    mars_language = yaml.safe_load(f)[\"_field\"]\n",
    "\n",
    "for k in mars_language.keys(): print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "be9074a8-a56f-4fd0-a466-de8904faaa1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9dd26fe4-5da5-48a5-9e43-83ac1085f7e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([Key(key='class', value='od', key_spec=class=od, reason='Matches'),\n",
       "  Key(key='stream', value=5, key_spec=stream, reason='Matches'),\n",
       "  Key(key='date', value='', key_spec=date, reason='Key Missing')],\n",
       " [Key(key='class', value='ensemble', key_spec=class=ensemble, reason='Matches'),\n",
       "  Key(key='number', value='2', key_spec=number, reason='Matches'),\n",
       "  Key(key='stream', value=5, key_spec=stream, reason='Matches'),\n",
       "  Key(key='date', value='', key_spec=date, reason='Key Missing')])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from fdb_schema import FDBSchemaFile\n",
    "schema = FDBSchemaFile(\"./test_schema\")\n",
    "\n",
    "r = {\n",
    "    \"class\" : [\"ensemble\", \"od\"],\n",
    "    \"number\" : \"2\",\n",
    "    \"stream\" : 5,\n",
    "}\n",
    "\n",
    "a, b = schema.match_all(r)\n",
    "a, b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f46268e3-e197-47b9-bb6e-94f06e0bf648",
   "metadata": {},
   "outputs": [],
   "source": [
    "([],\n",
    " [[Key(key='class', value='od', key_spec=class=od, reason='Matches'),\n",
    "   Key(key='stream', value=5, key_spec=stream, reason='Matches'),\n",
    "   Key(key='date', value='', key_spec=date, reason='Key Missing')],\n",
    "  \n",
    "  [Key(key='class', value='ensemble', key_spec=class=ensemble, reason='Matches'),\n",
    "   Key(key='number', value='2', key_spec=number, reason='Matches'),\n",
    "   Key(key='stream', value=5, key_spec=stream, reason='Matches'),\n",
    "   Key(key='date', value='', key_spec=date, reason='Key Missing')]])"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python [conda env:micromamba-ionbeam]",
   "language": "python",
   "name": "conda-env-micromamba-ionbeam-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}