{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a1e27a82-7792-4544-939a-b3ca2adc87fd",
   "metadata": {},
   "source": [
    "# Build a catalog for CESM1 timeseries output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beb0521b-922f-48f2-960c-bb2526ba1671",
   "metadata": {},
   "source": [
    "## Imports\n",
    "First, we import the `Builder` object and the parser we are using, in this case, `parse_cesm_timeseries`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "94483fcd-b5ec-4668-985f-291675c52637",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ecgtools import Builder\n",
    "from ecgtools.parsers.cesm import parse_cesm_timeseries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63fad45a-3a88-415c-bc2e-6df9af0802a6",
   "metadata": {},
   "source": [
    "## Setup the Builder\n",
    "In this example, we are using sample CESM model output stored in `/glade/work/mgrover/cesm_test_data/b.e20.B1850.f19_g17.test/`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d771fac5-09b2-498c-9de7-d579f447f1d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "b = Builder(\n",
    "    # Where to look for model output\n",
    "    \"/glade/campaign/univ/udeo0005/cesmLE_no_pinatubo/\",\n",
    "    depth=5,\n",
    "    exclude_patterns=[\"*/hist/*\", \"*/rest/*\"],\n",
    "    njobs=-1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06b6ba6d-b468-41ba-9c63-228e16febf6a",
   "metadata": {},
   "source": [
    "## Configuring the parser\n",
    "Since we are working with CESM1 model output, we will specify the stream information to ensure the parser is adding the correct information to our catalog"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79849523-c095-480e-b1e7-df74cf1d6624",
   "metadata": {},
   "source": [
    "Notice how the parser takes in two arguments, the `file` and `user_streams_dict`\n",
    "\n",
    "The `user_streams_dict` allows us to customize the stream information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fb05360f-6f4f-458e-91e9-0cbb262164dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mparse_cesm_timeseries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muser_streams_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m Parser for CESM Timeseries files\n",
       "\u001b[0;31mFile:\u001b[0m      /glade/work/mgrover/git_repos/ecgtools/ecgtools/parsers/cesm.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?parse_cesm_timeseries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7cc1e7d-a526-40cf-89bb-11063a7b7fdb",
   "metadata": {},
   "source": [
    "We setup a dictionary formatted in the following way - this is a good practice to investigate which streams are included in your CESM model output, their associated components, and the frequency. Since this CESM1, the `frequency` information is not stored within the attributes, which means the parser will use the [`default_streams` dictionary](https://github.com/mgrover1/ecgtools/blob/main/ecgtools/parsers/cesm.py#L12#L58) to parse the files and assign metadata within the catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9aa1b599-e1fd-489f-bd3f-7d017e8ab3ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "stream_info = {\n",
    "    'cam.h0': {'component': 'atm', 'frequency': 'month_1'},\n",
    "    'cam.h1': {'component': 'atm', 'frequency': 'day_1'},\n",
    "    'cam.h2': {'component': 'atm', 'frequency': 'hour_6'},\n",
    "    'cice.h': {'component': 'ice', 'frequency': 'month_1'},\n",
    "    'cice.h1': {'component': 'ice', 'frequency': 'day_1'},\n",
    "    'clm2.h0': {'component': 'lnd', 'frequency': 'month_1'},\n",
    "    'clm2.h1': {'component': 'lnd', 'frequency': 'day_1'},\n",
    "    'pop.h.ecosys.nyear1': {'component': 'ocn', 'frequency': 'year_1'},\n",
    "    'pop.h.nday1': {'component': 'ocn', 'frequency': 'day_1'},\n",
    "    'pop.h': {'component': 'ocn', 'frequency': 'month_1'},\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29ed06bc-7f6c-41f7-b1e0-918ace433294",
   "metadata": {},
   "source": [
    "## Build the catalog\n",
    "Now that we setup our `stream_info` dictionary, we feed the parser (`parse_cesm_timeseries`) and the `stream_info` dictionary into the `.build()` call, using the following syntax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5cfe931a-f39e-4311-aaf2-2f38c350af70",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 36 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 out of   1 | elapsed:    1.9s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 36 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  90 tasks      | elapsed:    1.6s\n",
      "[Parallel(n_jobs=-1)]: Done 216 tasks      | elapsed:    2.1s\n",
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      "[Parallel(n_jobs=-1)]: Done 15876 tasks      | elapsed:  1.0min\n",
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      "[Parallel(n_jobs=-1)]: Done 24444 tasks      | elapsed:  1.6min\n",
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      "[Parallel(n_jobs=-1)]: Done 28620 tasks      | elapsed:  1.8min\n",
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      "[Parallel(n_jobs=-1)]: Done 38733 out of 38733 | elapsed:  2.2min finished\n",
      "/glade/work/mgrover/git_repos/ecgtools/ecgtools/builder.py:180: UserWarning: Unable to parse 1950 assets/files. A list of these assets can be found in `.invalid_assets` attribute.\n",
      "  parsing_func, parsing_func_kwargs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Builder(root_path=PosixPath('/glade/campaign/univ/udeo0005/cesmLE_no_pinatubo'), extension='.nc', depth=5, exclude_patterns=['*/hist/*', '*/rest/*'], njobs=-1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.build(parse_cesm_timeseries, parsing_func_kwargs={'user_streams_dict': stream_info})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "294685b5-695e-4027-a7b8-7193d9aef30b",
   "metadata": {},
   "source": [
    "## Inspect the catalog"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1693590d-8516-4651-baad-c567c23e75be",
   "metadata": {},
   "source": [
    "It looks like the primary files missed were zonally averaged timeseries files, which for now, we are not concerned about dealing with "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e6af3ae2-8367-47a7-999d-748f197abe77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved catalog location: /glade/work/mgrover/intake-esm-catalogs/pinatubo-LE.json and /glade/work/mgrover/intake-esm-catalogs/pinatubo-LE.csv\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/glade/u/home/mgrover/miniconda3/envs/cesm2-marbl/lib/python3.7/site-packages/ipykernel_launcher.py:17: UserWarning: Unable to parse 1950 assets/files. A list of these assets can be found in /glade/work/mgrover/intake-esm-catalogs/invalid_assets_pinatubo-LE.csv.\n"
     ]
    }
   ],
   "source": [
    "b.save(\n",
    "    '/glade/work/mgrover/intake-esm-catalogs/pinatubo-LE.csv',\n",
    "    # Column name including filepath\n",
    "    path_column_name='path',\n",
    "    # Column name including variables\n",
    "    variable_column_name='variable',\n",
    "    # Data file format - could be netcdf or zarr (in this case, netcdf)\n",
    "    data_format=\"netcdf\",\n",
    "    # Which attributes to groupby when reading in variables using intake-esm\n",
    "    groupby_attrs=[\"component\", \"stream\", \"case\"],\n",
    "    # Aggregations which are fed into xarray when reading in data using intake\n",
    "    aggregations=[\n",
    "        {\n",
    "            \"type\": \"join_existing\",\n",
    "            \"attribute_name\": \"time_range\",\n",
    "            \"options\": {\"dim\": \"time\", \"coords\": \"minimal\", \"compat\": \"override\"},\n",
    "        }\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c002400b-33e5-4b91-a63f-0c72d00df923",
   "metadata": {},
   "source": [
    "## Test the Catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2a60872d-c08d-4b5d-b208-2984022cb875",
   "metadata": {},
   "outputs": [],
   "source": [
    "import intake"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c28576f5-71ac-42ad-a08c-9e5bb5134ea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "col = intake.open_esm_datastore('/glade/work/mgrover/intake-esm-catalogs/pinatubo-LE.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cfd0ce51-8414-4301-ad66-fc70df171213",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['cam.h1', 'cam.h0', 'pop.h.ecosys.nday1', 'pop.h.nday1',\n",
       "       'pop.h.ecosys.nyear1', 'pop.h', 'clm2.h1', 'clm2.h0', 'rtm.h1',\n",
       "       'rtm.h0', 'cice.h1', 'cice.h'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col.df.stream.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c93534fc-2377-4853-a018-aaa743918e7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat = col.search(variable='TEMP', frequency='month_1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f796b5e3-4411-492b-9be8-c6e46f6017d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'component.stream.case'\n"
     ]
    },
    {
     "data": {
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       "                border: none;\n",
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       "      100.00% [38/38 00:03<00:00]\n",
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     "metadata": {},
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   ],
   "source": [
    "dsets = cat.to_dataset_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1f8efa71-d323-49d6-9645-f046e478f546",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = dsets['ocn.pop.h.b.e11.BRCP85C5CNBDRD_no_pinatubo.f09_g16.009']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8ca5ca5e-c708-45fe-8856-4fecc4f4c048",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2af4d74fc210>"
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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.TEMP.isel(time=0, z_t=0).plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:miniconda3-cesm2-marbl]",
   "language": "python",
   "name": "conda-env-miniconda3-cesm2-marbl-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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