matt-long/cmip-ap-search.ipynb

## cmip-ap-search.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "41b026d4-ef89-429b-9497-b1081f603a84",
   "metadata": {},
   "source": [
    "# Demonstrate CMIP-AP access using [intake-esm](https://intake-esm.readthedocs.io/en/latest/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6a6a75d1-755a-471f-ae53-3f945eaea9ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import intake"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8b117a5-795e-4fac-b9d3-d4c749dfa6ab",
   "metadata": {},
   "source": [
    "## Load the catalog\n",
    "\n",
    "Note that we're updating catalog files manually, so they can occasionally be out of date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2f7adc2a-4b4f-476e-aa3a-9544ae0d4d25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong>glade-cmip6 catalog with 5112 dataset(s) from 2139741 asset(s)</strong>:</p> <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>activity_id</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>institution_id</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_id</th>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment_id</th>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member_id</th>\n",
       "      <td>421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>table_id</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable_id</th>\n",
       "      <td>1070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grid_label</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <td>581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_range</th>\n",
       "      <td>20941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path</th>\n",
       "      <td>2139741</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "catalog_json = '/glade/collections/cmip/catalog/intake-esm-datastore/catalogs/glade-cmip6.json'\n",
    "cat = intake.open_esm_datastore(catalog_json)\n",
    "cat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e0a4d09-d923-486f-a228-2bcde98072c9",
   "metadata": {},
   "source": [
    "## Search for a particular dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fd356cff-12d1-467a-b4b5-b1995d6a9cc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong>glade-cmip6 catalog with 28 dataset(s) from 4479 asset(s)</strong>:</p> <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>activity_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>institution_id</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_id</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member_id</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>table_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grid_label</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_range</th>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path</th>\n",
       "      <td>4479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_sub = cat.search(table_id=\"Omon\", variable_id=\"thetao\", experiment_id=\"historical\", grid_label=\"gn\")\n",
    "cat_sub"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd01006f-00d2-4bba-ae8e-a1cf151b8944",
   "metadata": {},
   "source": [
    "Display a unique list of models that have data for this search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1be8fba1-f8cf-4261-8c47-0263e5c2c79c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['AWI-CM-1-1-MR', 'BCC-CSM2-MR', 'BCC-ESM1', 'CAMS-CSM1-0',\n",
       "       'FGOALS-f3-L', 'CanESM5', 'CNRM-CM6-1', 'CNRM-ESM2-1',\n",
       "       'EC-Earth3-Veg', 'EC-Earth3', 'IPSL-CM6A-LR', 'MIROC-ES2L',\n",
       "       'MIROC6', 'HadGEM3-GC31-LL', 'UKESM1-0-LL', 'GISS-E2-1-G-CC',\n",
       "       'GISS-E2-1-G', 'CESM2-FV2', 'CESM2-WACCM-FV2', 'CESM2-WACCM',\n",
       "       'CESM2', 'NorCPM1', 'NorESM2-LM', 'GFDL-CM4', 'GFDL-ESM4', 'NESM3',\n",
       "       'SAM0-UNICON', 'MCM-UA-1-0'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_sub.df.source_id.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8065fa28-2079-4b7b-9d85-dfd47963d377",
   "metadata": {},
   "source": [
    "Load the data for a particular model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a4b6df3f-cc20-4fe6-8071-330665fb722d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/glade/work/mclong/miniconda3/envs/sno/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'thetao' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  decode_timedelta=decode_timedelta,\n"
     ]
    },
    {
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    {
     "data": {
      "text/plain": [
       "{'CMIP.NCAR.CESM2.historical.Omon.gn': <xarray.Dataset>\n",
       " Dimensions:    (member_id: 11, time: 1980, lev: 60, nlat: 384, nlon: 320, d2: 2, vertices: 4)\n",
       " Coordinates:\n",
       "     lat        (nlat, nlon) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
       "   * lev        (lev) float64 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05\n",
       "     lon        (nlat, nlon) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
       "   * nlat       (nlat) int32 1 2 3 4 5 6 7 8 ... 377 378 379 380 381 382 383 384\n",
       "   * nlon       (nlon) int32 1 2 3 4 5 6 7 8 ... 313 314 315 316 317 318 319 320\n",
       "   * time       (time) object 1850-01-15 13:00:00.000007 ... 2014-12-15 12:00:00\n",
       "   * member_id  (member_id) <U9 'r10i1p1f1' 'r11i1p1f1' ... 'r8i1p1f1' 'r9i1p1f1'\n",
       " Dimensions without coordinates: d2, vertices\n",
       " Data variables:\n",
       "     thetao     (member_id, time, lev, nlat, nlon) float32 dask.array<chunksize=(1, 600, 60, 384, 320), meta=np.ndarray>\n",
       "     time_bnds  (time, d2) object dask.array<chunksize=(600, 2), meta=np.ndarray>\n",
       "     lat_bnds   (nlat, nlon, vertices) float32 dask.array<chunksize=(384, 320, 4), meta=np.ndarray>\n",
       "     lon_bnds   (nlat, nlon, vertices) float32 dask.array<chunksize=(384, 320, 4), meta=np.ndarray>\n",
       "     lev_bnds   (lev, d2) float32 dask.array<chunksize=(60, 2), meta=np.ndarray>\n",
       " Attributes: (12/46)\n",
       "     source_type:             AOGCM BGC\n",
       "     source_id:               CESM2\n",
       "     source:                  CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite ...\n",
       "     sub_experiment:          none\n",
       "     Conventions:             CF-1.7 CMIP-6.2\n",
       "     license:                 CMIP6 model data produced by <The National Cente...\n",
       "     ...                      ...\n",
       "     parent_activity_id:      CMIP\n",
       "     variant_label:           r9i1p1f1\n",
       "     contact:                 cesm_cmip6@ucar.edu\n",
       "     institution:             National Center for Atmospheric Research, Climat...\n",
       "     parent_mip_era:          CMIP6\n",
       "     intake_esm_dataset_key:  CMIP.NCAR.CESM2.historical.Omon.gn}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsets = cat_sub.search(source_id=\"CESM2\").to_dataset_dict()\n",
    "dsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f280d92a-f696-4047-ac98-7115acddca9c",
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 6,
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     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dsets['CMIP.NCAR.CESM2.historical.Omon.gn'].thetao.isel(member_id=0, time=0, lev=0).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d5052de-0272-46a7-b391-7f23dcd609be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9295aac-f35a-415c-8b06-e66ee01ae1d9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:miniconda3-sno]",
   "language": "python",
   "name": "conda-env-miniconda3-sno-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": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
No results found