examples/intake-esm-s3-nc-simple-access.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Authors: A.Radhakrishnan, GFDL\n",
    "Ack: Anderson Banihirwe for intake-esm updates, GFDL colleagues for data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from netCDF4 import Dataset\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "import xarray as xr\n",
    "import intake,yaml\n",
    "import intake_esm\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "esgf-world.json is the ESM collections spec file for the netCDF data in the S3 bucket esgf-world. \n",
    "The catalog is updated on an on-demand basis for now. \n",
    "You can refer to https://github.com/aradhakrishnanGFDL/gfdl-aws-analysis/tree/community/esm-collection-spec-examples for the most recent catalogs\n",
    "More examples can be found in https://github.com/aradhakrishnanGFDL/gfdl-aws-analysis/tree/community/examples \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_url = \"https://cmip6-nc.s3.us-east-2.amazonaws.com/esgf-world.json\" \n",
    "#col_url = \"https://raw.githubusercontent.com/aradhakrishnanGFDL/gfdl-aws-analysis/community/esm-collection-spec-examples/esgf-world.json\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = intake.open_esm_datastore(col_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>project</th>\n",
       "      <th>institution_id</th>\n",
       "      <th>source_id</th>\n",
       "      <th>experiment_id</th>\n",
       "      <th>frequency</th>\n",
       "      <th>modeling_realm</th>\n",
       "      <th>table_id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>grid_label</th>\n",
       "      <th>variable_id</th>\n",
       "      <th>temporal_subset</th>\n",
       "      <th>version</th>\n",
       "      <th>path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>AS-RCEC</td>\n",
       "      <td>TaiESM1</td>\n",
       "      <td>histSST-piNTCF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AERmon</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>gn</td>\n",
       "      <td>ps</td>\n",
       "      <td>185001-201412</td>\n",
       "      <td>v20200318</td>\n",
       "      <td>s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>AS-RCEC</td>\n",
       "      <td>TaiESM1</td>\n",
       "      <td>histSST-piNTCF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CFmon</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>gn</td>\n",
       "      <td>ta</td>\n",
       "      <td>185001-201412</td>\n",
       "      <td>v20200318</td>\n",
       "      <td>s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>AS-RCEC</td>\n",
       "      <td>TaiESM1</td>\n",
       "      <td>histSST-piNTCF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LImon</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>gn</td>\n",
       "      <td>snc</td>\n",
       "      <td>185002-201412</td>\n",
       "      <td>v20200318</td>\n",
       "      <td>s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>AS-RCEC</td>\n",
       "      <td>TaiESM1</td>\n",
       "      <td>histSST-piNTCF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LImon</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>gn</td>\n",
       "      <td>snd</td>\n",
       "      <td>185002-201412</td>\n",
       "      <td>v20200318</td>\n",
       "      <td>s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>AS-RCEC</td>\n",
       "      <td>TaiESM1</td>\n",
       "      <td>histSST-piNTCF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LImon</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>gn</td>\n",
       "      <td>snw</td>\n",
       "      <td>185002-201412</td>\n",
       "      <td>v20200318</td>\n",
       "      <td>s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1151014</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>UA</td>\n",
       "      <td>MCM-UA-1-0</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>mon</td>\n",
       "      <td>atmos</td>\n",
       "      <td>Amon</td>\n",
       "      <td>r1i1p1f2</td>\n",
       "      <td>gn</td>\n",
       "      <td>rlut</td>\n",
       "      <td>201501-210012</td>\n",
       "      <td>v20190731</td>\n",
       "      <td>s3://esgf-world/CMIP6/ScenarioMIP/UA/MCM-UA-1-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1151015</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>UA</td>\n",
       "      <td>MCM-UA-1-0</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>mon</td>\n",
       "      <td>atmos</td>\n",
       "      <td>Amon</td>\n",
       "      <td>r1i1p1f2</td>\n",
       "      <td>gn</td>\n",
       "      <td>rtmt</td>\n",
       "      <td>201501-210012</td>\n",
       "      <td>v20190731</td>\n",
       "      <td>s3://esgf-world/CMIP6/ScenarioMIP/UA/MCM-UA-1-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1151016</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>UA</td>\n",
       "      <td>MCM-UA-1-0</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>mon</td>\n",
       "      <td>ocean</td>\n",
       "      <td>Omon</td>\n",
       "      <td>r1i1p1f2</td>\n",
       "      <td>gn</td>\n",
       "      <td>sos</td>\n",
       "      <td>201501-210012</td>\n",
       "      <td>v20190731</td>\n",
       "      <td>s3://esgf-world/CMIP6/ScenarioMIP/UA/MCM-UA-1-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1151017</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>UA</td>\n",
       "      <td>MCM-UA-1-0</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>mon</td>\n",
       "      <td>ocean</td>\n",
       "      <td>Omon</td>\n",
       "      <td>r1i1p1f2</td>\n",
       "      <td>gn</td>\n",
       "      <td>tos</td>\n",
       "      <td>201501-210012</td>\n",
       "      <td>v20190731</td>\n",
       "      <td>s3://esgf-world/CMIP6/ScenarioMIP/UA/MCM-UA-1-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1151018</th>\n",
       "      <td>CMIP6</td>\n",
       "      <td>UA</td>\n",
       "      <td>MCM-UA-1-0</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fx</td>\n",
       "      <td>r1i1p1f2</td>\n",
       "      <td>gn</td>\n",
       "      <td>areacella</td>\n",
       "      <td>NaN</td>\n",
       "      <td>v20190731</td>\n",
       "      <td>s3://esgf-world/CMIP6/ScenarioMIP/UA/MCM-UA-1-...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1151019 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        project institution_id   source_id   experiment_id frequency  \\\n",
       "0         CMIP6        AS-RCEC     TaiESM1  histSST-piNTCF       NaN   \n",
       "1         CMIP6        AS-RCEC     TaiESM1  histSST-piNTCF       NaN   \n",
       "2         CMIP6        AS-RCEC     TaiESM1  histSST-piNTCF       NaN   \n",
       "3         CMIP6        AS-RCEC     TaiESM1  histSST-piNTCF       NaN   \n",
       "4         CMIP6        AS-RCEC     TaiESM1  histSST-piNTCF       NaN   \n",
       "...         ...            ...         ...             ...       ...   \n",
       "1151014   CMIP6             UA  MCM-UA-1-0          ssp585       mon   \n",
       "1151015   CMIP6             UA  MCM-UA-1-0          ssp585       mon   \n",
       "1151016   CMIP6             UA  MCM-UA-1-0          ssp585       mon   \n",
       "1151017   CMIP6             UA  MCM-UA-1-0          ssp585       mon   \n",
       "1151018   CMIP6             UA  MCM-UA-1-0          ssp585       NaN   \n",
       "\n",
       "        modeling_realm table_id member_id grid_label variable_id  \\\n",
       "0                  NaN   AERmon  r1i1p1f1         gn          ps   \n",
       "1                  NaN    CFmon  r1i1p1f1         gn          ta   \n",
       "2                  NaN    LImon  r1i1p1f1         gn         snc   \n",
       "3                  NaN    LImon  r1i1p1f1         gn         snd   \n",
       "4                  NaN    LImon  r1i1p1f1         gn         snw   \n",
       "...                ...      ...       ...        ...         ...   \n",
       "1151014          atmos     Amon  r1i1p1f2         gn        rlut   \n",
       "1151015          atmos     Amon  r1i1p1f2         gn        rtmt   \n",
       "1151016          ocean     Omon  r1i1p1f2         gn         sos   \n",
       "1151017          ocean     Omon  r1i1p1f2         gn         tos   \n",
       "1151018            NaN       fx  r1i1p1f2         gn   areacella   \n",
       "\n",
       "        temporal_subset    version  \\\n",
       "0         185001-201412  v20200318   \n",
       "1         185001-201412  v20200318   \n",
       "2         185002-201412  v20200318   \n",
       "3         185002-201412  v20200318   \n",
       "4         185002-201412  v20200318   \n",
       "...                 ...        ...   \n",
       "1151014   201501-210012  v20190731   \n",
       "1151015   201501-210012  v20190731   \n",
       "1151016   201501-210012  v20190731   \n",
       "1151017   201501-210012  v20190731   \n",
       "1151018             NaN  v20190731   \n",
       "\n",
       "                                                      path  \n",
       "0        s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES...  \n",
       "1        s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES...  \n",
       "2        s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES...  \n",
       "3        s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES...  \n",
       "4        s3://esgf-world/CMIP6/AerChemMIP/AS-RCEC/TaiES...  \n",
       "...                                                    ...  \n",
       "1151014  s3://esgf-world/CMIP6/ScenarioMIP/UA/MCM-UA-1-...  \n",
       "1151015  s3://esgf-world/CMIP6/ScenarioMIP/UA/MCM-UA-1-...  \n",
       "1151016  s3://esgf-world/CMIP6/ScenarioMIP/UA/MCM-UA-1-...  \n",
       "1151017  s3://esgf-world/CMIP6/ScenarioMIP/UA/MCM-UA-1-...  \n",
       "1151018  s3://esgf-world/CMIP6/ScenarioMIP/UA/MCM-UA-1-...  \n",
       "\n",
       "[1151019 rows x 13 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col.df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Examples to just search for what we want from the catalog\n",
    "expname_filter = ['historical']\n",
    "table_id_filter = 'Amon'\n",
    "model_filter = 'GFDL-ESM4'\n",
    "variable_id_filter = \"tas\"\n",
    "ens_filter = \"r1i1p1f1\"\n",
    "version_filter = \"v20190726\"\n",
    "cat = col.search(experiment_id=expname_filter, table_id=table_id_filter,source_id=model_filter,variable_id=variable_id_filter,version=\"v20190726\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong>gfdltest catalog with 1 dataset(s) from 2 asset(s)</strong>:</p> <div>\n",
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       "      <th></th>\n",
       "      <th>unique</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>project</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>institution_id</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>source_id</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>experiment_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frequency</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>modeling_realm</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>table_id</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>member_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>variable_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temporal_subset</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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   "source": [
    "cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'project.institution_id.source_id.experiment_id.table_id'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
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   ],
   "source": [
    "dset_dict = cat.to_dataset_dict(cdf_kwargs={'chunks': {'time': 1}},storage_options={'anon':True})\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CMIP6.NOAA-GFDL.GFDL-ESM4.historical.Amon': <xarray.Dataset>\n",
       " Dimensions:    (bnds: 2, lat: 180, lon: 288, member_id: 1, time: 1980)\n",
       " Coordinates:\n",
       "   * bnds       (bnds) float64 1.0 2.0\n",
       "     height     float64 ...\n",
       "   * lat        (lat) float64 -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
       "   * lon        (lon) float64 0.625 1.875 3.125 4.375 ... 355.6 356.9 358.1 359.4\n",
       "   * time       (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "   * member_id  (member_id) <U8 'r1i1p1f1'\n",
       " Data variables:\n",
       "     lat_bnds   (lat, bnds) float64 dask.array<chunksize=(180, 2), meta=np.ndarray>\n",
       "     lon_bnds   (lon, bnds) float64 dask.array<chunksize=(288, 2), meta=np.ndarray>\n",
       "     tas        (member_id, time, lat, lon) float32 dask.array<chunksize=(1, 1, 180, 288), meta=np.ndarray>\n",
       "     time_bnds  (time, bnds) object dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       " Attributes: (12/47)\n",
       "     history:                 File was processed by fremetar (GFDL analog of C...\n",
       "     experiment_id:           historical\n",
       "     forcing_index:           [1]\n",
       "     data_specs_version:      01.00.27\n",
       "     frequency:               mon\n",
       "     parent_mip_era:          CMIP6\n",
       "     ...                      ...\n",
       "     branch_time_in_parent:   [36500.]\n",
       "     external_variables:      areacella\n",
       "     experiment:              all-forcing simulation of the recent past\n",
       "     source_type:             AOGCM AER CHEM BGC\n",
       "     branch_time_in_child:    [0.]\n",
       "     intake_esm_dataset_key:  CMIP6.NOAA-GFDL.GFDL-ESM4.historical.Amon}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "hxr_gfdl_esm4 = dset_dict[\"CMIP6.NOAA-GFDL.GFDL-ESM4.historical.Amon\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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       "  <text x=\"120.294118\" y=\"93.356296\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,120.294118,93.356296)\">1980</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-82f48ad5-41bb-40e2-84df-f1ba71a31291' class='xr-section-summary-in' type='checkbox'  checked><label for='section-82f48ad5-41bb-40e2-84df-f1ba71a31291' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3efa188e-909e-4158-8c9d-970632f49d9c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3efa188e-909e-4158-8c9d-970632f49d9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56c13de7-33f1-46ae-8851-b8fd8b954dc9' class='xr-var-data-in' type='checkbox'><label for='data-56c13de7-33f1-46ae-8851-b8fd8b954dc9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>description :</span></dt><dd>~2 m standard surface air temperature and surface humidity  height</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-e81fc6c2-5869-4cef-80ab-23babdb056c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e81fc6c2-5869-4cef-80ab-23babdb056c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98950fc4-f396-468a-9589-476d67383375' class='xr-var-data-in' type='checkbox'><label for='data-98950fc4-f396-468a-9589-476d67383375' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.625 1.875 3.125 ... 358.1 359.4</div><input id='attrs-073b2654-0971-4a85-8955-01d3c7036cae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-073b2654-0971-4a85-8955-01d3c7036cae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b68dcdb0-1830-4d3b-b098-7b2f0a149a5e' class='xr-var-data-in' type='checkbox'><label for='data-b68dcdb0-1830-4d3b-b098-7b2f0a149a5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><pre>array([  0.625,   1.875,   3.125, ..., 356.875, 358.125, 359.375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-02e9d457-61e3-457a-b9b7-151b2c31f94e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02e9d457-61e3-457a-b9b7-151b2c31f94e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11636bb9-a4ab-40a3-adf2-3fcc08c7637d' class='xr-var-data-in' type='checkbox'><label for='data-11636bb9-a4ab-40a3-adf2-3fcc08c7637d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>noleap</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>description :</span></dt><dd>Temporal mean</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-7c28f4e4-2399-4e76-b1b3-400f83e46b9f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c28f4e4-2399-4e76-b1b3-400f83e46b9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c3e84fb-86b8-449f-adeb-c55c597268c7' class='xr-var-data-in' type='checkbox'><label for='data-0c3e84fb-86b8-449f-adeb-c55c597268c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;r1i1p1f1&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-867e046d-b20a-4518-8e19-556a06cec607' class='xr-section-summary-in' type='checkbox'  checked><label for='section-867e046d-b20a-4518-8e19-556a06cec607' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>interp_method :</span></dt><dd>conserve_order2</dd><dt><span>original_name :</span></dt><dd>tas</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tas' (member_id: 1, time: 1980, lat: 180, lon: 288)>\n",
       "dask.array<broadcast_to, shape=(1, 1980, 180, 288), dtype=float32, chunksize=(1, 1, 180, 288), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    height     float64 ...\n",
       "  * lat        (lat) float64 -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
       "  * lon        (lon) float64 0.625 1.875 3.125 4.375 ... 355.6 356.9 358.1 359.4\n",
       "  * time       (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "  * member_id  (member_id) <U8 'r1i1p1f1'\n",
       "Attributes:\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    units:          K\n",
       "    cell_methods:   area: time: mean\n",
       "    cell_measures:  area: areacella\n",
       "    standard_name:  air_temperature\n",
       "    interp_method:  conserve_order2\n",
       "    original_name:  tas"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hxr_gfdl_esm4.tas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "! GLOBAL MEAN \n",
    "let hgtas = tas[x=@ave,y=@ave,d=1,l=529:780@ave]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat1 = 60\n",
    "lat2 = 90"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "tas = hxr_gfdl_esm4.tas\n",
    "weights = np.cos(np.deg2rad(tas.lat))\n",
    "weights.name = \"weights\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "!!!!!!!!!!!!!!!! COMPUTE BASELINE (1994-2014) TAS !!!!!!!!!!!!!!!!!!!!!!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[287.16173045]\n"
     ]
    }
   ],
   "source": [
    "hgtas = hxr_gfdl_esm4.tas.sel(time=slice(\"1994\", \"2014\"))\n",
    "hgtas = hgtas.weighted(weights)\n",
    "hgtas = hgtas.mean(dim=['lon','lat','time'])\n",
    "hgtas = hgtas.values\n",
    "print(hgtas) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[264.3833672]\n"
     ]
    }
   ],
   "source": [
    "hatas = hxr_gfdl_esm4.tas.sel(time=slice(\"1994\", \"2014\"),lat=slice(lat1, lat2))\n",
    "hatas = hatas.weighted(weights)\n",
    "hatas = hatas.mean(dim=['lon','lat','time'])\n",
    "hatas = hatas.values \n",
    "print(hatas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  54238.,  294657.,  313952.,  822221.,  868236., 1102418.,\n",
       "        2664606., 2776262., 4056464.,  110626.]),\n",
       " array([199.64807, 211.30887, 222.96968, 234.63048, 246.29129, 257.9521 ,\n",
       "        269.61288, 281.27368, 292.9345 , 304.5953 , 316.2561 ],\n",
       "       dtype=float32),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hgtas_hist = tas.sel(time=slice(\"1994\", \"2014\"))\n",
    "hgtas_hist.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hgtas2d = tas.isel(time=1).plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:enso_ml]",
   "language": "python",
   "name": "conda-env-enso_ml-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": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}