notebooks/cmip6_aws_zarr.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "642d5bb1-f56f-44f2-983b-c85dabae97f7",
   "metadata": {},
   "source": [
    "# Access CMIP6 zarr data from AWS using the osdf protocol and plot surface air temperature\n",
    "- This workflow is inspired by https://gallery.pangeo.io/repos/pangeo-gallery/cmip6/global_mean_surface_temp.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2ee9f844-c4a4-4327-94a1-260a1aa1d689",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/glade/derecho/scratch/harshah/tmp/ipykernel_59105/2032592841.py:6: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from tqdm.autonotebook import tqdm\n"
     ]
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import dask\n",
    "from dask.diagnostics import progress\n",
    "from tqdm.autonotebook import tqdm\n",
    "import intake\n",
    "import fsspec\n",
    "import seaborn as sns\n",
    "import re\n",
    "import aiohttp\n",
    "from dask_jobqueue import PBSCluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "18afd4d0-1d32-4078-85a9-eb7d305cd96a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import fsspec.implementations.http as fshttp\n",
    "from pelicanfs.core import OSDFFileSystem,PelicanMap "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f4325202-4ce3-4e1e-9fdf-11dbd228f0a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "rda_scratch = '/gpfs/csfs1/collections/rda/scratch/harshah'\n",
    "rda_url     =  'https://data.rda.ucar.edu/'\n",
    "cat_url     = rda_url +  'harshah/intake_catalogs/cmip6-aws/cmip6-osdf-zarr.json'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2055d157-7e3b-41db-8d67-fe2e52daf385",
   "metadata": {},
   "source": [
    "### Spin up cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c2071ec-1edc-4b0f-abf3-0ef6abbe7c76",
   "metadata": {},
   "source": [
    "#### Select the Dask cluster type\n",
    "The default will be LocalCluster as that can run on any system.\n",
    "\n",
    "If running on a HPC computer with a PBS Scheduler, set to True. Otherwise, set to False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f4a68af9-f9c2-4eed-9a12-d0d5e5dc1c6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "USE_PBS_SCHEDULER = True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58c04f89-4484-46d5-96ea-3250488f4674",
   "metadata": {},
   "source": [
    "If running on Jupyter server with Dask Gateway configured, set to True. Otherwise, set to False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d2dc9399-cbe9-4847-aab1-02d246511907",
   "metadata": {},
   "outputs": [],
   "source": [
    "USE_DASK_GATEWAY = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c48d9879-9f31-485a-8bb4-60907a274d49",
   "metadata": {},
   "source": [
    "#### Python function for a PBS cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "24fbe212-e8d1-4dd2-9163-cbb9611dd4dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a PBS cluster object\n",
    "def get_pbs_cluster():\n",
    "    \"\"\" Create cluster through dask_jobqueue.   \n",
    "    \"\"\"\n",
    "    from dask_jobqueue import PBSCluster\n",
    "    cluster = PBSCluster(\n",
    "        job_name = 'dask-osdf-24',\n",
    "        cores = 1,\n",
    "        memory = '4GiB',\n",
    "        processes = 1,\n",
    "        local_directory = rda_scratch + '/dask/spill',\n",
    "        log_directory = rda_scratch + '/dask/logs/',\n",
    "        resource_spec = 'select=1:ncpus=1:mem=4GB',\n",
    "        queue = 'casper',\n",
    "        walltime = '3:00:00',\n",
    "        #interface = 'ib0'\n",
    "        interface = 'ext'\n",
    "    )\n",
    "    return cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27c2b856-adc4-4d8a-b8ce-1c5d7407c43d",
   "metadata": {},
   "source": [
    "#### Python function for a Gateway Cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b021dc5a-f2be-45f4-ae43-3589c87ba484",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_gateway_cluster():\n",
    "    \"\"\" Create cluster through dask_gateway\n",
    "    \"\"\"\n",
    "    from dask_gateway import Gateway\n",
    "\n",
    "    gateway = Gateway()\n",
    "    cluster = gateway.new_cluster()\n",
    "    cluster.adapt(minimum=2, maximum=4)\n",
    "    return cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b678447b-a43a-4fce-abab-bcaa8ff9ee60",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_local_cluster():\n",
    "    \"\"\" Create cluster using the Jupyter server's resources\n",
    "    \"\"\"\n",
    "    from distributed import LocalCluster, performance_report\n",
    "    cluster = LocalCluster()    \n",
    "\n",
    "    cluster.scale(6)\n",
    "    return cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30f65a4e-059e-4043-bac3-38fdbe1f8ee6",
   "metadata": {},
   "source": [
    "#### Python logic for a Local Cluster\n",
    "This uses True/False boolean logic based on the variables set in the previous cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "20253001-b812-433e-9c23-43b1e11e8d3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/glade/u/home/harshah/venvs/osdf/lib/python3.10/site-packages/distributed/node.py:187: UserWarning: Port 8787 is already in use.\n",
      "Perhaps you already have a cluster running?\n",
      "Hosting the HTTP server on port 33173 instead\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Obtain dask cluster in one of three ways\n",
    "if USE_PBS_SCHEDULER:\n",
    "    cluster = get_pbs_cluster()\n",
    "elif USE_DASK_GATEWAY:\n",
    "    cluster = get_gateway_cluster()\n",
    "else:\n",
    "    cluster = get_local_cluster()\n",
    "\n",
    "# Connect to cluster\n",
    "from distributed import Client\n",
    "client = Client(cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "55ff8442-8385-4574-9466-4c8dcd87ade2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
       "    </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">PBSCluster</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">d9815a4a</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard:</strong> <a href=\"https://jupyterhub.hpc.ucar.edu/stable/user/harshah/proxy/33173/status\" target=\"_blank\">https://jupyterhub.hpc.ucar.edu/stable/user/harshah/proxy/33173/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Workers:</strong> 0\n",
       "                </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total threads:</strong> 0\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total memory:</strong> 0 B\n",
       "                </td>\n",
       "            </tr>\n",
       "            \n",
       "        </table>\n",
       "\n",
       "        <details>\n",
       "            <summary style=\"margin-bottom: 20px;\">\n",
       "                <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
       "            </summary>\n",
       "\n",
       "            <div style=\"\">\n",
       "    <div>\n",
       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
       "        <div style=\"margin-left: 48px;\">\n",
       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-0ea55c41-2a87-4697-baa7-bc3de9087deb</p>\n",
       "            <table style=\"width: 100%; text-align: left;\">\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Comm:</strong> tcp://128.117.208.96:38063\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Workers:</strong> 0\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Dashboard:</strong> <a href=\"https://jupyterhub.hpc.ucar.edu/stable/user/harshah/proxy/33173/status\" target=\"_blank\">https://jupyterhub.hpc.ucar.edu/stable/user/harshah/proxy/33173/status</a>\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total threads:</strong> 0\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Started:</strong> Just now\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total memory:</strong> 0 B\n",
       "                    </td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "\n",
       "    <details style=\"margin-left: 48px;\">\n",
       "        <summary style=\"margin-bottom: 20px;\">\n",
       "            <h3 style=\"display: inline;\">Workers</h3>\n",
       "        </summary>\n",
       "\n",
       "        \n",
       "\n",
       "    </details>\n",
       "</div>\n",
       "\n",
       "        </details>\n",
       "    </div>\n",
       "</div>"
      ],
      "text/plain": [
       "PBSCluster(d9815a4a, 'tcp://128.117.208.96:38063', workers=0, threads=0, memory=0 B)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scale the cluster and display cluster dashboard URL\n",
    "cluster.scale(8)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34631dec-365d-4f0e-850f-d97e1c7d2e3a",
   "metadata": {},
   "source": [
    "### Load catalog and select data subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8dcef760-8b9c-4068-ad42-77c36b5959e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong>pangeo-cmip6 catalog with 7780 dataset(s) from 522217 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>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>institution_id</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_id</th>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment_id</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member_id</th>\n",
       "      <td>657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>table_id</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable_id</th>\n",
       "      <td>709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grid_label</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zstore</th>\n",
       "      <td>522217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <td>744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>derived_variable_id</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col = intake.open_esm_datastore(cat_url)\n",
    "col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9cb806e6-b742-4dda-b0a8-cf0591c0392b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['esm-ssp585-ssp126Lu',\n",
       " 'ssp126-ssp370Lu',\n",
       " 'ssp370-ssp126Lu',\n",
       " 'ssp585',\n",
       " 'ssp245',\n",
       " 'ssp370-lowNTCF',\n",
       " 'ssp370SST-ssp126Lu',\n",
       " 'ssp370SST',\n",
       " 'ssp370pdSST',\n",
       " 'ssp370SST-lowCH4',\n",
       " 'ssp370SST-lowNTCF',\n",
       " 'ssp126',\n",
       " 'ssp119',\n",
       " 'ssp370',\n",
       " 'esm-ssp585',\n",
       " 'ssp245-nat',\n",
       " 'ssp245-GHG',\n",
       " 'ssp460',\n",
       " 'ssp434',\n",
       " 'ssp534-over',\n",
       " 'ssp245-aer',\n",
       " 'ssp245-stratO3',\n",
       " 'ssp245-cov-fossil',\n",
       " 'ssp245-cov-modgreen',\n",
       " 'ssp245-cov-strgreen',\n",
       " 'ssp245-covid',\n",
       " 'ssp585-bgc']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[eid for eid in col.df['experiment_id'].unique() if 'ssp' in eid]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4b82caf2-d4b7-400e-849e-3ff746627cd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong>pangeo-cmip6 catalog with 81 dataset(s) from 81 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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>institution_id</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_id</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment_id</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member_id</th>\n",
       "      <td>1</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>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zstore</th>\n",
       "      <td>81</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>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>derived_variable_id</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# there is currently a significant amount of data for these runs\n",
    "expts = ['historical', 'ssp245', 'ssp370']\n",
    "\n",
    "query = dict(\n",
    "    experiment_id=expts,\n",
    "    table_id='Amon',\n",
    "    variable_id=['tas'],\n",
    "    member_id = 'r1i1p1f1',\n",
    "    #activity_id = 'CMIP',\n",
    ")\n",
    "\n",
    "col_subset = col.search(require_all_on=[\"source_id\"], **query)\n",
    "col_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d0bbd94d-804f-48ec-9144-e3d97530b26b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>experiment_id</th>\n",
       "      <th>variable_id</th>\n",
       "      <th>table_id</th>\n",
       "      <th>activity_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACCESS-CM2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AWI-CM-1-1-MR</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BCC-CSM2-MR</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMS-CSM1-0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAS-ESM2-0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CESM2-WACCM</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMCC-CM2-SR5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMCC-ESM2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CanESM5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EC-Earth3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EC-Earth3-Veg</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EC-Earth3-Veg-LR</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FGOALS-f3-L</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FGOALS-g3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GFDL-ESM4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IITM-ESM</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INM-CM4-8</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INM-CM5-0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IPSL-CM6A-LR</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KACE-1-0-G</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIROC6</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPI-ESM1-2-HR</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPI-ESM1-2-LR</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MRI-ESM2-0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NorESM2-LM</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NorESM2-MM</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TaiESM1</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  experiment_id  variable_id  table_id  activity_id\n",
       "source_id                                                          \n",
       "ACCESS-CM2                    3            1         1            2\n",
       "AWI-CM-1-1-MR                 3            1         1            2\n",
       "BCC-CSM2-MR                   3            1         1            2\n",
       "CAMS-CSM1-0                   3            1         1            2\n",
       "CAS-ESM2-0                    3            1         1            2\n",
       "CESM2-WACCM                   3            1         1            2\n",
       "CMCC-CM2-SR5                  3            1         1            2\n",
       "CMCC-ESM2                     3            1         1            2\n",
       "CanESM5                       3            1         1            2\n",
       "EC-Earth3                     3            1         1            2\n",
       "EC-Earth3-Veg                 3            1         1            2\n",
       "EC-Earth3-Veg-LR              3            1         1            2\n",
       "FGOALS-f3-L                   3            1         1            2\n",
       "FGOALS-g3                     3            1         1            2\n",
       "GFDL-ESM4                     3            1         1            2\n",
       "IITM-ESM                      3            1         1            2\n",
       "INM-CM4-8                     3            1         1            2\n",
       "INM-CM5-0                     3            1         1            2\n",
       "IPSL-CM6A-LR                  3            1         1            2\n",
       "KACE-1-0-G                    3            1         1            2\n",
       "MIROC6                        3            1         1            2\n",
       "MPI-ESM1-2-HR                 3            1         1            2\n",
       "MPI-ESM1-2-LR                 3            1         1            2\n",
       "MRI-ESM2-0                    3            1         1            2\n",
       "NorESM2-LM                    3            1         1            2\n",
       "NorESM2-MM                    3            1         1            2\n",
       "TaiESM1                       3            1         1            2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_subset.df.groupby(\"source_id\")[[\"experiment_id\", \"variable_id\", \"table_id\",\"activity_id\"]].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bcd3d850-239a-411e-8a16-b201222df416",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activity_id</th>\n",
       "      <th>institution_id</th>\n",
       "      <th>source_id</th>\n",
       "      <th>experiment_id</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>CSIRO-ARCCSS</td>\n",
       "      <td>ACCESS-CM2</td>\n",
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       "      <td>osdf:///aws-opendata/us-west-2/cmip6-pds/CMIP6...</td>\n",
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       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
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       "      <td>osdf:///aws-opendata/us-west-2/cmip6-pds/CMIP6...</td>\n",
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       "      <td>20191108</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>CSIRO-ARCCSS</td>\n",
       "      <td>ACCESS-CM2</td>\n",
       "      <td>ssp370</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>osdf:///aws-opendata/us-west-2/cmip6-pds/CMIP6...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20191108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>AWI</td>\n",
       "      <td>AWI-CM-1-1-MR</td>\n",
       "      <td>ssp245</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>osdf:///aws-opendata/us-west-2/cmip6-pds/CMIP6...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20190529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>AWI</td>\n",
       "      <td>AWI-CM-1-1-MR</td>\n",
       "      <td>ssp370</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>osdf:///aws-opendata/us-west-2/cmip6-pds/CMIP6...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20190529</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>NCC</td>\n",
       "      <td>NorESM2-MM</td>\n",
       "      <td>ssp370</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>osdf:///aws-opendata/us-west-2/cmip6-pds/CMIP6...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20191108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>NCC</td>\n",
       "      <td>NorESM2-MM</td>\n",
       "      <td>ssp245</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>osdf:///aws-opendata/us-west-2/cmip6-pds/CMIP6...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20191108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>AS-RCEC</td>\n",
       "      <td>TaiESM1</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>osdf:///aws-opendata/us-west-2/cmip6-pds/CMIP6...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20200623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>AS-RCEC</td>\n",
       "      <td>TaiESM1</td>\n",
       "      <td>ssp370</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>osdf:///aws-opendata/us-west-2/cmip6-pds/CMIP6...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20201014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>AS-RCEC</td>\n",
       "      <td>TaiESM1</td>\n",
       "      <td>ssp245</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>osdf:///aws-opendata/us-west-2/cmip6-pds/CMIP6...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20201124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    activity_id institution_id      source_id experiment_id member_id  \\\n",
       "0          CMIP   CSIRO-ARCCSS     ACCESS-CM2    historical  r1i1p1f1   \n",
       "1   ScenarioMIP   CSIRO-ARCCSS     ACCESS-CM2        ssp245  r1i1p1f1   \n",
       "2   ScenarioMIP   CSIRO-ARCCSS     ACCESS-CM2        ssp370  r1i1p1f1   \n",
       "3   ScenarioMIP            AWI  AWI-CM-1-1-MR        ssp245  r1i1p1f1   \n",
       "4   ScenarioMIP            AWI  AWI-CM-1-1-MR        ssp370  r1i1p1f1   \n",
       "..          ...            ...            ...           ...       ...   \n",
       "76  ScenarioMIP            NCC     NorESM2-MM        ssp370  r1i1p1f1   \n",
       "77  ScenarioMIP            NCC     NorESM2-MM        ssp245  r1i1p1f1   \n",
       "78         CMIP        AS-RCEC        TaiESM1    historical  r1i1p1f1   \n",
       "79  ScenarioMIP        AS-RCEC        TaiESM1        ssp370  r1i1p1f1   \n",
       "80  ScenarioMIP        AS-RCEC        TaiESM1        ssp245  r1i1p1f1   \n",
       "\n",
       "   table_id variable_id grid_label  \\\n",
       "0      Amon         tas         gn   \n",
       "1      Amon         tas         gn   \n",
       "2      Amon         tas         gn   \n",
       "3      Amon         tas         gn   \n",
       "4      Amon         tas         gn   \n",
       "..      ...         ...        ...   \n",
       "76     Amon         tas         gn   \n",
       "77     Amon         tas         gn   \n",
       "78     Amon         tas         gn   \n",
       "79     Amon         tas         gn   \n",
       "80     Amon         tas         gn   \n",
       "\n",
       "                                               zstore  dcpp_init_year  \\\n",
       "0   osdf:///aws-opendata/us-west-2/cmip6-pds/CMIP6...             NaN   \n",
       "1   osdf:///aws-opendata/us-west-2/cmip6-pds/CMIP6...             NaN   \n",
       "2   osdf:///aws-opendata/us-west-2/cmip6-pds/CMIP6...             NaN   \n",
       "3   osdf:///aws-opendata/us-west-2/cmip6-pds/CMIP6...             NaN   \n",
       "4   osdf:///aws-opendata/us-west-2/cmip6-pds/CMIP6...             NaN   \n",
       "..                                                ...             ...   \n",
       "76  osdf:///aws-opendata/us-west-2/cmip6-pds/CMIP6...             NaN   \n",
       "77  osdf:///aws-opendata/us-west-2/cmip6-pds/CMIP6...             NaN   \n",
       "78  osdf:///aws-opendata/us-west-2/cmip6-pds/CMIP6...             NaN   \n",
       "79  osdf:///aws-opendata/us-west-2/cmip6-pds/CMIP6...             NaN   \n",
       "80  osdf:///aws-opendata/us-west-2/cmip6-pds/CMIP6...             NaN   \n",
       "\n",
       "     version  \n",
       "0   20191108  \n",
       "1   20191108  \n",
       "2   20191108  \n",
       "3   20190529  \n",
       "4   20190529  \n",
       "..       ...  \n",
       "76  20191108  \n",
       "77  20191108  \n",
       "78  20200623  \n",
       "79  20201014  \n",
       "80  20201124  \n",
       "\n",
       "[81 rows x 11 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_subset.df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "88bb6d56-47d2-41aa-8dc5-b6fc50f2feb2",
   "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"
     ]
    },
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      "\n",
      "Dataset dictionary keys:\n",
      " dict_keys(['CMIP.NCC.NorESM2-MM.historical.Amon.gn', 'CMIP.CSIRO-ARCCSS.ACCESS-CM2.historical.Amon.gn', 'ScenarioMIP.CSIRO-ARCCSS.ACCESS-CM2.ssp245.Amon.gn', 'ScenarioMIP.NCAR.CESM2-WACCM.ssp245.Amon.gn', 'ScenarioMIP.AWI.AWI-CM-1-1-MR.ssp245.Amon.gn', 'CMIP.CCCR-IITM.IITM-ESM.historical.Amon.gn', 'CMIP.CAMS.CAMS-CSM1-0.historical.Amon.gn', 'CMIP.CAS.FGOALS-g3.historical.Amon.gn', 'ScenarioMIP.MIROC.MIROC6.ssp370.Amon.gn', 'ScenarioMIP.AS-RCEC.TaiESM1.ssp245.Amon.gn', 'CMIP.NCAR.CESM2-WACCM.historical.Amon.gn', 'ScenarioMIP.CAMS.CAMS-CSM1-0.ssp245.Amon.gn', 'CMIP.INM.INM-CM5-0.historical.Amon.gr1', 'ScenarioMIP.NCC.NorESM2-MM.ssp370.Amon.gn', 'CMIP.CMCC.CMCC-CM2-SR5.historical.Amon.gn', 'CMIP.AS-RCEC.TaiESM1.historical.Amon.gn', 'ScenarioMIP.CMCC.CMCC-CM2-SR5.ssp370.Amon.gn', 'ScenarioMIP.DKRZ.MPI-ESM1-2-HR.ssp245.Amon.gn', 'ScenarioMIP.NCC.NorESM2-LM.ssp370.Amon.gn', 'ScenarioMIP.AWI.AWI-CM-1-1-MR.ssp370.Amon.gn', 'ScenarioMIP.MIROC.MIROC6.ssp245.Amon.gn', 'CMIP.CCCma.CanESM5.historical.Amon.gn', 'ScenarioMIP.CCCR-IITM.IITM-ESM.ssp245.Amon.gn', 'ScenarioMIP.INM.INM-CM4-8.ssp245.Amon.gr1', 'ScenarioMIP.EC-Earth-Consortium.EC-Earth3.ssp245.Amon.gr', 'ScenarioMIP.INM.INM-CM5-0.ssp370.Amon.gr1', 'CMIP.MPI-M.MPI-ESM1-2-LR.historical.Amon.gn', 'ScenarioMIP.CMCC.CMCC-CM2-SR5.ssp245.Amon.gn', 'ScenarioMIP.MPI-M.MPI-ESM1-2-LR.ssp370.Amon.gn', 'ScenarioMIP.MRI.MRI-ESM2-0.ssp245.Amon.gn', 'ScenarioMIP.CCCma.CanESM5.ssp370.Amon.gn', 'CMIP.CAS.FGOALS-f3-L.historical.Amon.gr', 'ScenarioMIP.CAS.FGOALS-f3-L.ssp370.Amon.gr', 'ScenarioMIP.MRI.MRI-ESM2-0.ssp370.Amon.gn', 'ScenarioMIP.NOAA-GFDL.GFDL-ESM4.ssp245.Amon.gr1', 'CMIP.IPSL.IPSL-CM6A-LR.historical.Amon.gr', 'CMIP.CMCC.CMCC-ESM2.historical.Amon.gn', 'ScenarioMIP.CAS.CAS-ESM2-0.ssp245.Amon.gn', 'CMIP.AWI.AWI-CM-1-1-MR.historical.Amon.gn', 'ScenarioMIP.CSIRO-ARCCSS.ACCESS-CM2.ssp370.Amon.gn', 'ScenarioMIP.NCC.NorESM2-MM.ssp245.Amon.gn', 'ScenarioMIP.EC-Earth-Consortium.EC-Earth3-Veg-LR.ssp370.Amon.gr', 'ScenarioMIP.INM.INM-CM4-8.ssp370.Amon.gr1', 'CMIP.MRI.MRI-ESM2-0.historical.Amon.gn', 'ScenarioMIP.CAMS.CAMS-CSM1-0.ssp370.Amon.gn', 'ScenarioMIP.CMCC.CMCC-ESM2.ssp370.Amon.gn', 'ScenarioMIP.MPI-M.MPI-ESM1-2-LR.ssp245.Amon.gn', 'ScenarioMIP.CAS.FGOALS-g3.ssp370.Amon.gn', 'CMIP.MPI-M.MPI-ESM1-2-HR.historical.Amon.gn', 'ScenarioMIP.IPSL.IPSL-CM6A-LR.ssp245.Amon.gr', 'ScenarioMIP.NCC.NorESM2-LM.ssp245.Amon.gn', 'CMIP.INM.INM-CM4-8.historical.Amon.gr1', 'ScenarioMIP.AS-RCEC.TaiESM1.ssp370.Amon.gn', 'ScenarioMIP.CCCma.CanESM5.ssp245.Amon.gn', 'ScenarioMIP.NIMS-KMA.KACE-1-0-G.ssp370.Amon.gr', 'ScenarioMIP.DKRZ.MPI-ESM1-2-HR.ssp370.Amon.gn', 'CMIP.NIMS-KMA.KACE-1-0-G.historical.Amon.gr', 'ScenarioMIP.CAS.CAS-ESM2-0.ssp370.Amon.gn', 'CMIP.EC-Earth-Consortium.EC-Earth3-Veg-LR.historical.Amon.gr', 'ScenarioMIP.EC-Earth-Consortium.EC-Earth3-Veg.ssp370.Amon.gr', 'CMIP.NCC.NorESM2-LM.historical.Amon.gn', 'ScenarioMIP.CAS.FGOALS-g3.ssp245.Amon.gn', 'CMIP.EC-Earth-Consortium.EC-Earth3-Veg.historical.Amon.gr', 'CMIP.NOAA-GFDL.GFDL-ESM4.historical.Amon.gr1', 'ScenarioMIP.NOAA-GFDL.GFDL-ESM4.ssp370.Amon.gr1', 'CMIP.CAS.CAS-ESM2-0.historical.Amon.gn', 'ScenarioMIP.IPSL.IPSL-CM6A-LR.ssp370.Amon.gr', 'ScenarioMIP.NIMS-KMA.KACE-1-0-G.ssp245.Amon.gr', 'CMIP.EC-Earth-Consortium.EC-Earth3.historical.Amon.gr', 'ScenarioMIP.CMCC.CMCC-ESM2.ssp245.Amon.gn', 'ScenarioMIP.CAS.FGOALS-f3-L.ssp245.Amon.gr', 'ScenarioMIP.EC-Earth-Consortium.EC-Earth3.ssp370.Amon.gr', 'ScenarioMIP.CCCR-IITM.IITM-ESM.ssp370.Amon.gn', 'ScenarioMIP.NCAR.CESM2-WACCM.ssp370.Amon.gn', 'ScenarioMIP.EC-Earth-Consortium.EC-Earth3-Veg.ssp245.Amon.gr', 'ScenarioMIP.INM.INM-CM5-0.ssp245.Amon.gr1', 'ScenarioMIP.EC-Earth-Consortium.EC-Earth3-Veg-LR.ssp245.Amon.gr', 'ScenarioMIP.BCC.BCC-CSM2-MR.ssp245.Amon.gn', 'CMIP.BCC.BCC-CSM2-MR.historical.Amon.gn', 'CMIP.MIROC.MIROC6.historical.Amon.gn', 'ScenarioMIP.BCC.BCC-CSM2-MR.ssp370.Amon.gn'])\n",
      "CPU times: user 8.84 s, sys: 1.42 s, total: 10.3 s\n",
      "Wall time: 6min 28s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "dsets_osdf  = col_subset.to_dataset_dict()\n",
    "print(f\"\\nDataset dictionary keys:\\n {dsets_osdf.keys()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b747dfe5-8ce4-428d-8836-371d50e9abf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_all_bounds(ds):\n",
    "    drop_vars = [vname for vname in ds.coords\n",
    "                 if (('_bounds') in vname ) or ('_bnds') in vname]\n",
    "    return ds.drop(drop_vars)\n",
    "\n",
    "def open_dset(df):\n",
    "    assert len(df) == 1\n",
    "    ds = xr.open_zarr(fsspec.get_mapper(df.zstore.values[0]), consolidated=True)\n",
    "    return drop_all_bounds(ds)\n",
    "\n",
    "def open_delayed(df):\n",
    "    return dask.delayed(open_dset)(df)\n",
    "\n",
    "from collections import defaultdict\n",
    "dsets = defaultdict(dict)\n",
    "\n",
    "for group, df in col_subset.df.groupby(by=['source_id', 'experiment_id']):\n",
    "    dsets[group[0]][group[1]] = open_delayed(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "909202ae-a905-431e-82df-b1b564471424",
   "metadata": {},
   "outputs": [],
   "source": [
    "dsets_ = dask.compute(dict(dsets))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b01f80a5-6df5-428c-a7ef-acf3b596a239",
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate global means\n",
    "def get_lat_name(ds):\n",
    "    for lat_name in ['lat', 'latitude']:\n",
    "        if lat_name in ds.coords:\n",
    "            return lat_name\n",
    "    raise RuntimeError(\"Couldn't find a latitude coordinate\")\n",
    "\n",
    "def global_mean(ds):\n",
    "    lat = ds[get_lat_name(ds)]\n",
    "    weight = np.cos(np.deg2rad(lat))\n",
    "    weight /= weight.mean()\n",
    "    other_dims = set(ds.dims) - {'time'}\n",
    "    return (ds * weight).mean(other_dims)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6b0729f-a509-4950-a1b8-fe0dce1f97cf",
   "metadata": {},
   "source": [
    "### Calculate Global Means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "147beaca-9d0e-4bd8-8614-3a6a7bca3cad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 27/27 [01:32<00:00,  3.43s/it]\n"
     ]
    }
   ],
   "source": [
    "expt_da = xr.DataArray(expts, dims='experiment_id', name='experiment_id',\n",
    "                       coords={'experiment_id': expts})\n",
    "\n",
    "dsets_aligned = {}\n",
    "\n",
    "for k, v in tqdm(dsets_.items()):\n",
    "    expt_dsets = v.values()\n",
    "    if any([d is None for d in expt_dsets]):\n",
    "        print(f\"Missing experiment for {k}\")\n",
    "        continue\n",
    "\n",
    "    for ds in expt_dsets:\n",
    "        ds.coords['year'] = ds.time.dt.year\n",
    "\n",
    "    # workaround for\n",
    "    # https://github.com/pydata/xarray/issues/2237#issuecomment-620961663\n",
    "    dsets_ann_mean = [v[expt].pipe(global_mean).swap_dims({'time': 'year'})\n",
    "                             .drop_vars('time').coarsen(year=12).mean()\n",
    "                      for expt in expts]\n",
    "\n",
    "    # align everything with the 4xCO2 experiment\n",
    "    dsets_aligned[k] = xr.concat(dsets_ann_mean, join='outer',dim=expt_da)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "02c849a2-ecf1-4fab-9208-ecadc7c82e3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "with progress.ProgressBar():\n",
    "    dsets_aligned_ = dask.compute(dsets_aligned)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "90619917-6997-45ae-9572-ed525b15c7ed",
   "metadata": {},
   "outputs": [
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       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 167kB\n",
       "Dimensions:        (source_id: 27, experiment_id: 3, year: 251)\n",
       "Coordinates:\n",
       "  * year           (year) float64 2kB 1.85e+03 1.851e+03 ... 2.099e+03 2.1e+03\n",
       "  * experiment_id  (experiment_id) &lt;U10 120B &#x27;historical&#x27; &#x27;ssp245&#x27; &#x27;ssp370&#x27;\n",
       "  * source_id      (source_id) &lt;U16 2kB &#x27;ACCESS-CM2&#x27; ... &#x27;TaiESM1&#x27;\n",
       "Data variables:\n",
       "    tas            (source_id, experiment_id, year) float64 163kB 287.0 ... 2...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0d910e0c-7af5-43d4-9d33-1b50ac936322' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0d910e0c-7af5-43d4-9d33-1b50ac936322' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>source_id</span>: 27</li><li><span class='xr-has-index'>experiment_id</span>: 3</li><li><span class='xr-has-index'>year</span>: 251</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5d10170f-352a-44e5-8dcf-7a275ae39b4b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5d10170f-352a-44e5-8dcf-7a275ae39b4b' class='xr-section-summary' >Coordinates: <span>(3)</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 class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.85e+03 1.851e+03 ... 2.1e+03</div><input id='attrs-008fe1f9-671e-4489-a148-82dc976977fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-008fe1f9-671e-4489-a148-82dc976977fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71d004fb-078f-46f6-97fd-d5960b84cfa8' class='xr-var-data-in' type='checkbox'><label for='data-71d004fb-078f-46f6-97fd-d5960b84cfa8' 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>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([1850., 1851., 1852., ..., 2098., 2099., 2100.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>experiment_id</span></div><div class='xr-var-dims'>(experiment_id)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;historical&#x27; &#x27;ssp245&#x27; &#x27;ssp370&#x27;</div><input id='attrs-40c271fd-6fd2-4f39-bdd6-616fe91fa394' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-40c271fd-6fd2-4f39-bdd6-616fe91fa394' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23ce19e3-b205-47a9-a129-9af0b364bb95' class='xr-var-data-in' type='checkbox'><label for='data-23ce19e3-b205-47a9-a129-9af0b364bb95' 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;historical&#x27;, &#x27;ssp245&#x27;, &#x27;ssp370&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>source_id</span></div><div class='xr-var-dims'>(source_id)</div><div class='xr-var-dtype'>&lt;U16</div><div class='xr-var-preview xr-preview'>&#x27;ACCESS-CM2&#x27; ... &#x27;TaiESM1&#x27;</div><input id='attrs-a2ae9ce0-3963-4a0f-a73e-5a1d002d65be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2ae9ce0-3963-4a0f-a73e-5a1d002d65be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12edca66-9a34-4b27-a67d-53e3e8e3e97c' class='xr-var-data-in' type='checkbox'><label for='data-12edca66-9a34-4b27-a67d-53e3e8e3e97c' 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;ACCESS-CM2&#x27;, &#x27;AWI-CM-1-1-MR&#x27;, &#x27;BCC-CSM2-MR&#x27;, &#x27;CAMS-CSM1-0&#x27;,\n",
       "       &#x27;CAS-ESM2-0&#x27;, &#x27;CESM2-WACCM&#x27;, &#x27;CMCC-CM2-SR5&#x27;, &#x27;CMCC-ESM2&#x27;, &#x27;CanESM5&#x27;,\n",
       "       &#x27;EC-Earth3&#x27;, &#x27;EC-Earth3-Veg&#x27;, &#x27;EC-Earth3-Veg-LR&#x27;, &#x27;FGOALS-f3-L&#x27;,\n",
       "       &#x27;FGOALS-g3&#x27;, &#x27;GFDL-ESM4&#x27;, &#x27;IITM-ESM&#x27;, &#x27;INM-CM4-8&#x27;, &#x27;INM-CM5-0&#x27;,\n",
       "       &#x27;IPSL-CM6A-LR&#x27;, &#x27;KACE-1-0-G&#x27;, &#x27;MIROC6&#x27;, &#x27;MPI-ESM1-2-HR&#x27;,\n",
       "       &#x27;MPI-ESM1-2-LR&#x27;, &#x27;MRI-ESM2-0&#x27;, &#x27;NorESM2-LM&#x27;, &#x27;NorESM2-MM&#x27;, &#x27;TaiESM1&#x27;],\n",
       "      dtype=&#x27;&lt;U16&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a59d448a-064d-4444-9eff-f30469854057' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a59d448a-064d-4444-9eff-f30469854057' class='xr-section-summary' >Data variables: <span>(1)</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>tas</span></div><div class='xr-var-dims'>(source_id, experiment_id, year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>287.0 287.0 287.2 ... 292.2 292.2</div><input id='attrs-c4044eeb-e438-40ca-8729-baf0d03dc322' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c4044eeb-e438-40ca-8729-baf0d03dc322' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d257bcf3-28a2-40da-8a72-80da618f6d09' class='xr-var-data-in' type='checkbox'><label for='data-d257bcf3-28a2-40da-8a72-80da618f6d09' 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([[[287.00749364, 287.01698452, 287.17454216, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ..., 290.4591316 ,\n",
       "         290.48987264, 290.47033396],\n",
       "        [         nan,          nan,          nan, ..., 291.82939421,\n",
       "         291.91089187, 291.92837357]],\n",
       "\n",
       "       [[         nan, 286.82136175, 286.83013242, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ..., 289.72906638,\n",
       "         289.74245177, 289.67149832],\n",
       "        [         nan,          nan,          nan, ..., 291.23628537,\n",
       "         291.1672382 , 291.25386248]],\n",
       "\n",
       "       [[287.53316044, 287.57944112, 287.74630242, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ..., 290.41228438,\n",
       "         290.38157755, 290.43605473],\n",
       "        [         nan,          nan,          nan, ..., 291.87989311,\n",
       "         291.96489922, 291.79178862]],\n",
       "...\n",
       "       [[287.58305201, 287.80131659, 287.70510569, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ..., 289.91781284,\n",
       "         289.82392539, 289.79675765],\n",
       "        [         nan,          nan,          nan, ..., 290.68411048,\n",
       "         290.88837198, 291.09227829]],\n",
       "\n",
       "       [[287.14579909, 287.16514246, 287.06030532, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ..., 289.25248647,\n",
       "         289.44535069, 289.06052785],\n",
       "        [         nan,          nan,          nan, ..., 290.38792547,\n",
       "         290.62864325, 290.55704909]],\n",
       "\n",
       "       [[286.88855234, 287.0176198 , 287.02504101, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ..., 290.86751411,\n",
       "         290.69018884, 290.81608993],\n",
       "        [         nan,          nan,          nan, ..., 292.14112625,\n",
       "         292.20628878, 292.21111936]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-081fc422-7d1f-4ea2-b635-15eca55d4c9d' class='xr-section-summary-in' type='checkbox'  ><label for='section-081fc422-7d1f-4ea2-b635-15eca55d4c9d' class='xr-section-summary' >Indexes: <span>(3)</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-index-name'><div>year</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-9640a409-4ec9-470c-b4be-4216dc51e7d5' class='xr-index-data-in' type='checkbox'/><label for='index-9640a409-4ec9-470c-b4be-4216dc51e7d5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1850.0, 1851.0, 1852.0, 1853.0, 1854.0, 1855.0, 1856.0, 1857.0, 1858.0,\n",
       "       1859.0,\n",
       "       ...\n",
       "       2091.0, 2092.0, 2093.0, 2094.0, 2095.0, 2096.0, 2097.0, 2098.0, 2099.0,\n",
       "       2100.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;year&#x27;, length=251))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>experiment_id</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-0f455362-1fca-4891-823a-2e24a1ed0530' class='xr-index-data-in' type='checkbox'/><label for='index-0f455362-1fca-4891-823a-2e24a1ed0530' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;historical&#x27;, &#x27;ssp245&#x27;, &#x27;ssp370&#x27;], dtype=&#x27;object&#x27;, name=&#x27;experiment_id&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>source_id</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-cefc0ff2-277c-4400-9bed-4a98822dcf71' class='xr-index-data-in' type='checkbox'/><label for='index-cefc0ff2-277c-4400-9bed-4a98822dcf71' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;ACCESS-CM2&#x27;, &#x27;AWI-CM-1-1-MR&#x27;, &#x27;BCC-CSM2-MR&#x27;, &#x27;CAMS-CSM1-0&#x27;,\n",
       "       &#x27;CAS-ESM2-0&#x27;, &#x27;CESM2-WACCM&#x27;, &#x27;CMCC-CM2-SR5&#x27;, &#x27;CMCC-ESM2&#x27;, &#x27;CanESM5&#x27;,\n",
       "       &#x27;EC-Earth3&#x27;, &#x27;EC-Earth3-Veg&#x27;, &#x27;EC-Earth3-Veg-LR&#x27;, &#x27;FGOALS-f3-L&#x27;,\n",
       "       &#x27;FGOALS-g3&#x27;, &#x27;GFDL-ESM4&#x27;, &#x27;IITM-ESM&#x27;, &#x27;INM-CM4-8&#x27;, &#x27;INM-CM5-0&#x27;,\n",
       "       &#x27;IPSL-CM6A-LR&#x27;, &#x27;KACE-1-0-G&#x27;, &#x27;MIROC6&#x27;, &#x27;MPI-ESM1-2-HR&#x27;,\n",
       "       &#x27;MPI-ESM1-2-LR&#x27;, &#x27;MRI-ESM2-0&#x27;, &#x27;NorESM2-LM&#x27;, &#x27;NorESM2-MM&#x27;, &#x27;TaiESM1&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;source_id&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-efba5fb3-f025-4e3f-9ae8-4d487c722d01' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-efba5fb3-f025-4e3f-9ae8-4d487c722d01' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 167kB\n",
       "Dimensions:        (source_id: 27, experiment_id: 3, year: 251)\n",
       "Coordinates:\n",
       "  * year           (year) float64 2kB 1.85e+03 1.851e+03 ... 2.099e+03 2.1e+03\n",
       "  * experiment_id  (experiment_id) <U10 120B 'historical' 'ssp245' 'ssp370'\n",
       "  * source_id      (source_id) <U16 2kB 'ACCESS-CM2' ... 'TaiESM1'\n",
       "Data variables:\n",
       "    tas            (source_id, experiment_id, year) float64 163kB 287.0 ... 2..."
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source_ids = list(dsets_aligned_.keys())\n",
    "source_da = xr.DataArray(source_ids, dims='source_id', name='source_id',\n",
    "                         coords={'source_id': source_ids})\n",
    "\n",
    "big_ds = xr.concat([ds.reset_coords(drop=True)\n",
    "                    for ds in dsets_aligned_.values()],\n",
    "                    dim=source_da)\n",
    "\n",
    "big_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0bf4e2f2-8178-4383-9f85-3ec22085f04c",
   "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>source_id</th>\n",
       "      <th>experiment_id</th>\n",
       "      <th>year</th>\n",
       "      <th>tas</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACCESS-CM2</td>\n",
       "      <td>historical</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>287.019917</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACCESS-CM2</td>\n",
       "      <td>historical</td>\n",
       "      <td>1901.0</td>\n",
       "      <td>286.966182</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACCESS-CM2</td>\n",
       "      <td>historical</td>\n",
       "      <td>1902.0</td>\n",
       "      <td>286.994328</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACCESS-CM2</td>\n",
       "      <td>historical</td>\n",
       "      <td>1903.0</td>\n",
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       "      <td>ACCESS-CM2</td>\n",
       "      <td>historical</td>\n",
       "      <td>1904.0</td>\n",
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       "    source_id experiment_id    year         tas\n",
       "0  ACCESS-CM2    historical  1900.0  287.019917\n",
       "1  ACCESS-CM2    historical  1901.0  286.966182\n",
       "2  ACCESS-CM2    historical  1902.0  286.994328\n",
       "3  ACCESS-CM2    historical  1903.0  286.797043\n",
       "4  ACCESS-CM2    historical  1904.0  286.803313"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = big_ds.sel(year=slice(1900, 2100)).to_dataframe().reset_index()\n",
    "df_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2398a5df-0d8d-4343-be09-9a176ccca101",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/glade/u/home/harshah/venvs/osdf/lib/python3.10/site-packages/seaborn/axisgrid.py:854: FutureWarning: \n",
      "\n",
      "The `ci` parameter is deprecated. Use `errorbar='sd'` for the same effect.\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1113.5x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(data=df_all,x=\"year\", y=\"tas\", hue='experiment_id',kind=\"line\", ci=\"sd\", aspect=2);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python:osdf",
   "language": "python",
   "name": "osdf"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}