Tutorials/COSIMA_CookBook_Tutorial.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# How to use the COSIMA Cookbook\n",
    "\n",
    "## <span style=\"color:red\">⚠️🚨WARNING🚨⚠️</span>\n",
    "### <span style=\"color:red\"> **`cosima-cookbook` is deprecated and no longer being developed. Use the [ACCESS-NRI Intake catalog](https://cosima-recipes.readthedocs.io/en/latest/Tutorials/ACCESS-NRI_Intake_Catalog.html) instead.**</span>\n",
    "\n",
    "This notebook is designed to help new users get to grips with the COSIMA Cookbook.\n",
    "\n",
    "It assumes that:\n",
    " * You have access to the COSIMA cookbook.\n",
    " * We recommend using the latest version of the cookbook available through the `conda/analysis3-unstable` module on NCI.\n",
    " * You can fire up a Jupyter notebook!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Before starting,** load in some standard libraries that you are likely to need:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import cmocean as cm\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import IPython.display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition, you **always** need to load the `cosima_cookbook` module. This provides a bunch of functions that we use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cosima_cookbook as cc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. The Cookbook Philosophy\n",
    "The COSIMA Cookbook is a framework for analysing ocean-sea ice model output.\n",
    "It is designed to:\n",
    "\n",
    "* Provide examples of commonly used diagnostics;\n",
    "* Write efficient, well-documented, openly accessible code;\n",
    "* Encourage community input to the code;\n",
    "* Ensure diagnostic results are reproducible;\n",
    "* Process diagnostics directly from the model output, minimising creation of intermediate files;\n",
    "* Find methods to deal with the memory limitations of analysing high-resolution model output.\n",
    "\n",
    "\n",
    "### 1.1 A database of experiments\n",
    "The COSIMA Cookbook relies on a database of experiments in order to load model output. This database effectively holds metadata for each experiment, as well as variable names, data ranges and so on. \n",
    "\n",
    "**NCI Projects**: Access to COSIMA ocean-sea ice model output requires that you are a member of NCI projects `hh5` and `ik11` and, potentially also of `cj50`, and `jk72`.\n",
    "\n",
    "With that sorted out, there are three different ways for you to access the database:\n",
    "\n",
    "1. Use the default database, which is periodically refreshed automatically. This database sits in `/g/data/ik11/databases/cosima_master.db` and should be readable for all users. It includes all experiments stored in the COSIMA data directories on NCI under various projects mentioned above. The examples in this tutorial use this database.\n",
    "\n",
    "2. Use some of the databases sitting in `/g/data/ik11/databases/`. Note that some may *not* be up to date.\n",
    "\n",
    "3. Make your own database, which is stored in your own path and includes only the experiments you are interested in. Please refer to the `Make_Your_Own_Database` tutorial for instructions on how to create this database.\n",
    "\n",
    "To access the default database, you need to start a database session each time you fire up a notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "session = cc.database.create_session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Inbuilt Database Functions\n",
    "\n",
    "We have constructed a few functions to help you operate the cookbook and to access the datasets. These functions all sit in the `cosima_cookbook` directory. The following functions query the data available in the database, without loading the data itself.\n",
    "\n",
    "They return `pandas` dataframes, which by default are truncated to show only the first and last 5 rows. To see more results, we need to set an option in `pandas` itself:\n",
    "\n",
    "```python\n",
    "import pandas as pd\n",
    "pd.set_option(\"display.max_rows\", n)\n",
    "```\n",
    "\n",
    "Where `n` is the maximum number of rows to display without truncation; if there are more rows, the result will again be truncated. Pass `None` to never truncate (but some dataframes can be very big!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`get_experiments` lists all of the experiments that are catalogued in the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experiment</th>\n",
       "      <th>ncfiles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>woa18</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>eac-zstar-v1</td>\n",
       "      <td>29</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>eac-zstar-v2</td>\n",
       "      <td>76</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>025deg_jra55_ryf9091_gadi_norediGM</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>025deg_jra55_ryf9091_gadi_noGM</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>01deg_jra55v140_iaf_cycle4_jra55v150_extension</td>\n",
       "      <td>8485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>025deg_jra55_ryf9091_GMhi</td>\n",
       "      <td>27588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>01deg_jra55v150_iaf_cycle1</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>basal_melt_outputs</td>\n",
       "      <td>2372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>01deg_jra55v150_iaf_cycle1_old</td>\n",
       "      <td>10540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>164 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         experiment  ncfiles\n",
       "0                                             woa18       24\n",
       "1                                      eac-zstar-v1       29\n",
       "2                                      eac-zstar-v2       76\n",
       "3                025deg_jra55_ryf9091_gadi_norediGM      312\n",
       "4                    025deg_jra55_ryf9091_gadi_noGM      316\n",
       "..                                              ...      ...\n",
       "159  01deg_jra55v140_iaf_cycle4_jra55v150_extension     8485\n",
       "160                       025deg_jra55_ryf9091_GMhi    27588\n",
       "161                      01deg_jra55v150_iaf_cycle1     6000\n",
       "162                              basal_melt_outputs     2372\n",
       "163                  01deg_jra55v150_iaf_cycle1_old    10540\n",
       "\n",
       "[164 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc.querying.get_experiments(session)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Internally, an experiment is a set of netCDF4 files as shown in the above table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`get_ncfiles` provides a list of all the netcdf filenames saved for a given experiment along with the time stamp for when that file was added to the cookbook database. Note that each of these filenames are present in some or all of the output directories -- **but the cookbook philosophy is that you don't need to know about the directories in which these files are stored**. To see the relevant files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>output000/ice/OUTPUT/iceh.1958-01.nc</td>\n",
       "      <td>2022-06-17 10:35:19.884907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>output000/ice/OUTPUT/iceh.1958-02.nc</td>\n",
       "      <td>2022-06-15 20:27:00.950424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>output000/ice/OUTPUT/iceh.1958-03.nc</td>\n",
       "      <td>2022-05-20 18:19:02.443077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>output000/ice/OUTPUT/iceh.1958-04.nc</td>\n",
       "      <td>2022-05-20 18:09:59.346391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>output000/ice/OUTPUT/iceh.1958-05.nc</td>\n",
       "      <td>2022-05-20 17:58:46.536143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4721</th>\n",
       "      <td>restart150/ice/mice.nc</td>\n",
       "      <td>2022-05-31 19:53:09.657403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4722</th>\n",
       "      <td>restart150/ice/monthly_sstsss.nc</td>\n",
       "      <td>2022-05-20 18:19:57.159255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4723</th>\n",
       "      <td>restart150/ice/o2i.nc</td>\n",
       "      <td>2022-06-15 20:17:46.383853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4724</th>\n",
       "      <td>restart150/ice/sicemass.nc</td>\n",
       "      <td>2022-06-17 10:45:46.842706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4725</th>\n",
       "      <td>restart150/ice/u_star.nc</td>\n",
       "      <td>2022-06-15 20:24:15.744271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4726 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    ncfile                 index_time\n",
       "0     output000/ice/OUTPUT/iceh.1958-01.nc 2022-06-17 10:35:19.884907\n",
       "1     output000/ice/OUTPUT/iceh.1958-02.nc 2022-06-15 20:27:00.950424\n",
       "2     output000/ice/OUTPUT/iceh.1958-03.nc 2022-05-20 18:19:02.443077\n",
       "3     output000/ice/OUTPUT/iceh.1958-04.nc 2022-05-20 18:09:59.346391\n",
       "4     output000/ice/OUTPUT/iceh.1958-05.nc 2022-05-20 17:58:46.536143\n",
       "...                                    ...                        ...\n",
       "4721                restart150/ice/mice.nc 2022-05-31 19:53:09.657403\n",
       "4722      restart150/ice/monthly_sstsss.nc 2022-05-20 18:19:57.159255\n",
       "4723                 restart150/ice/o2i.nc 2022-06-15 20:17:46.383853\n",
       "4724            restart150/ice/sicemass.nc 2022-06-17 10:45:46.842706\n",
       "4725              restart150/ice/u_star.nc 2022-06-15 20:24:15.744271\n",
       "\n",
       "[4726 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc.querying.get_ncfiles(session, '025deg_jra55v13_iaf_gmredi6')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More usefully, `get_variables` provides a list of all the variables available in a specific experiment. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>long_name</th>\n",
       "      <th>units</th>\n",
       "      <th>frequency</th>\n",
       "      <th>ncfile</th>\n",
       "      <th>cell_methods</th>\n",
       "      <th># ncfiles</th>\n",
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       "      <th>time_end</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANGLE</td>\n",
       "      <td>angle grid makes with latitude line on U grid</td>\n",
       "      <td>radians</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ice/OUTPUT/iceh.2257-12.nc</td>\n",
       "      <td>None</td>\n",
       "      <td>3599</td>\n",
       "      <td>1958-01-01 00:00:00</td>\n",
       "      <td>2258-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANGLET</td>\n",
       "      <td>angle grid makes with latitude line on T grid</td>\n",
       "      <td>radians</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ice/OUTPUT/iceh.2257-12.nc</td>\n",
       "      <td>None</td>\n",
       "      <td>3599</td>\n",
       "      <td>1958-01-01 00:00:00</td>\n",
       "      <td>2258-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HTE</td>\n",
       "      <td>T cell width on East side</td>\n",
       "      <td>m</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ice/OUTPUT/iceh.2257-12.nc</td>\n",
       "      <td>None</td>\n",
       "      <td>3599</td>\n",
       "      <td>1958-01-01 00:00:00</td>\n",
       "      <td>2258-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HTN</td>\n",
       "      <td>T cell width on North side</td>\n",
       "      <td>m</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ice/OUTPUT/iceh.2257-12.nc</td>\n",
       "      <td>None</td>\n",
       "      <td>3599</td>\n",
       "      <td>1958-01-01 00:00:00</td>\n",
       "      <td>2258-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCAT</td>\n",
       "      <td>category maximum thickness</td>\n",
       "      <td>m</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ice/OUTPUT/iceh.2257-12.nc</td>\n",
       "      <td>None</td>\n",
       "      <td>3599</td>\n",
       "      <td>1958-01-01 00:00:00</td>\n",
       "      <td>2258-01-01 00:00:00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>vvel_m</td>\n",
       "      <td>ice velocity (y)</td>\n",
       "      <td>m/s</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ice/OUTPUT/iceh.2257-12.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>3599</td>\n",
       "      <td>1958-01-01 00:00:00</td>\n",
       "      <td>2258-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>xt_ocean</td>\n",
       "      <td>tcell longitude</td>\n",
       "      <td>degrees_E</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_snapshot.nc</td>\n",
       "      <td>None</td>\n",
       "      <td>308</td>\n",
       "      <td>1957-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>xu_ocean</td>\n",
       "      <td>ucell longitude</td>\n",
       "      <td>degrees_E</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_snapshot.nc</td>\n",
       "      <td>None</td>\n",
       "      <td>308</td>\n",
       "      <td>1957-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>yt_ocean</td>\n",
       "      <td>tcell latitude</td>\n",
       "      <td>degrees_N</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_snapshot.nc</td>\n",
       "      <td>None</td>\n",
       "      <td>308</td>\n",
       "      <td>1957-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>yu_ocean</td>\n",
       "      <td>ucell latitude</td>\n",
       "      <td>degrees_N</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_snapshot.nc</td>\n",
       "      <td>None</td>\n",
       "      <td>308</td>\n",
       "      <td>1957-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>192 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "         name                                      long_name      units  \\\n",
       "0       ANGLE  angle grid makes with latitude line on U grid    radians   \n",
       "1      ANGLET  angle grid makes with latitude line on T grid    radians   \n",
       "2         HTE                      T cell width on East side          m   \n",
       "3         HTN                     T cell width on North side          m   \n",
       "4        NCAT                     category maximum thickness          m   \n",
       "..        ...                                            ...        ...   \n",
       "187    vvel_m                               ice velocity (y)        m/s   \n",
       "188  xt_ocean                                tcell longitude  degrees_E   \n",
       "189  xu_ocean                                ucell longitude  degrees_E   \n",
       "190  yt_ocean                                 tcell latitude  degrees_N   \n",
       "191  yu_ocean                                 ucell latitude  degrees_N   \n",
       "\n",
       "     frequency                                ncfile cell_methods  # ncfiles  \\\n",
       "0    1 monthly  output153/ice/OUTPUT/iceh.2257-12.nc         None       3599   \n",
       "1    1 monthly  output153/ice/OUTPUT/iceh.2257-12.nc         None       3599   \n",
       "2    1 monthly  output153/ice/OUTPUT/iceh.2257-12.nc         None       3599   \n",
       "3    1 monthly  output153/ice/OUTPUT/iceh.2257-12.nc         None       3599   \n",
       "4    1 monthly  output153/ice/OUTPUT/iceh.2257-12.nc         None       3599   \n",
       "..         ...                                   ...          ...        ...   \n",
       "187  1 monthly  output153/ice/OUTPUT/iceh.2257-12.nc   time: mean       3599   \n",
       "188  1 monthly     output153/ocean/ocean_snapshot.nc         None        308   \n",
       "189  1 monthly     output153/ocean/ocean_snapshot.nc         None        308   \n",
       "190  1 monthly     output153/ocean/ocean_snapshot.nc         None        308   \n",
       "191  1 monthly     output153/ocean/ocean_snapshot.nc         None        308   \n",
       "\n",
       "              time_start             time_end  \n",
       "0    1958-01-01 00:00:00  2258-01-01 00:00:00  \n",
       "1    1958-01-01 00:00:00  2258-01-01 00:00:00  \n",
       "2    1958-01-01 00:00:00  2258-01-01 00:00:00  \n",
       "3    1958-01-01 00:00:00  2258-01-01 00:00:00  \n",
       "4    1958-01-01 00:00:00  2258-01-01 00:00:00  \n",
       "..                   ...                  ...  \n",
       "187  1958-01-01 00:00:00  2258-01-01 00:00:00  \n",
       "188  1957-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "189  1957-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "190  1957-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "191  1957-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "\n",
       "[192 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc.querying.get_variables(session, experiment='025deg_jra55v13_iaf_gmredi6', frequency='1 monthly')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since this is a pretty big list, we can search the dataframe for variable names which contain a specific string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>name</th>\n",
       "      <th>long_name</th>\n",
       "      <th>units</th>\n",
       "      <th>frequency</th>\n",
       "      <th>ncfile</th>\n",
       "      <th>cell_methods</th>\n",
       "      <th># ncfiles</th>\n",
       "      <th>time_start</th>\n",
       "      <th>time_end</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TEMP</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>restart080/ice/monthly_sstsss.nc</td>\n",
       "      <td>None</td>\n",
       "      <td>17</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>surface_temp</td>\n",
       "      <td>Conservative temperature</td>\n",
       "      <td>deg_C</td>\n",
       "      <td>1 daily</td>\n",
       "      <td>output153/ocean/ocean_daily.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>neutral_diffusion_temp</td>\n",
       "      <td>rho*dzt*cp*explicit neutral diffusion tendency...</td>\n",
       "      <td>Watts/m^2</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>neutral_gm_temp</td>\n",
       "      <td>rho*dzt*cp*GM stirring (heating)</td>\n",
       "      <td>Watts/m^2</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>pot_temp</td>\n",
       "      <td>Potential temperature</td>\n",
       "      <td>degrees K</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>surface_temp</td>\n",
       "      <td>Conservative temperature</td>\n",
       "      <td>deg_C</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>154</td>\n",
       "      <td>1957-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>temp</td>\n",
       "      <td>Conservative temperature</td>\n",
       "      <td>deg_C</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>temp_advection</td>\n",
       "      <td>cp*rho*dzt*advection tendency</td>\n",
       "      <td>Watts/m^2</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>temp_eta_smooth</td>\n",
       "      <td>surface smoother for temp</td>\n",
       "      <td>Watts/m^2</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>temp_global_ave</td>\n",
       "      <td>Global mean temp in liquid seawater</td>\n",
       "      <td>deg_C</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_scalar.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>154</td>\n",
       "      <td>1957-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>temp_nonlocal_KPP</td>\n",
       "      <td>cp*rho*dzt*nonlocal tendency from KPP</td>\n",
       "      <td>Watts/m^2</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>temp_rivermix</td>\n",
       "      <td>cp*rivermix*rho_dzt*temp</td>\n",
       "      <td>Watt/m^2</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>temp_submeso</td>\n",
       "      <td>rho*dzt*cp*submesoscale tendency (heating)</td>\n",
       "      <td>Watts/m^2</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>temp_surface_ave</td>\n",
       "      <td>Global mass weighted mean surface temp in liqu...</td>\n",
       "      <td>deg_C</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_scalar.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>154</td>\n",
       "      <td>1957-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>temp_tendency</td>\n",
       "      <td>time tendency for tracer Conservative temperature</td>\n",
       "      <td>Watts/m^2</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>temp_tendency_expl</td>\n",
       "      <td>explicit in time tendency for tracer Conservat...</td>\n",
       "      <td>Watts/m^2</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>temp_vdiffuse_diff_cbt</td>\n",
       "      <td>vert diffusion of heat due to diff_cbt</td>\n",
       "      <td>Watts/m^2</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>temp_vdiffuse_diff_cbt_conv</td>\n",
       "      <td>vert diffusion of heat due to diff_cbt_conv</td>\n",
       "      <td>Watts/m^2</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>temp_vdiffuse_impl</td>\n",
       "      <td>implicit vert diffusion of heat</td>\n",
       "      <td>Watts/m^2</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>temp_vdiffuse_k33</td>\n",
       "      <td>vert diffusion of heat due to K33 from neutral...</td>\n",
       "      <td>Watts/m^2</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>temp_vdiffuse_sbc</td>\n",
       "      <td>vert diffusion of heat due to surface flux</td>\n",
       "      <td>Watts/m^2</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>temp_xflux_adv_int_z</td>\n",
       "      <td>z-integral of cp*rho*dyt*u*temp</td>\n",
       "      <td>Watts</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>temp_xflux_gm_int_z</td>\n",
       "      <td>z-integral cp*gm_xflux*dyt*rho_dzt*temp</td>\n",
       "      <td>Watt</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>temp_xflux_ndiffuse_int_z</td>\n",
       "      <td>z-integral cp*ndiffuse_xflux*dyt*rho_dzt*temp</td>\n",
       "      <td>Watt</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>temp_xflux_submeso_int_z</td>\n",
       "      <td>z-integral cp*submeso_xflux*dyt*rho_dzt*temp</td>\n",
       "      <td>Watt</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>temp_yflux_adv_int_z</td>\n",
       "      <td>z-integral of cp*rho*dxt*v*temp</td>\n",
       "      <td>Watts</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>temp_yflux_gm_int_z</td>\n",
       "      <td>z-integral cp*gm_yflux*dyt*rho_dzt*temp</td>\n",
       "      <td>Watt</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>temp_yflux_ndiffuse_int_z</td>\n",
       "      <td>z-integral cp*ndiffuse_yflux*dxt*rho_dzt*temp</td>\n",
       "      <td>Watt</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>temp_yflux_submeso_int_z</td>\n",
       "      <td>z-integral cp*submeso_yflux*dxt*rho_dzt*temp</td>\n",
       "      <td>Watt</td>\n",
       "      <td>1 monthly</td>\n",
       "      <td>output153/ocean/ocean_month.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>34</td>\n",
       "      <td>2197-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>pot_temp</td>\n",
       "      <td>Potential temperature</td>\n",
       "      <td>degrees K</td>\n",
       "      <td>1 yearly</td>\n",
       "      <td>output153/ocean/ocean.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>154</td>\n",
       "      <td>1957-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>temp</td>\n",
       "      <td>Conservative temperature</td>\n",
       "      <td>deg_C</td>\n",
       "      <td>1 yearly</td>\n",
       "      <td>output153/ocean/ocean.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>154</td>\n",
       "      <td>1957-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>temp_xflux_adv</td>\n",
       "      <td>cp*rho*dzt*dyt*u*temp</td>\n",
       "      <td>Watts</td>\n",
       "      <td>1 yearly</td>\n",
       "      <td>output153/ocean/ocean.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>154</td>\n",
       "      <td>1957-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>temp_yflux_adv</td>\n",
       "      <td>cp*rho*dzt*dxt*v*temp</td>\n",
       "      <td>Watts</td>\n",
       "      <td>1 yearly</td>\n",
       "      <td>output153/ocean/ocean.nc</td>\n",
       "      <td>time: mean</td>\n",
       "      <td>154</td>\n",
       "      <td>1957-12-30 00:00:00</td>\n",
       "      <td>2257-12-30 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            name  \\\n",
       "1                           TEMP   \n",
       "107                 surface_temp   \n",
       "185       neutral_diffusion_temp   \n",
       "186              neutral_gm_temp   \n",
       "191                     pot_temp   \n",
       "228                 surface_temp   \n",
       "234                         temp   \n",
       "235               temp_advection   \n",
       "236              temp_eta_smooth   \n",
       "237              temp_global_ave   \n",
       "238            temp_nonlocal_KPP   \n",
       "239                temp_rivermix   \n",
       "240                 temp_submeso   \n",
       "241             temp_surface_ave   \n",
       "242                temp_tendency   \n",
       "243           temp_tendency_expl   \n",
       "244       temp_vdiffuse_diff_cbt   \n",
       "245  temp_vdiffuse_diff_cbt_conv   \n",
       "246           temp_vdiffuse_impl   \n",
       "247            temp_vdiffuse_k33   \n",
       "248            temp_vdiffuse_sbc   \n",
       "249         temp_xflux_adv_int_z   \n",
       "250          temp_xflux_gm_int_z   \n",
       "251    temp_xflux_ndiffuse_int_z   \n",
       "252     temp_xflux_submeso_int_z   \n",
       "253         temp_yflux_adv_int_z   \n",
       "254          temp_yflux_gm_int_z   \n",
       "255    temp_yflux_ndiffuse_int_z   \n",
       "256     temp_yflux_submeso_int_z   \n",
       "321                     pot_temp   \n",
       "330                         temp   \n",
       "331               temp_xflux_adv   \n",
       "332               temp_yflux_adv   \n",
       "\n",
       "                                             long_name      units  frequency  \\\n",
       "1                                                 None       None       None   \n",
       "107                           Conservative temperature      deg_C    1 daily   \n",
       "185  rho*dzt*cp*explicit neutral diffusion tendency...  Watts/m^2  1 monthly   \n",
       "186                   rho*dzt*cp*GM stirring (heating)  Watts/m^2  1 monthly   \n",
       "191                              Potential temperature  degrees K  1 monthly   \n",
       "228                           Conservative temperature      deg_C  1 monthly   \n",
       "234                           Conservative temperature      deg_C  1 monthly   \n",
       "235                      cp*rho*dzt*advection tendency  Watts/m^2  1 monthly   \n",
       "236                          surface smoother for temp  Watts/m^2  1 monthly   \n",
       "237                Global mean temp in liquid seawater      deg_C  1 monthly   \n",
       "238              cp*rho*dzt*nonlocal tendency from KPP  Watts/m^2  1 monthly   \n",
       "239                           cp*rivermix*rho_dzt*temp   Watt/m^2  1 monthly   \n",
       "240         rho*dzt*cp*submesoscale tendency (heating)  Watts/m^2  1 monthly   \n",
       "241  Global mass weighted mean surface temp in liqu...      deg_C  1 monthly   \n",
       "242  time tendency for tracer Conservative temperature  Watts/m^2  1 monthly   \n",
       "243  explicit in time tendency for tracer Conservat...  Watts/m^2  1 monthly   \n",
       "244             vert diffusion of heat due to diff_cbt  Watts/m^2  1 monthly   \n",
       "245        vert diffusion of heat due to diff_cbt_conv  Watts/m^2  1 monthly   \n",
       "246                    implicit vert diffusion of heat  Watts/m^2  1 monthly   \n",
       "247  vert diffusion of heat due to K33 from neutral...  Watts/m^2  1 monthly   \n",
       "248         vert diffusion of heat due to surface flux  Watts/m^2  1 monthly   \n",
       "249                    z-integral of cp*rho*dyt*u*temp      Watts  1 monthly   \n",
       "250            z-integral cp*gm_xflux*dyt*rho_dzt*temp       Watt  1 monthly   \n",
       "251      z-integral cp*ndiffuse_xflux*dyt*rho_dzt*temp       Watt  1 monthly   \n",
       "252       z-integral cp*submeso_xflux*dyt*rho_dzt*temp       Watt  1 monthly   \n",
       "253                    z-integral of cp*rho*dxt*v*temp      Watts  1 monthly   \n",
       "254            z-integral cp*gm_yflux*dyt*rho_dzt*temp       Watt  1 monthly   \n",
       "255      z-integral cp*ndiffuse_yflux*dxt*rho_dzt*temp       Watt  1 monthly   \n",
       "256       z-integral cp*submeso_yflux*dxt*rho_dzt*temp       Watt  1 monthly   \n",
       "321                              Potential temperature  degrees K   1 yearly   \n",
       "330                           Conservative temperature      deg_C   1 yearly   \n",
       "331                              cp*rho*dzt*dyt*u*temp      Watts   1 yearly   \n",
       "332                              cp*rho*dzt*dxt*v*temp      Watts   1 yearly   \n",
       "\n",
       "                               ncfile cell_methods  # ncfiles  \\\n",
       "1    restart080/ice/monthly_sstsss.nc         None         17   \n",
       "107    output153/ocean/ocean_daily.nc   time: mean         34   \n",
       "185    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "186    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "191    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "228    output153/ocean/ocean_month.nc   time: mean        154   \n",
       "234    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "235    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "236    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "237   output153/ocean/ocean_scalar.nc   time: mean        154   \n",
       "238    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "239    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "240    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "241   output153/ocean/ocean_scalar.nc   time: mean        154   \n",
       "242    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "243    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "244    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "245    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "246    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "247    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "248    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "249    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "250    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "251    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "252    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "253    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "254    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "255    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "256    output153/ocean/ocean_month.nc   time: mean         34   \n",
       "321          output153/ocean/ocean.nc   time: mean        154   \n",
       "330          output153/ocean/ocean.nc   time: mean        154   \n",
       "331          output153/ocean/ocean.nc   time: mean        154   \n",
       "332          output153/ocean/ocean.nc   time: mean        154   \n",
       "\n",
       "              time_start             time_end  \n",
       "1                   None                 None  \n",
       "107  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "185  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "186  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "191  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "228  1957-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "234  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "235  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "236  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "237  1957-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "238  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "239  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "240  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "241  1957-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "242  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "243  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "244  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "245  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "246  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "247  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "248  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "249  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "250  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "251  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "252  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "253  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "254  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "255  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "256  2197-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "321  1957-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "330  1957-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "331  1957-12-30 00:00:00  2257-12-30 00:00:00  \n",
       "332  1957-12-30 00:00:00  2257-12-30 00:00:00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vars_025deg = cc.querying.get_variables(session, experiment='025deg_jra55v13_iaf_gmredi6')\n",
    "vars_025deg[vars_025deg['name'].str.lower().str.contains('temp')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Omitting the `frequency` would give variables at all temporal frequencies.  To determine what frequencies are in a given experient, we can use `get_frequencies`. Leaving off the `experiment` gives all possible frequencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>frequency</th>\n",
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      "text/plain": [
       "   frequency\n",
       "0       None\n",
       "1    1 daily\n",
       "2  1 monthly\n",
       "3   1 yearly\n",
       "4     static"
      ]
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   "source": [
    "cc.querying.get_frequencies(session, experiment='025deg_jra55v13_iaf_gmredi6')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 Loading data from a netcdf file\n",
    "\n",
    "Python has many ways of reading in data from a netcdf file ... so we thought we would add another way. This is done via the `querying.getvar()` function, which is the most commonly used function in the cookbook. This function queries the database to find a specific variable, and loads some or all of that file.\n",
    "\n",
    "Let's take now a little while to get to know this function. In it's simplest form, you need just three arguments: `experiment`, `variable`, and database's `session`. \n",
    "\n",
    "You can see all the available options using the inbuilt help function, which brings up the function's documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function getvar in module cosima_cookbook.querying:\n",
      "\n",
      "getvar(expt, variable, session, ncfile=None, start_time=None, end_time=None, n=None, frequency=None, attrs=None, attrs_unique=None, return_dataset=False, **kwargs)\n",
      "    For a given experiment, return an xarray DataArray containing the\n",
      "    specified variable.\n",
      "    \n",
      "    expt - text string indicating the name of the experiment\n",
      "    variable - text string indicating the name of the variable to load\n",
      "    session - a database session created by cc.database.create_session()\n",
      "    ncfile -  an optional text string indicating the pattern for filenames\n",
      "              to load. All filenames containing this string will match, so\n",
      "              be specific. '/' can be used to match the start of the\n",
      "              filename, and '%' is a wildcard character.\n",
      "    start_time - only load data after this date. specify as a text string,\n",
      "                 e.g. '1900-01-01'\n",
      "    end_time - only load data before this date. specify as a text string,\n",
      "               e.g. '1900-01-01'\n",
      "    n - after all other queries, restrict the total number of files to the\n",
      "        first n. pass a negative value to restrict to the last n\n",
      "    frequency - specify frequency to disambiguate identical variables saved\n",
      "                at different temporal resolution\n",
      "    attrs - a dictionary of attribute names and their values that must be\n",
      "            present on the returned variables\n",
      "    attrs_unique - a dictionary of attribute names and their values that\n",
      "            must be unique on the returned variables. Defaults to\n",
      "            {'cell_methods': 'time: mean'} and should not generally be\n",
      "            changed.\n",
      "    return_dataset - if True, return xarray.Dataset, containing the\n",
      "                     requested variable, along with its time_bounds,\n",
      "                     if present.  Otherwise (default), return\n",
      "                     xarray.DataArray containing only the variable\n",
      "    \n",
      "    Note that if start_time and/or end_time are used, the time range\n",
      "    of the resulting dataset may not be bounded exactly on those\n",
      "    values, depending on where the underlying files start/end. Use\n",
      "    dataset.sel() to exactly select times from the dataset.\n",
      "    \n",
      "    Other kwargs are passed through to xarray.open_mfdataset, including:\n",
      "    \n",
      "    chunks - Override any chunking by passing a chunks dictionary.\n",
      "    decode_times - Time decoding can be disabled by passing decode_times=False\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(cc.querying.getvar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may like to note a few things about this function:\n",
    "1. The data is returned as an xarray DataArray, which includes the coordinate and attribute information from the netcdf file (more on xarray later). \n",
    "2. The variable time does not start at zero - and if you don't like it you can introduce an offset to alter the time axis.\n",
    "3. By default, we load the whole dataset, but we can load a subset of the times (see below).\n",
    "4. Other customisable options include setting the variable chunking and incorporating a function to operate on the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  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.DataArray &#x27;temp_global_ave&#x27; (time: 3600, scalar_axis: 1)&gt;\n",
       "dask.array&lt;concatenate, shape=(3600, 1), dtype=float32, chunksize=(1, 1), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * scalar_axis  (scalar_axis) float64 0.0\n",
       "  * time         (time) datetime64[ns] 1958-01-14T12:00:00 ... 2257-12-14T12:...\n",
       "Attributes:\n",
       "    long_name:      Global mean temp in liquid seawater\n",
       "    units:          deg_C\n",
       "    valid_range:    [ -10. 1000.]\n",
       "    cell_methods:   time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT\n",
       "    standard_name:  sea_water_potential_temperature\n",
       "    ncfiles:        [&#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_i...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'temp_global_ave'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3600</li><li><span class='xr-has-index'>scalar_axis</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c76f2d06-93b7-443f-80e8-938e4f66a123' class='xr-array-in' type='checkbox' checked><label for='section-c76f2d06-93b7-443f-80e8-938e4f66a123' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 14.06 kiB </td>\n",
       "                        <td> 4 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3600, 1) </td>\n",
       "                        <td> (1, 1) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 3600 chunks in 309 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "  <line x1=\"0\" y1=\"12\" x2=\"25\" y2=\"12\" />\n",
       "  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" />\n",
       "  <line x1=\"0\" y1=\"31\" x2=\"25\" y2=\"31\" />\n",
       "  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n",
       "  <line x1=\"0\" y1=\"44\" x2=\"25\" y2=\"44\" />\n",
       "  <line x1=\"0\" y1=\"50\" x2=\"25\" y2=\"50\" />\n",
       "  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n",
       "  <line x1=\"0\" y1=\"63\" x2=\"25\" y2=\"63\" />\n",
       "  <line x1=\"0\" y1=\"69\" x2=\"25\" y2=\"69\" />\n",
       "  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n",
       "  <line x1=\"0\" y1=\"82\" x2=\"25\" y2=\"82\" />\n",
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       "  <line x1=\"0\" y1=\"94\" x2=\"25\" y2=\"94\" />\n",
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       "  <line x1=\"0\" y1=\"113\" x2=\"25\" y2=\"113\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
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       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">3600</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-b367f4a3-1db5-4bed-9c36-43c9093f0785' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b367f4a3-1db5-4bed-9c36-43c9093f0785' class='xr-section-summary' >Coordinates: <span>(2)</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'>scalar_axis</span></div><div class='xr-var-dims'>(scalar_axis)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-81f4da93-2adc-477e-a2e2-c78d78ed934d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81f4da93-2adc-477e-a2e2-c78d78ed934d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cd411f7-3e80-42b9-b4a7-568494a5ce52' class='xr-var-data-in' type='checkbox'><label for='data-6cd411f7-3e80-42b9-b4a7-568494a5ce52' 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>none</dd><dt><span>units :</span></dt><dd>none</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([0.])</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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1958-01-14T12:00:00 ... 2257-12-...</div><input id='attrs-f117d115-fdb9-4319-80ca-b574523f7221' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f117d115-fdb9-4319-80ca-b574523f7221' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-543f8d55-b6c9-46e4-9c91-e14f07a844a2' class='xr-var-data-in' type='checkbox'><label for='data-543f8d55-b6c9-46e4-9c91-e14f07a844a2' 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>cartesian_axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>GREGORIAN</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1958-01-14T12:00:00.000000000&#x27;, &#x27;1958-02-13T00:00:00.000000000&#x27;,\n",
       "       &#x27;1958-03-14T12:00:00.000000000&#x27;, ..., &#x27;2257-10-14T12:00:00.000000000&#x27;,\n",
       "       &#x27;2257-11-14T00:00:00.000000000&#x27;, &#x27;2257-12-14T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-74b2909c-23f9-42c0-9453-54c4fcf17461' class='xr-section-summary-in' type='checkbox'  ><label for='section-74b2909c-23f9-42c0-9453-54c4fcf17461' class='xr-section-summary' >Indexes: <span>(2)</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>scalar_axis</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3699c377-fbc5-4870-9f7b-055ba95dfa68' class='xr-index-data-in' type='checkbox'/><label for='index-3699c377-fbc5-4870-9f7b-055ba95dfa68' 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(Float64Index([0.0], dtype=&#x27;float64&#x27;, name=&#x27;scalar_axis&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1256b4e9-7eea-4c02-b849-949187dfb079' class='xr-index-data-in' type='checkbox'/><label for='index-1256b4e9-7eea-4c02-b849-949187dfb079' 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(DatetimeIndex([&#x27;1958-01-14 12:00:00&#x27;, &#x27;1958-02-13 00:00:00&#x27;,\n",
       "               &#x27;1958-03-14 12:00:00&#x27;, &#x27;1958-04-14 00:00:00&#x27;,\n",
       "               &#x27;1958-05-14 12:00:00&#x27;, &#x27;1958-06-14 00:00:00&#x27;,\n",
       "               &#x27;1958-07-14 12:00:00&#x27;, &#x27;1958-08-14 12:00:00&#x27;,\n",
       "               &#x27;1958-09-14 00:00:00&#x27;, &#x27;1958-10-14 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2257-03-14 12:00:00&#x27;, &#x27;2257-04-14 00:00:00&#x27;,\n",
       "               &#x27;2257-05-14 12:00:00&#x27;, &#x27;2257-06-14 00:00:00&#x27;,\n",
       "               &#x27;2257-07-14 12:00:00&#x27;, &#x27;2257-08-14 12:00:00&#x27;,\n",
       "               &#x27;2257-09-14 00:00:00&#x27;, &#x27;2257-10-14 12:00:00&#x27;,\n",
       "               &#x27;2257-11-14 00:00:00&#x27;, &#x27;2257-12-14 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=3600, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fd462f2c-ddcf-4f93-b2cd-297df4f1524f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fd462f2c-ddcf-4f93-b2cd-297df4f1524f' 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>Global mean temp in liquid seawater</dd><dt><span>units :</span></dt><dd>deg_C</dd><dt><span>valid_range :</span></dt><dd>[ -10. 1000.]</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>ncfiles 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      ],
      "text/plain": [
       "<xarray.DataArray 'temp_global_ave' (time: 3600, scalar_axis: 1)>\n",
       "dask.array<concatenate, shape=(3600, 1), dtype=float32, chunksize=(1, 1), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * scalar_axis  (scalar_axis) float64 0.0\n",
       "  * time         (time) datetime64[ns] 1958-01-14T12:00:00 ... 2257-12-14T12:...\n",
       "Attributes:\n",
       "    long_name:      Global mean temp in liquid seawater\n",
       "    units:          deg_C\n",
       "    valid_range:    [ -10. 1000.]\n",
       "    cell_methods:   time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT\n",
       "    standard_name:  sea_water_potential_temperature\n",
       "    ncfiles:        ['/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_i..."
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiment = '025deg_jra55v13_iaf_gmredi6'\n",
    "variable = 'temp_global_ave'\n",
    "\n",
    "darray = cc.querying.getvar(experiment, variable, session)\n",
    "darray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that this operation loads the globally averaged potential temperature  from the model output. The time axis runs from 1900 to 2198. For some variables (particularly 3D variables that might use a lot of memory) you may prefer to restrict yourself to a smaller time window:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "  * time         (time) datetime64[ns] 2000-01-14T12:00:00 ... 2051-12-14T12:...\n",
       "Attributes:\n",
       "    long_name:      Global mean temp in liquid seawater\n",
       "    units:          deg_C\n",
       "    valid_range:    [ -10. 1000.]\n",
       "    cell_methods:   time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT\n",
       "    standard_name:  sea_water_potential_temperature\n",
       "    ncfiles:        [&#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_i...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'temp_global_ave'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 624</li><li><span class='xr-has-index'>scalar_axis</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-48cca741-7705-4fad-8bcc-3580e2809242' class='xr-array-in' type='checkbox' checked><label for='section-48cca741-7705-4fad-8bcc-3580e2809242' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "                        <td> 2.44 kiB </td>\n",
       "                        <td> 4 B </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (624, 1) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 624 chunks in 53 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-12787bff-468c-4ff6-bfb9-95347b57663b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-12787bff-468c-4ff6-bfb9-95347b57663b' class='xr-section-summary' >Coordinates: <span>(2)</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'>scalar_axis</span></div><div class='xr-var-dims'>(scalar_axis)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-d196646a-8499-49d1-9ac8-879a81443acc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d196646a-8499-49d1-9ac8-879a81443acc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24abd167-9763-4f7c-a6e6-33a2a04188fe' class='xr-var-data-in' type='checkbox'><label for='data-24abd167-9763-4f7c-a6e6-33a2a04188fe' 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>none</dd><dt><span>units :</span></dt><dd>none</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([0.])</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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-14T12:00:00 ... 2051-12-...</div><input id='attrs-fb1a7c12-5f78-4de3-8efd-fc0c329e4335' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb1a7c12-5f78-4de3-8efd-fc0c329e4335' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fe4303f-1bbd-4da3-b624-b7417f3aeffb' class='xr-var-data-in' type='checkbox'><label for='data-0fe4303f-1bbd-4da3-b624-b7417f3aeffb' 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>cartesian_axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>GREGORIAN</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-14T12:00:00.000000000&#x27;, &#x27;2000-02-13T12:00:00.000000000&#x27;,\n",
       "       &#x27;2000-03-14T12:00:00.000000000&#x27;, ..., &#x27;2051-10-14T12:00:00.000000000&#x27;,\n",
       "       &#x27;2051-11-14T00:00:00.000000000&#x27;, &#x27;2051-12-14T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-66b06962-094c-469b-9d7e-46104a82a2db' class='xr-section-summary-in' type='checkbox'  ><label for='section-66b06962-094c-469b-9d7e-46104a82a2db' class='xr-section-summary' >Indexes: <span>(2)</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>scalar_axis</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-381a7f81-b295-487e-9350-1843ce1da197' class='xr-index-data-in' type='checkbox'/><label for='index-381a7f81-b295-487e-9350-1843ce1da197' 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(Float64Index([0.0], dtype=&#x27;float64&#x27;, name=&#x27;scalar_axis&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b150fafa-b468-4e88-bb91-fb084d21c7b1' class='xr-index-data-in' type='checkbox'/><label for='index-b150fafa-b468-4e88-bb91-fb084d21c7b1' 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(DatetimeIndex([&#x27;2000-01-14 12:00:00&#x27;, &#x27;2000-02-13 12:00:00&#x27;,\n",
       "               &#x27;2000-03-14 12:00:00&#x27;, &#x27;2000-04-14 00:00:00&#x27;,\n",
       "               &#x27;2000-05-14 12:00:00&#x27;, &#x27;2000-06-14 00:00:00&#x27;,\n",
       "               &#x27;2000-07-14 12:00:00&#x27;, &#x27;2000-08-14 12:00:00&#x27;,\n",
       "               &#x27;2000-09-14 00:00:00&#x27;, &#x27;2000-10-14 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2051-03-14 12:00:00&#x27;, &#x27;2051-04-14 00:00:00&#x27;,\n",
       "               &#x27;2051-05-14 12:00:00&#x27;, &#x27;2051-06-14 00:00:00&#x27;,\n",
       "               &#x27;2051-07-14 12:00:00&#x27;, &#x27;2051-08-14 12:00:00&#x27;,\n",
       "               &#x27;2051-09-14 00:00:00&#x27;, &#x27;2051-10-14 12:00:00&#x27;,\n",
       "               &#x27;2051-11-14 00:00:00&#x27;, &#x27;2051-12-14 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=624, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0c2e0c8f-6d95-4885-ae09-447a4c638583' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0c2e0c8f-6d95-4885-ae09-447a4c638583' 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>Global mean temp in liquid seawater</dd><dt><span>units :</span></dt><dd>deg_C</dd><dt><span>valid_range :</span></dt><dd>[ -10. 1000.]</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>ncfiles :</span></dt><dd>[&#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output021/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output022/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output023/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output024/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output025/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output026/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output027/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output028/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output029/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output030/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output031/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output032/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output033/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output034/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output035/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output036/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output037/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output038/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output039/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output040/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output041/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output042/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output043/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output044/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output045/ocean/ocean_scalar.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output046/ocean/ocean_scalar.nc&#x27;]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'temp_global_ave' (time: 624, scalar_axis: 1)>\n",
       "dask.array<concatenate, shape=(624, 1), dtype=float32, chunksize=(1, 1), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * scalar_axis  (scalar_axis) float64 0.0\n",
       "  * time         (time) datetime64[ns] 2000-01-14T12:00:00 ... 2051-12-14T12:...\n",
       "Attributes:\n",
       "    long_name:      Global mean temp in liquid seawater\n",
       "    units:          deg_C\n",
       "    valid_range:    [ -10. 1000.]\n",
       "    cell_methods:   time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT\n",
       "    standard_name:  sea_water_potential_temperature\n",
       "    ncfiles:        ['/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_i..."
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "darray = cc.querying.getvar(experiment, variable, session,\n",
    "                            start_time = '2000-01-01',\n",
    "                            end_time = '2050-12-31')\n",
    "darray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will see that the time boundaries are not exact here. `cc.querying.getvar` loads all files that include any dates within the specified range.  You can use `.sel(time = ...)` to refine this selection if required (see below)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Exercises\n",
    "OK, this is a tutorial, so now you have to do some work. Your tasks are to:\n",
    "* Find and load sea-surface height (ssh) from an experiment (an experiment... perhaps choose a 1° configuration for start)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Just load the last 10 files from an experiment (any variable you like)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Load potential temperature from an experiment (again, 1° would be quickest). Can you chunk the data differently from the default?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. How to manipulate and plot variables with xarray\n",
    "We use the python package `xarray` (which is built on `dask`, `pandas`, `matplotlib` and `numpy`) for many of our diagnostics. `xarray` has a a lot of nice features, some of which we will try to demonstrate for you. \n",
    "\n",
    "### 2.1 Plotting\n",
    "`xarray`'s `.plot()` method does its best to figure out what you are trying to plot, and plotting it for you. Let's start by loading a 1-dimensional variable and plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 288,
       "width": 403
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "experiment = '025deg_jra55v13_iaf_gmredi6'\n",
    "variable = 'temp_global_ave'\n",
    "darray = cc.querying.getvar(experiment, variable, session)\n",
    "darray.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  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.DataArray &#x27;temp_global_ave&#x27; (time: 3600, scalar_axis: 1)&gt;\n",
       "dask.array&lt;concatenate, shape=(3600, 1), dtype=float32, chunksize=(1, 1), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * scalar_axis  (scalar_axis) float64 0.0\n",
       "  * time         (time) datetime64[ns] 1958-01-14T12:00:00 ... 2257-12-14T12:...\n",
       "Attributes:\n",
       "    long_name:      Global mean temp in liquid seawater\n",
       "    units:          deg_C\n",
       "    valid_range:    [ -10. 1000.]\n",
       "    cell_methods:   time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT\n",
       "    standard_name:  sea_water_potential_temperature\n",
       "    ncfiles:        [&#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_i...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'temp_global_ave'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3600</li><li><span class='xr-has-index'>scalar_axis</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f3eaba07-1359-47ab-b61c-dba2ad2aa797' class='xr-array-in' type='checkbox' checked><label for='section-f3eaba07-1359-47ab-b61c-dba2ad2aa797' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 14.06 kiB </td>\n",
       "                        <td> 4 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3600, 1) </td>\n",
       "                        <td> (1, 1) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 3600 chunks in 309 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"6\" x2=\"25\" y2=\"6\" />\n",
       "  <line x1=\"0\" y1=\"12\" x2=\"25\" y2=\"12\" />\n",
       "  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" />\n",
       "  <line x1=\"0\" y1=\"31\" x2=\"25\" y2=\"31\" />\n",
       "  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n",
       "  <line x1=\"0\" y1=\"44\" x2=\"25\" y2=\"44\" />\n",
       "  <line x1=\"0\" y1=\"50\" x2=\"25\" y2=\"50\" />\n",
       "  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n",
       "  <line x1=\"0\" y1=\"63\" x2=\"25\" y2=\"63\" />\n",
       "  <line x1=\"0\" y1=\"69\" x2=\"25\" y2=\"69\" />\n",
       "  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n",
       "  <line x1=\"0\" y1=\"82\" x2=\"25\" y2=\"82\" />\n",
       "  <line x1=\"0\" y1=\"88\" x2=\"25\" y2=\"88\" />\n",
       "  <line x1=\"0\" y1=\"94\" x2=\"25\" y2=\"94\" />\n",
       "  <line x1=\"0\" y1=\"101\" x2=\"25\" y2=\"101\" />\n",
       "  <line x1=\"0\" y1=\"107\" x2=\"25\" y2=\"107\" />\n",
       "  <line x1=\"0\" y1=\"113\" x2=\"25\" y2=\"113\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">3600</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-bf03eef8-048a-432a-b87b-c12114585ca7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bf03eef8-048a-432a-b87b-c12114585ca7' class='xr-section-summary' >Coordinates: <span>(2)</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'>scalar_axis</span></div><div class='xr-var-dims'>(scalar_axis)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-1bdd67fc-da77-4604-8551-98318ed71d9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1bdd67fc-da77-4604-8551-98318ed71d9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7e27f8e-4e27-4569-aa0d-3878a898dea1' class='xr-var-data-in' type='checkbox'><label for='data-d7e27f8e-4e27-4569-aa0d-3878a898dea1' 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>none</dd><dt><span>units :</span></dt><dd>none</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([0.])</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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1958-01-14T12:00:00 ... 2257-12-...</div><input id='attrs-0c94206f-c39f-41cc-9268-a8ffb2d6db8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c94206f-c39f-41cc-9268-a8ffb2d6db8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de5bc72e-8947-40e5-bd01-8a9ea94f5f20' class='xr-var-data-in' type='checkbox'><label for='data-de5bc72e-8947-40e5-bd01-8a9ea94f5f20' 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>cartesian_axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>GREGORIAN</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1958-01-14T12:00:00.000000000&#x27;, &#x27;1958-02-13T00:00:00.000000000&#x27;,\n",
       "       &#x27;1958-03-14T12:00:00.000000000&#x27;, ..., &#x27;2257-10-14T12:00:00.000000000&#x27;,\n",
       "       &#x27;2257-11-14T00:00:00.000000000&#x27;, &#x27;2257-12-14T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b4a39bbc-c0a3-4fa6-a3a6-2d871c923f01' class='xr-section-summary-in' type='checkbox'  ><label for='section-b4a39bbc-c0a3-4fa6-a3a6-2d871c923f01' class='xr-section-summary' >Indexes: <span>(2)</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>scalar_axis</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-02d0eb87-f417-42fc-af6c-5a3675ad8d04' class='xr-index-data-in' type='checkbox'/><label for='index-02d0eb87-f417-42fc-af6c-5a3675ad8d04' 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(Float64Index([0.0], dtype=&#x27;float64&#x27;, name=&#x27;scalar_axis&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e7c72e95-7e19-446b-8484-cf6669ccaba0' class='xr-index-data-in' type='checkbox'/><label for='index-e7c72e95-7e19-446b-8484-cf6669ccaba0' 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(DatetimeIndex([&#x27;1958-01-14 12:00:00&#x27;, &#x27;1958-02-13 00:00:00&#x27;,\n",
       "               &#x27;1958-03-14 12:00:00&#x27;, &#x27;1958-04-14 00:00:00&#x27;,\n",
       "               &#x27;1958-05-14 12:00:00&#x27;, &#x27;1958-06-14 00:00:00&#x27;,\n",
       "               &#x27;1958-07-14 12:00:00&#x27;, &#x27;1958-08-14 12:00:00&#x27;,\n",
       "               &#x27;1958-09-14 00:00:00&#x27;, &#x27;1958-10-14 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2257-03-14 12:00:00&#x27;, &#x27;2257-04-14 00:00:00&#x27;,\n",
       "               &#x27;2257-05-14 12:00:00&#x27;, &#x27;2257-06-14 00:00:00&#x27;,\n",
       "               &#x27;2257-07-14 12:00:00&#x27;, &#x27;2257-08-14 12:00:00&#x27;,\n",
       "               &#x27;2257-09-14 00:00:00&#x27;, &#x27;2257-10-14 12:00:00&#x27;,\n",
       "               &#x27;2257-11-14 00:00:00&#x27;, &#x27;2257-12-14 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=3600, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-32fd3541-0f12-459b-ae13-3810f243ba37' class='xr-section-summary-in' type='checkbox'  checked><label for='section-32fd3541-0f12-459b-ae13-3810f243ba37' 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>Global mean temp in liquid seawater</dd><dt><span>units :</span></dt><dd>deg_C</dd><dt><span>valid_range :</span></dt><dd>[ -10. 1000.]</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>ncfiles 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      ],
      "text/plain": [
       "<xarray.DataArray 'temp_global_ave' (time: 3600, scalar_axis: 1)>\n",
       "dask.array<concatenate, shape=(3600, 1), dtype=float32, chunksize=(1, 1), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * scalar_axis  (scalar_axis) float64 0.0\n",
       "  * time         (time) datetime64[ns] 1958-01-14T12:00:00 ... 2257-12-14T12:...\n",
       "Attributes:\n",
       "    long_name:      Global mean temp in liquid seawater\n",
       "    units:          deg_C\n",
       "    valid_range:    [ -10. 1000.]\n",
       "    cell_methods:   time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT\n",
       "    standard_name:  sea_water_potential_temperature\n",
       "    ncfiles:        ['/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_i..."
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "darray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see that `xarray` has figured out that this data is a timeseries, that the x-axis is representing time and that the y-axis is `temp_global_ave`. You can always modify aspects of your plot if you are unhappy with the default xarray behaviour:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 288,
       "width": 392
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "darray.plot()\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Temperature (°C)')\n",
    "plt.title('Globally Averaged Temperature');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because `xarray` knows about dimensions, it has plotting routines which can figure out what it should plot. By way of example, let's load a single time slice of `surface_temp` and see how `.plot()` handles it: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 392
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "experiment = '025deg_jra55v13_iaf_gmredi6'\n",
    "variable = 'surface_temp'\n",
    "darray = cc.querying.getvar(experiment, variable, session, n=-1)\n",
    "darray.mean('time').plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, you can customise this plot as you see fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 384
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_C = darray - 273.15 # convert from Kelvin to Celsius\n",
    "temp_C.mean('time').plot.contourf(levels=np.arange(-2, 32, 2), cmap=cm.cm.thermal);\n",
    "plt.ylabel('latitude')\n",
    "plt.xlabel('longitude');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Slicing and dicing\n",
    "\n",
    "There are two different ways of subselecting from a DataArray: `isel` and `sel`. The first of these is probably what you are used to -- you specify the value of the index of the array. In the second case you specify the value of the coordinate you want to select. These two methods are demonstrated in the following example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 409
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "darray = cc.querying.getvar('025deg_jra55v13_iaf_gmredi6', 'pot_rho_2', session)\n",
    "density = darray.isel(time = 200).sel(st_ocean = 1000, method='nearest')\n",
    "density.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above example, a 300-year dataset is loaded. We then use `isel` to select the 201st year (time index of 200) and use `sel` to select the $z$ level that is about 1000m deep. The `sel` method is very flexible, allowing us to use similar code in differing model resolutions or grids. In addition, both methods allow you to slice a range of values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 396
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "darray = cc.querying.getvar('1deg_jra55v13_iaf_spinup1_B1', 'v', session, ncfile=\"ocean.nc\")\n",
    "v = darray.isel(time = 100).sel(st_ocean=50, method='nearest').sel({'xu_ocean': slice(-230, -180),\n",
    "                                                                    'yu_ocean': slice(-50, -20)})\n",
    "v.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we have taken meridional velocity, and sliced out a small region of interest for our plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Averaging along dimensions\n",
    "\n",
    "We often perform operations such as averaging on dataarrays. Again, knowledge of the coordinates can be a big help here, as you can instruct the `mean()` method to operate along given coordinates. The case below takes a temporal and zonal average of potential density.\n",
    "\n",
    "#### IMPORTANT\n",
    "To be precise, it is actually a mean in the $i$-grid direction, which is only zonal outside the tripolar region in the Arctic, i.e., *south of 65N* in the ACCESS-OM2 models. To compute the zonal mean correctly one needs to be a bit more carefull; see the [`Examples/True_Zonal_Mean.ipynb`](https://cosima-recipes.readthedocs.io/en/latest/examples/True_Zonal_Mean.html#gallery-examples-true-zonal-mean-ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mwfgDaTW0oExkFZG8JaO5e+bMHRNzfDb1uzvllZfujLL3yld+hs5yVX1dpZ/idIi7bZ+uw91a/EC/oRivUaYz0YnsQlNxq6g873QA77Td7f2xH51+5WKnTbf2jk1eUcnqsUxKTEhsKL/Hsuc9HV3WFa/rjh2xXXHydCO4Pbu4Nl00/3YhfRj1PuS6vQpLdyKvlPVbnrqh3msy3e0woS7rs1awfBxI/pqUxyFv2SjTK1taOrP86M4rOdXKR9kFbz4THCyHYce12urkdUpaEhGXAfsDW0iVkdcBewHvBx4VEdeNrnQrT7Vbl1mu2pVs3A0/3me2NGZvZTKzlW26ZizQXFZSS4bZUnNLSYsi4ufAC5cyDxVdsqqqLR697bPUO+viNKWT7y+vZMzaYtJrWRnsxtXN0umXMQbCzOjyRd56UsTJf+XVdd/KC501eVfzHMhPM7dB6k5Qm3D+BZNdmcxbeCYrV7f697OWmvzqaTQ9iSzb7P5A96lKPS+yq7oDLSX51e58QHDlu2/gwnqW0WC4+mM2IPuUGZxqtbiSng/Kramr9rpl9Vo5itaB4kBMTAzurytbr+Uib8kop9+taaUoW1XKloqGZIvEZpY5DV5NadROuhCD+TR246JMo59v2VrS7eZhZpazP8A5ilaRbF8nBrqP9fZpsEWpV5ZuVo6i/NUZgOaqeOXlGciTwVaLiV43r3xbXvb+/eFaR+bentLN06pvHclnw5rRUpKP22noMpN/xkzm3bTylpKB+/Xdt+oM5DPH9MR1V8/LFpq67lu9165y8teFLT8vb5ueZH3RTau8vdO6OxrLX8ab7AR77XjDjPSv+PWOWTnqPx7zqRw66k9unVpJ+i/+NNH7DN55YqLXcjKpTvZ9oN5kLN0INhTlmyC4jew8DvrNoVlLSUcTvfJ3Q0xFpzeAbtt0p/c9MVmdCaNX/uJ925v4RQP3h2n17RC1lZqOotdSsqIqPRHtD3Rv44qQLRtXSlYh0el34VoCeVeuOcO20I0r31btxjWgqftWlOXOvoBrunINjG3PKw1ZupPUV1C62bTB+ZfMdF75yrt0NPT/zsPkH6Wdhpdy1nVG8rVr8gpKXimZbS2UgcpLfeUjBo753BWUgSzKU2M6Pxbl7WD3poFyl92zanMYTL7XLalTVkz6P7KqlZQZZQ0GDtaMclTKPHB/oHvhPL8Uq2WJuq5k/W5beZg8/2qlZMb4o4YfM3mlpPc2LdIvKyLR7U84UA078/Wqf35NA9N75R4YxJ5XDLLt+XiRgcrH3OFnjAlpmK53YLxLQ1cuqFRKqDfZUKnJx33kFZF1+cWOmPmeyM+9yYHxIfUVlN622krJ3OFm+wGbd78C2DgxNbPLUM13UvVHebUb2GT2nu1P6zvYVau+PP3xIR36FQyASTpMFZ/l01l343XqsK44GfPtqD+d/YRSl7NyEPxE9Lv+TnS7MJmaeTd0u71JVMrK/FTRpXZS/UHw6yemZ6mY9O9PVCoY/cpP8+tWPR65qSgrRf7RbiuHKyVmZmZmNlLB4AyfbaVp48OVkjFXrlsCRXcqpRaMsrWkty8LVz6eS3fIK7y13bjKy6YN3bjqZ+GKgTAD66nMaE3pm64pZ35VqLwGmV9Zq+vKNbh+S9NV//orkPmVrsieW37VMx/QPpFdJp4euBqfFSyfHjcrQX6FefaXKCtTtjUG5o0d6Aw2S1pZy1J+tS6fkjbPo2YWrWp25SHLi1NeiK7tbtb0XCutHb3Dr/6VRnWK7b2wWbcUMbOlRDPza5p5q3YAffWUqWtNqcsnL8tsVzDzJirNHJzebwnqP8+61Z5hsBWmOpi/N7VzlF24itaRbn/fjBaSbv32XrK9stc/v07WutV7PnlrR/ZcB1o+GgaxN3bFmmX2rbx1JJ+ud3BtkcG+hpM1M1lV5a0jTQPaB7fn0+7lB2Tm/rpB63lZ6tZbmm2mrWqcpjD9fUWZehNRzGzlWVf0z5zufZ5H7Xoi/W5XwXbF/t23v4n7bEzdv/LFcx+zy/Vcflsa1r5hYFISMVm8qOvU6d0vTRa5TNL/Psxb0Ndl4bsRbOhM9O536PZaR27vdikXtZ9Q2gfQzZ7XtmJgerf4JJ/qdnrHYLvO9IzWoZTW4LHoKAa+w8pjkM8yVu0f0dSa1KE/NXJdC9koeaD72uaB7mvMMJWRpvB1cecz3XDTeikD+yqVjw6q3ZZusy+vXjr1J3Xdh3NH/ebwiexLZSL7wu7tJ/pfttmP3Q792VImO93+/P95OlmYiU5kM890ez8qJtTt9+nudHtf6J1std88/bxsdeF7/Z0novdDbCDvyW7vB1YephpfE9Gb6jW/3+n0fzwOxJ/s/1DM709MRn9RvPx+sRbFxHp6q2VPlrcb0j/Auo3pdv2mym2xXsKG7WPgdv2m/hft+h0Gv3TXb5r/l96GOy1+Yobtd1786P0ddt7WQjnml8bGHQef+8Y7dbN93eK2OP47NN2m8BvK16t4fTZsSv/V++XrvW5jdn9DsK5YP2RyQ/TWEmm8vz6YXN8/z/rnWvRWaS/PxfJ8nJzs9hYynFjXZaJYRyV/v0xOTvcqJOV7snxf5vHXTfYX3JuY6PYqJJOd6V53rcmJbq9L18DnQSdqZ7oa+Ewq81Q3G49R/xlU7l8/Md0b31H3WdJLZyDNGLjtffYVt+sUvR/o62rGpWwo8tmuyLe83ZB1WSvHa2zIfpxvzH7Ub84qmfmsWvln/T7b9d9feYViXeX7pqnikW+fHLhoVr8d6M3UBbBjVhPeOavU7lDp5rdzVpndcaL/Xtycxdl5shon25d9md0lm2J958qX3F0m+2XdJRuUld/Pw5itBG4pGUPlYPP+VMAauNzcH/PB4ErjZf/aISsmc4VfjgpJqa5C0t/HjG25pgpJdf9Ew1XDiYErjHm5669MDqRTM71jul9/xTS/ujgwZWq+fWAq0frwMHhVeCDvfJG3hlWzgf66E5X7gwOU8yvP1N6faFjnIa+QlPIKSWmhFZIN20fv6vf67WOgRWH9pui1JMwYW9Jw5bD8cV5t6Wga1Jwtd9C7X1ZMhhkULsVA+mXaO9x52+C2Iuxs7+peI09xxXr7nbb1B8dHf6B8ucZLWb5yDMmGO01DMS0wpIpJObZ3MRWSUn4/r5D0tmWLGU4Oc3/9zPMM6FVGgBmLH+arqpeVkRSuvnWkeoU/j58vetg0dmRgfEn+edAw7e7AZ1JWIakrT/4ZlFdIqmE7NeEG05y7QlKqVkgms0pRU4Vku4np3mdkXjEpP5N3nOiyqXgP339jf5D8RmUt4PQrJBPqX8SazMaHdNBApSOVtzNwW8YpVSsj5VTkk+qwvtNf/Hf7icler4IdO2JD8Zx3nIheW/WOE4OdknaanOq1VGzqdNlUfI5unoze91hZESkfb56AdcUbP6+A7DzRH9C/S7G9HEvTVBnZZXKyV/51s31wLLslGOjuDlxjxdVkMzMzMzMbKbeUjJkOHaZ7CyVWWkuA6grvaRLB9q48VMemLDidgdmvyrQHZ+gaGMsxS5blc8/HZtSNH6kbZ9Jcvjz9LPOoDzSVT8qVX33Mrl5OZ61ZGrjq2b+frz6r7IplPv1q4/bKlVs1tYhNZYsqDlwtrp4neXmzUHnLQBYlv8CVL4JYu+jgwPbhzqfqWIt8ocLa/Z3+NK/VsSODs3DVtEhU0xwoR1HeSguFilaXPH6damtEdarXgXTyfDRzf+951OVT3g4c85l5R9FSMtBiEml+uF74rooZt9RLvLyfz8g18Pyqj3t5loWpOxcajnknavd3mlrzGmfuysJXWxbzlsm8dSNvARmYxrfSUlKzcjrQW/QWZmstrW+lzVtaBxYkrcx0VU2jrmW1fkzJzPJOzPJ+zKe4reZTNds4ie2yeOWV+nLfdPS3beoMTuub2zbweZ919y3u5+NIJkjjS3ozBMbcPQY62fTAA3qtRCntDRG9VpR8wcVuRC+/6QimInrfO91O9FpKNqg/FiZ/juVzaepWNljWwV4G+fb+dPz5eaVeS0k1zigFHui+1rmlxMzMzMzMRsotJWNI6hDRzRYrHLy60G896fRm4krBlmZWi7p1SwYWUoT+1Dblwoua6K3q3o/f3H7Rya6EQWWdkXK++Cx83ZxSeer1LSn9ayrTWfpNM85MR32YgfVIsnwGZtXJrnhFw9XG6ezSaCcr8PRAq0kWpnLFvbq4XZ28xWaqehUubxGZrs8zzyOfMavTcBW8djBFQzFnzohVaSEpZ2mqvD69K96V1o/BtTAGx2LUtU5U065d+HFGy8vg2I6OYsbiidUjkLdkVFs+OllrSF1+OSlqF9ir31a/vyxr2UpS7ut2B8ecdLvqh53RYlJpGak8rst32IUX0/3sPMtbUCqLQ9aHqX+fVvMYGEfSqW8dqY79qGt1SAnXt47UtVykcjSUMbubt1hM9MZ7zN7aMri/KM8sa5jUqZ21qxImb7GpjufrVLZ3mNlSskGD4wQnKp/9pbJ1YVt0a8cfVseUpJaPpOlqfKcypiR/vD5LK6VRvgZZyzXBhuJ7bFv0Z7/sZos1ThO9VoqynPnCjnmrR/V7r1MpwzDlbtLtrbuysq5Nt9ETw8aXKyU2ElOzVEDM5jlJ3JrgY2JVTYPil8vK+jm78k0voDNR00K0q1EQS9B9y5WcceLPFDMzMzMzGym3lIyTgS4MRZesshtX0YWrHIheduEajD5YB63rzjVMV6+ySbmpmbUu76pJTWTN34MLK9Z20ZrlalG+cFR1G8y9eGKdvAtCtftNL0w+CLWhu1c+veZ0dqk77xIycBgHuhg1dNnK+onEQDe26oDpmd2CqvenB/q8DEQfeB7TE9lCYllXrhjovtWPW9dlKG3PyzezPINPoD7cjG5b1f30H9dNnVumWe2yVXa9yrtMzVyUsNrlZ/YTqa51o3oMqm+j3mJ3+Ws2j6t9gRBB0yTBTedzdRBztYtXbwphYLrbycINdt/KB8Pn++sG9vfGvNd0YavbVpU/k7m62lUjzPbaTQwMaM+7X+VdoAY/4/KuWY3HuGGK8KYuU905uohG9D+rmtKoG9Q+UdOla7ayzHb1srr4Xm9NJs2M1x90Ty9MdZHACbIuTI3T0fe3r6MzMLg9//7oTw88UYk/MWPgd9pePyVw03Zg4JzqEr1eABsZ7BFQdpeaiu5Ai8kGTdSmP1uXrKYuWvVxBxc3LrcNLHa8grQ/JbCNk5V1Ntqa0XWTqs1m7fRYWFZNP5ZHwZ8Aizfq1bhHnf+4mVrAD+611H3LzC0lZmZmZjZaMThxQFtp2vhwpcTMzMzMRsoD3c2VkjHTkQYWSCynB4Z8KuByzAe9aYHrVMeYVPfNdwrh+UwNnE//259icW4TvbDZeIte/+hsW81CinOPI+nfz8MOHKWGsSZ5f+1O1PcVz/uQD0wjmUfOVh0cmO43n4a3ZjG8apkBIj8enayPcRZwemDcyOAr0O3240xPZ2NgsnEkA1MC10wtO5u6hf/qxnL0x5I0TcNbfdxPt1PdloXNx5x0FL1xG6rpg79U5hqTksoxe5imY900rmfY8tTFL0+dtLhi+TlTP2akaSxJPu3wbHnNZ8rghZhtNrOB920+viSf1rsy89XAVL4N24fRNFXwYF4z9zeNZaub/neYqX1zTeNGBsMMPp6sKXsZJh9Hko8lSWXqfzMNjuuYOe0vwLpOJxtL0V9ssKP+uIvquIludGeMMynzaxpjUYafrTtVlxgY11EdU1I3jfBkNqZkxtiXxnEtnYHt1XGe9QtDVp9veix3D7MVxJUSMzMzMxs5r1Oytnmgu5mZmZmZjZRbSszMzMxs5NoeU2LjxZWSMdON6I0rgfQGlrIepkXLZ5duNsZk5liPYcy1Zsls65XUzY0+uD8fNFD0+y3HhlRu8/DVW8j6F+fjSGrzbLhf9n3Ox4jkfaYHxprUrxuQB8+GWVSeQ9NYk/oVVgaTnzlGBmZfv2VwjEe3dvt0lth0VNaxyZ9TNr4k3z6db6c+fJPqGBDoH+qBvu6dqA1f168+pdEPl48LSY9j4DEMvi4LXTW9PCbDxp9v+OpzHGbMzmy9ILq991F1XMRwaeXnXXU8yIz1S6pjTSpjUarpV8ekzFWWpnJUyzMf+XtyomEcSfU1yY9l02fARKfpfZ+FbxgnUpfX4JiQPF4/7Dp1a8LOVDe9b/X8qI4bqUunzKcaNg8/sE5JTZiJhvF0EzVjTNI4kGxtknx7zfdFiltdt6RTG26wXKqE7X/H5WM8uvl4nnwdqcbXUkxq5k+x/niYfhpN40DLsKp831bHx9R9L6+kMSVBtD77lge6jxd33zIzMzMzs5FypcTMzMzMRi7otvrfBkmHSjpO0lck3SgpJJ00R5wDJJ0p6XpJt0q6RNKrpZnTvkl6sKQTJH1H0i8l3S7p55K+KOlZ0sw27KIMc/0/v5UDsIzcfcvMzMzMrN6RwEOBm4ArgX1mCyzpmcBpwG3AKcD1wNOB9wIHAs+pRNkPOAS4ELgA2ArsVsQ5Dfg4cHglzjEN2d8JeC0wBfznXE9spXGlxMzMzMxGboVOCfwaUmXkUuAg4NymgJJ2BD4MTAMHR8S3iu1vBs4BDpV0WEScnEX7RERsaUjrQuD5ko6PiIvKfRFxdEP+f17cPSMirhn6Ga4QrpSMoQ4dKAfTZQspzjVAbDED3mdrBp25cFN/wcbeAMCy8bGyiGK+bZjB7OUA23zAY2/Q7cAA8ExRrm7D/vJ+vvjgfBdSzOXlnsxyyhdJHBigni2YmGfQGWLg7sQCBvE1DTCeEa5hMPOwizfWhalbSK46GD1tax6QXm3InjHguCZub98KGfTYNNC87fQhHf/qcShfk4Uen2EGulcfl+daGXeucyo/nwYWUqQp3tzlnm1ByXxfJxuEPpktnriu018Mb7bzMD+skw2D5psGtOfpNk2OUd5fn5VnXeNCiuXA87kHsqdwMzZl4SuP8wk7ZhkEX92XL57YTyufaEQztlUHuTcumFjczxclbFoQMc+rdl++YGE2wL15IUPRzSYMmW0mqcG0Z5ahHNTelNdgWuV3YN0ULzXha6eCGb2VOtA9InqVkJqeVFWHArsCHysrJEUat0k6EvgS8FLg5Gzf7Q353ijp88ADgPsCF9WFq/iz4vafhwi74nhMiZmZmZnZ4j2uuD27Zt/5wC3AAZI2zJWQpE1Zet8bIvx+wL7AFcAXhinsSuOWEjMzMzMbuSVap2QfSRfX7YiI/VrO6/7F7Y9q8pqSdDnwQOA+wPfz/ZL2Bp5Hmin7bsBTgd2Bd0TEJUPkXbaSfDhiZfaDm4srJWZmZmZmi7e5uN3asL/cvlPNvr2Bo7LHdwBvAN49V6aSdgD+iDTA/SPDFHQlcqXEzMzMzEYqYgnGlKQGgx8sQYtI6yLibECS1gH3Ap4LvB04SNKzI+KOWaL/EWnmrf83jgPcS66UjBEh1nXW0VEapjbdnQJ16UZ/8Hq+umx1AHw+6K/t1d1Tuv0B73MNqh9Y6bYXv35l98FtMwfB9+4PDEjPV2Qv4kSeHjPuD67QPvfq7nmYfGTrRMzMu5r+pPLB7VlpskHvTau1Nw20WymDuOdrrhWsZwwoHvJ5Nq2GPZty4HQZdyGrgbet7nkMU66m86cxn0WeP9X8ZqwC3xvArhnh6wa9Nw1in+8EDINlmPUp9OSTL2ycnOrdz1dhn+145WVc3zA4fuAzYCDvpgHr+SD5dLuuYeD8uppzpm7bQL5zlKVOPoC9Gr9uX3WA+wT5yuxlmMHXbt6ruGffc5PFwO9y+7CTwfTLnX8PNA+a7zSsQF+uHF+dUWq2VdqrK8Y3xZuvDp2l6hplg8qWkM0N+8vtNzQlEBHbgMuAt0q6A3gH8Erg2FnyLbtu/cvQJV2BPNDdzMzMzEau3aUTR3Kh7ofF7f2qOyRNAvcmdbH6yZDpnVXcHtwUQNLDgP2ByxnTAe4lV0rMzMzMbOS60W31fwTOKW6fVLPvMcAm4IKmaYBr3KO4nZolTLk2yQnjOsC95EqJmZmZmdninQpcCxwmaf9yo6SNwNuKhx/MI+ThKtt3Bd5ZPPxcQ5jtgT9mzAe4lzymxMzMzMxGKqD1LldtpCbpEOCQ4uFuxe2jJW0p7l8bEa+H3oKHLyZVTs6TdDJwPfAM0nTBpwKnVLI4QdIupMURf0ZaDX5P4CnAdsDpNFc4DgN2ZMwHuJdcKTEzMzMzq/cw4AWVbfcp/gF+Cry+3BERp0s6CHgT8GxgI3Ap8FrgAzVdrI4lVXr2BZ4IrCe1tpwDfBz45CzdslbFAPeSKyVjpIOY1CQTncnUX1Jdut1pprtT3BHbijAdukw3zrzVS2uO2bGa5DOFNM3E1ZF6s4308i3fTtnkITPKVjwuZ0zJlWHLmVjy2W16s7Nkaa/LyrmtFzdLLy9HzXPYll1fWZclvG1gZq0sQjbbz2Q2Qw/dfjkGZ9zKC5Pdz2bi6iyya2jTRC0LmWWpaRaeYWa4mivMXOVZyCxaa9lcM3PVzcpVfQ0Wc8yb8i/zrdvfrZlFa7phZq2B+w0zcQ1VnlmOU/4e3jS5rXd/rpni6tLOZ9+aa4as2fJo6ms9oZmzFU7UPLWBGQeHfH3r0mkqSzVsp2EbDH4W18/61Z9ZK9/Wm31R6u3rSAOzbPXuq9ML3/tOqZk5axgddbJZvZrPG9HpfS/m35WdykxrTTNupecwdz4zyjdkT/y6cJrnLH1Lq/0pgdtoK4mIo4Gj5xnna6SWjmHCngScNO+CpbiPXEi8lcqVEhuJEQ1AM1vT5jtNsK1sE66w2yqyUrtv2fLxQHczMzMzMxspt5SYmZmZ2ci5F8Xa5pYSMzMzMzMbKbeUmJmZmdmILcUq7B5VMk5cKTEzMzOzkQra777lKsl4caVknEhMdCaZ0CQTgtumb2Fb9w7uiG1MxxQAXTqD0wuqM+ubvDpVcG6uD4fqtIb5FMHldIZNUwN31OlNW1xOBUxlauBONlNQ3TTBVfmUmNuysvSnB86eT83Uvk1Homl64PyKzkDekU0D3DA98EBZss35NMBzTe1aNds0n0NN3TvLx3fzlMD9+xMN8eecEnjOKYPbN669ljvMv+zDnkejmHp5tumBp/P3WszcPyMe9dMGL0R+LLafmOrdn1Q+7W7z8cqnM16XTQebT5HbdF7neTc/19nTqXst55omuE7TlMCzpTFR83la/fQuvx/StMGDmUyqMzDdbz8fsa74jpjMp+hFvfuTmhiYBnjGVPjZ/WF+/A5+lw53TlW/F5u21eZBpzGfYaf9NRtnrpTYSHTH9meh2Wistp8ki6042OjPiVHnb6tP+923bJz4M8XMzMzMzEbKLSVmZmZmNlIR/S7fbaZp48MtJWZmZmZmNlJuKTEzMzOzEQumPSXwmuZKiZmZmZmNVJoSuOXuW62mZkvN3bfMzMzMzGyk3FIyTrIrCBFdbp2+lTu623rrgzTNhT7bWiSzyeMNM6d7mf8w65V0yNZPKWcGraxXkq+bMcyJ2tTsWzfFYL7GQLk2yLaG+Plc/duy12A6C56vqTKRrUuwLZv2tJOtWdLJ1zHI02xYm2EhOg0zrjatJzIzftTeHwiTp9sYZu7jOlfaNn8LmXR7JR3zpvJPN5y++Toli5WfsztM5GuTDBc/L+O6fC2fIeIPPr9szZKGMOX2+aw5MuzrvKB1Sqr7B9Yaqaavgc/OFH/wcbl/nTpMZuuRlGtXddSpXZukuq5H9fusfJxPT59vHyzD4r5D674/q+uTpG3V576S3pHLw1MCr21r74y3FaHtVVttdfEHk9ncRv0pOtf7dNgFB83MwC0lZmZmZrYCtD2mxMaLKyVmZmZmNlJBczfsxaRp48O9JMzMzMzMbKTcUmJmZmZmIxZL0H3LbSXjxC0lZmZmZmY2Um4pMTMzM7ORCtqfEtjtJOPFlRIzMzMzG7lpz761prlSMma60aUbdwAwFdNzLpzYltkWgaoSnYEFFFP8+kUUgf6ljMoiiuXiWABTZTpF4LoTN1/wLF+Aa7pY1C9fsDDvt9opytrJyryuYcHEfHu+gFkeJn/m2XqJdLPFBQcXP6ufy3865p7jf2IgzeHWBJjvIocpTh4uu9+Q1jALtQ27EN1c6awEo14vYj7yc6/6GqzUYzzM8W3zx0x+XDZl9ydnWXcjL2P++TJbnLq4+cdBd2Bh1Wz7Ipf/GHrxxGHSmrHgXyWNgcUTZxa8bi2TclsH9e5PqsP6zrri/sTA90N/8cRJJjtpe/U7Mf+u7Ej976JsId9hFhqu+66tft/NfD7NeXSK8pT32zTUwsleSsZWEFdKzMzWiKbV0M0WYq7FESdW2OKJS33xbi5DVRLWsNR9q/00bXz4HWJmZmZmZiPllhIzMzMzG61YghXd3VQyVtxSYmZmZmZmI+WWEjMzMzMbqWBwQoe20rTx4UqJmZmZmY3cOM1kaO0bu+5bkg6VdJykr0i6UVJIOmmOOAdIOlPS9ZJulXSJpFdLapzxUNLTJJ0naaukmyR9Q9IL5sjnBZIuKsJvLeI/baHP1czMzMxsLRjHlpIjgYcCNwFXAvvMFljSM4HTgNuAU4DrgacD7wUOBJ5TE+flwHHAdcBJwB3AocAWSQ+OiNfXxDkWeF1Rpg8D64HDgDMkvSIijl/IkzUzMzNb7YL2py13963xMnYtJcBrgPsBOwIvnS2gpB1JFYRp4OCIeFFEvAF4GPB14FBJh1Xi7AkcS6q87B8RL4uI1wAPAS4DXifp0ZU4B5AqJJcBD4mI10TEy4D9inSOLdI1MzMzM7OKsWspiYhzy/uae2GmQ4FdgY9FxLeyNG6TdCTwJVLF5uQszp8AG4C/j4grsji/kvR24F+Bl5AqNaWXFLd/FxG/yuJcIekfgTcDLwSOGvJp1qs83250l30xqHzxp9lWd29aTTdfbGvG6u4NK7vD4OruKczErPlPZcPlypO8bpV3gMlI+a/L0pvOrq9sGFitPV9BvX4V94HV3TtNq7jn6sMst9lWWM9fzaFWa29KZ4UtprYS5FNgtnl8aqfWnCP5cX592pxKND8OG7LPvGGPT16WdVn86SGu2w48jyy7bbN83s1Io2ZbG98Usy2GWLdaOwwes4lhV3QvwnWkXpyOOr3vgfWaHPguKldCn+xMMKHJgW11pmKq9zkWdOkU6c52Ds32XZvvK8tV/X6qW8k9bde8V3JfzYswekzJ2rZ6z+zkccXt2TX7zgduAQ6QtGHIOGdVwiwmzpq22B8Qs1VIzMxs9Ma5kmvLr+y+1ea/u2+Nl9VeKbl/cfuj6o6ImAIuJ11Iv8+Qca4Gbgb2kLQJQNL2wD2Am4r9VT8ubu+3kCdgZmZmZrbajV33rXnaXNxubdhfbt9pnnG2L8LdssA8Gkm6uGHXrAP6zczMzMaZ+0Csbau9pcTMzMzMzFa41d5SUrZSbG7YX26/oRLnLsW+62aJs7VyO588GkXEfnXbixaUfYdJw8zMzGyceEpgW+0tJT8sbmeM55A0CdwbmAJ+MmScu5O6bl0ZEbcARMTNwC+AHYr9VfctbmeMUTEzMzOzpBtq9d/Gy2qvlJxT3D6pZt9jgE3ABRFx+5BxnlwJs5g4ZmZmZmbG6q+UnApcCxwmaf9yo6SNwNuKhx+sxDkRuB14eb7goaSdgb8pHn6oEqd8/KYiXBlnT+BlRXonLuaJmJmZma1WQRro3ua/u2+Nl7EbUyLpEOCQ4uFuxe2jJW0p7l8bEa8HiIgbJb2YVDk5T9LJpBXWn0Ga+vdU4JQ8/Yi4XNIbgA8A35J0CnAHaSHGPYB3R8TXK3EukPQe4LXAJZJOBdYDfwjcGXhFvhCjmZmZmZn1jV2lBHgY8ILKtvvQX2vkp8Dryx0Rcbqkg4A3Ac8GNgKXkioQH4iYuYpfRBwn6YoincNJLUr/AxwZER+tK1REvE7S90gtI39GqqR/G3hXRHx2Qc/UzMzMbC2I9ge6u6lkvIxdpSQijgaOnmecrwFPmWecM4Az5hlnC7BlPnHMzMzMbGWSdChwEOmi+EOBOwH/FhHPmyXOAcCRwKOA7UgLaX8EOC4ipithHwy8CtiP1CNnR+D/SBMv/RPwqboL6EXc3YA3kn7j3gu4jbQw+Bci4q8W9oxHZ+wqJdasozREqBvd3v3lyK/UjeZlj1QMX4piaaSO6mbFKNKLXqS+Iu1JTaS8mH1WjbxsZbnqtgF0lTJcF/3909nllW72WdBt2J4bJu50Q9xcm4tILeRsmKh9jaAzx7GHptfXllPT+Vnl1yrJj1d+TNZlnxuTs3yuDrzX1b8/OfC5M8Rrkr0c+WdG0+fTSjFR+VyY7bwa5jOkLk75+T+pyYHP8zKvCU32wnRm+dTrSL3Xoku3930wof53lCrxm75TZ/vea34unYHjsxzf1+MgjSlp9/OopXfKkaTKyE3AlcyxmLWkZwKnkSoIp5CGDTwdeC9wIPCcSpT9SMMSLgQuIC01sVsR5zTg46ReO9V8DgQ+S5q06UzgU6QK0N7AYYArJbZ0NLErG+78sv6Gq948usKYmZmZtWh6ZU7j+xpSZeRSUovJuU0BJe0IfBiYBg6OiG8V299MmoX1UEmHRcTJWbRPFD1t6tK6EHi+pOMj4qJs327Ap0kVmEdGxI8qcdct5ImO2oIqJZLu1ULeN0TEjS2kY2ZmZmbWuojoVUI0d8vyocCuwMfKCkmRxm2SjgS+BLwUODnbd/uMVOhN1vR54AGkNe8uynb/DbAL8LxqhaSIu22ugq5EC20puYLFt4odA7x1kWmYmZmZ2SrQZrflEXlccXt2zb7zgVuAAyRtaKqMlCRtytL7XmX3HwG/Aj4v6TeBx5O6cV0GnB0RNy2w/CO1mO5blwDfXUA8UdM3zszMzMysZftIurhuR0Ts13Je9y9u61ovpiRdDjyQNGPs9/P9kvYGngdMAHcDngrsDrwjIi7Jwt0buAvwTdI4lVdVsrpO0uERcWYrz2gZLaZS8qmIWFBLhyRXSszMzMwMgEB0Wx5TEi0PnB/C5uJ2a8P+cvtONfv2Bo7KHt8BvAF4dyXcXYvbfYEHAS8HPkn6Tf884O3AaZL2jYjvM0YWWim5mXSwFmqx8c3MzMxsFWm7UlL4wRK0iLQuIs4GVAxSvxfwXFIF4yBJz46I8ndzOV3bBPDWiPjHLJl3FYPgXwu8GvjzZSl8SxZUKYmIOy0m08XGNzMzMzNbYcqWkM0N+8vtNzQlUAxSvwx4q6Q7gHcArwSOrYn7qZokPkWqlPzWUCVeQTw5tpmZmZmNVADTqNX/Eazo88Pi9n7VHZImgXsDU8BPhkzvrOL24GzbZUUaUF+5+VVxu92QeawYS14pkbSzpO2XOh8zMzMzsxE6p7h9Us2+x5BmyLpgrpm3MvcobstKCEU3rq8UDx9UE6fcdvmQeawYrVRKJD1e0j9I2jnbdldJXwauBa6X9J428jIzMzOz1acb7f6PwKmk372HSdq/3ChpI/C24uEH8wh5uMr2XYF3Fg8/V9l9XHH71vzCv6SdgHJl7U8soPwj1daK7q8AHhQRf5ltOxb4HdIKmDsAr5J0YUR8sqU8zczMzGwViGh/oHu0UDGRdAhwSPFwt+L20ZK2FPevjYjXp/ziRkkvJlVOzpN0MnA98AzSdMGnAqdUsjhB0i6kxRF/RloNfk/gKaQuWKcDHxl8XvEpSScCLwS+J+ks0sD3p5FaV04DTlrcM19+bVVKHgp8uXwgaTvSqpb/GRFPlHQn0sIvLyFNW2ZLoBtp2aGORjNUqMy3LEcdFY1zUbNEUqe3UmpR/vzDpNxVpp09x7qTuJtF7tZMCdhVtr9Is25bdftAGg2fdtN53pGXY+5Px6Y0h9GZe6XZhae9/NMqrhjDHtfFvHYLyW823Qjm85J1pF75q/fbtpRpD5t3raw4ednW5Z81mpgl7ewzI3uv5++dps+SYco4PUTcudLolWmWY18Xt+mza7bPhbZe3+mmvLP0y++VTvGX9jd/D3bo9F7vqZiim3Ua6RbfTZ1hO5I0PM08/9nSGjofG6WHAS+obLtP8Q/wU+D15Y6IOF3SQcCbgGcDG0kX6F8LfCBixpvsWFKlZ1/gicB6UmvLOcDHgU/WxAF4EXABaYatI0hn4/+QBsZ/MGKWH2MrVFuVkrsCV2WPH0l6EbYARMSvJX0W+P2W8jMzMzOzVaTuIuKoRcTRwNHzjPM1UkvHMGFPYgGtGkVF5YTif1Voq4p+O4Oj/H+HdJ37/GzbjcCdW8rPzMzMzMxWibZaSi4HHpc9fjbw44j4RbbtnqTmKDMzMzOzTPsrus+rH6uNXFstJR8FHizpG5K+AjwY+PdKmIfQn7/ZzMzMzMwMaK+l5IPAo4A/JFVLzwD+vtwp6UGkispbWsrPzMzMzFaJYAlm32o1NVtqrVRKImIb8MeSXpIexq8rQa4BHg5c0UZ+ZmZmZra6tN99y8ZJK5USSR8BvhcR763bHxHX4vEkZmZmZmZWo60xJX9MmhbYzMzMzGxegjQlcJv/7r41XtqqlFyBKyVmZmZmZrYAbVVK/h14sqSdW0rPzMzMzNaQiHb/bby0VSl5B/At4FxJT5N0t5bSNTMzM7PVLtJA9zb/3X9rvLQ1JfBtxa2ATwNItTMoRES0laeZmZmZma0CbVUQvoLroyPV0fCNXp2igaxLd8nL0o36PJQ10kWlHJ1ehTZ7TuXZpRl76BYbu9kpWLefgW0xY3++DU3M+Ry6qj/l8/BNYQbD5/nOGXxBOvUXCaxNK+kQZ2Upz6/8HKjbNlD+pXwuozxOC8h7vfpfk7N9zuafM4OfRfXbm9Op/7yZ68t68PNrzmyAhrINEbc7S7+Y+XzWzJZONb2OOr3y5q+D6DBZvEYdiYnO/H7WrNN6prtTqTx06TAxI0yHTuPrkocf5vmU5ezfb6vDyviLJVjRPVbUB7PNpa11Sg5uIx0zMzMzM1t73JXKzMzMzEaurmeDrR2tV0okbQ/cD9ghIr7SdvpmZmZmtvqEV3Rf01rrzChpD0mnAb+imIkr2/fbkv5H0sFt5WdmZmZmZqtDKy0lku4OfAO4G/AZ0kKKj86CfKPY9ofAeW3kaWZmZmarQ8ASDHS3cdJWS8lRpErH70XEs4D/zHdGxDbSDF0HtpSfmZmZmZmtEm2NKXkK8JmIOHeWMD8Dfqel/MzMzMxsFWm7pcTGS1stJXcDfjxHmG3A9i3lZ2ZmZmZmq0RbLSXXA/ecI8z9gGtays/MzMzMVouAIdefnFeaNj7aqpR8DXiGpN0iYkbFQ9J9gScBJ7WUn5mZmZmtEoFaX6fEK7qPl7a6b70L2Ah8WdKTgU2Q1iwpHp8BdIF3t5SfmZmZmZmtEq20lETENyT9OfBB4LPZrhuL2yngTyLi/2sjPzMzMzNbXbx44trW2oruEfERSV8B/gJ4FLALsBW4EDg+In7YVl42Uze6dFTf8NVpaBDLt3fpLkm5yjJ1ozl9FeWIWcrQe25Z/9CyzOW+/FkO5Fd3XLL9HU0U6dV3Pm1qTm4KT5HejHI0xV3mz+COm7OXROP5UNFBdIklfx165anJpkv6vFiOc2HY47JUFvsc88/Vyey9XZV/TuTPudOwfZh0FqounzZe69nOqVnjNXwOdjQzoaZjNxXTA69F+b3RUWfgdSm/1+bznZZ/v/S+Vyrfm3Xfo9U8OhLdOQZFdCQ6dBq/r+toHmHNxllrlRKAiPgx8Jo207ThzOcDbjVYqkqUmZnZXBbyHVRXCZvLWquQeErgta2Vs13SWyQ9Zo4wvyPpLW3kZ2ZmZmarS4Ra/bfx0lYV/Gjg4DnCPIa08ruZmZmZmVlPq9235rAO3OfGzMzMzAYF0G15CJqXKRkvy9lZcV/g2mXMz8zMzMzMxsCCW0oknVPZdISkg2uCTpBWe/8N4BMLzc/MzMzMVqlYgimB3VQyVhbTfevg7H4Aexb/VV3gOuAUPDOXmZmZmc2gJViB3YPdx8mCKyUR0ev6JakLHB0Rb22lVGZmZmZmtma0NdD9hcB3WkrLzMzMzNaQNNC93ZYN994aL61USiLio22kY2ZmZmZma09rs29J6kh6haQLJW2VNJXte7ikf5J0v7byMzMzM7PVI6Ldfxsvba3ovh74T+B9wF7ArxkcXXQ58CfAc9vIz8zMzMzMVo+2WkreADwWOAa4G3BCvjMibgDOB57YUn5mZmZmtopEqNV/Gy9tDXR/LvC1cvYtSXWNZpcDT28pPzMzMzNbRVyRWNvaqpTcG/jcHGGuB+7cUn5W6Ki5saszj4awatgu3QWXqTb9opzdaE5XRRmiyLuj/odTt+gc2nu+NdXevMwDx6Uuz7rj1lC2jiayPPoZNx3d/DnmcQfLurydXTtraK72YY5t9Xi08XrMp9m5tcF8C8yji2b97Fiuciy3hbwP8uM0W/zmzwnVbm/6LJzPZ0ZT2nMd84V+Hiz4fVJ5TrN9DzTpqNP7nprQRO916dDp3Z9Q/yfNfL7/6MWH6ZiaI3Ch4Tso/96qysukZXr/mY2Ttt4VtwE7zRHmXsANLeVnwL12/9tRF8HMzMxs8Voe5B6B5wQeM21VSr4LPKEY8D6DpM2k8SQXtZSfmZmZmZmtEm1VSv4FuCfwb5J2zHdI2gnYAuwMfKil/MzMzMxslQjaH+juhpLx0kqlJCI+Qap4PBv4JfBSAEnfAq4Gngn8U0Sc2UZ+ZmZmZraaiG60+08L4yklHSrpOElfkXSjpJB00hxxDpB0pqTrJd0q6RJJr5ZmDhqT9GBJJ0j6jqRfSrpd0s8lfVHSs6SZA5UkHV2Uo+n/SYt+4iPQ1kB3IuJPJJ0PvAp4COlM2Bf4/4D3RMSJbeVlZmZmZrYMjgQeCtwEXAnsM1tgSc8ETiONtz6FNNHT04H3AgcCz6lE2Q84BLgQuADYCuxWxDkN+DhweEN2HwWuqNl+6azPaIVqrVICEBFbgC2StiN119oaETe3mYeZmZmZrT4rtLvVa0iVkUuBg4BzmwIWQxg+DEwDB0fEt4rtbwbOAQ6VdFhEnJxF+0Tx+7kurQuB50s6PiLqxmVviYjzFvSsVqAlmZMuIm6NiKtcITEzMzOzcRUR50bEjyNimDrTocCuwMllhaRI4zZSiwsUQxyyfbc35Hsj8Pni4X3nXfAx1GpLiZmZmZnZQqyCxRMfV9yeXbPvfOAW4ABJG5oqIyVJm7L0vtcQ7Lcl7Q9MkLpxfSkirp13qVeI1iolkn4LeCPwMGCPhrQjIlwRMjMzM7OecvatttNcZvcvbn9U3RERU5IuBx4I3Af4fr5f0t7A80gVjLsBTwV2B94REZc05FddsO52Se8C3jJky86K0koFQdKhwMmk7mBXkNYjGXJZVDMzMzOzJbGPpIvrdkTEfi3ntbm43dqwv9y+U82+vYGjssd3AG8A3l0T9r+APwHOI81ye1fgCcDbSN3EJoC/Gb7YK0NbrRZHAzcDT42Ir7aUppmZmZmtBeUq7C2nOS4i4mxAktYB9wKeC7wdOEjSsyPijizspyrRfwacIOnbpMHxr5f0nnHrytVWpWRv0gwArpCYmZmZ2UrxgyVoEWlStoRsbthfbr+hKYGI2AZcBrxV0h3AO4BXAsfOlXlEfFvSRaSphx8NnDFcsVeGtmbfugbY1lJaZmZmZrbGtL2i+wj8sLi9X3WHpEng3qThDT8ZMr2zituD51GGXxa3288jzorQVqXkP4Dfk7S+pfRqSdpF0p9K+pSkS4tVMrdK+qqkF0mqfT7zWVkzi/M0SecV6d8k6RuSXjBH+V4g6aIi/NYi/tMW+7zNzMzMbMU7p7itW1H9McAm4IK5Zt7K3KO4HWqcdtH1a9/i4bAVnxWjre5bRwG/A3xS0qsi4qctpVv1HOCDpEE955L60N0NeBZwAvBkSc/JZxxYwMqaSHo5cBxwHXASabDRoaSFIR8cEa+viXMs8DrSAjsfBtYDhwFnSHpFRBzfxgFYLp151Fe7dIdPN6s3dmM+8VTEicZ0Gstcd7GkLu/6Ou1gObL7TeXvNNR1u1nn1iVZIMiAhR3b1fp6zHWOdoY459e6TvYBMtl8HWvg/Z1/vgwc4/z1yLbneXQbOsHP9Uotxys5TB7DfK43fUbWplccjw4aOJbl5/2kJphQe5N6zpVW+X1XeyyGGL/QUQepM6/v2Jl5d+b1vTtOxm++qBlOBf4eOEzScdniiRtJg9Ah/Y7tkbR/vqZJtn1X4J3Fw89l2+8E7B4RP6yEX0/6bXsv4AfAjDRXulbeyRFxi6Q/I1UUfiLpBupnHoiI2GsRWf0IeAbwuYj+J5+kvyHN+PVsUgXltGL7vFfWlLQnqd/e9cD+EXFFsf2twDeB10k6LSK+nsU5gFQhuQx4RET8qtj+LuBi4FhJny3TsvlVSMzMzMadLwLMbSWuUyLpEOCQ4uFuxe2jJW0p7l9bXqyOiBslvZhUOTlP0smk35PPIE0XfCrpAnnuBEm7kH7H/oz0m3VP4CnAdsDpwEey8LsA35f0LdK0wleTFmx8LKl72LXAH+W/k8dFK+8QSb8NXADsTDqYt5CuT1f/F5VfRJwTEWdUD3REXAN8qHh4cLZr3itrkqZY2wAcn1ciiorG24uHL6nEKR//XVkhKeJcAfxjkd4L536GZmZmZraCPAx4QfH/xGLbfbJth+aBI+J04CDSYonPBl5BGnf9WuCwmvVDjgW+ATwceDFpUPsBpIvnhwHPiojpLPz1wPFAtyjPa4E/BH5NaqV5YER8d1HPeETaavP8e2AdcDjw7yOqnZUD7fN+dwtZWXO2OGdVwgyTz1nAm4swR9XsNzMzM7MV2H0rIo4mLX0xnzhfI7V0DBP2JNJQgWHTvpFUcVl12qqUPBT4RHFgl10xo8HhxcO8YrCQlTVni3O1pJuBPSRtKrqtbU8aiHRTRFxdU7wfF7czZmJoeC61C/wA+wwT38zMzMxs3LRVKbmJ1Jw0Ku8EHgScGRGfz7YvZGXNYeJsX4S7ZYF5mJmZmVlhKabxXYljVKxZW5WSM0n955adpFeSBpn/AHj+KMrQpqYFfooWlH3r9pmZmZmNu1Uw+5YtQltTQfwVsKOkfyy6My2LYure9wP/Azw2IqqtNQtZWXPYOFsrtwtevdPMzMzMbC1rq6XkZNKo/5cAh0v6Ec1TAj++jQwlvZo0H/N/A4+PiP+rCfZDYH/SeI6BsRqzrKz5Q+AuRZyvV+LcndR168qIuAUgIm6W9AvgHpLuXjOu5L7F7YwxKmZmZmaWuLvV2tZWS8nBpCnTRPrR/vBiW93/okl6I6lC8l1SC0ldhQQWtrLmbHGeXAmzmDhmZmZmZkZLlZKI6Az5P/wyrg2KhQ/fSWr5eHxEXDtL8FNJi8gcJmn/LI3GlTWBE4HbgZcXCymWcXYG/qZ4+KFKnPLxm4pwZZw9gZcV6Z0413MzMzMzW6ui5X8bL21131oWkl4AvJW0QONXgFdKM5r6roiILbCwlTUj4nJJbwA+AHxL0inAHaTFcfYA3p2v5l7EuUDSe0gL2Fwi6VRgPWkxmzsDr/Bq7mZmZmbN3H1rbRurSglpDAjABPDqhjBfBraUDyLidEkHAW8iray5EbiUVIH4QM3KmkTEcZKuAF5PWv+kQxpMf2REfLQu04h4naTvkVpG/oy00ua3gXdFxGfn9SzNzMzMzNaQBVVKJP0WabD3VcsZfyGrahbxhl5ZM4tzBnDGPONsIasQmZmZmdmQ3OdqTVvomJKvA3+6iHwXG9/MzMzMzFaJhXbfWmynP3caNDMzMzMgLZzY/orurSZnS2wxY0peLemIBcb1abKKdGoa3Lp0Z4+jmjiR4qhIL+ZIo5pWGb+pfHmZ6vKnIf5AGbNTtzaN2QyRvlmbOpoYOGdn7Pf1oTnl7/NZ3/ORf740TDSZpzVEOmNpvp+Ls+hGd+AcLT/jN3Q2MFkc43Wd9fNOtzNzgpyG/Ge+d8p8u9n3ShTlmu38kDq9uMOq5p9/19Z97y6EVthngCsRoydpPWl89MGktfzOAk6MiOmasK8CXhUR92kj74VWSn5Gqlgs9Gz+GV7h3MzMzMxsRZC0DvgScAD93/iHAC+R9OyI+Gklyk7Ab7SV/4IqJRGxZ1sFMDMzMzNzP5qRewVwIGn22HcD24AXAU8Evibp4Ii4dKkyb6+d1czMzMzMxtUfAVcDB0XEJyLi1Ih4MvBK4G7AuZJa6apVx5USMzMzMxu5CLX6b/N2f+AzEXFzvjEijictCH5X4BxJrXXZyrlSYmZmZmZmHeBXdTsi4v+RKia7k1pM7rkUmZuZmZmZjVSaFri9f5u3nwN7N+2MiNOB5wL3BM4B9mgz88VMCWxmZmZmtnhB+wPdXTGZr28DT5A0GRFTdQEi4j+KaYM/CrQ6vsQtJWZmZmZmdiawC/Cc2QJFxL+RZuVqtdrnlhIzMzMzG7GlGJzuwe7z9CngycA1cwWMiI9K+imjXqfEzMzMzMxWj4i4Bfj8PMKfV90m6V7AnhFx/nzzb7VSImmCNJ3YzsBEXZiFFNLMzMzMVq+g/cHpHlIyEi8E3kJDPWA2rVVKJL0ZeA2weY6g8y6kmZmZmZmtXq1USiT9JXAMsBX4OGlKsdpR+2ZmZmZmZrm2WkpeDPwC2DciftlSmmZmZma2RngV9rWtrSmB7wmc7gqJmZmZmZnNV1stJf/bYlq2CnQa6rtdus1xlOJ0I4VRlkYU8TqaeRWlW4yMK+PP3N8dqkxzxU9pLOIqTkP6a0l+/LojHoJYlmXU5VhqHSrn8Bo+D4d9/+bnxLBxhjqu2evQdN6N6+vT/5wd/jOy7jgPbKsci6lsLbdyX913QltmS3tS5fDYiSWbdbZTpDvb9+Zq41XY17a2Pv0+CfyepA0tpWdmZi0b1x+8ZmvVWqqQ9FZ0b/vfxkZb31BHAVcDp0q6d0tpmpmZmZnZGrCgLleSflKzeR2wO/AUSVuBG2rCRETstZA8zczMzGz1cvettW2h40A6zGwUmwJ+lj2u62XpaRXMzMzMzFYnscDf+wuqlETEnguJZ2ZmZmZWy1MCrwbvBU5cSETPmGVmZmZmZjNIehTwp8DDgZ1IC6VfDJwYERdUw0fE1iLMvLUy0F3SOZIOnyPM8ySd00Z+ZmZmZra6RLT7b4sj6W3A14A/IVVK7g08DHgR8BVJb28zv7Zm3zoY2HOOML8BHNRSfmZmZma2mng64BVD0nOAvyGNF/9T4D7AdsXtnxbb3yjpD9rKczknrd+ONBjezMzMzMxWrleQFkd/RER8JCKuiIjbi9uPAI8Afgm8rK0M26yU1NZJlfwG8BTg5y3mZ2ZmZmarQNtdt9yFa9EeCpwaEdfW7Sy2/wepO1crFlwpkdSVNC1puth0dPk4/ye1jvyEVOiTF19kMzMzMzNbQpPALXOEuYUWJ81aTELn028deQypb9kVNeGmgeuALwEnLCI/MzMzM1utPCXwSnIZ8DRJfx0R3epOSR1SL6jL2spwwZWSiDi4vC+pS5oa7K1tFMrMzMzM1hZ3t1pR/h14O/BpSa+NiB+XOyTtBbwL+E3gTW1l2FaTy72BG1pKy8zMzMzMRuc9wJOApwJPlnQVcDWwG3AP0hCQrxbhWtFKpSQiflrel7QOeACwmbR4yvcjYlsb+ZiZmZnZ6uSWkpUjIu6Q9HvA60nrlOwF7FHsvgz4CHBsm7/xW5t9S9Iukj5MajH5DnBecXuDpA9LuktbeZmZmZmZLTVJh0o6TtJXJN0oKSSdNEecAySdKel6SbdKukTSqyVN1IR9sKQTJH1H0i8l3S7p55K+KOlZkuYcaCPpLpKuLsr21cU831xEbIuId0TEfYEdgXsCO0bEfYvtrTY6tNJSIulupBUf70NqHbkIuIbUxPMw0sqPj5V0YET8bxt52uw6y7oEzfDKcnWZMWaqH0ZFmGxclYp4UROvU7xfuw2XWOrSqytTLi9fGX9Yzfl4AF9upRyPlVKOJVVzDq+J571ATccmf2/P93OhGieP3fSZ0YZuMR/Nkr/eCzkec2yrHpepSJN9bj+5ad55tWU+362a5zGpGUu85N/l/hQYypGk6XFvAq4E9pktsKRnAqcBtwGnANcDTwfeCxwIPKcSZT/gEOBC4ALS7+jdijinAR8HDp+jjP8M7DDk81mQiLiJdAyWTFtjSt5OqpC8Dzg6Im4sd0jaETgGeBXwd6RVIM3MzMzMelZo963XkCojlwIHAec2BSx+836YNPPswRHxrWL7m4FzgEMlHRYR+RIZn4iILQ1pXQg8X9LxEXFRQ56HA88C/gL4p/k/vZWjrSr404CvRMRr8woJQETcGBGvIbWkPL2l/MzMzMzMllREnBsRP44Yqsp0KLArcHJZISnSuI3U4gLw0kr6tzfkeyPw+eLhfevCSLoX8AHgX4GzhijfvEg6SNJnJf2fpG116xFKmmorv7ZaSu5EGoE/m68A+7aUn5mZmZmtFkH765Qsf8vL44rbs2v2nU9abPAASRuaKiMlSZuy9L5Xs1/AFlJ3r9cCd15gmZvyfypwOjBBWovwh6QF0ZdMW5WSHwB3nyPM3UlPyMzMzMxsOewj6eK6HRGxX8t53b+4/VFNXlOSLgceSBry8P18v6S9geeRKgF3I03Fuzvwjoi4pCavVwMHA0+IiBsltVopAY4GtgFPjYgvtJx2rbYqJe8H/knS++oOnKSHAX9ApcnKzMzMzAxW7JiS+dhc3G5t2F9u36lm397AUdnjO4A3AO+uBpT0m6Tx3B+KiC8uqKRzexCpG9qyVEigvUrJ5cB/AhdJ+hipiep/STW9g4Dnk/q6XSHpMXnEiDi/pTKYmZmZmeV+sAQtIq2LiLNJvbLWAfcCnkuqeBwk6dkRcQf01gP8OGkhw79cwiLdRJo5bNm0VSk5j9RzT6TZtV6U7Ss7CD6j+K+aMWezmZmZma0x499SUraEbG7YX26/oSmBYu2Py4C3SroDeAfwSuDYIshfAw8HHltM07tUvgQ8egnTn6GtSslbWQ2nkpmZmZktu6D97lsj+GH6Q2B/4H7AwDgWSZPAvUmDxX8yZHpnkSolB9OvlOxLuuB/XsO6igdKCmBrROw0v+IPeCOpB9SRwN8NOfvYorRSKYmIo9tIx8zMzMxsTJ1D6nb1JOATlX2PATYB588181bmHsVtPuvVfwLX1oTdAfhD0vCJz5Jm+hqapI/UbP7/SGsN/omk71LfwhMR8aKa7fPWVkuJmZmZmdnCjX+fm1OBvwcOk3RctnjiRuBtRZgP5hEk7Z+vaZJt3xV4Z/Hwc+X2iPjHuowl7UmqlFwaEQtZqPyIWfbtWfzXCQaHbSxYq5WSYvDN44EHADtExN8W2zcCOwLXRkS3zTzNzMzMzJaCpEOAQ4qHuxW3j5a0pbh/bUS8HtKCh5JeTKqcnCfpZNJg8WeQpgs+FTilksUJknYBLiKtBzJNqgA8BdiOtFZIXStG2+69DHnMqrVKiaQnkVaU3I3U1y2Avy12P4y0ovvzmNmcZWZmZmZrWSzBlMDtpPcw4AWVbfcp/gF+Cry+l2XE6ZIOAt4EPBvYCFxKWuDwAzVjM44lVXr2BZ4IrCd1zzqHNMvWJ5djPEdE/DR/XMyWe2NEfHep8y61UimRtD+pJnct8Brgt4A/KvdHxIXFgjG/jyslZmZmZla1ArtvFeOmj55nnK+RWjqGCXsScNK8CzYznSvoz3jbhnOBfwb+osU0Z9VpKZ03kwbU7B8RHwB+XBPmm8BDW8rPzMzMzMyWxrXArcuZYVuVkgOB0yPimlnC/By4e0v5mZmZmdkqEtHuvy3KecABy5lhW5WSHaifniy3qcX8zMzMzMxsaRwJ3F/S3xYTWS25tga6/wJ44BxhHsbwi8XYPHXGrL6Xl7dL/YRsHWVhiknbVMSLmjid+kWE6BaXS/L0BvfXpDXE8Rym3Gaj1o0unYZuxj5XF24hn7nz+aybLcx8LMUrXP+Z2U5X9oHnqYkZ++/obmsln/mqvt5agvdOW2l6klNryV8D/w38DfAiSf8FXMPMkT8rbp2Ss4CXSPrtiPhqdaekJ5OagN45I6aZmZnZEB54r/ey9ZfvGnUxbEmIiDbHaac0bcGOyO7vRn865KoVt07JO4DDgC9IOo5igRVJTyWtYPky4GrgPS3lZ2ZmZmZmS2PZ1y1ppVISEb+Q9ATgk8Absl2fIVVTLwOeFRFzjTsxMzMzs7XIg9NXjOq6JcOStCOwU0T8bL5xW1s8MSK+Len+wFOBRwO7AFuBC4FPR8RUW3mZmZmZmdmK8xrgLcDMQWFzaK1SAhAR06TWkc+0ma6ZmZmZrW6exndt89QrZmZmZmY2UgtqKZF0+EIzjIiPLTSumZmZma1CQftjStzyMlYW2n1rC4MvtZj7pS/DuFJiZmZmZj1B+923XCcZLwutlLywZtuzgKcDXyYtTX8NaU7jx5KmBf4M8KkF5mdmZmZmZqvUgiolEfHR/LGkpwBPAp4ZEWdUgh8j6Zmk6YI/tKBSmpmZmdnq5qaNNa2tge5vAj5VUyEBICI+DZwOvLml/MzMzMzMbJVoq1LyUODSOcJcCjykpfzMzMzMbBWJaPffxktb65TcQaqYzOahwLaW8jMzMzOzVcT1iFVBxf+8tdVS8iXgKZJeLmmgIEpeATwZ+OJiM5L095K+JOnnkm6VdL2k70g6StIuDXEOkHRmEfZWSZdIerWkxtUmJT1N0nmStkq6SdI3JL1gjrK9QNJFRfitRfynLfY5m5mZmZktF0mHS5q1h5OkB9UsE3IiaZKreWurUvJXwK+A9wM/lrSlqDxsAX4MvA+4vgi3WK8Btgf+s8jv34Ap4GjgEkn3zAMXg+zPJ80A9ingeGA98F7g5LoMJL0cOAN4EHAS8GFgd2CLpGMb4hxLmir57kX4k4AHA2cU6ZmZmZlZk2j53xZjC3DIHGGeSaqE9ETETyPiywvJsJXuWxFxmaRHAf8E/C5wn0qQ/wReFhE/aSG7HSPitupGSX8H/A3w18BfFNt2JFUQpoGDI+JbxfY3A+cAh0o6LCJOztLZEziWVInaPyKuKLa/Ffgm8DpJp0XE17M4BwCvAy4DHhERvyq2vwu4GDhW0mfLtMzMzMzMxtwELVb/2hpTQkRcCjxB0j2AhwObga3AdyLiFy3mM6NCUvgkqVJy32zbocCuwMfKCkmZhqQjSd3OXspgi8mfABuAv88rERHxK0lvB/4VeAnw9SzOS4rbvysrJEWcKyT9I2nWsRcCR83jqQ6l01pj1+iUz6FLtzmMijCRwmiO5x1ZWh3N7NrYzUbAlWkPo8wfVsexHweznRcLVffalfnk++abd4dObToL1cZzn9RkYzrVMi7FsR7GangvNX2OzPszY4ie2KM6Xr1zex6fmcNaKefiMLSA5z/sMcvPl4WaX/kW1PV/aSzF4HS3liy1+5F6SrWitUpJqaiAtFYJmYenF7eXZNseV9yeXRP+fOAW4ABJGyLi9iHinFUJM0w+Z5EqJY9jCSolZmZmq8199/iHxn2bd30DW3/5rmUpx+Zd3zDw+Ne/fPey5LtmuRIxUpI+Utl0SNGDqGoCuBfwO8Dn2sq/9UrJcpH0emAHUovM/sBvkyok78yC3b+4/VE1fkRMSboceCCpu9n3h4hztaSbgT0kbYqIWyRtD9wDuCkirq4p6o+L2/sN+bwubti1zzDxzczMzMwW4IjsfgAPK/7rBPAN0ljvVoxtpQR4PXC37PHZwBER8cts2+bidmtDGuX2neYZZ/si3C0LzMPMzMzMMl5bZOTuXdwK+Alpoqr314SbBn4VETe3mfnYVkoiYjcASXcDDiC1kHxH0tMi4tsjLdwiRMR+dduLFpR9l7k4ZmZmZrYGRMRPy/uSjgHOzbcttbGtlJQi4n+BT0n6NqnL1cdIU/lCv5Vic13cbPsN2batwF2KfdfNEmdr5XY+eZiZmZlZzi0lK0ZEHLPceY7/lCeFoib3P8ADJd2l2PzD4nbGeA5Jk6RmqilSExVDxLk7qevWlRFxS5HvzaSB/TsU+6vK2cBmjFExMzMzM1upJD1K0gmSLpZ0maRvS/pwsRxGq1ZNpaSwe3E7XdyeU9w+qSbsY4BNwAXZzFtzxXlyJcxi4piZmZkZxXqH0fL/qJ/UmJP0NuBrpOUyHk66mP8w4EXAV4qlMlrTSqVE0rSkf2sjrTnyuZ+kGd2kJHWKxRPvSqpklHMmnwpcCxwmaf8s/EbgbcXDD1aSOxG4HXh5Pg2apJ1J66AAfKgSp3z8piJcGWdP4GVFeidiZmZmZjO1XSHxqu6LIuk5pN+9PwP+lDRT7XbF7Z8W298o6Q/ayrOtMSW/JhVuqT0FeIekrwKXk8Z83A04iHSQrgFeXAaOiBslvZhUOTlP0smkldqfQZr691TglDyDiLhc0huADwDfknQKcAdpIcY9gHfnq7kXcS6Q9B7gtcAlkk4F1gN/CNwZeIVXczczMzOzMfEK4H+BR0TEtdn2K4CPSPoM8N+ki++fbCPDtiol3wF+s6W0ZvNFYG/SmiQPJ02zezNpvMbHgQ9ExPV5hIg4XdJBwJuAZwMbgUtJFYgPRMycgC4ijpN0BWna4cNJLUr/AxwZER+tK1hEvE7S90gvzp8BXeDbwLsi4rOLe9pmZmZmq90KWmHeHgp8rFIh6YmIayX9B+l3civaqpT8PXCGpN+LiP9sKc0ZIuK/gZcvIN7XSK0s84lzBnDGPONsAbbMJ46ZmZmZ2QozSVqPbza30OJMvm0ldFfS4oVnSTod+CapK1VdK8THWsrTzMzMzFYJL564olwGPE3SX0dEt7pTUod0wf+ytjJsq1KyhVQBEfCs4h8GKyUqHrtSYmZmZmYDXClZUf4deDvwaUmvjYgflzsk7QW8izR0401tZdhWpeSFLaVjZmZmZmaj9R7SUhdPBZ4s6SrgamA34B6k8dZfLcK1opVKSdPgbzMzMzOzobilZMWIiDsk/R5p0qc/AfYizUILqcvWR4BjI2JbW3m2NjjFzMzMzMxWh6LC8Q7Schw7AJuBrRFx01Lk50qJzUtHw03X111Ax9BOZS3PLjPGVdFRp0h/5r6catYFjSy9YZ9Hyqv/XMr8x81cx2slq54Xy5nPQvJus7zL9dznm1/de3O+aYyjhbz/8zjDvA9X8vEryzbb67/QNFebhX5XzPd8WW08pmTlKSojv09aimMzsFXSt4HT266ctFYpkbQ98BfAE0l9zTbUBIuI2KutPA12v/tRXHX1MaMuhpmZ2aLstcc7hw67edc3zNi29ZfvWnDedenVudOur+Oma9+74HysWW8V9pbTtIUrVnX/EGldwPxqbgA3SPrziDi1rfxaqZRI2ok02OU3gRuBHYGtpFXNtyuCXQW01u/MzMzMzMzaV4wn+QRpMfCPAeeRlvvYDXgs8MfAJyTdEBFfbCPPttpNjyRVSF4E7Fxsey+wA3AAaWXzy4AHtJSfmZmZma0m0fK/LcZbgNuBR0XECyPioxHx+eL2CNLv+21FuFa0VSl5BnB+RJwY0W8si+RC0uIq+9DiXMZmZmZmZrYkHg6cEhHfrtsZEd8CPgns21aGbVVK7glcnD3uko0piYj/A84CDmspPzMzMzNbRdxQsqLcTlqXZDZXFeFa0Val5BYYmI5jK6nPWe5/SQPgzczMzMxs5foKcOAcYQ4Ezm8rw7YqJT8ntZaU/gd4jDQwJ95vkwbImJmZmZkNKGfgauu/DZIOlXScpK9IulFSSDppjjgHSDpT0vWSbpV0iaRXS5qoCftgSSdI+o6kX0q6XdLPJX1R0rOkmWsYSDpE0imSfiDpV0UeP5b0CUn7t/PMeSPwEEnvLGbYzfPfXtI/AA8C/qql/FqbEvjLwB9IUjGm5BTgA8CZks4ADgYeBXywpfzMzMzMbDVZmX2ujgQeCtwEXEkaI91I0jOB04DbSL+HrweeTpoA6kDgOZUo+wGHABcCF9DvbfT0Ip2PA4dX4jwTeATwTVIXqjuAvUnrifyhpD+LiBPm/UwHvRG4BHgD8GfF2iT/C9yNNI5kM6mV5I2VelNExIsWkmFblZKPkqb/3YPUavIh4HGkg/yEIszXSC+smZmZmdk4eA2pMnIpcBBwblNASTsCHwamgYOLweBIejNwDnCopMMi4uQs2iciYktDWhcCz5d0fERclO1+aUTcVhPnwaSKyrGSPhYRd8zvqQ44Iru/E+l3fdVBxX8uSLPxzlsrlZJiZP5Ls8dTwLMk7UequV0BfDNiDS5PamZmZmZzWomLHUZErxJS05Oq6lBgV+BjZYWkSOM2SUcCXyL9Xj4521c7UDwibpT0edJyGvcFLsr2zaiQFNu/J+n7wMOKcvxirgLP4t6LiLsgra3oXiciLmZwVi4zMzMzs9WobE04u2bf+aSJoQ6QtKGpMlKStClL73vDZC7pfsD9gWuZe+asWUXETxcTfyHaWtH9J8D7IuIDs4R5GfC6iLhPG3mamZmZ2SrR4uD0PE1gH0m1F8gjYr+Wc7x/cfujmrymJF0OPBC4D/D9fL+kvYHnAROkcRtPBXYH3hERl9RlJul3SRNJrSe1bDy92PWn49g7qa2Wkj1J/c1msxPwGy3lZ2ZmZmaryQrsvjVPm4vbrQ37y+071ezbGzgqe3wHaZD5u2fJ73dJA9JL1wBHRMTn5yzpCtTWlMDDuBPpAJuZmZmZLYcfRMR+df+jLlguIs6OCJFaPfYG/g54O/AZSesb4vxVEWcH0oxY5wBnSXrTMhW7VQtuKZF0r8qmnWq2QWqGuhfwbOAnC83PzMzMzFanoP3uWyNoeClbQjY37C+339CUQERsAy4D3irpDuAdwCuBY2eJczPwHeC5ku4M/K2kL0TEN+dX/NFaTEvJFcDlxT/Aq7LH+f+lpJrbXqRp0szMzMzMVpsfFrf3q+6QNEka9zHF8BfpzypuD55HGc4GxMypele8xYwp+RipEirSoi6XAN+tCTcNXAd8KSK+sIj8zMzMzGyVCuaccnelOwd4LvAk4BOVfY8BNgHnzzXzVuYexe3UPMqwkDgrwoIrJRFxRHlf0uHApyLirW0Uylaeztxzc88avruANtlO1pDXZXASiY6Gb+TrFhNQaB4Ng5HlN9/n3paFHLMm8zlebetGfixnL0d3mSYLqStHXTmHLc9SHd+2jken5eGDrac3wvNzObX1PJfrfdKkfP2rn8vDyss/xXQrZVpOHXUaX4M2z+WmtEb9+i+llbhOyTydCvw9cJik47LFEzcCbyvCfDCPIGn/fE2TbPuuwDuLh5/Ltm8A9omI/6qJ8wjgJaQGgbppiVe0thZPXBvfKGZmZrYibd71DWz95bvmFd5sLpIOAQ4pHu5W3D5a0pbi/rUR8XroLXj4YlLl5DxJJwPXA88gTRd8KnBKJYsTJO1CWhzxZ6QKxZ7AU4DtgNOBj2ThtwO+K+kS4L9Jq81vIi2yWK5r8oaI+MEinvZILOniiZL2AZ5MWizm5IhomiLNzMzMzNayldlS8jDgBZVt9yn+AX4KvL7cERGnSzoIeBNpkqeNpPHVrwU+EDGjPehYUqVnX+CJpNm3riV1Bfs48MlKnJuBN5PGjBwE3IV05H4BnAT8Y0R8Y8HPdoTaWjzxLcBLgQdGxPXFtt8FziAdXIC/lPRbEXFdG3mamZmZmS2liDgaOHqecb5GaukYJuxJpMrEsGlvI3UFe9tcYcdNW92unkyaB/r6bNs7SDW3o0j95+5NmqHLzMzMzKyvWNG9zf8V2vJiDdqqlOwJfL98IOkewH7AP0XE2yLi5aRmqENays/MzMzMzFaJtiolO5MG8pQOJNVPP5ttu5i0iKKZmZmZ2YDWW0psrLQ10P2X9OdFBngssA3IB9qsp71KkJmZmZmtEqtkRXdbhLYqJd8FniHpQcBtwB8CX42IW7MwewJXt5SfmZmZmZmtEm1VSv4BOBfIF3J5d3lH0gSpS9d/tpSfmZmZma0ibtlY29paPPErkp4GvJh0Tv1bRJyVBTmANH/yp9rIz8zMzMzMVo/WFk+MiLNpWNI+Ir4CPLytvMzMzMxslXFTyZrW1uKJjwFuiYhvzRLmXsCeEXF+G3mamZmZ2eoRoVEXwUaordmwzgO+IenYWcK8kDTuxMzMzMzMrKfNKXpvB14j6TRJG1tM18zMzMxWM6/ovua1WSl5N/Bx4PeB8yTt2mLaZmZmZma2SrVZKdkWEUcARwO/BVwo6f4tpm9mZmZmq5RXdF/bWpt9qxQRb5V0GfCvwAWSnh0R57Wdj7WrozS4rLtE7+Iy/dx88upU6s9duvPIu1PkN3wcLaK+HlnZ8nRiHmWG+mPWhqV6jZuUx7/tsG2ry7u6rTyHlqucozweVfN5/9RZSc9l3C33say+9rN9/g5znnRXUZ+aUZzXs+U52/Ff6e9Br+hurVdKACLi3yRdSVqX5GxJf7YU+Viy+92P4qqrjxl1MeZluX8Ym5nZyvOAe7577kDzsHnXN9Ru3/rLd826fz427fKq3v1brnv/otMzs2RJKiUAEfFlSQcAnwNOBH6yVHmZmZmZ2Xjz9cq1bUnb8iLiB8AjgYuAvZYyLzMzMzMzG09ttZQ8FriibkdEXCvpYOBVwHYt5WdmZmZmZqtEK5WSiPjyHPtvB/6hjbzMzMzMbPXxiu5r28qeisHMzMzMzFa9JRvobmZmZmY2lKVYW8QD58eKW0rMzMzMzGyk3FJiZmZmZiPnKYHXNreUmJmZmZnZSLmlxMzMzMxGKmi/pcQNL+PFlRIzMzMzGzl331rbFlQpkXT4QjOMiI8tNK6ZmZmZma0+C20p2cL8W8VUxHGlxMzMzMwygtYXT/RijONkoZWSF7ZaCjMzMzMzW7MWVCmJiI+2XRAzMzMzW6O8eOKa54HuZmZmZjZyHui+trlSsoZ1NLOvZd22pc67u4BPoc4cS+x06dbkubBleboxM63ZqKFsTduHETXPZz7p5/GX4zVeyGs6DhZ6Dq0G+XOf7T2xlo/RapC/tk2vZd3nb/mZO6nBnxVTMTWwbSqm2iimNfD7z8bZQmff+skC84uI2GuBcW0Wu9/9KACuuvqYEZfEzMwsuf89j+WyK/9q4PEobN71DUuS7qZdXrUk6da55br3L1teoxIemL6mLbSlpMPCeur5bDMzMzMzswELHei+Z8vlMDMzM7M1yiu6m8eUmJmZmdnouRaxpi3JiChJO0u651KkbWZmZmZmq0trlRJJO0h6t6RrgGuBy7N9j5R0pqR928rPzMzMzFaPiHb/bby0UimRtBn4OvAa4Crg+wwOav8e8DvAH7WRn5mZmZmZrR5ttZS8CXggcERE7Av8R74zIm4Bvgw8vqX8eiQ9T1IU/3/aEOZpks6TtFXSTZK+IekFc6T7AkkXFeG3FvGfNkv4CUmvkXSJpFslXV+0Dh2w2OdoZmZmtqoFRKjVf49RGS9tVUqeBXw+Ij42S5ifAvdoKT8AinErxwM3zRLm5cAZwIOAk4APA7sDWyTVTphebN8C3L0IfxLwYOCMIr1qeAEnA+8B1hdl+hTwGOB8Sc9c2DM0MzMzM1v92qqU7AFcMkeYm4DNLeVXVgROBK4DPtQQZk/gWOB6YP+IeFlEvAZ4CHAZ8DpJj67EOQB4XbH/IRHxmoh4GbBfkc6xRbq5w4BDgQuAh0XEGyLiRcBjgWngw5LutPhnbWZmZrY6eUzJ2tZWpeTXwF3nCHNv0gD4trwSeBzwQuDmhjB/AmwAjo+IK8qNEfEr4O3Fw5dU4pSP/64IV8a5AvjHIr0XVuK8tLg9MiJuy+J8EzgF2JVUaTEzMzOzOtHyv42Vtiol3wSe1tQaIOnuwFOAr7aRmaQHAO8E3h8R588S9HHF7dk1+86qhFlQHEkbgQOAW4CvzCMfMzMzM1vBJB0q6ThJX5F0YzGG+aQ54hxQjCu+vhhnfImkV0uaqAn7YEknSPqOpF9Kul3SzyV9UdKzip5BeXhJelJRpu9K+pWk2yT9UNL7JN2t7WOwXNpaPPH9pB/fZ0r6s3xHUYH4MLAR+MBiM5I0CXwc+BnwN3MEv39x+6Pqjoi4WtLNwB6SNkXELZK2J417uSkirq5J78fF7f2ybXsBE8BPImJqyDiNJF3csGufYeKbmZmZjaMIzR1o+R0JPJQ0DOFK5vg9VowjPg24jdRb5nrg6cB7gQOB51Si7AccAlxIGgawFditiHMa6Tfv4Vn4DaTf3HcA5wNfJP0OfRzwKuAwSb8TET9mzLRSKYmIz0s6BjgK+G9gG4Cka4GdSdMDvzEiLmghu7cADwd+OyJunSNsOYZla8P+rcD2RbhbhgwPsNM886jGMTMzM7OV7zWkysilwEHAuU0BJe1IuhA/DRwcEd8qtr8ZOAc4VNJhEXFyFu0TEbGlIa0LgedLOj4iLip2TZMqSv+UDzOQ1AH+Cfhz0sRLT1/Y0x2d1hZPjIhjSFP+fgb4FemgBXAm8LsR8a7F5iHpkaTWkXdHxNcXm95KFBH71f0DPxh12czMzMyWQtD+QPc2hpVExLkR8eOIoYbOH0oaR3xyWSEp0riNVJGA/jjkct/tDfneCHy+eHjfbPu2iBgY91xs7wJvLR4ePERZV5y2um8B6YVjlhrkYhTdtj5G6or15iGjbQXuQmrNuK5mf7WVY2tle1P4Gyp5zDeOmZmZmZUCaLv71vIPdp9tXPL5pF45B0ja0FQZKUnalKX3vSHz31bc1g0nWPFaaylZBjuQxmU8ALgtWzAxSN3GIE29G5LeVzz+YXE7YzxHMfh+e+DKYnFHIuJm4BfADsX+qrKmmo9RuYzUKnSfouI0TBwzMzMzW11mG8s8BVxOahC4T3W/pL0lHS3pbyX9C2lM8kOAd0TEXMtulP6kuK2rFK14rbSUSHo88FzSlLhX1ezfHXgb8LGIOG+B2dwO/GvDvn1J40y+SqqIlF27ziENKnpStq305CxM7hzg+UWcE+eKExG3SboA+J3iv9pS1JTPyHUj6Gj0g8rKMnRbnFS8k9W3u3QXl5aytGJxaS2UFnn9II8fizwewxjmvFro6z2fc7aax3zKVQ07bFpN8Ycp37jI3xMrRX68x/W4rgQLfW07s3xG7bXHO7nsyr9aaJGssGmXV3HLde8fdTGW1BK9dfdpmkSo6B7fpsWMM96b/kV2SAPZ3wC8e5iMJT2iiP9r+l3Fxkpb3bdeAexTVyEBiIirikUKNwPnLSSDYlD7n9btk3Q0qVLy0Yg4Idt1IvCXwMslnViuVSJpZ/ozd1UXXvwQqVLyJkmnl332igUTX0aqHFUrKx8kVUjeJunx5VolxQnyh8AvSTMoLLnd794/n6+6+pjlyNLMzGxWe+3xzlEXYVXYtMurAFZ95WQtioizSTP+rgPuRbrY/3bgIEnPjog7muJKuh9wBrAOOCwiLluOMretrUrJvqQpyWbzVeAJLeU3lIi4XNIbSFMRf0vSKaSa56GkVehnDJiPiAskvQd4LXCJpFOB9aTKxZ2BV+QLMRZOBp5VpPsdSWcAuxRxJoAXFwOWzMzMzKzO0rSU/GAJWkSaLHqccURsIw0NeKukO4B3kBYMP7YufFEhOZf0G/WwiPjM/Iu9MrTVBn9XoLaVJPO/zL3qe+si4jjgGcD/R5rn+c+Aa4AjIuL1DXFeR1q1/Zoi/OFF/KdHxPE14QP4I1JFZorUcvQs0qCmx0TEp1t+WmZmZmarSoRa/R+B2cYyTwL3Jv1O/MmQ6ZULcB9ct7NYC/A80qROz4mIZemVs1TaainZCtxzjjD3BG5uKb8BEXE0cPQs+88gNWvNJ80twJZ5hJ8iLYzz3vnkY2ZmZmarwjmkbldPAj5R2fcYYBNw/lwzb2XuUdzOmE1L0oNJvZQ2A8+KiM8tqMQrSFstJRcBh0jarW5nMdD9kCKcmZmZmdmgaPl/+Z0KXEtaVX3/cqOkjaQJnyCNQybbtz81JO0KlIOxPlfZ9zBSl607Ac9cDRUSaK+l5DjgqcBXJL0O+HxE3C5pA6m2+G7SlL4faCk/MzMzM7MlJekQ0oV1gPLi+6MlbSnuX1sOB4iIGyW9mFQ5OU/SycD1pGEE9y+2n1LJ4gRJu5Au3P+MtMzEnsBTgO2A04GPZOXZGfgSaQzJl4qyPLqm6O+LiBsW8JRHppVKSUR8QdLfkhY1/BQQkn4F7Ayo+P/bYmYBMzMzM7O+0BIsnthKeg8DXlDZdh/6a438FOiNUY6I0yUdBLwJeDawEbiUNO74AzUrwx9LqvTsCzyRNLnStaSuYB8HPlmJs5lUIQF4fPFfZwtjtnB3ayu6R8RRkr5GGuT9SNIczNcDFwLHRcR/tpWXmZmZmdlSm2vcckOcr5FaOoYJexJw0jzSvoJ0sX/Vaa1SAqnFBPhCm2mamZmZ2erndU/XtlYGuks6XNJD5gjzYEmHt5GfmZmZma0yZReutv5trLQ1+9YW+oOAmjyDmSuhm5mZmZnZGtdq9605TDCqCdrMzMzMbGXzr8Q1ra2WkmHcD/jVMuZnZmZmZmZjYMEtJZI+Utl0iKQ9a4JOAPcCfofK4i9mZmZmZoDHgaxxi+m+dUR2P0jzOD+sIWwA3wBes4j8zMzMzGw1WopV2N0dbKwsplJy7+JWwE+A9wHvrwk3DfwqIm5eRF5mZmZmZrZKLbhSEhE/Le9LOgY4N99mK19Ha6OZtFMMnerSXXxa6g/D6ka3dnuTPPwoaZFDyaKF4wjLc/4tJI+mOB2JbjGJ/mzpDptn1xPyL8gwxzcPkx/n2eI2hWt6nYZNa1yslM8nW9vk7ltrWiuzb0XEMW2kY+3a/e5HNe676mq/ZGZm1p699njnqIuwZmza5VXzCn/LdXUdWcxWluWcEtjMzMzMrN74NTJai1wpMTMzM7PRc/etNW051ykxMzMzMzObwS0lZmZmZjZycvetNc0tJWZmZmZmNlJuKTEzMzOz0QqhbstjSjxGZay4pcTMzMzMzEbKLSVmZmZmNnpu2VjTXCkxMzMzs5ES7Q90dxVnvLj7lpmZmZmZjZRbSszMzMxs5OTuW2uaW0rMzMzMzGyk3FJiZmZmZqMVLMGUwO0mZ0vLlRIzMzMzGzmv6L62ufuWmZmZmZmNlFtKzMzMzGzkPNB9bXOlxFaMjtKHUTfGo/22o7XZ0KiGBtagu8wlWX7lObpU6XYjBvJYqvdCR+rlVc0zV+ZfhhuVxRz3YeM2hVuq13yUurH636tmNn5cKVmjdr/7UQOPr7nmrSMqiZmZjau99njnqItgQ9i0y6tqt3cmPwZcubyFaaAAtVxf9hiV8eJKiZmZmZmNmJag+9bqa+lczdZm/xMzMzMzM1sx3FJiZmZmZiPnge5rm1tKzMzMzMxspNxSYmZmZmYj1/ZAdxsvbikxMzMzM7ORckuJmZmZmY1WLMGYEk8JPFZcKTEzMzOzkRLQ6bZbKfGw+fHi7ltmZmZmZjZSbikxMzMzs5HzCuxrm1tKzMzMzMxspNxSYmZmZmajFaCWx5R4oPt4caXEzMzMzEbO65Ssbe6+ZWZmZmZmI+WWEjMzMzMbKSE6La9TIk8KPFbcUmJmZmZmZiPlSomtOB2Jjtq7utEt/kato868/seN6Mzrf60rz/P8XK+e922+D3LdiN7tbHnk5asrb1vytOv+x0E3ovc/mvy7Q/+brUiRxpS0+d/GQHdJh0o6TtJXJN0oKSSdNEecAySdKel6SbdKukTSqyVN1IR9sKQTJH1H0i8l3S7p55K+KOlZ0swPQUkPkHSMpE9L+llRppA01j2gxrrw1p7ddnsLANdc89YRl8TMzFaqvfZ456iLYKtY67NvteNI4KHATcCVwD6zBZb0TOA04DbgFOB64OnAe4EDgedUouwHHAJcCFwAbAV2K+KcBnwcOLwS54nAW4Bp4MdFXhsX8NxWFFdKzMzMzMzqvYZUGbkUOAg4tymgpB2BD5MqCwdHxLeK7W8GzgEOlXRYRJycRftERGxpSOtC4PmSjo+Ii7LdZwFfBy6JiFslXQH8xsKf4srgPhRmZmZmNlICOt12/9tod4mIcyPixxFD9c08FNgVOLmskBRp3EZqcQF4aSX92xvyvRH4fPHwvpV9P4yIb0TErUM+jbHgSomZmZmZ2eI9rrg9u2bf+cAtwAGSNsyVkKRNWXrfa6d4K5u7b5mZmZnZaAWo7XkiUnr7SLq4dnfEfi3neP/i9kc1eU1Juhx4IHAf4Pv5fkl7A88DJoC7AU8FdgfeERGXtFzOFcmVEjMzMzOzxdtc3G5t2F9u36lm397AUdnjO4A3AO9upWRjwJUSMzMzMxs5Lc2M1T9YghaR1kXE2YAkrQPuBTwXeDtwkKRnR8QdIy3gMnClxMzMzMxGrjP+y+iULSGbG/aX229oSiAitgGXAW+VdAfwDuCVwLEtlXHF8kB3MzMzM7PF+2Fxe7/qjmJhw3sDU8BPhkzvrOL24EWXbAy4UmJmZmZmI6UAdaPd/7YHzs/tnOL2STX7HgNsAi5omga4xj2K26nFFmwcuFJiZmZmZrZ4pwLXAodJ2r/cKGkj8Lbi4QfzCHm4yvZdgXcWDz/XflFXHo8pMTMzM7MRS60bbae5WJIOAQ4pHu5W3D5a0pbi/rUR8XpICx5KejGpcnKepJOB64FnkKYLPhU4pZLFCZJ2AS4CfkZaDX5P4CnAdsDpwEcqZboLg2NM7lLc/qvUax96Z0T8YN5PeIRcKTEzMzOzkdNQi6Yvu4cBL6hsu0/xD/BT4PXljog4XdJBwJuAZwMbgUuB1wIfqFkZ/lhSpWdf4InAelJryznAx4FP1sTZoaZMAIdn97cArpSYmZmZmY27iDgaOHqecb5GaukYJuxJwEnzTP8KQPOJMw5cKTEzMzOz0SoGuredpo0PD3Q3MzMzM7ORckuJmZmZmY2cYvxXT7SFG7uWEklXSIqG/2sa4hwg6UxJ10u6VdIlkl4taWKWfJ4m6TxJWyXdJOkbkuoGFeVxXiDpoiL81iL+0xb7nJfTbru9pXdbd385daQZ/23pRnfW/84Qf0uto07vv01Bt/fftL26bymIzoz/pcpjoftXsrbeE92I3n/T9nJfXbhqORbynm0qw0pTPSajLnf+mbUc9trjnXMHMluwgGj53/23xsq4tpRsBd5Xs/2m6gZJzwROA24jTcN2PfB04L3AgcBzauK8HDgOuI40+OgO4FBgi6QHl1O/VeIcC7wOuBL4MGn2hMOAMyS9IiKOn/ezHJG8AlK9f801bx1FkczMbIRcITGzpTaulZIbitkQZiVpR1IFYRo4OCK+VWx/M2mqtUMlHRYRJ2dx9iRNz3Y9sH8xwwGS3gp8E3idpNMi4utZnANIFZLLgEdExK+K7e8CLgaOlfTZMi0zMzMz6xPtd99addNTrXLj2W9heIcCuwInlxUSgIi4DTiyePjSSpw/ATYAx+eViKKi8fbi4UsqccrHf1dWSIo4VwD/WKT3wsU8ETMzMzOz1WpcKyUbJD1P0t9IepWkxzaMD3lccXt2zb7zgVuAAyRtGDLOWZUwi4ljZmZmZpCGf0S35f9RPymbj3HtvrUbaZXL3OWSXhgRX8623b+4/VE1gYiYknQ58EDSqpzfHyLO1ZJuBvaQtCkibpG0PXAP4KaIuLqmrD8ubu83zBOTdHHDrn2GiW9mZmZmNm7GsaXkRODxpIrJ9sCDgX8G9gTOkvTQLOzm4nZrQ1rl9p0WEGdz5XY+eZiZmZlZT5CGALf576aScTJ2LSURcUxl038DL5F0E2mw+dHA7y93udoSEfvVbS9aUPZd5uKYmZmZLQ+vU7KmjWNLSZMPFbePybZVWzWqyu03LCDO1srtfPIwMzMzM7PCaqqU/LK43T7b9sPidsZ4DkmTwL2BKeAnQ8a5e5H+lRFxC0BE3Az8Atih2F913+J2xhgVMzMzM4PU1arb8r+7b42T1VQpeVRxm1cwzilun1QT/jHAJuCCiLh9yDhProRZTBwzMzMzM2PMKiWSHlDMdlXdvidQrph+UrbrVOBa4DBJ+2fhNwJvKx5+sJLcicDtwMuLdMs4OwN/Uzz8UCVO+fhNRbi8XC8r0jtx9mdnZmZmtobFdLv/NlbGbaD7H5JWVD8f+Cnwa2Av4KnARuBM0mrsAETEjZJeTKqcnCfpZNJK7c8gTf17KnBKnkFEXC7pDcAHgG9JOgW4g7QQ4x7Au/PV3Is4F0h6D/Ba4BJJpwLri/LeGXiFV3M3MzMza1J232o7TRsX41YpOZdUmXg4cCBpfMcNwFdJ65Z8PCIGzsCIOF3SQcCbgGeTKi+XkioQH6iGL+IcJ+kK4PXA4aQWpf8BjoyIj9YVLCJeJ+l7pJaRPyO9s74NvCsiPru4p21mZmZmtnqNVaWkWBjxy3MGnBnva8BT5hnnDOCMecbZAmyZTxwzMzMzg7S2iK1VYzWmxMzMzMzMVp+xaikxMzMzs9UolmDxRI8pGSeulNhY6UgAdGcOBRrQbX2wXE1ZsobGxeTXzT6EO+rUbo8sfQ3ZwBlDlGm2ME37hs1/JSifw1xlFh2Cbu92Ico8qq/VQtMrdaSB8718DzTtX4zZ0mnaV26vlqutMlmz/POiSXcRP/KGSd+sXe6+tZa5UmLzsttubxkq3DXXvHWJS2JmZmZmq4UrJWZmZmY2UkEsumW5Lk0bH26bNTMzMzOzkXJLiZmZmZmNnldhX9PcUmJmZmZmZiPllhIzMzMzG7GA1mfO9JiSceJKiZmZmZmNXHhK4DXN3bfMzMzMzGyk3FJiZmZmZiPm7ltrnVtKzMzMzMxspNxSYmZmZmajFRBtTwnshpKx4kqJmZmZmY2Yu2+tde6+ZWZmZmZmI+WWEjMzMzMbuWi9pcTGiVtKzMzMzMxspNxSYmZmZmYjFtD64okeUzJOXCkxMzMzs9ELd99ayxThWuQ4kHTddtttd+cHPOABoy7KULZtu3rURZhVzHH1RGiZSjJYljzfucpoZjaXxXyO5J9H69ft3kZxbIX5/ve/z6233np9ROwyynJIulhM7rthcudW07196lcEU9+OiP1aTdiWhFtKxseNt956K9/+9revmCXMPsXtD5ahPKuRj9/i+PgtnI/d4vj4Lc4Qx++aZSnImBrn829P4MZRFwL4QTDFbVO/XJK0lyJRa59bSlYRSRcD+IrAwvj4LY6P38L52C2Oj9/i+Pgtjo+fWTs8+5aZmZmZmY2UKyVmZmZmZjZSrpSYmZmZmdlIuVJiZmZmZmYj5UqJmZmZmZmNlGffMjMzMzOzkXJLiZmZmZmZjZQrJWZmZmZmNlKulJiZmZmZ2Ui5UmJmZmZmZiPlSomZmZmZmY2UKyVmZmZmZjZSrpSYmZmZmdlIuVIy5iRtkRRz/H+pEueIOcK/ZFTPZ7lJ2nOOY3HyLHFfIOkiSTdJ2irpPElPW87yj5qk+0p6o6RzJP1c0h2S/lfSpyU9tiGOz7+CpD0kfUTSVZJul3SFpPdJ2nnUZVsJJO0i6U8lfUrSpZJuLd5rX5X0IkmdSvgFv59Xq+Kcajoe1zTEOUDSmZKuL475JZJeLWliucs/SkN8VoWk6Sy8zz+zRZgcdQFs0U4HrmjY93zgPsBZDfs/DXy3Zvu3FluoMfRfpGNZ9d91gSUdC7wOuBL4MLAeOAw4Q9IrIuL4JSrnSvO3wB8C/wOcCVwP3B94BvAMSa+KiA80xF3T55+kvYALgLuSjsUPgN8CXgU8SdKBEXHdCIu4EjwH+CBwNXAu8DPgbsCzgBOAJ0t6TsxcBXhe7+c1YCvwvprtN1U3SHomcBpwG3AK6T39dOC9wIGk12St+C5wTMO+3wEeR/33q88/swXwiu6rlKSdgKuACeAeEXFttu8I4ETghRGxZRTlWykk7QlcDnw0Io4YMs4BwNeAy4BHRMSvsrQuBrYH9omIK9ov8cpSnEv/FRHfqWw/CPhPIIA9I+LqSpw1f/5J+jzwBOCVEXFctv09wGuAf46INdNqVEfS40jvp89FRDfbvhtwEXBP4NCIOK3YvifzfD+vdpKuAIiIPYcIuyNwKbAZODAivlVs3wicAzwa+KOIWPNX/CV9HXgU8MyI+EyxbU98/pktmLtvrV7PB7YD/l9eIbFWlD8U/66skAAUlZB/BDYALxxBuZZdRGypVkiK7V8GziO1IB2w3OVa6YpWkieQWjn/sbL7KOBm4PmStl/moq0oEXFORJyRV0iK7dcAHyoeHrzsBVu9DgV2BU4uKyQAEXEbcGTx8KWjKNhKIunBpArJL4DPjbg4ZquGu2+tXi8ubv9lljAPk/RqYCPpw/XciLhyqQu2Qu0u6c+BXYDrgK9HxCUNYR9X3J5ds+8s4M1FmKNaL+V42VbcTjXsX8vnXzne5gs1P7h/LelrpErLo4AvVSMbMPv5NZ/381qwQdLzgHuRKryXAOdHxHQl3GyfbecDtwAHSNoQEbcvWWlXvj8rbv+15hiCzz+zBXGlZBWS9GjgwcCPIuLcWYK+qvJ4WtIJwKuLK2Nrye8V/z2SzgNeEBE/y7ZtD9wDuCnvkpT5cXF7vyUq51iQ9BvA40k/Ys5vCLaWz7/7F7c/atj/Y1Kl5H64UjKDpEng8OJh3Q/ood7Pa8huwMcr2y6X9MKiVbPUeF5GxJSky4EHksYqfn9JSrrCSdoOeB4wTRrXVMfnn9kCuPvW6lRexflww/7LgVeQvoC2B3YH/oDUleTPgY8scflWkltIg7X3A3Yu/g8iDao9GPhSpQvN5uJ2a0N65fad2i7ouJC0Afg3Uje2o/MubgWffz6PFuudwIOAMyPi89n2+b6f14ITSRcIdiO93x4M/DOwJ3CWpIdmYX1ezu0PSM//7Ij4eWWfzz+zxYgI/4/4n/RjLObxf9IsaW0mNc/fDtxlnuW4J2mmlQAeOurjMorjl6U5CVxYhH9Vtn33YtuVDfHWFftvH/VxGdH5NwF8sgh3MsVkGqv5/FvgMf+X4nn+acP+vyv2//Woy7rS/oFXFsfm+8Cdh4xT+35ey//AscXx+FS27UfFtr0b4nyt2P/oUZd/hMetPAZPn0ccn3/+9/8Q/+6+tTJcRpp+cVhXzbLvecAm0kDFeQ1wj4ifSzoTeC7wGNK0huOgzeMH9LoqnAA8knQs3l/sKq8Ubq6N2N9+wzzKM2qtHL9iDYOTSFOGfhJ4XkQMPb3fGJ9/C7Eaz6MlJ+nlpPfi/wCPj4jrh4k3y/t5LfsQaVrzx2TbfF7OQtIDSRN3XEmaAn0oPv/MhuNKyQoQEY9vMblygPs/LzD+L4vbsWlibvn45WYci4i4WdIvgHtIunvMHFdy3+K2aazAitPG8ZO0jtRl6znAvwOHR/0A0LmM3fm3QD8sbpvGHo3debTUikkR3kta6+HxEfF/80xirZxbw6o7Hj8E9iedlxfngYtxPPcmTSzwk+Uo4Ao01wD32fj8M5uDx5SsIpIeCTyUNMD9vAUm88jidq1+6eQeVdxWj8U5xe2TauI8uRJm1ZO0HvgPUoXkY8DzF1ghgbVz/pUTUDyhZlXyO5EWqbuF1OVjzZP0RlKF5LvAYxdQIYHm9/NaVXc8ZvtsewypFf6CWIMzbxVrtTyfNMD9XxeQhM8/szm4UrK6lFdxZpsGGEn712zrSPpr0uJY11I/o82qI2nf6o/CYvvjSQvYQeqSlCvXR3iTpJ2zOHsCLyON5zmx/dKuPMWg9k8BzyR9Ub8wKlPc1sRZ8+dfRFwGfIE02Phlld3HkK6mfjwibl7moq04kt5MGth+MamFpLFb6gLfz6uWpAfUDawuPquOLx7mx+NU0vvvsPx9Wvwgf1vx8INLU9oV7zmkgetnxcwB7oDPP7PF8oruq0SxEu9VpC55e8zxxR2kLhD/RVofYjPpyuyDSFdnfz8ivrDkhV4Bimka7wtcQOonDPAQ+vP1vzki3lYT793Aa4s4p5IWCfxD0rz0r4iI46txViNJJwJHkH7I/BNpIGfVeXnLnc+/pFhA8QLgrsCnSQO3H0law+RHwAERcd3oSjh6kl4AbCFdnT6O+lmhroiILUX481jA+3m1knQ0adzI+cBPgV8DewFPJa0PdCbp/XZHFucQ0mfabaTJKq4HnkGaLe9U4A/mM1ZstZD0FeC3gWdExBkNYc7D55/ZgrlSskpIeinpR+HJEfFHc4R9F/BbpA/POwNd4GfAF4H3RMSaaV6W9CLg90k/iO9Cmj3rf4GvA8dHxFdmiXsE6Sr3b5KO4beBd0XEZ5e42CtG8SV80BzBjomIo7M4Pv8Kku4JvJXUXWYX4GpSy9MxMXMq5TWn+FE91yKkX46Ig4vwC34/r0aSDgJeAjyc/pTAN5C6wX2c1Bo340eApAOBN5FaLjcCl5Km6v7AIrpmji1JDyBNrnAlsGfTMfD5Z7Y4rpSYmZmZmdlIeUyJmZmZmZmNlCslZmZmZmY2Uq6UmJmZmZnZSLlSYmZmZmZmI+VKiZmZmZmZjZQrJWZmZmZmNlKulJiZmZmZ2Ui5UmJmZmZmZiPlSomZmZmZmY2UKyVmZmZmZjZSrpSYmZmZmdlIuVJiZktK0hWSrqhsO0JSSDpiyDSOLsIf3H4JB/I5T1IsZR5DlGFex2YB6R9cpF/+/2Aecfcs4mxZirLZ8CQdWnkdzxt1mczMFsOVErNVzj8k+yRtKY7FnvOIU/6IP3rpSjYSXwaOAY4fdUHWqkoF8ZMNYcr371cru/6H9Pods+QFNTNbBpOjLoCZ2QpyOLBp1IVYJudFxNGjLoT1PEfSoyLiwmECR8T/AEcDSDpqKQtmZrYc3FJiZlaIiJ9FxNDdmcxacllxe+xIS2FmNkKulJitYkWXo8uLhy+o9EE/ohL2CZLOkPR/km6X9HNJn5b0uzXpPlHSmZKuLcJeJuldknZa8ic1WI5DJJ0k6UeSbi7+L5b0SkmdStgAXlA8vDw7DldkYQbGlBRd3s4tHh5VOX4HF2Eax7vM1nVO0t6S/kPSr4pyXyDpqXM83z0kHS/pJ8Vxv07SZyQ9Ys6DNQ+S7iTpPZKulHSbpB9Iei2zfGdI2iTpryV9t3g+N0n6uqQ/agi/oTh25XO5XNLbiu0zxkjkx1nSH0v6RpHHFQstQxFn6HNZ0kMkfUJpnNTtkn4p6duS3idp3dxHttGFwKeBAyU9exHpmJmNLXffMlvdzgN2Al4F/Bdwerbvu+UdSccAbwFuKsL8HNgdOAB4HvDFLOxRpG4j1wOfBf4PeAjweuApkh4dETcuxZOp8U6gC3wD+AWwGXgc8H7gEcDzs7DHAIcADy3231Bsv4Fmpxe3LyCNwTgv23fFQgst6b7A14FdgLNIr8XeRX5nNcTZF/gCcGfg88D/A+5Cek5flfT7EXHmQsuU5bMB+BLp+P0X8G+kc+jNwEENcXYCzgEeDnwb+AipAvNE4N8lPTAijszCCzgNeCrwY9K4lnXAEcAD5yji64DfA84gVRg3L6QMRZyhz2VJDyGdZwF8hlTZ35H0uv0FcCSwbY6yz+YvScfjnZI+ExGLScvMbPxEhP/97/9V/A/sSfohtaVh/xOK/T8B7lGzf4/s/mOLsBcAO1XCHVHse29l+xXAFQ1hjxjyORxdhD+4sn2vmrAd4KNF+EdW9m0ptu/ZkM956WNxYNvBRZyj51O22Y49qXIRwKsq259ZbB84NqQLSJcCtwEHVeLsTqqQXQ1sGOJYzvV8/qbYfxrQybbfm/Tjve75lMf1LyvbNwJnkyqOD8u2P78Ifz6wPtu+E/CDYt95Dcf5ZuDhNeWebxnmdS4D7y62PbMm753zYzXsf/ZanFQ8Pr54/Mqac+irs6Qz43j53//+9/+4/bv7lpm9orh9XUT8orozIq7MHr6yuH1xRNxQCbeFdMX/ue0XsV5EXFazrUtqCYF0lXxFkbQH6Ur/5VRmvoqIT5NaZKqeCuwFHBcRX67EuQr4B2A34PEtFPGFpB/wf1kcyzKfy4EPVANL2oXUmvatiPiHStluA94ICPjjbFfZje7IiLgjC38D8LdzlO9fIuI7LZRhoefyrdUNEfGr/FgtwjHAjcBbJG1uIT0zs7Hh7ltm9ijSldazhwj7aFIXledIek7N/vXArpJ2iYjrWixjreLH6BuApwD3AbavBLnHUpdhAR5e3H41IqZr9p/HzG5Sjy5uf0P1UxPft7h9ALDgLlyS7kTqjvTzugpfUbbqTE+PACaApmmTy7EWD8i2PZxU8bmgJnx16tuqi2q2LaQM8z2XTyF1gzxd0qmkLo1fazhOCxIRv5T0TuDtwJtIXbrMzNYEV0rMbCfgVxEx4wpwjV1InxtzTUG6A7CklZJiDME3Sd2KLgI+RupeNEV/HM2GpSzDApVXwP+3Yf81Ndt2KW7rfjzndlhQifoWU7ZHFP9N8rJtBq6PiKmacE15t12GeZ3LEXGRpN8hVRYO5f9v795CrajiOI7/fiSSFGUKWZZpaQT2EJFCisZJKKw0KcPsoYcgglAifIiCboZRPXihy0OJCNoFQsmMoptCJaZCBWFe6qFjUA8aqHnBk9C/h/9sPR5nn7P3uTi6/X5gGJhZs/Y6s0f3/GfWWv9ivJLt3ZIWRsQHPdTTqKWSHpf0hO23+qlOADjrEZQAOCBpuO0hDQQmB5V954cNfLN69KgyIFkYXfJt2J6kDErOhFq3nbL/T4eWbDtYrEfUqe+Kbo6ZFRHrG29a0/rStqURsaDBz/lH0jDbg0oCk3qfXRMl23rThqav5Yj4XtKMYjKAWyRNV3Z/fN/2voj4utsKGvuMY7afVY6Lqr0xAYCWx5gSoPXVughdUGf/FmV/++kN1LVF0mW2e5oh6UwYV6zXluwrnSVKPZ+L3hyzv1iPKtk3oWRbbTzEFNtldbaVbKsl1Jtapw39IiIOKQfUX2V7bEmRtpJt25SBWTNt+0n5+zO5ZN+UJurpSxt6fS1HREdEbI6I53VybMqsZuvpxmrlOXpI5dcQALQcghKg9e1XPl2+ps7+N4r1YtunjcHosm1psV5ue2RJ2Yts39qXxjahvVi3dWnDzZKeqXNMrUtZvXPRm2NqYxwesX3ibYntUcpplk9RTBzwlfItz/zO+2zPUnlA9bEywd4823eXNcL2JNv9kY1+pfK34TV3yvVi+1qdvAE/ISL2KqcNnmD7ubJAy/bY4viaVcV6ke3Bncpdqpx6uCm9bENT17LtybaHlHx87c3O0WbbXU9EhHJaYkt6pb/qBYCzGd23gBYXEYdtb5U01fZ7kn5VPv1fHxE/R8SXthcp8yzstL1OmadkhPKp9RblFKmKiA22n1beKP1m+zPlLFIXSxqtvKHepMbeuvTVKuUg92W2b1fmu7he0gxlDo8HS47ZUByz3PZaSYckHYiIN0vK1uxWTrk71/ZxSXuUQd7qiNgTEVttfyvpNknbbG9UnruZynwiZW9Q5inzlCyzfacyH8g4Sfcp82/M7Fw4Io7bvr+o71Pbm5WzQx0t6p+oHOh/pfp+c7xYmftktqQfbX+h7IY2RzmF770lx8xXnvuXJD1se5NybMhI5eDyicqn/rVEnqskzVVeJ9ttr1cORp+tHCd0g052i2tUU23oxbX8lKRptr8ryh1W5lS5Sxn4v9Nke7sVERuLNpUGoQDQcqqek5iFhWXgF+UN7yfKp/7/qSRHiPLm53PlYPEOZWDykaRpJfVNkfShpL8k/Stpn/ImeYmkCV3Ktmvg8pSMVyay26vMX/GDcqzJGNXJzSJpgaSdxd8YndumkjwlxfaJyoDmYKfz19Zp/1BJy4t2dEjaLumxHtoxTtIa5ZieI8og5Z7uzo2ky5UJI7crg4/DymBsjXJK3EENnMs2dZOnpChzSfFd/qnMjbJLmbTwum7+nsHKwGBzcZ46JP1RnLcnJQ3vUv5CZQDxe1G2XdLLyhnTQtK6Rq6BvrShmWtZmc9npaQdRd1HlAHr65JG9/LfZe27eLfO/vHKiRvIU8LCwtLyiyPKxgwCAFqR7TZlJvTTJgg4G9i+Q5lc8tWIqNcND53YDknfRERb1W0BgN5iTAkAnJ9esB22d1Xx4XXGcQxXvgmS8i0d6rD9QPH98WQRQEtgTAkAnF/alZnDa/6uqB1LbN+k7Gq1T9LVyvEZwyS9HRFlSRJx0g6d+j22V9QOAOgXdN8CAJxxtucokwTeqByTc0zSL5JWSFoR5+CPU9E1rq2BogciYtlAtgUAzjUEJQAA9APbL6rnDPGStCcixgxsawDg3EJQAgAAAKBSDHQHAAAAUCmCEgAAAACVIigBAAAAUCmCEgAAAACVIigBAAAAUCmCEgAAAACVIigBAAAAUCmCEgAAAACVIigBAAAAUCmCEgAAAACVIigBAAAAUCmCEgAAAACVIigBAAAAUKn/AdgPZcOiwTMHAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 266,
       "width": 402
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "darray = cc.querying.getvar('1deg_jra55v13_iaf_spinup1_B1', 'pot_rho_2', session, n=-10)\n",
    "darray.mean({'time', 'xt_ocean'}).plot(cmap=cm.cm.haline)\n",
    "plt.gca().invert_yaxis();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Resampling\n",
    "\n",
    "`xarray` uses `datetime` conventions to allow for operations such as resampling in time. This resampling is simple and powerful. Here is an example of re-plotting the figure from 2.1 with annual averaging:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 288,
       "width": 403
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "darray = cc.querying.getvar('025deg_jra55v13_iaf_gmredi6', 'temp_global_ave', session)\n",
    "meandata = darray.resample(time='A').mean(dim='time')\n",
    "meandata.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 Exercises\n",
    "\n",
    " * Pick an experiment and plot a map of the temperature of the upper 100m of the ocean for one year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " * Now, take the same experiment and construct a timeseries of spatially averaged (regional or global) upper 700m temperature, resampled every 3 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. More Advanced Stuff\n",
    "\n",
    "### 3.1 Making a map with cartopy\n",
    "Refer to [map tutorial](https://cosima-recipes.readthedocs.io/en/latest/tutorials/Making_Maps_with_Cartopy.html#gallery-tutorials-making-maps-with-cartopy-ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Distributed computing\n",
    "\n",
    "Many of our scripts use multiple cores for their calculations, usually via the following . It sets up a local cluster on your node for distributed computation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-530d6848-688d-11ed-8bbd-0000075bfe80</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "\n",
       "        <tr>\n",
       "        \n",
       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
       "        \n",
       "        </tr>\n",
       "\n",
       "        \n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard: </strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\"></td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        </table>\n",
       "\n",
       "        \n",
       "            <details>\n",
       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
       "            <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;\">LocalCluster</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">5abe1b0c</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard:</strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Workers:</strong> 4\n",
       "                </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total threads:</strong> 12\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total memory:</strong> 48.00 GiB\n",
       "                </td>\n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "    <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
       "    <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
       "</tr>\n",
       "\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-1056c99d-5a82-40e4-bd05-98d6ad118bb5</p>\n",
       "            <table style=\"width: 100%; text-align: left;\">\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Comm:</strong> tcp://127.0.0.1:42889\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Workers:</strong> 4\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Dashboard:</strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total threads:</strong> 12\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> 48.00 GiB\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",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:44119\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 3\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/44067/status\" target=\"_blank\">/proxy/44067/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 12.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:32781\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /jobfs/64294669.gadi-pbs/dask-worker-space/worker-jxmozt4j\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:43563\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 3\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/38045/status\" target=\"_blank\">/proxy/38045/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 12.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:37411\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /jobfs/64294669.gadi-pbs/dask-worker-space/worker-6zbdy1zl\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:37537\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 3\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/45429/status\" target=\"_blank\">/proxy/45429/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 12.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:40245\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /jobfs/64294669.gadi-pbs/dask-worker-space/worker-76b6w2al\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:46561\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 3\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/36967/status\" target=\"_blank\">/proxy/36967/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 12.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:39909\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /jobfs/64294669.gadi-pbs/dask-worker-space/worker-pv1g_p8x\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "\n",
       "    </details>\n",
       "</div>\n",
       "\n",
       "        </details>\n",
       "    </div>\n",
       "</div>\n",
       "            </details>\n",
       "        \n",
       "\n",
       "    </div>\n",
       "</div>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:42889' processes=4 threads=12, memory=48.00 GiB>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask.distributed import Client\n",
    "\n",
    "client = Client()\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dashboard link should allow you to access information on how your work is distributed between the cores on your local cluster."
   ]
  }
 ],
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