dask_zarr_rechunking_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6ffe0c79",
   "metadata": {},
   "source": [
    "## Reading in a large netCDF array, converting to Zarr, and rechunking for the purpose of calculating quantiles along a single dimension\n",
    "\n",
    "Load in a bunch of modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bfb11ddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "import sys\n",
    "sys.path.append('/home/bwallace/scripts/functions/')\n",
    "import main as pwrf\n",
    "import xarray as xr\n",
    "from matplotlib import gridspec\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from matplotlib import ticker\n",
    "from netCDF4 import Dataset\n",
    "from datetime import datetime, timedelta, date\n",
    "import matplotlib.dates as mdates\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6bd3047",
   "metadata": {},
   "source": [
    "### Call in your Dask distributed client\n",
    "\n",
    "We'll also specify the specific path we want for our dashboard so we can monitor the job progress."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9b3949ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask\n",
    "from distributed import Client\n",
    "\n",
    "dask.config.set({'distributed.dashboard.link':'https://jupyter.alcf.anl.gov/theta/user/{USER}/proxy/{port}/status'})\n",
    "\n",
    "client = Client()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4855be63",
   "metadata": {},
   "source": [
    "You can view the dashboard by printing out the client object and clicking on the corresponding link"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "92daecbc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-e8a0fcbb-a51d-11ed-a58b-98f2b3eb912a</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=\"https://jupyter.alcf.anl.gov/theta/user/bwallace/proxy/8787/status\" target=\"_blank\">https://jupyter.alcf.anl.gov/theta/user/bwallace/proxy/8787/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\"></td>\n",
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       "        \n",
       "\n",
       "        </table>\n",
       "\n",
       "        \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;\">a96c7584</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard:</strong> <a href=\"https://jupyter.alcf.anl.gov/theta/user/bwallace/proxy/8787/status\" target=\"_blank\">https://jupyter.alcf.anl.gov/theta/user/bwallace/proxy/8787/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Workers:</strong> 8\n",
       "                </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total threads:</strong> 64\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total memory:</strong> 503.60 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-5018b3a0-eeb2-492a-8f8a-a432c1e27b65</p>\n",
       "            <table style=\"width: 100%; text-align: left;\">\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Comm:</strong> tcp://127.0.0.1:39860\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Workers:</strong> 8\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Dashboard:</strong> <a href=\"https://jupyter.alcf.anl.gov/theta/user/bwallace/proxy/8787/status\" target=\"_blank\">https://jupyter.alcf.anl.gov/theta/user/bwallace/proxy/8787/status</a>\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total threads:</strong> 64\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> 503.60 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://127.0.0.1:38253\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 8\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"https://jupyter.alcf.anl.gov/theta/user/bwallace/proxy/38895/status\" target=\"_blank\">https://jupyter.alcf.anl.gov/theta/user/bwallace/proxy/38895/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 62.95 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:38063\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> /tmp/dask-worker-space/worker-poogpe07\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://127.0.0.1:39414\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 8\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"https://jupyter.alcf.anl.gov/theta/user/bwallace/proxy/40646/status\" target=\"_blank\">https://jupyter.alcf.anl.gov/theta/user/bwallace/proxy/40646/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 62.95 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:45266\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> /tmp/dask-worker-space/worker-2_j341dn\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://127.0.0.1:46098\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 8\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"https://jupyter.alcf.anl.gov/theta/user/bwallace/proxy/46753/status\" target=\"_blank\">https://jupyter.alcf.anl.gov/theta/user/bwallace/proxy/46753/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 62.95 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:39800\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> /tmp/dask-worker-space/worker-7llxero7\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://127.0.0.1:36399\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 8\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"https://jupyter.alcf.anl.gov/theta/user/bwallace/proxy/45619/status\" target=\"_blank\">https://jupyter.alcf.anl.gov/theta/user/bwallace/proxy/45619/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 62.95 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:38778\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> /tmp/dask-worker-space/worker-op57nwo8\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: 4</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:46801\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 8\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"https://jupyter.alcf.anl.gov/theta/user/bwallace/proxy/38065/status\" target=\"_blank\">https://jupyter.alcf.anl.gov/theta/user/bwallace/proxy/38065/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 62.95 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43096\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> /tmp/dask-worker-space/worker-fl2qjutg\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: 5</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:37522\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 8\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"https://jupyter.alcf.anl.gov/theta/user/bwallace/proxy/36541/status\" target=\"_blank\">https://jupyter.alcf.anl.gov/theta/user/bwallace/proxy/36541/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 62.95 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:42552\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> /tmp/dask-worker-space/worker-93ednpem\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: 6</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:41082\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 8\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"https://jupyter.alcf.anl.gov/theta/user/bwallace/proxy/42132/status\" target=\"_blank\">https://jupyter.alcf.anl.gov/theta/user/bwallace/proxy/42132/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 62.95 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:42685\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> /tmp/dask-worker-space/worker-l94mlc7l\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: 7</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:46811\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 8\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"https://jupyter.alcf.anl.gov/theta/user/bwallace/proxy/33001/status\" target=\"_blank\">https://jupyter.alcf.anl.gov/theta/user/bwallace/proxy/33001/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 62.95 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:37662\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> /tmp/dask-worker-space/worker-74fuwrye\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://127.0.0.1:39860' processes=8 threads=64, memory=503.60 GiB>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72cbbe93",
   "metadata": {},
   "source": [
    "The next three cells gather all your paths together. This could be done a lot easier with glob.glob, but sometimes specifically picking out dates gives you more control"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "52eb930f",
   "metadata": {},
   "outputs": [],
   "source": [
    "###THIS NEEDS TO BE CHANGED WHEN CHANGING SCENARIO\n",
    "\n",
    "dates_daily=[]\n",
    "dates_daily.append(\\\n",
    "    [\\\n",
    "    entry for entry in np.arange(datetime(2085,10,1),datetime(2100,10,1),timedelta(days=1)).astype(datetime)\\\n",
    "    ]\\\n",
    "                 )\n",
    "dates_daily=np.array(dates_daily).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "edd93d42",
   "metadata": {},
   "outputs": [],
   "source": [
    "scenario='end_of_century_8p5' #historical or end_of_century_8p5 - DONT FORGET TO CHANGE DATES ABOVE!!!\n",
    "\n",
    "path_to_files='/eagle/climate_severe/bwallace_scratch/hourly_resampled/'+scenario+'/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "24d51d66",
   "metadata": {},
   "outputs": [],
   "source": [
    "paths=[]\n",
    "for sd in dates_daily:\n",
    "    sdyr,sdmo,sddy=str(sd.year),str(sd.month).zfill(2),str(sd.day).zfill(2)\n",
    "    \n",
    "    possible_path=path_to_files+'AFWA_TOTPRECIP_'+sdyr+sdmo+sddy+'.nc'\n",
    "    if os.path.exists(possible_path):\n",
    "        paths.append(possible_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c641bf0",
   "metadata": {},
   "source": [
    "Next we'll call in our data array using open_mfdataset(). I typically have more success using combine='nested' and specifying the concat_dim I'd like xarray to concatenate along. In this case, it's Time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a027966a",
   "metadata": {},
   "outputs": [],
   "source": [
    "mfds=xr.open_mfdataset(paths,combine='nested',concat_dim='Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a245f1ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "mfds.AFWA_TOTPRECIP.encoding={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b668badb",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "Dimensions:         (Time: 131040, south_north: 899, west_east: 1399)\n",
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       "Dimensions without coordinates: south_north, west_east\n",
       "Data variables:\n",
       "    AFWA_TOTPRECIP  (Time, south_north, west_east) float32 dask.array&lt;chunksize=(24, 899, 1399), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d9186ebf-7494-4ff9-a40b-62a4a0375a11' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d9186ebf-7494-4ff9-a40b-62a4a0375a11' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>Time</span>: 131040</li><li><span>south_north</span>: 899</li><li><span>west_east</span>: 1399</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1cb25196-d3b4-4ab0-a971-021c1f3c1bbd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1cb25196-d3b4-4ab0-a971-021c1f3c1bbd' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span 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'>2085-10-01 ... 2100-09-29T23:00:00</div><input id='attrs-53f1f1bd-d147-45f4-ab9e-3d0fbb128a82' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-53f1f1bd-d147-45f4-ab9e-3d0fbb128a82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c9db134-19de-4821-9034-baac11baa275' class='xr-var-data-in' type='checkbox'><label for='data-0c9db134-19de-4821-9034-baac11baa275' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2085-10-01T00:00:00.000000000&#x27;, &#x27;2085-10-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;2085-10-01T02:00:00.000000000&#x27;, ..., &#x27;2100-09-29T21:00:00.000000000&#x27;,\n",
       "       &#x27;2100-09-29T22:00:00.000000000&#x27;, &#x27;2100-09-29T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7da70227-d203-4518-b662-7c62807a57f0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7da70227-d203-4518-b662-7c62807a57f0' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>AFWA_TOTPRECIP</span></div><div class='xr-var-dims'>(Time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(24, 899, 1399), meta=np.ndarray&gt;</div><input id='attrs-45c0dc02-0699-4ca4-8db3-d84553ca3040' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45c0dc02-0699-4ca4-8db3-d84553ca3040' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0257be46-0e3d-45fe-a4a3-ecabd7dcd249' class='xr-var-data-in' type='checkbox'><label for='data-0257be46-0e3d-45fe-a4a3-ecabd7dcd249' 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>Description :</span></dt><dd>Hourly precipitation accumulation starting at the current hour. e.g. 02 UTC accumulation is rain that has fallen between 02 &amp; 03 UTC</dd><dt><span>Units :</span></dt><dd>mm hr$^{-1}$</dd></dl></div><div class='xr-var-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> 613.96 GiB </td>\n",
       "                        <td> 115.15 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (131040, 899, 1399) </td>\n",
       "                        <td> (24, 899, 1399) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 5460 chunks in 10921 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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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-298b6250-9d8e-478b-82dd-05291b403e29' class='xr-section-summary-in' type='checkbox'  ><label for='section-298b6250-9d8e-478b-82dd-05291b403e29' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>Time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c1d09fee-7d3d-4bbe-a437-7597f8d5f073' class='xr-index-data-in' type='checkbox'/><label for='index-c1d09fee-7d3d-4bbe-a437-7597f8d5f073' 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;2085-10-01 00:00:00&#x27;, &#x27;2085-10-01 01:00:00&#x27;,\n",
       "               &#x27;2085-10-01 02:00:00&#x27;, &#x27;2085-10-01 03:00:00&#x27;,\n",
       "               &#x27;2085-10-01 04:00:00&#x27;, &#x27;2085-10-01 05:00:00&#x27;,\n",
       "               &#x27;2085-10-01 06:00:00&#x27;, &#x27;2085-10-01 07:00:00&#x27;,\n",
       "               &#x27;2085-10-01 08:00:00&#x27;, &#x27;2085-10-01 09:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2100-09-29 14:00:00&#x27;, &#x27;2100-09-29 15:00:00&#x27;,\n",
       "               &#x27;2100-09-29 16:00:00&#x27;, &#x27;2100-09-29 17:00:00&#x27;,\n",
       "               &#x27;2100-09-29 18:00:00&#x27;, &#x27;2100-09-29 19:00:00&#x27;,\n",
       "               &#x27;2100-09-29 20:00:00&#x27;, &#x27;2100-09-29 21:00:00&#x27;,\n",
       "               &#x27;2100-09-29 22:00:00&#x27;, &#x27;2100-09-29 23:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Time&#x27;, length=131040, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-439b32a2-19f5-4df7-98ef-62cd57bdaff8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-439b32a2-19f5-4df7-98ef-62cd57bdaff8' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:         (Time: 131040, south_north: 899, west_east: 1399)\n",
       "Coordinates:\n",
       "  * Time            (Time) datetime64[ns] 2085-10-01 ... 2100-09-29T23:00:00\n",
       "Dimensions without coordinates: south_north, west_east\n",
       "Data variables:\n",
       "    AFWA_TOTPRECIP  (Time, south_north, west_east) float32 dask.array<chunksize=(24, 899, 1399), meta=np.ndarray>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mfds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d165da3f",
   "metadata": {},
   "source": [
    "#### When we print out our mfdataset, we can get some info about how the data is chunked based off the netCDF array. We can see that our field of interest, AFWA_TOTPRECIP, is chunked on the same interval as the data is stored, with 24 chunks in the Time dimension corresponding to the 24 hourly accumulated rainfall readings, and no chunks in the spatial dimensions.\n",
    "\n",
    "These chunks would be particularly well suited to performing some operation along the spatial dimension for all times. For example, taking the mean of the spatial dimensions across all times and producing a time series.\n",
    "\n",
    "In our use case, we want to calculate the quantile along the time dimension, which needs to consider all possible values along the time dimension. Having frequent chunks in time and no chunks in space is not optimal for addressing this problem. \n",
    "\n",
    "We could rechunk our mfdataset array during the open_mfdataset() call, but the behavior of specifying chunks within is limited to chunking WITHIN each input file. This means you can only specify a maximum chunk of 24 along the time dimension.\n",
    "\n",
    "We could chunk after the fact, but this is not recommended, and for large file sizes with a lot of chunks, it takes A LOT of memory (more than your dataset) and produces a lot of tasks in the task graph.\n",
    "\n",
    "So our solution is to utilize Zarr and Rechunker. Zarr is a highly compressible data storage format built around chunking and it's compatible with xarray in the same way that netCDF is. We can write our array as a Zarr file, and then rearrange its chunks using Rechunker.\n",
    "\n",
    "To write a zarr array, we can just use .to_zarr(path_to_file)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3d7bec18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.backends.zarr.ZarrStore at 0x7f02d4ca11c0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mfds.to_zarr('/eagle/climate_severe/bwallace_scratch/ZARR/eoc8p5_AFWA_TOTPRECIP.zarr')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ac396be",
   "metadata": {},
   "source": [
    "If you have your dask dashboard open, you'll notice that doing this is efficient and uses relatively little memory.\n",
    "\n",
    "We can read in this created zarr file by importing the zarr module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4c4b5e0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import zarr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "942427f7",
   "metadata": {},
   "source": [
    "Next we'll use zarr.open() and print out some information on the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "91525eeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/\n",
      " ├── AFWA_TOTPRECIP (131040, 899, 1399) float32\n",
      " └── Time (131040,) int64\n"
     ]
    }
   ],
   "source": [
    "source_group=zarr.open('/eagle/climate_severe/bwallace_scratch/ZARR/eoc8p5_AFWA_TOTPRECIP.zarr')\n",
    "print(source_group.tree())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc333a8a",
   "metadata": {},
   "source": [
    "We can see that the total size of the zarr array is identical to the size of our netCDF array and that it also contains information for the Time dimension.\n",
    "\n",
    "The chunks in this Zarr array are also kept identical to how the chunks were oriented in the original netCDF array.\n",
    "\n",
    "To rechunk, we can import rechunker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f19df53d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rechunker import rechunk"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "162f1c16",
   "metadata": {},
   "source": [
    "Using rechunker, we can specify our target chunks and the file will be rewritten with the new chunk structure.\n",
    "\n",
    "These new chunks are a little bigger but have a different orientation, and are now oriented for efficient analysis across the time dimension. \n",
    "\n",
    "We also want to specify the location of our target file and our intermediate temporary file. We need to do this because of the way rechunker works. I find that it's easier to understand how this operation works based on the image below.\n",
    "\n",
    "<img src=\"https://miro.medium.com/v2/resize:fit:1400/format:webp/1*skmMOIOawvuBNH29ECgcqg.png\" alt=\"Alternative text\" />\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b2d396e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "target_chunks={\n",
    "    'AFWA_TOTPRECIP':{'Time':131040,'south_north':40,'west_east':40},\n",
    "    'Time': None,\n",
    "}\n",
    "\n",
    "target_store='/eagle/climate_severe/bwallace_scratch/ZARR/eoc8p5_AFWA_TOTPRECIP_rechunked.zarr'\n",
    "temp_store='/eagle/climate_severe/bwallace_scratch/ZARR/eoc8p5_AFWA_TOTPRECIP_rechunked-tmp.zarr'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab9e643b",
   "metadata": {},
   "source": [
    "We also want to specify our max memory. From my experience, this can't be smaller than the size of an individual chunk. I haven't played around with this too much, but I think a higher value allows the operation to move faster at the cost of more memory. For now, I'm specifying a max_mem that's slightly bigger than 1 chunk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "86725abb",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_mem='850MB'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "427993f9",
   "metadata": {},
   "source": [
    "Next, we create our rechunk plan, specifying the source array, the target chunks we'd like our new array to have, the max memory, where our new rechunked array will be stored, and where intermediate temporary data is stored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7df79686",
   "metadata": {},
   "outputs": [],
   "source": [
    "array_plan=rechunk(source_group,target_chunks,max_mem,target_store,temp_store=temp_store)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "367da8be",
   "metadata": {},
   "source": [
    "Finally, to initiate our job, we'll use .execute() on our array plan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2eccaae",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bwallace/.conda/envs/geo_env/lib/python3.11/site-packages/distributed/worker.py:2988: UserWarning: Large object of size 43.61 MiB detected in task graph: \n",
      "  (\"('copy_read_to_intermediate-1b215299a2c501a9828f ... 48c', 219721)\")\n",
      "Consider scattering large objects ahead of time\n",
      "with client.scatter to reduce scheduler burden and \n",
      "keep data on workers\n",
      "\n",
      "    future = client.submit(func, big_data)    # bad\n",
      "\n",
      "    big_future = client.scatter(big_data)     # good\n",
      "    future = client.submit(func, big_future)  # good\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "array_plan.execute()"
   ]
  },
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   "id": "6c8cda30",
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   "source": [
    "From my experience, rechunking our 650 GB rainfall array to be continuous in time and chunked in space took about 12 or so hours to complete.\n",
    "\n",
    "We can open this newly completed rechunked Zarr array using xarray's open_zarr() function and print out some information about it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c96344bd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2257/2410216854.py:1: RuntimeWarning: Failed to open Zarr store with consolidated metadata, but successfully read with non-consolidated metadata. This is typically much slower for opening a dataset. To silence this warning, consider:\n",
      "1. Consolidating metadata in this existing store with zarr.consolidate_metadata().\n",
      "2. Explicitly setting consolidated=False, to avoid trying to read consolidate metadata, or\n",
      "3. Explicitly setting consolidated=True, to raise an error in this case instead of falling back to try reading non-consolidated metadata.\n",
      "  xrds=xr.open_zarr('/eagle/climate_severe/bwallace_scratch/ZARR/eoc8p5_AFWA_TOTPRECIP_rechunked.zarr')\n"
     ]
    }
   ],
   "source": [
    "xrds=xr.open_zarr('/eagle/climate_severe/bwallace_scratch/ZARR/eoc8p5_AFWA_TOTPRECIP_rechunked.zarr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ce0bd588",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:         (Time: 131040, south_north: 899, west_east: 1399)\n",
       "Coordinates:\n",
       "  * Time            (Time) datetime64[ns] 2085-10-01 ... 2100-09-29T23:00:00\n",
       "Dimensions without coordinates: south_north, west_east\n",
       "Data variables:\n",
       "    AFWA_TOTPRECIP  (Time, south_north, west_east) float32 dask.array&lt;chunksize=(131040, 40, 40), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ead83c15-f983-4f6b-a85f-97e34e140a57' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ead83c15-f983-4f6b-a85f-97e34e140a57' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>Time</span>: 131040</li><li><span>south_north</span>: 899</li><li><span>west_east</span>: 1399</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7479ab11-9499-42f1-8425-4146f7e77cb4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7479ab11-9499-42f1-8425-4146f7e77cb4' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span 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'>2085-10-01 ... 2100-09-29T23:00:00</div><input id='attrs-7e95b19b-9969-4a68-8706-de9bba2ca97b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e95b19b-9969-4a68-8706-de9bba2ca97b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1999b235-ab96-4085-935d-5b928e2a506e' class='xr-var-data-in' type='checkbox'><label for='data-1999b235-ab96-4085-935d-5b928e2a506e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2085-10-01T00:00:00.000000000&#x27;, &#x27;2085-10-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;2085-10-01T02:00:00.000000000&#x27;, ..., &#x27;2100-09-29T21:00:00.000000000&#x27;,\n",
       "       &#x27;2100-09-29T22:00:00.000000000&#x27;, &#x27;2100-09-29T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b3996fe2-9716-4f71-92e0-7723765c452c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b3996fe2-9716-4f71-92e0-7723765c452c' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>AFWA_TOTPRECIP</span></div><div class='xr-var-dims'>(Time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(131040, 40, 40), meta=np.ndarray&gt;</div><input id='attrs-39ba01bd-6e4d-42ee-a7a8-1f0ba7aa2972' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39ba01bd-6e4d-42ee-a7a8-1f0ba7aa2972' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad3892c6-4f64-4b41-8a0e-3b82fdc12d23' class='xr-var-data-in' type='checkbox'><label for='data-ad3892c6-4f64-4b41-8a0e-3b82fdc12d23' 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>Description :</span></dt><dd>Hourly precipitation accumulation starting at the current hour. e.g. 02 UTC accumulation is rain that has fallen between 02 &amp; 03 UTC</dd><dt><span>Units :</span></dt><dd>mm hr$^{-1}$</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                        <td> </td>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 613.96 GiB </td>\n",
       "                        <td> 799.80 MiB </td>\n",
       "                    </tr>\n",
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       "               &#x27;2085-10-01 02:00:00&#x27;, &#x27;2085-10-01 03:00:00&#x27;,\n",
       "               &#x27;2085-10-01 04:00:00&#x27;, &#x27;2085-10-01 05:00:00&#x27;,\n",
       "               &#x27;2085-10-01 06:00:00&#x27;, &#x27;2085-10-01 07:00:00&#x27;,\n",
       "               &#x27;2085-10-01 08:00:00&#x27;, &#x27;2085-10-01 09:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2100-09-29 14:00:00&#x27;, &#x27;2100-09-29 15:00:00&#x27;,\n",
       "               &#x27;2100-09-29 16:00:00&#x27;, &#x27;2100-09-29 17:00:00&#x27;,\n",
       "               &#x27;2100-09-29 18:00:00&#x27;, &#x27;2100-09-29 19:00:00&#x27;,\n",
       "               &#x27;2100-09-29 20:00:00&#x27;, &#x27;2100-09-29 21:00:00&#x27;,\n",
       "               &#x27;2100-09-29 22:00:00&#x27;, &#x27;2100-09-29 23:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Time&#x27;, length=131040, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f22f9772-4e11-4103-9cc1-93449cb266b4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f22f9772-4e11-4103-9cc1-93449cb266b4' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:         (Time: 131040, south_north: 899, west_east: 1399)\n",
       "Coordinates:\n",
       "  * Time            (Time) datetime64[ns] 2085-10-01 ... 2100-09-29T23:00:00\n",
       "Dimensions without coordinates: south_north, west_east\n",
       "Data variables:\n",
       "    AFWA_TOTPRECIP  (Time, south_north, west_east) float32 dask.array<chunksize=(131040, 40, 40), meta=np.ndarray>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xrds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce096fd6",
   "metadata": {},
   "source": [
    "We can see that this newly rechunked array is continuous in time and chunked in space, and that the size of an individual chunk is a fairly large 800 MiB. This new orientation is ideal for our analysis!\n",
    "\n",
    "We'll calculate a quantile along the Time dimension and load it into memory for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbab8e48",
   "metadata": {},
   "outputs": [],
   "source": [
    "quantile=xrds.AFWA_TOTPRECIP.quantile(q=0.95,dim='Time').compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "88fa3a88",
   "metadata": {},
   "outputs": [],
   "source": [
    "#quantile.to_netcdf('/eagle/climate_severe/bwallace_scratch/quantiles/AFWA_TOTPRECIP_q95_eoc8p5.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03888cea",
   "metadata": {},
   "source": [
    "We can plot the 95% quantile for hourly rainfall in the EoC8p5 runs and can see that the resulting values are fairly reasonable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d87277f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "quantile.plot(vmin=0,vmax=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e9362a5",
   "metadata": {},
   "source": [
    "Lastly, we can do a quick confirmation that operations performed on this Zarr array are identical to operations performed on the netCDF array. Since all we're doing is rechunking the data to make it more compatible with distributed analysis, there's no approximation or modification done to the data itself.\n",
    "\n",
    "We can do a quick and dirty comparison by calculating the 99% quantile for the first 24 hours between the unoptimally chunked netCDF array vs the optimally chunked Zarr array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1c4f0b98",
   "metadata": {},
   "outputs": [],
   "source": [
    "ncq=mfds.AFWA_TOTPRECIP[:24].quantile(q=0.99,dim='Time').compute()\n",
    "zrq=xrds.AFWA_TOTPRECIP[:24].quantile(q=0.99,dim='Time').compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "522b9632",
   "metadata": {},
   "source": [
    "Differencing these two approximations and plotting the result shows that there is zero difference between the two fields!\n",
    "\n",
    "This makes sense, as we are just rechunking the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "7333c5c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f02b3e9cd50>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(ncq-zrq).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2542deb",
   "metadata": {},
   "source": [
    "On disk, Zarr takes up substantially less space than netCDF. For a 650 GB hourly rainfall array, the Zarr array on disk takes up about 95 GB. Through modules such as rechunker, the entire array can be effortlessly rechunked into different configurations for different analyses. It's also natively compatible with xarray and so can easily slot into a lot of pre-existing workflows!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b59e6b6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "jhub_geoenv",
   "language": "python",
   "name": "jhub_geoenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}