{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Part 1: Geostrophic balance\n",
    "Andrew Delman, updated 2025-07-24.\n",
    "\n",
    "## Objectives\n",
    "\n",
    "To use ECCO state estimate output to illustrate the concept of geostrophic balance; where it does and doesn't explain oceanic flows well.\n",
    "\n",
    "By the end of the tutorial, you will be able to:\n",
    "\n",
    "* Download or access ECCO fields and query their attributes using ```xarray```\n",
    "* Plot ECCO fields on a single tile\n",
    "* Carry out spatial differencing and interpolation on the ECCO native model grid\n",
    "* Compare the two sides of the geostrophic balance equations\n",
    "* Compute geostrophic velocities\n",
    "* Apply masks in 2-D spatial plots\n",
    "* Use the ```ecco_v4_py``` package to plot global maps of ECCO fields\n",
    "* Use a statistical measure (normalized difference) to assess the latitude and depth dependence of geostrophic balance\n",
    "\n",
    "\n",
    "## Introduction\n",
    "\n",
    "### Conservation of momentum\n",
    "\n",
    "Geostrophic balance originates from the conservation of momentum, as expressed by the momentum equation:\n",
    "\n",
    "$$\n",
    "\\frac{D\\bf{u}}{Dt} = -\\frac{1}{\\rho}{\\nabla}p + {\\nu}{\\nabla^2}\\bf{u} + \\bf{F_b}\n",
    "$$\n",
    "\n",
    "following roughly the notation used in Kundu and Cohen (2008) and Vallis (2006). This equation is a simplification of the Navier-Stokes equation for an incompressible fluid. The material derivative $D/Dt \\equiv \\partial{}/\\partial{t} + {\\bf{u}}\\cdot\\nabla$ is the derivative in time *following a fluid parcel*. The symbol $\\nu \\equiv \\mu/\\rho$ indicates the kinematic viscosity, and $F_b$ represents body forces (real or apparent) exerted on the fluid.\n",
    "\n",
    "The equation above applies to any incompressible fluid, even the water in a fish tank or swimming pool. However, the body forces $\\bf{F_b}$ are dependent upon the environment of the fluid and the scales being considered. In a physical oceanography context, three forces that are typically very important are (a) gravity, (b) the Coriolis force from rotation of the earth, and (c) friction from surface winds or topography. So the equation above can be rewritten as\n",
    "\n",
    "$$\n",
    "\\frac{\\partial{\\bf{u}}}{\\partial{t}} + {\\bf{u}}\\cdot\\nabla{\\bf{u}} = -\\frac{1}{\\rho}{\\nabla}p + {\\nu}{\\nabla^2}{\\bf{u}} + g\\bf{\\hat{k}} - 2{\\bf{\\Omega}}\\times{\\bf{u}} + \\bf{F_f}\n",
    "$$\n",
    "\n",
    "Of those last three terms: \n",
    "1. Gravity $g\\bf{\\hat{k}}$ effectively only applies to vertical momentum, and can be neglected in the horizontal momentum equations\n",
    "1. The Coriolis force $-2{\\bf{\\Omega}}\\times{\\bf{u}}$ can be approximated by its vertical component, $2{\\Omega}\\sin\\theta(v{\\bf{\\hat{i}}} - u{\\bf{\\hat{j}}})$ where $\\Omega$ is the rotation rate of Earth in radians and $\\theta$ is latitude\n",
    "1. Friction $\\bf{F_f}$ from wind and topography is negligible in the ocean interior.\n",
    "\n",
    "To simplify, $f$ is defined as $f \\equiv 2{\\Omega}\\sin\\theta$. So using subscript notation for derivatives ($u_t$ is the derivative of $u$ with respect to $t$) the two horizontal components of momentum conservation are:\n",
    "\n",
    "$$\n",
    "u_t + {\\bf{u}}\\cdot\\nabla{u} = -\\frac{1}{\\rho}{p_x} + {\\nu}{\\nabla^2}u + fv\n",
    "$$\n",
    "\n",
    "$$\n",
    "v_t + {\\bf{u}}\\cdot\\nabla{v} = -\\frac{1}{\\rho}{p_y} + {\\nu}{\\nabla^2}v - fu\n",
    "$$\n",
    "\n",
    "### Geostrophic balance\n",
    "\n",
    "The two horizontal momentum equations still have a number of terms, but in the global oceans most of the flow is explained by a balance between just two terms. In steady state (or for very slowly-varying ocean features) the time derivatives $u_t$, $v_t$ are negligible, and at the large scales of major ocean currents viscosity is relatively small as well (inviscid approximation). This leaves three terms. The 2nd term on the left-hand side is usually negligible at large scales as well (we'll return to this later), so large-scale ocean flows generally follow **geostrophic balance**:\n",
    "\n",
    "$$\n",
    "fv = \\frac{1}{\\rho}{p_x}\n",
    "$$\n",
    "$$\n",
    "fu = -\\frac{1}{\\rho}{p_y}\n",
    "$$\n",
    "\n",
    "(cf. Vallis ch. 2.8, Kundu and Cohen ch. 14.5, Gill ch. 7.6)\n",
    "\n",
    "If you've looked at weather maps that show the clockwise or counter-clockwise flow of winds around areas of high or low pressure, you've encountered geostrophic balance in the atmosphere. But what does it look like in the ocean? You're about to see it in action...using output from the ECCO state estimate.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Access the ECCO output\n",
    "\n",
    "### Looking for specific variables\n",
    "\n",
    "These notebooks use the `ecco_access` Python package to download ECCO output (or access it within the AWS Cloud if you are working on AWS). If you haven't been through the `ecco_access` Python package [intro tutorial](https://ecco-access.readthedocs.io/en/latest/Using_ECCO_access.html) tutorial yet, I recommend going through it before proceeding further with this tutorial.\n",
    "\n",
    "What fields will we need? Look at the geostrophic balance equations. On the left-hand side, the Coriolis parameter $f$ is a function of latitude which we can compute, so we only need to download horizontal velocities $u$ and $v$. On the right-hand side we need density $\\rho$ and pressure $p$, as well as the grid parameters that allow us to compute spatial derivatives.\n",
    "\n",
    "How do we get those fields, while staying in our comfortable Jupyter notebook environment? There are two ways. \n",
    "\n",
    "The first option is to consult the following variable lists to find the NASA Earthdata ShortName of the datasets for the variables that we want:\n",
    "\n",
    "[ECCO v4r4 llc90 Grid Dataset Variables - Monthly Means](https://raw.githubusercontent.com/ECCO-GROUP/ECCO-v4-Python-Tutorial/master/varlist/v4r4_nctiles_monthly_varlist.txt)\n",
    "\n",
    "[ECCO v4r4 llc90 Grid Dataset Variables - Daily Means](https://raw.githubusercontent.com/ECCO-GROUP/ECCO-v4-Python-Tutorial/master/varlist/v4r4_nctiles_daily_varlist.txt)\n",
    "\n",
    "[ECCO v4r4 llc90 Grid Dataset Variables - Daily Snapshots](https://raw.githubusercontent.com/ECCO-GROUP/ECCO-v4-Python-Tutorial/master/varlist/v4r4_nctiles_snapshots_varlist.txt)\n",
    "\n",
    "[ECCO v4r4 0.5-Deg Interp Grid Dataset Variables - Monthly Means](https://raw.githubusercontent.com/ECCO-GROUP/ECCO-v4-Python-Tutorial/master/varlist/v4r4_latlon_monthly_varlist.txt)\n",
    "\n",
    "[ECCO v4r4 0.5-Deg Interp Grid Dataset Variables - Daily Means](https://raw.githubusercontent.com/ECCO-GROUP/ECCO-v4-Python-Tutorial/master/varlist/v4r4_latlon_daily_varlist.txt)\n",
    "\n",
    "[ECCO v4r4 Time Series and Grid Parameters](https://raw.githubusercontent.com/ECCO-GROUP/ECCO-v4-Python-Tutorial/master/varlist/v4r4_tseries_grid_varlist.txt)\n",
    "\n",
    "Let's have a look at the llc90 grid monthly means variable list. A text search for \"velocity\" shows us that **UVEL** and **VVEL** are the horizontal velocity variables we need. Importantly, we need to know the ShortName of the datasets containing these variables, which is **ECCO_L4_OCEAN_VEL_LLC0090GRID_MONTHLY_V4R4**.\n",
    "\n",
    "Searching for \"density\" and we get **RHOAnoma** (in-situ seawater density anomaly), and \"pressure\" gives us **PHIHYD** and **PHIHYDcR** (ocean hydrostatic pressure anomaly)--in this analysis we specifically need to use **PHIHYDcR**, the pressure anomaly at constant depth. These fields are all found in datasets with ShortName **ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4**.\n",
    "\n",
    "The second option, if we don't know the ShortNames already, is to use *ecco_access* top-level functions to \"query\" the variable lists and find the datasets that we need. You will see examples of these queries below.\n",
    "\n",
    "\n",
    "### Download output\n",
    "\n",
    "Make sure to [install](https://ecco-access.readthedocs.io/en/latest/Installation.html) the `ecco_access` Python package if you have not already done so.\n",
    "\n",
    "*ecco_access* provides a convenient function, `ecco_podaac_to_xrdataset` that accesses the requested output from PO.DAAC's cloud storage **and** opens an *xarray* dataset.\n",
    "\n",
    "We also can specify a \"mode\" option, which tells the function how to access these datasets. The default mode is `download_ifspace`, which downloads these tutorials to your local machine (under `~/Downloads/ECCO_V4r4_PODAAC/`) *if* the downloaded files would occupy less than 50% of available disk space. See the [access modes](https://ecco-access.readthedocs.io/ECCO_access_modes.html) tutorial for more information on these modes.\n",
    "\n",
    "> Tip: If you are working in the AWS Cloud, you can specify `incloud_access = True`, which will then use mode = `s3_get_ifspace` by default. The `s3_get_ifspace` mode downloads the files to your local instance if they would occupy less than 50% of available disk space; otherwise the data are accessed remotely (typically slower).\n",
    "\n",
    "\n",
    "Now let's use it to get access to the ECCO output we need, starting with the monthly velocity fields for Jan 2000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# specify incloud_access = True if working in the AWS Cloud, otherwise False\n",
    "incloud_access = False\n",
    "\n",
    "from os.path import expanduser,join\n",
    "user_home_dir = expanduser('~')\n",
    "\n",
    "if incloud_access:\n",
    "    access_mode = 's3_open_fsspec'\n",
    "    download_root_dir = None\n",
    "    # specify location to store json files that will speed up S3 data access\n",
    "    jsons_root_dir = join(user_home_dir,'MZZ')\n",
    "else:\n",
    "    access_mode = 'download_ifspace'\n",
    "    # specify location to store downloaded files\n",
    "    # ~/Downloads/ECCO_V4r4_PODAAC is also the default path used for download_root_dir; \n",
    "    # change as needed\n",
    "    download_root_dir = join(user_home_dir,'Downloads','ECCO_V4r4_PODAAC')\n",
    "    jsons_root_dir = None\n",
    "\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import xmitgcm\n",
    "import xgcm\n",
    "import glob\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# import ecco_v4_py and ecco_access\n",
    "import ecco_v4_py as ecco\n",
    "import ecco_access as ea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ShortName Options for query \"velocity\":\n",
      "                  Variable Name     Description (units)\n",
      "\n",
      "Option 1: ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_MONTHLY_V4R4    *native grid,monthly means*\n",
      "                  SIuice            Sea-ice velocity in the model +x direction (m/s)\n",
      "                  SIvice            Sea-ice velocity in the model +y direction (m/s)\n",
      "\n",
      "Option 2: ECCO_L4_OCEAN_VEL_LLC0090GRID_MONTHLY_V4R4    UVEL,VVEL should not be used in volume flux calculations due to time-variable grid cell thicknesses (z* coordinates). Use UVELMASS,VVELMASS instead.\n",
      "                  UVEL              Horizontal velocity in the model +x direction\n",
      "                                    (m/s)\n",
      "                  VVEL              Horizontal velocity in the model +y direction\n",
      "                                    (m/s)\n",
      "                  WVEL              Vertical velocity (m/s)\n",
      "\n",
      "Option 3: ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4    *native grid,monthly means*\n",
      "                  UVELMASS          Horizontal velocity in the model +x direction per\n",
      "                                    unit area of the grid cell \\'u\\' face. Use this in\n",
      "                                    volume flux calculations as it accounts for\n",
      "                                    partial cells and time-varying grid cell\n",
      "                                    thickness. (m/s)\n",
      "                  VVELMASS          Horizontal velocity in the model +y direction per\n",
      "                                    unit area of the grid cell \\'v\\' face. Use this in\n",
      "                                    volume flux calculations as it accounts for\n",
      "                                    partial cells and time-varying grid cell\n",
      "                                    thickness. (m/s)\n",
      "                  WVELMASS          Grid cell face-averaged vertical velocity in the\n",
      "                                    model +z direction. (m/s)\n",
      "                                    \n",
      "\n",
      "Option 4: ECCO_L4_BOLUS_LLC0090GRID_MONTHLY_V4R4    *native grid,monthly means*\n",
      "                  UVELSTAR          Gent-McWilliams parameterized bolus velocity in\n",
      "                                    the model +x direction scaled by time-varying grid\n",
      "                                    cell thickness (m/s)\n",
      "                  VVELSTAR          Gent-McWilliams parameterized bolus velocity in\n",
      "                                    the model +y direction scaled by time-varying grid\n",
      "                                    cell thickness (m/s)\n",
      "                  WVELSTAR          Gent-McWilliams parameterized bolus velocity in\n",
      "                                    the model +z direction (m/s)\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Please select option [1-4]:  2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using dataset with ShortName: ECCO_L4_OCEAN_VEL_LLC0090GRID_MONTHLY_V4R4\n",
      "Size of files to be downloaded to instance is 0.029 GB,\n",
      "which is 0.02% of the 143.844 GB available storage.\n",
      "Proceeding with file downloads via NASA Earthdata URLs\n",
      "DL Progress: 100%|###########################| 1/1 [00:02<00:00,  2.65s/it]\n",
      "\n",
      "=====================================\n",
      "total downloaded: 30.6 Mb\n",
      "avg download speed: 11.54 Mb/s\n",
      "Time spent = 2.65132212638855 seconds\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# open dataset containing Jan 2000 monthly velocities\n",
    "\n",
    "# ds_vel_mo = ea.ecco_podaac_to_xrdataset(\"ECCO_L4_OCEAN_VEL_LLC0090GRID_MONTHLY_V4R4\",\\\n",
    "#                                               StartDate=\"2000-01\",EndDate=\"2000-01\",\\\n",
    "#                                               mode=access_mode,\\\n",
    "#                                               download_root_dir=download_root_dir,\\\n",
    "#                                               jsons_root_dir=jsons_root_dir,\\\n",
    "#                                               prompt_request_payer=False)\n",
    "ds_vel_mo = ea.ecco_podaac_to_xrdataset(\"velocity\",grid=\"native\",time_res=\"monthly\",\\\n",
    "                                              StartDate=\"2000-01\",EndDate=\"2000-01\",\\\n",
    "                                              mode=access_mode,\\\n",
    "                                              download_root_dir=download_root_dir,\\\n",
    "                                              jsons_root_dir=jsons_root_dir,\\\n",
    "                                              prompt_request_payer=False)    # enter \"2\" when prompted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now do the same for the Jan 2000 monthly density and pressure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ShortName Options for query \"density\":\n",
      "                  Variable Name     Description (units)\n",
      "\n",
      "Option 1: ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4    *native grid,monthly means*\n",
      "                  SSH               Dynamic sea surface height anomaly. Suitable for\n",
      "                                    comparisons with altimetry sea surface height data\n",
      "                                    products that apply the inverse barometer\n",
      "                                    correction. (m)\n",
      "                  SSHIBC            The inverted barometer correction to sea surface\n",
      "                                    height due to atmospheric pressure loading. (m)\n",
      "                  SSHNOIBC          Sea surface height anomaly without the inverted\n",
      "                                    barometer correction. Suitable for comparisons\n",
      "                                    with altimetry sea surface height data products\n",
      "                                    that do NOT apply the inverse barometer\n",
      "                                    correction. (m)\n",
      "                  ETAN              Model sea level anomaly, without corrections for\n",
      "                                    global mean density changes, inverted barometer\n",
      "                                    effect, or volume displacement due to submerged\n",
      "                                    sea-ice and snow. (m)\n",
      "\n",
      "Option 2: ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4    *native grid,monthly means*\n",
      "                  RHOAnoma          In-situ seawater density anomaly (kg/m^3)\n",
      "                  DRHODR            Density stratification ((kg/m^3)/m)\n",
      "                  PHIHYD            Ocean hydrostatic pressure anomaly (m^2/s^2)\n",
      "                  PHIHYDcR          Ocean hydrostatic pressure anomaly at constant\n",
      "                                    depths (m^2/s^2)\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Please select option [1-2]:  2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using dataset with ShortName: ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4\n",
      "Size of files to be downloaded to instance is 0.029 GB,\n",
      "which is 0.02% of the 143.816 GB available storage.\n",
      "Proceeding with file downloads via NASA Earthdata URLs\n",
      "DL Progress: 100%|###########################| 1/1 [00:02<00:00,  2.45s/it]\n",
      "\n",
      "=====================================\n",
      "total downloaded: 30.98 Mb\n",
      "avg download speed: 12.65 Mb/s\n",
      "Time spent = 2.4490714073181152 seconds\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# open dataset containing Jan 2000 monthly density/pressure\n",
    "\n",
    "# ds_denspress_mo = ea.ecco_podaac_to_xrdataset(\"ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4\",\\\n",
    "#                                               StartDate=\"2000-01\",EndDate=\"2000-01\",\\\n",
    "#                                               mode=access_mode,\\\n",
    "#                                               download_root_dir=download_root_dir,\\\n",
    "#                                               jsons_root_dir=jsons_root_dir,\\\n",
    "#                                               prompt_request_payer=False)\n",
    "ds_denspress_mo = ea.ecco_podaac_to_xrdataset(\"density\",grid=\"native\",time_res=\"monthly\",\\\n",
    "                                              StartDate=\"2000-01\",EndDate=\"2000-01\",\\\n",
    "                                              mode=access_mode,\\\n",
    "                                              download_root_dir=download_root_dir,\\\n",
    "                                              jsons_root_dir=jsons_root_dir,\\\n",
    "                                              prompt_request_payer=False)    # enter \"2\" when prompted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also access the *daily* velocity and density/pressure output on 01 Jan 2000, changing the `time_res` option to `daily`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ShortName Options for query \"velocity\":\n",
      "                  Variable Name     Description (units)\n",
      "\n",
      "Option 1: ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_DAILY_V4R4    *native grid,daily means*\n",
      "                  SIuice            Sea-ice velocity in the model +x direction (m/s)\n",
      "                  SIvice            Sea-ice velocity in the model +y direction (m/s)\n",
      "\n",
      "Option 2: ECCO_L4_OCEAN_VEL_LLC0090GRID_DAILY_V4R4    UVEL,VVEL should not be used in volume flux calculations due to time-variable grid cell thicknesses (z* coordinates). Use UVELMASS,VVELMASS instead.\n",
      "                  UVEL              Horizontal velocity in the model +x direction\n",
      "                                    (m/s)\n",
      "                  VVEL              Horizontal velocity in the model +y direction\n",
      "                                    (m/s)\n",
      "                  WVEL              Vertical velocity (m/s)\n",
      "\n",
      "Option 3: ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4    *native grid,daily means*\n",
      "                  UVELMASS          Horizontal velocity in the model +x direction per\n",
      "                                    unit area of the grid cell \\'u\\' face. Use this in\n",
      "                                    volume flux calculations as it accounts for\n",
      "                                    partial cells and time-varying grid cell\n",
      "                                    thickness. (m/s)\n",
      "                  VVELMASS          Horizontal velocity in the model +y direction per\n",
      "                                    unit area of the grid cell \\'v\\' face. Use this in\n",
      "                                    volume flux calculations as it accounts for\n",
      "                                    partial cells and time-varying grid cell\n",
      "                                    thickness. (m/s)\n",
      "                  WVELMASS          Grid cell face-averaged vertical velocity in the\n",
      "                                    model +z direction. (m/s)\n",
      "                                    \n",
      "\n",
      "Option 4: ECCO_L4_BOLUS_LLC0090GRID_DAILY_V4R4    *native grid,daily means*\n",
      "                  UVELSTAR          Gent-McWilliams parameterized bolus velocity in\n",
      "                                    the model +x direction scaled by time-varying grid\n",
      "                                    cell thickness (m/s)\n",
      "                  VVELSTAR          Gent-McWilliams parameterized bolus velocity in\n",
      "                                    the model +y direction scaled by time-varying grid\n",
      "                                    cell thickness (m/s)\n",
      "                  WVELSTAR          Gent-McWilliams parameterized bolus velocity in\n",
      "                                    the model +z direction (m/s)\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Please select option [1-4]:  2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using dataset with ShortName: ECCO_L4_OCEAN_VEL_LLC0090GRID_DAILY_V4R4\n",
      "Size of files to be downloaded to instance is 0.029 GB,\n",
      "which is 0.02% of the 143.788 GB available storage.\n",
      "Proceeding with file downloads via NASA Earthdata URLs\n",
      "DL Progress: 100%|###########################| 1/1 [00:01<00:00,  1.97s/it]\n",
      "\n",
      "=====================================\n",
      "total downloaded: 30.68 Mb\n",
      "avg download speed: 15.56 Mb/s\n",
      "Time spent = 1.9719030857086182 seconds\n",
      "\n",
      "\n",
      "ShortName Options for query \"density\":\n",
      "                  Variable Name     Description (units)\n",
      "\n",
      "Option 1: ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4    *native grid,daily means*\n",
      "                  SSH               Dynamic sea surface height anomaly. Suitable for\n",
      "                                    comparisons with altimetry sea surface height data\n",
      "                                    products that apply the inverse barometer\n",
      "                                    correction. (m)\n",
      "                  SSHIBC            The inverted barometer correction to sea surface\n",
      "                                    height due to atmospheric pressure loading. (m)\n",
      "                  SSHNOIBC          Sea surface height anomaly without the inverted\n",
      "                                    barometer correction. Suitable for comparisons\n",
      "                                    with altimetry sea surface height data products\n",
      "                                    that do NOT apply the inverse barometer\n",
      "                                    correction. (m)\n",
      "                  ETAN              Model sea level anomaly, without corrections for\n",
      "                                    global mean density changes, inverted barometer\n",
      "                                    effect, or volume displacement due to submerged\n",
      "                                    sea-ice and snow. (m)\n",
      "\n",
      "Option 2: ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_DAILY_V4R4    *native grid,daily means*\n",
      "                  RHOAnoma          In-situ seawater density anomaly (kg/m^3)\n",
      "                  DRHODR            Density stratification ((kg/m^3)/m)\n",
      "                  PHIHYD            Ocean hydrostatic pressure anomaly (m^2/s^2)\n",
      "                  PHIHYDcR          Ocean hydrostatic pressure anomaly at constant\n",
      "                                    depths (m^2/s^2)\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Please select option [1-2]:  2\n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using dataset with ShortName: ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_DAILY_V4R4\n",
      "Size of files to be downloaded to instance is 0.029 GB,\n",
      "which is 0.02% of the 143.75900000000001 GB available storage.\n",
      "Proceeding with file downloads via NASA Earthdata URLs\n",
      "DL Progress: 100%|###########################| 1/1 [00:02<00:00,  2.46s/it]\n",
      "\n",
      "=====================================\n",
      "total downloaded: 31.2 Mb\n",
      "avg download speed: 12.67 Mb/s\n",
      "Time spent = 2.4622464179992676 seconds\n",
      "\n",
      "\n"
     ]
    }
   ],
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    "# open dataset containing Jan 2000 monthly velocities\n",
    "\n",
    "# ds_vel_daily = ea.ecco_podaac_to_xrdataset(\"ECCO_L4_OCEAN_VEL_LLC0090GRID_DAILY_V4R4\",\\\n",
    "#                                               StartDate=\"2000-01\",EndDate=\"2000-01\",\\\n",
    "#                                               mode=access_mode,\\\n",
    "#                                               download_root_dir=download_root_dir,\\\n",
    "#                                               jsons_root_dir=jsons_root_dir,\\\n",
    "#                                               prompt_request_payer=False)\n",
    "ds_vel_daily = ea.ecco_podaac_to_xrdataset(\"velocity\",grid=\"native\",time_res=\"daily\",\\\n",
    "                                              StartDate=\"2000-01-01\",EndDate=\"2000-01-01\",\\\n",
    "                                              mode=access_mode,\\\n",
    "                                              download_root_dir=download_root_dir,\\\n",
    "                                              jsons_root_dir=jsons_root_dir,\\\n",
    "                                              prompt_request_payer=False)    # enter \"2\" when prompted\n",
    "\n",
    "# ds_denspress_daily = ea.ecco_podaac_to_xrdataset(\"ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_DAILY_V4R4\",\\\n",
    "#                                                     StartDate=\"2000-01\",EndDate=\"2000-01\",\\\n",
    "#                                                     mode=access_mode,\\\n",
    "#                                                     download_root_dir=download_root_dir,\\\n",
    "#                                                     jsons_root_dir=jsons_root_dir,\\\n",
    "#                                                     prompt_request_payer=False)\n",
    "ds_denspress_daily = ea.ecco_podaac_to_xrdataset(\"density\",grid=\"native\",time_res=\"daily\",\\\n",
    "                                                  StartDate=\"2000-01-01\",EndDate=\"2000-01-01\",\\\n",
    "                                                  mode=access_mode,\\\n",
    "                                                  download_root_dir=download_root_dir,\\\n",
    "                                                  jsons_root_dir=jsons_root_dir,\\\n",
    "                                                  prompt_request_payer=False)    # enter \"2\" when prompted"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "And the grid parameters file (leave out `time_res` specification in this case):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ShortName Options for query \"geometry\":\n",
      "                  Variable Name     Description (units)\n",
      "\n",
      "Option 1: ECCO_L4_GEOMETRY_LLC0090GRID_V4R4    *for native LLC90 grid*\n",
      "                  CS                Cosine of tracer grid cell orientation vs\n",
      "                                    geographical north (-1 to 1)\n",
      "                  SN                Sine of tracer grid cell orientation vs\n",
      "                                    geographical north (-1 to 1)\n",
      "                  rA                Area of tracer grid cell (m^2)\n",
      "                  dxG               Distance between 'southwest' and 'southeast'\n",
      "                                    corners of the tracer grid cell (m)\n",
      "                  dyG               Distance between 'southwest' and 'northwest'\n",
      "                                    corners of the tracer grid cell (m)\n",
      "                  Depth             Model seafloor depth below ocean surface at rest\n",
      "                                    (m)\n",
      "                  rAz               Area of vorticity 'g' grid cell (m^2)\n",
      "                  dxC               Distance between centers of adjacent tracer grid\n",
      "                                    cells in the 'x' direction (m)\n",
      "                  dyC               Distance between centers of adjacent tracer grid\n",
      "                                    cells in the 'y' direction (m)\n",
      "                  rAw               Area of 'v' grid cell, staggered between adjacent\n",
      "                                    tracer grid cells in the 'x' direction (m^2)\n",
      "                  rAs               Area of 'u' grid cell, staggered between adjacent\n",
      "                                    tracer grid cells in the 'y' direction (m^2)\n",
      "                  drC               Distance between the centers of adjacent tracer\n",
      "                                    grid cells in the 'z' direction (m)\n",
      "                  drF               Distance between the upper and lower interfaces of\n",
      "                                    the model grid cell (m)\n",
      "                  PHrefC            Reference ocean hydrostatic pressure at tracer\n",
      "                                    grid cell center (m^2/s^2)\n",
      "                  PHrefF            Reference ocean hydrostatic pressure at tracer\n",
      "                                    grid cell top/bottom interface (m^2/s^2)\n",
      "                  hFacC             Vertical open (wet) fraction of tracer grid cell\n",
      "                                    (fraction from 0 to 1)\n",
      "                  hFacW             Vertical open (wet) fraction of tracer grid cell\n",
      "                                    'west' face (fraction from 0 to 1)\n",
      "                  hFacS             Vertical open (wet) fraction of tracer grid cell\n",
      "                                    'south' face (fraction from 0 to 1)\n",
      "                  maskC             Wet/dry boolean mask for tracer grid cell (True\n",
      "                                    for hFacC > 0, otherwise False)\n",
      "                  maskW             Wet/dry boolean mask for 'west' face of tracer\n",
      "                                    grid cell (True for hFacW > 0, otherwise False)\n",
      "                  maskS             Wet/dry boolean mask for 'south' face of tracer\n",
      "                                    grid cell (True for hFacS > 0, otherwise False)\n",
      "\n",
      "\n"
     ]
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     "output_type": "stream",
     "text": [
      "Proceed with option 1? [y/n]:  y\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using dataset with ShortName: ECCO_L4_GEOMETRY_LLC0090GRID_V4R4\n",
      "Size of files to be downloaded to instance is 0.008 GB,\n",
      "which is 0.01% of the 143.73 GB available storage.\n",
      "Proceeding with file downloads via NASA Earthdata URLs\n",
      "DL Progress: 100%|###########################| 1/1 [00:02<00:00,  2.21s/it]\n",
      "\n",
      "=====================================\n",
      "total downloaded: 8.57 Mb\n",
      "avg download speed: 3.88 Mb/s\n",
      "Time spent = 2.2117831707000732 seconds\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# ds_grid = ea.ecco_podaac_to_xrdataset(\"ECCO_L4_GEOMETRY_LLC0090GRID_V4R4\",\\\n",
    "#                                         mode=access_mode,\\\n",
    "#                                         download_root_dir=download_root_dir,\\\n",
    "#                                         jsons_root_dir=jsons_root_dir,\\\n",
    "#                                         prompt_request_payer=False)\n",
    "ds_grid = ea.ecco_podaac_to_xrdataset(\"geometry\",grid=\"native\",\\\n",
    "                                        mode=access_mode,\\\n",
    "                                        download_root_dir=download_root_dir,\\\n",
    "                                        jsons_root_dir=jsons_root_dir,\\\n",
    "                                        prompt_request_payer=False)     # enter \"y\" when prompted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Open/view ECCO files\n",
    "\n",
    "### View and plot density/pressure anomalies on a single \"tile\"\n",
    "\n",
    "Now let's load one of our datasets and create a couple plots. First we'll load some Python packages that will help us read and plot the data, then we'll load the monthly density/stratification/pressure dataset into our workspace.\n",
    "\n",
    "> Tip: If you have any errors involving reading the netCDF file in the code below, try installing the ```netCDF4``` or ```h5netcdf``` packages, using pip install {pkgname} or conda install -c conda-forge {pkgname}."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have just loaded the contents of the netCDF file into the workspace using the package ```xarray```. We can look at a summary of the contents of this xarray DataSet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 89MB\n",
       "Dimensions:    (i: 90, i_g: 90, j: 90, j_g: 90, k: 50, k_u: 50, k_l: 50,\n",
       "                k_p1: 51, tile: 13, time: 1, nv: 2, nb: 4)\n",
       "Coordinates: (12/22)\n",
       "  * i          (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * i_g        (i_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j          (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j_g        (j_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * k          (k) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n",
       "  * k_u        (k_u) int32 200B 0 1 2 3 4 5 6 7 8 ... 41 42 43 44 45 46 47 48 49\n",
       "    ...         ...\n",
       "    Zu         (k_u) float32 200B dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;\n",
       "    Zl         (k_l) float32 200B dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;\n",
       "    time_bnds  (time, nv) datetime64[ns] 16B dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n",
       "    XC_bnds    (tile, j, i, nb) float32 2MB dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;\n",
       "    YC_bnds    (tile, j, i, nb) float32 2MB dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;\n",
       "    Z_bnds     (k, nv) float32 400B dask.array&lt;chunksize=(50, 2), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: nv, nb\n",
       "Data variables:\n",
       "    RHOAnoma   (time, k, tile, j, i) float32 21MB dask.array&lt;chunksize=(1, 25, 7, 45, 45), meta=np.ndarray&gt;\n",
       "    DRHODR     (time, k_l, tile, j, i) float32 21MB dask.array&lt;chunksize=(1, 25, 7, 45, 45), meta=np.ndarray&gt;\n",
       "    PHIHYD     (time, k, tile, j, i) float32 21MB dask.array&lt;chunksize=(1, 25, 7, 45, 45), meta=np.ndarray&gt;\n",
       "    PHIHYDcR   (time, k, tile, j, i) float32 21MB dask.array&lt;chunksize=(1, 25, 7, 45, 45), meta=np.ndarray&gt;\n",
       "Attributes: (12/62)\n",
       "    acknowledgement:                 This research was carried out by the Jet...\n",
       "    author:                          Ian Fenty and Ou Wang\n",
       "    cdm_data_type:                   Grid\n",
       "    comment:                         Fields provided on the curvilinear lat-l...\n",
       "    Conventions:                     CF-1.8, ACDD-1.3\n",
       "    coordinates_comment:             Note: the global &#x27;coordinates&#x27; attribute...\n",
       "    ...                              ...\n",
       "    time_coverage_duration:          P1M\n",
       "    time_coverage_end:               2000-02-01T00:00:00\n",
       "    time_coverage_resolution:        P1M\n",
       "    time_coverage_start:             2000-01-01T00:00:00\n",
       "    title:                           ECCO Ocean Density, Stratification, and ...\n",
       "    uuid:                            166a1992-4182-11eb-82f9-0cc47a3f43f9</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-cb393d03-6b9b-4981-81b1-687afebbd8fb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cb393d03-6b9b-4981-81b1-687afebbd8fb' 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'>i</span>: 90</li><li><span class='xr-has-index'>i_g</span>: 90</li><li><span class='xr-has-index'>j</span>: 90</li><li><span class='xr-has-index'>j_g</span>: 90</li><li><span class='xr-has-index'>k</span>: 50</li><li><span class='xr-has-index'>k_u</span>: 50</li><li><span class='xr-has-index'>k_l</span>: 50</li><li><span class='xr-has-index'>k_p1</span>: 51</li><li><span class='xr-has-index'>tile</span>: 13</li><li><span class='xr-has-index'>time</span>: 1</li><li><span>nv</span>: 2</li><li><span>nb</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e20bcbc8-9fb2-4567-b9f3-6da86b7039eb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e20bcbc8-9fb2-4567-b9f3-6da86b7039eb' class='xr-section-summary' >Coordinates: <span>(22)</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'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-8a1db1b6-5fdd-4919-ba9a-a7eedfd5c575' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a1db1b6-5fdd-4919-ba9a-a7eedfd5c575' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a344a57-0855-4017-a358-bbf90dc2fed7' class='xr-var-data-in' type='checkbox'><label for='data-2a344a57-0855-4017-a358-bbf90dc2fed7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>grid index in x for variables at tracer and &#x27;v&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>XC</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, tracer (e.g., THETA) and &#x27;v&#x27; variables (e.g., VVEL) have the same x coordinate on the model grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>i_g</span></div><div class='xr-var-dims'>(i_g)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-4a4e3d45-4300-4125-a28c-849d539c6000' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a4e3d45-4300-4125-a28c-849d539c6000' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cad7d01-063d-4514-a81a-ff58237a411e' class='xr-var-data-in' type='checkbox'><label for='data-2cad7d01-063d-4514-a81a-ff58237a411e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>grid index in x for variables at &#x27;u&#x27; and &#x27;g&#x27; locations</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd><dt><span>swap_dim :</span></dt><dd>XG</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, &#x27;u&#x27; (e.g., UVEL) and &#x27;g&#x27; variables (e.g., XG) have the same x coordinate on the model grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j</span></div><div class='xr-var-dims'>(j)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-1a5b17f9-52de-4e1e-9633-fa2984674728' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a5b17f9-52de-4e1e-9633-fa2984674728' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-076539d0-1ab8-42c2-bd51-fccc5bdfab39' class='xr-var-data-in' type='checkbox'><label for='data-076539d0-1ab8-42c2-bd51-fccc5bdfab39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>grid index in y for variables at tracer and &#x27;u&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>YC</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, tracer (e.g., THETA) and &#x27;u&#x27; variables (e.g., UVEL) have the same y coordinate on the model grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j_g</span></div><div class='xr-var-dims'>(j_g)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-dccdf7f6-7b8d-40ba-a40e-c4e58a39723c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dccdf7f6-7b8d-40ba-a40e-c4e58a39723c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ce2cd25-9382-472d-82e5-03f6c8fb5ebc' class='xr-var-data-in' type='checkbox'><label for='data-3ce2cd25-9382-472d-82e5-03f6c8fb5ebc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>grid index in y for variables at &#x27;v&#x27; and &#x27;g&#x27; locations</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd><dt><span>swap_dim :</span></dt><dd>YG</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, &#x27;v&#x27; (e.g., VVEL) and &#x27;g&#x27; variables (e.g., XG) have the same y coordinate.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 44 45 46 47 48 49</div><input id='attrs-52f47bc4-93ea-4c56-8ccb-f04ee7a9cc10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52f47bc4-93ea-4c56-8ccb-f04ee7a9cc10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8f353f7-7108-4857-8b9b-4968bab8288f' class='xr-var-data-in' type='checkbox'><label for='data-b8f353f7-7108-4857-8b9b-4968bab8288f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>grid index in z for tracer variables</dd><dt><span>swap_dim :</span></dt><dd>Z</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k_u</span></div><div class='xr-var-dims'>(k_u)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 44 45 46 47 48 49</div><input id='attrs-f2ae5e09-8317-4f65-9194-9f65242f1d00' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2ae5e09-8317-4f65-9194-9f65242f1d00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ff13b60-13b6-4c57-b00d-08c2d7efc0ed' class='xr-var-data-in' type='checkbox'><label for='data-5ff13b60-13b6-4c57-b00d-08c2d7efc0ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>c_grid_axis_shift :</span></dt><dd>0.5</dd><dt><span>swap_dim :</span></dt><dd>Zu</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>grid index in z corresponding to the bottom face of tracer grid cells (&#x27;w&#x27; locations)</dd><dt><span>comment :</span></dt><dd>First index corresponds to the bottom surface of the uppermost tracer grid cell. The use of &#x27;u&#x27; in the variable name follows the MITgcm convention for ocean variables in which the upper (u) face of a tracer grid cell on the logical grid corresponds to the bottom face of the grid cell on the physical grid.</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k_l</span></div><div class='xr-var-dims'>(k_l)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 44 45 46 47 48 49</div><input id='attrs-5bb51801-9bea-4c7c-956f-12412ee88cbb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bb51801-9bea-4c7c-956f-12412ee88cbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf8b1141-daa7-4430-9474-fcdfe0097247' class='xr-var-data-in' type='checkbox'><label for='data-bf8b1141-daa7-4430-9474-fcdfe0097247' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd><dt><span>swap_dim :</span></dt><dd>Zl</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>grid index in z corresponding to the top face of tracer grid cells (&#x27;w&#x27; locations)</dd><dt><span>comment :</span></dt><dd>First index corresponds to the top surface of the uppermost tracer grid cell. The use of &#x27;l&#x27; in the variable name follows the MITgcm convention for ocean variables in which the lower (l) face of a tracer grid cell on the logical grid corresponds to the top face of the grid cell on the physical grid.</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k_p1</span></div><div class='xr-var-dims'>(k_p1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 45 46 47 48 49 50</div><input id='attrs-8c04ba92-b00c-4117-86cd-216ff9dc98c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c04ba92-b00c-4117-86cd-216ff9dc98c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-268baac2-e4dd-4129-8996-0211e2445e62' class='xr-var-data-in' type='checkbox'><label for='data-268baac2-e4dd-4129-8996-0211e2445e62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>grid index in z for variables at &#x27;w&#x27; locations</dd><dt><span>c_grid_axis_shift :</span></dt><dd>[-0.5  0.5]</dd><dt><span>swap_dim :</span></dt><dd>Zp1</dd><dt><span>comment :</span></dt><dd>Includes top of uppermost model tracer cell (k_p1=0) and bottom of lowermost tracer cell (k_p1=51).</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>tile</span></div><div class='xr-var-dims'>(tile)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-f7c44e4e-2c06-4423-a594-a0e4d3a59ab4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7c44e4e-2c06-4423-a594-a0e4d3a59ab4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b619dffb-fe24-430d-bcd9-ac28aa8f5d9e' class='xr-var-data-in' type='checkbox'><label for='data-b619dffb-fe24-430d-bcd9-ac28aa8f5d9e' 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>lat-lon-cap tile index</dd><dt><span>comment :</span></dt><dd>The ECCO V4 horizontal model grid is divided into 13 tiles of 90x90 cells for convenience.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], dtype=int32)</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'>2000-01-16T12:00:00</div><input id='attrs-0e353f76-0b14-4a3d-8138-847d199dfb46' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e353f76-0b14-4a3d-8138-847d199dfb46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-105938f2-04f9-4fc0-a826-46e95d54799a' class='xr-var-data-in' type='checkbox'><label for='data-105938f2-04f9-4fc0-a826-46e95d54799a' 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>center time of averaging period</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-16T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-25785ba9-54be-46cf-91ba-babcb9540eae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25785ba9-54be-46cf-91ba-babcb9540eae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c55f222d-266a-4336-9d50-31ff5343c529' class='xr-var-data-in' type='checkbox'><label for='data-c55f222d-266a-4336-9d50-31ff5343c529' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of tracer grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>coordinate :</span></dt><dd>YC XC</dd><dt><span>bounds :</span></dt><dd>XC_bnds</dd><dt><span>comment :</span></dt><dd>nonuniform grid spacing</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-246e5802-fcc0-42b2-8a90-7527e22b43ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-246e5802-fcc0-42b2-8a90-7527e22b43ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d06e7cab-4615-4831-992b-2ad67ad4ee1a' class='xr-var-data-in' type='checkbox'><label for='data-d06e7cab-4615-4831-992b-2ad67ad4ee1a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of tracer grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>coordinate :</span></dt><dd>YC XC</dd><dt><span>bounds :</span></dt><dd>YC_bnds</dd><dt><span>comment :</span></dt><dd>nonuniform grid spacing</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <td> 411.33 kiB </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XG</span></div><div class='xr-var-dims'>(tile, j_g, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-fc9379df-7d98-4232-8b38-9a1bf8661cad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc9379df-7d98-4232-8b38-9a1bf8661cad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a4f91f6-502c-4147-a931-8fbd5a888452' class='xr-var-data-in' type='checkbox'><label for='data-4a4f91f6-502c-4147-a931-8fbd5a888452' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of &#x27;southwest&#x27; corner of tracer grid cell</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>coordinate :</span></dt><dd>YG XG</dd><dt><span>comment :</span></dt><dd>Nonuniform grid spacing. Note: &#x27;southwest&#x27; does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Zu</span></div><div class='xr-var-dims'>(k_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-343b8fcc-f7f1-4d5e-b95a-1baf663ccc98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-343b8fcc-f7f1-4d5e-b95a-1baf663ccc98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e63b9c4a-9b87-4f3e-a6c7-d2629b49a1a7' class='xr-var-data-in' type='checkbox'><label for='data-e63b9c4a-9b87-4f3e-a6c7-d2629b49a1a7' 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>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>long_name :</span></dt><dd>depth of the bottom face of tracer grid cells</dd><dt><span>comment :</span></dt><dd>First element is -10m, the depth of the bottom face of the first tracer grid cell. Last element is the depth of the bottom face of the deepest grid cell. The use of &#x27;u&#x27; in the variable name follows the MITgcm convention for ocean variables in which the upper (u) face of a tracer grid cell on the logical grid corresponds to the bottom face of the grid cell on the physical grid. In other words, the logical vertical grid of MITgcm ocean variables is inverted relative to the physical vertical grid.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nv)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-28c064b2-d716-43f2-bfa2-3d5e7d0281ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28c064b2-d716-43f2-bfa2-3d5e7d0281ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-690f88ca-08f3-4cf5-a728-d04e3ea7b625' class='xr-var-data-in' type='checkbox'><label for='data-690f88ca-08f3-4cf5-a728-d04e3ea7b625' 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>comment :</span></dt><dd>Start and end times of averaging period.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>time bounds of averaging period</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> 16 B </td>\n",
       "                        <td> 16 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1, 2) </td>\n",
       "                        <td> (1, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XC_bnds</span></div><div class='xr-var-dims'>(tile, j, i, nb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;</div><input id='attrs-2ae578e8-dbf2-440e-8b1c-59a2f9e586f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ae578e8-dbf2-440e-8b1c-59a2f9e586f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e2e42eb-00b3-497f-9ad2-17e82e0c1e3e' class='xr-var-data-in' type='checkbox'><label for='data-9e2e42eb-00b3-497f-9ad2-17e82e0c1e3e' 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>comment :</span></dt><dd>Bounds array follows CF conventions. XC_bnds[i,j,0] = &#x27;southwest&#x27; corner (j-1, i-1), XC_bnds[i,j,1] = &#x27;southeast&#x27; corner (j-1, i+1), XC_bnds[i,j,2] = &#x27;northeast&#x27; corner (j+1, i+1), XC_bnds[i,j,3]  = &#x27;northwest&#x27; corner (j+1, i-1). Note: &#x27;southwest&#x27;, &#x27;southeast&#x27;, northwest&#x27;, and &#x27;northeast&#x27; do not correspond to geographic orientation but are used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitudes of tracer grid cell corners</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> 1.61 MiB </td>\n",
       "                        <td> 1.61 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90, 4) </td>\n",
       "                        <td> (13, 90, 90, 4) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 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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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YC_bnds</span></div><div class='xr-var-dims'>(tile, j, i, nb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;</div><input id='attrs-413e2c1d-df9d-4ba4-a4a2-c29dcc0397f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-413e2c1d-df9d-4ba4-a4a2-c29dcc0397f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f6e6a7b-485f-4257-8b42-66fb10879bdf' class='xr-var-data-in' type='checkbox'><label for='data-8f6e6a7b-485f-4257-8b42-66fb10879bdf' 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>comment :</span></dt><dd>Bounds array follows CF conventions. YC_bnds[i,j,0] = &#x27;southwest&#x27; corner (j-1, i-1), YC_bnds[i,j,1] = &#x27;southeast&#x27; corner (j-1, i+1), YC_bnds[i,j,2] = &#x27;northeast&#x27; corner (j+1, i+1), YC_bnds[i,j,3]  = &#x27;northwest&#x27; corner (j+1, i-1). Note: &#x27;southwest&#x27;, &#x27;southeast&#x27;, northwest&#x27;, and &#x27;northeast&#x27; do not correspond to geographic orientation but are used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitudes of tracer grid cell corners</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> 1.61 MiB </td>\n",
       "                        <td> 1.61 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90, 4) </td>\n",
       "                        <td> (13, 90, 90, 4) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 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",
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       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"40\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Z_bnds</span></div><div class='xr-var-dims'>(k, nv)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50, 2), meta=np.ndarray&gt;</div><input id='attrs-0346c040-e2d1-4e17-b404-c1b88c0f472c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0346c040-e2d1-4e17-b404-c1b88c0f472c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d370640b-dc0b-427b-a64e-56c61888428c' class='xr-var-data-in' type='checkbox'><label for='data-d370640b-dc0b-427b-a64e-56c61888428c' 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>comment :</span></dt><dd>One pair of depths for each vertical level.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>depths of tracer grid cell upper and lower interfaces</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> 400 B </td>\n",
       "                        <td> 400 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50, 2) </td>\n",
       "                        <td> (50, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
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       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "                        <td> 20.08 MiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DRHODR</span></div><div class='xr-var-dims'>(time, k_l, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 25, 7, 45, 45), meta=np.ndarray&gt;</div><input id='attrs-2d55545d-8781-4181-bb3b-3f22e3cd4016' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d55545d-8781-4181-bb3b-3f22e3cd4016' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c294e4e3-6308-4601-aaa2-ae045bc2eced' class='xr-var-data-in' type='checkbox'><label for='data-c294e4e3-6308-4601-aaa2-ae045bc2eced' 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>Density stratification</dd><dt><span>units :</span></dt><dd>kg m-3 m-1</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>Density stratification: d(sigma) d z-1. Note: density computations are done with in-situ density. The vertical derivatives of in-situ density and locally-referenced potential density are identical  The equation of state is a modified UNESCO formula by Jackett and McDougall (1995), which uses the model variable potential temperature as input assuming a horizontally and temporally constant pressure of $p_0=-g \\rho_{0} z$.</dd><dt><span>valid_min :</span></dt><dd>-0.7393171787261963</dd><dt><span>valid_max :</span></dt><dd>3.208351699868217e-05</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PHIHYD</span></div><div class='xr-var-dims'>(time, k, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 25, 7, 45, 45), meta=np.ndarray&gt;</div><input id='attrs-fc4c70ca-99fd-4395-b3bd-d7defc10a8ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc4c70ca-99fd-4395-b3bd-d7defc10a8ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a546ac8-82a2-4cf2-ab75-9349849f9446' class='xr-var-data-in' type='checkbox'><label for='data-6a546ac8-82a2-4cf2-ab75-9349849f9446' 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>Ocean hydrostatic pressure anomaly</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>PHIHYD = p(k) / rhoConst - g z*(k,t), where p = hydrostatic ocean pressure at depth level k, rhoConst = reference density (1029 kg m-3), g is acceleration due to gravity (9.81 m s-2), and z*(k,t) is model depth at level k and time t. Units: p:[kg m-1 s-2], rhoConst:[kg m-3], g:[m s-2], H(t):[m]. Note: includes atmospheric pressure loading. Quantity referred to in some contexts as hydrostatic pressure anomaly. PHIBOT accounts for the model&#x27;s time-varying grid cell thickness (z* coordinate system). See PHIHYDcR for hydrostatic pressure potential anomaly calculated using time-invariant grid cell thicknesses. PHIHYD is NOT corrected for global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH). </dd><dt><span>valid_min :</span></dt><dd>79.53374481201172</dd><dt><span>valid_max :</span></dt><dd>783.2001953125</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PHIHYDcR</span></div><div class='xr-var-dims'>(time, k, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 25, 7, 45, 45), meta=np.ndarray&gt;</div><input id='attrs-d5d18813-5b71-4d61-b353-14275a606edc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5d18813-5b71-4d61-b353-14275a606edc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdb90f04-7609-48d9-975e-31b6d87cfeea' class='xr-var-data-in' type='checkbox'><label for='data-fdb90f04-7609-48d9-975e-31b6d87cfeea' 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>Ocean hydrostatic pressure anomaly at constant depths</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>PHIHYD = p(k) / rhoConst - g z(k,t), where p = hydrostatic ocean pressure at depth level k, rhoConst = reference density (1029 kg m-3), g is acceleration due to gravity (9.81 m s-2), and z(k,t) is fixed model depth at level k. Units: p:[kg m-1 s-2], rhoConst:[kg m-3], g:[m s-2], H(t):[m]. Note: includes atmospheric pressure loading. Quantity referred to in some contexts as hydrostatic pressure potential anomaly. PHIHYDcR is calculated with respect to the model&#x27;s initial, time-invariant grid cell thicknesses. See PHIHYD for hydrostatic pressure anomaly calculated using model&#x27;s time-variable grid cell thicknesses (z* coordinate system). PHIHYDcR is is NOT corrected for global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH). </dd><dt><span>valid_min :</span></dt><dd>78.17041778564453</dd><dt><span>valid_max :</span></dt><dd>783.6217041015625</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> 20.08 MiB </td>\n",
       "                        <td> 1.35 MiB </td>\n",
       "                    </tr>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1, 50, 13, 90, 90) </td>\n",
       "                        <td> (1, 25, 7, 45, 45) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 16 chunks in 2 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-76b48743-a993-4fc9-9838-b8ecce6b2d56' class='xr-section-summary-in' type='checkbox'  ><label for='section-76b48743-a993-4fc9-9838-b8ecce6b2d56' class='xr-section-summary' >Indexes: <span>(10)</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>i</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-08bf577c-b4da-4481-8ee0-9bae57f4981e' class='xr-index-data-in' type='checkbox'/><label for='index-08bf577c-b4da-4481-8ee0-9bae57f4981e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;i&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>i_g</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-77d3c965-d4a5-431f-ba1a-b44c3c3b7fa1' class='xr-index-data-in' type='checkbox'/><label for='index-77d3c965-d4a5-431f-ba1a-b44c3c3b7fa1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;i_g&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>j</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-59cba802-ffc8-483e-b542-72b7545abf13' class='xr-index-data-in' type='checkbox'/><label for='index-59cba802-ffc8-483e-b542-72b7545abf13' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;j&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>j_g</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-75af3ba9-85de-4d25-9061-0a9c8b7a7c19' class='xr-index-data-in' type='checkbox'/><label for='index-75af3ba9-85de-4d25-9061-0a9c8b7a7c19' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;j_g&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ad473b38-e1da-4f66-a13a-fac76108ad9b' class='xr-index-data-in' type='checkbox'/><label for='index-ad473b38-e1da-4f66-a13a-fac76108ad9b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;k&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k_u</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7a01568b-896c-44ad-a0c4-dc0d6e45a82c' class='xr-index-data-in' type='checkbox'/><label for='index-7a01568b-896c-44ad-a0c4-dc0d6e45a82c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;k_u&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k_l</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e2773dbf-e9b8-4ae2-97ac-01cd6f9ef363' class='xr-index-data-in' type='checkbox'/><label for='index-e2773dbf-e9b8-4ae2-97ac-01cd6f9ef363' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;k_l&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k_p1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-21d579f1-a6b4-42b1-80e4-6b34a8f36df8' class='xr-index-data-in' type='checkbox'/><label for='index-21d579f1-a6b4-42b1-80e4-6b34a8f36df8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;k_p1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>tile</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-580663f9-c254-4864-9d12-e45afa1698bd' class='xr-index-data-in' type='checkbox'/><label for='index-580663f9-c254-4864-9d12-e45afa1698bd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=&#x27;int32&#x27;, name=&#x27;tile&#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-ddd1d2b3-5c01-459c-9c90-b13de82273e6' class='xr-index-data-in' type='checkbox'/><label for='index-ddd1d2b3-5c01-459c-9c90-b13de82273e6' 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;2000-01-16 12:00:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a2411c93-09c5-45a6-b4b0-5431a9a372ce' class='xr-section-summary-in' type='checkbox'  ><label for='section-a2411c93-09c5-45a6-b4b0-5431a9a372ce' class='xr-section-summary' >Attributes: <span>(62)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>acknowledgement :</span></dt><dd>This research was carried out by the Jet Propulsion Laboratory, managed by the California Institute of Technology under a contract with the National Aeronautics and Space Administration.</dd><dt><span>author :</span></dt><dd>Ian Fenty and Ou Wang</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>Fields provided on the curvilinear lat-lon-cap 90 (llc90) native grid used in the ECCO model.</dd><dt><span>Conventions :</span></dt><dd>CF-1.8, ACDD-1.3</dd><dt><span>coordinates_comment :</span></dt><dd>Note: the global &#x27;coordinates&#x27; attribute describes auxillary coordinates.</dd><dt><span>creator_email :</span></dt><dd>ecco-group@mit.edu</dd><dt><span>creator_institution :</span></dt><dd>NASA Jet Propulsion Laboratory (JPL)</dd><dt><span>creator_name :</span></dt><dd>ECCO Consortium</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_url :</span></dt><dd>https://ecco-group.org</dd><dt><span>date_created :</span></dt><dd>2020-12-18T14:40:54</dd><dt><span>date_issued :</span></dt><dd>2020-12-18T14:40:54</dd><dt><span>date_metadata_modified :</span></dt><dd>2021-03-15T21:55:10</dd><dt><span>date_modified :</span></dt><dd>2021-03-15T21:55:10</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>variable</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>180.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>-180.0</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>variable</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_vertical_max :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_min :</span></dt><dd>-6134.5</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>up</dd><dt><span>geospatial_vertical_resolution :</span></dt><dd>variable</dd><dt><span>geospatial_vertical_units :</span></dt><dd>meter</dd><dt><span>history :</span></dt><dd>Inaugural release of an ECCO Central Estimate solution to PO.DAAC</dd><dt><span>id :</span></dt><dd>10.5067/ECL5M-ODE44</dd><dt><span>institution :</span></dt><dd>NASA Jet Propulsion Laboratory (JPL)</dd><dt><span>instrument_vocabulary :</span></dt><dd>GCMD instrument keywords</dd><dt><span>keywords :</span></dt><dd>EARTH SCIENCE &gt; OCEANS &gt; OCEAN PRESSURE &gt; WATER PRESSURE, EARTH SCIENCE SERVICES &gt; MODELS &gt; EARTH SCIENCE REANALYSES/ASSIMILATION MODELS, EARTH SCIENCE &gt; OCEANS &gt; OCEAN PRESSURE, EARTH SCIENCE &gt; OCEANS &gt; SALINITY/DENSITY &gt; DENSITY, EARTH SCIENCE &gt; OCEANS &gt; SALINITY/DENSITY</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>license :</span></dt><dd>Public Domain</dd><dt><span>metadata_link :</span></dt><dd>https://cmr.earthdata.nasa.gov/search/collections.umm_json?ShortName=ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4</dd><dt><span>naming_authority :</span></dt><dd>gov.nasa.jpl</dd><dt><span>platform :</span></dt><dd>ERS-1/2, TOPEX/Poseidon, Geosat Follow-On (GFO), ENVISAT, Jason-1, Jason-2, CryoSat-2, SARAL/AltiKa, Jason-3, AVHRR, Aquarius, SSM/I, SSMIS, GRACE, DTU17MDT, Argo, WOCE, GO-SHIP, MEOP, Ice Tethered Profilers (ITP)</dd><dt><span>platform_vocabulary :</span></dt><dd>GCMD platform keywords</dd><dt><span>processing_level :</span></dt><dd>L4</dd><dt><span>product_name :</span></dt><dd>OCEAN_DENS_STRAT_PRESS_mon_mean_2000-01_ECCO_V4r4_native_llc0090.nc</dd><dt><span>product_time_coverage_end :</span></dt><dd>2018-01-01T00:00:00</dd><dt><span>product_time_coverage_start :</span></dt><dd>1992-01-01T12:00:00</dd><dt><span>product_version :</span></dt><dd>Version 4, Release 4</dd><dt><span>program :</span></dt><dd>NASA Physical Oceanography, Cryosphere, Modeling, Analysis, and Prediction (MAP)</dd><dt><span>project :</span></dt><dd>Estimating the Circulation and Climate of the Ocean (ECCO)</dd><dt><span>publisher_email :</span></dt><dd>podaac@podaac.jpl.nasa.gov</dd><dt><span>publisher_institution :</span></dt><dd>PO.DAAC</dd><dt><span>publisher_name :</span></dt><dd>Physical Oceanography Distributed Active Archive Center (PO.DAAC)</dd><dt><span>publisher_type :</span></dt><dd>institution</dd><dt><span>publisher_url :</span></dt><dd>https://podaac.jpl.nasa.gov</dd><dt><span>references :</span></dt><dd>ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., &amp; Ponte, R. M. 2020. Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate (Version 4 Release 4). doi:10.5281/zenodo.3765928</dd><dt><span>source :</span></dt><dd>The ECCO V4r4 state estimate was produced by fitting a free-running solution of the MITgcm (checkpoint 66g) to satellite and in situ observational data in a least squares sense using the adjoint method</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>summary :</span></dt><dd>This dataset provides monthly-averaged ocean density, stratification, and hydrostatic pressure on the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea-ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.</dd><dt><span>time_coverage_duration :</span></dt><dd>P1M</dd><dt><span>time_coverage_end :</span></dt><dd>2000-02-01T00:00:00</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1M</dd><dt><span>time_coverage_start :</span></dt><dd>2000-01-01T00:00:00</dd><dt><span>title :</span></dt><dd>ECCO Ocean Density, Stratification, and Hydrostatic Pressure - Monthly Mean llc90 Grid (Version 4 Release 4)</dd><dt><span>uuid :</span></dt><dd>166a1992-4182-11eb-82f9-0cc47a3f43f9</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 89MB\n",
       "Dimensions:    (i: 90, i_g: 90, j: 90, j_g: 90, k: 50, k_u: 50, k_l: 50,\n",
       "                k_p1: 51, tile: 13, time: 1, nv: 2, nb: 4)\n",
       "Coordinates: (12/22)\n",
       "  * i          (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * i_g        (i_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j          (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j_g        (j_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * k          (k) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n",
       "  * k_u        (k_u) int32 200B 0 1 2 3 4 5 6 7 8 ... 41 42 43 44 45 46 47 48 49\n",
       "    ...         ...\n",
       "    Zu         (k_u) float32 200B dask.array<chunksize=(50,), meta=np.ndarray>\n",
       "    Zl         (k_l) float32 200B dask.array<chunksize=(50,), meta=np.ndarray>\n",
       "    time_bnds  (time, nv) datetime64[ns] 16B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "    XC_bnds    (tile, j, i, nb) float32 2MB dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>\n",
       "    YC_bnds    (tile, j, i, nb) float32 2MB dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>\n",
       "    Z_bnds     (k, nv) float32 400B dask.array<chunksize=(50, 2), meta=np.ndarray>\n",
       "Dimensions without coordinates: nv, nb\n",
       "Data variables:\n",
       "    RHOAnoma   (time, k, tile, j, i) float32 21MB dask.array<chunksize=(1, 25, 7, 45, 45), meta=np.ndarray>\n",
       "    DRHODR     (time, k_l, tile, j, i) float32 21MB dask.array<chunksize=(1, 25, 7, 45, 45), meta=np.ndarray>\n",
       "    PHIHYD     (time, k, tile, j, i) float32 21MB dask.array<chunksize=(1, 25, 7, 45, 45), meta=np.ndarray>\n",
       "    PHIHYDcR   (time, k, tile, j, i) float32 21MB dask.array<chunksize=(1, 25, 7, 45, 45), meta=np.ndarray>\n",
       "Attributes: (12/62)\n",
       "    acknowledgement:                 This research was carried out by the Jet...\n",
       "    author:                          Ian Fenty and Ou Wang\n",
       "    cdm_data_type:                   Grid\n",
       "    comment:                         Fields provided on the curvilinear lat-l...\n",
       "    Conventions:                     CF-1.8, ACDD-1.3\n",
       "    coordinates_comment:             Note: the global 'coordinates' attribute...\n",
       "    ...                              ...\n",
       "    time_coverage_duration:          P1M\n",
       "    time_coverage_end:               2000-02-01T00:00:00\n",
       "    time_coverage_resolution:        P1M\n",
       "    time_coverage_start:             2000-01-01T00:00:00\n",
       "    title:                           ECCO Ocean Density, Stratification, and ...\n",
       "    uuid:                            166a1992-4182-11eb-82f9-0cc47a3f43f9"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_denspress_mo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the summary above, the **boldface** coordinates are also dimensions, used to index and locate the other variables in the dataset. For the remaining variables, the 2nd column indicates the dimensions of each gridded variable. For example, most of the \"data variables\" are indexed on coordinates (time, k, tile, j, i). The global grid of ECCO is made up of 13 \"tiles\" with 90x90 horizontal grid cells each (see [this tutorial](../doc/fields.rst) for more information on the ECCO grid). Time has length 1 (Jan 2000), k (depth) has length 50, tile has length 13, and j and i each have lengths 90.\n",
    "\n",
    "Now let's plot density anomaly at the surface on a single tile. Python indexing (unlike Matlab or some other programming languages) starts at zero, so the surface index is ```k=0```, and we will plot ```tile=1``` (the 2nd of 13 tiles). For this plot we'll use the `'seismic'` colormap.\n",
    "\n",
    "> Tip: The Python plotting package we are using (Matplotlib) provides a number of [named colormaps](https://matplotlib.org/stable/gallery/color/colormap_reference.html), and you can also [create your own](https://matplotlib.org/stable/tutorials/colors/colormap-manipulation.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "densanom = ds_denspress_mo.RHOAnoma\n",
    "densanom_surf = densanom.isel(k=0)\n",
    "\n",
    "plt.rcParams[\"font.size\"] = 12    # set default font size for plots in this tutorial\n",
    "densanom_surf_plot = (densanom_surf.isel(tile=1)).plot(cmap='seismic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So that's one quick and easy way to get a look at a map of ECCO fields (at least on a single tile). The ```xarray``` package is very convenient to use with netCDF files as it retains data and coordinate labels/attributes even as you plot and manipulate the data.\n",
    "\n",
    "But wait a moment...why are all of the density values negative?? The variable that we just plotted is the \"in-situ seawater density anomaly\". But anomaly relative to what? Let's consult the attributes for this variable, contained in the variable's DataArray.\n",
    "\n",
    "> Tip: When a variable is subsetted from an xarray DataSet, an xarray DataArrray object is created. An xarray DataArray contains only one data variable, but retains coordinates and attributes from the parent Dataset."
   ]
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  {
   "cell_type": "code",
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   "outputs": [
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       "    comment:                In-situ seawater density anomaly relative to the ...</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'>'RHOAnoma'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>k</span>: 50</li><li><span class='xr-has-index'>tile</span>: 13</li><li><span class='xr-has-index'>j</span>: 90</li><li><span class='xr-has-index'>i</span>: 90</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ad6a8d2c-f913-49b5-af1b-1afea94a5c97' class='xr-array-in' type='checkbox' checked><label for='section-ad6a8d2c-f913-49b5-af1b-1afea94a5c97' 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, 25, 7, 45, 45), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-cb839824-676d-4940-ba94-1fd85f9e769a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cb839824-676d-4940-ba94-1fd85f9e769a' class='xr-section-summary' >Coordinates: <span>(8)</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'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-9e7aa6c5-6e62-4865-9242-d6ee036d81bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e7aa6c5-6e62-4865-9242-d6ee036d81bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c591245f-2c23-4abd-b2eb-1fc23606cf5b' class='xr-var-data-in' type='checkbox'><label for='data-c591245f-2c23-4abd-b2eb-1fc23606cf5b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>grid index in x for variables at tracer and &#x27;v&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>XC</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, tracer (e.g., THETA) and &#x27;v&#x27; variables (e.g., VVEL) have the same x coordinate on the model grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j</span></div><div class='xr-var-dims'>(j)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-708a254d-9762-487a-a4b2-149a9bed33a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-708a254d-9762-487a-a4b2-149a9bed33a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e501451-f0ce-40d7-8c11-8da0e1f7b33a' class='xr-var-data-in' type='checkbox'><label for='data-0e501451-f0ce-40d7-8c11-8da0e1f7b33a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>grid index in y for variables at tracer and &#x27;u&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>YC</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, tracer (e.g., THETA) and &#x27;u&#x27; variables (e.g., UVEL) have the same y coordinate on the model grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 44 45 46 47 48 49</div><input id='attrs-1a2080eb-c3d0-4d4d-882a-f3a9c25a04a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a2080eb-c3d0-4d4d-882a-f3a9c25a04a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb67af8d-0114-4cbb-8e3b-539c199dc8cf' class='xr-var-data-in' type='checkbox'><label for='data-cb67af8d-0114-4cbb-8e3b-539c199dc8cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>grid index in z for tracer variables</dd><dt><span>swap_dim :</span></dt><dd>Z</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>tile</span></div><div class='xr-var-dims'>(tile)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-c8756692-f5be-48d4-be04-9a8b14427e98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8756692-f5be-48d4-be04-9a8b14427e98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06ab52e8-1992-454b-b22a-1de4b3255f4a' class='xr-var-data-in' type='checkbox'><label for='data-06ab52e8-1992-454b-b22a-1de4b3255f4a' 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>lat-lon-cap tile index</dd><dt><span>comment :</span></dt><dd>The ECCO V4 horizontal model grid is divided into 13 tiles of 90x90 cells for convenience.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], dtype=int32)</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'>2000-01-16T12:00:00</div><input id='attrs-cb324457-94cf-4949-80b7-ba8eb50559b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb324457-94cf-4949-80b7-ba8eb50559b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f3d07be-ef34-4276-8512-3891e8d64849' class='xr-var-data-in' type='checkbox'><label for='data-9f3d07be-ef34-4276-8512-3891e8d64849' 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>center time of averaging period</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-16T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-1d131233-d903-468d-80a2-1310804413b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d131233-d903-468d-80a2-1310804413b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33218054-a158-483d-adf5-c355b8c7abfe' class='xr-var-data-in' type='checkbox'><label for='data-33218054-a158-483d-adf5-c355b8c7abfe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of tracer grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>coordinate :</span></dt><dd>YC XC</dd><dt><span>bounds :</span></dt><dd>XC_bnds</dd><dt><span>comment :</span></dt><dd>nonuniform grid spacing</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-ac8c880d-9d47-44bf-be61-54fc8eb81d10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac8c880d-9d47-44bf-be61-54fc8eb81d10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5ea8baf-17d1-4b99-b22f-fde2ee0bbdcc' class='xr-var-data-in' type='checkbox'><label for='data-b5ea8baf-17d1-4b99-b22f-fde2ee0bbdcc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of tracer grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>coordinate :</span></dt><dd>YC XC</dd><dt><span>bounds :</span></dt><dd>YC_bnds</dd><dt><span>comment :</span></dt><dd>nonuniform grid spacing</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "  <text x=\"173.836907\" y=\"83.836907\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,173.836907,83.836907)\">90</text>\n",
       "  <text x=\"11.918454\" y=\"151.918454\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.918454,151.918454)\">13</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Z</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-b379cbec-9979-4a33-bc4f-333389e0f706' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b379cbec-9979-4a33-bc4f-333389e0f706' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-982d2578-1283-43ad-b617-140ed76a6dd1' class='xr-var-data-in' type='checkbox'><label for='data-982d2578-1283-43ad-b617-140ed76a6dd1' 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>depth of tracer grid cell center</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>bounds :</span></dt><dd>Z_bnds</dd><dt><span>comment :</span></dt><dd>Non-uniform vertical spacing.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>depth</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> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 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=\"170\" height=\"79\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"29\" x2=\"120\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,29.030629010473877 0.0,29.030629010473877\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"49.030629\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >50</text>\n",
       "  <text x=\"140.000000\" y=\"14.515315\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,14.515315)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-27098039-1c12-4d08-9ee5-dc9232084914' class='xr-section-summary-in' type='checkbox'  ><label for='section-27098039-1c12-4d08-9ee5-dc9232084914' class='xr-section-summary' >Indexes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>i</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-50fdf644-bc85-4a44-bf4c-5c0ae143b17f' class='xr-index-data-in' type='checkbox'/><label for='index-50fdf644-bc85-4a44-bf4c-5c0ae143b17f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;i&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>j</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7ac6ba96-d302-4f8c-b22d-043058c278b4' class='xr-index-data-in' type='checkbox'/><label for='index-7ac6ba96-d302-4f8c-b22d-043058c278b4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;j&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f55c492e-1b37-4e9b-b356-23661ede00b2' class='xr-index-data-in' type='checkbox'/><label for='index-f55c492e-1b37-4e9b-b356-23661ede00b2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;k&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>tile</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d53f0b8e-79b9-450b-ab18-98d013d9caa9' class='xr-index-data-in' type='checkbox'/><label for='index-d53f0b8e-79b9-450b-ab18-98d013d9caa9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=&#x27;int32&#x27;, name=&#x27;tile&#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-fa324277-468c-42c7-a3a2-9bd0e4d93206' class='xr-index-data-in' type='checkbox'/><label for='index-fa324277-468c-42c7-a3a2-9bd0e4d93206' 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;2000-01-16 12:00:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4a07c51a-3e8c-434f-aa18-5fc1d4a99c7e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4a07c51a-3e8c-434f-aa18-5fc1d4a99c7e' class='xr-section-summary' >Attributes: <span>(6)</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>In-situ seawater density anomaly</dd><dt><span>units :</span></dt><dd>kg m-3</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>valid_min :</span></dt><dd>-18.81316375732422</dd><dt><span>valid_max :</span></dt><dd>25.540061950683594</dd><dt><span>comment :</span></dt><dd>In-situ seawater density anomaly relative to the reference density, rhoConst. rhoConst = 1029 kg m-3</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'RHOAnoma' (time: 1, k: 50, tile: 13, j: 90, i: 90)> Size: 21MB\n",
       "dask.array<open_dataset-RHOAnoma, shape=(1, 50, 13, 90, 90), dtype=float32, chunksize=(1, 25, 7, 45, 45), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * i        (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j        (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * k        (k) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n",
       "  * tile     (tile) int32 52B 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "  * time     (time) datetime64[ns] 8B 2000-01-16T12:00:00\n",
       "    XC       (tile, j, i) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    YC       (tile, j, i) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    Z        (k) float32 200B dask.array<chunksize=(50,), meta=np.ndarray>\n",
       "Attributes:\n",
       "    long_name:              In-situ seawater density anomaly\n",
       "    units:                  kg m-3\n",
       "    coverage_content_type:  modelResult\n",
       "    valid_min:              -18.81316375732422\n",
       "    valid_max:              25.540061950683594\n",
       "    comment:                In-situ seawater density anomaly relative to the ..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "densanom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the attribute \"comment\" at the bottom. The density anomaly is relative to a constant value, 1029 kg m-3. So if we need the actual density value, we add 1029 to the density anomaly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rhoConst = 1029.\n",
    "dens = rhoConst + densanom\n",
    "dens_surf = dens.isel(k=0)\n",
    "dens_surf_plot = (dens_surf.isel(tile=1)).plot(cmap='RdYlBu_r')   # trying out another colormap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The map looks the same, but the colorbar labels are now actual density values. However the colorbar label, which previously was assigned automatically by xarray's plot function, got confused since we created a new data array (density) from the old array (density anomaly). We can update variable name and attributes in the **dens** DataArray accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dens.name = 'RHO'\n",
    "dens.attrs.update({'long_name': 'In-situ seawater density', 'units': 'kg m-3'})\n",
    "\n",
    "# re-plot surface density\n",
    "dens_surf = dens.isel(k=0)\n",
    "dens_surf_plot = (dens_surf.isel(tile=1)).plot(cmap='RdYlBu_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's plot pressure anomaly, and for a different depth level, ```k=20``` (the 21st depth level counting from the surface)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mDKysrNC3b1+EhIRg1apVCvetVq0a/Pz8sH37dpw6dUoa3BW8D0LvHwCkpaXJbCeWJt77gu+KotJJYr4rn7enyndP3ud//PjxsLS0xJo1a/DLL79g2bJlYBgGXl5eWLRoEVxcXAo9VsHrXpCPK+a4qvw2FlB0HHmfg8KI/S0hpLhRcKdlQlcFCn4wxo0bh6VLl4pqR1dXF2PGjMGYMWPw5s0bnDlzBn/88Qd2796NW7du4ebNmzAwMAAAuLu7IyoqCtnZ2fjnn39w5MgRrFy5Ev369UPFihWlyeMFf+Xm5ubyjqdoNFhh53XgwAF06dJF1HnJs3TpUiQnJ2Pjxo28shc7d+7kDYxQRUF/X716hXr16vHWv3z5UmY7TdPkOTZr1gxNmjSRDqw4ePAgUlJSEBwcLHoAze+//45Hjx5hxowZvKsm58+fxy+//CK6P+ro0KEDAChVYLZSpUoAIHOlsXbt2gCAe/fuCe5TcDWrVq1aqnRTLZ8HnmZmZhprT5XvXmFXLgcOHIiBAwfi/fv3OHfuHCIjIxEeHo727dvj9u3bhV7FK+jT69evBde/evVK7j7K/DYWUHScovoeE6INVAqlBHJzc4OOjg5Onz6t0v6VKlVC9+7dsWvXLnh7e+P+/fu4efMmbztDQ0O0aNECYWFhWLFiBViWxf79+6XrC0aBPn36lLcvt3yAGM2bNwcAlc/rcw8ePACQPyqXS1Oj1Bo3bgxAOIh4//49rl27BiMjI9StW1cjx+PS9DmOHDkSmZmZ2Lp1K3777TcwDIMffvihSPujq6sLQLNXMQpu/QoVMJbn4sWLACC9gg0ADg4OsLW1xb179/Do0SPePocPHwYApUbKaooq35XCXmtNfveEmJubo1OnTli/fj0GDx6M5ORkhccqW7YsatasiefPnwuWtRH63qnz23jmzBnB8ioFxyn4vgNF87klpDhRcFcCVapUCd9++y0uX76MWbNmCV45S0hIkP6DlJ2djejoaN7t4JycHLx79w4AYGRkBCD/x13o9kPBX7UF2wH/z7X5vEYXAMTHx6t0laZr165wcHDA6tWrpeUsuM6fP4+MjAyFbRWUJ4iJiZFZfvToUfz+++9K903IgAEDoK+vj5UrV0oDmwLTp09HWloaBgwYIKqEiCo0fY79+vWDubk55s2bh3PnzqF9+/YywY6q/bl69SrmzZsnuE+FChUACP+BUJiLFy8Kfg7S09MxZswYAODlhgn9g8+yLBYuXIhz587B0tJSetUPyL8iNWLECAD5JS0+/4f/wIEDOH36NJycnJTOw9KE0aNHQ19fH+PGjRO8svjp0yfe+Rb2Wmvyu1fgyJEjgr9Nb968ASD7WyLPkCFDIJFIEBQUJPP6P3r0CCtWrOBtr+xv4+fu37+PNWvWyCw7cOAATp48iZo1a0pLoQCqf24JKSnotmwJtWrVKty/fx8hISHYunUrWrVqhcqVK+PFixe4ffs24uLisHPnTtjb2yMzMxNt27ZF9erV0axZM9jZ2SErKwvHjh3D7du30blzZzg5OQEAlixZgr///huenp6oUaMGypQpg3///ReHDx+Gubk5fvzxR2kfunbtitq1a2Pnzp149uwZmjVrhsTERBw4cABdu3bFrl27lDonfX197Nu3D76+vvDz80OLFi3g7OwMExMTPH36FHFxcXj48CFevnwJExOTQtsKCAjAxo0b0bt3b/To0QM2Nja4efMmjhw5gt69eyMiIkL5F52jevXqWL58OUaNGgUXFxdpnbuTJ0/i/PnzqFOnDhYsWKD2ceTR9DmamJhg0KBB0sBcmYEUQP4tuEWLFmHcuHGIjY2Fo6Mj7t+/j6ioKHTv3l2wPz4+Pli0aBF++OEH9OjRA2XKlIG5uTlGjx5d6LHmzZuH2NhYtGnTBra2ttLPyOHDh/H+/Xu0aNECkydPltnHw8MDtWrVgqurK2xsbJCamopz584hPj4eJiYm2L59O+8W5/jx4xEVFYU9e/agWbNm8PHxQWJiInbv3g0TExOEh4drZXaKOnXqIDw8HEOHDkW9evXQoUMH1KpVCzk5OUhMTMTp06dRsWJFmYEDPj4+2L17N7p3746OHTvC2NgYdnZ2+O677zT63SvQt29fGBkZoVWrVqhevTpYlsXp06cRFxcHFxcXtG3bVmEbEyZMwP79+7F37164uLjA19cXqampiIiIgIeHBw4ePMjbR5nfxs916NABEyZMwOHDh9GoUSNpnTsjIyNs2LBB5n328vKCjo4OJk+ejPj4eOldjGnTpol6bQjROi2O1C11IKfOnRBFQ+1ZlmWzs7PZlStXsu7u7qyZmRlrYGDAVqtWjfX29maXLVvGJiUlsSzLsp8+fWIXLFjAdujQga1WrRpraGjIWlpass2aNWN//fVXNjs7W9rm0aNH2cGDB7N169ZlzczMWBMTE7ZWrVrsTz/9JFiG49mzZ2yfPn3Y8uXLs0ZGRmzTpk3ZvXv3KiyFUpjXr1+zQUFBbL169VhjY2PW1NSUrVmzJtujRw9269atvFpv8pw9e5b18vJizc3N2TJlyrAtW7ZkIyMjVepbQY20jRs38tYdPXqUbdeuHWtubs4aGBiwDg4O7MSJE2VqmYk5RmFlQeR9HjR5jizLstevX2cBsFWqVBH9On/u33//Zf39/dmKFSuyJiYmrIuLC7t+/Xr20aNHgjXJWJZllyxZwtapU4c1MDBgAbB2dnYKjxMVFcX279+fdXR0ZM3MzFg9PT22YsWKrI+PD/vbb78J9j0wMJD18PBgra2tWUNDQ9bY2JitXbs2O2rUKDYhIUHusTIyMtiQkBC2Zs2arIGBAWtpacn27NmT/ffff5V5aTT+3rMsy964cYMdNGgQa2tryxoYGLDly5dn69Wrx/7444+8kiu5ubns5MmTWXt7e2n5pTZt2shso8x3T9Fv1K+//sp+8803rL29PWtsbMyWL1+edXZ2ZhcsWMCmpaUV+lp9LjU1lR03bhxbpUoV1tDQkK1duza7ePFiNiEhQe5nSuxvI8vKlm06d+4c6+Pjw5YtW5YtU6YM265dO/bSpUuC/dq6dSvbqFEj1sjIiPfbrkqZKEKKE8OySgztJIR80cLDwzFs2DBMnz4dYWFh2u4OIUUuNjYWXl5eggOBCCmtKOeOkK9Ebm4uli1bBn19faVvyRJCCPlyUM4dIaXcqVOnEBMTg9jYWNy8eRNjxoyBjY2NtrtFCCGkiFBwR0gpd+LECYSGhqJChQoYMWIE5s+fr+0uEUIIKUKUc0cIIYQQUopQzh0hhBBCSClCwR0hhBBCSClCOXcqkkgkePHiBcqWLVvo3IuEEEIIy7L48OEDqlSpUqSFsbOysvDp0ye12zEwMBA1ywgpmSi4U9GLFy9QrVo1bXeDEELIF+Tp06eoWrVqkbSdlZUFa+MyeA/158S1srLCo0ePKMD7QlFwp6KyZcsCABIPj4OZadHMLfrF0NHelUumQbDWjk0IIfLk7ZWtJZmWmYPqo/ZK/+0oCp8+fcJ75GEVYw9jNbKuMiHB6FeP8OnTJwruvlAU3Kmo4FasmakhzMp87cGd9lI3Gc5coYQQUhLkmRgILi+ONB5j6MCE0VW9Aaqh8cWj4I4QQggpRXR01LuhosMCGrizS7SIgruvmRavuGkKe13x/KhMo5Bi6Akh5GuWFzFIdgE3uirG9BUK7ggFd4QQQkgpopHgjnzRvvxLN4QQQgghRIqu3BFCCCGliA6j5pU7zXWFaAkFd6VZKcipI4SQL4IWS0Jx0W1ZQv/6E0IIIYSUInTljhBCCClF6ModoeCOEEIIKUUouCMU3H2pKJ9ONKqFRwjRNIV17QjRIgruCCGEkFKEUfPKHUNX7r54FNwRQgghpYgOo97NHR2J5vpCtIPu7RFCCCGElCJ05a4kony6YqcoL6805OSJyT3kKg3nTUiRKME5djo6al65U2GfS5cuYfr06Th37hxYloWrqytmz56Nli1bymzHMPJft9q1a+POnTuFHsfT0xMnT57kLff19cWRI0dU6HnpRMEdIYQQUooUd3AXFxcHDw8PuLm5YevWrWBZFgsXLoSPjw9iYmLg7u4u3fb8+fO8/S9evIixY8eiW7duoo5Xo0YNbN++XWaZubm5kr0u3Si4I4QQQkoRhmEKvUImZn9lTJ8+Hebm5jhy5AhMTEwAAG3btkWNGjUQGBiIs2fPSrdt3rw5b//ffvsNDMNg2LBhoo5nbGws2A75P7r/RwghhBCVnT17Fp6entLADgDKli0LDw8PnDt3Di9fvpS774cPH7B79260adMGNWvWLI7ufhXoyp261B2WRL5q7NWZvGVMY/4ypdtVIb9ObDuUh0dIyVbct2U/ffoEQ0ND3vKCZfHx8bC2thbc948//sDHjx/x/fffiz5eQkICLCwskJaWBjs7O/Tt2xfTpk2DsbGxkj0vvSi4I4QQQkoRTQV3aWlpMssNDQ0FgzgnJydcuHABEokEOv8dODc3FxcvXgQAJCcnyz3Whg0bYG5ujh49eojqW6tWrdCnTx/UqVMHmZmZOHz4MBYuXIgzZ84gJiZGevyvHb0KhBBCCOGpVq0aypUrJ33MmzdPcLuffvoJ9+7dw+jRo/H8+XM8ffoUI0aMwJMnTwBAbsD177//4uLFi/j2229hZGQkqk+zZ8/GyJEj4eXlhU6dOmHlypWYP38+Tp06hQMHDqh2oqUQBXeEEEJIKVJw5U6dBwA8ffoUqamp0sfkyZMFjzd06FDMnz8fW7duRdWqVWFra4tbt24hMDAQAGBjYyO434YNGwBAqVuyQgYMGAAAuHDhglrtlCZ0W5YQETSVw8Yj8BdtkR2LEKKSvN2DFW8kUTBnl6L1GqSp27JmZmYwMzMTtU9QUBDGjh2L+/fvo2zZsrCzs8Pw4cNhamqKJk2a8Lb/9OkTtm7diiZNmsDZ2Vn1zn7eb7olK0XBHSGEEELUZmhoiPr16wMAEhMTERERgR9++EFwoMPBgweRlJSEsDD1/5jdvHkzAOEyK18rCu4IIYSQUqS4R8vevHkTe/fuRdOmTWFoaIjr169j/vz5cHR0xKxZswT32bBhA4yNjdG/f3+57erp6aFNmzaIjo4GAJw+fRpz5sxBt27dUKNGDWRlZeHw4cNYt24dvL294e/vr2TPSy8K7gghhJBShFEzuFO2/LGBgQFOnDiBFStWID09Hba2thgxYgSCg4NhamrK2/7p06f4+++/MWDAAJQrV05uu3l5ecjLy5M+t7a2hq6uLmbNmoWkpCQwDANHR0eEhYVhwoQJdFv2MxTcEUKUQnPUktJOIzl2X5FatWoJzvcqT7Vq1WSCNnlYVvY1rlmzJg4dOqR0/75GFNwRQgghpYi6tfV1KG794lFwRwghhJQiaufcUXD3xaPgjhBCCClFdJj8hzr7ky8bZR8SQgghhJQipebK3dWrVxEaGopLly7h/fv3sLW1Rf/+/REYGAgTExPpdleuXMGkSZNw4cIF6OnpwdvbG4sXL0aNGjW02HtCSjfeIAyJhLcN03hm8XSGEGV9YYMn6LYsKRVX7m7duoUWLVrg8ePHWL58OaKiotC3b1+EhYWhX79+0u3u3LkDT09PfPr0Cbt27UJ4eDju3buH1q1b4+3bt1o8A0IIIUQzNDX9GPlylYordzt27EBWVhb27t0LBwcHAIC3tzdevnyJdevWISUlBeXLl0dISAgMDQ0RFRUlnVKlSZMmcHR0xOLFi7FgwQJtngYhhBBCiNpKRXyur68PALxiiObm5tDR0YGBgQFyc3MRFRWFHj16yMyVZ2dnBy8vL0RGRhZrnwkhhJCiQFfuSKm4cjdo0CAsX74cI0eOxIIFC1CxYkWcPHkSv/32G0aNGgVTU1PcvXsXmZmZaNiwIW//hg0b4tixY8jKyoKRkZEWzoAQQog25EUM0nYXNE5Hh4GOGkNedVgaLvulKxXBXfXq1XH+/Hl069ZNelsWAH7++WcsX74cAJCcnAwAsLCw4O1vYWEBlmWRkpICa2trwWNkZ2cjOztb+jwtLU2DZ0AIIYQQohmlIrh7/Pgx/P39UblyZezZswcVK1bExYsXMXv2bKSnp2PDhg3SbRlG/l8kha2bN28eQkNDNdpvQgghRNMYXQaMrupX3xilZ5clJU2pCO6Cg4ORlpaGa9euSScp9vDwgKWlJYYOHYqBAwfCysoKwP+v4H3u3bt3YBgG5ubmco8xefJkjB8/Xvo8LS0N1apV0+yJEEIIIWpidBgwatyWZei27BevVAR3165dg5OTkzSwK+Dq6goAuHnzJlq2bAljY2PEx8fz9o+Pj0fNmjULzbczNDSEoaGhZjtOyFeKatoRbSmNOXaEcJWKMTFVqlTBv//+i/T0dJnl58+fBwBUrVoVenp68Pf3x759+/DhwwfpNomJiYiJiUH37t2Ltc+EEEJIkWAY6dU7VR4oJEWJfBlKRXA3duxYJCUloV27dti1axdOnDiBuXPnYvz48XByckLHjh0BAKGhocjIyEDnzp1x+PBhREZGws/PD5aWlpgwYYKWz4IQQghRX0HOnToP8mUrFcFdly5dEB0dDTMzM4wZMwadO3fG5s2bMXz4cJw6dQoGBgYAgDp16iA2Nhb6+vro2bMnBg8ejJo1a+LUqVOoWLGils+CEEIIUZ86V+3UzdcjJUOpyLkDAC8vL3h5eSncrkmTJjh+/Hgx9IgQQkiJww1cvrB5YwkRo9QEd4QQQgjRQCkUGi37xaPgjhBCCClF1J6hgm7LfvFKRc4dIYQQQgjJR1fuCCGElEpfa007tYsY05W7Lx4Fd4QQQkgpQjl3hG7LEkIIIYSI9Pz5c1y8eBEpKSmC65OSkrBly5Zi7pUsCu4IIYSQUoRhdMDoqPFgKDQQIpFI8P3338PW1hYtWrSAlZUVJk6ciJycHJntEhISMGTIEC31Mh/dliWEEEJKEbVvy0rotqyQDRs2YNOmTQgICICbmxuio6OxbNkyXLx4EYcOHULZsmW13UUpCs8JIYQQQhT47bffMHbsWKxcuRLfffcdNm3ahL/++gvx8fFo164dUlNTtd1FKQruCCGEkFKEph8rGvfv35fOVV+gffv2iI6OxoMHD9C2bVu5eXjFjYI7QgghpBSh4K5oGBgY8PLrAMDFxQXHjh3Do0eP4OPjg+TkZC30Thbl3BFCCCkVvta6dlyMLtTMudNgZ0oRR0dHnD9/Hh06dOCta9y4MY4dO4Z27dph0CDtfw7pyh0hhBBCiALe3t7YvHkz8vLyBNc3btwYx48fB8uyxdwzPrpyRwghhJQiNLds0Rg9ejTc3d2Rnp6OcuXKCW7j7OyMc+fO4fz588XcO1kU3BFCCCGlCE0/VjSsrKzg5+encLtatWqhVq1axdAj+ei2LCGEEEJIKUJX7gghhJBShIoYF728vDysXLkS27dvx5MnT5CVlSWznmEYrda9o+COEEIIKUUYRs3bsgwFd4oEBwdjyZIlcHZ2Rrt27WBgYKDtLsmg4I4QQgghRAnbt29HcHAw5s6dq+2uCKLgjhBCCClN1LwtC3X2/UpkZmaibdu22u6GXDSgghBCCClFGB0dtR+kcO3bt8fFixe13Q256B0khBBCiFouXboEX19flC1bFmXKlIGXlxfOnj3L227w4MH5OYGcR506dUQf6/jx43B3d4eJiQksLS0xePBgvHnzRpOno9CKFSuwd+9erFixAg8ePMC7d+94D22i27KEEEJIKVLcde7i4uLg4eEBNzc3bN26FSzLYuHChfDx8UFMTAzc3d1ltjc2NsaJEyd4y8Q4efIkOnbsCD8/Pxw4cABv3rxBUFAQfHx8cPnyZRgaGirVd1WZmZmhdu3aGDduHMaNGye4jbyZLIoDBXeEEEJIKaKjy0BHjbw5ZfedPn06zM3NceTIEZiYmAAA2rZtixo1aiAwMJB3BU9HRwfNmzdXqW8TJ05ErVq1sGfPHujp5Ycw9vb2aNmyJcLDwzFy5EiV2lXWiBEjsGvXLnTt2hV169al0bKEkK8P0yhE210gpVBehIIJ2oWuQEm0P+9nUSvuK3dnz56Fn5+fNLADgLJly8LDwwP79u3Dy5cvYW1trXJ/Cjx//hxxcXGYN2+eNLADgBYtWqBWrVqIjIwstuBu//79mDdvHgIDA4vleMqinDtCCCGEqOzTp0+Ct0MLlsXHx8ssz8zMhJWVFXR1dVG1alWMHj1aVI7azZs3AQANGzbkrWvYsKF0fXHQ19dH48aNi+14ytLYlbusrCwYGRlpqjlCCCGEqEDtGSr+2zctLU1muaGhoWAQ5+TkhAsXLkAikUDnv5G2ubm50tGkycnJ0m0bNWqERo0aoX79+gDyc+iWLVuG6OhoxMXFoUyZMnL7VdCOhYUFb52FhYXMcRS5c+cOTp48iaSkJAwbNgxWVlZ48eIFypcvLyr/r3v37jh69Ch8fHxEH7M4KR3cRUREIDk5GQEBAQCABw8eoEuXLrh79y5atGiBgwcPonz58hrvKCGEEEIU09Rt2WrVqsksnzFjBmbOnMnb/qeffsKwYcMwevRoTJ06FRKJBKGhoXjy5AkASAM+ALzBB+3atUPjxo3Rs2dPrF+/Xu7gBJn+yZlBQ8zMGnl5efjxxx+xadMmsCwLhmHQsWNHWFlZYfjw4WjcuDHCwsIUttOvXz/88MMPyMnJgZ+fn2DA6eLiorCdoqL0bdnFixfj48eP0ucTJ05ESkoKxowZgzt37milWrO8odUFjwsXLki3vXLlCtq2bYsyZcrA3Nwc3bt3x8OHD4u9z4R8TdjrYTIPQoqEDiP7IGp5+vQpUlNTpY/JkycLbjd06FDMnz8fW7duRdWqVWFra4tbt25J89FsbGwKPU63bt1gamoq82+1kAoVKgCA4BW6d+/eCQZYXHPmzMGOHTuwaNEi3Lx5Eyz7/xzMjh074siRIwrbAAAfHx88fPgQv/zyC3x9feHq6ip9NG3aFK6urqLaKSpKX7l7+PCh9HJqVlYWjh49irVr12LgwIGoXbs2Fi9ejEWLFmm8o4WZPn06RowYwVvu7+8PQ0ND6Yt8584deHp6wtnZGbt27UJWVhZCQkLQunVrXLt2DRUrVizWfhNCCCEap8OoN8vEf4GxmZkZzMzMRO0SFBSEsWPH4v79+yhbtizs7OwwfPhwmJqaokmTJgr3Z1lW5gqfkILYIz4+Hp06dZJZFx8fL11fmE2bNmH69OkYP348r1SJvb09Hj16pLANANi4caOo7bRF6eAuIyMDpqamAICLFy8iOzsbHTt2BJB/3/358+ea7aEIDg4OcHBwkFlWcC992rRp0NXVBQCEhITA0NAQUVFR0g9skyZN4OjoiMWLF2PBggXF3ndCCCFEk4p7tGwBQ0NDaYCVmJiIiIgI/PDDDwpz2Pbs2YOMjAyF5VFsbGzg5uaGbdu2ITAwUPpv+4ULF3D37l2MHTtWYR+fP3/Oq7tXwMjICB8+fFDYBgAMGqRgpLaWKX1b1traGteuXQMAHDlyBLVr15Ze8UpJSZEZCq1NGzZsAMMwGDp0KID85M6oqCj06NFD5i8ROzs7eHl5ITIyUltdJYQQQr5YN2/eRGhoKA4dOoTjx49jyZIl0gsns2bNkm735MkTtGzZEitXrsThw4dx5MgRTJ48GYMGDUK9evXw/fffy7Srp6fHG7CwYMEC3LlzB7169cLx48exY8cO9O7dG/Xr18eQIUMU9rVSpUpyU7Hu3r2LqlWrqvAKlDxKX7nr3r07pk6dipMnT+Lw4cMICgqSrrtx4wbvCpo2pKamYs+ePfDx8YG9vT0AICEhAZmZmXKHUB87doxG/BJCSAmlsKadWNyrUqWx7p2uTv5Dnf2VYGBggBMnTmDFihVIT0+Hra0tRowYgeDgYOmdPiD/Nm/lypWxdOlSvH79Gnl5ebCzs8PPP/+MKVOmyGwL5A9+4N469fT0xF9//YWQkBD4+/vDxMQEnTt3xqJFi0TNTtGpUyfMmTMHHTp0gJWVFYD8gRipqalYsWIF/P395e67dOlSfPvtt6hcubLo12bp0qX47rvvij3tS+ngbtasWUhPT8e5c+fQv39/TJo0SbouKioKbdu2Vbkzmgqudu7ciczMTAwbNky6TNEQapZlkZKSIrfQYnZ2NrKzs6XPuUPECSGEkBJB3QElSu5bq1YtnDx5UuF25cuXx759+0S3+/lgh8+1a9cO7dq1E93O58LCwnD48GE4OTnBy8sLDMNgypQpuHnzJvT19TF9+nS5+06cOBGtWrUSHdzl5eVh4sSJ8PT0LPnBnbGxMdauXSu4TtFIlwJFXU5lw4YNqFChArp168ZbV9hQ6cLWzZs3D6GhoSr3iRBCCCHaVblyZcTFxWHGjBk4dOgQdHV1cf36dXTu3BlhYWGFjrhlWRYbNmwQPaJWIpFoqttKU6uI8d27d5GUlARnZ2fe5dTCLF68GL1795Y+/7ycytatWzF37lyVR9zeuHEDly9fxpgxY2Qu0SoaQs0wDMzNzeW2O3nyZIwfP176PC0tjVcDiBBCCNE2RhdqFjHWYGdKkKysLISFhaFHjx5yL1Ipsn79eg33qmiodFN+y5YtqFq1KpycnODh4YG7d+8CAHr37i3qxIXKqSxYsABLly7F7NmzsX//flW6BSD/qh0AXmKmg4MDjI2NedOgAPlDqGvWrFnoLWFDQ0PpsHCZ4eESFpBI8h+EEEKKD9W1E8Z9XVR5lEJGRkZYtmyZTK1eZUgkEpUe2ihmrHRwt3v3bgwePBguLi5YtWqVzD1xFxcX7Nq1S2EbRVVOJTs7G9u2bYObmxuv3o2enh78/f2xb98+maHOiYmJiImJQffu3VU6JiGEEFKi6DLqP0qpunXriq5l9yVTOribN28ehgwZgoMHD+LHH3+UWVe3bl3cunVLYRtFVU5l//79ePfuHe+qXYHQ0FBkZGSgc+fOOHz4MCIjI+Hn5wdLS0tMmDBBpWMSQggh5Mswffp0zJ49GwkJCdruSpFSOufu9u3bcov9ip24t6jKqWzYsAGmpqbo27ev4Po6deogNjYWQUFB6NmzJ/T09ODt7Y3FixfT7BSEEEJKBYZRs4ixiDlav1QbN25ERkYG6tati4YNG8La2lrmfBmGwYEDB7TYQ81QOrgzMTFBamqq4Lrnz5+LGuVaVOVU/v77b4XbNGnSBMePH1epfYWE8u4UTKdCvjKays2kzxUhRJ5irnP3Jblx4wYMDAxgY2OD5ORk3gWp0hLYKh3ctWzZEqtWrUKPHj146zZt2gRPT0+FbWiinAohhBBCiDIeP36s7S4UC6WDu5CQELRq1Qpubm7o378/GIbBvn37MGPGDJw6dQqXLl0qin4SQgghRARtzS1LSg6lg7umTZvi8OHDCAgIkA5CmDt3LhwdHfHXX3/xRqnKc+bMGezYsQNPnjxBZmamzDqGYRAdHa1s1wghhBCi7ojXUjxaNjExUeE2tra2CrfJysrCp0+fZOaq37VrF65cuYJ27drx5sQtbioVMfby8sLt27eRkJCA169fw9LSErVq1RK9/8aNGzFs2DBYWFigVq1avPng5E05UiJJ2P/PTSj01w43x+pLy5UqafX7SvLrV5yvlSrHKkGvHXs9jLeMaRSihZ6Qkipvx0DZBUUVcAj+bn9B/wYRpVSvXl1hXh13Plsh3333HUxNTbFp0yYAwIoVKzB27FgAwKJFi/Dnn3+iU6dO6nZXZWrNUOHg4KDSyNaFCxeid+/e2Lx5s6iJfgkhhBAiEl25kys8PJwX3CUlJeHgwYN49uwZpk2bJqqdS5cuyVQOWbFiBQYMGIBVq1Zh2LBhWLx4cckP7k6dOqVUox4eHoWuf/LkCVauXEmBHSGEEKJhlHMn3+DBgwWXT5gwAb169cLTp09FtfP27VvY2NgAAB49eoSHDx9i586dMDMzw7BhwzBw4EAFLRQtUcGdp6enqOHBLMuCYRiFlzTr1q2L169fi+shIYQQQkgRGzx4MEaOHImQEMUpIp+XhTt9+jTKlCmDpk2bAsif5iw9Pb1I+6qIqOAuJiZGowedO3cuAgMD4enpKY18v1RM4ylg/kuoZP+ZIWIPqoWnFm3lmpW03ENViDkHLX4WuXl4lINHZAhcTWIU3D5k81TMnRNz5aok5+XRbVmV5Obm4v3796K2bdCgAVavXg07OzusWbMGXl5e0otgiYmJsLKyKsKeKiYquGvTpo1GD7p69WqkpqaiVq1acHZ2RoUKFWTWl5YK0YQQQkixY3TU+0ON+bouOOTk5ODGjRuYMWMGGjVqJGqf6dOno3PnznB2doaBgYHM5AiHDh2Ci4tLUXVXFLUGVLx48QLJycmoUKECqlSpInq/GzduQFdXF5UqVcKLFy/w4sULmfWlpUI0IYQQUtwYXUbhVU1F+5dWOjo6cmOM8uXL4+jRo6La8fb2xu3bt/HPP//A2dkZNWrUkFnn7Oysie6qTKXgbt++fZg8eTIePHggXebg4IC5c+eiZ8+eCvf/WipEE0IIIaTkCAkJ4QV3RkZGqF69Ojp16oSyZcuKbsvOzg52dna85cOHD1e7n+pSOriLiIhAv379UKdOHYSEhMDKygovX75EREQE+vTpgx07dqBPnz5F0dcSj2kSqnAb4by8ElwLT6gvX1r+2ZfWX20q4Xl55CuigatHQlegVM7D41J2RGlxjkDVYdQ7XikeLTtz5kyNtvfmzRvByRgAxZVDipLSwV1YWBg6duyIP//8Ezqf/ciHhITAz88PYWFhooK7nJwcbNmyBdHR0UhOToalpSXatm2LAQMGQF9fX9luEUIIIQSgARVKevr0Kf7991+4urryxgDI8/LlS3z33XeCA07FVg4pSkoHdwkJCVi4cKFMYAfk38cOCAhAr169FLaRmpoKHx8fXLlyBaamprCyssK5c+ewc+dOrFmzBtHR0TJTehBCCCGEqGvatGn4+PEjli1bBgA4fvw4/P39kZ2djfLly+PUqVOoV6+ewnZGjx6Nq1evYsGCBWjYsGGJq9ur9P0VOzs7ZGRkCK7LyMhAtWrVFLYxdepU3L17FxEREfjw4QPu37+PDx8+YNeuXbh79y6mTp2qbLcIIYQQgv8XMVbnUVrt3bsXTk5O0ufTpk1Dw4YNsX//ftjZ2WH27Nmi2jl58iQWL16MwMBAtG/fHm3atOE9tEnpK3cTJkxAWFgYvLy8YGlpKV3+5s0bzJ49G4GBgQrb2L9/P8LCwnhX+Xr27InExEQsXboUK1euVLZrXwahnCYd3eLvhzpUybmivLfSQ9F7qaGcPPbqTJnnTOOZgtuRL1/u5gG8ZYw+53OkoYBDlZGgGsvTKy66OvkPdfYvpZ4/f46aNWsCAJKTkxEXF4e//voLvr6+yMrKwoQJE0S1wzCMqItZ2iIquPv5559lnqelpaF69erw8fGBlZUVXr16hejoaFhaWuLWrVsK23v79i0aNmwouK5Ro0ZISkoS0y1CCCGEENFYloXkvz9Qz549C11dXenAB2tra9HxR69evRAVFYW2bdsWWV/VISq4W7VqleDyP//8U+Z5YmIiVq1ahV9++aXQ9mxsbHDmzBn4+Pjw1p09e1apmnmEEEII+Ywu1BxQobGelDgODg6IioqCj48P/vjjD7i5ucHY2BhA/iCJ8uXLy933ypUr0v/v3bs3fvjhB0gkEvj7+wsOxNBmIWNRwZ1Ew7fU+vTpg7lz56Js2bIYNGgQKlSogOTkZGzbtg1z587F+PHjNXo8Qggh5GvBMOrlzZXmiQSGDx+OUaNGYcuWLXj//j3Cw8Ol686ePSuTj8fVtGlTmdeGZVmsWrUKq1evltnuixwtqwkzZ87E1atXERgYiIkTJ0JPTw+5ublgWRa+vr4ar0NToggFyhJubknxdKVYlfTaaJQTqDmCeaUqvP8l/TNDNEZv0DbesrwdA7XQE2FFWi+PFKuRI0eifPnyOHfuHNzc3DBgwP/zPTMzMzF48GC5+27cuLEYeqgZWgnuDA0NceTIERw9ehQxMTHSKcx8fHzQrl07bXSJEEIIKR2ozl2h+vbti759+/KWr1u3rtD9Bg0aVFRd0jiVgrtt27Zh+fLluH37NrKysnjrxV6K9PX1ha+vrypdIIQQQogQmqFCrqysLHz69Emmlu6uXbtw5coVtGvXTnAsgBBvb2+sWbMGderU4a27d+8eRowYgRMnTmis38pS+r7HwYMHMWTIEDRu3BiZmZkYMmQI+vXrB1NTUzg6OiIkJKQo+kkIIYQQERhdRu1HafXdd9/JVABZsWIF+vbti4ULF6J9+/b466+/RLUTGxuLtLQ0wXUfPnzAyZMnNdJfVSkd3M2fPx/jx4/H2rVrAQABAQHYtm0b7t27h7y8PLl1X2rUqIHr168DAOzt7VGjRg25DwcHBzVOiRBCCCGE79KlS+jQoYP0+YoVKzBgwAC8f/8e3bt3x+LFi9U+xsuXL2FiYqJ2O+pQ+rbs3bt3ERoaKh0xkpubCwCwsrLCtGnTsGjRIgwdOpS3X5s2baSXQdu0aVOqR+MUhmk2h7eMjZsuu0DCeW0osbzoUWHmosV9regzTRRgJbIDFhgJfwADd0lxXnFSdCytDrjQ0VHvO1aKv59v376FjY0NAODRo0d4+PAhdu7cCTMzMwwbNgwDB8ofyHPgwAEcOHBA+nzWrFmoWLGizDaZmZmIjY1F48aNi+YERFI6uMvLy4OBgQF0dHRgamqKV69eSdfZ2tri4cOHgvt9Pspk06ZNyveUEEIIIYpRcCeXiYkJUlNTAQCnT59GmTJl0LRpUwCAkZER0tPT5e5769Yt7N69G0B+uZgTJ05Ah/NaGRoaokGDBgrr/RY1pd9Be3t7vHjxAkD+bBI7d+6UrtuzZw+sra0VtrFlyxYkJycLrnv37h22bNmibLcIIYQQQgrVoEEDrF69GvHx8VizZg28vLykdxITExNhZWUld9/Jkyfjw4cP+PDhA1iWRUxMjPR5wSMpKQkxMTFyZ+EqLkoHdz4+Pjh+/DgAYMyYMYiIiEDNmjXh5OSEtWvXYsSIEQrbGDJkCBISEgTXPXr0CEOGDFG2W4QQQggB/n/lTp1HKTV9+nScPHkSzs7OuH79OiZNmiRdd+jQIdGzSkgkEri5uRVVN9Wm9G3ZOXPmIDs7G0D+3Gq6urrYvn07GIbBpEmTCi0AWIBl5eciZGVlQVdXtblPzpw5g7lz5+L8+fPIyspC1apVMXDgQEyf/v+ctitXrmDSpEm4cOEC9PT04O3tjcWLF6NGjRoqHVNZkjMTecsYQyPZBaX4i1WqiHmfKC9PmAqFjtnrYbxlTCManV9qcXLWWPA/M9xZGFhuvrIQDZX5UJRzx11frCNQqRSKXN7e3rh9+zb++ecfODs7y/zb7+3tDWdnZ6XaO3/+vEy9Xk9PT7Ro0ULDvVae0sGdoaEhDA0Npc+7d++O7t27K9wvMTERjx8/lj6/evUqr0ZeZmYm1q1bB1tbW2W7hR07duC7775D7969sWXLFpQpUwYJCQnSW8gAcOfOHXh6esLZ2Rm7du1CVlYWQkJC0Lp1a1y7do2XGEkIIYSQ0sXOzg52dna85cOHDxfdRmZmJvr27YuoqCiZC1YMw6BTp07YtWuXdM5abSi2GSo2btwoHWXLMAwCAgJ42xS8QMomIj5//hw//vgjhg8fjjVr1kiXe3l5yWwXEhICQ0NDREVFSUfuNmnSBI6Ojli8eDEWLFig7GkRQgghJQuj5q1VpnTfPcrOzsamTZsQGxuLpKQkrFmzBo6Ojjhw4AAaNGgg6k7epEmTcPjwYcyePRv9+/eHlZUVXr16he3bt2PmzJmYNGkSVq5cWQxnI0xUcDd06FBMnz4d9vb2gmVOPscwDDZs2MBb3rt3b9SvXx8sy6J3796YO3cuHB0dZbYxNDRE/fr1Ub16dfFnAOD333/Hx48fERQUJHeb3NxcREVFYeDAgTKVqe3s7ODl5YXIyEgK7gghhHz5tDBa9tKlS5g+fTrOnTsHlmXh6uqK2bNno2XLltJt8vLy8Msvv+Dvv//GzZs38e7dO9jZ2aFr164IDg6Gubm5wuN4enoKFgj29fXFkSNHFO6flJQELy8v/Pvvv7CyssLr16/x4cMHAMD+/ftx9OhRmYtE8kRERGD69OmYPHmydJmdnR2mTJmCnJwcrFq1quQHdzExMRgzZgwA4MSJE4XWqJO3rm7duqhbty6A/Kt4nTt3RoUKFZTtr6BTp07BwsICd+7cQdeuXXHz5k1YWFige/fuWLhwIczMzJCQkIDMzEzBESwNGzbEsWPHkJWVBSMjI4EjaJDQl0bRF0lofXHlcmkz/6805KtR/TxCNEOgbhyvllwOZwMReW7cvD1BAu1w6/ApylPTat27IhYXFwcPDw+4ublh69atYFkWCxcuhI+PD2JiYuDu7g4g/1bmzJkz0a9fP3z//fewtLTElStXMHv2bPz555+4fPmyqFuZNWrUwPbt22WWiQkMgfwrbu/fv8fly5fRsGFDGBgYSNd5eXmJvsiTkZEhN7euZcuWWLRokah2ioqo4O7Ro0fS//88b05Vmp589/nz58jIyECvXr0wefJkLF++HHFxcZgxYwZu3ryJ06dPS0uvWFhY8Pa3sLAAy7JISUmRW8olOztbOpAEgNxpRwghhBCtKuYrd9OnT4e5uTmOHDkinZmhbdu2qFGjBgIDA3H27FkAgLGxMR49eiRzYcfT0xO2trbo1asX9u7diwEDBig8nrGxMZo3b65UHwtERUVhwYIFcHFxQV5ensy6qlWr4tmzZ6Laad68OeLi4gTnoo2Li9P6SFqlcu6ysrIQFhaGHj16oEmTJmod+N27d9ixYwdu376NzMxMmXXybu3KI5FIkJWVhRkzZiA4OBhA/gfGwMAAY8eORXR0tPQDp8pVRwCYN28eQkNDRfeJEEII0QodRs3gTrnRsmfPnoWfn5/MlFtly5aFh4cH9u3bh5cvX8La2hq6urqCd+wKAqGnT5+q3meR0tLSBAdTAEBOTo501i1FVqxYAT8/P5QtWxb9+/dH+fLlkZKSgu3bt2PdunWIiorSZLeVplRwZ2RkhGXLlsnMy6aKxMREuLq6IiMjAxkZGbC0tMS7d++Ql5eH8uXLo1y5ckq1V6FCBdy/fx++vr4yyzt27IixY8fiypUr6Nq1KwAIFk9+9+4dGIYp9LLu5MmTMX78eOnztLQ0ufPoEkIIIVpTzFfuPn36JFNFo0DBsvj4+EInODhx4gQAoF69eqKOl5CQAAsLC2mg1rdvX0ybNk3ULV17e3ucP38e3t7evHWXLl1C7dq1RfWhWbNmyMnJwc8//4yff/4Zenp60sBQX19feisayL9wVDArRnFRerRs3bp18ejRI3h4eKh80ODgYNSrVw9RUVEoU6YMDh8+jPr162P9+vWYO3cuDh06pFR7DRs2xIULF3jLC0bf6ujowMHBAcbGxoiPj+dtFx8fj5o1axaab8ctASOWJHa87AIj5dso1hysklRjr6j6UtJz2r7W+nk0/yz5jN6gbTLPc8P787bh5b2JyWvj5M8J7SEuD4/z+eTWtdPnrBeYG7ek46Yfyft30MnJCRcuXIBEIpFOx5Wbm4uLFy8CEL6oUuD58+cIDg5G06ZN0blzZ4V9atWqFfr06YM6deogMzMThw8fxsKFC3HmzBnExMTwpgPj+vbbb7FgwQLUr18ffn5+APKDr7i4OPzyyy+YOnWqwj4AQI8ePQq926dtSgd306dPx6RJk9CqVSs4ODiodNDz589j4cKF0mCKZVkYGBhg1KhReP36NSZOnKjUJc0ePXpg3bp1OHz4sMxkvX/99ReA/Hvjenp68Pf3x759+7Bw4UKULVsWQP5VxJiYGIwbN06lcyGEEEJKFA1duePenZoxYwZmzpzJ2/ynn37CsGHDMHr0aEydOhUSiQShoaF48uTJf80J9+Xdu3fo1KkTWJZFRESEwsAMAGbPni3zvFOnTqhevToCAwNx4MABdOvWrdD9g4KCcPbsWXTr1g3ly5cHkD/SNjk5GR06dJAOHlVk06ZNorbTFqWDu40bNyIjIwN169ZFw4YNYW1tLRO9MgyDAwcOFNrG69evYW1tDR0dHejq6sr8ddCmTRusWLFCqT61b98e/v7+CAsLg0QiQfPmzXH58mWEhoaic+fOaNWqFQAgNDQUrq6u6Ny5M4KDg6VFjC0tLTFhwgSljkkIIYSUSBqaoeLp06cypcPk3b0aOnQo3r59i9mzZ+PXX38FALi7uyMwMBALFiyAjY0Nb5+UlBS0a9cOz58/x4kTJ9SaJWrAgAEIDAzEhQsXFAZ3+vr6+OuvvxAREYFDhw7h9evXsLS0ROfOndG3b19RAeaXQOmzuHHjBgwMDGBjY4Pk5GTcvHkT8fHxMg9FKleujHfv3gEAqlevjsuXL0vXPX78GHp6ytdWjoiIwNixY7Fu3Tp07NgRv/76K8aNG4c9e/ZIt6lTpw5iY2Ohr6+Pnj17YvDgwahZsyZOnTpFs1MQQgghnzEzM5N5FJaaFBQUhKSkJMTHx+Px48c4d+4cUlJSYGpqyhuAmZKSgrZt2+LRo0c4duyYYIkyVSgKzDIzM9GyZUtER0ejb9++2Lp1K/7++2/s2LED/fv3Vzqwu3PnDvr16wdra2sYGBjgypUrAPIvJMXExKh8HpqgdBSliVIozZs3x9WrV9GlSxd0794dYWFhyM7OhoGBARYtWiSY6KiIsbEx5s+fj/nz5xe6XZMmTXD8+HFVu14oyYmx/IUG+jJPGaHK3yVpHr+vIe+pNOTyKTqH0piTR75qvPw6AMiR/ZyzObKlLQRz8ETUvuPtJaIdRp8zJ7qJ7G8/m13Mvw/FXMQY+P9EBEB+ylNERAR++OEHmYEOBYHdw4cPcezYMZlUKlVt3rwZABSWRynIu1flAhLXtWvX0Lp1a5QtWxaenp7YtWuXdF16ejrWrl3LmyWrOBXb9GOfCwwMlAaJISEhuH37NmbMmAGWZeHh4aH09GOEEEII+U8xB3c3b97E3r170bRpUxgaGuL69euYP38+HB0dMWvWLOl2mZmZ8PX1xdWrV7F8+XLk5ubKDIasWLGiTC6/np4e2rRpg+joaADA6dOnMWfOHHTr1g01atRAVlYWDh8+jHXr1sHb2xv+/v4K++ru7o5Lly7B09NTqXPkCg4Olk6AYGBggIiICOk6Nzc37N27V6321aVWcPf27VtejToAsLW1LXS/Jk2aSC/Tmpqa4uDBg0hLSwPDMNKBDoQQQggp+QwMDHDixAmsWLEC6enpsLW1xYgRIxAcHAxTU1Ppdq9fv0ZcXBwACA5cGDRokMxAhby8PJlCwwW18mbNmoWkpCQwDANHR0eEhYVhwoQJom6rLlmyBF27doWVlRW6d++OMmXKqHTOZ8+exbZt22BiYsIrhly5cmW8evVKpXY1RaXgbvbs2VixYoXc4c3cExXj86RNQgghhKiGYXSEU4CU2F8ZtWrVEpzvlat69erSEmVicLetWbOm0qXSuNzd3fHp0ycMGTIEQ4YMgYmJCW9QqJiadAVVPoSkpKSoVDpNk5QO7sLDwzF//nwEBwcjJCQEU6dOBcuy2Lp1K4yNjREUFKSwjQULFuDZs2eCk+r+9NNPsLOzQ2BgoLJd0wpJzCRITP97g4XeaO69fQ3c688/cDHlxgnlbpXGPDxNKEnzyGpzPmJCigl3vlaWk4NnOGEfb5/shYWPphRiOClS4TbcdnU4udRslriZDzSCUfO2rBqBYUmnqfp0DRs2RGRkJDp27Mhbd+TIEbVn8VKX0pHG6tWrMWXKFAQFBSEkJATdunWDi4sLpk6dCg8PDyQlJSlsY/PmzXJryTRq1Ai//PLLFxPcEUIIIeTLoKn6dGPGjEH//v1hamqK7777DkD+IJITJ04gPDxcplKHNigd3D148ADNmzeX3tv+9OkTgPxRKBMmTMD06dMxceLEQtt48uQJatWqJbiuZs2aGhmRSwghhHyVtDRa9mvSp08fJCQkYObMmdLavD169ICenh5CQ0NFDe4oSkoHdwVDiBmGgZmZGZ49eyZdZ2lpiefPnytsQ19fH2/evBFc9/r16xI9pQchhBBSommoiHFp9fjxY8ybNw8xMTFITk5GhQoV4OXlheDgYNjb24tuZ8qUKRg4cCCOHj0qLYbs6+sLOzu7Iuy9OEoHd46Ojnj69CkAwNXVFevXr0fXrl2ho6ODdevWoXr16grbaNq0KdavX48+ffrw1q1fvx5NmzZVtlvao8P8/68cgS+ESkmtquRGaSqfShPzmtJffeIV5zyyJSknkJBiIJhfJ2L+WcGaeor24dXcK/x5kfr83yVV9y+lrl27Bi8vL2RkZKBFixZo0qQJXr16hU2bNiEiIgKxsbFwdnYW3V7VqlUxbNiwouuwipQO7jp16oRTp05h0KBBmDx5Mnx9fWFubg49PT2kp6cjPDxcYRuBgYHw8/ODp6cnAgICYGNjg2fPnmHt2rU4deqUdE5YQgghhBBNGTt2LCpWrIjjx4/LlG178uQJ2rVrh3HjxsmdXSIxMVGpYykqC1eUlA7uQkJCpP/v7e2Nc+fO4Y8//gDDMPDz8xNVkblDhw5Yt24dJkyYgL59+4JhGLAsi3LlymH9+vXw9fVVtluEEEIIASjnrhCXLl3Chg0beIGXnZ0dZs6cie+//17uvtWrV1cqbUyVsnCaonZdDldXV7i6uiq937Bhw9C3b1+cPXsWSUlJqFixIlq0aCFT8JAQQgghSqLgTq5y5cqhXLlyguvMzc0LrbkbHh4uDe5ycnIwe/ZsmJiYoE+fPrCyssLLly8RERGBjIwMmQth2qB0cNezZ08MHToUHTp0UHqSXS5TU1O0b99erTa0Tt0vEQCokN9RdFTIC+Gev6p5WqX4B0UtxZmXV5KOzcE00u6PJSmBFH32VMyvM5p6QPm+5HH6wp3nNld7V3HI//Xv3x+///47OnXqxFu3fv169OvXT+6+gwcPlv7/lClT4OTkhKioKJlYKCQkBH5+frh//75G+60spYO78+fPIzIyEpUrV8agQYMwaNAg1KlTR+kDv3v3DsuWLUN0dDSSk5NhaWmJtm3bYuzYsShfvrzS7RFCCCEEdOWuEC4uLtizZw/c3NzQr18/WFlZ4dWrV9i5cyfevHmDXr16Yd++/xe/7t69u2A7W7Zswdq1a3kXuXR0dBAQEIARI0Zg/vz5RXouhVE6uHv69CmOHj2KTZs2Yfny5Vi4cCGaNWuGYcOGoXfv3qLmhn3+/DlatmyJxMRE1K1bF7a2tnjx4gVmzZqFLVu24OzZs6hSpYpKJ0QIIYR81Si4k6ug4PDTp09x+fJlwfUF054xDCM3by45ORmZmZmC6zIzM5GSkqKhHqtG6eBOR0cHHTt2RMeOHfH+/Xvs2LEDGzduxA8//IAxY8agR48e2Lx5c6FtTJkyBZmZmbh48aJMvl5cXBz8/f0xZcoUjVWRJoQQQggBIHckrLJcXFwQFhYGLy8vWFpaSpe/ffsWYWFhaNy4sUaOoyq1BlSYm5sjICAAAQEBOHPmDPr3749t27YpDO6OHDmC2bNn8wZiuLq6IiwsDNOnT1enW8VLIvl/3oeE/9cOy8rmYTC5AvMLlqi/klTpiwby9ADVcrdK1GunRZrKe/zSjk3I51TIsVMpv06oXW5du6y8Qp8XKSpiLFebNm000s6SJUvQtm1bVK9eHT4+PtLbu9HR0QCA48ePa+Q4qlJ7tOyxY8ewceNG7N+/H1lZWWjRooXCfVJTU+UWO7a3t0dqaqq63SKEEEK+Toyat2VVKb7/lWnevDni4uIQGhqK2NhY6UwX/v7+mDZtGurVq6fV/qkU3CUkJGDTpk3YvHkznj9/Dmtra4wZMwZDhw6Fo6Ojwv3t7e1x6NAhtGvXjrfu8OHDSk3/QQghhBBS3OrWrYs//vhD290QpHRw16ZNG5w5cwb6+vrw9/fH0KFD4evrq1RZlCFDhiA4OBgSiQSDBg2CtbU1Xr58iW3btmHlypVaHWFCCCGEfNFoQMVXT+ng7sOHD1i+fDm+/fZbWFhYqHTQiRMnIiEhAatWrcLq1auly1mWxY8//ojAwECV2iWEEEK+ehTcffWUDu6uXLmi9kEZhsFvv/2G8ePHIyYmRnqv2tvbG7Vq1VK7/WKVmwcUDJIQ+kJwBlCwQtuwRfNFYlTJm+Amw4sqYst5LuqHQUOFjrVZvLck09SAleI89pf+mhONyt32newCocESnGXcQQ2aGiwhioTbl7xCnxcphlEvb06JKbZIyaT2gAp11K5dG7Vr19ZmFwghhBBCShX6U5kQQggpTRgd9R+l1Pjx4/Htt98KrhswYAAmTpxYzD0qGsX2Duro6EBXV1fUQ09PqxcUCSGEkC9XQc6dOo9S6uDBg3LntG/fvj0OHBB3Kz8sLAwvXrwQXPfy5UuEhYWp3EdNKLYoKiQkBExpvI8vU8RYIK+IW7NY6BVXlI+k4heN5ea1qdIOdzJsqJjLxyV0yqLy+zh5K6oU2xR1HA3lp5XiH8mixjQK0XYXSDHJ2zGQt4zN4uQr5/C/k1rNseNguTmB3KLGuVTcuyR4/vy53Dq7dnZ2ePbsmah2QkND0aFDB8GpUl+8eIHQ0FCEhGjvN0yp4O7Tp0+IjY1FnTp1YGtrq9SBZs6cqdT2hBBCCFEBw6g3KKI0Xoj5j6mpKZ4+fSq4LjExEUZGRqLaKZh/Vkh6ejr09fVV6p+mKBXc6enpoXPnzjh8+LDSwR0hhBBCioG6eXOlOOfO3d0dS5YsQZ8+fWQCsJycHCxbtqzQWbZu3LiBa9euSZ//9ddfuHPnjsw2mZmZ2L59OxwcHDTed2UoFdzp6OigatWqSEtLK6r+EEIIIYQUiWnTpsHDwwP169fHsGHDYGNjg2fPniE8PBxPnjzB2rVr5e4bGRmJ0NBQAPkl3eTl1RkbG2Pjxo1F0n+xlM65GzZsGFavXo0uXbpAV1e3KPr05RKTp8XNwQMU52Vx88wA1XLNVKlhJ0BhWb6iytMTpEq7xZhPV0T5lIR8ybg17Lj5dQB4OWsQUScua05XmefarXPHybnLkX8bT+Poyp1czZo1w8GDBzFq1CgEBwdLlzs4OODgwYNwc3OTu++PP/6Izp07g2VZuLm5YePGjahfv77MNoaGhnBwcICxsXGRnYMYSgd3BgYGuHv3LurWrYsuXbrA2tpaZqAEwzAYN26cRjupSGxsLLy8vATXnT9/Hs2bN5c+v3LlCiZNmoQLFy5AT08P3t7eWLx4MWrUqFFc3SWEEEKKDs1QUShfX188ePAA9+/fx9u3b1GxYkU4Ojoq3M/a2hrW1tYAgJiYGDRp0gRlypQp6u6qROngLigoSPr/S5cu5a3XRnBXYO7cubwg7/Oo+s6dO/D09ISzszN27dqFrKwshISEoHXr1rh27RoqVqxY3F0mhBBCiBY4OjqKCuqEtGnTRsO90Sylg7tHjx4VRT80wtHRUeYqHVdISAgMDQ0RFRUFMzMzAECTJk3g6OiIxYsXY8GCBcXVVUIIIaRo0G1ZGadOnYKLiwvKlCmDU6dOKdzew8NDVLvbtm3Djh078OTJE2RmZsqsYxgGCQkJKvVXE5QO7uzs7IqiH0UuNzcXUVFRGDhwoDSwA/LPx8vLC5GRkaoFd6yCOndiFNt8rtw2VMzlU6G/oqbP5eTqicrT08RrJ3Y/UQ0p2W5RzblaXPPIiiVmTmDOMvbqTJnnTGPZ5+TLwa1jx6thlyXwW8Sbm1XgMyM032wxyZjUQea5orlj2WyhhOsiQsGdDE9PT1y4cAFubm7w9PSUW3OXZVkwDIO8PMX5nQsWLMDkyZPh5OSERo0awdDQUNPdVovKRYwfPHiAEydOIDk5GZaWlvDy8kLNmjXlbq+joyPzgop58ZQ1atQo9O3bFyYmJnB3d8f06dPRqlUrAEBCQgIyMzPRsGFD3n4NGzbEsWPHkJWVJbfGTXZ2NrKzs6XPacQwIYSQEonq3MmIiYmBk5MTAODEiRMamVBh3bp1GDVqFFauXKl2W0VB6eCOZVn89NNPWLt2LSSf/aWto6ODgIAArFixQnC/8PDwIpuholy5chgzZgw8PT1RoUIFPHjwAIsWLYKnpycOHToEX19fJCcnAwAsLCx4+1tYWIBlWaSkpEiTJbnmzZsnHQJNCCGEkC/D5/lxnp6eGmnz1atX6Natm0baKgpKB3fLli3DmjVrMHLkSAwePBhVqlTBixcvsHnzZqxZswb29vaCAyoGDx6sif4Katy4MRo3bix93rp1a3Tr1g0NGjTApEmT4OvrK11XWIBZ2LrJkydj/Pjx0udpaWmoVq2amj0nhBBCNI1R89Zq6bpy9zlvb2+sWbMGderU4a27d+8eRowYgRMnTihsp0mTJkhISIC3t3dRdFNtSgd3v//+O3766Sf88ssv0mU2NjZwdXWFrq4u1q9fr7XRsp8zNzdH586dsXbtWmRmZqJChQoAIL2C97l3796BYRiYm5vLbc/Q0FDxPXVt5jkJHVulPLIiyj1TJU9PqF0F+zHF+haIea00UOeupH2uFBFzTkLblLQ8QaKSvN2DecskGTkyz9kPn2Sfi8inYyXay68TxK3nqaC/xTq3LJVCkSs2NlZuWtWHDx9w8uRJUe0sXboUAwYMgIuLC5o0aaLJLmqE0sHdw4cP0blzZ8F1nTt3xm+//aawDXlVnQswDIPp06cr2zWegrnfGIaRFhWMj4/nbRcfH4+aNWuKnlOOEEIIIaXLy5cvYWJiImrbIUOGIDk5GW5ubrCyspJeQCrAMAyuX79eFN0URengrly5cnjy5InguidPnsiMRJVn5syZha7XRHCXkpKCqKgoODs7S4M2f39/7Nu3DwsXLkTZsmUB5E8UHBMTUyKuNhJCCCHqYhgdtWYFKroZhbTjwIEDOHDg/7OVzJo1i1fXNjMzE7GxsTIpXoWpUKECLC0tNdpPTVI6uGvXrh2mTZuGxo0by1yKvHbtGmbMmCGT3yaPROD2y7t377B//34sX74chw4dUqpP/fv3h62tLZo2bQpLS0vcv38fS5YswevXr7Fp0ybpdqGhoXB1dUXnzp0RHBwsLWJsaWmJCRMmKHVMQgghpESiUigybt26hd27dwPIv3h04sQJ6HBuPRsaGqJBgwYyKWeFiY2N1XQ3NYphC+5divT06VO4u7vj5cuXcHJygrW1NV6+fIlbt26hSpUqOH/+PKpWrapyh+bPn4+LFy8iMjJSqX0iIiLw6NEjpKenw8LCAq1atcLkyZPh6uoqs+0///yDoKAgnD9/Xmb6MQcHB6X6mZaWhnLlyiFl9xCYmRjkL1Q110iVbcTQRDuqtKHqcVWZL7eIXk9Rf7lq4tiqnLPYYytSnDluRVSrj2rflTzceWMBQPJOtsCr5AMnB0/EvLGM0HdFt/DPlabmls2c2om3TMLJG8zLLvwc0j7lotqmM0hNTRV1h0sVBf8upT7dBDMzcbcXhdvJQLlqg4u0r9qio6MjrXlXmin9i1utWjVcu3YNkyZNgqmpKR49egRTU1MEBwfj6tWragV2AODm5obo6Gil9ik49vv375Gbm4s3b95g3759vMAOyB/hcvz4cXz8+BGpqamIjIxUOrAjhBBCSqyCK3fqPJR06dIl+Pr6omzZsihTpgy8vLxw9uxZwW2vXLmCtm3bokyZMjA3N0f37t3x8OFD0cc6fvw43N3dYWJiAktLSwwePBhv3rwRta9EItFoYJeamoq4uDicOnWK99AmUbdlx48fj3HjxqFatWpITEyEtbU15s2bVyQdun79eomdiJcQQggp8Yq5iHFcXBw8PDzg5uaGrVu3gmVZLFy4ED4+PoiJiYG7u7t0W3XneD958iQ6duwIPz8/HDhwAG/evEFQUBB8fHxw+fLlYpspIjc3FyNGjMCWLVvkTspQFJM1iCUquFu+fDn69u2LatWqwd7eHufPn1cr8t2yZQtvWXZ2Nm7cuIHw8HAMGDBA5bYJIYQQUnymT58Oc3NzHDlyRDratG3btqhRowYCAwNlruCpO8f7xIkTUatWLezZswd6evkhjL29PVq2bInw8HCMHDlSYX+3bduG5cuX4/bt28jKyuKtFxOULVu2DH/++SfCw8MxcOBArF69Gvr6+li/fj1SU1PlTuhQXEQFd+XLl8fr168B/H/uNXXIK2hsZGSEAQMGYPHixWq1X6wkbP4j/4lWu8KjyryrRXFcscdWZb5c7vy4Qnk5JWkuXB5V35MS9lkjpMAn/j+M3LljuXPLQqiGHee7LJQcznC+B0Yhf4rqorJ4/QU/xy6PU6uP5fzu5H4qxrlli7nO3dmzZ+Hn5ydTRqRs2bLw8PDAvn378PLlS1hbW6s9x/vz588RFxeHefPmSQM7AGjRogVq1aqFyMhIhcHdwYMHMWTIEAwePBhXrlzB0KFDkZWVhYMHD6JKlSro16+fqHPeunUrpk6din79+mHgwIFo1qwZXFxc8P3338PX1xcxMTFo37694L5dunQRdQyu5cuXo0aNGqK2FRXcNW/eHMOGDZNerZswYYLcgr8Mw8gMORby6NEj3jIjIyNUrlxZTHcIIYQQIk8xj5b99OmT4O3QgmXx8fGwtrZWe473mzdvSrcV2l9ejt/n5s+fj/Hjx2Pu3LnYsGEDAgIC4OLiglevXqF169aiZ556+PAhGjVqJB11+/kVwBEjRmDMmDFy09eioqJgZWWl1C3kxMREhWXkPicquFuzZg3Gjh2Lf//9FwzD4MGDB3I7Jeaqnp2dnegOEkIIIUQJGgruuDM5yJupycnJCRcuXIBEIpEGO7m5ubh48SKA/88Mpe4c74r2F5qBiuvu3bsIDQ2Vxiq5uflXVK2srDBt2jQsWrQIQ4cOVdiOqakpPn36BIZhYGFhgSdPnqBFixYAAGNjY4V92b9/v+j0ttzcXBgYGIjatoCod9/Ozg6RkZG4d+8eWJbF/v378ejRI8GHMiNeCCGEEFIyVatWDeXKlZM+5F2J+umnn3Dv3j2MHj0az58/x9OnTzFixAjphAfcmnKqzvGuaBsx++bl5cHAwAA6OjowNTXFq1evpOtsbW1FxzB16tSR3oVs0aIFli5dimfPnuHNmzdYuHAhateuLXffRo0awdTUVNRxgPzXr1GjRqJnzwBUKGIcExMDJycnZXeDvb29zAtfaoLA3Fwg978PrlCeAm9Z0czDKooq7WpqztriOrbQy8vdh5unB/Bz9TSVp6coL6848xOLiqq1+hS2K+K1Il8E7tyxvLlkBXLuGH3O+6+rvcnshea+zc2WzaGTCM2P+3kbxTm3rIau3D19+lQmN07eHbuhQ4fi7du3mD17Nn799VcAgLu7OwIDA7FgwQLY2NgAgNpzvCvaX+iKHpe9vT1evHgBID/I2rlzpzQHbs+ePXKvGnL16dMH9+7dA5A/QYKHh4f0rqS+vj727dsnd9+rV6+KOkYBHR0dpfdROrhr06aNsrsAAAYNGqT2QAxCCCGEKKDDqDmgIv/fajMzM9FFjIOCgjB27Fjcv38fZcuWhZ2dHYYPHw5TU1PpbFbqzvFev3596badOskWlo6Pj5euL4yPjw+OHz+Ofv36YcyYMejTpw/i4uJgYGCAu3fvYv78+aLONyAgQPr/jRs3xq1bt7B//34wDIN27doVeuWuOCgd3KlKmURAQgghhHxZDA0NpQFWYmIiIiIi8MMPP8DY2BgAoKenp9Yc7zY2NnBzc8O2bdsQGBgIXV1dAMCFCxdw9+5djB07VmEf58yZg+zsbABAr169oKuri+3bt4NhGEyaNEluNQ+ugpq/+vr6APJvYf/0008A8nPkEhMTYWtrK6qtolC6JpAjhBBCvnbFPEPFzZs3ERoaikOHDuH48eNYsmSJtHbdrFmzZLYNDQ1FRkYGOnfujMOHDyMyMhJ+fn6Cc7zr6enBx8dHZtmCBQtw584d9OrVC8ePH8eOHTvQu3dv1K9fH0OGDFHYV0NDQ5mrkd27d8fevXuxZ88e0YEdkH97V96t0uvXr8Pe3r7Q/W/fvo0ff/wRHTt2xMSJE/H8+XPeNteuXRNd+oSr2K7clVoSiZK5QKrkq2moyrWivDLBfQT6q2g/VfPpiurYYvLnFOWwqZKnJ9AOyz2QUF/yFL82onL3FFF5DmDusTn5UhqqNUhKEe5nWqiuncI2FNfCywrzl3muqbp3rEB/uTl2uQrmls0VMX+uxhTzDBUGBgY4ceIEVqxYgfT0dNja2mLEiBEIDg7mDRyoU6cOYmNjERQUhJ49e8rM8c6dnSIvL49XUNjT0xN//fUXQkJC4O/vDxMTE3Tu3BmLFi0SVVqkRo0aiIyMRKNGjXjrbt68iS5duogaE8Cy8j/DeXl5haahPXjwAM2aNUN2djaqVq2KY8eO4ffff8fWrVvRuXNn6XbZ2dnSQSnKouCOEEIIISqrVasWTp48KXr7gjneFZEXQLVr1w7t2rUTfbzPPX78WHpblisrK0upYEoogMvOzsbhw4dhaWkpd7+ZM2eiWrVqOH78OKytrfHw4UMMGzYM3bt3x+bNm0UXUi6M0n8+L1myBCkpKWofmBBCCCFFoJhvy35p5F1Ve/jwoTQPUEhoaCh0dXWhq6sLhmHQvHlz6fOCh4mJCcLCwtC1a1e57Zw9exZTp06VjsytUaOGdJDHwIEDBadoVZbSV+6CgoIQEhKC/v37Y/To0YKXNgkhhBCiJcU8Q0VJt3nzZmzevFn6fOTIkbxRwJmZmbh+/XqhFUHc3NwQEBAAlmWxZs0a9OzZkzezlqGhIRo0aID+/fvLbef169e8mTB0dXWxefNm6OnpYejQocjNzUW9evWUOU0ZSgd3CQkJWL16NTZu3Ijw8HC4u7tj9OjR0nvnhBBCCCElRUZGBt6+fQsg/6rd+/fvebdmDQ0N0adPH4SGhsptp2PHjujYsSMA4OPHjwgJCVE4cEKIjY0NHj58iNatW/PWbdiwAQDw448/ihogIg/DFpYVWIjs7Gxs374da9aswZUrV2BlZYUff/wRw4cPFywCmJiYqFT72hxCLEZaWhrKlSuHd+F9YWby37QgAonkDLfwpqYSyYsqIV2VdouriK2m9hFVbFpD7SrcR8XXroheG95ADVXaEXx9i+h7wBmowTSeqZl2iWiSgz/IPM9NTOVtk/cyXXafjByZ54zQ90BfV3YbblFjgFfYmNuOqgMqMiZ1kHn+6V0Wb5us97LL8jgDKrh9+ZCThzqHryA1NVV07ThlFfy79D71EMzMxM+AwG/nI8zL+RVpX7XF3t4e+/fvL7K7joXNjVugf//+yMzMRGRkpNxtvv/+e4SHh4NhGN6gEjFU/oU1NDTE0KFDcfnyZZw9exa1atXCrFmzUL16dfTr1w/Xr1+X2b569eqwt7cX/SCEEEKI8lhWR+1HafXo0SONBHYRERFYs2aN9PmDBw/g5OQEU1NTtG7dutCxCb169cLLly+RlJQkd5vff/8do0aNUvlCl9rv4PHjx7FgwQKcOXMGFhYW+Pbbb3Hq1Ck0bdoUv//+u3S78PBw6eO3335DtWrVULt2bYSEhGDNmjWYPn06atWqhapVq2LdunXqdosQQgj5KrHQVftRWt24cQOnTp2SPk9PT0dAQACaN2+OkJCQQkucfG7x4sX4+PGj9PnEiRORkpKCMWPG4M6dO5g7d67cfbt164YLFy4UOqIWAFauXCmdv1ZZKiXJffjwARs3bsSaNWtw79491K9fH2vXrsWAAQNgZGSEnJwcjBgxAjNnzsT3338PADLFAadMmQInJydERUXJTCgcEhICPz8/3L9/X6WTIYQQQgiRZ/z48XBxcYGHhwcAYOrUqVi/fj0aNGiAefPmoWLFitKZJgrz8OFD6WwcWVlZOHr0KNauXYuBAweidu3aWLx4MRYtWlSk51IYpYO7kSNHYvv27dIK07/++iu8vLxkttHX18eQIUOwceNGwTa2bNmCtWvXygR2QP7kuAEBARgxYoTo+d20jc2VSCeVFsobYSXcZUKFbgvPu+Ll7QFFN4l6UeW5qUJHhXRQHRWLI2tzG5X24eRgiOqL4kLCYoosM4oKPoshouAz0T7JkQDZBRmZvG3yOPloklR+DTE2K1f2eQ73cyaUr6ygWLYAVRLIM4I78pblcs4hJ/0Tbxtujp1EQZHivNziK2Ks7q3V0nxb9ubNmxg9ejSA/Dp627dvR2hoKKZMmYJp06YhPDxcVHCXkZEhLdB88eJFZGdnSwdbODk5Cc44oUheXh4MDAwQFxcHFxcXpff/nNLv4B9//IEffvgB9+/fx/79+3mBXYE6derIDe6Sk5ORmcn/kQDyhyNTHT1CCCFENSwYsNBR41FEg+RKgPfv30tvh16/fh0pKSno3bs3AMDHx0fU7BQAYG1tjWvXrgEAjhw5gtq1a0tn2EhJSYGJiYlK/VNxjCuP0lfunj17xptORIilpSUGDRokuM7FxQVhYWHw8vKSuef89u1bhIWFoXHjxsp2ixBCCCGkUBUqVMDTp08BADExMahcuTJq1qwJAPj06ZPo4Kp79+6YOnUqTp48icOHDyMoKEi67saNG3BwcNB855WgdHAnJrBTZMmSJWjbti2qV68OHx8fWFlZ4dWrV4iOjgYAUdOSEEIIIYRPwupAosatVXX2Lelat26NmTNnIikpCcuWLYOfn5903f3793nFheWZNWsW0tPTce7cOfTv3x+TJk2SrouKikLbtm013ndlaKXqcPPmzREXF4fQ0FDExsYiOTkZFSpUgL+/P6ZNm6ZWVeZi90kC6ObnUrDcHBEAyOE8F8qf4+Dm7rHcNkS2oxJV8ty4+V8CBPMGNUGlvDeB/iraRkw9Om3WMOTuI6aWn9B7LeI8ub/7vBw8Qdxja+jzQHl6RYpN/SjzPO8tP51G8k52WV4KvyZczgehH7H/0xH4fdDh5NgJ1bnj5TlzfoO59eoAgOXkxuVl5PK2yebk3H36yO9/LqdWn4TTXx1O3yS5RZQnLUDdEa+lebTsvHnz0LFjR4wZMwYODg4ICQmRrtu9ezeaN28uqh1jY2OsXbtWcN2FCxdU6puuri42btyokXJwWptSom7duvjjjz+0dXhCCCGEfGXs7e1x584dvHv3DhYWFjLrVq1aBSsrq2Ltz4kTJ5CcnIxevXoBADp06IBvv/0WV65cQfv27bFu3TqFRZGF0HxhhBBCSClCo2UV4wZ2ANCgQQOl2jhz5gx27NiBJ0+e8AaJMgwjTTUrTEhICNq1ayd9PmnSJJw+fRrt2rXDnj174OjoiOnTpyvVL0ADRYxVdebMGQQEBMDPzw/e3t4yDx8fH211ixBCCPmiqTdSNv9RmiUkJOC7775DlSpVYGhoCBsbGwwaNAgJCQmi29i4cSM8PDywa9cupKSkgGVZmYdEZLmye/fuScue5ObmIjIyEgsWLMC+ffsQFhaGnTt3qnSOWrlyt3HjRgwbNgwWFhaoVasWDA0NZdZraihwcWCz88AW5P2IqbEmkFvCy7Hj7SMil0/ksYqC4LyQHIJ5g5qgK6aunYg8x5I8B7CovEIR/ReTl6fH+UkQzMuTfc1Zzj4q5eAB4NWAVOG1Y6/O5C2j+WZVJ3kvm3uW++wDb5tPSbJXLHIz+V92CaeuHfc3QygnV4eTY8eo8nkQ+AeW25fcbH4OLreundA23HZ4x+acY14x5twR+e7cuQN3d3dkZWXB29sbVapUwYsXL7Br1y5ERUXh7NmzqFOnjsJ2Fi5ciN69e2Pz5s28GEYZaWlpMDc3BwD8888/+PjxI7p06QIAcHNzw8yZM1VqV6Xg7vHjx9i1a5fcS5EbNmwodH9NvSiF+f333/HDDz/A1NQU6emyk1ZfuXIFkyZNwoULF6Cnpwdvb28sXrwYNWrUKJK+EEIIIcVFAh1I1Lj6ps6+Jd2UKVNQoUIFxMbGomrVqtLlz549g7e3N6ZOnYq9e/cqbOfJkydYuXKl2jFMpUqVcP/+fbRu3RrHjx+HnZ2dtF8fPnyAvr6+Su0qHdwdOnQI3bt3R15eHipVqsQ7MYZRfJVEUy+KPM+fP0dgYCCqVKmC1NRUmXV37tyBp6cnnJ2dsWvXLmRlZSEkJAStW7fGtWvXpEUICSGEkC8R5dzJd/LkSaxYsUImsAOAqlWrIiQkBD///LOodurWrYvXr1+r3Z8OHTpgypQp+Pfff7Fp0yaZ+sB37txB9erVVWpX6eBu6tSpaNmyJf744w9UqlRJpYNq6kWRZ8SIEfDw8ICFhQX27Nkjsy4kJASGhoaIioqCmZkZAKBJkyZwdHTE4sWLsWDBgiLrFyGEEFLUqBSKfBkZGahQoYLgOktLS7mzZ3HNnTsXgYGB8PT0hI2Njcr9mTt3LhITE7F+/Xq4ublh2rRp0nU7duxAixYtVGpX6eDu/v372Ldvn8qBHaC5F0XItm3bcPLkSdy6dUvmRQLykxWjoqIwcOBAaWAHAHZ2dvDy8pImMiqDzc0Dm1PIXzncPBFVatYJ5fKJyacrqjw3DjEZkmLy8lQilI/I20ZMjTruNpwcmyKr06dauwpzBMXk3OkJ/IBzc5S4OXjy2v4MKzQfraifGu5+KuTgUd07jZJw5o3NfM7PuctM5swtKzDHKvfzys2fE8q5U+U3g+XUmmPz+L9O3Dw8oXy6vCzZZdx2hZbx8gi1WOeOyFe7dm1s374dHTrwayDu3LlTVL4dAKxevRqpqamoVasWnJ2deQEjwzA4cOCAwnYsLS1x5MgRwXUxMTEqlUEBVAju7OzseDlsytLUi8L15s0bjB07FvPnz+ddcgXyR8hkZmaiYcOGvHUNGzbEsWPHkJWVJfhiZmdnIzv7/8nFaWlpSvePEEIIKWosy6h5W7b0zi37888/4/vvv0dqaioGDRoEa2trvHz5Etu2bcPBgwfx+++/i2rnxo0b0NXVRaVKlfDixQu8ePFCZr2YFDVFPr8IpSylg7spU6Zg8eLF6Nixo8oT4xbVixIQEIDatWtj5MiRguuTk5MBCNe3sbCwAMuySElJgbW1NW/9vHnzEBoaqlK/CCGEkOKibjmT0lwKZejQoXj9+jVmz56NQ4cOAciv0GFsbIw5c+ZgyJAhotp5/PhxEfZSfUoHd5cuXcKbN29Qs2ZNeHl5CV51++WXXwptoyhelL179+LPP//E1atXFQaHha2Xt27y5MkYP3689HlaWproOegIIYQQol15eXlISEjAyJEjERAQgPPnz0unP3V3d0e5cuW03UWNUTq4W7VqlfT/hYrriQnuNC09PR2jRo3CTz/9hCpVquD9+/cAgE+f8msVvX//Hvr6+tJAtOAK3ufevXsHhmGk9Wa4DA0NBUf3sjkSsIXWWiu8vlN+G5wFonLEFP9lpWg+V6F8lKKiypHEzUcrYg5YlXLuRBCT76cKVfrL/QwKnA/v9cwVUedOqBAn97Mn4ewjkKfHe/8Fbhnx8/IU5OAJ9YWoJW/3YJnnuS9lU3DSX/BTcrLTPvGWcenoFZ5jJ6pWpkDeG28bzm+amFw5od9B7jyxYr4G3LlkuXX6UIz1WyWsDiRq3JZVZ9+SjGVZODk54c8//0THjh0F8+6UkZOTgy1btiA6OhrJycmwtLRE27ZtMWDAAJVLmGiK0sGd2KrLYr19+1ZwdIqtra3oNpKSkvD69WssWbIES5Ys4a0vX748unbtij179sDY2Bjx8fG8beLj41GzZk2VkxcJIYSQkkG90bIopaNl9fT0YGVlpZE4JjU1FT4+Prhy5QpMTU1hZWWFc+fOYefOnVizZg2io6PVyplTl9bC89mzZ6NSpUqwsrKCvb0976EMKysrxMTE8B6+vr4wMjJCTEwMZs+eDT09Pfj7+2Pfvn348OH/o74SExMRExOD7t27a/o0CSGEEFJC9O3bF1u2bFG7nalTp+Lu3buIiIjAhw8fcP/+fXz48AG7du3C3bt3MXXqVA30VnVamX4sPDwc8+fPR3BwMEJCQjB16lSwLIutW7fC2NgYQUFBSrVnZGQET09P3vJNmzZBV1dXZl1oaChcXV3RuXNnBAcHS4sYW1paYsKECWqeGSGEEKJdVMRYPmdnZ0RERMDb2xvdu3eHtbU1L9dezIWe/fv3IywsDL169ZJZ3rNnTyQmJmLp0qVYuXKlRvuuDFHBXY0aNRAZGYlGjRrB3t5e4YAERZPvrl69GlOmTEFQUBBCQkLQrVs3uLi4YOrUqfDw8EBSUpJyZ6GEOnXqIDY2FkFBQejZs6fM9GMqzU7xSSJuTtn/sKrUpxOsASUi/0SFOndi8lqKi1BPFObmqJqvqEodOyXed6WolHPHOSehNhTl6QFguLcrVMnLE7rlwc3LE3gPWD1Ofqqu4n208+dp6SA5EsBblnNbNh/5/SPZGX7S38rWtAOALNnpZwXLJ+px3ltVUiUFP1YKvoIaziKSwSsbqcf5xeLMPSvJK746dzRaVr6BAwcCyJ/JKjY2lreeYRjk5fFrH3K9fftWsKwaADRq1KhI4xgxRP00tmnTRnrvuE2bNmrXb3nw4AGaN28Onf++HQUDH4yNjTFhwgRMnz4dEydOVOsYQP6Vu02bNvGWN2nSBMePH1e7fUIIIYR8OWJiYjTSjo2NDc6cOQMfHx/eurNnz6JKlSoaOY6qRAV3GzdulP6/ULCk9EH/+8ufYRiYmZnh2bNn0nWWlpZ4/vy52scghBBCvkZ0W1a+Nm3aaKSdPn36YO7cuShbtiwGDRqEChUqIDk5Gdu2bcPcuXNlSqdpg1beQUdHRzx9+hQA4OrqivXr1yMnJwd5eXlYt26dyhPlEkIIIV87CXTVfpDCzZw5E15eXggMDESlSpVgaGiISpUqYdy4cfDy8sLMmTO12j+tZKx06tQJp06dwqBBgzB58mT4+vrC3Nwcenp6SE9PR3h4uDa6pRJWwiqXpyawrUpzKGqiRp2INkpSDh4AxfXohHLNeMuEilapUudOA1P0CCUfcXIlher9sdz+cue5zBPYh5uXJxHoP/czISYvj5tPJ5TolJsr+1xozlpOO9wcPKF9eGcgkPDFXp0pu0/jmbxtvgaS4z/LPM+5+oK3zfsbb2WfP5GdS/a9bApefruct8nAgL+NnuIUJoXE5M+pkmMnZhpmwRxBbnoq5yOuw8mLzi3OuqI0/ZgMb29v0dsyDIPo6GiF2xkaGuLIkSM4evQoYmJipMWQfXx80K5dO3W6qxFaCe5CQkKk/+/t7Y1z587hjz/+AMMw8PPzg5eXlza6RQghhJBSRiKRyIwVuHv3Ll69egU7OztYWVnh1atXePLkCaytrVG7dm2l2vb19YWvr6+mu6y2EjHWzNXVFa6urtruBiGEEPLFo9Gysj4fFXvkyBH88MMPOHv2LNzd3aXLz507hz59+iAwMFBUmxcuXEBiYiJ69+7NW7dr1y7Y2dmhWbNmavddVaXrHSSEEEK+cgUDKtR5lFbTpk3DzJkzZQI7AGjRogVmzJghuvjwlClTBGe7AoBbt25h2rRpavdVHSXiyt3XTlFem6ayH0Tlz4moxaTNPDzukXn5ihIROWxC8/uqkj+nypzAHKLqFQrlxnGSgBjONrycPKDo8vL0Oe1yk48Afr5crkASlp6CvDwDgXlCOfldjGACFee1uR4mu0+jEJQ2knP8QvC5t2Rz7FKvvuZt8/bOe5nnSZxpuHNy+O8BPxWS/5kR+kgoS1M168Tk06m0Dfel4XzEP2kg75Co799//0W1atUE19na2uLOnTui2rlx44bciQ+aNWuGNWvWqNxHTVA6PP+8LApXbm6u1of/EkIIIV8zGi0rX+XKlbF3717Bdbt370blypVFtfPx40dpWTcuHR0dmSlOtUHp4G7YsGEYPHgwMjMzZZY/efIELVu2xOrVqzXWOUIIIYQoh27LyhcQEID169ejV69eiIyMxPnz5xEZGYmePXsiPDwcAQH8mVuE2Nvbyy2IHBMTAzs7O012W2lKv4Ph4eHYs2cPXF1dcevWLQBAZGQkGjdujNevX+PkyZOF7p+Xl4d79+7h/fv3KnWYEEIIISXL1atX8c0336BKlSowMTFBnTp1EBYWhoyMDOk2DMPIfdSpU0fhMTw9PQX37dChg+h+Tpo0CSEhITh06BB69uyJVq1aoUePHvjrr78wdepUTJo0SVQ7ffv2xbJly3h3Mzdt2oTly5ejX79+ovtUFJTOuRs8eDBcXV3Ru3dvuLm5oUOHDoiMjIS/vz82bdoEc3PzQvdnWRZOTk74888/0bFjR1X7XXLkSQrPU+PmMKlAKMdNTG08hblxqubXqVKvSVN5etz8KW5fRLxWrFDui1CuXiFtCBLzuugq6L/ANsJHln3vuKPbxGT/CfZWIvviCOblGcjesmE4c2gK5gjmySYoCtXuE1Uvj4v7vjAC76OBvuJ2ShnJff4sP+lXZHPsXt9K4W3z8pXspyKLM5WsuDlhBb6Dak5ZqUlFlXOnaH1u8U0tW+yjZW/duoUWLVqgdu3aWL58OSwtLXHq1CmEhYXhn3/+wYEDBwAA58+f5+178eJFjB07Ft26dRN1rBo1amD79u0yyxTFHVwzZ87EuHHjcP78eWl9uubNmyvVTnBwMGJjYzFs2DCMHj0aVapUwYsXL5CVlQVPT09MnjxZqT5pmkoDKurVq4fdu3ejadOm2LdvH5o1a4bIyEhRX2A9PT1YWVlBUpQzOhNCCCFfKQnLQsKq/ge1svvu2LEDWVlZ2Lt3LxwcHADk17B9+fIl1q1bh5SUFJQvXx7Nmzfn7fvbb7+BYRgMGzZM1LGMjY0F2xHr1atXsLKyQrly5ZS64sdlYGCAY8eOYceOHThy5Ajevn0LNzc3dOzYEf369YOurnbzFlUK7aOiotC6dWtUrFgRo0aNQlxcHNq3b483b96I2r9v377YsmWLKocmhBBCSAmir59/dbxcuXIyy83NzaGjowMDoWlLAHz48AG7d+9GmzZtULNmzSLvJ5A/IrZfv344e/as2m3p6uriu+++w/bt2/H3339j+/btGDBggNYDO0CF4G7SpEno0qUL3N3dcfXqVaxcuRKHDx9GfHw8nJ2dFebcAYCzszPOnTsHb29vrFq1Cnv37sW+fftkHoQQQghRnoRV/6GMQYMGwdzcHCNHjsTDhw/x4cMHREVF4bfffsOoUaNgamoquN8ff/yBjx8/4vvvvxd9rISEBFhYWEBPTw8ODg6YOnUqb4BnYaZNm4bTp0/Dw8MDzs7O2LBhg1L7fykYllXu+quBgQFmz57NSzp89eoV+vbti7NnzyInh1sATJaOgmQFhmGQl1eyiwKlpaWhXLlyeD3ZF2ZGheT0aCDnTgjl3MkhkMslKl9Owfukyvy/wscR0xdOzp3QsbntcOveCR2H246Ivggem5tzJ6ZdzjaC/eOWFeDOEyv017+RoWy7+gLbcHPuuK9VKaxzl7t5AG9ZWswTmefPL7/lbaNKzh33bRGqDkE5d0C6JA/eDx8gNTUVZmZmijuqgoJ/l64kvkIZNY6RnpYGF1srPH36VKavhoaGMDQ0FNznzp076Natm0yduJ9//hnLly+X+/43b94cd+/excuXL2FkZKSwX9OmTYONjQ3q1KmDzMxMHD58GGvXrkWLFi0QExOjMLYokJeXh71792LVqlU4c+YMypcvjyFDhmDkyJHS28pfOqVz7mJiYtCyZUvecisrK5w4cQIzZswQ1UappMVATsx+vEBNqL+cgE+w4K+iAwsFfwomuhdNhf0UFj4G+IEu57VRtXAz71jc10ZEgCU4oIa3pPABFsL7COAO+BA6Nqcaq5jCx/zPoohBFyrk5bICP+5MLmeZAee9jZ/N36eBdqvLK0tyRLZ8w6d/k3jbJN9+J/OcG8gBwPvUwo9jJPDvOvdtEn7blP/+iBpPo2SAld8uw3mueD8xMYOibXKKMc1clatv3P0B8Ir9zpgxAzNnzuRt//jxY/j7+6Ny5crYs2cPKlasiIsXL2L27NlIT0/Hhg0bePv8+++/uHjxIkaNGiUqsAOA2bNlv6udOnVC9erVERgYiAMHDogelKGrq4vevXujd+/euHHjBlatWoW1a9di+fLl6NChA3766acSOV+sMpQO7oQCuwI6OjqYNWuWwjbatGmj7GEJIYQQUoyErtwJCQ4ORlpaGq5duya9Bevh4QFLS0sMHToUAwcO5P27XxDwKXNLVsiAAQMQGBiICxcuiA7uPtegQQN07NgRN27cwKVLlxAdHY3Dhw+jcePG2LFjB2rVqqVW/7RFpUtNd+7cQb9+/WBtbQ0DAwNcuXIFABAaGqrUVbnU1FQcPXoU27dvR0oKf1g+IYQQQpRTMFpWnQcAmJmZyTzkBXfXrl2Dk5MTL7fO1dUVAHDz5k2Z5Z8+fcLWrVvRpEkTODs7a+Scxd6SLZCUlIR58+bB3t4ePXv2hJ6eHiIiIpCWlob9+/fjw4cPGDx4sEb6pg1KB3fXrl2Dq6srTp48CU9PT5ncuPT0dKxdu1ZUO7NmzUKVKlXQsWNHDBw4EI8ePQIA+Pj4YP78+cp2ixBCCCHQXHAnVpUqVfDvv/8iPT1dZnlBXbuqVavKLD948CCSkpJElz8pzObNmwFAdHmUixcvYuDAgahWrRpCQ0PRpk0bxMXF4cyZM+jVqxf09fXh7++P5cuX459//pHbjre3t9x5aO/duwdvb2/lT0aDlL4tGxwcjIYNG+LYsWMwMDBARESEdJ2bm5vcOds+t2bNGoSGhiIgIAAdO3aEn5+fdF3nzp2xb98+BAcHK9s17dDVUTvXTmPJ+lyiiuFy6MgmsQvmXHFHeXPz1YTy9FRJABGzjwqvnapFoRURN3BD+eOIGlChKSrkBPIH5vC/D7y8RxFdYXM4BZVF7COUzc/Nw2MknA+wkn/xFzfJuSD+wjeyOXXcHLt3N/k5d69eyL6eaQJTX+bmyj7nvpxiXipVSpiqWvaUux+3f8L5dCznOX8b7ng+bp6eEEWvTXEWMS5uY8eOxTfffIN27dph3LhxsLS0xIULFzBv3jw4OTnxJizYsGEDjI2N0b9/f7lt6unpoU2bNoiOjgYAnD59GnPmzEG3bt1Qo0YNZGVl4fDhw1i3bh28vb3h7+8vqq/u7u6wsrJCcHAwRo4ciUqVKgluV716dbRo0UJuO7GxsUhLSxNc9+HDB1GVQ4qS0sHd2bNnsW3bNpiYmPBGtFauXBmvXr1S2MaqVaswfvx4LFy4kNeGo6Mj7t+/r2y3CCGEEAIgD6oVNfh8f2V06dIF0dHRmD9/PsaMGYPU1FRUq1YNw4cPx+TJk2Xq3D19+hR///03BgwYwKuLJ9OHvDyZ+MDa2hq6urqYNWsWkpKSwDAMHB0dERYWhgkTJoi+Lbtlyxb06dNHWptPnrp166o8+PPly5cwMTFRaV9NUTq4Y1lWbkHClJQUuffkP/fw4UO5I1HKli1L884SQgghKiruGSoAwMvLC15eXgq3q1atmqhSZ9wqbTVr1sShQ4eU7hfXgAH8MkFiHThwQDqVGpCfXlaxYkWZbTIzMxEbG4vGjRurfBxNUDq4a9iwISIjIwXnhT1y5AiaNGmisI1y5crh9evXguseP34s9zIpIYQQQog6Pnz4gMOHD+PJkye8AsYMw2D69OmC+926dQu7d++WbnfixAneFUNDQ0M0aNAAv/zyS9F0XiSlg7sxY8agf//+MDU1xXfffQcASExMxIkTJxAeHo49e/YobMPHxwcLFy5E165dpfVtGIZBbm4ufv311y+qvgyjw2g+Z05T+VTcdkTlT8n+tSRYbJZLzPlz89yErojncSeg11DhYw7hHDYNFDEuqgLFQhQVAi+qnDzw3xd+LT+h5CJObTmBLRT1mJuDl39szuvArbor1I6I47K3ZQd1MXU1kwMsucCZTDxDoDI+5xwkb9N5m+Q8ei/z/MOtZJnnyY/5+6Smyp45N78O4H+suPWfhQoUi6kJV1x5eIpy8MRuw1/G/8Ryt+FejOIW7i3O6dQ1VeeuNLp48SL8/Pzw7t07wfWFBXeTJ0/G5Mn532EdHR3ExMTAzc2tyPqqDqWDuz59+iAhIQEzZ87EihUrAAA9evSAnp4eQkNDRSU1hoWFwdXVFU5OTujWrRsYhsGqVatw9epVJCYmYteuXcqfCSGEEEK0clv2SzFu3DjY2NjgyJEjaNiwodw0M0UkxRmtq0Dp4A4ApkyZgoEDB+Lo0aN4/fo1LC0t4evrCzs7O1H716xZE2fPnsX48eOxZs0asCyLLVu2wMvLC9u3b4etra0q3SKEEEK+enTlTr74+Hjs2LEDTZs21XZXipRKwR2QX7dGnRo1Tk5OOHLkCLKzs5GcnIzy5cvD2NhY5fYIIYQQQgrDHQChjm3btmH58uW4ffs2sgRSQsQMHCkqooK7xMREpRpV5sqboaEhqlSpolT7JYqBDm+uypJKVN6YiG34c9SKmICeu0DE/LOCuVAa+JNSpfw5MeeoSrsihu9rLO+xiCjMwQNUqoXHvXzA6Avsky1iPloFk5+yAvswnGNzc/AAxXl4vPw6gFefTpLCz7nLS8qQff6Cnz+X/lS2SN37R7KTwgrNEauohh3Az7HjTvkptI+8SeE/x60tV1Q5eGLmuVWlVp8qpRC558wd/VmUWDVvyxZnX4vbTz/9hLVr16Jz586iPrvyHDx4EEOGDMHgwYNx5coVDB06FFlZWTh48CCqVKmCfv36abDXyhMV3FWvXl2pF0FRtDp+/Hi8fv0a27dv560bMGAArK2tsWjRItHHu3btGqZOnYr4+Hi8ffsWxsbGqF27NkaNGsUb9nzlyhVMmjQJFy5cgJ6eHry9vbF48WLUqFFD9PEIIYSQkopy7mQtXbpU5vnt27fRuHFj+Pn5oUKFCjLrGIbBuHHjFLY5f/58jB8/HnPnzsWGDRsQEBAAFxcXvHr1Cq1bt0a1atU0eg7KEhXchYeHS4O7nJwczJ49GyYmJujTpw+srKzw8uVLREREICMjAyEhIQrbO3jwoNzRKO3bt8fs2bOVCu7ev3+PatWqoV+/frCxscHHjx+xfft2fPfdd3j8+DGmTZsGIH9OXE9PTzg7O2PXrl3IyspCSEgIWrdujWvXrmn0ci0hhBBCtC8wMFBw+Y0bN3jLxAZ3d+/eRWhoqDQ2yv3v0riVlRWmTZuGRYsWYejQoWr0Wj2igrvPJ8+dMmUKnJycEBUVJVPfJSQkBH5+fqJml3j+/DmqV68uuM7Ozg7Pnj0T0y0pT09PeHp6yizr3LkzHj16hHXr1kmDu5CQEBgaGiIqKgpmZmYAgCZNmsDR0RGLFy/GggULlDouIYQQUtLQgApZBXPXa1JeXh4MDAygo6MDU1NTmdm5bG1t8fDhQ40fUxlKD6jYsmUL1q5dyyvcp6Ojg4CAAIwYMQLz5/PzUz5namqKp0+fCq5LTEyU1r5Tl6WlJd68eQMgP6qOiorCwIEDpYEdkB9Menl5ITIyUqXgjtHXEcwFUoo688QoQ0O1z1SqocY9R6G8LE6+j+AcsIraVZWi+nNC56yJfLninI9WhKKqLcgjohYeFwuB3DjOeQp+ZrgJVLmctJFPn/jHMsiWXZCZwd+Gm1OXJbsP+5ZfRyvvmWyuXF4SP+cuh7Ps4xv+sT+++ijzPPW97DnmCXwvuD+rQh9fQ0PZ15NbJULVuWW5c7OKyY3jnoPQNkK1+pQlJi9PE1UvirfOHd2W/ZzYSh7KsLe3x4sXLwAAjRo1ws6dO9GlSxcAwJ49e2Btba3xYypD6agkOTmZV9G5QGZmJlJSUhS24e7ujiVLliAnRzYZOicnB8uWLSt0st7CSCQS5Obm4u3bt1izZg2OHj2KoKD8SbcTEhKQmZmJhg0b8vZr2LAhHjx4IDjapUB2djbS0tJkHoQQQgj5+vj4+OD48eMA8id3iIiIQM2aNeHk5IS1a9dixIgRWu2f0lfuXFxcEBYWBi8vL1haWkqXv337FmFhYaLmU5s2bRo8PDxQv359DBs2DDY2Nnj27BnCw8Px5MkTrF27VtluAQACAgLw22+/AQAMDAywYsUKDB8+HEB+UAoAFhYWvP0sLCzAsixSUlLkRtvz5s1DaGioSv0ihBBCigvdli16c+bMQXZ2/tX6Xr16QVdXF9u3bwfDMJg0aZJMOps2KB3cLVmyBG3btkX16tXh4+MDKysrvHr1CtHR0QAgjWQL06xZMxw8eBCjRo1CcPD/ywk4ODjg4MGDKk/nMWXKFHz//fd48+YN/vzzT4wePRofP36USaYsbNRvYesmT56M8ePHS5+npaVpfTQMIYQQwkW3ZYueoaEhDA0Npc+7d++O7t27a7FHspQO7po3b464uDiEhoYiNjYWycnJqFChAvz9/TFt2jTUq1dPVDu+vr548OAB7t+/j7dv36JixYpwdHRU+gQ+Z2trK62x16lTJwD5QdmgQYOkw50LruB97t27d2AYBubm5nLb5r6RBRgDPTCGSryMQn8ScXPNVMkjE5PQoUqxJk1R4dCCNct47arwWqkwd6tg7pw28+UUzIWrKkZXhZ0E8+c00A73HAW+F7zvSg5vE7A53P7JJmox+vycO+jI5rkJvf/cY7MZsu3mvZbNiwOA3JeyNeuyBHLust7L5u5lpfDTRbI/yh6L+9U2Nub319RU9rlQzTojQ+42yn9+JQK/cdzcOF7OncDXODeX4TwX2qbw50K5hyV81ihCNEKlGSrq1q2LP/74QyMdcHR0VDuok8fNzQ1r167Fw4cP0aRJExgbGyM+Pp63XXx8PGrWrKmxgRyEEEKIttCVO6Ly9GOacPPmTdy+fVtwgMbAgQPVbj8mJgY6OjqoUaMG9PT04O/vj3379mHhwoUoW7YsgPzRuTExMaLq2hBCCCElHeXcEZWCuzNnzmDHjh148uQJLzBjGEaafydPRkYGunTpghMnToBhGOlUJ5/nvCkT3P34448wMzODm5sbKleujKSkJOzevRsRERGYOHGitDhxaGgoXF1d0blzZwQHB0uLGFtaWmLChAmij0cIIYSUVBIWyFPryp0GO1MCqRvDfAmUDu42btyIYcOGwcLCArVq1eLloYmZk27WrFl4/PgxTp48iTZt2mDfvn0oW7Ys1q5di/j4eERERCjVJ3d3d2zcuBGbN2/G+/fvUaZMGTRq1Ahbt26VmX6sTp06iI2NRVBQEHr27Ckz/Ziqs1MwpnpgjP9LmhOTKycmP0nMN4t7LIlmatgVGZXqrokpqqWhXyFF/RPIudJmbpyiXD5V69yJwZ9bWESinirfDc5zlWvwZRW+n5h2hfJg2WzZBC/JB9ncPck7fq5cZrLsPyTZafx8v9wMgcRBDgNj2ddc31Dx/L46nHqc3OcAoKtXeK4pI5C3y52bV/C14rzGeTmK31tuO5Jc/m9nTrbssk+cl+7TJ/7roCj/T2iZmDw97r993AF6umrMY0o0RxMxzJeAYZU8k7p166JRo0bYvHmz4AADMZycnDBu3DgMHToU+vr6uHz5MlxcXAAA/fv3h5mZmcrlUIpLWloaypUrh+R1vWFm/F+lTw0Fd6L+EeMFdyX8A1lUwQYFd4KKNbgTQwPfDZWDO0XHLuHBXW42f65uiYLgiIK7/54LjJXRVnD3UZIH/zcPkJqaKlNIX5MK/l3adf0RTP5LPVJFxocP6N3Ivkj7qi2aiGEAYO/evejWrRtvQoeSQulePXnyBN9//71aL8rjx49Rp04d6OrqgmEYZGT8f2Tat99+i/3796vcNiGEEPI1KxhQoc6jtNJEDAPk17azs7PDnDlzpDNhlSRKB3d169bF69ev1Tqoubk5Pn7MLxNQqVIlmfloc3JypOsIIYQQQjRFEzEMAMTGxsLd3R2hoaGwtbXFd999hwsXLmigh5qhdM7d3LlzERgYCE9PT9jY2Kh00AYNGuDevXvo0KEDvLy8MHfuXDg6OsLAwABhYWFo1KiRSu1qg46xHnRM8l9GlerTAbzbQgy3fpbQbSPusTR1u7eYFOVtQy4x562wP0K3VzU0V68i4m7/Ft/rqdLcwjoiPtPcvzXF5KeKSE/gfS+5txF5dfAAfOJuw781ynJul3Jvy35K5+fOcW+nCtHl1M3kPgeEbpfKPhdzy1VoGzG3YblUuS3LW6/CrVwAyOO8B3mcW+VCt7R5+wi0y6ufx2+Gd6uWW5ePu15T5SDFoFIo8mkihgEADw8PeHh44OXLl/j111/x+++/Y8eOHXB2dsZPP/2Efv36qX11UB1KB3erV69GamoqatWqBWdnZ2lx4AIMw+DAgQOFtjFs2DDp1bo5c+agVatWaNOmDYD8q3p//fWXst0ihBBCCKgUSmE0EcN8ztraGmFhYQgJCcHu3buxdOlSDBs2DBMnTsT333+Pn3/+We60pkVJ6eDuxo0b0NXVRaVKlfDixQu8ePFCZn1hU3gV6N27t/T/7e3tce/ePWlZlBYtWgjO/0oIIYQQog5NxDBCHj16hIsXL+L+/fvQ1dVFgwYN8Msvv2DVqlXYsWMH/P39NdF90ZQO7h4/fqzWATMzMzFs2DAEBASgVatWAABTU9NiP3FCCCGkNJJAzduyKL2X7tSNYT7Hsiz+/PNPrF69GtHR0bCwsMDo0aMREBCAKlWq4M2bNxg2bBjGjRtX8oM7dRkbG+PAgQMYMWJEcR+6aBgZAkb5pVBExftirndzkjWE5ljl5QCJyMvj9k+lkiuaIqa0iJjcLjH9456nKuekaikUTRAxr61KeXDFiOXk3DFCnz1uXh5nNQOB74Eq7yUvX1Ug34vz/eLm1wEAm8UphSIin46b52aob8Dbhp8/x68jqKugrAkjlE/HbUfg86uJz5HKuceKCOVTct4Dbj5djkDNQDE5d9yyK3lZ/Ilt83jbcHIwOesN81ggkddMkZBI1JtDl+bfVWzBggVYu3Ytnjx5gkaNGmH9+vXo37+/TJ5dpUqVMHHiRHh5eRV7/7Qy/ZizszNu3rwJDw8PbRyeEEIIIV+5t2/fCk5/amtrq3DfadOmoUuXLti0aZN0zIAQBwcHhISEqNVPVYgK7nR1dXH+/Hm4ublBR0en0HvSDMMglzvUiGP+/Pn47rvvUK9evUJfFEIIIYQoRyJhIVFjVIQ6+34JZs+ejRUrViA5OVlwfV6ewPBojgcPHsDOzk7hdjY2NpgxY4bSfVSXqOAuJCQEVatWlf6/qgmHBQICApCeng5vb2+UL18e1tbWMm0yDIPr16+rdQxCCCHka0SlUOQLDw/H/PnzERwcjJCQEEydOhUsy2Lr1q0wNjZGUFCQqHbEBHbaJCq4+zzqnDlzptoHrVChAiwtLdVuhxBCCCGyJBIWeXTlTtDq1asxZcoUBAUFISQkBN26dYOLiwumTp0KDw8PJCUlyd03LCxM9HEYhsH06dM10WWVaCXnLjY2VhuHLRrGxoCJmoUKFU1kKJDdyhtkIabKppiBGqoMUFCFmGRuoQKq3GViJoHkzmMpkJivEZoa1CBmsAT3dVBlfsNizJpmuN0T+uxxB11wzpvNEfjMcNsQ+GxyB2KwEs5e3OcAv2i1jsBrxRmgoGMke2wDgUENrInsT67QoBzGiLONIX9ABXfABHcfwQFAAv3hUeFzJGoQhoLBUoKDk7jvgUAVYG4Bau58v4Yf+SlCvMEyAgMqFLULABLOAIqcTM6cwBmy++jm5AJXec2QYvbgwQM0b95cOifsp/8mIDY2NsaECRMwffp0TJw4UXBfZS5uaTu408qMt1u2bJF7r/vdu3fYsmVLMfeIEEIIKR0Kcu7UeZRWenr5fwgxDAMzMzM8e/ZMus7S0hLPnz+Xu69EIhH9EJO3V5S0EtwNGTIECQkJgusePXqEIUOGFHOPCCGEkNKhIOdOnUdp5ejoiKdPnwIAXF1dsX79euTk5CAvLw/r1q1D9erVtdtBDdHKbVm2kA9OVlYWdHX5tyEIIYQQQtTRqVMnnDp1CoMGDcLkyZPh6+sLc3Nz6OnpIT09HeHh4druokYUW3CXmJgoUxn66tWryMrKktkmMzMT69atE1VjpqRgjI3BGKuXc8eyCnKfhHKjuDl2gtvkFr6NmFw+ocvzYvLcVKHH+TjqCQT53G34s3fzdmF0uIVMhfJwuLN8c85b1Xw6BYWOVc6n4742msq5K67bMUKfPW5eG3cbodeSm3smWJCY004OZx+hnCvOsVih/DnufibK/5zyCgsDYIx0Oc8F2uUVLebsIya/TkRxbF7fNPV55X3XBc5RzGda0fdfqCyXmN9OzjKhvDzJh08yzw05z7nrkZUD7OcfqihQKRT5Pq855+3tjbNnzyIiIgIMw8DPz0+pgsOnTp3CihUrcPv2bV69PIZh5N6hLA7FFtxt3LgRoaGhYBgGDMMgICCAt03BFb1ffvmluLpFCCGElCoU3Inn5uYGNzc3pfc7c+YMfHx84Onpidu3b6NDhw748OEDzp8/jxo1aqBly5ZF0FvxRAV3iYnKzZkidOWtd+/eqF+/PliWRe/evTF37lw4OjrKbGNoaIj/tXfmYVVV6x//7sM5nMMQyCAgOKA4IBlXzQFNUzRDBYxwulmpYLdSyyEpZwXStLKbdm/enIecZxO1MsGhQtOrdjXT1NQUp0BUkPFw1u8Pfpzcay84m8OM7+d5zvO411577XevPfiy1ne9b6tWrWrNnDdBEARBENWPc+fO4eDBg0hNTcWIESPg5eWFGzduwMXFBXZ2dhaPnzlzJqKiovCf//wHOp0Os2bNQtu2bfG///0PvXv3RmRkZCVcRfGocu58fX1LFbhYtEqkZcuWaNmyJYDCUbywsDC4ubmpbpMgCIIgCMvQyF3xFBQU4PXXX8fKlSvBGIMkSejTpw+8vLzwxhtvoE2bNqri2Z05cwYxMTFm36jI7wkMDMT06dMRHx+P8PDwCr2WklDl3C1fvtx8Afn5+Zg1axbs7e0xePBgeHl54ebNm9i4cSOysrJU5VAbNmxY2ayuTtgZAHtDmZpQuM1qNG38yyfQlii0fOUQG094Lms0eEJdDvc42iqTqlvUmuUpE4UjK0u2KYrvx2vsrPq0WRO7z1o9Hd9Xatq15j6Vl55S0Y5AT2lB7yn889IKHSkr4PpOpLmz18kLRDHW+HdQTYxIPr6bSHPH6+UE+jnFcYrnQU3sORVxJPltNdo4gVZW0tmWXEf0rlsTy7G8vkUckuD7qsnJlW2zhw/lFTLl27ZZnAavAqmKDBUnT55EXFwcfvrpJ9y7dw8NGzbEkCFDEBMTA3t7ewDA8OHDsWrVKsWxLVq0wLlz51Sd57vvvsP06dPx888/w97eHmFhYfjoo4/g4eGh6vjZs2dj3bp1+Pjjj9G7d2+0atXKvK9Pnz5YuXKlKucuKysLjo6O0Gg00Ov1suDH/v7+OHv2rCp7KgpVzt3w4cPN/54yZQoCAgKQkJBgDgIIFIoUQ0NDceHChXI3kiAIgiCI6snZs2fRuXNntGjRAvPnz4e7uzsOHTqE+Ph4/Pe//8XOnTvNde3s7JCYmCg7Xs00KAAcPHgQffr0QWhoKHbu3Ik7d+5g4sSJ6NmzJ44fPw693vLixpUrV2L69Ol45513FLOMjRs3xuXLl1XZ0rBhQ9y+fRsAEBAQgN27d6NPnz5mO6t6ZrLUCypWr16NL774QubYAYBGo8GoUaPw5ptvYu7cueVmIEEQBEEQ6ilgZUs/VlDKkbt169YhJycHW7duhZ+fH4DClag3b97E4sWLkZ6eDhcXFwCFvkJQUJBVdr377rto3rw5tmzZYg5G3LhxYzzzzDNYvnw5Ro4cabGNlJQUdOrUSbjPYDAgIyNDlS3du3fHgQMHMGDAAPzjH//AqFGj8Ouvv0Kv1+Pbb7/FhAkT1F9YBVDq+AlpaWmKJb9FZGdnIz09vcxGEQRBEARhHZWdoUKnK5QxODs7y8rr1KkDjUYDW9G0eylJSUnBsWPH8Oqrr5odOwDo3Lkzmjdvju3bt6tqx8PDA7///rtw3/nz51G/fn1V7cTFxWHMmDEAgDfffBPz5s3DvXv3cOfOHUybNg2zZs1S1U5FUeqRu7Zt2yI+Ph7BwcFwd3c3l//555+Ij49HmzZtytXAao9BX/irSKyMNaeMWadGT2c5BhQr4GNJlT5ulBqtmWRnr6zD97VCc6fUtTAVGjZJw/3Bkm85dYyqXLiWtFCqNExW6Jys0SdZXYd7Pq1ttzz0fSrasPheAJDUXJM19paX5tIaPVo5nFuyETyLtpw+UfS88nX4/+w1gmP4YPaKBMXFlMnaFfUd/04K9J9q+jNfrrmT8rhvSFamfH+mPK5rRcLKuKBClKO5JIYNG4b58+dj5MiR+PDDD1G3bl0cPHgQixYtwujRo+Hg4GCum52dDS8vL/z555+oV68eIiIiEB8fD1dX1xLPcebMGQCFCxZ4AgMD8cMPP6iytW/fvpg9ezZ69+4NLy8vAIUx6e7fv4/PPvtM9SIId3d3mQ/0zjvv4J133lF1bGVQaufuk08+wXPPPQdfX1/07NkTXl5euHXrFvbv3w+gUOxoifj4eLz22mvw9vZW7Lt58yaWLFmiamEGQRAEQRAVw4MHD2Tber1eqGvz9fVFcnIyXnzxRfO0LACMGTMG8+fPN2//7W9/w9/+9jfzIoaDBw/i008/xf79+3Hs2DE4OjoWa0tRPnqRE+jq6lpsvnqe+Ph47N27FwEBAQgODoYkSZgyZQrOnDkDnU6H6dOnq2qnulNq5y4oKAjHjh1DXFwcDhw4gLS0NLi5uSE8PBzTpk3Dk08+abGNuLg49O7dW+jc3bhxA3FxceTcEQRBEIQVlNdq2QYNGsjKZ86cidjYWEX9K1euIDw8HJ6entiyZQvq1q2Lo0ePYtasWcjMzMSyZcsAAOPHj5cd16tXL7Rp0wYDBgzAkiVLFPtFFBeWTW24Nk9PTxw7dgwzZ87E7t27YWNjg59//hlhYWGqRhAf5eTJk1i3bh2uXr2qyLglSZJsIUllY1WGipYtW2LDhg1Wn7Sk3LKZmZnm+XuCIAiCIEpHecW5u3btGpycnMzlxa1GnTRpEh48eIBTp06Zp2CfffZZuLu7Izo6GkOHDkW3bt2Ex7744otwcHDAkSNHSrSpaPWpaITu7t27pXLKPD098cUXX6iuL2L16tWIioqCRqOBh4eHQldYmtjAFUGlpR/73//+h1OnTpm39+zZo4hrk52djbVr18qGdas9Otu/9COW9B8ViSg/rTWaOxXx8xQxn6zR3Ing9XT2guXxBk6Hp+X+EDAptXIS345eGZeQGeQxqSReuyfKUalGw2RJPyeKR6ZG51QemjtrsZR/WI1Os7zi51mDmvzJlo5Rg7X3wNoYdaVE1XNVHno6QKlz499boS1cmeg/S+44if8Gi7R8/Lm1AnttVAww8N8aI/fNcOI0eQ/k8TZrAk5OTjLnrjhOnTqFgIAAmbYOANq3bw+gUC9XnHMHFA748BE4eIqmck+fPo2+ffvK9p0+fVoWr64k8vPzkZeXp7AVAB4+fAhbW1tVA0yzZ89GaGgoVq1aZV4JXJ1Q5dw9qpGzFNxPkiThnPX27dsRFxdnrlNcO3Z2dlixYoUas8wkJiZizZo1+PHHH3Ht2jXUqVMH7dq1w4wZM/D000/L6p44cQLvvfcejhw5Aq1Wix49emDevHlo0qRJqc5JEARBENURk6lsWSZK+7eMt7c3zpw5g8zMTJluLjk5GQBKXIG6ZcsWZGVlWQyP4uPjgw4dOmDNmjWIiYmBzf8vuDly5AjOnz+PcePGqbL1H//4B3Jzc7F+/XrFvtdffx12dnZYunSpxXZSUlLw+eefV0vHDlDp3MXGxpo1cqL59kcpzrl7/fXXERYWBsYYOnTogBUrVig8bb1eDz8/P9UBDYv4z3/+g7S0NIwdOxYBAQH4888/8cknnyAoKAjffPMNevToAaAwl1z37t3RunVrbNq0CTk5OZgxYwa6du2KU6dOoW7duqU6L0EQBEFUNwoYK3WsOv740jBu3DhERESgV69eGD9+PNzd3XHkyBHMmTMHAQEB6NOnD65evYohQ4bg73//O5o2bQpJknDw4EHMnz8fTz75JF577TVZm1qtFt26dTMv1gSADz/8EL169cLAgQMxatQo3LlzB5MmTUKrVq0QFRWlytakpKRiY/GGh4dj8uTJqtpp06YNUlJSVNWtClQ5d6ZH3HiTldMp9erVQ7169QAUdu7TTz9d4sqY0vD5558rUo/07t0bTZs2xQcffGB27mbMmAG9Xo+EhATzUPPTTz+NZs2aYd68efjwww/LxR6CIAiCeFzo168f9u/fj7lz52Ls2LG4f/8+GjRogDfeeAOTJ0+Gra0tnJyc4OnpiX/+85+4ffs2CgoK0KhRI4wZMwZTpkxRTJMWFBQoMkh0794de/bswYwZMxAeHm5OP/bxxx+ryk4BALdv3zb7IjxF0T/U8PHHHyMqKgpt2rQRhmepakqlucvJycHq1avRtWtXtGzZ0uqTljT3bg2inHKOjo4ICAjAtWvXAABGoxEJCQkYOnSoTEPQqFEjBAcHY/v27dY5d1qdWLNRHCLdSFVp9UQ6Pf4vNl4rJSqzpMED1MW54zV3BqUmAnZPyDYlLXeM4C9OZsdpRhwfKupIOVxZFhezSqS5K4+8ldbEJxOVWRMbzVp7LGk3BfdfodOsqLhxaqjMnLqVhTWxBtXEueP1c7y+DijUHT+KUD/H58Ll49wJYs1JKrSn/LeT19OJNHc6+TfDpFFqcAuYnttWXrckyZ9zG1v5My7p5fegwKT87lQUhdOyZTu+tAQHByM4OLjY/S4uLti2bZvq9opbeNmrVy/06tWr1PYVUadOHVy8eBHdu3dX7Lt48SKeeOIJ5UECgoKCEBkZiTZt2qBevXqKBR2SJOHnn3+22s6yUirnzmAwYMyYMfjmm2/K5NwBwIULF7Bo0SL8+uuviowXkiTJhmKt4f79+zhx4oR51O7SpUvIzs4uNgDivn37kJOTA4NB+aIDQG5uLnJz/xLI8vF/CIIgCKI6UF6rZWsjwcHBmDNnDiIjI2UO2d27dzF37lyzz2CJDz/8EHPmzEHdunXRqFGjcsnCUZ6UerVskyZNVA9bFseZM2cQFBQEHx8fXLx4EYGBgUhNTUVKSgoaNGhQLqtlR48ejYcPH2Lq1KkALAdAZIwhPT292OHaOXPmmBeEEARBEARR84iNjUX79u3RrFkzDB48GD4+Prh+/To2b96M/Px81f/PL1iwANHR0Vi0aJF5cUd1otRzGmPHjsXcuXPLNHI1ZcoUhISE4JdffgFjDMuWLcO1a9ewa9cu5OTklDkn2/Tp07F27Vp8+umnitWyJcWeKWnf5MmTcf/+ffOvaLqXIAiCIKoTlZ1btibRokULHD58GK1bt8aSJUswffp0LF26FK1bt8bhw4fRokULVe08ePAAQ4YMqZaOHWDFyN0vv/yC1NRU+Pr6okePHqhXr57MKZIkCQsWLCixjRMnTmDhwoXmuDZFizRCQ0MRExODyZMn4+DBg6U1DUBh9otZs2Zh9uzZeOutt8zllgIgSpKEOnXqFNtucWlXIEl/6eisyX0IKLUv/DHCdq0IkMi3I9Lc8Yh0DyZeY8fFdxIeo0Jzx2lhJINA+8Bp7kxauS7PJNDGaKR8+bZJkOORzwvJbTMuj2ThyVToyPg+5rcLLOewFaJ4ZrjnQc2zqEavZo0eVKjTzFdRR1BmCWuuQc1zX5lx7aw5t0LDaDk+pap2+Th2auLc8Vo4kX6Or6PmG2fpuyg6F19HRfw80TeD19gZmXLKjbGS7zeD3LaHRrHkpyJgKFuGCoba69wBhWnQ9u/fj+zsbKSnp8PV1bVYSVZxdOnSBWfPnlU9jVvZlNq5+/e//23+t0gcqca5K+pMjUYDnU6H9PR087527dpZjKVXHHFxcYiNjUVsbCymTJki21cUYuX06dOK406fPo2mTZuW+uYSBEEQRHWDNHclwxhDWloaJElSDFCpZcGCBejfvz8aNGiAPn36VDvNXan/1DSZTCX++KXLInx8fJCamgoAaNq0KQ4dOmTe97///c+qECnvv/8+YmNjMW3aNMycOVOxX6vVIjw8HNu2bUNGRoa5/I8//kBSUhIiIyNLfU6CIAiCIGoGycnJeOGFF8xhWTw8PODk5ISIiAgcPXq0VG21a9cOFy9eRGRkJOzt7c3ZPIp+zs7OFXQV6qi09GOP0qVLF/z444+IiIjAyy+/jJkzZ+LmzZuwtbXFypUr8corr5SqvU8++QQzZsxA7969ERoaqshRVxT5Oi4uDu3bt0dYWBgmTZpkDmLs7u6OCRMmlNv1EQRBEERVUWBiKCjD6FtZjq2uLFy4EGPHjgVQGN+2cePGYIzhypUr2L17N3bv3o0FCxZg1KhRqtrr379/leePLYkqce6mTp2KGzduAAAmTpyIW7duYe3atZAkCYMGDcK8efNK1d6uXbsAAF9//TW+/vprxf6ieDn+/v44cOAAJk6ciAEDBsjSj1mdnUKj/UvDIdLcWKOfs5Qv0dp2eUT28rockW6D0ywxXnumRjslsFfiY1/ZKTV3BVp5zLrsAvlfR0aTMrsJr5+RoBxd1mrlukGtTq6xs5GUmjutRn6MDQS6PCNXls/p/YycFg2C/hRppSzou4TPjKKSiudKJBa2RofHXSfj+wFQ6vJ4bZyafqjMmJGVdS6RRpB/x/i+43WwgECnJ2iX19TxmjU+jhygjCWnJs6dCv2n4hmuwtzdIn3dE7qSw4Gl5coX3Rn597oCoWlZOUeOHMGYMWPQt29fLFy4UJEO7fr16xg5ciTGjh2Ldu3aoUOHDhbbXLlyZQVZWz5IrLhIgcWg0WiK9VaLFiW0a9cOEydOLDGgYU3nwYMHcHZ2xr3fPoPTE//vUJBzV0hNdO44R00rkXMHgJy7kiDnrpDKcu5EAYktfDuFAea5wOdGpvxmGLkgxvkmpR67tM5dxoMMNPZ4Evfv35cF0i9Piv5fGr8uGXp76zNA5WZl4tMhnSrU1spk4MCBuHHjBg4fPmxeyMljMpnQtWtXeHt7Y/PmzZVsYflT6q/TjBkz0KhRI7i6umLYsGF477338Oqrr8LV1RUNGzbEK6+8guvXr6NXr17Yt29fRdhMEARBEEQxmBgr86828f3332P06NHFOnZA4cDVqFGj8P3336tu99KlS3j11Vfh7e0NvV4PHx8fDBs2DJcuXSoPs8tEqadlXV1d4eXlhdOnT8tywWVmZqJXr17w8fHBqVOn0KtXL8yePbtMaUIIgiAIgigdNC0r5+7du2jYsKHFeo0aNcLdu3dVtXnu3Dl06tQJOTk56NGjB7y9vXHjxg1s2rQJCQkJ+OGHH+Dv719W062m1M7dZ599ho8++kiR5NfR0RExMTGIiYnBu+++izfffBP/+Mc/ys3QaovW9q+hfxV5Y1XlR+SnMFTEalI1JazYr0IMqmJaVuKniIQ5a7kykW3cFArTKadL8ozy5y6vQL6dW6CcQsgz8cco7eO/ZRqua7R8AQCtRj69q9NkK+oYbOTBvg36+7Jtvb0yGLjET+WqictWXvDPnmAqrABcbk7Gxw1T3lutgZvCZsq+UsQaVBM/kUfNM11e8f0qSxMmuv/81D0Xh5HxfQkI+lPQrqU4cTaCaVleRiKcPrXiW2TNfeLPXYU6PaL64ObmhqtXr6JLly4l1vvjjz/MMXEtMWXKFLi5ueHAgQMyDd/169fRo0cPTJ06FVu3bi2T3WWh1E/+9evXodMJXnAUhhspSk1Wr1495Ocr9UQEQRAEQVQclKFCTpcuXbBw4UJzwgQRJpMJ//73v9G1a1dVbR48eBBxcXGKxRn169fHjBkzkJSUVCaby0qpnbsWLVpgwYIFMHLiXKPRiAULFphTd9y8edP6FagEQRAEQVgFOXdy3nnnHRw9ehSRkZG4efOmYv+NGzcQGRmJY8eOqQ6LlpWVVewon7u7O7KzBaPnlUipp2Xj4+PRv39/NG3aFBEREfD09MTt27exY8cOpKSkmIch9+3bh06dOpW7wQRBEARBEGoJCgrCp59+ivHjx2PPnj1o164dGjduDAC4fPkyjh8/DpPJhPnz56sKgwIUDnStXbsWvXv3Vuxbv359lertACucuxdeeAEJCQmYMWMG/vWvf4ExBkmS0K5dOyxatAghISEAgKVLl5a7sQRBEARBlIypgMFUUIYFFWU4trry9ttvo23btpgzZw4OHDhgTnZgb2+PkJAQTJ48GZ07d1bd3pgxY/Daa6/h/v37GDZsGOrVq4ebN29izZo1+Oqrr6rcB7IqiHHv3r3Ru3dvZGVlIT09HS4uLrC3ty9v22oEks4Ayfb/hf9WxLADYDlOlCARN+OOEQXZ5BNXW0p0LUS0RkTiFlRoC0rcDwASHxtP0Fe8ffkFovhT8kUXfNJvPj4VAGTly+3LyFPGuePrGDlthmiWgl9kYa9TxtRyMdSTbTvZ1pEfo1WuzOIXZgj7k78HXOw+jegYWF6YwTilhrFA2Z/8ApV8LrYgv8Ci0B65jMPW5qGijl4nX1xia5DXkdQEgVUhoOefPTXvjuia+IUj/DHlhSguow3Xn1q+r0QLKvKtWKjDx7UTLZbgv1fWxNy0Fgv3m/9OAspvRoHg3qr5Vj7I/83CMcp3p9IwmcBK0JepOb428swzzyAhIQEmk8mcAtXd3b3EECnFER0djdu3b2PWrFnYvXs3gMKECXZ2dpg9ezaioqLK1fbSUqYMFfb29o+tU0cQBEEQ1RETK2MolFoW545Ho9HAw8OjzO1MnjwZo0aNQnJyMtLS0uDm5oZOnTpVeV5ZoIrSjxEEQRAEQdR0nJ2dhbq7qoacO4IgCIKoRVAQ48qBMYZjx47h6tWrwtWxQ4cOrQKrCiHnrqzYOwH2DsXv57UmVgSF5XOjAoDJyAWOFUS1saQbsVYjJNIAyfYL9F4aFXov/hp4bQwAGE285s6y9jCf04/kGJX2Z+TJNUyZOfLt7Fyl3kvDae4cDEp7M7l23e3l9jvr5TGSAEBvI8+7KtLK8X1sI+Vz28o8t7xOS3SfFLpHQa7e3AJ5zt8so/y6RUGiNdx74KBzVdThAz7rbTJk2zYaQe5eFfDXpEaLyuuwRM8ir0/k2zGVkwZPI3jfLGkYbXVKTaNOz2k5LbzHgDp9Gl+H75f/Lyw1ajSiluowo0hPyd8nZR3FNQl1mfz3it/mn6HK+++WnLuK57fffkO/fv1w4cIFMME0tiRJ5NwRBEEQBEHUFEaPHo2cnBxs3LgRgYGB0OurcAGNAHLuCIIgCKIWYSooWzgTk+VB3ceen376CUuWLMGAAQOq2hQh5NwRBEEQRC2CpmUrHkdHRzg5KXOZVxfIuSsj2fCADo7F7ue1GgXG0sddEmnj1GjNFMdUUBwuHpFGiEek9+IRa6HkfcX3Q75JGU+LX9ZvFHy4cvLl9mQ8lCdZz8jikq5D+QG8r1P2b7aTQbady/01nSmIuWdrI29HK9Bp8jH29DZy3adBq+w7LafDE90Dvn/zCpTX9JCLCfggV943Is0dj72gr5z1T3B16si2eZ0ZoO5Z4597pQZPpLmy/MzwZdaEj1AzuGIjCBGn5eJyGbTusm0+VqKoTNSflvpGqP8th2+R6D4qYzla1p7y7aj5zogoj++p8ttEudZrE1FRUVi3bl21XCkLkHNHEARBELUKZmJgZRh9K8uxjwutWrXC+vXr0a9fP4SHhwvzzEZGRlaBZYWQc0cQBEEQtQiTyQRTGbJMlOXYx4UhQ4YAKMxNm5CQoNgvSRIKCqpOvEjOHUEQBEEQRClISkqqahNKhJy7MnI/zwemvOJFlWo0KwpdHpOLbEQj5DUtPQwf58xa+Ovm+yZX8JdSjlH+V6hQP8XpxHI5XdnDbKU+ia+jE+jc+HbzeL2anfJ5sNXJ2+H1dYVl8jp23LlFmjZbG94+kdaMs7dAed2WcvXm5Qvi3HHXkKFVtsvHBDRoOV2W4BFS91zJ7TMxI7etPMLScwYo8w+rwZrZLjXXzd9bvu8K68i/UyItHw+vCRS9O2r0icpjLOcJ1kj8e6C8Jv7d0HHb/HtS2C6/XTF5b/l+yMyrvFAZtKCi4unWrVtVm1Ai5NwRBEEQRC3CZGJlC4VCzl2Nh5w7giAIgqhFsDKO3NGCCnVcuHABixYtwq+//qpIPyZJEvbv319FlpFzRxAEQRAEUSrOnDmDoKAg+Pj44OLFiwgMDERqaipSUlLQoEED+Pn5Val95NyVkVtZtsi0KcwXKtLB8X8AGU1KrQlfxxo9nRrdiEi7Ux5UlGbFGoQx7LhcsnycNkCZO5bXxvH6OgDIyrYct4pvl9fuOdgJYthx+imtQMun4zVWenk7dnplu7Zay/dJ8bwKpnYs9RWvMxRhK9AE3ueuidfp2Qge4AIVIwyi4yyhpt2qnLri+0bP9adWIKjjdXlqukXNt0lNvD9enqim7/hrFMjnFNek598doV6Va1fw/aoIXd7D3MqLc0eau4pnypQpCAkJwcaNG2Fra4tly5ahbdu22L17N6KjozFr1qwqtc9ypEaCIAiCIGoMRXHuyvIjSubEiRMYNmwYNP//V0dR+JjQ0FDExMRg8uTJVWkeOXcEQRAEQRClIT09Ha6urtBoNNDpdEhPTzfva9euHU6cOFGF1pFzRxAEQRC1iqJp2bL8iJLx8fFBamoqAKBp06Y4dOiQed///vc/ODoWn5a0MiDNXRm5dj8H9iZlrLIieM2SSI9kSd8j0gzxGhBen1JYZuEYK2UkynbKR3PH22PN90UUe4zXBIli1vE6suxcPnabUnOXy2nuRB/E3Gz5ReVw587IEsTu4nRDovh5lnR5ep3lY0TPDH8NQo0VFzewgI8JJ5Dc8c+ijYpnRk275aXdKg/UXJOoz61BcU3cufW2lp8ra2wR9Tdfli/4ximeKyvy8vL2A8p3g9dyirSdvF5VK9Ci8v0p0u6V9ruXJfjuVBSmgjKGQinDsY8LXbp0wY8//oiIiAi8/PLLmDlzJm7evAlbW1usXLkSr7zySpXaV2tG7jIyMvDee+/h+eefR926dSFJEmJjY4V1T5w4geeeew6Ojo6oU6cOIiMj8fvvv1euwQRBEARB1EimTp2K8PBwAMDEiRMxcuRIbN++HZs2bcKgQYMwb968KrWv1jh3aWlpWLx4MXJzcxEREVFsvXPnzqF79+7Iy8vDpk2bsHz5cvz222/o2rUr/vzzz8ozmCAIgiAqgKLcsmX5lZaTJ08iIiIC3t7esLe3h7+/P+Lj45GVlQUAKCgowD//+U/07t0b9evXh729PVq2bIlJkybh3r17qs7RvXt3SJKk+PXu3bvU9pYVPz8/dO3aFQBgY2ODzz77DGlpaUhNTcXKlSvh5FR85qrKoNZMyzZq1Ajp6emQJAmpqalYunSpsN6MGTOg1+uRkJBg7vynn34azZo1w7x58/Dhhx9WptkEQRAEUa5UdiiUs2fPonPnzmjRogXmz58Pd3d3HDp0CPHx8fjvf/+LnTt3Ijs7G7GxsXjppZfw2muvwd3dHSdOnMCsWbOwa9cuHD9+HHZ2dhbP1aRJE6xdu1ZWVqdOnVLZ+zhQa5w7SYX+wWg0IiEhAUOHDpV51Y0aNUJwcDC2b99eaucuJTUTdv+vqxK9ELzGTqxHKfkcIo0QrxsRaku0fFwrNTq90uuG1LSrBl5bqCbWGI+ae8Dr6wBlzDpFLLdcpeaOLyswKuvw5HK6GxuRnk5nWRuliAHH3X81x5SX/qu84O8dr+3LF9w3vo4I/jp5faKkQoSnEcSNs9SfwntQXvpUCzEARfo0/rpFl61GN8jDayONRsu6PEXuXiu0k4BIe8rlmhX1gxVxJEV1LGkY+XuSlVN5ce4qm3Xr1iEnJwdbt241B+/t0aMHbt68icWLFyM9PR1OTk64fPky3NzczMd1794dDRs2xMCBA7F161ZVOjU7OzsEBQVV2LXUFmqNc6eGS5cuITs7G4GBgYp9gYGB2LdvH3JycmAwGBT7c3NzkZuba95+8OBBhdpKEARBENZgYmUcuStlIH2drnBRobOzs6y8Tp060Gg0sLW1hY2NjcyxK6JDhw4AgGvXrllpLSGi1mju1JCWlgYAcHV1VexzdXUFY0wWq+ZR5syZA2dnZ/OvQYMGFWorQRAEQVhDZYdCGTZsGOrUqYORI0fi999/R0ZGBhISErBo0SKMHj0aDg4OxR6bmJgIAHjyySdVnevSpUtwdXWFVquFn58fpk6dqsjrSjxmI3dFlDSFW9y+yZMn45133jFvP3jwgBw8giAIotrBChhYGcKZFB3Lz1Dp9Xro9XpFfV9fXyQnJ+PFF1+U5VQdM2YM5s+fX+x5UlJSMGnSJLRr1w5hYWEW7erSpQsGDx4Mf39/ZGdnY+/evfjoo4/w/fffIykpyZwtgnjMnLuiIeGiEbxHuXv3LiRJKlaYWdxDfeduNvQ5hXonNXovkUaogIuhxmuARHofW1s+l6TyoVbkm7RCc6NGuyU6t6VjKgo190CUJzbHQly7/Dyl3ovX2Il0ebw9qnRZNmXXxqn5yElW6CuFdQTPpzUY8zmNXZ5l3WMB9z6J4nPx9vE6R1XPuCBuIN/HfH+W1721pg6vwQSU77+auHFq4LWxwlh4FjR2auLnWaMjFeaN5eqoiSNpp7ccN5DvX15zl52Zi5oGP4Axc+ZMYYixK1euIDw8HJ6entiyZQvq1q2Lo0ePYtasWcjMzMSyZcsUx9y9exd9+/YFYwwbN25U9c3i87X27dsXvr6+iImJwc6dO/Hiiy+W7gJrMY+Vc+fn5wc7OzucPn1ase/06dNo2rSpUG9HEARBEDUFZir9ilf+eKBQB/fo4kPRAAcATJo0CQ8ePMCpU6fMU7DPPvss3N3dER0djaFDh6Jbt27m+unp6ejVqxdSUlKQmJiIJk2aWG3rK6+8gpiYGBw5coScu0d4rMYwtVotwsPDsW3bNmRkZJjL//jjDyQlJSEyMrIKrSMIgiCIslNemjsnJyfZrzjn7tSpUwgICFBo69q3bw8AOHPmjLksPT0dzz33HC5fvox9+/YJFzhaA03JyqlVI3d79+7Fw4cPzY7b2bNnsWXLFgCFw7f29vaIi4tD+/btERYWhkmTJiEnJwczZsyAu7s7JkyYUJXmEwRBEESNw9vbG2fOnEFmZqYsp2pycjIAoH79+gD+cux+//137Nu3D23atCnzuVetWgUAFB6Fo1Y5dyNHjsTVq1fN25s3b8bmzZsBAJcvX4avry/8/f1x4MABTJw4EQMGDIBWq0WPHj0wb9481K1bt9TnfHA/G7Z5JWjuOB0RrxECACOn3VLqSJR/kfC6IVuBJkSnl99epebOupy1vEZFTc7KyvqjSk3+UVF+X0tx7fj7KGpXdP8Zn3fTirhsarBmCkbNedTo8qyB7xdA2cd5nOYuX6i5sxxbUKFhVejT1MSEE8RYsxBbUNR31mguRVj6RojiJ/LfCButQEcm6IvSItTPcVpIxr2o5ZWo3pp2RDHs9HbyfOF5+cr/KvnYopZi7OVUYpw7ZjIp+ri0x5eGcePGISIiAr169cL48ePh7u6OI0eOYM6cOQgICECfPn2QnZ2NkJAQnDx5EvPnz4fRaMSRI0fMbdStW1e2GEOr1aJbt27Yv38/AODw4cOYPXs2XnzxRTRp0gQ5OTnYu3cvFi9ejB49ephTgRGF1Crn7sqVK6rqPf300/juu+8q1hiCIAiCqALKa7WsWvr164f9+/dj7ty5GDt2LO7fv48GDRrgjTfewOTJk2Fra4srV67g2LFjAICxY8cq2hg2bBhWrlxp3i4oKEBBwV9/vNWrVw82NjZ4//33kZqaCkmS0KxZM8THx2PChAk0LctRq5w7giAIgiAqn+DgYAQHBxe739fXF6wUwZH5uk2bNsXu3buttu9xg5w7giAIgqhFMBMTSh9KczxRsyHnrow8zMhDvrEwfpG1mjtLOhGRLofX1OTmCGJW2XKaO2tylgp0ObwmqLy0fHxcKGsQ5aO1lNcSUMYf5LVc1mqCFOcu4HV6lrUt1pzbWnv5+6KmHb6OaEpHVVwzLragiXt3mCA+If+fkOjcEve8SrxWThTDjisT5Z9V5KhVEcNOleZORZxLS/o+kQaX//bo9MpnLz+vYvIPq7n/VrXL3e/Fr3WUbUct/MFiG6JvHP89EMUnNXCaZkta5Jxsy/rQ8oKZyjgtS85djYcmqQmCIAiCIGoRNHJHEARBELUImpYlyLkjCIIgiNpEganwV5bjiRoNOXcEQRAEUYugkTuCnLsykptjhAmFwVZFL4QigK5AFG6N2Fi5iEGwoEIvP5fiGBVCbTUBlC0tsBC1K0psbukYa7G0qAFQBu0UCajLxxbLwVuteR7UiKfLa2GGIgBtfsnbgHIxhMhexeII/hiRLSquW7HQgVssYRItGuIXVAieVxNfR0XwYUsBlUVlonb5d0zLXYMwSLSWf8aV3yK+ncoMYl0e8Aso1ARULq/FHcaCkhdU5HJBuQmiIiHnjiAIgiBqEZUdxJiofpBzRxAEQRC1CJqWJSgUCkEQBEEQRC2CRu7KyPK3u8DJyanY/YM+TLLYhiKArkCzxMPXEelyeE0Nr6exJjiqqA6/LUo+zgcK1WiU+hPeHjV6JJ7yCjasDD4t0EpW0NRFeQUF5rEmkbjw3LzGLtdY4rbwGNEzzq3QUxOgmK8jel75d8OkCGIs0Aia5J9GjVB7WLJuVKxXkz9HoveWP04j0Pvx77+RuwZbgW2afPl7WWBUvqd5Grl9aoIuW0N56dzKI+i2xiS6ptLr4wo4HaSRu2+5gveiwjCZCn9lOZ6o0ZBzRxAEQRC1CMpQQdC0LEEQBEEQRC2CRu4IgiAIohZBCyoIcu4qmE0Tg2XbkbO+U9Thh88VmiuB/oFXgIkSm/O6nHyNZb2PNRobNVo+nS2vuRPE7hJo9ZT2VcxgMx9/jofX4BUew98nyzG1LLWhtg6vAbRmCkbNB1xNzDpeY1eQI9An8ro8Ubv8NXD3xJqYdgAAPracoh3RZ5DTR5mUz53EtcOfW80dEeryuPhowudKoTXj4twJ+sqGe7+MRsvvtpq4l4pjVMT7Ky+9qppzWUL5tJZPTEgtr+OtxDh3rICBaSgUyuMMTcsSBEEQBEHUImjkjiAIgiBqETQtS5BzRxAEQRC1iQIGaMoQzoSmZWs85NyVkWGfHoLO4ABArCPb8G73UrdpKXenGIEmzIJeThxjS43uTX4cr+XjtT2AMqeuVhC7Kz/Pcow9S7aUF+rixlmONWcpl6xQT6cmxl5+yXq08vrLm9fXic6tsEVwjMI+Fe1a0uABUOjpJEHMMonTHjFeGyeyRXkm5am5bcZLqqzUh0r8f8oFAr2fruRcskygETQK4trxWHqfRPvV5NStKKx5/63RFeepaNdS3MOKylctgkbuCNLcEQRBEARB1CJo5I4gCIIgahG0WpYg544gCIIgahE0LUuQc1dGjPkFkGwKNTuiGFADPkiUbYt1WSXn0FSnuSs91ua+VMbY4zQsRkEuTD7vokB/Yo0WpqLi3lmDKFaeImetijzCvMZOFDeO14mpycNqDUyF3k9N3liLejpBmUgLp6CAqyPKE8rr0/i+E+nION2bqI7JgjKP1/qphnvnCgTfDD7XrUKDJ7z/pX+3edToYMtLg2eN/lfNfhNnn1arPA8fh08juJei2JePwveVMZ8cJqLyIOeOIAiCIGoTZcwtCxq5q/GQc0cQBEEQtQmTSby6vDTHEzWa6jOnRRAEQRAEQZQZGrkrI3n5BWDmOG+WNUKioXKLOUoLBHk4y2HYXNQCrxNh+YJKXO5LxTEC2/i4W6KYW9Zo7tToeyoqFh6PSE/J329ec2cS6MoKcjlNmJqYcBY0eNYi1MbxuWVVaO4sHSOqY801iJ4HxTXouP0iHSxnn+ivYFEsudIi0rDxsfpE2j2+9yzluVV7bquwsdwPls4ltld+laI2+JiFqnR6/FlE/auITym6T5w22kKsycpcpMAKGJhEq2UfZx7LkbvMzEyMGzcO3t7eMBgMaN26NTZs2FDVZhEEQRBEmSlaLVuWH1GzeSxH7iIjI3Hs2DHMnTsXzZs3x7p16/DSSy/BZDJhyJAhVW0eQRAEQRCE1Tx2zt2ePXuwb98+s0MHAMHBwbh69SreffddDB48GDY2JaeRIQiCIIjqCk3LEo+dc7d9+3Y4Ojpi4MCBsvKoqCgMGTIER48eRefOnVW3l5eZD1N+8ZkH1WhfLObqVKHlqqhhdLEmiDuXQnOnnO3n7ZVsBPHdVJxbYYsVmhsR5aHLE2ruLOQJFsaEy5EnKbUm1py1Ok01z5WleHTCuIwmFXHuykNHKmhX0liooyIeneiaFO+BNYh0ZKaSNa2ic2u4fM7loQcUIXwnub4RfvP442x4e0X3zZp3krtPmvL5Q53X0wGAjYX/PZW6vUpcgUqrZR97Hjvn7syZM2jZsiW0WvmlBwYGmveLnLvc3Fzk5uaatx88eFCxhhIEQRCEFdDIHfHYLahIS0uDq6uroryoLC0tTXjcnDlz4OzsbP41aNCgQu0kCIIgCIKwhsdu5A4AJKn44f7i9k2ePBnvvPOOefv+/fto2LAhjHlZJZ/LmmlZY8lhLoAqnpZVTIWWPOUiqqNqyvVxmJYVpGEz5amYllWEVOGnZa2b9lT1XPEhHjj7RNekalqWlf0Z5sOIAIAkcam5uHlafj8ARRozNSFLrEH4jFtxbg3jnvsKSstnbfoxNd8IS8cI+4Gvw52bFSjvLT9NbxLJSLhQTcI0h0zetolLY2bi2sjPeVh4WDk855YwGrPLdJ6CgpxytIaoCh47587NzU04Onf37l0AEI7qAYBer4derzdvF03Lfr80ugKsJAiCIGojGRkZcHZ2rpC2bW1t4eXlhV9OTStzW15eXrC1tS0Hq4iq4LFz7p566imsX78eRqNRprs7ffo0AKBVq1aq2vH29sa1a9fAGEPDhg1x7do1ODk5VYjNjzMPHjxAgwYNqH8rCOrfiof6uGKpKf3LGENGRga8vb0r7BwGgwGXL19GXl7xi/zUYmtrC4PBUA5WEVXBY+fcvfjii1iyZAm2bt2KwYMHm8tXrVoFb29vdOzYUVU7Go0G9evXN4/gOTk5VesPS02H+rdiof6teKiPK5aa0L8VNWL3KAaDgZwy4vFz7vr06YNevXph5MiRePDgAZo2bYr169fj66+/xpo1ayjGHUEQBEEQNZrHzrkDgG3btmHq1KmYMWMG7t69C39/f6xfvx5///vfq9o0giAIgiCIMvFYOneOjo5YsGABFixYUOa29Ho9Zs6cKVtsQZQf1L8VC/VvxUN9XLFQ/xKEEolVxrpsgiAIgiAIolJ47IIYEwRBEARB1GbIuSMIgiAIgqhFkHNHEARBEARRiyDnjiAIgiAIohZBzp2VZGZmYty4cfD29obBYEDr1q2xYcOGqjarxpGYmIjo6Gj4+/vDwcEBPj4+eOGFF/Df//5XUffEiRN47rnn4OjoiDp16iAyMhK///57FVhds1m6dCkkSYKjo6NiH/WxdXz//ffo27cvXFxcYGdnh2bNmuH999+X1aG+tY6TJ08iIiIC3t7esLe3h7+/P+Lj45GVJc/rTf1LEH9Bzp2VREZGYtWqVZg5cyb27t2L9u3b46WXXsK6deuq2rQaxX/+8x9cuXIFY8eOxZ49e7BgwQLcuXMHQUFBSExMNNc7d+4cunfvjry8PGzatAnLly/Hb7/9hq5du+LPP/+swiuoWaSkpCAmJkaYAon62DrWrVuHbt26wdnZGatXr8aePXswceJEWeJ26lvrOHv2LDp37owrV65g/vz5SEhIwN///nfEx8fjpZdeMtej/iUIDkaUmt27dzMAbN26dbLyXr16MW9vb2Y0GqvIsprH7du3FWUZGRnM09OT9ezZ01w2cOBA5u7uzu7fv28uu3LlCtPpdOy9996rFFtrA2FhYSw8PJwNGzaMOTg4yPZRH5ee69evMwcHBzZy5MgS61HfWsfUqVMZAHbx4kVZ+euvv84AsLt37zLGqH8JgodG7qxg+/btcHR0xMCBA2XlUVFRuHHjBo4ePVpFltU8PDw8FGWOjo4ICAjAtWvXAABGoxEJCQno37+/LHdko0aNEBwcjO3bt1eavTWZNWvW4ODBg1i4cKFiH/WxdSxduhQPHz7ExIkTi61DfWs9Op0OgDIna506daDRaGBra0v9SxACyLmzgjNnzqBly5bQauUJPgIDA837Ceu5f/8+Tpw4gSeffBIAcOnSJWRnZ5v791ECAwNx8eJF5OTkVLaZNYo7d+5g3LhxmDt3LurXr6/YT31sHYcOHYKrqyvOnTuH1q1bQ6vVwsPDA2+++SYePHgAgPq2LAwbNgx16tTByJEj8fvvvyMjIwMJCQlYtGgRRo8eDQcHB+pfghBAzp0VpKWlwdXVVVFeVJaWllbZJtUqRo8ejYcPH2Lq1KkA/urP4vqcMYb09PRKtbGmMWrUKLRo0QIjR44U7qc+to6UlBRkZWVh4MCBGDx4ML777ju8++67WL16Nfr27QvGGPVtGfD19UVycjLOnDkDPz8/ODk5ITw8HMOGDTOnj6T+JQglj2Vu2fJAkiSr9hElM336dKxduxb/+te/8PTTT8v2UZ9bx9atW7Fr1y6cPHnSYj9RH5cOk8mEnJwczJw5E5MmTQIAdO/eHba2thg3bhz2798Pe3t7ANS31nDlyhWEh4fD09MTW7ZsQd26dXH06FHMmjULmZmZWLZsmbku9S9B/AU5d1bg5uYmHJ27e/cuAPFfkIRl4uLiMGvWLMyePRtvvfWWudzNzQ2AeET07t27kCQJderUqSwzaxSZmZkYPXo03n77bXh7e+PevXsAgLy8PADAvXv3oNPpqI+txM3NDRcuXEBISIisvE+fPhg3bhxOnDiBF154AQD1rTVMmjQJDx48wKlTp+Dg4AAAePbZZ+Hu7o7o6GgMHToUXl5eAKh/CeJRaFrWCp566in8+uuvMBqNsvLTp08DAFq1alUVZtVo4uLiEBsbi9jYWEyZMkW2z8/PD3Z2dub+fZTTp0+jadOmMBgMlWVqjSI1NRW3b9/GJ598AhcXF/Nv/fr1ePjwIVxcXPDyyy9TH1uJSOcFwBwGRaPRUN+WgVOnTiEgIMDs2BXRvn17ADBP11L/EoQccu6s4MUXX0RmZia2bt0qK1+1ahW8vb3RsWPHKrKsZvL+++8jNjYW06ZNw8yZMxX7tVotwsPDsW3bNmRkZJjL//jjDyQlJSEyMrIyza1ReHl5ISkpSfELCQmBwWBAUlISZs2aRX1sJf379wcA7N27V1a+Z88eAEBQUBD1bRnw9vbGL7/8gszMTFl5cnIyAKB+/frUvwQhoirjsNRkevXqxVxcXNjixYtZYmIi+8c//sEAsDVr1lS1aTWKefPmMQCsd+/eLDk5WfEr4tdff2WOjo7s2WefZXv27GHbtm1jrVq1Yt7e3uzOnTtVeAU1E1GcO+pj6wgPD2d6vZ69//77bN++fWzOnDnMYDCwsLAwcx3qW+vYuXMnkySJBQUFsY0bN7L9+/ez2bNnM0dHRxYQEMByc3MZY9S/BMFDzp2VZGRksDFjxjAvLy9ma2vLAgMD2fr166varBpHt27dGIBif49y/Phx1rNnT2Zvb8+cnJxYRESEIrgpoQ6Rc8cY9bE1ZGVlsYkTJ7IGDRowrVbLGjZsyCZPnsxycnJk9ahvrSMxMZE9//zzzMvLi9nZ2bHmzZuzCRMmsNTUVFk96l+C+AuJsUdy5BAEQRAEQRA1GtLcEQRBEARB1CLIuSMIgiAIgqhFkHNHEARBEARRiyDnjiAIgiAIohZBzh1BEARBEEQtgpw7giAIgiCIWgQ5dwRBEARBELUIcu6IWokkSYiNjbVYb+XKlZAkCVeuXCmxXmxsLCRJKh/jSnnu8mb48OHw9fWt1HOWxJUrVxAaGgpXV1dIkoRx48ZVtUnlhq+vL4YPH27VsZIk4a233rJY78CBA5AkCQcOHLDqPARB1D60VW0AQVQEycnJqF+/frm199prr6F3797l1l5VMn36dIwdO7aqzTAzfvx4HD16FMuXL4eXlxfq1atX1SaVG9u3b4eTk1NVm0EQxGMGOXdErYExhpycHNjZ2SEoKKhc265fv365OotViZ+fX1WbIOPMmTPo0KEDIiIiyqW9goICGI1G6PX6cmnPGrKzs2FnZ4c2bdpUmQ0EQTy+0LQsUe3YuXMnAgMDodfr0aRJEyxYsEA4LVo0bfXFF1+gZcuW0Ov1WLVqlXkfPy175MgRPPPMMzAYDPD29sbkyZORn5+vyibR+X19fREWFoavv/4abdu2hZ2dHfz9/bF8+XLF8aU598aNG9GpUyc4ODjA0dERISEhOHnypHn/999/D51Oh5iYGNlxRdO8y5YtK/FaRNOyRX355ZdfomXLlrC3t8ff/vY3JCQklNgWALz55pswGAz473//ay4zmUzo2bMnPD09cfPmTeFxRdOJFy9exN69eyFJkmya+o8//sArr7wCDw8P6PV6tGzZEp988glMJpO5jStXrkCSJHz00UeYNWsWGjduDL1ej6SkJOE527Rpg65duyrKCwoK4OPjg8jISHNZXFwcOnbsCFdXVzg5OaFt27ZYtmwZ+IyNRc/Btm3b0KZNGxgMBsTFxZn3PTotm5OTgwkTJqB169ZwdnaGq6srOnXqhJ07dxbbv4sWLULz5s2h1+sREBCADRs2FFv3UY4fP45+/frB1dUVBoMBbdq0waZNm2R1srKyEBMTg8aNG8NgMMDV1RXt2rXD+vXrVZ2DIIhqStWmtiUIOXv37mUajYZ1796dbd++nW3evJl17NiR+fr6Mv5xBcB8fHxYYGAgW7duHUtMTGRnzpwx75s5c6a57i+//MLs7e1ZQEAAW79+Pdu5cycLCQlhDRs2ZADY5cuXS7Rr5syZivM3atSI1a9fnwUEBLDVq1ezb775hg0cOJABYAcPHrTq3LNnz2aSJLHo6GiWkJDAtm3bxjp16sQcHBzYL7/8Yq43d+5cBoDt3LmTMcbYmTNnmL29PXvllVcs9vGwYcNYo0aNFH3p6+vLOnTowDZt2sT27NnDunfvzrRaLbt06VKJ7WVnZ7PWrVuzJk2asPT0dMYYYzNmzGAajYZ9++23xR53//59lpyczLy8vNgzzzzDkpOTWXJyMsvJyWF37txhPj4+rG7duuyLL75gX3/9NXvrrbcYADZy5EhzG5cvXzY/B8HBwWzLli3s22+/LfZ+LliwgAFgv/32m6x8z549DAD76quvzGXDhw9ny5YtY/v27WP79u1j77//PrOzs2NxcXGyYxs1asTq1avHmjRpwpYvX86SkpLYTz/9ZN43bNgwc9179+6x4cOHsy+//JIlJiayr7/+msXExDCNRsNWrVolaxcAa9Cggfm5+eqrr1jv3r0ZALZ582ZzvaSkJAaAJSUlmcsSExOZra0t69q1K9u4cSP7+uuv2fDhwxkAtmLFCnO9N954g9nb27N//vOfLCkpiSUkJLC5c+eyf/3rX8XeN4Igqj/k3BHVivbt27MGDRqw3Nxcc1lGRgZzc3MTOnfOzs7s7t27inZ4527w4MHMzs6O3bp1y1xmNBqZv79/mZw7g8HArl69ai7Lzs5mrq6u7I033ij1uf/44w+m1WrZ22+/LTtPRkYG8/LyYoMGDTKXmUwm1rdvX1anTh125swZFhAQwPz9/VlmZmaJ18FY8c6dp6cne/Dggbns1q1bTKPRsDlz5lhs88KFC8zJyYlFRESw7777jmk0GjZt2jSLxzFW2I+hoaGyskmTJjEA7OjRo7LykSNHMkmS2Pnz5xljfzl3fn5+LC8vz+K5UlNTma2tLZsyZYqsfNCgQczT05Pl5+cLjysoKGD5+fksPj6eubm5MZPJJLPfxsbGbBN/bY86dzxGo5Hl5+ezESNGsDZt2sj2ASj2uWnatKm5TOTc+fv7szZt2iiuJywsjNWrV48VFBQwxhhr1aoVi4iIKNY+giBqJjQtS1QbHj58iOPHjyMiIgK2trbmckdHR4SHhwuP6dGjB1xcXCy2nZSUZJ4mLMLGxgaDBw8uk82tW7dGw4YNzdsGgwHNmzfH1atXS33ub775BkajEUOHDoXRaDT/DAYDunXrJlsNKUkSVq9ejSeeeALt2rXD5cuXsWnTJjg4OFh9LcHBwXjiiSfM256envDw8JBdS3E0bdoUS5YswY4dOxAWFoauXbuqWq1cHImJiQgICECHDh1k5cOHDwdjDImJibLyfv36QafTWWzXzc0N4eHhWLVqlXl6Nz09HTt37sTQoUOh1f4lQ05MTMRzzz0HZ2dn2NjYQKfTYcaMGUhLS8OdO3dk7QYGBqJ58+aqrm3z5s145pln4OjoCK1WC51Oh2XLluHXX39V1C3uubl48SKuX78ubP/ixYs4d+4cXn75ZQCQPUt9+/bFzZs3cf78eQBAhw4dsHfvXkyaNAkHDhxAdna2qmsgCKJ6Q84dUW1IT08HY0z2n1kRojIAqldWpqWlwcvLS1EuKisNbm5uijK9Xi/7T1LtuW/fvg0AaN++PXQ6ney3ceNGpKamKs7dr18/5OTkoHfv3njqqacq/FpKIjQ0FJ6ensjJycE777wDGxsbq21JS0sT3ltvb2/z/kcpzQrb6OhopKSkYN++fQCA9evXIzc3V6aN++mnn/D8888DAJYsWYIffvgBx44dw9SpUwFA0Sdqz79t2zYMGjQIPj4+WLNmDZKTk3Hs2DFER0cjJydHUb+k54bvgyKKnqOYmBjFczRq1CgAMD9Ln332GSZOnIgdO3YgODgYrq6uiIiIwIULF1RdD0EQ1RNaLUtUG1xcXCBJkvk/p0e5deuW8Bi1sefc3NyEbRTXbnmi9tzu7u4AgC1btqBRo0YW2923bx/+85//oEOHDti+fTu2bt2K/v37l4/RVvDmm28iIyMDTz75JMaMGYOuXbuqGlUV4ebmJlyIcePGDQB/9VURpYlBGBISAm9vb6xYsQIhISFYsWIFOnbsiICAAHOdDRs2QKfTISEhAQaDwVy+Y8cOYZtqz79mzRo0btwYGzdulB2Tm5srrF/ScyNyxoG/+mby5MmyBSKP0qJFCwCAg4MD4uLiEBcXh9u3b5tH8cLDw3Hu3DlV10QQRPWDRu6IaoODgwPatWuHHTt2IC8vz1yemZmpatVmSQQHB2P//v0yx7GgoAAbN24sU7vlee6QkBBotVpcunQJ7dq1E/6KuHnzJl555RV069YNP/74I/r164cRI0bg8uXLFX49IpYuXYo1a9bg3//+N7766ivcu3cPUVFRVrfXs2dPnD17FidOnJCVr169GpIkITg42Oq2bWxs8Oqrr2LHjh04fPgwjh8/jujoaFkdSZKg1Wplo4/Z2dn48ssvrT5vUbu2trYyx+7WrVvFrpYt7rnx8/MrNjRPixYt0KxZM/z888/FPkePTr8X4enpieHDh+Oll17C+fPnkZWVVaZrJQii6iDnjqhWxMfHIyUlBSEhIdixYwe2bt2K5557Do6OjmXKEDFt2jQAhRq9jRs3YteuXQgNDcXDhw/Ly/Qyn9vX1xfx8fGYOnUq3nzzTezYsQMHDx7Epk2bEBMTg5kzZwIo/A/+pZdegiRJWLduHWxsbLBy5Uo4Oztj8ODBMse4Mjh9+jTGjBmDYcOGISoqCk2aNMGyZcuwc+dOzJ8/36o2x48fDx8fH4SGhmLJkiX49ttvMXbsWCxcuBAjR45UrW8rjujoaOTm5mLIkCGws7NT6B9DQ0ORmZmJIUOGYN++fdiwYQO6du1a5th5YWFhOH/+PEaNGoXExESsWrUKXbp0KXZa193dHT169MCGDRuwa9cuhIWF4dy5c5g9e3aJ51m0aBH279+PkJAQrF+/HocOHcKOHTswZ84cDBw40FyvY8eOeP/997Fz504cOnQIixYtwpdffolOnTrB3t6+TNdKEEQVUtUrOgiCZ/v27eypp55itra2rGHDhmzu3LlszJgxzMXFRVYPABs9erSwDXCrZRlj7IcffmBBQUFMr9czLy8v9u6777LFixeXabUsv8qTMca6devGunXrZvW5d+zYwYKDg5mTkxPT6/WsUaNGbMCAAey7775jjDE2depUptFo2P79+2XH/fjjj0yr1bKxY8eWeC3FrZYV9aWl1Z6ZmZnM39+fBQQEsIcPH8r2jR49mul0OsWKV9E5RP149epVNmTIEObm5sZ0Oh1r0aIF+/jjj80rPRn7a7Xsxx9/XOI5RHTu3JkBYC+//LJw//Lly1mLFi2YXq9nTZo0YXPmzGHLli1T3LPi7C/ax/ff3Llzma+vL9Pr9axly5ZsyZIlwuer6J4sXLiQ+fn5MZ1Ox/z9/dnatWtl9USrZRlj7Oeff2aDBg1iHh4eTKfTMS8vL9ajRw/2xRdfmOtMmjSJtWvXjrm4uJivc/z48Sw1NdVC7xEEUZ2RGOMichJENSM/Px+tW7eGj48Pvv3226o2hyAIgiCqNbSggqh2jBgxAr169UK9evVw69YtfPHFF/j111+xYMGCqjaNIAiCIKo95NwR1Y6MjAzExMTgzz//hE6nQ9u2bbFnzx4899xzVW0aQRAEQVR7aFqWIAiCIAiiFkGrZQmCIAiCIGoR5NwRBEEQBEHUIsi5I2olN27cQGxsLE6dOqWq/tmzZxEbG4srV65UqF21nZUrV0KSJIv9GBsbC19fX6vOIUkSVq5cWWKdK1euqKpXGXzwwQfCzBYHDhyAJEmynMEEQRDlATl3RK3kxo0biIuLK5VzFxcXR84dUe4U59y1bdsWycnJaNu2beUbRRBErYZWyxKEFWRlZVWrCP75+fnmlFlEzcDJyQlBQUFVbQZBELUQGrkjahQXL15EVFQUmjVrBnt7e/j4+CA8PBynT5821zlw4ADat28PAIiKioIkSZAkCbGxscI2V65caU7JFBwcbK5fNKXXvXt3tGrVCocOHULnzp1hb29vzkW6ceNGPP/886hXrx7s7OzQsmVLTJo0SZjW7OjRowgPD4ebmxsMBgP8/Pwwbtw4WZ0LFy5gyJAh8PDwgF6vR8uWLfH555/L6hRN53355ZeYMGECfHx8oNfrcfHixWL7LS4uDh07doSrqyucnJzQtm1bLFu2DPxieV9fX4SFheHrr79G27ZtYWdnB39/fyxfvlzR5pEjR/DMM8/AYDDA29sbkydPRn5+frE2lESbNm3QtWtXRXlBQQF8fHwQGRlpVbs833//PXr27IknnngC9vb26Ny5M3bv3q2ol5KSgtdffx0NGjSAra0tvL29MWDAAHOe15ycHEyYMAGtW7eGs7MzXF1d0alTJ0WOWEmS8PDhQ6xatcr8XHXv3h1A8dOyX331lTn91xNPPIFevXohOTlZVic2NhaSJOGXX37BSy+9BGdnZ3h6eiI6Ohr379+X1d28eTM6duwIZ2dn2Nvbo0mTJopcugRB1C7oz3yiRnHjxg24ublh7ty5qFu3Lu7evYtVq1ahY8eOOHnyJFq0aIG2bdtixYoViIqKwrRp0xAaGgoAxSZaDw0NxQcffIApU6bg888/N0+T+fn5mevcvHkTr7zyCt577z188MEH0GgK/y66cOEC+vbti3HjxsHBwQHnzp3Dhx9+iJ9++gmJiYnm47/55huEh4ejZcuW+Oc//4mGDRviypUrsowbZ8+eRefOndGwYUN88skn8PLywjfffIMxY8YgNTXVnFu2iMmTJ6NTp0744osvoNFo4OHhUWy/XblyBW+88QYaNmwIoNAxe/vtt5GSkoIZM2bI6v7888+YMGECJk2aBE9PTyxduhQjRoxA06ZN8eyzz5pt7dmzJ3x9fbFy5UrY29tj4cKFWLduXck38P+JjY2VOdtRUVEYO3YsLly4gGbNmpnLv/32W9y4cQNRUVHmMmujNx08eBC9evVCYGAgli1bBr1ej4ULFyI8PBzr168355dNSUlB+/btkZ+fjylTpiAwMBBpaWn45ptvkJ6eDk9PT+Tm5uLu3buIiYmBj48P8vLy8N133yEyMhIrVqzA0KFDAQDJycno0aMHgoODMX36dACFI3bFsW7dOrz88st4/vnnsX79euTm5uKjjz5C9+7dsX//fnTp0kVWv3///hg8eDBGjBiB06dPY/LkyQBgdsaTk5MxePBgDB48GLGxsTAYDLh69ars2SQIohZSlbnPCKKsGI1GlpeXx5o1a8bGjx9vLj927BgDwFasWKGqnc2bNwvzczJWmCsWgCKXK4/JZGL5+fns4MGDDAD7+eefzfv8/PyYn58fy87OLvb4kJAQVr9+fXb//n1Z+VtvvcUMBgO7e/cuY+yvXKLPPvusqmvjKSgoYPn5+Sw+Pp65ubkxk8lk3teoUSNmMBjY1atXzWXZ2dnM1dWVvfHGG+aywYMHMzs7O3br1i1zmdFoZP7+/qpy9fKkpqYyW1tbNmXKFFn5oEGDmKenJ8vPzy9Ve0U5Zx+9/0FBQczDw4NlZGTIbG7VqhWrX7++uR+io6OZTqdjZ8+eVX0+o9HI8vPz2YgRI1ibNm1k+xwcHIT5efmcsAUFBczb25s99dRTsvy5GRkZzMPDg3Xu3NlcVpSL9qOPPpK1OWrUKGYwGMzXMm/ePAaA3bt3T/W1EARR86FpWaJGYTQa8cEHHyAgIAC2trbQarWwtbXFhQsX8Ouvv1bYeV1cXNCjRw9F+e+//44hQ4bAy8sLNjY20Ol06NatGwCY7fntt99w6dIljBgxAgaDQdh+Tk4O9u/fjxdffBH29vYwGo3mX9++fZGTk4MjR47Ijunfv79q+xMTE/Hcc8/B2dnZbOeMGTOQlpaGO3fuyOq2bt3aPMIHAAaDAc2bN8fVq1fNZUlJSejZsyc8PT3NZTY2NubRr9Li5uaG8PBwrFq1CiaTCQCQnp6OnTt3YujQoWXWEj58+BBHjx7FgAED4OjoKLP51VdfxfXr13H+/HkAwN69exEcHIyWLVuW2ObmzZvxzDPPwNHREVqtFjqdDsuWLbP6OTx//jxu3LiBV1991TwyDACOjo7o378/jhw5gqysLNkx/fr1k20HBgYiJyfHfE+L5AmDBg3Cpk2bkJKSYpVtBEHULMi5I2oU77zzDqZPn46IiAjs2rULR48exbFjx/C3v/0N2dnZFXbeevXqKcoyMzPRtWtXHD16FLNmzcKBAwdw7NgxbNu2DQDM9vz5558Aip8WBoC0tDQYjUb861//gk6nk/369u0LAEhNTbVok4iffvoJzz//PABgyZIl+OGHH3Ds2DFMnTpVZmcRbm5uijb0er2sXlpaGry8vBT1RGVqiY6ORkpKCvbt2wcA5mnJ4cOHW91mEenp6WCMCfvM29sbQOE1AYX3q6R7BQDbtm3DoEGD4OPjgzVr1iA5ORnHjh1DdHQ0cnJyrLKx6PzF2WgymZCeni4r5++VXq8H8Nc9ffbZZ7Fjxw4YjUYMHToU9evXR6tWrbB+/XqrbCQIomZAmjuiRrFmzRoMHToUH3zwgaw8NTUVderUqbDzSpKkKEtMTMSNGzdw4MAB82gdANy7d09Wr27dugCA69evF9u+i4uLeRRp9OjRwjqNGze2aJOIDRs2QKfTISEhQTZyKArPoRY3NzfcunVLUS4qU0tISAi8vb2xYsUKhISEYMWKFejYsSMCAgKsbrMIFxcXaDQa3Lx5U7Hvxo0bAAB3d3cAhferpHsFFD6HjRs3xsaNG2X3ITc312obixy14mzUaDRwcXEpdbsvvPACXnjhBeTm5uLIkSOYM2cOhgwZAl9fX3Tq1MlqewmCqL7QyB1Ro5AkyTw6UcTu3bsV0038CIYlSlu/yJZHjy1i0aJFsu3mzZvDz88Py5cvL/Y/f3t7ewQHB+PkyZMIDAxEu3btFD/RiJpaO7VaLWxsbMxl2dnZ+PLLL61qDyhcVbx//37z6lGgcGXrxo0brW6zyLndsWMHDh8+jOPHj5fbqk4HBwd07NgR27Ztk91jk8mENWvWoH79+mjevDkAoE+fPkhKSjJP04qQJAm2trYyx+7WrVuK1bKActSzOFq0aAEfHx+sW7dOtmjk4cOH2Lp1q3kFrbXo9Xp069YNH374IQDg5MmTVrdFEET1hpw7okYRFhaGlStXYv78+UhMTMTHH3+MqKgoxTSan58f7OzssHbtWhw4cADHjx83j9CIaNWqFQBg8eLF+P7773H8+HHzNFlxdO7cGS4uLnjzzTexfft2JCQk4KWXXsLPP/+sqPv555/j6tWrCAoKwurVq3HgwAGsXr0aL7/8srnOggUL8Mcff6Br165YuXIlDhw4gF27duHTTz8V6v3UEhoaiszMTAwZMgT79u3Dhg0b0LVrV4VTWhqmTZsGAOjRowc2btyIXbt2ITQ0VBgCpjRER0cjNzcXQ4YMgZ2dndUaPhFz5sxBWloagoODsWXLFnz11Vfo27cvzpw5g3nz5pkdtfj4eLi7u+PZZ5/FggULkJiYiG3btuH111/HuXPnABQ+h+fPn8eoUaOQmJiIVatWoUuXLsIp1aeeesp8L48fP16s06jRaPDRRx/h1KlTCAsLw1dffYXNmzcjODgY9+7dw9y5c0t9zTNmzEB0dDTWrl2LgwcPYufOnRg/frxMG0oQRC2kqld0EERpSE9PZyNGjGAeHh7M3t6edenShR0+fJh169aNdevWTVZ3/fr1zN/fn+l0OgaAzZw5s8S258+fzxo3bsxsbGxkKy27devGnnzySeExP/74I+vUqROzt7dndevWZa+99ho7ceKEcKVucnIy69OnD3N2dmZ6vZ75+fnJVvgyVrjKMzo6mvn4+DCdTsfq1q3LOnfuzGbNmmWuU7TKcvPmzar6jDHGli9fzlq0aMH0ej1r0qQJmzNnDlu2bJliZWujRo1YaGio4nhR//7www8sKCiI6fV65uXlxd599122ePFiq1bLPkrnzp0ZAPbyyy9b3YZotSxjjB0+fJj16NGDOTg4MDs7OxYUFMR27dqlOP7atWssOjqaeXl5MZ1Ox7y9vdmgQYPY7du3zXXmzp3LfH19mV6vZy1btmRLliwxr2J9lFOnTrFnnnmG2dvbMwDmfuRXyxaxY8cO1rFjR2YwGJiDgwPr2bMn++GHH2R1is7z559/yspXrFgh6/+EhATWp08f5uPjw2xtbZmHhwfr27cvO3z4cCl6kyCImobEmJVBowiCIAiCIIhqB03LEgRBEARB1CLIuSMIgiAIgqhFkHNHEARBEARRiyDnjiAIgiAIohZBzh1BEARBEEQtgpw7giAIgiCIWgQ5dwRBEARBELUIcu4IgiAIgiBqEeTcEQRBEARB1CLIuSMIgiAIgqhFkHNHEARBEARRi/g/6sb/KDl2vzcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pressanom = ds_denspress_mo.PHIHYDcR\n",
    "k_plot = 20\n",
    "pressanom_plot = (pressanom.isel(tile=1,k=k_plot)).plot(cmap='RdYlBu_r')\n",
    "# change plot title to show depth of plotted level\n",
    "depth_plot_level = -pressanom.Z[k_plot].values      # sign change, so depth is given as positive number\n",
    "plt.title('Pressure anomaly at ' + str(int(depth_plot_level)) + ' meters depth')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the units are different from standard pressure units (which would be N m-2 or kg m-1 s-2). Consult the attributes for this variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;PHIHYDcR&#x27; (time: 1, k: 50, tile: 13, j: 90, i: 90)&gt; Size: 21MB\n",
       "dask.array&lt;open_dataset-PHIHYDcR, shape=(1, 50, 13, 90, 90), dtype=float32, chunksize=(1, 25, 7, 45, 45), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * i        (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j        (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * k        (k) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n",
       "  * tile     (tile) int32 52B 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "  * time     (time) datetime64[ns] 8B 2000-01-16T12:00:00\n",
       "    XC       (tile, j, i) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    YC       (tile, j, i) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    Z        (k) float32 200B dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    long_name:              Ocean hydrostatic pressure anomaly at constant de...\n",
       "    units:                  m2 s-2\n",
       "    coverage_content_type:  modelResult\n",
       "    comment:                PHIHYD = p(k) / rhoConst - g z(k,t), where p = hy...\n",
       "    valid_min:              78.17041778564453\n",
       "    valid_max:              783.6217041015625</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'>'PHIHYDcR'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>k</span>: 50</li><li><span class='xr-has-index'>tile</span>: 13</li><li><span class='xr-has-index'>j</span>: 90</li><li><span class='xr-has-index'>i</span>: 90</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4cd0c46c-74ff-454b-a646-f8c6a95b3e57' class='xr-array-in' type='checkbox' checked><label for='section-4cd0c46c-74ff-454b-a646-f8c6a95b3e57' 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, 25, 7, 45, 45), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-8b0f6d0f-886e-4168-9f74-ac78dd50294e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8b0f6d0f-886e-4168-9f74-ac78dd50294e' class='xr-section-summary' >Coordinates: <span>(8)</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'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-7dc983ce-39e8-456b-972e-2da700da9a1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7dc983ce-39e8-456b-972e-2da700da9a1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-798d1957-a42a-4c19-bc25-879813a238cf' class='xr-var-data-in' type='checkbox'><label for='data-798d1957-a42a-4c19-bc25-879813a238cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>grid index in x for variables at tracer and &#x27;v&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>XC</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, tracer (e.g., THETA) and &#x27;v&#x27; variables (e.g., VVEL) have the same x coordinate on the model grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j</span></div><div class='xr-var-dims'>(j)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-3067ec63-09a6-4df3-87e0-4a2ac2caa210' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3067ec63-09a6-4df3-87e0-4a2ac2caa210' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-311bf062-161e-4619-b990-fd4ca3e7d1fc' class='xr-var-data-in' type='checkbox'><label for='data-311bf062-161e-4619-b990-fd4ca3e7d1fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>grid index in y for variables at tracer and &#x27;u&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>YC</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, tracer (e.g., THETA) and &#x27;u&#x27; variables (e.g., UVEL) have the same y coordinate on the model grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 44 45 46 47 48 49</div><input id='attrs-6e1e8a64-7624-420b-9825-a0df088c3909' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e1e8a64-7624-420b-9825-a0df088c3909' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c03c5845-a7b4-4a79-9170-1477fc89f456' class='xr-var-data-in' type='checkbox'><label for='data-c03c5845-a7b4-4a79-9170-1477fc89f456' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>grid index in z for tracer variables</dd><dt><span>swap_dim :</span></dt><dd>Z</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>tile</span></div><div class='xr-var-dims'>(tile)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-75a06edc-c939-44af-bf3a-548710a1673a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75a06edc-c939-44af-bf3a-548710a1673a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c308cca-a632-4d3f-9b57-3e1fce62f744' class='xr-var-data-in' type='checkbox'><label for='data-3c308cca-a632-4d3f-9b57-3e1fce62f744' 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>lat-lon-cap tile index</dd><dt><span>comment :</span></dt><dd>The ECCO V4 horizontal model grid is divided into 13 tiles of 90x90 cells for convenience.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], dtype=int32)</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'>2000-01-16T12:00:00</div><input id='attrs-313e4e4e-8480-4f44-9d2c-6b3405c461e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-313e4e4e-8480-4f44-9d2c-6b3405c461e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e645972-dc35-4cc8-b40c-22b6ac74f5a6' class='xr-var-data-in' type='checkbox'><label for='data-3e645972-dc35-4cc8-b40c-22b6ac74f5a6' 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>center time of averaging period</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-16T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-6c67c351-e528-4f35-8675-c853cce2baf1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c67c351-e528-4f35-8675-c853cce2baf1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-020b396a-9ff0-492b-9b55-45241c1965b2' class='xr-var-data-in' type='checkbox'><label for='data-020b396a-9ff0-492b-9b55-45241c1965b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of tracer grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>coordinate :</span></dt><dd>YC XC</dd><dt><span>bounds :</span></dt><dd>XC_bnds</dd><dt><span>comment :</span></dt><dd>nonuniform grid spacing</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-408410dd-aa59-4e93-8cf4-ec9d87e45026' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-408410dd-aa59-4e93-8cf4-ec9d87e45026' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1238e8dd-b81d-4b7d-a5d3-9c2679c97754' class='xr-var-data-in' type='checkbox'><label for='data-1238e8dd-b81d-4b7d-a5d3-9c2679c97754' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of tracer grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>coordinate :</span></dt><dd>YC XC</dd><dt><span>bounds :</span></dt><dd>YC_bnds</dd><dt><span>comment :</span></dt><dd>nonuniform grid spacing</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
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       "      dtype=&#x27;int32&#x27;, name=&#x27;i&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>j</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7e39dad8-ad8c-4c35-b517-82a92af8b03f' class='xr-index-data-in' type='checkbox'/><label for='index-7e39dad8-ad8c-4c35-b517-82a92af8b03f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
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       "      dtype=&#x27;int32&#x27;, name=&#x27;j&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4c9f9fab-7def-49b9-bf8b-fe7bfb770e2b' class='xr-index-data-in' type='checkbox'/><label for='index-4c9f9fab-7def-49b9-bf8b-fe7bfb770e2b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
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       "      dtype=&#x27;int32&#x27;, name=&#x27;k&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>tile</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-83196c6f-0b45-4868-bf03-c1890bf7173e' class='xr-index-data-in' type='checkbox'/><label for='index-83196c6f-0b45-4868-bf03-c1890bf7173e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=&#x27;int32&#x27;, name=&#x27;tile&#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-b7bc11e5-c35f-400c-aac9-c5d79f39d7f7' class='xr-index-data-in' type='checkbox'/><label for='index-b7bc11e5-c35f-400c-aac9-c5d79f39d7f7' 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;2000-01-16 12:00:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-69430598-7941-42be-a5fe-bffb66222d50' class='xr-section-summary-in' type='checkbox'  checked><label for='section-69430598-7941-42be-a5fe-bffb66222d50' class='xr-section-summary' >Attributes: <span>(6)</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>Ocean hydrostatic pressure anomaly at constant depths</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>PHIHYD = p(k) / rhoConst - g z(k,t), where p = hydrostatic ocean pressure at depth level k, rhoConst = reference density (1029 kg m-3), g is acceleration due to gravity (9.81 m s-2), and z(k,t) is fixed model depth at level k. Units: p:[kg m-1 s-2], rhoConst:[kg m-3], g:[m s-2], H(t):[m]. Note: includes atmospheric pressure loading. Quantity referred to in some contexts as hydrostatic pressure potential anomaly. PHIHYDcR is calculated with respect to the model&#x27;s initial, time-invariant grid cell thicknesses. See PHIHYD for hydrostatic pressure anomaly calculated using model&#x27;s time-variable grid cell thicknesses (z* coordinate system). PHIHYDcR is is NOT corrected for global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH). </dd><dt><span>valid_min :</span></dt><dd>78.17041778564453</dd><dt><span>valid_max :</span></dt><dd>783.6217041015625</dd></dl></div></li></ul></div></div>"
      ],
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       "<xarray.DataArray 'PHIHYDcR' (time: 1, k: 50, tile: 13, j: 90, i: 90)> Size: 21MB\n",
       "dask.array<open_dataset-PHIHYDcR, shape=(1, 50, 13, 90, 90), dtype=float32, chunksize=(1, 25, 7, 45, 45), chunktype=numpy.ndarray>\n",
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       "  * k        (k) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n",
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       "  * time     (time) datetime64[ns] 8B 2000-01-16T12:00:00\n",
       "    XC       (tile, j, i) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    YC       (tile, j, i) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
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       "Attributes:\n",
       "    long_name:              Ocean hydrostatic pressure anomaly at constant de...\n",
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       "    coverage_content_type:  modelResult\n",
       "    comment:                PHIHYD = p(k) / rhoConst - g z(k,t), where p = hy...\n",
       "    valid_min:              78.17041778564453\n",
       "    valid_max:              783.6217041015625"
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     "execution_count": 12,
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   "source": [
    "That's a long comment! But what we need to know is that **PHIHYDcR** is related to the actual in-situ ocean pressure $p$ by\n",
    "\n",
    "$$\n",
    "{\\rm{PHIHYDcR}} = \\frac{p}{\\rm{rhoConst}} - gz\n",
    "$$\n",
    "\n",
    "where $z$ is a depth coordinate, and rhoConst and $g$ are constants. Therefore, the $gz$ term does not impact the computation of ***horizontal*** gradients of pressure, which is what we need for geostrophic balance.\n",
    "\n",
    "### Look at velocity file\n",
    "\n",
    "Lastly we will need the actual ECCO velocity fields to compute the other side of the geostrophic balance equations. The velocity dataset has 3 data variables (UVEL,VVEL,WVEL) corresponding to the components of three-dimensional velocities. You'll notice that the velocity fields are each centered in a different location corresponding to an outer face of a grid cell, rather than the center (since the output is from an [Arakawa C-grid model](https://www.youtube.com/watch?v=iF_D2gnDJDU))."
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       "}\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.Dataset&gt; Size: 68MB\n",
       "Dimensions:    (i: 90, i_g: 90, j: 90, j_g: 90, k: 50, k_u: 50, k_l: 50,\n",
       "                k_p1: 51, tile: 13, time: 1, nv: 2, nb: 4)\n",
       "Coordinates: (12/22)\n",
       "  * i          (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * i_g        (i_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j          (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j_g        (j_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * k          (k) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n",
       "  * k_u        (k_u) int32 200B 0 1 2 3 4 5 6 7 8 ... 41 42 43 44 45 46 47 48 49\n",
       "    ...         ...\n",
       "    Zu         (k_u) float32 200B dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;\n",
       "    Zl         (k_l) float32 200B dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;\n",
       "    time_bnds  (time, nv) datetime64[ns] 16B dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n",
       "    XC_bnds    (tile, j, i, nb) float32 2MB dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;\n",
       "    YC_bnds    (tile, j, i, nb) float32 2MB dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;\n",
       "    Z_bnds     (k, nv) float32 400B dask.array&lt;chunksize=(50, 2), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: nv, nb\n",
       "Data variables:\n",
       "    UVEL       (time, k, tile, j, i_g) float32 21MB dask.array&lt;chunksize=(1, 25, 7, 45, 45), meta=np.ndarray&gt;\n",
       "    VVEL       (time, k, tile, j_g, i) float32 21MB dask.array&lt;chunksize=(1, 25, 7, 45, 45), meta=np.ndarray&gt;\n",
       "    WVEL       (time, k_l, tile, j, i) float32 21MB dask.array&lt;chunksize=(1, 25, 7, 45, 45), meta=np.ndarray&gt;\n",
       "Attributes: (12/62)\n",
       "    acknowledgement:                 This research was carried out by the Jet...\n",
       "    author:                          Ian Fenty and Ou Wang\n",
       "    cdm_data_type:                   Grid\n",
       "    comment:                         Fields provided on the curvilinear lat-l...\n",
       "    Conventions:                     CF-1.8, ACDD-1.3\n",
       "    coordinates_comment:             Note: the global &#x27;coordinates&#x27; attribute...\n",
       "    ...                              ...\n",
       "    time_coverage_duration:          P1M\n",
       "    time_coverage_end:               2000-02-01T00:00:00\n",
       "    time_coverage_resolution:        P1M\n",
       "    time_coverage_start:             2000-01-01T00:00:00\n",
       "    title:                           ECCO Ocean Velocity - Monthly Mean llc90...\n",
       "    uuid:                            32bd652c-4182-11eb-bd5c-0cc47a3f82e7</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-572f13e8-d5f6-470c-b9b5-193d1557c0d4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-572f13e8-d5f6-470c-b9b5-193d1557c0d4' 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'>i</span>: 90</li><li><span class='xr-has-index'>i_g</span>: 90</li><li><span class='xr-has-index'>j</span>: 90</li><li><span class='xr-has-index'>j_g</span>: 90</li><li><span class='xr-has-index'>k</span>: 50</li><li><span class='xr-has-index'>k_u</span>: 50</li><li><span class='xr-has-index'>k_l</span>: 50</li><li><span class='xr-has-index'>k_p1</span>: 51</li><li><span class='xr-has-index'>tile</span>: 13</li><li><span class='xr-has-index'>time</span>: 1</li><li><span>nv</span>: 2</li><li><span>nb</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-00fa78ea-0793-483d-9b85-c1bae4c2cdbd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-00fa78ea-0793-483d-9b85-c1bae4c2cdbd' class='xr-section-summary' >Coordinates: <span>(22)</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'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-616b011c-f10f-4eee-83cf-8fa642ec265f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-616b011c-f10f-4eee-83cf-8fa642ec265f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-090cbec0-2686-43f2-b15e-4cfa0a870089' class='xr-var-data-in' type='checkbox'><label for='data-090cbec0-2686-43f2-b15e-4cfa0a870089' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>grid index in x for variables at tracer and &#x27;v&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>XC</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, tracer (e.g., THETA) and &#x27;v&#x27; variables (e.g., VVEL) have the same x coordinate on the model grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>i_g</span></div><div class='xr-var-dims'>(i_g)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-fdd84384-c086-40bf-8383-42e0dd77ffe8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdd84384-c086-40bf-8383-42e0dd77ffe8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70d67f8b-fb65-48ff-b322-7bb58b3d89b9' class='xr-var-data-in' type='checkbox'><label for='data-70d67f8b-fb65-48ff-b322-7bb58b3d89b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>grid index in x for variables at &#x27;u&#x27; and &#x27;g&#x27; locations</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd><dt><span>swap_dim :</span></dt><dd>XG</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, &#x27;u&#x27; (e.g., UVEL) and &#x27;g&#x27; variables (e.g., XG) have the same x coordinate on the model grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j</span></div><div class='xr-var-dims'>(j)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-3db9b6e5-bd62-4f8f-9738-b6737dac4a8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3db9b6e5-bd62-4f8f-9738-b6737dac4a8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-322fa840-10a8-4b05-81c2-6a20369902cd' class='xr-var-data-in' type='checkbox'><label for='data-322fa840-10a8-4b05-81c2-6a20369902cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>grid index in y for variables at tracer and &#x27;u&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>YC</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, tracer (e.g., THETA) and &#x27;u&#x27; variables (e.g., UVEL) have the same y coordinate on the model grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j_g</span></div><div class='xr-var-dims'>(j_g)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-04479cc0-ef32-4bf9-8432-4e53a5c4c829' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04479cc0-ef32-4bf9-8432-4e53a5c4c829' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70a1738c-ca71-4f12-9ea0-6a72970522f6' class='xr-var-data-in' type='checkbox'><label for='data-70a1738c-ca71-4f12-9ea0-6a72970522f6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>grid index in y for variables at &#x27;v&#x27; and &#x27;g&#x27; locations</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd><dt><span>swap_dim :</span></dt><dd>YG</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, &#x27;v&#x27; (e.g., VVEL) and &#x27;g&#x27; variables (e.g., XG) have the same y coordinate.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 44 45 46 47 48 49</div><input id='attrs-7053df3c-f991-46a6-864a-00782993b0c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7053df3c-f991-46a6-864a-00782993b0c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2c8251f-9f6e-4be8-89f3-6cc6b8ebfeae' class='xr-var-data-in' type='checkbox'><label for='data-f2c8251f-9f6e-4be8-89f3-6cc6b8ebfeae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>grid index in z for tracer variables</dd><dt><span>swap_dim :</span></dt><dd>Z</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k_u</span></div><div class='xr-var-dims'>(k_u)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 44 45 46 47 48 49</div><input id='attrs-9ac8735e-6dce-4a23-bbf5-668d1a598fd7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ac8735e-6dce-4a23-bbf5-668d1a598fd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8844b46b-3ef0-4363-beb2-36bc9306648d' class='xr-var-data-in' type='checkbox'><label for='data-8844b46b-3ef0-4363-beb2-36bc9306648d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>c_grid_axis_shift :</span></dt><dd>0.5</dd><dt><span>swap_dim :</span></dt><dd>Zu</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>grid index in z corresponding to the bottom face of tracer grid cells (&#x27;w&#x27; locations)</dd><dt><span>comment :</span></dt><dd>First index corresponds to the bottom surface of the uppermost tracer grid cell. The use of &#x27;u&#x27; in the variable name follows the MITgcm convention for ocean variables in which the upper (u) face of a tracer grid cell on the logical grid corresponds to the bottom face of the grid cell on the physical grid.</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k_l</span></div><div class='xr-var-dims'>(k_l)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 44 45 46 47 48 49</div><input id='attrs-44f4d8e5-d70d-4442-8f99-50046de82f2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44f4d8e5-d70d-4442-8f99-50046de82f2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44ac2c90-e776-4564-aa3a-76792e0da273' class='xr-var-data-in' type='checkbox'><label for='data-44ac2c90-e776-4564-aa3a-76792e0da273' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd><dt><span>swap_dim :</span></dt><dd>Zl</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>grid index in z corresponding to the top face of tracer grid cells (&#x27;w&#x27; locations)</dd><dt><span>comment :</span></dt><dd>First index corresponds to the top surface of the uppermost tracer grid cell. The use of &#x27;l&#x27; in the variable name follows the MITgcm convention for ocean variables in which the lower (l) face of a tracer grid cell on the logical grid corresponds to the top face of the grid cell on the physical grid.</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k_p1</span></div><div class='xr-var-dims'>(k_p1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 45 46 47 48 49 50</div><input id='attrs-2d81a809-0d76-42a2-839f-6bc80bc75bcb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d81a809-0d76-42a2-839f-6bc80bc75bcb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bb6a963-519a-4058-9996-57054bf84dba' class='xr-var-data-in' type='checkbox'><label for='data-5bb6a963-519a-4058-9996-57054bf84dba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>grid index in z for variables at &#x27;w&#x27; locations</dd><dt><span>c_grid_axis_shift :</span></dt><dd>[-0.5  0.5]</dd><dt><span>swap_dim :</span></dt><dd>Zp1</dd><dt><span>comment :</span></dt><dd>Includes top of uppermost model tracer cell (k_p1=0) and bottom of lowermost tracer cell (k_p1=51).</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>tile</span></div><div class='xr-var-dims'>(tile)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-5e9c60cd-0073-4f96-a40a-76801ecdec37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e9c60cd-0073-4f96-a40a-76801ecdec37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e37cc760-4d4b-45c6-991e-631740d81d8f' class='xr-var-data-in' type='checkbox'><label for='data-e37cc760-4d4b-45c6-991e-631740d81d8f' 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>lat-lon-cap tile index</dd><dt><span>comment :</span></dt><dd>The ECCO V4 horizontal model grid is divided into 13 tiles of 90x90 cells for convenience.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], dtype=int32)</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'>2000-01-16T12:00:00</div><input id='attrs-7e017582-3af8-4dac-a663-7d6089feb287' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e017582-3af8-4dac-a663-7d6089feb287' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6632baeb-9572-4739-bd2e-553e1e87b74c' class='xr-var-data-in' type='checkbox'><label for='data-6632baeb-9572-4739-bd2e-553e1e87b74c' 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>center time of averaging period</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-16T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-e637f957-ab0c-4911-9c61-b6c6e5b0a23e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e637f957-ab0c-4911-9c61-b6c6e5b0a23e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea7be1d4-440e-4c98-832a-1b82633519a7' class='xr-var-data-in' type='checkbox'><label for='data-ea7be1d4-440e-4c98-832a-1b82633519a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of tracer grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>coordinate :</span></dt><dd>YC XC</dd><dt><span>bounds :</span></dt><dd>XC_bnds</dd><dt><span>comment :</span></dt><dd>nonuniform grid spacing</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>longitude</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",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
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       "                    \n",
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       "                        <td> (13, 90, 90) </td>\n",
       "                        <td> (13, 90, 90) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 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><li class='xr-var-item'><div class='xr-var-name'><span>YC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-20c0a7a7-814a-4465-a7d9-eec3290b0807' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20c0a7a7-814a-4465-a7d9-eec3290b0807' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10e222da-abfe-4c11-9a7d-00800be5434b' class='xr-var-data-in' type='checkbox'><label for='data-10e222da-abfe-4c11-9a7d-00800be5434b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of tracer grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>coordinate :</span></dt><dd>YC XC</dd><dt><span>bounds :</span></dt><dd>YC_bnds</dd><dt><span>comment :</span></dt><dd>nonuniform grid spacing</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                    <tr>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XG</span></div><div class='xr-var-dims'>(tile, j_g, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-0ce5143d-4613-4f77-b9a5-b135ddb8dbb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ce5143d-4613-4f77-b9a5-b135ddb8dbb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d801b8e-3ab3-4c22-ba57-18abad5e3a17' class='xr-var-data-in' type='checkbox'><label for='data-8d801b8e-3ab3-4c22-ba57-18abad5e3a17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of &#x27;southwest&#x27; corner of tracer grid cell</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>coordinate :</span></dt><dd>YG XG</dd><dt><span>comment :</span></dt><dd>Nonuniform grid spacing. Note: &#x27;southwest&#x27; does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 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",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Zl</span></div><div class='xr-var-dims'>(k_l)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-f039b56f-d9f3-4aa1-97d4-9419ab2b60cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f039b56f-d9f3-4aa1-97d4-9419ab2b60cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a27ca5f-61f9-48a2-ae54-3b3eebbfaaf4' class='xr-var-data-in' type='checkbox'><label for='data-9a27ca5f-61f9-48a2-ae54-3b3eebbfaaf4' 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>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>long_name :</span></dt><dd>depth of the top face of tracer grid cells</dd><dt><span>comment :</span></dt><dd>First element is 0m, the depth of the top face of the first tracer grid cell (ocean surface). Last element is the depth of the top face of the deepest grid cell. The use of &#x27;l&#x27; in the variable name follows the MITgcm convention for ocean variables in which the lower (l) face of a tracer grid cell on the logical grid corresponds to the top face of the grid cell on the physical grid. In other words, the logical vertical grid of MITgcm ocean variables is inverted relative to the physical vertical grid.</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> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 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=\"170\" height=\"79\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nv)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-4dfc25f1-d1bb-4dd1-bc98-9941d4f9f036' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4dfc25f1-d1bb-4dd1-bc98-9941d4f9f036' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32e435fd-d945-4833-a0d0-957150ffbfd9' class='xr-var-data-in' type='checkbox'><label for='data-32e435fd-d945-4833-a0d0-957150ffbfd9' 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>comment :</span></dt><dd>Start and end times of averaging period.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>time bounds of averaging period</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> 16 B </td>\n",
       "                        <td> 16 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1, 2) </td>\n",
       "                        <td> (1, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XC_bnds</span></div><div class='xr-var-dims'>(tile, j, i, nb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;</div><input id='attrs-248460b6-33d5-476d-ae5b-a19f95a3f458' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-248460b6-33d5-476d-ae5b-a19f95a3f458' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b874784d-c5c1-4971-9a91-9bfb916987c6' class='xr-var-data-in' type='checkbox'><label for='data-b874784d-c5c1-4971-9a91-9bfb916987c6' 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>comment :</span></dt><dd>Bounds array follows CF conventions. XC_bnds[i,j,0] = &#x27;southwest&#x27; corner (j-1, i-1), XC_bnds[i,j,1] = &#x27;southeast&#x27; corner (j-1, i+1), XC_bnds[i,j,2] = &#x27;northeast&#x27; corner (j+1, i+1), XC_bnds[i,j,3]  = &#x27;northwest&#x27; corner (j+1, i-1). Note: &#x27;southwest&#x27;, &#x27;southeast&#x27;, northwest&#x27;, and &#x27;northeast&#x27; do not correspond to geographic orientation but are used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitudes of tracer grid cell corners</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> 1.61 MiB </td>\n",
       "                        <td> 1.61 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90, 4) </td>\n",
       "                        <td> (13, 90, 90, 4) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 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>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YC_bnds</span></div><div class='xr-var-dims'>(tile, j, i, nb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;</div><input id='attrs-ab31c115-d96c-43e2-b793-10aad2d55897' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab31c115-d96c-43e2-b793-10aad2d55897' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bc39d4b-c4b6-4cd8-8533-e0f47d2d91c5' class='xr-var-data-in' type='checkbox'><label for='data-9bc39d4b-c4b6-4cd8-8533-e0f47d2d91c5' 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>comment :</span></dt><dd>Bounds array follows CF conventions. YC_bnds[i,j,0] = &#x27;southwest&#x27; corner (j-1, i-1), YC_bnds[i,j,1] = &#x27;southeast&#x27; corner (j-1, i+1), YC_bnds[i,j,2] = &#x27;northeast&#x27; corner (j+1, i+1), YC_bnds[i,j,3]  = &#x27;northwest&#x27; corner (j+1, i-1). Note: &#x27;southwest&#x27;, &#x27;southeast&#x27;, northwest&#x27;, and &#x27;northeast&#x27; do not correspond to geographic orientation but are used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitudes of tracer grid cell corners</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> 1.61 MiB </td>\n",
       "                        <td> 1.61 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90, 4) </td>\n",
       "                        <td> (13, 90, 90, 4) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
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       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Z_bnds</span></div><div class='xr-var-dims'>(k, nv)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50, 2), meta=np.ndarray&gt;</div><input id='attrs-66ec6d32-8902-4fbb-a088-bab83b083363' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66ec6d32-8902-4fbb-a088-bab83b083363' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8b7f1f6-f9be-4841-9f32-79030e080894' class='xr-var-data-in' type='checkbox'><label for='data-c8b7f1f6-f9be-4841-9f32-79030e080894' 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>comment :</span></dt><dd>One pair of depths for each vertical level.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>depths of tracer grid cell upper and lower interfaces</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 400 B </td>\n",
       "                        <td> 400 B </td>\n",
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       "                        <td> (50, 2) </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-225d3b8d-42f0-47fe-8147-684febf7b3f1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-225d3b8d-42f0-47fe-8147-684febf7b3f1' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>UVEL</span></div><div class='xr-var-dims'>(time, k, tile, j, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 25, 7, 45, 45), meta=np.ndarray&gt;</div><input id='attrs-5aecfcb8-970e-46da-ba88-951cbdc89250' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5aecfcb8-970e-46da-ba88-951cbdc89250' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12464596-c927-490d-a392-9767354063c4' class='xr-var-data-in' type='checkbox'><label for='data-12464596-c927-490d-a392-9767354063c4' 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>Horizontal velocity in the model +x direction</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>mate :</span></dt><dd>VVEL</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases volume</dd><dt><span>standard_name :</span></dt><dd>sea_water_x_velocity</dd><dt><span>comment :</span></dt><dd>Horizontal velocity in the +x direction at the &#x27;u&#x27; face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal velocities are staggered relative to the tracer cells with indexing such that +UVEL(i_g,j,k) corresponds to +x fluxes through the &#x27;u&#x27; face of the tracer cell at (i,j,k). Do NOT use UVEL for volume flux calculations because the model&#x27;s grid cell thicknesses vary with time (z* coordinates); use UVELMASS instead. Also, the model +x direction does not necessarily correspond to the geographical east-west direction because the x and y axes of the model&#x27;s curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles. See EVEL and NVEL for zonal and meridional velocity.</dd><dt><span>valid_min :</span></dt><dd>-1.2937312126159668</dd><dt><span>valid_max :</span></dt><dd>1.776294231414795</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.08 MiB </td>\n",
       "                        <td> 1.35 MiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (1, 50, 13, 90, 90) </td>\n",
       "                        <td> (1, 25, 7, 45, 45) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>VVEL</span></div><div class='xr-var-dims'>(time, k, tile, j_g, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 25, 7, 45, 45), meta=np.ndarray&gt;</div><input id='attrs-f5b599a6-2303-4e6f-8204-cfb5da2bde42' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5b599a6-2303-4e6f-8204-cfb5da2bde42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-919b2ce2-fee2-4bfc-89ec-e00c4242e9fe' class='xr-var-data-in' type='checkbox'><label for='data-919b2ce2-fee2-4bfc-89ec-e00c4242e9fe' 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>Horizontal velocity in the model +y direction</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>mate :</span></dt><dd>UVEL</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases volume</dd><dt><span>standard_name :</span></dt><dd>sea_water_y_velocity</dd><dt><span>comment :</span></dt><dd>Horizontal velocity in the +y direction at the &#x27;v&#x27; face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal velocities are staggered relative to the tracer cells with indexing such that +VVEL(i,j_g,k) corresponds to +y fluxes through the &#x27;v&#x27; face of the tracer cell at (i,j,k). Do NOT use VVEL for volume flux calculations because the model&#x27;s grid cell thicknesses vary with time (z* coordinates); use VVELMASS instead. Also, the model +y direction does not necessarily correspond to the geographical north-south direction because the x and y axes of the model&#x27;s curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles. See EVEL and NVEL for zonal and meridional velocity.</dd><dt><span>valid_min :</span></dt><dd>-1.2608345746994019</dd><dt><span>valid_max :</span></dt><dd>1.4756959676742554</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>WVEL</span></div><div class='xr-var-dims'>(time, k_l, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 25, 7, 45, 45), meta=np.ndarray&gt;</div><input id='attrs-8c41f444-12d8-4f32-b2ca-0ae1b6a4a84c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c41f444-12d8-4f32-b2ca-0ae1b6a4a84c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ab23a90-4c1e-4777-b9dd-f0099cb67fe2' class='xr-var-data-in' type='checkbox'><label for='data-0ab23a90-4c1e-4777-b9dd-f0099cb67fe2' 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>Vertical velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 decreases volume</dd><dt><span>standard_name :</span></dt><dd>upward_sea_water_velocity</dd><dt><span>comment :</span></dt><dd>Vertical velocity in the +z direction at the top &#x27;w&#x27; face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, vertical velocities are staggered relative to the tracer cells with indexing such that +WVEL(i,j,k_l) corresponds to upward +z motion through the top &#x27;w&#x27; face of the tracer cell at (i,j,k). WVEL is identical to WVELMASS.</dd><dt><span>valid_min :</span></dt><dd>-0.0012012795777991414</dd><dt><span>valid_max :</span></dt><dd>0.0005706523661501706</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Chunk </th>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.08 MiB </td>\n",
       "                        <td> 1.35 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-47ffba41-5cf8-4243-8ba8-56a1d2c3bf68' class='xr-section-summary-in' type='checkbox'  ><label for='section-47ffba41-5cf8-4243-8ba8-56a1d2c3bf68' class='xr-section-summary' >Indexes: <span>(10)</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>i</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f917766c-1fa8-440a-9f74-949b62c55a79' class='xr-index-data-in' type='checkbox'/><label for='index-f917766c-1fa8-440a-9f74-949b62c55a79' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;i&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>i_g</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b5dc61f1-5155-4503-b5cb-fd42b3dcff08' class='xr-index-data-in' type='checkbox'/><label for='index-b5dc61f1-5155-4503-b5cb-fd42b3dcff08' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;i_g&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>j</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0dbc33ee-8cea-4ba7-8d50-d047320d1a15' class='xr-index-data-in' type='checkbox'/><label for='index-0dbc33ee-8cea-4ba7-8d50-d047320d1a15' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;j&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>j_g</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d21f9010-0dea-4276-ad32-726f81d89f4e' class='xr-index-data-in' type='checkbox'/><label for='index-d21f9010-0dea-4276-ad32-726f81d89f4e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;j_g&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5e545727-ba37-4754-b32f-93a898647ffc' class='xr-index-data-in' type='checkbox'/><label for='index-5e545727-ba37-4754-b32f-93a898647ffc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;k&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k_u</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5b20c899-6f0f-4223-8c5f-c71a958475a9' class='xr-index-data-in' type='checkbox'/><label for='index-5b20c899-6f0f-4223-8c5f-c71a958475a9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;k_u&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k_l</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-65d1e2f6-6df2-4d53-8b8e-b81a6b137775' class='xr-index-data-in' type='checkbox'/><label for='index-65d1e2f6-6df2-4d53-8b8e-b81a6b137775' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;k_l&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k_p1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d7b2c0f9-e917-4ff7-88a3-0fcb217343f8' class='xr-index-data-in' type='checkbox'/><label for='index-d7b2c0f9-e917-4ff7-88a3-0fcb217343f8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;k_p1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>tile</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b7fd3827-4f63-4048-8d79-9298b2b03f15' class='xr-index-data-in' type='checkbox'/><label for='index-b7fd3827-4f63-4048-8d79-9298b2b03f15' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=&#x27;int32&#x27;, name=&#x27;tile&#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-42645d63-2ac4-4f5e-abca-7081f46e892a' class='xr-index-data-in' type='checkbox'/><label for='index-42645d63-2ac4-4f5e-abca-7081f46e892a' 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;2000-01-16 12:00:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3a3ea400-cd1e-43b5-836a-097ab9ceadd9' class='xr-section-summary-in' type='checkbox'  ><label for='section-3a3ea400-cd1e-43b5-836a-097ab9ceadd9' class='xr-section-summary' >Attributes: <span>(62)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>acknowledgement :</span></dt><dd>This research was carried out by the Jet Propulsion Laboratory, managed by the California Institute of Technology under a contract with the National Aeronautics and Space Administration.</dd><dt><span>author :</span></dt><dd>Ian Fenty and Ou Wang</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>Fields provided on the curvilinear lat-lon-cap 90 (llc90) native grid used in the ECCO model.</dd><dt><span>Conventions :</span></dt><dd>CF-1.8, ACDD-1.3</dd><dt><span>coordinates_comment :</span></dt><dd>Note: the global &#x27;coordinates&#x27; attribute describes auxillary coordinates.</dd><dt><span>creator_email :</span></dt><dd>ecco-group@mit.edu</dd><dt><span>creator_institution :</span></dt><dd>NASA Jet Propulsion Laboratory (JPL)</dd><dt><span>creator_name :</span></dt><dd>ECCO Consortium</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_url :</span></dt><dd>https://ecco-group.org</dd><dt><span>date_created :</span></dt><dd>2020-12-18T14:41:41</dd><dt><span>date_issued :</span></dt><dd>2020-12-18T14:41:41</dd><dt><span>date_metadata_modified :</span></dt><dd>2021-03-15T21:55:00</dd><dt><span>date_modified :</span></dt><dd>2021-03-15T21:55:00</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>variable</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>180.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>-180.0</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>variable</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_vertical_max :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_min :</span></dt><dd>-6134.5</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>up</dd><dt><span>geospatial_vertical_resolution :</span></dt><dd>variable</dd><dt><span>geospatial_vertical_units :</span></dt><dd>meter</dd><dt><span>history :</span></dt><dd>Inaugural release of an ECCO Central Estimate solution to PO.DAAC</dd><dt><span>id :</span></dt><dd>10.5067/ECL5M-OVE44</dd><dt><span>institution :</span></dt><dd>NASA Jet Propulsion Laboratory (JPL)</dd><dt><span>instrument_vocabulary :</span></dt><dd>GCMD instrument keywords</dd><dt><span>keywords :</span></dt><dd>EARTH SCIENCE &gt; OCEANS &gt; OCEAN CIRCULATION, EARTH SCIENCE SERVICES &gt; MODELS &gt; EARTH SCIENCE REANALYSES/ASSIMILATION MODELS, EARTH SCIENCE &gt; OCEANS &gt; OCEAN CIRCULATION &gt; OCEAN CURRENTS</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>license :</span></dt><dd>Public Domain</dd><dt><span>metadata_link :</span></dt><dd>https://cmr.earthdata.nasa.gov/search/collections.umm_json?ShortName=ECCO_L4_OCEAN_VEL_LLC0090GRID_MONTHLY_V4R4</dd><dt><span>naming_authority :</span></dt><dd>gov.nasa.jpl</dd><dt><span>platform :</span></dt><dd>ERS-1/2, TOPEX/Poseidon, Geosat Follow-On (GFO), ENVISAT, Jason-1, Jason-2, CryoSat-2, SARAL/AltiKa, Jason-3, AVHRR, Aquarius, SSM/I, SSMIS, GRACE, DTU17MDT, Argo, WOCE, GO-SHIP, MEOP, Ice Tethered Profilers (ITP)</dd><dt><span>platform_vocabulary :</span></dt><dd>GCMD platform keywords</dd><dt><span>processing_level :</span></dt><dd>L4</dd><dt><span>product_name :</span></dt><dd>OCEAN_VELOCITY_mon_mean_2000-01_ECCO_V4r4_native_llc0090.nc</dd><dt><span>product_time_coverage_end :</span></dt><dd>2018-01-01T00:00:00</dd><dt><span>product_time_coverage_start :</span></dt><dd>1992-01-01T12:00:00</dd><dt><span>product_version :</span></dt><dd>Version 4, Release 4</dd><dt><span>program :</span></dt><dd>NASA Physical Oceanography, Cryosphere, Modeling, Analysis, and Prediction (MAP)</dd><dt><span>project :</span></dt><dd>Estimating the Circulation and Climate of the Ocean (ECCO)</dd><dt><span>publisher_email :</span></dt><dd>podaac@podaac.jpl.nasa.gov</dd><dt><span>publisher_institution :</span></dt><dd>PO.DAAC</dd><dt><span>publisher_name :</span></dt><dd>Physical Oceanography Distributed Active Archive Center (PO.DAAC)</dd><dt><span>publisher_type :</span></dt><dd>institution</dd><dt><span>publisher_url :</span></dt><dd>https://podaac.jpl.nasa.gov</dd><dt><span>references :</span></dt><dd>ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., &amp; Ponte, R. M. 2020. Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate (Version 4 Release 4). doi:10.5281/zenodo.3765928</dd><dt><span>source :</span></dt><dd>The ECCO V4r4 state estimate was produced by fitting a free-running solution of the MITgcm (checkpoint 66g) to satellite and in situ observational data in a least squares sense using the adjoint method</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>summary :</span></dt><dd>This dataset provides monthly-averaged ocean velocity on the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea-ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.</dd><dt><span>time_coverage_duration :</span></dt><dd>P1M</dd><dt><span>time_coverage_end :</span></dt><dd>2000-02-01T00:00:00</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1M</dd><dt><span>time_coverage_start :</span></dt><dd>2000-01-01T00:00:00</dd><dt><span>title :</span></dt><dd>ECCO Ocean Velocity - Monthly Mean llc90 Grid (Version 4 Release 4)</dd><dt><span>uuid :</span></dt><dd>32bd652c-4182-11eb-bd5c-0cc47a3f82e7</dd></dl></div></li></ul></div></div>"
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       "<xarray.Dataset> Size: 68MB\n",
       "Dimensions:    (i: 90, i_g: 90, j: 90, j_g: 90, k: 50, k_u: 50, k_l: 50,\n",
       "                k_p1: 51, tile: 13, time: 1, nv: 2, nb: 4)\n",
       "Coordinates: (12/22)\n",
       "  * i          (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * i_g        (i_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j          (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j_g        (j_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
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       "  * k_u        (k_u) int32 200B 0 1 2 3 4 5 6 7 8 ... 41 42 43 44 45 46 47 48 49\n",
       "    ...         ...\n",
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       "Dimensions without coordinates: nv, nb\n",
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       "    VVEL       (time, k, tile, j_g, i) float32 21MB dask.array<chunksize=(1, 25, 7, 45, 45), meta=np.ndarray>\n",
       "    WVEL       (time, k_l, tile, j, i) float32 21MB dask.array<chunksize=(1, 25, 7, 45, 45), meta=np.ndarray>\n",
       "Attributes: (12/62)\n",
       "    acknowledgement:                 This research was carried out by the Jet...\n",
       "    author:                          Ian Fenty and Ou Wang\n",
       "    cdm_data_type:                   Grid\n",
       "    comment:                         Fields provided on the curvilinear lat-l...\n",
       "    Conventions:                     CF-1.8, ACDD-1.3\n",
       "    coordinates_comment:             Note: the global 'coordinates' attribute...\n",
       "    ...                              ...\n",
       "    time_coverage_duration:          P1M\n",
       "    time_coverage_end:               2000-02-01T00:00:00\n",
       "    time_coverage_resolution:        P1M\n",
       "    time_coverage_start:             2000-01-01T00:00:00\n",
       "    title:                           ECCO Ocean Velocity - Monthly Mean llc90...\n",
       "    uuid:                            32bd652c-4182-11eb-bd5c-0cc47a3f82e7"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the contents of the monthly velocity dataset\n",
    "ds_vel_mo"
   ]
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   "source": [
    "## Compute and map geostrophic balance\n",
    "\n",
    "Now that we are aware of the corrections needed to get actual in-situ density and pressure (gradients) from ECCO, we are ready to assess geostrophic balance. Let's consider the ```k=20``` depth level again; we are deliberately avoiding plotting at the surface for reasons that will be apparent later! \n",
    "\n",
    "The right-hand side of the geostrophic balance equations are $(1/\\rho){p_x}$ and $-(1/\\rho){p_y}$, and $p_x$ and $p_y$ can be computed as\n",
    "\n",
    "$$\n",
    "p_x = ({\\rm{rhoConst}})\\frac{\\partial}{\\partial{x}}{\\rm{PHIHYDcR}}\n",
    "$$\n",
    "$$\n",
    "p_y = ({\\rm{rhoConst}})\\frac{\\partial}{\\partial{y}}{\\rm{PHIHYDcR}}\n",
    "$$\n",
    "\n",
    "with ${\\rm rhoConst} = 1029 \\, {\\rm kg \\, m}^{-3}$.\n",
    "\n",
    "Now we use the grid parameters to help us compute derivatives:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "\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.Dataset&gt; Size: 89MB\n",
       "Dimensions:  (i: 90, i_g: 90, j: 90, j_g: 90, k: 50, k_u: 50, k_l: 50,\n",
       "              k_p1: 51, tile: 13, nb: 4, nv: 2)\n",
       "Coordinates: (12/20)\n",
       "  * i        (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * i_g      (i_g) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j        (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j_g      (j_g) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * k        (k) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n",
       "  * k_u      (k_u) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n",
       "    ...       ...\n",
       "    Zp1      (k_p1) float32 204B dask.array&lt;chunksize=(51,), meta=np.ndarray&gt;\n",
       "    Zu       (k_u) float32 200B dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;\n",
       "    Zl       (k_l) float32 200B dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;\n",
       "    XC_bnds  (tile, j, i, nb) float32 2MB dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;\n",
       "    YC_bnds  (tile, j, i, nb) float32 2MB dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;\n",
       "    Z_bnds   (k, nv) float32 400B dask.array&lt;chunksize=(50, 2), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: nb, nv\n",
       "Data variables: (12/21)\n",
       "    CS       (tile, j, i) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    SN       (tile, j, i) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    rA       (tile, j, i) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    dxG      (tile, j_g, i) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    dyG      (tile, j, i_g) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    Depth    (tile, j, i) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    ...       ...\n",
       "    hFacC    (k, tile, j, i) float32 21MB dask.array&lt;chunksize=(25, 7, 45, 45), meta=np.ndarray&gt;\n",
       "    hFacW    (k, tile, j, i_g) float32 21MB dask.array&lt;chunksize=(25, 7, 45, 45), meta=np.ndarray&gt;\n",
       "    hFacS    (k, tile, j_g, i) float32 21MB dask.array&lt;chunksize=(25, 7, 45, 45), meta=np.ndarray&gt;\n",
       "    maskC    (k, tile, j, i) bool 5MB dask.array&lt;chunksize=(25, 7, 45, 45), meta=np.ndarray&gt;\n",
       "    maskW    (k, tile, j, i_g) bool 5MB dask.array&lt;chunksize=(25, 7, 45, 45), meta=np.ndarray&gt;\n",
       "    maskS    (k, tile, j_g, i) bool 5MB dask.array&lt;chunksize=(25, 7, 45, 45), meta=np.ndarray&gt;\n",
       "Attributes: (12/58)\n",
       "    acknowledgement:                 This research was carried out by the Jet...\n",
       "    author:                          Ian Fenty and Ou Wang\n",
       "    cdm_data_type:                   Grid\n",
       "    comment:                         Fields provided on the curvilinear lat-l...\n",
       "    Conventions:                     CF-1.8, ACDD-1.3\n",
       "    coordinates_comment:             Note: the global &#x27;coordinates&#x27; attribute...\n",
       "    ...                              ...\n",
       "    references:                      ECCO Consortium, Fukumori, I., Wang, O.,...\n",
       "    source:                          The ECCO V4r4 state estimate was produce...\n",
       "    standard_name_vocabulary:        NetCDF Climate and Forecast (CF) Metadat...\n",
       "    summary:                         This dataset provides geometric paramete...\n",
       "    title:                           ECCO Geometry Parameters for the Lat-Lon...\n",
       "    uuid:                            87ff7d24-86e5-11eb-9c5f-f8f21e2ee3e0</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-0b187dc2-a734-4056-85c5-a4c2be034b0e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0b187dc2-a734-4056-85c5-a4c2be034b0e' 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'>i</span>: 90</li><li><span class='xr-has-index'>i_g</span>: 90</li><li><span class='xr-has-index'>j</span>: 90</li><li><span class='xr-has-index'>j_g</span>: 90</li><li><span class='xr-has-index'>k</span>: 50</li><li><span class='xr-has-index'>k_u</span>: 50</li><li><span class='xr-has-index'>k_l</span>: 50</li><li><span class='xr-has-index'>k_p1</span>: 51</li><li><span class='xr-has-index'>tile</span>: 13</li><li><span>nb</span>: 4</li><li><span>nv</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-70620b9c-d3e4-482e-91a4-3bd4eae4ec79' class='xr-section-summary-in' type='checkbox'  checked><label for='section-70620b9c-d3e4-482e-91a4-3bd4eae4ec79' class='xr-section-summary' >Coordinates: <span>(20)</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'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-d7ec1e62-ada8-43df-9784-382a437e7500' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7ec1e62-ada8-43df-9784-382a437e7500' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84efc76b-9840-47ae-b806-5c3371a48189' class='xr-var-data-in' type='checkbox'><label for='data-84efc76b-9840-47ae-b806-5c3371a48189' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>grid index in x for variables at tracer and &#x27;v&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>XC</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, tracer (e.g., THETA) and &#x27;v&#x27; variables (e.g., VVEL) have the same x coordinate on the model grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>i_g</span></div><div class='xr-var-dims'>(i_g)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-266b5e49-dac5-420f-86c8-1a26844921dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-266b5e49-dac5-420f-86c8-1a26844921dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7f7e5ce-f9b7-44bf-a0af-0d4d9ce679c3' class='xr-var-data-in' type='checkbox'><label for='data-d7f7e5ce-f9b7-44bf-a0af-0d4d9ce679c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>grid index in x for variables at &#x27;u&#x27; and &#x27;g&#x27; locations</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd><dt><span>swap_dim :</span></dt><dd>XG</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, &#x27;u&#x27; (e.g., UVEL) and &#x27;g&#x27; variables (e.g., XG) have the same x coordinate on the model grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j</span></div><div class='xr-var-dims'>(j)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-3dd6e78c-5c90-44eb-8dee-76eaeb8788b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3dd6e78c-5c90-44eb-8dee-76eaeb8788b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b793afe-18fc-4f66-b5d6-e827b3f266de' class='xr-var-data-in' type='checkbox'><label for='data-7b793afe-18fc-4f66-b5d6-e827b3f266de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>grid index in y for variables at tracer and &#x27;u&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>YC</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, tracer (e.g., THETA) and &#x27;u&#x27; variables (e.g., UVEL) have the same y coordinate on the model grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j_g</span></div><div class='xr-var-dims'>(j_g)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-d62ff660-ba1b-4915-99cd-162d0b8a3014' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d62ff660-ba1b-4915-99cd-162d0b8a3014' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53d3ec73-1242-444c-99df-f509d6ad50c5' class='xr-var-data-in' type='checkbox'><label for='data-53d3ec73-1242-444c-99df-f509d6ad50c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>grid index in y for variables at &#x27;v&#x27; and &#x27;g&#x27; locations</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd><dt><span>swap_dim :</span></dt><dd>YG</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, &#x27;v&#x27; (e.g., VVEL) and &#x27;g&#x27; variables (e.g., XG) have the same y coordinate.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 44 45 46 47 48 49</div><input id='attrs-0c64b688-9769-4ee7-906a-12cc91e2ba2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c64b688-9769-4ee7-906a-12cc91e2ba2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3645d579-d92b-4783-b9fd-941143a21299' class='xr-var-data-in' type='checkbox'><label for='data-3645d579-d92b-4783-b9fd-941143a21299' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>grid index in z for tracer variables</dd><dt><span>swap_dim :</span></dt><dd>Z</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k_u</span></div><div class='xr-var-dims'>(k_u)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 44 45 46 47 48 49</div><input id='attrs-f8cb0e4f-8c90-4013-96f4-fb6eb13f3dde' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8cb0e4f-8c90-4013-96f4-fb6eb13f3dde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48d39c2e-84ad-4f84-9d81-8dc1831decc4' class='xr-var-data-in' type='checkbox'><label for='data-48d39c2e-84ad-4f84-9d81-8dc1831decc4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>grid index in z corresponding to the bottom face of tracer grid cells (&#x27;w&#x27; locations)</dd><dt><span>c_grid_axis_shift :</span></dt><dd>0.5</dd><dt><span>swap_dim :</span></dt><dd>Zu</dd><dt><span>comment :</span></dt><dd>First index corresponds to the bottom face of the uppermost tracer grid cell. The use of &#x27;u&#x27; in the variable name follows the MITgcm convention for naming the bottom face of ocean tracer grid cells.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k_l</span></div><div class='xr-var-dims'>(k_l)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 44 45 46 47 48 49</div><input id='attrs-b39bad8e-b9f7-436c-9438-860aded85031' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b39bad8e-b9f7-436c-9438-860aded85031' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75c26ca4-2920-407d-b7db-9e442ce15d94' class='xr-var-data-in' type='checkbox'><label for='data-75c26ca4-2920-407d-b7db-9e442ce15d94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>grid index in z corresponding to the top face of tracer grid cells (&#x27;w&#x27; locations)</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd><dt><span>swap_dim :</span></dt><dd>Zl</dd><dt><span>comment :</span></dt><dd>First index corresponds to the top face of the uppermost tracer grid cell. The use of &#x27;l&#x27; in the variable name follows the MITgcm convention for naming the top face of ocean tracer grid cells.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k_p1</span></div><div class='xr-var-dims'>(k_p1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 45 46 47 48 49 50</div><input id='attrs-6464f7f0-9a59-4064-8b75-94cabf848e14' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6464f7f0-9a59-4064-8b75-94cabf848e14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38ebe156-b14f-40e0-9d65-513f90cce48a' class='xr-var-data-in' type='checkbox'><label for='data-38ebe156-b14f-40e0-9d65-513f90cce48a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>grid index in z for variables at &#x27;w&#x27; locations</dd><dt><span>c_grid_axis_shift :</span></dt><dd>[-0.5  0.5]</dd><dt><span>swap_dim :</span></dt><dd>Zp1</dd><dt><span>comment :</span></dt><dd>Includes top of uppermost model tracer cell (k_p1=0) and bottom of lowermost tracer cell (k_p1=51).</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>tile</span></div><div class='xr-var-dims'>(tile)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-b94cb292-3dba-4176-9837-805060fd0b74' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b94cb292-3dba-4176-9837-805060fd0b74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca99f745-9202-4178-9a5c-7a02ed170237' class='xr-var-data-in' type='checkbox'><label for='data-ca99f745-9202-4178-9a5c-7a02ed170237' 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>lat-lon-cap tile index</dd><dt><span>comment :</span></dt><dd>The ECCO V4 horizontal model grid is divided into 13 tiles of 90x90 cells for convenience.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-8f986600-53a5-43c9-bf6f-a6df0e15e09d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f986600-53a5-43c9-bf6f-a6df0e15e09d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-924c3c5d-4399-4c00-b44a-8fbcf20879e2' class='xr-var-data-in' type='checkbox'><label for='data-924c3c5d-4399-4c00-b44a-8fbcf20879e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of tracer grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>coordinate :</span></dt><dd>YC XC</dd><dt><span>bounds :</span></dt><dd>XC_bnds</dd><dt><span>comment :</span></dt><dd>nonuniform grid spacing</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>longitude</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> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90) </td>\n",
       "                        <td> (13, 90, 90) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 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></li><li class='xr-var-item'><div class='xr-var-name'><span>YC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-b6f9844e-2201-4da9-8119-12b4eae10204' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6f9844e-2201-4da9-8119-12b4eae10204' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7f7910c-80f5-4e89-9282-da95eb5b4496' class='xr-var-data-in' type='checkbox'><label for='data-a7f7910c-80f5-4e89-9282-da95eb5b4496' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of tracer grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>coordinate :</span></dt><dd>YC XC</dd><dt><span>bounds :</span></dt><dd>YC_bnds</dd><dt><span>comment :</span></dt><dd>nonuniform grid spacing</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                <thead>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                    <tr>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XG</span></div><div class='xr-var-dims'>(tile, j_g, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-6a41394b-1d44-4b89-82af-c9fad5988443' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a41394b-1d44-4b89-82af-c9fad5988443' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca6e1e91-1314-4646-8340-d35d11f8052d' class='xr-var-data-in' type='checkbox'><label for='data-ca6e1e91-1314-4646-8340-d35d11f8052d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of &#x27;southwest&#x27; corner of tracer grid cell</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>coordinate :</span></dt><dd>YG XG</dd><dt><span>comment :</span></dt><dd>Nonuniform grid spacing. Note: &#x27;southwest&#x27; does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "            <table style=\"border-collapse: collapse;\">\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
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       "                        <td> (13, 90, 90) </td>\n",
       "                        <td> (13, 90, 90) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 204 B </td>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Zl</span></div><div class='xr-var-dims'>(k_l)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-51f37a78-5a64-491b-88d3-a26d9588d34a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51f37a78-5a64-491b-88d3-a26d9588d34a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-355e4c22-965d-4256-894b-4d1bebb15bf7' class='xr-var-data-in' type='checkbox'><label for='data-355e4c22-965d-4256-894b-4d1bebb15bf7' 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>depth of top face of tracer grid cell</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>comment :</span></dt><dd>First element is 0m, the depth of the top face of the uppermost tracer grid cell (i.e., the ocean surface). Last element is the depth of the top face of the deepest grid cell. The use of &#x27;l&#x27; in the variable name follows the MITgcm convention for naming the top face of ocean tracer grid cells.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>depth</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> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 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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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XC_bnds</span></div><div class='xr-var-dims'>(tile, j, i, nb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;</div><input id='attrs-d8a47ed5-95e7-4933-974f-d45c13c2febe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8a47ed5-95e7-4933-974f-d45c13c2febe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b04cdc4a-867d-46e7-9d20-1618b8abc58a' class='xr-var-data-in' type='checkbox'><label for='data-b04cdc4a-867d-46e7-9d20-1618b8abc58a' 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>comment :</span></dt><dd>Bounds array follows CF conventions. XC_bnds[i,j,0] = &#x27;southwest&#x27; corner (j-1, i-1), XC_bnds[i,j,1] = &#x27;southeast&#x27; corner (j-1, i+1), XC_bnds[i,j,2] = &#x27;northeast&#x27; corner (j+1, i+1), XC_bnds[i,j,3]  = &#x27;northwest&#x27; corner (j+1, i-1). Note: &#x27;southwest&#x27;, &#x27;southeast&#x27;, northwest&#x27;, and &#x27;northeast&#x27; do not correspond to geographic orientation but are used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitudes of tracer grid cell corners</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> 1.61 MiB </td>\n",
       "                        <td> 1.61 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90, 4) </td>\n",
       "                        <td> (13, 90, 90, 4) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YC_bnds</span></div><div class='xr-var-dims'>(tile, j, i, nb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;</div><input id='attrs-e2290773-ead9-4331-8fb1-0ed0377c70af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2290773-ead9-4331-8fb1-0ed0377c70af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec36e8f8-7ea1-4262-bb52-4e5b12ffabaa' class='xr-var-data-in' type='checkbox'><label for='data-ec36e8f8-7ea1-4262-bb52-4e5b12ffabaa' 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>comment :</span></dt><dd>Bounds array follows CF conventions. YC_bnds[i,j,0] = &#x27;southwest&#x27; corner (j-1, i-1), YC_bnds[i,j,1] = &#x27;southeast&#x27; corner (j-1, i+1), YC_bnds[i,j,2] = &#x27;northeast&#x27; corner (j+1, i+1), YC_bnds[i,j,3]  = &#x27;northwest&#x27; corner (j+1, i-1). Note: &#x27;southwest&#x27;, &#x27;southeast&#x27;, northwest&#x27;, and &#x27;northeast&#x27; do not correspond to geographic orientation but are used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitudes of tracer grid cell corners</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> 1.61 MiB </td>\n",
       "                        <td> 1.61 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90, 4) </td>\n",
       "                        <td> (13, 90, 90, 4) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 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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       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Z_bnds</span></div><div class='xr-var-dims'>(k, nv)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50, 2), meta=np.ndarray&gt;</div><input id='attrs-aec378c1-9315-424b-835a-bf5346f150dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aec378c1-9315-424b-835a-bf5346f150dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1f25e82-c810-467b-8b40-eaa59cdbb9e8' class='xr-var-data-in' type='checkbox'><label for='data-f1f25e82-c810-467b-8b40-eaa59cdbb9e8' 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>comment :</span></dt><dd>One pair of depths for each vertical level.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>depths of top and bottom faces of tracer grid cell</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> 400 B </td>\n",
       "                        <td> 400 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50, 2) </td>\n",
       "                        <td> (50, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 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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       "        </td>\n",
       "        <td>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 411.33 kiB </td>\n",
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       "                        <td> (13, 90, 90) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                        <td> 411.33 kiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dxG</span></div><div class='xr-var-dims'>(tile, j_g, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-e6e1d2bd-0a46-41b2-975b-c0b785971663' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6e1d2bd-0a46-41b2-975b-c0b785971663' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3361166-9eec-4e81-92c3-407cb12e52c7' class='xr-var-data-in' type='checkbox'><label for='data-d3361166-9eec-4e81-92c3-407cb12e52c7' 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>distance between &#x27;southwest&#x27; and &#x27;southeast&#x27; corners of the tracer grid cell</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coordinate :</span></dt><dd>YG XC</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>Alternatively, the length of &#x27;south&#x27; side of tracer grid cell. Note: &#x27;south&#x27;, &#x27;southwest&#x27;, and &#x27;southeast&#x27; do not correspond to geographic orientation but are used for convenience to describe the computational grid. See MITgcm documentation for details.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>drC</span></div><div class='xr-var-dims'>(k_p1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(51,), meta=np.ndarray&gt;</div><input id='attrs-59ae53a6-6333-453d-8bfe-636966f52241' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-59ae53a6-6333-453d-8bfe-636966f52241' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3db8b675-8a36-4f37-950d-b1ccfaaaf52f' class='xr-var-data-in' type='checkbox'><label for='data-3db8b675-8a36-4f37-950d-b1ccfaaaf52f' 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>distance between the centers of adjacent tracer grid cells in the &#x27;z&#x27; direction</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>The first element corresponds to the distance between the depth of the center of the uppermost model grid cell and the surface.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>drF</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-8af542ae-9e43-4c98-9a8e-833f162caa44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8af542ae-9e43-4c98-9a8e-833f162caa44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e953b88e-f27e-4b6e-9ca7-a198385388f5' class='xr-var-data-in' type='checkbox'><label for='data-e953b88e-f27e-4b6e-9ca7-a198385388f5' 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>distance between the upper and lower interfaces of the model grid cell</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>standard_name :</span></dt><dd>cell_thickness</dd><dt><span>comment :</span></dt><dd>Nominal grid cell thickness. Note: in the z* coordinate system used in ECCO V4, actual tracer grid cell thickness, h, varies through time as h(i,j,k,t)= drF(k) hfacC(i,j,k,t).</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PHrefC</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50,), meta=np.ndarray&gt;</div><input id='attrs-e4dd1590-844f-4600-86e6-13b322038a3f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4dd1590-844f-4600-86e6-13b322038a3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-604797d8-6f4c-431b-bda7-31beb158d53f' class='xr-var-data-in' type='checkbox'><label for='data-604797d8-6f4c-431b-bda7-31beb158d53f' 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>reference ocean hydrostatic pressure at tracer grid cell center</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>PHrefC = p_ref (k) / rhoConst = rhoConst g z(k) / rhoConst = g z(k), where p_ref(k) is reference hydrostatic ocean pressure at center of tracer grid cell k, rhoConst is reference density (1029 kg m-3), g is acceleration due to gravity (9.81 m s-2), and z(k) is depth at center of tracer grid cell k. Units: p:[kg m-1 s-2], rhoConst:[kg m-3], g:[m s-2], z_m(t):[m]. Note: does not include atmospheric pressure loading. Quantity referred to in some contexts as hydrostatic pressure potential. PHIHYDcR is anomaly of PHrefC.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PHrefF</span></div><div class='xr-var-dims'>(k_p1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(51,), meta=np.ndarray&gt;</div><input id='attrs-d7280acf-2c57-4e3f-a216-f0cbae8ccb08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7280acf-2c57-4e3f-a216-f0cbae8ccb08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f2a1122-6e58-4253-8395-77ed7bcb81bf' class='xr-var-data-in' type='checkbox'><label for='data-0f2a1122-6e58-4253-8395-77ed7bcb81bf' 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>reference ocean hydrostatic pressure at tracer grid cell top/bottom interface</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>PHrefF = p_ref (k_l) / rhoConst = rhoConst g z(k_l) / rhoConst = g z(k_l), where p_ref(k_l) is reference hydrostatic ocean pressure at lower interface of tracer grid cell k, rhoConst is reference density (1029 kg m-3), g is acceleration due to gravity (9.81 m s-2), and z(k) is depth at center of tracer grid cell k. Units: p:[kg m-1 s-2], rhoConst:[kg m-3], g:[m s-2], z_m(t):[m]. Note: does not include atmospheric pressure loading. Quantity referred to in some contexts as hydrostatic pressure potential. See PHrefC</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hFacC</span></div><div class='xr-var-dims'>(k, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 7, 45, 45), meta=np.ndarray&gt;</div><input id='attrs-2acfbede-f308-4520-8d65-63432853ad57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2acfbede-f308-4520-8d65-63432853ad57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31ab0f27-bed1-47fa-83e2-91ceafb1efa1' class='xr-var-data-in' type='checkbox'><label for='data-31ab0f27-bed1-47fa-83e2-91ceafb1efa1' 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>vertical open fraction of tracer grid cell</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>comment :</span></dt><dd>Tracer grid cells may be fractionally closed in the vertical. The open vertical fraction is hFacC. The model allows for partially-filled cells to represent topographic variations more smoothly (hFacC &lt; 1). Completely closed (dry) tracer grid cells have hFacC = 0. Note: the model z* coordinate system allows hFacC to vary through time. A time-invariant hFacC field is provided for reference.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>maskC</span></div><div class='xr-var-dims'>(k, tile, j, i)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 7, 45, 45), meta=np.ndarray&gt;</div><input id='attrs-3b27a9ee-65f4-4054-9b54-1100a9f95334' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b27a9ee-65f4-4054-9b54-1100a9f95334' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-684c30d7-5044-4a24-8563-b97073d70cee' class='xr-var-data-in' type='checkbox'><label for='data-684c30d7-5044-4a24-8563-b97073d70cee' 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>wet/dry boolean mask for tracer grid cell</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>True for tracer grid cells with nonzero open vertical fraction (hFacC &gt; 0), otherwise False. Although hFacC can vary though time, cells will never close if starting open and will never open if starting closed: hFacC(i,j,k,t) &gt; 0 for all t, if hFacC(i,j,k,t=0) and hFacC(i,j,k,t) = 0 for all t, if hFacC(i,j,k,t=0) = 0. Therefore, maskC is time invariant.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> bool numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>maskW</span></div><div class='xr-var-dims'>(k, tile, j, i_g)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 7, 45, 45), meta=np.ndarray&gt;</div><input id='attrs-b14c5c12-9d6b-4bd3-90a4-8da18ec138f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b14c5c12-9d6b-4bd3-90a4-8da18ec138f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-159bec9d-a6b6-484a-8b9a-131f17df8e59' class='xr-var-data-in' type='checkbox'><label for='data-159bec9d-a6b6-484a-8b9a-131f17df8e59' 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>wet/dry boolean mask for &#x27;west&#x27; face of tracer grid cell</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>True for grid cells with nonzero open vertical fraction along their &#x27;west&#x27; face (hFacW &gt; 0), otherwise False. Although hFacW can vary though time, cells will never close if starting open and will never open if starting closed: hFacW(i,j,k,t) &gt; 0 for all t, if hFacW(i,j,k,t=0) and hFacW(i,j,k,t) = 0 for all t, if hFacW(i,j,k,t=0) = 0. Therefore, maskW is time invariant. Note: </dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 5.02 MiB </td>\n",
       "                        <td> 346.07 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50, 13, 90, 90) </td>\n",
       "                        <td> (25, 7, 45, 45) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> bool numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>maskS</span></div><div class='xr-var-dims'>(k, tile, j_g, i)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(25, 7, 45, 45), meta=np.ndarray&gt;</div><input id='attrs-10311609-f628-489f-8c87-096d49660145' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10311609-f628-489f-8c87-096d49660145' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d878b54c-d97f-4cbe-b916-5b9365e4229d' class='xr-var-data-in' type='checkbox'><label for='data-d878b54c-d97f-4cbe-b916-5b9365e4229d' 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>wet/dry boolean mask for &#x27;south&#x27; face of tracer grid cell</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>True for grid cells with nonzero open vertical fraction along their &#x27;south&#x27; face (hFacS &gt; 0), otherwise False. Although hFacS can vary though time, cells will never close if starting open and will never open if starting closed: hFacS(i,j,k,t) &gt; 0 for all t, if hFacS(i,j,k,t=0) and hFacS(i,j,k,t) = 0 for all t, if hFacS(i,j,k,t=0) = 0. Therefore, maskS is time invariant. Note: </dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
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       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-f2507d81-1beb-4cef-a944-dea3cfa8a759' class='xr-section-summary-in' type='checkbox'  ><label for='section-f2507d81-1beb-4cef-a944-dea3cfa8a759' class='xr-section-summary' >Indexes: <span>(9)</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>i</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ad42dc6b-000d-4094-a7f5-89952ab0f288' class='xr-index-data-in' type='checkbox'/><label for='index-ad42dc6b-000d-4094-a7f5-89952ab0f288' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;i&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>i_g</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-65141e15-9f55-4f6f-a906-97c99c6582c7' class='xr-index-data-in' type='checkbox'/><label for='index-65141e15-9f55-4f6f-a906-97c99c6582c7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;i_g&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>j</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-894cb202-55f6-4aeb-a8cf-3b64a2f4437e' class='xr-index-data-in' type='checkbox'/><label for='index-894cb202-55f6-4aeb-a8cf-3b64a2f4437e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;j&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>j_g</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7f22f362-88a5-4da8-88b9-6adb3726faa1' class='xr-index-data-in' type='checkbox'/><label for='index-7f22f362-88a5-4da8-88b9-6adb3726faa1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;j_g&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0c7ed332-4701-4739-8fca-026c211f3ef8' class='xr-index-data-in' type='checkbox'/><label for='index-0c7ed332-4701-4739-8fca-026c211f3ef8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;k&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k_u</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-410e0230-c486-42f9-b232-3f90a0c6a7a0' class='xr-index-data-in' type='checkbox'/><label for='index-410e0230-c486-42f9-b232-3f90a0c6a7a0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;k_u&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k_l</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5bdf8e34-f0e8-451a-b02e-16e2c29f5c83' class='xr-index-data-in' type='checkbox'/><label for='index-5bdf8e34-f0e8-451a-b02e-16e2c29f5c83' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;k_l&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k_p1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-44f56f5f-0287-4a54-80be-fcc77fbae775' class='xr-index-data-in' type='checkbox'/><label for='index-44f56f5f-0287-4a54-80be-fcc77fbae775' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;k_p1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>tile</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e98ef31a-319f-4960-a487-ae7dd89e48a6' class='xr-index-data-in' type='checkbox'/><label for='index-e98ef31a-319f-4960-a487-ae7dd89e48a6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=&#x27;int32&#x27;, name=&#x27;tile&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-93ebfbc5-612d-4f82-a55b-18b4ecef74d7' class='xr-section-summary-in' type='checkbox'  ><label for='section-93ebfbc5-612d-4f82-a55b-18b4ecef74d7' class='xr-section-summary' >Attributes: <span>(58)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>acknowledgement :</span></dt><dd>This research was carried out by the Jet Propulsion Laboratory, managed by the California Institute of Technology under a contract with the National Aeronautics and Space Administration.</dd><dt><span>author :</span></dt><dd>Ian Fenty and Ou Wang</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>Fields provided on the curvilinear lat-lon-cap 90 (llc90) native grid used in the ECCO model.</dd><dt><span>Conventions :</span></dt><dd>CF-1.8, ACDD-1.3</dd><dt><span>coordinates_comment :</span></dt><dd>Note: the global &#x27;coordinates&#x27; attribute describes auxillary coordinates.</dd><dt><span>creator_email :</span></dt><dd>ecco-group@mit.edu</dd><dt><span>creator_institution :</span></dt><dd>NASA Jet Propulsion Laboratory (JPL)</dd><dt><span>creator_name :</span></dt><dd>ECCO Consortium</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_url :</span></dt><dd>https://ecco-group.org</dd><dt><span>date_created :</span></dt><dd>2021-03-16T22:56:35</dd><dt><span>date_issued :</span></dt><dd>2021-03-16T22:56:35</dd><dt><span>date_metadata_modified :</span></dt><dd>2021-03-16T22:56:35</dd><dt><span>date_modified :</span></dt><dd>2021-03-16T22:56:35</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>variable</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>180.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>-180.0</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>variable</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_vertical_max :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_min :</span></dt><dd>-6134.5</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>up</dd><dt><span>geospatial_vertical_resolution :</span></dt><dd>variable</dd><dt><span>geospatial_vertical_units :</span></dt><dd>meter</dd><dt><span>history :</span></dt><dd>Inaugural release of an ECCO Central Estimate solution to PO.DAAC</dd><dt><span>id :</span></dt><dd>10.5067/ECL5A-GRD44</dd><dt><span>institution :</span></dt><dd>NASA Jet Propulsion Laboratory (JPL)</dd><dt><span>instrument_vocabulary :</span></dt><dd>GCMD instrument keywords</dd><dt><span>keywords :</span></dt><dd>EARTH SCIENCE SERVICES &gt; MODELS &gt; EARTH SCIENCE REANALYSES/ASSIMILATION MODELS</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>license :</span></dt><dd>Public Domain</dd><dt><span>metadata_link :</span></dt><dd>https://cmr.earthdata.nasa.gov/search/collections.umm_json?ShortName=ECCO_L4_GEOMETRY_LLC0090GRID_V4R4</dd><dt><span>naming_authority :</span></dt><dd>gov.nasa.jpl</dd><dt><span>platform :</span></dt><dd>ERS-1/2, TOPEX/Poseidon, Geosat Follow-On (GFO), ENVISAT, Jason-1, Jason-2, CryoSat-2, SARAL/AltiKa, Jason-3, AVHRR, Aquarius, SSM/I, SSMIS, GRACE, DTU17MDT, Argo, WOCE, GO-SHIP, MEOP, Ice Tethered Profilers (ITP)</dd><dt><span>platform_vocabulary :</span></dt><dd>GCMD platform keywords</dd><dt><span>processing_level :</span></dt><dd>L4</dd><dt><span>product_name :</span></dt><dd>GRID_GEOMETRY_ECCO_V4r4_native_llc0090.nc</dd><dt><span>product_time_coverage_end :</span></dt><dd>2018-01-01T00:00:00</dd><dt><span>product_time_coverage_start :</span></dt><dd>1992-01-01T12:00:00</dd><dt><span>product_version :</span></dt><dd>Version 4, Release 4</dd><dt><span>program :</span></dt><dd>NASA Physical Oceanography, Cryosphere, Modeling, Analysis, and Prediction (MAP)</dd><dt><span>project :</span></dt><dd>Estimating the Circulation and Climate of the Ocean (ECCO)</dd><dt><span>publisher_email :</span></dt><dd>podaac@podaac.jpl.nasa.gov</dd><dt><span>publisher_institution :</span></dt><dd>PO.DAAC</dd><dt><span>publisher_name :</span></dt><dd>Physical Oceanography Distributed Active Archive Center (PO.DAAC)</dd><dt><span>publisher_type :</span></dt><dd>institution</dd><dt><span>publisher_url :</span></dt><dd>https://podaac.jpl.nasa.gov</dd><dt><span>references :</span></dt><dd>ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., &amp; Ponte, R. M. 2020. Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate (Version 4 Release 4). doi:10.5281/zenodo.3765928</dd><dt><span>source :</span></dt><dd>The ECCO V4r4 state estimate was produced by fitting a free-running solution of the MITgcm (checkpoint 66g) to satellite and in situ observational data in a least squares sense using the adjoint method</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>summary :</span></dt><dd>This dataset provides geometric parameters for the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Parameters include areas and lengths of grid cell sides, horizontal and vertical coordinates of grid cell centers and corners, grid rotation angles, and global domain geometry including bathymetry and land/ocean masks. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea-ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.</dd><dt><span>title :</span></dt><dd>ECCO Geometry Parameters for the Lat-Lon-Cap 90 (llc90) Native Model Grid (Version 4 Release 4)</dd><dt><span>uuid :</span></dt><dd>87ff7d24-86e5-11eb-9c5f-f8f21e2ee3e0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 89MB\n",
       "Dimensions:  (i: 90, i_g: 90, j: 90, j_g: 90, k: 50, k_u: 50, k_l: 50,\n",
       "              k_p1: 51, tile: 13, nb: 4, nv: 2)\n",
       "Coordinates: (12/20)\n",
       "  * i        (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * i_g      (i_g) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j        (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j_g      (j_g) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * k        (k) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n",
       "  * k_u      (k_u) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n",
       "    ...       ...\n",
       "    Zp1      (k_p1) float32 204B dask.array<chunksize=(51,), meta=np.ndarray>\n",
       "    Zu       (k_u) float32 200B dask.array<chunksize=(50,), meta=np.ndarray>\n",
       "    Zl       (k_l) float32 200B dask.array<chunksize=(50,), meta=np.ndarray>\n",
       "    XC_bnds  (tile, j, i, nb) float32 2MB dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>\n",
       "    YC_bnds  (tile, j, i, nb) float32 2MB dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>\n",
       "    Z_bnds   (k, nv) float32 400B dask.array<chunksize=(50, 2), meta=np.ndarray>\n",
       "Dimensions without coordinates: nb, nv\n",
       "Data variables: (12/21)\n",
       "    CS       (tile, j, i) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    SN       (tile, j, i) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    rA       (tile, j, i) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    dxG      (tile, j_g, i) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    dyG      (tile, j, i_g) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    Depth    (tile, j, i) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    ...       ...\n",
       "    hFacC    (k, tile, j, i) float32 21MB dask.array<chunksize=(25, 7, 45, 45), meta=np.ndarray>\n",
       "    hFacW    (k, tile, j, i_g) float32 21MB dask.array<chunksize=(25, 7, 45, 45), meta=np.ndarray>\n",
       "    hFacS    (k, tile, j_g, i) float32 21MB dask.array<chunksize=(25, 7, 45, 45), meta=np.ndarray>\n",
       "    maskC    (k, tile, j, i) bool 5MB dask.array<chunksize=(25, 7, 45, 45), meta=np.ndarray>\n",
       "    maskW    (k, tile, j, i_g) bool 5MB dask.array<chunksize=(25, 7, 45, 45), meta=np.ndarray>\n",
       "    maskS    (k, tile, j_g, i) bool 5MB dask.array<chunksize=(25, 7, 45, 45), meta=np.ndarray>\n",
       "Attributes: (12/58)\n",
       "    acknowledgement:                 This research was carried out by the Jet...\n",
       "    author:                          Ian Fenty and Ou Wang\n",
       "    cdm_data_type:                   Grid\n",
       "    comment:                         Fields provided on the curvilinear lat-l...\n",
       "    Conventions:                     CF-1.8, ACDD-1.3\n",
       "    coordinates_comment:             Note: the global 'coordinates' attribute...\n",
       "    ...                              ...\n",
       "    references:                      ECCO Consortium, Fukumori, I., Wang, O.,...\n",
       "    source:                          The ECCO V4r4 state estimate was produce...\n",
       "    standard_name_vocabulary:        NetCDF Climate and Forecast (CF) Metadat...\n",
       "    summary:                         This dataset provides geometric paramete...\n",
       "    title:                           ECCO Geometry Parameters for the Lat-Lon...\n",
       "    uuid:                            87ff7d24-86e5-11eb-9c5f-f8f21e2ee3e0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at grid parameters dataset\n",
    "ds_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operations on the native model grid\n",
    "\n",
    "If you expand the \"Data variables\" section in the above Dataset summary, you'll see a number of grid parameters, and you can look at their descriptions by clicking on the \"sheet of paper\" icon to the right of each one. To compute $x$- and $y$-derivatives of pressure, we recall that the pressure variable (PHIHYD) is located on coordinates (time,k,tile,j,i). The (j,i) coordinates indicate that pressure is \"centered\" in a given grid cell. The distance *between* the centers of grid cells is given by the variables **dxC** and **dyC** (the \"C\" indicates distance between grid cell centers).\n",
    "\n",
    "Now we can compute the derivatives of pressure and the right-hand side of the geostrophic balance equations. Because the native grid of ECCOv4 is composed of 13 tiles, when we difference or interpolate values across the tile edges, things can get complicated! Fortunately the ```xgcm``` package that was imported previously can help us with those operations. First we specify the connections between tiles in a Python dictionary, then create an xgcm Grid object that will allow us to carry out differencing and interpolation across the tile edges. We will need to both difference and interpolate to end up with pressure gradient values that are located at the center (i,j) of the grid cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg\n",
      "/srv/conda/envs/notebook/lib/python3.11/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;dp_dx&#x27; (time: 1, k: 50, tile: 13, j: 90, i: 90)&gt; Size: 21MB\n",
       "array([[[[[            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          ...,\n",
       "          [-1.78578217e-03, -2.80201435e-04,  4.48737061e-04, ...,\n",
       "            2.56811129e-03,  2.54054624e-03,  2.38151103e-03],\n",
       "          [-1.97579130e-03, -1.17824820e-04,  6.02442888e-04, ...,\n",
       "            2.95511540e-03,  3.09964363e-03,  3.09713813e-03],\n",
       "          [-1.60034164e-03,  3.31102929e-04,  6.47389097e-04, ...,\n",
       "            3.22869234e-03,  3.55401146e-03,  3.75702744e-03]],\n",
       "\n",
       "         [[-8.98729253e-04,  7.29348627e-04,  2.39784946e-04, ...,\n",
       "            3.42662539e-03,  3.89235793e-03,  4.29460499e-03],\n",
       "          [-2.40144931e-04,  7.03949365e-04, -7.84797710e-04, ...,\n",
       "            3.54899094e-03,  4.10596048e-03,  4.68219491e-03],\n",
       "          [ 5.17581939e-05,  1.75516907e-04, -2.09984998e-03, ...,\n",
       "            3.58781964e-03,  4.15722188e-03,  4.85306140e-03],\n",
       "...\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan]],\n",
       "\n",
       "         [[            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          ...,\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan]]]]],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "  * i        (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j        (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * k        (k) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n",
       "  * tile     (tile) int32 52B 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "Dimensions without coordinates: time</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'>'dp_dx'</div><ul class='xr-dim-list'><li><span>time</span>: 1</li><li><span class='xr-has-index'>k</span>: 50</li><li><span class='xr-has-index'>tile</span>: 13</li><li><span class='xr-has-index'>j</span>: 90</li><li><span class='xr-has-index'>i</span>: 90</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-cca31d9a-2d5c-44d9-9612-362b3746b808' class='xr-array-in' type='checkbox' checked><label for='section-cca31d9a-2d5c-44d9-9612-362b3746b808' 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>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[[[            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          ...,\n",
       "          [-1.78578217e-03, -2.80201435e-04,  4.48737061e-04, ...,\n",
       "            2.56811129e-03,  2.54054624e-03,  2.38151103e-03],\n",
       "          [-1.97579130e-03, -1.17824820e-04,  6.02442888e-04, ...,\n",
       "            2.95511540e-03,  3.09964363e-03,  3.09713813e-03],\n",
       "          [-1.60034164e-03,  3.31102929e-04,  6.47389097e-04, ...,\n",
       "            3.22869234e-03,  3.55401146e-03,  3.75702744e-03]],\n",
       "\n",
       "         [[-8.98729253e-04,  7.29348627e-04,  2.39784946e-04, ...,\n",
       "            3.42662539e-03,  3.89235793e-03,  4.29460499e-03],\n",
       "          [-2.40144931e-04,  7.03949365e-04, -7.84797710e-04, ...,\n",
       "            3.54899094e-03,  4.10596048e-03,  4.68219491e-03],\n",
       "          [ 5.17581939e-05,  1.75516907e-04, -2.09984998e-03, ...,\n",
       "            3.58781964e-03,  4.15722188e-03,  4.85306140e-03],\n",
       "...\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan]],\n",
       "\n",
       "         [[            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          ...,\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan]]]]],\n",
       "      dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-5d75b09e-2285-4559-aaac-9e650ef2a378' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5d75b09e-2285-4559-aaac-9e650ef2a378' class='xr-section-summary' >Coordinates: <span>(4)</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'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-463ba319-ad38-4131-a9d7-e8bf0cadc8e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-463ba319-ad38-4131-a9d7-e8bf0cadc8e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3f141cb-309e-4e8d-a61e-795e232fcb29' class='xr-var-data-in' type='checkbox'><label for='data-d3f141cb-309e-4e8d-a61e-795e232fcb29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>grid index in x for variables at tracer and &#x27;v&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>XC</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, tracer (e.g., THETA) and &#x27;v&#x27; variables (e.g., VVEL) have the same x coordinate on the model grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j</span></div><div class='xr-var-dims'>(j)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-5dcc842a-6a80-4145-80fa-f73c60ed88c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5dcc842a-6a80-4145-80fa-f73c60ed88c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c56f9c93-39c4-4337-9e26-021876590a37' class='xr-var-data-in' type='checkbox'><label for='data-c56f9c93-39c4-4337-9e26-021876590a37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>grid index in y for variables at tracer and &#x27;u&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>YC</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, tracer (e.g., THETA) and &#x27;u&#x27; variables (e.g., UVEL) have the same y coordinate on the model grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 44 45 46 47 48 49</div><input id='attrs-a84efad1-a65c-4760-a23d-f790783820ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a84efad1-a65c-4760-a23d-f790783820ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d1842f0-5387-4e73-af44-5c3dd2de4851' class='xr-var-data-in' type='checkbox'><label for='data-9d1842f0-5387-4e73-af44-5c3dd2de4851' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>grid index in z for tracer variables</dd><dt><span>swap_dim :</span></dt><dd>Z</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>tile</span></div><div class='xr-var-dims'>(tile)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-601b2eda-1857-46d8-9984-dcd7593621b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-601b2eda-1857-46d8-9984-dcd7593621b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33f9919e-467b-4a88-8b91-a1439d8a9c9e' class='xr-var-data-in' type='checkbox'><label for='data-33f9919e-467b-4a88-8b91-a1439d8a9c9e' 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>lat-lon-cap tile index</dd><dt><span>comment :</span></dt><dd>The ECCO V4 horizontal model grid is divided into 13 tiles of 90x90 cells for convenience.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ea998017-13c9-4e32-9b0e-449ed62c9bd4' class='xr-section-summary-in' type='checkbox'  ><label for='section-ea998017-13c9-4e32-9b0e-449ed62c9bd4' class='xr-section-summary' >Indexes: <span>(4)</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>i</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-715b5c51-ed80-4cbc-9b0b-7717292cca1f' class='xr-index-data-in' type='checkbox'/><label for='index-715b5c51-ed80-4cbc-9b0b-7717292cca1f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;i&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>j</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7da79323-0837-4c95-bd10-fdb36d3e934b' class='xr-index-data-in' type='checkbox'/><label for='index-7da79323-0837-4c95-bd10-fdb36d3e934b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;j&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-038c2085-f2d0-4bdf-b4e2-6ce6fd26b7dd' class='xr-index-data-in' type='checkbox'/><label for='index-038c2085-f2d0-4bdf-b4e2-6ce6fd26b7dd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;k&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>tile</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d3bfb131-da30-42b8-930b-30fc4063e8ab' class='xr-index-data-in' type='checkbox'/><label for='index-d3bfb131-da30-42b8-930b-30fc4063e8ab' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=&#x27;int32&#x27;, name=&#x27;tile&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8bf5b0c6-d703-486a-8ab3-74730270137b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8bf5b0c6-d703-486a-8ab3-74730270137b' 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.DataArray 'dp_dx' (time: 1, k: 50, tile: 13, j: 90, i: 90)> Size: 21MB\n",
       "array([[[[[            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          ...,\n",
       "          [-1.78578217e-03, -2.80201435e-04,  4.48737061e-04, ...,\n",
       "            2.56811129e-03,  2.54054624e-03,  2.38151103e-03],\n",
       "          [-1.97579130e-03, -1.17824820e-04,  6.02442888e-04, ...,\n",
       "            2.95511540e-03,  3.09964363e-03,  3.09713813e-03],\n",
       "          [-1.60034164e-03,  3.31102929e-04,  6.47389097e-04, ...,\n",
       "            3.22869234e-03,  3.55401146e-03,  3.75702744e-03]],\n",
       "\n",
       "         [[-8.98729253e-04,  7.29348627e-04,  2.39784946e-04, ...,\n",
       "            3.42662539e-03,  3.89235793e-03,  4.29460499e-03],\n",
       "          [-2.40144931e-04,  7.03949365e-04, -7.84797710e-04, ...,\n",
       "            3.54899094e-03,  4.10596048e-03,  4.68219491e-03],\n",
       "          [ 5.17581939e-05,  1.75516907e-04, -2.09984998e-03, ...,\n",
       "            3.58781964e-03,  4.15722188e-03,  4.85306140e-03],\n",
       "...\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan]],\n",
       "\n",
       "         [[            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          ...,\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan],\n",
       "          [            nan,             nan,             nan, ...,\n",
       "                       nan,             nan,             nan]]]]],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "  * i        (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j        (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * k        (k) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n",
       "  * tile     (tile) int32 52B 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "Dimensions without coordinates: time"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # create xgcm Grid object\n",
    "xgcm_grid = ecco.get_llc_grid(ds_grid)\n",
    "\n",
    "# convert DataArray content from dask to numpy arrays\n",
    "pressanom = pressanom.compute()\n",
    "\n",
    "# compute derivatives of pressure in x and y\n",
    "d_press_dx = (xgcm_grid.diff(rhoConst*pressanom,axis=\"X\",boundary='extend'))/ds_grid.dxC\n",
    "d_press_dy = (xgcm_grid.diff(rhoConst*pressanom,axis=\"Y\",boundary='extend'))/ds_grid.dyC\n",
    "d_press_dx = d_press_dx.compute()\n",
    "d_press_dy = d_press_dy.compute()\n",
    "\n",
    "\n",
    "# interpolate (vector) gradient values to center of grid cells\n",
    "import warnings\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")    # use this to ignore future warnings caused by interp_2d_vector function\n",
    "    press_grads_interp = xgcm_grid.interp_2d_vector({\"X\":d_press_dx,\"Y\":d_press_dy},boundary='extend')\n",
    "dp_dx = press_grads_interp['X']\n",
    "dp_dy = press_grads_interp['Y']\n",
    "dp_dx.name = 'dp_dx'\n",
    "dp_dy.name = 'dp_dy'\n",
    "\n",
    "dp_dx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the DataArray **dp_dx** has (j,i) horizontal coordinates, indicating that it is centered in the grid cells (after the interpolation operation). You can check that the same is true of **dp_dy**.\n",
    "\n",
    "### Right-hand side\n",
    "\n",
    "Now we use density to compute and plot the right-hand side of the geostrophic balance equations. Note that these plots adjust the colormap in order to mask land areas in black."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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d0bhxY0RFRSE9PV223rFjx9C5c2c4OzvD1dUVvXv3xsWLF0usnhKY4lNS+ypNyqM2z6ciKK57prjKZXl5yk9OjuxDiEXJy1N+8p9+FPQUhF9ekgHAon2b+rEwlldjgdOnT6Nt27a4fPkyPv/8c8TGxuLVV19FVFQUXnvtNeN6Z86cQWBgIB49eoSNGzdi+fLl+Pvvv9GuXTvcu3evFI+AEEIIMZMK2JgpFzEz69atQ2ZmJr7//nvUr18fANCxY0fcunULS5YsQVJSEqpUqYLw8HAYDAbExsbCxcUFANCyZUs0aNAAc+fOxaefflqah0EIIYQQHSyv+SVga2sLAKhcubJsvqurK6ysrGBnZ4ecnBzExsaiT58+xoYMANSpUwdBQUGIiYkp0ToTQgghxaICPpmxvBoLDBo0CK6urhgxYgQuXryI1NRUxMbG4uuvv8bIkSPh5OSECxcuICMjA/7+/ort/f39cf78eWRmZpq8b3PEdJgjbqEkY3F4pRnbUhFjaICyFQ+liYX/UJKKhY/50qQsNRAqYGOmXHQz+fj44NChQ3j55ZeN3UwA8MEHH+Dzzz8HACQkJAAA3NzcFNu7ubmBMYakpCR4eXkJ95GVlYWsrCzjdEpKihmPgBBCCCF6lYvGzOXLlxEaGorq1atj06ZNqFq1Ko4cOYIZM2YgLS0Ny5YtM64rSQX/9V7YslmzZiEyMtKs9SaEEELMrgLmmSkXjZkJEyYgJSUFJ06cgJOTEwCgffv28PDwwNChQzFw4EB4enoC+PcJzZMSExMhSRJcXV0L3MfEiRMxZswY43RKSgpq1apl3gMhhBBCiqqoA03qGJOttJWLxsyJEyfg5+dnbMjkCwgIAACcOnUKzz//PBwcHHDy5EnF9idPnoSvry/s7e0L3IfBYIDBYDBvxQtgETEQpEwp6/cMDT5Jyqq8PMF3Jy/X9ILU7nH6DhSrcnF2vb298eeffyItLU02/9ChQwCAmjVrwsbGBqGhodi8eTNSU1ON61y9ehVxcXHo3bt3idaZEEIIKRYVMADY8mosMHr0aNy/fx9dunTBxo0bsX//fsycORNjxoyBn58funfvDgCIjIxEeno6QkJCsHPnTsTExKBnz57w8PDA2LFjS/koCCGEEDOgxoxl6tWrF/bt2wcXFxeMGjUKISEhWLVqFd59910cPHgQdnZ2AIDGjRsjPj4etra26Nu3LwYPHgxfX18cPHgQVatWLeWjIIQQQsygAjZmykXMDAAEBQUhKChIdb2WLVti7969JVCjx/jcJ2U9tsHS0PkkhBSF8DfEAv8zr+jKTWOGEEIIISj60xULbMxRY4YQQggpTypgY8byakwIIYQQ8gR6MkMIIaTC0DzWUlH3w8VLlujYcRXwyQw1ZgghhJDypAI2ZiyvxoQQQgghT6AnM4QQQkh5UgGfzFBjpoSJ+k315EopS/lr+H0XZ98w5ZUhhJRFJRoTo6aoA01KZehYNLK85hchhBBCyBPoyQwhhBBSnlTAbibLqzEhhBBCClYGxmb65ptvIEkSnJ2dzXBA6ujJTBlgjr5Wc8XiEEJIeZGXJ/oNrAC/i6X8ZObGjRsYN24cvL29kZycXKSytKInM4QQQggxm+HDh6N9+/bo0qVLie2TGjOEEEJIeVKK3Uxr167FgQMHsGjRIjMekDrqZiKEEELKEzN1M6WkpMhmGwwGGAyGAje7e/cuRo8ejdmzZ6NmzZr6968DPZkhZieBKT6lWQ4hhBDT1apVC5UrVzZ+Zs2aVej6YWFhaNSoEUaMGFFCNfwXPZkhhBBCyhMzPZm5du0aXFxcjLMLeyrz/fffY/v27Th+/DikUki6R40ZQgghpDwxU2PGxcVF1pgpSFpaGkaOHIn3338f3t7eePDgAQDg0aNHAIAHDx7A1tYWTk5O+uukVuViK5kQQggh5d79+/dx584dzJs3D1WqVDF+1q9fj4cPH6JKlSp4/fXXi7UO9GSmDFCLBdGSh6YsxZOYa4wSyp1DCCmrytRYTLwSzjPj6emJuLg4xfzZs2fjwIED2LlzJzw8PPTXRwNqzBBCCCHlSQkPNGlvb4/AwEDF/JUrV8La2lq4zNyom4kQQgghFo0aM4QQQkh5UgbGZgIeP5lJS0szS1lqqJuJEEIIKU8q4KjZ1Jgpory8xx9A2/XXE8Ba1oNeSzIQjt9XWT83FZFkgT+EpHx49Ih+DwBUyMaM5dWYEEIIIeQJZnsyk5mZCXt7e3MVRwghhBA96MmMuujoaNlomOfPn4efnx+cnJzQrl07JCUlmbWChBBCCDFBGQkALkkm13ju3Ll4+PChcfqjjz5CUlISRo0ahTNnzmDmzJlmraAWgwcPhiRJBX4OHz5sXPfYsWPo3LkznJ2d4erqit69e+PixYu6950fM1PQh6ccOrEMJ16yAHQuyx6Wl6f4EFIahIPVqv1o0/1qkUzuZrp48SKaNm0K4HHX0u7du7F48WIMHDgQjRo1wty5c/Gf//zH7BUtzNSpUzF8+HDF/NDQUBgMBgQEBAAAzpw5g8DAQDRr1gwbN25EZmYmwsPD0a5dO5w4cQJVq1Yt0XoTQgghZlcBu5lMbsykp6cbB4s6cuQIsrKy0L17dwCAn58fbty4Yd4aalC/fn3Ur19fNu/AgQO4f/8+pkyZAmtrawBAeHg4DAYDYmNjjYNntWzZEg0aNMDcuXPx6aeflnjdCSGEELOqgI0Zk2vs5eWFEydOAAB27dqFRo0aGZ9oJCUlwdHR0awV1GvZsmWQJAlDhw4FAOTk5CA2NhZ9+vSRjQJap04dBAUFISYmprSqSgghhJAiMPnJTO/evTF58mTj4FHjx483Lvvjjz8UT0hKQ3JyMjZt2oROnTqhbt26AIALFy4gIyMD/v7+ivX9/f2xZ88eXW9kmdrFaoENXotSHgenNFcskKWfB0J4KSnye5p+X/9RAZ/MmNyYmT59OtLS0vDLL79gwIAB+Pjjj43LYmNj0blzZ92VMdfr3evXr0dGRgaGDRtmnJeQkAAAcHNzU6zv5uYGxhiSkpLg5eUlLDMrKwtZWVnG6ZSUlCLXkxBCCDG7Eh5osiwwuTHj4OCAxYsXC5c9+dZQYaKjo5GQkICwsDAAj1/v7tWrF86ePYu2bdti27ZtqFKliqlVM1q2bBnc3d3x8ssvK5ZJhVykwpbNmjULkZGRuutECCGEkOJRpGdJZ8+exc8//yx7VVuL4ny9+48//sBvv/2GN954AwaDwTjf3d0dwL9PaJ6UmJgISZLg6upaYLkTJ05EcnKy8XPt2jXddSSEEEKKDeWZ0Wb16tWoWbMm/Pz80L59e5w9exYA0K9fPyxdulR1e9Hr3Z9++ik+++wzzJgxA1u2bNFTLQCPn8oAwFtvvSWbX79+fTg4OODkyZOKbU6ePAlfX99Cu7gMBgNcXFxkHwDIyfn3oyV9geijyJWip5BSpMjjQIqkuHLnUE4eUt6o/n9cxn87iw01ZtR99913GDx4MFq0aIGFCxeCsX//82rRogU2btyoWkZxvd6dlZWFtWvXonXr1sbGUj4bGxuEhoZi8+bNSE1NNc6/evUq4uLi0Lt3b137JIQQQsoUasyomzVrFoYMGYJt27bhnXfekS1r0qQJTp8+rVpGcb3evWXLFiQmJiqeyuSLjIxEeno6QkJCsHPnTsTExKBnz57w8PDA2LFjde2TEEIIIaXL5MbMX3/9hVdffVW4zM3NTRiTwst/vbtPnz6YP38++vfvb1xWlNe7ly1bBicnpwLr17hxY8THx8PW1hZ9+/bF4MGD4evri4MHD1L2X0IIIeVDBXwyY/LbTI6OjkhOThYuu3Hjhqa3kIrr9e4ff/xRdZ2WLVti7969uspXI+p+5eeJ7hEb7ipY8ytp6dcVrWOBN2Rx4ONDKK7nMS1xM3SuSFmm+tOYk6NeiOh3UssPtwq+iBINz6E8M+qef/55LFy4EH369FEsW7lyJQIDA1XLMMfr3YQQQgghgI7GTHh4OF544QW0bt0aAwYMgCRJ2Lx5M6ZNm4aDBw/i119/LY56EkIIIUQLejKjrlWrVti5cyfCwsKMQbMzZ85EgwYNsGPHDsVbRAX56aefsG7dOly5cgUZGRmyZZIkYd++faZWjRBCCCHUmNEmKCgIf/31Fy5cuIA7d+7Aw8MDDRs21Lz9ihUrMGzYMLi5uaFhw4ay5HYAZK97l3Xe3pWN/05LU6+3qAuX70u1t+djPAS09Omao5NWx00tirOgvCaWyVxxNYy7FyUL/LEkpevyZfX7THEvaokl1BlvyO+LfuNKl67GTL769evrevNozpw56NevH1atWqVoyBBCCCGkCOjJjNjBgwdNKrR9+/aFLr9y5QoWLFhADRlCCCHE3GigSbHAwMBCB2HMxxiDJEnIzc0tdL0mTZrgzp072mpICCGEEFIITY2ZuLg4s+505syZGDduHAIDA1GjRg2zll2anJ2VDb4HD+T9qqKYGX4e36A2FKkzsGwoS/3Lon1TPhX99MTV8DE0AMXRENMpbqPiSuaioVz+/rWykrhps9aocNTNJNahQwez7vTLL79EcnIyGjZsiGbNmhlHtM4nSRK2bt1q1n0SQgghFQI1Zkxz8+ZNJCQkwN3dHd7e3pq3++OPP2BtbY1q1arh5s2buHnzpmy5li4tQgghhAhQY0abzZs3Y+LEiTh//rxxXv369TFz5kz07dtXdfvLly/r2S0hhBBCiILJjZno6Gi89tpraNy4McLDw+Hp6Ylbt24hOjoa/fv3x7p162QDR1Z0rq7qT5n4/Al8o9jOxVqxjaSnb1jPmE/FpDRz0RRbfIyW81lCf/GUtaG6aHwsUlRaxr7TtIKWL4Ke77JKLqUSvefpyYy6qKgodO/eHdu3b4fVEwccHh6Onj17IioqSlNjJjs7G6tXr8a+ffuQkJAADw8PdO7cGW+88QZsbW1NrRYhhBBCgArZmDG5xhcuXEBYWJisIQMAVlZWCAsLw4ULF1TLSE5OxnPPPYe3334b27dvx8WLF7Ft2zYMGzYMbdu2RUpKiqnVIoQQQkgFZXJjpk6dOkhPTxcuS09PR61atVTLmDx5Ms6ePYvo6Gikpqbi3LlzSE1NxcaNG3H27FlMnjzZ1GoRQgghBPj3yUxRPhbG5G6msWPHIioqCkFBQfDw8DDOv3v3LmbMmIFx48aplrFlyxZERUXhlVdekc3v27cvrl69is8++wwLFiwwtWoWi28b2tvLpx0dlduYpSOujN2wmsb4KUvjnxRTzJE5cvKUsUurCeMSLkk25SDBEtHtt9/k34NHj5Tr8Dm6mJU8vrBY7yFTv/8lGaNYAbuZNF3pDz74QDadkpICHx8fdOrUCZ6enrh9+zb27dsHDw8PnD59WrW8e/fuwd/fX7jsmWeewf3797VUixBCCCFEW2Nm4cKFwvnbt2+XTV+9ehULFy7E/PnzCy2vRo0a+Omnn9CpUyfFsp9//tmknDWEEEIIeQI9mRHLM/Pjsf79+2PmzJmoVKkSBg0aBHd3dyQkJGDt2rWYOXMmxowZY9b9EUIIIRUGDTRZMiIiInD8+HGMGzcOH330EWxsbJCTkwPGGIKDgxEREVEa1So1ajEzLi7KbWztNNyoFti6VmPx+Un4Pww0XKPSzMmjhZ6UHMLxscrh/Ur0a9VKfo+cPq38HvAxM/y0rbliZnQluSni+sQkpdKYMRgM2LVrF3bv3o24uDjjkAidOnVCly5dSqNKhBBCSPlA3UzarF27Fp9//jn++usvZGZmKpbn5uZqKic4OBjBwcF6qkAIIYQQkQrYmDG5xtu2bcOQIUPQvHlzZGRkYMiQIXjttdfg5OSEBg0aIDw8vDjqSQghhBAtKM+MutmzZ2PMmDGYOXMmli1bhrCwMLRo0QK3b99Gu3btCkyaV69ePcTExOCZZ55B3bp1Cx0ZW5IkTZmECSGEEEJMbsycPXsWkZGRxsZIzj8RV56enpgyZQr+85//YOjQoYrtOnToAJd/Ilk7dOhQaGOmouED3U6dkge6mStuTEvQqMUH2FoanQPhqV2nshQgDGgNEi5bdSZlH39f8Yn1rOyVg/Rqeeig+H6JNuJ3bup0caqA3UwmN2Zyc3NhZ2cHKysrODk54fbt28ZltWvXxsWLF4XbrVixwvjvlStXml5TQgghhKirgI0Zk2tct25d3Lx5E8DjbL3r1683Ltu0aRO8vLxUy1i9ejUSEhKEyxITE7F69WpTq0UIIYSQCsrkxkynTp2wd+9eAMCoUaMQHR0NX19f+Pn5YfHixRg+fLhqGUOGDCkwJubSpUsYMmSIqdUihBBCCEABwFp88sknyMrKAgC88sorsLa2xrfffgtJkvDxxx9j8ODBqmUwVnB/f2ZmJqytlf2cWvz000+YOXMmDh06hMzMTNSsWRMDBw7E1KlTjescO3YMH3/8MQ4fPgwbGxt07NgRc+fORb169XTt0xw2bpSfDz7xk1564ib0bFNh42z4L7y5+sT1ZKHjmOua6Lkf9FZfGWIgPwaKqanYnJ2V8+zs5NP8PSTIHKK498RfJfV7zYob1NLWhitINDJmSamA3UwmN2YMBgMMBoNxunfv3ujdu7fqdlevXsXly5eN08ePH1fkqMnIyMCSJUtQu3ZtU6uFdevW4c0330S/fv2wevVqODs748KFC8YuMQA4c+YMAgMD0axZM2zcuBGZmZkIDw9Hu3btcOLECVStWtXk/RJCCCGkdJVYBuAVK1YY34KSJAlhYWGKdfKf2KgNVMm7ceMG3nnnHbz77rtYtGiRcX5QUJBsvfDwcBgMBsTGxhrfrGrZsiUaNGiAuXPn4tNPPzX1sAghhJCypYSfzOzfvx9r167FL7/8gmvXrsHV1RWtWrVCeHg4WrZsqb8eJtDUmBk6dCimTp2KunXrCl+7fpIkSVi2bJlifr9+/dC0aVMwxtCvXz/MnDkTDRo0kK1jMBjQtGlT+Pj4aD8CAN988w0ePnyI8ePHF7hOTk4OYmNjMXDgQGNDBgDq1KmDoKAgxMTEUGOGEEKI5SvhgSa/+uorJCQkYNSoUfDz88O9e/cwb948tGnTBrt370bHjh3110UjTY2ZuLg4jBo1CsDjFphawjuRJk2aoEmTJgAeP6UJCQmBu7u7qfUVOnjwINzc3HDmzBm8+OKLOHXqFNzc3NC7d2/MmTMHLi4uuHDhAjIyMuDv76/Y3t/fH3v27EFmZibs+VEeSwA/FhpfBeFYaToGLDRDKIYQ5a8pBWoX00x93lqum5ZYltJMuUHKBw8P5Tz+t5G/r0Txh3piEkX3K18O/z1wdjbIprOt5NPlyZdffolq1arJ5nXr1g2+vr6YOXNm2WnMXLp0yfjvJ+Ne9Bo0aFCRy3jSjRs3kJ6ejldeeQUTJ07E559/jqNHj2LatGk4deoU/u///s/4Kribm5tiezc3NzDGkJSUVOCr5VlZWcbAZwBISUkx6zEQQgghZlHC3Ux8QwYAnJ2d4efnh2vXrumvhwlMipnJzMxEVFQU+vTpU+R+sMTERKxbtw5//fUXMjIyZMsK6qoqSF5eHjIzMzFt2jRMmDABABAYGAg7OzuMHj0a+/btg6Ojo7HsghS2bNasWYiMjNRcJ0IIIaRUlIG3mZKTk3Hs2LESeSoDmNiYsbe3x3//+19069atSDu9evUqAgICkJ6ejvT0dHh4eCAxMRG5ubmoUqUKKleubFJ57u7uOHfunGIE7u7du2P06NE4duwYXnzxRQAQJutLTEyEJElwdXUtcB8TJ07EmDFjjNMpKSkFjkNFCCGElBozNWb4Hgj+bebCjBw5Eg8fPsTkyZP118MEJr/N1KRJE1y6dAnt27fXvdMJEybgqaeeQmxsLJydnbFz5040bdoUS5cuxcyZM/HDDz+YVJ6/vz8OHz6smJ//dpSVlRXq168PBwcHnDx5UrHeyZMn4evrW2i8jCkXsTCLFiljEPhcCf88RCpwOQCA7/cV3Lh64hL0bKPlO8PH1Zgthqa4AoEsXQmeF/5aimJo9MTMVMTLRgrm6Ki8r9jdu/xKskkrOyfFNunp8mlRTCJ/74ny1Tx4IJ9OSyu0KorlloD/g33atGmIiIhQ3W7q1Kn49ttvsWDBghJ7m8nkn4upU6dixowZRRrV+tChQxgxYoSx8cAYg52dHUaOHIlhw4bho48+Mqm8Pn36AAB27twpm79jxw4AQJs2bWBjY4PQ0FBs3rwZqampxnWuXr2KuLg4TblyCCGEkDLPTBmAr127huTkZONn4sSJqruOjIzEjBkz8Mknn+C9994r7iM1MvnJzIoVK5Ceno4mTZrA398fXl5eslgTSZKwdevWQsu4c+cOvLy8YGVlBWtra9mjrA4dOuCLL74wqU5du3ZFaGgooqKikJeXhzZt2uC3335DZGQkQkJC8MILLwB4fJIDAgIQEhKCCRMmGJPmeXh4YOzYsSbtkxBCCCmTzNTN5OLiIktloiYyMhIRERGIiIjApEmT9O9fB5OP9o8//oCdnR1q1KiBhIQEnDp1CidPnpR91FSvXh2JiYkAAB8fH/z222/GZZcvX4aN8F3kwkVHR2P06NFYsmQJunfvjq+++goffvghNm3aZFyncePGiI+Ph62tLfr27YvBgwfD19cXBw8epOy/hBBCiE7Tp09HREQEpkyZgmnTppX4/k1uNZjj1ew2bdrg+PHj6NWrF3r37o2oqChkZWXBzs4O//nPf3RFPzs4OGD27NmYPXt2oeu1bNnSOFBmSVi7Vh5PIIp/4d9q48cgkfJylRtxrW4tuV6KK05BT7lmy01TXOMjaVFc+zJHuVoupGg/ZjifkmDffByNqFgtOUIIkeH/P+ICVWyFY+45yKZEoZLWacnyLRyVK928KY+h/OMP+XL+a8DH6hSrEn6bad68eQgPD0e3bt3Qs2dPRQxrmzZt9NdFoxIbzuBJ48aNMzaKwsPD8ddff2HatGlgjKF9+/YmD2dACCGEkH+UcGNm+/btAIBdu3Zh165diuWFDS5tLkVqzNy7d0+RIwaA6kCRLVu2NEY4Ozk5Ydu2bUhJSYEkSahUqVJRqkQIIYSQEhQfH1/aVdDXmJkxYwa++OILYc4WAMjNFXSLqDAlyIgQQgghBSgDSfNKmsmNmeXLl2P27NmYMGECwsPDMXnyZDDGsGbNGjg4OBQ62GO+Tz/9FNevX8eCBQsUy95//33UqVMH48aNM7VqpW7zZuWjND4OQDS+iFrMjDDAgAuS1hKDoEVpjpmjJa5GNY6mJL+EpRmvo0ZLPIzW7cywbysr66KXSwjv9m3ZpNSrl8lFMEFwllRIAlWt3ntP/lv16FGRi9SuAjZmTK7xl19+iUmTJhnfN3/55ZcxY8YMnDlzBpUqVcL9+/dVy1i1ahWaNm0qXPbMM89g1apVplaLEEIIIRWUyY2Z8+fPo02bNrD6p+X26J/mpoODA8aOHYslS5aolnHlyhU0bNhQuMzX19csb0wRQgghFZIkFS1hXiHjFJZVJncz5eeAkSQJLi4uuH79unGZh4cHbty4oVqGra0t7vJpqP9x586dQgd8JIQQQkghKmA3k8mNmQYNGhiH9A4ICMDSpUvx4osvwsrKCkuWLIGPj49qGa1atcLSpUvRv39/xbKlS5eiVatWplar1Gzdmgwnp8fBy9yYXACU8S+imBk3N/m0rRUfQF1yN1ZZCvsQEY378ySzjfmkh+gHwByDXZnrovDllOAPVnGN30QqOC4Qhf30k3y56P8j/ke4mBLAlOr9TI0ZdT169MDBgwcxaNAgTJw4EcHBwXB1dYWNjQ3S0tKwfPly1TLGjRuHnj17IjAwEGFhYahRowauX7+OxYsX4+DBg8YxlQghhBBC1JjcmAkPDzf+u2PHjvjll1+wYcMGSJKEnj17IigoSLWMbt26YcmSJRg7dixeffVVSJIExhgqV66MpUuXIjg42NRqEUIIIQSgJzN6BAQEICAgwOTthg0bhldffRU///wz7t+/j6pVq6Jt27ZwclIO2U4IIYQQjagxo65v374YOnQounXrZnyjSS8nJyd07dq1SGWUJaJxl/iYGUUOmQK2U6NlbCM1evtw1bYzV+iIHmoxNVqZLfZG7TtSmoEhenPR6ClXsRv1vDOOjvQiAFGRmSmf1nL/ctuYI6eMCD/mE73XUrxM/uU6dOgQQkNDUbNmTUycOBFnzpzRtePExERMnToVbdu2RaNGjfD8889j2rRpSEpK0lUeIYQQQlC017KL+lSnlJhc42vXriE2Nhbt2rXD559/jqeeegpt27bFsmXLkJqaqqmMGzduoEWLFvjkk0+QnJyM2rVr48GDB5g+fTpatGiBmzdvmnwghBBCCAE1ZjRtYGWF7t27Izo6Grdu3cKCBQuQnZ2Nt99+G15eXhg0aJBqGZMmTUJGRgaOHDmCP//8E3v27MGff/6JI0eOICMjA5MmTdJ1MIQQQgipeIoUAOzq6oqwsDCEhYXhp59+woABA7B27VrV4Qh27dqFGTNmKAKHAwICEBUVhalTpxalWqXGRnA2+XgY0ToKGlrFenIYmGudkiijOGn5o8McMUkiilgcvX8BqZ1kLRdBS3CTaJ2yPCYVqVjS0gpfLvjBLa4YGR4/djIf3lOsKADYdHv27MGKFSuwZcsWZGZmom3btqrbJCcnF5hcr27dukhOTi5qtQghhJCKqQI2ZnTV+MKFC5g6dSpq166Nbt264eDBgxg1ahTOnj2Ln/gMjAJ169bFDz/8IFy2c+dO1K1bV0+1CCGEEFIBmfxkpkOHDvjpp59ga2uL0NBQDB06FMHBwSa9pj1kyBBMmDABeXl5GDRoELy8vHDr1i2sXbsWCxYswOzZs02tFiGEEEKAfweaLMr2Fsbkxkxqaio+//xzvP7663DjBxXS6KOPPsKFCxewcOFCfPnll8b5jDG88847GDdunK5yCSGEkAqvAnYzmdyYOXbsWJF3KkkSvv76a4wZMwZxcXFISEiAu7s7OnbsiIYNGxa5/JL04ouVjf/eu1eZaE1LwC8fP5mbp94qLqlg3pIsV8++Le07pyWpn64gYT0XpbiS5uksg+KISWFYVJRyJjfQpKQhZrOkeHvLpzMySnDn1JgpWY0aNUKjRo1KswqEEEIIsXCl2pghhBBCiJnRk5niY2VlBUljUJEkScjJySnmGhFCCCHlEDVmik94eLjmxoylcnRUztMyiCTfbtMTZ6NHacbd6N2Xpedr05aTTv49Mdugl8VFx0URHbezc/n+fSCmYStXymcIss5Jw4eXTGV0qFdPPv3wYenUo6IwqTHz6NEjxMfHo3Hjxqhdu7ZJO4qIiDBpfUIIIYToUAGfzJhUYxsbG4SEhODcuXPFVR9CCCGEFAUNNKmyspUVatasiZSUlOKqDyGEEEKISUyOmRk2bBi+/PJL9OrVC9bW1sVRJ4slipmxt5dPl1Q8TEmWW5LK0jHoHcvRVKLcNJKegkvq5FngX3WkdLGNG5Uz+R9PQQAi44bPkV54wZzVKhI+EkNtTEyzqoDdTCY3Zuzs7HD27Fk0adIEvXr1gpeXlyywV5IkfPjhh2atpJr4+HgEBQUJlx06dAht2rQxTh87dgwff/wxDh8+DBsbG3Ts2BFz585FPT5aixBCCLFE1JhRN378eOO/P/vsM8Xy0mjM5Js5c6aiUdO0aVPjv8+cOYPAwEA0a9YMGzduRGZmJsLDw9GuXTucOHECVatWLekqE0IIIaSITG7MXLp0qTjqYRYNGjSQPYXhhYeHw2AwIDY2Fi4uLgCAli1bokGDBpg7dy4+/fTTkqoqIYQQUjxooEl1derUKY56FLucnBzExsZi4MCBxoYM8Ph4goKCEBMTU+TGDN/FCyi7eUUxM2U5/EGP4hpKSAstww2ZaxgjPduZ6+mtMhdN6WEwPS+OaB3GnSxdcUHEIrBFi+QzRAGH1arJp5/43TZydjZfpYqI3b0rm07l/j8o0fdmKmA3k+4anz9/HkuWLMGsWbOwdOlSnD9/vvAdWVnB2tra+CkOI0eOhI2NDVxcXBAcHIyfnggOu3DhAjIyMuDv76/Yzt/fH+fPn0emIClTvqysLKSkpMg+hBBCSJlTAV/NNvnJDGMM77//PhYvXoy8J/6SsrKyQlhYGL744gvhdsuXLy+2DMCVK1fGqFGjEBgYCHd3d5w/fx7/+c9/EBgYiB9++AHBwcFISEgAALi5uSm2d3NzA2MMSUlJ8PLyEu5j1qxZiIyMLJb6E0IIIUQ/kxsz//3vf7Fo0SKMGDECgwcPhre3N27evIlVq1Zh0aJFqFu3rjAAePDgweaor1Dz5s3RvHlz43S7du3w8ssv4+mnn8bHH3+M4OBg47LCGlSFLZs4cSLGjBljnE5JSUGtWrWKWHNCCCHEzCpgN5PJjZlvvvkG77//PubPn2+cV6NGDQQEBMDa2hpLly4ttbeZnuTq6oqQkBAsXrwYGRkZcHd3BwDjE5onJSYmQpIkuLq6FliewWCAwWBQzP/992RUqiToy/0HHyMjHounwM0LVJpxKbzSrL+WmBQ9cSvFNW6VtrGZ5NNachNZl+CPDx8jo2W5pvGlynIgGCkS9s47ha/Ax8cAgI+PfFoUH1OW/tN98EA2ae8jfzv20aMSrAs1ZtRdvHgRISEhwmUhISH4+uuvVcuIiooqdLkkSZg6daqpVVNgjBnLq1+/PhwcHHDy5EnFeidPnoSvry/sRRG8hBBCCCnTTG7MVK5cGVeuXBEuu3LliuxNoYKoDTppjsZMUlISYmNj0axZM2MjJTQ0FJs3b8acOXNQqVIlAMDVq1cRFxdXJp4mEUIIIUVGT2bUdenSBVOmTEHz5s3RsmVL4/wTJ05g2rRpsviUguQJHicnJiZiy5Yt+Pzzz/HDDz+YVKcBAwagdu3aaNWqFTw8PHDu3DnMmzcPd+7cwconhpGPjIxEQEAAQkJCMGHCBGPSPA8PD4wdO9akfRJCCCFlEjVm1M2aNQvx8fFo3bo1/Pz84OXlhVu3buH06dPw9vbGrFmzdFXEzc0NQ4cOxd27d/HBBx8gJiZG87b+/v6Ijo7G4sWLkZaWBjc3N7zwwgtYs2YNAgICjOs1btwY8fHxGD9+PPr27SsbzkBv9l97+3/zy2jJcVKS94g58qtoKbck66K2XUnmmSmpEA9tY0BxuV7Ml9BGdRVd9dMQQ8O4IANJMDYPsRB8Kgs+/qVmTcUmGc7y32RR5ozSjAxg/Ju76emySf57QSFhxcvkxkytWrVw4sQJzJs3D3Fxcbh06RLc3d0xYcIEfPjhh/Dw8ChShVq3bo2ZM2eatM2ECRMwYcIETeu2bNkSe/fu1VM1QgghpOyjJzNiY8aMwYcffohatWrh6tWr8PLy0v0ERs3vv/8O5zKU1ZEQQgixKNSYEfv888/x6quvolatWqhbty4OHTqE1q1b697p6tWrFfOysrLwxx9/YPny5XjjjTd0l00IIYSQikVTY6ZKlSq4c+cOgMevOxc1k29BCfTs7e3xxhtvYO7cuUUqvyQ92QDW2ydaXHE1pdVHq6X+WvLBmIu2mI6SqYse+vLtCL6jVuYZRiQnp/B9iyhz54hy0ZBy6/p1+TQf/+Tpqdjk9mX5tOg+8/UtxbuGrxCXEIqP8SlktBzzo4Emxdq0aYNhw4YZn8aMHTu2wARzkiRh69athZYnGnnb3t4e1atX11IdQgghhBSEupnEFi1ahNGjR+PPP/+EJEk4f/68MBsuUPiQAPksdeRtQgghpMyjxoxYnTp1jK9KW1lZYcuWLUWKmSGEEEIIMReTX82Oi4uDn5+fyTuqW7eu7KnNxYsXTS6jrONjCYDiG7eIZ64Yj7IUK6JnDCU98RtatitL50VEy3nQExekJb+OnnMuugaKiJ6yftKJdjdvyqe5+JLkFOUTfT41jZYxykpUWpp8mqsgPxZTdnYx1+dJ9GRGXYcOHXTtaNCgQUUOHCaEEEKICmrMFB+18ZgIIYQQQvSwvOYXIYQQQgqW/2SmKB8TpaWlYfTo0fD29oa9vT2aNWuGDRs2FMPBiZW1XkiLk5Pzb6xMccWt6M3bolZuWVNSuV5K8jyU1NNaPfEv5oqZEcWK8TTFzNiV4WQ/pGgSE+XT3KBKfPgJoBjqCI6OynWuX5eP8VWzZgmGMty+LZ/m7lf+e6Hle2I2pdDN1Lt3bxw9ehSzZ89Gw4YNsW7dOrz22mvIy8vDgAED9NdFI2rMEEIIIUS3HTt2YM+ePcYGDAAEBQXhypUr+Oijj9C/f39YW5snaWdBTG5+zZs3D0lJScVRF0IIIYQUVQl3M8XExMDZ2RmvvPKKbP6QIUNw8+ZNHDlyxJxHJ2RyY2b8+PGoWbMm3n77bfz+++/FUSdCCCGE6FXCjZlTp06hSZMmsOFeT/f39zcuL24mN2YuXLiAkSNHYsuWLWjRogVeeOEFbNiwATkl2iFICCGEkOKUkpIi+2RlZQnXS0hIgJubm2J+/ryEhIRirSegI2amTp06mDNnDqZPn45vv/0WixYtwoABAzBmzBi88847ePfdd+Hl5aXY7urVqybtp3bt2qZWrVTk5RU9TlFt+7L+yn9xBSiXZnK74hr8s6QGEdUSuKtlOz0BwFoS9onwg09acxsxPosaAMnFRb1gUqKYaGbNmvJpbmBJwaVVJJ3jYoZLFGvcWDmTz+LHTesJuDcXBgmsCEO35m9bq1Yt2fxp06YVmGalsDxyJZFjTncAsMFgwNChQzF06FAcOnQIEydOxPTp0zFr1iz07t0bEyZMwDPPPGNc38fHx6QDys3N1Vs1QgghpMIq6h/Z+dteu3YNLk/8wVDQmIzu7u7Cpy+J/7zFJnpqY25Ffptp7969WLhwIX766Se4ubkhNDQUu3fvxqZNm/DVV1/hrbfeAgAsX77c2JjJzs7GjBkz4OjoiP79+8PT0xO3bt1CdHQ00tPTER4eXtRqEUIIIRWSuRozLi4ussZMQZ5++mmsX78eOTk5sriZkydPAgCaNm2qvzIa6WrMpKamYsWKFVi0aBH+/vtvNG3aFIsXL8Ybb7wBe3t7ZGdnY/jw4YiIiDA2ZgYPHmzcftKkSfDz80NsbCysnniUHB4ejp49e+LcuXNFOypCCCGElIiXX34ZS5cuxffff4/+/fsb569atQre3t549tlni70OJjdmRowYgW+//Rbp6ekICQnBV199haCgINk6tra2GDJkCFasWCEsY/Xq1Vi8eLGsIQM8HpE7LCwMw4cPx+zZs02tWql4MvDbzk65nO9WFbWW+ZgDLduYYwDL4urD1RsXoqV+5hosUa1cLcwVK8TTE7dijm0KmmeOffNE54WPkXCw474I9JJBqWNduypnxserb8hv5+Mjm0wTjDusJRYrM1N913qwtm3lM7gYHwCAr698mnuCkcfdzyUZM2OuJzNade/eHV26dMGIESOQkpICX19frF+/Hrt27cLatWuLPccMoKMxs2HDBrz99tt47733ULdu3QLXa9y4cYGNmYSEBGRkZAiXZWRkUB4bQgghRKeSbswAwObNmzF58mSEh4cjMTERjRs3xvr16/Hqq6/qr4gJTG7MXL9+HU5OTqrreXh4YNCgQcJlLVq0QFRUFIKCguDh4WGcf+/ePURFRaF58+amVosQQgghpcTZ2Rnz58/H/PnzS2X/JjdmtDRk1MybNw+dO3eGj48POnXqBE9PT9y+fRv79u0D8DiomBBCCCGmK40nM6WtVMZmatOmDY4ePYrIyEjEx8cjISEB7u7uCA0NxZQpU/DUU0+VRrV0sbMTx8rk0xLjoScUQE8elNLMlVLWc+WoEdXfHOdcy4+G6P7Qk+tFyzollRuDj48BlPEPdnbyfnY+7wwpBb/+qpzH55Bp0UK5TseOssmH9u6qu+IHlhTlmeHXycyUZ7kxPLij3Ij/Y1n0xzN/M/LxMQDA556pVk02mXNZvrgkQ76oMVOCmjRpUqLDgxNCCCGkfKJRswkhhJByhJ7MlKCffvoJ69atw5UrVxRvNkmSZIyfIYQQQoh21JgpIStWrMCwYcPg5uaGhg0bKlIkMyYc3aNMMhj+7cs1V24PLbENKsOCACi9uJqSDG3QM+aTlnL0nCtz1UXLPcPHnPD3jJ44G637VhtDy1zxRfw9XclZGTTB0tJk05Kzs/qOiH4PHijntW4tn+7bV7lOYKBs8p8s90Z87AugjEUUJaLls+TbpifLZ/A7ApQ3Vr16ynX4m9HPT7kOFzOTkCgfrof/ftEIPcVLV2Pm8uXL2LhxY4FPVZYtW1bo9nPmzEG/fv2watWqAsd6KKpvvvkGb7/9NpycnJDG/eAdO3YMH3/8MQ4fPgwbGxt07NgRc+fORT3RTU0IIYRYEMaK9nTFgp4nGJncmPnhhx/Qu3dv5Obmolq1aorGiJbBJK9cuYIFCxYUW0Pmxo0bGDduHLy9vZGcLG+pnzlzBoGBgWjWrBk2btyIzMxMhIeHo127djhx4gSqVq1aLHUihBBCSgJ1M2kwefJkPP/889iwYQOqca+iadWkSRPcuSN4Zc5Mhg8fjvbt28PNzQ2bNm2SLQsPD4fBYEBsbKxxAK2WLVuiQYMGmDt3Lj799NNiqxchhBBS3Kgxo8G5c+ewefNm3Q0ZAJg5cybGjRuHwMBA1KhRQ3c5ImvXrsWBAwdw+vRpTJkyRbYsJycHsbGxGDhwoGwk0Dp16iAoKAgxMTEmN2bs7f+NmRHFIJgjlkFvrAvfNcyvY22lfJbIoP5kzRw3ut4xlcw1DpCpzDXeFM9c58FcMTN6yuGJ4re0jDfGU8bQKO9NB1HyEVKy+Bws3bopVrl0V55slb/+fOwLoIyjEYVDWWc+lM9IT5dPi260J7LOAxDfsHyAjiDPTJaH/P+ulJvKYkjJMbkxU6dOHUUMiqm+/PJLJCcno2HDhmjWrBnc3eUJlCRJwtatW00u9+7duxg9ejRmz56NmnwiJwAXLlxARkYG/P39Fcv8/f2xZ88eZGZmwl7wA5mVlYWsrCzjdEpKisn1I4QQQoobPZnRYNKkSZg7dy66d+8OR1EIugZ//PEHrK2tUa1aNdy8eRM3b8qbtFribkTCwsLQqFEjjBgxQrg8ISEBAOAm+DPAzc0NjDEkJSXBy8tLsXzWrFmIjIzUVS9CCCGkpFBjRoNff/0Vd+/eha+vL4KCgoRPVdQGmrp8+bKpu1X1/fffY/v27Th+/LhqY6iw5QUtmzhxIsaMGWOcTklJQa1atfRVlhBCCCFmY3JjZuHChcZ/r1+/XrFcS2PG3NLS0jBy5Ei8//778Pb2xoN/ciE8+icZx4MHD2Bra2tseOU/oXlSYmIiJEmCq6urcB8Gg0H49pWNjbjLNZ+WWAu1vDKiuAU9cTRSHpfoQND8VjTlBDtSGyNHFHdjrvwqxTV2UHHlximu+KLiOg/8vSYaQ0ktpktvfdXidYTH6GotmEnMRdMbuq1aySYv3K+sWIVP9+LtLZ8WxcwYrLLlM9IzlSvxNyh/k4h6D/gQhNq1letw/w9kuyrfcr17Wz7N36/8/wvWJXir0pMZDfLMfJT37t1T5KoBgNqiG6wA9+/fx507dzBv3jzMmzdPsbxKlSp48cUXsWnTJjg4OODkyZOKdU6ePAlfX19hvAwhhBBiKagxU4JmzJiBL774QviUBAByTUiX6Onpibi4OMX82bNn48CBA9i5cyc8PDxgY2OD0NBQbN68GXPmzEGlSpUAAFevXkVcXBw+/PBDfQdDCCGEkFJTKo2Z5cuXY/bs2ZgwYQLCw8MxefJkMMawZs0aODg4YPz48SaVZ29vj0AuXTYArFy5EtbW1rJlkZGRCAgIQEhICCZMmGBMmufh4YGxY8cW8cgIIYSQ0kVPZgpQr149xMTE4JlnnkHdunVVA2gvXLhQaHlffvklJk2ahPHjxyM8PBwvv/wyWrRogcmTJ6N9+/a4f/++aUdhgsaNGyM+Ph7jx49H3759ZcMZFDX7ryh2Rm8ekSdpGetGtI4iRkatf1lrwSrrSMIAFNPfUNMTe2GuvDN6Ymi0nM7iih1S229B83j8PZwpCFNQq59oP3ry1/DliOoiKofopyuLfceOssnrl5WrqOWMMSALCnyMjJYvDx8moGXQJ8E6qWny36uUu8pi+J9T/n7ld8NPFydqzBSgQ4cOxiRzHTp00P3qdL7z58+jTZs2sPrn6ucH6jo4OGDs2LGYOnUqPvrooyLtA3j8ZGblypWK+S1btsTevXuLXD4hhBBCSp+mxsyKFSuM/xY1Dkze6T9//kmSBBcXF1y/ft24zMPDAzdu3CjyPgghhJCKqCIONFlML6QWrkGDBrh27RoAICAgAEuXLkV2djZyc3OxZMkS+Pj4lEa1CCGEEIuX381UlI+lKZUA4B49euDgwYMYNGgQJk6ciODgYLi6usLGxgZpaWlYvnx5aVRLlycvvN7YFj3j1vDbCPtjM7lOXT0xM1oG2tHAykqeZEHvl0Ut94iWQxIpqTwzesZH0hM7pOWyaYnx0rKOnjAr0XHzMTH8tKhcfiiev/9W/knZsGHRusXLK11/fI8erZh1zaqObJq/JoByOCRFBgwtwU9afjz5aUGqjYfp8vshXRCiyd97onxLaj+VWr5vxYViZkpIeHi48d8dO3bEL7/8gg0bNkCSJPTs2RNBQUGlUS1CCCGEWKBSyzPzpICAAAQEBJR2NQghhBCLR09mCCGEEGLRqDFDTKZ20+iJJ9DSt8qvo8gpk1+5wqb1Dvqkto3gAPioBRsb5UAlenKGlFSeGS3baNm3nngYvXE1PC3HxMdeiUb3ULs/tdy/Wo47La3waUA55o8oF82ZM/LokMaNK2YMja4YmWbN5NNPhAjkO/2rfFoUv/dPZg8jgx1XG9F3X0vQCTcvF/LflXTBPcPH9GgZf0xLDBp/3PxyW1tlGcR8TP6f68nXtHk5OTmykaUJIYQQUrIq4ttMJjdmhg0bhsGDBysGh7xy5Qqef/55fPnll2arHCGEEEJMQ40ZDZYvX45NmzYhICAAp0+fBgDExMSgefPmuHPnDg4cOFDo9rm5ufj777/x4MEDXRUmhBBCCHmSyTEzgwcPRkBAAPr164fWrVujW7duiImJQWhoKFauXAlXV9dCt2eMwc/PD9u3b0f37t311rvMMLUVqze1i9o2ZqMlYIOnZQAi7qAkK+U6NjbyTmVRMXryzPD05G0R0RNHo+X0muOvIr311xIzo1a23jHKRLELTxLFzNy9q74OKQIu59fuX6soVuHjlho2VBaj+G9BbWAjQBkPY6UMOuG/P3yxWsbz0vBzpStvk564O3OpiAHAuk7vU089he+++w55eXnYvHkzWrdujZiYGNWGDPB4KANPT0/kWeLZIoQQQso46mbSKDY2Fu3atUPVqlUxcuRIHD16FF27dsVd/s+kArz66qtYvXq1nl0TQgghhMiY3M308ccfY+7cuejRowdWr14NNzc39OrVC2+++SaaNWuG9evXo0OHDoWW0axZM0RHR6Njx47o3bs3vLy8FCNx9+7d29SqEUIIIRVeRRxo0uTGzOeff47Zs2fj448/Ns7r0qULTpw4gVdffRWdO3dGdnZ2oWUMHDgQAHDjxg3Ex8crlkuShNxcQd4UQgghhBSqIsbMmNyYiYuLw/PPP6+Y7+npif3792PatGmayiiPSvUG0DOCpd4Kq5UjqouGLFQSV46dnUF113yxWgJq9QQAiw5JS7lq9dNSFz2XVm+wIb+dngBgLedKhD83zs7yaVEQZkpK4dMie/fK/+zs3Ln8JdHT/Yf1lCmyyZ/Tm8um/3mBVaZ2bfm0m5tynUrOXI3SuYut4SbP0ZDcTk8ySdF9pWe8XbWAXwoALl4mN2ZEDZl8VlZWmD59umoZat1QhBBCCCFa6WornjlzBq+99hq8vLxgZ2eHY8eOAQAiIyNNeuqSnJyM3bt349tvv0VSUpKeqhBCCCHkCfQ2kwYnTpxAQEAADhw4gMDAQFlsS1paGhYvXqypnOnTp8Pb2xvdu3fHwIEDcenSJQBAp06dMHv2bFOrRQghhBBUzMaMyd1MEyZMgL+/P/bs2QM7OztER0cbl7Vu3Rrff/+9ahmLFi1CZGQkwsLC0L17d/Ts2dO4LCQkBJs3b8aECRNMrVqpsLH5t+9US0I8PbTEVeTmKfv9rUUjvj1JS2yLnrtaS6I90Tp8Yj3Bvu3tHWTT/Cp6k2Spxb/oTcZnjj59LcnttFDr49daH56WnIlazicfn8MPTiiKxeATtomyQ1y8KJ9WS85nCYrtZRMuZubwQvVNPDzk06LrpBjdUctIjtw6fFJNQD1WTMvAvnrXUWOuEEWijcmNmZ9//hlr166Fo6Oj4o2j6tWr4/bt26plLFy4EGPGjMGcOXMUZTRo0ADnzp0ztVqEEEIIAQUAa8IYg10BfxYmJSXBYFC+gcK7ePEigoODhcsqVapE4zYRQgghOlXExozJD8/8/f0RExMjXLZr1y60bNlStYzKlSvjzp07wmWXL19GtWrVTK0WIYQQQiook5/MjBo1CgMGDICTkxPefPNNAMDVq1exf/9+44jaajp16oQ5c+bgxRdfhP0/HeWSJCEnJwdfffVVgU9tyiJrKwZrK/bPv9XXZ1DPaaEnPkOYX8VKvi8bLm+LpKWz2FyjMmqJo+GDGQTl8nE0jo5OqlUxR54ZUfXNcaq0xFlpyYOhZbmWmBn+OM2VG0NLrBD/wJfPM8PHZgDKUAxRPAw/+CTfEz5jhjICZcqUksk9w4YOVc786Sf59N9/F8/OFy1SzNq5X/4bwZ+7evWUxdSsKZ+u7CxIeJqYrpz3JA0JoqztlDeNgz3fSyC/blq+k3qZWk5JPu2oiE9mTG7M9O/fHxcuXEBERAS++OILAECfPn1gY2ODyMhIhIaGqpYRFRWFgIAA+Pn54eWXX4YkSVi4cCGOHz+Oq1evYuPGjaYfCSGEEEKoMaPVpEmTMHDgQOzevRt37tyBh4cHgoODUadOHU3b+/r64ueff8aYMWOwaNEiMMawevVqBAUF4dtvv0VtPqUkIYQQQjShsZlMULNmTQwbNkz3jv38/LBr1y5kZWUhISEBVapUgYODg/qGhBBCCCFP0NSYuXr1qkmFmvJkxWAwwNvb26Tyy5RHjwpPXMEFHYh64q35YAYb9X5fc+ROsbNT5m1QxNGYK8BErQyd5fD15fPQiIoR5aJROyQtY7OI6Ilt0ZLjQi3+RUt9Revwxy26tdUui96/CPXk9tBy3HyMDB8HwsfdAEBYmPxP00WLzBNDo/iDVxRjqGWAKT1695ZNpr4xQrHK+ZXyaT5njJaYGdy/r1yJP8laLja/jejicsmJHPhAK3v13DRaxnPTQm2bkhw7uSJ2M2kK7/Px8UHdunU1f9SMGTMGr7/+unDZG2+8gY8++sikgzhx4gR69uyJ2rVrw8HBAW5ubnjuueewdu1axbrHjh1D586d4ezsDFdXV/Tu3RsX+axahBBCiIWiDMAFWL58OSTp8V8m2dnZmDFjBhwdHdG/f394enri1q1biI6ORnp6OsLDw1XL27ZtG6ZOnSpc1rVrV8yYMQP/+c9/NB/EgwcPUKtWLbz22muoUaMGHj58iG+//RZvvvkmLl++jCn/ZLU8c+YMAgMD0axZM2zcuBGZmZkIDw9Hu3btcOLECVStWlXzPgkhhBBSNmhqzAwePNj470mTJsHPzw+xsbGweuLRYHh4OHr27Kkpe++NGzfg4+MjXFanTh1cv35dS7WMAgMDERgYKJsXEhKCS5cuYcmSJcbGTHh4OAwGA2JjY+HyT670li1bokGDBpg7dy4+/fRTk/ZLCCGElDWW0M20f/9+rF27Fr/88guuXbsGV1dXtGrVCuHh4Zry1fFMDgBevXo1Fi9eLGvIAICVlRXCwsIwfPhw1YEinZyccO3aNeGyq1evGnPPFJWHhwfu/jNYS05ODmJjYzFw4EBjQwZ43HgKCgpCTEyMvsZMTo62GJF8GgbEsebWUcTUALCyKno+BfHwSNayaWEuGrUdaQnyEO1cTyAQF9RhK8hOzR+TiFp+Fb1jKPHztIypZI6xY0Tb2NpwERuCa2DNHaiDnfLeY/by86nlsvGxN1puEf5cic6do6N8mh/PCQD4HJx8SAcfQwMoj2nbNuXrHaF+F+QzOneWT1++rCyYV1zxMX37Kud99plsMj5euQp/7fi/OUUxM4b0JPkM0THpGRBLT3Ii7gZwEAwUZeUsz02ldzw3NaU5NpMlNGa++uorJCQkYNSoUfDz88O9e/cwb948tGnTBrt370bHjh1NKs/kxkxCQgIyMjKEyzIyMpCUlCRc9qTnnnsO8+bNQ//+/WFr+2+AVnZ2Nv773/+ibdu2plYLAJCXl4e8vDwkJSXhu+++w+7du7Fw4eOR0i5cuICMjAz4+/srtssfODMzM7PAhlRWVhaysrKM0ynF9SNECCGElHNffvmlItt/t27d4Ovri5kzZxZ/Y6ZFixaIiopCUFAQPJ5IyXnv3j1ERUWhefPmqmVMmTIF7du3R9OmTTFs2DDUqFED169fx/Lly3HlyhUsXrzY1GoBAMLCwvD1118DAOzs7PDFF1/g3XffBfC4EQYAboKWupubGxhjSEpKgpeXl7DsWbNmITIyUle9CCGEkJJiCU9mRMMWOTs7w8/Pr8Cem8KY3JiZN28eOnfuDB8fH3Tq1Amenp64ffs29u3bBwDYu3evahnPPvsstm3bhpEjR2LChAnG+fXr18e2bdvQunVrU6sF4HE8z1tvvYW7d+9i+/bteO+99/Dw4UOMGzfOuE5+ILNIYcsmTpyIMWPGGKdTUlJQq1YtXfUkhBBCiou5GjN8D4TBYNA0mLReycnJOHbsmMlPZQAdjZk2bdrg6NGjiIyMRHx8PBISEuDu7o7Q0FBMmTIFTz31lKZygoODcf78eZw7dw737t1D1apV0aBBA5MP4Em1a9c25rjp0aMHgMeNkEGDBsHd3R3Av09onpSYmAhJkuDq6lpg2QVeRFPvGi1xIBoCIPg4GlFcTa6VPLZBz5hP4sw4clbcfiQ79bggYc4IvqNaS1yNhuV8zIyeXWvZRhTTwc8z19BXWsKUVOnM9SNBPs9WkexFWRkDd0/k5invK7X7U0u+JdH4TXweFD59iShmgr9uwvcVNh1Tr6AefIX53CmAMniFewEC/fopNvnfXfkfX4mJymL5P5b54/b0VG6D61zQkSg+Ru2mFm3DXxhRcBNfDh9EJbghDNxBGAS/+7lW8vw0Wn6K+EPQEyZU1vB/sE+bNg0RERHFtr+RI0fi4cOHmDx5ssnb6soA3KRJE2zYsEHPpgoNGjQociOmIK1bt8bixYtx8eJFtGzZEg4ODjh58qRivZMnT8LX19dsgceEEEJIaTHXk5lr167JXpgp6KlMfHw8goKCNJV9/PhxNGvWTDF/6tSp+Pbbb7FgwYKSeZvJnE6dOoW//vpLGFA8cODAIpcfFxcHKysr1KtXDzY2NggNDcXmzZsxZ84cVKpUCcDjt6fi4uLw4YcfFnl/hBBCSGkzV2PGxcVF1pgpSKNGjbB06VJNZYtGCIiMjMSMGTPwySef4L333jOprvl0NWZ++uknrFu3DleuXFE0RCRJMsbPFCQ9PR29evXC/v37IUkS2D+jWj0Zs2JKY+add96Bi4sLWrdujerVq+P+/fv47rvvEB0djY8++siYDC8yMhIBAQEICQnBhAkTjEnzPDw8MHbsWM37I4QQQsqqkg4A9vLywltvvaVrX5GRkYiIiEBERAQmTZqkqwxAR2NmxYoVGDZsGNzc3NCwYUPFYyemYbjN6dOn4/Llyzhw4AA6dOiAzZs3o1KlSli8eDFOnjyJ6Ohok+r03HPPYcWKFVi1ahUePHgAZ2dnPPPMM1izZg3eeOMN43qNGzdGfHw8xo8fj759+8LGxgYdO3bE3Llz9Wf/LY6YGS15azQkMLHmus34uBpmpZ5/RcuhKXO0CEeg4lZS7tuKG0dFyhMMZsJ3RGtICMGfGi3jFpmSOiifntwpWnKyaBlLSm2/AMC4+CdJy+BMImrxDhoGuxHdeYp5GmLHDIp1BPW34erjzK0jCElRBNb8Ihif7swZrhxRQRw+PqN9e+U6rVrJp319les0biyfbtpUNvnn38oxiW7elE+L/uDmY2L4YfOk9IfKjZSDvqmvoyenlGgQLT5FBr+NKDCI30bwVo01dy1FMYkG7jidnOXxOqmZ6nE3Fd306dMRERGBKVOmYNq0aUUqy+TGzJw5c9CvXz+sWrVKd1Tz1q1bMX78eGM+mdq1a6NFixbo1KkTBgwYgK+++sqk17OHDBmCIUOGaFq3ZcuWmt64IoQQQiwRY0V7MqPhmUSRzZs3D+Hh4ejWrRt69uyJw4cPy5a3adPGpPJMbsxcuXIFCxYsKNLrWZcvX0bjxo1hbW0NSZKQ/kSL+/XXX8ewYcN055ohhBBCKjJLyDOzfft2AMCuXbuwa9cuxXItvTxPMvmFziZNmuDOnTumbibj6uqKhw8fP66sVq2abDyn7Oxs4zJCCCGElD/x8fFgjBX4MZXJT2ZmzpyJcePGITAwEDVq1DB5hwDw9NNP4++//0a3bt0QFBSEmTNnokGDBrCzs0NUVBSeeeYZXeVaLL4zlQ+S0NLZKnqtnN+OW0cYM8HnrxHEKfCxF1pCfvSMdWJnp4ysMPB11hBgwh+Do6P6U0UtOSL4qohOJx8+wMcB8Tl6AG3pdrSk7VElurYa4qgkqPzQiE4enyNEy4A4PNE9zh+46MbiYy34uohiMfh1/hnjrdB1+NwvfL4YAPDzk0+LkoNxSUOT7asrVrl9Wz79gEt5I0rJwp8+LTEzihQs6YJrxBesZQAyLXlm+HUePFCuw187Ph5GyzhRon3z+Wq04E5oJS7giOWV3BA4lvBkxtxM/gn88ssvkZycjIYNG6JZs2bGZHT5JEnC1q1bCy1j2LBhxqcxn3zyCV544QV06NABwOOnNjt27DC1WoQQQggBNWY0+eOPP2BtbY1q1arh5s2buMmFyBc2JEC+fk9kp6xbty7+/vtv42vabdu2FY6fRAghhBAiYnJj5rKWYe0LkZGRgWHDhiEsLAwvvPACAMDJyQmhoaFFKpcQQggh9GSmRDg4OGDr1q0YPnx4Se+6eNjY/Ntvr2/wI/WgE1GfPk/U76sWqKIl4YpgHT7WxsZGnk9BFA6hpZtaJcQHAGBw4+qjZfwWbp5B9OTP0Uk2aY7UP1pW0jIkjeh8qu1LFLYg5WSrVE55bbNzRE9a5fNs+Z2JDoqvsGgd/j7XMviVlpgZ/gRqiatQi7MBlEEnfH4Y0UBG3DpZTZVp2//4Qz7Nx8cA6t8VUcgHX13RUHT8Ooqvv+hLqSORkyLnkeje5MsVXSeelnhD/lqKytXyu8LfI/xJ57cRlVFMKmJjRs/wdEXWrFkznDp1qjR2TQghhJByRlNjxtraGr/++uvjDaysYG1tXeDHRsNrFbNnz8acOXNw4MCBotWeEEIIITL5T2aK8rE0mrqZwsPDUfOf1wzDw8M1BfkWJiwsDGlpaejYsSOqVKkCLy8vWZmSJOH3338v0j4IIYSQiqgidjNpasw8OWZCREREkXfq7u4ODw+PIpdDCCGEEDlqzJSQ+Pj40tht8TA1AFhELZpTFMTGB1BqyZqmZUA4vi4aMsFJXFCgnZ0yKZ2eeDpRrKGLCxd8ygfvXRUMCMgfEz96HgADNyx9po2DbFpLwLKWRHt88KwouJc/D6L4b/7S8bGHwoBKLYHk3Em3slFeS/64c/Pkx2Qtuq/4eVqSPGpZriVSWy1JnpaEbaJBJPlAcn5aMEBkqmcD2fSxw4pVwL8wKrpH+Orw11+UEI8P+BUFCfNfd+W+1e+HdA3x1MoAZuXAmDVrypOySqJBI/kD5a+16F7kT57oXuQPXBSFff26ct6T+PpmZBS+PimSUgkAXr16NRISEoTLEhMTsXr16hKuESGEEFI+5A80qfdTEgNNmlupNGaGDBmCCxcuCJddunRJ8wjYhBBCCJGriAHApdKYKWwQqczMTFhbq48PQwghhBAClGDMzNWrV2XZg48fP45Mrl8yIyMDS5YsQW0ufqFMU4uZ0ZOoTkv2NS2xAqLO9idpSW4mqgtfX67P2VrQYW/DxaCIwjf4bmnRrvmiq/N95GfOKDfidya6v/jBKH3ksQ1axqsTnW5Rd7zaNnrGZBSFdKjuTMOfX9bOovtV/seGpmSBKveMsCD+BGuJmRHd03zchJa4MC1Z6PggFC4WK9u7jmKTi6fl0/fvK4vVEg7HV0cthgbQNv6jWmyLlp8Z0XeFv6f5YxTdv/xlqiEauJMffJI/cNGJ4GPmROvwP0ai35Xz5wuvC39xtQXVmQUFABejFStWIDIyEpIkQZIkhIWFKdbJf2Izf/78kqoWIYQQUq5QY6YAV0VviBRC9GSlX79+aNq0KRhj6NevH2bOnIkGDeR//RoMBjRt2hQ+Pj4m7Y8QQgghFZemxoyPj49JifJyc3MV85o0aYImTZoAePyUJiQkBO7u7prLJIQQQog6ejJTgOXLlxsbM9nZ2ZgxYwYcHR3Rv39/eHp64tatW4iOjkZ6ejrCw8NVyxs0aFDRal2GMCtrMKvHMQSSKHiAvytEdwnfma02DWgb+VDPNmqDUwLKzmw+vkCwjb2Het6Wmzfl06K4GkXMDH8H8/3YAHDxonxa9OSPOwbbatW4FSorNuHjAESnSm3cTi3jIopiZvjt+HOV66HM22GtZbBHDYFAijwyjzQk3NGS44ifp+W7oyXZj9r3SXSC+QulJc8MN7AkH0IBaEtfoyWkhw/X4adFp1ftngGUp49fR3SqdIz1qinkj0/T4uZWRbGOQ+PG8hn8d1swmuZDq0qq+67MXxjRWIL8QfE9GFoCjooJNWYKMHjwYOO/J02aBD8/P8TGxsLqiW9deHg4evbsiXPnzpm9koQQQgghBTH51ezVq1cjLCxM1pABHg9AGRYWhjVr1pitcoQQQggxTUXMM2Py20wJCQnIKCAtc0ZGBpKSkopcKUIIIYToQ91MGrRo0QJRUVEICgqSDRZ57949REVFoXnz5matYFmXk/NvV6iVlShImsvJYaNMCCjxKTe09LXyHetaYlt4OmMmFPviY2YE+7Xl4gkyM5Xniu9y5mNoAGVKiICO3IClokCFEyfUC+aDcbg38lx9lfc1n0ZCFCvAnwq1lCeAtrAl/jLx+xbl+qjCxwFoSRCjJbEIP60lf5Eotwc/T0uAkZb7VW1cKC3BTqL6cuczlxvHSkueJFE8DH/KteSZUctnBKjfM4Dy66Ml55GWS8AfE39utAypJRoeycWlqrwu3Lm6y4XLicoRnd9mzbxk0+716ilX4uPq+B8ELV/2YlIRGzMmn9158+bh0qVL8PHxwYsvvoh3330XL774IurWrYvLly9j3rx5qmVERUXhpug/FAC3bt1CVFSUqdUihBBCSAVlcmOmTZs2OHr0KEJCQnDkyBEsX74cR44cQWhoKI4cOYI2bdqolhEZGYnrBYw4evPmTURGRppaLUIIIYSgYg40qSsDcJMmTbBhwwbdOy1sbKa0tDTY2ipfKyWEEEKIuorYzVRiwxn88ccfOPFE7MKOHTtwhhvvIiMjA99++y3q169fUtUqskeP/u0z1jOkEgDY2Mgbbwa+Y11UsFpODhEteQ74xBKiZBRquT1E8QVc/VJSlLFDfDoY0XAo/GH361dLNu0gyCuhCCDhE1gAihwhaNhQNllJkJuGz3uhZeggtbwzgL5QFv4SiEKH7KrJc/04uQqSkWgJgOCDGfidaYlb0RBQkgV5DIroDrd3kR+TJDqhfH21nGC+vhpifHK4UyfK9cKnphHdrmpVEc3jD0l0CfjbXvTV5tfhL63eVCnmiB0T3dP8PL7+omiGu3fl06J4I37e8/7+ypVat5ZPq13crCwgPl5ZDjELTY2ZqKgovPXWW/D29laNZ5EkCVOnTlXMj4mJMXYfSZJUYDkODg5YsWKFlmoZ7d+/H2vXrsUvv/yCa9euwdXVFa1atUJ4eDhatmwpW/fYsWP4+OOPcfjwYdjY2KBjx46YO3cu6okCvAghhBALQ09mChAREYFu3brB29sbERERha5bUGPmnXfeQUhICBhjaN26NVasWIGmTZvK1jEYDKhfvz4cHBwU2xfmq6++QkJCAkaNGgU/Pz/cu3cP8+bNQ5s2bbB792507NgRAHDmzBkEBgaiWbNm2LhxIzIzMxEeHo527drhxIkTqFq1qsqeCCGEkLKNGjMFyHviyPJ0HqWXlxe8vB6/7hYXF4eWLVvCWZTLW4cvv/wS1bjX5Lp16wZfX1/MnDnT2JgJDw+HwWBAbGwsXP55pN2yZUs0aNAAc+fOxaeffmqW+hBCCCGk5JgUM5OZmYnVq1ejXbt2xkEj9ejQoYPubUX4hgwAODs7w8/PD9euXQMA5OTkIDY2FgMHDjQ2ZACgTp06CAoKQkxMjK7GzJMxMyJahpfh2dnJ40kcHZXjAtnyDUEtARt8RUUd1aKOdF5hB1xAXXK5fDui/m8+z8zp0+pVOXhQPh0sepvul1/k03//rVyHf7uOH4tFEDNTh+szd3Y2KNbhaUmdoiVnDB8ToWXYJf6cP3JUxi3Z28ufijo4C34i+Arx94woJklLnAoXM/PISn4+tYwl5OLipFhH4u9HUUAJT8P4aLl58lxJakOWidYRnRYpJ1s+QxgAw90k3DHa8nF3AHJc5L8jorwt/DnWMv4Yf5yi41ZL9aNlLClRXhy+vnw8jOilWT4djKi+/OmrV89LsY5X587yGS1ayKefyMMG4PEBlNAfzPRkRoW9vT0++OAD7N69u0iNGQA4d+4cvv76a/z111+KjMKSJGHfvn1FKj85ORnHjh0zPpW5cOECMjIy4C8I5PL398eePXuQmZkJ+wKyT2VlZSErK8s4nSL6H4YQQggpZRWxMWNynpl69erhtqhJb4JTp06hefPm2L59O3bt2oWkpCScO3cO8fHxuHDhQqGvbms1cuRIPHz4EJMnTwbweBgGAHDjI87/mccYK3QohlmzZqFy5crGT61atQpclxBCCCElx+TGzKhRozB79uwiPZmYNGkSgoOD8eeff4IxhmXLluHatWvYvn07MjMzMWPGDN1lA8DUqVPx7bff4r///a/ibSZJEg05oL5s4sSJSE5ONn7yu68IIYSQsoQGmtTgzz//xP379+Hj44OOHTvCy8tL1giQJAnz588vtIxjx45h0aJFxpG384OKe/bsiXHjxmHixIk4cOCAqVUD8Di78IwZM/DJJ5/gvffeM853d3cH8O8TmiclJiZCkiS4FpL0wWAwwGBQxkRkZxceQsIvE63L3zha+t4duXgHG8GYT3x8hp2zPB7CWtRhz/fPixqtajEzgnL5sAVR/AO/K9Guf/tNPh0bK58OnNtfsY2Bf5K4d6+yYL5Dnu98FwXwcMfpLohTUJxP/mLz/eoAqnDxOXfslNeWPzdq+WwA5TUQnV/+NDg7KxNYVuFj1PhgHNGTWy1JTrjACWc/eYyHlpgJ0XE7aRmLicdtk2ulPA9qOVdEcSCKeJhHGsa+EsUg8eeYv5iC37Iq3Nujzs6VlOVy1MZUArSl5OFD/DSEJGmKA+NPFX+PiG4zfhvRdbx8WT7Nh90BQMeO8lALRy67Bx92l5ZWcqEJFbGbyeTGzMKFC43/3rx5s2K5lsZMUlIS3NzcYGVlBVtbW1n3TqtWrXSPzRQZGYmIiAhERERg0qRJsmX5r3yfPHlSsd3Jkyfh6+tbYLwMIYQQYikqYmPG5G6mvLy8Qj+5ubmqZdSoUQP3/wkp9/X1xcEnXkn5448/dL2yPX36dERERGDKlCmYNm2aYrmNjQ1CQ0OxefNmpKamGudfvXoVcXFx6N27t8n7JIQQQkjpK7HhDJ70wgsv4JdffsFLL72E119/HdOmTcOtW7dgZ2eHlStX4o033jCpvHnz5iE8PBzdunVDz549cfjwYdny/MEvIyMjERAQgJCQEEyYMMGYNM/DwwNjx4412/ERQgghpSV/oMmibG9pSqUxM3nyZNz8Z9CM8ePH4/bt2/j2228hSRL69euHuXPnmlTe9u3bAQC7du3Crl27FMvz345q3Lgx4uPjMX78ePTt21c2nIHe7L85Of/2uYr6XrUMdaSW/kUcM1P4NKDeL+3iouwzl1y4CoriiNSCBTTkFBH1kWvp5eN3vWOHfLpmTeU2w4ePkk1X9vNTrvTrr/JptfGHAODYMfV1+EQX/Lnh4hgAAO3byyarC9axsZEHq6vFXQHK+0wUg8KHXojCX1xd5bFXDRo3lq/AxxsByoG2+KAEQPHlkLib2qVaXcUm/CkXhuK4ymPdbF3Uf/aycuRxSpmCc8XTMu6WIvpJ9D+OloG2+PPHJ2kSPd3mKuRZL0Cxyvnz8mktYyjxvz2i0DF+Hv9d1xLjJaKIC9QQi8PPE+2Hz0XTt2/BL4eURRWxm8nkxoyVlVWBb/3kB9G2atUK48ePR1BQkHC9+vXrGweTtLa2xhdffIEvvvjC1KoYxZsweFfLli2xVxQASgghhBCLZHJjJjw8HKtXr0ZqaipCQ0NRvXp13Lp1Cz/88AMqVaqEXr16Ye/evejSpQt27tyJLl26FEe9CSGEECJAT2Y0cHNzg6enJ06ePAknp39Th6elpaFLly6oUaMGTpw4gS5duuCTTz6hxgwhhBBSgqgxo8EXX3yBOXPmyBoywOOxkMaNG4dx48bho48+wvDhw/H222+braJl1ZM3jWgIFS3j7PD9/Hy/tCgehu/nFd18ou2eJKqvA9+5LQqs4HfO9/ELOqoNdvKIsmrVlF2Vvr7yaVFqFz7lxsWL8ulvvlFuw4cX9OqlbGB3n9BRPoMLIlcEE4gquH+/ch0+MQ5/cbt1U27D554RjD3mzq+jIcAgC+pjHfGxAqLQFn5X91vIy31OFLjEx7LxSTgA4J84OiMusMKpqzJ7d7qdPBeNKBcJ/53jczKJTp2WXCR8vAb/fRPGzNhoGJyJ/zKLLhR/rvjxxkTBIlz2c3d+LCEAzs7yc6MltoUPzxGF2fHztJwrLbmp1LbRktdL9BOnZSgxUraY3Ji5fv06bG2VCaSAx68/5w914OXlhezsbOF6hBBCCCkeFfHJjMntz0aNGmH+/PnI4ZrAOTk5mD9/Pho1agQAuHXrlu43hAghhBCiDw1noEFUVBT69OkDX19fvPTSS6hevTru3LmDLVu24MaNG/j+++8BAHv27MFzzz1n9goTQgghhDzJ5MbMiy++iNjYWISHh2PBggVgjEGSJLRq1Qpff/01goODAQDfiIIXCCGEEFKsKmI3k66ked26dUO3bt2Qnp6OpKQkVKlSBY5q0abllJ3dv7F2WhLiaRksjye6sfj4PlHCObUbUrjcXpFZT7mOWpSdKJqPi6j08XFQrNK6tXxay0CTfK4wUZwuP08Up7s3RB742Lfv87Lp5wIFQa18ADBfORH+pJ84oVyna1f5tGigQf6mESVW4xi8vWXT9vbVFevwAcCiQ+LzAPKJ9dpEyQfgAwCJv0dEx81Hc/OBzw0bKjZxbviMbJqvP6D8zvGXQBQIryXYlP/J44NGxQnb5PeZJPquaBlhUS27oahcPpmh4J5xdnaXTfO/K6Jj0hMAzG+jJWGm6JqoJQ/VknBU9DvDn74tW5Qpcfnbkf8t4ocufPQoBStXygPWiws1Zkzk6OhYYRsxhBBCSFlUERsz9AIaIYQQQiwaNWYIIYSQciR/oEm9n9IYaPKbb76BJElwFo0rpkGpDDRZnlSq9G9YiSgehn9cJ0q+xc/TkrBJy2NAvhwt5TIrrk9f1OnMx9FoiZnh1vH2VsbMtGqlXgyfK+6XX+TTolxsPD6/GAB89lnh5URE1FFs85yPj/rO1IgCNviLK+rU5wNX+HgT0cX295dN2vsqY2b4XYnO1R9/yKf5y8+PKQkATfj7SBTcwscG8UEIgm0cmubKpvmEeIDyFPOxF6LTy3+XtXx3+B530XddmV9SmbNLEUcj2jlfuJZRGTVklFOLxdMyQKzoJ4Of52Anv254ILgI3IVzEvwgOLnJ/+Pz8JD/rrgp8ywqfkNEv9v8rkShg6Lfpyfx94O18tYsNpbWzXTjxg2MGzcO3t7eSE5O1lUGPZkhhBBCSKkZPnw42rdvX6Thj6gxQwghhJQjlpQ0b+3atThw4AAWLVpUpHKom4kQQggpR8zVzZTC9cEaDAYYDAbBFvrcvXsXo0ePxuzZs1FTNKabCagxU0QOyIADHvd729sr40D4flVRn7PaNqJ4KC35FNT6u9X6fIUbiSqkZdRLTiXHXMU8X9/CB7kD1AesE/V/iwZLVPPjj/JpQYoTPPfOC/IZfGc8II4NeZKnp3KeloPig1n4IB/RyeP2ZdNYuQofRqElBwufrkR4yHzOGFEwA48/BlHsCFdhR0fld5DHH5PoduVvaS3pYPTEuolCXWz575ye5C5aRoQUVFgtZkZ0jPw8Xf+Jim40/kYSJZrhjtPAHWMtwSCtnp5VZNOiFE38MWgZD5S/BPz/zaJDLOtq1aolm542bRoiIiLMVn5YWBgaNWqEESNGFLksaswQQggh5Yi5nsxcu3YNLk9EPxf0VCY+Ph5BQUGayj5+/DiaNWuG77//Htu3b8fx48chSZL+yv6DGjOEEEJIOWKuxoyLi4usMVOQRo0aYenSpZrKrl27NtLS0jBy5Ei8//778Pb2xoN/Ho89+udJ64MHD2BrawsnJyfNdabGDCGEEEJ08/LywltvvaV5/cuXL+POnTuYN28e5s2bp1hepUoVvPjii9iyZYvmMqkxU1RJScaObxubGqqri0IZ+K5sflrUMObniUaVUOvvFnWrS9CQLUlLHgw1gmABV1d5zIyoWL5Pn//rQxSvoSdmhrdjh3LeuHFPy6brhIQoV1q5svCCfX2V8/gbQBQzw4/Fc/OmfFoUk8Kdc9H55e8ZUUgPX+XateXTwlgsfqU2bZTr8BeXTzzEjS0FQHEQou+K2vdAS4oWLSEo/HdQFOqi6avCbyg6KP7C8OdXtCM+qEOwDn+u+GMS/bWvZawr/hbmx6gyiE4WXxB/zwPKOBr+APjxqADYcueuqijWTcPFzHok7xrhw3P474mW8b7MpaznmfH09ERcXJxi/uzZs3HgwAHs3LkTHqLrUghqzBBCCCHlSFlvzNjb2yMwMFAxf+XKlbC2thYuU0ONGUIIIaQcKeuNmeJASfMIIYQQUupWrlyJNFG3ugb0ZKaoHj0y5rp4JGjNKsdiUa7Ddw3y3eGi8Acp/WHhOwKATK6tqgg40ZA8QUsnuZ6EGwJSnjz3jKOjcjAT/lzVqyefbtFCWe758/Lpw4dNrxs/9BEA7Noln353wADlSnw/P/9FFcXM8NdJFNTBn2O+j5+PjwAUQR6iy8SHpfBhK4DynPNDVIlCWwAupkMUX8QHHXA7z/WW57wQsU5LVcwzcOevOjeej42NcnwkLfEv/Cmu5MzFm4muGxd8k5sneCXVivtZFl1LtSQmon3zsTeCdfhV+GlR3AdfjJbYEP7eq+4qSKbFByqJChbExMiI/mPkz5VoHf7ABcm++Jw21arJ37zhvyc6/4/WJX+gyaJsb2moMUMIIYSUI9TNRAghhBBiYejJDCGEEFKOVMQnM9SYKSpnZ2N/6gNBGgS+n1Q0zhIfE+Nux/X7n7mu3IgbAExILY5CVBmeKBaHv9O1DEDFz9M0yItyFX5XfNc2n24DAPz95dN8DA2gPoSSyIkT8umMgcrh6x34/C/8zgVjx2gaEIePmeC3EQVacfNEOY/4Mag0pKtRxDGJx4vjAmkEF+qhqzxPEz/8FATXiI/XqZKjYYwf7nvhLrgGLi7yMZ6EOZlSkuUzbnMxHRoS+ViLEkTx24nyzPCBSVriQPhyBTEoHlycB19sYqKyWH4dUZ4ZtVw+Li7KNPkO/O+TloHt+B2JKsMPxiT6jePX0fDD7cTdRz4+lWTTWn6yzaUiNmbKTTdTamoqPv74Y3Tt2hVVq1aFJEkFDoh17NgxdO7cGc7OznB1dUXv3r1xURThSQghhJAyr9w0ZhISErBkyRJkZWXhpZdeKnC9M2fOIDAwEI8ePcLGjRuxfPly/P3332jXrh3u3btXchUmhBBCikH+k5mifCxNuelmqlOnDpKSkiBJEu7fv49vvvlGuF54eDgMBgNiY2ONA2i1bNkSDRo0wNy5c/Hpp5+WZLUJIYQQs6qI3UzlpjGjZQjxnJwcxMbGYuDAgbKRQOvUqYOgoCDExMSY3JjJreKB3H/Kun9KuZzvehV1+ypiF/jcCaLBhfg4AFH/PB/woCWHDE/U58xvpzagi6h+gvoyyK+hlhQ3/PkUpeTgwzOaNlWuEx+vnKeGjx/47TflOu06dpTP4K+JKJhAbbAuUTlaEhpxJ8f6UYZiFV9feawInysDUF4667xs+Yw0QT4QLrDmVopyNNzDP8qn+dteFF7Eh5NUEQ3wy8eP8NOCa2CrZVAitXJEgTZaytWS5IY/cP7GF+adUg+Akbhy3NzcZdOi21XL2Ex8dbSE+DjwxyS6Afid8xXUEmcjCgTif4NFAWZ8fbh9V+LimhiUOZCI+ZSbxowWFy5cQEZGBvz5iFAA/v7+2LNnDzIzM2EvuHGzsrKQlZVlnE4pyWguQgghRKOK+GSm3MTMaJGQkAAAcBO8ouHm5gbGGJKSkoTbzpo1C5UrVzZ+atVSz0ZKCCGElLSKGDNToRoz+Qrrkipo2cSJE5GcnGz8XLt2rbiqRwghhOhWERszFaqbyd39cf9v/hOaJyUmJkKSJLiKgi4AGAwGGAzKXAh37vybrkHU78v3FWtJ26JYSdSlxcfMiO4+tTgKUZ8+T8tgLKIYGR6/L8G+JcgHBLGyUjYs+XgNvhhReAk/1pVoOCT+dPLxGqJUH7zrgnRAyf7yJ3iV/bnrJOqv57s5RTvnzzl/YrQENwhuWGukcdMaaIiHyrWXx8hcP60shj/n/PkUhYUpDsFDECPBHzd/T4vOFX8NtARw8bTEa2ihZ6wz0Q8Nf9yie4/PnVJbfu+5uCjHseJ3JTot/Cnm1xFVF9wYWsIkUvyXW0vBfDwMP36aaJ7oGqiFGvDblOTgTBVQhWrM1K9fHw4ODjh58qRi2cmTJ+Hr6yuMlyGEEEIsRUUcaLJCdTPZ2NggNDQUmzdvRmrqv5HlV69eRVxcHHr37l2KtSOEEEKKjrqZLNzOnTvx8OFDY0Pl9OnT2LRpEwCgR48ecHR0RGRkJAICAhASEoIJEyYgMzMT4eHh8PDwwNixY0uz+oQQQgjRoVw1ZkaMGIErV64Yp7/77jt89913AIBLly7Bx8cHjRs3Rnx8PMaPH4++ffvCxsYGHTt2xNy5c1G1alWT9/nwIZAfMywKQdEyzI6iW5ePhxB1ffEFiZI7qOXXEPXh8gchipnhK6wlvwY/T8PYTDZ2yhgltWGgtMTMiHKn8N3f/CXQkjJE1IXOj5LRuHEd2bQDP7ARoPyzSBCTlJUjj2ZRpP4RhNlIaVyeC9E9wxPFfXD1YTbyOApRCMoDLjxDtGv+luBve1FVFH9BilbiL55a0hNRZUQ3AL8vLV92LWMHaaH2XdbyvRXdsPw8rhxHx8qKTbTEk/G71hBCpzy/gpiZW7flcXX8KRelppFOHJfPEP0OinJ78fibj4+35L+3Dx+ql2kmeXn//r+kd3tLU64aM5e13IB4nPF37969xVsZQgghpBRUxMZMhYqZIYQQQkj5U66ezBBCCCEVXUV8MkONGTPS0ncs6tJXdG9X4zIUi+Iq+L5tLXeflqQ3WnJaqA3GIuqL15ErQ/RdtOPiaPjzKboGfMyMqIucPwS+u1t0qvh9icIf+HQVfFiFh4dyjCL+mPIEp5MPvdCQ6gUGLYFA3M6T05SZZhK5/C/8+dRyqUXr8NeJr66WsZk05dfhL6YovoQnilvjYyT4dUTbaMmDohaTBigHfeMvgpaEVqJyVc6NgyAoLcdZPRsR/93gr78wVRVXv4w8ZQydt7fp/2MzUUIonpZcRPw6/DT/O0gxM8WKupkIIYQQYtHoyQwhhBBSjlTEJzPUmCGEEELKEWrMEEIIIcSiUWOGmMxg+DfOjxujDYC+MSM9PSvJpp34yEhRQVpGd9MSmckHhWoJYtQSAMzXRUugpoCtqzxA1c5O/o0VJc1Tq4po11oS4mkZp5OPy+THuBPFaWrJvcbvm6+v6BhtbLhEezYOinX45HaiWEl+Hn9uREHuarGygDLAl497F32/FLHxWqK7+WktAcAakjwqDkoU1aolCJ+vjyiynA8AVouwBdST/IloCJZ2dpb/Xom+xmo/PcLvEleQg12uYpXERPkgQlevypeLvrfZ3H1mKxrA8vx5+bToGqidP5UEhMS8qDFDCCGElCMVcaBJaswQQggh5UhRu4kssZuJXs0mhBBCiEWjJzNF9PTTyoHXCvP558rnd3zMDN8V+0xDb2VBfJ+5qCnN93fzASWiPl8tib94WmJm+HW0dKyLgi+4Y+KT6GmJL9GC33VionId/vSKqsvTcqr4eVoGueTXEZ1efp6oC5+/F2/eVK7DxyXwt6KovnzYlygMjE+Axx+jKHdkZcds+YybD5Qr6RmUUS0eRrSOlngzfhvRvvmAJy1xQDxR8Bg/TxSExNeZr4ugvhJ33I6OylgsNeKYGm6mIL7I1VW+L766oiSaivtTdB68ud9c0XXiA8H4Lxj/o5GRoSyjmFTEJzPUmCGEEELKkYrYmKFuJkIIIYRYNHoyQwghhJQjFfHJDDVmStjo0cpMRmFh8jgavp/X21vZB11VFHTAUwvqEOXBEI74plIuT9S/zPfxa4iH0TIQnq2zvBwbG+X55b+YWtLt8NUTdavzsS2i6vL70hIOISqHpxb+pGXQUy1hVqL68fNEcT88LeFQ/DHwIQnCHEL8zrUEIfH3ouh+5iuoJRiL30ZLsJZoHS0DQvLr8CdPdLL4m5g/waJyeBry4ti6KI/J3t5WNq0tBZbKlweAlCmPQ6nqIa+/m5vy90BKTFDfOX+utPxW8r97fMyMWpyTGVXExgx1MxFCCCHEotGTGUIIIaQcqYhPZqgxQwghhJQj1JghJktOTITLPwEKfL4Frfixbi5elE/7+iq3qerPxcyI+n3VYlBE9eX7zEV3tdpxaklyouXbomW8Ke4YJcF5sObmOTpaK9bhqcVvAMr8KqJcNHw3uo6wIE2nUy3lBaAe4iFaR8uQRGpxQYDyPIhCW9SOQZjHJ4UrWEu8lpagJC1BPmoxMloSD+n8zVDNaaNlICstSVi0jCWlIR7O4Cwv18pKHssiPA2PNMQOqeTBsRZtw39R9Q6QxlOLmcnKUi/DTCpiY4ZiZgghhBBi0ejJDCGEEFKO0ECThBBCCLFoeXmApHwrXTNqzFRAqenWkGwex2AwQSDA2ZuVZNPffKMs4/x5+fTt24VPA0BCbXnch3u1asqV+D5cPbEDWmiJL9Ay1o2W3B58nfljEnW+c+WI4mqcHOX7treX/xKI0kyohRcAykvA52gRpZ5QS+MDKE+NlqGvpLxc2bSdnTJ2iL8souPmU5jwl0R0W2kJH1HLBySOq9AQ06H2J6qWwa9EJ4I/WVryzPB1Ea2jJXCJ3xd/UUTxMPw8US4atYAoEf6YtOSi4Y8xU3CN+Jtay5eFnxZtw/9Oi+qrZbAzlfg9xXQ2N44YMStqzBBCCCHlCD2ZIYQQQohFq4iNGXqbiRBCCCEWjZ7MFFFGxr/dq5UE3eq1a8unW7dWrsN36/Ld1KKcHHyOE3t7ZfyDo7M8XkfRUBfl5NAyLgxPy8BAfB+0aB0tgRV8ffTEIGjI7WHNrSM6v/xhi4pVSYOhaWwmUbl6QgVyIT8GURiAltAm/tJpSZWiFuIhKpcPU7G1ksf8AFC/HwD1gxIdgJb4ErUYLy3jOWlJ5KPlBKvF74jK0RBfpinvlJ7YFp6WHDKiMtTWEdWF/xJqGVRNdJ3UYrH4a1CCjzvoyUwFkZaWhtGjR8Pb2xv29vZo1qwZNmzYUNrVIoQQQoosL6/oH0tTIZ/M9O7dG0ePHsXs2bPRsGFDrFu3Dq+99hry8vIwYMCA0q4eIYQQQkxQ4RozO3bswJ49e4wNGAAICgrClStX8NFHH6F///6wtlZPd08IIYSURRWxm6nCNWZiYmLg7OyMV155RTZ/yJAhGDBgAI4cOYK2bdtqLi8t7d+bprq98tmcQ06qbLpp00qKdfiYGH5a1LWtpZua7+Z14Pv9Rc8S+SAOLf3JfLmiCvP9x6KYGS3joajF9GhJcqKlXG7aykbZwNUS2qAWpqDnEAHl9ebvGdE4UVpCL/iQA1HohVo4iWgbPv5FFILCDyfEDyUkDDDiKyw6ofzO+ftMFBjEDxSlJWaGp2VQLREtN5baOloutpb4Ii205JlR+55qyeOi5Xyq5aEqqH48PfFPaoFhtrbq+zWTitiYqXAxM6dOnUKTJk1gw92I/v7+xuUiWVlZSElJkX0IIYSQsqYixsxUuMZMQkIC3AQjyubPS0hIEG43a9YsVK5c2fipVatWsdaTEEIIIdpUuG4mAJAKef5W0LKJEydizJgxxunk5GTUrl0baWn/PqFJsRM8reEeT6alKZ/fZWTIp/kno6I3qPkn7qmpynX4J67Zj7h9i54uPXwonxY9kuWHsucrKBrqXkt3QG6u+jo8tRz4gPLxr5b05dw0s1E+IubPuagXhD81atdaNE90SPwTay2vfDs4KOfx+O3420G0Ly3dYloeW/PHxPf+5OYI7ldzVFj0ZyhfGVG3jVq3gZbXjUVfXC3HpNYNYjAot+F/20T144+TX4e/gQHl+RV9v/jvdnF1M/H7EX3B+GMQ/cDy64iOSe01cO53MOWfaVYCfTiMpRSxq8jyeh4qXGPG3d1d+PQl8Z8gA9FTGwAwGAwwPPEDkd/N1K4dPaEhhBCiTWpqKipXrlwsZdvZ2cHT0xO3bxf9/yVPT0/Yacn9VUZUuMbM008/jfXr1yMnJ0cWN3Py5EkAQNOmTTWV4+3tjWvXroExhtq1a+PatWtwEQW1kiJJSUlBrVq16PwWEzq/xY/OcfGylPPLGENqaiq8vb2LbR/29va4dOkSHmkJclZhZ2cHe1FwfBlV4RozL7/8MpYuXYrvv/8e/fv3N85ftWoVvL298eyzz2oqx8rKCjVr1jQ+oXFxcSnTXyRLR+e3eNH5LX50jouXJZzf4noi8yR7e3uLaoSYS4VrzHTv3h1dunTBiBEjkJKSAl9fX6xfvx67du3C2rVrKccMIYQQYmEqXGMGADZv3ozJkycjPDwciYmJaNy4MdavX49XX321tKtGCCGEEBNVyMaMs7Mz5s+fj/nz5xe5LIPBgGnTpsmCg4n50PktXnR+ix+d4+JF55cAgMRK4j0xQgghhJBiUuGS5hFCCCGkfKHGDCGEEEIsGjVmCCGEEGLRqDFDCCGEEItGjRmd0tLSMHr0aHh7e8Pe3h7NmjXDhg0bSrtaFmf//v0YOnQoGjduDCcnJ9SoUQMvvvgi/ve//ynWPXbsGDp37gxnZ2e4urqid+/euHjxYinU2rJ98803kCQJzs7OimV0jvX56aef0KNHD1SpUgUODg5o0KABpk+fLluHzq0+x48fx0svvQRvb284OjqicePGiIqKQjo3phKd34qNGjM69e7dG6tWrcK0adOwc+dOBAQE4LXXXsO6detKu2oW5auvvsLly5cxatQo7NixA/Pnz8fdu3fRpk0b7N+/37jemTNnEBgYiEePHmHjxo1Yvnw5/v77b7Rr1w737t0rxSOwLDdu3MC4ceOEKdXpHOuzbt06dOjQAZUrV8bq1auxY8cOjB8/XjagIJ1bfU6fPo22bdvi8uXL+PzzzxEbG4tXX30VUVFReO2114zr0fklYMRkP/zwAwPA1q1bJ5vfpUsX5u3tzXJyckqpZpbnzp07inmpqamsevXqrFOnTsZ5r7zyCvPw8GDJycnGeZcvX2a2trbs448/LpG6lgchISEsNDSUDRo0iDk5OcmW0Tk23fXr15mTkxMbMWJEoevRudVn8uTJDAA7f/68bP4777zDALDExETGGJ1fwhg9mdEhJiYGzs7OeOWVV2TzhwwZgps3b+LIkSOlVDPLU61aNcU8Z2dn+Pn54dq1awCAnJwcxMbGok+fPrKxV+rUqYOgoCDExMSUWH0t2dq1a3HgwAEsWrRIsYzOsT7ffPMNHj58iPHjxxe4Dp1b/WxtbQEoxzRydXWFlZUV7Ozs6PwSANTNpMupU6fQpEkT2ajbAODv729cTvRLTk7GsWPH8NRTTwEALly4gIyMDOP5fZK/vz/Onz+PzMzMkq6mRbl79y5Gjx6N2bNno2bNmorldI71OXjwINzc3HDmzBk0a9YMNjY2qFatGoYPH24chJbOrX6DBg2Cq6srRowYgYsXLyI1NRWxsbH4+uuvMXLkSDg5OdH5JQCoMaNLQkIC3NzcFPPz5yUkJJR0lcqVkSNH4uHDh5g8eTKAf89nQeecMYakpKQSraOlCQsLQ6NGjTBixAjhcjrH+ty4cQPp6el45ZVX0L9/f+zduxcfffQRVq9ejR49eoAxRue2CHx8fHDo0CGcOnUK9evXh4uLC0JDQzFo0CDjcDR0fglQQcdmMgdJknQtI4WbOnUqvv32WyxYsAAtW7aULaNzrs/333+P7du34/jx46rnic6xafLy8pCZmYlp06ZhwoQJAIDAwEDY2dlh9OjR2LdvHxwdHQHQudXj8uXLCA0NRfXq1bFp0yZUrVoVR44cwYwZM5CWloZly5YZ16XzW7FRY0YHd3d34dOXxMREAOK/EIi6yMhIzJgxA5988gnee+8943x3d3cA4ideiYmJkCQJrq6uJVVNi5KWloaRI0fi/fffh7e3Nx48eAAAePToEQDgwYMHsLW1pXOsk7u7O86dO4fg4GDZ/O7du2P06NE4duwYXnzxRQB0bvWYMGECUlJScOLECTg5OQEA2rdvDw8PDwwdOhQDBw6Ep6cnADq/FR11M+nw9NNP46+//kJOTo5s/smTJwEATZs2LY1qWbTIyEhEREQgIiICkyZNki2rX78+HBwcjOf3SSdPnoSvry/s7e1LqqoW5f79+7hz5w7mzZuHKlWqGD/r16/Hw4cPUaVKFbz++ut0jnUSxWkAML6WbWVlRee2CE6cOAE/Pz9jQyZfQEAAABi7n+j8EmrM6PDyyy8jLS0N33//vWz+qlWr4O3tjWeffbaUamaZpk+fjoiICEyZMgXTpk1TLLexsUFoaCg2b96M1NRU4/yrV68iLi4OvXv3LsnqWhRPT0/ExcUpPsHBwbC3t0dcXBxmzJhB51inPn36AAB27twpm79jxw4AQJs2bejcFoG3tzf+/PNPpKWlyeYfOnQIAFCzZk06v+Sx0nwv3JJ16dKFValShS1ZsoTt37+fvf322wwAW7t2bWlXzaLMnTuXAWDdunVjhw4dUnzy/fXXX8zZ2Zm1b9+e7dixg23evJk1bdqUeXt7s7t375biEVgmUZ4ZOsf6hIaGMoPBwKZPn8727NnDZs2axezt7VlISIhxHTq3+mzdupVJksTatGnDoqOj2b59+9gnn3zCnJ2dmZ+fH8vKymKM0fkljFFjRqfU1FT2wQcfME9PT2ZnZ8f8/f3Z+vXrS7taFqdDhw4MQIGfJ/3222+sU6dOzNHRkbm4uLCXXnpJkUyLaCNqzDBG51iP9PR0Nn78eFarVi1mY2PDateuzSZOnMgyMzNl69G51Wf//v2sa9euzNPTkzk4OLCGDRuysWPHsvv378vWo/NbsUmMPZFzmxBCCCHEwlDMDCGEEEIsGjVmCCGEEGLRqDFDCCGEEItGjRlCCCGEWDRqzBBCCCHEolFjhhBCCCEWjRozhBBCCLFo1Jgh5ZIkSYiIiFBdb+XKlZAkCZcvXy50vYiICLOPvKt13+Y2ePBg+Pj4lOg+C3P58mX07NkTbm5ukCQJo0ePLu0qmY2Pjw8GDx6sa1tJkmQDrhYkPj4ekiQhPj5e134IKQ9o1GxSLh06dAg1a9Y0W3lvvfUWunXrZrbyStPUqVMxatSo0q6G0YcffogjR45g+fLl8PT0hJeXV2lXyWxiYmLg4uJS2tUgpNyjxgwpNxhjyMzMhIODA9q0aWPWsmvWrGnWxlFpql+/fmlXQebUqVNo3bo1XnrpJbOUl5ubi5ycHBgMBrOUp0dGRgYcHBzQvHnzUqsDIRUJdTORMmfr1q3w9/eHwWBAvXr1MH/+fGE3T/5j+MWLF6NJkyYwGAxYtWqVcRnfzXT48GE8//zzsLe3h7e3NyZOnIjs7GxNdRLt38fHByEhIdi1axdatGgBBwcHNG7cGMuXL1dsb8q+o6Oj8dxzz8HJyQnOzs4IDg7G8ePHjct/+ukn2NraYty4cbLt8rutli1bVuixiLqZ8s/lmjVr0KRJEzg6OuKZZ55BbGxsoWUBwPDhw2Fvb4///e9/xnl5eXno1KkTqlevjlu3bgm3y+8eOX/+PHbu3AlJkmTdblevXsUbb7yBatWqwWAwoEmTJpg3bx7y8vKMZVy+fBmSJGHOnDmYMWMG6tatC4PBgLi4OOE+mzdvjnbt2inm5+bmokaNGrIRliMjI/Hss8/Czc0NLi4uaNGiBZYtWwZ+BJj8+2Dz5s1o3rw57O3tERkZaVz2ZDdTZmYmxo4di2bNmqFy5cpwc3PDc889h61btxZ4fr/++ms0bNgQBoMBfn5+2LBhQ4HrPum3335Dr1694ObmBnt7ezRv3hwbN26UrZOeno5x48ahbt26sLe3h5ubG1q1aoX169dr2gchZUbpDg1FiNzOnTuZlZUVCwwMZDExMey7775jzz77LPPx8VEMPAmA1ahRg/n7+7N169ax/fv3s1OnThmXTZs2zbjun3/+yRwdHZmfnx9bv34927p1KwsODma1a9dmANilS5cKrde0adMU+69Tpw6rWbMm8/PzY6tXr2a7d+9mr7zyCgPADhw4oGvfn3zyCZMkiQ0dOpTFxsayzZs3s+eee445OTmxP//807je7NmzGQC2detWxhhjp06dYo6OjuyNN95QPceDBg1iderUUZxLHx8f1rp1a7Zx40a2Y8cOFhgYyGxsbNiFCxcKLS8jI4M1a9aM1atXjyUlJTHGGAsPD2dWVlbsxx9/LHC75ORkdujQIebp6cmef/5540jpmZmZ7O7du6xGjRqsatWqbPHixWzXrl3svffeYwDYiBEjjGVcunTJeB8EBQWxTZs2sR9//LHA6zl//nwGgP3999+y+Tt27GAA2LZt24zzBg8ezJYtW8b27NnD9uzZw6ZPn84cHBxYZGSkbNs6deowLy8vVq9ePbZ8+XIWFxfHfv31V+OyQYMGGdd98OABGzx4MFuzZg3bv38/27VrFxs3bhyzsrJiq1atkpULgNWqVct432zbto1169aNAWDfffedcb24uDgGgMXFxRnn7d+/n9nZ2bF27dqx6OhotmvXLjZ48GAGgK1YscK43rvvvsscHR3ZZ599xuLi4lhsbCybPXs2W7BgQYHXjZCyiBozpEwJCAhgtWrVYllZWcZ5qampzN3dXdiYqVy5MktMTFSUwzdm+vfvzxwcHNjt27eN83Jycljjxo2L1Jixt7dnV65cMc7LyMhgbm5u7N133zV531evXmU2Njbs/fffl+0nNTWVeXp6sn79+hnn5eXlsR49ejBXV1d26tQp5ufnxxo3bszS0tIKPQ7GCm7MVK9enaWkpBjn3b59m1lZWbFZs2aplnnu3DnjSMV79+5lVlZWbMqUKarbMfb4PPbs2VM2b8KECQwAO3LkiGz+iBEjmCRJ7OzZs4yxfxsz9evXZ48ePVLd1/3795mdnR2bNGmSbH6/fv1Y9erVWXZ2tnC73Nxclp2dzaKiopi7uzvLy8uT1d/a2tpYJ/7YnmzM8HJyclh2djYbNmwYa968uWwZgALvG19fX+M8UWOmcePGrHnz5orjCQkJYV5eXiw3N5cxxljTpk3ZSy+9VGD9CLEU1M1EyoyHDx/it99+w0svvQQ7OzvjfGdnZ4SGhgq36dixI6pUqaJadlxcnLHbI5+1tTX69+9fpDo3a9YMtWvXNk7b29ujYcOGuHLlisn73r17N3JycjBw4EDk5OQYP/b29ujQoYPsbRVJkrB69WpUqlQJrVq1wqVLl7Bx40Y4OTnpPpagoCBUqlTJOF29enVUq1ZNdiwF8fX1xdKlS7FlyxaEhISgXbt2mt4mK8j+/fvh5+eH1q1by+YPHjwYjDHs379fNr9Xr16wtbVVLdfd3R2hoaFYtWqVsbsqKSkJW7duxcCBA2Fj828Y4f79+9G5c2dUrlwZ1tbWsLW1RXh4OBISEnD37l1Zuf7+/mjYsKGmY/vuu+/w/PPPw9nZGTY2NrC1tcWyZcvw119/KdYt6L45f/48rl+/Liz//PnzOHPmDF5//XUAkN1LPXr0wK1bt3D27FkAQOvWrbFz505MmDAB8fHxyMjI0HQMhJQ11JghZUZSUhIYY7If73yieQA0v/mSkJAAT09PxXzRPFO4u7sr5hkMBtl/Clr3fefOHQBAQEAAbG1tZZ/o6Gjcv39fse9evXohMzMT3bp1w9NPP13sx1KYnj17onr16sjMzMSYMWNgbW2tuy4JCQnCa+vt7W1c/iRT3oAaOnQobty4gT179gAA1q9fj6ysLFlsy6+//oquXbsCAJYuXYqff/4ZR48exeTJkwFAcU607n/z5s3o168fatSogbVr1+LQoUM4evQohg4diszMTMX6hd03/DnIl38fjRs3TnEfhYWFAYDxXvriiy8wfvx4bNmyBUFBQXBzc8NLL72Ec+fOaToeQsoKepuJlBlVqlSBJEnGH+Mn3b59W7iN1twv7u7uwjIKKtectO7bw8MDALBp0ybUqVNHtdw9e/bgq6++QuvWrRETE4Pvv/8effr0MU+ldRg+fDhSU1Px1FNP4YMPPkC7du00PTUTcXd3FwYO37x5E8C/5yqfKTmAgoOD4e3tjRUrViA4OBgrVqzAs88+Cz8/P+M6GzZsgK2tLWJjY2Fvb2+cv2XLFmGZWve/du1a1K1bF9HR0bJtsrKyhOsXdt+IGp/Av+dm4sSJsoDmJzVq1AgA4OTkhMjISERGRuLOnTvGpzShoaE4c+aMpmMipCygJzOkzHByckKrVq2wZcsWPHr0yDg/LS1N01s1hQkKCsK+fftkDaXc3FxER0cXqVxz7js4OBg2Nja4cOECWrVqJfzku3XrFt544w106NABv/zyC3r16oVhw4bh0qVLxX48It988w3Wrl2LhQsXYtu2bXjw4AGGDBmiu7xOnTrh9OnTOHbsmGz+6tWrIUkSgoKCdJdtbW2NN998E1u2bMH//d//4bfffsPQoUNl60iSBBsbG9nTpYyMDKxZs0b3fvPLtbOzkzVkbt++XeDbTAXdN/Xr1y8wVUCjRo3QoEED/P777wXeR092J+arXr06Bg8ejNdeew1nz55Fenp6kY6VkJJEjRlSpkRFReHGjRsIDg7Gli1b8P3336Nz585wdnYuUgbeKVOmAHgcYxMdHY3t27ejZ8+eePjwobmqXuR9+/j4ICoqCpMnT8bw4cOxZcsWHDhwABs3bsS4ceMwbdo0AI//Q3vttdcgSRLWrVsHa2trrFy5EpUrV0b//v1lDcGScPLkSXzwwQcYNGgQhgwZgnr16mHZsmXYunUrPv/8c11lfvjhh6hRowZ69uyJpUuX4scff8SoUaOwaNEijBgxQnN8SkGGDh2KrKwsDBgwAA4ODor4pZ49eyItLQ0DBgzAnj17sGHDBrRr167IuWtCQkJw9uxZhIWFYf/+/Vi1ahVeeOGFArupPDw80LFjR2zYsAHbt29HSEgIzpw5g08++aTQ/Xz99dfYt28fgoODsX79ehw8eBBbtmzBrFmz8MorrxjXe/bZZzF9+nRs3boVBw8exNdff401a9bgueeeg6OjY5GOlZASVdoRyITwYmJi2NNPP83s7OxY7dq12ezZs9kHH3zAqlSpIlsPABs5cqSwDHBvMzHG2M8//8zatGnDDAYD8/T0ZB999BFbsmRJkd5m4t/CYYyxDh06sA4dOuje95YtW1hQUBBzcXFhBoOB1alTh/Xt25ft3buXMcbY5MmTmZWVFdu3b59su19++YXZ2NiwUaNGFXosBb3NJDqXam/jpKWlscaNGzM/Pz/28OFD2bKRI0cyW1tbxRtJon2IzuOVK1fYgAEDmLu7O7O1tWWNGjVi//nPf4xv4jD279tM//nPfwrdh0jbtm0ZAPb6668Lly9fvpw1atSIGQwGVq9ePTZr1iy2bNkyxTUrqP75y/jzN3v2bObj48MMBgNr0qQJW7p0qfD+yr8mixYtYvXr12e2trascePG7Ntvv5WtJ3qbiTHGfv/9d9avXz9WrVo1Zmtryzw9PVnHjh3Z4sWLjetMmDCBtWrVilWpUsV4nB9++CG7f/++ytkjpGyRGOMyQBFSxmRnZ6NZs2aoUaMGfvzxx9KuDiGEkDKGAoBJmTNs2DB06dIFXl5euH37NhYvXoy//voL8+fPL+2qEUIIKYOoMUPKnNTUVIwbNw737t2Dra0tWrRogR07dqBz586lXTVCCCFlEHUzEUIIIcSi0dtMhBBCCLFo1JghhBBCiEWjxgwpl27evImIiAicOHFC0/qnT59GREQELl++XKz1Ku9WrlwJSZJUz2NERAR8fHx07UOSJKxcubLQdS5fvqxpvZIwc+ZMYebg+Ph4SJIkG3OLEKIPNWZIuXTz5k1ERkaa1JiJjIykxgwxu4IaMy1atMChQ4fQokWLkq8UIeUMvc1EiA7p6ellKkNqdna2MQU/sQwuLi5o06ZNaVeDkHKBnswQi3L+/HkMGTIEDRo0gKOjI2rUqIHQ0FCcPHnSuE58fDwCAgIAAEOGDIEkSZAkCREREcIyV65caUzxHhQUZFw/v4siMDAQTZs2xcGDB9G2bVs4Ojoax/KJjo5G165d4eXlBQcHBzRp0gQTJkwQDpNw5MgRhIaGwt3dHfb29qhfvz5Gjx4tW+fcuXMYMGAAqlWrBoPBgCZNmuDLL7+UrZPfPbFmzRqMHTsWNWrUgMFgwPnz5ws8b5GRkXj22Wfh5uYGFxcXtGjRAsuWLQP/MqOPjw9CQkKwa9cutGjRAg4ODmjcuDGWL1+uKPPw4cN4/vnnYW9vD29vb0ycOBHZ2dkF1qEwzZs3R7t27RTzc3NzUaNGjQIHTDTVTz/9hE6dOqFSpUpwdHRE27Zt8cMPPyjWu3HjBt555x3UqlULdnZ28Pb2Rt++fY3jJGVmZmLs2LFo1qwZKleuDDc3Nzz33HOKMZYkScLDhw+xatUq430VGBgIoOBupm3bthmHE6hUqRK6dOmCQ4cOydaJiIiAJEn4888/8dprr6Fy5cqoXr06hg4diuTkZNm63333HZ599llUrlwZjo6OqFevnmIsKkIsXqnmHybERAcOHGBjx45lmzZtYgcOHGAxMTHspZdeYg4ODuzMmTOMMcaSk5PZihUrGAA2ZcoUdujQIXbo0CF27do1YZl3795lM2fOZADYl19+aVz/7t27jLHHwxO4ubmxWrVqsQULFrC4uDh24MABxhhj06dPZ//973/ZDz/8wOLj49nixYtZ3bp1WVBQkGwfu3btYra2tszf35+tXLmS7d+/ny1fvpy9+uqrxnX+/PNPVrlyZfb000+z1atXsx9//JGNHTuWWVlZsYiICON6+enra9Sowfr27cu2bdvGYmNjWUJCQoHnbfDgwWzZsmVsz549bM+ePWz69OnMwcGBRUZGytarU6cOq1mzJvPz82OrV69mu3fvZq+88goDYDzm/Lo6OjoyPz8/tn79erZ161YWHBzMateurWl4CN78+fMZAPb333/L5u/YsYMBYNu2bTOpvPxhDlasWGGcFx8fz2xtbVnLli1ZdHQ027JlC+vatSuTJIlt2LDBuN7169eZl5cX8/DwYJ999hnbu3cvi46OZkOHDmV//fUXY4yxBw8esMGDB7M1a9aw/fv3s127drFx48YxKysrtmrVKmNZhw4dYg4ODqxHjx7G++rPP/9kjImHIfj2228ZANa1a1e2ZcsWFh0dzVq2bMns7OzY//3f/xnXyx/+oFGjRiw8PJzt2bOHffbZZ8xgMLAhQ4YY1/vll1+YJEns1VdfZTt27GD79+9nK1asYG+++aZJ55OQso4aM8Si5eTksEePHrEGDRqwDz/80Dj/6NGjiv/MCvPdd98Jx7dh7HFjBoBiLCReXl4ey87OZgcOHGAA2O+//25cVr9+fVa/fn2WkZFR4PbBwcGsZs2aLDk5WTb/vffeY/b29iwxMZEx9u9/gu3bt9d0bLzc3FyWnZ3NoqKimLu7O8vLyzMuq1OnDrO3t2dXrlwxzsvIyGBubm7s3XffNc7r378/c3BwYLdv3zbOy8nJYY0bN9bVmLl//z6zs7NjkyZNks3v168fq169OsvOzjapPFFjpk2bNqxatWosNTVVVuemTZuymjVrGs/D0KFDma2tLTt9+rTm/eXk5LDs7Gw2bNgw1rx5c9kyJycn4fhWfGMmNzeXeXt7s6efflo2/lRqaiqrVq0aa9u2rXFefmNmzpw5sjLDwsKYvb298Vjmzp3LALAHDx5oPhZCLBF1MxGLkpOTg5kzZ8LPzw92dnawsbGBnZ0dzp07h7/++qvY9lulShV07NhRMf/ixYsYMGAAPD09YW1tDVtbW3To0AEAjPX5+++/ceHCBQwbNgz29vbC8jMzM7Fv3z68/PLLcHR0RE5OjvHTo0cPZGZm4vDhw7Jt+vTpo7n++/fvR+fOnVG5cmVjPcPDw5GQkIC7d+/K1m3WrBlq165tnLa3t0fDhg1x5coV47y4uDh06tQJ1atXN86ztrZWjD6tlbu7O0JDQ7Fq1Srk5eUBAJKSkrB161YMHDiwyLFADx8+xJEjR9C3b184OzvL6vzmm2/i+vXrOHv2LABg586dCAoKQpMmTQot87vvvsPzzz8PZ2dn2NjYwNbWFsuWLdN9H549exY3b97Em2++CSurf3+anZ2d0adPHxw+fBjp6emybXr16iWb9vf3R2ZmpvGa5ne39uvXDxs3bsSNGzd01Y2Qso4aM8SijBkzBlOnTsVLL72E7du348iRIzh69CieeeYZZGRkFNt+vby8FPPS0tLQrl07HDlyBDNmzEB8fDyOHj2KzZs3A4CxPvfu3QMA1KxZs8DyExISkJOTgwULFsDW1lb26dGjBwDg/v37qnUS+fXXX9G1a1cAwNKlS/Hzzz/j6NGjmDx5sqye+dzd3RVlGAwG2XoJCQnw9PRUrCeap9XQoUNx48YN7NmzBwCwfv16ZGVlYfDgwbrLzJeUlATGmPCceXt7A3h8TMDj61XYtQKAzZs3o1+/fqhRowbWrl2LQ4cO4ejRoxg6dCgyMzN11TF//wXVMS8vD0lJSbL5/LUyGAwA/r2m7du3x5YtW5CTk4OBAweiZs2aaNq0KdavX6+rjoSUVfTqA7Eoa9euxcCBAzFz5kzZ/Pv378PV1bXY9itJkmLe/v37cfPmTcTHxxufxgDAgwcPZOtVrVoVAHD9+vUCy69SpYrxKcHIkSOF69StW1e1TiIbNmyAra0tYmNjZU+GRK8La+Xu7o7bt28r5ovmaRUcHAxvb2+sWLECwcHBWLFiBZ599ln4+fnpLjNflSpVYGVlhVu3bimW3bx5EwDg4eEB4PH1KuxaAY/vw7p16yI6Olp2HbKysnTXMb9hUlAdraysUKVKFZPLffHFF/Hiiy8iKysLhw8fxqxZszBgwAD4+Pjgueee011fQsoSejJDLIokSca/PvP98MMPisfn/F+oakxdP78uT26b7+uvv5ZNN2zYEPXr18fy5csL/M/O0dERQUFBOH78OPz9/dGqVSvFR/TERGs9bWxsYG1tbZyXkZGBNWvW6CoPePzW1759+4xv9wCP3zyKjv7/du4lJLUtDgP41wnbqYMIKiKDXtCDaqzZQ7KR6TioRllENGvQLAoiyB5QDZoUhUlP7GHZLDDDHhMpgwY1rCZFRA1q0KT/HRyS49Uup7hw7z58v6GsvcS1HXy61rfXvj3nR5jz+XwIhUIIh8P/WutGr9fDaDRic3Mz5h6/v79jcXERubm5KC4uBgDYbDbs7+9Ht50SSUpKQkpKSkyQubu7i2szAfH/an2mpKQEBoMBy8vLMS2z19dXbGxsRBtO36UoCiwWC0ZGRgAAZ2dn356L6P+GYYZUxeFwwO12Y3JyEoFAAGNjY2hra4vbFigqKoJWq8XS0hKCwSDC4XD0F3giFRUVAICZmRkcHh4iHA5H//b/jNlsRnp6Orq6urC1tYXd3V00Nzfj/Pw8buz09DSur69hMpng8XgQDAbh8XjQ2toaHTM1NYWbmxvU1tbC7XYjGAzC7/djYmIi4Xmd32W32/Hy8oKWlhbs7e1hdXUVtbW1cSHsK/r6+gAAVqsVa2tr8Pv9sNvtCSvpX+F0OvH29oaWlhZotdpvn8FJZHh4GI+Pj6ivr8f6+jp2dnbQ2NiIi4sLjI+PR4PJ4OAgMjIyUFdXh6mpKQQCAWxubqKzsxOXl5cAfn4Pr66u0N3djUAggIWFBdTU1CTcIqqsrIzey3A4/GlI+vHjB0ZHRxGJROBwOLCzswOv14v6+no8Pz/D5XJ9+TP39/fD6XRiaWkJBwcH2N7eRk9PT8zZLqI/wn99ApnoK56enqS9vV2ysrJEp9NJTU2NhEIhsVgsYrFYYsaurKxIaWmpaDQaASADAwP/OPfk5KQUFBRIcnJyTBPGYrFIeXl5wmuOj4+lqqpKdDqdZGZmSkdHh5yeniZsUp2cnIjNZpO0tDRRFEWKiopiGlgiP1s4TqdTDAaDaDQayczMFLPZLENDQ9ExHy0Yr9f7W2smIjI/Py8lJSWiKIoUFhbK8PCwzM3NxTWP8vLyxG63x12faH2Pjo7EZDKJoiiSnZ0tvb29MjMz860206/MZrMAkNbW1m/PkajNJCISCoXEarWKXq8XrVYrJpNJ/H5/3PW3t7fidDolOztbNBqN5OTkSFNTk9zf30fHuFwuyc/PF0VRpKysTGZnZ6Mto19FIhGprq4WnU4nAKLrmKiaLSLi8/nEaDRKamqq6PV6aWhokKOjo5gxH+/z8PAQ8/rHIwk+1n93d1dsNpsYDAZJSUmRrKwsaWxsjKl5E/0JkkT+9tQsIiIiIhXhNhMRERGpGsMMERERqRrDDBEREakawwwRERGpGsMMERERqRrDDBEREakawwwRERGpGsMMERERqRrDDBEREakawwwRERGpGsMMERERqdpfgeRH4asvtTUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute right-hand side of geostrophic balance equations\n",
    "geos_bal_RHS_1 = dp_dx/dens\n",
    "geos_bal_RHS_2 = -dp_dy/dens\n",
    "\n",
    "geos_bal_RHS_1 = geos_bal_RHS_1.where(ds_grid.maskC,np.nan)    # mask land areas with NaNs\n",
    "geos_bal_RHS_2 = geos_bal_RHS_2.where(ds_grid.maskC)           # note that 'where' also masks with NaNs by default\n",
    "\n",
    "# plot RHS of 1st geostrophic balance equation\n",
    "curr_plot = (geos_bal_RHS_1.isel(tile=1,k=20)).plot(cmap='bwr')\n",
    "# render NaNs (land areas) as black\n",
    "cmap_nanmasked = curr_plot.cmap.copy()\n",
    "cmap_nanmasked.set_bad('black')\n",
    "curr_plot.cmap = cmap_nanmasked\n",
    "# Note: Matplotlib plot text (such as the title below) can include LaTeX,\n",
    "# but special characters such as backslash \\ must include an extra \"escape\" backslash \\\n",
    "# in order to render correctly\n",
    "plt.title('$(1/\\\\rho)p_x$')\n",
    "# adjust colormap limits (multiply by 0.3)\n",
    "curr_clim = np.asarray(curr_plot.get_clim())\n",
    "curr_plot.set_clim(0.3*curr_clim)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Left-hand side\n",
    "\n",
    "To compute the left-hand side of the geostrophic balance equations we need to center the velocity fields in the grid cells and compute $f$. With the tools we've covered so far, this is fairly straightforward.\n",
    "\n",
    "The code below also includes a function to scale the colormaps/colorbars based on the range of values present in the maps. As you write more code in Python, you will likely find yourself relying more and more on functions, rather than scripts or code blocks that run linearly top to bottom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Note: the model velocities UVEL/u and VVEL/v are defined on the model grid axes.\n",
    "On some tiles (like tile=1 which we plot below) \n",
    "u and v will be essentially the eastward and northward velocity respectively.\n",
    "But on other tiles the model axes are very different from geographic axes.\n",
    "What matters for now is that these two components are always locally perpendicular.\n",
    "\"\"\"\n",
    "# interpolate velocities to center of grid cells\n",
    "ds_vel_mo.UVEL.data = ds_vel_mo.UVEL.values\n",
    "ds_vel_mo.VVEL.data = ds_vel_mo.VVEL.values\n",
    "import warnings\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    vel_interp = xgcm_grid.interp_2d_vector({'X':ds_vel_mo.UVEL,'Y':ds_vel_mo.VVEL},boundary='extend')\n",
    "u = vel_interp['X']\n",
    "v = vel_interp['Y']\n",
    "\n",
    "# compute f from latitude of grid cell centers\n",
    "lat = ds_grid.YC\n",
    "Omega = (2*np.pi)/86164\n",
    "lat_rad = (np.pi/180)*lat    # convert latitude from degrees to radians\n",
    "f = 2*Omega*np.sin(lat_rad)\n",
    "\n",
    "# compute left-hand side of geostrophic balance equations\n",
    "geos_bal_LHS_1 = f*v\n",
    "geos_bal_LHS_2 = f*u\n",
    "\n",
    "geos_bal_LHS_1 = geos_bal_LHS_1.where(ds_grid.maskC)    # mask land areas with NaNs\n",
    "geos_bal_LHS_2 = geos_bal_LHS_2.where(ds_grid.maskC)\n",
    "\n",
    "\n",
    "# Introduce function to scale colormap\n",
    "def cmap_zerocent_scale(plot,scale_factor):\n",
    "    \"\"\"\n",
    "    Center colormap at zero and scale relative to existing |maximum| value, \n",
    "    given plot object and scale_factor, a number of type float.\n",
    "    Returns new colormap limits as new_clim.\n",
    "    \"\"\"\n",
    "    curr_clim = plot.get_clim()\n",
    "    new_clim = (scale_factor*np.max(np.abs(curr_clim)))*np.array([-1,1])\n",
    "    plot.set_clim(new_clim)\n",
    "    return new_clim\n",
    "\n",
    "\n",
    "# tile and k indices to plot\n",
    "tile_plot = 1\n",
    "k_plot = 20\n",
    "# dictionary to select tile and k indices (can then pass this to isel to save space)\n",
    "isel_dict = {'tile':tile_plot,'k':k_plot}\n",
    "\n",
    "\n",
    "# create figure\n",
    "fig,axs = plt.subplots(2,2,figsize=(11,8))\n",
    "\n",
    "# set NaN masking in colormap in advance \n",
    "# (in contrast to previous method setting NaN mask after plot is created)\n",
    "seismic_nanmasked = plt.get_cmap('seismic').copy()\n",
    "seismic_nanmasked.set_bad('black')\n",
    "\n",
    "\n",
    "curr_ax = axs[0,0]\n",
    "curr_plot = curr_ax.pcolormesh((geos_bal_LHS_1.isel(tile=tile_plot,k=k_plot)).squeeze(),cmap=seismic_nanmasked)\n",
    "plot_clim = cmap_zerocent_scale(curr_plot,0.3)   # centers colormap at zero and scales extent by 0.3\n",
    "curr_ax.set_title('$fv$')\n",
    "fig.colorbar(curr_plot,ax=curr_ax)\n",
    "curr_ax = axs[0,1]\n",
    "curr_plot = curr_ax.pcolormesh((geos_bal_RHS_1.isel(tile=tile_plot,k=k_plot)).squeeze(),cmap=seismic_nanmasked)\n",
    "curr_plot.set_clim(plot_clim)    # set RHS colormap limits to same values as LHS\n",
    "curr_ax.set_title('$(1/\\\\rho)p_x$')\n",
    "fig.colorbar(curr_plot,ax=curr_ax)\n",
    "\n",
    "curr_ax = axs[1,0]\n",
    "curr_plot = curr_ax.pcolormesh((geos_bal_LHS_2.isel(tile=tile_plot,k=k_plot)).squeeze(),cmap=seismic_nanmasked)\n",
    "plot_clim = cmap_zerocent_scale(curr_plot,0.3)\n",
    "curr_ax.set_title('$fu$')\n",
    "fig.colorbar(curr_plot,ax=curr_ax)\n",
    "curr_ax = axs[1,1]\n",
    "curr_plot = curr_ax.pcolormesh((geos_bal_RHS_2.isel(tile=tile_plot,k=k_plot)).squeeze(),cmap=seismic_nanmasked)\n",
    "curr_plot.set_clim(plot_clim)    # set RHS colormap limits to same values as LHS\n",
    "curr_ax.set_title('$-(1/\\\\rho)p_y$')\n",
    "fig.colorbar(curr_plot,ax=curr_ax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the left and right plots in each row are nearly identical!\n",
    "\n",
    "We can also plot the equation divided by $f$ so each side is in units of velocity. Actual velocities $v$, $u$ are on the left, and *geostrophic* velocities $v_g$, $u_g$ are on the right--the velocities that are \"explained\" by geostrophic balance.\n",
    "\n",
    "$$\n",
    "v_g = \\frac{1}{f{\\rho}}p_x\n",
    "$$\n",
    "$$\n",
    "u_g = -\\frac{1}{f{\\rho}}p_y\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute geostrophic velocities\n",
    "v_g = geos_bal_RHS_1/f\n",
    "u_g = geos_bal_RHS_2/f\n",
    "\n",
    "v_g = v_g.where(ds_grid.maskC)    # mask land areas with NaNs\n",
    "u_g = u_g.where(ds_grid.maskC)\n",
    "\n",
    "\n",
    "# tile and k indices to plot\n",
    "tile_plot = 1\n",
    "k_plot = 20\n",
    "# dictionary to select tile and k indices (can then pass this to isel to save space)\n",
    "isel_dict = {'tile':tile_plot,'k':k_plot}\n",
    "\n",
    "\n",
    "# identify depth level for plot titles\n",
    "depth_plot_level = -ds_grid.Z[k_plot].values\n",
    "# convert depth level (rounded to nearest meter) to a string for plot titles\n",
    "depth_str = str(int(depth_plot_level))\n",
    "\n",
    "\n",
    "# plot LHS and RHS of equations for tile = 1, k = 20 in a figure with 2x2 subplots\n",
    "# arrays need to have singleton (time) dimension removed with .squeeze() before plotting\n",
    "\n",
    "# create figure\n",
    "fig,axs = plt.subplots(2,2,figsize=(11,8))\n",
    "\n",
    "curr_ax = axs[0,0]\n",
    "curr_plot = curr_ax.pcolormesh((v.isel(isel_dict)).squeeze(),cmap=seismic_nanmasked)\n",
    "plot_clim = cmap_zerocent_scale(curr_plot,0.3)\n",
    "curr_ax.set_title('$v$, ' + depth_str + ' m depth')\n",
    "cbar = fig.colorbar(curr_plot,ax=axs[0,0])\n",
    "# cbar.set_label('Velocity [m s$^{-1}$]')\n",
    "curr_ax = axs[0,1]\n",
    "curr_plot = curr_ax.pcolormesh((v_g.isel(isel_dict)).squeeze(),cmap=seismic_nanmasked)\n",
    "curr_plot.set_clim(plot_clim)\n",
    "curr_ax.set_title('$v_g$, ' + depth_str + ' m depth')\n",
    "cbar = fig.colorbar(curr_plot,ax=axs[0,1])\n",
    "cbar.set_label('Velocity [m s$^{-1}$]')\n",
    "\n",
    "curr_ax = axs[1,0]\n",
    "curr_plot = curr_ax.pcolormesh((u.isel(isel_dict)).squeeze(),cmap=seismic_nanmasked)\n",
    "plot_clim = cmap_zerocent_scale(curr_plot,0.3)\n",
    "curr_ax.set_title('$u$, ' + depth_str + ' m depth')\n",
    "cbar = fig.colorbar(curr_plot,ax=axs[1,0])\n",
    "# cbar.set_label('Velocity [m s$^{-1}$]')\n",
    "curr_ax = axs[1,1]\n",
    "curr_plot = axs[1,1].pcolormesh((u_g.isel(isel_dict)).squeeze(),cmap=seismic_nanmasked)\n",
    "curr_plot.set_clim(plot_clim)\n",
    "curr_ax.set_title('$u_g$, ' + depth_str + ' m depth')\n",
    "cbar = fig.colorbar(curr_plot,ax=axs[1,1])\n",
    "cbar.set_label('Velocity [m s$^{-1}$]')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice some weirdness in the geostrophic velocity maps (right column) near the equator. Hmm, why would that be...?\n",
    "\n",
    "We can also visualize (actual) velocities as vectors relative to pressure anomaly contours. This looks much like maps of the atmospheric circulation that you might see in a weather report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pressanom_plot = pressanom.where(ds_grid.maskC)    # mask land areas with NaNs\n",
    "\n",
    "# tile and k indices to plot\n",
    "tile_plot = 1\n",
    "k_plot = 20\n",
    "# dictionary to select tile and k indices (can then pass this to isel to save space)\n",
    "isel_dict = {'tile':tile_plot,'k':k_plot}\n",
    "\n",
    "# identify depth level for plot titles\n",
    "depth_plot_level = -ds_grid.Z[k_plot].values\n",
    "# convert depth level (rounded to nearest meter) to a string for plot titles\n",
    "depth_str = str(int(depth_plot_level))\n",
    "\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(10,8))\n",
    "\n",
    "# create 'bwr' colormap with NaN masking\n",
    "bwr_nanmasked = plt.get_cmap('bwr').copy()\n",
    "bwr_nanmasked.set_bad('black')\n",
    "\n",
    "# plot filled contours, contour lines, and land mask\n",
    "# (plots with higher zorder values are visible over lower zorder values)\n",
    "curr_plot = (pressanom_plot.isel(isel_dict).squeeze()).plot.contourf(cmap=bwr_nanmasked,levels=29,vmin=86,vmax=100,zorder=50)\n",
    "curr_contours = (pressanom_plot.isel(isel_dict).squeeze()).plot.contour(colors='black',levels=29,vmin=86,vmax=100,alpha=0.5,\\\n",
    "                                                                             linewidths=0.5,zorder=100)\n",
    "curr_plot_mask = (pressanom_plot.isel(isel_dict).squeeze()).plot(cmap=bwr_nanmasked,add_colorbar=False,vmin=86,vmax=100,zorder=0)\n",
    "\n",
    "# plot velocity vectors using quiver\n",
    "u_plot = (u.isel(isel_dict)).squeeze()\n",
    "v_plot = (v.isel(isel_dict)).squeeze()\n",
    "arrow_spacing = 3     # plot arrows spaced out (otherwise there is an arrow for every grid point)\n",
    "ind_arrow_plot = np.r_[1:u_plot.shape[-1]:arrow_spacing]\n",
    "curr_velplot = ax.quiver(ind_arrow_plot,ind_arrow_plot,u_plot[ind_arrow_plot,ind_arrow_plot],\\\n",
    "                        v_plot[ind_arrow_plot,ind_arrow_plot],scale=1.5,zorder=150)\n",
    "ax.set_title('Pressure anomaly (shading) and velocity (arrows), ' + depth_str + ' m depth')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assessment of geostrophic balance\n",
    "\n",
    "### Normalized difference maps\n",
    "\n",
    "How can we can assess the variation in geostrophic balance and how well it accounts for ocean velocity globally? One way is to plot the normalized difference between actual and geostrophic velocities. We could do this for $v$ and $u$ components separately, but to get the most information in one plot, we can do this with the magnitude of difference between the velocity vectors.\n",
    "\n",
    "$$\n",
    "\\vert{\\Delta{\\bf u}}\\vert_{\\rm norm} = \\frac{\\vert{{\\bf u} - {\\bf u_g}}\\vert}{\\vert{\\bf u}\\vert}\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# difference between actual and geostrophic velocity vectors (in complex plane)\n",
    "\n",
    "u_diff = u - u_g\n",
    "v_diff = v - v_g\n",
    "vel_diff_complex = u_diff + (1j*v_diff)    # in Python, imaginary number i is indicated by 1j\n",
    "vel_complex = u + (1j*v)\n",
    "\n",
    "# normalize magnitude of difference vector by magnitude of actual velocity\n",
    "vel_diff_abs = np.abs(vel_diff_complex)\n",
    "vel_abs = np.abs(vel_complex)\n",
    "vel_diff_norm = vel_diff_abs/vel_abs\n",
    "\n",
    "\n",
    "# tile and k indices to plot\n",
    "tile_plot = 1\n",
    "k_plot = 20\n",
    "# dictionary to select tile and k indices (can then pass this to isel to save space)\n",
    "isel_dict = {'tile':tile_plot,'k':k_plot}\n",
    "\n",
    "# identify depth level for plot titles\n",
    "depth_plot_level = -ds_grid.Z[k_plot].values\n",
    "# convert depth level (rounded to nearest meter) to a string for plot titles\n",
    "depth_str = str(int(depth_plot_level))\n",
    "\n",
    "\n",
    "# plot normalized difference\n",
    "fig,ax = plt.subplots(figsize=(10,8))\n",
    "array_plot = (vel_diff_norm.isel(isel_dict)).squeeze()\n",
    "# let's use a logarithmic color scale for this plot!\n",
    "import matplotlib.colors as colors\n",
    "# set axis limits to 0.01 and 1 (values outside this range will be saturated)\n",
    "curr_plot = ax.pcolormesh(array_plot,norm=colors.LogNorm(vmin=0.01,vmax=1),cmap=seismic_nanmasked)\n",
    "fig.colorbar(curr_plot,ax=ax)\n",
    "ax.set_title('|Delta u|_norm, actual vs. geostrophic velocity,' + depth_str + ' m depth')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A normalized difference of 0.1 (white on the above map) can be considered an approximate threshold for where the geostrophic velocity is a decent predictor of actual velocity. Therefore, what the above map shows is areas where geostrophic velocity is a good predictor of actual velocity (blue), and areas where the geostrophic velocity is a poor predictor (red). Note that where the actual velocity magnitude is very small, the map may show red even though the velocity difference itself isn't large. To focus on areas with more substantial velocities, we can remap with the small-velocity points masked out.\n",
    "\n",
    "To help us out with this, we'll introduce a new function that plots a mask. Note that we can also use this function (instead of masking with `set_bad`) to mask land areas, and we'll do this below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Introduce function to plot mask as pcolormesh\n",
    "def plot_mask(*args,ax=None,color):\n",
    "    \"\"\"\n",
    "    Plot mask, given input parameters:\n",
    "    - X, Y: (optional) coordinates as 1-D or 2-D NumPy arrays or xarray DataArrays\n",
    "    - mask: 2-D array of boolean values (True/False or 1/0), NumPy or xarray\n",
    "    - axes: axes to plot on, defaults to current axes\n",
    "    - color: a string indicating a color in Matplotlib, or a 3-element tuple or NumPy array indicating RGB color values\n",
    "    \n",
    "    Returns plot_obj, the plot object of the mask \n",
    "    \"\"\"\n",
    "    if len(args) == 1:\n",
    "        mask = args[0]\n",
    "    else:\n",
    "        X = args[0]\n",
    "        Y = args[1]\n",
    "        mask = args[2]\n",
    "    # set alpha values to 1 where mask is plotted, 0 otherwise\n",
    "    if str(type(mask))[0:5] == 'xarray':\n",
    "        mask = mask.values\n",
    "    # get color for mask\n",
    "    if isinstance(color,str):\n",
    "        import matplotlib.colors as mcolors\n",
    "        color_rgb = mcolors.to_rgb(color)\n",
    "    elif (isinstance(color,tuple)) and (len(color) == 3):\n",
    "        color_rgb = np.asarray(color)\n",
    "    elif (isinstance(color,np.ndarray)) and (len(color) == 3):\n",
    "        color_rgb = color\n",
    "    else:\n",
    "        raise TypeError(\"input parameter 'color' has incorrect type or number of elements\")\n",
    "    # create a colormap using a 2x4 array with two RGBA entries\n",
    "    # the RGB entries are the same in each row\n",
    "    # in the 1st row alpha=0, in the 2nd row alpha=1\n",
    "    cmap_array = np.hstack((np.tile(color_rgb,(2,1)),np.array([[0],[1]])))\n",
    "    from matplotlib.colors import ListedColormap\n",
    "    colormap = ListedColormap(cmap_array)\n",
    "    # get axis limits of existing plot\n",
    "    if ax is None:\n",
    "        ax = plt.gca()\n",
    "    existing_xlim = ax.get_xlim()\n",
    "    existing_ylim = ax.get_ylim()\n",
    "    # plot mask using colormap just created, with alpha=1 where mask=1 or True\n",
    "    if len(args) == 1:\n",
    "        plot_obj = ax.pcolormesh(mask,cmap=colormap,vmin=0.,vmax=1.,zorder=50)\n",
    "    else:\n",
    "        plot_obj = ax.pcolormesh(X,Y,mask,cmap=colormap,vmin=0.,vmax=1.,zorder=50)\n",
    "    # set axis limits of mask to axis limits of existing plot\n",
    "    ax.set_xlim(existing_xlim)\n",
    "    ax.set_ylim(existing_ylim)\n",
    "    \n",
    "    return plot_obj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll create two plots side-by-side, one without and one with the small-velocity mask."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load land mask (a boolean array) from grid parameters\n",
    "# Note: maskC has True (1) over water and False (0) over land\n",
    "# we want the reverse for a land_mask, so ~ switches the boolean values\n",
    "land_mask = ~ds_grid.maskC\n",
    "\n",
    "# small velocity mask\n",
    "mask_threshold = 0.005    # mask out velocities <0.5 cm s-1\n",
    "# Note: the .values attribute contains the content of the data variable as a NumPy array.\n",
    "# This allows additional operations not permitted in xarray,\n",
    "# such as boolean indexing used here to mask with NaNs.\n",
    "mask_smallvel = (vel_abs < mask_threshold)\n",
    "\n",
    "\n",
    "# dictionary to select tile and k indices (can then pass this to isel to save space)\n",
    "isel_dict = {'tile':tile_plot,'k':k_plot}\n",
    "\n",
    "# plot normalized difference without and with mask\n",
    "fig,axs = plt.subplots(1,2,figsize=(15,6))\n",
    "curr_ax = axs[0]\n",
    "curr_ax.tick_params(labelsize=14)\n",
    "array_plot = vel_diff_norm.isel(isel_dict).squeeze()\n",
    "curr_plot = curr_ax.pcolormesh(array_plot,norm=colors.LogNorm(vmin=0.01,vmax=1),cmap='seismic')\n",
    "# plot land mask in black\n",
    "plot_mask(land_mask.isel(isel_dict).squeeze(),ax=curr_ax,color='black')\n",
    "curr_ax.set_title('|Delta u|_norm, actual vs. geostrophic velocity',fontsize=14)\n",
    "fig.colorbar(curr_plot,ax=curr_ax)\n",
    "curr_ax = axs[1]\n",
    "curr_ax.tick_params(labelsize=14)\n",
    "array_plot = vel_diff_norm.isel(isel_dict).squeeze()\n",
    "curr_plot = curr_ax.pcolormesh(array_plot,norm=colors.LogNorm(vmin=0.01,vmax=1),cmap='seismic')\n",
    "plot_mask(land_mask.isel(isel_dict).squeeze(),ax=curr_ax,color='black')\n",
    "# plot small velocity mask in gray color, RGB=(0.5,0.5,0.5)\n",
    "plot_mask(mask_smallvel.isel(isel_dict).squeeze(),ax=curr_ax,color=(0.5,0.5,0.5))\n",
    "curr_ax.set_title('|Delta u|_norm, actual vs. geostrophic velocity, with mask',fontsize=14)\n",
    "fig.colorbar(curr_plot,ax=curr_ax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Global maps\n",
    "\n",
    "Having mastered mapping on a single tile of the model grid, we can now plot maps that show the model's entire horizontal global domain. We'll use functions that are part of the ```ecco_v4_py``` package to do this in two ways here: (1) plotting 13 tiles side-by-side, and (2) plotting the model fields regridded to a global lat-lon projection.\n",
    "\n",
    "First the 13 tiles map. The Plotting Tiles tutorial shows a number of configurations that tiles can be plotted; here we use the **latlon** layout with the Arctic tile adjacent to tile=10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1006.67 with 17 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# k index (depth level) to plot\n",
    "k_plot = 20\n",
    "\n",
    "# identify depth level for plot titles\n",
    "depth_plot_level = -ds_grid.Z[k_plot].values\n",
    "# convert depth level (rounded to nearest meter) to a string for plot titles\n",
    "depth_str = str(int(depth_plot_level))\n",
    "\n",
    "\n",
    "# 13 tiles map using ecco_v4_py function plot_tiles\n",
    "# (we'll leave out the small-velocity mask for the global maps)\n",
    "curr_obj = ecco.plot_tiles(vel_diff_norm.isel(k=k_plot).squeeze(),\\\n",
    "                            cmap=seismic_nanmasked,show_colorbar=False,fig_size=10,\\\n",
    "                            layout='latlon',rotate_to_latlon=True,\\\n",
    "                            show_tile_labels=False,\\\n",
    "                            Arctic_cap_tile_location=10)\n",
    "curr_fig = curr_obj[0]\n",
    "# loop through tiles to adjust color scaling and add mask\n",
    "tile_order = np.array([-1,-1,-1,6, \\\n",
    "                         2,5,7,10,  \\\n",
    "                         1,4,8,11, \\\n",
    "                         0,3,9,12])\n",
    "for idx,curr_ax in enumerate(curr_fig.get_axes()):\n",
    "    # set color scaling to lognormal range we used previously\n",
    "    if len(curr_ax.get_images()) > 0:\n",
    "        (curr_ax.get_images()[0]).norm = colors.LogNorm(vmin=0.01,vmax=1)\n",
    "\n",
    "# add colorbar\n",
    "cbar_ax = curr_fig.add_axes([0.95,0.2,0.025,0.6])\n",
    "cbar = curr_fig.colorbar(plt.cm.ScalarMappable(\\\n",
    "                            norm=colors.LogNorm(vmin=0.01,vmax=1),\\\n",
    "                            cmap='seismic'),cax=cbar_ax)\n",
    "# add title\n",
    "curr_fig.suptitle('|Delta u|_norm, actual vs. geostrophic velocity, ' + depth_str + ' m depth')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And on a global map projection (here the Robinson projection is used):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# k index (depth level) to plot\n",
    "k_plot = 20\n",
    "\n",
    "# global map projection using ecco_v4_py function plot_proj_to_latlon_grid\n",
    "# Query help(ecco.plot_proj_to_latlon_grid) for a full list of input arguments.\n",
    "curr_obj = ecco.plot_proj_to_latlon_grid(ds_grid.XC,ds_grid.YC,\\\n",
    "                                        vel_diff_norm.isel(k=k_plot).squeeze(),\\\n",
    "                                        cmap=seismic_nanmasked,show_colorbar=False,\\\n",
    "                                        show_land=True,projection_type='robin',\\\n",
    "                                        user_lon_0=-67,plot_type='pcolormesh')\n",
    "curr_fig = curr_obj[0]\n",
    "curr_ax = curr_obj[1]\n",
    "curr_fig.set_figwidth(15)\n",
    "curr_fig.set_figheight(10)\n",
    "import matplotlib as mpl\n",
    "# set color scaling to lognormal range\n",
    "for handle in curr_ax.get_children():\n",
    "    if isinstance(handle,mpl.collections.QuadMesh):\n",
    "        handle.norm = colors.LogNorm(vmin=0.01,vmax=1)\n",
    "# add colorbar with correct scaling\n",
    "cbar_ax = curr_fig.add_axes([0.95,0.2,0.025,0.6])\n",
    "cbar = curr_fig.colorbar(plt.cm.ScalarMappable(\\\n",
    "                            norm=colors.LogNorm(vmin=0.01,vmax=1),\\\n",
    "                            cmap=seismic_nanmasked),cax=cbar_ax)\n",
    "# add title\n",
    "curr_ax.set_title('|Delta u|_norm, actual vs. geostrophic velocity, ' + depth_str + ' m depth')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Latitude and depth dependence\n",
    "\n",
    "We can also plot $\\vert{{\\Delta}{\\bf u}}\\vert_{\\rm norm}$ along a transect to consider its variation with depth. Let's plot an example along ```i=40``` on ```tile=1``` (which follows the line of longitude ${\\rm 2.5}^{\\circ}{\\rm E}$):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# tile and i indices to plot\n",
    "tile_plot = 1\n",
    "i_plot = 40\n",
    "# dictionary to select tile and i (i_g) indices\n",
    "isel_dict = {'tile':tile_plot,'i':i_plot}\n",
    "isel_dict_ig = {'tile':tile_plot,'i_g':i_plot}\n",
    "\n",
    "# compute land and small velocity masks\n",
    "curr_land_mask = land_mask.isel(isel_dict).squeeze()\n",
    "curr_mask_smallvel = mask_smallvel.isel(isel_dict).squeeze()\n",
    "\n",
    "\n",
    "# plot normalized difference without and with mask\n",
    "# here we'll plot top 25 and bottom 25 depth levels separately to accommodate different scales\n",
    "fig,axs = plt.subplots(2,2,figsize=(15,10))\n",
    "curr_ax = axs[0,0]\n",
    "\n",
    "# Note: when specifying coordinates with a data array of size MxN\n",
    "# Matplotlib will only use \"flat\" shading if coordinate dimensions are one larger than data array\n",
    "# i.e., length M+1 and N+1, or (M+1)x(N+1), otherwise defaults to \"nearest\" shading\n",
    "# bounding coordinates for upper pcolormesh plots\n",
    "lat_plot = np.hstack(((ds_grid.YG.isel(isel_dict_ig)).squeeze(),\\\n",
    "                      ds_grid.YC_bnds.isel({**isel_dict,'j':-1,'nb':3})))\n",
    "depth_plot = -ds_grid.Zp1[0:26].squeeze()    # top 25 depth levels\n",
    "\n",
    "array_plot = (vel_diff_norm.isel(isel_dict)).squeeze()\n",
    "curr_plot = curr_ax.pcolormesh(lat_plot,depth_plot,array_plot[0:25,:],\\\n",
    "                               norm=colors.LogNorm(vmin=0.01,vmax=1),cmap='seismic')\n",
    "plot_mask(lat_plot,depth_plot,curr_land_mask[0:25,:],ax=curr_ax,color='black')\n",
    "curr_ax.set_ylim(curr_ax.get_ylim()[::-1])\n",
    "curr_ax.set_ylabel('Depth [m]')\n",
    "curr_ax.set_title('|Delta u|_norm, actual vs. geostrophic velocity',fontsize=14)\n",
    "fig.colorbar(curr_plot,ax=curr_ax)\n",
    "curr_ax = axs[0,1]\n",
    "array_plot = (vel_diff_norm.isel(isel_dict)).squeeze()\n",
    "curr_plot = curr_ax.pcolormesh(lat_plot,depth_plot,array_plot[0:25,:],\\\n",
    "                               norm=colors.LogNorm(vmin=0.01,vmax=1),cmap='seismic')\n",
    "plot_mask(lat_plot,depth_plot,curr_land_mask[0:25,:],ax=curr_ax,color='black')\n",
    "plot_mask(lat_plot,depth_plot,curr_mask_smallvel[0:25,:],ax=curr_ax,color=(0.5,0.5,0.5))\n",
    "curr_ax.set_ylim(curr_ax.get_ylim()[::-1])\n",
    "curr_ax.set_ylabel('Depth [m]')\n",
    "curr_ax.set_title('|Delta u|_norm, actual vs. geostrophic velocity, with mask',fontsize=14)\n",
    "fig.colorbar(curr_plot,ax=curr_ax)\n",
    "\n",
    "curr_ax = axs[1,0]\n",
    "\n",
    "# bounding coordinates for lower pcolormesh plots\n",
    "lat_plot = np.hstack(((ds_grid.YG.isel(isel_dict_ig)).squeeze(),\\\n",
    "                      ds_grid.YC_bnds.isel({**isel_dict,'j':-1,'nb':3})))\n",
    "depth_plot = -ds_grid.Zp1[25:].squeeze()    # bottom 25 depth levels\n",
    "\n",
    "array_plot = (vel_diff_norm.isel(isel_dict)).squeeze()\n",
    "curr_plot = curr_ax.pcolormesh(lat_plot,depth_plot,array_plot[25:,:],\\\n",
    "                               norm=colors.LogNorm(vmin=0.01,vmax=1),cmap='seismic')\n",
    "plot_mask(lat_plot,depth_plot,curr_land_mask[25:,:],ax=curr_ax,color='black')\n",
    "curr_ax.set_ylim(curr_ax.get_ylim()[::-1])\n",
    "curr_ax.set_ylabel('Depth [m]')\n",
    "curr_ax.set_xlabel('Latitude')\n",
    "fig.colorbar(curr_plot,ax=curr_ax)\n",
    "curr_ax = axs[1,1]\n",
    "array_plot = (vel_diff_norm.isel(isel_dict)).squeeze()\n",
    "curr_plot = curr_ax.pcolormesh(lat_plot,depth_plot,array_plot[25:,:],\\\n",
    "                               norm=colors.LogNorm(vmin=0.01,vmax=1),cmap='seismic')\n",
    "plot_mask(lat_plot,depth_plot,curr_land_mask[25:,:],ax=curr_ax,color='black')\n",
    "plot_mask(lat_plot,depth_plot,curr_mask_smallvel[25:,:],ax=curr_ax,color=(0.5,0.5,0.5))\n",
    "curr_ax.set_ylim(curr_ax.get_ylim()[::-1])\n",
    "curr_ax.set_ylabel('Depth [m]')\n",
    "curr_ax.set_xlabel('Latitude')\n",
    "fig.colorbar(curr_plot,ax=curr_ax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can you see some patterns in the above plots? Most depths 100-1500 meters have a lot of blue, meaning geostrophic velocities are very good approximations of the actual velocities. But (1) close to the ocean surface, (2) close to the ocean bottom, and (3) near the equator there is a lot more red. So in those areas, some of the other terms in the momentum equations that we earlier considered to be negligible, may not actually be negligible. We'll explore some of these other balances in upcoming tutorials.\n",
    "\n",
    "For now, let's explore these patterns in a more systematic way globally. The following code computes the normalized difference (weighted by the area of each grid cell) as a function of latitude and depth, for the global oceans. **Note: the ```mean_weighted_binned``` function may take a few minutes to run over the global domain.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bins of latitude\n",
    "lat_bin_spacing = 2\n",
    "lat_bin_bounds = np.c_[-90:90:lat_bin_spacing] + np.array([[0,lat_bin_spacing]])\n",
    "lat_bin_centers = np.mean(lat_bin_bounds,axis=-1)\n",
    "\n",
    "# bins of depth\n",
    "depth_bin_bounds = -ds_grid.Z_bnds.values\n",
    "\n",
    "vel_diff_abs = np.abs(vel_diff_complex)\n",
    "vel_abs = np.abs(vel_complex)\n",
    "\n",
    "# function to loop through and compute weighted mean in bins\n",
    "def mean_weighted_binned(value_field,weighting,bin_field,bin_bounds):\n",
    "    \"\"\"\n",
    "    Compute normalized difference in bins, given:\n",
    "    - value_field: field of values to average\n",
    "    - weighting: weighting of individual grid cells\n",
    "    - bin_field: field to use in binning\n",
    "    - bin_bounds: bound values of bins to use, as numpy array of size (N,2)\n",
    "    \"\"\"\n",
    "    mean_weighted_inbins = np.empty((bin_bounds.shape[0]))\n",
    "    mean_weighted_inbins.fill(np.nan)\n",
    "    bin_field_broadcast_flat = (np.ones(value_field.shape)*bin_field).flatten()\n",
    "    weighting_flat = weighting.flatten()\n",
    "    value_field_flat = value_field.flatten()\n",
    "    bin_idx_sorted = np.argsort(bin_field_broadcast_flat)   # flatten and sort bin field values\n",
    "    sort_idx = (bin_field_broadcast_flat[bin_idx_sorted] >= bin_bounds[0,0]).nonzero()[0][0]\n",
    "    curr_idx = bin_idx_sorted[sort_idx]\n",
    "    curr_bin_val = bin_field_broadcast_flat[curr_idx]\n",
    "    curr_bin_num = ((bin_bounds[:,0] <= curr_bin_val) & (bin_bounds[:,1] > curr_bin_val)).nonzero()[0][0]\n",
    "    value_sum_inbin = 0.\n",
    "    weighting_sum_inbin = 0.\n",
    "    while curr_bin_val < bin_bounds[-1,1]:\n",
    "        if np.logical_and(~np.isnan(value_field_flat[curr_idx]),~np.isinf(value_field_flat[curr_idx])):\n",
    "            value_sum_inbin += weighting_flat[curr_idx]*value_field_flat[curr_idx]\n",
    "            weighting_sum_inbin += weighting_flat[curr_idx]\n",
    "        sort_idx += 1\n",
    "        if sort_idx >= len(bin_idx_sorted):\n",
    "            if weighting_sum_inbin > 0:\n",
    "                mean_weighted_inbins[curr_bin_num] = value_sum_inbin/weighting_sum_inbin\n",
    "            break\n",
    "        curr_idx = bin_idx_sorted[sort_idx]\n",
    "        curr_bin_val = bin_field_broadcast_flat[curr_idx]\n",
    "        if curr_bin_val >= bin_bounds[curr_bin_num,1]:\n",
    "            if weighting_sum_inbin > 0:\n",
    "                mean_weighted_inbins[curr_bin_num] = value_sum_inbin/weighting_sum_inbin\n",
    "            curr_bin_num = ((bin_bounds[:,0] <= curr_bin_val) & (bin_bounds[:,1] > curr_bin_val)).nonzero()[0][0]\n",
    "            value_sum_inbin = 0.\n",
    "            weighting_sum_inbin = 0.\n",
    "    return mean_weighted_inbins\n",
    "\n",
    "\n",
    "# compute norm diff, weighting by horizontal area of grid cells and masks\n",
    "\n",
    "curr_weighting = ((~mask_smallvel)*(ds_grid.maskC)*(ds_grid.rA)).values\n",
    "\n",
    "diff_norm_lat = mean_weighted_binned(vel_diff_norm.values,curr_weighting,\\\n",
    "                                    ds_grid.YC.values,lat_bin_bounds)\n",
    "diff_norm_depth = mean_weighted_binned(vel_diff_norm.values,curr_weighting,\\\n",
    "                                    np.expand_dims(-ds_grid.Z.values,axis=(-3,-2,-1)),\\\n",
    "                                    depth_bin_bounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NNzB37lzLdTU1NTh58iSCg4MtJed/pVQqkZKSYvn7sWPHAMAyl6S+bt26ITo6GpmZma2K7Wr35efnhwEDBmDXrl2tui9b8/DwwLx58/Dpp59aZn4BwOHDh5GcnIzZs2c3Gr6dlZWF119/Hdu3b0dWVlajmSXNfT+HDx/eptiqq6uxYsUKrF+/HufPn0dlZWWDGSS2eN1Y+/7x9/dH9+7dG90mOjoaAFBaWtqqx580aRI+++wznDp1Cv3798cff/yB6OhoDBs2DIMGDcL27dsBAIWFhThz5gyuueaaRm0e1urZsyd8fHyafQ5lZWVNXt9ae/fuxYoVK7B//34UFBQ0mGUCGL9/TbVPiMma9/3+/fuhUCjw3XffNXmfGo0GhYWFKC4uRlBQkO2DJqJOi+eCbcNzQevwXJDngs09h9acC1rz2t6/fz8A43y/Q4cONbpdQUEBDAYDLly40OjnTFOPM3bsWMjlcsv3si0GDBgAmUzW6PLo6GhLnOR8mMwjUY0cORJz587F999/j++++67B7JH6ysvLAQDh4eFNXh8REYGsrCyUl5c3+AcoLCys2ZNCAM3OATHPpWjqevN1Wq3WctmBAwcwadIky3yHG264Ab6+vpBKpThx4gQ2bNjQ7MDhtli8eDG2bNmC+++/H0899VSD60pLSyEIAgoLC1s9TNr8dW1ulkd4eHirT+Bac19iWrhwIT799FOsXr3acgJnXiywcOHCBsdeunQJw4YNQ2lpKcaOHYtrrrkGfn5+kMlklhkuzX0/2/I8tVotJk2ahEOHDqFPnz6YP38+QkJCLHNsli9fbpPXjbXvn5beH3q9vlWPP3nyZHz22WfYvn07+vTpg127dllm3EyePBlvvfUWysvL8ccff0AQBJvO7bDVc2jK+vXrMXfuXLi7u2Pq1Kno3r07vLy8IJVKsXPnTuzatcsm3z9bs+Z9X1xcDJ1O1+LPlqqqKibziKhNeC7YNjwXtB7PBXkuWF9bnoM1r+3i4mIAwJtvvnnV+66qqmp0WVOPI5PJEBQUhIKCghbj/aurfQ0MBkOb748cA5N5JLrXXnsNGzZswJIlSzB79uwmjzH/AMrLy2vy06Hc3NwGx5ld7eTNll5++WXU1tZix44djQaOvvrqq9iwYUO7H+ONN97Axx9/jGuvvbbJLWrm5z5w4EDLp0ctMd8mPz8fvXv3bnR9Xl5eq+Orf19Nact92cOYMWPQvXt3bNiwAWVlZfDy8sK3336L4OBgywmd2b///W8UFxdj1apVjTZirVmzxnLi15S2vOY2bNiAQ4cOYeHChY02m+Xm5rZ5w19zrH3/2MqkSZMAANu3b8eYMWNQVlZmOUmbPHkyXn/9dezYscPyqaz5eEf3wgsvwM3NDUeOHEFiYmKD6+6///4Oqz6QSqWNKgLNmtoya8373s/PDwaDASUlJe0LloioCTwXbB2eC7YPzwV5Lmgta17b5tuUl5fD19e3TY+Xn5/fqLNDr9ejuLi4zfdFnZdU7ACIunfvjgcffBDp6el4//33mzxm4MCBAIxlyn+VlpaGnJwcdO3atd0bHq2VlpaGwMDAJrdS2uIX+u+//x7PPvss+vfvj++++67JMmlvb2/07t0bZ8+ebfUv3IMGDWo2xkuXLiE7O7vVMV7tvsrLy3HixIlW35e93HHHHVCr1Vi7di02btyI4uJiLFiwoMFGN8D4/QSM28j+ypYJGmsex/y9b0tFmdjvn/DwcCQlJWHPnj3YunUrgLqTtDFjxkCpVGL79u34448/EBAQYIm3JTKZrF2Vde2VlpaGpKSkRok8g8GAP//8s8nbSKXSNsfc0vc8ICAA+fn5DSpEzI4cOdLoMmve9yNGjEBpaSnOnj3bptiJiFqD54It47mgbfBccGeTMfBc8OqseW2PGDECALBnz542P15Tj7Nnzx7odLpGXxtrzi2pc2AyjxzC0qVL4e/vj3/9619NlhrfddddAIyfehYWFlou1+v1+L//+z8YDAbcfffdHRbvX8XFxaGkpASnTp1qcPn//vc/yz9Y1jpw4ABuv/12REZGYuPGjVed6fDEE09Ao9HgrrvuarIip7S0tMEntbfddhsUCgXef/99ZGRkWC43GAx46qmn2lR2feONNyIgIADffPNNowTCsmXLLOXpYlq4cCEkEgm++OILfPHFFwDQ6NNWwPj9BIAdO3Y0uHzr1q349NNPbRZPc49z6dIlPPPMM03extzC2JaTa0d4/0yaNAmVlZX44IMPkJiYiMjISADGGTYjR47Ed999h4sXL2LChAmQSlv3T1NQUBAKCwsbzSTqKHFxcbhw4UKDWTaCIGD58uU4d+5ck7cJCgpq0/fOfBug+e/58OHDodPpsGrVqgaXf/7559i7d2+j46153z/++OMAgHvvvRdXrlxpdH11dTUOHDjQ6udERPRXPBdsHs8FbYfngjwXtIY1r+2HHnoICoUCjz/+OM6fP9/oeo1G02yi75///GeDeYQqlQpLliwBACxatKjBsdacW1LnwDZbcgiBgYH4xz/+gaeffrrJ60eNGoWnn34ab7zxBvr06YO5c+fCy8sLv/76K86cOYMxY8Y0mhvSkR577DFs3boVY8aMwbx58+Dn54cjR47gzz//tMyBsdZdd90FlUqF4cOHN3ny4O/vj8cee8xy7NGjR/Hhhx+ie/fumDZtGmJiYlBSUoL09HTs3r0bixYtwkcffQTAeALx2muv4cknn8TAgQMxf/58+Pn5YevWrSgrK0O/fv0anZQ2x9vbG5988gnmz5+PsWPHYv78+YiIiMCff/6JM2fOYNy4cdi9e7fVXwdbiI2Nxfjx47Fz507I5XL07du3yU/+HnzwQaxatQrz5s3DnDlzEBUVhTNnzmDLli2YN28e1q5da5N4rr/+esTHx+Odd97BmTNnMHDgQGRlZWHjxo2YOXMmsrKyGt1m4sSJkEqlWLJkCU6fPo2AgAAAwPPPP9/s4zjC+2fy5Mn44IMPUFBQgHnz5jW6zvxJcVtmpEyePBmHDx/G9OnTMXbsWLi5uaF///64/vrrbRl6sx5//HH8/e9/x6BBgzBnzhwoFArs3bsX586dw/XXX49ffvmlyZi//fZb3HjjjRg4cCDkcjnGjRuHcePGNfs4kydPxptvvol7770Xc+bMgbe3N/z9/fHQQw8BAB555BGsWrUKDzzwALZv347o6GicPHkS+/btw3XXXYeNGzc2uD9r3veTJ0/Ga6+9hiVLlqBHjx6YMWMGunbtiqqqKmRmZmLXrl0YM2YMtmzZYoOvLBG5Ip4LNo/ngrbDc0GeC1rDmtd2QkICPvvsM9x1113o3bs3rr32WvTs2RNarRZZWVnYs2cPQkJCGiykMUtKSkLv3r0xd+5cKBQKbNiwARcvXsTMmTNx++23N/oatPXckjoJETfpkgsCIERFRTV5nUqlEuLi4gQAAgBBq9U2OmbNmjXC6NGjBW9vb0GpVApJSUnCyy+/LNTW1jY6tqVV5Ve73rwWvClNrXsXBEH45ZdfhOHDhwve3t6Cn5+fMHXqVGHXrl1tWqXe1LHmtfDN/WnqOfzyyy/CzJkzhZCQEEGhUAhhYWHC0KFDheeee05ITk5udPw333wjDBw4UFAqlUJwcLBw2223CZcvX77q16E5v/32mzB69GjBw8ND8Pf3F2644QYhOTm5ya+beV36woULG9yHefX6iy++2Oj+m7tNa5m/xgCEt956q9nj9u7dK0ycOFHw9/cXvL29hdGjRwvr169vNraWvlbNvQ6ysrKEBQsWCJGRkYK7u7uQlJQkvP7664JWq2121fyXX34p9O/fX3B3d7c8F7PmXp+CYLv3z4svvigAEHbs2NHs8/2r0tJSQSqVCgCEH3/8scF1+/btszyPc+fONbptc1+7qqoq4e9//7sQFRUlyGSyRq+L5r5+gnD1r1NTmvt6rFq1Sujfv7/g6ekpBAUFCbNmzRJOnTrV7NcoPz9fuPXWW4XQ0FDL18P8Wrraa/vtt98WEhISBDc3tybf93v37hXGjRsneHh4CD4+PsKMGTOEkydPXvV7Zc37fs+ePcLNN98sRERECAqFQggODhb69+8vPP7448Lhw4db8ZUkIlfHc8GrPz7PBY14Lshzwfoc4VxQENr22jY7deqUsHDhQiEmJkZwc3MTAgIChN69ewv33XefsH379gbHml9DKpVKeO6554S4uDjBzc1N6Nq1q7Bs2TJBpVI1un9rzy3rPx45J4kg1Nt7TUREREREREREHWrChAnYtWsXmKKh1nDpmXmFhYWYOXMmvLy80LNnT/z+++9ih0RERERERERERNQsl56Zt3jxYoSHh6OwsBDbtm3DvHnzkJaWZhkqSkRERERERERE5EhctjKvqqoKP/30E5YtWwZPT0/ccMMN6N+/PzZs2CB2aERERERERERERE1ymmReZWUlnn76aVxzzTUICQmBRCLBsmXLmjy2qqoKjz32GCIjI+Hu7o4BAwbg22+/bXDMhQsX4O3tjejoaMtlffv2xdmzZ+35NIiIiIiIiIiIGti5cyfn5VGrOU0yr7i4GJ988gnUajVmzZp11WNvuukmrF69Gi+++CJ+/fVXDB06FLfeeiu++eYbyzFVVVXw9fVtcDtfX19UVVXZI3wiIiIiIiIiIqJ2c5qZebGxsSgtLYVEIkFRURE+/fTTJo/bvHkzfv/9d3zzzTe49dZbAQATJ05EZmYmnnrqKcyfPx8ymQze3t6oqKhocNuKigp4e3vb/bkQERERERERERFZw2mSeRKJpFXHrV+/Ht7e3rj55psbXL5o0SIsWLAABw8exKhRo9CjRw9UVVUhJycHXbp0AQCcOXMGt99+e7P3rVaroVarLX83GAwoKSlBUFBQq+MjIiIi1yYIAiorKxEZGQmp1GmaJFyOwWDAlStX4OPjw/M8IiIiapWOOs9zmmRea505cwaJiYmQyxs+tX79+lmuHzVqFLy9vXHjjTdi2bJleP/997F9+3acOHEC69ata/a+X331VSxfvtyu8RMREZFryM7OtnygSI7nypUrDWYrExEREbWWvc/zOl0yr7i4GN26dWt0eWBgoOV6sw8//BALFy5EUFAQoqKisHbtWgQHBzd730uWLMETTzxh+Xt5eTliYmKQnZ3daP4eETmnh9ccw46UQrxwfSLmD4nBiRMnMH78ePz3u414aV8NIvzc8fsT48UOk4icWEVFBaKjo+Hj4yN2KHQV5u8Pz/OI6K8SEhKQm5uLiIgIpKSkiB0OETmQjjrP63TJPODqLbn1rwsJCcHmzZtbfb9KpRJKpbLR5b6+vjzJI+osFJ6QKj3h7+sHX19fyxzNAF8fSJWAXq7k+52IbIKtm47N/P3heR4R/ZW5dU4qlfLnAxE1yd7neZ1uUEtQUFCD6juzkpISAHUVekRETdHoDAAAN3nDH48K09/VWn2Hx0RERERERERk1umSeX379kVycjJ0Ol2Dy0+fPg0A6NOnjxhhEZGT0OiNyTzlX5J55r+rdEzmERERERERkXg6XTJv9uzZqKqqwg8//NDg8tWrVyMyMhLDhw8XKTIicgbmyjylXNbgcjfT37V6AXqD0OFxEREREREREQFONjPv119/RXV1NSorKwEA586dw/fffw8AmDFjBjw9PTF9+nRMnToVDzzwACoqKhAfH481a9Zgy5Yt+OqrryCTya72EETk4tSmyru/ttm6ySQNjvF0c6ofn0RERERERNRJONVvow888AAyMzMtf1+3bh3WrVsHAEhPT0dcXBwA4Mcff8Rzzz2HpUuXoqSkBAkJCVizZg1uueUWMcImIifS3Mw8t3qVeiqtAZ5uHRoWEREREREREQAnS+ZlZGS06jhvb2+sWLECK1assG9ARNTpWJJ5sobJPJlUAoVMAq1esFTvEREREREREXW0Tjczj4ioPcwLMP5amQfUzdFTaQ0dGhMRERERERGRGZN5RET1qHVNb7MFAHeF1HQMK/OIiIiIiIhIHEzmERHVo25mZh7AyjwiImdRVVWFxx57DJGRkXB3d8eAAQPw7bffih0WERERkU041cw8IiJ7EgSh2QUYAKA0VeaptKzMIyJyZDfddBMOHz6M1157DT179sQ333yDW2+9FQaDAQsWLBA7PCIiIqJ2YTKPiMhEqxcs/6+UyRpd726qzDNX7xERkePZvHkzfv/9d0sCDwAmTpyIzMxMPPXUU5g/fz5kTfyMJyIiInIWbLO1wsqVK5GUlIShQ4eKHQoR2ZB5+QXAyjwiIme1fv16eHt74+abb25w+aJFi3DlyhUcPHhQpMiIiIiIbIPJPCssXrwY586dw+HDh8UOhYhsSKO7ejLP3TIzj8k8IiJHdebMGSQmJkIub9iA0q9fP8v1RERERM6MbbZERCbmZJ5cKoFMKml0fd02W7bZEhE5quLiYnTr1q3R5YGBgZbrm6JWq6FWqy1/r6iosE+ARERERO3EyjwiIhO1zlhx11RVHlC3zVbNyjwiIocmkTT+QKal61599VX4+flZ/kRHR9srPCIiIqJ2YTKPiMjkaptsgbrKPJWWlXlERI4qKCioyeq7kpISAHUVen+1ZMkSlJeXW/5kZ2fbNU4iIiIiazGZR0RkYm6fdZM1l8wzb7NlZR4RkaPq27cvkpOTodPpGlx++vRpAECfPn2avJ1SqYSvr2+DP0RERESOiMk8IiIT8zbb5ttsWZlHROToZs+ejaqqKvzwww8NLl+9ejUiIyMxfPhwkSIjIiIisg0uwCAiMjG32SqbbbNlZR4RkaObPn06pk6digceeAAVFRWIj4/HmjVrsGXLFnz11VeQyWRih0hERETULkzmERGZWNps5U3/oqc0JfNYmUdE5Nh+/PFHPPfcc1i6dClKSkqQkJCANWvW4JZbbhE7NCIiIqJ2YzKPiMikpQUYdW22rMwjInJk3t7eWLFiBVasWCF2KEREREQ2x5l5REQmljbbFhdgsDKPiIiIiIiIxMFkHhGRiUZvrLhjZR4RERERERE5KibziIhMWmqzNVfmqViZR0RERERE5LJ2phYgq7hGtMdnMo+IyKTlbbbGy9WszCMiIiIiInJJWr0BT3x3EuPe3IGjmSWixMBkHhGRibrFBRiszCMiIiIiInJlf14oQkm1BsHebujfxV+UGJjMIyIysSTzml2Awco8IiIiIiIiV7bhxGUAwHX9IiFv5ndHe2MyzworV65EUlIShg4dKnYoRGRDrZ2Zx222RERERERErqdGo8Nv5/IBADcMiBQtDibzrLB48WKcO3cOhw8fFjsUIrIhjb6lNltusyUiIiIiInJV25ILUKPRIybQEwOj/UWLg8k8IiKTugUYsiavZ2UeERERERGR69pw3Nhie+OASEgkEtHiYDKPiMhErTNW3DXbZmtegMHKPCIiIiIiIpdSWq3BrvOFAIzJPDExmUdEZFJXmddMm62irs1WEIQOi4uIiIiIiIjEtflMLnQGAUkRvogP9RE1FibziIhMNC1tszVV5hkEQGdgMo+IiIiIiMhVbDhxBQAwa6C4VXkAk3lERBYtLsBQ1F3OVlsiIiIiIiLXcKWsFofSSyCRANf3ZzKPiMhhWCrzWthmCwAqLZdgEBERERERuYKfTxqr8obFBSLCz0PkaJjMIyKyULcwM08ikViuMy/LICIiIiIios7N3GJ744AokSMxYjKPiMhE3UJlHlCX6GNlHhERERERUed3Pr8SybkVUMgkmNE3XOxwADCZR0Rk0dICDABwVxiXYLAyj4iIiIiIqPP72VSVN75nKPw93USOxojJPCIik5Zm5gF1yTxW5hEREREREXVugiBgw8nLAIAbB4i/+MKMyTwiIpOWttkCdW22am6zJSIiIiIi6tSOZZUhu6QWXm4yTEkMEzscCybziIhMNJYFGLJmj6lrs2VlHhERERERUWf28wljVd603uHwcGv+98SOxmQeEZGJeQ5ec9tsAcBdYV6Awco8IiIiIiKizkqnN2DjqVwAwA0O1GILMJlHRGTRmpl55qo9FRdgEBERERERdVp7LxajuFqDIC83jI4PFjucBpjMIyIyad02W/PMPLbZEhERERERdVYbjhtbbGf2i4DiKr8jisGxoiEiElGrFmBYttmyMo+IiIiIiKgzqtXosfVsHgDgxgFRIkfTGJN5Vli5ciWSkpIwdOhQsUMhIhsxGARo9QKAVm6z5QIMIiIiIiKiTml7Sj6qNXp0CfDAoBh/scNphMk8KyxevBjnzp3D4cOHxQ6FiGzEXJUHtLQAw1yZx2QeERERERFRZ1NYqcYHf6QBAG4cEAmJRCJyRI3JxQ6AiMgR1K+0a01lHhdgEBERERERdS6ZxdW447NDyCyuQZCXGxYMjxU7pCYxmUdEhLrlF0BLCzCMlXlcgEFERERERNR5nLlcjjtXHUJRlQbRgR744q7hiPL3EDusJjGZR0SEessvZNKrllG7y01ttqzMIyIiIiIi6hT+vFCE+788gmqNHkkRvvj8rqEI9XEXO6xmMZlHRIS6yryrtdgCgFJharPlNlsiIiIiIiKn9/PJK3jyuxPQ6gWM6h6Ej28fDB93hdhhXRWTeUREqEvmXW35BQC4c5stERERERFRp/DZn+l4aeM5AMDMfhH497z+UJq6sRwZk3lERADUprbZlirz6mbmsTKPiIiIiIjIGQmCgDe2puI/Oy8CAO4cFYel1yVBKnW8zbVNYTKPiAjWtNmyMo+IiIiIiMjZaPUGPPvDafxwLAcA8NS0XnhwQverzk53NEzmERGhXjLvKptsgboFGGouwCAiIiIiInIqNRodFn99DDtSCyGTSvDq7L6YNzRa7LDajMk8IiIAan3rKvPMbbaszCMiIiIiInIepdUaLPr8ME5kl8FdIcXKBYMwOTFM7LCswmQeERHa0GZrWYDByjwiIiIiIiJnkFNagzs+O4RLhdXw81DgszuHYnBsgNhhWY3JPCIitH6brZKVeURERERERE4jJa8CCz87hPwKNSL93PHF3cMQH+ojdljtwmQeEREAtaUy7+pryM3JPhW32RIRERERETm0g5eKcc8XR1Cp0qFnmDdW3zUMEX4eYofVbkzmERGhDQswFOYFGKzMIyIiIiIiclRbzuThkW+PQ6MzYGhcAD69Yyj8PBVih2UTTOYREQHQmGbgtdRm665gZR4REREREZEj+/pgJl746QwMAjA1KQzv3zrQUpjRGTCZR0QEQNPKbbZKeV1lniAIkEgkdo+NiIiIiIiIWiYIAlZsv4B3t10AANw6LBr/vLEP5C10YDkbJvOIiND6BRjmyjzAmNDrTJ/uEBEREREROSu9QcALG87gm4NZAIBHJsXj8ak9O2UBBpN5RESovwCjdTPzAECtZTKPiIiIiIhIbCqtHo9+exxbz+ZDIgFeurEPbh8RK3ZYdsNkHhERWr8AQy6VQCoBDAKg0unhh84xQJWIiIiIiMgZlddqce/qIziUUQI3mRQrbhmA6X0jxA7LrjpX03AHWblyJZKSkjB06FCxQyEiG2ltZZ5EIqnbaKvlRlsiIiIiIiKx5JWrMP/j/TiUUQIfpRxf3D2s0yfyACbzrLJ48WKcO3cOhw8fFjsUIrKR1i7AAOpabVU6brQlIiIiIiISQ1pBFeb8Zx9S8ioR6qPE2vtHYkS3ILHD6hBssyUiQv0FGC3PwDMvyWBlHhERERERUcc7llWKuz8/jNIaLboFe2H1XcMQHegpdlgdhsk8IiLUm5nHyjwiIiIiIiKHVFajwft/pOGL/RnQ6gX0j/bHZwuHIMhbKXZoHYrJPCIiAGpTYq41yTxzZZ5Ky2QeERERERGRvWl0BnyxPwPv/5GG8lotAGBa7zC8M38APN1cL7Xles+YiKgJljbbFrbZAoCSCzCIiIiIiIjsThAEbDmTh9e2pCCzuAYAkBDug3/MSMS4niEiRyceJvOIiNDGBRjmyjy22RIREREREdnF8axS/GtTMo5klgIAQnyUeHJqT9w8JBoyqUTk6MTFZB4REdo2M89cmadiZR4REREREZFNZZfU4I2tqfjl5BUAgLtCivvGdcf947rBS8k0FsBkHhERgPrbbFtfmadmZR4REREREZFNlNdq8eGONKzamwGN3gCJBJg7qAuevKYXwv3cxQ7PoTCZR0QEQG3NNltW5hEREREREbWLVm/A1wcysWL7BZTWGJdbjI4Pwj9mJKJ3pJ/I0TkmJvOIiFCvzbY1CzBYmUdERERERNQugiDg93P5eO3XFFwqqgYAxId647kZiZjQKwQSiWvPxbsaJvOIiMDKPCIiIiIioo5yOqccL286h4PpJQCAIC83PD61J24ZGg15KwosXB2TeUREaOM2W4WpMk/LyjwiIiIiIqLWulJWize3pmL98csAjF1P94ztir+P7w4fd4XI0TkPJvOIiFB/AYasxWPNx5ir+YiIiIiIiKh5lSot/rPzIv73Z7rl96ibBkbhyWm9EOXvIXJ0zofJPCIitHGbrakyT8XKPCIiIiIiombp9AZ8ezgb7247j6IqDQBgeNdAPD8zCX27cLmFtZjMIyJC3TKL1rTZmivzmMwjIiIiIiJqTBAE7EgtwCubU5BWUAUA6BbshWenJ2BqUhiXW7QTk3lE5PJ0egMMgvH/W7PN1jIzj222REREREREDZy9Uo5XNidjb1oxACDAU4HHpvTEguExUHC5hU0wmUdELs+8/AJoZWWegpV5RERERERE9eWVq/DWb6n44VgOBMFYKLFoTBwWT4yHL5db2BSTeUTk8jS6Nibz5OaZeazMIyIiIiIi11at1uHj3Zfw392XUGsqeLi+fySentYL0YGeIkfXOTGZR0Quz5zMk0oAubTl2Q3uCvM2W1bmERERERGRa9IbBKw7ko23fz+Pwko1AGBIbACem5mIgTEBIkfXuTGZR0Quzzz7zk0ubdUgVndLmy0r84iIiIiIyPXsPl+IVzYnIyWvEgAQG+SJJdMTMK13OJdbdAAm84jI5VmSea0cxmpus2VlHhERERERuZLUvEr8a3Mydp8vBAD4eSjwyOQeuH1EbKtGFpFtMJlHRC5PY6nMk7XqeFbmERERERGRKymoVOGd389j7eFsGARAIZNg4cg4PDQpHv6ebmKH53KYzCMil2feZqts5SdJ7gpW5hERERERUedXq9Hjv3su4aNdF1GjMf7+M6NvOJ65NgGxQV4iR+e6mMwjIpdnrsxrbTJPaargU7Myj4iIiIiIOiGDQcCPxy/jra2pyKtQAQAGxvjj+ZmJGBwbKHJ0xGSeFVauXImVK1dCr2dVDlFnYK6wa+2MB3NlnoqVeURERERE1MnsuVCIVzanIDm3AgDQJcADz1ybgOv6RXC5hYNgMs8KixcvxuLFi1FRUQE/Pz+xwyGidtLU22bbGu6myjytXoDeIEAm5T9oRERERETk3M5eKcdrv6Zgz4UiAICPuxwPT4rHwlFxlu4kcgxM5hGRy9O0dZutou44tU4PTzf+KCUiIiIiIud0uawWb/+WivXHL0MQjL8X3TEyFosnxiPAi8stHBF/AyUil2degNHayrz6n0qptAZweRMRERERETmb8lotPtyZhlV7MywFDjcOiMT/XdML0YGeIkdHV8NkHhG5PHUb22xlUgkUMgm0eoEbbYmIiIiIyKmodXp8uT8TH+xIQ1mNFgAwslsQ/jEjEX27cJSYM2Ayj4hcXlu32QLGuXlavQ4qbrQlIiIiIiInYDAI+OXUFby5NRU5pbUAgF5hPnh2RgIm9AzhcgsnwmQeEbm8usq81g91VSpkqFTroNKyMo+IiIiIiBzbvotFeHVzCk5fLgcAhPkq8eQ1vTBnUBcu9HNCTOYRkctr6wIMoK6Kz5wIJCIiIiIicjSpeZV47ddk7EgtBAB4K+V4YEJ33DW6KzzcuKHWWTGZR0QuT9PGmXkA4G7aaMvKPCIiIiIicjR55Sr8+/dUfH80BwYBkEsl+NuIWDw8KR5B3kqxw6N2YjKPiFyeRm9MyLVpZp7C+CkWk3lEREREROQoKlRafLzrIv73Z7plvvfMvhF4alovxAV7iRwd2QqTeUTk8qxZgME2WyIiIiIichQanQHfHMzEe3+koaRaAwAYGheAJTMSMSgmQOToyNaYzCMil6e2qs2WlXlERERERCQuQRCw+XQe3tiagsziGgBA9xAvPDs9EVMSQ7mhtpNiMo+IXJ41CzDMyTy1lpV5RERERETU8Q6ll+CVzck4kV0GAAj2VuKJqT0xb0gXyNvwuw05HybziMjlWbMAo67NlpV5RERERETUcdIKKvHar6nYlpwPAPB0k+H+cd1xz9iu8FIyzeMK+F0mIpen1renzZaVeUREREREZH8FFSq8s+0C1h7OgkEAZFIJbh0WjUcm90Coj7vY4VEHYjKPiFweK/OIiIiIiMhRVal1+GT3Jfx39yXUmmZ2X5MUhqevTUB8qLfI0ZEYmMwjIpdXt81W1urbsDKPiIiIiIjsSas3YO3hbLy77QKKqtQAgIEx/vjHjEQMjQsUOToSE5N5ROTyzNV1barMUxiP5TZbIiIiIiKyJUEQsPVsPt7YkoJLRdUAgLggTzxzbQKu7RPODbXEZB4RkTXbbM1VfGodK/OIiIiIiMg2jmaW4JXNKTiaWQoACPJyw6NTeuDWYTFQcEMtmTCZR0QuT6M3t9m2ZQEGK/OIiIiIiMg2LhVW4Y0tqdhyNg+A8feNe8d2w33jusHHXSFydORomMwjIpdnzQIMd1NlnoqVeUREREREZKXCSjXe234B3xzKgt4gQCoB5g2JxmNTeiLcjxtqqWlM5hGRy6tbgNH2mXlqVuYREREREVEb1Wh0+HRPOj7edRHVGuPvFJMTQvHM9AT0DPMROTpydEzmEZHLU7Myj4iIiIiIOoDBIGD98ct4Y2sK8iuMG2r7dfHDs9MTMKp7sMjRkbNgMo+IXJ5VbbYKUzKPlXlERERERNQKhzNK8M+N53AqpxwA0CXAA09fm4Dr+kZAKuWGWmo9JvOIyOVZt83W1GbLyjwiIiIiIrqK7JIavPZrCjadzgUAeCvlWDwxHotGx1mKBIjagsk8InJ5ar31lXmcmUdERERERE2pUuvw4Y40fPpnOjQ6A6QSYP7QaDwxtRdCfJRih0dOjMk8InJpgiBY1WZrWYDByjwiIiIiIqpHbxCw7kg23vrtPIqqjHPxRnUPwvMzk5AU6StydNQZMJlHRC5Nqxcs/6+Ut77E3bIAg5V5RERERERksu9iEf65MRnJuRUAgLggTzw3MwlTEkMhkXAuHtlG68tQiIg6IbWuLhmnbFObrfFYJvOIiOxv586dkEgkTf45cOBAg2OPHTuGKVOmwNvbG/7+/rjppptw6dIlkSInIiJXkVFUjfu+OIIF/z2I5NwK+LjL8fzMRPz2+HhMTQpjIo9sipV5Vli5ciVWrlwJvZ6/xBM5O029Ntm2LcAwzcxjmy0RUYd55ZVXMHHixAaX9enTx/L/KSkpmDBhAgYMGIDvvvsOKpUKS5cuxdixY3HixAmEhIR0dMhERNTJlddq8cEfF/D5vgxo9QJkUgluGx6Dx6b0RKCXm9jhUSfFZJ4VFi9ejMWLF6OiogJ+fn5ih0NE7aAxLb+QSyVtWgdfvzJPEAR+0kZE1AF69OiBESNGNHv90qVLoVQqsXHjRvj6GmcSDR48GD169MBbb72F119/vaNCJSKiTk6nN2DN4Wy88/t5lFRrAADje4bg+ZmJ6BHmI3J01NkxmUdELs2a5RcAoDRtszUIxrl7bnIm84iIxKTT6bBx40bccccdlkQeAMTGxmLixIlYv349k3lERGQTu88X4uVN53A+vwoAEB/qjedmJmJir1CRIyNXwZl5ROTSzMm8tszL++vx9efuERGR/SxevBhyuRy+vr6YNm0a/vzzT8t1Fy9eRG1tLfr169fodv369UNaWhpUKlVHhktERJ1MWkEV7vr8MO747BDO51fB31OB5Tf0xq+PjmUijzoUK/OIyKWpra3Mk0shkQCCAKi0Bvi42yM6IiICAD8/Pzz66KOYMGECgoKCkJaWhjfffBMTJkzApk2bMG3aNBQXFwMAAgMDG90+MDAQgiCgtLQUERERTT6GWq2GWq22/L2iosI+T4aIiJxOWY0G7267gK8OZEJnECCXSrBwVBwemdQDfp4KscMjF8RkHhG5NGuTeRKJBEq5FCqtgZV5RJ1Mcm4FQnyUCPZWih0KmQwcOBADBw60/H3s2LGYPXs2+vbti6effhrTpk2zXHe1GaZXu+7VV1/F8uXLbRMwERF1Clq9AV8dyMS72y6gvFYLAJiSGIp/zEhEtxBvkaMjV8Y2WyJyaZaZeW3YZGtm3mir0nKjLVFn8uwPpzDk5W3YciZP7FDoKvz9/XHdddfh1KlTqK2tRVBQEABYKvTqKykpgUQigb+/f7P3t2TJEpSXl1v+ZGdn2yt0IiJycIIg4I+UfEx7dzeW/3IO5bVaJIT74Ku7h+PThUOZyCPRsTKPiFyaeZutmykx1xbuCinKa40bbYmocyip1uDU5XIAwIBof3GDoRYJggDAWHHXvXt3eHh44PTp042OO336NOLj4+Hu3vxMBKVSCaWS1ZhERK4uNa8SL286hz0XigAAQV5uePKaXpg/NBoyKZfekWNgZR4RuTRrF2AYb2NMAJpbdYnI+f2ZVgRBAHqF+SDcj8MwHVlpaSk2btyIAQMGwN3dHXK5HNdffz1+/PFHVFZWWo7LysrCjh07cNNNN4kYLRERObriKjWeW38a01fsxp4LRXCTSXH/+G7Y8dQELBgew0QeORRW5hGRS9NYOTMPMFbmAYCalXlkZ9VqHd7cmorr+0dgcGzj4f5kO7vPFwIAxvUMFjkSqm/BggWIiYnBkCFDEBwcjAsXLuDtt99Gfn4+Pv/8c8txy5cvx9ChQ3Hdddfh2WefhUqlwtKlSxEcHIwnn3xSvCdAREQOS63TY/W+DLy/PQ2Vah0AYHqfcCyZnoiYIE+RoyNqGpN5ROTSzMsrrKnMc1eYZuZxAQbZ2er9Gfh8XwbOXanAd38fKXY4nZYgCNhzwZjMG98zVORoqL5+/fph7dq1+Oijj1BVVYXAwECMGTMGX375JYYOHWo5LiEhATt37sQzzzyDuXPnQi6XY9KkSXjrrbcQEhIi4jMgIiJHIwgCfjuXj1c2JyOzuAYA0DvSFy9cl4QR3YJEjo7o6pjMIyKX1r4FGObKPLbZkn39etq4iCE1vxKCIFx1IydZLzW/EvkVargrpBgSFyB2OFTPs88+i2effbZVxw4ePBjbtm2zc0REROTMzl4pxz83nsOBSyUAgBAfJZ6e1gtzBnWBlO205ASYzCMil1a3AIOVeeSYsktqcNq0kKG8VouiKg1CfDik3x7MLbYjugVZ3t9ERETUeRRUqvD21vP47mg2BMH44fy9Y7vhgQnd4aVkeoScB1+tROTS2jMzz7wAQ8XKPLKjLWfyGvz9QkElk3l2sss8L68H2zGJiIg6E5VWj//9mY4Pd6ShWmP8IP76/pF45tpe6BLAuXjkfJjMIyKXpm7PNlsuwKAO8OuZXACATCqB3iAgraAKo7pzOYOt1Wh0OJxeCgAY15PJPCIios5AEARsOp2L135NQU5pLQCgf7Q/ll6XyKVi5NSYzCMil6ZuzzZbc2WejpV5ZB+55bU4llUGALixfyR+PH4ZaQVV4gbVSR28VAKN3oAofw90D/ESOxwiIiJqp7IaDf5v3SlsS84HAET4ueOZaxNwQ/9IzsUjp8dkHhG5tLoFGG2fj+Wu4AIMsq+tphbbIbEBGBUfjB+PX8aFfCbz7MHSYtszmAtGiIiInNzRzBI8/M1xXClXwU0mxYMTu+P+cd3h4caZuNQ5MJlHRC7NJjPzuACD7GSzKZl3bZ9w9Aj1BgBcYGWeXey+wHl5REREzs5gEPDJnkt4c2sq9AYBcUGe+GDBIPSJ8hM7NCKbYjKPiFyaRm9MxFm3zdZ4GxVn5pEdFFSqcDijBAAwvW8E/DwUAICiKjVKqzUI8HITM7xOJae0BpcKqyGTSjAqnvMIiYiInFFxlRpPrjuJnanGD+hu6B+JV27qC29uqaVOqO2/vRIRdSKadizAcFcYK/PUnJlHdvDb2XwIgnFIc5S/B7yVckT5ewAA0grFq847llWKk9lloj2+tY5mlqKsRtPkdbvPFwEABkb7W5KmRERE5DwOXirGjPf2YGdqIZRyKV67qS9W3DKAiTzqtJjMIyKX1p5knvk2rMwjezBvsZ3eJ9xyWby51VakuXmf7rmEmz7ch1s+OYAqtU6UGKyxL60Ic/6zDzd8sBcl1Y0TervOFwDgFlsiIiJnozcIeH/7Bdz63wPIr1Cje4gXNjw0GrcMi+EMXOrUmMwjIpfWrm225so8J1yAcamwChfyK8UOg5pRUq3BgUumFtt6yTzz3LyO3mhrMAh4eeM5vLwpGQBQq9U7VXXej8cvAwCySmrw96+OWpL4AKDVG7AvrRgAk3lERETOpKBShTs+O4i3fz8PgwDMGdQFvzw8BgnhvmKHRmR3TOYRkUur22ZrfWWe2skWYOj0Bsz9aD9mvLcHZ6+Uix0ONeH3c3nQGwQkRfgiNsjLcrmlMq+g4xKxap0ej3x7HJ/+mQ4AiPBzB2BsW3UGGp0BW88aF4koZBIcSi/Bc+tPQxAEAMCJ7DJUqnXw91SgL4djExEROYW9aUWYseJP7E0rhodChrdu7o+35/WHpxvbask1MJlHRC5No29/ZZ7KySrzCqvUKKnWQKsX8H/rTjWoUiLH8Ktpi+2MvuENLu8R1rGVeeW1Wiz87BA2nsqFQibBu/MH4O/juwNwnmTen2mFqFTpEOqjxCe3D4FUAqw7moOPd18CAOw+bxySPSY+GDIp23GIiIgcmd4g4N+/peJv/zuIoio1eoX54JeHR2Pu4C5ih0bUoZjMIyKX1r42W+eszMsrV1n+Pzm3Ait3pIkYDf1Vea0We9OMCxmu7RPR4Lr4EB8AQG65CpUqrV3jyCtXYf7H+3HgUgm8lXKsunMYZg2MwuDYAADGRRgGg2DXGGxh40nj7MEZfSMwMSEUL17fGwDw+pYUbD2bZ0nmscWWiIjIseVXqLDgvwfw3h9pEATg1mHR2PDQaMSH+ogdGlGHYzKPiFxa3QIMWZtva76Ns1Xm5VcYk3mebsb4V+5IY7utA9menA+tXkDPMG9LW62Zn6cCoT5KAPavznv+pzNIyatEiI8Sa+8fgTE9ggEACeE+8HSToVKlw4UOnt3XViqtHr+fywcAXNfPmBhdOCoOd4yMhSAAj317AqcuG1/743owmUdEROSodqYWYPqKPTiYXgIvNxlW3DIAr97Uz9IpQ+RqmMwjIpfWnso8pcI5t9maK/PG9wzB9D7h0BkEPPndSbbbOojNp40tttP/UpVnZm61tWciTRAEHMk0LuD46G+D0DuybpacXCbFgGh/AI7farvnQhEq1TqE+7pjUEyA5fKl1yVhbI9g1Gr1EASgV5gPwk2zAImIiMhxaPUGvL4lBXeuOoySag2SInyx8ZGxuHFAlNihEYmKyTwicmkaU4usNQswLNtsnSwJllehBgCE+brjn7P6INDLDSl5lfjgjwsiR0a1Gj12XzC2fU7/y7w8sx6mVpKLdkzmFVSqUVajhVSCBok8M3OrraMn8zadugLA2GIrrTcPTy6T4oMFgyyVjxMSWJVHRETkaK6U1eKWTw7gPzsvAgDuGBmLHx8cha7BXi3ckqjzYzKPiFxaexZgmLfZOltlnrnNNtzPHcHeSrx0o3GG2MqdF3HmMtttxXQypwwanQHhvu7oFdb0/Je6jbb2S+Yl51YAALqFeDfZvjKo3tw8R1W/xXZmv8ZVjn4eCnxzz3A8PzMRD06I7+jwiIiI6Cq2ncvHjPf24GhmKXzc5fjPbYPw0o192FZLZMJkHhG5tLqZee3ZZutcyTxzm224r7Gt8Lp+kZjRNxx6g4D/W8d2WzGZk2ODYwMgkTS9WbWHJZlXabc4UvOM990rvOmE4qBoYzIvvagaxVVqu8XRHrvOF6Jao0eknzsGmtqC/yrU1x33jO0GPw9FxwZHRERETdLoDHh54znc88URlNVo0b+LHzY9PBbT+zY9foTIVTGZR0QuzRbJPGdrszVX5oX51s0Ie+nGunbblzaehU7vXM+psziWWQYAGBjj3+wx5sq8nNJa1Gh0dokjxZTMS2wmmefnqbAkFY9lldklhvbadMq4xXZmv4YttkREROSYsktqcPPH+/Hpn+kAgLtGd8W6v49CTJCnyJEROR4m84jIpWnaswDDdBu1zgBBEGwal70IgoC8em22ZsHeSrw8qw8A4KsDWbj1vwdwpaxWlBhdlSAIOG6qzBtYb1nDXwV5KxHo5QZBAC4VVtsllhRLZZ5vs8cMiTPGaF6U4UhUWj22JZtbbCNFjoaIiIhasuVMLma8twcns8vg56HAf+8YgqXXJ1l1jk7kCvjOICKX1p5ttvVndjhLdV6lWocajbEtONy34fbOGX0jsOKWAfBWynE4oxTTV+zBb2fzxAjTJWWX1KK4WgM3mRR9oppPogH15+bZvtVWqzcgzXS/Cc1U5gGwbIc95oBLMHamFqBGo0eUvwf6d2m8wIOIiIgcg1qnx4sbzuDvXx1DpUqHQTH+2PTIGExNChM7NCKHxmQeEbksg0GAzmCsqLNmm2391ly11jmSefmmeXm+7nJ4uDUeIHzjgChsfHgM+kb5obxWi/u+PIplP591urmAzsg8L693lC+U8qsPdza3uKbZYQlGelE1tHoB3ko5ugR4NHuceaPtyZxyh5uzuNHUYntdv4hmZw8SERGRuDKKqjHnP/uwen8mAOD+8d2w9v6R6BLAtlqiljCZR0QuS1NvLpw1lXkKmRQy0ywulc45kl1Ntdj+VVywF354YBTuHdsVAPD5vgzc9OE+FFSqOiRGV2VO5g26SoutmWUJRr7tk3nmTba9wn2umgjrGuyFAE8FNDoDzl5xnC3ItRo9ticXAGh6iy0RERGJ75eTV3Dd+3/izOUKBHq5YdWioVgyPREKKz5gJ3JFfKcQkcuq3xpr7TwOd/PcPCepzDNvsg3zbT6ZBxi/Hs/NTMKqO4ci0MsN53Ir8OGOix0Ross6ZpmX59/isT3CjO2v9qjMa2mTrZlEIrFU5x11oFbbHakFqNXqER3ogb5RbLElIiJyJCqtHkt+PI2H1xxHlVqHYXGB2PzIWEzsFSp2aEROhck8InJZ9VsDrWmzBQClaW6es1TmmTfZ/nVeXnMmJoTildl9ARjnkJF91Gh0SM41JtHaUpmXUVwNtY1fey1tsq1vcGwggLpEpCOwbLHtG8kWWyIiIgeSVlCFWSv3Ys2hLEgkwMOT4vHNvcOv2jFCRE2Tix0AEZFYzEkQN7nU6l/6zZV5zjJTztxm21JlXn2j44Mgl0qQUVyDjKJqxAV72Ss8l3Uqpxx6g4BwX3dE+jc/p84sxEcJH3c5KlU6pBdVI+EqW2fbKrUVm2zNzJV5RzJKIQiC6MmzSpUW21OMW2yvY4stERGRw8guqcHslXtRqdYh2FuJd+cPwJgewWKHReS0WJlHRC7LXJmnbMdsDvNGW2fZZptXrgYAhLXhE1AfdwWGxBmTNqzOs4/jWWUAgEGx/q06XiKR2GVuXnmtFpfLagG03GYLAP26+EEulaCgUo2c0lqbxWGtDSeuQKU1ID7UG70jbZfgJCIiIusJgoB/rD+NSrUO/br4YfOjY5jII2onJvOssHLlSiQlJWHo0KFih0JE7WBegGHtvLz6t3WWyry2ttmaTTDNMdl1vtDmMVHbll+Y9Qi1/dy88/nGqrxIP3f4eShaPN5dIUNv01w6sVttBUHANwezAAC3DosRvUqQiIiIjH44dhl7LhRBKZdixS0DEerDtlqi9mIyzwqLFy/GuXPncPjwYbFDIaJ2MFfmtSeZZ67MUznLAgyrk3khAID9l4qdJnHpLARBwHHL8os2JPPCjJV5tkzmpZg22SZEtL6qbXCMYyzBOH25HOdyK+Aml+KmgVGixkJERERGhZVq/HPjOQDAY1N6oivHtRDZBJN5ROSyLG227UjmBXq5AQCKqtQ2icmetHqDJc4wP2WbbtsrzAfhvu5QaQ04mF5ij/BcVnZJLYqqNFDIJG1qDY03t9kWVNoslpRWbrKtz1E22q45ZKzKm94nHAGm9yURERGJa9kvZ1Feq0XvSF/cO7ar2OEQdRpM5hGRy7JFZV6XAOOygpzSGpvEZE+FlWoIAiCXShDs1bZknkQisVTncW6ebZnbU3tH+lkqPVujR5gx4ZZeVA2t3jaVoeZkXkIbknnmeYrJuRWoVutsEkdbVal1+PnEFQDGFlsiIiIS3+/n8rHpVC5kUglen9MP8nbMqSaihvhuIiKXpbZhMi+7RPzh/y0xt9iG+ighlbZ9npg5mbcrlXPzbMmaeXmAca6dl5sMWr2AzOL2J5MFQbBssm3LdtwwX3dE+XvAIAAnssvaHYc1fjl5BdUaPboFe2F410BRYiAiIqI6FSotnv/pNADg3rHd0Mc0Y5eIbIPJPCJyWZZkXjs+JYwO8ATgHJV5+eXGZF5bNtnWNzo+GHKpBJeKqpFlg+QRGbV1k62ZRCJBd8tG2/a32uaU1qJKrYNCJkG3kLbNsxG71dbcYsvFF0RERI7htV9TkF+hRtdgLzw2pYfY4RB1OkzmEZHLssU22y6mZF52qfNU5rV1+YWZj7vCkrTZeZ6ttrZQq9Ej2bR0oq2VeYCxNRcAPt59qd2ttuaqvO4h3lC0McFtfl0czuj4eYpnLpfjVE45FDIJbhrExRdERERiO3Cp2LJh/tWb+rZpjAgRtQ6TeUTksupm5ll/ghEdaGyzLaxUO/yW1/wK0/ILK5N5ADChVygAYCdbbW3iVE4ZdAYB4b7uiPT3aPPtH54UD193OU5kl+H97RfaFUtKnjGpmNiGTbZmw0ytrUczSy3vq47y7WHjLwvTeocjyLttsyCJiIjItlRaPZb8aGyvvXVYDEZ0CxI5IqLOick8InJZtthm6+ehgLdSDgC4XObY1Xn55so8K9tsgbq5efsuFjl88tIZHDO12A6M8bfq9pH+Hnjlpr4AgA92pLWrMs6aTbZmvcJ8EOjlhhqNHqdyyqyOoa1qNDr8dNy4+GIBF18QERGJbsX2C0gvqkaYrxJLZiSIHQ5Rp8VkHhG5LLXOmIxqT5utRCKptwTDsefI5ZW3r80WMG45Dfd1h0prwKH0jm+p7GysXX5R33X9IjFnUBcYBOCxb0+gvFZr1f1Ys8nWTCqVYGR34yfve9OKrXp8a2w8mYsqtQ5xQZ785J+IiEhkZy6X45PdlwAA/7yxD3zdFSJHRNR5MZlHRC7LUpnXjgUYQN3cvBwHn5tnrsxrT5utRCLB+J7G6jy22raPIAg4bk7mtXH5xV8tv7E3YgI9cbmsFks3nGnysbYn52POf/bh5Y3nIAhCg+tVWj3Si6oBtG2TbX2jTMm8fReLrLq9Nb4xLb6YPzTGqg3NREREZBs6vQHP/ngKeoOAmX0jcE3vcLFDIurUmMwjIpdVNzOvvck8U2WeA2+0FQShbgFGO9psgbpWWy7BaJ+c0loUVWmgkEksiyys5a2U491bBkAmlWDDiSv46fhly3XnrlTgb/87iLtXH8HRzFJ8+mc6vj2c3eD2aQVV0BsE+HsqEOZr3dy5Ud2DARi389Zq7N+CnZxbgRPZZZBLJZg7uIvdH4+IiIia9+mf6ThzuQJ+Hgosu6G32OEQdXpM5hGRy7LFNlsAiA50/Mq8SrUONaYES3vabAFgdI9gyKUSXCqsdvjWYkdmbrHtHelnky1vg2IC8OjkHgCA5386g6OZpXjm+1OY+f4e7E0rhptMirE9jAm3ZT+ftWyvBeo22SaE+0Aisa7CLS7IE5F+7tDoDTiSaf8W7K8PZgIArukdhhAfLr4gIiISS3pRNd75/TwA4PmZifx3magDMJlHRC7LFgswgLrKPEdO5uWb5uX5usvh4da+xJGvuwKDYo0z3namsjrPWuZlFe2Zl/dXD07ojiGxAahS6zDnP/uw9kg2BAGY2S8C258cj9WLhmFCrxCodQYs/uYYajQ6AHWbbK1tsQWMLdgjTdV5+y7ad25edkkNvjucAwD42/BYuz4WERERNU8QBCz58RTUOgPG9ghmtTxRB2Eyj4hcltrGbbY5DlylZqsWWzNLqy3n5rWJ3iBg69k8zP94P746YJz31t55efXJZVK8M38AfEwblvtH++OHB0Zi5YJBiA70hFQqwds390eojxJpBVVY9vNZAO1bflHf6HjT3Lw0+87Ne3NrKjR6A8bEB1sWbxAREVHH+/ZwNg5cKoGHQoZXZve1usKfiNpGLnYARERisSTzZO2rVDMvwCiu1qBGo4Onm+P9aDVvsm3P8ov6JvQMxRtbUrHvYjFUWr1N2kQ7s0qVFt8dycHn+9KRXWKs4JRLJbihfySmJIbZ9LGiAz2xfvFo5JbXYnT34EaLIYK8lXj3lgG47dOD+O5IDkZ1D7Yk83q1M5lnTqydvlyO8lot/Dxsv8XuVE4Zfj55BQDw7PQE/tJAREQkkvwKFV7ZnAwAePKanpbRM0Rkf473GycRUQex1QIMPw8FfN3lqFDpkFNai55h7UuI2IN5k2175+WZJUb4INRHiYJKNU7llGNY10Cb3G9ndDijBItWHUaV2tjS6u+pwIJhMbh9ZCwi/Dzs8pjxod6ID/Vu9vpR3YPx8KQeeG/7BSz58TRqtXpIJGj3azfCzwPdgr1wqagah9JLMDXJtolKQRAsvzTMHhiFPlHtWxxCRERE1hEEAS/8dAaVKh36R/tj0eiuYodE5FLYZktELstWCzCAuuq8HAfdaGvrNluJRGJJpKTmV7ZwtGt7Y0sKqtQ6dAvxwiuz+2L/s5Px9LUJdkvktdajk3tgeNdA1GqNi1FiAj3hpWz/Z3yjTK22e+3QarsjtQAHLpXATS7Fk9f0tPn9ExERUev8eiYPv53Lh1wqwetz+kImZaU8UUdiMo+IXJZGZ0xi2CKZFx3o2Esw8srVAGzXZgsAPcKMlV8XmMxr1rkrFTicUQq5VII1947AguEx7V5AYisyqQQrbhmIQC83AO2fl2c2yrQEY7+Nl2Do9Aa8ujkFALBoVJwlgU5EREQdq6xGg6UbjHN3H5zQvV0LtIjIOkzmEZHLstU2W6CuMi/bQZdg2LrNFgB6hhqTP+eZzGvWlwcyAADTeofbNJFqK+F+7vjg1oFICPfBLUNjbHKfI7oZK/NS8ytRWKlu8piSag1S89r2uvnhWA4uFFTBz0OBByfEtztOIiIiss6/NiWjqEqN+FBvLJ7Ef5OJxMBkHhG5LLUNk3nRAQ5emWfjNlugbr7a+fwqm91nZ1Jeq8VPx42LGm4fGStyNM0bFR+MLY+Nw8SEUJvcX6CXG5IijJ/Q77/UuDqvqEqNae/uxrR3d2PTqdxW3WeNRod//34eAPDwpHj4edp+sQYRERG1bOvZPKw7mgOJBHh9Tl8o5Y7RcUDkapjMIyKXZVmAIbNhZZ4DzszT6g0oqrJ9m218qDckEmOVlfn+qc73R3NQq9WjV5gPhrvYgpBRpq22+y82nJsnCAKe/eG0pWLvqe9Ptqqy83970pFfoUaXAA+HTowSERF1Zmcul+Oxb08AAO4a3RWDY13r/IbIkTCZR0Quy6YLMBx4Zl5hpRqCAMilEgSZ5qPZgoebDNGmJCZbbRsyGAR8dSATgLEqTyJxraHQo+ONc/P2pjWszPv2cDa2JefDTSZF/y5+qNHocf+XR1Feq232vgoqVfho10UAwFPTerECgIiISAQFFSrc+8UR1Gr1GNsjGM9OTxA7JCKXxmQeEbksS2WeDWfmldVoUalqPjEhBnOLbaiPElIbbxrraVmCwVbb+v5MK0J6UTV8lHLMHhgldjgdbmjXQMikEmSV1FjmSGYUVeOfG88BAP5vWk+sWjQMUf4eSC+qxpPfnYDBIDS6n1M5ZZi9ch+qNXr0jfLD9f0iO/R5EBEREaDS6nHvF0eQW65C9xAvfLBgEBQ26GwhIuvxHUhELqtuAUb7K328lXIEmOZ4OVp1Xn65MZkXZsN5eWZ1c/NYmVffF/uNVXlzBneBl1IucjQdz1spR/8ufgCMc/N0egMeW3sCNRo9RnQLxD1juiHQyw0f/W0w3ORSbEsuwPt/pFluLwgCvjyQibn/2Y/LZbWIDfLEO/P72zwZTURERFdnMAh4ct1JnMwph7+nAv9bOBR+HpxdSyQ2JvOIyGWpbViZBwDRgcbqPEdL5uXZYZOtmTmZx8q8OtklNdiekg8A+NsI153vZm613ZdWhA92pOFEdhl83OV4e94AS1Kubxc//GtWHwDAu9vP44+UfFSrdXhs7Qm88NMZaPQGXJMUhp8fGoN40/ZkIiIi6jjvbr+ATadyoZBJ8NHfBiMu2EvskIgIgOuVCxARmahtuAADALoEeOBUTrmlrdBRmJN5tlx+YdbD1GZ7vqASgiC43Gy4pnx9MAuCAIyJD0Z8qLfY4YhmZPcgvP9HGrYnF6BGqwcAvDyrD6L8PRocd/OQaJzKKceXBzLx6LcnEObrjrSCKsikEjx7bQLuGduVrysiIiIRbDhxGe9tvwAA+NesvhjRLUjkiIjIjMk8InJJWr0B5bUaAICvh21+FJrn5jlaZZ65zTbcDm223UO8IZUYZwUWVqkR6mP7x3AmKq0eaw9nAYDLb10dFBMApVyKSrUOAHBD/0jcOKDp+YEvXJeEc7kVOJpZikpVFUJ9lFh52yAMjeOWPCIiIjEcyyrFU9+fAgDcP64b5g2NFjkiIqqPbbZE5JIyi6uh1QvwdJMh0s+j5Ru0QnSAeaOtY1bm2aPN1l0hQ2yQsd3ifB5bbTedykVpjRaRfu6YnBAqdjiiclfIMCQuAAAQ6eeOf97Yp9lj3eRSfHjbIPTv4oepSWHY9MhYJvKIiIhEklNag/u+OAKNzoApiWF4+lpuriVyNKzMIyKXdN40461HmI/NhuqbK/OyHa0yr0INwD5ttgDQI9Qb6UXVOJ9fiTE9gu3yGM7iiwPGxRe3jYiFnFvecO/YbqhS6bD0+t7w87z6sOwwX3dseGhMB0VGRERETalS63DP6iMoqtIgMcIXK24ZABkXUBE5HCbziMgpVKt1OJVTjhPZZTiRXYpTOeWICfTEJ7cPaTFJ0BTz9tWeNpxp1sUBK/MEQUCeHdtsAeMSjN/O5eNCgWtvtE3Nq8TJ7DK4yaSYz1YUAMCEXqGY0Mu1KxSJiIichd4g4NE1x5GSV4lgbyU+XTgEXkqmDIgcEd+ZROTQNp/OxXvbL+B8fiUMQsPrcstV+Nv/DuKre4bDz6NtCT1LMi/MdhsyzZV5lSodymu0ViUZba1CpUOtafmAPdpsgXpLMFx8o+3JnDIAwODYAAR7K8UNhoiIiKiNXvs1GdtTCuAml+K/dwxutLSKiBwHe4CIyKG99msKUvKMibwIP3fM6BuOf8xIwH9uG4RALzecvlyOO/53EBUqbZvut67N1naVeR5uMgR7uwEAsh2kOi/fNC/P110ODzeZXR7DnBA9n2/caOuqUnKNCeLECF+RIyEiIiJqm7WHs/DfPekAgLdv7o+BMQEiR0REV8PKPCJyWEVVamSV1EAiAXb+3wTLogWzuGAvLPjvAZzMKcfCzw7hi7uGwce95Wo4jc6AjKJqALatzAOM1XlFVRrklNaiT5SfTe/bGvZusQWAbiFekEklqFTpkF+htutjObLU/AoAQEK4bV9TRERERPa0/2Ixnlt/BgDw2JQeuL5/pMgREVFLWJlHRA7rRFYZACA+xLtRIg8wVkB9dc9w+HsqcDyrDAs/O4Qqta7F+00vqobOIMBHKUeEjRNPjjY3z7zJ1l7LLwBAKZchLsjYYmxuX3ZF5sq8XkzmERERkZNIL6rGA18fhc4g4Pr+kXh0cg+xQyKiVmAyj4gc1rGsUgDAoKuU+feO9MNXdxtn5h3LKsOdrUjomRNOPcK8IZHYdjuXeW5ejoNstM03V+bZMZkHNGy1dUWFlWoUV2sgkdi+2pOIiIjIHsprtLj788Moq9FiQLQ/3pzbz+bnxkRkH0zmOZlt5/Kx4L8HcDSzVOxQiOzuuKkyb2CM/1WP6xNlTOj5ustxJLMU//zl3FWPv2CH5Rdm0YHGyrzsEseqzLN362sPF0/mpeQZW2zjgrzsNpuQiIiIyFa0egMe/OYoLhVVI9LPHZ/cMRjuCp7DEDkLJvOchMEg4J3fz+OeL45g38VifHUgU+yQiOxKbxAs20FbM4C3bxc/rLhlIABge0rBVRcxpFoq82yfzHO4yrwOaLMFgJ4uvtE2Nc/4muK8PCIiInJ0giBg2c9nsTetGJ5uMny6cChCfVxz5jGRs+ICDCdQodLiibUnsS0533JZSp5rVr+Q60jNq0SNRg9vpRzxoa3bODuyexDcZFIUVamRUVyDrsGN5+wBwAVTwqmnDTfZmkXXm5knCILorQrmpGKUv4ddH8dc5ZhWUOUQz7ujJXNeHhERETmJz/dl4OuDWZBIgPduGYikSF+xQyKiNmJlnoNLK6jCrJV7sS05H25yKR6f0hMAcLGgClq9QeToiOzneLaxlXxAtD9k0tYlhtwVMvTrYtwgezijpMljVFo9MoqNm2x72aEyL9KUNKvW6FFao7X5/beFIAjILDa2+8aaFlTYS1yQF+RSCarUOlwxzelzJXWbbHkyTERERI5rR2oB/rnROJJmyfQETEkKEzkiIrIGk3kO7Pdz+Zi1ci8uFVYjws8d6+4fiYcnxcPLTQaN3oCMomqxQySym9bOy/uroV0DAQBHmknmXSqshkEA/DwUCPFRtifEJrkrZAg13a/YG20LK9Wo1eohldS1/9qLm1xqqYR0tbl5Or3BUu3JNlsiIiJyVOfzK/HwN8dhEIB5Q7rg3rHdxA6JiKzEZJ6DWn88B/d+cQRVah2GdQ3Ezw+NQf9of0ilEvQ0/bLIVlvqzI6bNtm2OZkXZ5yvdzij6SUxFwrMyy9sv8nWLDrQmDjLLhF3bl6maQlHpL8H3OT2/3FvbrW94GLJvIziGqh1BngoZIgJtG/SlIiIiMgaxVVq3PX5YVSpdRjeNRAvz+rrcmNRiDoTJvMc1LgeIYjy98Cdo+Lw9T3DG1QQmSs/UpnMo06qrEaDi4XGytMB0S0vv6hvcEwgJBIgvagahZXqRteb3zf2WH5h1qXe3Dwxmat37d1ia9bDRZdgmF9TPcN9IG1lSzgRERFRR3rq+1PIKa1FbJAnPvrb4A75oJeI7IcLMBxUkLcSmx4ZA39Pt0bXmed8sTKPOqsT2WUAgK7BXgj0avweuBo/TwV6hfkgJa8SRzJKML1vRIPrzYmmnq1cqmGNumSeuJV5WSXmeXlNLwKxNXNlnqu12abkmebl2TFBTERERGStshoNdqYWAAA+vn0wAtp4fk1EjofpeAfWVCIPAHqZBqybf4Ek6mws8/Ki/a26/dA449y8plptLW22dpxtFm2aT5ctdmWeeflFB7V+1rXZVsFgEDrkMR2B+YOVhAgm84iIiMjx/JlWBINgHDPDZV1EnQOTeU7I3GabU1qLKrVO5GiIbO+YlfPyzIZY5uY1XIJRq9FbqtV62rXN1pg8E70yr9jcZtsxlXlxQZ5wk0lRq9Xjcpm4z70jmT9Y6cXlF0REROSAdqUWAgDG9wwRORIishUm85xQgJcbwnyNM/Q4N486G4NBsLTZDoxp27w8s2GmjbZnr5Q3SHhfLKyCIACBXm4I9rb9Jluz6MC6mXn6VlSoVal12Hw6F1q9waZxWCrzOmhmnlwmRbcQ19poW6XWWRad8JNuIiIicjSCIGDXeWMybxyTeUSdBpN5TsrcastkHnU2l4qqUKnSwV0htVShtlWEnwei/D1gEOq24gJ1CaYedpyXBxi3x/q6y6HSGrDx1JUWj39i7Qk8+PUxvLElxWYxlNVoUF6rBdBxyTygbrGIqyzBMP8MDvVRtnm+IxEREZG9peRVoqBSDXeF1DKKhoicH5N5Tqpuoy3n5lHncsw0L69fF3/IZdb/iDJX59Wfm5dqSubZs8UWABQyKe4b1w0A8O/fz1+14u5Qegl+O5cPAPh8Xwayim0zZy/TdD8hPkp4unXcriPzYpELLlKZl2qZl8eqPCIiInI8u01VeSO7BcFdIRM5GiKyFSbznBQ32lJndbyd8/LMzHPzjtSbm3fBvMm2A2abLRrdFcHebsgsrsF3R7KbPEYQBPxrczIAQCGTQKsX8MZW21TnZZpmA8Z1YFUeUK8yr8A1fjaZP1CxtoqUiIiIyJ7MLbacl0fUuTCZ56TMg9ZT8yshCK6zNZI6v7pNttbNyzMztxEczyqzVMaZ22x72rnNFgC8lHIsnhgPAHhv+wWotPpGx2w6nYuT2WXwdJPhfwuHQiIBNp7KtSwAaY/Moo5dfmGWGGGuGq5EWY2mQx9bDMmmD1R62bnak4iIiKitqtU6y0K48b1CRY6GiGyJyTwnFR/qDZlUgrIaLQoq1WKHQ2QTVWqdpRV2UDsr8+JDvOHvqUCtVo+zVypQrdZZtsvau83WbMHwGET5eyC/Qo0v9mc0uE6t0+ONLakAgPvGdcO4niGYO6gLAOCVTcntTtKbK/NiAzu2Mi82yAtJEb7Q6gVsONHyvEBHd6WsFl8eyIRa1zgZKwhCvTZbJvOIiIjIsey/WAytXkB0oEeHd2sQkX0xmeek3BUyyw9kttpSZ3EquwyCAET5eyDU171d9yWVSjAk1ljddzi9BGkFxhbbYG8lAjpoUYFSLsNjU3oAAD7ceREVKq3luq8OZCGrpAYhPkrcO9Y4X+/Ja3rBXSHFkcxSbD2b167Hziw2VeYFd2xlHgDMG2JMSq493HR7sTN55odTeOGnM3j7t/ONrsurUKG8VguZVIL4Dqj2JCIiImqL+i22EolE5GiIyJaYzHNiCZaNtlyCQZ3DMRvNyzMzt9oeziipt/yiY5MuswdGoXuIF8pqtPh0TzoAoLxWi/f/uAAAeGJqT3gpjQsqwv3cLYm9135NgUbX/OKMlpgXYHR0ZR4A3DggCm4yKc7lVuDM5fIOf3xbuVxWiz/TigAYl5PklDZcTmL+IKVbsBeUcg6UJiIiIsey+4I5mccWW6LOhsk8K6xcuRJJSUkYOnSoqHGY5+axMo86C8u8vJj2zcszG2JK5h3JLMX5vI7ZZPtXcpkU/3dNLwDA//ZcQnGVGh/uTENZjRbxod64eXCXBsffP747gr3dkFFcg28OZlr1mDUanaX9Pq6DZ+YBQICXG6b2DgMArGtm+YczWH8sB+ZuZ43OgH//pTovJdc0L4/LL4iIiMjBZBRVI7O4BgqZBCO7B4kdDhHZGJN5Vli8eDHOnTuHw4cPixqHJZmXy2QeOT9BEHA8uwxA++flmfWN8oNSLkVJtQa/ncsH0PHJPAC4tk84+kb5oVqjx9Kfz2LV3gwAwJLpCZDLGv4Y9lbK8fjUngCAFdsvoLxW+9e7a1GWaV6en4cCfp6K9gVvpXlDogEAP5240uTyD0cnCAJ+OHYZALBwZCwAYP2Jyw0qDc1V0YkRvh0fIBEREdFVmFtsh8QGwtvUBUJEnQeTeU4s0dRmm1ZYBZ3e+nY8IkeQWVyDkmoN3GRSJEXaJjniJpdiQLQ/gLoEV0e32QKARCLBU9OM1XmbTuVCozNgRLdATEpouuVh/pBoxId6o7RGiw93prX58TKKjM9VzEHHY+KDEeHnjvJaLX43JVKdybGsUqQXVcPTTYanr03ADf0jIQjA61tSLMekcJMtEREROSjLvLxeISJHQkT2wGSeE+sS4AFPNxk0OgMyTMPuiZzV8WzjvLzeUb42nT82rGtgg7/3ECnxMrZHMEZ0q4vlHzMSmx1ELJdJsWR6AgBg1d4MFFSo2vRYWSXGnwcxIrTYmsmkEsw1tRB/54Sttt8fzQEATO8TAS+lHE9N6wWFTII9F4qw+3whtHoDLhYal6qwzZaIiIgciUqrx/6LxQCAcT2YzCPqjJjMc2JSqcTSMsi5eeTstpwxbm81b6C1FfPcPAAI81XCz0OctlOJRILnZiTB002G20fEol8X/6sePykhFH2j/KDRGbAtuaBNj2VefiFmZR4ASzLvz7QiXC6rFTWWtlBp9dh4MhcAMGdwFAAgOtATd4yMAwC8+msK0gqqoNUL8FbK0SXAQ6xQiYiIiBo5klGKWq0eIT5KJEbwQ0eizojJPCeXYKoISWUyj5xYdkmNpRXTPGvNVgbF+ENqKoATY15efX27+OHMsml46cbeLR4rkUgwNcm4RGJHqnXJvBgRNtnWFxvkhRHdAiEIwA+mSjdnsPVsHirVOkT5e2BE17qB0Q9NjIePuxzJuRWWdtte4T7NVlgSERERiaFui20Iz1OIOikm85wcN9pSZ/DVgUwYBGMrqq3bYH3cFZYFBWIn8wBjRW1rT6rMM/X2phVBrWv9EolMU5ttXLB4bbZm84cak7PrjmbDYBBEjqZ1zC22cwZFQSqt+14FeLlh8cR4AMDOVONJcgJbbImIiMjB7EqtS+YRUefEZJ6T68XKPHJyNRod1hzKAgAsNLUx2tqcQV0gkQDXmCrdnEXvSF+E+ihRo9Hj4KWSVt1GozPgcqmxpTVW5Mo8ALi2dwR8lHJkl9TiQHqx2OG0KK9chb1pRQCAOaY24fruHBWHSD93y9+ZzCMiIiJHkltei9T8SkglxoVkRNQ5MZnn5BJMG22zSmpQrdaJHA1R2/10/AoqVDrEBHpiYjPbXdtr0eg4pP1rBoZ3C2r5YAcikUgwsZfxa/JHSutabS+X1cIgAB4KGUJ8lPYMr1U83GS4fkAkAGDdEcdvtf3xeA4MAjAsLhCxTSwQcVfI8OQ1vSx/T4iwzeZlIiIiIlvYbdpi26+LPwK83ESOhojshck8Jxfo5Wb5hf18PqvzyLkIgoDV+zIAAHeMjIVMap+ZHhKJxG73bW/mBOeO1AIIQsttqubN1rFBng4zI8U8B3Hz6VxUqLQiR9M8QRDqWmxNiy+aMmtgFMb1DEFCuA/6Rvl1VHhERERELdp1ni22RK6AybxOgEswyFntv1SM1PxKeLrJcLONF190FmN6BEMhkyCzuAaXiqpbPD7LtPwiVuRNtvX17+KHnmHeUOsM+OXkFbHDadaJ7DJcKqyGu0KKGX0jmj1OJpVg9aKh2PLYOLgrZB0YIREREVHziqrU2HPBOC5kfC8m84g6MybzOoFeYVyCQc7p870ZAIwz7fw8FOIG46C8lXIMN21U3dGKVtu6yjzxl1+YSSQS3DzYmKz98dhlkaNpnrkqb3qfCPi4X/316ChVj0RERESAcVbevI/3o1KlQ5cAD/Tv4i92SERkR0zmdQLmmU0peRUiR0LUetklNdiWnA8AWDgqVuRoHJu51bY1c/McsTIPAG4YEAmJBDiaWYqc0hqb379Ob2jX7VVavaVqcM6gxosviIiIiBxVZnE15v5nPy4VViPSzx1f3DXMaUfMEFHrMJnXCdRvs23NTC0iR/DlgUwYBGBsj2DEh3Ij6NVMMiXzDmeUoLKFmXOWyrxAx6nMA4AwX3cM7xoIAPjlZK5N73v1vgz0emELNpywvupv46lcVKh0iPRzx8juzrUohYiIiFzX+fxK3PzRflwuq0VckCfWPTAK3UK8xQ6LiOyMybxOID7UG1IJUFqjRWGlWuxwiFpUo9Hh20NZAIybZunqugZ7oWuwF7R6AXvTipo9Tm8QkF1SC8DxKvMA4Ib+xqUSP9twbt6pnDL8c+M56A0CPt51yar7UGn1eOf38wCA20bYbxELERERkS2dyinDvI/3o6BSjYRwH3z395GI8vcQOywi6gBM5nUC7goZ4oKNVTicm0fOYP3xy6hQ6RAb5IkJPUPFDscpTDANMb5aq21ehQoavQEKmQSRDngiN71POORSCZJzK5BW0P6fVdVqHR5Zcxw6g7Ei+VxuBc5dafu4gVV7M3C5rBYRfu64a3TXdsdFREREZG+H0kuw4L8HUVajRf9of3x73wiE+riLHRYRdRAm8zoJc6vt+Xwm88ixCYKA1fsyAAB3jIyDlFVQrWJutd2RWgiDoel2+kxTi210gKdDVpcFeLlhXE9jUvLnE+2vzlv281lkFNcgws8dY3sEAwB+OJbTpvsorlLjwx1pAICnpvWChxu30xJ1pMrKSjz99NO45pprEBISAolEgmXLljV57LFjxzBlyhR4e3vD398fN910Ey5daroi9/3330dCQgKUSiW6du2K5cuXQ6u9+pgCIiJnsTO1AHd8dhBVah1GdAvE1/cMh7+nm9hhEVEHYjKvk+gZxmQeOYd1R3JwPr8Knm4y3DyEiwZaa1jXQHi6yVBYqcbZZqrPMk3LL2IcsMXW7Ib+kQCMrbbtmfH5y8krWHc0BxIJ8O78AbhzVBwA4Kfjl6FtwzKMFdsvoFKtQ58oX8waEGV1PERkneLiYnzyySdQq9WYNWtWs8elpKRgwoQJ0Gg0+O677/DZZ5/h/PnzGDt2LAoLCxsc+69//QuPPvoobrrpJmzduhUPPvggXnnlFSxevNjOz4aIyP5+PZ2Le784ApXWgIm9QvD5omHwVsrFDouIOhjf9Z1EL1MyLzW/SuRIiJqXnFuBFzacAQAsnhgPX3eFyBE5D6VchjHxwfjtXD7+SClA3y5+jY4xJ/Pighxr+UV9U5PC4K6QIqO4Bqcvl6NfF/8230dOaQ3+sf40AOChifEY3i0IWr0Bwd5uKKrSYFdqIaYkhbV4P2kFVfj6oHF24z9mJLJKlEgEsbGxKC0thUQiQVFRET799NMmj1u6dCmUSiU2btwIX19fAMDgwYPRo0cPvPXWW3j99dcBGJODL7/8Mu6991688sorAIAJEyZAq9Xi+eefx2OPPYakpKSOeXJERDb2w9EcPPX9SRgEYGbfCLwzfwDc5KzPIXJFfOd3Ej1MybwL+ZXNtuARialKrcPir49BrTNgfM8QPDC+u9ghOR1zq+0fqU3PzTO32cYEOm5lnpdSjsmJxkSbNa22Or0Bj317ApUqHQbG+OORyT0AAAqZ1FJZ9/3R1rXavvZrCvQGAVMSQzGqe3CbYyGi9pNIJJBIrp5I1+l02LhxI+bMmWNJ5AHGRODEiROxfv16y2VbtmyBSqXCokWLGtzHokWLIAgCfvrpJ5vGT0TUUb7cn4En1xkTefOGdMF7tw5kIo/IhfHd30nEBXnCTSZFjUaPy2W1YodD1IAgCFjy42lcKqpGhJ873pk/gFVQVphoSuadyilDUVXjzdWWyrxgx03mAXWtthtP5bb5w4cPdqThSGYpvJVyrJg/EApZ3T9jcwYb27a3p+SjtFpz1fvZf7EY25LzIZNK8Oz0xDY+AyLqSBcvXkRtbS369evX6Lp+/fohLS0NKpUKAHDmjLH6u2/fvg2Oi4iIQHBwsOV6IiJn8uHONLyw4SwA4M5RcXjtpn4OOR+ZiDoOk3mdhFwmRfdQbwBAKjfakh2dz69EtVrXptt8dTALv5y8ArlUgg8WDESgFwf0WiPM1x29I30hCMCu1IYzogRBqFeZ57httoBxM6+Puxx5FSocyihp9e3OXinHe9svAABentWn0WzAxAhfJEX4QqsX8Mup5qv+DAYB/9p8DgCwYFgM4k0/O4nIMRUXFwMAAgMDG10XGBgIQRBQWlpqOVapVMLLq/HPwcDAQMt9NUWtVqOioqLBHyIiMQmCgDe2pOCNLakAgIcnxePF65P4oTgRMZnXmfQKMyXzuASD7OS3s3m45p3dmPjWTmw5k9uqBQZnLpfjn78YEyfPTk/A4NjGv4xR65lbbdcfv4z8CpXl8uJqDao1ekgkQHSgh1jhtYpSLsO1vcMBGBdhtNZbW1NhEIAZfcMxa2DTyyrmmqrzrtZqu+HkZZy5XAFvpRyPTunRhsiJSExXa8etf11rj/urV199FX5+fpY/0dHR1gVKRGQDBoOAZT+fxYc7LwIwnkc/eU2vFkcTEJFrYDKvE+kZzo22ZF/mBElBpRp//+oY7vvyKHLLm2/rLq/V4sGvj0GjN2BqUhjuHtO1o0LttMzz5v5MK8LwV7bj+vf/xDu/n8dvZ/MBAJF+HlDKZWKG2Co3DDC22v56OrdV22cPZ5RgR2ohZFIJnp6W0OxxNw6IhFwqwamc8iZ/FuaW11o+3X5wYncEeyutfAZE1FGCgoIAoMmqupKSEkgkEvj7+1uOValUqKmpafLYpqr7zJYsWYLy8nLLn+zsbNs8ASKiNtLpDXjq+1NYvT8TEomxI+HvnDdNRPUwmdeJWDbass2W7KBWo8fuC8bWzrmDu0AuleD3c/mY+u/dWL0vA3qDAEEQkF1Sg02ncvHqr8mY//F+ZJXUoEuAB96a25+fJNpA/y5+WH5DbwyI9odEApy+XI4V2y9YtrvGBjn2vDyzkd2CEOzthtIaLf68UHTVYwVBwJumBNy8IdGIC26+jTjIW2mZLfjDX6rz0goqMefDfcgtVyE60AN3jWZymcgZdO/eHR4eHjh9+nSj606fPo34+Hi4u7sDqJuV99dj8/LyUFRUhD59+jT7OEqlEr6+vg3+EBF1NLVOj4fXHMcPx3Igk0rw73n98bcRsWKHRUQOhsm8TqSnKZl3qbC6VZUuRG2x63whVFoDovw98Obcftj0yFgMivFHlVqHF38+i8lv78TAf/6OsW/swOJvjuHjXZeQklcJpVyKD28bBD9PhdhPoVOQSCRYOCoOPy0ejUP/mII35vbDtN5h8HQzVuMNjXOONma5TIqZfSMAtNxqu/tCEQ5llMBNLsUjk+NbvO85g4yttuuPX4bO9LPwaGYp5n60H1fKVegW4oU1946Au8LxKxiJCJDL5bj++uvx448/orKy7gPLrKws7NixAzfddJPlsmuvvRbu7u74/PPPG9zH559/DolEglmzZnVQ1EREbWMwCDhwqRiLVh3Gr2fy4CYznkPPHthF7NCIyAHJxQ6AbCfK3wNebjJUa/TILK5GfKiP2CFRJ/LbuTwAwLTe4ZBIJOgV7oPv/z4KXx/Kwuu/piDDtElVIZMgMcIXfaP80K+LH8b0CEGUv2PPcHNWIT5KzBsSjXlDoqHW6ZFZXIOuV6laczQ3DIjE6v2Z+O1sHmo1eni4NU6uCYKAN7emAADuGBGLCL+WX0uTEkIR4KlAQaUae9KKIAgCHvz6GFRaAwZE++OzO4dyCQuRA/n1119RXV1tSdSdO3cO33//PQBgxowZ8PT0xPLlyzF06FBcd911ePbZZ6FSqbB06VIEBwfjySeftNxXYGAgnn/+ebzwwgsIDAzENddcg8OHD2PZsmW45557kJSUJMpzJCJqTlpBJX48dhkbTlzB5TLj+BoPhQyf3DEYY3uEiBwdETkqJvM6EalUgh5hPjiRXYbUvCom88hmtHoDticXAACm9Q6zXC6VSnD7iFhMSwrDwfQSxAV5oWe4t1PMbOtslHKZpTrXWQyKCUCUvwcul9Xi9S0pWHpd4+1sW87k4czlCni5yfDAhNbNinGTS3HjgCh8vi8DL288h4ziGugNAib0CsGHtw2Cpxv/6SNyJA888AAyMzMtf1+3bh3WrVsHAEhPT0dcXBwSEhKwc+dOPPPMM5g7dy7kcjkmTZqEt956CyEhDX/Zfe655+Dj44OVK1firbfeQnh4OJ599lk899xzHfq8iIiaU1ipxs8nr2D98RycuVy3OdtHKceMvhG4c3QcEiPY6k9EzeNvNJ1ML3MyL78SMxEhdjjUSRxKL0F5rRZBXm4Y0kQbZ6ivO67vHylCZOTMJBIJHp3cA0//cAqf78tAYaUab8/rb2l/1RsEvPWbcVbe3WO7IagNyyrmDOqCz/dl4GJhNQDgpkFReH1OPyhknC5B5GgyMjJaddzgwYOxbdu2Vh37yCOP4JFHHmlHVEREtlWr0eO3c3n48dhl/JlWBL1BAADIpRJM6BWC2QO7YHJiKMeAEFGrMJnXyVg22nIJBtnQ1rPGFtspiWGQSbnEgmxn3tBouMmleOr7k9h0Ohf5FSr8944hCPByw/rjl3GxsBr+ngrcM7Ztyyr6RPmiT5QvzlyuwP3ju+HZaxO4gIWIiIg6lN4gYP/FYvx4PAdbz+ShWqO3XDcg2h83DYrCzL4RbfrAkogIaGUy77333sMtt9yC0NBQe8dD7WTeaHs+n8k8sg2DQcBvZ/MBANP6hLVwNFHbzRoYhVBfJe7/8iiOZJZizkf78MntQ/DO7+cBAA+M7w5f97YtUJFIJFh15zBkl9ZgUEyAPcImIiIialJybgV+On4ZP524jPwKteXy6EAPzB7YBbMGRKJbiLeIERKRs2tVMu/xxx/HiBEjEBoaCplMhv3792PYsGH2jo2s0DPc+I9CRnE1VFo9y7Sp3U5dLkdehQpebjKM6h4sdjjUSY3qHozv/z4Ki1YdwqXCakxfsRtavYAwXyUWjoqz6j5DfJQI8eEn3URERGR/eeUq/HzyMn48dhkp9bqk/DwUuK5fBGYPjMLg2AB2ChCRTbQqmefj44OysjIAxs2C5LhCvJUI8FSgtEaLtIIq9InyEzskcnLmFtsJCZzhQfbVK9wH6xePxqJVh3Eu1zgM+uFJPfi6IyIiIockAPjhaA7WH7+MvReLYP5V2U0mxaSEUMwaGIWJCSFcDkdENteqZN6AAQPwyCOPYMqUKQCAt99+G2FhTbfbSSQSrFixwnYRUptIJBL0DPPBwfQSnM+vbDKZV6PRYcW2C7i2TzgGsv2MWmBO5k3rHS5yJOQKwnzd8d3fR2L5z2eh0Rswb0i02CERERERNaDVG7N2BRUqPLnupOXyoXEBmDXQOAfP39NNrPCIyAW0Kpn37rvvYtGiRfjkk08gkUiwbt26Zo9lMk98vcLNybyqJq//3550fLz7EnadL8SWx8Z1cHTkTNIKKnGpsBpuMikm9goROxxyEd5KOd68ub/YYRARERE1YDAI+Gj3RRRXG+fgCQLQNdgLswdGYdaAKMQEeYocIRG5ilZX5h0/fhwAIJVKceDAAc7Mc2A9r7IEw2AQsPZINgAgJa8SmcXViA3y6tD4yHlsNS2+GBUfBJ82LiAgIiIiIuosSqs1eOK7E9iRWmjsrwUQ5K3EH0+O5xw8Iupw0tYc9MQTTyA725gAWrVqFbp27WrXoKh9eoUbk3mpeY2TefsuFiOntNbyd3MLJVFT2GJLRERERK7uaGYpZr63BztSC6GUS+HnafyQWyGTMJFHRKJoVTLv3XffRW5uLgDgrrvuQnp6ul2DovbpGWpM5l0uq0WlStvgujWHswAAQV7GGQ7myiuiv7pSVotTOeWQSIApiU3PyCQiIiIi6qwEQcCney5h/sf7caVcha7BXvhp8Wh4cDkXEYmsVcm8gIAA5Ocbkz6CIPDTBwfn56lAuK87AOBCQd3cvJJqDX4zVVq9MbcfAOBYVikKKlUdHyQ5PPNrZUhsAEJ8lCJHQ0RERETUccprtbj/y6N4eVMydAYB1/WLwM8PjUZihK/YoRERtW5m3ogRI3D33Xdb5uQ9+eST8Pf3b/JYiUSCDRs22CxAsk7PcB/kVahwPq8Sg0wba388lgOtXkDfKD9MTgxD/2h/nMwuw+/n8nHb8FiRIyZHY67avCaJLbZERERE5DpO55TjwW+OIrukFm4yKV64LhF/GxHLohYichitSuZ9+OGHeOyxx3D27FlIJBKkpaVBqWy6Uoc/4BxDz1Bv7D5fiFTTEgxBELD2sHHu4fyh0QCAab3DcDK7DFvPMplHDZXXaHEoowQA5+URERERkWsQBAFfHcjEPzcmQ6M3IDrQAx8uGIy+XfzEDo2IqIFWJfNiY2Oxfv16AMZttj/99BO32Tq4nuENN9oeyyrDhYIquCukuGFAJABjkuaNLanYl1aE8lot/Dy4rZSMDqQXQ28Q0D3ECzFBnmKHQ0RERERkVyqtHv+37iQ2njLOir8mKQxv3tyfvyMRkUNq1cy8+nbs2IGkpCR7xEI21CvMvNHWODPv20PGxRcz+0bC1934D1L3EG/Eh3pDZxCwI6VAnEDJIe2/WAwAGNk9SORIiIiIiIjsb82hLGw8lQu5VILnZybi49sHM5FHRA6rzcm88ePHw9vb2x6xkA31CDN+j4qq1MgqrrF8wnTrsOgGx11raqHcalp2QAQABy6ZknndgkWOhIiIiIjI/o5llQEAHpncA/eM7cbxUUTk0FrVZjtp0iR8+OGHSEhIwKRJk656rEQiwfbt220SHFnP002OmEBPZJXU4K3fUlGr1aN7iBcGxwY0OG5a73B8sCMNO1MLodLq4c416y6vuEqNlDxje/aIboEiR0NEREREZH/nrpQDAPpxPh4ROYFWVeYJgmD5f4PBAEEQmv1jMBjsFiy1TU9Tq+3PJ68AAG4ZGtPoE6Y+Ub6I8vdArVaPPReKOjxGcjwH042LL3qF+SDIu+lFN0REREREnUWNRodLRdUAgKRIX5GjISJqWasq83bs2GH5/507d9orFrKxXuHe2JacDwBQyCSYPSiq0TESiQRTk8Lw+b4MbDmTh6lJYR0dJjkYzssjIiIiIleSklcJQQBCfJQI9XEXOxwioha1eWYeOQ9zZR4ATE0KQ3AzVVbTTHPztqfkQ6dnZaWr22+alzeiG5N5RERERNT5nbtSAQBIimBVHhE5h1ZV5mVlZbXpTmNiYqwKhmyrV3hdMm/+0Oa/J0PjAhDo5YaSag0OpZdgVDyXHriqgkoV0gqqIJFwXh4RERERuYZzuaZkHltsichJtCqZFxcX16ZtPnq93uqAyHbiQ7wxJDYAbnIpxlwlQSeXSTElMRTfHcnB1rN5TOa5sAOXjPPyEsJ94e/pJnI0RERERET2Z67M681kHhE5iVYl8z777DNLMk+r1eLll1+Gp6cn5s+fj/DwcOTm5mLt2rWoqanB0qVL7RowtZ5cJsX3D4xq1bHTeofjuyM5+O1cPpbd0Jur2F2UZV4eW2yJiIiIyEWkm5ZfhPlyXh4ROYdWJfPuvPNOy///4x//QFJSEjZu3AiptG7k3tKlSzFz5kxcuHDB5kGS/Y2OD4aXmwy55SoczSzFkDi2WLqig5e4/IKIiIiIXMvQuEBsS87Hx7suYih/DyIiJ9DmBRhffPEFHnzwwQaJPACQSqV48MEH8eWXX9osOOo47goZJicaN9ne88UR7E0rEjki6mj5FSpcKqqGVAIM68qTGCKizqykpMSqPwYDF2URUeezZEYC5FIJtiUXYM+FQrHDISJqUZuTecXFxaitrW3yutraWpSWlrY7KBLH8zMT0b+LH8pqtLj9fwfx6Z5LEARB7LCog5hbbHtH+sHPQyFyNEREZE/BwcEICQlp05/Q0FCcOHFC7NCJiGyue4g37hgZBwD458Zz0On5wQURObZWtdnWN2jQILz00kuYOHEigoPrFiUUFhbipZdewsCBA20aIHWcUF93rL1/JJ5bfwY/HMvBy5uScfZKBV69qS/cFTKxwyM7s8zLY4stEZFLuOuuu9ClS5dWHavX6/Gvf/3LzhEREYnn0ck9sP54Ds7nV2HN4WzcPiJW7JCIiJrV5mTe22+/jSlTpiAuLg6TJ09GeHg48vLysH37dgDAtm3bbB4kdRx3hQxv3dwPfaJ88fKmZKw/fhkXCirx8e1DEOXvIXZ4ZEf7L3H5BRGRK7n33nsxbNiwVh2r1+vx8ssv2zkiIiLx+Hkq8MTUnnhhw1n8+7dU3NAvEn6e7FYhIsfU5mTeiBEjcPjwYSxfvhw7d+5EcXExgoKCcP311+P5559H79697REndSCJRIJFo7uiV7gPFn99DGcuV2Ds638gzNcdkf4eiPAz/jfSzx2TE8MQHegpdsjUTpfLapFVUgOZVIKhnJdHRNTpvfPOO4iLi2v18TKZDO+88w5iYmLsFxQRkchuHRaDL/Zn4kJBFd774wJeuC5J7JCIiJrU5mQeACQmJuLbb7+1dSzkYEZ1D8bPD43BQ98cw8mccuSWq5BbrmpwzJpD2dj6+DiRIiRbMbfY9o3yg7fSqh8LRETkRB599NEOuQ0RkTORy6R44bok3PHZIazel4HbhsegW4i32GERETXC39rpqqIDPfHT4tEoqFTjSlktcstVuGKq4jJ+alUJjc4AN3mbd6lQKxkMAg6mlyCjuBpzB3eBQmb7rzXn5RERERERAeN6hmBSQij+SCnAK5uT8enCoWKHRETUCJN51CKJRIIwX3eE+brDvN5EEASsO5KDWq0eOaU1/MTKDnLLa/H9kRysO5qDrJIa02UqPDG1p00fRxAEHOC8PCIil6ZWq7Ft2zZkZmZCpWpYhS+RSPD444+LFBkRUcfrE+WHP1IKcCi9BIIgQCKRiB0SEVEDTOaRVSQSCWKDPJGSV4nMEibzbEUQBGw9m481h7Kw+0IhBMF4uVIuhVpnwKd7LuFvw2MQ6utus8fMLqnF5bJayKUSDI4NsNn9EhGRczh69Ciuv/565OfnQzD/w1MPk3lE5EoOXCrGyh1pAIBnpycykUdEDonJPLJaTKAxmZdVXCN2KJ3GlwcysXTDWcvfh3cNxLwh0bi2Tzhu+/QgTmSX4Z1tF/DqTX1t9pjmqrz+0f7w4rw8IiKX8+CDD8LX1xcfffQREhMT4ebmJnZIRESiKKhU4eE1x6E3CJg9MAq3DosWOyQioibxN3eyWmyQcYttJpN5NlFeq8W/fz8PAFgwPAb3je2GuGAvy/X/mJGIeR/vx9rDWbh7TBziQ31s8rj72WJLROTSzp49i2+++QY33HCD2KEQEYlGpzfg0TUnUFipRs8wb/xrdh9W5RGRw+LWArJabJAx0ZRZXC1yJJ3DhzvSUFajRY9Qb7x0Q+8GiTwAGNY1EFOTwmAQgNd+TbXJYwqCgD/TigBw+QURkauKiYkROwQiItG9s+089l8qhqebDB/eNhiebqx7ISLH1aqfUF988UWb7vSOO+6wKhhyLpbKvBJW5rVXTmkNVu3LAAAsmZEAeTMba5+5NgF/pBRgW3I+Dl4qxvB2VtOdvVKBwko1PN1kGBLHeXlERK7o6aefxltvvYVp06ZBqVSKHQ4RUYf7IyUfK3dcBAC8Nqcf4kM5D5yIHFurknl33nlng7+by43rD0muX4LMZJ5riA00Vo5lldTAYBAglYpThq7W6VGt1iPQy3ln/Lz923lodAaM7BaEib1Cmz0uPtQbtwyNxtcHs/DKryn46cFR7Sr/35laAAAYHR8MpVxm9f0QEZHzuvPOO5GRkYHu3btjwoQJCAwMbHC9RCLBihUrRIqOiMi+sktq8PjakwCAhSNjcUP/SJEjIiJqWauSeenp6Zb/z8vLw/z58zFt2jQsWLAA4eHhyMvLw9dff43ffvsNa9eutVuw5Fgi/d0hl0qg0RmQV6FCpL9Hh8dwOqccf//qKIqr1fjjyQlWx7DhxGX8cvIKlsxIRPcO3sx75nI51h+/DMA4F6+l5NyjU3pg/fHLOJldhk2nc3FdP+tPOHakFgIAJvQKsfo+iIjIuW3atAmvvvoqtFotvvnmm0bXM5lHRJ2VWqfH4m+OobxWi/7R/vjHzESxQyIiapVWzcyLjY21/Hn33Xcxe/ZsfPzxxxg/fjx69eqF8ePH45NPPsHs2bPx73//294xk4OQy6ToEmBMnomxBGPdkWzM+WgfLpfVQqU1YHtyvlX3czSzBE98dxLbkguw4L8HOnQGoCAIeGVzMgBg1oBI9O3i1+JtQn3ccd+4bgCAN7akQqMzWPXYZTUaHM8qBQBMuEo1IBERdW5PPfUUBg0ahBMnTkCtVsNgMDT4o9frxQ6RiMguXt6YjFM55fD3VGDlgoHsVCEip9HmBRi//vorZs6c2eR1M2bMwNatW9sdFDmP/2/vvuOqrPs/jr8PW0BAhgoquEVSnKRWDtyampq7MvVue5dNtRxpOarb+mVlwzK1aVlqZanlyjRT1HLkzL0HIHtzfn8gpwhQxoGLA6/n43EexXV9z3U+5+rqOoc33xHokz3UtvQCsNT0TE1ZsU/PfLVHqemZ8nXPmt/n58NXCn2s6IRUPfpZ1vLzjvYmXYxN0Yj3t+nc1SRrl52njYcu69ejkXKyt9PTPRoV+Hn3t68rv8rOOhWVqE+3nSzSa286ckWZZqlRtcqqYUCvSgBA2XDixAlNmzZNoaGhcnR0NLocACgVO09G6ePfsr5H/9/Q5qpZxdXgigCg4Aod5mVmZurIkSN57jty5EiOefRQ/gV5X1sEo5R65l2KTdaI93+zfPA+0bWhFo4KkyRtPXqlUL3UMjPNemrpbp2LSVYdXzetebyD6vi66ezVJI14/zddik0ukfeQLT0jU7NXZfXKG3Vr7UJ9gXBzdtATXRtKkt5Yd0QJKemFfv2NB7Pmy2OILQBUbMHBwYqNjTW6DAAoVT/uzxrV069ZwHXnrAaAsqjQYV7Pnj01adIkff/99zm2r1y5UpMnT1aPHj2sVhzKPsuKtqUQ5u07G6M+b27WjpPRquzioAX3tta4rg10U4CHfNyclJCaoV3Xho0WxPu/HNP6g5fk5GCnt0a0UF0/d316XxvV8KqkE5GJuuuDbYqMTymx9/PVzjM6fDFenpUcNbZT/UI/f0jrmgr0dlV0Ypq+33u+UM/NzDTr58PZ8+Xx5QUAKrIXX3xRM2bM0IULF4wuBQBKzaZro3q6NOa7MADbU+gwb+7cuapevbr69esnLy8vNWrUSF5eXrrjjjtUtWpVJkiuYIKuDbM9WcLDbK8mpuqBj3boUlyKGlR117f/vU1dGleTJNnZmdS+ga8kWQKqG9l5MkqvrDkkSXq+b4huCsiaqy7Aq5I+v7+tqnu46MileN2zYLtiEtOs/n6iE1L12k+HJUmPdq4vT9fCD2tysLfT0LBakqQvIk4X6rl7z8YoMiFV7s4Oal27SqFfGwBQfrz33nuKjo5W/fr1ddttt6lfv345HnfccYfRJQKAVV2KTdaB87EymaTb6vsaXQ4AFFqBVrP9J39/f+3atUuLFi3Sxo0bFRkZqRYtWig8PFwjR45UpUrMvVWR/LNnntlsvuFKrEVhNpv11JdZw2Fr+7jq60dukYdLzvCrQ0M/rfjjnDYdvqwJPYOve7zohFT999o8ef2aBWjEzYE59gf6uOrT+9to6Htbtf98rPq+tVmdg6uqde0qah3kreqeLpa2GZlmHb4Ypz9OX9Xvp6J1OipJT3ZvqLDa3vm+fmp6ph76ZKcuxaWoto+r7mkXVISzkmVQq5p67afD2nkyWkcuxqlBtcoFet6GQ1lDbNs38JWjfaEzfQBAObJnzx7Z29vLz89PZ8+e1dmzZ3PsL4nPdgAw0qYjWb3ymtbwlM+1+bcBwJYUOsyTJBcXFz300EN66KGHrF0PbEzgtTnz4pLTdTUxTVXcnKz+Gh/8clzrLMNhW+YK8iSpfYOsed/+PBery3Ep8quc94dy9jx552OSVdfXTbMGNs3zl5R6fu765L42GvH+Np2KStSiX09o0a8nJEk1q1RSi8AquhKXoj1nriohNecqfyMXbNfC0WFqW9cn13HNZrMmr9irbcej5O7soPkjWxdr1axqHi4Kb1RVaw9c1BcRpzW5T0iBnrfhUPYQW+bLA4CK7sSJE0aXAAClatO10TwdGvBdGIBtKnKXnIMHD+q9997TzJkzLXOsnDt3TklJpbMKKMoGF0d7VffI6ql2ItL6Q213nozWy6sPSpKm9glRkxqeebbzq+ysmwI8JEmb/8p/qO3Snae1/uAlOTvYad5dLeXunH+eHVzdQ+ue7Kg3hrfQve2CdFOAh+xM0pnoJH23+5y2HotUQmqG3J0ddEs9H40Nr6fb6vsqKS1DoxdG6NejuVfXff+XY/pyxxnZmaQ3R7RQwwL2pLue4TdnDbVd9vtZpaRn3KC1FBmfFUJKzJcHAACAiiUj06xfjlwL8xoS5gGwTYXumZeRkaEHHnhAixYtsgyr7NWrl6pXr64HH3xQLVq00AsvvFAStaKMCvRx1YXYZJ2KSlSLQOvNv3Y1MVWPff670jPN6hPqr7vaBF63fYeGfvrzXKw2Hb6iAS1q5tqfmWnWuz8fkyQ91b2hGvt73LCGKm5O6tcsQP2aBUiS4pLT9Mfpq9pzJkY+bk5qEVhF9au6y94uq3dfclqGHvx4p34+fFljFkXow3vDdMu1eTh+2n9Rs1dlBZNT+oRYbdWsjg39VM3DWRdjU7R2/yXdHup/3fabjlyW2SyF+HuomofLddsCAAAA5cm+szGKTkxTZWcHtQj0MrocACiSQvfMmzlzpj777DP973//0759+2Q2my37evXqpdWrV1u1QJR9Qd7WX9HWbDbr6aW7dfZqkmr7uGp2PsNh/ym7m/ymw5eVmWnOtX/tgYs6fiVBHi4OuqtN0eapq+ziqPYN/DQ2vL6G3RyoRtUrW4I8Kaun4nv3tFKnRn5KTsvUmMUR2vLXFf15Lkbjlvwus1m6u22gRt1Su0ivnxcHezsNbpXVO29JxKkbtt9wkCG2AFDReXh4aOfOnQVun5mZKQ8PD+3evbsEqwKAkpc9xPaW+j7MHQ3AZhX67rVo0SJNmTJFTz75pBo1apRjX506dXT8+HGrFQfb8M9FMKxlwebjWnvgkpzss+bJq5zHPHn/1iqoityc7BWZkKr952Nz7Z+/KatX3t1tg+R2neG1xZUd6HUOrpoV6C2K0OiFEUpMzdBt9X31fN+brD6Z+JDWWWHe5r+u6HRU/v8dMjLN2nRtWEF4MENsAaCiio+PV2ZmZoHbm81mxcfHKyPjxtM5AEBZ9vNhhtgCsH2FDvPOnj2rdu3a5bnPxcVFcXFxxS4KtiXIx02SdNIKc+Ylp2XozXVH9FL2cNS++c+T929ODnZqVy9r0YnsD+lsO09Ga8fJaDnZ21m1V1x+nB3s9c7dLdUluKpS0jN1KS5F9fzcNO+uliXyF8BAH1fdWt9HZrO0dOeZfNv9cfqqriamycPFQS1qeVm9DgCA7Wjbtq3s7e0L9HBycmJVWwA2LzY5Tb+fviqJxS8A2LZCd0+qWrWqjh07pvDw8Fz7Dh06pJo1c89VhvLN0jPvOj3CbsRsNmvlnvN6adVBnb2atYjKgBY1dPcN5sn7tw4N/bT2wCVtOnxZY8PrW7Z/8EtWr7z+LQJUtZTmiXN2sNfbd7fUc8v2af/5WL1zV0t5VrpxD8OiGhoWqC1/RWrpjtMa16VBjuG/2TYeuiRJat/QTw4MKwCACuv5558v0vMCAgKsXAkAlJ7tx6KUkWlWXV831bo2VRAA2KJCh3m9e/fWzJkz1bNnT1WvXl2SZDKZFBMTozfeeEN9+/a1epEo24K8s3rmXY5LUWJqulydcl9We8/EaOWecwrwqqTavm6q6+umAK9Ksrczac+Zq3rhu/3acTJakhTg6aKJvRurb6h/oXsBdLzWXX7nyWjFp6TL3dlBJ64kaPWfWSsu39e+bnHeaqE5O9jr1SHNSuW1uodUk5ero87HJGvTkct5LrCx4VqYZ63FNwAAtqmoYR4A2LKIE1GSpDZ1vQ2uBACKp9Bh3gsvvKBVq1YpJCRE4eHhMplMeu6557Rv3z45OjpqypQpJVEnyjBPV0d5VnJUTFKaTkUlKrh67lVin1r6hw5fjM+xzcneTjWqVNLxK1nDcys52uvhTvV0f/u6quRkX6RagnzcFOTjqpORifr1ryvqflN1Ldh8XGazFN7ITw2rVS7ScW2Bi6O9BrSooYVbTuiL7adzBXaX4pK172zWXIIdmSMEAAAAFcy241lhXlhtwjwAtq3Q4+yqVaumiIgIDR8+XDt37pS9vb12796tXr166ddff5W3NzfGiqj2taG2J67kHmr716V4Hb4YL0d7k7qHVFPDau5ycrBTakamJcgb2KKG1j/dUY91aVDkIC+bZVXbI5cVlZCqpTtPS5Ie6FCvWMe1BcPCsoYlrz1wUZfjUiRJ564macXvZzVp+T5JUmhNT/lVdjasRgAAAKC0Jaama9/ZGEmEeQBsX5GW9KxWrZreffdda9cCGxbo46bdZ2J0Kir3Ihir952XJN1a31fzR7aWlLWq6rmrSToRmaDqHi5qYMUecx0a+unj305q0+Er8nN3UXJapprW8FTbCtCdvlH1ymoR6KXfT13VfR/tUGR8is5EJ+Vo0+Om6gZVBwAAABjjj1NXlZ5plr+ni2pWqWR0OQBQLIUO8z766CM1atRIbdq0ybXvypUr+uGHHzRy5EirFAfbEXRtAtmTkbl75mXPV9eryd8hkr2dSbW8XUtk4tl29XzkYGfSqahEzd90VJJ0f4e6FWYVvmFhtfT7qavafW2lLns7k5oEeOjmOt5qU8dH4cHMlwcAAICKZfuJv4fYVpTfCwCUX4UO80aNGiUHBwe9++67GjNmTI59R48e1ejRownzKqBAn7zDvNNRidp3NlZ2Jqlr42qlUou7s4NaBVXRtuNRSkjNUA2vSurdpOL0RhvQoqZORibKzmRSm7reahlYRW7OReqECwAAAJQL27Pny6tT/kfrACj/Cj1nniR16tRJ999/v6ZNm2blckrfO++8o5YtW8rR0bFcvB+j1PbJWtH25L+G2a7el9Urr00dH/m4l948bR3+scDDf26rIwf7Il3qNsnJwU7jewbr6R6N1L6BH0EeAAAAKrS0jEz9fuqqJKkNYR6AcqBIv+XPmDFD3bt314QJE3T69GnNnz9f9vbFW7TAKP7+/po+fbo++ugjo0uxaUHXeuadu5qstIxMOV4Lz1Zdmy+vV9PS7RnXpXFV/W/NIXm5OmpIWK1SfW0AAGxNXFycVq1apZMnTyopKedcqyaTSVOmTDGoMgAovn1nY5SUliEvV0fV93M3uhwAKLYid9l5+umnVaNGDY0ePVrnzp3TV199Zc26Sk3//v0lSd98842xhdi4qpWd5eJop+S0TJ2NTlJtXzddjE3Wrmt/AeseUrphXnB1D3005mZV9XCWOz3TAADI17Zt23T77bcrKioqz/2EeQBsXcS1+fJaB3nLzo758gDYvmKNPRw+fLhWrVqlrVu3qmPHjrpw4UKRjhMXF6fx48ere/fu8vPzk8lkynfIa3x8vB5//HEFBATIxcVFzZs315IlS4rxLmANJpNJQd5ZQ21PRGYNtV1zbeGLloFequ7pUuo1dWjop+DqHqX+ugAA2JInnnhCNWrU0Pbt25WcnKzMzMwcj4yMDKNLBIBi2X48WpJ0c50qBlcCANZR7InEwsPDtWnTJl24cEF33313kY4RGRmp+fPnKyUlxdJTLj8DBw7U4sWL9fzzz2vVqlUKCwvT8OHD9dlnnxXptWE92YtgnIrKWgRj1d7sVWz9DasJAABc3969ezVjxgy1bt1aTk5ORpcDAFb3+6msMK91bebLA1A+FDrM69ixozw8cvZ2Cg0N1a+//qrAwMAiFREUFKTo6Gj9/PPPmj17dr7tfvjhB/300096++239eCDDyo8PFzvv/++unXrpmeeeSbHX467dOkiFxeXPB8MFSkZQd5/r2gbGZ+ibccjJUk9K9BKsgAA2Bo/P78bNwIAG2Z/bWitA0NsAZQThZ5MbMOGDXluDwwM1J9//lmkIkymgt1Uly9fLnd3dw0ePDjH9tGjR2vEiBHatm2bbrnlFknSunXrilTL9aSkpCglJcXyc2xsrCQpODhYdnYVZ7XU/CSmZig2KU0vzbfX6452iklMk6O9ndp9wl/5YbtSU1MlST179qTHCgCryMzMNLqEHB599FG9++676tOnT4G/kwGALanl7apLcSk6HZWk0JpeRpcDAMVmUysD7Nu3T40bN5aDQ86yQ0NDLfuzw7yCSk9PV3p6ujIyMpSenq7k5GQ5OjrmuTrv7NmzNX369Fzbz58/X6jXLO8Srz0kKUPS2RgDiwGs5PLly0aXAABW89prr+X4+cCBA2rRooVuv/12+fj45NhnMpn0xBNPlGZ5AGBVNatU0s6T0ToTnXjjxgBgAwoU5o0ZM0ZTpkxRnTp1NGbMmOu2NZlMWrBggVWK+7fIyEjVrVs313Zvb2/L/sKaMWNGjoBu5syZWrhwoUaNGpWr7bPPPqsnn3zS8nNsbKxq1aolf39/euZJysg063JcikwmySxJZsm3sjPd2WHTUlNTdfnyZfn5+dEzD4BVZGZmGv6HwKeffjrP7Xv27Mm1jTAPgK2rVSVrOqDThHkAyokChXkbNmzQuHHjJEnr16+/7hCMkh6eYe3XnjZtWr4r5/6bs7OznJ2dc20/ePBgrnkEK6K0jEwFT1mtjEyzJKl+VXetfbKjwVUBxbNr1y61atVKq1evVsuWLY0uB0A5EBsbK09PT0NrOH78uKGvDwClqWaVSpKkM9FJBlcCANZRoDDvn1/4Tpw4UVK13JCPj0+eve+ioqIk/d1DD8ZwtLdTDa9KltVse7HwBQAAZVJQUJDRJQBAqal5rWceYR6A8sKmxoY2bdpUBw4cUHp6eo7te/fulSQ1adLEiLLwD0E+rpZ/ZxVbAADKPnt7e23fvj3PfTt37sxzHmEAsCW1vLN75iXKbDYbXA0AFJ9NhXkDBgxQfHy8vv766xzbFy9erICAALVp08agypAtO8yr5V1JIf4MPQYAoKy73i+2mZmZrHALwOb5e1aSySQlp2XqSnyq0eUAQLEVaJhtnTp1CvxFzmQy6ejRo4UuZNWqVUpISFBcXJwkaf/+/frqq68kSb1795arq6t69eqlbt266eGHH1ZsbKzq16+vzz//XKtXr9Ynn3zCX47LgNvq++qT307prjZBfPkHAMBG5PeZvXPnTsPn9wOA4nJysJO/h4vOxSTrdHSi/CrnngcdAGxJgcK8jh07lngw8/DDD+vkyZOWn5cuXaqlS5dKypqzr3bt2pKkZcuWadKkSZo6daqioqIUHByszz//XMOGDSvR+lAwPW6qru2TusjPnQ9IAADKqrlz52ru3LmSsoK8/v3751rkKykpSZcuXdKgQYOMKBEArKpmFVedi0nWmegktQysYnQ5AFAsBQrzFi1aVMJlFHxhDXd39xxfQFG2mEwmVa3sYnQZAADgOqpWraqbbrpJUtZ3sLp168rLyytHG2dnZzVt2lTjxo0zoEIAsK6aVSpp+wnp9LXF+gDAlhUozAMAAED5MXz4cA0fPlySFB4ernfeeUfBwcEGVwUAJaemNyvaAig/ihzmxcTE6PDhw0pKyn0z7NChQ7GKAgAAQOnYsGGD0SUAQInz98waPXQ5LtngSgCg+Aod5qWnp+uhhx7SRx99pIyMjDzb5LcdAAAAZU9sbKzmzZunDRs2KDIyUj4+PgoPD9fDDz+ca/gtANiiSo5ZiyUmp2UaXAkAFJ9dYZ/wf//3f/ruu+/04Ycfymw266233tJ7772n1q1bq0GDBlq1alVJ1AkAAIAScPz4cYWGhmrSpEk6cuSInJycdOTIEU2aNEnNmjXTsWPHjC4RAIrNxTHrV9/kNDqeALB9hQ7zPv74Y02aNMkyz0qbNm103333adu2bQoKCmKoBgAAgA0ZN26ckpOTtWXLFh0/flxbt27V8ePHtXnzZqWkpOjxxx83ukQAKDbn7J556YR5AGxfocO8Y8eOqVmzZrKzu/aXjeS/5xx46KGH9Omnn1qvujJq3rx5CgkJUVhYmNGlAAAAFMv69es1c+ZMtWvXLsf2W265RTNmzND69esNqgwArMfFgWG2AMqPQod5bm5uSk1Nlclkkre3t06ePGnZV6lSJUVGRlq1wLJo7Nix2r9/vyIiIowuBQAAoFicnZ1Vq1atPPcFBgbK2dm5lCsCAOtjmC2A8qTQYV5wcLCOHz8uKesvtq+99prOnDmjS5cu6ZVXXlGjRo2sXiQAAABKxh133KGlS5fmuW/p0qXq06dPKVcEANbnwgIYAMqRQq9mO3ToUB0+fFiSNH36dHXo0EFBQUGSJEdHRy1btsy6FQIAAKDEjBgxQv/5z380ePBgjRgxQtWrV9eFCxf06aefaseOHVqwYIF27dplad+yZUsDqwWAonF2yOrHkkLPPADlQKHDvEceecTy7y1atND+/fu1YsUKmUwmdevWjZ55AAAANqR79+6SpNOnT+f4o6zZbM6x32w2y2QyKSODX4QB2B4XFsAAUI4UOsz7t1q1aunRRx+1Ri0AAAAoZQsXLjS6BAAocdlhXlqGWRmZZtnbmQyuCACKrlhhXmJiYo7VbLN5e3sX57AAAAAoJffee6/RJQBAicteAEPKWgTDzbnY/VoAwDCFvoMlJibqueee06effqqoqKg82zD8AgAAwPYcOnRIV65cUfPmzeXm5mZ0OQBgNS4O9pZ/J8wDYOsKfQf773//q48//lh9+/ZV48aN5eTkVBJ1AQAAoJR89NFHeu6553T+/HlJUkREhFq2bKkhQ4aoW7duuv/++w2uEACKx87OJCd7O6VmZCo5nRVtAdi2Qod53333nWbPnq2nn366JOoBAABAKVq6dKlGjRqlPn36qFevXho7dqxlX8uWLfXll18S5gEoFxztTUrNkNII8wDYOLsbN8mtRYsW1q4DAAAABpg9e7ZGjx6tb7/9Vg888ECOfY0bN9b+/fsNqgwArCvlWojn7FikX4MBoMwo9F1s4MCB+vHHH0uiFgAAAJSyAwcOaNiwYXnu8/b2VmRkZClXBADWl56RqfRMs6Sc8+cBgC0q9DDbV199VXfeeaeefPJJ9e7dO8+Va1u2bGmV4gAAAFCyXF1dFRMTk+e+s2fPqkqVKqVcEQBY3z/nyXNxJMwDYNsKHeYlJSUpPT1dr7/+uubOnZtjn9lslslkYjVbAAAAG3Hrrbfqrbfe0p133plr36JFi9SpU6fSLwoArCw57e/fUZ0dGGYLwLYVOsz7z3/+o4iICD3++OOsZgsAAGDjpk6dqttuu00333yzRowYIZPJpGXLlun555/Xpk2btH37dqNLBIBiyw7znBzsZGdnMrgaACieQod5GzZs0GuvvcaqZgAAAOVA69attWrVKj3yyCN66qmnJEmzZs1SgwYN9MMPP6hJkyYGVwgAxZecljXM1oVeeQDKgUKHeZUrV1bt2rVLoBTbMW/ePM2bN4/hxAAAoFwIDw/XgQMHdPToUV28eFG+vr5q2LCh0WUBgNVk98xjvjwA5UGh/ywxcuRILVmypCRqsRljx47V/v37FRERYXQpAAAAVlOvXj3dcsstBHkAyp2UdMI8AOVHoXvmNWvWTJMmTdKAAQN0++2357ma7cCBA61SHAAAAErO5cuX9d5772nTpk06d+6cJCkgIEDh4eF64IEH5OPjY3CFAGAdlmG2jgyzBWD7Ch3m3XXXXZKkEydO6Jtvvsm1n9VsAQAAyr5169bpzjvvVGxsrOzt7eXr6yuz2axDhw5p7dq1mjNnjpYvX64OHToYXSoAFBvDbAGUJ0VaAAMAAAC26/Llyxo6dKg8PT31wQcfqHfv3nJ1dZUkJSYmauXKlXr66ac1aNAgHThwgB56AGze3wtgEOYBsH2FCvOSk5O1Zs0a3XnnnWrVqlVJ1QQAAIAStGDBAmVkZGjLli2qWbNmjn2urq4aMmSI2rZtq2bNmmnBggUaP368QZUCgHVk98xzZpgtgHKgUHcyFxcX/d///Z8SEhJKqh4AAACUsB9//FFjxozJFeT9U2BgoEaPHq3Vq1eXYmUAUDJS0rN65qVc66EHALas0H+WaNy4sY4fP14StQAAAKAUHDhwQLfddtsN27Vv314HDhwohYoAoGS1DPKSnUnafiJK3+4+Z3Q5AFAshQ7zpkyZohkzZujo0aMlUQ8AAABK2NWrV1W1atUbtqtataquXr1a8gUBQAkLru6h/3ZuIEmavHyvzl1NMrgiACi6Qi+AsXDhQiUmJqpx48YKDQ2Vv7+/TCaTZb/JZMpzlVsAAACUDSkpKXJ0dLxhOwcHB6WmppZCRQBQ8h7tXF8/H7qk3Wdi9PTS3frkP21kZ2e68RMBoIwpdJi3Z88eOTk5qUaNGoqMjFRkZGSO/f8M9gAAAFA2HTp0SA4O1/8qePDgwVKqBgBKnqO9nf5vaHPd/sZm/Xo0Uh9uOa772tc1uiwAKLRCh3knTpwogTIAAABQmkaNGnXDNmazmT/UAihX6vq5a3Kfxpq0fJ9eWX1ItzXwVXB1D6PLAoBCKXSYBwAAANu2cOFCo0sAAMOMuDlQ6w9c0rqDl/T4kj+0YuytcnG0N7osACiwIoV5aWlp+uijj7Ru3TpFRkbK19dXXbt21d13312g+VcAAABgnHvvvdfoEgDAMCaTSS/dGaqer2/SwQtxevXHQ5p0e4jRZQFAgRV6NduYmBi1a9dO999/v7777jsdO3ZM3377rf7zn//olltuUWxsbEnUCQAAAACAVfhVdtbLd4ZKkt7/5bh+/euKwRUBQMEVOsybNGmSDh06pC+++EJxcXE6cuSI4uLi9OWXX+rQoUOaNGlSSdQJAAAAAIDVdA2ppuE3B0qSnlq6WzGJaQZXBAAFU+gwb8WKFXrhhRc0ePDgHNsHDRqkadOmafny5VYrDgAAAACAkjKlT2PV8XXT+ZhkTf5mn9HlAECBFDrMu3z5skJDQ/Pc16xZM125QvdkAAAAAEDZ5+rkoP8b2lz2diZ9t/ucvvnjrNElAcANFTrMq1GjhjZv3pznvi1btiggIKDYRZV18+bNU0hIiMLCwowuBQAAAABQDM1reemxzg0kSZNX7NOZ6ESDKwKA6yt0mDd06FDNmjVLr732miIjIyVJkZGRmjt3rmbNmqVhw4ZZvciyZuzYsdq/f78iIiKMLgUAAAAAUExjw+upRaCX4pLTNf6rPUaXAwDXVegwb9q0aQoPD9fTTz+tqlWrytnZWVWrVtUTTzyh8PBwTZs2rQTKBAAAAACgZDjY22n2wKaSpF+PRio5LcPgigAgfw6FfYKzs7NWr16tNWvWaMOGDYqMjJSPj4+6dOmibt26lUSNAAAAAACUqKTUrACvamVnuTjaG1wNAOSv0GFeth49eqhHjx7WrAUAAAAAAEMcvhgnSWpUvbLBlQDA9RV6mC0AAAAAAOXNoQvxkqSG1QjzAJRtBeqZFxoaWuADmkwm7d69u8gFAQAAAABQ2iw98wjzAJRxBQrzvL29ZTKZrtsmPj5eO3fuvGE7AAAAAADKmkPXwrwG1dwNrgQArq9AYd7GjRvz3Zeenq758+frhRdekMlk0ogRI6xVGwAAAAAAJS46IVWX41IkSQ3omQegjCvWnHlLly5VSEiIHn30UTVr1kw7d+7Uxx9/bK3aAAAAAAAocdlDbGtWqSR35yKvEwkApaJIYd7GjRvVpk0bDR06VB4eHvrxxx+1Zs0aNW/e3MrlAQAAAABQspgvD4AtKVSYt3fvXvXu3VtdunRRZGSkPvvsM+3YsUNdunQpqfoAAAAAAChR2fPlNaxOmAeg7CtQmHf69Gnde++9atmypXbu3KnXX39dBw4c0LBhw0q6PgAAAAAAStThC/GS6JkHwDYUaDKAhg0bKjU1VT179tT48eNVuXJl7d27N9/2LVu2tFqBAAAAAACUFLPZzEq2AGxKgXrmpaSkyGw2a9WqVercubPCwsLyfLRu3VphYWElXTMAAADKgY0bN8pkMuX5+O2333K03bVrl7p27Sp3d3d5eXlp4MCBOnbsWJ7HffPNNxUcHCxnZ2fVqVNH06dPV1paWmm8JQA26FJcimKS0mRnkur5EeYBKPsK1DNv4cKFJV0HAAAAKqhZs2YpPDw8x7YmTZpY/v3gwYPq1KmTmjdvri+//FLJycmaOnWq2rdvrz/++EN+fn6WtjNnztSUKVM0ceJEde/eXREREZo8ebLOnj2r+fPnl9p7AmA7jl1OkCQFervKxdHe4GoA4MYKFObde++9JV0HAAAAKqgGDRqobdu2+e6fOnWqnJ2dtXLlSnl4eEiSWrVqpQYNGmjOnDl6+eWXJUmRkZGaMWOG7r//fs2aNUuS1KlTJ6WlpWny5Ml6/PHHFRISUvJvCIBNiUxIkSRVrexicCUAUDCFWs0WAAAAKE3p6elauXKl7rzzTkuQJ0lBQUEKDw/X8uXLLdtWr16t5ORkjR49OscxRo8eLbPZrBUrVpRW2QBsSGR8qiTJ283J4EoAoGAI8wAAAGCosWPHysHBQR4eHurRo4c2b95s2Xf06FElJSUpNDQ01/NCQ0P1119/KTk5WZK0b98+SVLTpk1ztPP395evr69lf15SUlIUGxub4wGgYohMuBbmuRPmAbANhHkAAAAwhKenp8aNG6f33ntPGzZs0Ny5c3X69Gl16tRJa9askZQ1dFaSvL29cz3f29tbZrNZ0dHRlrbOzs5yc3PLs232sfIye/ZseXp6Wh61atWyxlsEYAOirg2z9aVnHgAbUaA585DTvHnzNG/ePGVkZBhdCgAAgM1q0aKFWrRoYfm5ffv2GjBggJo2barx48erR48eln0mkynf4/xzX0Hb/duzzz6rJ5980vJzbGwsgR5QQUQlMMwWgG2hZ14RjB07Vvv371dERITRpQAAAJQrXl5e6tOnj/bs2aOkpCT5+PhIUp696qKiomQymeTl5SVJ8vHxUXJyshITE/Nsm1fvvmzOzs7y8PDI8QBQMVjmzHN3NrgSACgYwjwAAACUKWazWVJWT7p69eqpUqVK2rt3b652e/fuVf369eXikrUCZfZcef9ue+HCBV25ckVNmjQp4coB2KLsnnk+9MwDYCMI8wAAAFBmREdHa+XKlWrevLlcXFzk4OCgvn37atmyZYqLi7O0O3XqlDZs2KCBAwdatvXs2VMuLi5atGhRjmMuWrRIJpNJ/fv3L6V3AcCWMMwWgK1hzjwAAAAYYsSIEQoMDFTr1q3l6+urI0eO6NVXX9XFixdzBHLTp09XWFiY+vTpo4kTJyo5OVlTp06Vr6+vnnrqKUs7b29vTZ48WVOmTJG3t7e6d++uiIgITZs2Tffdd59CQkIMeJcAyrKMTLOiE+mZB8C2EOYBAADAEKGhofriiy/07rvvKj4+Xt7e3rrtttv08ccfKywszNIuODhYGzdu1IQJEzRo0CA5ODioc+fOmjNnjvz8/HIcc9KkSapcubLmzZunOXPmqHr16po4caImTZpU2m8PgA24mpiqzKyR/apCmAfARhDmAQAAwBATJ07UxIkTC9S2VatWWrt2bYHaPvbYY3rssceKUxqACiJ7iK1nJUc52jMLFQDbwN0KAAAAAFAhRTJfHgAbRJgHAAAAAKiQfN2zQryz0UmKTU4zuBoAKBjCPAAAAABAhVTPz10NqrorNSNTP/150ehyAKBACPMAAAAAABWSyWRSn9AASdJ3e84ZXA0AFAxhHgAAAACgwurTzF+StPnIFcuCGABQlhHmAQAAAAAqrHp+7ropwEPpmWat3nfB6HIA4IYI8wAAAAAAFVr2UNuVDLUFYAMI8wAAAAAAFVqf0KyhtluPRepSbLLB1QDA9RHmAQAAAAAqtFrermpey0tms/TD3vNGlwMA10WYBwAAAACo8Po2yx5qS5gHoGwjzAMAAAAAVHi3N/WXySTtOBmts1eTjC4HAPJFmAcAAAAAqPCqe7oorLa3JOl7FsIAUIYR5gEAAAAAoL+H2n6/94LBlQBA/gjzAAAAAACQ1Dm4qiRp39kYJadlGFwNAOSNMA8AAAAAAEkBni7ydnNSRqZZhy7EGV0OAOSJMA8AAAAAAEkmk0k3BXhIkvadizG4GgDIG2EeAAAAAADXNKnhKUnadzbW4EoAIG+EeQAAAAAAXNMkICvM+5OeeQDKKMK8Ipg3b55CQkIUFhZmdCkAAAAAACtqUiNrmO3B83FKy8g0uBoAyI0wrwjGjh2r/fv3KyIiwuhSAAAAAABWFOjtqsouDkrNyNSRi/FGlwMAuRDmAQAAAABwjclksgy1ZREMAGURYR4AAAAAAP+QPdT2z7OEeQDKHsI8AAAAAAD+wbKi7TlWtAVQ9hDmAQAAAADwDzddG2a7/1ysktMyDK4GAHIizAMAAAAA4B/q+LopwNNFSWkZmr/pmNHlAEAOhHkAAAAAAPyDvZ1JE3s3liS9vfEvnYlONLgiAPgbYR4AAAAAAP/SN9Rfbep4KzktUzO/P2B0OQBgQZgHAAAAAMC/mEwmTb/jJtnbmbRq3wX9cuSy0SUBgCTCPAAAAAAA8hRc3UP3tA2SJE379k+lpmcaXBEAEOYBAAAAAJCvJ7o1lI+bk45eTtDiX08YXQ4AEOYBAAAAAJAfz0qOmtArWJL0+trDyjSbDa4IQEVHmAcAAAAAwHUMallTzWt5KSE1Q3HJ6UaXA6CCI8wDAAAAAOA67OxMmt7vJplMUlJqhtHlAKjgCPMAAAAAALiBZrW8NLR1LaPLAADCPAAAAAAACuKZHo1kMpkkSakZrGwLwBiEeQAAAAAAFICPu7NcHLN+jU5JI8wDYAzCPAAAAAAACsjZ4VqYl06YB8AYhHkAAAAAABSQk4O9JCk9I1MXY5MNrgZARUSYBwAAAABAAdmZ/v73nw9dNq4QABUWYR4AAAAAAEWw8fAlo0sAUAER5gEAAAAAUAS/HLmidFa1BVDKCPMAAAAAACgkOzuT4pLT9fvpq0aXAqCCIcwDAAAAAKCQnK6tarvxEENtAZQuwjwAAAAAAArJ+VqYt+nwFYMrAVDREOYBAAAAAFBITvZZv04fvBCrNObNA1CKCPMAAAAAACgkezuT3JzslZZh1okrCUaXA6ACIcwDAAAAAKAIGlavLEk6dDHO4EoAVCSEeUUwb948hYSEKCwszOhSAAAAypy4uDiNHz9e3bt3l5+fn0wmk6ZNm5Zn2127dqlr165yd3eXl5eXBg4cqGPHjuXZ9s0331RwcLCcnZ1Vp04dTZ8+XWlpabnaXbp0SaNGjZKvr69cXV3Vrl07rVu3zppvEQAkSY2qXQvzLhDmASg9hHlFMHbsWO3fv18RERFGlwIAAFDmREZGav78+UpJSVH//v3zbXfw4EF16tRJqamp+vLLL/Xhhx/q8OHDat++vS5fvpyj7cyZMzVu3DgNHDhQa9as0SOPPKJZs2Zp7NixOdqlpKSoS5cuWrdunebOnatvvvlG1apVU8+ePfXzzz+XxNsFUIE1JMwDYAAHowsAAABA+RIUFKTo6GiZTCZduXJFH3zwQZ7tpk6dKmdnZ61cuVIeHh6SpFatWqlBgwaaM2eOXn75ZUlZ4eCMGTN0//33a9asWZKkTp06KS0tTZMnT9bjjz+ukJAQSdKCBQu0b98+/frrr2rXrp0kKTw8XM2aNdP48eO1bdu2kn77ACqQ4GvDbA8zzBZAKaJnHgAAAKzKZDLJZDJdt016erpWrlypO++80xLkSVlBYHh4uJYvX27Ztnr1aiUnJ2v06NE5jjF69GiZzWatWLHCsm358uVq1KiRJciTJAcHB919993avn27zp49W8x3BwB/y54z72RUopJSMwyuBkBFQZgHAACAUnf06FElJSUpNDQ0177Q0FD99ddfSk5OliTt27dPktS0adMc7fz9/eXr62vZn902v2NK0p9//plnPSkpKYqNjc3xAIAb8XV3lo+bk8xm6cgleucBKB2EeQAAACh1kZGRkiRvb+9c+7y9vWU2mxUdHW1p6+zsLDc3tzzbZh8ru21+x/zn6/7b7Nmz5enpaXnUqlWr8G8KQIXU6FrvvF+OXDG4EgAVBWEeAAAADHO94bj/3FfQdoVtm+3ZZ59VTEyM5XH69Ol8jwEA/zSgRQ1J0rs/H1V0QqrB1QCoCAjzAAAAUOp8fHwk5d1TLioqSiaTSV5eXpa2ycnJSkxMzLPtP3vi+fj45HtMKe+egJLk7OwsDw+PHA8AKIiBLWuqsb+H4pLT9cb6I0aXA6ACIMwDAABAqatXr54qVaqkvXv35tq3d+9e1a9fXy4uLpL+nivv320vXLigK1euqEmTJpZtTZs2zfeYknK0BQBrsLczaVLvxpKkj7ee1PErCQZXBKC8I8wDAABAqXNwcFDfvn21bNkyxcX9PWn8qVOntGHDBg0cONCyrWfPnnJxcdGiRYtyHGPRokUymUzq37+/ZduAAQN08OBBbdu2zbItPT1dn3zyidq0aaOAgIASe08AKq7bGviqUyM/pWea9crqg0aXA6CcczC6AAAAAJQ/q1atUkJCgiWo279/v7766itJUu/eveXq6qrp06crLCxMffr00cSJE5WcnKypU6fK19dXTz31lOVY3t7emjx5sqZMmSJvb291795dERERmjZtmu677z6FhIRY2o4ZM0bz5s3T4MGD9dJLL6lq1ap6++23dejQIa1du7Z0TwKACuXZXo216fBlrdp3QTtPRqlVUN7D+gGguAjzAAAAYHUPP/ywTp48afl56dKlWrp0qSTp+PHjql27toKDg7Vx40ZNmDBBgwYNkoODgzp37qw5c+bIz88vx/EmTZqkypUra968eZozZ46qV6+uiRMnatKkSTnaOTs7a926dRo/frweffRRJSYmqnnz5lq1apU6duxY8m8cQIXVqHplDQ2rpc+3n9aM7w9o2cO3XHdBHgAoKsI8AAAAWN2JEycK1K5Vq1YF7jH32GOP6bHHHrthu2rVqmnx4sUFOiYAWNMTXRvqmz/O6fdTV/XD3gu6PdTf6JIAlEPMmQcAAAAAgBVU9XDRgx3qSZLm/3LM4GoAlFeEeQAAAAAAWEmXxlUlSRdikgyuBEB5RZgHAAAAAICVeFZylCTFJqUbXAmA8oowDwAAAAAAK/FwyQrzktIylJqeaXA1AMojwjwAAAAAAKzE3eXvdSbjktMMrARAeUWYBwAAAACAldjbmVTZOSvQi01mqC0A6yPMAwAAAADAijws8+bRMw+A9RHmAQAAAABgRZWvDbWNIcwDUAII8wAAAAAAsKJa3q6SpF2nog2uBEB5RJgHAAAAAIAV9bypuiTp+z3nDa4EQHlEmAcAAAAAgBV1u6manOztdORSvA5fjDO6HADlDGEeAAAAAABW5OHiqA4N/SRJK3efM7gaAOUNYR4AAAAAAFbWJ9RfkrRy73mZzWaDqwFQnhDmAQAAAABgZV0aV5WTg52OXU7QwQsMtQVgPYR5AAAAAABYWWUXR4U3yhpq+/n2UwZXA6A8IcwDAAAAAKAEDLs5UJL00daTWrWXlW0BWAdhHgAAAAAAJSC8UVXdd1sdSdJTS3frEMNtAVgBYR4AAAAAACVkYq9g3VrfR4mpGXrg4x2KSUwzuiQANo4wDwAAAACAEuJgb6c3h7dUDa9KOhmZqEeX/K6MTFa3BVB0hHkAAAAAAJQgbzcnzR/ZSi6Odtp0+LLm/HjI6JIA2DDCPAAAAAAASthNAZ56ZVAzSdI7G49q5Z5zBlcEwFYR5gEAAAAAUAr6NQvQAx3qSpImfr1Xp6MSDa4IgC0izAMAAAAAoJSM79FIrYOqKD4lXU8v3a1M5s8DUEiEeQAAAAAAlBIHezu9OqSZXJ3ste14lD7cctzokgDYGMI8AAAAAABKUZCPmybfHiJJemXNIR2+GGdwRQBsCWEeAAAAAAClbPjNtRTeyE+p6Zl64os/lJqeaXRJAGwEYV4RzJs3TyEhIQoLCzO6FAAAAACADTKZTHr5zlBVcXXUn+di9ca6I0aXBMBGEOYVwdixY7V//35FREQYXQoAAAAAwEZV9XDRzAFNJUlvb/xLpyJZ3RbAjRHmAQAAAABgkN5N/RXi76FMs5g7D0CBEOYBAAAAAGCg6p4ukqTIhBSDKwFgCwjzAAAAAAAwkI+bkyTpSnyqwZUAsAWEeQAAAAAAGMjH3VmSFEmYB6AACPMAAAAAADBQds88htkCKAjCPAAAAAAADOTjnhXmHbucoKTUDIOrAVDWEeYBAAAAAGCg2r5ukqS9Z2MUPmejlu06o8xMs8FVASirCPMAAAAAADBQy8AqevuulqrhVUkXYpP15Je71f/tLYo4EWV0aQDKIMI8AAAAAAAM1rupv9Y91VHjezaSu7OD9pyJ0eB3t+qRT3fqVGSi0eUBKEMI8wAAAAAAKANcHO31SKf62vB0Jw2/OVB2JumHvRfU9bWfNXvVAcUmpxldIoAygDAPAAAAAIAyxK+ys2YPbKofxrXXbfV9lZqRqfd+Pqbw/23UJ7+dVHpGptElAjAQYR4AAAAAAGVQcHUPffyfm/XhqNaq6+emyIRUTV6xT73f+EWbDl82ujwABiHMAwAAAACgjDKZTOocXE1rHu+gaX1D5OXqqMMX4zXyw+0a8t5WbTp8WWYzK98CFQlhHgAAAAAAZZyjvZ1G3VpHPz8drjG31pGTvZ22H4/SyA+3q/+8LfrxzwvKzCTUAyoCwjwAAAAAAGyEp6ujpvYN0abxWaGei6Oddp+J0QMf71Svub/omz/OKoNQDyjXCPMAAAAAALAx1T1dNLVviDZP6KxHOtWTu7ODDl2M07glf6jLqxv1RcQppaazUAZQHhHmAQAAAABgo3zdnTW+Z7C2TOisJ7s1lJero05EJmrC13vV6X8btPjXE0pOyzC6TABWRJgHAAAAAICN83R11GNdGmjLhM6a1Lux/Co761xMsp7/9k/d9vIGvffzUcWnpBtdJgArIMwDAAAAAKCccHN20P0d6uqX8eF68Y6bVMOrkq7Ep2j2qoO69aX1en3tYcUkphldJoBiIMwDAAAAAKCccXG01z3tamvjM530v0GhquPrppikNL2+9oh6zt2kM9GJRpcIoIgI8wAAAAAAKKcc7e00uHUtrX2yo94c3kJBPq46H5OskR9uV3RCqtHlASgCwjwAAAAAAMo5ezuT+jYL0JIH2srf00XHLidozOIIJaWyOAZgawjzAAAAAACoIPw9K+mjMTfLs5Kjfj91Vf/9bJfSMzKNLgtAIRDmAQAAAABQgTSoVlkfjmotZwc7rTt4Sc8t3yuz2Wx0WQAKiDAPAAAAAIAKplWQt94a0VJ2JunLHWc058dDRpcEoIAI8wAAAAAAqIC6hVTTrAFNJUnzNhzV4l9PGFsQgAIhzAMAAAAAoIIadnOgnuzWUJI07bs/tfHQJYMrAnAjhHkAAAAAAFRgj3aur8b+HjKbpd9PXTW6HAA3QJgHAAAAAEAFdiY6SQcvxEqS+reoYXA1AG6EMA8AAAAAgArs022nZDZL7Rv4qo6vm9HlALgBwjwAAAAAACqo5LQMfbnjtCTpnrZBBlcDoCAI8wAAAAAAqKBW7TuvqIRUBXi6qHNwVaPLAVAAhHkAAAAAAFRQH289KUka0SZQDvZEBIAt4P9UAAAAAAAqoH1nY7Tr1FU52ps0JKyW0eUAKCDCPAAAAAAAKqCVe85LkjxcHHX0UoLB1QAoKMI8AAAAAAAqoFvr+8jNyV6RCaka/v5vum9xhP66FGd0WQBugDAPAAAAAIAKqH0DP/08Plz3tA2SvZ1Jaw9cUo/Xf9Gk5Xt1OS7F6PIA5IMwDwAAAACACsrX3Vkv9m+iNY93ULeQasrINOvTbafU6X8b9Ma6I0pMTTe6RAD/QpgHAAAAAEAFV7+qu94f2VpfPNBWzWp6KiE1Q6/9dFjhczbqy4jTysg0G10igGsI8wAAAAAAgCSpTV0fLX/kVr0xvIVqVqmki7EpGv/1Ht3+xi/6+fBlo8sDIMI8AAAAAADwD3Z2JvVrFqB1T3XUpN6N5eHioIMX4nTvh9t1z4Jt2n8u1ugSgQqNMA8AAAAAAOTi7GCv+zvU1abx4brvtjpysrfTL0eu6PY3f9FTX+5WVEKq0SUCFRJhHgAAAAAAyJeXq5Mm9wnR2ic7qk+ov8xm6etdZ9Tvrc06eIFeekBpI8wDAAAAAAA3FOjjqrdGtNTyR25RkI+rzkQn6c63f9WPf14wujSgQiHMAwAAAAAABdYisIq+GXurbq3vo4TUDD34yU7N2/CXzGZWvAVKA2EeAAAAAAAoFC9XJy0afbNGtguS2Sz9b80hjVvyh5LTMowuDSj3CPMAAAAAAEChOdrb6YU7mmjmgCZysDPp293nNOS9rboQk2x0aUC5RpgHAAAAAACK7K42Qfr4P23k5eqoPWdi1O+tzfrj9FWjywLKLcI8AAAAAABQLO3q+ejbsbepYTV3XYpL0ZD3tuqbP84aXRZQLhHmAQAAAACAYgv0cdXXD9+iLsFVlZqeqXFL/tBLqw4qI5OFMQBrIswDAAAAAABWUdnFUfNHttZDHetJkt79+ajuWbBNV+JTDK4MKD8I8wAAAAAAgNXY25k0sVew5g5rrkqO9vr1aKRuf+MX7TgRZXRpQLlAmFcE8+bNU0hIiMLCwowuBQAAAACAMumO5jX07X9vVT0/N12MTdHQ+b/pg1+OyWxm2C1QHIR5RTB27Fjt379fERERRpcCAAAAAECZ1aBaZX3739vUt1mAMjLNmvH9AT3y6S7FJacZXRpgswjzAAAAAABAiXFzdtAbw5prer+b5Ghv0qp9F9TvrS06eCHW6NIAm0SYBwAAAAAASpTJZNK9t9TWlw+2U4Cni45fSVD/eVv01c4zRpcG2BzCPAAAAAAAUCpaBFbRysfaq0NDPyWnZerppbv17LI9Sk7LMLo0wGYQ5gEAAAAAgFLj7eakhaPC9HjXBjKZpM+3n9ad7/yqfWdjjC4NsAmEeQAAAAAAoFTZ25n0eNeGWjz6ZlVxddSf52LV763Nmvbtn4plcQzgugjzAAAAAACAITo09NPqxzuob7MAZZqlRb+eUOc5P2v572dkNpuNLg8okwjzAAAAAACAYap5uOjN4S306X1tVNfPTVfiU/TEF7s1bP5vOnwxzujygDKHMA8AAAAAABju1vq+WjWuvZ7p0UgujnbadjxKvef+olk/HFBCSrrR5QFlBmEeAAAAAAAoE5wd7DU2vL7WPtlR3UOqKT3TrPmbjqnraz/rh73nGXoLiDAPAAAAAACUMTWruGr+yNb6cFRr1fKupPMxyXrk010a+eF2Hb+SYHR5gKEI8wAAAAAAQJnUObiafnqiox7r0kBO9nb65cgV9fi/TXrtx0NKTsswujzAEIR5AAAAAACgzHJxtNeT3RpqzRMd1KGhn1IzMvXG+r/Ua+4vuhSbbHR5QKkjzAMAAAAAAGVeHV83LR4dpnfuaqlqHs46fiVB9320Q0mp9NBDxUKYBwAAAAAAbILJZFKvpv764oF2quLqqD1nYvTEF38oM5OFMVBxEOYBAAAAAACbUtvXTfNHtpaTvZ1W/3lBL685aHRJQKkhzAMAAAAAADYnrLa3XhkUKkl67+djWrL9lMEVAaWDMA8AAAAAANik/i1qaFyXBpKkySv2actfVwyuCCh5hHkAAAAAAMBmPd61ge5oHqD0TLMe+mSn/roUZ3RJQIkizAMAAAAAADbLZDLp5TtD1TqoiuKS0zV6UYQi41OMLgsoMYR5AAAAAADAprk42mv+yNYK9HbV6agkTf9uv9ElASWGMA8AAAAAANg8bzcnvT6suSTpp/0XlZyWYWxBQAkhzAMAAAAAAOVCi1peCvB0UVJahjYfYTEMlE+EeQAAAAAAoFwwmUzqflN1SdKP+y8YXA1QMgjzAAAAAABAudH9pmqSpLUHLik9I9PgagDrI8wDAAAAAADlxs21veVZyVFRCanaeTLa6HIAqyPMAwAAAAAA5YaDvZ26NK4qSfpx/0WDqwGsjzAPAAAAAACUK91DsubNW73vghJS0g2uBrAuwjwAAAAAAFCudGjoq8rODjp7NUm95v6ibccijS4JsBrCPAAAAAAAUK64Ojnog3tbq4ZXJZ2KStSw93/Tiyv3Kzktw+jSgGIjzAMAAAAAAOVOm7o+Wv14ew1tXUtms7Rg83H1fuMX/XH6qtGlAcVCmAcAAAAAAMqlyi6OenlQqD4c1VpVKzvr2OUEDXx7i+asOaTU9EyjywOKhDAPAAAAAACUa52Dq+nHJzrojuYByjRLb234S/3e2qz952KNLg0oNMI8AAAAAABQ7nm5OmnusBZ6566W8nZz0sELcbpj3ma9tf6I0jPopQfbQZgHAAAAAAAqjF5N/fXjEx3UPaSa0jLMmvPjYd35zq/661Kc0aUBBUKYBwAAAAAAKhRfd2fNu6ul+oT6S5J2n4nRiPe3yWw2G1wZcGMORhcAAAAAAABQWs5EJ2rJ9tP6csdpXYpLkSSZTFKnRn4ymUwGVwfcGGEeAAAAAAAo19IzMrXh0GV9uu2kfj58Wdkd8HzdnTWkdU0NvzlQtbxdjS0SKCDCPAAAAAAAUC6dj0nSku2n9UXEaV2ITbZsv62+r0a0CVTXxtXk5MAMZLAthHkAAAAAAKDcyMg06+fDl/TZtlNaf/CSMq/1wvN2c9LgVlm98Gr7uhlbJFAMhHkAAAAAAMDmXYxN1hcRWb3wzl5NsmxvW9dbI9oEqcdN1eTsYG9ghYB1EOYBAAAAAACblJlp1i9/XdFn205q7YFLyrjWDc/L1VF3tszqhVe/qrvBVQLWRZgHAAAAAABsjtls1qB3f9WuU1ct28JqV9GINoHq1cRfLo70wkP5RJgHAAAAAABsjtksHboQJ0nqFlJNz/RopIbVKhtcFVDyWLIFAAAAVrNx40aZTKY8H7/99luOtrt27VLXrl3l7u4uLy8vDRw4UMeOHcvzuG+++aaCg4Pl7OysOnXqaPr06UpLS8vV7tKlSxo1apR8fX3l6uqqdu3aad26dSXyXgEAxrKzM6lf8wBJkquTPUEeKgzCPAAAAFjdrFmztHXr1hyPJk2aWPYfPHhQnTp1Umpqqr788kt9+OGHOnz4sNq3b6/Lly/nONbMmTM1btw4DRw4UGvWrNEjjzyiWbNmaezYsTnapaSkqEuXLlq3bp3mzp2rb775RtWqVVPPnj31888/l8r7BgCUrmFhgZKkVfsu6GpiqsHVAKWDYbYAAACwugYNGqht27b57p86daqcnZ21cuVKeXh4SJJatWqlBg0aaM6cOXr55ZclSZGRkZoxY4buv/9+zZo1S5LUqVMnpaWlafLkyXr88ccVEhIiSVqwYIH27dunX3/9Ve3atZMkhYeHq1m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",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot normalized difference, binned by latitude and by depth\n",
    "fig,axs = plt.subplots(1,2,figsize=(15,8))\n",
    "curr_ax = axs[0]            \n",
    "curr_ax.plot(lat_bin_centers,diff_norm_lat)\n",
    "curr_ax.set_ylim(0.01,1)\n",
    "curr_ax.set_yscale('log')\n",
    "curr_ax.axhline(y=0.1,color='black',lw=2)\n",
    "curr_ax.axvline(x=0,color='black',lw=1)\n",
    "curr_ax.set_xlabel('Latitude')\n",
    "curr_ax.set_ylabel('Normalized diff')\n",
    "curr_ax.set_title('Normalized diff variation with latitude')\n",
    "curr_ax = axs[1]\n",
    "curr_ax.plot(diff_norm_depth,-ds_grid.Z)\n",
    "# Note: in older versions of Matplotlib\n",
    "# may need to use linthreshy instead of linthresh\n",
    "curr_ax.set_yscale('symlog',linthresh=100)\n",
    "curr_ax.set_ylim(curr_ax.get_ylim()[::-1])\n",
    "curr_ax.set_yticks([0,50,100,500,1000,5000])\n",
    "curr_ax.set_yticklabels(['0','50','100','500','1000','5000'])\n",
    "curr_ax.set_xlim(0.01,1)\n",
    "curr_ax.set_xscale('log')\n",
    "curr_ax.axvline(x=0.1,color='black',lw=2)\n",
    "curr_ax.set_xlabel('Normalized diff')\n",
    "curr_ax.set_ylabel('Depth [m]')\n",
    "curr_ax.set_title('Normalized diff variation with depth')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the normalized diff = 0.1 threshold discussed before, geostrophic balance doesn't hold particularly well at any latitude, and only at depths 100-1000 m. But let's try this again, masking for only 100-1000 m depths in the latitude plot, and only latitudes more than 5 degrees from the equator in the depth plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# latitude and depth masks\n",
    "\n",
    "# create depth array that will broadcast correctly across horizontal dimensions\n",
    "depth_expand_dims = np.expand_dims(-ds_grid.Z,axis=(-3,-2,-1))\n",
    "\n",
    "depth_mask = np.logical_and(depth_expand_dims > 100,\\\n",
    "                            depth_expand_dims < 1000)\n",
    "lat_mask = np.expand_dims((np.abs(ds_grid.YC) > 5),axis=0)\n",
    "\n",
    "\n",
    "# compute norm diff with additional masks\n",
    "\n",
    "curr_weighting = (depth_mask*(~mask_smallvel)*(ds_grid.maskC)*(ds_grid.rA)).values\n",
    "diff_norm_lat_moremask = mean_weighted_binned(vel_diff_norm.values,curr_weighting,\\\n",
    "                                                ds_grid.YC.values,lat_bin_bounds)\n",
    "\n",
    "curr_weighting = (lat_mask*(~mask_smallvel)*(ds_grid.maskC)*(ds_grid.rA)).values\n",
    "diff_norm_depth_moremask = mean_weighted_binned(vel_diff_norm.values,curr_weighting,\\\n",
    "                                                depth_expand_dims,depth_bin_bounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot normalized difference, binned by latitude and by depth\n",
    "fig,axs = plt.subplots(1,2,figsize=(15,8))\n",
    "curr_ax = axs[0]            \n",
    "curr_ax.plot(lat_bin_centers,diff_norm_lat,color=(0.3,0.3,0.3),label='All depths')\n",
    "curr_ax.plot(lat_bin_centers,diff_norm_lat_moremask,color='blue',label='100-1000 m depth')\n",
    "curr_ax.set_ylim(0.01,1)\n",
    "curr_ax.set_yscale('log')\n",
    "curr_ax.axhline(y=0.1,color='black',lw=2)\n",
    "curr_ax.axvline(x=0,color='black',lw=1)\n",
    "curr_ax.set_xlabel('Latitude')\n",
    "curr_ax.set_ylabel('Normalized diff')\n",
    "curr_ax.set_title('Normalized diff variation with latitude')\n",
    "curr_ax.legend()\n",
    "curr_ax = axs[1]\n",
    "curr_ax.plot(diff_norm_depth,-ds_grid.Z,color=(0.3,0.3,0.3),label='All latitudes')\n",
    "curr_ax.plot(diff_norm_depth_moremask,-ds_grid.Z,color='blue',label='>5 deg from equator')\n",
    "# Note: in older versions of Matplotlib\n",
    "# may need to use linthreshy instead of linthresh\n",
    "curr_ax.set_yscale('symlog',linthresh=100)\n",
    "curr_ax.set_ylim(curr_ax.get_ylim()[::-1])\n",
    "curr_ax.set_yticks([0,50,100,500,1000,5000])\n",
    "curr_ax.set_yticklabels(['0','50','100','500','1000','5000'])\n",
    "curr_ax.set_xlim(0.01,1)\n",
    "curr_ax.set_xscale('log')\n",
    "curr_ax.axvline(x=0.1,color='black',lw=2)\n",
    "curr_ax.set_xlabel('Normalized diff')\n",
    "curr_ax.set_ylabel('Depth [m]')\n",
    "curr_ax.set_title('Normalized diff variation with depth')\n",
    "curr_ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above plots, we can see that for areas in the 100-1000 m depth range, more than 5 degrees from the equator and in low-to-mid latitudes, velocities are overwhelmingly geostrophic. Outside of these areas, geostrophy is less predictive of velocity. The bin-averaged normalized differences show the loss of geostrophic balance at the equator, near the ocean surface, and in the deep ocean.\n",
    "\n",
    "Before we finish, you can use the code below to save the binned normalized differences plotted above. This will be useful in the next tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save norm diff bin-averaged values (in numpy .npz format) for future use\n",
    "diff_norm_inbins_filename = \"diff_norm_geostr_bal.npz\"\n",
    "np.savez(diff_norm_inbins_filename,lat_bin_bounds=lat_bin_bounds,\\\n",
    "                                   diff_norm_lat=diff_norm_lat,\\\n",
    "                                   diff_norm_lat_moremask=diff_norm_lat_moremask,\\\n",
    "                                   depth_bin_bounds=depth_bin_bounds,\\\n",
    "                                   diff_norm_depth=diff_norm_depth,\\\n",
    "                                   diff_norm_depth_moremask=diff_norm_depth_moremask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "Having made it to the end of the tutorial, you're now ready to try tweaking some of the code above to explore the ECCO data on your own. Here are a couple of suggested exercises, or you can develop your own ideas!\n",
    "\n",
    "1. All of the mapping we just did was with monthly data for January 2000, but we also downloaded daily files for 2000-01-01. Repeat the calculations and plots that we did, but this time with the daily files. How does geostrophic balance on daily timescales compare with monthly timescales? Why might they be different?\n",
    "\n",
    "1. You probably noticed that geostrophic balance does not seem to be as good of an approximation for the flow in the Arctic as in other ocean basins. Focus on the monthly data in the Arctic tile (```tile=6```). Where is geostrophic balance the least effective, after masking out the smallest velocities? You'll probably need to change a few parameters to get informative plots in this region of lower velocities. Pick a grid cell (i,j) where there are high normalized differences (preferably one where the ocean is >500 m deep), and make a plot comparing the depth profiles of geostrophic and actual u and v. For extra credit, also plot the depth profile of the *angle* of difference between the geostrophic and actual velocity vectors.\n",
    "\n",
    "> Tip: make a copy of this notebook before you edit it, so that you can try your own innovations while being able to easily return to the original notebook for comparison."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ecco_po_tutorials module\n",
    "\n",
    "There are some functions used in this tutorial that it will be helpful to have access to for future tutorials (or your own analysis with ECCOv4). Hence these functions have been put into a ecco_po_tutorials module (a Python file) that will be imported in future tutorials. This module also includes a function to compute geostrophic velocities quickly, given the file containing density and pressure anomalies.\n",
    "\n",
    "[ecco_po_tutorials module](https://raw.githubusercontent.com/ECCO-GROUP/ECCO-v4-Python-Tutorial/master/Intro_to_PO_Tutorials/ecco_po_tutorials.py)\n",
    "\n",
    "I recommend downloading it to the same directory where your tutorials are located. Some functions from this module may also be incorporated into the ```ecco_v4_py``` package in the future."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "Gill, A.E. (1982). ***Atmosphere-Ocean Dynamics***. Academic Press, Elsevier.\n",
    "\n",
    "Kundu, P.K. and Cohen, I.M. (2008). ***Fluid Mechanics*** (4th ed.). Elsevier.\n",
    "\n",
    "Vallis, G.K. (2006). ***Atmospheric and Oceanic Fluid Dynamics***. Cambridge University Press."
   ]
  }
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