{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ADCP Discharge Example\n",
    "\n",
    "This example notebook overviews how to calculate discharge from an ADCP transect. The example data used in this notebook was taken by a Teledyne RDI RiverPro ADCP, an instrument purpose-built for calculating discharge from a survey transect, but the following workflow can be used with any ADCP's data, given that there is GPS information and/or bottom track measurements stored in the ADCP file.\n",
    "\n",
    "The basic steps that the notebook conducts are to\n",
    "1. Read in the binary ADCP file\n",
    "2. Rotate the dataset into the Earth coordinate system (\"East\", \"North\", \"Up\")\n",
    "3. Correct vessel motion using stored bottom track (You can also use the GPS's velocity measurement, found from the VTG sentence and converted from speed and direction to velocity-east and velocity-north)\n",
    "4. Rotate the dataset into the water current's principal coordinate system (\"streamwise\", \"cross-stream\", \"vertical\")\n",
    "5. Calculate the distance from the ADCP to the riverbed/seabed. (We use the bottom track ping here, but can also use the ADCP's altimeter, if available)\n",
    "6. Finally, calculate the water discharge. Additional parameters that are not availble within the ADCP's dataset are required here.\n",
    "\n",
    "We'll start by immediately jumping through steps 1 and 2. Note if that quality control is required, that should be done here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reading file c:\\users\\mcve343\\mhkit-python\\examples\\data\\dolfyn/RiverPro_test01.PD0 ...\n"
     ]
    }
   ],
   "source": [
    "from mhkit import dolfyn\n",
    "\n",
    "ds = dolfyn.read_example(\"RiverPro_test01.PD0\")\n",
    "ds.velds.set_declination(18)  # Set declination to 18 degrees East\n",
    "ds.velds.rotate2(\"earth\")\n",
    "\n",
    "# # Note, if the range coordinate has not been adjusted given the depth of the ADCP \n",
    "# # below the waterline, do so using the following line.\n",
    "# ds = dolfyn.adp.clean.set_range_offset(ds, x.x)  # Set range offset to x.x m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the dataset below so we know what we are looking at"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Depth [m]')]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(1,2, figsize=(10,3), constrained_layout=True)\n",
    "e = ax[0].pcolormesh(ds[\"time\"].values, -ds[\"range\"].values, ds[\"vel\"][0], cmap=\"coolwarm\", vmin=-2, vmax=2)\n",
    "n = ax[1].pcolormesh(ds[\"time\"].values, -ds[\"range\"].values, ds[\"vel\"][1], cmap=\"coolwarm\", vmin=-2, vmax=2)\n",
    "fig.colorbar(e, ax=ax[0], label=\"East Velocity (m/s)\")\n",
    "fig.colorbar(n, ax=ax[1], label=\"North Velocity (m/s)\")\n",
    "for a in ax:\n",
    "    a.set(xlabel=\"Time (UTC)\", ylim=(-10, 0))\n",
    "ax[0].set(ylabel=\"Depth [m]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step we need to do is to make sure we correct the ADCP measurement for the vessel motion. This is not done natively in the raw ADCP file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correct velocity\n",
    "ds[\"vel_bt\"] = ds[\"vel_bt\"].where((ds[\"vel_bt\"] < 5) & (ds[\"vel_bt\"] > -5))\n",
    "ds[\"vel\"] -= ds[\"vel_bt\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then rotate into principal coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rotate to principal reference frame\n",
    "ds.attrs[\"principal_heading\"] = dolfyn.calc_principal_heading(ds[\"vel\"].mean(\"range\"))\n",
    "ds.velds.rotate2(\"principal\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And replot...\n",
    "\n",
    "The sign associated with the streamwise velocity is a byproduct of the principal direction calculation; it should be associated with ebb or flood tide based on visual observation. The sign associated with the cross-stream (transverse) velocity is related to the streamwise velocity by the right-hand-rule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Depth [m]')]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5+OCDBxxPY2Mj1dXVA55OCCGEGKkkVw8dachvxjVnVvU7O7N5Jqdc3stVp1UX32+v/hwxYfNl3+NTcAJWr0oBEApbpHryANQ3xig4FgCZjEv7ujS1dTFAPa4h8AP8wC+V5TouRvHL0NPRTShsE4lHSp8blgFATX2MtjW9APiuT3WjWjYrZBbL8QiFLbrWdhAKjQOga2038Zo4ruuVysumskTiEexICDus4sym86xb3UUsoeabzzmku1Olaeyorebr+RRy+dIZtmgiimmZZHrTAHieh2mpeILiMqY6e9SymwbNk5rVMhk6mq7h5B1VT1097Dh7ZwDaWztZ4/lYtlWK17ItUt3p4vJaBEFAKGwX69TAtEycgirLsi1CYYuONd3F8UNYIZNEwi7WcQpd0whHQ6reXJ9EdYyONV3Fusio8oqxAbh5h5723tJ7yw4RTcSKdaXqo+/EZCFXIBy1S3XmeT6+65NOFQDoWNtTLMMgHLVKZfrFddS37jMZB9MyaBpXX1pHvuvT26XqwR9XRSxm0d2dJ5dRsVbXRjCL9yyl06r+I7EQhYJHPu8RjofpWtuxfjlCJoahl+I2NtgpW3YIz1Ex+QGldVE3qpqWNWuJVcVKZfSt/8axdXieX1rHQaAm7En5FHIqxuioGLat5tnZkgNMqqrVuujtdYhEDBxHTReLGXR3q5hymQLhiEE4rKb1PZ9IWKNtlSrXtnWyOY9wRNWpU/CIx3S89Zs+rhtgmhodHWpdTBtv0tGtY5o6enGbrqtX9d/UsH7XaugaTc2xYrk+IUuj4KrP8oUATVPLCGBZOtVx9b9pahiGhqZB8WtBxPZxXB2tWNWmoabPO9BQpYJd02kQCwesaVfjjGowKa4mTAPaU33fMahPqkAKrk6x6sm7OmHLZ+5uql6fWdJL3jXBAsdX+5OoWSDvWdiGg+MbGJqq87BZIKQ77DK1iXL09BhljVeugfaE29PT02+4bdvYtj1k8cyYMYNcLsdOO+3Ed7/7XebMmTNkZQuldqc9PpSrdySxifHqgPfe3Y6ZU2rVgGlnEgbW/+4ydaNpUs/9hXX1OwLQsO5N8lVN2N1rAAjM9ftfAp9IxwdoNaMB0DyHcEZ9Ab1EMW/56ruWqpmAFvi00QhAQuslXujCK37J82aUardNTauZuLr6Hi5PjwJgTKwNA4+Q4ZZmn9aT9BRiaARYqP1TxgkRNgpk3TAApubj6DYmarqMFyFirt9PdRViREy1Pwy5WTxTp0AxD5k2eTNKKBQHQA88vOKdmbV6Ow42uqZ2IFVuO22eqkufGJblYKLKdfUQgbb+VzjDyeJbKj473U4y2kVGi5c+d30T11DLnw/CBGhUWyqXFgILjYC8p9aDG+i4gYFWPHaI+L04hkXUV+OHnRQEAU5xvXm+gRvomMW4fUNH89bn7XwojqU5+MXlNPAwNZdUoLY1TQvIaxECtFKdOIGFbfql9RgDNN/FMdbvU7xAIxOqIig+VsvUHDoyYXI18VKckWwHBVPlEi3w8dHR8cmgxkmEYvjo9Lgxkna1Ksd3cAJVVxoBYUNtByYOKT9BwVPrOmTkSQfR0nIBpPMWga3h6apuYrkOYlYeXfPRHFVO3/rdcDnUOtZLdaBpATkzXlquD/OL694zbMzAKQ4zCBku2SAKQFzrIKqniafVd6Anuh2G5lEwo6XxTa9Ab1h9j2vcLOlQFRoqB2W8CFVBG9lQkoyn6jCkO0S9HqxcD6mIOibK+zY1zhqcsI3lqePvbque2kKrqpNQNbrvktNV7o77XZh+gV5HlRk1C1A8Psj7Iar0bgLdoBAUjxXtOuJOV2l7BAiMGPFAHVtmNLWXCvtpPE2tm3WhMdS5av6xQhcxugBI2TVoxQOpDfcJgaaRdcJMn6r2Me++9x5eYBAKchRXCR4qx4a8HHld1aERuASBjq/pTJk8eaP1tCkfzpWflOTqoSP3yAshhBgR+nrCLecFMG7cOKqqqkqvK6+8ckjiaG5u5pe//CULFy7knnvuYfvtt+eLX/wiTzzxxJCUL4QQQmyrJFcPHflFXgghxIgw0LP8K1eu7Pdr7lCd4d9+++3ZfvvtS+9nz57NypUrueaaa9hnn32GZB5CCCHEtkhy9dCRX+SFEEKMCAPtCTeZTPZ7DeWleh+255578vbbb2+x8oUQQohtgeTqoSO/yAshhBgRNrwU7+PG+7S9/PLLNDc3b5Gy8/k8zz//PO+//z6ZTIaGhgZmzJjBpEmTtsj8hBBCiMGq1Fy9JUhDXgghxIgw0Mv1ypVKpXjnnXdK75ctW8Yrr7xCbW0t48ePZ8GCBaxatYo777wTgOuuu46JEycyffp0CoUCd911FwsXLmThwoUDW6CP8cwzz3D99ddz7733UigUqK6uJhKJ0NHRQT6fZ/LkyZx22mnMmzePRGJTXcAJIYQQn65Ky9VbkjTkhRBCjAhb6iz/iy++2K8X2/nz5wNw4okncscdd9DS0sKKFStKnxcKBc4991xWrVpFJBJh+vTp/O1vf+OQQw4Z0Hw/ymGHHcYLL7zAcccdx0MPPcSsWbOIRqOlz9977z2efPJJfve733Httddy5513csABBwzZ/IUQQojBqKRcvaVJQ14IIcTIoGmUns33ceMNwH777Vd6HOKm3HHHHf3en3/++Zx//vkDmsdAzZ07lz/+8Y+EQqFNfj558mQmT57MiSeeyBtvvMHq1au3aDxCCCFEWSooV3+Y4zi0traWboOrra39ROVJQ14IIcSIoGllXq43wIODrdEZZ5xR9rjTp09n+vTpWzAaIYQQojyVlKtBXfL/m9/8ht/97nc8//zz5PP50mdjx45l7ty5nHbaaey+++4DLlt6rRdCCDEiDPTZtCPFypUr+eCDD0rvn3/+ec4++2x++ctfDmNUQgghxMYqKVf/5Cc/YeLEifzqV79i//3355577uGVV15h6dKlPPvss1x88cW4rssBBxzAQQcdNOAe8+UXeSGEECPClupAZ2t33HHHcdppp3H88cfT2trKAQccwPTp07nrrrtobW3l+9///nCHKIQQQgCVlaufeeYZHnvsMXbZZZdNfv65z32Or3/969x0003ceuutLFq0iO22267s8qUhPwRuuah+QOOfe0TfGaaqjT67+WEYPcoCoHWtQ7dpEI4YAHR25jBNHTvc/57I9tZuAEZNGIWma+jFS1E8z6dQvFdE1zTiyTAA1XUxQpYq07RULEteXoFlWzj5Am8vWaemMXV8z6eqJk6h4AHg5h0iTTWYloHrqGGhsIo33ZsFIAgCapvrAPA9n3R3GoBkreo1ORKPqPgcj3DULg3vWtdNqlMti+cWy46oZ0UWsnkS1ZHSMhdy6y9LsWMRDEMtR/faDmisJdXRA0C8Nkk4apPq7FXxRMNE4mG6e1VM6Y5ufM9j1IQmVcfrunEKLpZtlZYlHDHp7soWlyGOaRkUcg4AuaxLPBnGdYrLsLaDWFW8tI5ymTzhaLg0/5pRtXS25tFNFe/oSU3oho5tq/dO3iESC5fWoRUycF2PXFbNL9WdZtzkBgqOV1qHfcJRC7M4zHV8sun1dWaHLWrqo6x6b21xvQQEZkA8HqJznaqL0WMT5POq3pPJYvxZD13XSvccheOqMy3D0An8oDTeWsvEDq3f4SbqkqX/0z2q7hI1cVzXw47apTijiTCRmCozn3MIR1R5gaFjFMdprNP5V7G+/AAcp//9T4WCD0A24xAEAbbdVy9G6bKsKTs0UFVl4bpBcb42UTuguUmt544utdzRqNolFkwdy9TQdfBU8QQBNNRodHerMrvSBo210N2tUZ1U8b33fp66OptcPihN5/lBaX1mMi5r2zSaalWM9TU6a9o8ipsv+YKaD0Ay6uN6Gp29Gn25zPN1LBNGN6oBkZCHaRo0VXtEQy4AqYhOJOQzeXSxTFcjElLB1EayZN3i9zVvYepquBny0ZLFfYIRMDG5FhgDwF47bqq38759UGQTnw2frfmRNlvS66+/zuc+9zkA/vCHP7Dzzjvz9NNP8/DDDzNv3jxpyBdNnjJlQOPH9/gK8b43U6b2+yz9zD3ontov+6EogWFhOmpfZ61YijdaPfrPMVQOi+S6APDMEBpRQpqa1sOkYITxKO63Agg0tX2m9SSmrr7XcXIAdBaSRM0cqXwIL6q+h2kvikZAgIZfLMf1NSzdJeWovB82XIzAxdZUOa5uYvgujqE+j1LA8Yv7AC9PYGhkffVZzopj+gVcU71PkyCqF/M9OhGvl25d5fyMmSASOKV6CtDQNbVT8zFwNbX/yUdqiHZ9gBupBkDzPfJahLacOi6qDqWx9QK2mwGg26hR5RX3pToBeT+Ebah5ZZwQKcLUhFXd5c0oVuCQ0dT+y9LzRDNthMIFAAq6RVc2Sk04XYrTqR5VqvusEScf2ER0Nf8eL4mpeWTdYn2ZWZzAwg/U+Lrv4qOzrlvlEC0Z4KYyGFYYPfBL9WEZPgVs3MAozdc01uc0x7CJACE3W6r7HidG0kpjaCpPWYU0WiTA8w1yVkzFE/TiBzphLYuLRU9B5dWqaDchvUBGU9uh5eXIexZVpjrW6vUSmEaAr60/ngh0g7xn0aS3kK0br+rLN6nTu0rjRCy1XVoU0AMVV1c+SmO4VZWrVZfG9XS1zguGyhdG4JY+M3wHQ/PQNVVHdraTwG6iM67yT8LvxmH9Ma+v6RTMCF6g6tkzbQzfJaep5Q0CjZRVTaLQQSSktvWcbxPLtuNEqjB9tb3ohk+33UDet4n4qeK6KZAJqe0vnu8ETcNEjR9y0rRHxhIL1LFnldmD6xfrVHPp8Grxozpxr7sUa18cAAU9ghcYuLpaFi8wCNDI6bHS+CEK2Hl17FoIxekIjQJAC4LS9yZMhpTft007RI0soL4bUyZPZltRSbn6j3/8Y1nj2bbN6aefPuDyt/0aEkIIIQBNX3+m/6Nfwx3p0HIcB9tWB5X/93//x6GHHgrADjvsQEtLy3CGJoQQQvRTqbn6w3p6erj33ntZsmTJoMsY4VUkhBCiUpR3YFDeJX3bkunTp3PTTTfx5JNP8sgjj3DQQQcBsHr1aurq6oY5OiGEEGK9Ss3VRx99ND//+c8ByGazzJo1i6OPPppdd9110M+ul4a8EEKIEUEzjLJfI8lVV13FzTffzH777cd//Md/sNtuuwFw3333lS65F0IIIbYGlZqrn3jiCb7whS8A8Oc//5kgCOjq6uJnP/sZP/jBDwZVptwjL4QQYkSopPvuQD3SJh6Ps99++9HW1kZPTw81NTWlz0877TSi0egwRiiEEEL0V2m5uk93d3fpufEPPvggRx55JNFolC9/+cucd955gypzxNTQ+++/zze+8Q0mTZpEJBJhypQpXHzxxRQKheEOTQghxKeg0i7Xq6+v5+CDD+bGG29kzZo1/RrxABMnTqSxsXGYots0ydVCCFHZKi1X9xk3bhzPPvss6XSaBx98kLlz5wLQ2dlJOBweVJkj5hf5t956C9/3ufnmm5k6dSqvv/46p556Kul0mmuuuWa4wxNCCLGlaTqUcwZ/hPSgs3TpUu677z4WLlzI2Wefza677sqhhx7KYYcdxq677jrc4W2S5GohhKhwFZar+5x99tl87WtfIx6PM2HCBPbbbz9AXXK/ucfTfZwR05A/6KCDSh38AEyePJmlS5dy4403fuTBQT6fJ59f/yiznp6eLRqnEEKILaTcM/gj5Cz/hAkT+Na3vsW3vvUturu7uf/++/nf//1ffvzjH1NTU1Nq1O+7774YW8m9hpKrhRCiwlVYru5z+umn87nPfY6VK1dywAEHoBdPZkyePHnQ98iPrFMdH7LhvQibc+WVV1JVVVV6jRs37lOKTgghxFDSNL3s10hTVVXFf/zHf/D73/+etrY2br75Znzf5+STT6ahoYHf/OY3wx3iZkmuFkKIylFpuXr27NlcddVVvPXWW8yaNYuvfvWrxOPx0udf/vKX2XvvvQdV9siooU149913uf7665k3b95HjrdgwQK6u7tLr5UrV35KEQohhBhSulb+awQzTZO5c+dy/fXXs3z5cv7+978zbdq04Q5rkyRXCyFEhamwXD1v3jyef/55dt99d6ZNm8Z5553Hk08+SRAEn7jsrf7S+ksuuYRLL730I8d54YUXmDVrVun96tWrOeiggzjqqKM45ZRTPnJa27axbXtIYhVCCDF8KrUnXIBcLsfixYtZu3Ytvu+Xhmuaxle+8pUtPn/J1UIIIcpRabn6xBNP5MQTTySfz/P3v/+d//3f/+WYY47BcRy+/OUvc9hhh3HggQcO6ikzW31D/swzz+TYY4/9yHEmTpxY+n/16tXMmTOH2bNn88tf/nILRyeEEGJrUW4vtyOtJ9wHH3yQE044gba2to0+0zQNz/O2eAySq4UQQpSjUnO1bdsccsghHHLIIdx8880899xz3HfffXz/+9/na1/7Gvvvvz8LFiwY0GX2W31Dvr6+nvr6+rLGXbVqFXPmzGHmzJncfvvtpU4EtiXfnLvhO4ubH64hnVWXXrSvy9DRkek3frI2QeAXP2/pYOzUZjrXqU6AapuqKOTUI3062tNU1UQA6FyXxi9ezjF2onpcUV1zDV1re7DsEO8vfhuAWG0V2Z4MNfXr7+MIxyNk0zmskEV1XQyAdCpPKGyx6m11qWNNUz01DWqa1uXtpWk71nRiR8M4eQcA3/PxPI9QOASAHbVp/yALgGmrYemuXvU+ZGIYan2GIyau42JaavONxKOlZfZcj2xPhlh1AqBUN+F4ZIN5+hSy6ztNskKh0s7Cd31ymQJVNeqsWFurg+uu/3XL93ywDFxn/YFxPudi2SqWmlF1AKXPTcskWRsvrYfA94nXJErLUtcQI593yeX84rJE6O1K0TB6/f2iobCFFTKK67SG3t48Y8claG1NF8dQy+Z5AdFEpDhfvbTsAL1dGaqqI8Rr4sX4fDIZh4aGCJ6n5p3Pe9i2gWlqpNNuqRxN07Dt9fUPEE3YmJZBZ2cOgER1/7OI2VSWcFQ9SiNRpWLq7gwo5BzymTxWyAKgeXSc1e+tAcAOW6XLjFzHwymoGBwXqmtV+b3deaIRVReZTAGIlNaP63iYiRDxuFlcHp+GBhVDR0cesLBDaj0nkzY9aY11HWoe6ZRLKLR++UxL/e/7AaCmCdsamkap/OqYx+p2g3DYoKlGre/elE0271NbbdKyVpUdsnRcVy1XKGQQj5t4xXWTyQXU1xpkit/xRBTautS8C05Ac31AbRLiYVV+KmcQs31cX8VUF8kwqi7B6EQPjq/qpTGpUW1nKfgqTtfX2W9ntQ6eWQIJS237X9wlDGyuA7Qxmxm+ldO08nq51UbWwcGZZ57JUUcdxfe//32ampqGJYZKy9WxvY7o977z1UVYuWIHfMkaNF99Z/VA/Q11twKQr26mjUYimtp36vj4moGBGk8jIOSqPBgzNXxNfUejhvre5v0QGTeMbXlkLZXnLN+lgEnczBIU91eJUIFeJ0oipMpKOxEM3yGtq2kc3yTi9ZbiMyyPhJkCoOBECdDIOGr/mQyHyfkRYqbKxwEaBm7p/4IRoSuvytW1GJqm9mdJM01tdhWuqcppN0cRR9WRY4QJDAtfV/upQNOI+Clgfe7z0XAMdRVGxrOJm1kMTcWbD0JoWoDvq20nZKh4UoGKY1R+OZ32qFKcuu+RjdQS9dT8W5x6DM0v7Tf7liUdqBxpaB5OYKL7KnZbK5APQiQtVQeWVsANLOpNdfLMyKsc37cp5/UIMcchlOkhUxMrzcPSPcJBhnavGoCI30sQQKTQW1z/PgQ+plc8RrHi2IZDwbewi9tAX30mQ2niThcAppsjZthkiWHiUB/uVpN7efJaGNtQx12G5+IFBlZf+aj68jAJBbliPeiEjQJ5PYoeUvWdzZj4YbVwXmBgFNexQ4iQpsqyDY9oXsWTi8RwfFWXOVPl75CXLa5rvbTdFYwIVuDiBOqYwLET+OgEgdqOQ0GOTBAjZKjYooUeNAJyuqrTnBUn5OXQdVWer+sE6OSsONXOOrVNWDX0xEYRdlL0mGr7snCwgjzpIErWUnHm/TD5QB17mlaBtF1Nt18FQNhKk/dt4qY67upyqwgX/+91o1RbvbiBVdyGwdNNqrOtOKbKvSmSpNwIUUtNE9Ey6IZPyo+hoeqyjnXU7LYvAKuXLi7VwY5Tt9F8/FEqNFd/2B577MEee+zB5Zdfzrvvvst9991HS0vLgMrY6hvy5Vq9ejX77bcf48eP55prrmHdunWlz0aNGjWMkQkhhPg0VOpZ/rVr1zJ//vxha8QPhORqIYSobJWaqzeUSqX63QbX0NDAOeecM+ByRkxD/uGHH+add97hnXfeYezYsf0+G4rOBIQQQmzl9DKfTbsN/gL8Uf793/+dxx9/nClTpgx3KB9LcrUQQlS4Cs3Vy5Yt48wzz+Txxx8nl8uVhgdBMOjb4EZMQ/6kk07ipJNOGu4whBBCDBNN09DKuBSvnHG2JT//+c856qijePLJJ9lll12wLKvf52edddYwRbYxydVCCFHZKjVXf+1rXwPgtttuo6mpaUiWb8Q05IUQQlQ4rcyz/CPk2bR9fvvb3/LQQw8RiUR4/PHH+x0caJq2VTXkhRBCVLgKzdWLFy/mn//8J9tvv/2QlTmyakgIIUTF6rvvrpzXQDzxxBN85StfYfTo0Wiaxr333vux0yxatIiZM2cSDoeZPHkyN9100yCX6uN997vf5bLLLqO7u5v333+fZcuWlV7vvffeFpuvEEIIMVCVmqt33313Vq5cOaRlyi/yQgghRgZNL7Mn3IGdw06n0+y2226cfPLJHHnkkR87/rJlyzjkkEM49dRTueuuu3j66ac5/fTTaWhoKGv6gSoUChxzzDHbZO/vQgghKkyF5upbbrmFefPmsWrVKnbeeeeNboPbddddB1ymNOSFEEKMDLqmXuWMNwAHH3wwBx98cNnj33TTTYwfP57rrrsOgB133JEXX3yRa665ZoscHJx44oncfffdXHjhhUNethBCCDGkKjRXr1u3jnfffZeTTz65NEzTNOnsTgghhNA0Ha2MM/h94/T09PQbbts2tm1/4jieffZZ5s6d22/YgQceyK233orjOBudhf+kPM/jRz/6EQ899BC77rrrRuVfe+21Qzo/IYQQYrAqNVd//etfZ8aMGfzud7+Tzu6EEEKIfgZ4ln/cuHH9Bl988cVccsklnziM1tbWjZ7p3tTUhOu6tLW10dzc/InnsaHXXnuNGTNmAPD666/3+2yk9forhBBiG1ehuXr58uXcd999TJ06dcjKlIa8EEKIEUEzDDTDKGs8gJUrV5JMJkvDh+IMf2keH2pA9z0jfUs0rB977LEhL1MIIYTYEio1V++///68+uqr0pAXQgghNqJr5T3SpniWP5lM9js4GCqjRo2itbW137C1a9dimiZ1dXVDPj8hhBBim1GhuforX/kK55xzDq+99hq77LLLRpfuH3rooQMuUxryW7lvzgXoOytUzY/vDXjj1XWlz03LIFEVBqC3K0Xb6k5qmqqKn63/ktTWxdANVU6iOoLn+QDksq4quTaKaRpUNyZ56x9vABCJRYkmo5iWQSisNpW21WCFLFzHxbTUmTLf88k5HqYdAsB1XAJfndHqWttBvCZZHM8jmojh5J3ieA6RUJSejl4AjA3Ozrn5QmkaACfv09uVBaC+sZZ0RzcTdlZntHo6erBs9WWI11Sh6RqFXL5UTk+HRi6VASBalSjWjVoe37WKf/1S7IVcgVBYxRwEAdl0gVhcnf2zqyN0dmRKy67pGp7nl5Y3WZugp6OXUFjVRTadxbQMClkVTygcIpvKUlWv1lFvbx7D0DGK60Y3dPQP3Tdk2yaGsX5Yssomn/co5NzSMNMOYdsGTkQtTyRsERtXTapXzTdZE8NxPapqogCEIwbhiEF3V4FEdQSAfN5D0zTqay1aW1X919aFMS0dy9IZN7mWjjWdxektOtvSNI1W9dQLJOLrY8ylMiRqEoTjsdLjQzzHJVIdw47apWGxmIEdtYvrMUXTuFpV735AOqViz2QDTFMvzteku1vF1lcnqd5CaV1kMg7Fk6lMm2JT3IxwXbU+ips9hqHRVOPRk1Jl1IxR465pzQFQ32Bjh8BxKZWhaRAPeyTjat0buke+UNzO02pYdRXEHB3bgsY6tY1ZJqxao9bV1Ek2alNR041t8Dl8d4M/PKveHz17wzPAGuu/+xvuqjc8i51ktloC+guxKXvtmNjk8BFD09SrnPG2oNmzZ/OXv/yl37CHH36YWbNmDdk9d/PmzeOiiy7a6JLDTbn77rtxXZevfe1rQzJvsbGa3fbt977rlccBiOS7AXAS9QCYhTR+RKfaUXk8ayVwNJteLw5And5GvHM5AJmmXUlk1XgdIXVQGTbyuH4E39fIBmp/3lmIEzPzOIFJMugCwAtq0dHIOOr4QNd8TN8hy/r3dq6bfFjlIktzqc23ANATrscpGERMtQ8uBDYRLUOkoHJ1ykriorbjcJAh0HTcQO1LXc+kNpwCIO1F8XWLgqHmGaCR1WJqfuQJNB1fX/99aKehdLWth45GgKurfVkocNEIMFDHBIbm0+tECelq35pzLUKGh+MX95VBgI+OXhw/bVdT07OCXGKMel8IEQRgGSopJMwMgW6ScVU+rLU68DQDU1PlawSYmosdqOOQHNHS/wBWrodwLItpBMVl1THCYTAtAtbvbzKORSEcRtfUfLusBlxPw9NV3HahFy0IsAppVQ+RJjQCLN3F9tVxTCEUxw90QloBy1UxOGYE283QadQSEKanoLaNuN2DoXlkneIxjJsiHCqQ19XnRuBjmy4eRilOTzfxAx3bzxAurnPf1wihtodOv6YUf4g8tqviCukumqvq28XENpxS3QHEsu3qve/RlRirtqUgQWc+zujwWgDyVpxeN069obZ7TzNxfYOsob4fjmFjeflSfRq+i2PY9FCtllfrpaXQRGOoDd1QseQDG0NzsXPdeNZoFatWIJFro9VsIqSp7bPaa+N9fxIA2VAcH506VBy+prPrdo288Y76juy+fTUU56n2wFXFiDa9P15/gXbVJj9XGkr/jd5+V0Z/xJjbvArK1RuaN28eAJdddtlGn0lnd0IIISqaputoZZzlL2ecDaVSKd55553S+2XLlvHKK69QW1vL+PHjWbBgAatWreLOO+8EVLL++c9/zvz58zn11FN59tlnufXWW/nd7343sAX6CA0NDey8887stddeHHroocyaNYvRo0cTDofp7OzkzTff5KmnnuL3v/89Y8aM4Ze//OWQzVsIIYQYrErK1RvyfX/Iy5SGvBBCiJFhCz2b9sUXX2TOnDml9/PnzwfUY9/uuOMOWlpaWLFiRenzSZMmcf/993POOefwi1/8gtGjR/Ozn/1sSB9n89///d9861vf4tZbb+Wmm27aqJO7RCLBl770JW655ZaNeuUVQgghhk0F5eotTRryQgghRgatzJ5wB3i53n777VfqAGdT7rjjjo2G7bvvvrz00ksDms9ANTY2smDBAhYsWEBXVxfLly8nm81SX1/PlClTpMd6IYQQW58KytW///3vOfbYY8sad+XKlaxYsYK999677PIHdqpDCCGE2Er1PZu2nNdIU11dzW677caee+7J1KlTpREvhBBiq1RJufrGG29khx124KqrrmLJkiUbfd7d3c3999/Pcccdx8yZM+no6BhQ+fKLvBBCiJFhgM+mFUIIIcSnrIJy9aJFi/jrX//K9ddfz4UXXkgsFqOpqanUn01raysNDQ2cfPLJvP766zQ2Ng6ofGnICyGEGBm20H13QgghhBgiFZar/+3f/o1/+7d/o729naeeeor333+/dBvcjBkzmDFjBvoAO/brIw15IYQQI8NW8kgbIYQQQmxGhebquro6DjvssCEtUxryQgghRgZdV69yxhNCCCHEp09y9ZCRGhJCCDEy9F2uV85rBLnkkktYvnz5cIchhBBCfLwKzdVbgtSQEEKIkaGvA51yXiPIX/7yF6ZMmcIXv/hFfvvb35LL5YY7JCGEEGLTKjRXbwlyaf025juHa3D4+h4NG0clmDrJBmDl2zqe59E8JgFAPG5SyLkAeL7P6NFRAN55J08u6wAwZmwcgHTGw/N8bDuCbhoAuI5L3agkfhCgo75MNQ1VhMIWa1e109mWAiASs2ldvo5QWMXheR6u4wOQqKvCMFR5uVQWTdewbAsA0zLJZXJE4hEAWt/7gHA8Vhw3rZahpgqA3vZOutp7iktdixUJk+pS8+9e214qw3NcwvEInqOW2zcM9OL8+z7P9GTIpTIABL5PNp0hn1PzTdTECYVD9HarA+Fw1Ka6NophqOU3TR3TMsjkVP05eZeejhTJWlWPVsikB+hu6wagqr4KTdMwLPVVi1VFCYVD2GGrOL2HnVz/NQxHQ+i6RiishiWTNnq1TcsqtayarmGaOrGYie+vf1amYRoYhk7fIN3QiMVNYnFVTtvarKrPpFpH6bSDaeqEwgbZ3Ppla2xQcYVCxXWW9YjFQ+TzHul0oVTPfdtPtvg33Z3DcRNYkXBpveuGTi6Vxi4uS3VDkmjMoq4hRj6v1s/q1Rm627oA2G329qXlsUI6fvFZoGFbI50uADB6dAzXVucfXVdtY7V1ap49PQXCYZNwRMXemw4Y26DGWbnaIxbVaaxV5QeBhRcERMJq2UfXB7T36PREVayJmE44FLC2PSAW0UrlAdQmi/ON5qivjtGT1qlLqOVZ120ytsHlKzM/vGtd//7Nd1az09TR/T49eracUx0SmlZmBzoj6+Dgn//8J4sXL+b222/nnHPO4YwzzuDYY4/l61//Orvvvvtwh1eRqj+zX7/3nqn2vXbnapyEQTTTBkCqqppcEEbX1H6llyrqdbW/MP1Cafo6vTg+SdqzUWzDIyjm5axj0mB30e3E6dVVzix4Js3RDtblqgGIWXlczaIrr44DomaBrsRY9MADwNA8rILKi75tkDTTGJr6zAsMAl3DMdQymJpLJFA52tNMTL+Aoan949p0iNHRPAA5QPN8CrraR9vkSzH7mk4hWoNRXEZft9AIiFkqV3m+gR/oaMV6sfUCeT+Eaah9bc6zcX2diKlitE2XqLm+vnrD9Rh4RN1eNb4ZI9ANclq0uEwaOUcnZqt40q4a7hent4ICvmbgsf74wcCjoKllWZluZGJsNQZq/ugGOj7hkKoHJ7Aw4lHwPCzNKZWRLRhYQb70K5obmJjG+lxu5XvJRetK6yXQNDQtwNIccpo6TrHdTGl7MYvrTAt88lYcnQBd89BQZRqBS6BpWLoqz8p245gGWnFJTc0lCDQMPHyteOxnhDACj26tloS3FoBoyMUvRq1rPqaupk8FCRwzpGJwA3K22v4SXifpYAwAcbcLGI9nqPFCXgrbVXGnzCSjw2vJBqr+I1qKqJ4lr6ljjSqnDcusoi1fA8A4ayW+ZmCgtgNXt9ADj5BeKNVD2ChQCGxyxTKSQScBOk4oRtZT6083fQpmlD13qAKqirU/mvWZOcGKt5cwfrsd2dD0qc2IIVChuXpLkKNHIYQQI0NfBzrlvEaYXXfdlZ/85CesWrWK2267jVWrVrH33nuzyy678NOf/pTu7u7hDlEIIYSo2Fz9+OOPD3mZ0pAXQggxMvR1oFPOa4TyfZ9CoUA+nycIAmpra7nxxhsZN24cd99993CHJ4QQotJVaK4+6KCDmDJlCj/4wQ9YuXLlkJQ5smpICCFE5arQs/ygLq8/88wzaW5u5pxzzmHGjBksWbKERYsW8dZbb3HxxRdz1llnDXeYQgghKl2F5urVq1fz7W9/m3vuuYdJkyZx4IEH8oc//IFCofDxE2+GNOSFEEKMDBXaE+6uu+7KnnvuybJly7j11ltZuXIlP/zhD5k6dWppnBNOOIF169YNY5RCCCEEFZura2trOeuss3jppZd48cUX2X777TnjjDNobm7mrLPO4tVXXx1wmSOrhoQQQlQu3QCjjJdufHxZ25CjjjqK999/n7/97W8cfvjhpQ5GN9TQ0IDv+5uYWgghhPgUVWiu3tBnPvMZLrjgAs444wzS6TS33XYbM2fO5Atf+AJvvPFG2eVIQ14IIcTIUKFn+YMgoKamZqPh2WyWyy67bBgiEkIIITajQnM1gOM4/OlPf+KQQw5hwoQJPPTQQ/z85z9nzZo1LFu2jHHjxnHUUUeVXd7IqyEhhBCVqULvu7v00ktJpVIbDc9kMlx66aXDEJEQQgixGRWaq7/1rW/R3NzMvHnzmDZtGi+//DLPPvssp5xyCrFYjHHjxvHDH/6Qt956q+wy5TnyQgghRoZye7kdYT3hBkGAtokDnldffZXa2tphiEgIIYTYjArN1W+++SbXX389Rx55JKFQaJPjjB49mscee6zsMqUhL4QQYkQINI2gjDP45YyzLaipqUHTNDRNY9q0af0a857nkUqlmDdv3jBGKIQQQvRXabm6z8UXX8xee+2FafZvfruuyzPPPMM+++yDaZrsu+++ZZcpDXkhhBAjg6aVd0/dCDk4uO666wiCgK9//etceumlVFVVlT4LhUJMnDiR2bNnD2OEQgghxIdUWK7uM2fOHFpaWmhsbOw3vLu7mzlz5uB53oDLlIa8EEKIkaHcznFGSAc6J554IgCTJk1ir732wrKsYY5ICCGE+BgVlqv7bO42uPb2dmKx2KDKlIb8Nq62xqLgqP+rG5PEk2E6O3PFT8PU1EUBqKpaf4DX2BSjo0ONk82qsz9OwSMatVjbmqJ5yjgAOlraaFnWRl1zNWZc3cthWOpREFbIQjfUF6yrrRfd0EufOXmnNK+ahipymQIAmZ4Uibok2VQWgFwqg+d5GKaarmniGDK96eJn6m+qs7tUVs+6LgBClo5lh2j/oBUA0w7RvmotAJqmo+kave3FcSM2hVyefDpbGtd1XIINHsMU+AGGqZbFMHWiiTDZdL64nCbdnVmqaiIqnp4s0ZiF76npTctA1zXCUVW/7a09WCELz+mrVwen4JJLZQDIZ+IYlkk0Yav3ORdd07BD6osdi9uYpoFRrFvX9cnlXOoa1HrM5UKsbU2h6QlixTIAfD/AsnQCPwBg6uQw2VxAb0rFmaiy0TWwQsVynYAgCIrbQ7RfOYGuUVdnF+taY+26Ap7no2tav3VbXRfFKq5zPwgIAnCyudI24IScYn37pXq2LIN83i0tn2FoxKripTIMQ9VDqrdALqPmVVflEwqp+ei6hl5cdWZxnXV25kvlh2sMDF2VURXX6M0axc9cTBNcT31WKHjkCjphuzi/rEZTtUc6YxbLBs+HKeMgZKh1ObmxgKV7hAwXgN23r+bzAGj035V+9G51p6mjP/JzMXhb8nK9G264gauvvpqWlhamT5/Oddddxxe+8IVNjvv4448zZ86cjYYvWbKEHXbYYcDz3pSenh6SySQAM2bMIJvNks1mNzlu33hi+EQ/WAKAUz+GMcYqzN52AMLxZiy9wCpX7RdGWy3k4g0ARPLd9EbU/x2e6usgrKv9nW26hDSVW3UNPAxiZo6Cr3KRpgXkfJtMQe2PggDS0Sqyjnofs/LktCgeah/Z60SJxNfvmzQtIOOrvBfTM+T9MCFN7d9tcviami7lJ6imHS9Q3ynLUPPtizVl12Kg9qERp4uMpbbFaL4Lw8mV5mf1thFJ5nADFV9e09A1H0dTZZm4ONr6fWvS7MXxq+ktqBijZoGokaXbUfnE0F18TUcP1LzDbppUtIFCoI5l6iIZ8iGLiKHyTMTMEUq3E46pOvU0k5S3/sA6QGOc+x6OaRfHryMXRNBZfyyhBx5hS73vcWOYdfU44Vi/ceK2S0ELU/DVskQJCJse8Z5VADjhJJ4RIvBV3FoQoBFgk8PyVH0Fmo4bmOT9ELmIelpFONtJEEriBCYGHp6vl2JyAxNDU+V5oRhxK4uDqgcdH0v3MHHQAhWnrxnknBAJO4VvqO3JCzTyQVitC83D1NS4jm+S1LtUXEEVQTH/poxqfEfFkA2pdWK6atvtiTfj6VapXl0soro65tNdj5CRL20zeTNKTyGGrq2Prd2vp05vA8B2M6TsGkzUejR8l4iRI0y2tG2Pn7pjqf4bSv/VAGP4KOO32/EjPxeDV0m5GuCII44AQNM0TjrpJGx7/fG753ksXryYvfbaa1Bll9WQ7+npGXDBcuAghBDiU7WFzvLffffdnH322dxwww3svffe3HzzzRx88MG8+eabjB8/frPTLV26tF8ubGho2Oy4A1VTU1O6RK+6unqTZ/n7zv4P5nI9IYQQYouooFwNlG57C4KARCJBJBIpfRYKhdhzzz059dRTB1V2WQ35zR0kbI6mafzrX/9i8uTJgwpKCCGEGLByH1czwLP81157Ld/4xjc45ZRTAHVv+kMPPcSNN97IlVdeudnp+hrZW8Kjjz5a6pH+0UcfHVCOFkIIIYZNBeVqgNtvvx2AiRMncu655w76MvpNKfvS+j/96U9lPcYmCAIOOeSQTxSUEEIIMWADfKTNh682s2273yVvAIVCgX/+859ccMEF/YbPnTuXZ5555iNnM2PGDHK5HDvttBPf/e53N3kJ32Bt2KvtfvvtN2TlCiGEEFtUBeXqDV188cVDXmZZDfkJEyawzz77UFdXV1ahkydPlk53hBBCfKoGet/duHHj+g2/+OKLueSSS/oNa2trw/M8mpqa+g1vamqitbV1k+U3Nzfzy1/+kpkzZ5LP5/mf//kfvvjFL/L444+zzz77DGCJynP77bcTj8c56qij+g3/4x//SCaTKXWKJ4QQQgy3SsrVn/3sZ/n73/9OTU0NM2bM+Mir51566aUBl19WQ37ZsmUDKvT1118fcCBCCCHEJzLA++5WrlzZ7764D5/h7zfJh5Lv5nqfBdh+++3ZfvvtS+9nz57NypUrueaaa7ZIQ/6HP/whN91000bDGxsbOe2006QhL4QQYutRQbn6sMMOK8V7+OGHD0mZGxqRvdbn83n22GMPXn31VV5++WU+85nPDHdIQgghtrBA0wnKODjoGyeZTH5sx6z19fUYhrHRGf21a9dudOb/o+y5557cddddZY8/EMuXL2fSpEkbDZ8wYQIrVqzYIvMcCpKrhRCi8lRSrt7wcvphu7T+w55//nkef/xx1q5di7/BY7xAdTQw3M4//3xGjx7Nq6++OtyhCCGE+LRsgQ50QqEQM2fO5JFHHuGrX/1qafgjjzzCYYcdVnY5L7/8Ms3NzWWPPxCNjY0sXryYiRMn9hv+6quvln1L3HCQXC2EEBWoQnP1Cy+8gO/77LHHHv2GP/fccxiGwaxZswZc5oAb8ldccQXf/e532X777Wlqaup3ucLW0GvuAw88wMMPP8zChQt54IEHhjscIYQQn5KAMs/yM7BH2syfP5/jjz+eWbNmMXv2bH75y1+yYsUK5s2bB8CCBQtYtWoVd955J6B6yp04cSLTp0+nUChw1113sXDhQhYuXDjwhSrDsccey1lnnUUikShdDrho0SK+/e1vc+yxx26ReX5SkquFEKIyVWquPuOMMzj//PM3asivWrWKq666iueee27AZQ64If/Tn/6U2267jZNOOmnAM9vS1qxZw6mnnsq9995LNBota5p8Pk8+ny+9/3DPiEIIIbYRW+iRNscccwzt7e1cdtlltLS0sPPOO3P//fczYcIEAFpaWvpdwl4oFDj33HNZtWoVkUiE6dOn87e//W2LPdHlBz/4AcuXL+eLX/wipqnSuu/7nHDCCVxxxRVbZJ6fhORqIYSoYBWaq998800++9nPbjR8xowZvPnmm4Mqc8ANeV3X2XvvvQc1sy0pCAJOOukk5s2bx6xZs3j//ffLmu7KK6/k0ksv3bLBCSGE2PI0rcwOdAZ+9djpp5/O6aefvsnP7rjjjn7vzz//fM4///wBz2OwQqEQd999N//93//Nq6++SiQSYZdddikdvGxNJFcLIUSFq9Bcbds2a9asYfLkyf2Gt7S0lE7CD9TArlkAzjnnHH7xi18MamaDcckll6Bp2ke+XnzxRa6//np6enpYsGDBgMpfsGAB3d3dpdfKlSu30JIIIYTYkvoeaVPOaySaNm0a//7v/86Xv/zlT70RL7laCCFEOSo1Vx9wwAGlXNanq6uLCy+8kAMOOGBQZQ64+X/uuefy5S9/mSlTprDTTjtt9Lz4e+65Z1CBbM6ZZ575sff4TZw4kR/84Af84x//2OiRBLNmzeJrX/sav/71rzc5rW3bH/kYg63duUdscC7mwHqu/d+Ari4HgHffWkc0EQagtrYG1w0AdRLss7vGAVj8ZgaAcMQkm3FoHpPAMFSZvueTqI6RyxSwQgYA+WwBTdPQdQ3TUsMs28JzPOyoqkfTMrFs9ZlTcBk1rhqA7rZuQuEQ7avWApCsr6FtZSuuo+L1fR/dMEqLE47HyKXSpfe1o+sBKDg+0UQMozhuursHN18AIJKMk89kMSy1aefTWexoBKO4nbr5Ak6+gG6qaXXDwLIt8lk1fThq4zoeenHnoenqb2ebiiMSC6FpGvmcijkaD/WL2XVcnLxDTZNa5p6OXqyQWYo9m8rRMLaOaDSk6i5kkM052KFIqQzD0AiHzdL8bdvEMIpxrEuj6RqBH+B56zua1HWNcFgvjadrEI9qdHapdZ7LukSjJqleFXcsZlFXa5HLB6RSLgDxuInvg+MGhCxVTmMtZLImXZ0FIlGLmoYqNX08RCxmEY2obSWXi6Hr63e40YS6XDZWnSBRtf775bo+uq6V6jcaNUnWJgCoq7Pp6zszn/eorVdlZPM6yaSqr+qkjuupcZLFbTsaVevWNDWyGY94XNVdwYXGKjVyVZVFMqYxrjYLwAn7qvp+dom6PNfQAmqsLvxgLACH724AWvHVJ4LYugWaQaAZZY030tx5551cffXVvP3224Bq1J933nkcf/zxn8r8JVd/tPAhp6m/QALIrF4MQN6MEnFSBIHa13i6ieGpfOSaYZJZlS9fz08EYFp1C83xHkK6Q11aXSK6jGYc38T1TRJWCoCsYeP5BqahckDeMygEIWyjuE+0UqTcWCm+nBvCL+Z0Axdd80j7ah9cn11Od6yZWLYDgLboeNxg/eFjoOml+AE8v5j/sYjRg2Oo9eYYNgWK/5sRDCe3fppokqwfxvVVuREjhxcYaKikEKDKLAQqF5iaS7oQwtTV5xk3RBBohHS3VKYVFIhm2gDoSoylizq8QOWsiJknZLg4xVjjWi9s0HlznjC2XsALiscKmo+GT8FQeSAUuES1NO2u6kwyF6khp0XRUfWtAxgmgRki7K8/junOWfhxnbCh1nFMSxEykwSeWi7DzeOFq/F0s1i3GobmE6CV5q0RkHIj+IFO3izm2sIq3IRF4GuEdIdQcT1rQYCpuQTF/OyEogSBhqF5pfrUNBWzEbjFutaImHnsIIvhFo+NQi5dBZWrq0Ip+moqYuTwi+umPRtlkq1yrGZUEyouYyFQ6zwTqVXrxcuTM9S2F6cXANtRx6J64OFiYaDimzB1eyYALy7tLNVhWM/TGaiyJu44jfL7Ixdbi0rN1T/+8Y/ZZ599mDBhAjNmzADglVdeoampif/5n/8ZVJkDbsh/61vf4rHHHmPOnDnU1dVt8Q7u6uvrqa+v/9jxfvazn/GDH/yg9H716tUceOCB3H333Rt1KiCEEGLkGegjbUaKa6+9lu9973uceeaZ7L333gRBwNNPP828efNoa2vjnHPO2eIxSK4WQghRjkrN1WPGjGHx4sX85je/Kd0Gd/LJJ/Mf//EfG/0wXq4BN+TvvPNOFi5cyJe//OVBzXBLGT9+fL/38bj6xXnKlCmMHTt2OEISQgjxadL0Mu+7G1kHB9dffz033ngjJ5xwQmnYYYcdxvTp07nkkks+lYZ8uSRXCyFEhavQXA0Qi8U47bTThqy8ATfka2trmTJlypAFIIQQQgyFcu+pG2n33bW0tLDXXnttNHyvvfaipaVlGCISQgghNq1SczXAu+++y3XXXceSJUvQNI0dd9yRb3/724NuWw/4VMcll1zCxRdfTCaTGdQMPy0TJ04kCAI+85nPDHcoQgghPgV9l+uV8xpJpk6dyh/+8IeNht99991st912wxBR+SRXCyFEZanUXP3QQw+x00478fzzz7Prrruy884789xzzzF9+nQeeeSRQZU54F/kf/azn/Huu+/S1NTExIkTN7qm/6WXXhpUIEIIIcQnsoWeTbu1u/TSSznmmGN44okn2HvvvdE0jaeeeoq///3vm2zgCyGEEMOmQnP1BRdcwDnnnMMPf/jDjYb/v//3/wbVc/2AG/KHH374gGcihBBCbHHlnsEfYWf5jzzySJ577jl+8pOfcO+99xIEQemsf1/PuEIIIcRWoUJz9ZIlSzZ5cv3rX/8611133aDKHHBD/uKLLx7UjIQQQogtKUAjoIz77soYZ1szc+ZM7rrrruEOQwghhPhIlZqrGxoaeOWVVza65e2VV16hsbFxUGUOuCEvhBBCbI0q6ZE2PT09ZY+bTCa3YCRCCCFE+SopV2/o1FNP5bTTTuO9995jr732Kt0Gd9VVV/Gd73xnUGWW1ZCvra3lX//6V1nPiAX1eJknn3ySCRMmDCooIYQQYsA0yrzvbotHssVVV1ejfcyyBkGApml4nvcpRSWEEEJ8jArK1Rv63ve+RyKR4Mc//jELFiwAYPTo0VxyySWcddZZgyqzrIZ8V1cXDzzwAFVVVWUV2t7eLgcOQgghPlUBOkEZD2MpZ5yt3WOPPTbcIQghhBADVkm5ekOapnHOOedwzjnn0NvbC0AikfhEZZZ9af2JJ574iWYkPh3zD9OAEAB//Ecz//d4FwA9vS5jm9Xwuhqdju4AgNpaGwDPD4hFw1imRneX2iwmTGvCtg3+9dpqmkarkzgda3spZAvYUZtwRD2xwHU87KhNqiul5tXeRbI2DoBurP8ShqNh0t1pdMMAIJfKoukaruMC0NRcx5oVawBINtRSP6aelUtXqHJ0DdNScXV15onEwxRyeVVuPEamS11mWsiqYX7xRJJuGhiWWXofSVRj2gWyPSpWwzTQNJ1wVNVDJGbjeX4p5lymQE1DnJZlbQCEwrXYtoFpqWWwbZN4VQTXUdM0jqnhvddXEIs3qHhyDoWcQ22jqr+ezjSFnIMfqPqvrrYpFHyyefW+rj7Ce2+3M2ac+mK3t+eIRC1sW81P0zWskIlhaPju+jgTtdVEwjqep8rZc/xq/rFiNIahFeM0MAyNoDhJPG6QiILrgmlusI5sDcuErl5VTndKw7Z1rJBOLufSPE4tRyik47o+4bBR2gZMo1QMvR09TNhxHImaGLU1ajvp7soxqinM8hUpRo0Kq7hCGvVNalkNQyMo1sv48TGyWRVsfcJhWfHMbRBAVE3KxPocEOHIL6QBeObtGr715Q+fvlVBHTqr732k36ezd9zwkuMqthvcYzzFVqKSnk277777DncI4hOI7nssAFOAnn8+xLik2k82LnsBUiqf5cftgGepHd64ZCcAMa8bW8/iaDad8TEAWGmPrGuT80zChsqBjqejYZC0swC4vknBtwiZKhcaeOS8EGGjAEAylEFD7X8tL8dKbzyGpt7n7CqyQZSwpfK6obmYOABkieJoIXrzKv76WA4/UDml4FsUzDBhV+2j82YUM1DTuUYIjYC8rfbB4Uy7Klsvxqd55HybXKD22QEaYT2Pjl+KoSacIeOq4xo/0AkZBUxNTZ8mQZQUmqfml9ejRMjiFxsGrbk6YmaevKfiNo0CvmXj+uo4I6LnqPLayZgqP3V7VeTNKH7xUl/HN9ADj4Knxg80Az/QidlqflVWD5gmqVgTvrZBcgQsrYCuqeUKeVmCYP39wrrvYvguvVZtabk0AvwNGj4FI0LgaUTNXOnS40K0hpweI/A1wlqWRPF4KQg0QuSxdLWeDTdPwu6hgDrm6XFiZJwQNfkP6I2pe3RN3yHr2RACw1GPmq6Kp8h6alvU8Uv1FPN7SOtqHdZEMmSNRGmcghcqrqviOvNV3dj5Xpp2nMnmNG1i2Kzta4r/1WziU7GtqaRcvTmftAHfp6yGvO/7Hz+SEEIIMYwq9b47gCeffJKbb76Z9957jz/+8Y+MGTOG//mf/2HSpEl8/vOfH+7whBBCCKCycvWMGTM+9ja4PoN5hLt0dieEEGJEqNSecBcuXMjxxx/P1772NV566SXyefWrbG9vL1dccQX333//MEcohBBCKJWUq7f0Y9ulIS+EEGJEqKSz/Bv6wQ9+wE033cQJJ5zA73//+9Lwvfbai8suu2wYIxNCCCH6q6RcvaUf277t15AQQgjB+vvuynmNJEuXLmWfffbZaHgymaSrq+vTD0gIIYTYjErN1aA6kL/llltYsGABHR0dgLqkftWqVYMqT36RF0IIMSJU0uV6G2pubuadd95h4sSJ/YY/9dRTTJ48eXiCEkIIITahUnP14sWL+dKXvkRVVRXvv/8+p556KrW1tfz5z39m+fLl3HnnnQMuU36RF0IIMSL0Xa5Xzmsk+eY3v8m3v/1tnnvuOTRNY/Xq1fzmN7/h3HPP5fTTTx/u8IQQQoiSSs3V8+fP56STTuLtt98mHA6Xhh988ME88cQTgypzUDXk+z7/+te/eOqpp3jiiSf6vYQQQojh4GPga2W8MD6+sA+54YYbmDRpEuFwmJkzZ/Lkk09+5PiLFi1i5syZhMNhJk+ezE033TTYxfpY559/Pocffjhz5swhlUqxzz77cMopp/DNb36TM888c4vNVwghhBioSs3VL7zwAt/85jc3Gj5mzBhaW1sHVeaAL63/xz/+wXHHHcfy5ctLz3zuo2kaXvF53UIIIcSnaUtdrnf33Xdz9tlnc8MNN7D33ntz8803c/DBB/Pmm28yfvz4jcZftmwZhxxyCKeeeip33XUXTz/9NKeffjoNDQ0ceeSRA5r3R/nMZz7DKaecwte+9jUuv/xyLrroIt58801832ennXYiHo8P2byEEEKIoVBpubpPOBymp6dno+FLly6loaFhUGUO+Bf5efPmMWvWLF5//XU6Ojro7Owsvfpu2hdCCCE+bapznHIu1xvYwcG1117LN77xDU455RR23HFHrrvuOsaNG8eNN964yfFvuukmxo8fz3XXXceOO+7IKaecwte//nWuueaaoVjMkj322IPvfve7jB49muOOO45nn32WWbNm8bnPfU4a8UIIIbZKlZar+xx22GFcdtllOI4DqB/AV6xYwQUXXDDoEwcDbsi//fbbXHHFFey4445UV1dTVVXV7yWEEEIMh76z/OW8AHp6evq9+p6/vqFCocA///lP5s6d22/43LlzeeaZZzYZx7PPPrvR+AceeCAvvvhiKYEPhZtvvpnW1lZ++ctf0trayty5c5k4cSKXXXYZK1asGLL5CCGEEEOl0nJ1n2uuuYZ169bR2NhINptl3333ZerUqSQSCS6//PJBlTnghvwee+zBO++8M6iZCSGEEFvKQB9pM27cuH4noq+88sqNymxra8PzPJqamvoNb2pq2uw9ba2trZsc33Vd2trahmhplXA4zPHHH8+jjz7KO++8w/HHH8+tt97K5MmTOfDAA/nDH/4wpPMTQgghPolKy9Vnn302r7/+OslkkqeeeoqFCxfywx/+kDPPPJP777+fRYsWEYvFBlV2WffIL168uPT/t771Lb7zne/Q2trKLrvsgmVZ/cbdddddBxWIGHpH7alz1J61pfcPv1oAwDZdVociALz9vurnQNPB9QKqk+s7lojFLEIhnUg8jG6oL1NtY4Jc1iGbymOF1LiJ6ghO3mPdB2sB8N31/SRU1URI9agzZ1X1CVa/24LruABYdohYVRLdVOeTdEMnEosCUD+mlo41XRiGmke8Jkk4EgIg1ZOnY00HXkGVE6tOkOlaf8+JGTLJp9WZtEgyiee4GJZZXE6NTFfv+nHtEKZlkunNAhAEAaGwhV/s/yHw1d9Qcd5OwSWf9zAMFXOqN4/n+ejF93bYJByPUF2r6rettRtN14gl1PTd7b3UNsbJ51XsNTU2iYRJTVIrzt8gngzT06PiD4dNLFMjHF5fR4WcQzrtEIvbpeXYdY+JxKIak6eqq2JGZd9gTVtTaTk8L8DzAkLh9etX1wPskEYur+Ydi6i/iaiPVuwp1DSg4EBdXYg1a3yaR6nlaO9wqau1CNtqmobGKK4HNaMbAahrrqWhMUou67HTBLUsa9ZYaLqGbZs01qs4Ort9Egm1D4mENcxiPbZ1uoxqUOusJpLi+1/b1NU+qo5nTqst/t3EKKKiBIFGEJRx311xnJUrV5JMJkvDbdve3CRoH7rELwiCjYZ93PibGj6UJk2axH//939z2WWXsXDhQr75zW/yf//3fxx99NFbbJ7ik0vOPJDSVrjdjuQevAWAUFcLuTp1X6eh+QDEU2vxDYtUpB7TV/tW19exDJ+erEXWUfvDkOETsQqlefgBWJpLVSitpglMDM3DD9Q+t7cQJhFNARAYGr6ro2kqlweahhuYuIba/xeC9d8TH51sEMXQ1PadcSxMu5ibjRw+Bm2aOlAenVtOW2g0AAmvA8338HW1n/fMsPoF7kPf3774+ualF+sh48ewjQJacb6eb+AHOraeASDtR6n2c+hODoCI10vejGIEKjbf1yj4JhFD1WFGT5AIJzF19bmOT7dZV4rH0lx03y/VeRBoZLUYlq7qKG9ECWtZXF/Vf9638da0YE3N4enrD7XrY3mihR5Wus0ANNgGBc/ACamDeKuQxteM0i+RGgGW5pALIpiais3DIGFlCGkFwgW1zjwjhIeBqbv96ilnxPADA8e3SuVHrW4MUx1rre4Zw4TqHszOHgpJta2FyBHCJZrvQvPV8q1O19IY7S4tR8TMF7cNnYTXCUA3ccZvtyMbU/m7aceZm/hMVKJKy9UPPvgg119/PTNnzuSUU07h2GOPZf/99x+SsstqyH/mM59B07R+ndt9/etfL/3f95l0dieEEGL46ARlXWimxkkmk/0ODjalvr4ewzA2OqO/du3ajc7k9xk1atQmxzdNk7q6ujLiG7zHHnuM22+/nXvuuQfTNDn11FO36PyEEEKIgamsXP3WW2/x9NNPc9ttt3Huuecyf/58jjzySL7xjW+wzz77fKKyy7q0ftmyZbz33nssW7Zsk6++z957771PFIwQQggxWAO9764coVCImTNn8sgjj/Qb/sgjj7DXXnttcprZs2dvNP7DDz/MrFmzNrqKbSisWLGCyy67jMmTJ/PFL36R5cuXc8MNN9DS0rJFH6UjhBBCDFQl5uq9996bW2+9ldbWVq6//nqWLVvGfvvtx3bbbccPf/hDVq9ePahyy2rIT5gwofRavnw5Y8aM6TdswoQJjBkzhuXLlw8qCCGEEOKT2hIHBwDz58/nlltu4bbbbmPJkiWcc845rFixgnnz5gGwYMECTjjhhNL48+bNY/ny5cyfP58lS5Zw2223ceutt3LuuecO6fL+9re/5YADDmDy5MncfPPNHHPMMfzrX/9i0aJFnHDCCUQikSGdnxBCCPFJVVqu3lA0GuXkk0/miSee4O233+boo4/mRz/6ERMnThxUeQN+jvycOXNoaWmhsbGx3/Du7m7mzJkjl9YLIYQYFlvq2bTHHHMM7e3tXHbZZbS0tLDzzjtz//33M2HCBABaWlr69RI/adIk7r//fs455xx+8YtfMHr0aH72s58N+XNpTzrpJL785S9z7733csghh6DrA+6/VgghhPhUVVqu3pR0Os2iRYtYtGgRXV1dbL/99oMqZ8AN+c11GtDe3j7oHveEEEKIT2pLHRwAnH766Zx++umb/OyOO+7YaNi+++7LSy+9NOD5DMQHH3yw0Ul1IYQQYmtWabl6Q0888QS33347f/rTnwA46qijuOqqq9h7770HVV7ZDfkjjjgCUB3bnXTSSf16DPQ8j8WLF2/2HgQhhBBiSxtoT7jbOmnECyGE2NZUWq7+4IMP+PWvf80dd9zBu+++yx577MFPfvITjj32WOLx+Ccqu+yGfFWVenxEEAQkEol+996FQiH23HNP6R1XCCHEsNmSZ/mFEEII8clVWq6eOHEidXV1HH/88XzjG99gxx039ZjGwSm7IX/77beXgjn33HPlMnohhBBblUo7OBBCCCG2NZWWq//whz9w6KGHYpoDvqP9Yw24xIsvvhhQz9lbunQpmqYxbdo0ucRPCCHEsKq0gwMhhBBiW1Npubrv9vQtYcBd3Pb09HD88cczZswY9t13X/bZZx/GjBnDf/7nf9Ld3b0lYhRCCCE+VoBWuvfuI18j5OBgQ11dXdxyyy0sWLCAjo4OAF566SVWrVo1zJEJIYQQ61Vyrh5qA27In3LKKTz33HP89a9/pauri+7ubv7617/y4osvyj3yQgghho2PVvZrJFm8eDHTpk3jqquu4pprrqGrqwuAP//5zyxYsGB4gxNCCCE2UKm5eksY8KX1f/vb33jooYf4/Oc/Xxp24IEH8qtf/YqDDjpoSIMTQ2vubqHifyGeeCMNwCtpdS4nnrCoqTJwvQDTUsMScYOdJnp0dCTxvQCAHXZI8u57aSZPrWHVyl4Apm1fxfvL08RrEgDk01kMQ5URBAFVNWEA0qkCurn+3FE+kwXAsCwAdE2jrrkGUE9H6G3vJl6TLI2fTecA8LwQ2Z4U8RrVAWOqc/2VIJqukU9nS+8t20LTdCJx1adDVX0VnavXluZphUK4jksoouoml8nj5B2iCdWZY6Q2RqY3XyqvujZKNGrS+oGKJRwN0TgqQTpVKMVtWiZOwQcglogQ+AG5jFMqI5m0SRXHj0V1RtVDMuwU68viHS+gqkrFF4/pFBzIZD0A7LBJEDKIxax+j4GcOjHEuNosr2ZUXbdHx5FKuYQjBgC+FxCLmXjF9ahrUHA0krGAfEGV4/kwus7D9TUKxXCLq5FkXKO728Qy1bjjRltk81AVU8vZkzAZ3+gyelITANtNq2bXKS7/eN1iWmI5AOt2mEI87BG2o+TyKo7aap10SJVZVwWuWkz+66ANd01VCFGOSrtcr8/8+fM56aST+NGPfkQikSgNP/jggznuuOOGMTIxGOGDTin9n3rjOQAa3NXqsxVvElTVkWuuIlzoASAWKmDpHtURnbRjlaY1tIBGYw0AH/hjyHk2uqb22QmrF0OLoWlqX+z4BhrqM9vL4AUafSU5uo2JW+pB2vEtvOLvQH6gU/BMoiEXgLW9NmNj6vgi64dJaF3kNfWUo7wVJaSpfKoHHka2Fz1aC4BnhbE0l143CkBId7D1AhoqPh+dlBuj2lL5XsfHCwwMTSWNHidCTbiAWzysrQ/WEM12oAVq+mSqhbaqyaW6sU0XP9CxTBVPgIYTimLrKjcbuOSCcGn+ET1LXo+iB2p+uuaT8SJETHUsYBcyxLwOfH8sAKbmouk6kZ5WQqFoca470dIdZkakjXHV6tgmEyRY22MR1OqlekibVeQDu7ScGgFhLUunq46P4maagm8RNdL4mlGMx8OiQDYIk/Lj6MW4ATwMcr4qz+pogVhDabl6MzqRuhxoGoav1mFGT9BEC3rg4YSTpfqK6ykAXCx68uoYaeYu6599PRYhylOpuXpLGPAv8nV1daUe7DdUVVVFTU3NkAQlhBBCDFRZl+qV+dibbckLL7zAN7/5zY2GjxkzhtbW1mGISAghhNi0Ss3Vfd555x0eeughsln1w2MQBB8zxeYNuCH/3e9+l/nz59PS0lIa1traynnnncf3vve9QQcihBBCfBJ+oOEHehmvkXVwEA6H6enp2Wj40qVLaWhoGIaIhBBCiE2r1Fzd3t7Ol770JaZNm8YhhxxSakufcsopfOc73xlUmQNuyN9444384x//YMKECUydOpWpU6cyfvx4nnnmGW6++WY++9nPll5CCCHEp6Xvcr1yXiPJYYcdxmWXXYbjqHtiNE1jxYoVXHDBBRx55JHDHJ0QQgixXqXm6nPOOQfTNFmxYgXRaLQ0/JhjjuHBBx8cVJkDvkf+8MMPH9SMhBBCiC2p3EvxRtrletdccw2HHHIIjY2NZLNZ9t13X1pbW5k9ezaXX375cIcnhBBClFRqrn744Yd56KGHGDu2f48S2223HcuXLx9UmYN+jrwQQgixNQmg2F3Xx483kiSTSZ566ikeffRRXnrpJXzf57Of/Sxf+tKXhjs0IYQQop9KzdXpdLrfL/F92trasG17UGUOuCEP6nm1f/rTn3j33Xc577zzqK2t5aWXXqKpqYkxY8YMKhAhhBDik6jUs/x99t9/f/bff3+A0iPohBBCiK1JpebqffbZhzvvvJP//u//BtRtcL7vc/XVVzNnzpxBlTnghvzixYv50pe+RFVVFe+//z6nnnoqtbW1/PnPf2b58uXceeedgwpECCGE+CQq9ZE2V111FRMnTuSYY44B4Oijj2bhwoWMGjWK+++/n912222YIxRCCCGUSs3VV199Nfvttx8vvvgihUKB888/nzfeeIOOjg6efvrpQZU54M7u+p5X+/bbbxMOh0vDDz74YJ544olBBTGU/va3v7HHHnsQiUSor6/niCOOGO6QhBBCfAoq9ZE2N998M+PGjQPgkUce4ZFHHuGBBx7g4IMP5rzzzhvm6DZNcrUQQlSmSs3VO+20E4sXL+Zzn/scBxxwAOl0miOOOIKXX36ZKVOmDKrMAf8i/8ILL3DzzTdvNHxreF7twoULOfXUU7niiivYf//9CYKA1157bVhjEkII8emo1LP8LS0tpYb8X//6V44++mjmzp3LxIkT2WOPPYY5uo1JrhZCiMpVqbkaYNSoUVx66aVDVt6AG/Jb6/NqXdfl29/+NldffTXf+MY3SsO33377YYtJCCHEp8cP1Kuc8UaSmpoaVq5cybhx43jwwQf5wQ9+AEAQBHieN8zR9Se5WgghKlul5uoHH3yQeDzO5z//eQB+8Ytf8Ktf/YqddtqJX/ziF9TU1Ay4zAE35PueV/uHP/wB2HqeV/vSSy+xatUqdF1nxowZtLa28pnPfIZrrrmG6dOnb3a6fD5PPp8vvd/USYqRaJ/pMQBefk99S957L8XU8XHSOY3mUREAIhEd8LBtk9oaC4B1bS6GoZPJeowZlwAgZGmMGR0l1aM2wN72bjxvfX+U4bDazAoFj7rmOnrWdarpIjaapuMVn33c25XGdVwAGsfWoel66X00ub6Xx0KugO969KzrAMCwrNJnibpqsj1pdNMAIFYVZ93KNTRPHlOaFijNM1YVo7dj/TpPd6fQDQPdMErDIrEQmq7OCjoFj17PJ5ZQt5XU1EUxzfVnDD3PJ5qIYFp6sfwwTn79gXTdqCoScYNQKFyszwK1VTbJahXXu1mTUMggElbTm4aGv8GezA6bNDZGME2NTHZ9uZ8ZvRZD83jj9RAAO01oxil4uK5aD+GIQTym096h6tOyNPwA4mGfnpBa1iAA2/TB1YkW75rJO1CV0IjaPs1NFomoiiUS8knGNBrjWVUPUZPpoSUs2W5XAL6y2ypWphvZfacABxXT9FHt5D2LcVUeBV9tE3vtmIAReMZVDI9KPct/xBFHcNxxx7HddtvR3t7OwQcfDMArr7zC1KlThzm6/iRXD0zDdHVFReof96kBgY+W6UUPPBxT5eqklSbvh5gSfp+l/mQAdD2gLRMjmkgCoGkBpu6SdVXPyD16khqri14vrt7nTLy42i/7moHn65iWyhc5LUreD2EbalrXNQjral0UsNA0g2o7BUBHxsajmH+NDJaTJ2l2AeDo63tl1n0XfA9PV/k7E6oiomfI6ir5mJpL1g9Tq7UDsNZrwtA93EDFGNazpL04tq5yp+Mb6ASkXHVsU+e14FgRTDNUqjcPs/TdDxsFuvNRgmL+DpHHznXTq6sTSraVU381tZyhIEcs10HGrgagKxchFioQC2cAMPwCmu9RU3wfoxc/X8APRchF1LFRFbCmHTp3Gk9D59sArK6eTsgMSusyY1ST9SN0FtR6aQ63YWguOT+Crql87gYmESNHl1dNLOgq1V+XW42u+eS8EDWW+n7ogU+IPL5evJPWMGi3mtE0lcvrEh4re+toSjRh+qouq9Mf0Jqchh7yMH11rLTvDlFgUmn97YAQg1epufq8887jqquuAuC1115j/vz5fOc73+HRRx9l/vz53H777QMuc8D3yF9zzTWsW7eu3/Nqp06dSiKRGNbn1b733nsAXHLJJXz3u9/lr3/9KzU1Ney77750dHRsdrorr7ySqqqq0qvv8kQhhBDblkq97+4nP/kJZ555JjvttBOPPPII8bhqBLS0tHD66acPc3T9Sa4WQojKVqm5etmyZey0006AusXsK1/5CldccQU33HADDzzwwKDKHHBDvu95tQsXLuSHP/whZ555Jvfffz+LFi0iFosNKoiPcskll6Bp2ke+XnzxRXxfnam86KKLOPLII5k5cya33347mqbxxz/+cbPlL1iwgO7u7tJr5cqVQ74MQgghtrwgKP81kliWxbnnnstPf/pTZsyYURp+9tlnc8opp3wqMUiuFkIIUY5KzdWhUIhMRl2183//93/MnTsXgNra2kFfZTao58hD/+fVbklnnnkmxx577EeOM3HiRHp7ewFKZzoAbNtm8uTJrFixYrPT2raNbdub/VwIIcS2wUfDL+NSvHLGGazOzk7OOuss7rtPXQp96KGHcv3111NdXb3ZaU466SR+/etf9xu2xx578I9//GOz09x3330cfPDBWJZVmtfmHHrooeUvwCBJrhZCCFGOSsrVG9p7772ZP38+e++9N88//zx33303AP/6178YO3bsoJZjQA153/e54447uOeee3j//ffRNI1Jkybx7//+7xx//PFo2tBXeH19PfX19R873syZM7Ftm6VLl5Y6EXAch/fff58JEyYMeVxCCCG2LuVeirclL9c77rjj+OCDD3jwwQcBOO200zj++OP5y1/+8pHTHXTQQf3ujwuFQh85/uGHH05rayuNjY0cfvjhmx1P07RPpcM7ydVCCCHKUUm5ekO/+MUvOOOMM/jTn/7EjTfeyJgxqv+uBx54gIMOOmgQSzGAhnwQBBx66KHcf//97Lbbbuyyyy4EQcCSJUs46aSTuOeee7j33nsHFcRQSCaTzJs3j4svvphx48YxYcIErr76agCOOuqoYYtLCCHEp6PcS/G21OV6S5Ys4cEHH+Qf//hH6bFvv/rVr5g9ezZLly79yJ7Zbdtm1KhRZc+r7xL1D/+/tZNcLYQQla2ScnUf13V57LHH+OUvf0lzc3O/z37yk58MuLw+ZTfk77jjDp544gn+/ve/M2fOnH6fPfrooxx++OHceeednHDCCYMO5pO6+uqrMU2T448/nmw2yx577MGjjz46qO78hRBCbFsG2hPuh+9J+6SXbz/77LNUVVX1e3b7nnvuSVVVFc8888xHHhw8/vjjNDY2Ul1dzb777svll19OY2NjWfPNZDJEo9GPH3ErIblaCCEqVyXmatM0+a//+i+WLFky6Lg3pezO7n73u99x4YUXbtSIB3W//AUXXMBvfvObIQ1uoCzL4pprrmHNmjX09PTwyCOPfOTjbIQQQowcfc+mLecFMG7cuH49oV955ZWfaP59l7p/WGNjI62trZud7uCDD+Y3v/kNjz76KD/+8Y954YUX2H///fs9bu2jVFdXs9dee3HhhRfy0EMPkU6nB70MnwbJ1UIIUbkqNVfvsccevPzyy4OOe1PK/kV+8eLF/OhHP9rs5wcffDA/+9nPhiQoIYQQYsDKfVxNcZyVK1eSTCZLgzd3hv+SSy7h0ksv/cgiX3jhBYBN9hUTBMFH9iFzzDHHlP7feeedmTVrFhMmTOBvf/sbRxxxxEfOF2DRokUsWrSIxx9/nJ///Ofkcjk++9nPst9++7HvvvuWnisvhBBCDLsKzdWnn3463/nOd/jggw+YOXPmRk9723XXXT+2jA8ruyHf0dFBU1PTZj9vamqis7NzwAEIIYQQQ8ELNLwyDg76xkkmk/0ODjan3B7ZFy9ezJo1azb6bN26dR+ZPz+submZCRMm8Pbbb5c1/uzZs5k9ezYXXHABnufxwgsvcNNNN/HjH/+Yq6+++lPp7E4IIYQoR6Xm6r4TAWeddVZpmKZppRMIg8nVZTfkPc/DNDc/umEYuK474ACEEEKIobClOtApt0f22bNn093dzfPPP8/nPvc5AJ577jm6u7vZa6+9yp5fe3s7K1eu3KhDnI/y1ltv8fjjj5d+mXcch6985Svsu+++ZZchhBBCbGmVmquXLVtWdtnlGlCv9SeddNJmL2co9/4AIYQQYksY7kfa7Ljjjhx00EGceuqp3HzzzYB6pM2//du/9es8Z4cdduDKK6/kq1/9KqlUiksuuYQjjzyS5uZm3n//fS688ELq6+v56le/WtZ8R40aheM47L///uy3335ceOGF7LLLLltkGYUQQohPolJz9ZZ4xGrZDfkTTzzxY8cZzh7rxeB8+yvqS/KLBxKkspDOBFiWGmaHYMU6i+pqn6qk6hexN+XR0BCm4PhsN0GNt7YT4jEd0zIAGLPdWOLJMAC5jENdnfo/FDKIxGyap44DoOWdlUyZMY2WZS0ApLtTuI66qsOa3IhhGGR7UwA0jl1/hs2OhuhuqyXd1QtANBlH01UsvudhhExiVXEAutZ2YlomnqsuV9E0jWh1Es9x1Hxsi2giVpqvHQ1jhSzS3aqzqHhVBDtslsq3bZNI1EQvvk8kLHp6CtQWlzGdcqmqidJ3i02mN084YtE4KlasA51RDRpr2vvq2ELToN5Wt6U4bpQxY2M01fUtbYDjagSBqlvXCTFlHDguPPVsqjhONbrm01lIMn1n1XN1R1qdXNMNNZ/q6hCOExCN6KV163mQKeilWPOFgGjIJePY1MRVfRhaQMj0iJoF4naEjKPimFDdhRcYxIwMAG+0jaLm/aeZNEbd3+MEFo6n0xjrpbqwFoCkZmB5Wep3nI0QW8KGneN83Hhbym9+8xvOOuss5s6dC8Chhx7Kz3/+837jLF26lO7ubkBdzfbaa69x55130tXVRXNzM3PmzOHuu+8mkUiUNc9Ro0axZMkSVqxYwYoVK/jggw+YNGkS8Xh8aBdODJv4nocCkHruL2ieg68ZhJ1inrJ60PU4WhAwOtZRmsZHJ47q7dkO5ehyq0lYap9t63lcTDRNfRmSYZe8r36oCXQNL9BLnxl4WJqLF6hDRkt36XFVTgsbBXRAR41bFSmg46+fzs3iGKpcHY8uvxqAenc5hWQjppsDoNUYi7HBYxRzvo3rm2i6GqZrPmE9j66p915gEtZz6KjcHjI8bH39D0p5PUos10GgF3OnFcX2M/iaeh83ABv8QOXENYUGGrylpB31TOgp7ho+0EYxLqyOM1Y7zYwOQzTfBUAsVMDz1/cXHaDhGSFipqrfmu4V6LEob0Y/x1TntdJ4o+oh7KbR3QIAkSBNXbyAq1sAFLQwPYUYMTNfKtcPDDJepLTsQaB6/E45YYxAlWMaBcYEy+kx6zA1Gws1POyk6LVqS/Mv1DST9DtoDdTzq2cm36RTbyCl1RIrLpvdvYbO8Cx22HEsQmwJlZqrAf7nf/6Hm266iWXLlvHss88yYcIErrvuOiZNmsRhhx024OUouyF/++23D7hwIYQQ4tMy3M+mBaitreWuu+76mPmvDyASifDQQw99onm+8sordHV18cQTT7Bo0SK+973v8cYbb7DrrrsyZ84cfvjDH36i8oUQQoihUqm5+sYbb+T73/8+Z599Npdffnnpnvjq6mquu+66QTXky378nBBCCLE163s2bTmvkaa6uppDDz2Uiy66iAsvvJCjjz6al156iauvvnq4QxNCCCFKKjVXX3/99fzqV7/ioosuwjCM0vBZs2bx2muvfcSUm1f2L/JCCCHE1synzMv1tngkn64///nPPP744zz++OO88cYb1NXV8YUvfIGf/OQnzJkzZ7jDE0IIIUoqNVcvW7aMGTNmbDTctm3S6fSgypSGvBBCiBFha7hcbzh885vfZJ999uHUU09lv/32Y+eddx7ukIQQQohNqtRcPWnSJF555ZWNOr174IEH2GmnnQZVpjTkhRBCjAiVenCwdu3a4Q5BCCGEKEul5urzzjuPM844g1wuRxAEPP/88/zud7/jyiuv5JZbbhlUmdKQF0IIMSL4gYZfxuNqyhlnW7R27VrWrl2L7/e/IHHXXXcdpoiEEEKI/io1V5988sm4rsv5559PJpPhuOOOY8yYMfz0pz/l2GOPHVSZ0pAXQggxIlTqWf5//vOfnHjiiSxZsqTUy66maQRBgKZppZ5xhRBCiOFWqbka4NRTT+XUU0+lra0N3/dpbGz8ROVJQ14IIcSIUKkHByeffDLTpk3j1ltvpampCU0bWb9iCCGEGDkqNVdfeuml/Od//idTpkyhvr5+SMqUhrwQQogRIQjK6wl3pB0cLFu2jHvuuYepU6cOdyhCCCHER6rUXL1w4UIuu+wydt99d/7zP/+TY445hoaGhk9UpjxHXgghxIgQBFrZr5Hki1/8Iq+++upwhyGEEEJ8rErN1YsXL2bx4sXsv//+XHvttYwZM4ZDDjmE3/72t2QymUGVKb/ICyGEGBEq9XK9W265hRNPPJHXX3+dnXfeGcuy+n1+6KGHDlNkQgghRH+VmqsBpk+fzhVXXMEVV1zB008/zW9/+1vOPvts5s2bR09Pz4DLk4a8AOCMg9Xfx17L0Z6xAXBc9SV6+90syXgMgJk76Wiaj+PpmLr6ho1r8OhKGxiGOnMWi9t8dtc4AEvfzROLqgs/Vq7IM3psgq62XgCmzdqBRHWEbKoGgEKuQKa1DYBsukCqs5todQIAOxrCKbgA6MUTdJ7jABCvSZDuTgFghUKEwjaGYRSnC2NaJnYkBEDn2i62220S61q6AAhHQ9hhC91QMaa60vh+QP2YWgBGjU6QzbhEwurzguMxZVSE1a0qBtPUsG2Drq68mp9tYoUMaqrVgXSqN0xVtU0opKYf1WCQjHi41Sq+ZDQgnYNqZx0AjbVjiNrQGFdn5lp7I9hWwLvLVWdVsZiaribmoOnrz1Q2FVbQ4uzGZ6eo6fKuybRpCVa3qLhcT62rxjoVR15VHWHLJ6oWlapInga7i5gVJWpk1ed6Fj8wSDgdjKpKkCcMQEN2BQBaQfWObTfmyD/5FjvupB6DtfPUUagnWdcWX0JseX6Zl+uVM8625JlnnuGpp57igQce2Ogz6exuZInv8RUA2t9eUjrKzWlRImRwNBsdtU/uduJM8d6iJ6Iu2yz4NiHdIaznALCDLHktQkxXOSMWz1AIVJ5szdYRMlyiusoDoSDH2kIdNSF1kNk3D4C0E8HxDaKmGtc2PFan1T5/p8i/yIUS5HR1/OCjU2N0AhBoGr4ZxjPUPGu0DlIkcX2Vo0xdQ9d8Io46XqiywxQCGy9QOdDUHHwMDNS2XW2nsCjQ46tjhrwZIR8dQ4OjljcTrsExbEKeeo8GcSNNh1Ollt/M4Zlhso46LG5NTiLiOWSDqBpdC3D19SfILN1jVVeUKjsCgGuGsdwsIU3lXDPdhT5qDG+2VrGr846aaJfPM6Wum1AhTapqLAA9VFNjp/GKZfvoNNltZH1VboCGE1hEjSzh4vooBDYGHuPCLXQwprQ+E7m12GYU03AoBOo4rmDZhMhTQNVzPlyFq4cY7a0EYPS0XYslbGCnWcz+8DAhhlCl5uoPi8ViRCIRQqEQvb29gypDLq0XQggxIvSd5S/nNZKcddZZHH/88bS0tOD7fr+XNOKFEEJsTSo1V4Pq0+byyy9np512YtasWbz00ktccskltLa2Dqo8+UVeCCHEiFCpl+u1t7dzzjnn0NTUNNyhCCGEEB+pUnP17Nmzef7559lll104+eSTS8+R/ySkIS+EEGJEqNTL9Y444ggee+wxpkyZMtyhCCGEEB+pUnP1nDlzuOWWW5g+ffqQlSkNeSGEECOC56lXOeONJNOmTWPBggU89dRT7LLLLht1dnfWWWcNU2RCCCFEf5Waq6+44op+7z3P47XXXmPChAnU1NQMqkxpyAshhBgRKvVyvVtuuYV4PM6iRYtYtGhRv880TZOGvBBCiK1Gpebqs88+m1122YVvfOMbeJ7HvvvuyzPPPEM0GuWvf/0r++2334DLlIa8EEKIEaFSDw6WLVs23CEIIYQQZanUXP3HP/6R//zP/wTgL3/5C8uWLeOtt97izjvv5KKLLuLpp58ecJnSa70QQogRwWf9vXcf+RruQIUQQogKVam5ur29nVGjRgFw//33c9RRRzFt2jS+8Y1v8Nprrw2qTPlFXgghxIgQBAFBGafwyxlnW/PBBx9w3333sWLFCgqFQr/Prr322mGKSgghhOivUnN1U1MTb775Js3NzTz44IPccMMNAGQyGQzDGFSZ8ou8EEKIEWFreDbt5Zdfzl577UU0GqW6urrMuAMuueQSRo8eTSQSYb/99uONN94oe55///vf2X777bnhhhv48Y9/zGOPPcbtt9/ObbfdxiuvvDK4BRFCCCG2gErN1SeffDJHH300O++8M5qmccABBwDw3HPPscMOOwxmMaQhL4QQYmQIfPDLeAVb8Hq9QqHAUUcdxX/913+VPc2PfvQjrr32Wn7+85/zwgsvMGrUKA444AB6e3vLmn7BggV85zvf4fXXXyccDrNw4UJWrlzJvvvuy1FHHTXYRRFCCCGGXKXm6ksuuYRbbrmF0047jaeffhrbtgEwDIMLLrhgUMshl9aLfubsEun3/tr/DRg3Nko0vH5YVThPyHBZ2ZUAoDmZJWHrfDAmDkB3d4F4RH37aqotHEedUhs3PoGmwYSpjQB4vo/r+FTXq3J6OlLUjm4AIBS2CMej1Bffd7en2GEXdV9Jy+oUobBNsqEWgPHbNbKuRQUYS0aoqY3w/tvr1PuqKNlUjmhcfW6adYTCJg3N1QCEoxaeFxCLqcc1RWMh1q7qJJ5U41dVmdi2TiarnoERiZo4bkBzk1UsD9JpGDcuBqizh51dDo6rlrmxKUpttUE2r96Prc0TsVy6M2r80cle/vRPi7kNqr7qEw6W4ZO00gDEazO4vok3rQqARNhlTLyTBN1MO6zvMpwqeu06vIJG3MoV6zbKbhPS6JqaT321T9x26c2paaY0ZMi6FrXhFEmjBwADj2R2LXU77wVUf2jLmPCh92P7vasFOO96qhBi+GwNHehceumlANxxxx1ljR8EAddddx0XXXQRRxxxBAC//vWvaWpq4re//S3f/OY3P7aMJUuW8Lvf/Q4A0zTJZrPE43Euu+wyDjvssAEdqIhtw/jtdiz9Xwu89+67tOSbqA6pA8oaq4e0WUO0oPbvGTNGXO/lg5zKo3Erx6hgFY6hDiRNv8Cq/FQAPuiMMH1UO06g8lwYyHkmuqbyVIBGzg0B4Hg69dFeEnQD0BEksE0XALuQIh2uLcWZ9qIYhsqleTtJqJCiK6KOB5JOOzVannVeNQB1oS7SfhTDU7eJGHjkfJu4oXKjGTjkNIuwkwKg10iiB+ufVRXyc2T1OIGmfq9aSzM1QSc5XeVEHx0DD0tX08T1XgwnS20yo+rLi7C8K8mMhg4AJrKGFm8cMUMtZ3smiqEH2LqKr6CFWWuMobGwCgC9kKV37M70vh9QGNVcqsdlHUkmVoXosepUnThhTG19a8UOsiSza/9/e3ce3lSZ7wH8e9KkSbqlLaEb1FJKKQyLYFEojFIWKzzAlPGCIlwufdwFdBhEBrwjlJkBhGFw9KqDzGXzPjODzx3tXGd8AFkrVUC2yiJba0sLtFTovqVNznv/iI2EbmlImu37eZ7zPM0573nP79fT9Jf35Cy4GRD/Yy6SAWq5AQH1lQAAk8IfDf7BiOuXBGtJCEdnotCz0zZEzuWrtRoAZsyY0WrevHnzbAu6DRzIExGRV2i5QY4t7QCgurraar5arbYcIe8uBQUFKC0tRVpamlUcLY+lseXDQWBgIAwGAwAgJiYG+fn5GDRoEADg1q1bzgmciIjIDr5aqwHzpXD79+9HWVkZZNn6lIOtW7d2OS6eWk9ERF6hq9fdxcbGQqfTWaa1a9d2e8ylpaUAzDfBuVNkZKRlWWdGjRpleWzNlClT8Oqrr2L16tV4+umnMWrUKMcGTEREdA98tVavWrUKaWlp2L9/P27duoWKigqryR78Rp6IiLyCkAWEDYf5W9oUFxcjJCTEMr+9I/yZmZmW0/Dac/z4cYwYMaIL0VqTJMk6RiFazWvPxo0bUVtrPsU4MzMTtbW1+Oijj9CvXz+89dZbdsdERETkaL5aqzdt2oTt27dj7ty5dm//bhzIExGRV+jq6XohISFWHw7as3DhQsyaNavDNn369LEhwtZanilbWlqK6Ohoy/yysrJWR/7b07dvX8vPAQEBlkfaEBERuRtfrdVNTU0YPXq0XdtvDwfyRETkFZx1Ax29Xg+9Xm9fUJ2Ij49HVFQU9u7di+HDhwMwF/vs7GysW7fO5n4qKyvx97//Hfn5+XjttdcQHh6OU6dOITIyEr169XJK7ERERF3lq7X62WefxV//+le88cYbDouLA3kiIvIKsiwg23CY35Y29ioqKkJ5eTmKiopgMpksz3Hv168fgoLMT/YYMGAA1q5di5///OeQJAmLFi3CmjVrkJiYiMTERKxZswYBAQGYPXu2Tds8c+YMJk6cCJ1Oh8LCQjz33HMIDw9HVlYWrl69ig8//NBZ6RIREXWJr9bqxsZGbN68Gfv27cPQoUOhUqmslm/cuLHLeXAgT0REXsEdHmmzYsUK7Nixw/K65cj9wYMHkZqaCgC4dOkSqqqqLG2WLl2KhoYGzJ8/HxUVFRg5ciQ+//xzBAcH27TNxYsXIyMjA+vXr7daZ/LkyTZ/wCAiIuoOvlqrz5w5g2HDhgEAzp07Z7XM1uvs78aBPBEReQV3+HCwffv2Tp9LK+4KQJIkZGZmIjMz065tHj9+HB988EGr+b169bL5brpERETdwVdr9cGDB+1aryMcyBMRkVeQhYBsQ+W3pY0n0Wg0rZ6zC5i/TejZs6cLIiIiImqbr9ZqZ/Cq58hfvnwZ6enp0Ov1CAkJwZgxY5xy9IOIiNyPkG2fvEl6ejp+85vfoLm5GYD5W4OioiIsW7YM//Zv/+bi6FpjrSYi8l2+WqsB8xl0S5cuxaxZs/D4449bTfbwqoH8lClTYDQaceDAAZw8eRLDhg3D1KlTeWohEZEPkGUBk6nzyZk30HGFDRs24Pvvv0dERAQaGhowduxY9OvXD8HBwVi9erWrw2uFtZqIyHf5aq3euXMnxowZg2+//RZZWVlobm7Gt99+iwMHDkCn09nVp9ecWn/r1i3k5eVh69atGDp0KADgzTffxPvvv4/z589bnv9HXbM43XzzhU17zK8HRtVACPO8PmHmUzn9JBPKjUGI6ukHAFAq1dAoTT/8rERDo/mQWss1MbGxGgBASWkTACOCVGoAgLHZhJBw850iY+8LRnV5FNQa8x0dA3UaqNXm405qjRJBYUHw9w8FAPTooUFEhBYAUF5ugE6nQmCI1pJD/wd7o77BHE9jgxGJCQH4rrABADB2hBK3alQICzICAKrqgxCQEogAf/Pr8lrA0OyH0u/NfenD/RCoEegZbDDHLCsQHyFDqTD3V/B9ABJ6+aG63vy7CAkwATBB/uF31mxSIDqwBqGB5pwbjf5IiNdCIczxDVefRbl/FIJNFebfrWyE0mSAXh9m/p336w8g8ofpR01QIyG4BArJ3I9OWYWiumgMiq0zx+FfD5VkRJ02AAAQpKyDXm1CUkIsgB539NQPRJ5KCNHqmrb22nmTkJAQ5OTk4MCBAzh16hRkWcYDDzyAiRMnujq0VlirnaNvQgL6AriYXwkA0Ih61CEYtUrzs5cVkKGv+g5VweYPi41Gf5QpoxGsqAEAVIkwGGVzjdUFmBDkV4fSRnNtCPGvgCxL8IO5vhiEGmo/89kfAUoBP8hQmcw1MUjVCI3C/PNN9EGIqIAkmd9vKsmIQKP5xlEB9bdQHhIH+Yfvk9TNtagKiEKwZK6lEgTCpdtQmMy1WNtcAz+/ZpQZzX8foUoTNKIeKqO5fYCqDtqmGvTwN9deyIA/DBAK8+sgvzpom2tQ6tfbnJOiGkKSYJLNy9Woh8k/EHpVOQDAX27EFyU6jA2+ZY5HNiFSuoEmP/Pnl/uCy+EXYrLEr2muQ7BahVKY+4+aMBwaAC8PAoDHLPupyShBkk2W12GqahTUROI+/3wAQLW6B2o1PSy/az/JCJNQIjbxJ+3vfCIP46u1es2aNXjrrbewYMECBAcH4+2330Z8fDxeeOEFq2fTd4XXDOR79OiBgQMH4sMPP8QDDzwAtVqNDz74AJGRkUhOTm53PYPBAIPBYHnd1nWGRETk/mRhnmxp5y2MRiM0Gg1yc3Mxfvx4jB8/3tUhdYi1mojIt/lirQaA/Px8TJkyBQCgVqtRV1cHSZLwy1/+EuPHj8eqVau63KfXDOQlScLevXuRnp6O4OBgKBQKREZGYvfu3QgNDW13vbVr19r1iyMiIvciZAFhQ+W3pY2nUCqViIuLg8lk6ryxG2CtJiLybb5YqwEgPDwcNTXms6B69eqFc+fOYciQIaisrER9fb1dfbr9NfKZmZmQJKnD6cSJExBCYP78+YiIiMDhw4fx9ddfIz09HVOnTkVJSUm7/S9fvhxVVVWWqbi4uBuzIyIiR2m5fMeWyZv8+te/xvLly1FeXu6yGFiriYjIFr5aqx9++GHs3bsXAPDEE0/gF7/4BZ577jk89dRTmDBhgl19uv038gsXLsSsWbM6bNOnTx8cOHAA//rXv1BRUYGQEPM1Ye+//z727t2LHTt2YNmyZW2uq1aroVarHR43ERF1L1m27eY43nYDnXfeeQd5eXmIiYlBXFwcAgMDrZafOnXK6TGwVhMRkS18tVa/++67aGxsBGA+OK1SqZCTk4PHH38cb7zxhl19uv1AXq/XQ6/Xd9qu5ZQEhcL6JAOFQgFZ9sLnFxARkRVfvYFOeno6JElyaQys1UREZAtfrNVGoxH//Oc/8dhj5ptfKhQKLF26FEuXLr2nft1+IG+rlJQUhIWFYd68eVixYgW0Wi3+/Oc/o6CgwHJjASIi8l62PnfW255Nm5mZ6eoQbMZaTUTk23yxViuVSrz00ku4cOGCQ/t1+2vkbaXX67F7927U1tZi/PjxGDFiBHJycvB///d/uP/++10dHhEROZkshM2TN+nbty9u377dan5lZSX69u3rgojax1pNROTbfLVWjxw5EqdPn3Zon17zjTwAjBgxAnv27HF1GERE5AK+eLoeABQWFrZ513qDwYBr1665IKKOsVYTEfkuX63V8+fPx6uvvopr164hOTm51f1shg4d2uU+vWogT0REvsvXbqDz6aefWn7es2cPdDqd5bXJZML+/fsRHx/vitCIiIja5Gu1+umnn8Yf//hHPPnkkwCAV155xbJMkiQIISBJkl2PkeVAnoiIvIKtj6vxloP806dPB2D+IDBv3jyrZSqVCn369MEf/vAHF0RGRETUNl+r1Tt27MCbb76JgoICh/fNgTwREXkFIQSEDUfwveV0vZa7vMfHx+P48eM23TWeiIjIlXytVrfkERcX5/C+JeEtvyUHqa6uhk6nQ1VVleUZt9Sx83kluFqth8rP/KGyoVlp+VkIwCib76l4uUiCLAB/lfkxSRq1hIuXaxHfx3yNSLReIDTACAAI9m+EnySgVJhfN8lKNJnMx52CVI1oNPlDpTCfghLoV4/K5mAAgFKSEaBswPcN5lNMQ9QN8JNMUMD8Z95oUkOrbESdUQMAkIUC/gqjZTvNsgpav0YISD9sVwV/RTOu14YBAEI1DQhSmvsEgBBTOQx+ATBCZe4PCjTIGgxPNH+gPpt3E0rJCAXMv4/yJh30/hVQohkAcMuox4NJofe+E4g8kKP+37b08/zaYvhrOu+nqbEam5fH8v+8B2Ot7rrqk3tQExwDADD4BUBIEozCXLtMws/SziD8EayoQb1srs2huA2jwh/N8AcAGIUSasn8LGRJElAb69HkpzWvC42lHwkCCsjQyrUAAIUwoVmhtlpuVJi3L4QESRJoEublShjRIGsRYboOAGhUBkJICkv//jBY4gEAFZpggAb+kuGHfJRQwAR/udGybVnyg8pksLyu9I9AUkIsAKDoygVI+PHjsKa5FtdV8dAozO39YEJiguM/hBN5Atbqe6NQKHDz5k307NnT8X07vEciIiIXELKwefIGx44dw65du6zmffjhh4iPj0dERASef/55GAwGF0VHRETUmq/VagDo378/wsPDO5zswVPriYjIK9ha+L3lw0FmZiZSU1MxefJkAMDZs2fxzDPPICMjAwMHDsTvf/97xMTEeNRz5omIyLv5Wq0GgFWrVlndkNZROJAnIiKvIAvzZEs7b5Cbm4vf/va3ltc7d+7EyJEj8ec//xkAEBsbi5UrV3IgT0REbsPXajUAzJo1CxEREQ7vlwN5IiLyCr52lL+iogKRkZGW19nZ2Zg0aZLl9YMPPoji4mJXhEZERNQmX6vVkiQ5rW9eI09ERF5BCGHz5CyrV6/G6NGjERAQgNDQUJvWycjIgCRJVtOoUaM6XS8yMtLyOJumpiacOnUKKSkpluU1NTVQqVR25UFEROQMvlarnZkHv5EnIiKvYDIJmEyyTe2cpampCTNnzkRKSgq2bNli83qTJk3Ctm3bLK/9/f07aP3jOsuWLcO6devwj3/8AwEBAXj44Ycty8+cOYOEhISuJUBEROREvlarWx4V6wwcyBMRkVfo6ul61dXVVvPVajXUanVbq9hs1apVAIDt27d3aT21Wo2oqKgurfO73/0Ojz/+OMaOHYugoCDs2LHD6kPF1q1bkZaW1qU+iYiInMnXarUz8dR6IiLyCl19pE1sbCx0Op1lWrt2rctiP3ToECIiItC/f38899xzKCsr63Sdnj174vDhw6ioqEBFRQV+/vOfWy3/3//9X6xcudJZIRMREXWZr9VqZ+I38kRE5BVkCMg2XIsmw9ymuLgYISEhlvn3eoTfXpMnT8bMmTMRFxeHgoICvPHGGxg/fjxOnjxpU0ztPdLG3ufSEhEROYuv1mpn4DfyRETkFbp6lD8kJMRqaq8QZ2ZmtrrBzd3TiRMn7I77ySefxJQpUzB48GBMmzYNu3btwuXLl/HZZ5/Z3ScREZE7Yq12HH4jT/dsUL9oDGo11691wxHA2bybGNLP/Liks3k38ZPeQQhQVgIAlAojAhQNAIBqYzCUCiOGJfZsY4uBVq/Kz5zFT4Y+fMecO7+d0raxftvfXnVkiOUn/7vW79Xxev0i25j741HF+C5HQkTtsfUut129g+zChQsxa9asDtv06dOnS312JDo6GnFxcbhy5YrD+iQKSX7sjurTsat5tzE00Vyhrl++CSEU8JfN9VmW/GCUfrgXgwCa/TToa8NNFb/Lz7epnTXHP3e5xZ3V+b7Ega2Wt/Xpg4juHWu143AgT0REXkHIArITnk2r1+uh1+vtDavLbt++jeLiYkRHR3fbNomIiLoDa7Xj8NR6IiLyCl09Xc8ZioqKkJubi6KiIphMJuTm5iI3Nxe1tbWWNgMGDEBWVhYAoLa2FkuWLMGRI0dQWFiIQ4cOYdq0adDr9a1uXkdEROTpWKsdh9/IExGRV3DW6XpdsWLFCuzYscPyevjw4QCAgwcPIjU1FQBw6dIlVFVVAQD8/Pxw9uxZfPjhh6isrER0dDTGjRuHjz76CMHBwU6Lk4iIyBVYqx2HA3kiIvIKQpYhZNmmds6yffv2Tp9Le+eHE61Wiz179jgtHiIiInfCWu04HMgTEZFXkG287s6WNkREROR4rNWOw4E8ERF5BXc4XY+IiIjax1rtOBzIExGRV7D15jjOvIEOERERtY+12nE4kCciIq/ADwdERETujbXacTiQJyIiryBDhiw6vzmODOfdQIeIiIjax1rtOBzIExGRVxCybUfwbfj8QERERE7AWu04HMgTEZFX4Ol6RERE7o212nE4kKduNaRfZBs/B95Tn+FDH76n9YnIO/BOuESOEdcvyfJzr/5DHNJn34QEh/RDRJ6NtdpxOJAnIiKvYDKaoFCYbGpHRERE3Y+12nE4kCciIq8ghAxhw0V1trQhIiIix2OtdhwO5ImIyCvwujsiIiL3xlrtOBzIExGRd7DxwwH44YCIiMg1WKsdhgN5IiLyCrKw8dm0PF2PiIjIJVirHYcDeSIi8go8XY+IiMi9sVY7DgfyRETkFYSQIWTeQIeIiMhdsVY7DgfyRETkFXiUn4iIyL2xVjsOB/JEROQV+EgbIiIi98Za7TgcyBMRkVeQZUC24Qi+DWf0ERERkROwVjsOB/JEROQVhGzjdXf8dEBEROQSrNWOo3B1ALZavXo1Ro8ejYCAAISGhrbZpqioCNOmTUNgYCD0ej1eeeUVNDU1dW+gRETkEi3X3dkyOUNhYSGeeeYZxMfHQ6vVIiEhAStXruy0DgkhkJmZiZiYGGi1WqSmpuL8+fNOidHZWKuJiKgjrNWO4zED+aamJsycORMvvfRSm8tNJhOmTJmCuro65OTkYOfOnfj444/x6quvdnOkRETkCi3X3dkyOcPFixchyzI++OADnD9/Hm+99RY2bdqE119/vcP11q9fj40bN+Ldd9/F8ePHERUVhUcffRQ1NTVOidOZWKuJiKgjrNWOIwkhPOqWgNu3b8eiRYtQWVlpNX/Xrl2YOnUqiouLERMTAwDYuXMnMjIyUFZWhpCQEJv6r66uhk6nQ1VVlc3rEBFR1znq/21LP6Mm74JSFdhpe2NzHY7umtwt/+d///vf409/+hO+++67NpcLIRATE4NFixbhV7/6FQDAYDAgMjIS69atwwsvvODU+JyFtZqIyDuwVrtvrfaaa+SPHDmCwYMHWz4YAMBjjz0Gg8GAkydPYty4cW2uZzAYYDAYLK+rqqoAmP/YiIjIeVr+zzrqeLKxqcama+pMxjqr7bdQq9VQq9UOiaVFVVUVwsPD211eUFCA0tJSpKWlWcUxduxYfPXVVx47kG8PazURkWdhrXbfWu01A/nS0lJERkZazQsLC4O/vz9KS0vbXW/t2rVYtWpVq/mxsbEOj5GIiFqrqamBTqeze31/f39ERUXhxP4nbF4nKCio1f/5lStXIjMz0+447pafn4//+q//wh/+8Id227TUp7vrV2RkJK5eveqwWNwFazURkWdirXa/Wu3SgXxmZmabhflOx48fx4gRI2zqT5KkVvOEEG3Ob7F8+XIsXrzY8lqWZZSXl6NHjx4drtee6upqxMbGori42OtP9/OVXH0lT4C5eit3zVUIgZqaGqtvZ+2h0WhQUFDQpRumtVUb2jvCb0+tunHjBiZNmoSZM2fi2Wef7TSeu2PprHZ1J9Zqz+YrufpKngBz9Vbumitr9Y/crVa7dCC/cOFCzJo1q8M2ffr0samvqKgoHDt2zGpeRUUFmpubWx09uVNbp2e0d6fdrggJCXGrN6Ez+UquvpInwFy9lTvmei9H9++k0Wig0Wgc0tfdulqrbty4gXHjxiElJQWbN2/ucL2oqCgA5qP90dHRlvllZWUd1q7uxFrtHXwlV1/JE2Cu3sodc2Wtds9a7dKBvF6vh16vd0hfKSkpWL16NUpKSiy/4M8//xxqtRrJyckO2QYREfmertSq69evY9y4cUhOTsa2bdugUHT8cJj4+HhERUVh7969GD58OADznd+zs7Oxbt26e47dEViriYjI3flirfaYx88VFRUhNzcXRUVFMJlMyM3NRW5uLmprawEAaWlp+MlPfoK5c+fi9OnT2L9/P5YsWYLnnnvO7Y5qERGR97lx4wZSU1MRGxuLDRs24Pvvv0dpaWmra78HDBiArKwsAObT9BYtWoQ1a9YgKysL586dQ0ZGBgICAjB79mxXpHFPWKuJiMideVOt9pib3a1YsQI7duywvG45GnLw4EGkpqbCz88Pn332GebPn48xY8ZAq9Vi9uzZ2LBhQ7fGqVarsXLlSoffTdEd+UquvpInwFy9lS/l6kqff/458vLykJeXh969e1stu/Nuv5cuXbLcdR0Ali5dioaGBsyfPx8VFRUYOXIkPv/8cwQHB3db7I7CWu1+fCVXX8kTYK7eypdydSVvqtUe9xx5IiIiIiIiIl/mMafWExEREREREREH8kREREREREQehQN5IiIiIiIiIg/CgTwRERERERGRB/HqgfzatWvx4IMPIjg4GBEREZg+fTouXbpk1UYIgczMTMTExECr1SI1NRXnz5/vsN/GxkZkZGRgyJAhUCqVmD59eoftv/zySyiVSgwbNqzTmF944QUkJCRAq9WiZ8+eSE9Px8WLF63aVFRUYO7cudDpdNDpdBg+fDgeeOABl+R56NAhSJLUaro75rsZDAa8/PLL0Ov1CAwMxM9+9jNcu3atwzznzp2LFStWuGyf2purPfvU1bkC5n30n//5n4iLi4NarUZCQgK2bt3aYd+euF/tzdXT3qsZGRlt/v0OGjSow77t3aeVlZUd9kvUwldqtav/13VnvfbEzyWeuE8B1mrW6h9/N6zVvserB/LZ2dlYsGABjh49ir1798JoNCItLQ11dXWWNuvXr8fGjRvx7rvv4vjx44iKisKjjz6Kmpqadvs1mUzQarV45ZVXMHHixA5jqKqqwn/8x39gwoQJNsWcnJyMbdu24cKFC9izZw+EEEhLS4PJZLK0mT17NnJzc7F7927s3r0beXl5kGXZpXleunQJJSUllikxMbHD9osWLUJWVhZ27tyJnJwc1NbWYurUqR3mmZubi02bNrl8n3Y1V3v2qTvk+sQTT2D//v3YsmULLl26hL/97W8YMGBAh7l66n61J1dPe6++/fbbVn+3xcXFCA8Px8yZMzvM0959Onfu3A77JWrhK7XaHf7XAd1Trz3xc4mn7lPWatZqgLXaZwkfUlZWJgCI7OxsIYQQsiyLqKgo8eabb1raNDY2Cp1OJzZt2mRTn/PmzRPp6entLn/yySfFr3/9a7Fy5Upx//33dznmb775RgAQeXl5Qgghvv32WwFAHD161NLmyJEjAoC4ePGiEKJ78zx48KAAICoqKmzOqbKyUqhUKrFz507LvOvXrwuFQiF2795tc56ekGtb7NmnQnRvrrt27RI6nU7cvn3b5rw8db/ak2tb3P29eresrCwhSZIoLCxst40j9ymRrXylVgvh/jXMUf8D3D3PtnjCPmWt7jrWatZqb+LV38jfraqqCgAQHh4OACgoKEBpaSnS0tIsbdRqNcaOHYuvvvrKMi8jIwOpqald3t62bduQn5+PlStXtrm85fSvwsLCNpfX1dVh27ZtiI+PR2xsLADgyJEj0Ol0GDlypKXdqFGjoNPpLDF3d54AMHz4cERHR2PChAk4ePBgh3mePHkSzc3NVvHExMRg8ODBlnhsydMTcr2bvfu0u3P99NNPMWLECKxfvx69evVC//79sWTJEjQ0NLSbq6fuV3tyvZsnvVdbbNmyBRMnTkRcXFy7eTpynxLZyldqtStyBVxTr909z7t5yj5lrWatBlirfZnS1QF0FyEEFi9ejJ/+9KcYPHgwAKC0tBQAEBkZadU2MjISV69etbyOjo6GLMtd2t6VK1ewbNkyHD58GEpl27/mgIAAJCUlQaVSWc1///33sXTpUtTV1WHAgAHYu3cv/P39LTFHRES06isiIgKlpaXdnmd0dDQ2b96M5ORkGAwG/M///A8mTJiAQ4cO4ZFHHmkzz9LSUvj7+yMsLKxVPC2xdpYn0P371J5cW9zLPnVFrt999x1ycnKg0WiQlZWFW7duYf78+SgvL7dcj+Yt+9WeXFt40nv1TiUlJdi1axf++te/Ws131j4lspWv1GpX5Oqqeu0JebbwtH3KWs1a3RIPa7Vv8pmB/MKFC3HmzBnk5OS0WiZJktVrIYTVvLVr13ZpWyaTCbNnz8aqVavQv3//dts99NBDbd58Zc6cOXj00UdRUlKCDRs24IknnsCXX34JjUbTZrx3xtydeQJAUlISkpKSLK9TUlJQXFyMDRs2WApme3m2l0N78d7dxpNyvZd96opcZVmGJEn4y1/+Ap1OBwDYuHEjZsyYgffeew9ardZr9uu95OpJ79U7bd++HaGhoa1utOOsfUpkK1+p1YBn1bCO8mgr3jvbeFKenrZPWatZqzvCWu39fOLU+pdffhmffvopDh48iN69e1vmR0VFAUCrI1FlZWWtjrJ1RU1NDU6cOIGFCxdCqVRCqVTiN7/5Db755hsolUocOHCgw/V1Oh0SExPxyCOP4O9//zsuXryIrKwsS8w3b95stc7333+Pffv2dWue7Rk1ahSuXLnS7vKoqCg0NTWhoqKi3Xg6yjMyMrLb92l7Osu1hb371FW5RkdHo1evXpZiCQADBw6EEKLVXVDvjMcT96s9ubbwxPeqEAJbt27F3LlzLd9ItMcR+5TIVr5Sqz2pht3r/wBP+VzSwtP2KWs1a3VLPKzVvsmrB/JCCCxcuBCffPIJDhw4gPj4eKvl8fHxiIqKwt69ey3zmpqakJ2djdGjR9u93ZCQEJw9exa5ubmW6cUXX0RSUhJyc3Otrk+xNQ+DwQDAfGS5qqoKX3/9tWX50aNHUVVVhbNnz3Zrnu05ffo0oqOj212enJwMlUplFU9JSQnOnTtniaetPI8dO4aqqirs27ev2/dpezrLtT2d7VNX5zpmzBjcuHEDtbW1lnmXL1+GQqGwKmZ38tT9ak+u7fGE92p2djby8vLwzDPPdNr2XvepM95z5H18pVa7+n9dW5xVr93hf92dWKt/xFrNWt2CtdoLOO6+ee7npZdeEjqdThw6dEiUlJRYpvr6ekubN998U+h0OvHJJ5+Is2fPiqeeekpER0eL6upqS5tly5aJuXPnWvV9/vx5cfr0aTFt2jSRmpoqTp8+LU6fPt1uLG3dCffYsWMiKSlJXLt2TQghRH5+vlizZo04ceKEuHr1qvjqq69Eenq6CA8PFzdv3rSsN2nSJDF06FBx5MgRceTIEREeHi6USqVL8nzrrbdEVlaWuHz5sjh37pxYtmyZACA+/vjjdvMUQogXX3xR9O7dW+zbt0+cOnVKjB8/Xtx///3CaDS2m+eQIUNEXFycy/apPbnau09dnWtNTY3o3bu3mDFjhjh//rzIzs4WiYmJ4tlnn/W6/WpPrp74Xm3x7//+72LkyJGt5jt6n06dOrXNbRDdzVdqtav/13Vnvfa0zyWeuk9Zq1mr73WfslZ7Nq8eyANoc9q2bZuljSzLYuXKlSIqKkqo1WrxyCOPiLNnz1r1M2/ePDF27FireXFxcW323Z62Phy0PCKloKBACGF+VMTkyZNFRESEUKlUonfv3mL27NmtHgtx+/ZtMWfOHBEcHCyCg4Ndmue6detEQkKC0Gg0IiwsTPz0pz8Vn332WYd5CiFEQ0ODWLhwoQgPDxdarVZMnTpVFBUVdZjnnDlzPC5Xe/epq3MVQogLFy6IiRMnCq1WK3r37i0WL15sVcS8Zb/ak6snvleFMD+iRqvVis2bN4u2OHKf3uujn8h3uPp9cSdn1mpX/6/rznrtaXl66j4VgrWatdqMtdo3SUIIASIiIiIiIiLyCF59jTwRERERERGRt+FAnoiIiIiIiMiDcCBPRERERERE5EE4kCciIiIiIiLyIBzIExEREREREXkQDuSJiIiIiIiIPAgH8kREREREREQehAN5IiIiIiIiIg/CgTyRg2VmZmLYsGEu2/4bb7yB559/3iXbNhgMuO+++3Dy5EmXbJ+IiMgWrNWs1USejgN5oi6QJKnDKSMjA0uWLMH+/ftdEt/Nmzfx9ttv4/XXX7fMS01NxaJFi1q1/cc//gFJkixtOsqrT58+AIDS0lK8/PLL6Nu3L9RqNWJjYzFt2jRLvmq1GkuWLMGvfvUrp+dKRETUFtZq1moiX6B0dQBEnqSkpMTy80cffYQVK1bg0qVLlnlarRZBQUEICgpyRXjYsmULUlJSLMXcVp988gmampoAAMXFxXjooYewb98+DBo0CADg5+eHwsJCjBkzBqGhoVi/fj2GDh2K5uZm7NmzBwsWLMDFixcBAHPmzMFrr72GCxcuYODAgQ7Nj4iIqDOs1azVRL6A38gTdUFUVJRl0ul0kCSp1by7T9fLyMjA9OnTsWbNGkRGRiI0NBSrVq2C0WjEa6+9hvDwcPTu3Rtbt2612tb169fx5JNPIiwsDD169EB6ejoKCws7jG/nzp342c9+1uW8wsPDLTn07NkTANCjRw+refPnz4ckSfj6668xY8YM9O/fH4MGDcLixYtx9OhRS189evTA6NGj8be//a3LcRAREd0r1mrWaiJfwIE8UTc4cOAAbty4gS+++AIbN25EZmYmpk6dirCwMBw7dgwvvvgiXnzxRRQXFwMA6uvrMW7cOAQFBeGLL75ATk4OgoKCMGnSJMvR+LtVVFTg3LlzGDFihMPjLy8vx+7du7FgwQIEBga2Wh4aGmr1+qGHHsLhw4cdHgcREZGzsFYTkSfhQJ6oG4SHh+Odd95BUlISnn76aSQlJaG+vh6vv/46EhMTsXz5cvj7++PLL78EYD5ar1Ao8N///d8YMmQIBg4ciG3btqGoqAiHDh1qcxtXr16FEAIxMTEOjz8vLw9CCAwYMMCm9r169er0GwkiIiJ3wlpNRJ6E18gTdYNBgwZBofjxuFlkZCQGDx5see3n54cePXqgrKwMAHDy5Enk5eUhODjYqp/Gxkbk5+e3uY2GhgYAgEajcXT4EEIAgOWGO53RarWor693eBxERETOwlpNRJ6EA3mibqBSqaxeS5LU5jxZlgEAsiwjOTkZf/nLX1r11XJd3N30ej0A82l7d7YJCQlBVVVVq/aVlZUICQmxKf7ExERIkoQLFy5g+vTpnbYvLy9vN04iIiJ3xFpNRJ6Ep9YTuaEHHngAV65cQUREBPr162c16XS6NtdJSEhASEgIvv32W6v5AwYMwIkTJ1q1P378OJKSkmyKJzw8HI899hjee+891NXVtVpeWVlp9frcuXMYPny4TX0TERF5ItZqInIlDuSJ3NCcOXOg1+uRnp6Ow4cPo6CgANnZ2fjFL36Ba9eutbmOQqHAxIkTkZOTYzV//vz5yM/Px4IFC/DNN9/g8uXLeO+997Blyxa89tprNsf0/vvvw2Qy4aGHHsLHH3+MK1eu4MKFC3jnnXeQkpJi1fbw4cNIS0vreuJEREQegrWaiFyJA3kiNxQQEIAvvvgC9913Hx5//HEMHDgQTz/9NBoaGjo8xe7555/Hzp07Laf9AUCfPn1w+PBh5OfnIy0tDQ8++CC2b9+O7du3Y+bMmTbHFB8fj1OnTmHcuHF49dVXMXjwYDz66KPYv38//vSnP1naHTlyBFVVVZgxY4Z9yRMREXkA1moiciVJtNwZg4g8nhACo0aNwqJFi/DUU0+5JIaZM2di+PDheP31112yfSIiInfGWk1EjsBv5Im8iCRJ2Lx5M4xGo0u2bzAYcP/99+OXv/ylS7ZPRETk7liricgR+I08ERERERERkQfhN/JEREREREREHoQDeSIiIiIiIiIPwoE8ERERERERkQfhQJ6IiIiIiIjIg3AgT0RERERERORBOJAnIiIiIiIi8iAcyBMRERERERF5EA7kiYiIiIiIiDwIB/JEREREREREHuT/AU0jKaenzlbuAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize=(10,3), constrained_layout=True)\n",
    "s = ax[0].pcolormesh(ds[\"time\"].values, -ds[\"range\"].values, ds[\"vel\"][0], cmap=\"coolwarm\", vmin=-2, vmax=2)\n",
    "t = ax[1].pcolormesh(ds[\"time\"].values, -ds[\"range\"].values, ds[\"vel\"][1], cmap=\"coolwarm\", vmin=-2, vmax=2)\n",
    "fig.colorbar(s, ax=ax[0], label=\"Streamwise Velocity (m/s)\")\n",
    "fig.colorbar(t, ax=ax[1], label=\"Transverse Velocity (m/s)\")\n",
    "for a in ax:\n",
    "    a.set(xlabel=\"Time (UTC)\", ylim=(-10, 0))\n",
    "ax[0].set(ylabel=\"Depth [m]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next step is to calculate the water depth from one of the ADCP's measurements. This can come from the bottom track ping, an altimeter ping, or an external depth sounder. You may need to do some quality control on this measurement, and make sure to add the `range_offset`, the depth of the ADCP below the waterline, to this array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the water depth based on the average bottom track pings\n",
    "water_depth = ds.attrs[\"range_offset\"] + ds[\"dist_bt\"].mean(\"beam\").values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can superimpose that on our plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Depth [m]')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize=(10,3), constrained_layout=True)\n",
    "s = ax[0].pcolormesh(ds[\"time\"].values, -ds[\"range\"].values, ds[\"vel\"][0], cmap=\"coolwarm\", vmin=-2, vmax=2)\n",
    "t = ax[1].pcolormesh(ds[\"time\"].values, -ds[\"range\"].values, ds[\"vel\"][1], cmap=\"coolwarm\", vmin=-2, vmax=2)\n",
    "ax[0].plot(ds[\"time\"].values, -water_depth, color=\"k\", label=\"Water Depth\")\n",
    "ax[1].plot(ds[\"time\"].values, -water_depth, color=\"k\", label=\"Water Depth\")\n",
    "fig.colorbar(s, ax=ax[0], label=\"Streamwise Velocity (m/s)\")\n",
    "fig.colorbar(t, ax=ax[1], label=\"Transverse Velocity (m/s)\")\n",
    "for a in ax:\n",
    "    a.set(xlabel=\"Time (UTC)\", ylim=(-10, 0))\n",
    "ax[0].set(ylabel=\"Depth [m]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use the `discharge` function to calculate discharge, among other values (including power [W], power density [W/m^2], and the channel's Reynolds Number). This function does quite a number of things internally:\n",
    "1. Linearly extrapolates velocity to the riverbed/seafloor (assumes velocity at the seafloor, specified by the \"water_depth\" input, is 0 m/s)\n",
    "2. Constant extrapolation of velocity to the water surface (water velocity at the uppermost bin is the same speed as that at the water surface)\n",
    "3. Remaps the velocity transect from \"time\" onto \"distance\" based on the GPS-measured location (`latitude_gps` and `longitude_gps` variables). It does this by converting the lat/lon to UTM and interpolating the UTM location onto the timegrid.\n",
    "4. Velocity data is then integrated over the cross-sectional area to find discharge [m^3/s], power [W], power density [W/m^2], and hydraulic depth [m]. The last is used to find the Reynolds Number. \n",
    "5. Values are saved into the returned dataset\n",
    "\n",
    "The inputs are as follows:\n",
    "1. `ds` - ADCP dataset\n",
    "2. `water_depth` - as calculated above\n",
    "3. `rho` - water density for the water current in question\n",
    "4. `mu` - kinematic viscosity, based on the water temperature and salinity. Can be found from a look-up table online.\n",
    "5. `surface_offset` - Location of water surface on a vertical datum. Typically will be 0 for a river. In a tidal channel, this will depend on the height of the tide relative to MLLW (or MSL or desired level) at the time of the transect measurement. If the water level during the transect is above MLLW, this value will be negative. If above MLLW, this value will be positive.\n",
    "6. `utm_zone` - UTM zone at the survey location. Can easily be found for a specific location by searching here: https://www.usgs.gov/media/images/mapping-utm-grid-conterminous-48-united-states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
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       "\n",
       ".xr-attrs dt:hover span {\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
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       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 236kB\n",
       "Dimensions:              (time: 262, time_gps: 262, range: 24, range_sl: 5,\n",
       "                          beam: 4, dir: 4, x1: 4, x2: 4, earth: 3, inst: 3)\n",
       "Coordinates:\n",
       "  * time                 (time) datetime64[ns] 2kB 2022-08-19T20:14:21.930000...\n",
       "  * time_gps             (time_gps) datetime64[ns] 2kB 2022-08-19T20:14:23.59...\n",
       "  * range                (range) float32 96B 0.95 1.43 1.91 ... 11.51 11.99\n",
       "  * range_sl             (range_sl) float64 40B 0.54 0.66 0.78 0.9 1.02\n",
       "  * beam                 (beam) int32 16B 1 2 3 4\n",
       "  * dir                  (dir) &lt;U10 160B &#x27;streamwise&#x27; &#x27;x-stream&#x27; &#x27;vert&#x27; &#x27;err&#x27;\n",
       "  * x1                   (x1) int32 16B 1 2 3 4\n",
       "  * x2                   (x2) int32 16B 1 2 3 4\n",
       "  * earth                (earth) &lt;U1 12B &#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;\n",
       "  * inst                 (inst) &lt;U1 12B &#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;\n",
       "Data variables: (12/47)\n",
       "    number               (time) uint32 1kB 398 399 400 401 ... 656 657 658 659\n",
       "    builtin_test_fail    (time) bool 262B False False False ... False False\n",
       "    c_sound              (time) float32 1kB 1.458e+03 1.458e+03 ... 1.459e+03\n",
       "    depth                (time) float32 1kB 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    pitch                (time) float32 1kB -1.21 -1.01 -1.46 ... -0.5 -0.57\n",
       "    roll                 (time) float32 1kB 1.97 0.49 1.07 ... -0.66 -0.53 0.72\n",
       "    ...                   ...\n",
       "    beam2inst_orientmat  (x1, x2) float32 64B 1.462 -1.462 0.0 ... -1.034 -1.034\n",
       "    orientmat            (earth, inst, time) float32 9kB -0.8992 ... 0.9999\n",
       "    discharge            float32 4B -1.136e+03\n",
       "    power                float32 4B -1.211e+06\n",
       "    power_density        float32 4B -1.307e+03\n",
       "    reynolds_number      float32 4B 6.2e+06\n",
       "Attributes: (12/46)\n",
       "    prog_ver:                  56.1\n",
       "    inst_make:                 TRDI\n",
       "    inst_type:                 ADCP\n",
       "    rotate_vars:               [&#x27;vel&#x27;, &#x27;vel_sl&#x27;, &#x27;vel_bt&#x27;]\n",
       "    has_imu:                   0\n",
       "    inst_model:                RiverPro\n",
       "    ...                        ...\n",
       "    range_offset:              0.37\n",
       "    blank_dist_sl:             0.05\n",
       "    fs:                        1.47\n",
       "    declination:               18\n",
       "    declination_in_orientmat:  1\n",
       "    principal_heading:         173.522</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-edc30d19-1df3-4c73-950c-50611da517d9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-edc30d19-1df3-4c73-950c-50611da517d9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 262</li><li><span class='xr-has-index'>time_gps</span>: 262</li><li><span class='xr-has-index'>range</span>: 24</li><li><span class='xr-has-index'>range_sl</span>: 5</li><li><span class='xr-has-index'>beam</span>: 4</li><li><span class='xr-has-index'>dir</span>: 4</li><li><span class='xr-has-index'>x1</span>: 4</li><li><span class='xr-has-index'>x2</span>: 4</li><li><span class='xr-has-index'>earth</span>: 3</li><li><span class='xr-has-index'>inst</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-15a59d4d-7c1a-43aa-a351-240223de5f7c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-15a59d4d-7c1a-43aa-a351-240223de5f7c' class='xr-section-summary' >Coordinates: <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-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'>2022-08-19T20:14:21.930000066 .....</div><input id='attrs-57e364a7-e2bc-4ce9-8616-2d6e7eede585' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57e364a7-e2bc-4ce9-8616-2d6e7eede585' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1455e039-cd59-4396-add3-1ff76a068f14' class='xr-var-data-in' type='checkbox'><label for='data-1455e039-cd59-4396-add3-1ff76a068f14' 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>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-08-19T20:14:21.930000066&#x27;, &#x27;2022-08-19T20:14:22.500000000&#x27;,\n",
       "       &#x27;2022-08-19T20:14:23.220000028&#x27;, ..., &#x27;2022-08-19T20:17:18.329999923&#x27;,\n",
       "       &#x27;2022-08-19T20:17:18.910000085&#x27;, &#x27;2022-08-19T20:17:19.450000047&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-08-19T20:14:23.599999904 .....</div><input id='attrs-74481117-7050-4b99-9d94-cd5a737d30bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74481117-7050-4b99-9d94-cd5a737d30bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a124b621-ae78-48ea-9787-24c808f7ec91' class='xr-var-data-in' type='checkbox'><label for='data-a124b621-ae78-48ea-9787-24c808f7ec91' 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>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-08-19T20:14:23.599999904&#x27;, &#x27;2022-08-19T20:14:24.200000047&#x27;,\n",
       "       &#x27;2022-08-19T20:14:25.000000000&#x27;, ..., &#x27;2022-08-19T20:17:20.000000000&#x27;,\n",
       "       &#x27;2022-08-19T20:17:20.400000095&#x27;, &#x27;2022-08-19T20:17:21.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.95 1.43 1.91 ... 11.51 11.99</div><input id='attrs-eca2edbc-d894-41dd-a978-8849c6606ce9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eca2edbc-d894-41dd-a978-8849c6606ce9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb3f937d-a402-45d8-81df-d3fb5ae8387f' class='xr-var-data-in' type='checkbox'><label for='data-fb3f937d-a402-45d8-81df-d3fb5ae8387f' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 0.95,  1.43,  1.91,  2.39,  2.87,  3.35,  3.83,  4.31,  4.79,  5.27,\n",
       "        5.75,  6.23,  6.71,  7.19,  7.67,  8.15,  8.63,  9.11,  9.59, 10.07,\n",
       "       10.55, 11.03, 11.51, 11.99], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range_sl</span></div><div class='xr-var-dims'>(range_sl)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.54 0.66 0.78 0.9 1.02</div><input id='attrs-7644bf78-5eed-4bd7-8fcb-cd9fa9334ea9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7644bf78-5eed-4bd7-8fcb-cd9fa9334ea9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cfd9fa4-3554-41d9-be21-b5457d382629' class='xr-var-data-in' type='checkbox'><label for='data-7cfd9fa4-3554-41d9-be21-b5457d382629' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([0.54, 0.66, 0.78, 0.9 , 1.02])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>beam</span></div><div class='xr-var-dims'>(beam)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-8254643f-2a6e-4b12-8b3e-8437153c2542' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8254643f-2a6e-4b12-8b3e-8437153c2542' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bb2ab1e-bbe9-44f8-98de-0e1e134a43c8' class='xr-var-data-in' type='checkbox'><label for='data-5bb2ab1e-bbe9-44f8-98de-0e1e134a43c8' 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>1</dd><dt><span>long_name :</span></dt><dd>Beam Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dir</span></div><div class='xr-var-dims'>(dir)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;streamwise&#x27; &#x27;x-stream&#x27; ... &#x27;err&#x27;</div><input id='attrs-16b5fe43-5aac-4714-b709-23656dbbcdc7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16b5fe43-5aac-4714-b709-23656dbbcdc7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7e52a6f-1ff6-41af-8145-9ab0fee2c1b0' class='xr-var-data-in' type='checkbox'><label for='data-e7e52a6f-1ff6-41af-8145-9ab0fee2c1b0' 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>1</dd><dt><span>long_name :</span></dt><dd>Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>ref_frame :</span></dt><dd>principal</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert&#x27;, &#x27;err&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x1</span></div><div class='xr-var-dims'>(x1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-32fed5e1-0f0c-4e0b-a33a-1033efdb7b7c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-32fed5e1-0f0c-4e0b-a33a-1033efdb7b7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf00c755-a990-4093-bd60-e796987ce774' class='xr-var-data-in' type='checkbox'><label for='data-bf00c755-a990-4093-bd60-e796987ce774' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x2</span></div><div class='xr-var-dims'>(x2)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-523ff7c5-65b8-4ddf-a8e5-230c738c200b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-523ff7c5-65b8-4ddf-a8e5-230c738c200b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67ca9b62-c099-4c46-9a78-ad69ee442a55' class='xr-var-data-in' type='checkbox'><label for='data-67ca9b62-c099-4c46-9a78-ad69ee442a55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>earth</span></div><div class='xr-var-dims'>(earth)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-5f3bc6ba-def4-4673-8287-61a29fa408f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f3bc6ba-def4-4673-8287-61a29fa408f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9766e962-08a5-4198-b79d-5dac66b3d606' class='xr-var-data-in' type='checkbox'><label for='data-9766e962-08a5-4198-b79d-5dac66b3d606' 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>1</dd><dt><span>long_name :</span></dt><dd>Earth Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>inst</span></div><div class='xr-var-dims'>(inst)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-48cf72de-87f8-4f52-b12a-2bd752a02a61' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48cf72de-87f8-4f52-b12a-2bd752a02a61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3accfdfb-81b7-48ac-9894-b9e71159e91d' class='xr-var-data-in' type='checkbox'><label for='data-3accfdfb-81b7-48ac-9894-b9e71159e91d' 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>1</dd><dt><span>long_name :</span></dt><dd>Instrument Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-739420bc-1597-42d7-a7ac-b9ebda745079' class='xr-section-summary-in' type='checkbox'  ><label for='section-739420bc-1597-42d7-a7ac-b9ebda745079' class='xr-section-summary' >Data variables: <span>(47)</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>number</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>398 399 400 401 ... 656 657 658 659</div><input id='attrs-e68ad34e-bf91-439c-a0fb-5ba5825fa1d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e68ad34e-bf91-439c-a0fb-5ba5825fa1d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2790d20c-27a4-48e8-8a93-456bce71df3b' class='xr-var-data-in' type='checkbox'><label for='data-2790d20c-27a4-48e8-8a93-456bce71df3b' 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>1</dd><dt><span>long_name :</span></dt><dd>Ensemble Number</dd><dt><span>standard_name :</span></dt><dd>number_of_observations</dd></dl></div><div class='xr-var-data'><pre>array([398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410,\n",
       "       411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423,\n",
       "       424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436,\n",
       "       437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449,\n",
       "       450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462,\n",
       "       463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475,\n",
       "       476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488,\n",
       "       489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501,\n",
       "       502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514,\n",
       "       515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527,\n",
       "       528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540,\n",
       "       541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553,\n",
       "       554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566,\n",
       "       567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579,\n",
       "       580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592,\n",
       "       593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605,\n",
       "       606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618,\n",
       "       619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631,\n",
       "       632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644,\n",
       "       645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657,\n",
       "       658, 659], dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>builtin_test_fail</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-21fa84c4-0eb2-4553-ac3c-b31321e50662' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21fa84c4-0eb2-4553-ac3c-b31321e50662' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec15f26a-d2a9-4940-9ca3-ba74c2fd9d44' class='xr-var-data-in' type='checkbox'><label for='data-ec15f26a-d2a9-4940-9ca3-ba74c2fd9d44' 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>1</dd><dt><span>long_name :</span></dt><dd>Built-In Test Failures</dd></dl></div><div class='xr-var-data'><pre>array([False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>c_sound</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.458e+03 1.458e+03 ... 1.459e+03</div><input id='attrs-f22b08e9-d6bb-43e7-93e3-87b9745589fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f22b08e9-d6bb-43e7-93e3-87b9745589fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3beda6d6-4ecb-40e1-a60f-205da4cce821' class='xr-var-data-in' type='checkbox'><label for='data-3beda6d6-4ecb-40e1-a60f-205da4cce821' 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 s-1</dd><dt><span>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>standard_name :</span></dt><dd>speed_of_sound_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1459., 1459., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1459., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
       "       1458., 1458., 1458., 1458., 1458., 1459., 1459., 1458., 1458.,\n",
       "       1459., 1459., 1459., 1459., 1459., 1458., 1458., 1458., 1458.,\n",
       "       1459., 1459., 1458., 1458., 1459., 1459., 1459., 1459., 1459.,\n",
       "       1459., 1459., 1459., 1459., 1459., 1459., 1459., 1459., 1459.,\n",
       "       1459.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-2d412c7d-ab87-48b4-8c7d-2dda6053c70e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d412c7d-ab87-48b4-8c7d-2dda6053c70e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cf0e520-aacc-43b5-884c-b6474e549b82' class='xr-var-data-in' type='checkbox'><label for='data-3cf0e520-aacc-43b5-884c-b6474e549b82' 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>long_name :</span></dt><dd>Depth</dd><dt><span>standard_name :</span></dt><dd>depth</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.21 -1.01 -1.46 ... -0.5 -0.57</div><input id='attrs-ce76c58a-5f51-44c1-9480-ec77485d5808' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce76c58a-5f51-44c1-9480-ec77485d5808' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f136827-92b5-454a-a64b-a812335d6f71' class='xr-var-data-in' type='checkbox'><label for='data-6f136827-92b5-454a-a64b-a812335d6f71' 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>degree</dd><dt><span>long_name :</span></dt><dd>Pitch</dd><dt><span>standard_name :</span></dt><dd>platform_pitch</dd></dl></div><div class='xr-var-data'><pre>array([-1.21, -1.01, -1.46, -1.61, -0.25, -0.25, -0.25, -0.58, -1.57,\n",
       "       -1.61, -2.01, -0.44,  0.07, -0.82, -0.69, -1.4 , -1.49, -1.01,\n",
       "       -1.23, -1.38, -0.82, -0.18, -0.7 , -0.83, -0.71, -1.47, -0.36,\n",
       "       -1.58, -2.57, -2.37, -0.7 , -0.37, -1.31, -0.64,  0.33, -1.54,\n",
       "        0.13, -0.37, -0.81,  0.26, -1.96, -0.44, -2.18, -0.12, -0.89,\n",
       "       -1.16, -0.12,  0.14,  0.97, -0.69,  0.01, -1.99, -0.69, -2.12,\n",
       "        0.52,  0.33, -0.32,  0.46,  0.33,  0.33,  0.33,  0.33,  0.52,\n",
       "        0.39,  0.14, -1.09, -2.62, -0.37, -2.54, -0.78, -0.94, -1.14,\n",
       "       -0.99, -0.99, -0.56, -0.56, -0.56, -1.89, -0.82, -2.95,  0.08,\n",
       "       -1.82, -3.25, -1.65, -2.7 , -0.56, -0.84,  0.52, -0.45, -1.99,\n",
       "       -0.43, -1.03,  0.46, -0.95, -0.58, -0.89, -0.7 ,  0.  ,  0.78,\n",
       "       -1.93, -1.41, -1.83, -0.5 ,  0.78, -0.87,  0.53,  1.04,  0.2 ,\n",
       "       -0.19, -0.46, -2.8 , -0.31, -1.86,  0.07,  1.1 , -1.01,  0.14,\n",
       "        1.23,  0.2 , -1.14,  0.26, -0.95,  1.68, -4.  , -0.98, -0.57,\n",
       "       -1.78, -3.35,  0.21,  0.39, -0.69, -0.88, -1.93,  0.33, -0.51,\n",
       "       -3.12, -1.93, -0.13, -0.44,  0.84, -0.44,  0.39,  0.71,  0.4 ,\n",
       "        1.09,  0.65,  1.99, -0.37,  1.93,  1.03,  2.13,  2.13,  2.13,\n",
       "       -0.7 , -0.05, -0.05, -0.05,  0.72, -1.19,  0.2 ,  0.46, -2.  ,\n",
       "       -0.37, -2.08,  1.29,  0.59, -1.08,  0.14, -1.01, -2.67, -0.58,\n",
       "        0.08,  0.4 ,  1.93,  0.14,  1.86, -1.73, -3.07,  0.39,  0.39,\n",
       "       -1.19, -0.31, -2.44, -0.63, -0.57, -1.16, -1.32, -1.87, -2.41,\n",
       "       -0.57, -2.4 , -1.44, -1.74, -1.2 ,  0.91, -1.29, -1.74,  0.65,\n",
       "        1.36, -1.96,  0.01, -0.31, -1.79, -0.37,  0.2 , -1.98,  0.14,\n",
       "        0.08,  0.39, -1.51,  1.17, -0.26, -0.18, -0.18, -0.18, -0.12,\n",
       "       -0.19,  0.14, -1.08, -0.06, -2.2 , -2.2 , -2.2 ,  0.52, -1.17,\n",
       "       -0.62, -0.44, -0.76, -1.78, -0.87, -1.26, -1.01, -0.44, -0.77,\n",
       "       -0.44, -0.44, -1.23, -0.31,  0.21, -0.19, -0.88, -0.64, -0.32,\n",
       "       -1.2 , -1.77, -0.88,  0.27,  0.71, -0.25, -0.3 , -0.5 , -0.13,\n",
       "       -1.13, -0.37, -0.12,  0.07, -0.96, -1.41, -1.46, -0.19, -0.5 ,\n",
       "       -0.57], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.97 0.49 1.07 ... -0.66 -0.53 0.72</div><input id='attrs-83914d03-45c5-4ce9-a42a-4d804101db30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83914d03-45c5-4ce9-a42a-4d804101db30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5d702ee-7a45-43f6-a589-089ae59ad724' class='xr-var-data-in' type='checkbox'><label for='data-c5d702ee-7a45-43f6-a589-089ae59ad724' 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>degree</dd><dt><span>long_name :</span></dt><dd>Roll</dd><dt><span>standard_name :</span></dt><dd>platform_roll</dd></dl></div><div class='xr-var-data'><pre>array([ 1.97,  0.49,  1.07,  1.05,  0.01,  0.01,  0.01, -0.47,  2.04,\n",
       "       -0.46,  0.97, -0.21,  0.18, -0.43, -0.43,  1.62,  1.74,  0.24,\n",
       "        0.6 ,  1.44, -0.15,  0.88,  0.66,  0.31, -1.3 ,  2.  ,  1.14,\n",
       "        1.61,  1.65,  1.66,  1.01,  1.35,  1.53,  0.3 ,  0.11,  1.44,\n",
       "        0.63,  0.42,  1.37,  0.37,  0.24, -0.05, -0.49,  0.62, -0.5 ,\n",
       "        0.28,  1.61,  0.01,  0.8 ,  1.32,  1.67,  1.98,  1.67,  0.05,\n",
       "       -0.31,  0.59, -1.75, -0.47, -0.05, -0.05, -0.05,  2.34,  1.18,\n",
       "        0.14,  1.73,  0.95, -0.65, -0.63,  0.32,  0.41,  1.32,  0.72,\n",
       "        0.13,  0.52,  1.79,  1.79,  1.79,  1.06,  0.98,  1.7 ,  1.87,\n",
       "        0.19,  0.39,  0.69, -1.  ,  2.83,  2.61,  1.54,  1.96,  2.17,\n",
       "        0.77,  1.8 ,  0.58,  0.33,  0.6 ,  0.85,  0.37,  0.11,  1.35,\n",
       "        0.76,  1.08,  0.58,  1.14, -0.86,  0.93,  2.13,  1.92,  2.16,\n",
       "        1.66, -1.76,  2.93, -0.08, -0.43, -0.53, -0.83,  2.42,  0.69,\n",
       "        2.91,  0.99,  1.33, -0.88,  0.27,  3.6 ,  1.87, -1.18,  0.47,\n",
       "        0.95,  0.96,  2.87,  2.13,  2.13,  0.87,  0.6 , -1.4 , -2.45,\n",
       "        0.23,  0.02, -0.24,  1.62,  1.12,  1.16, -0.79,  0.79,  4.73,\n",
       "       -0.76, -0.98,  0.63,  1.22, -0.41,  0.07, -2.18, -2.18, -2.18,\n",
       "       -0.34,  0.94,  0.94,  0.94,  1.06,  1.05, -0.37,  0.32,  1.44,\n",
       "        1.31,  0.36,  1.08,  1.06,  1.96, -0.44, -0.18,  1.65,  0.57,\n",
       "        1.18,  1.12, -0.51, -0.41, -3.35, -1.45,  0.18, -0.57, -0.76,\n",
       "        1.86,  1.27, -0.62,  0.39,  1.01,  1.7 ,  0.55, -0.46,  0.66,\n",
       "       -1.07,  1.1 ,  0.29,  2.38,  0.36, -0.34, -0.11,  1.09,  1.17,\n",
       "        1.58,  0.22, -0.69,  0.17,  1.63, -0.28,  0.2 ,  1.65,  1.9 ,\n",
       "        2.5 , -0.66,  0.2 , -0.25, -0.43,  0.2 ,  0.2 ,  0.2 , -2.13,\n",
       "       -0.02, -1.2 , -0.37, -0.21,  0.87,  0.87,  0.87,  0.59,  0.9 ,\n",
       "        0.7 ,  0.01,  2.11,  1.01, -0.09,  0.92,  0.97,  1.42,  1.56,\n",
       "        0.4 ,  0.37, -0.53, -0.28,  0.57,  1.08,  0.55,  0.43,  0.53,\n",
       "        0.2 ,  0.43,  1.64,  0.23, -0.41,  0.56,  0.84,  0.72, -0.27,\n",
       "        1.38,  0.72, -0.24, -0.18,  1.21,  0.76,  1.3 , -0.66, -0.53,\n",
       "        0.72], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-154.2 -146.9 ... 159.4 155.4</div><input id='attrs-43be276b-b839-4a49-b08f-fb687d768b0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43be276b-b839-4a49-b08f-fb687d768b0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3aafdb77-1324-4291-8eda-7285e5d6f19c' class='xr-var-data-in' type='checkbox'><label for='data-3aafdb77-1324-4291-8eda-7285e5d6f19c' 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>degree</dd><dt><span>long_name :</span></dt><dd>Heading</dd><dt><span>standard_name :</span></dt><dd>platform_orientation</dd></dl></div><div class='xr-var-data'><pre>array([-154.16, -146.94, -149.73, -149.08, -142.38, -142.38, -142.38,\n",
       "       -138.54, -150.82, -140.2 , -145.69, -138.74, -138.54, -137.62,\n",
       "       -137.32, -146.94, -147.46, -141.54, -145.16, -150.39, -147.23,\n",
       "       -152.72, -155.97, -157.79, -153.27, -170.08, -169.21, -172.79,\n",
       "       -173.52, -174.86, -174.83, -178.64, -177.88, -174.07, -174.06,\n",
       "       -178.08, -177.42, -175.4 , -179.11, -176.44, -172.87, -174.05,\n",
       "       -170.72, -178.3 , -172.07, -175.75,  176.65, -176.27,  178.85,\n",
       "        178.7 ,  175.97,  178.48,  177.12, -175.92, -177.85,  177.34,\n",
       "       -171.18, -178.37,  179.31,  179.31,  179.31,  167.96,  172.52,\n",
       "        177.7 ,  170.57,  175.68, -176.88,  178.71,  178.44,  174.43,\n",
       "        171.22,  173.2 ,  175.78,  174.36,  169.04,  169.04,  169.04,\n",
       "        174.78,  173.5 ,  174.49,  167.44,  175.53,  175.54,  170.25,\n",
       "        177.2 ,  157.25,  157.76,  157.99,  158.78,  161.56,  161.43,\n",
       "        158.78,  159.33,  163.81,  163.14,  163.47,  166.03,  166.89,\n",
       "        160.38,  169.02,  166.81,  170.04,  165.43,  169.3 ,  166.31,\n",
       "        159.1 ,  158.09,  159.45,  162.64,  176.71,  164.01,  168.79,\n",
       "        173.7 ,  170.36,  169.52,  162.49,  165.44,  154.91,  164.57,\n",
       "        166.59,  171.5 ,  169.89,  152.04,  171.17, -179.85,  171.93,\n",
       "        170.65,  173.46,  159.55,  161.73,  163.4 ,  167.76,  171.29,\n",
       "        174.45, -178.8 ,  174.32,  173.31,  171.61,  165.1 ,  164.11,\n",
       "        166.82,  173.13,  166.24,  154.54,  173.9 ,  176.31,  165.78,\n",
       "        168.37,  170.27,  169.04,  176.61,  176.61,  176.61,  173.82,\n",
       "        166.56,  166.56,  166.56,  164.95,  168.67,  171.44,  166.63,\n",
       "        167.48,  163.93,  170.73,  159.4 ,  161.15,  161.24,  165.7 ,\n",
       "        165.39,  165.08,  163.16,  159.8 ,  159.5 ,  161.17,  166.81,\n",
       "        176.11,  173.99,  170.09,  163.81,  163.81,  158.22,  156.79,\n",
       "        167.65,  158.87,  156.31,  155.09,  158.19,  162.2 ,  159.09,\n",
       "        159.77,  158.65,  159.81,  154.  ,  158.14,  154.61,  160.49,\n",
       "        158.01,  151.21,  147.92,  162.34,  160.3 ,  157.92,  157.12,\n",
       "        160.22,  156.76,  157.91,  151.12,  149.55,  158.79,  161.03,\n",
       "        154.09,  160.07,  158.06,  158.06,  158.06,  166.28,  158.86,\n",
       "        162.37,  162.58,  159.29,  160.53,  160.53,  160.53,  151.05,\n",
       "        156.2 ,  155.22,  156.83,  150.5 ,  156.62,  157.63,  154.88,\n",
       "        154.28,  151.08,  150.79,  153.06,  153.4 ,  157.91,  154.43,\n",
       "        150.07,  149.9 ,  153.38,  152.86,  152.34,  156.03,  157.2 ,\n",
       "        151.1 ,  152.18,  152.47,  153.35,  152.24,  153.47,  156.04,\n",
       "        154.19,  154.04,  156.93,  156.44,  154.83,  157.14,  155.93,\n",
       "        158.46,  159.43,  155.37], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>13.13 13.13 13.13 ... 13.19 13.25</div><input id='attrs-1c6e3e97-0f7f-4d4e-b624-5898ce3c2da9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c6e3e97-0f7f-4d4e-b624-5898ce3c2da9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c44b9712-ab2f-439c-88af-9ed68c398c85' class='xr-var-data-in' type='checkbox'><label for='data-c44b9712-ab2f-439c-88af-9ed68c398c85' 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>degree_C</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd></dl></div><div class='xr-var-data'><pre>array([13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.06, 13.06, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.19, 13.19, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.19, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.19, 13.19, 13.13, 13.13,\n",
       "       13.19, 13.19, 13.19, 13.19, 13.19, 13.13, 13.13, 13.13, 13.13,\n",
       "       13.19, 13.19, 13.13, 13.13, 13.19, 13.19, 13.19, 13.19, 13.19,\n",
       "       13.19, 13.19, 13.19, 13.19, 13.19, 13.19, 13.19, 13.19, 13.19,\n",
       "       13.25], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-6512a3b6-ba8e-4b50-8966-078461c566a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6512a3b6-ba8e-4b50-8966-078461c566a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69e7ea10-5bbb-453c-a3fd-0ffb40c40993' class='xr-var-data-in' type='checkbox'><label for='data-69e7ea10-5bbb-453c-a3fd-0ffb40c40993' 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>psu</dd><dt><span>long_name :</span></dt><dd>Salinity</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>min_preping_wait</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>420.8 420.4 420.1 ... 600.7 600.3</div><input id='attrs-7cfd4a04-5369-4acc-8f2e-2e3da99c239e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7cfd4a04-5369-4acc-8f2e-2e3da99c239e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fb16855-49be-4413-8bd1-b9ab14211e09' class='xr-var-data-in' type='checkbox'><label for='data-4fb16855-49be-4413-8bd1-b9ab14211e09' 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>s</dd><dt><span>long_name :</span></dt><dd>Minimum Pre-Ping Wait Time Between Measurements</dd></dl></div><div class='xr-var-data'><pre>array([420.78, 420.39, 420.07, 420.79, 420.46, 420.37, 420.05, 420.71,\n",
       "       420.43, 420.1 , 420.81, 420.49, 420.22, 420.91, 420.53, 420.1 ,\n",
       "       420.71, 420.37, 420.09, 420.81, 420.61, 420.3 , 420.1 , 420.9 ,\n",
       "       420.56, 420.3 , 420.97, 420.66, 420.32, 420.03, 420.72, 420.46,\n",
       "       420.08, 420.76, 420.42, 420.13, 420.81, 420.55, 420.26, 420.01,\n",
       "       420.78, 420.57, 420.38, 420.22, 420.06, 420.66, 420.3 , 420.9 ,\n",
       "       420.54, 420.14, 420.8 , 420.41, 420.09, 420.73, 420.42, 420.06,\n",
       "       420.76, 420.44, 420.15, 420.02, 420.71, 420.38, 420.08, 420.75,\n",
       "       420.46, 420.13, 420.85, 420.54, 420.27, 420.95, 420.68, 420.36,\n",
       "       420.08, 420.77, 420.5 , 420.36, 480.05, 480.72, 480.44, 480.11,\n",
       "       480.84, 480.51, 480.22, 480.88, 480.6 , 480.27, 480.98, 480.64,\n",
       "       480.36, 480.02, 480.72, 480.39, 480.1 , 480.77, 480.48, 480.13,\n",
       "       480.83, 480.49, 480.19, 480.85, 480.55, 480.21, 480.91, 480.57,\n",
       "       480.28, 480.94, 480.64, 480.31, 480.  , 480.67, 480.38, 480.04,\n",
       "       480.75, 480.42, 480.12, 480.8 , 480.5 , 480.14, 480.83, 480.47,\n",
       "       480.15, 480.79, 480.48, 480.11, 480.76, 480.39, 480.05, 480.68,\n",
       "       480.34, 480.99, 480.67, 480.32, 480.01, 480.65, 480.34, 480.98,\n",
       "       480.66, 480.31, 480.99, 480.63, 480.29, 480.9 , 480.56, 480.17,\n",
       "       480.81, 480.41, 480.05, 480.66, 480.31, 480.91, 480.56, 480.36,\n",
       "       480.99, 480.6 , 480.26, 480.06, 480.69, 480.31, 480.94, 480.53,\n",
       "       480.16, 480.75, 480.39, 480.96, 480.78, 480.58, 540.39, 540.19,\n",
       "       540.98, 540.76, 540.48, 540.24, 540.95, 540.69, 540.39, 540.12,\n",
       "       540.8 , 540.54, 540.22, 540.96, 540.63, 540.36, 540.03, 540.73,\n",
       "       540.39, 540.09, 540.74, 540.43, 540.08, 540.78, 540.44, 540.14,\n",
       "       540.8 , 540.49, 540.13, 540.81, 540.45, 540.12, 540.74, 540.41,\n",
       "       540.02, 540.69, 540.3 , 540.94, 540.54, 540.28, 540.96, 540.69,\n",
       "       540.39, 540.13, 540.73, 540.46, 540.13, 540.02, 540.7 , 540.35,\n",
       "       540.02, 540.63, 540.29, 540.9 , 540.58, 540.4 , 540.04, 540.67,\n",
       "       540.34, 540.95, 540.61, 540.23, 540.95, 540.57, 540.36, 540.08,\n",
       "       540.84, 540.62, 540.4 , 540.17, 540.88, 540.64, 540.34, 540.09,\n",
       "       540.77, 540.43, 540.04, 540.7 , 540.32, 540.97, 540.57, 540.2 ,\n",
       "       540.78, 540.41, 540.98, 540.59, 540.14, 540.75, 600.32, 600.92,\n",
       "       600.47, 600.07, 600.62, 600.21, 600.74, 600.33], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-092b1426-c7e2-42b7-b483-5c1b30f215cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-092b1426-c7e2-42b7-b483-5c1b30f215cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9fbc7f2-e477-462e-a668-562159993e9c' class='xr-var-data-in' type='checkbox'><label for='data-b9fbc7f2-e477-462e-a668-562159993e9c' 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>degree</dd><dt><span>long_name :</span></dt><dd>Heading Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-0a8e2fe7-658b-48e5-aba0-bc226099dbe0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a8e2fe7-658b-48e5-aba0-bc226099dbe0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d48ed86-61f5-425f-9d93-30251f873931' class='xr-var-data-in' type='checkbox'><label for='data-9d48ed86-61f5-425f-9d93-30251f873931' 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>degree</dd><dt><span>long_name :</span></dt><dd>Pitch Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-120969eb-f708-4532-a80b-a2b68be59b9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-120969eb-f708-4532-a80b-a2b68be59b9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16e8d9a0-802e-4761-a040-72490bc2c885' class='xr-var-data-in' type='checkbox'><label for='data-16e8d9a0-802e-4761-a040-72490bc2c885' 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>degree</dd><dt><span>long_name :</span></dt><dd>Roll Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-5c17f3e5-e347-4d95-bdb8-5a7c0bcf5cbe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c17f3e5-e347-4d95-bdb8-5a7c0bcf5cbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9dafba6f-628d-40fc-b1d0-bec5f82cbf17' class='xr-var-data-in' type='checkbox'><label for='data-9dafba6f-628d-40fc-b1d0-bec5f82cbf17' 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>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-7d56cc68-5fb4-4388-a167-c1b2bf5d7771' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d56cc68-5fb4-4388-a167-c1b2bf5d7771' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7eeabdff-751d-43ba-87a0-c865489455d6' class='xr-var-data-in' type='checkbox'><label for='data-7eeabdff-751d-43ba-87a0-c865489455d6' 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>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel</span></div><div class='xr-var-dims'>(dir, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan -1.378 -1.597 ... nan nan nan</div><input id='attrs-96ab13ea-ad02-4bb4-87c0-cb9045c89ab5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96ab13ea-ad02-4bb4-87c0-cb9045c89ab5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15484545-0b2a-4442-8477-b2167bb10e30' class='xr-var-data-in' type='checkbox'><label for='data-15484545-0b2a-4442-8477-b2167bb10e30' 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 s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[[           nan, -1.3780606e+00, -1.5968075e+00, ...,\n",
       "         -2.6226619e-01, -2.5654507e-01, -2.2308642e-01],\n",
       "        [           nan, -2.5692660e-01, -2.6959109e-01, ...,\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,  2.6142585e-01, -4.9651265e-03, ...,\n",
       "          3.5546001e-02,  3.3569042e-02, -1.2026111e-02],\n",
       "        [           nan,  4.8740420e-02, -8.3826878e-04, ...,\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, -3.6425743e-01, -7.1455896e-02, ...,\n",
       "          5.0781541e-02, -3.7472174e-03,  1.1241753e-02],\n",
       "        [           nan, -6.7912415e-02, -1.2063992e-02, ...,\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]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>145 149 147 147 149 ... 0 0 0 0 0</div><input id='attrs-a01e6990-2294-4050-a493-124de4bea17a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a01e6990-2294-4050-a493-124de4bea17a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b265b6a-83a7-4698-bbe0-3e7fb37fa2e5' class='xr-var-data-in' type='checkbox'><label for='data-8b265b6a-83a7-4698-bbe0-3e7fb37fa2e5' 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>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[145, 149, 147, ..., 133, 131, 134],\n",
       "        [138, 147, 148, ...,  85,  87,  86],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
       "\n",
       "       [[145, 153, 151, ..., 137, 138, 136],\n",
       "        [155, 164, 170, ...,  93,  90,  85],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
       "\n",
       "       [[142, 145, 143, ..., 128, 128, 130],\n",
       "        [154, 157, 155, ...,  85,  86,  87],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
       "\n",
       "       [[141, 147, 147, ..., 135, 132, 132],\n",
       "        [141, 139, 138, ...,  85,  88,  83],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0]]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>135 113 114 107 117 ... 0 0 0 0 0</div><input id='attrs-a0fdb2be-e418-4afc-bdee-b07a28b4691b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0fdb2be-e418-4afc-bdee-b07a28b4691b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-946e07ec-ee76-4912-b942-decc794f612b' class='xr-var-data-in' type='checkbox'><label for='data-946e07ec-ee76-4912-b942-decc794f612b' 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>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[135, 113, 114, ..., 241, 245, 246],\n",
       "        [110, 111, 115, ..., 136, 149, 145],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
       "\n",
       "       [[173, 118, 110, ..., 240, 245, 247],\n",
       "        [133, 110, 124, ..., 141, 143, 145],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
       "\n",
       "       [[128, 114, 112, ..., 245, 244, 246],\n",
       "        [134, 110, 109, ..., 138, 145, 148],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
       "\n",
       "       [[151, 117, 118, ..., 241, 244, 246],\n",
       "        [108, 101, 112, ..., 141, 150, 142],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0]]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dist_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.3 1.18 1.16 ... 1.04 0.96 0.96</div><input id='attrs-74bfbac9-9a38-4797-a9f1-4f2e852d1de8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74bfbac9-9a38-4797-a9f1-4f2e852d1de8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a57dfd9-09ae-4a2b-b7f9-a160f59f0d22' class='xr-var-data-in' type='checkbox'><label for='data-3a57dfd9-09ae-4a2b-b7f9-a160f59f0d22' 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>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Bottom Track Measured Depth</dd></dl></div><div class='xr-var-data'><pre>array([[1.3 , 1.18, 1.16, ..., 0.98, 0.96, 0.96],\n",
       "       [1.11, 1.13, 1.09, ..., 0.97, 0.95, 0.97],\n",
       "       [1.17, 1.12, 1.12, ..., 0.95, 0.97, 0.92],\n",
       "       [1.24, 1.25, 1.26, ..., 1.04, 0.96, 0.96]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel_bt</span></div><div class='xr-var-dims'>(dir, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.03986 0.02038 ... 0.007236</div><input id='attrs-7c96ceed-47ac-4ceb-b6c0-89ff3a31c8a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c96ceed-47ac-4ceb-b6c0-89ff3a31c8a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d08fa1b7-d508-4789-b104-906f4a32c718' class='xr-var-data-in' type='checkbox'><label for='data-d08fa1b7-d508-4789-b104-906f4a32c718' 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 s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.03985795,  0.02037775,  0.03288013, ..., -0.01411782,\n",
       "        -0.01381912,  0.04580029],\n",
       "       [-0.07396869, -0.08312948, -0.07563345, ..., -0.6168041 ,\n",
       "        -0.6167893 , -0.5712449 ],\n",
       "       [ 0.00555705, -0.00484052, -0.00232305, ...,  0.01484554,\n",
       "         0.0183713 ,  0.00577757],\n",
       "       [ 0.00413489,  0.00206744, -0.00103372, ...,  0.00206745,\n",
       "        -0.00516861,  0.00723603]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>251 251 253 249 ... 222 254 248 223</div><input id='attrs-c96e8494-21c7-4b76-8546-d25184170aa1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c96e8494-21c7-4b76-8546-d25184170aa1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c632b10d-0163-4bba-94ef-d113a6472841' class='xr-var-data-in' type='checkbox'><label for='data-c632b10d-0163-4bba-94ef-d113a6472841' 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>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[251, 251, 253, ..., 234, 236, 231],\n",
       "       [252, 251, 250, ..., 229, 252, 232],\n",
       "       [249, 250, 243, ..., 224, 239, 228],\n",
       "       [249, 252, 243, ..., 254, 248, 223]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>35 31 29 33 39 ... 46 40 60 48 52</div><input id='attrs-4d27f779-dff1-450c-9053-8b6ec40b9cf6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d27f779-dff1-450c-9053-8b6ec40b9cf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71873448-50d1-4a42-918c-0baa0b4c6b18' class='xr-var-data-in' type='checkbox'><label for='data-71873448-50d1-4a42-918c-0baa0b4c6b18' 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>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[35, 31, 29, ..., 61, 74, 58],\n",
       "       [36, 51, 54, ..., 47, 56, 52],\n",
       "       [43, 56, 52, ..., 64, 51, 62],\n",
       "       [42, 53, 48, ..., 60, 48, 52]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prcnt_gd_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1</div><input id='attrs-d06de2db-bf71-420c-8751-192b629abecb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d06de2db-bf71-420c-8751-192b629abecb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02d877ef-d6ed-4721-9f19-41debc04eb75' class='xr-var-data-in' type='checkbox'><label for='data-02d877ef-d6ed-4721-9f19-41debc04eb75' 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>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Percent Good</dd><dt><span>standard_name :</span></dt><dd>proportion_of_acceptable_signal_returns_from_acoustic_instrument_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [1, 1, 1, ..., 1, 1, 1]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alt_dist</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.1 1.08 1.07 ... 1.02 0.98 0.97</div><input id='attrs-52453374-61ee-421f-954e-531aa8a454d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52453374-61ee-421f-954e-531aa8a454d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5455f622-78a1-46d3-9e7e-dbf0e98aa3e6' class='xr-var-data-in' type='checkbox'><label for='data-5455f622-78a1-46d3-9e7e-dbf0e98aa3e6' 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>long_name :</span></dt><dd>Altimeter Range</dd><dt><span>standard_name :</span></dt><dd>altimeter_range</dd></dl></div><div class='xr-var-data'><pre>array([1.1  , 1.08 , 1.07 , 1.06 , 1.06 , 1.31 , 1.31 , 1.29 , 1.26 ,\n",
       "       1.271, 1.22 , 1.21 , 1.21 , 1.13 , 1.22 , 1.21 , 1.23 , 1.37 ,\n",
       "       1.45 , 1.46 , 1.4  , 1.64 , 2.03 , 2.217, 2.379, 2.52 , 2.75 ,\n",
       "       2.99 , 3.54 , 3.58 , 3.83 , 4.179, 4.453, 4.52 , 4.729, 5.79 ,\n",
       "       5.914, 5.966, 0.   , 0.   , 7.651, 7.394, 7.53 , 7.56 , 7.65 ,\n",
       "       7.793, 7.893, 8.042, 8.172, 8.31 , 8.66 , 8.67 , 8.75 , 8.82 ,\n",
       "       8.88 , 8.96 , 8.97 , 9.01 , 9.05 , 9.09 , 9.13 , 9.16 , 9.2  ,\n",
       "       9.27 , 9.27 , 8.96 , 9.04 , 9.18 , 8.93 , 9.09 , 9.05 , 9.01 ,\n",
       "       8.97 , 8.97 , 8.94 , 8.88 , 8.86 , 8.86 , 8.84 , 8.88 , 8.86 ,\n",
       "       8.77 , 8.78 , 8.78 , 8.78 , 8.59 , 8.77 , 8.73 , 8.69 , 8.71 ,\n",
       "       8.77 , 8.78 , 8.698, 8.698, 8.73 , 8.67 , 8.61 , 8.63 , 8.67 ,\n",
       "       8.69 , 8.65 , 8.641, 8.61 , 8.584, 8.617, 8.698, 8.811, 8.887,\n",
       "       8.96 , 8.99 , 8.925, 9.01 , 8.754, 8.78 , 8.071, 8.635, 8.565,\n",
       "       8.4  , 8.35 , 8.27 , 7.58 , 8.142, 8.15 , 7.46 , 8.07 , 8.136,\n",
       "       8.243, 8.261, 7.905, 8.408, 7.922, 7.959, 7.926, 8.408, 8.35 ,\n",
       "       7.709, 8.275, 8.173, 8.17 , 8.047, 7.905, 7.86 , 7.295, 7.668,\n",
       "       7.544, 7.444, 7.314, 6.99 , 7.391, 7.252, 7.236, 7.375, 7.576,\n",
       "       7.644, 7.577, 7.444, 6.774, 6.566, 6.509, 6.737, 6.86 , 6.75 ,\n",
       "       6.71 , 6.61 , 6.63 , 6.73 , 6.124, 6.084, 5.772, 5.444, 5.294,\n",
       "       5.294, 5.163, 4.956, 4.78 , 4.7  , 4.66 , 5.127, 5.213, 4.831,\n",
       "       4.593, 4.465, 4.425, 4.054, 4.026, 4.216, 4.26 , 4.26 , 4.292,\n",
       "       4.292, 4.273, 4.168, 4.06 , 3.95 , 3.89 , 3.81 , 3.797, 3.996,\n",
       "       3.614, 3.54 , 3.52 , 3.49 , 3.41 , 3.24 , 3.11 , 3.06 , 3.14 ,\n",
       "       3.22 , 3.22 , 3.15 , 3.027, 2.9  , 2.82 , 2.7  , 2.58 , 2.49 ,\n",
       "       2.43 , 2.41 , 2.39 , 2.38 , 2.37 , 2.4  , 2.4  , 2.38 , 2.36 ,\n",
       "       2.31 , 2.3  , 2.29 , 2.29 , 2.29 , 2.28 , 2.25 , 2.21 , 2.19 ,\n",
       "       2.13 , 2.08 , 2.01 , 1.95 , 1.86 , 1.76 , 1.69 , 1.6  , 1.53 ,\n",
       "       1.46 , 1.41 , 1.38 , 1.33 , 1.3  , 1.26 , 1.23 , 1.18 , 1.16 ,\n",
       "       1.14 , 1.12 , 1.11 , 1.09 , 1.06 , 1.   , 0.96 , 1.02 , 0.98 ,\n",
       "       0.97 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alt_rssi</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>154 143 145 140 ... 154 132 136 136</div><input id='attrs-7c7749a4-9f3c-48fc-a6be-c43a91fd5af1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c7749a4-9f3c-48fc-a6be-c43a91fd5af1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc7bbe47-5f13-451d-81c9-71f59b3a0e64' class='xr-var-data-in' type='checkbox'><label for='data-dc7bbe47-5f13-451d-81c9-71f59b3a0e64' 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>dB</dd><dt><span>long_name :</span></dt><dd>Altimeter Recieved Signal Strength Indicator</dd></dl></div><div class='xr-var-data'><pre>array([154, 143, 145, 140, 122, 140, 150, 147, 119, 142, 141, 135, 139,\n",
       "       120, 134, 139, 142, 143, 139, 126,  98, 121, 136, 111, 127, 121,\n",
       "       130, 105, 105, 118, 109, 104, 174, 175, 163, 131, 156, 154, 154,\n",
       "       147, 139, 151, 153, 149, 161, 156, 154, 155, 165, 154, 159, 155,\n",
       "       160, 155, 157, 158, 156, 151, 157, 155, 160, 164, 159, 154, 157,\n",
       "       156, 164, 149, 160, 161, 153, 160, 162, 150, 162, 163, 162, 169,\n",
       "       154, 170, 164, 165, 158, 147, 163, 171, 174, 162, 161, 158, 161,\n",
       "       161, 159, 163, 161, 158, 154, 156, 143, 150, 160, 147, 153, 153,\n",
       "       152, 150, 140, 150, 151, 160, 160, 136, 152, 155, 157, 152, 154,\n",
       "       163, 151, 142, 154, 154, 154, 154, 149, 149, 142, 137, 154, 153,\n",
       "       146, 146, 144, 147, 151, 156, 146, 156, 131, 149, 142, 146, 139,\n",
       "       141, 142, 140, 138, 145, 143, 161, 147, 146, 151, 160, 151, 157,\n",
       "       157, 147, 145, 159, 150, 168, 163, 168, 174, 166, 154, 155, 147,\n",
       "       150, 151, 145, 141, 152, 175, 175, 158, 153, 170, 150, 155, 148,\n",
       "       153, 162, 156, 159, 168, 160, 147, 159, 153, 150, 162, 196, 121,\n",
       "       116, 106, 101,  91, 123,  92,  86, 104,  98, 101, 124, 114, 116,\n",
       "       117, 111,  98, 110, 127,  95, 103, 115, 125, 133, 104, 116, 108,\n",
       "       127, 128, 124, 129, 117, 133, 132, 132, 129, 107, 122, 131, 122,\n",
       "       122, 122, 129, 124, 129, 125, 120, 146, 128, 124, 143, 127, 133,\n",
       "       135, 126, 133, 111, 102, 115, 148, 146, 148, 139, 121, 154, 132,\n",
       "       136, 136], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alt_eval</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>60 60 54 46 58 ... 73 100 70 77 72</div><input id='attrs-6eb6b6fa-1702-440b-8260-8e7873cf3db7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6eb6b6fa-1702-440b-8260-8e7873cf3db7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c52bc71-0f80-4b28-9f07-6efaa6597bec' class='xr-var-data-in' type='checkbox'><label for='data-1c52bc71-0f80-4b28-9f07-6efaa6597bec' 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>dB</dd><dt><span>long_name :</span></dt><dd>Altimeter Evaluation Amplitude</dd></dl></div><div class='xr-var-data'><pre>array([ 60,  60,  54,  46,  58,  36,  41,  40,  42,  46,  47,  50,  51,\n",
       "        56,  62,  59,  53,  49,  51,  43,  41,  32,  45,  46,  59,  57,\n",
       "        52,  38,  40,  51,  42,  44,  53,  60,  40,  39,  47,  45,  28,\n",
       "        27,  34,  45,  49,  53,  55,  54,  53,  56,  59,  58,  58,  61,\n",
       "        61,  58,  60,  60,  64,  60,  63,  66,  60,  63,  61,  61,  53,\n",
       "        60,  60,  55,  61,  61,  60,  62,  66,  58,  59,  60,  61,  62,\n",
       "        55,  68,  65,  65,  61,  62,  63,  66,  62,  61,  60,  57,  57,\n",
       "        53,  51,  50,  53,  50,  51,  52,  53,  55,  52,  53,  47,  56,\n",
       "        52,  53,  52,  53,  53,  58,  49,  48,  44,  48,  46,  46,  42,\n",
       "        54,  50,  56,  48,  55,  59,  52,  51,  50,  46,  39,  38,  41,\n",
       "        39,  40,  41,  43,  49,  48,  48,  51,  49,  51,  49,  49,  46,\n",
       "        49,  52,  50,  45,  42,  46,  51,  53,  49,  48,  50,  50,  45,\n",
       "        44,  41,  42,  53,  54,  52,  59,  59,  60,  57,  51,  47,  47,\n",
       "        47,  34,  45,  47,  54,  63,  60,  53,  43,  47,  45,  44,  45,\n",
       "        53,  49,  44,  51,  59,  57,  52,  54,  53,  56,  47,  83,  57,\n",
       "        66,  45,  45,  45,  62,  49,  47,  47,  49,  63,  59,  48,  50,\n",
       "        61,  48,  45,  56,  57,  42,  54,  59,  62,  67,  56,  58,  56,\n",
       "        55,  59,  58,  57,  63,  61,  61,  62,  59,  53,  60,  62,  63,\n",
       "        65,  64,  67,  65,  64,  68,  71,  75,  67,  71,  71,  60,  72,\n",
       "        68,  69,  76,  63,  57,  59,  71,  77,  82,  70,  73, 100,  70,\n",
       "        77,  72], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alt_status</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>1 1 1 2 2 1 1 1 ... 1 1 1 1 2 1 1 1</div><input id='attrs-fc4390a0-a07d-43db-889e-b566ffb3e364' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc4390a0-a07d-43db-889e-b566ffb3e364' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-283e5b21-dff4-4bc3-8d0c-6fe1361fbbc3' class='xr-var-data-in' type='checkbox'><label for='data-283e5b21-dff4-4bc3-8d0c-6fe1361fbbc3' 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>bit</dd><dt><span>long_name :</span></dt><dd>Altimeter Status</dd></dl></div><div class='xr-var-data'><pre>array([1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 5, 5, 5, 5, 5, 4, 4, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6,\n",
       "       6, 5, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1],\n",
       "      dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>64.56 64.56 64.56 ... 64.56 64.56</div><input id='attrs-8b6a71b6-9ba8-4a2b-9b4a-057dc6553541' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b6a71b6-9ba8-4a2b-9b4a-057dc6553541' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-619b07f2-9bed-45b4-a6a7-3ce607400ba9' class='xr-var-data-in' type='checkbox'><label for='data-619b07f2-9bed-45b4-a6a7-3ce607400ba9' 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>degree_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([64.56195 , 64.56195 , 64.56195 , 64.56195 , 64.56195 , 64.56195 ,\n",
       "       64.56195 , 64.56195 , 64.56195 , 64.56195 , 64.56195 , 64.56195 ,\n",
       "       64.56195 , 64.56195 , 64.56195 , 64.56195 , 64.56195 , 64.56195 ,\n",
       "       64.56195 , 64.56195 , 64.56196 , 64.56196 , 64.56196 , 64.56196 ,\n",
       "       64.56196 , 64.56196 , 64.561966, 64.561966, 64.561966, 64.561966,\n",
       "       64.561966, 64.561966, 64.561966, 64.561966, 64.56197 , 64.56197 ,\n",
       "       64.56197 , 64.56197 , 64.56197 , 64.56198 , 64.56198 , 64.56198 ,\n",
       "       64.56199 , 64.56199 , 64.56199 , 64.56199 , 64.562   , 64.562   ,\n",
       "       64.562   , 64.562   , 64.562   , 64.562   , 64.562   , 64.562   ,\n",
       "       64.562   , 64.562   , 64.562   , 64.562004, 64.562004, 64.562004,\n",
       "       64.562004, 64.562004, 64.56201 , 64.56201 , 64.56201 , 64.56201 ,\n",
       "       64.56201 , 64.56201 , 64.56202 , 64.56202 , 64.56202 , 64.56202 ,\n",
       "       64.56202 , 64.56203 , 64.56203 , 64.56203 , 64.56203 , 64.562035,\n",
       "       64.562035, 64.56204 , 64.56204 , 64.56204 , 64.56205 , 64.56205 ,\n",
       "       64.56206 , 64.56206 , 64.56206 , 64.562065, 64.562065, 64.56207 ,\n",
       "       64.56208 , 64.56208 , 64.56209 , 64.56209 , 64.562096, 64.562096,\n",
       "       64.5621  , 64.5621  , 64.56211 , 64.56211 , 64.56212 , 64.56212 ,\n",
       "       64.562126, 64.562126, 64.56213 , 64.56213 , 64.56214 , 64.56214 ,\n",
       "       64.56214 , 64.56215 , 64.56215 , 64.56216 , 64.56216 , 64.562164,\n",
       "       64.562164, 64.56217 , 64.56217 , 64.56218 , 64.56218 , 64.56218 ,\n",
       "...\n",
       "       64.56225 , 64.562256, 64.562256, 64.562256, 64.562256, 64.562256,\n",
       "       64.562256, 64.562256, 64.562256, 64.562256, 64.562256, 64.56226 ,\n",
       "       64.56226 , 64.56226 , 64.56226 , 64.56226 , 64.56226 , 64.56226 ,\n",
       "       64.56227 , 64.56227 , 64.56227 , 64.56227 , 64.56227 , 64.56228 ,\n",
       "       64.56228 , 64.56228 , 64.56228 , 64.56229 , 64.56229 , 64.56229 ,\n",
       "       64.56229 , 64.56229 , 64.56229 , 64.562294, 64.562294, 64.562294,\n",
       "       64.562294, 64.5623  , 64.5623  , 64.5623  , 64.5623  , 64.56231 ,\n",
       "       64.56231 , 64.56231 , 64.56232 , 64.56232 , 64.56232 , 64.562325,\n",
       "       64.562325, 64.562325, 64.56233 , 64.56233 , 64.56233 , 64.56233 ,\n",
       "       64.56234 , 64.56234 , 64.56234 , 64.56235 , 64.56235 , 64.56235 ,\n",
       "       64.56235 , 64.562355, 64.562355, 64.562355, 64.56236 , 64.56236 ,\n",
       "       64.56236 , 64.56237 , 64.56237 , 64.56237 , 64.56237 , 64.56237 ,\n",
       "       64.56238 , 64.56238 , 64.56238 , 64.56238 , 64.562386, 64.562386,\n",
       "       64.562386, 64.562386, 64.56239 , 64.56239 , 64.56239 , 64.56239 ,\n",
       "       64.56239 , 64.5624  , 64.5624  , 64.5624  , 64.56241 , 64.56241 ,\n",
       "       64.56241 , 64.56241 , 64.562416, 64.562416, 64.562416, 64.562416,\n",
       "       64.56242 , 64.56242 , 64.56242 , 64.56242 , 64.56243 , 64.56243 ,\n",
       "       64.56243 , 64.56243 , 64.56243 , 64.56243 , 64.56244 , 64.56244 ,\n",
       "       64.56244 , 64.56244 , 64.56244 , 64.56244 , 64.56244 , 64.56244 ,\n",
       "       64.56244 , 64.56244 , 64.56245 , 64.56245 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-149.1 -149.1 ... -149.1 -149.1</div><input id='attrs-bcfec285-3699-4cc0-a990-84442f8ac73d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcfec285-3699-4cc0-a990-84442f8ac73d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd470c68-02a5-4c59-9932-62930d02f3a0' class='xr-var-data-in' type='checkbox'><label for='data-cd470c68-02a5-4c59-9932-62930d02f3a0' 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>degree_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-149.06721, -149.06721, -149.06721, -149.06721, -149.06721,\n",
       "       -149.06721, -149.06721, -149.06721, -149.06721, -149.06721,\n",
       "       -149.0672 , -149.0672 , -149.0672 , -149.0672 , -149.0672 ,\n",
       "       -149.0672 , -149.0672 , -149.0672 , -149.0672 , -149.0672 ,\n",
       "       -149.06718, -149.06718, -149.06718, -149.06717, -149.06717,\n",
       "       -149.06715, -149.06715, -149.06714, -149.06714, -149.06712,\n",
       "       -149.06712, -149.06712, -149.06711, -149.0671 , -149.0671 ,\n",
       "       -149.06708, -149.06708, -149.06706, -149.06706, -149.06705,\n",
       "       -149.06705, -149.06703, -149.06703, -149.06702, -149.06702,\n",
       "       -149.06702, -149.067  , -149.067  , -149.06699, -149.06699,\n",
       "       -149.06697, -149.06697, -149.06696, -149.06696, -149.06694,\n",
       "       -149.06693, -149.06693, -149.06691, -149.06691, -149.0669 ,\n",
       "       -149.0669 , -149.06688, -149.06686, -149.06686, -149.06685,\n",
       "       -149.06683, -149.06682, -149.06682, -149.0668 , -149.06679,\n",
       "       -149.06677, -149.06677, -149.06676, -149.06674, -149.06673,\n",
       "       -149.06671, -149.06671, -149.0667 , -149.06668, -149.06667,\n",
       "       -149.06667, -149.06665, -149.06664, -149.06662, -149.0666 ,\n",
       "       -149.06659, -149.06659, -149.06656, -149.06656, -149.06654,\n",
       "       -149.06653, -149.06651, -149.0665 , -149.06648, -149.06647,\n",
       "       -149.06645, -149.06645, -149.06642, -149.06642, -149.0664 ,\n",
       "...\n",
       "       -149.06529, -149.06526, -149.06523, -149.06522, -149.06519,\n",
       "       -149.06517, -149.06514, -149.06512, -149.0651 , -149.06508,\n",
       "       -149.06506, -149.06503, -149.06502, -149.065  , -149.06499,\n",
       "       -149.06496, -149.06494, -149.06493, -149.06491, -149.0649 ,\n",
       "       -149.06487, -149.06485, -149.06483, -149.06482, -149.0648 ,\n",
       "       -149.06479, -149.06477, -149.06476, -149.06474, -149.06473,\n",
       "       -149.0647 , -149.06468, -149.06467, -149.06465, -149.06464,\n",
       "       -149.06462, -149.0646 , -149.06459, -149.06456, -149.06454,\n",
       "       -149.06453, -149.06451, -149.06448, -149.06447, -149.06445,\n",
       "       -149.06444, -149.0644 , -149.06439, -149.06438, -149.06435,\n",
       "       -149.06433, -149.06432, -149.0643 , -149.06429, -149.06427,\n",
       "       -149.06425, -149.06424, -149.06422, -149.06421, -149.0642 ,\n",
       "       -149.06418, -149.06416, -149.06415, -149.06413, -149.0641 ,\n",
       "       -149.06409, -149.06407, -149.06406, -149.06404, -149.06403,\n",
       "       -149.06401, -149.06398, -149.06396, -149.06395, -149.06393,\n",
       "       -149.06393, -149.06392, -149.0639 , -149.06389, -149.06387,\n",
       "       -149.06386, -149.06386, -149.06384, -149.06383, -149.06383,\n",
       "       -149.06381, -149.0638 , -149.0638 , -149.06378, -149.06378,\n",
       "       -149.06377, -149.06377, -149.06375, -149.06374, -149.06374,\n",
       "       -149.06374, -149.06372], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fix_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>2 2 2 2 2 2 2 2 ... 2 2 2 2 2 2 2 2</div><input id='attrs-2cb855c7-2028-4e6d-9b65-7a05eedb25f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cb855c7-2028-4e6d-9b65-7a05eedb25f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f61daf69-febf-4f5c-836c-fabac000b2ac' class='xr-var-data-in' type='checkbox'><label for='data-f61daf69-febf-4f5c-836c-fabac000b2ac' 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>1</dd><dt><span>long_name :</span></dt><dd>GPS Fix</dd></dl></div><div class='xr-var-data'><pre>array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
       "      dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_sat_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>31 31 31 31 31 ... 31 31 31 31 31</div><input id='attrs-a1345f9b-3f90-4ccf-92bb-e459cca5f313' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1345f9b-3f90-4ccf-92bb-e459cca5f313' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84f59b02-cfee-4548-b8a8-6640df058dec' class='xr-var-data-in' type='checkbox'><label for='data-84f59b02-cfee-4548-b8a8-6640df058dec' 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>count</dd><dt><span>long_name :</span></dt><dd>Number of Satellites</dd></dl></div><div class='xr-var-data'><pre>array([31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
       "       31, 31, 30, 30, 30, 30, 30, 30, 30, 30, 31, 31, 31, 31, 30, 31, 31,\n",
       "       30, 30, 30, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
       "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
       "       30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30,\n",
       "       30, 30, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 30, 30,\n",
       "       30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30,\n",
       "       30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30,\n",
       "       30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30,\n",
       "       30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 31, 31, 31, 31, 31,\n",
       "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
       "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
       "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
       "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
       "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
       "       31, 31, 31, 31, 31, 31, 31], dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hdop_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.4 0.4 0.4 0.4 ... 0.4 0.4 0.4 0.4</div><input id='attrs-3a74b34e-0e0c-4227-869e-3c83667a35a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a74b34e-0e0c-4227-869e-3c83667a35a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49cf059b-2d65-435a-b2a5-25a7ea20fda2' class='xr-var-data-in' type='checkbox'><label for='data-49cf059b-2d65-435a-b2a5-25a7ea20fda2' 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>1</dd><dt><span>long_name :</span></dt><dd>Horizontal Dilution of Precision</dd></dl></div><div class='xr-var-data'><pre>array([0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.4, 0.4, 0.4, 0.4, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
       "       0.4, 0.4], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>114.9 114.9 114.9 ... 115.1 115.1</div><input id='attrs-e7fd5cf8-1953-40dd-8d0b-edee85f79337' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7fd5cf8-1953-40dd-8d0b-edee85f79337' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d99ceb1-6b50-4475-afb9-3f8427b605ab' class='xr-var-data-in' type='checkbox'><label for='data-8d99ceb1-6b50-4475-afb9-3f8427b605ab' 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>long_name :</span></dt><dd>Elevation above MLLW</dd></dl></div><div class='xr-var-data'><pre>array([114.878, 114.878, 114.881, 114.884, 114.88 , 114.888, 114.884,\n",
       "       114.883, 114.893, 114.893, 114.895, 114.895, 114.9  , 114.912,\n",
       "       114.914, 114.922, 114.924, 114.919, 114.925, 114.933, 114.935,\n",
       "       114.938, 114.928, 114.933, 114.94 , 114.945, 114.947, 114.941,\n",
       "       114.942, 114.944, 114.95 , 114.952, 114.948, 114.939, 114.941,\n",
       "       114.944, 114.945, 114.95 , 114.945, 114.95 , 114.952, 114.955,\n",
       "       114.953, 114.955, 114.961, 114.966, 114.973, 114.973, 114.979,\n",
       "       114.983, 114.98 , 114.981, 114.977, 114.971, 114.984, 114.997,\n",
       "       115.004, 115.   , 115.007, 115.01 , 115.023, 115.02 , 115.021,\n",
       "       115.012, 115.006, 115.008, 115.011, 115.015, 115.028, 115.028,\n",
       "       115.019, 115.028, 115.029, 115.033, 115.029, 115.015, 115.016,\n",
       "       115.029, 115.024, 115.034, 115.031, 115.019, 115.018, 114.993,\n",
       "       114.989, 114.982, 114.986, 114.977, 114.961, 114.95 , 114.949,\n",
       "       114.946, 114.93 , 114.935, 114.927, 114.937, 114.927, 114.911,\n",
       "       114.898, 114.905, 114.916, 114.918, 114.909, 114.901, 114.907,\n",
       "       114.902, 114.895, 114.894, 114.884, 114.888, 114.892, 114.893,\n",
       "       114.896, 114.897, 114.892, 114.891, 114.917, 114.923, 114.923,\n",
       "       114.913, 114.916, 114.93 , 114.931, 114.907, 114.893, 114.899,\n",
       "       114.908, 114.924, 114.922, 114.91 , 114.905, 114.902, 114.896,\n",
       "       114.9  , 114.897, 114.896, 114.907, 114.899, 114.893, 114.9  ,\n",
       "       114.902, 114.914, 114.939, 114.948, 114.951, 114.952, 114.943,\n",
       "       114.94 , 114.944, 114.948, 114.947, 114.928, 114.927, 114.938,\n",
       "       114.947, 114.959, 114.956, 114.954, 114.945, 114.943, 114.945,\n",
       "       114.948, 114.944, 114.939, 114.933, 114.925, 114.915, 114.91 ,\n",
       "       114.925, 114.925, 114.915, 114.918, 114.922, 114.918, 114.906,\n",
       "       114.906, 114.906, 114.906, 114.914, 114.916, 114.911, 114.904,\n",
       "       114.913, 114.922, 114.926, 114.929, 114.919, 114.925, 114.943,\n",
       "       114.95 , 114.953, 114.95 , 114.954, 114.959, 114.961, 114.962,\n",
       "       114.973, 114.984, 114.985, 114.977, 114.987, 114.994, 115.002,\n",
       "       115.011, 115.013, 115.015, 115.021, 115.025, 115.02 , 115.045,\n",
       "       115.05 , 115.046, 115.043, 115.041, 115.044, 115.048, 115.049,\n",
       "       115.053, 115.06 , 115.04 , 115.039, 115.053, 115.066, 115.062,\n",
       "       115.061, 115.052, 115.049, 115.055, 115.06 , 115.058, 115.073,\n",
       "       115.076, 115.063, 115.067, 115.065, 115.057, 115.055, 115.06 ,\n",
       "       115.057, 115.049, 115.052, 115.049, 115.053, 115.057, 115.055,\n",
       "       115.054, 115.06 , 115.057, 115.052, 115.055, 115.057, 115.056,\n",
       "       115.06 , 115.063, 115.061, 115.062, 115.063, 115.057, 115.06 ,\n",
       "       115.061, 115.063, 115.062], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rtk_age_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.6 6.2 7.0 6.6 ... 7.4 7.0 7.4 7.0</div><input id='attrs-38b7ceab-8b44-4c23-866a-ec34f8604759' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38b7ceab-8b44-4c23-866a-ec34f8604759' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd92539c-6ef1-4d85-8108-85d22402cfbc' class='xr-var-data-in' type='checkbox'><label for='data-fd92539c-6ef1-4d85-8108-85d22402cfbc' 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>s</dd><dt><span>long_name :</span></dt><dd>Age of Received Real Time Kinetic Signal</dd></dl></div><div class='xr-var-data'><pre>array([5.6, 6.2, 7. , 6.6, 7.4, 7.2, 3.8, 4.6, 5.2, 6. , 6.6, 7.4, 7. ,\n",
       "       6.6, 7.2, 3.8, 4.6, 5.2, 6. , 6.8, 7.4, 7.2, 7. , 3.8, 4.4, 5.2,\n",
       "       5.8, 6.6, 7.2, 7. , 6.6, 7.2, 4. , 4.6, 5.2, 6. , 6.8, 7.4, 7.2,\n",
       "       7. , 3.8, 4.6, 5.4, 6.2, 6.8, 7.4, 7. , 6.6, 7.4, 4. , 4.6, 5.2,\n",
       "       6. , 6.6, 7.2, 6.8, 6.6, 7.2, 4. , 3.8, 4.6, 5.2, 6. , 6.6, 7.4,\n",
       "       7. , 3.8, 4.4, 5.2, 5.8, 6.6, 7.2, 7. , 6.6, 7.4, 4.2, 4.8, 5.6,\n",
       "       6.2, 7. , 6.8, 7.4, 7. , 3.8, 4.4, 5.2, 5.8, 6.6, 7.2, 6.8, 6.6,\n",
       "       7.2, 4. , 4.6, 5.4, 6. , 6.6, 7.4, 7. , 6.8, 7.4, 4. , 4.8, 5.4,\n",
       "       6.2, 6.8, 6.6, 7.2, 6.8, 3.6, 4.2, 4.8, 5.6, 6.2, 7. , 6.6, 7.4,\n",
       "       7. , 3.6, 4.4, 5. , 5.6, 6.2, 7. , 6.6, 7.2, 6.8, 7.4, 4.2, 4.8,\n",
       "       5.6, 6.2, 6.8, 6.6, 7.2, 6.8, 3.6, 4.2, 4.8, 5.4, 6. , 6.8, 7.4,\n",
       "       7. , 6.6, 7.2, 3.8, 4.4, 5. , 5.8, 6.4, 7.2, 6.8, 7.4, 7. , 3.8,\n",
       "       4.6, 5. , 5.8, 6.4, 7. , 6.6, 7.2, 7. , 3.8, 4.6, 5.4, 6.2, 7. ,\n",
       "       6.6, 7.4, 7.2, 3.8, 4.6, 5.2, 6. , 6.8, 7.4, 7.2, 6.8, 3.6, 4.2,\n",
       "       4.8, 5.6, 6.2, 7. , 6.6, 7.2, 7. , 3.6, 4.2, 5. , 5.6, 6.4, 7. ,\n",
       "       6.6, 7.2, 7. , 3.6, 4.2, 4.8, 5.4, 6. , 6.8, 7.4, 7.2, 6.8, 3.6,\n",
       "       4.2, 5. , 5.6, 6.4, 7. , 6.8, 6.6, 7.2, 3.8, 4.4, 5. , 5.8, 6.4,\n",
       "       7.2, 6.8, 7.4, 7.2, 3.8, 4.4, 5. , 5.8, 6.6, 7.2, 7. , 6.8, 3.6,\n",
       "       4.4, 5. , 5.8, 6.6, 7.2, 7. , 6.6, 7.2, 3.8, 4.6, 5.2, 5.8, 6.4,\n",
       "       7. , 6.6, 7.2, 6.8, 7.4, 4. , 4.6, 5. , 5.6, 6.2, 6.8, 7.4, 7. ,\n",
       "       7.4, 7. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_over_grnd_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.09774 0.09774 ... 0.6019 0.5761</div><input id='attrs-2669b692-2a8a-493c-9ae5-1e9ff6596e8c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2669b692-2a8a-493c-9ae5-1e9ff6596e8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-663dc6cf-8486-42ab-a163-8eb6ba0821bb' class='xr-var-data-in' type='checkbox'><label for='data-663dc6cf-8486-42ab-a163-8eb6ba0821bb' 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 s-1</dd><dt><span>long_name :</span></dt><dd>Platform Speed over Ground</dd><dt><span>standard_name :</span></dt><dd>platform_speed_wrt_ground</dd></dl></div><div class='xr-var-data'><pre>array([0.09773663, 0.09773663, 0.07201646, 0.0925926 , 0.0771605 ,\n",
       "       0.0308642 , 0.08744856, 0.06687243, 0.03600823, 0.05144033,\n",
       "       0.08230452, 0.0617284 , 0.05144033, 0.05658436, 0.0617284 ,\n",
       "       0.08744856, 0.16460904, 0.13374485, 0.25720164, 0.28806585,\n",
       "       0.38065845, 0.39609054, 0.42695472, 0.49382716, 0.47325104,\n",
       "       0.4835391 , 0.47325104, 0.52469134, 0.5504115 , 0.6069959 ,\n",
       "       0.5606996 , 0.5606996 , 0.56584364, 0.5761317 , 0.62757206,\n",
       "       0.5555556 , 0.5812757 , 0.5298354 , 0.5401234 , 0.5761317 ,\n",
       "       0.54526746, 0.5761317 , 0.58641976, 0.5555556 , 0.52469134,\n",
       "       0.5092593 , 0.4989712 , 0.56584364, 0.63271606, 0.622428  ,\n",
       "       0.6532922 , 0.70987654, 0.70987654, 0.7253086 , 0.6893004 ,\n",
       "       0.63271606, 0.5967078 , 0.6018518 , 0.63271606, 0.58641976,\n",
       "       0.66872424, 0.66358024, 0.75617284, 0.7613169 , 0.8076132 ,\n",
       "       0.8281893 , 0.7973251 , 0.8436214 , 0.7921811 , 0.9259259 ,\n",
       "       0.8847737 , 0.9053498 , 0.89506173, 0.9002058 , 0.81790125,\n",
       "       0.7973251 , 0.77160496, 0.81790125, 0.8384774 , 0.936214  ,\n",
       "       0.9722222 , 1.0288066 , 1.0853909 , 1.0905349 , 1.0545267 ,\n",
       "       1.0339506 , 1.100823  , 1.1522634 , 1.1985596 , 1.1831275 ,\n",
       "       1.2191358 , 1.1882715 , 1.1728395 , 1.1728395 , 1.1779835 ,\n",
       "       1.244856  , 1.1676955 , 1.1471194 , 1.1162552 , 1.1625514 ,\n",
       "...\n",
       "       1.5895061 , 1.5226338 , 1.5020577 , 1.5020577 , 1.5020577 ,\n",
       "       1.5072017 , 1.5020577 , 1.4866256 , 1.4866256 , 1.4351852 ,\n",
       "       1.4146091 , 1.394033  , 1.3734568 , 1.3683128 , 1.3271605 ,\n",
       "       1.3065844 , 1.3220165 , 1.3117284 , 1.3014404 , 1.3271605 ,\n",
       "       1.3065844 , 1.244856  , 1.1934156 , 1.1985596 , 1.1779835 ,\n",
       "       1.2139918 , 1.2551441 , 1.2860082 , 1.2962962 , 1.3477366 ,\n",
       "       1.3323045 , 1.3220165 , 1.3734568 , 1.3837448 , 1.3837448 ,\n",
       "       1.3374485 , 1.3734568 , 1.3734568 , 1.388889  , 1.3631687 ,\n",
       "       1.3786008 , 1.3786008 , 1.3528807 , 1.3168724 , 1.3271605 ,\n",
       "       1.399177  , 1.3837448 , 1.3837448 , 1.3683128 , 1.3168724 ,\n",
       "       1.2860082 , 1.2139918 , 1.2191358 , 1.234568  , 1.1779835 ,\n",
       "       1.1265433 , 1.1471194 , 1.2139918 , 1.2294239 , 1.244856  ,\n",
       "       1.1779835 , 1.1985596 , 1.2088476 , 1.1574074 , 1.1162552 ,\n",
       "       1.1471194 , 1.1419753 , 1.1625514 , 1.1676955 , 1.1471194 ,\n",
       "       1.1625514 , 1.1111112 , 1.1162552 , 1.1265433 , 1.0493827 ,\n",
       "       1.0339506 , 1.0185186 , 0.9876543 , 0.9516461 , 0.9259259 ,\n",
       "       0.9104938 , 0.9104938 , 0.8436214 , 0.84876543, 0.8333333 ,\n",
       "       0.7973251 , 0.77160496, 0.80246913, 0.781893  , 0.73045266,\n",
       "       0.7458848 , 0.73559666, 0.6841564 , 0.6430041 , 0.63271606,\n",
       "       0.6018518 , 0.5761317 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dir_over_grnd_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>72.72 44.35 43.78 ... 73.83 80.23</div><input id='attrs-225e4f84-75c0-41be-bf8b-fc10b966ef94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-225e4f84-75c0-41be-bf8b-fc10b966ef94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97818d1d-3850-42ae-ba6d-a95f41d49298' class='xr-var-data-in' type='checkbox'><label for='data-97818d1d-3850-42ae-ba6d-a95f41d49298' 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>degree</dd><dt><span>long_name :</span></dt><dd>Platform Direction over Ground</dd><dt><span>standard_name :</span></dt><dd>platform_course</dd></dl></div><div class='xr-var-data'><pre>array([ 72.72,  44.35,  43.78,  49.87,  46.01,  97.11, 104.02,  60.97,\n",
       "        32.95,  93.34,  49.23,  50.77,  40.85,   6.39,  20.07,  45.72,\n",
       "        50.2 ,  50.69,  58.9 ,  57.84,  62.46,  59.65,  63.37,  62.99,\n",
       "        67.92,  69.65,  72.65,  72.21,  74.13,  72.15,  73.66,  75.01,\n",
       "        74.6 ,  69.57,  70.5 ,  67.84,  61.23,  67.05,  60.61,  58.55,\n",
       "        55.72,  52.02,  50.42,  50.28,  50.22,  57.5 ,  60.96,  66.68,\n",
       "        79.81,  84.9 ,  87.21,  91.27,  88.96,  80.19,  76.88,  72.07,\n",
       "        70.25,  67.17,  61.13,  67.11,  74.79,  79.83,  82.53,  85.06,\n",
       "        80.79,  73.74,  71.15,  68.83,  72.07,  68.26,  72.83,  71.99,\n",
       "        73.01,  74.41,  70.12,  62.63,  60.16,  56.99,  56.71,  60.35,\n",
       "        59.81,  63.25,  63.99,  60.47,  58.38,  56.72,  56.57,  59.46,\n",
       "        57.19,  58.53,  54.36,  53.07,  51.32,  51.63,  54.26,  56.79,\n",
       "        59.69,  61.14,  58.39,  57.56,  59.36,  58.53,  59.86,  60.52,\n",
       "        64.01,  64.71,  64.53,  65.43,  66.07,  64.94,  64.24,  62.6 ,\n",
       "        59.83,  59.2 ,  56.48,  55.96,  61.52,  62.71,  64.38,  66.06,\n",
       "        71.77,  78.84,  81.8 ,  75.95,  72.45,  69.59,  68.75,  71.24,\n",
       "        70.67,  70.58,  67.8 ,  64.3 ,  64.67,  61.71,  59.79,  57.44,\n",
       "        59.57,  57.96,  56.76,  55.99,  57.98,  61.39,  71.72,  78.45,\n",
       "        80.64,  83.65,  83.97,  84.37,  88.13,  89.6 ,  87.59,  83.46,\n",
       "        80.34,  83.03,  82.44,  82.74,  82.57,  81.85,  82.39,  82.42,\n",
       "        83.09,  83.16,  82.29,  84.85,  83.7 ,  78.19,  79.66,  79.22,\n",
       "        79.29,  80.13,  79.34,  81.14,  84.08,  83.23,  81.84,  80.06,\n",
       "        78.68,  78.3 ,  79.28,  78.33,  79.25,  78.43,  77.09,  76.28,\n",
       "        76.99,  72.75,  69.48,  67.48,  67.77,  71.86,  70.99,  69.07,\n",
       "        70.22,  70.32,  72.33,  72.61,  71.88,  73.91,  74.14,  72.15,\n",
       "        73.1 ,  75.5 ,  74.3 ,  74.19,  73.77,  73.66,  74.78,  73.75,\n",
       "        70.26,  72.  ,  75.1 ,  77.64,  78.61,  79.36,  79.65,  79.38,\n",
       "        77.87,  75.41,  75.99,  72.76,  69.83,  72.26,  75.56,  79.4 ,\n",
       "        78.22,  77.92,  78.05,  76.88,  74.82,  72.37,  71.78,  73.08,\n",
       "        73.98,  74.32,  76.55,  74.07,  72.79,  74.25,  73.88,  74.37,\n",
       "        73.39,  72.57,  71.65,  72.7 ,  71.16,  72.53,  74.24,  74.79,\n",
       "        72.56,  74.71,  74.95,  75.83,  77.18,  78.42,  79.11,  79.43,\n",
       "        80.85,  77.13,  79.9 ,  78.86,  73.83,  80.23], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dist_nmea</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.3 1.3 1.3 1.3 ... 1.2 1.1 1.1 1.1</div><input id='attrs-5e25944f-ec8f-414a-af10-b191ce3ff482' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e25944f-ec8f-414a-af10-b191ce3ff482' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28aa2c6c-e94f-4bdc-97b5-86c0605e3b3b' class='xr-var-data-in' type='checkbox'><label for='data-28aa2c6c-e94f-4bdc-97b5-86c0605e3b3b' 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>long_name :</span></dt><dd>Depth Sounder Range</dd></dl></div><div class='xr-var-data'><pre>array([1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.4, 0. , 1.4,\n",
       "       1.4, 1.4, 1.4, 1.5, 1.5, 1.7, 1.7, 1.7, 0. , 1.8, 2. , 2.6, 2.6,\n",
       "       2.8, 2.7, 2.7, 3.8, 3.8, 3.8, 4.2, 4.2, 4.2, 4.5, 4.8, 6.5, 6.5,\n",
       "       6.4, 6.4, 6.8, 6.9, 0. , 7.7, 7.9, 7.9, 7.9, 7.9, 8.1, 8.2, 8.2,\n",
       "       8.6, 8.9, 8.9, 9. , 9.1, 9.1, 9.2, 9.2, 9.3, 9.3, 9.3, 9.4, 9.4,\n",
       "       9.5, 9.5, 9.5, 9.5, 9.3, 9.3, 9.3, 9.3, 9.2, 9.2, 9.2, 9.2, 9.1,\n",
       "       9.1, 9.1, 9.1, 9.1, 9.1, 9. , 9. , 9. , 9. , 9. , 9. , 8.9, 8.9,\n",
       "       8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9,\n",
       "       8.9, 8.8, 8.8, 8.8, 9. , 9.1, 9.1, 9.2, 9.2, 9.2, 9.1, 9.1, 9.1,\n",
       "       8.9, 8.8, 8.8, 8.6, 8.4, 8.4, 8.3, 8.3, 8.3, 8.3, 8.4, 8.4, 8.4,\n",
       "       8.5, 8.5, 8.6, 8.7, 8.7, 8.6, 8.6, 8.6, 8.5, 8.4, 8.4, 8.3, 8.3,\n",
       "       7.5, 7.7, 7.7, 7.7, 7.7, 7.7, 7.7, 7.7, 7.6, 7.5, 7.5, 7.5, 7.7,\n",
       "       7.7, 7.7, 7.7, 7.7, 7.1, 7.1, 7.1, 7. , 7. , 7. , 7. , 7. , 7. ,\n",
       "       6.6, 6.6, 6. , 5.7, 5.6, 5.6, 5.2, 5. , 4.9, 4.9, 5.1, 5.1, 5.1,\n",
       "       4.9, 4.7, 4.7, 4.6, 4.6, 4.6, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.3,\n",
       "       4.1, 4.1, 3.9, 3.9, 3.9, 3.8, 3.8, 3.8, 3.3, 3.3, 3.3, 3.3, 3.3,\n",
       "       3.3, 3.4, 3.4, 3.4, 3.1, 3.1, 3.1, 3.1, 2.8, 2.8, 2.7, 2.5, 2.5,\n",
       "       2.5, 2.6, 2.6, 2.6, 2.6, 2.6, 2.5, 2.5, 2.5, 2.5, 2.5, 2.4, 2.4,\n",
       "       2.4, 2.4, 2.3, 2.2, 2.2, 2.1, 2.1, 2.1, 1.8, 1.8, 1.8, 1.7, 1.7,\n",
       "       1.6, 1.5, 1.5, 1.5, 1.5, 1.3, 1.3, 1.3, 1.3, 1.3, 1.2, 1.2, 1.1,\n",
       "       1.1, 1.1], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel_sl</span></div><div class='xr-var-dims'>(dir, range_sl, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.338 -1.327 -1.275 ... nan nan</div><input id='attrs-86fccae0-3ebf-452c-9773-3e363f23b875' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86fccae0-3ebf-452c-9773-3e363f23b875' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32f84030-2848-4592-98ff-96c59a1b2e3d' class='xr-var-data-in' type='checkbox'><label for='data-32f84030-2848-4592-98ff-96c59a1b2e3d' 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 s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[[-1.3384625e+00, -1.3271410e+00, -1.2751875e+00, ...,\n",
       "         -2.3546405e-01, -2.2787344e-01, -1.6161118e-01],\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",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]],\n",
       "\n",
       "       [[ 1.0810345e-03,  8.8868886e-03,  2.0653188e-01, ...,\n",
       "         -7.0343661e-01, -4.8634797e-01, -5.6815118e-01],\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",
       "        [           nan,            nan,            nan, ...,\n",
       "...\n",
       "         -3.2227486e-02, -2.9505478e-02, -1.6433744e-02],\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",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]],\n",
       "\n",
       "       [[ 4.7551185e-02,  7.8045934e-02, -7.5461626e-02, ...,\n",
       "          6.4090714e-02,  1.5505850e-03,  8.5798822e-02],\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",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr_sl</span></div><div class='xr-var-dims'>(beam, range_sl, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>144 173 184 165 186 ... 0 0 0 0 0</div><input id='attrs-fb995f4f-3611-4628-8bcf-9ccbcd7fe939' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb995f4f-3611-4628-8bcf-9ccbcd7fe939' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efb58985-7a37-4c09-a663-5f30f159f6f2' class='xr-var-data-in' type='checkbox'><label for='data-efb58985-7a37-4c09-a663-5f30f159f6f2' 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>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[144, 173, 184, ..., 145, 151, 161],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
       "\n",
       "       [[151, 174, 156, ..., 118, 159, 179],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
       "\n",
       "       [[160, 166, 188, ..., 146, 133, 170],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
       "\n",
       "       [[169, 160, 138, ..., 172, 212, 125],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0]]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp_sl</span></div><div class='xr-var-dims'>(beam, range_sl, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>137 143 142 143 144 ... 0 0 0 0 0</div><input id='attrs-e6986ed3-25e2-44b1-9ca9-21315afece08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6986ed3-25e2-44b1-9ca9-21315afece08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d399ae10-fab9-459c-b555-ab3adb2fd865' class='xr-var-data-in' type='checkbox'><label for='data-d399ae10-fab9-459c-b555-ab3adb2fd865' 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>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[137, 143, 142, ..., 128, 127, 130],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
       "\n",
       "       [[140, 148, 144, ..., 134, 130, 136],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
       "\n",
       "       [[133, 139, 140, ..., 125, 122, 129],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
       "\n",
       "       [[135, 142, 139, ..., 129, 135, 125],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0],\n",
       "        [  0,   0,   0, ...,   0,   0,   0]]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hdwtime_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-08-19T20:14:21.930000066 .....</div><input id='attrs-1b03ff73-41c6-4d3d-9dc4-aa510b73149f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1b03ff73-41c6-4d3d-9dc4-aa510b73149f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24b9dee8-45f6-4f1d-aeb1-e214f7c8fb2e' class='xr-var-data-in' type='checkbox'><label for='data-24b9dee8-45f6-4f1d-aeb1-e214f7c8fb2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-08-19T20:14:21.930000066&#x27;, &#x27;2022-08-19T20:14:22.500000000&#x27;,\n",
       "       &#x27;2022-08-19T20:14:23.220000028&#x27;, &#x27;2022-08-19T20:14:23.900000095&#x27;,\n",
       "       &#x27;2022-08-19T20:14:24.609999895&#x27;, &#x27;2022-08-19T20:14:25.279999971&#x27;,\n",
       "       &#x27;2022-08-19T20:14:26.180000066&#x27;, &#x27;2022-08-19T20:14:26.859999895&#x27;,\n",
       "       &#x27;2022-08-19T20:14:27.539999961&#x27;, &#x27;2022-08-19T20:14:28.250000000&#x27;,\n",
       "       &#x27;2022-08-19T20:14:28.920000076&#x27;, &#x27;2022-08-19T20:14:29.640000104&#x27;,\n",
       "       &#x27;2022-08-19T20:14:30.329999923&#x27;, &#x27;2022-08-19T20:14:31.059999942&#x27;,\n",
       "       &#x27;2022-08-19T20:14:31.640000104&#x27;, &#x27;2022-08-19T20:14:32.250000000&#x27;,\n",
       "       &#x27;2022-08-19T20:14:32.819999933&#x27;, &#x27;2022-08-19T20:14:33.519999980&#x27;,\n",
       "       &#x27;2022-08-19T20:14:34.200000047&#x27;, &#x27;2022-08-19T20:14:34.970000028&#x27;,\n",
       "       &#x27;2022-08-19T20:14:35.720000028&#x27;, &#x27;2022-08-19T20:14:36.450000047&#x27;,\n",
       "       &#x27;2022-08-19T20:14:37.210000038&#x27;, &#x27;2022-08-19T20:14:38.009999990&#x27;,\n",
       "       &#x27;2022-08-19T20:14:38.710000038&#x27;, &#x27;2022-08-19T20:14:39.420000076&#x27;,\n",
       "       &#x27;2022-08-19T20:14:40.119999885&#x27;, &#x27;2022-08-19T20:14:40.769999980&#x27;,\n",
       "       &#x27;2022-08-19T20:14:41.470000028&#x27;, &#x27;2022-08-19T20:14:42.140000104&#x27;,\n",
       "       &#x27;2022-08-19T20:14:42.869999885&#x27;, &#x27;2022-08-19T20:14:43.569999933&#x27;,\n",
       "       &#x27;2022-08-19T20:14:44.230000019&#x27;, &#x27;2022-08-19T20:14:44.869999885&#x27;,\n",
       "       &#x27;2022-08-19T20:14:45.569999933&#x27;, &#x27;2022-08-19T20:14:46.240000009&#x27;,\n",
       "       &#x27;2022-08-19T20:14:46.960000038&#x27;, &#x27;2022-08-19T20:14:47.670000076&#x27;,\n",
       "       &#x27;2022-08-19T20:14:48.410000085&#x27;, &#x27;2022-08-19T20:14:49.130000114&#x27;,\n",
       "...\n",
       "       &#x27;2022-08-19T20:16:55.450000047&#x27;, &#x27;2022-08-19T20:16:56.099999904&#x27;,\n",
       "       &#x27;2022-08-19T20:16:56.720000028&#x27;, &#x27;2022-08-19T20:16:57.380000114&#x27;,\n",
       "       &#x27;2022-08-19T20:16:58.059999942&#x27;, &#x27;2022-08-19T20:16:58.720000028&#x27;,\n",
       "       &#x27;2022-08-19T20:16:59.480000019&#x27;, &#x27;2022-08-19T20:17:00.250000000&#x27;,\n",
       "       &#x27;2022-08-19T20:17:00.970000028&#x27;, &#x27;2022-08-19T20:17:01.750000000&#x27;,\n",
       "       &#x27;2022-08-19T20:17:02.529999971&#x27;, &#x27;2022-08-19T20:17:03.299999952&#x27;,\n",
       "       &#x27;2022-08-19T20:17:04.039999961&#x27;, &#x27;2022-08-19T20:17:04.759999990&#x27;,\n",
       "       &#x27;2022-08-19T20:17:05.509999990&#x27;, &#x27;2022-08-19T20:17:06.210000038&#x27;,\n",
       "       &#x27;2022-08-19T20:17:06.930000066&#x27;, &#x27;2022-08-19T20:17:07.559999942&#x27;,\n",
       "       &#x27;2022-08-19T20:17:08.200000047&#x27;, &#x27;2022-08-19T20:17:08.819999933&#x27;,\n",
       "       &#x27;2022-08-19T20:17:09.480000019&#x27;, &#x27;2022-08-19T20:17:10.089999914&#x27;,\n",
       "       &#x27;2022-08-19T20:17:10.730000019&#x27;, &#x27;2022-08-19T20:17:11.329999923&#x27;,\n",
       "       &#x27;2022-08-19T20:17:11.950000047&#x27;, &#x27;2022-08-19T20:17:12.539999961&#x27;,\n",
       "       &#x27;2022-08-19T20:17:13.140000104&#x27;, &#x27;2022-08-19T20:17:13.710000038&#x27;,\n",
       "       &#x27;2022-08-19T20:17:14.299999952&#x27;, &#x27;2022-08-19T20:17:14.869999885&#x27;,\n",
       "       &#x27;2022-08-19T20:17:15.480000019&#x27;, &#x27;2022-08-19T20:17:16.039999961&#x27;,\n",
       "       &#x27;2022-08-19T20:17:16.630000114&#x27;, &#x27;2022-08-19T20:17:17.190000057&#x27;,\n",
       "       &#x27;2022-08-19T20:17:17.779999971&#x27;, &#x27;2022-08-19T20:17:18.329999923&#x27;,\n",
       "       &#x27;2022-08-19T20:17:18.910000085&#x27;, &#x27;2022-08-19T20:17:19.450000047&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beam2inst_orientmat</span></div><div class='xr-var-dims'>(x1, x2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.462 -1.462 0.0 ... -1.034 -1.034</div><input id='attrs-00e74a63-3d77-44ea-8f49-b95f2f1fdbd3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00e74a63-3d77-44ea-8f49-b95f2f1fdbd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fc973d5-423f-4b82-849b-428f67c68ac1' class='xr-var-data-in' type='checkbox'><label for='data-7fc973d5-423f-4b82-849b-428f67c68ac1' 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>1</dd><dt><span>long_name :</span></dt><dd>Rotation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[ 1.4619021 , -1.4619021 ,  0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        , -1.4619021 ,  1.4619021 ],\n",
       "       [ 0.26604444,  0.26604444,  0.26604444,  0.26604444],\n",
       "       [ 1.033721  ,  1.033721  , -1.033721  , -1.033721  ]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat</span></div><div class='xr-var-dims'>(earth, inst, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.8992 -0.838 ... 0.9999 0.9999</div><input id='attrs-71d27077-dd47-4576-8328-f69328bab1e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71d27077-dd47-4576-8328-f69328bab1e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a61d61a6-6896-46aa-82ad-413c3784a397' class='xr-var-data-in' type='checkbox'><label for='data-a61d61a6-6896-46aa-82ad-413c3784a397' 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>1</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-0.8991666 , -0.8379869 , -0.86326927, ..., -0.9300857 ,\n",
       "         -0.93617517, -0.9089982 ],\n",
       "        [ 0.43625483,  0.5456233 ,  0.5043985 , ..., -0.36716184,\n",
       "         -0.35141215, -0.41661045],\n",
       "        [-0.03436856, -0.00855068, -0.01866788, ...,  0.01151885,\n",
       "          0.00924976, -0.01256542]],\n",
       "\n",
       "       [[-0.4357624 , -0.5454322 , -0.5039119 , ...,  0.36714867,\n",
       "          0.35133827,  0.41673636],\n",
       "        [-0.89981425, -0.8379696 , -0.8633793 , ..., -0.9301563 ,\n",
       "         -0.9362079 , -0.908973  ],\n",
       "        [-0.02110444, -0.01762627, -0.02547461, ..., -0.0033159 ,\n",
       "         -0.00872616, -0.00994743]],\n",
       "\n",
       "       [[-0.04013224, -0.01678251, -0.02896681, ...,  0.01193181,\n",
       "          0.01172618, -0.00727742],\n",
       "        [-0.00399988, -0.01010676, -0.01258448, ...,  0.00114506,\n",
       "         -0.00491942, -0.01427866],\n",
       "        [ 0.99918634,  0.9998081 ,  0.99950117, ...,  0.9999282 ,\n",
       "          0.9999191 ,  0.99987155]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>discharge</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.136e+03</div><input id='attrs-5bb89513-6938-4519-8163-5c115c412004' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bb89513-6938-4519-8163-5c115c412004' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6eeb58e0-210f-4db9-969a-13d85dea4ed2' class='xr-var-data-in' type='checkbox'><label for='data-6eeb58e0-210f-4db9-969a-13d85dea4ed2' 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>m3 s-1</dd><dt><span>long_name :</span></dt><dd>Discharge</dd></dl></div><div class='xr-var-data'><pre>array(-1136.2273, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>power</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.211e+06</div><input id='attrs-5eefdae6-e3ce-4f2d-921f-005eaa0c866c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5eefdae6-e3ce-4f2d-921f-005eaa0c866c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8e85d43-72c7-4325-88f7-b8a95555300b' class='xr-var-data-in' type='checkbox'><label for='data-d8e85d43-72c7-4325-88f7-b8a95555300b' 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>W</dd><dt><span>long_name :</span></dt><dd>Power</dd></dl></div><div class='xr-var-data'><pre>array(-1210604.4, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>power_density</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.307e+03</div><input id='attrs-b025b8d7-e39c-466c-8def-4276cb77675c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b025b8d7-e39c-466c-8def-4276cb77675c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43fb84d8-465d-44c9-8e12-cde076511f19' class='xr-var-data-in' type='checkbox'><label for='data-43fb84d8-465d-44c9-8e12-cde076511f19' 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>W m-2</dd><dt><span>long_name :</span></dt><dd>Power Density</dd></dl></div><div class='xr-var-data'><pre>array(-1306.6486, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reynolds_number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>6.2e+06</div><input id='attrs-90042812-fee4-4e70-b18a-d53ae43eb033' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90042812-fee4-4e70-b18a-d53ae43eb033' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67609ab3-f559-452a-9658-1e985d8989f0' class='xr-var-data-in' type='checkbox'><label for='data-67609ab3-f559-452a-9658-1e985d8989f0' 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>1</dd><dt><span>long_name :</span></dt><dd>Reynolds Number</dd></dl></div><div class='xr-var-data'><pre>array(6199513.5, dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4d459e0d-cff0-4086-8e66-7015c8815624' class='xr-section-summary-in' type='checkbox'  ><label for='section-4d459e0d-cff0-4086-8e66-7015c8815624' 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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ebb3b6c0-1725-4d0a-8fb1-625eef1a82b2' class='xr-index-data-in' type='checkbox'/><label for='index-ebb3b6c0-1725-4d0a-8fb1-625eef1a82b2' 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;2022-08-19 20:14:21.930000066&#x27;,\n",
       "                  &#x27;2022-08-19 20:14:22.500000&#x27;,\n",
       "               &#x27;2022-08-19 20:14:23.220000028&#x27;,\n",
       "               &#x27;2022-08-19 20:14:23.900000095&#x27;,\n",
       "               &#x27;2022-08-19 20:14:24.609999895&#x27;,\n",
       "               &#x27;2022-08-19 20:14:25.279999971&#x27;,\n",
       "               &#x27;2022-08-19 20:14:26.180000066&#x27;,\n",
       "               &#x27;2022-08-19 20:14:26.859999895&#x27;,\n",
       "               &#x27;2022-08-19 20:14:27.539999961&#x27;,\n",
       "                  &#x27;2022-08-19 20:14:28.250000&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-08-19 20:17:14.299999952&#x27;,\n",
       "               &#x27;2022-08-19 20:17:14.869999885&#x27;,\n",
       "               &#x27;2022-08-19 20:17:15.480000019&#x27;,\n",
       "               &#x27;2022-08-19 20:17:16.039999961&#x27;,\n",
       "               &#x27;2022-08-19 20:17:16.630000114&#x27;,\n",
       "               &#x27;2022-08-19 20:17:17.190000057&#x27;,\n",
       "               &#x27;2022-08-19 20:17:17.779999971&#x27;,\n",
       "               &#x27;2022-08-19 20:17:18.329999923&#x27;,\n",
       "               &#x27;2022-08-19 20:17:18.910000085&#x27;,\n",
       "               &#x27;2022-08-19 20:17:19.450000047&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=262, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_gps</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fd9b0b0d-edb4-468e-a219-7589a34805d7' class='xr-index-data-in' type='checkbox'/><label for='index-fd9b0b0d-edb4-468e-a219-7589a34805d7' 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;2022-08-19 20:14:23.599999904&#x27;,\n",
       "               &#x27;2022-08-19 20:14:24.200000047&#x27;,\n",
       "                         &#x27;2022-08-19 20:14:25&#x27;,\n",
       "               &#x27;2022-08-19 20:14:25.599999904&#x27;,\n",
       "               &#x27;2022-08-19 20:14:26.400000095&#x27;,\n",
       "               &#x27;2022-08-19 20:14:27.200000047&#x27;,\n",
       "               &#x27;2022-08-19 20:14:27.799999952&#x27;,\n",
       "               &#x27;2022-08-19 20:14:28.599999904&#x27;,\n",
       "               &#x27;2022-08-19 20:14:29.200000047&#x27;,\n",
       "                         &#x27;2022-08-19 20:14:30&#x27;,\n",
       "               ...\n",
       "                         &#x27;2022-08-19 20:17:16&#x27;,\n",
       "               &#x27;2022-08-19 20:17:16.599999904&#x27;,\n",
       "                         &#x27;2022-08-19 20:17:17&#x27;,\n",
       "               &#x27;2022-08-19 20:17:17.599999904&#x27;,\n",
       "               &#x27;2022-08-19 20:17:18.200000047&#x27;,\n",
       "               &#x27;2022-08-19 20:17:18.799999952&#x27;,\n",
       "               &#x27;2022-08-19 20:17:19.400000095&#x27;,\n",
       "                         &#x27;2022-08-19 20:17:20&#x27;,\n",
       "               &#x27;2022-08-19 20:17:20.400000095&#x27;,\n",
       "                         &#x27;2022-08-19 20:17:21&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_gps&#x27;, length=262, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-64b1e991-51d9-43b7-a8c4-041513b52806' class='xr-index-data-in' type='checkbox'/><label for='index-64b1e991-51d9-43b7-a8c4-041513b52806' 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([ 0.949999988079071, 1.4299999475479126,  1.909999966621399,\n",
       "       2.3899998664855957,  2.869999885559082, 3.3499999046325684,\n",
       "       3.8299999237060547,  4.309999942779541,  4.789999961853027,\n",
       "        5.269999980926514,               5.75,  6.230000019073486,\n",
       "        6.710000038146973,  7.190000057220459,  7.670000076293945,\n",
       "        8.149999618530273,  8.630000114440918,  9.109999656677246,\n",
       "         9.59000015258789, 10.069999694824219, 10.550000190734863,\n",
       "       11.029999732971191, 11.510000228881836, 11.989999771118164],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;range&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range_sl</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e24555c3-c469-41da-b7f7-c6968a9469ef' class='xr-index-data-in' type='checkbox'/><label for='index-e24555c3-c469-41da-b7f7-c6968a9469ef' 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([             0.54, 0.659999997317791, 0.779999994635582,\n",
       "       0.899999991953373, 1.019999989271164],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;range_sl&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>beam</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7fb297f7-f44f-480f-9338-6fe1aa7f694d' class='xr-index-data-in' type='checkbox'/><label for='index-7fb297f7-f44f-480f-9338-6fe1aa7f694d' 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([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;beam&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dir</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-10561123-efb5-4e06-8f1e-15e11a08adda' class='xr-index-data-in' type='checkbox'/><label for='index-10561123-efb5-4e06-8f1e-15e11a08adda' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert&#x27;, &#x27;err&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dir&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-61de4b44-42d1-4ee2-867a-5a781f64d477' class='xr-index-data-in' type='checkbox'/><label for='index-61de4b44-42d1-4ee2-867a-5a781f64d477' 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([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;x1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e5aa512a-169c-4d92-a774-7144a769a242' class='xr-index-data-in' type='checkbox'/><label for='index-e5aa512a-169c-4d92-a774-7144a769a242' 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([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;x2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>earth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4d4fc557-3b54-459d-994f-787d959b7cf1' class='xr-index-data-in' type='checkbox'/><label for='index-4d4fc557-3b54-459d-994f-787d959b7cf1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;object&#x27;, name=&#x27;earth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>inst</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-dcba92da-4702-4545-8ab7-08b0dc28da3e' class='xr-index-data-in' type='checkbox'/><label for='index-dcba92da-4702-4545-8ab7-08b0dc28da3e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;inst&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ace2899d-5862-448b-badb-3234f6b40582' class='xr-section-summary-in' type='checkbox'  ><label for='section-ace2899d-5862-448b-badb-3234f6b40582' class='xr-section-summary' >Attributes: <span>(46)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>prog_ver :</span></dt><dd>56.1</dd><dt><span>inst_make :</span></dt><dd>TRDI</dd><dt><span>inst_type :</span></dt><dd>ADCP</dd><dt><span>rotate_vars :</span></dt><dd>[&#x27;vel&#x27;, &#x27;vel_sl&#x27;, &#x27;vel_bt&#x27;]</dd><dt><span>has_imu :</span></dt><dd>0</dd><dt><span>inst_model :</span></dt><dd>RiverPro</dd><dt><span>beam_angle :</span></dt><dd>20</dd><dt><span>freq :</span></dt><dd>1200</dd><dt><span>beam_pattern :</span></dt><dd>convex</dd><dt><span>orientation :</span></dt><dd>down</dd><dt><span>n_beams :</span></dt><dd>5</dd><dt><span>n_cells :</span></dt><dd>24</dd><dt><span>pings_per_ensemble :</span></dt><dd>1</dd><dt><span>cell_size :</span></dt><dd>0.48</dd><dt><span>blank_dist :</span></dt><dd>0.1</dd><dt><span>profiling_mode :</span></dt><dd>3</dd><dt><span>min_corr_threshold :</span></dt><dd>64</dd><dt><span>n_code_reps :</span></dt><dd>2</dd><dt><span>min_prcnt_gd :</span></dt><dd>1</dd><dt><span>max_error_vel :</span></dt><dd>9.999</dd><dt><span>sec_between_ping_groups :</span></dt><dd>0.0</dd><dt><span>coord_sys :</span></dt><dd>principal</dd><dt><span>use_pitchroll :</span></dt><dd>no</dd><dt><span>use_3beam :</span></dt><dd>no</dd><dt><span>bin_mapping :</span></dt><dd>no</dd><dt><span>heading_misalign_deg :</span></dt><dd>0.0</dd><dt><span>magnetic_var_deg :</span></dt><dd>0.0</dd><dt><span>sensors_src :</span></dt><dd>01011101</dd><dt><span>sensors_avail :</span></dt><dd>00011101</dd><dt><span>transmit_pulse_m :</span></dt><dd>0.03</dd><dt><span>water_ref_cells :</span></dt><dd>[1, 1]</dd><dt><span>false_target_threshold :</span></dt><dd>255</dd><dt><span>transmit_lag_m :</span></dt><dd>0.96</dd><dt><span>bandwidth :</span></dt><dd>0</dd><dt><span>power_level :</span></dt><dd>0</dd><dt><span>serialnum :</span></dt><dd>2888</dd><dt><span>surface_layer :</span></dt><dd>1</dd><dt><span>n_cells_sl :</span></dt><dd>5</dd><dt><span>cell_size_sl :</span></dt><dd>0.12</dd><dt><span>sourceprog :</span></dt><dd>WinRiver2</dd><dt><span>range_offset :</span></dt><dd>0.37</dd><dt><span>blank_dist_sl :</span></dt><dd>0.05</dd><dt><span>fs :</span></dt><dd>1.47</dd><dt><span>declination :</span></dt><dd>18</dd><dt><span>declination_in_orientmat :</span></dt><dd>1</dd><dt><span>principal_heading :</span></dt><dd>173.522</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 236kB\n",
       "Dimensions:              (time: 262, time_gps: 262, range: 24, range_sl: 5,\n",
       "                          beam: 4, dir: 4, x1: 4, x2: 4, earth: 3, inst: 3)\n",
       "Coordinates:\n",
       "  * time                 (time) datetime64[ns] 2kB 2022-08-19T20:14:21.930000...\n",
       "  * time_gps             (time_gps) datetime64[ns] 2kB 2022-08-19T20:14:23.59...\n",
       "  * range                (range) float32 96B 0.95 1.43 1.91 ... 11.51 11.99\n",
       "  * range_sl             (range_sl) float64 40B 0.54 0.66 0.78 0.9 1.02\n",
       "  * beam                 (beam) int32 16B 1 2 3 4\n",
       "  * dir                  (dir) <U10 160B 'streamwise' 'x-stream' 'vert' 'err'\n",
       "  * x1                   (x1) int32 16B 1 2 3 4\n",
       "  * x2                   (x2) int32 16B 1 2 3 4\n",
       "  * earth                (earth) <U1 12B 'E' 'N' 'U'\n",
       "  * inst                 (inst) <U1 12B 'X' 'Y' 'Z'\n",
       "Data variables: (12/47)\n",
       "    number               (time) uint32 1kB 398 399 400 401 ... 656 657 658 659\n",
       "    builtin_test_fail    (time) bool 262B False False False ... False False\n",
       "    c_sound              (time) float32 1kB 1.458e+03 1.458e+03 ... 1.459e+03\n",
       "    depth                (time) float32 1kB 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    pitch                (time) float32 1kB -1.21 -1.01 -1.46 ... -0.5 -0.57\n",
       "    roll                 (time) float32 1kB 1.97 0.49 1.07 ... -0.66 -0.53 0.72\n",
       "    ...                   ...\n",
       "    beam2inst_orientmat  (x1, x2) float32 64B 1.462 -1.462 0.0 ... -1.034 -1.034\n",
       "    orientmat            (earth, inst, time) float32 9kB -0.8992 ... 0.9999\n",
       "    discharge            float32 4B -1.136e+03\n",
       "    power                float32 4B -1.211e+06\n",
       "    power_density        float32 4B -1.307e+03\n",
       "    reynolds_number      float32 4B 6.2e+06\n",
       "Attributes: (12/46)\n",
       "    prog_ver:                  56.1\n",
       "    inst_make:                 TRDI\n",
       "    inst_type:                 ADCP\n",
       "    rotate_vars:               ['vel', 'vel_sl', 'vel_bt']\n",
       "    has_imu:                   0\n",
       "    inst_model:                RiverPro\n",
       "    ...                        ...\n",
       "    range_offset:              0.37\n",
       "    blank_dist_sl:             0.05\n",
       "    fs:                        1.47\n",
       "    declination:               18\n",
       "    declination_in_orientmat:  1\n",
       "    principal_heading:         173.522"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate discharge, power, power density, and the Reynolds number of the channel\n",
    "dolfyn.adp.api.discharge(ds, water_depth, rho=1020, mu=0.0012, surface_offset=0, utm_zone=5)"
   ]
  }
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