03_process_anagada_sections.ipynb

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    "# process Anagada Passage Sections\n",
    "\n",
    "In this notebook we are going to start creating some `science` products from our RU29 data!\n",
    "\n",
    "So far we have:\n",
    "1. identified all the start and end times of the crossings (`01_get_anagada_sections`)\n",
    "1. gridded and split the glider data into sections, and saved them as individual netcdf files (`02_create_anagada_netcdfs`)\n",
    "\n",
    "Next let's start to apply some of our physical Oceanographic knowledge to \"derive\" important quantities from the data. \n",
    "\n",
    "We will start with calculating the geostrophic velocity using the thermal wind equations:\n",
    "\n",
    "$$\n",
    "\\frac{\\partial v}{\\partial z} = \\frac{-g}{\\rho f}\\frac{\\partial \\rho}{\\partial x} \\\\\n",
    "\\frac{\\partial u}{\\partial z} = \\frac{g}{ \\rho f}\\frac{\\partial \\rho}{\\partial y}\n",
    "$$\n",
    "\n",
    "How do you actually calculate those equations from real data?\n",
    "\n",
    "Talk through this, and ask Nick to draw on his ipad\n",
    "\n",
    "remember, from our glider data we can measure things on the right hand side:\n",
    "\n",
    "$$\n",
    "\\frac{-g}{\\rho f}\\frac{\\partial \\rho}{\\partial x} \\\\\n",
    "\\frac{g}{ \\rho f}\\frac{\\partial \\rho}{\\partial y}\n",
    "$$\n",
    "\n",
    "at least we can measure $\\frac{\\partial \\rho}{\\partial x}$ in the direction the glider is flying (not in the cross flight direction), so we can calculate the left hand side, or how the velocity changes with depth: \n",
    "\n",
    "$$\n",
    "\\frac{\\partial v}{\\partial z} \n",
    "$$\n",
    "\n",
    "# comparison with earlier results:\n",
    "\n",
    "[Bill Johns et al in 2002](https://doi.org/10.1016/S0967-0637(01)00041-3) give an overview of the inflow of water to the carrabean. \n",
    "\n",
    "We can compare to this paper!\n",
    "\n",
    "![](./johns_2002.png)"
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   "source": [
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import seawater as sw\n",
    "import numpy as np\n",
    "from scipy import integrate\n",
    "\n",
    "from scipy import interpolate\n",
    "\n",
    "import cartopy.crs as ccrs"
   ]
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       "Dimensions:                (depth: 199, distance: 29)\n",
       "Coordinates:\n",
       "    date                   (distance) datetime64[ns] 2020-10-01T00:26:24.1823...\n",
       "  * depth                  (depth) float64 2.5 7.5 12.5 ... 982.5 987.5 992.5\n",
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       "    v                      (distance) float64 nan 0.01699 nan ... 0.01935 nan\n",
       "    potential_temperature  (depth, distance) float64 29.85 29.84 nan ... nan nan\n",
       "    salinity               (depth, distance) float64 34.73 34.72 nan ... nan nan\n",
       "    cdom                   (depth, distance) float64 nan 0.0 nan ... nan nan nan\n",
       "    chlorophyll_a          (depth, distance) float64 nan 0.0 nan ... nan nan nan\n",
       "    beta_700nm             (depth, distance) float64 nan 0.0 nan ... nan nan nan</pre><div class='xr-wrap' hidden><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-e066267d-dcba-4de5-9265-050264094c26' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e066267d-dcba-4de5-9265-050264094c26' 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'>depth</span>: 199</li><li><span class='xr-has-index'>distance</span>: 29</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2bb668d6-ab7c-4f37-aeeb-b86980251ba9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2bb668d6-ab7c-4f37-aeeb-b86980251ba9' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>date</span></div><div class='xr-var-dims'>(distance)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-10-01T00:26:24.182305792 .....</div><input id='attrs-16b6d819-738b-4fdd-9a32-9b1fd5187cfb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-16b6d819-738b-4fdd-9a32-9b1fd5187cfb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb592054-5830-43e8-bf7d-d7606d40cff7' class='xr-var-data-in' type='checkbox'><label for='data-bb592054-5830-43e8-bf7d-d7606d40cff7' 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;2020-10-01T00:26:24.182305792&#x27;, &#x27;2020-10-01T02:13:28.027615232&#x27;,\n",
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       "       &#x27;2020-10-02T16:34:24.742997760&#x27;, &#x27;2020-10-02T18:21:03.848529152&#x27;,\n",
       "       &#x27;2020-10-02T19:56:18.201492224&#x27;, &#x27;2020-10-02T21:33:46.426829824&#x27;,\n",
       "       &#x27;2020-10-02T23:06:42.728592384&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.5 7.5 12.5 ... 982.5 987.5 992.5</div><input id='attrs-97a002cf-a4b8-4c20-a490-751244ace1b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-97a002cf-a4b8-4c20-a490-751244ace1b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a023b47-1b42-4a8c-a62b-cef26fb3ec2c' class='xr-var-data-in' type='checkbox'><label for='data-4a023b47-1b42-4a8c-a62b-cef26fb3ec2c' 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([  2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,\n",
       "        52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5,  92.5,  97.5,\n",
       "       102.5, 107.5, 112.5, 117.5, 122.5, 127.5, 132.5, 137.5, 142.5, 147.5,\n",
       "       152.5, 157.5, 162.5, 167.5, 172.5, 177.5, 182.5, 187.5, 192.5, 197.5,\n",
       "       202.5, 207.5, 212.5, 217.5, 222.5, 227.5, 232.5, 237.5, 242.5, 247.5,\n",
       "       252.5, 257.5, 262.5, 267.5, 272.5, 277.5, 282.5, 287.5, 292.5, 297.5,\n",
       "       302.5, 307.5, 312.5, 317.5, 322.5, 327.5, 332.5, 337.5, 342.5, 347.5,\n",
       "       352.5, 357.5, 362.5, 367.5, 372.5, 377.5, 382.5, 387.5, 392.5, 397.5,\n",
       "       402.5, 407.5, 412.5, 417.5, 422.5, 427.5, 432.5, 437.5, 442.5, 447.5,\n",
       "       452.5, 457.5, 462.5, 467.5, 472.5, 477.5, 482.5, 487.5, 492.5, 497.5,\n",
       "       502.5, 507.5, 512.5, 517.5, 522.5, 527.5, 532.5, 537.5, 542.5, 547.5,\n",
       "       552.5, 557.5, 562.5, 567.5, 572.5, 577.5, 582.5, 587.5, 592.5, 597.5,\n",
       "       602.5, 607.5, 612.5, 617.5, 622.5, 627.5, 632.5, 637.5, 642.5, 647.5,\n",
       "       652.5, 657.5, 662.5, 667.5, 672.5, 677.5, 682.5, 687.5, 692.5, 697.5,\n",
       "       702.5, 707.5, 712.5, 717.5, 722.5, 727.5, 732.5, 737.5, 742.5, 747.5,\n",
       "       752.5, 757.5, 762.5, 767.5, 772.5, 777.5, 782.5, 787.5, 792.5, 797.5,\n",
       "       802.5, 807.5, 812.5, 817.5, 822.5, 827.5, 832.5, 837.5, 842.5, 847.5,\n",
       "       852.5, 857.5, 862.5, 867.5, 872.5, 877.5, 882.5, 887.5, 892.5, 897.5,\n",
       "       902.5, 907.5, 912.5, 917.5, 922.5, 927.5, 932.5, 937.5, 942.5, 947.5,\n",
       "       952.5, 957.5, 962.5, 967.5, 972.5, 977.5, 982.5, 987.5, 992.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(distance)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>17.82 17.82 17.83 ... 18.15 18.16</div><input id='attrs-3fe7bc5f-eba7-428b-8cb2-926900de722e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3fe7bc5f-eba7-428b-8cb2-926900de722e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78289f1f-e067-4223-9b26-b30052ce4982' class='xr-var-data-in' type='checkbox'><label for='data-78289f1f-e067-4223-9b26-b30052ce4982' 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([17.81733479, 17.82005104, 17.82663472, 17.83991212, 17.85441662,\n",
       "       17.87240011, 17.88846738, 17.90527031, 17.91695131, 17.93659766,\n",
       "       17.95035585, 17.96473181, 17.97935669, 17.99445091, 18.00647208,\n",
       "       18.02121178, 18.03397729, 18.04551587, 18.05809021, 18.06870211,\n",
       "       18.08104569, 18.09186443, 18.10324313, 18.1134997 , 18.12312013,\n",
       "       18.13126797, 18.1407412 , 18.14835452, 18.15594933])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(distance)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-64.77 -64.79 ... -64.8 -64.79</div><input id='attrs-830dc8b6-087f-47b3-9d56-760539322b77' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-830dc8b6-087f-47b3-9d56-760539322b77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d5b016d-bd95-43ec-99bc-e91db30a20ff' class='xr-var-data-in' type='checkbox'><label for='data-8d5b016d-bd95-43ec-99bc-e91db30a20ff' 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([-64.77311504, -64.78810841, -64.79582686, -64.80017555,\n",
       "       -64.80165625, -64.80526166, -64.80597782, -64.80859873,\n",
       "       -64.80855714, -64.81425133, -64.81481875, -64.81568595,\n",
       "       -64.81479802, -64.81481845, -64.81344955, -64.81459779,\n",
       "       -64.81454014, -64.81501724, -64.81453187, -64.81538996,\n",
       "       -64.81301015, -64.81375278, -64.81074703, -64.81014525,\n",
       "       -64.80756735, -64.80583473, -64.80170122, -64.79864358,\n",
       "       -64.79084852])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>distance</span></div><div class='xr-var-dims'>(distance)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.618 2.618 ... 36.74 37.52</div><input id='attrs-9fbf7a61-9af0-49d8-b8c9-e82822aa813e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9fbf7a61-9af0-49d8-b8c9-e82822aa813e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4818f5d8-e53e-4728-a4a7-33067e65e921' class='xr-var-data-in' type='checkbox'><label for='data-4818f5d8-e53e-4728-a4a7-33067e65e921' 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([ 0.      ,  1.617668,  2.618486,  3.804336,  5.098768,  6.982429,\n",
       "        8.60899 , 10.433949, 11.647754, 13.901182, 15.371676, 16.92603 ,\n",
       "       18.468421, 20.094978, 21.365402, 22.989064, 24.376265, 25.642203,\n",
       "       27.005573, 28.179434, 29.491865, 30.688338, 31.89441 , 33.013379,\n",
       "       34.040511, 34.918564, 35.922498, 36.736966, 37.524972])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3d44e702-ac99-4361-ac29-279e428efb68' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3d44e702-ac99-4361-ac29-279e428efb68' class='xr-section-summary' >Data variables: <span>(7)</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>u</span></div><div class='xr-var-dims'>(distance)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan -0.04459 nan ... -0.1964 nan</div><input id='attrs-39509795-c9c3-4e1a-92ea-379066e43b9e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-39509795-c9c3-4e1a-92ea-379066e43b9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69881a6d-897b-4b0e-beb9-bd5a83c69fa9' class='xr-var-data-in' type='checkbox'><label for='data-69881a6d-897b-4b0e-beb9-bd5a83c69fa9' 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([        nan, -0.0445943 ,         nan, -0.05114395,         nan,\n",
       "               nan,         nan, -0.11040045,         nan,         nan,\n",
       "               nan, -0.163118  ,         nan, -0.164581  ,         nan,\n",
       "       -0.163713  ,         nan, -0.1763145 ,         nan, -0.1907955 ,\n",
       "               nan, -0.17689   ,         nan, -0.185215  ,         nan,\n",
       "       -0.193428  ,         nan, -0.196416  ,         nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(distance)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan 0.01699 nan ... nan 0.01935 nan</div><input id='attrs-1993c174-e6b1-49a2-9260-3f02e26bcfca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1993c174-e6b1-49a2-9260-3f02e26bcfca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49c89fca-c5aa-462f-8af3-0b08cf03ec95' class='xr-var-data-in' type='checkbox'><label for='data-49c89fca-c5aa-462f-8af3-0b08cf03ec95' 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([       nan, 0.0169883 ,        nan, 0.00630039,        nan,\n",
       "              nan,        nan, 0.0280509 ,        nan,        nan,\n",
       "              nan, 0.01026402,        nan, 0.0312954 ,        nan,\n",
       "       0.0191639 ,        nan, 0.01468995,        nan, 0.02032915,\n",
       "              nan, 0.036074  ,        nan, 0.0189874 ,        nan,\n",
       "       0.01622555,        nan, 0.0193468 ,        nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>potential_temperature</span></div><div class='xr-var-dims'>(depth, distance)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>29.85 29.84 nan ... nan nan nan</div><input id='attrs-6fa453a9-4568-4fa9-8693-5806eb5aae05' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6fa453a9-4568-4fa9-8693-5806eb5aae05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b26a96d4-812e-4a8b-b376-4f40958573df' class='xr-var-data-in' type='checkbox'><label for='data-b26a96d4-812e-4a8b-b376-4f40958573df' 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([[29.854816  , 29.840055  ,         nan, ..., 29.459057  ,\n",
       "        29.420052  , 29.434092  ],\n",
       "       [29.85893575, 29.843912  , 29.78201775, ..., 29.45415933,\n",
       "        29.4348305 , 29.43889967],\n",
       "       [29.85064275, 29.847231  , 29.7874735 , ..., 29.45029   ,\n",
       "        29.448156  , 29.43551767],\n",
       "       ...,\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity</span></div><div class='xr-var-dims'>(depth, distance)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>34.73 34.72 nan ... nan nan nan</div><input id='attrs-99b563f2-31fc-4e31-b54d-ec8d9245b90a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-99b563f2-31fc-4e31-b54d-ec8d9245b90a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24b8372e-59b5-4341-8ed5-1b129589eadc' class='xr-var-data-in' type='checkbox'><label for='data-24b8372e-59b5-4341-8ed5-1b129589eadc' 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([[34.73492   , 34.72451   ,         nan, ..., 35.02837   ,\n",
       "        35.025505  , 35.03164   ],\n",
       "       [34.734878  , 34.732948  , 34.75355725, ..., 35.02504233,\n",
       "        35.0239735 , 35.032272  ],\n",
       "       [34.752083  , 34.738129  , 34.786775  , ..., 35.025915  ,\n",
       "        35.023697  , 35.033325  ],\n",
       "       ...,\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cdom</span></div><div class='xr-var-dims'>(depth, distance)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan 0.0 nan 0.0 ... nan nan nan nan</div><input id='attrs-42237d49-0d27-4b8c-adea-6d82ba1d8db4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42237d49-0d27-4b8c-adea-6d82ba1d8db4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9321a6c7-32a1-4578-82b9-72d96b3ce51d' class='xr-var-data-in' type='checkbox'><label for='data-9321a6c7-32a1-4578-82b9-72d96b3ce51d' 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([[    nan, 0.     ,     nan, ...,     nan, 0.     ,     nan],\n",
       "       [    nan, 1.12625,     nan, ...,     nan, 0.8109 ,     nan],\n",
       "       [    nan, 0.94605,     nan, ...,     nan, 1.0812 ,     nan],\n",
       "       ...,\n",
       "       [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "       [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "       [    nan,     nan,     nan, ...,     nan,     nan,     nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chlorophyll_a</span></div><div class='xr-var-dims'>(depth, distance)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan 0.0 nan 0.0 ... nan nan nan nan</div><input id='attrs-fff62cbb-ab8f-43e9-b302-2e70637bf06c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fff62cbb-ab8f-43e9-b302-2e70637bf06c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ff3ac74-7b92-401b-8e36-9365ce0b4451' class='xr-var-data-in' type='checkbox'><label for='data-8ff3ac74-7b92-401b-8e36-9365ce0b4451' 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([[   nan, 0.    ,    nan, ...,    nan, 0.    ,    nan],\n",
       "       [   nan, 0.1296,    nan, ...,    nan, 0.1296,    nan],\n",
       "       [   nan, 0.126 ,    nan, ...,    nan, 0.1224,    nan],\n",
       "       ...,\n",
       "       [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "       [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "       [   nan,    nan,    nan, ...,    nan,    nan,    nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_700nm</span></div><div class='xr-var-dims'>(depth, distance)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan 0.0 nan 0.0 ... nan nan nan nan</div><input id='attrs-981d547c-6543-4ce8-8c82-3ee55c64a2df' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-981d547c-6543-4ce8-8c82-3ee55c64a2df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ffdc35f-01ac-4718-9c01-c067737263d9' class='xr-var-data-in' type='checkbox'><label for='data-5ffdc35f-01ac-4718-9c01-c067737263d9' 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([[       nan, 0.        ,        nan, ...,        nan, 0.        ,\n",
       "               nan],\n",
       "       [       nan, 0.00219699,        nan, ...,        nan, 0.00020723,\n",
       "               nan],\n",
       "       [       nan, 0.00045899,        nan, ...,        nan, 0.00015057,\n",
       "               nan],\n",
       "       ...,\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4880120e-bf50-4bb8-b9a0-4227a6f73f29' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4880120e-bf50-4bb8-b9a0-4227a6f73f29' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                (depth: 199, distance: 29)\n",
       "Coordinates:\n",
       "    date                   (distance) datetime64[ns] 2020-10-01T00:26:24.1823...\n",
       "  * depth                  (depth) float64 2.5 7.5 12.5 ... 982.5 987.5 992.5\n",
       "    lat                    (distance) float64 17.82 17.82 17.83 ... 18.15 18.16\n",
       "    lon                    (distance) float64 -64.77 -64.79 ... -64.8 -64.79\n",
       "  * distance               (distance) float64 0.0 1.618 2.618 ... 36.74 37.52\n",
       "Data variables:\n",
       "    u                      (distance) float64 nan -0.04459 nan ... -0.1964 nan\n",
       "    v                      (distance) float64 nan 0.01699 nan ... 0.01935 nan\n",
       "    potential_temperature  (depth, distance) float64 29.85 29.84 nan ... nan nan\n",
       "    salinity               (depth, distance) float64 34.73 34.72 nan ... nan nan\n",
       "    cdom                   (depth, distance) float64 nan 0.0 nan ... nan nan nan\n",
       "    chlorophyll_a          (depth, distance) float64 nan 0.0 nan ... nan nan nan\n",
       "    beta_700nm             (depth, distance) float64 nan 0.0 nan ... nan nan nan"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fname = './processed_sections/section1.nc'\n",
    "\n",
    "ds = xr.open_dataset(fname)\n",
    "\n",
    "# make distance the main dim:\n",
    "ds = ds.swap_dims({'date':'distance'})\n",
    "\n",
    "# sort so the data is always S to N:\n",
    "ds = ds.sortby(ds.distance)\n",
    "\n",
    "\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "crucial-train",
   "metadata": {},
   "source": [
    "# make a map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "herbal-facial",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.gridliner.Gridliner at 0x7fe4cdeadd90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grab bathymetry data for a map\n",
    "\n",
    "lat_lims = [16, 18.9]\n",
    "lon_lims = [-66, -61]\n",
    "\n",
    "# grab some bathymetry\n",
    "\n",
    "url = 'http://geoport.whoi.edu/thredds/dodsC/bathy/etopo1_bed_g2'\n",
    "\n",
    "etopo1 = xr.open_dataset(url).sel(lat = slice(lat_lims[0], lat_lims[1]), lon = slice(lon_lims[0], lon_lims[1] ))\n",
    "\n",
    "\n",
    "ax = plt.subplot( projection= ccrs.Mercator())\n",
    "\n",
    "dpth_contours = np.append(np.arange(-5000, -500, 500), np.arange(-500, 0, 50)) \n",
    "\n",
    "plt.contourf( etopo1.lon, etopo1.lat, etopo1.topo, dpth_contours , \n",
    "                 transform=ccrs.PlateCarree(), cmap='Blues_r', zorder=0)\n",
    "\n",
    "ax.coastlines()\n",
    "\n",
    "plt.plot(ds.lon, ds.lat, 'k.', \n",
    "         transform=ccrs.PlateCarree())\n",
    "\n",
    "plt.quiver(ds.lon.values, ds.lat.values, ds.u.values, ds.v.values,\n",
    "           scale=0.5, transform=ccrs.PlateCarree(), color='r')\n",
    "\n",
    "ax.set_extent((-65.0,-64.5, 17.6, 18.5) )\n",
    "\n",
    "\n",
    "ax.gridlines(draw_labels=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fifty-delhi",
   "metadata": {},
   "source": [
    "# how big is DAC?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "asian-relation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'DAV mag [m/s]')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "( (ds.u**2 + ds.v**2)**0.5 ).plot( marker='o')\n",
    "\n",
    "plt.ylabel('DAV mag [m/s]')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "flying-vitamin",
   "metadata": {},
   "source": [
    "# get $\\rho$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "registered-plant",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
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       "\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                (depth: 199, distance: 29)\n",
       "Coordinates:\n",
       "    date                   (distance) datetime64[ns] 2020-10-01T00:26:24.1823...\n",
       "  * depth                  (depth) float64 2.5 7.5 12.5 ... 982.5 987.5 992.5\n",
       "    lat                    (distance) float64 17.82 17.82 17.83 ... 18.15 18.16\n",
       "    lon                    (distance) float64 -64.77 -64.79 ... -64.8 -64.79\n",
       "  * distance               (distance) float64 0.0 1.618 2.618 ... 36.74 37.52\n",
       "Data variables:\n",
       "    u                      (distance) float64 nan -0.04459 nan ... -0.1964 nan\n",
       "    v                      (distance) float64 nan 0.01699 nan ... 0.01935 nan\n",
       "    potential_temperature  (depth, distance) float64 29.85 29.84 nan ... nan nan\n",
       "    salinity               (depth, distance) float64 34.73 34.72 nan ... nan nan\n",
       "    cdom                   (depth, distance) float64 nan 0.0 nan ... nan nan nan\n",
       "    chlorophyll_a          (depth, distance) float64 nan 0.0 nan ... nan nan nan\n",
       "    beta_700nm             (depth, distance) float64 nan 0.0 nan ... nan nan nan\n",
       "    density                (depth, distance) float64 1.022e+03 1.022e+03 ... nan</pre><div class='xr-wrap' hidden><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-d4296ac7-d476-41f5-a091-de50b435fbf1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d4296ac7-d476-41f5-a091-de50b435fbf1' 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'>depth</span>: 199</li><li><span class='xr-has-index'>distance</span>: 29</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6befa747-ccd9-4a88-9964-390f9be2061a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6befa747-ccd9-4a88-9964-390f9be2061a' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>date</span></div><div class='xr-var-dims'>(distance)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-10-01T00:26:24.182305792 .....</div><input id='attrs-755f24ab-91ab-4d9f-808b-30a2190546da' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-755f24ab-91ab-4d9f-808b-30a2190546da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe67eb16-3493-4464-a7ac-b191e721e5d1' class='xr-var-data-in' type='checkbox'><label for='data-fe67eb16-3493-4464-a7ac-b191e721e5d1' 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;2020-10-01T00:26:24.182305792&#x27;, &#x27;2020-10-01T02:13:28.027615232&#x27;,\n",
       "       &#x27;2020-10-01T03:52:12.756130816&#x27;, &#x27;2020-10-01T05:39:53.966352896&#x27;,\n",
       "       &#x27;2020-10-01T07:02:38.177451776&#x27;, &#x27;2020-10-01T09:02:56.244776192&#x27;,\n",
       "       &#x27;2020-10-01T10:35:48.917067776&#x27;, &#x27;2020-10-01T12:26:06.721575936&#x27;,\n",
       "       &#x27;2020-10-01T13:47:13.102471168&#x27;, &#x27;2020-10-01T16:13:55.199094016&#x27;,\n",
       "       &#x27;2020-10-01T17:49:57.708368896&#x27;, &#x27;2020-10-01T19:32:51.237538816&#x27;,\n",
       "       &#x27;2020-10-01T21:06:36.944635392&#x27;, &#x27;2020-10-01T22:54:39.338535936&#x27;,\n",
       "       &#x27;2020-10-02T00:17:50.120293376&#x27;, &#x27;2020-10-02T02:05:35.068047872&#x27;,\n",
       "       &#x27;2020-10-02T03:41:10.333333504&#x27;, &#x27;2020-10-02T05:17:30.637037568&#x27;,\n",
       "       &#x27;2020-10-02T06:50:03.791476480&#x27;, &#x27;2020-10-02T08:31:08.007278080&#x27;,\n",
       "       &#x27;2020-10-02T10:05:30.537444608&#x27;, &#x27;2020-10-02T11:43:45.247422464&#x27;,\n",
       "       &#x27;2020-10-02T13:20:10.357447168&#x27;, &#x27;2020-10-02T15:01:52.723496191&#x27;,\n",
       "       &#x27;2020-10-02T16:34:24.742997760&#x27;, &#x27;2020-10-02T18:21:03.848529152&#x27;,\n",
       "       &#x27;2020-10-02T19:56:18.201492224&#x27;, &#x27;2020-10-02T21:33:46.426829824&#x27;,\n",
       "       &#x27;2020-10-02T23:06:42.728592384&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.5 7.5 12.5 ... 982.5 987.5 992.5</div><input id='attrs-7cb61c9e-93d9-47ef-86ea-8069cd48c6fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7cb61c9e-93d9-47ef-86ea-8069cd48c6fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc132999-9d3b-4e7d-8037-0404943c0c2c' class='xr-var-data-in' type='checkbox'><label for='data-fc132999-9d3b-4e7d-8037-0404943c0c2c' 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([  2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,\n",
       "        52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5,  92.5,  97.5,\n",
       "       102.5, 107.5, 112.5, 117.5, 122.5, 127.5, 132.5, 137.5, 142.5, 147.5,\n",
       "       152.5, 157.5, 162.5, 167.5, 172.5, 177.5, 182.5, 187.5, 192.5, 197.5,\n",
       "       202.5, 207.5, 212.5, 217.5, 222.5, 227.5, 232.5, 237.5, 242.5, 247.5,\n",
       "       252.5, 257.5, 262.5, 267.5, 272.5, 277.5, 282.5, 287.5, 292.5, 297.5,\n",
       "       302.5, 307.5, 312.5, 317.5, 322.5, 327.5, 332.5, 337.5, 342.5, 347.5,\n",
       "       352.5, 357.5, 362.5, 367.5, 372.5, 377.5, 382.5, 387.5, 392.5, 397.5,\n",
       "       402.5, 407.5, 412.5, 417.5, 422.5, 427.5, 432.5, 437.5, 442.5, 447.5,\n",
       "       452.5, 457.5, 462.5, 467.5, 472.5, 477.5, 482.5, 487.5, 492.5, 497.5,\n",
       "       502.5, 507.5, 512.5, 517.5, 522.5, 527.5, 532.5, 537.5, 542.5, 547.5,\n",
       "       552.5, 557.5, 562.5, 567.5, 572.5, 577.5, 582.5, 587.5, 592.5, 597.5,\n",
       "       602.5, 607.5, 612.5, 617.5, 622.5, 627.5, 632.5, 637.5, 642.5, 647.5,\n",
       "       652.5, 657.5, 662.5, 667.5, 672.5, 677.5, 682.5, 687.5, 692.5, 697.5,\n",
       "       702.5, 707.5, 712.5, 717.5, 722.5, 727.5, 732.5, 737.5, 742.5, 747.5,\n",
       "       752.5, 757.5, 762.5, 767.5, 772.5, 777.5, 782.5, 787.5, 792.5, 797.5,\n",
       "       802.5, 807.5, 812.5, 817.5, 822.5, 827.5, 832.5, 837.5, 842.5, 847.5,\n",
       "       852.5, 857.5, 862.5, 867.5, 872.5, 877.5, 882.5, 887.5, 892.5, 897.5,\n",
       "       902.5, 907.5, 912.5, 917.5, 922.5, 927.5, 932.5, 937.5, 942.5, 947.5,\n",
       "       952.5, 957.5, 962.5, 967.5, 972.5, 977.5, 982.5, 987.5, 992.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(distance)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>17.82 17.82 17.83 ... 18.15 18.16</div><input id='attrs-ee5017a6-b04c-4c26-9ce0-f8a2fecc11d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee5017a6-b04c-4c26-9ce0-f8a2fecc11d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac1ad724-c2ae-4077-a0a7-3b320651c945' class='xr-var-data-in' type='checkbox'><label for='data-ac1ad724-c2ae-4077-a0a7-3b320651c945' 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([17.81733479, 17.82005104, 17.82663472, 17.83991212, 17.85441662,\n",
       "       17.87240011, 17.88846738, 17.90527031, 17.91695131, 17.93659766,\n",
       "       17.95035585, 17.96473181, 17.97935669, 17.99445091, 18.00647208,\n",
       "       18.02121178, 18.03397729, 18.04551587, 18.05809021, 18.06870211,\n",
       "       18.08104569, 18.09186443, 18.10324313, 18.1134997 , 18.12312013,\n",
       "       18.13126797, 18.1407412 , 18.14835452, 18.15594933])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(distance)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-64.77 -64.79 ... -64.8 -64.79</div><input id='attrs-de8eb70e-5450-4737-a444-31d6e05cde77' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-de8eb70e-5450-4737-a444-31d6e05cde77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42416527-74a8-4793-a6bb-a147177a69c2' class='xr-var-data-in' type='checkbox'><label for='data-42416527-74a8-4793-a6bb-a147177a69c2' 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([-64.77311504, -64.78810841, -64.79582686, -64.80017555,\n",
       "       -64.80165625, -64.80526166, -64.80597782, -64.80859873,\n",
       "       -64.80855714, -64.81425133, -64.81481875, -64.81568595,\n",
       "       -64.81479802, -64.81481845, -64.81344955, -64.81459779,\n",
       "       -64.81454014, -64.81501724, -64.81453187, -64.81538996,\n",
       "       -64.81301015, -64.81375278, -64.81074703, -64.81014525,\n",
       "       -64.80756735, -64.80583473, -64.80170122, -64.79864358,\n",
       "       -64.79084852])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>distance</span></div><div class='xr-var-dims'>(distance)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.618 2.618 ... 36.74 37.52</div><input id='attrs-ae967f66-6f74-4552-9426-21397b7316d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae967f66-6f74-4552-9426-21397b7316d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2462a428-124e-4832-b136-fe400b3a0f89' class='xr-var-data-in' type='checkbox'><label for='data-2462a428-124e-4832-b136-fe400b3a0f89' 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([ 0.      ,  1.617668,  2.618486,  3.804336,  5.098768,  6.982429,\n",
       "        8.60899 , 10.433949, 11.647754, 13.901182, 15.371676, 16.92603 ,\n",
       "       18.468421, 20.094978, 21.365402, 22.989064, 24.376265, 25.642203,\n",
       "       27.005573, 28.179434, 29.491865, 30.688338, 31.89441 , 33.013379,\n",
       "       34.040511, 34.918564, 35.922498, 36.736966, 37.524972])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e78e163e-cf41-488e-9724-8863c147bfa5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e78e163e-cf41-488e-9724-8863c147bfa5' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(distance)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan -0.04459 nan ... -0.1964 nan</div><input id='attrs-149c1835-bc77-48c6-ab53-7eca7e9ad77e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-149c1835-bc77-48c6-ab53-7eca7e9ad77e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2e0609d-50a9-4c26-8d95-5384886b4b28' class='xr-var-data-in' type='checkbox'><label for='data-f2e0609d-50a9-4c26-8d95-5384886b4b28' 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([        nan, -0.0445943 ,         nan, -0.05114395,         nan,\n",
       "               nan,         nan, -0.11040045,         nan,         nan,\n",
       "               nan, -0.163118  ,         nan, -0.164581  ,         nan,\n",
       "       -0.163713  ,         nan, -0.1763145 ,         nan, -0.1907955 ,\n",
       "               nan, -0.17689   ,         nan, -0.185215  ,         nan,\n",
       "       -0.193428  ,         nan, -0.196416  ,         nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(distance)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan 0.01699 nan ... nan 0.01935 nan</div><input id='attrs-366a10cf-7070-4a96-a653-de74058aa088' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-366a10cf-7070-4a96-a653-de74058aa088' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-799bb26b-c983-4be1-9e88-5458f20ff09a' class='xr-var-data-in' type='checkbox'><label for='data-799bb26b-c983-4be1-9e88-5458f20ff09a' 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([       nan, 0.0169883 ,        nan, 0.00630039,        nan,\n",
       "              nan,        nan, 0.0280509 ,        nan,        nan,\n",
       "              nan, 0.01026402,        nan, 0.0312954 ,        nan,\n",
       "       0.0191639 ,        nan, 0.01468995,        nan, 0.02032915,\n",
       "              nan, 0.036074  ,        nan, 0.0189874 ,        nan,\n",
       "       0.01622555,        nan, 0.0193468 ,        nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>potential_temperature</span></div><div class='xr-var-dims'>(depth, distance)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>29.85 29.84 nan ... nan nan nan</div><input id='attrs-cc92673a-f3e6-48a1-a954-2b27dd660656' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc92673a-f3e6-48a1-a954-2b27dd660656' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dba71f0f-a889-4682-a22d-c207b2a9b1b3' class='xr-var-data-in' type='checkbox'><label for='data-dba71f0f-a889-4682-a22d-c207b2a9b1b3' 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([[29.854816  , 29.840055  ,         nan, ..., 29.459057  ,\n",
       "        29.420052  , 29.434092  ],\n",
       "       [29.85893575, 29.843912  , 29.78201775, ..., 29.45415933,\n",
       "        29.4348305 , 29.43889967],\n",
       "       [29.85064275, 29.847231  , 29.7874735 , ..., 29.45029   ,\n",
       "        29.448156  , 29.43551767],\n",
       "       ...,\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity</span></div><div class='xr-var-dims'>(depth, distance)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>34.73 34.72 nan ... nan nan nan</div><input id='attrs-3579e4b7-76ad-4dfc-b69d-b157363411f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3579e4b7-76ad-4dfc-b69d-b157363411f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbe24e1d-e603-47d4-91e7-77b23ba9e88b' class='xr-var-data-in' type='checkbox'><label for='data-bbe24e1d-e603-47d4-91e7-77b23ba9e88b' 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([[34.73492   , 34.72451   ,         nan, ..., 35.02837   ,\n",
       "        35.025505  , 35.03164   ],\n",
       "       [34.734878  , 34.732948  , 34.75355725, ..., 35.02504233,\n",
       "        35.0239735 , 35.032272  ],\n",
       "       [34.752083  , 34.738129  , 34.786775  , ..., 35.025915  ,\n",
       "        35.023697  , 35.033325  ],\n",
       "       ...,\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cdom</span></div><div class='xr-var-dims'>(depth, distance)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan 0.0 nan 0.0 ... nan nan nan nan</div><input id='attrs-0ae064c1-3954-4ea8-a2d8-9ee21c79460d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ae064c1-3954-4ea8-a2d8-9ee21c79460d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-abe66a6c-d48c-4870-8c65-60e1c6a3d2b8' class='xr-var-data-in' type='checkbox'><label for='data-abe66a6c-d48c-4870-8c65-60e1c6a3d2b8' 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([[    nan, 0.     ,     nan, ...,     nan, 0.     ,     nan],\n",
       "       [    nan, 1.12625,     nan, ...,     nan, 0.8109 ,     nan],\n",
       "       [    nan, 0.94605,     nan, ...,     nan, 1.0812 ,     nan],\n",
       "       ...,\n",
       "       [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "       [    nan,     nan,     nan, ...,     nan,     nan,     nan],\n",
       "       [    nan,     nan,     nan, ...,     nan,     nan,     nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chlorophyll_a</span></div><div class='xr-var-dims'>(depth, distance)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan 0.0 nan 0.0 ... nan nan nan nan</div><input id='attrs-5c030ac2-cdbc-4843-81fd-7710b99d325a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c030ac2-cdbc-4843-81fd-7710b99d325a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b36f6f00-5d61-402a-b972-3061b5f5481e' class='xr-var-data-in' type='checkbox'><label for='data-b36f6f00-5d61-402a-b972-3061b5f5481e' 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([[   nan, 0.    ,    nan, ...,    nan, 0.    ,    nan],\n",
       "       [   nan, 0.1296,    nan, ...,    nan, 0.1296,    nan],\n",
       "       [   nan, 0.126 ,    nan, ...,    nan, 0.1224,    nan],\n",
       "       ...,\n",
       "       [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "       [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "       [   nan,    nan,    nan, ...,    nan,    nan,    nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_700nm</span></div><div class='xr-var-dims'>(depth, distance)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan 0.0 nan 0.0 ... nan nan nan nan</div><input id='attrs-95e11eb5-be3e-45b3-9dde-d56cfc60081e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-95e11eb5-be3e-45b3-9dde-d56cfc60081e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22a14c85-3f8a-4690-8c90-fa1be0453090' class='xr-var-data-in' type='checkbox'><label for='data-22a14c85-3f8a-4690-8c90-fa1be0453090' 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([[       nan, 0.        ,        nan, ...,        nan, 0.        ,\n",
       "               nan],\n",
       "       [       nan, 0.00219699,        nan, ...,        nan, 0.00020723,\n",
       "               nan],\n",
       "       [       nan, 0.00045899,        nan, ...,        nan, 0.00015057,\n",
       "               nan],\n",
       "       ...,\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>density</span></div><div class='xr-var-dims'>(depth, distance)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.022e+03 1.022e+03 nan ... nan nan</div><input id='attrs-023a4d92-27da-495b-8c11-eadfb95e75f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-023a4d92-27da-495b-8c11-eadfb95e75f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a85f4b3e-7434-4667-9688-d7cf44809c41' class='xr-var-data-in' type='checkbox'><label for='data-a85f4b3e-7434-4667-9688-d7cf44809c41' 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([[1021.57692834, 1021.5741438 ,           nan, ..., 1021.93115493,\n",
       "        1021.94216904, 1021.94203543],\n",
       "       [1021.57549534, 1021.57915867, 1021.61564853, ..., 1021.93031159,\n",
       "        1021.93603356, 1021.94088718],\n",
       "       [1021.59121615, 1021.58191438, 1021.63870354, ..., 1021.93227261,\n",
       "        1021.9313287 , 1021.94281868],\n",
       "       ...,\n",
       "       [          nan,           nan,           nan, ...,           nan,\n",
       "                  nan,           nan],\n",
       "       [          nan,           nan,           nan, ...,           nan,\n",
       "                  nan,           nan],\n",
       "       [          nan,           nan,           nan, ...,           nan,\n",
       "                  nan,           nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b771a2e5-5547-4679-9ef5-3b17994eacb8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b771a2e5-5547-4679-9ef5-3b17994eacb8' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                (depth: 199, distance: 29)\n",
       "Coordinates:\n",
       "    date                   (distance) datetime64[ns] 2020-10-01T00:26:24.1823...\n",
       "  * depth                  (depth) float64 2.5 7.5 12.5 ... 982.5 987.5 992.5\n",
       "    lat                    (distance) float64 17.82 17.82 17.83 ... 18.15 18.16\n",
       "    lon                    (distance) float64 -64.77 -64.79 ... -64.8 -64.79\n",
       "  * distance               (distance) float64 0.0 1.618 2.618 ... 36.74 37.52\n",
       "Data variables:\n",
       "    u                      (distance) float64 nan -0.04459 nan ... -0.1964 nan\n",
       "    v                      (distance) float64 nan 0.01699 nan ... 0.01935 nan\n",
       "    potential_temperature  (depth, distance) float64 29.85 29.84 nan ... nan nan\n",
       "    salinity               (depth, distance) float64 34.73 34.72 nan ... nan nan\n",
       "    cdom                   (depth, distance) float64 nan 0.0 nan ... nan nan nan\n",
       "    chlorophyll_a          (depth, distance) float64 nan 0.0 nan ... nan nan nan\n",
       "    beta_700nm             (depth, distance) float64 nan 0.0 nan ... nan nan nan\n",
       "    density                (depth, distance) float64 1.022e+03 1.022e+03 ... nan"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate potential density\n",
    "\n",
    "\n",
    "pden = sw.eos80.dens0(ds.salinity, ds.potential_temperature)\n",
    "\n",
    "ds.update({'density' :  (('depth', 'distance'), pden)})\n",
    "\n",
    "# ds.density.sel(depth = 50, method='nearest').plot()\n",
    "# plt.plot(ds.depth, ds.density)\n",
    "\n",
    "ds.density.plot()\n",
    "plt.ylim([1000, 0])\n",
    "\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "breeding-middle",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/beaird/anaconda3/envs/FM2020/lib/python3.8/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(a, axis=axis, dtype=dtype)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe4ce9ff6d0>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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go5fN4ScffBv5WamhuuHNBAKT68tNpK/8uYbtR1r58ruWMrdoGgDl+cH1TnUJ0v0UXkPh5GI7cDhRiMg1IrJPRA6IyH1DvL5IRLaKiF9EPuNEjEPZtM9Lf0C5OrRX0NLy4EKtnZNsnGJzbTMXnVUwKabAjsacwixWzMrjie11k6J1WNPQzg0PPs+2Iyf52q3L+Nx1SwbWiVw2v5DmDj81DT6Ho4yNZ3c38IMtr/OBi2fzzmUzB54PJ4pE6X5yulZ2mGOJQkQ8wIPAtcAS4HYRWTLosBPAJ4Gvxjm8Ea3d20jhtDSWlecBsKAkm1RPErsm0TjF0RNdvN7cOSW6nSLdsqKMfY0+9tS3Ox3KhPz5tQZu+c4L+HsDPHrXRbzr/PK3vD5QN7x28nU/HWnp4jOPvcq5Zbl87rrFb3mtPD8TIGEGtBvaghMOpmyiAC4ADqjqIVXtAX4N3Bh5gKo2qerLQK8TAQ6lpy/Ac/u8XL24mKTQVNHU5CQWz5hcA9rhOg1TYSA70vVLZ5LiEZ7YnphrKgIB5Vvrarn7F9uYX5LNHz6xcsjde0ty0llUmj3pxim6e/v5+CPbEOA77z2PtOS3toYLp6WSnpKUMC2KhvZuCrKcK4Ea5uTVy4CjEY+PhZ4bFxG5S0SqRaTa643dP/4XD7XQ4e8b6HYKO7c8l9fq2iZNn+/m/V7K8jKYW5TldChxlZ+VyuULi3ny1eOuLLg0kk5/H/c8sp1vrNvPLSvKePSui0b8JnrZ/EKqD5+kqycxBnZH47+e3sNrde187T3LqZieedrrIkJ5fmbCjCc2tju/2A6cTRRDrdwa96esqj6kqlWqWlVUFLtvwev2NpKeksTK+YVveX5pWR4+fx+vt3TG7Nrx0tsf4IWDLaxaUDglt96+ZUUZXp+fvybQCt6jJ7p413df4NndDXz+usV87T3Lzji2tGpBET39AV46NDkW3z25o45fvHiEu1adxZolJcMeV56fwbHWBGlRtDm/hgKcTRTHgIqIx+WAq6ebqCrr9jRy2fyi0/4TLq0IDmhPhnGKV4600uHvm3LjE2FXLi4mJz2ZJ7YnxpqKrQdbuOGB56lrPcVPPnQBH7lsdLU13lYZrBs+GcrBHmjq4LOP7+L82fn8r79bOOKxwbUU1qIYCycTxcvAfBGZIyKpwG3AUw7Gc0Z76ts53tY9UBkt0ryiaaSnJE2KcYrN+714koRL5hWe+eBJKLilx0ye3d1Ip8vn2/9pVz3v/9FLTM9K5cl7LmX1GMaUwnXDt0yCAe1/f2o3aclJPPAPK0g5ww7A5fmZtHb14ut2zdDnkHr6ArQ4XAI1zLFEoap9wL3As8Be4DequltE7haRuwFEpFREjgGfBj4vIsdEJMepmNftaUIErlhUfNpryZ4kzp6ZOym28thc62VFRZ6j2xo77ZbzyjjV28+zu929pcdvqo8yIy+dJ+65lLNCawXGYtX8Qg56O6lrTYxv2ENpO9XL1kMt3H7BLGbknrkuzMBaCpf/mZvCi+1yna8U6ehQuqo+o6oLVHWuqn4p9Nz3VPV7ofsNqlquqjmqmhe679i8xXV7G1lRkUdR9tB/ceeW5fJaXTv9CTygfaKzh111bVNuttNgVbPzqZie4fodZb0dfuYVTSMnfXxJfWCabAJ3P22pDa5runKIL3BDGZgi6/IB7fAaiuKp3KJINPVtp9hV18bVIwySLS3P5VRvPwe9HXGMLLq21HpRnXrTYgcTEW5eXsZfDzQP/Id1o6Z2P8XZ4/8gmV88jdKc9ITuftpQ00RuRgrLK/JGdXyiLLoLr6GY0l1PiWb93mCxk6HGJ8LCK7RfPdoaj5Bi4rn9XvIyUzjXyoJy83nlBDQ4m8aN+gNKS2fPsC3c0RARVi0o5Pna5oSbDgzBdSPP7fOyekHRqKsTFmSlkpHi4ajLB7TDtbItUSSQtXsamV2Qybzi4fuBzyqcRlaqh111iTmgrapsqW1m5bzCSVl3YqzmFGaxvCKPx126+O5kVw/9AZ1QogC4bH4R7d19vJqAEzF21rXR0tkz6m4nCK+lcP8uso3t3aQmJ5GX6fxYoSWKUejw97H1YAtrFpeMOO0wKUk4pyw3YWc+7a334fX5p3y3U6RbziujpsHHXhdu6RGuJzHRRLFyXiEiJGT304aa4ASTscz2gsSYItvQFixY5Ia1TJYoRmHLfi89/YERxyfClpbnsqe+nd4EbMaH9/2ZqusnhnL90pkkJ4krB7WjlSjys1JZWp6XkAPaG2uaWFGRR36o0uRolednuj9RuKCyXZglilFYu7eR3IwUqmafebvtc8vz6OkLsL8x8Xbl3Lzfy8KSyV/Nbiymh7f02FHnutlsA4li2sSnT66aX8iOo620dbl7bUGkJl83u+raxtTtFFaen0HbqV7aXbyWoqm9mxKX/F+0RHEGff0BNtY0ceWi4lENli0tS8wV2l094Wp2U3OR3UhuOa+MxnY/Lxx0V0U4b0d0WhQQnOUWUFz3ZxxJuJ77UOuaziQ8RdatdSkGSqA6XIcizBLFGWw/0srJrt7TNgEczuyCTLLTkxNuYHCgmp2NT5zmykXFZKcnu25HWa/PT2aqh6y05Amfa3lFHtlpyQm17fjGmiZKctJYMmPsa3DfnCLrzkTRfqqP7l7nS6CGWaI4g3V7G0nxyKi/aYsIS8sTb4X25v3NpKck8bbK6U6H4jrpKR6uXzqDP+9ucNVOq00+P8VRaE0ApHiSuHhuAZv3NydE0aaevgBbapu5YmHxuAZ73b6WosElBYvCLFGMQFVZu6eRi84qIHsMK1+Xluexr8FHd29/DKOLrs37vVOqmt1Y3bS8jK4ed23p4fV1R6XbKWzVgiLqWk9xqNn9OyBXv3GCDn/fuLqdIDj2lJnqce124wNrKGyMwv0Oejt5vblzxC2Lh7K0LJfefmVfgpSZPHqii0NTsJrdWLytcjpleRmuWlPh9fmjmihWJ9B2Hhtrmkj1JLFynBtXun0tRWObexbbgSWKEa3b2wgw6vGJsHMHamgnxjjFwLRYG58YVlKScPOK4JYeTS7Z0sPr80dlxlNYxfRMKgsyEyJRbKhp4sKzpk9ofMbNU2QbBvZ5ssFs11u3p5GzZ+YwM+/MO1JGKsvLYHpWKruOtcYmsCibqtXsxurm88pCW3o4Xzalu7ef9u6+qLYoIPhl4cVDJ/D3ubfb9EhLFwe9nVy+cHzdTmFublHUt52iICv1tFKuTrFEMYyWDj/bjpwcc2sCgs3acxNkhXZvf4AXDkzdanZjMbdoGsvKc3l6V73TodAcxamxkVbNL+JUbz/bDp+M6nmjaeO+4L5r41k/Eak8P4P27j7aTrlvLcW+Bt+I2wXFmyWKYWyoaUKVMY9PhC0tz2V/o49TPe79ZgbBana+KVzNbqzOLsvlyAnnv4VGa1X2YBfNLSA5SXjOxdNkN9Q0MacwizmFE2sBu3UtRSAQHN9cPI5pv7FiiWIY6/Y2UpqTztkzx/eXtbQ8j4DCnnp3tyqmejW7sSrNSedEZ4/jXTNNA6uyozvYOS0tmfNn57NlvzsX3nX19LH1UAtXTLDbCdw7Rbau9RSdPf0sLM12OpQBliiG0N3bz+b9zVy9ZHxztOHNLcfd3v20udbL8ilezW4swrNQmtr9jsYRblHEYrBz1YIi9tS3D1zDTbYebKGnL8AViybeAh4oYOSyFkV4A8pFlijc7YWDzZzq7R/X+ERYSU46xdlprt7KY6CanXU7jVr4g9npYkZenx+R4HqAaAv/e3DjbrIbaprITPVwwZyJLwzNz0whK9XDUZe1KGoafIjAghJLFACIyDUisk9EDojIfUO8LiJyf+j1nSJyXjziWruniaxUDxfPLZjQeZaW57p6iuyb1eys22m0wgugGp1uUXT4mZ6ZSsooi/WMxdkzcyjISmVLrbu6n1SVjTVNrJxXGJXZQMG1FO6bIruvwcfs6ZlR2ZolWkb1r0xErheRqP6LFBEP8CBwLbAEuF1Elgw67Fpgfuh2F/DdaMYwlEBAWb+3kdULiyb8j/HcsjwOejvo8Ltn24dIm/c3k5eZwtLyPKdDSRjhrqcGF7Qooj2QHZaUJKycX8iWWi8BF+2Yu6/Rx/G27gnPdorkxroUexvaXTU+AaNvUdwG1IrIV0RkcZSufQFwQFUPqWoP8GvgxkHH3Aj8TINeBPJEZEaUrj+kXXVtNPn8E+p2CltanosqvObCVkWwmp3XqtmNUW5GCqnJSa7oeopVooBg1bvmjh72uKhg08aaYFfYRNdPRHLbWopTPf0cbu5kUal7ZjzBKBOFqr4PWAEcBH4iIltF5C4RmUjaKwOORjw+FnpurMcAEIqnWkSqvd7x962u29tIkhCVWRXhFdpuHKeoafDRZNXsxkxEKM1Jd0eiiOKq7MFWzQ92R7qp+2ljTRNLZuREdf+j8vxMfC5aS1Hb5COgsHhGYrYoUNV24HcEv/nPAG4GtovIJ8Z57aG+xg5u547mmHB8D6lqlapWFRWN/8Nv7Z5Gqiqnj7li1lAKp6VRlpfBS6+fcN2OnOFtGmwge+xKc9JpaHMuUagq3o7YtiiKc9JZVJrtmu082rp62XbkZFS7ncB9U2RrQvvDLUzEFoWI3CAiTwAbgBTgAlW9FlgGfGac1z4GVEQ8LgcG740wmmOi5uiJLmoafKyJQrdT2JolJazb28iHfvqyox8ug22utWp241Wck+Zoi6L9VB89fYGYJgoITpOtfuMEnS4YY3uu1kt/QMe9W+xw3DZFtqbeR0aKh1nTM50O5S1G26J4N/ANVV2qqv+jqk0AqtoFfHic134ZmC8ic0QkleA4yFODjnkK+EBo9tNFQJuqxmz/hIFNAMe5Gnso//f6JXzhnUt48VALa77xHL/bdszx1kVXTx8vv27V7MYr2PXkd+zv0dsRTFIxTxTzi+jtV156vSWm1xmNTTVN5GemsLwiL6rnDbcojrpgtT1ATUM7C0qzXTduONpEUa+qmyOfEJH/BlDV9eO5sKr2AfcCzwJ7gd+o6m4RuVtE7g4d9gxwCDgA/AD4+HiuNVrr9jYyt2jiWwNESkoSPnTpHP70qVUsLMnmXx57lY/+rJomn3PfSF86dMKq2U1ASU46p0Kb8jmhKUbbdwxWVZlPekoSmx1epd0fUDbt97J6QVHUP0DzMlOYlpbsihaFqlLT4GORi9ZPhI02UawZ4rlrJ3pxVX1GVReo6lxV/VLoue+p6vdC91VV7wm9fq6qVk/0msM51dNP9eGTUW1NRJpTmMWj/3gxn79uMVtqm3n7Nzbz5I46R76VPrffa9XsJiBc8N6p7cYHVmXHOFGkp3i4cE6B4+MUrx5r5URnT9S7nSCyLoXzicLb4edEZw+LXDaQDWdIFCLyMRHZBSwKLXgL314HdsYnxPjISPXw0r9dxUcvOytm1/AkCR+57Cye/uRlVBZk8alf7+Bjv9g+sBNovGze7+XCOVbNbrycXkvhjdE+T0NZtaCIQ82djnbNbKppIkneLKwUbW6ZIltTHxzIdtvUWDhzi+IR4J3Ak6Gf4dv5oSmzk0peZiqFMZxyGDaveBq/+9gl3HftIjbUNPH2b2zm6Z3x2bp6oJqddTuNW0loGw+nJid4O/ykepLIyYj9yt2rQt/i/xinf59D2bCvifNm5ZOXGf3tSiA4oF138pTjY4fhiphu2uMp7EyJQlX1MHAP4Iu4ISLWbzEBniTh7tVzefqTKynPz+CeR7bzh1djXxAnXM1utQ1kj1u44H2TQ5vmhRfbxaN+SGVhFm+rzOexbUcd+SBtau/mtbr2mHQ7hZXnZ+Dz99F+ytnZXXsb2inJSYvK1PxoG02LAmAbUB36uS3isZmg+SXZPP6xSyjLy4hL5bQ3q9m5pyhKoklP8ZCXmeJci8LnpzDG4xORbq2q4JC3k+1H4l/MKFpFikYyMPPJ4e6nmnqfK7ud4AyJQlWvD/2co6pnhX6Gb7HrzJ9ikj1JXL24mOcPeGNa6Miq2UVPSXa6o2MUsR7IjnTduTPITPXwm5ePRe2cqjqqFsrGGi8zctNj2h3jhrUUff0BDjR1uLLbCWBUnZwicimwQ1U7ReR9wHnAN1X1SEyjm0LWLCnl4a1vsKXWy9vPLo3JNba9cdKq2UVJSW66o7OezpudH7frZaUlc925M/jjzuN84YYlZKZObGykpy/AdfdvoaGtm9mFmVQWZAVvhVlUFmRSWZhFQVYqvf3K8weaeeeymTH9YuOG1dmvN3fS0x9w5YwnGGWiILhr6zIRWQb8K/Aj4OfA6lgFNtVceNZ0stOTWbunMWaJ4odbDpGbkcJlNpA9YaU5aexriP+Geb39AU509cR0n6eh3FpVwWPbjvHMrgbefX75hM71+PZj1DZ1cMOymbSd6mVXXRt/eq2B/oidarPTkinOSaPD3xfTbicIbvSY7fBair0N7p3xBKNPFH2qqiJyI/AtVf2RiNwRy8CmmhRPElcsLGZDTRP9AY36wqLX6tpYt7eJT69ZwDQX7XOfqEpy0vH6/PT1B0iOQU2I4Zzo7EE19ovtBntbZT6VBZn8pvrohBJFb3+ABzYeYFl5Lt+6bflAS6G3P8Cxk6c43NzJ4ZbO0M8uZuZlsDLGZXpFhDKHp8jW1LeTnCSuHTsc7SeGT0Q+C7wPWBWqJWG1M6Ps7WeX8NSrx9l+5GTUF8N9c10tOenJfPDSyqied6oqyUknoNDS2TMwCyoevHFalT2YiHBrVQX/8+w+Djd3UjnO3Que2F7HsZOn+I8bz35Ld1KKJ4k5hdHdFWEsggWMnEsU+xp8zC2aRmqyO4uOjjaqvwf8wJ2q2kBwq+//iVlUU9TqBUWkeIS1exqjet5ga6KRO1eeRU665fdoCCeHeM98cipRALzrvHKSBH67bXyD2uHWxNLy3Khs4x9N4dXZTq2lqGnwuXZ8AkZfj6JBVb+uqltCj4+o6s9iG9rUk52ewsVzC/nL7oao/oO9f30t2daaiCqnVme/uSo7/omiNDedVQuK+O22Y28ZTxit379Sx5ETXXzyyvmum3VXnp9Bh9+ZuhRtp3qpaz3l2vEJGP0247eISK2ItIlIu4j4RMQ9pa8mkTVLSjjc0sWBpo6onG/38Tb+sqeRO1fOITfDWhPRUpIb/KCO98wnb4dzLQqAW8+voKG9m+cPjG2jwL5Qa+KcshyuWuyu1gQ4O0V2f6N7V2SHjbbr6SvADaqaq6o5qpqtqu5NfwksXAvjL1Hqfgq3Jj506ZyonM8EFWSl4UmSuLcomtq7yUlPdmyfrquXFJOXmcJvqo+e+eAIT+44zhst7mxNgLNTZGtC5WYTvusJaFTVvTGNxADB5v3S8tyojFPsOd7Os7sb+fCl1pqINk+SUJydRmN7fLfxiHVluzNJS/Zw0/Iy1u5upLWrZ1S/E25NLJ6Rw5oY7c48URWhFsXRE/FvUext8JGbkTLQnelGo00U1SLyqIjcHuqGukVEbolpZFPYmsUl7DjaOuFujfvX15KdlsyHrTUREyUO1M4O7/PkpFuryunpD4x6y5k/7DzO682dfOqqea5sTQDkZqaQnZ7sSItiX4OPhaXZrn1vYPSJIgfoAt7OmzvIXh+roKa6NWcHv3Wt29s07nPsrW/nz7sb+NClleRmWmsiFkpy0hyZ9VSU7ew3z7Nn5nL2zJxRdT/1B5RvbzjAotJs3r4kNgtJoyU4RTa+LYpAQNnX4GOxi8cnYPSznj40xG28JVDNGSwsyWbW9EzW7mkY9zm+vSHUmlhprYlYKXWqReHAjKfB3lNVwe7j7ew+3jbicX/ceZxD3k4+edV8klxW3nMwJwoY1bWeosPfx6IZ7h7yHe2spwUisl5EXgs9Xioinx/vRUVkuoisDc2kWisiQ25cIyI/FpGm8HWnChFhzZIS/nqwhY5xFLavaWjnmV0NfPDSypjt4W+C+z21d/fFdCPHSJ3+Pjp7+h3vegK4cflMUj1JPFY9/JqK/oBy//paFpZkc02MtqWJpnABo3iupagJbd2xcDK0KAjWq/4s0AugqjuB2yZw3fuA9ao6H1gfejyUnwLXTOA6CWvNkhJ6+gLjKkP57fUHmJaWzJ3Wmoipkuz4rqVodnhqbKS8zFTWnF3C73fU4e8bOlE+vaueg95OPnHVPNe3JiDY9dTZ009rV/zWUoRnPC10YZ3sSKNNFJmq+rdBz02kyseNwMOh+w8DNw11kKpuBk5M4DoJq2p2PnmZKWOe/bSvwcczr9XzwUusNRFrpaHa2fHqfnJyVfZQbj2/nNauXtYPMZYWCCjfXl/L/OJpvOOcGQ5EN3ZvTpGNX/dTTYOP2QWZZLl8/7XRJopmEZkLKICIvBuYSG3EElWtBwj9nPAKHBG5S0SqRaTa63W2GHw0JHuSuHJRcJPA3v7AqH/v/g21ZKZ4rDURB+GSqPFKFOGKevGsRTGSy+YXUZqTPuSg9p9ea6C2qYNPJMDYRJgTBYz2NrS7eqFd2GgTxT3A94FFIlIH/BNw90i/ICLrROS1IW43TizkoanqQ6papapVRUWTYxvtty8poe1ULy8fHl2jan+jj2d21fPBSytdWU5xson3fk9ua1F4koR3n1/O5v3et7wHgdDYxNyiLK47NzFaExC5Ojs+iaK7t5/DzZ0sdPHWHWEjtndE5NMRD58BNhJMLp3Au4CvD/e7qnr1COdtFJEZqlovIjOA8c8DncQum19EanISa/c0csncM2+1fP/6YGviIyut+GA8ZKenkJXqiduiO6/PjydJyHdRl+K7zy/ngY0H+N32Y9xzxTwAnt3dwL5GH9+6bXnUt8uPpdyMFHLS41eXoraxg4Di+qmxcOYWRXboVgV8DMgH8gi2JpZM4LpPAeF6FncAT07gXJNWVloyK+cVsnZP4xlnYtQ2+nh6Vz0fuMRaE/EUz0V3Xp+fgqxUV334VhZmccGc6TxWfRRVJRBQvrW+lrOKsrh+6UynwxuzeK6l2NsQ3rrD/S2KM9XM/qKqfhEoBM5T1c+o6r8A5wMTKXP1ZWCNiNQCa0KPEZGZIvJM+CAR+RWwFVgoIsdE5M4JXDMhvX1JCcdOnhqYRjecb284QEaKh49eZq2JeCrJiV/tbKe37xjOreeXc7ili+o3TvKXPY3UNPj4xJXzXJXQRqs8jgWM9jX4SE9JYtb0zLhcbyJGO0YxC4jc2KUHqBzvRVW1RVWvUtX5oZ8nQs8fV9V3RBx3u6rOUNUUVS1X1R+N95qJ6qrFJYgw4uynA00+/rDzOB+4uJLp1pqIq9Lc+LYo3Jgo3nHuDLJSPTz68lHuX1/LnMIs3pmArQl4s0URj7UUNQ3tLCzJToiEOtpE8XPgbyLy7yLyBeAl3pzeamKoKDuNFRV5IyaKb284QHqyh49eZjOd4q04J42mdn9cPljcsip7sKy0ZK5fOpPfbT/Gnvp27r1iXlzLw0ZTeX4GXT39nIzDWoqaep+ra1BEGu0WHl8CPgScBFqBD6nq/4thXCbCmiWl7Kpr43jr6X2nB5o6+MOrx/nAJbMpcOGHyGRXmpNOT38g5h8sgYDS3OGnOMedf8e3VpWjCrMLMrlxeWK2JiB+2417fX5aOntcvyI7bNRpX1W3q+q3QrdXYhmUeavw1szr9p7eqnhgQy1pyR7usrEJR5TGaYrsya4e+gLqyhYFwPmz83nvhbP44g1nJ2xrAt6cIhvr7cZrGtxfgyJS4v6NTiHziqdxVmHWad1Ph7wdPPXqcd5/sbUmnFKcE5/V2W9WtnNnzQIR4Us3n8vlLquFPVbl0+PToqipD1e1m0RdT8Z5a5aU8OKhFtq73+zieGDDAVKTk7hrlbUmnBKvbTzctthusspJTyE3IyXmU2RrGnwUZ6clzOQTSxQJYs2SEnr7lU37gtuTHPJ28Psddbz/otkUWmvCMeGuoFhPkbVEET+xniLr7+vnuf1NnDdryE2zXckSRYJYMSufwmmpA91PD2wMtybmOhzZ1JaanEThtFRrUUwisa5L8efXGmju6OH2C2fF7BrRZokiQXiShKsWlbCpponaRh+/f6WO91042z44XCC4Oju223h4fX4yUjxkpXpieh0T+7UUP9v6BpUFmVw278zb8riFu/e2NW+xZkkJj1Yf5e5fbCPFk8Rdq21swg1KctJjPuspvCrbzXWVJ4vy/AxO9fazv7GDvkAAr88fvHX4aWoP/vT6/HR09/GVdy/lnLLcUZ979/E2tr1xks9ftzhhdtUFSxQJZeX8QjJSPBz0dnLnyjkUu3QGzFRTkpPOq0dbY3qNpna/a7YXn+wqQlNk/+6bm097LTstmaLsNAqz06hvO8W//nYnT9176ainBP986xukpyRx6/kVUY051ixRJJD0FA+rFhSyaZ+Xf7TWhGuU5qTT0tlDT1+A1OTY9OZ6O/zML54Wk3Obt7psQSGfv24xaSkeiqalUZSdRnF2GoXT0siI6Pr70656PvbL7fz0hcN8ZBTrmNq6evn9jjpuWl5GbmZKLP8IUWeJIsF88YZzaGzvttaEi4QLGDX5ugcWbEWb1+fnkrkFMTm3eau0ZM+oPvivOaeUqxYV87W/7Oeac0rP+Hf/2LajdPcGeP/Fs6MVatzYYHaCKc1NZ1lFntNhmAglMV5L4e/rp+1Ur2tXZU9VIsIXbzwbgC88uXvEwe9AQPnFi29w/ux8zp45+jENt7BEYcwElWSHE0VsZj41dwQ3brYZbu5Tnp/Jp9csYH1NE8/ubhj2uC0Hmjnc0sUHErA1AZYojJmw8OrsWM18sjUU7vahSytZPCOHLzy1G1/30JtD/nzrYQqnpXLNOaVxji46LFEYM0H5mSmkepJo9FmimIqSPUn8v1vOpcnn52t/2X/a60dPdLG+ponb3jaLtOTEXAdjicKYCRIRinPSaLQWxZS1vCKPD1w0m4e3HmbHoKnSv3zpCAL8QwKtxB7MEoUxUVAaw5KoTaGWiu3p5W6f+buFFGen8dnHd9HXHwCgu7efR18+wpolJczMy3A4wvFzJFGIyHQRWSsitaGfp+2OJSIVIrJRRPaKyG4R+ZQTsRozGiW56TTFaDDb6/MzPSuVlASu8zAVZKen8MUbzmZvfTs/+ethAJ7eWc/Jrl4+cHGlo7FNlFP/8u4D1qvqfGB96PFgfcC/qOpi4CLgHhFZEscYjRm1kuxgiyIW+wO5tQSqOd3fnV3K1YuL+fra/Rw72cXPXnyDs4qyEn4NjFOJ4kberLn9MHDT4ANUtV5Vt4fu+4C9QFm8AjRmLEpz0+jq6cfn74v6ucP7PBn3C66tOAcR+MjD1bx6tJX3XzQ74ffocipRlKhqPQQTAjBiWSwRqQRWAC+NcMxdIlItItVerzeasRpzRiWhSndNMRin8PosUSSSsrwMPr1mATUNPjJTPbzr/HKnQ5qwmG3hISLrgKEmDX9ujOeZBvwO+CdVbR/uOFV9CHgIoKqqKjb7AxszjJKB2tl+5hVHrw6yqlqiSEAfvKSSDTXB4kQ56Ym1r9NQYpYoVPXq4V4TkUYRmaGq9SIyA2ga5rgUgknil6r6eIxCNWbCSsOJIsotCp+/D39fwMYoEkyyJ4lHPnqR02FEjVNdT08Bd4Tu3wE8OfgACXbq/QjYq6pfj2NsxoxZuEUR7f2ebA2FcQOnEsWXgTUiUgusCT1GRGaKyDOhYy4F3g9cKSI7Qrd3OBOuMSPLSPWQk54c9UQRnnJrtSiMkxzZZlxVW4Crhnj+OPCO0P3ngcSeKmCmlNLc6Fe683ZYi8I4z1bwGBMlJTnpNPqiu+jOup6MG1iiMCZKSnLSo77fk9fnJ8Uj5GYk/swZk7gsURgTJaU56Xg7/PQHojc7O7wqO9EXbJnEZonCmCgpyUmjP6C0dESv+8lWZRs3sERhTJSUxGAthS22M25gicKYKCnNjX5J1GCiSI/a+YwZD0sUxkRJtFsUff0BWjqtRWGcZ4nCmCgpnJZGkhC1mU8nOntQtamxxnmWKIyJEk+SUJSdFrXV2U3hNRS2z5NxmCUKY6IomiVRbVW2cQtLFMZEUUlOetRaFOFV2bbPk3GaJQpjoiiYKKIz6ymcKAqt68k4zBKFMVFUmptO26leunv7J3wur89PdloyGameKERmzPhZojAmit6sdDfx7ievz09RjrUmjPMsURgTRSWhD/ZojFOE93kyxmmWKIyJomiWRLV9noxbWKIwJopKQtt4NEVhQNv2eTJu4UiiEJHpIrJWRGpDP/OHOCZdRP4mIq+KyG4R+aITsRozFtlpyWSkeCbcoujq6aPD32eJwriCUy2K+4D1qjofWB96PJgfuFJVlwHLgWtE5KL4hWjM2IlIsCTqBBNFs68HsFXZxh2cShQ3Ag+H7j8M3DT4AA3qCD1MCd2iVxHGmBgpzk6jaYKJwtsR/H1rURg3cCpRlKhqPUDoZ/FQB4mIR0R2AE3AWlV9abgTishdIlItItVerzcWMRszKtFoUVitbOMmybE6sYisA0qHeOlzoz2HqvYDy0UkD3hCRM5R1deGOfYh4CGAqqoqa3kYx5SGVmer6rhLmDYNbN9htSiM82KWKFT16uFeE5FGEZmhqvUiMoNgi2Gkc7WKyCbgGmDIRGGMWxTnpNPTF6C1q5f8rNRxncPr85MkMH2cv29MNDnV9fQUcEfo/h3Ak4MPEJGiUEsCEckArgZq4hWgMeMVjbUUXp+fgmlpeJLG1yIxJpqcShRfBtaISC2wJvQYEZkpIs+EjpkBbBSRncDLBMco/uhItMaMQWlucFxhoonCZjwZt4hZ19NIVLUFuGqI548D7wjd3wmsiHNoxkxYeFxhIjOfbFW2cRNbmW1MlL25MeD4V2fbqmzjJpYojImy1OQkCrJSx931dKSli8b2biryM6McmTHjY4nCmBgozkkfd9fTgxsPkOxJ4rYLKqIclTHjY4nCmBgozUkbV4vi6Ikufrf9GP9wwayBLixjnGaJwpgYGG9J1Ac3HiApSbh79dwYRGXM+FiiMCYGSnLSaen009sfGPXvHD3RxW+3HeP2t1VQmmutCeMeliiMiYHS3HRU39yKYzS+s+kgSSLcfbm1Joy7WKIwJgbGWhL12MkuHqs+ym0XVDAjNyOWoRkzZpYojImB8EB0Y9voEkW4NfExa00YF7JEYUwMjGW/p7rWUzxWfZT3vK3cWhPGlSxRGBMD+ZmppHhkVDOfvrvpAAAfv3xerMMyZlwsURgTA0lJQnF2+hnHKI63nuLRl4/ynqoKZuZZa8K4kyUKY2KkNDedhjOMUXx300EAPn6FtSaMe1miMCZGSnLSaPQNnyjq24KtiXefX0GZtSaMi1miMCZGSnLSR5z19N1NBwmocs8VNtPJuJslCmNipCQnnc6efnzdvae91tDWza//dpRbq8opt11ijctZojAmRsJTZIea+fS954KtCZvpZBKBJQpjYmRg0d2gmU+N7d088rcjvPv8ciqmW2vCuJ8jiUJEpovIWhGpDf3MH+FYj4i8IiJWL9sklOG28fjupoMEAso9NtPJJAinWhT3AetVdT6wPvR4OJ8C9sYlKmOiKLwDbOTq7Kb2bn71tyPccl6ZtSZMwnAqUdwIPBy6/zBw01AHiUg5cB3ww/iEZUz0ZKYmk52e/JaZT9997iB9AeXeK+Y7GJkxY+NUoihR1XqA0M/iYY77JvCvwBk39ReRu0SkWkSqvV5v1AI1ZiIiCxg1tXfzyEtHuGVFGbMKrDVhEkdyrE4sIuuA0iFe+twof/96oElVt4nI5Wc6XlUfAh4CqKqq0tFHakzslOakD3Q9fX/zoWBr4kobmzCJJWaJQlWvHu41EWkUkRmqWi8iM4CmIQ67FLhBRN4BpAM5IvILVX1fjEI2JupKctI5eLCZJl83v3jxDW5aXsbsgiynwzJmTJzqenoKuCN0/w7gycEHqOpnVbVcVSuB24ANliRMoinJSaPJ5+d7m4KtiU9Ya8IkIKcSxZeBNSJSC6wJPUZEZorIMw7FZEzUleam0x9Qfrb1MDcun0llobUmTOKJWdfTSFS1BbhqiOePA+8Y4vlNwKaYB2ZMlBVnB6fIBlT5xJU208kkJluZbUwMhddS3LS8jDnWmjAJypEWhTFTxZIZOXxk5RzuvGyO06EYM26WKIyJodTkJD5//RKnwzBmQqzryRhjzIgsURhjjBmRJQpjjDEjskRhjDFmRJYojDHGjMgShTHGmBFZojDGGDMiSxTGGGNGJKqTr3SDiHiBN8b564VAcxTDiTa3xwfuj9Ht8YHFGA1ujw/cFeNsVS0a6oVJmSgmQkSqVbXK6TiG4/b4wP0xuj0+sBijwe3xQWLECNb1ZIwx5gwsURhjjBmRJYrTPeR0AGfg9vjA/TG6PT6wGKPB7fFBYsRoYxTGGGNGZi0KY4wxI7JEYYwxZkSWKEJE5BoR2SciB0TkPqfjGYqIHBaRXSKyQ0SqnY4HQER+LCJNIvJaxHPTRWStiNSGfua7LL5/F5G60Pu4Q0ROq9Mex/gqRGSjiOwVkd0i8qnQ8256D4eL0RXvo4iki8jfROTVUHxfDD3vpvdwuBhd8R6eiY1RACLiAfYDa4BjwMvA7aq6x9HABhGRw0CVqrplgQ4isgroAH6mqueEnvsKcEJVvxxKuvmq+r9dFN+/Ax2q+lUnYookIjOAGaq6XUSygW3ATcAHcc97OFyM78EF76OICJClqh0ikgI8D3wKuAX3vIfDxXgNLngPz8RaFEEXAAdU9ZCq9gC/Bm50OKaEoKqbgRODnr4ReDh0/2GCHyqOGCY+11DVelXdHrrvA/YCZbjrPRwuRlfQoI7Qw5TQTXHXezhcjAnBEkVQGXA04vExXPQfIYICfxGRbSJyl9PBjKBEVesh+CEDFDscz1DuFZGdoa4px7okIolIJbACeAmXvoeDYgSXvI8i4hGRHUATsFZVXfceDhMjuOQ9HIkliiAZ4jk3ZvtLVfU84FrgnlC3ihm77wJzgeVAPfA1R6MBRGQa8Dvgn1S13el4hjJEjK55H1W1X1WXA+XABSJyjlOxDGeYGF3zHo7EEkXQMaAi4nE5cNyhWIalqsdDP5uAJwh2mblRY6hfO9y/3eRwPG+hqo2h/7QB4Ac4/D6G+qx/B/xSVR8PPe2q93CoGN32PoZiagU2Eez7d9V7GBYZoxvfw6FYogh6GZgvInNEJBW4DXjK4ZjeQkSyQgOJiEgW8HbgtZF/yzFPAXeE7t8BPOlgLKcJf3iE3IyD72NokPNHwF5V/XrES655D4eL0S3vo4gUiUhe6H4GcDVQg7vewyFjdMt7eCY26ykkNC3tm4AH+LGqfsnZiN5KRM4i2IoASAYecUOMIvIr4HKC2yU3Al8Afg/8BpgFHAFuVVVHBpSHie9ygk19BQ4D/xjuy3YgvpXAFmAXEAg9/W8ExwDc8h4OF+PtuOB9FJGlBAerPQS//P5GVf9DRApwz3s4XIw/xwXv4ZlYojDGGDMi63oyxhgzIksUxhhjRmSJwhhjzIgsURhjjBmRJQpjjDEjSnY6AGMSRXgzQSAH2Kyq64Y57iZgv9s2lTRmvKxFYcwYqer/HS5JhNwELIlTOMbEnCUKY0YgIp+TYJ2SdcDC0HM/FZF3h+5/WUT2hDZ1+6qIXALcAPxPqL7AXBH5qIi8HKpF8DsRyYw4z/0i8oKIHAqfM/Tav0qw9sirIvLl0HNzReTPoU0ht4jIori/IWZKsq4nY4YhIucT3M5lBcH/K9sJ1mIIvz6d4LYLi1RVRSRPVVtF5Cngj6r629Bxrar6g9D9/wLuBL4dOs0MYCWwiOCWE78VkWsJtkouVNWu0HUAHgLuVtVaEbkQ+A5wZezeAWOCLFEYM7zLgCdUtQsglAAitQPdwA9F5Gngj8Oc55xQgsgDpgHPRrz2+9CGcHtEpCT03NXAT8LXVdUToZ1bLwEeC269BEDaRP5wxoyWJQpjRjbsHjeq2iciFwBXEWx53MvQ3/B/Ctykqq+KyAcJ7jUV5o+4LxE/B183CWgNbVNtTFzZGIUxw9sM3CwiGaGde98Z+WLoW36uqj4D/BPBzd0AfEB2xKHZQH1oq+73juK6fwE+HDGWMT1U/+F1Ebk19JyIyLJx/8mMGQNLFMYMI1T+81FgB8FaDFsGHZIN/FFEdgLPAf8cev7XwP8SkVdEZC7wfwjuBruW4PbXZ7runwmOV1SHKqJ9JvTSe4E7ReRVYDdWrtfEie0ea4wxZkTWojDGGDMiSxTGGGNGZInCGGPMiCxRGGOMGZElCmOMMSOyRGGMMWZEliiMMcaM6P8D3fLvATnI0UAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at the density anomaly on depths\n",
    "\n",
    "danom = ds.density - ds.density.mean(dim='distance')\n",
    "\n",
    "danom.plot()\n",
    "plt.ylim([1000,0])\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "danom.sel(depth = 50, method='nearest').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "assisted-amino",
   "metadata": {},
   "source": [
    "# get $\\frac{\\partial \\rho}{\\partial y}$\n",
    "\n",
    "\n",
    "What are potential issues with this?\n",
    "\n",
    "How might we deal with them? - a potential GDA project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "neither-stranger",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000.0, 0.0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "dRHOdy = ds.density.differentiate('distance')  / 1000 # convert km to m\n",
    "\n",
    "dRHOdy.plot( cbar_kwargs = {'label' : r'$\\frac{\\partial \\rho}{\\partial y}$'})\n",
    "# plt.pcolormesh( dRHOdy.distance, dRHOdy.depth, dRHOdy, cmap='bwr')\n",
    "# plt.colorbar()\n",
    "\n",
    "plt.ylim([1000, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tutorial-spring",
   "metadata": {},
   "source": [
    "# get $\\frac{\\partial u}{\\partial z}$\n",
    "\n",
    "ok, now we have all the components of the right hand side to give us how the velocity changes with depth :\n",
    "\n",
    "$$\n",
    "\\frac{g}{\\rho f}\\frac{\\partial \\rho}{\\partial y}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "independent-click",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000.0, 0.0)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # # calculate the thermal wind\n",
    "CF = sw.extras.f(ds.lat.mean())\n",
    "g = sw.eos80.g(ds.lat.mean(), 0)\n",
    "\n",
    "therm_wind = (1/CF)*(g/1027)*dRHOdy\n",
    "\n",
    "\n",
    "therm_wind.plot()\n",
    "plt.ylim([1000, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "valued-florence",
   "metadata": {},
   "source": [
    "# Integrate vertically to get $u$\n",
    "\n",
    "So far we have $\\frac{\\partial u}{\\partial z}$, or how velocity changes with depth. But what  we would really like to know is the total velocity $u$. \n",
    "\n",
    "We can integrate the shear to get this!\n",
    "\n",
    "$$\n",
    "u = \\int \\frac{du}{dz}dz\n",
    "$$\n",
    "\n",
    "\n",
    "or actually each little step in $v$ looks like this (just re arrainging the equation):\n",
    "\n",
    "$$\n",
    "\\Delta u = \\frac{\\Delta u}{\\Delta z} * \\Delta z\n",
    "$$\n",
    "\n",
    "if you sub in the thermal wind equation it looks like this:\n",
    "\n",
    "$$\n",
    "\\Delta u = \\frac{-g}{\\rho f}\\frac{\\Delta \\rho}{\\Delta y} * \\Delta z\n",
    "$$\n",
    "\n",
    "and if we want the total $u$ we can sum up all the little $\\Delta u$'s from the water column to get a profile of $u$\n",
    "\n",
    "This actually just gives us a `relative geostrophic velocity` something we will have to reference to get the absolute velocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "blond-hunter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000.0, 0.0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# we can take the integral by cumulativly summing the thermal wind\n",
    "# we must multiply by the thickness of the depth bin\n",
    "\n",
    "zbin = np.diff(therm_wind.depth).mean()\n",
    "\n",
    "\n",
    "# replace nan with zeros for integration\n",
    "therm_wind = therm_wind.fillna(0)\n",
    "\n",
    "# flip to integrate from bottom, then flip back\n",
    "rel_Vg = integrate.cumulative_trapezoid(therm_wind.values[::-1,:], \n",
    "                                        dx=zbin, initial=0, axis=0)[::-1,:]\n",
    "\n",
    "ds.update({'V_rel' :  (('depth', 'distance'), rel_Vg)})\n",
    "\n",
    "# plt.pcolormesh(ds.date, ds.depth, rel_Vg, vmin =-1.5, vmax = 1.5, cmap='RdBu_r')\n",
    "ds.V_rel.plot( cbar_kwargs = {'label' : 'v'})\n",
    "# plt.colorbar()\n",
    "plt.ylim([1000, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "experimental-house",
   "metadata": {},
   "source": [
    "# adjust the relative velocity\n",
    "\n",
    "So far we assume that the velocity starts at `0` at 1000m and get `ds.V_rel`. This probably isn't true. \n",
    "\n",
    "Luckily we know what the depth-average (0-1000m average) velocity is. We can just set the depth average of `ds.V_rel` equal to that measurement!\n",
    "\n",
    "There are a couple of steps:\n",
    "* remember we actually only get the component of velocity in the direction perpendicular to the glider flight path, so we need just that component of the DAC to use as a reference. We do this by first identifying the direction the glider is going, then taking the projection of the DAC in the perpendicular direction\n",
    "* we also don't have DAC everywhere right now, so we will probably need to do some interpolation\n",
    "\n",
    "### note: \n",
    "The glider doesn't fly in a totally straight line. We could 'map' the glider data onto an actual straight line to make the calculations easier. We can think about this later- could be a GDA project\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "alike-chance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.gridliner.Gridliner at 0x7fe4cee896a0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SgwcPllraTmJiYuTVV1+VJk2ayO233y7vv/++nD59usRpHzp0SJ599lmpU6eOTJkyReLi4oqdZmxsrHh5ecmHH34o33zzjaxbt05OnDiRef2DDz6Q/v37Fzt9kYopvApb1UxMTOTkyZNllr5SiqFDh7J//3569uxJjx49mDhxInFxcaWS/jvvvEPLli05cuQI33//PRs3bmT06NGl0mkSEhLCu+++y+7du4mJiSEkJIQ33nijWFXQevXq8c9//pMrV67wzDPPMGfOHJYtW5bp765Xr14cOnSoxDZXOKxWflkFf39/CQ4Olujo6Pz+kRabvKqD586dk9GjR0tQUJDMnTtX0tLSip32rl27pH79+nL8+PESWppFSkpKvh0g+/fvlwEDBshDDz1U7PQ3bNggR44ckSVLlsjDDz8sDRs2lHnz5smxY8ckODi42OmKVMwSz3IDyip07dpV3n//fbn55pvljz/+yO9vmifR0dE5NmvNTUE9eLt375Y+ffpI27ZtJSIiokjPFdEbw3bu3Fk++uijIsfNi0OHDsnQoUMFkDlz5sikSZNk8+bN171fXFyc1KxZU2JjY4v1nNy/yfbt26Vbt24yadIkqVevXnHNF5GKKbwKO44HMHr0aGJjY7n77rtZsGAB7du3JyAgIPN6XFwcu3fvZufOnVy+fJlJkyZx6dIlWrZsydq1a+nXr18BqedNp06dWLduHV9++SX33Xcf+/btK9L6tXPnztGoUSNGjiwd/yx//PEHq1ev5siRIxw8eJCYmBhGjhxJUlIS9913H/fffz/VqlUjKiqK+vXrs3jxYsaOHVt4woXQtWtX3n//ffr165e5Ma4hC5d8J3gi3bp1k+3btyMiTJ8+nSVLlnDo0CFuvvlmmjVrxv79+4mNjaVTp07ccsstJCYm8uOPP9KuXTvOnDlD9erV+fXXX/OcDeKqI8VRo0bh7+/Pe++955LNe/fuZf369dx7773ccMMNRX3lfJkyZQo7d+5kypQphIeHIyJERUWxbNkyli9fjojQvn172rdvz/Dhw/PdMawg8vtNHn30Ue68886S7AyNUipf3wkei9VFblmFvHo1k5OTZefOnfL111/LgQMHMgegnfzvf/+T0NBQOX78uLRp00bWrl17XRoirg8WX7p0SRo1auRylfPWW2+VpUuXunRvUUhLS5OePXvKF198UeppOzED6EULlaoO4O/vzy233MLQoUNp1aoV3t7eOa7fcccdbNmyhRtvvJGpU6fy0ksvISWoEdSuXZu33nqL0aNHuzSQ3KlTJy5evMjFixeL/cy88PHxYf78+cTExJRquobiU6mEVxTuv/9+vLy8uOuuuzhy5EiR4p46dYrLly8D8OCDDxIcHMysWbMKjffBBx9QvXp1evbsyalTp/K9Lzk5mePHj7N582a++eYb5s2bx8yZMzl79my+cfbv31+aPugMJaRCd66UBG9vb2w2G2+//Tbdu3fnb3/7G0899RRNmzbN834R4dNPP2XBggXs27ePtLQ0evbsSe3atTl69Ci7du3K4XgkL5RSNG7cmCeeeILbb7+dp59+mpiYGM6dO8fZs2c5d+4c586dIzExkQYNGtCwYUMaNWpEw4YNycjIoH379owZM4bJkydf16Hz448/0rdv31L7fQwlpCT1VOBDtA/o7P7JOgObgd1oj5qhecSrgnZltAfYh/ZZ7bwWDKwHvgUCHeemoX2YBWW7L6Eg20pz5srRo0dlzJgxUqdOHenXr58sX75crl27luOeyZMnS9u2beWbb76RlJQUuXLlinz55ZeyaNEi2bx583WOOPLD2VZatmyZPPfcc/Lmm2/KZ599Jj/99JPs27dPLl68mO9Qx4kTJ+Qvf/mL1K9fX2bOnCmJiYkioidRN2vWTFavXl38H8FFu8sC8mnjuXP+KyyUVHi9gC65Xvx/QH/H9wGALY94KttL+QJbgNscx9OBdsAgYEy2Fz8JvOHqi5fFlLGkpCRZsmSJ/Pe//5XatWvLk08+KRs3bpRff/1VGjZsKDExMSV+Rmlk4KioKPnzn/8sTZo0kQULFsiBAwckODi4wq1OcOf8V1goURtPiun83fF7JjgOfR3B2YvhdPJuJ6eT9w+B4Uopy/ZjqFKlCg888AAtWrQgKiqKVq1aMXr0aHr37s3cuXOpX7++VabloF27dqxYsYKlS5fyySefEBoaWqwxSXfHk/NfWbTxxuOC83ellDewA2gBzBWRLY5L7wGfol3qZveikYB++XFoB4SWEhwczPPPP89zzz3HyZMni+2FtSwJCwvDZrPx448/csMNN3D+/HmrTSoPxuMB+a8shPc0MEGynL8vBK77dysiGUBnpVQtYIVSqr2IRInICXQVIi/mALuVUm8VZkRqaio2m62471AgCQkJ16V97NixMku7pPj5+XH+/PkySdtJWaZdRNwi/xVKSeqpjrpuM4ro/D2PNF5C+6HO7/o053XgNeAFLGjjOfHUtpKnpk0BA+jumv8KC2Uxjud0/g75OH9XStV3/KdBKRWA/o90wMX0Z6H9UpuhEENeeET+K5HwHM7fI4BWSqnTSqkn0c7f31JK7UH/d8h0/q6UWuWI2gjYoJSKBLYBP4rISleeKSIXgBWAf0lsN3g+npz/SqRaEXkon0vXbT8sIn+gu3cRkUjgliI8Z1qu44nARJcNNVRIPDn/mSljBoMFGOEZDBZghGcwWIARnsFgAUZ4BoMFGOEZDBZghGcwWIARnsFgAUZ4BoMFGOEZDBZghGcwWIARnsFgAUZ4BoMFGOEZDBZghGcwWIARnsFgAUZ4BoMFGOEZDBZghGcwWIARnsFgAUZ4BoMFGOEZDBZghGcwWIARnsFgAUZ4BoMFGOEZDBZghGcwWEChwlNKfaiUilFKRWU711kptVkptVsptV0pFepqXMf5aUqpM474u5VSA7Jdm+lIs7fjuJlSSpRSY7Pd855S6vFivK+hAuHJedOVEm8RcHeuczPQDts7A1Mdx67GdTJbRDo7wioApVRrx7VewDPZ7o0Bximl/Fyw1+OJiIjg9ddfJyIiwmpT3J1FeGjeLNRbkIj8opRqlvs0hfiZLiBuQTj9Tws5/U/HAr8BI4H/FiE9jyIiIoJZs2bxv7VrSU9Px8/fn3Xr1hEWFma1aW6JJ+fN4rbxxgMzlVKngDeBKcVI41mlVKSjyK8NICL7gKrAr8C8XPdPByY5fFdXOCIiIujbty8rV64kJTWVDLu9TN1JV2DG4wF5s7j+8VzyM10A84CX0f89XgbeAp4AEJGxeUUQkWNKqa3kdAifL+XtA72kzJo1i+TkZKfrX5RS+Hh5UaNGjVJ7lvGB7hJlnjedkcrUz3nuuCW5DrQGooD/AI8XZLMn+UDftGmT+Pn5ieOPLb4gY7p3l029eol8/HGpPcf4QHePvCkl8IFeqJ/pglBKNcp2ONRhcKGIyAFgPzCwKM9zd2w2GxkZGYAu6Z7s25d5nTsTdu0aTJ0KZ85YbKFH4RF505XhhOL6mc4vLsAMpdRehw/qPsAEV4x18CrQpAj3uz1169bFy8sLLy8v/Hx9GeHnB+vXQ4cOkJwM/fqBQ5iGLDw6bxZWJHpq8JSq5qZNmyQgIEC8QHy8vGTiM8+IpKeLxMaKzJgh0qKFSLVqIk2aiBw+XKJnVcSqpqcGM3PFYmw2GykpKdgBuwhXzp6FkBB44QVo0wZ274YFCyA2Ftq1gwcfhPh4q802lBAjPIupW7cudrsd0MKrERQEGzbAkCHwzTe6urlsGYweDR07QlQUNGwII0bAvn3WGm8oNsUdTjCUErt27cr87qUUVy5ehD//GVJSoEcP+Oc/oVo12LIFDh6E4GB46CFYuRJ++w2aNIFnntFx/CrFxJ4KgSnxLCQiIoIPP/ww89jXx4fOPXvCrl2waRMMGwaHD8N77+nSr08fiImB9HRo3Bji4uDoUVi0CNq21T2gJ09qUTpKUYN7Yko8C7HZbKSnpwN6wOkvTZvSztsbbr4ZmjfXYmrbFgYM0OeOHYOaNWHVKl0KduwICQm6B7RGDfj5Z5g7F65ehbvugs8/1+cNbocRnoVkb98JcMvkydCyJRw6pEuy/ft1O27ePP398mUtwHr1oFMnLa4LF+CGG+B//4Nt23QV1W7X4uzSBdau1SI2uBVGeBZy8eJFvLy8sNvteAEXp0+HadN01bJ1a+jcGQYPBi9HiyAjQ5d6W7fC3/+uRXbqFFy8qEu/yMisKqaILu0uXTLCc0OM8CwkPDwcX19fUlNT8fXzI3zGDFL8/HQptmIFHDgAJ07o6mXr1jq0aqU/58/XAt22TSd2/Dj88ovu/czIgEaNYMwY6NrV0nc05I0RnsXo8WHHZ82a4OsLEyfmvCkuTvdoHjgAe/fq4YWDB3Vp16YN3HmnFmPTptCzpxbf0KGml9ONMcKzEJvNRkZ6OiJCRmoqtpEjCXvxRXjqKahfX4/XOUOjRvpz6FD92aABXLkCoaF6YP38ed2es9l0Z0vLlvDyy3D//aBUobYYyhcjPAsJDw/Hx9cXe2oqPn5+hC9fTkpKii7NYmPh3LmcYf9+/Xn2rP5MTdVDC+PHa6ElJ+t23Z//rMf2unY1onNTjPAsRkRABElLg1mzdIn21ltaQM7QvDncckvWcfXqWdXId96BSZO0CO12CArSYtuxQw9FVK1q7Qsa8sQIz0Kcy4EEyABsAQGEVamihRUfr9twV67kHdLSdCLe3nDTTXDkiK5y7tkD/v66zRcXZ4TnphjhWYhzOZCI4OfvT/jTT+uq5r33up5IeroeMN+7V0+mrlMHevWCbt2gVq0ys91QMozwLCIiIoLx48eTkZaGl1K83akTYZ9/ji0sDF55BQIDXQs+PlC7NoSF6Wlm//d/ur1ncGuM8Cwi+3IglOJihw56alhKip4InZCg52UePaq/O8PVqzmPHVPOAL1SfcoU3any7LN6BYOXmY7rjhjhWUSO5UB2O3Vr19btsfR0PZRQs6buMElJce3z6lVYvVq3Dc+ehZ07YeBAPb3M4HYY4VnEddPF1q7VJVyfPrB9u+619PfP+Vmliu7VzH0++/Vr1+C++3TpGRho9Wsa8sEIzyKumy42b55up9lsuopYHHr00G0+g9tj/koWkmO62OnTEB2tq46HDjlvyArZj/O79uqrEBCgq5h33qmrqwa3xAjPIjLH8JzTxaZMIeymm+CBB/RAunPGiVJZIftxXtf27tWdMbt3w++/6xktZj2eW2KEZxHh4eH4+Phgt9v1dLFPP82qaq5dW7xEp07V43cDB5reTDfHCM9CMquaGRnw/fd6PV2dOjB7tp6Z4kpIT8/6fuiQnhhdr57eNuLf/9ZTyAxuhxGeReSoamZkYNu2jbCaNfVsEx8f3Vbz9S08+Phkff/oI11q3nuv3gS3ShWrX9OQD0Z4FnFdVfPf/86qag4fXrxEW7bUAuzQwYjOzTHCs5AcVc3163WvZkAAzJkDSUl6mY/zM/v3/D7j4vRWD0rBk0/C66+bAXQ3xQjPIq6raq5dS1h4uF7Kk5GhhwIaNNBCrFKl8M8qVWDhQt3WGzZML5Y1uC9lvUc88CHaXW12V0qdgc3AbmA7EJrt2kzHud6O42boTbjGZrvnPTzcTZfTZ4K3t7cEBATIpk2bSi3t/PDUtMnHd4JVeas0Qnn0OS/CRT/VlckHelhYGOvWrWPUqFGMHDmydBJ1DqZXHhbhoXmrzKuaUjQ/1ZXOB/rHH31EakoKH//3v6y76y7dsTJ+fFb10d/f9c/162HzZj0I37+/roZWYDw5b1nVxhsPrFVKvYneRr4HaD/TSimnn+nnc8WZDqxWSn1IBcFms5Gank4GkCqCLTiYsFq19ETp5GQ9fcz5ee2a7jjJfd7Z8ZKSoqed7dypt3vv3x9mzNCehyoX4/GAvGWV8PL1Uy2VyAf6hQsXAPDy8sLH15canTuT4OWFLTCwePMsExJ0dTMwUPdsnjmTw5tsZfeBXlp5qzSwSngjgXGO78uABS7Gew34CvilsBv9/PwIDw8vlnGFYbPZSpx2REQE8+bNQzIy8FKKOe3bM3rPHmy1ahG+d68eSM8vVKmS9+5hv/2md5+uVq3M7M6Psky7iJR53ioNrBKe00+1jSL4qRaRA0opp5/prWVmXTlgs9lITU3FDigvLy527653GLt6VY+9Xb6sq46XL18fkpJ0Ir6+etsHpyCjovTO0/fdByNH6r1XKt+cTY/IW2UuPIev6XCgnlLqNPAS2k/1O0opHyAZh59qF3kV2FXoXW5OjpkrPj6EP/po1syVoUNdSyQ1NacgJ07Ucz0bNtRtwqSkfEu/ioAn563y6NV8KJ9LLm3qLyLHgfbZjvdQQfz6Sfb1eJcu6W37nGvsXNmI1s9PT4IOCtJx3nhDexGqJCvPPTlvmZkrFnHderwxYwirWxdGjdLTvZw4x+ZyDx/kHm6oUkVvYvv773rbh/vvh3vuqTQi9DSM8CwiPDwcPz8/UlNTdUfQ0qVZVc2jR3PeLKKngjmHDnIPJTiPjx/XJV7bttrXgnFa4rYY4VlEWFgYb7/9NsuXL2fYsGGEhYXlf7NSWkR+fvmvKLfb9XDCnXea3aM9ACM8i3BuaJuanMzGdevo8N13hAUFQe/e8MQT10dwpc33ww96e7++ffWavMce0z2fBrfDCM8inMMJGSJ61kqjRoQ98ogutXLvMubKHEwRvS3gn/4EgwbB3Xcb0bkxRngWkWM4wdeX8CeeyGrjFXUgOiNDl3Tdu+tju10PJ9SuXdpmG0oJIzwLyRxOsNt1aRUbq/dQmT9fD6TnDs4t3J3fMzJ0SeftrcfrLl7UU8QiIrSnoTFjjOMSN8UIzyJyDCekpWH7+GPCbr9de3i9ckUPAzRsqF12ZQ+BgVmfPj5ZvZnR0fDww1n3eXmZThY3xgjPIq4bTnj33ayq5sO55urGx2v/d1FR+tMZTp7UAmvWTDuvvPFGXcI9/7wew6t808U8BiM8i3AuhP3kk090dTE2Fn75RVcX/+//ssR1/rxeqdC8eVa4/3792bRpzg6Uo0fh5puteymDyxjhWczHCxeSmpamF8LecYee3NyqlV5P17y53nfFVT/mRnQegxGehdhsNlLtdr0QVilsvXsT1qhR0Xs1DR6HEZ6FXNfOCw/XrphLwuef657NLl10qFOndIw1lCpGeBaSo52XFwcOQOvWWcepqbrNd+6cdj559mzWd+fnyZP6e5Uq8NBD8NJLutPF4FYY4bkBH3/8sd7w6KOPmDltGuG7d2sRLV2qRXPxoh678/PTbb5GjfRQQ6NG+nr37lnndu7UPhRGjDAD6G6MEZ7F2Gw2UlNSyLDbSU1NZfeuXdrBZJcu8Mkn2vvPSy/psTlX6NZNz9U0KxPcGiM8iwkPD8fH1xd7aio+fn507tULnnlGl3LnzsHXX+vdwnKTlqbH95zh8mX9uWkTzJ2rS8GePfVk6c6dy/u1DIVghOcGZE4dy8jQ/g9eeQWOHdPDA15e2vNPYqIWVlKSHl7w9dXje04PQ87vycl6XLBRIz0g3759wQ83WIIRnsXk9qGwe98+LbTWrXWPZHw8PPdclrAK8wJ07hxMn248wbo5RngWk3tIofPtt2dt/RAYCN9+q91v5ce5c1qcrVrp4wYNYNw4PY+za1fdVmzZUk+kNrgNRngWk3slesvsImvYUAsL9FKfixdzDiMcOKBXMvTqpauo587ptt+VK3rzpNBQvULhxhvNhGk3wwjPYjJXoqeksNFmY+bUqYSvX69FdPy47iy56SbdrqtXL2sYoWpV+OwzvdnR//t/+nyDBvr4iy90CdjVpc22DBZghGcxmSvR7XZS09LYvXev3iWsZ0+oX1/vsXnkyPUrDZYvh/fe0xOmQ0NzXgsJ0SsWDG6LEZ7FXNfG69nz+q0f8pokHRkJ//wnDB6sq5YnT+rFrydPwoIFemFtcDAMGaJ7Sc3UMbfCCM9iCp02FhioRXb5cpa4fv9dj+81awYffaR7MG+4QS8TatJED7a3b68FN3Cg6VhxQ4zw3ISPFy0iNSUFH19fuixfTti1axATo8Nf/6qHF5zi+vlnmDJF9146Z7QkJMDLL8MHH+jvu3drERrcEiM8NyD7jmOSno6tTRvCXnpJt/Eef1wPL/TqpW+OjIR587Twspdk58/DypXa0cnKlbBxo27rtWrl+nQzQ7lR6N4ASqkPlVIxSqmobOeWKKV2O8JxpdTufOJOUErtU0pFKaUWK6WqOM5PU0qdyZbGgGxxZiqltiulejuOmymlRCk1Nts97ymlHi/+a7sX4eHh+Pn74+3tja+vr3ZgEhSk23bZhxRAb+vwxhtZohOBhQv1oPujj2qhrlunS8Nbb9U9na+9plc2VDA8OW+6UuItQjtkz2yEiMjwbA96C4jP48UaA38H2opIklJqKfCgIz2A2SLyZq442f1ULwJ+dhw7/VS/LyIVLgdlH8tr3759zl2lncJLT9fDB5cuweHDsHo17N+vhxsyMvTW7dHR0KKFrmJ26KDbeePH67agq6vYPYtFeGjeLFR4+fiZdhqjgAfQfsjySz9AKZUGVCXLH3V+uJWf6vIicywvNZWff/6Z+xo3JszXVwvp5591h8o77+j23l136T0za9TQnSyvvaYnVSulS79Tp7Qf9F27tE/0hQth6lSYMMHq1yx1PDlvlnQbqp7AeRG5zvmfiJwB3gROAmeBeBH5X7ZbnlVKRTqqC7Udcfahf4RfgXm5kpwOTFJKVbguusw2XkYGaWlp2JYu1TNQ+vaFv/9d90y++CI8+KAu9c6ehTVrtK/zdu101XPoUL1Hy4MP6n01u3TRnTLR0TB5cmXcVdqt82ZJO1ceAhbndcFh8BDgJuAysEwp9aiIfIY2/GX0f4+XgbeAJ8C9/FSXF9nH8nx8fAifPVuvLABdnVyyBF5/XYuvZUuoW1dXLx9+WK+/695dz17p2DFrHd7ly5V9M1u3zpvFFp7D4+a95O8EsB9wTERiHfd/DfQAPhOR89nS+S+w0sXHuuynOjU1FZvN5mKyRSMhIaHU0545cyZr164lPT2dnTt3knLtmq5SXr2qq5egJ0O//LJeWV6tWs5ezYQE3d4rZ7vLI+2i4u55E9BrwQoLQDMgKte5u4GfC4jTHXAWzwr4GBjruNYo230TgC9dfTawFF1FeLwgm7t27SplxYYNG0o9zU2bNklAlSripZQEKCWbWrUS+dvfRP79b+0j9qGHROLjS/SMsrC7PNIGtksFypsi4tJwwmIgAmillDqtlHK6K32QXEW5UipYKbXKIegt6P8AO4G96PbkB45bZyil9iqlIoE+jhd0lVeBCjcybLPZSE1Lwy5CqpcXtpEj9RDBm2/qcbjPPjNr7HLhyXnTlV7NPP1Mi8jjeZz7AxiQ7fgltEP43Pc95opxjnuPU0F9oGfH2c5LSUnBSynq/vCD7jxp0kSvRLh4UQ+oGzLx5LxZ4TKwp+Icy1NKkZGezvitW4lQCmbN0mN5588XnojBYzDCcyMuXryIiGAHUjIymHb4MBE1ahChFK/Pnk1ERITVJhpKCTNX040IDw/H19eXtLQ07HY7P8XF8XOfPkh6Ohki+C1ezLp16wr2l27wCEyJ50aEhYXx1ltv0a9fP7y8vHRHS2oqaXZ75r6b7tJlbygZpsRzM9q1a0eXLl3YuHFj5oC6OHYgc/pXMHg+RnhuiHNxrM1myxSa87upZlYMjPDclLCwsBwiM4KrWJg2nsFgAUZ4HkB6ejpXrlyx2gxDKWKqmm5IbGwsERERmWHHjh14e3uzYsUK+vTpY7V5hlLAlHhuxJ49ezh48CAhISHMnTsXf39/pkyZwqlTp/jqq6945JFHGD9+PImJiVabaighRnhuQFJSEi+++CJ33HEHdevW5eLFi6xdu5Zp06Zx1113UatWLfr168fevXuJjY2lc+fObCpkCZDBvTHCKwXEMdBdHDZs2EDHjh2Jjo4mMjKSevXq4Z3PPph169bl888/5/XXX2fYsGE8//zzJCcnl8R0g0UY4ZWQ9PR0HnzwQWrWrMk999zD+++/zx9/FLZ9B+zfv58RI0YwYsQI3nrrLZYuXUrDhg1deuawYcOIjIzk+PHj3HLLLWzdurWkr2EoZ0znSgnIyMhgxIgRxMfHc+LECTZs2MB3333HlClTaN68OYMHD2bw4MF07NgRpRQiwi+//MLMmTPZtm0bzzzzDO+99x41irHOrn79+ixdupSlS5cyaNAgnnzySV566SX8/f1L7f3i4+PZunUrW7ZsYcuWLSQlJVGjRo3rQvXq1WnQoAHff/89nTp1omnTpqVmQ0XFCK8EfP/99xw8eJBff/2VgIAAhg8fzvDhw0lLS+PXX3/lu+++Y+jQodjtdgYMGMD27du5fPkykyZNYtmyZQQEBJTo+Uophg8fTu/evRkzZgzdunVj6tSpDBw4sMhpp6enExUVxebNmzOFdvLkSbp27Ur37t0ZOXIkNWvW5MqVK1y9epUrV65w5coVLly4wNGjR+nevTtfffUVW7du5cYbb2To0KEMHTqUNm3aoCrm1oIlo7Al6p4aymPrh4kTJ8qrr75a4L12u12ioqLkjTfekK+//lrS09NdSruo2O12WbZsmdxxxx1Sq1Yteeyxx2T16tWSlpZWaNo//vij1K9fX9q0aSOPP/64zJ8/X3bt2pUjbmE4005LS5MNGzbI2LFj5YYbbpCWLVvKCy+8IFu2bJGMjIxivRsFbP3gqcFyA8oqlIfwQkNDxWazlUnaJeHs2bMyZ84cue222yQoKEhee+01SUhIuC5tu90uc+bMkQYNGpT4PfKy2263y7Zt2+TFF1+U1q1bS+/evYuVthGeB4XyEF7VqlXl8uXLZZJ2abF//365//77pVGjRrJ8+fLM8ykpKTJq1Chp3769HD16tMTPKcju+Ph4WbdunXh5eRUr7YooPNPGKwHe3t76v5cb06ZNG5YuXcqOHTuIiIjgtddeY9SoUdx3333UrFmTTZs2Ub2UnJokJCSwatUq1q5dy8mTJzl9+jRnzpwhIyODoKAgJlTA3ayLixFeCfD39y/2+F1507VrVy5dusRzzz3HjBkzePrpp3nllVfyHTN0lfj4eFauXElycjKDBw+mR48eDBo0iAceeIAmTZrQuHFjatasaTpYcmGEVwKcuz97Cr6+vvzyyy/s2rWrxAtqDx48mDn+2KtXL0aOHMmJEyeoXbt26RhbwTED6CXA39+flJQUq80oEjVr1iyR6CIiIrj33nvp2bMnwcHBHD58mO+++466desa0RUBU+KVAE8r8YqL3W7nhx9+YMaMGZw5c4aJEyfy6aefUq1aNatN81iM8EqAJ7XxikNKSgqff/45M2fOpGrVqkyePJlhw4bh42OyTUkxv2AJcO78XJEQEQ4cOMDy5cuZN28eHTp0YO7cufTp08d0kJQiRngloKJUNTMyMti8eTPffvst33zzDUlJSQwZMoRVq1bRqVMnq82rkBjhlQBPqmqKCOnp6URERHD48GGio6MzPw8dOsSNN97IkCFDWLx4MV26dDGlWxlTIuEppT4EBgIxItLecW4J0MpxSy3gsoh0zhWvFbAk26mbgaki8rZSKhj4DLgKPCIiCUqpacBkoJmIxDjSSBCRwJLYX1LsdrtbZVARITY2NoeonJ/R0dFMmzaNL774gpCQEFq0aME999xDixYtCAkJoW7dulabX2Q8Of+VtMRbRDGcv4vIQaCz4x5v4AywwnH578BY9I/xKDDfcf4CMAn4RwltLjViY2OpX84efJziyi0s56e3t3emsEJCQhg4cGDm8d69exk/fny52lvGLMJD81+JhCclc/7upC9wREROOI6dTt7t5HTy/iHwuFLqDRG5VBK7S4sLFy5Qr169Uk9XRIiJicm35PLx8ckhrkGDBmUe16lTp9TtcVc8Of+VZRsvX+fvucjtRPA94FP0f6rsPqUT0C8/jjz8mpU3SUlJxMXFFbuKJiKcP3/+OnH16dOHwYMH4+fnlymsFi1aMGTIEFq0aFHpxFUC3Dr/qZJO8nX8x1nprGNnOz8PiBaRtwqI6wf8AbSTbL6n87hvGvrFFwC7gY7AHwXVsTt27Chz5sxx/UWKQEJCAikpKVy6dImQkJAC701PTyc5OZmUlJTMT+d3Ly8v/P398ff3p0qVKvj7++Pj40O1atVKPIcyP7sDA8umWVyWaffp02eHiHTL65q75r/CKJMSzwXn7076AzsLeunsiMhlpdQXwN8Ku7csHXzYbDZeffVVpkyZQnh4OImJiezYsSPPamGVKlVylFwhISF06NCBFi1a5DnFKru/hLKw2xPTLirukP8Ko6yqmv2AAyJyupD7HiKXr2oXmAVsw8KhkKSkJE6cOIG3tzcjRozg+++/p2XLlrRq1YqQkBDuvffeTKHVqlXLKjMrM26f/0o6nLAYCAfqKaVOAy+JyELycf4OLBCRAY7jqsAdwF+L8kwRuaCUWkHRnMKXKhcuXODs2bO8/PLLPPLII8ycOZMGDRpYZU6lxZPzX0l7NUvi/D0RcKlnQkSm5TqeCEwsgqmlSlBQENHR0TRv3twqEwx4dv4zy4KKgb+/vxGdoUQY4RkMFmCEZzBYgBGewWABRngGgwUY4RkMFmCEZzBYgBGewWABRngGgwUY4RkMFmCEZzBYgBGewWABRngGgwUY4RkMFmCEZzBYgBGewWABRngGgwUY4RkMFmCEZzBYQIn31XRXlFLbrbbBUGpcEJG7rTaiNKmwwjMY3BlT1TQYLMAIz2CwACM8g8ECjPAMBguodMJTStVSSn2llDqglPpdKRWW7dpzSilRSuXp9E4pNU4pFaWU2qeUGp/tfLBSar1S6lulVKDjGRcdPtpQSoU50m3iOK6plLqklHL59y+u3UqpVkqp3dnCFaft5WG3IW8q4w/4DrBGRFoDnYDfAZRSN6D30j+ZVySlVHtgFBDqiDdQKeX00eX0IroAeFRELgPngDaO6z2AXY5PgNuALSJiL2u7ReSgiHR2uCPuCiRyvffTsrTbkAeVSnhKqRpAL2AhgIikOjIbwGy0n+v8xlfaAJtFJFFE0oGfgaGOa3l5Ef2NrAzbw5F+9uNN5WR3dlzxflpqdhvyp1IJD+3XOhb4SCm1Sym1QClVTSk1GDgjInsKiBsF9FJK1XV4mhkA3OC49h7wPjAG7bgedAZ1ZtibgWWA07liD3QGLw+7s5OX99OytNuQHyJSaQI6A6UD3R3H7wAzgS1ATce540C9fOI/CewEfkE7pZ9dwLNCgAPATcAKx7nfgEDgEhBYXnY7rvsBF4AGhTyr1Ow2If9Q2Uq808BpEdniOP4K6ILOZHuUUseBJsBOpVTD3JFFZKGIdBGRXuhMmK9/bdG+t2sDg4AIx+kdwF+AYyKSUF52O3DJ+2kp223Ih0olPBE5B5xSSrVynOqLzoxBItJMRJqhM3kXx705UEoFOT6bol39FuZNNALtrD4i2/F4ithOKqndDori/bRU7DYUgNVFbnkHoDOwHYgEvgFq57p+HEeVDQgGVmW7thHYD+wB+rrwrOeBVCDAcdwM3QnyUDnbXRW4iKNaWp52m5B3MJOkDQYLqFRVTYPBXTDCMxgswAjPYLAAIzyDwQKM8AwGCzDCMxgswAjPYLCA/w8Ba7JftwVzTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first let's interpolate all the DAC u and v:\n",
    "ds['u'] = ds.u.interpolate_na(dim='distance')\n",
    "ds['v'] = ds.v.interpolate_na(dim='distance')\n",
    "\n",
    "# next we need to identify just the 'cross-track component'\n",
    "\n",
    "# let's get the glider direction\n",
    "dist, ang= sw.dist( ds.lat.values, ds.lon.values)\n",
    "across_transect_dir = ang - 90\n",
    "\n",
    "# unit vectors in the cross track direction:\n",
    "# they are at the mid-points of the glider dives\n",
    "i_across_t = np.real(np.exp( 1j * (across_transect_dir)*np.pi/180 ))\n",
    "j_across_t = np.imag(np.exp( 1j * (across_transect_dir)*np.pi/180 ))\n",
    "\n",
    "\n",
    "# get onto the same grid as the dac\n",
    "mid = (ds.distance[0:-1].values + ds.distance[1:].values)/2\n",
    "f = interpolate.interp1d(mid, i_across_t, fill_value='extrapolate')\n",
    "\n",
    "i_across_t = f(ds.distance)\n",
    "\n",
    "f = interpolate.interp1d(mid, j_across_t, fill_value='extrapolate')\n",
    "\n",
    "j_across_t = f(ds.distance)\n",
    "\n",
    "# project DAC onto the unit vectors:\n",
    "\n",
    "Vdac_cross = ds.u*i_across_t + ds.v*j_across_t\n",
    "\n",
    "ax = plt.subplot( projection= ccrs.Mercator())\n",
    "\n",
    "ax.coastlines()\n",
    "\n",
    "plt.plot(ds.lon, ds.lat, 'k.', \n",
    "         transform=ccrs.PlateCarree())\n",
    "\n",
    "plt.quiver(ds.lon.values, ds.lat.values, i_across_t, j_across_t,\n",
    "           scale=5, transform=ccrs.PlateCarree(), color='r')\n",
    "\n",
    "\n",
    "# plt.quiver(ds.lon.values, ds.lat.values, ds.u.values, ds.v.values,\n",
    "#            scale=0.5, transform=ccrs.PlateCarree(), color='y')\n",
    "\n",
    "ax.set_extent((-65.0,-64.5, 17.6, 18.5) )\n",
    "\n",
    "\n",
    "ax.gridlines(draw_labels=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "looking-plant",
   "metadata": {},
   "source": [
    "# add the reference velocity\n",
    "\n",
    "$$\n",
    "V_{abs}(x,z) = V_{rel}(x,z) + V_{ref}(x)\n",
    "$$\n",
    "\n",
    "The reference, $V_{ref}$, is calculated from the DAC ($V_{dac}$) such that:\n",
    "\n",
    "$$\n",
    "V_{ref}(x) = V_{dac} - \\frac{1}{h}\\int_h V_{g}(x,z)dz\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "early-provider",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000.0, 0.0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gCxn9FhEQtQu9NJCrJVWaSBYWyk30nOtbF7gyn7Fe+oauTB3Gay26GHhGofGLolexzNAqen8XWujm+szYhLFMoAu9SKbHW/OCFIOka0eEMbu0jAuEQDWruu7eUKWhBMMxlvj9Onz0lvsQcomFThF6LEWCXmaMFc2AbSIJXSlSXz56ETkW+BBwNFlnbKuq/uE454wu9EU/QkQOAz4KHA98DfgpVf33/DuXAOeTLfz2DlW9tq7y1LGWq0mrqohPxTKl4F8dFbNLq4JFX2WSkdV108Q9toQAimGwuK4Ml2MTeEahBngUf3siE2PzqJvaGpdl4L+p6h0isgG4XUSuV9W7Rz1hGxb90B8BvBW4UVUvF5GLgYuBi0TkVOBc4KXAC4AbROTFqhV8IWMSeh9NIpBIhUwNMYbkVckxrxVMXPMKgUalrGJdW6pNLA+ESaJC2RlDrpvA6Ytzy1cPr+zHzgwo1DYYq6q7gF35/58UkXuAzUB3hL7kR5wNvDo/7ArgJuCifP+VqroPuF9EdgCnA5+rpzzjn6PtqJe2rz8WRote+ivobHjtAJiu7J8pEXLtNJF+oCyFcUyk2szYjSKyfeDvraq6deh5RY4HXg58fpzyteqjP+BHHJU3AqjqLhE5Mj9sM3DLwNd25vuGne8C4AKA4447zlQGiz93Rsq7iF3W2bape41VAK3QfWrz2cX0ItfSKw1eJBRVM/4lrER36VSbMLVbVbcETymyHvg48E5VfWKc4rU2jlfhRwy7e0Mfo6puVdUtqrrliCOOqKOYQDp+wGmmPx9e29dxWiP30Vs20+lE5sj08cOq+olxi9eKRV/wIx4WkU25Nb8JeCTfvxM4duDrxwAP1VWWOjS87rwaVabR59+o9fpRaSAaqU3XTd0LtZuXWgwQZTA2EBbcxHNpLBtlZaS2FaYk6179BXCPqr6njnNGt+hLfsQ1wHn5/88Drh7Yf66ILIjICcDJwK2xymtBJLw5BWjftlU5pap5axMRMWxx6lZPwhvSK900tCX8LMYlW2GqZ9oMfC/wc8BrReTOfHv9OOVrw6Jf/RH/LCJ35vt+A7gc2CYi5wMPAOcAqOpdIrKNbMR5GbiwzoibWmKQO15JW8Uo4lV6OU0Mz1l92HUbmLXNnk2gjjbxXJKx6EXozdcjp6r6T9Q8fNRG1E3Zjzij4DuXAZc1UR5LHH1w0kqDk6GcZomezrYisUpnuk5oMDzguhi1rSmbVFck9PEnqdbnummCqZ8ZW9OSnM6oGBbIrkoTxqs53r7COS3uiqQaoqDQNyMnZVZ70Wft5LpJN3XJ1At9HYy74MI008S9q9Kbt7s02hFci87b3Erlv9Oii+OmQAi1gkWN2soIwQaWjLJ1IognNUsZ24vUfDkGScCdGo+ONJKpuIJHpZaeQciib+FZFlr0Laww1XPXTbpYkkZF7wUm1FtPhSoD3m1GcNQ+GFvv6cYjUDFDFn/IS1fkV++P8AK2sUSlW/QTjmXGpkg3LFcnHpYaYZGOaJoWqueBBGyjNsCjWOexLXoRoTeXrpymW7JImFw3zRdjLLruVrAw8+TDrKy3zXauMp2/SrqENqgra2YdpwkllmtqrCrhNO/fRtxH7zRMp5OaVUrfbBWS+l84qzWa6vhKHXckNJehqWpYZp234aIZSr1pimvHhd7AinbEqnCAjjd8B2D5LfVF5pQTmpzUG3epwY7ji4N3HBf59umv39h2ERynEBGfMOXA+AsrOxNHXTXCcp46coaEvFLhoIT6/VpFrpvoxlmNKRCaIN2SJURSsxOnlUoDffU3ql2PmarDnRVy3YQWWBr1Hpb54ZPx0YNb9I590YxRSKeqp8Ektct1/ZY6Fh4JJhAL1vHR+hVlWl4k9K2EV3oKBMddN93GGk3ThuVvEeleDeFAKQYUFbpufMLUc3ChT5BUQ/S6QhOvW+o5cULUEXUTMuirL5gzPsUWfeSCeHilAxVfAhd6pwIWUUthUt2oMlhW9rkCv3jR/ubwqJukqcV6NrhlmrTSExqPqk4DVmBds0mbpi43j+XXhupIyis8lY0NzBWE1xTtbwrpedTNxGOy1tN9j5waqVsvU7IRx20MQg1w0QBqmdusaE3d+Ba9R904VFvKzcM5p4O6GgVLj24lAUOjiZ5nUd752BY9IkhgcfQ2caGvgYR7veljjEbq7X2c/pqDbce22E6m4AtPlZD9UjSwurRcfFOLVpJqxVZyoe82+1eUec+D4HSYUAMUo3o34dgo6v1a1pmoFwmumdsmLvQGXOTbx2rNO04r+Jqx3aeKf70NqroLUmq3mpgxXOXnJXQrOk+VVcCqUD4YW1CWFnLdMDsf+aJ2pl7ofdyzZRro7lbx0VsHvlNv7MclxthCOOqm+TI0hXgcvVOVSRcVJ2PajIwmhLzQR9+Ci76uwVgR+QDwY8AjqnpaHed0oY9ElZDJqRL6Kc4BVNcvt1jjwRj4CK1OyGVYFBMfTKY2hPiNqNQZdfNB4E+AD9V1Qhd6AyGRNs2XmiLtduoj1izfGNVz1AlTXaEu142q3iwix9dyshwXeqcbVLH8K6iWWVuM56zkMkhI2GL46EP3ZpScYMnko5delcHYjSKyfeDvraq6tYFSPYsLvYGE3sfJo4mom448sLqKadHoUF6dWtIYh3q+gWuMYtEXfSd6D7paeOVuVd3SZHEOZOqFvg6DwBJWph7INxVU0arpHZ0YTpGPvnSFqWReK58w1XmCj8+wzF2lPBgVrZFUeq+TTBMGYl09D9tgbOAchqIEV4QNXCNksUfPT1MnNUbdNIELvdMJZGkv/fl1toNbHPiu26brSq75OqjTdROf+pKaichfA68m8+XvBN6lqn8xzjld6A2ELC8xWPRKuq290w519cQs6YNj5LoJhQWPGr02ipi3k9Sstqibt9RyogFc6AlPg9+/0mdxtuQhWlw3Hl85nAYGYxekzz5tx1+6Z6nPhvnuNeor/dGiXgZ5eqnPmrnik6yoFvrhodhH/6JD1/D1x/YO/04qfkvpIZ4CIV0s1aRU5J0o9OfWmI9tS+SBToo8jC/yQKnIQ7GQhygS+aQQfDB2GCIyA2wHHlTVHxORw4CPAscDXwN+SlX/PT/2EuB8YAV4h6peW1c5LN3CWrqBBqv/26RbYaaVROzGkQm7d5pfPFwCgydFxvlIvvvIT0yQpLNXtqkovwLcM/D3xcCNqnoycGP+NyJyKnAu8FLgLOC9eSMRjV5gQzW8Oc4BiGHrEqpavkHpNiMyfOsVbz1h6Bad1agby9YCrVj0InIM8KPAZcB/zXefTTbSDHAFcBNwUb7/SlXdB9wvIjuA04HPRSxv+QEmH73dou9VrgzdbUiaSFNcBevVTesCt0Rdi4yPS9CiDw0IFzyMouUC06LWXDe105br5g+AXwc2DOw7SlV3AajqLhE5Mt+/Gbhl4Lid+b7nISIXABcAHHfccbUVNlTPpG+JunEcp4yi16wsvn6l4NVrIx+9zM5Fvqid6EIvIqvpN28XkVdbvjJk31DdzPNFbAXYsmWLa2sXaNmit05aCvmXp4GQORO6Q6FxguKUw8XPqMiIbsd9k+7YWhsW/fcCbxCR1wOLwEEi8n+Bh0VkU27NbwIeyY/fCRw78P1jgIdiFjhYZyxumUqDsdXoSm6XsWgoqZmTYRlGarqaFZ2/LFqnqBGI/0ZI0kIfvWSqeomqHqOqx5MNsv6dqv4scA1wXn7YecDV+f+vAc4VkQUROQE4Gbg1ZpmLBnyeHfjRfnhzkiT0bFsb3OsgfS3fmmBuRoZuPSnemkKlZ9raIKU4+suBbSJyPvAAcA6Aqt4lItuAu4Fl4EJVXWmvmEMwWfRuZjrTzahiXybORdZ+fB89SVv0rQq9qt5EFl2Dqn4TOKPguMvIInRaIegaMQzGVmG6VpiyvZHVol7s9y91r1fixWudokidUSdnjY4kXZlSsug7iynXjbtvxqK3vI/+7EJ71zceZ8k78ywJC0MThAdrh+8vm7VbJOix3W0K6Ey6cppuyRLisWdWOGxNSYxsP+xJqhJH74xHlZc83c52RizBsrhVxk181oSfvngAt/5rlSJpD8a60BsoFXknCm1a845jwoW+29QSXlmBKfLQt449NLWdp2IrnyFNceDzrpoyRZZ7/JDjCbHo8/wyRw1+R1UfaKJQqRHMo+0++tExvhxtj09bhWOSn/K4MhYavxglCKEoTXEbIbFtp/MowyT0IvLLwLuAh/l2XVbgZQ2Vq1u4iHeW1GPk61ucJHBA4vehiCJpbWVVwq4LPVmmyZfkIZDOKHhjkCRWPWiiQeiai65pd0jR/SjrUReVKbrrRiYjqdk3gMebLEjKBOtMyykQnOcSM7rjQNp2MRURdouML4yhxnAltED5CPeuKPTSXTfPpVToRWQ1hfB9wE0i8mlg3+rnqvqeBsvWHWoW8UlZ7LkLWAXB+grXPWU7JY9KcI5AC/MCip5LK7luOrzC1Goa4QfybT7foHs9TydB2raCzNkrzcE58V+LVHsRdVF274sHY2O7buiuj15VfxtARM5R1Y8NfiYi5zRZsFhEqw81p0mYNqYqLYRjpuj1jd+5SDu80lqyS4z7nBqwZlT0zIoOhLNG1uUK7Ae2Nih6J0S1cGsK7c2atjYI+eh/BHg9sFlE/mjgo4PIMkk6zngYraBZYHnCjHpvo5/LKEZLYdRNP7I8dTwFwkPAduANwO0D+58EfrWpQnWNuvPY+GBsPMyDsQmrcqVEagWk/Pt6JU1iYbz8SguZzGv0F4nIWcAfkk1a/nNVvXyc84V89F8EvigiHyEzQE4hG4S9V1X3j3PhLvHo08scWZLvZmnjSczt3hGxRGkiIpVF55kVZbHm2S0zEg7lW6WvNpGbnxH2G05qPS41nlnuszg7nkW61NfShbxnesLKCFbMmjlh71K17y3NLDC/9NTz9jfnOqnPos+zEPwp8DqyFfZuE5FrVPXuUc9p/dWvA/4M+FcywT9BRH5RVT876oW7RJnIAy7yOaNYllaRr+K2qaKzVkvWKt5dFHlgbJEHSkUeGEnkgcoiDwwVeWjWpVNjBNnpwA5VvQ9ARK4EziZbfGkkrEL/HuA1qrojv/CJwKeBqRD6IB5RMzLmBUU86qaQWLWvaQ/0KBE0hWGUsX30UMWi3ygi2wf+3qqqWwf+3kw2SXWVncCrximaVegfWRX5nPv49uLdncb1oxtUeUy++Ed1YtyGJhqkwnJHnomuCH378PpuVd1S8vmwE42lVFahv0tEPgNsyy94Dpnf6M0AqvqJcQqRPKFK463FxNNWc9ClmtXUPSrzCBWGS0bvZWudcz12AscO/H0MWWDMyFiFfpEsc+UP5n8/ChwG/DhZXZxooffVoRxnfGIGH0YPr6TWRvk24GQROQF4EDgX+OlxTmgSelV92zgXSZlauqzeECRFlTG/dPMNdo9WQjSL3r3IvWylvrBoVV0WkbcD15JV0Q+o6l3jnNOaj/7FwPuAo1T1NBF5GfAGVf3dcS6eAqb6EBByt/hHpyter5TjzGPdw6ZT/xbnpyn+gYXvXgvvZB3zGQbO9RngM3Wdz9qbej9ZyoOlvBBfIutOOEZE++bNGQ+tsLVJ6uWrioiMtY2E9odusd+vVYs+VjqKqlh99GtV9dYDHsb0pEAItdQuzo3T1AtilZfU15YNkWapnkvRLR6pDYj9Tmq1+RuxsQr97jx2XgFE5CeBXY2Vqmv4wiPOFNC0+2qkwdoiI6zjrpu6sQr9hcBW4BQReRC4H/iZxkrlOAcw1xOWjGZ9E9Z/yj76VAjdo1HvYVmum0Kix9GnvTC8dYUpyAYG/p6s4X0K+AmyGbMTz7/tn+Xo+TE9VQm39m1SJfY4ZYtpFGK2HV1op4rdYyXPvUDQ2xjrSrl6WleYegnwH4CryerMzwE3N1iupBhb5J2xWfaUnk7ipFxFrStMXQe8QlWfzP9+N/Cxkq86TiewLjm3krK5NuGUPaI2JkYNQzXtOmL10R8HDKYl3g8cX3tpukrfkPvaB2PHooq11ISLpwuuj6kkofcqYZ03C/1fAbeKyCfJHGZvAq5orFRTTtVBq5S7jI5jpajel74PRUIfOddNFkef7otoTYFwmYh8Fvj+fNfbVPULzRWrY/TCE+l9ItRwrK9GU6+QNUZ7pMgPZ6pIV+btFj2qegdwR4Nl6S4W140Tja40qR6yaaP0NhVZ7i1Y1yn3rNtZktxxKtL2S9T1tPXWQecmab8EzZKw58aFPhruuolGpXVHjAdbV8Kqoqd1JQmznCZlERqHVFyiqpp01E3MFNHPIiKHiMhVIvIvInKPiPxHETlMRK4Xka/m/x46cPwlIrJDRO4VkR9uo8xOu6iqeWsCUTVt04wEtkkn5aRmrQg98IfA36rqKcB3AvcAFwM3qurJwI3534jIqWSZMl8KnAW8N18lPR4FGfKe3RwncUTKt86SyDupZL0my9YG0V03InIQ8APAWwFUdT+wX0TOBl6dH3YFcBNwEdnq51eq6j7gfhHZQbZK+ueiFnxcvEEYitXCWZjtsXfZdg8bsZoaeH6Wwdgua/CBTPrgcz/huJs2LPoXkS1F+Jci8gUR+XMRWUe2qMkugPzfI/Pjh62IvnnYiUXkAhHZLiLbH3300fpKLL3yzXGcxuiJFG4pkbJF34ZKzQKvAN6nqi8nS5B2ccnx5hXRVXWrqm5R1S1HHHHE+CV1ksFqzTtOG6xOmLJsbdBG1M1OYKeqfj7/+yoyoX9YRDap6i4R2QQ8MnB8rSuiO05TTHP/LmRhhw3wEUSwSDgjW/uqsJTwyiPR66Wq/hvwDRF5Sb7rDOBu4BrgvHzfeWSZMsn3nysiC/mq6CcDt0Yscj2EBnR9cLcUa7c42+qP0El5GcieYZtYknmHsvBKy9YGbcXR/zLwYRGZB+4D3kZWH7eJyPnAA8A5AKp6l4hsI2sMloELVdWnok4ZbU+Yaou6fnYdYj+tz8DCROS6qRtVvRPYMuSjMwqOvwy4rMkyNU0qEzucA6j5udQ1CWoSGTXqpjRNcSrvlcJKIkUZhs+MdSaOhN83Z0Jxi34aMGSvdMajK8sITnqseHIkYtErmNc0boOJHqepjTqmFfb79s1JDx9IH5tRUyR0YpBZYaWvpm0cROQcEblLRPoiMsz9PRS36J2JoyPG/8QRtHkCz6XLwxtKtBj5LwNvBv6sypdc6OvAZ8c2Tlds5Q5rlTMmMcLoVfUeqD7o70LvOE7SlIpaIu6yioOxG0Vk+8DfW1V1a/2l+jZTL/SmhtEt9tY5eGGGx/f59IlRmdiwz0SEftVHb2S3qhb610XkBuDoIR9dqqpXD9kfZOqF3sLD+4SjFkoeojcE3cWfXfJ0IZKpzqgbVT2zlhMN4EJvoFTknSi4Ne+kjMfRO04NJPwOdYLwPITmzebU0grXiir9CHH0IvIm4I+BI4BPi8idqhpcdW/qhb6Ojrt6999x0iKyZaBEi7r5JPDJqt+beqF32qWJGa8pd6HbIjQYm4KpUlSGfkc6AinXOxf6OqjZou9IvY5KlZeokoug5qiNKgOHlkMtv3piI2oMpJLULMtHn0ZZhjH1Qm95STR0zBS/aI7jxHPdjMrUC73jOE4duOvGccak9VfI+hJ75652Uhg/CKG0t3qUhakX+lomY/QMVdGzUjpOPGK7U6vNjI3O1At9iqRbXepnmgcSnedSVBe6sBaB4kLvOGNz+JpZvrl3ue1iOC3QBWNAFfYvp9trd6F3nCkg5KLsgpgOo2iyokRObK2Mv6hIk7jQO53ArXknadxHnzYdNWScEtp8pl21jC10IYvks1gCJGrEffSOU4JVPLqkMc5oFNWFhPXzWdQtesdxhmFJ1ZBybHYsutKTcKHvOKnk03CmD4vG1SGEdZxjmtukvir7POomXWKlKa4SBZCwYeB0lBSGDkIpAkYqYtG714Jx5ha9E31waNJofdGKBoTD9JPS1Q5nAPfRO04JCRiaU0EdZkbTz2ok91GBRd/GYkApj6dMvdBPcjic030sPZk6qnAdb0FT2Ru78I76hKlJwAdjnQYQgzDqBPV5gqvWjiLoRZZ7ZIveUyA4gK8rOy4dMOqcMak1jDK20AMrCWeonXqh70qM7rRz2OIM33pmpe1iBKlUnww9RenNjF6YyDTluCi7pcW5biKj7rrpPHt0jvWyFO16VdOydsGHmSxT0tOa2DpSNBhbdHhDblhPgTABxBR5ZzhdsOad9GnKhaoKywkLfSvmjIj8qojcJSJfFpG/FpFFETlMRK4Xka/m/x46cPwlIrJDRO4VkR+utSyGzXGc5hnp/RMp2HrFWwOsWvSWrQ2iW/Qishl4B3Cqqu4VkW3AucCpwI2qermIXAxcDFwkIqfmn78UeAFwg4i8WFW7ZeJNiYugKq1PhEocixevjpplGVsIadS4GjZTUIbSOlI4MzauPKhqlKgbEfk94MeB/cC/Am9T1cdC32tLfWaBNSIyC6wFHgLOBq7IP78CeGP+/7OBK1V1n6reD+wATo9b3ABl1kODVoRTA9o3bWLdsPUSYzdvPSnfYp2j/PwydCtDpTd0K7b0m7vzkSz664HTVPVlwFeASyxfim7Rq+qDIvL7wAPAXuA6Vb1ORI5S1V35MbtE5Mj8K5uBWwZOsTPf9zxE5ALgAoDjjjvOVJ5oUTcu9k5DTOxAq4Wi1CIaP44+hltGVa8b+PMW4Cct32vDdXMomZV+AvAY8DER+dmyrwzZN/SOqupWYCvAli1bTHfd9JKkO8biDKEzTWrExj9GUxCKFgu9ayO1VwmlQFC70G8Uke0Df2/NtasqvwB81HJgG1E3ZwL3q+qjACLyCeB7gIdFZFNuzW8CHsmP3wkcO/D9Y8hcPRNLVcOgyLfpTAddmQsSbAgKPtZRfPSxV5hS6Ntf3N2quqXoQxG5ATh6yEeXqurV+TGXAsvAhy0XbEPoHwC+W0TWkrluzgC2A08B5wGX5/9enR9/DfAREXkP2WDsycCtdRXG9JIE5253xoZ0JpCO6PzIWFJFPP9Lsd9JrTz/pfBMqmeWfS4i5wE/Bpyhxou24aP/vIhcBdxB1iJ9gczdsh7YJiLnkzUG5+TH35VH5tydH39hnRE3k/6SOA6EDRqLCzOkKSGDNmhUFU1mKhPtVPLRK6zEibo5C7gI+EFVfdr6vVYmTKnqu4B3HbB7H5l1P+z4y4DLmihLiuF9PiTgpEiwMRjToi2atVp21sIUCC3kuonUtvwJsABcnz+PW1T1l0JfmvqZsQnqvDOEjWtm2b13ue1iJInF/ZhCZE7QhV3jYGwb7tS6XDeBa5w0yvemXugdx0mEApO4VP8LGrDoUTfVBmOj40LvdAK35p200SrhldGZeqEfaUT/eScxWA++eMlQEvAodJ4U3DJgCE4LnqDoDOm/O6qwspJuOade6B1nGgj58RM2Rssp9NHH/0Fu0aeMW9rTjc+BAOpJajY2heGVxYuvFEfdxH+vXegdF5QpoVq4blgYLAtum6Juxi6JU4aq+mBsyjS14ozjTBshnRs5VUdZYzdl4ZWjMvVC7zgmGjAISnO4PHtQOuIxbk6d4GBt0YSpEtEuuodtDE+nbDO60Cf4dBLuAXaCNp9o3QEwXhVGI3YcvUZKgTAqLvSO45gs4BQbnaIOT3SLXn0wNm0sXeNE4pSdFmnAQqxlDoeR0CCxZdC3U29BC9krLfewLVzoHcdCSy4+i5E48esRlNz7InGNnaM/S2rmQp8ulhe4JI63CaqP3k/6mw5HrJ3l0acnKw2CaTA2ksOkniyu46UxrvOKtntbI+66mQD6K9AbT+zbWNqsCyTc2x2JKu6Y6GKUOiPko09JWz2OvuuMKfLO+HhSMydlVJW+57pJF8uEKcWF3uk2Da8Zkp9jvMXB66SNnqJb9E4l0rUL2qPKi+sekeokcc9GcG8WNy7xf5D2a1vhtHZc6GNFU7iPPhqTdKctA/O93iT94o6i6kLvOMmS4MzoNogR099ELphUvCWKC33aJPiiT1okShkpTzJxqhF6kya636FKf2l/26UoxIU+Fkk4QZ3GqWA4JDXIn6DB0yncdZM4XsGdhKnLNRHHNVP+eX/CbR0X+pTppyf0qfgdnQHcIDCEIreQAz76FYfjPvpJIBAx47NenVFIaXyijgV4QgZK8C3pssWvbtEnjW3CVB0X8sZgHA5fM8M399pepJgTc5537SqCaagTlroXLWVOghZ9EfEbUaUfQehF5H8CZ5ONfT8CvFVVHwp9b+qFPsUuecpLkjn1EfUph+p5gu+BhVTcnKpKfzlK1M3vqepvAojIO4DfAn4p9CUXeqcTWK15x2kFVXSl+Tqqqk8M/LkOo73gQh8J9+OnSeqLw1ss1pgLmHSFNu5IBR/9RhHZPvD3VlXdav2yiFwG/DzwOPAay3dc6BN8SdKWnnqpJw96N4npdqilQUvwXUkm1021OPrdqrql6EMRuQE4eshHl6rq1ap6KXCpiFwCvB14V+iCLvSJW3TOdGMar6kn9eT450iI+L77+sIrVfVM46EfAT6NC31CeOIpp+u4UVRItpRg8/dHRE5W1a/mf74B+BfL91zoHcdxxiVe1M3lIvISMg/v1zFE3IALveM4zvhonDh6Vf2JUb7nQu84jjMmClHCK0elMcexiHxARB4RkS8P7DtMRK4Xka/m/x468NklIrJDRO4VkR8e2P9KEfnn/LM/kjanPTqO4wwjj7qxbG3Q5AjhB4GzDth3MXCjqp4M3Jj/jYicCpwLvDT/zntFZDWH6/uAC4CT8+3AczqO47RM2kLfmOtGVW8WkeMP2H028Or8/1cANwEX5fuvVNV9wP0isgM4XUS+Bhykqp8DEJEPAW8EPltXOeeOPnH8k6zbMP45Bjiu1rNNBi9c03IBan7GAAu1n7FdNo99hnWVv3Hk2NesiXiDsSMR20d/lKruAlDVXSKy+pw2A7cMHLcz37eU///A/UMRkQvIrH+APSJy7wGHbAR2j178WkmlLKmUA7wsw0ilHJBOWZooxwvH+bLu/ea1S3f+5Ubj4dHvYSqDscP87lqyfyj5NOLCqcQisr1sRlpMUilLKuUAL0vK5YB0ypJKOQZR1aRdyrFn8TwsIpsA8n8fyffvBI4dOO4Y4KF8/zFD9juO4zhGYgv9NcB5+f/PA64e2H+uiCyIyAlkg6635m6eJ0Xku/Nom58f+I7jOI5joDHXjYj8NdnA60YR2UmWj+FyYJuInA88AJwDoKp3icg24G5gGbhQVVeHp/8LWQTPGrJB2HEGYs0Z4iKQSllSKQd4WYaRSjkgnbKkUo7OIL7IheM4zmTjmbYcx3EmHBd6x3GcCWdqhF5EzsrTK+wQkYtbLMfX8pQOdx6wykyMa1dKS9FCWd4tIg/m9+ZOEXl9hHIcKyJ/LyL3iMhdIvIr+f7o96WkLFHvi4gsisitIvLFvBy/ne9v454UlSV6XekyU+Gjz9MpfAV4HVnI5m3AW1T17hbK8jVgi6pGnzQhIj8A7AE+pKqn5fv+N/AtVb08bwAPVdWLWirLu4E9qvr7TV9/oBybgE2qeoeIbABuJ5t9/VYi35eSsvwUEe9LHuG2TlX3iMgc8E/ArwBvJv49KSrLWUSuK11mWiz604Edqnqfqu4HriRLuzBVqOrNwLcO2H02WToK8n/f2GJZoqOqu1T1jvz/TwL3kM2+jn5fSsoSFc3Yk/85l29KO/ekqCxOBaZF6DcD3xj4uzSVQsMocJ2I3J6nbGib56SloP30IW8XkS/lrp0obqRV8txMLwc+T8v35YCyQOT7IiIzInIn2aTG61W1tXtSUBZosa50jWkR+kqpFBrme1X1FcCPABfmLgwn433AicB3AbuA/xPrwiKyHvg48E5VfSLWdY1liX5fVHVFVb+LbDb66SJyWtPXrFiW1upKF5kWoS9KsRAdVX0o//cR4JNkbqU2KUpLER1VfTh/qfvA+4l0b3Lf78eBD6vqJ/LdrdyXYWVp677k136MLMvsWbRcVwbL0uY96SLTIvS3ASeLyAkiMk+W+/6a2IUQkXX5IBsisg74IeDL5d9qnKK0FNFZFZGcNxHh3uSDfX8B3KOq7xn4KPp9KSpL7PsiIkeIyCH5/9cAZ5ItQt3GPRlaljbqSpeZiqgbgDz86g+AGeADqnpZC2V4EZkVD1n6iY/ELIcMpKUAHiZLS/E3wDayNPgPAOeoauODpAVleTVZV1yBrwG/uOoTbrAc3wf8I/DPZAsuA/wGmW886n0pKctbiHhfRORlZIOtM2TG4DZV/R0ROZz496SoLH9F5LrSZaZG6B3HcaaVaXHdOI7jTC0u9I7jOBOOC73jOM6E40LvOI4z4bjQO47jTDipLA7uOEFWk54BBwE3q+oNBce9EfhKG0nrHCdF3KJ3Ooeq/laRyOe8ETg1UnEcJ3lc6J2kEZFLJVtH4AbgJfm+D4rIT+b/v1xE7s6TW/2+iHwP8Abg9/I85SeKyH8WkdvynOYfF5G1A+f5IxH5fyJy3+o5889+XbJ1A74oIpfn+04Ukb/NE9L9o4icEv2GOM4IuOvGSRYReSVZuoqXk9XVO8hytK9+fhjZ9PdTVFVF5BBVfUxErgE+papX5cc9pqrvz///u8D5wB/np9kEfB9wCtkU/6tE5EfIegWvUtWn8+tAtij1L6nqV0XkVcB7gdc2dwccpx5c6J2U+X7gk6r6NEAu4IM8ATwD/LmIfBr4VMF5TssF/hBgPXDtwGd/kyfGultEjsr3nQn85ep1VfVbeUbJ7wE+lqWkAWBhnB/nOLFwoXdSpzBHh6oui8jpwBlklv/bGW5hfxB4o6p+UUTeSpZTZ5V9A/+XgX8PvG4PeCxPl+s4ncJ99E7K3Ay8SUTW5Fk/f3zww9zKPlhVPwO8kyzJFcCTwIaBQzcAu/IUwD9juO51wC8M+PIPy/PC3y8i5+T7RES+c+Rf5jgRcaF3kiVfVu+jwJ1kOdr/8YBDNgCfEpEvAf8A/Gq+/0rg10TkCyJyIvCbZNkorydLtxu67t+S+eu35ysb/ff8o58BzheRLwJ3MYXLUTrdxLNXOo7jTDhu0TuO40w4LvSO4zgTjgu94zjOhONC7ziOM+G40DuO40w4LvSO4zgTjgu94zjOhPP/Abm3Lwc9S0yLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vref = Vdac_cross - ds.V_rel.mean(dim='depth')\n",
    "\n",
    "\n",
    "Vgeo = ds.V_rel + vref\n",
    "\n",
    "\n",
    "Vgeo.plot()\n",
    "plt.ylim([1000, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "seventh-background",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}