dfo_mooring_plots_copy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "735e4235-62d0-4294-a0aa-b101e40f8fb3",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Downloading DFO Historical Mooring Data\n",
    "### https://data.cioospacific.ca/erddap/tabledap/IOS_CTD_Moorings.htm\n",
    "## Constraints:\n",
    "Limited the search area to the mooring of interest E01. (49.1 - 49.3 & 125.99 - 126.7) <br>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e3b6a09-f343-4770-ba8e-a99249afa6f4",
   "metadata": {},
   "source": [
    "## DFO Pacific Mooring Sites (E01 is in blue)"
   ]
  },
  {
   "attachments": {
    "8b114a42-d5c2-4b11-8d29-e7c6fbd6d217.png": {
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KJUuW/Mr27SupVasad29dYcrw9jy8dxMABwc7OnRoyfHjZxgx4n+U8vBl56kE\nTBXJtKuWTovKGiq46qjgqqOiq46G5TW8UzuDLjXTaVUlg+oeWuwsJErY6ahVRktbn4xC4/F44sTz\nY0MWNtRKKO+io121DLxL6LhyT87OiyoexmV1FS6K+sktT+vGwsKcpb+PxdvqGqvnfUPDNj14mFHK\naBtGUUJcO3mDMAAFRZLG7T5k2OS/sHN0IS0tnbJla2dZoq1ZsyphYZdxcXE2Ko+NjefsWX8AEuKi\nyY4PhnzLzDWXad9rZLbHFQoF48Z9RqdOrXF2dsLLqwzp6el06/YRLi5V8PZuwIABvbC1fbyx96uv\nfuDq1eBs23sSuVxOhQrlqFChHBpNhtGxgIBrlC5diy+/nPrEOH2ytFG3bk327FlDly5tCX9k/AFE\nRcXwzz/raNeuB3///S8JcTFMHd6O2h4pWJs9Qx4ZmKr1+8jKFdfhW1ZLdQ8tZZ11qApxaI+ihFIB\nPqW0tKmagZlKYn+gilM3nj0bKHg+MpmMpn718Smt4p85X9Kg9fvsv5DE/AV/c/asP4XQf1PwhhEG\nYBGkfNXnLycWFd4f8i1Wto6YWtgYlavVajZu/CvbWHh9+gxh8OAvALI9nknojQC2rZhteF+lSgUu\nXNhHTEwwq1cvRCaTsXTpHK5ePcb27Suxs3ssQ2paBmPHTiIhwTjeYLFiTrka17Fj2zl2bDsffvg4\nDIxSbUL/z2dTsmwlFEo1ZmamhIScY+7c6YY6U6fOZupUvcy3boVgZmaGubnesrOwsqNkmYqUqVCb\nth8Mp2xFfYaSvxZNx97uxT3QChuNG9fLbxHyHWszaFZJvz8wJFLO8WClwRlI6OfZ5KSbyZO/YNLY\n7mxfPoMmHfqQaNWAjwZPplv3gdy5c+8NSpl/iGsnbxAGYBEkIiwkv0UoMHhW8uXHf87iWNwdgOLF\ni7Fo0SxCQs5iaZl9GsEbN24bXtds1OGZbd+79TiWn0ymz87Ro8cgduzYS7duH1GjxuOYfnZ2tvTp\n8x5yuQylUkXf0T8jk8tRqk2p07wroI9XaG9v+0Lj++23H+jYsRUAmvQ0fJu+w4RftuLq4UVaWjqm\npiaGuuvXb2XGjLnMmDEXL6961KrVipUr15OSkopj8VIMmbSID4Z8x5gZa+jUZyw6nRaZTIZarX4h\nmQort27dyW8RCgSyR/sD65fTO4lcf6j/mhH6eTbP002dOjX45fu+rPntM5Akxs/ejKWrLzVqNGf7\n9qyOaIUNce3kDcIALIJEhxeNX40vQq3GekMuLi6e9PSMHPMqduigN6hcS1egQo1Gz6xXt0VX5m0P\nYd72EH7dEETNxh24ciWYnj0/BUCj0dCx44cALF78D7/8shCdTqJtj8+o7deZOZtvMHtdIP3GzERt\nYpqtTN9/Pws7u3LY2ZWjVq2WhIU95NCh4+zcuc/g2Xzo0HEAirk+9qSr07wrOp0OZ+dKODiUx8Wl\nCiNHPnbuiIiMws7Jlc9/Xs/v227z3eLDlKlQ03DtaDQa0lKSkCSJtm0/wNe3NRMnTnump3NRIDRU\n3FdP4movUdZZi3+IgqQ0oZ+cyI1u7Oxs+fP3r1k3dyT7Ny2h7QfDad9zJIsXryz0y8Hi2skbhAFY\nBFEoC8mu+9dI83cG0qX/BFJSUpk/f2mOdadP/xonJwfCQq7lun2lWk3PYVNp1mUgji7u2Dvpw7Bk\nZtR45512uLvrs4rsXjMvi5Gu1WqIjo7h0KHj+PsHcPVqMIsW/cPff/9rqHPjxm0qVmxI58596NFj\nEB4etWja9B3i4xOxsS/G5IUHDHWr+LbEq0pdLG0cUJtaoDSxQKOTY2Jmgam5FXaOJShftT7FXMsa\nyZF57Zw7vIX7T4w/OPgmc+Ysonz5+uzYsTfXeilMqNXivnqaqqW0qJRw8IoKmVzo51nk9tpxdHRg\n44YlnPnvb07uXU/r94dx4248Q4eOJy0tLY+lzD/EvZU3iEwgAsEjdDodP47qTEjwRRo3rsumTcue\nWbd8+XrExiXyy4bnG4HxsZGsnPM//I/vRpJ0jJu9mX0bF3H6wCY8PUtz+vRuAFxdq5KcrPf6tS/m\nypS/jhnaWPD9IC4c25lt+zKZnBFTV5AYF821i8dwLF6K8Hu3OHd4G+npqVjbOjL4m0W4ldWnivvn\ntwkc3vHPo3P13pqZjwGZTIaVlSUajYbk5BSsbR2Z/s/ZLH2mJifyRc+aZKSn0qJFY06ePEdCwuMg\n1seObX+uXgRFg7BYGQevqHCw1FGvnAZLkdb1lQkIuEbTZl35fslRZHIFX/Sozh9//PxKWXkEbx8i\nFVw2CAMwZ84e2krNxs/eu1bUmfRxMx7eu8Hixb/wzjvtsq3Tp89QtmzZzW8bg1E+Zw/cXz+N4uS+\n7MM3WFiYc/euPxqNBienCoA+m8iIqf9QyrOyUd3bQf5cv3yK1JREMtLTKFWuChVqNMb8Ba7xhVMH\nc+7Idjw83Fi6dA4+PhXR6XTs33+UpKRk2rVrjlKpd81t1Kgjly9fZfj3y6hYozFgfO1cDzjFz593\np3//Hvz449eUKVObxET9snDNmlWpV68WkZFRtG/fkjZtmhnaLaxs2LD9mddLUedOlIwFf+2kfrMO\nVCqppZSjDnnWSDFFlpe5dj7+ZAyhUUr6jZ3FkhmfcWr/Jvr2+4DZs77LIynzD3FvZY9IBSd4YXQ6\nEZshJ5p27o9CoeSjj0awdm32adwCAq4ik8mea/z9NrGvwfgbMeJjQ7mTkz6wqY2NNX5+XXB1fRyK\nxcTMAscS7lnaunP9Ege2LCXw7CHio8OJi3pI+N2bpCYnkhgfS3xsJDrd46DKyYlxRqnqNiyZxrkj\n26lWrTJnz/6Hj48+dZxcLqd580Z06tTaYKR9991MLl++ilyhwMnFnejwe/w7fxK71s5j2shOTBvR\nkYNb9TOkbm6uKJVK5syZZphJPHvWnzlzFrFq1UZ69x6Kk1MFevT4JEddve1kBhUXZKWUg4SncwYq\nhcSJ60oOXVGiLRzxv18LL3PtfDZ8ICf3rSchNoqew6ehMjFl6V+rCAq6kQcS5i/i3sobhAFYBHEs\nXur5lYowpTwrM2XpcWQyGR9/PBpXVx/Cwh4ajkdERHLz5p1cbby+c/0ioF9a/eWXhQAcPryFvXvX\nY2ZmyvDhAwgMvEZ6uj5en7m5GTER9zm2c1WWtvyP7SbqYSi3rp7jxN51rFkwmekjOzGqWyU+/6Aq\n43rWZGiH0gzr5MmQ9h6Mec+HEV3KM3t8D3Q6HTcC9MFUo6JiWLVqY45y37hxC4Bun3yDg7MbU4e3\nY//mJYRev8y9mwGEBF/k3OFtABw4cASATp1as2nT3zg760PVqFQqKlf2NrS5f//R5+rrbcbDwy2/\nRcgXtDqITZIRFiPjYZzsmYZdFe+SNK+soZi1jgdxcu7HiCnATF7m2qlSpQLNWzRhwfcfo1Kb8r85\nO3As7kaTJp1JSkp+5nk6nc6wXeNtoajeW3mNMACLIPbFSuS3CAUa+2IlsLEvxnuDv0WlNiU5OYXP\nP59kOG5iog+dYmbx/Cn3xEeBop80Fk1M1Hz88ShSUlKZNOknNE9Ey01OTgGgYs0mRu3odDpiox4n\nht+zZw2rVy/k88+H0qfPe/Tv3wMrK33YGq0mAx+fivTp8x41alTh2sVjrP3jWz77/m8q+zbjfthD\nhg4dh4+PH6dOnc8ic2pqKlWq6GcHd/zzCzPGvENSQizvvNOOK1eOEhFxhUaN6hhmkps0eRxXsnHj\nely9eox79/wJDw/k8OEtbN6snylMS0vn4MFjWforLJQsWXTuK0mCiHgZJ64rWH9Kxc6LKg5eVbE/\nUMXW8/qUcDceygmNknEvWsa9GBlKK1eOByuJSJBRrrgWF9tCt/vopXnZa2fe79Mxkyfzy5c9sHcq\nwTcL9lG5TiuaNns323v74MFjVPHxo1Sp6m+Vs1ZBubeS0+D6QznBD+SkpOe3NK9O4d6UI8iWoIsn\nqO3XOb/FKLBk6sevQx9qNerA1wMbs23bf/z88zwGDOhlWMpMTU54bltNO/Vn/+YlgD6O37p1i/H3\nD+D06QsASJIOCwtzEhMfB3yu0bAdJdy9jNoJuxNk8LodNKgPtWpVA6BVKz9Dnc6d29ClS18Ali37\nHTe3EiQmJuLmVp39m5dw6fQ+2vX4jEFfLmD5r+M4tX8DrVu/R9WqlWjY0JcHDyIICLhGcPDNR0su\nMlzLVOTq+cP4+tbgzz9nsX79Nrp168iZM/6GfjPzGj8ZFNvc3NzwulGjupQsWYK7d+/TrdtHnDq1\ni9Klsy5xP01qaioajeaZ8RgLGkeOnKRbt475LUaek5AKJ68riUyQY2EiUclNi5OVhLmJRLpGxrUw\n/RdkusZ4hu/0gTPUadqJup7aQpP/+XXxsteOk5MDW7cup1mzrnzRsxY//H2CAePmcPbQFjp16stX\nX43gk096o1KpOHLkJF27fmRYTi1b1uM1jyLvyI97Ky0DwmLlJKbqM93IgEuhCrQ6fazLy3cVVHLV\notWBjbmErbnE/Vj9M7CUgw71W2BdCSeQIsjpA5uEAZgDT+sn9EYAU4dn3YBsZmHFzDWXc2wrNuoh\nE3r7IpfLOXx4CxUrelG/fjtDjmBzczNCQy/w3nsD2bv3MGoTM37ZcDXbtsb1qk1iXCRXrx7Fyckx\n1+NZsmQlixatIDAwCLWJGbPXXzHI9ucPQ7kReAbQPwaUKjVqEzOSE+MM56tUKi5dOoizsxOffjqW\nGzdCOHPmAgByhRKdVoOtrTWffNKHbt06Uq5cmSwy3Llzj+rVmxn2KCoUCrRafSBpmUyGQiHH0dGB\nKlUqYGKi5vDhE8TGxgPw1VejGTNmcK7Hm1+sXbul0BuAGi3s8FchA2qU1uBiKyF7xkquToIMjX62\nUAI2rN9C9+4dUSnepMRvB6967Zw6dZ7Wrd/D1NyS9wdPpk6zrsRFh7Pg+08IDw1CoVSQEP/4B+vf\nf8+hY8fWr0P0N8KburdS0+FujJzQKDnhcTIkZJiqJDRa0OjA3VFHzdJadBIcD1YSHidDLgetTn8T\nyGT6i91UDTU9NFiY6mfKXex0WOWB97vwAs4GYQDmTGzkA2wdi+e3GAWW7PRzPeAM8yYPQKFUkhCr\nD3Zcs0lHBo6b89z2RnWtiFaTjqOjPQkJiWg0GlJT03ArW4nQGwEMHdqfuXP1s4RtPxhOpz5js21n\n/aKp7Fm3wCh0i5ubK23aNCMhIZGUlBRkMhmVK1dgxIiPs3jdVqjQgAcPwnlv0CSadu5vKNfpdETc\nu4WVQzHMza3Y9s8vbF0+kzp1ajB8+Mc0alQHa2sr9u49TLduHxm1KZfLjRxPQG8wfvhhN7p0aUvj\nxvVYsGApEyZMealgtWZmpqxf/xd169Z84XPfNPfvP6BEicJ9X92NlnHkmoq2VTOwMX+xz7Mo6Odl\neVXdSJLExInTCQ29x549B6nZpDPvD/4OhVJF2J0grvkf50HodQ5tW8asWd/Rr98Hr1H6109ERBTx\n8QmoVEpKlizBgwfheXrt6HQQeE9OwD0FSFDMRqKkvY6S9jrM1I9+xEjwdObPzEfa3WgZElDcRiJD\nC6duKHkY97iyUi5RwVWLh5MOczXP/NH0oggDMBuEAZgzt69dwKN8tfwWo8CSk35CbwTw4yj97OAP\ny05haWP/3PbmfN2PgDP7jcqsbB3p1GcsK34dz+jRg1m5cj1hYQ+ZOG9PluXfTO7dvsqOVXO4fvkk\nDs5uqNQmXLt4/PFT6Anq1KnBzp2rAX3WDp1OR//+I9i+/T9cSnnx9fw9z5R3WCdPtJoMjh/fjrd3\nOUO5TqejR49BREVF4+7uxtdfj8HKyorJk2dw+fJVwsIe8uBBuJGhN3fudL777mcePAgH9A8+D49S\neHuXo3JlbywszB+1LREUdJ3Ll69y924Y5uZmTJ8+0ZB15W3g7Fl/atasmt9i5CkR8TL2BqhoUTkD\nR6sX++ooCvp5WV5WNzdvhmBra429vZ2hbP/+Iwwb9iUJSWmM+GEVrh7lAfhv/UJO7lzCpUsHXpPU\nr44h3qi1FZIksXbtFpavPYhDyUqoVCbcu32VpOgQunRoRJfObdBqddjZ2WBnZ/tK/cYly7gfIyM+\nRUZCqv5/hgYquOoo76LF5BXjTkuSvo80jX55OOCufk+sTpIhk0lk2n9KBdiaS7g+MjbN1XqnKo2O\nR7OOMlQKCVMVyGVwI1xOXJKMuBQZMUkykhMT+KxrlZc2AN+CVWrB6yYiLEQYgDmQk37cylbit83X\nX6g9n7otDAagUqlEo9GQEBvFnnULAOjb931mz16ASm1CMdesy6eZ/PZVb+Ki9YZU5n8ABwc7Gjas\nQ3DwTQIDgwAMSeJ1Oh2urj4GL2MLK1tGTluZo7wKpRKtJgOdzvgLXi6X0717pyxLMb/8MsXw+tq1\n6/Tv/5lhiXvo0HF6GZ1LEvXwLpGR14z2Cr5ujh07xcCBo4mNjeP8+X0Gj+Q3wa1bdwq9gWNlqr8m\nohJlL2wAFgX9vCwvo5tZsxbw7bc/AeDrW4OFC3+mVKmSNG3akKNHt/DJoLFMGdqa9r1G0vydj2nW\n5SMuntjDggVLGTSob14Mw4i7d8OIiIikWrXKpKWlEx0dQ3R0LFHRMZw+7U+5ch5s2bKH9eu30vW9\n7ti6VMC+VDV6jOxGdMR90lKSadKhD3KFgiM7V7LxFCTGxxEdHsitKycpaaejZ4/O1KlTw7Aq8jzS\nNXD2loKQSAVKuYSNuYSlqYSzjYSbgw7bF5zVfhYyGdhaPG6rZmktVdy0hMfJSMl4LGu6BqIS5PiH\nKDh/W4l+s8Tzx+Jiq6OCq5aUpFcLjyMMwCKITCacv3PideundPnqhtcXLuyjZcvuhIU9RC7Xb4aq\nWtUPgAbNu2YbLPl6wBnWLvzWyOjLxMurDCdP7jK8t7PTz9g5OupnJuVyOc7OToSG3gfg/cHfcu/m\nFR6qVGxZNpOmXT6iev02Rm2+89GX/Dvva5o1ewcTExMSEhKpUKEcDg52JCWlcOvWHT7/fGgWWY4c\nOUHHjr2NyspVqUPbDz7jyI4VRD28m2fGn06no2XLbpw7d8lQlumt/abIS8O2oBAerx+jq92LO3EU\nBf28LC+jm5iYWMPrU6fOUbVqUz777GM++aQ3rq4urFq5gJkz5zNjxlx2rprLxPl7+GzKcuZ/9zEB\nAUF89914bGyevUL2ZHagF0WSJJo06Ux0dAw1G3XAs7IvxUuWxaG4G7YOXpSs34QUoMVHnWjx0W+A\nPt1l4NmD7Fz2LaWdlTRpUo9DBzZy9OwtUlPTSElKwMLalhLu5ant15nkxDgWrF7Bjz8t5PtvR1Ox\nYvYrJ6Dfj3onUs7FOwoytOBbVoO7ow7FG7wk1Uoo6SCRud/6CelI1+gdTjRa/aygUi6hVIBCrt9H\nm5ohQyeBuVrC2VYyBFGPj381ZyqxBCwQvAHG9apFfEyEUdkv668Qduc6/21YSDHX0rTvOTLLF0Fy\nYhwTP2pEcmIcdnY2mJqakp6eTlpaOsWLF2Pr1hWYmJiwZ88BOnZsReXKjYmKigFg5coFtGnTjPT0\ndGrVapVtQnWZTE6l2n6kJiXSb+xMHJz18bYuHN/Ngu/0gavVJmbodBq0Wi3So/1+DRvWoWvXjvTr\n9/5jWZOTcXXVz2KYWVjTfdA31GvRDYA1f3zHvo1/smvXv/j6Vud1smjRP0ydOpvo6BhD2X//rRWz\nTXlAdKKM3ZdUlHfRUqmk9q3wdCzs3Llzl6pVmxqV1W/gyztd2mFpacGKf9Zx5PAJPhzxIzUbtUdt\nas62FbM4vnMZ7du3oFSpEsTHJ3Li1EUsnbwoU6kOjsXd0GgyiI+JJCYyjJiI+1ibK3Atbo23Zwmq\nVi5HuXKlMTV99o+sPn2GkaQoSfdPvibsTjAPQq8TERZCcnw0ri4OxMUnkpqaQlx8IrFRD7l7/SI7\ntv1ttO0E9D/uEhOTiI2NJy4unnv3wth78DwxGQ40aNuD9NQUvhvcgu8mj6F//x5G50oS3I+RcfGO\ngrgUOa52OmqU1mDxZn8b5hliD2A2CAMwZ84f3UH1Bm3zW4wCS17oR6PRcGrfepbN/hwAuVzB3K03\nczznesApfvuqD+lpKQwbNoDvvhtvOHbxYiDt2vUgJSUVmUyGVqvFzMwUnU5Cq5PQZKQzZEh/pkz5\n0tD/r78uJCYmDnt7O3bv3o+Hh1uWgNBWto40aP0Bnft+zjX/Y1zzP0aHD0cbDNMZY7tyM1AfUFom\nkxEdHWR0/g8//MKPP86hvE99o6XmqIehfNW/Ie++255Fi2bnWm8HDx7DxMSEYcPGU7asB6tXL0Sn\n07Fy5XrWrNnM+fOXiX/Cu1GlUtGtW0d+/316rvt4XWzZsuut8qx8WS6FKrhyT46dhUSLyppcb2gv\nKvp5GV6Hbi5fvsr06b+xdevubI+bmJqRkZ5Op76f06rbp4Tfv8WJPWtISoylvE99qtRpidrElNvX\nLnA/JAiFUom1nRP2TiWwcyyB2tTM0JYmI53YqIdEPbzLvVuBKHWJ1PQpTcP6VTl/9gKJyekc839I\nq/eGsevf30kJO8XChTMNsUqf5MGDcFat2oCvbw3q16+da/0EBFxj0ZrTFPOowfzvPqZlszosXDjT\ncDxdA+duKbgdqaCYtQ6fUtoX3rZQ0HlVA1D8fiuCaDIKQQTLPCQv9KNUKqnf6j2CLh7n9IGNlK9W\nP8f6t4P8mfnFeygUckOS97lzF/PgQTgbN+7g7t37yORyfVYXSaJCjcac+G8N6Wlp1GzSibMHN3Pg\nwOOgy0qlktGjH4dSGTVqEAAjRnxCWpp+vNOn/8ahQ8fZuXoOgWcP8PnMTZSvaiynX4c+KOQKgi+f\npGrVSkbHdDodV67oDcJrF48R9TCU1JQk9m1cTOSDOwA8fGg8C/osLl4MpEePQdy//zj49Y0bt6le\nvRnh4ZGGgNmZyOVy1q5dRNOmDXPVfl6QqcfCThU3LcWsdewPVHLlvpyKrrlbhioq+nkZXoduKlf2\nZtmyudy7F8avvy5k9erN1KhRhfT0DFRqFU396lO7dnX+/HM5c7/px9DJf9G53zjD+fdDgpg3+SOa\nNqrBH3/8ZChPT08nJiaGyOg7RMSkcDs0kpB70dy5F429sxuVazejmGtpAM6HA27uWALNPTXsWDWH\ngd2r0qjhwGfKXbx4MUaOHJTj2LLTT6VK5Rnu5M3JGyq+nr2S2hXtuP5Azr0YOdFJeqcOuQzqemrw\ncBJxJ7NDGIBFEHunghFVvaDyOvSj0+nYu+FP7ly/hEptSuP2H1LcrSxV6rbAxsGZKr4tjOqnpyaz\n7s+pnDm0GZVKjdrUHEmSGDSoD/Xr12bChCnMn/8XoHfS8PKpR+e+n1OmwuPwKD2Gfg/ApqUzAAgL\ne8DzeHK55Z9/5qPT6ejXbzhbtuxmTPdKVKjRmIHj5hpyHts7lTC8vnDhMs2bv8s333zOjBlzCQi4\nSkzM4/iBu9cu4PD25UZ7ibLbO5jJ1q27GT36G2JiYtBo9DECHV3cSYyLwsLanuT4GG7fDn2mvv/6\na1W+GYAbN+7gm2+ms2nTDpYt+z1fZHiTONtIeDrruHJPQYUSulzNAhaUbA4FkdepG1dXF6ZP/5rp\n07/Ocuzo0ZOsX78Nl1KeWY6FBPkT+SCUjz6aYVSuVqtxdnbE2Tn72KMJCYmcu3CUY6eucOPWfdIz\ndLiXKo6zvQnTxnfGxcX5lcf0LP14OEnoJA3B5mUICJcjQ8LRSqKcs357Qkl7HeaFZLk3LxBLwEWQ\nhLgorGwc8luMAsvr0M/fsz7n+J5/n3nc1MyCWesCDe+/HtCYiLAQ1GoV6ekZuHtV4871i4Y9d5nY\nF3Nl8h8HDEbYk2g0GtYsmMSZg5tJToyjQQNfhg0bwOrVG1m4cGa2DibP4pdf/mD+/KX6+Fvu5fls\nynJs7IsZdBMSdJGNf03n6oUjj8dkbkkZ75rcu32F5MR4hn+3lJnjPsDe3paAgEP6Oqb6aKg6nY4V\nK9Zx//4D0tMzWLz4H2Jj45DJ5djYOxMbGWZoV6FQoFaryMjQULKkC5980pv+/XsQFRXDV1/9wMaN\nOwx1mzSpz/Llc99o9pDo6Fi8vOoaMiwEBh55LV96BZ2d/kp0koy2VTNyZQBGRkbh6CieO9nxpnTj\n4lKZ1NQ0AHz9OtN37GzD9o601GRGvluBunVrsWNHzpEC3jTP048kQUq63tFCWYQCjYslYMELc/X8\nEZEJJAdeh35qNGr3TANQLlfQ5v1hRmWm5nqDpVevbixZshJrWwd+WnWBwLMH+W/Dn5StWBu3MhWp\n0bh9tsYfwJyJfbjmf9Tw/ujRUxw9egoAL6+yTJgwItfyjxjxCSNGfELbth9w4sRZxn9Ym4nz9nDv\n1hVq+3XG3cuHEVNXcOH4bsJCrlGrSRecXIwTth/ZuRKQkMtlmJqaotPpaN78Xc6du4RKpSQjQ2Oo\nq1KbULd5V94f/C2XTu1l8Y+fAeDm5kqXLm3p1q0jPj4Vjdp3dXVhyZJfadBgOZ9/PhmZXM7Bg8fw\n8fEjMPCIwdjMS8LCHtKoUUeD8ffdd+OLhPGXmAqxyXJqeOR+D+CBA8cKfaaUl+VN6ebIka0MHDgK\nZ2cndu3axKkDmwBo0PoD3hs0Cc+KtThx4gzDh0/g++8nYGPz4kZFXvA8/chkiJm+l0D45QsEeUDl\nWk1RKIx/Xzk4l2T87C3M3XqT1u8NMTrWb+xsTEzNWLJkJQqlko69x2BuaUOtJp0YP3sz7T8cyf7N\nixnfqzYTetdhyrC2LJkxkuQn8hEHXzqRRQ4TUwuALHvmcsuOHav44IMuAHw/tDV3rhunvqtWrxVt\nPxiexfgD+HfeNwB8+eVIQ5m/fwCAwfhzcHbjwxE/0uHDMZzav5HR3Svz18+jDPVDQ+/x229/0qTJ\nsw3ygQM/ZNas75F0OpQqNTExcTRv3o2HDyP49deF3LsX9sxzX4WdO/fh4+Nn8Lpu27Y5w4YNyJO+\nChKX78rZel6NiVKimHWhW0Aq1JQt68H+/RtYteoPvvpqtKH86K5VLJs9lq6f6JeNly9fS81aLQkL\ne5hfogreAGIJuAgSHX4P+2Ku+S1GgeV16WfT0hkc2/0v8THhlKtch9E/PntJOJNr/sdwK1sJc0sb\no7IF3w8iJSneUKZSqcjIyEChUOJRvjpKpYq7t6+QFK83RuztbYmOjkUmkyNJOvbsWUOtWtVeeiwX\nLwbSqlV30tMz+GLWJjy8sg+xsmfdH2z6a5phRgygUaM6bN683PD+2LFTzJjxO/7+AcTExOJSygu1\niSkhwRezbVMmk/HNN2MZMeKTHGVcsWItw4ZNQK5QoHuifw8PN86f3/ciw30uT+7JBBg58hM++qgX\nbm6Fe5+bJMH60ypK2Olzor5IGJjQ0PuFXj8vS37oRpIkRoz4imXLHj+X6rd6n6ad+rF52c9cOvkf\n//77Jy1bNnmjcmWHuHayRywBC16YxPhoYQDmwOvST+e+n9O57+do0tOfuWz7NE973QKcPbTFYPwt\nXTqHy5evoFQq+eGHX9BqNdwIPJ3lnOjoWABKlSrBTz9NeiXjD8DHpyK7dv2Ln18XFk0fzneLDhmO\n6XQ6Zox5h5iI+yTGRT8KytyE9PQMrK0tmTXrO6O26tf3ZcMGX0JCQqlWrRmWNva4lPJ8pgFYo0aV\n5xp/oF8+v3z5GvPn/0X1Bm05f1S/N7BcuWdnV3kZgoNvGow/mUzGH3/8TLduHfH3DygSX1IqBdyN\nlpOYKkOtBLVSwuTRf7USHK0k7C2zzitER8cUCf28DPmhG5lMxq+/TiEyMpodO/4D4OTetSQlRNO5\nz+cE+R/lz0UraN68Ub4H8RbXTt4gloCLIA/v5hx/rqjzuvWTW+PvWXQb9A1Wj3IO//rrH3z4YXfc\n3bMuuT6NnZ0N587tpUWL1/MLvmrVSqjVKmLC76FJfxyWQZOeyu1rF4iLDker1SBJEn/99SsbNy7l\n77/n4uBgnC956NBx2NmVo1YtfZ7f5l0GcGjbcrLDwsKCrVtX5FrGceOGY2ZmyvmjO6jTvBtm5lbs\n2XPwmbHRXob9+x/vs3z//c6GvUnBwYX/vpLJoFmlDLxL6LA202ckSE6T8TBOxvWHCs7dVrL7koqz\ntxQcD1YQcFdOYqr+3KKgn5clP3Uzd+4Phtdz5vzAtfOH2b7yFwZOmMee3QeoW68dXbsNICEhMd9k\nFNdO3iAMQIGggKNWmzJ16UnK+9Tn7NmLVK3qx5AhXwBQ268zXfqPN6q/Zcsy/vtvLUFBJ177L/em\nTRui1Wq4evGoYf+h2tQcv479DHVcXV1Q52D02tnpl7c1Gg0fffFrjnmpBw3q80LOHLa21pw8uYvi\nxYtxcu9aajftAsBXX03LdRvPo337xyF8/v13E99/P+u1tf02YGmqjwVYx1NLI28NzStraFtNQx1P\nDWZq/cxf8AMFEfFyrt5XsO2CipPXFaSKMIAFEjs7W65ePcaECZ/RrVtH/vjjJ84d2c62FbOQJIng\noBvs23uIQZ9+TkREVH6LK3iNiD2ARRCdTpfvU/oFmYKsn9AbAWxf+SvxsZF4VqpN+x6fkZ6Wyuc9\n9OnVKlQox7Fj23NsIzY2nm3b9DNibdo0yzJDlxN167bl2rXrhvfe1RsxYop+9m7l3K84tG0ZO3as\nom7dx/EJe/cewtate2jXrgVLl+rzfpYrV4eUNA2z110hPT2VEV3KA2BmZsrIkYP44gtjL2nQfy7f\nffczPXt2fe6yrkajwcmpAkqVGk1GOs2aNWLdusW5Hufz+OqrqcyduwQACwtz7t71L9DXTV6TnAab\nz6mxs9BRxU2DVifD1U5CJ8GNh3Ku3FeQkqbD3QnKu+iwMZdyDNeR+a30Emlo30oK2rWzZcsu1q/f\nhqmpKfv2HyX84eM85Fu3LqdBgzpvVJ6Cpp+CwqvuAXxljd6+fRuZTGb4a9OmDX/99ZdR2bRpWX99\nP6vOli1bKFmyJJMmTQJg0qRJyGQyvv5a75301VdfvVRyasFjLp3am98iFGgKsn7cylZi0FcL+Pyn\ndbzTfzxqU3MyMvRxvdRqNV26tCM29rGziEajYePGHXz44WDKl6+Hk1MFSpeuybBhExg2bALe3jln\nJAG4c+ceJUpUwcWlilHgaAcHe66eP0xqsn5pqO0HwwH47bc/jc5v21Y/Y7Z9+384O1fCyakCsbHx\n+Pp10bf/aO9fhQrl+PLLkXz6ad9s5Rgz5htmz/6D3r2fHUw6E6VSSb9+H6DJSMfE1Iz9+48Y6eVV\n6dv3A8PrsmU9ANi5c79RHX//ADw9fRk2zHiGtjCilUAhk5AkKGEHbg4Scrk+Jlv5Ejo61sggOWQP\nkQly9lxWsfaUmk1nVOy9rOTkdf1ScVCYnHO3FGw7r2L1CbX+77iKf0+oWHtSxU5/JRHxhfPZ//S1\nk9907NiaJUt+Zd68Hzl3dg//+99Iw7EOHT7k888ns2DBUi5fvvpG5Clo+iksvDaT+vjx44SGhrJ8\nuX42oGTJkoSGhhIaGsrQodk/sLOrs3jxYpYtW8auXbuM6s6dO5ekpKTXJW6RJj01Ob9FKNC8bfqx\nc3Sh+TsD0Wi1/PDDL5QuXZOyZX3x9PSlWLGK9O//Gdu2/Ud4eCQajT78ipmZKY6O9rmKDbh69UZS\nUlJJTU1l06YdlC/vyeLFv/DDD/8DYOnMMQDYOjhj5+jCrl37jfbc9ez5Ln/88TOg/yUPUN6nPj2H\n6/ceeZSvgZ2jC1euBDNx4rRnhnzJTAsXGnrvuTLrdDqWL18LQLMuHyNJEqdOnXvuebnls8++NLxu\n374lAMnJxtfNrVt3iIqKYcWKdYSG3n9tfRdErEyhRmktsclyMrRZjyvkYKNOon31DJpXyqBOWQ1l\niukwU0vEJcu4FqbgQoiC+zFy1EqJ0k5aapfRULOMluoeWqq4aVHIYV+AkmNBCqISC5ch+PS1U5Cw\nsDBn7NihBAWdMOx3/fPP5Ywf/32WH3t5RUHWz9vMazMA27dvT+vWrTl79iwADx48oHr16vTu3ZuI\niOzzf2ZXx8/Pj+bNm1OyZEmjug4ODixe/PqWcIoytg7F81uEAk1B0s/Sn0czrJMnkz9twe+TBzDn\n636cP/I480VcdDgTP2rEgS1/IUkScoUCEzML0jUyMnQqSnvX4N0B/6NBa/2MVcWKXmza9Df3718i\nOPikUX7gZ3Ho0HHDa5lMzrVr1/nzz+UEBFzF2tqKC8d28tdPo9i5ei7vDPwfMpmc3r2H4u5eg08+\nGcOtWyF07doBhUL/uHl3wP8Y8u0SQ5tKpZIu/cdhYW0HQM2axiFmNBoNgYFB9O7dHdDHNFy2bE2O\nMm/f/p/B2C3uVhaAGzduPXesuSUu7vFsYmZmhacDQHfq9Dh5/ZQpMynsFLPWIUPi3C0F2mxSr7q4\nOKOQg5O1ROliOqqU0lLfS0srHw3v1s7gvboZdKiRQcsqGup4ainrrMPTWUe54jrKl9DRvLKG6h5a\nohLl7Lmk4r/LSu5FFw5D8G0IHu7k5MDChTM5eHAjDo+ycty4cZuLFwPyvO+3QT9vI6+8BzA6Opod\nO3ZQqVIlRo4cyeXLl9m1axeRkZFYWFjQvXt36tSpw8aNG43OO3v27DPrRERE4ODggFwuZ9KkSUye\nPJl58+Yxffp0PvjgA6ZNm0ZOYos9gDmTlBCLhZVtfotRYCko+tHpdAztUDpLuZWNAyU8vLl55Qwm\nphYkxkcjl8upVasaiYmJBAYGGcUdvHx6H3O/+QgHB1uuXz/1wnKsXr2JTz8dC4BnJV+uB+TQhkxG\nzUbtUavNuHhyD0kJsQA4OtoTHR2LTqfj538vGuIcRoSF8M+c/3H1/GFUKhVVqniza9e/KJVKUlNT\n6dHjUw4c0HvdTpgwgrlzFxMfn4BSqSQk5Czm5ubZirFkyUpGj/6aDh+ORqvJYMeq31i7djHNmzd6\n4fFnx+efT+bPP/WrHebmZly5chStVoudna1RvWLFKpCRocHS0oLQ0Auvpe+CzK1wOadvKrAyk6jn\nqcXW4vFzOiYmNot+XgadBPejZVwMVRKfIqNZpYy3PiD169LNm+LMmQu0bNnd8P727XPY2OTdd+3b\npp83Rb7HAbS3t6dXr14AvP/++xw8eBAXFxdq1tRvAm/cuDHnz5/Pcl7m8ezqODk5Zanfu3dvJk+e\nzLp163It27nD21CbmFGtQRuuXThKSlICVrYOeHhVM+zzcvOsjKTTcfemPi9r1bqtuB54mqT4GCys\nbPGsXAf/4/rlaNfSFVAolNy5fgmAyr7NCAm6SEJsJKbmllSs0ZhzR/Qb8F3cvTAxteD2Nf24KtZs\nwv3b14iNeoDa1Jwqvs05e2gLAM4ly2Bpbc+NwDMAeFdvSPjdm0RH3EepUlO9QVvOHNyCJOlwcnHH\n1qE4wZdPAuDlU5fo8PtEPriDXK6gZuMOnDuyHa0mA/tirji5uHPN/xgAZSvVJiE2iuN71lC2Yk1q\n+3XmwrFdZKSnYuvogkupclw5p4/vVqZCDVKSEgi7EwxAjUbtCTxzkNSURKztnCjlWZnLp/X7Mty9\nqpKRnsb92/r9INXqtybo4gmSE+OwtLandIUaXDqpjzPlVrYSoHdmAKhSpwW3rpwjMT4ac0sbvHzq\ncuGYXt8lPLxRqU0ICfLX67t2U+5cv0x8TASmZpZUrNWEc4e36fVdqhxmFlbcvKJf5qtQozFhd4KJ\njQxDpTalWv3WnH6U9qiYaxmsbB24EaCPn1e+an0iwkKIDr+HQqlCq8lALleg02lxLF4K+2IlCLqo\nz7JRrnIdYqMeEBEWgkwmp1aTjpw/ugNNRjr2TiUoVrIMV8/r8+OWrViLxPhoQ1iZmo07cunUXtJT\nk7F1KE4Jj/IEnj0IgEf56qSlJhEWEgSAj28Lvh74OHzLzJnf8vff/3LhwmUSE2K55n8UU1MTEuOj\nAb2xmJqaasj3Gx8byekDmyhZpiKr530DSIwdO4SEhETOnPEnKioaa2srmjSpx5Yt+uXaChW8MDc3\n5ezZi9y8GcK//27k5s07Rj+2njb+zC1tSE6Me1wgSZw9tBX3cj68P+Q7oh/e5cq5wwQHnESn01Gz\nUQcy0tMIunSCpT+PITr8rv7eci3OmDGDqVy5AlFRMRw+fIKZM+dz5UqQoWkbG2s++qgHs2f/gUaj\nYfr03/jss48NYVl8fWsQGxtLUNBNNmzQ34dW1vYc3K431GrUqMLatfp7rmZNH5KTUw3td+zYioMH\njxMfn4CTkwM1aviwa5f++q5atRI6nY5Ll64A0K5dC9q2bcbOnXu5ezeM5OQUqlZtyjvvtOXjj/ug\nUim5cEGfMeXDD7uxZMkqEhOTeOedvvz775/Mn7+U+PgEGjTwRafTMXbsJJKSkjl2bBtXr14nLOwh\n5ubmtGnTlPXr9dd3uXJlsLe34+RJ/QqLn199rl+/zd279zExUdOxY2vWr9+GTqejdOlSuLg4c+yY\n/vpu2LAOd+/e5/btUBQKBe+8047Nm3eSnp6Bm5srpUuXMszy1q1bi8jIKK5f18+WduvWkW3b9pCS\nkkqJEsXx9vZk374jj/Rdnbi4BINTUJcubbl27hCpUcncSXcmOrYyqof/IZdDjRo+bNmyC1dXFwA6\ndGjFkSMniY2Nw8HBjtq1q7Nzpz5Yd2bKv4sX9c/kNm2acfr0eaKiYrC1taFhwzqc2L+b1HQIl1Xi\ncIoKKfoCAC1b+nHxYgAPH0ZgaWlBixaNDbmiy5f3xMbGilOn9M/kZs0acvXqde7ff4CZmSnt27c0\nXB+enqVxdHTgxAn9M7lx43rcunWH0NB7qNUqOnVqw4YN29FqtXh4uFGyZAmOHNE/k+vXr01Y2ENu\n3bqDXC7n3Xfbs2XLLtLS0ilZsgSenh4cOKB/JtepU5Po6BiWL19LnTo1ePfd9uzcuZ/k5GRcXJyp\nWNGLvXsPA1CrVlUSE5O5elX/TO7cuQ379h0hISGRYsUcqVatMrt3HwCgWrXKZGRoCAjQP5Pbt2/J\nsWOniYmJxd7elrp1a7F9u/6ZXKVKBeRyuSFLT+vWTTl37iIREVHPfEbcvh3KwIG9+PPPFY/G3J7B\ng/syeHA/w73n5VUGW1tbw9aLpk0bEBR0k3v3wjA1NaFDh1asW7cVSZIoW9aDYsUcOX5cr+9Gjepy\n585dQkLuolQq0Wg0hv/u7iUpVaokhw/rn8n16tUiPDySGzf0/ghdu3Zg69bdpKam4erqgpdXmWyf\nEQDvvNOO3bsPkJSUjItLMSpXrsCePQdfyzPixIkzREfrDdf69WuzbdseACpV8jZ6RrRq5ceFC5cJ\nD4/EysqSZs0asmnTTgC8vcthaWnOmTP678DmzRsRGBhkeEbUr1+bV+GVZwB3795NWFgYvr6+DB8+\nnAsXLjB58mTKlSuHvb09nTt3xtfXlw0bNhATE0NaWhrFixfn999/x9PTM0udp8mcAczIyGDGjBl8\n+aV+742YAXx5Th/YJHIB50BB0M/obpVJeSLNm1wuN+yfA/2s07hxw9m0aQfnzl3CzMLaECxaqVJj\nX6wkHXqNpLZfZ9Yu/I69G4z36piamhAWZpzW7UlKlKhCSkqqUdmcOT+QkJBIiRIuJCQkMGzYBKPj\nDs5uRD0MBfS5fX/d+Nh402g0pKcmGWb+tiybyfaVv2BlZcnu3f8aOZdk4uPTxLB3btq0rxg0qC8b\nN+6gf399nuCqVStx4MDGbOUfMeJ//P33v3w+cyNLfvyMlIRI7t71f+Z4X4aLFwPx8+tieBY1b96Y\ntWsXGdXJzBiSmZHFy6uM4cvnaUJDz2NpaflaZcwv4lNg+wU1jb0zKGGn18/atVtee77boDA5524r\naVs1Axvzt3cWMC908yb47ruZzJw5D9A7ofXt+z4fftgVH59Kr7Wft1U/eU2+ewFbWloyffp0qlev\nzr1791i1ahVyuZyPPvqIxo0bU65cOWbN0sfJGjVqFLVq1QL0Ucizq5MTgwcPxspKGHSvikf56vkt\nQoGmIOinYm0/7JxK8MlXC+gxbAoly1bC3aua4XhycgrffPMj585dwtLaji9/2061+m0A0GSkE37v\nJrvW6B/M3T6eaHC4yMTTM+vS8pN0797J8PrXX6cSFHScxo3rM2HCFPr2HZbF+APQajLoM/pnPLyq\n0bH3WKNjSqXSYPw9vHeL7St/ASAhIZFevT41zGo8SZcu7QyvV6xYx8OHEXz++SRDWVDQDSpXbsyK\nFWuNzhs58itWrdL/mDyyYwUKhdLIeH5d+PhUZM2axwbfpUuBRsf//nu1IWPIx/+b90jmrMZf8eLF\nWLlyQaEx/iQJ4pL1e/NSMx7v0atVK/v0ga9CWWcdKoXExTsK8uAjfmPkhW7eBP/730jatWuJUqXG\nxd2bhQuX0aRJF+zsyhntHX5V3lb9FHREHMAiyN1bVyhZukJ+i1FgKYj6+WfOlxzerl9uqVWrKrdv\nhxIZqV/+dS1dga/m7iQ5MY4v+9QhPS3tUfy7NCRJQiaTYWXrgFJlQnS43oPW1NQUBwc7TE1N8PBw\n4++/5xjtp9NoNMyatYDBg/saDJNt2/7jww+zOo2YmZliaWlBREQUljb2lKlQk3f7T8D5kfPF08z5\nuh8BZ4zDOpiamrBz52qqVn08c6DT6ahatSl37+bsQatQKIiMfByOwtOzDlFRet007dSfsDvBXPM/\nSnR0UJZzX0d8MUdHb0Pu45iYx4asg4MXOp3+8TprbQBffdSQpPgY6tSpwc6dq1+pz4KKJMHpmwpu\nhitwttHRwEtjyBd8+fJVKlf2fu193omUcyxYie8jz+K3kbzSzZsgMTGJtm17cPnyFaPygwc3vraZ\nwLdZP3lJvs8ACt4+MveZCbKnIOrnyrnDhtdnzvgTE/N43513tQZsXTGLuZM+wsahOGUq1CQjPRVT\nUxMaN66LtbUlKrmW+OiHhnNSU1O5dy+MGzdus3fvYerXb2/Un1Kp5PPPhxrNSrVv34KBA3tx4sQO\noqKu0aFDS2bO/Jb79y8RFHSC4cMHkpIYx8UTe5gyvO0zx5K5tL1mzZ8MGNATmUxGamoafn5d8PHx\n4/PPJxsMM7ValeX8Jk3qI5PJkCuUKJWqLAaci0sxAPw69qNTn7EUc/VAkiTu3Qsz1Ll3L4xKlRrh\n4FCeH3+cYyhPTk7m888ns2TJymfK/zRPxiU9f/6S4XWVKhUNrxdM+ZSk+BjMzc355ZcpuW77bSMp\nDW6GK3C109G04mPjD8h2lvd1UMpRR0l7HRduK0h5S7ON5JVu3gSWlhZs27YCFxfj6Ana7FzBX5K3\nWT8FmVd2AhEIBHnLzHHvE/ngDgCVKpUnIOAakgRt3h+GTqth99r5RvXD7+k38KekpPLbb9M4deoc\nJUuWwNe3OnPmLOLixUAcHOwwMzPF3t6OW7fu8M477bL0mx116tSkfHlPAJYt+93o2LffjuPbb8dR\nrVozQkJCmfN1P4Z9+1eWNirWaMzNwDM4ONjz00+TGTduON27D8TfP4DQ0Hv8+edyVq/eSPHixbh5\nM8To3Hr1arFx41IaNOhAYOA1dEDXrh0Mx/39A7h8+SplKtTk/cGTOblvAw9D9cuulSs3znZMP/zw\nC40a1eXIkZNMm/YrOp0Oa2sr+vfvkSudNGlSz7BRv0WLbuzatZpatarRsKEv/v4BOLm4c/X8YUqV\ncuX8+X2FOqOBuQmYKKU3nsGjhoeG87eVJKXJDOnoBG8Oa2srevV6l1mz/kCr1YdfunIliOrVq+Sz\nZIKcEEvARRCtJgOFMuvMikBPQdPPrn9/Z+Nf07GxscbW1pp79x4ya/1VlEolg9u5A/pl0F27VtOi\nRbcc28r0EpYkybBs2afPe/zzz3o8PNw4eXKnwUC5ejUYKytLg+cmQEZGBipVVt3Exyewe/cBOnRo\nSUREND4+eg/mqvVaERLkj5mFDaN/XIOltS2n9m9gyYyRTJr0OYMH9+PmzRDCwh7y7rv9AXAs7k7k\nA73hpzY1NwTmLlOmFG5urty6dYc7d/RL2Wq1isDAI4Z0du+8048DB47y7aJD7Fw9l2O7c7fU6uBg\nR1RUjOH9nDk/0KtXzrrMxN8/gObNuxr02aPHu3zzzVj8/Lrw4MHjFFrr1y+hadOGuWrzbSUmScau\niyoals+gpL3xV8uzrh1B4dBNQkIiNWq2JDIiEoDDh7e8tmXbwqCfvCDfw8AI3j4Czx2iim/z/Baj\nwFLQ9NP6vSHcuX6Jc0e2ExcXj0f56gZDLhNrayvOn7+Ek5ODUcL2TO/TTLRabRYP+hUr1qHVag2h\nPwDOnvWnRYtuODrac+3acYNRuG/fEVq3bppFxpEjvzKEf3gS/+O7MTU1JTbqIX9MGcTo6aup1aQz\nf88cy/Lla9mwYTv+/gFGy6gSj+XNNP4UShU3b4Zy61aokfzbt6/EwcGeoUPHsWnTTpKSkrF3cmXq\nsLakpiTh6OjAvn3ruXlTHyJCLtf3I5fLkcnkyGQy0tPT6dy5zyN9yViw4CcjJ5hnERb2kD59hnH2\nrL9BJhMTNSNGfEzVqn6kpenXI4sXL8bYsUMLvfEHGDJ0ZDcB+KxrR1A4dGNlZck3X49m+HB9pI45\ncxYxf/6M19J2YdBPQaTwrkUInklm7lZB9hRE/QwYP5e6zbtS3K0sfUb/ZCi3ddBHyI+JieXzzycb\n5btVqU2RJB1mZqaGsuwm/GVyffYQgJMn9TG7MmfYIiOjadXqPUPdhITsdXPlSjZ7dGQyTE1NCA4+\nTokSxbl7Ux9nTC6X416+Gtev36Jjx9YGuUzNLfll4zWQ9DOaZ87s5vDhLajVarSaDECibMXa+NRt\n+VgvA0YxYMBI/vlnPWnpWjwr1yE64h6ajDS++GIY/v77cHMrQZMm9WncuB4NG9alYcO61K/vS506\nNTh71t9g/AGsWvVHrow/gMmTZ3DmzAWDTosXL8bVq8fZtWu/wfhzdnbiypWjDBjQM1dtvq1EJsjY\ndl7FmZtKSjtpcbHLep0969oRFB7d9OzZ1fDaxET92totLPopaAgDsAhiZeuY3yIUaAqifuRyOX3H\nzOSbBftwcfM0lA+auBBHF3c8ylen9XtDsbJzplKtptRv/QEZ6fo4fjKZjC++GAboY/U1bNMDn7ot\nady+N6Omr0GlNiEtRZ9nu127HlSq1BCtVouFhd4r+OxZf2rV0htdxYplr5sBA3qiUCjo0KEla9cu\npmbNqlT1qciGDUuJi0sgPDwSU7PHDiWdeuvzB0+dOttQ1ub94ajVptT264xWq8XXtw2enh6EhJxl\n48alqNVqbl87z+Cv/+S3jcHUaNiO0ND7rF+/DQtLG76YuYF7twIxMzPlwoV9TJgwwsizOT4+weAI\nEhp6nxIlqvDVV/rwOEqlkgsX9tGqlV+uPo9RoyayerU+sLharWbGjG9YsOAnUlJSjJxK/ve/Ublq\n720mXQNHg5SolRKNvTPwLatFns0U4LOuHUHh0Y1cLqdDh1YA/P33vyxe/M9rabew6KegIfYAFkFS\nkhOEXnKgMOjnyrnD/PrVhwA4ONhz8eJ+ypb1JTU1DStbRz6d+AdlKtQ07C+sU6cGGRkazp27+Mw2\nFy2aRYsWTbC2zr1uNBoNzs76KPkDv/ydmg0fexufP7aTVXP/R3yMfs/Q3K23kMvlnDm4mUXThwN6\nj9+NG5cC8O23PzNr1nx6Dv+BRm31M2rJyYkkxkZQrIQ+ruHo7pUxN1URFPR42To5OZlOnfpw9qw/\ncrmco0e30qBBB0NswFmzvqdfv/dzPaYbN25Rq5b+S04ul7N//wZ8fCqyd+9hunX7yFDvgw+6MG/e\n61kCy2/SNXA7Qs6dKDlaHdg9SvEmSRCbLCMxVUabqhlYmDy7jfj4hBe6dooShUk3GRkZrF27hQkT\npjBu3DAGD+7/ym0WJv28TkQYGMELc/nUvvwWoUBTGPRj71zS8DoqKho3t+qkpqYBkBAbyaLp+mwa\nxUqWAeDChQC8vMpw6tQuFAoFSpUav07GD+4BA0Yxffpvuep/69bd2Nt7Ua1aM4OhVdLdeEN49fpt\nmLL0JM26DKTHsCkGg23xj59l22Zm8Op7t/Qx/36fPIAx3SoxZWgbUlP0S0TV6rclKiqaSpUa0bfv\nMNzcquHqWpWzZ/VZQHQ6HfXqtTPItG/f+hcy/q5eDcbXt43h/dKlv+HjU5H//jtoZPwNHdr/rTX+\nktLg1A0F286r2HhGxdpTKtafVnP+tgITJdiYScQkyYhNkpGQKsNECY28NTkaf4AhTZkgK4VJNyqV\nih493uX27bOvxfiDwqWfgoRwAhEICiFPhhoxNTXB1tbGyCM1Ovwuf88ay4cjfsTarhjxMeGsWrWR\nn3+ezNSp/2PcuG8Jupg1kv/KlRto374F9ev75th/v36fGcXea9imR7aBoZVKJd0/mWh4nxgXbbRP\n8eDBY+zff4SmTRtiaanfpxgS5E/ojQBDfun0tFQiw+5QskxF+oyagYmpOQe2/MXmzbsM7chkMiws\nzElMTDKUtWnT9IXDVAwf/uVjg7ZkCTp0aEVqaiq9ew99QuZNhny2bxNpGRASKediqAKlHNzsdZio\nJJQKMFGCs40O8+cYeQKB4O1BzAAWQUp5ithMOVEY9OPk4k7xRwZXhQpeBAQcNuwDzCQpIZbda+YR\nH/PYMGzXridBQTcAiI8Op7JvM6NzYmJimT37DyZMmEKHDr2ypFg7c+YCI0d+ZQiJAmBqbsWRnSuZ\n0DtnoxFgwfeDAH3Q6Uz69RvOsmVr2LFDb/CZW9rg7FYW93I++goyGfbF3Qz13x88GcfipZDJZAwd\n2p8TJ3YQHR3EtWvH+PXXqSxdOocdO1axYoVx/MSc0Ol0dO8+gDNnLqBUqlEqVdy9e5+zZ/0ZOHCU\nYXbVysqS69dvcuTICYKDb5KYmHXzur9/ALduhWQpz09uPJSz5ZyKc7eVuNnraFctg5pltFR20+Fd\nQkfpYq9u/FWrVvn1CFsIEbrJGaGfvEHMABZBMgN1CrKnsOhn4rz/+HF0F86f9+fLL6cwder/sLW1\n5r//DnPs2GkCzuyn57CpnNq/kbA7+uwn/v4B+PsHYGNfjGHfLiUk+FKWJfG4uARDjtsnZxr9/Lrg\n7x+QRY70VP2sW0pSwnNlTkqIBfRp51QqNWYW1sTHRvLZZ/rQEuaW1jTp2IcZo9/h7s1AvKs1YtBX\n8zE1f+xgcj3gjCFw9hdfDDfsHTI3N6d37+6kp6cjl8tfKCDz7NkL+O+/Q8hkcjSax+kmno67mJCQ\nyIABz3f8cHMrwcWLB3Pdf15y/raCa2EKyhTTUsVNi9nrc940IiOjcNxXeYHQTc4I/eQNYgawCHLv\n1pXnVyrCFBb9yOVyQ07jv/5aTZcufahWrQrr1i2mc+fWaDUafv6iO4O/Wcy87SH8/O8lavl1ok7z\nroyZsZaSZSpydNfjlGjm5mYAnDp1zlD2ZMJ3a+vHRlgmjRrVwcVFH6om7VFMv5z4cs4OqjdoS42G\n7fhptT8DJ8zDxt4ZE1O9N29yYjzzJg/g7s1AAO7dCjQy/gBKlauMdzV9zL1y5epmSSPl4VGTkiWr\nEh0d+1x5QB/u5bvvZgIgSTrmzp2Ora01pmaWuGaTM/ppebLj66/H5qrvvCZdA9fCFJR30eJbNu+M\nP4CAgKvPr1REEbrJGaGfvEEYgAJBIablu58AkJaWxuHDJ2nXrgfJycmGvW8R92/z7af6Zd6JHzXg\nzIHNnNy7jq8HNOaPKZ/S+r0hBo/o5OSULO0/GUNv8+blxMQEc/36SX76aTJBQcdZuHAW9erVAjAY\ncTmhVCr55H/z+fjLeahNzflnzgTioh+SlppMzZpV6d69E126tOXMmd1YWVmSEBfFnK/7GrWhVpsy\nYuoKVGoT0tPTWb16o9HxlJRU0tLSWbly/XPl+eWXP5g9+w9Av7l98eJfOHToGLGx8XhVrUdCTAQy\nmYwRIz42nJOanIiJiQlffDGMmJhgAgOPcPnyIWJigg1/3bp1fG7fbwLdo+2WCvFNIBAUOcQScBGk\nar3W+S1CgaYw6cfZrSy/bQxm5vj3uXVVP3O3YMEyRo0aRIMGdWjUqCOajHQ+e6c8GWmpRueeP7qD\n80d3MHr6asytbJnzdV9iIx8AUKpcFe4EXwLAwaE8nTu3YfHiXx69tzcEPi5btrZhpq1U5p69F6B6\ng7bs+vd3dDotFy8G8PHHvZHLZQwcOBq1Wp8a6pr/sWzPtXV0IeL+berUqZnt8bi4+GzLdTodX345\nhWXL1pKcrJ+19PYux9aty2nZsju3bt3B1qE4V84dIiNdv/cvMjKaypW9iYuLZ9Omvyld2t3QXuYM\naEEkcwvnwzgZKenk6Qxg+/Ytn1+piCJ0kzNCP3mD+N1XBLl++WR+i1CgKej6SU6M49rFE7mur1Sr\n+WLmBgZNXIhMJuPbb3/i449HM3HiNMaP/0wfA/AJ469x47qUL/842HRs1ENcPbyZ8tdxeo/6mblb\nbzHhl61MWqDfG6jT6diwYTshIaFZ+m7cuJ7hdXT43SxOI8+jU5+xzN16k4Ff/k5GhoZPPx3LJ5+M\nwf9iILFxCbi4ezF2xrpsz23fcySgn8XLJD093RDg+s8/l5Oens7u3QdwdfWhZs0WrF69iXLl6rBg\nwd8kJycjk8GYMYM5fnw7EydO59atO9Ro1J7YqAcG4w+gXLkyTJr0BRcvHjQy/go65iZQ11NDUpqM\n/YEqktKef87LcuzY6bxr/C1H6CZnhH7yBjEDWATJ3GgvyJ6Crp9f/teLO8GXaNfjMzo+yqiRG6rV\na8WUpcf5X996rF27BdDv4QsLu0TJktXIyMh4VGZsXNb26wzo9xSqVCqD80TY3RtG9WrUaMG3345j\n6NDH8fCWLPmVkSMD8PPrQtTDu1w5f5hKNZu88JhrNmxPqUWH2LthEUqVik69x6B+zpJylUcezElJ\nj/cetmr1nuF9TEwc9eu358aN2wDcvBnCp58+3pvn6GjPhg1LWbduC6VL1yI2Ng5La3sUSuNpsmHD\nBjBixCcGnb5teDjpsDaTOHhFyU5/Fa19MrA0ff55L0pMTOzrb7SQIHSTM0I/eYOYASyCWFjb5bcI\nBZqCrp9Mb9rzR3e88Ll2ji5M/vMwKrU+poeZmSkPHkTw4MFlDhzYiEwmA4zzeO3btNjw+kndbFz8\ng1E9nU6Xbeqn77+faXgdev3yC8uciZOLOx8M+ZZuH098rvEH+nAxDs5uXLp0BR8fPwYOHJXFSznT\n+MuOhIQkOnfuzezZf5CarsXazonE+GhO798AQOnSpQgKOs53340HwN7e9qXHlt/YW0q0r67/AXAr\nXJE3fbzF+slrhG5yRugnbxAGYBHEs2Lt/BahQFPQ9VPFVx8jr/gTOYFfBCcXN/43dxfOrmVISkqm\nalU/6tRpQ5UqFR4FYZawtitmqB8X/ThO4JO6+WreHsMycCZDhnzE0xw/fgaAMhVrUb917rNuvA4m\nLdhH3eZduXcvjHXrtmJpY8cPy06hNtF7NKvUpihV+hk9W1sb5s37kQsX9GNKS0sjPj4JW0cXUpMT\niY+JMGp7w4a/cHJ6nKO0bt1ab2hUecPV+woytDJMVHmTHfRt109eInSTM0I/eYMwAIsg/id257cI\nBZqCrp/un0xkxip/Bk74/aXbcHYtzaSF+xn4pb6N69dv8c03PxqOx8eE07H3WNr3Gkn7XiMN5U/q\nRqlU4uxWFrni8YzR2LHfUKlSIxwcvLC392LYsPGGzB5jflyDte2bTequVKvpO2YmJzp9RAQQFxfD\nuN6+HGrbg0ETF/LjP2fp8KF+GT02No5hwyZQrZp+6XjwpCUgkxMbGZZt29WqNeP99z/mxImzAGzf\n/t8bGVNeEBQmJ/CegsolNXgWf7F9mrnlbdZPXiN0kzNCP3mDMAAFgrcQS2vbFwpk/CxqNmyPq4c+\nR++cOYuMjgX5H6NDr1Go1TlvCBs4bi5VfJsz/Z+z1GvZnQcPItDpJCRJYsWKdSQnp2Bhbfda5H1Z\nam38EwceP/B8Ny6mWr1WmJpb0rr7p3y9YC/FXEujMjHH1tGFmo07UKpsJTQZj70i5HI5VlbGMf52\n7z5A27YfsGhR1qXvt4HUdDhyTcm520rKu+gzf8hlzz9PIBC8/cikJxNvFhLi4+OxsbFh5trLhhhm\ngseE3QnGpVS5/BajwFLU9JOanMitq+fRSTrW/zSSGvExHJckpqtMsPxlMzGPDETInW40Gg3odCjV\nauKiw3lw9yYupTxfy+yf3e2rdBr/AabxsaRa27J52iqDfOH3b3Piv7WUr9aQ8j51AQg4e5CDW5bS\n69Re6gOewFqgK7B5+7PTsZ3av4ELx3Zx/ugOrK2tuHTpoCGjCEBo6H1++mku69ZtNTiVlC/vSbt2\nLTAzM8XMzJQ+fd4zOqegodXBjgsqMrRQs7QWNwcdsjw0/q5du27kXS54jNBNzgj9ZE98fALu7jWI\ni4vD2tr6hc8XBmAR5MHdGxQvWTa/xSiwFHb9VFs+mzr/zDIqO/fOJ5z++H/0/aAapvExyAAJSLW2\nY+mqC4Z6+a2bnOT7sk9dYiLDkMnl/LoxGJ1Ow4gu5bNtZw6gyMEAfDouopdXWRYtmk3lyt5G9eLj\nE6hevTnR0TFZ2ujWrSMLF87MUl5QiIiXsTdARYvKGTha5f3XQHDwTcqVK5Pn/byNCN3kjNBP9ryq\nASiWgIsgr+KJWRQo7PrJNP6e9PetsUEfK880PtZQJgNMnwqJk5+6cT+6w2D8QVb5MtL1BpvpIw/h\nuzf1Kf0aN67LmqYN2QJMe1R33XN+GLqVNU4+HxR0g0aNOmaJY2htbcWNG6dYuXIBjRvXo3Jlb5yd\nnahatRLffz/hZYaZp0gSRCbICLgr5/RNJSYqCVvzNzMHkF2eaIEeoZucEfrJG4QBKBAUQWTPeJ1q\nbUumOSABSBJ9P6iG3e28y8VZ/OJxPu5YlkHt3BnUzp13RnbC7CmPW4A2Uz41ei8BqVa2hvcjp62i\nfa+RfP/3CZRKJQu+HYhMJuOnnybTYv0SDn0+lK8fZQ8pPWxKjjLdvnoeAAsLc7p372Qob9GiW7bB\nrMuXL4uzsxO7dq3m6tVjHDiwEWdnp9yqIM9ISoNbEXIexslISIFjwQr+u6zi6n0FVqYSfhU0KPMm\n6otAICjgiEDQRZAqvs3zW4QCTVHQj8Rjw+/J+Z/N01Y92mOnX9KUAabxMXQa/wFLV13Iohv3ozuM\nDLOd/5tPSIO2LyRLx/99iFyrMchTLMifpjPHsP27v7PUfXqL2uZpqwyvXT28DQ4tAClJ8VSoUI6j\nR0/Tvn0vIiIiUShVtO850hDc+llISHh7l+P48e0AWFpasGTJSs6fv0Tdum3ZuXM19va2HDp0nHff\n7Y9WqwUgPDySjRuXvtD48wKdBFfuybkcqkB6QmsKuUS9chrcHN68s0fr1k3fbIdvEUI3OSP0kzeI\nGcAiyO2gC/ktQoGmoOnH7vZV+n5QjUHtPF7LbNzJnqMAveGXafyde+cTAGI8vB/tqZMZL7XGxzCo\nnTsZK2YbtZVp/Mmeev8iPGn8ZbblFHwx27rSU/+fdFB5Ep1OR0ZGOoGBQYwa9RWRkVEAaDUZbF/1\nK2cPbX2mXtNTk1GpTbl6NRhXVx9iY+OZOfNbevZ8F9DvR/L09GXz5l107z7QYPwBfPxx7xce/+sk\nPgVO3VCw9byKS6EKKrjqeLd2Om2qZtCkQgbtqmXg7pg/nr7nzmX/mQqEbp6H0E/eIGYAiyAJsVH5\nLUKBpqDpJ3NG7unZuJflwocjufDhyBzrpFrbGjlbZP533bCQ+I+/Mqr7pKH4MrvJdAqlkREoARHl\nfLLU2/m/+bSZ8qmhj53/m59juyam5khI+Pp14cjOlQCUK1ea4OBb/DltKL0USkwf9WsaH0OnkZ1Z\nsv4K/+vfgPRUvWdvcnIKMTEx2NpaM3fudGrUqMrYsd8gSRJ9+w4z9PXLL99jZmZG+/YtXkIDL4Yk\nQWyyjLvRcmKTZNhaSCjlEhEJcu7HyDBTg5u9jlKOOoNzh1opYfv85Cl5SkREwbqvChJCNzkj9JM3\nCAOwCGJmITyjc6Kg6ed5jhl5wZNLwU/2bQPEP1X3SQPxWeQUvmXLlOWGZWCAcK+q7B/9c5Y2Qhq0\nZUEOnrtPIpfLmb1e7wSSnJzAw7s3Cb58kuDgW4Y6TbUazgHVH8lvmp5K2J0gEuOiadmyCfPm/Yid\n3eN4i1269OHEiXPs2bOG9u17kp6eYWjrww+7899/h3Il28ui08HDeBlX7yt4GCdHKZewt5S4/kCO\nTgJbc4laZbSUdtKhKIBrOwU5JE5+I3STM0I/eYMIA1MEychIQ6UyyW8xCiwFTT/PC82Slwxq5w48\nNvDSgE2/7zIYb7ndA5ifY8hEp9NxdnwPFl8+YShbA3R79FoCpv19gi/71MXTszQ/jf+MNhN/IDUs\nnF1AZhK7mJhgoqKi8fN7h7t37wP6kC+//joVM7Ocg2a/CJKkd+KITZJxP1bO3Wg56RoZNmY6qrhp\ncbGTCqSh9yzS0tIwMSk491VBQugmZ4R+skeEgRG8MBeO7sxvEQo0BU0/m6etItXaDkkmI9Xazsjx\n4U3w5AzgevRL0plkzspl/j3LASQ/ZjGfxCwmgg7f9GP+5RP8ATQGPgVaPjqe+Su45+wvcLC25/r1\nW3QZOAqHsHAceWz8ASQnJ+PgYI+//34aNPAFYO3aLZQoUYWSJasRHR370nKmZcCdSDlnbirYeFbF\n1vNqjgSpCI+T4+mso7VPBm2qaijp8HYZfwBbthTsFIv5idBNzgj95A1iCVggKOA8dsx486Ra2+UY\ney/37RjvKUSSKLdrNcGt33/Oma+HpjPHUPL8EeTAx4/+wNgRRga4XThKNYWSvY/Kkh79fwc4CEQD\nc+Ys5osvhiGXy9m6dQXz5i3hyy+nAvqZiri4OOztbV9YxodxMo4GKUnXyLAwkfBw1FHcRoethYSp\nijzN0iEQCIoewgAsgpRwzz47gkBPYdRPenoqM794j/C7N3CXyZmcFE839EbPvhE/PtMQ2zxtFe8N\naW3Y51cJQJJwP7rjhcK9PN2OBDT75Qsiy1d9pifv66RYkD9ynTbbY2lWtgajVq7T8rOJKfVNTElO\njDPUiUNv/AH88MMvaDQavvxyJACDB/dn8OD+BARco1Il/bVz7dp1jh8/S0REJKVKlaRhQ19cXV2M\n+tVoNMydu5gNG7bz4ZD/YVOqPo7WEnXKZmBeCFe7KlTwym8RCixCNznj7e3FjYd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sWQJP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1OQ9j4k6OSHZ20cYkf/7Kwzxz3yXETb2uck5cFbeCPK584sbT9uXi5sGjL2tFpNVqRVEUXFxO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\nQCGEEEIIByMFoBBCCCGEg/l/fMJFePCuwQUAAAAASUVORK5CYII=\n"
    }
   },
   "cell_type": "markdown",
   "id": "1046259b-b772-4700-9ad7-ff64767518be",
   "metadata": {},
   "source": [
    "![DFO_Mooring_Locations.png](attachment:8b114a42-d5c2-4b11-8d29-e7c6fbd6d217.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5a7da35d-84b6-403a-9a1c-0e877c638b64",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_16973/2798140817.py:11: FutureWarning: In a future version of pandas all arguments of StringMethods.split except for the argument 'pat' will be keyword-only.\n",
      "  df[['latdeg', 'latmin', 'dir']] = df['Latitude'].str.split(' ', 2, expand=True)\n",
      "/tmp/ipykernel_16973/2798140817.py:14: FutureWarning: In a future version of pandas all arguments of StringMethods.split except for the argument 'pat' will be keyword-only.\n",
      "  df[['londeg', 'lonmin', 'dir']] = df['Longitude'].str.split(' ', 2, expand=True)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter, LatitudeLocator\n",
    "import cartopy\n",
    "\n",
    "def plot_moorings():\n",
    "    df = pd.read_csv(\"Master_Mooring_List.csv\", skiprows=2)\n",
    "    \n",
    "    # convert deg min to decimal\n",
    "    df[['latdeg', 'latmin', 'dir']] = df['Latitude'].str.split(' ', 2, expand=True)\n",
    "    df['latdeg'] = pd.to_numeric(df['latdeg'])\n",
    "    df['latmin'] = pd.to_numeric(df['latmin'])\n",
    "    df[['londeg', 'lonmin', 'dir']] = df['Longitude'].str.split(' ', 2, expand=True)\n",
    "    df['londeg'] = pd.to_numeric(df['londeg'])\n",
    "    df['lonmin'] = pd.to_numeric(df['lonmin'])\n",
    "    df['latsig'] = df['latmin']/60\n",
    "    df['Lat'] = df['latdeg'] + df['latsig']\n",
    "    df['lonsig'] = df['lonmin']/60\n",
    "    df['Lon'] = (df['londeg'] + df['lonsig'])*-1\n",
    "    \n",
    "    lon = df.Lon.values\n",
    "    lat = df.Lat.values\n",
    "    \n",
    "    eo1lon = -126.6\n",
    "    eo1lat = 49.3\n",
    "    \n",
    "    left_lon, right_lon, bot_lat, top_lat = -140, -120, 45, 57\n",
    "    \n",
    "    Map = plt.axes(projection=ccrs.PlateCarree())\n",
    "    Map.set_extent(\n",
    "        [left_lon, right_lon, bot_lat, top_lat]\n",
    "    )  # try left_lon, right_lon, bot_lat, top_lat\n",
    "    x, y = (lon, lat)\n",
    "    \n",
    "    Map.coastlines()\n",
    "    Map.add_feature(cartopy.feature.OCEAN)\n",
    "    Map.add_feature(cartopy.feature.LAND, edgecolor='black')\n",
    "    Map.add_feature(cartopy.feature.LAKES, edgecolor='black')\n",
    "    Map.add_feature(cartopy.feature.RIVERS)\n",
    "    gl = Map.gridlines(\n",
    "        crs=ccrs.PlateCarree(),\n",
    "        linewidth=0.5,\n",
    "        color=\"black\",\n",
    "        alpha=0.5,\n",
    "        linestyle=\"--\",\n",
    "        draw_labels=True,\n",
    "    )\n",
    "    gl.top_labels = False\n",
    "    gl.left_labels = True\n",
    "    gl.bottom_labels = True\n",
    "    gl.right_labels = False\n",
    "    gl.ylocator = LatitudeLocator()\n",
    "    gl.xformatter = LongitudeFormatter()\n",
    "    gl.yformatter = LatitudeFormatter()\n",
    "\n",
    "    gl.xlabel_style = {\"color\": \"black\", \"weight\": \"bold\", \"size\": 6}\n",
    "    gl.ylabel_style = {\"color\": \"black\", \"weight\": \"bold\", \"size\": 6}\n",
    "    \n",
    "    \n",
    "\n",
    "    cax = plt.scatter(x, y, transform=ccrs.PlateCarree(), marker=\".\", color=\"red\", s=25)\n",
    "    plt.scatter(eo1lon, eo1lat, color='blue', s=30)\n",
    "    plt.title(\"DFO Pacific Mooring Locations\")\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(\"DFO_Mooring_Locations.png\")\n",
    "    plt.show()\n",
    "    plt.close()\n",
    "  \n",
    "plot_moorings()\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "47c2d6d5-b0b4-4ee4-88af-b807b8ba07e5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/home/jovyan/ohw23_proj_fancymoorings/IOS_CTD_Moorings_all_yrs.nc'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/xarray/backends/file_manager.py:199\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 199\u001b[0m     file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_key\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    200\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/xarray/backends/lru_cache.py:53\u001b[0m, in \u001b[0;36mLRUCache.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[0;32m---> 53\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m     54\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache\u001b[38;5;241m.\u001b[39mmove_to_end(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: [<class 'netCDF4._netCDF4.Dataset'>, ('/home/jovyan/ohw23_proj_fancymoorings/IOS_CTD_Moorings_all_yrs.nc',), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('persist', False))]",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 6\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mxarray\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mxr\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# have a look at the dataset:\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m#ds = xr.open_dataset(\"IOS_CTD_Moorings_9614_794f_0026.nc\")\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[43mxr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mIOS_CTD_Moorings_all_yrs.nc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m#ds = xr.open_dataset(\"IOS_CTD_Moorings_allyrs2.nc\")\u001b[39;00m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m#print(ds)\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m#print(ds.PSALST01.data)\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;66;03m#print(ds.variables)\u001b[39;00m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;66;03m#print(ds.time)\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/xarray/backends/api.py:495\u001b[0m, in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, backend_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    483\u001b[0m decoders \u001b[38;5;241m=\u001b[39m _resolve_decoders_kwargs(\n\u001b[1;32m    484\u001b[0m     decode_cf,\n\u001b[1;32m    485\u001b[0m     open_backend_dataset_parameters\u001b[38;5;241m=\u001b[39mbackend\u001b[38;5;241m.\u001b[39mopen_dataset_parameters,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    491\u001b[0m     decode_coords\u001b[38;5;241m=\u001b[39mdecode_coords,\n\u001b[1;32m    492\u001b[0m )\n\u001b[1;32m    494\u001b[0m overwrite_encoded_chunks \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moverwrite_encoded_chunks\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 495\u001b[0m backend_ds \u001b[38;5;241m=\u001b[39m \u001b[43mbackend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    496\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    497\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdrop_variables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop_variables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    498\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdecoders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    499\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    500\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    501\u001b[0m ds \u001b[38;5;241m=\u001b[39m _dataset_from_backend_dataset(\n\u001b[1;32m    502\u001b[0m     backend_ds,\n\u001b[1;32m    503\u001b[0m     filename_or_obj,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    510\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    511\u001b[0m )\n\u001b[1;32m    512\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
      "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/xarray/backends/netCDF4_.py:553\u001b[0m, in \u001b[0;36mNetCDF4BackendEntrypoint.open_dataset\u001b[0;34m(self, filename_or_obj, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, use_cftime, decode_timedelta, group, mode, format, clobber, diskless, persist, lock, autoclose)\u001b[0m\n\u001b[1;32m    532\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mopen_dataset\u001b[39m(\n\u001b[1;32m    533\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    534\u001b[0m     filename_or_obj,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    549\u001b[0m     autoclose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    550\u001b[0m ):\n\u001b[1;32m    552\u001b[0m     filename_or_obj \u001b[38;5;241m=\u001b[39m _normalize_path(filename_or_obj)\n\u001b[0;32m--> 553\u001b[0m     store \u001b[38;5;241m=\u001b[39m \u001b[43mNetCDF4DataStore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    554\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    555\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    556\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    557\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    558\u001b[0m \u001b[43m        \u001b[49m\u001b[43mclobber\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclobber\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    559\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdiskless\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdiskless\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    560\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpersist\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpersist\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    561\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    562\u001b[0m \u001b[43m        \u001b[49m\u001b[43mautoclose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautoclose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    563\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    565\u001b[0m     store_entrypoint \u001b[38;5;241m=\u001b[39m StoreBackendEntrypoint()\n\u001b[1;32m    566\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m close_on_error(store):\n",
      "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/xarray/backends/netCDF4_.py:382\u001b[0m, in \u001b[0;36mNetCDF4DataStore.open\u001b[0;34m(cls, filename, mode, format, group, clobber, diskless, persist, lock, lock_maker, autoclose)\u001b[0m\n\u001b[1;32m    376\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\n\u001b[1;32m    377\u001b[0m     clobber\u001b[38;5;241m=\u001b[39mclobber, diskless\u001b[38;5;241m=\u001b[39mdiskless, persist\u001b[38;5;241m=\u001b[39mpersist, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mformat\u001b[39m\n\u001b[1;32m    378\u001b[0m )\n\u001b[1;32m    379\u001b[0m manager \u001b[38;5;241m=\u001b[39m CachingFileManager(\n\u001b[1;32m    380\u001b[0m     netCDF4\u001b[38;5;241m.\u001b[39mDataset, filename, mode\u001b[38;5;241m=\u001b[39mmode, kwargs\u001b[38;5;241m=\u001b[39mkwargs\n\u001b[1;32m    381\u001b[0m )\n\u001b[0;32m--> 382\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmanager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mautoclose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautoclose\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/xarray/backends/netCDF4_.py:330\u001b[0m, in \u001b[0;36mNetCDF4DataStore.__init__\u001b[0;34m(self, manager, group, mode, lock, autoclose)\u001b[0m\n\u001b[1;32m    328\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group \u001b[38;5;241m=\u001b[39m group\n\u001b[1;32m    329\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m mode\n\u001b[0;32m--> 330\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mds\u001b[49m\u001b[38;5;241m.\u001b[39mdata_model\n\u001b[1;32m    331\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mds\u001b[38;5;241m.\u001b[39mfilepath()\n\u001b[1;32m    332\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_remote \u001b[38;5;241m=\u001b[39m is_remote_uri(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/xarray/backends/netCDF4_.py:391\u001b[0m, in \u001b[0;36mNetCDF4DataStore.ds\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    389\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m    390\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mds\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 391\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_acquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/xarray/backends/netCDF4_.py:385\u001b[0m, in \u001b[0;36mNetCDF4DataStore._acquire\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    384\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_acquire\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m--> 385\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_manager\u001b[38;5;241m.\u001b[39macquire_context(needs_lock) \u001b[38;5;28;01mas\u001b[39;00m root:\n\u001b[1;32m    386\u001b[0m         ds \u001b[38;5;241m=\u001b[39m _nc4_require_group(root, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode)\n\u001b[1;32m    387\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
      "File \u001b[0;32m/opt/conda/lib/python3.9/contextlib.py:119\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 119\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    120\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m    121\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenerator didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt yield\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/xarray/backends/file_manager.py:187\u001b[0m, in \u001b[0;36mCachingFileManager.acquire_context\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    184\u001b[0m \u001b[38;5;129m@contextlib\u001b[39m\u001b[38;5;241m.\u001b[39mcontextmanager\n\u001b[1;32m    185\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21macquire_context\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m    186\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Context manager for acquiring a file.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 187\u001b[0m     file, cached \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_acquire_with_cache_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43mneeds_lock\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    188\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    189\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m file\n",
      "File \u001b[0;32m/opt/conda/lib/python3.9/site-packages/xarray/backends/file_manager.py:205\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    203\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m    204\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode\n\u001b[0;32m--> 205\u001b[0m file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_opener\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    206\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    207\u001b[0m     \u001b[38;5;66;03m# ensure file doesn't get overridden when opened again\u001b[39;00m\n\u001b[1;32m    208\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2464\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2027\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/home/jovyan/ohw23_proj_fancymoorings/IOS_CTD_Moorings_all_yrs.nc'"
     ]
    }
   ],
   "source": [
    "import xarray as xr\n",
    "\n",
    "# have a look at the dataset:\n",
    "\n",
    "#ds = xr.open_dataset(\"IOS_CTD_Moorings_9614_794f_0026.nc\")\n",
    "ds = xr.open_dataset(\"IOS_CTD_Moorings_all_yrs.nc\")\n",
    "#ds = xr.open_dataset(\"IOS_CTD_Moorings_allyrs2.nc\")\n",
    "#print(ds)\n",
    "#print(ds.PSALST01.data)\n",
    "#print(ds.DOXYZZ01)\n",
    "#print(ds.DOXMZZ01)\n",
    "#print(ds.filename.data)\n",
    "#print(ds.variables)\n",
    "#print(ds.time)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d875fc9-85bf-446d-9d71-3e722be465e3",
   "metadata": {},
   "source": [
    "## Combine what was learned Below - use a merged Temperature variable for the time series - do not plot Oxygen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1631164f-997b-4645-8036-e901252ccdce",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def plot_dfo_mooring(dpth, mind, maxd, fstrt1, fstrt2):  #was mooring_depth but that doesn't work with whole time series\n",
    "    \"\"\" function to plot mooring data.  Must know the sensor depths and find data in the depth range of\n",
    "    the sensor.  Recent filenames would allow a string split to find the depths but this isn't the case\n",
    "    historically.  We know for E01 that the depths are 35, 75 and 90 in recent years.  \n",
    "    For this function we are going to grab data with 'x'm of the sensor depths. \"\"\"\n",
    "\n",
    "    df = pd.DataFrame()\n",
    "    \n",
    "    df['Salt'] = ds.sea_water_practical_salinity.data\n",
    "    df['Temp1'] = ds.sea_water_temperature.data\n",
    "    df['Temp2'] = ds.TEMPST01.data\n",
    "    # Get a final temp\n",
    "    df['Temp'] = np.where(df['Temp1'].isnull(), df['Temp2'], df['Temp1'])\n",
    "    df['depth'] = ds.depth.data\n",
    "    df['Time'] = ds.time.data\n",
    "    \n",
    "    #df['Time'] = pd.to_datetime(df['Time'].dt.strftime('%Y-%m-%dT%H:%M:%SZ')) # this doesn't change anything\n",
    "    \n",
    "    \n",
    "    df['lat'] = ds.latitude.values\n",
    "    df['lon'] = ds.longitude.values\n",
    "    #latmx = df['lat'].max()\n",
    "    #print(latmx)\n",
    "    #latmn = df['lat'].min()\n",
    "    #print(latmn)\n",
    "    #lonmx = df['lon'].max()\n",
    "    #print(lonmx)\n",
    "    #lonmn = df['lon'].min()\n",
    "    #print(lonmn)\n",
    "    #print(df['Salt'].min())\n",
    "    #print(df['Time'][0])\n",
    "    \n",
    "    \n",
    "    #  Need to figure a better way to capture sensor depths - using this from the filename for now\n",
    "    df['filename'] = ds.filename.data\n",
    "    #uniq = df['filename'].unique()\n",
    "    #print(uniq)\n",
    "    \n",
    "    drop_files = ['tof1_20150801_20160714_0032m.ctd', 'cyp1_20160714_20171004_0062m_L1.ctd',\n",
    "                 'fortune1_20171006_20181011_0090m_L2.ctd',\n",
    "                 'millar1_20171006_20181011_0017m_L2.ctd'] \n",
    "    \n",
    "    # keep only the data with filename starting with e01 or E01\n",
    "    #df = df[(df['filename'].str[0:3] == fstrt1) or (df['filename'].str[0:3] == fstrt2)]\n",
    "    # fix this to make it work\n",
    "    \n",
    "    df = df[~df['filename'].isin(drop_files)]\n",
    "    \n",
    "    #  Look at one file only \n",
    "    \n",
    "    #df= df[df['filename'] == 'e01_20150801_20160712_0035m.ctd']\n",
    "    #print(df)\n",
    "\n",
    "    \n",
    "    \n",
    "    #df['file_depth'] = df['filename'].str[-10:-8]#.astype(int)  #doesn't work with all filenames \n",
    "    #print(df['file_depth'])\n",
    "    \n",
    "    \n",
    "    # This method doesn't work with the whole time series\n",
    "    #df_depth = df[df['file_depth'] == mooring_depth]\n",
    "    \n",
    "    # Use a max/min range to capture data around the sensor depth\n",
    "    df_depth = df[df['depth'].between(mind, maxd)]\n",
    "    \n",
    "    df.sort_values('Time', ascending=True)\n",
    "    \n",
    "    #print(df_depth['filename'].unique())\n",
    "\n",
    "    x = df_depth.Time\n",
    "    salt = df_depth.Salt\n",
    "    temp = df_depth.Temp\n",
    "\n",
    "   \n",
    "    #  try to put labels on the blank shared x axis\n",
    "    #tcks = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
    "    #tklbls = [\"2018-01\", \"2018-07\", \"2019-01\", \"2019-07\", \"2020-01\", \"2020-07\", \"2021-01\", \"2021-07\", \"2022-01\", \"2022-01\"]\n",
    "    \n",
    "    fig, ax = plt.subplots(2, figsize=(15, 8), sharex=True)\n",
    "   \n",
    "    ax[0].plot(x, salt, linewidth=0.05, c='blue')\n",
    "    ax[0].set_title('Salinity (PSU)')\n",
    "   \n",
    "    ax[1].plot(x, temp, linewidth=0.5, c='orange')\n",
    "    ax[1].set_title(\"Temperature (C)\")\n",
    "    \n",
    "    \n",
    "    fig.subplots_adjust(hspace=0.5)\n",
    "    plt.suptitle(\"DFO Mooring Station E01 at depth {} metres\".format(str(dpth)))\n",
    "    plt.show()\n",
    "    \n",
    "plot_dfo_mooring(dpth = 35, mind = 32, maxd=38, fstrt1 = 'e01', fstrt2='E01')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcd637fe-3419-4774-b53b-bf43a3467e6a",
   "metadata": {},
   "source": [
    "### Have a look at the various salinity variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94eddde2-007a-4bfd-b05a-99c65a4d31ef",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def plot_dfo_salt(dpth, mind, maxd, fstrt1, fstrt2):\n",
    "    \n",
    "\n",
    "    df = pd.DataFrame()\n",
    "    \n",
    "    df['sea_water_practical_salinity'] = ds.sea_water_practical_salinity.data\n",
    "    df['PSALST01'] = ds.PSALST01.data\n",
    "    df['PSALST02'] = ds.PSALST02.data\n",
    "    df['SSALST01'] = ds.SSALST01.data\n",
    "    df['Time'] = ds.time.data\n",
    "    df['depth'] = ds.depth.data\n",
    "    # method for all files\n",
    "    \n",
    "    drop_files = ['tof1_20150801_20160714_0032m.ctd' 'cyp1_20160714_20171004_0062m_L1.ctd',\n",
    "                 'fortune1_20171006_20181011_0090m_L2.ctd'\n",
    "                 'millar1_20171006_20181011_0017m_L2.ctd'] \n",
    "\n",
    "    df['filename'] = ds.filename.data\n",
    "    df = df[~df['filename'].isin(drop_files)]\n",
    "    \n",
    "    \n",
    "    df_depth = df[df['depth'].between(mind, maxd)]\n",
    "    \n",
    "    \n",
    "    # another method but only works when files are all same format\n",
    "    \n",
    "    #  Need to figure a better way to capture sensor depths - using this from the filename for now\n",
    "    #df['filename'] = ds.filename.data\n",
    "    #df['file_depth'] = df['filename'].str[-10:-8].astype(int)\n",
    "\n",
    "    # isolate the sensor depth\n",
    "    #df_depth = df[df['file_depth'] == mooring_depth]\n",
    "\n",
    "    x = df_depth.Time\n",
    "    var1 = df_depth.sea_water_practical_salinity\n",
    "    var2 = df_depth.PSALST01\n",
    "    var3 = df_depth.PSALST02\n",
    "    var4 = df_depth.SSALST01\n",
    "   \n",
    "    fig, ax = plt.subplots(4, figsize=(15, 8), sharex=True)\n",
    "   \n",
    "    ax[0].plot(x, var1, linewidth=0.05, c='blue')\n",
    "    ax[0].set_title('sea_water_practical_salinity')\n",
    "    ax[1].plot(x, var2, linewidth=0.5, c='orange')\n",
    "    ax[1].set_title(\"PSALST01\")\n",
    "    ax[2].plot(x, var3, linewidth=0.5, c='purple')\n",
    "    ax[2].set_title(\"PSALST02\")\n",
    "    ax[3].plot(x, var3, linewidth=0.5, c='purple')\n",
    "    ax[3].set_title(\"SSALST01\")\n",
    "    fig.subplots_adjust(hspace=0.5)\n",
    "    plt.suptitle(\"DFO Mooring Station E01 Salinity variables at depth {} metres\".format(str(dpth)))\n",
    "    plt.show()\n",
    "    \n",
    "plot_dfo_salt(dpth = 75, mind = 72, maxd=78, fstrt1 = 'e01', fstrt2='E01')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2fdcfb3-214f-43d2-a619-7d94017ca82d",
   "metadata": {},
   "source": [
    "### Have a look at the various temperature variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "127f964c-4196-479a-8537-ec90f1f818a7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_dfo_temp(dpth, mind, maxd, fstrt1, fstrt2):\n",
    "    \n",
    "    df = pd.DataFrame()\n",
    "    \n",
    "    df['sea_water_temperature'] = ds.sea_water_temperature.data\n",
    "    df['TEMPST01'] = ds.TEMPST01.data\n",
    "    df['TEMPS601'] = ds.TEMPS601.data\n",
    "    df['TEMPS602'] = ds.TEMPS602.data\n",
    "    #df['TEMPS902'] = ds.TEMPS902.data - ? doesn't exist\n",
    "    df['TEMPS901'] = ds.TEMPS901.data\n",
    "    df['Time'] = ds.time.data\n",
    "    df['filename'] = ds.filename.data\n",
    "    df['depth'] = ds.depth.data\n",
    "    \n",
    "    #  Need to figure a better way to capture sensor depths - using this from the filename for now\n",
    "    #df['file_depth'] = df['filename'].str[-10:-8].astype(int)\n",
    "    #df['depth'] = ds.depth.data\n",
    "    \n",
    "    #  Saw some data gaps - populated the nans with TEMPST01\n",
    "    #  Merge the two temp columns\n",
    "    df['merge_temp'] = np.where(df['sea_water_temperature'].isnull(), df['TEMPST01'], df['sea_water_temperature'])\n",
    "    \n",
    "    # isolate the sensor depth\n",
    "    #df_depth = df[df['file_depth'] == mooring_depth]\n",
    "    \n",
    "    drop_files = ['tof1_20150801_20160714_0032m.ctd' 'cyp1_20160714_20171004_0062m_L1.ctd',\n",
    "                 'fortune1_20171006_20181011_0090m_L2.ctd'\n",
    "                 'millar1_20171006_20181011_0017m_L2.ctd'] \n",
    "\n",
    "    df['filename'] = ds.filename.data\n",
    "    df = df[~df['filename'].isin(drop_files)]\n",
    "    \n",
    "    \n",
    "    df_depth = df[df['depth'].between(mind, maxd)]\n",
    "    \n",
    "    \n",
    "    # look at the depth range for 75m sensors - can we use this in the erddap option?\n",
    "    # print(df_depth['depth'].max()) # 80.15737  \n",
    "    # print(df_depth['depth'].min()) # 68.238\n",
    "\n",
    "    x = df_depth.Time\n",
    "    var1 = df_depth.sea_water_temperature\n",
    "    var2 = df_depth.TEMPST01\n",
    "    var3 = df_depth.TEMPS601\n",
    "    var4 = df_depth.TEMPS602\n",
    "    var5 = df_depth.TEMPS901\n",
    "    var6 = df_depth.merge_temp\n",
    "    \n",
    "   \n",
    "    fig, ax = plt.subplots(6, figsize=(15, 8), sharex=True, sharey=True)\n",
    "   \n",
    "    ax[0].plot(x, var1, linewidth=0.05, c='blue')\n",
    "    ax[0].set_title('sea_water_temperature')\n",
    "    ax[1].plot(x, var2, linewidth=0.5, c='orange')\n",
    "    ax[1].set_title(\"TEMPST01\")\n",
    "    ax[2].plot(x, var3, linewidth=0.5, c='purple')\n",
    "    ax[2].set_title(\"TEMPS601\")\n",
    "    ax[3].plot(x, var4, linewidth=0.5, c='green')\n",
    "    ax[3].set_title(\"TEMPS601\")\n",
    "    ax[4].plot(x, var5, linewidth=0.5, c='red')\n",
    "    ax[4].set_title(\"TEMPS901\")\n",
    "    ax[5].plot(x, var6, linewidth=0.5, c='red')\n",
    "    ax[5].set_title(\"merge_temp\")\n",
    "    fig.subplots_adjust(hspace=0.5)\n",
    "    plt.suptitle(\"DFO Mooring Station E01 Temperature variables at depth {} metres\".format(str(dpth)))\n",
    "    plt.show()\n",
    "    \n",
    "plot_dfo_temp(dpth = 35, mind = 32, maxd=38, fstrt1 = 'e01', fstrt2='E01')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afcf6bbb-30de-434a-b208-bf767bc2b26a",
   "metadata": {},
   "source": [
    "### Have a look at the various oxygen variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3eceb45-5c63-4ffe-b927-5c52840a2e60",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#  Not seeing any oxy in this time frame at any depth.\n",
    "\n",
    "def plot_dfo_oxy(mooring_depth):\n",
    "    \n",
    "    df = pd.DataFrame()\n",
    "    \n",
    "    df['DOXYZZ01'] = ds.DOXYZZ01.data\n",
    "    df['DOXMZZ01'] = ds.DOXMZZ01.data\n",
    "    df['Time'] = ds.time.data\n",
    "    df['filename'] = ds.filename.data\n",
    "    \n",
    "    #  Need to figure a better way to capture sensor depths - using this from the filename for now\n",
    "    df['file_depth'] = df['filename'].str[-10:-8].astype(int)\n",
    "    df['depth'] = ds.depth.data\n",
    "\n",
    "    # isolate the sensor depth\n",
    "    df_depth = df[df['file_depth'] == mooring_depth]\n",
    "    \n",
    "\n",
    "    x = df_depth.Time\n",
    "    var1 = df_depth.DOXYZZ01\n",
    "    var2 = df_depth.DOXMZZ01\n",
    "    \n",
    "   \n",
    "    fig, ax = plt.subplots(2, figsize=(15, 8), sharex=True, sharey=True)\n",
    "   \n",
    "    ax[0].plot(x, var1, linewidth=0.05, c='blue')\n",
    "    ax[0].set_title('DOXYZZ01')\n",
    "    ax[1].plot(x, var2, linewidth=0.5, c='orange')\n",
    "    ax[1].set_title(\"DOXMZZ01\")\n",
    "\n",
    "    fig.subplots_adjust(hspace=0.5)\n",
    "    plt.suptitle(\"DFO Mooring Station E01 Oxygen variables at depth {} metres\".format(str(mooring_depth)))\n",
    "    plt.show()\n",
    "    \n",
    "plot_dfo_oxy(90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb659d01-4804-4e95-8120-bff0ef9bbac0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "291f3e62-cde2-49ea-859b-f4504069a52c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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