Greatcircle_stuff.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import netCDF4 as nc\n",
    "import matplotlib.pylab as plt \n",
    "from mpl_toolkits.basemap import Basemap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def onclick(event):\n",
    "    ix, iy = event.xdata, event.ydata\n",
    "    xpti, ypti = m(ix, iy,inverse=True)\n",
    "    string = '(%5.1fW,%3.1fN)' % (xpti, ypti)\n",
    "    print(string)\n",
    "    annotation.xy = (ix, iy)\n",
    "    point.set_data([ix], [iy])\n",
    "    annotation.set_text(string)\n",
    "    plt.gcf().canvas.draw_idle()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "grdfile = '/Users/elizabethdrenkard/ANALYSES/CCS/Inputs/Grid/CCS_grd_high_res_bathy_jerlov.nc'\n",
    "fid = nc.Dataset(grdfile)\n",
    "mask_rho = fid.variables['mask_rho'][:]\n",
    "rlat = fid.variables['lat_rho'][:]\n",
    "rlon = fid.variables['lon_rho'][:]\n",
    "plat = fid.variables['lat_psi'][:]\n",
    "plon = fid.variables['lon_psi'][:]\n",
    "m_offset = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7dl28eFG1atVydSkA3BRhCrc3Y8YMPfvsszIMw9WlAHBTTCDBrZnNZs2cOVPz5s1zdSkA3BhPpnBr69evV8GCBVW9enVXlwLAjRGmcGs3Fh4xxAvAkQhTuLWtW7eqWrVqri4DgJsjTOHWOnbsqLFjx7q6DABujjCFW4uKitLmzZuVnJys+Lhohfb2kdfbhkJ7+yg+LtrV5QFwE4Qp3Fr+/PkVHR2tEYPbqNuROB3wT5fZkA74p6v9iThF963i6hIBuAHCFG7PZDJpUekUXcx38+dmQ4orkEygArAZYQq3V6JECR0tksmXfwVqQD+DYV8AOUaYwiOUPe+d+ZeGdN5P6ngsjkAFkCOEKTzC0Hu6yTBbbpPuLXU/FOecggC4FcIUHiHSFKuokyFSFoF6wVfMoQLINsIUHmPyZ/vUdLuv5UBlDhVADhCm8BiGYahDq/FqllQgy0BlDhVAdhCm8Cht27bVjvXF1On3clkO+TKHCsBahCk8iq+vr3r27Km01NoyXbrPqjlUhnwBZIUwhcfp1q2bvvvuO/XruTjrQP1ryDeK3ZIAWECYwuMEBgaqffv2+uyzzxQ7LMmqJ9QbC5N4QgVwO4QpPNLLL7+sL774QufPn89WoDKHCuB2CFN4pHLlyqlBgwaaMGGCJFkdqMyhArgdwhQeq2/fvoqJidHRo0clWRmozKECuA3CFB4rPDxc0dHRateundLS0iRdD9RpJU0yrmVxMXOoAP6BMIVH69+/vwoVKqT+/ftnfBZpitXUu0zMoQKwGmEKj+bl5aVp06bpyy+/1Lx58zI+jzTFMocKwGqEKTxe8eLF9eWXX6pr167au3dvxufMoQKwFmEKSKpZs6YGDBigNm3a6M8//8z4nDlUANYgTIG/9OzZU+XLl9crr7xy0+fZmUNtf5zN8QFPRJgCfzEMQ1988YWWL1+uqVOn3vSdtXOoZi+GfAFPRJgC/1C4cGHNnj1br732mpKSkm76jq0HAWSGMAX+pVq1avrwww/VunVrnTt3TpJ0+fJlHTp0SF3aTtF7lyOsmkPltRnAc/i4ugAgN+rYsaPWrFmj8uXL6/Lly7p48aJKlCihO++8UyVLllRH/wqaVHW3ZGTex43XZsaUNSnSFOu84gE4nWE2ZzVm9bewsDBzYmKiA8sBco/09HTt3btXxYsXV9GiRWUYNydndN8qiiuQbDFQJUlmyXTpPsUOS8qiIYDcxjCMTWazOSyrdgzzApnw9vZWhQoVFBgYeFOQHjp0SCaTSUP6rua1GQCSCFMg286cOaOxY8fqwQcfVGj1SA0415itBwEPR5gC2RQcHKyCBQtq9OjRat26tYyAR1R/k8HWg4AHI0yBbCpSpIgMw1CdOnWUmJiolStX6ofvpKd/DbRq68GOx9jYAXA3hCmQA8HBwTp48KDKlCmj5cuXa+DAgdq6pohVc6jp3gz5Au6GMAVy4EaYStcXKr399tv6+eefrd568IKv2CUJcCOEKZADZcuWzQjTG3x9fSVZufXgXyt8m7wc6MAqATgLYQrkQPny5ZWQkKDM3tO29vi2hMAzBCrgBghTIAeio6O1f/9+DR06NNM22QlUhnyBvI0wBXKgUKFCWrBggT7//HN9+eWXmbazNlDjCiQTqEAeRpgCOVS6dGl9++236tmzp9atW5dpOwIVcH+EKWCD6tWra9KkSWrdurX27t2babvYYUlqnFqUQAXcFGEK2Kh58+Z666231Lx5c6WmpmbabtnIVAIVcFOEKWAH0dHRevzxx9W6dWtduXIl03bLRqZaPeTLLklA3kGYAnby4YcfKiAgQD169Mj0lRnp+pCvf+Z5e50htT/OtoNAXkGYAnbi7e2t+Ph4bd26Ve+//77FtmPKmuSdbrk/s5cUdSKOIV8gDyBMATvy9/fXt99+q7i4OIuvzESaYjW5lEmFLoshX8ANGJaGo/4tLCzMnJiY6MByAPewbds2NWnSRPPnz1etWrUstg3oZ+i8n+X+Cl2Wzg+1/t9VAPZhGMYms9kcllU7nkwBB7j//vs1efJktWrVyuIrM9L1IV/OQgXyNsIUcJDmzZurf//+ioiIsPjKjLUb43MWKpB7EaaAA7300kt67LHH1KZNG4uvzMQOS+IsVCAPI0wBB/voo49UqFAhmUwmi6/McBYqkHcRpoCDeXt7a/r06dqyZYuGDRtmsS1noQJ5E2EKOIG/v78WLFig0aNHa9asWRbbchYqkPcQpoCTBAUFacGCBYqOjtaPP/5osS1noQJ5C2EKONEDDzygSZMmqWXLltq3b5/Fttk5uo0VvoBrEaaAk0VERKhfv36KiIjQmTNnLLa1NlDZxxdwLcIUcIGePXuqadOmatOmjdLS0iy2teYsVPbxBVyLMAVcZMSIESpQoECWr8xI2TsLlSdUwPkIU8BFvL29NWPGDG3atEnDhw/Psv2ykalWHd3Gpg6A8xGmgAvdOGXm448/VlJSUpbtrd3Hl1dmAOciTAEXCwoKUsWKFXXq1Kks21q7qQPvoALORZgCuYBhGFnOm95g1T6+vIMKOBVhCuQCXl5eVoepZOU+vn8tSCJQAccjTIFcIDtPpjdkZx9fAhVwLMIUyAUMw9C1a1mcv3Yb1ryDSqACjkeYArmAYRj66aefrFqE9G+8gwq4HmEK5AImk0lr1qxRuXLlVL16dfXq1Utz5syxOlyXjUy1asiXd1ABxzCyM08TFhZmTkxMdGA5gGdLS0vT5s2btXLlSq1atUpr165VSEiIGjRooAYNGqhevXoqXrx4ptcH9DN03s/CDcyS6dJ9ih2W9TutACTDMDaZzeawLNsRpkDudfXq1YxwXblyZZbhGh8XrY7H4pTubaFTs9Q4taiWjUx1/A8A8jjCFHBD1oTr4lkD1f54nMyWJnEIVMAqhCngAf4ZrqtWrdKaNWsUEhKiWtXMGlthp2RYuJghXyBLhCngga5evaotW7Zo5cqVWrFxsBbedyHLQJ1W0qRIU6zTagTyEsIUgEx9qmhMwWSLgWpck6beRaACt2NtmPJqDODG4oZbd7B4x2NxvIMK2IAwBdxcpps6bH9O+nif9Ha60kftU/fvz7ikPsAdEKaAB7jlYPHtz0kLxklnQyV5SWdDdeHbaYqustI1BQJ5HGEKeIibDhZPGCqlFfpXCy/FJdcnUIEcIEwBD3HTKTNngzNpZRCoQA4QpoAHiR2WdD1Qixy00IpABbKLMAU8TOywJJlqTZdk6cg3Q3HJ9RQfvcZZZQF5GmEKeKDYhf1kuu8HWT5mxkvd46o7qyQgTyNMAQ8Vm9RA/jpvsc0FBahJ4EYnVQTkXYQp4MHGmLbJW2kWWhhKOBNGoAJZIEwBDxYZW0eTTT/JyGL+NOFMGAuSAAsIU8DDRcbW0VTTOlmeP2VBEmAJYQpAkbF1ZLpvlW4J1Grx0iuh0iAv6ZVy6r5mpyvKA3I9whSApOsLkhoXTVRGoFaLl57oJhU9IBlmqegBXWhlUpOXA11aJ5AbEaYAMixLDf87UBv3l3wv3tzAkBICzxCowL8QpgBusiw1/PorM5ntkvRXoEb3reLcwoBcjDAFcIsxpm3S2bKZNzCkuALJBCrwF8IUwC0iY+vIdPzRrBb4EqjAXwhTALcVO2Pc7Q8V/ycCFZBEmAKwYNnIVKsDNT4u2ml1AbkNYQrAomUjU/8+BzUzhtT9UJzTagJyG8IUQJZihyXJ/4rlNhd8xXAvPBZhCsAqY8qa5J1uoUEO50+PHTum9957T7t27bKtQMCFCFMAVok0xWpyKZMMy3viZztQT5w4ocGDB6thw4Z6+OGH9dlnn+nUqVO2Fww4EWEKwGqRplhNvctk1wVJVapUUb58+ZSUlKTBgwdr/fr1Kl++vJ566inNnj1bly9ftk/xgAMRpgCyJdIUq86p99htQVK+fPn0wAMPaNu2bWrWrJni4+N18OBBPf300xo9erRKly6tHj16aN26dTKbLd0UcB3CFEC2jR+5x64LksLDw7Vhw4aMvxcuXFidOnXS8uXLtWXLFoWEhKhLly6qUKGCBg8erL1799pSPmB3hCmAHHmvUDu7LUgKDw/Xxo0bb/tdcHCw3nzzTSUnJ2vGjBk6ffq0atasqbp162rs2LE6c+ZMDn8BYD+EKYAc6dV/pp7bXs4uC5Jq1KiRaZhmdGUYCg8P16hRo3T48GH17t1bS5cuVUhIiNq2basFCxYoLS0tB78EsB1hCiDH+rz7jRotzG/zgqTy5cvrjz/+0IkTJ6y6r6+vr5588knNmjVL+/fvV5MmTTRs2DAFBQWpV69eSkxMZH4VTkWYAsixatWqqUjZ5mpz8E6bFiQZhqGwsLAsn05vJzAwUN26ddOaNWu0fv163XHHHXr66ac1derUbPcF5BRhCsAmAwcO1JqFXjYvSLJmqDcr99xzjwYNGqRx48bpk08+4ekUTkOYArDJ/fffr5o1a8r0e22bFiRZWoSUXY899pjOnj2rn376yS79AVkhTAHYbODAgZo2f5/Gl+hq1YKk282f3ng9xh5Pk15eXjKZTBo9erTNfQHWIEwB2OzBBx9UeHi4zpqrWrVD0u3mT4OCguTr66v9+/fbpabOnTtrwYIFOnnypF36AywhTAHYxcCBAzVs2DC17jQix/On9pg3vaFYsWJq1aqVxo8fb5f+AEsIUwB28dBDD+k///mPvvjiixyfMGPPeVNJio6O1pgxY5SebqkYwHaEKQC7GTRokIYNG6Y2nT/O0Qkz/95W0FZhYWEqWbKkvv/+e7v1CdwOYQrAbsLCwlS9enVNmDAhRyfMhIWFacuWLXZ9kvzvf//LQiQ4HGEKwK4GDRqkmJgYXb58WZGmWJku3Wf1gqRixYqpZMmSdj0ovG3bttq8ebN2795ttz6BfyNMAdhVjRo1VKVKFU2aNEmSFDssKVsLkuw9b5o/f3516tRJY8aMsVufwL8RpgDs7sbT6ZUr11M0OwuSatSoYdd5U0nq0aOHJk+erIsXL9q1X+AGwhSA3dWsWVOVKlXS5MmTJV0/UNzaBUlb9oyy65OpJN19992qWbOmZs6cadd+gRsIUwAOMWjQIA0dOjTjWDRrFyRNKb5PBdI22/0A8BsLkdivF45AmAJwiNq1a6t8+fKaMmVKxmfWLkja3Pya6tatq23bttmtnscee0xnzpxhv144BGEKwGEGDhyo995776ZDu61dkNT12Spq2rSpVq1aZZdabuzXGxsba5f+gH8iTAE4TN26dXX33Xerb9++N707as2CpHf8l+q1rjXVpk0bffPNN3aph/164SiEKQCHmjlzprZt26ZmzZrp1KlTkqxbkGT2kgZ4L1C/l+rLZDLZZY/dYsWKqWXLluzXC7sjTAE4VIkSJbR48WKFhYXpoYceynjtxZoFSene0luXZ2vVqlUaMmSI3n//fZsXELFfLxyBMAXgcD4+PoqJidEnn3yiFi1aaOzYsTKbzYo0xVo1f/rJ+JZau3at4uPj9dprr+naNUvv2FjGfr1wBMIUgNO0bNlSa9as0ahRo9SlSxddunRJY8pat3/vinlD9MMPP2jjxo3q0KHDTYuasov9emFvhCkAp6pYsaJ+/PFHXbp0SY888ohqN+tt1esy7Y/H6fuZ/bVkyRKdPXtWTz75pC5cuJCjGm7s17tnz56c/QjgXwhTAE7n7++v6dOn64UXXlDNmjX1RIMPswxUs5fU8Vic5k7+n+bOnau77rpLjRs31unTp7N9/xv79cbFxdnwK4C/EaYAXMIwDPXq1UuzZ89W165dVbJgW/W4aDlQ072vnzDj4+OjCRMmqH79+qpbt64OHTqU7fuzXy/siTAF4FJ16tTRxo0blZCQoINJoVYtSIqPi5ZhGBo2bJg6d+6sOnXqKCUlJVv3Zb9e2BNhCsDlSpUqpYSEBFWqVEmPrPS3+vxTSfrf//6nd999Vw0bNsz2VoGvvvqqBg0axFmnsBlhCiBXyJcvn0aMGKHOr4xX/U2GxUC94Cs1eTkw4+8dOnTQ+PHj1aJFCy1evNjqezZu3FiDBw9Ww4YNlZycbEv58HCEKYBcpW3btoodtlONt/pkHqiGlBB45qZAjYiI0Lx589ShQwfNmDHD6vt17txZw4cPV+PGjbV161a/prlHAAAXp0lEQVQbq4enIkwB5Dr33Xef5kw5bXn+9K9Aje5bJeOj2rVrKyEhQX369NGoUaOsvt/zzz+vzz77TM2aNbP7WarwDIQpgFypcOHCVm/oEB8XnfFR1apVtWbNGo0ePVoDBgywevvB1q1b64svvlBERITWrl1rY/XwNIQpgFzL2vNP/7kgSZJCQkK0Zs0aLV68WN27d7d6H94WLVooPj5eLVu21IoVK2yoHJ6GMAWQq8UOS1Lj1KJZLkj653CvdH2D/eXLl+unn37K1qKkpk2batasWWrXrp0WLVqU07LhYQhTALne0k9+V9g+Cyt8/xru/XegBgQEKCIiIuOkGmvVr18/YzHTvHnzclg1PAlhCiDXW79+vc6suyfLBUn/nj+VpPDwcCUmJmb7nrVq1dLChQvVvXt3ffXVV9m+Hp6FMAWQ68XFxalHjx5WLUj69/xpeHi4Nm7cmKNzUB966CEtWbJEr7zyiqZMmZLt6+E5CFMAudqpU6e0YMECvfDCC1aff/rP4d6goCAZhpGj/XslqXr16kpISFC/fv00duzYHPUB9+fj6gIAwJJJkybpySef1B133CFJGlPWpI7H4pTunckFfw33PhIXrUhTrAzDyHg6DQ4OzlEN9957r1auXKkmTZpo8+bNqlq1qsqUKaOyZcuqTJkyKlGihLy8eDbxZPyvDyBX279/v/bu3avjx49Luv66zORSJhnXLFxkSJ2P/D3ceyNMbVG+fHmtXr1aISEhSklJ0YQJE9S1a1dVq1ZNBQoU0N13362EhASb7oG8iydTAC5jNps1a9YsFShQQMWLF1fx4sVVokQJFSlSRIZhSJJGjhypd955R2FhYZo5c6bq1KmjSFOsFCdFnYiTjNv3fcXn+v69y0amKjw8XEOGDJHZbM7oNyfKli2rN99885bP//zzT0VFRenw4cM57ht5G0+mAFzm6tWr+uijj/Tkk0+qQ4cOevzxxxUSEiJfX1/dddddqlatmpo0aaLk5GT5+/urQYMGGj9+vCQrNnT4x3aD9evXV1pamnr27Klr1yw90uZM/vz5ZRiGChYsaPe+kTfwZArAZfLly6fVq1drwIABmj59uqZOnaqGDRvqypUrOn36tE6ePKlTp07p1KlTatiwoU6ePKn8+fNnXB87LElT+xk675fJDW7Mn07+n5YuXarHH39cJpNJcXFxdp/jvHjxImHqwQhTAC7l6+ur4cOHq0mTJoqMjFTnzp319ttvq1SpUipVqlSW148pa7I43HvjdZnzRWK1ePFiNW/eXC+++KLGjRsnb+/MVjFlH2Hq2RjmBZArPProo9qyZYs2btyoevXqaf/+/VZdF2mKtXq7wYCAAC1atEj79+/XCy+8oKtXr9qneBGmno4wBZBrlCxZUgsXLlTLli1Vo0YNff3111Zdt2xkquVA/cd2g4UKFdK3336rEydOqH379kpLS7NL7RcvXpSvr69d+kLeQ5gCyFW8vLzUu3dvfffdd3rjjTfUvXt3Xbx4Mcvrlo1MtXq7wYIFC2r+/Pn6448/9Nxzz+nKlSx2grBC06ZN1a5dO85D9VCEKYBcKTw8XJs3b9b58+cVHh6uHTt2ZHlNdrYbzJ8/v+bMmaO0tDQ988wzunz5sk31jhgxQu+++64iIiI0ZMgQuw4hI/cjTAHkWoULF9a0adPUp08fNWrUSHFxcRb32M3udoN+fn6aNWuWfHx81KpVK/3555821duuXTtt3rxZK1euVP369bV3716b+kPeQZgCyNUMw1DHjh21Zs0ajRs3Tq1bt9bvv/9+27YpKSl6fHNpeVs6C/xfx7X5+vpq5syZCggI0JNPPmnVkLIlZcqU0ZIlS9SmTRs9/PDDmjhxYo422UfeQpgCyBMqVaqk9evXKzg4WA8++KBWr16d8d25c+fUu3dv1atXT3Wa91X0oYey3G7wn8e15cuXT9OmTVPJkiXVokULXbhwwaZavby89Oqrr2r58uX6+OOP1aZNG50+fdqmPpG7EaYA8gw/Pz998sknGj16tJ555hm98847io+PV+XKlXXq1Cnt3LlTvXr10qiJiXr5SI1sHdfm4+OjSZMmKTQ0VI8//rjOnTtnc73VqlXThg0bFBoaqvvvv1+LFy+2uU/kTkZ2hh/CwsLMOTlkFwDs7ejRo+rUqZNSU1M1cuRI1apV65Y2z3Quqa+Df8t8QwezZLp0n2KHJWV8dO3aNZlMJu3YsUMLFy5UkSJF7FJvQkKCXnjhBbVq1Urvv/++ChQoYJd+4ViGYWwym81hWbXjyRRAnlS6dGktXrxYGzZsuG2QStKsCSesfl3mBi8vL40ZM0YPPfSQHn30UaWmptql3saNG2vbtm06fvy4wsLCtHXrVrv0i9yBMAXg1rLzukzGR4ahUaNG6ZFHHlHz5s3ttoCoWLFimjlzpt588001bdpUw4cPV3q6pdVSyCsIUwBuLcvTZXTz6zI3GIahjz76SKdOndKmTZvsVo9hGIqKitLGjRv17bffqnHjxjp48KDd+odrEKYA3F7ssKQsj2v75+syGR8bhp5//nlNnz7d7jWFhoZqxYoVatasmcLCwhxyDzgPC5AAeIwAS8e1SZJZmlbSdP3w8b/s2rVLjRo10qFDh+x6ysw/LV26VBEREUpNTVWhQoUccg/kDAuQAOBfcjJ/WrlyZZUqVUqrVq1yWF0JCQnq0qULQZqHEaYAPIa12w3+c3WvJIcN9UrSb7/9pnHjxqlfv34O6R/OQZgC8Chjypqy3G6w/fG4mwK1Xbt2mjNnjs2b4d/OBx98oOeee05ly5a1e99wHsIUgEeJNMVqcimTxe0GzV5Sx2N/B2qZMmV0//33a+HChXat5fjx4xo/frzefPNNu/YL5yNMAXicSFOspt5lef403fvm+dPIyEjFx8fbtY7hw4erffv2CgoKsmu/cD5W8wLwWNlZ3ZuamqrQ0FAdOnRIhQsXtvnex48fV4UKFZSSkqIyZcrY3B8cg9W8AJCFxzaVsnp1b2BgoBo2bKi5c+fa5d5Xr15V1apVVb9+fY0ePdrmk2rgWoQpAI912fchRZ4oa/XuSPZc1VumTBmtX79eU6dOVUJCgkJDQzVgwAAdP37cLv3DuQhTAB4rICBAj9eJsXp3pBYtWmjDhg06ceKE3WqoXbu25syZo3Xr1un333/XvffeqxdffFHJycl2uwccjzAF4LH8/f117tw5xQ5Lsup0mbmT/6cnnnhCX331ld1rqVChgmJjY7V7926FhISoUaNGioiI0IoVK+y20T4chzAF4LECAgJ0/vx5SdbvjuTIDRwkqXjx4nrrrbe0b98+Pf300zKZTAoLC9OMGTOUlpbmsPvCNoQpAI8VEBCgc+fOSbL+dJmTv36jX3/9Vb/++qtDaytQoIC6du2q5ORkDR48WJ9//rnKly+vESNG6I8//nDovZF9hCkAj3VjmPcGa4Z7TUc+V9u2bTVjxgzHF6jrh5W3aNFCK1eu1Ndff60NGzbo7rvv1scff+yU+8M6hCkAj/XPYd4bshruveAr7TRPVnx8vNPnMsPDwzVz5kwtW7ZMH374oVPvDcsIUwAe65/DvDdkOdxrSKtKnFehGru0bds2xxd5G9WqVdPJkyd15UoWu/bDaQhTAB6rSJEiSk5OVmpq6k2fWzPcu+luadyITo4tMBM+Pj666667dPToUZfcH7ciTAF4rCZNmqh+/fqqVq3aLZvYW7O6d3LQVl27ZmHHfAcqW7asDh065JJ741aEKQCP5efnp1GjRmnKlCkymUzq1q1b9lb3+kmdet3jpGpvVq5cOc2bN493UHMJwhSAx2vUqJG2b9+ua9euqXr16lqxYoWk68O9jVOLWpw/nVJ8/y2HiTvD+++/r2XLlqlHjx66evWq0++PmxGmACCpcOHC+uKLL/TZZ5+pffv2evnll3Xx4kUtG5mq/JayypA6H4mz0MAxgoKCtHr1ah04cEBPPfXULauS4VyEKQD8Q0REhLZv365Tp07pgQce0Pr16/VFkOX50ys+UqOeRZxX5F8CAgK0YMEClS5dWvXr19exY8ecXgOuI0wB4F+KFSum+Ph4xcTEqGXLltpxoLB6XLjX4nDvijv+UKumJfXpp5/adSP8rOTLl09jx45Vq1atVKtWLSUlJTnt3vgbYQoAmWjdurW2b9+un3/+WWsWeWf5usyS8N+0YcMGVapUSY8++qgmTpyos2fPOrxOwzDUv39/DRkyRI0aNcqY84XzEKYAYMGdd96pOXPmqG/fvqqxzC/L3ZECSm/W0aNH1bVrV82fP1/BwcFq1arVLa/eOEJUVJRmzpypZ599VtOmTXP4/fA3IzvLqsPCwsyJiYkOLAcAcq8jR46o1Vsh2hCcLhmZNDJLpkv3KXbY9eHWM2fOaM6cOerdu7c2bdqk0NBQh9eZlJSkiIgIde3aVf369ZNhZFYssmIYxiaz2RyWVTueTAHASkFBQfpxfFqWq3vjCiRnvC5TtGhRde7cWS+88IJiY2OdUmeVKlW0fv16zZ49W926dePoNicgTAEgGwzDuP3q3u3PSR/vk95Olz7Zp+7fn7np6//+97+aOHGiLl686JQ6S5UqpR9++EFHjhzRE088ccsexLAvwhQAsumW3ZG2Pyd9M0E6GyrJSzobqgvfT1B89JqMa8qVK6eaNWs69GDxf/P399f8+fMVEhKievXqsZevAxGmAJADN22Gv3CkdC3/zQ2u5Vf3uOo3fdSzZ099+umnTt0C0MfHR2PGjFG7du1Uq1Yt7dy502n39iSEKQDkUMZm+JeK3/b7CwpQdJWVGX9v2rSprly5otWrVzunwL8YhqE33nhDMTExatSokRISEpx6f09AmAJADmUM9/5TtXipd3FpkCEN8lJcRBtFP9dV0vVQe+mllzRq1CgXVCs9//zzmjVrlp5//nlNmTLFJTW4K16NAQAbBfic0vn04teD9KlOks+/Vs+me2laqe6KNMXq3LlzCg0N1datW1W2bFmX1JuSkqLmzZurU6dOeuutt3h1xgJejQEAJxnTbZcks9S4/61BKkne19T90PXN8AMCAhQVFaW4OOdvjn/Dvffeq/Xr12v+/Pnq0qULr87YAWEKADaKjK0j032rpCIHM21zwVeK7ltFklSvXj2NGzfOpWeR3nXXXVq5cqVOnjypiIgI/fHHHy6rxR0QpgBgB7FJDeR/NjDzBoY0pkCyOrWpqJ49e+r99993+fCqv7+/5s6dq/Lly6tu3bo6fPiwS+vJywhTALCTMRXbWdy712xIy0L26Oeff1aXLl2cV5gFPj4+Gj16tCIjI1W7dm1t377d1SXlSYQpANjJLZs53MaRALMCAgKcV5QVDMNQnz599MEHH6hJkyZaunSpq0vKcwhTALCj2GFJFgM1+IK3cwvKhnbt2mn27Nl69tlndeDAAVeXk6cQpgBgZzcC1fhXoBZMk94r1801RVmpbt26+u9//6sBAwa4upQ8hTAFAAeIHZakqSVNCjnvLcMshZz31tggkyJNzjk5xha9e/fWsmXLtHnzZleXkmewaQMA4BZjxozRV199pYSEBJevOnYlNm0AAOTYiy++qGPHjun77793dSl5AmEKALiFj4+Phg8frj59+ujqVUunoUMiTAEAmWjRooVKlCihCRMmuLqUXI8wBQDclmEY+vDDD/X222/r/Pnzri4nVyNMAQCZCgsLU8OGDfXhhx+6upRcjTAFAFj03nvv6dNPP9WxY8dcXUquRZgCACwKDQ1V586dNXDgQFeXkmsRpgCALPXr10/z5s1TUlKSq0vJlQhTAECWAgMD1a9fP/Xp08fVpeRKhCkAwCrR0dHatWuXEhISXF1KrkOYAgCs4uvrq5iYGPXu3VvXrl1zdTm5CmEKALDaM888I19fX8XHx7u6lFyFMAUAWM0wDNWsWVM7d+50dSm5io+rCwAA5B0nTpzQlClTtH37dleXkqvwZAoAsNqwYcPUvn17lSlTxtWl5Co8mQIArHLs2DFNmjSJd01vgydTAIBV3n//fb3wwgsqVaqUq0vJdXgyBQBk6fDhw5o6dapSUlJcXUquxJMpACBLMTEx6tKli0qWLOnqUnIlnkwBABYdP35cEyZM4KnUAp5MAQAWFS5cWC1btlSjRo20dOlSV5eTKxGmAACLChYsqOnTp+uzzz5Thw4d1K1bN5nNZleXlaswzAsAsOjChQuaPXu2JkyYoPT0dBUvXtzVJeU6hCkAIFO9e/fW+PHjVbt2bfXq1UstWrSQr6+vq8vKdQhTAECmNm3apHfeeUcvvfSSq0vJ1ZgzBQBkqk+fPho7dixzpFkgTAEAmXrsscfk5eWlhQsXurqUXI0wBQBkyjAM9e3bV0OHDuVAcAsIUwCARc8884y8vLz07LPP6tKlS64uJ1ciTAEAFvn4+GjJkiXKly+fGjVqpN9++83VJeU6hCkAIEv58+fXtGnT9Oijj6pmzZpKTk52dUm5CmEKALCKYRgaPHiwBg8erIYNG2rZsmWuLinXIEwBANnSvn17zZo1S1FRUfriiy9cXU6uwKYNAIBsq1evnlavXq2IiAjt2bNHQ4cOlZeX5z6fee4vBwDYpEKFClq/fr3WrVuntm3b6uLFi64uyWUIUwBAjt1xxx1aunSpChQooIYNG+r48eOuLsklCFMAgE38/Pw0ZcoUNW/eXLVq1VJSUpKrS3I6whQAYDPDMDRo0CANGTJEDRs29LhDxAlTAIDdREZGavbs2Wrfvr3Gjh3r6nKchtW8AAC7qlu3rtasWaOIiAjt3r1bw4YNc/uVvu796wAALlG+fHmtW7dOGzZsUJs2bdx+pS9hCgBwiDvuuENLliyRv7+/GjRo4NYrfQlTAIDD+Pn5afLkyXryySdVs2ZN7dixw9UlOQRhCgBwKMMwNGDAAMXExKhx48ZatGiRq0uyO8IUAOAUzz33nObOnatOnTopLi7O1eXYFWEKAHCaRx55RGvWrNHIkSP1+uuvKz093dUl2QVhCgBwqnvuuUfr16/X5s2b1bp1a/3++++uLslmhCkAwOkCAwO1ePFilSpVSiEhIapataq6deumiRMn6ueff5bZbHZ1idliZKfgsLAwc2JiogPLAQB4mrS0NG3fvj3jBJp169bp/PnzqlWrlmrVqqXatWsrPDxchQoVcnpthmFsMpvNYVm2I0wBALnN0aNHbwrX7du3q3Llyqpdu3bGf4KDg2UYhkPrIEwBAG7jzz//1ObNm7Vu3TqtX79ea9eulZeXV0awNm3aVNWqVbP7fa0NU/bmBQDkevnz588ITkkym83av39/xtNr06ZN9eqrr6pPnz4Of1q9HZ5MAQB53uHDh9W6dWuVLVtWEydOVEBAgF36tfbJlNW8AIA8r0yZMlq1apUCAwP18MMP6+eff3bq/QlTAIBbyJ8/v8aNG6dXX31VdevW1bx585x2b8IUAOBWunbtqgULFuill17SW2+95ZRdlghTAIDbefjhh5WYmKgffvhBLVq0cPguS4QpAMAtlSxZUsuWLVPlypUVHh6ubdu2OexehCkAwG3ly5dPH3/8sd599101adJEo0aNUmpqqt3vQ5gCANze888/r4SEBK1evVqhoaFq1aqV5syZo8uXL9ulf8IUAOARqlevrlmzZunAgQOKiIjQp59+qtKlS6t79+5avXq1rl27luO+CVMAgEcpWrSounTpohUrVmjLli0qV66cTCaTypUrp/79+yslJSXbfRKmAACPFRwcrL59+2rHjh2aN2+erly5oiZNmuihhx7Sxx9/bHU/hCkAwOMZhqH7779fH3zwgQ4ePKjhw4drx44d1l/P3rwAANwee/MCAOAkhCkAADYiTAEAsBFhCgCAjQhTAABsRJgCAGAjwhQAABsRpgAA2IgwBQDARoQpAAA2IkwBALARYQoAgI0IUwAAbESYAgBgI8IUAAAbEaYAANiIMAUAwEaEKQAANiJMAQCwkWE2m61vbBgnJR1wXDkAAOQqIWazuURWjbIVpgAA4FYM8wIAYCPCFAAAGxGmAADYiDAFAMBGhCkAADYiTAEAsBFhCgCAjQhTAABsRJgCAGCj/wPK19lT/2taTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,8))\n",
    "ax = fig.add_subplot(111, aspect='equal', autoscale_on=False)\n",
    "m = Basemap(llcrnrlat=15,urcrnrlat=50, llcrnrlon=-140, urcrnrlon=-100,resolution='c')\n",
    "#m = Basemap(llcrnrlat=np.min(plat)-m_offset,urcrnrlat = np.max(plat)+m_offset,llcrnrlon=np.min(plon)-m_offset,urcrnrlon=np.max(plon)+m_offset, resolution='i', ax=ax)\n",
    "m.drawcoastlines()\n",
    "A = m.drawgreatcircle(-124, 40, -110, 23, del_s=10, color='r',marker='o')\n",
    "ax = plt.gca()\n",
    "line = ax.lines[0]\n",
    "\n",
    "x = line.get_xdata()\n",
    "y = line.get_ydata()\n",
    "\n",
    "for npt in range(len(x)):\n",
    "    if m.is_land(x[npt],y[npt]):\n",
    "        m.plot(x[npt],y[npt],'go')\n",
    "    else:\n",
    "        m.plot(x[npt],y[npt],'bo')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# OBJECTIVES:\n",
    "#   Get First lat/lon from click\n",
    "#   Query the resolution from the user\n",
    "#   Get Second lat/lon from click  \n",
    "#   Define great circle equation\n",
    "#      - use to provide forward azimuth \n",
    "#      - from starting point, work from 1st point adding distance\n",
    "#   Question: how does great circle deal with spacing? \n",
    "#   Question: how to calculate orthoganol points (along great circles)?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Result:', 9.960521706052505)\n",
      "('Result:', 9.960641701896481)\n",
      "('Result:', 9.960761727224629)\n",
      "('Result:', 9.960881780869938)\n",
      "('Result:', 9.961001861655182)\n",
      "('Result:', 9.961121968410056)\n",
      "('Result:', 9.961242099955447)\n",
      "('Result:', 9.961362255122094)\n",
      "('Result:', 9.961482432730968)\n",
      "('Result:', 9.961602631610702)\n",
      "('Result:', 9.961722850582982)\n",
      "('Result:', 9.96184308847606)\n",
      "('Result:', 9.96196334410876)\n",
      "('Result:', 9.96208361630774)\n",
      "('Result:', 9.96220390389753)\n",
      "('Result:', 9.962324205698314)\n",
      "('Result:', 9.96244452053592)\n",
      "('Result:', 9.962564847232645)\n",
      "('Result:', 9.962685184609033)\n",
      "('Result:', 9.962805531490657)\n",
      "('Result:', 9.962925886698937)\n",
      "('Result:', 9.963046249051342)\n",
      "('Result:', 9.963166617375977)\n",
      "('Result:', 9.963286990493419)\n",
      "('Result:', 9.963407367218245)\n",
      "('Result:', 9.963527746382)\n",
      "('Result:', 9.963648126802733)\n",
      "('Result:', 9.963768507293857)\n",
      "('Result:', 9.963888886686885)\n",
      "('Result:', 9.964009263799817)\n",
      "('Result:', 9.964129637448131)\n",
      "('Result:', 9.964250006459505)\n",
      "('Result:', 9.964370369653654)\n",
      "('Result:', 9.964490725846018)\n",
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    }
   ],
   "source": [
    "from math import sin, cos, sqrt, atan2, radians\n",
    "\n",
    "# approximate radius of earth in km\n",
    "R = 6373.0\n",
    "\n",
    "for nt in range(len(x)-1):\n",
    "  lat1 = radians(y[nt])\n",
    "  lon1 = radians(x[nt])\n",
    "  lat2 = radians(y[nt+1])\n",
    "  lon2 = radians(x[nt+1])\n",
    "\n",
    "  dlon = lon2 - lon1\n",
    "  dlat = lat2 - lat1\n",
    "\n",
    "  a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\n",
    "  c = 2 * atan2(sqrt(a), sqrt(1 - a))\n",
    "\n",
    "  distance = R * c\n",
    "\n",
    "  print(\"Result:\", distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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