{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate Well Trajectory\n",
    "\n",
    "Calculate the well trajectory points from a json document with only \n",
    "wellname, md, inc, azim, and surface latitude and longitude or surface x and y.\n",
    "\n",
    "Directional surveys come in a variety of formats, these formats are often missing critical information the user needs for their analysis. The welltrajconvert package allows the user to take the bare minimum required wellbore survey information and convert that into its latitude and longitude points along the wellbore and a host of other common parameters.\n",
    "\n",
    "welltrajconvert requires the wellId, measured depth, inclination angle, azimuth degrees, surface_latitude, and surface_longitude points or surface x and y points to calculate various points along the wellbore using a minimum curvature algorithm.\n",
    "\n",
    "**welltrajconvert calculates the following:**\n",
    "\n",
    "* **tvd:**\n",
    "    * true vertical depth from surface to the survey point.\n",
    "* **n_s_deviation:**\n",
    "    * north south deviation for each point in the wellbore path.\n",
    "* **e_w_deviation:**\n",
    "    * east west deviation for each point in the wellbore path.\n",
    "* **dls:**\n",
    "    * Dogleg severity is a measure of the change in direction of a wellbore over a defined length, measured in degrees per 100 feet of length.\n",
    "* **surface_x:**\n",
    "    * Surface Easting component of the UTM coordinate\n",
    "* **surface_y:**\n",
    "    * Surface Northing component of the UTM coordinate\n",
    "* **x_points:**\n",
    "    * Easting component of the UTM coordinate\n",
    "* **y_points:**\n",
    "    * Northing component of the UTM coordinate\n",
    "* **zone_number:**\n",
    "    * Zone number of the UTM coordinate\n",
    "* **zone_letter:**\n",
    "    * Zone letter of the UTM coordinate\n",
    "* **latitude_points:**\n",
    "    * The latitude value of a location in the borehole. A positive value denotes north. Angle subtended with equatorial plane by a perpendicular from a point on the surface of a spheriod.\n",
    "* **longitude_points:**\n",
    "    * The longitude value of a location in a borehole. A positive value denotes east. Angle measured about the spheroid axis from a local prime meridian to the meridian through the point.\n",
    "* **isHorizontal:**\n",
    "    * Array of strings, Vertical Or Horizontal depending on Inclination angle point.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from welltrajconvert.wellbore_trajectory import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Wellbore Trajectory object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "get the path to the json file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./data/wellbore_survey.json'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json_path = './data/wellbore_survey.json'\n",
    "json_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Open the json, just to take a look at it. the `from_json()` takes care of this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(json_path) as json_file:\n",
    "    json_obj = json.load(json_file)\n",
    "json_file.close()\n",
    "# take a look at the json\n",
    "#print(json.dumps(json_obj, indent=4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "using the WellboreTrajectory class, use `from_json` to take the path and convert it to a deviation survey object. This step will validate if the json contains the correct data for the minimum curvature calculation.\n",
    "\n",
    "**Create Wellbore_Survey_Object:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DeviationSurvey(wellId='well_C', md=array([    0.  ,    35.  ,   774.81,   800.  ,   900.  ,  1000.  ,\n",
       "        1100.  ,  1200.  ,  1300.  ,  1400.  ,  1500.  ,  1600.  ,\n",
       "        1700.  ,  1800.  ,  1900.  ,  2000.  ,  2100.  ,  2200.  ,\n",
       "        2300.  ,  2400.  ,  2500.  ,  2600.  ,  2700.  ,  2800.  ,\n",
       "        2900.  ,  3000.  ,  3100.  ,  3200.  ,  3300.  ,  3400.  ,\n",
       "        3500.  ,  3600.  ,  3700.  ,  3800.  ,  3900.  ,  4000.  ,\n",
       "        4100.  ,  4200.  ,  4300.  ,  4400.  ,  4500.  ,  4600.  ,\n",
       "        4700.  ,  4800.  ,  4900.  ,  5000.  ,  5100.  ,  5200.  ,\n",
       "        5300.  ,  5400.  ,  5500.  ,  5600.  ,  5700.  ,  5800.  ,\n",
       "        5900.  ,  6000.  ,  6100.  ,  6200.  ,  6300.  ,  6400.  ,\n",
       "        6450.67,  6532.  ,  6625.  ,  6720.  ,  6813.  ,  6909.  ,\n",
       "        7002.  ,  7098.  ,  7191.  ,  7286.  ,  7379.  ,  7475.  ,\n",
       "        7569.  ,  7663.  ,  7758.  ,  7851.  ,  7947.  ,  8040.  ,\n",
       "        8135.  ,  8229.  ,  8324.  ,  8418.  ,  8512.  ,  8606.  ,\n",
       "        8700.  ,  8794.  ,  8859.  ,  8924.  ,  9018.  ,  9112.  ,\n",
       "        9206.  ,  9292.  ,  9386.  ,  9481.  ,  9574.  ,  9669.  ,\n",
       "        9764.  ,  9858.  ,  9952.  , 10047.  , 10142.  , 10236.  ,\n",
       "       10330.  , 10424.  , 10517.  , 10613.  , 10707.  , 10802.  ,\n",
       "       10851.  , 10890.  ]), inc=array([ 0.  ,  0.  ,  0.46,  0.13,  0.57,  0.49,  0.29,  0.6 ,  0.53,\n",
       "        0.83,  0.61,  0.87,  1.17,  1.27,  1.21,  1.18,  1.24,  1.07,\n",
       "        1.14,  1.16,  0.86,  0.95,  1.22,  0.82,  0.83,  0.98,  0.87,\n",
       "        1.25,  1.06,  1.07,  1.34,  1.03,  1.17,  1.34,  1.37,  1.39,\n",
       "        1.16,  1.61,  1.58,  1.78,  1.78,  1.81,  1.86,  1.63,  1.51,\n",
       "        1.57,  1.09,  0.99,  0.88,  0.91,  0.83,  0.77,  0.64,  0.91,\n",
       "        0.94,  0.66,  1.09,  0.91,  0.95,  0.8 ,  4.91, 10.35, 15.28,\n",
       "       20.67, 26.19, 34.23, 40.44, 44.43, 45.39, 48.78, 51.19, 53.89,\n",
       "       56.1 , 58.69, 61.62, 64.38, 67.3 , 70.15, 72.39, 75.47, 78.67,\n",
       "       81.46, 84.59, 86.2 , 87.65, 89.13, 90.27, 89.72, 89.9 , 90.02,\n",
       "       90.05, 90.03, 89.94, 90.06, 90.37, 90.24, 90.06, 89.97, 89.88,\n",
       "       90.15, 90.03, 89.91, 89.97, 90.  , 89.88, 89.88, 90.  , 89.72,\n",
       "       90.06, 90.  ]), azim=array([227.11, 227.11, 227.11, 163.86, 230.43, 254.47, 268.25, 147.67,\n",
       "       236.56, 190.05, 342.93, 184.69, 187.06, 193.66, 195.77, 185.77,\n",
       "       214.81, 203.82, 189.81, 194.18, 109.65, 214.46, 205.35, 190.63,\n",
       "       187.75, 193.45, 188.19, 205.4 , 130.09, 218.09, 247.16, 245.17,\n",
       "       239.46, 234.52, 232.45, 228.1 , 199.28, 305.98, 213.22, 211.63,\n",
       "       207.08, 209.84, 204.39, 208.  , 208.46, 201.98, 187.95, 185.23,\n",
       "       165.95, 163.86, 138.75, 253.13, 132.16, 189.07, 141.79, 143.34,\n",
       "       306.53, 111.26, 117.69,  87.41,  55.44,  48.23,  50.93,  49.37,\n",
       "        48.77,  48.96,  48.41,  48.88,  49.58,  49.67,  49.73,  49.78,\n",
       "        49.14,  49.25,  49.5 ,  49.12,  48.63,  48.79,  48.83,  49.02,\n",
       "        49.49,  49.55,  49.29,  47.62,  47.22,  47.69,  48.36,  48.41,\n",
       "        44.97,  42.69,  40.86,  40.48,  40.36,  39.41,  38.74,  40.09,\n",
       "        38.63,  35.85,  33.71,  32.12,  30.91,  29.75,  28.78,  28.53,\n",
       "        29.28,  27.59,  26.69,  26.72,  27.04,  27.35]), surface_latitude=29.90829444, surface_longitude=47.68852083, tvd=None, n_s_deviation=None, e_w_deviation=None, dls=None, surface_x=None, surface_y=None, x_points=None, y_points=None, zone_number=None, zone_letter=None, latitude_points=None, longitude_points=None, isHorizontal=None)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a wellbore deviation object from the json path\n",
    "dev_obj = WellboreTrajectory.from_json(json_path)\n",
    "# take a look at the data\n",
    "dev_obj.deviation_survey_obj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see above, there is only input data for wellId, md, inc, azim, and surface latitude and longitude.\n",
    "The rest of the data is missing and needs to be calculated.\n",
    "\n",
    "**Calculate Survey Points:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DeviationSurvey(wellId='well_C', md=array([    0.  ,    35.  ,   774.81,   800.  ,   900.  ,  1000.  ,\n",
       "        1100.  ,  1200.  ,  1300.  ,  1400.  ,  1500.  ,  1600.  ,\n",
       "        1700.  ,  1800.  ,  1900.  ,  2000.  ,  2100.  ,  2200.  ,\n",
       "        2300.  ,  2400.  ,  2500.  ,  2600.  ,  2700.  ,  2800.  ,\n",
       "        2900.  ,  3000.  ,  3100.  ,  3200.  ,  3300.  ,  3400.  ,\n",
       "        3500.  ,  3600.  ,  3700.  ,  3800.  ,  3900.  ,  4000.  ,\n",
       "        4100.  ,  4200.  ,  4300.  ,  4400.  ,  4500.  ,  4600.  ,\n",
       "        4700.  ,  4800.  ,  4900.  ,  5000.  ,  5100.  ,  5200.  ,\n",
       "        5300.  ,  5400.  ,  5500.  ,  5600.  ,  5700.  ,  5800.  ,\n",
       "        5900.  ,  6000.  ,  6100.  ,  6200.  ,  6300.  ,  6400.  ,\n",
       "        6450.67,  6532.  ,  6625.  ,  6720.  ,  6813.  ,  6909.  ,\n",
       "        7002.  ,  7098.  ,  7191.  ,  7286.  ,  7379.  ,  7475.  ,\n",
       "        7569.  ,  7663.  ,  7758.  ,  7851.  ,  7947.  ,  8040.  ,\n",
       "        8135.  ,  8229.  ,  8324.  ,  8418.  ,  8512.  ,  8606.  ,\n",
       "        8700.  ,  8794.  ,  8859.  ,  8924.  ,  9018.  ,  9112.  ,\n",
       "        9206.  ,  9292.  ,  9386.  ,  9481.  ,  9574.  ,  9669.  ,\n",
       "        9764.  ,  9858.  ,  9952.  , 10047.  , 10142.  , 10236.  ,\n",
       "       10330.  , 10424.  , 10517.  , 10613.  , 10707.  , 10802.  ,\n",
       "       10851.  , 10890.  ]), inc=array([ 0.  ,  0.  ,  0.46,  0.13,  0.57,  0.49,  0.29,  0.6 ,  0.53,\n",
       "        0.83,  0.61,  0.87,  1.17,  1.27,  1.21,  1.18,  1.24,  1.07,\n",
       "        1.14,  1.16,  0.86,  0.95,  1.22,  0.82,  0.83,  0.98,  0.87,\n",
       "        1.25,  1.06,  1.07,  1.34,  1.03,  1.17,  1.34,  1.37,  1.39,\n",
       "        1.16,  1.61,  1.58,  1.78,  1.78,  1.81,  1.86,  1.63,  1.51,\n",
       "        1.57,  1.09,  0.99,  0.88,  0.91,  0.83,  0.77,  0.64,  0.91,\n",
       "        0.94,  0.66,  1.09,  0.91,  0.95,  0.8 ,  4.91, 10.35, 15.28,\n",
       "       20.67, 26.19, 34.23, 40.44, 44.43, 45.39, 48.78, 51.19, 53.89,\n",
       "       56.1 , 58.69, 61.62, 64.38, 67.3 , 70.15, 72.39, 75.47, 78.67,\n",
       "       81.46, 84.59, 86.2 , 87.65, 89.13, 90.27, 89.72, 89.9 , 90.02,\n",
       "       90.05, 90.03, 89.94, 90.06, 90.37, 90.24, 90.06, 89.97, 89.88,\n",
       "       90.15, 90.03, 89.91, 89.97, 90.  , 89.88, 89.88, 90.  , 89.72,\n",
       "       90.06, 90.  ]), azim=array([227.11, 227.11, 227.11, 163.86, 230.43, 254.47, 268.25, 147.67,\n",
       "       236.56, 190.05, 342.93, 184.69, 187.06, 193.66, 195.77, 185.77,\n",
       "       214.81, 203.82, 189.81, 194.18, 109.65, 214.46, 205.35, 190.63,\n",
       "       187.75, 193.45, 188.19, 205.4 , 130.09, 218.09, 247.16, 245.17,\n",
       "       239.46, 234.52, 232.45, 228.1 , 199.28, 305.98, 213.22, 211.63,\n",
       "       207.08, 209.84, 204.39, 208.  , 208.46, 201.98, 187.95, 185.23,\n",
       "       165.95, 163.86, 138.75, 253.13, 132.16, 189.07, 141.79, 143.34,\n",
       "       306.53, 111.26, 117.69,  87.41,  55.44,  48.23,  50.93,  49.37,\n",
       "        48.77,  48.96,  48.41,  48.88,  49.58,  49.67,  49.73,  49.78,\n",
       "        49.14,  49.25,  49.5 ,  49.12,  48.63,  48.79,  48.83,  49.02,\n",
       "        49.49,  49.55,  49.29,  47.62,  47.22,  47.69,  48.36,  48.41,\n",
       "        44.97,  42.69,  40.86,  40.48,  40.36,  39.41,  38.74,  40.09,\n",
       "        38.63,  35.85,  33.71,  32.12,  30.91,  29.75,  28.78,  28.53,\n",
       "        29.28,  27.59,  26.69,  26.72,  27.04,  27.35]), surface_latitude=29.90829444, surface_longitude=47.68852083, tvd=array([   0.        ,   35.        ,  774.80205236,  799.9916839 ,\n",
       "        899.98957066,  999.98528439, 1099.98291708, 1199.97977187,\n",
       "       1299.97490562, 1399.96774509, 1499.95980744, 1599.95135856,\n",
       "       1699.93539853, 1799.91271696, 1899.88929431, 1999.86754393,\n",
       "       2099.8452397 , 2199.8248857 , 2299.80628255, 2399.78613986,\n",
       "       2499.77051364, 2599.75802178, 2699.73999928, 2799.7239516 ,\n",
       "       2899.71358513, 2999.70108231, 3099.68803535, 3199.67073753,\n",
       "       3299.65039143, 3399.63311651, 3499.6109049 , 3599.58939615,\n",
       "       3699.5709424 , 3799.54691757, 3899.51895345, 3999.48994863,\n",
       "       4099.46512692, 4199.43556378, 4299.39682997, 4399.35379463,\n",
       "       4499.30554098, 4599.25646956, 4699.20518489, 4799.15874306,\n",
       "       4899.12118453, 4999.0850605 , 5099.05782961, 5199.04134366,\n",
       "       5299.02801386, 5399.01581265, 5499.004277  , 5598.99453025,\n",
       "       5698.9869478 , 5798.9777011 , 5898.96466764, 5998.95482054,\n",
       "       6098.94287286, 6198.92763525, 6298.9144603 , 6398.90277221,\n",
       "       6449.49892068, 6530.07840696, 6620.73385719, 6711.06365715,\n",
       "       6796.36247083, 6879.25635883, 6953.16472562, 7024.00253299,\n",
       "       7089.86588017, 7154.54311968, 7214.33658158, 7272.71906108,\n",
       "       7326.6385766 , 7377.28559394, 7424.55766084, 7466.7746503 ,\n",
       "       7506.06181579, 7539.80284576, 7570.306201  , 7596.32330294,\n",
       "       7617.57772512, 7633.79396604, 7645.20749536, 7652.75404791,\n",
       "       7657.79630452, 7660.43720229, 7660.77749063, 7660.78311918,\n",
       "       7661.09477093, 7661.16033202, 7661.10284739, 7661.04275014,\n",
       "       7661.06729601, 7661.06723206, 7660.71819198, 7660.2124211 ,\n",
       "       7659.96364797, 7659.93897552, 7660.06195799, 7660.03702311,\n",
       "       7659.88773352, 7659.93688854, 7660.03526184, 7660.05980771,\n",
       "       7660.15713449, 7660.35813173, 7660.45650502, 7660.6885695 ,\n",
       "       7660.7826096 , 7660.76216298]), n_s_deviation=array([ 0.00000000e+00, -1.37853123e-17, -2.02120304e+00, -2.11747423e+00,\n",
       "       -2.54331148e+00, -2.97465803e+00, -3.09687233e+00, -3.54702524e+00,\n",
       "       -4.24431864e+00, -5.21236307e+00, -5.41667042e+00, -5.66445225e+00,\n",
       "       -7.43430965e+00, -9.52436356e+00, -1.16173152e+01, -1.36578743e+01,\n",
       "       -1.55707206e+01, -1.73132762e+01, -1.91476665e+01, -2.11092738e+01,\n",
       "       -2.23430190e+01, -2.32789031e+01, -2.49244904e+01, -2.65898398e+01,\n",
       "       -2.80107915e+01, -2.95601783e+01, -3.11433404e+01, -3.28801005e+01,\n",
       "       -3.44610833e+01, -3.57916156e+01, -3.69803351e+01, -3.78116235e+01,\n",
       "       -3.87078339e+01, -3.99052784e+01, -4.13125015e+01, -4.28510661e+01,\n",
       "       -4.46165238e+01, -4.47466470e+01, -4.50746457e+01, -4.75503611e+01,\n",
       "       -5.02555752e+01, -5.30082836e+01, -5.58562012e+01, -5.85900167e+01,\n",
       "       -6.10041327e+01, -6.34328142e+01, -6.56451723e+01, -6.74474774e+01,\n",
       "       -6.90527190e+01, -7.05604547e+01, -7.18677961e+01, -7.26073384e+01,\n",
       "       -7.31771963e+01, -7.43362244e+01, -7.57649125e+01, -7.68714604e+01,\n",
       "       -7.67673139e+01, -7.64890862e+01, -7.71622488e+01, -7.75159265e+01,\n",
       "       -7.62693198e+01, -6.94230110e+01, -5.61261178e+01, -3.73044917e+01,\n",
       "       -1.30713494e+01,  1.86716088e+01,  5.59039697e+01,  9.86865303e+01,\n",
       "        1.41558635e+02,  1.86620485e+02,  2.32685134e+02,  2.81911427e+02,\n",
       "        3.31957433e+02,  3.83699227e+02,  4.37342727e+02,  4.91364006e+02,\n",
       "        5.48969383e+02,  6.06148249e+02,  6.65394128e+02,  7.24734956e+02,\n",
       "        7.85158398e+02,  8.45260340e+02,  9.05948465e+02,  9.68081147e+02,\n",
       "        1.03158981e+03,  1.09512217e+03,  1.13859248e+03,  1.18176022e+03,\n",
       "        1.24620960e+03,  1.31400750e+03,  1.38410060e+03,  1.44932911e+03,\n",
       "        1.52089238e+03,  1.59378648e+03,  1.66598252e+03,  1.73937026e+03,\n",
       "        1.81281578e+03,  1.88762789e+03,  1.96482110e+03,  2.04456368e+03,\n",
       "        2.12554687e+03,  2.20667702e+03,  2.28867662e+03,  2.37116358e+03,\n",
       "        2.45257604e+03,  2.53698503e+03,  2.62063246e+03,  2.70549867e+03,\n",
       "        2.74920416e+03,  2.78389282e+03]), e_w_deviation=array([ 0.00000000e+00, -1.48400066e-17, -2.17584236e+00, -2.24198402e+00,\n",
       "       -2.59387761e+00, -3.38929521e+00, -4.05423896e+00, -4.02718020e+00,\n",
       "       -4.13311241e+00, -4.64545267e+00, -4.92810207e+00, -5.14643276e+00,\n",
       "       -5.33399368e+00, -5.72119085e+00, -6.26985886e+00, -6.66033428e+00,\n",
       "       -7.38153427e+00, -8.37630369e+00, -8.92288458e+00, -9.34034125e+00,\n",
       "       -8.88154478e+00, -8.64385623e+00, -9.56872314e+00, -1.01565171e+01,\n",
       "       -1.03861865e+01, -1.06827693e+01, -1.09898326e+01, -1.15658473e+01,\n",
       "       -1.13260729e+01, -1.11944362e+01, -1.28480229e+01, -1.47413241e+01,\n",
       "       -1.64363521e+01, -1.82678247e+01, -2.01677492e+01, -2.20182850e+01,\n",
       "       -2.32552747e+01, -2.47263008e+01, -2.66183940e+01, -2.81881843e+01,\n",
       "       -2.97097016e+01, -3.12025354e+01, -3.26585056e+01, -3.39963761e+01,\n",
       "       -3.52919696e+01, -3.64325906e+01, -3.70768846e+01, -3.72871869e+01,\n",
       "       -3.71795109e+01, -3.67723413e+01, -3.60740414e+01, -3.62395027e+01,\n",
       "       -3.64685225e+01, -3.61797082e+01, -3.57975182e+01, -3.49462669e+01,\n",
       "       -3.53666805e+01, -3.53909200e+01, -3.39168180e+01, -3.24853682e+01,\n",
       "       -3.03452994e+01, -2.20237849e+01, -6.26921560e+00,  1.61908433e+01,\n",
       "        4.41039232e+01,  8.04614299e+01,  1.22790651e+02,  1.71410194e+02,\n",
       "        2.21135813e+02,  2.74132499e+02,  3.28451128e+02,  3.86610837e+02,\n",
       "        4.45115521e+02,  5.05050118e+02,  5.67585921e+02,  6.30408504e+02,\n",
       "        6.96378191e+02,  7.61488266e+02,  8.29188492e+02,  8.97273922e+02,\n",
       "        9.67414937e+02,  1.03783540e+03,  1.10869046e+03,  1.17880536e+03,\n",
       "        1.24791863e+03,  1.31714307e+03,  1.36546419e+03,  1.41405959e+03,\n",
       "        1.48242772e+03,  1.54751178e+03,  1.61012728e+03,  1.66617329e+03,\n",
       "        1.72712153e+03,  1.78803807e+03,  1.84665796e+03,  1.90697156e+03,\n",
       "        1.96721460e+03,  2.02408247e+03,  2.07769323e+03,  2.12931067e+03,\n",
       "        2.17896642e+03,  2.22643208e+03,  2.27238229e+03,  2.31745844e+03,\n",
       "        2.36240983e+03,  2.40811628e+03,  2.45099457e+03,  2.49368712e+03,\n",
       "        2.51584102e+03,  2.53366475e+03]), dls=array([0.00000000e+00, 0.00000000e+00, 6.21781757e-02, 1.31232310e+00,\n",
       "       4.39221317e-01, 8.04228773e-02, 2.00071540e-01, 3.05418485e-01,\n",
       "       7.54425181e-02, 2.97606656e-01, 2.36700951e-01, 2.42136276e-01,\n",
       "       2.99985041e-01, 9.98282346e-02, 6.00182291e-02, 3.03785574e-02,\n",
       "       5.67899502e-02, 1.70424766e-01, 6.93668432e-02, 1.99329254e-02,\n",
       "       3.15750269e-01, 7.20972968e-02, 2.69745086e-01, 4.00573314e-01,\n",
       "       9.98500555e-03, 1.49929930e-01, 1.10062745e-01, 3.79150580e-01,\n",
       "       2.07259038e-01, 9.10249869e-03, 2.66848255e-01, 3.10014770e-01,\n",
       "       1.39895762e-01, 1.69898507e-01, 2.99791190e-02, 1.99042910e-02,\n",
       "       2.33485281e-01, 4.08046160e-01, 7.65236333e-02, 1.99981263e-01,\n",
       "       1.74220101e-04, 2.99348167e-02, 4.97345106e-02, 2.30105145e-01,\n",
       "       1.20001478e-01, 5.97357686e-02, 4.80891203e-01, 1.00021295e-01,\n",
       "       1.10852783e-01, 2.99907267e-02, 8.12457823e-02, 7.57578344e-02,\n",
       "       1.43026417e-01, 2.65385225e-01, 2.51993303e-02, 2.80004182e-01,\n",
       "       4.05427058e-01, 2.14009347e-01, 3.99051249e-02, 1.51809635e-01,\n",
       "       8.09081900e+00, 6.68023804e+00, 5.29784122e+00, 5.67160924e+00,\n",
       "       5.93496227e+00, 8.37492511e+00, 6.67638890e+00, 4.15534146e+00,\n",
       "       1.02996749e+00, 3.56838401e+00, 2.59138009e+00, 2.81248790e+00,\n",
       "       2.34851585e+00, 2.75524166e+00, 3.08378139e+00, 2.96666935e+00,\n",
       "       3.03985353e+00, 3.06431010e+00, 2.35788341e+00, 3.27628910e+00,\n",
       "       3.36649771e+00, 2.96805503e+00, 3.32917200e+00, 1.68704987e+00,\n",
       "       1.54107353e+00, 1.57242053e+00, 1.74782160e+00, 8.46188075e-01,\n",
       "       8.16646613e-02, 7.94058502e-02, 8.27467516e-04, 2.47210927e-02,\n",
       "       9.58784409e-02, 1.18025764e-01, 3.29121479e-01, 1.53582345e-01,\n",
       "       2.09053372e-01, 1.67478606e-01, 1.38255244e-01, 2.60989359e-01,\n",
       "       1.39764497e-01, 1.40151347e-01, 5.50950458e-02, 3.13346899e-02,\n",
       "       1.34310492e-01, 2.59607171e-02, 1.20140067e-01, 2.94745341e-01,\n",
       "       6.92054431e-01, 1.55996606e-01]), surface_x=759587.9344401711, surface_y=3311661.864849136, x_points=array([759587.93444017, 759587.93444017, 759587.27124342, 759587.25108344,\n",
       "       759587.14382628, 759586.90138299, 759586.69870814, 759586.70695564,\n",
       "       759586.67466751, 759586.5185062 , 759586.43235466, 759586.36580747,\n",
       "       759586.3086389 , 759586.1906212 , 759586.02338719, 759585.90437028,\n",
       "       759585.68454853, 759585.38134281, 759585.21474495, 759585.08750416,\n",
       "       759585.22734532, 759585.29979279, 759585.01789336, 759584.83873376,\n",
       "       759584.76873053, 759584.67833208, 759584.58473918, 759584.40916992,\n",
       "       759584.48225316, 759584.52237603, 759584.01836278, 759583.44128459,\n",
       "       759582.92464004, 759582.36640719, 759581.78731022, 759581.22326689,\n",
       "       759580.84623245, 759580.39786367, 759579.82115369, 759579.34268161,\n",
       "       759578.87892312, 759578.42390738, 759577.98012767, 759577.57234474,\n",
       "       759577.17744785, 759576.82978656, 759576.63340573, 759576.56930561,\n",
       "       759576.60212525, 759576.72623055, 759576.93907234, 759576.88863974,\n",
       "       759576.8188345 , 759576.9068651 , 759577.02335663, 759577.28281802,\n",
       "       759577.15467595, 759577.14728775, 759577.59659405, 759578.03289993,\n",
       "       759578.68519291, 759581.22159055, 759586.02358326, 759592.86940922,\n",
       "       759601.37731597, 759612.45908399, 759625.3610306 , 759640.18026739,\n",
       "       759655.33663587, 759671.49002588, 759688.04634402, 759705.77342333,\n",
       "       759723.60565099, 759741.87371618, 759760.93462884, 759780.08295207,\n",
       "       759800.19051272, 759820.03606369, 759840.67109264, 759861.4235316 ,\n",
       "       759882.802513  , 759904.2666706 , 759925.86329204, 759947.23431319,\n",
       "       759968.30003949, 759989.39964669, 760004.12792385, 760018.93980192,\n",
       "       760039.77840835, 760059.61603204, 760078.70123438, 760095.78405957,\n",
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       "       760307.99695571, 760321.92828112, 760334.99758426, 760348.01027549,\n",
       "       760354.7627828 , 760360.19545591]), y_points=array([3311661.86484914, 3311661.86484914, 3311661.24878645,\n",
       "       3311661.21944299, 3311661.0896478 , 3311660.95817337,\n",
       "       3311660.92092245, 3311660.78371584, 3311660.57118081,\n",
       "       3311660.27612087, 3311660.21384799, 3311660.13832409,\n",
       "       3311659.59887155, 3311658.96182312, 3311658.32389146,\n",
       "       3311657.70192905, 3311657.11889351, 3311656.58776254,\n",
       "       3311656.02864039, 3311655.4307425 , 3311655.05469696,\n",
       "       3311654.76943947, 3311654.26786448, 3311653.76026595,\n",
       "       3311653.32715989, 3311652.85490678, 3311652.37235899,\n",
       "       3311651.8429945 , 3311651.36111094, 3311650.9555647 ,\n",
       "       3311650.593243  , 3311650.3398663 , 3311650.06670136,\n",
       "       3311649.70172028, 3311649.27279868, 3311648.8038442 ,\n",
       "       3311648.26573267, 3311648.22607114, 3311648.12609713,\n",
       "       3311647.37149908, 3311646.54694983, 3311645.70792429,\n",
       "       3311644.83987901, 3311644.00661204, 3311643.27078948,\n",
       "       3311642.53052736, 3311641.85620062, 3311641.30685803,\n",
       "       3311640.81758037, 3311640.35802254, 3311639.95954488,\n",
       "       3311639.73413239, 3311639.56043972, 3311639.20716795,\n",
       "       3311638.7717038 , 3311638.43442801, 3311638.46617186,\n",
       "       3311638.55097566, 3311638.34579571, 3311638.23799473,\n",
       "       3311638.61796045, 3311640.70471538, 3311644.75760844,\n",
       "       3311650.49444008, 3311657.88070182, 3311667.55595549,\n",
       "       3311678.90437911, 3311691.94450356, 3311705.01192119,\n",
       "       3311718.74677303, 3311732.78727812, 3311747.79145201,\n",
       "       3311763.04547471, 3311778.81637349, 3311795.16691218,\n",
       "       3311811.63259822, 3311829.19071712, 3311846.6188354 ,\n",
       "       3311864.67697941, 3311882.76406374, 3311901.18112871,\n",
       "       3311919.50020064, 3311937.99794123, 3311956.9359827 ,\n",
       "       3311976.29342274, 3311995.658087  , 3312008.90783771,\n",
       "       3312022.06536487, 3312041.7095345 , 3312062.37433424,\n",
       "       3312083.73871144, 3312103.62036305, 3312125.43284558,\n",
       "       3312147.65096739, 3312169.65632205, 3312192.02490452,\n",
       "       3312214.41110004, 3312237.21382894, 3312260.74232019,\n",
       "       3312285.04786023, 3312309.73153467, 3312334.46000463,\n",
       "       3312359.45348241, 3312384.59550716, 3312409.41002751,\n",
       "       3312435.137885  , 3312460.63362334, 3312486.5008434 ,\n",
       "       3312499.82227807, 3312510.39538081]), zone_number=38, zone_letter='R', latitude_points=array([29.90829449, 29.90829449, 29.90828908, 29.90828882, 29.90828767,\n",
       "       29.90828654, 29.90828625, 29.90828501, 29.9082831 , 29.90828047,\n",
       "       29.90827993, 29.90827926, 29.90827441, 29.90826869, 29.90826298,\n",
       "       29.90825739, 29.90825219, 29.90824746, 29.90824246, 29.90823709,\n",
       "       29.90823367, 29.90823109, 29.90822662, 29.90822209, 29.9082182 ,\n",
       "       29.90821396, 29.90820963, 29.90820489, 29.90820053, 29.90819687,\n",
       "       29.90819371, 29.90819155, 29.90818919, 29.90818602, 29.90818228,\n",
       "       29.90817817, 29.9081734 , 29.90817313, 29.90817235, 29.90816565,\n",
       "       29.90815832, 29.90815085, 29.90814312, 29.90813569, 29.90812914,\n",
       "       29.90812254, 29.90811651, 29.90811157, 29.90810715, 29.90810298,\n",
       "       29.90809934, 29.90809732, 29.90809577, 29.90809257, 29.90808862,\n",
       "       29.90808552, 29.90808584, 29.9080866 , 29.90808466, 29.90808359,\n",
       "       29.90808688, 29.90810516, 29.90814068, 29.90819095, 29.90825574,\n",
       "       29.90834062, 29.9084402 , 29.90855463, 29.90866923, 29.90878963,\n",
       "       29.90891271, 29.90904422, 29.90917797, 29.90931628, 29.90945965,\n",
       "       29.90960404, 29.90975808, 29.90991099, 29.91006942, 29.91022809,\n",
       "       29.91038959, 29.9105502 , 29.91071239, 29.91087859, 29.91104864,\n",
       "       29.91121875, 29.91133508, 29.91145056, 29.91162324, 29.91180533,\n",
       "       29.91199389, 29.9121695 , 29.9123622 , 29.91255857, 29.91275316,\n",
       "       29.91295092, 29.91314884, 29.91335073, 29.91355937, 29.91377515,\n",
       "       29.91399446, 29.91421432, 29.91443666, 29.9146604 , 29.9148812 ,\n",
       "       29.91511018, 29.91533725, 29.91556768, 29.91568634, 29.9157805 ]), longitude_points=array([47.68852083, 47.68852083, 47.68851382, 47.6885136 , 47.68851246,\n",
       "       47.68850992, 47.68850782, 47.68850787, 47.68850748, 47.6885058 ,\n",
       "       47.68850489, 47.68850418, 47.68850346, 47.68850208, 47.6885002 ,\n",
       "       47.68849882, 47.6884964 , 47.68849313, 47.68849128, 47.68848981,\n",
       "       47.68849117, 47.68849185, 47.68848881, 47.68848684, 47.68848601,\n",
       "       47.68848496, 47.68848387, 47.68848193, 47.68848257, 47.68848288,\n",
       "       47.68847758, 47.68847155, 47.68846613, 47.68846027, 47.68845417,\n",
       "       47.68844822, 47.68844419, 47.68843954, 47.68843355, 47.68842842,\n",
       "       47.68842342, 47.68841851, 47.68841371, 47.68840928, 47.68840502,\n",
       "       47.68840124, 47.68839905, 47.68839825, 47.68839847, 47.68839965,\n",
       "       47.68840175, 47.68840117, 47.68840041, 47.68840124, 47.68840234,\n",
       "       47.68840494, 47.68840362, 47.68840356, 47.68840816, 47.68841265,\n",
       "       47.68841949, 47.68844624, 47.68849691, 47.68856914, 47.68865896,\n",
       "       47.68877598, 47.68891223, 47.68906873, 47.68922872, 47.6893992 ,\n",
       "       47.68957391, 47.68976098, 47.6899492 , 47.69014205, 47.69034324,\n",
       "       47.69054537, 47.69075769, 47.69096727, 47.69118517, 47.69140429,\n",
       "       47.69162998, 47.69185653, 47.69208449, 47.69231022, 47.6925329 ,\n",
       "       47.69275593, 47.69291155, 47.69306801, 47.69328841, 47.6934987 ,\n",
       "       47.69370137, 47.69388296, 47.69408049, 47.69427802, 47.69446825,\n",
       "       47.69466391, 47.69485935, 47.69504425, 47.69521906, 47.69538777,\n",
       "       47.69555038, 47.6957061 , 47.6958571 , 47.69600538, 47.69615319,\n",
       "       47.6963036 , 47.69644504, 47.69658598, 47.6966591 , 47.69671789]), isHorizontal=array(['Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
       "       'Horizontal', 'Horizontal', 'Horizontal', 'Horizontal',\n",
       "       'Horizontal', 'Horizontal', 'Horizontal', 'Horizontal',\n",
       "       'Horizontal', 'Horizontal', 'Horizontal', 'Horizontal',\n",
       "       'Horizontal', 'Horizontal', 'Horizontal', 'Horizontal',\n",
       "       'Horizontal', 'Horizontal', 'Horizontal', 'Horizontal',\n",
       "       'Horizontal', 'Horizontal', 'Horizontal', 'Horizontal',\n",
       "       'Horizontal'], dtype='<U10'))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now you can calculate the survey points using a minimum curvature algorithm\n",
    "dev_obj.calculate_survey_points()\n",
    "# take a look at the data that was just calculated\n",
    "dev_obj.deviation_survey_obj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see above, the data for latitude, longitude points and various other points along the wellbore have been calculated. \n",
    "\n",
    "**Serialize Dict:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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3312486.5008433997, 3312499.8222780693, 3312510.3953808066], \"surface_x\": 759587.9344401711, \"surface_y\": 3311661.864849136, \"isHorizontal\": [\"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\"]}'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finally, you can serialize the data object back to a json string to export or be used in a other applications.\n",
    "json_ds = dev_obj.serialize()\n",
    "json_ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you have done it, you can check out your calculated points in a dataframe for easy viewing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wellId</th>\n",
       "      <th>md</th>\n",
       "      <th>inc</th>\n",
       "      <th>azim</th>\n",
       "      <th>tvd</th>\n",
       "      <th>e_w_deviation</th>\n",
       "      <th>n_s_deviation</th>\n",
       "      <th>dls</th>\n",
       "      <th>surface_latitude</th>\n",
       "      <th>surface_longitude</th>\n",
       "      <th>longitude_points</th>\n",
       "      <th>latitude_points</th>\n",
       "      <th>zone_number</th>\n",
       "      <th>zone_letter</th>\n",
       "      <th>x_points</th>\n",
       "      <th>y_points</th>\n",
       "      <th>surface_x</th>\n",
       "      <th>surface_y</th>\n",
       "      <th>isHorizontal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>well_C</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>227.11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.908294</td>\n",
       "      <td>47.688521</td>\n",
       "      <td>47.688521</td>\n",
       "      <td>29.908294</td>\n",
       "      <td>38</td>\n",
       "      <td>R</td>\n",
       "      <td>759587.934440</td>\n",
       "      <td>3.311662e+06</td>\n",
       "      <td>759587.93444</td>\n",
       "      <td>3.311662e+06</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>well_C</td>\n",
       "      <td>35.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>227.11</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>-1.484001e-17</td>\n",
       "      <td>-1.378531e-17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.908294</td>\n",
       "      <td>47.688521</td>\n",
       "      <td>47.688521</td>\n",
       "      <td>29.908294</td>\n",
       "      <td>38</td>\n",
       "      <td>R</td>\n",
       "      <td>759587.934440</td>\n",
       "      <td>3.311662e+06</td>\n",
       "      <td>759587.93444</td>\n",
       "      <td>3.311662e+06</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>well_C</td>\n",
       "      <td>774.81</td>\n",
       "      <td>0.46</td>\n",
       "      <td>227.11</td>\n",
       "      <td>774.802052</td>\n",
       "      <td>-2.175842e+00</td>\n",
       "      <td>-2.021203e+00</td>\n",
       "      <td>0.062178</td>\n",
       "      <td>29.908294</td>\n",
       "      <td>47.688521</td>\n",
       "      <td>47.688514</td>\n",
       "      <td>29.908289</td>\n",
       "      <td>38</td>\n",
       "      <td>R</td>\n",
       "      <td>759587.271243</td>\n",
       "      <td>3.311661e+06</td>\n",
       "      <td>759587.93444</td>\n",
       "      <td>3.311662e+06</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>well_C</td>\n",
       "      <td>800.00</td>\n",
       "      <td>0.13</td>\n",
       "      <td>163.86</td>\n",
       "      <td>799.991684</td>\n",
       "      <td>-2.241984e+00</td>\n",
       "      <td>-2.117474e+00</td>\n",
       "      <td>1.312323</td>\n",
       "      <td>29.908294</td>\n",
       "      <td>47.688521</td>\n",
       "      <td>47.688514</td>\n",
       "      <td>29.908289</td>\n",
       "      <td>38</td>\n",
       "      <td>R</td>\n",
       "      <td>759587.251083</td>\n",
       "      <td>3.311661e+06</td>\n",
       "      <td>759587.93444</td>\n",
       "      <td>3.311662e+06</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>well_C</td>\n",
       "      <td>900.00</td>\n",
       "      <td>0.57</td>\n",
       "      <td>230.43</td>\n",
       "      <td>899.989571</td>\n",
       "      <td>-2.593878e+00</td>\n",
       "      <td>-2.543311e+00</td>\n",
       "      <td>0.439221</td>\n",
       "      <td>29.908294</td>\n",
       "      <td>47.688521</td>\n",
       "      <td>47.688512</td>\n",
       "      <td>29.908288</td>\n",
       "      <td>38</td>\n",
       "      <td>R</td>\n",
       "      <td>759587.143826</td>\n",
       "      <td>3.311661e+06</td>\n",
       "      <td>759587.93444</td>\n",
       "      <td>3.311662e+06</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   wellId      md   inc    azim         tvd  e_w_deviation  n_s_deviation  \\\n",
       "0  well_C    0.00  0.00  227.11    0.000000   0.000000e+00   0.000000e+00   \n",
       "1  well_C   35.00  0.00  227.11   35.000000  -1.484001e-17  -1.378531e-17   \n",
       "2  well_C  774.81  0.46  227.11  774.802052  -2.175842e+00  -2.021203e+00   \n",
       "3  well_C  800.00  0.13  163.86  799.991684  -2.241984e+00  -2.117474e+00   \n",
       "4  well_C  900.00  0.57  230.43  899.989571  -2.593878e+00  -2.543311e+00   \n",
       "\n",
       "        dls  surface_latitude  surface_longitude  longitude_points  \\\n",
       "0  0.000000         29.908294          47.688521         47.688521   \n",
       "1  0.000000         29.908294          47.688521         47.688521   \n",
       "2  0.062178         29.908294          47.688521         47.688514   \n",
       "3  1.312323         29.908294          47.688521         47.688514   \n",
       "4  0.439221         29.908294          47.688521         47.688512   \n",
       "\n",
       "   latitude_points  zone_number zone_letter       x_points      y_points  \\\n",
       "0        29.908294           38           R  759587.934440  3.311662e+06   \n",
       "1        29.908294           38           R  759587.934440  3.311662e+06   \n",
       "2        29.908289           38           R  759587.271243  3.311661e+06   \n",
       "3        29.908289           38           R  759587.251083  3.311661e+06   \n",
       "4        29.908288           38           R  759587.143826  3.311661e+06   \n",
       "\n",
       "      surface_x     surface_y isHorizontal  \n",
       "0  759587.93444  3.311662e+06     Vertical  \n",
       "1  759587.93444  3.311662e+06     Vertical  \n",
       "2  759587.93444  3.311662e+06     Vertical  \n",
       "3  759587.93444  3.311662e+06     Vertical  \n",
       "4  759587.93444  3.311662e+06     Vertical  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json_ds_obj = json.loads(json_ds)\n",
    "df = pd.DataFrame(json_ds_obj)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "df.plot(kind=\"scatter\", x=\"longitude_points\", y=\"latitude_points\", alpha=0.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig = pyplot.figure()\n",
    "ax = Axes3D(fig)\n",
    "ax.scatter(df['longitude_points'].values, df['latitude_points'].values, df['tvd'].values*-1)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finished"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate From WellId, MD, INC, AZIM and Suface X,Y\n",
    "\n",
    "Sometimes the latitue and longitude coordinates are not provided.\n",
    "\n",
    "In the case that only X,Y surface coordinates are provided you can still calculate the survey points with\n",
    "one additional step. \n",
    "\n",
    "The user must find the CRS coordinate system and provide that in the calculation.\n",
    "\n",
    "* **Transformation Tested with latitude and longitude points error less than 0.001%**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get data that has only surface x and y, wellid, md, inc, azim\n",
    "json_path = './data/well_export.json'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(json_path) as json_file:\n",
    "    json_obj = json.load(json_file)\n",
    "json_file.close()\n",
    "#create a dict that has only the surface x and y\n",
    "well_dict = {'wellId':json_obj['wellId'],\n",
    "             'md':json_obj['md'],\n",
    "            'inc':json_obj['inc'],\n",
    "            'azim':json_obj['azim'],\n",
    "            'surface_x':json_obj['surface_x'],\n",
    "            'surface_y':json_obj['surface_y']}\n",
    "#well_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we already have a dictionary created, we do not need the `from_json`\n",
    "Enter the dict into the `WellboreTrajectory(data)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DeviationSurvey(wellId='well_C', md=array([    0.  ,    35.  ,   774.81,   800.  ,   900.  ,  1000.  ,\n",
       "        1100.  ,  1200.  ,  1300.  ,  1400.  ,  1500.  ,  1600.  ,\n",
       "        1700.  ,  1800.  ,  1900.  ,  2000.  ,  2100.  ,  2200.  ,\n",
       "        2300.  ,  2400.  ,  2500.  ,  2600.  ,  2700.  ,  2800.  ,\n",
       "        2900.  ,  3000.  ,  3100.  ,  3200.  ,  3300.  ,  3400.  ,\n",
       "        3500.  ,  3600.  ,  3700.  ,  3800.  ,  3900.  ,  4000.  ,\n",
       "        4100.  ,  4200.  ,  4300.  ,  4400.  ,  4500.  ,  4600.  ,\n",
       "        4700.  ,  4800.  ,  4900.  ,  5000.  ,  5100.  ,  5200.  ,\n",
       "        5300.  ,  5400.  ,  5500.  ,  5600.  ,  5700.  ,  5800.  ,\n",
       "        5900.  ,  6000.  ,  6100.  ,  6200.  ,  6300.  ,  6400.  ,\n",
       "        6450.67,  6532.  ,  6625.  ,  6720.  ,  6813.  ,  6909.  ,\n",
       "        7002.  ,  7098.  ,  7191.  ,  7286.  ,  7379.  ,  7475.  ,\n",
       "        7569.  ,  7663.  ,  7758.  ,  7851.  ,  7947.  ,  8040.  ,\n",
       "        8135.  ,  8229.  ,  8324.  ,  8418.  ,  8512.  ,  8606.  ,\n",
       "        8700.  ,  8794.  ,  8859.  ,  8924.  ,  9018.  ,  9112.  ,\n",
       "        9206.  ,  9292.  ,  9386.  ,  9481.  ,  9574.  ,  9669.  ,\n",
       "        9764.  ,  9858.  ,  9952.  , 10047.  , 10142.  , 10236.  ,\n",
       "       10330.  , 10424.  , 10517.  , 10613.  , 10707.  , 10802.  ,\n",
       "       10851.  , 10890.  ]), inc=array([ 0.  ,  0.  ,  0.46,  0.13,  0.57,  0.49,  0.29,  0.6 ,  0.53,\n",
       "        0.83,  0.61,  0.87,  1.17,  1.27,  1.21,  1.18,  1.24,  1.07,\n",
       "        1.14,  1.16,  0.86,  0.95,  1.22,  0.82,  0.83,  0.98,  0.87,\n",
       "        1.25,  1.06,  1.07,  1.34,  1.03,  1.17,  1.34,  1.37,  1.39,\n",
       "        1.16,  1.61,  1.58,  1.78,  1.78,  1.81,  1.86,  1.63,  1.51,\n",
       "        1.57,  1.09,  0.99,  0.88,  0.91,  0.83,  0.77,  0.64,  0.91,\n",
       "        0.94,  0.66,  1.09,  0.91,  0.95,  0.8 ,  4.91, 10.35, 15.28,\n",
       "       20.67, 26.19, 34.23, 40.44, 44.43, 45.39, 48.78, 51.19, 53.89,\n",
       "       56.1 , 58.69, 61.62, 64.38, 67.3 , 70.15, 72.39, 75.47, 78.67,\n",
       "       81.46, 84.59, 86.2 , 87.65, 89.13, 90.27, 89.72, 89.9 , 90.02,\n",
       "       90.05, 90.03, 89.94, 90.06, 90.37, 90.24, 90.06, 89.97, 89.88,\n",
       "       90.15, 90.03, 89.91, 89.97, 90.  , 89.88, 89.88, 90.  , 89.72,\n",
       "       90.06, 90.  ]), azim=array([227.11, 227.11, 227.11, 163.86, 230.43, 254.47, 268.25, 147.67,\n",
       "       236.56, 190.05, 342.93, 184.69, 187.06, 193.66, 195.77, 185.77,\n",
       "       214.81, 203.82, 189.81, 194.18, 109.65, 214.46, 205.35, 190.63,\n",
       "       187.75, 193.45, 188.19, 205.4 , 130.09, 218.09, 247.16, 245.17,\n",
       "       239.46, 234.52, 232.45, 228.1 , 199.28, 305.98, 213.22, 211.63,\n",
       "       207.08, 209.84, 204.39, 208.  , 208.46, 201.98, 187.95, 185.23,\n",
       "       165.95, 163.86, 138.75, 253.13, 132.16, 189.07, 141.79, 143.34,\n",
       "       306.53, 111.26, 117.69,  87.41,  55.44,  48.23,  50.93,  49.37,\n",
       "        48.77,  48.96,  48.41,  48.88,  49.58,  49.67,  49.73,  49.78,\n",
       "        49.14,  49.25,  49.5 ,  49.12,  48.63,  48.79,  48.83,  49.02,\n",
       "        49.49,  49.55,  49.29,  47.62,  47.22,  47.69,  48.36,  48.41,\n",
       "        44.97,  42.69,  40.86,  40.48,  40.36,  39.41,  38.74,  40.09,\n",
       "        38.63,  35.85,  33.71,  32.12,  30.91,  29.75,  28.78,  28.53,\n",
       "        29.28,  27.59,  26.69,  26.72,  27.04,  27.35]), surface_latitude=None, surface_longitude=None, tvd=None, n_s_deviation=None, e_w_deviation=None, dls=None, surface_x=759587.9344401711, surface_y=3311661.864849136, x_points=None, y_points=None, zone_number=None, zone_letter=None, latitude_points=None, longitude_points=None, isHorizontal=None)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import dict data\n",
    "well_obj = WellboreTrajectory(data = well_dict)\n",
    "well_obj.deviation_survey_obj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see above, no surface latitude and longitude are provided. Only the surface X and Y.\n",
    "\n",
    "**CRS Transformation:**\n",
    "Since we are only given surface x and y we must translate this to the WGS:84 projection system.\n",
    "To do this use `crs_transform()` and enter in the EPSG coordinate system.\n",
    "\n",
    "Find your CRS here: https://epsg.io/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# must import crs transform string\n",
    "well_obj.crs_transform(crs_in='epsg:32638')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Calculate Survey Points:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DeviationSurvey(wellId='well_C', md=array([    0.  ,    35.  ,   774.81,   800.  ,   900.  ,  1000.  ,\n",
       "        1100.  ,  1200.  ,  1300.  ,  1400.  ,  1500.  ,  1600.  ,\n",
       "        1700.  ,  1800.  ,  1900.  ,  2000.  ,  2100.  ,  2200.  ,\n",
       "        2300.  ,  2400.  ,  2500.  ,  2600.  ,  2700.  ,  2800.  ,\n",
       "        2900.  ,  3000.  ,  3100.  ,  3200.  ,  3300.  ,  3400.  ,\n",
       "        3500.  ,  3600.  ,  3700.  ,  3800.  ,  3900.  ,  4000.  ,\n",
       "        4100.  ,  4200.  ,  4300.  ,  4400.  ,  4500.  ,  4600.  ,\n",
       "        4700.  ,  4800.  ,  4900.  ,  5000.  ,  5100.  ,  5200.  ,\n",
       "        5300.  ,  5400.  ,  5500.  ,  5600.  ,  5700.  ,  5800.  ,\n",
       "        5900.  ,  6000.  ,  6100.  ,  6200.  ,  6300.  ,  6400.  ,\n",
       "        6450.67,  6532.  ,  6625.  ,  6720.  ,  6813.  ,  6909.  ,\n",
       "        7002.  ,  7098.  ,  7191.  ,  7286.  ,  7379.  ,  7475.  ,\n",
       "        7569.  ,  7663.  ,  7758.  ,  7851.  ,  7947.  ,  8040.  ,\n",
       "        8135.  ,  8229.  ,  8324.  ,  8418.  ,  8512.  ,  8606.  ,\n",
       "        8700.  ,  8794.  ,  8859.  ,  8924.  ,  9018.  ,  9112.  ,\n",
       "        9206.  ,  9292.  ,  9386.  ,  9481.  ,  9574.  ,  9669.  ,\n",
       "        9764.  ,  9858.  ,  9952.  , 10047.  , 10142.  , 10236.  ,\n",
       "       10330.  , 10424.  , 10517.  , 10613.  , 10707.  , 10802.  ,\n",
       "       10851.  , 10890.  ]), inc=array([ 0.  ,  0.  ,  0.46,  0.13,  0.57,  0.49,  0.29,  0.6 ,  0.53,\n",
       "        0.83,  0.61,  0.87,  1.17,  1.27,  1.21,  1.18,  1.24,  1.07,\n",
       "        1.14,  1.16,  0.86,  0.95,  1.22,  0.82,  0.83,  0.98,  0.87,\n",
       "        1.25,  1.06,  1.07,  1.34,  1.03,  1.17,  1.34,  1.37,  1.39,\n",
       "        1.16,  1.61,  1.58,  1.78,  1.78,  1.81,  1.86,  1.63,  1.51,\n",
       "        1.57,  1.09,  0.99,  0.88,  0.91,  0.83,  0.77,  0.64,  0.91,\n",
       "        0.94,  0.66,  1.09,  0.91,  0.95,  0.8 ,  4.91, 10.35, 15.28,\n",
       "       20.67, 26.19, 34.23, 40.44, 44.43, 45.39, 48.78, 51.19, 53.89,\n",
       "       56.1 , 58.69, 61.62, 64.38, 67.3 , 70.15, 72.39, 75.47, 78.67,\n",
       "       81.46, 84.59, 86.2 , 87.65, 89.13, 90.27, 89.72, 89.9 , 90.02,\n",
       "       90.05, 90.03, 89.94, 90.06, 90.37, 90.24, 90.06, 89.97, 89.88,\n",
       "       90.15, 90.03, 89.91, 89.97, 90.  , 89.88, 89.88, 90.  , 89.72,\n",
       "       90.06, 90.  ]), azim=array([227.11, 227.11, 227.11, 163.86, 230.43, 254.47, 268.25, 147.67,\n",
       "       236.56, 190.05, 342.93, 184.69, 187.06, 193.66, 195.77, 185.77,\n",
       "       214.81, 203.82, 189.81, 194.18, 109.65, 214.46, 205.35, 190.63,\n",
       "       187.75, 193.45, 188.19, 205.4 , 130.09, 218.09, 247.16, 245.17,\n",
       "       239.46, 234.52, 232.45, 228.1 , 199.28, 305.98, 213.22, 211.63,\n",
       "       207.08, 209.84, 204.39, 208.  , 208.46, 201.98, 187.95, 185.23,\n",
       "       165.95, 163.86, 138.75, 253.13, 132.16, 189.07, 141.79, 143.34,\n",
       "       306.53, 111.26, 117.69,  87.41,  55.44,  48.23,  50.93,  49.37,\n",
       "        48.77,  48.96,  48.41,  48.88,  49.58,  49.67,  49.73,  49.78,\n",
       "        49.14,  49.25,  49.5 ,  49.12,  48.63,  48.79,  48.83,  49.02,\n",
       "        49.49,  49.55,  49.29,  47.62,  47.22,  47.69,  48.36,  48.41,\n",
       "        44.97,  42.69,  40.86,  40.48,  40.36,  39.41,  38.74,  40.09,\n",
       "        38.63,  35.85,  33.71,  32.12,  30.91,  29.75,  28.78,  28.53,\n",
       "        29.28,  27.59,  26.69,  26.72,  27.04,  27.35]), surface_latitude=29.90829443997491, surface_longitude=47.68852083021084, tvd=array([   0.        ,   35.        ,  774.80205236,  799.9916839 ,\n",
       "        899.98957066,  999.98528439, 1099.98291708, 1199.97977187,\n",
       "       1299.97490562, 1399.96774509, 1499.95980744, 1599.95135856,\n",
       "       1699.93539853, 1799.91271696, 1899.88929431, 1999.86754393,\n",
       "       2099.8452397 , 2199.8248857 , 2299.80628255, 2399.78613986,\n",
       "       2499.77051364, 2599.75802178, 2699.73999928, 2799.7239516 ,\n",
       "       2899.71358513, 2999.70108231, 3099.68803535, 3199.67073753,\n",
       "       3299.65039143, 3399.63311651, 3499.6109049 , 3599.58939615,\n",
       "       3699.5709424 , 3799.54691757, 3899.51895345, 3999.48994863,\n",
       "       4099.46512692, 4199.43556378, 4299.39682997, 4399.35379463,\n",
       "       4499.30554098, 4599.25646956, 4699.20518489, 4799.15874306,\n",
       "       4899.12118453, 4999.0850605 , 5099.05782961, 5199.04134366,\n",
       "       5299.02801386, 5399.01581265, 5499.004277  , 5598.99453025,\n",
       "       5698.9869478 , 5798.9777011 , 5898.96466764, 5998.95482054,\n",
       "       6098.94287286, 6198.92763525, 6298.9144603 , 6398.90277221,\n",
       "       6449.49892068, 6530.07840696, 6620.73385719, 6711.06365715,\n",
       "       6796.36247083, 6879.25635883, 6953.16472562, 7024.00253299,\n",
       "       7089.86588017, 7154.54311968, 7214.33658158, 7272.71906108,\n",
       "       7326.6385766 , 7377.28559394, 7424.55766084, 7466.7746503 ,\n",
       "       7506.06181579, 7539.80284576, 7570.306201  , 7596.32330294,\n",
       "       7617.57772512, 7633.79396604, 7645.20749536, 7652.75404791,\n",
       "       7657.79630452, 7660.43720229, 7660.77749063, 7660.78311918,\n",
       "       7661.09477093, 7661.16033202, 7661.10284739, 7661.04275014,\n",
       "       7661.06729601, 7661.06723206, 7660.71819198, 7660.2124211 ,\n",
       "       7659.96364797, 7659.93897552, 7660.06195799, 7660.03702311,\n",
       "       7659.88773352, 7659.93688854, 7660.03526184, 7660.05980771,\n",
       "       7660.15713449, 7660.35813173, 7660.45650502, 7660.6885695 ,\n",
       "       7660.7826096 , 7660.76216298]), n_s_deviation=array([ 0.00000000e+00, -1.37853123e-17, -2.02120304e+00, -2.11747423e+00,\n",
       "       -2.54331148e+00, -2.97465803e+00, -3.09687233e+00, -3.54702524e+00,\n",
       "       -4.24431864e+00, -5.21236307e+00, -5.41667042e+00, -5.66445225e+00,\n",
       "       -7.43430965e+00, -9.52436356e+00, -1.16173152e+01, -1.36578743e+01,\n",
       "       -1.55707206e+01, -1.73132762e+01, -1.91476665e+01, -2.11092738e+01,\n",
       "       -2.23430190e+01, -2.32789031e+01, -2.49244904e+01, -2.65898398e+01,\n",
       "       -2.80107915e+01, -2.95601783e+01, -3.11433404e+01, -3.28801005e+01,\n",
       "       -3.44610833e+01, -3.57916156e+01, -3.69803351e+01, -3.78116235e+01,\n",
       "       -3.87078339e+01, -3.99052784e+01, -4.13125015e+01, -4.28510661e+01,\n",
       "       -4.46165238e+01, -4.47466470e+01, -4.50746457e+01, -4.75503611e+01,\n",
       "       -5.02555752e+01, -5.30082836e+01, -5.58562012e+01, -5.85900167e+01,\n",
       "       -6.10041327e+01, -6.34328142e+01, -6.56451723e+01, -6.74474774e+01,\n",
       "       -6.90527190e+01, -7.05604547e+01, -7.18677961e+01, -7.26073384e+01,\n",
       "       -7.31771963e+01, -7.43362244e+01, -7.57649125e+01, -7.68714604e+01,\n",
       "       -7.67673139e+01, -7.64890862e+01, -7.71622488e+01, -7.75159265e+01,\n",
       "       -7.62693198e+01, -6.94230110e+01, -5.61261178e+01, -3.73044917e+01,\n",
       "       -1.30713494e+01,  1.86716088e+01,  5.59039697e+01,  9.86865303e+01,\n",
       "        1.41558635e+02,  1.86620485e+02,  2.32685134e+02,  2.81911427e+02,\n",
       "        3.31957433e+02,  3.83699227e+02,  4.37342727e+02,  4.91364006e+02,\n",
       "        5.48969383e+02,  6.06148249e+02,  6.65394128e+02,  7.24734956e+02,\n",
       "        7.85158398e+02,  8.45260340e+02,  9.05948465e+02,  9.68081147e+02,\n",
       "        1.03158981e+03,  1.09512217e+03,  1.13859248e+03,  1.18176022e+03,\n",
       "        1.24620960e+03,  1.31400750e+03,  1.38410060e+03,  1.44932911e+03,\n",
       "        1.52089238e+03,  1.59378648e+03,  1.66598252e+03,  1.73937026e+03,\n",
       "        1.81281578e+03,  1.88762789e+03,  1.96482110e+03,  2.04456368e+03,\n",
       "        2.12554687e+03,  2.20667702e+03,  2.28867662e+03,  2.37116358e+03,\n",
       "        2.45257604e+03,  2.53698503e+03,  2.62063246e+03,  2.70549867e+03,\n",
       "        2.74920416e+03,  2.78389282e+03]), e_w_deviation=array([ 0.00000000e+00, -1.48400066e-17, -2.17584236e+00, -2.24198402e+00,\n",
       "       -2.59387761e+00, -3.38929521e+00, -4.05423896e+00, -4.02718020e+00,\n",
       "       -4.13311241e+00, -4.64545267e+00, -4.92810207e+00, -5.14643276e+00,\n",
       "       -5.33399368e+00, -5.72119085e+00, -6.26985886e+00, -6.66033428e+00,\n",
       "       -7.38153427e+00, -8.37630369e+00, -8.92288458e+00, -9.34034125e+00,\n",
       "       -8.88154478e+00, -8.64385623e+00, -9.56872314e+00, -1.01565171e+01,\n",
       "       -1.03861865e+01, -1.06827693e+01, -1.09898326e+01, -1.15658473e+01,\n",
       "       -1.13260729e+01, -1.11944362e+01, -1.28480229e+01, -1.47413241e+01,\n",
       "       -1.64363521e+01, -1.82678247e+01, -2.01677492e+01, -2.20182850e+01,\n",
       "       -2.32552747e+01, -2.47263008e+01, -2.66183940e+01, -2.81881843e+01,\n",
       "       -2.97097016e+01, -3.12025354e+01, -3.26585056e+01, -3.39963761e+01,\n",
       "       -3.52919696e+01, -3.64325906e+01, -3.70768846e+01, -3.72871869e+01,\n",
       "       -3.71795109e+01, -3.67723413e+01, -3.60740414e+01, -3.62395027e+01,\n",
       "       -3.64685225e+01, -3.61797082e+01, -3.57975182e+01, -3.49462669e+01,\n",
       "       -3.53666805e+01, -3.53909200e+01, -3.39168180e+01, -3.24853682e+01,\n",
       "       -3.03452994e+01, -2.20237849e+01, -6.26921560e+00,  1.61908433e+01,\n",
       "        4.41039232e+01,  8.04614299e+01,  1.22790651e+02,  1.71410194e+02,\n",
       "        2.21135813e+02,  2.74132499e+02,  3.28451128e+02,  3.86610837e+02,\n",
       "        4.45115521e+02,  5.05050118e+02,  5.67585921e+02,  6.30408504e+02,\n",
       "        6.96378191e+02,  7.61488266e+02,  8.29188492e+02,  8.97273922e+02,\n",
       "        9.67414937e+02,  1.03783540e+03,  1.10869046e+03,  1.17880536e+03,\n",
       "        1.24791863e+03,  1.31714307e+03,  1.36546419e+03,  1.41405959e+03,\n",
       "        1.48242772e+03,  1.54751178e+03,  1.61012728e+03,  1.66617329e+03,\n",
       "        1.72712153e+03,  1.78803807e+03,  1.84665796e+03,  1.90697156e+03,\n",
       "        1.96721460e+03,  2.02408247e+03,  2.07769323e+03,  2.12931067e+03,\n",
       "        2.17896642e+03,  2.22643208e+03,  2.27238229e+03,  2.31745844e+03,\n",
       "        2.36240983e+03,  2.40811628e+03,  2.45099457e+03,  2.49368712e+03,\n",
       "        2.51584102e+03,  2.53366475e+03]), dls=array([0.00000000e+00, 0.00000000e+00, 6.21781757e-02, 1.31232310e+00,\n",
       "       4.39221317e-01, 8.04228773e-02, 2.00071540e-01, 3.05418485e-01,\n",
       "       7.54425181e-02, 2.97606656e-01, 2.36700951e-01, 2.42136276e-01,\n",
       "       2.99985041e-01, 9.98282346e-02, 6.00182291e-02, 3.03785574e-02,\n",
       "       5.67899502e-02, 1.70424766e-01, 6.93668432e-02, 1.99329254e-02,\n",
       "       3.15750269e-01, 7.20972968e-02, 2.69745086e-01, 4.00573314e-01,\n",
       "       9.98500555e-03, 1.49929930e-01, 1.10062745e-01, 3.79150580e-01,\n",
       "       2.07259038e-01, 9.10249869e-03, 2.66848255e-01, 3.10014770e-01,\n",
       "       1.39895762e-01, 1.69898507e-01, 2.99791190e-02, 1.99042910e-02,\n",
       "       2.33485281e-01, 4.08046160e-01, 7.65236333e-02, 1.99981263e-01,\n",
       "       1.74220101e-04, 2.99348167e-02, 4.97345106e-02, 2.30105145e-01,\n",
       "       1.20001478e-01, 5.97357686e-02, 4.80891203e-01, 1.00021295e-01,\n",
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       "       47.6963036 , 47.69644504, 47.69658599, 47.6966591 , 47.69671789]), isHorizontal=array(['Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
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       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
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       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
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       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
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       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
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       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
       "       'Vertical', 'Vertical', 'Vertical', 'Vertical', 'Vertical',\n",
       "       'Horizontal', 'Horizontal', 'Horizontal', 'Horizontal',\n",
       "       'Horizontal', 'Horizontal', 'Horizontal', 'Horizontal',\n",
       "       'Horizontal', 'Horizontal', 'Horizontal', 'Horizontal',\n",
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       "       'Horizontal', 'Horizontal', 'Horizontal', 'Horizontal',\n",
       "       'Horizontal', 'Horizontal', 'Horizontal', 'Horizontal',\n",
       "       'Horizontal'], dtype='<U10'))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well_obj.calculate_survey_points()\n",
    "# check out object\n",
    "well_obj.deviation_survey_obj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see above, all the survey points have been calculated.\n",
    "\n",
    "**Deserialize Dict:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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3311638.434425707, 3311638.4661695533, 3311638.550973351, 3311638.3457934014, 3311638.237992423, 3311638.6179581434, 3311640.704713079, 3311644.7576061315, 3311650.494437772, 3311657.8806995205, 3311667.5559531883, 3311678.904376811, 3311691.9445012566, 3311705.0119188856, 3311718.7467707256, 3311732.7872758145, 3311747.791449701, 3311763.045472407, 3311778.816371181, 3311795.166909879, 3311811.6325959126, 3311829.19071482, 3311846.6188330953, 3311864.6769771096, 3311882.7640614365, 3311901.1811264046, 3311919.5001983377, 3311937.997938923, 3311956.9359803936, 3311976.293420434, 3311995.6580846966, 3312008.907835404, 3312022.0653625648, 3312041.7095322, 3312062.374331935, 3312083.738709136, 3312103.6203607474, 3312125.4328432726, 3312147.6509650885, 3312169.65631975, 3312192.0249022143, 3312214.411097739, 3312237.213826633, 3312260.7423178838, 3312285.0478579258, 3312309.7315323697, 3312334.4600023227, 3312359.4534801045, 3312384.595504853, 3312409.4100252073, 3312435.137882693, 3312460.633621033, 3312486.5008410956, 3312499.8222757652, 3312510.3953785026], \"surface_x\": 759587.9344606006, \"surface_y\": 3311661.864846832, \"isHorizontal\": [\"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Vertical\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\", \"Horizontal\"]}'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# serialize\n",
    "json_ds = well_obj.serialize()\n",
    "json_ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you have done it, you can check out your calculated points in a dataframe for easy viewing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wellId</th>\n",
       "      <th>md</th>\n",
       "      <th>inc</th>\n",
       "      <th>azim</th>\n",
       "      <th>tvd</th>\n",
       "      <th>e_w_deviation</th>\n",
       "      <th>n_s_deviation</th>\n",
       "      <th>dls</th>\n",
       "      <th>surface_latitude</th>\n",
       "      <th>surface_longitude</th>\n",
       "      <th>longitude_points</th>\n",
       "      <th>latitude_points</th>\n",
       "      <th>zone_number</th>\n",
       "      <th>zone_letter</th>\n",
       "      <th>x_points</th>\n",
       "      <th>y_points</th>\n",
       "      <th>surface_x</th>\n",
       "      <th>surface_y</th>\n",
       "      <th>isHorizontal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>well_C</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>227.11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.908294</td>\n",
       "      <td>47.688521</td>\n",
       "      <td>47.688521</td>\n",
       "      <td>29.908294</td>\n",
       "      <td>38</td>\n",
       "      <td>R</td>\n",
       "      <td>759587.934461</td>\n",
       "      <td>3.311662e+06</td>\n",
       "      <td>759587.934461</td>\n",
       "      <td>3.311662e+06</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>well_C</td>\n",
       "      <td>35.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>227.11</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>-1.484001e-17</td>\n",
       "      <td>-1.378531e-17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.908294</td>\n",
       "      <td>47.688521</td>\n",
       "      <td>47.688521</td>\n",
       "      <td>29.908294</td>\n",
       "      <td>38</td>\n",
       "      <td>R</td>\n",
       "      <td>759587.934461</td>\n",
       "      <td>3.311662e+06</td>\n",
       "      <td>759587.934461</td>\n",
       "      <td>3.311662e+06</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>well_C</td>\n",
       "      <td>774.81</td>\n",
       "      <td>0.46</td>\n",
       "      <td>227.11</td>\n",
       "      <td>774.802052</td>\n",
       "      <td>-2.175842e+00</td>\n",
       "      <td>-2.021203e+00</td>\n",
       "      <td>0.062178</td>\n",
       "      <td>29.908294</td>\n",
       "      <td>47.688521</td>\n",
       "      <td>47.688514</td>\n",
       "      <td>29.908289</td>\n",
       "      <td>38</td>\n",
       "      <td>R</td>\n",
       "      <td>759587.271264</td>\n",
       "      <td>3.311661e+06</td>\n",
       "      <td>759587.934461</td>\n",
       "      <td>3.311662e+06</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>well_C</td>\n",
       "      <td>800.00</td>\n",
       "      <td>0.13</td>\n",
       "      <td>163.86</td>\n",
       "      <td>799.991684</td>\n",
       "      <td>-2.241984e+00</td>\n",
       "      <td>-2.117474e+00</td>\n",
       "      <td>1.312323</td>\n",
       "      <td>29.908294</td>\n",
       "      <td>47.688521</td>\n",
       "      <td>47.688514</td>\n",
       "      <td>29.908289</td>\n",
       "      <td>38</td>\n",
       "      <td>R</td>\n",
       "      <td>759587.251104</td>\n",
       "      <td>3.311661e+06</td>\n",
       "      <td>759587.934461</td>\n",
       "      <td>3.311662e+06</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>well_C</td>\n",
       "      <td>900.00</td>\n",
       "      <td>0.57</td>\n",
       "      <td>230.43</td>\n",
       "      <td>899.989571</td>\n",
       "      <td>-2.593878e+00</td>\n",
       "      <td>-2.543311e+00</td>\n",
       "      <td>0.439221</td>\n",
       "      <td>29.908294</td>\n",
       "      <td>47.688521</td>\n",
       "      <td>47.688512</td>\n",
       "      <td>29.908288</td>\n",
       "      <td>38</td>\n",
       "      <td>R</td>\n",
       "      <td>759587.143847</td>\n",
       "      <td>3.311661e+06</td>\n",
       "      <td>759587.934461</td>\n",
       "      <td>3.311662e+06</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   wellId      md   inc    azim         tvd  e_w_deviation  n_s_deviation  \\\n",
       "0  well_C    0.00  0.00  227.11    0.000000   0.000000e+00   0.000000e+00   \n",
       "1  well_C   35.00  0.00  227.11   35.000000  -1.484001e-17  -1.378531e-17   \n",
       "2  well_C  774.81  0.46  227.11  774.802052  -2.175842e+00  -2.021203e+00   \n",
       "3  well_C  800.00  0.13  163.86  799.991684  -2.241984e+00  -2.117474e+00   \n",
       "4  well_C  900.00  0.57  230.43  899.989571  -2.593878e+00  -2.543311e+00   \n",
       "\n",
       "        dls  surface_latitude  surface_longitude  longitude_points  \\\n",
       "0  0.000000         29.908294          47.688521         47.688521   \n",
       "1  0.000000         29.908294          47.688521         47.688521   \n",
       "2  0.062178         29.908294          47.688521         47.688514   \n",
       "3  1.312323         29.908294          47.688521         47.688514   \n",
       "4  0.439221         29.908294          47.688521         47.688512   \n",
       "\n",
       "   latitude_points  zone_number zone_letter       x_points      y_points  \\\n",
       "0        29.908294           38           R  759587.934461  3.311662e+06   \n",
       "1        29.908294           38           R  759587.934461  3.311662e+06   \n",
       "2        29.908289           38           R  759587.271264  3.311661e+06   \n",
       "3        29.908289           38           R  759587.251104  3.311661e+06   \n",
       "4        29.908288           38           R  759587.143847  3.311661e+06   \n",
       "\n",
       "       surface_x     surface_y isHorizontal  \n",
       "0  759587.934461  3.311662e+06     Vertical  \n",
       "1  759587.934461  3.311662e+06     Vertical  \n",
       "2  759587.934461  3.311662e+06     Vertical  \n",
       "3  759587.934461  3.311662e+06     Vertical  \n",
       "4  759587.934461  3.311662e+06     Vertical  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json_ds_obj = json.loads(json_ds)\n",
    "dfXY = pd.DataFrame(json_ds_obj)\n",
    "dfXY.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "dfXY.plot(kind=\"scatter\", x=\"longitude_points\", y=\"latitude_points\", alpha=0.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig = pyplot.figure()\n",
    "ax = Axes3D(fig)\n",
    "ax.scatter(dfXY['longitude_points'].values, dfXY['latitude_points'].values, dfXY['tvd'].values*-1)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finished"
   ]
  }
 ],
 "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": "3.7.9"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}