{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "inner-sheriff",
   "metadata": {},
   "source": [
    "# Well log wedge\n",
    "\n",
    "Let's try to make a wedge model from a well log!\n",
    "\n",
    "We'll try linear wedges and a sort of sigmoid thing.\n",
    "\n",
    "**Watch out, this is all rather experimental.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "incredible-belief",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "violent-roulette",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7ff2327c8670>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wIHCJu+/6sN3di6L3xcDTwKR4vJ9Ie3HHs6toaHR+evFonTMvgTnqoDezgcBTwBfdfW2T9gwz6/LhY+BcoNkzd0TCaP7aEp5btp0bzhrGgB46Z16C0+LUjZk9DkwGssysELgNSAJw9xnArUBP4IHoEUt99Ayb3sDT0bZE4DF3f6EVxiDS5uyvb+Cnc1eQ2zOdr54xJOhypIOL5aybK1rovxa4tpn2DcC4j+8hEn4PvrmRDaWVzLpmIqlJCUGXIx2cvhkrEmfb9lbzh/+s47zRvZk8olfQ5Ygo6EXi7c7nVgFwy0WjAq5EJEJBLxJHbxWU8tyy7Xz9zGG6aJm0GQp6kTipa2jkp3NXMKBHGtedqQ9gpe1Q0IvEySNvb2ZdcQW3XDhKH8BKm6KgF4mD0or93PPyWs44LptzRukbsNK2KOhF4uDuF9ZQXdfAbVNH6Ruw0uYo6EWO0rLCMp5YtJVrTs9laHbnoMsR+RgFvchRcHdum7ucnhnJfPPs4UGXI9IsBb3IUZizeBvvb9nLD6eMpGtqUtDliDRLQS9yhCr313PX86sZl9ONyyZovR1puxT0IkfogdcL2LlvP7ddPJpOWgNW2jAFvcgR2LKrij+/uZFLx/dnwsDMoMsROSQFvcgRuHPeKhI7GT+cMjLoUkRapKAXOUwL1pfywoodfGPyUPp0Sw26HJEWKehFDkN9QyM/+/dKcjLTuPaTup6NtA8tBr2ZPWRmxWbW7DKAFvF7Mysws6VmNqFJ3xQzWxPtuymehYsEYfbCrazeUc6PLzhe17ORdiOWI/pZwJRD9J8PDI/epgN/BDCzBOD+aP8o4Aoz0wW6pd0qq6rjNy+t4ZQhPTh/TJ+gyxGJWYtB7+7zgd2H2OQS4BGPeAfobmZ9gUlAgbtvcPdaYHZ0W5F26d5X11FWXcetF43W9WykXYnHHH1/YGuT54XRtoO1i7Q7BcUVPPL2Ji6fOJBR/boGXY7IYYlH0Dd3aOOHaG/+Rcymm1m+meWXlJTEoSyR+LnjuZWkJSfw/XOPC7oUkcMWj6AvBAY0eZ4DFB2ivVnuPtPd89w9Lzs7Ow5licTHa2uKeX1NCd86ezg9O6cEXY7IYYtH0M8FroqefXMKUObu24GFwHAzG2xmycC06LYi7UZtfSM/f3YlQ7IyuOrU3KDLETkiiS1tYGaPA5OBLDMrBG4DkgDcfQYwD7gAKACqgGuiffVmdgPwIpAAPOTuK1phDCKt5pG3N7GhpJK/fmkiyYn62om0Ty0Gvbtf0UK/A9cfpG8ekf8IRNqd0or93PvqOiaPyOaskb2CLkfkiOkQReQgfvPSWqprG7j5Qn39Q9o3Bb1IM1YUlTF74RauPi2XYb20PKC0bwp6kQO4O7fPXUlmejI3anlACQEFvcgBnl26nfc27eb7546gW5qWB5T2T0Ev0kRVbT13zlvF6H5duXzigJZ3EGkHFPQiTcx4fT3by2q4bepoErQ8oISEgl4kauvuKv40fwNTx/Vj0uAeQZcjEjcKepGoO55biRn86HwtDyjhoqAXAd5cV8KLK3Zyw1nD6Nc9LehyROJKQS8dXm19Iz+du4JBPdO1PKCEkoJeOryHF2xifUklt140SssDSigp6KVDK95Xwz2vrOWsEdmcfXzvoMsRaRUKeunQ7np+NXUNzq1TRwddikirUdBLh/Xuhl089cE2pp8xhMFZGUGXI9JqFPTSIdU1NHLrMyvo3z2N688aFnQ5Iq1KQS8d0sMLNrFmZzm3Th1FWrI+gJVwU9BLh7NzXw33vLKOs0Zkc+4ofQAr4RdT0JvZFDNbY2YFZnZTM/0/MLPF0dtyM2swsx7Rvk1mtizalx/vAYgcrjueW0VtQyM/vXg0ZrqejYRfLGvGJgD3A+cAhcBCM5vr7is/3Mbd7wbujm4/FfiOu+9u8jJnuXtpXCsXOQLz15bw7yVFfPvTwxnUUx/ASscQyxH9JKDA3Te4ey0wG7jkENtfATwej+JE4qmmroFbnlnOkKwMvj55aNDliBwzsQR9f2Brk+eF0baPMbN0YArwrybNDrxkZovMbPrB3sTMpptZvpnll5SUxFCWyOF54LUCNu+q4o7PjCElUR/ASscRS9A3N4npB9l2KvDWAdM2p7v7BOB84HozO6O5Hd19prvnuXtednZ2DGWJxK6guII/vrGez47vz2nDsoIuR+SYiiXoC4GmS+3kAEUH2XYaB0zbuHtR9L4YeJrIVJDIMePu3DxnGWlJCfz4guODLkfkmIsl6BcCw81ssJklEwnzuQduZGbdgDOBZ5q0ZZhZlw8fA+cCy+NRuEisnsjfyjsbdvOjC44nu0tK0OWIHHMtnnXj7vVmdgPwIpAAPOTuK8zsa9H+GdFNPwu85O6VTXbvDTwdPYUtEXjM3V+I5wBEDqW4vIZfPLeKSYN7cHme1oCVjqnFoAdw93nAvAPaZhzwfBYw64C2DcC4o6pQ5CjcPnclNfWN/PLSE+ikNWClg9I3YyW0Xlm5k+eWbefGTw1jaHbnoMsRCYyCXkJpX00dN89ZzojeXZh+hs6Zl44tpqkbkfbmF8+uori8hj998SSSE3U8Ix2bfgMkdOavLeEf+VuZfsZQxg3oHnQ5IoFT0EuoVOyv50dPLWNodgbf/vTwoMsRaRM0dSOh8st5qygqq+bJr52mhb5FonREL6HxxtoS/v7uFq79xGBOGpQZdDkibYaCXkKhrKqOHz65hOG9OvO9c0cEXY5Im6KpGwmFW+cuZ1dFLX+5eqKmbEQOoCN6afeeXVrEM4uL+NbZwxnTv1vQ5Yi0OQp6ade2l1Vz85zljBvQXYuJiByEgl7arYZG5zv/WExtfSP3XH4iiQn6cRZpjubopd2a8cZ63tmwm7svG8vgLK3/KnIwOgSSdumDLXv47ctrmTquH5edlBN0OSJtmoJe2p2y6jpunP0BfbqmcsdnxhBd70BEDkJTN9KuuDs/fHIJ2/fW8I/rTqVbWlLQJYm0eTEd0ZvZFDNbY2YFZnZTM/2TzazMzBZHb7fGuq/I4fjrW5t4ccVO/nfKSH37VSRGLR7Rm1kCcD9wDpGFwhea2Vx3X3nApm+6+0VHuK9Ii97fsoc7563inFG9ufaTg4MuR6TdiOWIfhJQ4O4b3L0WmA1cEuPrH82+Ih/ZXVnLNx/7gD7dUvn1ZeM0Ly9yGGIJ+v7A1ibPC6NtBzrVzJaY2fNmNvow98XMpptZvpnll5SUxFCWdBT1DY3c8Nj7lFTs54EvTKBbuublRQ5HLEHf3KGTH/D8fWCQu48D/gDMOYx9I43uM909z93zsrOzYyhLOoq7nl/NgvW7+MVnxjA2p3vQ5Yi0O7EEfSEwoMnzHKCo6Qbuvs/dK6KP5wFJZpYVy74ih/LM4m08+H8bufrUQfxP3oCWdxCRj4kl6BcCw81ssJklA9OAuU03MLM+Fp00NbNJ0dfdFcu+IgeztHAv//uvpUzK7cHNF40KuhyRdqvFs27cvd7MbgBeBBKAh9x9hZl9Ldo/A7gM+LqZ1QPVwDR3d6DZfVtpLBIi28uqufbhfHpmpHD/FyaQpOvYiBwxi+Rx25KXl+f5+flBlyEBqdxfz//MeJstu6v419dPY0SfLkGXJNLmmdkid89rrk+HSdKmNDQ635q9mNU79nHf58cr5EXiQEEvbYa789O5K3hl1U5umzqaySN6BV2SSCgo6KXNuO8/BTz6zmauO2MIV5+WG3Q5IqGhoJc2YfZ7W/jNy2u5dHx//nfKyKDLEQkVBb0E7oXl2/nx08s487hsfnXZWDp10uUNROJJQS+B+s/qnXzz8Q84cUB3HtBplCKtQr9VEpj/W1fK1/72PiP7dGXWlyeRkaLlEURag4JeAvHOhl189ZF8hmRl8MiXJ9E1VRcqE2ktCno55uavLeFLf32PnMw0/nbtyWRmJAddkkioKejlmHp11U6ufTifwVmdmT39FLI6pwRdkkjoKejlmHl2aRHXPbqIkX278PhXT6anQl7kmNCnX3JMzHprI7c/u5KTBmby0DUTNScvcgwp6KVVuTt3v7iGB15fz7mjevP7K8aTmpQQdFkiHYqCXlpNTV0DN/1rKXMWF/H5kwfy80vGkKAvQ4kccwp6aRXF5TVc9+giPtiylx+cN4JvTB6qBb1FAqKgl7hbvq2M6Y/ks6eqjhlXTmDKmL5BlyTSocV01o2ZTTGzNWZWYGY3NdP/BTNbGr0tMLNxTfo2mdkyM1tsZlpNJOSeyN/K5/64gEaHf37tVIW8SBvQ4hG9mSUA9wPnEFnse6GZzXX3lU022wic6e57zOx8YCZwcpP+s9y9NI51SxtTU9fAbc+s4B/5WzltaE9+f8V4nSMv0kbEMnUzCShw9w0AZjYbuAT4KOjdfUGT7d8BcuJZpLRta3eWc+PjH7B6RznXnzWU754zQh+6irQhsQR9f2Brk+eF/PfR+oG+Ajzf5LkDL5mZA39y95nN7WRm04HpAAMHDoyhLAmau/PI25u5c94qOqck8tCX8vjUyN5BlyUiB4gl6Js7NGt2RXEzO4tI0H+iSfPp7l5kZr2Al81stbvP/9gLRv4DmAmRxcFjqEsCtG1vNT9+ahlvrC1h8ohs7r5sHNldNFUj0hbFEvSFwIAmz3OAogM3MrOxwIPA+e6+68N2dy+K3heb2dNEpoI+FvTSPjQ2Oo8v3MIv562modG5/eLRXHXqIJ06KdKGxRL0C4HhZjYY2AZMAz7fdAMzGwg8BXzR3dc2ac8AOrl7efTxucDP4lW8HFtrd5Zzy5zlvLtxN6cP68ldl45lQI/0oMsSkRa0GPTuXm9mNwAvAgnAQ+6+wsy+Fu2fAdwK9AQeiB7Z1bt7HtAbeDralgg85u4vtMpIpNWU19Rx7yvr+OuCTXRJTeSuS0/g8okDdBQv0k6Ye9ubDs/Ly/P8fJ1yH7T6hkaeyC/kd6+spbRiP9MmDuAH542kh64fL9LmmNmi6AH2x+ibsfIx7s6rq4r51QurWVdcwUmDMvnzVXmcOKB70KWJyBFQ0MtH3J3X15ZwzyvrWLJ1L4OzMphx5QTOG91H0zQi7ZiCXmhsdF5auZMZb6xn8da99O+exl2XnsDnTsohKUFr04i0dwr6Dqy6toGnP9jGg29uYENpJQN6pHHnZ0/gspNySE5UwIuEhYK+A9q8q5JH397ME/lb2VdTzwn9u3Hf58czZXQfEnUELxI6CvoOorq2gRdWbOef+YUsWL+LxE7GeWP6cNUpg5g0uIfm4EVCTEEfYvUNjSxYv4u5S4p4cfkOyvfXM7BHOt895zgunziA3l1Tgy5RRI4BBX3I7K9vYMH6Xby0Ygcvr9xJaUUtXVISOW9MHy47KYdJuT3opCtLinQoCvoQ2FFWwxtri3l9TQlvriulYn89GckJTB7Zi6lj+zF5RLYW5BbpwBT07dDeqlre27ibBet3sWB9KWt3VgDQp2sqF43ty7mje3Pa0CyFu4gACvo2r7HR2VBayZKte1m0ZQ/5m3Z/FOypSZ2YmNuDSyfkMHlENiN6d9GHqiLyMQr6NqSuoZGNpZWsLNrHiqIylm/bx/KiMspr6gHokpLIhEGZXDyuHxNze3DiwO6kJOqoXUQOTUEfgKraejaVVrGhtIKC4grWl1Sydkc5G0orqGuIXGQuObETI/t0Yeq4fpw4oDsnDujO0OzOWqJPRA6bgr4V1NQ1sL2shqK91WzbW03hnmoKd1exdU8Vm3dVUVy+/6NtzSAnM43hvbrwqeN7MaJ3F0b27cLQ7M66/ICIxIWCPkY1dQ3srapjd2Utuytr2VW5n9KKWkrK90duFfvZWVbDzvIa9lbV/de+nQz6dkujf2Yak0dkM6hnBrk9MxiSncHgrAx9aCoirSr0QV/f0Eh1XQNVtR/e6qnc30Dl/noq9td/dF9eE7ntq6mjvKaOsuo69lbVsa+6jj1VdVTXNTT7+kkJRnbnFLK6pDCwZzoTB2fSp2sq/bqn0bdbGv26p9K3W5quHSMigYkp6M1sCnAvkRWmHnT3uw7ot2j/BUAV8CV3fz+WfePpoj+8yb7qemrqGqiua6CmruGjOe9YZCQn0C0tia5pSXRNTWJAj3S6pSXRPS2JzIxkMtOTyUxPomfnFHpkJNMzI5nu6Uk600VE2rQWg97MEoD7gXOILBS+0MzmuvvKJpudDwyP3k4G/gicHOO+cTO8VxfcndSkBFKTEkhLTiAtKXJLT0kgPTmB9OREMpITyUhJICMlkYyURLqkRtr0QaeIhFEsR/STgAJ33wBgZrOBS4CmYX0J8IhH1iV8x8y6m1lfIDeGfePmd5ef2BovKyLSrsUycdwf2NrkeWG0LZZtYtkXADObbmb5ZpZfUlISQ1kiIhKLWIK+ufmMAye+D7ZNLPtGGt1nunueu+dlZ2fHUJaIiMQilqmbQmBAk+c5QFGM2yTHsK+IiLSiWI7oFwLDzWywmSUD04C5B2wzF7jKIk4Bytx9e4z7iohIK2rxiN7d683sBuBFIqdIPuTuK8zsa9H+GcA8IqdWFhA5vfKaQ+3bKiMREZFmWeREmbYlLy/P8/Pzgy5DRKTdMLNF7p7XXJ++rikiEnIKehGRkGuTUzdmVgJsPsLds4DSOJbTHnTEMUPHHHdHHDN0zHEf7pgHuXuz56a3yaA/GmaWf7B5qrDqiGOGjjnujjhm6JjjjueYNXUjIhJyCnoRkZALY9DPDLqAAHTEMUPHHHdHHDN0zHHHbcyhm6MXEZH/FsYjehERaUJBLyIScqEJejObYmZrzKzAzG4Kup7WYmYDzOw1M1tlZivM7FvR9h5m9rKZrYveZwZda7yZWYKZfWBmz0afd4QxdzezJ81sdfTf/NSwj9vMvhP92V5uZo+bWWoYx2xmD5lZsZktb9J20HGa2Y+i+bbGzM47nPcKRdA3WbLwfGAUcIWZjQq2qlZTD3zP3Y8HTgGuj471JuBVdx8OvBp9HjbfAlY1ed4Rxnwv8IK7jwTGERl/aMdtZv2BG4E8dx9D5GKI0wjnmGcBUw5oa3ac0d/xacDo6D4PRHMvJqEIeposd+jutcCHSxaGjrtv/3DhdXcvJ/KL35/IeB+ObvYw8JlACmwlZpYDXAg82KQ57GPuCpwB/AXA3WvdfS8hHzeRq+qmmVkikE5kDYvQjdnd5wO7D2g+2DgvAWa7+35330jkSsGTYn2vsAR9zEsWhomZ5QLjgXeB3tE1AIje9wqwtNZwD/BDoLFJW9jHPAQoAf4anbJ60MwyCPG43X0b8GtgC7CdyNoWLxHiMR/gYOM8qowLS9DHvGRhWJhZZ+BfwLfdfV/Q9bQmM7sIKHb3RUHXcowlAhOAP7r7eKCScExZHFR0TvoSYDDQD8gwsyuDrapNOKqMC0vQx7LcYWiYWRKRkP+7uz8Vbd5pZn2j/X2B4qDqawWnAxeb2SYi03KfMrO/Ee4xQ+TnutDd340+f5JI8Id53J8GNrp7ibvXAU8BpxHuMTd1sHEeVcaFJeg7zJKFZmZE5mxXuftvm3TNBa6OPr4aeOZY19Za3P1H7p7j7rlE/m3/4+5XEuIxA7j7DmCrmY2INp0NrCTc494CnGJm6dGf9bOJfA4V5jE3dbBxzgWmmVmKmQ0GhgPvxfyq7h6KG5GlDNcC64GfBF1PK47zE0T+ZFsKLI7eLgB6EvmUfl30vkfQtbbS+CcDz0Yfh37MwIlAfvTfew6QGfZxA7cDq4HlwKNAShjHDDxO5HOIOiJH7F851DiBn0TzbQ1w/uG8ly6BICIScmGZuhERkYNQ0IuIhJyCXkQk5BT0IiIhp6AXEQk5Bb2ISMgp6EVEQu7/A2e9bAbI7BE0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def sigmoid(start, stop, num):\n",
    "    \"\"\"\n",
    "    Nonlinear space following a logistic function.\n",
    "    \n",
    "    The function is asymptotic; the parameters used in the sigmoid\n",
    "    gets within 0.5% of the target thickness in a wedge increasing\n",
    "    from 0 to 2x the original thickness.\n",
    "    \"\"\"\n",
    "    x = np.linspace(-5.293305, 5.293305, num)\n",
    "    return start + (stop-start) / (1 + np.exp(-x))\n",
    "\n",
    "left, right = 0.01, 2\n",
    "y = sigmoid(left, right, 100)\n",
    "plt.plot(y)\n",
    "plt.axhline(right, c='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "explicit-coach",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pad_func(before, after):\n",
    "    \"\"\"\n",
    "    Padding function. Operates on vector *in place*,\n",
    "    as per the np.pad documentation.\n",
    "    \"\"\"\n",
    "    def pad_with(x, pad_width, iaxis, kwargs):\n",
    "        x[:pad_width[0]] = before[-pad_width[0]:]\n",
    "        x[-pad_width[1]:] = after[:pad_width[1]]\n",
    "        return\n",
    "    return pad_with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "exact-cisco",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.ndimage as sn\n",
    "\n",
    "def get_strat(strat, thickness, mode='stretch', kind='nearest', position=1, wedge=None, zoom_mode='nearest'):\n",
    "    \"\"\"\n",
    "    Take a 'stratigraphy' (either an int, a tuple of ints, or a list-like of\n",
    "    floats) and expand or compress it to the required thickness.\n",
    "    \n",
    "    'mode' can be 'stretch' or 'crop'\n",
    "    \n",
    "    `kind` can be 'nearest', 'linear', 'quadratic', or 'cubic'.\n",
    "    \"\"\"\n",
    "    orders = {'nearest': 0, 'linear': 1, 'quadratic': 2, 'cubic': 3}\n",
    "    order = orders.get(kind, 0)\n",
    "    \n",
    "    if isinstance(strat, int) and order==0:\n",
    "        out = np.repeat([strat], thickness)\n",
    "    elif isinstance(strat, float) and order==0:\n",
    "        out = np.repeat([strat], thickness)\n",
    "    elif isinstance(strat, tuple) and order==0:\n",
    "        out = np.repeat(strat, int(round(thickness/len(strat))))\n",
    "    else:\n",
    "        if position == 0:\n",
    "            wedge_zoom = wedge[1]/len(wedge[0])\n",
    "            strat = strat[-int(thickness/wedge_zoom):]\n",
    "        elif position == -1:\n",
    "            wedge_zoom = wedge[1]/len(wedge[0])\n",
    "            strat = strat[:int(thickness/wedge_zoom)]\n",
    "        zoom = thickness / len(strat)\n",
    "        out = sn.zoom(strat, zoom=zoom, order=order, mode=zoom_mode)\n",
    "       \n",
    "    # Guarantee correct length by adjusting bottom layer.\n",
    "    missing = int(np.ceil(thickness - out.size))\n",
    "    if out.size > 0 and missing > 0:\n",
    "        out = np.pad(out, [0, missing], mode='edge')\n",
    "    elif out.size > 0 and missing < 0:\n",
    "        out = out[:missing]\n",
    "\n",
    "    return out\n",
    "\n",
    "get_strat((0, 1), 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "physical-november",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_conforming(strat, thickness, conformance):\n",
    "    \"\"\"\n",
    "    Function to deal with top and bottom conforming wedges.\n",
    "    \"\"\"\n",
    "    thickness = int(np.ceil(thickness))\n",
    "    if thickness == 0:\n",
    "        return np.array([])\n",
    "    if strat.size == thickness:\n",
    "        return strat\n",
    "    elif strat.size > thickness:\n",
    "        return strat[:thickness] if conformance == 'top' else strat[-thickness:]\n",
    "    else:\n",
    "        if conformance == 'top':\n",
    "            return np.pad(strat, [0, thickness-strat.size], mode='wrap')\n",
    "        else:\n",
    "            return np.pad(strat, [thickness-strat.size, 0], mode='wrap')\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "preceding-european",
   "metadata": {},
   "outputs": [],
   "source": [
    "thick = 25\n",
    "s = get_strat((0, 1, 0, 1, 0), thick)\n",
    "assert s.size == thick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "assisted-storm",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = get_strat([0, 1, 0, 1, 0], thick)\n",
    "assert s.size == thick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "first-actor",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import namedtuple\n",
    "\n",
    "\n",
    "def wedge(depth=(30, 40, 30),\n",
    "          width=(10, 80, 10),\n",
    "          breadth=None,  # Not implemented.\n",
    "          strat=(0, 1, 2),\n",
    "          thickness=(0.0, 1.0),\n",
    "          mode='linear',\n",
    "          conformance='both',\n",
    "         ):\n",
    "    \"\"\"\n",
    "    Generate a wedge model.\n",
    "    \n",
    "    Args:\n",
    "        depth (int or tuple): The vertical size of the model. If a 3-tuple, then\n",
    "            each element corresponds to a layer. If an integer, then each layer\n",
    "            of the model will be 1/3 of the thickness. Note that if the 'right'\n",
    "            wedge thickness is more than 1, then the total thickness will be\n",
    "            greater than this value.\n",
    "        width (int or tuple): The width of the model. If a 3-tuple, then each\n",
    "            element corresponds to a 'zone' (left, middle, right). If an integer,\n",
    "            then the zones will be 10%, 80% and 10% of the width, respectively.\n",
    "        breadth (int or tuple): Not implemented. Raises an error.\n",
    "        strat (tuple): Stratigraphy above, in, and below the wedge. This is the\n",
    "            'reference' stratigraphy. If you give integers, you get 'solid' layers\n",
    "            containing those numbers. If you give arrays, you will get layers of\n",
    "            those arrays, expanded or squeezed into the layer thicknesses implied\n",
    "            in `depth`. \n",
    "        thickness (tuple): The wedge thickness on the left and on the right.\n",
    "            Default is (0.0, 1.0) so the wedge will be thickness 0 on the left\n",
    "            and the wedge thickness specified in the depth argument on the right.\n",
    "            If the thickness are equal, you'll have a flat, layer-cake model.\n",
    "        mode (str): What kind of interpolation to use. Default: 'linear'. Other\n",
    "            option is 'sigmoid', which makes a clinoform-like body.\n",
    "        conformance (str): 'top', 'bottom', or 'both' (the default). How you want\n",
    "            the layers inside the wedge to behave. For top and bottom conformance,\n",
    "            if the layer needs to be thicker than the reference\n",
    "        \n",
    "    Returns:\n",
    "        namedtuple[ndarray, ndarray, ndarray, int]: A tuple containing the\n",
    "            2D wedge model, the top 'horizon', the base 'horizon', and the\n",
    "            position at which the wedge has thickness 1 (i.e. is the thickness\n",
    "            specfied by the middle layer depth and/or strat).\n",
    "        \n",
    "    TODO\n",
    "    - Nearest interp ints, but linear interp floats (e.g. rock properties).\n",
    "    - Breadth argument implements the third dimension.\n",
    "    - If the wedge layer is a tuple of two ints, e.g. (1, 2, 1, 2, 1), then\n",
    "        you are making a 'binary wedge', which has special features.\n",
    "    \"\"\"\n",
    "    if breadth is not None:\n",
    "        raise NotImplementedError(\"The breadth argument is not implemented yet.\")\n",
    "\n",
    "    # Allow wedge to be thin-thick or thick-thin.\n",
    "    left, right = thickness\n",
    "    if left > right:\n",
    "        left, right = right, left\n",
    "        flip = True\n",
    "    else:\n",
    "        flip = False\n",
    "    \n",
    "    if isinstance(depth, int):\n",
    "        L1, L2, L3 = 3 * [depth//3]  # Sizes if depth is just a number.\n",
    "        L3 += 1\n",
    "    else:\n",
    "        L1, L2, L3 = map(int, depth)\n",
    "    L3 += int(right * L2)  # Adjust bottom layer.\n",
    "    \n",
    "    if isinstance(width, int):\n",
    "        Z1, Z2, Z3 = width // 10, int(0.8 * zones), zones // 10\n",
    "    else:\n",
    "        Z1, Z2, Z3 = width  # Z1 and Z3 are the bookends.\n",
    "        \n",
    "    if mode == 'linear':\n",
    "        zooms = np.linspace(left, right, Z2)\n",
    "    elif mode in ['clinoform', 'sigmoid']:\n",
    "        zooms = sigmoid(left, right, Z2)\n",
    "    else:\n",
    "        raise TypeError(\"Mode not recognized.\")\n",
    "\n",
    "    # Get the reference stratigraphy in each layer.\n",
    "    # The 'well log' case is tricky, because layer1 and layer3\n",
    "    # need to know about the amount of zoom on the wedge layer.\n",
    "    # There must be an easier way to do this.\n",
    "    layer1 = get_strat(strat[0], L1, position=0, wedge=(strat[1], L2))\n",
    "    layer2 = get_strat(strat[1], L2, position=1)\n",
    "    layer3 = get_strat(strat[2], L3, position=-1, wedge=(strat[1], L2))\n",
    "\n",
    "    padder = pad_func(layer1, layer3)\n",
    "\n",
    "    # Collect wedge pieces, then pad top & bottom, then stack, then pad left & right.\n",
    "    if conformance in ['top', 'bottom', 'base']:\n",
    "        wedges = [get_conforming(layer2, z*L2, conformance) for z in zooms]\n",
    "    else:\n",
    "        wedges = [get_strat(layer2, thickness=z*L2) for z in zooms]\n",
    "    padded = [np.pad(w, [L1, L3-w.size], mode=padder) for w in wedges] \n",
    "    wedge = np.pad(np.stack(padded), [[Z1, Z3], [0, 0]], mode='edge')\n",
    "\n",
    "    # Make the top and base 'horizons'.\n",
    "    top = np.ones(np.sum(width)) * L1\n",
    "    base = np.pad(L1 + zooms * L2, [Z1, Z3], mode='edge')\n",
    "    \n",
    "    # Calculate the reference profile ('well' position).\n",
    "    if left <= 1 <= right:\n",
    "        ref = Z1 + np.argmin(np.abs(zooms-1))\n",
    "    elif left == right == 1:\n",
    "        ref = Z1 + Z2//2\n",
    "    else:\n",
    "        ref = -1\n",
    "        \n",
    "    if flip:\n",
    "        wedge = np.flipud(wedge)\n",
    "        base = base[::-1]\n",
    "        ref = sum(width) - ref\n",
    "    \n",
    "    Wedge = namedtuple('Wedge', ['wedge', 'top', 'base', 'reference'])\n",
    "    return Wedge(wedge.T, top, base, ref)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "forward-suite",
   "metadata": {},
   "source": [
    "## Floats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "effective-liver",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7ff225d78b20>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w, top, base, ref = wedge(depth=(4., 20, 4),\n",
    "                          width=(4, 21, 4),\n",
    "                          strat=(1.48, (2.10, 2.25, 2.35, 2.20, 2.40), 2.65),\n",
    "                          thickness=(1, 0.),\n",
    "                          mode='linear',\n",
    "                         )\n",
    "\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.imshow(w, aspect='auto', interpolation='none')\n",
    "plt.axvline(ref, color='k', ls='--')\n",
    "plt.plot(top, 'r-', lw=4)\n",
    "plt.plot(base, 'r-', lw=4)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "median-queens",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff225c76a30>]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w, top, base, ref = wedge(depth=(20, 60, 20),\n",
    "                          width=(0, 61, 0),\n",
    "                          strat=(0, (1,2), 3),\n",
    "                          mode='linear',\n",
    "                          conformance='bottom',\n",
    "                          thickness=(0, 2)\n",
    "                         )\n",
    "\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.imshow(w, aspect='auto', interpolation='none')\n",
    "plt.axvline(ref, color='k', ls='--')\n",
    "plt.plot(top, 'r-', lw=4)\n",
    "plt.plot(base, 'r-', lw=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "amino-convert",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 600 180 [200.         203.35195531 206.70391061 210.05586592 213.40782123\n",
      " 216.75977654 220.11173184 223.46368715 226.81564246 230.16759777\n",
      " 233.51955307 236.87150838 240.22346369 243.57541899 246.9273743\n",
      " 250.27932961 253.63128492 256.98324022 260.33519553 263.68715084\n",
      " 267.03910615 270.39106145 273.74301676 277.09497207 280.44692737\n",
      " 283.79888268 287.15083799 290.5027933  293.8547486  297.20670391\n",
      " 300.55865922 303.91061453 307.26256983 310.61452514 313.96648045\n",
      " 317.31843575 320.67039106 324.02234637 327.37430168 330.72625698\n",
      " 334.07821229 337.4301676  340.78212291 344.13407821 347.48603352\n",
      " 350.83798883 354.18994413 357.54189944 360.89385475 364.24581006\n",
      " 367.59776536 370.94972067 374.30167598 377.65363128 381.00558659\n",
      " 384.3575419  387.70949721 391.06145251 394.41340782 397.76536313\n",
      " 401.11731844 404.46927374 407.82122905 411.17318436 414.52513966\n",
      " 417.87709497 421.22905028 424.58100559 427.93296089 431.2849162\n",
      " 434.63687151 437.98882682 441.34078212 444.69273743 448.04469274\n",
      " 451.39664804 454.74860335 458.10055866 461.45251397 464.80446927\n",
      " 468.15642458 471.50837989 474.8603352  478.2122905  481.56424581\n",
      " 484.91620112 488.26815642 491.62011173 494.97206704 498.32402235\n",
      " 501.67597765 505.02793296 508.37988827 511.73184358 515.08379888\n",
      " 518.43575419 521.7877095  525.1396648  528.49162011 531.84357542\n",
      " 535.19553073 538.54748603 541.89944134 545.25139665 548.60335196\n",
      " 551.95530726 555.30726257 558.65921788 562.01117318 565.36312849\n",
      " 568.7150838  572.06703911 575.41899441 578.77094972 582.12290503\n",
      " 585.47486034 588.82681564 592.17877095 595.53072626 598.88268156\n",
      " 602.23463687 605.58659218 608.93854749 612.29050279 615.6424581\n",
      " 618.99441341 622.34636872 625.69832402 629.05027933 632.40223464\n",
      " 635.75418994 639.10614525 642.45810056 645.81005587 649.16201117\n",
      " 652.51396648 655.86592179 659.21787709 662.5698324  665.92178771\n",
      " 669.27374302 672.62569832 675.97765363 679.32960894 682.68156425\n",
      " 686.03351955 689.38547486 692.73743017 696.08938547 699.44134078\n",
      " 702.79329609 706.1452514  709.4972067  712.84916201 716.20111732\n",
      " 719.55307263 722.90502793 726.25698324 729.60893855 732.96089385\n",
      " 736.31284916 739.66480447 743.01675978 746.36871508 749.72067039\n",
      " 753.0726257  756.42458101 759.77653631 763.12849162 766.48044693\n",
      " 769.83240223 773.18435754 776.53631285 779.88826816 783.24022346\n",
      " 786.59217877 789.94413408 793.29608939 796.64804469 800.        ] 180\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff2260dec40>]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w, top, base, ref = wedge(depth=(200, 600, 200), width=(20, 180, 20), strat=(0, 1, 2), mode='linear')\n",
    "\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.imshow(w, aspect='auto', interpolation='none')\n",
    "plt.axvline(ref, color='k', ls='--')\n",
    "plt.plot(top, 'r-', lw=4)\n",
    "plt.plot(base, 'r-', lw=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "included-willow",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((100, 60), (60,))"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.shape, top.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "concerned-barbados",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matt/anaconda3/envs/py39/lib/python3.9/site-packages/welly/well.py:193: FutureWarning: From v0.5 the default will be 'original', keeping whatever is used in the LAS file. If you want to force conversion to metres, change your code to use `index='m'`.\n",
      "  warnings.warn(m, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from welly import Well\n",
    "\n",
    "w = Well.from_las('../data/R-39.las')\n",
    "\n",
    "log, top, bot = 'GR', 2620, 2625\n",
    "\n",
    "log_before = w.data[log].to_basis(stop=top)\n",
    "log_wedge = w.data[log].to_basis(start=top, stop=bot)\n",
    "log_after = w.data[log].to_basis(start=bot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "prime-shark",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2802,), (33,), (5008,))"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_before.shape, log_wedge.shape, log_after.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "raising-grounds",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff22620ba90>]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w, top, base, ref = wedge(depth=(200, 600, 400),\n",
    "                          width=(20, 260, 20),\n",
    "                          strat=(log_before, log_wedge, log_after),\n",
    "                          mode='sigmoid', conformance='bottom',\n",
    "                          thickness=(0, 1)\n",
    "                         )\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.imshow(w, aspect='auto', cmap='summer_r', interpolation='none')\n",
    "plt.axvline(ref, color='k', ls='--')\n",
    "plt.plot(top, 'r-', lw=4)\n",
    "plt.plot(base, 'r-', lw=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cathedral-benefit",
   "metadata": {},
   "source": [
    "---\n",
    "&copy; 2021 Agile Scientific, licensed CC-BY / Apache 2.0"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py39",
   "language": "python",
   "name": "py39"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}