Moving sand proportion.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ceramic-principle",
   "metadata": {},
   "source": [
    "# Moving sand proportion with `striplog`\n",
    "\n",
    "We'd like to calculate the proportion of sand in a moving window. The data here represent beds of sandstone. Each one has a top, base, and an ID number. There are lots of gaps; the assumption is that there's shale in the gaps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "surgical-soviet",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"\"\"top,base,number\n",
    "24.22,24.17,20\n",
    "24.02,23.38,19\n",
    "22.97,22.91,18\n",
    "22.67,22.62,17\n",
    "21.23,21.17,16\n",
    "19.85,19.8,15\n",
    "17.9,17.5,14\n",
    "17.17,15.5,13\n",
    "15.18,14.96,12\n",
    "14.65,13.93,11\n",
    "13.4,13.05,10\n",
    "11.94,11.87,9\n",
    "10.17,10.11,8\n",
    "7.54,7.49,7\n",
    "6,5.95,6\n",
    "5.3,5.25,5\n",
    "4.91,3.04,4\n",
    "2.92,2.6,3\n",
    "2.22,2.17,2\n",
    "1.9,1.75,1\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "clear-fortune",
   "metadata": {},
   "source": [
    "You need the latest and greatest `striplog`, install like so:\n",
    "\n",
    "    python -m pip install --upgrade https://github.com/agile-geoscience/striplog/archive/develop.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "united-carrier",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.8.9'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import striplog\n",
    "\n",
    "striplog.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "obvious-graham",
   "metadata": {},
   "source": [
    "## Make a striplog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "stunning-catering",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from striplog import Striplog, Component\n",
    "\n",
    "s = Striplog.from_csv(text=text)\n",
    "\n",
    "s.plot(aspect=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fewer-soviet",
   "metadata": {},
   "source": [
    "This is not a nice plot... it would normally be easy to make a nicer one, but there's something weird going on with the 'elevation' mode of striplog here. It's bug, I'll fix it asap."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "changed-generic",
   "metadata": {},
   "source": [
    "## Make a sand flag log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "alternate-contractor",
   "metadata": {},
   "outputs": [],
   "source": [
    "start, stop, step = 0, 25, 0.01\n",
    "\n",
    "L = s.to_log(start=start, stop=stop, step=step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "chemical-teddy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd670987a30>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(L)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "backed-customer",
   "metadata": {},
   "source": [
    "## Convolve with running window\n",
    "\n",
    "Convolution with a boxcar filter computes the mean in a window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "joined-martial",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-5.0, 105.0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Define a window length in m.\n",
    "window_length = 2\n",
    "\n",
    "# Compute the number of samples in the window.\n",
    "N = int(window_length / step)\n",
    "\n",
    "# Make the boxcar filter: divide by N to get the mean,\n",
    "# and multiply by 100 to get percentage not proportion.\n",
    "boxcar = 100 * np.ones(N) / N\n",
    "\n",
    "# Make a linear 'space' (range of numbers) representing\n",
    "# depth. (We only really need this for the plot.)\n",
    "z = np.linspace(start, stop, L.size)\n",
    "\n",
    "# Convolve the log with the boxcar. This computes the\n",
    "# running mean. (Convolution does a running weighted sum\n",
    "# in the window; the weights are the boxcar array.)\n",
    "prop = np.convolve(L, boxcar, mode='same')\n",
    "\n",
    "plt.plot(z, prop)\n",
    "plt.grid(c='k', alpha=0.2)\n",
    "plt.ylim(-5, 105)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "satellite-mining",
   "metadata": {},
   "source": [
    "## Plot everything together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "indirect-reducing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '% sand, 2 m')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 8), ncols=3, sharey=True)\n",
    "\n",
    "s.plot(ax=ax[0])\n",
    "ax[0].set_title('Striplog')\n",
    "\n",
    "# Fake a striplog.\n",
    "ax[1].fill_betweenx(z, 0.5, 0, color='grey')\n",
    "ax[1].fill_betweenx(z, L, 0, color='gold', lw=0)\n",
    "ax[1].set_title('Faked with log')\n",
    "\n",
    "ax[2].plot(prop, z, 'r', lw=2)\n",
    "ax[2].set_title(f'% sand, {window_length} m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "parallel-bones",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "welly",
   "language": "python",
   "name": "welly"
  },
  "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
}