DLISIO_oDa.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DLISIO for oDa 2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dlisio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import plotly_express as px\n",
    "import hvplot\n",
    "import hvplot.pandas\n",
    "import holoviews as hv\n",
    "import panel as pn\n",
    "\n",
    "##Let's load the plotting extensions we need for Jupyter\n",
    "\n",
    "hv.extension('bokeh')\n",
    "pn.extension()\n",
    "\n",
    "##And let's make our Jupyter cells a bit bigger and easier to look at\n",
    "\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "display(HTML(data=\"\"\"\n",
    "<style>\n",
    "    div#notebook-container    { width: 95%; }\n",
    "    div#menubar-container     { width: 65%; }\n",
    "    div#maintoolbar-container { width: 60%; }\n",
    "</style>\n",
    "\"\"\"))\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We will use a file representing signals acquired in the subsurface inside of a well from the open source NOPIMS Australia Dataset (https://nopims.dmp.wa.gov.au/Nopims/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = r\"PE010408.dlis\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = dlisio.dlis.load(filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is the origin metadata?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for d in file:\n",
    "    for origin in d.origins:\n",
    "        print(origin.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "## Let's get the actual well log data now..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for d in file:\n",
    "    frame_count = 0\n",
    "    for frame in d.frames:\n",
    "        if frame_count == 0:\n",
    "            curves = frame.curves() ## Magic is here\n",
    "            df = pd.DataFrame(curves, index=curves[frame.index])\n",
    "            frame_count = frame_count + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Well log data liberated from a 1982 file format..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "curve_list = list(df.columns)\n",
    "curve_list.remove('DEPT')\n",
    "curve_list.remove('FRAMENO')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def curve_plot(log, df, depthname):\n",
    "  aplot = df.hvplot(x=depthname, y=log, invert=True, flip_yaxis=True, shared_axes=True,\n",
    "                       height=600, width=300).opts(fontsize={'labels': 16,'xticks': 14, 'yticks': 14})\n",
    "  return aplot;\n",
    "\n",
    "plotlist = [curve_plot(x, df=df, depthname='DEPT') for x in curve_list]\n",
    "well_section = hv.Layout(plotlist).cols(len(curve_list))\n",
    "well_section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}