Example 1 - Using Loop's Sources.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loop Workflow Example 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* High level approach to making a 3D model from just a bounding box (in Australia only for now). Simply run all the cells of the notebook. \n",
    "* This requires an internet connection to access remote data, for a local data version see Example 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#if not already installed:\n",
    "#!conda install -c loop3d map2loop loopstructural pyamg meshio\n",
    "#!pip install loopstructuralvisualisation[jupyter]\n",
    "\n",
    "# note on versions:\n",
    "\n",
    "## map2loop version = v3.2.0\n",
    "## LoopStructural version = 1.6.5\n",
    "## LoopProjectFile = 0.2.2\n",
    "## loopstructural-visualisation = 0.1.10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optional:\n",
    "Create a config dictionary that specifies the column names and description texts to process the shape files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "config_dict = {\n",
    "    \"structure\" : {\n",
    "        \"orientation_type\": \"strike\",\n",
    "        \"dipdir_column\": \"strike\",\n",
    "        \"dip_column\": \"dip\",\n",
    "        \"description_column\": \"feature\",\n",
    "        \"bedding_text\": \"Bed\",\n",
    "        \"overturned_column\": \"structypei\",\n",
    "        \"overturned_text\": \"BEOI\",\n",
    "        \"objectid_column\": \"geopnt_id\",\n",
    "    },\n",
    "    \"geology\" : {\n",
    "        \"unitname_column\": \"unitname\",\n",
    "        \"alt_unitname_column\": \"code\",\n",
    "        \"group_column\": \"group_\",\n",
    "        \"supergroup_column\": \"supersuite\",\n",
    "        \"description_column\": \"descriptn\",\n",
    "        \"minage_column\": \"min_age_ma\",\n",
    "        \"maxage_column\": \"max_age_ma\",\n",
    "        \"rocktype_column\": \"rocktype1\",\n",
    "        \"alt_rocktype_column\": \"rocktype2\",\n",
    "        \"sill_text\": \"is a sill\",\n",
    "        \"intrusive_text\": \"intrusive\",\n",
    "        \"volcanic_text\": \"volcanic\",\n",
    "        \"objectid_column\": \"objectid\",\n",
    "        \"ignore_codes\": [\"cover\"],\n",
    "    },\n",
    "    \"fault\" : {\n",
    "        \"structtype_column\": \"feature\",\n",
    "        \"fault_text\": \"Fault\",\n",
    "        \"dip_null_value\": \"0\",\n",
    "        \"dipdir_flag\": \"num\",\n",
    "        \"dipdir_column\": \"dip_dir\",\n",
    "        \"dip_column\": \"dip\",\n",
    "        \"orientation_type\": \"dip direction\",\n",
    "        \"dipestimate_column\": \"dip_est\",\n",
    "        \"dipestimate_text\": \"gentle,moderate,steep\",\n",
    "        \"name_column\": \"name\",\n",
    "        \"objectid_column\": \"objectid\",\n",
    "    },\n",
    "    \"fold\" : {\n",
    "        \"structtype_column\": \"feature\",\n",
    "        \"fold_text\": \"Fold axial trace\",\n",
    "        \"description_column\": \"type\",\n",
    "        \"synform_text\": \"syncline\",\n",
    "        \"foldname_column\": \"name\",\n",
    "        \"objectid_column\": \"objectid\",\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Map2Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\angel\\anaconda3\\envs\\ls\\Lib\\site-packages\\map2loop\\__init__.py:112: UserWarning: dependencies.txt not found. No dependencies checked for map2loop.\n",
      "  warnings.warn(\n",
      "WARNING: 2024-12-17 16:05:31,596: mapdata.py:466 -- Datatype FAULT_ORIENTATION is not set and so cannot be loaded\n",
      "\n",
      "WARNING: 2024-12-17 16:05:38,012: interpolators.py:352 -- Detected 12 collocated point clusters. Aggregating these points.\n",
      " \n",
      "WARNING: 2024-12-17 16:05:44,907: thickness_calculator.py:407 -- Thickness Calculator InterpolatedStructure: Cannot calculate thickness between Jeerinah_Formation and Bunjinah_Formation\n",
      "\n",
      "WARNING: 2024-12-17 16:05:44,907: thickness_calculator.py:407 -- Thickness Calculator InterpolatedStructure: Cannot calculate thickness between Bunjinah_Formation and Pyradie_Formation\n",
      "\n",
      "WARNING: 2024-12-17 16:05:44,907: thickness_calculator.py:407 -- Thickness Calculator InterpolatedStructure: Cannot calculate thickness between Pyradie_Formation and Fortescue_Group\n",
      "\n",
      "WARNING: 2024-12-17 16:05:45,185: project.py:659 -- No fault orientation data found, skipping fault orientation calculation\n",
      "C:\\Users\\angel\\anaconda3\\envs\\ls\\Lib\\site-packages\\pandas\\core\\series.py:1031: RuntimeWarning: invalid value encountered in cast\n",
      "  arr = np.asarray(values, dtype=dtype)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from map2loop.project import Project\n",
    "from map2loop.m2l_enums import VerboseLevel, Datatype\n",
    "from map2loop.sorter import SorterAlpha, SorterAgeBased, SorterUseHint, SorterUseNetworkX, SorterMaximiseContacts, SorterObservationProjections\n",
    "from map2loop.thickness_calculator import ThicknessCalculatorAlpha, InterpolatedStructure, StructuralPoint\n",
    "from map2loop.sampler import SamplerSpacing\n",
    "\n",
    "\n",
    "# Set the region of interest for the project\n",
    "bbox_3d = {\n",
    "    \"minx\": 515687.31005864,\n",
    "    \"miny\": 7493446.76593407,\n",
    "    \"maxx\": 562666.860106543,\n",
    "    \"maxy\": 7521273.57407786,\n",
    "    \"base\": -3200,\n",
    "    \"top\": 3000,\n",
    "}\n",
    "\n",
    "# Specify minimum details (Australian state, projection, bounding box and output file)\n",
    "# Optional: the config information manually created in the config_dict can be used by\n",
    "# setting the parameter config_dictionary\n",
    "loop_project_filename=\"wa_output.loop3d\"\n",
    "proj = Project( \n",
    "    use_australian_state_data = \"WA\",\n",
    "    config_dictionary = config_dict,\n",
    "    working_projection = \"EPSG:28350\",\n",
    "    bounding_box = bbox_3d,\n",
    "    verbose_level = VerboseLevel.NONE,\n",
    "    loop_project_filename = loop_project_filename,\n",
    "    overwrite_loopprojectfile = True\n",
    ")\n",
    "\n",
    "# Specify\n",
    "proj.set_ignore_lithology_codes([\"cover\", \"Fortescue_Group\", \"A_FO_od\"])\n",
    "\n",
    "# Set the distance between sample points for arial and linestring geometry\n",
    "proj.set_sampler(Datatype.GEOLOGY, SamplerSpacing(200.0))\n",
    "proj.set_sampler(Datatype.FAULT, SamplerSpacing(200.0))\n",
    "\n",
    "# Choose which thickness calculator to use\n",
    "# proj.set_thickness_calculator(ThicknessCalculatorAlpha())\n",
    "proj.set_thickness_calculator(InterpolatedStructure())\n",
    "# proj.set_thickness_calculator(StructuralPoint())\n",
    "\n",
    "# Choose which stratigraphic sorter to use or run_all with \"take_best\" flag to run them all\n",
    "# proj.set_sorter(SorterAlpha())\n",
    "# proj.set_sorter(SorterAgeBased())\n",
    "# proj.set_sorter(SorterUseHint())\n",
    "# proj.set_sorter(SorterUseNetworkX())\n",
    "# proj.set_sorter(SorterMaximiseContacts())\n",
    "# proj.set_sorter(SorterObservationProjections())\n",
    "column = [\n",
    "    # youngest\n",
    "    'Turee_Creek_Group',\n",
    "    'Boolgeeda_Iron_Formation',\n",
    "    'Woongarra_Rhyolite',\n",
    "    'Weeli_Wolli_Formation',\n",
    "    'Brockman_Iron_Formation',\n",
    "    'Mount_McRae_Shale_and_Mount_Sylvia_Formation',\n",
    "    'Wittenoom_Formation',\n",
    "    'Marra_Mamba_Iron_Formation',\n",
    "    'Jeerinah_Formation',\n",
    "    'Bunjinah_Formation',\n",
    "    'Pyradie_Formation',\n",
    "    'Fortescue_Group',\n",
    "    # oldest\n",
    "]\n",
    "proj.run_all(user_defined_stratigraphic_column=column)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract Information from project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw overlay of point data on geology map (can also try 'contacts','orientations','faults')\n",
    "proj.draw_geology_map(overlay=\"contacts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Turee_Creek_Group', 'Boolgeeda_Iron_Formation', 'Woongarra_Rhyolite', 'Weeli_Wolli_Formation', 'Brockman_Iron_Formation', 'Mount_McRae_Shale_and_Mount_Sylvia_Formation', 'Wittenoom_Formation', 'Marra_Mamba_Iron_Formation', 'Jeerinah_Formation', 'Bunjinah_Formation', 'Pyradie_Formation', 'Fortescue_Group']\n"
     ]
    }
   ],
   "source": [
    "# Extract map2loop estimate of the stratigraphic column\n",
    "print(proj.stratigraphic_column.column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Projection is : epsg:3857\n"
     ]
    }
   ],
   "source": [
    "# Save geology map as a raster map in a specified projection (useful for overlays on online maps)\n",
    "proj.save_geotiff_raster(filename=\"geol.tif\", projection=\"epsg:3857\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loop Structural"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: 2024-12-17 16:05:48,248: __init__.py:86 -- Surfe is not installed, SurfeRBFInterpolator will not be available\n"
     ]
    }
   ],
   "source": [
    "import LoopProjectFile as LPF\n",
    "import LoopStructural\n",
    "from LoopStructural.modelling.input.project_file import LoopProjectfileProcessor as LPFProcessor\n",
    "from LoopStructural.visualisation import Loop3DView\n",
    "import numpy as np\n",
    "\n",
    "# LoopStructural Modelling\n",
    "fault_params = {\n",
    "    'interpolatortype':'FDI',\n",
    "    'nelements':1e4,\n",
    "}\n",
    "foliation_params = {\n",
    "    'interpolatortype':'FDI' , # 'interpolatortype':'PLI',\n",
    "    'nelements':1e5,  # how many tetras/voxels\n",
    "    'regularisation':5,\n",
    "}\n",
    "projFile = LPF.ProjectFile(loop_project_filename)\n",
    "processedData = LPFProcessor(projFile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Turee_Creek_Group': inf,\n",
       " 'Boolgeeda_Iron_Formation': 436.53165648221125,\n",
       " 'Woongarra_Rhyolite': 620.048633031626,\n",
       " 'Weeli_Wolli_Formation': 446.2193181653078,\n",
       " 'Brockman_Iron_Formation': 861.1421464230768,\n",
       " 'Mount_McRae_Shale_and_Mount_Sylvia_Formation': 240.25476954067562,\n",
       " 'Wittenoom_Formation': 486.0072783721006,\n",
       " 'Marra_Mamba_Iron_Formation': 332.4297770640236,\n",
       " 'Jeerinah_Formation': 991.8848915029621,\n",
       " 'Bunjinah_Formation': -1.0}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processedData.thicknesses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: 2024-12-17 16:05:54,366: _fault_builder.py:153 -- No orientation data for fault\n",
      "                    Defaulting to a dip of 90vertical fault\n",
      "WARNING: 2024-12-17 16:05:54,379: _fault_builder.py:208 -- There is no slip vector data for the fault, using vertical slip vector\n",
      "                          projected onto fault surface estimating from fault normal\n",
      "WARNING: 2024-12-17 16:05:54,491: _fault_builder.py:153 -- No orientation data for fault\n",
      "                    Defaulting to a dip of 90vertical fault\n",
      "WARNING: 2024-12-17 16:05:54,506: _fault_builder.py:208 -- There is no slip vector data for the fault, using vertical slip vector\n",
      "                          projected onto fault surface estimating from fault normal\n",
      "WARNING: 2024-12-17 16:05:54,628: _fault_builder.py:153 -- No orientation data for fault\n",
      "                    Defaulting to a dip of 90vertical fault\n",
      "WARNING: 2024-12-17 16:05:54,638: _fault_builder.py:208 -- There is no slip vector data for the fault, using vertical slip vector\n",
      "                          projected onto fault surface estimating from fault normal\n",
      "WARNING: 2024-12-17 16:05:54,749: _fault_builder.py:153 -- No orientation data for fault\n",
      "                    Defaulting to a dip of 90vertical fault\n",
      "WARNING: 2024-12-17 16:05:54,749: _fault_builder.py:208 -- There is no slip vector data for the fault, using vertical slip vector\n",
      "                          projected onto fault surface estimating from fault normal\n",
      "WARNING: 2024-12-17 16:05:54,867: _fault_builder.py:153 -- No orientation data for fault\n",
      "                    Defaulting to a dip of 90vertical fault\n",
      "WARNING: 2024-12-17 16:05:54,885: _fault_builder.py:208 -- There is no slip vector data for the fault, using vertical slip vector\n",
      "                          projected onto fault surface estimating from fault normal\n",
      "WARNING: 2024-12-17 16:05:55,047: _fault_builder.py:153 -- No orientation data for fault\n",
      "                    Defaulting to a dip of 90vertical fault\n",
      "WARNING: 2024-12-17 16:05:55,057: _fault_builder.py:208 -- There is no slip vector data for the fault, using vertical slip vector\n",
      "                          projected onto fault surface estimating from fault normal\n",
      "WARNING: 2024-12-17 16:05:55,174: _fault_builder.py:153 -- No orientation data for fault\n",
      "                    Defaulting to a dip of 90vertical fault\n",
      "WARNING: 2024-12-17 16:05:55,190: _fault_builder.py:208 -- There is no slip vector data for the fault, using vertical slip vector\n",
      "                          projected onto fault surface estimating from fault normal\n",
      "WARNING: 2024-12-17 16:05:55,293: _fault_builder.py:153 -- No orientation data for fault\n",
      "                    Defaulting to a dip of 90vertical fault\n",
      "WARNING: 2024-12-17 16:05:55,305: _fault_builder.py:208 -- There is no slip vector data for the fault, using vertical slip vector\n",
      "                          projected onto fault surface estimating from fault normal\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d02213af844a447180d25472a62cefc7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/25 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "processedData.foliation_properties['sg'] = foliation_params\n",
    "processedData.fault_properties['interpolatortype'] = fault_params['interpolatortype']\n",
    "processedData.fault_properties['nelements'] = fault_params['nelements']\n",
    "\n",
    "model = LoopStructural.GeologicalModel.from_processor(processedData)\n",
    "model.nsteps=np.array([100,100,50])\n",
    "model.update()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6ceb06e2e9a24819b158f7f5e0cd6d28",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Widget(value='<iframe src=\"http://localhost:52050/index.html?ui=P_0x24a6ad6de10_0&reconnect=auto\" class=\"pyvis…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "view = Loop3DView(model)\n",
    "view.plot_model_surfaces()\n",
    "view.display()"
   ]
  }
 ],
 "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": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}