SA-Geophysics-Reference-Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\"\"\"\n",
    "GOCAD data tools.\n",
    "paulbourke.net/dataformats/gocad/gocad.pdf\n",
    "from https://github.com/elygeo/coseis/blob/main/cst/gocad.py\n",
    "https://github.com/esys-escript/esys-escript.github.io/blob/master/doc/examples/usersguide/voxet_reader.py\n",
    "\"\"\"\n",
    "import os\n",
    "import numpy as np\n",
    "import pyvista as pv\n",
    "\n",
    "def header(buff, counter=0, casters=None):\n",
    "    \"\"\"\n",
    "    GOCAD header reader\n",
    "    \"\"\"\n",
    "    if casters is None:\n",
    "        casters = {\n",
    "            int: ('pclip', 'field'),\n",
    "            bool: ('imap', 'ivolmap', 'parts', 'transparency'),\n",
    "            float: ('contrast', 'low_clip', 'high_clip', 'transparency_min'),\n",
    "        }\n",
    "    cast = {}\n",
    "    for c in casters:\n",
    "        for k in casters[c]:\n",
    "            cast[k] = c\n",
    "    header = {}\n",
    "    while counter < len(buff):\n",
    "        line = buff[counter]\n",
    "        counter += 1\n",
    "        if '}' in line:\n",
    "            return header, counter\n",
    "        k, v = line.split(':')\n",
    "        k = k.split('*')[-1]\n",
    "        header[k] = v\n",
    "        if k in cast:\n",
    "            f = v.split()\n",
    "            if len(f) > 1:\n",
    "                try:\n",
    "                    header[k] = tuple(cast[k](x) for x in f)\n",
    "                except ValueError:\n",
    "                    print('Could not cast %s %s to %s' % (k, v, cast[k]))\n",
    "            else:\n",
    "                try:\n",
    "                    header[k] = cast[k](v)\n",
    "                except ValueError:\n",
    "                    print('Could not cast %s %s to %s' % (k, v, cast[k]))\n",
    "    raise Exception('Error in header')\n",
    "    return\n",
    "\n",
    "\n",
    "def voxet(path, load_props=[], alternate='', no_data_value=None, buff=None):\n",
    "    \"\"\"\n",
    "    GOCAD voxet reader\n",
    "    \"\"\"\n",
    "    if buff is None:\n",
    "        buff = open(path).read()\n",
    "    if no_data_value in ('nan', 'NaN', 'NAN'):\n",
    "        no_data_value = float('nan')\n",
    "    buff = buff.strip().split('\\n')\n",
    "    cast = {}\n",
    "    casters = {\n",
    "        int: ('N', 'ESIZE', 'OFFSET', 'SIGNED', 'PAINTED_FLAG_BIT_POS'),\n",
    "        str: (\n",
    "            'NAME', 'FILE', 'TYPE', 'ETYPE', 'FORMAT', 'UNIT',\n",
    "            'ORIGINAL_UNIT'\n",
    "        ),\n",
    "        float: (\n",
    "            'O', 'D', 'U', 'V', 'W', 'MIN', 'MAX', 'NO_DATA_VALUE',\n",
    "            'SAMPLE_STATS'\n",
    "        ),\n",
    "    }\n",
    "    for c in casters:\n",
    "        for k in casters[c]:\n",
    "            cast[k] = c\n",
    "    voxet = {}\n",
    "    counter = 0\n",
    "    while counter < len(buff):\n",
    "        line = buff[counter].strip()\n",
    "        counter += 1\n",
    "        f = line.replace('\"', '').split()\n",
    "        if len(f) == 0 or line.startswith('#'):\n",
    "            continue\n",
    "        elif line.startswith('GOCAD Voxet'):\n",
    "            id_ = f[2]\n",
    "            axis, prop = {}, {}\n",
    "        elif f[0] == 'HEADER':\n",
    "            hdr, counter = header(buff, counter)\n",
    "        elif len(f) > 1:\n",
    "            k = f[0].split('_', 1)\n",
    "            if k[0] == 'AXIS':\n",
    "                axis[k[1]] = tuple(cast[k[1]](x) for x in f[1:])\n",
    "            elif f[0] == 'PROPERTY':\n",
    "                prop[f[1]] = {'PROPERTY': f[2]}\n",
    "            elif k[0] == 'PROP':\n",
    "                if len(f) > 3:\n",
    "                    prop[f[1]][k[1]] = tuple(cast[k[1]](x) for x in f[2:])\n",
    "                else:\n",
    "                    prop[f[1]][k[1]] = cast[k[1]](f[2])\n",
    "        elif f[0] == 'END':\n",
    "            for p in load_props:\n",
    "                print(prop)\n",
    "                p = prop[p]\n",
    "                n = axis['N']\n",
    "                print(n)\n",
    "                f = os.path.join(os.path.dirname(path), p['FILE'] + alternate)\n",
    "                print(f)\n",
    "                if os.path.exists(f):\n",
    "                    print('exists')\n",
    "                    dtype = '>f%s' % p['ESIZE']\n",
    "                    data = numpy.fromfile(f, dtype)\n",
    "                    if no_data_value is not None:\n",
    "                        data[data == p['NO_DATA_VALUE']] = no_data_value\n",
    "                    p['DATA'] = data.reshape(n[::-1]).T\n",
    "            voxet[id_] = {'HEADER': hdr, 'AXIS': axis, 'PROP': prop}\n",
    "    return voxet\n",
    "\n",
    "\n",
    "def tsurf(buff):\n",
    "    \"\"\"\n",
    "    GOCAD triangulated surface reader\n",
    "    \"\"\"\n",
    "    buff = buff.strip().split('\\n')\n",
    "    tsurf = []\n",
    "    counter = 0\n",
    "    # casters = {\n",
    "    #     int: ('ATOM', 'PATOM', 'TRGL', 'BORDER', 'BSTONE'),\n",
    "    #     float: ('VRTX', 'PVRTX'),\n",
    "    # }\n",
    "    while counter < len(buff):\n",
    "        line = buff[counter].strip()\n",
    "        counter += 1\n",
    "        f = line.split()\n",
    "        if len(f) == 0 or line.startswith('#'):\n",
    "            continue\n",
    "        elif line.startswith('GOCAD TSurf'):\n",
    "            meta0, meta, tri, x, t, b, s, a = None, {}, [], [], [], [], [], []\n",
    "        elif f[0] in ('VRTX', 'PVRTX'):\n",
    "            x.append([float(f[2]), float(f[3]), float(f[4])])\n",
    "        elif f[0] in ('ATOM', 'PATOM'):\n",
    "            i = int(f[2]) - 1\n",
    "            a.append([len(x), i])\n",
    "            # x.append(x[i])\n",
    "            x.append([float('nan'), float('nan'), float('nan')])\n",
    "        elif f[0] == 'TRGL':\n",
    "            t.append([int(f[1]) - 1, int(f[2]) - 1, int(f[3]) - 1])\n",
    "        elif f[0] == 'BORDER':\n",
    "            b.append([int(f[2]) - 1, int(f[3]) - 1])\n",
    "        elif f[0] == 'BSTONE':\n",
    "            s.append(int(f[1]) - 1)\n",
    "        elif f[0] == 'TFACE':\n",
    "            if t != []:\n",
    "                tri.append(numpy.array(t, 'i'))\n",
    "            t = []\n",
    "        elif f[0] == 'END':\n",
    "            tri.append(numpy.array(t, 'i'))\n",
    "            x = numpy.array(x, 'f')\n",
    "            b = numpy.array(b, 'i')\n",
    "            s = numpy.array(s, 'i')\n",
    "            for i, j in a:\n",
    "                tri[tri == i] = j\n",
    "                b[b == i] = j\n",
    "                s[s == i] = j\n",
    "            data = {'vtx': x, 'tri': tri, 'border': b, 'bstone': s}\n",
    "            meta.update(meta0)\n",
    "            tsurf.append([meta, data])\n",
    "        elif f[0] == 'PROPERTY_CLASS_HEADER':\n",
    "            meta[f[1]], counter = header(buff, counter)\n",
    "        elif f[0] == 'HEADER':\n",
    "            meta0, counter = header(buff, counter)\n",
    "    return tsurf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#r'J:\\McArthur\\Id1507107909\\DIP012\\Digital_Data\\REGIONAL_MODEL\\GOCAD_OBJECTS\\GMcAB_OBJECTS\\Voxets (.vo)\\SEISMIC_CUBE_DIP_DATA\\DIP_DATA_VOXET.vo'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "hfile = r'H:\\GDP00037\\Data\\SA_Geophysic_Reference_Model\\SGrid\\SA_Geophysics_Reference_Model_SGrid.sg'\n",
    "propfile1 = r'H:\\GDP00037\\Data\\SA_Geophysic_Reference_Model\\SGrid\\\\SA_Geophysics_Reference_Model_SGrid__flags@@'\n",
    "propfile2 = r'H:\\GDP00037\\Data\\SA_Geophysic_Reference_Model\\SGrid\\\\SA_Geophysics_Reference_Model_SGrid__points@@'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR:root:Cannot read cell data array \"Gravity_Inversion_Constrained\" from PointData in piece 0.  The data array in the element may be too short.\n"
     ]
    }
   ],
   "source": [
    "#junk = voxet(hfile)\n",
    "junk=pv.read(r'J:\\ogs-6.4.1-79-g69a2996020-Windows-10.0.19043-python-3.8.2-utils\\bin\\SA_Geophysics_Reference_Model.vtu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1': {'HEADER': {'name': ' DIP_DATA_VOXET',\n",
       "   'smoothed': ' on',\n",
       "   'use_private_colormaps': ' off',\n",
       "   'probe_type': ' Sections',\n",
       "   'size': ' 0 0 0 49 49 49',\n",
       "   'fence0': ' 3 1 2 0 0 0 49 49 49',\n",
       "   'nb_fences': ' 1',\n",
       "   'sections': ' 3 1 0 0 2 0 0 3 0 0',\n",
       "   'VoxetIntersectionAssociation': ' off',\n",
       "   'variable': ' Dip',\n",
       "   'painted': ' off',\n",
       "   'ascii': ' off'},\n",
       "  'AXIS': {'NAME': ('axis-1', 'axis-2', 'axis-3'),\n",
       "   'UNIT': ('unitless', 'unitless', 'unitless'),\n",
       "   'O': (270217.0, 7923111.8125, -15583.5),\n",
       "   'U': (382516.0, 0.0, 0.0),\n",
       "   'V': (0.0, 620896.188, 0.0),\n",
       "   'W': (0.0, 0.0, 31500.0),\n",
       "   'MIN': (0.0, 0.0, 0.0),\n",
       "   'MAX': (1.0, 1.0, 1.0),\n",
       "   'N': (50, 50, 50),\n",
       "   'TYPE': ('even', 'even', 'even')},\n",
       "  'PROP': {'1': {'PROPERTY': 'Dip',\n",
       "    'ORIGINAL_UNIT': 'unitless',\n",
       "    'UNIT': 'unitless',\n",
       "    'NO_DATA_VALUE': -99999.0,\n",
       "    'SAMPLE_STATS': (480.0, 7.91667, 144.068, 0.0, 70.0),\n",
       "    'ESIZE': 4,\n",
       "    'ETYPE': 'IEEE',\n",
       "    'PAINTED_FLAG_BIT_POS': 6,\n",
       "    'FORMAT': 'RAW',\n",
       "    'OFFSET': 0,\n",
       "    'FILE': 'DIP_DATA_VOXET_Dip@@'}}}}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "junk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'1': {'PROPERTY': 'Dip', 'ORIGINAL_UNIT': 'unitless', 'UNIT': 'unitless', 'NO_DATA_VALUE': -99999.0, 'SAMPLE_STATS': (480.0, 7.91667, 144.068, 0.0, 70.0), 'ESIZE': 4, 'ETYPE': 'IEEE', 'PAINTED_FLAG_BIT_POS': 6, 'FORMAT': 'RAW', 'OFFSET': 0, 'FILE': 'DIP_DATA_VOXET_Dip@@'}}\n",
      "(50, 50, 50)\n",
      "J:\\McArthur\\Id1507107909\\DIP012\\Digital_Data\\REGIONAL_MODEL\\GOCAD_OBJECTS\\GMcAB_OBJECTS\\Voxets (.vo)\\SEISMIC_CUBE_DIP_DATA\\DIP_DATA_VOXET_Dip@@\n",
      "exists\n"
     ]
    }
   ],
   "source": [
    "junk = voxet(hfile, load_props=['1'], no_data_value=-99999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1': {'HEADER': {'name': ' DIP_DATA_VOXET',\n",
       "   'smoothed': ' on',\n",
       "   'use_private_colormaps': ' off',\n",
       "   'probe_type': ' Sections',\n",
       "   'size': ' 0 0 0 49 49 49',\n",
       "   'fence0': ' 3 1 2 0 0 0 49 49 49',\n",
       "   'nb_fences': ' 1',\n",
       "   'sections': ' 3 1 0 0 2 0 0 3 0 0',\n",
       "   'VoxetIntersectionAssociation': ' off',\n",
       "   'variable': ' Dip',\n",
       "   'painted': ' off',\n",
       "   'ascii': ' off'},\n",
       "  'AXIS': {'NAME': ('axis-1', 'axis-2', 'axis-3'),\n",
       "   'UNIT': ('unitless', 'unitless', 'unitless'),\n",
       "   'O': (270217.0, 7923111.8125, -15583.5),\n",
       "   'U': (382516.0, 0.0, 0.0),\n",
       "   'V': (0.0, 620896.188, 0.0),\n",
       "   'W': (0.0, 0.0, 31500.0),\n",
       "   'MIN': (0.0, 0.0, 0.0),\n",
       "   'MAX': (1.0, 1.0, 1.0),\n",
       "   'N': (50, 50, 50),\n",
       "   'TYPE': ('even', 'even', 'even')},\n",
       "  'PROP': {'1': {'PROPERTY': 'Dip',\n",
       "    'ORIGINAL_UNIT': 'unitless',\n",
       "    'UNIT': 'unitless',\n",
       "    'NO_DATA_VALUE': -99999.0,\n",
       "    'SAMPLE_STATS': (480.0, 7.91667, 144.068, 0.0, 70.0),\n",
       "    'ESIZE': 4,\n",
       "    'ETYPE': 'IEEE',\n",
       "    'PAINTED_FLAG_BIT_POS': 6,\n",
       "    'FORMAT': 'RAW',\n",
       "    'OFFSET': 0,\n",
       "    'FILE': 'DIP_DATA_VOXET_Dip@@',\n",
       "    'DATA': array([[[-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            ...,\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.]],\n",
       "    \n",
       "           [[-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            ...,\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.]],\n",
       "    \n",
       "           [[-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            ...,\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.]],\n",
       "    \n",
       "           ...,\n",
       "    \n",
       "           [[-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            ...,\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.]],\n",
       "    \n",
       "           [[-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            ...,\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.]],\n",
       "    \n",
       "           [[-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            ...,\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "            [-99999., -99999., -99999., ..., -99999., -99999., -99999.]]],\n",
       "          dtype=float32)}}}}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "junk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HEADER': {'name': ' DIP_DATA_VOXET', 'smoothed': ' on', 'use_private_colormaps': ' off', 'probe_type': ' Sections', 'size': ' 0 0 0 49 49 49', 'fence0': ' 3 1 2 0 0 0 49 49 49', 'nb_fences': ' 1', 'sections': ' 3 1 0 0 2 0 0 3 0 0', 'VoxetIntersectionAssociation': ' off', 'variable': ' Dip', 'painted': ' off', 'ascii': ' off'}, 'AXIS': {'NAME': ('axis-1', 'axis-2', 'axis-3'), 'UNIT': ('unitless', 'unitless', 'unitless'), 'O': (270217.0, 7923111.8125, -15583.5), 'U': (382516.0, 0.0, 0.0), 'V': (0.0, 620896.188, 0.0), 'W': (0.0, 0.0, 31500.0), 'MIN': (0.0, 0.0, 0.0), 'MAX': (1.0, 1.0, 1.0), 'N': (50, 50, 50), 'TYPE': ('even', 'even', 'even')}, 'PROP': {'1': {'PROPERTY': 'Dip', 'ORIGINAL_UNIT': 'unitless', 'UNIT': 'unitless', 'NO_DATA_VALUE': -99999.0, 'SAMPLE_STATS': (480.0, 7.91667, 144.068, 0.0, 70.0), 'ESIZE': 4, 'ETYPE': 'IEEE', 'PAINTED_FLAG_BIT_POS': 6, 'FORMAT': 'RAW', 'OFFSET': 0, 'FILE': 'DIP_DATA_VOXET_Dip@@'}}}\n"
     ]
    }
   ],
   "source": [
    "for key in junk.keys():\n",
    "    print(junk[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 50, 50)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "junk['1']['PROP']['1']['DATA'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-9.9999e+04,  0.0000e+00,  1.0000e+00,  2.0000e+00,  3.0000e+00,\n",
       "        4.0000e+00,  5.0000e+00,  6.0000e+00,  8.0000e+00,  1.0000e+01,\n",
       "        1.4000e+01,  1.5000e+01,  2.0000e+01,  2.6000e+01,  3.0000e+01,\n",
       "        3.2000e+01,  3.5000e+01,  5.0000e+01,  7.0000e+01], dtype=float32)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.unique(junk['1']['PROP']['1']['DATA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-67-a7e86292ca8b>:19: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  cells = np.full((len(points)-1, 3), 2, dtype=np.int)\n",
      "<ipython-input-67-a7e86292ca8b>:20: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  cells[:, 1] = np.arange(0, len(points)-1, dtype=np.int)\n",
      "<ipython-input-67-a7e86292ca8b>:21: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  cells[:, 2] = np.arange(1, len(points), dtype=np.int)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1024x768 at 0x1EE146640A0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[(13.719394954848525, 14.57901445433668, 13.719394954848525),\n",
       " (4.440892098500626e-16, 0.8596194994881534, 0.0),\n",
       " (0.0, 0.0, 1.0)]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyvista as pv\n",
    "import numpy as np\n",
    "pv.set_plot_theme('doc')\n",
    "\n",
    "def make_points():\n",
    "    \"\"\"Helper to make XYZ points\"\"\"\n",
    "    theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)\n",
    "    z = np.linspace(-2, 2, 100)\n",
    "    r = z**2 + 1\n",
    "    x = r * np.sin(theta)\n",
    "    y = r * np.cos(theta)\n",
    "    return np.column_stack((x, y, z))\n",
    "\n",
    "\n",
    "def lines_from_points(points):\n",
    "    \"\"\"Given an array of points, make a line set\"\"\"\n",
    "    poly = pv.PolyData()\n",
    "    poly.points = points\n",
    "    cells = np.full((len(points)-1, 3), 2, dtype=np.int)\n",
    "    cells[:, 1] = np.arange(0, len(points)-1, dtype=np.int)\n",
    "    cells[:, 2] = np.arange(1, len(points), dtype=np.int)\n",
    "    poly.lines = cells\n",
    "    return poly\n",
    "\n",
    "points = make_points()\n",
    "\n",
    "line = lines_from_points(points)\n",
    "\n",
    "p = pv.Plotter()\n",
    "p.add_mesh(points, color='red')\n",
    "p.add_mesh(line, color='blue',)\n",
    "p.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1024x768 at 0x1EE11EBB7F0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1024x768 at 0x1EE11EAA730>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[(630460.6466856378, 8673647.271685638, 78.20070175106548),\n",
       " (629812.8125, 8672999.4375, -569.6334838867188),\n",
       " (0.0, 0.0, 1.0)]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#https://docs.pyvista.org/api/utilities/_autosummary/pyvista.lines_from_points.html\n",
    "import numpy as np\n",
    "import pyvista\n",
    "points = np.array([[0, 0, 0], [1, 0, 0], [1, 1, 0]])\n",
    "points = np.array([[629607.40625, 8673138.9375, -419.00299072265625 ], [629607.40625, 8673138.9375, -720.26397705078125 ], [630018.21875, 8672859.9375, -709.30902099609375 ]])\n",
    "poly = pyvista.lines_from_points(points, close=True)\n",
    "poly.plot(line_width=5)\n",
    "\n",
    "poly = pyvista.lines_from_points(points, close=False)\n",
    "poly.plot(line_width=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1024x768 at 0x1EE14633C10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[(6.731479005868078, 6.731479005868078, 6.231479005868078),\n",
       " (1.5, 1.5, 1.0),\n",
       " (0.0, 0.0, 1.0)]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points = np.array([[0, 0, 0], [1, 0, 0], [1, 1, 0], [2,2, 2], [3, 2, 2], [3, 3, 2]]  )\n",
    "poly = pyvista.lines_from_points(points)\n",
    "poly.plot(line_width=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "points = np.array([[0, 0, 0],\n",
    "                   [1, 0, 0],\n",
    "                   [1, 1, 0],\n",
    "                   [0, 1, 0]])\n",
    "\n",
    "lines = np.array(([0, 1, 2, 3]))\n",
    "\n",
    "pdata = pv.PolyData(points)\n",
    "pdata.lines = lines\n",
    "\n",
    "pl = pv.Plotter()\n",
    "pl.add_mesh(pdata)\n",
    "pl.camera_position = 'xy'\n",
    "pl.add_point_labels(points, range(4), font_size=20)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 1, 2, 1, 2], dtype=int64)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdata.lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "https://docs.pyvista.org/examples/00-load/create-surface-draped.html\n",
    "https://www.opengeosys.org/docs/tools/fileio/gocadtsurfacereader/    \n",
    "https://github.com/AuScope/geomodel-2-3dweb/blob/master/scripts/converters/gocad2webasset.py    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:gempy2021]",
   "language": "python",
   "name": "conda-env-gempy2021-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}