Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'paraview'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-0d0b35761e96>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpygmsh\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mparaview\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'paraview'"
     ]
    }
   ],
   "source": [
    "import pygmsh as pg\n",
    "import paraview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyvista as pv\n",
    "from pyvista import examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/nijso/anaconda3/lib/python3.7/site-packages/pyvista/examples/airplane.ply'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a sample file\n",
    "filename = examples.planefile\n",
    "filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1024x768 at 0x7FD122429950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mesh = pv.read(filename)\n",
    "cpos = mesh.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pyvista/utilities/fileio.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(filename, attrs, file_format)\u001b[0m\n\u001b[1;32m    208\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 209\u001b[0;31m             \u001b[0mreader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_reader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    210\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mstandard_reader_routine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pyvista/utilities/fileio.py\u001b[0m in \u001b[0;36mget_reader\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m     83\u001b[0m     \u001b[0mext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_ext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mREADERS\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mext\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Get and instantiate the reader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: '.msh'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-556ca64ef3cd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcgnsfile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/home/nijso/bs.msh'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pyvista/utilities/fileio.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(filename, attrs, file_format)\u001b[0m\n\u001b[1;32m    214\u001b[0m                 \u001b[0;32mfrom\u001b[0m \u001b[0mmeshio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exceptions\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mReadError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mread_meshio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    217\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mReadError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m                     \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pyvista/utilities/fileio.py\u001b[0m in \u001b[0;36mread_meshio\u001b[0;34m(filename, file_format)\u001b[0m\n\u001b[1;32m    323\u001b[0m     \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpanduser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    324\u001b[0m     \u001b[0;31m# Read mesh file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 325\u001b[0;31m     \u001b[0mmesh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmeshio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_format\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    326\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mfrom_meshio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmesh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/meshio/_helpers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(filename, file_format)\u001b[0m\n\u001b[1;32m     66\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mReadError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mreader_map\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfile_format\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/meshio/gmsh/main.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m     15\u001b[0m     \"\"\"\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0mmesh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_buffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmesh\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/meshio/gmsh/main.py\u001b[0m in \u001b[0;36mread_buffer\u001b[0;34m(f)\u001b[0m\n\u001b[1;32m     45\u001b[0m                 )\n\u001b[1;32m     46\u001b[0m             )\n\u001b[0;32m---> 47\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mreader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_buffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_ascii\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/meshio/gmsh/_gmsh22.py\u001b[0m in \u001b[0;36mread_buffer\u001b[0;34m(f, is_ascii, data_size)\u001b[0m\n\u001b[1;32m     53\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0menviron\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"Elements\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m             has_additional_tag_data, cell_tags = _read_cells(\n\u001b[0;32m---> 55\u001b[0;31m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcells\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpoint_tags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_ascii\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m             )\n\u001b[1;32m     57\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0menviron\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"Periodic\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/meshio/gmsh/_gmsh22.py\u001b[0m in \u001b[0;36m_read_cells\u001b[0;34m(f, cells, point_tags, is_ascii)\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[0mcell_tags\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mis_ascii\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m         \u001b[0m_read_cells_ascii\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcells\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell_tags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_num_cells\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    124\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m         \u001b[0m_read_cells_binary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcells\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell_tags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_num_cells\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/meshio/gmsh/_gmsh22.py\u001b[0m in \u001b[0;36m_read_cells_ascii\u001b[0;34m(f, cells, cell_tags, total_num_cells)\u001b[0m\n\u001b[1;32m    164\u001b[0m         \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"utf-8\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m         \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_gmsh_to_meshio_type\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    167\u001b[0m         \u001b[0mnum_nodes_per_elem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_nodes_per_cell\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 0"
     ]
    }
   ],
   "source": [
    "cgnsfile=pv.read('/home/nijso/bs.msh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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