pyuff Showcase.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jun 2018: Janko Slavič (janko.slavic@fs.uni-lj.si), Matej Razpotnik (matej.razpotnik@gmail.com), Blaž Starc (sbtlaarzc@gmail.com), Matjaž Mršnik (matjaz.mrsnik@gmail.com), Matija Brumat (matija.brumat@gmail.com)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pyuff Showcase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This module is part of the [www.openmodal.com](www.openmodal.com) project and defines an UFF class to manipulate with the UFF (Universal File Format) files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "UFF file format was originally developed by the Structural Dynamics Research Corporation (SDRC) to standardize data transfer mainly between various modal analysis, measurement or CAD geometry software products. UFF file is composed into several *datasets*, most important being the *dataset* 58 for measurement data and *dataset* 55 for modal data. More info on specific *datasets* can be obtained at http://www.sdrl.uc.edu/sdrl/referenceinfo/universalfileformats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If required, install ``pyuff`` and ``matplotlib``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#!!pip install pyuff\n",
    "#!!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To analyse UFF file we first load the uff module and example file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyuff\n",
    "uff_file = pyuff.UFF('data/beam.uff')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we can check which *datasets* are written in the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 151.,  164., 2420., 2411.,   58.,   58.,   58.])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uff_file.get_set_types()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we see that first 4 *datasets* are *151: Header, 164: Units, 2420: Coordinate Systems* and *2411: Nodes - Double Precision*. Next we have several *datasets* 58 containing measurement data. To check what is written in the header (first *dataset*) use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 151,\n",
       " 'model_name': 'NewModel',\n",
       " 'description': 'DefaultDecription',\n",
       " 'db_app': 'modaldata v1',\n",
       " 'date_db_created': '29-Jan-16',\n",
       " 'time_db_created': '14:48:16',\n",
       " 'version_db1': 0,\n",
       " 'version_db2': 0,\n",
       " 'file_type': 0,\n",
       " 'date_db_saved': '29-Jan-16',\n",
       " 'time_db_saved': '14:48:16',\n",
       " 'program': 'Open Modal',\n",
       " 'date_file_written': '29-Jan-16',\n",
       " 'time_file_written': '14:48:16'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uff_file.read_sets(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that each *dataset* consists number of dictionary-like *keys*. We read and write directly to *keys*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading from the UFF file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To load all *datasets* from the UFF file to `data` object use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = uff_file.read_sets()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first *dataset* 58 (this is the fifth in the example file) contains following *keys*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['type', 'binary', 'id1', 'id2', 'id3', 'id4', 'id5', 'func_type', 'func_id', 'ver_num', 'load_case_id', 'rsp_ent_name', 'rsp_node', 'rsp_dir', 'ref_ent_name', 'ref_node', 'ref_dir', 'ord_data_type', 'num_pts', 'abscissa_spacing', 'abscissa_min', 'abscissa_inc', 'z_axis_value', 'abscissa_spec_data_type', 'abscissa_len_unit_exp', 'abscissa_force_unit_exp', 'abscissa_temp_unit_exp', 'abscissa_axis_lab', 'abscissa_axis_units_lab', 'ordinate_spec_data_type', 'ordinate_len_unit_exp', 'ordinate_force_unit_exp', 'ordinate_temp_unit_exp', 'ordinate_axis_lab', 'ordinate_axis_units_lab', 'orddenom_spec_data_type', 'orddenom_len_unit_exp', 'orddenom_force_unit_exp', 'orddenom_temp_unit_exp', 'orddenom_axis_lab', 'orddenom_axis_units_lab', 'z_axis_spec_data_type', 'z_axis_len_unit_exp', 'z_axis_force_unit_exp', 'z_axis_temp_unit_exp', 'z_axis_axis_lab', 'z_axis_axis_units_lab', 'x', 'data'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[4].keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To read specific *key* form the desired *dataset* use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[4]['func_type']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "e.g. *key* `'func_type'`: 4 in the *dataset* 58 means that the function type in this *dataset* is frequency response function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most important *keys* are 'x': x_axis and 'data': y_axis that define the stored response:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.semilogy(data[4]['x'], np.abs(data[4]['data']))\n",
    "plt.xlabel('Frequency  [Hz]')\n",
    "plt.ylabel('FRF Magnitude [dB m/N]')\n",
    "plt.xlim([0,1000])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other keys in the *dataset* 58 are explained here: http://www.sdrl.uc.edu/sdrl/referenceinfo/universalfileformats/file-format-storehouse/universal-dataset-number-58 Similarly we could read the data from *dataset* 55 that is used for modal data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write measurement data to UFF file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you can see a minimal working example for writing three measured accelerance FRF data to the UFF file *measurement_123.uff*. First we load the accelerances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "measurement_point_1 = np.genfromtxt('data/meas_point_1.txt', dtype=complex)\n",
    "measurement_point_2 = np.genfromtxt('data/meas_point_2.txt', dtype=complex)\n",
    "measurement_point_3 = np.genfromtxt('data/meas_point_3.txt', dtype=complex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "measurement_point_1[0] = np.nan*(1+1.j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([        nan       +nanj, -0.17515115-0.12042372j,\n",
       "        0.09362347+0.02570918j, ..., -0.54654555+0.00535394j,\n",
       "       -0.5316    +0.00659625j, -0.53797177+0.01138392j])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measurement_point_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "measurement = [measurement_point_1, measurement_point_2, measurement_point_3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next step we create a UFF file where we add *dataset* 58 for each measurement point consisting of the dictionary-like *keys* containing the measurement data and the information about the mesurement point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding point 1\n",
      "Adding point 2\n",
      "Adding point 3\n"
     ]
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    print('Adding point {:}'.format(i + 1))\n",
    "    response_node = 1\n",
    "    response_direction = 1\n",
    "    reference_node = i + 1\n",
    "    reference_direction = 1\n",
    "    acceleration_complex = measurement[i]\n",
    "    frequency = np.arange(0, 1001)\n",
    "    name = 'TestCase'\n",
    "    data = {'type':58, \n",
    "            'func_type':4, \n",
    "            'rsp_node': response_node, \n",
    "            'rsp_dir': response_direction, \n",
    "            'ref_dir': reference_direction, \n",
    "            'ref_node': reference_node,\n",
    "            'data': acceleration_complex,\n",
    "            'x': frequency,\n",
    "            'id1': 'id1', \n",
    "            'rsp_ent_name': name,\n",
    "            'ref_ent_name': name,\n",
    "            'abscissa_spacing':1,\n",
    "            'abscissa_spec_data_type':18,\n",
    "            'ordinate_spec_data_type':12,\n",
    "            'orddenom_spec_data_type':13}\n",
    "    uffwrite = pyuff.UFF('./data/measurement.uff')\n",
    "    uffwrite._write_set(data,'add')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: If we run upper example twice we add the data two-times! `._write_set` Does not check if the nodal responses are allready written in the file, it just writes the defined dataset at the end of choosen file!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you would like to write is as dataset 58b, all you need to add is the key `binary: 1` (which is by default `0`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding point 1\n",
      "Adding point 2\n",
      "Adding point 3\n"
     ]
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    print('Adding point {:}'.format(i + 1))\n",
    "    response_node = 1\n",
    "    response_direction = 1\n",
    "    reference_node = i + 1\n",
    "    reference_direction = 1\n",
    "    acceleration_complex = measurement[i]\n",
    "    frequency = np.arange(0, 1001)\n",
    "    name = 'TestCase'\n",
    "    data = {'type': 58,\n",
    "            'binary': 1,\n",
    "            'func_type':4, \n",
    "            'rsp_node': response_node, \n",
    "            'rsp_dir': response_direction, \n",
    "            'ref_dir': reference_direction, \n",
    "            'ref_node': reference_node,\n",
    "            'data': acceleration_complex,\n",
    "            'x': frequency,\n",
    "            'id1': 'id1', \n",
    "            'rsp_ent_name': name,\n",
    "            'ref_ent_name': name,\n",
    "            'abscissa_spacing':1,\n",
    "            'abscissa_spec_data_type':18,\n",
    "            'ordinate_spec_data_type':12,\n",
    "            'orddenom_spec_data_type':13}\n",
    "    uffwrite = pyuff.UFF('./data/measurement_58b.uff')\n",
    "    uffwrite._write_set(data,'add')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test functions\n",
    "\n",
    "Currently, test functions shown below are prepared. Check out the pyuff.py for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 15,\n",
       " 'node_nums': [16, 17, 18, 19, 20],\n",
       " 'def_cs': [11, 11, 11, 12, 12],\n",
       " 'disp_cs': [16, 16, 17, 18, 19],\n",
       " 'color': [1, 3, 4, 5, 6],\n",
       " 'x': [0.0, 1.53, 0.0, 1.53, 0.0],\n",
       " 'y': [0.0, 0.0, 3.84, 3.84, 0.0],\n",
       " 'z': [0.0, 0.0, 0.0, 0.0, 1.83]}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyuff.prepare_test_15()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding point 1\n",
      "Adding point 2\n",
      "Adding point 3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'type': 58,\n",
       "  'binary': 0,\n",
       "  'func_type': 4,\n",
       "  'rsp_node': 1,\n",
       "  'rsp_dir': 1,\n",
       "  'ref_dir': 1,\n",
       "  'ref_node': 1,\n",
       "  'data': array([ 1.76405235+0.14404357j,  0.40015721+1.45427351j,\n",
       "          0.97873798+0.76103773j,  2.2408932 +0.12167502j,\n",
       "          1.86755799+0.44386323j, -0.97727788+0.33367433j,\n",
       "          0.95008842+1.49407907j, -0.15135721-0.20515826j,\n",
       "         -0.10321885+0.3130677j ,  0.4105985 -0.85409574j]),\n",
       "  'x': array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
       "  'id1': 'id1',\n",
       "  'rsp_ent_name': 'TestCase',\n",
       "  'ref_ent_name': 'TestCase',\n",
       "  'abscissa_spacing': 1,\n",
       "  'abscissa_spec_data_type': 18,\n",
       "  'ordinate_spec_data_type': 12,\n",
       "  'orddenom_spec_data_type': 13},\n",
       " {'type': 58,\n",
       "  'binary': 1,\n",
       "  'func_type': 4,\n",
       "  'rsp_node': 1,\n",
       "  'rsp_dir': 1,\n",
       "  'ref_dir': 1,\n",
       "  'ref_node': 2,\n",
       "  'data': array([-2.55298982+0.15494743j,  0.6536186 +0.37816252j,\n",
       "          0.8644362 -0.88778575j, -0.74216502-1.98079647j,\n",
       "          2.26975462-0.34791215j, -1.45436567+0.15634897j,\n",
       "          0.04575852+1.23029068j, -0.18718385+1.20237985j,\n",
       "          1.53277921-0.38732682j,  1.46935877-0.30230275j]),\n",
       "  'x': array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
       "  'id1': 'id1',\n",
       "  'rsp_ent_name': 'TestCase',\n",
       "  'ref_ent_name': 'TestCase',\n",
       "  'abscissa_spacing': 1,\n",
       "  'abscissa_spec_data_type': 18,\n",
       "  'ordinate_spec_data_type': 12,\n",
       "  'orddenom_spec_data_type': 13},\n",
       " {'type': 58,\n",
       "  'binary': 0,\n",
       "  'func_type': 4,\n",
       "  'rsp_node': 1,\n",
       "  'rsp_dir': 1,\n",
       "  'ref_dir': 1,\n",
       "  'ref_node': 3,\n",
       "  'data': array([-1.04855297-0.89546656j, -1.42001794+0.3869025j ,\n",
       "         -1.70627019-0.51080514j,  1.9507754 -1.18063218j,\n",
       "         -0.50965218-0.02818223j, -0.4380743 +0.42833187j,\n",
       "         -1.25279536+0.06651722j,  0.77749036+0.3024719j ,\n",
       "         -1.61389785-0.63432209j, -0.21274028-0.36274117j]),\n",
       "  'x': array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
       "  'id1': 'id1',\n",
       "  'rsp_ent_name': 'TestCase',\n",
       "  'ref_ent_name': 'TestCase',\n",
       "  'abscissa_spacing': 1,\n",
       "  'abscissa_spec_data_type': 18,\n",
       "  'ordinate_spec_data_type': 12,\n",
       "  'orddenom_spec_data_type': 13}]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyuff.prepare_test_58()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 82,\n",
       " 'trace_num': 2,\n",
       " 'n_nodes': 7,\n",
       " 'color': 30,\n",
       " 'id': 'Identification line',\n",
       " 'nodes': array([ 0, 10, 13, 14, 15, 16, 17])}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyuff.prepare_test_82()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 151,\n",
       " 'model_name': 'Model file name',\n",
       " 'description': 'Model file description',\n",
       " 'db_app': 'Program which created DB',\n",
       " 'date_db_created': '27-Jan-16',\n",
       " 'time_db_created': '14:38:15',\n",
       " 'version_db1': 1,\n",
       " 'version_db2': 2,\n",
       " 'file_type': 0,\n",
       " 'date_db_saved': '28-Jan-16',\n",
       " 'time_db_saved': '14:38:16',\n",
       " 'program': 'OpenModal',\n",
       " 'date_db_written': '29-Jan-16',\n",
       " 'time_db_written': '14:38:17'}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyuff.prepare_test_151()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 164,\n",
       " 'units_code': 1,\n",
       " 'units_description': 'SI units',\n",
       " 'temp_mode': 1,\n",
       " 'length': 3.2808398950131235,\n",
       " 'force': 0.22480894309971047,\n",
       " 'temp': 1.8,\n",
       " 'temp_offset': 459.67}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyuff.prepare_test_164()"
   ]
  }
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