pyuff Showcase.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jul 2017: 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": {
    "collapsed": true
   },
   "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": 3,
   "metadata": {
    "collapsed": true
   },
   "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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  151.,   164.,  2420.,  2411.,    58.,    58.,    58.])"
      ]
     },
     "execution_count": 4,
     "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'date_db_created': '29-Jan-16',\n",
       " 'date_db_saved': '29-Jan-16',\n",
       " 'date_file_written': '29-Jan-16',\n",
       " 'db_app': 'modaldata v1',\n",
       " 'description': 'DefaultDecription',\n",
       " 'file_type': 0,\n",
       " 'model_name': 'NewModel',\n",
       " 'program': 'Open Modal',\n",
       " 'time_db_created': '14:48:16',\n",
       " 'time_db_saved': '14:48:16',\n",
       " 'time_file_written': '14:48:16',\n",
       " 'type': 151,\n",
       " 'version_db1': 0,\n",
       " 'version_db2': 0}"
      ]
     },
     "execution_count": 5,
     "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": 6,
   "metadata": {
    "collapsed": true
   },
   "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": 7,
   "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": 7,
     "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 8,
     "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V/PCZlTzqjvH5+fPvd9rmlEmDWbB+L02t7WRJ54qzi6eN5IUVOzrNhgpwzt1z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\nLoZhGIbv/H+L5/y0TtpClgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1775d356f28>"
      ]
     },
     "metadata": {},
     "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": 10,
   "metadata": {
    "collapsed": true
   },
   "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": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "measurement_point_1[0] = np.nan*(1+1.j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([        nan       +nanj, -0.17515115-0.12042372j,\n",
       "        0.09362347+0.02570918j, ..., -0.54654555+0.00535394j,\n",
       "       -0.53160000+0.00659625j, -0.53797177+0.01138392j])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measurement_point_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "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": 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",
    "            '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": [
    "# 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": [
       "{'color': [1, 3, 4, 5, 6],\n",
       " 'def_cs': [11, 11, 11, 12, 12],\n",
       " 'disp_cs': [16, 16, 17, 18, 19],\n",
       " 'node_nums': [16, 17, 18, 19, 20],\n",
       " 'type': 15,\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": [
       "[{'abscissa_spacing': 1,\n",
       "  'abscissa_spec_data_type': 18,\n",
       "  'binary': 0,\n",
       "  'data': array([ 1.76405235+0.14404357j,  0.40015721+1.45427351j,\n",
       "          0.97873798+0.76103773j,  2.24089320+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 ,  0.41059850-0.85409574j]),\n",
       "  'func_type': 4,\n",
       "  'id1': 'id1',\n",
       "  'orddenom_spec_data_type': 13,\n",
       "  'ordinate_spec_data_type': 12,\n",
       "  'ref_dir': 1,\n",
       "  'ref_ent_name': 'TestCase',\n",
       "  'ref_node': 1,\n",
       "  'rsp_dir': 1,\n",
       "  'rsp_ent_name': 'TestCase',\n",
       "  'rsp_node': 1,\n",
       "  'type': 58,\n",
       "  'x': array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])},\n",
       " {'abscissa_spacing': 1,\n",
       "  'abscissa_spec_data_type': 18,\n",
       "  'binary': 1,\n",
       "  'data': array([-2.55298982+0.15494743j,  0.65361860+0.37816252j,\n",
       "          0.86443620-0.88778575j, -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",
       "  'func_type': 4,\n",
       "  'id1': 'id1',\n",
       "  'orddenom_spec_data_type': 13,\n",
       "  'ordinate_spec_data_type': 12,\n",
       "  'ref_dir': 1,\n",
       "  'ref_ent_name': 'TestCase',\n",
       "  'ref_node': 2,\n",
       "  'rsp_dir': 1,\n",
       "  'rsp_ent_name': 'TestCase',\n",
       "  'rsp_node': 1,\n",
       "  'type': 58,\n",
       "  'x': array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])},\n",
       " {'abscissa_spacing': 1,\n",
       "  'abscissa_spec_data_type': 18,\n",
       "  'binary': 0,\n",
       "  'data': array([-1.04855297-0.89546656j, -1.42001794+0.3869025j ,\n",
       "         -1.70627019-0.51080514j,  1.95077540-1.18063218j,\n",
       "         -0.50965218-0.02818223j, -0.43807430+0.42833187j,\n",
       "         -1.25279536+0.06651722j,  0.77749036+0.3024719j ,\n",
       "         -1.61389785-0.63432209j, -0.21274028-0.36274117j]),\n",
       "  'func_type': 4,\n",
       "  'id1': 'id1',\n",
       "  'orddenom_spec_data_type': 13,\n",
       "  'ordinate_spec_data_type': 12,\n",
       "  'ref_dir': 1,\n",
       "  'ref_ent_name': 'TestCase',\n",
       "  'ref_node': 3,\n",
       "  'rsp_dir': 1,\n",
       "  'rsp_ent_name': 'TestCase',\n",
       "  'rsp_node': 1,\n",
       "  'type': 58,\n",
       "  'x': array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])}]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyuff.prepare_test_58()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'color': 30,\n",
       " 'id': 'Identification line',\n",
       " 'n_nodes': 7,\n",
       " 'nodes': [0, 10, 13, 14, 15, 16, 17],\n",
       " 'trace_num': 2,\n",
       " 'type': 82}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyuff.prepare_test_82()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'date_db_created': '27-Jan-16',\n",
       " 'date_db_saved': '28-Jan-16',\n",
       " 'date_db_written': '29-Jan-16',\n",
       " 'db_app': 'Model file description',\n",
       " 'description': 'Model file description',\n",
       " 'file_type': 0,\n",
       " 'model_name': 'Model file name',\n",
       " 'program': 'OpenModal',\n",
       " 'time_db_created': '14:38:15',\n",
       " 'time_db_saved': '14:38:16',\n",
       " 'time_db_written': '14:38:17',\n",
       " 'type': 151,\n",
       " 'version_db1': 1,\n",
       " 'version_db2': 2}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyuff.prepare_test_151()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'force': 0.22480894309971047,\n",
       " 'length': 3.2808398950131235,\n",
       " 'temp': 1.8,\n",
       " 'temp_mode': 1,\n",
       " 'temp_offset': 459.67,\n",
       " 'type': 164,\n",
       " 'units_code': 1,\n",
       " 'units_description': 'SI units'}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyuff.prepare_test_164()"
   ]
  }
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