pyuff_Showcase.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sep 2023: Janko Slavič (janko.slavic@fs.uni-lj.si), Klemen Zaletelj (klemen.zaletelj@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": [
    "See the [documentation](https://pyuff.readthedocs.io/en/latest/index.html) for the ``pyUFF`` package!"
   ]
  },
  {
   "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 https://www.ceas3.uc.edu/sdrluff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If required, install ``pyuff`` and ``matplotlib``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "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": 3,
   "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": 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": [
       "{'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": 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": {},
   "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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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "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": {},
   "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": {},
   "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",
       "       -0.5316    +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": {},
   "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_sets(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`). \n",
    "\n",
    "The data dictionary in the example below is built using the ``prepare_58()`` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding point 1\n",
      "Adding point 2\n",
      "Adding point 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\_d\\prg\\PycharmProjects\\pyuff\\pyuff\\pyuff.py:425: ResourceWarning: unclosed file <_io.TextIOWrapper name='./data/measurement_58b.uff' mode='at' encoding='cp1252'>\n",
      "  _write58(fh, dset, mode, _filename=self._filename, force_double=force_double)\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\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",
    "    \n",
    "    data = pyuff.prepare_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",
    "    \n",
    "    uffwrite = pyuff.UFF('./data/measurement_58b.uff')\n",
    "    uffwrite.write_sets(data, 'add')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The ``prepare_xx()`` functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The new ``prepare_xx()`` functions enable easy dictionary construction. \n",
    "\n",
    "Each function contains the arguments that are available for a certain dataset and the arguments are documented in the docstring along with the reference to the **record** and **field** in the UFF file.\n",
    "\n",
    "All of the docstrings are available in the ``pyUFF`` [documentation](https://pyuff.readthedocs.io/en/latest/index.html). To see the docstring for, e.g., ``prepare_15()`` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mSignature:\u001b[0m\n",
       " \u001b[0mpyuff\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprepare_15\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mnode_nums\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mdef_cs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mdisp_cs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mreturn_full_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mDocstring:\u001b[0m\n",
       "Name: Nodes\n",
       "\n",
       "R-Record, F-Field\n",
       "\n",
       ":param node_nums: R1 F1, node label\n",
       ":param def_cs: R1 F2, deformation coordinate system numbers, optional\n",
       ":param disp_cs: R1 F3, displacement coordinate system numbers, optional\n",
       ":param color: R1 F4, color, optional\n",
       ":param x: R1 F5, Dimensional coordinate of node in the definition system\n",
       ":param y: R1 F6, Dimensional coordinate of node in the definition system\n",
       ":param z: R1 F7, Dimensional coordinate of node in the definition system\n",
       "\n",
       ":param return_full_dict: If True full dict with all keys is returned, else only specified arguments are included\n",
       "\n",
       "**Test prepare_15**\n",
       "\n",
       ">>> save_to_file = 'test_pyuff'\n",
       ">>> dataset = pyuff.prepare_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],  # I10,\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])\n",
       ">>> if save_to_file:\n",
       ">>>     if os.path.exists(save_to_file):\n",
       ">>>         os.remove(save_to_file)\n",
       ">>>     uffwrite = pyuff.UFF(save_to_file)\n",
       ">>>     uffwrite._write_set(dataset, 'add')\n",
       ">>> dataset\n",
       "\u001b[1;31mFile:\u001b[0m      c:\\_d\\prg\\pycharmprojects\\pyuff\\pyuff\\datasets\\dataset_15.py\n",
       "\u001b[1;31mType:\u001b[0m      function"
      ]
     },
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    }
   ],
   "source": [
    "? pyuff.prepare_15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The ``get_structure_xx()`` functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ``get_strucutre_xx()`` is used to get the structure of a particular dataset, e.g. see:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Universal Dataset Number: 15\n",
      "\n",
      "Name:   Nodes\n",
      "-----------------------------------------------------------------------\n",
      " \n",
      "             Record 1: FORMAT(4I10,1P3E13.5)\n",
      "                       Field 1 -    node label\n",
      "                       Field 2 -    definition coordinate system number\n",
      "                       Field 3 -    displacement coordinate system number\n",
      "                       Field 4 -    color\n",
      "                       Field 5-7 -  3 - Dimensional coordinates of node\n",
      "                                    in the definition system\n",
      " \n",
      "             NOTE:  Repeat record for each node\n",
      " \n",
      "------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pyuff.get_structure_15()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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