Demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pyMapVBVD Demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from mapVBVD import mapVBVD\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load VB file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Software version: VB\n",
      "TODO\n",
      "UTC: TODO\n",
      "526464\n",
      "Scan 1/1, read all mdhs:\n",
      "14.00 % read in 0.00 s;\n",
      "estimated time left: 0.00 s\n",
      "\n",
      "43.00 % read in 0.00 s;\n",
      "estimated time left: 0.00 s\n",
      "\n",
      "71.00 % read in 0.00 s;\n",
      "estimated time left: 0.00 s\n",
      "\n",
      "     3.5MB read in    0 s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "vbfile = 'tests/test_data/meas_MID311_STEAM_wref1_FID115674.dat'\n",
    "twixObj = mapVBVD.mapVBVD(vbfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Show file contents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MDH: image\n",
      "***twix_map_obj***\n",
      "File: tests/test_data/meas_MID311_STEAM_wref1_FID115674.dat\n",
      "Software: vb\n",
      "Number of acquisitions read 2\n",
      "Data size is [4096,32,1,1,1,1,1,1,2,1,1,1,1,1,1,1]\n",
      "Squeezed data size is [4096,32,2] (['Col', 'Cha', 'Rep'])\n",
      "NCol = 4096\n",
      "NCha = 32\n",
      "NLin  = 1\n",
      "NAve  = 1\n",
      "NSli  = 1\n",
      "NPar  = 1\n",
      "NEco  = 1\n",
      "NPhs  = 1\n",
      "NRep  = 2\n",
      "NSet  = 1\n",
      "NSeg  = 1\n",
      "NIda  = 1\n",
      "NIdb  = 1\n",
      "NIdc  = 1\n",
      "NIdd  = 1\n",
      "NIde  = 1\n",
      "\n",
      "\n",
      "MDH: phasecor\n",
      "***twix_map_obj***\n",
      "File: tests/test_data/meas_MID311_STEAM_wref1_FID115674.dat\n",
      "Software: vb\n",
      "Number of acquisitions read 1\n",
      "Data size is [4096,32,1,1,1,1,1,1,1,1,1,1,1,1,1,1]\n",
      "Squeezed data size is [4096,32] (['Col', 'Cha'])\n",
      "NCol = 4096\n",
      "NCha = 32\n",
      "NLin  = 1\n",
      "NAve  = 1\n",
      "NSli  = 1\n",
      "NPar  = 1\n",
      "NEco  = 1\n",
      "NPhs  = 1\n",
      "NRep  = 1\n",
      "NSet  = 1\n",
      "NSeg  = 1\n",
      "NIda  = 1\n",
      "NIdb  = 1\n",
      "NIdc  = 1\n",
      "NIdd  = 1\n",
      "NIde  = 1\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mdh_flags = twixObj.MDH_flags()\n",
    "for mdh in mdh_flags:\n",
    "    print(f'MDH: {mdh}')\n",
    "    print(twixObj[mdh])\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### View data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 3)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa = np.random.randn(10,10)\n",
    "aa[:,[1,3,5]].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4096, 32, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1)\n",
      "(4096, 32, 2)\n",
      "(2000, 8)\n",
      "(4096, 32, 2)\n"
     ]
    },
    {
     "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": [
    "# You can retrieve all of the data\n",
    "twixObj.image.squeeze = False\n",
    "out = twixObj.image['']\n",
    "print(out.shape)\n",
    "\n",
    "# Which can be squeezed\n",
    "twixObj.image.squeeze = True\n",
    "out = twixObj.image['']\n",
    "print(out.shape)\n",
    "\n",
    "# Or you can select a subset of the data\n",
    "twixObj.image.squeeze = True\n",
    "out = twixObj.image[0:2000,0:31:4,1]\n",
    "print(out.shape)\n",
    "\n",
    "# Or you can retrieve the unsorted data (not very interesting in this example)\n",
    "out_unsorted = twixObj.image.unsorted()\n",
    "print(out_unsorted.shape)\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(np.real(out))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### View header information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MeasYaps:\n",
      "\t('sTXSPEC', 'asNucleusInfo', '0', 'tNucleus'): \"1H\"\n",
      "\t('sTXSPEC', 'asNucleusInfo', '0', 'lFrequency'): 297219948.0\n",
      "\t('sTXSPEC', 'asNucleusInfo', '0', 'flReferenceAmplitude'): 225.0\n",
      "\t('sTXSPEC', 'asNucleusInfo', '0', 'flAmplitudeCorrection'): 1.0\n",
      "\t('sTXSPEC', 'asNucleusInfo', '1', 'flAmplitudeCorrection'): 1.0\n",
      "\t('sTXSPEC', 'asNucleusInfo', '1', 'lRFPAIndex'): -1.0\n",
      "Phoenix:\n",
      "\t('sTXSPEC', 'asNucleusInfo', '0', 'tNucleus'): \"1H\"\n",
      "\t('sTXSPEC', 'asNucleusInfo', '0', 'lFrequency'): 297219948.0\n",
      "\t('sTXSPEC', 'asNucleusInfo', '0', 'bFrequencyValid'): 1.0\n",
      "\t('sTXSPEC', 'asNucleusInfo', '0', 'flReferenceAmplitude'): 225.0\n",
      "\t('sTXSPEC', 'asNucleusInfo', '0', 'bReferenceAmplitudeValid'): 1.0\n",
      "\t('sTXSPEC', 'asNucleusInfo', '0', 'flAmplitudeCorrection'): 1.0\n",
      "\t('sTXSPEC', 'asNucleusInfo', '0', 'bAmplitudeCorrectionValid'): 1.0\n",
      "\t('sTXSPEC', 'asNucleusInfo', '0', 'bRFPAIndexValid'): 1.0\n",
      "\t('sTXSPEC', 'asNucleusInfo', '1', 'bFrequencyValid'): 1.0\n",
      "\t('sTXSPEC', 'asNucleusInfo', '1', 'bReferenceAmplitudeValid'): 1.0\n",
      "\t('sTXSPEC', 'asNucleusInfo', '1', 'flAmplitudeCorrection'): 1.0\n",
      "\t('sTXSPEC', 'asNucleusInfo', '1', 'bAmplitudeCorrectionValid'): 1.0\n",
      "\t('sTXSPEC', 'asNucleusInfo', '1', 'lRFPAIndex'): -1.0\n",
      "\t('sTXSPEC', 'asNucleusInfo', '1', 'bRFPAIndexValid'): 1.0\n"
     ]
    }
   ],
   "source": [
    "found_keys = twixObj.search_header_for_keys(('TXSPEC', 'asNucleusInfo'),top_lvl=['MeasYaps','Phoenix'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['\"1H\"']\n"
     ]
    }
   ],
   "source": [
    "value = twixObj.search_header_for_val('MeasYaps',('sTXSPEC', 'asNucleusInfo', '0', 'tNucleus'))\n",
    "print(value)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}