Demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pyMapVBVD Demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from mapvbvd import mapVBVD as mapVBVD\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load VB file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Scan 1/1, read all mdhs:  71%|███████▏  | 2.51M/3.52M [00:00<00:00, 979MB/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pymapVBVD version 0.3.2+44.g33ddc7b.dirty\n",
      "Software version: VB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "vbfile = 'tests/test_data/meas_MID311_STEAM_wref1_FID115674.dat'\n",
    "twixObj = mapVBVD(vbfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Show file contents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "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,\n",
      "    1,   1]\n",
      "Squeezed data size is [2048,32,2] (['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,\n",
      "    1,   1]\n",
      "Squeezed data size is [2048,32] (['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": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2048, 32, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1)\n",
      "(2048, 32, 2)\n",
      "(2000, 8)\n",
      "(2048, 32, 2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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7kWXLgMlowpUTr8TSi5filPxT8Jnrn/jd6NtQNWcnyvbX4oVfrMeHz+9Cc4O7Zw/WaED61ZPgPCkPAY8fZX/YDHdR31f+JYSQbuseWh69R5Vdd81N+zZ9htd/e7/WTzJm5pxjzpU2luKhrQ9p1ojDnYRv1H0fCQXDJE1RWJePxcyzR4ry6Zlub9lbjaNPfKn5c340B5ZsfTFHQgjpJbQ8YkC4fopR02fBbLOhcPOGr5zLdebil4t+iUfPeRSWRAOeyfwNXp36e3jyq/Hp64V4449bZLhvzywI+4Q0pC+ZpPnLHtyE+n9y0UVCSGxgs1UMMUuz1bAJk1G0a/uxJ2qLgLf+XTpOJuPk5bfiA8tk3HPirbAO8+GJkffhg3EvoOhAJZb98jOsf7MQ/h7sOKgWW8z6XrCZrfbt/aiT0ViEEBJtqDxiTP7kqag4dAAtjboVUSvWwPNXARufBOpLgOoDsH65HEvevBsrTrgVSy9aipbxpXh25r2oSijBxhUH8MSP/4lDOyojvqdtTAqyf6jPRl95ABVPfsktbwkhUYXKI8YMP2Ga6hxB8Z6dQYvjb2cDNQeBJS8A99UC/68G+NarQMYE4KVvY9qOd7Q1s56+/AkUnLMKa8cug7vZi3/86XOsfPwL1FU2R3Rfa54TefcsgCnNBteeahT91zr46nrWGU8IIV3BDvMY43G14C83LsHciy7Dqa4XgZJtwE3vAHkzjr3QJZbJs5cBRTKc+Kqngalf04JXHViFR955BlMKT0du/VjpZwGyxjsxeeZwZAxPRP7E1G77XlSnfs1rBWj8rBTGJAvSLhsPu9oGl3uDEEL60GFO5REHXvjZj2ForsI1SWJhXP4IMPObnV/YXA08cR5QuQ+4+jlg8oVacJ27Dst2LcPT65/HCSULMbZqJlJbcrRzgQQPkpITkJKRgLxRaTJs2IakDDsyhjmRmBbce12hhu9WvbgL3nKxXJQCunm61rxFCCG9UR6cYR4H8idNwZa3X4XnlGmwTF/S9YWONODf3geeuRRYfpM0Z70CjF4ER8CKsRt8uPqjDHjd2+G2fIEaoxFq4no9RqHMOAOpR/NQtF2awUIxB5A60obcYWlIcNox+euTYFxRCPeBOlQ8ug0GqwlJZ41A4sK8LlfzJYSQzjBHSTMtFuePIiaRx0VLPdDhvEE/rz6lm0RukGs2dxdXwtPFkXYeqI0sDoh8Q85V6+fuFuc7ImohqTskfGU0fkesGJ5Qh40BA0rHfAsjpNLvFlsS8M2XgKekWJ67AtXnPownH3yq7fQcaf7yeX0o+OxfaHBVIan6IEz+o8CsfGwb3QhPpQnGomSYmq3IbMpHXVEaagqDfR2bVx5EILEZycktmBNIQ4oYIXXvHtBEKRK1wKJF+kqsI5JglGNCCImZ8pCKXNUyD4mcK6ImFmyQsDelQm/ftxW4QER6hDVZIKI2xlgQJu5dImuUMpEw5VfyU/FPEVd9vk8VGSayWsImynXBFQnjhd+vNhwXlRdmKRCfB/kHnhFPPoqaUzEikrQTs6RfZCWaH78Mz/5ebTRlxPxLr8Qp37yhra/i7JtukSz4ULhpAza+9SqKPtyBk0eMwpxzz8XUeQmoPfAB9hx8Cr8w1iDNa8bwypmo8Q1DgjR3pVfnoV4UhxkezHWakGIywO72aQsshtKMRpiMZtQ6a3Ek6TBSMhPRYvZj75EqZKclwWA04cv9pbCaTXCLQmslPT0daWlpqK+v1/pcKiqCG2qFYrVaMXLkSPh8PjQ0NGjXjBgxAk6nE7t27cLUqdPQ1NyExKRkpKSkYNTY8UhJcqKyrgm5GalIcibAbAoqYnUPNbnSaDSEnbjZen2kfT4RpacPpVaz/ZXf3+CBwW7SZvwri85T3gRLpiOYliW4yZe6zlfdAlOqHQYp/9Z7aemwP4oMEctjvkiBvPiF+ou/TBzp+UWo8lDHzwaC/x2fyjWpInm6VdFVXOWeocdXte+HIj/Vw5dJHLXa4H6JU6Dn4ZMo/JbIUMNt//FDyflq4M4vIG1DXV+76WnY6/cjM/tEHNkdWiRhcGbinYYz4A18jovyd2Jy8X8DH0uz1HgZrZU+FrAkwNhcg/HOcoybWY8vHAnYsGMPVj55EOstzTgnrwDzEmrwtqqXJooVM/lErVms3iQVfuNebD/6Bj6ocKG8cQya6pNh89nF9JMKzRS0UlRTWbPB3bagI6qUtC8tX1HX0uYPVRyK6qNVMgq5Bj6zDwapJFXlGJCKNRS3242DO7aLEpbkbVb18HH4cPuclO3bgzPlW1n30drOyykgCYQue3zMuQAyvQnIcFmRYE7CIdNR1Mnvyw+kI8NtR7IpFY0tFUix56DF0wCv/GWYM2GFRVr8jBBjUcMnmfRL2SRImWjFYfDBGbBpYepMg6FFztlUB6KUoRFNRo923Cjh6lxSQBSHxCsxViE50D7rv8ngwh5TCVL9TpjkZgb5HW7JQ1rAiSpDA+wBE+okfjqSUGNoRL2hWe4BZAdS4JMPl6ZAC2pMzUiBE2a/AYfMVRjlz5RmzFq5d3AxzmyPU/s9iXCgRf5lmsxelBvrMNyXgfpAPWqlPDL8Yu1Kej6/lIBR3gF5Vuq3OH1m7fdUyPs02p8lHx81qLS2rwSd7DbBDhuM8ghK7U1I9zqQakiCLWDBQZTB5jchzZQCr9cNt8kPiy+AImsDrH4jEv12pIjss1Uiq1nK22SDw2+BUcrgkKUa2b5kKTMpT68fVpNdPgzkI0byq67xyhOxy29KNabCbfCizFMBv+Q7z5iJJn+LtgWP3WjT3t9yQ61WtvXGFgzzpUo5NiHHn4xy1MAkHy5pxjTtI+ywrRYjvRkwyfdsi7deKkYLWoxyH5MDlYY6pPsT0eyXzykpv1xfijzLarjlh6d45TmbPUjxOVBhaoBT8tdoUv8wQKYvCUdN9dIyYJAylSZkKR/V32iUD0+/tEAkyK9wGTzaO+KS36HKS70nbrmvVr5SPnWS7wS/Va60olregYC8AFmSbpWxUX5HCkrl49CvXgohxS91gtygWs515Ae33oiMnFGd/5/0kj53mEvl/XVxFks639WPrxNngRzfHnLNW+I8IGHr9OM1uiIY3VVc8deImxqSRrUcp4n7Fzn8VPzP6eHq0/wdOV7eSd5uFkeJ+sqdc/BgFHbfaygHfqcMqBCufxMYe3rn1snjUtk3VWJN8vfx5Yfv4dbHnoPVLi9RGA5s24JXfv1znPz1b+KkE6Tje93v5T9URmqh6zrUlzcfn5fZsfGIHQ1iWqg1t6bMkQ526XNJMZjhr66Cr7YW1ZVVWFNTjeJuvnBzSsrQkJgIm1v+6W0paE6wYpIrRyq2RCSaErXK0Sr/YqqyU//I6h9dqpxjvtZVZaQsF0VFyxF4/C6p9LwY4ZwEt69FqxQUxU37kGhO1f7pm+Uf120xQUvdaJHKzo/dgQNIlQrVbzZr/7RNUimoilxVqCofdcbIhi+TnmESBeozBFeUtgXMWgUXFlWddHitjFJ5t1Zw5PhwwcLTsWDxmf2uw7yzGqjjm9LVNZHE7c39goGBwGPiPNa6qm6YdCPj4ZO+GvasdHAvuBU4U7pi7CEjmFb8RGpG6dq5+A+YlDgfW1e+hb3r/4Wpp4tCCTO8d/Xf/oLkrGzMu/wqsTKkcpahu3Uv/x31b78OR1YA7kr5qt1dAW9V6FfGEfmaARZJCRVLs1JRQzM2VK/AhjUyNFiwyD9x4/CxcCVLx7yuOCxyr+TKMhhqq5EizUdlifKNI1+ILvkyMtYENKMj+B0F7OqQT4vBCod80Y9KnAK7OQVjE6eiwVsnX0fyTS5fdUpZJNnS5WvOAqdYPR6fGynypadoVRyKYQntW2mlWtp3a9SQuivfmK5VZC3y1TfXPU6+5IzwilXjtnqR0KQsAD+aEluQ2CDNdVnNqE6pR1pmCho9jTC4DXA75MtOrmluki9Khxn2BKcoWwuKy8uQ4bDgcF0DUm1mFFXWI9HpQH6qE0kJNhSU1SFL/B6/GVXSXNYiZZWbm43C/YcxJj8DFoddU5TFJVXISE9FXmY6ahqbse3L3RiZn4vs9DQUlVUgNysVXp8fVVXVmHzCCXDVVcGWkgZHQir27NsPZ4IDSaKoHQkWWOSLOT0rC021NTAmJMLd2AKPxLNJWH2zC3Yp0+SMTFgSpdlQnpdD8tfc1CD/FAE4kqXspCHY65LyECsx0eqHOSlJFLJ6haxyfRVcorwNYoU4JL67tBiGpFTYHFYZiOFCfW0jEoflwWY2ora+Bk6bAw21HtikibHlcB2c41JRW9KI5DSr5KlK0k5AndcFa5J85Use7VZlAwnSVOeXdxbltTCYE9CcZIZZPl48LjcSxUisKJffJi9VqtkGl9RAjhSxTBr8UkYmNNjcaKirxajhw9EoTakuaZo0NDWirqkFqVJGqfJeu0QRVclHUJLZIV/xAZSXHIHZKgNDEq1wOWxSqRnhTMqVl8wDd50HRmlWNdslP/JlX1lSAbvkK5CfCoszCYEWtbunlJP8fk+jX4zzRDFk5f0Xy9gvVrrFqJobG+FvMiB5fK6UrQd+nxe11eUyylHiawrWIGWQCG9Ng1hQZvlAaharxQib1wiTvKdqUVS/UZonbTaxwuQ9qqyB1WJDoNwPQ5pYuAkGeZ/FypLWXbNK01APf30AllQ/ymvqkZ6Sg2aTWKRGeU5NTWgQa8jnbkGaNOM2SLnYxdKrk484VcEZ5Tq/yyjvrEdeBbGiCsuAlhpM7qXi6I5oKA/VVxHalD9cpDjCa6zdxC1TTVvyz1miN3GV9+B+0UdZaC9+S6wI6ZxWfOc9aYf4PKggFOsfCTZjnXs/kCD/xMVbgI1iFDmk33/WdVIBmpCak4fta9eEVR4fv/gcaqVi+/pP70PN44+jYd3HaN60qe282uDWJP0Apiz5B6mSYb0hWMeNQ8DrwbSZMzFL+hSam5txpLkOW4sPo7zVbBUzPeHALhhd0gwihy6xDuT1xc60PGR4qnEoIRfN5kQ45YXOqS2EcdppMDdWISE1HbNPmo+srExtXxO79Ec4U+Sf0G7vWVGqPd6lOL1Vcn+xMgxW+QcShWW0iV8qLg29H6Cn7f/qZYgWi3oZ72LVsBoh02cFVwL4CtL/E5ZsNaZEKuGQ7ZE1pAbPSG//iJFGqSCpycdel3bscYr6L9NxiELT4rba/nnB9FIy9NTyg/duW086Z1gn+Qvmq635QCd5VPZXr+2E4B06Jze7PbP541QDRhd0+MlJ6UnB3tIwtJVZB6x6q2NiZocyVyS0x+p6ELxT+vCkT1PR8RFntnraV+lOyukQknFs3pwZYe43vpsm9X6gPNSqfxPkn3yMuEV6Z3bHiQzSroPb9T4N1WFeqyuFim7iqjjfFlGjr5T7Rkj4CxLnQXHVG6vakD6Lwu8I38+xS7W+6YyQbhYl86TFbadkab0YOAelVW7ZNe3X5Iuld91rUhGqyhma0vj4paBiSMnW34oOVB45hM0r3sCEjBy4//0naCqTLweFfMlk3vZ92MaNh3PRyTDJF2WkNO7ciapXZNiv14szzj4PhZ5UfLKvEu/vrZJvNNXGbcbEnETMG5OJUSNScd2ETGQn90wh9IRWBaH2ZCeEDEz6rDxECXilIlf9G2q4rBo99aSEbZewW/TzfxVnhT5Mt0Afqntjd3H1pJXSeEnOqyG5h0Su0uOotF/SO9VVI+xtsRxpVfnEEzBlZCB1Vkhlf8Xf2v3qy3iKfGqOlb79N+Sn7F0FjJJv1pPFP+oUtfVg26VTTj8LH7/8vPR9rMaib1yrhXk8HqxcuRKzJ06E5cMP8c5rS2GSppORH2+E/ZRTkfyTnyD5gsVS4fbuUW3duhWvv/467IkpqMicgxveVqOdq6VpxoELZ43E4qm5mD8mHVnS9EAIIZHCGeZhKLz0MlhGjpBWhLelN1eGHN36LzHRu7Z7A9JWWrtiBZo++QS+unrYRCmkXHYpbGNlhJSwXDrBS/ftwc1/fgIlTz2Np47qzWDCvLUfYFd2EmaJWbvo7nthG6MMst7z4itvYOcX0nwmLG2ZCau0YS8an4kl80fg9IlZHBJKCAkLZ5j3El99PVyrZXDYEjVWVcgY3+l13upqVJbuP/sAABSiSURBVImVUvPa6/BVtq+A2/DBB6h89FGY8/LgXLAA45sbcLCxEavPOxuHp86Qhrf2tuKtJ5+MYa46nPGbP0nHV+8m6fmkA/H1LUVY/tFWTKoNKo5NSQtxx+njcdOiMXBw8h8hJApwTYoweEtKNLexzArn5TLqV0aIhKJGFVU9/QzK//d/tePEM8+ERUaKZNzwbRilM7nx449R99ZbaJF+h9o33pCOyASkjs1F4dTJOCqK41Tp2D7zkkvw9G//W9rmZMTI7DN6rDjUfh8f7a3A8k1HsGp7mYwLb8Cl1h0ywsOM+Rd8HffNn9yj9AghJBxUHmHIvO02HH3oIRz6IBPj7v4eLH4ZXqcvMdLw0Uc4fPP32q7N/8PvkbxYrbbSTvL552nSqmhUx7fr/ZV4Y6WM1hKO7tiCpz54G9WlJTBPnYeC4tJwWWqjutGNZz45gCfX7UddS3AM/nmTMzHswHrN/93v3IDhosgIISTaUHmEIevqs9G8/EGxPGzYd975beGm1FT4aoIzrhOkOWrkU0+2KZWuMOgWxeh5J8P3z09hqSrDkbLgrGq1ZLt/+Dh8JApp3759GCdDbruirsWD/3x5G97dXtrWZ/+LS6figmm5WL/2PWw6AJxxxhlUHISQmGGOWcqDheeuxPBTqlC040Q07Gzv3G5VHKNfXAbHjA57c4ShsDC4htQVN/0bHEYD0nLzZOhurrYWlFIeH0tTV1fK49PCStyxdAvK612YPzod914yBVOHJWud36tXr8amTZuwcOFCTXkQQkisoPIIR3M1jJYARvziDnhyT4e7cD+MSYmwSH+F0WqF0dnzuQrFxcWwWCyYNH2mtGK1WytJSUk466yz8P7772sLBWZl6ZOJBDWT+d43tuPvnx7EsBQ73rx9EaYPb59+tVP6VJTSSUxMxLnnqnUmCSEkdnAb2nB41LQUISkXluxsOBcugGPqVJjT0nqlOBSlpaXIzc09RnG0Mnv2bJikeWvDBjX3Moha2uIn0kylFMeUvGS8c+dpxyiOqqoqvPzyy5qyueOOO7T4hBASS6g8IiW5k+UXeoFfOtxblUdnKMthqign1fxUV1cHjyiOW57bjFc2H8EPzhqPt+84BSmO9mXg1fIjL774oqaIrrjiCm2pc0IIiTVstoqUjK47sHuCao5Sy5EPC5nf0RHVX7Ft2zaslf6PFTV5WL2zDD8+dyJuF+URutaT2gvj0UcfRY30vyxZsqRLhUQIIdGGyiMcd2wF3F9dH7+3FBQUBNcrG9/5ZMPWzZSmTJmKjZs2Y23zibj34pm46ZRjZ5u7XC4sX75cUxzz58/H5Mmcy0EIiR9UHuFI79sSIR0pKSnRdsZTneNd4fL68ElLHhL9O3B9fgVuOPnYTVyqq6vxxz+qnXuBiy66CPPmzYtqHgkhJBzs84gzSnnk5YWsfd3JxL9L/rwOr+2oReLoE9FSWYxly5ahpaUFtbW1+Mc//oGHH35Yu1aNqqLiIIQcD2h5xBGlACorKzF9+vROz6/aXoqb/x7ct+NnF52AmxaNlqYpL3bs2IEHHlCLDAdRyufyyy9HTk7ny7oTQkisofKII2X63hwdLY9mtw//885OPPtJcJvc129bhJkjgkNxr7zySqTJsGA161yN0jrllFNw+umna/NECCHkeEHlEecmq47KQy01cv0Tn2Hr4Rpcu2Akfn7xFNgt7fM01JwN1TzFiX+EkP4ElUeclYeax9HaWd7i8eHOZVs1xfGHq2fi8ln58cwOIYT0GnaYx1l5hM7FuP+tHXh/Vzl+/bVpVByEkAEFlUecUNvNqgmCrU1W//i8GC+sP4TvnT5WmquOHYpLCCH9HSqPOFFeXq4tbqgsj9LaFvzn8m2YPjwFPz53UryyQAghUYPKI06olXQValmSB9/bDa/fjz9fMwtWMx8BIWTgwZorTqhhtg6HA7urfNp2sdefNBqjMnq3Ki8hhBxvqDzixNGjR5GanoEfyOiq0aI07jxnQrxuTQghUYdDdeOoPIqRhkaXF899ZwGS7JzkRwgZuNDyiAO19Y1obGzEnhoDfnbRFEzK7XpRREIIGQjQ8ogRPn8Aj3xYgN+t2oN8SyPONQFnzhiLby4YGatbEkJI3KDyCINflEBAXJOxfROmcKjd/9TKuLtK67Xjhfl26TEHbjjrxF4/KEII6U+w2SoM5/3hI9yxdEvEBarmcvxu1W5NcXxr4Ujs+uViXDwpSdsBMDW1fd9xQggZyNDyCIMyOPyiECLlmX8dwKNrC3HZzGH41eVBS6OqqkrbAMpsZnETQgYHtDzCFZBYDJEqjwYZSfV/0scxY0SqttBhK2oPD7W1LCGEDBaoPMKgmpukCyMi/rxmL+pFgdx/6VQtXivK8qDyIIQMJvqkPKSCTBd5T2Sv7qZ1cd1ikd0iBSJ3RRJf/Hfr16t454eEf6iHbdUluy+/IRwmY7AfIxy7Suvw6EeFOHtytmZ5tNLU1KTtIEjlQQgZTPTV8lCKYI1Urmq69Br9+Bikclc7Gz0kcoHIFJFrJGxKd/H180tEpoosFnlYT6eVayXOTF3K+/gbotJs9ermIs2956ITjglXK+kqMjIyop85QggZoMrjMpFndL9yL+/kmvkiBVLJF4q4xb9Mj9ddfBW+TK53iexX8fV04o5qfpLRumGH876+pQjnnJCDsVmJnS6ImJ/PjZ4IIYOHviqPHKnctb1VdbezJiRVax4OOT6ih3UXv7s4iqf0Jqufi3Q5AUNO3SyyUUmrBRCL0VaPrytEeb0Ll8w4dm9yRXV1NaxWK5xOLoJICBk8hB07KhXvanHat79r554I79FZ5R6uHai7OKrJqkjypdb4eEXkOpFnO0tErntMHCWYO3du5ONtQzCFabY6XNWEB97ZhQVj0nHhiZ0rj7S0tGM60AkhZNArD6mAz+nqnFSIZSJ5ympQrgR11v+grIYRIcfDRYJtOUBX8buMoxSH7tZLnBf05qxOlUfU+jy6GW314Ht7tGat+2SElUX1rndAjbTKysqKVfYIIWRANlu9KfJt3a/cNzq5ZoPIBKnox4hY9Y7wN8PEV+FL5Hqbiqfii3wmfrNIprpAXLUs7cUiX/bxN3SLMhi6sjwOHG3Ea9LXoZiU89XFDtUoKzXHo3XrWUIIGSz0dcrzAyIvSUX+HXEPiVylAuV4mDiPi3VwoYhXjm+X45UiasTUkxK2vbv46ryEvSTeHSJekdskzCdhquNgpa44VFqqSe1vffwNYS0PtetfZ1z853Wa+/SN82DsZO2rkpKStt0DCSFkMNEn5SEVeqU4Z3cSrpqYLgw5XiHOikjj6+d+Lc6vO4Q1ijOnL3nuKZ8UVraNqApVEGrV3Ca30mvAaROywm49SwghgwnOMI+Qp/914Jjjt7YVa30dv7tqRqdWh+LgwYPaYogcaUUIGWxQeURIQUVDm3/ZZ4fww2VbNf/XZnU+f8Pj8aCgoADjxo3r4yMihJD+B5d5jRC1fazX58es+9/T1q9S/HTx5C73+Thy5Ig0dfkxceLE6DwpQgjpR1B5RMgbW4s1aeU/zp+EW8/o2qpQVofRaMTIkdw5kBAy+GCzVS/4+pzhuO3M8d1es2HDBuTk5MDhcPTqwRBCSH+GyiMMv7x8GmaNPHYHwN9cOb3bOMrqcLvdSE5O7tvTIYSQfor5eGegv3PdwlGaKMrrWpDssHS7n7lavn3VqlWa/6KLLopLHgkhJN5QefSA7GR72GsOHDiA8vJyTXHQ8iCEDFbYbBVFGhsb8cwzwRXmZ8yYEc2kCSGkX0HLIwrU1dXhwQcfbDueO3eutgw7IYQMVqg8eolaav3o0aNYu3Yt6uvrjznHvg5CyGCHyqOXPP7441ozVUeuvvpq7t1BCBn0UHn0ko6KY/78+dqmT5MmTerzQyGEkP4OlUcvUMNxQ1mwYAHOP/98bUY5IYQMBag8ekFRUXADqFYuuOCCqDwMQggZKFB59ILly5dr7nnnnYeUlJSoPhBCCBkIUHn0ELVSbk1NjeafN28eLBa1qSEhhAwt2EjfQ9S+5K1QcRBChipUHj1k27Ztmsu5HISQoQyVRw959913NVdtL0sIIUMVKo9ekpmZGc3nQAghAwp2mPeQ0aNHo6GhQZsQSAghQxVaHhH0cezdu7ft2OVyUXEQQoY8VB5h+Oijj7BlyxbNf/jwYZSUlHS6phUhhAwlqDzCYDAY2pYjWbp0qeaWlZXF9qkQQkg/h8ojAuWhUP0cTU1Nmt9ms8X2qRBCSD+HyiMClOXRqjgU3/rWt2L2QAghZCBA5RFhs1XozPJhw4bF9KEQQkh/h8ojQlqVx3e/+92YPQxCCBkoUHlEaHmoIboK9ncQQkgflYdUrOki74ns1d1OZ85J+GKR3SIFIneFiy9uhsgHIg0if+mQ1hyRL/S0/mRo7dGOEa3Jt66ka7fbY3k7QggZEpaHUgRr5Mt8gnL1446Vr0mch0TUjklTRK6RsClh4qs2op+L/KSTez4icrOIiqNkcR9/Q1iU5bFmjcoelQchhERDeVwm8ozuV+7lnVwzX6RAKuBCEbf4l+nxuowv1zWKrBNvey+1IEonT5xkOfdJIDj54tku7hmTeR4KLsNOCCF9Vx45UrGWKI/uZndyTb7I4ZDjI3pYpPE7pnWki7Q6q/hvFtmopKKiIkzSke9ZTgghQ52wCyNKxbtanNxOTt0T4T0665PobW3co7Sk0n9MHCWYO3duoLeWx759+zQ/h+gSQkiEykMq4HO6qVjLVFOSshr0JqXyTi5T1sGIkOPhIsW6P5L4HdMa3kVaMSG0P15tQUsIIaTvzVZvinxb9yv3jU6u2SAyQSrhMSJW8S/R40Uavw29aate0lmoj7K6PlycaHLJJZfE61aEEDKolccDIueqobbK1Y/V1/owkRV6he8V53aRlSI7RV6SsO3dxdfTOCDOgyI3iP9IyAitW0UeFykQUe1J7/TxN0RseWRnh+uSIYSQoUGfNoMSJVApztmdhKumpAtDjpUiWRFpfP3c6C7CN4ozrZdZ7jVOp5MjrQghRIczzCO0PLiHByGEtEPlEYYYT2AnhJABCZUHIYQQKo9oc/DgQc0dN25c1NMmhJCBCi2PCGmdKEgIIYTKI2JOPPFEvi+EEKJDyyNC5syZE+mlhBAy6KHyIIQQQuVBCCEk9tDyiBCHwxHL50AIIQMKKo8IycnJieVzIISQAQWVByGEECoPQgghsYeWByGEECoPQgghsYeWByGEECoPQggh/XwnwaHAddddh6ampuOdDUII6VdQeYSBS7ETQshXYZ8HIYSQHkPlQQghhMqDEEJI7KHlQQghhMqDEEJI7KHlQQghhMqDEEJI7KHlQQghpMcYAoFAjyMNRAwGQ4U4B3sZPVPkaBSzEy2YL5YX36/+Q+YgrSdGiZ7IGrLKo4+KZ6OU09zjnY+OMF8sL75f/QfDEKsn2GxFCCGEyoMQQkjsoeURGY/F9Cn0HuaL5cX3q//w2PHOQDzzxT4PQgghPYaWByGEECoPQgghsYeWR/dD3BaL7BYpELkrzvceIfKByE6R7SI/1MPvEykS2arLhSFx7tbzqvJ8fgzzdkDkC/3+G/WwdJH3RPbqblo88yXpTgopEyV1Incer/KS9J4UKRf5MiSsx2Uk/jl6WatzfxIxxCBf/yuyS2SbyGsiqXr4aJHmkLL7a5zz1eNnF6d8vRiSJ/W/sDWe5dVN3RDf90vN86B8tQwEk8g+kbEiVpHPRabEq6yEPJHZuj9JZI+6v8h9Ij/p5Popeh5tImP0vJtilLcDIpkdwn4rcpfuV4r2N/HOV4dnVyoy6niVl3Caen4iX/aljITPRE5S/+ci74hcEIN8nSdi1v2/CcnX6NDrOqQTj3z1+NnFI18dzv+fyL3xLK9u6oa4vl+0PLpmvkiBFFKhiFv8y0Qu6+b6qCL3LBHZrPvrxdkpkt9NFJW3ZXKtS2S/+Av03xAv1P2f0f3Kvfw45utskX1yv+5WFIhpviTNj8Sp6ksZyUegqiSSJeyTQPA//dmQOFHLl4StEvHqh5+KDO8ujXjlqxuOa3m1YhDE+YbI0u7SiHa+uqkb4vp+UXl0jXoYh0OOj+hhcUcesvqimSWyXg+6XW9ieDLENI1nftWLtkruvUnkZj0sR73U2smgm30c8tXKkg7/0Me7vNDLMsrX/fHM4036F2grY6TMtoisFTlVD4tnvnry7OJdXqeKlMmz3Hu8yqtD3RDX94vKo5vn0klY3NdykZcjUZxXRO6UF6JO3EdExonMFCnRzWbt0jjmd5HkRZnyF4jcJnlUpn2/KEfJi2pivFTkZT2oP5RXOLrKS7zL7h5xlAXyvB6kymukPGtVOf1I5AW5JjmO+erps4v3M72mw0dKXMurk7qhy0u7uH+f8kXl0TVKC48IOVamfHGkBRsN5OWw6C/H8/JyvKrCxFVfOj4Rvxz+LaSpJW75lXtr6YpbLs5reh7KdDO41Uwvj3e+dJRC26zKqb+UVwg9LaMjHZqQYpZHyc+3xblY5Fq9CQN6M0el7t+kt5VPjFe+evHs4lleZnGuEHkxJL9xK6/O6oZ4v19UHl2zQWSCPIQx+tesagp5M9KC7St6e+oTIjvl5XgwJFx7OXS+JtI6CkTlbYmct6k8i3+C3hkW7Xw5RZJa/Xpn65f6/VUFpFDuG/HMV1dfg8e7vDrQozLSmx7qJWyh/j5cHxInakjSi8X5qcilcs+mkPAsEZPuH6vnqzCO+erRs4tXvnTOEdkl9zwS7/Lqqm6I+/vV2x7/oSDChfpIBvUFcU+c732Kejwi20TUUMCten7+LvKFHq5eiryQOPfoed3d11Em3eRrrD5yQ8n21nIRMkTWiKj2X+WmxzNf+n0SRNSXX0pI2HEpL12BqX9Oj4iqYL7TmzIS5uqVpjr3l9ZVIaKcrwK9Tbz1Pfurfu2V+jNWz1p10F4S53z1+NnFI196+NMit3S4Ni7l1U3dENf3i8uTEEII6TFstiKEEELlQQghJPbQ8iCEEELlQQghJPbQ8iCEEELlQQghJPbQ8iCEENJj/j99CQLYqWKDtgAAAABJRU5ErkJggg==\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[0:2000,0:32:4,1]\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": 5,
   "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": 6,
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}