docs/notebooks/02_afni_deoblique.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AFNI Deoblique\n",
    "\n",
    "This notebook explores the implementation of *deobliquing* operations in AFNI, in order to be able to correctly manipulate transforms generated by the tool, and to produce transforms that can be correctly applied with AFNI.\n",
    "Most of the preparation comes from the previous notebook.\n",
    "\n",
    "This notebook is the test bed for resolving [issue #45](https://github.com/poldracklab/nitransforms/issues/45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preamble\n",
    "Prepare a Python environment and use a temporal directory for the outputs. After that, fetch the actual file from NiBabel documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "/tmp/tmppk0m5zaq\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "from pathlib import Path\n",
    "from tempfile import TemporaryDirectory\n",
    "\n",
    "import numpy as np\n",
    "import nibabel as nb\n",
    "import nitransforms as nt\n",
    "from nipype.interfaces.afni import Warp\n",
    "\n",
    "cwd = TemporaryDirectory()\n",
    "os.chdir(cwd.name)\n",
    "print(os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-11-13 09:34:55--  https://nipy.org/nibabel/_downloads/62985f4c43f499609fa65cb2eb955b79/someones_anatomy.nii.gz\n",
      "Resolving nipy.org (nipy.org)... 185.199.108.153, 185.199.110.153, 185.199.111.153, ...\n",
      "Connecting to nipy.org (nipy.org)|185.199.108.153|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 191449 (187K) [application/gzip]\n",
      "Saving to: ‘someones_anatomy.nii.gz’\n",
      "\n",
      "someones_anatomy.ni 100%[===================>] 186.96K  --.-KB/s    in 0.007s  \n",
      "\n",
      "2019-11-13 09:34:55 (25.2 MB/s) - ‘someones_anatomy.nii.gz’ saved [191449/191449]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://nipy.org/nibabel/_downloads/62985f4c43f499609fa65cb2eb955b79/someones_anatomy.nii.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load in the image\n",
    "Let's first visualize the example image, and retain some copies of the original header and affine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<OrthoSlicer3D: someones_anatomy.nii.gz (57, 67, 56)>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the example\n",
    "nii = nb.load('someones_anatomy.nii.gz')\n",
    "hdr = nii.header.copy()\n",
    "aff = nii.affine.copy()\n",
    "data = np.asanyarray(nii.dataobj)\n",
    "nii.orthoview()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating variants\n",
    "Playing with the affine header and the data array ordering of axes and elements we generate several versions of the dataset with different orientations (LAS, LPS, oblique, LPS oblique)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create LAS version\n",
    "las_aff = aff.copy()\n",
    "las_aff[0, 0] *= -1.0\n",
    "las_aff[0, 3] = aff.dot(np.hstack((np.array(nii.shape[:3]) - 1, 1.0)))[0]\n",
    "\n",
    "las = nb.Nifti1Image(np.flip(data, 0), las_aff, nii.header)\n",
    "las.to_filename('someones_anatomy_LAS.nii.gz')\n",
    "\n",
    "# Create LPS version\n",
    "lps_aff = aff.copy()\n",
    "lps_aff[0, 0] *= -1.0\n",
    "lps_aff[1, 1] *= -1.0\n",
    "lps_aff[:2, 3] = aff.dot(np.hstack((np.array(nii.shape[:3]) - 1, 1.0)))[:2]\n",
    "\n",
    "lps = nb.Nifti1Image(np.flip(np.flip(data, 0), 1), lps_aff, nii.header)\n",
    "lps.to_filename('someones_anatomy_LPS.nii.gz')\n",
    "\n",
    "# Create version not aligned to canonical\n",
    "R = nb.affines.from_matvec(nb.eulerangles.euler2mat(x=0.09, y=0.001, z=0.001))\n",
    "M = R.dot(nii.affine)\n",
    "new = nb.Nifti1Image(data, M, hdr)\n",
    "new.header.set_qform(M, 1)\n",
    "new.header.set_sform(M, 1)\n",
    "new.to_filename('someones_anatomy_rot.nii.gz')\n",
    "\n",
    "# Create version not aligned to canonical (LPS)\n",
    "lpsM = R.dot(lps.affine)\n",
    "lpsnew = nb.Nifti1Image(np.flip(np.flip(data, 0), 1), lpsM, hdr)\n",
    "lpsnew.header.set_qform(lpsM, 1)\n",
    "lpsnew.header.set_sform(lpsM, 1)\n",
    "lpsnew.to_filename('someones_anatomy_lpsrot.nii.gz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print affines of interest\n",
    "Output some affines that we will want to compare to their counterparts generated by AFNI and stored in the NIfTI header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2.74999725e+00 -2.74999817e-03  2.74999954e-03 -7.79999220e+01]\n",
      " [ 2.98603540e-03  2.73886840e+00 -2.47165887e-01 -8.25373961e+01]\n",
      " [-2.49170222e-03  2.47168626e-01  2.73886865e+00 -9.87400142e+01]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  1.00000000e+00]]\n",
      "[[ 2.74999725e+00 -2.74999817e-03  2.74999954e-03 -7.79999220e+01]\n",
      " [ 2.98603540e-03  2.73886840e+00 -2.47165887e-01 -8.25373961e+01]\n",
      " [-2.49170222e-03  2.47168626e-01  2.73886865e+00 -9.87400142e+01]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  1.00000000e+00]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-77.99992205, -82.53739614, -98.74001417])"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(new.affine)\n",
    "print(R.dot(nii.affine))\n",
    "R[:3, :3].dot(nii.affine[:3, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-9.99999000e-01  9.99999333e-04 -9.99999833e-04  0.00000000e+00]\n",
      " [-1.08583106e-03 -9.95952145e-01  8.98785043e-02  0.00000000e+00]\n",
      " [-9.06073535e-04  8.98795002e-02  9.95952235e-01  0.00000000e+00]\n",
      " [-0.00000000e+00 -0.00000000e+00  0.00000000e+00  1.00000000e+00]]\n"
     ]
    }
   ],
   "source": [
    "print(np.linalg.inv(np.diag([-1, -1, 1, 1]).dot(R)).T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run `3dWarp -deoblique`\n",
    "Generate one example image after \"deobliquing\", to then see if we can generate equivalent composite transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "191113-09:35:00,729 nipype.interface INFO:\n",
      "\t stderr 2019-11-13T09:35:00.729908:++ 3dWarp: AFNI version=AFNI_16.0.01 (Jan 27 2016) [64-bit]\n",
      "191113-09:35:00,731 nipype.interface INFO:\n",
      "\t stderr 2019-11-13T09:35:00.729908:++ Authored by: RW Cox\n",
      "191113-09:35:00,732 nipype.interface INFO:\n",
      "\t stderr 2019-11-13T09:35:00.732404:** AFNI converts NIFTI_datatype=2 (UINT8) in file someones_anatomy_rot.nii.gz to FLOAT32\n",
      "191113-09:35:00,733 nipype.interface INFO:\n",
      "\t stderr 2019-11-13T09:35:00.732404:     Warnings of this type will be muted for this session.\n",
      "191113-09:35:00,733 nipype.interface INFO:\n",
      "\t stderr 2019-11-13T09:35:00.732404:     Set AFNI_NIFTI_TYPE_WARN to YES to see them all, NO to see none.\n",
      "191113-09:35:00,734 nipype.interface INFO:\n",
      "\t stderr 2019-11-13T09:35:00.732404:++ Using minimum spacing of 2.750000 mm for new grid spacing\n",
      "191113-09:35:00,785 nipype.interface INFO:\n",
      "\t stderr 2019-11-13T09:35:00.785593:++ Output dataset ./deob.nii.gz\n",
      "191113-09:35:00,786 nipype.interface INFO:\n",
      "\t stdout 2019-11-13T09:35:00.786511:# mat44 Obliquity Transformation ::\n",
      "191113-09:35:00,786 nipype.interface INFO:\n",
      "\t stdout 2019-11-13T09:35:00.786511:      0.999999     -0.001000     -0.001000      -0.016129\n",
      "191113-09:35:00,787 nipype.interface INFO:\n",
      "\t stdout 2019-11-13T09:35:00.786511:      0.001086      0.995952      0.089879       9.124008\n",
      "191113-09:35:00,788 nipype.interface INFO:\n",
      "\t stdout 2019-11-13T09:35:00.786511:      0.000906     -0.089880      0.995952       6.948074\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<nipype.interfaces.base.support.InterfaceResult at 0x7f547e582550>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#!3dWarp -deoblique -prefix deoblps.nii.gz someones_anatomy_lpsrot.nii.gz\n",
    "Warp(in_file='someones_anatomy_rot.nii.gz', deoblique=True, verbose=True, out_file='deob.nii.gz').run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating AFNI's \"deobliquing\" matrices\n",
    "\n",
    "Based on the image's information, we need to reconstruct AFNI's `WARPDRIVE_MATVEC_FOR_000000` affine, and then check that the results of applying such a matrix ends in the same outcome with AFNI and NiTransforms tools."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nibabel.affines import from_matvec\n",
    "B = np.ones((2, 2))\n",
    "AFNI_SIGNS = np.block([[B, -1.0 * B], [-1.0 * B, B]])\n",
    "def _afni_warpdrive_for(oblique, plumb, offset=True, inv=False):\n",
    "    \"\"\"\n",
    "    Calculate AFNI's ``WARPDRIVE_MATVEC_FOR_000000`` (de)obliquing affine.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    oblique : 4x4 numpy.array\n",
    "        affine that is not aligned to the cardinal axes.\n",
    "    plumb : 4x4 numpy.array\n",
    "        corresponding affine that is aligned to the cardinal axes.\n",
    "\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    plumb_to_oblique : 4x4 numpy.array\n",
    "        the matrix that pre-pended to the plumb affine rotates it\n",
    "        to be oblique.\n",
    "\n",
    "    \"\"\"\n",
    "    R = np.linalg.inv(plumb[:3, :3]).dot(oblique[:3, :3])\n",
    "    origin = oblique[:3, 3] - R.dot(oblique[:3, 3])\n",
    "    if offset is False:\n",
    "        origin = np.zeros(3)\n",
    "    matvec_inv = from_matvec(R, origin) * AFNI_SIGNS\n",
    "    if not inv:\n",
    "        return np.linalg.inv(matvec_inv)\n",
    "    return matvec_inv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspecting affines and headers\n",
    "Generate a rotation affine to apply on the original affine of an image, and try to reconstruct the inverse of it as AFNI calculates it.\n",
    "\n",
    "The following cells are just prior information useful for when we arrive to the next section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 9.99999000e-01  1.08583106e-03  9.06073535e-04  0.00000000e+00]\n",
      " [-9.99999333e-04  9.95952145e-01 -8.98795002e-02  0.00000000e+00]\n",
      " [-9.99999833e-04  8.98785043e-02  9.95952235e-01  0.00000000e+00]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  1.00000000e+00]]\n"
     ]
    }
   ],
   "source": [
    "R = nb.affines.from_matvec(nb.eulerangles.euler2mat(x=0.09, y=0.001, z=0.001))\n",
    "# D = _afni_to_oblique(new.affine, nii.affine)\n",
    "# print(R)\n",
    "# print(D)\n",
    "print(_afni_warpdrive_for(new.affine, nii.affine, offset=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<?xml version='1.0' ?>\n",
      "<AFNI_attributes\n",
      "  self_idcode=\"XYZ_5BVhie9GEKQyZ5eTUAHGAw\"\n",
      "  NIfTI_nums=\"58,72,62,1,1,16\"\n",
      "  ni_form=\"ni_group\" >\n",
      "<AFNI_atr\n",
      "  ni_type=\"String\"\n",
      "  ni_dimen=\"1\"\n",
      "  atr_name=\"HISTORY_NOTE\" >\n",
      " \"[oesteban@dendrite: Tue Nov 12 08:53:31 2019] 3dWarp -deoblique -prefix deob.nii.gz -verb someones_anatomy_rot.nii.gz\"\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"float\"\n",
      "  ni_dimen=\"12\"\n",
      "  atr_name=\"WARPDRIVE_MATVEC_FOR_000000\" >\n",
      " 0.999999\n",
      " 0.001085831\n",
      " 0.0009060735\n",
      " -7.40746e-05\n",
      " -0.0009999992\n",
      " 0.9959521\n",
      " -0.0898795\n",
      " -8.462602\n",
      " -0.0009999998\n",
      " 0.0898785\n",
      " 0.9959521\n",
      " -7.740018\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"float\"\n",
      "  ni_dimen=\"12\"\n",
      "  atr_name=\"WARPDRIVE_MATVEC_INV_000000\" >\n",
      " 0.999999\n",
      " -0.0009999993\n",
      " -0.0009999998\n",
      " -0.01612854\n",
      " 0.001085831\n",
      " 0.9959522\n",
      " 0.08987851\n",
      " 9.124008\n",
      " 0.0009060736\n",
      " -0.08987951\n",
      " 0.9959523\n",
      " 6.948074\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"String\"\n",
      "  ni_dimen=\"1\"\n",
      "  atr_name=\"TYPESTRING\" >\n",
      " \"3DIM_HEAD_ANAT\"\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"String\"\n",
      "  ni_dimen=\"1\"\n",
      "  atr_name=\"IDCODE_STRING\" >\n",
      " \"XYZ_5BVhie9GEKQyZ5eTUAHGAw\"\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"String\"\n",
      "  ni_dimen=\"1\"\n",
      "  atr_name=\"IDCODE_DATE\" >\n",
      " \"Tue Nov 12 08:53:31 2019\"\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"int\"\n",
      "  ni_dimen=\"8\"\n",
      "  atr_name=\"SCENE_DATA\" >\n",
      " 0\n",
      " 11\n",
      " 0\n",
      " -999\n",
      " -999\n",
      " -999\n",
      " -999\n",
      " -999\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"String\"\n",
      "  ni_dimen=\"1\"\n",
      "  atr_name=\"LABEL_1\" >\n",
      " \"zyxt\"\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"String\"\n",
      "  ni_dimen=\"1\"\n",
      "  atr_name=\"LABEL_2\" >\n",
      " \"zyxt\"\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"String\"\n",
      "  ni_dimen=\"1\"\n",
      "  atr_name=\"DATASET_NAME\" >\n",
      " \"zyxt\"\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"int\"\n",
      "  ni_dimen=\"3\"\n",
      "  atr_name=\"ORIENT_SPECIFIC\" >\n",
      " 0\n",
      " 3\n",
      " 4\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"float\"\n",
      "  ni_dimen=\"3\"\n",
      "  atr_name=\"ORIGIN\" >\n",
      " -77.35988\n",
      " -98.75681\n",
      " -99.20933\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"float\"\n",
      "  ni_dimen=\"3\"\n",
      "  atr_name=\"DELTA\" >\n",
      " 2.75\n",
      " 2.75\n",
      " 2.75\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"float\"\n",
      "  ni_dimen=\"12\"\n",
      "  atr_name=\"IJK_TO_DICOM\" >\n",
      " 2.75\n",
      " 0\n",
      " 0\n",
      " -77.35988\n",
      " 0\n",
      " 2.75\n",
      " 0\n",
      " -98.75681\n",
      " 0\n",
      " 0\n",
      " 2.75\n",
      " -99.20933\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"float\"\n",
      "  ni_dimen=\"12\"\n",
      "  atr_name=\"IJK_TO_DICOM_REAL\" >\n",
      " 2.75\n",
      " 0\n",
      " 0\n",
      " -77.35988\n",
      " 0\n",
      " 2.75\n",
      " 0\n",
      " -98.75681\n",
      " 0\n",
      " 0\n",
      " 2.75\n",
      " -99.20933\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"float\"\n",
      "  ni_dimen=\"2\"\n",
      "  atr_name=\"BRICK_STATS\" >\n",
      " 0\n",
      " 95.66843\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"int\"\n",
      "  ni_dimen=\"8\"\n",
      "  atr_name=\"DATASET_RANK\" >\n",
      " 3\n",
      " 1\n",
      " 0\n",
      " 0\n",
      " 0\n",
      " 0\n",
      " 0\n",
      " 0\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"int\"\n",
      "  ni_dimen=\"5\"\n",
      "  atr_name=\"DATASET_DIMENSIONS\" >\n",
      " 58\n",
      " 72\n",
      " 62\n",
      " 0\n",
      " 0\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"int\"\n",
      "  ni_dimen=\"1\"\n",
      "  atr_name=\"BRICK_TYPES\" >\n",
      " 3\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"float\"\n",
      "  ni_dimen=\"1\"\n",
      "  atr_name=\"BRICK_FLOAT_FACS\" >\n",
      " 0\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"String\"\n",
      "  ni_dimen=\"1\"\n",
      "  atr_name=\"TEMPLATE_SPACE\" >\n",
      " \"ORIG\"\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"int\"\n",
      "  ni_dimen=\"1\"\n",
      "  atr_name=\"INT_CMAP\" >\n",
      " 0\n",
      "</AFNI_atr>\n",
      "\n",
      "</AFNI_attributes>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(deob.header.extensions[0].get_content().decode())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 9.99999000e-01  1.08583106e-03 -9.06073535e-04  0.00000000e+00]\n",
      " [-9.99999333e-04  9.95952145e-01  8.98795002e-02  0.00000000e+00]\n",
      " [ 9.99999833e-04 -8.98785043e-02  9.95952235e-01  0.00000000e+00]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  1.00000000e+00]]\n",
      "[ 7.79544740e-05  8.46260386e+00 -7.74001417e+00  0.00000000e+00]\n",
      "[-7.79544740e-05 -8.46260386e+00  7.74001417e+00  0.00000000e+00]\n",
      "[ 0.01612466 -9.12400945  6.94806988  0.        ]\n"
     ]
    }
   ],
   "source": [
    "print(R.T)\n",
    "print(R.dot(nii.affine[:, 3]) - nii.affine[:, 3])\n",
    "print(nii.affine[:, 3] - new.affine[:, 3])\n",
    "print(new.affine[:, 3] - np.linalg.inv(R).T.dot(new.affine[:, 3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.999999   -0.001       0.001       0.01612466]\n",
      "[ 1.08583106e-03  9.95952145e-01 -8.98785043e-02 -9.12400945e+00]\n",
      "[-9.06073535e-04  8.98795002e-02  9.95952235e-01  6.94806988e+00]\n",
      "[0. 0. 0. 1.]\n"
     ]
    }
   ],
   "source": [
    "print('\\n'.join([str(row) for row in _afni_deoblique(new.affine, nii.affine)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 7.79544740e-05  8.46260386e+00 -7.74001417e+00  0.00000000e+00]\n",
      "[[ 2.74999725e+00 -2.74999817e-03  2.74999954e-03 -7.79999220e+01]\n",
      " [ 2.98603540e-03  2.73886840e+00 -2.47165887e-01 -8.25373961e+01]\n",
      " [-2.49170222e-03  2.47168626e-01  2.73886865e+00 -9.87400142e+01]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  1.00000000e+00]]\n",
      "[[ 2.74999725e+00 -2.74999817e-03  2.74999954e-03 -7.79999220e+01]\n",
      " [ 2.98603540e-03  2.73886840e+00 -2.47165887e-01 -8.25373961e+01]\n",
      " [-2.49170222e-03  2.47168626e-01  2.73886865e+00 -9.87400142e+01]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  1.00000000e+00]]\n",
      "[[ 2.74999714e+00 -2.74999812e-03  2.74999952e-03 -7.79999237e+01]\n",
      " [ 2.98603531e-03  2.73886847e+00 -2.47165889e-01 -8.25373993e+01]\n",
      " [-2.49170233e-03  2.47168630e-01  2.73886871e+00 -9.87400131e+01]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  1.00000000e+00]]\n"
     ]
    }
   ],
   "source": [
    "print(new.affine[:, 3] - nii.affine[:, 3])\n",
    "\n",
    "print(nb.as_closest_canonical(new).affine)\n",
    "print(new.affine)\n",
    "print(new.header.get_best_affine())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-2.74999725e+00  2.74999817e-03 -2.74999954e-03  7.79999220e+01]\n",
      " [-2.98603540e-03 -2.73886840e+00  2.47165887e-01  8.25373961e+01]\n",
      " [-2.49170222e-03  2.47168626e-01  2.73886865e+00 -9.87400142e+01]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  1.00000000e+00]]\n"
     ]
    }
   ],
   "source": [
    "afni_dicom = np.diag([-1, -1, 1, 1]).dot(new.affine)\n",
    "print(afni_dicom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  2.75   0.     0.   -78.  ]\n",
      " [  0.     2.75   0.   -91.  ]\n",
      " [  0.     0.     2.75 -91.  ]\n",
      " [  0.     0.     0.     1.  ]]\n",
      "[[ 2.74999725e+00 -2.74999817e-03  2.74999954e-03 -7.79999220e+01]\n",
      " [ 2.98603540e-03  2.73886840e+00 -2.47165887e-01 -8.25373961e+01]\n",
      " [-2.49170222e-03  2.47168626e-01  2.73886865e+00 -9.87400142e+01]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  1.00000000e+00]]\n",
      "[[ -2.75        -0.          -0.          77.35987854]\n",
      " [ -0.          -2.75        -0.          98.75680542]\n",
      " [  0.           0.           2.75       -99.2093277 ]\n",
      " [  0.           0.           0.           1.        ]]\n"
     ]
    }
   ],
   "source": [
    "deob = nb.load('deob.nii.gz')\n",
    "\n",
    "print(nii.affine)\n",
    "print(new.affine)\n",
    "print(deob.affine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-77.99992205 -82.53739614 -98.74001417   1.        ]\n",
      "[ 77.16183513 107.35783795 -90.00163308   1.        ]\n"
     ]
    }
   ],
   "source": [
    "print(new.affine[:, -1])\n",
    "print(R.dot(deob.affine[:, -1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 77.6875  91.4375 -76.3125]\n",
      "[ 1.6875  0.9375 14.6875]\n"
     ]
    }
   ],
   "source": [
    "center = lps.affine[:3, :3].dot(np.array(lps.shape) - 0.5) * -0.5\n",
    "origin = lps.affine[:3, -1]\n",
    "\n",
    "print(center)\n",
    "print(center - origin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.70124831 -0.38455306 14.71078148]\n"
     ]
    }
   ],
   "source": [
    "center = lpsnew.affine[:3, :3].dot(np.array(lpsnew.shape) - 0.5) * -0.5\n",
    "origin = lpsnew.affine[:3, -1]\n",
    "print(center - origin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 9.99999000e-01  1.08583106e-03 -9.06073535e-04  1.35987854e+00]\n",
      " [-9.99999333e-04  9.95952145e-01  8.98795002e-02  8.25680542e+00]\n",
      " [ 9.99999833e-04 -8.98785043e-02  9.95952235e-01 -8.20932770e+00]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  1.00000000e+00]]\n",
      "[[ 9.99999000e-01  1.08583106e-03 -9.06073535e-04  0.00000000e+00]\n",
      " [-9.99999333e-04  9.95952145e-01  8.98795002e-02  0.00000000e+00]\n",
      " [ 9.99999833e-04 -8.98785043e-02  9.95952235e-01  0.00000000e+00]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  1.00000000e+00]]\n"
     ]
    }
   ],
   "source": [
    "R_afni = deob.affine.dot(np.linalg.inv(lpsnew.affine))\n",
    "print(R_afni)\n",
    "print(np.linalg.inv(R))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<?xml version='1.0' ?>\n",
      "<AFNI_attributes\n",
      "  self_idcode=\"XYZ_OAi4s_U-_iTkOb4zdPzWjg\"\n",
      "  NIfTI_nums=\"58,72,62,1,1,16\"\n",
      "  ni_form=\"ni_group\" >\n",
      "<AFNI_atr\n",
      "  ni_type=\"String\"\n",
      "  ni_dimen=\"1\"\n",
      "  atr_name=\"HISTORY_NOTE\" >\n",
      " \"[oesteban@dendrite: Tue Nov 12 08:52:11 2019] 3dWarp -deoblique -prefix deoblps.nii.gz -verb someones_anatomy_lpsrot.nii.gz\"\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"float\"\n",
      "  ni_dimen=\"12\"\n",
      "  atr_name=\"WARPDRIVE_MATVEC_FOR_000000\" >\n",
      " 0.999999\n",
      " 0.001085831\n",
      " 0.0009060735\n",
      " 0.1815765\n",
      " -0.0009999992\n",
      " 0.9959521\n",
      " -0.0898795\n",
      " -7.895141\n",
      " -0.0009999998\n",
      " 0.0898785\n",
      " 0.9959521\n",
      " 8.433576\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"float\"\n",
      "  ni_dimen=\"12\"\n",
      "  atr_name=\"WARPDRIVE_MATVEC_INV_000000\" >\n",
      " 0.999999\n",
      " -0.0009999993\n",
      " -0.0009999998\n",
      " -0.1810379\n",
      " 0.001085831\n",
      " 0.9959522\n",
      " 0.08987851\n",
      " 7.104988\n",
      " 0.0009060736\n",
      " -0.08987951\n",
      " 0.9959523\n",
      " -9.109215\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"String\"\n",
      "  ni_dimen=\"1\"\n",
      "  atr_name=\"TYPESTRING\" >\n",
      " \"3DIM_HEAD_ANAT\"\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"String\"\n",
      "  ni_dimen=\"1\"\n",
      "  atr_name=\"IDCODE_STRING\" >\n",
      " \"XYZ_OAi4s_U-_iTkOb4zdPzWjg\"\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"String\"\n",
      "  ni_dimen=\"1\"\n",
      "  atr_name=\"IDCODE_DATE\" >\n",
      " \"Tue Nov 12 08:52:11 2019\"\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"int\"\n",
      "  ni_dimen=\"8\"\n",
      "  atr_name=\"SCENE_DATA\" >\n",
      " 0\n",
      " 11\n",
      " 0\n",
      " -999\n",
      " -999\n",
      " -999\n",
      " -999\n",
      " -999\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"String\"\n",
      "  ni_dimen=\"1\"\n",
      "  atr_name=\"LABEL_1\" >\n",
      " \"zyxt\"\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"String\"\n",
      "  ni_dimen=\"1\"\n",
      "  atr_name=\"LABEL_2\" >\n",
      " \"zyxt\"\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"String\"\n",
      "  ni_dimen=\"1\"\n",
      "  atr_name=\"DATASET_NAME\" >\n",
      " \"zyxt\"\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"int\"\n",
      "  ni_dimen=\"3\"\n",
      "  atr_name=\"ORIENT_SPECIFIC\" >\n",
      " 0\n",
      " 3\n",
      " 4\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"float\"\n",
      "  ni_dimen=\"3\"\n",
      "  atr_name=\"ORIGIN\" >\n",
      " -77.35988\n",
      " -98.75681\n",
      " -99.20933\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"float\"\n",
      "  ni_dimen=\"3\"\n",
      "  atr_name=\"DELTA\" >\n",
      " 2.75\n",
      " 2.75\n",
      " 2.75\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"float\"\n",
      "  ni_dimen=\"12\"\n",
      "  atr_name=\"IJK_TO_DICOM\" >\n",
      " 2.75\n",
      " 0\n",
      " 0\n",
      " -77.35988\n",
      " 0\n",
      " 2.75\n",
      " 0\n",
      " -98.75681\n",
      " 0\n",
      " 0\n",
      " 2.75\n",
      " -99.20933\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"float\"\n",
      "  ni_dimen=\"12\"\n",
      "  atr_name=\"IJK_TO_DICOM_REAL\" >\n",
      " 2.75\n",
      " 0\n",
      " 0\n",
      " -77.35988\n",
      " 0\n",
      " 2.75\n",
      " 0\n",
      " -98.75681\n",
      " 0\n",
      " 0\n",
      " 2.75\n",
      " -99.20933\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"float\"\n",
      "  ni_dimen=\"2\"\n",
      "  atr_name=\"BRICK_STATS\" >\n",
      " 0\n",
      " 95.66844\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"int\"\n",
      "  ni_dimen=\"8\"\n",
      "  atr_name=\"DATASET_RANK\" >\n",
      " 3\n",
      " 1\n",
      " 0\n",
      " 0\n",
      " 0\n",
      " 0\n",
      " 0\n",
      " 0\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"int\"\n",
      "  ni_dimen=\"5\"\n",
      "  atr_name=\"DATASET_DIMENSIONS\" >\n",
      " 58\n",
      " 72\n",
      " 62\n",
      " 0\n",
      " 0\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"int\"\n",
      "  ni_dimen=\"1\"\n",
      "  atr_name=\"BRICK_TYPES\" >\n",
      " 3\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"float\"\n",
      "  ni_dimen=\"1\"\n",
      "  atr_name=\"BRICK_FLOAT_FACS\" >\n",
      " 0\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"String\"\n",
      "  ni_dimen=\"1\"\n",
      "  atr_name=\"TEMPLATE_SPACE\" >\n",
      " \"ORIG\"\n",
      "</AFNI_atr>\n",
      "\n",
      "<AFNI_atr\n",
      "  ni_type=\"int\"\n",
      "  ni_dimen=\"1\"\n",
      "  atr_name=\"INT_CMAP\" >\n",
      " 0\n",
      "</AFNI_atr>\n",
      "\n",
      "</AFNI_attributes>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(deob.header.extensions[0].get_content().decode())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reverse-engineering matrix compositions of oblique datasets\n",
    "Calculating AFNI's affines based on the oblique image information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-9.99999000e-01 -1.08583106e-03  9.06073535e-04 -6.40121460e-01]\n",
      " [ 9.99999333e-04 -9.95952145e-01 -8.98795002e-02  7.75680542e+00]\n",
      " [ 9.99999833e-04 -8.98785043e-02  9.95952235e-01 -8.20932770e+00]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  1.00000000e+00]]\n",
      "[[ 9.99999000e-01  1.08583106e-03 -9.06073535e-04  0.00000000e+00]\n",
      " [-9.99999333e-04  9.95952145e-01  8.98795002e-02  0.00000000e+00]\n",
      " [ 9.99999833e-04 -8.98785043e-02  9.95952235e-01  0.00000000e+00]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  1.00000000e+00]]\n"
     ]
    }
   ],
   "source": [
    "R_afni = nb.load('deob.nii.gz').affine.dot(np.linalg.inv(new.affine))\n",
    "print(R_afni)\n",
    "print(np.linalg.inv(R))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-9.99999000e-01  9.99999333e-04  9.99999833e-04 -6.39668294e-01]\n",
      " [-1.08583106e-03 -9.95952145e-01 -8.98785043e-02  6.98686984e+00]\n",
      " [ 9.06073535e-04 -8.98795002e-02  9.95952235e-01  8.87385606e+00]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  1.00000000e+00]]\n",
      "[[ 9.99999000e-01 -9.99999333e-04  9.99999833e-04  0.00000000e+00]\n",
      " [ 1.08583106e-03  9.95952145e-01 -8.98785043e-02  0.00000000e+00]\n",
      " [-9.06073535e-04  8.98795002e-02  9.95952235e-01  0.00000000e+00]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  1.00000000e+00]]\n"
     ]
    }
   ],
   "source": [
    "R_afni = new.affine.dot(np.linalg.inv(nb.load('deob.nii.gz').affine))\n",
    "print(R_afni)\n",
    "print(R)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visually test whether we correctly apply dual transforms with both tools\n",
    "Load images generated with AFNI and with NiTransforms to check whether NiTransforms is operating as expected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<OrthoSlicer3D: deob-for2.nii.gz (58, 72, 62)>"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#!3dWarp -deoblique -prefix deob.nii.gz -NN someones_anatomy_rot.nii.gz\n",
    "#!3dAllineate -base someones_anatomy.nii.gz -input someones_anatomy.nii.gz -master deob.nii.gz -1Dmatrix_apply for2.afni -prefix deob-for2.nii.gz -final NN\n",
    "nb.load('deob.nii.gz').orthoview()\n",
    "# nb.load('moved-for-T-inv.nii.gz').orthoview()\n",
    "# nb.load('moved-inv-T-for.nii.gz').orthoview()\n",
    "# nb.load('deob-for.nii.gz').orthoview()\n",
    "nb.load('deob-for2.nii.gz').orthoview()\n",
    "# nb.load('moved-affine-oblique-inv-T-for.nii.gz').orthoview()\n",
    "# nb.load('moved-affine-oblique-inv-T-for-master.nii.gz').orthoview()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If both derivatives are resampled in the same space, the following figure should be almost (or fully) blank."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<OrthoSlicer3D: (58, 72, 62)>"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVxA4AUE3sAADVZq219QZ20My8m+T1JFeS/FuSF9Za/7PtKgD4/5zscFK31lp7a61nkvwmyZe3HgQA9yJ2OAuvJvnQ1iMA4F7EDqcyM1eSfDq3L2kBwIVzZesB7KzHZmb/4ONXk3xzyzEAcBg3KHMiM3NzrXV16x0AcD8uYwEA1cQOAFDNZSwAoJqTHQCgmtgBAKqJHQCgmtgBAKqJHQCgmtgBAKqJHQCgmtgBAKr9H5zsm4ec0kZKAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "deob = nb.load('deob.nii.gz')\n",
    "nb.Nifti1Image(np.abs(deob.get_fdata() - nb.load('deob-for2.nii.gz').get_fdata()), deob.affine, deob.header).orthoview()"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}