NA Vs A.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "QRY_PATH=\"/home/global/anaconda2/tract_querier/queries/freesurfer_queries.qry\"\n",
    "#SUBJ_ID=\"T02S01\"\n",
    "SUBJ_ID=\"p_DG21_a\"\n",
    "NAS=\"tupac\"\n",
    "#protocole=\"healthy_volunteers\"\n",
    "protocole=\"alexcis\"\n",
    "DIR_PATH=\"/NAS/\"+NAS+\"/protocoles/\"+protocole\n",
    "FS_PATH=DIR_PATH+\"/FS53/\"\n",
    "DTI_PATH=FS_PATH+SUBJ_ID+\"/dti/\"\n",
    "TRACTO=\"whole_brain_6_1500000\"\n",
    "#SEG = DTI_PATH+\"raparc+aseg.nii\"\n",
    "SEG = DTI_PATH+\"raparc.a2009s+aseg.nii\"\n",
    "MESH = \"lh.white.ras\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nibabel import trackvis as tv\n",
    "from dipy.viz import fvtk\n",
    "from dipy.segment.metric import ResampleFeature\n",
    "from dipy.segment.metric import AveragePointwiseEuclideanMetric\n",
    "from dipy.segment.metric import CenterOfMassFeature\n",
    "from dipy.segment.metric import EuclideanMetric\n",
    "from dipy.segment.metric import IdentityFeature\n",
    "from dipy.segment.clustering import QuickBundles\n",
    "from dipy.io.image import load_nifti, save_nifti\n",
    "from dipy.tracking.streamline import set_number_of_points\n",
    "from dipy.tracking.utils import density_map\n",
    "from nibabel import freesurfer as fs\n",
    "from dipy.utils.optpkg import optional_package\n",
    "import dipy.io.vtk as io_vtk\n",
    "import dipy.viz.utils as ut_vtk\n",
    "from dipy.viz import window, actor, ui\n",
    "from dipy.tracking import utils\n",
    "import nipype.interfaces.mrtrix as mrt\n",
    "from dipy.tracking.streamline import select_random_set_of_streamlines\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import nibabel as nib\n",
    "import random\n",
    "from dipy.tracking.streamline import transform_streamlines\n",
    "from dipy.denoise.enhancement_kernel import EnhancementKernel\n",
    "from dipy.tracking.fbcmeasures import FBCMeasures\n",
    "from nibabel.streamlines import Field\n",
    "from dipy.io import read_bvals_bvecs\n",
    "from dipy.core.gradients import gradient_table\n",
    "from dipy.reconst.dti import TensorModel\n",
    "from dipy.viz import colormap\n",
    "from dipy.reconst.dti import fractional_anisotropy, color_fa, lower_triangular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The kernel already exists. Loading from /tmp/kernel_d33@1.00_d44@0.02_t@1.00_numverts100.npy\n",
      "<class 'nibabel.nifti1.Nifti1Image'>\n",
      "data shape (128, 128, 66)\n",
      "affine: \n",
      "[[  -2.            0.            0.          137.89001465]\n",
      " [   0.            2.            0.         -128.83609009]\n",
      " [   0.            0.            2.          -32.35711288]\n",
      " [   0.            0.            0.            1.        ]]\n",
      "metadata:\n",
      "<class 'nibabel.nifti1.Nifti1Header'> object, endian='<'\n",
      "sizeof_hdr      : 348\n",
      "data_type       : \n",
      "db_name         : \n",
      "extents         : 0\n",
      "session_error   : 0\n",
      "regular         : r\n",
      "dim_info        : 0\n",
      "dim             : [  3 128 128  66   1   1   1   1]\n",
      "intent_p1       : 0.0\n",
      "intent_p2       : 0.0\n",
      "intent_p3       : 0.0\n",
      "intent_code     : none\n",
      "datatype        : int32\n",
      "bitpix          : 32\n",
      "slice_start     : 0\n",
      "pixdim          : [-1.  2.  2.  2.  0.  0.  0.  0.]\n",
      "vox_offset      : 0.0\n",
      "scl_slope       : nan\n",
      "scl_inter       : nan\n",
      "slice_end       : 0\n",
      "slice_code      : unknown\n",
      "xyzt_units      : 10\n",
      "cal_max         : 0.0\n",
      "cal_min         : 0.0\n",
      "slice_duration  : 0.0\n",
      "toffset         : 0.0\n",
      "glmax           : 0\n",
      "glmin           : 0\n",
      "descrip         : spm - realigned\n",
      "aux_file        : \n",
      "qform_code      : aligned\n",
      "sform_code      : aligned\n",
      "quatern_b       : 0.0\n",
      "quatern_c       : 1.0\n",
      "quatern_d       : 0.0\n",
      "qoffset_x       : 137.890014648\n",
      "qoffset_y       : -128.836090088\n",
      "qoffset_z       : -32.3571128845\n",
      "srow_x          : [  -2.            0.            0.          137.89001465]\n",
      "srow_y          : [   0.            2.            0.         -128.83609009]\n",
      "srow_z          : [  0.           0.           2.         -32.35711288]\n",
      "intent_name     : \n",
      "magic           : n+1\n",
      "<class 'nibabel.nifti1.Nifti1Image'>\n",
      "data shape (128, 128, 66)\n",
      "affine: \n",
      "[[  -2.            0.            0.          137.89001465]\n",
      " [   0.            2.            0.         -128.83609009]\n",
      " [   0.            0.            2.          -32.35711288]\n",
      " [   0.            0.            0.            1.        ]]\n",
      "metadata:\n",
      "<class 'nibabel.nifti1.Nifti1Header'> object, endian='<'\n",
      "sizeof_hdr      : 348\n",
      "data_type       : \n",
      "db_name         : \n",
      "extents         : 0\n",
      "session_error   : 0\n",
      "regular         : r\n",
      "dim_info        : 0\n",
      "dim             : [  3 128 128  66   1   1   1   1]\n",
      "intent_p1       : 0.0\n",
      "intent_p2       : 0.0\n",
      "intent_p3       : 0.0\n",
      "intent_code     : none\n",
      "datatype        : float32\n",
      "bitpix          : 32\n",
      "slice_start     : 0\n",
      "pixdim          : [-1.  2.  2.  2.  1.  0.  0.  0.]\n",
      "vox_offset      : 0.0\n",
      "scl_slope       : nan\n",
      "scl_inter       : nan\n",
      "slice_end       : 0\n",
      "slice_code      : unknown\n",
      "xyzt_units      : 10\n",
      "cal_max         : 1.0\n",
      "cal_min         : 0.0\n",
      "slice_duration  : 0.0\n",
      "toffset         : 0.0\n",
      "glmax           : 0\n",
      "glmin           : 0\n",
      "descrip         : FSL5.0\n",
      "aux_file        : \n",
      "qform_code      : scanner\n",
      "sform_code      : scanner\n",
      "quatern_b       : 0.0\n",
      "quatern_c       : 1.0\n",
      "quatern_d       : 0.0\n",
      "qoffset_x       : 137.890014648\n",
      "qoffset_y       : -128.836090088\n",
      "qoffset_z       : -32.3571128845\n",
      "srow_x          : [  -2.            0.            0.          137.89001465]\n",
      "srow_y          : [   0.            2.            0.         -128.83609009]\n",
      "srow_z          : [  0.           0.           2.         -32.35711288]\n",
      "intent_name     : \n",
      "magic           : n+1\n",
      "(0.47324979391687483, 0.6479161435320323, 0.34229195441836624)\n",
      "('Nb. clusters:', 1)\n",
      "('Cluster sizes:', [2256])\n",
      "(0.3033616394229661, 0.9807392842549993, 0.413930663151993)\n",
      "('Nb. clusters:', 1)\n",
      "('Cluster sizes:', [2256])\n"
     ]
    }
   ],
   "source": [
    "D33 = 1.0\n",
    "D44 = 0.02\n",
    "t = 1\n",
    "k = EnhancementKernel(D33, D44, t)\n",
    "\n",
    "r = fvtk.ren()\n",
    "\n",
    "nii2 = nib.load(DTI_PATH+\"raparc.a2009s+aseg.nii\")\n",
    "raparc = fvtk.slicer(nii2.get_data())#,nii2.affine)\n",
    "print(nii2)\n",
    "raparc.display(60, None, None)\n",
    "#fvtk.add(r,raparc)\n",
    "\n",
    "data_colorfa = nib.load(DTI_PATH+\"dti_finalcor_FA.nii.gz\")\n",
    "print(data_colorfa)\n",
    "dti_fa = fvtk.slicer(data_colorfa.get_data())#,data_colorfa.affine)\n",
    "dti_fa.display(64, None, None)\n",
    "#fvtk.add(r,dti_fa)\n",
    "\n",
    "#Lecture de la surface\n",
    "vtk, have_vtk, setup_module = optional_package('vtk')\n",
    "white_mesh=fs.read_geometry(FS_PATH+SUBJ_ID+\"/surf/\"+MESH)\n",
    "my_polydata = vtk.vtkPolyData()\n",
    "test=actor.apply_affine(np.linalg.inv(nii2.affine), white_mesh[0]) \n",
    "\n",
    "ut_vtk.set_polydata_vertices(my_polydata, test)\n",
    "ut_vtk.set_polydata_triangles(my_polydata, white_mesh[1])\n",
    "\n",
    "#transform = vtk.vtkTransform()\n",
    "#transform.SetMatrix(np.reshape(data_colorfa.affine,16).tolist())\n",
    "#print(transform)\n",
    "#print(data_colorfa.affine)\n",
    "#print(my_polydata)\n",
    "\n",
    "#transformFilter=vtk.vtkTransformPolyDataFilter()\n",
    "#transformFilter.SetTransform(transform)\n",
    "#transformFilter.SetInputConnection(my_polydata)\n",
    "#transformFilter.Update()\n",
    "\n",
    "stream_actor = ut_vtk.get_actor_from_polydata(my_polydata)\n",
    "stream_actor.GetProperty().SetOpacity(.6) \n",
    "#r.add(stream_actor)\n",
    "#fvtk.add(r, vol_actor)\n",
    "\n",
    "li = ['cc_1','cc_3']#,'cc_4'#,'cc_2']#,'cc_7'] \n",
    "for BUNDLE in li:\n",
    "    color=(random.uniform(0, 1),random.uniform(0, 1),random.uniform(0, 1))\n",
    "    print(color)\n",
    "    trkS = nib.streamlines.load(FS_PATH+SUBJ_ID+\"/bundle_\"+BUNDLE+\".trk\")\n",
    "    #Track ouverture\n",
    "    #streams, hdr = tv.read(tractogram)    \n",
    "    #input_spacetmp=np.array(tv.aff_from_hdr(hdr));\n",
    "    #input_space=np.array([[1.,0.,0.,-input_spacetmp[0,3]],[0.,1.,0.,input_spacetmp[1,3]],[0.,0.,1.,input_spacetmp[2,3]],[0.,0.,0.,1.]])\n",
    "    \n",
    "    #tracto = [i[0] for i in streams]    \n",
    "    #plot_tracto = select_random_set_of_streamlines(tracto, 1000)\n",
    "    \n",
    "    #plot_tracto=list(utils.move_streamlines(plot_tracto,input_space))\n",
    "    #fvtk.add(r, fvtk.streamtube(plot_tracto,color,linewidth=0.1,opacity=0.1));\n",
    "    #fvtk.add(r, fvtk.streamtube(tracto,color,linewidth=0.1,opacity=0.5));\n",
    "\n",
    "    #Track ouverture & Affichage    \n",
    "    trk = nib.streamlines.load(FS_PATH+SUBJ_ID+\"/bundle_\"+BUNDLE+\"_clean.trk\")\n",
    "    #trk.header.update(trkS.header)\n",
    "    #trk.header[Field.VOXEL_TO_RASMM]=trkS.header[Field.VOXEL_TO_RASMM]\n",
    "    #trk.header[Field.VOXEL_SIZES]=trkS.header[Field.VOXEL_SIZES]\n",
    "    #trk.header[Field.ORIGIN]=trkS.header[Field.ORIGIN]\n",
    "    bundle_native3 = transform_streamlines(trkS.tractogram.streamlines,np.linalg.inv(data_colorfa.affine))\n",
    "    #fvtk.add(r,actor.line(bundle_native3,fvtk.colors.white,linewidth=0.1,opacity=0.2))\n",
    "    \n",
    "    #bundle_native = transform_streamlines(trk.tractogram.streamlines,np.linalg.inv(data_colorfa.affine))\n",
    "    #bundle_native2 = transform_streamlines(bundle_native,np.linalg.inv(data_colorfa.affine))    \n",
    "    #fvtk.add(r,actor.line(bundle_native2,color,linewidth=0.2,opacity=0.5))    \n",
    "    \n",
    "    fbc = FBCMeasures(bundle_native3, k)\n",
    "    fbc_sl_orig2, clrs_orig2, rfbc_orig2 = fbc.get_points_rfbc_thresholded(0, emphasis=0.01)\n",
    "    fbc_sl_thres2, clrs_thres2, rfbc_thres2 = fbc.get_points_rfbc_thresholded(0.125, emphasis=0.01)\n",
    "    fvtk.add(r, actor.line(fbc_sl_orig2, clrs_orig2, linewidth=0.2))\n",
    "    fvtk.add(r, actor.line(fbc_sl_thres2, clrs_thres2, linewidth=0.2))\n",
    "   \n",
    "    #Track cluster\n",
    "    feature = ResampleFeature(nb_points=240)\n",
    "    metric = AveragePointwiseEuclideanMetric(feature=feature)  # a.k.a. MDF    \n",
    "    qb = QuickBundles(threshold=100, metric=metric)\n",
    "    qb.max_nb_clusters = 1\n",
    "    cluster = qb.cluster(bundle_native2) \n",
    "    print(\"Nb. clusters:\", len(cluster))\n",
    "    print(\"Cluster sizes:\", map(len, cluster))\n",
    "\n",
    "    #clus=list(utils.move_streamlines(cluster.centroids,input_space))\n",
    "    clus=list(cluster.centroids)\n",
    "    #fvtk.add(r, fvtk.streamtube(clus,data_colorfa.get_data(),linewidth=1));    \n",
    "     \n",
    "a=fvtk.axes()\n",
    "fvtk.add(r,a)\n",
    "fvtk.add(r, actor.line(fbc_sl_orig, clrs_orig, linewidth=0.2))\n",
    "fvtk.add(r, actor.line(fbc_sl_thres, clrs_thres, linewidth=0.2))\n",
    "\n",
    "fvtk.record(r, out_path=FS_PATH+SUBJ_ID+\"/dti/clean.png\", size=(600,600))\n",
    "#fvtk.show(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'origin': array([ 0.,  0.,  0.], dtype=float32), 'reserved': '', 'voxel_sizes': array([ 2.,  2.,  2.], dtype=float32), 'property_name': array(['', '', '', '', '', '', '', '', '', ''],\n",
      "      dtype='|S20'), 'invert_y': '', 'invert_x': '', 'invert_z': '', 'dimensions': array([128, 128,  66], dtype=int16), 'swap_yz': '', 'magic_number': 'TRACK', 'nb_scalars_per_point': 0, 'version': 2, 'endianness': '<', 'hdr_size': 1000, 'pad1': '', 'voxel_order': 'LAS', 'pad2': '', '_offset_data': 1000, 'nb_properties_per_streamline': 0, 'voxel_to_rasmm': array([[  -2.        ,    0.        ,    0.        ,  137.89001465],\n",
      "       [   0.        ,    2.        ,    0.        , -128.83609009],\n",
      "       [   0.        ,    0.        ,    2.        ,  -32.35711288],\n",
      "       [   0.        ,    0.        ,    0.        ,    1.        ]], dtype=float32), 'swap_zx': '', 'swap_xy': '', 'scalar_name': array(['', '', '', '', '', '', '', '', '', ''],\n",
      "      dtype='|S20'), 'image_orientation_patient': array([ 1.,  0.,  0.,  0., -1.,  0.], dtype=float32), 'nb_streamlines': 1371}\n",
      "{'origin': array([ 0.,  0.,  0.], dtype=float32), 'reserved': '', 'voxel_sizes': array([ 1.,  1.,  1.], dtype=float32), 'property_name': array(['', '', '', '', '', '', '', '', '', ''],\n",
      "      dtype='|S20'), 'invert_y': '', 'invert_x': '', 'invert_z': '', 'dimensions': array([1, 1, 1], dtype=int16), 'swap_yz': '', 'magic_number': 'TRACK', 'nb_scalars_per_point': 0, 'version': 2, 'endianness': '<', 'hdr_size': 1000, 'pad1': '', 'voxel_order': 'RAS', 'pad2': '', '_offset_data': 1000, 'nb_properties_per_streamline': 0, 'voxel_to_rasmm': array([[ 1.,  0.,  0.,  0.],\n",
      "       [ 0.,  1.,  0.,  0.],\n",
      "       [ 0.,  0.,  1.,  0.],\n",
      "       [ 0.,  0.,  0.,  1.]], dtype=float32), 'swap_zx': '', 'swap_xy': '', 'scalar_name': array(['', '', '', '', '', '', '', '', '', ''],\n",
      "      dtype='|S20'), 'image_orientation_patient': array([ 0.,  0.,  0.,  0.,  0.,  0.], dtype=float32), 'nb_streamlines': 638}\n",
      "[[ 12.76728821  40.67550659  33.31148911]\n",
      " [ 12.62088013  40.59831238  33.42376328]\n",
      " [ 12.48191833  40.48794556  33.51600266]\n",
      " [ 12.34153748  40.38980103  33.61925125]\n",
      " [ 12.19544983  40.32208252  33.73788834]\n",
      " [ 12.07102203  40.2244873   33.86032486]\n",
      " [ 11.96191406  40.10221863  33.97497177]\n",
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      " [ 11.68389893  39.86984253  34.14290237]\n",
      " [ 11.55233002  39.74169922  34.22208786]\n",
      " [ 11.41609192  39.61941528  34.30263138]\n",
      " [ 11.29905701  39.47026062  34.36629868]\n",
      " [ 11.17147827  39.32583618  34.41981888]\n",
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      " [ 109.75666809  -79.44772339   50.72953415]\n",
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      " [ 110.33256531  -78.89852905   50.788311  ]\n",
      " [ 110.65751648  -78.68167114   50.87417221]\n",
      " [ 110.9964447   -78.4765625    50.92962265]\n",
      " [ 111.3507843   -78.30096436   50.98960495]\n",
      " [ 111.68554688  -78.12036133   51.11338425]\n",
      " [ 112.03407288  -77.93481445   51.17739487]\n",
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      " [ 112.75260925  -77.58770752   51.12777328]\n",
      " [ 113.09786987  -77.41436768   51.02404404]\n",
      " [ 113.40007019  -77.18060303   50.90559006]\n",
      " [ 113.68301392  -76.91238403   50.81623459]\n",
      " [ 113.96273804  -76.63418579   50.75007248]\n",
      " [ 114.27508545  -76.40823364   50.64339828]\n",
      " [ 114.56213379  -76.19500732   50.46412277]\n",
      " [ 114.84138489  -75.95657349   50.3054924 ]\n",
      " [ 115.09863281  -75.68252563   50.16865158]\n",
      " [ 115.36430359  -75.43121338   50.00660324]\n",
      " [ 115.63134766  -75.17388916   49.85673141]\n",
      " [ 115.92247009  -74.92962646   49.73182297]\n",
      " [ 116.19949341  -74.6789856    49.58884811]\n",
      " [ 116.50241089  -74.45425415   49.45569992]\n",
      " [ 116.82183838  -74.2492981    49.32940292]\n",
      " [ 117.10253906  -74.025177     49.15339279]\n",
      " [ 117.3526001   -73.80310059   48.93394089]\n",
      " [ 117.64453125  -73.59994507   48.75089645]\n",
      " [ 117.92666626  -73.35778809   48.60343552]\n",
      " [ 118.16162109  -73.10180664   48.40525436]\n",
      " [ 118.43688965  -72.84933472   48.26215744]\n",
      " [ 118.71417236  -72.58712769   48.14229965]\n",
      " [ 118.9680481   -72.30905151   48.0073204 ]\n",
      " [ 119.20025635  -72.02844238   47.84196091]\n",
      " [ 119.43252563  -71.7409668    47.68891525]\n",
      " [ 119.65316772  -71.43109131   47.56527328]\n",
      " [ 119.89007568  -71.16015625   47.39071274]\n",
      " [ 120.10327148  -70.84347534   47.27137375]\n",
      " [ 120.35046387  -70.54873657   47.16169357]\n",
      " [ 120.58340454  -70.24514771   47.04517746]\n",
      " [ 120.81695557  -69.92819214   46.97457504]\n",
      " [ 121.027771    -69.59707642   46.89762497]\n",
      " [ 121.2003479   -69.23706055   46.87269211]\n",
      " [ 121.3911438   -68.89093018   46.81113815]\n",
      " [ 121.55004883  -68.52490234   46.78355026]\n",
      " [ 121.74569702  -68.18359375   46.7112236 ]\n",
      " [ 121.96472168  -67.87750244   46.57584763]\n",
      " [ 122.17349243  -67.56896973   46.43014145]\n",
      " [ 122.41622925  -67.31332397   46.24110031]\n",
      " [ 122.60305786  -67.04708862   46.00828171]\n",
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      " [ 122.95162964  -66.5899353    45.45643234]\n",
      " [ 123.09487915  -66.28887939   45.23540878]\n",
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      " [ 123.39935303  -65.61965942   44.92507553]\n",
      " [ 123.54702759  -65.31100464   44.71787643]\n",
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      " [ 123.86410522  -64.62020874   44.4794426 ]\n",
      " [ 124.08166504  -64.28625488   44.44564438]\n",
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      " [ 124.49591064  -63.6111145    44.33607101]\n",
      " [ 124.74475098  -63.30923462   44.25259018]\n",
      " [ 124.94763184  -62.96850586   44.20022202]\n",
      " [ 125.19326782  -62.65365601   44.17751694]\n",
      " [ 125.38833618  -62.31097412   44.11027145]\n",
      " [ 125.61306763  -61.99050903   44.02778244]\n",
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      " [ 126.59707642  -60.32357788   43.55750656]\n",
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      " [ 127.17868042  -59.34713745   43.18174362]\n",
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      " [ 127.7366333   -57.88763428   42.86486435]\n",
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      " [ 127.99783325  -57.14941406   42.70911789]\n",
      " [ 128.14822388  -56.77914429   42.69247055]\n",
      " [ 128.32058716  -56.41848755   42.70701218]\n",
      " [ 128.44952393  -56.04180908   42.66845322]\n",
      " [ 128.50662231  -55.64846802   42.62336349]\n",
      " [ 128.52960205  -55.2494812    42.60645676]\n",
      " [ 128.48147583  -54.85238647   42.6056633 ]\n",
      " [ 128.41638184  -54.45779419   42.59891891]\n",
      " [ 128.34796143  -54.06637573   42.55311203]\n",
      " [ 128.21829224  -53.69631958   42.47404099]\n",
      " [ 128.08331299  -53.33535767   42.36680222]\n",
      " [ 128.0116272   -52.94934082   42.29034042]\n",
      " [ 127.92584229  -52.5586853    42.28350449]\n",
      " [ 127.8326416   -52.16976929   42.28940964]\n",
      " [ 127.74969482  -51.78292847   42.34858322]\n",
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      " [ 127.52410889  -51.03549194   42.51090622]\n",
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      " [ 127.34362793  -49.46517944   42.59711838]\n",
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      " [ 127.30123901  -44.15084839   43.94753647]\n",
      " [ 127.31860352  -43.75494385   44.00179672]\n",
      " [ 127.35913086  -43.35797119   44.02959824]\n",
      " [ 127.39849854  -42.96072388   44.05521774]\n",
      " [ 127.412323    -42.56256104   44.01966476]\n",
      " [ 127.44525146  -42.16452026   44.04151535]\n",
      " [ 127.50769043  -41.76953125   44.05143356]\n",
      " [ 127.54504395  -41.37261963   44.08399582]\n",
      " [ 127.52560425  -40.97506714   44.1235466 ]\n",
      " [ 127.5171814   -40.57562256   44.14319992]\n",
      " [ 127.45108032  -40.18148804   44.15993881]\n",
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      " [ 127.29608154  -39.40774536   44.2782402 ]\n",
      " [ 127.15377808  -39.04522705   44.36945724]\n",
      " [ 127.00939941  -38.68215942   44.45508957]\n",
      " [ 126.83981323  -38.32626343   44.52282333]\n",
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      " [ 126.50836182  -37.5987854    44.53580856]\n",
      " [ 126.30230713  -37.25769043   44.5013237 ]\n",
      " [ 126.15228271  -36.89065552   44.44843674]\n",
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      " [ 125.94692993  -36.1366272    44.28275681]\n",
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      " [ 125.66226196  -35.43380737   44.03750229]\n",
      " [ 125.49703979  -35.08135986   43.94535446]\n",
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      " [ 124.19351196  -32.63796997   43.64948654]\n",
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      " [ 123.71405029  -32.01275635   43.53504562]\n",
      " [ 123.46984863  -31.70266724   43.47013474]\n",
      " [ 123.26342773  -31.36209106   43.43278122]\n",
      " [ 123.07723999  -31.02484131   43.3251152 ]\n",
      " [ 122.87515259  -30.70324707   43.19965744]\n",
      " [ 122.66976929  -30.39657593   43.04545212]\n",
      " [ 122.44750977  -30.13528442   42.83970261]\n",
      " [ 122.1930542   -29.87435913   42.67489243]\n",
      " [ 121.89590454  -29.65164185   42.52619553]\n",
      " [ 121.58908081  -29.41760254   42.4209404 ]\n",
      " [ 121.25701904  -29.22229004   42.31328964]\n",
      " [ 120.97268677  -28.98052979   42.16944504]\n",
      " [ 120.66372681  -28.73873901   42.09138107]\n",
      " [ 120.3553772   -28.49978638   42.00297165]\n",
      " [ 120.03689575  -28.271698     41.92202377]\n",
      " [ 119.74066162  -28.05126953   41.76826096]\n",
      " [ 119.41860962  -27.8800354    41.60401535]\n",
      " [ 119.08132935  -27.70437622   41.48006821]\n",
      " [ 118.76660156  -27.51934814   41.31663132]\n",
      " [ 118.45150757  -27.37002563   41.12061691]\n",
      " [ 118.13082886  -27.24765015   40.91520309]\n",
      " [ 117.84542847  -27.08047485   40.69027328]\n",
      " [ 117.59521484  -26.88259888   40.44895554]\n",
      " [ 117.33557129  -26.67947388   40.22240829]\n",
      " [ 117.10113525  -26.42562866   40.02087021]\n",
      " [ 116.8913269   -26.13137817   39.8494072 ]\n",
      " [ 116.64877319  -25.86590576   39.6742363 ]\n",
      " [ 116.38104248  -25.6164856    39.5126152 ]]\n"
     ]
    }
   ],
   "source": [
    "tractogram=FS_PATH+SUBJ_ID+\"/bundle_cc_1.trk\"\n",
    "trkS = nib.streamlines.load(tractogram)\n",
    "tractogram=FS_PATH+SUBJ_ID+\"/bundle_cc_1_clean.trk\"\n",
    "trkD = nib.streamlines.load(tractogram)\n",
    "#trkD.header[Field.VOXEL_TO_RASMM]=trkS.header[Field.VOXEL_TO_RASMM]\n",
    "#trkD.header.update(trkS.header)\n",
    "print(trkS.header)\n",
    "print(trkD.header)\n",
    "print(trkS.tractogram.streamlines[0])\n",
    "print(trkD.tractogram.streamlines[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linda Marrakchi-Kacem (Thèse 2011)\n",
    "Connectivité anatomique des noyaux gris centraux : développements méthodologiques et application aux troubles moteurs"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "http://nipy.org/dipy/examples_built/reconst_dti.html\n",
    "http://pe.sfrnet.org/Data/ModuleConsultationPoster/pdf/2008/1/17944e9e-c986-4f72-b01e-89333da7d343.pdf\n",
    "(présentation interessante pour la sequenocgram de la diff)\n",
    "trace= coefficient de diffusion \"moyen\" (distance moyenne des molécules d'eau) ADC = (D1+D2+D3)/3\n",
    "FA haute signifie diffusivité dans une direction privilégié \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(128, 128, 66)\n",
      "vtkImageActor (0x4113000)\n",
      "  Debug: Off\n",
      "  Modified Time: 11189\n",
      "  Reference Count: 1\n",
      "  Registered Events: (none)\n",
      "  Dragable: On\n",
      "  Pickable: On\n",
      "  AllocatedRenderTime: 10\n",
      "  EstimatedRenderTime: 0\n",
      "  NumberOfConsumers: 0\n",
      "  RenderTimeMultiplier: 1\n",
      "  Visibility: On\n",
      "  PropertyKeys: none.\n",
      "  useBounds: 1\n",
      "  IsIdentity: true\n",
      "  Position: (0, 0, 0)\n",
      "  Orientation: (0, 0, 0)\n",
      "  Origin: (0, 0, 0)\n",
      "  Scale: (1, 1, 1)\n",
      "  Bounds: \n",
      "    Xmin,Xmax: (-125.658, 128.342)\n",
      "    Ymin,Ymax: (-29.9284, -29.9284)\n",
      "    Zmin,Zmax: (-58.1077, 71.8923)\n",
      "  UserTransform: (none)\n",
      "  UserMatrix: (none)\n",
      "  Property:\n",
      "    Debug: Off\n",
      "    Modified Time: 10358\n",
      "    Reference Count: 1\n",
      "    Registered Events: (none)\n",
      "    ColorWindow: 255\n",
      "    ColorLevel: 127.5\n",
      "    UseLookupTableScalarRange: Off\n",
      "    LookupTable: 0\n",
      "    Opacity: 1\n",
      "    Ambient: 1\n",
      "    Diffuse: 0\n",
      "    InterpolationType: Linear\n",
      "    LayerNumber: 0\n",
      "    Checkerboard: Off\n",
      "    CheckerboardSpacing: 10 10\n",
      "    CheckerboardOffset: 0 0\n",
      "    Backing: Off\n",
      "    BackingColor: 0 0 0\n",
      "  Mapper:\n",
      "    Debug: Off\n",
      "    Modified Time: 11188\n",
      "    Reference Count: 2\n",
      "    Registered Events: (none)\n",
      "    Executive: 0x41137f0\n",
      "    ErrorCode: Success\n",
      "    Information: 0x4114f90\n",
      "    AbortExecute: Off\n",
      "    Progress: 1\n",
      "    Progress Text: (None)\n",
      "    TimeToDraw: 0\n",
      "    ClippingPlanes: (none)\n",
      "    SlicePlane: 0x3eeba50\n",
      "    SliceAtFocalPoint: Off\n",
      "    SliceFacesCamera: Off\n",
      "    Border: On\n",
      "    Background: Off\n",
      "    NumberOfThreads: 8\n",
      "    Streaming: On\n",
      "    SliceNumber: 50\n",
      "    SliceNumberMinValue: 0\n",
      "    SliceNumberMaxValue: 127\n",
      "    Orientation: 1\n",
      "    Cropping: On\n",
      "    CroppingRegion: 0 127 50 50 0 65\n",
      "    Points: 0\n",
      "  Bounds: (-125.658, 128.342) (-29.9284) (-29.9284) (-58.1077) (71.8923)\n",
      "  Input: 0x412b780\n",
      "  Interpolate: On\n",
      "  Opacity: 1\n",
      "  DisplayExtent: (0, 127, 50, 50, 0, 65)\n",
      "\n",
      "\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'trk' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-cf79182d74b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0mtrk4\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstreamlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFS_PATH\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"p_AR28_na/bundle_cc_4.trk\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;31m#trk = nib.streamlines.load(tractogram)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrk2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrk3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'trk' is not defined"
     ]
    }
   ],
   "source": [
    "hue = [0.0, 0.0]  # red only\n",
    "saturation = [0.0, 1.0]  # white to red\n",
    "lut_cmap = actor.colormap_lookup_table(hue_range=hue,saturation_range=saturation)\n",
    "\n",
    "data, affine = load_nifti(FS_PATH+\"p_AR28_na/dti/dti.nii.gz\")\n",
    "bvals, bvecs = read_bvals_bvecs(FS_PATH+\"p_AR28_na/dti/dti.bval\",FS_PATH+\"p_AR28_na/dti/dti.bvec\")\n",
    "gtab = gradient_table(bvals, bvecs)\n",
    "tenmodel = TensorModel(gtab)\n",
    "tenfit = tenmodel.fit(data)\n",
    "color_fa = fvtk.slicer(tenfit.color_fa,affine)\n",
    "fa = fvtk.slicer(tenfit.fa,affine)\n",
    "fa.display(None, 50, None)\n",
    "print(fa.shape)\n",
    "print(fa)\n",
    "\n",
    "md = fvtk.slicer(tenfit.md,affine)\n",
    "rd = fvtk.slicer(tenfit.rd,affine)\n",
    "ad = fvtk.slicer(tenfit.ad,affine)\n",
    "\n",
    "#trk2 = nib.streamlines.load(FS_PATH+\"p_AR28_na/dti/whole_brain_6_1500000.tck\")\n",
    "trk3 = nib.streamlines.load(FS_PATH+\"p_AR28_na/bundle_thalamo_frontal.left.trk\")\n",
    "trk4 = nib.streamlines.load(FS_PATH+\"p_AR28_na/bundle_cc_4.trk\")\n",
    "#trk = nib.streamlines.load(tractogram)\n",
    "print(trk.header)\n",
    "print(trk2.header)\n",
    "print(trk3.header)\n",
    "print(trk4.header)\n",
    "#plot_tracto = select_random_set_of_streamlines(trk.tractogram.streamlines, 10000)\n",
    "\n",
    "r = fvtk.ren()\n",
    "fvtk.add(r,fa)\n",
    "\n",
    "\n",
    "vtk, have_vtk, setup_module = optional_package('vtk')\n",
    "white_mesh=fs.read_geometry(FS_PATH+\"p_AR28_na/surf/lh.white.ras\")\n",
    "my_polydata = vtk.vtkPolyData()\n",
    "#print(white_mesh.shape)\n",
    "#test=actor.apply_affine([[1,0,0,0][0,1,0,0][0,0,1,0][0,0,0,1]], white_mesh[0]) \n",
    "\n",
    "ut_vtk.set_polydata_vertices(my_polydata, white_mesh[0])\n",
    "ut_vtk.set_polydata_triangles(my_polydata, white_mesh[1])\n",
    "\n",
    "stream_actor = ut_vtk.get_actor_from_polydata(my_polydata)\n",
    "stream_actor.GetProperty().SetOpacity(0.7) \n",
    "r.add(stream_actor)\n",
    "\n",
    "nii2 = nib.load(FS_PATH+\"p_AR28_na/dti/t1_dti_ras.nii\")\n",
    "raparc2 = fvtk.slicer(nii2.get_data(),nii2.affine)\n",
    "raparc2.display(120, None, None)\n",
    "fvtk.add(r,raparc2)\n",
    "\n",
    "nii = nib.load(FS_PATH+\"p_AR28_na/dti/dti_finalcor_FA.nii.gz\")\n",
    "raparc = fvtk.slicer(nii.get_data(),nii.affine)\n",
    "raparc.display(60, None, None)\n",
    "fvtk.add(r,raparc)\n",
    "\n",
    "#test=actor.apply_affine(nii.affine,nii.get_data()) \n",
    "\n",
    "fvtk.add(r, actor.line(plot_tracto,raparc2.display(None, None, None),linewidth=0.1,opacity=0.3));\n",
    "#bundle_native = transform_streamlines(trk4.tractogram.streamlines,np.linalg.inv(nii.affine))\n",
    "\n",
    "#test = density_map(trk4.streamlines,fa.shape,affine=nii.affine)\n",
    "#print(test.shape)\n",
    "#print(type(test))\n",
    "#fvtk.add(r, actor.line(trk4.streamlines,linewidth=0.1,opacity=0.3,lookup_colormap=lut_cmap));\n",
    "#fvtk.add(r, actor.line(trk3.streamlines,fa.display(None, None, None),linewidth=0.1,opacity=0.3,lookup_colormap=lut_cmap));\n",
    "\n",
    "bar = actor.scalar_bar()\n",
    "#fvtk.add(r,bar)\n",
    "a=fvtk.axes()\n",
    "fvtk.add(r,a)\n",
    "\n",
    "#fvtk.add(r,fvtk.streamtube(trk3.streamlines,raparc.get,linewidth=1))\n",
    "\n",
    "#fvtk.add(r, actor.line(fbc_sl_orig, clrs_orig, linewidth=0.2))\n",
    "#fvtk.add(r, actor.line(fbc_sl_thres, clrs_thres, linewidth=0.2))\n",
    "fvtk.show(r)\n",
    "#fvtk.record(r, out_path=FS_PATH+\"p_AR28_na/dti/orient.png\", size=(600,600))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<dipy.reconst.dti.TensorFit object at 0x7f0ae12ffad0>\n",
      "(array([592033,  92785,  82417,  57552,  40175,  27619,  19204,  44022,\n",
      "        34208,  91329]), array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ]))\n",
      "(array([1081245,      51,      22,       4,       3,       3,       4,\n",
      "             5,       4,       3]), array([  1.00001000e-09,   1.12731393e+00,   2.25462787e+00,\n",
      "         3.38194180e+00,   4.50925573e+00,   5.63656967e+00,\n",
      "         6.76388360e+00,   7.89119753e+00,   9.01851146e+00,\n",
      "         1.01458254e+01,   1.12731393e+01]))\n",
      "(array([1081245,      51,      22,       4,       3,       3,       4,\n",
      "             5,       4,       3]), array([  1.00001000e-09,   1.12717168e+00,   2.25434335e+00,\n",
      "         3.38151503e+00,   4.50868671e+00,   5.63585838e+00,\n",
      "         6.76303006e+00,   7.89020174e+00,   9.01737341e+00,\n",
      "         1.01445451e+01,   1.12717168e+01]))\n",
      "(array([1081245,      51,      22,       4,       3,       3,       4,\n",
      "             5,       4,       3]), array([  1.00001000e-09,   1.12721910e+00,   2.25443819e+00,\n",
      "         3.38165729e+00,   4.50887638e+00,   5.63609548e+00,\n",
      "         6.76331457e+00,   7.89053367e+00,   9.01775276e+00,\n",
      "         1.01449719e+01,   1.12721910e+01]))\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'ImageActor' object has no attribute 'header'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-921dd7188585>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtenfit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtenfit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdensity_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrk4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstreamlines\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maffine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maffine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'ImageActor' object has no attribute 'header'"
     ]
    }
   ],
   "source": [
    "print(tenfit)\n",
    "print(np.histogram(tenfit.fa))\n",
    "print(np.histogram(tenfit.ad))\n",
    "print(np.histogram(tenfit.rd))\n",
    "print(np.histogram(tenfit.md))\n",
    "test = density_map(trk4.streamlines,fa.shape,affine=fa.header.affine)\n",
    "print(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from dipy.viz import actor, window\n",
    "ren = window.Renderer()\n",
    "lines = [np.random.rand(10, 3), np.random.rand(20, 3)]\n",
    "print(np.shape(lines))\n",
    "print(lines)\n",
    "colors = np.random.rand(2, 3)\n",
    "print(np.shape(colors))\n",
    "print(colors)\n",
    "\n",
    "c = actor.line(lines, colors)\n",
    "ren.add(c)\n",
    "window.show(ren)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tenfit.fa\n",
      "[[  -2.            0.            0.          128.34214783]\n",
      " [   0.            2.            0.         -129.92842102]\n",
      " [   0.            0.            2.          -58.10770035]\n",
      " [   0.            0.            0.            1.        ]]\n",
      "[[  -2.            0.            0.          128.34214783]\n",
      " [   0.            2.            0.         -129.92842102]\n",
      " [   0.            0.            2.          -58.10770035]\n",
      " [   0.            0.            0.            1.        ]]\n",
      "[[  -2.            0.            0.          128.34214783]\n",
      " [   0.            2.            0.         -129.92842102]\n",
      " [   0.            0.            2.          -58.10770035]\n",
      " [   0.            0.            0.            1.        ]]\n",
      "(128, 128, 66)\n",
      "(4, 4)\n",
      "0.0\n",
      "<type 'numpy.ndarray'>\n",
      "<class 'dipy.viz.actor.ImageActor'>\n"
     ]
    }
   ],
   "source": [
    "print(\"tenfit.fa\")\n",
    "print(trk4.affine)\n",
    "print(fa_img.affine)\n",
    "print(affine)\n",
    "print(np.shape(FA))\n",
    "print(np.shape(affine))\n",
    "print(FA[10,10,10])\n",
    "#print(np.histogram(fa_img.get_data()))\n",
    "#print(np.histogram(tenfit.fa))\n",
    "#print(fa.display(20,None,None))\n",
    "\n",
    "print(type(fa_img.get_data()))\n",
    "print(type(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(35.00191116333008, 93.64488220214844, 46.35918426513672, 76.9970474243164, 28.019742965698242, 60.99980926513672)\n",
      "(0.0, 0.0, 0.0)\n",
      "(-58.94761657714844, 58.33832550048828, -37.210052490234375, 24.065673828125, -2.0682144165039062, 63.89191818237305)\n",
      "(0.0, 0.0, 0.0)\n",
      "<type 'vtkRenderingLODPython.vtkLODActor'>\n",
      "<type 'builtin_function_or_method'>\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "SetInputConnection argument 1: method requires a vtkAlgorithmOutput, a vtkLODActor was provided.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-76-34e39656b81d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0mtransformFilter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvtk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvtkTransformPolyDataFilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0mtransformFilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSetTransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m \u001b[0mtransformFilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSetInputConnection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbundle_native\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m \u001b[0mtransformFilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUpdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: SetInputConnection argument 1: method requires a vtkAlgorithmOutput, a vtkLODActor was provided."
     ]
    }
   ],
   "source": [
    "#trk4 = nib.streamlines.load(FS_PATH+\"p_AR28_na/bundle_cc_4.trk\")\n",
    "import vtk as vtk\n",
    "\n",
    "fa = fvtk.slicer(tenfit.fa,affine)\n",
    "\n",
    "FA = fractional_anisotropy(tenfit.evals)\n",
    "FA[np.isnan(FA)] = 0\n",
    "fa_img = nib.Nifti1Image(FA.astype(np.float32),affine)\n",
    "\n",
    "#ad = fvtk.slicer(tenfit.ad,affine)\n",
    "#v = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0],[0, 0, 0, 1]])\n",
    "#c = colormap.orient2rgb(v)\n",
    "\n",
    "r = fvtk.ren()\n",
    "test = fvtk.slicer(fa_img.get_data(),affine)\n",
    "#test.display(None, None, None)\n",
    "\n",
    "#test2=actor.apply_affine(affine,FA) \n",
    "\n",
    "trk_shifted = transform_streamlines(trk4.tractogram.streamlines,np.linalg.inv(affine))\n",
    "bundle_native = actor.line(trk_shifted,tenfit.fa,linewidth=0.1,opacity=0.3);\n",
    "fvtk.add(r,bundle_native);\n",
    "print(bundle_native.GetBounds())\n",
    "print(bundle_native.GetPosition())\n",
    "\n",
    "\n",
    "bundle=actor.line(trk4.streamlines,tenfit.fa,linewidth=0.1,opacity=0.3)\n",
    "fvtk.add(r,bundle);\n",
    "print(bundle.GetBounds())\n",
    "print(bundle.GetPosition())\n",
    "\n",
    "transform = vtk.vtkTransform()\n",
    "transform_matrix = vtk.vtkMatrix4x4()\n",
    "transform_matrix.DeepCopy(np.reshape(affine,16).tolist())\n",
    "transform.SetMatrix(transform_matrix)\n",
    "\n",
    "print(type(bundle_native))\n",
    "print(type(bundle_native.GetActors))\n",
    "\n",
    "transformFilter=vtk.vtkTransformPolyDataFilter()\n",
    "transformFilter.SetTransform(transform)\n",
    "transformFilter.SetInputConnection(bundle_native)\n",
    "transformFilter.Update()\n",
    "\n",
    "actor.SetTransform(transform)\n",
    "fvtk.add(r,bundle_native);\n",
    "\n",
    "#fvtk.add(r,);\n",
    "fvtk.add(r,actor.scalar_bar())\n",
    "fvtk.add(r,fvtk.axes())\n",
    "fvtk.show(r)"
   ]
  }
 ],
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