{
 "metadata": {
  "name": "",
  "signature": "sha256:6cc2746f2365ff4ea5d7a14018252680a0fa59d38a04efc6bef0d8bc8400b12d"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import tempfile\n",
      "\n",
      "import scipy.stats as stats\n",
      "\n",
      "import dipy.core.geometry as geo\n",
      "import dipy.sims.phantom as dps\n",
      "import dipy.core.sphere as sphere\n",
      "import osmosis.model.base as dwi\n",
      "import osmosis.model.dti as dti\n",
      "import osmosis.model.sparse_deconvolution as ssd\n",
      "import osmosis.model.analysis as oza\n",
      "\n",
      "import osmosis.utils as ozu\n",
      "import osmosis.viz.maya as maya\n",
      "import osmosis.viz.mpl as mpl\n",
      "import osmosis.simulation as sim\n",
      "\n",
      "import osmosis.tensor as ozt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "import osmosis as oz\n",
      "import osmosis.io as oio\n",
      "oio.data_path = os.path.join(oz.__path__[0], 'data')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_sims = 500\n",
      "subject = 'SUB1'\n",
      "\n",
      "bvecs = []\n",
      "bvals = []\n",
      "data = []\n",
      "b_idx = []\n",
      "\n",
      "#bvals_to_test = [500, 800, 1000, 1200, 1500, 1800, 2000]#, 2500, 3000, 3500, 4000, 4500]\n",
      "bvals_to_test = [1000, 2000, 4000]\n",
      "\n",
      "# Always use 1000 data for the noise and S0:\n",
      "data1, data2 =  oio.get_dwi_data(1000, subject)\n",
      "\n",
      "for bval in bvals_to_test:\n",
      "    bvals.append(np.loadtxt(data1[2])/1000. * bval)     \n",
      "    bvecs.append(np.loadtxt(data1[1]))\n",
      "    b_idx.append(np.where(bvals[-1]>0)[0])\n",
      "\n",
      "mask = oio.get_wm_mask(oio.data_path + '/%s/%s_wm_mask.nii.gz'%(subject, subject), resample=data1[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We use the b=1000 to estimate the noise - it probably doesn't matter becasue it's all the same anyway:\n",
      "DWI1 = dwi.DWI(*data1, mask=mask)\n",
      "DWI2 = dwi.DWI(*data2, mask=mask)\n",
      "noise_sig = (DWI1.data[DWI1.mask]- DWI2.data[DWI2.mask])/2.0\n",
      "fig = mpl.probability_hist(ozu.rms(noise_sig))\n",
      "S0 = [np.mean([stats.scoreatpercentile(DWI1._flat_S0, 25), stats.scoreatpercentile(DWI2._flat_S0, 25)]), \n",
      "      np.mean([np.median(DWI1._flat_S0), np.median(DWI2._flat_S0)]), \n",
      "      np.mean([stats.scoreatpercentile(DWI1._flat_S0, 75), stats.scoreatpercentile(DWI2._flat_S0, 75)])]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Loading from file: /home/arokem/usr/lib/python2.7/site-packages/osmosis/data/SUB1/SUB1_b1000_1.bvals\n",
        "Loading from file: /home/arokem/usr/lib/python2.7/site-packages/osmosis/data/SUB1/SUB1_b1000_2.bvals\n",
        "Loading from file: /home/arokem/usr/lib/python2.7/site-packages/osmosis/data/SUB1/SUB1_b1000_1.nii.gz\n",
        "Loading from file: /home/arokem/usr/lib/python2.7/site-packages/osmosis/data/SUB1/SUB1_b1000_2.nii.gz"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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sJdSphBoB5dQZrjED3+l0wmw2w2QyoaysbNQxa9asgclkQnZ2NlpaWsa1bTD7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\nsXp38f6yfAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x92d3490>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Likewise here, we take the first fascicle to point at one of the vectors from the b=4000 measurement:\n",
      "vec1 = bvecs[-1][:, b_idx[-1][0]]\n",
      "R = geo.rodrigues_axis_rotation(ozu.null_space([vec1, -vec1, [0,0,0]])[:,0], 90)\n",
      "vec2 = np.array(np.dot(R.T, vec1)).squeeze()\n",
      "n_bvecs=150"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "angles = np.linspace(10, 90, 11)\n",
      "angles = np.linspace(0,90,5)\n",
      "weights = np.linspace(0.5, 0.5, 1)\n",
      "sphere = np.array(S0) #np.linspace(S0,S0,1)\n",
      "vol = []\n",
      "vecs2keep = [] "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weights"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "array([ 0.5])"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vec1, vec2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "(array([-0.0209 ,  0.81153, -0.58394]),\n",
        " array([ -9.99361940e-01,   5.55111512e-17,   3.57685114e-02]))"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#deg_45 = np.pi/4\n",
      "#n_vecs = 15\n",
      "#vecs = []\n",
      "#odf_weights = np.random.rand(n_vecs)\n",
      "#odf_weights/= np.sum(odf_weights)\n",
      "for bval_idx in range(len(bvals)):\n",
      "    vol.append(np.empty((angles.shape[0], weights.shape[0], sphere.shape[0], n_bvecs + 10)))\n",
      "    vecs2keep.append(np.empty((angles.shape[0], weights.shape[0], sphere.shape[0], 2, 3)))\n",
      "\n",
      "    for i, angle in enumerate(angles):\n",
      "        if angle==0:\n",
      "            odf_vecs = np.array([vec1, vec1])\n",
      "        else:\n",
      "            # Rotate around the null-space of the vector and its inverse: \n",
      "            R = geo.rodrigues_axis_rotation(ozu.null_space([vec1, -vec1, [0,0,0]])[:,0], angle)\n",
      "            vec2 = np.array(np.dot(R.T, vec1)).squeeze()\n",
      "            odf_vecs = np.array([vec1, vec2])\n",
      "        for j, w in enumerate(weights):\n",
      "            for k, sph in enumerate(sphere):\n",
      "                odf = sim.ODF(odf_vecs, [w, 1-w])\n",
      "                vecs2keep[-1][i,j,k] = odf_vecs\n",
      "\n",
      "                v = sim.Voxel(bvecs[bval_idx][:, 10:], bvals[bval_idx][10:], odf)\n",
      "                S0 = 0 + np.ones(10) * sph\n",
      "                vol[-1][i,j,k] = np.concatenate((S0, v.signal(np.mean(S0)))) \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sfm_pdd_rel = []\n",
      "sfm_pdd_acc = []\n",
      "dtm_pdd_rel = []\n",
      "dtm_pdd_acc = []\n",
      "sfm_rrmse = [] \n",
      "dtm_rrmse = []\n",
      "\n",
      "alpha = 0.0005\n",
      "l1_ratio = 0.8\n",
      "solver_params = dict(alpha=alpha,\n",
      "                     l1_ratio=l1_ratio,\n",
      "                     fit_intercept=False,\n",
      "                     positive=True)\n",
      "\n",
      "TM2keep = []\n",
      "SFM2keep = []\n",
      "\n",
      "for bval_idx in range(len(bvals)):\n",
      "    \n",
      "    this_vol = vol[bval_idx]\n",
      "    this_vecs = vecs2keep[bval_idx]\n",
      "    \n",
      "    sfm_pdd_rel.append(np.zeros(vol[bval_idx].shape[:3] + (n_sims,)))\n",
      "    dtm_pdd_rel.append(np.zeros(vol[bval_idx].shape[:3] + (n_sims,)))\n",
      "    sfm_pdd_acc.append(np.zeros(vol[bval_idx].shape[:3] + (n_sims,)))\n",
      "    dtm_pdd_acc.append(np.zeros(vol[bval_idx].shape[:3] + (n_sims,)))\n",
      "    sfm_rrmse.append(np.zeros(vol[bval_idx].shape[:3] + (n_sims,)))\n",
      "    dtm_rrmse.append(np.zeros(vol[bval_idx].shape[:3] + (n_sims,)))\n",
      "    \n",
      "    for sim_idx in xrange(n_sims): \n",
      "    \n",
      "        noise_idx1 = int(np.random.rand() * noise_sig.shape[0])\n",
      "        vol_noise1 = np.abs(this_vol + noise_sig[noise_idx1]) #abs is important - remember that the signal cannot go negative!\n",
      "        \n",
      "        noise_idx2 = int(np.random.rand() * noise_sig.shape[0])\n",
      "        # Just making sure the two noise samples are actually different:\n",
      "        while noise_idx1 == noise_idx2:\n",
      "            noise_idx2 = int(np.random.rand() * sig_diff.shape[0])\n",
      "\n",
      "        vol_noise2 = np.abs(this_vol + noise_sig[noise_idx2])\n",
      "        \n",
      "    \n",
      "        TM1 = dti.TensorModel(vol_noise1,\n",
      "                         bvecs[bval_idx],\n",
      "                         bvals[bval_idx],\n",
      "                         params_file = 'temp')\n",
      "\n",
      "        TM2 = dti.TensorModel(vol_noise2,\n",
      "                         bvecs[bval_idx],\n",
      "                         bvals[bval_idx],\n",
      "                         params_file = 'temp')\n",
      "\n",
      "        SFM1 = ssd.SparseDeconvolutionModel(vol_noise1,\n",
      "                                        bvecs[bval_idx],\n",
      "                                        bvals[bval_idx],\n",
      "                                        solver_params=solver_params,\n",
      "                                        axial_diffusivity=1.5,\n",
      "                                        radial_diffusivity=0.5,\n",
      "                                        params_file = 'temp')\n",
      "\n",
      "        SFM2 = ssd.SparseDeconvolutionModel(vol_noise2,\n",
      "                                        bvecs[bval_idx],\n",
      "                                        bvals[bval_idx],\n",
      "                                        solver_params=solver_params,\n",
      "                                        axial_diffusivity=1.5,\n",
      "                                        radial_diffusivity=0.5,\n",
      "                                        params_file = 'temp')\n",
      "    \n",
      "        \n",
      "        for i, angle in enumerate(angles):\n",
      "            for j, w in enumerate(weights):\n",
      "                for k, sph in enumerate(sphere):\n",
      "                    # Calculate the accuracy:\n",
      "                    real_vec = vecs2keep[bval_idx][i,j,k]\n",
      "                    model_vec = SFM1.principal_diffusion_direction[i,j,k]\n",
      "                    model_vec = model_vec[np.unique(np.where(~np.isnan(model_vec))[0])]\n",
      "                    if model_vec.shape[0] == 0:\n",
      "                        sfm_pdd_acc[bval_idx][i,j,k, sim_idx] = np.mean(np.min(this_angs, 0)) \n",
      "                    else:\n",
      "                        cross_vecs = np.dot(model_vec, real_vec.T)\n",
      "                        cross_vecs[cross_vecs>1] = 1\n",
      "                        this_angs = np.rad2deg(np.arccos(cross_vecs))\n",
      "                        this_angs[this_angs>90] = 180 - this_angs[this_angs>90]\n",
      "                        sfm_pdd_acc[bval_idx][i,j,k, sim_idx] = np.mean(np.min(this_angs, 0)) \n",
      "                        \n",
      "                    dt_vec = TM1.principal_diffusion_direction[i,j,k]\n",
      "                    dt_cross = np.dot(dt_vec, real_vec.T)\n",
      "                    dt_cross[dt_cross>1] = 1\n",
      "                    dt_angs = np.rad2deg(np.arccos(dt_cross))\n",
      "                    dt_angs[dt_angs>90] = 180 - dt_angs[dt_angs>90]\n",
      "                    dtm_pdd_acc[bval_idx][i,j,k, sim_idx] = np.mean(np.min(dt_angs))\n",
      "                    \n",
      "                    # Calculate the reliability:\n",
      "                    this_sfm_ang = np.rad2deg(ozu.vector_angle(SFM1.principal_diffusion_direction[i,j,k][0], \n",
      "                                                               SFM2.principal_diffusion_direction[i,j,k][0]))\n",
      "                    \n",
      "                    sfm_pdd_rel[bval_idx][i,j,k,sim_idx] = np.min([this_sfm_ang, 180-this_sfm_ang])\n",
      "                \n",
      "                    this_dt_ang = np.rad2deg(ozu.vector_angle(TM1.principal_diffusion_direction[i,j,k], \n",
      "                                                          TM2.principal_diffusion_direction[i,j,k]))\n",
      "                \n",
      "                    dtm_pdd_rel[bval_idx][i,j,k,sim_idx] = np.min([this_dt_ang, 180-this_dt_ang])\n",
      "                    \n",
      "                    dtm_rrmse[bval_idx][i,j,k,sim_idx] = oza.relative_rmse(TM1, TM2)[i,j,k]\n",
      "                    sfm_rrmse[bval_idx][i,j,k,sim_idx] = oza.relative_rmse(SFM1, SFM2)[i,j,k]\n",
      "                    \n",
      "    TM2keep.append(TM1)\n",
      "    SFM2keep.append(SFM1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "SparseDeconvolutionModel.model_params [*****************93%***************   ]  14 of 15 complete "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Fitting TensorModel params using dipy\n",
        "Predicting signal from TensorModel\n",
        "Predicting signal from TensorModel\n",
        "Predicting signal from SparseDeconvolutionModel with ElasticNet(alpha=0.0005, copy_X=True, fit_intercept=False, l1_ratio=0.8,\n",
        "      max_iter=1000, normalize=False, positive=True, precompute=auto,\n",
        "      rho=None, tol=0.0001, warm_start=False)\n",
        "Predicting signal from SparseDeconvolutionModel with ElasticNet(alpha=0.0005, copy_X=True, fit_intercept=False, l1_ratio=0.8,\n",
        "      max_iter=1000, normalize=False, positive=True, precompute=auto,\n",
        "      rho=None, tol=0.0001, warm_start=False)\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "oza.relative_rmse(TM1, TM2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "array([[[ 0.72456259,  0.72891933,  0.72633835]],\n",
        "\n",
        "       [[ 0.73412484,  0.7338664 ,  0.73407156]],\n",
        "\n",
        "       [[ 0.75639224,  0.76431061,  0.7765528 ]],\n",
        "\n",
        "       [[ 0.79616833,  0.82196619,  0.85550541]],\n",
        "\n",
        "       [[ 0.82045467,  0.85502097,  0.89918745]]])"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "median_rel_sfm = []\n",
      "median_rel_dtm = []\n",
      "median_acc_sfm = []\n",
      "median_acc_dtm = []\n",
      "median_rrmse_sfm = []\n",
      "median_rrmse_dtm = []\n",
      "\n",
      "median_rel_sfm_e = []\n",
      "median_rel_dtm_e = []\n",
      "median_acc_sfm_e = []\n",
      "median_acc_dtm_e = []\n",
      "median_rrmse_sfm_e = []\n",
      "median_rrmse_dtm_e = []\n",
      "\n",
      "for bval_idx in range(len(bvals)):\n",
      "    median_rel_sfm.append(np.zeros(sfm_pdd_rel[bval_idx].shape[:2]))\n",
      "    median_rel_dtm.append(np.zeros(dtm_pdd_rel[bval_idx].shape[:2]))\n",
      "    median_acc_sfm.append(np.zeros(sfm_pdd_acc[bval_idx].shape[:2]))\n",
      "    median_acc_dtm.append(np.zeros(dtm_pdd_acc[bval_idx].shape[:2]))\n",
      "    median_rrmse_sfm.append(np.zeros(sfm_rrmse[bval_idx].shape[:2]))\n",
      "    median_rrmse_dtm.append(np.zeros(dtm_rrmse[bval_idx].shape[:2]))\n",
      "\n",
      "    \n",
      "    median_rel_sfm_e.append(np.zeros(sfm_pdd_rel[bval_idx].shape[:2]))\n",
      "    median_rel_dtm_e.append(np.zeros(dtm_pdd_rel[bval_idx].shape[:2]))\n",
      "    median_acc_sfm_e.append(np.zeros(sfm_pdd_acc[bval_idx].shape[:2]))\n",
      "    median_acc_dtm_e.append(np.zeros(dtm_pdd_acc[bval_idx].shape[:2]))\n",
      "    median_rrmse_sfm_e.append(np.zeros(sfm_rrmse[bval_idx].shape[:2]))\n",
      "    median_rrmse_dtm_e.append(np.zeros(dtm_rrmse[bval_idx].shape[:2]))\n",
      "\n",
      "    for i, angle in enumerate(angles):\n",
      "            for j, w in enumerate(weights):\n",
      "                for k, sph in enumerate(sphere):\n",
      "                    median_rel_sfm[-1][i,j] = stats.nanmedian(sfm_pdd_rel[bval_idx][i,j,k,:])\n",
      "                    median_rel_dtm[-1][i,j] = stats.nanmedian(dtm_pdd_rel[bval_idx][i,j,k,:])\n",
      "                    median_acc_sfm[-1][i,j] = stats.nanmedian(sfm_pdd_acc[bval_idx][i,j,k,:])\n",
      "                    median_acc_dtm[-1][i,j] = stats.nanmedian(dtm_pdd_acc[bval_idx][i,j,k,:])\n",
      "                    median_rrmse_sfm[-1][i,j] = stats.nanmedian(sfm_rrmse[bval_idx][i,j,k,:])\n",
      "                    median_rrmse_dtm[-1][i,j] = stats.nanmedian(dtm_rrmse[bval_idx][i,j,k,:])\n",
      "                    \n",
      "                    median_rel_sfm_e[-1][i,j] = stats.scoreatpercentile(sfm_pdd_rel[bval_idx][i,j,k,:], 84) - stats.scoreatpercentile(sfm_pdd_rel[bval_idx][i,j,k,:], 16) \n",
      "                    median_rel_dtm_e[-1][i,j] = stats.scoreatpercentile(dtm_pdd_rel[bval_idx][i,j,k,:], 84) - stats.scoreatpercentile(dtm_pdd_rel[bval_idx][i,j,k,:], 16)\n",
      "                    median_acc_sfm_e[-1][i,j] = stats.scoreatpercentile(sfm_pdd_acc[bval_idx][i,j,k,:], 84) - stats.scoreatpercentile(sfm_pdd_acc[bval_idx][i,j,k,:], 16)                    \n",
      "                    median_acc_dtm_e[-1][i,j] = stats.scoreatpercentile(dtm_pdd_acc[bval_idx][i,j,k,:], 84) - stats.scoreatpercentile(dtm_pdd_acc[bval_idx][i,j,k,:], 16)\n",
      "                    median_rrmse_sfm_e[-1][i,j] = stats.scoreatpercentile(sfm_rrmse[bval_idx][i,j,k,:], 84) - stats.scoreatpercentile(sfm_pdd_acc[bval_idx][i,j,k,:], 16)                    \n",
      "                    median_rrmse_dtm_e[-1][i,j] = stats.scoreatpercentile(dtm_rrmse[bval_idx][i,j,k,:], 84) - stats.scoreatpercentile(dtm_pdd_acc[bval_idx][i,j,k,:], 16)\n",
      "\n",
      "median_rel_sfm = np.array(median_rel_sfm)\n",
      "median_rel_dtm = np.array(median_rel_dtm)\n",
      "median_acc_sfm = np.array(median_acc_sfm)\n",
      "median_acc_dtm = np.array(median_acc_dtm)\n",
      "median_rrmse_sfm = np.array(median_rrmse_sfm)\n",
      "median_rrmse_dtm = np.array(median_rrmse_dtm)\n",
      "\n",
      "median_rel_sfm_e = np.array(median_rel_sfm_e)\n",
      "median_rel_dtm_e = np.array(median_rel_dtm_e)\n",
      "median_acc_sfm_e = np.array(median_acc_sfm_e)\n",
      "median_acc_dtm_e = np.array(median_acc_dtm_e)\n",
      "median_rrmse_sfm_e = np.array(median_rrmse_sfm_e)\n",
      "median_rrmse_dtm_e = np.array(median_rrmse_dtm_e)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for yy, w_bal in enumerate(weights):\n",
      "    fig = plt.figure()\n",
      "    for ang_idx, ang in enumerate(angles):\n",
      "        for bval_idx, bval in enumerate(bvals):\n",
      "            lw = 3\n",
      "            ax  = fig.add_subplot(len(bvals), len(angles), bval_idx * (len(angles)) + ang_idx+1, polar=True)\n",
      "            ax.plot([0,0],[0,1], linewidth=lw, color='k')\n",
      "            ax.plot([np.deg2rad(ang),np.deg2rad(ang)], [0,1], 'k', linewidth=lw)\n",
      "            ax.set_yticklabels('')\n",
      "            ax.set_yticks([])\n",
      "            ax.set_xticklabels('')\n",
      "            ax.bar(np.deg2rad(median_acc_dtm[bval_idx,ang_idx,yy])-np.deg2rad(median_rel_dtm[bval_idx,ang_idx,yy])/2, 1, np.deg2rad(median_rel_dtm[bval_idx,ang_idx,yy]), alpha=0.1, color='g')\n",
      "            ax.bar(np.deg2rad(-median_acc_sfm[bval_idx,ang_idx,yy])-np.deg2rad(median_rel_sfm[bval_idx,ang_idx,yy])/2, 1, np.deg2rad(median_rel_sfm[bval_idx,ang_idx,yy]), alpha=0.1, color='r')\n",
      "            # Plot a stick in the middle for each one of these :\n",
      "            \n",
      "            ax.plot([np.deg2rad(median_acc_dtm[bval_idx, ang_idx,yy]), #+ np.deg2rad(median_rel_dtm[bval_idx,ang_idx,yy])/2, \n",
      "                     np.deg2rad(median_acc_dtm[bval_idx, ang_idx,yy])],# + np.deg2rad(median_rel_dtm[bval_idx,ang_idx,yy])/2, \n",
      "                     [0,1], color='g', linewidth=2)\n",
      "\n",
      "            ax.plot([-np.deg2rad(median_acc_sfm[bval_idx, ang_idx,yy]), #+ np.deg2rad(median_rel_sfm[bval_idx,ang_idx,yy])/2,  \n",
      "                     -np.deg2rad(median_acc_sfm[bval_idx, ang_idx,yy])], #+ np.deg2rad(median_rel_sfm[bval_idx,ang_idx,yy])/2, \n",
      "                     [0,1], color='r', linewidth=2)\n",
      "\n",
      "\n",
      "fig.set_size_inches([10,6])\n",
      "fig.savefig('figures/sim_small_mults.svg')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Ip4hZFKOTswMAwcHB2LRpE+dRrpycHJw8eRKvvfYap+2ouXLlitEid81x6dIl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\n+oVUKqXvv/+eLC0tafPmzRpfbKFph0eO3Bd45513tDraoxWHp6ioiN5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DwzFy5EjExsai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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108deef50>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for yy, w_bal in enumerate(weights):\n",
      "    fig, ax = plt.subplots(1,len(angles), sharex=True, sharey=True)\n",
      "    for xx, ang in enumerate(angles):\n",
      "        ax[xx].plot(bvals_to_test, median_rel_dtm[:,xx,yy],'o-', label='Tensor')\n",
      "        ax[xx].plot(bvals_to_test, median_rel_sfm[:,xx,yy],'o-', label='SFM')\n",
      "        #ax[xx].errorbar(bvals_to_test, median_rel_dtm[:,xx,0], color='k', yerr=median_rel_dtm_e[:,xx,0])\n",
      "        #ax[xx].errorbar(bvals_to_test, median_rel_sfm[:,xx,0], color='k', yerr=median_rel_sfm_e[:,xx,0])\n",
      "        ax[xx].set_xticks([1000, 2000, 4000])\n",
      "        ax[xx].set_xlim([0, 5000])\n",
      "        #ax[xx].text(675, 42, str(int(ang)))\n",
      "        ax[xx].grid()\n",
      "        ax[xx].set_xlabel('b value')\n",
      "    ax[0].set_ylabel('Reliability (degrees)')\n",
      "    #ax[-1].legend()\n",
      "    fig.set_size_inches([13, 4])\n",
      "    fig.savefig('figures/sim_rel.svg')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CUKMPvwBhA7mCDeQKNpAr2HBGNxRbv3690tLSdPnll0uS1q5dq8mTJwd3hCFC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\nAAAAAESh/w+s6pOtLhrZGgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10990bd90>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for yy, w_bal in enumerate(weights):\n",
      "    fig, ax = plt.subplots(1,len(angles), sharex=True, sharey=True)\n",
      "    for xx, ang in enumerate(angles):\n",
      "        ax[xx].plot(bvals_to_test, median_acc_dtm[:,xx,yy],'o-', label='Tensor')\n",
      "        ax[xx].plot(bvals_to_test, median_acc_sfm[:,xx,yy],'o-', label='SFM')\n",
      "        #ax[xx].errorbar(bvals_to_test, median_acc_dtm[:,xx,0], color='k', yerr=median_acc_dtm_e[:,xx,0])\n",
      "        #ax[xx].errorbar(bvals_to_test, median_acc_sfm[:,xx,0], color='k', yerr=median_acc_sfm_e[:,xx,0])\n",
      "        ax[xx].set_xticks([1000, 2000, 4000])\n",
      "        ax[xx].set_xlim([0, 5000])\n",
      "        #ax[xx].text(675, 42, str(int(ang)))\n",
      "        ax[xx].grid()\n",
      "        ax[xx].set_xlabel('b value')\n",
      "    ax[0].set_ylabel('Accuracy (degrees)')\n",
      "    #ax[-1].legend()\n",
      "    fig.set_size_inches([13, 4])\n",
      "    fig.savefig('figures/sim_acc.svg')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AhISE4Ndff8Xq1atl9tWutJ6/5mz0VItozJZ6eqlDBuZKPb3UIQNzpZ5e6pDB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\nECAiIiIickH/H5fj8aSQDCqnAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1086baf50>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for yy, w_bal in enumerate(weights):\n",
      "    fig, ax = plt.subplots(1,len(angles), sharex=True, sharey=True)\n",
      "    for xx, ang in enumerate(angles):\n",
      "        ax[xx].plot(bvals_to_test, median_rrmse_dtm[:, xx, yy],'o-', label='Tensor')\n",
      "        ax[xx].plot(bvals_to_test, median_rrmse_sfm[:, xx, yy],'o-', label='SFM')\n",
      "        ax[xx].errorbar(bvals_to_test, median_rrmse_dtm[:,xx,0], color='k', yerr=median_rrmse_dtm_e[:,xx,0])\n",
      "        ax[xx].errorbar(bvals_to_test, median_rrmse_sfm[:,xx,0], color='k', yerr=median_rrmse_sfm_e[:,xx,0])\n",
      "        ax[xx].set_xticks([1000, 2000, 4000])\n",
      "        ax[xx].set_xlim([0, 5000])\n",
      "        #ax[xx].text(675, 42, str(int(ang)))\n",
      "        ax[xx].grid()\n",
      "        ax[xx].set_xlabel('b value')\n",
      "    ax[0].set_ylabel('Accuracy (degrees)')\n",
      "    #ax[-1].legend()\n",
      "    fig.set_size_inches([13, 4])\n",
      "    fig.savefig('figures/sim_acc.svg')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/lib/pymodules/python2.7/matplotlib/axes.py:5370: RuntimeWarning: invalid value encountered in double_scalars\n",
        "  in cbook.safezip(y,yerr)]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MTMs6ABxb/P/zevXqaeHChWn5/zy+joYNG+qtt95KaR0JVwVTp05VXl6e7rrr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\nDIRbfOkrMhBu8aWv4BZf+oo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       "text": [
        "<matplotlib.figure.Figure at 0x960a8d0>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}