{ "metadata": { "name": "motion_correction" }, "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": [ "\n", "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.kernel.zmq.pylab.backend_inline].\n", "For more information, type 'help(pylab)'.\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import matplotlib.pyplot as plt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "import os" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "I'm expecting the data to be in your home directory, as for the workflow notebook." ] }, { "cell_type": "code", "collapsed": false, "input": [ "DATA_PATH = os.path.join(os.path.expanduser('~'), 'data', 'ds105')\n", "os.listdir(DATA_PATH)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 4, "text": [ "['study_key.txt',\n", " 'release_history.txt',\n", " 'task_key.txt',\n", " 'models',\n", " 'references.txt',\n", " 'sub005',\n", " 'license.txt',\n", " 'sub004',\n", " 'sub003',\n", " 'scan_key.txt',\n", " 'sub001',\n", " 'sub006',\n", " 'README',\n", " 'sub002']" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get the filenames for the functional runs of the first subject" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import glob" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "run_fnames = glob.glob(os.path.join(DATA_PATH, 'sub001', 'BOLD', 'task*', 'bold.nii.gz'))\n", "run_fnames.sort()\n", "run_fnames" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 6, "text": [ "['/home/mb312/data/ds105/sub001/BOLD/task001_run001/bold.nii.gz',\n", " '/home/mb312/data/ds105/sub001/BOLD/task001_run002/bold.nii.gz',\n", " '/home/mb312/data/ds105/sub001/BOLD/task001_run003/bold.nii.gz',\n", " '/home/mb312/data/ds105/sub001/BOLD/task001_run004/bold.nii.gz',\n", " '/home/mb312/data/ds105/sub001/BOLD/task001_run005/bold.nii.gz',\n", " '/home/mb312/data/ds105/sub001/BOLD/task001_run006/bold.nii.gz',\n", " '/home/mb312/data/ds105/sub001/BOLD/task001_run007/bold.nii.gz',\n", " '/home/mb312/data/ds105/sub001/BOLD/task001_run008/bold.nii.gz',\n", " '/home/mb312/data/ds105/sub001/BOLD/task001_run009/bold.nii.gz',\n", " '/home/mb312/data/ds105/sub001/BOLD/task001_run010/bold.nii.gz',\n", " '/home/mb312/data/ds105/sub001/BOLD/task001_run011/bold.nii.gz',\n", " '/home/mb312/data/ds105/sub001/BOLD/task001_run012/bold.nii.gz']" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is how to do realignment AND slice time correction on a single subject in NIPY:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from nipy.algorithms.registration import FmriRealign4d\n", "from nipy import load_image, save_image" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "N_SLICES = 40\n", "TR = 2.5\n", "SLICE_AXIS = 0\n", "space_to_time = list(range(0, N_SLICES, 2)) + list(range(1, N_SLICES, 2))\n", "time_to_space = np.argsort(space_to_time)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "# Let's use only the first 2 runs for now\n", "run_fnames = run_fnames[:2]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "# Make filenames into images\n", "run_imgs = [load_image(fname) for fname in run_fnames]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "R = FmriRealign4d(run_imgs, slice_order=time_to_space, slice_info=(0, 1))\n", "# Estimate motion within- and between- sessions\n", "R.estimate(loops=1, refscan=None)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Resampling scan 1/121\n", "Resampling scan 2/121\n", "Resampling scan 3/121\n", "Resampling scan 4/121\n", "Resampling scan 5/121\n", "Resampling scan 6/121\n", "Resampling scan 7/121\n", "Resampling scan 8/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 9/121\n", "Resampling scan 10/121\n", "Resampling scan 11/121\n", "Resampling scan 12/121\n", "Resampling scan 13/121\n", "Resampling scan 14/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 15/121\n", "Resampling scan 16/121\n", "Resampling scan 17/121\n", "Resampling scan 18/121\n", "Resampling scan 19/121\n", "Resampling scan 20/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 21/121\n", "Resampling scan 22/121\n", "Resampling scan 23/121\n", "Resampling scan 24/121\n", "Resampling scan 25/121\n", "Resampling scan 26/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 27/121\n", "Resampling scan 28/121\n", "Resampling scan 29/121\n", "Resampling scan 30/121\n", "Resampling scan 31/121\n", "Resampling scan 32/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 33/121\n", "Resampling scan 34/121\n", "Resampling scan 35/121\n", "Resampling scan 36/121\n", "Resampling scan 37/121\n", "Resampling scan 38/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 39/121\n", "Resampling scan 40/121\n", "Resampling scan 41/121\n", "Resampling scan 42/121\n", "Resampling scan 43/121\n", "Resampling scan 44/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 45/121\n", "Resampling scan 46/121\n", "Resampling scan 47/121\n", "Resampling scan 48/121\n", "Resampling scan 49/121\n", "Resampling scan 50/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 51/121\n", "Resampling scan 52/121\n", "Resampling scan 53/121\n", "Resampling scan 54/121\n", "Resampling scan 55/121\n", "Resampling scan 56/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 57/121\n", "Resampling scan 58/121\n", "Resampling scan 59/121\n", "Resampling scan 60/121\n", "Resampling scan 61/121\n", "Resampling scan 62/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 63/121\n", "Resampling scan 64/121\n", "Resampling scan 65/121\n", "Resampling scan 66/121\n", "Resampling scan 67/121\n", "Resampling scan 68/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 69/121\n", "Resampling scan 70/121\n", "Resampling scan 71/121\n", "Resampling scan 72/121\n", "Resampling scan 73/121\n", "Resampling scan 74/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 75/121\n", "Resampling scan 76/121\n", "Resampling scan 77/121\n", "Resampling scan 78/121\n", "Resampling scan 79/121\n", "Resampling scan 80/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 81/121\n", "Resampling scan 82/121\n", "Resampling scan 83/121\n", "Resampling scan 84/121\n", "Resampling scan 85/121\n", "Resampling scan 86/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 87/121\n", "Resampling scan 88/121\n", "Resampling scan 89/121\n", "Resampling scan 90/121\n", "Resampling scan 91/121\n", "Resampling scan 92/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 93/121\n", "Resampling scan 94/121\n", "Resampling scan 95/121\n", "Resampling scan 96/121\n", "Resampling scan 97/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 98/121\n", "Resampling scan 99/121\n", "Resampling scan 100/121\n", "Resampling scan 101/121\n", "Resampling scan 102/121\n", "Resampling scan 103/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 104/121\n", "Resampling scan 105/121\n", "Resampling scan 106/121\n", "Resampling scan 107/121\n", "Resampling scan 108/121\n", "Resampling scan 109/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 110/121\n", "Resampling scan 111/121\n", "Resampling scan 112/121\n", "Resampling scan 113/121\n", "Resampling scan 114/121\n", "Resampling scan 115/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 116/121\n", "Resampling scan 117/121\n", "Resampling scan 118/121\n", "Resampling scan 119/121\n", "Resampling scan 120/121\n", "Resampling scan 121/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Estimating motion at time frame 1/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.307333\n", " Iterations: 11\n", " Function evaluations: 41\n", " Gradient evaluations: 11\n", " Hessian evaluations: 11\n", "translation : [-0.07324246 -0.01781421 -0.05081702]\n", "rotation : [-0.00117747 -0.00436287 0.00253694]\n", "\n", "Estimating motion at time frame 2/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.307975\n", " Iterations: 9\n", " Function evaluations: 10\n", " Gradient evaluations: 9\n", " Hessian evaluations: 9\n", "translation : [-0.08398673 -0.02948134 -0.04710506]\n", "rotation : [-0.00092927 -0.00304771 0.00258669]\n", "\n", "Estimating motion at time frame 3/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.308489\n", " Iterations: 11\n", " Function evaluations: 12\n", " Gradient evaluations: 11\n", " Hessian evaluations: 11\n", "translation : [-0.07913381 -0.02934382 -0.03703903]\n", "rotation : [-0.00145616 -0.00194298 0.00278976]\n", "\n", "Estimating motion at time frame 4/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.308728\n", " Iterations: 11\n", " Function evaluations: 19\n", " Gradient evaluations: 11\n", " Hessian evaluations: 11\n", "translation : [-0.08058752 -0.05550932 -0.03430978]\n", "rotation : [-0.00062274 -0.00130803 0.00026999]\n", "\n", "Estimating motion at time frame 5/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.309119\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.07132394 -0.08823757 -0.04714596]\n", "rotation : [-0.00116533 -0.00094461 -0.00062282]\n", "\n", "Estimating motion at time frame 6/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.309504\n", " Iterations: 10\n", " Function evaluations: 11\n", " Gradient evaluations: 10\n", " Hessian evaluations: 10\n", "translation : [-0.05987917 -0.07832279 -0.03477728]\n", "rotation : [-0.0011507 -0.00199486 0.00065904]\n", "\n", "Estimating motion at time frame 7/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.310003\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [-0.08691466 -0.10696442 -0.04342204]\n", "rotation : [-0.00135826 -0.00053823 0.00024895]\n", "\n", "Estimating motion at time frame 8/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.310624\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [-0.10180971 -0.06978421 -0.07831779]\n", "rotation : [-0.00167853 0.00054596 0.00018842]\n", "\n", "Estimating motion at time frame 9/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.311148\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.09542805 -0.02189536 -0.06644728]\n", "rotation : [ -2.04894031e-03 6.03084174e-05 4.92245406e-04]\n", "\n", "Estimating motion at time frame 10/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.311708\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [-0.07754244 -0.02470438 -0.03649362]\n", "rotation : [-0.00222406 -0.00052415 0.00214415]\n", "\n", "Estimating motion at time frame 11/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.312414\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [-0.09532827 -0.01793094 -0.03474164]\n", "rotation : [-0.00232173 -0.0012524 0.00270095]\n", "\n", "Estimating motion at time frame 12/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.312862\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.09382274 0.01549692 -0.02228395]\n", "rotation : [-0.00113201 -0.00169712 0.00250279]\n", "\n", "Estimating motion at time frame 13/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.313621\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.0869105 -0.03892984 -0.01481042]\n", "rotation : [-0.00159046 -0.00190578 0.00347667]\n", "\n", "Estimating motion at time frame 14/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.314481\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [-0.07134044 -0.04673073 -0.0145218 ]\n", "rotation : [-0.00260689 -0.00132065 0.00273099]\n", "\n", "Estimating motion at time frame 15/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.315213\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.10111364 -0.03781175 -0.0248575 ]\n", "rotation : [-0.00212531 -0.0016123 0.00242911]\n", "\n", "Estimating motion at time frame 16/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.315547\n", " Iterations: 9\n", " Function evaluations: 10\n", " Gradient evaluations: 9\n", " Hessian evaluations: 9\n", "translation : [-0.08270239 0.0037101 -0.024433 ]\n", "rotation : [-0.001459 -0.00166781 0.00131127]\n", "\n", "Estimating motion at time frame 17/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.315879\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.05740523 0.04182546 -0.08923144]\n", "rotation : [-0.00130093 -0.00043597 0.0008489 ]\n", "\n", "Estimating motion at time frame 18/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.316213\n", " Iterations: 8\n", " Function evaluations: 14\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [-0.07335987 -0.01124886 -0.02539189]\n", "rotation : [-0.00187203 -0.00033619 -0.00016958]\n", "\n", "Estimating motion at time frame 19/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.317202\n", " Iterations: 12\n", " Function evaluations: 13\n", " Gradient evaluations: 12\n", " Hessian evaluations: 12\n", "translation : [-0.0317631 -0.03433274 -0.01444959]\n", "rotation : [-0.00181308 -0.00464039 0.00191192]\n", "\n", "Estimating motion at time frame 20/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.318441\n", " Iterations: 9\n", " Function evaluations: 10\n", " Gradient evaluations: 9\n", " Hessian evaluations: 9\n", "translation : [-0.03563717 -0.05989017 -0.02295496]\n", "rotation : [-0.00063952 -0.00443947 0.00311445]\n", "\n", "Estimating motion at time frame 21/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.319041\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.07666668 -0.0508486 -0.00621835]\n", "rotation : [-0.00133174 -0.00185588 0.00253229]\n", "\n", "Estimating motion at time frame 22/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.320206\n", " Iterations: 10\n", " Function evaluations: 11\n", " Gradient evaluations: 10\n", " Hessian evaluations: 10\n", "translation : [-0.06035183 -0.01963408 -0.03390768]\n", "rotation : [-0.00178705 -0.00464661 0.0030958 ]\n", "\n", "Estimating motion at time frame 23/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.321080\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [-0.08371508 -0.01150348 -0.06142091]\n", "rotation : [-0.00262428 -0.00211742 0.00215308]\n", "\n", "Estimating motion at time frame 24/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.321710\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [-0.08964347 -0.04793891 -0.00234433]\n", "rotation : [-0.00145692 -0.00179118 0.00248898]\n", "\n", "Estimating motion at time frame 25/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.322613\n", " Iterations: 8\n", " Function evaluations: 11\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [-0.06097266 -0.04686847 -0.02180358]\n", "rotation : [-0.00220004 -0.00274606 0.00222926]\n", "\n", "Estimating motion at time frame 26/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.323960\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [-0.0445997 -0.05418781 -0.03167271]\n", "rotation : [-0.00241491 -0.0037994 0.0025703 ]\n", "\n", "Estimating motion at time frame 27/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.324735\n", " Iterations: 9\n", " Function evaluations: 19\n", " Gradient evaluations: 9\n", " Hessian evaluations: 9\n", "translation : [-0.05035372 -0.04114466 -0.02360156]\n", "rotation : [-0.00180287 -0.0022798 0.00289602]\n", "\n", "Estimating motion at time frame 28/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.325610\n", " Iterations: 9\n", " Function evaluations: 10\n", " Gradient evaluations: 9\n", " Hessian evaluations: 9\n", "translation : [-0.05328104 -0.04706176 -0.01420262]\n", "rotation : [-0.00180678 -0.00298788 0.00292667]\n", "\n", "Estimating motion at time frame 29/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.326420\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [-0.05707794 -0.02020597 -0.02568743]\n", "rotation : [-0.00286204 -0.00166395 0.00207471]\n", "\n", "Estimating motion at time frame 30/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.327089\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [-0.05564961 -0.03840093 -0.01301082]\n", "rotation : [-0.00281111 -0.00084431 0.00119604]\n", "\n", "Estimating motion at time frame 31/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.327505\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.04085094 -0.03958773 -0.03239269]\n", "rotation : [-0.00151893 -0.00136029 0.00173726]\n", "\n", "Estimating motion at time frame 32/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.327608\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.04940055 -0.02370535 -0.01459365]\n", "rotation : [ 2.37727686e-05 -9.15644375e-04 1.10325283e-03]\n", "\n", "Estimating motion at time frame 33/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.328711\n", " Iterations: 10\n", " Function evaluations: 11\n", " Gradient evaluations: 10\n", " Hessian evaluations: 10\n", "translation : [-0.02035479 -0.01514685 -0.02274351]\n", "rotation : [ -1.48859092e-05 -4.84350834e-03 2.98356786e-03]\n", "\n", "Estimating motion at time frame 34/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.329056\n", " Iterations: 11\n", " Function evaluations: 12\n", " Gradient evaluations: 11\n", " Hessian evaluations: 11\n", "translation : [-0.03439524 -0.0443216 -0.056892 ]\n", "rotation : [-0.0007847 -0.00120838 0.00229997]\n", "\n", "Estimating motion at time frame 35/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.329276\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.05973343 -0.06040037 -0.06068807]\n", "rotation : [-0.0001338 0.00027317 0.0004736 ]\n", "\n", "Estimating motion at time frame 36/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.329630\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.05847767 -0.05467099 -0.02239585]\n", "rotation : [-0.00156391 -0.00078187 0.00104831]\n", "\n", "Estimating motion at time frame 37/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.330223\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [-0.04583661 -0.00060643 -0.04549142]\n", "rotation : [-0.00254505 -0.00127479 0.00168809]\n", "\n", "Estimating motion at time frame 38/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.330719\n", " Iterations: 8\n", " Function evaluations: 13\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [-0.06886391 0.03675957 -0.02685298]\n", "rotation : [-0.00133262 -0.00196698 0.00248277]\n", "\n", "Estimating motion at time frame 39/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.331424\n", " Iterations: 5\n", " Function evaluations: 6\n", " Gradient evaluations: 5\n", " Hessian evaluations: 5\n", "translation : [-0.06344813 0.05248288 -0.00236703]\n", "rotation : [-0.00206883 -0.00150391 0.00323764]\n", "\n", "Estimating motion at time frame 40/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.332068\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [-0.05817704 0.04050488 0.00172802]\n", "rotation : [-0.00270139 -0.00021641 0.00248832]\n", "\n", "Estimating motion at time frame 41/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.332556\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [-0.04310825 0.0678692 -0.01390054]\n", "rotation : [-0.00279281 -0.00078861 0.00129305]\n", "\n", "Estimating motion at time frame 42/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.333027\n", " Iterations: 9\n", " Function evaluations: 14\n", " Gradient evaluations: 9\n", " Hessian evaluations: 9\n", "translation : [-0.06854416 0.07364128 -0.00495403]\n", "rotation : [-0.0015759 -0.00107647 0.00284884]\n", "\n", "Estimating motion at time frame 43/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.333752\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [-0.06137069 0.08797754 -0.00525977]\n", "rotation : [-0.00206654 -0.00224306 0.00274956]\n", "\n", "Estimating motion at time frame 44/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.334314\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [-0.06985808 0.05276079 -0.03439053]\n", "rotation : [-0.00206969 -0.00186585 0.00200181]\n", "\n", "Estimating motion at time frame 45/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.334659\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.03030906 0.02972735 -0.04298656]\n", "rotation : [-0.00079602 -0.00133197 0.00242659]\n", "\n", "Estimating motion at time frame 46/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.335193\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [-0.01221156 0.00102452 -0.04022707]\n", "rotation : [-0.00125354 -0.00205158 0.00233021]\n", "\n", "Estimating motion at time frame 47/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.335613\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.04341035 -0.0049739 -0.02799616]\n", "rotation : [-0.00100477 -0.00200897 0.00216562]\n", "\n", "Estimating motion at time frame 48/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.335890\n", " Iterations: 10\n", " Function evaluations: 18\n", " Gradient evaluations: 10\n", " Hessian evaluations: 10\n", "translation : [-0.03538467 -0.01102595 -0.01825483]\n", "rotation : [-0.0003946 -0.00063465 0.00252902]\n", "\n", "Estimating motion at time frame 49/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.336013\n", " Iterations: 5\n", " Function evaluations: 6\n", " Gradient evaluations: 5\n", " Hessian evaluations: 5\n", "translation : [-0.03717283 -0.02013523 -0.02840619]\n", "rotation : [ -9.54284089e-04 -6.14318301e-04 8.52956183e-05]\n", "\n", "Estimating motion at time frame 50/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.336206\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [-0.04748687 0.00804308 -0.02270663]\n", "rotation : [-0.00154504 -0.00035799 0.00044997]\n", "\n", "Estimating motion at time frame 51/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.336584\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.05698963 0.01673125 -0.05518179]\n", "rotation : [-0.00082174 -0.00118267 0.0023395 ]\n", "\n", "Estimating motion at time frame 52/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.336860\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.05609593 0.03396686 -0.03862095]\n", "rotation : [-0.00121126 -0.00128972 0.00154146]\n", "\n", "Estimating motion at time frame 53/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.337013\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.04869735 0.01402427 -0.00464874]\n", "rotation : [-0.00062059 -0.00166526 0.00062867]\n", "\n", "Estimating motion at time frame 54/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.337092\n", " Iterations: 7\n", " Function evaluations: 16\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.05003204 0.00339976 -0.01276425]\n", "rotation : [-0.00031503 -0.00090519 0.00045563]\n", "\n", "Estimating motion at time frame 55/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.337227\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [-0.04349731 -0.00353608 -0.01696442]\n", "rotation : [-0.00103095 -0.00085542 0.00051787]\n", "\n", "Estimating motion at time frame 56/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.337596\n", " Iterations: 5\n", " Function evaluations: 6\n", " Gradient evaluations: 5\n", " Hessian evaluations: 5\n", "translation : [-0.05004906 -0.00653723 -0.03967419]\n", "rotation : [ -1.78973078e-03 5.05032084e-06 1.57957490e-03]\n", "\n", "Estimating motion at time frame 57/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.337847\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [-0.01191324 -0.03160111 -0.04529673]\n", "rotation : [-0.00092493 0.00028858 0.00178701]\n", "\n", "Estimating motion at time frame 58/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.338220\n", " Iterations: 8\n", " Function evaluations: 10\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [-0.0279708 -0.01948132 -0.03606258]\n", "rotation : [-0.00070885 -0.00148397 0.00256994]\n", "\n", "Estimating motion at time frame 59/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.338618\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [-0.01433163 -0.01881631 -0.02049285]\n", "rotation : [-0.00100585 -0.00245132 0.00141272]\n", "\n", "Estimating motion at time frame 60/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.338858\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [-0.00108222 -0.02292137 -0.02298462]\n", "rotation : [-0.00116241 -0.00130386 0.00087814]\n", "\n", "Estimating motion at time frame 61/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.339148\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.02598379 -0.03919426 -0.02248411]\n", "rotation : [-0.00012567 -0.00132818 0.00230574]\n", "\n", "Estimating motion at time frame 62/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.339286\n", " Iterations: 9\n", " Function evaluations: 21\n", " Gradient evaluations: 9\n", " Hessian evaluations: 9\n", "translation : [ 0.01164239 -0.02974702 -0.05085832]\n", "rotation : [-0.00030313 -0.00070785 0.00075973]\n", "\n", "Estimating motion at time frame 63/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.339431\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.02263601 -0.05478358 -0.01399595]\n", "rotation : [-0.00080768 -0.00011674 -0.00089079]\n", "\n", "Estimating motion at time frame 64/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.339564\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.01657645 -0.03539572 -0.0170142 ]\n", "rotation : [-0.00108849 0.00011566 0.00053684]\n", "\n", "Estimating motion at time frame 65/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.339675\n", " Iterations: 5\n", " Function evaluations: 6\n", " Gradient evaluations: 5\n", " Hessian evaluations: 5\n", "translation : [-0.01250229 0.00553144 -0.03246694]\n", "rotation : [-0.00083816 -0.00027319 0.00113302]\n", "\n", "Estimating motion at time frame 66/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.339718\n", " Iterations: 5\n", " Function evaluations: 6\n", " Gradient evaluations: 5\n", " Hessian evaluations: 5\n", "translation : [ 0.00418873 0.00421107 -0.02256135]\n", "rotation : [-0.00076903 0.00020674 -0.00034165]\n", "\n", "Estimating motion at time frame 67/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.339762\n", " Iterations: 7\n", " Function evaluations: 10\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.00200664 0.00601005 -0.00235788]\n", "rotation : [-0.00065247 -0.00062159 0.00032407]\n", "\n", "Estimating motion at time frame 68/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.339887\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [-0.02740161 0.01438967 -0.03739854]\n", "rotation : [-0.00103639 0.00040474 0.00068106]\n", "\n", "Estimating motion at time frame 69/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.340114\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.02629211 0.03814909 -0.03221215]\n", "rotation : [ -1.79513063e-03 -9.32601530e-04 9.79540465e-05]\n", "\n", "Estimating motion at time frame 70/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.340211\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [-0.02957896 0.05199633 -0.00323779]\n", "rotation : [-0.00082505 -0.00090535 0.0005457 ]\n", "\n", "Estimating motion at time frame 71/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.340300\n", " Iterations: 10\n", " Function evaluations: 17\n", " Gradient evaluations: 10\n", " Hessian evaluations: 10\n", "translation : [-0.00718381 0.02910407 -0.00346897]\n", "rotation : [-0.0010776 0.0001562 -0.00107883]\n", "\n", "Estimating motion at time frame 72/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.340442\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.00775969 0.05139811 -0.02021846]\n", "rotation : [ -1.42667468e-03 -8.39688651e-04 -6.75493063e-05]\n", "\n", "Estimating motion at time frame 73/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.340712\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.04260424 0.01795161 -0.01386799]\n", "rotation : [-0.00199605 -0.000318 0.00088944]\n", "\n", "Estimating motion at time frame 74/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.341122\n", " Iterations: 32\n", " Function evaluations: 33\n", " Gradient evaluations: 32\n", " Hessian evaluations: 32\n", "translation : [ 0.00545324 -0.00057299 -0.0383464 ]\n", "rotation : [ 0.00237527 -0.00361484 -0.00148203]\n", "\n", "Estimating motion at time frame 75/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.342133\n", " Iterations: 11\n", " Function evaluations: 12\n", " Gradient evaluations: 11\n", " Hessian evaluations: 11\n", "translation : [-0.00985614 -0.03716704 0.03692394]\n", "rotation : [ 0.0046797 -0.00062828 -0.00015836]\n", "\n", "Estimating motion at time frame 76/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.342270\n", " Iterations: 9\n", " Function evaluations: 10\n", " Gradient evaluations: 9\n", " Hessian evaluations: 9\n", "translation : [ 0.00119713 -0.00800735 -0.03200265]\n", "rotation : [ -1.20977598e-03 2.36094444e-05 -1.35211622e-03]\n", "\n", "Estimating motion at time frame 77/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.342411\n", " Iterations: 10\n", " Function evaluations: 11\n", " Gradient evaluations: 10\n", " Hessian evaluations: 10\n", "translation : [ 0.02337243 -0.02762587 -0.014732 ]\n", "rotation : [-0.0004642 -0.00130514 -0.00032328]\n", "\n", "Estimating motion at time frame 78/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.342502\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [ 0.01057088 -0.04693108 -0.01642626]\n", "rotation : [-0.00050957 -0.00031952 -0.00074765]\n", "\n", "Estimating motion at time frame 79/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.342584\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [ 0.0192872 -0.0267163 -0.0065161]\n", "rotation : [ 0.00074673 -0.00032648 -0.0011559 ]\n", "\n", "Estimating motion at time frame 80/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.342656\n", " Iterations: 7\n", " Function evaluations: 9\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [ 0.01390969 -0.04160831 -0.01860688]\n", "rotation : [ 1.12263547e-03 1.71786680e-04 -1.14631625e-05]\n", "\n", "Estimating motion at time frame 81/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.342757\n", " Iterations: 9\n", " Function evaluations: 10\n", " Gradient evaluations: 9\n", " Hessian evaluations: 9\n", "translation : [-0.00437215 -0.04398502 0.02068755]\n", "rotation : [ 0.0008381 -0.0005034 -0.00122234]\n", "\n", "Estimating motion at time frame 82/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.343193\n", " Iterations: 13\n", " Function evaluations: 22\n", " Gradient evaluations: 13\n", " Hessian evaluations: 13\n", "translation : [ 0.03858513 0.00710725 0.00365776]\n", "rotation : [ -5.70756825e-05 -2.62682955e-04 -4.09164774e-03]\n", "\n", "Estimating motion at time frame 83/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.343384\n", " Iterations: 10\n", " Function evaluations: 13\n", " Gradient evaluations: 10\n", " Hessian evaluations: 10\n", "translation : [ 0.04302645 0.00604935 0.0029121 ]\n", "rotation : [-0.00088037 0.00096013 -0.00217519]\n", "\n", "Estimating motion at time frame 84/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.343506\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [ 0.02755571 0.00887969 0.01481617]\n", "rotation : [-0.00119192 0.00040358 -0.00118636]\n", "\n", "Estimating motion at time frame 85/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.343582\n", " Iterations: 9\n", " Function evaluations: 10\n", " Gradient evaluations: 9\n", " Hessian evaluations: 9\n", "translation : [ 0.03266299 -0.00223022 0.00973307]\n", "rotation : [-0.00055669 -0.00062248 -0.00044822]\n", "\n", "Estimating motion at time frame 86/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.343593\n", " Iterations: 11\n", " Function evaluations: 18\n", " Gradient evaluations: 11\n", " Hessian evaluations: 11\n", "translation : [ 0.00668905 0.01747903 0.01249129]\n", "rotation : [ 0.00010769 0.00010073 -0.00017251]\n", "\n", "Estimating motion at time frame 87/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.343683\n", " Iterations: 9\n", " Function evaluations: 10\n", " Gradient evaluations: 9\n", " Hessian evaluations: 9\n", "translation : [-0.01824415 -0.01919846 0.02590943]\n", "rotation : [-0.00019907 -0.0001975 -0.00140736]\n", "\n", "Estimating motion at time frame 88/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.343944\n", " Iterations: 10\n", " Function evaluations: 11\n", " Gradient evaluations: 10\n", " Hessian evaluations: 10\n", "translation : [ 0.02928954 -0.02091411 -0.00105839]\n", "rotation : [-0.00121313 0.00041463 -0.00257489]\n", "\n", "Estimating motion at time frame 89/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.344066\n", " Iterations: 10\n", " Function evaluations: 15\n", " Gradient evaluations: 10\n", " Hessian evaluations: 10\n", "translation : [ 0.04198033 -0.02848598 -0.00457596]\n", "rotation : [-0.00054806 0.00046423 -0.00153011]\n", "\n", "Estimating motion at time frame 90/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.344229\n", " Iterations: 11\n", " Function evaluations: 12\n", " Gradient evaluations: 11\n", " Hessian evaluations: 11\n", "translation : [ 0.02558585 -0.05281025 0.00853285]\n", "rotation : [-0.00077661 0.00101494 -0.00153907]\n", "\n", "Estimating motion at time frame 91/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.344396\n", " Iterations: 10\n", " Function evaluations: 11\n", " Gradient evaluations: 10\n", " Hessian evaluations: 10\n", "translation : [ 0.01860164 -0.04016875 0.00232094]\n", "rotation : [-0.00084075 0.00110776 -0.00169809]\n", "\n", "Estimating motion at time frame 92/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.344508\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [ 0.02455082 -0.00546642 0.00193785]\n", "rotation : [-0.00091901 -0.00026672 0.00108196]\n", "\n", "Estimating motion at time frame 93/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.344586\n", " Iterations: 8\n", " Function evaluations: 13\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [ 0.00135557 -0.01954893 -0.00635197]\n", "rotation : [-0.00056059 0.00115841 -0.00065839]\n", "\n", "Estimating motion at time frame 94/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.344629\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [ 0.01062316 -0.01098263 -0.02124509]\n", "rotation : [-0.0005415 -0.00014713 -0.00071951]\n", "\n", "Estimating motion at time frame 95/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.344728\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [ 0.01529853 0.01883825 -0.03095839]\n", "rotation : [-0.00112225 -0.00047648 0.00023511]\n", "\n", "Estimating motion at time frame 96/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.344866\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [ 0.02516524 0.02191945 0.01434922]\n", "rotation : [-0.00062199 -0.00121234 -0.00080541]\n", "\n", "Estimating motion at time frame 97/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.345081\n", " Iterations: 15\n", " Function evaluations: 16\n", " Gradient evaluations: 15\n", " Hessian evaluations: 15\n", "translation : [ 0.07724476 -0.00928004 0.00800545]\n", "rotation : [-0.00078025 -0.00052142 -0.00131838]\n", "\n", "Estimating motion at time frame 98/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.345146\n", " Iterations: 16\n", " Function evaluations: 21\n", " Gradient evaluations: 16\n", " Hessian evaluations: 16\n", "translation : [ 0.03526418 -0.01193457 0.00198287]\n", "rotation : [-0.00045877 0.00060504 -0.00132524]\n", "\n", "Estimating motion at time frame 99/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.345239\n", " Iterations: 14\n", " Function evaluations: 15\n", " Gradient evaluations: 14\n", " Hessian evaluations: 14\n", "translation : [ 0.02234311 -0.01560546 0.01374774]\n", "rotation : [ 0.00091415 -0.00016373 -0.00129539]\n", "\n", "Estimating motion at time frame 100/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.345424\n", " Iterations: 15\n", " Function evaluations: 20\n", " Gradient evaluations: 15\n", " Hessian evaluations: 15\n", "translation : [ 0.08633341 0.03612261 -0.01768435]\n", "rotation : [ 0.00029836 0.00036578 -0.00111784]\n", "\n", "Estimating motion at time frame 101/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.345635\n", " Iterations: 16\n", " Function evaluations: 17\n", " Gradient evaluations: 16\n", " Hessian evaluations: 16\n", "translation : [ 0.05806817 -0.0091085 0.00919831]\n", "rotation : [-0.00022839 -0.00031016 -0.00267494]\n", "\n", "Estimating motion at time frame 102/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.345845\n", " Iterations: 18\n", " Function evaluations: 19\n", " Gradient evaluations: 18\n", " Hessian evaluations: 18\n", "translation : [ 0.08425597 -0.0130324 -0.02093176]\n", "rotation : [-0.00031547 0.00030767 -0.00212074]\n", "\n", "Estimating motion at time frame 103/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.345935\n", " Iterations: 10\n", " Function evaluations: 11\n", " Gradient evaluations: 10\n", " Hessian evaluations: 10\n", "translation : [ 0.04253547 -0.02230563 -0.00254413]\n", "rotation : [ 0.00028129 -0.00022252 0.00093933]\n", "\n", "Estimating motion at time frame 104/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.346007\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.00385728 -0.05512885 0.03305142]\n", "rotation : [ 0.00077336 0.00020023 -0.0002717 ]\n", "\n", "Estimating motion at time frame 105/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.346294\n", " Iterations: 11\n", " Function evaluations: 12\n", " Gradient evaluations: 11\n", " Hessian evaluations: 11\n", "translation : [ 0.03692673 -0.02080838 -0.01621432]\n", "rotation : [ 0.00043149 0.00145525 -0.00306504]\n", "\n", "Estimating motion at time frame 106/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.346638\n", " Iterations: 12\n", " Function evaluations: 13\n", " Gradient evaluations: 12\n", " Hessian evaluations: 12\n", "translation : [ 0.07380531 -0.00593262 -0.04807677]\n", "rotation : [ 0.00061397 0.00075902 -0.00300646]\n", "\n", "Estimating motion at time frame 107/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.347007\n", " Iterations: 13\n", " Function evaluations: 14\n", " Gradient evaluations: 13\n", " Hessian evaluations: 13\n", "translation : [ 0.0614357 0.01523702 -0.05920555]\n", "rotation : [ 0.00144608 0.00102253 -0.00220432]\n", "\n", "Estimating motion at time frame 108/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.347731\n", " Iterations: 14\n", " Function evaluations: 15\n", " Gradient evaluations: 14\n", " Hessian evaluations: 14\n", "translation : [ 0.06925861 0.02664948 -0.04258225]\n", "rotation : [ 0.00078746 0.0016764 -0.00474793]\n", "\n", "Estimating motion at time frame 109/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.348176\n", " Iterations: 13\n", " Function evaluations: 14\n", " Gradient evaluations: 13\n", " Hessian evaluations: 13\n", "translation : [ 0.06925041 0.02416178 -0.03660971]\n", "rotation : [-0.00024309 0.00145994 -0.00389087]\n", "\n", "Estimating motion at time frame 110/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.348700\n", " Iterations: 12\n", " Function evaluations: 13\n", " Gradient evaluations: 12\n", " Hessian evaluations: 12\n", "translation : [ 0.07377188 0.04080285 -0.05015953]\n", "rotation : [ 0.00027193 0.00075089 -0.00404898]\n", "\n", "Estimating motion at time frame 111/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.349091\n", " Iterations: 12\n", " Function evaluations: 13\n", " Gradient evaluations: 12\n", " Hessian evaluations: 12\n", "translation : [ 0.08894641 0.02113464 -0.05958744]\n", "rotation : [-0.00014429 0.00040784 -0.00303715]\n", "\n", "Estimating motion at time frame 112/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.349372\n", " Iterations: 10\n", " Function evaluations: 11\n", " Gradient evaluations: 10\n", " Hessian evaluations: 10\n", "translation : [ 0.03493004 0.01593474 -0.0605416 ]\n", "rotation : [-0.00061866 0.00149317 -0.00225942]\n", "\n", "Estimating motion at time frame 113/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.349599\n", " Iterations: 12\n", " Function evaluations: 18\n", " Gradient evaluations: 12\n", " Hessian evaluations: 12\n", "translation : [ 0.05790233 -0.00254756 -0.01894323]\n", "rotation : [-0.00018387 0.00053087 -0.00272223]\n", "\n", "Estimating motion at time frame 114/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.350425\n", " Iterations: 12\n", " Function evaluations: 13\n", " Gradient evaluations: 12\n", " Hessian evaluations: 12\n", "translation : [ 0.08222547 -0.00657179 -0.0299611 ]\n", "rotation : [-0.00055494 0.00091665 -0.00546929]\n", "\n", "Estimating motion at time frame 115/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.351343\n", " Iterations: 11\n", " Function evaluations: 12\n", " Gradient evaluations: 11\n", " Hessian evaluations: 11\n", "translation : [ 0.08182988 0.00209713 -0.0534267 ]\n", "rotation : [-0.0003781 0.00171993 -0.00576221]\n", "\n", "Estimating motion at time frame 116/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.351808\n", " Iterations: 13\n", " Function evaluations: 14\n", " Gradient evaluations: 13\n", " Hessian evaluations: 13\n", "translation : [ 0.07784003 -0.00942977 -0.03611797]\n", "rotation : [ -8.82453007e-05 1.72219284e-03 -3.83960302e-03]\n", "\n", "Estimating motion at time frame 117/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.352379\n", " Iterations: 21\n", " Function evaluations: 44\n", " Gradient evaluations: 21\n", " Hessian evaluations: 21\n", "translation : [ 0.06423001 -0.0313237 -0.02518151]\n", "rotation : [ 0.00025566 0.00163512 -0.00435512]\n", "\n", "Estimating motion at time frame 118/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.352803\n", " Iterations: 11\n", " Function evaluations: 12\n", " Gradient evaluations: 11\n", " Hessian evaluations: 11\n", "translation : [ 0.0682333 -0.04534227 -0.01405482]\n", "rotation : [ 0.00028889 0.0014607 -0.00348953]\n", "\n", "Estimating motion at time frame 119/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.353074\n", " Iterations: 9\n", " Function evaluations: 19\n", " Gradient evaluations: 9\n", " Hessian evaluations: 9\n", "translation : [ 0.07402611 -0.0579042 -0.01680984]\n", "rotation : [-0.00014409 0.0012798 -0.002003 ]\n", "\n", "Estimating motion at time frame 120/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.353512\n", " Iterations: 13\n", " Function evaluations: 22\n", " Gradient evaluations: 13\n", " Hessian evaluations: 13\n", "translation : [ 0.04356846 -0.0694887 -0.00971056]\n", "rotation : [ 0.00011774 0.00180913 -0.00304784]\n", "\n", "Estimating motion at time frame 121/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.353641\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [ 0.03954592 -0.01692439 -0.00413662]\n", "rotation : [-0.00054916 0.00102438 -0.00154371]\n", "\n", "Resampling scan 1/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 2/121\n", "Resampling scan 3/121\n", "Resampling scan 4/121\n", "Resampling scan 5/121\n", "Resampling scan 6/121\n", "Resampling scan 7/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 8/121\n", "Resampling scan 9/121\n", "Resampling scan 10/121\n", "Resampling scan 11/121\n", "Resampling scan 12/121\n", "Resampling scan 13/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 14/121\n", "Resampling scan 15/121\n", "Resampling scan 16/121\n", "Resampling scan 17/121\n", "Resampling scan 18/121\n", "Resampling scan 19/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 20/121\n", "Resampling scan 21/121\n", "Resampling scan 22/121\n", "Resampling scan 23/121\n", "Resampling scan 24/121\n", "Resampling scan 25/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 26/121\n", "Resampling scan 27/121\n", "Resampling scan 28/121\n", "Resampling scan 29/121\n", "Resampling scan 30/121\n", "Resampling scan 31/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 32/121\n", "Resampling scan 33/121\n", "Resampling scan 34/121\n", "Resampling scan 35/121\n", "Resampling scan 36/121\n", "Resampling scan 37/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 38/121\n", "Resampling scan 39/121\n", "Resampling scan 40/121\n", "Resampling scan 41/121\n", "Resampling scan 42/121\n", "Resampling scan 43/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 44/121\n", "Resampling scan 45/121\n", "Resampling scan 46/121\n", "Resampling scan 47/121\n", "Resampling scan 48/121\n", "Resampling scan 49/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 50/121\n", "Resampling scan 51/121\n", "Resampling scan 52/121\n", "Resampling scan 53/121\n", "Resampling scan 54/121\n", "Resampling scan 55/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 56/121\n", "Resampling scan 57/121\n", "Resampling scan 58/121\n", "Resampling scan 59/121\n", "Resampling scan 60/121\n", "Resampling scan 61/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 62/121\n", "Resampling scan 63/121\n", "Resampling scan 64/121\n", "Resampling scan 65/121\n", "Resampling scan 66/121\n", "Resampling scan 67/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 68/121\n", "Resampling scan 69/121\n", "Resampling scan 70/121\n", "Resampling scan 71/121\n", "Resampling scan 72/121\n", "Resampling scan 73/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 74/121\n", "Resampling scan 75/121\n", "Resampling scan 76/121\n", "Resampling scan 77/121\n", "Resampling scan 78/121\n", "Resampling scan 79/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 80/121\n", "Resampling scan 81/121\n", "Resampling scan 82/121\n", "Resampling scan 83/121\n", "Resampling scan 84/121\n", "Resampling scan 85/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 86/121\n", "Resampling scan 87/121\n", "Resampling scan 88/121\n", "Resampling scan 89/121\n", "Resampling scan 90/121\n", "Resampling scan 91/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 92/121\n", "Resampling scan 93/121\n", "Resampling scan 94/121\n", "Resampling scan 95/121\n", "Resampling scan 96/121\n", "Resampling scan 97/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 98/121\n", "Resampling scan 99/121\n", "Resampling scan 100/121\n", "Resampling scan 101/121\n", "Resampling scan 102/121\n", "Resampling scan 103/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 104/121\n", "Resampling scan 105/121\n", "Resampling scan 106/121\n", "Resampling scan 107/121\n", "Resampling scan 108/121\n", "Resampling scan 109/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 110/121\n", "Resampling scan 111/121\n", "Resampling scan 112/121\n", "Resampling scan 113/121\n", "Resampling scan 114/121\n", "Resampling scan 115/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 116/121\n", "Resampling scan 117/121\n", "Resampling scan 118/121\n", "Resampling scan 119/121\n", "Resampling scan 120/121\n", "Resampling scan 121/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Estimating motion at time frame 1/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.228570\n", " Iterations: 15\n", " Function evaluations: 16\n", " Gradient evaluations: 15\n", " Hessian evaluations: 15\n", "translation : [ 0.0928405 0.0824929 -0.05528911]\n", "rotation : [-0.00367965 0.00331068 -0.00667309]\n", "\n", "Estimating motion at time frame 2/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.229909\n", " Iterations: 17\n", " Function evaluations: 18\n", " Gradient evaluations: 17\n", " Hessian evaluations: 17\n", "translation : [ 0.08420573 0.06461878 -0.05077613]\n", "rotation : [-0.00289566 0.0036068 -0.00644165]\n", "\n", "Estimating motion at time frame 3/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.231306\n", " Iterations: 13\n", " Function evaluations: 14\n", " Gradient evaluations: 13\n", " Hessian evaluations: 13\n", "translation : [ 0.09774265 0.09652555 -0.00988314]\n", "rotation : [-0.00355086 0.00332522 -0.00420032]\n", "\n", "Estimating motion at time frame 4/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.232190\n", " Iterations: 16\n", " Function evaluations: 49\n", " Gradient evaluations: 16\n", " Hessian evaluations: 16\n", "translation : [ 0.08357231 0.0591144 0.01588115]\n", "rotation : [-0.00263493 -0.00123514 -0.00372646]\n", "\n", "Estimating motion at time frame 5/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.234254\n", " Iterations: 23\n", " Function evaluations: 24\n", " Gradient evaluations: 23\n", " Hessian evaluations: 23\n", "translation : [ 0.136809 0.07515738 -0.07231451]\n", "rotation : [-0.00549132 0.00318958 -0.00609163]\n", "\n", "Estimating motion at time frame 6/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.235647\n", " Iterations: 14\n", " Function evaluations: 15\n", " Gradient evaluations: 14\n", " Hessian evaluations: 14\n", "translation : [ 0.13276624 0.08510397 -0.01282122]\n", "rotation : [-0.00393035 0.00319551 -0.00344242]\n", "\n", "Estimating motion at time frame 7/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.235902\n", " Iterations: 9\n", " Function evaluations: 10\n", " Gradient evaluations: 9\n", " Hessian evaluations: 9\n", "translation : [ 0.05725949 0.06158076 0.04304677]\n", "rotation : [-0.00133241 0.00028594 -0.00137611]\n", "\n", "Estimating motion at time frame 8/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.236225\n", " Iterations: 10\n", " Function evaluations: 11\n", " Gradient evaluations: 10\n", " Hessian evaluations: 10\n", "translation : [ 0.03468581 0.05803126 0.01005308]\n", "rotation : [-0.00163166 0.00101116 -0.0022182 ]\n", "\n", "Estimating motion at time frame 9/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.236733\n", " Iterations: 9\n", " Function evaluations: 10\n", " Gradient evaluations: 9\n", " Hessian evaluations: 9\n", "translation : [ 0.04121436 0.06689968 0.02258828]\n", "rotation : [-0.00196964 0.00177967 -0.00244442]\n", "\n", "Estimating motion at time frame 10/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.237286\n", " Iterations: 10\n", " Function evaluations: 12\n", " Gradient evaluations: 10\n", " Hessian evaluations: 10\n", "translation : [ 0.04295512 0.09884585 0.00501165]\n", "rotation : [-0.00120433 0.00196761 -0.00267496]\n", "\n", "Estimating motion at time frame 11/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.238035\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [ 0.04222575 0.07366685 0.00739301]\n", "rotation : [-0.00046409 0.00278182 -0.00305266]\n", "\n", "Estimating motion at time frame 12/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.238657\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [ 0.05338695 0.06695331 0.04210176]\n", "rotation : [-0.00150384 0.00244329 -0.00240441]\n", "\n", "Estimating motion at time frame 13/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.239197\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [ 0.065621 0.05101491 0.03134735]\n", "rotation : [-0.00114364 0.001687 -0.00297959]\n", "\n", "Estimating motion at time frame 14/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.239457\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.08133029 0.00903434 0.06158855]\n", "rotation : [ -8.41927920e-05 6.33589359e-04 -1.26310163e-03]\n", "\n", "Estimating motion at time frame 15/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.239630\n", " Iterations: 9\n", " Function evaluations: 10\n", " Gradient evaluations: 9\n", " Hessian evaluations: 9\n", "translation : [ 0.07564025 0.04449152 0.02422122]\n", "rotation : [-0.00091793 0.00041904 -0.00050127]\n", "\n", "Estimating motion at time frame 16/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.239928\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [ 0.08402639 0.04136186 0.01026489]\n", "rotation : [-0.00139998 0.00088771 -0.00160433]\n", "\n", "Estimating motion at time frame 17/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.240442\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [ 0.09475403 0.03110026 0.01365793]\n", "rotation : [-0.00179355 0.00155799 -0.0021775 ]\n", "\n", "Estimating motion at time frame 18/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.241114\n", " Iterations: 10\n", " Function evaluations: 29\n", " Gradient evaluations: 10\n", " Hessian evaluations: 10\n", "translation : [ 0.04055847 -0.00072108 0.03240029]\n", "rotation : [-0.00122351 0.00266859 -0.0026741 ]\n", "\n", "Estimating motion at time frame 19/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.241326\n", " Iterations: 7\n", " Function evaluations: 14\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [ 0.05106579 0.0271263 0.00055459]\n", "rotation : [-0.00122936 0.00170747 -0.00069849]\n", "\n", "Estimating motion at time frame 20/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.241532\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.06391328 0.06223062 -0.00997077]\n", "rotation : [-0.00147092 0.00083823 -0.00069011]\n", "\n", "Estimating motion at time frame 21/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.242245\n", " Iterations: 11\n", " Function evaluations: 48\n", " Gradient evaluations: 11\n", " Hessian evaluations: 11\n", "translation : [ 0.04988672 0.06107668 0.02360532]\n", "rotation : [-0.00107107 0.00226381 -0.00322457]\n", "\n", "Estimating motion at time frame 22/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.242863\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.06071055 0.08175623 0.04275734]\n", "rotation : [-0.0004427 0.00216792 -0.00262428]\n", "\n", "Estimating motion at time frame 23/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.243391\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [ 0.04808851 0.10466316 0.00936531]\n", "rotation : [-0.00193719 0.0016874 -0.00222875]\n", "\n", "Estimating motion at time frame 24/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.244223\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [ 0.04936955 0.13153549 0.0194837 ]\n", "rotation : [-0.0019608 0.00248492 -0.00259026]\n", "\n", "Estimating motion at time frame 25/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.244689\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [ 0.04101567 0.09032737 0.03450237]\n", "rotation : [-0.00058128 0.00189287 -0.00216156]\n", "\n", "Estimating motion at time frame 26/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.244920\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [ 0.04742626 0.086552 0.04277334]\n", "rotation : [-0.0008517 0.00085971 -0.00095615]\n", "\n", "Estimating motion at time frame 27/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.245172\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.04978739 0.08637279 0.01726275]\n", "rotation : [-0.00100685 0.00104889 -0.00138639]\n", "\n", "Estimating motion at time frame 28/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.245714\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.03279571 0.09615505 0.00593713]\n", "rotation : [-0.00103507 0.00189267 -0.00259049]\n", "\n", "Estimating motion at time frame 29/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.246218\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.04396378 0.07894392 -0.00125892]\n", "rotation : [-0.00115627 0.00143768 -0.00298827]\n", "\n", "Estimating motion at time frame 30/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.246637\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.0660508 0.05286477 0.01305228]\n", "rotation : [-0.00114233 0.00196406 -0.00207181]\n", "\n", "Estimating motion at time frame 31/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.247084\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.09729784 0.04194111 0.0015678 ]\n", "rotation : [-0.00121301 0.0019433 -0.00195455]\n", "\n", "Estimating motion at time frame 32/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.247752\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [ 0.12783477 0.01569704 0.01534836]\n", "rotation : [-0.00191041 0.00143087 -0.00242989]\n", "\n", "Estimating motion at time frame 33/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.248190\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.0946993 0.0083408 0.01147504]\n", "rotation : [-0.00160914 0.00095296 -0.00219423]\n", "\n", "Estimating motion at time frame 34/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.248398\n", " Iterations: 5\n", " Function evaluations: 6\n", " Gradient evaluations: 5\n", " Hessian evaluations: 5\n", "translation : [ 0.06401379 0.0031143 0.00774993]\n", "rotation : [-0.00121185 0.00145649 -0.00059329]\n", "\n", "Estimating motion at time frame 35/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.248688\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.07207341 0.03531322 0.00315252]\n", "rotation : [-0.00134662 0.00193067 -0.00060946]\n", "\n", "Estimating motion at time frame 36/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.248979\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.06527982 0.03665195 0.01239606]\n", "rotation : [-0.00144513 0.00161157 -0.0012237 ]\n", "\n", "Estimating motion at time frame 37/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.249391\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [ 0.06753383 0.04112264 -0.01473138]\n", "rotation : [-0.00138357 0.00206777 -0.00198453]\n", "\n", "Estimating motion at time frame 38/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.250104\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.06207367 0.07646453 -0.00823514]\n", "rotation : [-0.00073724 0.00283231 -0.00281993]\n", "\n", "Estimating motion at time frame 39/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.250589\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.04420616 0.08443218 0.00565777]\n", "rotation : [-0.00023606 0.00206399 -0.00232473]\n", "\n", "Estimating motion at time frame 40/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.250831\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.05981024 0.08125197 0.02907032]\n", "rotation : [-0.00070944 0.00095977 -0.0012444 ]\n", "\n", "Estimating motion at time frame 41/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.251002\n", " Iterations: 5\n", " Function evaluations: 6\n", " Gradient evaluations: 5\n", " Hessian evaluations: 5\n", "translation : [ 0.04562481 0.08388892 0.0173778 ]\n", "rotation : [-0.00090366 0.00039676 -0.00086676]\n", "\n", "Estimating motion at time frame 42/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.251219\n", " Iterations: 5\n", " Function evaluations: 6\n", " Gradient evaluations: 5\n", " Hessian evaluations: 5\n", "translation : [ 0.04141787 0.07179302 0.01945288]\n", "rotation : [-0.00156528 0.00028405 -0.00107883]\n", "\n", "Estimating motion at time frame 43/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.251528\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.05849286 0.05966207 0.00888164]\n", "rotation : [-0.00170638 0.00127568 -0.00139644]\n", "\n", "Estimating motion at time frame 44/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.251746\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.06150251 0.03359313 0.01072147]\n", "rotation : [-0.0011392 0.00116635 -0.00132572]\n", "\n", "Estimating motion at time frame 45/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.251988\n", " Iterations: 5\n", " Function evaluations: 6\n", " Gradient evaluations: 5\n", " Hessian evaluations: 5\n", "translation : [ 0.06719649 0.01640111 0.02186403]\n", "rotation : [-0.00152089 0.00016341 -0.00100615]\n", "\n", "Estimating motion at time frame 46/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.252244\n", " Iterations: 6\n", " Function evaluations: 10\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.07364443 0.00486995 0.02715408]\n", "rotation : [-0.00139725 -0.00019255 0.00010548]\n", "\n", "Estimating motion at time frame 47/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.252393\n", " Iterations: 5\n", " Function evaluations: 6\n", " Gradient evaluations: 5\n", " Hessian evaluations: 5\n", "translation : [ 0.05410682 0.01228795 0.00757718]\n", "rotation : [-0.00103179 0.00118967 -0.0006771 ]\n", "\n", "Estimating motion at time frame 48/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.252682\n", " Iterations: 5\n", " Function evaluations: 6\n", " Gradient evaluations: 5\n", " Hessian evaluations: 5\n", "translation : [ 0.06903241 -0.00041679 -0.00171434]\n", "rotation : [-0.00050884 0.00198261 -0.00156823]\n", "\n", "Estimating motion at time frame 49/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.253007\n", " Iterations: 5\n", " Function evaluations: 6\n", " Gradient evaluations: 5\n", " Hessian evaluations: 5\n", "translation : [ 0.07318316 0.01540697 -0.00072898]\n", "rotation : [-0.00115023 0.00163357 -0.00183896]\n", "\n", "Estimating motion at time frame 50/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.253261\n", " Iterations: 5\n", " Function evaluations: 6\n", " Gradient evaluations: 5\n", " Hessian evaluations: 5\n", "translation : [ 0.08036641 0.02275932 -0.00038109]\n", "rotation : [-0.00136086 0.00102118 -0.0011162 ]\n", "\n", "Estimating motion at time frame 51/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.253512\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.09510265 0.02879701 -0.00359076]\n", "rotation : [-0.00105605 0.0001095 -0.00063819]\n", "\n", "Estimating motion at time frame 52/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.253910\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.0890555 0.08316911 0.00321044]\n", "rotation : [-0.00180929 0.00042326 -0.00146206]\n", "\n", "Estimating motion at time frame 53/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.254253\n", " Iterations: 5\n", " Function evaluations: 6\n", " Gradient evaluations: 5\n", " Hessian evaluations: 5\n", "translation : [ 0.05301937 0.08832589 0.02252406]\n", "rotation : [-0.00163929 0.0006754 -0.00167777]\n", "\n", "Estimating motion at time frame 54/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.254787\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [ 0.07665849 0.07812911 0.01488443]\n", "rotation : [-0.00175515 0.00152688 -0.00239155]\n", "\n", "Estimating motion at time frame 55/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.255345\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.10615686 0.06677708 0.01971862]\n", "rotation : [-0.00164334 0.0022423 -0.00154506]\n", "\n", "Estimating motion at time frame 56/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.255738\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.07953384 0.09492814 -0.00124501]\n", "rotation : [-0.00169357 0.00142822 -0.00129684]\n", "\n", "Estimating motion at time frame 57/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.255972\n", " Iterations: 5\n", " Function evaluations: 6\n", " Gradient evaluations: 5\n", " Hessian evaluations: 5\n", "translation : [ 0.05493044 0.08681759 0.00869778]\n", "rotation : [ -1.68440039e-04 -4.37408821e-05 -1.36115783e-03]\n", "\n", "Estimating motion at time frame 58/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.256279\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.07012222 0.09738251 0.01148545]\n", "rotation : [-0.00059864 0.00058505 -0.00165494]\n", "\n", "Estimating motion at time frame 59/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.256656\n", " Iterations: 9\n", " Function evaluations: 10\n", " Gradient evaluations: 9\n", " Hessian evaluations: 9\n", "translation : [ 0.06365562 0.08515206 0.00557216]\n", "rotation : [-0.00149721 0.00120295 -0.0022745 ]\n", "\n", "Estimating motion at time frame 60/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.256924\n", " Iterations: 7\n", " Function evaluations: 11\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [ 0.05140845 0.06887741 0.01422709]\n", "rotation : [-0.00162994 0.00108643 -0.00118339]\n", "\n", "Estimating motion at time frame 61/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.257164\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.08863064 0.03957472 -0.00403691]\n", "rotation : [-0.00066222 0.0013764 -0.00103643]\n", "\n", "Estimating motion at time frame 62/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.257431\n", " Iterations: 5\n", " Function evaluations: 6\n", " Gradient evaluations: 5\n", " Hessian evaluations: 5\n", "translation : [ 0.09079453 0.08681764 -0.00580222]\n", "rotation : [-0.00027271 0.00080759 -0.00046947]\n", "\n", "Estimating motion at time frame 63/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.257632\n", " Iterations: 5\n", " Function evaluations: 6\n", " Gradient evaluations: 5\n", " Hessian evaluations: 5\n", "translation : [ 0.02440901 0.09803694 0.0226198 ]\n", "rotation : [-0.00095008 0.00074704 -0.00080478]\n", "\n", "Estimating motion at time frame 64/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.257860\n", " Iterations: 5\n", " Function evaluations: 6\n", " Gradient evaluations: 5\n", " Hessian evaluations: 5\n", "translation : [ 0.02816799 0.09626948 0.02524006]\n", "rotation : [-0.0010581 0.00098978 -0.00082998]\n", "\n", "Estimating motion at time frame 65/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.258101\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.06821181 0.09119939 0.00503771]\n", "rotation : [-0.00092308 0.00063371 -0.00107866]\n", "\n", "Estimating motion at time frame 66/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.258379\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.061189 0.08960167 -0.01081194]\n", "rotation : [ 0.0002089 0.00109098 -0.00120167]\n", "\n", "Estimating motion at time frame 67/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.258815\n", " Iterations: 7\n", " Function evaluations: 14\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [ 0.03994102 0.11743324 0.00371308]\n", "rotation : [ 0.00050593 0.00132677 -0.00142582]\n", "\n", "Estimating motion at time frame 68/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.259011\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [ 0.05100474 0.07967833 0.03467865]\n", "rotation : [ -8.99534312e-04 5.84183275e-05 -8.87998991e-04]\n", "\n", "Estimating motion at time frame 69/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.259306\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.06313383 0.04910031 0.01508577]\n", "rotation : [-0.00204349 0.00057117 -0.00085268]\n", "\n", "Estimating motion at time frame 70/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.259727\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.04063606 0.05659403 -0.00179231]\n", "rotation : [-0.00197692 0.00114706 -0.0021358 ]\n", "\n", "Estimating motion at time frame 71/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.259934\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [ 0.01356458 0.06548348 -0.0082509 ]\n", "rotation : [-0.00025811 0.00047022 -0.00197593]\n", "\n", "Estimating motion at time frame 72/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.260125\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [ 0.03655798 0.04230064 -0.00288208]\n", "rotation : [ 4.69256344e-06 4.47242752e-04 -2.04594078e-03]\n", "\n", "Estimating motion at time frame 73/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.260345\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.01883579 0.04224777 -0.02157423]\n", "rotation : [-0.00108987 0.00122011 -0.00159342]\n", "\n", "Estimating motion at time frame 74/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.260594\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.00915303 0.03577594 -0.02508677]\n", "rotation : [-0.00059007 0.00151025 -0.00158937]\n", "\n", "Estimating motion at time frame 75/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.260736\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.03518211 0.02491359 -0.01051802]\n", "rotation : [-0.00076327 0.00118711 -0.00122177]\n", "\n", "Estimating motion at time frame 76/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.260976\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.02385258 -0.00376197 -0.00824012]\n", "rotation : [-0.00075588 0.00194379 -0.00109114]\n", "\n", "Estimating motion at time frame 77/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.261256\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [ 0.0038264 0.01507509 -0.05237516]\n", "rotation : [-0.00113635 0.00185182 -0.00095407]\n", "\n", "Estimating motion at time frame 78/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.261430\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [-0.00901972 0.01384602 -0.03408313]\n", "rotation : [-0.00130392 0.00099368 -0.00075183]\n", "\n", "Estimating motion at time frame 79/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.261771\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.00855141 -0.00746364 -0.04099544]\n", "rotation : [-0.00119573 0.00168016 -0.00169187]\n", "\n", "Estimating motion at time frame 80/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.261971\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [-0.00172786 0.03210097 -0.04348428]\n", "rotation : [-0.00125879 0.0012676 -0.00085442]\n", "\n", "Estimating motion at time frame 81/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.262416\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.00481503 0.05004232 -0.04384281]\n", "rotation : [-0.00192991 0.00200978 -0.00112746]\n", "\n", "Estimating motion at time frame 82/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.262634\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.01712107 0.05893355 -0.02231939]\n", "rotation : [-0.00121284 0.00041921 -0.00154504]\n", "\n", "Estimating motion at time frame 83/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.262785\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.00314738 0.03581211 -0.00719289]\n", "rotation : [-0.00033494 0.00107851 -0.001358 ]\n", "\n", "Estimating motion at time frame 84/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.262927\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [ 0.00550873 0.0301759 -0.03876229]\n", "rotation : [ 0.00011474 0.00104103 -0.00115769]\n", "\n", "Estimating motion at time frame 85/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.262960\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.00383364 -0.00571865 -0.02815694]\n", "rotation : [-0.0002396 0.00012852 -0.00070836]\n", "\n", "Estimating motion at time frame 86/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.263113\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [-0.00844033 -0.00251681 -0.0560241 ]\n", "rotation : [-0.00079988 0.00013673 -0.00141199]\n", "\n", "Estimating motion at time frame 87/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.263424\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [-0.04397335 0.04087934 -0.0488733 ]\n", "rotation : [-0.00048643 0.00112468 -0.00147537]\n", "\n", "Estimating motion at time frame 88/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.263847\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.05443355 0.01102065 -0.04163745]\n", "rotation : [-0.00147874 0.00151981 -0.00073655]\n", "\n", "Estimating motion at time frame 89/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.264227\n", " Iterations: 9\n", " Function evaluations: 10\n", " Gradient evaluations: 9\n", " Hessian evaluations: 9\n", "translation : [ 0.0137017 0.00155435 -0.06099358]\n", "rotation : [-0.00153089 0.00187436 -0.00139481]\n", "\n", "Estimating motion at time frame 90/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.264396\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [ 0.03369056 -0.01212639 -0.05550729]\n", "rotation : [ 0.00064029 0.00081929 -0.00136932]\n", "\n", "Estimating motion at time frame 91/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.264576\n", " Iterations: 7\n", " Function evaluations: 9\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [ 0.01273073 -0.03935784 -0.03533869]\n", "rotation : [-0.00066981 0.00046175 -0.00175187]\n", "\n", "Estimating motion at time frame 92/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.264799\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [-0.00123724 -0.00244461 -0.01482723]\n", "rotation : [-0.00131161 0.00122619 -0.00121196]\n", "\n", "Estimating motion at time frame 93/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.265075\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.01687181 -0.02739558 -0.04418942]\n", "rotation : [-0.00116768 0.00134481 -0.0011268 ]\n", "\n", "Estimating motion at time frame 94/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.265293\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [-0.02550994 -0.00388278 -0.05670067]\n", "rotation : [-0.00048232 0.0010919 -0.00111134]\n", "\n", "Estimating motion at time frame 95/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.265602\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [-0.04333048 0.0242291 -0.02942412]\n", "rotation : [ 0.0005366 0.00128512 -0.00129498]\n", "\n", "Estimating motion at time frame 96/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.265652\n", " Iterations: 11\n", " Function evaluations: 12\n", " Gradient evaluations: 11\n", " Hessian evaluations: 11\n", "translation : [ 0.0038564 0.00696313 -0.03488493]\n", "rotation : [-0.00017782 0.00061746 -0.00074607]\n", "\n", "Estimating motion at time frame 97/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.265676\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [ 0.01580987 -0.00526898 -0.01970099]\n", "rotation : [-0.00040239 0.00035874 -0.00015379]\n", "\n", "Estimating motion at time frame 98/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.265775\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.03939152 -0.00252149 -0.04152317]\n", "rotation : [-0.00010382 0.00047695 -0.00027781]\n", "\n", "Estimating motion at time frame 99/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.265969\n", " Iterations: 10\n", " Function evaluations: 11\n", " Gradient evaluations: 10\n", " Hessian evaluations: 10\n", "translation : [-0.02264445 -0.03513545 -0.03775616]\n", "rotation : [-0.00087369 -0.00145602 -0.00037422]\n", "\n", "Estimating motion at time frame 100/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.266219\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [-0.00522399 -0.01944848 -0.04969151]\n", "rotation : [-0.00124307 -0.00104335 -0.00123545]\n", "\n", "Estimating motion at time frame 101/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.266386\n", " Iterations: 8\n", " Function evaluations: 14\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [-0.03083065 -0.01543366 -0.06430479]\n", "rotation : [-0.00048379 -0.00111901 -0.00055372]\n", "\n", "Estimating motion at time frame 102/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.266607\n", " Iterations: 10\n", " Function evaluations: 11\n", " Gradient evaluations: 10\n", " Hessian evaluations: 10\n", "translation : [-0.01287267 -0.02221365 -0.07095766]\n", "rotation : [ -2.81356820e-05 -1.65226936e-03 1.21998863e-05]\n", "\n", "Estimating motion at time frame 103/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.266746\n", " Iterations: 10\n", " Function evaluations: 11\n", " Gradient evaluations: 10\n", " Hessian evaluations: 10\n", "translation : [-0.01140426 -0.04935094 -0.02589236]\n", "rotation : [ -1.19033106e-04 -1.47053143e-03 4.63962468e-05]\n", "\n", "Estimating motion at time frame 104/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.266997\n", " Iterations: 9\n", " Function evaluations: 11\n", " Gradient evaluations: 9\n", " Hessian evaluations: 9\n", "translation : [ 0.01189447 -0.0749376 -0.04867406]\n", "rotation : [ -1.56001033e-04 -1.45246059e-03 -6.05109042e-05]\n", "\n", "Estimating motion at time frame 105/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.267223\n", " Iterations: 12\n", " Function evaluations: 13\n", " Gradient evaluations: 12\n", " Hessian evaluations: 12\n", "translation : [ 0.01074166 -0.07342572 -0.06813963]\n", "rotation : [ 0.00068774 -0.00103643 -0.00011251]\n", "\n", "Estimating motion at time frame 106/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.267454\n", " Iterations: 12\n", " Function evaluations: 13\n", " Gradient evaluations: 12\n", " Hessian evaluations: 12\n", "translation : [ 0.01612614 -0.04847578 -0.02404427]\n", "rotation : [ 0.00030053 -0.00172206 0.00075894]\n", "\n", "Estimating motion at time frame 107/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.267548\n", " Iterations: 8\n", " Function evaluations: 16\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [ 0.00270652 -0.04660136 -0.04077648]\n", "rotation : [ 0.000855 -0.00040705 0.00012317]\n", "\n", "Estimating motion at time frame 108/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.267728\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [-0.01133377 -0.07824919 -0.06101175]\n", "rotation : [ 0.00107868 0.00015115 0.00028908]\n", "\n", "Estimating motion at time frame 109/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.267855\n", " Iterations: 12\n", " Function evaluations: 13\n", " Gradient evaluations: 12\n", " Hessian evaluations: 12\n", "translation : [-0.00098519 -0.05691265 -0.05627393]\n", "rotation : [-0.00018433 0.00014404 0.00051418]\n", "\n", "Estimating motion at time frame 110/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.267983\n", " Iterations: 10\n", " Function evaluations: 11\n", " Gradient evaluations: 10\n", " Hessian evaluations: 10\n", "translation : [ 0.01807697 -0.01898413 -0.02839673]\n", "rotation : [-0.00051442 -0.00095525 0.00082246]\n", "\n", "Estimating motion at time frame 111/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.268156\n", " Iterations: 11\n", " Function evaluations: 12\n", " Gradient evaluations: 11\n", " Hessian evaluations: 11\n", "translation : [-0.0287268 -0.03453731 -0.05730409]\n", "rotation : [ -5.94014656e-05 -2.90817307e-04 1.55955499e-03]\n", "\n", "Estimating motion at time frame 112/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.268471\n", " Iterations: 13\n", " Function evaluations: 14\n", " Gradient evaluations: 13\n", " Hessian evaluations: 13\n", "translation : [-0.0189768 -0.08990759 -0.048834 ]\n", "rotation : [ 0.00033243 -0.00124208 0.00197982]\n", "\n", "Estimating motion at time frame 113/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.268848\n", " Iterations: 15\n", " Function evaluations: 16\n", " Gradient evaluations: 15\n", " Hessian evaluations: 15\n", "translation : [ 0.02122867 -0.08448683 -0.03509547]\n", "rotation : [ -7.79673167e-05 -1.77331728e-03 1.48254840e-03]\n", "\n", "Estimating motion at time frame 114/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.269001\n", " Iterations: 12\n", " Function evaluations: 13\n", " Gradient evaluations: 12\n", " Hessian evaluations: 12\n", "translation : [-0.01779981 -0.04777772 -0.04415282]\n", "rotation : [ 0.00063578 -0.00122383 0.00099418]\n", "\n", "Estimating motion at time frame 115/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.269204\n", " Iterations: 14\n", " Function evaluations: 15\n", " Gradient evaluations: 14\n", " Hessian evaluations: 14\n", "translation : [-0.05455476 -0.06039205 -0.07032252]\n", "rotation : [ 0.0004251 -0.00063709 0.00070238]\n", "\n", "Estimating motion at time frame 116/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.269426\n", " Iterations: 14\n", " Function evaluations: 15\n", " Gradient evaluations: 14\n", " Hessian evaluations: 14\n", "translation : [-0.00323338 -0.0143498 -0.0499116 ]\n", "rotation : [-0.00060287 -0.00200077 0.00069335]\n", "\n", "Estimating motion at time frame 117/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.269687\n", " Iterations: 16\n", " Function evaluations: 17\n", " Gradient evaluations: 16\n", " Hessian evaluations: 16\n", "translation : [-0.06525326 -0.01488982 -0.06911156]\n", "rotation : [-0.00045193 -0.00142579 0.00145967]\n", "\n", "Estimating motion at time frame 118/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.270018\n", " Iterations: 12\n", " Function evaluations: 14\n", " Gradient evaluations: 12\n", " Hessian evaluations: 12\n", "translation : [-0.0656423 -0.05013085 -0.09102045]\n", "rotation : [-0.00061034 -0.00033217 0.00113868]\n", "\n", "Estimating motion at time frame 119/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.270278\n", " Iterations: 13\n", " Function evaluations: 14\n", " Gradient evaluations: 13\n", " Hessian evaluations: 13\n", "translation : [-0.03092427 -0.04232494 -0.07515309]\n", "rotation : [-0.00038759 -0.00023646 0.00178322]\n", "\n", "Estimating motion at time frame 120/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.270518\n", " Iterations: 10\n", " Function evaluations: 11\n", " Gradient evaluations: 10\n", " Hessian evaluations: 10\n", "translation : [-0.02451544 -0.07225927 -0.07883558]\n", "rotation : [ 6.77486104e-04 -5.53343155e-06 1.01158048e-03]\n", "\n", "Estimating motion at time frame 121/121...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.270815\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 8\n", " Hessian evaluations: 8\n", "translation : [-0.0696038 -0.07214494 -0.05812192]\n", "rotation : [ 1.35152485e-03 -4.90508790e-05 9.62765446e-04]\n", "\n", "Gridding..." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 1/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 2/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 3/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 4/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 5/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 6/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 7/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 8/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 9/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 10/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 11/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 12/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 13/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 14/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 15/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 16/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 17/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 18/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 19/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 20/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 21/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 22/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 23/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 24/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 25/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 26/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 27/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 28/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 29/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 30/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 31/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 32/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 33/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 34/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 35/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 36/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 37/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 38/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 39/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 40/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 41/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 42/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 43/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 44/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 45/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 46/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 47/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 48/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 49/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 50/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 51/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 52/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 53/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 54/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 55/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 56/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 57/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 58/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 59/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 60/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 61/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 62/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 63/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 64/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 65/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 66/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 67/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 68/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 69/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 70/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 71/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 72/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 73/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 74/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 75/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 76/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 77/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 78/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 79/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 80/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 81/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 82/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 83/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 84/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 85/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 86/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 87/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 88/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 89/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 90/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 91/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 92/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 93/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 94/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 95/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 96/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 97/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 98/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 99/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 100/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 101/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 102/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 103/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 104/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 105/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 106/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 107/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 108/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 109/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 110/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 111/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 112/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 113/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 114/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 115/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 116/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 117/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 118/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 119/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 120/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 121/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Gridding..." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 1/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 2/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 3/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 4/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 5/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 6/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 7/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 8/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 9/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 10/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 11/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 12/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 13/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 14/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 15/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 16/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 17/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 18/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 19/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 20/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 21/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 22/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 23/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 24/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 25/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 26/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 27/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 28/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 29/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 30/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 31/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 32/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 33/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 34/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 35/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 36/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 37/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 38/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 39/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 40/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 41/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 42/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 43/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 44/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 45/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 46/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 47/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 48/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 49/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 50/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 51/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 52/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 53/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 54/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 55/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 56/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 57/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 58/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 59/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 60/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 61/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 62/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 63/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 64/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 65/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 66/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 67/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 68/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 69/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 70/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 71/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 72/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 73/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 74/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 75/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 76/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 77/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 78/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 79/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 80/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 81/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 82/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 83/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 84/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 85/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 86/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 87/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 88/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 89/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 90/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 91/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 92/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 93/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 94/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 95/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 96/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 97/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 98/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 99/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 100/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 101/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 102/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 103/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 104/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 105/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 106/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 107/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 108/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 109/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 110/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 111/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 112/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 113/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 114/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 115/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 116/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 117/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 118/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 119/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 120/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 121/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 1/2" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Resampling scan 2/2\n", "Estimating motion at time frame 1/2...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.883084\n", " Iterations: 6\n", " Function evaluations: 7\n", " Gradient evaluations: 6\n", " Hessian evaluations: 6\n", "translation : [ 0.10473759 0.00573426 0.04484346]\n", "rotation : [ -9.62102575e-06 1.04809448e-04 1.31232903e-03]\n", "\n", "Estimating motion at time frame 2/2...\n", "Optimization terminated successfully." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " Current function value: -6.886895\n", " Iterations: 7\n", " Function evaluations: 8\n", " Gradient evaluations: 7\n", " Hessian evaluations: 7\n", "translation : [ 0.00294511 -0.00222695 -0.01274325]\n", "rotation : [-0.0001774 -0.00063486 -0.00053491]\n", "\n" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Resample first run using motion estimates:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "resample_run0 = R.resample(0)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Gridding...\n", "Fully resampling scan 1/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 2/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 3/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 4/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 5/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 6/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 7/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 8/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 9/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 10/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 11/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 12/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 13/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 14/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 15/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 16/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 17/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 18/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 19/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 20/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 21/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 22/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 23/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 24/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 25/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 26/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 27/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 28/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 29/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 30/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 31/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 32/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 33/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 34/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 35/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 36/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 37/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 38/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 39/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 40/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 41/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 42/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 43/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 44/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 45/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 46/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 47/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 48/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 49/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 50/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 51/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 52/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 53/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 54/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 55/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 56/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 57/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 58/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 59/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 60/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 61/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 62/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 63/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 64/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 65/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 66/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 67/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 68/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 69/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 70/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 71/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 72/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 73/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 74/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 75/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 76/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 77/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 78/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 79/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 80/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 81/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 82/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 83/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 84/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 85/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 86/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 87/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 88/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 89/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 90/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 91/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 92/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 93/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 94/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 95/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 96/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 97/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 98/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 99/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 100/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 101/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 102/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 103/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 104/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 105/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 106/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 107/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 108/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 109/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 110/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 111/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 112/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 113/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 114/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 115/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 116/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 117/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 118/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 119/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 120/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Fully resampling scan 121/121" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "transforms0 = R._transforms[0]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "motion_trans_rotvec = np.vstack([t.param * t.precond[:6] for t in transforms0])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "from nipy.externals.transforms3d import taitbryan\n", "from nipy.algorithms.registration.affine import rotation_vec2mat" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "motion_trans_euler = motion_trans_rotvec.copy()\n", "for scan_no in range(motion_trans_rotvec.shape[0]):\n", " rot_mat = rotation_vec2mat(motion_trans_rotvec[scan_no, 3:])\n", " euler_angles = taitbryan.mat2euler(rot_mat)\n", " motion_trans_euler[scan_no, 3:] = euler_angles" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "fig, axes = plt.subplots(2, 1)\n", "axes[0].plot(motion_trans_rotvec[:, :3])\n", "axes[0].set_ylabel('translation (mm)')\n", "axes[1].plot(motion_trans_rotvec[:, 3:])\n", "axes[1].set_xlabel('time (TR)')\n", "axes[1].set_ylabel('rotation (radians)')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 18, "text": [ "" ] }, { "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAZwAAAEICAYAAABrtkJsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncDicWuH/AamHruqXLhoYAAAAAElFTkSuQmCC\n", "text": [ "" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make standard deviation images before and after registration:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "before_var = np.std(run_imgs[0].get_data(), axis=-1)\n", "after_var = np.std(resample_run0.get_data(), axis=-1)\n", "fig = figure()\n", "fig.set_size_inches(10.5,18.5)\n", "plt.imshow(np.hstack((before_var[..., 40], after_var[..., 40])), cmap=\"gray\", interpolation='nearest')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 19, "text": [ "" ] }, { "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAADTCAYAAADEdrRbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text": [ "" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "from subprocess import check_call" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "check_call('fsl4.1-mcflirt -in %s -plots' % run_fnames[0], shell=True)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 21, "text": [ "0" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "pth, fname = os.path.split(run_fnames[0])\n", "par_fname = os.path.join(pth, 'bold_mcf.par')\n", "params = np.loadtxt(par_fname)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "fix, axes = plt.subplots(2, 1)\n", "axes[0].plot(params[:, :3])\n", "axes[1].plot(params[:, 3:])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 23, "text": [ "[,\n", " ,\n", " ]" ] }, { "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAD9CAYAAACvMECaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text": [ "" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "mcsampled_run0 = load_image(os.path.join(pth, 'bold_mcf.nii.gz'))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "after_var = np.std(mcsampled_run0.get_data(), axis=-1)\n", "fig = figure()\n", "fig.set_size_inches(10.5,18.5)\n", "plt.imshow(np.hstack((before_var[..., 40], after_var[..., 40])), cmap=\"gray\", interpolation='nearest')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 25, "text": [ "" ] }, { "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAADTCAYAAADEdrRbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nP1zxHUugV2muAAAAAElFTkSuQmCC\n", "text": [ "" ] } ], "prompt_number": 25 } ], "metadata": {} } ] }