{ "cells": [ { "cell_type": "markdown", "id": "6a2d644b-3249-4737-8221-da2deabda1a2", "metadata": { "jupyter": { "is_executing": true } }, "source": [ "Fieldtrip: KIT oddball analysis with source localization using beamformer\n", "=========================================================================\n", "\n", "Lead authors: Hadi Zaatiti hadi.zaatiti@nyu.edu, Osama Abdullah, osama.abdullah@nyu.edu\n", "\n", "This notebook is to be run in MATLAB, while having fieldtrip library installed.\n", "It is a pipeline for processing the `oddball` experiment raw data acquired from KIT-MEG system at NYUAD, run frequency analysis in source space, source localization using Beamformer technique.\n", "The `oddball` code experiment in Psychtoolbox can be found here:\n", "\n", "[Oddball experiment page](https://meg-pipeline.readthedocs.io/en/latest/3-experimentdesign/experiments/6-exp-sound.html)\n", "\n", "[Oddball PsychToolBox code for KIT system](https://github.com/hzaatiti-NYU/meg-pipeline/blob/bc99896a4e271b902282b1432c12e338060f4fa0/experiments/psychtoolbox/sound/oddball/run_oddball_KIT.m)" ] }, { "cell_type": "markdown", "id": "9abbc62f-f227-4f1c-b29a-b0a316442ad0", "metadata": {}, "source": [ "Importing data and preprocessing\n", "--------------------------------\n", "The data used in this notebook is hosted on `NYU BOX`. Permissions are given upon request.\n", "\n", "- Install the BOX app from [here](https://www.box.com/resources/downloads)\n", "- Set an environment variable with name `MEG_DATA` to the path of the Data folder e.g.,\n", " - `C:\\Users\\user_name\\Box\\MEG\\Data`\n", " - or `C:\\Users\\user_name\\Box\\Data`\n", "\n", "Each experiment run using the KIT system generates a `.con` and several `.mrk`. Find more details about these files in the other chapters.\n", "In the following setup the variables pointing to your data, headshape and MRI scan." ] }, { "cell_type": "code", "execution_count": 64, "id": "e3c19093-d8f5-4759-b7e0-0b2b628850e1", "metadata": {}, "outputs": [], "source": [ "% Read the environment variable to NYU BOX\n", "MEG_DATA_FOLDER = getenv('MEG_DATA');\n", "\n", "% Set path to KIT .con file of sub-03\n", "DATASET_PATH = [MEG_DATA_FOLDER,'oddball\\sub-03\\meg-kit\\sub-03-raw-kit.con'];\n", "\n", "% Set path to computed .mat variables, these has been obtained by executing this pipeline and \n", "% will allow you to skip steps if you wish to execute a particular cell\n", "LOAD_PATH = [MEG_DATA_FOLDER, 'oddball\\derivatives\\kit_oddball_pipeline_fieldtrip\\sub-03\\'];\n", "\n", "% Experiment your own test and save your variables in a folder of your choice, choose the folder where to save your variables\n", "% We will also use it to copy variables from LOAD_PATH and use them in the notebook if needed\n", "SAVE_PATH = 'docs\\source\\5-pipeline\\notebooks\\fieldtrip\\fieldtrip_oddball_kit_data\\';\n", "\n", "% It is important that you use T1.mgz instead of orig.mgz as T1.mgz is normalized to [255,255,255] dimension\n", "MRI_FILE = fullfile([MEG_DATA_FOLDER,'oddball\\sub-03\\anat\\sub-003\\sub-003\\mri\\T1.mgz']);\n", " \n", "laser_surf = fullfile([MEG_DATA_FOLDER,'oddball\\sub-03\\anat\\digitized-headshape\\sub-03-basic-surface.txt']);\n", "%The cleaned stylus points removes the last three columns (dx, dx, dz) and\n", "%keeps only x,y,z\n", "laser_points = [MEG_DATA_FOLDER, 'oddball\\sub-03\\anat\\digitized-headshape\\sub-03-stylus-cleaned.txt'];\n", "mrkfile1 = [MEG_DATA_FOLDER,'oddball\\sub-03\\meg-kit\\240524-1.mrk'];\n", "mrkfile2 = [MEG_DATA_FOLDER, 'oddball\\sub-03\\meg-kit\\240524-2.mrk'];\n", "\n", "try\n", " cd(SAVE_PATH)\n", "catch\n", "end" ] }, { "cell_type": "markdown", "id": "1048657d-2e20-43b1-8095-f548494af4f4", "metadata": {}, "source": [ "Read the digitized headshape and stylus points.\n", "More information on those in other chapters of the documentation.\n", "The `read_head_shape_laser` function can be found in `pipeline/field_trip_pipelines/matlab_functions`. You can add it to your MATLAB path." ] }, { "cell_type": "code", "execution_count": 22, "id": "12945bab-128e-42f5-8b36-6d997e1b81d1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
shape = struct with fields:\n",
       "      pos: [1681x3 double]\n",
       "      fid: [1x1 struct]\n",
       "    label: []\n",
       "
" ], "text/plain": [ "shape = struct with fields:\n", " pos: [1681x3 double]\n", " fid: [1x1 struct]\n", " label: []\n" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "headshape = read_head_shape_laser(laser_surf,laser_points);" ] }, { "cell_type": "code", "execution_count": 24, "id": "70ed1c93-8465-491e-b61d-f5e6665ff25d", "metadata": {}, "outputs": [], "source": [ "headshape = ft_convert_units(headshape, 'mm');" ] }, { "cell_type": "markdown", "id": "96b2195b-5a73-4d16-8381-899bf2fa7e38", "metadata": {}, "source": [ "Plot initial digitized headshape." ] }, { "cell_type": "code", "execution_count": 25, "id": "80834384-8f44-4bdc-b808-47f0046779c4", "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "The axes are 150 mm long in each direction\n", "The diameter of the sphere at the origin is 10 mm\n" ] }, { "data": { "text/html": [ "
ans = struct with fields:\n",
       "      pos: [1681x3 double]\n",
       "      fid: [1x1 struct]\n",
       "    label: []\n",
       "     unit: 'mm'\n",
       "
" ], "text/plain": [ "ans = struct with fields:\n", " pos: [1681x3 double]\n", " fid: [1x1 struct]\n", " label: []\n", " unit: 'mm'\n" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ft_determine_coordsys(headshape, 'interactive', 'no')" ] }, { "cell_type": "markdown", "id": "1b50b97d-6ad1-4b17-881a-342d152ecede", "metadata": {}, "source": [ "Let us now define a coordinate system for the digitized headshape based on the known fiducials from the laser_shape.\n", "CTF is an ALS coordinate system meaning:\n", "- the X-coordinate axis points to A (Anterior)\n", "- the Y-coordinate axis points to L (Left)\n", "- the Z-coordinate axis points to S (Superior)\n", "\n", "The first, 4th and 5th fiducials in the stylus points correspond to Nasion, Left Ear and Right Ear.\n", "More information on digitized headshape fiducials is found [here.](https://meg-pipeline.readthedocs.io/en/latest/2-operationprotocol/operationprotocol.html)\n", "Define the transformation as follows:" ] }, { "cell_type": "code", "execution_count": 26, "id": "e467031a-f159-4897-b5ce-629d676166ba", "metadata": {}, "outputs": [], "source": [ "laser2ctf = ft_headcoordinates(headshape.fid.pos(1,:),headshape.fid.pos(4,:),headshape.fid.pos(5,:),'ctf');" ] }, { "cell_type": "markdown", "id": "72878d78-cfdf-4c9e-9c40-ea1dc30be856", "metadata": {}, "source": [ "Apply the transformation to the digitized headshape." ] }, { "cell_type": "code", "execution_count": 27, "id": "8e64f3b5-1bc6-4e20-928d-b6c202fb690a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
headshape = struct with fields:\n",
       "      pos: [1681x3 double]\n",
       "      fid: [1x1 struct]\n",
       "    label: []\n",
       "     unit: 'mm'\n",
       "
" ], "text/plain": [ "headshape = struct with fields:\n", " pos: [1681x3 double]\n", " fid: [1x1 struct]\n", " label: []\n", " unit: 'mm'\n" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "headshape = ft_transform_geometry(laser2ctf, headshape)" ] }, { "cell_type": "markdown", "id": "17619865-fbe5-4cea-aa16-501babbc8c7a", "metadata": {}, "source": [ "Plot to verify the new coordinate system and ensure that the new coordinate system is indeed CTF." ] }, { "cell_type": "code", "execution_count": 28, "id": "06de4e88-3bdd-41ef-9e03-068a34713d03", "metadata": {}, "outputs": [ { "data": { "image/png": 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DIKHysp+24IiKP3H/8zc+07XjHK5ATf3G4Y9AgrtCzcFztF1LolLF0+ECPuuAQ3D0geAIAgmp8Dkf3tn8RZ7ym+qpiCCFR6t22FHByGeqMuiIlF0SIs5dM1CzCCTUBJ/VIiLvnkGBxeJkcJbLcnte1eQzM4JwgggkpC1Io5PqTLmUWrpEDhG/blnOYQu7AkW+svXkaiSIQEJVS7X9ijAZr+RPnW9mk7q5VJA+FqesUAqBhIQFXFY1gwOF3Styp6d8C2tkrS0VSz4XBgUtv7sIC9cmlSKEDeKoq3QFAITgf6+NshMN/K+j6tzGc0sgbfSQkK60L2hN46Dp8VnqtGAD/2uDfPp2nXMXI89QoJeDSiGQkLVs5lbFPIr/SjxBbhihYC27/8WncV6f+EOacuCOf6gpBBISllnvJOY1TP4/CtUKdyZWQf+m/fGOBVYjtOz5BZa6lDX+5cM+J6uCLIiXFGf7tcgM55Cq3O4/6aXfdnvk0EG99Fu1vO2x8cG9dsOScOW3tRaWH4DJE2Vj26aXHwh70DjVC1XhnLLnWroKNJLxbtmPOkwzxxb+O+F9HqUN7GOmjCz5dIp7ZsaY+jrNHBvw2SSjzujCpkyPWAHB33ER3m6K+I6LJeW3WygEUpXre4QWfU2Pfq/rkd9fpWd/pb5HeGy8+BvmvZZw5be2aN6sOBWM8lHa1GnVXXbzY+q559jtndPsndNGDNQVH27/t+C70xZ8d9qCz2rqKI/tf3KO+tWH60P0rdeCz3od2uvG58VhHOqMnf3NJW2blh1qs5eM1xknlNu4xG8zwseCCgj+jovwdlMC77jQ8t5ulWdR5dq2/Lf9fl3blv+21ratutPeOMLue8Njux1r7P/9UOjS391jrw/9RxLqb8z7z3LnOjt3fMC94lcvSGlltwnydvtfp2vSiK4SCsu87wv2X8fZd/dYa+2ONZ0/GneUnv2SR1WLn0j+BgMalPvd+VfJ56n5H6hwx7njrbVNg/XCV7xL9tnXZ7PK++M/tm19Kv+BQO+4aG83G/EdF1e5t1tm6CFVIWPy/5ljP6ZH28xPPqbhxtz5eTv7HvUf4rHX8p9p4hUyRkcb/XleCTOMHWFkjFpbtGCOnr9Fc0/S3JO0/OeFB/2Y0dlGxqi30SyjDxtdaXSl0eSOz7M7VuueWfrJkQVHtsd1HOvPjT2h62uNMlowx/s5Htmk3ofZUabgyXb9C/MShdhR3T+ohzlikEQ643iNGFgiJE41euoOfedF9RkoY3T0SZ0/+dvJ+rfnJEk7X7x3dtce9tOaeIz61uv2mfriBOV+HX9zauGzkWT/30R75wX2X8d1brz6q7JflT1Vuac2/mjd9+fa+Q3pUjN1lDHG3DzNTD62vZDfzNZpx7d/fe9saVD730CunNVf1d+cKn11td209OW3tO+gzhzZdejiFyroL6J4l7C/0Mg75tv0uNn9WrdSR3xUZ3zb/OZz2v68eeAr3u+43NstZ+eLmp/3a7v/cvvaM2XebjlP3qhHrvPbsvMdd/vU9uHBP1xtty5v/+m8i+yf/tj+9fzZ2rutZFG5t9umpUFfk9QwqaFHWCqdKP2ltEzmx2d4b/Mt6RZJsv1l8gf6G2VelCQd1k/flG69Q0ulodLnvqYZX9M70nWSMTpLGivdIUmqk8ZLh6Q7cltKQ432SVdJS2SeLjxy3ZaOr+Z3PWhHSPdLz95hpyt3RNOct48xOkdmnLSx/LPvdoY/wNa2+31cA2ZJyUOWOOj3vvc9SY2NjZK+8PlLBvbrLaneqF9vqY+RpINdG59xgvQp2V/LdJyJyC//M+P1ryskyU46afZf5B2uUUNf1Lo6XTJeBzt+HT//nJZu1As7unb/+7Ok5/9Hd8js04CG9o0/eYdOGaoHPqcPb9RR+/TYF/W9Jfrsf+rdE/W7z2nK7Wpr02fG6aktmnysPj1Gbx7QE5t18tGaPEJmjwY06K3x+nheOdooM3KKlf5pkz4zTss2erwmoS42Tn7LUDseek+tLZJ0qNW27jcH90pSw4D2n075gV5drH//mM76e3Pc6R67r5mnSVflvrQH3jAvLez6UfMi84GLdWSrVt2tXg2a87C2r9RdF2jkFA07uWuzZddr7QLNeVhtJbbcv1O3nqGzfqDZ8/TqYnP3dHvZo0Z1ZtU8jfio3brcrFto+g/Rcadr+wva8pQGDNfBvSUPOvJMs2qeTjgr4ouZEHpIPcU2qbfsLslaj3/7dqivtP2QXwl79qhemr9Lu61esRrxYS1boD17JOmx/61x0u3Svo6N66XF0h6pWdouLVugR/9FY8/UiqLxn9ElDre2o5zOIxbs+c93yuv0fhnWylojdf7z3MDjkeLHC/bLGxMrvpio+MEXX8zlvA4cOPD8fT99+zv93vp230++v9ets+r2XKONX+nafsRA/Wa2/naR6jZ61Pmow3R4X63eKUl6bJn6NHQ+tc7P7b3rdW3er2P04K7dv/0JzR4nDZPe3JW/8bY9WtSs57dr9GDtGKcjtuvmp/XuIZmXdctz+upH9MArmjpaks4ZrbtXt3d6po/VgrXyLEeDpTunydqVr+v4CL+49heyXcBfSm6fkv8i72it1i/RDcN1w3BtWGLu/0vdMFxzx3fbfdgEtbbYw4/3KHn/Tr27W0PHe/yoWwVadd5PNWC4Gs/XsA/pnfVdP3r8h1ozX5ctVf+jSm655l4NPUWTr1avBjVdqAmXmxVzNWa6Ni6RZNY/rA9eok3LJOmVhRo7y/+gdtgp2r25TIXTRyBVoU9J35K+JV3T8cgY6YPSIplp0u4/eeyyfaXqGmQC/Lr7DGr/Iv8k7Zr5GiQ1dN/y3Y4vDnQcwvPNucH7OHZd4RHt7WfbtQu6Gv36/uaukrmSP+7s/4Q8wyn47t3KCXy+PVfos88++8wzz6xZs2bFW++7f9wdC0/6j80NY/tc8p+DfqxRHYMlDb10/yX6/Su6aYV3UacM1bvvqa1cNXcX/Do6zB6n4wfp+H+W6Xt48cZvH5CkXw6VdnXt8j/bdERfLdmgxsEaPlBTR+pfntbRh2n4QJ1/ou5fp/xyrGSliQckyXz+98aYA+/plKFlalsFGs/VdXt03R41nquL7tZ1e/S1jV0/XbdQa+7RJ28wD17p8Y6L+XaTtGa+3tmsXLes1Jbd33H2mIk68LZGTdVbzdq7TeuX2MlXad8O7d2mVxbZsTP8D2rq+2v7yvIVThmBVIV+vVZ/9V/6q/+yf/OorNWuzfri+/TF+/XfVh+aqfmf8dhl0LGyrZJkrVmxSg31XZ8Ej36fHnywzMfJy5/QlGv0szPbd8l1mzpLOOdsSTpipA7u89i3RFNqNnSUU4I91H2GUkHXJ9pATbBPzd3iKvjH7e7fzp/fNUC5fPnytWvXXnzxxWtWr5k1q9scql9N16E2feXB7qXtXNv55Zbdqu/V/rXpO1i2rfNH/Xp332vtAiM93P2x02/RL5/Vr2f61X3jLumzf6Zl38/98Rx/uA60qs3qsAmf2frTU6c29nl6qx5q1qyxOuloLbms2765y3cfljqfVZ96Nb/lfaDiDwcFHwi8f63+XRkfkXf0t/tP+u2lmnm7Pn6tTjjL4x3X+XbLPanuvzW1HijcvtjlT+gjX9J9X/Dbpvs7zuzarN79ZOrUNN2++pC2Pm2OOVUnnqe192nnGjNqqv8B7aEWDW4sX7GUEUhV6MixGn2ORp9jTjhLtk33zta42RrzaUn69L/p7fVadn3hLkOapDq92dz+tW3VjtWS7Kalanmn/BEbBmjqj7Rrs566ueQ2Y2Zq3ULtWG2ttft22J+Nalt5e+4nbavvtv80xM7/jLXWPvfv9od97YJLyx7TvNVcdpucCN2dbgfqPts47OTjUgc1xjzwwAM33XTTTTfdJOm73/mupLkr9MLrXdt8dZLOPVEXz1ebVUOv9n/1dbnfV7uX31Rbmxpzn2WHNMm2jj9aks4cqSP66cEHH9yzZ48ke/vZntXYe1DfWaLjD9dVk2Stbd947YL8at//krRuoZ75pW2adtRhuuwU/b5Zkj77/XlvvrxC9j1Ji9frhnN1/0seT7nghWocrC27g7xyHvu66CNXamjeqZ0g77j8t5tSf8dJ0v6deu422zRNkv3ATPPQ19V0gSQ1nqvFX9eYGSUL6WDeatagY8tuljYmNVS5//qGDu7Rp25q/7b/EP3Zf+iOT9rG882Ij3ZtZurUeK7d+qQZ0qheDTrtm/rFRA0cboaM0SjvhqxQfV9N/6XumaGmC3TY0R4bHDXOzviVufUM863O99ul1s6RZN5/oVo+r1FTJGn0eWpt0dj2T+zmeun6jkUECprUjY/718iUXlwn/8H8Jq/b9IekL2zyKfDWW27NffH7jjbKXp/7n2S0+e+6b33yF/JHe9qsFq/Xx45X89sy9X1+fI6e+Sv1ed/ROuqD2viIT3327NmjfxwoqaVVX35Av/2s9PZ6z9/diztl63q9s2Prczde+urVunG57lgpSQ++rDv/TLlP94ub1a+37l+nyyZ0K7zYGcfp9hd86lVtmqZ1+zbIOy7/7SbFfcdNnuK9Qcc7TsMmaOvTOu1ac3KZt5ufjY/bCZdW/NNBxHtIo/o0L9KTN2hOx4jOoYNqa1Xv/gkfxbaZul5d35U9wVMqIfa+rl9M0DVbCgbiiz9Qh/oD9jxcwYNBQivsNu3nlPIfKFvtjt+XMeb8Rl37MX3yjvafNPTSuwf25X53QXoY7efk/FfnazvUq1ev/r21v+h8VakXyvPoQw/Ts1/Wcf9c/qSXf22rXsHbTSm+4/TefvXuH+iUVSkl3m7ZY8iuZjSer9YWvfFS+7e9GpJ/b0iJ/UGvuFmf+HZxaZHH5SLzHMHLHyQ0RRI5lnn/p8ylj+RKW9SsvvUa23F918FDUu/+Ca9oYOrarPYeDBokpY5+1an60eM1n0YqerspzXdcw4C477sSb7fsVb4GyM6M27TBb5DHFW2t5uwfmVOvqsgqMjHPSOUkW21r7en/5xXPJYUCViZsffKffvDd6+t0WG/NLboQLb/MUq9q9h81Ulc9bzcd3KdJV1a6HhJDdkhV5HM2We7ov0vZAj1HET3G66SCW8YGDwn/WsXh/xIl/mmg8JUJWR/0eExqAMKJEpYm95+4tzhSvJAodeInM1Uwpw4VRSChhyubH/4JUTY/kv1Qn30XIchUhXzF8z6IGSSFQELySrVQwfsW1T50023eudcCRtFEOBVUatZ7ZnUIW3i1/+oRB4GEdKXUvpTKtvSaM/+GOKkrnEodJcHnlZ9SEc5jFewSOZ/oWqEYgYSE1UIrE2QuQLdtbOGMBoU8o1Pcdci+QU/wcHSD4IlAQg+XSNsXoS32nGiXcR2CyM9Fn0PUwucMVByBhOzwuThBpRatKHi8eBUlF6Il1TWcUL0IJKQovbamgq2Yz1S9ZM8zBRyUK7VwX/C9IuAMENJAICFhEVq6ND4vV+QzePBsSLY19ywt4Lp8YXWulpRIaWJmHfIQSHCdC8M7EVZw6JqWltC071KdsESyIVo3C0gWgQRIqcVeZxpFiKXEuw5BSvM8BRX8Yib/oTwG+uCPQEIWXOjlVFy0lXsinHkKW7H0eF6ZSyyhFAIJlZdGG5r4bO+w3Ytu2+etZeep1GWnpbYse/1ssuN4kRdF9dyLNEIpBBJc59RH/k6hllUNVWyWFwNlOfMCKItAQooya9HiDwkmHnuROzHxVx7yuU1G8EG/UL+7/MKDj8i5+VEDFUQgISMFrU8tn1UK3tZXal3taAdiAjdiIpCQCgdHeyJEYNnNEozVOEUVZ1Wpadxpfw7IZVJ6dxREz0YgISPBh4yyF+G2sAkK3nwXD6bFX3Ah1MbBl4Itu5ebfwmoLAIJPUEGN7lwnM8rkNRFr0HWgwDiIJBQGT4NqH8PoAecfEqvEU/qxQk4fTzsLAbAH4GEVDiyFE3a6RWkX5K7/Kj4fkjF24ednJa/plzAkcZS+2YZ81X6MQIZIJCQOvd63CALAAANXklEQVQboLDnjTw7ECWfZt5VsfmXmgapUsF8hOxfyWi9H0c+jqDqEEhwnYNjdDEb2VLPKNlnF/kCW/9elyfPjhcQFoGECvDPGP92sCKZFPBa0Qg9A//WP1QHJfJFS6W2LKhb/KRx5PMEnEUgoYfwDLlqbAF90rrgRwEnhhQ8Hu2GVUFWq6vGVxtOIZBQo0Lc0Mh3GCpQK2zLT2oIUn4Gl0OldAiyCkEQSMhI5JbO2bassoshBX89Qw3fhV13HEgQgQRXuDP4k0hNCqZ9l90svmj3nuiMq4KlNDxvZZRSHYAcAglZyKB5yibDUj1KSpFc8OIXBE/MWd35uZU71UQUITICCVUs5oKkMbcMOPsgd/LItWa67Mw6+Z6+cu3poGcgkJC1lD5ER7h6JlSxCZaceDmeY27xRfg1+awNAZRFIKECSn30Tq/xCnWUsvduSOpOEyndACLxMsWlr8gEgYQsVPBeQZXn23oHv+pIXp3LCNPnQu1VsEv1vfioKgQSqljic7oid6RUYolS032SXcDhyiAneFLCogyoIAIJPUHk5i/U5bHFe6V6MWlYzHBDtSOQUGEZ3FvPf5WdgJLqjaW38kKp20n4HytsjJWtP30jREYgwQlxllt1ikdVbef/oywa6/94/JgMdXKo7FKw0eoA5BBIqBgHx5eSXw0o/ykGLsA/hMpO8o45XTu/Qxn5HhZABAQSKiPsuf2UPn1HKDazCdBBnr5/BdI7q0QaIQ0EEnqmtIePEmmRiyuZ0lmlgsMFvEEfkDECCU5IY5qcfw/DhWubKrJAeG5ELuDd/AL2sTh7hEQQSKg8Z5uzRObm5S5EKpjRELYXEue61LSngzv760PVIZBQGUFasWpp6cLWM8GJ40HKjLMuatlzVMGLAsoikFATEl8+zqccj4G4vAUbMlj8NGyXKPigHKeXkCoCCU4re+mlT/vufw4/QjB4rAyU6K38wtbBfxvPpYwClhAE3SMkjkCCW1yYa5ClgM+RDgpqAYEEOCHxsMndv7X48ThzHGrhIwIqiEBCz5f8+gu++eG5epD/ukGeFwwVLOcaraplhyuZ1Q13EEhwWtkZZYm0lWXPteQvHBfkJt9dhXTMZTAy/pkUqpIxd8nFm8+ciGiHA2IikNAzuXwuqmx94gypMWcB1YtAApLXlQrhW/VoodKZHwGH4+gAwUEEEtxSdhCsWNjOUMzP/jEX2PasbfzuSKll6/IfIYTgOAIJ1SH/rL5/8x2ne+HzYHpjgOmVHGryAmN0qDgCCT1Z1TWyudxN8K6y9IpQRQgkVI349weKX77nlgEPGvy24vFThDXoUI0IJLjLp60PGwnFG2TXKHc/ctklfOjToGYRSHBa5MGrzPImyxtJBMT8BVQpAglVL73GN07J7ZeXimAAgiKQUDWCf/CPfCO7CHuVEuQ24dl0ZVh5AdWCQEIPEfBCJf8tU2Fz/8n0tq2e1yGld3QgERFXbAQqJcG7yZW90ijgscps1hFIAQuMg7czqho9JNS0stOjQy2G7b2xyf2HETOgjLpKVwCIzudqpOB3Vi17iDi3gk27y0KXCD0JQ3aoSu6coo+5tF38w7m8rjkQCkN2qHo+p4I8t4wwTFd2x1CpUGokMOY630C1I5CAbjwjIdkeT9jSiBzUCAIJPU3ZfkaCS5d6SvwWeQzKoUYQSKhK/u1y/FY7+DBg2DL9V9Ujb1DLCCRUh1K9hJi9hyxWMeioVCUXeAWqAYGEnq9saAWZhqCQueWzll3ZiXkEFWoTgQQE5ZMToSKkM3siBE/+nXOBHoZAgov8uwvBeyqRJ2pXtr/izlVWQJZYqQHVweYpeLzza8fb8QwWbgCqGj0kuKKKFh6N1n9yPC+BiiOQgGQET6nIswGBno1AQsX4tOBlbwwRUNlpCLmSi28rnmoAeD4dLoMFCCRUvSCXJdHKA+4jkJAd/1TI7yLEmRidngipZmVzlyLZ9lv0FZbjsYtLTxnIEoGE1CV+Mj9g1yeRVAteeYIEiIlAQgIi3/y7c3f/8Ei2rffMsCy7ZUQX4IlAQmVE6DYFGTErdTfxUJcrBVkNIcGTUpzfAnIIJCQsSGvuf4cI//lv/of23DhUIT4SXImVa5KAYgQSUheqHY9287oI93j1P6JnrJYqLanAA2ocgYRYSrXRYVvnsJ0Pnwxg+VGgShFISF78UbtSuyj2YFfB7tFKK3WmKhriE8ghkJCWgnP1qa6XUyrenG3rna0YUEEEEiqjVH6kdLbffw6Fz71c075XOoBOBBJiCTudumwhQY7YWU7n2KDPQFzYkEv4mqeOO8Yama6lGgB4IZCQmMS7C2kvtUD/BnAKgYS0RLurt0pPXig1PztS7bohmQAXEEiojLBxlVRmkD2AswgkZCe9RU597p8EoFoQSHBUnBl3dIOAakQgwQlECAACCe7yn1NOhgE9DIGE6kD8AD1eXaUrAACARCABqepcnYFlGoCyCCQAgBMIJACAEwgkAIATCCQAgBMIJACAEwgkAIATCCQAgBNYqQEoo/heTQDSQCABIYQNJ25hDgTHkB0AwAkEEgDACQzZAWWUugtGgjdWByB6SEAoJBCQHgIJAOAEAgkA4ATOIQHhMGoHpIQeEgDACQQSAMAJBBKQIm5hDgRHIAEAnEAgAQCcQCABAJxAIAEAnEAgAQCcQCABAJxAIAEAnEAgAQCcQCABKcq/hXllawK4j0ACADiBQAIAOIFAAgA4gUACADiBQAIAOIFAArLVvNi+9kzhg+sW6u31Hhsf3Gs3LAlXflurXvptxLpFZtv08gNZHxQ9DoEEZGvXBvPrs7V3W+cDduVtWniF+hzusfHib5j3WsKV39qiebPiVTE8U6dVd9nNj2V9XPQsBBKQrYlf1jGT9LsvtX+7d5tZ9HeacZv6DynccueL2rJcTdMyrmBEZ15vHryy0pVAdSOQgMzNvFUbltrVd0nS7/5a42Z7p87yn2niFZK080XNn931+P2Xtw/6tbZowRw9f4vmnqS5J2n5zwtLePJGPXKd35Y7VuueWfrJkbp9atfY4B+utluXt3897yL7pz+2fz1/trSx5BGPbFLvw+ympdFeEkBSfaUrAPRk3ncuH3SczrvBLLrGtrWa11fqotXeO6+Zp0lXSbIH3jAvLex6vHmR+cDFktTWqlV3q1eD5jys7St11wUaOUWDR7dvtux6rV2gOQ+X3HLQcN16hs76gWbP06uLzd3T7WWPmmNOlerMqnka8VG7dblZt9D0H6LjTtf2F7TlKR080qOcYSe3H3HkmWbVPJ1wViIvHWoQPSSgEiZ+WUePNwsutRffpYYBHhvs36l3d2vo+DLl2Fad91MNGK7G8zXsQ3qnY2bE4z/Umvm6bKn6H1VyyzX3augpmny1ejWo6UJNuNysmCtJY6Zr4xJJZv3D+uAl2rRMkl5ZqLGz/I4o2WGnaPfmOK8KahyBBKTvD1frHwbqHwbqxmM6H7MTr1C/IebYj3vvsn2l6hpkArxD+wxq/6LvEV0Prpmvdzbr4F6/Lbev1OHHd9XnmIk68LYkjZqqt5q1d5vWL7GTr9K+Hdq7Ta8ssmNn+B1RMvX9tX1l+QoDJTBkB6Rv0pUaM0OS7VUfdI3VQcfKtua+NH0Hy7Z1/aj1QPndL39CS/9e931Bly0tuc0RI7X1fzq/M7s2q3c/STJ1appuX33IbH3aHHOqTjxPa+/TzjVm1NTChOvOHmoxgxvL1w0ogR4SkL4jx2r0ORp9jgl+fmVIk1SnN5vbv7at2rFakt20VC3vlN+9YYCm/ki7Nuupm0tuM2am1i3MFav9O/XcbbZjboX9wEzz0NfVdIEkNZ6rxV/PBao/81azBh1bvm5ACfSQACeZOjWea7c+aYY0qleDTvumfjFRA4ebIWM06uxAJdT31fRf6p4ZarpAhx3tscFR4+yMX5lbz9CwCdr6tE671pw8p/3g779QLZ/XqCmSNPo8tbZo7MzyR9z4uJ1wKbfZQGTGWq9ZQAAqrnmRnryhfZqcpEMH1daq3v0TPopt03v71bt/oPNVPva+rl9M0DVb4paDGsafDuCqxvPV2qI3Xmr/tldD8mkkydSpYUACKbLiZn3i26QR4uCvB3DYjNu04ZFKVyKAtlYd3KdJrNSAWBiyAwA4gR4SAMAJBBIAwAkEEgDACQQSAMAJBBIAwAkEEgDACQQSAMAJBBIAwAkEEgDACQQSAMAJBBIAwAkEEgDACQQSAMAJBBIAwAkEEgDACQQSAMAJBBIAwAkEEgDACQQSAMAJBBIAwAkEEgDACQQSAMAJBBIAwAkEEgDACQQSAMAJBBIAwAkEEgDACQQSAMAJBBIAwAkEEgDACQQSAMAJBBIAwAkEEgDACQQSAMAJBBIAwAkEEgDACQQSAMAJBBIAwAkEEgDACQQSAMAJBBIAwAkEEgDACQQSAMAJBBIAwAkEEgDACQQSAMAJBBIAwAkEEgDACQQSAMAJBBIAwAkEEgDACQQSAMAJBBIAwAkEEgDACQQSAMAJBBIAwAkEEgDACQQSAMAJBBIAwAkEEgDACQQSAMAJBBIAwAkEEgDACf8fWbnW/EF/+qgAAAAASUVORK5CYII=" }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "The axes are 150 mm long in each direction\n", "The diameter of the sphere at the origin is 10 mm\n" ] }, { "data": { "text/html": [ "
ans = struct with fields:\n",
       "      pos: [1681x3 double]\n",
       "      fid: [1x1 struct]\n",
       "    label: []\n",
       "     unit: 'mm'\n",
       "
" ], "text/plain": [ "ans = struct with fields:\n", " pos: [1681x3 double]\n", " fid: [1x1 struct]\n", " label: []\n", " unit: 'mm'\n" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ft_determine_coordsys(headshape, 'interactive', 'no')" ] }, { "cell_type": "markdown", "id": "26a75654-4797-4914-912e-d07638edf661", "metadata": {}, "source": [ "Apply a z-plane cut to remove unncessary points from digitized-headshape under a certain z-value." ] }, { "cell_type": "code", "execution_count": 29, "id": "a532bdb7-f08c-4dfa-953d-879e48cde350", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Use the mouse to rotate the geometry, and click \"redisplay\" to update the light.\n", "Close the figure when you are done.\n", "the template coordinate system is unknown, selecting the viewpoint is not possible\n", "The axes are 150 mm long in each direction\n", "The diameter of the sphere at the origin is 10 mm\n", "\n", "==================================================================================\n", "Press \"h\" to show this help.\n", "Press \"q\" or close the window when you are done.\n", "Press \"v\" to update the light position.\n", "the call to \"ft_interactiverealign\" took 2 seconds\n" ] }, { "data": { "text/html": [ "
ans = logical\n",
       "   1\n",
       "
" ], "text/plain": [ "ans = logical\n", " 1\n" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/html": [ "
ans = logical\n",
       "   1\n",
       "
" ], "text/plain": [ "ans = logical\n", " 1\n" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "keeping 1251 and removing 430 vertices in the mesh\n", "the call to \"ft_defacemesh\" took 2 seconds\n" ] }, { "data": { "image/png": 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" }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "The axes are 150 mm long in each direction\n", "The diameter of the sphere at the origin is 10 mm\n" ] }, { "data": { "text/html": [ "
ans = struct with fields:\n",
       "      pos: [1251x3 double]\n",
       "      fid: [1x1 struct]\n",
       "    label: []\n",
       "     unit: 'mm'\n",
       "      cfg: [1x1 struct]\n",
       "
" ], "text/plain": [ "ans = struct with fields:\n", " pos: [1251x3 double]\n", " fid: [1x1 struct]\n", " label: []\n", " unit: 'mm'\n", " cfg: [1x1 struct]\n" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "% Deface the laser mesh under a certain plan (change planecut) Define the configuration for ft_defacemesh\n", "planecut = -5;\n", "cfg = [];\n", "cfg.method = 'plane'; % Use a plane for exclusion\n", "cfg.translate = [0 0 planecut]; % A point on the plane (adjust z_value as needed)\n", "cfg.rotate = [0 0 0]; % Rotation vector, modify if the plane is not axis-aligned\n", "cfg.selection = 'outside'; % Remove points below the plane\n", "\n", "% Apply ft_defacemesh to remove points below the plane\n", "mesh = ft_defacemesh(cfg, headshape);\n", "\n", "% Plot the resulting mesh to check the results\n", "figure\n", "ft_determine_coordsys(mesh, 'interactive', 'no')" ] }, { "cell_type": "markdown", "id": "01f36b79-4f96-4db0-a4ec-6566b3d00770", "metadata": {}, "source": [ "When you are happy with the result set your headshape as the defaced mesh. Notice the new `headshape.pos` has less points after defacing." ] }, { "cell_type": "code", "execution_count": 30, "id": "4c5e6aa4-0beb-4b04-8e05-3c05b613dd2c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
headshape = struct with fields:\n",
       "      pos: [1251x3 double]\n",
       "      fid: [1x1 struct]\n",
       "    label: []\n",
       "     unit: 'mm'\n",
       "      cfg: [1x1 struct]\n",
       "
" ], "text/plain": [ "headshape = struct with fields:\n", " pos: [1251x3 double]\n", " fid: [1x1 struct]\n", " label: []\n", " unit: 'mm'\n", " cfg: [1x1 struct]\n" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "headshape = mesh\n", "save headshape headshape" ] }, { "cell_type": "markdown", "id": "e8d29376-e8f5-4aa4-8042-229d64335982", "metadata": {}, "source": [ "Read subjets MRI T1w scan." ] }, { "cell_type": "code", "execution_count": 66, "id": "2686e8c3-188a-4a36-8126-6a20e717ed13", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "extracting compressed dataset to C:\\Users\\hz3752\\AppData\\Local\\Temp\\x2001df3da49bb0c143cbbaaf3ffbe706\\...\n", "extracted dataset is located at C:\\Users\\hz3752\\AppData\\Local\\Temp\\x2001df3da49bb0c143cbbaaf3ffbe706\\T1\n" ] } ], "source": [ "%% read mri and mri-headshape\n", "mri = ft_read_mri(MRI_FILE); % read mri file\n", "mri = ft_convert_units(mri, 'mm'); %make sure units mm\n", "save mri mri" ] }, { "cell_type": "markdown", "id": "2a13b3bd-2ab9-47eb-9076-75c48f790903", "metadata": {}, "source": [ "Inspect visually the MRI." ] }, { "cell_type": "code", "execution_count": 45, "id": "7d11cf3b-adcc-4041-8b5d-94446b213c36", "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "The axes are 150 mm long in each direction\n", "The diameter of the sphere at the origin is 10 mm\n" ] } ], "source": [ "mri = ft_determine_coordsys(mri, 'interactive', 'no');" ] }, { "cell_type": "code", "execution_count": null, "id": "3543a526-2f9b-4f4e-9889-8aac52134aeb", "metadata": {}, "outputs": [], "source": [ "Notice that the MRI is not in CTF coordinate system (which is an ALS type)." ] }, { "cell_type": "code", "execution_count": 46, "id": "76d3499d-4f75-4eaf-befe-2325939bcdbf", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Error connecting to MATLAB. Check the status of MATLAB by clicking the \"Open MATLAB\" button. Retry after ensuring MATLAB is running successfully" ] } ], "source": [ "%Skip this cell if you had already set the MRI to CTF in a previous run and saved the result in mri_init\n", "cfg = [];\n", "cfg.method = 'interactive';\n", "cfg.coordsys = 'ctf'; %use CTF coordinates (pos x toward nose, +y to left)\n", "mri_init = ft_volumerealign(cfg,mri)\n", "ft_determine_coordsys(mri_init, 'interactive', 'no'); % sanity check, should be CTF\n", "\n", "save mri_init mri_init" ] }, { "cell_type": "markdown", "id": "be78e25c-3c34-4c7f-bf86-baddd5857e7e", "metadata": {}, "source": [ "The MRI data is now in the CTF coordinate system (based on ALS).\n", "Plot and check the final result." ] }, { "cell_type": "code", "execution_count": 48, "id": "5a422785-c709-417b-a2af-f8b4d98fbc18", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The positive x-axis is pointing towards anterior\n", "The positive y-axis is pointing towards the left\n", "The positive z-axis is pointing towards superior\n" ] }, { "data": { "image/png": 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60pf+5m/+hnbGvI6MjNTr9Wq16vf7MUsiEonU6/VIJGK646ampgzDkCTJYrGMjIzY7XZM8JMkCRNvnU4nNpn6+/s1TTNTWTFRyVSXVqsFBdqb/pDNZvG0RqMRj8cffPDB+fn5nopFZ5hOWJCY3iISiUQikUQiYeZeC4IQDAZxG+1NTTp79uzGxkanGESj0Xw+/8Ybbzz33HOw1RFRPB5vtVpmAqz5uGEYbrcbE82r1arP58OPBEFoNBqlUqlSqTgcDhRS5XIZlwKv01n03DKLCHl3siyrqnr27Fm/3w9j+ubm5oFcC4a5B1iQmN4iFotZrdZMJoMbrsfjkWU5EoksLi52+9A+lNOnTyO2bnR0dH19fXx8vFQqvfzyy5qmDQwMQD9EURQEwbRxq6oKR5yiKJCfVqsly3Imk7FYLGNjY0TkdDpVVV1cXMxms7B6733rVqslCILFYvmwfiwEQ8CJhwXAT3/600T085//PJVKHcz1YJi7hPeQmN5iaGio2WwWCoVSqYS461gsFolEejml7fLly5lMJhqNbm1tiaL4iU98AhPKRVF0u90o9RqNhq7r9h10Xd/Y2MCA81arhX9DdUKhUDgcRi/RwMAAEVmtVlmWMaB9aGhoenoaM/2AYRjtdrtzGwl0Fk+GYVSr1aWlJQieuWzIMD0F/3fJ9BZOpzOdTlcqFSxAGYYxNDQEZ1rPUi6XFUXBLInTp0/n8/mNjQ1BEEZGRlwu13e/+9+I6M/+7D82Go1Wq2XbweVyobLRNK3VarndblEUseHkdrvdbjcqp6mpqf7+fkmSdF3XdT2Xy2maFovFxsfHO0Vob4W0dzpUpVJZ2eHRRx+dmZk52OvCMB8TXrJjeguv1/vee+9hZh1C25xOZzgc7vZxfSjf+ta3/uEf/oF29rfC4fDq6moikSCi06dPd4b3aJpmGAZKPZfLpet6o9EwDEMQBE3TdF0/ceJEKBSyWCw4fTzTarXabDas+BFRoVBoNBp2u93v94+Nja2trd0mPMnpdHYeQLlcrtfr8ERMTEzMzc2pqgoXBsP0AixITA8hCIIsy/l8HhWSzWbz+/2iKCIRpze5evUq7cyrfeSRR3K53PLycigUUhQlFot1RvDVajVJkiA2VqsVsycgM9gHslqtGMSH1TZBEOAtVBRF13X8os1mUxQFi3t2u31sbCyfz3/Y9dmbZadpGraONjY2yuXymTNnJEnC5EOG6Tq8ZMf0EAgyUFUVxYGmabIsO53OXUEDCDvoBdxudy6Xq1arKFOCwSA2hAqFQiQS0TStUxLq9booiuiThVA5nc7O1UibzSZJkt1uRyFlDkDCT7GxJIoifqVSqeRyuXq9jm222dnZ2x9qZzMTEb3yyiuIL5qbmxseHt6Xq8Ew9wgLEtMToGKIxWKiKLZaLSxhEZEkST6fDwkIJh6Pp0uHuZsnnnjCHBv43HPPoTyKRCI2m62vrw92A/PJoihiHwhfyLIMucK54zkQJOwhVatVRVEikcjJkydDoRBCZr1eL34L7bG5XK5cLheLxVKpFI1Gb3OoFoul0wqB393e3obZjzWJ6QVYkJieIBQKhUKhWCwGd4DL5QoEArquS5KERS0TxO106zg7OXfunK7r5sA9n88HhwIEZu+Rq6qqqmq9Xtd13WKx2O12rMLtwufzwfUAlwR+S5ZliFY6nXa73U6nE6WSw+Gw2+2appXLZb/f36ltncCnB3nrDHqo1+s/+9nPnnjiiSeeeCIQCOzn1WGYjw8LEtMToB82HA7ncjkzYicQCASDwV39niMjI7cvBQ6NqakpczarzWZDKjk2daBMzWZzY2PDfH6z2azX66VSqV6v44tbhibYbDboCqwcmqbVajXsIcHg4PV6n3jiiaeeegoGbsyQFQQhnU5PTk5OTk6aLbcmKNSgWwMDA6YmIaYIC4mf+tSnbilmDHNosKmB6QmgMfV6vVwuVyqVdDo9MjLi9Xrb7TZcYSa7PuN3EdQu+BqBdcvLy5///Oez2awkSTdu3Nja2urcIkLrq/m7jUbDarWa8XedmKOPoBCVSkUQBDwoyzLsDA6HAy2u8/Pz6XRakiRZlrG26ff77Xb7LdVOVVVMbEomk1j6q1QqSG2YnJz84he/+KMf/ehOhgQyzEHAFRLTfTB+m4jQRqqqqsVikWVZluVYLIaQUBOv19sLbUlnz5599913q9UqzAInT568evXq008/3dfXd/78+YmJCbvdXiqVkslk52/V63VFURRFwdlhqhOW5jqfhtLH6XS22238Cmogu91utVrNttZgMBgMBlEVeTwei8VSKBQKhcLtFQWaFIvF4vE4Htna2tra2srn82NjYx9pjmCYg4MFiek+wWBQEIRoNNput8vlsqZpbrcbGyQul2tX6tr8/PyHjao7TM6ePYvdo0ajMT4+3mq1yuVyIBCA5y0Wi7nd7na77fF4TpyYJKILF57UNA1+bghSu91GgALYG8XtcDhUVW21WqIowi8uCILD4YDwwBZvs9mi0ejU1BQR6boON0Q4HLbb7QMDA7s2sUygScPDw0iCQCjGysrK9vb23NzcgV43hrkNvGTHdJ9YLGZ2faqqir2W06dPE5FpRTPxeDy39AIcMltbW4jfjsVio6OjP/rRj2KxGDTDarW6XC6Xy4WwBiyjpVIph8PRaDQwjk8QBOwzGYYBUzsyGjqBJQ+7a9vb28ViEZtJu2TGYrG43e5YLNZut7GYefLkyUqlUq1WvV5vJpOhnVEUnaiqurS0ZLfbz58//+qrrxJRqVTKZrOPPfbYF7/4xRdffJGIbjN4iWEOgu7/j80woigijQ27IK1Wy+FwoNnI9LCZ7Oqn6Qput3tlZQUdQjMzM/CnnTt3DkoJ23ooFCoWiydOnMBejqIomUzGLINUVTUHQ2CkbCqV6u/v70yZ03V9fHx8fX1dlmWY4D0eT6FQsNlsDofDVGVVVTVN8/v99Xodauf1eg3D8Hg8J06cQMPT9773vb1ngSDw8fFxuCeQH5jJZCYnJx9//HEi+tnPfnZAF5BhbgkLEtN9hoaGPB5PrVaDyxkb+KgD9mYN4AndOMxf88QTT/z85z/H14FA4MaNG36/3+12I6/BMAxVVf1+/+zsrNPphMZAG9rtNlYgDcOA7TscDuMcs9msx+PZ1WKFl8rn86qqootofHzc7/d3Rs1qmra1tVWtViVJwsByi8UyODioqmq1WoVuPfnkkwsLC+ZEpU4ymQwcGQjISKfTwWBwaGiIiIaGhnhKBXOY8B4S02VEUbRarYqieDwejD7C406n0+l07jUiV6tVlAvdQhCEZrOJwayiKOZyuYWFBWzzwEeHhTiXy4VuU5R0kUhkbGwsHo/jvCRJwrC+dDqdz+fz+XyhUFhfX+88NYfDEQqFYPW22WzuHWw2m9PpxBc2m61QKFSrVYvF0t/fjxfHwWA2IKQrHA6Pj4/fMk11aWkJRhK4+DKZDIZRqar6wAMPHNI1ZRgi4gqJ6Toul6vRaMCngCmreBwf7ff6F2q1WndNDV/4wheQnUpEjz322LVr18xe3Xa7XavVfD6fw+FAkddsNqGpgiA4nU7DMCKRCBE5HA4Ugslk0ky3oz0TykVRhHphkgURYXQF4oWwHOdwOFqtFqpM8xe9Xq8oingEe1Qej0dV1b1TZYno2rVrOJeLFy9Wq9VcLoedp73bWgxzoHCFxHQZ3KmJqFarKYqCAQ1+v39+fn5+fn7vDAU48bpxpO8TDoex+yKKYq1WK5VKPp8vEAiYYd5IL4VtoVwuo/dIVVUY5GBVwJOJqK+vD2UNlubK5fKuovDcuXOIdZAkSZIkM+Cu3W6jjikWizabLZfLNRqNdruNus1isWiaJgiC1+vt6+uzWCyQwMnJyb1nBN/50tJSJBJptVq5XG5zcxOLdRwpxBwmLEhMlymVSh6PB+EFMENj6g+Mare0h+2KtjtMxsfHl5eX0V00MDAAUdF1HRaMSqWi6/qbb74JSdB1vVKpYA9JVVXTmICeIazFSZKEhTi8fqVS2bVt1mq1RkZGxsfHMdmv3W5bLBYELKHrCM9HU5HP5/P5fKFQqNlsInBI0zSv1ytJEmQP1vO9FnMiMgfI1nfQdX1ubq4XPI3MMYEFiek+NpvNbAWlHdsYBGlvMWSxWPaq1KFx+vRpDGtA5ZHP5wVBgMZgQQzfYrSEzWarVCpwvkmSZHq+cRaKomD0EcDsor0VIXWk2wE82Gg0yuVyuVzG7pQkSQMDA/CIw3Tn8/mwmaRpmsVige9O1/VTp06dOnXqlonpJ0+exHu1Wq1Wq5VOp0Oh0ODg4EFdTYb5ICxITPfRdb3ZbDabTbNCkiQJgmRuKXUyPj5++AcJZFkul8s2my0ej1ssllQqZbFYpqennU6nIAiw/6FYkWVZVdVwOPzqq5eI6J/+6R+JyOv1OndwOBzZbPbtt99Gch3EzOl07rVsIJvO6/Xi12kneQi1Dh5HNC2KrWAwiPoM+Q6SJKFKg2cPS3+3LH0Qi6dpGnwWtVqt1WpNT0938YIzxwoWJKb7NBoNCFKj0TDn8qFmQkNMJ4IgdGsR6TOf+Yy5nhYIBLAsNjo6ipEQoihqmuZwOIaGhhqNBsSpc9YtNnUGBgYQbwqnnCRJi4uLi4uL2WwW8al7G61Q6+ASKYoCKcLUc7fbHQgEJEkaHBx0OByo1cwYVlEUFxYWsJtltVrR1IV9pmq1ujeU4cUXX5yeno5EItidWlpaWltb8/l8HN/AHA4sSEz3WVxcxBfm3obX68XH/72WMAxXPdTj2+HTn/40NlrMZDkiMle0ZFlG9rbFYmm1Wu12++rVq50tU6iiUKBAk1wuVygUMjd1IpGIx+O55QYPETV2KJfL+XwefjyXyzUzM9PX1+dyuer1erFYLBaLzWYT1w2NRwsLC8ViEbWmKIrXrl2DrU7XdfQbdZJOp0dHR5H/3W63s9lssVhkawNzOLAgMT0BPrZjtapzlFznCHATMwz7kLl69Sr80MPDw+VyeWNjA4pSrVZN4Wk2m/BtF4tF2DTMXy+Xy4ZhQFRQ5ImiGI1G4/F4PB6XZTmVSsGDt/et2+02KqR6vY5lvdgOgiC4XK7FxcXV1VWzdoQgmaMuYL1DNiBGTzkcjkqlgrW+zjfa3t7G9lhfX1+z2SwWi5lMZlfgOsMcEOyfYbqP0+mEyVhRFIzuNu/LnZEEoHPow2HyyCOPXLt2DUt20Wh0bW0tm80ODw8LgpBIJNCCapZNmqZtbm56PJ7O0UTI+8EZFYvF/v7+eDxeq9Xg2bPZbDDF3VKQTEcfVA3hs6ZFAut4RJRIJEZGRmhHs5vNJo7HnInudrvRnORyufx+/9ramt/vh+pjQU9RFMxMSqfTaKsqFAqbm5tPPvkkEZn5FAxzELAgMd3H4/Ggqsjn87quY9wqbql7tccwjFKpdPgH6fP5VldXTbXAAT/66KOqqpbL5WazabPZ6vU6nGw3b94sFAqPP/74LkHNZrOwPPj9fkVRcJqmLxxjJuDQ2/XuoihCaTRNu3HjBjqf4JRD0DgOzGazJRKJUCiEkUjhcNg0c7darWazCbcI7SjWyMiIxWJBr645bDCVSo2Pj0OTiKjZbG5tbWEzjwWJOVBYkJjuA5cdvsaevBnXtnfJTtO0W3qjDxRRFLe2thB5QETVanV+fh7GAUVRRFFEd1S5XHa5XJcvXy6VSna73e12d+6BIb/O3ACTJAlVCxbNSqWS1Wr1+/0fdna4IFardXJyErpl/ghqhEwHRVHQAwsPBX4LIYGtVsvr9aLnqV6vi6IYCAQMw8AB3Lx5E376TCZz9uxZInK73ZVKpV6vu1yu7sY1MccE3kNiuozL5bJarQ888MADDzzQarVUVUVswYc9H1NWD/MIiejLX/7y1taW+S20xOFwQG/K5fL8/DzyhMrlsiiKo6OjWKzrtMwhbQG7ZbSjIiajo6NTU1N3YiA08xpMIEW4kuiBxZ6W3+8Ph8PhcDgYDEJ1TDmEUGHoFAyNp04bd0pPAAAgAElEQVSdwo/a7XYul/P7/cPDw+YAp2QymUwmzUVChjkIWJCYLmOz2Xw+HzoxEXiDEd24cXdWSLBQBwKBWzYnHRwjIyOhUMjsxh0aGsKgIEy5RWhQuVxGhN3CwsLIyMjQ0JDVahUEoXOkELzXuPtThzYASZLuehIuGpswvs/MFoKoYzQtNt4ikcjW1haCgqA0MDWgEWpgYGB0dBQvmEwmoUlYFVQUBVMEv/KVr9zdETLMncCCxHQZl8ulKAo6MXEPdblcDocDvobO7aJ4PB4Oh2dnZycmJg7zCH/zN39zaWmJdvZdxsbG5ufniWhyctJisbTbbXgZxsfHQ6FQNBqVJAmx5WhU+p3f+V0i+tf/+ltOpxPtR2bZ1JkS1Gq1IAx3cYSSJFUqFZ/Ph35btGqZ9goYJSwWi6qqqC8xllcURSTpoaVXFEXTBb65uZlMJj0ez6lTpzBVdmFhYWFhYXl52RSt+wnhg3T7cI4vLEhM98EOv6IouB2bDrFms2k6vPFZPh6P9/f3j42NHfIRYlSdqqrxeFwQBFmWY7EY7vhYP3zkkUeIyOl0RiIRJAbBvGDWIsiU29X0ul/DBgOBALpZO18TpRj8dci1wxYXzOITExMYV1EsFk3jYqvVwrBBItrc3IQrb2BgwGq1olmqXC7fcobFfQaLU7dgQWK6jCiKmCNns9kwEgl7JKiQTL8ZIglCoVBnntshcPbsWSzWYU8oFotZrdZAIDAzMyOKoq7r1Wo1HA7Dv2CxWHALW1pacjgchmE4nc7OBUYUIvveR9VqtaLRaDgcxjonro8sy8FgEM2zNpvN6/UGAoGRkZHBwcHBwcFYLBYMBjFG3VwdzWQyMK/jZRVFQTArFieJqFAo3DIE7z6Gi6fDhF12TJdxuVxIsCYip9NZq9WQB4q7tmmbxmyeQCCAEUGHdnif+9znfvjDH5rf9vf3v/baa4ODg263G8emKAoCSdHxKkmSLMsnT56sVqter1fTNOgZ9pNKpRJ2lYaHhw/CK9g5SwnWOBQ0CHLd3t5G+ioR6bru8XhkWRZFUZZlQRDy+Twcd3a7HX7CarU6MTGBvAw0S4VCoeXlZZfLdWxbZXdp0t4+OeZe4AqJ6TKqqsIeZrVa4VQ2t+U7P62bt1o87dAOD3mvRGTaE2RZjkQiGIakaVqxWJyensZ4clQbiUTC6/UahqHruiiKDz74IE6nXq9jMKsoisvLy7cModh38HY+n8/j8YyOjoZCIbPp2Gq1ulyuvr4+mAZdLlc0GnW73WYYEmZbmGmtFosFwUVnzpw5hCM/EnDxtL+wIDFdpjObTpZlOM1EUdw1FmhrawuLddgXObTDe+GFF9bW1rAhNDo6ev36ddzNK5UK1GVqakqWZXP0ERKyDcPAg9VqNRaLEdEDDzxw+vTpaDSKkUWBQMD01B2aabBzpCyw2WyhUCgSiSBhr6+vLxwOm0oJ7wMmXIBms4klysM54KMFL+7dOyxITPdRVRX1EBE1Gg3sxGARzCyGIpEImmbQAHQ4B+bz+dbX11utFlJKJyYmUqkURBELdFardXV1FUt2mqY1Go1sNms263S2wdKOFRuyaob1AWgSBkEdxIlUq1VzqMTGxsbGxgaKUcyi7e/vR83ndDqDwaC5Ioq4WK/X+9577+Hi4z4bCoVM7wPzYbA+3QUsSEyXMQzDHGOKDFDkC2B4nflh3O/3wzB9mHtIX/va1xRFMT0UyCc9efIkEc3Pz9tsNl3XsZuCL3D7Hh8fbzabfr/farXefnWrc6yRLMubm5tvvvmmoigI7d6XU5B3SKfTyWQym80ibnV7e3tlZWVpaQkC39/fj0nniGrFPbTZbGI4r67rEDNMsvB6vYdvdDzqsDjdCWxqYLqMy+XCNFUiQt0AO0Or1bLb7WacAXKyiSgQCHRu3R8c09PTFy9eJKJIJJJKpbxe7+LiYl9fXywWg4Xa7XZj0ANGrDabzfn5+ccffxwuapfL1Wg0IpHIh5kX0B506dKlEydOENHVq1cTiYSiKLIsI1UB4dz3cgrVahVZdrVajYiQHoQbIlRfEITV1VWv1xsOh30+X7FYxAVHkVcqlfL5/MzMjCAI8IVj8vprr73GFdK9wM6ID4MFieky6IFFN4+iKLjFY3Rp5628UqnAVhAIBA5nHlIkEkkkEsPDwxsbG0Q0Pj6+trYWiUTsdnulUnE6nZiKhBqu3W4XCoVTp04JO1NfRVHMZrMIQ8ILopZKpVITExOyLDcajcXFxbW1NezZrK+vl0olVVVfe+21yclJIgoGg8PDw3fXKovaa2NjA53FxWIR2XqmjT6dTiPWQZZlr9ebz+f9fn8wGMxkMmYAa6lUSiQS4+PjLpcLfwu73W4YhqIoXCHtI6xPJrxkx3SZUqmEplGv14uhq6+88grKjs4BfeZCFswCh3Bg58+fx7RyIkJ/qM1mGxwctNlsLpdrbm4ORmrkkxIR+pMQkOrxeBqNxoULF4jo3//7f0dEf/In/xbGvEuXLq2srDz//PP/+I//+M477zgcjlQqlUqlZFmGDGC/R9O0uy6ParXatWvXstmsLMtYD8RBtlotURRxRsgUl2W5Wq2WSqVSqYSuWLvd7vF40DyLylUQhP7+frgeHA6HqqqYMQ93xn5dbcbkOC/ucYXEdBm73Q6rAhHV63VN05xOJ4ZwExH2MBB2gK/7+vrMWLmDBgFuRDQ4OIh6yOFw4LaObh708RCRmTHhdruz2awgCChKOl9N1/X+/v4zZ85cunRpfn4ewdsDAwNIZR0eHsbbRSIRGPO2t7fv3M+GoLy1tTUiymazqDXN8UuGYaDoRDMv7eQ4wGSPBb1arWaeC/x4drsdBavf78ePMKm9VqvBJn5su5EODWjS8amZWJCYLoNxPrjf+Xw+s0mWiMxsN1RLZiGCUXIHynPPPfed73zH/NbhcNTrdUmSLBYLAo2sVmuhUMDilaZp1Wr1zJkzbrcb9/1GozE7O1utVjuLuZ/+9Kezs7PZbBbPdLlcU1NTuq6bgzYee+yxb3/7216vt7+/n3Zy0O/kaK1Way6XCwaDuVyOiHRdr9VqPp8PFSeeI4oisvVg87NarU6nE84RXE8z4kHXdfgYkYGENb1ddsH19XWI1qF9OGCOAyxITJdBiIDFYtF1HbvuECfcSX0+X6VSwSY/bpfNZvMQljJGRkZ+/OMfm9+Ojo6+8sorgUAglUqhi4iInE5nq9XCzb1UKk1NTTmdznq9bhjG3Nyc0+ksFAqdivLGG2+srKzMzc1ls9lIJOJyuSBd2DFSVTUajX7961+HThBRKBRqNBofZinMZDJYMbPZbMlkEuIHJcOmF7IBTTOCruvCTuhqq9XSdd3r9UYiEYvFggveuUDaaXxQVRWebyLCu2B+lSiKexubmH3n+JRHxILEdB3M7TbvkjAWJ5NJrHfhhq5pmsfjgfVL1/VDqJCWlpba7XY4HM7n8+fOndve3rbZbKqqZrPZZrPp8/mq1arL5TLv4JAoBPBgOjjGPfzlX/4l0X/Ecx599NGpqalEIoGyKRgMYmIeNAMbNjMzM+YamsfjwQjdvZtJqqomk0n8IvwULpdrc3MTr4xRgdFoFIYRIkKhE41GbTYbQvl8Ph8OQFEUHDyCwOF6gERBltrttiRJiG5Kp9NwDzYaDb/ff/iDqZj7GxYkpvuYJi4iMpteUQ/hW4/H4/F4cPfHhv+BHk88Hn/++eeJKJ/PE9HAwMDFixf9fn+9Xtd1vVAoxONxLHlhXgMkCnMFBwcHBwYGVFVNpVJmfjkIh8NWqxX+NDRdOZ1OxN8RkaqqzWazXC6bIQ6qqvp8vkwmY7fbd+XJQiqwQIfmrXq9brZMYWcLU2UhZuVyuVQqwZQBsfF4PKqq5nI5M5cBDWH4Gg57iBnak81CDYt+iqLMzc0d0PVnTI5VeUTssmO6Dixe6DnF/DpBENCjgyk+RIT9f8jSIRwSBnibNBoNh8MRDodPnjzZ19fncDjW1tYCgUCr1cIEB6SVi6IIj7UoiolEIh6PX7p0qTORAXOe4J6CVw0zCZFSgbu/oij1eh2trPitcDi8N90cHVqY0IEMPSgQLtrw8HC9XseOEZ6PnG+32411UVEULRZLuVyGZSOXy+VyuXw+D995q9VCDYRfVxSl1Wq53W7MPscJwqE3PDx8ENefAcdNjYgFiekFFEVxOByiKAYCAQTzmNk8AwMD+GyO7XeMl9338Q27eOyxx2hnFY6IUB/UajW/3z83N3fmzJnNzU0cA8IaMHlPEITR0dGxsbF2u+3xeFKpFOyC5steuXIFuzLIoXA4HKaZkIgweqPdbiMlz9SSW/oa0NOKS4SBsDhIcwaSy+XyeDyuHZxOJ2ZkoLSCHBIRNr0wsk+W5Uql0m63zUdwDFevXrVarQhpxToqHkde30H9DZhjCQsS031WVlbQ44KP4WajDKaA446Mey7WlLB1f0AMDAy8/vrrRIS1rL6+PoQdmB5uTFg3DKPdbiuKUi6XHQ5HPB6vVCp9fX1EhCkPv/jFLwzDMKsK2pn8BEEyY+4gTpjd7vf78cp4xGxi3YthGMPDw2jearVa9Xod0ohLZDoXTMFwuVx4O5j6DMNAMYofQdiwZ4bdO7yyGcpXq9VMqUO2EJYZDcPYFcrH7BfHsDwi3kNiegEY5yRJ8ng8+GBut9txy9N1Hc002Ckhomq1ig32A+LZZ5/9yU9+QjvzJh5++OHl5WUi0jSt2WxardZGo4GtI1jsHA6H3+8vlUoQEiISRfGVV15pt9snTpzQNG18fGJ1deX8+ceLxV/Pm0CpBDdH55zcTg27/S0J1gna2WZDD6/pP7Rarfl8HjWlJElmPadpGuqbVCqF8LpSqYTrCe0/ceIEco+ISJZliHGz2fR6vViZbLVa1WrV5/PhZHeFsjP7wvFUI+IKiekFcFMTBAHZNk6nEzWE3W73+/1ut1tVVSxntdttwzAymcyBHk+hUDBtZouLi9jKQsqcYRipVGpqaqrdbmOmkc/n6+vrKxaLExMTRKQoysDAAO1EYvf3929tbRGRy+X6xCc+gbqkc4KGxWJRd4AEoh/oI6fiSpI0MjKCCbAul0tV1ZWVlUQikUgkCoWC2+2GAR0GBNjHUeRh8c1qtRaLRWximRARlh/NsbZmeSqKImqmdrutqqo5MuMrX/nKgfwNmGMJCxLTfaA3yEFQVRX7K3gEuxfJZDKXy8H+oOv6wX0q93q9mMiHMmJubm5paQl5dLVaDTNYUSjoup7L5VCdWCyWcDjs8Xja7fYbb7whiuLKygpihIgITVSiKMKWJu5gsViw84RtHuSWQvwQyP2RmgTdwh6bz+dzOBywOciyXKvV+vr64D5H1pHH40HzE/IgMPKDiCRJwuvA6OjxeMyLPzAwgFLphRdeQLsSzhfHuWuCIrNfHNvyiFiQmF7A3IewWCyVSsXtdmMsLHaMnE6n1WqtVqvr6+vr6+v5fP7gwlVHR0exQGceWDQaRfkCV0WpVBocHDQMo1KpzMzM9PX19ff3r6+vYyT5xsbG0NBQMpkMhUJoA3I6ndgKCgaDWBAzh2hg38gEg6AgwHj3O9Rdh8MhSZLdbp+cnBwdHR0dHc3n89VqNRQKWSwW1JeYwISZsHg7nBQMjZ0re0gJwk/hNMG7wGwCfcIyY6VSMRNvGWZf4D0kpvuYfu5isWgYBu6J5oa82+0ulUrIYcMjCLU7CD7zmc+8+OKL5rd+vx+bPU6n8+zZs61WK5/Pz87OYvxEPB4Ph8OSJOGOb7Vab9y48aUvfelnP/vZwMCAmR13/vz5H/yAotFoo9FotVooRFqtFgoaRIMTkSAIcBt2atId4na7kZ1qNjBhhwmKSEThcBgljt1uR76DmYiBcoeIUDxBdfDnqFarbre7M04wFArBmIfoIyLifNX95TiXR8SCxHQXfH5PJBK4OWazWdwQG40GPnpjh1/TNJ/Ph0LqIxey7oVsNovcHSIaGhr66U9/GggEpqamEHqtaRq8f0hJcDgcCCTF7pEsy0tLSxgj6/f7a7VaKBSSZfmhhx76wQ/eXwqDLYJ2XOw+n6/VaqEQRHMPiqd2u43cijs8bI/H4/V66/U6VhohNkjjhpCYHV1EhAGDmUwGlnGr1Qr/iCiKhmHgkPBMl8tl6g1myw4ODiaTSUgUXIiVSgVeif24/MedY65GxILEdJfx8XFBEG7evIl6KJ1OY8dF13XcSTFeCBN6zKDVg7v9LS0tmZVZLBbb3NyE7Q1dohaLZWJiAktwCFywWq2pVOoTn/gEEV28ePFb3/pWrVaDqEiSpGna/Pz8k08+STsudqvVigBvwzCq1arVau1MkHM6nbjLo6z5WINx0XuEi4b0B+xOaZoWiURMFcf+HBHF43FVVdfX101DI0bxYkEPWmi1WvHnIKJf/epXDz/8sNPpxMhBM9/B6/VKkmR6HBjmXuA9pJ5k6Xlj+1e7H7z5QyqudONoDhbcdldWVlZWVmq12tTUlMPhMNtgdV0PhUKI1jbbRQ9omegb3/gGHHHA5XIFAgG/34/lODjucGz1eh37KFhng0Vtfn4eUQhjY2PY8Nc0rVwu//M//zMRYZXP4/FAlqAKGGnh9/v9fj+M1OjHoo+pRiYobvr6+kxx+jDDHkbFx+PxgYGB8A7RaBR5DfitdrvduY30q1/9ShTFmzdvlsvlZrOJ5Uezn4m5R7g8IhakHqW8Knz7N6iWNB8wrvwX+uG3yOHv4kEdBEhg6+/vf/XVV1999dW+vj5JkjoHTKB6CIVCrVYLd3naM2FzvzBHGRHR0NBQpVLxer1oOYLvDqt2RFSpVDRNg7UBsQ5/93d/p2kaVLNQKKDuMQyjXC6jyDCPGRY7p9Pp9Xo7031gKbz34g9ObjRI3eZpcNlBt3BIhUKhWCzmcrlqtYqhEqVSKZPJYNUUnntE7Zl2O7zO3c20ZTphNQIsSD3JQ39E8XP0z//T+9/WksJP/jf67f9Crjsd13ZUSCQSNput2WyGQqFQKDQ8PNxoNBKJBHJCTYaHh9fX12u1GkbDmetF+8v169dp59YQi8Xm5+f7+/sxAMkwjEKhMDQ0hJy3c+fOYU0M1jhN0xYXF2dnZ0VRxG4TNmYqlUqr1frBD75PRP/pP/2ZOeiBiKrVKhwH6OxB71G73YZuwV+37wa2zk0p7Bipqtput2EWx1HBTQetzeVyxWIRbVUwgqNDGT3CqAvNQDyGuXdYkHoFgYQP/PPNf6HVnxvv/lcion/+n2nud2j6N7t9jPuPaXQ2+5AQpKYoChpjUZcgDSGbzdLOhO+DOJjOZDaXy2V+aMWSl9frxSDwra0tLNwJgjA9PU1Eb7/9NhE9/PDD9Xq9M5Xc6/V2xv90TpHAK2AFD0ZwxPM0m03sV5l523etvrquyx3UarVyuSwIAgJY8RzoK9qeXC7X0NDQ0NAQdrYMw4hEIoODgzgF7C1BjZCagT8Zvri7I2QAl0cmbGroXYR/VyP6OtHX6feJiAz6q24f0f6D/krTp7C4uNjf34+VLnPWDj6wj4yMIGIuGo0eULhqMvn+GmkoFEI8HZYKm83m9vb26OiozWbL5XKbm5sjIyNOpxMThgzDuHTpks1mi0Qi5gj2QqHg9XodDoeZ0EpE5tBbE03T6vU69mCwI2VOxkPqNgbrVSoVl8t1J/ZCGPnQO4xBUzAsIIDV6/Xmcjm8DvaukOSN35Vl2el0OhyOgYEB9BihZ7ZzFAVGbCAvA+di/jrD3DssSEcZOdvtI7hXkE8qSRKcxJjIgB/hEcMwsGQE69ra2prb7b797sjdEQqFzPamqamp9957Lx6PQx4KhQLeXdf1hYUF1BlEJMsybv25XM7n8+EeDe81YuV++MMf/sEf/MFbb73/FtVqddf4DLPUoJ30WFQwRIT9Kpi/UUjdyVk0m02Hw5HJZLa2ttrtdl9f3+bmJhGhrCEi5HkTkcViOX36NM4L11xRFPwoGo1WKhWfzwe/htVqxbIqXt8cJA9hw8yLe778xxcujzrhJbtewfjxv7n9E8zVvF8/lLpysMd0KKAdB/sopl8LexXtdhuzU4nI4XAEg0FRFG/cuNEZprBffO1rXzOTEeLx+MbGhtfrxXoaBkxgXCxS4FDSxWKxZrP5k5/8xGq1ut1uRVFSqRSs0giXazabncNt9w5zQkgPlvJQI2IubTqdTqfTsDyYKeN3AnaDMB9WUZTt7W0sx9VqNU3TUqlUvV5HdBBKvWKxqGkasosQG9iZkUFEqF+JCPnf+Esh8dbMxLuHq37cYTXaBVdIPcO5/9VY+W0iMqw2YfTJDwgPEREZZODBX/9oYvcz9vzS0cBqteKz+cbGBvZRFEXBKAesfQ0ODtJOKFwymdz3/43dbncikfD5fHCXbW1tTU9PY5Q41AIzbZeWlsLhsJn5ferUqUaj8c477/j9/lAotLW1BWMC7te5XG5oaOgj91ckSVIUBVtW6P+lneRv1EyQSay8EVG73cauW7VaxYvvqheRZWeaEvEcc5hFp69vcXER5WYkEiGi4eFh1DrmHhg+E5gFHxHpug7pNXXI4XDcZkwGw3wsWJB6hsgsRWZpR1MMMojIuP4d4b//G/qTHHU8aCKQQLvuzMYHfnwkaDabgUDAnPyG4sNms6G2wM48Jv1MTk4uLS0lk0m4G/YRtNfgxmq1Wt99910Y6gqFAvJPiahUKs3NzSGFAW46SZJ+/OMfw/c8NDS0urr68MMPwxqnKMprr7124cKFO9li0TRNURR0xUJrUReiuIlEIjDyQS2q1WogEIDkdDauAuiZy+WCMR1BDLiwkiS1Wi24EmjHro2scTNlFc285vg+CFKlUjHLuHa7feHChe9+97u1Wg3Clk6nzUU/5mPB5dFeWJCOMAZ1ZC3v/W971yO9qk/NZtOMrCaiRqMxMzOTyWTgeRsYGEBxAJv15OSkKIqXL1/e32O4cOHCm2++CQn8rd/6re9973ter9cwDFQ5/f396XQ6n88/9NBD2PLRdR1FGxEhfQ6j7XBzRzp4PB43Q+FuA5QGu0fmX3N9fR3/LpVKp0+fHh8fbzQaKysrRDQ5OYmlNiyaEVEkEkHmEO1YDLDxA10xrX26rguCYLFY8ExJkvL5vKqq6Px1OBxbW1v9/f12ux3eevyWoihLS0ud64rY9wqHw5jGlEwmWY2Y/YIFqacRHvg9euD3un0UBwuistF9KcsyVr0sFguS6zweDwKzMaZPFMW+vj6MQ93HYxBFMZ1O4+vOgCJN07LZLLIMLl68SEQYLEREo6Oj1Wr16tWrSAkaGBiYn5+nHWuAIAgzMzOdMQcmqH5MoTIrQtpJZ9B1HXl66+vr5kxCsyLJZrPY9Wm1WoIgpNNpRKnuGiEBi51ZcsFQh7U+vJcsyz6fD6+DVzYrKvTt4mRRjMJtSDslLP4QqMagnczHhcujW8KmhvsF4YP/7MX44D+9BOa/YR0smUxik8b0K2OfH12cuCGao7X3C3O9joiuX78+OTkpSZLT6RQEoVwud4oKukFhyM7lchh0G4lEqtXq8PAwUg8Mw8BspHw+X6/X//N//r+ICP+mjmhzEyTIwXAIbUALsCiKJ06cWFlZuXLlChFhtES9Xvf5fLgCKHqKxeLa2ho867iMfr9/YmJibGwMzvVQKIRlOofDgbAiHLbP58PcCpRT7R1Mcwc6f4moXC7jxK1WK3anMCDKtJwwHwtWow+DBek+Za847fp2rz51T6uKxaK5YKVp2o0bN5rNJioPdHGitRN3W7vdvr8jzB999NGNjQ1kntpstkQiQTtOAU3TzMFFWAGz2Wx2ux2j9p5//nm4BoLBYCgUWlhYwCAi5NoR0auvvvrSSy/BKGHGLtzyPo4qUJKkWq1WrVYhDE899dTs7Gw0Gp2enhZFERUbEsRNmdR1HTaHzq5e2qnzOjuOsV1kDtaz2+1QXGwd4fnNZjOXy21tbe2aEIg1PbwILgvqJLQJ7+PfgjnmsCAdA3bVTLespTp16NBrKfMWbK4LJRKJK1euQJPwhGQyCUGCQXkf3/3pp58ulUqokL74xS9ardazZ88ioEhV1Wg0ipt4LBZD9g+G3RHR8vIyBn7TThIdrOGtVisSiTidzlKptLy8jFMolUpo5emskBDAA9e11+u1WCzJZPI73/nO6urq6uqqYRgOh2NyctJisciyDEt3rVbDXCVsVhERqpa9+T39/f0TExOoqyKRiN1uh8BASNDhhKAgWB48Hg8G17bb7WQymUwmM5mM6bjDxYcmud1uSZLQvMxNSB8XLo9uA+8hHWH2J7Jlx9X3oZg/OjBbBDxm+Bq5DAgqvXLlysjIiNVqLZVKqVQKhREGyO7ju6PaKJVKRITcBEVR0AjlcrngWVAUZWhoyDAMuNJpx35drVbj8bjX60XpVi6XnU4n7vtXrlxxOp1mfl0gENjbz+twOCqVCp6Af9fr9VwuZ049x/QNh8NRLBYhOWbWHGL0sJOEV8tkMv39/Z2vj7EdROTz+crlMiQfGiMIAnaPYHNABJ+u616vt1arFYtFHAD0Bm9KO2OcbDYbJtISEeo/5g5hNbo9LEhHlX0OEPtIWTJ/egCyBAczEVmt1s7otkKh4PF4ms3m4OCgy+VaWFggokAgsL/jJ27cuIGhtDB/ox7SNC0QCIyMjJi9ug6HA7dyLLglk0mr1To4OIi1L5vNdvLkyUQiMTAwAP/b5uYmgn/w62YjkQl2iSAtRNRsNt1u9/b2diQSwW81Go1QKISJf8Fg0OFw5HK5druNcARs5BARmoQcDkc6nd4lSLRzB7TZbKOjo6qqKoqCF8/n8zA3opLDmqGu67CNYAEQE2yJyOPxdFZX2OHDK5tdTQxz77AgMR3sEptdXU3GrR7sfPLdahVCVGnnY7skSY1Gw+/3l0qlra0txMFZrVak27lcLhig9wukesNgvbGxgU4ddKoii6HZbMLrjNCjzf4AACAASURBVIkSKCz+6q/+CotX6AoiopGRkdXVVXgukDFxe883rGuQFlwEZAWhI5iIlpaWYEnAvFcUkcViEUdoZig4HA6v1+v3+2/evHmbtwuHw5AiwzDW1tai0ShW3pDxSjvSYhjGxsYGPu7Y7XacmplvhJA9XdcdDocZccSf+u8QvlAfCQvSkeSQ8pWF2367d4fpbttysfREO/dEM2UgEAioqprP5y0Wy8zMDIqVarUKj/i+gP4hrGuNjo7euHHDXFiD+xxFjDnmTlXVZ555xkycw6KW0+lcWFgYHR31eDyIJ8dSm6qqTz311Ie9NYZ/YwgeEWEJ7uTJk9euXYNpAn4B7CSZq2dOpzOfz3u93mAwiPA9LBg6HI4TJ07c5kw7S5mxsTG8msVi2djYwOk3Gg1N0ywWS39/Pxbi2u12NptF+LdpOcEFWV9fD4VCsVjMtJWbYbgMc9ewIDF3y67yaBcfpy13enoabUC4u5ltntiz8Xq9uq6bVRGShPYrzu7BBx986aWX8PXCwoKmaR6PB0uI2DiBLCGoG2EKNptteXkZS2eCIMRiMezE2Gy2eDxeKpVQvsCJcOrUqQ97a5fLVSqVDMOANphzK1CFEFFfX5+iKHgc8obYCOixruuopSwWS6VSqVar0Wj0Ds/aLG5SqdTm5ibWSzEDyWKxDAwM4PNBrVYLBAIYHm/u21UqFavV6vV6U6kUHsEVuIuLf6zg8uhOYJfd0aMXx8985BHd9n9Gc26QGd4TDAYxIZuIIpHIyMiIqqowfxcKhb37MXfN9PR0f38/MhoSiQRabcz5sLquY1vL4XCgWcps0xFFMRgMImGBdoo8dE35fL733nvPMIxPfvKTtxndhC4i2ultIiJsRPn9fuSfou8KkdtYpkOKEgalI3212Wym02nEp1YqFUVROsf6tdttzIa/5QFANfv7+3FhsVhqsVgKhQIewZgJhBXBCqjrut/vt1gsY2Nj1Wo1lUph9G08HsdoXeaWsBrdIVwhHTF6UY3A7Qsm+tAFPSwB1et1cyEuEAi43W4ztkBV1WQyOTExgUUhczP/3gmHw8hUTSaTgUCgVCphowV+NqvVikgCrKrVajWr1RqLxZxO57e//W1VVR0OhxnV43a7sd6FI3zjjTc8Hs/nP//5QqHwH/7D/0FEf/In/9bsje08AFjeaWdaktfrHR8fv3HjBhGNjY1hC6fVaiHQwe/32+32er0+Pz+PaeJEBDWNRCJbW1v4zwOCLQhCIpHARVtbW0PejyiKqqqansZIJBIMBvEcGPbQYGR6MRRFQU2GU4MVEIt4kiRVq9WxsbFKpRIKhdrtNkZdMMxdw4LEHAB7d5t2NTx1iJNKqv2L9nwun6c8EUkuye/3Y04P7fST6rqeSCRw97fZbGhivXcQ1IaKB6qgKMrJkyczmYwgCM1mE3Y+u91eLBZhZ5iZmUEZZLPZ0LVjjt2DcXxhYcHn82Hpj25lruuk1Wo5HA7oH0RCluXx8XFsDl27dm1qagpjI2w2WygUKpfLXq93a2sLkpzP54nI5/PZ7fZKpbJrUAU24XABEYFaKpX8fr9hGH19fZgHiO0rzL1F8+/y8jJOCofn8Xh8Ph/867j4sizb7fZCoYA/UH9/P3bXDmiM730Al0d3DgvSUaJ3y6PbI3zI1/S+Mv3ov//IfECRlRX69RLTI48+grigWq32sSYD3Qmf+9znnn/+eVQYc3Nzr7/++vnz54lIVdWlpaV4PJ5IJGZmZpADBOudmXEXCAQsFktfX1+r1XK5XPV63YzWfvfddzF/nW4rSCjFTDc5QFbeyMgIES0tLRWLxXq93mg0HA4H7HbFYhEKanY4oTXKarVCOYioWq2idRezDfFIo9HQdb1UKkE/YPCTJGloaAi1aT6fl2V5amqqXq9DxgqFAt7XbKJCFehyuZxOJ4wMfr9fkqSNjY19z3O6P2A1+liwIB0Zjqoa3R6Bnn32WU3TXvyXF2/58zdefwNfzMy+77JrtVr7FVdz/vz573//+/h6dnb23XffxcS5cDiMG7dhGLVarb+/H8Y2uNGwgFYulyORyMDAgK7rdrsdhYvNZrNarYlE4k5m2uLObs4WKhaLzWYzGo12zsfL5XKwnrdaLfg+EGUElwEuiCzLSOzuLByr1Sq0EA96PB6kviL1Z3t7G2uhROR2u6FeiqKUSiUIlTlXEJM1BEGARFmtVo/HI0kSkpz8fn8sFkNqAx4h7kxi7gE2NTBdxul0ZjKZoeGh2zsjbs7ffO/Ge06nE2Gg+/LWP/3pT80KJpfLTU9PQ/VFUTx16lQ8Hne73T6fD3lxNpttYmKCiDBVaHJyEpk9RCTLci6Xw/YMFrWmpqb8fv9HHqeZvW2CvCIEKdntdkyIwKdsxPo1m020K5mjXdHfGgqFzJ0h2rHMoamWiMLhMEoxtHypqopR60S0vLxcKBQKhQJmD+bzeQSKoxhSVRXaDOcFXhCiiONXFCUYDKZSqVKphFfelz/N/QGXRx8XrpCOBvdneUT02GOPZTKZX2+BON6f6BMIBErF0t7nv/P2O+9/dc/Xw2q1vvPOO7FYDA7ylZUVj8eD/id458xcH9xk0atLRMvLyzA7DA8Po75ptVoIDarVaqZjEFlwH7nd5XK5sIdksVgcDodhGIVCAaMu8NaXL1+ORCLmuAdJkprNZrVaNYdI6boOVcCiHJ7TaDSazSZMCpIkYWaH1WpFji2egzrPYrHAjDAwMBCJRJLJpKZpkDHIT6VScbvdOCkY83Rdx3YaEZVKpUajceLEiX/5l3+51z8Jc+xhQWK6ydtvv33u3LnFxUX6oH2uVCpNz0xjGyadTqOMGBkZWV9fx9pRme61DfPs2bPvvfce7vIjIyOZTObEiROm7xx9SLhr48FdKaJzc3O4L6ONt1wuN5vNEydOwDuO56yurnburMA4vvdIzBUzRVEkSdre3jZT++LxuCzLV69eNeskOLDN5B7acUPgILHUhgxyURTh3h4YGBAEwev14pkwX+A5Xq8Xga14qUgkYkZ6g0Qikc1mYcwzyzK8yOjoKBFtb29PTExgdMg9/kXuM7g8ugt4ye4IcL+WR4IgfPKTn2y32x6Pp1qtmlk4RGSz2TY2NtLpdLVaNeO933rrrVwuZ46hu0fq9boZf3Dq1KlgMGi1WqExWKHSdX1sbMx8vqklbrdbFEWv15vJZHDvVhSlUqmsra3puo5xq6qqut3uK1eudE6F2OtD67yJo46B/KDBaHR0dGRkZGpqCiVRIpFIJBLr6+uZTCaTyaBDyExcVRSlWCxCgVKpFEooi8VSr9fT6XQmkykUCqlUyuVyYTnOzFY375uQJXPYhyzL+Xwe9RlOChl65XK5VCrl83lzHwvKd+9/kfsJVqO7gyukXud+VSMiMgxje3t7enoa2dLm/Rqeb1mW6/W6+SMsImFbZV/e/bd/+7f/4i/+AgNP+/r6rl+/bgbEoSW22WxiuDg2b5BbisWxZ599Np1OI50B92XMYshms5lMptVqBQKBSqWSz+c70073Ou523bZkWb5+/bqiKLB9z87O2my2ra0tnDtKQ0VRUqnU2bNnU6kUrhhmCUIS8O9IJKJp2rVr1+Dh/vSnPw3TQa1W0zRtdnaWiFRVRfuR2+021yTRbGu2HuPVAoEABlLEYjG8CLKRsK4IJ/rbb7+9L38U5pjDgsR0jUAggGSdWCy2urpqPt5oNFA6dH7unp6eRjWzuroaDAbNieN3zbVr18xQg0wmg+YhImo2m1jaeuedd86dO4fC6MqVK0gikCRpcHBwaGjopZdegk0A6lWv11E9VKtV5GcjgK7dbv/5n//ff/zH/7vZFYs3xR0fcyXMeYO5XO6tt94yG2xff/11XddXV1fNabYmly9fNr+en59/5JFHMK8ILw7PwhtvvIFre/HixbNnz25sbGDGBHaMpqambDYb1B2FKX690WgoioIZ6q1WSxTFgYEBr9ebTqeTyWS5XB4cHDx9+vRbb7315ptv4hQ6D4YhLo/uARaknuY+Lo+IqFQqWSwWjLnDDRof1fcubcXj8XK5XCgUdF3fr3Gxq6urXq8Xd2S0vsZisXq9jk5Vr9dbr9fR9NpoNAzDmJqaMn8Xw8sFQcDMpLW1tTNnzszMzBSLxbfeest0shFRoVDAFhERXbt27fTp051LZBjrkMvliOgXv/jFN7/5TSzW+f1+pAFtb2/fyblcvnwZqeGwm/f19WUyGYjTysrKxMQEjBiIlsBrIo8VvgkcKpIpUqmU2SxMROPj4zC7RyKRXC6HIAl8UEA7rdVqXVtb26+ylTnmsCD1Lve3GhHRU089Jcvy+vq60+mECHk8Hmy578Lr9WJeERGhGfMe39rv92ezWbfbnc/nBwYG4LFutVrQHiLCHgw6UhOJhMvlCgaDpufi5s2bbre71WolEonp6en5+fmHH344EAjk83nMnrh27dro6GgoFPrrv/7r3//93yeil19+GdKFs8CyJHxxv/rVr/Cyf/qnfxqNRoPBIIKU7jyJx9xUw5XBOqSJGWfndrtDoRDqoWq1WigU4HfAKcM6SB/c2RoZGTGHNkUikUKhsLq6urS0hC9ox0zBmHB5dC+wIDFdI5fLpdNp7J/DFVar1RCFgCcIggC/w82bN0OhECbU0QfvmHfHhQsXLl26lEwm8S0WABuNBmwC8Xg8m8329fXB9VAqlcyJTQBzxKEo2O1PJpOXL1/GaAzIEsZJxGKxv/3bvyX683q9/tZbbw0PD6OIGRoagk/vpZdeQnmB+D673f7WW28JgjA+Pv5xvRs+n6/Vatnt9qGhoVuKWb1er9frmPQaDAZdLhc89yhP4Wk8c+bMu+++i+cja9X8YITVvGq1ms/nMSrw4130YwCr0T3CgtSj3Pfl0Te+8Y3FxUUsB8EXsPf+K4pis9kMh8PoztmvCDsievTRRy9fvgwl+NSnPnXx4sVAIABBQjQctAF2Zww17/x1jBGKRqOZTObKlSt+v397e3tra2ti4v9v783D2zqvc993A9iYZ4IkSHEQB5HUQMqSLMnWYNnybCmyHdupnTROm7qnadLhxGnT+9w8N216entOclsnPW192rpJnLpxk9iJZ8WWh9iWLVuSNU8UxREECQ4gQMwz9v1jUdsQJ3EWSK7f48cPsLGxsfcm9L1Y37fWu6o3bNjQ0tJCKQPpdDocDtN10RGOHDlClzwwMBAIBLq7u2keDIBer3c6nWSrWlBQ0NHRUVZWRsbbU78u0vLJbeVIeARBIKsIuTiXOqOP2tNgMFDy4cDAAG1MJBImk2mu2n8wTC6c9s1cA/bt2xcIBMrLy7u7uymrTZIkyhbTarUGg0GtVpN76XXXXZdKpWiEncOFCurUQI+1Wm15eblWq43FYtXV1SUlJU6ns7y8nAZuOjEqAiXcbrcgCGvWrPF6vStWrBAEIRKJ1NbWAvD5fDfccAOZw+p0OjIhlW14aDaSSpfi8Xhra+vFixdFUTQYDGQWLgjC2rVr6+vrZWfuaalRMBiMRqOkghPtQ02bSktLHQ4HmcNKkkTGEACy2WzuTXY6naIodnd3X7p0iX40dHV1rVy5cufOnfLCGCPD4dHs4QgpH1ny4RG11XnhhReoSTYlT1MHPHkZw2azkccoFdzkvn32bbNfe+21+vr6wcFBAFardXBwcOXKlefPn6+urqZWeNQRgzKeyRlI/ujTp0+LoqjT6QYHB2+99dbjx4+n0+nbb7/d7/eTkc+lS5fIb5SchwBIEoCP6O3kZJpMJleuXLly5UqVSkVbLly4QBWmfX19wWDQbDZTqttEKJVKWjGivAm6RVRFpNFoJlIy8g6nE6itrdXr9eRUBIDartOVUlMonU7ndrsp4Zv2qa+vb21tPX78eENDAzkKTjHtYsnDajQnsCDlHUtejW677TYaB6nWh+xtqBG4bIC9YsUKAKlUitKg6Y12u50yu+64446//uu/ns05dHd3y1VB7e3tJAm1tbWUqxaJRGTloKV+mmejnS0WSzqdpnAqEAiQ+emZM2csFgtZo/b19ZnNZuoITponI0kSVSYVFBQMDQ25XK5IJEJj/f33369QKM6ePetyuchLSY4dR03BGQwGp9O5devWgwcPAti0adPGjRtfeumlbDbb0tKCq32FSEJ6e3uLiopEUTx06BCJ2a5du9577z3ah3Lq1Go1GU+k02mqEbZarT/60Y+SyWQ6nWYpYuYcFiRmQbnjjjsMBoPRaDxw4AD1lKOfltQUVfYqFQRhcHCwu7ubxuK1a9euXbu2o6OjpKTktttum+U5mEymVCol57ZdunSJ8hfMZnMmk6HohHYzGo3UG4lKU2npSBRFGvGpbZ1Go+no6GhrazMajTfeeGNra6vJZDKbzTTlOPbTBwYGamtr4/G42+0mszgKdF599VW9Xu9wOJqampqbm2W3HmoRS56tAFKpFKWGRyKRHTt2ACDDoUQi4XA46urqWlpa5ElCGZ1ON2oZDMA771zhsC6rEYCamhp6EAgEyAePIqeOjg5yuMhN5BNFsbCwcDnrE4dHcwULUn6x5MMj6noXj8ep0w8l19ECO9nthEKh9evX+/1+lUplNBq3bdsWi8WamprIQCiTybS2tsrD5YyhBgr02OFwULREnVIjkUhRURG1X1IoFHL3PJfLFQ6HRVEMhUKiKGq12kwmk0gkyGJHEIQHH3yQGsuaTCayWpjI7TuVSvX29kqSRN0caMJNEIRgMFhYWFhSUtLa2gqA+i3pdLpgMEjdxHHZJcjj8bzxxhvU7qisrOzEiRN2u72ysrK8vBwAxUkEnYMoitQQdipt9KxWa0lJCZ1nUVERZeXRFZFU5yYcbt26NZVKkb1QMBic/p+CYT6FBYlZONatWxeLxcxm829+8xvZBjSdTtMKeSQSkSRp/fr1ANRqtewdV1paqtFoqAFER0fHrl27yE9oxmzcuJFylwE4nc54PG4ymSjTzO1200p+IpGgpAYaZFUqldvtpmQEkqJUKhWPxyORCGXTqVSqyspKj8fz9ttvDw4OqtVqjUaT2w9CRoCAnDIhsVAcGBhIJpIArDarKIpFRUXkSUFRY39/P7WvJZFQKBSdnZ1kRkdhSjKZrK6uvvXWWy0Wi9fr3bNnTygUkjPaaT6QOgpqNBqLxUI57iQwYykuLqY/QUlJSV9fX3FxcTKZpJgSQFFR0Re+8IWf/vSnACj+q66ubmpqCofDy9Y9iMOjOYQFKY9Y8uGRQqHYsmXLgQMHck2vJUkio2savslgDQCZbSsUClqnSSaTfX19JpPp1KlTuZ6nM6CkpEQu0yG/UYPBUFBQQKv0er0+EAgEg0Gn05lMJmOxmMlkInft0tJSMianeC4YDJ4+fZqOQzZCoii2t7fr9fpwOKzRaMbNjVaqlGQfF4vGAHgHvfJLw/7hYQxfwAX8BwCUrSrz+XyBQEAQBLPZLDcsNxgM8l0CMDw8fOLEic7OzkAgQLJKapSb+iH3odBoNNRbXa1WU3aDjFarLSkpaWhoKCoqovCxsLAwnU57PB4KCgHU1NSsWbPm+PHjg4ODFJ8ZjcbVq1d3dnYuz3F5eV71/MGClC8seTWqqqratm1bOp3WarXyOK7VajUaDQ2vVO5Kg3h5eTmFTRcuXNi2bRvtIIpiIBCor6+fpdt3aWlpeXk5fZDFYkkmkwqFglIMmpqacLnTBPU6UiqVVVVVCoXCaDT6/X6ypyOzOHmpRqfTNTQ0DA8Py9OA1O/c6XT29fcjZ8zS6/U0+SZJUuXKSiqejUQiwcA4k12tl1rpgQQpIAQ2bNgAwOv10toSSThV5nZ1dZ09e1alUuV2qqVpOnpML0mSRKZH9Kper89NxkulUlVVVTfddBO5xNLNT6fToVDIbrfTEtHp06dXrVq1fv36vr6+wsJCAJ2dnV1dXUeOHJnKZCDDTA4LErMQlJeXp9Npg8Fw+PDhCxcuUFYxhURKpdJkMtFIKmdXUzqD3++nOiSNRkNOByUlJcFgcJYdY/v7+y9cuECPKysr3W730NAQpSdQ19RsNhsMBqmB0OrVq6mdBOmQRqOhmMnj8cgZdLFYbOvWrR6PR17qpxrevv5+AOTaIIoiCZher9fr9fF4nASsoKAgGAxCACb/qS3hlm/cQrfozJkzNptt3bp1gUDg4MGDlDoPILf3+ahC42g0WldX5/P5QqEQ3T2yEbLb7QMDA3RuK1eurK6uliTJbDZTjHX27NlIJEIiSjJ27Nixnp6empqaQCBAdkT19fXf/va3N2zYMCpuWw5weDTnsCDlBUs4PCIroMLCwo0bN77wwgtWq5U6giuVylQqJQiCz+fT6/WxWCwSici+QbSGpNVqL1y4QL3AyS6hp6cnGo3OMqmhpaVFNgvXaDSpVIoWVKj61W63Uz9ycs0hJzdKAQ+Hw3a7PZlMBoPBwcHB3Ly16urqrq4uMsBWKpU1NTWHjxwBYDaZUskkgFQqpdPpyB8Il/uR2+12SZKsVqtKpUIhdu7cWV1d7ff7f/TDH4097Sf+/oncp5V3Ver1+p07d/b397vd7tw1IaVSKTeiJWgFqL6+vqqqyuVyAYhGo9TKqKGhQalUtrS0iKI4ODgoCEI0GqX1qtLSUr/fT9pJk4GxWMztdnd3d/f19dGX1uv1lpWVqdXq5ZZlx2o0H7AgXXuWsBoBMJvNDz/8MLmI+nw+2WqaBmgA6XRanjiSm6IC8Pv9q1evBqDX6/v7+6mZAoCioqLZREh33303rQMR5FZAp+dwOIxGo9Vq9Xq91M4cABUDZTKZeDxOwqlQKAYGBiKRSG6yWSKRkOchQ6EQqVGhw2FSqwEolUqyGgJgs9moUImukWyKampqampqrFYrZXvfe9+9JMwej8dkMlGBMJl/p1Kp48eOA3jj9TcmusaMkKFbKvdfp3MIBoOpVIrWkKiaShCEZDIZCoXq6uoEQejr6+vr6xsYGKCeSW1tbfR22RYdgCAIw8PDNFtIfzKLxSJfO8PMBhYkZh6hZQ+j0Xj8+HGKHijpi36bp9NpCpJwuf6Uhj/SAFEUXS7XLbfc4vF4qNdDKpVau3YtDdkzPqWVK1c2NzfLT+PxuMFg8Pl8RUVFZrPZ4XBIkpRMJrVaLSWjk11pIBBQKBSUxkZ9g0aV9Xz00UeysYL8y3nQ662uqspmsyqVqqamZmhoiFoQUSCYTCZpXeeee+6hK5JbXVRWViaTSbfbXV9fHwgESktLM5lMcXFxNBp1u90lpSUAyKf14PsHx7lICRlkAKSQ0ul1AJLJJBUwaTSaRCIBoLS01Gw2DwwMUJI6zR/SdKVOpyP5l8UsN9gSRZG6Zoz0kg8ELl26NK5H+xKGw6N5ggXpGrNUwyOa4bn++uudTud//ud/AqC8bXqVJqzkf9VUkUOpBAC8Xq/dbg8Gg42Njc3NzUVFRYcOHaKf/D6fb9euXWNrPKdOa2trbo4fLvduqK+vpw4Ucpcg2SUhm82Kotjf36/VavV6vdfrpdJd+fwpH33kWq78uBtvvLG7u5vaEel0OipQVSqVlFleWFi4evVqKvdJJpNGozEcDre1tfX395eXl+t0ukwmU1RURCXDkUikvLw8k8msXbs2mUxGo9FgMFi6otRoNLZcbMEEUC6fwWggCwxy2APQ0dFBNbk2m83pdPb19cViMcp4pMqniQ6YTCblpT4AarV6uakRM3+wIDHzxVe/+tVUKuX1euPxuMfjka16qIaGIiSTyURjX24vA7PZTL/l+/v7KyoqPvroI3pVpVJt27aNRvMZn9WWLVtGLbfEYjFSIMr2TqVStL7l9XoLCgpUKhVZ2zkcjlQqRQnfyWQy9zdyOp0+d+7c2M+qrKjYEInYbLYNGzaoVCqfz0ezheFweGBggLrlNjQ0kMkQdYl99dVXqaz1448/3rx5M4DKykoy+gMQjUa1Wi1lTEQikb6+PvJYKisvo9bpE2VGRMKRFrQA2LFzB/0soNz0YDBI6SRkehSNRoeHhxOJBK2ZTUVpZpnxuBjh8Gj+YEG6lizV8EitVn/xi1+kIOOXv/wlZRDEYjHK1wIgZzSMfS8tqlssFqqGGR4eTiaTiUSisbERAKnFbDKM/X5/rvhRWGA0GsmQlPqXU5wEYHBw0OFwRKPRFStWUIhGrfwmagWUO1AJQGEsdujQIXIGok53q1at6urqovWY5ubmb3/723KbO+oSS0emFaZPPvnks5/9rNlsliQpEAio1ep0Oq3T6T755BO1Wk0ZfXKwODJKCgAgiqJarTabzZ7ekfJYk9kUCoYAfHDwg9wT3rhpIwCTyfTeu+9t274tGo2q1WoypMBli9UZ3+olCavRvMKCdM1YqmokNxi1WCz/8R//QRuVSiW1A6cuD5RlR8Zxo95OnbYdDkc2m1UoFPJ6j9/vX79+fTweV6vVtCw/M06fPk1zVgSl0pWWlioUinA4TKEbyR4lmBUWFtpstng8Hg6HdTpdOp0OBAJWq3UqKc6UF07/b21tJUs6AKWlpSUlJQMDA//2b/+WTCZvvfVWAFar9fvf/z5Fb5ScfdNNN9GSlV6vv3jxIoDq6upcAwhSx2g0SqIFIJ1O030GEIvFdt60kw44oB2wr7SrVKpkMkne6lTkRPkRxKEPD8mP44gbjAZWI2aB4X5IzByTSqVuv/126ifk9/tpvYE67litVqvVSj49Ew12FotFpVJ1dXWRaMmy7Xa7XS6X1WoVBGE2HWObm5srKyvlp3IwlMlkLl26ROZApElWq7W8vFyv19PSDmVVeL1ejUYzke+OzLi/NSKRSDQajcVi1G51xYoVlEpw7NgxlUoVCoUee+wxURRNJhO1ezh//rzH4+ns7PR4PCtWrCgtLSVPcY1Gk81mKysrKb2CpLGsrGzv3r3r1q0rLy93Op2rV6/etm2bxWJpbm4+d+6c1+sluzwymPB4PGaLudhZ3NjUuHrN6nEvIRKOKvH1swAAIABJREFUQMLIfwwADo/mH46Qrg1LNTwCUF9f39bW1tTU9N3vfpe2UDBEiV7UsGeSt+v1+kQiQf0p3G63PASYzeaqqqpQKGQymabVti4XnU5Hjq70VKVSUWkRNf92OBwqlYo6oobD4eLiYoVCQZ49NI+XTqfj8XhFRYWcwjCWyStcKXEjm8329fVRMjeA4eHhgwcPtre3U4/ajRs39vT0fPOb36TKUyrD8vv9NLX4yiuvrFq1Sq/XDw0NdXd3U4QXi8XS6fSbb76ZzWatVit1uDAYDM8995wgCCaTSRCEQCBARnzkcgQgnU53dXU1NjZu37H9ww8+nOzGSRNoLMPMKSxI14AlrEYAXC7XnXfe2dzcTDNL8qJ3eXl5MpmcfFmCSmGqqqpEUaRJKoJccKLRaGFhoSRJ47Z1mApNTU2nT5+WjUfJd7y0tJTqhGpra3t7e2l5X6FQJBKJqqqqwcHBRCJhsVgkSVKr1bQMRpnTo5AH7Un+urnXfuzYMVqqicViPp+PUuRXrVpFdbXd3d0mk4mulHoYqtXqTZs2tba2fvTRRyRs1OyVLHzki+rv77dYLIWFhdFotLKy0u/3U7qEwWAgtyHKWaC/C61Feb1eWkyi9bkTx0+Mc+qjZHYpf4XHh8OjBYCn7Jg5JhaLVVVV0doP9fuh0ZNcq30+3yQ5cmTy1t7eTvNL8p6pVMputxcVFVGy9Ywzuw4fPmyxWGQ98/l8ZWVllNvt8Xi0Wq3T6TSZTJSBjZz6G2qjLknSihUrRvLZRp35dE6DbFstFovRaCwtLaVWTAaDoaKiIp1OJxKJWCxGtUoDAwMDAwPkJk4pDx988AEtdMnjYzgczm0DKAjC0aNH9Xo92SsUFhZS09tUKkWZfuTDRIXJiUQiEAgkEonW1tZsNkveSFXVVWXlZWXlZVtv2FpYVAjAWeKEAAhQqi6XJEu4YkJv1NMlB6vRwsAR0kKztMMjAF/+8pdPnTpVWlpKaWDkT0ojrGwvPVEPcrJgyGQy1BwPOa19ent7X3vttVtuuQWzSDUuKSlRqVTyEpQgCFS1evHiRUp0zmazcsZEeXl5Z2enz+crLS0dGBjQarXUYvXkyZPjHvyqf1ez2UxFSMPDw1arlfLgk8mkwWAg1yIAiUSCylGpEzlttNlssp0PIZdJAYjFYnJxMf1/9+7doihWVlY2NTW9//77ZMtNPZYA+P1+m81GqeTRaJSCtmAweOnSpZtvvrmzs1Ov1w8MDNjtdkEQNmzYoNqjwkYIgnDp0iWtVmu72QbgxIkTOp2usbHxrTffGi1Co3INGWbKsCAxc0lTU9Mnn3zymc985sc//jFlJNMQTCnLlZWVpFKUazf27TQ41tbWZjKZ6upq2TCNhtpAIHDs2DGj0SgP39Oluro6Ho/LBgRGo5F8g8Lh8KpVqxQKhV6v9/v91L6PIhJy9aakcIVCEY1Gc8tCian8eKb+e7hsfxAKhVQq1d13301Pe3t7aeYNwPDwcDgcRs6v8v7+/nA4fOTIEXkac5Scj3ra2dkZiUQsFks2m73zzjspdcJsNp8/f55SReT5PVzun2symerq6rRabVlZmdfrJbfArq4ustqj89fr9ZSOmEqlPvOZz1RVVdXX1//JK3+y7zP7Jrxs+bwWszJxeLRgsCAtKEs+PDp//vz9998fCAQ++9nP/tM//RNtzGQy2Wz2nnvuAeByucxm80Q501TyWV5eTp1PU6mUxWKh8kxqyUo/9mfsKr1q1SqPxyM7q1IHVapMonQGURSDwWAikUin05FIxGAwkL94UVERZd/5fL7clIqpj7ejBrVsNkuBlyiKbW1tK1asoOlN6uuqVCrNZrNWq6U5Q51OFwgEhoeHi4uL5ZOfhPb2dkqIALB58+b169cbjcYNGzbU19cfP36cGqVTwERlttQIipIjwuGwVqvt7+83mUxOp5PaVVCsJghCS0sLxZd+vz+VSn3nO99xu93PBJ7JZDI/+MEPTp4YP3b89E4twq8/q9FCwoK0cCx5NQKQTqc//PDDu+++OxAIPPjgg88//zwAym6IRCJHjhyhGs+xb6yurvZ4PEVFRdXV1QCo4dDZs2dpGamsrGz9+vUqlYr8e2acZVdWVjY8PCzPHFLDOlpcociJ7PV8Ph+VylJXdapjdTgcQ0NDo5raETP4u8Zise7u7pKSEmqE8dFHH1kslo0bN6pUKpobpEQ4p9NJpUsXLlygNLnpftDRo0ePHj368MMPAwiFQhs3bqQjb9q0KZPJkNJUVFSEw2GHwwFArVafPXsWwMmTJzUaDX2ixWKprKx0OBw2m+3QoUOCINCte/rppz/3uc/97Gc/A/1KGHUjRsn12BUmIWfPpf+Pg7k6LEjMHOP3+0+ePLljx45jx47p9Xpa77FarT6fr6KiItfYNJeampr6+nq/369SqSwWi1xGSilhfr8/kUiQzym1+JvZuUUikXQ6LVfj0gJSIBAwGo2dnZ12u52iIq/Xa7PZ1Go1GbVVV1eHQqHGxsaLFy+SrTUVKsmj68yG0z179gAIhUIdHR0kBgcPHty5c2c0GiWLIPJreO+996LR6CybO5BmUHxZV1cXDoepOdP69eup2tdoNAYCAZqN7O/vp8hM1r9AIHD69Gm73Z5KpSglhOyO1q9f/8EHH7S2tsrGslcgTPxUyvk/xjwe+95rBIdHCwwL0gKxHMIjIhaLtbW1rVu3rqamJhaLdXR0KBQK8q8bu/oCQBCEffv2AUin006nk4x2MplMOp2mlQyr1drb20uVSeR/OuNzGxoa+uijjx588EGykPB6vQ6HQ61Wq1Sq9evX02xYKBQimx86bYvFQtZBPT09FD8ByFWjGeNyuVQq1aFDhwAolUpSiOeff15O6KioqKCYcgYHH9UNlqALpCiTxIZyRhoaGqqqqgC43e729vaJPtHn86nV6lAoRD8IdDpdd3d3Q0PD9u3bSfCmwUSx1ERblsu/nuUOCxIz9wwPD//kJz/ZunVrb2+vRqMhO1RK3SYP09ydJUnav3//7t27yYAgHA5T4ji9mkqlUqlUWVmZKIrZbJZ6fs94ys7pdMod04lsNms2m8kZIZFIFBcXnzt3rrq6OhaL0XxdKpUym802m62zs/PAgQPjHnYqo+WoxELqMFRTUzP2ckKhEAUoHo+nvr7e4/GQf8S0rnTyWzTqaM3NzSS9nZ2dk5w5tUkk5yFKVadcD9LUpQeHRwsP1yEtBMsnPJIxmUznz5+vrq5WqVTUC5zaDpEzm9yHgpZMUqnUG2+84XK54vF4PB6nbOx0Ok0GBJQ4ns1mqZXc2bNn5RX76WI0GjOZjGz8Q73JLRaLTqfzeDyUZUCZfmq1urW1lTLKYrGY3+/PzUybQWIzzb/JhEIhv9/vcrnq6uomedfFixeDweBsrJJwOZkeQGVlZU1NjVKplO+/DNU5UQfbUW+U1QiX67GoXaFOpxNF8Z133rmqkdLVEXL+GxcJC1zqxGp0TeAIad5ZhmoEIBQKiaJ47ty58vJysoADYLFY2tvb7Xa7TqcbGhoiXwD5/pw8edJut1dUVFDZjcFgkI29BUG4cOFCW1ub3W6fzfAnCEI4HM6d9KP25FSOCsDj8ezYsUM2Ko1Go0ajkQywx18mGQMZDuWqFxEIBAoLC8lHtamp6YknnggGg6tWrXK73Vc95nRdtykThFaMaHWKbFjpqUqlohWp3LZ7zc3NhYWFuQW2uNJXgh7TTKYoilVVVeQTMfWzmipjZ/MmX3wSZvQDgclLWJCY+SKVSoVCoe7u7qKiIovFEgwGS0tLKfTRaDTUZoIcBOS3+Hw+8tIWBGFoaKihoaG/vz8YDJJoqdXqWf4YlyQpm83mtvmh5nvUr5Za2MkSWFVVlc1mI5GIUqlsa2sjU7tR2pA7+imVyuLiYhIDuREfLg/llMOt1Wppweyxxx57/fXXPR6P3W6/qiaNGvdHnQPFXtFoVKfT2e12uihyDiRTPqqoraurowwOm81mNBqPHTsmH0Gv11PPclz2JidIw3L1m2btaEI1lUpNlKIyl0ySGYHxAqa5ECcOj64VLEjzy/IMj2QymQxVZTocDlEUT58+rdVqKVkA461zUOYCCYYkSZcuXRrb+mjsD/mpQ6N27gwYlaCSZU5/f/+qVaskSaK/msPhyGQykiQNDQ1ls1m5keDYiprVq1dTjjgA6oRrsVgsFsvQ0JAst/F4fMeOHa2traFQqL+//80331Qqlc3NzXq9Ple3psKoPakGljo2ZbNZclb1er3FxcWkVaIo2u32ZDJJnaXITZxKmgwGg9PpDIfDFoulu7vbYrHk3ttR+SN0tFQqRdW1C6FGV0VOKB+XGZXlshpdQ3gNaR5Z5mokk0wme3t7A4FAeXk5mX6qVCqdTrd161Z5HyoFpRUm6lVB6xz0f61WK6d6Dw4Oyj/nZ4Ber9+1a5f8NBqNUsAhSRKZqOJyIKXRaCjFORgMUrq5/K7cv+uGDRsGBwfdbrfH4zl37tyFCxduuOGGhoYG8qajkIXo6uqqqKh47rnn3n//fbvd7vf7qZUGXe/UL0GtVlMb2dLS0s2bN1dWVhqNRkq+SKfTdru9uLh41apVBoNBr9cHAgFyo4hGo+FwmGyczGbzXXfd5XA4ksmkKIpr166lJAWXyyU39s2F7n8ymQwGg/X19el0mtLf84VJFp+IpWuyt8TgCIlZICKRSHd3d0VFRUVFRW9vb0VFxeDgYHV1NWUoULM+h8NBLekAGAwGGqxFUUwkEkajURTFeDxeU1Mj27JNF7PZTD/w5S2UTUeaRI0tYrGYwWAQRZEyAykbOxKJUG5e7rC2ffv2vr6+SCRC5ywHQ08//fS4n97d3f3SSy9RoZU8MSiHiVNcKKJmTmazmXzBKT+eEuHMZnNpaSk1NQ8EApFIxO/3a7XacDiczWZp/k2SJGrTl0wmt2/ffuzYMYoRo9Foe3s79TU3mUy0oka920lTQ6EQXaBCoSD/p7xjklRyYdKyXHlngcOjawwL0nzB4dFYAoFAMBikgKCjo6O+vn54eNjhcMhtxSn3QaVSiaI4PDxMA67BYFCr1RqNZtOmTcPDw9ls1mazzWzWTq/Xk2l37sZQKEQW4DR29/f3X3fddXJeA4BoNHqFnTZQWVlpMpkCgYBWqz137hwu91a/qqJIkrR69WoAa9eu3b9/P3W1iMVipMGTv51m9hQKBWUr9Pf3FxcXp1Ip6mlbVlZGDkCBQIDy6OjgtCZHIaAc5JGHLPVi93g8cnEVqS8lpMg3ilRTpVIJgvDAAw/s379fdnDIa6ZVlnt5u5Czn8RR1YLDgsQsKF1dXdR8obGxkexTAQiC4Pf7AVB7VnkZyWg0BoPB4uLi7du3k3+PSqUaGhpav379W2+9NYNPpwWkUetSfr+frL6p65LBYKDWR+fPn6fZraGhIUpIo/FJqVS63W6DwSD7ghsMBlrnDwQCsViMll7GdmcHEI1Gjx49unHjxtOnT+t0ut7eXhrZczM7iFxXWcJms1FPv0AgEAqFVq1aBaCkpKSrq4v8UuWcETpgd3e3bFJXWFiYzWYp3KRQKZvNvv/++5QNGAgEqOmGHOfR/6lhoM/n02q1kiR9/vOfB0BOdzO4+XnEVctyR/YSLr8+U2VqPSDp7ULp9VdsvPgyitbBVn3FxmRY6jkiVO1GdAjdH6J+Yr/aRFBqeRlSVmh6FACkLAQFpCwu7Ufd3hmeZ97AgjQvcHg0Cel02mw2d3V12e32QCDgcrkAiKJYW1sLQJIk2ZBbFMVbb701FAq1tLR0dnYqlUrKV57xcrrX65X7M8n09/fbbDbqWadUKq1Wa2dnp81m6+vro0a3iUTiitXxTEalUkWjURr6admJ2quTLd7Q0NAkMz+ZTObo0aNlZWVUAkXhC2kYBTcUJ406ArXXowwLatpEfWCTySTZv1KuvNlsTiQSpEMXL160WCxr164FYDQafT4fBUBms9lsNh88eFDuC2Wz2ejaKc5Tq9VUgEwrealUqr6+/tSpUxqNZv/+/YtejRaSQIfw/EP4o2YYR1qHSKeeFg58E1+7MHrPA38u1H0GgOQ9J/ziIfw/43SAHOHVrwhDF1G6BU2P4le/jet+B9W3QVDgzLOS1ixU3DRf17IgsCDNPaxGk0D53DabjRZsyFKaogTKTh4eHtbr9U6nU6/XDw4ODg0NnThxApfvKi0Cjdu6YirQosjYjcPDw2q1OhaL6XS6U6dO6fV6CjXIHiL346gqJp1O6/V6Wlzxer1qtToYDAaDQZPJRFZ7NKBTq9ax0Q8At9tNmXj19fWUcUD9xScZ7tesWWMwGDKZTElJiTyZZjababIxEAhQ/3KKco4cOVJaWipJUkdHh91uDwaDer0+GAxWVFTQncxkMq2trdTzQqvVDg8PUz2yXq+nLu+ZTEan00mSpFQqLRbL448//vzzz9MVLTUEAJAkSZggNWLU9skCpg//l1S1WyjdMvJ00x/g3HN45b/hkVcAIOwRXv867v8p9AVXvGvwPNwfY+//mdLZth3AfU+PBEPNL+G63xnZvuuvhOcewB+emdJB8hUWJGZBoQbkNPh2d3fTcpEoilSwCUCv16vV6r6+vmAwePr0aVndaQgOBoNk+kA/9qcLrVGN1bPu7m5KmiCTnng8HggE7HZ7JpPx+/1jK1soz8Ln85GbkZxWkFtqetXcuUAgQKkHjY2NK1euBOB2u0tKSs6ePTtqUtFsNlODc0mSnE4nTRWSpQVFOSaTyWazDQ0NJZNJj8dz5MgRAKSLSqUyEAgUFBSo1WrqI5XNZltaWkpLS5ubmwcHB8kqggqnTCYTfaLc+k+pVN52220NDQ1UOzyu2fkSYKS34ZVKM3bKjrZMts7UdVAoaEBpzpb7fox/WiOdfVZY93m88hWseQh194z++I9/gE2PjXNa/na89Rfo+A2Km6Sbvy1U3owXHkUihIN/i0AX4n6k43j3ryRRK5TvgKMOokHqeleovHkGdyBPYEGaYzg8uirU/JSSwdxud0FBAc1Hkbs29T8Nh8OUfScHNNQliJKzZ/zRfX19Go2mra1t7EuJRILSK+gpJTjodLroeNEY5YVTgpzJZMrVoWmh1Wqpy0ZhYSFV+YRCIZo8BEAlqLScls1mqX8gAHlKM5FIUCo5ALo5NP+p0+nk0Ccej9fV1VFkQ77m5Nr34Ycf0jlks9ne3l6axqREBrkuSqlUbtq0ye/3m0ymH/3oR0szPJqYsZHQWMUicZKyKaTjAJBJS+mokAwDgNoIAOZy3Pl3wuuPS9m00H8KD5wd55PO/Ryb/2j0xmQYP9yGHX+B+59B57vCz+7Hlz/EnidxaT+2/RlW3QMAB7+LHf+XsOKGkbes3CWc+TlYkBiC1WiKeDwetVpdV1dnMBj6+voSiYRGoyGxkSTpN7/5zdi3yEliarWaPOhm8Lk2my2bzY7bvaKjo4OiBHpqNBqpW13uPvJfl6bj5K5FmUxmZn6vNDcoSdLAwIDf75ckiabsqO5HFMX+/n6j0bhixQqSomw2S/OElAVHbkaHDh1qbGyk2CWRSFy8eFGn0w0ODpaUlFBnv9OnTxcWFlLj11Qq5XK55Cqi8vJymiMNBAK0gITLtb10XbFYbM+ePTQPOWMLwaXKp/rUfgC/eAAA0nHB9T4EFfQF+O+dI69u+gOce0544UvS730oqI2jjxIdRCKI4nWjD37xRcFSgRu+DgC1d6H+Xhx7Cnd9HwCUWqguf4dVWihGhnHJuV44/dM5vcSFhgWJuTYkEokzZ86sWrWKVDyRSKhUqnEz00Yxm/YT9PN/3N8NkiR5PB451kmlUtFoNDVBMaxKpaKzra6uHhgYoHCkv79/WsnQJSUljY2NAD755BO1Wk2mc9SHQu7FZzAYGhsbqV6YNDgQCOj1ehKM1atXv/jii9Fo1OPxyB0lAGQymTVr1lAqYCKRiEajfr9/69atRqPx5Zdfpn0KCgrIvja3iIrqf8lKQ5IkkqtsNvvBBx+wGk1G7R34v0MA8OwebPx9NNw36nVp02NC30mhbNs47+07BYUawujoU+g5Bs8J/K3p001rHpj8LASVHn2npn3y+QQL0pzB4dG06Ovrczgcer2+oqLC6/VGo9FEIhGJRGjSaZ4oLi7OZDIT/aWCwaC8eENJdKN8O+WBm+pynE6nfLYzaKDn8Xh0Oh3lv6lUKoPBUFpaKopia2ur3PYpEonE43G9Xp9IJHQ6XTabpextSZJouaupqenw4cOSJCkUCio8Kigo8Pv9HR0d1HCvoKCAKreMRiPJbWFhoUqlomQN2cMbgMlkopsjSRL5J/l8vn379rW3t5eWlk5+LczMMZdBGu2PBQC2atTchi/8euRpKvppVDQBUiYu2Gvn+vwWlOU1KczkFV6v99y5czqdrry8fMWKFVRPI3dCmg/S6TT1s5joVfnxSOeFy09VOQM3QfkO4XDY5/NNpEZylweacKNiVap5opZC8Xic8iyoe+Hp06eHhoZKS0tNJpPD4TAYDCUlJW63mzoTAlAoFDqdjpILfD6fx+MJh8MajYbauldWVlJwQ0kZ9NGSJDU2NpaVlb355psff/xxRUWFTqfT6XTBYFBeBiOoAJaKagVBUKlUGzZs6O7urqmpefXVV2dwt5cj138NxU3Te0tBHaDA0Bjzkdq70faW1PMxACTDeGozDv/DOG/P5gTxvlaYy6b36XkGR0hzA4dHM8PtdisUim3btr3zzjvRaLS/v588C+YPKiGafB9Kfb7CRHVMvpwkSRNl+slJAbKrgiiKFRUV9LlU2dPd3Z3JZEjJSLdIQuj/lNhNZhCSJNFsnslkKiwspIzz/v7+M2fOUFJcPB6nKUStVltTU9Pc3CwrLlmY9/X1uVwu8hyilBCKrnIz4LVareweS8FZKpU6fvz4V7/61Y6Ojn379v3iF7+Y4h1e1ozNoLsqggK1d0g9h4SCK4ObglrpgWeEZ/ehcA0GzqJ+38h6Ui4N9+LZPdj3FK77MgB0HpQ2fGlRj0RXdLFkZgar0Wwgywav1xsIBGiAzp06m1u2bdvW0tKi1+snnxgsLy/v7u7ObcSTu0JzVex2e27XpcbGRopvyNiUQqKxDZNGQdN0lPxmMpnkwmGqNAKQyWTa29tpacdqtRYXFxcUFAA4fvy4vBJGvRBpwk0umNVoNKPSAukLTL2O5MtUKpXUQv6+++771a9+BWDGFoL5zzUeBltfx6G/w6PjmY9IWaSiUKqhVI//3mx6JKkh3I9/3YDH3WOXoxYRi/jUmaWB3+/v6+sbHh6WJKm4uLixsVGhUNAIPucolUqyJJh8N1IjmYKCAqvV6nQ6y8quMh+yadOm6upqrVZLMQ1x5syZQCBAtq0A9Hr9VdUIQDQapbUiSZKCweDQ0NDAwIBaraYeE9Q8t7CwsLy8vKysrKqqSq/X6/X69vZ2EhudTmcymSg1w+VytbW1ud1u8lTNVSNKtZckSafTyVl2giCIokhzknfeeWcsFrv11luXsBpde2rvQjoO73j+I4ICauOEagTIKXY48o+46VuLWo3AU3azh8OjWaJUKvv6+oqLi2mgdLvd119/PTXiGx4eppcAVFVVtbW10WLMjN2mTSYTNTydJOJRqVQUn6mUSmQyVCpUXFycTCYnLwt95JFHgsGg0+k8ceJE7qoS+SSRltAv8VxrcEEQqL7nqsasQ0NDWq22qKiISonJ8KK2tjYajer1eqrfcjqdHo+HlspIaahol1Ih6DiU9U6Xn8lkyAic0urkycxUKkXxVjKZfP7558lIkJlH7n0a7W/AMdO+Ktk0khFs/tqcntM1gKfsZgsL0iyhfnEA7Ha71+ulRY6SkhLyawAgimJuIY5CoYjFYm+++eYMPmvv3r2HDx+mUtZJ/MJzJ+t0Op3D4ZD7l1Nfu1xFAVBXV2exWIqLi7PZ7IcffignFBAqlYpinXE/i64rkUhMpfcEBV6VlZUrV66kGl4K+ORJvFgsNjw8TF5/8lLWKPWVi40KCgqo0Ep+NbcSq7i4+Ld/+7d/+MMfAsjtsbsk4WEwT+AIaVawGs2eUChE9gThcJhaxkWj0eHhYY1GA6C/vz8cDvf09KjVapVKRYU7My5FosRohUJht9vHFaTcYUmr1ZaWlsZiMYfDEYlEKGjLZrMkAFSamkwmr7vuOgCJRMJsNn/yySfFxcW5glRZWUmJGw6HY+vWraQiJpNJp9NdvHgRgNVqPX78OEUtV/VD8nq969at0+v1AwMDTqeTapIApFKpgYEBAO3t7XKUQ7YXyNEbQlYvKoaVd6ZGGPR4xYoVe/fupT7oM3NpYpgZwBHSzGE1mnN0Oh1lRSeTSZqAoseyKwEAsiogeZgun/vc59555x2tVltbW/vuu++O3eGKpSO7Xa/X+3y+3I4MuadKw/edd9554cKFTZs2Aejs7CQrWFEULRbLDTfcAKC/v7+9vV2e9aqtraWTD4VC9957Lw337e3tlOwwdnJsbB+KdevWyS3+iouLaUXq+PHj02o7K4oiHTmVSpGJH223WCwbN24EUFVV9eMf/3iZjA/L5DLzH46QmDwiFouRqR1NK3V3d1MvcKVSWVFRQSNvZWXlwMDAzARJpVKR2Sh9xKgR/IoaWJVKk0hM8inUVW/z5s1vvPEGgPr6egq5rFZrQ0MDZXJ3dHTISdgUA2Uyma6uLqpvBfD888+LokhZcI2NjUNDQ/TGUZ7fo4bLs2fP0qLR2BWpqSB75ckiRMtISqXy+uuv7+rqqqqqOnTo0LgGTgwzr3CENEM4PJpX5MwCAAUFBTTLpNFotmzZQlNSr7322syO/Mgjj7z22muVlZVWq9Xj8cjJY6P+GRgNBkw6h6bT6QA0NjYeOXJk3boRIzIyEwJw7ty5UVOC5IAwyRaHw7Fu3bpYLOZyuci3WxRFckwg/70ZT53J0ut0OhUKhUaj6ejoAEC8gSnyAAAUpUlEQVR+EHQa5H50zz33HDx4cBm6BPEwmCdwhDQTWI3mm9w6pNxZLDK3lrskzACqS6VUNAB1dXUtLS2j9hEAbSYzeeHRjTfeODAwQI0ePB5PRUUFWcxFIpGjR4+O3X/skDdqi9fr/eCDD3bv3k1OqT09PXTharXaYDDM5iunVqu3bNlCIuT3++UMQIqQqJnF9ddfbzKZYrGY2+2e8QcxzCxhQWIWGUNDQ7PJQqYKJ6/Xq9frw+Ew+ReM6nikUqkmUSNBEG688UZ5WC8sLKRuRqlUqrOzczYpAOl0+sCBA7t27bLZbGazubm52ev1UvHQLAXp5MmTo3L/AFBid1FR0Y4dOwA888wz0zKHZZg5hwVp2nB4dG2ZZdIXVd3SYlU8HqfM8lEmqqN8InLTpg0Gg9lspmiDmmiQh6nJZOrt7dXpdLPPSfvggw927NjR1dWl0+nMZjM5+tDc3aigKreiaCyCIND84aj+T3a7/frrrwcQjUap718mk3nyySdnedoMM3tYkJjlBTmcBgKBUCikVCopHeAK27rL0BIL5VVTH1sSgIKCgmQy6XK5tFptIpEguYrH46IoTsWC4apkMpn33nuvqakpEAiIolhQUEDJDtQ7g+bZSkpKvF4vNd6V9TJXn0wmk9Vqdblc5L9Aa0irV68GYLPZysvLz507B2D//v3jtitkmGsCC9L04PBoseNyuaqrq10uVyKRKC4uVigUuWok51g3NTVR9vPLL7+cSqVCoVA0Gm1sbKyoqLh06RLVFVEXolgsplKpioqK5vAk165dOzw8TGVGfX19ZrM5k8lEIhHSFaVSabfbh4eHtVptcXFxT08PrYTZ7XaLxRKNRru6uqh5BNVLVVdXNzY2fvzxx42NjalU6u2337ZarceOHZtZk0OGmT9YkKZHbkfta3smzMx47bXX9u7dC6ChoWH79u3k+JDry7BlyxaLxUKeOuRRFI/HyeCHMq3Xrl2byWTOnz9vMBhUKlVhYSH1c5qT8AiAxWKhzu4AaEYxt2+h0+mknG+FQpHNZj0ej0KhaGhoIHOgeDyeTCa3bt0qtzCnb2xra2tHRweZMwWDwf3798/JqTLM3MKCNENyZ/NZnBYXr776qlKppJim87Itnkqluv3228mbJ5FIDAwMKJVKq9Xq8/kMBkMqlcpkMm63u6GhIZVKmc3miooKSkan3np+v9/pdE5udjdFAoEAGRQBsNlslOYuCIIgCBUVFQCam5tzp+n0er2c19fU1ATgwIEDNpttbBEV1d4yTN7CdUhzD+tT/mO1Wvfs2fOfP/0pgN233NLa2kp+erfffrvL5bJarQDi8fjbb79Na0h9fX00mptMpr1791LGWjAYJN0C4HQ629vbx3V9paWdoqKiqTTDpcJYg8FArn0Aksmk7C83irFpDszM4NuYJ/AXet5hfcpDKioqulwuAP/f975HhTjBYLC3tzfXwhVAT09PNBo9f/48AFqbIXW55ZZbtFot5dpFo9GSkpIVK1YA+OSTT8itLhqNRiIRi8UCoK6uTqPR0D+0vr4+ysqb6MQEQVAqlXL3DXmZh9d75hUeBvMEFqQFhcUpT8j90j/1b/8mSZLH46FpMTLA7u7uvnjxIk3BFRcXy452ZGGXyWQqKyvT6bTf708kEk6ns7a2du3atQBeeuklMkuNx+N2u72wsJDUhQwXyLtocHBwkmhJrVbbbDZ6EAwGqR/SvN4NhofBPIEF6VrC+nStyP3Sl5aU/MEf/MH58+ebm5v37NnT0tLy7rvvxmIxpVIp97IrKSmRcxaof6sgCOSd2tra2t7evnPnzi1btlit1nPnzh0/fpySqmn+jbzmqPuqx+MheyGXyzVJV1xaKxocHJxoso6ZW3gYzBNYkPII1qeFYZQvAzHWa3UqkIlRKBTSarWUvCdJ0ptvvskxzeKCh8E8gQUpT2FxmidGfd3n9i47HA6KhA4fPjynB2bmFx4G8wQWpMUB69NcMW54NIcUFhbSatOS77K6lOBhME9gQVqUsD7NGPnrPn93kDyH5u3wzNzDw2CewIK06GFxmhbSZStVvmuMDA+DeQI7NSx6xrZ9u1Znkv/IOsT3iGHyEBakpQbr00Twb2CGyXNYkJY47LmXy3K/fobJb1iQlhHLOXji8Ihh8h8WpOXLMtGnBUirYxhmTmBBYkbgyT2GYa4tLEjMOCyZ4Iln6hhmEcGCxFydRapPrEYMs7hgQWKmzWKc3FscZ8kwyxsWJGZW5G3wNN+edQzDzDksSMxckrf6xDBM/sOCxMwj+TC5x5LIMIsFFiRmgVjI4Ik+iaWIYRYXLEjMtWG+9YnViGEWHSxITF4wh/rE2d4Ms0hhQWLykVkuPnF4xDCLERYkJt+ZVvDE4RHDLF5YkJhFBunTWFliE1WGWeworvUJMMwcw0ESwyxSWJCYpQD7MjDMEoAFiVn0cEjEMEsDFiRmSXHV8Oihhx4yGAwLcSrM3CFd5lqfCDO/sCAxi5tpTdbpdDqXyxWJRAA0NjZaLBa73W42m+fv9JjZk6tDUg7X8JSYeYIFiVncTGvFqLGxcXh4WBRFAD09PQaDwefzxePxz3/+8/N0esz8IV3JtT4dZg5gQWIWN7Jt3VSUyel0Dg8P02OfzxcIBHQ6nSiKzz777PydITMbpq40rE9LABYkZhEzXRPV7u7ueDyeSqXoaTKZXLly5a5du+bj3JjZMxtdYXFajLAgMYubUWpUbsF9DVdsUStxXwNsOgAYHh6ORqO03ajGLVUoKCiYwYcqpqaBKsXok5n8mHvrZnAuzNXh4GmxwILELFbGHVf8MXz/Tnzn5k+3/OPdeGwj/LGRpyrViDvJ925DbeWKdevW7d+/f1qf+8z92F115ZlMMMxpVXjh4akeNivhkXXYWTmtc1nKzJ9ysD7lLSxIzCJGAP5iBzav+HRLOImHf4lv3YStZQDwSCPuWYUvvTDyajabTSaTANYU4oYyHBq0/su//Mt0P/Te+vG3jx3mwkkIfzWNI3/nXfzzPdM9HWa2sCblDyxIzKJEHkJ2VmCF6YqXDrvx/76PZz+L65z4P3vw8C8xdDk8ymazRqMRwJ9uxb+fQFtbG22/ZSVe/TwG/xwnv4K/2AEAWhV+ch9+dwPOfhVnv4o/2TJyhG/dBK0Kf7kL2ytQbcMvHoL03QL8ZLfU9e7IHskwXngUB/9G+n5lNvvGrz53HyAB0roi/OpzGPxzvP0oblkJAEY1fnIfvnUTOv4Uu1YCQIsPkeTI42UOi8TyhAWJWZSICpjUMKqhEqATYVTDqP701b98FwMRHPo9/M8P8KHr0+2ZTObhhx82m82/tQ7HBnShUAhAuQWvfQH/+2OseAJ/+Br+x83YvAIqBR5Zh21luP0Z/NkBPHEXmooA4O8PIZ7Gdz/EuSA+/DIeevwJPO7G9m8KP7sfg+cBIJ3Amf/CYDPufEIQqu9f8yKAQsPg+79rvf/x/+34n4nd33rllc9j8wpolHhkHRoK8I0DaBkaOcP3uvBbaxboHuYtrEbLFhYkZlGyeyV6H0fv49hdhR/uQ+/jOPOHV+xwwgOdCFfgio2JRAJAgS5r0aLZP7KYFE/jzv/EgXYkM2jxoic0EnKJKnzjADwhvN6Kk32oto/sTP/fUwFXALjh61BpUXsX6u/FsadGPkZKY8+TWP1ZJJ204aE1vzjVtx5b/xhKNer2Gnb80ZF/+pLX6xVV+O3/CvzqAjyhkbee6keFdR7uF8MsBrj9BLMoOdAO8/8CgFc/j38/jhebr3h1Xz0ebsQ3Xsc/34MPXOi+LEvpdPr8+fNr7KlECoHgiAgMRvBba/A3t2CDEy0+FBs/PU4wMfJAzomQ2eTElnIV/jZnunDNAyMPFGpornB/WF98yhWokJ9KpZuE8y/Ie14REDS/KKy+f8q3YQnC4dFyhgWJWWqUW/D0ffidF/HyReyowM8ewPYfjbyUTqfdbvemylUq5Vl5/4fW4i924MHncLQHWQmDfz6lT2n3AzW34Qu/HnmeikKlnWjnzsDKTc5j8lMh4IKoG3dPKROXfrwLv/PuyJ6zaOXOMIsOnrJjFjdPHsHp/k+fKgT84kE8dw4vXwSA338F1Tb85c0jr2azWY/H86v3zmazqLWNbGxwoNWHw25kJexaCccUnFdVCvzjry+h7S2p52MASIbx1GYc/oeJ9n+p+b599S9j4CwARAdx4mmpbvx0OsHXCnOZ/HS5JSgvh2tkJoEjJGZxs7/1iqffux0mDf709ZGnQzF88QW8+Sheb8VhNyRJisfjAA6048YKtPoB4N+P4wuNOPkVRJLwhPBOO2rteKt9wk986SJ+/UUFut+XHnhGeHYfCtdg4Czq9+GGr0/0lvODa37/1aeeNu6EcwN6jmL7N4SmRxEdGmfXzoPShi9NFBZNq5v7ooPViBH4S8AsRmY5Ft9Vi2/ciNuf+XSLXkQ6i2RmSm+XMikoVAAgZZGKQqmGUj3unrmnKWWzSEUh6iFMMDMR7se/bsDj7gl3mJglIE48FjE8ZccsI+RR+/VWaFVocHz6UjQ1ZTWSpBE1AiAooDZOpEYAJAk0zErS5Z0nEZsj/4ibvjUDNcLin9xbjOfMzDksSMwyInfU+90XRzsATfcIc0w2jWQEm782JwdbXPqU/2fILAw8ZccsSq7VDNXM/r0IAvLn31keTu7xKMQQnNTAMFNlaYyb+ZYZsTTuKjMnsCAxzLIm3/SJWc6wIDHMlFgmP+RzL3MBxGmZ3FVmirAgMczVWZ7j5nwHT8vzrjKTwILEMMyU4Mk9Zr5hQWKYq8A/5MdllpN7fFeZsbAgMcw8Ig/UeZX5PedMN3hiNWLGhQWJYSaDh84ZwJN7zMxgQWKYCWE1mhNGTe7xXWUmgq2DGIZZOFiNmElgQWKY8eGhk2EWGBYkhhkHViOGWXhYkBiGYZi8gAWJYUbD4RHDXBNYkBiGYZi8gAWJYa5gbsOjKzrGMgwzKSxIDPMpPFnHMNcQFiSGYRgmL2BBYpgRODximGsLCxLDAKxGDJMHsCAxDMMweQELEsNweMQweQELEsMwDJMXsCAxyx0OjxgmT2BBYpY1861GgjDSNJZ71DHMVWFBYhiGYfICFiRm+cKTdQyTV7AgMQzDMHkBCxKzTOHwiGHyDdW1PgGGuQZcSzWKDsL14eiNJRtgqRy9MRmWeo4IVbuRTaPlVTTcN+ExE0Gp5WVIWaHp0aufwFWPJiNlcWk/6vZefU+GmQtYkBhmYQn24PhTV2y5tB/3/hDXfXn0ngf+XKj7DACk4/j5/fjLiUX01a8IQxdRugVTEaSrHk1GUODMs5LWLFTcdPWdGWbWCDxxwSxGhFmkUS/kdz73NMf/2BcehecYHjsMtfGK7YPn8csv4CsnpvQx33PgvqfnJZTxtuC5B/CHZ+b+yAwzBl5DYpYX+fUL7Og/o+UVPPLKaDUC8PEPsOmxkcfpOH52/8iDFx7FyR/hybV4ci0+/gcAeOFRJEI4+Lc4+s8A4G/Hcw/hew78ZLfU9S4AJMN44VEc/Bv8YKXU9e6nRwMwcBY/u39k5453xtnZUQfRMHIchplnWJAYZh6hjrFy39grXnK9j1//ifS5X8JWPc47z/0c5TtHHmfTuPjiyIMz/wXXITz6Fu74exx4HH2nsedJaEzY9mfY8HtIhvHDbSjfhsfd2P5N4Wf3Y/A80gmc+S8MNuPOJ4SC+k+PFh3Ej3eiaje+0YsbHxf+6zNS75HROwNYuUs48/P5ukEMkwMLErOMyKPwKNgj/PxB3PGEULV7nFejg0gEUbxunJekNO58AsYS1N4F53UYbh+JrpRaqLTSxRdhqcANX4dKi9q7UH8vjj018q49T2L1Z2Es+fRQ536B4vXY+sdQqlG3Fxu+LBx5cuzOknM9gq45vwEMMxZOamCY+efXf4wTTwOAxoRv9CKTxM/2YdU9uOFPx9+/7xQUaggT/F7UmEceaG2jXhF6jsFzAn9r+nTTmgcAQKH+9F25n2KpkJ9JpZuE8y+M3VlQ6dF3atLLY5i5gQWJWS5cy/Bo89dQfy8ASakSALz8GAQl9v7LhPubyyClZ/JBtmrU3IYv/HrkaSoKlRYx/wQ7r0TPMfmZEHBB1I3dS8rEBXvtTE6GYaYJT9kxy4JrPFnnaED1bai+Tai8GUf/GW0H8Fu/hKBAJjnyX/ZK+SmoAxQYap32B9Xejba3pJ6PASAZxlObcfgfJty5/j5cfBkDZwEgOogTT0t194zdS/C1wlw27TNhmOnDERLDLCxtbyHSj+9XXLGx6Yu4/z8+fSooUHuH1HNIKJhmaFJQKz3wjPDsPhSuwcBZ1O/DDV9HdGj8nQvXSPc+Jfx4J5wb0HMU278hND06zs6dB6UNX2KzcmYB4DokZlEyrTqkRfklb30dh/4Oj741k/dKWaSiUKqhVE91Z1E//pJVuB//ugGPuydc0GKYuYO/ZMwSZ1GqEYDau5COw9s8k/cKCqiNU1IjeeeJ9ObIP+Kmb7EaMQsDf88YJl+592l0vH0tTyCbRjKCzV+7lufALCd4yo5ZlExxyo6/3gyziOAIiWEYhskLWJCYJQuHRwyzuGBBYpYmrEYMs+hgQWIYhmHyAhYkZgnC4RHDLEZYkJilBqsRwyxSWJAYhmGYvIAFiVlScHjEMIsXFiSGYRgmL2BBYpYOHB4xzKKGBYlZIrAaMcxihwWJYRiGyQtYkJilAIdHDLMEYEFiGIZh8gIWJGbRw+ERwywNWJCYxQ2rEcMsGViQGIZhmLyABYlZxHB4xDBLCRYkZrHCasQwSwwWJIZhGCYvEPhnJsMwDJMPcITEMAzD5AUsSAzDMExewILEMAzD5AUsSAzDMExewILEMAzD5AUsSAzDMExewILEMAzD5AUsSAzDMExewILEMAzD5AUsSAzDMExewILEMAzD5AUsSAzDMExewILEMAzD5AUsSAzDMExewILEMAzD5AUsSAzDMExewILEMAzD5AUsSAzDMExewILEMAzD5AUsSAzDMExewILEMAzD5AUsSAzDMExewILEMAzD5AUsSAzDMExe8P8DMTcd7ulQsqwAAAAASUVORK5CYII=" }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "The axes are 150 mm long in each direction\n", "The diameter of the sphere at the origin is 10 mm\n" ] } ], "source": [ "load mri_init mri_init\n", "\n", "ft_determine_coordsys(mri_init, 'interactive', 'no');" ] }, { "cell_type": "markdown", "id": "c89f241c-0c0a-4d25-a068-564b28c8d6bc", "metadata": {}, "source": [ "Coregistration: Laser Headshape and sensors\n", "-------------------------------------------\n", "\n", "Now we would like to coregister the sensors with the digitized headshape.\n", "Each `.mrk` file holds the positions of five points obtained from HPI coils that had been placed on the participants head in specific locations.\n", "Read more about the positions of the points in https://meg-pipeline.readthedocs.io/en/latest/2-operationprotocol/operationprotocol.html\n", "\n", "The order in which they appear in a `.mrk` are the following:\n", "\n" ] }, { "cell_type": "code", "execution_count": 31, "id": "905abae1-2f00-487e-a470-ebdd83e25f47", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning: adding C:\\Users\\hz3752\\Documents\\fieldtrip\\external\\yokogawa_meg_reader toolbox to your MATLAB path" ] }, { "name": "stdout", "output_type": "stream", "text": [ "The input file is an original one: only marker-coil positions are loaded\n", "The input file is an original one: only marker-coil positions are loaded\n" ] } ], "source": [ "%% Align MEG Dewar to Laser scan Head model\n", "% now we want to align the 3 markers in the *.con file with the 3 markers\n", "% in the headshape, where 1:5 markers match to the 4:9 headshape\n", "% fiducials\n", "mrk1 = ft_read_headshape(mrkfile1);\n", "mrk1 = ft_convert_units(mrk1, headshape.unit);\n", "mrk2 = ft_read_headshape(mrkfile2);\n", "mrk2 = ft_convert_units(mrk2, headshape.unit);" ] }, { "cell_type": "markdown", "id": "45472020-d935-4dc6-a94c-72deb3e8c8a5", "metadata": {}, "source": [ "mrk1 has been recorded prior to the experiment and mrk2 just after the end.\n", "Compute the average positions of the five points over both time points." ] }, { "cell_type": "code", "execution_count": 32, "id": "b4b1ec36-7433-45eb-9242-c9c1f93b6650", "metadata": {}, "outputs": [], "source": [ "% Define the average marker positions, mrk1 correspond to HPI coils at the\n", "% beginning and end of the experiment\n", "mrka = mrk1;\n", "mrka.fid.pos = (mrk1.fid.pos+mrk2.fid.pos)/2;" ] }, { "cell_type": "code", "execution_count": 33, "id": "279fd8d2-d5a7-4a07-83bd-6e44e2e9f432", "metadata": {}, "outputs": [], "source": [ "% pcoils holds all the marker points \n", "p_coils = mrka.fid.pos(1:5,:);\n", "p_headscan = headshape.fid.pos;" ] }, { "cell_type": "markdown", "id": "b7d59548-41e7-4edf-94c3-d0c82c6945ed", "metadata": {}, "source": [ "Compute the transformation to bring both HPI coils positions and headscan fiducials to the same coordinate system.\n", "Note: there is no HPI coil on the nasion, but they are on forehead, so the X-axis will point to the forehead and not the nasion." ] }, { "cell_type": "code", "execution_count": 34, "id": "e242bb38-5348-472f-bdbb-be8a2c79f88a", "metadata": {}, "outputs": [], "source": [ "t1 = ft_headcoordinates(p_coils(1,:), p_coils(2,:), p_coils(3,:), 'ctf');%J p_coils(1,:) is not exactly the nasion, so do not interpret the X-axis as going through the nasion\n", "t2 = ft_headcoordinates(p_headscan(6,:), p_headscan(4,:), p_headscan(5,:), 'ctf');%J" ] }, { "cell_type": "markdown", "id": "7bec658e-060b-402e-905c-362d39052bf0", "metadata": {}, "source": [ "Compute the quotient transformation allowing to coregister the HPI coils poisition to laser fiducials." ] }, { "cell_type": "code", "execution_count": 35, "id": "5cca7fd3-b369-41ab-9f10-d85d85c2af56", "metadata": {}, "outputs": [], "source": [ "% t2\\t1 is interpreted as the transformation t that, if you apply t to a\n", "% point, then you apply t1 on the resulting point, becomes as if you\n", "% applied t2 on that point, this means the composition t1(t(point)) = t2\n", "\n", "transform_mrk2laser = t2\\t1;\n", "% p1t = ft_warp_apply(transform_mrk2laser, p1)\n", "\n", "grad = ft_read_sens(DATASET_PATH,'senstype','meg');\n", "grad = ft_convert_units(grad,'mm');\n", "grad = ft_transform_geometry(transform_mrk2laser, grad);\n", "\n", "\n", "save grad grad" ] }, { "cell_type": "code", "execution_count": 37, "id": "2d37490f-eee2-4dc7-a831-352af3e4e100", "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "figure\n", "ft_plot_headshape(headshape)\n", "hold on\n", "ft_plot_sens(grad)\n", "hold off" ] }, { "cell_type": "markdown", "id": "7cc09b71-5f5e-4246-a26e-bdc9b46cd3f6", "metadata": {}, "source": [ "Plot with different views `view([azimuth, elevation])` where azimuth defines the rotation of object around the Z-axis, and elevation defines the angle of elevation of the observing camera." ] }, { "cell_type": "code", "execution_count": 66, "id": "aee352af-429a-4813-88dd-7d4338a1b40d", "metadata": {}, "outputs": [ { "data": { "image/png": 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}, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "view([0 0])" ] }, { "cell_type": "code", "execution_count": 68, "id": "b44175c6-c91c-433d-a3af-c586c5ed4185", "metadata": {}, "outputs": [ { "data": { "image/png": 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sMgMzAIJUH/w6c5JyAsmZpE5/njhXWUhNDbWkkjdYXpmyQwfd6lE1/nf9QJBAClIZDregsvDyR4SIWyZUnbVVLoULO9WoAk0qA14/USt0n5CEL4NoPd62AOLJG/Qc896NUqjTdoBSwEOqGLqaxMomZfAd5pPtNEes2TEiGGE/ae0/B0gHBAmEkjmMuxxFogD6Ouhb3xzZH4KbUq6uQbWDHsDOpGJAkKokx1akKFKtBEzugZdDGZumYXRMm0P2B5e9Pl6FE4fWkzUzIEjUoTJWDWaUhKo0UL+6aWnfyf18Q09RCOZs+WPCWk7teo0gFd4oQJCAFr+NSspjyow0SVqNpnt3uEaPLfYu5yqRL4tG5k9FaD+gVKSi0kmCRBUAglQfBebrhPkZ+3NIJKY5DBoLFUBN0wT+QHH3JCbuRgGCRBrdI6Hcu8cS7dGRC480nc2yXCa0sGoZM7LoLheLVrgTCu4Yywr/aBQQdehTGSBIU4BcjMOSrkG8vYBPsKB/rw+duDvz7+L7qyVULMlJaorcVLhRywNBqpJ8j0rCZ1u3pD+j91wE11ip76J36IVPLDeAzrnsz5LSR2GgMw0gSNWTW0KckM4yFOVhPsaeF8pNAveIfBc1Kvmx3wCCJtkuU3FTpe4N7EYqAASpPuqOAueprsGZiJkNCzsrvuerywehx7r+mnCBFpiBIFHH/BiUeaoVtRitW4lW0fYM7Aa3j6Wm1/g02LZbiV0k/6CeqUBiRM7xXGhSbiBIIBrK2jBKe/qABaONyyXbqcoM+Fmp/fqJwGRdMSBIdHGPmsv6qIz5HFb+/Lf99tjVR6FpeLR1VNxFWr9kjOwepGYRZwsEqQJGzB5kmayLLJOCa5W5DWI4/oirEXyfWzMAJd9CkLSfdeIBUakdCBLQQnBj04p6jI7sIQ0YV5WL6LFf6ilzwDeYNBCk6kk4JOzNR/ickm2VPr61FWxjim8eBd/RsQExTXV019SNUkhd2L3RF2WWT7hfBYAg0YVP+68j+ROS5KkbFMK1X7klxRE+02tlYbi1tDMPi5vB5wS/kot0L81MKNMDggREcmyzlXMIdX87liOc26WJW5m5mAbXqBZhnqvX6cm7JU+b+dEJ3JcJAEGqmPEfP/28v+DeBTfUNIVCKhFcMOP+iJG1uzkNOqc2yc/X32kxjwP0jAgQJJAC/QA26yuRsmtSQnvtVVSNeZWMbVDOPw9uj7g9wsmXUZOUBnyBIAFPfJ/ViGc73DRQM9aAMUbBp9djlSJsjy0ABKkCiD4ARBJpV7rUrOy9+Ig13bn+P5DCmymMLnUQzccBpACCRBoE9gAnUt8ncpY5dWTjtLYzuzhJqeoCSiBIYDxGz9Cam+Ttl3ssw5BFnQRB/5W5LK4I1YnEfuLqb7nKgSABorgsL2sP0J2FmR8bLusosQt76HmgAYIEUpPJ6CcvVldgGdHK4S5MOACEbMNAUiBI1MEcgg7L23RGpGRjClzssIrsgdEuvTf6TwzyAEEC42EzKzrb55o6iJRKJSHmKkbpjdBgyIY7qy3vv9qYwgInSSBIpCF90+vMBE/0G1GVOcTCu0VnH73aScEmCm2g0CQzPq1abXZOkYUBVAQECTjBqwKfVK5A1aWN0dxs3yjXq1fQQeoNt3fvgskAQZoFMemxxXObJjBbT9ZRPClrpWyMy+XTd3Qyo1sX9E4ZDuoEgjR9+oc8eJgpnEUuq6nOjhdYe3B+l89gipDX+GJ7n92uWly3I6ORrTSVB6YHBGni8MYlID7K+3WfMYxl+8Ky8/kIiTi65zKNBl6nMmNCxPvuhoWZ7hPLLVEg6B9MFwgSUZJMmkcWgon7JCy6Uf+GDp3h5w/N1jo1hvjGNLeEUIjhteWQojkBQQIhhMzapbWqjnZqbHO2suC+jubYLVfEN7r7RvnACGnSQJAqICwkIZd/I6wuyGYoONdZfJPcD+OvIiyljWGMv/xK5xvxx4TvM83vQpX3jzMF1K1eFGkrOSb8B8QDQQKedLmfDe5Rt6dHpzSGRz23FXAWnkGMe0W2qcy22cKZFJT3ko8Y8wpnVjtl+I/uFExo5wCCBGhTdilFHiCPNmT2jD0ZnlqboeRUJ+uuI19/tDsY2lMMCBJ1+keC/4TVaHTS4jtN5zA3qLA7y4iD9CbJEAvnmYWhkW6Gce6QTNUtLz9sp0HYDwcRGgsIUmXEbypK0IaEm5ACFpzS7ip1m8yxD9vdv0q1+M/fBsNrH/0OCUFykhinqQnjJSA2lIEgzQjf51D96HaGo//QsIpAZk+lqQHO8kDQkKnbY5j7Mvwi/hLuh+fN0Gvq4k/hREq/AkgIBAn4UfrVA2k3o2jMYuLEEwmXQ/QXTk0dE6CZLx0d5Vhkap1PAwhSTXjZIOEpiplMFz5xP7mvWshL5tuM0YhuqsKWSes9ah80RRsIenWBjLQ1uHClAIJUATE2JT5MyP3Ehx9++OTJk4yxa/btWzWAsbZtn3jiiUt+dck22+6atL29zRh785vffPHFF4e0SWmvqZrd2Lc6udXBl5ugwKyQmss1Mh1FrwT0NVF6a8WbrTCPJ8sTpbIpO3bsuPDCC9/0pjdtHzy4OIox9ifWrrXN3zWb/7x56PDh/vPNzc09e/bceuut1mIVnwdbNN2UnVsXuWYd1WySFUqLzAgX0mYiOP58iXRL7gHHrUX8YE75VXCTgA50K1ESClLaZik+5ARpe3t7c3OT3zzbPMTYtaxl4lKQHKzsWm+GcIkCgmQt2veUlIIk157ViRkWfvDgwT179uzatathTXefNE8027/aZoztY/s6r5pF+NPugsRUjxgEqSSYsgOZeZUxxhoWkdnakYDcEKtDiszM1DNVVY6muWb5z7ZtF5r0KLvnn+85zA6f89Nz/pr99QUXXMAY27NnT5ggYdqtIvA7EYX3kJReEUEPSXCAbrnllr//2t+3r2hKYqnfPOslSJLLZX3XjkcIdcDEo7XBLk3yP0DdMOUnmZAqapabjlqWzBWOyR4ED6kk6Fa6yE8CLUHS74BhukDqtr3hhhsYYwcOHBi8diH4QlwW8N0EiSnbrOvtMEMZIEjKipxnPnMJUkK54q9FKqk1/Lye9XrlV5WfO+FECFImMGVHHZfVV5CElq0yAnSd627FousOj4FMkP1TPiy5z+oyccokDyldiobg/RKgJGtjNwDMjgMPPGDybLpNS8OtS060rfo/rzKW/zVL56NdZuJJQGirbKW2jYpuvknsyeDu9afh/jO0vmHsB4891s/XdbLUsrZJmTDID3kPrOAeFW/RXIAg1URveiobvhmscFrr7G5tBW2Q2tDmDsGQ2xBVkoIkbbSj6e1OEVcC3zRWO96wpm1WflXL2maL3bC1hQCQ+QBBqoyitkZV/WAlQy8AnZNxw9bWf/yLvzh+/Hi+9qyG4U1jGYz7FGvRCYOWCN0S6Y4oK8rjaXm3So8y0U5ry33QrxgtZKlpttjWoEbd6hqYCrWNtecEXU9IZQsObm+fcsopZ5111rvf/e7lUYuMDIyxtbW19fX1Sy+99A1veAMf0va/Hnnkqquu0hbutv4snNssTnWOQZAvx1CvtXkGQ+kSXuFVlxlbQETCeAS+qEaVgHzwlVhMc/To0csuu4zt2MEYWx9+e8PW1oEDB9SVSlUXoNyy4ixBUANIw/79+0+ePPnoo49edNFF3/ve99bW1roR8SWXXLJr167D7PDuF3Y/88wzJ+4/wZYP8/vf//4nt7a+//3vv/Od71wVFB303Bq/nRG6OVIvksZ/6+IFrrjiitUxkXWAmpn9Q0sYuiZVvxvpN7/5zbnnnrt///5rr72WaZZ///SnP62trT3//PPnn3/+zTff/Ic//OHrX/+6fxN8tuB4BTFHRjwn8ZBi/IDo7U3a0hxKWHW73n966aWXdu3axZ918ODBLinD2trah6++Wm72c88999xzz1111VVEPCSiD2b9YA2JItTDeIZP4+FDh/pPzjjjjPPOO+/VV1/tvz1y5Ei/tP3UU08xxk6cOMEYu//++z/ykY984hOf+Na3vvXKK5rds1FtHClbc0SMnzfJQ+aKxOA9/fTTwif79u275O8u2XfPvm984xvNbsUpn/70p7e2tn7yk5/kaxWgANUx+LyJT9GdHc5g/Y+//dv3vOc9jLGTJ08ePXr0a1/72rFjx9761rcugqb6M1rG/htj/8TYL1jL2nPPPffee+/9wAc+cPHFF29tbX32s591r5H1nWMcvwd2IIUEP45tcPSurOWsyhuca+1hZQlWz5VJHkbTNEeOHNnY2FiEen+ZvfF/vvG3v/3t2toaY2zgTN91l1d7kgMPKS/KgFEwLhX8LlxEL//fjTfe+PTTTysPY+vsnHPOeeyxx9q2PXr06Nlnn90dcuedd+7evdurxlX/8LUrzmC6ElwvbSximqr5aRzqFI+XP3EqxHZAdw/oLoS1jD2luQTGXn75ZZdm5ANmMyuYsgMpeeaZZ8477zzlV88+9ewvf/HLvXv3MsbuvffekydPbmxsbGxsPPDAA8eOHXv88ccXx8XMGoWdK5wVEFucvM0xk37CnKFLeLrGs2lbU+6ejOzXfnP//fcXbIeCrpdctlWBAAhPCs0Y0pN1PaoH8rw/+7ONjY27775bcUz3JLPmd7//3Zlnnrl///63vOUt3Td33HHH6aef/u1vf1t5irIo9ZSd8hhNCabPfWftgmf5hD4s+aNzVZsS4jmknZXPMNy9TdM89thjey+/fPXR8Idr1tk5Z5zzi1/+Unn6O3bv7lYiRwSR3/lA2DdIyd13333llVd+/OMf37t378DKtMupNtb+44F/PP/886+//vr+2+PHj3/wgx/86le/esYZZ2iLThLEXJgwYfPdrhTXDHN6VsYWe5z9qtSH2FlPbY8zplEjxljnTF922WVezQG1gCm76cDnMStd93JeaGNjY2tr66abbjJkZ7jvvvs++clP8p9sbGycffbZ3/zmNxO2SDFOj5kHiw8/cz89c5BbbgIm+hrGvnfkiPzT/P53v3vdKYNB8+bm5l18XAOYFvCQJkU/wT1O9U3DGFvsql8f7LhvuRH2T3/6U/nUF154YXmok1q0yijEgOUfZY2+ikXcUVNijcqLKbvPl7H8hH+/Vxf378KBAwfOP/981s/RNc2hw4cZY+zBB6vsc2ADgjQReOusNtbpalr92zEiOQ+Lyxx+xOoKzM2pCuPSXVgXL8GGPwefl2Fzc/NQ9yNeeSVj4h0lO9Ng2tSweD4/AkxquTdaOuZeE7AmTQhdI5HdwWQXnjDlQcy+osyob5XhtS/WkGLWrnR4bXWisEUMQQ05gYdElzpi7Tr0yYRiS7MVIntI6kIiEwJF/hCOp/PtbJoyyX5cW+VY1ORcPVASCNI0yTtrVxJD4Fmqwr2OjGkJjQF+Beg6KmmnwdEhCAQJeMKv3CjVovDjnby6sM2tPbVZN+vYxWNko9xWZe1Pr2m6RF3dX5T9tYEVLUnWDwQJeMDHSjHfvSkdugfbHCCX1U/SIZg/R2J8qZHmuxSa1PZDjqR+trzomMT19MQ9AqjPKsl/UqKJcwWCRJF2rEzVRgaPcfdJ/++01sQ8snYcI/tGTDgO5xNiblVwlwaduBpkcFLEYjyD/kQ+nsUgP/RueAM0n9AJAEEC4fTbTQakmr9yVIiY6nizwpWjLoXOpE2eGUJhCivWMYr7XRSFFKcfgU1nRZY8EKRpkvz5UUzpLGft4uvKGLptq1dZkfcV0TCgJpzlQbxwL13ReaVJuqUrPPrGgLRQBoJUDdUHBenzm8lXtPow28UaDNNqRJw2ZYO7QQ9aXFFOImW/V8LExhxHrixzlHVEPbU+hrSBINWEYeaa9KyC3mbp2my6nIBrNAie9oz8/ZnVhWWLbvfOizoWhq3T9PxOus9a5UCQpgMvVyWflsWsHdcO1zO71mpGvq6SEH+lZYK2E9ai1Fe+Cu6AdixN8o1RpKc6oDwQpMowm+miozZzoLYDA0NJbEKGyV1tHrmHia75AAAgAElEQVR7tTw4C4O6MOnVUEyM0bC+Y0I4RREjB0B+IEggFjHDKTNaah8DFzJvFpYYzb3MtG3QkTyMXhVMmCyxEI+yIt3xumAQ35B9MBUgSCCUmPc7uJQpW6s52CNP+5t3MSNS3ZV6k9Q7DEAY5bh0IFaMigFBAk5YnBVrxJThRAqLB77mJsCqWq80sh8CLGZubXC8Il1iDjA/IEhEqWUr+GodKLi10WtRFkLnfMRIAZm0QeG+xOcFFwguZBnOtyoprJiIc71wDP8hHbk6USBIwBX5+dSa6iTRuu7+hHJZwoj4cj+rTZevKNE6WWkiXVLVVYtC4p/qkC9BLUupu9RdZoR7vpaRYqVAkIAHwtOofard8v20ygQQ3AEL9yuPfVe8cHbQQO1XJXC+ZKeo7pwGVBnS0kvL4CvNRQmHqc8dKQjTsEmucEtmAgQJ+GFKLaM5IV9jItFZc3UgNXOeXTQ4T8X8Kq79Ysx32ohzl5Z4lWBwr+ntDQBpgSDNCyf/Jhj/mbp22SR5KK1enUqRpkFsgFSLd9ai+FUc6zXKBywPUzia/Hmj7IqVUc716XdQOXl+YHJAkKohfolVXAGisWDLrxwsEF58YAgpNuZ6cKi7ZQFWL9V0Tbrxvu7esNj0AKX32kYGUQGeQJAqI6GK5AoiClo2d1lsyIijaQ6QolL7OpULGx4/blAuV9G/MY8hdCQJgQGTAIJUE8nFI4EmKc9N0s5xQ8BjhMSw1GTM8RMJBX93gVmZMgdZcK0g0yHADQgSKIWLGQrYeDsuySMROvQrRkWxVmr8TQdjHUmkLTF4YdkRS01KQ+oyAUGaC7keTuuMjUulwQ0b0S6k2os64lRV2AybAX5VLG55L2DxiciaKIgBgjQXdLNz42xHF5YNxs5v5rGk79tm+ZiANEVh52ZavupzHMTFLHicHirbOe5tyF5WIEiAKjkee/cy42t31IPIqSov1SmYOTtGq7Sbk8ynQCfqB4IE0mE2pvwEkSGfZlqzQublBSEhcL7hZ8X3jZp2Fg//ZkzRMPMOKuLU23LKQJBAHI7JC5KTQ2l8y3SeaVRvEipp0XQ/TXQ3rnYWc2U67SHrTx++DIJ1Uo0o8FkCQaobr3eWK6fURx7oBZke0dvwn+HxrW9Qsvl1Ccrdqar2tHIS0oQx9GkNurEHAveQ8WtRPrcxBeAeZQKCVDH9UzGpVI/K6R3uAhvVCLr7o1Ud71pjcpY/jfUFB/JZvrV4XEKqtTGdZ8x7jc6tgnEHHRCkKeAeTRTwukyvdqz+bYhGi6hRt1TeGr4dz9gpulfypSzBZgbXhC959BhFF+0pvsQFP6Y6IEgTwVeTCjQpL6pheGQg8qBYHZmCL/jCk5dsjfEL2DOU/BYiE34CRgSCRJpMQ7zSw8be0imNjvMMj5MD1DlkaXd6WlEGEAbsbUpIho1HaY537J+ALV9iPXCP6gOCBDIQNoFjHsgH5Os01MsfplsOifQVJuCGypiXrORvDccbHLKA5Lx43fgkgCCRg+ZLIsoRl4uotGHyNZ1hMQvBpzNviRWmc9P0o8EJTppbtkxm1SnMeFMFgkSLzpiGmVQMEgfkWJMI2HRlmG5K3sLgArlYTWELkTZ80T+UrgDFbn48ZZmAIIEUWE0hPzkWasIUMQu5V8LjgrDVQRZSwLqpjlJL/bqhjGLf6/A0xYeOMRQuZLb7ogaDsYEgkaZzerTGgqxLJAca8CNrF48hgOWI3tIhMd+a4cM0hrs+Sb9f3BY74Bq76N515pnYUi5XPxthPoZBsQoCQQIl6B961ydbs3Zitu+5cPT/NOjaXEiNXNaNrOGLaWMXdVUUpP9F3Ed13WET2TVBFQgSXXzNbhlL7ZHlZXkkb+9ch5z9aF3eSaq3IAPD6tsVvpNj8jH6Tazq94vLhURaukyh3ksrPEiQYQ3FjgtOyYqXArkfD+KBINVN4Vk7dR5MZjIuwug7wRPeNK1qBX6c6a+E2XHc12PkIyNugJT9Ng/XAeKUDwgSdczLSEwz+s6B0AYXadEt6atnsXwmhdplk4T26Jo+qKUMFZmt3E31cqfIA4cpHxAkivR+j6PSlJmpS1tLEt+u5af1UuTdWU4yDt2vgFqE8Ohiihg2UWbLnjCOCc5To+98Xf9njsYAAQhSNdAcl7mu6DgXZ/8kD4sNN5INUve5eXot3ni5RSLw7XG9NxzT8OiOifw56N3AgBQQJFrQjeTWY2qzMYxqcaLueH19/m20oGy/63JX7wMF65BvMgWueVwZ0kuhHN1rfuigcpUGPWBoquw7hnUI7ZsfrlJWIEgUkVdranwMXIXKipe9NqcSUHpyDq8sSjlECPsp+ZBFw6a0mIapq424dmqrekF4TZ6DSCBIIDO+2a8DMMyV2eyIi8FVaJK7eeL9hmijZp4FbXUHmKNF2nYVtShU569GYiq8gB89KLd3MNVNSEwbCFIdzHGMNuL1xu2ETVB1qjW5Hpuz0i6rGHzoq0bS5qpAc19WkwAdIEjkMId3jzWaC6tdO2YfFh3YpoBpnxSReGpifCDzue5ZXPsOD71A+4nC3li+ftUP7XrbSD6cx/5rT/gmBWcxTtsk0ANBIo11ExJ9DJrkcV0ULj9+7tE3YUHSDbD2ws1fGas2DDvUmmSc/+Q1NV82OZfbr+pHr0YgSMCVECepbVm/DXb4zeITpZUP8DN0ro9SAwosa6UuvFAeCt+e7/0Ma5luvukigHBQQ7gro8PlTp7jJDkBIEjAA+WTbHm2udQMQlFO5s/Xq0iCe/nurbJerNk+umlSoG65zDeav1WGgFvL0YQLmipKhGMtEwh2rQsIEvCjfyz5f2iP5lP7cEWkaopQhfgnX5EmEm8gsZpz04dTyz3gEkZhvtL+EiJ10Uq2acOY1a8ckGrMTIAgUadfRmJknpCiKZCVBjTpnJvTVOQoPe8fcj2YBbWWYyjfHEkfH0enrJGw/0Hk0Zs8ECQQSIJH1Gooveb0lMieh35XKVNtxGFkjJFZOBPMJgXvVjYf2PeqOSwC4QMAglQLU35WHZdhvFTKn25NK3IjzqCRghuXzpkTGtYwxoSXFckEd1pYtwshKmU3ui6bkL5SLCPlBoI0F1zf1FA7MZfmEAWcoCJlIc5+Xj9fKnwSS1h+JqZ5k8jqQE3Adx6zLryvK9V9Du+tGBCkOoh8JORc+nU8YL6NrCdDWiQBqWwV+Do9niqizcrqWXjYCyNyAPcoNxCkOVJjTvGOZjg2t+Mz2A/RsBjJ5P+s7rfg46FVYuB3g8Vn+ZOqS3WTQ4RKAkECOXEZILvFQCvWTpqGkZLVeP8sOBmS9RSvpSBfmeReKs9V6NcDuqm/SkdOIAwIUgXkWEot7STFrLhomuodfR4vGCUto3/+ngQIU2e6rU79/w79pOQtHH2yTh4GQR2zAkGqhlkMFa26Kx1gXlR3KjOYhEtWyRupi/cz+6O+zXA/3sFFkwde07/hwRAIUn3MQpk48l5v4Z4UXJCAkLPkgRu+oRBWAlrY9v7VjG5sIANBqpIkNnrk0IZ0hrUNyO7jluvTsx1J5cFRG5JEn+tKDnCAJgSpbdEzAYJUB8JsRr1hcguC06pGUjg/TUxdSsfFPf9p8nQ+/f7WtL9Ltns4+QOCcLsCQJDqQKlG7o8cdQGTjZ0LSssol5BvM1Oq/szxu4SJk8v+XPNm3piU4fSg+8hMFAhSTaQVlXEkSmeSOk0aJXI6Hq9KKctYWsZrYfy9rfSHoE+5gSBVyQSdJD32lndKJgcIKBeKSPVAYfk0x1NoPun+jrTuMWf715bmPq/xYakdCNKM4C17rRPiddmIJBEHfKy2NQGPsgT3Svl9x8tC+LSz/LZkdb1CgY6tjUNeYU1Vck+tz0ttQJDqQOcl+Po9Tm/Vo4f1zQsp98ZW1TNaIiesOu3hfKaW/9yxXp0Rz9DDye/nuh6QyQBBqoxeToKn4EZ+0nQGyxbUMIjt5gfyyve9uqB8LYJVsbxydWfdM5QNg+o43XWTcCaUyfHGasx8gCDVR28UAlaSqqZdrmcw6/xMLb1RJnDD5/gGlnc57JvJY0UKCFJNTHwi28Fuyh6StqjqTEmkO5UwU58xuao4AGqkVxFOjok/d5SAIFWG4BXhFZauTLWXYrLPCfqRZMnHJQEEbfBAjQgEqRp47ZH3yY7UqDhcHB33g5PUm9Y1CVijcqwoFUOvyBSzUOk9FkTFz1TlQJCqpHtg+pmTWcx0W2fh8qUFylqsLnTCJUmEoWE65Yu+lmkvMvWrR2x4mRO+ZFJAkKpkrOFbXy+h5zOdFzXy1UUKakybhXrDEjgpv6rfz4CrVBIIUmWMmGKVryvxZqYCz7y+qSsdWh7pdHWOseZZczEEFMg3W3LRujjGlg9SGF7gYk7PEMWQdjp0DDBfNyIQpJrgt7V2U3bFqhaUz/ttrf71LTIUJEnWqVeFxSUMEw61y6+Yl+i6HBm2ZUpVvkduglRRcPLPYd3LVSF90NDYDZkjEKTKIPWolHDRAkK8HBPt2ARVIbpWdXRsrUuSCKOIsqEIKbTTuRPEJmhCG7qlFVG8p0UvRcrhFygABKk+hMjvyW7fU675j5LMOzmhV6Fz2lwdVl0YxVAI1RuQdU0V3K/K5Qru0bhAkOpm9CcnjSKOPu7WN2BwgUpTHplC1FOcVks4KlkSf4uArUVd3Ka1qV5UNXSAezQiEKSKmdRQTje+DtjIaSifO1GcmHIf4I/kpVle0963qkyWitHHEKnhV2fHbst8gSBVCT8WntQILt2av/qTHDgm0fG9KGvwRUKS5GhIenru10kY6p3UA1UbEKSKITKUS7+OZZh0SrdQoVu9V5O2q5PuVPUmlQslxE30E4nRvppwO5URiVY5AgBlgSABJ2qKnkg1pWbIpBCJsoUuWUodJy17JYjUb6GdRX59CrcZkaHeDIEg1Qqdye68FkRZclh1kgy0cuMlC66YK3Opvfw6k9eEXoDWOm5mirsnk7yF0qs6pvGNiDxccwOCVDeRz2rA22ZHHr2mNvSLi9KU6TGnx2wTceWn6dxr0fWkb4hHHIXvLoPwjH+fzxUIUsVEjuP4B9Lx8ZM1Kdejq7wu54p4B8faPOViUrP8yrfqNMjOnMNowE8+g9EnFhIhb9PlLoV7NC4QpOoJc1yEID33EoTHlcRAcmi+G2UWA2MB8lZQwUp1JVurVnyors82D6kqQfFDCyMDS61Gxt5xTMc9AiMCQQKMearaaCJkTQXEFC5Rr7hMKTP9YcujHetyPSZpXyl/ppWPm7A9yilHOZFdfEVObbHfmV6DJF2Kk4mnPqkBCFLdWEd5vq5PBY+isYW69nefaie1dHuYhklXXRpgJ26rr/yLa38yL3GtGTkPvePBgBoQpCmg05KKR3z+m0AbY1odtyIoJcrrFVESwlYQS6/tPnz/OPawS+HZNu3aV86kTD+OHpXymCoflgkBQaoeYcjMP1G+CVhpqZduTUW2vy7mVRfnrUMQtrBlocLIbYgPtBOOIeZs+d6uhscBq0oUgCBNBD5eTn7eaCkNEdAbBniNVybLYNIoQXlMHKNEOhSrDshAkKaAHPlmfbSmIFHmxZh8l+Y7K6jMdyCk2+m/rZqI9gs3pMtuBIOjYzi++tt+0kCQJoJOk+R/jNjIcFrxpa4e57qvDHmtrIyOb0CELnCDDPw9nOlGdXklIxiRtbEbAJLRq07/p5BRXylaciFEp9FbSy61tm0H7fa9ii58oHdZ+OpckmHzp8fQlyMXFVB+fyG+ptZQV6poQxXtEuuRAatHzHh7Q40oAA9p1pTLvFCA5atODa1XXx1NAU5FjtDBuDwa+TDcvfx2NEAWCNKUcZm4q3gqT9VmqyblqNR0jLyZtPvWscN126HcThftr7w72P1y6jfliKOjDwRpaugExiA8BebuHbFv+XRYOGmFTHR8pgaDVKQi4YyWVwt1y/tSIWKavnhojGbM4ypENFQBBGlGGLYlUXhKTUlxPJHnZ6IuUHduDklTLlzpfBT9dJxp8ooNks/am1GDS2FVI/5/KdztQAkEaYLwkiPID9kpC51Xp/vKhJDRwOeUlGQNPbfUzPWYSszsE5skbxIeOUIn4KweqBQRIEizgHc1yGqSjgQzLcpUoamK6gtMks3BUEgqi9lLFL3MC1443hLW6ei6Hodpg7DvaSKrDp2FIhnHbbzKL5TFOdnZgJDoSMvFZ59T/mc4UT5A+WfTLN5DIZwSHP/NcXD45//71a8iCyyAddYXvhEp8GNMDV3m48DpryLEZMN0cimCPSQvp0dXi8tqUP+tuUZrdU0jzsUZ0xZoi+oPa1nL2oY1rXRI/wHBO6pHWDRVbnKg+VDME0zZTRB+AUn4kObjF9Wq5IFzwWkgeBxsfQjF16XahjHWCMtNizi94biH2k3FEMVQIRAkMDMMgeORZiuJ1Suy6SfYRgtSxLg/ydp9XjjhHhEHa0hTQ7cjXZdAKC3NkEy1eDXIsvSSr1Jz+h9+UaflMq7KZ+nMpbKEzgnuvxUa0zSLRaYIE9wydvDgwSeeeCK4hDKQuP2AJxgdTBDD0E83mZ6wXvMnXqcHF8WfpvhQN8unW/LRHenyoQ75WhwXlhw2Ra38GNWuWDm43trgpmU7T9v5yiuvbG5udp/s2bPn1ltvHdZPzpgI66lwj+iDKbsJMkpgt296CJnEBqKMJ9RDya4JXjK/EzZgjzFjjLHmlVdeOXTo0Ic2P9TmTMwEZg4EabLoMjIUjm4I0CQmrVJ4NfXhhx8+efLkNYYjmubg9vbi39vbl1xyydlnn+3SONO3vvrnPi9nPlffqt5DSpjcr1I1gntUCxCkaWJwkso/ir6axKQFAK/WfvSjH73wwgtNgsTYPffc0//7zDPPtAuSlzOkC0wwT46FbVN1CBMXtN2l1EcZu4wx9tpr7PjxtbU19jq2c+dO77YRgGwEIFACQZoyOiUwJLVLXldwaTGnf/GLX2Qf+lD/Z3Mqe2j/Q9ded13/yaFDh1am+fDhgcbkMF6OZeo0yeV0fgXLMcFr/62koFd0/7djB2OsXzcyQNPbELwi/hOaDQYQpMkiOElCmobC60wjzJBwdf2nt7/9ulevu5b/Vhl6Zw1hEL7SRzEcPHhQcNEOHz78rne9S+uILRvwf/7lX8778z/XHVUMecr05ZdfHqsx8fTDr/4TqBFNIEgTR140Um6bTVtX2mID+PGPf7y+vs4Yu/LklewoYy8wdjlrunadxtpXNKfZHAsxNEDvzezbt+9973vf448/vjisaWQvo/nP7I1PvvH3//qv/Sf33XffZz7zmf9yzTWvf/3rn3zyybW1taeUuqgUQpfhhXni0bgcZfhlifzoOmjmtgdqWjBp+F+Zce5RvuoCvkrL+vr6zp07d+3adeqpp66trd14441PP/00a1n3386dO5f/dP6P78bhkozy4O749fX1r3zlK+pvW/bzn/+cncpuueUWocDvfve7/YW8973vlc/VomuM/qK8UJoL+jZEuPkBcUgPbUAS5N0YWYe0usKLjaN37Nixvb29ubl5/Pjx66677te//vUPH/1he2rbsKZlbXNao/WQVIi5CYaOiDqArW2bprntttvuuOOO144fF75anHh7w/43Y/+dsbcztizkdaeccuLEieWB7bFjx3a/4x3iuS7bpNzx/EVigk1GIevGO5AcZGqYPvJz2LotIIXlXHAsvADr6+sPPPDAD1/84V/e/Jf9hzvZMFrMaKQabnytpOXm8QQ2NjY+9alPSQUu8iWw+xh7kLG3M3YRY3eypmWsbW+77bbeg2maZvfu3ZYrLI4wni1QY0ziD6hRdUCQ5kXLbfQxP+H9ytPClfbXJIFRLMLOnTuP3H3knnvu+cEPftB/ePjQoUG6HUeUSX26ztGc8eUvf5n/8+D2dlfr0UePsv+7UJ47t+7cfffubn/Pl770pVXZjDVN89yzz7o21fdyRsJLYGJuQqhRjUCQZkeYBxOgSeVH00o2Nja2trZuuumm146/lqxQm/VvGdt7+eU7TzuNMbZjff25Z5/9D7t29d9ece8VjDG2wa7YuOLmB24+duxYF/5w8uRJoZC3ve1t3pELhPESGAjJDMFPPkccB4+RuemqwfA2IP3qES9ITdMIUVzKMg9ub19zzTXN7xv279mDDz74sbd87DH2GGPs8jsu/9jpH/vwhz98yy23/Pz558UG8HUJC0jWXbGG44v/jsqbx2vF0f32g3tUKfCQ5kjwMg+d9aGUtFLObB5d3u5Q/uHAPzDGrr/+eraX7d27d+/evUf+65GHHnro4osv/uMf//iZv/qr3770klNBVf0QXsIQqUYxp4BxwQ82X/jdgrrbIJVpqJSB66P3MBQdoveQLrjggp/97GeG4/nIPZOHZGVsl6jHaw9TfJRmgVBSkAn8ZrNGCHBw1B7ij3rKDflNo81MqkxC4/gyC75Y5WF8UlTdJVjFicZv5LujNv6WM4+xAGWQqWF28M+2IEXKPe2OHxJBaFgvTsGtbY3ZshculJx5aPk1V5Db6F6ZT9uxHJK/iBdJ7iuadyZwAYI0R2RNapdb2euSHwFl43VfucNvNuILZHqhci9z/4EDJ06cuOlHrL1o+d1SRVvGDm5vMyktXt+U7uDQJpTA1z2KPBLUDn7pOSLMachehXLOxHAKBaxNCmmz6o2rHa1+qs3pT6lAHmNEuX6xitgvwqLn6zD5NjcQZTdH+gm6/k9h3UUOpes3joTtmadASIigEIBn3lZl1gbd66kYa9v2xRdfPOOMM9g9zPsVeIb4QKo4bpTuNy0FVxHUOjAmmLKbL9bJOuUpRZo2FRyj45rmTYz95ruMvdGttJox32b8oMFFsZSHQYrqhdzECyiGEB3rMnFHGccGJ74une1ziAKXT2mWE3jqZERV/Rw9gjwk6Xw+OlSOGk9YESgMpuzmi3mmrro9sNU0uG2/89BD8sfNMsKuZW2zk1180UXCWTF1NhIxpXnRDokv0DCVBzWqHQjSrDEvJgE/DMs5w6/OOuusf7e2duLf/o0/5MVfv9i/e2In2/mj23+UZH2oX7ARqPSH5l0iXYg/qBcI0tyZkib1YRe6A9LPQwZpxmWXXXb66ad/5zvf4T+8/fbbr7766v7PQ+xQfOsMzkSNP7Tjzwf3qF4gSEDEatYpw0cDCl/RWRVbW1u77bbbPv/5z/Mf3nXXXV/4whd8g+wM0LneJJjXODFZNw0QZQdWCtQ/zLU/1YLbx39IhM997nMnTpxgf/M3/SePPPLIpZdemqr8iamRANRoqkz5rgVgtrhv9CnTnkisS0eO+xYAcTBlBwAgjUsgA9RoGmDKDtCC8jzblKjRghvaXN21ACUQJEAI3T7H0c2NLsRj9IaFUZ0aKW8DLB1Nj8ruSzBtdIZyXFkyZwhlVA0ishiA6oAggfQoc7M6nmg4cpRxfdXRAXTmP4UkIGM1AxAHU3YgC/LYfMJmiGyIF4UmyU4kzb4CFIAggRK0+rek+xYySVs2sTWqnqn+XiATECSQHqVy9JtVDRbKKjmT1CTzGlWlF2t4N0SlVwQKgH1IoCjWFGpVJy4KwEWAS7YnIRAe4AsECWQhxpKSSkft0pLgUb/LiaN3RcBLKwzRklApYAD3B8hIpGFSWsBR7ljDAlhWNYqsIpKweASoEQgGa0ggBMcIhchYBjr2S5mtlf8qH2MFuzPVpVnX8KBGIAYIEgjEUWx4U167Saq9/Y6kFY9p/PSgDBi2gEB6s+U19dT/Gzcec+i68o5F/N5kOrtxQXVAkEA4AZoEBEhlaSMokGBW4PYCUfCaxDAcDoWIV1EydRPcZSADQQKxGF5XA+qiTHJbvjRljbiLZguCGkAsfOTVJDMpzJnkjq/VCUtbHagLCBJIQJJUdcWYauK4eORNuGn7RHCmhfIruoVAJjCYBSnpLA5lJ6nSlxvVjtC3ync1oecBbgKQDPrL1FW/3Kgj+F1TIyJ0qfwnq+EqQAEwZQeSUdfEnQ6aHp4hEJx4n2OaDrgDQQIpIR4CTlNsrFgzgluPGR3ddivKbQblQbZvkB5D5rcqGD3BNs8ErHZ3CYJjNIHrAsmBIIEskNWk1uF9S7CVCRGkiBF2oMHo4MEDeSFrfZSrMgRbO4FADAAcwX0MskPQyvMQDw5EfjkwH3Arg0LAbgZDKgErAPmAjQCgDogkYAUgHxAkAAAAJECUHSCENf4NADBhsDEW0ILfAwT3HYBZgSk7QAtzphkAwISBIAFymHNxAgCmCh51QBH5bQUsnatUY8JsIgizqXBhQVogSIAu1pfohJWpS5jNYFs18P1DfB8xqBoIEqCOYA1j7liXrAesKjub29tTuqrJawGgA4IE6JJjgmgaqeHMr0dK0nhIESgP9QcPgOSm0EXYyGqStfHxLYcUgbEg+tQBIJBJlpQFUlaj3FOOfRXQIVAeog8eADqSL/PkW4ZJa9PNapSqW6BDYEQgSKBKfO1vsVBvZRBaqsg0WZOgH2BKQJBAxbjLksGUO5bg2J7cERPI+Q0mDAQJzAWDehWL3/M9EoBZgQcDzIussQzT2+cEQEkgSGCmKOe+UsVM68DjBoABCBIAjE3Id8F8IKgX3LsAVIwyehB7iUClQJDArMkUDl4mxZxcLBKZg6qBIAEwIMammyMmAgrUVSG8L0o+DM81qBEIEgAWvHY7WY+MXKwyBE3gWQa1A0ECIDEItAMgDAgSAAAAEqyN3QAAAACAMcZOGbsBAEwQ86ydAGYpAOjAlB0AAAASYMoOAAAACSBIAAAASABBAgAAQAIIEgAAABJAkAAAAJAAggQAAIAEECQAAAAkgCABAAAgAQQJAAAACSBIAAAASABBAgAAQAIIEgAAABJAkAAAAJAAggQAAIAEECQAAAAkgCABAAAgAQQJAAAACSBIAAAASABBAgAAQAIIEgAAABJAkAAAAJAAggQAAIAEECQAAAAkgCABAGHwePMAAAA1SURBVAAgAQQJAAAACSBIAAAASABBAgAAQAIIEgAAABJAkAAAAJAAggQAAIAEECQAAAAk+P+SucybVX7VAQAAAABJRU5ErkJggg==" }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "view([90 90]) % Top view" ] }, { "cell_type": "code", "execution_count": 71, "id": "f0985193-bdcb-4147-8971-64910db5dbfe", "metadata": {}, "outputs": [ { "data": { "image/png": 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pPEF3cDf8Om4wXl6qGjXqCtW+bfxl7yvNFjc5MHihOLgPaW70BoPL3aAe5Uxr/duGGmy2B0lG1fj2PJtAed0ptPnHUrYvAt0Yn948XlexpmsgWUMPaZ6YA96kKdPoGFb77WjMggGPhaVAd6etxfXiK49XBjvo/GDHkz0PnAOernDOMGg1LlMJoV69gcl8QM4mEQMUpEXQ85MmbIkHEeQzejR5g03oQZd2Hs+m/GGBsUIzhoEPA17T+Vts8qLhjLSSVnLMu9xStJC4QkGaIQZnSOwEhW4bEzyWKFZbgiwN1s5PNlvjVqOS6fyGjYvpda/78Q7inhf1g23zK+XnoiYMlgwFaUGY8wk1f0zYM4dKGRjpMPQLPQvUuSZxAxaGdZk/hEnzbFfydQdE3J6VBfP5MrqhFChIc0PX5SxzzU3bY90Cu3uBBsb9OvXOn52zc9rHak+K6UH7+cDur5vz7R6y+jwyNlOR6aEmzQAK0pwZ7j0KzO4ji8zbXXuVWu5kUoYeDI+xKWr04AG97EERHMSSkTlZTbow7HvRDNWo4E5rYW3VZ6cL6Z4H5rD1wDK7CHhsDE9vE2tX6thrMdBDmg+9ZZJR98icDlwcY/aun9zaO7S6uzWnjaUeXdqJddEM5XhU4d1Cw7pRoqnOnSJt1zJdJ9/a+G+biWsyFRSkmdAbGNpM1pWERQIew/q+Zy3CMSynKY8xxykIeDZ6a5lIEGJjlrGy+8gsoCDNgV4wt1XqzBn0vbjORJR6bY43yIAwhXAmYKts74G0GUUp1cUgOYxrkA8FaSaYPSH2QAWlW/+k6C6OIU95zxuLcUkziwe1anIoSMXTc4+U22At+5is3mi/+cYlKnonQ4HlBh37SbxIJjgITSYh56hxm81JMvBbSTK4VgnaSBygIM2B0TmK/E0Kxds0hJysqAuln/4K2j7c29g7rE7XEnMtyUz56DPcO8DygdcVW3CXmQUUpLIZTQVk37WK6YfGhQrPsxiWGbYByITN0pf+hjokWFrMeN/JuaHkSIaCRABHO+6QIy4uuvDrjUP65qbqZGrol7Y6onew+niv9sSdytMN6jGMeje0p/NL7WHDuT6lQpv9qkgnbiMhTs+hbuKOUQ+TQ0EqHr/O0+17CHCkRHTd3mIA1taz6p6axSC6Vi66uIbzjQrVaO4G9Y9ML5WobdTRwNQ3cZL4hTx1EXsoSAUTmLrbwwQ4RdkGEW9wHaWc3CF5kuMke6tQWZykROicpAmbtHAoSGUTaKcC1WgCYjdg9KQqy+k7jLk4+c2cvWsY6FpFOjU5ThJn7aaCglQqcsZxaXuv/eyWZSQ3NjbTGCbB2gP6yzM23lJ+W+b0PHg0z/InYfqkfJbSPV2G2NQU1ZFRKEjEipIGjBnC4SKW7DQZaPn2DZdlMxMGtU52cSR4J9SkqaAgFYyQbhPffIzuicmP5ZYdS6a9aynW1dpiY4hW78HOI069reXFDL/mBQWpSDx2ApZBiKU27yVKd4mcAqmHP7ErVjFzGJHU061eUaCRG2Nd76w6VGlQkApGgnsUDftEQd2vnFL7tL/qavloA5yalJKRwO52N1Ke5s3p2QOwkqJZ9akCoSCVzeSzdnEs4ORWwDIzgvkNDqMl6zYtWVPt/EL9E8W90GmYXcbxfoSh5SnrqivE7fDLRUSiQEEqj+72ozn3lmDzLZeAxRWocgXZ7m7WHaCPfu6lsVBUYSmBhTD5CG/hUJAKQ1RvySGHTutAQ+M4LGTj+5HXtcEpA4L9pTC83MGitK4sdT/RVhGJtOtYAugmLqGTNAkUpJLovoIPeUdzwy6KpCvPlrNDlmphNO59I+ueTmkHm8QEUaI2sBPmYPvDeGF1/YLmaKbpJE0IBakwur0l88CtW3Xker0XJ+zROx89lYXlVW3kZ1QRk94gjzBC1129riGLvcOG6VnFaxjVaEIoSEUy1SBO4sSF9yZTRUmTnl3gDQ05d6VyWJZjFx9RLnSYckJBKpLenNIi5riTzobZVx2xFstNS0psJjPtD3Zkxs9bNws+l5HyQ0Eqht4idm89qUicQqIj7nI1eAAetdjPX9mXaa4o4h33231V7iNnR8F9qnAoSIXRe4FY4BsoFkSGZapJcFWpUbdsWI5roKO5JTahH5PCDjUhFKSSmHlXsbZT44nOZJs8NYHuYJTtyXVdja2l7QyG8qRlksHM+50kKEjlMXSPlja7Pf7K2lLMpV87PWIW2urGAhZqGKc0l8Eyu5UEKEiFEb7KmnwL0Vj12s+N427dyH1tO1wn5fxcK+UBumZHv8hTC63VU5dirSs7w9OkRGWAglQGukRB3u8gn1iWHDGfpsN4NjCOuSBinMUwV8V4QfaJzKNe2+ivq6D8TAIFaUF0+1ip0+JlqYXyCnsIp+WdCryhg4btSFH4WRgSvCYQjxRaUmp/KQ0KUpF4rB6VO+Ibb3mT0mZoL5ym1yYhcwPModtt7ol09Wax6bFWgMrtMuVCQSqJuN1jmpVb3UpP+DadbjmZcao0liKKj5+esIXhz7bSJaJEpYaCVAbDFHazco9cE6YNDx5+6Fem5TF+JefEJbJujfmMDBOJ7VfmfUiju5SEIb3jzA4KUhn0BmvF9xMbw5TCcmW2hqNzhsNPzLNbo+Y+LvYNC6wiwcMcfQKAy0gZoCAVyRziiOJVvY4ID4z5Dm1HDIfJ1eR5Vzoqlk4tmePWpWFGO5IaClJ5LK17pF3ryjzt1rPyHiPupG5QFDxauPKTei7I0h51QkEqhkV0zlFHx2O7YroQL8l3RCe05ovgusZj75haHBO+zY6UDgWpAFLMXOfu6mEzS913S3eKdNzbG36+6dypoWVXqkjquLXek2YTxdAvYLWFNkY7LZ3jdD4030ORGQoSSYnr2Fkf/L1O39d+ojlyMnSOSFLJtPyJh9/jcnxrpttfehjukdRQZAFQkJZIuT28n2d6FKdXLnngEVMuOVjcns5ZKNMMuglJc3BAc4bVxXrIGVyXEwpSGQR2rV6nKkaN0m0hKhz13XS1m/ZrSx6F90TCMPs3VvjkE3e9KtKVTyhIS6EYEQokxP/YHPVjeNGiXEOPhEbKlbPOwtLoS4w8W2U42G58oxCJlAa9KxhxdyDFKoqYoSCVQVnJud1IsQriTm/KyDMnukFXIqXfNi20GH7p7UX5XfauXLX/zPL0Ju0jdI9SQ0EinkSYHvELTbYsRPeJ6njFW1B3vq8R5UxjMPoOjlDNdpLM7qWzL9/JlyLLg4Ikne4gXUiPbZuUw29TDu2j1mh1VSeJRHBfQnN7QgzpFay3gkUTkrCghtQI6Xqzh4JE3Oi9VAnmvqoatkeTMaf39Ggs7EbjNb+N4HwYGqOPdO/9ZBjHNixzZ37MUFr4NuGI2jzYfSXK7otqzEKgIBEHlF10ZIw8EK26Lcquyp1CZG7j9dtjZM5T7lRsFGzEyaxkvcYbTtZ4atkWnCzFZrgllstISaEgEVt8BoxtyNPgG5OMDQbO4wx/Yigkj7kvcb+R65Vv3TJjmSGuRoppYZsXuFB+JoGCJJo5B9c5LT9MnUw6Qlw19L6F0bnc+FVE78Hg8WRYohsbOqSI3u5h8/gx1CIzFCRx6ERo2o4hMd+2R3vSXcCQofRowlOb8jd3JilKcGmJehsWBjdrrFXOA4512anuVG8R1OPBpkSlg4JUBkucOghffg+vWkfqLKvRMTS4XeTb+MwzqrParMvTcNPcLxUKEknIxkxXOiNujlEOTkuj26Vkhcd6WHTMtevXgTwciAiuQ1414qScKChIEul1kkLdI1M/d3qNQuA8Xm8uyzFKML61ctr0o/qVclI3MDmpvtoAez3mlhUBQxtyQkGSRYmDtRHh8fuh8WfrvyNdK+UmX9uIkmHAhV+S0+ZXFmEL7Thl3YS2ncqLY98eVb2mHKm931ZVBF9w6gAWM9SnpFCQikGmUBlExae55uCrlFega+W1S/rDlgxD4KLgaJQdng3LI22yOskYTJA5QUGSyPp1Z6u15dHjmz+mjZF1Ikppid6p0S/Kw4xONUNl2NelwSaifZqFFqd5XWv8XmZBxygPFCTpjE4ctd0mdYdRrmw5GylD+JZ7+zciJgxexSSD8UK8geZ+rP6Rpp2jxXrHjEyBzLmKeUBBKpvM49Za974Za+MbYatvVxcnt2K6izBgfd10P1caZeWFFWsQra9G0ZS40FsKFCS5eETcZugqPssVHhNreuPrs9fS5njX62ZOkDrIO6AOf++5JoFeY/S0p+uCB4HvNk6P5YfZcZq1A12ijFCQiEh6wWZp1opssQy00x82pUXzVqnuNUcajycwJjCAVpPsx3B5JsYXDgVJNOYBmtypA3N6NJ2JDJyF6yxumS6L2UCHOBntIrx35p5JMKzqOWEfg6DL6ZcXmzgFof1rvlCQSAxsfIgUC9ep7YWrtDhtcXXcD5sUz+kp+3Wv4Vc2JI62oN5Ig4Iki5B1ILkO0ySkuBRjOardmtF16aIs/3j/sJM9SB180asiygbYBCTaBqCsiH0tBRQkcfQe9MU997rFcGP6n5bclsLV7XMVHkN2bb8dUeOHRwjHdygh3tJR79anexK4JykdFCSSABsz7bR+4z3VMywq7rpR7yebRSXKLzclFnlabY8f9R1dbjpdlnlAQRJNoj6WbWajraP/x2Zrggrf/HnkeDBLyxgWCpHoHq//Dg8Bd7qkZqdH+W30UcJU2SVIGBSkmWDf92aQRxxIFpQ1uoreWmeLLKjOJLKelq6naziDrhw/qByEgjQP/NQI0UeR5hGx8jD3qivdevtoq7KbPMXldW3D6Hxjw7RpiizVSML1J7KhIBXMPBdXx3b4142sWhYSPQ7bnjZ0TR+FoTiRkKgH61bZFq6kNr5moqe+ds3LkB2YFAEFqWycOrBytDjxKNJLA5zT2ASiC1uwboDyCqfKgdQeGX0tbdieAQ6bmVQbmUtJ1UPfKxEUJBLGVC5aClsQOJ9mOlDhy+YOT4c+g0a8VbEaFuGFnWZUm9ehTc9DS79MKEgkHua5L/Ou0kTWWcwYNvTduHZ1OFeRBnsXtlwno9yWS4aCRKSSQq7sywyv3fJXvYpct+CEhMZJpa9nGL8dnEObBxSkpaDrrtN0Y2npRy3lx0Olwv3CkOiDFHTXfpJUMMB3WjjFs03NSwoFaSmkGkKOBcVZ4R36NWG4c5R9SNMGSSbNdZvdZaGTNAMoSCQXNpFaOis/3V6iEeI2rD39BJkL/NtjqNQYy1c7vds3hjr2gkfSiROVLxEUpJKIHhQb5y04LZZZYSxJsT3IKWzaG0ODzXFuYUwcyNfFJmV4mkk/6kTRUJAKI+K8RKpRXoy0zZgkKtryML/EbolR3M3u9tXRZri3sx6Gd/vd+vlt7ia+UJCKIVw/sk1oONE0qNuU2pwLYOdnMZywzQ0xGw0wEMunmdzTDYxEl/H8kDlBQVoWaUXIO+3C2BE+jWlx3xCzY9/tjW/4VfW4CPLGFiYMsdqqI7k3dplQkIgbfR9rdKUnRVbsxKgNJWJkEY0bU25gs6kbLlS8ihQbhgwt0WiScsuRefGJzBUKEnGgb9d0tmZ0UqsdBQ+XPVS1qmsJwzwGtzK16Yir4knPZVi4rrqQ+ziRFCmXM2eY0VgMFCRii8NCRZQea1Y1XUvshv99NRo9L110XEAbpiHw1ticTgp7HdvPts8v3gsjoholhYIklPKee5ttRkpSn2lA3LliMalLyKsiIi07pXqdlWtGoqIe157A6C4gNxvlh4JErBjpnAZbZogjQHo1ssR1NikwPs1QpvcF8ZCT1NbWcieZctKvRcgTQrJAQSK+2G9vDAhXqywmVWaF48mmzZfjsY+4e6+93h2VGo+XJjMpUTYoSCQUxaTWqC9l17d9rECgDTUcmSgDt5J0EXfRraphkW80tGF0LrQJ42z/WWnfvUvmAQWpMMw2OuuroOPOpcgzNP1LPWxhwOqU5w/VhWl283QD9GExLan8XHKABpkdFKT5YLlUmwTdpP/YBF2O3hVOAAAfdklEQVSEJeVsLy4KJGItg1OWOKfkOl6xd1jJfKEglYQh9E6cPeqin/LSWVLT6cR4KdGoBc9xPaM6H8O3pEt9GjSY5/eEIbevFQ4FqRiK7wOa9g8tKbonm2zKyKBJ6pcmhLtro6WFnaxtGGRcbMYN9r/qfSt4wlD0ELBYKEjzJPocTr/A2KP7wBK861V6FTsrLvatkjqQX2N9Lv1lSKdbY97FHHiVmjsS/OBJnN4kKyhIJDYp9mkmqq4bCzBqNOUk5UvThl6anNCXgITdF//fxqOVLgpYNihIEpGZpqH1JxSdM2Vi0GE7nAuxCR3Of83NJ+Id4ux1L4Y2tw7MBNG237I9RW2GldlDZwAFiTiwNlLeRejMUxQRiouf0QlpqmFrTkoMWW7rdNmJdiqY4M52nR7z2SkmdVVLniQWFCTiyHCaa3CA6dsE7Vn/HaVGj/jjkudzYuYp6Amq5QU038HeJ5Eutb2ucLIuJxSkeTKfWe+kZ+GarCFWdcRMxIh/UyW8HeKgIMmlpA6jjAgIyR7txcYSl9PIerTGQFNo+fMoBjf6Y9P4xJZrWkUtBRFpUJCEUpIaOeGUt2aM3mpWuyqAKBcw3Lba/2r0yBCtCkkEHuvE/WLo59oLiAYK0kywX6cNr2nnj0r/xljkWJ/XnWaOixCLJfgQylECVYeooCDNijnE/9iZKrXehA/nUyRokEknji4D7SjhxIkT11577cWb395zzz3NH7t27doD/DzwL7tfV9XXga8D/xblXF7iRQmjyOVR8OheN80Ve8ZmXJCGsWE2EedxBckpV5D9kVGm7JItaxleD98MmM6cOXPd9dd3D6iqCtdg60sAcN9LsPsxHAeO1KhXlb8aeAT4PPByAf0ia079hbFr6gaQsrkXuGf130ngy1/+8vq7uq5O7HxV3Vt95jOfefLJJyusDdzXv/71kydP2tbUzBB25wl1H8qhrtf/2R8fpSiX38Z3rDU1Giz46S/hvhqnADyKD3zwg0d+Cs+sGvVt4CHg7cCHI7eSiKOEkfjyKMBDWpmwvcArgecBAC4AD+7bd9VVV91///27du1CY+muwdaXcN8WXvKdlzz2tcfuOn7XW9/61ua3r371qx955JHPf/7zL3/5y4clA1pvo2pNmy4dA2R4SDbI+O3OtQq+FKOPblVVZ86cue6663ofngYOATu+1WPAlXga2AMAeD/wDeB3gVcCT/z4x/v27RttRlLoIaWDHhIJ5T8DJ4ATwF8A3/zmNx966KFPf/rT7bfNyBen8Nijj33wAx+85ZZbLly4AODb3/72Qw899Pa3v/3DH94c+KZ0Bax+5ReTFrfNIZ5f97eGcjbrVW4Uzbkkefg01jN9HwNegL31zid3AkeBK4ArgePHj2drkpI2OoNqlAIKklyKiVA4dao1bfv377/88sufeuopAM3s3OHT6wN/+7W//cMf/vBZ558F4Pjx42984xtvvvnmj370o9vb204VeqZvCZGNBdCzsOnc9Ou7C0gr9t247+LnXoznAnuBT+LMn59pbtWDDzxw/sCBX6xr1PWRP/7j23/jN0RP0pIwGGVHQnn44Yf37NkD4MKFCw888MB3vvOdxuLUqCtUp3H6MA43R972sds++YJP/t2evwNw55133nHHHVdcccWVV155/PjxW265ZaQapXG0tJhmp8fJJYo+m2d5QHYRDVUj/XmdOXNmePh73vOedx9892mcPnzZ4frfoLru+iai4Y6bbrpw4cKhQ4cAPPnkk48BXwRe1a2Cw4sZIX6tYsHIXUnq2Jq9wB5gL/BPF1107ty5m2+++bbbbrviiitWB1bYB2wDFwM/AF4A/F/Udf3ggw++9a1vffzxxwH8yZ/8ye233/7oo486NmF8rcJ214txIWrkYFXF2jbZC1KsjaV+JehKc1pD0vxQt4aE06gP1QAqVI0UVTX+scIlu3d/7GMfe+ELX9gceez6658DfKL747x9JNq2a6KCHhIJ5W5gCzj3ox+9+c1v/trXvnb55Zd3v/3D9/zhwYMHD+Pw31z2Ny960YsqVFVV3dQb9j722Be/+MVXvepV9pWqM36GT+MIGXQPbbrrbzOgkhybTKznz58/d+5c8/euXbt2794N4DRONwW2v6wr/BHw4he/+Fd+5Vfa354DXgv8D2B/vPPwQO5gsXB4WeUi96Hf9JBOAFsA6np7e/ulL33poUOHbr/9dqzGkqdPnz506FCFql6Zmv9XVZcAHwNeCODMGQDHjh17znOe84lPfKJX+Kht9Y+j05zOeL15PCTLusyEbD/yLr+9Kapvhyt/W1tbp06dqqrq9OnThw4f7n37MuDohz70zne+s1vpZcA7gHfFOgtHGGKXFKkmjxQoSKiq+4HDwBmgmZGpVIL0R1V1B7AzQ1fXAO6///7Xvva1jz/++P79+z3miNaHd7/IGUKtKyekPeFMJEjQa5Lykb7nnnuuvfbaiy++uHv8TuaGEyfaTy699NKrr746Zvt94axdOqSaPKLpvRN3hoE52Lt374kTJ7a2ttpvjwIPAV8F9jSCBBzabO3Lquoo8M5OIQAuu+yyd7zjHe9617uCQgxi7csZLSSbWYyeZCFKyy18yl6+hmrnKEWN61WlTrE3bG0BuO8r970AL3jZy16G++67tusVhTQ+GApSOihIchkKUqb0qcY2rf8eNODeqrqw+ns38Hzg4Oqffwk8jtWA98Ybmw8vBa4eFpU05k33Q1e3JrpZd6pI5XkMf6r2F7O13PqwbphD40m3/z9SAcDHlT+jIM0RBjWQaLypk7Xhntdj3724Cvj8M6ifhQeBd29h6yMf+cpXvoIXrMe8Vw9LiThsDyww+oJBGr9KNzpRfy7NjDbXpNmZVNeNDmGlTEehuafJG7Vx6XoKxLeYp4MeklyaXtHtG7q/c7Zp/feg9r1VtbOe1Bz7PVx0CZ66G/Wbd0a8qOujR48C+PjHP55q7miITSjBmJjZRk+Y8XDLxjykyiiWtg+Ja8agqAtRFXAGuF5ZzFEAqO9SfeVYr/3YQtnvej+Uu75bOPSQhFLAEKxtoXJ4DtT76+qKCk+t1Kj7k7uUNsYdne2OVeb6s81o5q6cVINXqUa018qfTzsoQRI3qxmvAGjT79aoj94V50raj+R08+QF9MdZwNRBBVBMZ9iz5+E/+IP7T5+uTgOfRfU7Fb4DXA8AVb3zX0sz5tz5b5ReTrYqOMl3vZlGqK7X7dH+IrZVqoNSGdmokVBLqr99bTTmztzdkSM4csT7EimxvCZCL93coYdEYnLs2LG9e/de9AOcA24Gjn8V+GkA2In5rqqj6eqONIJOCCd5hjS+5pkzuO667t4ArDTpCI4E1sDptYKghzR/Yg70xkb0d9999/e///0fAa8Hjv8cfvKzQDva7ZekWdoJdH3MpC7fVHOfja973lLmdg59tTDvzZUmd8PT554+d+7c+fPndz6tqiN3AXfdle1mUbomh4Ikl/C+0Ri+JJMPRoO1B7jnx/jXD+E32sMbTaprHDlyFyT5ClWFZseM6yVyMdnNXeihkKUoGMRsOj02V/2a17xm74o3vOENUaulxpQEp+wKwCY/mOG3gSX0jKZVIXVdA/efvv/w4cP4Aqrrd0po/KQjR0InYawC58Sgu/Ixt5SVtdqxeb6G01fvQMoI9SwzFKSScNKV3pF+mmTYnDvKocOHa+wENbQ2qAn7LoZgkRvJALtylbQx5WFtmI89bS5CxnNhcN0kzOV5nR29pAzDHA1u71/QfGLfDJ9ycmWpCarOZcON6wVUHz+a4c06H5L6XQ/dA3r+x4ROpFdail7+IY82Oz3Aw+jw4Z6k9lswWUMC6CEtCFcnSWdP1ysulma0++1UfVhnELsGaKQAcaM39Q01bLgxtN9eq/xUzX0khMbid6trt3xJnZ4lgVCQCqOdRpBiHAPnNJwsi/kAvwvSbuAfk5yRC244kXQRg03CusHOXFlPiCUeS5We9YgbWJAWCpJ0hhPZ7E6AtZK5iIFC7NPJv6FAe7dmhYgnJJHXMvS23X4dZ+mU5IGCRGST1y6063O9TybAxb4Xbz2Hu7JS1eOmNCHhqcQDChJxZHTRpTnAow+nXhiwLrNUAxRyAeOuBkUMiutlkrVvw4ruHIP5zirnw3U/KfUhkQ0FqQC8dyAlGdx1Fl2aaoJKM4Q7uzdppD3dwIpw8euVoNlpNH4LdEEio6Q3iL0xhzmzeDQSVGHfbMrMtFCQSC7iOkCWStYeNtGGkrUmucY9T82w2SP6agxAj4KncpNyoCAJJcpILdBJ4gR6FHYu4+Dzqg2T0/wsZaNGMGSXiJ9aouNfblwoSfJM8kBBmjnKTbV+Pzdh46NYVGZ7ZFxcB93uhrLnamwsVPiZXevNs15lj4S/j/tJ0QmLtSOlQEGaP+Fbl6T7SdY7//NVPfIjxc5cILFPYHc1+vda5n3nxN1MoSAtghA56UVChxTk/dNxRG3dVzbGplWTt3xq+ottK0aTJJF5QEEiVkzoISnWWpajbRmY5HwtK/KdaiaFQkESjeiuqDNkidYzdGswjsWpPxcWb+3cBglNMuP1VBSZA4kEQEEi0zFmpELfJDQ/Q5Zn62tEwmbYFPdXxj2lRiaCgiQd0U7SpGyEe4m6RDkbk2HCLewdHM7YZ4KwP54UAgWJxEZSPJtPgXkMnNKkBtpZv3d8FGHQqUDLgIJEhGIzEtfuiRl7+xHRYd5mFOQe8eKTMShIZDosMl2WPV0ZfVyfZdOS4co73w6nLdUC7nXxj1zhUJBEM3wZEiEKYicysH0L11RykqYu3QvLuweAEQ0poSAVgNBR27RNCt+oOy3KqxdySc2/dVeO6R85Yc4TyQAFiTjiaiYCzIr/rB3tl0hEvNlWw+iTRvcoAxQkEgON6mhf4lcKgYN0b+NlU680y1hV5hR5GFrzeD5QrFkEobMRi4GCVDDTD9n06xbrtgVvjZz+NJMy7cRUuOJa3N+kVr59PEJqoQ4JgYJE+sTqnMNC+iXbWcNudtfh36GUuFDh0ebuVieb7UrRL0tIm02lrp+oOYRlLh4Kkly6Qz/dMdE7YRtohFivq+gUUgeoXXwpykaJmhcPxQtzx36w/ntsUSfbk9DtFyQdFKTiiahJbTn+1t97K6tt8XkNul914QokSreMi0OBkYHeD4Pyh35Pl+UbLOl+ZYCCRLSIHhKW6XYoNH7axieu3cGIm5+0Mm83cYWCVAC6Lp1h0lxRRfN3gFCty5RgWTJY5I3appt4dLLpsXfaxr3OuseeTkzpUJDkYu+gJFWmKdeKyx8XB70+w4aSL5H2CkxxIhQzCVCQSDCSu/F0r0MdNXCplD7WKXv8tmR1NFBkKE2ZUJCkY7ZZedwX7cTdhK2SbR3GT799L7s5ZKBcxk5w/IlynDPMsA+JypQaClJ5RIwvmpiZDqidcX2PkW4vl82vmuOb/4dc87B7J+pZtXnLSZ6WEApS8USUIv+iBubJUJRDFTQEo5RwiYZLoeMPQO8nY8crHqrYwx26RxmgIBVJESEMyg22uYfG05oPb5sYYkwnPGWN66aQCrNTqHP7NCneRflbJAQK0hwQu2Fox0hUFYCmfTQcO7SBD1M3ZI1ZBV01MuKN1vjZne+TXEXqXH4oSKLRKc3Qj4nYc4aeTZSeqShhvr195wbZHZq6MUmqm/reZZYKpg7KAwWpPDJ0jO7+Tdh0/qnNUxATxVZwAG6F9yUKu7bUnkmgIBEtbuZywg4sMlrPvBo3okYWIeNWR8agagO4Y+E9vsl1c5X+EAcQGaAgSWfCvEFxkN/CZHSNWnf+E9lnnKzYbFKv5RGaLXLcoKOMzjU7KEhFIm46O1ZjirJZNqyNWjdCTM6pGV71u9lIn3RHZd5NLhdNCAWJ+DKbHhtoK8s0uwYMqpPbL5exvCfXqZ0dFKSCET1rJ3mzUekSknKFaYInqvTbQeJBQSoVubMKEW0KTVVe0qpRCXeQztC0UJBIDDLubunJcDG2Q0g7exo/eQJcmYOqAcU8ZoVDQSqPdhAX6CQ5bDNSMkXA1XAIn2lIa2hwrKrpDg7JvvsVqmeJapQNClLZRMm3X8o0xbrNg7QxbnNN4s90jYdKBZxd3a1xkqs0fFcFdXpJUJCKJG44g6enlcFSWGdlFR3f0aVw87pxkSc5F1WlEUdU0TZdES8oSOWRIpwh1KDbvF8nfbqjMjRpWgKuT5LLO3B5negqh+F1J6uqRhrPh2dyKEilEmh/lelZ8xn04nq+ZYMnem2ELgOvd4HKKkILtLw41i8PtGlSe4z9GI7u0YRQkGZOaORCjBbEL1PIts0MBNhln0vRnbBaFbL6Jn08nvJRMc7TmkvtqpFl+5mmYVooSEXi1Gd0fTLUSQqbbHFlJmITu/0psirYrqNMci+sBWYOT8vyoCAVjI3FSW7Hu0vcwwAtxxdRJ8FupijDrFd04t/99G+9S0SUh7x1j8o69zlBQSoV14kFZU+beCXJkdG2+bV87QoMpMu5wIzXText0jLaWpdZNftjyrtQC4aCtCAki40l6lPoJtL2LdahOi8qZSNLvhdyoHs0GyhIZRM+a1dc/uauaxjLDI1WF1JRfzEm/4K5zTUvZBmf7tG8oSAVSdPNPMKBDBN3um+b+rpH64pWf5jA0vVzTIwdHV6dt11LMceYnJAmOcV2x6jR/7W8wceT6FCQSsVJk0ZNqmt0rLJBynKTWrd6GOlnvYtFQcYQuFWFad3TncIL8X48UOY29FtHZLS3BChIxWPZ/VrbZzCCpQ4P8waghxJLocePnfl4P673OfvLVQQUpFIpdQG2uAb7Ynl3zGEazREOP1TWbnPNC7kvNiuIfqkWyutKc4SCVCR+0ws2TpLul4oPXZfKRXZ4f10PPLUY/tzQOk+fmMNpV7VnDSOzoMMPRy9L9/Mix3lzgYJUMJbGtNsbE06Uj4pWrDKjHLzxuyTbm/LQj++Q2s4RUg5cnNLZcRlpWihI86cnQg42y8NMiHeJlHTjDHtfeauRcqet9kjrQrst2yiinKudE6d0dgxtmBwK0lIQt+Y0oXSpqtYNoqe5YnJuU4+4d02e6ZfVR5YHBalUWoGx70KRNcm8XalMQtfVXGoqaJJNnWbC7pfrv7OfKd2j4qAgLQL/cIYQIvVtz/lGaaw2SGWNOwjWg3xpJlIuHRX82CwMClLBJB/QhXTjGPsxhz5EQV4FoNggldZDjUphl3oTv01IlK7JoSAthUROksN4v6o2bKhdgqJesd2VHv9TmM7oiLN3GhdK+4R0gvoinEn6q0GNKQsKUtk06hK0B9MSleXqZbFEVINrTghrPoCEUJAbp8R1so7ukRwoSEskRfdTmIDEGX12zmLzo0R1BTFhq9JUXYTtLqKRpAcFqXicel3qkaBhQ49fUdGa6re8X+a2qhCS2/Huitrqj+g1uq6tUr2EQEFaHB7x4h7ld/8dvYpuXTOINd8gs3BOaoW7870p/HUbGO0tCgrSzFH6Kz49UNXDdaqmVTtHu8mZfYE435FNlyjDvXR9YPiMyYGCNH+UZj11D8ymJZXA0DUlKbzGNC8/9HzlnV2GcmkebRkPz2KgIJEg8ixK6SIXHKr2ayGt1QrnpUGd8MTLFkjmBwVp5qReMYLHxJ2+IF35SusW64yk5K9b1Z37h4qSFDO61AySAQrSUphEk1IQN/WOstlBgYKzCMyLeysN06p0j0gXCtL88VMLV7s/DPhWVBrD9MSxX8ZsoUk23qYwu11XZtOniXuVmhJHG+NevMMVnv7dgyQ9FKQF4apMrna5m9cHcQ3HFDZIfoyfbfRBfqetV+NgAtDJB+0e7LHByL4iMjkUpEXgkcjO4yftDx1aprQvYsxHRE3KmVoJstXU1THy3q7EPUbFQUFaCiEWSrJ1E8fmVer5i7HcR0G3wy7a2+8dIiGnKegSEWsoSIvDw0ly+lUZZDkdw/avEIcp342IGrnn+pPeafr5VbN6aBcABWlBeIhKkZpkt2SS9HRG9WYnlr1tatKr2l1MylyjL1HUiBQHBWlZeEypp9WkSQ1H6lD4FMUugVhqRGUqDgrS/OmN1sVpkhOxA8aG0eqdqkQGaDV5K1YvRNcfNdG0XoyXplONFgsFaRH0VMSjoxYzX+eOLp5442TL2e4aV0eVY5dsj4H3IzfXZ3X2UJDmT6slgb3UOxDcj54pdKjON5LQ41dBdNP0jXk8SpQbdEwnYln+WERctrvvEeSdrEUkBxSkBRFLk5DefA/buVOv5uj13zJMks2l3jkgYJdMV4oUdSW7LOs5w9glNwQ+pVSmcqEgLYKIPs1UXX3E+MqjLzab6yLwPovBr2RdjeDGBEpRt4SCnhbSQEFaFgUvBXU8pAlOIUYcc6cwudd/PYvoe8CEDKMhmKahLChISyHzClBkButJ8U8h8byftrUCb8Ro8J5IK+8dm0fkQEFaEAVJkVtTSzijCWguSyMedkET5mu+/pYXnKSBgrREilAmcyNHTsH6daVpkX2FrejNNMqLHxnCvEHlQkFaFpNIkXcA9zqob/PTWA0Tx9TmvoiRihmqUdFQkBZHrG1JrpW2fzvFPuULrpNvvFLLVav9nRGE+IuyAUWodChISyRz9JEyT4THO5b8t8qmY0KfxrLqwG3CMZ6T/OHXVKZCoSAtFJvumtSOeOyxTZ2sIRPJNCxJZHmvnLBcEklFgpN1M4CCREzEmtlTlpPWfIzZ/WmztKnxrX0o7ZPvCZ2qAVSjoqEgkRHiahIGRmqqOAtdlra2VTnbY4W+ScPW+oh9pFNW3uU87hHVqHR4/4iJFNMgyjx10Z5D5ZqHR3XpFocilTzigjjVEu9kldc25P5aelqTe4QkCvSQiIkU+R2GrtJkY9upw6wD8bxiKRexIqqR0/oTpWgeUJCILXE1CRnSu/ltqi0Bge3XJmj3VaP2hwJPliSCgkRG6JrvuKZ8QitTd99FJJwC3bjACTQq0GKhIJFx0mlSfJyCyA2bbISfphFnPYgazuBWtaqQXrhgoXeBeMCbTWxpzdzMbMTajHY/9LCqiVwZ40uVkG3+047AZlCNFg49JGJLd3l5TpZCeRryz64nTRu5FVK+zlWH8uV4riVQjRYOBYm44Z12KGlg7rBJZdgyG6fK4kSmPdkod5ZqREBBIh6ETMWkkCVdtHHzR1q7FjElYFWVErPQZaqNR2SWUJCIlog2vbufCVmMjmfQsM2RQl625EtEbzJWqHc7DxzSGDID6BcTE3FXjNJNy5iLimzp7AVJOSNnDO0z/TAMw/DC70bEzbjBOToCekjEhlij13rwHopYUmFWzejZj6JhDj2PR/RzH44tEHYflb8VEjpIskFBIiaGU21RNAkr+xJRKiae8/GuNHFrR6+Jx8XvpVEwl+8NXyexQChIZITou2KHVibWrGBvR04qK1aOcUw0k9mNtEwXNkkRWiC868SKRGm/4R19YF1+S2D6AN1XEdscvRbdJZUcPpD6qSCS4c0mtiSaQpG/TpAhYsJmYs2vFqXIybzO4G6kxcP7TRxY4LS+5ZlmCDBbwjVnku+Fs2vqBpCS6AYO+OVrKA7LMw1JYEE1aqAaEd514oz3grbkpQsz6abUkHkTlVSoRgQUJJKNpNmD8pAutCFP0IRYZvBskChQkEg+8uxfIdPiEaWSKfEgEQ8FiWQl+g5/IgdGbJNA+MSQ3MTNgUYkMBxY8J4SD/jQkAmg/ZoTHGGQWPC5IZPBXZAzgGpEIsJHh0xJbzVbrC2L+A6hOcEVQRIXJlclU9ILuhOIzshGDwwzXwGBVp4xkyQ6QgekZH4UarNGnbbw87IpQeDVk5+EkBQHBYnkoOidjzYTid6TjUwdREgLn3KSiaJneEYn6EIEyVCszQGEzAYKEsnHDNbAdSs9TB3UwJwLJAQKEskKo4RnyVCHeFuJB3xoSG6oSXNC6ebyhhI/+NyQCWCmhnmgvGu8lcQbPjpkMmLl4ky0rjNXYr3UnGpEosONsWQyuoHggeXojGO3ooVjuBoet0A576osnBB7OJwhM8FgEBduK82n7+HTzCBaksiEgkRmhdkP8H7aez5EQb1Gd9Yh7mO5V4MIh1N2ZFYok+OF65Byeiqw8AzoJtYQ1mzJp0yKhh4SmT/ec0qWSYP8Cu+VoCS8e9KbIQVBQSLERLpEdjbOCoPWyKLglB0pklixy6M0IXzRy7dUGl0AISGzhIJESkUXvhzdfI++tClFnm/GrJMFwsEXKRVz/FgRD/ZscqoSEgUKEikY894jPttK6HsRsbDTkuIxvGWcj3eDMk6d14dIg2tIpHh6KYhaI8uIABQ1gUnI0rsrmR+l7FpNTfSMQYSkhg8lISOUuOhi0Bv6TEQsnLIjRI3yLajdAwqy6fQaSRHQQyJEzeikVlxXI/pbnShCpDgoSISosdSbwMWY0flAzrCR5cApO0LUdIP3EumBjZh1cxcRMm8oSISYGH2tbVLfhe4RWRScsiNkSszeD7snWRQUJEIIISLYNXUDCCGEEICCRAghRAgUJEIIISKgIBFCCBEBBYkQQogIKEiEEEJEQEEihBAiAgoSIYQQEVCQCCGEiICCRAghRAQUJEIIISKgIBFCCBEBBYkQQogIKEiEEEJEQEEihBAiAgoSIYQQEVCQCCGEiICCRAghRAQUJEIIISKgIBFCCBEBBYkQQogIKEiEEEJEQEEihBAiAgoSIYQQEVCQCCGEiICCRAghRAQUJEIIISKgIBFCCBEBBYkQQogIKEiEEEJEQEEihBAigv8PPzkYsfCZg08AAAAASUVORK5CYII=" }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "view([90 -90]) % Bottom view" ] }, { "cell_type": "code", "execution_count": 76, "id": "10d46a0f-f19f-4cf1-8eb6-f9f71ae61c21", "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "view([80 0]) % Front view" ] }, { "cell_type": "markdown", "id": "a10e3a89-40b4-4dec-9c81-4ee76d79226d", "metadata": {}, "source": [ "At this point, we have coregistered the sensors locations with the digitized headshape.\n", "We computed the transformation from sensor space to digitized headshape space knowing which points from the HPI coils should match which stylus points from the laser scan.\n", "Then, this transformation was applied to the sensors.\n", "As a sanity check, lets plot the sensors, headshape, HPI coils locations and the stylus points.\n", "`ft_transform_geometry` does not allow us to apply the transformation to the HPI_coils position themselves, we will need to apply the transformation differently.\n", "We propose two ways below" ] }, { "cell_type": "code", "execution_count": 38, "id": "29ac4ba3-59d2-4c6a-bae1-ff7862d0b2ce", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rotation matrix:\n", " 0.9602 -0.0355 0.2771\n", " 0.0384 0.9993 -0.0051\n", " -0.2767 0.0155 0.9608\n", "\n", "Translation vector:\n", " 16.7548\n", " -5.3949\n", " 42.4524\n", "\n" ] } ], "source": [ "% Extract the first 3x3 submatrix corresponding to the rotation matrix\n", "rotation_matrix = transform_mrk2laser(1:3, 1:3);\n", "\n", "% Extract the translation vector corresponding to the last column and removing the last element\n", "translation_vector = transform_mrk2laser(1:3, 4);\n", "\n", "% Display the results\n", "disp('Rotation matrix:');\n", "disp(rotation_matrix);\n", "\n", "disp('Translation vector:');\n", "disp(translation_vector);" ] }, { "cell_type": "code", "execution_count": 39, "id": "2532e9c6-9853-4f7c-aa9d-25c8b0e4b288", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
p_coils_t = 3x5 double\n",
       "  104.4093   -1.9115    1.9115   87.3524   97.7621\n",
       "    4.7914   74.7645  -74.7645   41.7716  -27.7891\n",
       "   21.3170   -0.5097    0.5097   24.0880   25.6129\n",
       "
" ], "text/plain": [ "p_coils_t = 3x5 double\n", " 104.4093 -1.9115 1.9115 87.3524 97.7621\n", " 4.7914 74.7645 -74.7645 41.7716 -27.7891\n", " 21.3170 -0.5097 0.5097 24.0880 25.6129\n" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "% Apply the transformation to the transpose of p_coils\n", "\n", "p_coils_t = rotation_matrix*p_coils'+ translation_vector" ] }, { "cell_type": "code", "execution_count": 40, "id": "c003ec2e-7545-4ab9-abe4-75845d3c9646", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
p_coils = 5x3 double\n",
       "  104.4093    4.7914   21.3170\n",
       "   -1.9115   74.7645   -0.5097\n",
       "    1.9115  -74.7645    0.5097\n",
       "   87.3524   41.7716   24.0880\n",
       "   97.7621  -27.7891   25.6129\n",
       "
" ], "text/plain": [ "p_coils = 5x3 double\n", " 104.4093 4.7914 21.3170\n", " -1.9115 74.7645 -0.5097\n", " 1.9115 -74.7645 0.5097\n", " 87.3524 41.7716 24.0880\n", " 97.7621 -27.7891 25.6129\n" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "% Set back the positions of the p_coils to the transformed coordinates\n", "p_coils = p_coils_t'" ] }, { "cell_type": "code", "execution_count": 41, "id": "5705989c-26ec-4120-9a9b-194326f8a0c6", "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "figure\n", "ft_plot_headshape(headshape)\n", "ft_plot_sens(grad)\n", "hold on\n", "plot3(p_coils(:,1), p_coils(:,2), p_coils(:,3), 'bo', 'MarkerSize', 10, 'MarkerFaceColor', 'b', 'LineStyle', 'none')\n", "%plot3(p_headscan(:,1), p_headscan(:,2), p_headscan(:,3), 'bo', 'MarkerSize', 10, 'MarkerFaceColor', 'b', 'LineStyle', 'none')\n", "view([50 20])\n", "hold on" ] }, { "cell_type": "code", "execution_count": 108, "id": "6b61af0f-a707-47e0-bf33-7b26b7a5abbf", "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ "view([140 20])" ] }, { "cell_type": "markdown", "id": "d48fc59b-9750-48b6-af56-34d1ee017376", "metadata": {}, "source": [ "Another way is to use what is described here: https://www.fieldtriptoolbox.org/faq/homogenous/, in which we would multiply the rigid body transformation matrix with the transpose of the p_coils (while adding an identity vector at the bottom row)." ] }, { "cell_type": "code", "execution_count": 81, "id": "89ddac8d-f440-436c-80c9-884a65f136a8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original p_coils:\n", " 90.4042 6.7430 3.9254\n", " -2.9621 80.0952 -46.8581\n", " -5.3097 -69.4416 -44.0606\n", " 74.6782 44.3433 1.6745\n", " 81.5830 -25.5108 6.3767\n", "\n", "p_coils with ones added and transposed:\n", " 90.4042 -2.9621 -5.3097 74.6782 81.5830\n", " 6.7430 80.0952 -69.4416 44.3433 -25.5108\n", " 3.9254 -46.8581 -44.0606 1.6745 6.3767\n", " 1.0000 1.0000 1.0000 1.0000 1.0000\n", "\n" ] } ], "source": [ "% Assume p_coils is already defined\n", "\n", "p_coils = mrka.fid.pos(1:5,:);\n", "\n", "% Create a column vector of ones\n", "num_points = size(p_coils, 1);\n", "ones_column = ones(num_points, 1);\n", "\n", "% Concatenate the column of ones to p_coils\n", "p_coils_with_ones = [p_coils, ones_column];\n", "\n", "% Transpose the resulting matrix\n", "p_coils_transposed = p_coils_with_ones';\n", "\n", "% Display the result\n", "disp('Original p_coils:');\n", "disp(p_coils);\n", "\n", "disp('p_coils with ones added and transposed:');\n", "disp(p_coils_transposed);" ] }, { "cell_type": "code", "execution_count": 82, "id": "dc7fb99a-013f-48d1-be20-31f79e59f0eb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
p_coils_t = 4x5 double\n",
       "  104.4093   -1.9115    1.9115   87.3524   97.7621\n",
       "    4.7914   74.7645  -74.7645   41.7716  -27.7891\n",
       "   21.3170   -0.5097    0.5097   24.0880   25.6129\n",
       "    1.0000    1.0000    1.0000    1.0000    1.0000\n",
       "
" ], "text/plain": [ "p_coils_t = 4x5 double\n", " 104.4093 -1.9115 1.9115 87.3524 97.7621\n", " 4.7914 74.7645 -74.7645 41.7716 -27.7891\n", " 21.3170 -0.5097 0.5097 24.0880 25.6129\n", " 1.0000 1.0000 1.0000 1.0000 1.0000\n" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p_coils_t = transform_mrk2laser * p_coils_transposed\n" ] }, { "cell_type": "code", "execution_count": 83, "id": "30e0ba57-9b26-4690-8af9-bf1177401e35", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
p_coils = 5x4 double\n",
       "  104.4093    4.7914   21.3170    1.0000\n",
       "   -1.9115   74.7645   -0.5097    1.0000\n",
       "    1.9115  -74.7645    0.5097    1.0000\n",
       "   87.3524   41.7716   24.0880    1.0000\n",
       "   97.7621  -27.7891   25.6129    1.0000\n",
       "
" ], "text/plain": [ "p_coils = 5x4 double\n", " 104.4093 4.7914 21.3170 1.0000\n", " -1.9115 74.7645 -0.5097 1.0000\n", " 1.9115 -74.7645 0.5097 1.0000\n", " 87.3524 41.7716 24.0880 1.0000\n", " 97.7621 -27.7891 25.6129 1.0000\n" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p_coils = p_coils_t'\n", "save p_coils p_coils" ] }, { "cell_type": "markdown", "id": "c31c4a82-35a8-42d8-a4ea-4f3272dcb232", "metadata": {}, "source": [ "Let us view display the order of appearence of each label" ] }, { "cell_type": "code", "execution_count": 123, "id": "a1a1fe44-68c9-4615-bde5-e54d78d93beb", "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ "figure\n", "ft_plot_headshape(headshape)\n", "ft_plot_sens(grad)\n", "hold on\n", "plot3(p_coils(:,1), p_coils(:,2), p_coils(:,3), 'bo', 'MarkerSize', 10, 'MarkerFaceColor', 'b', 'LineStyle', 'none')\n", "% Add row numbers near each point\n", "for i = 1:size(p_coils, 1)\n", " text(p_coils(i, 1), p_coils(i, 2), p_coils(i, 3), num2str(i), 'FontSize', 15, 'Color', 'magenta', 'HorizontalAlignment', 'left', 'VerticalAlignment', 'bottom');\n", "end\n", "for i = 1:size(p_headscan, 1)\n", " text(p_headscan(i, 1), p_headscan(i, 2), p_headscan(i, 3), num2str(i), 'FontSize', 15, 'Color', 'cyan', 'HorizontalAlignment', 'left', 'VerticalAlignment', 'bottom');\n", "end\n", "%plot3(p_headscan(:,1), p_headscan(:,2), p_headscan(:,3), 'bo', 'MarkerSize', 10, 'MarkerFaceColor', 'b', 'LineStyle', 'none')\n", "view([50 20])" ] }, { "cell_type": "markdown", "id": "878766c4-9bdc-4b10-9d0f-237ec8438195", "metadata": {}, "source": [ "Remove the last column in p_coils that was added to match the dimensions." ] }, { "cell_type": "code", "execution_count": 103, "id": "285da26c-0532-46c3-9070-0a4f84881a13", "metadata": {}, "outputs": [], "source": [ "p_coils = p_coils(:, 1:end-1);\n", "save p_coils p_coils" ] }, { "cell_type": "markdown", "id": "c21a234b-ef17-47ba-95f1-924cf2a1940d", "metadata": {}, "source": [ "Let us measure the relative error of coregistration as the average distance between stylus points and HPI coil positions\n", "The correspondance between stylus point order and .mrk order (remind that MATLAB start indexing at 0):\n", "Point 4 stylus is HPI coil position 2 in mrk\n", "5 is 3\n", "6 is 1\n", "7 is 4\n", "8 is 5" ] }, { "cell_type": "code", "execution_count": 5, "id": "15dda250-0a24-4788-8823-046967b84254", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Relative error (in mm): 6.811\n" ] } ], "source": [ "relative_error = norm(p_coils(1,:) - p_headscan(6,:)) + ...\n", " norm(p_coils(2,:) - p_headscan(4,:)) + ...\n", " norm(p_coils(3,:) - p_headscan(5,:)) + ...\n", " norm(p_coils(4,:) - p_headscan(7,:)) + ...\n", " norm(p_coils(5,:) - p_headscan(8,:));\n", "\n", "disp(['Relative error (in mm): ', num2str(relative_error/5)]);" ] }, { "cell_type": "markdown", "id": "a1b88fa7-3dc4-428a-914b-d45071c2e3cc", "metadata": {}, "source": [ "Coregistration: MRI and headshape \n", "---------------------------------\n", "\n", "Now that the digitized headshape is aligned with the sensors, we will proceed to align the MRI with the headshape.\n", "Two strategies are possible\n", "- use manually picked fiducials on the MRI for coregistration\n", "- use an interative algorithm (ICP) to match MRI headshape with laser headshape\n", "- use fiducials followed by ICP" ] }, { "cell_type": "markdown", "id": "f05332c7-fbfc-48b4-bb1f-47e1c7a33f0a", "metadata": {}, "source": [ "**Strategy 1: manually picked fiducials on MRI**\n", "\n", "Remind that the headshape coordinate system has been set as `CTF` which is a RAS coordinate system." ] }, { "cell_type": "code", "execution_count": null, "id": "306f4f33-3746-4786-969d-f7484f7d7f0b", "metadata": {}, "outputs": [], "source": [ "load headshape headshape" ] }, { "cell_type": "code", "execution_count": 49, "id": "8f79dfd7-2173-42c9-95e6-ecc678ac0ec6", "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "The axes are 150 mm long in each direction\n", "The diameter of the sphere at the origin is 10 mm\n" ] }, { "data": { "text/html": [ "
ans = struct with fields:\n",
       "      pos: [1251x3 double]\n",
       "      fid: [1x1 struct]\n",
       "    label: []\n",
       "     unit: 'mm'\n",
       "      cfg: [1x1 struct]\n",
       "
" ], "text/plain": [ "ans = struct with fields:\n", " pos: [1251x3 double]\n", " fid: [1x1 struct]\n", " label: []\n", " unit: 'mm'\n", " cfg: [1x1 struct]\n" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ft_determine_coordsys(headshape, 'interactive', 'no')" ] }, { "cell_type": "code", "execution_count": 58, "id": "dd3afdc9-5487-4498-9222-988fed73dce0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
cfg = struct with fields:\n",
       "         grad: [1x1 struct]\n",
       "    headshape: [1x1 struct]\n",
       "
" ], "text/plain": [ "cfg = struct with fields:\n", " grad: [1x1 struct]\n", " headshape: [1x1 struct]\n" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/html": [ "
cfg = struct with fields:\n",
       "         grad: [1x1 struct]\n",
       "    headshape: [1x1 struct]\n",
       "          mri: [1x1 struct]\n",
       "         mesh: [1x1 struct]\n",
       "         axes: 'yes'\n",
       "
" ], "text/plain": [ "cfg = struct with fields:\n", " grad: [1x1 struct]\n", " headshape: [1x1 struct]\n", " mri: [1x1 struct]\n", " mesh: [1x1 struct]\n", " axes: 'yes'\n" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "the template coordinate system is \"ctf\"\n", "the positive X-axis is pointing to anterior\n", "the positive Y-axis is pointing to the left\n", "the positive Z-axis is pointing to superior\n", "plotting anatomy\n", "The axes are 150 mm long in each direction\n", "The diameter of the sphere at the origin is 10 mm\n", "\n", "==================================================================================\n", "Press \"h\" to show this help.\n", "Press \"q\" to quit.\n", "Click and hold the left mouse button to rotate.\n", "the call to \"ft_geometryplot\" took 1 seconds\n" ] } ], "source": [ "load mri_init mri_init\n", "load headshape headshape\n", "load grad grad\n", "\n", "cfg = [];\n", "cfg.grad = grad; %structure, see FT_READ_SENS\n", "cfg.headshape = headshape %structure, see FT_READ_HEADSHAPE\n", "cfg.mri = mri_init;\n", "cfg.mesh = headshape;\n", "cfg.axes = 'yes'\n", "\n", "ft_geometryplot(cfg)" ] }, { "cell_type": "markdown", "id": "c2ded2a3-2aec-4f8d-8f13-9992ec4fa414", "metadata": {}, "source": [ "Since the headshape and MRI are both in CTF coordinates, no further adjustments are needed." ] }, { "cell_type": "code", "execution_count": 55, "id": "2cad59f0-656d-4868-868d-c0bc98212174", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "the input is volume data with dimensions [256 256 256]\n", "voxel size along 1st dimension (i) : 1.000000 mm\n", "voxel size along 2nd dimension (j) : 1.000000 mm\n", "voxel size along 3rd dimension (k) : 1.000000 mm\n", "volume per voxel : 1.000000 mm^3\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning: defaulting to \"\" coordinate system" ] }, { "name": "stdout", "output_type": "stream", "text": [ " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_volumerealign.m' at line 296\n", "\n", "the input is volume data with dimensions [256 256 256]\n", "voxel size along 1st dimension (i) : 1.000000 mm\n", "voxel size along 2nd dimension (j) : 1.000000 mm\n", "voxel size along 3rd dimension (k) : 1.000000 mm\n", "volume per voxel : 1.000000 mm^3\n", "creating scalpmask ... using the anatomy field for segmentation\n", "smoothing anatomy with a 2-voxel FWHM kernel\n", "thresholding anatomy at a relative threshold of 0.100\n", "the call to \"ft_volumesegment\" took 1 seconds\n", "triangulating the boundary of compartment 1 (scalp) with 20000 vertices\n", "the call to \"ft_prepare_mesh\" took 1 seconds\n", "doing interactive realignment with headshape\n", "Use the mouse to rotate the geometry, and click \"redisplay\" to update the light.\n", "Close the figure when you are done.\n", "the template coordinate system is unknown, selecting the viewpoint is not possible\n", "\n", "==================================================================================\n", "Press \"h\" to show this help.\n", "Press \"q\" or close the window when you are done.\n", "Press \"v\" to update the light position.\n", "the call to \"ft_interactiverealign\" took 126 seconds\n", "doing iterative closest points realignment with headshape\n", "the input is mesh data with 20000 vertices and 39996 triangles\n", "the input is source data with 20000 brainordinates\n", "the input is source data with 1187 brainordinates\n", "interpolating distance\n", "the call to \"ft_sourceinterpolate\" took 0 seconds\n", "the input is mesh data with 20000 vertices and 39996 triangles\n", "the input is source data with 20000 brainordinates\n", "the input is source data with 1187 brainordinates\n", "interpolating distance\n", "the call to \"ft_sourceinterpolate\" took 0 seconds\n", "the call to \"ft_volumerealign\" took 132 seconds\n" ] } ], "source": [ "%% check how good the co-registration is\n", "cfg = [];\n", "cfg.method = 'headshape';\n", "cfg.headshape.headshape = headshape;\n", "cdf.headshape.icp = 'no';\n", "[mri_aligned] = ft_volumerealign(cfg, mri_init);" ] }, { "attachments": { "e19de2ec-f10a-4edc-b998-a421530a9125.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "c6aeab94-8bf0-4b84-818c-f617e99c5431", "metadata": {}, "source": [ "![alignmentMRIHeadshape.png](attachment:e19de2ec-f10a-4edc-b998-a421530a9125.png)" ] }, { "cell_type": "code", "execution_count": 56, "id": "4262651b-722c-4c9e-9b32-5c7ac985fed6", "metadata": {}, "outputs": [], "source": [ "save mri_aligned mri_aligned" ] }, { "cell_type": "code", "execution_count": 57, "id": "12390ed1-908a-4297-9d7e-8ac8ee4c347d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
cfg = struct with fields:\n",
       "         grad: [1x1 struct]\n",
       "    headshape: [1x1 struct]\n",
       "
" ], "text/plain": [ "cfg = struct with fields:\n", " grad: [1x1 struct]\n", " headshape: [1x1 struct]\n" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/html": [ "
cfg = struct with fields:\n",
       "         grad: [1x1 struct]\n",
       "    headshape: [1x1 struct]\n",
       "          mri: [1x1 struct]\n",
       "         mesh: [1x1 struct]\n",
       "         axes: 'yes'\n",
       "
" ], "text/plain": [ "cfg = struct with fields:\n", " grad: [1x1 struct]\n", " headshape: [1x1 struct]\n", " mri: [1x1 struct]\n", " mesh: [1x1 struct]\n", " axes: 'yes'\n" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "the template coordinate system is \"ctf\"\n", "the positive X-axis is pointing to anterior\n", "the positive Y-axis is pointing to the left\n", "the positive Z-axis is pointing to superior\n", "this returns a light skin, you can also explicitly specify 'skin_light',' skin_medium_light', 'skin_medium', 'skin_medium_dark', or 'skin_dark'\n", "plotting anatomy\n", "The axes are 150 mm long in each direction\n", "The diameter of the sphere at the origin is 10 mm\n", "\n", "==================================================================================\n", "Press \"h\" to show this help.\n", "Press \"q\" to quit.\n", "Click and hold the left mouse button to rotate.\n", "the call to \"ft_geometryplot\" took 1 seconds\n" ] } ], "source": [ "cfg = [];\n", "cfg.grad = grad; %structure, see FT_READ_SENS\n", "cfg.headshape = headshape %structure, see FT_READ_HEADSHAPE\n", "cfg.mri = mri_aligned;\n", "cfg.mesh = headshape;\n", "cfg.axes = 'yes'\n", "\n", "ft_geometryplot(cfg)" ] }, { "cell_type": "markdown", "id": "becea8a5-3c44-45b8-aa7f-2cfd0378e449", "metadata": {}, "source": [ "A good alignment is visually seen at this stage from MRI, headshape and sensor space." ] }, { "cell_type": "markdown", "id": "1394802d-12cc-42dc-b798-b1a68140df2e", "metadata": {}, "source": [ "**Strategy 2: Iterative ICP to match two meshes**\n", "\n", "This is not an exact operation since ICP is an optimisation algorithm and can fall into local minima rather than the needed global minima" ] }, { "cell_type": "code", "execution_count": 67, "id": "fa4eaa19-a54a-4fcf-b1cf-853e84b24ea9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "cfg =\n", "\n", " []\n", "\n" ] }, { "data": { "text/html": [ "
cfg = struct with fields:\n",
       "       method: 'headshape'\n",
       "    headshape: [1x1 struct]\n",
       "
" ], "text/plain": [ "cfg = struct with fields:\n", " method: 'headshape'\n", " headshape: [1x1 struct]\n" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/html": [ "
cfg = struct with fields:\n",
       "       method: 'headshape'\n",
       "    headshape: [1x1 struct]\n",
       "
" ], "text/plain": [ "cfg = struct with fields:\n", " method: 'headshape'\n", " headshape: [1x1 struct]\n" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "the input is volume data with dimensions [256 256 256]\n", "voxel size along 1st dimension (i) : 1.000000 mm\n", "voxel size along 2nd dimension (j) : 1.000000 mm\n", "voxel size along 3rd dimension (k) : 1.000000 mm\n", "volume per voxel : 1.000000 mm^3\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning: defaulting to \"\" coordinate system" ] }, { "name": "stdout", "output_type": "stream", "text": [ " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_volumerealign.m' at line 296\n", "\n", "the input is volume data with dimensions [256 256 256]\n", "voxel size along 1st dimension (i) : 1.000000 mm\n", "voxel size along 2nd dimension (j) : 1.000000 mm\n", "voxel size along 3rd dimension (k) : 1.000000 mm\n", "volume per voxel : 1.000000 mm^3\n" ] }, { "data": { "image/png": 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" }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "The axes are 150 mm long in each direction\n", "The diameter of the sphere at the origin is 10 mm\n", "The coordinate system is not specified.\n", "creating scalpmask ... using the anatomy field for segmentation\n", "smoothing anatomy with a 2-voxel FWHM kernel\n", "thresholding anatomy at a relative threshold of 0.100\n", "the call to \"ft_volumesegment\" took 7 seconds\n", "triangulating the boundary of compartment 1 (scalp) with 20000 vertices\n", "the call to \"ft_prepare_mesh\" took 1 seconds\n", "doing interactive realignment with headshape\n", "Use the mouse to rotate the geometry, and click \"redisplay\" to update the light.\n", "Close the figure when you are done.\n", "the template coordinate system is unknown, selecting the viewpoint is not possible\n", "this returns a light skin, you can also explicitly specify 'skin_light',' skin_medium_light', 'skin_medium', 'skin_medium_dark', or 'skin_dark'\n", "\n", "==================================================================================\n", "Press \"h\" to show this help.\n", "Press \"q\" or close the window when you are done.\n", "Press \"v\" to update the light position.\n", "the call to \"ft_interactiverealign\" took 13 seconds\n", "No extrapolation!\n", "No extrapolation!\n", "No extrapolation!\n", "doing iterative closest points realignment with headshape\n", "the input is mesh data with 20000 vertices and 39996 triangles\n", "the input is source data with 20000 brainordinates\n", "the input is source data with 1187 brainordinates\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning: could not determine dimord of \"icp\" in:\n", "\n", " fid: [1x1 struct]\n", " label: []\n", " unit: 'mm'\n", " cfg: [1x1 struct]\n", " interactive: 'no'\n", " icp: 'yes'\n", " distance: [1187x1 double]\n", " pos: [1187x3 double]\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 789\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 739\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdatfield.m' at line 45\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_datatype_source.m' at line 266\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_checkdata.m' at line 293\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_sourceinterpolate.m' at line 168\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_volumerealign.m' at line 782\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning: could not determine dimord of \"interactive\" in:\n", "\n", " fid: [1x1 struct]\n", " label: []\n", " unit: 'mm'\n", " cfg: [1x1 struct]\n", " interactive: 'no'\n", " icp: 'yes'\n", " distance: [1187x1 double]\n", " pos: [1187x3 double]\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 789\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 739\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdatfield.m' at line 45\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_datatype_source.m' at line 266\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_checkdata.m' at line 293\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_sourceinterpolate.m' at line 168\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_volumerealign.m' at line 782\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning: could not determine dimord of \"icp\" in:\n", "\n", " fid: [1x1 struct]\n", " label: []\n", " unit: 'mm'\n", " cfg: [1x1 struct]\n", " interactive: 'no'\n", " icp: 'yes'\n", " distance: [1187x1 double]\n", " pos: [1187x3 double]\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 789\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 739\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_datatype_source.m' at line 268\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_checkdata.m' at line 293\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_sourceinterpolate.m' at line 168\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_volumerealign.m' at line 782\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning: could not determine dimord of \"interactive\" in:\n", "\n", " fid: [1x1 struct]\n", " label: []\n", " unit: 'mm'\n", " cfg: [1x1 struct]\n", " interactive: 'no'\n", " icp: 'yes'\n", " distance: [1187x1 double]\n", " pos: [1187x3 double]\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 789\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 739\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_datatype_source.m' at line 268\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_checkdata.m' at line 293\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_sourceinterpolate.m' at line 168\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_volumerealign.m' at line 782\n", "\n", "interpolating distance\n", "the call to \"ft_sourceinterpolate\" took 0 seconds\n", "the input is mesh data with 20000 vertices and 39996 triangles\n", "the input is source data with 20000 brainordinates\n", "the input is source data with 1187 brainordinates\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning: could not determine dimord of \"icp\" in:\n", "\n", " fid: [1x1 struct]\n", " label: []\n", " unit: 'mm'\n", " cfg: [1x1 struct]\n", " interactive: 'no'\n", " icp: 'yes'\n", " distance: [1187x1 double]\n", " pos: [1187x3 double]\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 789\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 739\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdatfield.m' at line 45\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_datatype_source.m' at line 266\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_checkdata.m' at line 293\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_sourceinterpolate.m' at line 168\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_volumerealign.m' at line 786\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning: could not determine dimord of \"interactive\" in:\n", "\n", " fid: [1x1 struct]\n", " label: []\n", " unit: 'mm'\n", " cfg: [1x1 struct]\n", " interactive: 'no'\n", " icp: 'yes'\n", " distance: [1187x1 double]\n", " pos: [1187x3 double]\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 789\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 739\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdatfield.m' at line 45\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_datatype_source.m' at line 266\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_checkdata.m' at line 293\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_sourceinterpolate.m' at line 168\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_volumerealign.m' at line 786\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning: could not determine dimord of \"icp\" in:\n", "\n", " fid: [1x1 struct]\n", " label: []\n", " unit: 'mm'\n", " cfg: [1x1 struct]\n", " interactive: 'no'\n", " icp: 'yes'\n", " distance: [1187x1 double]\n", " pos: [1187x3 double]\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 789\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 739\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_datatype_source.m' at line 268\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_checkdata.m' at line 293\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_sourceinterpolate.m' at line 168\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_volumerealign.m' at line 786\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning: could not determine dimord of \"interactive\" in:\n", "\n", " fid: [1x1 struct]\n", " label: []\n", " unit: 'mm'\n", " cfg: [1x1 struct]\n", " interactive: 'no'\n", " icp: 'yes'\n", " distance: [1187x1 double]\n", " pos: [1187x3 double]\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 789\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 739\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_datatype_source.m' at line 268\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_checkdata.m' at line 293\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_sourceinterpolate.m' at line 168\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_volumerealign.m' at line 786\n", "\n", "interpolating distance\n", "the call to \"ft_sourceinterpolate\" took 0 seconds\n", "the call to \"ft_volumerealign\" took 24 seconds\n" ] }, { "data": { "text/html": [ "
mri_aligned_icp = struct with fields:\n",
       "              dim: [256 256 256]\n",
       "          anatomy: [256x256x256 double]\n",
       "              hdr: [1x1 struct]\n",
       "        transform: [4x4 double]\n",
       "             unit: 'mm'\n",
       "    transformorig: [4x4 double]\n",
       "         coordsys: 'unknown'\n",
       "              cfg: [1x1 struct]\n",
       "
" ], "text/plain": [ "mri_aligned_icp = struct with fields:\n", " dim: [256 256 256]\n", " anatomy: [256x256x256 double]\n", " hdr: [1x1 struct]\n", " transform: [4x4 double]\n", " unit: 'mm'\n", " transformorig: [4x4 double]\n", " coordsys: 'unknown'\n", " cfg: [1x1 struct]\n" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%% align MRI and Laser\n", "load mri mri\n", "cfg = []\n", "cfg.method = 'headshape';\n", "cfg.headshape = headshape;\n", "cfg.headshape.interactive = 'no'\n", "cfg.headshape.icp = 'yes'\n", "mri_aligned_icp = ft_volumerealign(cfg,mri)" ] }, { "cell_type": "markdown", "id": "66a5bb14-23e7-44b6-9e39-9d158a0ae4ff", "metadata": {}, "source": [ "**Strategy 3: Fiducial fitting then ICP**" ] }, { "cell_type": "code", "execution_count": 68, "id": "42adb80c-8af8-4bbd-9533-be94e6f603b1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "cfg =\n", "\n", " []\n", "\n" ] }, { "data": { "text/html": [ "
cfg = struct with fields:\n",
       "       method: 'headshape'\n",
       "    headshape: [1x1 struct]\n",
       "
" ], "text/plain": [ "cfg = struct with fields:\n", " method: 'headshape'\n", " headshape: [1x1 struct]\n" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/html": [ "
cfg = struct with fields:\n",
       "       method: 'headshape'\n",
       "    headshape: [1x1 struct]\n",
       "
" ], "text/plain": [ "cfg = struct with fields:\n", " method: 'headshape'\n", " headshape: [1x1 struct]\n" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "the input is volume data with dimensions [256 256 256]\n", "voxel size along 1st dimension (i) : 1.000000 mm\n", "voxel size along 2nd dimension (j) : 1.000000 mm\n", "voxel size along 3rd dimension (k) : 1.000000 mm\n", "volume per voxel : 1.000000 mm^3\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning: defaulting to \"\" coordinate system" ] }, { "name": "stdout", "output_type": "stream", "text": [ " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_volumerealign.m' at line 296\n", "\n", "the input is volume data with dimensions [256 256 256]\n", "voxel size along 1st dimension (i) : 1.000000 mm\n", "voxel size along 2nd dimension (j) : 1.000000 mm\n", "voxel size along 3rd dimension (k) : 1.000000 mm\n", "volume per voxel : 1.000000 mm^3\n", "creating scalpmask ... using the anatomy field for segmentation\n", "smoothing anatomy with a 2-voxel FWHM kernel\n", "thresholding anatomy at a relative threshold of 0.100\n", "the call to \"ft_volumesegment\" took 1 seconds\n", "triangulating the boundary of compartment 1 (scalp) with 20000 vertices\n", "the call to \"ft_prepare_mesh\" took 2 seconds\n", "doing interactive realignment with headshape\n", "Use the mouse to rotate the geometry, and click \"redisplay\" to update the light.\n", "Close the figure when you are done.\n", "the template coordinate system is unknown, selecting the viewpoint is not possible\n", "this returns a light skin, you can also explicitly specify 'skin_light',' skin_medium_light', 'skin_medium', 'skin_medium_dark', or 'skin_dark'\n", "\n", "==================================================================================\n", "Press \"h\" to show this help.\n", "Press \"q\" or close the window when you are done.\n", "Press \"v\" to update the light position.\n", "the call to \"ft_interactiverealign\" took 11 seconds\n", "doing iterative closest points realignment with headshape\n", "the input is mesh data with 20000 vertices and 39996 triangles\n", "the input is source data with 20000 brainordinates\n", "the input is source data with 1187 brainordinates\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning: could not determine dimord of \"icp\" in:\n", "\n", " fid: [1x1 struct]\n", " label: []\n", " unit: 'mm'\n", " cfg: [1x1 struct]\n", " interactive: 'no'\n", " icp: 'yes'\n", " distance: [1187x1 double]\n", " pos: [1187x3 double]\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 789\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 739\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdatfield.m' at line 45\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_datatype_source.m' at line 266\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_checkdata.m' at line 293\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_sourceinterpolate.m' at line 168\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_volumerealign.m' at line 782\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning: could not determine dimord of \"interactive\" in:\n", "\n", " fid: [1x1 struct]\n", " label: []\n", " unit: 'mm'\n", " cfg: [1x1 struct]\n", " interactive: 'no'\n", " icp: 'yes'\n", " distance: [1187x1 double]\n", " pos: [1187x3 double]\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 789\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 739\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdatfield.m' at line 45\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_datatype_source.m' at line 266\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_checkdata.m' at line 293\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_sourceinterpolate.m' at line 168\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_volumerealign.m' at line 782\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning: could not determine dimord of \"icp\" in:\n", "\n", " fid: [1x1 struct]\n", " label: []\n", " unit: 'mm'\n", " cfg: [1x1 struct]\n", " interactive: 'no'\n", " icp: 'yes'\n", " distance: [1187x1 double]\n", " pos: [1187x3 double]\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 789\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 739\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_datatype_source.m' at line 268\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_checkdata.m' at line 293\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_sourceinterpolate.m' at line 168\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_volumerealign.m' at line 782\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning: could not determine dimord of \"interactive\" in:\n", "\n", " fid: [1x1 struct]\n", " label: []\n", " unit: 'mm'\n", " cfg: [1x1 struct]\n", " interactive: 'no'\n", " icp: 'yes'\n", " distance: [1187x1 double]\n", " pos: [1187x3 double]\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 789\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 739\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_datatype_source.m' at line 268\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_checkdata.m' at line 293\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_sourceinterpolate.m' at line 168\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_volumerealign.m' at line 782\n", "\n", "interpolating distance\n", "the call to \"ft_sourceinterpolate\" took 0 seconds\n", "the input is mesh data with 20000 vertices and 39996 triangles\n", "the input is source data with 20000 brainordinates\n", "the input is source data with 1187 brainordinates\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning: could not determine dimord of \"icp\" in:\n", "\n", " fid: [1x1 struct]\n", " label: []\n", " unit: 'mm'\n", " cfg: [1x1 struct]\n", " interactive: 'no'\n", " icp: 'yes'\n", " distance: [1187x1 double]\n", " pos: [1187x3 double]\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 789\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 739\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdatfield.m' at line 45\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_datatype_source.m' at line 266\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_checkdata.m' at line 293\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_sourceinterpolate.m' at line 168\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_volumerealign.m' at line 786\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning: could not determine dimord of \"interactive\" in:\n", "\n", " fid: [1x1 struct]\n", " label: []\n", " unit: 'mm'\n", " cfg: [1x1 struct]\n", " interactive: 'no'\n", " icp: 'yes'\n", " distance: [1187x1 double]\n", " pos: [1187x3 double]\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 789\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 739\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdatfield.m' at line 45\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_datatype_source.m' at line 266\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_checkdata.m' at line 293\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_sourceinterpolate.m' at line 168\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_volumerealign.m' at line 786\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning: could not determine dimord of \"icp\" in:\n", "\n", " fid: [1x1 struct]\n", " label: []\n", " unit: 'mm'\n", " cfg: [1x1 struct]\n", " interactive: 'no'\n", " icp: 'yes'\n", " distance: [1187x1 double]\n", " pos: [1187x3 double]\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 789\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 739\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_datatype_source.m' at line 268\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_checkdata.m' at line 293\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_sourceinterpolate.m' at line 168\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_volumerealign.m' at line 786\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning: could not determine dimord of \"interactive\" in:\n", "\n", " fid: [1x1 struct]\n", " label: []\n", " unit: 'mm'\n", " cfg: [1x1 struct]\n", " interactive: 'no'\n", " icp: 'yes'\n", " distance: [1187x1 double]\n", " pos: [1187x3 double]\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 789\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\private\\getdimord.m' at line 739\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_datatype_source.m' at line 268\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\utilities\\ft_checkdata.m' at line 293\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_sourceinterpolate.m' at line 168\n", " In 'C:\\Users\\hz3752\\Documents\\fieldtrip\\ft_volumerealign.m' at line 786\n", "\n", "interpolating distance\n", "the call to \"ft_sourceinterpolate\" took 0 seconds\n", "the call to \"ft_volumerealign\" took 17 seconds\n" ] }, { "data": { "text/html": [ "
mri_aligned_icp_fid = struct with fields:\n",
       "              dim: [256 256 256]\n",
       "          anatomy: [256x256x256 double]\n",
       "              hdr: [1x1 struct]\n",
       "        transform: [4x4 double]\n",
       "             unit: 'mm'\n",
       "              cfg: [1x1 struct]\n",
       "    transformorig: [4x4 double]\n",
       "         coordsys: 'ctf'\n",
       "
" ], "text/plain": [ "mri_aligned_icp_fid = struct with fields:\n", " dim: [256 256 256]\n", " anatomy: [256x256x256 double]\n", " hdr: [1x1 struct]\n", " transform: [4x4 double]\n", " unit: 'mm'\n", " cfg: [1x1 struct]\n", " transformorig: [4x4 double]\n", " coordsys: 'ctf'\n" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%% align MRI and Laser\n", "load mri_init mri_init\n", "cfg = []\n", "cfg.method = 'headshape';\n", "cfg.headshape = headshape;\n", "cfg.headshape.interactive = 'no'\n", "cfg.headshape.icp = 'yes'\n", "mri_aligned_icp_fid = ft_volumerealign(cfg,mri_init)\n", "save mri_aligned_icp_fid mri_aligned_icp_fid" ] }, { "cell_type": "markdown", "id": "ab146502-f062-48a7-9e00-99bc85ed997c", "metadata": {}, "source": [ "The following window should open, make a sanity check of the correct alignment." ] }, { "attachments": { "51a63be9-0807-4412-a6c2-464c212cf6c8.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "bade68b3-31e3-4e9a-8a89-a9c947817105", "metadata": {}, "source": [ "![mri-headshape-align.png](attachment:51a63be9-0807-4412-a6c2-464c212cf6c8.png)" ] }, { "cell_type": "markdown", "id": "b4a74040-ae5e-4557-a50b-07ae793b9d69", "metadata": {}, "source": [ "Computing the head conductor model\n", "==================================" ] }, { "cell_type": "markdown", "id": "e7bacd09-2114-4cc6-a400-1933c45bf264", "metadata": {}, "source": [ "Segmentation of brain, skull and scalp boundaries given the aligned MRI." ] }, { "cell_type": "code", "execution_count": 70, "id": "97f3a1b9-62b5-4467-8d6c-606d0602d8f8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
mri_aligned = struct with fields:\n",
       "              dim: [256 256 256]\n",
       "          anatomy: [256x256x256 double]\n",
       "              hdr: [1x1 struct]\n",
       "        transform: [4x4 double]\n",
       "             unit: 'mm'\n",
       "              cfg: [1x1 struct]\n",
       "    transformorig: [4x4 double]\n",
       "         coordsys: 'ctf'\n",
       "
" ], "text/plain": [ "mri_aligned = struct with fields:\n", " dim: [256 256 256]\n", " anatomy: [256x256x256 double]\n", " hdr: [1x1 struct]\n", " transform: [4x4 double]\n", " unit: 'mm'\n", " cfg: [1x1 struct]\n", " transformorig: [4x4 double]\n", " coordsys: 'ctf'\n" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "the input is volume data with dimensions [256 256 256]\n", "voxel size along 1st dimension (i) : 1.000000 mm\n", "voxel size along 2nd dimension (j) : 1.000000 mm\n", "voxel size along 3rd dimension (k) : 1.000000 mm\n", "volume per voxel : 1.000000 mm^3\n", "using 'OldNorm' normalisation\n", "the coordinate system appears to be 'mni152'\n", "Smoothing by 0 & 8mm..\n", "Coarse Affine Registration..\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning: Using 'state' to set RAND's internal state causes RAND, RANDI, and RANDN to use legacy random number generators. This syntax is not recommended. See Replace Discouraged Syntaxes of rand and randn to use RNG to replace the old syntax." ] }, { "name": "stdout", "output_type": "stream", "text": [ "Fine Affine Registration..\n", "performing the segmentation on the specified volume, using the old-style segmentation\n", "\n", "SPM12: spm_preproc 16:27:18 - 29/07/2024\n", "========================================================================\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND = 1.464403e-17.Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND = 1.873292e-17.Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND = 1.873964e-17.Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND = 1.039698e-20." ] }, { "name": "stdout", "output_type": "stream", "text": [ "Completed : 16:27:40 - 29/07/2024\n", "creating scalpmask ... using the anatomy field for segmentation\n", "smoothing anatomy with a 5-voxel FWHM kernel\n", "thresholding anatomy at a relative threshold of 0.100\n", "creating brainmask ... using the sum of gray, white and csf tpms\n", "smoothing brainmask with a 5-voxel FWHM kernel\n", "thresholding brainmask at a relative threshold of 0.500\n", "creating skullmask ... using the brainmask\n", "the call to \"ft_volumesegment\" took 51 seconds\n" ] } ], "source": [ "load mri_aligned_icp_fid mri_aligned_icp_fid\n", "mri_aligned = mri_aligned_icp_fid\n", "%% segmentation MRI\n", "cfg = [];\n", "cfg.output = {'brain', 'skull', 'scalp'};\n", "segmentedmri = ft_volumesegment(cfg, mri_aligned);\n", "\n", "save segmentedmri segmentedmri" ] }, { "cell_type": "code", "execution_count": 71, "id": "467fadbb-3f02-42aa-af08-7a0792301f53", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "triangulating the boundary of compartment 1 (brain) with 3000 vertices\n", "the call to \"ft_prepare_mesh\" took 1 seconds\n", "the call to \"ft_prepare_headmodel\" took 1 seconds\n" ] } ], "source": [ "cfg = [];\n", "cfg.method='singleshell';\n", "mriskullmodel = ft_prepare_headmodel(cfg, segmentedmri);" ] }, { "cell_type": "code", "execution_count": 72, "id": "ec9ab3c5-0e5f-4320-8acd-de70eb5a4ba6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "triangulating the boundary of compartment 1 (brain) with 3000 vertices\n", "triangulating the boundary of compartment 2 (skull) with 2000 vertices\n", "triangulating the boundary of compartment 3 (scalp) with 1000 vertices\n", "the call to \"ft_prepare_mesh\" took 3 seconds\n" ] } ], "source": [ "cfg = [];\n", "cfg.tissue = {'brain', 'skull', 'scalp'};\n", "cfg.numvertices = [3000 2000 1000];\n", "mesh = ft_prepare_mesh(cfg, segmentedmri);\n", "% ft_plot_mesh(mesh(3), 'facecolor', 'none'); % scalp\n", "\n", "save mriskullmodel mriskullmodel" ] }, { "cell_type": "markdown", "id": "294e3e77-c03e-49dc-8b5a-d43a3829ff41", "metadata": {}, "source": [ "Plot the final coregistration result and make sure everything aligns well visually." ] }, { "cell_type": "code", "execution_count": 73, "id": "a194c6f3-1f32-4ba1-877a-663bd74ff122", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
cfg = struct with fields:\n",
       "         grad: [1x1 struct]\n",
       "    headshape: [1x1 struct]\n",
       "
" ], "text/plain": [ "cfg = struct with fields:\n", " grad: [1x1 struct]\n", " headshape: [1x1 struct]\n" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/html": [ "
cfg = struct with fields:\n",
       "         grad: [1x1 struct]\n",
       "    headshape: [1x1 struct]\n",
       "    headmodel: [1x1 struct]\n",
       "
" ], "text/plain": [ "cfg = struct with fields:\n", " grad: [1x1 struct]\n", " headshape: [1x1 struct]\n", " headmodel: [1x1 struct]\n" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/html": [ "
cfg = struct with fields:\n",
       "         grad: [1x1 struct]\n",
       "    headshape: [1x1 struct]\n",
       "    headmodel: [1x1 struct]\n",
       "          mri: [1x1 struct]\n",
       "         mesh: [1x1 struct]\n",
       "         axes: 'yes'\n",
       "
" ], "text/plain": [ "cfg = struct with fields:\n", " grad: [1x1 struct]\n", " headshape: [1x1 struct]\n", " headmodel: [1x1 struct]\n", " mri: [1x1 struct]\n", " mesh: [1x1 struct]\n", " axes: 'yes'\n" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "the template coordinate system is \"ctf\"\n", "the positive X-axis is pointing to anterior\n", "the positive Y-axis is pointing to the left\n", "the positive Z-axis is pointing to superior\n", "this returns a light skin, you can also explicitly specify 'skin_light',' skin_medium_light', 'skin_medium', 'skin_medium_dark', or 'skin_dark'\n", "plotting anatomy\n", "The axes are 150 mm long in each direction\n", "The diameter of the sphere at the origin is 10 mm\n", "\n", "==================================================================================\n", "Press \"h\" to show this help.\n", "Press \"q\" to quit.\n", "Click and hold the left mouse button to rotate.\n", "the call to \"ft_geometryplot\" took 1 seconds\n" ] } ], "source": [ "load grad grad\n", "cfg = [];\n", "% cfg.elec = structure, see FT_READ_SENS\n", " cfg.grad = grad;%structure, see FT_READ_SENS\n", "% cfg.opto = structure, see FT_READ_SENS\n", " cfg.headshape = mesh(3)%structure, see FT_READ_HEADSHAPE\n", " cfg.headmodel = mriskullmodel% structure, see FT_PREPARE_HEADMODEL and FT_READ_HEADMODEL\n", "% cfg.sourcemodel = structure, see FT_PREPARE_SOURCEMODEL\n", "% cfg.dipole = structure, see FT_DIPOLEFITTING\n", " cfg.mri = mri_aligned;\n", " cfg.mesh = headshape;\n", " cfg.axes = 'yes'\n", "\n", "ft_geometryplot(cfg)" ] }, { "cell_type": "markdown", "id": "75d6c836-29e5-4c7a-a8ee-0947fbb58b31", "metadata": {}, "source": [ "Defining trials and epoching\n", "============================" ] }, { "cell_type": "markdown", "id": "897f7cb0-b6d8-499b-9c64-ec37e4d1127b", "metadata": {}, "source": [ "Ways to improve this notebook\n", "-----------------------------\n", "\n", "- Find a tolerable threshold for the relative_error of coregistration\n", "- To align the MRI with the headshape, ICP is used, it would be better to find the transformation based on fiducials (headshape fiducial are known, MRI fiducials are manually picked in previous steps) and apply the transformation to coregister, we can then apply ICP after the fiducial fitting, this would help us avoid wrong local minima\n", "- generate a coregistration report, showing different metrics (e.g., relative_error) the measured value, the minimum required thresholds, and how many metrics verify or not the requirements\n", "- check how the MRI slices are indexed from 0 to some value, whether the 0 slice correspond to the right or left part of the head, this can impact the selection of fiducials on the MRI" ] }, { "cell_type": "code", "execution_count": null, "id": "e34d6c12-2823-484b-a673-9e4895bf2f15", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "MATLAB Kernel", "language": "matlab", "name": "jupyter_matlab_kernel" }, "language_info": { "file_extension": ".m", "mimetype": "text/x-matlab", "name": "matlab" } }, "nbformat": 4, "nbformat_minor": 5 }