From eea92cc4c80e1bcc6a6c51e9c6087bbdcbfd828e Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 6 Apr 2021 02:31:09 -0400 Subject: [PATCH 01/73] animated plotting of tracking example --- .../src/gerry01_planar_tracking.ipynb | 7462 +++++++++++++++++ .../cablerobot/src/gerry01_planar_tracking.py | 48 +- 2 files changed, 7498 insertions(+), 12 deletions(-) create mode 100644 gtdynamics/cablerobot/src/gerry01_planar_tracking.ipynb diff --git a/gtdynamics/cablerobot/src/gerry01_planar_tracking.ipynb b/gtdynamics/cablerobot/src/gerry01_planar_tracking.ipynb new file mode 100644 index 00000000..d89cb255 --- /dev/null +++ b/gtdynamics/cablerobot/src/gerry01_planar_tracking.ipynb @@ -0,0 +1,7462 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "adolescent-campus", + "metadata": {}, + "outputs": [], + "source": [ + "import gtdynamics as gtd\n", + "import gtsam\n", + "import numpy as np\n", + "from gtsam import Pose3, Rot3\n", + "\n", + "from cdpr_planar import Cdpr\n", + "from cdpr_planar_controller import CdprController\n", + "from cdpr_planar_sim import CdprSimulator" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "absolute-royalty", + "metadata": {}, + "outputs": [], + "source": [ + "# problem parameters\n", + "Tf = 1\n", + "dt = 0.01\n", + "N = int(Tf / dt)\n", + "cdpr = Cdpr()\n", + "# set up controller\n", + "x_des = [\n", + " gtsam.Pose3(gtsam.Rot3(),\n", + " (1.5 + np.cos(2 * np.pi * i / N), 0, 1.5 + np.sin(2 * np.pi * i / N)))\n", + " for i in range(N)\n", + "]\n", + "# x_des[0] = x_des[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "traditional-running", + "metadata": {}, + "outputs": [], + "source": [ + "# initial state\n", + "x0 = gtsam.Values()\n", + "gtd.InsertPose(x0, cdpr.ee_id(), 0, x_des[0])\n", + "gtd.InsertTwist(x0, cdpr.ee_id(), 0, x_des[0].localCoordinates(x_des[1])*N)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "northern-pottery", + "metadata": {}, + "outputs": [], + "source": [ + "# controller\n", + "controller = CdprController(cdpr,\n", + " x0,\n", + " x_des,\n", + " dt=dt,\n", + " Q=np.array([0, 1, 0, 1e3, 0, 1e3]),\n", + " R=np.array([1e-3]))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "enclosed-capacity", + "metadata": {}, + "outputs": [], + "source": [ + "# run simulation\n", + "sim = CdprSimulator(cdpr, x0, controller, dt=dt)\n", + "result = sim.run(N=N, verbose=False)\n", + "poses = [gtd.Pose(result, cdpr.ee_id(), k) for k in range(N+1)]\n", + "posesxy = np.array([[pose.x() for pose in poses], [pose.z() for pose in poses]])\n", + "desposesxy = np.array([[pose.x() for pose in x_des], [pose.z() for pose in x_des]])\n", + "torques = np.array([[gtd.TorqueDouble(result, ji, k) for ji in range(4)] for k in range(N)])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "missing-formation", + "metadata": {}, + "outputs": [], + "source": [ + "# plot utils\n", + "boxinds = np.array([0,1,2,3,0]).reshape(5,1)\n", + "def ee_coords(k):\n", + " return (posesxy[:, k]+cdpr.params.b_locs[boxinds, [0,2]]).T\n", + "frame_coords = cdpr.params.a_locs[boxinds, [0,2]].T\n", + "def cable_coords(k, ji):\n", + " return np.array([[cdpr.params.a_locs[ji][0], posesxy[0][k]+cdpr.params.b_locs[ji][0]],\n", + " [cdpr.params.a_locs[ji][2], posesxy[1][k]+cdpr.params.b_locs[ji][2]]])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "announced-pittsburgh", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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u4qZ0SAmrn4ED86DXO+R3eY6/Tsez+UQsF65lcDEhg7TsfEK89VT31tO6hhdj2tck0NO58PYu/A2/TQRpgFFzoH4v2z4eC5FS8tn+z/j5+M9Maj6J51pNhVNr4MwmSDgLCecgLxu8aoBnDajdDdpOBDe/QttafHoxH+37iECXQL7o+QWNfBrZ4VGVb0W9ZqsBdDlQmQcLilIalfn/RAUaQAcBQVLKA0IId2A/MAx4CEiUUn4ohHgN8JZSvlpUO5Z4zQ6LDmPSpkk80OQBXuvwGoBpEP3IJHJOn6b6t9/g1r17mfqwlwV7L/Hm8mMMa+LDm9lHSVmyhLzISIRej1O9ejjWqI7Wy4v86BhyIyPJPX8eHBzw6NuXapMfw7nRrQOe8/HpPDY3nMikTL4e25Z+zQPt8MjKaNNbsOtrsjs/z5dyDEv2RxGfloOn3pFGAe7UquaCu7MjV5OzuJyYycmYVDRCcG/TAJ7p1YAmQR63tpl0ERaNMy02HPEjtBhl84dVVj8c/oFvDn3D/XWH8GaGRBxeBFmJ4OpvqkLi2wAc9aYFl4kREHMEtDpoOgx6vl7oDP6huEO8+NeLpOWl8VmPz+gW0s32D6wcUwPocqwyDxYUpTQq8/+JijKAvpkQYiXwjfnoIaWMNg+y/5JSFjltZanX7A/2fsDCUwuZ3Xc27QPbA2BISeHyw4+Qc+4c1b/7DrduXcvcjy0djkxm5Hc7edQQwciwZeTHxODSsSNeI4bj3qcPGheXW67JvXSJpIWLSF62DGN2NgEvvYj3hAm3lLJLyczj4Z/DOByVwif3tWR4m+q2elhld3wF/D6R0zXHMCZyBCnZ+fRqEsDo0Br0aOSHo/bW7NNLCRks2HuZxfsiyco18Gr/xjzStfatJf6yU2HRGLi0CwZ/Ce0m3tJWefVX5F88vfVpBnk04r+nwtDkZUDjQdB2AtTtWXjZvvgzsG8mHFoACBjypSlH/ObTMuN5YssTnE06y7Qu0xhWf5i1H06FoQbQ5VhlHiwoSmlU5v8TFXEALYSoDfwNNAcuSym9zLcLIKng++vOnwxMBqhZs2a7S5culTmGzLxM7lt9H0ZpZNnQZegd9AAYkpO59PAj5EZEUOP7Gbh27lzmvmwhPSefMR+tZeJfP9P86imcGjcm8M03cGnfvljX5yclEf3mW6Rv3Ypbjx4EffA/HLy9bzgnIyefR+eGs+dCAv8d1oJxHWta46FYVkoUhu+6cN4QwID0t2hXx59pQ5oVPqNciIT0HF5deoQ/TsbRo5EfX9zfGi8X3Y0n5WbCb+Ph3B/Q7yPoNMUKD8Sy4jPjGblyGP7Z6Sy8dBFdvXug/8emGefiSL4MSyZBVJgppWPAJ6YFl9dJz03nhb9eYHf0bp5p8wyPtni06tUYL0RRr9lqEaGiKIpSJCGEG7AUeE5KeUMZA/POsbfMwkgpf5RShkopQ/38bs29LA0XRxemdZlGVHoU3x367p/btV5e1Jw9C12tWkROfYKMsDCL9Gdt33/+K28s/S/NrkUQ8J+3qLN0SbEHzwAO3t5U//YbAt56i4ydO7n8yCQMqTdWmXB1cmDOw+3p0dCPN5YfZfG+y5Z+GJZlNJAwfyLZ2Tm8kP8Un41tz6+TOxV78AxQzc2JnyaEMn1oM3adS2D8rDBSs/NuPEnnAmMWmmZvN7xqKnVXjhmlkTc3PEpWdgofJ6aju28uPLis+INnAK+a8PA66PY8HJgLyx41LdK8jpvOjW97fcuguoP46uBXfLb/MyraJKstqQG0oiiKUighhCOmwfMCKeUy882x5tSNgjzpOFvF0z6wPSMbjGTeiXkcv3b8n9sdvL2pOWc2jiEhRE6ZSub+/bYKqcSklGyf9gn9f/4fjl6e1P39d3weeAChLfkmKEIIfB58gOrffUfOuXNEPjYZY0bGDec4O2r5fnw77m7ox2vLjrL8YJSlHopFGYySrTNfp9q1cH7yeJLvn72PIa2CSzUDKoRgfOfafD++LadiUnl4zj4ycm4q7efgZFpM2LA/rHvJtFixPDLkM3/paHanRfCK9KLuo39Ds2FQmplhrSP0ngZ9/2eqq736mVsqdjhqHflvt/8yptEYfj7+M+/ufhfDTQNtxUQNoBVFUZRbmNMzZgEnpZSfXXfXKqAgcXQisNKWcb0Q+gK+zr68vett8oz/ziw6VKtGrZ/n4OjvT+Tkx8k6dMiWYRWLzMvj8utv4vvrLI40aE+r1ctwbtSwzO263dWNkM8+JevYMSKfeBJjTs4N9zs5aPlhfDs61anGi78dZu2R6DL3aUmZufm8MXsNXa7M5rhXT5545k2qe9+a/11S9zQO4OuxbTgUmcykufvIyb9pIOigg9FzoV4vWPUMHF5c5j4tKiedq4tG8XXaSXo6BTJqwlbwDCl7u52fhLtfhYO/mBZr3kQjNLzR8Q0ea/EYS88u5eW/XybXkFv2fisZNYBWFEVRCtMVGA/cI4Q4ZD4GAB8CfYQQZ4He5u9txkPnwZud3uRM0hnmHb9x1tDBz4+ac39GW60alx99jKyjR20Z2m0Z0tKIfPxxMlcsZ2Gj3jT99gt0Hu4Wa9+jTx+CP/yAzL17ifvoo1vud3bUMuuhUNrW9ObZXw+y9VSsxfoui7jUbEb/sJsel75C66Cl2cPforPgluT9mgfx6X2t2BORyIfrT916goMTjFlgKve2YiqcXG2xvsskNRrm9OfT1KMIrY43Bs9H3JSzXCY9XocOj8Pub0w1sm8ihOCZts/wUuhLbL60mae2PEVmXiWoLW5BagCtKIqi3EJKuUNKKaSULaWUrc3HOillgpSyl5SygZSyt5Qy0dax3VPzHnrW6MkPR34gJiPmhvscAwKo9fMctJ6eXJ70KFlHj9k6vFvkX7vGpQkTydgbxhft7idn/KO0qul95wtLyHPwYHwefpikhYtI3bz5lvtddA7Mfrg9TYI8mPLLAXadu2bxGEri4rUMRszYhV/8Xvprw3C8+yXLzLDeZFibEB7qUps5Oy+y+UQhbxwc9TB2EQS3gSWPwDk773J57RzMupd96ZfZ5OrCI60mE+hq4VKEQphSOaq3h9XPm8reFWJis4lM7zqdsJgwHt30KInZNv/vXm6pAbSiKIpS4bza4VWM0sgn4Z/ccp9jcDA1585F6+7O5UmT7DqIzo2K4uK4B8i9eJHfRjzPrvqdeblvY6v15//8czg3a0b0W/8h7+rVW+73cHZk7iMdqF3NhUfnhbP/kn0GRMeupDDq+11kZ+fwnc9i8KoFnZ+2Wn+vD2hMs2APXl5ymKvJWbee4OQODy4B30bw6wNwcafVYrmtq4dgdl8MeZl8VKc5Qa5BPNzsYev0pXUw1cOWBlj++C2LCgsMqz+Mz3p8xpmkM4xbO45zSeesE08FowbQiqIoSoUT4hbCoy0eZePFjey+uvuW+3XVQ+w+iM4+fZpLY8dhTEkh4d1PmZMXyLO9G+Dn7mS1PoVOR8hnn0JeHldefgVpuHVQ5OOq45dJHfF3d2Li7H0cvFzkRpJWsScigbE/7kGn1bDurvPok8+YZkMdi9hF0AKcHLR8M64teflGnvv1EEZjIdUl9N4wfrlpF78F98HlPVaLp1AX/oafB4GjnqW9XuB0+mVeDH0RZwfr/VzwqQsD/g8u7YSdXxR52j0172FO3znkGHIYv348O67ssF5MFYQaQCuKoigV0sPNH6aGew0+CPuAPEPeLffrqodQa555EP3QQ2Tu22ez2DLDw7n04HjQaKg5fx4fX3akureeiV1qW71vXa1aBPznP2Tt30/yklvzWwH8PZxZNLkT1dx0TJgVxuHIZKvHBbDhWDQTZocR4OnMksfa4X/wW6jZBRoPtHrfdXxdeWdIM8IuJrLkQBHVSNz8YOJq8AiCX0ZCpI3KIh5fYerPM4Sciav54fwy2vi34d5a91q/71ZjoelQ+Osj026NRWjh14JFAxcR4hbCE388wRf7vyj0/11VoQbQiqIoSoXkpHXitQ6vcSHlAr+d+a3QcxxDQqi14BccAgK4/OhjpNlgc560LVu4/MgkHPz8qL1oIbulF4ejUniqZ/1Cd9GzBs9hQ3EJDSX+888xJCcXek6Qp55Fj3XC21XHg7P2sv+SdWeiF+69zBMLDtA82IMlUzoTfGEJpF2FHq+WrixbKYxqW512tbz5aP0pUjKLGPy5B5oG0W7+MH8EXLTybOu+mfD7Q6Yc7IfXsyIujLisOJ5o/YRtNjIRAvp+ABotbH77tqcGugYyr/88RjQYwaxjsxi/fjwXUi5YP8ZySA2gFUVRlArrrpC76BjYkR8O/0B6bnqh5zgGBlLrl/k41a9P1FNPk7xsuVVikVKS8PPPRD39DE5NGpsG7kFBfPnHWUK89Ixoa7vttIUQBPznLQypqcR/9VWR5wV76U0z0a46Hpi5xyrVOQxGyf/WneSN5Ue5u6EfCx7thJdOwvbPoUZHqHO3xfssikYjeG9oM5Iyc/ls8+miT/QIholrTP/OHwEn11g+GKMBNr4Ja1+Ehn1h/ArynNyZeWwmrf1a0zGwo+X7LIpnCHR9zlQf+g5vGAo2Nfq8x+dEpUcxYtUIPgr7iKRs26YC2ZsaQCuKoigVlhCC50OfJykniTnH5xR5noOPDzXn/oxrh/ZEv/EGsR99XGh+cGnJ3Fxi3n6HuA8/wr1XL2rNmYODtzfbz17jUGQyT/asj87Btn9ynRs1wnvcOJJ+XUz2iRNFnhfipWfJ1C408HfnsXn7+T288IoMpZGWncdj88L58e8IJnSuxY8TQtHrtHBoIaRGmeoR23i76GbBnjzQsRbz91zi+NWUok/0DIFHNkBgC9PW3+FFP79KLDsFFo42lZHrMBnuXwA6F1aeX0lMRgxTWk2x/TbaXZ4Gj+qw/rUiFxRer3et3qwYuoKh9Yay8NRCBiwbwBf7v+BiykXrx1oOqAG0oiiKUqE1q9aM/rX7M//EfOIz44s8T+vmRo0ffsD7gQdInDOHyKlTyU8q+6xZbtQVLj30MMm//061xx8n5Msv0Li4IKXkyy1nCfZ0ZlQ7280+X8/vmafRenkR89//3XZbZl83JxZN7kTnutV4eckR3lpxlOy8sr3BOBqVwrBvd7LtTDzThzXnvaHNTSks+bmw/TMICYV695Spj9J66d5GeLnomL6m6DcWALj4wMRVpjjXPAcrn4TcMtZDvnIAfroHIv6CQZ+bFvFpHcgz5jHz6Exa+LagS3CXsvVRGjoXuPc9iD0KB+cX6xJfvS/Tukxj2ZBldAzqyJzjcxi8YjAT1k9g1tFZHI0/Sr4x/84NVUBqAK0oiqJUeE+3fZo8Yx7fHf7utucJR0cC//MWgdPeIWP3HiIGDS60ZnJxSClJXr6CC0OHknPmDMGffIL/888hNKY/rWEXEtl/KYmpPerZfPa5gNbDA7+nnyJr/34ydu267bluTg7Mfqg9k7vX5Zc9lxn+3S7OxRWeFnM7+QYjX205y/DvdpKek8/8SR0Y36nWvyccWwopl+HuV2w++1zA08WRp++pz56IRHadv0M9bJ0rjF0Md70EBxfAzF4Qe4eBd2EMefDn/2Bmb9MgfMJKCH3kn7vXRazjSvoV+8w+F2g2wlQb+u9PTG90iqmeVz2+6PkFf4z6g+fbPU9abhpfHPiCcevG0e3Xbjy26TG+Pvg1u67uumEH0YpMDaAVRVGUCq+Gew3ub3Q/y88uJzL1zikI3mPGUGfJ7zgE+HPl6WeIev55cs4Vv75t5sGDRE6aRPTrr+PcpAl1V67Ac9CNlSRm77yAt4sj94XWKPHjsSTPkSNxCAri2tff3HYWGkDnoOGNAU2Y/VAoMSlZ9P/yb95eeYy4tOw79mMwSlYfvsqAr7bz2eYzDGwZxKbn7qZLPd9/T5IS9nwHfo2hgQ0qTNzG2A41CfBw4ovNZ+/4c0HrAL3+Y6oVnR4L33eD1c+adgy8E0M+HPnddM22j6DFffDEbtPuh2ZSSn45+Qv1vepzV8hdZXxkZSCEKa0mJRKO/Friy/1c/Hik+SMsH7qcP0f/yf91/z8G1R1Eck4yM4/O5PHNj9NjcQ/e3vk2h+MPW+EB2I6DvQNQFEVRFEuY1HwSS84sYeaxmbzb5d07nu/cqBF1Fi/m2o8/kjBzFmnrN+B2zz143z8afdu2aN1v3Go7PzGRjJ27SFm1iozt29H6+BDwxht4PzAOob1x++nIxEw2nYhl6t31cLbg1tSlodHp8H18MjHT3iVj5y7cunW94zX3NA5g43Pd+WLLWRbsvczv4VEMaRXM3Y386FrPF08XRwDyDEaORCWz+3wCyw9e4Xx8BvX93fj+wbb0ax50a8OXd0PMEVPqgr1mWc2cHbU80aM+76w6zq7zCXSt73vni+r3hifDYNvHED4bDi+GpkNMbwbq3WNK+QDIy4Yr++HSLji0AJIugF8TGLMIGg+4pdn9sfs5lXiKaZ2n2W/2uUD93hDUypRm02qc6c1DKfjqfelXpx/96vQDIDMvk73Re9l0aRObLm1i+bnl3N/ofp5r+xxuOjdLPgKbEHd811XOhIaGyvDwcHuHYVE9evQA4C8blFdSlIqgMv+fEELsl1KG2jsOW7H1a/YHez/gt9O/sWbEGkLcir8tdH5SEkm/LCDpl18wpKSAEDg1aIDW2xuZl4cxLc00Qy0lWh8fqj3yMN7jxqFxcSm0vffXnODnXRfZ/mpPgjz1lnp4pSZzcznXt59pq/NFC0s0SLt4LYOvtp5l8/FY0nLyEQJ0Wg1ajSDfIMk1GAFoWd2Tx7vXo3/zQDSaItpfPN60YcgLJ005t3aWnWegx//9RYi3niVTOpds8Jp4AbZ/CqfWQpZ5R0ehBa0OjHlQkPsbEgrdnodGA0BT+Af/z//5PPti97F51Gb0DvZ/vnByNSx+EEb8BC1HW7z5zLxMvj74NQtOLsDfxZ//u/v/aOPfxuL9WEJRr9lqBlpRFEWpNB5u/jC/n/mdWUdn8Xbn29e0vZ6Dtzd+Tz9FtUcnkXXwIJkHDpJ1+DDGrEyEkw5H92Dc+/fDrVs3nJs1u2XG+XrpOfksDo+kf4ugcjF4BtMOhb6PP07MtGnFnoUuUNvXlc9GtybPYORwpGm2OT0nH4NRotUIWtfwokMdH6q53WGHxeTLcGqNqdpDORg8g2kW+sme9fjPyuNsP3uN7g39in+xTx0Y+g0M/tK0MPDSDsjNAEMuaBxMA+eanf6dlS7ClfQrbI3cysPNHi4fg2eARgPBv6kpF7r5qCIH/qXl4ujCqx1epW/tvryx4w2e2vIUCwcupJZHrTtfXE5YbQAthKgBzAMCAAn8KKX88qZzegArgYIq3MuklO9ZKyZFuUF0NIwZA4sXQ2Dgrd8Xdo6iKOVaoGsgw+sPZ9m5ZUxuOZlA15L9v9Xo9bh26YJrl9JXQVi6P4q07Hwe7lq71G1Yg9eI4Vz78QcSvv++RAPoAo5aDaG1fQitffsBYZHCfgIEtH+sdNdbyej2Nfj2z/N8v+18yQbQBTRaqNHedJTCr6d+RSAY03hMqa63Co0G7noRlk4yvelpOsQq3bT2b80PfX7ggbUP8NSWp/hlwC94OnlapS9Ls+YiwnzgRSllU6AT8KQQomkh522XUrY2H2rwrFhOdDTcfTfExBT+/fTpsGMHvPde4d8Xdtud2lQUxe4mtZgEEmYdnWXzvo1GydxdF2lVw4u2Nb1t3v/tCJ0On/ETyAwPJ+v4cdt2npsJB+ZBk0HgZd9FlTdzctAysUttdp1P4MTVVJv2nZmXydKzS+ldq3eJ3+xZXbPh4FUT9n5v1W5quNfg856mTVle2vZShanSYbUBtJQyWkp5wPx1GnASKH5CmqKUVDEHyBkhIZwTgh0zZrDSaGTxjBnMFYIfZszgG6ORn2bMYL4Q/C4Ea2bMIMxo5PKMGeQIATVq3H7QrQbUimJ3wW7BDK43mBXnVpCcnWzTvvdEJBBxLYOJncvnR9Feo0aicXEhad4823Z8YgVkJ5s2DSmHxnWoid5Ry+ydtt2WesPFDaTlpjGu8Tib9lssGq3p04JLOyHmqFW7ahfQjnc6v8Oe6D0sPLnQqn1Zik3K2AkhagNtgL2F3N1ZCHFYCLFeCNGsiOsnCyHChRDh8fFFF8lXqpiiBszVq2MUguMzZrDQaOTtGTMYLQQtZszA3WjEzWikAXAXMAwYAzwETAGeBiYDE4DRwGCgI1ALcAZ8DAY6GI1MmDGD/wnBshkzuGg0ImfMMK0ov3mArQbUimIXE5pOINuQze9nfrdpv7/ui8TD2YEBLQqpQFEOaN3d8Rw5kpR168mLi7Ndx/vnQrX6UKvkqSO24OniyKh21Vl16GqxSvZZytKzS6nrWbfcLqCjzYPgoIe9P1i9q2H1h9EpqBOzj80mM6+Mm9XYgNUH0EIIN2Ap8JyU8ubPRg4AtaSUrYCvgRWFtSGl/FFKGSqlDPXzK0V+klI5XTdgzhGCP2bM4G2jkXsNBryB5sADwH+BQw4O1NFoeBT4yNGRuQEBbATCdTqOC8H5+vW5IgRxTk5ECcHZceM4Ono0e4RgtaMjPwHTfXwY4+CAJ/CnELwJjATqAMHAcOBjg4F9RiPGogbUilJBCCFmCyHihBDHrrttmhDiihDikPm4tR5XOVHfuz5dgruw8NRCcg3F3xCiLJIyctlwLIYRbavbvXTd7fiMfxDy80latMg2Hcadgsg90HaC3UvX3c7DXWuTazDyy57LNunvbNJZjsQfYWSDkfYvXVcUFx9TFY6jv0NmotW7e7L1kyRmJ7L49GKr91VWVh1ACyEcMQ2eF0gpl918v5QyVUqZbv56HeAohChGIUalSiqYzXV2BiG4OmMGPxqNDDMYqAb0wTRYjhOCcZ6ezAWO6XRkCsGZhg1ZBXzu7MwrBgMTXFy494knaBcWRtOpU6mbkUHw1Kn47d1LyNSp1M/KonleHh2nTmXQvn08+sQTvOXhwXdGI5udnYkUgtRGjQgTgm8dHOgNHHV05FWgAxAITAR+NRhIMRqhYEDt7KxmpJWK4megXyG3f37dupV1No6pRCY0ncC1rGtsuLjBJv0tO3iFXIOR+9uXrxzfm+lq1sTtnntI/nUxxmwbzLYenA8aR1NN4XKsrp8bvRr7s2DPpTJvY14cS88uxVHjyOB6g63eV5l0fBzys0057FbW2r81XYK7MOfYnHI/C221AbQwvZ2aBZyUUn5WxDmB5vMQQnQwx5NgrZiUCqaQFI2U7duZ3bYtvQICqA48DhwSggmenqwGkp2cOCQEM3x8mPDEEzQLC8Np6lRISoIpU2DPHtO/rVvDt99Cq1amf69evfH7ZctMx/W3tWlzQxvuqam0nzqVJ8LDmf/EE5wLCSFWCH5xdKQPsFajYSymMjT3abWs6N6dnPvu+3dGWqV3KOWYlPJvwPpTTlbUJbgL9TzrMe/4vDvvNFdGUkp+DbtMqxpeNAnysGpfluAzYQKGpCRS16yxbkf5OXBooWnzELfy/wnypG51SMjIZdXhq1btJ8eQw+rzq+lVsxfezuVrsektAppBrW6wbxYYrf/G4onWT5CUk8SiUzb6hKS0pJRWOYBumMrXHQEOmY8BmFJNp5jPeQo4DhwG9gBd7tRuu3btZGVz9913y7vvvtveYZQ/U6dKqdFIo0Yjd4McC9LJ9JyS9UG+A/K4TieNQkhZp46UTzwh5aFDpn+HD7d9vMOH3xBDfq1acpcQ8hmtVvqb4/YC+QzIM6YNbU3H1Km2j7Wcq8z/J4BwaaXXXUsfQG3g2HXfTwMuml/XZwPeRVw3GQgHwmvWrGnxn2FJLD2zVDb/ubncc3WPVfsJv5gga726Ri7ae8mq/ViK0WiU5wcNkhGj7rNuR0eXSPmOh5Rn/7BuPxZiNBplz0/+lMO/3WHVftacXyOb/9xc7r6626r9WMzxFabf46n1NuluyuYpsuuirjIjN8Mm/d1OUa/Z1qzCsUNKKaSULeV1H/dJKb+XUn5vPucbKWUzKWUrKWUnKeUua8WjVBDR0ab6k0KQO2MG841GOhiNdAbWAo8Be52cOBMSwrSHHqJpWBhi6tRbZ5SX3ZIxZH03zVhr27al89SpfLl/P1fGj2e9ry/9tVpmAA0xvZvcAP8uQFTpHUr5NwOoB7QGooFPCztJlqN1KwPrDsTH2YcFJxdYtZ9FYZG46rQMbhVs1X4sRQiB132jyT56lOyTJ63X0f654FkT6va0Xh8WJIRgXIeaHLiczKkY65W0W3p2KdXdqtMhsIPV+rCoRgPA1c+UjmMDjzR/hJScFP6M/NMm/ZWGTapwKEqxTZ9OrpT86OdHAyGYAKQLwbd+flwBvnZ2pkNeHmLIEJgzx74D5ju5bkDtMG8e/UaNYqGUXNbpmAYcBPoDoUKw+u67kfffrxYcKuWalDJWSmmQUhqBnzCl/JdrTlonhtcfzraobcRkWOfNaUZOPuuORjOoZTCuThVng1/PIYMROh3Jv1upUklypGnb7jYPWnwnO2sa0bY6Oq2GX8MirdJ+ZFok+2L2MbzBcDSigvxctI7Q8n44swHSrV+9pV1AO/z1/my8uNHqfZVWBfnNKZXWdQsDDUIwa8YMGgGPx8cTJCXrMOX4POHmhtsTT/ybw1wRZ2ljY2HKFALDwninWTMuAXMcHEiRkiHbttFh3jzWX18ST81IK+WMEOL62mzDgWNFnVuejGw4Eikly85a5432hmMxZOYaGBVa3SrtW4vWywv3fn1JWbUaY1aW5Ts4shiQ0Op+y7dtRT6uOvo1D2TZgSirLCZcE7EGgWBw3XK+ePBmbSeAMR8O/2r1rjRCw72172XHlR2k56Zbvb/SUANoxb7Mpej+7tGDdl5ePAr4Aes0GnbXr0//P/5AU15SNMrq+hSPhg3RPfEED4WHc3L8eGZ7e5MgBAOAgRoNpwcPNm0hrmakFTsRQiwCdgONhBBRQohJwMdCiKNCiCNAT+B5uwZZTDXca9AlpAtLzywl35hv8faXHYyipo8LobXK+WKwQnjfdx/G9HRS11u4UomUpoFWra7gXduybdvA2A41Sc02fbJgSVJKVp9fTYfADgS5lc9a4UXyawTVO8DBX0y/XyvrW7sveca8cpvGoQbQin3o9SAEkTNmMMZo5O6NG0lKTuY3TDnO/QHRpw/06lVxB8y3c91g2nHePB6+/35OA587OLDTaKT56tW8PHcuadeXwNPr7R21UoVIKcdKKYOklI5SyupSyllSyvFSyhbmtS1DpJSWHV1Y0eiGo4nLimNb1DaLtns1OYtd5xMY0Tak/NbyvQ19aCi6OnUsn8Zx5QAknIVWYyzbro10qutDHV9XFoVZtib04fjDRKZFlv/SdUVpOx6unYaofVbvqqVfSwJdA8ttGocaQCu2Fx2NbNWKmW3a0AxYCUxzdORkcDD3PfQQYu/eipumUVqxsThOncpz4eGcHT2aiS4ufIppM5gtTk4wYoRpFr4q/UwUxYK6V++Ov4s/v5+27EBx+cErSAkj2lSs9I0CpsWE95F18CA5Z89aruHDi8DBGZoOtVybNiSEYGyHGuy7mMS5OMulEKw6vwq9g57etXpbrE2bajYcHF1tUhNaIzT0rdWXnVd3kpKTYvX+SkoNoBWbi3r1Vfrv3ctjBw8SCpzQ6XjHYMBl6NDyvzDQWq6bkfZfvJiZEyawUwichaB3Tg5P/vkn6Xv3qnQORSklB40DoxqMYufVnUSmWmZxmJSSZQei6FDbh5rVXCzSpj14DhsKDg4kr1hhmQbzc+DYEmg8EJw9LdOmHQxrE4JWI1h2IMoi7eUYcthwcQO9avbC1dHVIm3anJO7aRB9fDnkZli9u761+5JvzC+XaRxqAK1Y33ULBZcLQfP589kOfAP8gWkr7Co343wnsbF0njqVgw4OPA/MSEqitZQcKkjn0GrVz0tRSmhEgxFohIbl55ZbpL3DUSmcj89gRNsQi7RnLw4+PrjddRepq1YjDRZYNHd2E2QlQauxZW/LjvzdnenewJflB69gNJY953db5DbSctMqbvpGgdZjITcdTll/I9Lmvs0JcQux2W6iJaEG0Ir1TZ9O/vbtvFyvHiOABhoNh4EnXVzQPPAAXLpU9Wac78Q8I+1y6RKfjRvHX05OZAGdgZ8DAkwLONRstKKUSIBrAF2Cu7Dy/EoMFthRbdmBKJwcNAxoWcEWgxXCc+hQ8uPjydi9p+yNHf4VXP0rTO3n2xnZrjrRKdnsjij7Jsmrz6/GX+9Px8COFojMjmp2Ac8a5ior1iWEoE+tPuyN3lvutvZWA2jFeswLBWNnzKCXlHxy4gRTgR1GI/WdnSE7Gzw8IDDQ3pGWX0FB4OFB97w8DgJdgIdjY3lMSnLU4kJFKbFh9YcRlxnHnuiyDRTzDEbWHImmd9MAPJwdLRSd/bj17IHGw4OUlSvL1lBWkmkGusUo0FacmthF6d0kAHdnB5aWMY0jOTuZHVd2MLDuQLQarYWisxONBlrcB+e32qQmdIfADuQb8zkcf9jqfZWEGkAr1hEdDa1bc7JXLzoKwT5gvk7Hd9Wr4/TQQxW7nrOtmetH+2/ezKb69XkDmAn012hIHjRILS5UlBLoWaMnnk6erDi3okzt7Dh3jcSMXIZWkJ0H70Tj5ITHgP6kbd6MIb0Mua0nV4Mh1zSArgScHbUMahnEhmMxZOSUvgTipkubyJf5DKw70ILR2VHL+0Ea4NhSq3fVxr8NWqElPDbc6n2VhBpAK9YxfTp/791Llz//JEtK/tbpeDA/HwYPrroLBUurYIFh795oe/fmvxoN8x0d2WE0ctfmzUSqxYWKUmw6rY4BdQaw9fLWMq3sX3nwCp56R3o08rdgdPblOXQoMjubtI1lKBt25DfwqQvBbS0XmJ2NbFudzFwDG46VfqJibcRa6nnWo6F3QwtGZkf+jSGolUXTOKTRSO6lS7fk4bvp3Gji04TwGDWAViozc9rG0hkz6CMlAUYje4BQUDPOlmCejX5QCNYDl3Ny6CwlJ1U6h6IU27D6w8g15rL+wvpSXZ+Zm8+mE7EMaBGEzqHy/BnVt26NY62apU/jSL0KF3dAi9Gm16NKol0tb2r6uJQ6jeNq+lUOxB1gQN0BFbJWeJFa3g9XD0L8mTI1kxcXx7Xvf+B8336c79uPSxMmkht15YZz2ge25+i1o2TlW2HHzFKqPP/zFfszp20s6dCB+4F2wC69njpqoaDlFMxGX7xIr3Hj2OHsjAHTlnAne/dW6RyKUgxNfJrQ0LthqdM4Np+IJTPXwLDWlSN9o4AQAs+hQ8kMCyPv6tWSN3BsGSArTfpGASEEw9uEsDsigZiU7BJfX/BGbUCdAZYOzb6ajwShKdMsdG5kJBGDhxD/xRc4BgXh+9RT5Jw6xYVhw0hZvfqf80IDQ8kz5nEk/oglIrcINYBWLGf6dJbs3cuYsDA6ARudnPDJyVELBa3BvLiwRW4uf+p0APT86y9OqnQORbkjIQTD6g/jeMJxziWdK/H1Kw9dJdjTmfa1fawQnX15DjTl6KauL8Xs/NHfIKg1+DawbFDlwNDWwUgJa46U/I3FugvraOXXiuruFXOznSK5B0LdHqbfeym29jZmZxP1zLMgJXVWrqTWvLn4PfUkdVauxKlRI66+/AoZe8MAUx60RmjKVR60GkArZWdO21g5YwZjpKQTsB5wl1KlbViTOZ2jMfAXQH4+PaXkvErnUJQ7GlBnAFqhZU3EmhJdl5iRy99n4hncOhiNphJ9HG+mq1UL55YtSVmztmQXxp+B6MPQcrR1ArOzun5utAjxZNXhkg2gzyad5UzSmco3+1ygxX2QfBmiSj6wjZk+nZyTJwn++COcG/2bG66rHkLN2bNwCAgg/osvkFLirnOnsU/jcpUHrQbQStnt3s1uLy/GYMp1Xq/X467SNqzvunSOxuPG8aezM/lAfyG4NnCgSudQlNuopq9Gl+AurL2wFqM0Fvu6tUejyTdKhraq2Jun3I7noIHknDxJzvnzxb/o2BJAQLMRVovL3oa2DuZIVAoXrhW/Ssm6C+vQCi19a/e1YmR21HgQaJ3Mv//iS166lJSly6g2dQruPXrccr/GyQnfqVPJOniQ9G3bAGgf0J4j8UfIMeRYIvIyUwNopczOfPwxg5OTqQ6sdnLCXaVt2JY5naNJbi6rdDouS8mQrVvJUukcinJbg+oOIiYjhv2x+4t9zapDV2jg70aTIHcrRmZf7v36gUZD6tpizkJLCUeXQO1u4FHxN5UpyqCWwQgBqw4VbxZaSsn6C+vpFNSJavpqVo7OTpw9oOG9pq29i7k5kSE9nbiP/w+X9u3xe+qpIs/zGjkCxxo1iP/yK6TRSGhgKLnG3HKTB60G0Erp6fUkCEH/RYvQABsAv5wcU/qAmvm0LXM6RxdgAbAnK4sJUiJVOoeiFKlnzZ64OLiwNqJ4A8UryVnsu5jE0NbBlauawk0c/f1x6diBlLVrkcXJbY0+BInnK93iwZsFejrTsY4PKw9fKdbP5XD8Ya6kX2FA3UqavlGg+ShIj4WL24t1etL8+RhSUvB/5RWEtuhNZYSjI35PPUnOyZOkbdpE24C2CES5SeNQA2ildKKjMbRqxQP+/kQBq4F6Li7wwAMQFaXSNmztunSOkePG8X+OjiwBPnV0hBEjVDqHohRC76Cnd63ebLq4qVgfC682578OriSbp9yO58CB5F26TPaxY3c++egS0DhCkyHWD8zOhrQKISI+g+NXU+947voL63HSOnFPjXtsEJkdNewLOnfT8+AODKmpJMz5GbdevdC3aH7H8z0GDUJXvx7xX3+Du6MpD7oknxhZkxpAK6UzfTrvh4WxMS6Or4Sgo9qau3wwp3O8kJ/PfRoNr+Xl8feBAxAWptI5KiEhRHUhxEtCiJVCiH1CiL+FEN8JIQYKIdTrezEMrDuQtLw0/o76+47nrjp0ldY1vKhVzdUGkdmXe58+CEdHUu+0mNBoNH18X78XuFS+qiQ36988EEetYOWhK7c9L9+Yz8aLG+levTtuOjcbRWcnjnpoPBBOroL8278RTfx5LsbUVPyeerJYTQutFp8JE8g9f56cU6do7tucEwknivfJiJWpF1ilZMwVNzbOmMG7UjIBmCyl6UVUVdwoH2JjEVOnMlOrpR5w/8WLxBiNoNI5KhUhxBxgNpALfASMBZ4A/gD6ATuEEN3L0P5sIUScEOLYdbf5CCE2CyHOmv/1LtujsL+OgR3x1fuy5vztq3Gci0vjRHQqQ6rA7DOA1tMT1+7dSV23Dmm8zSLLy7sh9YqpGkMV4O2qo3sDP9YcicZoLHoQty9mHwnZCfSv09+G0dlRi1GQnQLnthR5iiE5mcS5c3G/916cmzQpdtPuvXuDRkPa5s00q9aMtLw0ItMiLRF1magBtFIyEREkjBzJRKAZMEOvR6iKG+WLOZ3D49Illg4YQArwKCD1elOKzYUL9o5QsYxPpZT3Sim/klLuklKek1Iek1Iuk1I+DfQASrEbxj9+xjQQv95rwBYpZQNgi/n7Ck2r0TKgzgD+vvL3bbf2XnXoKhoBg1pW3kVyN/MY0J/8+Hiy9t/mI/NjS8DRBRpVkYEiphSe6JRsDlxOKvKcdRfW4eroyl0hd9kwMjuq2wP0PretxpE4bz7GzEx8izn7XMDBxweX0FDSNm+mabWmAJxIOFGWaC1CDaCVEnt67VoSgPk6HS6q4kb5FRRE85o1+UAI1gJzs7Jg61Z7R6VYiJTytsmpUspcKWXJdwn59/q/gcSbbh4KzDV/PRcYVtr2y5MBdQeQb8znj0t/FHq/lJJVh6/SuV41/D2cbRyd/bj36IFwdiZl3brCTzDkwfEVpsGzrvKntRTo3TQAJwfNPznxN8s15LLl0hZ61eyFs0MVeb5oHaHZMDi9HnJvLfMnc3NJ+u033Lp3x7lhw1uvvwP3Pn3IOXuOmklaHDWOHE84boGgy0YNoJUSWfrQQyzKzuZtPz9ah4WptI3yLjaWp6dMoXu7djyr0RAZHa1yoSsJIcSfQoitRRxFf45aNgFSymjz1zFAQBGxTRZChAshwuPj460UiuU09WlKbY/arLtQ+EDxSFQKFxMyq0z6RgGNqytuPXqQtnETMj//1hMi/oKsRFMVhirEzcmBexr7s+5YDIZC0ji2X9lOWl5a1UnfKNB8JORlmgbRN0n74w8M167h/cC4UjXt3rsXAFlb/qKRd6PKPQMthKhhfoE/IYQ4LoR4tpBzhBDiKyHEOSHEESFEW2vFo5SRXk+yEDyxaRPtgNfi402VHWbPVmkb5dmyZWjmzGHO/v0YjEamgMqFrjxeAl6+6VgK1AM8rN25NK3iKTQJVEr5o5QyVEoZ6ufnZ+1QykwIwYA6A9gXs4/YjNhb7l956Co6rYZ+zatO+kYBjwH9MSQmkhkWduudR5eAsxfU723zuOxtUMtg4tNy2Hsh4Zb71kWsw8fZh45BHe0QmR3V7ALuQXBs6S13JS1chGP16rh261aqph2DgnBu2fKfNI4TCSdKtAGSNVhzBjofeFFK2RToBDwphGh60zn9gQbmYzIww4rxKGWxezfT9HquATMBx4KSdSqftvyLiKDuuHG86+jIOmCNTgf+/rB3r70jU8pASrm/4ADc+Hch4RQpZXsrdRsrhAgCMP8bZ6V+bK5/nf5IJBsvbrzhdoNRsvrIVXo29sNT72in6OzHrXt3NC4ut6Zx5GbCqTXQdCg46OwTnB3d09gfF52W1Yejb7g9PTedbVHbuLfWvThqqtjzRaMx7UR5djNk/Zsfnn3mDJnh4XiPHYPQlH7Y6d6nN9nHjtHKGEJ6XrrdFxJabQAtpYyWUh4wf50GnARu3vt0KDBPmuwBvApenJXy5fiHH/JNVhaTgdaqZF3FYi5t93R+Po2F4LncXHLi4uD77+0dmVJGQoi+QojtwH+A/0opu0kpb/381HJWARPNX08EVlqxL5uq7VmbptWa3pLGsft8AvFpOQxrXXm37r4djbMzbr16kbb5D2Ru7r93nNkAuemVfvOUouh1Wno3CWDDsWjyDP/OhG6N3EqOIYeBdQfaMTo7ajESjHlw8t+qNsm//orQ6fAcUbZt3j369AGgwRHTrL+90zhskgMthKgNtAFunvIKAa5/CxHFrYPsCpdPV6no9UgheHbxYjyA6WAaPKvdBiuW2Fh0Gg1fSsl54DNQqRwVnBBiH/AD8CvwCpAihGhbcFig/UXAbqCRECJKCDEJ+BDoI4Q4C/Q2f19pDKgzgOMJx7mUeumf21YcuoK7kwM9G/vbMTL78ujfH2NKChm7d/9749Elpo/ra3W1X2B2NqhlEEmZeew6/28ax7qIdYS4hdDKr5UdI7Oj4LbgXeefNA5DegYpK1biMWAADt5lq3qpq10bpwYN0O85ik6j4/g1+y4ktPoAWgjhhikv7zkp5Z237ilERcunq1QiIvijZ0+2ANMAX7XbYMW0bBlERnLvuHEM0Wr5AEhUZe0qugzgAjAK+AT49Lrjk7I2LqUcK6UMklI6SimrSylnSSkTpJS9pJQNpJS9pZQ3V+mo0PrV7odA/DMLnZ1nYMOxGPo1D8TZsegthys7125d0Xh4kFqQxpGVBGc3mRaNaaruz+XuRn64Ozv8U43jWtY19kTvoX+d/pV6q/fbEsL0vLiwDdLjSF27FmNmJt5jx1ikedcunck+dISmHg04kViJZ6CFEI6YBs8LpJSFjbauADWu+766+TalnJBS8s6OHdQAHndyUqkbFZk5leN9o5E04DNV1q5Ck1L2kFL2NB/3XPd1TyllJd872DoCXAMIDQxlXcQ6pJT8eSqO9Jx8hlbR9I0CGp0O9969SftjC8acHDi52vQxfRVN3yjg5KDl3qaBbDweQ06+gU0XN2GQBgbUGWDv0OyrxSiQph0qU5Ytw6lBfZxbtrRI0y7t2yNzcuiS6s/JhJN2XUhozSocApgFnJRSflbEaauACeZqHJ2AlOtKJCnlwObHH2d3Xh5vBAXhtHevKltX0cXG0mLqVEbfey9fajRcU2XtKiwhxG2XswshPIQQzW0VT2XRv05/LqZe5FTiKVYcuoKfuxOd61Wzd1h259G/P8aMDDK2b4ejv0O1+hDU2t5h2d2gVkGkZeez/cw11l1YRwPvBjTwbmDvsOzLvwkENCfnr4VkHT6M5/ARFpuR17drB0DTSEF6XjqXUy9bpN3SsOYMdFdgPHCPEOKQ+RgghJgihJhiPmcdEAGcA37CtA2tUh6Yt+x+d80aagKPREersnWVwbJlMHs272zaRIbRyKegcqErrpFCiF1CiLeFEAOFEB2EEN2FEI8IIeYDawD1Sy2hPjX74CAcWHF2DX+eimdwy2C0mir6cfx1XDt1ROvlReqqZXBhu6n2c1VNU7hOt/q+eLk48vvhwxyOP6xmnwu0uI+UPedBq8Fz8CCLNevg7Y1Tg/r4n7kG2HchoTWrcOyQUgopZUspZWvzsU5K+b2U8nvzOVJK+aSUsp6UsoWUMtxa8SglFBHB3nvvZRem4rI6Vbau8oiIoOm4cYzSavkeyFC50BWSlPJ5YBAQDdyHaY3vC5jKgv4gpewupdxnxxArJC9nL7qEdGHN+fXkGvIZ0bZqp28UEI6OuPftS9pf2zHmAy1H2zukcsFRq6Ffs0B2xJjKH6oBtIlsMpyUCy64NQvBwcJr1/ShoYijp3HGkZOJJy3adkmonQiVIn399994ABNV7nPlYs6FftZoJBmYr3KhKywpZaKU8icp5UNSyr5SymFSytellDvsHVtF1r9Of1Lz46kVHE+zYKvvSVNhePTvj8zNJz27KVSrZ+9wyo2BLYLA9QB13VoS7Fa1dqssSsaxi+Rna/EMjgNZ6H5LpeYSGorMyKBLZginE09btO2SUANopVAxr7/Ob9nZPOzjg7vKfa58YmPpMmUK7Zo25SsnJ6TKhVaUf9R37Yg0OhIccqrqVlMohEsNZ7TOBlJjVE749dw8r6BxuoY2K9TeoZQbycuWo3XX4+5+AaIPWbRtl1DTPlEdol04k3TGom2XhIPdelbKJ70esrOZiWkryacSE025z87OkJVl5+AUi1m2DKHX80x2NhOBLUDvGTNM+dDqd61UceuPJJGf3phI5z3kG/Nx0Kg/lQDi+O941Mwh+ehlDOkZaN1c7R1SubDuwlo0OHLibC0ycvJxdarazxdDcjLpW7fidd9whPgOjvwOwW0s1r5jgD+OtWpS52I2CfUSuJZ1DV+9r8XaLy41A63cKCICOXYs84SgB1Bf5T5XXhERjL7/fjyBeQDqd60oSClZfvAK9V26kZKbRFh0mL1DKh+MBji6BI+uLZE5uaRv3WLviMqFPEMeGy5uoJ1vN7JznNhyqtLsbl9qKevWIfPy8Bp1PzS417SpitFg0T5cQkPxPHUFISVnk85atO3iUgNo5UZBQYQnJ3NWSh5wcFC5z5VZUBDO3t7cBywDMjIzwcFB/a4rICFEFyHEOCHEhILD3jFVVPsvJXE5MZMHW/bD3dGdtRfW2juk8uHC35AWjX7AQzgEBZG6dt2dr6kCdlzZQXJOMhNajCTQw5lVh67aOyS7S1m5EqeGDXFq0sS02DQ9BiL+tGgfLqHt0aRmUP0adkvjUANo5RYLdu5EB4wcOFDlPld2sbGMHzaMDGAFwN9/2zcepcTMJes+AboB7c2HSsYspWUHr+DsqGFQy5r0rtWbLZe3kJ2fbe+w7O/IYnDyQDQeiMeA/qTv3El+UpK9o7K7NRFr8HH2oWv1LgxqGcS2M3GkZObZOyy7yYm4QPbhI3gOHWpaP9CwH+i94dBCi/bj0t70Etch2lUNoJVyQK/HKASLU1MZBHitXAnffQfr19s7MsVa1q+n24oV1AIWgil9Q9WErmhCga5SyieklE+bj2fsHVRFlJVrYPWhq/RvHoSbkwMD6g4gIy+DbVHb7B2afeWkw4lV0HQoODrjOXAg5OeTtmmzvSOzq5ScFP6K/Iv+dfrjqHFkSOtg8gySDcer7n5wKStXgkaDR0HtZwcnaHEfnFxj2gLeQhxDQnDw96dVnLMaQCvlQEQEB/r2JQYYBiontiqIiEAzbhzDHRzYgqoJXUEdA1TejQVsOB5NWk4+o0NrANA+oD1+ej/WRlTxNI4TKyAvA9qMB8CpSRN0deqQurZq/1zWX1hPrjGXYfWHAdAixJPa1VxYdbhqpnFIo5GUVatw7doVR3//f+9o/QAYcky50BYihEDfqiU1LmdzPvk8eUbbz/qrAbTyr6Ag1sfEIIC+Op3Kf64KzDWhBxoM5ABbs7JUHnTF4wucEEJsFEKsKjjsHVRF9Nu+KGr6uNCxjg8AWo2W/nX6s/3KdlJyUuwcnR0d/AWqNYAaHQDT4MVj4EAy9+0jLzbWzsHZz/Jzy2ni04TGPo0B089lSKtgdp9PIC6t6qX9ZIaFkR8djeewoTfeEdQKAprDwQUW7c+5ZUtcY1NxSs/lUsoli7ZdHGoArdxg3alTtAf8hw5V+c9VRWws3SdPxs3BgbWg8qArnmmYPjT6H/DpdYdSApcSMtgdkcB97aqjuW7r7gF1B5BvzGfzpSqarnDtHFzeDW0euGHrbo+BA0BKUtdVzRS/04mnOZFw4p/Z5wJDWgdjlLD2SNVL40hZsRKNmxvuvXrdeIcQplnoqwcgznI7B+pbtgKgfrS0SxqHGkArJno9yUIQlpNDf4Dff1f5z1XF+vXofviBPvn5rAeVB13BSCm3AacAd/Nx0nybUgJL9kchBIwKrX7D7U19mlLbozbrLlTRqhOHFoDQQMsxN9zsVKcOzk2bVtk0juXnluOocWRg3YE33F7f350mQR5VLo3DmJFB6qZNuPfri8bZ+dYTWo4GjYPp0wwL0TdvBhoNjaKFGkArdhQRwZ4ePTAC3UHlP1clEREwbhw9HR25DEQ6O6vffQUihBgNhAH3AaOBvUKIUfaNqmIxGCVL9kfRvYEfQZ43vnEUQjCg7gDCY8KJyahin8gZDXB4EdTvAx5Bt9ztMXAg2ceOkXvxou1js6NcQy5rItbQq2YvPJ08b7l/SKtgDl5O5nJCph2is4/UjZuQmZl4jRhR+AmuvqaKHEcWQ36uRfrUuLriVL8+LeL0agCt2FFQEHsSE9EA7VX+c9VizoPukp8PwK7sbJUHXbG8CbSXUk6UUk4AOgD/sWaHQoiLQoijQohDQohwa/ZlC9vPxhOdks397WsUev+gOoOQyKq3mPD8VkiLNqVvFMJj4AAQgpTVa2wcmH39GfknKTkpt6RvFBjSOhiA5Qev2DAq+0pZvhxdrVro29xmx8F2D0FGPJy03BINfauW1IrK4XTiKYu1WVxqAK38Y/fZszQH3FX+c9UTG0vLyZNx0WrZBSoPumLRSCmv3/4sAdu8tveUUraWUlb4mtOLwi7j46qjVxP/Qu+v4VGD1n6tWX1+NVJKG0dnRwfmgd4HGvYv9G7HwEBcOnUkZXXV+rksP7ucAJcAOgV1KvT+EC89ner6sPxgVJX4ueRGRpK5bx+ew4ebaj8XpV4v8K4D+2ZarG/nli1xyshDcyWO5Oxki7VbHGoArYBejxSCsKwsOoHKf66K1q/H8Ycf6GAwsBtUHnTFssFcgeMhIcRDwFqgiibsllx0ShZ/nIzjvtDqODloizxvcL3BnE85z4nEEzaMzo7SYuD0OtPss4OuyNM8hwwl7/Jlsg4esl1sdhSZFsmuq7sY3mA4Wk3Rz5cRbapzMSGTg5HJtgvOTlKWrwAh8Bw65PYnajTQfpJpUWrMMYv0XbCQsMFV2y8kVANoBSIiuDpsGMlAS1D5z1WROQ+6tYMDxwGjqgddYUgpXwZ+xPTftyXwo5TyVWt3C2wSQuwXQky++U4hxGQhRLgQIjw+Pt7KoZTNorBIjFLyQIdatz2vb+2+6DQ6Vp9fbaPI7OzAfDDmQ7uHb3uae58+CGdnUlattFFg9rXkzBI0QsPIBiNve17/FoE4OWhYUcnTOKTRSMqKFbh26YJj0K158rdo/QA4OFtsFtqpfj2Ei576VyWnk05bpM3icrBpb0r5FBTEqfR0ABo7Oqr850rq+PYrnAm7Tc1W98E06B/KY0gWA84ugbDgKlD0avKGHQJodleIxWNVSkZKuRSw3C4Fd9ZNSnlFCOEPbBZCnJJS/pP3I6X8EdOgntDQ0HL7GXaewcivYZe5u6EfNau53PZcTydPetTowbqIdbwY+iKOGkcbRWkHRgPs/xnq9oBq9W57qtbNFffevUldv4GAN95Aoyt6trqiyzXksvzscu6ufjeBrrf/++ju7EifpgGsPnyVtwY2RedQOecrM8PCyLt6Fb8XXijeBS4+0GIUHPkN+rwLzrcuwiwJodWib96CJlcOssPGedCV8zeqlNipgwcBaNK3r8p/rqTOhMVyLSq96BNy83Dx9QUgEyD59htHXItKv/2AXLEqIcQO879pQojU6440IUSqNfuWUl4x/xsHLMe0cLHC+eNELHFpOYzvdPvZ5wJD6g0hKSeJHVE7rByZnZ3dBKlREDqpWKd7Dh2CMSWF9G2Vu3ri5kubScpJ4v5G9xfr/BFtQ0jKzGPbmfL9KUxZJC9bhsbdHffeve58coH2j5p2tjy0yCIx6Fu1pEZMPudibZtepWagqzq9HrKz/ykiG7TGvJq6sDqOSoXnW92N4S+2LfxOfVcSsrN5GNMuHJMAfsf0XMjKuuX05Z8esF6gyh1JKbuZ/3W3Zb9CCFdMCxfTzF/fC7xnyxgsZf6eS4R46enRqPDFgzfrEtIFH2cfVkespmfNnlaOzo7CZ4NbIDQqfPHgzVw7d0br60vqqlV49Olj5eDs57fTv1HDvQadggtfPHizuxr4Uc1Vx/KDUfRpGmDl6GzPkJxM2oaNeI4YXnjt56IEt4Hq7WHv96bBtLZsQ1Hnli3RGiTGMxHkGnLRaW3zKcgdZ6CFEP5CiOFCiCeFEI8IIToIIdTMdWVhzn2N0mqpCQiV/1x1RURQbdw43IBIULnwFYQQop4Qwsn8dQ8hxDNCCC8rdhkA7BBCHMZUf3qtlHKDFfuzinNx6ew6n8C4jjXRam5TOeA6jhpHBtQZwF+Rf1Xerb2TLsLZzdB2AmiLl6YiHBzwHDiQtL+2kZ+UZN347ORs0lkOxB3gvob3oSnmEMhRq2Fwq2D+OBFHUoZlah+XJymrViNzc/G+v3gz8jfo+iwkXYATK8och76VaSFhvah8ziWfK3N7xVXks0AI0VMIsRHTiu7+QBDQFHgLOCqEeFcI4WGbMBWrMdcAjjEYCATTTKPKf66azM+FICAa1HOh4lgKGIQQ9THlHdcAFlqrMyllhJSylfloJqX8r7X6sqafd11Ap9UwOrTw2s9FGVZ/GHnGPNZEVNLax+GzTRV42k4o0WWeI0ZAXh6pqyvnIsvFpxfjqHEssvZzUUaH1iDXYGTFocq1mFBKSdJvi3Fu2RLnJk1K3kCjgeDXGLZ/CkZjmWJx9PdHBAXQ8IrklA3zoG/3NmoA8JiUsr2UcrKU8i0p5UtSyiFAK+AgUHk/q6lKYmOJcXQ0DaCbNlX5z1VZbCxBLi6mAbR6LlQURillPjAc+NpclaMYy+GrruTMXJbsj2JYm2D83J1KdG0jn0Y0q9aMpWeXVr4avznppsWDTQaDV8neWDg3aohzixYk/76k0v1cUnJSWHV+FQPrDsTb2btE1zYN9qBFiCeL90VWqp9L1sGD5J47j/fo+0rXgEYD3V6AuBNwpuwlc93ahdL4Cpy8Zrs86CIH0FLKl6WUl4u4L19KucK88lupyPR6WL6c2Lw8AgCOH4fly1X936rI/FwIysw0DaDVc6GiyBNCjAUmAgXTopW4RETZLdh7mew8I5O61S3V9SMajOBs0lmOJxy3cGR2dngRZKdApydLdbnXyJHknD1L9tGjFg7Mvn4/8ztZ+VmMbzq+VNePDq3OqZg0jl2x6tpem0pe/BsaV1c8+hcvT75QzUeCd23TLHQZ31y4tGmDd5ok+oLtnnvFyYH2MufUfSaE+KrgsEVwig1ERJA/ZgxZgDeovNeqzJwP7+3gQAqo50LF8TDQGfivlPKCEKIOMN/OMZVbuflG5u66yF0NfGkUWLr1lwPqDEDvoGfp2Uo0h2Q0wp4ZENwWapSuqIrHwAEIZ2eSl1Sen0ueIY9FJxfRKagTDb0blqqNIa1DcHLQsDi80DnJCseQkkLqhg14DB6ExtW19A1pHaDrc3BlP0T8VaaYXMxbiGuOnsUoy5YSUlzFyYRfB9QGjgL7rztuSwgxWwgRJ4QodLsZ82KXFCHEIfPxdgniViwlKIhs85fOWq2qAV2VmXOgnfPzTc8JlQNdIUgpT0gpn5FSLjJ/f0FK+ZG94yqv1hy5SlxaDpO61Sl1G246N+6tdS/rItaRmZdpwejs6OwmSDwPnZ805UCXgtbdHY++fUlduxZjZuX4uWy8tJG4rDgmNC1ZTvj1PPWO9G8eyMpDV8nOM1gwOvtIWbkKmZOD9+jRZW+s9TjwCIEt75YpF9qpYUOMzjpqX84mMi2y7HEVQ3EG0M5SyheklHOklHMLjmJc9zPQ7w7nbJdStjYfFbIMUmWQvWsXYCoFo2pAV3GxsTj7+poG0CoHukIQQnQVQmwWQpwRQkQIIS4IISLsHVd5JKVk5vYLNPB34+6GfmVqa2TDkWTmZ7Lx4kYLRWdne741DWSaDi1TM16jRmLMyCB14yYLBWY/UkrmHZ9HHc86dA3pWqa2RrevQVp2PuuPRVsoOvuQRiNJCxbg3Kolzk2blr1BByfo9Q5cPQhHfi11M8LBAU3ThjS8IjmZeLLscRVDcQbQ84UQjwkhgoQQPgXHnS4y70qVWPYQFavR60EIsi+bPlZyPngQvvsO1pc9oV+pgMw50M7XrpELGI8f55fly7noVLJFVorNzQI+A7oB7YFQ87/KTXacu8aJ6FQmdauDKOUsa4HWfq2p41mncqRxxByFC39Dh8eKXbquKPrQUHS1apG8ZImFgrOf8NhwTiaeZHzT8cUuXVeUTnWqUdPHhUVhtpkdtZaM7dvJvXQJn/Gln5G/RYv7ICQU/njXtJC1lLxDO1M7Fs5esU0edHGeEbnA/wG7+Td9I9xC/XcWQhwWQqwXQjQr6iQhxGQhRLgQIjw+vvLu6GNz5pxXWVAAXadTOa9Vmfn5EKzT0QK47OzMeGDRiy/aOzLl9lKklOullHFSyoSCw95BlUdfbz1HoIczw9uWfft5IQQjG4zkcPxhm5bOsortn4HOHdo9VOamhBB4jR5N1v79ZJ8+U/bY7GjW0Vn4OPswqO6gMrel0QjGdaxJ2IVETsekWSA6+0icNx8Hf388+t5ruUY1Guj3IaTHwI7PSt2MW7t2aCUkH75jlrFFFGcA/SJQX0pZW0pZx3yUbunyjQ4AtaSUrYCvgRVFnSil/FFKGSqlDPXzK9vHbsp1CnJec3IAyMnNBQcHlfNaVZmfD4/l5XFECP7MNmXHDyhNkXzFlv4UQvyfEKKzEKJtwWHvoMqbsAuJhF1IZHL3ujg5aC3S5rD6w3DWOvPrqdJ/9Gx3187B8eXQ4VHQl6xEW1G8Ro5AODmRtNBq5cit7ti1Y+y8upMJTSegd7BMJaLRoTXQOWiYv+eiRdqztZzz58nYuRPvsWMQjhYu9FOjPbQYDbu+gcTSTeIVbKjiePy8JSMrUnEG0OcAi68GkFKmSinTzV+vAxyFEL6W7ke5g9hYnCdNAjDlvf79t13DUewsNtaU+wys8/Ag2NmZli1b2jko5Q46Ykrb+B+mXdg/BT6xa0Tl0Dd/nqOaq46xHWparE1PJ08G1h3I2oi1FXdnwh2fm/JQOz1hsSa1Xl54DBxIyurVGNIq5mzrj0d+xEPnwf2NLDeB4OOqY3DLYJYfuEJadp7F2rWVxF9+Qeh0eFlrUqX3NNDqYPkUMOSX+HKtlxdZ1X2pfjGD2IxYy8d3k+IMoDOAQ0KIHyxZxk4IESjMSWhCiA7mWNTHjra2fj1OM2cC5gH0hQumFdiq9m/VY86B5vhx8qRkU2oqA7KzTdu7K+WWlLJnIcc99o6rPDkSlczfZ+KZdFcd9DrLzD4XGNt4LNmGbFacW2HRdm0i+bJp4VbbieDmb9GmvR8Yh8zMJGX5Cou2awtnks7wZ+SfPNDkAdx0bhZte0LnWmTkGlh2oGLtTGhITSVlxUo8Bg3CweeOy+BKxzMEBn0GkXtge+nmAPStW9HwimRv9B4LB3er4gygVwD/BXZRsjJ2izDlTTcSQkQJISYJIaYIIaaYTxkFHBNCHAa+AsbIyrRNT0UREYHj2LHogFRQtX+rMnMONC4u7Mb0fOh/113quVDOCSEChBCzhBDrzd83FUJMsndc5ck3W8/h4ezA+E61LN52I59GtPVvy6+nfrVZ/VmL2fkVIKDrMxZvWt+sGfpWrUhauBBZxq2abW3mkZm4OLjwQJMHLN52qxpetKruyfw9lyrUzoRJvy5GZmXhM/5B63bUcjS0vB+2fQSXSz4IDuzSE/dsOLl3gxWCu9EdB9DXl64rSRk7KeVYKWWQlNJRSlldSjlLSvm9lPJ78/3fSCmbSSlbSSk7SSl3WeIBKSUUFITw9CQAiAVV+7cqM+dAk5XFOsAB6N2woXoulH8/AxuBYPP3Z4Dn7BVMeXPsSgqbTsTySLc6uDtbZ4PGsY3HEpUexY4rO6zSvlWkXoWD86HVGPCsbpUuvB8YR+7Fi2Ts3m2V9q0hIiWCDRc3MKbxGDydPK3Sx/jOtTkXl87u8xXjQ3djdjaJc+fi2q0bzk2aWL/DAZ+AV01Y+hikl6xwhHuPnhgFOGwLs/ob2iIH0EKI1UKIwUKIW15xhBB1hRDvCSEesWp0im3ExhKo1xMDqvZvVWfOgV4H3OXqikeiqkRZAfhKKX8DjABSynyg4u/WYCGfbDqNl4tjmTZOuZNeNXvhp/dj4ckKtGju7/8DowG6v2S1Ltz79UNbrRpJ8yrOxpjfHPwGvYOeic0mWq2PQS2D8HHVMWtHxfh0L3npUgwJCVSb/JhtOnT2gJGzISMe5g+HzOL/HXLw8SG7ZX1aH8vkdIJ1q+Pcbgb6MeAu4KQQYp8QYp0Q4k8hxAXgB2C/lHK2VaNTrM+c9xqUlWUaQB8/bsqDVTnQVY/5uRB5/DhHgQEZGeq5UDFkCCGqARJACNEJqKAr2ixr38VE/jodz5S761lt9hnAUevI2MZj2Xl1J6cTT1utH4tJjIAD86DdRPCubbVuNDod3uPGkr5tGznnzlmtH0s5nnCczZc2M6HZBHycrZTnCzg7ahnfqRZbTsVxNrZ8L7KUeXkkzpqNvk0bXNrbsLx89XYwZgFcOw0LRkF2arEv9Rs4lOBEOLhnpRUDvM0AWkoZI6V8BVN+8mRgOvA80ExK2UdKad3IFNsoqP3r4MBlQOr1Kge6qjI/F9brdAD0d3ZWz4WK4QVgFVBPCLETmAdYPqm1gpFS8n8bTuPn7sTEzrWt3t/oRqPRO+j5+fjPVu+rzP76EDSO0P1lq3flPW4cQq8nYfYcq/dVVl8f+BovJy8mNrXe7HOBCZ1r4eSg4aft5XvT0NR168i7epVqkx8r8+ZDJVa/F9w3F6IPwy8jTWlHxRA8cDhGAdmbt1o1PIdinOMP/I6pbvNs4LBVI1Jsy5z32shgIAm4lpWFn6oFXWldi0pn+acHij7BZSCxfdvxCnAGwRm3YFhwFbj1hevq2WRrhamUzHHgbqARIIDTFG+BeKX299lrhF1M5L2hzSxeeaMwnk6ejGo4ioUnF/J0m6cJdgu+80X2EHsCjvwGXZ8Fd+u/zjt4e+M1YgRJv/2G37PP4BgQYPU+S2NfzD52Xt3JS6EvWbzyRmGquTlxX2h1ftsXxUv3NsLfw9nqfZaUNBq59tNPODVsiFuPHvYJovEAGDXbVNpuRlcY9h006n/bSxyqVSOpSRA1w6+QkZuBq87VKqEVZxHhW0ADTNvFPgScFUL8TwhRzyoRKbYXG0vjgQMBOAWqFnQl1bBDAL7Vb/+HwZiSTBJQTa+H4GDIzbVNcEpZ7JZS5kspj0spj0kp8zBVQLIaIUQ/IcRpIcQ5IcRr1uyrNIxGyccbThHipWdMe8vVfb6T8U3GIxDMP1GOc363vg9O7qYBtI34PPwQGAwkzS+fPxcpJV8e+BJ/F3+L1n2+k0e71SXPaOTnXRdt1mdJpG3YQO6581SbPNn2s8/XazoUHv/bVOZu0RjTYDru5G0v0ffpTUiC5NDeVVYLqzgz0EgppRAiBogB8gFvYIkQYrM5zUOpyNavp4l517lTwF0FtaCdnU1VOZRKodldITS7q4gtjPV6yM5mA/A5sA7oD+o5UI4JIQKBEEAvhGiDafYZwAOwWvFuIYQW+BboA0QB+4QQq6SUJ6zVZ0mtOHSF41dT+XJMa3QOtpuMD3ILon+d/iw9u5QpraZYrYpDqV3YDqfXwj3/ARfr5fjeTFe9Oh79+pL062KqTZmC1s36M7wlsenSJg7HH2Za52k4O9huJri2ryv9mgXyy55LPNGzPm5OxRqS2YTMzyf+q69xatAAjwG3n/G1Cd8G8OgW+PO/sPdHOLwI6veGZsMhJBR8G5q2BDdrPHwiF7+cT9yaFXDXWKuEdMdXFiHEs0KI/cDHwE6ghZRyKtAOGGmVqBTbioigxtixuAJHQdWCrorM+c9rHBzQAz1ULnxF0BfTjoPV+XcHwk8xrVV5w4r9dgDOSSkjpJS5wK/AUCv2VyLZeQY+2XialtU9GdzS9mkUDzV/iKz8LBadWmTzvm/LaICNr4NnDej8pM2793lkEsb0dJIWlq+fS44hh8/CP6ORdyOG1R9m8/4fv7seqdn5zN99yeZ9307KylXkXryI7zNPIzTlJCPMwQn6vAfPH4d73oKYo7DySfiuI3xYA75oCd91gZ/uwXX5aK5Uh7prj7CjQxN2dGjC0hGtLRtOMc7xAUZIKW/47UopjUKIQRaNRrGPoCA0np6EAnuEgMxMUHnQVUtQEFKjYWV+Pn01GvQ5OaoeeDlnrsc/VwgxUkq51IZdhwCR130fhWk78X8IISZjWnxOzZq2S6EAmL3zAldTsvns/tZoNLb/2Lmhd0N6VO/BvBPzeKDJA7jr3G0eQ6EOLTQNOEbOAkfbV9bRN2+Ga/e7SJw1C+9xY8vNLPT8E/O5mnGVWV1nodVYP1f+Zq1reHF3Qz9+/Ps84zvXKhez0DI3l2vffotz8+a49+5t73Bu5VrNtAC224uQcA6i9pkWGmYnQ24G5GWRp3EiroWGZPdUtOb0k7ygahYNozg50O/cPHi+7r7bJ6EoFUdsLJ3btOEgkAUqD7oKOrB1K1HA0HbtYMoUVQ+8grDx4LlYpJQ/SilDpZShfn5+Nus3IT2H7/48T5+mAXSqa9k/liXxROsnSMtN45cTv9gthhvkpMHW6VC9PTS33wfHfk8/jSElhaRfysfPJT4znp+O/MQ9Ne6hQ1AHu8XxfJ+GJGXmMbec5EInLVlC3tWr+D37rH1zn+9EowG/htDmARjwMYz40VT6bvwyvg14l7ecXqPpN38zctkhRi47xJhvt1i2e4u2plRc69fT6eBB8qU07dNekAetagBXfno9CMGKq1fRAIP27YPvvoP16+0dmVI+XQFqXPd9dfNtdvf5H2fIyjPwWv/Gdo2jSbUm9KrZi/kn5pOSUw5Kcu/4HNJjoe8Hptd1O9G3aIFbz54kzPkZQ5r96x9/dfArco25vBj6ol3jaF3Di56N/Pjx7wjSsvPsGosxI4NrM2agb9cO125d7RpLaaVk5jFr+wX6NgugeYj11iGoAbRiEhFB5xEjANgOKg+6KjHnPy8Tgm6Ar/rdK7e3D2gghKgjhNABYzDVobark9GpLNx7mfGdalHPz/7pAVNbTSUtL83+FTkSzsOur6HFaKhhw40wiuD71JMYU1JInDfPrnEcijvEinMrGN9kPDU9bJtmVJjn+zQkJSuPn3detGsc12bOxBB/jYCXXyrfs8+3MXNHBGk5+TzXu6FV+1EDaMUkKAh/f39aAxtUHnTVEhTE8bQ0TkjJfQ4OkJ2t8p8rECGEixDiP0KIn8zfN7Dm+hTzVuFPARuBk8BvUsrj1uqvmDHx3uoTeOgdea53A3uG8o9GPo3oU6sPv5z8heTsZPsEISWsewkcnOHe9+0Tw030zZrh1rsXiT/PxZBin9n5fGM+0/dMJ8AlgCmtptglhpu1rO5F7yb+/LQ9gpRM+8xC50VHkzh7Dh4DBqBv3douMZRVUkYuc3ZeZECLQJoEeVi1LzWAVv4VG8uAtm3ZCSSDyoOuQn7bvh0NMKp/f5X/XPHMAXKAzubvrwBWHS1JKddJKRtKKetJKf9rzb6KY+PxGHZHJPBin4Z4uejsHc4/nmj1BFn5Wfx09Cf7BHByFZzfCj3fBPfys4GJ39NPY0xP59oPP9ql/19P/cqZpDO81uE1XBytVvGxxF7o04i0nHy++fOsXfqP++xzkBL/F1+wS/+WsPRAFOk5+TzTy/pvpNUAWvnX+vUMOHAAg5T8ASoPuirQ65FCsDg5mbuBwNWrVf5zxVNPSvkxkAcgpczk35rQlV52noH3156kUYA7YzvY/6P469X3rs+w+sNYeGohkamRd77AknLSYcPrENAC2j9q277vwLlRIzyHDydp/nxyo6Js2ndcZhzfHPqGriFd6VWzl037vpOmwR6MaludubsucTkh06Z9Zx05Qurq1fg89BCOIUXsF1ABbDoRS+NAdxoHWnf2GdQAWrleRAQdx4zBB1gBKg+6KoiI4FC/fpwG7gf1O6+YcoUQekACmHeJzbFvSLYza8cFopKyeHtwUxy05e9P2pOtn8RR48jnBz63bcfbPoTUKzDwE9DavzTazfyefRYcHIj79FOb9vvxvo/JM+TxRoc3ymWO74v3NkKrEXy08ZTN+pRGI7H//R/aatWoNnmyzfq1tMSMXMIvJtKnqW0+bSl/rzaK/QQF4eDlxUhMA+gMlQdd+QUFMefCBZyA0Tqdyn+umN4BNgA1hBALgC1AldghNjoli2+2nqNvswC61ve1dziF8nfx5+FmD7P50mYOxh20TadXD8Hub6HtRKjZyTZ9lpBjgD/VHnmEtPUbyDxom5/LtshtbLy4kcktJ5eLhYOFCfR05rHudVl7JJr9l5Js0mfK8uVkHT6M/0svoXVztUmf1rD1VBxGiRpAK3YSG8sDQ4eSgXlZvcqDrtRycnJYcPYswwDvoUNV/nMFJKXcDIwAHgIWAaFSyr/sGZOtfLj+FAYpeWtgU3uHclsTm03EX+/PJ/s+QUpp3c4M+bD6WXDxhT7vWrevMqr2yMNo/XyJ+/AjpNFo1b4y8jKYvmc69b3q80jzR6zaV1k93r0uvm5OvL/2BEajdZ8vhuRk4j75FH3btngOHWLVvqxt84kYAj2caWHF0nXXUwNo5Ubr13PXypXUAH4BlQddmen1rHZ2JtFo5GGA339X+c8ViBCibcEB1AKigatATfNtlVr4xURWHrrK493rUsOn/CwEK4yLowtPt32aI9eOsDpitXU7C/sBog9B/49A723dvspI4+qK/3PPk3X4MCkrVlq1r68OfEVcZhzTukzDUeto1b7KytXJgVf6NeLg5WSWHLBujnjcl19iSEkh8O3/lJ8tu0shO8/A32eu0bupv81ScyruT0uxjogINOPG8YCDAxuBKGdnlRNbWUVE8GNgINWFoDeo/OeK59PbHJ/YMS6rMxgl01YfJ8jTmak96tk7nGIZUm8Irfxa8Wn4p9bbXCXpEmx9Hxr0hWbDrdOHhXkOH4a+TRvi/u//MCQnW6WPw/GHWXRqEWMaj6GVXyur9GFpo9pWJ7SWNx+sO0lSRq5V+sg6dpzkXxfj/eADODe27+ZDZbXz3DWy8gz0aWq79EM1gFZuFBQEHh48bjAggRnZ2bB1q72jUqzgxJkzbI6JYaqUaJ2dVf5zBSOl7Hmb4x57x2dNS/dHcexKKq/1b4yLrvwtkCuMRmj4T6f/kJyTzNcHv7Z8B1LCmudAaEwLB8vhArnCCI2GwGnvYEhNJe7zLyzefq4hl3d2voO/iz/PtHnG4u1bi0YjeH94c1Kz8/nYCgsKZX4+Me+8g9a3Gn5PP23x9m1t84lY3Jwc6FTXx2Z9qgG0cqvYWGpPncrgHj34UaslOzoa3nvP3lEpFvbNk0/iBDzWqBHs2aPynysoIYSzEOIFIcQyIcRSIcRzQghne8dlLek5+Xy88TRta3oxpFWwvcMpkUY+jRjXeBy/nf6NY9eOWbbxw4tMNZ97vQNe5XOBXFGcGzXC58EHSf7tN7IOH7Zo2z8d/YnzKed5u/PbuOnsv0NlSTQO9OCRrrVZFBbJgcuWXVCYtGAB2cePE/jGG2jd3S3atq0ZjZI/TsZxdyM/nBy0NutXDaCVWy1bBrNn88xff3HNYGARwIwZKhe6stDrSRaCucePMw7wO30aWreG2bNNv3ulopkHNAO+Br4xf23n/aOtZ8Zf57iWnsN/BjUtl2XI7uTJ1k/iq/flvd3vkW/Mt0yj6XGmms81OpW7ms/F5fv00zj4+RH9zjRknmV24judeJqZR2YyqO4gulfvbpE2be3Z3g0J8nTmjWVHyc23zELLvOho4r78Ctfud+Her59F2rSno1dSuJaeQ58mtt0sSA2glcJFRNBz7FhaCsHHgEGvV/mxlUVEBN+0bEkm8Ayo3OeKr7mUcpKU8k/z8RimQXSlE5mYyU/bLzCsdTBtapbvBXJFcdO58WqHVzmZeJJ5J+ZZptF1L0NeJgz5GiroQjCtmyuBb/+HnFOnSJg1q8zt5RvzeWfXO3g4efBK+4pb1dHNyYH3hzXnVEwa3/11rsztSSmJmf4+GI0Evv1OhXwTerNd5xMA6FK/mk37rZj/0xTrCwpCeHryH+AU8GtWlsqFriTS0tP57OhRBgOtVe5zZXBACPFPsV8hREcg3I7xWM1HG06hEfBKv4q94OneWvfSq2Yvvj34LRdSyvjG9eQaOLEC7n4F/BpaJD57ce/dG48B/bn27XfknC3bdta/nPiF4wnHeb3D63g7V8w3WwV6NQlgWOtgvtl6jpPRqWVqK23zZtK3bsXv6afQVa+4Ow5eb3dEAvX93fB3t23mmtUG0EKI2UKIOCFEoYlewuQrIcQ5IcSRqlB2qcKJjWXElCm0bNiQd3U68lUudKXw7fjxJEnJf+rWVbnPFZgQ4qgQ4gjQDtglhLgohLgA7AZC7Rud5R2KTGbNkWgm31WXYK+KnUomhODNjm/i5ODEtF3TMMpSfjSflQxrXzRt1931OUuGaDcBb72Fxs2Nq2++hTQYStXG5dTLfHPoG3rW6Enf2n0tHKF9vDO4GV4ujryy5Aj5htI9XwypqcROfx+nJk3wmTDBwhHaR57BSPjFRDrXte3sM1h3Bvpn4HbJNf2BBuZjMjDDirEopbFsGZo5c3jnzBnO5uaakipVLnTFpdeTJgSf7t1Lf6B9RITKfa7YBgGDMb3O1gHuBnqYv+5vv7AsT0rJh+tPUs1Vx+S7K0bZujvxc/HjlfavcCDuAItOLSpdI5v/AxlxMPRrKOe1jYvLwceHgDffJPvIERJ/nlvi643SyDu73kGn0fFWp7cqRYoCgLerjveGNufolRS+33a+VG3EffoZ+QkJBE2fjnCsHM+XI1HJZOYa6FyvEg2gpZR/A4m3OWUoME+a7AG8hBBB1opHKaWICIaNHUsHjYY3gDRnZ/D3h7177R2ZUlK7d/OBXs814F1Quc8VnJTy0vUHkAXI645KY9uZePZEJPL0PfVxc6oYZeuKY2i9oXQN6coX+7/gUuqlkl0csQ0OzIPOT0FwG+sEaCceAwfg1rsX8V98QfaZMyW6dunZpYTHhvNi6Iv4u/hbKUL7GNAiiEEtg/jij7Mcu1KyWuKZ4eEkL16Mz8SJ6JtXniUSu835z50q2Qz0nYQAkdd9H2W+7RZCiMlCiHAhRHh8fLxNglPMgoLQeHrylZTEAP/Nzoa4OPj+e3tHppTQxU8+4bOsLB4E2qvc50pDCDFECHEWuABsAy4ClWY7SaNR8tGG09Tw0TOuYy17h2NRQgje7fwuOq2ON3a8UfyqHHlZpu26vetAj9etG6QdCCEIevddNO7uXH31NWRu8TYSic2I5bPwz+gQ2IERDUZYOUr7eH9Yc6q56Xh+8SGy84qX4mLMzSX67XdwDAnB7+mnrByhbe2OSKBxoDs+rjqb910hFhFKKX+UUoZKKUP9/PzsHU7VExtLR42Gh4DPgLOgUjkqEr0ehOC1BQvQAB+AafAshMp9rhymA52AM1LKOkAvYI99Q7KcVYevcjI6lZfubYTOoUL8ySqRANcA3ur0FkfijzDn2JziXbTtY0i6AIO/BF353sa8tByqVSNo+nvknDxJ/LffFeuaD8I+IM+YxzudK0d1icJ4uej4eFQrzsal838bTxfrmoQffiQ3IoLAadPQuFSe50tOvoHwi0l2Sd8A+w6grwA1rvu+uvk2pbxZtgwiI/lg+HD0wBTAqMraVRwREfxxzz0sBl4BqhekbkRFqdznyiFPSpkAaIQQGinln1hpEaEQYpoQ4ooQ4pD5GGCNfgrkG4x8/scZmgZ5MLhlxdo0pST61+lPv9r9+O7Qd5xMOHn7k2OOwa6voPWDUPdu2wRoJ+69euE5YgQJP/1E5oGDtz13y6UtbLm8hamtplLTo2JtJFNSdzf0Y0LnWszacYGd567d9tyc8+dJ+PFHPAYNwu2ubjaK0DYOXU4mJ99olwWEYN8B9CpggrkaRycgRUoZbcd4lNsJCiIwIIBPhGAr8L0qa1dhZGRm8ti2bTQEXnVyUqkblU+yEMIN+BtYIIT4EsiwYn+fSylbm491VuyHlYeucikhk+d6N0CjqZwzigXe6vQW3s7evLb9NbLzsws/yWgwpW44e8G9020an70EvPE6jkFBXH31VQzphT+t03PT+d/e/9HIuxETmlWO6hJ38nr/JtTzc+WF3w6RlFF4ios0Gol+5x2EiwsBr79m4witb3dEAkJAxzqVbAAthFiEqZxSIyFElBBikhBiihBiivmUdUAEcA74CXjCWrEoFhIby6NTpnBv5868LAQRqqxdhfDm8OFcNBiYWbs2+r17Vdm6ymcokAk8D2wAzmOqzlGh5RuMfPPnOZoGedCnqW13GLMHTydP3u/6PhEpEXy+//PCTwqfDVfCod+H4OJj2wDtROvmRvD/fUzelSvE/ve/hZ7z5YEvic+KZ1qXaThqKkd1iTvR67R8OaYNiRm5vLbsCFLeum44eelSssL3E/DKyzhUs88g05p2n0+gWbAHni72+Z1bswrHWCllkJTSUUpZXUo5S0r5vZTye/P9Ukr5pJSynpSyhZSyUhb+r1SWLUPMmcPM3btxkJKJQL7KhS6/9Hq2C8FXR4/yJHDXxYuqbF0lIoSoL4ToKqXMkFIapZT5Usq5wAHAy4pdP2Wu3T9bCFHoDhWWWPi9+shVLlzL4JleDSptPuvNuoR04cEmD7Lw1EJ2XNlx451psbDlPajbE1qMsk+AduLSti3VHp9MyvLlpG7YcMN9x68dZ/HpxYxtPJbmvs3tFKF9NA/x5OW+jdh4PJbfwiNvuC8/IYG4Tz7FpX17PEdUvgWV2XkGDkYm08lOs89QQRYRKuVIRAQ1xo3jO52OHcCbDg4wYoRpYKZmNcuP6GjimzZlrLMz9YQwLRxUZesqmy+AwrYlSzHfVypCiD+EEMcKOYZiqtdfD2gNRAOfFtZGWRd+G4ySr7eeo3GgO/dWgdnn6z3X7jnqe9XnrR1vkZh9XSXYTW9BfjYM/NQ0aVHF+D3xBM4tWxL99jvkmf/WGIwG3tvzHr56X55u87SdI7SPR7vVpUu9akxbdYKI+PR/bo/75FOMGRkETqucCyqPRKWQm2+ko53yn0ENoJWSCgoCDw8eyM/nca2Wj/PzWblvH4SFqXSOcsT43ntMOHCAazk5/Aa4q7J1lVGAlPLozTeab6td2kallL2llM0LOVZKKWOllAYppRFT6l2H0odftDVHrhIRn8GzvSp/7vPNnLROfHjXh6TmpjJt1zTTR/MX/oajv0G356Fa5dhIpqSEoyMhH3+EzM/n6muvI41Gfj/zOycSTvBy+5dx07nZO0S70GgEn41ujZOjhucWHyLPYCQzPJyU5cup9vDDONWrnM+XsAum+s+htey3TbsaQCslFxsLU6bwhUZDO2BiZCTnjEZV2q48MJes+/D779kAfCElbaQEo1HlPlc+Xre5zyr/CW/a7Go4cMwa/Tg5aOjV2J++zarmm71GPo14tu2z/Bn5J8tO/2bartu7tmkAXYXpatcm4PXXyNyzh8ifvuWrA1/RMagj/WrfbtPjyi/Q05kPhrfgSFQKX248Qcy77+IYHIzvE1PtHZrVhF1MomGAG952qP9cQA2glZJbtgy+/RbnS5dYMmQIDsAA4JrapdC+oqOhVSuWdujAm8BY4PGCcoOXLsG336rc58olXAjx2M03CiEeBfZbqc+PhRBHhRBHgJ6YFi5aXL/mQcx6qH2Vm32+3vim4+kY1JGPwj7kUnIE9P8/cFSTE16jRuHepzepX80g4Eomb3Z8s1KmKJRU/xZB3B9ag+jZc8k5e46At95EU0knswxGyYFLSXSoY9+FtGoArZReUBC1g4NZLQSRwJDsbLLULoX2M306e8PCeHDfPjoBs5ycEDk5Km2j8noOeFgI8ZcQ4lPzsQ2YBDxrjQ6llOPNi75bSimHqNKj1qMRGt5v9SyOhlxer1mPvPo97R1SuSCEIPHZMaToJW+s11PLKejOF1URb3by48HTmzlcvRnGznfZOxyrORmdSnpOPu1rqwG0UpHFxtJZo+EXTFufPYiqzGFz5rSNCzNmMERKgqRkJaCXUqVtVGLmfOQuwLuYtu++CLwrpewspVS/9EogcPcM3klI4ajMYuaRmfYOp1wwSiMfnv6WBSN8cLuaTPznRZT8q4LSv/kKJ4x803QI7646bu9wrGbvBdPiWjUDrVRs5l0KR44bx+eOjiwDJmq1GIYPV5U5bMGctnGpb1/uEYI8YJ2zM/4qbaPKkFL+KaX82nyo3Y0qi6j9cGgB97aaxKC6g/jhyA8cv1Z5B0XFtfr8ao5eO0q/+1/F+4EHSJw7j4zdu+0dlt1lHT5MyooV+D40kZGDO7Hs4BXWHa2cHxDtu5BIdW89QZ72naRTA2il7MyVOZ41GPifgwMLDQYmbN2KYe9eVZnD2qZPJzIsjJ5bt5IsJX/odDTOzVVpG4pSkRmNsP4VcAuA7i/xesfX8dX78vqO14vepbAKyMjL4IsDX9DStyWD6g7C/6UX0dWpw9XX38CQWlhFx6pBGo3EvP9fHPz8qPb4FJ66pz4tq3vyxvKjxKVWrueLlJJ9FxPtPvsMagCtWIq5MsfrGg3/AxampDBBSvJUOod1mNM2Ls+YQU8pScjLYxPQFlTahlLxpUTBnhn2jsJ+ji8z7TjY6x1wcsdD58H0rtO5kHKBLw58Ye/o7GbW0Vlcy7rGax1eQyM0aPR6gj/+iPz4eGLef9/e4dlNyoqVZB89iv/LL6F1c8VRq+Hz+1uTnWfgtWVHC92lsKKKuJZBQkYuHeyc/wxqAK1YirkyBxcv8vq4cXzg6MhCYLBGQ9rgwSqdw5LMaRuH77mHzkJwDdjo5ER7lbahVBYH5sGG1+DkGntHYnv5ObDlXQhsAa3G/nNz5+DOjGs8jgUnF7AvZp8dA7SPmIwY5p2YR/86/Wnh1+Kf2/UtWuD7+OOkrlpN2pYtdozQPoxZWcR/8QXOLVviMXjwP7fX83Pjlb6N2XoqjmUHrtgxQsvaZ85/bq9moJVKx5zO8ZrBwEwHB/4wGum+eTPRKp3DcqZPZ0tYGHf99RdCSnbodHTKy1NpG0rlcddLENQaVj0NqZUzj7NIYT9C8mXoMx00N/6Jfrbts9Rwr8HbO98mMy/TTgHaxzcHv8EojTzb9tYCM75THsepcWOip03DkJxs++DsKPHnn8mPiyPg1VduKef3UJfadKjtw7urjxNbSVI5wi4k4uumo66vq71DUQNoxQrM6RyTNBpWA2ezs+koJeEF6RzOznD33WpGuiSio0GrRQrBjzNm0F9KahmN7AGag0rbUCoXBx2MnGnaunrFFFNOcFWQmQh//x/U7w31bi1b5+Lowntd3iMqPYqvDn5lhwDt43TiaVadX8W4xuMIcQu55X6h0xH8wf8wJCUT89//2SFC+8iPj+faTzNx79MHl3btbrlfoxF8NKoluQYjr1eSVI6wi4mE1vIpF7W/1QBasbzr0jn6jxvH387OCKAr8EOHDsj774cdO9SMdElMn06m0cjD7u48DtwDbNfrqa7SNpTKyrcB9PsQIv6CPd/aOxrb2P4p5KRBn6JfG0MDQ/9J5QiPCbdhcPbz2f7PcNe581jLW/YN+odzkyamVI7VVSeVI/6bb5G5ufi/+EKR59TxdeVlcyrHikMVO5XjSnIWUUlZdKxr//QNUANoxZrM6Rxtc3PZ7+RET2BKWBgT580jXW39XTzmxYJnZsygMzAvLY13gLWAl9okRans2k6AxoNgy3SIP2PvaKwr+bIpfaP1OAhodttTn237LNXdqjNt9zRyDDk2CtA+9kTvYdfVXUxuORlPJ8/bnuv7+GScGjcm5t33MKSl2ShC+8g5f57k33/He8wYdLVr3/bch7vUpm1NL6avOUliRq5tArSCsAsJAHSsU83OkZioAbRiXeZ0Dt+9e1k7fjzTPDz4BWgFbHdyghEj1ALDokRHY2zZkq9btqQ1EAmsFYJpDRqg/eMPlbahVH5CwKD/b+++46Mqs8ePf07qJISEEkoCoQcLSJMi0mwooqKL6676dVddFElELCuiq6IrFlxcUWmCff3ZOyqrotIE6b0TQHpJ6AESksz5/TETN2KAhMydm0nO+/WaF1PufZ5zJ8nlzDPnPs9IiIqFL+4Eb4HbETln2rOAwAUPnXLT2MhYhnYeyqaDmxi/ZLzzsblEVXlp4UvUrVKX68+8/pTbS1QUScOGkZ+Vxe7nnw9ChO7JfOFFwmJiSExPO+W2YWHC033P4eDRPJ6ZtCoI0Tlj7sa9xHsiOKNuVbdDASyBNk4rLOdo3Zrw//yHx268kakiIEKP3FzunTqVo3aBYbF+GTyYS+bOZdDSpVwALIuO5nIRuOQSuPhiK9swlUNcbbj8X7B1LsypoMli1jpY/C50uA0S6pdol87JnenTtA9vLH+DdfvWORygO37c8iPLspaR1jqN6PDoEu0Tc05LavzlL+x/732OLFzocITuOLpsGYcmT6bGrbcSUaNk5Qxn1o3n9u5N+GjBVn5ev8fhCJ0xZ8NeOjSqQXiY+/XPYAm0CbZdu+ielsaSiAjSgBf27qWlKhPHjUMr+wWGO3ZAjx7kREfztAgt33mH+cCr+Eo26tnS3KayOuc6aN4LfngC9m5wO5rAm/IURMZCtxPXshbn/vb3UzWqKo///DgFFWx0vsBbwOhFo2kU34g+TfuUat9ag+4iMjmZHY8OxXssdEsWTiRz5EjCq1enxi23lGq/uy9OpUGNWB7+bBk5eaH1+7L7UA4bsg6Xm/pnsATaBJt/RDpu0ybG3HgjP0ZH4wGuBi5PSmL15ZdX2gsM9YknmDhjBi2iongYuDQ8nGVAv9hYxC4WNJVZYSlHeCR8eQ9UgNkEfrVjCaz4DM5LhyqJpdq1uqc6gzsMZmnmUj5c+6FDAbpj0sZJZOzP4M62dxIRFlGqfcOqVKHuPx/n2Pr17Hn1VYcidMfh2bM5POtnat7Rn/C40k3l5okM58lrWrIh6zCvzgitD6LzNu4DoGM5qX8GS6CNW/wXGF6Yl8fi6GheAGbv2ME5n3/OHV4vmwovMAwLq7gjrv4RZzwepolw4csvc7UqnuxsJgOfFhTQ0OOBnBy7WNCY+GS4eChsnAbLP3E7msD58UnwVIPzB57W7lc2uZJOSZ0YtXAUWUezAhubS/K8eYxdPJYza5zJpQ0vPa024rp1o2qvXuwZP4FjW7YEOEJ3qCq7R44kIimJ6jfccOoditG9eS0ub1mX0VMy2Lb/aIAjdM6cjXuIjQqnRXK826H8yhJo4x7/BYaRc+Zw9803sy4piTsiIngTSAUGxMezWfV/o9GFCWcoJ9RFj2HYMGbMmMFF1atzAbAGGAUsjonhkvr14eabYfZsK9swplD7v0FyW/j2H5BzwO1oym7rfFj3HXQZBJ6TzzBxIiLCw50e5mjBUUYuGBngAN3x5fov2Zq9lYFtBhImp5+m1HnoQSQ8nF1PPlUh5kDOnjqVnCVLSUxPIyy6ZDXhxXn4irMAePrr0LmgcO7GvZzbsDqR4eUnbS0/kZjKp8gFhrz5JrWuvprRXi8ZwG3A6wcP0hS4ftw4Zoqg9euHfnnHsGEcmz6dd5KS6DRuHN1VWblzJyOBDcBAj4fI3Fy46ip4803fe2NlG8b4hIXDFc9D9m748Sm3oym7qcMhpgZ07F+mZhonNOaWFrcwcf1EFuxaEKDg3JFXkMeEpRNoWbMl3et3L1NbkXXqkDhwINnTppH9448BitAdqkrmqFFEpqRQ7ZprytRW/eqxpF/QjK+X7WBWRvn/1mLf4WOs3nmITuVg+e6iLIE25Yd/RDpl8mTGpqaSERbG3cA3QFfgXK+XV7xe9ofSioZFyjQyRHhs3DgaAjcBB/CNOG+IieGe+vWJsRFnU06IyHUiskJEvCLS/rjXHhKRDBFZIyKXBT24eu18s1XMewW2Lw569wGzZR5kTPaNPkeXfVqu28+5naQqSTw5+0nyvHkBCNAdE9dPZFv2NtLapAVktbkaf7mJ6NRm7HzqKbxHQnf58+wffiB35SoS09ORyMgyt9e/exNSasTw2MQV5BeU75U+5/2yFyhf9c9gCbQpTwpHpP3TtDUAnvN42Aa8XLs2x0ToD9QB+qak8EnnzuTMmFG+SjyOi2HXQw8xasYMzqtalVRgGHAu8E1YGCuBgdHRxNqIsyl/lgN9gelFnxSRs4HrgRZAL2CsiIQHPbqLHvGN3P53SOheUDhtOMTWhA4nXl2vNGIjYxnScQgZ+zP4YPUHAWkz2ApHn1sltqJbvW4BaVMiI6k7dCj523ew59XXAtJmsKnXS+boMUQ1bEjCVVcGpE1PZDiPXHE263Zn89688l0jPmfjXqIiwmidcnplTk6xBNqUT/7RaGbPpkp6OnfExrIMmBsVRRowa8sW/jh1KjVVuWrcOMaKsPH4Eo/jE+pAJNinaNP7xBPMnTGDfyYn00mEpLfeYpAqOVlZ/AvYDHzl8XCZ10tYixYwZ46NOJtyR1VXqeqaYl66GnhfVXNVdSOQAXQMbnRATDXfBYVbZofmBYVb5kLG93D+IIiOC1izF6VcxPnJ5zN2yVj25uwNWLvB8vn6z9l+eHvARp8LxXboQHzvy9nz2mvkbQu95awPTf6e3NWrSbwzHYko3YwkJ3Pp2XXo2LgGL0xey6Gc8vutxcyMLDo0qk50RPA/q5+Mowm0iPTyf82XISIPFvP6LSKSKSKL/bfbnIzHhJCi9dFjxkDbtkhaGh3mzuWFm29ma716fBcdzd+AlcCdQBOvlyZeL/83bhyjRZhfvz7Hio5QDxtW+gT7+OeOa+PAI4/w/YwZDEtO5goRar38Mp1U+acqYcDjwDJgcWwsg+vXp35hmUZ6OjRvbiPOJtTUw7coZqGt/ud+Q0T6i8h8EZmfmZnpTCRtb4K6rWDyUDgWYl/NT3vWN/rcMTCjz4VEhAc6PMCRvCOMWTQmoG07La8gj1eWvkKrWq3oktwl4O3XHjwYRNj1rxEBb9tJ6vWSNXo0UY0bE3/FFQFtW0R45Iqz2HP4GGOnrg9o24GSlZ3L6p2HOL9p6aZ4DApVdeQGhAPrgSZAFLAEOPu4bW4BRpem3XPPPVcrmh49emiPHj3cDiO0DBigGham6vGoF3RNw4Y6MjJS+4ImgeK/hYM2B70adAjoeNCJoHNAN4WFabaIFgwY4GszLc3XZlqaqqrm5eXpgX79dJ2IzggL049AXwS9E/Ri0OQi/QB6Nmg/0HdAM2NiVFNTVUVUPZ7ftGtOrSL/TQDz1aHzbmluwPf4SjWOv11dZJupQPsij0cDNxV5/Brwx5P14+g5+5eZqo/Fq/74tHN9BNqW+b6YZzzvWBfD5wzXVm+10tV7VjvWR6B9svYTbflmS52+ZbpjfewePVpXnnGmZv8827E+Au3AN9/qyjPO1P0TJzrWxz3vL9LUhyfp1n1HHOvjdH2xeJs2HPKVLt68z7UYTnTOdnIEuiOQoaobVPUY8D6+r/+MKbsiJR6Snk5zEe4pKOATf8305kaN+CAqigeBc8LCyIiM5HngDqAP0Alo6PUSp0r4yy8TJUL8uHHU9HqJGzeOCBEiIyNJeO01UlXp5vVyHXA38DZwSIRLqlTh6YgIvgX2xcSwIjWVV0W40eMhMTcX8vMhLc0uDDTlkqpeoqoti7l9cZLdtgEpRR7X9z/njobnQ4u+MPMF2F++6zh/NX0ExFT3XQjpkAGtBxAfFc/wucNDYvq2fG8+ryx9hRY1W9C1XlfH+qnZrx+RycnsevpptKD8r8SnXi9ZY8cS1agR8b17O9bP4MvOQIAR36x2rI/TNXNdFvGeCFrWK1/1zwCBK6b5veK+6utUzHbXikh3YC1wr6r+7iwoIv2B/gANGjRwIFQTcoqWPIwZA337Qu/e0L8/MmECKZMmkZKfz588Hjh2DFJTyV+1ih1RUezKzWVnUhI79+wh69gxciMiyKlShZzsbPIKCvBEROBp2hRPWBgxGRnUzsujbnQ0devUoe7mzSRGRyN5edCwIaxe7ZsNpGjC3L8/TJjgK/8YM+Z/MRoT+iYC74rI80Ayvinb57oaUc8nYM0k3zLf177iaiintGMJrP0vXPhwQGbeOJGE6ATuansXw2YP44fNP3BJw0sc6ysQJm2cxNbsrQzpOCSgtc/HC/N4qP3AYLbdcy/7P/mE6n/6k2N9BUL2jz+Su2YNyc8OR8Kdq/9NrhbD37o2ZtzU9dzevQktkstHsqqq/JSRReemNQkPc+734nS5fRHhl0AjVW0FTAbeKm4jVZ2gqu1VtX2tWrWCGqAJEcXUTBeOUDNgAOzbR0RaGilz5tA+PZ0ro6O5LT+fBz0eHvN6eaZePUaqMtrj4TmvlycvuohHevTg7wUF/MXjoWdeHueEh1MrPR0pvPBv377f9tGmzW9jsLpmE6JE5A8ishXoDHwtIt8CqOoK4EN8lx58A9ypqu4O5VVL8S2DvexD2FbO50CePgKi48s873NJ9E3tS7NqzXh+wfMcKzjmeH+nq8BbwISlEzizxpn0qN/D8f6qXnYZMe3akfniSxRkH3a8v9OlqmSOHUtkwwYBr30uzoAeTUmIieS5b4u7dtgdm/ceYdv+o3RtVg7rn3E2gT7lV32qukdVc/0PX8U3w5cxZXd8Qr19+ykT7N883rnzN2UixSbIx7dpCbOpIFT1M1Wtr6rRqlpHVS8r8tpTqtpUVc9Q1f+6Geevut4LVWrBt4+U32ntdq2EVV9CpwG+WUQcFhEWweD2g9lyaAvvrX7P8f5O1ze/fMOmg5u4o9Udjo4+FxIR6jw4hII9e9jzSvn9xiJ7ylTfvM93DAjozBsnkhATSdoFTZmyJpO5G8vHDC4zM/YAcH45TaCd/KnMA1JFpDG+xPl64MaiG4hIkqru8D/sA4TOupImtB1fAlK0xKK4cgsrwTCm/PLEwwUPwdf3weqv4Kyr3I7o92Y8B1FxcF5a0Lo8v975dK3XlfFLxtOnaR+qe6oHre+S8KqXCUsn0KxaMy5qcFHQ+o1p1Yr4q65i7xtvUP1P1xFZ73cTybhKVckaO5bIlJSAzftcEjd3bsQbMzfyr29W89GAzkH5QHMyMzOySErw0CSxiqtxnIhjI9Cqmg8MBL7Flxh/qKorROQJEenj32yQf7WrJcAgfLNyGGOMMaXT7maodaZvWrv8claykLkWln/qu3AwNrjLEd/f/n6O5B9h7OKxQe23JCZvmsyGAxu4o/UdhElwK0pr33cviLD7+ZFB7bckDk+fTs7y5SQOuCMgqw6WVExUOHdf3Jz5m/bxw6rdQeu3OF6vMmt9Fuc3TXQ9kT8RR39jVXWSqjb3f933lP+5oao60X//IVVtoaqtVfVCVS1/l4AaY4wp/8IjoOcw2LsBFrzhdjS/NePfEBkDnQcGveum1Zryx+Z/5KO1H7HhwIag938iXvUyful4Gic0pmeDnkHvPzIpiRp/u5WDX3/N0aVLg97/iagqmWPGElmvHgl9+px6hwC7rn19GtWM5bnv1uD1ulcOtXLHQfYdyaNravlavrsoty8iNMYYYwIjtSc07g5Th0POAbej8dmz3neBY/u/QZw7F8GntU7DE+Fh5ILyM9o6ZcsU1u1bR/9W/QkPc2eFuZr9biM8MZFdw58tN9P9Hf7pJ3KWLqXmHf2DOvpcKDI8jHt7Nmf1zkNMWr7j1Ds4ZNpa3wJMXcrjAip+lkAbY4ypGER8o9BH98JP5SRZ/Ol5CIuE8+9yLYSaMTW57ZzbmLplKvN2znMtjkKqyvgl42kY35BejXq5Fkd4XBVqDbqLowsXcui7ya7FUUhVyRo9hojkJKpdc41rcVzZKpnmdeIYOXktBS6NQn+/ahet6ydQO97jSv8lYQm0McaYiiO5DbT6M8weBwe2uhvLvk2w5H0492aoWtfVUG466yaSqiQxYt4IvOp1NZZpW6exau8qbj/ndiLCnJ9h4mSqXXst0amp7P73v9Fj7tbOH541i6NLlpDYvz8SFeVaHOFhwn09m7M+8zBfLA7+Okm7D+WweMt+LjmrTtD7Lg1LoI0xxlQsF/mns/thmLtxTB8BEg5d7nE3DsAT4WFQu0Gs2ruKrzZ85VocqsqYxWNIqZpC7ybOra5XUhIeTu0HHiBv82b2vvOua3GoKpkvvUREUhIJffu6Fkehy1rUpUVyPC98v468guB+4JqyejeqcLEl0MYYY0wQVWvgmy5u6fuwbaE7MezdAIvfhXNvgYTyMU1a78a9aVmzJS8ueJEjeUdcieGHzT+weu9q0lqnERkW/Brf4sR160qVbt3IGjuW/H37XIkhe9o0cpYsJXHAAMJcHH0uJCL8/dLmbN57hI8XBPebnO9X7aZetRjOSnJutc5AsATaGGNMxdPtPohNhO9cWlxl+nMQHulb5KWcCJMwHuj4ALuP7uaNFcGfqcSrXsYsHkOj+EZc3vjyoPd/MnWGPID3yBGyRo0Ket+Fo8+RKSlU6/uHoPd/IheeUZu2Daox6od15OYHZ8HRnLwCZqzL5OKzapfb6esKWQJtjDGm4vEkwIX/gE0zfYurBNOe9bDkPWjfD+KTgtv3KbSt3ZZejXrxxvI32JEd3FkWvtv0HRn7M0hrneZ67fPxops1o/r117Pv/Q/IWbs2qH0fmjzZt+pgerorM2+ciIhw/6VnsP1ADu/P3RKUPmdmZJGT5y339c9gCbQxxpiKqnBxle8eDe7iKtOehfBo6HpP8PoshXvP9Y2Kv7DwhaD1WeAtYNzicTRNaMpljS479Q4uSBx4J2FxcewO4rR26vWSNWo0UY0aBXXVwZI6v2lNOjWuwegpGRw95vwo9PerdhMXHUGnJsFdcOh0WAJtjDGmYgqPgEufgn0bYe744PS5czks/RA63g5xtYPTZyklxyVzc4ubmbRxEot3Lw5Kn1+s/4INBzaQ3ibdtXmfTyWienVq3ZnO4VmzyJ4yNSh9HvzyS3LXrSNx4EAkonyNykNhLfQZZB7K5e3Zvzjal9er/LBqF92bJxIdUT5/R4qyBNoYY0zFlXoJNOsJU5+FQzud72/yUF/5SLf7nO+rDPq17Eft2No8Necp8r35jvZ1JO8IoxeNplWtVvRsGPxVB0uj+o03EtWsKbueegrv0aOO9uU9epTdz4/E07Il8b3LV014UR0b16B781qMm7qe7FznflcWbt7H7kO5IVG+AZZAG2OMqegufxYKcn2lHE7K+AHW/wDdB0NMdWf7KqPYyFiGdBjC6r2r+WDNB4729dbKt8g8msng9oPL/YVhEhlJ3aFDydu2jazxzn5rsffNN8nftYs6Dw5Bwsp3Ovb3ns3ZdySPV6Y7txz8Jwu3EhsVzmUt3J0zvaTK90/MGGOMKauaTX1zMS/7EH75yZk+vAW+0edqDX3lGyGgZ8OedKnXhVGLRrH7yG5H+sg8kskby9+gZ8OetKndxpE+Aq1Kx44kXN2HPa+9Tu6GjY70kZ+ZSdYrr1K1Z09i27d3pI9Aap1Sjd7n1OXVGRvIPJQb8PZz8gr4askOerWsS5Xo8lfKUhxLoI0xxlR8Xe+FhAbw9f1QkBf49pe8D7uWwyWPQUR04Nt3gIjwcMeHySvIY8S8EY70MWbxGPK8edzT7h5H2ndK7cGDCfN42DnsCUcuKMx86SU0L4/a9/894G075f5LzyAn38voH9cFvO1vV+zkUG4+fzy3fsDbdool0MYYY34lIteJyAoR8YpI+yLPNxKRoyKy2H972c04Sy0qFi4fDpmrYNZLgW37yF74/nGo1x5auL+KXGmkxKdwW6vb+OaXb5i+dXpA2168ezGfrvuUG868gQbxDQLattMiEhOpde89HPl5Nvs//jigbR9ZtIj9H39CjRtvJKphw4C27aQmteL4c4cU3p27mU17Dge07Y8XbKVetRjOa1wzoO06yRJoY4wxRS0H+gLFZVPrVbWN/zYgyHGV3Rm94exrYMozsHNZ4Nr97hE4sgeuHAnlvMa3OP1a9qN59eYMnTmUvTl7A9JmbkEuQ2cNpW6VugxsMzAgbQZb9euvJ/a889j1zHCObd4ckDa9ubnsePgRIpLqknjXXQFpM5juvjiV8DDh398Fbq7sHQeO8lNGFte2q0dYWOj8/VgCbYwx5lequkpV17gdhyNE4IrnIbYGfDYA8gNQy7n+R1j8DnS5G5Jalb09F0SFR/FMt2c4eOwgj896PCAlCxOWTmDjgY081vkxYiNjAxBl8ElYGMnPPI2Eh7P9wYfQgrLPg5w1egzHNmwg6YlhhMdVCUCUwVUn3kO/ro2ZuGQ7CzYF5sPWZ4u2oQrXhlD5BlgCbYwxpuQai8giEZkmIt1OtJGI9BeR+SIyPzMzM5jxnVqVmtBnlK9eeeozZWvr2GH48m6o2Qx6DAlMfC5pXr05d7e7mylbpvBZxmdlamvN3jW8vux1+jTtQ5d6XQIUoTsik5KoO/RRji5cyJ5XXytTW0eXLWfP66+TcG1f4rqG7vuSfkEzkhI8PPr5Cgq8Zfuw5fUqH8/fSsdGNWhYM7Q+UFgCbYwxlYyIfC8iy4u5XX2S3XYADVS1LXAf8K6IxBe3oapOUNX2qtq+Vq1aThxC2TS/DNr9FWa+COsmn14bqvDfIbB/sy8hj/QENkYX/OXsv9CpbieGzx3Oyj0rT6uNQ8cOMWT6EOKj4xncfnCAI3RH/JVXUvXyXmS+9BLZ00+vTrzgwAG2DxlCRM2a1BkS2h+2qkRH8MgVZ7Nyx0HembOpTG19tWwHG7IOc1Pn0KkFL2QJtDHGVDKqeomqtizm9sVJ9slV1T3++wuA9UDzYMUccJc9A3Vawke3wI6lpd//5zGw6G3o9ndoeH7Aw3NDmITxTLdnqBZdjfTv09l6aGup9s/35nP/tPvZdHATI7qPoJqnmjOBBpmIkDTsSaLPaM7We+7l6IoVpdpfjx1j66C7ObZlC8nPjSA8vtjPnSGl9zl16doskRHfriEr+/RKofILvLzw/VrOqFOVK89JCnCEzrME2hhjzCmJSC0RCfffbwKkAs6tquC06Di48UPwVIN3/wQHSpEsrvmv78LBs/rAhY84FqIbasXW4uVLXibPm0fa92nsy9lXov1UleFzhzNr+ywe7fwoHZM6OhxpcIXHVSFl3MuEV0tgy4AB5G3fXqL9VJUdjw7lyJw5JD/1JFU6Voz3RUR4vE8LcvIKePrrVafVxheLt7Mh8zD39kwNqYsHC1kCbYwx5lci8gcR2Qp0Br4WkW/9L3UHlorIYuBjYICqBuYqIrfEJ8H/feirZX67L2RlnHqfjTPg436Q1Br+MB7K+Qpyp6NJtSaMumgU27O3039yf7Yc3HLS7fO8eYxcMJIP1nzArS1upW9qaE3lV1KRdWrTYPx4NCeXX278P44sXHTS7b25uewaNowDX3xB4l0DSejTJ0iRBkez2nGk9WjKp4u28fmibaXaN6/Ay0s/ruPspHguPTs0Vh48XsX7yzfGGHPaVPUzVa2vqtGqWkdVL/M//4mqtvBPYddOVb90O9aAqNMCbngPDu+GCT1g2Qnm/PUWwJSn4a2rfIn3De/75pauoNrVaccLF77AtuxtXPfVdXy14atit9uevZ1bv7mVN1a8wXXNr+PudncHOdLgik5NpcGbbyAREWz661/Z89rrqNf7u+1y1q7ll+v+xL5336PGLbeQmJ7uQrTOG3RxKh0b1eAfny1jfWZ2iff7dOFWNu05wn09m4fk6DOAOLHCjpPat2+v8+fPdzuMgLrgggsAmDp1qqtxGFNeVOS/CRFZoKrlf+3eAAmZc/aBrfDx32DLHGjeC864HJpcCLkHYfNsWPaR77XWN0Dv53wlIJXAjuwdPDjjQRbuXkibWm3onNyZdnXase3QNhbsWsDULVPx4uXxzo/Tq3Evt8MNmoKDB9nxyKMc+u47IurWJa57d2I7tCdv61aOLl3G4ZkzCataleSnnyKuRw+3w3XUjgNH6f3iDOrEe/j8zi54IsNPuv2GzGyuGTOTJrXi+Cz9fKScz51+onN2aCw4bowxxjgpoT7c8jVMHwEL34a13/z29fh6cM3L0OYGd+JzSVJcEq9d9hpvr3yb7375jvFLx+NV34hrDU8NutTrwqC2g0iJT3E50uAKj4+n3osvcHDSJA598y0Hv/6a/R9+CEBUkyYk/OEP1Bp0FxE1Q2dlvdOVlBDD839uw61vzOOu9xYx6oa2J0yiDxzJo99b84kID2PUDW3LffJ8Mo4m0CLSC3gRCAdeVdXhx70eDfwHOBfYA/xZVX9xMiZjjDGmWOGRcOE/4IKHIHM1bJwOMdWhwXmQkBKSqwwGQkRYBLe2vJVbW97KwWMHWZa5jKS4JBrHNw7pBKisRISEK64g4Yor0GPHyM3IILJ+/Qoxy0ZpXXhGbR6/6mz++dVKbnhlNq/8tT2JcdG/2SavwEv6uwvYtu8o79zeiZQaoV0C5VgC7b9aewzQE9gKzBORiapadHLJfsA+VW0mItcDzwJ/diomY4wx5pREoPZZvpv5jfio+JBfHMUJEhWF5+yz3Q7DVbd0aUzdhBjufn8RfcfOYuCFzeiamkjNuCi+XLKD13/ayModB3nuutZ0aFTD7XDLzMkR6I5AhqpuABCR94GrgaIJ9NXA4/77HwOjRUQ01AqzA2Dx4sW/1n0aU9lNmzbN7RCMMcaUUq+WdXm//3kMfHcRD3zim189JjKco3kFpNaO44U/t+GatvVcjjIwnEyg6wFF577ZCnQ60Taqmi8iB4CaQFbRjUSkP9AfoEGDBk7F65obb7zR7RCMMcYYY8qsbYPq/DTkQtbsOsRP67JYn5lN73OS6NossUKV/ITERYSqOgGYAL4rul0OJ+D69+9P//793Q7DGGOMMabMRIQz68ZzZt2KWw/u5DzQ24Cil+XW9z9X7DYiEgEk4LuY0BhjjDHGmHLJyQR6HpAqIo1FJAq4Hph43DYTgZv99/8I/FgZ65+NMcYYY0zocKyEw1/TPBD4Ft80dq+r6goReQKYr6oTgdeAt0UkA9iLL8k2xhhjjDGm3HK0BlpVJwGTjntuaJH7OcB1TsZgjDHGGGNMIDlZwmGMMcYYY0yFYwm0McYYY4wxpWAJtDHGGGOMMaVgCbQxxhhjjDGlIKE2a5yIZAKb3I7DAYkctwJjJWTvgY+9Dz4V9X1oqKq13A4iWMpwzq6oP/9CdnyhzY4vtJXm+Io9Z4dcAl1Rich8VW3vdhxusvfAx94HH3sfKreK/vO34wttdnyhLRDHZyUcxhhjjDHGlIIl0MYYY4wxxpSCJdDlxwS3AygH7D3wsffBx96Hyq2i//zt+EKbHV9oK/PxWQ20McYYY4wxpWAj0MYYY4wxxpSCJdDGGGOMMcaUgiXQLhORXiKyRkQyRORBt+Nxg4i8LiK7RWS527G4SURSRGSKiKwUkRUicrfbMblBRDwiMldElvjfh3+6HZNxzqnOgSISLSIf+F+fIyKNXAjztJXg+O7z/80vFZEfRKShG3GerpL+HyYi14qIikhITY1WkuMTkT8VOW+/G+wYy6IEv58N/P8vLfL/jvZ2I87TcarcQnxe8h/7UhFpV6oOVNVuLt2AcGA90ASIApYAZ7sdlwvvQ3egHbDc7Vhcfh+SgHb++1WBtZX090GAOP/9SGAOcJ7bcdnNkZ/1Kc+BQDrwsv/+9cAHbscd4OO7EIj130+raMfn364qMB2YDbR3O+4A//xSgUVAdf/j2m7HHeDjmwCk+e+fDfzidtylOL6T5hZAb+C//v9zzgPmlKZ9G4F2V0cgQ1U3qOox4H3gapdjCjpVnQ7sdTsOt6nqDlVd6L9/CFgF1HM3quBTn2z/w0j/za52rphKcg68GnjLf/9j4GIRkSDGWBanPD5VnaKqR/wPZwP1gxxjWZT0/7BhwLNATjCDC4CSHN/twBhV3QegqruDHGNZlOT4FIj3308AtgcxvjIpQW5xNfAf//85s4FqIpJU0vYtgXZXPWBLkcdbqYQJk/k9/9fUbfGNvlY6IhIuIouB3cBkVa2U70MlUJJz4K/bqGo+cACoGZToyq605/h++EbEQsUpj8//tXiKqn4dzMACpCQ/v+ZAcxGZKSKzRaRX0KIru5Ic3+PATSKyFZgE3BWc0IKiTDmYJdDGlDMiEgd8AtyjqgfdjscNqlqgqm3wjcZ1FJGWLodkjKNE5CagPTDC7VgCRUTCgOeBv7sdi4Mi8JVxXADcALwiItXcDCjAbgDeVNX6+Eoe3vb/XCs9exPctQ1IKfK4vv85U0mJSCS+5PkdVf3U7Xjcpqr7gSlAKI3qmJIryTnw121EJALf18h7ghJd2ZXoHC8ilwAPA31UNTdIsQXCqY6vKtASmCoiv+CrM50YQhcSluTntxWYqKp5qroR37UrqUGKr6xKcnz9gA8BVPVnwAMkBiU655UpB7ME2l3zgFQRaSwiUfgukJnockzGJf66zteAVar6vNvxuEVEahWO4IhIDNATWO1qUMYpJTkHTgRu9t//I/Cj+q8ACgGnPD4RaQuMx5c8h1L9LJzi+FT1gKomqmojVW2Er8a7j6rOdyfcUivJ7+fn+EafEZFEfCUdG4IYY1mU5Pg2AxcDiMhZ+BLozKBG6ZyJwF/9s3GcBxxQ1R0l3TnCubjMqahqvogMBL7FdzXs66q6wuWwgk5E3sN3Akr011k9pqqvuRuVK7oAfwGW+et/Af6hqpPcC8kVScBbIhKO70P+h6r6lcsxGQec6BwoIk8A81V1Ir4PlW+LSAa+C4Kudy/i0inh8Y0A4oCP/NdGblbVPq4FXQolPL6QVcLj+xa4VERWAgXAYFUNiW9ISnh8f8dXlnIvvgsKbwmVD7DF5Rb4LkpHVV/GV9PdG8gAjgC3lqr9EHkfjDHGGGOMKReshMMYY4wxxphSsATaGGOMMcaYUrAE2hhjjDHGmFKwBNoYY4wxxphSsATaGGOMMcaYUrAE2lRaIpIkIqWaHk1EnhORi5yKyRhjKjoRqSYi6UUeJ4vIxw71dY2IDD3J6+eIyJtO9G0qNpvGzlRaIjIC+ElVvyjFPg2BV1T1UuciM8aYiktEGgFfqWrLIPQ1C9/iLVkn2eZ74G+qutnpeEzFYSPQpsITkQ4islREPCJSRURWiEhL4FrgG/82t4jI5yIyWUR+EZGBInKfiCwSkdkiUgNAVTcBNUWkrouHZIwxoWw40FREFovICBFpJCLLoeTnYhFpKiLfiMgCEZkhImce34mINAdyC5NnEblORJaLyBIRmV5k0y8JoQV6TPlgCbSp8FR1Hr4lO58E/gX8P+AwsE9Vc4ts2hLoC3QAngKOqGpb4Gfgr0W2W4hv1UBjjDGl9yCwXlXbqOrgYl4vybl4AnCXqp4L3A+MLaadLvjO14WGApepamug6GqP84FuZTgeUwnZUt6msngCmAfkAIOATkDmcdtMUdVDwCEROYBvVAJgGdCqyHa7gWRnwzXGmErrpOdiEYkDzud/y58DRBfTThK/Pc/PBN4UkQ+BT4s8b+d0U2qWQJvKoiYQB0QCHuCo/9+iio5Ge4s89vLbv5XC/Y0xxgTeqc7FYcB+VW1zinaOAgmFD1R1gIh0Aq4AFojIuaq6Bzunm9NgJRymshgPPAq8AzwLrAUanWZbzYHlgQnLGGMqnUNA1dPdWVUPAhtF5DoA8WldzKargGaFD0SkqarOUdWh+EamU/wv2TndlJol0KbCE5G/Anmq+i6+i1c64CvhWC8izU668+/bisR3Qp4f8ECNMaYS8I/6zvRf0DfiNJv5P6CfiCwBVgBXF7PNdKCt/K/OY4SILPNfsDgLWOJ//kLg69OMw1RSNo2dqbRE5A/Auar6SCn3aaeqjzoXmTHGmEAQkReBL1X1+xO8Hg1MA7qqan5QgzMhzUagTaWlqp8Bv5Rytwjg34GPxhhjjAOeBmJP8noD4EFLnk1p2Qi0McYYY4wxpWAj0MYYY4wxxpSCJdDGGGOMMcaUgiXQxhhjjDHGlIIl0MYYY4wxxpSCJdDGGGOMMcaUwv8H4TW24AP3rlEAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot\n", + "import matplotlib.pyplot as plt\n", + "fig = plt.figure(1, figsize=(12,4))\n", + "# xy plot\n", + "plt.subplot(1,2,1)\n", + "plt.plot(*frame_coords, 'k-')\n", + "plt.plot(*desposesxy, 'r*') # desired trajectory\n", + "ltraj, = plt.plot(*posesxy, 'k-') # actual trajectory\n", + "lscables = plt.plot(np.zeros((2,4)), np.zeros((2,4)))\n", + "lee, = plt.plot(*ee_coords(0))\n", + "plt.axis('equal')\n", + "plt.xlabel('x(m)');plt.ylabel('y(m)');plt.title('Trajectory')\n", + "# controls\n", + "plt.subplot(1,2,2)\n", + "lsctrl = plt.plot(np.arange(0,Tf,dt), torques)\n", + "plt.xlabel('time (s)');plt.ylabel('Cable tension (N)');plt.title('Control Inputs');" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "faced-compact", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# animate\n", + "plt.rcParams[\"savefig.dpi\"] = 80\n", + "import matplotlib.animation as animation\n", + "from IPython.display import HTML\n", + "\n", + "def update_line(num):\n", + " ltraj.set_data(posesxy[0][:num], posesxy[1][:num])\n", + " for ji in range(4):\n", + " lsctrl[ji].set_data(np.arange(0,Tf,dt)[:num], torques[:num, ji])\n", + " lscables[ji].set_data(cable_coords(num, ji))\n", + " lee.set_data(*ee_coords(num))\n", + " lines = [ltraj, *lsctrl, *lscables, lee]\n", + " return lines\n", + "\n", + "anim = animation.FuncAnimation(fig, update_line, len(posesxy[0]),\n", + " interval=100, blit=True)\n", + "HTML(anim.to_html5_video())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/gtdynamics/cablerobot/src/gerry01_planar_tracking.py b/gtdynamics/cablerobot/src/gerry01_planar_tracking.py index a85d8733..b57451a7 100644 --- a/gtdynamics/cablerobot/src/gerry01_planar_tracking.py +++ b/gtdynamics/cablerobot/src/gerry01_planar_tracking.py @@ -18,11 +18,13 @@ from cdpr_planar import Cdpr from cdpr_planar_controller import CdprController -from cdpr_planar_sim import cdpr_sim +from cdpr_planar_sim import CdprSimulator + +import cProfile def main(): Tf = 1 - dt = 0.05 + dt = 0.01 N = int(Tf / dt) cdpr = Cdpr() # set up controller @@ -32,9 +34,11 @@ def main(): for i in range(N) ] x_des[0] = x_des[1] + # initial state x0 = gtsam.Values() gtd.InsertPose(x0, cdpr.ee_id(), 0, x_des[0]) gtd.InsertTwist(x0, cdpr.ee_id(), 0, np.zeros(6)) + # controller controller = CdprController(cdpr, x0, x_des, @@ -42,19 +46,39 @@ def main(): Q=np.array([0, 1, 0, 1e3, 0, 1e3]), R=np.array([1e-3])) # run simulation - result = cdpr_sim(cdpr, x0, controller, dt=dt, N=N, verbose=True) - poses = [gtd.Pose(result, cdpr.ee_id(), k) for k in range(N)] + sim = CdprSimulator(cdpr, x0, controller, dt=dt) + result = sim.run(N=N, verbose=False) - # print(poses) + # extract useful variables as lists + poses = [gtd.Pose(result, cdpr.ee_id(), k) for k in range(N+1)] + posesxy = np.array([[pose.x() for pose in poses], [pose.z() for pose in poses]]) + desposesxy = np.array([[pose.x() for pose in x_des], [pose.z() for pose in x_des]]) + torques = np.array([[gtd.TorqueDouble(result, ji, k) for ji in range(4)] for k in range(N)]) - plt.figure(1) - plt.plot([pose.x() for pose in x_des], [pose.z() for pose in x_des], 'r-') - plt.plot([pose.x() for pose in poses], [pose.z() for pose in poses], 'k--') - plt.plot([*cdpr.params.a_locs[:, 0], cdpr.params.a_locs[0, 0]], - [*cdpr.params.a_locs[:, 2], cdpr.params.a_locs[0, 2]], 'k-') + # plot utils + boxinds = np.array([0,1,2,3,0]).reshape(5,1) + def ee_coords(k): + return (posesxy[:, k]+cdpr.params.b_locs[boxinds, [0,2]]).T + frame_coords = cdpr.params.a_locs[boxinds, [0,2]].T + def cable_coords(k, ji): + return np.array([[cdpr.params.a_locs[ji][0], posesxy[0][k]+cdpr.params.b_locs[ji][0]], + [cdpr.params.a_locs[ji][2], posesxy[1][k]+cdpr.params.b_locs[ji][2]]]) + # plot + fig = plt.figure(1, figsize=(12,4)) + # xy plot + plt.subplot(1,2,1) + plt.plot(*frame_coords, 'k-') + plt.plot(*desposesxy, 'r*') # desired trajectory + ltraj, = plt.plot(*posesxy, 'k-') # actual trajectory + lscables = plt.plot(np.zeros((2,4)), np.zeros((2,4))) + lee, = plt.plot(*ee_coords(0)) plt.axis('equal') - plt.show() + plt.xlabel('x(m)');plt.ylabel('y(m)');plt.title('Trajectory') + # controls + plt.subplot(1,2,2) + lsctrl = plt.plot(np.arange(0,Tf,dt), torques) + plt.xlabel('time (s)');plt.ylabel('Cable tension (N)');plt.title('Control Inputs'); if __name__ == '__main__': - main() + cProfile.run('main()') From 14fe322e99638817a013253d0beccaa27db0fe99 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 6 Apr 2021 02:35:14 -0400 Subject: [PATCH 02/73] faster simulation --- gtdynamics/cablerobot/src/cdpr_planar_sim.py | 45 +++++++++++++------ .../cablerobot/src/gerry01_planar_tracking.py | 6 ++- 2 files changed, 37 insertions(+), 14 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_planar_sim.py b/gtdynamics/cablerobot/src/cdpr_planar_sim.py index cae014ee..d1e4a7e3 100644 --- a/gtdynamics/cablerobot/src/cdpr_planar_sim.py +++ b/gtdynamics/cablerobot/src/cdpr_planar_sim.py @@ -133,21 +133,40 @@ def step(self, verbose=False): Returns: gtsam.Values: The new values object containing the current state and next Pose+Twist. - """ - if verbose: - print('time step: {:4d} -- EE position: ({:.2f}, {:.2f}, {:.2f})'.format( - self.k, - *gtd.Pose(self.x, self.cdpr.ee_id(), self.k).translation()), end=' -- ') - self.update_kinematics(self.cdpr, self.fg, self.x, self.k) - if self.k == 0: - self.x.insertDouble(0, self.dt) - u = self.controller.update(self.x, self.k) + """ + # setup + x, lid, k, dt = self.x, self.cdpr.ee_id(), self.k, self.dt + xk = gtsam.Values() + gtd.InsertPose(xk, lid, k, gtd.Pose(x, lid, k)) + gtd.InsertTwist(xk, lid, k, gtd.Twist(x, lid, k)) + + # kinematics + fg, xk = self.update_kinematics(self.cdpr, gtsam.NonlinearFactorGraph(), xk, k) + # controller + u = self.controller.update(x, k) + # dynamics + xk.insertDouble(0, dt) + self.update_dynamics(self.cdpr, fg, xk, u, k, dt) + + # update full self.x solution + for ji in range(4): + gtd.InsertJointAngleDouble(x, ji, k, gtd.JointAngleDouble(xk, ji, k)) + gtd.InsertJointVelDouble(x, ji, k, gtd.JointVelDouble(xk, ji, k)) + gtd.InsertTorqueDouble(x, ji, k, gtd.TorqueDouble(xk, ji, k)) + gtd.InsertTwistAccel(x, lid, k, gtd.TwistAccel(xk, lid, k)) + gtd.InsertPose(x, lid, k + 1, gtd.Pose(xk, lid, k + 1)) + gtd.InsertTwist(x, lid, k + 1, gtd.Twist(xk, lid, k + 1)) + + # debug if verbose: + print('time step: {:4d}'.format(k), end=' -- ') + print('EE position: ({:.2f}, {:.2f}, {:.2f})'.format( + *gtd.Pose(x, lid, k).translation()), end=' -- ') print('control torques: {:.2e}, {:.2e}, {:.2e}, {:.2e}'.format( - *[gtd.TorqueDouble(u, ji, self.k) for ji in range(4)])) - self.update_dynamics(self.cdpr, self.fg, self.x, u, self.k, self.dt) + *[gtd.TorqueDouble(u, ji, k) for ji in range(4)])) + self.k += 1 - return self.x + return x def run(self, N=100, verbose=False): """Runs the simulation @@ -158,7 +177,7 @@ def run(self, N=100, verbose=False): Returns: gtsam.Values: The values object containing all the data from the simulation. - """ + """ for k in range(N): self.step(verbose=verbose) return self.x diff --git a/gtdynamics/cablerobot/src/gerry01_planar_tracking.py b/gtdynamics/cablerobot/src/gerry01_planar_tracking.py index b57451a7..b1c3ac2c 100644 --- a/gtdynamics/cablerobot/src/gerry01_planar_tracking.py +++ b/gtdynamics/cablerobot/src/gerry01_planar_tracking.py @@ -49,6 +49,9 @@ def main(): sim = CdprSimulator(cdpr, x0, controller, dt=dt) result = sim.run(N=N, verbose=False) + return Tf, dt, N, cdpr, x_des, result + +def plot(Tf, dt, N, cdpr, x_des, result): # extract useful variables as lists poses = [gtd.Pose(result, cdpr.ee_id(), k) for k in range(N+1)] posesxy = np.array([[pose.x() for pose in poses], [pose.z() for pose in poses]]) @@ -81,4 +84,5 @@ def cable_coords(k, ji): if __name__ == '__main__': - cProfile.run('main()') + cProfile.run('vals = main()') + plot(*vals) From 49f21c6fb56f03b88a2df350118f0007eb6c73f2 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 6 Apr 2021 03:11:14 -0400 Subject: [PATCH 03/73] a little refactoring --- gtdynamics/cablerobot/src/cdpr_planar_sim.py | 96 +++++++++++--------- 1 file changed, 54 insertions(+), 42 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_planar_sim.py b/gtdynamics/cablerobot/src/cdpr_planar_sim.py index d1e4a7e3..c950ffc5 100644 --- a/gtdynamics/cablerobot/src/cdpr_planar_sim.py +++ b/gtdynamics/cablerobot/src/cdpr_planar_sim.py @@ -56,71 +56,87 @@ def __init__(self, cdpr, x0, controller, dt=0.01): self.reset() @staticmethod - def update_kinematics(cdpr, fg, x, k): - """Runs IK to solve for the cable lengths and velocities at time step k + def update_kinematics(cdpr, x, k): + """Runs IK to solve for the cable lengths and velocities at time step k. Modifies x + inplace!!! Args: - fg (gtsam.NonlineaFactorGraph): any previous factors, if applicable - x (gtsam.Values): Values object containing the current Pose and current Twist, plus any - other values that may be needed (as initial guesses) for the `fg` argument. + x (gtsam.Values): Values object containing at least the current Pose and current Twist k (int): current time step Returns: - tuple(gtsam.NonlinearFactorGraph, gtsam.Values): the updated factor graph and values + tuple(gtsam.NonlinearFactorGraph, gtsam.Values): the factor graph used to perform IK + and the solution values which includes the current Pose, Twist, JointAngles, and + JointVels. """ + fg = gtsam.NonlinearFactorGraph() + lid = cdpr.ee_id() + # local copy of values + xk = gtsam.Values() + gtd.InsertPose(xk, lid, k, gtd.Pose(x, lid, k)) + gtd.InsertTwist(xk, lid, k, gtd.Twist(x, lid, k)) # IK for this time step, graph fg.push_back(cdpr.kinematics_factors(ks=[k])) fg.push_back( cdpr.priors_ik(ks=[k], - Ts=[gtd.Pose(x, cdpr.ee_id(), k)], - Vs=[gtd.Twist(x, cdpr.ee_id(), k)])) + Ts=[gtd.Pose(xk, cdpr.ee_id(), k)], + Vs=[gtd.Twist(xk, cdpr.ee_id(), k)])) # IK initial estimate for j in range(4): - gtd.InsertJointAngleDouble(x, j, k, 0) - gtd.InsertJointVelDouble(x, j, k, 0) + gtd.InsertJointAngleDouble(xk, j, k, 0) + gtd.InsertJointVelDouble(xk, j, k, 0) # IK solve - result = gtsam.LevenbergMarquardtOptimizer(fg, x).optimize() + result = gtsam.LevenbergMarquardtOptimizer(fg, xk).optimize() assert abs(fg.error(result)) < 1e-20, "inverse kinematics didn't converge" - x.update(result) - return fg, x + xk.update(result) + return fg, xk @staticmethod - def update_dynamics(cdpr, fg, x, u, k, dt): + def update_dynamics(cdpr, x, u, k, dt): """Runs ID to solve for the twistAccel, and also runs collocation to get the next timestep - Pose/Twist + Pose/Twist. Modifies x inplace!!! Args: cdpr (Cdpr): the cable robot - fg (gtsam.NonlinearFactorGraph): a factor graph containing any previous factors - x (gtsam.Values): Values object containing at least the current Pose and Twist, and any - other values that may be needed (as initial guesses) for the `fg` argument + x (gtsam.Values): Values object containing at least the current Pose and Twist u (gtsam.Values): The current joint torques k (int): The current time index dt (float): the time slice duration Returns: - tuple(gtsam.NonlinearFactorGraph, gtsam.Values): the factor graph with added factors, - and the solution Values which adds the TwistAccel, next Pose, and next Twist to the `x` - argument. + tuple(gtsam.NonlinearFactorGraph, gtsam.Values): the factor graph used to update the + dynamics, and the solution Values which consists of the Pose, Twist, torque, TwistAccel, + next Pose, and next Twist. """ + fg = gtsam.NonlinearFactorGraph() + lid = cdpr.ee_id() + # local copy of values + xd = gtsam.Values() + xd.insertDouble(0, dt) + gtd.InsertPose(xd, lid, k, gtd.Pose(x, lid, k)) + gtd.InsertTwist(xd, lid, k, gtd.Twist(x, lid, k)) # ID for this timestep + collocation to next time step fg.push_back(cdpr.dynamics_factors(ks=[k])) fg.push_back(cdpr.collocation_factors(ks=[k], dt=dt)) - # ID priors (torque inputs) + # priors (pose/twist and torque inputs) + fg.push_back( + cdpr.priors_ik(ks=[k], + Ts=[gtd.Pose(xd, cdpr.ee_id(), k)], + Vs=[gtd.Twist(xd, cdpr.ee_id(), k)])) fg.push_back( cdpr.priors_id(ks=[k], torquess=[[gtd.TorqueDouble(u, ji, k) for ji in range(4)]])) # ID initial guess for ji in range(4): - gtd.InsertTorqueDouble(x, ji, k, gtd.TorqueDouble(u, ji, k)) - gtd.InsertWrench(x, cdpr.ee_id(), ji, k, np.zeros(6)) - gtd.InsertPose(x, cdpr.ee_id(), k+1, gtsam.Pose3(gtsam.Rot3(), (1.5, 0, 1.5))) - gtd.InsertTwist(x, cdpr.ee_id(), k+1, np.zeros(6)) - gtd.InsertTwistAccel(x, cdpr.ee_id(), k, np.zeros(6)) + gtd.InsertTorqueDouble(xd, ji, k, gtd.TorqueDouble(u, ji, k)) + gtd.InsertWrench(xd, cdpr.ee_id(), ji, k, np.zeros(6)) + gtd.InsertPose(xd, cdpr.ee_id(), k+1, gtsam.Pose3(gtsam.Rot3(), (1.5, 0, 1.5))) + gtd.InsertTwist(xd, cdpr.ee_id(), k+1, np.zeros(6)) + gtd.InsertTwistAccel(xd, cdpr.ee_id(), k, np.zeros(6)) # optimize - result = gtsam.LevenbergMarquardtOptimizer(fg, x).optimize() + result = gtsam.LevenbergMarquardtOptimizer(fg, xd).optimize() assert abs(fg.error(result)) < 1e-20, "dynamics simulation didn't converge" - x.update(result) - return fg, x + xd.update(result) + return fg, xd def step(self, verbose=False): """Performs one time step of the simulation, which consists of: @@ -136,26 +152,22 @@ def step(self, verbose=False): """ # setup x, lid, k, dt = self.x, self.cdpr.ee_id(), self.k, self.dt - xk = gtsam.Values() - gtd.InsertPose(xk, lid, k, gtd.Pose(x, lid, k)) - gtd.InsertTwist(xk, lid, k, gtd.Twist(x, lid, k)) # kinematics - fg, xk = self.update_kinematics(self.cdpr, gtsam.NonlinearFactorGraph(), xk, k) + _, xk = self.update_kinematics(self.cdpr, x, k) # controller - u = self.controller.update(x, k) + u = self.controller.update(xk, k) # dynamics - xk.insertDouble(0, dt) - self.update_dynamics(self.cdpr, fg, xk, u, k, dt) + _, xd = self.update_dynamics(self.cdpr, x, u, k, dt) - # update full self.x solution + # update x for ji in range(4): gtd.InsertJointAngleDouble(x, ji, k, gtd.JointAngleDouble(xk, ji, k)) gtd.InsertJointVelDouble(x, ji, k, gtd.JointVelDouble(xk, ji, k)) - gtd.InsertTorqueDouble(x, ji, k, gtd.TorqueDouble(xk, ji, k)) - gtd.InsertTwistAccel(x, lid, k, gtd.TwistAccel(xk, lid, k)) - gtd.InsertPose(x, lid, k + 1, gtd.Pose(xk, lid, k + 1)) - gtd.InsertTwist(x, lid, k + 1, gtd.Twist(xk, lid, k + 1)) + gtd.InsertTorqueDouble(x, ji, k, gtd.TorqueDouble(u, ji, k)) + gtd.InsertTwistAccel(x, lid, k, gtd.TwistAccel(xd, lid, k)) + gtd.InsertPose(x, lid, k + 1, gtd.Pose(xd, lid, k + 1)) + gtd.InsertTwist(x, lid, k + 1, gtd.Twist(xd, lid, k + 1)) # debug if verbose: From dffa7043f661a5818f785f495b3bbba9a6a0bbdd Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 6 Apr 2021 03:31:32 -0400 Subject: [PATCH 04/73] unit test easier priors adding functions --- gtdynamics/cablerobot/src/test_cdpr_planar.py | 26 ++++++++++++++----- 1 file changed, 20 insertions(+), 6 deletions(-) diff --git a/gtdynamics/cablerobot/src/test_cdpr_planar.py b/gtdynamics/cablerobot/src/test_cdpr_planar.py index ab30f47a..d628ce32 100644 --- a/gtdynamics/cablerobot/src/test_cdpr_planar.py +++ b/gtdynamics/cablerobot/src/test_cdpr_planar.py @@ -49,15 +49,22 @@ def zeroValues(): self.assertEqual(0.0, kfg.error(values)) # try optimizing IK ikgraph = gtsam.NonlinearFactorGraph(kfg) - ikgraph.push_back(cdpr.priors_ik( - [0], [gtd.Pose(values, cdpr.ee_id(), 0)], [gtd.Twist(values, cdpr.ee_id(), 0)])) + ik1 = cdpr.priors_ik(ks=[0], + Ts=[gtd.Pose(values, cdpr.ee_id(), 0)], + Vs=[gtd.Twist(values, cdpr.ee_id(), 0)]) + ik2 = cdpr.priors_ik(ks=[0], values=values) + self.gtsamAssertEquals(ik1, ik2) + ikgraph.push_back(ik1) ikres = gtsam.LevenbergMarquardtOptimizer(ikgraph, zeroValues()).optimize() self.gtsamAssertEquals(ikres, values) # should match with full sol # try optimizing FK fkgraph = gtsam.NonlinearFactorGraph(kfg) - fkgraph.push_back(cdpr.priors_fk([0], - [[gtd.JointAngleDouble(values, ji, 0) for ji in range(4)]], - [[gtd.JointVelDouble(values, ji, 0) for ji in range(4)]])) + fk1 = cdpr.priors_fk(ks=[0], + ls=[[gtd.JointAngleDouble(values, ji, 0) for ji in range(4)]], + ldots=[[gtd.JointVelDouble(values, ji, 0) for ji in range(4)]]) + fk2 = cdpr.priors_fk(ks=[0], values=values) + self.gtsamAssertEquals(fk1, fk2) + fkgraph.push_back(fk1) params = gtsam.LevenbergMarquardtParams() params.setAbsoluteErrorTol(1e-20) # FK less sensitive so we need to decrease the tolerance fkres = gtsam.LevenbergMarquardtOptimizer(fkgraph, zeroValues(), params).optimize() @@ -88,7 +95,10 @@ def testDynamicsInstantaneous(self): dfg.push_back( cdpr.priors_ik([0], [gtd.Pose(values, cdpr.ee_id(), 0)], [gtd.Twist(values, cdpr.ee_id(), 0)])) - dfg.push_back(cdpr.priors_fd([0], [gtd.TwistAccel(values, cdpr.ee_id(), 0)])) + fd1 = cdpr.priors_fd(ks=[0], Vas=[gtd.TwistAccel(values, cdpr.ee_id(), 0)]) + fd2 = cdpr.priors_fd(ks=[0], values=values) + self.gtsamAssertEquals(fd1, fd2) + dfg.push_back(fd1) # redundancy resolution dfg.push_back( gtd.PriorFactorDouble( @@ -105,6 +115,10 @@ def testDynamicsInstantaneous(self): gtd.InsertTorqueDouble(init, ji, 0, -1) results = gtsam.LevenbergMarquardtOptimizer(dfg, init).optimize() self.gtsamAssertEquals(results, values) + # check ID priors functions + id1 = cdpr.priors_id(ks=[0], torquess=[gtd.Torque(results, ji, 0) for ji in range(4)]) + id2 = cdpr.priors_id(ks=[0], values=results) + self.gtsamAssertEquals(id1, id2) def testDynamicsCollocation(self): """Test dynamics factors across multiple timesteps by using collocation. From 4252df80d4dbd2e14b38547d5b68ff5434bd9c8f Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 6 Apr 2021 04:49:52 -0400 Subject: [PATCH 05/73] values-based priors functions --- gtdynamics/cablerobot/src/cdpr_planar.py | 31 ++++++++++++++++---- gtdynamics/cablerobot/src/cdpr_planar_sim.py | 12 ++------ 2 files changed, 28 insertions(+), 15 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_planar.py b/gtdynamics/cablerobot/src/cdpr_planar.py index 15a85f9e..cf8b8571 100644 --- a/gtdynamics/cablerobot/src/cdpr_planar.py +++ b/gtdynamics/cablerobot/src/cdpr_planar.py @@ -202,20 +202,25 @@ def collocation_factors(self, ks=[], dt=0.01): dfg.push_back(gtd.PriorFactorDouble(0, dt, self.costmodel_dt)) return dfg - def priors_fk(self, ks=[], ls=[[]], ldots=[[]]): + def priors_fk(self, ks=[], ls=[[]], ldots=[[]], values=None): """Creates prior factors which correspond to solving the forward kinematics problem by specifying the joint angles and velocities. To be used with kinematics_factors to optimize - for the Pose and Twist. + for the Pose and Twist. Either supply ks/ls/ldots, or ks/values. If values is supplied, ls + and ldots will be ignored. Args: ks (list, optional): Time step indices. Defaults to []. ls (list, optional): List of list joint angles for each time step. Defaults to [[]]. ldots (list, optional): List of list of joint velocities for each time step. Defaults to [[]]. + values (gtsam.Values, optional): Values object containing all the needed key/values. Returns: gtsam.NonlinearFactorGraph: The forward kinematics prior factors """ + if values is not None: + ls = [[gtd.JointAngleDouble(values, ji, k) for ji in range(4)] for k in ks] + ldots = [[gtd.JointVelDouble(values, ji, k) for ji in range(4)] for k in ks] graph = gtsam.NonlinearFactorGraph() for k, l, ldot in zip(ks, ls, ldots): for ji, (lval, ldotval) in enumerate(zip(l, ldot)): @@ -225,19 +230,24 @@ def priors_fk(self, ks=[], ls=[[]], ldots=[[]]): ldotval, self.costmodel_prior_ldot)) return graph - def priors_ik(self, ks=[], Ts=[], Vs=[]): + def priors_ik(self, ks=[], Ts=[], Vs=[], values=None): """Creates prior factors which correspond to solving the inverse kinematics problem by specifying the Pose/Twist of the end effector. To be used with kinematics_factors to - optimize for the joint angles and velocities. + optimize for the joint angles and velocities. Either supply ks/Ts/Vs, or ks/values. If + values is supplied, Ts and Vs will be ignored. Args: ks (list, optional): Time step indices. Defaults to []. Ts (list, optional): List of Poses for each time step. Defaults to [[]]. Vs (list, optional): List of Twists for each time step. Defaults to [[]]. + values (gtsam.Values, optional): Values object containing all the needed key/values. Returns: gtsam.NonlinearFactorGraph: The inverve kinematics prior factors """ + if values is not None: + Ts = [gtd.Pose(values, self.ee_id(), k) for k in ks] + Vs = [gtd.Twist(values, self.ee_id(), k) for k in ks] graph = gtsam.NonlinearFactorGraph() for k, T, V in zip(ks, Ts, Vs): graph.push_back(gtsam.PriorFactorPose3(gtd.internal.PoseKey(self.ee_id(), k).key(), @@ -249,20 +259,24 @@ def priors_ik(self, ks=[], Ts=[], Vs=[]): # note: I am not using the strict definitions for forward/inverse dynamics. # priors_fd solves for torques given twistaccel (no joint accel) # priors_id solves for twistaccel (no joint accel) given torques - def priors_id(self, ks=[], torquess=[[]]): + def priors_id(self, ks=[], torquess=[[]], values=None): """Creates factors roughly corresponding to the inverse dynamics problem. While strictly inverse dynamics in Lynch & Park refers to the problem of calculating joint accelerations given joint torques, temproarily this function is more convenient which directly relates constrains joint torques (to obtain twist accelerations when used with dynamics_factors). + Either supply ks/torquess, or ks/values. If values is supplied, torquess will be ignored. Args: ks (list, optional): Time step indices. Defaults to []. torquess (list, optional): List of list of joint torques for each time step. Defaults to [[]]. + values (gtsam.Values, optional): Values object containing all the needed key/values. Returns: gtsam.NonlinearFactorGraph: The inverse dynamics prior factors """ + if values is not None: + torquess = [[gtd.TorqueDouble(values, ji, k) for ji in range(4)] for k in ks] graph = gtsam.NonlinearFactorGraph() for k, torques in zip(ks, torquess): for ji, torque in enumerate(torques): @@ -270,19 +284,24 @@ def priors_id(self, ks=[], torquess=[[]]): torque, self.costmodel_prior_tau)) return graph - def priors_fd(self, ks=[], VAs=[]): + def priors_fd(self, ks=[], VAs=[], values=None): """Creates factors roughly corresponding to the forward dynamics problem. While strictly forward dynamics in Lynch & Park refers to the problem of calculating joint torques given joint accelerations, temproarily this function is more convenient which directly relates constraints TwistAccelerations (to obtain joint torques when used with dynamics_factors). + Either supply ks/twistaccels, or ks/values. If values is supplied, twistaccels will be + ignored. Args: ks (list, optional): Time step indices. Defaults to []. VAs (list, optional): List of twist accelerations for each time step. Defaults to [[]]. + values (gtsam.Values, optional): Values object containing all the needed key/values. Returns: gtsam.NonlinearFactorGraph: The forward dynamics prior factors """ + if values is not None: + torquess = [gtd.TwistAccel(values, self.ee_id(), k) for k in ks] graph = gtsam.NonlinearFactorGraph() for k, VA in zip(ks, VAs): graph.push_back(gtsam.PriorFactorVector6( diff --git a/gtdynamics/cablerobot/src/cdpr_planar_sim.py b/gtdynamics/cablerobot/src/cdpr_planar_sim.py index c950ffc5..f669043b 100644 --- a/gtdynamics/cablerobot/src/cdpr_planar_sim.py +++ b/gtdynamics/cablerobot/src/cdpr_planar_sim.py @@ -77,10 +77,7 @@ def update_kinematics(cdpr, x, k): gtd.InsertTwist(xk, lid, k, gtd.Twist(x, lid, k)) # IK for this time step, graph fg.push_back(cdpr.kinematics_factors(ks=[k])) - fg.push_back( - cdpr.priors_ik(ks=[k], - Ts=[gtd.Pose(xk, cdpr.ee_id(), k)], - Vs=[gtd.Twist(xk, cdpr.ee_id(), k)])) + fg.push_back(cdpr.priors_ik(ks=[k], values=xk)) # IK initial estimate for j in range(4): gtd.InsertJointAngleDouble(xk, j, k, 0) @@ -119,12 +116,9 @@ def update_dynamics(cdpr, x, u, k, dt): fg.push_back(cdpr.dynamics_factors(ks=[k])) fg.push_back(cdpr.collocation_factors(ks=[k], dt=dt)) # priors (pose/twist and torque inputs) + fg.push_back(cdpr.priors_ik(ks=[k], values=xd)) fg.push_back( - cdpr.priors_ik(ks=[k], - Ts=[gtd.Pose(xd, cdpr.ee_id(), k)], - Vs=[gtd.Twist(xd, cdpr.ee_id(), k)])) - fg.push_back( - cdpr.priors_id(ks=[k], torquess=[[gtd.TorqueDouble(u, ji, k) for ji in range(4)]])) + cdpr.priors_id(ks=[k], values=u)) # ID initial guess for ji in range(4): gtd.InsertTorqueDouble(xd, ji, k, gtd.TorqueDouble(u, ji, k)) From 1b32c9d722d3a33f77310a1c099c5ae49f0a9bf8 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 6 Apr 2021 04:55:33 -0400 Subject: [PATCH 06/73] bug fix for some reason --- gtdynamics/cablerobot/src/test_cdpr_planar.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/gtdynamics/cablerobot/src/test_cdpr_planar.py b/gtdynamics/cablerobot/src/test_cdpr_planar.py index d628ce32..37fc9b77 100644 --- a/gtdynamics/cablerobot/src/test_cdpr_planar.py +++ b/gtdynamics/cablerobot/src/test_cdpr_planar.py @@ -95,7 +95,7 @@ def testDynamicsInstantaneous(self): dfg.push_back( cdpr.priors_ik([0], [gtd.Pose(values, cdpr.ee_id(), 0)], [gtd.Twist(values, cdpr.ee_id(), 0)])) - fd1 = cdpr.priors_fd(ks=[0], Vas=[gtd.TwistAccel(values, cdpr.ee_id(), 0)]) + fd1 = cdpr.priors_fd(ks=[0], VAs=[gtd.TwistAccel(values, cdpr.ee_id(), 0)]) fd2 = cdpr.priors_fd(ks=[0], values=values) self.gtsamAssertEquals(fd1, fd2) dfg.push_back(fd1) @@ -116,7 +116,7 @@ def testDynamicsInstantaneous(self): results = gtsam.LevenbergMarquardtOptimizer(dfg, init).optimize() self.gtsamAssertEquals(results, values) # check ID priors functions - id1 = cdpr.priors_id(ks=[0], torquess=[gtd.Torque(results, ji, 0) for ji in range(4)]) + id1 = cdpr.priors_id(ks=[0], torquess=[[gtd.TorqueDouble(results, ji, 0) for ji in range(4)]]) id2 = cdpr.priors_id(ks=[0], values=results) self.gtsamAssertEquals(id1, id2) From b50166b96b1f2cb2db8d13fe7539b64bd0d5d5c5 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 6 Apr 2021 04:56:55 -0400 Subject: [PATCH 07/73] move cdpr controller ilqr part 1 --- ..._controller.py => cdpr_controller_ilqr.py} | 32 ++++--------------- .../src/test_cdpr_planar_controller.py | 4 +-- 2 files changed, 9 insertions(+), 27 deletions(-) rename gtdynamics/cablerobot/src/{cdpr_planar_controller.py => cdpr_controller_ilqr.py} (76%) diff --git a/gtdynamics/cablerobot/src/cdpr_planar_controller.py b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py similarity index 76% rename from gtdynamics/cablerobot/src/cdpr_planar_controller.py rename to gtdynamics/cablerobot/src/cdpr_controller_ilqr.py index 3c45f4ed..fb05e616 100644 --- a/gtdynamics/cablerobot/src/cdpr_planar_controller.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py @@ -15,28 +15,9 @@ import gtdynamics as gtd import numpy as np import utils +from cdpr_planar_controller import CdprControllerBase -class CdprControllerBase: - """Interface for cable robot controllers - """ - @property - def update(self, values, t): - """gives the new control input given current measurements - - Args: - values (gtsam.Values): values object will contain at least the current Pose and Twist, - but should often also include the current joint angles and velocities - t (int): The current time index (discrete time index) - - Returns: - gtsam.Values: A values object which contains the joint torques for this time step. - - Raises: - NotImplementedError: Derived classes must override this function - """ - raise NotImplementedError("CdprControllers need to implement the `update` function") - -class CdprController(CdprControllerBase): +class CdprControllerIlqr(CdprControllerBase): """Precomputes the open-loop trajectory then just calls on that for each update. """ @@ -56,13 +37,14 @@ def __init__(self, cdpr, x0, pdes=[], dt=0.01, Q=None, R=np.array([1.])): self.pdes = pdes self.dt = dt + # initial guess + x0 = utils.zerovalues(cdpr.ee_id(), ts=range(len(pdes)), dt=dt) # create iLQR graph fg = self.create_ilqr_fg(cdpr, x0, pdes, dt, Q, R) - # initial guess - init = utils.zerovalues(cdpr.ee_id(), range(len(pdes)), dt=dt) # optimize - self.optimizer = gtsam.LevenbergMarquardtOptimizer(fg, init) - self.result = self.optimizer.optimize() + self.optimizer = gtsam.LevenbergMarquardtOptimizer(fg, x_guess) + # self.result = self.optimizer.optimize() + self.result = x_guess self.fg = fg def update(self, values, t): diff --git a/gtdynamics/cablerobot/src/test_cdpr_planar_controller.py b/gtdynamics/cablerobot/src/test_cdpr_planar_controller.py index 22d5b481..1ef315c2 100644 --- a/gtdynamics/cablerobot/src/test_cdpr_planar_controller.py +++ b/gtdynamics/cablerobot/src/test_cdpr_planar_controller.py @@ -17,7 +17,7 @@ from gtsam import Pose3, Rot3 import numpy as np from cdpr_planar import Cdpr -from cdpr_planar_controller import CdprController +from cdpr_controller_ilqr import CdprControllerIlqr from cdpr_planar_sim import CdprSimulator from gtsam.utils.test_case import GtsamTestCase @@ -33,7 +33,7 @@ def testTrajFollow(self): x_des = [Pose3(Rot3(), (1.5+k/20.0, 0, 1.5)) for k in range(9)] x_des = x_des[0:1] + x_des - controller = CdprController(cdpr, x0=x0, pdes=x_des, dt=0.1) + controller = CdprControllerIlqr(cdpr, x0=x0, pdes=x_des, dt=0.1) sim = CdprSimulator(cdpr, x0, controller, dt=0.1) result = sim.run(N=10) From 32ce160150a7d589c195efe87629bf4d40523ff8 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 6 Apr 2021 05:05:06 -0400 Subject: [PATCH 08/73] move cdpr controller ilqr part 2 --- gtdynamics/cablerobot/src/cdpr_controller.py | 37 + .../cablerobot/src/cdpr_controller_ilqr.py | 9 +- gtdynamics/cablerobot/src/cdpr_planar.py | 2 +- .../cablerobot/src/gerry00_planar_sim.py | 7 +- .../src/gerry01_planar_tracking.ipynb | 14056 ++++++++-------- .../cablerobot/src/gerry01_planar_tracking.py | 6 +- ...roller.py => test_cdpr_controller_ilqr.py} | 4 +- .../cablerobot/src/test_cdpr_planar_sim.py | 4 +- 8 files changed, 7083 insertions(+), 7042 deletions(-) create mode 100644 gtdynamics/cablerobot/src/cdpr_controller.py rename gtdynamics/cablerobot/src/{test_cdpr_planar_controller.py => test_cdpr_controller_ilqr.py} (95%) diff --git a/gtdynamics/cablerobot/src/cdpr_controller.py b/gtdynamics/cablerobot/src/cdpr_controller.py new file mode 100644 index 00000000..631aec9e --- /dev/null +++ b/gtdynamics/cablerobot/src/cdpr_controller.py @@ -0,0 +1,37 @@ +""" +GTDynamics Copyright 2021, Georgia Tech Research Corporation, +Atlanta, Georgia 30332-0415 +All Rights Reserved +See LICENSE for the license information + +@file cdpr_controller.py +@brief Optimal controller for a cable robot. Solved by creating a factor graph and adding state +objectives and control costs, then optimizing +@author Frank Dellaert +@author Gerry Chen +""" + +import gtsam +import gtdynamics as gtd +import numpy as np +import utils + +class CdprControllerBase: + """Interface for cable robot controllers + """ + @property + def update(self, values, t): + """gives the new control input given current measurements + + Args: + values (gtsam.Values): values object will contain at least the current Pose and Twist, + but should often also include the current joint angles and velocities + t (int): The current time index (discrete time index) + + Returns: + gtsam.Values: A values object which contains the joint torques for this time step. + + Raises: + NotImplementedError: Derived classes must override this function + """ + raise NotImplementedError("CdprControllers need to implement the `update` function") diff --git a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py index fb05e616..9c17dd3d 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py @@ -4,7 +4,7 @@ All Rights Reserved See LICENSE for the license information -@file cdpr_planar_controller.py +@file cdpr_controller_ilqr.py @brief Optimal controller for a cable robot. Solved by creating a factor graph and adding state objectives and control costs, then optimizing @author Frank Dellaert @@ -15,7 +15,7 @@ import gtdynamics as gtd import numpy as np import utils -from cdpr_planar_controller import CdprControllerBase +from cdpr_controller import CdprControllerBase class CdprControllerIlqr(CdprControllerBase): """Precomputes the open-loop trajectory @@ -38,13 +38,12 @@ def __init__(self, cdpr, x0, pdes=[], dt=0.01, Q=None, R=np.array([1.])): self.dt = dt # initial guess - x0 = utils.zerovalues(cdpr.ee_id(), ts=range(len(pdes)), dt=dt) + x_guess = utils.zerovalues(cdpr.ee_id(), ts=range(len(pdes)), dt=dt) # create iLQR graph fg = self.create_ilqr_fg(cdpr, x0, pdes, dt, Q, R) # optimize self.optimizer = gtsam.LevenbergMarquardtOptimizer(fg, x_guess) - # self.result = self.optimizer.optimize() - self.result = x_guess + self.result = self.optimizer.optimize() self.fg = fg def update(self, values, t): diff --git a/gtdynamics/cablerobot/src/cdpr_planar.py b/gtdynamics/cablerobot/src/cdpr_planar.py index cf8b8571..2eeb1be6 100644 --- a/gtdynamics/cablerobot/src/cdpr_planar.py +++ b/gtdynamics/cablerobot/src/cdpr_planar.py @@ -301,7 +301,7 @@ def priors_fd(self, ks=[], VAs=[], values=None): gtsam.NonlinearFactorGraph: The forward dynamics prior factors """ if values is not None: - torquess = [gtd.TwistAccel(values, self.ee_id(), k) for k in ks] + VAs = [gtd.TwistAccel(values, self.ee_id(), k) for k in ks] graph = gtsam.NonlinearFactorGraph() for k, VA in zip(ks, VAs): graph.push_back(gtsam.PriorFactorVector6( diff --git a/gtdynamics/cablerobot/src/gerry00_planar_sim.py b/gtdynamics/cablerobot/src/gerry00_planar_sim.py index c633e1c3..f9b50af1 100644 --- a/gtdynamics/cablerobot/src/gerry00_planar_sim.py +++ b/gtdynamics/cablerobot/src/gerry00_planar_sim.py @@ -17,8 +17,8 @@ from gtsam import Pose3, Rot3 from cdpr_planar import Cdpr -from cdpr_planar_controller import CdprControllerBase -from cdpr_planar_sim import cdpr_sim +from cdpr_controller import CdprControllerBase +from cdpr_planar_sim import CdprSimulator class DummyController(CdprControllerBase): @@ -43,7 +43,8 @@ def main(): gtd.InsertPose(x0, cdpr.ee_id(), 0, Pose3(Rot3(), (1.5, 0, 1.5))) gtd.InsertTwist(x0, cdpr.ee_id(), 0, np.zeros(6)) # run simulation - result = cdpr_sim(cdpr, x0, controller, dt=dt, N=N) + sim = CdprSimulator(cdpr, x0, controller, dt=dt) + result = sim.run(N=N) poses = [gtd.Pose(result, cdpr.ee_id(), k) for k in range(N)] plt.figure(1) diff --git a/gtdynamics/cablerobot/src/gerry01_planar_tracking.ipynb b/gtdynamics/cablerobot/src/gerry01_planar_tracking.ipynb index d89cb255..fa630cab 100644 --- a/gtdynamics/cablerobot/src/gerry01_planar_tracking.ipynb +++ b/gtdynamics/cablerobot/src/gerry01_planar_tracking.ipynb @@ -13,7 +13,7 @@ "from gtsam import Pose3, Rot3\n", "\n", "from cdpr_planar import Cdpr\n", - "from cdpr_planar_controller import CdprController\n", + "from cdpr_controller_ilqr import CdprControllerIlqr\n", "from cdpr_planar_sim import CdprSimulator" ] }, @@ -59,12 +59,12 @@ "outputs": [], "source": [ "# controller\n", - "controller = CdprController(cdpr,\n", - " x0,\n", - " x_des,\n", - " dt=dt,\n", - " Q=np.array([0, 1, 0, 1e3, 0, 1e3]),\n", - " R=np.array([1e-3]))" + "controller = CdprControllerIlqr(cdpr,\n", + " x0,\n", + " x_des,\n", + " dt=dt,\n", + " Q=np.array([0, 1, 0, 1e3, 0, 1e3]),\n", + " R=np.array([1e-3]))" ] }, { @@ -86,7 +86,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "missing-formation", + "id": "automatic-columbia", "metadata": {}, "outputs": [], "source": [ @@ -148,7 +148,7 @@ "data": { "text/html": [ "" diff --git a/gtdynamics/cablerobot/src/gerry01_planar_tracking.py b/gtdynamics/cablerobot/src/gerry01_planar_tracking.py index b1c3ac2c..533e0c01 100644 --- a/gtdynamics/cablerobot/src/gerry01_planar_tracking.py +++ b/gtdynamics/cablerobot/src/gerry01_planar_tracking.py @@ -17,7 +17,7 @@ from gtsam import Pose3, Rot3 from cdpr_planar import Cdpr -from cdpr_planar_controller import CdprController +from cdpr_controller_ilqr import CdprControllerIlqr from cdpr_planar_sim import CdprSimulator import cProfile @@ -39,7 +39,7 @@ def main(): gtd.InsertPose(x0, cdpr.ee_id(), 0, x_des[0]) gtd.InsertTwist(x0, cdpr.ee_id(), 0, np.zeros(6)) # controller - controller = CdprController(cdpr, + controller = CdprControllerIlqr(cdpr, x0, x_des, dt=dt, @@ -80,7 +80,7 @@ def cable_coords(k, ji): # controls plt.subplot(1,2,2) lsctrl = plt.plot(np.arange(0,Tf,dt), torques) - plt.xlabel('time (s)');plt.ylabel('Cable tension (N)');plt.title('Control Inputs'); + plt.xlabel('time (s)');plt.ylabel('Cable tension (N)');plt.title('Control Inputs') if __name__ == '__main__': diff --git a/gtdynamics/cablerobot/src/test_cdpr_planar_controller.py b/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py similarity index 95% rename from gtdynamics/cablerobot/src/test_cdpr_planar_controller.py rename to gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py index 1ef315c2..b438ab6b 100644 --- a/gtdynamics/cablerobot/src/test_cdpr_planar_controller.py +++ b/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py @@ -4,7 +4,7 @@ All Rights Reserved See LICENSE for the license information -@file test_cdpr_planar_controller.py +@file test_cdpr_controller.py @brief Unit tests for CDPR. @author Frank Dellaert @author Gerry Chen @@ -21,7 +21,7 @@ from cdpr_planar_sim import CdprSimulator from gtsam.utils.test_case import GtsamTestCase -class TestCdprPlanar(GtsamTestCase): +class TestCdprControllerIlqr(GtsamTestCase): def testTrajFollow(self): """Tests trajectory tracking controller """ diff --git a/gtdynamics/cablerobot/src/test_cdpr_planar_sim.py b/gtdynamics/cablerobot/src/test_cdpr_planar_sim.py index 35ffa0b7..ea201cb3 100644 --- a/gtdynamics/cablerobot/src/test_cdpr_planar_sim.py +++ b/gtdynamics/cablerobot/src/test_cdpr_planar_sim.py @@ -17,11 +17,11 @@ from gtsam import Pose3, Rot3 import numpy as np from cdpr_planar import Cdpr -from cdpr_planar_controller import CdprControllerBase +from cdpr_controller import CdprControllerBase from cdpr_planar_sim import CdprSimulator from gtsam.utils.test_case import GtsamTestCase -class TestCdprPlanar(GtsamTestCase): +class TestCdprSimulator(GtsamTestCase): def testSim(self): """Tests the simulation: given a controller and initial state, it will run through and simulate the system over multiple timesteps From a4aca11ebc75ef50f590f8962035f41913c4dff7 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 6 Apr 2021 05:41:45 -0400 Subject: [PATCH 09/73] unit test for a tension distribution conroller --- .../src/test_cdpr_controller_ilqr.py | 2 +- .../src/test_cdpr_controller_tension_dist.py | 54 +++++++++++++++++++ 2 files changed, 55 insertions(+), 1 deletion(-) create mode 100644 gtdynamics/cablerobot/src/test_cdpr_controller_tension_dist.py diff --git a/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py index b438ab6b..d0b8979d 100644 --- a/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py @@ -4,7 +4,7 @@ All Rights Reserved See LICENSE for the license information -@file test_cdpr_controller.py +@file test_cdpr_controller_ilqr.py @brief Unit tests for CDPR. @author Frank Dellaert @author Gerry Chen diff --git a/gtdynamics/cablerobot/src/test_cdpr_controller_tension_dist.py b/gtdynamics/cablerobot/src/test_cdpr_controller_tension_dist.py new file mode 100644 index 00000000..acf56c72 --- /dev/null +++ b/gtdynamics/cablerobot/src/test_cdpr_controller_tension_dist.py @@ -0,0 +1,54 @@ +""" +GTDynamics Copyright 2021, Georgia Tech Research Corporation, +Atlanta, Georgia 30332-0415 +All Rights Reserved +See LICENSE for the license information + +@file test_cdpr_controller_tension_dist.py +@brief Unit tests for CDPR. +@author Frank Dellaert +@author Gerry Chen +""" + +import unittest + +import gtdynamics as gtd +import gtsam +from gtsam import Pose3, Rot3 +import numpy as np +from cdpr_planar import Cdpr +from cdpr_controller_tension_dist import CdprControllerTensionDist as CdprController +from cdpr_planar_sim import CdprSimulator +from gtsam.utils.test_case import GtsamTestCase + +class TestCdprControllerTensionDist(GtsamTestCase): + def testTrajFollow(self): + """Tests trajectory tracking controller + """ + cdpr = Cdpr() + + x0 = gtsam.Values() + gtd.InsertPose(x0, cdpr.ee_id(), 0, Pose3(Rot3(), (1.5, 0, 1.5))) + gtd.InsertTwist(x0, cdpr.ee_id(), 0, np.zeros(6)) + + x_des = [Pose3(Rot3(), (1.5+k/20.0, 0, 1.5)) for k in range(9)] + x_des = x_des[0:1] + x_des + controller = CdprController(cdpr, x0=x0, pdes=x_des, dt=0.1) + + sim = CdprSimulator(cdpr, x0, controller, dt=0.1) + result = sim.run(N=10) + pAct = [gtd.Pose(result, cdpr.ee_id(), k) for k in range(10)] + + if False: + print() + for k, (des, act) in enumerate(zip(x_des, pAct)): + print(('k: {:d} -- des: {:.3f}, {:.3f}, {:.3f} -- act: {:.3f}, {:.3f}, {:.3f}' + + ' -- u: {:.3e}, {:.3e}, {:.3e}, {:.3e}').format( + k, *des.translation(), *act.translation(), + *[gtd.TorqueDouble(result, ji, k) for ji in range(4)])) + + for k, (des, act) in enumerate(zip(x_des, pAct)): + self.gtsamAssertEquals(des, act) + +if __name__ == "__main__": + unittest.main() From decb82b892383ca49eeae84c2ed198ba550cf4b1 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 6 Apr 2021 05:42:15 -0400 Subject: [PATCH 10/73] tension distribution controller - not quite working yet --- .../src/cdpr_controller_tension_dist.py | 139 ++++++++++++++++++ gtdynamics/cablerobot/src/utils.py | 19 +++ 2 files changed, 158 insertions(+) create mode 100644 gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py diff --git a/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py b/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py new file mode 100644 index 00000000..1955dc91 --- /dev/null +++ b/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py @@ -0,0 +1,139 @@ +""" +GTDynamics Copyright 2021, Georgia Tech Research Corporation, +Atlanta, Georgia 30332-0415 +All Rights Reserved +See LICENSE for the license information + +@file cdpr_controller_tension_dist.py +@brief Optimal controller for a cable robot. Solved by creating a factor graph and adding state +objectives and control costs, then optimizing +@author Frank Dellaert +@author Gerry Chen +""" + +import gtsam +import gtdynamics as gtd +import numpy as np +import utils +from cdpr_controller import CdprControllerBase + +class CdprControllerTensionDist(CdprControllerBase): + """Precomputes the open-loop trajectory + then just calls on that for each update. + """ + def __init__(self, cdpr, x0, pdes=[], dt=0.01, Q=None, R=np.array([1.])): + """constructor + + Args: + cdpr (Cdpr): cable robot object + x0 (gtsam.Values): initial state + pdes (list, optional): list of desired poses. Defaults to []. + dt (float, optional): time step duration. Defaults to 0.01. + R (np.ndarray, optional): Control cost (as a 1-vector). Defaults to np.array([1.]). + """ + self.cdpr = cdpr + self.pdes = pdes + self.dt = dt + self.R = R + N = len(pdes) + + # # initial guess + # lid = cdpr.ee_id() + # x_guess = gtsam.Values() + # x_guess.insertDouble(0, dt) + # for k, T in enumerate(pdes): + # gtd.InsertPose(x_guess, lid, k, T) + # utils.InsertTwist(x_guess, lid, 0, x0) + # for k in range(N-1): + + # utils.InsertJointAngles(x_guess, k, xk) + # utils.InsertJointVels(x_guess, k, xk) + # utils.InsertTorques(x_guess, k, xk) + # utils.InsertWrenches(x_guess, lid, k, xk) + # utils.InsertTwist(x_guess, lid, k+1, xk) + # utils.InsertTwistAccel(x_guess, lid, k, xk) + # for ji in range(4): + # gtd.InsertJointAngleDouble(x_guess, ji, N-1, 0) + # gtd.InsertJointVelDouble(x_guess, ji, N-1, 0) + # gtd.InsertTorqueDouble(x_guess, ji, N-1, 0) + # gtd.InsertWrench(x_guess, lid, ji, N-1, np.zeros(6)) + # gtd.InsertTwistAccel(x_guess, lid, N-1, np.zeros(6)) + # # create iLQR graph + # fg = self.create_ilqr_fg(cdpr, x0, pdes, dt, Q, R) + # # optimize + # self.optimizer = gtsam.LevenbergMarquardtOptimizer(fg, x_guess) + # # self.result = self.optimizer.optimize() + # self.result = x_guess + # self.fg = fg + + def update(self, values, t): + """New control: returns the entire results vector, which contains the optimal open-loop + control from the optimal trajectory. + """ + return self.solve_one_step(self.cdpr, + self.cdpr.ee_id(), + self.pdes[t], + t, + lldotnow=values, + dt=self.dt, + R=self.R) + + @staticmethod + def solve_one_step(cdpr, lid, Tgoal, k, TVnow=None, lldotnow=None, dt=0.01, R=np.ones(1)): + """Creates the factor graph for the tension distribution problem. This essentially consists + of creating a factor graph that describes the CDPR dynamics for this one timestep, then + adding control cost factors. Either the current pose/twist may be specified, or the current + cable lengths/velocities may be specified (in which case FK will be used to calculate the + pose/twist). + + Args: + cdpr (Cdpr): cable robot object + Tgoal (gtsam.Pose3): goal pose for the next time step + TVnow (gtsam.Values, optional): The current pose and twist + lldotnow (gtsam.Values, optional): The current cable lengths / speeds + dt (float): time step duration + R (np.ndarray): The control cost + + Returns: + gtsam.Values: a Values object containing the control torques + """ + fg = gtsam.NonlinearFactorGraph() + # IK: either solve for current pose T given measurements, or use open-loop solution + if lldotnow is not None: + fg.push_back(cdpr.kinematics_factors(ks=[k])) + fg.push_back(cdpr.priors_fk(ks=[k], values=lldotnow)) + else: + fg.push_back(cdpr.priors_ik(ks=[k], values=TVnow)) + # pose constraints: must reach next pose T + fg.push_back(gtsam.PriorFactorPose3(gtd.internal.PoseKey(lid, k).key(), + Tgoal, cdpr.costmodel_prior_pose)) + # collocation: given current+next Ts, solve for current+next Vs and current VAs + fg.push_back(cdpr.collocation_factors(ks=[k])) + # dynamics: given VA, solve for torque/wrenches + fg.push_back(cdpr.dynamics_factors(ks=[k])) + # redundancy resolution: control costs + for ji in range(4): + fg.push_back( + gtd.PriorFactorDouble(gtd.internal.TorqueKey(ji, k).key(), 0.0, + gtsam.noiseModel.Diagonal.Precisions(R))) + # tmp initial guess + xk = gtsam.Values() + xk.insertDouble(0, dt) + if lldotnow is not None: + utils.InsertJointAngles(xk, k, lldotnow) + utils.InsertJointVels(xk, k, lldotnow) + gtd.InsertPose(xk, lid, k, gtsam.Pose3()) + gtd.InsertTwist(xk, lid, k, np.zeros(6)) + else: + utils.InsertPose(xk, lid, k, TVnow) + utils.InsertTwist(xk, lid, k, TVnow) + gtd.InsertPose(xk, lid, k+1, Tgoal) + gtd.InsertTwist(xk, lid, k+1, np.zeros(6)) + gtd.InsertTwistAccel(xk, lid, k, np.zeros(6)) + for ji in range(4): + gtd.InsertTorqueDouble(xk, ji, k, 0) + gtd.InsertWrench(xk, lid, ji, k, np.zeros(6)) + # optimize and update + result = gtsam.LevenbergMarquardtOptimizer(fg, xk).optimize() + print(fg.error(result)) + return result diff --git a/gtdynamics/cablerobot/src/utils.py b/gtdynamics/cablerobot/src/utils.py index bd89d54c..bf58ecb0 100644 --- a/gtdynamics/cablerobot/src/utils.py +++ b/gtdynamics/cablerobot/src/utils.py @@ -37,3 +37,22 @@ def zerovalues(lid, ts=[], dt=0.01): gtd.InsertTwist(zero, lid, t, np.zeros(6)) gtd.InsertTwistAccel(zero, lid, t, np.zeros(6)) return zero + +def InsertPose(dest, link_id, k, source): + gtd.InsertPose(dest, link_id, k, gtd.Pose(source, link_id, k)) +def InsertTwist(dest, link_id, k, source): + gtd.InsertTwist(dest, link_id, k, gtd.Twist(source, link_id, k)) +def InsertTwistAccel(dest, link_id, k, source): + gtd.InsertTwistAccel(dest, link_id, k, gtd.TwistAccel(source, link_id, k)) +def InsertJointAngles(dest, k, source): + for ji in range(4): + gtd.InsertJointAngleDouble(dest, ji, k, gtd.JointAngleDouble(source, ji, k)) +def InsertJointVels(dest, k, source): + for ji in range(4): + gtd.InsertJointVelDouble(dest, ji, k, gtd.JointVelDouble(source, ji, k)) +def InsertTorques(dest, k, source): + for ji in range(4): + gtd.InsertTorqueDouble(dest, ji, k, gtd.TorqueDouble(source, ji, k)) +def InsertWrenches(dest, link_id, k, source): + for ji in range(4): + gtd.InsertWrench(dest, link_id, ji, k, gtd.Wrench(source, link_id, ji, k)) From 7599c33e6099016f360d7970d1cafe370698e185 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Thu, 8 Apr 2021 06:16:43 -0400 Subject: [PATCH 11/73] PriorFactorVector6 - use gtd verison --- gtdynamics/cablerobot/src/cdpr_planar.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gtdynamics/cablerobot/src/cdpr_planar.py b/gtdynamics/cablerobot/src/cdpr_planar.py index 2eeb1be6..5e2ff430 100644 --- a/gtdynamics/cablerobot/src/cdpr_planar.py +++ b/gtdynamics/cablerobot/src/cdpr_planar.py @@ -304,7 +304,7 @@ def priors_fd(self, ks=[], VAs=[], values=None): VAs = [gtd.TwistAccel(values, self.ee_id(), k) for k in ks] graph = gtsam.NonlinearFactorGraph() for k, VA in zip(ks, VAs): - graph.push_back(gtsam.PriorFactorVector6( + graph.push_back(gtd.PriorFactorVector6( gtd.internal.TwistAccelKey(self.ee_id(), k).key(), VA, self.costmodel_prior_twistaccel)) return graph From b9decde9f3a468317ccb234ad7a75440181e71c3 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Thu, 8 Apr 2021 06:45:05 -0400 Subject: [PATCH 12/73] paint parsing unit test --- gtdynamics/cablerobot/src/data/test_traj.h | 20 +++++++++ gtdynamics/cablerobot/src/paint_parse.py | 2 + gtdynamics/cablerobot/src/test_paint_parse.py | 44 +++++++++++++++++++ 3 files changed, 66 insertions(+) create mode 100644 gtdynamics/cablerobot/src/data/test_traj.h create mode 100644 gtdynamics/cablerobot/src/paint_parse.py create mode 100644 gtdynamics/cablerobot/src/test_paint_parse.py diff --git a/gtdynamics/cablerobot/src/data/test_traj.h b/gtdynamics/cablerobot/src/data/test_traj.h new file mode 100644 index 00000000..4757dc3e --- /dev/null +++ b/gtdynamics/cablerobot/src/data/test_traj.h @@ -0,0 +1,20 @@ +bool painton[] = { + 1, 1, 1, 1, 1, 1 +}; +uint8_t colorinds[] = { + 0, 0, 1, 1, 2, 3 +}; +uint8_t colorpalette[][3] = { + {4, 49, 75}, + {209, 4, 32}, + {236, 237, 237}, + {0, 0, 0} +}; +float traj[][2] = { + { 0.100, 1.100}, + { 0.200, 1.000}, + { 0.300, 0.900}, + { 0.400, 1.100}, + { 0.500, 1.200}, + { 0.100, 1.150} +}; diff --git a/gtdynamics/cablerobot/src/paint_parse.py b/gtdynamics/cablerobot/src/paint_parse.py new file mode 100644 index 00000000..ed673574 --- /dev/null +++ b/gtdynamics/cablerobot/src/paint_parse.py @@ -0,0 +1,2 @@ +def ParseFile(fname): + return None, None, None, None diff --git a/gtdynamics/cablerobot/src/test_paint_parse.py b/gtdynamics/cablerobot/src/test_paint_parse.py new file mode 100644 index 00000000..95b31592 --- /dev/null +++ b/gtdynamics/cablerobot/src/test_paint_parse.py @@ -0,0 +1,44 @@ +""" +GTDynamics Copyright 2021, Georgia Tech Research Corporation, +Atlanta, Georgia 30332-0415 +All Rights Reserved +See LICENSE for the license information + +@file test_paint_parse.py +@brief Unit tests for parsing a paint trajectory in a .h file. +@author Frank Dellaert +@author Gerry Chen +""" + +import unittest + +from paint_parse import ParseFile + +import numpy as np +from gtsam.utils.test_case import GtsamTestCase + + +class TestPaintParse(GtsamTestCase): + """Unit tests for paint trajectory file parsing""" + def testParse(self): + e_painton = np.array([1, 1, 1, 1, 1, 1]) + e_colorinds = np.array([0, 0, 1, 1, 2, 3]) + e_colorpalette = np.array([[4, 49, 75], + [209, 4, 32], + [236, 237, 237], + [0, 0, 0]]) + e_traj = np.array([[0.1, 1.1], + [0.2, 1.0], + [0.3, 0.9], + [0.4, 1.1], + [0.5, 1.2], + [0.1, 1.15]]) + a_painton, a_colorinds, a_colorpalette, a_traj = ParseFile('data/test_traj.h') + + np.testing.assert_equal(e_painton, a_painton) + np.testing.assert_equal(e_colorinds, a_colorinds) + np.testing.assert_equal(e_colorpalette, a_colorpalette) + np.testing.assert_equal(e_traj, a_traj) + +if __name__ == "__main__": + unittest.main() From 10531d64963f4dd22ada479c51159582253cf4b9 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Sun, 11 Apr 2021 10:28:28 -0400 Subject: [PATCH 13/73] paint file parsing works --- gtdynamics/cablerobot/src/paint_parse.py | 57 +++++++++++++++++++++++- 1 file changed, 56 insertions(+), 1 deletion(-) diff --git a/gtdynamics/cablerobot/src/paint_parse.py b/gtdynamics/cablerobot/src/paint_parse.py index ed673574..caaf3c98 100644 --- a/gtdynamics/cablerobot/src/paint_parse.py +++ b/gtdynamics/cablerobot/src/paint_parse.py @@ -1,2 +1,57 @@ +""" +GTDynamics Copyright 2021, Georgia Tech Research Corporation, +Atlanta, Georgia 30332-0415 +All Rights Reserved +See LICENSE for the license information + +@file paint_parse.py +@brief Parses a .h file containing the paint trajectory. +@author Frank Dellaert +@author Gerry Chen +""" + +import numpy as np +import re + +UINTEXPR = '([0-9]*?)' +FLOATEXPR = '(-?[0-9]*?\.?[0-9]*?)' + def ParseFile(fname): - return None, None, None, None + with open(fname) as f: + # painton + assert next(f) == 'bool painton[] = {\n', "variable `painton` not found" + paintons = [bool(e) for e in next(f).strip().split(',')] + assert next(f) == '};\n', "parse error on variable `painton`" + + # colorinds + assert next(f) == 'uint8_t colorinds[] = {\n', "variable `colorinds` not found" + colorinds = [int(e) for e in next(f).strip().split(',')] + assert next(f) == '};\n', "parse error on variable `colorinds`" + + # colorpalette + assert next(f) == 'uint8_t colorpalette[][3] = {\n', "variable `colorpalette` not found" + colors = [] + while True: + matches = re.search(f'\\{{{UINTEXPR}, {UINTEXPR}, {UINTEXPR}\\}},?', next(f)) + if matches is None: + break # next(f) was '};\n' + colors.append([int(g) for g in matches.groups()]) + colors = np.array(colors) + + # trajectory + assert next(f) == 'float traj[][2] = {\n', "variable `traj` not found" + traj = [] + while True: + matches = re.search(f'\{{{FLOATEXPR},{FLOATEXPR}}},?', + re.sub('\s', '', next(f))) # remove any whitespace + if matches is None: + break # next(f) was '};\n' + traj.append([float(g) for g in matches.groups()]) + traj = np.array(traj) + + # EOF + try: + next(f) + except StopIteration: + return paintons, colorinds, colors, traj + assert False, 'End of file expected' From 3d2c17a4f1394248a5c9ef9d5d6252f647c7a99a Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Sun, 11 Apr 2021 10:43:02 -0400 Subject: [PATCH 14/73] fix boolean parsing --- gtdynamics/cablerobot/src/data/test_traj.h | 2 +- gtdynamics/cablerobot/src/paint_parse.py | 2 +- gtdynamics/cablerobot/src/test_paint_parse.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/gtdynamics/cablerobot/src/data/test_traj.h b/gtdynamics/cablerobot/src/data/test_traj.h index 4757dc3e..f9d434d4 100644 --- a/gtdynamics/cablerobot/src/data/test_traj.h +++ b/gtdynamics/cablerobot/src/data/test_traj.h @@ -1,5 +1,5 @@ bool painton[] = { - 1, 1, 1, 1, 1, 1 + 1, 1, 0, 1, 1, 1 }; uint8_t colorinds[] = { 0, 0, 1, 1, 2, 3 diff --git a/gtdynamics/cablerobot/src/paint_parse.py b/gtdynamics/cablerobot/src/paint_parse.py index caaf3c98..4aa2e8c6 100644 --- a/gtdynamics/cablerobot/src/paint_parse.py +++ b/gtdynamics/cablerobot/src/paint_parse.py @@ -20,7 +20,7 @@ def ParseFile(fname): with open(fname) as f: # painton assert next(f) == 'bool painton[] = {\n', "variable `painton` not found" - paintons = [bool(e) for e in next(f).strip().split(',')] + paintons = [bool(int(e)) for e in next(f).strip().split(',')] assert next(f) == '};\n', "parse error on variable `painton`" # colorinds diff --git a/gtdynamics/cablerobot/src/test_paint_parse.py b/gtdynamics/cablerobot/src/test_paint_parse.py index 95b31592..67a71243 100644 --- a/gtdynamics/cablerobot/src/test_paint_parse.py +++ b/gtdynamics/cablerobot/src/test_paint_parse.py @@ -21,7 +21,7 @@ class TestPaintParse(GtsamTestCase): """Unit tests for paint trajectory file parsing""" def testParse(self): - e_painton = np.array([1, 1, 1, 1, 1, 1]) + e_painton = np.array([1, 1, 0, 1, 1, 1]) e_colorinds = np.array([0, 0, 1, 1, 2, 3]) e_colorpalette = np.array([[4, 49, 75], [209, 4, 32], From b35f1f3d7d6f3dd20fd705193c6d7b8cedb0e413 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Sun, 11 Apr 2021 10:43:37 -0400 Subject: [PATCH 15/73] begin trajectory tracking script --- gtdynamics/cablerobot/src/data/iros_logo_2.h | 13144 ++++++++++++++++ .../cablerobot/src/gerry02_traj_tracking.py | 39 + 2 files changed, 13183 insertions(+) create mode 100644 gtdynamics/cablerobot/src/data/iros_logo_2.h create mode 100644 gtdynamics/cablerobot/src/gerry02_traj_tracking.py diff --git a/gtdynamics/cablerobot/src/data/iros_logo_2.h b/gtdynamics/cablerobot/src/data/iros_logo_2.h new file mode 100644 index 00000000..7a80c008 --- /dev/null +++ b/gtdynamics/cablerobot/src/data/iros_logo_2.h @@ -0,0 +1,13144 @@ +bool painton[] = { + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 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2.163, 1.451}, + { 2.158, 1.451}, + { 2.153, 1.450}, + { 2.148, 1.450}, + { 2.143, 1.449}, + { 2.139, 1.449}, + { 2.134, 1.448}, + { 2.129, 1.448}, + { 2.124, 1.447}, + { 2.119, 1.446}, + { 2.115, 1.446} +}; diff --git a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py new file mode 100644 index 00000000..8e993932 --- /dev/null +++ b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py @@ -0,0 +1,39 @@ +""" +GTDynamics Copyright 2021, Georgia Tech Research Corporation, +Atlanta, Georgia 30332-0415 +All Rights Reserved +See LICENSE for the license information + +@file gerry01_planar_tracking.py +@brief quick test of open-loop trajectory tracking for planar cdpr. +@author Frank Dellaert +@author Gerry Chen +""" + +import gtdynamics as gtd +import gtsam +import matplotlib.pyplot as plt +import numpy as np +from gtsam import Pose3, Rot3 + +from cdpr_planar import Cdpr +from cdpr_controller_ilqr import CdprControllerIlqr +from cdpr_planar_sim import CdprSimulator +from paint_parse import ParseFile + +import cProfile + +def main(fname='data/iros_logo_2.h'): + isPaints, colorinds, colorpalette, traj = ParseFile(fname) + def paintString(isPaint, colori): + return '{:d}, {:d}, {:d}'.format(*colorpalette[colori]) if isPaint else 'paint off' + i = 0 + print(traj.shape) + for isPaint, colori, point in zip(isPaints, colorinds, traj): + print('{:5.2f}, {:5.2f}\t-\t{}'.format(*point, paintString(isPaint, colori))) + if i > 100: + break + i += 1 + +if __name__ == '__main__': + main() \ No newline at end of file From 7d2c0380d5c8b5956bd72ec20f75eed4f34e13f7 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Mon, 12 Apr 2021 11:45:52 -0400 Subject: [PATCH 16/73] clean up ipython notebook --- .../src/gerry01_planar_tracking.ipynb | 7359 +---------------- 1 file changed, 42 insertions(+), 7317 deletions(-) diff --git a/gtdynamics/cablerobot/src/gerry01_planar_tracking.ipynb b/gtdynamics/cablerobot/src/gerry01_planar_tracking.ipynb index fa630cab..8d476287 100644 --- a/gtdynamics/cablerobot/src/gerry01_planar_tracking.ipynb +++ b/gtdynamics/cablerobot/src/gerry01_planar_tracking.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "adolescent-campus", "metadata": {}, "outputs": [], @@ -13,13 +13,14 @@ "from gtsam import Pose3, Rot3\n", "\n", "from cdpr_planar import Cdpr\n", - "from cdpr_controller_ilqr import CdprControllerIlqr\n", + "from cdpr_controller_ilqr import CdprControllerIlqr as CdprController\n", + "# from cdpr_controller_tension_dist import CdprControllerTensionDist as CdprController\n", "from cdpr_planar_sim import CdprSimulator" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "absolute-royalty", "metadata": {}, "outputs": [], @@ -40,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "traditional-running", "metadata": {}, "outputs": [], @@ -53,72 +54,69 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "northern-pottery", "metadata": {}, "outputs": [], "source": [ "# controller\n", - "controller = CdprControllerIlqr(cdpr,\n", - " x0,\n", - " x_des,\n", - " dt=dt,\n", - " Q=np.array([0, 1, 0, 1e3, 0, 1e3]),\n", - " R=np.array([1e-3]))" + "controller = CdprController(cdpr,\n", + " x0,\n", + " x_des,\n", + " dt=dt,\n", + " Q=np.array([0, 1, 0, 1e3, 0, 1e3]),\n", + " R=np.array([1e-3]))" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "enclosed-capacity", "metadata": {}, "outputs": [], "source": [ "# run simulation\n", - "sim = CdprSimulator(cdpr, x0, controller, dt=dt)\n", - "result = sim.run(N=N, verbose=False)\n", - "poses = [gtd.Pose(result, cdpr.ee_id(), k) for k in range(N+1)]\n", - "posesxy = np.array([[pose.x() for pose in poses], [pose.z() for pose in poses]])\n", - "desposesxy = np.array([[pose.x() for pose in x_des], [pose.z() for pose in x_des]])\n", - "torques = np.array([[gtd.TorqueDouble(result, ji, k) for ji in range(4)] for k in range(N)])" + "sim = CdprSimulator(cdpr, x0, controller, dt=dt);\n", + "result = sim.run(N=N, verbose=False);" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "automatic-columbia", + "execution_count": null, + "id": "copyrighted-model", "metadata": {}, "outputs": [], "source": [ "# plot utils\n", + "poses = [gtd.Pose(result, cdpr.ee_id(), k) for k in range(N+1)]\n", + "posesxy = np.array([[pose.x() for pose in poses], [pose.z() for pose in poses]])\n", + "desposesxy = np.array([[pose.x() for pose in x_des], [pose.z() for pose in x_des]]);\n", + "torques = np.array([[gtd.TorqueDouble(result, ji, k) for ji in range(4)] for k in range(N)]);\n", "boxinds = np.array([0,1,2,3,0]).reshape(5,1)\n", "def ee_coords(k):\n", - " return (posesxy[:, k]+cdpr.params.b_locs[boxinds, [0,2]]).T\n", + " points = []\n", + " for corner in [0,1,2,3,0]:\n", + " pose = poses[k]\n", + " point = pose.transformFrom(cdpr.params.b_locs[corner])\n", + " points.append(point)\n", + " points = np.array(points)\n", + " return points[:, [0,2]].T\n", + " # return (posesxy[:, k]+cdpr.params.b_locs[boxinds, [0,2]]).T\n", "frame_coords = cdpr.params.a_locs[boxinds, [0,2]].T\n", "def cable_coords(k, ji):\n", + " point = poses[k].transformFrom(cdpr.params.b_locs[ji])\n", + " return np.array([[cdpr.params.a_locs[ji][0], point[0]],\n", + " [cdpr.params.a_locs[ji][2], point[2]]])\n", " return np.array([[cdpr.params.a_locs[ji][0], posesxy[0][k]+cdpr.params.b_locs[ji][0]],\n", " [cdpr.params.a_locs[ji][2], posesxy[1][k]+cdpr.params.b_locs[ji][2]]])" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "announced-pittsburgh", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plot\n", "import matplotlib.pyplot as plt\n", @@ -128,7 +126,9 @@ "plt.plot(*frame_coords, 'k-')\n", "plt.plot(*desposesxy, 'r*') # desired trajectory\n", "ltraj, = plt.plot(*posesxy, 'k-') # actual trajectory\n", - "lscables = plt.plot(np.zeros((2,4)), np.zeros((2,4)))\n", + "lscables = []\n", + "for ji in range(4):\n", + " lscables.append(plt.plot(*cable_coords(0, ji))[0])\n", "lee, = plt.plot(*ee_coords(0))\n", "plt.axis('equal')\n", "plt.xlabel('x(m)');plt.ylabel('y(m)');plt.title('Trajectory')\n", @@ -140,7287 +140,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "faced-compact", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": { + "scrolled": false + }, + "outputs": [], "source": [ "# animate\n", "plt.rcParams[\"savefig.dpi\"] = 80\n", From 5113538aba85db68463a5e5ed16e3f1587e3012d Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Mon, 12 Apr 2021 12:08:05 -0400 Subject: [PATCH 17/73] working simulation with iros logo --- gtdynamics/cablerobot/src/draw_cdpr.py | 100 ++++++++++++++++++ .../src/gerry02_traj_tracking.ipynb | 82 ++++++++++++++ .../cablerobot/src/gerry02_traj_tracking.py | 71 +++++++++++-- 3 files changed, 247 insertions(+), 6 deletions(-) create mode 100644 gtdynamics/cablerobot/src/draw_cdpr.py create mode 100644 gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb diff --git a/gtdynamics/cablerobot/src/draw_cdpr.py b/gtdynamics/cablerobot/src/draw_cdpr.py new file mode 100644 index 00000000..c20baca3 --- /dev/null +++ b/gtdynamics/cablerobot/src/draw_cdpr.py @@ -0,0 +1,100 @@ +""" +GTDynamics Copyright 2021, Georgia Tech Research Corporation, +Atlanta, Georgia 30332-0415 +All Rights Reserved +See LICENSE for the license information + +@file draw_cdpr.py +@brief Utility functions for plotting a cable robot trajectory +@author Frank Dellaert +@author Gerry Chen +""" + +import gtdynamics as gtd +import gtsam +import matplotlib.pyplot as plt +import matplotlib.animation as animation +import numpy as np +from gtsam import Pose3, Rot3 + +from cdpr_planar import Cdpr + +BOXINDS = np.array([0,1,2,3,0]).reshape(5,1) + +def pose32xy(x): + """Extracts just the planar translational component of a Pose3""" + return x.translation()[[0, 2]] +def a_coords(cdpr): + """Returns a 2x5 array of the xy coordinates of the frame corners""" + return cdpr.params.a_locs[BOXINDS, [0,2]].T +def b_coords(cdpr, x): + """Returns a 2x5 array of the xy coordinates of the end-effector mounting points""" + return (pose32xy(x) + cdpr.params.b_locs[BOXINDS, [0, 2]]).T +def ab_coords(cdpr, x, ji): + """Returns a 2x2 arrays of the xy coordinates of the specified cable's start/end locations.""" + b = b_coords(cdpr, x) + return np.array([[cdpr.params.a_locs[ji][0], b[0, ji]], + [cdpr.params.a_locs[ji][2], b[1, ji]]]) +def draw_cdpr(ax, cdpr, x): + """Draws the CDPR in the specified axis and returns the line objects: + l_a - the frame + l_b - the end effector + l_abs - a 4-list containing the 4 cable lines + """ + l_a, = ax.plot(*a_coords(cdpr)) + l_b, = ax.plot(*b_coords(cdpr, x)) + l_abs = [ax.plot(*ab_coords(cdpr, x, ji))[0] for ji in range(4)] + ax.axis('equal') + ax.set_xlabel('x(m)');ax.set_ylabel('y(m)');ax.set_title('Trajectory') + return l_a, l_b, l_abs +def redraw_cdpr(l_a, l_b, l_abs, cdpr, x): + """Updates the lines for the frame, end effector, and cables""" + l_a.set_data(*a_coords(cdpr)) + l_b.set_data(*b_coords(cdpr, x)) + [l_abs[ci].set_data(*ab_coords(cdpr, x, ci)) for ci in range(4)] + return l_a, l_b, *l_abs + +def plot_all(cdpr, result, Tf, dt, N, x_des, step=1): + """Animates the cdpr and controls in side-by-side subplots. + + Args: + cdpr (Cdpr): cable robot object + result (gtsam.Values): the data from the simulation including poses and torques + Tf (float): final time + dt (float): time step interval + N (int): number of discrete samples + x_des (List[gtsam.Pose3]): the desired trajectory as a list of gtsam Pose3 objects + step (int, optional): number of time steps to update the animation by each time. 1 doesn't skip any frames, and e.g. 10 would skip 9 frames at a time and update at a period of 10*dt. Defaults to 1. + + Returns: + matplotlib.animation.FuncAnimation: a matplotlib animation object + """ + # extract useful variables as lists + act_T = [gtd.Pose(result, cdpr.ee_id(), k) for k in range(N+1)] + act_xy = np.array([pose32xy(pose) for pose in act_T]).T + des_xy = np.array([pose32xy(pose) for pose in x_des]).T + torques = np.array([[gtd.TorqueDouble(result, ji, k) for ji in range(4)] for k in range(N)]) + + # plot + fig = plt.figure(1, figsize=(12,4)) + # xy plot + ax1 = plt.subplot(1,2,1) + cdpr_lines = draw_cdpr(ax1, cdpr, gtd.Pose(result, cdpr.ee_id(), 0)) + plt.plot(*des_xy, 'r-') # desired trajectory + ltraj, = plt.plot(*act_xy, 'k-') # actual trajectory + # controls + ax2 = plt.subplot(1,2,2) + lsctrl = plt.plot(np.arange(0,Tf,dt), torques) + plt.xlabel('time (s)');plt.ylabel('Cable tension (N)');plt.title('Control Inputs') + + # animate + plt.rcParams["savefig.dpi"] = 80 + + def update_line(num): + lines_to_update = redraw_cdpr(*cdpr_lines, cdpr, gtd.Pose(result, cdpr.ee_id(), num)) + for ji in range(4): + lsctrl[ji].set_data(np.arange(0,Tf,dt)[:num], torques[:num, ji]) + return [*lines_to_update, *lsctrl] + + return animation.FuncAnimation(fig, update_line, frames=range(0, N, step), + interval=dt*step*1e3, blit=True) diff --git a/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb b/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb new file mode 100644 index 00000000..5775f46e --- /dev/null +++ b/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb @@ -0,0 +1,82 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "impressive-plumbing", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "%aimport -gtsam\n", + "%aimport -gtdynamics as gtd\n", + "\n", + "from draw_cdpr import plot_all\n", + "from gerry02_traj_tracking import main" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fifteen-crawford", + "metadata": {}, + "outputs": [], + "source": [ + "cdpr, controller, result, N, dt, pdes = main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "generic-calibration", + "metadata": {}, + "outputs": [], + "source": [ + "anim = plot_all(cdpr, result, dt*N, dt, N, pdes, step=10);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "pursuant-enemy", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from IPython.display import HTML\n", + "HTML(anim.to_html5_video())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "amateur-abraham", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py index 8e993932..e63f16c2 100644 --- a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py +++ b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py @@ -16,24 +16,83 @@ import numpy as np from gtsam import Pose3, Rot3 -from cdpr_planar import Cdpr +from cdpr_planar import Cdpr, CdprParams from cdpr_controller_ilqr import CdprControllerIlqr from cdpr_planar_sim import CdprSimulator from paint_parse import ParseFile +from draw_cdpr import plot_all import cProfile +from pstats import SortKey -def main(fname='data/iros_logo_2.h'): - isPaints, colorinds, colorpalette, traj = ParseFile(fname) +def print_data(isPaints, colorinds, colorpalette, traj, N=100): + """Prints out the paint trajectory for sanity check""" def paintString(isPaint, colori): return '{:d}, {:d}, {:d}'.format(*colorpalette[colori]) if isPaint else 'paint off' i = 0 print(traj.shape) for isPaint, colori, point in zip(isPaints, colorinds, traj): print('{:5.2f}, {:5.2f}\t-\t{}'.format(*point, paintString(isPaint, colori))) - if i > 100: - break + if i > N: + return i += 1 +def xy2Pose3(traj): + """Converts an Nx2 numpy array into a list of gtsam.Pose3 objects""" + des_T = [] + for xy in traj: + des_T.append(gtsam.Pose3(gtsam.Rot3(), (xy[0], 0, xy[1]))) + return des_T + +def main(fname='data/iros_logo_2.h', debug=False): + # cdpr object + aw, ah = 2.32, 1.92 + bw, bh = 0.15, 0.30 + params = CdprParams() + params.a_locs = np.array([[aw, 0, 0], [aw, 0, ah], [0, 0, ah], [0, 0, 0]]) + params.b_locs = np.array([[bw, 0., -bh], [bw, 0., bh], [-bw, 0., bh], [-bw, 0, -bh]]) / 2 + params.b_locs = params.b_locs - [0, 0, bh * 0.4] + cdpr = Cdpr(params) + + # import data + isPaints, colorinds, colorpalette, traj = ParseFile(fname) + dt = 0.01 # this is a hardcoded constant. TODO(gerry): include this in the .h file. + N = 500 # only simulate a subset of the trajectory, since the trajectory is very large + if debug: + print_data(isPaints, colorinds, colorpalette, traj, N=100) + des_T = xy2Pose3(traj[:N, :]) + + + # initial configuration + x0 = gtsam.Values() + # gtd.InsertPose(x0, cdpr.ee_id(), 0, gtsam.Pose3(gtsam.Rot3(), (1.5, 0, 1.5))) + gtd.InsertPose(x0, cdpr.ee_id(), 0, des_T[0]) + gtd.InsertTwist(x0, cdpr.ee_id(), 0, (0, 0, 0, 0, 0, 0)) + + # controller + controller = CdprControllerIlqr(cdpr, x0, des_T, dt, np.ones(6)*1e2, np.ones(1)*1e-2) + # feedforward control + xff = np.zeros((N, 2)) + uff = np.zeros((N, 4)) + for t in range(N): + xff[t, :] = gtd.Pose(controller.result, cdpr.ee_id(), t).translation()[[0, 2]] + uff[t, :] = [gtd.TorqueDouble(controller.result, ji, t) for ji in range(4)] + if debug: + print(xff) + print(uff) + + # simulate + sim = CdprSimulator(cdpr, x0, controller, dt=0.01) + result = sim.run(N=N) + if debug: + print(result) + + return cdpr, controller, result, N, dt, des_T + +def plot(cdpr, controller, result, N, dt, des_T): + """Plots the results""" + plot_all(cdpr, result, dt*N, dt, N, des_T, step=1) + if __name__ == '__main__': - main() \ No newline at end of file + cProfile.run('results = main()', sort=SortKey.TIME) + plot(*results) From 6bf0e850cb64960576c7e76fde14c31d4c7fa79c Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Mon, 12 Apr 2021 12:39:57 -0400 Subject: [PATCH 18/73] Pott09's mid-tension iLQR --- gtdynamics/cablerobot/src/cdpr_controller_ilqr.py | 3 ++- gtdynamics/cablerobot/src/cdpr_planar.py | 2 ++ 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py index 9c17dd3d..fe2a878c 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py @@ -77,10 +77,11 @@ def create_ilqr_fg(cdpr, x0, pdes, dt, Q, R): # dynamics fg.push_back(cdpr.all_factors(N, dt)) # control costs + tmid = (cdpr.params.tmin + cdpr.params.tmax) / 2 for k in range(N): for ji in range(4): fg.push_back( - gtd.PriorFactorDouble(gtd.internal.TorqueKey(ji, k).key(), 0.0, + gtd.PriorFactorDouble(gtd.internal.TorqueKey(ji, k).key(), tmid, gtsam.noiseModel.Diagonal.Precisions(R))) # state objective costs cost_x = gtsam.noiseModel.Constrained.All(6) if Q is None else \ diff --git a/gtdynamics/cablerobot/src/cdpr_planar.py b/gtdynamics/cablerobot/src/cdpr_planar.py index 5e2ff430..145bb875 100644 --- a/gtdynamics/cablerobot/src/cdpr_planar.py +++ b/gtdynamics/cablerobot/src/cdpr_planar.py @@ -25,6 +25,8 @@ def __init__(self): self.mass = 1.0 self.inertia = np.eye(3) self.gravity = np.zeros((3, 1)) + self.tmin = 0.1 / (0.0254 / 2) # tension = torque / radius + self.tmax = 1.2 / (0.0254 / 2) class Cdpr: """Utility functions for a planar cable robot; mostly assembles together factors. From ae12b38bf067199e4fdf65827cd344d2668fcafc Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Mon, 12 Apr 2021 13:17:40 -0400 Subject: [PATCH 19/73] refactor plotting --- gtdynamics/cablerobot/src/draw_cdpr.py | 77 ++++++++++++++++++-------- 1 file changed, 54 insertions(+), 23 deletions(-) diff --git a/gtdynamics/cablerobot/src/draw_cdpr.py b/gtdynamics/cablerobot/src/draw_cdpr.py index c20baca3..7889bf05 100644 --- a/gtdynamics/cablerobot/src/draw_cdpr.py +++ b/gtdynamics/cablerobot/src/draw_cdpr.py @@ -36,30 +36,65 @@ def ab_coords(cdpr, x, ji): return np.array([[cdpr.params.a_locs[ji][0], b[0, ji]], [cdpr.params.a_locs[ji][2], b[1, ji]]]) def draw_cdpr(ax, cdpr, x): - """Draws the CDPR in the specified axis and returns the line objects: - l_a - the frame - l_b - the end effector - l_abs - a 4-list containing the 4 cable lines + """Draws the CDPR in the specified axis. + Args: + ax (plt.Axes): matplotlib axis object + cdpr (Cdpr): cable robot object + x (gtsam.Pose3): current pose of the end effector + + Returns: + tuple(): matplotlib line objects: + l_a - the frame + l_b - the end effector + ls_ab - a 4-list containing the 4 cable lines """ - l_a, = ax.plot(*a_coords(cdpr)) - l_b, = ax.plot(*b_coords(cdpr, x)) - l_abs = [ax.plot(*ab_coords(cdpr, x, ji))[0] for ji in range(4)] + l_a, = ax.plot(*a_coords(cdpr), 'k-') + l_b, = ax.plot(*b_coords(cdpr, x), color='#caa472') + ls_ab = [ax.plot(*ab_coords(cdpr, x, ji))[0] for ji in range(4)] ax.axis('equal') ax.set_xlabel('x(m)');ax.set_ylabel('y(m)');ax.set_title('Trajectory') - return l_a, l_b, l_abs -def redraw_cdpr(l_a, l_b, l_abs, cdpr, x): + return l_a, l_b, ls_ab +def redraw_cdpr(l_a, l_b, ls_ab, cdpr, x): """Updates the lines for the frame, end effector, and cables""" l_a.set_data(*a_coords(cdpr)) l_b.set_data(*b_coords(cdpr, x)) - [l_abs[ci].set_data(*ab_coords(cdpr, x, ci)) for ci in range(4)] - return l_a, l_b, *l_abs + [ls_ab[ci].set_data(*ab_coords(cdpr, x, ci)) for ci in range(4)] + return l_a, l_b, *ls_ab +def draw_traj(ax, cdpr, des_xy, act_xy): + """Draws the desired and actual x/y trajectories""" + l_des, = plt.plot(*des_xy, 'r-') # desired trajectory + l_act, = plt.plot(*act_xy, 'k-') # actual trajectory + ax.axis('equal') + ax.set_xlabel('x(m)');ax.set_ylabel('y(m)');ax.set_title('Trajectory') + return l_des, l_act +def redraw_traj(l_des, l_act, des_xy, act_xy, N=None): + """Updates the lines for the trajectory drawing""" + if N is None: + N = des_xy.shape[1] + l_des.set_data(*des_xy[:, :N]) + l_act.set_data(*act_xy[:, :N]) + return l_des, l_act +def draw_ctrl(ax, cdpr, tensions, Tf, dt): + """Draws the control tension signals""" + ls_ctrl = plt.plot(np.arange(0,Tf,dt), tensions) + plt.plot([0, Tf], [cdpr.params.tmin,]*2, 'r--') + plt.plot([0, Tf], [cdpr.params.tmax,]*2, 'r--') + plt.xlabel('time (s)');plt.ylabel('Cable tension (N)');plt.title('Control Inputs') + return ls_ctrl, +def redraw_ctrl(ls_ctrl, tensions, Tf, dt, N=None): + """Updates the lines for the tensions plot""" + if N is None: + N = tensions.shape[0] + for ji in range(4): + ls_ctrl[ji].set_data(np.arange(0,Tf,dt)[:N], tensions[:N, ji]) + return *ls_ctrl, def plot_all(cdpr, result, Tf, dt, N, x_des, step=1): """Animates the cdpr and controls in side-by-side subplots. Args: cdpr (Cdpr): cable robot object - result (gtsam.Values): the data from the simulation including poses and torques + result (gtsam.Values): the data from the simulation including poses and tensions Tf (float): final time dt (float): time step interval N (int): number of discrete samples @@ -73,28 +108,24 @@ def plot_all(cdpr, result, Tf, dt, N, x_des, step=1): act_T = [gtd.Pose(result, cdpr.ee_id(), k) for k in range(N+1)] act_xy = np.array([pose32xy(pose) for pose in act_T]).T des_xy = np.array([pose32xy(pose) for pose in x_des]).T - torques = np.array([[gtd.TorqueDouble(result, ji, k) for ji in range(4)] for k in range(N)]) + tensions = np.array([[gtd.TorqueDouble(result, ji, k) for ji in range(4)] for k in range(N)]) # plot fig = plt.figure(1, figsize=(12,4)) # xy plot ax1 = plt.subplot(1,2,1) cdpr_lines = draw_cdpr(ax1, cdpr, gtd.Pose(result, cdpr.ee_id(), 0)) - plt.plot(*des_xy, 'r-') # desired trajectory - ltraj, = plt.plot(*act_xy, 'k-') # actual trajectory + traj_lines = draw_traj(ax1, cdpr, des_xy, act_xy) # controls ax2 = plt.subplot(1,2,2) - lsctrl = plt.plot(np.arange(0,Tf,dt), torques) - plt.xlabel('time (s)');plt.ylabel('Cable tension (N)');plt.title('Control Inputs') + ctrl_lines = draw_ctrl(ax2, cdpr, tensions, Tf, dt) # animate plt.rcParams["savefig.dpi"] = 80 - def update_line(num): - lines_to_update = redraw_cdpr(*cdpr_lines, cdpr, gtd.Pose(result, cdpr.ee_id(), num)) - for ji in range(4): - lsctrl[ji].set_data(np.arange(0,Tf,dt)[:num], torques[:num, ji]) - return [*lines_to_update, *lsctrl] - + cdpr_to_update = redraw_cdpr(*cdpr_lines, cdpr, gtd.Pose(result, cdpr.ee_id(), num)) + traj_to_update = redraw_traj(*traj_lines, des_xy, act_xy, num if num > 0 else None) + ctrl_to_update = redraw_ctrl(*ctrl_lines, tensions, Tf, dt, num if num > 0 else None) + return [*cdpr_to_update, *traj_to_update, *ctrl_to_update] return animation.FuncAnimation(fig, update_line, frames=range(0, N, step), interval=dt*step*1e3, blit=True) From 66c58ec30afab6049dc2f13c7e8fc020bc7655a8 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Mon, 12 Apr 2021 13:18:19 -0400 Subject: [PATCH 20/73] expose more options to python notebook --- .../src/gerry02_traj_tracking.ipynb | 10 ++-- .../cablerobot/src/gerry02_traj_tracking.py | 53 ++++++++++++++++--- 2 files changed, 50 insertions(+), 13 deletions(-) diff --git a/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb b/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb index 5775f46e..014b2d99 100644 --- a/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb +++ b/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb @@ -9,8 +9,8 @@ "source": [ "%load_ext autoreload\n", "%autoreload 2\n", - "%aimport -gtsam\n", - "%aimport -gtdynamics as gtd\n", + "import numpy as np\n", + "%aimport -np\n", "\n", "from draw_cdpr import plot_all\n", "from gerry02_traj_tracking import main" @@ -23,7 +23,7 @@ "metadata": {}, "outputs": [], "source": [ - "cdpr, controller, result, N, dt, pdes = main()" + "cdpr, controller, result, N, dt, pdes = main(Q=np.ones(6)*1e3, R=np.ones(1)*1e-3, N0=9700, N=1000, dN=5)" ] }, { @@ -33,7 +33,7 @@ "metadata": {}, "outputs": [], "source": [ - "anim = plot_all(cdpr, result, dt*N, dt, N, pdes, step=10);" + "anim = plot_all(cdpr, result, dt*N, dt, N, pdes, step=2);" ] }, { @@ -52,7 +52,7 @@ { "cell_type": "code", "execution_count": null, - "id": "amateur-abraham", + "id": "terminal-brass", "metadata": {}, "outputs": [], "source": [] diff --git a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py index e63f16c2..f31ba0b8 100644 --- a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py +++ b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py @@ -44,9 +44,42 @@ def xy2Pose3(traj): des_T.append(gtsam.Pose3(gtsam.Rot3(), (xy[0], 0, xy[1]))) return des_T -def main(fname='data/iros_logo_2.h', debug=False): +def main(fname='data/iros_logo_2.h', + debug=False, + Q=np.ones(6) * 1e2, + R=np.ones(1) * 1e-2, + N0=0, + N=500, + dN=1): + """Runs a simulation of the iLQR controller trying to execute a predefined trajectory. + + Args: + fname (str, optional): The trajectory filename. Defaults to 'data/iros_logo_2.h'. + debug (bool, optional): Whether to print debug information. Defaults to False. + Q (np.ndarray, optional): Vector of weights to apply to the state objectives. The real + weight matrix will be diag(Q). Defaults to np.ones(6)*1e2. + R (np.ndarray, optional): Vector of weights to apply to the control costs. The real weight + matrix will be diag(R). Defaults to np.ones(1)*1e-2. + N0 (int, optional): The initial timestep of the trajectory to start at, since running the + full trajectory is very time consuming. Defaults to 0. + N (int, optional): The number of timesteps of the trajectory to run, since running the full + trajectory is very time consuming. Defaults to 500. + dN (int, optional): Skips some timesteps from the input trajectory, to make things faster. + The controller and simulation both will only use each dN'th time step. Defaults to 1. + + Returns: + tuple(Cdpr, CdprControllerIlqr, gtsam.Values, int, float, list[gtsam.Pose3]): The relevant + output data including: + - cdpr: the cable robot object + - controller: the controller + - result: the Values object containing the full state and controls of the robot in + open-loop + - N: the number of time steps (equal to N passed in) + - dt: the time step size + - des_T: the desired poses + """ # cdpr object - aw, ah = 2.32, 1.92 + aw, ah = 2.92, 2.32 bw, bh = 0.15, 0.30 params = CdprParams() params.a_locs = np.array([[aw, 0, 0], [aw, 0, ah], [0, 0, ah], [0, 0, 0]]) @@ -55,12 +88,16 @@ def main(fname='data/iros_logo_2.h', debug=False): cdpr = Cdpr(params) # import data + dt = 0.01 * dN # this is a hardcoded constant. TODO(gerry): include this in the .h file. + N = int(N/dN) # scale time by dN + N0 = int(N0/dN) isPaints, colorinds, colorpalette, traj = ParseFile(fname) - dt = 0.01 # this is a hardcoded constant. TODO(gerry): include this in the .h file. - N = 500 # only simulate a subset of the trajectory, since the trajectory is very large + traj = (traj - [aw/2, ah/2]) * 0.8 + [aw/2, ah/2] # rescale trajectory to be smaller + traj = traj[::dN, :] if debug: print_data(isPaints, colorinds, colorpalette, traj, N=100) - des_T = xy2Pose3(traj[:N, :]) + # only simulate a subset of the trajectory, since the trajectory is very large + des_T = xy2Pose3(traj[N0:N0+N, :]) # initial configuration @@ -70,7 +107,7 @@ def main(fname='data/iros_logo_2.h', debug=False): gtd.InsertTwist(x0, cdpr.ee_id(), 0, (0, 0, 0, 0, 0, 0)) # controller - controller = CdprControllerIlqr(cdpr, x0, des_T, dt, np.ones(6)*1e2, np.ones(1)*1e-2) + controller = CdprControllerIlqr(cdpr, x0, des_T, dt, Q, R) # feedforward control xff = np.zeros((N, 2)) uff = np.zeros((N, 4)) @@ -80,9 +117,9 @@ def main(fname='data/iros_logo_2.h', debug=False): if debug: print(xff) print(uff) - + # simulate - sim = CdprSimulator(cdpr, x0, controller, dt=0.01) + sim = CdprSimulator(cdpr, x0, controller, dt=dt) result = sim.run(N=N) if debug: print(result) From e8f8929bb04eb2f14411589ce4df7980d538c2b7 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 13 Apr 2021 11:11:37 -0400 Subject: [PATCH 21/73] unit test for local feedback gains --- .../cablerobot/src/cdpr_controller_ilqr.py | 16 ++++++++++++ .../src/test_cdpr_controller_ilqr.py | 26 +++++++++++++++++++ 2 files changed, 42 insertions(+) diff --git a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py index fe2a878c..3d32ef2b 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py @@ -45,6 +45,8 @@ def __init__(self, cdpr, x0, pdes=[], dt=0.01, Q=None, R=np.array([1.])): self.optimizer = gtsam.LevenbergMarquardtOptimizer(fg, x_guess) self.result = self.optimizer.optimize() self.fg = fg + # gains + self.gains = self.extract_gains(cdpr, fg, self.result) def update(self, values, t): """New control: returns the entire results vector, which contains the optimal open-loop @@ -91,3 +93,17 @@ def create_ilqr_fg(cdpr, x0, pdes, dt, Q, R): gtsam.PriorFactorPose3( gtd.internal.PoseKey(cdpr.ee_id(), k).key(), pdes[k], cost_x)) return fg + + @staticmethod + def extract_gains(cdpr, fg, openloop_results): + """Extracts the locally linear optimal feedback control gains + + Args: + cdpr (Cdpr): cable robot object + fg (gtsam.NonlinearFactorGraph): The iLQR factor graph + openloop_results (gtsam.Values): The open-loop optimal trajectory and controls + """ + return [[{gtd.internal.PoseKey(cdpr.ee_id(), t).key(): np.zeros((1, 6)), + gtd.internal.TwistKey(cdpr.ee_id(), t).key(): np.zeros((1, 6))} + for ji in range(4)] + for t in range(3)] diff --git a/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py index d0b8979d..e198cb21 100644 --- a/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py @@ -50,5 +50,31 @@ def testTrajFollow(self): for k, (des, act) in enumerate(zip(x_des, pAct)): self.gtsamAssertEquals(des, act) + def testGains(self): + """Tests locally linear, time-varying feedback gains + """ + cdpr = Cdpr() + dt = 0.01 + + x0 = gtsam.Values() + gtd.InsertPose(x0, cdpr.ee_id(), 0, Pose3(Rot3(), (1.5, 0, 1.5))) + gtd.InsertTwist(x0, cdpr.ee_id(), 0, np.zeros(6)) + x_des = [Pose3(Rot3(), (1.5, 0, 1.5)), + Pose3(Rot3(), (1.5, 0, 1.5)), + Pose3(Rot3(), (1.5, 0, 1.5))] # don't move + controller = CdprControllerIlqr(cdpr, x0=x0, pdes=x_des, dt=dt) + print(gtd.str(controller.result)) + print(controller.fg.error(controller.result)) + actual_gains = controller.gains[0][0][gtd.internal.PoseKey( + cdpr.ee_id(), 0).key()] # time 0, cable 0, pose gain + + # notation: x_K_y means xstar = K * dy + expected_1v_K_0x = np.diag([0, -1, 0, -1, 0, -1]) / dt # v at t=1 in response to x at t=0 + expected_0c0_K_1v = np.array([0, 1e9, 0, + -1 / np.sqrt(2), 0, -1 / np.sqrt(2)]).reshape((1, -1)) * \ + cdpr.params.mass # cable 0 tension at t=0 in response to v at t=1 + expected_gains = -expected_0c0_K_1v @ expected_1v_K_0x + self.gtsamAssertEquals(actual_gains[:, 3:], expected_gains[:, 3:]) + if __name__ == "__main__": unittest.main() From 5750d5f0079824b0cbc33cf07706f3c664f82ef8 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 13 Apr 2021 11:21:17 -0400 Subject: [PATCH 22/73] expand unit test to include twist and feedforward terms --- .../src/test_cdpr_controller_ilqr.py | 20 ++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py index e198cb21..ef810cd1 100644 --- a/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py @@ -65,16 +65,26 @@ def testGains(self): controller = CdprControllerIlqr(cdpr, x0=x0, pdes=x_des, dt=dt) print(gtd.str(controller.result)) print(controller.fg.error(controller.result)) - actual_gains = controller.gains[0][0][gtd.internal.PoseKey( - cdpr.ee_id(), 0).key()] # time 0, cable 0, pose gain # notation: x_K_y means xstar = K * dy + # position gain (Kp) - time 0, cable 0, pose gain + actual_0c0_K_0x = -controller.gains[0][0][gtd.internal.PoseKey(cdpr.ee_id(), 0).key()] expected_1v_K_0x = np.diag([0, -1, 0, -1, 0, -1]) / dt # v at t=1 in response to x at t=0 expected_0c0_K_1v = np.array([0, 1e9, 0, -1 / np.sqrt(2), 0, -1 / np.sqrt(2)]).reshape((1, -1)) * \ - cdpr.params.mass # cable 0 tension at t=0 in response to v at t=1 - expected_gains = -expected_0c0_K_1v @ expected_1v_K_0x - self.gtsamAssertEquals(actual_gains[:, 3:], expected_gains[:, 3:]) + cdpr.params.mass / dt # cable 0 tension at t=0 in response to v at t=1 + expected_0c0_K_0x = expected_0c0_K_1v @ expected_1v_K_0x + self.gtsamAssertEquals(actual_0c0_K_0x[:, 3:], expected_0c0_K_0x[:, 3:]) + + # velocity gain (Kd) - time 0, cable 0, twist gain + actual_0c0_K_0v = -controller.gains[0][0][gtd.internal.TwistKey(cdpr.ee_id(), 0).key()] + expected_0c0_K_0v = expected_0c0_K_1v + self.gtsamAssertEquals(actual_0c0_K_0v[:, 3:], expected_0c0_K_0v[:, 3:]) + + # feedforward term (uff) - time 0, cable 0, feedforward term + actual_0c0_ff = -controller.gains[0][0][gtd.internal.TorqueKey(0, 0).key()] + expected_0c0_ff = np.zeros(1) + self.gtsamAssertEquals(actual_0c0_ff[:, 3:], expected_0c0_ff[:, 3:]) if __name__ == "__main__": unittest.main() From 7a11d033534f152c4b1d5d9236a69a7940ff7405 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Wed, 14 Apr 2021 00:20:30 -0400 Subject: [PATCH 23/73] sequential elimination that preserves ordering in bayes net --- gtdynamics/cablerobot/CMakeLists.txt | 2 +- gtdynamics/cablerobot/cablerobot.i | 4 ++ .../cablerobot/tests/testCustomWrap.cpp | 53 +++++++++++++++++++ gtdynamics/cablerobot/utils/CustomWrap.cpp | 46 ++++++++++++++++ gtdynamics/cablerobot/utils/CustomWrap.h | 23 ++++++++ 5 files changed, 127 insertions(+), 1 deletion(-) create mode 100644 gtdynamics/cablerobot/tests/testCustomWrap.cpp create mode 100644 gtdynamics/cablerobot/utils/CustomWrap.cpp create mode 100644 gtdynamics/cablerobot/utils/CustomWrap.h diff --git a/gtdynamics/cablerobot/CMakeLists.txt b/gtdynamics/cablerobot/CMakeLists.txt index 6c2c74dd..9b57efba 100644 --- a/gtdynamics/cablerobot/CMakeLists.txt +++ b/gtdynamics/cablerobot/CMakeLists.txt @@ -1,5 +1,5 @@ # add cablerobot subfolders to gtdynamics' SOURCE_SUBDIRS list -list(APPEND SOURCE_SUBDIRS cablerobot/factors) +list(APPEND SOURCE_SUBDIRS cablerobot/factors cablerobot/utils) set(SOURCE_SUBDIRS ${SOURCE_SUBDIRS} PARENT_SCOPE) # add wrapper interface file diff --git a/gtdynamics/cablerobot/cablerobot.i b/gtdynamics/cablerobot/cablerobot.i index 7de4255e..73cc34b7 100644 --- a/gtdynamics/cablerobot/cablerobot.i +++ b/gtdynamics/cablerobot/cablerobot.i @@ -39,4 +39,8 @@ class PriorFactor : gtsam::NonlinearFactor { const gtsam::KeyFormatter &keyFormatter); }; +#include +gtsam::GaussianBayesNet *EliminateSequential(gtsam::GaussianFactorGraph graph, + const gtsam::Ordering &ordering); + } // namespace gtdynamics diff --git a/gtdynamics/cablerobot/tests/testCustomWrap.cpp b/gtdynamics/cablerobot/tests/testCustomWrap.cpp new file mode 100644 index 00000000..df9eb075 --- /dev/null +++ b/gtdynamics/cablerobot/tests/testCustomWrap.cpp @@ -0,0 +1,53 @@ +/** + * @file testCustomWrap.cpp + * @brief test utilities in custom wrap file + * @author Frank Dellaert + * @author Gerry Chen + */ + +#include + +#include + +#include +#include +#include + +#include +#include + +using namespace std; +using namespace gtsam; +using namespace gtdynamics; +using boost::assign::list_of; + +/** + * Test eliminate sequential + */ +TEST(EliminateSequential, fullelimination) { + GaussianFactorGraph gfg; + gfg.add(0, Vector1(1), Vector1(0), noiseModel::Unit::Create(1)); + gfg.add(1, Vector1(1), Vector1(0), noiseModel::Unit::Create(1)); + gfg.add(2, Vector1(1), // + 0, Vector1(1), // + Vector1(0), noiseModel::Unit::Create(1)); + + // specify the ordering as 0 -> 1 -> 2 + Ordering ordering(list_of(0)(1)(2)); + auto actual = EliminateSequential(gfg, ordering); + + // expected Bayes net + GaussianBayesNet expected; + expected.push_back(GaussianConditional(0, Vector1(0), Vector1(sqrt(2)), // + 2, Vector1(1) / sqrt(2))); + expected.push_back(GaussianConditional(1, Vector1(0), Vector1(1))); + expected.push_back(GaussianConditional(2, Vector1(0), Vector1(1 / sqrt(2)))); + + // check if the result matches + EXPECT(assert_equal(expected, *actual)); +} + +int main() { + TestResult tr; + return TestRegistry::runAllTests(tr); +} diff --git a/gtdynamics/cablerobot/utils/CustomWrap.cpp b/gtdynamics/cablerobot/utils/CustomWrap.cpp new file mode 100644 index 00000000..348aefe0 --- /dev/null +++ b/gtdynamics/cablerobot/utils/CustomWrap.cpp @@ -0,0 +1,46 @@ +/** + * @file CustomWrap.cpp + * @brief Custom wrap utilities + * @author Frank Dellaert + * @author Gerry Chen + */ + +#include + +#include +#include +#include + +using namespace gtsam; + +namespace gtdynamics { + +/// Performs sequential elimination and preserves correct bayes net order +GaussianBayesNet::shared_ptr EliminateSequential(GaussianFactorGraph graph, + const Ordering& ordering) { + // setup + VariableIndex variableIndex(graph); // maps keys to factor indices + auto bn = boost::make_shared(); + + // loop + for (auto key : ordering) { + // collect factors + GaussianFactorGraph factors; + for (size_t factorindex : variableIndex[key]) { + factors.push_back(graph.at(factorindex)); + graph.remove(factorindex); + } + // eliminate + auto [conditional, newfactor] = + EliminationTraits::DefaultEliminate( + factors, boost::assign::list_of(key)); + bn->push_back(conditional); + // add new joint factor + graph.push_back(newfactor); + variableIndex.augment(GaussianFactorGraph(newfactor)); + } + return bn; +} + + +} // namespace gtdynamics diff --git a/gtdynamics/cablerobot/utils/CustomWrap.h b/gtdynamics/cablerobot/utils/CustomWrap.h new file mode 100644 index 00000000..b2fe313e --- /dev/null +++ b/gtdynamics/cablerobot/utils/CustomWrap.h @@ -0,0 +1,23 @@ +/** + * @file CustomWrap.h + * @brief Custom wrap utilities + * @author Frank Dellaert + * @author Gerry Chen + */ + +#pragma once + +#include +#include +#include + +namespace gtdynamics { + +using BlockOrdering = std::vector; + +/// Performs sequential elimination and preserves correct bayes net order +gtsam::GaussianBayesNet::shared_ptr EliminateSequential( + gtsam::GaussianFactorGraph graph, const gtsam::Ordering& ordering); + + +} // namespace gtdynamics From c2ea56f8418d69040b730c1daddee1e2624e302d Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Wed, 14 Apr 2021 00:36:45 -0400 Subject: [PATCH 24/73] block elimination --- gtdynamics/cablerobot/cablerobot.i | 4 ++- .../cablerobot/tests/testCustomWrap.cpp | 27 ++++++++++++++++++ gtdynamics/cablerobot/utils/CustomWrap.cpp | 28 +++++++++++++++++++ gtdynamics/cablerobot/utils/CustomWrap.h | 6 +++- 4 files changed, 63 insertions(+), 2 deletions(-) diff --git a/gtdynamics/cablerobot/cablerobot.i b/gtdynamics/cablerobot/cablerobot.i index 73cc34b7..2309b9b9 100644 --- a/gtdynamics/cablerobot/cablerobot.i +++ b/gtdynamics/cablerobot/cablerobot.i @@ -40,7 +40,9 @@ class PriorFactor : gtsam::NonlinearFactor { }; #include -gtsam::GaussianBayesNet *EliminateSequential(gtsam::GaussianFactorGraph graph, +gtsam::GaussianBayesNet* EliminateSequential(gtsam::GaussianFactorGraph graph, const gtsam::Ordering &ordering); +gtsam::GaussianBayesNet* BlockEliminateSequential( + gtsam::GaussianFactorGraph graph, const BlockOrdering &ordering); } // namespace gtdynamics diff --git a/gtdynamics/cablerobot/tests/testCustomWrap.cpp b/gtdynamics/cablerobot/tests/testCustomWrap.cpp index df9eb075..74433fa8 100644 --- a/gtdynamics/cablerobot/tests/testCustomWrap.cpp +++ b/gtdynamics/cablerobot/tests/testCustomWrap.cpp @@ -47,6 +47,33 @@ TEST(EliminateSequential, fullelimination) { EXPECT(assert_equal(expected, *actual)); } +/** + * Test block eliminate sequential + */ +TEST(EliminateSequential, block) { + GaussianFactorGraph gfg; + gfg.add(0, Vector1(1), Vector1(0), noiseModel::Unit::Create(1)); + gfg.add(1, Vector1(1), Vector1(0), noiseModel::Unit::Create(1)); + gfg.add(2, Vector1(1), // + 0, Vector1(1), // + Vector1(0), noiseModel::Unit::Create(1)); + + // specify the ordering as (0;1) -> 2 + BlockOrdering ordering{list_of(0)(1), list_of(2)}; + auto actual = BlockEliminateSequential(gfg, ordering); + + // expected Bayes net + GaussianBayesNet expected; + vector> terms{make_pair(0, Vector2(sqrt(2), 0)), + make_pair(1, Vector2(0, 1)), + make_pair(2, Vector2(1 / sqrt(2), 0))}; + expected.push_back(GaussianConditional(terms, 2, Vector2(0, 0))); + expected.push_back(GaussianConditional(2, Vector1(0), Vector1(1 / sqrt(2)))); + + // check if the result matches + EXPECT(assert_equal(expected, *actual)); +} + int main() { TestResult tr; return TestRegistry::runAllTests(tr); diff --git a/gtdynamics/cablerobot/utils/CustomWrap.cpp b/gtdynamics/cablerobot/utils/CustomWrap.cpp index 348aefe0..61b5122c 100644 --- a/gtdynamics/cablerobot/utils/CustomWrap.cpp +++ b/gtdynamics/cablerobot/utils/CustomWrap.cpp @@ -11,6 +11,8 @@ #include #include +#include + using namespace gtsam; namespace gtdynamics { @@ -42,5 +44,31 @@ GaussianBayesNet::shared_ptr EliminateSequential(GaussianFactorGraph graph, return bn; } +/// Performs sequential elimination and preserves correct bayes net order +GaussianBayesNet::shared_ptr BlockEliminateSequential( + GaussianFactorGraph graph, const BlockOrdering &ordering) { + // setup + VariableIndex variableIndex(graph); // maps keys to factor indices + auto bn = boost::make_shared(); + + // loop + for (auto keys : ordering) { + // collect factors + GaussianFactorGraph factors; + for (auto key : keys) + for (size_t factorindex : variableIndex[key]) { + factors.push_back(graph.at(factorindex)); + graph.remove(factorindex); + } + // eliminate + auto [conditional, newfactor] = + EliminationTraits::DefaultEliminate(factors, keys); + bn->push_back(conditional); + // add new joint factor + graph.push_back(newfactor); + variableIndex.augment(GaussianFactorGraph(newfactor)); + } + return bn; +} } // namespace gtdynamics diff --git a/gtdynamics/cablerobot/utils/CustomWrap.h b/gtdynamics/cablerobot/utils/CustomWrap.h index b2fe313e..6013505d 100644 --- a/gtdynamics/cablerobot/utils/CustomWrap.h +++ b/gtdynamics/cablerobot/utils/CustomWrap.h @@ -13,11 +13,15 @@ namespace gtdynamics { -using BlockOrdering = std::vector; +using BlockOrdering = std::vector; /// Performs sequential elimination and preserves correct bayes net order gtsam::GaussianBayesNet::shared_ptr EliminateSequential( gtsam::GaussianFactorGraph graph, const gtsam::Ordering& ordering); +/// Performs sequential block elimination and preserves correct bayes net order +gtsam::GaussianBayesNet::shared_ptr BlockEliminateSequential( + gtsam::GaussianFactorGraph graph, + const BlockOrdering& ordering); } // namespace gtdynamics From a1e6d4e3fca4addb24773a6f30a0ffb6b751b1a0 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Wed, 14 Apr 2021 00:40:30 -0400 Subject: [PATCH 25/73] reduce copy-pasta in EliminateSequential --- gtdynamics/cablerobot/cablerobot.i | 2 +- gtdynamics/cablerobot/utils/CMakeLists.txt | 0 gtdynamics/cablerobot/utils/CustomWrap.cpp | 26 ++++------------------ 3 files changed, 5 insertions(+), 23 deletions(-) create mode 100644 gtdynamics/cablerobot/utils/CMakeLists.txt diff --git a/gtdynamics/cablerobot/cablerobot.i b/gtdynamics/cablerobot/cablerobot.i index 2309b9b9..4b192718 100644 --- a/gtdynamics/cablerobot/cablerobot.i +++ b/gtdynamics/cablerobot/cablerobot.i @@ -43,6 +43,6 @@ class PriorFactor : gtsam::NonlinearFactor { gtsam::GaussianBayesNet* EliminateSequential(gtsam::GaussianFactorGraph graph, const gtsam::Ordering &ordering); gtsam::GaussianBayesNet* BlockEliminateSequential( - gtsam::GaussianFactorGraph graph, const BlockOrdering &ordering); + gtsam::GaussianFactorGraph graph, const gtdynamics::BlockOrdering &ordering); } // namespace gtdynamics diff --git a/gtdynamics/cablerobot/utils/CMakeLists.txt b/gtdynamics/cablerobot/utils/CMakeLists.txt new file mode 100644 index 00000000..e69de29b diff --git a/gtdynamics/cablerobot/utils/CustomWrap.cpp b/gtdynamics/cablerobot/utils/CustomWrap.cpp index 61b5122c..d521ff73 100644 --- a/gtdynamics/cablerobot/utils/CustomWrap.cpp +++ b/gtdynamics/cablerobot/utils/CustomWrap.cpp @@ -20,28 +20,10 @@ namespace gtdynamics { /// Performs sequential elimination and preserves correct bayes net order GaussianBayesNet::shared_ptr EliminateSequential(GaussianFactorGraph graph, const Ordering& ordering) { - // setup - VariableIndex variableIndex(graph); // maps keys to factor indices - auto bn = boost::make_shared(); - - // loop - for (auto key : ordering) { - // collect factors - GaussianFactorGraph factors; - for (size_t factorindex : variableIndex[key]) { - factors.push_back(graph.at(factorindex)); - graph.remove(factorindex); - } - // eliminate - auto [conditional, newfactor] = - EliminationTraits::DefaultEliminate( - factors, boost::assign::list_of(key)); - bn->push_back(conditional); - // add new joint factor - graph.push_back(newfactor); - variableIndex.augment(GaussianFactorGraph(newfactor)); - } - return bn; + BlockOrdering blockOrdering; + for (auto k : ordering) + blockOrdering.push_back(boost::assign::list_of(k)); + return BlockEliminateSequential(graph, blockOrdering); } /// Performs sequential elimination and preserves correct bayes net order From c27e2585c612e0205d97d2e749bd2fb82172035e Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Mon, 19 Apr 2021 12:13:24 -0400 Subject: [PATCH 26/73] preliminary gains calculation --- .../cablerobot/src/cdpr_controller_ilqr.py | 68 +++++++++++++++++-- .../src/test_cdpr_controller_ilqr.py | 35 +++++++--- 2 files changed, 86 insertions(+), 17 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py index 3d32ef2b..9f12bbb5 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py @@ -16,6 +16,8 @@ import numpy as np import utils from cdpr_controller import CdprControllerBase +from scipy.linalg import solve_triangular + class CdprControllerIlqr(CdprControllerBase): """Precomputes the open-loop trajectory @@ -46,12 +48,12 @@ def __init__(self, cdpr, x0, pdes=[], dt=0.01, Q=None, R=np.array([1.])): self.result = self.optimizer.optimize() self.fg = fg # gains - self.gains = self.extract_gains(cdpr, fg, self.result) + self.gains = self.extract_gains(cdpr, fg, self.result, len(self.pdes)) def update(self, values, t): """New control: returns the entire results vector, which contains the optimal open-loop control from the optimal trajectory. - """ + """ return self.result @staticmethod @@ -95,15 +97,67 @@ def create_ilqr_fg(cdpr, x0, pdes, dt, Q, R): return fg @staticmethod - def extract_gains(cdpr, fg, openloop_results): + def extract_bayesnets(cdpr, fg, openloop_results, N): + lid = cdpr.ee_id() + gfg = fg.linearize(openloop_results) + + # ordering + ordering = [] + # ordering.append(gtsam.Ordering()) + for t in range(N - 1, -1, -1): + # stuff we don't care about + ordering.append(gtsam.Ordering()) + for ji in range(4): + ordering[-1].push_back(gtd.internal.JointAngleKey(ji, t).key()) + for ji in range(4): + ordering[-1].push_back(gtd.internal.JointVelKey(ji, t).key()) + # immediate control variables + ordering.append(gtsam.Ordering()) + for ji in range(4): + ordering[-1].push_back(gtd.internal.TorqueKey(ji, t).key()) + # intermediate control variables + ordering.append(gtsam.Ordering()) + for ji in range(4): + ordering[-1].push_back(gtd.internal.WrenchKey(lid, ji, t).key()) + ordering[-1].push_back(gtd.internal.TwistAccelKey(lid, t).key()) + # measurement inputs + ordering.append(gtsam.Ordering()) + ordering[-1].push_back(gtd.internal.TwistKey(lid, t).key()) + ordering[-1].push_back(gtd.internal.PoseKey(lid, t).key()) + ordering.append(gtsam.Ordering()) + ordering[-1].push_back(0) + + # eliminate + return gtd.BlockEliminateSequential(gfg, ordering), reversed(range(3, 4*N, 4)) + + @staticmethod + def extract_gains(cdpr, fg, openloop_results, N): """Extracts the locally linear optimal feedback control gains Args: cdpr (Cdpr): cable robot object fg (gtsam.NonlinearFactorGraph): The iLQR factor graph openloop_results (gtsam.Values): The open-loop optimal trajectory and controls + Returns: + gains (List[Tuple[np.ndarray, np.ndarray]]): The feedback gains in the form u = Kx """ - return [[{gtd.internal.PoseKey(cdpr.ee_id(), t).key(): np.zeros((1, 6)), - gtd.internal.TwistKey(cdpr.ee_id(), t).key(): np.zeros((1, 6))} - for ji in range(4)] - for t in range(3)] + lid = cdpr.ee_id() + net, u_inds = CdprControllerIlqr.extract_bayesnets(cdpr, fg, openloop_results, N) + # print(net.__repr__("net", gtd.KeyFormatter())) + utils.print_bayesnet_reduced(net) + + # extract_gains + gains = [None for t in range(N)] + # for t, neti in enumerate(u_inds): + for t, (neti, netu) in enumerate(zip(reversed(range(2, 4*N, 4)), + reversed(range(1, 4*N, 4)))): + ucond = net.at(netu) + icond = net.at(neti) + u_K_F = solve_triangular(ucond.R(), -ucond.S()[:, :24]) + u_K_p = solve_triangular(ucond.R(), -ucond.S()[:, 24:]) + F_K_x = solve_triangular(icond.R(), -icond.S())[:24, -12:] + u_K_x = u_K_F @ F_K_x + u_K_x[:, 6:] += u_K_p + gains[t] = u_K_x, 0#solve_triangular(R, gc.d()) + print(gains[0][0]) + return gains diff --git a/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py index ef810cd1..c7e62c59 100644 --- a/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py @@ -22,6 +22,7 @@ from gtsam.utils.test_case import GtsamTestCase class TestCdprControllerIlqr(GtsamTestCase): + @unittest.skip def testTrajFollow(self): """Tests trajectory tracking controller """ @@ -62,29 +63,43 @@ def testGains(self): x_des = [Pose3(Rot3(), (1.5, 0, 1.5)), Pose3(Rot3(), (1.5, 0, 1.5)), Pose3(Rot3(), (1.5, 0, 1.5))] # don't move - controller = CdprControllerIlqr(cdpr, x0=x0, pdes=x_des, dt=dt) - print(gtd.str(controller.result)) + controller = CdprControllerIlqr(cdpr, x0=x0, pdes=x_des, dt=dt, Q=np.ones(6)*1e12, R=np.array([1.])) print(controller.fg.error(controller.result)) # notation: x_K_y means xstar = K * dy + # where xstar is optimal x and dy is (desired_y - actual_y) + # note: when multiplying gain matrices together, be mindful of negative signs # position gain (Kp) - time 0, cable 0, pose gain - actual_0c0_K_0x = -controller.gains[0][0][gtd.internal.PoseKey(cdpr.ee_id(), 0).key()] + actual_0c0_K_0x = controller.gains[0][0][0:1, 6:] expected_1v_K_0x = np.diag([0, -1, 0, -1, 0, -1]) / dt # v at t=1 in response to x at t=0 expected_0c0_K_1v = np.array([0, 1e9, 0, - -1 / np.sqrt(2), 0, -1 / np.sqrt(2)]).reshape((1, -1)) * \ + -1 / np.sqrt(2) / 2, 0, 1 / np.sqrt(2) / 2]).reshape((1, -1)) * \ cdpr.params.mass / dt # cable 0 tension at t=0 in response to v at t=1 - expected_0c0_K_0x = expected_0c0_K_1v @ expected_1v_K_0x - self.gtsamAssertEquals(actual_0c0_K_0x[:, 3:], expected_0c0_K_0x[:, 3:]) + expected_0c0_K_0x = -expected_0c0_K_1v @ expected_1v_K_0x + self.gtsamAssertEquals(actual_0c0_K_0x[:, 3:], expected_0c0_K_0x[:, 3:], tol=1/dt) # velocity gain (Kd) - time 0, cable 0, twist gain - actual_0c0_K_0v = -controller.gains[0][0][gtd.internal.TwistKey(cdpr.ee_id(), 0).key()] - expected_0c0_K_0v = expected_0c0_K_1v - self.gtsamAssertEquals(actual_0c0_K_0v[:, 3:], expected_0c0_K_0v[:, 3:]) + actual_0c0_K_0v = controller.gains[0][0][0:1, :6] + expected_0c0_K_0v = 2 * expected_0c0_K_1v + self.gtsamAssertEquals(actual_0c0_K_0v[:, 3:], expected_0c0_K_0v[:, 3:], tol=dt) # feedforward term (uff) - time 0, cable 0, feedforward term - actual_0c0_ff = -controller.gains[0][0][gtd.internal.TorqueKey(0, 0).key()] + actual_0c0_ff = controller.gains[0][1] expected_0c0_ff = np.zeros(1) self.gtsamAssertEquals(actual_0c0_ff[:, 3:], expected_0c0_ff[:, 3:]) + + def testRun(self): + """Tests that controller will not "compile" (aka run without errors) + """ + cdpr = Cdpr() + dt = 0.01 + + x0 = gtsam.Values() + gtd.InsertPose(x0, cdpr.ee_id(), 0, Pose3(Rot3(), (1.5, 0, 1.5))) + gtd.InsertTwist(x0, cdpr.ee_id(), 0, np.zeros(6)) + x_des = [Pose3(Rot3(), (1.5, 0, 1.5))] + controller = CdprControllerIlqr(cdpr, x0=x0, pdes=x_des, dt=dt) + if __name__ == "__main__": unittest.main() From 9d45d7fa468cf7a1688035c7afd2d6f8df247c06 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Mon, 19 Apr 2021 12:14:49 -0400 Subject: [PATCH 27/73] feedforward calculation for gains --- gtdynamics/cablerobot/src/cdpr_controller_ilqr.py | 8 ++++---- gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py | 9 ++++----- 2 files changed, 8 insertions(+), 9 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py index 9f12bbb5..6bd46298 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py @@ -143,8 +143,6 @@ def extract_gains(cdpr, fg, openloop_results, N): """ lid = cdpr.ee_id() net, u_inds = CdprControllerIlqr.extract_bayesnets(cdpr, fg, openloop_results, N) - # print(net.__repr__("net", gtd.KeyFormatter())) - utils.print_bayesnet_reduced(net) # extract_gains gains = [None for t in range(N)] @@ -155,9 +153,11 @@ def extract_gains(cdpr, fg, openloop_results, N): icond = net.at(neti) u_K_F = solve_triangular(ucond.R(), -ucond.S()[:, :24]) u_K_p = solve_triangular(ucond.R(), -ucond.S()[:, 24:]) + u_k = solve_triangular(ucond.R(), ucond.d()) F_K_x = solve_triangular(icond.R(), -icond.S())[:24, -12:] + F_k = solve_triangular(icond.R(), icond.d())[:24] u_K_x = u_K_F @ F_K_x u_K_x[:, 6:] += u_K_p - gains[t] = u_K_x, 0#solve_triangular(R, gc.d()) - print(gains[0][0]) + u_k += u_K_F @ F_k + gains[t] = u_K_x, u_k return gains diff --git a/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py index c7e62c59..76197e2e 100644 --- a/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py @@ -22,7 +22,6 @@ from gtsam.utils.test_case import GtsamTestCase class TestCdprControllerIlqr(GtsamTestCase): - @unittest.skip def testTrajFollow(self): """Tests trajectory tracking controller """ @@ -51,7 +50,7 @@ def testTrajFollow(self): for k, (des, act) in enumerate(zip(x_des, pAct)): self.gtsamAssertEquals(des, act) - def testGains(self): + def testGainsNearConstrained(self): """Tests locally linear, time-varying feedback gains """ cdpr = Cdpr() @@ -84,10 +83,10 @@ def testGains(self): self.gtsamAssertEquals(actual_0c0_K_0v[:, 3:], expected_0c0_K_0v[:, 3:], tol=dt) # feedforward term (uff) - time 0, cable 0, feedforward term - actual_0c0_ff = controller.gains[0][1] + actual_0c0_ff = controller.gains[0][1][0:1] expected_0c0_ff = np.zeros(1) - self.gtsamAssertEquals(actual_0c0_ff[:, 3:], expected_0c0_ff[:, 3:]) - + self.gtsamAssertEquals(actual_0c0_ff, expected_0c0_ff, tol=1e-6) + def testRun(self): """Tests that controller will not "compile" (aka run without errors) """ From f40df137eff11182c1be8cc1dbf40456d0df1a69 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 20 Apr 2021 17:28:05 -0400 Subject: [PATCH 28/73] repackage controller gains with ff terms --- .../cablerobot/src/cdpr_controller_ilqr.py | 80 +++++++++++++++++-- .../src/test_cdpr_controller_ilqr.py | 9 +-- 2 files changed, 75 insertions(+), 14 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py index 6bd46298..552b5da6 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py @@ -48,13 +48,24 @@ def __init__(self, cdpr, x0, pdes=[], dt=0.01, Q=None, R=np.array([1.])): self.result = self.optimizer.optimize() self.fg = fg # gains - self.gains = self.extract_gains(cdpr, fg, self.result, len(self.pdes)) + self.gains_ff = self.extract_gains_ff(cdpr, fg, self.result, len(self.pdes)) def update(self, values, t): """New control: returns the entire results vector, which contains the optimal open-loop control from the optimal trajectory. """ - return self.result + K, uff, Vff, Tff = self.gains_ff[t] + # compute dx + Vhat = gtd.Twist(values, self.cdpr.ee_id(), t) + That = gtd.Pose(values, self.cdpr.ee_id(), t) + dx = np.hstack((Vhat - Vff, Tff.localCoordinates(That))) + #compute u + u = K @ dx + uff + # populate into values object + result = gtsam.Values() + for ji in range(4): + gtd.InsertTorqueDouble(result, ji, t, u[ji]) + return result @staticmethod def create_ilqr_fg(cdpr, x0, pdes, dt, Q, R): @@ -138,8 +149,9 @@ def extract_gains(cdpr, fg, openloop_results, N): cdpr (Cdpr): cable robot object fg (gtsam.NonlinearFactorGraph): The iLQR factor graph openloop_results (gtsam.Values): The open-loop optimal trajectory and controls + N (int): The total number of timesteps in the iLQR factor graph Returns: - gains (List[Tuple[np.ndarray, np.ndarray]]): The feedback gains in the form u = Kx + gains (List[np.ndarray]): The feedback gains in the form u = K*(x-xff) + uff """ lid = cdpr.ee_id() net, u_inds = CdprControllerIlqr.extract_bayesnets(cdpr, fg, openloop_results, N) @@ -153,11 +165,65 @@ def extract_gains(cdpr, fg, openloop_results, N): icond = net.at(neti) u_K_F = solve_triangular(ucond.R(), -ucond.S()[:, :24]) u_K_p = solve_triangular(ucond.R(), -ucond.S()[:, 24:]) - u_k = solve_triangular(ucond.R(), ucond.d()) F_K_x = solve_triangular(icond.R(), -icond.S())[:24, -12:] - F_k = solve_triangular(icond.R(), icond.d())[:24] u_K_x = u_K_F @ F_K_x u_K_x[:, 6:] += u_K_p - u_k += u_K_F @ F_k - gains[t] = u_K_x, u_k + gains[t] = u_K_x return gains + + @staticmethod + def extract_uff(results, N): + """Extracts the feedforward control terms in a more python-friendly format + + Args: + results (gtsam.Values): contains the result of optimizing an iLQR factor graph + N (int): The total number of timesteps in the iLQR factor graph + + Returns: + List[np.ndarray]: Feedforward control terms. + """ + uff = [] + for t in range(N): + uff.append(np.array([gtd.TorqueDouble(results, ji, t) for ji in range(4)])) + return uff + + @staticmethod + def extract_xff(cdpr, results, N): + """Extracts the feedforward trajectory terms in a more python-friendly format + + Args: + cdpr (Cdpr): cable robot + results (gtsam.Values): contains the result of optimizing an iLQR factor graph + N (int): The total number of timesteps in the iLQR factor graph + + Returns: + List[Tuple[np.ndarray, gtsam.Pose3]]: Feedforward twist and pose terms, where each + element of the list contains a tuple of (Twist, Pose). + """ + xff = [] + for t in range(N): + xff.append(( + gtd.Twist(results, cdpr.ee_id(), t), # + gtd.Pose(results, cdpr.ee_id(), t))) + return xff + + @staticmethod + def extract_gains_ff(cdpr, fg, openloop_results, N): + """Extracts both the gains and the feedforward controls/trajectory terms of the form + u = K*(x-xff) + uff + + Args: + cdpr (Cdpr): cable robot object + fg (gtsam.NonlinearFactorGraph): The iLQR factor graph + openloop_results (gtsam.Values): The open-loop optimal trajectory and controls + N (int): The total number of timesteps in the iLQR factor graph + + Returns: + List[Tuple[np.ndarray, np.ndarray, np.ndarray, gtsam.Pose3]]: Gains and feedforward + terms as a list of tuples: (K, tension_ff, twist_ff, pose_ff) where u = K*(x-xff) + uff + and x stacks twist on top of pose. + """ + return list( + zip(CdprControllerIlqr.extract_gains(cdpr, fg, openloop_results, N), + CdprControllerIlqr.extract_uff(openloop_results, N), + *zip(*CdprControllerIlqr.extract_xff(cdpr, openloop_results, N)))) diff --git a/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py index 76197e2e..a4135e4c 100644 --- a/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py @@ -69,7 +69,7 @@ def testGainsNearConstrained(self): # where xstar is optimal x and dy is (desired_y - actual_y) # note: when multiplying gain matrices together, be mindful of negative signs # position gain (Kp) - time 0, cable 0, pose gain - actual_0c0_K_0x = controller.gains[0][0][0:1, 6:] + actual_0c0_K_0x = controller.gains_ff[0][0][0:1, 6:] expected_1v_K_0x = np.diag([0, -1, 0, -1, 0, -1]) / dt # v at t=1 in response to x at t=0 expected_0c0_K_1v = np.array([0, 1e9, 0, -1 / np.sqrt(2) / 2, 0, 1 / np.sqrt(2) / 2]).reshape((1, -1)) * \ @@ -78,14 +78,9 @@ def testGainsNearConstrained(self): self.gtsamAssertEquals(actual_0c0_K_0x[:, 3:], expected_0c0_K_0x[:, 3:], tol=1/dt) # velocity gain (Kd) - time 0, cable 0, twist gain - actual_0c0_K_0v = controller.gains[0][0][0:1, :6] + actual_0c0_K_0v = controller.gains_ff[0][0][0:1, :6] expected_0c0_K_0v = 2 * expected_0c0_K_1v self.gtsamAssertEquals(actual_0c0_K_0v[:, 3:], expected_0c0_K_0v[:, 3:], tol=dt) - - # feedforward term (uff) - time 0, cable 0, feedforward term - actual_0c0_ff = controller.gains[0][1][0:1] - expected_0c0_ff = np.zeros(1) - self.gtsamAssertEquals(actual_0c0_ff, expected_0c0_ff, tol=1e-6) def testRun(self): """Tests that controller will not "compile" (aka run without errors) From acf2376d3a2a08d5652be983ed125fd8883d4fff Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 20 Apr 2021 17:32:39 -0400 Subject: [PATCH 29/73] bugfix in unit test: set "median" tension to 0 --- gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py index a4135e4c..e66c0bcb 100644 --- a/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py @@ -54,6 +54,8 @@ def testGainsNearConstrained(self): """Tests locally linear, time-varying feedback gains """ cdpr = Cdpr() + cdpr.params.tmin = -1 + cdpr.params.tmax = 1 dt = 0.01 x0 = gtsam.Values() @@ -63,7 +65,6 @@ def testGainsNearConstrained(self): Pose3(Rot3(), (1.5, 0, 1.5)), Pose3(Rot3(), (1.5, 0, 1.5))] # don't move controller = CdprControllerIlqr(cdpr, x0=x0, pdes=x_des, dt=dt, Q=np.ones(6)*1e12, R=np.array([1.])) - print(controller.fg.error(controller.result)) # notation: x_K_y means xstar = K * dy # where xstar is optimal x and dy is (desired_y - actual_y) From a7fcd2a327a951fd7e313246cfe966340b789940 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 20 Apr 2021 17:33:04 -0400 Subject: [PATCH 30/73] unit test gains using infinite horizon approximation --- .../src/test_cdpr_controller_ilqr.py | 40 +++++++++++++++++++ 1 file changed, 40 insertions(+) diff --git a/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py index e66c0bcb..0a4019bf 100644 --- a/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py @@ -83,6 +83,46 @@ def testGainsNearConstrained(self): expected_0c0_K_0v = 2 * expected_0c0_K_1v self.gtsamAssertEquals(actual_0c0_K_0v[:, 3:], expected_0c0_K_0v[:, 3:], tol=dt) + def testGainsLongHorizon(self): + """Approximate infinite horizon problem by doing very long horizon. Compare with matlab. + """ + cdpr = Cdpr() + cdpr.params.tmin = -1 + cdpr.params.tmax = 1 + dt = 0.01 + + x0 = gtsam.Values() + gtd.InsertPose(x0, cdpr.ee_id(), 0, Pose3(Rot3(), (1.5, 0, 1.5))) + gtd.InsertTwist(x0, cdpr.ee_id(), 0, np.zeros(6)) + x_des = [Pose3(Rot3(), (1.5, 0, 1.5)) for t in range(1000)] + controller = CdprControllerIlqr(cdpr, x0=x0, pdes=x_des, dt=dt, + Q=np.array([0,1,0,2,0,3]), R=np.array([0.12])) + ''' matlab code: + dt = 0.01; + m = 1; + + A = [zeros(2), eye(2);... + zeros(2), zeros(2)]; + B_F = [zeros(2);... + eye(2) / m]; + F_T = [1, 1, -1, -1;... + -1, 1, 1, -1] / sqrt(2); + B = B_F * F_T; + Q = diag([2, 3, 0, 0]); + R = diag(repmat([0.12], 4, 1)); + [K, ~, ~] = lqrd(A, B, Q, R, dt) + ''' + expected_0c_K = -np.array([ # minus sign because matlab uses u=-Kx convention + # vx vy x y + [ 2.0069, -2.4534, 1.1912, -1.3171], + [ 2.0069, 2.4534, 1.1912, 1.3171], + [-2.0069, 2.4534, -1.1912, 1.3171], + [-2.0069, -2.4534, -1.1912, -1.3171] + ]) + actual_0c_K = controller.gains_ff[0][0] + actual_0c_K = actual_0c_K[:, [9, 11, 3, 5]] + self.gtsamAssertEquals(expected_0c_K, actual_0c_K, 2e-2) + def testRun(self): """Tests that controller will not "compile" (aka run without errors) """ From 7499a7d2278a6f7d3f3aedb604e1b720217596d8 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 20 Apr 2021 19:44:41 -0400 Subject: [PATCH 31/73] full IROS trajectory with iLQR --- gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb | 11 +++++++---- gtdynamics/cablerobot/src/gerry02_traj_tracking.py | 5 +++-- 2 files changed, 10 insertions(+), 6 deletions(-) diff --git a/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb b/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb index 014b2d99..076a36d9 100644 --- a/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb +++ b/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb @@ -20,10 +20,13 @@ "cell_type": "code", "execution_count": null, "id": "fifteen-crawford", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ - "cdpr, controller, result, N, dt, pdes = main(Q=np.ones(6)*1e3, R=np.ones(1)*1e-3, N0=9700, N=1000, dN=5)" + "cdpr, controller, result, N, dt, pdes = main(Q=np.ones(6)*1e3, R=np.ones(1)*1e-3, debug=False)\n", + "# cdpr, controller, result, N, dt, pdes = main(Q=np.ones(6)*1e3, R=np.ones(1)*1e-3, N0=9700, N=2000, dN=5, debug=False)" ] }, { @@ -33,7 +36,7 @@ "metadata": {}, "outputs": [], "source": [ - "anim = plot_all(cdpr, result, dt*N, dt, N, pdes, step=2);" + "anim = plot_all(cdpr, result, dt*N, dt, N, pdes, step=50);" ] }, { @@ -52,7 +55,7 @@ { "cell_type": "code", "execution_count": null, - "id": "terminal-brass", + "id": "weird-transport", "metadata": {}, "outputs": [], "source": [] diff --git a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py index f31ba0b8..c36490ab 100644 --- a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py +++ b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py @@ -49,7 +49,7 @@ def main(fname='data/iros_logo_2.h', Q=np.ones(6) * 1e2, R=np.ones(1) * 1e-2, N0=0, - N=500, + N=None, dN=1): """Runs a simulation of the iLQR controller trying to execute a predefined trajectory. @@ -88,10 +88,11 @@ def main(fname='data/iros_logo_2.h', cdpr = Cdpr(params) # import data + isPaints, colorinds, colorpalette, traj = ParseFile(fname) + N = len(traj) if N is None else N dt = 0.01 * dN # this is a hardcoded constant. TODO(gerry): include this in the .h file. N = int(N/dN) # scale time by dN N0 = int(N0/dN) - isPaints, colorinds, colorpalette, traj = ParseFile(fname) traj = (traj - [aw/2, ah/2]) * 0.8 + [aw/2, ah/2] # rescale trajectory to be smaller traj = traj[::dN, :] if debug: From 6d0ea28c56aa0e9e55529cc54700dc8ac29b4215 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 20 Apr 2021 19:49:00 -0400 Subject: [PATCH 32/73] format comment line in customwrap.cpp --- gtdynamics/cablerobot/utils/CustomWrap.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/gtdynamics/cablerobot/utils/CustomWrap.cpp b/gtdynamics/cablerobot/utils/CustomWrap.cpp index d521ff73..f55bb7ed 100644 --- a/gtdynamics/cablerobot/utils/CustomWrap.cpp +++ b/gtdynamics/cablerobot/utils/CustomWrap.cpp @@ -17,7 +17,7 @@ using namespace gtsam; namespace gtdynamics { -/// Performs sequential elimination and preserves correct bayes net order +/******************************************************************************/ GaussianBayesNet::shared_ptr EliminateSequential(GaussianFactorGraph graph, const Ordering& ordering) { BlockOrdering blockOrdering; @@ -26,7 +26,7 @@ GaussianBayesNet::shared_ptr EliminateSequential(GaussianFactorGraph graph, return BlockEliminateSequential(graph, blockOrdering); } -/// Performs sequential elimination and preserves correct bayes net order +/******************************************************************************/ GaussianBayesNet::shared_ptr BlockEliminateSequential( GaussianFactorGraph graph, const BlockOrdering &ordering) { // setup From 822e27c4d7cb68d985921c53c49bc0da343c5772 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 20 Apr 2021 20:15:17 -0400 Subject: [PATCH 33/73] minor plotting improvements --- gtdynamics/cablerobot/src/draw_cdpr.py | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/gtdynamics/cablerobot/src/draw_cdpr.py b/gtdynamics/cablerobot/src/draw_cdpr.py index 7889bf05..417d51c4 100644 --- a/gtdynamics/cablerobot/src/draw_cdpr.py +++ b/gtdynamics/cablerobot/src/draw_cdpr.py @@ -52,7 +52,7 @@ def draw_cdpr(ax, cdpr, x): l_b, = ax.plot(*b_coords(cdpr, x), color='#caa472') ls_ab = [ax.plot(*ab_coords(cdpr, x, ji))[0] for ji in range(4)] ax.axis('equal') - ax.set_xlabel('x(m)');ax.set_ylabel('y(m)');ax.set_title('Trajectory') + ax.set_xlabel('x(m)');ax.set_ylabel('y(m)');ax.set_title('Trajectory');ax.grid() return l_a, l_b, ls_ab def redraw_cdpr(l_a, l_b, ls_ab, cdpr, x): """Updates the lines for the frame, end effector, and cables""" @@ -76,15 +76,18 @@ def redraw_traj(l_des, l_act, des_xy, act_xy, N=None): return l_des, l_act def draw_ctrl(ax, cdpr, tensions, Tf, dt): """Draws the control tension signals""" - ls_ctrl = plt.plot(np.arange(0,Tf,dt), tensions) - plt.plot([0, Tf], [cdpr.params.tmin,]*2, 'r--') - plt.plot([0, Tf], [cdpr.params.tmax,]*2, 'r--') - plt.xlabel('time (s)');plt.ylabel('Cable tension (N)');plt.title('Control Inputs') + ls_ctrl = ax.plot(np.arange(0,Tf,dt), tensions) + ax.plot([0, Tf], [cdpr.params.tmin,]*2, 'r--') + ax.plot([0, Tf], [cdpr.params.tmax,]*2, 'r--') + ax.set_xlabel('time (s)');ax.set_ylabel('Cable tension (N)');ax.set_title('Control Inputs') + ax.grid() return ls_ctrl, def redraw_ctrl(ls_ctrl, tensions, Tf, dt, N=None): """Updates the lines for the tensions plot""" if N is None: N = tensions.shape[0] + else: + ls_ctrl[0].axes.set_xlim(max(0, dt*N-10), max(10, dt*N)) for ji in range(4): ls_ctrl[ji].set_data(np.arange(0,Tf,dt)[:N], tensions[:N, ji]) return *ls_ctrl, From 918daa70e87e4ca82e6f13e672fe5bcbffda27e7 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 20 Apr 2021 20:15:27 -0400 Subject: [PATCH 34/73] iros trajectory - higher framerate --- .../cablerobot/src/gerry02_traj_tracking.ipynb | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb b/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb index 076a36d9..1f695e87 100644 --- a/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb +++ b/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb @@ -36,18 +36,18 @@ "metadata": {}, "outputs": [], "source": [ - "anim = plot_all(cdpr, result, dt*N, dt, N, pdes, step=50);" + "anim = plot_all(cdpr, result, dt*N, dt, N, pdes, step=10);" ] }, { "cell_type": "code", "execution_count": null, - "id": "pursuant-enemy", - "metadata": { - "scrolled": true - }, + "id": "broad-profit", + "metadata": {}, "outputs": [], "source": [ + "import matplotlib\n", + "matplotlib.rcParams['animation.embed_limit'] = 25.0\n", "from IPython.display import HTML\n", "HTML(anim.to_html5_video())" ] @@ -55,7 +55,7 @@ { "cell_type": "code", "execution_count": null, - "id": "weird-transport", + "id": "joint-batch", "metadata": {}, "outputs": [], "source": [] From 8cb50458b64abc6e6078eccc39e553d795acd886 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Wed, 21 Apr 2021 14:26:22 -0400 Subject: [PATCH 35/73] id/fd were flip flopped --- gtdynamics/cablerobot/src/cdpr_planar.py | 8 +++---- gtdynamics/cablerobot/src/cdpr_planar_sim.py | 7 +++--- gtdynamics/cablerobot/src/test_cdpr_planar.py | 22 +++++++++---------- 3 files changed, 18 insertions(+), 19 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_planar.py b/gtdynamics/cablerobot/src/cdpr_planar.py index 145bb875..cfa40196 100644 --- a/gtdynamics/cablerobot/src/cdpr_planar.py +++ b/gtdynamics/cablerobot/src/cdpr_planar.py @@ -259,9 +259,9 @@ def priors_ik(self, ks=[], Ts=[], Vs=[], values=None): return graph # note: I am not using the strict definitions for forward/inverse dynamics. - # priors_fd solves for torques given twistaccel (no joint accel) - # priors_id solves for twistaccel (no joint accel) given torques - def priors_id(self, ks=[], torquess=[[]], values=None): + # priors_fd solves for twistaccel (no joint accel) given torques + # priors_id solves for torques given twistaccel (no joint accel) + def priors_fd(self, ks=[], torquess=[[]], values=None): """Creates factors roughly corresponding to the inverse dynamics problem. While strictly inverse dynamics in Lynch & Park refers to the problem of calculating joint accelerations given joint torques, temproarily this function is more convenient which directly relates @@ -286,7 +286,7 @@ def priors_id(self, ks=[], torquess=[[]], values=None): torque, self.costmodel_prior_tau)) return graph - def priors_fd(self, ks=[], VAs=[], values=None): + def priors_id(self, ks=[], VAs=[], values=None): """Creates factors roughly corresponding to the forward dynamics problem. While strictly forward dynamics in Lynch & Park refers to the problem of calculating joint torques given joint accelerations, temproarily this function is more convenient which directly relates diff --git a/gtdynamics/cablerobot/src/cdpr_planar_sim.py b/gtdynamics/cablerobot/src/cdpr_planar_sim.py index f669043b..0408bfc3 100644 --- a/gtdynamics/cablerobot/src/cdpr_planar_sim.py +++ b/gtdynamics/cablerobot/src/cdpr_planar_sim.py @@ -112,14 +112,13 @@ def update_dynamics(cdpr, x, u, k, dt): xd.insertDouble(0, dt) gtd.InsertPose(xd, lid, k, gtd.Pose(x, lid, k)) gtd.InsertTwist(xd, lid, k, gtd.Twist(x, lid, k)) - # ID for this timestep + collocation to next time step + # FD for this timestep + collocation to next time step fg.push_back(cdpr.dynamics_factors(ks=[k])) fg.push_back(cdpr.collocation_factors(ks=[k], dt=dt)) # priors (pose/twist and torque inputs) fg.push_back(cdpr.priors_ik(ks=[k], values=xd)) - fg.push_back( - cdpr.priors_id(ks=[k], values=u)) - # ID initial guess + fg.push_back(cdpr.priors_fd(ks=[k], values=u)) + # FD initial guess for ji in range(4): gtd.InsertTorqueDouble(xd, ji, k, gtd.TorqueDouble(u, ji, k)) gtd.InsertWrench(xd, cdpr.ee_id(), ji, k, np.zeros(6)) diff --git a/gtdynamics/cablerobot/src/test_cdpr_planar.py b/gtdynamics/cablerobot/src/test_cdpr_planar.py index 37fc9b77..104ef741 100644 --- a/gtdynamics/cablerobot/src/test_cdpr_planar.py +++ b/gtdynamics/cablerobot/src/test_cdpr_planar.py @@ -76,7 +76,7 @@ def testDynamicsInstantaneous(self): cdpr = Cdpr() dfg = cdpr.dynamics_factors(ks=[0]) values = gtsam.Values() - # things needed to define kinematic + # things needed to define IK gtd.InsertPose(values, cdpr.ee_id(), 0, Pose3(Rot3(), (1.5, 0, 1.5))) gtd.InsertTwist(values, cdpr.ee_id(), 0, np.zeros(6)) # things needed to define ID @@ -95,10 +95,10 @@ def testDynamicsInstantaneous(self): dfg.push_back( cdpr.priors_ik([0], [gtd.Pose(values, cdpr.ee_id(), 0)], [gtd.Twist(values, cdpr.ee_id(), 0)])) - fd1 = cdpr.priors_fd(ks=[0], VAs=[gtd.TwistAccel(values, cdpr.ee_id(), 0)]) - fd2 = cdpr.priors_fd(ks=[0], values=values) - self.gtsamAssertEquals(fd1, fd2) - dfg.push_back(fd1) + id1 = cdpr.priors_id(ks=[0], VAs=[gtd.TwistAccel(values, cdpr.ee_id(), 0)]) + id2 = cdpr.priors_id(ks=[0], values=values) + self.gtsamAssertEquals(id1, id2) + dfg.push_back(id1) # redundancy resolution dfg.push_back( gtd.PriorFactorDouble( @@ -115,10 +115,10 @@ def testDynamicsInstantaneous(self): gtd.InsertTorqueDouble(init, ji, 0, -1) results = gtsam.LevenbergMarquardtOptimizer(dfg, init).optimize() self.gtsamAssertEquals(results, values) - # check ID priors functions - id1 = cdpr.priors_id(ks=[0], torquess=[[gtd.TorqueDouble(results, ji, 0) for ji in range(4)]]) - id2 = cdpr.priors_id(ks=[0], values=results) - self.gtsamAssertEquals(id1, id2) + # check FD priors functions + fd1 = cdpr.priors_fd(ks=[0], torquess=[[gtd.TorqueDouble(results, ji, 0) for ji in range(4)]]) + fd2 = cdpr.priors_fd(ks=[0], values=results) + self.gtsamAssertEquals(fd1, fd2) def testDynamicsCollocation(self): """Test dynamics factors across multiple timesteps by using collocation. @@ -133,8 +133,8 @@ def testDynamicsCollocation(self): # initial state fg.push_back( cdpr.priors_ik(ks=[0], Ts=[Pose3(Rot3(), (1.5, 0, 1.5))], Vs=[np.zeros(6)])) - # torque inputs (ID priors) - fg.push_back(cdpr.priors_id(ks=[0, 1, 2], torquess=[[1,1,0,0],]*3)) + # torque inputs (FD priors) + fg.push_back(cdpr.priors_fd(ks=[0, 1, 2], torquess=[[1,1,0,0],]*3)) # construct initial guess init = gtsam.Values() init.insertDouble(0, 0.01) From 2a551aab8938770b5676efb7062c7545b699a596 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Wed, 21 Apr 2021 14:35:41 -0400 Subject: [PATCH 36/73] update cdpr unit test with correct ID/FD and inertia ID/FD should be lddot <-> torque, rather than TwistAccel <-> torque --- gtdynamics/cablerobot/src/test_cdpr_planar.py | 23 +++++++++++++------ 1 file changed, 16 insertions(+), 7 deletions(-) diff --git a/gtdynamics/cablerobot/src/test_cdpr_planar.py b/gtdynamics/cablerobot/src/test_cdpr_planar.py index 104ef741..1bb38daa 100644 --- a/gtdynamics/cablerobot/src/test_cdpr_planar.py +++ b/gtdynamics/cablerobot/src/test_cdpr_planar.py @@ -74,28 +74,34 @@ def testDynamicsInstantaneous(self): """Test dynamics factors: relates torque to wrench+twistaccel. Also tests ID solving """ cdpr = Cdpr() + cdpr.motor_inertia = 1.23 dfg = cdpr.dynamics_factors(ks=[0]) values = gtsam.Values() # things needed to define IK gtd.InsertPose(values, cdpr.ee_id(), 0, Pose3(Rot3(), (1.5, 0, 1.5))) gtd.InsertTwist(values, cdpr.ee_id(), 0, np.zeros(6)) + # gtd.InsertTwistAccel(values, cdpr.ee_id(), 0, (0, 0, 0, 0, 0, -np.sqrt(2))) # things needed to define ID - for j, tau in zip(range(4), [1, 0, 0, 1]): + for j, tau in zip(range(4), [1 + 1.23, 0 - 1.23, 0 - 1.23, 1 + 1.23]): gtd.InsertTorqueDouble(values, j, 0, tau) # things needed to define FD - gtd.InsertTwistAccel(values, cdpr.ee_id(), 0, (0, 0, 0, 0, 0, -np.sqrt(2))) + for j, lddot in zip(range(4), [1, 0, 0, 1]): + gtd.InsertJointAccelDouble(values, j, 0, lddot) # things needed intermediaries gtd.InsertWrench(values, cdpr.ee_id(), 0, [0,0,0,1/np.sqrt(2),0,-1/np.sqrt(2)]) gtd.InsertWrench(values, cdpr.ee_id(), 1, [0,0,0,0,0,0]) gtd.InsertWrench(values, cdpr.ee_id(), 2, [0,0,0,0,0,0]) gtd.InsertWrench(values, cdpr.ee_id(), 3, [0,0,0,-1/np.sqrt(2),0,-1/np.sqrt(2)]) + gtd.InsertTwistAccel(values, cdpr.ee_id(), 0, (0, 0, 0, 0, 0, -np.sqrt(2))) # check FD/ID is valid self.assertAlmostEqual(0.0, dfg.error(values), 10) - # build FD graph + # build ID graph dfg.push_back( - cdpr.priors_ik([0], [gtd.Pose(values, cdpr.ee_id(), 0)], - [gtd.Twist(values, cdpr.ee_id(), 0)])) - id1 = cdpr.priors_id(ks=[0], VAs=[gtd.TwistAccel(values, cdpr.ee_id(), 0)]) + cdpr.priors_ik([0], + Ts=[gtd.Pose(values, cdpr.ee_id(), 0)], + Vs=[gtd.Twist(values, cdpr.ee_id(), 0)])) + id1 = cdpr.priors_id(ks=[0], + lddotss=[[gtd.JointAccelDouble(values, ji, 0) for ji in range(4)]]) id2 = cdpr.priors_id(ks=[0], values=values) self.gtsamAssertEquals(id1, id2) dfg.push_back(id1) @@ -124,6 +130,7 @@ def testDynamicsCollocation(self): """Test dynamics factors across multiple timesteps by using collocation. """ cdpr = Cdpr() + cdpr.motor_inertia = 1.23 # kinematics fg = cdpr.kinematics_factors(ks=[0, 1, 2]) # dynamics @@ -134,7 +141,8 @@ def testDynamicsCollocation(self): fg.push_back( cdpr.priors_ik(ks=[0], Ts=[Pose3(Rot3(), (1.5, 0, 1.5))], Vs=[np.zeros(6)])) # torque inputs (FD priors) - fg.push_back(cdpr.priors_fd(ks=[0, 1, 2], torquess=[[1,1,0,0],]*3)) + torques = [1 + 1.23, 1 + 1.23, 0 - 1.23, 0 - 1.23] + fg.push_back(cdpr.priors_fd(ks=[0, 1, 2], torquess=[torques,]*3)) # construct initial guess init = gtsam.Values() init.insertDouble(0, 0.01) @@ -142,6 +150,7 @@ def testDynamicsCollocation(self): for j in range(4): gtd.InsertJointAngleDouble(init, j, t, 1) gtd.InsertJointVelDouble(init, j, t, 1) + gtd.InsertJointAccelDouble(init, j, t, 1) gtd.InsertTorqueDouble(init, j, t, 1) gtd.InsertWrench(init, cdpr.ee_id(), j, t, np.ones(6)) gtd.InsertPose(init, cdpr.ee_id(), t, Pose3(Rot3(), (1.5, 1, 1.5))) From 340978f85e697f8872812cf43f9eeef54a952984 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Wed, 21 Apr 2021 14:53:41 -0400 Subject: [PATCH 37/73] python side of ID/FD with joint angles and winch inertia --- gtdynamics/cablerobot/src/cdpr_planar.py | 79 ++++++++++++++++++------ 1 file changed, 60 insertions(+), 19 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_planar.py b/gtdynamics/cablerobot/src/cdpr_planar.py index cfa40196..f89a925b 100644 --- a/gtdynamics/cablerobot/src/cdpr_planar.py +++ b/gtdynamics/cablerobot/src/cdpr_planar.py @@ -27,6 +27,7 @@ def __init__(self): self.gravity = np.zeros((3, 1)) self.tmin = 0.1 / (0.0254 / 2) # tension = torque / radius self.tmax = 1.2 / (0.0254 / 2) + self.motor_inertia = 0 class Cdpr: """Utility functions for a planar cable robot; mostly assembles together factors. @@ -38,12 +39,15 @@ def __init__(self, params=CdprParams()): self.robot = gtd.Robot({'ee' : ee}, {}) self.costmodel_l = gtsam.noiseModel.Constrained.All(1) self.costmodel_ldot = gtsam.noiseModel.Constrained.All(1) - self.costmodel_wrench = gtsam.noiseModel.Constrained.All(6) + self.costmodel_lddot = gtsam.noiseModel.Constrained.All(1) + self.costmodel_winch = gtsam.noiseModel.Constrained.All(1) self.costmodel_torque = gtsam.noiseModel.Constrained.All(6) + self.costmodel_wrench = gtsam.noiseModel.Constrained.All(6) self.costmodel_twistcollo = gtsam.noiseModel.Constrained.All(6) self.costmodel_posecollo = gtsam.noiseModel.Constrained.All(6) self.costmodel_prior_l = gtsam.noiseModel.Constrained.All(1) self.costmodel_prior_ldot = gtsam.noiseModel.Constrained.All(1) + self.costmodel_prior_lddot = gtsam.noiseModel.Constrained.All(1) self.costmodel_prior_tau = gtsam.noiseModel.Constrained.All(1) self.costmodel_prior_pose = gtsam.noiseModel.Constrained.All(6) self.costmodel_prior_twist = gtsam.noiseModel.Constrained.All(6) @@ -64,7 +68,7 @@ def ee_id(self): Returns: int: The end effector's link id - """ + """ return self.eelink().id() def all_factors(self, N, dt): @@ -79,7 +83,7 @@ def all_factors(self, N, dt): Returns: gtsam.NonlinearFactorGraph: the factor graph - """ + """ fg = gtsam.NonlinearFactorGraph() fg.push_back(self.kinematics_factors(ks=range(N))) fg.push_back(self.dynamics_factors(ks=range(N))) @@ -140,8 +144,8 @@ def kinematics_factors(self, ks=[]): def dynamics_factors(self, ks=[]): """Creates factors necessary for dynamics calculations. Specifically, consists of the generalized version of F=ma and calculates wrenches from cable tensions. - Primary variables: Torque <--> TwistAccel - Intermediate variables: Wrenches + Primary variables: Torque <--> lengthddot + Intermediate variables: Wrenches, TwistAccel Prerequisite variables: Pose, Twist @@ -168,10 +172,26 @@ def dynamics_factors(self, ks=[]): for ji in range(4): dfg.push_back( gtd.CableTensionFactor( - gtd.internal.TorqueKey(ji, k).key(), + gtd.internal.TensionKey(ji, k).key(), gtd.internal.PoseKey(self.ee_id(), k).key(), gtd.internal.WrenchKey(self.ee_id(), ji, k).key(), self.costmodel_torque, self.params.a_locs[ji], self.params.b_locs[ji])) + dfg.push_back( + gtd.CableAccelFactor( + gtd.internal.JointAccelKey(ji, k).key(), + gtd.internal.PoseKey(self.ee_id(), k).key(), + gtd.internal.TwistKey(self.ee_id(), k).key(), + gtd.internal.TwistAccelKey(self.ee_id(), k).key(), + self.costmodel_lddot, self.params.a_locs[ji], self.params.b_locs[ji])) + dfg.push_back( + gtd.WinchFactor( + gtd.internal.TorqueKey(ji, k).key(), + gtd.internal.TensionKey(ji, k).key(), + gtd.internal.JointVelKey(ki, k).key(), + gtd.internal.JointAccelKey(ki, k).key(), + self.costmodel_winch, self.params.motor_inertia + ) + ) return dfg def collocation_factors(self, ks=[], dt=0.01): @@ -258,15 +278,12 @@ def priors_ik(self, ks=[], Ts=[], Vs=[], values=None): V, self.costmodel_prior_twist)) return graph - # note: I am not using the strict definitions for forward/inverse dynamics. - # priors_fd solves for twistaccel (no joint accel) given torques - # priors_id solves for torques given twistaccel (no joint accel) + # priors_fd solves for joint accel given torques + # priors_id solves for torques given joint accel + # priors_id_va solves for torques given twist accel (since this may optimize easier) def priors_fd(self, ks=[], torquess=[[]], values=None): - """Creates factors roughly corresponding to the inverse dynamics problem. While strictly - inverse dynamics in Lynch & Park refers to the problem of calculating joint accelerations - given joint torques, temproarily this function is more convenient which directly relates - constrains joint torques (to obtain twist accelerations when used with dynamics_factors). - Either supply ks/torquess, or ks/values. If values is supplied, torquess will be ignored. + """Creates factors roughly corresponding to the forward dynamics problem. Either supply + ks/torquess, or ks/values. If values is supplied, torquess will be ignored. Args: ks (list, optional): Time step indices. Defaults to []. @@ -275,7 +292,7 @@ def priors_fd(self, ks=[], torquess=[[]], values=None): values (gtsam.Values, optional): Values object containing all the needed key/values. Returns: - gtsam.NonlinearFactorGraph: The inverse dynamics prior factors + gtsam.NonlinearFactorGraph: The forward dynamics prior factors """ if values is not None: torquess = [[gtd.TorqueDouble(values, ji, k) for ji in range(4)] for k in ks] @@ -286,9 +303,33 @@ def priors_fd(self, ks=[], torquess=[[]], values=None): torque, self.costmodel_prior_tau)) return graph - def priors_id(self, ks=[], VAs=[], values=None): - """Creates factors roughly corresponding to the forward dynamics problem. While strictly - forward dynamics in Lynch & Park refers to the problem of calculating joint torques given + def priors_id(self, ks=[], lddotss=[[]], values=None): + """Creates factors roughly corresponding to the inverse dynamics problem. Either supply + ks/lddotss, or ks/values. If values is supplied, lddotss will be ignored. + + Args: + ks (list, optional): Time step indices. Defaults to []. + lddotss (list, optional): List of list of joint accelerations for each time step. + Defaults to [[]]. + values (gtsam.Values, optional): Values object containing all the needed key/values. + + Returns: + gtsam.NonlinearFactorGraph: The inverse dynamics prior factors + """ + if values is not None: + lddotss = [[gtd.JointAccelDouble(values, ji, k) for ji in range(4)] for k in ks] + graph = gtsam.NonlinearFactorGraph() + for k, lddots in zip(ks, lddotss): + for ji, lddot in enumerate(lddots): + graph.push_back( + gtd.PriorFactorDouble( + gtd.internal.JointAccelKey(self.ee_id(), k).key(), lddot, + self.costmodel_prior_lddot)) + return graph + + def priors_id_va(self, ks=[], VAs=[], values=None): + """Creates factors roughly corresponding to the inverse dynamics problem. While strictly + inverse dynamics in Lynch & Park refers to the problem of calculating joint torques given joint accelerations, temproarily this function is more convenient which directly relates constraints TwistAccelerations (to obtain joint torques when used with dynamics_factors). Either supply ks/twistaccels, or ks/values. If values is supplied, twistaccels will be @@ -300,7 +341,7 @@ def priors_id(self, ks=[], VAs=[], values=None): values (gtsam.Values, optional): Values object containing all the needed key/values. Returns: - gtsam.NonlinearFactorGraph: The forward dynamics prior factors + gtsam.NonlinearFactorGraph: The inverse dynamics prior factors """ if values is not None: VAs = [gtd.TwistAccel(values, self.ee_id(), k) for k in ks] From 761168e5130271bd553d3a1a3de3c3f3049cc79f Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Wed, 21 Apr 2021 21:28:54 -0400 Subject: [PATCH 38/73] winch factor --- gtdynamics/cablerobot/factors/WinchFactor.h | 140 ++++++++++++++++++ .../cablerobot/tests/testWinchFactor.cpp | 74 +++++++++ 2 files changed, 214 insertions(+) create mode 100644 gtdynamics/cablerobot/factors/WinchFactor.h create mode 100644 gtdynamics/cablerobot/tests/testWinchFactor.cpp diff --git a/gtdynamics/cablerobot/factors/WinchFactor.h b/gtdynamics/cablerobot/factors/WinchFactor.h new file mode 100644 index 00000000..e812cfde --- /dev/null +++ b/gtdynamics/cablerobot/factors/WinchFactor.h @@ -0,0 +1,140 @@ +/** + * @file WinchFactor.h + * @brief Maps motor input torque to cable tension + * @author Gerry Chen + * + * Applies a mapping from input torque to cable tension based on: + * - winch radius + * - motor inertia + * - static friction + * - viscous friction + * + * Input torque is given by (current) * (motor torque constant) + * External torques are given by + * (tension) * (radius) + + * (static friction) + + * (viscous friction) + * These together cause: + * (motor inertia) * (angular acceleration) + **/ +#pragma once + +#include +#include +#include +#include + +#include +#include +#include + +namespace gtdynamics { + +/** + * WinchParams holds parameters relevant to winches + */ +struct WinchParams { + double radius_; // winch radius in m + double inertia_; // motor inertia in kg.m^2 + double staticFriction_; // static friction of the winch in N.m + double viscousFriction_; // viscous friction of the winch in N.m / rad + WinchParams(double radius, double inertia, double staticFriction, + double viscousFriction) + : radius_(radius), + inertia_(inertia), + staticFriction_(staticFriction), + viscousFriction_(viscousFriction) {} +}; + +/** + * WinchFactor is a 4-way factor which converts motor input torque to cable + * tension, taking into account winch radius, inertia, and friction. + * + * The relationship is given by + * $$ tau = tension * radius - inertia * acceleration / radius + friction * radius $$ + * where + * $$ friction = - (static * sign(v) + viscous * v) $$ + * + * This is just rewriting $\sum{torques} = I * alpha$ and adjusting for sign + * convention (torque is backwards...) + * + * Note that static friction causes a major discontinuity, so instead it is + * approximated as tanh(v/eps) as is done in Gouttefarde15TRO, with eps=1/50. + */ +class WinchFactor + : public gtsam::NoiseModelFactor4 { + private: + typedef WinchFactor This; + typedef gtsam::NoiseModelFactor4 Base; + const WinchParams params_; + + public: + /** Winch factor + * @param torque -- key for motor torque input + * @param tenison -- key for cable tension + * @param cableVelocity -- key for cable velocity + * @param cableAcceleration -- key for cable acceleration + * @param cost_model -- noise model (1 dimensional) + * @param params -- winch parameters + */ + WinchFactor(gtsam::Key torque, gtsam::Key tension, gtsam::Key cableVelocity, + gtsam::Key cableAcceleration, + const gtsam::noiseModel::Base::shared_ptr &cost_model, + WinchParams params) + : Base(cost_model, torque, tension, cableVelocity, cableAcceleration), + params_(params) {} + virtual ~WinchFactor() {} + + public: + /** Winch factor + * @param torque -- motor torque input + * @param tension -- cable tension + * @param vel -- cable velocity + * @param acc -- cable acceleration + * @return cable torque minus computed cable torque + */ + gtsam::Vector evaluateError( + const double &torque, const double &tension, const double &vel, + const double &acc, + boost::optional H_torque = boost::none, + boost::optional H_tension = boost::none, + boost::optional H_vel = boost::none, + boost::optional H_acc = boost::none) const override { + const double &r = params_.radius_; + const double &I = params_.inertia_; + const double &mu = params_.staticFriction_; + const double &b = params_.viscousFriction_; + + double sign_vel = tanh(50 * vel); // tanh approximation to sign(vel) + + if (H_torque) *H_torque = gtsam::Vector1(1); + if (H_tension) *H_tension = gtsam::Vector1(-r); + if (H_vel) + *H_vel = gtsam::Vector1(mu * 50 * (1 - sign_vel * sign_vel) + b / r); + if (H_acc) *H_acc = gtsam::Vector1(I / r); + + return gtsam::Vector1(torque - // tau = + tension * r + // F * r - + I * acc / r - // I * alpha + + (-mu * sign_vel) - // static friction + + (-b * vel / r)); // viscous friction + } + + // @return a deep copy of this factor + gtsam::NonlinearFactor::shared_ptr clone() const override { + return boost::static_pointer_cast( + gtsam::NonlinearFactor::shared_ptr(new This(*this))); + } + + private: + /** Serialization function */ + friend class boost::serialization::access; + template + void serialize(ARCHIVE &ar, const unsigned int version) { + ar &boost::serialization::make_nvp( + "NoiseModelFactor4", boost::serialization::base_object(*this)); + ar &BOOST_SERIALIZATION_NVP(params_); + } +}; + +} // namespace gtdynamics diff --git a/gtdynamics/cablerobot/tests/testWinchFactor.cpp b/gtdynamics/cablerobot/tests/testWinchFactor.cpp new file mode 100644 index 00000000..b185e0fa --- /dev/null +++ b/gtdynamics/cablerobot/tests/testWinchFactor.cpp @@ -0,0 +1,74 @@ +/** + * @file testWinchFactor.cpp + * @brief test winch factor + * @author Gerry Chen + */ + +#include + +#include +#include + +#include +#include +#include +#include + +#include +#include + +using namespace std; +using namespace gtsam; +using namespace gtdynamics; + +/** + * Test winch factor + */ +TEST(WinchFactor, error) { + // noise model + noiseModel::Gaussian::shared_ptr cost_model = + noiseModel::Isotropic::Sigma(1, 1.0); + WinchParams params(0.12, // radius + 1.23, // inerita + 0.34, // static friction + 0.56); // viscous friction + + Symbol torqueKey = symbol('q', 0); + Symbol tensionKey = symbol('t', 0); + Symbol jointVelKey = symbol('v', 0); + Symbol jointAccKey = symbol('a', 0); + WinchFactor factor(torqueKey, tensionKey, jointVelKey, jointAccKey, + cost_model, params); + + // q t v a + EXPECT(assert_equal(Vector1(0), factor.evaluateError( 0, 0, 0, 0))); + EXPECT(assert_equal(Vector1(0), factor.evaluateError(.12, 1, 0, 0))); + // inertia cancels out tension exactly + EXPECT(assert_equal(Vector1(0), + factor.evaluateError(0, 1, 0, 0.12 * 0.12 / 1.23))); + // viscous friction + EXPECT(assert_equal( + Vector1(0), // forward velocity -> extending cable -> negative torque + factor.evaluateError(-0.4621171573 * 0.34 - 0.01 / .12 * 0.56, 0, .01, + 0))); + EXPECT(assert_equal(Vector1(0), + factor.evaluateError(-0.34 - 1 / 0.12 * 0.56, 0, 1, 0))); + EXPECT(assert_equal(Vector1(0), + factor.evaluateError(+0.34 + 1 / 0.12 * 0.56, 0, -1, 0))); + // viscous friction with tension + EXPECT(assert_equal( + Vector1(0), + factor.evaluateError(0.12 + 0.34 + 1 / 0.12 * 0.56, 1, -1, 0))); + + Values values; + values.insertDouble(torqueKey, 1); + values.insertDouble(tensionKey, 2); + values.insertDouble(jointVelKey, .03); + values.insertDouble(jointAccKey, 4); + EXPECT_CORRECT_FACTOR_JACOBIANS(factor, values, 1e-7, 1e-3); +} + +int main() { + TestResult tr; + return TestRegistry::runAllTests(tr); +} From 72d13d0449953b93116bf2fb75572e70efafab2c Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Wed, 21 Apr 2021 22:43:56 -0400 Subject: [PATCH 39/73] cable acceleration factor unit test --- .../factors/CableAccelerationFactor.h | 122 ++++++++++++++++++ .../tests/testCableAccelerationFactor.cpp | 81 ++++++++++++ 2 files changed, 203 insertions(+) create mode 100644 gtdynamics/cablerobot/factors/CableAccelerationFactor.h create mode 100644 gtdynamics/cablerobot/tests/testCableAccelerationFactor.cpp diff --git a/gtdynamics/cablerobot/factors/CableAccelerationFactor.h b/gtdynamics/cablerobot/factors/CableAccelerationFactor.h new file mode 100644 index 00000000..72937e5e --- /dev/null +++ b/gtdynamics/cablerobot/factors/CableAccelerationFactor.h @@ -0,0 +1,122 @@ +/** + * @file CableAccelerationFactor.h + * @brief Cable acceleration factor: relates cable length acceleration with end + * effector pose/twist/twist acceleration. The cable mounting locations on the + * stationary frame and on the end-effector are passed as parameters. + * @author Gerry Chen + */ + +#pragma once + +#include + +#include +#include +#include +#include + +#include + +#include +#include + +namespace gtdynamics { + +/** CableAccelerationFactor is a 4-way nonlinear factor which enforces relation + * amongst cable acceleration, end-effector pose, end-effector twist, and + * end-effector twist acceleration + */ +class CableAccelerationFactor + : public gtsam::NoiseModelFactor4 { + private: + using Point3 = gtsam::Point3; + using Vector3 = gtsam::Vector3; + using Pose3 = gtsam::Pose3; + using Vector6 = gtsam::Vector6; + using This = CableAccelerationFactor; + using Base = gtsam::NoiseModelFactor4; + + Point3 wPa_, xPb_; + + public: + /** Cable factor + * @param lddot_key -- key for cable speed + * @param wTx -- key for end effector pose + * @param Vx -- key for end effector twist + * @param VAx -- key for end effector twist acceleration + * @param cost_model -- noise model (1 dimensional) + * @param wPa -- cable mounting location on the fixed frame, in world coords + * @param xPb -- cable mounting location on the end effector, in the + * end-effector frame (wPb = wTx * xPb) + */ + CableAccelerationFactor(gtsam::Key lddot_key, gtsam::Key wTx_key, + gtsam::Key Vx_key, gtsam::Key VAx_key, + const gtsam::noiseModel::Base::shared_ptr &cost_model, + const Point3 &wPa, const Point3 &xPb) + : Base(cost_model, lddot_key, wTx_key, Vx_key, VAx_key), + wPa_(wPa), + xPb_(xPb) {} + virtual ~CableAccelerationFactor() {} + + private: + /** Computes the cable acceleration that will result from some + * twist/twistaccel + * @param wTx the pose of the end effector + * @param Vx the twist of the end effector in the end effector's frame + * @param VAx the twist of the end effector in the end effector's frame + * @return double: calculated length acceleration change + */ + double computeLddot( + const Pose3 &wTx, const Vector6 &Vx, const Vector6 &VAx, + boost::optional H_wTx = boost::none, + boost::optional H_Vx = boost::none, + boost::optional H_VAx = boost::none) const; + + public: + /** Cable acceleration factor + * @param lddot -- cable speed (lddot) + * @param wTx -- end effector pose + * @param Vx -- end effector twist + * @param VAx -- end effector twist + * @return given lddot minus expected/calculated cable speed + */ + gtsam::Vector evaluateError( + const double &lddot, const Pose3 &wTx, const Vector6 &Vx, + const Vector6 &VAx, boost::optional H_lddot = boost::none, + boost::optional H_wTx = boost::none, + boost::optional H_Vx = boost::none, + boost::optional H_VAx = boost::none) const override { + double expected_lddot = computeLddot(wTx, Vx, VAx, H_wTx, H_Vx, H_VAx); + if (H_lddot) *H_lddot = gtsam::I_1x1; + if (H_wTx) *H_wTx = -(*H_wTx); + if (H_Vx) *H_Vx = -(*H_Vx); + if (H_VAx) *H_VAx = -(*H_VAx); + return gtsam::Vector1(lddot - expected_lddot); + } + + // @return a deep copy of this factor + gtsam::NonlinearFactor::shared_ptr clone() const override { + return boost::static_pointer_cast( + gtsam::NonlinearFactor::shared_ptr(new This(*this))); + } + + /** print contents */ + void print(const std::string &s = "", + const gtsam::KeyFormatter &keyFormatter = + GTDKeyFormatter) const override { + std::cout << s << "cable accel factor" << std::endl; + Base::print("", keyFormatter); + } + + private: + /** Serialization function */ + friend class boost::serialization::access; + template + void serialize(ARCHIVE &ar, const unsigned int version) { + ar &boost::serialization::make_nvp( + "NoiseModelFactor4", boost::serialization::base_object(*this)); + } +}; + +} // namespace gtdynamics diff --git a/gtdynamics/cablerobot/tests/testCableAccelerationFactor.cpp b/gtdynamics/cablerobot/tests/testCableAccelerationFactor.cpp new file mode 100644 index 00000000..4adc76bc --- /dev/null +++ b/gtdynamics/cablerobot/tests/testCableAccelerationFactor.cpp @@ -0,0 +1,81 @@ +/** + * @file testCableAccelerationFactor.cpp + * @brief test cable accel factor + * @author Frank Dellaert + * @author Gerry Chen + */ + +#include + +#include + +#include +#include + +#include +#include +#include +#include + +#include + +using namespace std; +using namespace gtsam; +using namespace gtdynamics; +using namespace gtdynamics::internal; + +/** + * Test cable factor + */ +TEST(CableAccelerationFactor, error) { + // noise model + noiseModel::Gaussian::shared_ptr cost_model = + noiseModel::Isotropic::Sigma(1, 1.0); + + int jid = 0; + int lid = 0; + Point3 frameLoc = Point3(0.1, 0.2, 0.3); + Point3 eeLoc = Point3(-0.15, 0, -0.15); + + CableAccelerationFactor factor(JointAccelKey(jid), PoseKey(lid), + TwistKey(lid), TwistAccelKey(lid), cost_model, + frameLoc, eeLoc); + + // Test configuration + Values values; + InsertJointAccel(&values, jid, 1.0); + InsertPose(&values, lid, Pose3(Rot3(), Point3(1.5, 0, 1.5))); + InsertTwist(&values, lid, (Vector6() << 0, 1, 0, 1.1, 0, 1.2).finished()); + InsertTwistAccel(&values, lid, + (Vector6() << 0, 1.3, 0, 1.4, 0, 1.5).finished()); + + // "expected" calculation + Vector3 dir = normalize(Point3(1.25, -.2, 1.05)); + Vector3 linaccel = Vector3(1.4, 0, 1.5); // translational part of VA + Vector3 rotaccel = 1.3 * normalize(Vector3(-0.15, 0, 0.15)); // rot of VA + Vector3 coraccel = + 0.9 * 0.9 * 0.15 * sqrt(2) * normalize(-eeLoc); // centripet + double expected_lddot = dot(dir, linaccel + rotaccel + coraccel); + Vector1 expected_errors{JointAccel(values, jid) - expected_lddot}; + + // evaluateError + Vector1 actual_errors = + factor.evaluateError(JointAccel(values, jid), Pose(values, lid), + Twist(values, lid), TwistAccel(values, lid)); + + EXPECT(assert_equal(expected_errors, actual_errors, 1e-4)); + + // jacobians + EXPECT_CORRECT_FACTOR_JACOBIANS(factor, values, 1e-7, 1e-3); + values.update(PoseKey(lid), Pose3(Rot3::Rz(0.4), Point3(0.2, 0.3, 0.4))); + values.update(TwistKey(lid), + (Vector6() << 0.12, 0.13, 0.14, 0.18, 0.19, 0.21).finished()); + values.update(TwistAccelKey(lid), + (Vector6() << 0.21, 0.22, 0.23, 0.24, 0.25, 0.26).finished()); + EXPECT_CORRECT_FACTOR_JACOBIANS(factor, values, 1e-7, 1e-3); +} + +int main() { + TestResult tr; + return TestRegistry::runAllTests(tr); +} From 9f8a0fe6b8a99e296373011174f0da21be9318e0 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Wed, 21 Apr 2021 23:14:39 -0400 Subject: [PATCH 40/73] CableAccelFactor passes unit test for simplified case without any rotation --- .../factors/CableAccelerationFactor.cpp | 69 +++++++++++++++++++ .../tests/testCableAccelerationFactor.cpp | 4 +- 2 files changed, 72 insertions(+), 1 deletion(-) create mode 100644 gtdynamics/cablerobot/factors/CableAccelerationFactor.cpp diff --git a/gtdynamics/cablerobot/factors/CableAccelerationFactor.cpp b/gtdynamics/cablerobot/factors/CableAccelerationFactor.cpp new file mode 100644 index 00000000..baafa659 --- /dev/null +++ b/gtdynamics/cablerobot/factors/CableAccelerationFactor.cpp @@ -0,0 +1,69 @@ +/** + * @file CableAccelerationFactor.cpp + * @brief Cable acceleration factor: relates cable length acceleration with end + * effector pose/twist/twist acceleration. The cable mounting locations on the + * stationary frame and on the end-effector are passed as parameters. + * @author Gerry Chen + */ + +#include "CableAccelerationFactor.h" + +#include +#include +#include + +#include +#include +#include + +using namespace gtsam; + +namespace gtdynamics { + +// Attention: This version currently ignores all rotational effects since +// they're relatively insignificant and it's just a pain to deal with them. +double CableAccelerationFactor::computeLddot( + const Pose3 &wTx, const Vector6 &Vx, const Vector6 &VAx, + boost::optional H_wTx, boost::optional H_Vx, + boost::optional H_VAx) const { + Matrix33 wAb_H_wRx; + Matrix33 wAb_H_xAb; + Matrix36 wAb_H_wTx; + Matrix36 wAb_H_VAx; + Matrix36 wPb_H_wTx; + Matrix33 dir_H_wPb; + Matrix13 H_dir; + Matrix13 H_wAb; + + // linear acceleration + Vector3 xAb = VAx.bottomRows<3>(); // acceleration of point b in x's frame + Vector3 wAb = wTx.rotation().rotate(xAb, H_wTx ? &wAb_H_wRx : 0, + H_VAx ? &wAb_H_xAb : 0); + if (H_wTx) wAb_H_wTx << wAb_H_wRx, Z_3x3; + if (H_VAx) wAb_H_VAx << Z_3x3, wAb_H_xAb; + // cable direction + Point3 wPb = wTx.transformFrom(xPb_, H_wTx ? &wPb_H_wTx : 0); + Vector3 dir = normalize(wPb - wPa_, H_wTx ? &dir_H_wPb : 0); + // lddot + double lddot = dot(dir, wAb, // + H_wTx ? &H_dir : 0, // + (H_wTx || H_VAx) ? &H_wAb : 0); + if (H_wTx) *H_wTx = H_dir * dir_H_wPb * wPb_H_wTx + H_wAb * wAb_H_wTx; + if (H_Vx) *H_Vx = Matrix16::Zero(); + if (H_VAx) *H_VAx = H_wAb * wAb_H_VAx; + return lddot; +} + +// below: pseudocode for full version +// static Vector3 compute_linear_acceleration_b(Pose3 x, Point3 xPe, Vector6 Vx, Vector6 VAx) { +// Vector6 VAe1 = xPe.inverse().AdjointMap() * VAx; +// Vector3 ae1 = VAe.bottom<3>(); + +// Vector6 Ve = xPe.inverse().AdjointMap() * Vx; +// Vector6 VAe2 = -Pose3::adjointMap(Ve, Ve); +// Vector3 ae2 = VAe2.bottom<3>(); + +// return ae1 + ae2; +// } + +} // namespace gtdynamics diff --git a/gtdynamics/cablerobot/tests/testCableAccelerationFactor.cpp b/gtdynamics/cablerobot/tests/testCableAccelerationFactor.cpp index 4adc76bc..4b541724 100644 --- a/gtdynamics/cablerobot/tests/testCableAccelerationFactor.cpp +++ b/gtdynamics/cablerobot/tests/testCableAccelerationFactor.cpp @@ -55,7 +55,9 @@ TEST(CableAccelerationFactor, error) { Vector3 rotaccel = 1.3 * normalize(Vector3(-0.15, 0, 0.15)); // rot of VA Vector3 coraccel = 0.9 * 0.9 * 0.15 * sqrt(2) * normalize(-eeLoc); // centripet - double expected_lddot = dot(dir, linaccel + rotaccel + coraccel); + // TODO(gerry): temporarily ignoring rotational effects because it's annoying + // and relatively insignificant + double expected_lddot = dot(dir, linaccel + 0 * rotaccel + 0 * coraccel); Vector1 expected_errors{JointAccel(values, jid) - expected_lddot}; // evaluateError From 1daaf242a86ab2a9f33c0777dbd680b95242d3e2 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Wed, 21 Apr 2021 23:56:04 -0400 Subject: [PATCH 41/73] wrap WinchFactor and CableAccelerationFactor --- gtdynamics/cablerobot/cablerobot.i | 25 +++++++++++++++++++++ gtdynamics/cablerobot/factors/WinchFactor.h | 10 ++++++++- 2 files changed, 34 insertions(+), 1 deletion(-) diff --git a/gtdynamics/cablerobot/cablerobot.i b/gtdynamics/cablerobot/cablerobot.i index 4b192718..de53a03f 100644 --- a/gtdynamics/cablerobot/cablerobot.i +++ b/gtdynamics/cablerobot/cablerobot.i @@ -45,4 +45,29 @@ gtsam::GaussianBayesNet* EliminateSequential(gtsam::GaussianFactorGraph graph, gtsam::GaussianBayesNet* BlockEliminateSequential( gtsam::GaussianFactorGraph graph, const gtdynamics::BlockOrdering &ordering); +#include +class WinchParams { + double radius_; + double inertia_; + double staticFriction_; + double viscousFriction_; + WinchParams(double radius = 1, double inertia = 0, double staticFriction = 0, + double viscousFriction = 0); +}; +class WinchFactor : gtsam::NonlinearFactor { + WinchFactor(gtsam::Key torque, gtsam::Key tension, gtsam::Key cableVelocity, + gtsam::Key cableAcceleration, + const gtsam::noiseModel::Base *cost_model, gtdynamics::WinchParams params); + void print(const string &s, const gtsam::KeyFormatter &keyFormatter); +}; + +#include +class CableAccelerationFactor : gtsam::NonlinearFactor { + CableAccelerationFactor(gtsam::Key ldot_key, gtsam::Key wTee_key, + gtsam::Key Vee_key, gtsam::Key VA_key, + const gtsam::noiseModel::Base *cost_model, + const gtsam::Point3 &wPb, const gtsam::Point3 &eePem); + void print(const string &s, const gtsam::KeyFormatter &keyFormatter); +}; + } // namespace gtdynamics diff --git a/gtdynamics/cablerobot/factors/WinchFactor.h b/gtdynamics/cablerobot/factors/WinchFactor.h index e812cfde..2a2e74e2 100644 --- a/gtdynamics/cablerobot/factors/WinchFactor.h +++ b/gtdynamics/cablerobot/factors/WinchFactor.h @@ -80,7 +80,7 @@ class WinchFactor WinchFactor(gtsam::Key torque, gtsam::Key tension, gtsam::Key cableVelocity, gtsam::Key cableAcceleration, const gtsam::noiseModel::Base::shared_ptr &cost_model, - WinchParams params) + const WinchParams ¶ms) : Base(cost_model, torque, tension, cableVelocity, cableAcceleration), params_(params) {} virtual ~WinchFactor() {} @@ -126,6 +126,14 @@ class WinchFactor gtsam::NonlinearFactor::shared_ptr(new This(*this))); } + /** print contents */ + void print(const std::string &s = "", + const gtsam::KeyFormatter &keyFormatter = + GTDKeyFormatter) const override { + std::cout << s << "winch factor" << std::endl; + Base::print("", keyFormatter); + } + private: /** Serialization function */ friend class boost::serialization::access; From 77bfd05ef86bbc94520e159bd304da2d2da94c51 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Thu, 22 Apr 2021 00:56:13 -0400 Subject: [PATCH 42/73] create and wrap TensionKey --- gtdynamics/cablerobot/cablerobot.i | 14 ++++++ gtdynamics/cablerobot/utils/CustomWrap.h | 57 ++++++++++++++++++++++++ 2 files changed, 71 insertions(+) diff --git a/gtdynamics/cablerobot/cablerobot.i b/gtdynamics/cablerobot/cablerobot.i index de53a03f..c12cb79d 100644 --- a/gtdynamics/cablerobot/cablerobot.i +++ b/gtdynamics/cablerobot/cablerobot.i @@ -45,6 +45,20 @@ gtsam::GaussianBayesNet* EliminateSequential(gtsam::GaussianFactorGraph graph, gtsam::GaussianBayesNet* BlockEliminateSequential( gtsam::GaussianFactorGraph graph, const gtdynamics::BlockOrdering &ordering); +// Tension Key Stuff +namespace cinternal { + gtdynamics::DynamicsSymbol TensionKey(int j, int t = 0); +} + +template +void InsertTension(gtsam::Values @values, int j, int t, T value); + +template +void InsertTension(gtsam::Values @values, int j, T value); + +template +T Tension(const gtsam::Values &values, int j, int t = 0); + #include class WinchParams { double radius_; diff --git a/gtdynamics/cablerobot/utils/CustomWrap.h b/gtdynamics/cablerobot/utils/CustomWrap.h index 6013505d..e03a980b 100644 --- a/gtdynamics/cablerobot/utils/CustomWrap.h +++ b/gtdynamics/cablerobot/utils/CustomWrap.h @@ -7,9 +7,13 @@ #pragma once +#include +#include + #include #include #include +#include namespace gtdynamics { @@ -24,4 +28,57 @@ gtsam::GaussianBayesNet::shared_ptr BlockEliminateSequential( gtsam::GaussianFactorGraph graph, const BlockOrdering& ordering); +///@name TensionKey +///@{ +namespace cinternal { + +/// Shorthand for t_j_k, for j-th tension at time step k. +inline DynamicsSymbol TensionKey(int j, int k = 0) { + return DynamicsSymbol::JointSymbol("t", j, k); +} + +} // namespace internal + +/** + * @brief Insert j-th tension at time k. + * + * @tparam T Tension type (default `double`). + * @param values Values pointer to insert tension into. + * @param j The joint id. + * @param k Time step. + * @param value The tension value. + */ +template +void InsertTension(gtsam::Values *values, int j, int k, T value) { + values->insert(cinternal::TensionKey(j, k), value); +} + +/** + * @brief Insert j-th tension at time 0. + * + * @tparam T Tension type (default `double`). + * @param values Values pointer to insert tension into. + * @param j The joint id. + * @param value The tension value. + */ +template +void InsertTension(gtsam::Values *values, int j, T value) { + values->insert(cinternal::TensionKey(j), value); +} + +/** + * @brief Retrieve j-th tension at time k. + * + * @tparam T Tension type (default is `double`). + * @param values Values dictionary containing the tension. + * @param j The joint id. + * @param k Time step. + * @return The tension. + */ +template +T Tension(const gtsam::Values &values, int j, int k = 0) { + return internal::at(values, cinternal::TensionKey(j, k)); +} +///@} + } // namespace gtdynamics From 7d19163c8d09d0ed2812f93daf8d1e6310e83e61 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Thu, 22 Apr 2021 00:57:54 -0400 Subject: [PATCH 43/73] cdpr with winch inertia passes unit tests :) (and squashed a bug ;) ) --- gtdynamics/cablerobot/src/cdpr_planar.py | 23 +++++++++++-------- gtdynamics/cablerobot/src/test_cdpr_planar.py | 16 ++++++++----- 2 files changed, 23 insertions(+), 16 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_planar.py b/gtdynamics/cablerobot/src/cdpr_planar.py index f89a925b..d4af3670 100644 --- a/gtdynamics/cablerobot/src/cdpr_planar.py +++ b/gtdynamics/cablerobot/src/cdpr_planar.py @@ -27,7 +27,11 @@ def __init__(self): self.gravity = np.zeros((3, 1)) self.tmin = 0.1 / (0.0254 / 2) # tension = torque / radius self.tmax = 1.2 / (0.0254 / 2) - self.motor_inertia = 0 + self.winch_params = gtd.WinchParams(inertia=0, + radius=1, + staticFriction=0, + viscousFriction=0) + class Cdpr: """Utility functions for a planar cable robot; mostly assembles together factors. @@ -146,7 +150,7 @@ def dynamics_factors(self, ks=[]): generalized version of F=ma and calculates wrenches from cable tensions. Primary variables: Torque <--> lengthddot Intermediate variables: Wrenches, TwistAccel - Prerequisite variables: Pose, Twist + Prerequisite variables: Pose, Twist, length, lengthdot Args: @@ -172,12 +176,12 @@ def dynamics_factors(self, ks=[]): for ji in range(4): dfg.push_back( gtd.CableTensionFactor( - gtd.internal.TensionKey(ji, k).key(), + gtd.cinternal.TensionKey(ji, k).key(), gtd.internal.PoseKey(self.ee_id(), k).key(), gtd.internal.WrenchKey(self.ee_id(), ji, k).key(), self.costmodel_torque, self.params.a_locs[ji], self.params.b_locs[ji])) dfg.push_back( - gtd.CableAccelFactor( + gtd.CableAccelerationFactor( gtd.internal.JointAccelKey(ji, k).key(), gtd.internal.PoseKey(self.ee_id(), k).key(), gtd.internal.TwistKey(self.ee_id(), k).key(), @@ -186,10 +190,10 @@ def dynamics_factors(self, ks=[]): dfg.push_back( gtd.WinchFactor( gtd.internal.TorqueKey(ji, k).key(), - gtd.internal.TensionKey(ji, k).key(), - gtd.internal.JointVelKey(ki, k).key(), - gtd.internal.JointAccelKey(ki, k).key(), - self.costmodel_winch, self.params.motor_inertia + gtd.cinternal.TensionKey(ji, k).key(), + gtd.internal.JointVelKey(ji, k).key(), + gtd.internal.JointAccelKey(ji, k).key(), + self.costmodel_winch, self.params.winch_params ) ) return dfg @@ -323,8 +327,7 @@ def priors_id(self, ks=[], lddotss=[[]], values=None): for ji, lddot in enumerate(lddots): graph.push_back( gtd.PriorFactorDouble( - gtd.internal.JointAccelKey(self.ee_id(), k).key(), lddot, - self.costmodel_prior_lddot)) + gtd.internal.JointAccelKey(ji, k).key(), lddot, self.costmodel_prior_lddot)) return graph def priors_id_va(self, ks=[], VAs=[], values=None): diff --git a/gtdynamics/cablerobot/src/test_cdpr_planar.py b/gtdynamics/cablerobot/src/test_cdpr_planar.py index 1bb38daa..3095ba1d 100644 --- a/gtdynamics/cablerobot/src/test_cdpr_planar.py +++ b/gtdynamics/cablerobot/src/test_cdpr_planar.py @@ -74,18 +74,17 @@ def testDynamicsInstantaneous(self): """Test dynamics factors: relates torque to wrench+twistaccel. Also tests ID solving """ cdpr = Cdpr() - cdpr.motor_inertia = 1.23 + cdpr.params.winch_params.inertia_ = 1.23 dfg = cdpr.dynamics_factors(ks=[0]) values = gtsam.Values() # things needed to define IK gtd.InsertPose(values, cdpr.ee_id(), 0, Pose3(Rot3(), (1.5, 0, 1.5))) gtd.InsertTwist(values, cdpr.ee_id(), 0, np.zeros(6)) - # gtd.InsertTwistAccel(values, cdpr.ee_id(), 0, (0, 0, 0, 0, 0, -np.sqrt(2))) # things needed to define ID for j, tau in zip(range(4), [1 + 1.23, 0 - 1.23, 0 - 1.23, 1 + 1.23]): gtd.InsertTorqueDouble(values, j, 0, tau) # things needed to define FD - for j, lddot in zip(range(4), [1, 0, 0, 1]): + for j, lddot in zip(range(4), [-1, 1, 1, -1]): gtd.InsertJointAccelDouble(values, j, 0, lddot) # things needed intermediaries gtd.InsertWrench(values, cdpr.ee_id(), 0, [0,0,0,1/np.sqrt(2),0,-1/np.sqrt(2)]) @@ -93,6 +92,10 @@ def testDynamicsInstantaneous(self): gtd.InsertWrench(values, cdpr.ee_id(), 2, [0,0,0,0,0,0]) gtd.InsertWrench(values, cdpr.ee_id(), 3, [0,0,0,-1/np.sqrt(2),0,-1/np.sqrt(2)]) gtd.InsertTwistAccel(values, cdpr.ee_id(), 0, (0, 0, 0, 0, 0, -np.sqrt(2))) + for ji, t in enumerate([1, 0, 0, 1]): + gtd.InsertTensionDouble(values, ji, 0, t) + for ji in range(4): + gtd.InsertJointVelDouble(values, ji, 0, 0) # for WinchFactor, ugh # check FD/ID is valid self.assertAlmostEqual(0.0, dfg.error(values), 10) # build ID graph @@ -108,11 +111,11 @@ def testDynamicsInstantaneous(self): # redundancy resolution dfg.push_back( gtd.PriorFactorDouble( - gtd.internal.TorqueKey(1, 0).key(), 0.0, + gtd.internal.TorqueKey(1, 0).key(), -1.23, gtsam.noiseModel.Unit.Create(1))) dfg.push_back( gtd.PriorFactorDouble( - gtd.internal.TorqueKey(2, 0).key(), 0.0, + gtd.internal.TorqueKey(2, 0).key(), -1.23, gtsam.noiseModel.Unit.Create(1))) # initialize and solve init = gtsam.Values(values) @@ -130,7 +133,7 @@ def testDynamicsCollocation(self): """Test dynamics factors across multiple timesteps by using collocation. """ cdpr = Cdpr() - cdpr.motor_inertia = 1.23 + cdpr.params.winch_params.inertia_ = 1.23 # kinematics fg = cdpr.kinematics_factors(ks=[0, 1, 2]) # dynamics @@ -152,6 +155,7 @@ def testDynamicsCollocation(self): gtd.InsertJointVelDouble(init, j, t, 1) gtd.InsertJointAccelDouble(init, j, t, 1) gtd.InsertTorqueDouble(init, j, t, 1) + gtd.InsertTensionDouble(init, j, t, 1) gtd.InsertWrench(init, cdpr.ee_id(), j, t, np.ones(6)) gtd.InsertPose(init, cdpr.ee_id(), t, Pose3(Rot3(), (1.5, 1, 1.5))) gtd.InsertTwist(init, cdpr.ee_id(), t, np.ones(6)) From fa05de8d2dc4da6776d6c2e820d049a70ede24e1 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Thu, 22 Apr 2021 01:16:50 -0400 Subject: [PATCH 44/73] update sim and iLQR to pass unit tests --- gtdynamics/cablerobot/src/cdpr_controller_ilqr.py | 13 ++++++++++--- gtdynamics/cablerobot/src/cdpr_planar_sim.py | 4 ++++ 2 files changed, 14 insertions(+), 3 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py index 552b5da6..43cb10be 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py @@ -122,6 +122,11 @@ def extract_bayesnets(cdpr, fg, openloop_results, N): ordering[-1].push_back(gtd.internal.JointAngleKey(ji, t).key()) for ji in range(4): ordering[-1].push_back(gtd.internal.JointVelKey(ji, t).key()) + for ji in range(4): + ordering[-1].push_back(gtd.internal.JointAccelKey(ji, t).key()) + for ji in range(4): + ordering[-1].push_back(gtd.cinternal.TensionKey(ji, t).key()) + ordering[-1].push_back(gtd.internal.TwistAccelKey(lid, t).key()) # immediate control variables ordering.append(gtsam.Ordering()) for ji in range(4): @@ -130,7 +135,6 @@ def extract_bayesnets(cdpr, fg, openloop_results, N): ordering.append(gtsam.Ordering()) for ji in range(4): ordering[-1].push_back(gtd.internal.WrenchKey(lid, ji, t).key()) - ordering[-1].push_back(gtd.internal.TwistAccelKey(lid, t).key()) # measurement inputs ordering.append(gtsam.Ordering()) ordering[-1].push_back(gtd.internal.TwistKey(lid, t).key()) @@ -163,8 +167,11 @@ def extract_gains(cdpr, fg, openloop_results, N): reversed(range(1, 4*N, 4)))): ucond = net.at(netu) icond = net.at(neti) - u_K_F = solve_triangular(ucond.R(), -ucond.S()[:, :24]) - u_K_p = solve_triangular(ucond.R(), -ucond.S()[:, 24:]) + if 0 in ucond.keys(): + u_K_F = solve_triangular(ucond.R(), -ucond.S()[:, 1:25]) + else: + u_K_F = solve_triangular(ucond.R(), -ucond.S()[:, :24]) + u_K_p = solve_triangular(ucond.R(), -ucond.S()[:, -6:]) F_K_x = solve_triangular(icond.R(), -icond.S())[:24, -12:] u_K_x = u_K_F @ F_K_x u_K_x[:, 6:] += u_K_p diff --git a/gtdynamics/cablerobot/src/cdpr_planar_sim.py b/gtdynamics/cablerobot/src/cdpr_planar_sim.py index 0408bfc3..6d812643 100644 --- a/gtdynamics/cablerobot/src/cdpr_planar_sim.py +++ b/gtdynamics/cablerobot/src/cdpr_planar_sim.py @@ -120,7 +120,11 @@ def update_dynamics(cdpr, x, u, k, dt): fg.push_back(cdpr.priors_fd(ks=[k], values=u)) # FD initial guess for ji in range(4): + gtd.InsertJointVelDouble(xd, ji, k, 0) + gtd.InsertJointAccelDouble(xd, ji, k, 0) gtd.InsertTorqueDouble(xd, ji, k, gtd.TorqueDouble(u, ji, k)) + gtd.InsertTensionDouble(xd, ji, k, + gtd.TorqueDouble(u, ji, k) / cdpr.params.winch_params.radius_) gtd.InsertWrench(xd, cdpr.ee_id(), ji, k, np.zeros(6)) gtd.InsertPose(xd, cdpr.ee_id(), k+1, gtsam.Pose3(gtsam.Rot3(), (1.5, 0, 1.5))) gtd.InsertTwist(xd, cdpr.ee_id(), k+1, np.zeros(6)) From b1a9259c62d73ab0993740f5dca93e5979e4d65d Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Thu, 22 Apr 2021 02:25:13 -0400 Subject: [PATCH 45/73] fix torque real values and plotting --- gtdynamics/cablerobot/src/cdpr_planar.py | 4 ++-- gtdynamics/cablerobot/src/draw_cdpr.py | 2 +- gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb | 11 +++++++---- gtdynamics/cablerobot/src/gerry02_traj_tracking.py | 4 +++- 4 files changed, 13 insertions(+), 8 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_planar.py b/gtdynamics/cablerobot/src/cdpr_planar.py index d4af3670..91567d83 100644 --- a/gtdynamics/cablerobot/src/cdpr_planar.py +++ b/gtdynamics/cablerobot/src/cdpr_planar.py @@ -25,8 +25,8 @@ def __init__(self): self.mass = 1.0 self.inertia = np.eye(3) self.gravity = np.zeros((3, 1)) - self.tmin = 0.1 / (0.0254 / 2) # tension = torque / radius - self.tmax = 1.2 / (0.0254 / 2) + self.tmin = 0.1 # minimum torque in N.m + self.tmax = 1.2 self.winch_params = gtd.WinchParams(inertia=0, radius=1, staticFriction=0, diff --git a/gtdynamics/cablerobot/src/draw_cdpr.py b/gtdynamics/cablerobot/src/draw_cdpr.py index 417d51c4..3c9d5117 100644 --- a/gtdynamics/cablerobot/src/draw_cdpr.py +++ b/gtdynamics/cablerobot/src/draw_cdpr.py @@ -79,7 +79,7 @@ def draw_ctrl(ax, cdpr, tensions, Tf, dt): ls_ctrl = ax.plot(np.arange(0,Tf,dt), tensions) ax.plot([0, Tf], [cdpr.params.tmin,]*2, 'r--') ax.plot([0, Tf], [cdpr.params.tmax,]*2, 'r--') - ax.set_xlabel('time (s)');ax.set_ylabel('Cable tension (N)');ax.set_title('Control Inputs') + ax.set_xlabel('time (s)');ax.set_ylabel('Motor torque (N.m)');ax.set_title('Control Inputs') ax.grid() return ls_ctrl, def redraw_ctrl(ls_ctrl, tensions, Tf, dt, N=None): diff --git a/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb b/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb index 1f695e87..5ebb4f67 100644 --- a/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb +++ b/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb @@ -10,7 +10,9 @@ "%load_ext autoreload\n", "%autoreload 2\n", "import numpy as np\n", + "import matplotlib.pyplot as plt\n", "%aimport -np\n", + "%aimport -plt\n", "\n", "from draw_cdpr import plot_all\n", "from gerry02_traj_tracking import main" @@ -25,8 +27,8 @@ }, "outputs": [], "source": [ - "cdpr, controller, result, N, dt, pdes = main(Q=np.ones(6)*1e3, R=np.ones(1)*1e-3, debug=False)\n", - "# cdpr, controller, result, N, dt, pdes = main(Q=np.ones(6)*1e3, R=np.ones(1)*1e-3, N0=9700, N=2000, dN=5, debug=False)" + "# cdpr, controller, result, N, dt, pdes = main(Q=np.ones(6)*1e3, R=np.ones(1)*1e-3, debug=False)\n", + "cdpr, controller, result, N, dt, pdes = main(Q=np.ones(6)*1e3, R=np.ones(1)*1e-3, N0=9700, dN=1, debug=False)" ] }, { @@ -36,13 +38,14 @@ "metadata": {}, "outputs": [], "source": [ - "anim = plot_all(cdpr, result, dt*N, dt, N, pdes, step=10);" + "anim = plot_all(cdpr, result, dt*N, dt, N, pdes, step=10);\n", + "plt.suptitle('Motor inertia: {:.1e} kg.m^2'.format(cdpr.params.winch_params.inertia_));" ] }, { "cell_type": "code", "execution_count": null, - "id": "broad-profit", + "id": "daily-visiting", "metadata": {}, "outputs": [], "source": [ diff --git a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py index c36490ab..c5a2c4fa 100644 --- a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py +++ b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py @@ -85,11 +85,13 @@ def main(fname='data/iros_logo_2.h', params.a_locs = np.array([[aw, 0, 0], [aw, 0, ah], [0, 0, ah], [0, 0, 0]]) params.b_locs = np.array([[bw, 0., -bh], [bw, 0., bh], [-bw, 0., bh], [-bw, 0, -bh]]) / 2 params.b_locs = params.b_locs - [0, 0, bh * 0.4] + params.winch_params.inertia_ = 9.26e-5 * 890 / 420 # https://bit.ly/3sOF2Wt + params.winch_params.radius_ = 0.0127 cdpr = Cdpr(params) # import data isPaints, colorinds, colorpalette, traj = ParseFile(fname) - N = len(traj) if N is None else N + N = len(traj) - N0 - 1 if N is None else N dt = 0.01 * dN # this is a hardcoded constant. TODO(gerry): include this in the .h file. N = int(N/dN) # scale time by dN N0 = int(N0/dN) From d618e654105384cfa4d5b3deb1dc98fd4d0accbd Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Thu, 22 Apr 2021 11:28:24 -0400 Subject: [PATCH 46/73] add functions to visualize gains --- gtdynamics/cablerobot/src/draw_cdpr.py | 2 +- gtdynamics/cablerobot/src/draw_controller.py | 155 ++++++++++++++++++ .../src/gerry02_traj_tracking.ipynb | 40 ++++- .../cablerobot/src/gerry02_traj_tracking.py | 7 +- 4 files changed, 195 insertions(+), 9 deletions(-) create mode 100644 gtdynamics/cablerobot/src/draw_controller.py diff --git a/gtdynamics/cablerobot/src/draw_cdpr.py b/gtdynamics/cablerobot/src/draw_cdpr.py index 3c9d5117..38a38185 100644 --- a/gtdynamics/cablerobot/src/draw_cdpr.py +++ b/gtdynamics/cablerobot/src/draw_cdpr.py @@ -92,7 +92,7 @@ def redraw_ctrl(ls_ctrl, tensions, Tf, dt, N=None): ls_ctrl[ji].set_data(np.arange(0,Tf,dt)[:N], tensions[:N, ji]) return *ls_ctrl, -def plot_all(cdpr, result, Tf, dt, N, x_des, step=1): +def plot_trajectory(cdpr, result, Tf, dt, N, x_des, step=1): """Animates the cdpr and controls in side-by-side subplots. Args: diff --git a/gtdynamics/cablerobot/src/draw_controller.py b/gtdynamics/cablerobot/src/draw_controller.py new file mode 100644 index 00000000..c3b434a2 --- /dev/null +++ b/gtdynamics/cablerobot/src/draw_controller.py @@ -0,0 +1,155 @@ +""" +GTDynamics Copyright 2021, Georgia Tech Research Corporation, +Atlanta, Georgia 30332-0415 +All Rights Reserved +See LICENSE for the license information + +@file draw_cdpr.py +@brief Utility functions for drawing a cable robot's controller using opencv +@author Frank Dellaert +@author Gerry Chen +""" + +import gtdynamics as gtd +import gtsam +import matplotlib.pyplot as plt +import matplotlib.animation as animation +import numpy as np +import cv2 +from gtsam import Pose3, Rot3 + +from cdpr_planar import Cdpr +from cdpr_controller_ilqr import CdprControllerIlqr +from draw_cdpr import pose32xy, a_coords, b_coords, ab_coords + +COLORS = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (127, 127, 127)] + +def x2img(x): + return x * 130 + 5 +def img2x(img): + return (img - 5) / 130 +def condition(pts): + """Rescales and reshapes a 2xN numpy matrix to be passed into cv2.polylines + """ + return x2img(pts.T.reshape((-1, 1, 2))).astype(np.int32) + +def draw_cdpr(img, cdpr, x): + """Draws the CDPR in the specified axis. + Args: + ax (plt.Axes): matplotlib axis object + cdpr (Cdpr): cable robot object + x (gtsam.Pose3): current pose of the end effector + + Returns: + tuple(): matplotlib line objects: + l_a - the frame + l_b - the end effector + ls_ab - a 4-list containing the 4 cable lines + """ + cv2.polylines(img, [condition(a_coords(cdpr))], False, (0, 0, 0)) + cv2.polylines(img, [condition(b_coords(cdpr, x))], False, (202, 164, 114)) + for ji in range(4): + cv2.polylines(img, [condition(ab_coords(cdpr, x, ji))], False, COLORS[ji]) + # ax.set_xlabel('x(m)');ax.set_ylabel('y(m)');ax.set_title('Trajectory');ax.grid() + return img + +def draw_traj(img, cdpr, controller, act_xy, N): + """Draws the desired and actual x/y trajectories""" + img = img.astype(np.float) + kw2 = 81 + kw = 40 + for k in range(N): + K, uff, Vff, Tff = controller.gains_ff[k] + K = np.abs(K) / 400 * 7 + # y, x = act_xy[k, 0, :] + y, x = x2img(Tff.translation()[[0, 2]]).astype(np.int) + for ci in range(4): + patch = np.zeros((kw2, kw2, 1)) + patch[kw, kw] = 1 + patch[:, :, 0] = cv2.GaussianBlur(patch, (kw2, kw2), sigmaX=K[ci, 9], sigmaY=K[ci, 11]) * (4-ci) + alph_src = img[x - kw:x + kw + 1, y - kw:y + kw + 1, 3:] + img_src = img[x - kw:x + kw + 1, y - kw:y + kw + 1, :3] + alph_dst = alph_src * (1 - patch) + patch + tmp = (img_src * alph_src) + img[x - kw:x + kw + 1, y - kw:y + kw + 1, :3] = ( + img_src * alph_src * (1 - patch) + COLORS[ci] * patch) / alph_dst + img[x - kw:x + kw + 1, y - kw:y + kw + 1, 3:] = alph_dst + img[:, :, 3] = img[:, :, 3]*255 + img = img.astype(np.uint8) + imgtraj = cv2.polylines(np.zeros(img.shape[:2]), [act_xy], False, (1)) + img[imgtraj!=0, :3] = 0 + return img + +def draw_controller_one(img, cdpr, controller: CdprControllerIlqr, k, cablei): + """Draws the controller torque response for every point x in the space, at time step k""" + xvals = img2x(np.arange(0, img.shape[0])) + yvals = img2x(np.arange(0, img.shape[1])) + K, uff, Vff, Tff = controller.gains_ff[k] + K = K * 100 # this needs to get scaled with the square of dN + dx = np.zeros((12, *img.shape[:-1])) + dx[9, :] = (xvals - Tff.translation()[0]).reshape((-1, 1)) + dx[11, :] = (yvals - Tff.translation()[2]).reshape((1, -1)) + uvals = np.einsum('i,ijk->jk', K[cablei, :], dx) + uff[cablei] + uvals = (uvals + 500) / 1000 + for ci in range(3): + img[:, :, ci] = np.uint8(uvals * COLORS[cablei][ci]) + return img + + +def draw_controller_anim(cdpr: Cdpr, + controller: CdprControllerIlqr, + result: gtsam.Values, + N, + step=1): + """Animates the cdpr and feedback gains in side-by-side subplots. + + Args: + cdpr (Cdpr): cable robot object + controller (CdprControllerIlqr): cable robot controller object + result (gtsam.Values): the data from the simulation including poses and tensions + N (int): number of discrete samples + step (int, optional): number of time steps to update the animation by each time. 1 doesn't skip any frames, and e.g. 10 would skip 9 frames at a time and update at a period of 10*dt. Defaults to 1. + + Returns: + matplotlib.animation.FuncAnimation: a matplotlib animation object + """ + # extract useful variables as lists + act_T = [gtd.Pose(result, cdpr.ee_id(), k) for k in range(N+1)] + act_xy = np.array([pose32xy(pose) for pose in act_T]).T + w, h = np.max(cdpr.params.a_locs, axis=0)[[0, 2]] + + # plot + def get_imgs(k): + if k == 0: + k = N - 20 + img_cdpr = 255 * np.ones((int(x2img(h)+5), int(x2img(w)+5), 3), dtype=np.uint8) + cv2.polylines(img_cdpr, [condition(act_xy[:, :k])], False, (0, 0, 0)) + draw_cdpr(img_cdpr, cdpr, act_T[k]) + img_cdpr = img_cdpr + imgs = [] + for i in range(4): + img = np.zeros((int(x2img(h)+5), int(x2img(w)+5), 3), dtype=np.uint8) + draw_controller_one(img, cdpr, controller, k, i) + cv2.polylines(img_cdpr, [condition(act_xy[:, :k])], False, (0, 0, 0)) + draw_cdpr(img, cdpr, act_T[k]) + imgs.append(img) + img_traj = 255 * np.ones((int(x2img(h)+5), int(x2img(w)+5), 4), dtype=np.uint8) + img_traj[:, :, 3] = 1 + img_traj = draw_traj(img_traj, cdpr, controller, condition(act_xy).astype(np.int), N) + return img_cdpr, imgs, img_traj + + fig, axes = plt.subplots(1, 5, figsize=(18, 3)) + def plot_imgs(axes, k): + img, imgs, _ = get_imgs(k) + axes[0].imshow(img, origin='lower') + for i in range(4): + axes[i+1].imshow(imgs[i], origin='lower') + plot_imgs(axes, N - 1) + def update_line(k): + plot_imgs(axes, k) + return [] + return animation.FuncAnimation(fig, + update_line, + frames=range(0, N, step), + interval=0.01 * step * 1e3, + blit=True) diff --git a/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb b/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb index 5ebb4f67..426e7b74 100644 --- a/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb +++ b/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb @@ -11,11 +11,14 @@ "%autoreload 2\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "from IPython.display import Image, display\n", "%aimport -np\n", "%aimport -plt\n", + "%aimport -display\n", "\n", - "from draw_cdpr import plot_all\n", - "from gerry02_traj_tracking import main" + "from draw_cdpr import plot_trajectory\n", + "from draw_controller import draw_controller_anim\n", + "from gerry02_traj_tracking import main, plot" ] }, { @@ -28,17 +31,20 @@ "outputs": [], "source": [ "# cdpr, controller, result, N, dt, pdes = main(Q=np.ones(6)*1e3, R=np.ones(1)*1e-3, debug=False)\n", - "cdpr, controller, result, N, dt, pdes = main(Q=np.ones(6)*1e3, R=np.ones(1)*1e-3, N0=9700, dN=1, debug=False)" + "cdpr, controller, result, N, dt, pdes = main(Q=np.ones(6)*1e3, R=np.ones(1)*1e-3, N0=9700, dN=10, debug=False)" ] }, { "cell_type": "code", "execution_count": null, "id": "generic-calibration", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ - "anim = plot_all(cdpr, result, dt*N, dt, N, pdes, step=10);\n", + "# Plot Trajectory\n", + "anim = plot_trajectory(cdpr, result, dt*N, dt, N, pdes, step=10);\n", "plt.suptitle('Motor inertia: {:.1e} kg.m^2'.format(cdpr.params.winch_params.inertia_));" ] }, @@ -49,6 +55,7 @@ "metadata": {}, "outputs": [], "source": [ + "# Animate Trajectory\n", "import matplotlib\n", "matplotlib.rcParams['animation.embed_limit'] = 25.0\n", "from IPython.display import HTML\n", @@ -61,6 +68,29 @@ "id": "joint-batch", "metadata": {}, "outputs": [], + "source": [ + "# Plot Controller Gains\n", + "anim2 = draw_controller_anim(cdpr, controller, result, N, step=50);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "expired-inclusion", + "metadata": {}, + "outputs": [], + "source": [ + "# Animate Controller Gains\n", + "from IPython.display import HTML\n", + "HTML(anim2.to_html5_video())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "other-calvin", + "metadata": {}, + "outputs": [], "source": [] } ], diff --git a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py index c5a2c4fa..58402773 100644 --- a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py +++ b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py @@ -20,7 +20,8 @@ from cdpr_controller_ilqr import CdprControllerIlqr from cdpr_planar_sim import CdprSimulator from paint_parse import ParseFile -from draw_cdpr import plot_all +from draw_cdpr import plot_trajectory +from draw_controller import draw_controller_anim import cProfile from pstats import SortKey @@ -131,8 +132,8 @@ def main(fname='data/iros_logo_2.h', def plot(cdpr, controller, result, N, dt, des_T): """Plots the results""" - plot_all(cdpr, result, dt*N, dt, N, des_T, step=1) + plot_trajectory(cdpr, result, dt*N, dt, N, des_T, step=1) if __name__ == '__main__': - cProfile.run('results = main()', sort=SortKey.TIME) + cProfile.run('results = main(N=100)', sort=SortKey.TIME) plot(*results) From 45599ad581d60bd60ead05138228c9eed3537a7b Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Mon, 26 Apr 2021 13:26:50 -0400 Subject: [PATCH 47/73] update "zeroValues" to include joint accel and tension --- gtdynamics/cablerobot/src/utils.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/gtdynamics/cablerobot/src/utils.py b/gtdynamics/cablerobot/src/utils.py index bf58ecb0..e1246d91 100644 --- a/gtdynamics/cablerobot/src/utils.py +++ b/gtdynamics/cablerobot/src/utils.py @@ -31,6 +31,8 @@ def zerovalues(lid, ts=[], dt=0.01): for j in range(4): gtd.InsertJointAngleDouble(zero, j, t, 0) gtd.InsertJointVelDouble(zero, j, t, 0) + gtd.InsertJointAccelDouble(zero, j, t, 0) + gtd.InsertTensionDouble(zero, j, t, 0) gtd.InsertTorqueDouble(zero, j, t, 0) gtd.InsertWrench(zero, lid, j, t, np.zeros(6)) gtd.InsertPose(zero, lid, t, gtsam.Pose3(gtsam.Rot3(), (1.5, 0, 1.5))) From c1173faec96391741fd457860a9f73af8b57449b Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Mon, 26 Apr 2021 13:27:28 -0400 Subject: [PATCH 48/73] write out gains to .h file --- .../src/data/iros_logo_2_controller.h | 2747 +++++++++++++++++ .../src/gerry02_traj_tracking.ipynb | 12 +- .../cablerobot/src/gerry02_traj_tracking.py | 5 +- gtdynamics/cablerobot/src/paint_parse.py | 25 + 4 files changed, 2787 insertions(+), 2 deletions(-) create mode 100644 gtdynamics/cablerobot/src/data/iros_logo_2_controller.h diff --git a/gtdynamics/cablerobot/src/data/iros_logo_2_controller.h b/gtdynamics/cablerobot/src/data/iros_logo_2_controller.h new file mode 100644 index 00000000..af4924a5 --- /dev/null +++ b/gtdynamics/cablerobot/src/data/iros_logo_2_controller.h @@ -0,0 +1,2747 @@ +// u = K * ([v;x]-[vff;xff]) + uff +float xffs[][2] = { + {1.064800, 1.284800}, + {1.064800, 1.284800}, + {0.985602, 1.297600}, + {0.946400, 1.303200}, + {0.906400, 1.309600}, + {0.866400, 1.316000}, + {0.827200, 1.322400}, + {0.787200, 1.328800}, + {0.748000, 1.335200}, + {0.708000, 1.340800}, + {0.668000, 1.347200}, + {0.628800, 1.353600}, + {0.588800, 1.360000}, + {0.548800, 1.366400}, + {0.509600, 1.372800}, + {0.469600, 1.379200}, + {0.430400, 1.384800}, + {0.402401, 1.381599}, + {0.402400, 1.340800}, + {0.402400, 1.300000}, + {0.403201, 1.260801}, + {0.443200, 1.260800}, + {0.484000, 1.260800}, + {0.524801, 1.260800}, + {0.564799, 1.260801}, + {0.564801, 1.300800}, + {0.564801, 1.341600}, + {0.564800, 1.382399}, + {0.532001, 1.389600}, + {0.491201, 1.389600}, + {0.451200, 1.389600}, + {0.410401, 1.389600}, + {0.403200, 1.360800}, + {0.404800, 1.319200}, + {0.406401, 1.277600}, + {0.408001, 1.236800}, + {0.408800, 1.195200}, + {0.403201, 1.164799}, + {0.400801, 1.119200}, + {0.404800, 1.080800}, + {0.404001, 1.040800}, + {0.410401, 1.001600}, + {0.431200, 0.967200}, + {0.462400, 0.940000}, + {0.498401, 0.922400}, + {0.538400, 0.913600}, + {0.565600, 0.932800}, + {0.564801, 0.973600}, + {0.564800, 1.013600}, + {0.564800, 1.053600}, + {0.564800, 1.094400}, + {0.565601, 1.133600}, + {0.561600, 1.173599}, + {0.521601, 1.176800}, + {0.481600, 1.176800}, + {0.441600, 1.176800}, + {0.412802, 1.171199}, + {0.439200, 1.140800}, + {0.466400, 1.110400}, + {0.492800, 1.080000}, + {0.520000, 1.048800}, + {0.546400, 1.018400}, + {0.573600, 0.988000}, + {0.600000, 0.957600}, + {0.627200, 0.926400}, + {0.657600, 0.914400}, + {0.697600, 0.914400}, + {0.737600, 0.914400}, + {0.777600, 0.914400}, + {0.796000, 0.936800}, + {0.796000, 0.976800}, + {0.796000, 1.016800}, + {0.813600, 1.039200}, + {0.853600, 1.039200}, + {0.894400, 1.039200}, + {0.934400, 1.039200}, + {0.974400, 1.039200}, + {1.016000, 1.039200}, + {1.043200, 1.020000}, + {1.042400, 0.980000}, + {1.042400, 0.940000}, + {1.060000, 0.916800}, + {1.100000, 0.916800}, + {1.140000, 0.916800}, + {1.180000, 0.916800}, + {1.208000, 0.928801}, + {1.209600, 0.969600}, + {1.210400, 1.011200}, + {1.204800, 1.050400}, + {1.188800, 1.084800}, + {1.180001, 1.111200}, + {1.201599, 1.143200}, + {1.208800, 1.183200}, + {1.208800, 1.224800}, + {1.208000, 1.263200}, + {1.205600, 1.304000}, + {1.192000, 1.340800}, + {1.163200, 1.369600}, + {1.127200, 1.384800}, + {1.088000, 1.389600}, + {1.047200, 1.389600}, + {1.007200, 1.389600}, + {0.967200, 1.389600}, + {0.927200, 1.389600}, + {0.887200, 1.389600}, + {0.846400, 1.389600}, + {0.806400, 1.389600}, + {0.766400, 1.389600}, + {0.726400, 1.389600}, + {0.686400, 1.389600}, + {0.645601, 1.388000}, + {0.631200, 1.356800}, + {0.632000, 1.312000}, + {0.635201, 1.273600}, + {0.665600, 1.257600}, + {0.708800, 1.259200}, + {0.747200, 1.260800}, + {0.787200, 1.260800}, + {0.827200, 1.260800}, + {0.868000, 1.260800}, + {0.908000, 1.260800}, + {0.948000, 1.260800}, + {0.987200, 1.261600}, + {1.028800, 1.259200}, + {1.052800, 1.232000}, + {1.047200, 1.192000}, + {1.016800, 1.175200}, + {0.971200, 1.175200}, + {0.933600, 1.176800}, + {0.893600, 1.177600}, + {0.853600, 1.177600}, + {0.812800, 1.178400}, + {0.772800, 1.179200}, + {0.732800, 1.179200}, + {0.694400, 1.180800}, + {0.650401, 1.180000}, + {0.633600, 1.152800}, + {0.635200, 1.111200}, + {0.635200, 1.070400}, + {0.636000, 1.030400}, + {0.636800, 0.990400}, + {0.637600, 0.950400}, + {0.637601, 0.914401}, + {0.671200, 0.936799}, + {0.704800, 0.959200}, + {0.738400, 0.981600}, + {0.772000, 1.004000}, + {0.805600, 1.026400}, + {0.839200, 1.048800}, + {0.872800, 1.071200}, + {0.906400, 1.093600}, + {0.940000, 1.116000}, + {0.973600, 1.138400}, + {1.007200, 1.160800}, + {1.040800, 1.183200}, + {1.074400, 1.204800}, + {1.108000, 1.227200}, + {1.141600, 1.249600}, + {1.175200, 1.272000}, + {1.208800, 1.294400}, + {1.242400, 1.316800}, + {1.276000, 1.339200}, + {1.309601, 1.361600}, + {1.343199, 1.384000}, + {1.323200, 1.381600}, + {1.287200, 1.359200}, + {1.265600, 1.328000}, + {1.255200, 1.291200}, + {1.252800, 1.250400}, + {1.254400, 1.209600}, + {1.255200, 1.169600}, + {1.253600, 1.129600}, + {1.253600, 1.089600}, + {1.256000, 1.048800}, + {1.256800, 1.008800}, + {1.265600, 0.972000}, + {1.292800, 0.940000}, + {1.328000, 0.922400}, + {1.367200, 0.916000}, + {1.408000, 0.914400}, + {1.448000, 0.914400}, + {1.488000, 0.914400}, + {1.528000, 0.914400}, + {1.568000, 0.914400}, + {1.608000, 0.914400}, + {1.648800, 0.914400}, + {1.688000, 0.913600}, + {1.728800, 0.913600}, + {1.768800, 0.915200}, + {1.808000, 0.923200}, + {1.842400, 0.943200}, + {1.868000, 0.973600}, + {1.878400, 1.011200}, + {1.877600, 1.052800}, + {1.877600, 1.092000}, + {1.877600, 1.132800}, + {1.877600, 1.172800}, + {1.877600, 1.212800}, + {1.877600, 1.252800}, + {1.876800, 1.293600}, + {1.867200, 1.331200}, + {1.841600, 1.363200}, + {1.806400, 1.381600}, + {1.768000, 1.388800}, + {1.728000, 1.389600}, + {1.687200, 1.389600}, + {1.647200, 1.389600}, + {1.607200, 1.389600}, + {1.567200, 1.389600}, + {1.526400, 1.389600}, + {1.486400, 1.389600}, + {1.446400, 1.389600}, + {1.406399, 1.391200}, + {1.365601, 1.390400}, + {1.368800, 1.381600}, + {1.407200, 1.367200}, + {1.444800, 1.352800}, + {1.483200, 1.338400}, + {1.520800, 1.324000}, + {1.559200, 1.309600}, + {1.596800, 1.296000}, + {1.635200, 1.281600}, + {1.672800, 1.267200}, + {1.707200, 1.253600}, + {1.723200, 1.220000}, + {1.724000, 1.179200}, + {1.723200, 1.139200}, + {1.723200, 1.099200}, + {1.716799, 1.060000}, + {1.680000, 1.048800}, + {1.639200, 1.050400}, + {1.599200, 1.050400}, + {1.559200, 1.050400}, + {1.518400, 1.050400}, + {1.479200, 1.049600}, + {1.437600, 1.050400}, + {1.409600, 1.072800}, + {1.408800, 1.112800}, + {1.408800, 1.153600}, + {1.408800, 1.193600}, + {1.408800, 1.233600}, + {1.436000, 1.255200}, + {1.479200, 1.254400}, + {1.518400, 1.253600}, + {1.558400, 1.253600}, + {1.599200, 1.253600}, + {1.639200, 1.253600}, + {1.680000, 1.256800}, + {1.714400, 1.260800}, + {1.751200, 1.278400}, + {1.788000, 1.296000}, + {1.824800, 1.313600}, + {1.861600, 1.330400}, + {1.898400, 1.348000}, + {1.936001, 1.365600}, + {1.972799, 1.383200}, + {1.961600, 1.380000}, + {1.936000, 1.349600}, + {1.923200, 1.311200}, + {1.920000, 1.271200}, + {1.920000, 1.231200}, + {1.920800, 1.191200}, + {1.927200, 1.152800}, + {1.946400, 1.117600}, + {1.979200, 1.096800}, + {2.020000, 1.092800}, + {2.060000, 1.092800}, + {2.100000, 1.092800}, + {2.140000, 1.092800}, + {2.180000, 1.092800}, + {2.220000, 1.092800}, + {2.257600, 1.093600}, + {2.302400, 1.096000}, + {2.338399, 1.083999}, + {2.339199, 1.047201}, + {2.304800, 1.033600}, + {2.261600, 1.034400}, + {2.223200, 1.034400}, + {2.183200, 1.034400}, + {2.143200, 1.034400}, + {2.103200, 1.034400}, + {2.063200, 1.034400}, + {2.023200, 1.034400}, + {1.983200, 1.034400}, + {1.943200, 1.034400}, + {1.920000, 1.012800}, + {1.931200, 0.974400}, + {1.951200, 0.939200}, + {1.984000, 0.917600}, + {2.023200, 0.912800}, + {2.064000, 0.913600}, + {2.103200, 0.914400}, + {2.143200, 0.914400}, + {2.183200, 0.914400}, + {2.224000, 0.914400}, + {2.264000, 0.914400}, + {2.303200, 0.913600}, + {2.344000, 0.912000}, + {2.384800, 0.912000}, + {2.424000, 0.917600}, + {2.463199, 0.936000}, + {2.487200, 0.964000}, + {2.498400, 1.000000}, + {2.501600, 1.039200}, + {2.501600, 1.080800}, + {2.500000, 1.120800}, + {2.491999, 1.159200}, + {2.471999, 1.192800}, + {2.437600, 1.215200}, + {2.398400, 1.220000}, + {2.358400, 1.220000}, + {2.318400, 1.220000}, + {2.278400, 1.220800}, + {2.237600, 1.221600}, + {2.197600, 1.222400}, + {2.160799, 1.221600}, + {2.115200, 1.220000}, + {2.077600, 1.231200}, + {2.076800, 1.262400}, + {2.114400, 1.272000}, + {2.160799, 1.270400}, + {2.197600, 1.269600}, + {2.237600, 1.269600}, + {2.277600, 1.269600}, + {2.317600, 1.269600}, + {2.357600, 1.269600}, + {2.397600, 1.269600}, + {2.436800, 1.267200}, + {2.479999, 1.269600}, + {2.490400, 1.301600}, + {2.479200, 1.340000}, + {2.455199, 1.370400}, + {2.420000, 1.388000}, + {2.380800, 1.392800}, + {2.340800, 1.393600}, + {2.300000, 1.392800}, + {2.259200, 1.391200}, + {2.219200, 1.389600}, + {2.180000, 1.389600}, + {2.140000, 1.391200}, + {2.099201, 1.392800}, + {2.058399, 1.393600}, +}; +float vffs[][2] = { + {0.000000, 0.000000}, + {-0.791980, 0.127997}, + {-0.392025, 0.056004}, + {-0.399995, 0.063999}, + {-0.400000, 0.064000}, + {-0.392000, 0.064000}, + {-0.399999, 0.064000}, + 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0.430803, 0.669134, 0.790079}, + {0.604132, 0.449798, 0.670756, 0.782670}, + {0.605869, 0.468707, 0.672683, 0.774529}, + {0.606961, 0.487488, 0.674883, 0.765654}, + {0.608217, 0.505183, 0.675808, 0.757387}, + {0.606484, 0.525418, 0.681454, 0.744379}, + {0.606925, 0.542259, 0.682479, 0.734908}, + {0.605662, 0.560079, 0.685450, 0.723173}, + {0.603861, 0.577544, 0.688530, 0.710750}, + {0.601543, 0.594606, 0.691688, 0.697660}, + {0.598729, 0.611221, 0.694891, 0.683929}, + {0.595440, 0.627349, 0.698105, 0.669585}, + {0.591711, 0.642924, 0.701317, 0.654657}, + {0.560449, 0.508137, 0.776834, 0.738556}, + {0.593605, 0.612098, 0.711784, 0.673499}, + {0.609588, 0.688855, 0.667244, 0.626529}, + {0.605187, 0.671041, 0.675709, 0.641123}, + {0.609491, 0.649386, 0.676526, 0.659000}, + {0.613954, 0.628005, 0.678622, 0.673488}, + {0.624370, 0.599898, 0.673757, 0.693535}, + {0.640596, 0.576978, 0.658202, 0.711020}, + {0.661347, 0.567412, 0.632197, 0.719823}, + {0.678084, 0.548290, 0.610533, 0.732697}, + {0.685824, 0.521053, 0.601280, 0.746437}, + {0.709355, 0.525832, 0.564332, 0.742447}, + {0.733652, 0.530959, 0.523877, 0.737508}, + {0.723623, 0.502212, 0.539302, 0.744679}, + {0.734393, 0.474053, 0.516392, 0.757594}, + {0.746310, 0.453412, 0.491468, 0.768298}, + {0.752365, 0.444935, 0.479355, 0.772573}, + {0.759698, 0.449527, 0.466627, 0.769939}, + {0.763788, 0.455969, 0.459910, 0.766147}, + {0.767684, 0.462573, 0.453375, 0.762177}, + {0.771412, 0.469372, 0.446988, 0.758002}, + {0.775973, 0.478083, 0.438890, 0.752590}, + {0.777433, 0.478862, 0.436950, 0.751683}, + {0.782862, 0.496197, 0.425771, 0.741055}, + {0.782307, 0.500137, 0.426149, 0.738331}, + {0.779591, 0.518095, 0.427012, 0.728268}, + {0.770514, 0.531466, 0.439369, 0.722000}, + {0.769512, 0.535103, 0.444879, 0.719662}, + {0.766134, 0.537711, 0.461424, 0.717861}, + {0.772560, 0.563147, 0.458660, 0.700016}, + {0.789748, 0.595678, 0.437758, 0.674894}, + {0.774785, 0.623763, 0.465663, 0.657236}, + {0.769988, 0.638250, 0.481647, 0.644609}, + {0.758933, 0.658417, 0.501827, 0.627929}, + {0.748396, 0.675440, 0.520179, 0.611935}, + {0.734745, 0.691312, 0.540950, 0.595715}, + {0.721055, 0.684321, 0.573190, 0.592929}, + {0.704426, 0.680885, 0.605425, 0.587044}, + {0.710366, 0.689653, 0.599798, 0.574675}, + {0.695627, 0.712222, 0.606728, 0.556674}, + {0.674447, 0.724199, 0.623723, 0.548915}, + {0.655031, 0.731884, 0.639256, 0.545968}, + {0.647734, 0.731281, 0.645116, 0.550897}, + {0.639676, 0.724344, 0.654920, 0.559850}, + {0.632698, 0.719022, 0.662327, 0.567442}, + {0.624925, 0.711994, 0.671130, 0.575727}, + {0.619824, 0.709787, 0.674328, 0.581083}, + {0.611987, 0.702418, 0.682636, 0.589178}, + {0.602657, 0.698845, 0.690939, 0.593671}, + {0.601103, 0.685857, 0.693238, 0.607619}, + {0.652423, 0.769735, 0.597875, 0.572944}, + {0.632150, 0.748073, 0.625778, 0.586724}, + {0.587762, 0.673940, 0.704781, 0.620875}, + {0.603133, 0.678107, 0.690778, 0.619326}, + {0.614479, 0.675085, 0.683173, 0.621433}, + {0.629324, 0.678896, 0.668249, 0.619704}, + {0.638916, 0.676882, 0.660987, 0.620458}, + {0.655650, 0.678105, 0.642995, 0.620642}, + {0.664319, 0.676438, 0.635329, 0.620702}, + {0.672246, 0.673127, 0.628642, 0.621730}, + {0.700189, 0.609388, 0.623071, 0.665124}, + {0.693693, 0.636743, 0.619999, 0.643994}, + {0.709879, 0.665757, 0.580612, 0.628902}, + {0.727315, 0.651669, 0.554931, 0.643300}, + {0.730123, 0.620619, 0.553806, 0.666844}, + {0.674385, 0.603794, 0.627739, 0.681910}, + {0.736394, 0.606540, 0.526410, 0.681547}, + {0.760938, 0.576291, 0.495532, 0.700973}, + {0.752993, 0.570763, 0.507791, 0.706456}, + {0.747593, 0.561732, 0.516579, 0.713642}, + {0.746879, 0.558109, 0.516826, 0.717502}, + {0.734250, 0.546539, 0.536543, 0.725255}, + {0.724413, 0.606889, 0.540523, 0.687355}, + {0.740784, 0.623006, 0.513433, 0.680432}, + {0.718433, 0.545267, 0.559352, 0.729758}, + {0.706312, 0.561868, 0.579568, 0.720627}, + {0.691118, 0.583644, 0.601279, 0.706089}, + {0.732729, 0.634874, 0.534070, 0.686246}, + {0.709944, 0.623575, 0.570383, 0.690833}, + {0.651753, 0.626807, 0.651849, 0.668666}, + {0.663402, 0.645120, 0.635323, 0.655432}, + {0.671018, 0.649961, 0.627172, 0.650456}, + {0.675366, 0.653083, 0.623767, 0.645568}, + {0.678485, 0.667977, 0.617327, 0.632362}, + {0.680139, 0.656080, 0.622886, 0.636762}, + {0.674399, 0.702558, 0.613104, 0.601305}, + {0.697150, 0.682563, 0.595969, 0.612625}, + {0.696285, 0.695094, 0.596038, 0.597425}, + {0.696913, 0.705814, 0.594670, 0.582712}, + {0.693555, 0.720086, 0.595507, 0.564715}, + {0.695686, 0.731018, 0.590793, 0.548516}, + {0.693935, 0.738597, 0.592361, 0.533461}, + {0.687656, 0.628107, 0.681696, 0.594923}, + {0.734079, 0.686799, 0.592624, 0.543532}, + {0.721891, 0.766143, 0.547249, 0.495131}, + {0.713253, 0.760129, 0.556058, 0.512216}, + {0.715496, 0.738370, 0.560676, 0.538128}, + {0.725922, 0.720811, 0.550460, 0.558890}, + {0.740337, 0.718515, 0.523350, 0.568180}, + {0.756598, 0.719620, 0.489772, 0.574073}, + {0.749709, 0.728412, 0.481594, 0.576102}, + {0.750879, 0.712827, 0.477250, 0.589909}, + {0.771029, 0.670877, 0.466857, 0.611268}, + {0.783471, 0.671898, 0.447690, 0.602331}, + {0.787411, 0.681267, 0.436245, 0.588388}, + {0.791174, 0.690768, 0.423972, 0.573191}, + {0.794702, 0.700336, 0.410778, 0.556610}, + {0.794377, 0.708032, 0.403659, 0.540977}, + {0.803665, 0.732956, 0.363178, 0.508559}, + {0.823430, 0.685144, 0.378426, 0.518660}, + {0.827262, 0.638577, 0.425012, 0.530770}, + {0.736496, 0.748951, 0.439962, 0.484839}, + {0.776036, 0.753895, 0.350191, 0.470720}, + {0.816526, 0.725571, 0.312971, 0.489634}, + {0.808928, 0.707999, 0.349474, 0.522773}, + {0.808546, 0.699931, 0.359788, 0.541559}, + {0.806314, 0.689084, 0.373971, 0.561391}, + {0.803679, 0.678210, 0.387102, 0.579463}, + {0.800732, 0.667426, 0.399280, 0.595934}, + {0.797545, 0.656816, 0.410607, 0.610953}, + {0.794175, 0.646444, 0.421169, 0.624666}, + {0.842570, 0.620817, 0.363454, 0.634044}, + {0.849739, 0.652599, 0.333538, 0.619989}, + {0.793396, 0.631809, 0.416770, 0.649164}, + {0.789629, 0.642058, 0.397946, 0.646791}, + {0.784665, 0.622801, 0.396239, 0.660453}, + {0.800394, 0.587183, 0.377323, 0.674676}, + {0.808202, 0.578398, 0.368843, 0.673812}, + {0.814469, 0.593597, 0.353499, 0.658490}, + {0.814704, 0.607021, 0.348134, 0.645341}, + {0.817675, 0.621472, 0.336356, 0.629211}, + {0.818080, 0.632724, 0.330347, 0.614490}, + {0.821075, 0.645231, 0.318139, 0.595855}, + {0.824287, 0.662723, 0.299187, 0.571816}, + {0.819220, 0.679515, 0.292287, 0.550242}, + {0.810424, 0.700249, 0.284467, 0.524879}, + {0.796390, 0.732513, 0.268441, 0.490418}, + {0.789852, 0.722040, 0.294211, 0.478342}, + {0.789842, 0.737249, 0.270880, 0.433326}, + {0.797185, 0.742556, 0.246284, 0.394348}, + {0.796760, 0.753532, 0.231483, 0.363068}, + {0.802489, 0.752117, 0.229403, 0.344700}, + {0.797428, 0.752612, 0.250459, 0.336477}, + {0.799282, 0.744338, 0.273102, 0.333560}, + {0.810074, 0.731634, 0.289234, 0.334607}, + {0.826981, 0.731058, 0.278973, 0.331873}, + {0.801124, 0.765770, 0.302252, 0.340756}, + {0.791384, 0.776073, 0.329103, 0.363743}, + {0.788899, 0.774675, 0.353880, 0.389883}, + {0.788026, 0.767649, 0.378225, 0.416182}, + {0.786081, 0.764760, 0.395319, 0.436949}, + {0.787222, 0.759097, 0.408076, 0.456578}, + {0.771571, 0.737046, 0.458652, 0.490393}, + {0.755070, 0.783117, 0.441670, 0.475904}, + {0.762340, 0.834326, 0.392381, 0.453669}, + {0.835241, 0.753537, 0.369365, 0.497747}, + {0.781050, 0.735130, 0.467164, 0.512989}, + {0.745126, 0.737258, 0.514457, 0.506705}, + {0.763296, 0.764134, 0.462354, 0.472613}, + {0.767440, 0.762852, 0.453451, 0.461133}, + {0.772536, 0.768321, 0.434543, 0.441952}, + {0.777071, 0.773241, 0.414144, 0.421252}, + {0.780874, 0.777434, 0.392177, 0.398948}, + {0.788281, 0.774629, 0.369267, 0.377449}, + {0.777489, 0.797820, 0.335135, 0.341566}, + {0.705705, 0.809980, 0.422349, 0.349703}, + {0.760688, 0.773408, 0.349448, 0.315889}, + {0.792791, 0.753493, 0.318175, 0.296000}, + {0.800784, 0.748712, 0.329713, 0.295846}, + {0.800593, 0.760987, 0.336587, 0.296673}, + {0.778854, 0.786839, 0.363741, 0.306626}, + {0.769305, 0.793020, 0.394412, 0.327643}, + {0.762951, 0.795212, 0.419634, 0.348404}, + {0.755859, 0.796154, 0.442968, 0.368450}, + {0.746038, 0.796522, 0.466775, 0.387577}, + {0.738034, 0.790620, 0.491400, 0.408077}, + {0.733843, 0.783804, 0.509289, 0.425803}, + {0.727709, 0.779707, 0.525195, 0.440524}, + {0.649999, 0.649999, 0.650000, 0.650000}, + {0.649999, 0.649999, 0.650000, 0.650000}, +}; +// vx, vy, x, y +float Ks[][4][4] = { + { + {-0.239070, 0.224067, -1.008631, 0.699905}, + {-0.475194, -0.222682, -2.126252, -0.663567}, + {0.369375, -0.348627, 1.678816, -1.394690}, + {0.205721, 0.350433, 0.845230, 1.375843}, + },{ + {-0.239070, 0.224066, -0.948100, 0.686394}, + {-0.475194, -0.222682, -2.160949, -0.695056}, + {0.369374, -0.348628, 1.659475, -1.402090}, + {0.205721, 0.350433, 0.844099, 1.419627}, + },{ + {-0.240216, 0.211127, -0.949489, 0.633659}, + {-0.483765, -0.206473, -2.162058, -0.609736}, + {0.355821, -0.357877, 1.590203, -1.474453}, + {0.197178, 0.364257, 0.787244, 1.492156}, + },{ + {-0.240855, 0.204329, -0.936647, 0.605095}, + {-0.488053, -0.198477, -2.167347, -0.573967}, + {0.348713, -0.362640, 1.550870, -1.513372}, + {0.192458, 0.370840, 0.754738, 1.535864}, + },{ + {-0.241531, 0.197269, -0.923036, 0.576731}, + {-0.492384, -0.190181, -2.169639, -0.537312}, + {0.341203, -0.367426, 1.508955, -1.553734}, + {0.187215, 0.377574, 0.719233, 1.581027}, + },{ + {-0.242264, 0.189992, -0.909116, 0.548853}, + {-0.496739, -0.181836, -2.169648, -0.500268}, + {0.333366, -0.372280, 1.466041, -1.594832}, + {0.181553, 0.384186, 0.681022, 1.625451}, + },{ + {-0.243045, 0.182662, -0.893006, 0.521892}, + {-0.501030, -0.173593, -2.167710, -0.465253}, + {0.325337, -0.377095, 1.421537, -1.636463}, + {0.175570, 0.390549, 0.641250, 1.670385}, + },{ + {-0.243921, 0.174950, -0.875793, 0.494884}, + {-0.505469, -0.165140, -2.164255, -0.429035}, + {0.316732, -0.382123, 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-0.277950, 1.991344}, + {-0.253153, -0.413310, -1.063476, -1.957748}, + {0.532255, -0.109235, 2.072676, -0.204613}, + {0.251031, 0.124179, 0.755602, 0.354558}, + },{ + {-0.126134, 0.429370, -0.326169, 1.948827}, + {-0.266052, -0.406528, -1.127491, -1.907298}, + {0.526630, -0.117669, 2.083093, -0.229653}, + {0.249458, 0.133727, 0.780758, 0.381681}, + },{ + {-0.135410, 0.425035, -0.375719, 1.905208}, + {-0.278293, -0.399748, -1.189508, -1.855954}, + {0.521146, -0.126146, 2.094319, -0.257658}, + {0.247926, 0.143248, 0.805032, 0.409251}, + },{ + {-0.144025, 0.420438, -0.425377, 1.861803}, + {-0.289719, -0.393083, -1.247863, -1.805070}, + {0.515854, -0.134475, 2.103983, -0.287654}, + {0.246479, 0.152585, 0.828535, 0.437161}, + },{ + {-0.151862, 0.415756, -0.471820, 1.819809}, + {-0.300242, -0.386598, -1.303488, -1.755774}, + {0.510785, -0.142465, 2.113538, -0.318484}, + {0.245139, 0.161602, 0.847979, 0.465433}, + },{ + {-0.158947, 0.411218, -0.513687, 1.778425}, + {-0.310081, -0.380055, -1.358513, -1.706870}, + {0.505782, -0.149985, 2.121834, -0.347745}, + {0.243870, 0.170447, 0.864934, 0.494674}, + },{ + {-0.165579, 0.406690, -0.554148, 1.736628}, + {-0.319679, -0.373156, -1.413154, -1.656567}, + {0.500664, -0.157327, 2.126887, -0.376852}, + {0.242605, 0.179502, 0.882109, 0.525990}, + },{ + {0.000000, 0.000000, 0.000000, -0.000000}, + {-0.000000, 0.000000, 0.000000, 0.000000}, + {-0.000000, -0.000000, 0.000000, 0.000000}, + {0.000000, -0.000000, 0.000000, -0.000000}, + },{ + {0.001775, 0.001243, 0.000000, 0.000000}, + {-0.001629, 0.001429, 0.000000, 0.000000}, + {-0.000893, -0.001975, 0.000000, -0.000000}, + {0.001068, -0.001886, 0.000000, 0.000000}, + }, +}; diff --git a/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb b/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb index 426e7b74..fcd71e5c 100644 --- a/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb +++ b/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb @@ -18,7 +18,7 @@ "\n", "from draw_cdpr import plot_trajectory\n", "from draw_controller import draw_controller_anim\n", - "from gerry02_traj_tracking import main, plot" + "from gerry02_traj_tracking import main, plot, save_controller" ] }, { @@ -91,6 +91,16 @@ "id": "other-calvin", "metadata": {}, "outputs": [], + "source": [ + "save_controller('data/iros_logo_2_controller.h', controller)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "obvious-girlfriend", + "metadata": {}, + "outputs": [], "source": [] } ], diff --git a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py index 58402773..5caf5450 100644 --- a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py +++ b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py @@ -19,7 +19,7 @@ from cdpr_planar import Cdpr, CdprParams from cdpr_controller_ilqr import CdprControllerIlqr from cdpr_planar_sim import CdprSimulator -from paint_parse import ParseFile +from paint_parse import ParseFile, writeControls from draw_cdpr import plot_trajectory from draw_controller import draw_controller_anim @@ -134,6 +134,9 @@ def plot(cdpr, controller, result, N, dt, des_T): """Plots the results""" plot_trajectory(cdpr, result, dt*N, dt, N, des_T, step=1) +def save_controller(fname, controller): + writeControls(fname, controller.gains_ff) + if __name__ == '__main__': cProfile.run('results = main(N=100)', sort=SortKey.TIME) plot(*results) diff --git a/gtdynamics/cablerobot/src/paint_parse.py b/gtdynamics/cablerobot/src/paint_parse.py index 4aa2e8c6..48cb61a6 100644 --- a/gtdynamics/cablerobot/src/paint_parse.py +++ b/gtdynamics/cablerobot/src/paint_parse.py @@ -55,3 +55,28 @@ def ParseFile(fname): except StopIteration: return paintons, colorinds, colors, traj assert False, 'End of file expected' + +def writeControls(fname, gains_ff): + with open(fname, 'w') as f: + Ks, uffs, vffs, xffs = zip(*gains_ff) + f.write('// u = K * ([v;x]-[vff;xff]) + uff\n') + f.write('float xffs[][2] = {\n') + for xff in xffs: + f.write('\t{{{:f}, {:f}}},\n'.format(*xff.translation()[[0, 2]])) + f.write('};\n') + f.write('float vffs[][2] = {\n') + for vff in vffs: + f.write('\t{{{:f}, {:f}}},\n'.format(*vff[[3, 5]])) + f.write('};\n') + f.write('float uffs[][4] = {\n') + for uff in uffs: + f.write('\t{{{:f}, {:f}, {:f}, {:f}}},\n'.format(*uff)) + f.write('};\n') + f.write('// vx, vy, x, y\n') + f.write('float Ks[][4][4] = {\n\t') + for K in Ks: + f.write('{\n') + for Krow in K: + f.write('\t {{{:f}, {:f}, {:f}, {:f}}},\n'.format(*Krow[[3,5,9,11]])) + f.write('\t},') + f.write('\n};\n') From 149501af3489cf9e3f05aac336659258d47fc7e0 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Wed, 5 May 2021 10:22:39 -0400 Subject: [PATCH 49/73] switch to ATL painting and make EE mass more realistic --- gtdynamics/cablerobot/src/data/ATL.h | 2929 ++++ .../src/gerry02_traj_tracking.ipynb | 12728 +++++++++++++++- .../cablerobot/src/gerry02_traj_tracking.py | 3 +- 3 files changed, 15645 insertions(+), 15 deletions(-) create mode 100644 gtdynamics/cablerobot/src/data/ATL.h diff --git a/gtdynamics/cablerobot/src/data/ATL.h b/gtdynamics/cablerobot/src/data/ATL.h new file mode 100644 index 00000000..244f6867 --- /dev/null +++ b/gtdynamics/cablerobot/src/data/ATL.h @@ -0,0 +1,2929 @@ +bool painton[] = { + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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2.004, 1.496}, + { 1.999, 1.496}, + { 1.994, 1.497}, + { 1.989, 1.497}, + { 1.984, 1.497}, + { 1.979, 1.497}, + { 1.974, 1.497}, + { 1.969, 1.497}, + { 1.964, 1.497}, + { 1.959, 1.497}, + { 1.954, 1.496}, + { 1.949, 1.495}, + { 1.945, 1.493}, + { 1.941, 1.491} +}; diff --git a/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb b/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb index fcd71e5c..b045d454 100644 --- a/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb +++ b/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "impressive-plumbing", "metadata": {}, "outputs": [], @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "fifteen-crawford", "metadata": { "scrolled": true @@ -31,17 +31,32 @@ "outputs": [], "source": [ "# cdpr, controller, result, N, dt, pdes = main(Q=np.ones(6)*1e3, R=np.ones(1)*1e-3, debug=False)\n", - "cdpr, controller, result, N, dt, pdes = main(Q=np.ones(6)*1e3, R=np.ones(1)*1e-3, N0=9700, dN=10, debug=False)" + "# cdpr, controller, result, N, dt, pdes = main(Q=np.ones(6)*1e3, R=np.ones(1)*1e-3, N0=9700, dN=10, debug=False)\n", + "# cdpr, controller, result, N, dt, pdes = main(Q=np.ones(6)*1e3, R=np.ones(1)*1e-3, N0=9700, debug=False)\n", + "cdpr, controller, result, N, dt, pdes = main(fname='data/ATL.h', Q=np.ones(6)*1e3, R=np.ones(1)*1e-3, dN=1, debug=False)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "generic-calibration", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Plot Trajectory\n", "anim = plot_trajectory(cdpr, result, dt*N, dt, N, pdes, step=10);\n", @@ -50,10 +65,11435 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "daily-visiting", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Animate Trajectory\n", "import matplotlib\n", @@ -64,10 +11504,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "joint-batch", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Plot Controller Gains\n", "anim2 = draw_controller_anim(cdpr, controller, result, N, step=50);" @@ -75,10 +11528,1257 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "expired-inclusion", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Animate Controller Gains\n", "from IPython.display import HTML\n", @@ -87,18 +12787,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "other-calvin", "metadata": {}, "outputs": [], "source": [ - "save_controller('data/iros_logo_2_controller.h', controller)" + "save_controller('data/ATL_controller_1e6.h', controller)" ] }, { "cell_type": "code", "execution_count": null, - "id": "obvious-girlfriend", + "id": "developmental-official", "metadata": {}, "outputs": [], "source": [] diff --git a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py index 5caf5450..544e294b 100644 --- a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py +++ b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py @@ -83,6 +83,7 @@ def main(fname='data/iros_logo_2.h', aw, ah = 2.92, 2.32 bw, bh = 0.15, 0.30 params = CdprParams() + params.mass = 0.5 params.a_locs = np.array([[aw, 0, 0], [aw, 0, ah], [0, 0, ah], [0, 0, 0]]) params.b_locs = np.array([[bw, 0., -bh], [bw, 0., bh], [-bw, 0., bh], [-bw, 0, -bh]]) / 2 params.b_locs = params.b_locs - [0, 0, bh * 0.4] @@ -96,7 +97,7 @@ def main(fname='data/iros_logo_2.h', dt = 0.01 * dN # this is a hardcoded constant. TODO(gerry): include this in the .h file. N = int(N/dN) # scale time by dN N0 = int(N0/dN) - traj = (traj - [aw/2, ah/2]) * 0.8 + [aw/2, ah/2] # rescale trajectory to be smaller + traj = (traj - [aw/2, ah/2]) * 0.85 + [aw/2, ah/2] # rescale trajectory to be smaller traj = traj[::dN, :] if debug: print_data(isPaints, colorinds, colorpalette, traj, N=100) From 103f564ab992abc628cfc82673275be40c9a1820 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Wed, 5 May 2021 10:39:38 -0400 Subject: [PATCH 50/73] add raw trajectories to source control (git lfs) --- gtdynamics/cablerobot/src/data/.gitattributes | 4 + .../cablerobot/src/data/ATL_controller_1e2.h | 3 + .../cablerobot/src/data/ATL_controller_1e4.h | 3 + .../cablerobot/src/data/ATL_controller_1e6.h | 3 + .../src/data/iros_logo_2_controller.h | 2750 +---------------- 5 files changed, 16 insertions(+), 2747 deletions(-) create mode 100644 gtdynamics/cablerobot/src/data/.gitattributes create mode 100644 gtdynamics/cablerobot/src/data/ATL_controller_1e2.h create mode 100644 gtdynamics/cablerobot/src/data/ATL_controller_1e4.h create mode 100644 gtdynamics/cablerobot/src/data/ATL_controller_1e6.h diff --git a/gtdynamics/cablerobot/src/data/.gitattributes b/gtdynamics/cablerobot/src/data/.gitattributes new file mode 100644 index 00000000..842f8d6e --- /dev/null +++ b/gtdynamics/cablerobot/src/data/.gitattributes @@ -0,0 +1,4 @@ +ATL_controller_1e2.h filter=lfs diff=lfs merge=lfs -text +ATL_controller_1e4.h filter=lfs diff=lfs merge=lfs -text +ATL_controller_1e6.h filter=lfs diff=lfs merge=lfs -text +iros_logo_2_controller.h filter=lfs diff=lfs merge=lfs -text diff --git a/gtdynamics/cablerobot/src/data/ATL_controller_1e2.h b/gtdynamics/cablerobot/src/data/ATL_controller_1e2.h new file mode 100644 index 00000000..4703d641 --- /dev/null +++ b/gtdynamics/cablerobot/src/data/ATL_controller_1e2.h @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b8d81744aa5d52dc78ff0dcb65b61aa8639c65e1ecacf7e1619be6d085b9aa8f +size 813508 diff --git a/gtdynamics/cablerobot/src/data/ATL_controller_1e4.h b/gtdynamics/cablerobot/src/data/ATL_controller_1e4.h new file mode 100644 index 00000000..1bb14b98 --- /dev/null +++ b/gtdynamics/cablerobot/src/data/ATL_controller_1e4.h @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:489781a2483bef0bbbde375248319dffcd0630bb36e9fa40d6a96242c4b41748 +size 836656 diff --git a/gtdynamics/cablerobot/src/data/ATL_controller_1e6.h b/gtdynamics/cablerobot/src/data/ATL_controller_1e6.h new file mode 100644 index 00000000..73b74c2c --- /dev/null +++ b/gtdynamics/cablerobot/src/data/ATL_controller_1e6.h @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7227e2223f93006c241105420621be75e8a050d81217af469b948f4e67b83324 +size 851266 diff --git a/gtdynamics/cablerobot/src/data/iros_logo_2_controller.h b/gtdynamics/cablerobot/src/data/iros_logo_2_controller.h index af4924a5..b9cf7179 100644 --- a/gtdynamics/cablerobot/src/data/iros_logo_2_controller.h +++ b/gtdynamics/cablerobot/src/data/iros_logo_2_controller.h @@ -1,2747 +1,3 @@ -// u = K * ([v;x]-[vff;xff]) + uff -float xffs[][2] = { - {1.064800, 1.284800}, - {1.064800, 1.284800}, - {0.985602, 1.297600}, - {0.946400, 1.303200}, - {0.906400, 1.309600}, - {0.866400, 1.316000}, - {0.827200, 1.322400}, - {0.787200, 1.328800}, - {0.748000, 1.335200}, - {0.708000, 1.340800}, - {0.668000, 1.347200}, - {0.628800, 1.353600}, - {0.588800, 1.360000}, - {0.548800, 1.366400}, - {0.509600, 1.372800}, - {0.469600, 1.379200}, - {0.430400, 1.384800}, - {0.402401, 1.381599}, - {0.402400, 1.340800}, - {0.402400, 1.300000}, - {0.403201, 1.260801}, - {0.443200, 1.260800}, - {0.484000, 1.260800}, - {0.524801, 1.260800}, - {0.564799, 1.260801}, - {0.564801, 1.300800}, - {0.564801, 1.341600}, - {0.564800, 1.382399}, - {0.532001, 1.389600}, - {0.491201, 1.389600}, - {0.451200, 1.389600}, - {0.410401, 1.389600}, - {0.403200, 1.360800}, - {0.404800, 1.319200}, - {0.406401, 1.277600}, - {0.408001, 1.236800}, - {0.408800, 1.195200}, - {0.403201, 1.164799}, - {0.400801, 1.119200}, - {0.404800, 1.080800}, - {0.404001, 1.040800}, - {0.410401, 1.001600}, - {0.431200, 0.967200}, - {0.462400, 0.940000}, - {0.498401, 0.922400}, - {0.538400, 0.913600}, - {0.565600, 0.932800}, - {0.564801, 0.973600}, - {0.564800, 1.013600}, - {0.564800, 1.053600}, - {0.564800, 1.094400}, - {0.565601, 1.133600}, - {0.561600, 1.173599}, - {0.521601, 1.176800}, - {0.481600, 1.176800}, - {0.441600, 1.176800}, - {0.412802, 1.171199}, - {0.439200, 1.140800}, - {0.466400, 1.110400}, - {0.492800, 1.080000}, - {0.520000, 1.048800}, - {0.546400, 1.018400}, - {0.573600, 0.988000}, - {0.600000, 0.957600}, - {0.627200, 0.926400}, - {0.657600, 0.914400}, - {0.697600, 0.914400}, - {0.737600, 0.914400}, - {0.777600, 0.914400}, - {0.796000, 0.936800}, - {0.796000, 0.976800}, - {0.796000, 1.016800}, - {0.813600, 1.039200}, - {0.853600, 1.039200}, - {0.894400, 1.039200}, - {0.934400, 1.039200}, - {0.974400, 1.039200}, - {1.016000, 1.039200}, - {1.043200, 1.020000}, - {1.042400, 0.980000}, - {1.042400, 0.940000}, - {1.060000, 0.916800}, - {1.100000, 0.916800}, - {1.140000, 0.916800}, - {1.180000, 0.916800}, - {1.208000, 0.928801}, - {1.209600, 0.969600}, - {1.210400, 1.011200}, - {1.204800, 1.050400}, - {1.188800, 1.084800}, - {1.180001, 1.111200}, - {1.201599, 1.143200}, - {1.208800, 1.183200}, - {1.208800, 1.224800}, - {1.208000, 1.263200}, - {1.205600, 1.304000}, - {1.192000, 1.340800}, - {1.163200, 1.369600}, - {1.127200, 1.384800}, - {1.088000, 1.389600}, - {1.047200, 1.389600}, - {1.007200, 1.389600}, - {0.967200, 1.389600}, - {0.927200, 1.389600}, - {0.887200, 1.389600}, - {0.846400, 1.389600}, - {0.806400, 1.389600}, - {0.766400, 1.389600}, - {0.726400, 1.389600}, - {0.686400, 1.389600}, - {0.645601, 1.388000}, - {0.631200, 1.356800}, - {0.632000, 1.312000}, - {0.635201, 1.273600}, - {0.665600, 1.257600}, - {0.708800, 1.259200}, - {0.747200, 1.260800}, - {0.787200, 1.260800}, - {0.827200, 1.260800}, - {0.868000, 1.260800}, - {0.908000, 1.260800}, - {0.948000, 1.260800}, - {0.987200, 1.261600}, - {1.028800, 1.259200}, - {1.052800, 1.232000}, - {1.047200, 1.192000}, - {1.016800, 1.175200}, - {0.971200, 1.175200}, - {0.933600, 1.176800}, - {0.893600, 1.177600}, - {0.853600, 1.177600}, - {0.812800, 1.178400}, - {0.772800, 1.179200}, - {0.732800, 1.179200}, - {0.694400, 1.180800}, - {0.650401, 1.180000}, - {0.633600, 1.152800}, - {0.635200, 1.111200}, - {0.635200, 1.070400}, - {0.636000, 1.030400}, - {0.636800, 0.990400}, - {0.637600, 0.950400}, - {0.637601, 0.914401}, - {0.671200, 0.936799}, - {0.704800, 0.959200}, - {0.738400, 0.981600}, - {0.772000, 1.004000}, - {0.805600, 1.026400}, - {0.839200, 1.048800}, - {0.872800, 1.071200}, - {0.906400, 1.093600}, - {0.940000, 1.116000}, - {0.973600, 1.138400}, - {1.007200, 1.160800}, - {1.040800, 1.183200}, - {1.074400, 1.204800}, - {1.108000, 1.227200}, - {1.141600, 1.249600}, - {1.175200, 1.272000}, - {1.208800, 1.294400}, - {1.242400, 1.316800}, - {1.276000, 1.339200}, - {1.309601, 1.361600}, - {1.343199, 1.384000}, - {1.323200, 1.381600}, - {1.287200, 1.359200}, - {1.265600, 1.328000}, - {1.255200, 1.291200}, - {1.252800, 1.250400}, - {1.254400, 1.209600}, - {1.255200, 1.169600}, - {1.253600, 1.129600}, - {1.253600, 1.089600}, - {1.256000, 1.048800}, - {1.256800, 1.008800}, - {1.265600, 0.972000}, - {1.292800, 0.940000}, - {1.328000, 0.922400}, - {1.367200, 0.916000}, - {1.408000, 0.914400}, - {1.448000, 0.914400}, - {1.488000, 0.914400}, - {1.528000, 0.914400}, - {1.568000, 0.914400}, - {1.608000, 0.914400}, - {1.648800, 0.914400}, - {1.688000, 0.913600}, - {1.728800, 0.913600}, - {1.768800, 0.915200}, - {1.808000, 0.923200}, - {1.842400, 0.943200}, - {1.868000, 0.973600}, - {1.878400, 1.011200}, - {1.877600, 1.052800}, - {1.877600, 1.092000}, - {1.877600, 1.132800}, - {1.877600, 1.172800}, - {1.877600, 1.212800}, - {1.877600, 1.252800}, - {1.876800, 1.293600}, - {1.867200, 1.331200}, - {1.841600, 1.363200}, - {1.806400, 1.381600}, - {1.768000, 1.388800}, - {1.728000, 1.389600}, - {1.687200, 1.389600}, - {1.647200, 1.389600}, - {1.607200, 1.389600}, - {1.567200, 1.389600}, - {1.526400, 1.389600}, - {1.486400, 1.389600}, - {1.446400, 1.389600}, - {1.406399, 1.391200}, - {1.365601, 1.390400}, - {1.368800, 1.381600}, - {1.407200, 1.367200}, - {1.444800, 1.352800}, - {1.483200, 1.338400}, - {1.520800, 1.324000}, - {1.559200, 1.309600}, - {1.596800, 1.296000}, - {1.635200, 1.281600}, - {1.672800, 1.267200}, - {1.707200, 1.253600}, - {1.723200, 1.220000}, - {1.724000, 1.179200}, - {1.723200, 1.139200}, - {1.723200, 1.099200}, - {1.716799, 1.060000}, - {1.680000, 1.048800}, - {1.639200, 1.050400}, - {1.599200, 1.050400}, - {1.559200, 1.050400}, - {1.518400, 1.050400}, - {1.479200, 1.049600}, - {1.437600, 1.050400}, - {1.409600, 1.072800}, - {1.408800, 1.112800}, - {1.408800, 1.153600}, - {1.408800, 1.193600}, - {1.408800, 1.233600}, - {1.436000, 1.255200}, - {1.479200, 1.254400}, - {1.518400, 1.253600}, - {1.558400, 1.253600}, - {1.599200, 1.253600}, - {1.639200, 1.253600}, - {1.680000, 1.256800}, - {1.714400, 1.260800}, - {1.751200, 1.278400}, - {1.788000, 1.296000}, - {1.824800, 1.313600}, - {1.861600, 1.330400}, - {1.898400, 1.348000}, - {1.936001, 1.365600}, - {1.972799, 1.383200}, - {1.961600, 1.380000}, - {1.936000, 1.349600}, - {1.923200, 1.311200}, - {1.920000, 1.271200}, - {1.920000, 1.231200}, - {1.920800, 1.191200}, - {1.927200, 1.152800}, - {1.946400, 1.117600}, - {1.979200, 1.096800}, - {2.020000, 1.092800}, - {2.060000, 1.092800}, - {2.100000, 1.092800}, - {2.140000, 1.092800}, - {2.180000, 1.092800}, - {2.220000, 1.092800}, - {2.257600, 1.093600}, - {2.302400, 1.096000}, - {2.338399, 1.083999}, - {2.339199, 1.047201}, - {2.304800, 1.033600}, - {2.261600, 1.034400}, - {2.223200, 1.034400}, - {2.183200, 1.034400}, - {2.143200, 1.034400}, - {2.103200, 1.034400}, - {2.063200, 1.034400}, - {2.023200, 1.034400}, - {1.983200, 1.034400}, - {1.943200, 1.034400}, - {1.920000, 1.012800}, - {1.931200, 0.974400}, - {1.951200, 0.939200}, - {1.984000, 0.917600}, - {2.023200, 0.912800}, - {2.064000, 0.913600}, - {2.103200, 0.914400}, - {2.143200, 0.914400}, - {2.183200, 0.914400}, - {2.224000, 0.914400}, - {2.264000, 0.914400}, - {2.303200, 0.913600}, - {2.344000, 0.912000}, - {2.384800, 0.912000}, - {2.424000, 0.917600}, - {2.463199, 0.936000}, - {2.487200, 0.964000}, - {2.498400, 1.000000}, - {2.501600, 1.039200}, - {2.501600, 1.080800}, - {2.500000, 1.120800}, - {2.491999, 1.159200}, - {2.471999, 1.192800}, - {2.437600, 1.215200}, - {2.398400, 1.220000}, - {2.358400, 1.220000}, - {2.318400, 1.220000}, - {2.278400, 1.220800}, - {2.237600, 1.221600}, - {2.197600, 1.222400}, - {2.160799, 1.221600}, - {2.115200, 1.220000}, - {2.077600, 1.231200}, - {2.076800, 1.262400}, - {2.114400, 1.272000}, - {2.160799, 1.270400}, - {2.197600, 1.269600}, - {2.237600, 1.269600}, - {2.277600, 1.269600}, - {2.317600, 1.269600}, - {2.357600, 1.269600}, - {2.397600, 1.269600}, - {2.436800, 1.267200}, - {2.479999, 1.269600}, - {2.490400, 1.301600}, - {2.479200, 1.340000}, - {2.455199, 1.370400}, - {2.420000, 1.388000}, - {2.380800, 1.392800}, - {2.340800, 1.393600}, - {2.300000, 1.392800}, - 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{0.598625, 0.411762, 0.667847, 0.796759}, - {0.601727, 0.430803, 0.669134, 0.790079}, - {0.604132, 0.449798, 0.670756, 0.782670}, - {0.605869, 0.468707, 0.672683, 0.774529}, - {0.606961, 0.487488, 0.674883, 0.765654}, - {0.608217, 0.505183, 0.675808, 0.757387}, - {0.606484, 0.525418, 0.681454, 0.744379}, - {0.606925, 0.542259, 0.682479, 0.734908}, - {0.605662, 0.560079, 0.685450, 0.723173}, - {0.603861, 0.577544, 0.688530, 0.710750}, - {0.601543, 0.594606, 0.691688, 0.697660}, - {0.598729, 0.611221, 0.694891, 0.683929}, - {0.595440, 0.627349, 0.698105, 0.669585}, - {0.591711, 0.642924, 0.701317, 0.654657}, - {0.560449, 0.508137, 0.776834, 0.738556}, - {0.593605, 0.612098, 0.711784, 0.673499}, - {0.609588, 0.688855, 0.667244, 0.626529}, - {0.605187, 0.671041, 0.675709, 0.641123}, - {0.609491, 0.649386, 0.676526, 0.659000}, - {0.613954, 0.628005, 0.678622, 0.673488}, - {0.624370, 0.599898, 0.673757, 0.693535}, - {0.640596, 0.576978, 0.658202, 0.711020}, - {0.661347, 0.567412, 0.632197, 0.719823}, - {0.678084, 0.548290, 0.610533, 0.732697}, - {0.685824, 0.521053, 0.601280, 0.746437}, - {0.709355, 0.525832, 0.564332, 0.742447}, - {0.733652, 0.530959, 0.523877, 0.737508}, - {0.723623, 0.502212, 0.539302, 0.744679}, - {0.734393, 0.474053, 0.516392, 0.757594}, - {0.746310, 0.453412, 0.491468, 0.768298}, - {0.752365, 0.444935, 0.479355, 0.772573}, - {0.759698, 0.449527, 0.466627, 0.769939}, - {0.763788, 0.455969, 0.459910, 0.766147}, - {0.767684, 0.462573, 0.453375, 0.762177}, - {0.771412, 0.469372, 0.446988, 0.758002}, - {0.775973, 0.478083, 0.438890, 0.752590}, - {0.777433, 0.478862, 0.436950, 0.751683}, - {0.782862, 0.496197, 0.425771, 0.741055}, - {0.782307, 0.500137, 0.426149, 0.738331}, - {0.779591, 0.518095, 0.427012, 0.728268}, - {0.770514, 0.531466, 0.439369, 0.722000}, - {0.769512, 0.535103, 0.444879, 0.719662}, - {0.766134, 0.537711, 0.461424, 0.717861}, - {0.772560, 0.563147, 0.458660, 0.700016}, - {0.789748, 0.595678, 0.437758, 0.674894}, - {0.774785, 0.623763, 0.465663, 0.657236}, - {0.769988, 0.638250, 0.481647, 0.644609}, - {0.758933, 0.658417, 0.501827, 0.627929}, - {0.748396, 0.675440, 0.520179, 0.611935}, - {0.734745, 0.691312, 0.540950, 0.595715}, - {0.721055, 0.684321, 0.573190, 0.592929}, - {0.704426, 0.680885, 0.605425, 0.587044}, - {0.710366, 0.689653, 0.599798, 0.574675}, - {0.695627, 0.712222, 0.606728, 0.556674}, - {0.674447, 0.724199, 0.623723, 0.548915}, - {0.655031, 0.731884, 0.639256, 0.545968}, - {0.647734, 0.731281, 0.645116, 0.550897}, - {0.639676, 0.724344, 0.654920, 0.559850}, - {0.632698, 0.719022, 0.662327, 0.567442}, - {0.624925, 0.711994, 0.671130, 0.575727}, - {0.619824, 0.709787, 0.674328, 0.581083}, - {0.611987, 0.702418, 0.682636, 0.589178}, - {0.602657, 0.698845, 0.690939, 0.593671}, - {0.601103, 0.685857, 0.693238, 0.607619}, - {0.652423, 0.769735, 0.597875, 0.572944}, - {0.632150, 0.748073, 0.625778, 0.586724}, - {0.587762, 0.673940, 0.704781, 0.620875}, - {0.603133, 0.678107, 0.690778, 0.619326}, - {0.614479, 0.675085, 0.683173, 0.621433}, - {0.629324, 0.678896, 0.668249, 0.619704}, - {0.638916, 0.676882, 0.660987, 0.620458}, - {0.655650, 0.678105, 0.642995, 0.620642}, - {0.664319, 0.676438, 0.635329, 0.620702}, - {0.672246, 0.673127, 0.628642, 0.621730}, - {0.700189, 0.609388, 0.623071, 0.665124}, - {0.693693, 0.636743, 0.619999, 0.643994}, - {0.709879, 0.665757, 0.580612, 0.628902}, - {0.727315, 0.651669, 0.554931, 0.643300}, - {0.730123, 0.620619, 0.553806, 0.666844}, - {0.674385, 0.603794, 0.627739, 0.681910}, - {0.736394, 0.606540, 0.526410, 0.681547}, - {0.760938, 0.576291, 0.495532, 0.700973}, - {0.752993, 0.570763, 0.507791, 0.706456}, - {0.747593, 0.561732, 0.516579, 0.713642}, - {0.746879, 0.558109, 0.516826, 0.717502}, - {0.734250, 0.546539, 0.536543, 0.725255}, - {0.724413, 0.606889, 0.540523, 0.687355}, - {0.740784, 0.623006, 0.513433, 0.680432}, - {0.718433, 0.545267, 0.559352, 0.729758}, - {0.706312, 0.561868, 0.579568, 0.720627}, - {0.691118, 0.583644, 0.601279, 0.706089}, - {0.732729, 0.634874, 0.534070, 0.686246}, - {0.709944, 0.623575, 0.570383, 0.690833}, - {0.651753, 0.626807, 0.651849, 0.668666}, - {0.663402, 0.645120, 0.635323, 0.655432}, - {0.671018, 0.649961, 0.627172, 0.650456}, - {0.675366, 0.653083, 0.623767, 0.645568}, - {0.678485, 0.667977, 0.617327, 0.632362}, - {0.680139, 0.656080, 0.622886, 0.636762}, - {0.674399, 0.702558, 0.613104, 0.601305}, - {0.697150, 0.682563, 0.595969, 0.612625}, - {0.696285, 0.695094, 0.596038, 0.597425}, - {0.696913, 0.705814, 0.594670, 0.582712}, - {0.693555, 0.720086, 0.595507, 0.564715}, - {0.695686, 0.731018, 0.590793, 0.548516}, - {0.693935, 0.738597, 0.592361, 0.533461}, - {0.687656, 0.628107, 0.681696, 0.594923}, - {0.734079, 0.686799, 0.592624, 0.543532}, - {0.721891, 0.766143, 0.547249, 0.495131}, - {0.713253, 0.760129, 0.556058, 0.512216}, - {0.715496, 0.738370, 0.560676, 0.538128}, - {0.725922, 0.720811, 0.550460, 0.558890}, - {0.740337, 0.718515, 0.523350, 0.568180}, - {0.756598, 0.719620, 0.489772, 0.574073}, - {0.749709, 0.728412, 0.481594, 0.576102}, - {0.750879, 0.712827, 0.477250, 0.589909}, - {0.771029, 0.670877, 0.466857, 0.611268}, - {0.783471, 0.671898, 0.447690, 0.602331}, - {0.787411, 0.681267, 0.436245, 0.588388}, - {0.791174, 0.690768, 0.423972, 0.573191}, - {0.794702, 0.700336, 0.410778, 0.556610}, - {0.794377, 0.708032, 0.403659, 0.540977}, - {0.803665, 0.732956, 0.363178, 0.508559}, - {0.823430, 0.685144, 0.378426, 0.518660}, - {0.827262, 0.638577, 0.425012, 0.530770}, - {0.736496, 0.748951, 0.439962, 0.484839}, - {0.776036, 0.753895, 0.350191, 0.470720}, - {0.816526, 0.725571, 0.312971, 0.489634}, - {0.808928, 0.707999, 0.349474, 0.522773}, - {0.808546, 0.699931, 0.359788, 0.541559}, - {0.806314, 0.689084, 0.373971, 0.561391}, - {0.803679, 0.678210, 0.387102, 0.579463}, - {0.800732, 0.667426, 0.399280, 0.595934}, - {0.797545, 0.656816, 0.410607, 0.610953}, - {0.794175, 0.646444, 0.421169, 0.624666}, - {0.842570, 0.620817, 0.363454, 0.634044}, - {0.849739, 0.652599, 0.333538, 0.619989}, - {0.793396, 0.631809, 0.416770, 0.649164}, - {0.789629, 0.642058, 0.397946, 0.646791}, - {0.784665, 0.622801, 0.396239, 0.660453}, - {0.800394, 0.587183, 0.377323, 0.674676}, - {0.808202, 0.578398, 0.368843, 0.673812}, - {0.814469, 0.593597, 0.353499, 0.658490}, - {0.814704, 0.607021, 0.348134, 0.645341}, - {0.817675, 0.621472, 0.336356, 0.629211}, - {0.818080, 0.632724, 0.330347, 0.614490}, - {0.821075, 0.645231, 0.318139, 0.595855}, - {0.824287, 0.662723, 0.299187, 0.571816}, - {0.819220, 0.679515, 0.292287, 0.550242}, - {0.810424, 0.700249, 0.284467, 0.524879}, - {0.796390, 0.732513, 0.268441, 0.490418}, - {0.789852, 0.722040, 0.294211, 0.478342}, - {0.789842, 0.737249, 0.270880, 0.433326}, - {0.797185, 0.742556, 0.246284, 0.394348}, - {0.796760, 0.753532, 0.231483, 0.363068}, - {0.802489, 0.752117, 0.229403, 0.344700}, - {0.797428, 0.752612, 0.250459, 0.336477}, - {0.799282, 0.744338, 0.273102, 0.333560}, - {0.810074, 0.731634, 0.289234, 0.334607}, - {0.826981, 0.731058, 0.278973, 0.331873}, - {0.801124, 0.765770, 0.302252, 0.340756}, - {0.791384, 0.776073, 0.329103, 0.363743}, - {0.788899, 0.774675, 0.353880, 0.389883}, - {0.788026, 0.767649, 0.378225, 0.416182}, - {0.786081, 0.764760, 0.395319, 0.436949}, - {0.787222, 0.759097, 0.408076, 0.456578}, - {0.771571, 0.737046, 0.458652, 0.490393}, - {0.755070, 0.783117, 0.441670, 0.475904}, - {0.762340, 0.834326, 0.392381, 0.453669}, - {0.835241, 0.753537, 0.369365, 0.497747}, - {0.781050, 0.735130, 0.467164, 0.512989}, - {0.745126, 0.737258, 0.514457, 0.506705}, - {0.763296, 0.764134, 0.462354, 0.472613}, - {0.767440, 0.762852, 0.453451, 0.461133}, - {0.772536, 0.768321, 0.434543, 0.441952}, - {0.777071, 0.773241, 0.414144, 0.421252}, - {0.780874, 0.777434, 0.392177, 0.398948}, - {0.788281, 0.774629, 0.369267, 0.377449}, - {0.777489, 0.797820, 0.335135, 0.341566}, - {0.705705, 0.809980, 0.422349, 0.349703}, - {0.760688, 0.773408, 0.349448, 0.315889}, - {0.792791, 0.753493, 0.318175, 0.296000}, - {0.800784, 0.748712, 0.329713, 0.295846}, - {0.800593, 0.760987, 0.336587, 0.296673}, - {0.778854, 0.786839, 0.363741, 0.306626}, - {0.769305, 0.793020, 0.394412, 0.327643}, - {0.762951, 0.795212, 0.419634, 0.348404}, - {0.755859, 0.796154, 0.442968, 0.368450}, - {0.746038, 0.796522, 0.466775, 0.387577}, - {0.738034, 0.790620, 0.491400, 0.408077}, - {0.733843, 0.783804, 0.509289, 0.425803}, - {0.727709, 0.779707, 0.525195, 0.440524}, - {0.649999, 0.649999, 0.650000, 0.650000}, - {0.649999, 0.649999, 0.650000, 0.650000}, -}; -// vx, vy, x, y -float Ks[][4][4] = { - { - {-0.239070, 0.224067, -1.008631, 0.699905}, - {-0.475194, -0.222682, -2.126252, -0.663567}, - {0.369375, -0.348627, 1.678816, -1.394690}, - {0.205721, 0.350433, 0.845230, 1.375843}, - },{ - {-0.239070, 0.224066, -0.948100, 0.686394}, - {-0.475194, -0.222682, -2.160949, -0.695056}, - {0.369374, -0.348628, 1.659475, -1.402090}, - {0.205721, 0.350433, 0.844099, 1.419627}, - },{ - {-0.240216, 0.211127, -0.949489, 0.633659}, - {-0.483765, -0.206473, -2.162058, -0.609736}, - {0.355821, -0.357877, 1.590203, -1.474453}, - {0.197178, 0.364257, 0.787244, 1.492156}, - },{ - {-0.240855, 0.204329, -0.936647, 0.605095}, - {-0.488053, -0.198477, -2.167347, -0.573967}, - {0.348713, -0.362640, 1.550870, -1.513372}, - {0.192458, 0.370840, 0.754738, 1.535864}, - },{ - {-0.241531, 0.197269, -0.923036, 0.576731}, - {-0.492384, -0.190181, -2.169639, -0.537312}, - {0.341203, -0.367426, 1.508955, -1.553734}, - {0.187215, 0.377574, 0.719233, 1.581027}, - },{ - {-0.242264, 0.189992, -0.909116, 0.548853}, - {-0.496739, -0.181836, -2.169648, -0.500268}, - {0.333366, -0.372280, 1.466041, -1.594832}, - {0.181553, 0.384186, 0.681022, 1.625451}, - },{ - {-0.243045, 0.182662, -0.893006, 0.521892}, - {-0.501030, -0.173593, -2.167710, -0.465253}, - {0.325337, -0.377095, 1.421537, -1.636463}, - {0.175570, 0.390549, 0.641250, 1.670385}, - },{ - {-0.243921, 0.174950, -0.875793, 0.494884}, - {-0.505469, -0.165140, -2.164255, -0.429035}, - {0.316732, -0.382123, 1.375709, -1.679561}, - {0.169009, 0.396865, 0.597613, 1.714987}, - },{ - 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{-0.151862, 0.415756, -0.471820, 1.819809}, - {-0.300242, -0.386598, -1.303488, -1.755774}, - {0.510785, -0.142465, 2.113538, -0.318484}, - {0.245139, 0.161602, 0.847979, 0.465433}, - },{ - {-0.158947, 0.411218, -0.513687, 1.778425}, - {-0.310081, -0.380055, -1.358513, -1.706870}, - {0.505782, -0.149985, 2.121834, -0.347745}, - {0.243870, 0.170447, 0.864934, 0.494674}, - },{ - {-0.165579, 0.406690, -0.554148, 1.736628}, - {-0.319679, -0.373156, -1.413154, -1.656567}, - {0.500664, -0.157327, 2.126887, -0.376852}, - {0.242605, 0.179502, 0.882109, 0.525990}, - },{ - {0.000000, 0.000000, 0.000000, -0.000000}, - {-0.000000, 0.000000, 0.000000, 0.000000}, - {-0.000000, -0.000000, 0.000000, 0.000000}, - {0.000000, -0.000000, 0.000000, -0.000000}, - },{ - {0.001775, 0.001243, 0.000000, 0.000000}, - {-0.001629, 0.001429, 0.000000, 0.000000}, - {-0.000893, -0.001975, 0.000000, -0.000000}, - {0.001068, -0.001886, 0.000000, 0.000000}, - }, -}; +version https://git-lfs.github.com/spec/v1 +oid sha256:d3adef8c76e6f542c3d354fc69a4ec23f5f49051e2f7c66361ec5741afc7f340 +size 1000617 From 6c8233f78b4febfe28915a410ca75a10b41a2132 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Sat, 28 Aug 2021 11:30:33 -0400 Subject: [PATCH 51/73] WIP: fix some unit tests: cdpr_planar, cdpr_planar_sim, and cdpr_controller_tension_dist all passing now --- .../cablerobot/src/cdpr_controller_ilqr.py | 2 +- .../src/cdpr_controller_tension_dist.py | 89 +++++++++++++++---- gtdynamics/cablerobot/src/cdpr_planar.py | 49 +++++++--- gtdynamics/cablerobot/src/cdpr_planar_sim.py | 11 ++- .../src/test_cdpr_controller_tension_dist.py | 5 +- gtdynamics/cablerobot/src/test_cdpr_planar.py | 8 +- .../cablerobot/src/test_cdpr_planar_sim.py | 2 +- .../cablerobot/tests/testWinchFactor.cpp | 8 +- 8 files changed, 128 insertions(+), 46 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py index 43cb10be..301866d4 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py @@ -99,7 +99,7 @@ def create_ilqr_fg(cdpr, x0, pdes, dt, Q, R): gtd.PriorFactorDouble(gtd.internal.TorqueKey(ji, k).key(), tmid, gtsam.noiseModel.Diagonal.Precisions(R))) # state objective costs - cost_x = gtsam.noiseModel.Constrained.All(6) if Q is None else \ + cost_x = gtsam.noiseModel.Isotropic.Sigma(6, 0.001) if Q is None else \ gtsam.noiseModel.Diagonal.Precisions(Q) for k in range(N): fg.push_back( diff --git a/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py b/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py index 1955dc91..1e494620 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py @@ -16,6 +16,7 @@ import numpy as np import utils from cdpr_controller import CdprControllerBase +from cdpr_planar import Cdpr class CdprControllerTensionDist(CdprControllerBase): """Precomputes the open-loop trajectory @@ -40,7 +41,7 @@ def __init__(self, cdpr, x0, pdes=[], dt=0.01, Q=None, R=np.array([1.])): # # initial guess # lid = cdpr.ee_id() # x_guess = gtsam.Values() - # x_guess.insertDouble(0, dt) + # x_guess.insert(0, dt) # for k, T in enumerate(pdes): # gtd.InsertPose(x_guess, lid, k, T) # utils.InsertTwist(x_guess, lid, 0, x0) @@ -72,9 +73,9 @@ def update(self, values, t): """ return self.solve_one_step(self.cdpr, self.cdpr.ee_id(), - self.pdes[t], + self.pdes[t+1], t, - lldotnow=values, + TVnow=values, dt=self.dt, R=self.R) @@ -97,7 +98,39 @@ def solve_one_step(cdpr, lid, Tgoal, k, TVnow=None, lldotnow=None, dt=0.01, R=np Returns: gtsam.Values: a Values object containing the control torques """ + print("My goal for k = {:d} is:".format(k), Tgoal.translation()) + + ## First solve for required TwistAccel + print("\tpose: ", gtd.Pose(TVnow, cdpr.ee_id(), k).translation()) + print("\tTwist: ", gtd.Twist(TVnow, cdpr.ee_id(), k)[3:]) + VAres = CdprControllerTensionDist.solve_twist_accel(cdpr, lid, Tgoal, k, TVnow, lldotnow, dt) + print("\tpose: ", gtd.Pose(VAres, cdpr.ee_id(), k).translation()) + print("\tpose: ", gtd.Pose(VAres, cdpr.ee_id(), k + 1).translation()) + print("\tTwist: ", gtd.Twist(VAres, cdpr.ee_id(), k)[3:]) + print("\tTwist: ", gtd.Twist(VAres, cdpr.ee_id(), k + 1)[3:]) + print("\tTwistAccel: ", gtd.TwistAccel(VAres, cdpr.ee_id(), k)[3:]) + + ## Now solve for required torques + result = CdprControllerTensionDist.solve_torques(cdpr, lid, k, VAres, VAres, dt) + print("\tTwistAccel: ", gtd.TwistAccel(result, cdpr.ee_id(), k)[3:]) + print("\tTorques: ", [gtd.TorqueDouble(result, ji, k) for ji in range(4)]) + + return result + + @staticmethod + def solve_graph(graph, init): + params = gtsam.LevenbergMarquardtParams() + params.setRelativeErrorTol(0) + params.setAbsoluteErrorTol(0) + params.setErrorTol(1e-15) + result = gtsam.LevenbergMarquardtOptimizer(graph, init, params).optimize() + print("\terror ", graph.error(result)) + return result + + @staticmethod + def solve_twist_accel(cdpr, lid, Tgoal, k, TVnow=None, lldotnow=None, dt=0.01): fg = gtsam.NonlinearFactorGraph() + # IK: either solve for current pose T given measurements, or use open-loop solution if lldotnow is not None: fg.push_back(cdpr.kinematics_factors(ks=[k])) @@ -105,20 +138,13 @@ def solve_one_step(cdpr, lid, Tgoal, k, TVnow=None, lldotnow=None, dt=0.01, R=np else: fg.push_back(cdpr.priors_ik(ks=[k], values=TVnow)) # pose constraints: must reach next pose T - fg.push_back(gtsam.PriorFactorPose3(gtd.internal.PoseKey(lid, k).key(), + fg.push_back(gtsam.PriorFactorPose3(gtd.internal.PoseKey(lid, k+1).key(), Tgoal, cdpr.costmodel_prior_pose)) # collocation: given current+next Ts, solve for current+next Vs and current VAs - fg.push_back(cdpr.collocation_factors(ks=[k])) - # dynamics: given VA, solve for torque/wrenches - fg.push_back(cdpr.dynamics_factors(ks=[k])) - # redundancy resolution: control costs - for ji in range(4): - fg.push_back( - gtd.PriorFactorDouble(gtd.internal.TorqueKey(ji, k).key(), 0.0, - gtsam.noiseModel.Diagonal.Precisions(R))) - # tmp initial guess + fg.push_back(cdpr.collocation_factors(ks=[k], dt=dt)) + xk = gtsam.Values() - xk.insertDouble(0, dt) + xk.insert(0, dt) if lldotnow is not None: utils.InsertJointAngles(xk, k, lldotnow) utils.InsertJointVels(xk, k, lldotnow) @@ -130,10 +156,35 @@ def solve_one_step(cdpr, lid, Tgoal, k, TVnow=None, lldotnow=None, dt=0.01, R=np gtd.InsertPose(xk, lid, k+1, Tgoal) gtd.InsertTwist(xk, lid, k+1, np.zeros(6)) gtd.InsertTwistAccel(xk, lid, k, np.zeros(6)) + + return CdprControllerTensionDist.solve_graph(fg, xk) + + @staticmethod + def solve_torques(cdpr: Cdpr, lid, k, VAnow, TVnow, dt=0.01): + fg = gtsam.NonlinearFactorGraph() + # priors + fg.push_back(cdpr.priors_ik(ks=[k], values=TVnow)) + fg.push_back(cdpr.priors_id_va(ks=[k], values=VAnow)) + + # dynamics: given VA, solve for torque/wrenches + fg.push_back(cdpr.dynamics_factors(ks=[k])) + # redundancy resolution: control costs + for ji in range(4): + fg.push_back( + gtd.PriorFactorDouble(gtd.internal.TorqueKey(ji, k).key(), 0.0, + gtsam.noiseModel.Isotropic.Sigma(1, 100000.))) + + # tmp initial guess + xk = gtsam.Values() + utils.InsertPose(xk, lid, k, TVnow) + utils.InsertTwist(xk, lid, k, TVnow) + gtd.InsertTwistAccel(xk, lid, k, np.zeros(6)) for ji in range(4): - gtd.InsertTorqueDouble(xk, ji, k, 0) + gtd.InsertJointVelDouble(xk, ji, k, 0) + gtd.InsertJointAccelDouble(xk, ji, k, 1) + gtd.InsertTorqueDouble(xk, ji, k, 1) + gtd.InsertTensionDouble(xk, ji, k, 50) gtd.InsertWrench(xk, lid, ji, k, np.zeros(6)) - # optimize and update - result = gtsam.LevenbergMarquardtOptimizer(fg, xk).optimize() - print(fg.error(result)) - return result + + # optimize + return CdprControllerTensionDist.solve_graph(fg, xk) diff --git a/gtdynamics/cablerobot/src/cdpr_planar.py b/gtdynamics/cablerobot/src/cdpr_planar.py index 8a1f79a7..52d97df2 100644 --- a/gtdynamics/cablerobot/src/cdpr_planar.py +++ b/gtdynamics/cablerobot/src/cdpr_planar.py @@ -31,6 +31,7 @@ def __init__(self): radius=1, staticFriction=0, viscousFriction=0) + self.collocation_mode = 0 class Cdpr: @@ -150,7 +151,7 @@ def dynamics_factors(self, ks=[]): generalized version of F=ma and calculates wrenches from cable tensions. Primary variables: Torque <--> lengthddot Intermediate variables: Wrenches, TwistAccel - Prerequisite variables: Pose, Twist, length, lengthdot + Prerequisite variables: Pose, Twist, lengthdot, tension Args: @@ -212,19 +213,39 @@ def collocation_factors(self, ks=[], dt=0.01): gtsam.NonlinearFactorGraph: the collocation factors """ dfg = gtsam.NonlinearFactorGraph() - for k in ks: - dfg.push_back( - gtd.EulerPoseCollocationFactor( - gtd.internal.PoseKey(self.ee_id(), k).key(), - gtd.internal.PoseKey(self.ee_id(), k + 1).key(), - gtd.internal.TwistKey(self.ee_id(), k).key(), 0, - self.costmodel_posecollo)) - dfg.push_back( - gtd.EulerTwistCollocationFactor( - gtd.internal.TwistKey(self.ee_id(), k).key(), - gtd.internal.TwistKey(self.ee_id(), k + 1).key(), - gtd.internal.TwistAccelKey(self.ee_id(), k).key(), 0, - self.costmodel_twistcollo)) + if self.params.collocation_mode == 1: + for k in ks: + dfg.push_back( + gtd.TrapezoidalPoseCollocationFactor( + gtd.internal.PoseKey(self.ee_id(), k).key(), + gtd.internal.PoseKey(self.ee_id(), k + 1).key(), + gtd.internal.TwistKey(self.ee_id(), k).key(), # + gtd.internal.TwistKey(self.ee_id(), k + 1).key(), # + 0, + self.costmodel_posecollo)) + dfg.push_back( + gtd.EulerTwistCollocationFactor( + gtd.internal.TwistKey(self.ee_id(), k).key(), + gtd.internal.TwistKey(self.ee_id(), k + 1).key(), + gtd.internal.TwistAccelKey(self.ee_id(), k).key(), # + 0, + self.costmodel_twistcollo)) + else: + for k in ks: + dfg.push_back( + gtd.EulerPoseCollocationFactor( + gtd.internal.PoseKey(self.ee_id(), k).key(), + gtd.internal.PoseKey(self.ee_id(), k + 1).key(), + gtd.internal.TwistKey(self.ee_id(), k).key(), # + 0, + self.costmodel_posecollo)) + dfg.push_back( + gtd.EulerTwistCollocationFactor( + gtd.internal.TwistKey(self.ee_id(), k).key(), + gtd.internal.TwistKey(self.ee_id(), k + 1).key(), + gtd.internal.TwistAccelKey(self.ee_id(), k).key(), # + 0, + self.costmodel_twistcollo)) dfg.push_back(gtd.PriorFactorDouble(0, dt, self.costmodel_dt)) return dfg diff --git a/gtdynamics/cablerobot/src/cdpr_planar_sim.py b/gtdynamics/cablerobot/src/cdpr_planar_sim.py index 6d812643..d4cb6248 100644 --- a/gtdynamics/cablerobot/src/cdpr_planar_sim.py +++ b/gtdynamics/cablerobot/src/cdpr_planar_sim.py @@ -109,7 +109,7 @@ def update_dynamics(cdpr, x, u, k, dt): lid = cdpr.ee_id() # local copy of values xd = gtsam.Values() - xd.insertDouble(0, dt) + xd.insert(0, dt) gtd.InsertPose(xd, lid, k, gtd.Pose(x, lid, k)) gtd.InsertTwist(xd, lid, k, gtd.Twist(x, lid, k)) # FD for this timestep + collocation to next time step @@ -130,8 +130,13 @@ def update_dynamics(cdpr, x, u, k, dt): gtd.InsertTwist(xd, cdpr.ee_id(), k+1, np.zeros(6)) gtd.InsertTwistAccel(xd, cdpr.ee_id(), k, np.zeros(6)) # optimize - result = gtsam.LevenbergMarquardtOptimizer(fg, xd).optimize() - assert abs(fg.error(result)) < 1e-20, "dynamics simulation didn't converge" + params = gtsam.LevenbergMarquardtParams() + params.setRelativeErrorTol(0) + params.setAbsoluteErrorTol(0) + params.setErrorTol(1e-15) + result = gtsam.LevenbergMarquardtOptimizer(fg, xd, params).optimize() + assert abs(fg.error(result)) < 1e-15, "dynamics simulation didn't converge: {:.5e}".format( + abs(fg.error(result))) xd.update(result) return fg, xd diff --git a/gtdynamics/cablerobot/src/test_cdpr_controller_tension_dist.py b/gtdynamics/cablerobot/src/test_cdpr_controller_tension_dist.py index f8f959ca..f05d9003 100644 --- a/gtdynamics/cablerobot/src/test_cdpr_controller_tension_dist.py +++ b/gtdynamics/cablerobot/src/test_cdpr_controller_tension_dist.py @@ -26,7 +26,7 @@ def testTrajFollow(self): """Tests trajectory tracking controller """ cdpr = Cdpr() - + cdpr.params.collocation_mode = 1 x0 = gtsam.Values() gtd.InsertPose(x0, cdpr.ee_id(), 0, Pose3(Rot3(), (1.5, 0, 1.5))) gtd.InsertTwist(x0, cdpr.ee_id(), 0, np.zeros(6)) @@ -36,10 +36,11 @@ def testTrajFollow(self): controller = CdprController(cdpr, x0=x0, pdes=x_des, dt=0.1) sim = CdprSimulator(cdpr, x0, controller, dt=0.1) - result = sim.run(N=10) + result = sim.run(N=9) pAct = [gtd.Pose(result, cdpr.ee_id(), k) for k in range(10)] if False: + [gtd.InsertTorqueDouble(result, ji, 9, np.nan) for ji in range(4)] print() for k, (des, act) in enumerate(zip(x_des, pAct)): print(('k: {:d} -- des: {:.3f}, {:.3f}, {:.3f} -- act: {:.3f}, {:.3f}, {:.3f}' + diff --git a/gtdynamics/cablerobot/src/test_cdpr_planar.py b/gtdynamics/cablerobot/src/test_cdpr_planar.py index 72520a9d..6a663c62 100644 --- a/gtdynamics/cablerobot/src/test_cdpr_planar.py +++ b/gtdynamics/cablerobot/src/test_cdpr_planar.py @@ -122,8 +122,12 @@ def testDynamicsInstantaneous(self): for ji in range(4): init.erase(gtd.internal.TorqueKey(ji, 0).key()) gtd.InsertTorqueDouble(init, ji, 0, -1) - results = gtsam.LevenbergMarquardtOptimizer(dfg, init).optimize() - self.gtsamAssertEquals(results, values) + params = gtsam.LevenbergMarquardtParams() + params.setRelativeErrorTol(0) + params.setAbsoluteErrorTol(0) + params.setErrorTol(1e-20) + results = gtsam.LevenbergMarquardtOptimizer(dfg, init, params).optimize() + self.gtsamAssertEquals(values, results) # check FD priors functions fd1 = cdpr.priors_fd(ks=[0], torquess=[[gtd.TorqueDouble(results, ji, 0) for ji in range(4)]]) fd2 = cdpr.priors_fd(ks=[0], values=results) diff --git a/gtdynamics/cablerobot/src/test_cdpr_planar_sim.py b/gtdynamics/cablerobot/src/test_cdpr_planar_sim.py index 2bbe3a8b..2f16e8ee 100644 --- a/gtdynamics/cablerobot/src/test_cdpr_planar_sim.py +++ b/gtdynamics/cablerobot/src/test_cdpr_planar_sim.py @@ -51,7 +51,7 @@ def update(self, values, k): xdot = 0 for k in range(10): pExp = Pose3(Rot3(), (x, 0, 1.5)) - self.gtsamAssertEquals(pExp, pAct[k], tol=1e-15) + self.gtsamAssertEquals(pExp, pAct[k], tol=1e-14) dx, dy = 3 - x - 0.15, 1.35 # (dx, dy) represents the cable vector xddot = 2 * dx / np.sqrt(dx**2 + dy**2) x += xdot * dt diff --git a/gtdynamics/cablerobot/tests/testWinchFactor.cpp b/gtdynamics/cablerobot/tests/testWinchFactor.cpp index b185e0fa..83780205 100644 --- a/gtdynamics/cablerobot/tests/testWinchFactor.cpp +++ b/gtdynamics/cablerobot/tests/testWinchFactor.cpp @@ -61,10 +61,10 @@ TEST(WinchFactor, error) { factor.evaluateError(0.12 + 0.34 + 1 / 0.12 * 0.56, 1, -1, 0))); Values values; - values.insertDouble(torqueKey, 1); - values.insertDouble(tensionKey, 2); - values.insertDouble(jointVelKey, .03); - values.insertDouble(jointAccKey, 4); + values.insert(torqueKey, 1.); + values.insert(tensionKey, 2.); + values.insert(jointVelKey, .03); + values.insert(jointAccKey, 4.); EXPECT_CORRECT_FACTOR_JACOBIANS(factor, values, 1e-7, 1e-3); } From e0508f0db4098f2a31d01bc2eeac9e382a3003da Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Sat, 28 Aug 2021 19:44:09 -0400 Subject: [PATCH 52/73] remove debug statements --- .../src/cdpr_controller_tension_dist.py | 47 ++++++++++--------- 1 file changed, 25 insertions(+), 22 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py b/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py index 1e494620..29fdd5bd 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py @@ -80,7 +80,7 @@ def update(self, values, t): R=self.R) @staticmethod - def solve_one_step(cdpr, lid, Tgoal, k, TVnow=None, lldotnow=None, dt=0.01, R=np.ones(1)): + def solve_one_step(cdpr, lid, Tgoal, k, TVnow=None, lldotnow=None, dt=0.01, R=np.ones(1), debug=False): """Creates the factor graph for the tension distribution problem. This essentially consists of creating a factor graph that describes the CDPR dynamics for this one timestep, then adding control cost factors. Either the current pose/twist may be specified, or the current @@ -98,22 +98,26 @@ def solve_one_step(cdpr, lid, Tgoal, k, TVnow=None, lldotnow=None, dt=0.01, R=np Returns: gtsam.Values: a Values object containing the control torques """ - print("My goal for k = {:d} is:".format(k), Tgoal.translation()) - - ## First solve for required TwistAccel - print("\tpose: ", gtd.Pose(TVnow, cdpr.ee_id(), k).translation()) - print("\tTwist: ", gtd.Twist(TVnow, cdpr.ee_id(), k)[3:]) - VAres = CdprControllerTensionDist.solve_twist_accel(cdpr, lid, Tgoal, k, TVnow, lldotnow, dt) - print("\tpose: ", gtd.Pose(VAres, cdpr.ee_id(), k).translation()) - print("\tpose: ", gtd.Pose(VAres, cdpr.ee_id(), k + 1).translation()) - print("\tTwist: ", gtd.Twist(VAres, cdpr.ee_id(), k)[3:]) - print("\tTwist: ", gtd.Twist(VAres, cdpr.ee_id(), k + 1)[3:]) - print("\tTwistAccel: ", gtd.TwistAccel(VAres, cdpr.ee_id(), k)[3:]) - - ## Now solve for required torques - result = CdprControllerTensionDist.solve_torques(cdpr, lid, k, VAres, VAres, dt) - print("\tTwistAccel: ", gtd.TwistAccel(result, cdpr.ee_id(), k)[3:]) - print("\tTorques: ", [gtd.TorqueDouble(result, ji, k) for ji in range(4)]) + + # First solve for required TwistAccel + VAres = CdprControllerTensionDist.solve_twist_accel(cdpr, lid, Tgoal, k, TVnow, lldotnow, + dt) + + # Now solve for required torques + result = CdprControllerTensionDist.solve_torques(cdpr, lid, k, VAres, VAres, dt, R) + + # Debug + if debug: + print("My goal for k = {:d} is:".format(k), Tgoal.translation()) + print("\cur pose: ", gtd.Pose(TVnow, cdpr.ee_id(), k).translation()) + print("\tcur Twist: ", gtd.Twist(TVnow, cdpr.ee_id(), k)[3:]) + print("\tcur pose VA: ", gtd.Pose(VAres, cdpr.ee_id(), k).translation()) + print("\tnext pose VA: ", gtd.Pose(VAres, cdpr.ee_id(), k + 1).translation()) + print("\tcur Twist VA: ", gtd.Twist(VAres, cdpr.ee_id(), k)[3:]) + print("\tnext Twist VA: ", gtd.Twist(VAres, cdpr.ee_id(), k + 1)[3:]) + print("\tcur TwistAccel VA: ", gtd.TwistAccel(VAres, cdpr.ee_id(), k)[3:]) + print("\tcur TwistAccel res: ", gtd.TwistAccel(result, cdpr.ee_id(), k)[3:]) + print("\tcur Torques res: ", [gtd.TorqueDouble(result, ji, k) for ji in range(4)]) return result @@ -124,7 +128,6 @@ def solve_graph(graph, init): params.setAbsoluteErrorTol(0) params.setErrorTol(1e-15) result = gtsam.LevenbergMarquardtOptimizer(graph, init, params).optimize() - print("\terror ", graph.error(result)) return result @staticmethod @@ -160,7 +163,7 @@ def solve_twist_accel(cdpr, lid, Tgoal, k, TVnow=None, lldotnow=None, dt=0.01): return CdprControllerTensionDist.solve_graph(fg, xk) @staticmethod - def solve_torques(cdpr: Cdpr, lid, k, VAnow, TVnow, dt=0.01): + def solve_torques(cdpr: Cdpr, lid, k, VAnow, TVnow, dt, R): fg = gtsam.NonlinearFactorGraph() # priors fg.push_back(cdpr.priors_ik(ks=[k], values=TVnow)) @@ -172,8 +175,8 @@ def solve_torques(cdpr: Cdpr, lid, k, VAnow, TVnow, dt=0.01): for ji in range(4): fg.push_back( gtd.PriorFactorDouble(gtd.internal.TorqueKey(ji, k).key(), 0.0, - gtsam.noiseModel.Isotropic.Sigma(1, 100000.))) - + gtsam.noiseModel.Diagonal.Precisions(R))) + # tmp initial guess xk = gtsam.Values() utils.InsertPose(xk, lid, k, TVnow) @@ -185,6 +188,6 @@ def solve_torques(cdpr: Cdpr, lid, k, VAnow, TVnow, dt=0.01): gtd.InsertTorqueDouble(xk, ji, k, 1) gtd.InsertTensionDouble(xk, ji, k, 50) gtd.InsertWrench(xk, lid, ji, k, np.zeros(6)) - + # optimize return CdprControllerTensionDist.solve_graph(fg, xk) From 88791464a6d271fc8ca2f9a98492aba1923bac88 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Sat, 28 Aug 2021 19:51:23 -0400 Subject: [PATCH 53/73] WIP: bugfix in gtsam python wrapper: see branch: bugfix/gerry/python_jacobianfactor_rtti Be sure to manually set linearSolverType to QR, or use the MyLMParams() function in utils, or else the optimizer won't work properly. --- gtdynamics/cablerobot/src/utils.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/gtdynamics/cablerobot/src/utils.py b/gtdynamics/cablerobot/src/utils.py index ee175385..a38ac16d 100644 --- a/gtdynamics/cablerobot/src/utils.py +++ b/gtdynamics/cablerobot/src/utils.py @@ -58,3 +58,11 @@ def InsertTorques(dest, k, source): def InsertWrenches(dest, link_id, k, source): for ji in range(4): gtd.InsertWrench(dest, link_id, ji, k, gtd.Wrench(source, link_id, ji, k)) + +def MyLMParams(abs_err_tol=1e-15): + params = gtsam.LevenbergMarquardtParams() + params.setRelativeErrorTol(0) + params.setAbsoluteErrorTol(0) + params.setErrorTol(abs_err_tol) + params.setLinearSolverType("MULTIFRONTAL_QR") + return params From 4c30d2cc3e5aacc7755bc9eecc26eaee7fde6574 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Sat, 28 Aug 2021 19:45:30 -0400 Subject: [PATCH 54/73] WIP: cdpr_planar (post gtsam bugfix) --- gtdynamics/cablerobot/src/cdpr_planar.py | 36 +++++++++---------- gtdynamics/cablerobot/src/test_cdpr_planar.py | 16 ++++----- 2 files changed, 24 insertions(+), 28 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_planar.py b/gtdynamics/cablerobot/src/cdpr_planar.py index 52d97df2..35e89afa 100644 --- a/gtdynamics/cablerobot/src/cdpr_planar.py +++ b/gtdynamics/cablerobot/src/cdpr_planar.py @@ -42,24 +42,24 @@ def __init__(self, params=CdprParams()): self.params = params ee = gtd.Link(1, "ee", params.mass, params.inertia, Pose3(), Pose3()) self.robot = gtd.Robot({'ee' : ee}, {}) - self.costmodel_l = gtsam.noiseModel.Isotropic.Sigma(1, 0.001) - self.costmodel_ldot = gtsam.noiseModel.Isotropic.Sigma(1, 0.001) - self.costmodel_lddot = gtsam.noiseModel.Isotropic.Sigma(1, 0.001) - self.costmodel_winch = gtsam.noiseModel.Isotropic.Sigma(1, 0.001) - self.costmodel_torque = gtsam.noiseModel.Isotropic.Sigma(6, 0.001) - self.costmodel_wrench = gtsam.noiseModel.Isotropic.Sigma(6, 0.001) - self.costmodel_twistcollo = gtsam.noiseModel.Isotropic.Sigma(6, 0.001) - self.costmodel_posecollo = gtsam.noiseModel.Isotropic.Sigma(6, 0.001) - self.costmodel_prior_l = gtsam.noiseModel.Isotropic.Sigma(1, 0.001) - self.costmodel_prior_ldot = gtsam.noiseModel.Isotropic.Sigma(1, 0.001) - self.costmodel_prior_lddot = gtsam.noiseModel.Isotropic.Sigma(1, 0.001) - self.costmodel_prior_tau = gtsam.noiseModel.Isotropic.Sigma(1, 0.001) - self.costmodel_prior_pose = gtsam.noiseModel.Isotropic.Sigma(6, 0.001) - self.costmodel_prior_twist = gtsam.noiseModel.Isotropic.Sigma(6, 0.001) - self.costmodel_prior_twistaccel = gtsam.noiseModel.Isotropic.Sigma(6, 0.001) - self.costmodel_planar_pose = gtsam.noiseModel.Isotropic.Sigma(3, 0.001) - self.costmodel_planar_twist = gtsam.noiseModel.Isotropic.Sigma(3, 0.001) - self.costmodel_dt = gtsam.noiseModel.Isotropic.Sigma(1, 0.001) + self.costmodel_l = gtsam.noiseModel.Constrained.All(1) + self.costmodel_ldot = gtsam.noiseModel.Constrained.All(1) + self.costmodel_lddot = gtsam.noiseModel.Constrained.All(1) + self.costmodel_winch = gtsam.noiseModel.Constrained.All(1) + self.costmodel_torque = gtsam.noiseModel.Constrained.All(6) + self.costmodel_wrench = gtsam.noiseModel.Constrained.All(6) + self.costmodel_twistcollo = gtsam.noiseModel.Constrained.All(6) + self.costmodel_posecollo = gtsam.noiseModel.Constrained.All(6) + self.costmodel_prior_l = gtsam.noiseModel.Constrained.All(1) + self.costmodel_prior_ldot = gtsam.noiseModel.Constrained.All(1) + self.costmodel_prior_lddot = gtsam.noiseModel.Constrained.All(1) + self.costmodel_prior_tau = gtsam.noiseModel.Constrained.All(1) + self.costmodel_prior_pose = gtsam.noiseModel.Constrained.All(6) + self.costmodel_prior_twist = gtsam.noiseModel.Constrained.All(6) + self.costmodel_prior_twistaccel = gtsam.noiseModel.Constrained.All(6) + self.costmodel_planar_pose = gtsam.noiseModel.Constrained.All(3) + self.costmodel_planar_twist = gtsam.noiseModel.Constrained.All(3) + self.costmodel_dt = gtsam.noiseModel.Constrained.All(1) def eelink(self): """Link object for the end-effector diff --git a/gtdynamics/cablerobot/src/test_cdpr_planar.py b/gtdynamics/cablerobot/src/test_cdpr_planar.py index 6a663c62..f15f4a58 100644 --- a/gtdynamics/cablerobot/src/test_cdpr_planar.py +++ b/gtdynamics/cablerobot/src/test_cdpr_planar.py @@ -18,6 +18,7 @@ import numpy as np from cdpr_planar import Cdpr from gtsam.utils.test_case import GtsamTestCase +from utils import MyLMParams class TestCdprPlanar(GtsamTestCase): """Unit tests for planar CDPR""" @@ -55,7 +56,7 @@ def zeroValues(): ik2 = cdpr.priors_ik(ks=[0], values=values) self.gtsamAssertEquals(ik1, ik2) ikgraph.push_back(ik1) - ikres = gtsam.LevenbergMarquardtOptimizer(ikgraph, zeroValues()).optimize() + ikres = gtsam.LevenbergMarquardtOptimizer(ikgraph, zeroValues(), MyLMParams()).optimize() self.gtsamAssertEquals(ikres, values) # should match with full sol # try optimizing FK fkgraph = gtsam.NonlinearFactorGraph(kfg) @@ -65,9 +66,8 @@ def zeroValues(): fk2 = cdpr.priors_fk(ks=[0], values=values) self.gtsamAssertEquals(fk1, fk2) fkgraph.push_back(fk1) - params = gtsam.LevenbergMarquardtParams() - params.setAbsoluteErrorTol(1e-20) # FK less sensitive so we need to decrease the tolerance - fkres = gtsam.LevenbergMarquardtOptimizer(fkgraph, zeroValues(), params).optimize() + # FK less sensitive so we need to decrease the tolerance to 1e-20 + fkres = gtsam.LevenbergMarquardtOptimizer(fkgraph, zeroValues(), MyLMParams(1e-20)).optimize() self.gtsamAssertEquals(fkres, values, tol=1e-5) # should match with full sol def testDynamicsInstantaneous(self): @@ -122,11 +122,7 @@ def testDynamicsInstantaneous(self): for ji in range(4): init.erase(gtd.internal.TorqueKey(ji, 0).key()) gtd.InsertTorqueDouble(init, ji, 0, -1) - params = gtsam.LevenbergMarquardtParams() - params.setRelativeErrorTol(0) - params.setAbsoluteErrorTol(0) - params.setErrorTol(1e-20) - results = gtsam.LevenbergMarquardtOptimizer(dfg, init, params).optimize() + results = gtsam.LevenbergMarquardtOptimizer(dfg, init, MyLMParams(1e-20)).optimize() self.gtsamAssertEquals(values, results) # check FD priors functions fd1 = cdpr.priors_fd(ks=[0], torquess=[[gtd.TorqueDouble(results, ji, 0) for ji in range(4)]]) @@ -165,7 +161,7 @@ def testDynamicsCollocation(self): gtd.InsertTwist(init, cdpr.ee_id(), t, np.ones(6)) gtd.InsertTwistAccel(init, cdpr.ee_id(), t, np.ones(6)) # optimize - optimizer = gtsam.LevenbergMarquardtOptimizer(fg, init) + optimizer = gtsam.LevenbergMarquardtOptimizer(fg, init, MyLMParams()) result = optimizer.optimize() # correctness checks: # timestep 0 From 23d2eda3f07b709033fa881d01deefe136a93d3f Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Sat, 28 Aug 2021 19:47:15 -0400 Subject: [PATCH 55/73] WIP: cdpr_planar_sim (post gtsam bugfix) --- gtdynamics/cablerobot/src/cdpr_planar_sim.py | 9 +++------ gtdynamics/cablerobot/src/test_cdpr_planar_sim.py | 2 +- 2 files changed, 4 insertions(+), 7 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_planar_sim.py b/gtdynamics/cablerobot/src/cdpr_planar_sim.py index d4cb6248..1cd59601 100644 --- a/gtdynamics/cablerobot/src/cdpr_planar_sim.py +++ b/gtdynamics/cablerobot/src/cdpr_planar_sim.py @@ -13,6 +13,7 @@ import gtsam import gtdynamics as gtd import numpy as np +from utils import MyLMParams class CdprSimulator: """Simulates a cable robot forward in time, given a robot, initial state, and controller. @@ -83,7 +84,7 @@ def update_kinematics(cdpr, x, k): gtd.InsertJointAngleDouble(xk, j, k, 0) gtd.InsertJointVelDouble(xk, j, k, 0) # IK solve - result = gtsam.LevenbergMarquardtOptimizer(fg, xk).optimize() + result = gtsam.LevenbergMarquardtOptimizer(fg, xk, MyLMParams()).optimize() assert abs(fg.error(result)) < 1e-20, "inverse kinematics didn't converge" xk.update(result) return fg, xk @@ -130,11 +131,7 @@ def update_dynamics(cdpr, x, u, k, dt): gtd.InsertTwist(xd, cdpr.ee_id(), k+1, np.zeros(6)) gtd.InsertTwistAccel(xd, cdpr.ee_id(), k, np.zeros(6)) # optimize - params = gtsam.LevenbergMarquardtParams() - params.setRelativeErrorTol(0) - params.setAbsoluteErrorTol(0) - params.setErrorTol(1e-15) - result = gtsam.LevenbergMarquardtOptimizer(fg, xd, params).optimize() + result = gtsam.LevenbergMarquardtOptimizer(fg, xd, MyLMParams()).optimize() assert abs(fg.error(result)) < 1e-15, "dynamics simulation didn't converge: {:.5e}".format( abs(fg.error(result))) xd.update(result) diff --git a/gtdynamics/cablerobot/src/test_cdpr_planar_sim.py b/gtdynamics/cablerobot/src/test_cdpr_planar_sim.py index 2f16e8ee..2bbe3a8b 100644 --- a/gtdynamics/cablerobot/src/test_cdpr_planar_sim.py +++ b/gtdynamics/cablerobot/src/test_cdpr_planar_sim.py @@ -51,7 +51,7 @@ def update(self, values, k): xdot = 0 for k in range(10): pExp = Pose3(Rot3(), (x, 0, 1.5)) - self.gtsamAssertEquals(pExp, pAct[k], tol=1e-14) + self.gtsamAssertEquals(pExp, pAct[k], tol=1e-15) dx, dy = 3 - x - 0.15, 1.35 # (dx, dy) represents the cable vector xddot = 2 * dx / np.sqrt(dx**2 + dy**2) x += xdot * dt From 8f46cef6e4ee5a49c4a8148ae51e6e12956e6068 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Sat, 28 Aug 2021 19:48:09 -0400 Subject: [PATCH 56/73] WIP: cdpr_controller_tension_dist (post gtsam bugfix) --- gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py b/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py index 29fdd5bd..d3376f2c 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py @@ -123,11 +123,7 @@ def solve_one_step(cdpr, lid, Tgoal, k, TVnow=None, lldotnow=None, dt=0.01, R=np @staticmethod def solve_graph(graph, init): - params = gtsam.LevenbergMarquardtParams() - params.setRelativeErrorTol(0) - params.setAbsoluteErrorTol(0) - params.setErrorTol(1e-15) - result = gtsam.LevenbergMarquardtOptimizer(graph, init, params).optimize() + result = gtsam.LevenbergMarquardtOptimizer(graph, init, utils.MyLMParams()).optimize() return result @staticmethod From 08997a56df687d9dea368e8470e364defa8fec4b Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Sat, 28 Aug 2021 19:50:40 -0400 Subject: [PATCH 57/73] WIP: cdpr_controller_ilqr (post gtsam bugfix) --- gtdynamics/cablerobot/src/cdpr_controller_ilqr.py | 6 +++++- gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py | 5 +++-- 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py index 301866d4..4bea1ac7 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py @@ -44,7 +44,7 @@ def __init__(self, cdpr, x0, pdes=[], dt=0.01, Q=None, R=np.array([1.])): # create iLQR graph fg = self.create_ilqr_fg(cdpr, x0, pdes, dt, Q, R) # optimize - self.optimizer = gtsam.LevenbergMarquardtOptimizer(fg, x_guess) + self.optimizer = gtsam.LevenbergMarquardtOptimizer(fg, x_guess, utils.MyLMParams()) self.result = self.optimizer.optimize() self.fg = fg # gains @@ -163,6 +163,10 @@ def extract_gains(cdpr, fg, openloop_results, N): # extract_gains gains = [None for t in range(N)] # for t, neti in enumerate(u_inds): + # 0 mod 4: ("stuff we do care about") -> ("stuff we don't care about") + # 1 mod 4: (wrenches, twist, pose) -> torques + # 2 mod 4: (twist, pose) -> wrenches + # 3 mod 4: (prev state) -> (twist, pose) aka collocation update for t, (neti, netu) in enumerate(zip(reversed(range(2, 4*N, 4)), reversed(range(1, 4*N, 4)))): ucond = net.at(netu) diff --git a/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py index 0a4019bf..7b6fec86 100644 --- a/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/test_cdpr_controller_ilqr.py @@ -48,7 +48,7 @@ def testTrajFollow(self): *[gtd.TorqueDouble(result, ji, k) for ji in range(4)])) for k, (des, act) in enumerate(zip(x_des, pAct)): - self.gtsamAssertEquals(des, act) + self.gtsamAssertEquals(des, act, tol=1e-3) def testGainsNearConstrained(self): """Tests locally linear, time-varying feedback gains @@ -56,6 +56,7 @@ def testGainsNearConstrained(self): cdpr = Cdpr() cdpr.params.tmin = -1 cdpr.params.tmax = 1 + cdpr.params.collocation_mode = 0 dt = 0.01 x0 = gtsam.Values() @@ -76,7 +77,7 @@ def testGainsNearConstrained(self): -1 / np.sqrt(2) / 2, 0, 1 / np.sqrt(2) / 2]).reshape((1, -1)) * \ cdpr.params.mass / dt # cable 0 tension at t=0 in response to v at t=1 expected_0c0_K_0x = -expected_0c0_K_1v @ expected_1v_K_0x - self.gtsamAssertEquals(actual_0c0_K_0x[:, 3:], expected_0c0_K_0x[:, 3:], tol=1/dt) + self.gtsamAssertEquals(expected_0c0_K_0x[:, 3:], actual_0c0_K_0x[:, 3:], tol=1/dt) # velocity gain (Kd) - time 0, cable 0, twist gain actual_0c0_K_0v = controller.gains_ff[0][0][0:1, :6] From a44e6a10be218fbc5af1b1f3b2736a87fd7ac15d Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Sat, 28 Aug 2021 20:34:18 -0400 Subject: [PATCH 58/73] cleanup --- .../cablerobot/src/cdpr_controller_ilqr.py | 4 +- .../src/cdpr_controller_tension_dist.py | 9 +-- gtdynamics/cablerobot/src/cdpr_planar_sim.py | 4 +- gtdynamics/cablerobot/src/utils.py | 63 +++++++++++++++++++ 4 files changed, 69 insertions(+), 11 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py index 4bea1ac7..49a56a5a 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py @@ -39,10 +39,10 @@ def __init__(self, cdpr, x0, pdes=[], dt=0.01, Q=None, R=np.array([1.])): self.pdes = pdes self.dt = dt - # initial guess - x_guess = utils.zerovalues(cdpr.ee_id(), ts=range(len(pdes)), dt=dt) # create iLQR graph fg = self.create_ilqr_fg(cdpr, x0, pdes, dt, Q, R) + # initial guess + x_guess = utils.ZeroValues(fg, dt) # optimize self.optimizer = gtsam.LevenbergMarquardtOptimizer(fg, x_guess, utils.MyLMParams()) self.result = self.optimizer.optimize() diff --git a/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py b/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py index d3376f2c..e46a4887 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py @@ -121,11 +121,6 @@ def solve_one_step(cdpr, lid, Tgoal, k, TVnow=None, lldotnow=None, dt=0.01, R=np return result - @staticmethod - def solve_graph(graph, init): - result = gtsam.LevenbergMarquardtOptimizer(graph, init, utils.MyLMParams()).optimize() - return result - @staticmethod def solve_twist_accel(cdpr, lid, Tgoal, k, TVnow=None, lldotnow=None, dt=0.01): fg = gtsam.NonlinearFactorGraph() @@ -156,7 +151,7 @@ def solve_twist_accel(cdpr, lid, Tgoal, k, TVnow=None, lldotnow=None, dt=0.01): gtd.InsertTwist(xk, lid, k+1, np.zeros(6)) gtd.InsertTwistAccel(xk, lid, k, np.zeros(6)) - return CdprControllerTensionDist.solve_graph(fg, xk) + return gtsam.LevenbergMarquardtOptimizer(fg, xk, utils.MyLMParams()).optimize() @staticmethod def solve_torques(cdpr: Cdpr, lid, k, VAnow, TVnow, dt, R): @@ -186,4 +181,4 @@ def solve_torques(cdpr: Cdpr, lid, k, VAnow, TVnow, dt, R): gtd.InsertWrench(xk, lid, ji, k, np.zeros(6)) # optimize - return CdprControllerTensionDist.solve_graph(fg, xk) + return gtsam.LevenbergMarquardtOptimizer(fg, xk, utils.MyLMParams()).optimize() diff --git a/gtdynamics/cablerobot/src/cdpr_planar_sim.py b/gtdynamics/cablerobot/src/cdpr_planar_sim.py index 1cd59601..1c6a6f39 100644 --- a/gtdynamics/cablerobot/src/cdpr_planar_sim.py +++ b/gtdynamics/cablerobot/src/cdpr_planar_sim.py @@ -13,7 +13,7 @@ import gtsam import gtdynamics as gtd import numpy as np -from utils import MyLMParams +from utils import MyLMParams, InitValues class CdprSimulator: """Simulates a cable robot forward in time, given a robot, initial state, and controller. @@ -84,7 +84,7 @@ def update_kinematics(cdpr, x, k): gtd.InsertJointAngleDouble(xk, j, k, 0) gtd.InsertJointVelDouble(xk, j, k, 0) # IK solve - result = gtsam.LevenbergMarquardtOptimizer(fg, xk, MyLMParams()).optimize() + result = gtsam.LevenbergMarquardtOptimizer(fg, InitValues(fg), MyLMParams()).optimize() assert abs(fg.error(result)) < 1e-20, "inverse kinematics didn't converge" xk.update(result) return fg, xk diff --git a/gtdynamics/cablerobot/src/utils.py b/gtdynamics/cablerobot/src/utils.py index a38ac16d..30ef51a4 100644 --- a/gtdynamics/cablerobot/src/utils.py +++ b/gtdynamics/cablerobot/src/utils.py @@ -40,6 +40,69 @@ def zerovalues(lid, ts=[], dt=0.01): gtd.InsertTwistAccel(zero, lid, t, np.zeros(6)) return zero +def InsertZeroByLabel(values, key, dt=None): + label, l, j, t = key.label(), key.linkIdx(), key.jointIdx(), key.time() + if label == "q": # JointAngleKey + gtd.InsertJointAngleDouble(values, j, t, 0.0) + elif label == "v": # JointVelKey + gtd.InsertJointVelDouble(values, j, t, 0.0) + elif label == "a": # JointAccelKey + gtd.InsertJointAccelDouble(values, j, t, 0.0) + elif label == "t": # TensionKey + gtd.InsertTensionDouble(values, j, t, 0.0) + elif label == "T": # TorqueKey + gtd.InsertTorqueDouble(values, j, t, 0.0) + elif label == "p": # PoseKey + gtd.InsertPose(values, l, t, gtsam.Pose3()) + elif label == "V": # TwistKey + gtd.InsertTwist(values, l, t, np.zeros(6)) + elif label == "A": # TwistAccelKey + gtd.InsertTwistAccel(values, l, t, np.zeros(6)) + elif label == "F": # WrenchKey + gtd.InsertWrench(values, l, j, t, np.zeros(6)) + elif (dt is not None) and (key.key() == 0): + values.insert(key.key(), dt) + else: + print(key) + raise RuntimeError("Unknown key label type: ", label) + +def ZeroValues(graph, dt=None): + z = gtsam.Values() + for key in graph.keyVector(): + key = gtd.DynamicsSymbol(key) + InsertZeroByLabel(z, key, dt) + return z + +def UpdateFromValues(src, dst, key): + label = key.label() + if label in "qvatT": # joint angle, vel, acc, tension, torque + dst.insert(key.key(), src.atDouble(key.key())) + elif label == "p": # PoseKey + dst.insert(key.key(), src.atPose3(key.key())) + elif label == "V": # TwistKey + gtd.InsertTwist(dst, key.linkIdx(), key.time(), gtd.Twist(src, key.linkIdx(), key.time())) + elif label == "A": # TwistAccelKey + gtd.InsertTwistAccel(dst, key.linkIdx(), key.time(), + gtd.TwistAccel(src, key.linkIdx(), key.time())) + elif label == "F": # WrenchKey + gtd.InsertWrench(dst, key.linkIdx(), key.time(), gtd.Wrench(src, key.linkIdx(), key.time())) + elif key.key() == 0: + dst.insert(key.key(), src.atDouble(key.key())) + else: + print(key) + raise RuntimeError("Unknown key label type: ", label) + +def InitValues(graph, values=None, dt=None): + init = gtsam.Values() + existing_keys = values.keys() if values is not None else [] + for key in graph.keyVector(): + key = gtd.DynamicsSymbol(key) + if key.key() in existing_keys: + UpdateFromValues(values, init, key) + else: + InsertZeroByLabel(init, key, dt) + return init + def InsertPose(dest, link_id, k, source): gtd.InsertPose(dest, link_id, k, gtd.Pose(source, link_id, k)) def InsertTwist(dest, link_id, k, source): From dc709fc618b3f3a33ef00b5710ebd1cfeb81b7cc Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Sun, 29 Aug 2021 11:45:49 -0400 Subject: [PATCH 59/73] parameter tweaking --- gtdynamics/cablerobot/src/cdpr_controller_ilqr.py | 2 +- gtdynamics/cablerobot/src/cdpr_planar_sim.py | 12 +++++++----- gtdynamics/cablerobot/src/gerry02_traj_tracking.py | 7 +++++-- 3 files changed, 13 insertions(+), 8 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py index 49a56a5a..23f043b8 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py @@ -42,7 +42,7 @@ def __init__(self, cdpr, x0, pdes=[], dt=0.01, Q=None, R=np.array([1.])): # create iLQR graph fg = self.create_ilqr_fg(cdpr, x0, pdes, dt, Q, R) # initial guess - x_guess = utils.ZeroValues(fg, dt) + x_guess = utils.InitValues(fg, dt=dt) # optimize self.optimizer = gtsam.LevenbergMarquardtOptimizer(fg, x_guess, utils.MyLMParams()) self.result = self.optimizer.optimize() diff --git a/gtdynamics/cablerobot/src/cdpr_planar_sim.py b/gtdynamics/cablerobot/src/cdpr_planar_sim.py index 1c6a6f39..e59c6397 100644 --- a/gtdynamics/cablerobot/src/cdpr_planar_sim.py +++ b/gtdynamics/cablerobot/src/cdpr_planar_sim.py @@ -81,10 +81,10 @@ def update_kinematics(cdpr, x, k): fg.push_back(cdpr.priors_ik(ks=[k], values=xk)) # IK initial estimate for j in range(4): - gtd.InsertJointAngleDouble(xk, j, k, 0) + gtd.InsertJointAngleDouble(xk, j, k, 1.5) gtd.InsertJointVelDouble(xk, j, k, 0) # IK solve - result = gtsam.LevenbergMarquardtOptimizer(fg, InitValues(fg), MyLMParams()).optimize() + result = gtsam.LevenbergMarquardtOptimizer(fg, xk, MyLMParams()).optimize() assert abs(fg.error(result)) < 1e-20, "inverse kinematics didn't converge" xk.update(result) return fg, xk @@ -127,8 +127,8 @@ def update_dynamics(cdpr, x, u, k, dt): gtd.InsertTensionDouble(xd, ji, k, gtd.TorqueDouble(u, ji, k) / cdpr.params.winch_params.radius_) gtd.InsertWrench(xd, cdpr.ee_id(), ji, k, np.zeros(6)) - gtd.InsertPose(xd, cdpr.ee_id(), k+1, gtsam.Pose3(gtsam.Rot3(), (1.5, 0, 1.5))) - gtd.InsertTwist(xd, cdpr.ee_id(), k+1, np.zeros(6)) + gtd.InsertPose(xd, cdpr.ee_id(), k + 1, gtd.Pose(x, lid, k)) + gtd.InsertTwist(xd, cdpr.ee_id(), k + 1, gtd.Twist(x, lid, k)) gtd.InsertTwistAccel(xd, cdpr.ee_id(), k, np.zeros(6)) # optimize result = gtsam.LevenbergMarquardtOptimizer(fg, xd, MyLMParams()).optimize() @@ -173,6 +173,8 @@ def step(self, verbose=False): print('time step: {:4d}'.format(k), end=' -- ') print('EE position: ({:.2f}, {:.2f}, {:.2f})'.format( *gtd.Pose(x, lid, k).translation()), end=' -- ') + print('next: ({:.2f}, {:.2f}, {:.2f})'.format( + *gtd.Pose(x, lid, k+1).translation()), end=' -- ') print('control torques: {:.2e}, {:.2e}, {:.2e}, {:.2e}'.format( *[gtd.TorqueDouble(u, ji, k) for ji in range(4)])) @@ -189,7 +191,7 @@ def run(self, N=100, verbose=False): Returns: gtsam.Values: The values object containing all the data from the simulation. """ - for k in range(N): + for _ in range(N): self.step(verbose=verbose) return self.x diff --git a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py index 544e294b..f3ebbf98 100644 --- a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py +++ b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py @@ -51,7 +51,8 @@ def main(fname='data/iros_logo_2.h', R=np.ones(1) * 1e-2, N0=0, N=None, - dN=1): + dN=1, + speed_multiplier=1): """Runs a simulation of the iLQR controller trying to execute a predefined trajectory. Args: @@ -84,6 +85,7 @@ def main(fname='data/iros_logo_2.h', bw, bh = 0.15, 0.30 params = CdprParams() params.mass = 0.5 + params.gravity = np.array([0, 0, -9.8]).reshape((3, 1)) params.a_locs = np.array([[aw, 0, 0], [aw, 0, ah], [0, 0, ah], [0, 0, 0]]) params.b_locs = np.array([[bw, 0., -bh], [bw, 0., bh], [-bw, 0., bh], [-bw, 0, -bh]]) / 2 params.b_locs = params.b_locs - [0, 0, bh * 0.4] @@ -94,7 +96,8 @@ def main(fname='data/iros_logo_2.h', # import data isPaints, colorinds, colorpalette, traj = ParseFile(fname) N = len(traj) - N0 - 1 if N is None else N - dt = 0.01 * dN # this is a hardcoded constant. TODO(gerry): include this in the .h file. + dt = (0.01 / speed_multiplier + ) * dN # this is a hardcoded constant. TODO(gerry): include this in the .h file. N = int(N/dN) # scale time by dN N0 = int(N0/dN) traj = (traj - [aw/2, ah/2]) * 0.85 + [aw/2, ah/2] # rescale trajectory to be smaller From 2b0e523e3278c4e4c60f86d67af148c946c73908 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 7 Sep 2021 19:51:37 -0400 Subject: [PATCH 60/73] minor refactoring --- .../cablerobot/src/gerry02_traj_tracking.py | 41 ++++++++++++------- 1 file changed, 27 insertions(+), 14 deletions(-) diff --git a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py index f3ebbf98..f1df1f65 100644 --- a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py +++ b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py @@ -4,10 +4,13 @@ All Rights Reserved See LICENSE for the license information -@file gerry01_planar_tracking.py -@brief quick test of open-loop trajectory tracking for planar cdpr. +@file gerry02_traj_tracking.py +@brief Computes the linearized feedforward controllers for tracking a trajectory. @author Frank Dellaert @author Gerry Chen + +Given a .h file containing the trajectory, we compute the time-varying linearized iLQR gains & +feedforward terms and write them out to a new .h file. """ import gtdynamics as gtd @@ -26,6 +29,25 @@ import cProfile from pstats import SortKey +FRAME_WIDTH, FRAME_HEIGHT = 2.92, 2.32 +EE_WIDTH, EE_HEIGHT = 0.15, 0.30 + +def create_cdpr(): + """Creates a cdpr with parameters matching the real CDPR in lab. + """ + # cdpr object + aw, ah = FRAME_WIDTH, FRAME_HEIGHT + bw, bh = EE_WIDTH, EE_HEIGHT + params = CdprParams() + params.mass = 0.5 + params.gravity = np.array([0, 0, -9.8]).reshape((3, 1)) + params.a_locs = np.array([[aw, 0, 0], [aw, 0, ah], [0, 0, ah], [0, 0, 0]]) + params.b_locs = np.array([[bw, 0., -bh], [bw, 0., bh], [-bw, 0., bh], [-bw, 0, -bh]]) / 2 + params.b_locs = params.b_locs - [0, 0, bh * 0.4] + params.winch_params.inertia_ = 9.26e-5 * 890 / 420 # https://bit.ly/3sOF2Wt + params.winch_params.radius_ = 0.0127 + return Cdpr(params) + def print_data(isPaints, colorinds, colorpalette, traj, N=100): """Prints out the paint trajectory for sanity check""" def paintString(isPaint, colori): @@ -81,17 +103,7 @@ def main(fname='data/iros_logo_2.h', - des_T: the desired poses """ # cdpr object - aw, ah = 2.92, 2.32 - bw, bh = 0.15, 0.30 - params = CdprParams() - params.mass = 0.5 - params.gravity = np.array([0, 0, -9.8]).reshape((3, 1)) - params.a_locs = np.array([[aw, 0, 0], [aw, 0, ah], [0, 0, ah], [0, 0, 0]]) - params.b_locs = np.array([[bw, 0., -bh], [bw, 0., bh], [-bw, 0., bh], [-bw, 0, -bh]]) / 2 - params.b_locs = params.b_locs - [0, 0, bh * 0.4] - params.winch_params.inertia_ = 9.26e-5 * 890 / 420 # https://bit.ly/3sOF2Wt - params.winch_params.radius_ = 0.0127 - cdpr = Cdpr(params) + cdpr = create_cdpr() # import data isPaints, colorinds, colorpalette, traj = ParseFile(fname) @@ -100,7 +112,8 @@ def main(fname='data/iros_logo_2.h', ) * dN # this is a hardcoded constant. TODO(gerry): include this in the .h file. N = int(N/dN) # scale time by dN N0 = int(N0/dN) - traj = (traj - [aw/2, ah/2]) * 0.85 + [aw/2, ah/2] # rescale trajectory to be smaller + width, _, height = cdpr.params.a_locs[1] - cdpr.params.a_locs[3] # rescale trajectory to be + traj = (traj - [width/2, height/2]) * 0.85 + [width/2, height/2] # smaller traj = traj[::dN, :] if debug: print_data(isPaints, colorinds, colorpalette, traj, N=100) From d1bb841f60167559588f3fe3ae70b5081f1bc7c2 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 7 Sep 2021 19:52:48 -0400 Subject: [PATCH 61/73] initial code: per-stroke optimization --- .../src/gerry03_stroke_tracking.ipynb | 99 +++++++++++ .../cablerobot/src/gerry03_stroke_tracking.py | 162 ++++++++++++++++++ gtdynamics/cablerobot/src/utils.py | 24 ++- 3 files changed, 278 insertions(+), 7 deletions(-) create mode 100644 gtdynamics/cablerobot/src/gerry03_stroke_tracking.ipynb create mode 100644 gtdynamics/cablerobot/src/gerry03_stroke_tracking.py diff --git a/gtdynamics/cablerobot/src/gerry03_stroke_tracking.ipynb b/gtdynamics/cablerobot/src/gerry03_stroke_tracking.ipynb new file mode 100644 index 00000000..e6b8e1d0 --- /dev/null +++ b/gtdynamics/cablerobot/src/gerry03_stroke_tracking.ipynb @@ -0,0 +1,99 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "id": "08dd84c0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from IPython.display import Image, display\n", + "%aimport -np\n", + "%aimport -plt\n", + "%aimport -display\n", + "\n", + "from draw_cdpr import plot_trajectory\n", + "from draw_controller import draw_controller_anim\n", + "import gerry03_stroke_tracking" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "72dcedc4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting stroke 0 of 145\n", + "Starting stroke 1 of 145\n", + "Starting stroke 2 of 145\n", + "Starting stroke 3 of 145\n", + "Starting stroke 4 of 145\n", + "Starting stroke 5 of 145\n", + "Starting stroke 6 of 145\n" + ] + } + ], + "source": [ + "# cdpr, all_controllers, all_results, strokes, dt = gerry03_stroke_tracking.main(fname='data/ATL_filled.h', Q=np.ones(6)*1e1, R=np.ones(1)*1e-3, dN=100, debug=False, speed_multiplier=1)\n", + "cdpr, all_controllers, all_results, strokes, dt = gerry03_stroke_tracking.main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "095ea4e5", + "metadata": {}, + "outputs": [], + "source": [ + "gerry03_stroke_tracking.plot(cdpr, all_results, strokes, dt)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c0951da", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/gtdynamics/cablerobot/src/gerry03_stroke_tracking.py b/gtdynamics/cablerobot/src/gerry03_stroke_tracking.py new file mode 100644 index 00000000..bb3a7543 --- /dev/null +++ b/gtdynamics/cablerobot/src/gerry03_stroke_tracking.py @@ -0,0 +1,162 @@ +""" +GTDynamics Copyright 2021, Georgia Tech Research Corporation, +Atlanta, Georgia 30332-0415 +All Rights Reserved +See LICENSE for the license information + +@file gerry03_stroke_tracking.py +@brief Computes the controllers stroke-by-stroke for tracking a trajectory. +@author Frank Dellaert +@author Gerry Chen +""" + +import gtdynamics as gtd +import gtsam +import matplotlib.pyplot as plt +import numpy as np +from gtsam import Pose3, Rot3 + +from cdpr_planar import Cdpr, CdprParams +from cdpr_controller_ilqr import CdprControllerIlqr +from cdpr_planar_sim import CdprSimulator +from paint_parse import ParseFile, writeControls +from draw_cdpr import plot_trajectory +from draw_controller import draw_controller_anim +import utils +import gerry02_traj_tracking + +import cProfile +from pstats import SortKey + +DT = 0.01 # hardcoded constant. TODO(gerry): include in .h file. + +def preprocessTraj(cdpr, is_paint_on, traj, dN): + """Preprocesses the trajectory to (1) rescale the trajectory, and (2) take every dN'th index. + + Args: + cdpr (Cdpr): CDPR object + traj ([type]): [description] + dN ([type]): [description] + """ + # rescale trajectory to be smaller + width, _, height = cdpr.params.a_locs[1] - cdpr.params.a_locs[3] # rescale trajectory to be + traj = (traj - [width/2, height/2]) * 0.85 + [width/2, height/2] # smaller + # extract the part of the trajectory we care about + return np.array(is_paint_on)[::dN], traj[::dN, :] + +def splitTrajByStroke(is_paint_on, all_des_poses): + prev_index = 0 + for transition_index in np.argwhere(np.diff(is_paint_on)) + 1: + yield all_des_poses[prev_index:transition_index[0]] + prev_index = transition_index[0] + yield all_des_poses[prev_index:] + +def main(fname='data/ATL_filled.h', + debug=False, + Q=np.ones(6) * 1e2, + R=np.ones(1) * 1e-2, + dN=1, + speed_multiplier=1): + """Runs a simulation of the iLQR controller trying to execute a predefined trajectory. + + Args: + fname (str, optional): The trajectory filename. Defaults to 'data/iros_logo_2.h'. + debug (bool, optional): Whether to print debug information. Defaults to False. + Q (np.ndarray, optional): Vector of weights to apply to the state objectives. The real + weight matrix will be diag(Q). Defaults to np.ones(6)*1e2. + R (np.ndarray, optional): Vector of weights to apply to the control costs. The real weight + matrix will be diag(R). Defaults to np.ones(1)*1e-2. + dN (int, optional): Skips some timesteps from the input trajectory, to make things faster. + The controller and simulation both will only use each dN'th time step. Defaults to 1. + + Returns: + tuple(Cdpr, CdprControllerIlqr, gtsam.Values, int, float, list[gtsam.Pose3]): The relevant + output data including: + - cdpr: the cable robot object + - controller: the controller + - result: the Values object containing the full state and controls of the robot in + open-loop + - N: the number of time steps (equal to N passed in) + - dt: the time step size + - des_T: the desired poses + """ + cdpr = gerry02_traj_tracking.create_cdpr() + dt = (DT / speed_multiplier) * dN + + # import data + is_paint_on, _, _, all_trajs = ParseFile(fname) + is_paint_on, all_trajs = preprocessTraj(cdpr, is_paint_on, all_trajs, dN) + all_des_poses = gerry02_traj_tracking.xy2Pose3(all_trajs) + + all_controllers = [] + all_results = [] + cur_x, cur_v = all_des_poses[0], (0, 0, 0, 0, 0, 0) + + strokes = list(splitTrajByStroke(is_paint_on, all_des_poses)) + k = 0 + for i, des_poses in enumerate(strokes): + print("Starting stroke {:d} of {:d}".format(i, len(strokes))) + if i > 5: + break + N = len(des_poses) + + # initial configuration + X_init = gtsam.Values() + gtd.InsertPose(X_init, cdpr.ee_id(), 0, cur_x) + gtd.InsertTwist(X_init, cdpr.ee_id(), 0, cur_v) + + # controller + controller = CdprControllerIlqr(cdpr, X_init, des_poses, dt, Q, R) + # feedforward control + xff = np.zeros((N, 2)) + uff = np.zeros((N, 4)) + for t in range(N): + xff[t, :] = gtd.Pose(controller.result, cdpr.ee_id(), t).translation()[[0, 2]] + uff[t, :] = [gtd.TorqueDouble(controller.result, ji, t) for ji in range(4)] + if debug: + print(xff) + print(uff) + + # simulate + sim = CdprSimulator(cdpr, X_init, controller, dt=dt) + result = sim.run(N=N) + if debug: + print(result) + cur_x = gtd.Pose(result, cdpr.ee_id(), N) + cur_v = gtd.Twist(result, cdpr.ee_id(), N) + + # shift timesteps + new = gtsam.Values() + for key in result.keys(): + utils.UpdateFromValues(result, new, gtd.DynamicsSymbol(key), shift_time_by=k) + result = new + + # store + all_controllers.append(all_controllers) + all_results.append(result) + k += N + + return cdpr, all_controllers, all_results, strokes, dt + +def plot(cdpr, all_results, strokes, dt): + """Plots the results""" + N = sum(len(s) for s in strokes[:len(all_results)]) + combined_results = gtsam.Values() + time_shifts = np.cumsum([len(des_T) for des_T in strokes]) + time_shifts = [0, *time_shifts] + for i, (results, time_shift) in enumerate(zip(all_results, time_shifts)): + # remove duplicates + overlap = set(combined_results.keys()) & set(results.keys()) + for key in overlap: + results.erase(key) + # insert + combined_results.insert(results) + plot_trajectory(cdpr, combined_results, dt*N, dt, N, sum(strokes, []), step=1) + +def save_controller(fname, controller): + writeControls(fname, controller.gains_ff) + +if __name__ == '__main__': + cProfile.run('cdpr, all_controllers, all_results, strokes, dt = main()', + sort=SortKey.TIME) + plot(cdpr, all_results, strokes, dt) diff --git a/gtdynamics/cablerobot/src/utils.py b/gtdynamics/cablerobot/src/utils.py index 30ef51a4..f118a29c 100644 --- a/gtdynamics/cablerobot/src/utils.py +++ b/gtdynamics/cablerobot/src/utils.py @@ -73,25 +73,35 @@ def ZeroValues(graph, dt=None): InsertZeroByLabel(z, key, dt) return z -def UpdateFromValues(src, dst, key): + +def UpdateFromValues(src, dst, key, shift_time_by=0): label = key.label() + if shift_time_by: + new_key = gtd.DynamicsSymbol.LinkJointSymbol(key.label(), key.linkIdx(), key.jointIdx(), + key.time() + shift_time_by) + else: + new_key = key + if label in "qvatT": # joint angle, vel, acc, tension, torque - dst.insert(key.key(), src.atDouble(key.key())) + dst.insert(new_key.key(), src.atDouble(key.key())) elif label == "p": # PoseKey - dst.insert(key.key(), src.atPose3(key.key())) + dst.insert(new_key.key(), src.atPose3(key.key())) elif label == "V": # TwistKey - gtd.InsertTwist(dst, key.linkIdx(), key.time(), gtd.Twist(src, key.linkIdx(), key.time())) + gtd.InsertTwist(dst, new_key.linkIdx(), new_key.time(), + gtd.Twist(src, key.linkIdx(), key.time())) elif label == "A": # TwistAccelKey - gtd.InsertTwistAccel(dst, key.linkIdx(), key.time(), + gtd.InsertTwistAccel(dst, new_key.linkIdx(), new_key.time(), gtd.TwistAccel(src, key.linkIdx(), key.time())) elif label == "F": # WrenchKey - gtd.InsertWrench(dst, key.linkIdx(), key.time(), gtd.Wrench(src, key.linkIdx(), key.time())) + gtd.InsertWrench(dst, new_key.linkIdx(), new_key.time(), + gtd.Wrench(src, key.linkIdx(), key.time())) elif key.key() == 0: - dst.insert(key.key(), src.atDouble(key.key())) + dst.insert(new_key.key(), src.atDouble(key.key())) else: print(key) raise RuntimeError("Unknown key label type: ", label) + def InitValues(graph, values=None, dt=None): init = gtsam.Values() existing_keys = values.keys() if values is not None else [] From d33f09ea42f520607548aacf6625bae36710482a Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 7 Sep 2021 21:23:56 -0400 Subject: [PATCH 62/73] optimize utils.InitValues --- gtdynamics/cablerobot/src/utils.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/gtdynamics/cablerobot/src/utils.py b/gtdynamics/cablerobot/src/utils.py index f118a29c..d6b5d227 100644 --- a/gtdynamics/cablerobot/src/utils.py +++ b/gtdynamics/cablerobot/src/utils.py @@ -104,13 +104,12 @@ def UpdateFromValues(src, dst, key, shift_time_by=0): def InitValues(graph, values=None, dt=None): init = gtsam.Values() - existing_keys = values.keys() if values is not None else [] - for key in graph.keyVector(): - key = gtd.DynamicsSymbol(key) - if key.key() in existing_keys: - UpdateFromValues(values, init, key) - else: - InsertZeroByLabel(init, key, dt) + existing_keys = set(values.keys()) if values is not None else set() + graphkeys = set(graph.keyVector()) + for key in graphkeys & existing_keys: + UpdateFromValues(values, init, gtd.DynamicsSymbol(key)) + for key in graphkeys - existing_keys: + InsertZeroByLabel(init, gtd.DynamicsSymbol(key), dt) return init def InsertPose(dest, link_id, k, source): From cee41cc3cc0ab4e01eeaa3c31ae2886fabc9fbbe Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 7 Sep 2021 21:33:50 -0400 Subject: [PATCH 63/73] iLQR "initial guess" - doesn't help that much --- .../cablerobot/src/cdpr_controller_ilqr.py | 31 +++++++++++++++++-- 1 file changed, 29 insertions(+), 2 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py index 23f043b8..123ab810 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py @@ -17,13 +17,22 @@ import utils from cdpr_controller import CdprControllerBase from scipy.linalg import solve_triangular +import time class CdprControllerIlqr(CdprControllerBase): """Precomputes the open-loop trajectory then just calls on that for each update. """ - def __init__(self, cdpr, x0, pdes=[], dt=0.01, Q=None, R=np.array([1.])): + def __init__(self, + cdpr, + x0, + pdes=[], + dt=0.01, + Q=None, + R=np.array([1.]), + x_guess=None, + debug=False): """constructor Args: @@ -42,13 +51,31 @@ def __init__(self, cdpr, x0, pdes=[], dt=0.01, Q=None, R=np.array([1.])): # create iLQR graph fg = self.create_ilqr_fg(cdpr, x0, pdes, dt, Q, R) # initial guess - x_guess = utils.InitValues(fg, dt=dt) + if x_guess is None: + x_guess = gtsam.Values() + for k, pdes_ in enumerate(pdes): + gtd.InsertPose(x_guess, cdpr.ee_id(), k, pdes_) + for ji in range(4): + l = np.linalg.norm(cdpr.params.a_locs[ji] - pdes_.translation()) + gtd.InsertJointAngleDouble(x_guess, ji, k, l) + x_guess = utils.InitValues(fg, x_guess, dt=dt) # optimize self.optimizer = gtsam.LevenbergMarquardtOptimizer(fg, x_guess, utils.MyLMParams()) self.result = self.optimizer.optimize() self.fg = fg + def optimize(optimizer): # so this shows up in the profiler + return optimizer.optimize() + tstart = time.time() + self.result = optimize(self.optimizer) + tend = time.time() # gains self.gains_ff = self.extract_gains_ff(cdpr, fg, self.result, len(self.pdes)) + # print debug info + if debug: + print("FG size: ", fg.size()) + print("\tNumber of variables: ", len(x_guess.keys())) + print("\tNumber of timesteps: ", len(pdes)) + print("\tTime to optimize: ", tend - tstart) def update(self, values, t): """New control: returns the entire results vector, which contains the optimal open-loop From a3ba8befc8c61badfabbc0fe4851b21df0f21a67 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 7 Sep 2021 21:33:59 -0400 Subject: [PATCH 64/73] make iLQR way faster by using less strict optim params --- gtdynamics/cablerobot/src/cdpr_controller_ilqr.py | 11 ++++++++--- gtdynamics/cablerobot/src/utils.py | 7 ++++--- 2 files changed, 12 insertions(+), 6 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py index 123ab810..4c3a6190 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py @@ -50,6 +50,7 @@ def __init__(self, # create iLQR graph fg = self.create_ilqr_fg(cdpr, x0, pdes, dt, Q, R) + # initial guess if x_guess is None: x_guess = gtsam.Values() @@ -59,17 +60,21 @@ def __init__(self, l = np.linalg.norm(cdpr.params.a_locs[ji] - pdes_.translation()) gtd.InsertJointAngleDouble(x_guess, ji, k, l) x_guess = utils.InitValues(fg, x_guess, dt=dt) + # optimize - self.optimizer = gtsam.LevenbergMarquardtOptimizer(fg, x_guess, utils.MyLMParams()) - self.result = self.optimizer.optimize() - self.fg = fg + params = utils.MyLMParams(None) + # params.setRelativeErrorTol(1e-10) + # params.setAbsoluteErrorTol(1e-10) + self.optimizer = gtsam.LevenbergMarquardtOptimizer(fg, x_guess, params) def optimize(optimizer): # so this shows up in the profiler return optimizer.optimize() tstart = time.time() self.result = optimize(self.optimizer) tend = time.time() + # gains self.gains_ff = self.extract_gains_ff(cdpr, fg, self.result, len(self.pdes)) + # print debug info if debug: print("FG size: ", fg.size()) diff --git a/gtdynamics/cablerobot/src/utils.py b/gtdynamics/cablerobot/src/utils.py index d6b5d227..99ded096 100644 --- a/gtdynamics/cablerobot/src/utils.py +++ b/gtdynamics/cablerobot/src/utils.py @@ -133,8 +133,9 @@ def InsertWrenches(dest, link_id, k, source): def MyLMParams(abs_err_tol=1e-15): params = gtsam.LevenbergMarquardtParams() - params.setRelativeErrorTol(0) - params.setAbsoluteErrorTol(0) - params.setErrorTol(abs_err_tol) + if abs_err_tol is not None: + params.setRelativeErrorTol(0) + params.setAbsoluteErrorTol(0) + params.setErrorTol(abs_err_tol) params.setLinearSolverType("MULTIFRONTAL_QR") return params From 6a8665c3f084f6f8a739ab7a75af3debde118358 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 7 Sep 2021 21:34:36 -0400 Subject: [PATCH 65/73] update stroke tracking --- .../src/gerry03_stroke_tracking.ipynb | 152 ++++++++++++-- .../cablerobot/src/gerry03_stroke_tracking.py | 192 +++++++++++------- 2 files changed, 251 insertions(+), 93 deletions(-) diff --git a/gtdynamics/cablerobot/src/gerry03_stroke_tracking.ipynb b/gtdynamics/cablerobot/src/gerry03_stroke_tracking.ipynb index e6b8e1d0..2cfbf9bc 100644 --- a/gtdynamics/cablerobot/src/gerry03_stroke_tracking.ipynb +++ b/gtdynamics/cablerobot/src/gerry03_stroke_tracking.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 8, + "execution_count": 23, "id": "08dd84c0", "metadata": {}, "outputs": [ @@ -18,6 +18,7 @@ "source": [ "%load_ext autoreload\n", "%autoreload 2\n", + "import time\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from IPython.display import Image, display\n", @@ -27,52 +28,171 @@ "\n", "from draw_cdpr import plot_trajectory\n", "from draw_controller import draw_controller_anim\n", + "import gerry02_traj_tracking\n", "import gerry03_stroke_tracking" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 27, + "id": "d191f422", + "metadata": {}, + "outputs": [], + "source": [ + "def timed(func, *args, **kwargs):\n", + " tstart = time.time()\n", + " rets = func(*args, **kwargs)\n", + " print(\"Time elapsed: \", time.time() - tstart)\n", + " return rets" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "cb4afa56", + "metadata": {}, + "outputs": [], + "source": [ + "fname='data/ATL_filled.h'" + ] + }, + { + "cell_type": "code", + "execution_count": 30, "id": "72dcedc4", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running per-stroke optimization...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████| 145/145 [03:05<00:00, 1.28s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Combining per-stroke results and running one final optimization...\n", + "Done.\n", + "Time elapsed: 363.07522892951965\n" + ] + } + ], + "source": [ + "cdpr, controller, result, des_poses, dt = timed(gerry03_stroke_tracking.main, fname=fname, Q=np.ones(6)*1e1, R=np.ones(1)*1e-3, speed_multiplier=1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "0151948c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Starting stroke 0 of 145\n", - "Starting stroke 1 of 145\n", - "Starting stroke 2 of 145\n", - "Starting stroke 3 of 145\n", - "Starting stroke 4 of 145\n", - "Starting stroke 5 of 145\n", - "Starting stroke 6 of 145\n" + "Time elapsed: 157.96959900856018\n" ] } ], "source": [ - "# cdpr, all_controllers, all_results, strokes, dt = gerry03_stroke_tracking.main(fname='data/ATL_filled.h', Q=np.ones(6)*1e1, R=np.ones(1)*1e-3, dN=100, debug=False, speed_multiplier=1)\n", - "cdpr, all_controllers, all_results, strokes, dt = gerry03_stroke_tracking.main()" + "cdpr_2, controller_2, result_2, N, dt, pdes = timed(gerry02_traj_tracking.main, fname=fname, Q=np.ones(6)*1e1, R=np.ones(1)*1e-3, dN=1, debug=False, speed_multiplier=1)\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "095ea4e5", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2, 13602)\n", + "(2, 13601)\n" + ] + }, + { + "data": { + "image/png": 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d1usxbcbQPq59I0ttYFDHHF4F314FIRFw8zxo1b+xJfoncSswX69ACHEHcAdAy5YtSXW+LIPkQOEBBgAFhYWYNElesvfs4WhqKqaKCvoXFPi0y9q1i6zUVEILC+mjU56xfTvHU1MJz86ml055+tat5MbGEpmWRg+d8sObN5MfEkLMvn10LSjAbrdToKl3YMMGiiwW4rZto7NO+33r1lFSUECzzZvpoFO+e/VqyjMzab51K+10yneuWkXl/v0kb99OG53y7StWYI2PJ2XXLlLUcq2MW5YuxRERQes9e2ih036Tep3a7d9Pc69ye3k5W9XyDgcP0syr3OpwsF0t75SWRrxXeWVoKDvV8q5HjhDjJV/Z0aPsUcu7Hz1KlFf7kiNH2KeW9zp2jHCv8sK0NA6q5X2OHye0qMijPP/gQQ6r5f3y8jBXVnqU5+7fT7paPtD72O129pxg95435eXlpKamnlD3npaSkhJSU1Mb/d4LhFPGGiOlbFJ/gwcPlg3F2LFj5YABAxpsf7Vl8eLFUkopy63lctmRZfLl1S/LC2ZdIPtO6yv7Tusrz//pfPnS3y/JpelLZbm1vNHlbAo0FVmbipxS1qGs23+V8rlkKd8dLGXeoRp1MXbsWDl27Fi/5fVxXoF18gR4lvr7AzoC26qoczqwE2heVX81eWZ/suUT2XdaX1lhq6h224akKfzuTnQZT3T5pDRkrAtOdPmk9C9jsM9sw0J9EhIREuFy+wBIL0p3heWbtXcWM3bNINwczpCUIa6sjR3iOjSy1AYG1WDNJzDvYWg7BK79HqISG1uifwxCiP7Ap8B4KWVuVfVrg/IuMzAwMDjxMRTqfwDt4tpxbdy1XNvrWirtlR6+16+seUWpE9uOUa1HMabtGIamDCUyJLKRpTYw0EFK+Ot5WPYmdB8PV3wOYVGNLdU/BiFEe2AWcL2Uck9V9Wu8H4w41AYGBk0LQ6H+hxFuDmdUm1GMajOKR3mU9OJ0l3L9y75f+G73d4SZwhiSMsRl5e4Y19FItGDQ+Nit8Nt9sOkbGHQjXPAWmI1HWF0ihPgWGAckCSGOAM8AoQBSyg+Bp4HmwBT1mWCTUg5pHGkNDAwMThyMt9E/nHax7bim5zVc0/MaKu2VrM9a73IPeW3ta7y29jXaxLRxTWwcmjKUqFDDImjQwFSWwA83wb6FMO5xGPsoGB95dY6U8poqym8DbmsgcQwMDAyaDIZCbeAi3BzOyDYjGdlmJI/yKEeKj7AiYwXLM5Yze/9sZu6eSZgpjMEtByvW67aj6RTXybBeG9QvJcdhxpWQuRkumgyDb2psiQwaCCMOtYGBQVPBUKgN/NI2ti1X9byKq3pehcVuYf2x9S73kNfXvc7r6153Wa9HtxnNsJRhhvXaoG7JO6DEmC7KhKtnQI/xjS2RQQNgfKQbGBg0NQyF2iAowsxhjGg9ghGtR/Dw0IfJKMlgRcYKlmUsc1mvQ02hLuv1mDZj6BRvWK8NakHGBvjmSpAOuHE2tBvW2BIZGBgYGBjoYijUBjWiTUwbJvSYwIQeE7DYLWzI3sDyI4r1+o11b/DGujdoHd2aUW1GMbrNaE5tdaphvTYInn1/wswbIKo5XD8Lkro1tkQGBgYGBgZ+MRRqg1oTZg7j1FancmqrU3lo6EMcLTnqcg2Ze2AuP+z5gRBTCINbKNbrcEs4UkrDem2gz6ZvYfY9kNxLSSUem9LYEhk0EkYcagMDg6ZCvSnUQoh2wFdACuAAPpZSTvaqI4DJwPlAGXCTlHJDfclk0DC0jmntsl5b7VbFeq0q2G+ufxOAL376wsN6HR0a3chSGzQ6UsLyt2HRs9BpLFw1HSLiGlsqg0bAiENtcDKzK28XXRO6EmIybJonE/V5NW3Ag1LKDUKIWGC9EGKhlHKHps54oJv6NxyYqv5rcJIQag5leKvhDG81nAeHPEhmSSafL/6c7Ohs5h2Yx497fiTEFMKgFoNckxu7JnQ1rNf/NBx2WPAYrPkY+l4Bl06FkLDGlsqgsSnJhugWRvIeg5OGA4UHuPK3K7mx9408NPQhAAorC7nytyt55/R36N28dyNLaFBT6k2hllJmApnqcrEQYifQBtAq1JcAX6m50v8WQiQIIVqpbQ1OQlrFtGJU7CjGjRuH1W5lY/ZGlmcsZ1nGMt5a/xZvrX+LlOgUJWtjmzEMbzWcmLCYxhbboD6xVsDPd8COX2HEPXD282AyNbZUBicC7w6ENkPgtj8bWxIDgzohtzwXgK05W13b1mWtI7M0k482f8TkMyb7a2pwgtMg4w1CiI7AKcBqr6I2QLpm/Yi6zVCo/wGEmkMZ1moYw1oN44EhD5BVmuVyDVlwaAE/7f2JEBHCKS1PcVmvuyV0M6zXJxPl+fDddXB4BZzzIoy8p7ElMjjROLK2sSU4eTm8Eiyl0O3sxpbEwKDJU+8KtRAiBvgJuF9KWeRdrNPEZxaKEOIO4A6Ali1bkpqaWtdi6lJQUIDdbm+w/dWWkpKSJiFrIDmTSOJScSkXtrqQg5UH2VG+gx25O3g7623eXv82CeYEekX2ondkb3pE9CDSFNlosp5INBU5wS1reEUO/bc8S2T5UXb1epBsS19o4GMoKCgA8HvumtJ5PZkwfKgbiC/UuO6TChtXDgODk4B6VaiFEKEoyvQ3UspZOlWOAO00622Bo96VpJQfAx8DDBkyRI4bN67uhdUhISGBgoICGmp/tSU1NbVJyFoTObNKs1h5dCXLM5az6ugqVpWsIkSEMLDFQJf1unuz7nVuvT6Zz2ljkZqayrjeLWH6v8FWBNfPonfnsTSG52BCQgKA33PXlM7rP4ZVU+D3x+HpPDCZG1saA4NqUZ2Pxe252zlceJjzO59fjxIZ1BX1GeVDAJ8BO6WUb/mpNhu4RwjxHcpkxELDf9pAj5ToFC7vdjmXd7scq8PK5uzNLveQdza8wzsb3qFFVAuXcn1qq1OJDYttbLENdIgv2A6f3wAhkXDLfEjp19giGTQlFj2r/Gu3QD2PUBkYNCZXz7kawFComwj1aaEeBVwPbBVCbFK3PQG0B5BSfgjMQwmZtw8lbN7N9SiPwUlCqCmUISlDGJIyhPsH3092WbYra+PCQwuZtXcWISKEAS0GuLI21of12qAG7JjNgM3PQGJHmPgTNOvQ2BIZGBgYGBjUmvqM8rEcfR9pbR0J/Ke+ZDD4Z9AiqgWXdbuMy7pdhtVhZcvxLS7r9eQNk5m8YTLJkcmuuNcjWo8gLsyIb9zgrPkE5j1McVx34m9dAFGJjS2RwQlOwLQu2qQv5fmw4WsYeS8YH84GBgaNgBFV3OCkItQUyuCWgxnccjD3DbqP42XHXcr1osOL+GXfL5iFmQHJA1zuIT0TexrW6/pESlj0HCx/C7qPZ3PLmznNUKYNAlDt3+PcB2HbT9CqP3QeVy8ynYxsDwtjZlwMk6QDkzBCVRqcPHy942sGJA+gf3L/BtunoVAbnNQkRyW7rNc2h83Dev3uxnd5d+O7JEUmMar1KEa3Hc2IViOID49vbLFPHuxWmP1f2DwDBt0IF7yFY9nyxpbK4GSjQo1SYbM0rhw1pLTSxpt/7OGR83oQEdpwEy3vTkkm32zm/soCEiOMj9ymwlvr3uLrnV+z8fqNjS3KCctra18DYOuNW6uoWXcYCrXBP4YQUwiDWg5iUMtB/HfQf8kpz3Ep13+l/8Wv+3/FLMz0T+7vsl47pKOxxW66VJbADzfCvj9h3BMw9hFjON6gDnDeQwEdQpoUHy3Zz+crDtIyLpw7x3ZpbHEM6pFAoy8yyHv6i+1f1JU4DUrBT7PI/N//6L76b8zxJ5/hylCoDf6xJEUmcWnXS7m066XYHDa25mxl2ZFlLM9Yznsb3+O9je8Ra4rl9OWnM6bNGEa0NqzXQVNyHGZcCZmb4aJ3YfCNjS2RQROkVirz7vmw9Ue44rO6EqfesNiVI7U5GucjQcqT5+OkqRCs8nwykTd9OgDWjAxDoTYwOFkJMYVwSotTOKXFKS7r9YqMFczaOIvU9FRm75+NSZjon6Rar9uOpldiL8PvUI+8A/D15VCcBVfPgB7jG1sig5MeHavft0rIsaagUBsYwMmb0MheUgKOk3+011CoDQx0SIpM4pKulxB/JJ7Rp41mW842l3vI+5ve5/1N75MYkcjoNqMZ1XoUI1uPJCEiobHFbnwyNsA3V4J0wI2/QbuhjS2RwcmG4TZU5xiTsg1qQu5nnyMtlSTdfbfH9sr9+zEnJBDSvDmOsjL2DBlKzFlnekbmOQkxFGoDgyoIMSkZGQe2GMg9p9xDbnkuK4+uZFnGMpYcWeKyXvdN6uuKe927ee9/nvV675/w/Q0Q1RyunwVJ3RpbIgODJkVj6bWGy0fDcTJZobNffx3AR6E+cMGFmGJi6LFuLfbiEgAi1qyF1q0bXMaG5B/2xjcwqD3NI5tzUZeLeO2011gyYQnTz5/OHf3vwOFwMHXTVK6Zew2nf386jy97nLkH5lJQUdDYItc/m2bAt1dBYme4baGhTBvUCUHHoQ6uhYEXJ49qd3JQW7/qcls5/b7sx9wDc+tIoprjKCkJWJ5dlk2/L/vxy75fGkagBsCwUBsY1AKzSYlpPSB5AP8Z+B/yKvJYkbHC5R4y58AcBIJ+Sf1ckUP6JPU5eazXUirxpRc9B53GwlXTIcJImtNUEUJ8DlwIZEsp++qUC2AySobbMuAmKeWGOpcjKFVPo3w4TbtVWVqzd0FsCkQm1FS0eqGxJqgZnx8nF9ll2QBM2TSFCzpf0MjSBEAIDhUeAmD2/tlc2vXSRhWnrjAUagODOiQxIpGLulzERV0uwu6wsz13u0u5nrp5KlM2T6FZeDNGthnp8r9uFtGsscWuGQ47zH8U1n4Cfa+AS6dCSFhjS2VQO6YB7wNf+SkfD3RT/4YDU9V/mwZThkOL3vDvVY0tiS4nkzuAQfU5Ua+/tFjY1X8AKZOeodnVV9esk3+An76hUBsY1BNmkxLTun9yf/498N/kVeSx8uhKlmcsZ2XGSuYemItAuHyvR7cZTZ/mfTCbGi6xQ42xVsCs22HnbBhxD5z9PJhOEqv7Pxgp5VIhRMcAVS4BvpKK0+3fQogEIUQrKWVmw0iooaZ+v9k76laOJszJr+I0LU7UUHr24mIAjk9+t+YKtRf1eay2/Px66zsQhkJtYNBAJEYkcmHnC7mw84XYHXZ25O5wWa8/3PwhUzdPJSE8gZGtVet1m1EnZvay8nz49lpIWwnnvgQj/tPYEhk0HG2AdM36EXWbh0IthLgDuAOgZcuWpKamVmsn+4r2ASAFIPFoP8ZuxwwsW74Me0g0AP1y82gObN26ldzMCADGqfW1bfW21YaSkpI66Ss9TcnweODAflI9Tm/tCUbGlStXEmuOrdP9BktdncP6pC5l3F+xH4DCwkJXn9vKtgGQk5Ojux9/+9ZuLykpYfXq1QCUl5fX6TkVRUW0AKw2q0e/Lf3Ip91uKiggGXA4JBk5RSQB69atY3tSGQAF+QV1KmvY1m00++AD+l1tYmsnU7X6ru11NhRqA4NGwGwy0y+5H/2S+3H3wLvJr8h3W6+PrmTewXkIBH2a92F0W8V63bd538a3XhcegelXQO4++Ndn0O+KxpXHoKHRM2r6mJqklB8DHwMMGTJEjhs3rlo7Sd+RDmvd6x7tV5jBAWNGj4YINTlExhTIg379+kEPtW6qTlu9bbUgNTW1TvpaVbYTDh6gc+cujBtXt5kSA8p4QPln5MiRNI9sXqf7DZa6Oof1SV3KGHcsDhZAXFycq0/7YTukQlJSkud+vlT+8dm3zvbU1FT6DeoHP0NkZGSdnlNbTg57gdDQMI9+d6Ivn3a79dgx9gFWCUUWSRIwZMgQQhOK4A9IaJZQp7Jmb9xELtD1KGztVL3fem2vs6FQGxicADSLaMYFnS/ggs4X4JAOduTuYFnGMlZkrODjLR/z4eYPSQhPYETrEYxpM4aRrRvhBXhsB0z/F1QWw8SfoPPYht2/wYnAEaCdZr0tcLRRJGngUG8VVjuVVgfxUaENul8DgxOGWvhB+/u5nqh+4zXBUKgNDE4wnDGt+yb15e4Bd1NQUcCqzFUu95D5B+cD0Kd5H0a1GcWYNmNwyHrOQnVoBXx3DYREwi3zIaVf/e7P4ERlNnCPEOI7lMmIhY3iPw2AhMMrITrZc1st2JVVRJnFzqD2vhOFL3l/BbuPFXPolRM4ekINOVF9d/9p1JVyWefX06kN19XEwpN0gqKhUBvUCY7CQta98QapU6dyxWOP0fmhhxpbpJOGhIgExncaz/hO43FIBzvzdrJ8/2KWH1vFp1s/5eMtHxNliuK0Jae5fK+TIpPqToAdv8JPt0NCe8Uy3axD3fVtcEIhhPgWxdU4SQhxBHgGCAWQUn4IzEMJmbcPJWzezfUpj0stKDkOH4+F6370rPCFmta+27l1sr/z3lkGoKs07z5WHLDtgeMldEqKrlHWQedxnqR6hoEG5/2hp/TW54eNlJJp26dxebfLiQ+Pr3ZboGazWOtaGT+BMRRqgxpTcvgwC99+mzk//8zctDSOqdttL7zAE9VQqKXNxpe33sq3c+aQXVlJTGgoLaKiaJmQQIvkZFq0aUPz5GQSO3WiWUoKiS1b0qxjR2JbtCAkMvKE+qFaLRZKs7IoKyigNC+P0mPHKMnNpbSwkNKcHEqLiyktLaWkoIDSkhJKKyooLS+nrLSUCquVcouF8spKKhwOyqWkQkriQ0NpFxfHmJEjkVYrn8yfz7bSUhKEoG9cLB1bJ+FIqWTvwHn80XsejlATvRJ7KVkb246hX1I/Qkw1/Kmv/hjmPwJth8K1MyHqBJwkaVBnSCmvqaJcAvU+C9XHUrdnPhRlwKoPtMLUtxhBs/ZQHld+uIoXL+vLdcP1PzgrrHYiQptABJ/MLRDTQonXbVDnNJaLw7pj63hr/VtszdnKW+Peql7jE+endkJjKNQGQSOlZNeiRSz86CPmLV7M4txcLEC8EIzv1o3zLrmEm954g/zu3avV76SzzuK5JUvoFR5Ol+hoSmw2duXksCQzk9wdgUNchQKRQJTJRKTJRJTZTGRoKFFmM1EhIYSZTEghkELgCAlBAg4pcUiJVP/VLkspcQAOhwMJWCwWzELotwEqHA7K7HbKHA7KAFs1z2kEEC0EUSYTEWYzkSYTEaGhRJrNJApBhBAUWK18e/gw3x4+DEA48ODQoeSUlLA1LY3vdx6gbCewGBKAIdERxLc+ytqeqcwdHkdJpxYMiuvBQEczRkV2DU4wKUlK+4OkI6nQ43xlAmJYVDWPzsCgjnC6NPn7eA42sYs3uxfA0Y1w+uM1lw3FOg2wKa1AV6Ges+Uo98zYyO/3n0aPlMaJphE0H42B0Cj4XyN58vxD8JfuPb8inw3ZGziz/Zm69WsyAgJgsSuRZEosgTMYBqK2HwON8SlxaOo7lL//Kb22b6v3fRkKtUFAsg4dYtGHH7Jwzhz+3L2bDJuiMnYPDeWeYcO46OabGXXzzYSGhwPwwdSprN69O+j+c3bv5tUlS7imXTu+OXQI4RXL2FZayvFdu8hLSyN/3z7yc3LIy80lPyeHkpISysrLKS8ro6ysjDLVultWWUmZzUZeZSWVDgdCSkyAyW5HACaUh5IJMAnlEWESArPONruUhIWHYzKZlDZCuJaFEESEhBAVFUVURATR4eFERUQQFR9PZFQUMdHRRDdvTnRCAtFxccSkpBDdrJmy3qoV0cnJmEOq/gm+3qEDS4rdw819IyN5aeVKTGpbh9XKrDffpOzQIZavXMnyAwf4c28e7IXQ344zyHyQ3OYb2NoniQEXTiCmT+cqY0abrSXEZ64mv+Uwmk34GszGo8KgibDha0ju4V6X0r8i/u1Vyr+1VKirUjQW7VQy2G0/WhhQoT5hxtqsZY0twUmLrsuH5sLf89c9bDm+heVXL/dod9OCm9iQvYGtN2712L42ay2PLn2UR5MerTeZm7KJunzyRw22L+MtaeBBaXExC197jZenTGFNXp5HWRshuKlDB84+91w6DR6sKJgmE5u/+calbCaFhjK/oICi48eJS072sxc3C6dMoRJ44LnnfJRpgJDoaFoNHkyrwYPr6hCrRWOHdMrZto1H0tIAaGEykWA2s768nL+/+YaRN94IgCk0lKRTT2XcY49xg9ouNzubld9/z/I5c1i+cSM/Zmdjzc5g8uK36WkyMbpDB0aPHs3oa66h87nn6p77fdKBLSyOZoYybdBoqIGo9VRNf9bo2ffUp0BuLKWYVKtfU5/U589aalD3OD++9M65RJJepMQhtzk8xzs3ZG/Q7e+dDe9wvPw4R61H6UUvj7IDBQcosZbQP7l/XYheM/zcW4F+M3aHnTv/vJM7+t3BsFbD6kSMgooC4sPja2zhDwbjTfkPx263s2HuXBZOn87CJUtYmZ2NxU/dDCmZdvgw0z7+uMp+X77gAl5es6bKep9/pHw9XnLLLYTecgug/y3sve1kreNNvuZhlO1wkO1Qhr5H3XQTrW6+GanpQ2r+vNfNgFXdvsvhYNfBg3x68CB8/TUA5yckcNbYsZx64YWcMmECEXFx2Ko5ccXAoK4I7qWn/fUEqB/IQl0bXmrN8LBEOPNgrbtqbIW22h8EX1wAMclw5bR6kaehsJeUUPzHQhIuv6zB9mkSivFCe85dIxzSvb26ip/ePXTJr5cA+Fi1q009RvnQG93JrchldeZq9hfsZ/GExTXaxfj1Dmaf6u57zMwxPDn8Sa7qeVWN+gsGQ6H+B3JgzRoWfvQRC//6i7/S0shXlbSBISHc17cv1tJS3jl4kCgh+Pm55zCHhCBVn2Hnj9bpT+zyKVZ9jotzc7n+/fd5Ze1aXg5Clj8rKwE4u2dPj+16P1vvB4xunSraVLdOaWkp0dH6M/d9+tGTJxiZ/TykpJS8v2WLbhnABb17I9T2ZRo5hWY/zvXKklwAImOV6B8OKdlx9Ch/5eQAsLyggHm//gq//krY7bdzSnw8A1om0L9fFyJiTqHD8OG6VmyDEwshxAhgIjAGaAWUA9uAucB0KWVhI4pXB2iUhqCV0PpTVsMtyiheXU00a+j51UI9NdVW6A+r7ghNVKFOzysjOTac3KefpmjefMK7dSWyX8OEAnVZqHUUaqn+p93WlJm24iAXt6m+munyF6/FOUgohQvWet7XyzOWGwq1Qe3IO3SIv6ZOZeGcOSzcu5eDVsVW2VYILm3fnrPHjePMG26gxdixYFZmofe88kru+vFHfpo5kw83b66WMnX9++8DYKusJET1ra6KaVVMPmwsGtvl433NG3buf//L+ZMnMyAykk4JCXyyzT3Joio5dy2cAkDPs//tt87R9etZ/d13/L1wIX/v28fXew5Tvucw9/w0khYmE4OaN2dgjx6cMmoUgy+7jM7DhtXr8JlB9RBCzEdJsvIr8CKQjTLvtTtwOvCrEOItKeXsxpOyLnDec9UY8ynKhLhW9SVQk0WeyD/f9LVwZA2M8Aoqs+UHJUNm93Nq1K2UkjGvLeb0HslMyj4OgKO8vLbSBo9r/qxGodbxq65rhdq7v/c3vs9HWz5iyw1bEEKQ+9lnRI8ZQ0Q1gwoEYtJvO9jdMZwbAFmNd0VdfVREVTbsyI+hUJ+EVBYVsfKTT1g4axYLN21ifVkZEogFTk9O5oHhwzn7ppvofsklCD+T4u78/nsODxvGy+vW0eH883liwYKg9z8+KYn5OTlMvfpq7v3557o5qH84kcDvCxZwPhBrMlFsq248kappPXgwlw0ejHPwc9svb3JoxWbSDlewev16Nmdk8Ofy5diWL4dXXyVBCAY1b87ofv046+KLGX777YRFR9e5XAZBc72UMsdrWwmwQf17UwhRhwHK6x/f16HmBRusVfXQcvjqYrji8zqSyj9VSXSiuiqfkD7gn52l/OutUM+6Tfl3UnCDLXmlFqLCzK6Qhc5rsHj3cSbVgZjVJZCFujb9VZePtijulg7pwCzMZL/+BmLyu/TcshmAFRkriA+Pp29SX83Oqr+v4gqr78Zgfwh1/cFXzx+QhkJ9EnB8507Wfvcdj734Ilvtdp/ycODsZs0Y1rs3IaGhFJeUMOv990G1JPsjRlWO/vf770wPDeX60aODkqdlfDzk5PDfX37h3iCP4a8XXnC5lGjdS7xdTZzrelTlyhHI/cJf3YMHD2JZurRWfQdVX6e9tcw9074ceHfPHhIHDWJFWRmUlbHohRcA5ZwcPHgQ27Jl7vOk6UtKyfH9awE4tCLbvV1Tjs7y8f3rAGjXYzDtevTgCqCyuJhta9eyfvdu1ufl8VdODn8tXsxzixfD//0frUwmls2aRZdLLsGgYfFWpoUQcWie8VLKPB2Fu4mh8YfWzQ6q88bMVBQE0jVzOooy69a/Qq+rZW9Bzwsh2W3x87fLxlK0RRD7XZmxkjv/vJO/rvyL5KiqJ5qfaAx6fiGntE/g6z42CufMJeXVVxtVHj0F2GWh1j6LG+gjx4EDk1T9ui3uGVR3/XkXoPpfN/ANWhcuH+D5HuycKaFtrbqrEkOhbqIcnj+fe6+7jpUFBeRWcbNXAnPy85mzYkWN97fTZuOJ1NRqt5MOR1DuImc+9VQNpPrnMWnjRtfyWTU6Z3Nr0GZ+0DUzHQ66XnopRTt2ENurV9UNDOocIcSdwHMo32HaOaqdG02o2uKhiVbX5cOVh9C96a2eOvWCx2Z3eL48vXdrKYNFz8LK9+BR96TFhlacV+3P5cnlZfw5KnBSmUA+1N/u+haArTlbOaP9GXUuY0OwMa2A9HeVZGMtX3mlUWUJpCg3ykiBdP2vYXYVVD2lpnMCp0+53U7el1/R7JqrMUVGBtXnK9Ps/G46BmdWXbemGAp1E+W/t9/On/n5XNOlC5/v3w/ArV278t7mzZjMdZuNq+DwYcb068dhi4WlX3zBwGsCJlMDYPqtt3LbN9+w7pdfGHr55X7rjQ0JYYXNxp8ffeSK7eyaWKddF+5vVee6Fu8Xgs+61379WWW913fu2kXPHj08ywP0VaN1P/19O3Uqn27ZwmUdOxISFcUPO3bQ0mTimDqJdOknn7ja7N69m57qxE7teXKSuV2ZKd267xmu7dpyPWt9xrZFCKBNv7M86+q0E0KQvmEDF7/5JgAbZs5k7KRJGDQKDwF9mrQ12q4zTAzeQy/u5UCJXVRLdk6phbryd7E5ZMCX56KdWZwJ2C1laJ/GfkfXapiXpiqe/nUbR0okx3aupEO0Dbqc7rlfl1z1oFAdSAVrOfQYX/d9+yG/1MK8bZlcHlPCoX9dQftp0xps33pIKSnfuJHIU07xsUL7C5vnfNM5dEdggt+vN+E/L+L7921MeUVxFxy434HJoViokZ6Kq72klO9ftvHVGSa4scZiVFtGJ85j92ehLpo3n+zXXsN2/DgtH30k6H0mHKt5UptgMBTqJsq+Y8c4OyWF4tJSAF457zyGP/ookVF1n82uZffu/LViBSNOPZVLb72VVV260HHMmIBtLrvvPu765ht+mjIloEJdaTJxRuvWjL3jjroWu06wpKYyqpEmJS744gvCgO+2bCE0JobV4eEMjo/nZzUyx5jbbnPVtaemMjrgpMQKAHqefXPQ+49dWKK2uT6o+ounT3ctD7juuqD3Y1Dn7AeadmaOHb8CfhRQoWehriJsHpBZWFFnCrWPLuC1+93HSjgTcEglZKVL4io05rqe4+vsr8OsC5UFL7/jerVLfnWJ7j7rgnIhGPZlPx4d+igTe090bb9/5iaW7DnOoFZK7P6SxYsBz+gddXnM248WcrSggrN7t9QtL/jxR7Keepo277xD3HnnKvsvOuJTTxs2zy1n7SU9d72DAy2VvsNnK0aV6CLFreOJ7xWl1THJgfdZsecpUaHO3eCl1Ks3VOmWzaRNuNq1Oeejj0m84Xpda7HO+JBCRSWXr3Bw9FL9Dwtld/o/CEe58nizFxfpljcWRhysJkqezcbsrCx+yMrijSuu4NH5wQ/L14S2Q4aw4OefqZCS8848k9wqsiEmDh3KGTEx/LRiRcCXSKnDQVRoaF2Le1KwZs8eTomOJiw2FiEE5/bqxZ85J67R8QH1Hvz8iitI6NatkaX5R/M4sFII8ZEQ4l3nX2MLVR1Eaa6fAtf//PhQ69Hww+huNUDZd6VdkfWleTsBZaLWi3N3UGnznfPSGASjvAXyZ00vSmfkjJGkF6fXpVh+KVTdCKdtn+axPbdUCcNqdwRwq6jD2+GCd5dz+1fr/JZbDijuPtYMVYnePR/HzIk+9TxH/YK3UEspGbEoi4d/1L+Pbv3DwYtfB77HdN/PVXzZFf35p8f68bff5vi77wXuX7sfKYn4ajZXL3XQZ0223zbe95wlLQ1HaWnDx5cMEsNC3RSx28nSrH4/bx4zo6Ox2e2YTSbdBB9aRFX/Ol0CdMoLpKTAaiWpZ08GhoURFRrqt59lqvV8+dSpjPm3fri2rTYbWw8f5vbOnXVlrdI9op7rl1dU8ElEhG792vbtxOXeoll2OBwsKShQyv08PCZ26OCasFlRWclH4eGeEzg1da0VJQgBoeEve/QZ6NpbK4oRQGjEq1W2OVrotkLdqCaLMWg0PgL+ArYCNR83bkz8zbvw5/KhW8G5ST0FdfgS9qeA+st/UWFRlJr8MsWVZfKfe/l0+UE6NI9m4qkdfNpXlymp+8gtsfDUhb2r1S5Yy3lVzD4wm2JrMXP2z+HugXfXuJ9KeyUZJRl0jq8jd3+da6577QIo4HUkiPJP1lbdO0cb+cP5nPVWqL96w8ayPsLDBUMiOWNuBgCrAGmz0TNdUhJg+or38Uuk3xvPZ9Kqq57veXWo73ufPjz2pflwKFdGTUNsvo8ofz7U+885l4gB/Um44govefRp6LCQhkLdBJGakGnnJie7foBWi4XwsDDAU0FzroOvsuUd6aGq7WdHR7PwuBK7c5PFwtiYGEJUJd67XTerlb0WC6f95z9IPwq1k3npbsuGT4SM6q5XFXGjGutSSkwBInLUVlbp/adGMdlv9eNDquHvzEx3n1IiTCa3Uu61P4dDeaELU5nvtXL+63WtXX5soqTKNmnqPbn0jTcwaT5ADBoFm5TygcYWolYI4akba32qXT8iHR9qPVz3dR0q1F7vcd/fuWcFb/FsqgJnsTl/Y54Vsosr+PtAHhcPaB2UPK8tUEYMvRVqv1bltNVgq3CtBpwoV3xUWbAE8D91OK2gtVNMn1rxFPMPzmflNSuJ1Wy/as5VHCo8xOrrVnvK5n2evY9Xz09Zs8mapZimcj76kOhTh9dKdl105s/k6Mxx0qYjv2ZOMdH5duS/PNtGWOHsTYHn41infMFz0+289e/KoEVUnvPeN7Sf8+g0qph07it/yq3T2uxlofbHztydLkVaz5hUsXkL4sor1X78dtMoGAp1E6S8SPEbemX8eB6dN8+1vSGTkEydMIF///AD3VJS+HjrVr+RPFxf2zYbJj8xrwEyglAgG4PGSuxyfqtWzM/KYvott3DdZ5+5trcKCyPLauWrG27g+i+/DFrOYBK71LTN3x9/zMg77+T/hg9nzIMPBt2/Qb2xWAhxB/AbSpAfAKSUeY0nUnVxpmdWmav9Pgjg8qE7KdE5fByA4mNwdAOdRCaRVK2MVDU6VZWTtVNPcPhRLG6ZtpZtGUWc1i2JhKiwKuXxh+53RvEx+FxNitKuTdWd5B9SLkdRpt8qMu1vZSF7Z3VF9GBtlhLes9xW7qFQ78jVT/xVYXVw8zNv8+4VZ1OxeDGIUxR5Alxte36+a9l27BgAZav+rpXcVaJeiK+LdvJ6SyX04O783Vw39zo+PPtDVzWJZOxaJcmMI4jBJe86jv2HAIgpCd6VyCEdOpZcVXF2W048/vUzPlP1zlyTbyV6v8is0iwmzJmgqV63JuZgwkTWBsOHuglSpn5VR8XFNZoMd8+cyf+GD+fTHTt49qyz/NYboco49bLLfMrs9ZCc5GRhvnqNL3vZM4H7sFZKtjd7ZfAWiPrEVl7Ov++/n1YmE5N++aWxxTFQuBbVjxpYr/75d/Q8AREmP5GKNk13K9Ibp+vX8SGIt+hXF8O3V7M4/EHmhT9RdY/+hsi9red+dh3hKOdQxLX0TvtWtzyzQLEeW+31oAG8qZMJr5q7KRMCi3ZDZbHyr8bq3RBUlpfxhZjEkauuImfKFEw+SqjvgRV+Oa1BZNOSVZrFQ0se4p38jR7bt+RsYeS3I3l3ozLFQWtx17p8+HXJqcntoesppT+i4qPOOhVqPQO1Q/8DQGjGNfU+dLSWa6vD07BWk0y8Huet2q1rh2GhboJY1DSp9uJiSvftcyVAsaalUbhrl2vd4XC43Qh0/rTohlKrYtt9H33E1rPO4tnFiwk/7zzuVEOmaet/+MknDLjqKu6ZMwevfFeUqT7CkQA6CqI/mfXk95ZPb73ayVWEgMpKZEWFbnlDEBURAUXumcxDu3Vjdloa69au5SbtOdPIqRcS0K76q9lKS/2GDPQZPiwtx2QSOMrLdUMVAnxy9dVsLC9n5gMPEJuSUosjNagrpJSdGluGWlOcBf4CFlWqv4dlb+oU+rdQByR3f7CSAf5dbtPzlOgD3iPi3j+dWLtiJe2TPgP4n08/rjB6esdTUQTHtkOHEdUROSBBTUrUHMPwju3oZLFSrRz25QXw5zNw5jMQlaju2L//bjCYUS2x3spcIA8g7cWr72e6emyz9//K73H6PsYA23O3A7A60+3SUmwpdi17+1Pf94udzESQE6tvK/Z2Rwro8uFpmPYt11BQkY+ug5LTWySIOUU/7P7BY/1g4UF3/SDDZO7N3+s3mk99+1TXm0IthPgcuBDIllL21SkfB/wKOM/YLCnlc/Ulz8lEebYyK/b/5s3j/zQuH43JE7//zhO//x6wTtGePcR1d1tHKtWht3LAHBHhMZGyKSL8LFd3XfuNLuLjdff1wYEDfFAjX+WauGS42wiUYS0BOMcX2gNXvvFGDfo1aCiEEClSyqyqa54gxLQERw3EDTBR0VuR0KsTiEtNy+lhSgcu8Ft99UF9rxrfeR6B9yu8FBoPpp0PWVvhsXSIqOYoZYF+FA6pUdj6dWrPdYXFPOZbyWP1YFg1ozNt/BrWT4OE9jBGfaZ8PJaxmVvhdOVdUNshfueQvgw0yVB7HEEkHasT/CQoCcRVc65yLVfYPS3/o3Y6XS/83D8BNnpbiYPyoQ7i95GalkrQU2L9dPfF9i8CtNE28q9Q2xwB3EfrWcGoTwv1NOB94KsAdZZJKS+sRxlOSvZu3epafvXyy12RIY4fP07LFi0QQmDyTpDi/NdrG/hOMPO7zXviorpeWlTE42rItAnt2zPy1FM9yjds387XO3bw9YMP8p/ffnP1Z1FTa7c0mbjttNM8ZAN8Zdccg7Nci49fY6DkLQHqasvz8/Np1qxZ9RLFBNm3v/JVu3aRqvr2BeKpsWNdSXzy8/NJTEx0lXko90JQmpsOCGKat3Vt05Z7t5NAcU4aUkJUYhsc6mRJh3P0Q0peXrIEgA+eeaZRrPcG1eIz4ILGFiJoEtpDXlbw779dc5R/V33gW1ZHcdLeCZviWvbn++zE2xrnU16FguX8NS3alc1FA1oTE655VWepz39b1W5f3hOqecfTtuX8yJCbv4XTHmfeASWT6jfxsS6FWlYUQlRw2ei0vNssngiHxJlh4JiliPEd2zGj6BCuPJWZm3WVv5pGHXEeT6DnkYdrQnWfW8d3g6UUUvqB2fODwpafj7RaCW3RQru36vXvh1NnnMr36nKZ1R1i3ie0Xg2ew4FGJ/y6fOhNSvSza9c1kZ578q5eWFmo2z6tKI32ce09RyECZUIKdO3rOehRvSnUUsqlQoiO9dX/P5niEmW29b9atOCRn35ybW+sCXQAt+7axagBA/gzPZ1JU6bQ6wLPd/ee+HjeXbCAuzWTD0vVKBVv3HknE6dM4USkMc7puxMnkvrNN2SkptJ67FiPsjAhXBbsyydOZKCa3KWhJyVOPu881/KFRkbEEx4pZdNRpgERHlt1JT2OrNHZ6G/cWluleoqPXu1rzIvY5lC8bYTwtIr7692f1dypEzw+ayvL9+bwwXWDlA15B9yVNs+AUfcFlDNoA+zSN+C0x/l186ee21W3QQj8kaBX8kmCMrrmVKiXlKZhFYKZZYd4Jkix/O5PQmKRpNzbLcjL6GMvKPTR3Pz5+npjd9gZ+PVAAB5t9ajS9wfD3BW8EtbsHTESgF67fCdm1qXB4awfzsI5Tb224Q7dfVRhoa7FfvSaWg4dImzhSqVcCKSUjP5utKv8rgF38eFmZbLmtO3TeHrE07ofQo4yXzeaQCPFOWXHa3QMwdLYPtQjhBCbgaPAQ1LK7XqV1BnrdwC0bNmS1NTUBhGuoKAAu93eYPsLlkMHlIeqzWbzkK2kpKRRZX3ptde45//+j3MvuYT3PvyQ+K5dXWWXn302j/70Ex/edhvtr7yS1NRUMv5WZlbn5OWdcOfYSWOc09SlS2kD7LHZ2OO1b+1g1ncffECBeo6rkjNC9VevzrH4a1NeUMD9qnvPj598Uu3z09j3qZaCKs7LiSTrP5pmHZVoEzXBlca4di4fHrV1tISXQ51qzr0+/XlX9+uDnV9GcYXVw/XhUK5GaXj3FPfywqerVqgDKXL9r4b8pYp8ArDbWFm0z1VcYasgYs8fLHNapxc9B4PvYmeuTiSPogwIq77LxlNJiRSbTLzjtb0qBdSyL5IP19h55nrvaBbOMITKWuGvv8KlXll9/Vioz3tnKZmFFWx+RomA8vjyx11lr2a+ysSpOqMfOljtDj5M3c9tYzq7E5TUoUJdbHX7Vv+2/zf6q8tZ1pp5dOm6fHijuYEtR45QvniJT5VOWZKyjRuJOuUUnzKAiPJSZHgzAI4+9JDHnZJd5pvcxckPe37g6RFPe1035Z/ihX9y5N57afXii5jVAAh1HRmkOjSmQr0B6CClLBFCnA/8AuimV5NSfgx8DDBkyBDZUBbDhIQECgoKGs3q64/jc5Vhub8LCqhYtMj1o00/fJgO7dsDnj8P7WS+qsq05cGUefd749ChvLZmDZfefjsf3nADcaobQkpLJTXru998w4c338y4ceNYtV+ZCLR11Spaq77CPu4RdeRKUVOXj8zMTFK8JtvVxv3Dia5ri+rW8rMak/vVf/3LI66092Pi1U2b6K+m+z6amUlrNQKIHvkZykuwWZsDfusE2+YmNYzfZW3b8i9N+vNgacyRFG8SEhIA/MpzIslaFUKIYty3nNZ7JwQIk1JW+bwXQpwHTEbJmP2plPIVr/J4YDqK63wI8IaUMoDjYx1hUofXJ3wF399Qvba6kxeDI6OgnPxSC33beM5l8FaIfXykfRRuP08Cr5B+0/9OY+W+3KAmCQZDQEUuJNxDurKfb/coTj2SynnfXw+d2quVFOX190O+c2VkWQ6Exfts95LGZ8svsTG6NauyvNqylfuhVZ5EG1+vKCQVGF7FYIS7UOC+Mruyij3qzT/olX042yt0X4V+2uufN2bw5sI9lFhs3OyxJ08u7Hwhcw4orkqj2oxiRcYKnzqD9zrIiRMcbql/HV9Y/YLLFeTbvG+5AkWZlVJyrLRqt0FQXD58b1f9EyiRHLz0MhwlvjHJO2bD4Wuu9bHQV+X+JAXkV+Z7ttFTijUKtfa+Ll74JxH9+pN0x+3OQs2+vTupX2W70RRqKWWRZnmeEGKKECJJSnni5lY+QWihKk7HHA7Gv/BCI0vjn7u+8nWf3223s3XGDMaNG8cGNcza52lpfP7xxw0s3YnPAk32QX9oY1QHx8oaSKLf5rt9+3S3GzQOUkoPPwkhRCzwb+BO4Oeq2gshzMAHwNnAEWCtEGK2lFKrSfwH2CGlvEgIkQzsFkJ8I6W06HRZCzQf/2Gaw2pWmwAm/l/sebsjkXZB816eisKoV/4C4NArnh4zPglF/CiuUkrKLXYP/SS7uIIZa9L5dzgUVVipKPKccHYgx3MYu6Cs5jH6o2QZb4R+qF/o5QN8Xf5KCHPHvH54ycOc593m+B7mHpxbQ2nq5iNBi7fCZDepz8xAHxIOSf/j+8iKSkRWw3K8NiKcoRUav/WvLgEe8qnnTHueX2pxHbLebkzCRGyZpDgSHDpuKKe1PY17XlbuvwmPB6equZJxAc+uepZ7der4ZErUc/lQ5ZHuSu4iHWXaH21KjhMW6/bL6V5wxKdOy/RSftzzY0AZIbDve/nmzZRt2EDUoEEesnpH9TCdrAq1ECIFOCallEKIYSjBA3IbS56mRIyapvuJ4cO56J57AOUHtGPnTnr36uUT7q7KUHheZXr1qlv/m5df5sU1axgYEcGMb77BFBLCjr/+4vLJk/nvJ59w78cfs2bXLgAePPVUbnnpJZ++9NarDHUHfstrEjZv/fr1DB48uPptA5Q5syG6ljV/D157LT8fOsTAiAhefOkln7oXP/QQnSMiePjGG7n7o49444ILuOjRR9mwYQODBg3CH4fXKvpUh6GX+a0TTJvJt97KlL17+XXyZMLCw/01NWhEhBAJwP3ADcAMYKiUMphn6zBgn5TygNrPd8AlgFahlkCsUG7qGCAPd8CXOsPDQtWyN/bSXMyKUO7tKf3cE/RqybENinXVW6H2RyCj255jxXzw115uiVAmL/Z6egE9Wro/Coa9uIi2msMY9tIiLj/Ff4KVjIJyOj42l0PPnuZbWJINMe5JcGm5ZcREhLD2UB53fr2eZyJ/4ArzUv2OTaEewxj7NMq0k0rvB2r632SVul0LNmVvYqDF7lZsD69SxAqQVbEmKs11f9lJKsIj7bZu3xI6iKyAF8jmcPDqCuUjwxIRRbCxSv6X3Jw/0o+6NxzdoC+D699A3rwQe7yUzybb+eoME8cvUZTFuLA4lJ8U3DfoPuz8FaR0CpUOC2EoCumyjGUuhXrDsQ34Sw/kwOFzvpzKq0miZGauhg/13rHjXMuf/vlqlfX7/X2M50+fCcANvW/gqx2+hrh9+fvoZHbf59Lq+cgpWbSIkkWLlJUr3C4+nb29YOrZG6Q+w+Z9C4wDkoQQR4BnQLl3pZQfAlcAdwshbCiR066WdeFh/w/AmRBl5OjRnDpxomt7eWoqw0+Q4ekXLr2UuHPO4dGFC/nkxRd5a/16elx8MUyeDEDGtm3kliqWmLGjRtH79NMbU1y/HKqooKMataQh+PXQIQDm/PknbUaN8iyUkk4PPcTILl24+a23ePCjjzhw4ADdx4zhqN1O9zFjfDtUcVQoikegOlW1ydm2jRl793JWq1ZcdK+e7cOgMRFCJKHEOLwK+Bw4RUpZ9TCHmzaANq7aEcA7H/P7wGyUeS+xwFVS+qYsrO28l2OaKDcFRSWUFJXSFli4Yh1nq9u3JF9G/6ytFMV2Ja646tGSHuk/VFnHiVPea82LiMBCamo04zRleRUOLlfXOz42l4m9wrhCXT/n7aU+cXB3HysmELM2Znisx1eW0LEok83Jbi/IPT8+j3dKli2/f0Ve8yGu9dNeX+xRfrP0PzCRnpHpsuAV+5m9uMNLyT4+9z5o39a1/sifj/DHntXIZqq7R2UBqamp7Cx3D/s7z6XcNQeSEnXnJTjXLRZloGPVqlVcqim7ZLV0LY/TtPO2ZEZZcpgb/gAbd49zKcoRtkpsmkRB+w9n4DwCu8Pho1CnpqbqupxkemX6LYrtChXuNi3V7bt2K2ngM7MyyThyhCigwuKb8MayR7nmp+yXzMrKYdBeB6eOmggoSV4yNmVQ3cj+GfkZdMJ9Hp1MWjyJpysqiQZsx/I85F21ahXJtmic6mpqaiphu3bRDEgsgV19+5F/339pBtgKCqvUSW1BRKjyx/GM4yQVSg6p70Eni/5eRE97G5eMmf/zjd3uJHbBWtfyoP2e19FmdwR8FtV2zkx9Rvm4pory91EezgbVxK5+PZobKoZmDXl4wQIyTjmFtzdsoM2FF/LgnDlMHj+e++bP59whQ4iNVCa7NPcIM/TPxqmZ+CjTAIWF5ADNExMJj4rijJYt+X3PnqCsB9LhAGv1DIk2Bx5Bhp65+mqKgLc//9wIk3dichg4DnwBlAG3aq+TlPKtKtrrXVTvm+tcYBNwBtAFWCiEWKZ14VP3Vat5L8f++BnUTNcJiUkUFyk+lr379gfVKN1/6GjYCnHxzSCwvgpASDUM6ePGjUOu/5Jx6kTDT80PuBS52E79uWnqKi7XhIGfvtPCC5r1wBMgq+aV5VPpWHyM8Ze647t33/uRT73+9q0w7iFYUA03jLAYsJTQrksPOKBERXm9hb7qdkNrz+1naJRpgEyrZzpyAYwb2o/fN/wO6jwz57WfuV5Zj42Jcc9L+NKzTvQ3YcRlS0694lRY7i5zqufjxo2DVM/9aUmSlRSlRRBryndN4P55zv/Yk+CWe1uO3aVQW6QJ52UbeXQrg7N3M/blz1lwaAGk+ZwOdiW2o2ee8s0ZV7yPcaZNpDoGMmzkaA6rdbp37wHbt9K6VSvaFLYlHyizlxJZISiPcEvcXJ1fJICr5h+n5yYHpZe4RypOP/1013F/ds5n8HIV5nkg355PJ1BMyxpaNGtB1HHlo/OqnzPp9t/+ONXeoT160jahPXvU9Z5Ll5I/wzODZ7PJipIvrDV3PwrE//1s5+3LzAwqNnPzFDtb/1vJgmh3ub2VnVHtR7I3iL4iS/xn6wwxmwPOiantnJnGjvJhUAPsxcrb44E33mDOnDk41LjAJWVlTI+M1P26rtI1orr1g+zPGa/1oblzeVEIrumpRCDdXlnpCgJ/02OP0fmVVzwmN2q9ujzcHpx/OhMs/clTnWP3LrPb7YRoLBOB6gZ0N6mqLmCxu2esB1JYJy9bxu7ERBaoiXG6mc10iInh1RD9n7N0OHCUlio7CSKtMoBEUGYThAoHkRFPsq2ggCNS8u9eveh7no9npcGJweu4fxI1iTt3BGinWW+LYonWcjPwijqauE8IcRDoCejFq6s97YbBhW/D+850BZrfRT1+1H27Jo1L5j5MlLqLF+bu5DZV8/rX1FUMEIEt4lUp1M9c1Af+0NSXDvrkHmRH8044hImOxYq60599bKGrn16AHb8ovrrVYdAN8PcUQCBMyjNji/roaGmzcczfc8SPbdI7zgbFR12T7QAsdgthZn8OB55c81sBwzfZcVytmaRWjYHrM/9KJ2NvImVtiz0SbWp9dx2aGaVxmpjOT61RtPvMp57ikW6/evQb43BQYjJxZbwgMaYNC9KPEiklz4R8SaplIL2f/h3nFMa41UuY/8tLSpgFdQT5xkUOblwEEx8yYwn1dI+MDYkhIlvJfmyq0FdYOyd0xhnw7az2ZwELdOs5j6zUWoriRauwOms1Qj3u6EIr/1v2BP9Vy8rOuZLiF1901fVWphuCEbskzUe9gPk75b6J25+NK3wJMH3HdG5vdYWf1sETVVG/ThCGQt0EWa4mR9kpJcd278aMO3udWfjOj612dIw6Xo8RghIpyQemqH7TAOVq6uzwiAjybTaPZDPC+aeNhIHGP81k8qjrb/96x+6vXO84bFISEiDaSTDrwZ7vdcVBmNlU8mw2mplM5Dsc7JOSOLtd98csrVakc/gv1Ow/XpdvSyokSIcg2mbjiHoMz37/fRXtDBoLKeWkWnaxFugmhOgEZABXA9d61UkDzgSWCSFaAj2A4EPHBInrN33h29C8izues1aJ1nxUO7eWxXYiqvhgrff/+KytXBPhP3HKgyGB3Ue+umWo4r0OfHHTUG6etpZu+ensTWjLzaM70TFJkdgs4Ie7RrD+1v8wNmMTAJ/2cec6G2vazBZHAIUaOOX5hbQoy6PSHEZhuDtqhvCXxCJUDYOnk1zmkdx8HmyZrNssv+0gsPlmWvwq3uvbbcVkj9XlGcs5o8Lmfu7lKdcnvdi3ry6HlWeVLHUrutVRqIfuVQZKTBb/sabPP/R3wD4Kf/wJvCYB/nokkzPbK9bjPLOZtJAQelitmHXOcZcp7vlAxX/+6VEWbgWL6mMSFaaYYM0mM1IdPTFrXFO0JEUmuRTqJ4Y/QY6OQt0urB1wCFDflQHO23/vXeKxHsiFoqEYtqWC9X5kLrYWUzTXdxQmZdIksqqRB2HcquAnVNYEQ6FuguSpvsevDB/Oo3+7Hw4ncoivgsOHGdOjB4crK5ny5JNMevVV9lutCGBLYSEitJppbBuIhjynL557Lk/98Qc5e/aQ2M03guS7Y8dy39Kl/GfUKN5fvhwpJV3Dw+kTF8cDP/7oIac1I4Oj/3uSsr//Jnr0aFq98DyhKdXzyFu8K5ubp63lgr/fY82S3wkBkvr2rbKdQeMghHgS+EBKme+n/AwgSko5R69cSmkTQtwD/I4SNu9zKeV2IcRdavmHwPPANCHEVpT39qP1EZkp/GgJI3c6kOd5KixSwlPWm2hGCTeUVpIEHC+udPlWHhj8BH1Tb8dKKKHU3fB0PJ4v4myauZZvHNGBL1cddq2/cGlfIkMVZVwAp/dsweBju3hh1ad81/1Mnnn1ffbv2e4qH9oxkRhVmQa4eXv1o2h8+YeixI2/9A3eveYU/vvtRlqhnwbd9fmho1CHBdBdl0VHg45H/ifx8SRpRtd27/4F2rhDeN63+D62Nj/btS7UCYtFlfph5wAlG6GLGlgVa2mIPL3d6SxOd/ujt7B72uGdH3DtTce51ryIGfYzdfuxZXnOihsb2ZY5ZNAtNJ4ecT2BeV79Vj3q0jyyOXo/uHRLuod1yaT56Xz/stvdyRyEUaWwbTzxR5SL3X7aF6TddHOVbWpLwROTENcqro56Ema/9prHevFjz9GhS8d6l6s6nNhOuAa6mFUrjb0JzeFM6NCB+UuXEm828/CLL3Jxf2U8R8IJq0w3NIvXr6d/eLiuMg2wS52o0bNPH0Cx3p/buzd/5eZiVz+ypJTk//ADBy6+hIotW0h57lnaffJxtZVpgHE9kunRIprXlihxZ9O2bKnBURk0IFuBOUKIRUKI14UQjwghnhZCfK0qwBcBqwN1IKWcJ6XsLqXsIqV8Ud32oapMI6U8KqU8R0rZT0rZV0o5vT4OJG5rNvf/6nCNqGgt1F/bz+Fd++Vkq+Hm8srcyoJQX2k7wvtTl/QyeTrUXtDDbZV95sLeHmUTT+3goxZd31F5xl29Z5EqZwC8XFlC9NI8+yFVruCi/ooy+3zoFz7l8+1DcSa60RNCG2asT6WnhX6/1LfYF5tNHAxzP8OvbK3zrFnnFd5TSo6WensTAQ5Vcd02y6OuN85TNGy3nZ3ftfYpjzheuyiO63cFjq4h2rgjKr0U+lmAmp50ClUmb54Z0cZr5FJZNlWRkj5QnSGFKZqslvDkdz7OOEHz4wODXcvR1ZyU3/WvRdWqv0+TPqH5OnWwKwh3rqxyB5FDhpD0b3cm39Rre1Vr33WNYaFugkSoCmhlkClUTxTaDhvGglmzGH3JJczctMm1PX/zZkDHNSJQYpZgy7xkCLadc71o+3aOhIdXb//o43Rh0YYYdC5by8pYlJvLKSEhLNPEFtf6Un+WprzUHaWl7PhRidvZrmVLSoHlL7xAYkYGBd9/T8WOnYR37Ur81VdTKSVpM2d6yKmXsEe7P+1f1OsPANDJHEKrfv38HJnBiYCU8lfgVyFEN2AU0AooQknEcoeUsrwx5asJvr8t9+/BpJvW27mtdraiCDyVR7v07C+ywp3C2GQponl0mIczccSxTQCECUXZ758sPHoUvhknXJg1QVOEhPvP7sb0P1b5rW9yuHdc/uvP8IriD5shvWONQDnhuM+YQOswc9+g+5DznwRgXGkZdxQUcW0bt3J8oEhx1fgoK5s7U/xPJNeL7VxgMnlep7Jc3loXYI5shdYUrml5fLdHtVP211xpDMRDP9l5+ga3epTe9lLeGXcD96feD4AjzDMhjfANdKOL9sxrNzpv8+pO9h7bdiygKLCPTDnCuq5K+0H7JX10JlXqMX1CEhO/97R5Z8oCj/Xwi8dTOVvxEm/9xhus+eoN2m7Rz8xoC4vQ3e4Pm+anFb9HmeTqEz9aY1U/GJdCp6IsMJkQQpD077vJmTIFgK+S93hEgWloDIW6CRIXodywz61bx1dmxefKYxKf5t+qlr3x8Et2OJSb1l95gGW/5UJQABRohtASBw70I80/j402G6c99VTAOvd98w18843Htkl//80kjfsPmUdhmZ/4szXglKe/RUppRPdoAkgp90JQE+KbDHp3nfNW1OrcznqBxu6khAPzk0nqXUJ8R/1vjCvNnj6m9/fMB9U1+6tbhsE8jTJRnseEoe2wLxWYQhSVv3nqIx7to7OzXAq1lNLl3yqQ2PL8uWbA0I7NGHNGN9b/OdNvnZZlnh4+Wc8+S7htAAdEis8b/nLzcqQcopwnr9/yLX1vYbGqUAN09BPRoV2ASA+y6zmQ6zs/9VColyBpqzhS4p4ouDhtMae3P91dvmsOrlmF2gv89WV+912X9PSMYsihjlcxrt0417ot3NNvfMgxt6K/YPCFnLde16sKgJZ5kt5zDiOuVd6BwlpGi6iWQDptY9v5zAL2Rpvg5Lqe1+JUqAHiypRzddam4EevNySXMlGz3nnePI5vuNO1Xm4rxzTsFFAV6vgLL+CNQ0/xjp8By8rwKP0Cfwj4/eounPvdfr9VTt/iPp7ciHg6FWVhc7i+QlxlJVGN+34yFOomyMAzz4RZs2grBGO6dHEpOeVlZURHKTezd1pr17Lah3YCoBNtVI1PnJMHHQ5uUyNzaMu922i3+530p5afUlbGgowMNNNOeOeaazzkdhIwUkagpC1+6lVZ5rWelZVFK9Vdojb9uJK3aNc1y3d+qcwwbxcaSoe4ONd2LSvUF+/n995LVHg4Qggc5eVc874SffKTnj2JGTuWkPh438Q8XnJ5bwd39BTnftcvXMiL69dzY7/BpJZFsmp/LiO7+lq9DAwaDM1wt3P6ltZqHYxCjQRLUShHVyf4Vag7x4P2ATXq4Huu5f5t46FYo1CX5XHPqT3Yc38rIhItdHoOTJWezsZ537ldGApmzkQMdruJ7B2pEyLTeTyVivIaiyJnSURrYio8Va7rdv3hsV7w3Ux+YSb2eBOM9+3zYE4pndXlbI07iUmYICJB2S8Qm9QTKPVu7jrHCXY7BWbPSXR7/QyC/B0ZQbzdrQjKmROhU3vO2OTgYIpg9v7ZnG71E0dEa/21+Q+JVhs2tOjOoOw9fsvt5kiPCYPWME+lsX+OO+rLhGHtKVrvf193LHDQ8nAhpdvUNrYKoiKiqATCg4iG4ihz35g9lx5GayduF+RshnlDBOevU34llQ4L0ddfQ+nXSnSP8M6dOL7CPQKTW55L6Dmn89Ffr7BtZCt+U9s4mXKBiX/PdV+jQoudO894iJGZ27hxp340kkdG3831xxbRb+8eJNBh/OXw3evuCl7vzyF7FFlzIuJc2wrKrbp1GxNDoW6CmKKV2cFfPvkkZzz3nGt7XU2gs1ks/BQRQZ6qWF11222c9eCDte7XSWpqKj+NG+ehdF585ZV0uqxhrA/VoaEmJToV6jSLf9+/h/v354Nt27j5XSUmaNGCBWQ9+xxPxMTwUkkJF0yfTqvBg/22ry5ffvwxCULw6oI5XPD5NqYu2W8o1AYNg/poOPfX//L06PcYrpPD2fn88FCeA7hSVId/jewN2gANXc6A/YpfbUSoGeya32lxJmGhSrTBirwwVQz/LgBZk54l8tfvsJSYETF+qwHQbNpSKi88wFvhH4GE1a0mYs56j+nxsXwc1RfTgb9ILi/QbWsu9JXBIs38vj2Lu0NAz+4vw2OBCqXkgjdgyd0e5Rd3vgiRrmQZFMDEwiKmx7uVnGN+MtB/0CyBJ3LclvhjqiJ+13xFxgkpf0KpWx6Pq6jNulimJPyUjrpVog7FtwqoUHsrbVZnpBSVcw4ryURmdTmNO0KCczfyiDdVhVKoNa4UzXFbv7Oefc6jXmSQruO/nmri/HWKhVwKKB/cA752l9sc7nkJuRW5JEcmM2e4iRjcbQDsAtZ3dcve5q036f96KsSl0KrUf3LWrUldmB5xlFf37kEKSEjpEFDewWqClj3N2hOu/vZ+33GMW+CEUqiNSYlNEGdCF3s9+VBvnjWLPCmZdtNNdDGb+e9TT7myM9YXZ11+edWVTlIqCnWmzuuQW15Oc5MJW34+GQ88SMb9/0domzZc8vzzACycOrXOZNo4bRq/5uXxwPjxtGydwq2jO7Fsbw5bjwQnq4FBrVD1Bxmew9MbrnVPSpRahdo9IvaK9Wp2ODq49GmtrbNAajJEBEncn54uG05lGiDcUeZZ9uckxT3OD44KX6uqfeFf7J/TEmtOYGXAVFbJgfMvIEQq1rivd8O9KcmsjowgW03e0T83+KiFe6Q7zLg22MNZKSMAkPFKaDgBEJnIPQPv8Wg/sOUpHm59D+QVeJQfr1TW78oP/Jy4pZWOD/a6zzROxhrh1vlOrpS2ulGiXjpTOb6f+uqY8nVoH6ucP1tIuMf2YtVi/XNXnfTwWkzojKegO/GyZInb7ahi2zbXctakZ4OSVcvkyz0n/ufHuoMqSiC7rzKxc0UvgdXh6dKTU57D9qNKRJYiNU621sdZK3nkue4cBY4qFF3tJW4elUSLRx7RrXdB5wtcy+8PuByTeq4ceI62nggYFuomSIiqUO/8+286zFKGEqWU5Gzbxq7cXI9JZz4T0dQ+dMtUN4RpaniasKgoJvTrx8ubNvHwaacx8T//AfRdHbz/9djmtX5s+3Z2HHcPKYESyPa1MWMwRUX5JG7RlV1n2R/+fm7+fojarXn5+axs1iyoulX17a/uZ4vd4ZlCtZnt1D/vV/X97doh7XZCkpMx7d+H3K8MHd742WdsWL/etS9vtx9vdx+fdU2dF9euJQT478cfAzDx1PZMSd3H1CX7mHJd3VnBDeoeIUR3YCrQUkrZVwjRH7hYSvlCFU1PGPz+rrQuCuq/UsKH9ov50H4xC1QXhfwyq8snxKmMv2S9hidCa5+0QhR7T8YSHgq1dDg85LcXFPj04dip+NxWHqieTSuk+9nAdwB8FhtJdaMH/2wfRZJQlKPZhw+7ti/PVp4bsv9VsPYlRf4WvehUfsSjfYtIT0XYOz7T0TIlIc2NhUV86ExHrvJJQgIAloROpDt8fRP8PsfXfIyPqlKLkYjNE+9jwPTJANxy75WMj+3IKe0SfOqFWiXWUM878eUxr3DdvOsocHiagtuUKseTE5kAFPjfuUbsNnFtKQTaajPYap7/lfvcPsUlqZ4+/dWlz5W3wyxl4l52vFehgOyybP6txt6+oMzz2uSW53L8mH6eKCk8letu/5uvKXUXXHn+c1yxN5Wr9v6lKXWfjMTwRGLPPMMVGk87sXXugbncqC7nR8RhUkd/LN4zF1UcJuExibEhMRTqJsixdesAuG/RIlhUvRA11eFadeYswNurVvH2Kv8zzeuCR5cvr9f+mwLBjAN8UVqqKMDp6ZCe7vHy/nzTJtfcfXAr5cGsO7c5p4t2Dwkhvo1isYqNCOWGER2Ykrqf/cdL6JJcxVi1QWPyCfAw8BGAlHKLEGIG0GQUaieB9CaTy0Lta7X26EP911ad112zTpB/0GPT0dUJFB6Mouf1GZ4Kf7thVB485Fq1ZWV5KJrpmrBeLlSlwJqln8jDH3/uOk6sGhlsm7X6o0VrHT0Zb1YmDS6qPOjSiCd0PF9ZUH14BYAQdGvmGcKzX3I/KrueA5XbA16bEKDtcUleLJSp6baLHILOmRJ7K/2n3N8RGquv9gSXZgO+ofFqSkUzt9taXGSIz+6cpBRAuleOmz35ilvI/7L+crmn1zR6bahJOfkyOwwSfDvRJoXJ+eCDavff+rVXOfrIowAsOLiAs9TtK3oLmkc0BzX5uARSj6S62mWXZ3v0M33ndE4Je9hjWzAeN5uT3QmJSsKiKAsN160nBcSFxxHWQaPp2yuJdDi4vaCI1O6nARtcRTO7n0m/nAOUtVf6X3soD+3bSCbEQl6AGOf1iKFQN0F2HVGsBl1NJibdf7/LEpmenk6H9u0BT4txoAlpetsufcmd6UnLiPBwHtX4UntPUtSbrKhXdjgtjfbt23OVV6D206OjmX3kCGjCtrmOwc+y9zZQfMDTNmzg8Jo15B48yNgLLyTpjDM89qUXMk5v+5IlSzjtNP1hPCkl9pwcDq5cSZtRo4hs3txv39ptlUVF7Fm2jG2LF7Nt40beWbtWt//OYWH0SUykV3ILOuTl8VzGEbqEhrK8pAQR5jl55ZWLL+bx335jyTffcMq13sntqofzXM6cNctj+82jOvHpsoN8vOQAr15Rt3F+DeqUKCnlGq9Rkvr12apjvI1Peu9v5+GVabLiWdXoQZ7T27x+j8EoQF7KNEDhQXUiWoFXCIjyfLJfd0+oOnzDjXQd7S6u3LHTpy/bX8sUUWoRLnlbqW+mwe5rVrNn2HC/bSoIc52ZCuG+JR7ue5uy4Ixe0VKJdd8pvpNH+8SIRLJiWkDldnVDZ/zdWm99aictCV68RZBvNvN/PzsYdEDyzeOxQAGjysrR2rj3hoW5JkvWh31xx1V303vmVIpT2lVdGWhRILE1s5OpScXeL0kJHdorqTczcnO4qKiYGG9rqLq6pM0AxmZs9ijyCAVQoh92zknl3uoF6qkYOJAITTjauAsucCnUituEMrFWCsUv2ml5kQJS01Nd7bLLPBXqg4UH2bo3jRj120pK6emt4ke5tpiDyy8hEcSEehloKgqYt6CAuHblyNEj0SrUG1t059n7P6O5CY4VVXDlh6vQ2sWTX3iWvH//X1D7rmsMhboJYlIndFzSsyfXvfmma3udTKCTElSF+u0LL0QIwf1qqvN9lZVc+MwzmMOqnokcCKec33/1FeuPH+eY3U45kFpaStbChXS98krddvaKCvLS0sjesYPjBw+SnZVF9uHDZGdlcezYMQ7m5HCgqIjDFovHI77LlCnsLijAHO891lU1oRERhEf5CQNksTC2e3eWWiyYgd7h4Qxo1Yr+vXvTf9Qoeg4fTllpKdtXrWL7mjVs27OH7dnZ7LFYtCFrXUwaPZo+Q4bQ96yz6HrmmQibnezXX6dg5kzCBgxgRkkx4TabjzIN0Oeii+C33/h9+vRaKdSbZ8xwLfe96CKPsqSYcK4a2o5v16Rx/9ndaBUf6d3c4MQgRwjRBfXVLoS4AshsXJFqi8u5FnPUPkwRR7FYT2N7WBj2crdC89vmo/RV6+Fq4dlDrSXJO+Kp4B/bRuVu9xPHeuSITxt/mCIl9mJB/L8up/CnWVU3qOK7yBwXF7D8oHRn0fg7RKOQe7uoNQ+Q7lydkKf4WTdDkOMxwe6UFqcgDilBkNvnwFvZOdzcqiXdjip1VlYeh3BYERXJfZrjeb15Mz5U1+tDoc44bTwPVnbh3tgE1zY9u8qm9pEMTCvn3PWSS0MzsQ+7g3VqWfdm3REIwkxhvJyYwKawUJ7ZWuzZgctFUUfTlNJ1cKLgkG+xZlK6oyS4NNnlYcpkxLKzzvRQqIUmAsut/W7lP5d9wIM/O9jfKrB52Vuh7hDXge2a9f3HS/1G8qoO2tTo3inXI3JLOL45joL9UZzZqQLv787c0kr2Hy9l+Eu+I/ThI/x/UNY3hkLdBAlXv5gr7XpqWe04vl356cQC9//2Gxtnz4bffqN3SAg7bDa+u+8+rqujyW/tWrRgTlaWxwuv24QJPHnOORzPyiKnsJDjxcUcLy0lx2ol1+Hw8ScGxZcySQg6RkYytHlzrmrfni5du9KxXz++ef99Pj9yhFK7ncCvmupTsH8/Sy0WLmrZkpiICL49fJithw7BoUMwb55um1Dgqs6dOf+ss+h/xhlk7NrF+EmT+O2pp7hQE7GldPUaMp94AuvRoyTecgvJ9/0XS4sWxIbo/2Rju3VjQEQEf6xcyWO1OKaB110HwNwX9L0Dbh/TmW9Wp/HZsoM86ZUhzuCE4T/Ax0BPIUQGSgTliYGbnJg4nw3aJ11Uh08B2FXcicfapHBensU5es3B4yXg9b3pmtCITlQQL6xlJioLQ4lppZ8VEMCRn+k5m78gjZq6JNiLlZ7McYE/9qVUdF5hLvfZXh2+vHUEW778AYCWjhiOmZxKm/PcBNFhlzMha55yXkc/wNdJ7Zg4z3173dj7RsTqX13rgys8z2W5vRz9MQfck1GBia1aUmQyMTujDr8FTfph/bSjOZ8PGsa7aUvY3kFwjZQQ3cI1kUUIQVRolGLhBQpNJo5t8rp26kXJadYSvAYzdIOma6jOx5iTjObQNROk2b/7ULg5nNU9Tdx3hyCzuWB0m9FAKgAXdbmY3wqXk1+ZD8CRomMebb0zM943e7qnVTrIOYHeLkIua72mfVlKPFFZhYRYlI0OmyAyKgYLUGl2y+GcR6aHKcB5qG8MhboJEqEqVVN376bt2WfjkBKHlOTm57NUnfihxft+DxRP+ecVKwAoBl466yz+p/poX9CnDzs2b2bihx+StmcPQnNDV6d/gPz8fNYlJlJWWIjea+uFP/4gGUgOCSEpPJzezZqRFB9PUosWtExKokW7drTo0IEWrVvTokcPEnv1whyhn53phscU9TJedceoD347dqzqSipWYMaBA8z4+GNQJ/wBXPT886BG6/Dh0UeUP5WOmmQ+LqQkTUqoqKC5EET7eeAEel2WaCZWnf8//elO7RKjuHhAa2asSeM/p3elWXTtRisM6h4p5QHgLCFENGCSUhZX1eZEw/uZUa4OT/++w61wZFsUH+L0cPd9Gx8VCjblPr+g8kVsmPkpbBLgx2Loxb7ZSsz5XlcfJa3VebTP9I2j68g7BiGRYKs68WRN/Wu9sVtMrA3pCSZPW521JEjl4c5lEJnAyIQWrJCKhbm9I1mjUAePaDUAUM9n93PpZPc8Dy2jW/pN7KXlvazjQDOvzt0Lm1Wf6kKvZ1n+viisRwJn45NhJoTFQVaC4gsNkF6xgdgeL5BZ+Yy7nk7b3BjFVeG6VAdcDQy+Cda6o2yUWks5WHjQJW5ijxIyVzfj0VF3efQ6uGNzcDdTSrQ7zHGH6XNGgjn+3vsBjyswVd/fmc2VOmd3OBupKtTNIptRkF3gqrMxwzPFYkFFgWZNkhb6ATGaQCDe/tQvXNqXp+YtBnsVI5g6eSt2330OpzzzA4WJpTinv5a8+g4AH1zo3tHYHsnsPub7WOuaEGBkpQGoUqEWQrRASWPbGihHuUXWSRlkrk2DOqdDN8WZyQ48ppm4UNf8TzPh8fXNbl+wJ/76S696ndFKCI7a7bWOLykdDh8DwcnAuO7dXcvOM1RaVobj6FGO2GyYTSbO7N49YDghvZLPdyq+nvP8KNNO7hrbhZ83ZvDlqkPcf1b3gHUNGh4hxNNe6wBIKZ/TbXACUuoVumtWnJlHCmDa6r2g+nJW2nxfQUVlVpeFertU/X+lVCy86qt7raMHcQT+xpASzHZFybGKMEI1zs6WjYsI01iwraW+H6/SAcIEDkv1nmEtHn6YwrlzfPyuHVZB1+h8zOZDHtuLM3wVy1YvPE/mk17ZVkMjIUGZX/OJ/QIqCWV/aI7Gk8Y5h8a/vGe0O0OtqqljDiXGa7i+WUQzaN4F1GQ0xKR4lAsgyuFgTHk5e7wUaqc47yQmuLY9m5TI7Zo6WesSqAqRHM4PnSpY2tfEex8q4xtZlco5zbb4xpvWHnVJhNd9FR049n5FvqKAH41R6jnnyxRV6LnnaNTH4gxQx02taYoSG96zB5aDvv77WkI7tMd6OHBe8dKhPQPLrEmQkxiZ5DEykV+Zr/o0FwBQ6JWkCNx+03oTU8d0SyKmy5tIR2D10hXiUnPyLQmKe6WpohTvoab0FoAaHOzz5frnaF/BvoD3cH3j94iFEKcDjwGJwEYgG4gALgW6CCF+BN6UUjbOdMp/MGEtlG+3eU88wbgnnsBkMmEymVi6dCmnn366R13vCXJVrQ+Jj2dLZSV39OnDx6r7x/sXX0xMYiJlpaX8+4cf6CYEW1T/rur2L6Vk2bJljBkzxqN8WMuWmEwmdpSXkykllWVlhEdXP36slh1qAPzPb7iBm9XEKUEjJYX79zP322+Jdzg4vHs3aQcPkpaZyeG8PNLKyjhqt/u4oCQLQYfISNrHx9OhRQs6dOxI+27d6NC/P+0HDaJ5r14u6/7O+fPpfb4yu/7zsWPZvGYNO2w2ttrtZOnEtR0ZHc2f+/cT2bKlT1lqaipfDRpEYnw81/TuzeStW6t1uLaKChbHxtIqKorxftw9nPRIieWsXi2YtvIQd5zWmagwY6DrBEOb3i4CuBDwnRl3AlMpPZWRTRFmKoRAhrgV4R+yloFP4ABf/9XCLVGk72qO6TK72rd7spQ/s5B0QEl0BwCywzuQePSQqyx9SXN6Xe3OVlhwwPc5ZbeYCIlwYKtUfuvRI4ZRumoNplCJwxr4hS/LfC3fx7fEkTiqEHOM50TE0Ghft7/Yc8/1Vag158NCKJ/aLyBW+jqH6bl8LEjP4IK2rXlo6ENqT57ye7sExIXFwdDbASUBFSVZ3Hnmi0jhjlRRZjLpqj3OvW/SRPxItrmPMViLvxDww5iqrfd6/YmQ6s0Uzd+jTKircGY5dHaqc4BCO4FRZ98lf1YdtUurTEePHAkHV7rWl940kNOmbcLaxjfOd5eYU9hfshGAlGj3R05ylGcok6OWDR7rDhwIk74LlHfYPIAOzZXfgzDZfOpq0bv+IeXKue+4wXfkMz3cbX22+QmLd0X3K7DbGydkHgS2UJ8P3C6l9PkUEkKEoDykzwZ+qifZDPyh/mCjIiOJ1Cid5tBQTH58bINFqJMinMo0wD2zZ3vU2Sslaz79lNP++98a7SMyOpoYr8kz140YwdOpqZwdF8fCoiL+PXQon+3YUaP+naz4QfEVHHPDDT5ldouFzI0bObxpE2nbtpG2bx9phw9zOCeHtOJi0ioq8P4uDwXahYTQPjqaMzp0oEPr1kTHxPDYggVc0bo1X23eTGRS8JkEn79dsbs827Ilp2Yd47wHH6LFo49gjokhc9s29vzxBwd37aKysJDOgwZxxn//iznS/zBaRFwcY5OT+X33brfTZZD8/OSTHLTZeOOuu6quDNw9rgv/mrqKb9ekc+voTlU3MGgwpJRvateFEG8As/1UP7FR341bI80M7dgOc5lb4cgOdyuXIjSXkJjdVJRV0q9Te27LsCj+VUDxXsWKG6J+pNqkW9Ha9X1rwuM9reEADpuJgvieOKyCophE7Fu8sr6ZQkG1ott1FOTSrHCO/t2MpD7KB0DcWWMpXbUGoeOJ1fyOOzwmoyVcfRXZr7zqUcdSYublqIewl5dCs79d2zNWJPr0J/TeAZpnQUJUKAVlvsfsjzY2O5sOpUOsV3QM7fNFSkxSiQEcGxaLLcZTSau0V+r40Lp55Fg+r7V0W6u1VWfEx3KpOlnRXsW4+NFWUbTOLCMyzIzrBvCiKj9xf8qjk4s6X8RvB5SJ+mVE4PTwLw31HC3o1zZBZ+fSHX9ZRwxpDeK6mEyuuOfNrr0GXnAr1PtObcPW41u4YOK5Ps2yCitdsdlPb3c621Q3quQonSQ7XoQmrvRYd2gs1LVVX7Xt7RHKx65Z/YbS3iN2zdfzsxf34ZnZ2qmSCrf3u113nlVD4Vf7klI+HKDMBvxSHwIZVI3L6lsPGYI6pqSQnZVFuRAUOBxs+vlnwjV+2atmzeKW995j7H33IWuoUOtx0V138VRqKpeMH8/CmTP5fOdOPqtln4tVd5gX776btJwc0kpKSLNafWYMe9PGZKJPTAwdmjcnMSKCvn360KF7d1K6d8ccqvzgtSEH31iwgHWZmdVSpqXFwrcZikNKp7AwDp82hszICHZMnqyUo1zn1u3aQbt2OCorWaiG5nIm5dE+iA4ePEhZaioOKdlttfLxVVfRoW9fV0hBnyQvmmQuSMnNb75Jl5AQV9bFqhjcIZFhnRL5dNkBrj+1A2FBpts1aBSiwBWRrElQ4PDNLgjgCMvX3R7TVfltFGYNBGB9XAV4jZ0ObBsHudCpRRwVzrFjoLLQN7yXpSgER66F3T+1ouCsKMIrvKydZzwJfyq+uGExvlbiYxuUiWo529UwdJpHdeJNN5E3bZpr3Rwfj82Z6EoI4s45x0ehDo22c+XYU/h2cdUufnpRgLT0aBnL6oN5umXBDpefvcHBod5uZf6mPx2cv04y4TH1PHXUhBrtd6VuKFHtnnrvEOA18PbeVBstC2DC4241ZU9udEAJc5MiaJ1Z5qeO8xlYhQpo0r/3nDiVaYAtjk6ERO7GVm5Gql9LkQMUH/PQPn19ojlpPyrsluCfmabYWBzFysdZxxnfcOjqawCIPessj+jyUsCS/iYuiNYxvGh2rnXbaaGjUI9sPRJY6t7g8PqNaOcTBqmG7E3w/CBzBSPQtC9PSfDbXgj3mzs+Uj8kX5g5zOcennq+ibvnNYyaHYwPdQJwA9BRW19KWXfalEG1sBxVhhvPe/JJxjgzCwE2m40XNNYJn1jQ3tt16i13hupR2/z7+ut9+nHSJyKCGC9riPD+1ztbIuCw2zGbzZ5lat/3zJzp6mvlK68w8rGax6z4LkuJ8zmtmvE8MxwOMkpKWOk8F7t3V9kmR0rWfvIJQ2+/vcq6Fbv3cFRzXDekp8Mnn1RLxqq484cfQLXQB8uEbt2qFRLx3+O6cNMXa/llUwYThgQX29Wg/hFCbMX9szYDyUCT8Z8GsPvxxTBZE7CH+I/fKwNkG3EmgumQFEtVv+iMlc0o6Z5AApBzLJEOCVYqCzQv8bg2rkWn8qzFW1kyx6hxdgWUqhO/XWUJGoUaMOm4uhWnRxIXHkKYl6VQD2E2sy+lDX0q9f1MQ8zOCArRmEJKPcqCifIhS8u4/XcHOWvy4U5l2/nrPNuJKM0k8FH3cZbJTqVwm0heHP0iHLzetR6X5qsgtSxQ/m2R7+67dFU8MT413WS1iqbf1ryASp5WUj2blDBVktlC0iq7ak3RHnMIW7nyEXH5oDYcK6og9vRT6bZ8Gftsvs9SoXFfydsdfHIsc7NmLoU6tEMH/UrC/Y7W+zAqLK8gRMeLMi7c9/4d02YMWoVaSvexWAsGERGpZtYkeAv1xhZe8210Gl7a7TLyna5CXgiz+0Pnr13ZhMSvwxyeTWX2+a7tSZFJ5Jd7zo+wNmDQj2D8A+YBfwNb8c2CbNAIrFi4EIAKoFhjfbSbTIR4+abpKbTOf4VOuZax8fGEhoT4tKNUeQjvqKzkvFh3StJgFHgpJVabjRC1Xz1lvUtoKPutVj796KMaK9RF2UoszZbAC9ddR6suXTCHhuqmYg+07eChQ65kOVpZtXX/mDWLT3ft4pZ772VrAIVa2mzkfvoZxz/4gAK1/eXt23Pv44/7TbajZ13WK9uxcyc9e/RAOhyMuO022pjNfP/RRz4p5p2Wbe32M9RMbg/Nrp5XwNjuyfRuFceHS/ZzxaC2mExVv4AMGoQLNcs24Jg6qthkMOv5RgD2yMDJMMpDS/2WHYtQXZPajwQ24ZFS1AtbuRnn7SwRlGR6OmtbKyMIBfJlcEpRRC+3MuGdsMM7Pr5JJ+59aJQNGdcKe3lbTGG5PuXetAkvUUOj+BIeIgiJ3eqjTAeN+rETVe6rDngbUgCIiGdgQntWa+p1Tejq4b9sO6qeX53r0TbHvTHGT2CVAynwzTgTPR1VGwQC+W4DmCMPY1H1e7tF4K2PvTT6JZ5Y/oTSl8aX960JA13LIUlJ9MR34kK/bw+Rrmpd4d4faV6YoqNxqO9a56RF53Z/uD6I9A7Sz8em0HluS5unS6YwK3IIAaEJG9CG5/KX2CUQPVrGIgp8H0kp0Slox6Bs2g9TjStOVJiZyNY/Ango1Kq0Hms1ka+mBKNQR0gpH6h3SQyCplANsfP2hRe6kq5A7RO7HM/IoEXbtgBMv/VWrvv0U916tvJyQtWH/nyNZSVY/Mn5+6RJnPfss7z7zDP8+tVXfLVnD68dOEBS5+qPVq/7UfmxTfvf/zivikl2NZFVyxXPP8+nZjPbKv373lUeOMDRxx6nYssWYsefx+pjx2D7Np6aPJmBl15aY/mclKamMlyV85bXXmPWnj0MnzgRc7h+ulcnf73qHlpO7Fq9kENCCO4e14V7v93IHzuyOK9vq6obGTQE3iEs4rQKjpRSf7z/BCLAvC4fSsxuRSEzcZ/fekeiesODu8HiAKYE7FOYYUC7BI4DfdokYNnqqVbZbBH4V4U8CY2yefg1h7Zv76kgaeeTSIkI9e3ZWhZCbPN2hMZv9inTo4Uji0oZ4uHr7eSY+JPItt95NqhWfD/1o16nSc/4nmoNzZUL980A0DyiORVJF6LNgKelZYG780tXVW3HS08SbO1kopfz8utZnqvsRa1nstMhQ6ld1uIaYr3KHZrRk4gALs9SSoojIFbjQRJ3tBIU+wzm0MDH5VSmAVImTSJr0iRFPp37w+cYdI42MSqUIkDavSLD6JjpcwvdLiPSHo4wl3hUrY2SmhsRx5sTBvDKB/pexXEXXUTx3NlIh3A5ay/sk4A53K1rpMRHgDqAHJq4VK+bRiEYJ56vhRC3CyFaCSESnX/1LpmBXyptypddRC0zFnqzd/Fi1/KD06YxNiaG02JiGBMdzZjoaEarf2M1vsJr3q9N3ExPxt1/PzHA7BkzuPfZZ6kAPrnvvhr19ffvvwMwzE/WxbpEG5O7+NAhjzJpt5P7xTQOXnY51sOHafPWm7R9+23+WrmSJJOJ/l7ZCOuCc84/nwJgrcZP0x9nqiMAf3/xRY32Nb5vCh2aRzE1db/fdO4GDc4GlABTe4C96vJ69W9dgHYnDI4ArhveZIXpKyaxvR4jou2XmmFVAbEpQd2n0g6haiKJiFDfMWNpU0MRAiGRdqKS3R/T4cmeCo8p3OGhtLSa9KRHuTk+QfMF4V9TaRkXgcPqOzyvhxBS0XpCo3z67dlWRwusxnwcV7hCnfP4r87/8m0Q7q2SQkJ4ArbQ9h7bmhW7+5s0w33VegYR+9QpfbSt6m9Fvavv7+il9FVeuzdzjzaEq6cypq9vGMb04nQ+PN+/ilV2PLCxQ0vUsGGuZSEEKc89S8fvvtWR17/Lh3NT+RGvSfpCYC12J+mSjhDCTZqRZ1ss0VFlzjXffXrsSv+3KEJzESEFvHT1s9x9xoOYhIAQ/RjopphoRZnWMGpvgdqRcsKba/IfRLT0SqIW4OctQur3HRWMhdoCvA78D8/R+yY1yeVkokKdCWwrL6dMtXRIKbFmZbkUOtfwvvdwv5d7g5b77r3XtXzMbueY+oXcJSyMOK/JeO1DQkiz2fhx6lR6nO8ecvFJ6qJZdy5Zs7IoOXTIs0xdHpOQwA87d/LWyJGMDA/nvXnzeMhiIbSaHw9fzZ0LwCs33eR2cUC5cR1a1we1zKE5H1oXi9LSUmbFxHgMZXq7ymiP+M3LL2fSBsXqYklL4+jjT1C+fj0xZ5xBq2cnEZKcjLRY+PrwYQBuV2OK15byigq+VpPb5Kq+3yPuuotbXn3Vb5tsTWrb4TfdVKP9hphN3HlaF574eSsr9+cyqmvwEzMN6o0FwGwp5TwAIcR44Cwp5YONK1bwVOfjrDKAWSg0didWIdSIts4+Xb4cNcZe6J7xaCs3E92qAlQFyRzmORWtMt/z2WWO9FTQzQnBKclSSmUoPtQ7/pAvwqR6ZoSE4509KzFax8JZjfNtUcOS6X3znNt+vO9Gk+8HSWG5pGzBRo9tXbcPAKFvsa4aRZgIu2fmx+q19qVow2GfDLtxYe4tPdOVliXZvspxpb2SvW1qbsptdvm55M9SDENhbdt4lk2YoNvm7gF3szvvAKe2PlWn1BldxPMHY5cgrW4bqbTFuz4mARy2WMwRJfpRSYTXZpP+lH/npOHj+R9QXFGsZP1Uy5qHt/Woa472daOqVH9CwlSBtIdyzbD2vOYbTlyRyWs9X9tdPbt/BKNQPwB0lVLm1K8oBsHSTA2ddu/8+dzrb4JCHbLfYgGL/g/l9R07eL1LlzrfZ7TmuJ466yxeWVq9YZ3dalr29zZtcvuLAyanQqzdpln28PfW/Hlvc617RdvYvG8f0uEg/7vvyH79DURICK1eeZn4Sy5xKeFf3323q/4f6Z5xZWuKlFI3kUug/o+oIx3zH6idR9flg9rw9p97mJK6z1CoTwyGSild8Q+llPOFEMGFbzlBqKmu27xQMnWKnR/Ot7JxgGdnUoAtPx/pmngd5NtVR9m05RcAYM1TFI9CTSxqW3EleDuEaH6bJrOnwm2O9bXg6lG5ezfmyOCeF8IkkXYBIfWQyTSANTstt5SEZgmusG4ADosFk5dBZO+xEhJ3HfbY9vDiDeT4eocEhd3l8K6GRgxyupcATBFHkD5qs4LD4tuPSeM3/8AvSrnD5vtVV2GroCBGkHFLPm0+b+baHhrkbIbIzm1c/sTCUhBUm793h7Fj7R3sHWJjsJdqUFRuhQhlTsD6w/mutCl7jpUgNVkNHdY4Vh/MY5S6Lu2xVMojTkGUbZpbQLv8zjWeCWWeuMFMaeCklpiEpxpqPeKbtGbDafFAKVGd3sOScwZmk7fftEYezc/1mevM7GwvaKjpf8Eo1NuBsiprGTQYXfr3h40bebBfP5I7dXJZTXNyckhOTvZvTdWxrjqZ8ttv7FR9gB/v3ZuUXr2QUrJqzRpmHjmCPx4fPJhkr0l7TrRWJm1ZTk4OzTWpwLVlx7OyeGP1aiKA/517Lk/9/juvLlvGK1WdFA3Z6qQfM1BeSzeE6viln5OYyN6yMtJuvZWyVX8TPWoUrV58gdAUz0xhD6muGGunTWPIjTfWSj5/cj41ZgwvL1/O1v37SWjf3qd+RVERHZo1Y3B8POe9+aZPeXWICDVz2+hOvDx/F5vTCxjQLqFW/RnUmhwhxJPAdJSf10Sg6plsgBDiPGAyys/nUymlz09PCDEOeAdFa8yRUo6tE6k98G8FDUTnLKVB3102fhjgNVpWWcTeESOD7qt8i5IcyZ7n60aQ9eLrNLsabEW+ipSlKICPqwST8HS5MJk064FcL/xM1AyLs/rsU5glDrvQtQ4HyqCqR2WRGWtJiCu6RqBr8vjP25j7cBsPrcZRWIgpORntaPt1n65mvk776j6tN3QWDDrgNmrsDLVzOnBANziq73Gnl+wnutP75FjPofntt1GYfhBYgsMkMTkEEd19B+JLK3yvQ2mfCrBZPD5gKtRMm3FeSbq6HyUgTr9rU4Tm2hVolEwp4dkEZXmS52jF5nRl/dUFu/n+zhEeZXbpcE2w3HqkgMHq8gvzduKwJri7t8Xz545jPOBaj8HhjOmtTgz0uE6a++moZSPQx7W+L4CF3n0feV71ogV/+NRtn5gOJGIKLSKi1S/YHE/71NGTR1GmG45gFGo7sEkIsRjN4JERNq/xKFddPp77/HOihgxxba/ppMSyvDxemjXLtf7yjh08Bjzx+++Y772XDVlZ7FWtmYkmE2/dey9n3norbfv1q5H8Vcm5MiaGCil5csEC3haCPOD7Z55hwrPPBtX/L+okxAntGi6Um5SS0e3a8cyWLWT9vZruzz5LwoQrdV9ex9UHbF0p03qc869/8cLy5SyeOpXLXn7Zp3z6ffeR7XDw4MN+w81Xi2uHt+eDxfuYmrqfD68fXHUDg/rkGuAZ4GeUt9VSdVtAhBBm4AOUhF1HgLVCiNlSyh2aOgkoM/rOk1KmCSGqzgpRA2rr6Vgc4gCv+AwF6TP1K/vBorplOcr1Q0uUZIXjVNISOpfqZkwESOzhaY8yCS8fjPKCoOQpXDAPEtzrZksYoG/uFGYUP9SgDQr+6x2YpwSI7uW9D50mpc502xol0pqVxfpiEyFInOcrxqJvozNV48Jf/6CZUTskgw64G9mEGbD7xH/2R5FF+ViqNKXR4sE3+WnzVNi0hIoQQZQFHKHNfdrklvgq60VtbWAt81Cos0qViDRh1TTqOC2+ZqHxy7ZoIrI4/Ju4nZ4aa/zEGXfuwKyJ7GGxO7AV9YfWSphVbz99afMdQXFUpgC+zu15lYGj8NhFIaHNViPlGL91Inp2p2KXpz/H4nhPM7fVEWg2aEAR6pVgJiX+ArwIrMQ9sWV9PcpkUAVlRYr/XkQ1EokEYsrNN5PpcJD65JOsnzWLG/v04ZUdO4hr1457f/mFZpGRfHv//VjKysi127nxnXdqrEwHw0WjRrGhrIyMjRtZ9tVXAFz1XPBhdOf9oXzhXlgPE/70sB7LJv2uu+hzTAnVd2jMGJpdNUFXmS7TWrus1qD/pMWCtFhwVFa6/uwVFdgrKrCVl2OvrMRaVub6G3TttYQDv/3wA9ayMuwVFUo2NqsVWVnJ2zNmMDAigjMefbROzkFsRCg3jOjI7zuy2JetP9nEoP5RleJ3pZT3SSlPkVIOklLeH2Rkj2HAPinlASmlBfgOuMSrzrXALGcGXSlldp0egEpN34mB7FG2KlM6eeIMX1fhJw59empzilYrQ+UJXd0KYnwnz3B0YbEWKHOffrHEyzBQUUgwR1zww48e63Fq2Do9i7jJrLh8uEcJg7PUiYpKnv/KRrOjvpPsfOrqiFxQoboEaLYdunICc7ZkEuGcd2mJ4+H1M3T7jAqcpNCDyjD31DunEmqSVavSerG2nVs6xnUE3Eqwo9RX8S8o91Xm5sVFg80zIcyv+34FINdcs0DIoZveca9s/d69bPHzfJVgq0Ha7bP7tPLw21BC5rnXHToKtfATunHm3i8D7isv5lMiUn7lWLl71NviJXPybdf6tJtY4nk//n7od586iwa4Jwo3FlVaqKWUgc+QQYNTkq94Vv327LMQF+d6aKYfOUK+amnWm3zo8v3VbCsqLORhNf5wVm4uWQsXct7Ikezdvp29wLUDBzL81FNxlJfzw//9n2ffGpkCbdMihCDz6FHSZ8xwuaA4tzvXy9Tju+HMM7nzGrdhLWPbNtr07Vvl+VmmJnS5ccoU/m/qVM/40drzoPnTW3fJrPkzea1r/5x2g3s/+ZirPv5IV7a/nnnGtRwSFuZ5vrz+rQu+2L+fLzRxS7V+4p9PmOARoaS23DyqI58uP8BHS/bz+pUDqm5gUOdIKe1CiGQhRJiqFFeHNoDWSfcIMNyrTncgVAiRCsQCk6WUX9VYYD84aumqpW3tfMbYRACrlp4MaiINWeapVIW1b401K8cjXbg2BFpMq0oKD7p/c6ZQCcXucX7RohdKageVigKoVJVwq68CFxJpx1ZuJnT8WcBP7mZ5Y4CFRPYvoXyL50QuZzp0aVeO33LkKMIaTWjLFvoTPs3KpLrwzftolwGRP+1WPp30CHBtKqwOdb+eim27xEjMKNclLP1Khh37WLd9ZHXvWC9RSk0mFH9ZnXePXnuvjdGhynVzRmJ0lPnG6i6psIHdxPWp7vtpaUyEj0J9evvTWZ21mkEVlQS22/pBK9vG6UBrZblSo1A7HJp6krlbM3W7cjg832wz16VzirpaZrF7WKz7tWpFYb42FrrvmbPknQqkBnccWjmEcj7tmg+f3FLPi155YL9Puxgvt5mnV3q6fDizad4Dwc+NqAf8KtRCiN+Aj4EFUkqrV1ln4CbgkJTy83qV0MCHD1cq2bIuDSIsWnW4eupUn22TN22CTZvqdD/B8ld+Pn9NcceL/eCOO3hpZdWZwvpFRLCkooJb+/cH3JFJXMo7XlE6vBV7TXlRURExsbEeCVEcUuKw2bBkZWEvLkZERhKSkgJhYXyydasmqbEvm3coo+d39OtHcmKia59OtHJo16valp+fT2KzZh59Tf3rLzIcDm7s1InOnTopckvJ86mpAEx4660Aklaf5jHhXDWkHTPWpPF/Z3endYJO+luDhuAQsEIIMRtwaQRSyqouuN6byFszCQEGA2cCkcAqIcTfUkqPMVohxB3AHQAtW7YkVb3ngqV5WRRQdTSL6pBrq17G1FI/z5rS5gmEZBzzOFkmTeg+rzlWmEIc7N6+GRPKyTxc7Gmx3LZuBRF7lgGQs2UhW1N70yrE4ZroZlKdjyu/ngmaNNwtygoAyAo34Rykd57nlnsUBTv/UCXNk2D/hNsAOPbhVDLyfIfqU9fvBHaSnp5OC8BiqXT35dV3dl42/VBuFu86SGVbyI4daJ0lXpq3C2cycmHzdY2ZNPxmJq3+wmd7TXE4HHgPwOcXFoAJCgsKXNv27VOUN7vNRmpqKpvKNikyhjvAZibnSAb7UlMpKSlxHeuqAxaG7QrhojVuRfDi0lLWrkilNKaja9u2wm0ARHspg1XhvMLW0DhlGMBLQVy7fBFD1eW9M9wjjOXl5fRIEGxRw0ekpqa6rsu3cxdrehBsy3BHqdl5II2oEPc+SsozyCvWuH04fCOYSD/+/M79+sPhcIAZtm3b5rdNp8NLPMriO5ZhD/IDOzU1lZLyUgKFSQgkn/Y614RAFurbUSJ8vC2EyEeJZRqJkoJ8H/C+lPLXGu/ZoMaUqjfX+pkzEeqwpBCCLVu2MGCA2zLorZxpl4UQ5KWnc9rtt9M9LIyff/vNbz3v/vyWB9gG7tB9GzdtYuCAAZ4h/fC0qt9/zTX8kZvLmpkziYyN5a6LL+bztWuZVFlJWBXJSjLtdi5v25YPN28OWC8Y9Py9ixb8Ttazz+IQJpJfvY/Em25CqMN6v5pMZEvJpp9+YuC/fGOypm7eTP/wcD7asqXWslUl59XLltH1tNMYOmgQ/1ET3SyfMsWlUEe3aUNdc/tpnfn678O8/cbnRK3+hd9XrWJPaSmnx8Xx9datROlMkDSoc46qfybwyUsRiCOAduJBW7Uf7zo5UspSoFQIsRQYgBLz2oWU8mMUgwxDhgyR1Z3bMe/zyYC+ta2mVHeCoz+SO3elYOMOj23mUHfnZm2iFhSFuEeHluwFBIIOKc08Muj17dKWYzEx5JFNUlISPcaNIy3ZQmmmfngEa1FfkCGEFSuJlMripEuhdp7nQ8mVlB8PJyrWM7rG2LFjWbF6Bd65153t9pRmYWcBKbEtGapu2+lVZ/+hvViAULt7m7OORDBs5Ggqi4o9rLKJ5e6Po+QI5Z2wrkUPhmQrghyNqaH7oh9jpEln5K1ZfDMOFkN8QoJrW9euXWALmENCGDduHNs2boPjEH5OPpU/J9EsLo6B48Z5PF+/SVtHlN3TGh2GZKjYCuNucm3buG4jYXkSzytQxeGYJLF2B3bMhCa2o+vF27FXmqD/VfCd8tE1tE9nVzT5bhG57FLbRkZGcX7baKYVX82VlmcYN+4O13X5IS3cbXb3YkmGHavZ5HpQ9Eg8g71H3FcvtKJ/NY5AvSf8+DU4QhVzU+/efdigOg5L8Hh32Qr+YO9cTeKjUEmYDO6jZNy4cRzPzSZQSLpAz6LaJsfzq1BLKbOAR4QQ6cByIAIoB/ZIKY2oHycAgy67DDRZkwqioxlQjZvh8REjEMCPM2bQ+5xz6l5AP6QDXauQ85GHHuKPxx/n6ObNXPLiizx5112Mf/99vn/pJSYGmJxYWVzMPquVCfUQTtCWn8+x51+gaN48Ivr2pfUrLxPulV3w8Ysv5v9+/ZXXH3qIb7wUaktZGStyc7m9Tx8agi5jxtA5JIQ/li3jP+q2Mf9Rlpa9916d7qswPZ3lH33EvF9/pXLHTt5w2DEBp8bEML5rV2bs28cZDz3Ef77/vsq+DGqHlPJZACFErLIqg3VqXwt0E0J0QplxdDW+A/+/Au8LIUKAMBSXkLfrRHANwQzamu2Smxc6mDXSRF6c+jEfQGmutjuBVp6ICKSaodYc7zlRzRQT4xGAw5SQDJoEyqZQqfpJq2iXvdeFAFslSb1K/CrU9vIOWPPGkGhW3Pvebh/HFwPysWuiTyR2LyXjeLiPtdyel4cpgHXRYWsGQGWF/++wQOdYIMktsVDx7rse21tpfMivLlHSGTqVaYD02Jb4Q8THITVxvz3wJ4t0T4CsDh9tUVz1jscLWreuwFbs60ueHBtOqZd+ZzXjE4Wl1FJITJCKoJO49uXuNPeWEkIjHYRGOsChcaFZ6HYbpP0IYJW6Irl6+x0kiBJuNP+Bw3G7q1pBmUXzae19XoTHtt/WWxCaD8S4yNAGDfNmauWZm6E4PIHWDj/XX4OtVInIckL7UKOM5vyAkn3rc6D2Zj+DuiGIFKT+yNqyhXf//purO3Sgn44ltbE57b//Jf6JJ5j9ww9c8uKLnPPSS/SYMoXJ773HdZMm+Q39tGfBAhzA0aNHWThpkjtWtI413N+yEyEEhw8fpvD337GkpVG6dCmOigpiBg8mqltXdn6pfIa73EaEoJ1qgZ1x6BDfeMm29qOPKAfe3b6dtUHGng0GCdjtdsw6k18O2GwcyM5mREwMVs2x7Vq7ll1+ooz4eyBZLRaOHzvGsdxc9mVksCg3N2B0zy5mM1KI/2/vrOOjutIG/JyxTFxJCEkgBCtSpKVoKakDdXf3fu1Wt7bV3crWu/Wt0lLfKqVuAYoUKMXdJZAQSEI8mZnz/XHvzNzRTIwkcJ7fb5Ir5977zpkr733PK6zXfdr/nDUrwm+kaAlCiEHAFCBFny8BLpZSLg+3nZTSIYS4AfgBLUXGW1LK5UKIa/X1r0opVwohvgeWoDmrviGlXBZ6r838Du7/YZ6OgzZJjvtL0qXcxWPnBJ77/bZJXIZ9XPZT5MqNrXcv6td5fTlN8XE4dYW61C840JzomxXBlNIVH4Xa4oK1P3kbrPoWn4GD2jLfg9dVEpNeT96kIk+GDSPOKk3hOGqNNnhgkpDW3/DOJCVCt0bKoZfBUm9Ad+3KVQhbaEWzpLKOVGBvdZi3jzDD7xLB9rIaEv1SrR5c4u3L+F3a/WBLXDrdKxuPabV2z6F+adhTN8Bt1uFqgBC24UiKBvVscNBgdeGsCnwXbXC4aNhxMlq+Bo1PEuK5b+NMn3aVteVNdvcwIso2az4B4Jvlo9BQ/OanB/COT0BSnXZO5Ihiquq92UBOGZrF8zPOw5Y6A1dtN59Kg0GO7DMXF2XBZkumrF47pxv2Dowom0XE+P12JukbmVq2KYpegxr/zep2HU+Dc9/kmw5FJEGJ9woh7gOOAy5Ds058ArwppQz0HlfsMy7r2dOnuEhNbS1v2u0BVQBxTxu2/UQv+LGquJhzuncP+s7qMx9EgW3uNrW1tbylV/ULt025lLy1di2O3FxMQrDa5YLSUn5++WWO/b//Ixhv3a8FK7y1cSNvRZhmr8n88IP2aSI/3XOPZ3pOZaRGw9ZhbpVvcM1V77Z6HFkAa51O1hosPF8WFqICLvYJrwG3Sil/A0/e6NeBRpMw69UVv/Vb9qrf/JNo1XPbDON9wFHZF0tcYFm0xuyP/5riGxiX0oRLzr9amzkuHucubSDZ5We1NCX6uXikZoLBqcNklbBzCaC5aFBfga9CXe77ElsfaBVNPHYs5T/NQkjpyRm8KXUQw7ZsIi22HoweCFJi0hVqV844CuL/R8ZHN2my2KMQrjA95+73IE12V9aRGheF8NNZnC6DEQJYV1zpyXHs5pKV34NueJX6i//fx/0fH3/ntbbaBw6kdnmg4tz1wYfYcsaZoWUOQmkzM2u4SXU6+dLUn4PLA3+L6non5mB6W68jfWar6suJc/kqgvMvPJTD3oswSZrxN1jzHZ6gRCMNVeTED4IdvgV/jjQvZlWZN91jrcOJbEijbufpAOyt9YbFSQHH9O/KH/r8347qzQszvVk84uxWRHS6R6EW5mrQPeSbH/8nQ78sN/g+q+IqKzEBSU4XZeYwqrwrij83l5KXEFr5Fm1sv47EQo2UUgohdgI70ZIZJAOfCiF+klLeEWwbIcRbwIlAsZQyIDWD0O6Y/wEmoRWOuVRK2dy6owcUNrR68L/qVgBPEJ3L5fEdE8aPX5nsLYbo9Bqnk8VFRT779z/lgr3RB7RpZN64TLpcCJMpouMAvLt5M90tFpKAMuC4G25AhlCon1uleZR9/tBDdMnJ8Qky9ClwY8gq4jOvL5NAzarVFL/3HqKmmrgjjyTh2GPBbA7w9/a3gD993318vm0bJcuXk2Zw75idkEBqbS1TX/XRT1qFNWvW0K9fP5wOByWbNlFcWEjRzp2sXr+e9/Vy9G7STCb2uFwtqh01wGbjmAEDGHvUUcQlJmIymzGZTJjMZsxmM9vL63hr9mYmHNyNIw7qysnXXssAP99SRZsR61amAaSUBUKI4EmSOyqGB7WUET2mQm7fHGr84y8MlsbUq65i9+uve+bNcfHQ/yTc2WTNab75700W6S7gF/xxbsxDLaUni4NbtzVZXdRu0iyPuTthiUsbmayrqsYpYF68zbdsj3R5LNSOomLMhpRjrppaj2IbjmDKzn9+Wcs/TxlErCXWo7+76uoodxjiZICHPvuLqUIEWLKF0NZnL9RcFPZGeU/J5BgrMcOHB1WoowxxFw4TWCK4cUWqNgm/E8VutlPrrMUMxFtrcVUFZvn4ZukOTvWzPKc4nTD3ZZjgzflfWV8ZYKE2FbVC5co+x8Fab/GTJHsytWwNaHbLx4txRyH/d/oGn3UNTulNOYhgZM9U/tiozY/r24UXZnp7cOWOvRzRqwvryzUXnfvG3M2/fnoPgLW6jv9+vollPSK/6OpdYUZAXE6t0qf+4rfm+BPoz+vEmCyUhXlqSWcUr8/YwGMntF+cTqN3KiHE34BLgBLgDeDvUsoGIYQJWAsEVaiBycCLQChT2ESgj/4ZCbxCYIomRRAGJSeTWVfHNL+LPVKH+qv69uXdtWtZO2sW3cdEXjmstYhUzj3r1pHepw93jRvHw3rpcbdSvH7JEq1iZAhOuz9MJaVGcFZWUfzkk5R9/DFxWVn0fv4/RIc5lj/S6eTzK69k5n//y2m6L2FDfT2zdu3i8j59GHPNNU2Sp7qoiOIVKyjavJniNWso2r6d4p07Kd65k6KyMoorKthRWUmJw0GJDJZl1ZdDk5NJT0wkIzWV9NRUMnr0QLhcLPnzT/5av56/ystxezzmWSyM7dmTMWPGMOb00xk4aRJmS2QKzrTX5vB5STU3XZHPsHvuoTxE+XpFq7NBH1Wcos9fCGxsR3majI990dV0hbq17VDuIi8ACRMn+CjUpthYGH4mboVapGT7bGvqMw7XNkMUoPB+u6ikhkCfaj3PsDXOSfrQchK617An7hDq1m6k+y7JEqu2fXx9DVV2zT3BB+mitlRTunfcdx/cfZdnVc1fCxGjI7BQB6GmPjDH855332XGoRM9RV+ElJy17regbiHhUkQf0j2Z6MxD4J3AaDazIe1nbRTE6YbXPvX12KSvJTqsf3eQr5asuz4kRWv9dWT3I1mx/Q9gC2vNWeTUFSEdgYVULH5fJt0R+OWqGipJ9+uH+OjueH2evaQNrKBkeXxkdXgavJZnV1KuZ1r4bbxtT5AUjDhw+Kl9Eoi3e5fF2nzX1ztcpNq7eOYnHTSIB38xcfclZgq1RFV8NVoz5KXaAwvhBKPaEWa4aOEUTBYXznrtt/24NJVTkiHaGgeO0L7U0hXFL6uKYdK+K+jmTyR3qjTgdCnlZuNCKaVLCHFiqI2klDOEELlh9nsK8K7UTHtzhRBJQohMKWXrhnbvh9TU1xNtDx6w0hhrv/+et9eu5fqhQ9tFmW4KKb17c3h8PFPnz+dhfdlrRx7J1b/9xoSxY33cCVqLqnnz2HHPP2jYvp2Uyy5j5bChHNwEZRrgsAsuIPrKKyn45RdO05ct+PhjqqUk/5hjcDoc7NmyheIlSyjasIHizZspLiykePt2iktLKS4ro6iiguK6OoodDkLdeuKBDKuVdLudnvHxHJGZSXpyMhndupGenU16VhYZAwbw2r338uyffxIDHJOZyZdbt1KxZAmzP/uM6T//zEfTp7NQrwaXKgTH5eZy/MSJHHvFFWQd2vyqh9fl9+aSt+bx1V+FWMxmHC3wJ1Q0icuBhwB3+dMZaGlOOx3C8Lej4C744jNvDEiLz/RZL2JSoGYP0EVTNA3KmMksdR9qTaETQvhYqFMP0owmLr1gSv5SF98OdWKWLvpFO6iINbHR5jcMLl3EZ9dS9CekXn4ZJQ3e4X1TbCymMAVuvDpZoGb3vz+3afnlDYF2e6d+za3ru3nKiEsEF60KLB0NIBtC/44DuyUQf6y36q+IiUZWa/ckY658syUKd8Hmz7fv5CFXEDcIgrsiGL9bav8KnPUm0uI0c333FO03rXfWY9MrEe6xaCNq/lbqvC6xDCr1NfOvirJBV9/nRGVDNT397nlFQw6j79TAip3WuNDVDwMwjGiYyjaBOEj/Wr7HumBUDzAc6mTTLJ63vcQpdf9ksTQE0wtBvN0bjxUbZcb/mkuO8irKSXbNXWl9t8BO3l27m4ogLkv+hFWoa0oxWaNx6qdp9wrNzz66aheEy/Al9e/Qwhz2LSESH+qQpj4p5cpQ6yIgWBGBLILkSmppTtPmUlZWhtPp3GfHi5SqujrMdnuAXJHkUHzh0kuxAcfceGO7fa+m5HocPWgQ/54zhy/+8x+Shwyh9113wW+/sa6ykh9//BGbzTuE5jRYEs5M1yoiGy8to7+5Z949LSXU1EBdnebWER2NfPIJkJKnje4ixo/wHTA0LqsBnl+xgtN++w2E4KO779bkeuUVCJLvO1JigC5mM12sVuxuP0GXi/K6Oio2b2bF5s2evOFu2Rbq/trVwNQdOzCFsDAnA4NiYykqKeHdKVOYMmWKz/pQgaCe9UHmcxzw/PMwq0a7yRb45XRtb8r0nLSh5OlIsjaBY6SUfzMuEEKchRZc3jkw+V9Z+5Yut93Krqe9abvjjjqKyl9/BXSLtAF/BZv4rr7zK74Ej9+y78NeSnQLtSFtXBCFJOHYoyj7/GsGboGHat9iePFqNuT0pyIqMN8y0oVZz4st7NEIw8hQ8ZNPId67MthX1mm8r43VAy3dMkmO8SpjmfE2SuyJpBms7n9k9Gdkka+qkP3yy/Cjdz/XH9nb5/4SddzR1H45LeDYPR9+jJ033eqZb4rqJDzuApL0IVofb/JrU+esw1alpXZLqNDum3UbfN0lauudRBleitxFReTOJT69t7O+jFF+CnV9fFITJA6B/4iGjvDzPf7v9LU+ZU6ft70EwJH2tSyu6Y2UbudGPwt1lAUQvHGcid1RiZwzPIfjewzg/TVvRCTeM382Xt9gddnS0CsHnIJpqjdGqaSblr0j2tXIr+03WuHG6rSwN9pBQg3Edm1CKc5m0AzntFYj2JUbtMdamtO0uSQlJVFWVtaivIRtwRaHgy27d7P42GM9y9zKovumFMynea1B4bwtiNtBYz7NTfWbDuVHbZTTjVFRNc5v0CtunX7zzfSxWHxOmg2ffsq1r3krbq3+4w/P9F/l5QH78kz7F3mREulwIKREmM2YhIC6OmRtLU7d39sjO6EVc//lALErV3LY9ddz287QtbLShSDdaiXFbteOHYTG+rbB4cBkCMQxynFwfDxzQljzh8TEEGuxeNq6H72t9ds7hIu6Bu9DJT8/P2KXn31Bkp6TNpQ8HUnWJnA3gcpzsGUdloSoxoPK4qxxgO8QcHNzTS/oLRi+zruxOc43KNF+UD+vQu2nQNetW4dW58YtmJ9CbcRPPukSusXRoFDXBVrvYkaNA2DxkASGL9bcR/K2hrBnSZdmMDcJXFVVARkZwr0YR9J9Ne9O9kxXTZ9B6akne+Yzk2JIqy1nc3wGPSq02Jx/H3YhX0z7h88+ovr2hR8XeebtVt/f2xTta4nsNqqUvVuiST5+IjsJVKgjkbt79XL+BLLrQ3g/vTWR383bPFbQ+DpN4a/fsAFSvRbawvJabE7tWVptcIl24B5ngIr6Chqkk/8lxHP/bm/GF2noe3tKPbV7tB006ZXRPyuMjrPO18UjzidS1UusFaiBB0dfzskbZuFC+CjUcVEWZEMyX3c/lobyQ7gnPY70GO2cNtU37p/86ZpPG20zbfOHAYGrgPaGue5nLTOOzpaeg6AS7I1anrUzfVNJpU/y/Xv3FJERZ6emJorkPoE+8a1JeyrUkRQRUIRhcLavr15tbS3R0d7qdP4X6dqN2o3kuPR0UuPigl7EjWXqaGpmj2DHqKmpIcbwUAoo7IJXOR0FfKBbCA7NyUECQxsa+GTbNl79+GOu+e9/Pcdc8f33AMx78UUOCxG0aMRVX0/JCy+w+823sHTNoNsjjxA7erRPm+YqVG9cfTVXvf46/3zgAT6/7DJWO53836BBvLg0zJt5C2hMznNycvh9xw621de3arnxxnC6JEc/XcDKO47aZ8c8UBFCTEQL8s4SQhgTASegPe87DRbDy6GrvkvQNk7d+tcaBVuePMOExQXvP6m9wAvDfRRARHld7ISfu13NX3+B01BMODrZd+cnPguf3eqzKC67jsptUVhiCelD7XP8aM0qPmRxcB9SJwa/c+lCCDBFWXFVVyMMI1KxY0b7BOINWe/iol9dyPPqEbbIAuaCleN2M6hOs+66lWmAWkvgML21awb/Pv1gY+Y5H/z7ODG3hsTcmoB2tXhdQCD48+aWq8zE1cJRdidUQU5ckEYC2DIbenoVxs965TN6xwqs3bppI5fgSctWZtWeX0+e6b2XOroc5FGo99YH/52kQUJTiEIrjVLnt2+HJpurztdoEkcNd4y9lsR6398rRk9RsiCjPwsyNO93o8tHlMUECOpLNINdtM2MlJKK1Q+SGR+s85pHcaLWF8szDC+Tiz+Cyp2YrCmeRWtKqsEO0RG6DH6yYCtXGOYPqa1DK6PS9mNd7alQTwVuEEJ8hBaMWK78pxtHGk6qjzf6vmmHU6oWvPEGH111FQ8edRQP/PJLW4rYKE1VUrsNH85//vyT//7+OwndNJ+5Y48+mqt+/ZWZX37JEadpnsor5mjBHgedGNK130PNsuXsuPsu6tauI+msM0m/884Aq1RLuOiZZ7jq9deZVlLCwk8+oQoYf1T7KZXHH3kkn0yZwrKvvuLg005rfINWwmwSXDO+F9P32REPaArRaqidjDtCTqMCuKVdJGombo8P1+YLqHcOIqrLTwFtapxei9zps1wcsczFR0c072VRmgQNhk2dhvLUoOWhdiPMvtZUER2Nx+ETwP+FNX1AwPESRvWn8tMNmO1WcNQY/KolVO8OaB8KR5T2CK80mUh0Pxv0fZnsNk35NRgvLF19/buv+d5F2l5w7NqF1VA51RwkEA+0wERzbk+fZd33ekff+jgbL8ABICwWspNjQq635GSHXNdUtqdpJ9PRZVvAGjbuEoB0/bvXWrQXDFdNjec3rWnQ+jZbr0C5ort3Z/MqNzFen3bp/swP7grzW7aSdtdQpKWUdO5YjCElNRdHz+TxLqcGtL+g5n3uEydg9KBIMFio/Y1iMTa3a6EdEUZldFTnYonZFLHcW9MFN15rZktNXx53L9ylZekyWXxfNha58rDLsrD7u3RMLpNnb+LXlUUehXrpRq3ioluqtvaubjOFWgjxIZAPpAkhtgEPoI+I6HlNv0WzpqxDc++8rK1k2Z+o3aPlX4gg85EXKfnHHXeQKgS3vO9fbqTjc9KFF/LUn3/y49NPc+bTTwNw/nPPcefgwfzn7ru9CvXKlXQ3mYhPS0PqfsPGtHZSSmR9PbvefIvdkydjTkkh48kniB09mrqaGmR1tWcbN3WlpVQVFwek3Gts2mq4KRV8+CEA2VlZrP/6a5/9h3OtCFgXYjspJcXLl7Ni9+6Q++jWRysG8fStt3Kb/sAIuT+C43aXCfrdw6wbUN8QbHeKVkZKuRhYLIT4QEq5f3R6bXewhleSpYBzZ2gKjMVkAeqxtdAeX7fKtza3OT50ysdujz4C4ZJQxqQhdAUhIacGEGDWLeC60obDoJAHUaillGxNgxy/msqL+1qBBn6OieaMSt0SqVskTdFRlH/2uVHHwlVdTYMryKnhp0TlbFsd2AaYta6EnB++81l25toCz3R8oZa74Ib8W3ix4FnP8uTzz6f0gw98tksJUlzE3kdSu1YQnz+G8n8GrG6ccBpT5U5ITiKcJpvqcDJeD4asM+sKdXUNxGkjBLW6Qp0xSwu8NLpwVBpepOr1F6wYw33VEuNgRWE5bkdNYfM+xa3x2gkbndpA1c4mPd3pNqCErTNTqI7y/V3Pc33D45yqz/l2TFyUhb213ovEbaF2NQSe59FWcxA3ocA+lPWp0ASFGqAoWSBrA/dl8suNmEoF0UFcPn4+82eO+fQYAB44aQCTZ28Kmmt6X0VhtJlCLaU8r5H1Emh8XF7hQ9kyrShZHbB22jScLhcu/bNj2TKW7g68GRe89x4/lpZySd++bO4A1ep2LF8eVM5QxGdo1cIefeUV+umZSaSU9LZY+Hz1ajYvXEiPQw7hgy3a26hoiqX55JMbb9NC7tKLwIy58842P1ZjvLNpE++cfXa7HPswk5lFW8va5dgHEvuNMo1BDZASkwSXIVhRS9G1y8flw6lnaDhkfctsUTHHHEX5l1965k1hhrpNsbEggweKARCbiskMfU7bidmqKQruoX9h0YfaHYZgqSAK9e7a3QHKNEBWXQxQ7utf6lGoAzNBuWqqw1YKbCzpZoPThWurb87j3uXbPdO5P30GwPpE3+wb0hmYVs5mMeF/ouZcPQnLn2+CX3n3Q2tfwYYjIOGcv6IUFUZ8p7u1K1AW99euFwJ31e1at0JdU+1RqGev036bqD27AnaR6XRpPsBC8ObSNwFYr1cz7n3yTkxWyYodFVRa7MQ5anWrt3Y+xKQ1kDexGFuCg5LlTRspjetWR/9zdlAq03yWr5Je95V4fN1l4u1Wj0JtEmC3mrBuOYfSul4B+3e7fPgQJABQSmvAskipqXcSbTN7suWYrL7H+1/sOdjlewHbpUZ7zxNj4gB/kvtWUb0riqjEtvV8a0+XD0UzqN+zxzPd96STmrTtO2vW8M6ZTas41ZH4q6aGwUHk//dRR/HsSm+Azv3HHONjMUVKHNu307BlKyarhajevbGmpQUWvTEGKwqBlJLdu3eTkprq699tDEj0nza0+3XePGaUex+07950k2faJ4eBv9+5YT6cT7px3datW+luKIAQrN2tzz7LVoeDd6+5hhi9XHKo/fkf1/+7BSvlbpw3brNo0SKeKiig19BjeaVgHee1X5pQRadBP4v08+qsmS7OmiW58HYz5TsvBqBH0jLAV7HpXdg6g7rOAb6KRUAmDwMiyg7hhqPtSQBYorxWt4ShmVTPm0f68QdB4Vqo8QauURWoUG8o2+ATaOUmvUFTqJOM/qW1e3WZowPay6pqHPpLh4VYIMyLALB4a5lPAe9tu8rJ9WvTc28QT03D/eODK0cSu8FK2cdaDredfYbQH0iItuD/TS2THodxN0Osr0K928fOHkhugwMwk+zyKqn+lLhddcxh3BYE2KTkJ+eh1OkvOzvvux9e1TIz3fzxIgDqevQiarNvoeg6pOZLb7HxZ5HmcVWo+69bYzSZqmobeHHoGdy14H1I6IYWSqbRmLKXclAltXtCK63drJU+kRK/OwcxzrSETLGbLOHb08YgRLNJILYtYKHzST4VRwDnEWMzU63nHY82BIy6nwv1u49AmOpwVPbDmjQfa+JiEuyWEGGQgeTE9WJrpbf/qusdukKt7d/f5ePEmBVcF+QaNInA0atgFuqEnFoSzm37ED2lUHcyHPpJdWxGBpeccw4mkwmzyYRJiAClCuCbL77g7fXrOTY1lWsua5lXjZQSh9NJvcNBXUMDQgiirFbsVqunQmM4XC4Xu0tLWfbXX1Q3NLC9uJjtFRVsr6ujrAm5I6OBLIuFrNhYppeX8155OQfp321iZiYP/eT1t6zbsIHCu+6mdus24q+8kq7334clJSXEngNpSZaHW3fs8Ph8n56aykXPPdes/URCJHLGpaQw8e67Sc/M5PgHHmgzWfxJe/FFniooYMjgg3h1eRHjkwMf9IrWQQhhBv4tpfx7e8vSIvzMTMcs0u4PsbWwu1KrPmpJ0fwtjTmHT5rXOgr1XuGbXsvkFyQX1b8/dfpLvGYJlsRn1xB36EGBOwvitGuymuk2sgykpuhYzeVAAub46KAW6tWlqxkesBRMG7cCFoqMft3f/V2XK9B1oHrBAhx6DmKryep5b9leVktukJTOO8pr6WGYn/zZHA4PIkc4+mcmkND7ONx27OLeWuHk9Hh7gEKN2QpJgZkkfr1tvMd/Oegx6htYsrGIG/RCJ8u7B/b5N3G6Qmay8pVucU73rJX8EhNNjcnEQnsUt5n/9Fiog2GuriR66FBgmWfZvGg7o/dug5Q8BqQOoLCqkIH1gWnaatxBmnEZGBVqAA4+C76YEfSYGUPD+6fHuHyDWW+3epP6bHH5Bvam2xrYQymjTCuZGTUediwC4Eyzduy4KItHoY4xFHrxWKqljbriEwAwRe3CmrgYASy5eAmD3228bkODy7dfvL+trlD7WahLdpdQEReoY/gr1H87ug8f/jKv0eO3FUqh7mTU6Enmr7rwQs566imfdf5KlauhgX+98AJ5VivfbNmCNYyVxUjRsmV88OijLFqwgE27drGzspKdDgcVBHdRiwPO7tWL2159lZLt29m+ZAnbN21i+/btmtJcVsb2ykp2NDQElBQQQFch6G23kxUbS1ZGhvbp3p2sXr3I6t+fbiNGgJSk5eZy66hRPD7HO/C3+uefGXjssdysu1VcdMklgBa8uefdd9n17HOY7HaynnmahEmTIvr+rUV8pjcA6O933RWm5b7hiGuuIeruu/nh88/3qUJdtG4dAMcdO5rJK018u7GB8/fZ0Q8spJROIcShQgghw43tdxLcrhEWkw13UsePrx7Fl4u2YynWlMjWyPLhz+NLn+Mmw7wwFJSQUmIyzIsoOzgk2YeXQn/fYfegjLgadCsxA06FHYtJ6VeJNdZB/MjeMPvbgE1W71lNSlfI88u+2XD/DdDwKg90SeV0tw914V8AmEL4nneJ1pSrIYkTEXoxzYVby8j1i52s27CR2gZfpfLSlb7+08HIfvkl+NHrYpAU42tVbY4/a16XUG4QwmequIvguuvNnOHcC35W7VqD0efeLpoF/PVyXaHdW8jNGVq/LI+y4YxKoqEmtHpk2VVEza4imOht80ZSIjfpl9yYrDH8vOVnjqkKzEziFdjQE2NuhMJFkDUcrQ6TLy5bPKZGCqbEuAIrI7pZIPvR3TCac6xzOvfb/kdvUyHHRo31qb4ImkJdXKEpvdH+hYNCYDGbEEJw36j7+Nfcf2ExWTyjIf7srPZ9kVhbXOkTpOqfAaWu36mMKX2ZX2LD6zC3HNOHBb99EZG8bYFSqDsZ1XqwXUwEyvGnt9zC4ro6ptx4Y8TK9PIvv2T8aaexG+gmBHlxcXRLSSFDSqqrq9lVW0uJ04nx0q0E3lq/nrcMebEBYoEss5ms6GjGde1KVno6WRkZyKgoRo4bR9ahh9J12DAs8cEGMwPJT07m64ULeVz3UwPod8wxXNa/P2/o1qLda9fy5hlnUL1wIY7de7B0zcDepzfigw/ggw+CuiT4P4+Nbh/FxcWsef11n2U+034uI/7bu/nyww+Z+sknPi4jnv8QVA7jvjzzQgRdV1FRwbeGfgzVrg54dskSokaMCFgXqXtJuPCUYO2enDsXgD5DB3FunGTKnE1sL6shK0lZqtuIv4CvhBD/Azw5s6SUn4fepPMwMi+VkXmpvPpq26V/nLVjtq9CbfUqhXtq9/ikdTNF26HCffVGoC466z2+okTFgyUa4aghoXstSBdU7wnYZE3pGqqHmMjb6evO0KP3obASxlUHKm4iuRsQmKbTXcluTJdTcFenr6oLVHyce3Zz86dFGFXowwt991djthHt9DWTWLOzgbVeOfwt9K35nufpdu8xdicKTDIH/xzlHoxKXqWe3s/Q5/8q2U3thJfg4/C/ZfShhwKLPfPjqmvAoTk9OHU/bXNA0LnwiQuI7VqLMEvocTgc9zD88Ro54/dQujYWs8FFSFqigxb8MRLnvdQD2Cp8hx8SzA1k6m4gsVZTQCq+2CivamgP4vIRjCP6ai+TZ/c7m4NSDqKuroLLf7k2rMxurO6XHf26ECbffksdfSEnfPJkUIU62hLNjcNu9Mj3gvUFioM6SLU9SqHuZFQXan5AT770En/qllr3Sb6ntJTfk7UcqA6Hg4d++w2AjStX8uiECVpbw778FUCXlNyjW3oThMBqsfBHRQUNfkVBBNAVyLJayUpIICs1lVfWaKl7MoTg1NxcunXpQnxqKphMPj62dUBJYSHTf/oJfvopwAfXH+PSpeXlFLlcXNanD717eX0c6wyldW/87DPfHVTshbVraW8eX7gQrf5U4x83/j0iI5iOtN2/58/HGmJdc4/VGHG9enFVNxdT5mzijZkbeOCkgU3YWtEEUoDdgDFPo8Rbirzjo59Y/q9nZrwvYXmJPQlmzWsNJuROALyV+mw9vaniyuvKsRoUahFlh71ugf1eSqN0C2/uONg0U5v+czIMv9zbKDYNyvVAv9oyjKXJ3awuXc3eIG4MVmEm3hJDTkOgslVfWBz0u324bAqYBTFWr5W9vCYwjrV64V+AVxEbnZca0ObOw6/j+en/8VlmSUnh4tE9QuaYbkssZhPgRJhtoROvrPsZkkP7ZMe6JPa4RAjjESzt0UQPHoxRoR5UV+9RqGds087L4B7P+u8oBN3zdUV+4wzoNwEWvUd0SgPRI8vAHAVOzUrssERjDrqv0BTLJNJFGQBplmqf/nAYgvlsVjP87s3IgpTeVHloPtTh3G3cxBpcQwZ3GczCBa9GLOs3S3dweJ80MOmjTnoXWbO0l59dNb4ZU4zMu8DXxcNOPb1PKvKvxL5PUAp1J6NAjzyfvmcP03/+OaJt7o+wnZFD7XaykpPJSk0lq1s3zQWjTx+6DRtG1/79tWT3hofHEytXEj9gAEVS8t+NG8EvR3ZrMnn9eli/Pui6xxISOWL8eLpcfx22NO2N2W1JDpfuzlNh0hhcJyXz589n+PDhPsvCTutySClZMHUq5z76KAB5Nhvr69qu7Gmkvt5Lv/qKwaeeypuXXsrlb7/dZvIYX5Dc/vUiKoqsKBiVaeGjeVu58ag+QVNnKVqGlLJlwRIdAU/FV+91CtDT6s0xPzJzJIW80yaH/37T9xhUXp9CHaV1pWQYLdT2KLxvAL5Wc+lOGdltqFehnvQUFK/wNjIq1FWB2SPAm9fYH8fuPcRaYqgyBSrbzvLgFtroOqiMgSP7dWWNvmy3Prxv3ukN3Nr1zDNwqtetMKFBG5dcn9iNXuVau7VJgfmiLWlpjO8bqIAVjzqS9Lm/0ZbZgEVSNuzdSGV8BpQHSYsClJoNv5E0vLnpk3aXxLx6GvH2CcEPIiWitsb7soT2YlMv8GRrmbld+60tfkpgvVHlMq5L0F9cUnvDDl1Jt8VwS/nlbJSZvO2aEjRVbq20YhfBk/o4DDUyL3D5lnFPpczzsiolvlb7db8QF+V94bBbzSTH2Dh7eDaXjMkNeqxgNOVXHtFTMwS6r5+EHjVU7ozCMkxbbDKJxkuP68SKOm14vB1QCnUnY1WRNkQ15bLLOP+FF3wUl+kzZnDEuHHUlZbSJzeXDLududu3e0tnB8k7bFz2yW23cenkycyfMoXhF17YJLni+vf37ufWWznmtttCKq2zZs9m3OGHh1zvjxACZ309cydP5qg77vBZl2c2k2uz8WuNNuT5ZnUVN703heiE0Hljm0JiaSlZQ4c2a9svH37YM72h3t97vH0YdNJJZJpM/PDzzz4KQ2sTbmhwUp6VWYU1TJ69iVuP7duGUhyYCCGygReAsWjPtd+Bm6SU28Ju2IEx6efTyJ7J1Cxdhqyt8VT8k6FPtZBMO0xQHSU4+/fIzFj3zbqP6/TpX7b8woUGZcoUHW1QjkK4NxiVp74ToNhQNjw+E81LB/grMDWYG3eBEiO2nrnELo+h2j1kbnBdSDzlFE0p9iO6XlOoU2K9LwUlVZoimPL2iyGPf3qFFgTqVqaBoAGXoPng+lOVkwdzf6PB3obajkUbwbCYQ+dy/jghvDuADQk5I4lbGlw96lJTBsCeN9+C27VlDdLJ79HRjN+9nGE9xnBsxkh+KvqDWP13f8t6Hl9WDqASo8uCoe8Gna79P+LvsEwfZbXG8IVLKzm/ca8kWf+Jy0UCiVJ7Wdoku3KQ8E1j6KabCHQdcpO/+QWqdRV9WeFedyFBjcK/sFuPZlXUJWyUmditkzCZBE+cOSRgP9FWM/efMJZH//yK7gm+waQyXG52PwrLfEcDTBZJ9thSPjCdBNUwMDOBPe1hcm4i+64GsaJVqKrVTrzEjAxMsbGY4+I8H1NMDJb4eN688UZ2SMmTjz5KVHIytsREbImJRCUleT725GTsyclEp6R4PhvXrkUAg08/vVmyzdeLxpz9zDMkd+tGUmYmSZmZJHbtSkJGBvHp6cSnp2NPSiI2LY2Y1FRiUlOJTknBnpzsI19UUhK1lZV89cADXDFsGDkZGT7K9H2HH87yd9/lt8sv5xQ9vdHTkyaxzuHgyX1YCTAcv82bR1+D72Wr+g42E2EycVzv3vy0bRvOmjABM21IVpyJ4wZk8M7sTVQG8d1UtJi30SrRdgOygK/1ZZ0I32slzqYpIucNPIVNZ53F5osu9ihz5mY8Z989xsyn4yJ//M3cNtMzPWXFFN9S5DYb0y0uDu7Zna19j9YWust9B1M4Y/0CF+MzA9u4ycuHy38IuTqqZ0/WV2zhJ7dvaYU3hZ0pOniMQow+UGYW3mH9ORs05Us0+Fo7rU7v9Zlcp/nofpM72rPMHiLwMT0hMAf2jqNO5sXBp7Fh1HGhvk6Tqddvr8LPt8IRxG0mHMazzSIlDDydHeWBLh/5/bqQq3dryhW+JonVUTYuXvo8LPuMuHW/kGGoNlkj7CyRgTme3UxfV07uXd9Q7vS+qEmLtw8bDLbPHXjdNVJEeL/qcARLLwdAQiZRFhN20UB/0xbM7tGPZwbCb4/67kPAeYMm8tqxr3HRgIt81jUlJvrJH/RCQkZ//Lu2smLQ30mJtZGeYKd7Q8d/VigLdSejUs/yEZsTPJlv1Y4dPPL11xyZksLRhrzHkbBlxw4yzWZsEQYw+jP8/PPhggsAWPP55/RthmK+5pdfmPb880ybOZOZpaU4gFQhmNSjByedeirxqalMvO8+8hISML/4EtV1dXxcpQVqTp2pPfQe+PVX3jKbyY2L8w1CNH78XDTc+AcUOh0OLPrDUfi38WtrXNbgdDKryjdIRJhMdNd9yv1pyTLQv0+Q3NHBti/Sl1tiYsjwDK237PigvZ0LNGuiMMwH47r8Xvy4oogP/9jCVUfkhdmrohl0kVIaFejJQoib20uYluB+6Jsw4QKiDUqGY5uWiK1PkeY32xhfjBacNqd5L7UO6fswN2b9APhw+68ALIyNJwfo9vi/Kbztdt3P1g+rn6IbTqE+5z0tcDEUVj9N8t1TPJOhcmefPtvFs6cFq34H1YeMJGbhH575lFpvnur4D98C4OUhp3HCJi1+J79vOjGjR1E9Rws8Ls/tB0DPtFgMNngApMXCN3ljudzcVG/g0BQMiCW6tp5rutVqp4D+ldzvWIMsCSxzhEk3t/STAC1ot8XqrWDpRs/z7XRJsi3aS0fMYYfBujcD91m0ggYhsEg0FwbpatSY8uZcbfBoXamTQ/VldSbvuT7StMozXeGM8phC3T7SLeGKUVmwyLDg+3uw9fV1EWHND7B3G0x/HI68x7PYobthjO42mgD0r3xoTS1/BikyZGTCwK76NvovZ40BewKltU6SorVzPEZKhtTWsdjetEqS+xKlUHcy3AF4F950EwP/8Q9cUhtYcUlJg9PJHF2JK9+zh6OSk32UP0+JaHyVQff8d6WlLZbv43vu4ZxHH6XfGWcwXi8eYsxmAVrApFtJdTidzK0KHZ0cBQyMj2d7aSmvvv02Lv3Gdtm333KU1YopOprZ+rLphuDJzS4XPUxaGh+jYheqD9xygq/SWA9YrVYf32hjG5+APcP3nLU38CaeBBzVV3NxCKZoBl0W5KEXrF1VdTVxsYFDqcHa7t67l8+2aTfwUw2uOpEeK1g793d3Gf/r5+SUtWu5PjfXp/2w7smMzkvljd83cPGYHkRZWu8hq6BECHEh8KE+fx4EpvztyASoH0HOudolSwCIronMGhmdkUVA3t9m0CuxF6YS34f64mLN7/XeWfdySu9TsKZrGY79FW8Pxu8T3zXM0cL7swRciwYf7JpFi4Ju02+bZO75cwOWV9U5fJRpCMzqAeAy+Ik/cdZgYk96ljWjNIWq8IgQfsdtQG7tBySYnmTqKBPXFuLzTnVIdDfeL13M32MP4pLy0HmJxa7VkJnhc77l6rmjD+oaz1d5h3PKht89CvHS7eUcsVd7zphDuRUKwTQ9zzW2eKgr99n/bru2nb1vL3cmSKRZSwvZYFCiHabgIwxVsukKpcvzSqpRG5WGrNW+x6C9foG9deXYLH4jDxt92+T360LB6l10SwytKLtdPkKdwXmJeWwo30CMzUyXeP07Jeo++edqZer31jSQaEi7aDRHuTN7dCSUQt3JyO/VizkrVpAZFUW1y4VJVxhNJpPHHQRgZGKi5/7iscq6XD6KrTFdW2s5I5z9yCOcowfi1Tqd2C0WH6VWAOUOB0urQ+fM7GE20yMuziOTC+2+42poQBoC+yqionxK2o5MSKDG4WCJvu977ryT41uY/7m5hV3sQlAHrP7qK7rn55OYmMhVhx3GE/PaJul8U+UcHheHXQheXb68TeRxU7RsGVMOPpgBhx0WsO76I3tx0Zvz+GLhds4dEVjMQdFsLgdeBJ5Fu7RnA50qUDHkcLQBx67gAXyhGJDan6Yo1E6zwOwMlGN9+XpElG+K0MoG36Ia0UOHEnfmaXS57nptQTgLZTALtSHDQ2Oc3edMPln7acByazfvfqP69+f+wav5x8cuMvKPJ9aqKXzCGQNUg4TFW0pJ9tvHlcv9LJV+JNitSEMAW2u6te1Mgq5lTdvG7VefYY1j6cYtcNjxjbTXkAb/3ESnNp2RYPcov+jPmbLqBmpKtHdTU1yI3NjGwNQoTaE29suGpCzm3fE0Fx+fDa/r2TVMmkX8hRnbGKW3qzd5FedNrgxyTVr8VLUhPHGtK4s+Jm/p91CY/PyZzc5aT5n5ETveD2gf5a9Qz/H1rb/y8Dz2rplFakKITE2VxchqLSg0lEI9PGM4G8o3UF3vZMrczfzr1EFeC3WGtt+y6gZS4wzxCobTKykqKcSe2w/lQ93ZSErCDCwoL2dWeTkzy8qYXlbGb6WlHJGnDZ0v/vRTppeVMUP/zCwr4/eyMmaVlzO7vJw5+mdueTl/7N3LvL17mR/EotpcfnrySQCuPvNMCkpL+W3PHl585x2OHzmSOpeL5XqAXoYQXNG3L1/ccw8V27d7smVscjh85C/YtImPrriSN3K68/4pp/LJVVcB8Pi//sU/rrkG0HJUzy0v5/Ijj/TI8eQTT7Tad2oq7kdh35NPxp6QwNDYWP5Yvbrd5PHn+GHDmFtZSXmIbCmtRbGesjA9Kytg3eG90xiUlcB/Z2zAGWEEtyIicqSUJ0spu0gp06WUpwKdsuC7DGOhrZo9u2n7ChMoGwynNfSoSZkMPaoGgMXCpD5f8/BGd+owv/N7yPlss5hpyDsCEoIo1G5Zw8gco7+kpsSkBR7h/+YTd/TRntnoIUNYla3tK7n/wd52UrP+CSTXvfxr+O8EdH3wwSCiGl3eWu863vD0tTxyY3rYNu5gVf/fVgYdQwwkmPu9ezxy+ppdDN2l3b9MhufjuO3aaIQpyIighiDF6WRkTa3HXSfVL5tRRW4fRFfD6KDuBjNrg3eUeGmR15/4U+cRnulq7Fxefzsn1T3MTMtIn/02RGmvRB9ZTg4hm/4dHZWYdZNbZYxfphZzVKCF2o+kGCufRz3IP0vvDt7gqT7IuVq59kavOmNlJrfPvsnCppIqCstqPC4fgI/ryOl9mhfr1ZYoC3Uno6KignghPBegm6JFi3hr1SrO7dmTwWec0U7SaRx96630/8c/uGLyZBbOn883a9awSXdVOSQ6mhsGDeLCW2/l0AsvxOTvA+hHxW+/seP++3GWlpF24w2kXX01XXbtwvb660ybMoXVmzcDMGnsWNZ98w03f/MNva1WLEIwo7SULbpftX+au1DL3LgfErsXL2aDlCFT7kGg60i9wXXmQ70k+oaqKkqA7x96CLPV6lvcJYRVJ6DQSpjCKxs2bqTBT8EI195i1m6n9550EqddfHGTjtUU2X7W84KnG/L4Gre5Pr8317+/kO+X7eSEwWF8SRVN4QXgkAiWdXgCFLQWWEAtld4g3PcmvUd1QzU8FjrXTbXZgVENuupGM3H6IGCtyRn24elW6L5a/xUPH/6wV+7jtdG7yvR+TMzJ4uRV7/LIIbc15+sgYjSXgCg9o0WdENjdx0nKwVzvzfIgLWZPAJ+jeBc7H36E9Ntu9e4LyfhtfzV6TEvXDAhTQKQ1uWLsTVwxNjAO6J/nmaixCR44YgAf7rCzI6g4kb08Tdf70HhWuateTxzUlUO+1BTq5Oee4+W0oQCsS8rmsOLVWFID83JrhxbEuVykOp1g0s6SUw/J5qOliSzepvmkS4lWZl0nSNZDyh3eM8wYlFgto/jVpV3K4+sW+yS7lrZ4qCullvA+ywA2oVvdE/rRfffv3hXOOlIbikJv2FDLoCxtVCKzdl2jxwlF0ExQLq9Cnf9UAQBJMfpVmHEw4PXpt5g6nvra8SRShKWipoY4wFFa6q30JyX3n3UW1cBdTzxB1c6dPuvAqwiadL/iYNMtpWjlSr554gmm/fgjK3Ur9EvLl3N8UhK3Hn44k266iawhQ/j9998ZOGYMNbt3B1VuAVxVVex64UUqvvuWqLxeZD76KPa+faktLcVitTI6MZHPFy1ir+4/fce0adwxTRueXGeIVO9xhPfNvj04f/Jkn/mJQSw87cmLK1fy4t0hrAytSEyIjAPHD+xKXlosr0xfx6SDu4ZNt6cIjxBiNDAG6CKEuNWwKgEiqwshhJgA/Edv/4aU8t8h2h0GzAXOkVIG+hu0FD0X3oUrfuT7EaeGtdRGSmrBMs/0kC5aCjD/wLmfhglSdWNkvd/TsTxOUK6P8tdbhefhuXK371587rtepwL9vzZf7dDc0mYXzoaxDxMa3++dduMNlLygDb+7gw53VWuuL6UmE5luFzizzcclYZej1GPF3fOOlrvbmml4gZVw3dKvwsihETtmDJdXrAtZtCXciEJrsSxXe1ZdNrYn//u8Zcf7KUjlPav+2x3aw+sAYynZzRPfayOMMY46MJkCjFpeBE4EZgns1hTyaIuJk4dmeRTqSE7nWul9nfNRqA0uH3X+pWNs2m9eKxpXqN3EOMoCluVWhnm5+vNtGBmmAmKFpowbv2KUy0VdCB0jJzkaj2HeoFC7ibfr00fcBgvuD33cDoBSqDsZ76zT3gitKSlB1w8966wW7b8pZTakw8Gijz9m2jvvMG3OHObpZdGzzWau7NOHN/Th/h/Kyvhh2jT+Ni28P15I1q+Hn35s0ibD4+K4+swzMZnNIUuFh1pmDEAsLCwkU3/w+GQHCVLIxf3/sXffZaPTyVFdu9IlLg4BzN26lU26//fMV1/1BomGyMHtb7VubH7V6tX06+vN6exvxwu2/Sn/93+USUnBSy/5rotg20jnj9UzzVjS/FKF6ZhNgmvG53HnZ0uZubaEI/p2CdpOERE2IA7tvm5MDbEXOLOxjYUQZuAl4Fg0Z+P5QoipUsoVQdo9DoTO59ZKTNg0j5Ozo3C1wDKdcsXl7HnzLXaePILc18KL/PoEr5KUXh66XaWo82QU/mGT7z4rGyqJsWhrPa4HngIivkqFQARqV30nwIbpnln39dQ/pT/WBK/rlNuHPMqiKVi7LGZNoY5JBZPZxyVhxs7Z4D9IJF2eaz1Y7qEnDj2PO/780GeZyWYjIyEwKK7w4BF0Wzov7AtxlO5CY0y11+vHH/jj999DbRKUqadODWKd9M+9FBlFenC88dtbDj4H0Mpv3zf6Cv41x5vJo2/pFi1IMQxSSnaZzZj1fNZs1H5Lj2JIZAMttpg4T/X0BsP7cLX0Ksvdop1gSEDjsmm/+QZTYEyKFCaEdDEnJp/R1QWe5ZklgQGqJrOhf7f4rxeBX+DFw+CQi2HMjVq1Tz++31rI5KQE3knUfNLvGnEXG8s3ArC1VBs5crkkpiAKdYy7AqMluFHGB2f7ptZTCnUn5Z/HH+/JWHHv998DcHGvXgzo18+byUL42kd8MjH4TVfU1vLk9Oncd+ihQY/npnrXLn594QWmff4501auZLvLhQBGxMTw8JFHcuKVVzL43HMRJhPL4+OZoyvZj0yc6JFp9+7dpKWm+sgJgNNJ/fr11G/fjikmhqj+/THFx/sorRJYumYNH6wLHGqyAlMfeIDj7r+/VSzu0LygxE+nTiWmqopfdnhzwkqHw+Pecrju992a1BcUMLaJcj4yYwb/99FHdMvLo8+EtonON990E06gXxjZTh2WxTM/reHlgnVKoW4BUsrpwHQhxGQp5WYhRLy2WFY2tq3OCGCdlHIDgBDiI+AUYIVfuxuBz4DASNM2wNJQ706GgDQEXkdK+i23kHrllcwv/DpAob7hWjNpeyXn9DuHj1d/HPE+V+xdg9u7NyHKN9vD5r2bOSjlIN8N3MFWIX19Daz53kd5qHVq3/n43OOpfO0Xz/KaBX8CcEj6IbzN215/4EladUNh8T7edzv3Eixkyi2NxRWYKWVVSo9A2YC+XQPT+DVEa5ZRKULfd88ZnkNJRR3XjvfmY7Z1746zW7eQ2wSjZ2Kg+5i3uE8zXYRqy0CvfiliNGNVjM1MqSFloUm6eGzWfxvd1f8q11Fv0jJ9/Cutr65QSxLsXmvynurGC33trPb2pcOoUBss1Be4vvbZpnTUXSR/fi7LTf2otiT5WJ/r7WlE1RSz1D7cR6GOrQkMauyZngjueHV35UY3398JI67yXVayBn68V1Oog5xLaS6XJ4/0sNpaLuh/AQ/P9R2Zqax3kBBEoY52v4CZrczcvI1xPbLpk9wn4Bjal2wkL/egtnWHVQp1J+U+XYle/fXXPPD999x46KGc8tRTzcpIAbCrsJAns7JI6hqYwmnb/Pl88/TTTCso4OeiImrRzGDHd+vGiSeeyMTrrydjyJCA7X7dupXoZG3Y7LoXXyRZD5oMpqRWz59P4T3/oMFkJuXe++hy802Y9BtcfU0NM958k6mTJ/P14sVscvi+hZ6dnc1Hmzd7KkK2Jw0NDfy+Zw+XGazF4Ptw6ygcd8018NFH/PDGG22mULtvrTHJ/vkDvERZzFw1Lo+Hv1nJwi2lHNI9dFtFRMQLIf4CUgCEECXAJVLKZeE3Iwswll3bBvhEPQkhsoDTgKMIo1ALIa4GrgbIyMigoKCgSV/AWl3lURvK9uzBXF+PGfjz009JatKetAqymEys27vOI7BbnuJkQXGy4Lrawzm8x+HcuFlLxdXd1p0VORsYELwIHZ9YF5OvT89d7WvBu/jbi3kyRwvMFggKCgqI3b6B0phoeq5dx/aaAsodmvm7vr5eux8att+afSrdCr/FDMyYOZNy/XVi88bNFFdW4Va1K049hYKCAt7epaUc/3dqMh8VFjF7u6C+RPt+GXpbZ5Bb4/r1GzzW7wszA7Mu7fArQLNz4DCKCgp8bMDufvzhkKPJKq5hV2o2KQW+xzb+9sOs8MdsQ6VFoLKyssnnh5tMVyZb2QqWLkAFFRXau+OWbVsYCmwvLCRozW4/HIZvVbnsexZET2DtTgfb4rxBkWMKl+LS2zVkZ/vIbBd2aqX24vPr7vUgwCEEW3cUkwOsX7+eDbHejEpvz9pEb1HE8oYrOMS0lj17Aqsa1oRw+WgwRXlurEvswxle7U1pN2ObiTvr3qGLSVBnsvjUZax2mokCdlY1bsXvMvcRz/TKjdvp77d+ekEB4/Vp4/lbUFBAl+JZhMj9AUDv+gYKCgrYvltT5EdlmplZCj8XzOSgTasYAHzwbYGn/eYN6yho2ExS6XKGulxMSbiasoSDg54z9poiT5aUYGzea2JjmHOtJeciKIW60/PAdddhB+5+/31WGCyiTaVWzwVtt9lwOZ0seO89pr32Gl8vXMgi3SrU02Lh6sGDOfGCCzjimmuISkwMt0vsSUncOXw4jy9YQEqvXkGD71y1tex69ln2vDsFa04OPaa8S8zw4ezZtIlvH3+cr7/5hu+3bmUvWnXUY9LSuOeYYzjh1lu5/tRT+aqwkB8LC7ltzJiQQYItmS8rK+PXpCTPvI+rSJDtSvbsoVpKXlq9mpdCDH9e4JeTuTWora3ldbu9WTH2N372GbNbSSYTWuS92dA/kXDeiO688Os6XilYz+sXD28VWQ5gXgNulVL+BiCEyNeXjWlku2A/mf8p9Rxwp5TSGW54X0r5mn5Mhg8fLpv6or/o81c807a1a7FkZOAA0r6eRlMHdcfn5yNMJrYs3+JZ5pHnneDzW+q30GAWuL++1WSlweWNzShJ9H73OZVzfI7XIBs44ogj4D3vvg/bfBO1GV1Y2rcvfUbkU1xdDP+DKFuUduzMj+FDzdUg58p3qH6kK08mJ3PbqGFM3fIzbIONlo1k9uhF2RzteH2GDiM5P5/4ongu/f5Sjq/SlOIxx5/mkcXt3e0I4u7bq1ceq4SW2eOIr94FYHL/iVy68jutKy4f4eMrbb38Og7PH+ezX3e/zalZyYs7o7izbz/y83sFbROK5qYmBRjtHM2m8k30iEqF7QtJWPcu7NpMTnYOrIOsrCwoaTw1aJEhD35cUgr5+fnI1cW8uGi+Z7lZujw5uK3btmky6+fLvWPu5d5Z9wKQGBcPVSV0a3CQk5MD26BXr16M6zGcJ+Z73UVSc/vzwe81fOA8GnYFWnVrDA6YDdIrn8keD/qpuDD7Ioav8SrUI0eOhJkFREdHY2owexRvl9lOfFwc7IEjRwwGb+FPjZhUqPamqo+p8eoS/Xt0hVW+zcePHw/6YfPz86HAMP3gKT5t/fOtCL3d73N/h9WAXdMjzF37MWDuFwDssGUB2ii0TOxGfv5A2GKHxTB08EDole8r0HODYci5MPRE8E2lrtF3Aqz5nh65ufQIc6615FwEpVB3Wr685RYWzpnDx9u3089qpeD559m6dSs7Pv5YyzdtDEo0TPsHK7rbrtELfVz7xRfca7NR5HJhAsYmJfH4kUdy4g030H/ixCYHjT32xx88rgdv3DBoEMccfzxbt26l7OuvaSgupmrm7zSUl7E7I4MZ8/6g7wUXMLewkPmVlbiAriYTZ/fpw0knn8wxf/sbMd29vmEXnnUWX/3nP5S5XLz+h3YV+V+8rf2/Nfh082ZyrNZWDd+Rxkwkzdh+9rZt2Fpo4XfnM3e6iw01YdvYKAuXjMnl+V/Wsraogj4ZYarDKRoj1q1MA0gpC4QQoXJ8GdmGb3q9bKDQr81w4CP9XEsDJgkhHFLKL1smsi8B7976ue0oCpN9IBStENBoVKYBoi3RQPDh5RR7SoArR61+Nex2VJNKYNwBfY6DXkfDqOsA+DAumk8T4kha8S7DMkcAcH7/8+kyehRlH2muKSY9Q0WKXXNR2GU2Q37wAONgCrWxX0SF5qz7Xe4oj0LdPzMeyz33UPToo0E29qOdsl5GmaPol6JVZ6TvcYh1U3wbBDHiJNgS2FvvmybWaKFmlJY7vK9+D1qc1oshJeu5fvEXuPzOpR/O+IHi6mLeWf6O4ZCaFmuTErrqKQrT+pIQ7atuNZYptM6gUFttXr9pc1Ss59T7ZlUZVwe5bQsBicZMHQIseiqRcYN6ByrUo66HX/8VXJAfgp1TTf/BfcNyvXRL1s7jJdvKOYVA1hTpX9aku8w4GwIblW3WqjjmHh784FmHaq5UbYxSqDsThpvDac8955le3dDAOS+/3CqHcAJHZmdz4oQJTLj9dlL7hPBVihBhMrH8u+8YOHEiLy1fzkuhComUlQHwXXk5o2NjuXfsWE649FKGX3opphDuEum6VfXHxx/n2DvuaJGcoQj2xtpoCj6Xi7nvvMMrTz7JR0F8veuB9Xomkh5CcOXo0Zx15530mTQp5HdtjpyR8OVdd3Ha448z+dlnyb+x9StPnZ6ZyVr9t22MS8fk8vqMDbwyfT3PnD201WU5gNgghLgPcGsXFwIbI9huPtBHCNET2A6cC5xvbCCl9DiwCiEmA9NaW5kGKIvpQVeWNHk7ERODDFE0anjXpo18mELoDCfkncCi4kWEUqj31O7xKNRCCB/l+eGdv/Es3lRwnnEckwku+tyz3Bg3Uu/UXD7yEvMwGSovCj1zTq1DG0GckpjAHfnBC1nV6bpI/PnnUPFBaF/xvTavk0B6vJ26Md6S0pEYUzpDkp7HRj/E/02/xWfZbmOlVqvWB7E2bdlLQ07ntV+eJKGhGkdiEtRD7Hgte1S3uG50i+vGs38+69m8Rv+9oqSEwedAxiDoOoj4Kl+/6cYCbY1+01FRNk9xA3OUN3vLsQf38Po6h8HkMMQemIK8XcUYkhwk5mh9UBKmbkIzgoRDGVj26P3y5u8buU9/bzCeRrcdp7tPugMlgynUbmrKmixXa6IU6k5EzW5tSOa8vDzGjhrFDR98wPm9e3P3f/6DyWRiydKlDB06FOFOh2cyeaYB77z7A6C3m//dd5xx771MvekmTjIo603B5XSydckS1s2Ywbr581m7di3rtm5lXRD/sGCMj43lk6+/Jt1QnCUceWO0Eez5v/4aUqGurahg1/Ll7N6wgT3r1rGnuJg9RUXs3rOHPXv3UlFZSb3DQYPL5flvBqJMJuxC4HQ4+DolBbvNRpTNhj0qiqj4eOx2O1FRUdhjYohKStKWR0Vhs9mw2mzEWa38/bbbuLKykqL16/npt9+YHKSwy2YpuW/2bO47xftubgUuPeggLr75ZkZefDHWECnnWoOjrrsO8+OP8+OHH7aJQl1ZW0tcI7nG3aTE2jh3RA5T5mzm1mP7kp0cmNJKERGXAw8Bn6M9m2YQQaVEKaVDCHEDWvYOM/CWlHK5EOJaff2rYXfQilREZxGuILc/1qwsGrZvR1itAbYz9/1vQOqAgDR5obhrxF1MX/ooB28OVBySo5Ipqyvj7WNMbOiq7dskTLgM1fbq9CqHUkoqGryK916/6odBgxLxLVTirsIYY43xKNEApmjt+nBbaC8deGnI7zO3n7Y/c5xx5CeI9uunEUf17m0QKrQS1U4G6mZxSPrQ8A30lwp3domt8RmeVZbyMgCqpvuW4j6196ksLF4IQLVDy1oRK11af3YdBPhm+QDYuCt8Pm+jQm212T0KtS3ae188dnBORAp1o0Qb4layDoVi/zhkAyaLN8i2CbgDR91n2DWDr2FH1Q6uPuhsfpq7MOR2ye481Gb9/8cXwO3rIC5IAHuQDCP7EqVQdyJK9SImR4wcyftff01Xk4nX58whRk9JVmy3c1Az/X82LtYieWN1f+FQOB0Oti5axLrp01m7ZAnrVq1i3datrC0tZUNtLcbHRRTQy2ajd3Iyx3ftirWigqkbN1IlJYOSk5l4+OEcf8YZ9D7nHIQ98ryZbrIPO4zjEhN54Icf+CMzk7TEREoqKymuqqK4uppdDQ1UhHkI2IEEkwmbEFiF8Px3AbVSUicltS4XtYWF1EnZZN9N/2Nd3bcvz/7+OzFdtBtBQ309f339NVMef5w3FyygRpe1AXh91Spev/ZauNab7/Po1FQuv/hiTr77buwJvpkFnPX1OOp8H9ZGfKqZGabjs7IYlZDAj3/9xaPOQD++llLV0EBsE37bq8blMWXOZt6YuZEHTw4X2qIIhZSyFPhbM7f9FvjWb1lQRVpKeWlzjtEcHDt3hl0vbNrDVkT48tYYMZYYlvXQrpNFPX2VzMSoRKoaqvjuMO9Yu8tPwSipKfFMl9aWeqarbU17ORZCUFmvKdTx1nifa7dhuxbUZRImTMKEzWwLug+AGru2nSU+IWSbuGOOaZJsnY3Hikt4MykBkwidkv2C8goo3QTQaLVAI4O7DPZMV9aVgUUQ6+fTYTX77u/pn9YE3VedtBAlHMx0eitaSnuiZ0DEbvcq1LYo7/lUh+/LZMmAS0hb4XVFIX2Alo3DFoum1hpaRxss1LVlWjsj2YfBNt2f3OXw8bduKu7CiF1iuvDS0S9R2xD+uePpN5Ph2n6qN5z1Diz9n/bfjbJQKyJlz1Yt5HzuvHn8XlHBS+ed51GmW0qlrqxHd+uG0+Fgy6JFrJs1i7V//sm6NWtYt20ba0tK2FBXh3Hgyo6mNPdLTOSE/v3p07s3vYcOpff48WSPHInJYqF29RoK776LuhUrufG221mXP57xJ5zQcqGF4OM//+SeM8/k12XLmF9cTBeLhfSoKPJSU+mSmkp6WhpdcnJI69aNlF69SMnOJqVHD1J69CA6ZOlYL0ZXCqfTSV1tLbW7d1NXVUVtRQV1FRXUlpRQW1tLXW0t9XV1NNTX09DQgHQ6SY6LI613b/oceSQ2v+NZbTZGnHEGI844gxf0ZVJKNs2Zw2ePPsrkn37ylGkH+GX3bn559ll49lnagmCZSEQzp93/6wGqIq+s1i0pmlOHZfHR/C3ceFRvUuMiCNFXACCEmBpuvZQyfD3iDkRTLZ6WLl2o37iRmEMPpeKHlqfHrnZUsydB8MQZJlZ0F5zR5ww+W6tV/UyMCh+MDbCzUnsBEELw2LzHPMuXla31aRcydPfgs2DLt2CyeCzUsTbf+0fsKG8CFrMw4wySriz12mvY+vknuLUxa7pvKe+UmnLPdPZzz8K9Yfquif4c5pQUXM1Ic9gayCBTJ1ZVc2JVNdVhvseNpWXQc3zI9W5ix43zmbdbvEaDKukArJrLRzMYWfcSSaKSQrzP9l0JB4OWdpyoaK/Lh81uVKj9XqisXsVbHnIpYsKjWrq7xGw4+ExNGXVjtFBvKAgUKmOgV6EGqAj/gguBdR38l7uJCvbiYviNzO4ykma/l+X/XaL911+AAGWhVkROyRYtSv2d9esB+HPuXK7t29eTn7mqpob39EwPxrzTPtMhLvL3N20CYMK111JzzTUYvZSigd5WK/2Tkzk5J4fevXrR+9BD6TN6NN1GjPDx6zMiHQ5KXnudkhdewBQfT9YLz5Nw7LGsbUFaGn+SevXi5b/+arX9hcNsNhMTG0tMBIp4cxFC0HPMGG6fNo3bDcurS0r47uGHeXPKFL5rxIVmUEwMh+Xm0rNrV387hM/v756au2IF3xcVcZDVytljx4ZtG8ly47J/z/SPfmmca8fn8dnCbUyevYnbjuvX5O0PYEajpb37EC3WvRN4tLYOIlpTaBJPOaVVFOoVu7Uh7wV9tYf9oLRBXoXaFqhQ90/pz8o9XoeS26Z7y4nP2j6ryceXqb1gC6BbqKMt0VhNvgqFMChTFpMFpwxUqNNvvplru86AOk2hNhsyM8l63xEtYbFwZL/AYfTSxC4kl+8K6/IxcVBXXpuxgSP7eRX2PjOmN8vXtiVEFjQfuk2slLoFVyPaaqamwcmzQ8/ilkVeBdTWM9dnu2hD3vAiPQi/uRdfGfGUyXgsJoFDt3InxHifsdExsbjLvxut1XXC9zlssnqVfGGL1b6XO2jP6udOZ/ShDobV75n3xlERfBNf3FU0/fvF/ZtZ9DHgP1y+Ody7Jel9669Qe3fgnW7MQt3G56NSqDsRH7/5ps/8tC1bfC2CUmJyF0txf/znCX+hH5OWRp/u3enTpw+9hwyh99ixdBs50jOkGil1GzZSePdd1C5eQvxxx9H1wQewhKjuqGicmLQ0znjuOc4w+Lc3lJayaupUFv7wAzNmzmR5SQmLamtZVl3NshUrSF21isPS0zls4EBGHHkkh519NhlBgkydDQ2kRUUxpnt3Hvrtt4D1LeHfzYhS6p0ez3EDMnhn9iauPiKPeHvrDOMfAHRFq3J4Hlow4TfAh1LK1vCy3Mc0Ux1pZLOM++4lppHiVQCHZx3O1PVeg3+c1WsVTIpKCmg/ousIH4XabVUORlVDVUjfaX8EgsqGSp/je9aZDFY8YcbhCu6UVuLwup+YDP630ul1UzHp1WCjLIHuEL+NP4vTp76MMyMzYJ2bYd2T2fRv31HH9sy9H65/jafIicQzzT+41GBtvnZ8L579eQ0/9hjho1Bn3HmnzyZ2s3ebWj1bUktVN4fBZSQx2nsPjI01KNSG37PekHBbgMcXHACrn6uRze98sid5p8+aDP+71Hf9tnmRCb3XPymQF2+WD0PPlG3RgiCB400LABhpWsXsYDsI5dJkfMaEslB7KpW2rY1BKdSdiD/WeocLnXV1mPyU3MYyPUgp2bNxI9tnz6Zw3Tp2bNpESVERy9esYf2GDdxy8ME8s6TpkfU+x3C52PPuu+x69jlMdjvdnn6KhEmTmpxuT9E41uRkDr7kEg6+5BJ66L99/a5dLPv8c+b98APz//qL+du38+Mvv+D65Re4915yLBZGZGZy2ODBHHbMMQw/+2wSunXj6Oxsfty4Eel0IsyhfQz3Fdfn9+aH5UV8OG8LVx/Rq/ENFEgtX9f3wPdCiCg0xbpACPFPKeUL4bfuWESqcAYj5/XXsOXksH7CxIB1KRdcENE+xnTzTdkdY7DoBXP5cC87Kucoft36q2d5MJeOx+c9zvVDr49IDoCK+gri/BUg0DKD6JhNoRXqMXFj+Kb8G23GOLpkKNNsGaDFK5w8NLBq4ap+I5h4ah7vJjTu6tLe9E3uy1/Ff5EUpky1MFRzdAU7zwwK9TdLC90b+e7D7x5pdPlw05q20ASDQh0XGwcUA1o1RzeZciebjTIafcUDFGo/i7NR/sTAsuUeH2qzDZxhqjzWetMR+p/5Af1RvBJeHgXH/gvoRZJopKBrsOwk4FuZsVEfaqVQK3T+qtGihy/s2zfk27/T6WT7pk2smzeP9YsXs/6vv1i/bh3ri4pYX13N3iBDHvFCcN1BB/FIC62T9Vu2UHjPPdQs+JO4/Hy6/vOhAJ89Rdti69KFQ665hkOuuQZ3OGPVli0s/N//mP/LL8xbsoT5hYV8tnUrfPMN4pZb6GezsUr31T42M5OJhx+OPSZGG93wKw/vGfUwFG4x6fMmITDp2WNMLXyBGpKTxNjeqbwxcyOXjMkNajlTBKIr0iegKdO5wPNo2T4ODIQgzs+/NRyfnfxZUBeOGIvvkPjOKq/PaDiFOs4Wx8FpB1PVUMWG8g1B3TC+WPdFgEJd3VDN6tLVDEsfFtDe30JtTkrCWVbmq1ALc9BjAWyt95Z7tGZne1c4vO3jb9FcVCYdnBmQCWUfe220iDsOu4NJPSfRa0vorBFGhdr44vZosW7JN7gWrClqRMnTMQUpud5WCnVMjFcZNhqqTEiykqLpkx7H/ScNIOGr67w78FegjdZrYfJ9YYiKJ4BR18HclzXLdk0Yl8O/poRcFZCH2u37vHkWeV0Gs3e3JtMXzrEEJVQwqTElYEgf6n1zEiuFuhOx8JZbOOTZZ3lvzRp+t9kYEBdHrMVCtZRUu1xsq6pic0ODT9CgFcg1meiVmMiYnBzyevUiZ+BAuvXtS2a/fnTp04e49PQWWZCllJR99BFFTz6FMJnIfPRREk87VVmlOwix3bsz7rbbGHeb16+zZPlyFnz2GfMLCpi/YgWri4qQwC+7dvHLF1+06vGHNvM8uG58by588w8+X7id80YEsZoofBBCvAMMAr4DHoqg1HiHJbokdNCTiIpC1tURe8Q4qmY03UffSN/kvkGXW/38NY3+y8EU6iizd7i9qKqI4priyATQL40bf72ReTvncdeIu7igv68VvbLeV6FOv/02dtx7H+Y47zIpJWtKA7NGbKvYxpIa76ijNd3rI12nx+IAWDIDLdOdEZvZxiEZh4BZVwoHnQnTfw7Z3qhmnahXmgzmFnBEtoW8775lw8RJIfd19eCreW3Ja575O8c/3iTZw5FgSLlnD5FGda2lH30sJn66VQ+qzDwY1uoVD/0t1Es/807b4vGx3Nr9MsHEpHqV2cbu5buC5K6Oz4SdYa4HKblrwkF8/75WUOh5x+kEjZ4O8tICgMMQCxDKQq1cPhT+DHvmGcrvuotP77mHb777jo0VFdTU1BBjMhFtMtE3Pp5TMzLonZtLr3796DVwIDlHHYW5Z882O5EaCgvZce+9VM2eQ+yYMWQ+8jDWzNC+doqOQdrAgUwYOJAJ998PaK46G77/np/feYc5f/zBjM2bfSqBZFgsjOvRg8PHjGHsySeTkZWlbSclLpfLE/Dq0itvulwu9u7axYhzz+W8445rloxje6cyODuR/05fz9nDc7zR3opQXITmXNkX+JvhhVYAUkoZOmdaB6P7z1+GXql/r1DB0G3BUd2P4v7Z2rUSzJ/ZaOkMpkxbTJaQLhkA83ZqPqovLXopQKGuaKggI9abCznpzDNJOvNMnzaldaWU7ipld81uUqNTPcuv+OEKn3bGWJjKX42uKaHplHaRtD7woJ7BZLrvKqOF2mn43YQwwzDfvv/gqpE8+u1KLhrgIKpnT8JhtFLP2bSVuDMCs4VYzYIGZ2TW0vF9uzB9jZbaw8eHOsR574zP8l0w5kZY+6M2vWUuHHqpd12loYpiVFx4C7UxpV5jLhPrfgpcltANWByksXdfa4oqeMamZecslknB7cmRKNSNZvlQCrXCQEJ6Ope/8QaXB1nX0jr0TUFKSfnnn1P02L+RLhddH3yQpHPOVlbpToowmeg1aRK9Jk3iGn1Z1Zo1zH33XWZ++y0zV63i2/Xr+XT9epgyhd7R0Yzr3ZtxY8cy7swz6ZWfH+BXuOSjjwDo1q95mTqEEFw3vhfXvb+Q75bt4MTB+4cVra2QUrasdnwHojy3H4mbwlRqA4S1aYHSLcFolfa/xx2eFaLcsYEoc5SPQh3KPaOiXguQM2bSqayvJN4WZBg+CLVO3zR1ZXVlEW0XTsXrTC4fkWD0a/f5ag8EujKM6ZXGtBvHUaBnpur1808hX+RMeC+/OCmD+vzazCYaIsz3HxflVc/i7VbG1z1DptjDk1HB1bZom1/wtjEzR6XfS94JT8MnF2nTVSW+CnVABpBU73ohoNfRsP6X4EIn9dDKgDeRiQdnauWngCqief6XtYGNQirUhnM+mIX6pOdh7/Ymy9Qc9psbsGLf0VBczLZrr2PHP+7FftBB5H31JcnnnqOU6f2M2L59Ofrhh3lw4UJ+qa6mbNs25j7+OE8efjgDrVamLl3K5a++Sp9jjiHdauWopCT+NmwYr19+Ob//9788fu+9AIw766xmy3D8wK7kdYnl5d/Wh0z5qNj/cMQ0nprSJ/NQO54b/hbrvw0LrKmTak/1mX/pr5c809UNvqXSX1n0is98qCwfwfhg5Qc+8+qeHAxvn7jznDzY+9yItrRlZ2PpEqRCH/Dy4pf9DhOoXtU7I68waAw4tFtNbJZdmesaQLQtuC+xNPkp2ka3pSP+7rsu1VAB0129c9ztcNVvgUMSMSn4WHaTc0MLHUSZbvzKlPRIiaAqbiiF2hgkKZ0+GUyeaDgbDr3EsI+2vR6UQq2IGCkl5V9PY8NJJ1M1dy4Z99xN93ffwZaT096iKfYB1qwsRt5xB7fPnMmX5eUU19Sw/NNPefWiizj1oIOodjp5a9Eirn77bcZdey0frF/PPUOH0uPwxi14oTCZBNce0YsVO/YyY21J4xso9g+CFCnxp7WqIrYUt1XZzcDUwAqfA1IH+Mx/veFrAIqri5lTOMdnnVExc0onNY6aiBXqd1e8G1G7AxqDTuXO8pEcYf82CX8FF0K6exzTX3Pp6ZvhlcNuNQedjrYGV6jronxf2nyKtcR39V3nH6QIcPR9kHWINm3MeR6dYlBEBSx4M2BTD2nBYxICqCz2UW4t5ghU0Ugs1OCbAtD9Y8uAkMg2QSnUiohw7N7N9ptupvDvfycqN5eeX3xBysUXI0zqFDpQMdntDDjjDK55911eX7GCuRUV7K2rY/133zHtvvtYMmUKj/z5Z4uPc+qwLDIT7bz827pWkFrRGRCuxi150UOHhl2ffvttmBLaxm38kPRDPNOzCmf5jJ4EC1oMlgXCzc0FNwcsc/tkVzVo+YaDps0Lw+3Tb+fjVR+3yqhOvl7spbvBitj7t1/p83vLAkLbC18fag1zmN8nUt6f9D4AU7fpqfaCpNK7/bjgCqfb6hxj8yrhxlLlxmqCQSsLAluyTvJdkGQwdIXLQ50zKnBnxiwgMcmB640YzzH/kuWh+PnByNoZCfUbbfrddz46yTM519Vfm0jVU6+m5DX9uE2gTbUhIcQEIcRqIcQ6IcRdQdbnCyHKhRCL9M/9bSmPonns/fFHNpx0MpW//UaX226lxwfvE5UXPkBDcWBistnImzCBE/75Tw6+8EKf1F7NxWYxceW4PP7YuIc/N5e2gpSKjk4ohTrWMNoRf+wxgdsZrF6pV15Jv3l/NFuG6DC5jGP8/Uzdx0cEVajNepaEB0c/2CQZ3AViIrVQu/lh0w88/MfDuKTWj32T+/Lvcf9u0j7cXHF4Txbcewy5aV6rpjUzE0taWpitOg7ZcdlcPsgbdWRUqIfoxVAyo1pedGxwl8EsvWQpPe8shDs3gTnQQn3SkOBxIHM37AZ8/aZ/XukNHDSmDfW35u7WY43NQY7n3cjP79tooc4KUujIqHDHpAauN9LEct8SPJU7PTSaP5rQ7hrGkujgsVC/6DiFhVJ/gRl8Dlz+o1ZyvQ1pM4VaaFnFXwImAgOA84QQA4I0nSmlHKp//tlW8iiajrOsjO23/53tf7sJa9eu5H72KWlXXdUhCn8oDizOPSyHpBgrrxSsb7yxotOTFhvcncM+yOBOYXzAtoELtbH6nT/GPNUPjXnIZ10whdqNfzq+xpi/U1MW6sMV0wiDO0hxfPZ4Tsg7oZHWwRFCkBa37zKqtDbfnfEdtxx6S9B1V5HEV9sK6RvbigHPlihfdwsDPVJj+c+5QwOWZydrL292q1clO3u4N2d4SqxvAK7x96i46HtmHPwYpnDGC39ruVHBtgY5z40Kd7SfD7U/z/i5OI24GoCBdfX0TurNrQM0H2aX8XoyZuaQEqZHkGIwlELdd4LvvG6hdhrVWyGg+8hO7UM9AlgnpdwgpawHPgJOacPjKVqRioICNpx0Mnu//560G28g9+OPsPeN0D9KoWhlYqMsXDoml59XFrF6Z0XjGyg6NbZQKRIlPg/FhEkTybjnnjaRIcrfqmfAaL3OivNNV+ZvTb5s4GW++zU3rpy6M1G4lfp+KZFnyikLYjFsqsvI/oxvMRRBXoODfVX4AzSl2p/zDtPy7BtlG9fHG/zonzL0u5vG8dX/aQVQcnsP5IgzGqm86X/O+WT1CDISY3Td8M9L7Y/uluShcBEAMVLyxSlfMDj5oMBt1v6Aj5K+5GMAFrmaURW33u/4BpePET1bPvLQFNoybV4WsNUwvw0YGaTdaCHEYqAQuF1Kudy/gRDiauBqgIyMDE8Km7amrKwMp9O5z47XUiorK1ssq6ipIf5/nxI9ezYN3bqx946/U9S9O8ya1TpC0jpy7is6i6ydRU5ovqy9XZIoMzz0yWyuHtw6FrOysjKAkPJ0pn7dn5DhghINRRqynnkGgMo28Oc1Vkf0x1hq2r+qon9mjWHpw/hps5afV0pJnbOOxnDvw51qLykqKSKZAfbUBaZ/a8xl5IDIoHPw2bD0k6Dl4PclriB9HSxzx+Ds0CMdXeKj6BLfhHtgOOt1CPclD/55qW9eCs8dHLr9tnm+88aAxlA0aFWgv3ceFl6WOzdpwZ7zXoNfdIcGvyw5bpePq8flcc1RI8Lvr5VpS4U6WO/5n0kLgR5SykohxCTgS6BPwEZSvga8BjB8+HC5r3ItJyUlUVZWts9yO7eUluahrpo9m8KH/omjqIjUq68m7Yb/w2Rr/Vyv+zJfdkvpLLJ2FjmhZbL+Vb+CybM38fiFI8iJJNVSIyQlJQGElKcz9et+RUTpxdrW5SP8kb3HDuVP7SbZntxo+rqx3cYyq1AzWhyfezwfrvoQgL31ewFfBb4xrCLQrcRY6dGWm0v9pk3EHXWUT3GX/Z5TX4FJT7a3FJ73wb4ZcZ7S5tYgWS5aJeXhpKcgthFf98bOLf8c6EmNVK3tMRY2BxrgAi7RrYb4Bl0pftMZuhIl4HWlMfp11xsUamH2vABEW80QIiNKW9GWLh/bAGM+tWw0K7QHKeVeKWWlPv0tYBVCdI5Ih/0IV1UVOx56iC2XX4HJbif3ww9Iv/WWNlGmFYqWcOW4npgEvDFzQ3uLomhDYg49xHd++HAAZG1teD/IVvSRfPv4t0OuW1bireputFAHU4KS7V5/Wonk6fFPB7QZmOb1Q/1h0w+U1GgpIt1BiUaFOBhn9vUGW7265NWA9Qk277B97scfkXb99WQ/96x3fXTHSEHYppgtEJ3k+xu1Q55u92iAzZCtIxIxHj51EA+fOqhpBxtxFQw8LXybYC4fY270TvtYqCN4cz3kYr8FIb7czKcC9tkQqY3XaOVf8pF32p4Y+nj7gLZUqOcDfYQQPYUQNuBcYKqxgRCiq9DPbiHECF2e3W0ok8KP6gUL2HDqaZR99DEpl1xCzy8+J3rIkPYWS6EISmZiNKcNy+Kj+VspqWx86FzROUm//XZKHvAmfXJWaH7ze7//3rOsrXWhoelDQ64bljHMMx1jjfEpPe6Pv7uGf07qSGjM5eOB0Q8wLF2Taer6qQHrjWn7zImJdPnbjT6FcYJZSA8o9qHLi/tIFoMbhvHwV40LnkHrwlE9uHBUj9YXKNgIS5LhOMEU7nD0Oc53vgUXanpT3FrAx396nw9b0YYKtZTSAdwA/ACsBD6RUi4XQlwrhLhWb3YmsEz3oX4eOFceEM5c7Y+rtpaix/7N5ou0t8keU94l4+67MNkjH1pUKNqDa8b3ot7pYvKsTe0tiqKNEFYrzsxMz3zuhx9gTkoib+pXhkbeB3XKpVomAfvAwKIqzcVisvDkEU/y0tEvBay7YegNnulw6fWAgLLhwbKAhHvspUWnYQ5Sxtqf8w86P+S6pmYXOXBoP2umJUTg7Z0TDmLx/ccFXdcm9BgTuGy4N82gZvVtAiFdoEL0tSO0YSTeHsJinXVI8OU+RV32PW3pQ+124/jWb9mrhukXgRfbUgZFIDWLF1N4193Ub9xI8vnnkX7bbZhiGy/1q1B0BHp1iWPCwK68M2cT14zPI96ulIX9HVNMDH3nzgm5Pm7sWPqvWtnqx53Qc0LQ5Ua/aZvZFjbQzb+oS7BS5TWOmpDbR5IVBOCI7CNCrhvTLYjSpOgwGI24FrOJxJh9OGIQlx64zPgCF5OiVTaMlIA0fMLwNwiGHNQpsTb2VHlTRIYs1Z45BO4phMknQuFC73IfC/W+5wAf5zmwcNXXU/z0M2w673xctbV0f+tNut5/v1KmFZ2O6/J7UVHr4P0/trS3KIp9TQccxPR3+fBPpWfE3886Oz47REuN7ZXbI5IhXHBkuEqNwYifGPxFYv+lfc6pDY9OYt0jE9vnlD7jTRh3+z47nAylURct1f5nHcovt44nNdbG1Bu0lIAJ4YwltljIPdw3JaA9qV384t0ohfoAoWb5cjadcSa7X3+dxNNOJW/qV8SOUVYLRedkcHYSh/dO483fN1LbECbFmqJJRFDd9gIhxBL9M1sIsV8FXGTEZDSpfaxJM0bkJWoljYd2GQrAvaPuDdo+mGtHVUOVRyG/cdiNAev3Nf1XrST72Wcbb7g/0I7KF4DJJAIqHxqJDZJOr9U4+Ew4+r62238AjfT1gFNIjrXx533HMjg7icfPOJg3L2kkjZ7ZCsaiR+1soW5Tlw9F+yMbGij572uUvPoqluRksl99hXiVBkyxH3B9fi/Of+MPPlu4jQtGtkGwzgGGobrtsWhZmuYLIaZKKVcYmm0ExkspS4UQE9HSmQarL9Ap+e7073ARSco+WHrJUk+O8qHpQ/nu9O9CWqbDuYT0SOjBtoptIduNzx4fkTyKyBmQOoAL+18Is99qb1HCUvD3I31cINod9wvh4bfC789EulFkzcb8zWf2nMMaSc8HYLb57t/oQ90OZn9lod6PqV2zhk3nnEvJiy+SMHEieV9PVcq0Yr9hdK9UhmQn8t/pG3BElLdY0QiNVreVUs6WUpbqs3PR0qG2D21gXbSarSF9lv930v94Lv+5kNtmx2d73Dly4nN81oXLKTw8Y3jYdtO3TQ8nsg/XDw2smJdlDe1+cqDy8Ykfc1Kvk9pbjEbpEh9Fv67xjTdsC+7YCPe6faf9zsvuo1v/eM25nv2DbaOTaNdA03Y7sqLNkE4nu996i5LnX8AUH0/W8/8h4bh9GDWsUOwDhBBcl9+ba9/7k2+X7eTkId3aW6TOTqTVbd1cAXwXbEVrVLetrKzE7YBh3D7d6UQAM2bOhH2cK9+MmYKNXlkiqaRZUFDAzhKt6uKqVaso2O7bfvr06Wwu3QzAhg0byLZls61+W8A+ImFXxa6AZZfGXRpy+2D9u69pz2qkg/fsIQVYsngJe7aFVodaU8bVezQXtfLycs8+lxdpFTFLSkqafZy27EdrfRljgfqGBmYXFJCyewmDQ7QtKCgg3zAdVbuL0dCoxbg5suds2YSxWPmOpQXU2jPpCWzevJmNTdxnS/tQKdT7GXUbNrLj7rupWbyY+OOOo+sD92NJTW18Q4WiE3LcgAx6dYnllYL1nDQ4s3Wqix24RFLdVmsoxJFoCvXhwda3RnVb44PNuP0qkwkJHDFuHKboJubIbWXCVdJ8bvNzpESnMCx9GL/O+hXWQd9+fcnvm88F8y7g/ZXvA9p3W/jnQlgGeXl5XJp9KWdMPcNnX5H2X/2mev43/X8+yzLiM0Ju786L0p7VQNu1GunWVCiFwYMHQ5/QMrSmjDEb98C8OSQlJZKfr8Ux1S7bCX/9SVpaGvn5wxvZQ9vLGEDlLpgNNqtVO8ZGMywN0u7/5pHfpR8UaLP5+flQtlUby9LvzTKEBblZsn/gW8QoM9oBuT1hE/To0YMeTdxnS/tQuXzsJ0iXi5hffmHjaadRt2kT3Z56iqz/PKeUacV+jckkuHZ8L1bu2EvBmkDrnKJJNFrdFkAIMRh4AzhFSqkKcYXg6B5He4qtCI8yob2f5CbkBt1GIOiZ6FvYwz+PdTiO7H5k4D7VS6ai1dDfr3ODvkdDWt+QW+6TszD/HsOM8qFWNIP6rVvZcvElxP/vU2JHjSJv6lQSTzxB3UgVBwSnDM0iM9HOKwXr21uUzk4k1W27A58DF0kp17SDjJji4tzCtMfhm4U72NCtUAf47xqe/f5lxqPNkVvhGytRrlA0C/9rTQj4x87G2/kQRsHNGdUssQKKzjS1CE0ro1w+OjFSSso+/piiJ55EmEyUX3wRB919t1KkFQcUNouJq8bl8c9pK/hz8x4O7ZHS3iJ1SqSUDiGEu7qtGXjLXd1WX/8qcD+QCrys32ccUsrmjVE3k9z336OioKBTV3WNtcby3JHP0SNey07jVrSD3bvTYtL2qWwHJvuw9HgYX+JO9eSOtCS5fk73a9ACx4fWBamMeOQ9gcsiIcbvXu9qaN5+Wglloe6kNOzYwdYrrmTngw8RM3QIeVO/onbMGKVMKw5Izh2RQ3KMlZd/U1bqliCl/FZK2VdK2UtK+Yi+7FV3hVsp5ZVSymQp5VD9s0+VaQBbbi6pl166rw/bItyZQyzCa8M6uvvR9E7u7dPObcl+YPQDnmXJ9uRmH3dC7oFWoKWJqOdlZNiTIKkHnNj8/OSH1Lv4rSaBCVXVWlEZI8HKn0eCf8Gi9AHeSo0RVhhtTZSFupMhpaT8iy8pevRRpMtF1wfuJ+ncczVFek27jMAqFO1OjM3CpWN68uzPa1i1cy8HdU1ob5EUCg83DruRKEsUJ+adGLad21J9bI9jeWjOQwA4XI5mH/eGYTewceHGZm+/33Pis/DbY9Bz3+X67pRGL7MFbl4Sev2Yv4GjNuwuhIQ0tw0+1fdFMiD9XaT496XZCiOuhtpyGPu34Nu0IcpC3YloKC5m23XXs+Oee7AfdBB5X31J8nnndc4LVKFoZS4Z04MYm5lXlS+1ooMRZ4vj1kNvxdoMxcHpan4l0FA5tRU6Sd3htFfAsu/SLwZz+UiI1mybXRM7qRvTcf+CSU823s791YWAIee1/Lj+FmoASxQcdW/kLimtiFKoOwFSSsqnfcOGk06mas4cMu6+i+7vvoMtJ6fxjRWKA4SkGBvnj+jO10t2sHVPdXuLo1A0G6NVuqkW6isPvtIz3TW2a6vJpGhdjFUxR+el8p9zh3LPpP7tKNG+RMBJz7d8Nx3shVEp1B0cx549bL/pZgpvvx1bbg96fvEFKZdcgjCpn06h8OfKcXmYBLw2Y0N7i6JQNBtjqjynbJqFOlT5c0XHRQjBKUOzsFvN7S1KGxBiBL01RtbH/g1GXtvy/bQSSivrwOz98Uc2nHgSlb/9RpdbbyX3/feJyuvZ+IYKxQFK10Q7pw/L5pMFW9lVESSaXKHogAg/pcNmtvHhCR8CTVeoR2VqKcgyYzNbRziFotUwuru0gkIdFQ8TH4drZ8E1M1q+vxaiFOoOiLO8nO1/v4Ptf7sJS9cMcj/7lLSrr0JYVAypQtEY14zPo97p4u1ZKhhL0XnpFtcNgEk9JzVpu4zYDIZ0GcI/x/6zLcRStJB9X26kx10Q3AAAE9xJREFUo2D45kK0boaVroMgc0jr7a+ZKA2tg1E5fTo77r0PR2kpaTfcQNo1VyOsKlm/QhEpeV3imDQokylzNnNtfi8S7Or6UXQ+UuwpLLxwIRZT0x7TVpOV9ya910ZSKRQRMPEJSNf9wUMpzsIE/U+CQy7Zd3K1MUqh7iA4Kyspeuwxyj/7nKg+vcl+9RWiBw5sb7EUik7Jdfm9+GbpDt6fu4Xr8nu1tzgKRUT4Z4BoTlYQhaLdGXlN8OXSz0p9zv714qdcPjoAVXPmsOHkkyn/4ktSr7qK3M8+U8q0QtECBmUlMq5PGm/+vpHahuanHVMoFIo24YDOdrt/fnlloW5HXFVVFD/9NKUffIgtN5fcD94neujQ9hZLodgvuC6/F+e//gef/rmNC0f1aG9xFE1k6M03Y9u5A0uXLpCfry08+2y4/nqoroZJQXyLL71U+5SUwJlnBq6/7jo45xzYuhUuuihw/W23wUknwerVcE0QK9u998Ixx8CiRXDzzQwtK4OkJO/6Rx+FMWNg9my4J0g55eeeg6FD4eef4eGHPYvPqtjGuKodbH5iJxwMfP01PP104PZTpkBODnz8MbzySuD6Tz+FtDSYPFn7gK+M334LMTHw8svwySf0jYsFp9PbvwUF2v+nnoJp03z3HR0N332nTf/rX/DLL77rU1Phs8+06bvvhjlzfNdnZ8N7ukXy5pu1PjTK17cvvPaatv7qqwMLlQ0dqvUfwIUXwrZtvutHj4bHHtOmzzgDdu/2XX/00XDffdr0xIlQU+O7/sQT4fbbtWl3f7gPXVamydQK5559RyEffXAXCdFW+NBQgKqJ554/CWeeqcndxHPPw3//C/36Ne3c21Sl/S/ID3ruAeCsh61VcGmiNj+/Hn68DGyxvvvfV+deG6IU6naiesECCu++h4Zt20i55GK63Hwzpuh9n4hcodhfGZ2XytCcJP47Yz3nHpaDxawG5DobMcrA0KaY4+LaW4QDjsHZiWxNiqZrQict4qIIjZSyU30OPfRQua8YP368HDJkSKvu01lTI3c+9m+54qD+cu3Rx8iqefNabd+//fZbq+2rLeksckrZeWTtLHJKuW9l/X7ZDtnjzmnyy7+2BV0/fvx4OX78+JDbt4WswALZAe6l++rT3Ht2ZzinW0vGpxc8LQdNHiTfWPJGq+zPSEfvx44un5RKxpA8kKB9wlG+XcoHEmTtIz2kfGm01n7Hkn0iXlMJ1YeR3rOVyWYfUrN4MRtPO509kyeTdO455H31JTGHHdbeYikU+y3H9s+gd3ocrxSsD1ryV6FQKBRticFfujVT5XVAlEK9D3DV11P8zLNsOu98XLW15Lz5BpkPPIApNrbxjRUKRbMxmQTXju/Fqp0VFKze1d7iKBRhkQdwlmLFfkowJXo/NW4ohbqNqV2xgk1nnsXu114j8dRTyZv6FXFjx7a3WArFAcMpQ7uRlRTNywXr2lsUhUKhUOynKIW6jZANDex66SU2nn0OjtI9ZL/yMt0efQRzfHx7i6ZQHFBYzSauGteT+ZtKmb9pT3uLo1AoFAcOdi27R2G3ie0sSNujFOo2oG7tWjadex4lL7xIwoQJ5E2dSvyRR7a3WArFAcs5h3UnJdbGKwXr21sUhSIAsZ/m5VUosEbDg+Vszj2H/TX/tBulULci0umk5PXX2Xj6GTQUFpL1n/+Q9dSTWJKT21s0heKAJtpm5rIxufy6qpiVO/a2tzgKhUJxALJ/+k67UQp1K1G3cSObL7iQXU8/Q1x+PnnTvibh+OPaWyyFQqFz8ehcYm1mXp2urNQKhUKhaF2UQt1CpMvFnnffZeNpp1O3cSPdnnySrOf/gyU1tb1FUygUBhJjrFwwqgdfLy5ky+7q9hZHofCgXD4UBwb793muFOoWUL9tG1suuZSiRx8jZuQI8qZOJfGkExH7ea5FhaKzcsXhPbGYTLw2U1mpFQqFQtF6KIW6GUgpKf3oYzacfAq1K1aQ+cjD5Lz6KtaM9PYWTaFQhCEjwc4Zh2bxyYJtFFfUtrc4CgUAp/Y+lWhLNBNyJ7S3KAqFopkohbqJNOzYwdYrr2Lngw8SM3QIeV9PJemMM5RVWqHoJFx9RC8cThdvz9rU3qJ0OIQQE4QQq4UQ64QQdwVZL4QQz+vrlwghDmkPOfc3chNzmXfBPLLjs9tbFIUici6Zpn2azP4ZnGhpbwE6C1JKyr/4kqJHH0U6nXR94H6Szj1XKdIKRSejZ1osEw/O5L05m0k22zA569tbpA6BEMIMvAQcC2wD5gshpkopVxiaTQT66J+RwCv6f4VCcaDRc1x7S9ChaFML9f5i7WgoLmbb9f/HjnvuIeqgfuR99SXJ552nlGmFopNy3fheVNQ52JsxtL1F6UiMANZJKTdIKeuBj4BT/NqcArwrNeYCSUKIzH0tqEKhUHQ02sxCvV9YO6RktNPJxpNOxlVbS/pdd5Jy8cUIk/KUUSg6M4OyEjmibxd+rx9Owo6F7S1ORyEL2GqY30bg/ThYmyxgh7GREOJq4GqAjIwMCgoKmixMZWVls7bblygZW05Hlw+UjK1BZWUlFVWVxAMLFvxJZXxpe4sUQEv7sC1dPjzWDgAhhNvaYVSoPdYOYK4QIkkIkSml3BG4u32LlJLLy8s5rMHBuuoq3klMpGjyZJg8ub1FC0lZWRlJSUntLUajdBY5ofPI2lnkhI4ja218Nq6B5/FXeVR7i9JRCDbk5u/sGEkbpJSvAa8BDB8+XObn5zdZmIKCApqz3b5EydhyOrp8oGRsDQoKCohP6gKVGxl+2GHQ9eD2FimAlvZhWyrUHcra0RzMOTl8sH4D35hMuCor98kxW4LT6aSsrKy9xWiUziIndB5ZO4uc0HFklWVlVIkCXDUVIe8pHd3q08psA3IM89lAYTPaKBQKRSBnTYaF70LGoPaWpE1oS4W6Q1k7mkV+PgUFBfyrA7/1Genob6huOouc0Hlk7SxygpK1AzMf6COE6AlsB84FzvdrMxW4QR9xHAmUd4QRRYVC0QlIzIIj725vKdqMtlSolbVDoVAoOglSSocQ4gbgB8AMvCWlXC6EuFZf/yrwLTAJWAdUA5e1l7wKhULRkWhLhVpZOxQKhaITIaX8Fk1pNi571TAtgf/b13IpFApFR6fNFGpl7VAoFAqFQqFQHAi0aWEXZe1QKBQKhUKhUOzvqITKCoVCoVAoFApFC1AKtUKhUCgUCoVC0QKUQq1QKBQKhUKhULQApVArFAqFQqFQKBQtQGhxgZ0HIcQuYPM+PGQaULIPj9cSOousnUVO6DyydhY5QcnaQ0rZpZX32WFpwT27M5wnSsaW09HlAyVja9DR5YPQMkZ0z+50CvW+RgixQEo5vL3liITOImtnkRM6j6ydRU5QsioiozP0vZKx5XR0+UDJ2Bp0dPmg5TIqlw+FQqFQKBQKhaIFKIVaoVAoFAqFQqFoAUqhbpzX2luAJtBZZO0sckLnkbWzyAlKVkVkdIa+VzK2nI4uHygZW4OOLh+0UEblQ61QKBQKhUKhULQAZaFWKBQKhUKhUChagFKoFQqFQqFQKBSKFqAUah0hxAQhxGohxDohxF1B1gshxPP6+iVCiEM6qJz5QohyIcQi/XN/O8n5lhCiWAixLMT6DtGfuiyNydpR+jRHCPGbEGKlEGK5EOKmIG06RL9GKGu796sQwi6EmCeEWKzL+VCQNh2iTw8kGrvPtYM8Qc9nIUSKEOInIcRa/X9yB5DVLIT4SwgxrSPKKIRIEkJ8KoRYpffn6I4koxDiFv03XiaE+FC/R7SrfMGeUeFkEkLcrV87q4UQx7ejjE/qv/MSIcQXQoikjiajYd3tQggphEhrtoxSygP+A5iB9UAeYAMWAwP82kwCvgMEMAr4o4PKmQ9M6wB9egRwCLAsxPp2788myNpR+jQTOESfjgfWdMTztAmytnu/6v0Up09bgT+AUR2xTw+UTyT3uXaQKej5DDwB3KUvvwt4vAP0363AB+5rq6PJCLwDXKlP24CkjiIjkAVsBKL1+U+AS9tbvmDPqFAy6eflYiAK6KlfS+Z2kvE4wKJPP94RZdSX5wA/oBWgSmuujMpCrTECWCel3CClrAc+Ak7xa3MK8K7UmAskCSEyO6CcHQIp5QxgT5gmHaE/gYhk7RBIKXdIKRfq0xXASrQHgJEO0a8Rytru6P1Uqc9a9Y9/pHaH6NMDiA53nwtzPp+CpiCi/z+1XQTUEUJkAycAbxgWdxgZhRAJaErNmwBSynopZRkdSEbAAkQLISxADFBIO8sX4hkVSqZTgI+klHVSyo3AOrRrap/LKKX8UUrp0GfnAtkdTUadZ4E78L33N1lGpVBrZAFbDfPbCHz4R9KmrYlUhtH6EPZ3QoiB+0a0JtMR+rMpdKg+FULkAsPQLKpGOly/hpEVOkC/6kPki4Bi4CcpZYfv0/2cDt3ffudzhpRyB2hKN5DejqIBPIemGLgMyzqSjHnALuBt3S3lDSFEbEeRUUq5HXgK2ALsAMqllD92FPn8CCVTR71+Lkcb6YMOJKMQ4mRgu5Rysd+qJsuoFGoNEWSZv5UqkjZtTSQyLESrOz8EeAH4sq2FaiYdoT8jpUP1qRAiDvgMuFlKudd/dZBN2q1fG5G1Q/SrlNIppRyKZj0ZIYQY5NekQ/XpAUCH7e9Gzud2RQhxIlAspfyzvWUJgwVtyP0VKeUwoArNXaFDoPshn4I2xN8NiBVCXNi+UjWZDnf9CCH+ATiA992LgjTb5zIKIWKAfwDB4neaLKNSqDW2ofnQuMlGG+Zpapu2plEZpJR73UPYUspvAavRyb4D0RH6MyI6Up8KIaxoD/T3pZSfB2nSYfq1MVk7Ur/qMpQBBcAEv1Udpk8PEDpkf4c4n4vc7j/6/+L2kg8YC5wshNiE5iZzlBDiPTqWjNuAbYZRoE/RFOyOIuMxwEYp5S4pZQPwOTCmA8lnJJRMHer6EUJcApwIXCB152Q6joy90F6eFuvXTTawUAjRlWbIqBRqjflAHyFETyGEDTgXmOrXZipwsdAYhTYUtKOjySmE6CqEEPr0CLTfePc+ljMSOkJ/RkRH6VNdhjeBlVLKZ0I06xD9GomsHaFfhRBd3JHnQohotAfqKr9mHaJPDyAiuR/vU8Kcz1OBS/TpS4Cv9rVsbqSUd0sps6WUuWh99quU8kI6low7ga1CiH76oqOBFXQcGbcAo4QQMfpvfjSav3xHkc9IKJmmAucKIaKEED2BPsC8dpAPIcQE4E7gZClltWFVh5BRSrlUSpkupczVr5ttaMHHO5slY7iIxQPpgxbJvwYtkvMf+rJrgWv1aQG8pK9fCgzvoHLeACxHi06dC4xpJzk/RPNBa9BP0is6Yn9GKGtH6dPD0YaclgCL9M+kjtivEcra7v0KDAb+0uVcBtyvL+9wfXogfYLd59pZnlDncyrwC7BW/5/S3rLq8ubjzfLRoWQEhgIL9L78EkjuSDICD6G9VC8DpqBleWhX+UI8o0LKhObGsB5YDUxsRxnXofkhu6+ZVzuajH7rN6Fn+WiOjKr0uEKhUCgUCoVC0QKUy4dCoVAoFAqFQtEClEKtUCgUCoVCoVC0AKVQKxQKhUKhUCgULUAp1AqFQqFQKBQKRQtQCrVCoVAoFAqFQtEClEKtOGARQmQKIaY1cZunhBBHtZVMCoVCsb8jhEgSQlxvmO8mhPi0jY51qhAiWCU89/qDhRCT2+LYigMLlTZPccAihHgS+F1KGXGSfiFED+B1KeVxbSeZQqFQ7L8IIXLR8mQP2gfHmo1WWKQkTJufgcullFvaWh7F/ouyUCv2e4QQhwkhlggh7EKIWCHEciHEIOAM4Hu9zaVCiC+FEF8LITYKIW4QQtwqhPhLCDFXCJECIKXcDKTqpUkVCoVC0XT+DfQSQiwSQjwphMgVQiyDyO/FQoheQojvhRB/CiFmCiEO8j+IEKIvUOdWpoUQZwkhlgkhFgshZhiafo1WXVKhaDZKoVbs90gp56OVEX0YeAJ4D6gCSqWUdYamg4DzgRHAI0C1lHIYMAe42NBuITB2H4iuUCgU+yN3AeullEOllH8Psj6Se/FrwI1SykOB24GXg+xnLNr92s39wPFSyiHAyYblC4BxLfg+CgWW9hZAodhH/BOYD9QCfwNGArv82vwmpawAKoQQ5WhWC9DKTQ82tCsGurWtuAqFQnHAEvZeLISIA8YA/xNCuLeJCrKfTHzv87OAyUKIT4DPDcvVPV3RYpRCrThQSAHiACtgB2r0/0aM1mqXYd6F77Xi3l6hUCgUrU9j92ITUCalHNrIfmqARPeMlPJaIcRI4ARgkRBiqJRyN+qermgFlMuH4kDhNeA+4H3gcWANkNvMffUFlrWOWAqFQnHAUQHEN3djKeVeYKMQ4iwAoTEkSNOVQG/3jBCil5TyDynl/UAJkKOvUvd0RYtRCrViv0cIcTHgkFJ+gBYMcxiay8d6IUTvsBsH7suKdoNe0OqCKhQKxQGAbhWepQcIPtnM3VwAXCGEWAwsB04J0mYGMEx4/UKeFEIs1QMgZwCL9eVHAt80Uw6FAlBp8xQHMEKI04BDpZT3NnGbQ6SU97WdZAqFQqFoDYQQ/wG+llL+HGJ9FDAdOFxK6dinwin2K5SFWnHAIqX8AtjUxM0swNOtL41CoVAo2oBHgZgw67sDdyllWtFSlIVaoVAoFAqFQqFoAcpCrVAoFAqFQqFQtAClUCsUCoVCoVAoFC1AKdQKhUKhUCgUCkULUAq1QqFQKBQKhULRApRCrVAoFAqFQqFQtID/B+2T/8l3Ej5hAAAAAElFTkSuQmCC\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gerry02_traj_tracking.plot(cdpr_2, controller_2, result_2, len(pdes), dt, pdes)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "6c0951da", "metadata": {}, "outputs": [], "source": [ - "gerry03_stroke_tracking.plot(cdpr, all_results, strokes, dt)\n", - "plt.show()" + "gerry03_stroke_tracking.save_controller('data/test_params2.h', all_controllers)" ] }, { "cell_type": "code", "execution_count": null, - "id": "6c0951da", + "id": "0feaf679", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "cProfile.run('_ = gerry03_stroke_tracking.main(fname=fname)', sort=SortKey.TIME)" + ] } ], "metadata": { diff --git a/gtdynamics/cablerobot/src/gerry03_stroke_tracking.py b/gtdynamics/cablerobot/src/gerry03_stroke_tracking.py index bb3a7543..d872787a 100644 --- a/gtdynamics/cablerobot/src/gerry03_stroke_tracking.py +++ b/gtdynamics/cablerobot/src/gerry03_stroke_tracking.py @@ -27,58 +27,134 @@ import cProfile from pstats import SortKey +import tqdm DT = 0.01 # hardcoded constant. TODO(gerry): include in .h file. -def preprocessTraj(cdpr, is_paint_on, traj, dN): - """Preprocesses the trajectory to (1) rescale the trajectory, and (2) take every dN'th index. - Args: - cdpr (Cdpr): CDPR object - traj ([type]): [description] - dN ([type]): [description] +def preprocessTraj(cdpr: Cdpr, is_paint_on: list[bool], traj: np.ndarray, dN: int): + """Preprocesses the trajectory to (1) rescale the trajectory, and (2) take every dN'th index. + `traj` is an Nx2 array. """ # rescale trajectory to be smaller width, _, height = cdpr.params.a_locs[1] - cdpr.params.a_locs[3] # rescale trajectory to be - traj = (traj - [width/2, height/2]) * 0.85 + [width/2, height/2] # smaller + traj = (traj - [width / 2, height / 2]) * 0.85 + [width / 2, height / 2] # smaller # extract the part of the trajectory we care about return np.array(is_paint_on)[::dN], traj[::dN, :] -def splitTrajByStroke(is_paint_on, all_des_poses): + +def splitTrajByStroke(is_paint_on: list[bool], all_des_poses: list[gtsam.Pose3]): + """Splits up a trajectory according to paint changes + """ prev_index = 0 for transition_index in np.argwhere(np.diff(is_paint_on)) + 1: yield all_des_poses[prev_index:transition_index[0]] prev_index = transition_index[0] yield all_des_poses[prev_index:] -def main(fname='data/ATL_filled.h', - debug=False, + +def combineResults(all_results: list[gtsam.Values]) -> gtsam.Values: + """Combines values objects, taking care to remove duplicates + """ + combined_results = gtsam.Values() + combined_keys = set() + for results in all_results: + # remove duplicates + new_keys = set(results.keys()) + for key in (combined_keys & new_keys): + results.erase(key) + combined_keys |= new_keys + # insert + combined_results.insert(results) + return combined_results + + +def optimizeStroke(cdpr: Cdpr, + stroke: list[gtsam.Pose3], + x0: gtsam.Pose3, + v0, + Q, + R, + dt: float, + x_guess: gtsam.Values = None): + """Computes the optimal controller for a single stroke + """ + N = len(stroke) + + # initial configuration + X_init = gtsam.Values() + gtd.InsertPose(X_init, cdpr.ee_id(), 0, x0) + gtd.InsertTwist(X_init, cdpr.ee_id(), 0, v0) + + # controller + controller = CdprControllerIlqr(cdpr, X_init, stroke, dt, Q, R, x_guess=x_guess) + # feedforward control + xff = np.zeros((N, 2)) + uff = np.zeros((N, 4)) + for t in range(N): + xff[t, :] = gtd.Pose(controller.result, cdpr.ee_id(), t).translation()[[0, 2]] + uff[t, :] = [gtd.TorqueDouble(controller.result, ji, t) for ji in range(4)] + + # simulate + sim = CdprSimulator(cdpr, X_init, controller, dt=dt) + result = sim.run(N=N) + xf = gtd.Pose(result, cdpr.ee_id(), N) + vf = gtd.Twist(result, cdpr.ee_id(), N) + + return xf, vf, controller, result + + +def optimizeStrokes(cdpr: Cdpr, strokes: list[list[gtsam.Pose3]], Q, R, dt: float): + """Finds the optimal controllers for a set of strokes + """ + all_controllers = [] + all_results = [] + cur_x, cur_v = strokes[0][0], (0, 0, 0, 0, 0, 0) + k = 0 + + for stroke in tqdm.tqdm(strokes): # tqdm is iteration timer + cur_x, cur_v, controller, result = optimizeStroke(cdpr, stroke, cur_x, cur_v, Q, R, dt) + + # shift timesteps + new = gtsam.Values() + for key in result.keys(): + utils.UpdateFromValues(result, new, gtd.DynamicsSymbol(key), shift_time_by=k) + result = new + + # save and update + all_controllers.append(controller) + all_results.append(result) + k += len(stroke) + + return cdpr, all_controllers, all_results, strokes, dt + + +def main(fname: str = 'data/ATL_filled.h', Q=np.ones(6) * 1e2, R=np.ones(1) * 1e-2, - dN=1, - speed_multiplier=1): + dN: int = 1, + speed_multiplier: float = 1): """Runs a simulation of the iLQR controller trying to execute a predefined trajectory. Args: fname (str, optional): The trajectory filename. Defaults to 'data/iros_logo_2.h'. - debug (bool, optional): Whether to print debug information. Defaults to False. Q (np.ndarray, optional): Vector of weights to apply to the state objectives. The real weight matrix will be diag(Q). Defaults to np.ones(6)*1e2. R (np.ndarray, optional): Vector of weights to apply to the control costs. The real weight matrix will be diag(R). Defaults to np.ones(1)*1e-2. dN (int, optional): Skips some timesteps from the input trajectory, to make things faster. The controller and simulation both will only use each dN'th time step. Defaults to 1. + speed_multiplier (float, optional): Makes the entire trajectory faster or slower Returns: - tuple(Cdpr, CdprControllerIlqr, gtsam.Values, int, float, list[gtsam.Pose3]): The relevant + tuple(Cdpr, CdprControllerIlqr, gtsam.Values, list[gtsam.Pose3], float): The relevant output data including: - cdpr: the cable robot object - controller: the controller - result: the Values object containing the full state and controls of the robot in open-loop - - N: the number of time steps (equal to N passed in) - - dt: the time step size - des_T: the desired poses + - dt: the time step size """ cdpr = gerry02_traj_tracking.create_cdpr() dt = (DT / speed_multiplier) * dN @@ -88,75 +164,37 @@ def main(fname='data/ATL_filled.h', is_paint_on, all_trajs = preprocessTraj(cdpr, is_paint_on, all_trajs, dN) all_des_poses = gerry02_traj_tracking.xy2Pose3(all_trajs) - all_controllers = [] - all_results = [] - cur_x, cur_v = all_des_poses[0], (0, 0, 0, 0, 0, 0) - + # run per-stroke + print("Running per-stroke optimization...") strokes = list(splitTrajByStroke(is_paint_on, all_des_poses)) - k = 0 - for i, des_poses in enumerate(strokes): - print("Starting stroke {:d} of {:d}".format(i, len(strokes))) - if i > 5: - break - N = len(des_poses) - - # initial configuration - X_init = gtsam.Values() - gtd.InsertPose(X_init, cdpr.ee_id(), 0, cur_x) - gtd.InsertTwist(X_init, cdpr.ee_id(), 0, cur_v) - - # controller - controller = CdprControllerIlqr(cdpr, X_init, des_poses, dt, Q, R) - # feedforward control - xff = np.zeros((N, 2)) - uff = np.zeros((N, 4)) - for t in range(N): - xff[t, :] = gtd.Pose(controller.result, cdpr.ee_id(), t).translation()[[0, 2]] - uff[t, :] = [gtd.TorqueDouble(controller.result, ji, t) for ji in range(4)] - if debug: - print(xff) - print(uff) - - # simulate - sim = CdprSimulator(cdpr, X_init, controller, dt=dt) - result = sim.run(N=N) - if debug: - print(result) - cur_x = gtd.Pose(result, cdpr.ee_id(), N) - cur_v = gtd.Twist(result, cdpr.ee_id(), N) + cdpr, all_controllers, all_results, strokes, dt = optimizeStrokes(cdpr, strokes, Q, R, dt) - # shift timesteps - new = gtsam.Values() - for key in result.keys(): - utils.UpdateFromValues(result, new, gtd.DynamicsSymbol(key), shift_time_by=k) - result = new + # combine and run together to smooth out + print("Combining per-stroke results and running one final optimization...") + x_guess = combineResults(all_results) + _, _, controller, result = optimizeStroke(cdpr, + all_des_poses, + all_des_poses[0], (0, 0, 0, 0, 0, 0), + Q, + R, + dt, + x_guess=x_guess) - # store - all_controllers.append(all_controllers) - all_results.append(result) - k += N + print("Done.") + return cdpr, controller, result, all_des_poses, dt - return cdpr, all_controllers, all_results, strokes, dt def plot(cdpr, all_results, strokes, dt): """Plots the results""" N = sum(len(s) for s in strokes[:len(all_results)]) - combined_results = gtsam.Values() - time_shifts = np.cumsum([len(des_T) for des_T in strokes]) - time_shifts = [0, *time_shifts] - for i, (results, time_shift) in enumerate(zip(all_results, time_shifts)): - # remove duplicates - overlap = set(combined_results.keys()) & set(results.keys()) - for key in overlap: - results.erase(key) - # insert - combined_results.insert(results) - plot_trajectory(cdpr, combined_results, dt*N, dt, N, sum(strokes, []), step=1) + plot_trajectory(cdpr, combineResults(all_results), dt * N, dt, N, sum(strokes, []), step=1) + + +def save_controller(fname, all_controllers): + gains_ff = sum((controller.gains_ff for controller in all_controllers), []) + writeControls(fname, gains_ff) -def save_controller(fname, controller): - writeControls(fname, controller.gains_ff) if __name__ == '__main__': - cProfile.run('cdpr, all_controllers, all_results, strokes, dt = main()', - sort=SortKey.TIME) - plot(cdpr, all_results, strokes, dt) + cProfile.run('cdpr, controller, result, des_poses, dt = main(dN=10)', sort=SortKey.TIME) + gerry02_traj_tracking.plot(cdpr, result, des_poses, dt) From 578610fe1d42265adfa31de3ae9eef833d1cf08d Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 7 Sep 2021 23:34:45 -0400 Subject: [PATCH 66/73] comments and easier running from command line --- gtdynamics/cablerobot/src/gerry02_traj_tracking.py | 5 ++++- gtdynamics/cablerobot/src/gerry03_stroke_tracking.py | 10 ++++++++-- 2 files changed, 12 insertions(+), 3 deletions(-) diff --git a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py index f1df1f65..c217580b 100644 --- a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py +++ b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py @@ -155,5 +155,8 @@ def save_controller(fname, controller): writeControls(fname, controller.gains_ff) if __name__ == '__main__': - cProfile.run('results = main(N=100)', sort=SortKey.TIME) + # cProfile.run('results = main(N=100)', sort=SortKey.TIME) + results = main(fname='data/ATL_filled.h', Q=np.ones(6)*1e1, R=np.ones(1)*1e-3, dN=1, debug=False, speed_multiplier=1) plot(*results) + plt.show() + save_controller('data/tmp.h', results[1]) diff --git a/gtdynamics/cablerobot/src/gerry03_stroke_tracking.py b/gtdynamics/cablerobot/src/gerry03_stroke_tracking.py index d872787a..0b41cdb0 100644 --- a/gtdynamics/cablerobot/src/gerry03_stroke_tracking.py +++ b/gtdynamics/cablerobot/src/gerry03_stroke_tracking.py @@ -112,6 +112,9 @@ def optimizeStrokes(cdpr: Cdpr, strokes: list[list[gtsam.Pose3]], Q, R, dt: floa cur_x, cur_v = strokes[0][0], (0, 0, 0, 0, 0, 0) k = 0 + # TODO(Gerry): figure out multithreading. Tried concurrent.futures.ThreadPoolExecutor but it + # didn't help at all due to GIL. Couldn't get ProcessPoolExecutor to work - it wouldn't + # actually execute the thread. for stroke in tqdm.tqdm(strokes): # tqdm is iteration timer cur_x, cur_v, controller, result = optimizeStroke(cdpr, stroke, cur_x, cur_v, Q, R, dt) @@ -196,5 +199,8 @@ def save_controller(fname, all_controllers): if __name__ == '__main__': - cProfile.run('cdpr, controller, result, des_poses, dt = main(dN=10)', sort=SortKey.TIME) - gerry02_traj_tracking.plot(cdpr, result, des_poses, dt) + # cProfile.run('cdpr, controller, result, des_poses, dt = main(dN=10)', sort=SortKey.TIME) + cdpr, controller, result, des_poses, dt = main(dN=1) + gerry02_traj_tracking.plot(cdpr, controller, result, len(des_poses), dt, des_poses) + plt.show() + gerry02_traj_tracking.save_controller('data/tmp.h', controller) From 9595f968c5a2da711d2e89a0720a8bcac61735ef Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Sun, 3 Oct 2021 13:06:45 -0400 Subject: [PATCH 67/73] LQR-style plots for ICRA paper --- .../cablerobot/src/gerry04_lqr_style.py | 35 +++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 gtdynamics/cablerobot/src/gerry04_lqr_style.py diff --git a/gtdynamics/cablerobot/src/gerry04_lqr_style.py b/gtdynamics/cablerobot/src/gerry04_lqr_style.py new file mode 100644 index 00000000..83e755a4 --- /dev/null +++ b/gtdynamics/cablerobot/src/gerry04_lqr_style.py @@ -0,0 +1,35 @@ +import gerry02_traj_tracking as gerry02 +import draw_cdpr + +import numpy as np +import gtdynamics as gtd +import matplotlib.pyplot as plt + +def plot(ax, cdpr, controller, result, N, dt, des_T): + """Plots the results""" + # plot_trajectory(cdpr, result, dt*N, dt, N, des_T, step=1) + + act_T = [gtd.Pose(result, cdpr.ee_id(), k) for k in range(N+1)] + act_xy = np.array([draw_cdpr.pose32xy(pose) for pose in act_T]).T + des_xy = np.array([draw_cdpr.pose32xy(pose) for pose in des_T]).T + + # ls = draw_cdpr.draw_cdpr(ax, cdpr, gtd.Pose(result, cdpr.ee_id(), 0)) + # ls[1].remove() + # for l in ls[2]: + # l.remove() + # for l in ls[1:]: + # l.remove() + ls = draw_cdpr.draw_traj(ax, cdpr, des_xy, act_xy) + # plt.legend(ls, [r'$\bf{x}_d$', r'$\bf{x}_{ff}$']) + # plt.legend(loc='upper right') + + +if __name__ == '__main__': + # cProfile.run('results = main(N=100)', sort=SortKey.TIME) + # fname = 'data/ATL_filled_output_7mps2.h' + # fname = 'data/concentric_diamonds2_output_2mps_20mps2.h' + fname = 'data/ATL_outline_output.h' + Q, R = 1e-1, 1e-3 + results = gerry02.main(fname=fname, Q=np.ones(6)*Q, R=np.ones(1)*R, dN=1, debug=False, speed_multiplier=1) + plot(*results) + plt.show() From a0122ab0bcc8ca398ac9b995ce03ba13996bcb95 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Thu, 21 Oct 2021 03:37:34 -0400 Subject: [PATCH 68/73] fix - don't need "intermediate control variables" - must have had a typo before --- .../cablerobot/src/cdpr_controller_ilqr.py | 37 ++++++++----------- 1 file changed, 15 insertions(+), 22 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py index 4c3a6190..83f77423 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py @@ -156,17 +156,15 @@ def extract_bayesnets(cdpr, fg, openloop_results, N): ordering[-1].push_back(gtd.internal.JointVelKey(ji, t).key()) for ji in range(4): ordering[-1].push_back(gtd.internal.JointAccelKey(ji, t).key()) + ordering[-1].push_back(gtd.internal.TwistAccelKey(lid, t).key()) + for ji in range(4): + ordering[-1].push_back(gtd.internal.WrenchKey(lid, ji, t).key()) for ji in range(4): ordering[-1].push_back(gtd.cinternal.TensionKey(ji, t).key()) - ordering[-1].push_back(gtd.internal.TwistAccelKey(lid, t).key()) - # immediate control variables + # control variables ordering.append(gtsam.Ordering()) for ji in range(4): ordering[-1].push_back(gtd.internal.TorqueKey(ji, t).key()) - # intermediate control variables - ordering.append(gtsam.Ordering()) - for ji in range(4): - ordering[-1].push_back(gtd.internal.WrenchKey(lid, ji, t).key()) # measurement inputs ordering.append(gtsam.Ordering()) ordering[-1].push_back(gtd.internal.TwistKey(lid, t).key()) @@ -195,23 +193,18 @@ def extract_gains(cdpr, fg, openloop_results, N): # extract_gains gains = [None for t in range(N)] # for t, neti in enumerate(u_inds): - # 0 mod 4: ("stuff we do care about") -> ("stuff we don't care about") - # 1 mod 4: (wrenches, twist, pose) -> torques - # 2 mod 4: (twist, pose) -> wrenches - # 3 mod 4: (prev state) -> (twist, pose) aka collocation update - for t, (neti, netu) in enumerate(zip(reversed(range(2, 4*N, 4)), - reversed(range(1, 4*N, 4)))): + # 0 mod 3: ("stuff we do care about") -> ("stuff we don't care about") + # 1 mod 3: (twist, pose) -> torques + # 2 mod 3: (prev state) -> (twist, pose) aka collocation update + for t, (neti, netu, netx) in enumerate( + zip( + reversed(range(0, 3 * N, 3)), # + reversed(range(1, 3 * N, 3)), + reversed(range(2, 3 * N, 3)))): ucond = net.at(netu) - icond = net.at(neti) - if 0 in ucond.keys(): - u_K_F = solve_triangular(ucond.R(), -ucond.S()[:, 1:25]) - else: - u_K_F = solve_triangular(ucond.R(), -ucond.S()[:, :24]) - u_K_p = solve_triangular(ucond.R(), -ucond.S()[:, -6:]) - F_K_x = solve_triangular(icond.R(), -icond.S())[:24, -12:] - u_K_x = u_K_F @ F_K_x - u_K_x[:, 6:] += u_K_p - gains[t] = u_K_x + u_K_x = solve_triangular(ucond.R(), -ucond.S()[:, -6:]) + u_K_v = solve_triangular(ucond.R(), -ucond.S()[:, -12:-6]) + gains[t] = np.hstack((u_K_v, u_K_x)) return gains @staticmethod From e2dc95dc7b0c8f55925dced3cd505e72643373d0 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 11 Jan 2022 21:19:31 -0500 Subject: [PATCH 69/73] Fix merge issues related to: * Key functions are no longer internal * values functions remove "Double" template parameter --- gtdynamics/cablerobot/cablerobot.i | 13 +++------ .../cablerobot/src/cdpr_controller_ilqr.py | 28 +++++++++---------- .../src/cdpr_controller_tension_dist.py | 16 ++++++----- gtdynamics/cablerobot/src/cdpr_planar.py | 14 +++++----- gtdynamics/cablerobot/src/cdpr_planar_sim.py | 10 +++---- gtdynamics/cablerobot/src/test_cdpr_planar.py | 8 ++---- gtdynamics/cablerobot/src/utils.py | 10 +++---- gtdynamics/cablerobot/utils/CustomWrap.h | 21 ++++---------- 8 files changed, 53 insertions(+), 67 deletions(-) diff --git a/gtdynamics/cablerobot/cablerobot.i b/gtdynamics/cablerobot/cablerobot.i index c12cb79d..4a6ed386 100644 --- a/gtdynamics/cablerobot/cablerobot.i +++ b/gtdynamics/cablerobot/cablerobot.i @@ -46,18 +46,13 @@ gtsam::GaussianBayesNet* BlockEliminateSequential( gtsam::GaussianFactorGraph graph, const gtdynamics::BlockOrdering &ordering); // Tension Key Stuff -namespace cinternal { - gtdynamics::DynamicsSymbol TensionKey(int j, int t = 0); -} +gtdynamics::DynamicsSymbol TensionKey(int j, int t = 0); -template -void InsertTension(gtsam::Values @values, int j, int t, T value); +void InsertTension(gtsam::Values @values, int j, int t, double value); -template -void InsertTension(gtsam::Values @values, int j, T value); +void InsertTension(gtsam::Values @values, int j, double value); -template -T Tension(const gtsam::Values &values, int j, int t = 0); +double Tension(const gtsam::Values &values, int j, int t = 0); #include class WinchParams { diff --git a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py index 83f77423..b9578f7b 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py @@ -58,7 +58,7 @@ def __init__(self, gtd.InsertPose(x_guess, cdpr.ee_id(), k, pdes_) for ji in range(4): l = np.linalg.norm(cdpr.params.a_locs[ji] - pdes_.translation()) - gtd.InsertJointAngleDouble(x_guess, ji, k, l) + gtd.InsertJointAngle(x_guess, ji, k, l) x_guess = utils.InitValues(fg, x_guess, dt=dt) # optimize @@ -96,7 +96,7 @@ def update(self, values, t): # populate into values object result = gtsam.Values() for ji in range(4): - gtd.InsertTorqueDouble(result, ji, t, u[ji]) + gtd.InsertTorque(result, ji, t, u[ji]) return result @staticmethod @@ -128,7 +128,7 @@ def create_ilqr_fg(cdpr, x0, pdes, dt, Q, R): for k in range(N): for ji in range(4): fg.push_back( - gtd.PriorFactorDouble(gtd.internal.TorqueKey(ji, k).key(), tmid, + gtd.PriorFactorDouble(gtd.TorqueKey(ji, k).key(), tmid, gtsam.noiseModel.Diagonal.Precisions(R))) # state objective costs cost_x = gtsam.noiseModel.Isotropic.Sigma(6, 0.001) if Q is None else \ @@ -136,7 +136,7 @@ def create_ilqr_fg(cdpr, x0, pdes, dt, Q, R): for k in range(N): fg.push_back( gtsam.PriorFactorPose3( - gtd.internal.PoseKey(cdpr.ee_id(), k).key(), pdes[k], cost_x)) + gtd.PoseKey(cdpr.ee_id(), k).key(), pdes[k], cost_x)) return fg @staticmethod @@ -151,24 +151,24 @@ def extract_bayesnets(cdpr, fg, openloop_results, N): # stuff we don't care about ordering.append(gtsam.Ordering()) for ji in range(4): - ordering[-1].push_back(gtd.internal.JointAngleKey(ji, t).key()) + ordering[-1].push_back(gtd.JointAngleKey(ji, t).key()) for ji in range(4): - ordering[-1].push_back(gtd.internal.JointVelKey(ji, t).key()) + ordering[-1].push_back(gtd.JointVelKey(ji, t).key()) for ji in range(4): - ordering[-1].push_back(gtd.internal.JointAccelKey(ji, t).key()) - ordering[-1].push_back(gtd.internal.TwistAccelKey(lid, t).key()) + ordering[-1].push_back(gtd.JointAccelKey(ji, t).key()) + ordering[-1].push_back(gtd.TwistAccelKey(lid, t).key()) for ji in range(4): - ordering[-1].push_back(gtd.internal.WrenchKey(lid, ji, t).key()) + ordering[-1].push_back(gtd.WrenchKey(lid, ji, t).key()) for ji in range(4): - ordering[-1].push_back(gtd.cinternal.TensionKey(ji, t).key()) + ordering[-1].push_back(gtd.TensionKey(ji, t).key()) # control variables ordering.append(gtsam.Ordering()) for ji in range(4): - ordering[-1].push_back(gtd.internal.TorqueKey(ji, t).key()) + ordering[-1].push_back(gtd.TorqueKey(ji, t).key()) # measurement inputs ordering.append(gtsam.Ordering()) - ordering[-1].push_back(gtd.internal.TwistKey(lid, t).key()) - ordering[-1].push_back(gtd.internal.PoseKey(lid, t).key()) + ordering[-1].push_back(gtd.TwistKey(lid, t).key()) + ordering[-1].push_back(gtd.PoseKey(lid, t).key()) ordering.append(gtsam.Ordering()) ordering[-1].push_back(0) @@ -220,7 +220,7 @@ def extract_uff(results, N): """ uff = [] for t in range(N): - uff.append(np.array([gtd.TorqueDouble(results, ji, t) for ji in range(4)])) + uff.append(np.array([gtd.Torque(results, ji, t) for ji in range(4)])) return uff @staticmethod diff --git a/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py b/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py index e46a4887..319a6800 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py @@ -132,7 +132,7 @@ def solve_twist_accel(cdpr, lid, Tgoal, k, TVnow=None, lldotnow=None, dt=0.01): else: fg.push_back(cdpr.priors_ik(ks=[k], values=TVnow)) # pose constraints: must reach next pose T - fg.push_back(gtsam.PriorFactorPose3(gtd.internal.PoseKey(lid, k+1).key(), + fg.push_back(gtsam.PriorFactorPose3(gtd.PoseKey(lid, k+1).key(), Tgoal, cdpr.costmodel_prior_pose)) # collocation: given current+next Ts, solve for current+next Vs and current VAs fg.push_back(cdpr.collocation_factors(ks=[k], dt=dt)) @@ -165,8 +165,10 @@ def solve_torques(cdpr: Cdpr, lid, k, VAnow, TVnow, dt, R): # redundancy resolution: control costs for ji in range(4): fg.push_back( - gtd.PriorFactorDouble(gtd.internal.TorqueKey(ji, k).key(), 0.0, - gtsam.noiseModel.Diagonal.Precisions(R))) + gtd.PriorFactorDouble( + gtd.TorqueKey(ji, k).key(), # + 0.0, + gtsam.noiseModel.Diagonal.Precisions(R))) # tmp initial guess xk = gtsam.Values() @@ -174,10 +176,10 @@ def solve_torques(cdpr: Cdpr, lid, k, VAnow, TVnow, dt, R): utils.InsertTwist(xk, lid, k, TVnow) gtd.InsertTwistAccel(xk, lid, k, np.zeros(6)) for ji in range(4): - gtd.InsertJointVelDouble(xk, ji, k, 0) - gtd.InsertJointAccelDouble(xk, ji, k, 1) - gtd.InsertTorqueDouble(xk, ji, k, 1) - gtd.InsertTensionDouble(xk, ji, k, 50) + gtd.InsertJointVel(xk, ji, k, 0) + gtd.InsertJointAccel(xk, ji, k, 1) + gtd.InsertTorque(xk, ji, k, 1) + gtd.InsertTension(xk, ji, k, 50) gtd.InsertWrench(xk, lid, ji, k, np.zeros(6)) # optimize diff --git a/gtdynamics/cablerobot/src/cdpr_planar.py b/gtdynamics/cablerobot/src/cdpr_planar.py index 47cf6290..c00ba035 100644 --- a/gtdynamics/cablerobot/src/cdpr_planar.py +++ b/gtdynamics/cablerobot/src/cdpr_planar.py @@ -180,7 +180,7 @@ def dynamics_factors(self, ks=[]): for ji in range(4): dfg.push_back( gtd.CableTensionFactor( - gtd.cinternal.TensionKey(ji, k).key(), + gtd.TensionKey(ji, k).key(), gtd.PoseKey(self.ee_id(), k).key(), gtd.WrenchKey(self.ee_id(), ji, k).key(), self.costmodel_torque, self.params.a_locs[ji], self.params.b_locs[ji])) @@ -194,7 +194,7 @@ def dynamics_factors(self, ks=[]): dfg.push_back( gtd.WinchFactor( gtd.TorqueKey(ji, k).key(), - gtd.cinternal.TensionKey(ji, k).key(), + gtd.TensionKey(ji, k).key(), gtd.JointVelKey(ji, k).key(), gtd.JointAccelKey(ji, k).key(), self.costmodel_winch, self.params.winch_params @@ -269,8 +269,8 @@ def priors_fk(self, ks=[], ls=[[]], ldots=[[]], values=None): gtsam.NonlinearFactorGraph: The forward kinematics prior factors """ if values is not None: - ls = [[gtd.JointAngleDouble(values, ji, k) for ji in range(4)] for k in ks] - ldots = [[gtd.JointVelDouble(values, ji, k) for ji in range(4)] for k in ks] + ls = [[gtd.JointAngle(values, ji, k) for ji in range(4)] for k in ks] + ldots = [[gtd.JointVel(values, ji, k) for ji in range(4)] for k in ks] graph = gtsam.NonlinearFactorGraph() for k, l, ldot in zip(ks, ls, ldots): for ji, (lval, ldotval) in enumerate(zip(l, ldot)): @@ -331,7 +331,7 @@ def priors_fd(self, ks=[], torquess=[[]], values=None): gtsam.NonlinearFactorGraph: The forward dynamics prior factors """ if values is not None: - torquess = [[gtd.TorqueDouble(values, ji, k) for ji in range(4)] for k in ks] + torquess = [[gtd.Torque(values, ji, k) for ji in range(4)] for k in ks] graph = gtsam.NonlinearFactorGraph() for k, torques in zip(ks, torquess): for ji, torque in enumerate(torques): @@ -355,13 +355,13 @@ def priors_id(self, ks=[], lddotss=[[]], values=None): gtsam.NonlinearFactorGraph: The inverse dynamics prior factors """ if values is not None: - lddotss = [[gtd.JointAccelDouble(values, ji, k) for ji in range(4)] for k in ks] + lddotss = [[gtd.JointAccel(values, ji, k) for ji in range(4)] for k in ks] graph = gtsam.NonlinearFactorGraph() for k, lddots in zip(ks, lddotss): for ji, lddot in enumerate(lddots): graph.push_back( gtd.PriorFactorDouble( - gtd.internal.JointAccelKey(ji, k).key(), lddot, self.costmodel_prior_lddot)) + gtd.JointAccelKey(ji, k).key(), lddot, self.costmodel_prior_lddot)) return graph def priors_id_va(self, ks=[], VAs=[], values=None): diff --git a/gtdynamics/cablerobot/src/cdpr_planar_sim.py b/gtdynamics/cablerobot/src/cdpr_planar_sim.py index 8a6bcf31..f6608748 100644 --- a/gtdynamics/cablerobot/src/cdpr_planar_sim.py +++ b/gtdynamics/cablerobot/src/cdpr_planar_sim.py @@ -81,8 +81,8 @@ def update_kinematics(cdpr, x, k): fg.push_back(cdpr.priors_ik(ks=[k], values=xk)) # IK initial estimate for j in range(4): - gtd.InsertJointAngle(x, j, k, 0) - gtd.InsertJointVel(x, j, k, 0) + gtd.InsertJointAngle(xk, j, k, 0) + gtd.InsertJointVel(xk, j, k, 0) # IK solve result = gtsam.LevenbergMarquardtOptimizer(fg, xk, MyLMParams()).optimize() assert abs(fg.error(result)) < 1e-20, "inverse kinematics didn't converge" @@ -161,9 +161,9 @@ def step(self, verbose=False): # update x for ji in range(4): - gtd.InsertJointAngleDouble(x, ji, k, gtd.JointAngleDouble(xk, ji, k)) - gtd.InsertJointVelDouble(x, ji, k, gtd.JointVelDouble(xk, ji, k)) - gtd.InsertTorqueDouble(x, ji, k, gtd.TorqueDouble(u, ji, k)) + gtd.InsertJointAngle(x, ji, k, gtd.JointAngle(xk, ji, k)) + gtd.InsertJointVel(x, ji, k, gtd.JointVel(xk, ji, k)) + gtd.InsertTorque(x, ji, k, gtd.Torque(u, ji, k)) gtd.InsertTwistAccel(x, lid, k, gtd.TwistAccel(xd, lid, k)) gtd.InsertPose(x, lid, k + 1, gtd.Pose(xd, lid, k + 1)) gtd.InsertTwist(x, lid, k + 1, gtd.Twist(xd, lid, k + 1)) diff --git a/gtdynamics/cablerobot/src/test_cdpr_planar.py b/gtdynamics/cablerobot/src/test_cdpr_planar.py index f8094c33..d373eda8 100644 --- a/gtdynamics/cablerobot/src/test_cdpr_planar.py +++ b/gtdynamics/cablerobot/src/test_cdpr_planar.py @@ -117,16 +117,14 @@ def testDynamicsInstantaneous(self): # redundancy resolution dfg.push_back( gtd.PriorFactorDouble( - gtd.internal.TorqueKey(1, 0).key(), -1.23, - gtsam.noiseModel.Unit.Create(1))) + gtd.TorqueKey(1, 0).key(), -1.23, gtsam.noiseModel.Unit.Create(1))) dfg.push_back( gtd.PriorFactorDouble( - gtd.internal.TorqueKey(2, 0).key(), -1.23, - gtsam.noiseModel.Unit.Create(1))) + gtd.TorqueKey(2, 0).key(), -1.23, gtsam.noiseModel.Unit.Create(1))) # initialize and solve init = gtsam.Values(values) for ji in range(4): - init.erase(gtd.internal.TorqueKey(ji, 0).key()) + init.erase(gtd.TorqueKey(ji, 0).key()) gtd.InsertTorque(init, ji, 0, -1) results = gtsam.LevenbergMarquardtOptimizer(dfg, init, MyLMParams(1e-20)).optimize() self.gtsamAssertEquals(values, results) diff --git a/gtdynamics/cablerobot/src/utils.py b/gtdynamics/cablerobot/src/utils.py index 4b7493ca..93e8b866 100644 --- a/gtdynamics/cablerobot/src/utils.py +++ b/gtdynamics/cablerobot/src/utils.py @@ -43,15 +43,15 @@ def zerovalues(lid, ts=[], dt=0.01): def InsertZeroByLabel(values, key, dt=None): label, l, j, t = key.label(), key.linkIdx(), key.jointIdx(), key.time() if label == "q": # JointAngleKey - gtd.InsertJointAngleDouble(values, j, t, 0.0) + gtd.InsertJointAngle(values, j, t, 0.0) elif label == "v": # JointVelKey - gtd.InsertJointVelDouble(values, j, t, 0.0) + gtd.InsertJointVel(values, j, t, 0.0) elif label == "a": # JointAccelKey - gtd.InsertJointAccelDouble(values, j, t, 0.0) + gtd.InsertJointAccel(values, j, t, 0.0) elif label == "t": # TensionKey - gtd.InsertTensionDouble(values, j, t, 0.0) + gtd.InsertTension(values, j, t, 0.0) elif label == "T": # TorqueKey - gtd.InsertTorqueDouble(values, j, t, 0.0) + gtd.InsertTorque(values, j, t, 0.0) elif label == "p": # PoseKey gtd.InsertPose(values, l, t, gtsam.Pose3()) elif label == "V": # TwistKey diff --git a/gtdynamics/cablerobot/utils/CustomWrap.h b/gtdynamics/cablerobot/utils/CustomWrap.h index e03a980b..1dfc98e8 100644 --- a/gtdynamics/cablerobot/utils/CustomWrap.h +++ b/gtdynamics/cablerobot/utils/CustomWrap.h @@ -30,54 +30,45 @@ gtsam::GaussianBayesNet::shared_ptr BlockEliminateSequential( ///@name TensionKey ///@{ -namespace cinternal { /// Shorthand for t_j_k, for j-th tension at time step k. inline DynamicsSymbol TensionKey(int j, int k = 0) { return DynamicsSymbol::JointSymbol("t", j, k); } -} // namespace internal - /** * @brief Insert j-th tension at time k. * - * @tparam T Tension type (default `double`). * @param values Values pointer to insert tension into. * @param j The joint id. * @param k Time step. * @param value The tension value. */ -template -void InsertTension(gtsam::Values *values, int j, int k, T value) { - values->insert(cinternal::TensionKey(j, k), value); +void InsertTension(gtsam::Values *values, int j, int k, double value) { + values->insert(TensionKey(j, k), value); } /** * @brief Insert j-th tension at time 0. * - * @tparam T Tension type (default `double`). * @param values Values pointer to insert tension into. * @param j The joint id. * @param value The tension value. */ -template -void InsertTension(gtsam::Values *values, int j, T value) { - values->insert(cinternal::TensionKey(j), value); +void InsertTension(gtsam::Values *values, int j, double value) { + values->insert(TensionKey(j), value); } /** * @brief Retrieve j-th tension at time k. * - * @tparam T Tension type (default is `double`). * @param values Values dictionary containing the tension. * @param j The joint id. * @param k Time step. * @return The tension. */ -template -T Tension(const gtsam::Values &values, int j, int k = 0) { - return internal::at(values, cinternal::TensionKey(j, k)); +double Tension(const gtsam::Values &values, int j, int k = 0) { + return at(values, TensionKey(j, k)); } ///@} From d89c5e05dc35fb9577d21337ee048f0df6f32379 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 11 Jan 2022 21:53:39 -0500 Subject: [PATCH 70/73] clean up large data-file diffs --- gtdynamics/cablerobot/src/data/.gitattributes | 1 - .../cablerobot/src/data/ATL_controller_1e2.h | 3 - .../cablerobot/src/data/ATL_controller_1e4.h | 3 - .../cablerobot/src/data/ATL_controller_1e6.h | 3 - gtdynamics/cablerobot/src/data/iros_logo_2.h | 13144 ---------------- .../src/data/iros_logo_2_controller.h | 3 - .../src/gerry02_traj_tracking.ipynb | 12716 +-------------- .../cablerobot/src/gerry02_traj_tracking.py | 4 +- .../src/gerry03_stroke_tracking.ipynb | 109 +- .../cablerobot/src/gerry03_stroke_tracking.py | 2 +- 10 files changed, 25 insertions(+), 25963 deletions(-) delete mode 100644 gtdynamics/cablerobot/src/data/ATL_controller_1e2.h delete mode 100644 gtdynamics/cablerobot/src/data/ATL_controller_1e4.h delete mode 100644 gtdynamics/cablerobot/src/data/ATL_controller_1e6.h delete mode 100644 gtdynamics/cablerobot/src/data/iros_logo_2.h delete mode 100644 gtdynamics/cablerobot/src/data/iros_logo_2_controller.h diff --git a/gtdynamics/cablerobot/src/data/.gitattributes b/gtdynamics/cablerobot/src/data/.gitattributes index 842f8d6e..5b1a61bf 100644 --- a/gtdynamics/cablerobot/src/data/.gitattributes +++ b/gtdynamics/cablerobot/src/data/.gitattributes @@ -1,4 +1,3 @@ ATL_controller_1e2.h filter=lfs diff=lfs merge=lfs -text ATL_controller_1e4.h filter=lfs diff=lfs merge=lfs -text ATL_controller_1e6.h filter=lfs diff=lfs merge=lfs -text -iros_logo_2_controller.h filter=lfs diff=lfs merge=lfs -text 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--git a/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb b/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb index b045d454..b8a2c8be 100644 --- a/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb +++ b/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb @@ -38,25 +38,12 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "generic-calibration", "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "image/png": 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LDG82x3grzjNUxULFBYEQYhXwnZTyF0/bAiCEWAH8JqX8ztO2KBQKxYWMEGIS0FZKeZunbVHUL1Qyc8V5jxAiAG3a8KinbQEQQsQCvamFggUKhUKhUCjcgwrnUJzXCCGi0MIyVgJrPGwOQoif0fJQP14hq4dCoVAoFIp6hArnUCgUCkVJ3vNf0Mo7S+AbKeWnFdoItBjP0WgZC8ZLKbdW7EuhUCguBFQ4h0KhUCgADMCTUsqt5rSNW4QQS6WUeyzajALamV/9gCnm/xUKheKCo96J6IiICNmyZctaGy83N5fAwPqR3rG+2Fpf7IT6Y2t9sROUrVu2bEmTUka6tFMXIKVMBBLNP2cLIfaiFbWwFNFXAb9IbQpzgxAiTAjRyHysVZy9Zten74k16rP99dl2qN/2K9s9hy37q7pm1zsR3bJlSzZv3lxr48XHxzNs2LBaG68m1Bdb64udUH9srS92grJVCFHn0xoKIVoCvYCKJe+bUL6i3CnztnIiWghxP3A/QHR0NB988IHDNuTk5BAUVF1xu7pLfba/PtsO9dt+ZbvnsGX/xRdfbPOaXe9EtEKhUCjchxAiCPgTbfFrljN9SCm/Ab4B6Nu3r3TmQaQ+PWxZoz7bX59th/ptv7Ldczhjv8rOoVAoFAoAzFXh/gR+lVL+ZaXJaSzKI6OVRnZHCWaFQqGo8ygRrVAoFIqSzBvfA3ullB/ZaDYXuENo9Acyq4qHVigUivMZFc6hUCgUCoBBaOWOdwohtpm3PQ80B5BSfgUsREtvdwgtxd1dtW+mQqFQ1A2UiFYoFAoFUso1gKimjQQeqR2LFAqFom6jwjkUCoVCoVAoFAoHUSJaoVAoFAqFQqFwEBXOUQWPf/c4K1asIHxSuKdNsYuMjAzCwsI8bUa11Bc7of7YWl/shLpjq7cwcX3TVKL7XcvVE970tDkKCxKSEliQsYAhpiHodXpPm6NQKBRWUZ7oKliVsorCroWeNkOhULgUyeCIDH6K28sDbc6Qu2Wmpw1SVOC/lP/4J/MfTJg8bYpCoVDYRHmiq8AryQufPj789c9fhPvVfW90fUl0Xl/shPpja32xEzxsa9Iu+OdZOLYNIjvy5LJitqSHcKtnrFEoFApFPUZ5oqvAO9EbgC3JWzxsiUKhqBG5aTD/f/D1EEjeBaM/gAfXsiU9xNOWKRQKhaKeojzRVeCV5gVFWnzepS0u9bQ5CoXCUYzFsOlbiH8HinIg9j4Y9iwE1P2ZJYVCoVDUbZSIrgIhBZyBhKgET5uiUCgc5cASWPw8nD0IbS6By9+GqI6etkrhCNLTBigUCoVtlIiuBnFKcKjlIc7mn6Whf0NPm6NQKKojdb8mng8tg/A2MG46tL8cRJV1RBR1CFF1zReFQqGoEygRXQ3ipEAi2Zy8mctbXu5pcxQKhS3y0yH+XUj4FrwD4LI3Ie5+8PLxtGUKhUKhOA9RIro6UiDAK4CEpAQlohWKuojRAFt+hH/f0oR0nzvh4hchKNLTlikUCoXiPEaJ6GoQJkHv6N4kJKm4aIWiznEkHv55DlL2QMshMPJtiOnmaasUCoVCcQGgUtzZQWxMLEcyj5CWn+ZpUxQKBcDZw/D7LfDLVVrWjRt/gTvnKQF9niHVykKFQlGHUZ5oO4iLiQNgc9JmRrYa6WFrFIoLmIIsWP0BbJgCOm8Y/jL0fwS8/TxtmcKFCLUIVKFQ1AOUiLaDjuEdCfQOJCEpQYlohcITmIyw7VdY/jrkpkDPWzUBHRzjacsUCoVCcYGiRLQdeOm86BPdh01JmzxtikJx4XF8nVaqO3E7NOsHt/wBTfp42iqFQqFQXOAoEW0nsdGxrDq1itS8VCID1Kp/hcLtZJyApS/D7r8hpAlc9z10vU7le1YoFApFnUCJaDuJbRQLaCXAR7ce7WFrFIrzmKJcWPMJrJsMCBj6LAx6DHwCPG2ZopZRCwsVCkVdRoloO+nYoCPB3sEkJCsRrVC4BZMJds6EZZMg+wx0vR4ufRVCm3raMoVCoVAoKqFEtJ3odXr6RPdR+aIVCndwagv8MxFOJUCjnnDDj9C8v6etUigUCoXCJkpEO0DfmL7En4onOTeZ6MBoT5ujUNR/ss7Asldhxx8QFA1XfQk9xoFOpbBXKBQKRd1GiWgHiI0xx0UnJzCm9RgPW6NQ1GOK82H957D6IzAZYPATMOQJ8A32tGWKOoSUKiZaoVDUXZSIdoAODToQ7BPM5qTNSkQrFM4gJZEpa+HzRyHzBHQaC5e+DuGtPG2Zog4hUBlYFApF3UeJaAcoiYtW+aIVCidI3A7/PEeX42shuitcPQ9aXeRpqxQKhUKhcAoVeOggcTFxnMw+SVJukqdNUSjqBzkpMPdR+HoopO5jf/uH4IFVSkArFAqFol6jRLSDlMZFqywdCkXVGIpg7WSY3Bu2/QYDHoFHt5LYeCTo9J62TqFQKBSKGqHCORykfYP2hPiEkJCUwNg2Yz1tjkJR95AS9i+CJS/AuSPQ7nK4/E2IaOdpyxT1DFVsRaFQ1GXc5okWQjQTQvwrhNgjhNgthHjMShshhJgshDgkhNghhOjtLntchU7o6BvdV3miFQprJO+BqVfDH+NA5w23/Qm3zlACWuEQwgOl3V9Y8wLf7vi21sdVKBT1F3d6og3Ak1LKrUKIYGCLEGKplHKPRZtRQDvzqx8wxfx/nSauURwrTq4gMSeRRkGNPG2OQuF58s7Bv2/B5h/ANwhGvgux94De29OWKRR2MffwXADu636fhy1RKBT1BbeJaCllIpBo/jlbCLEXaAJYiuirgF+klgx0gxAiTAjRyHxsnaVvdF9Ayxd9ZdCVHrZGofAgxmJI+B7i34bCLOh7D1z8PASEe9oyhUKhUCjcSq0sLBRCtAR6ARsr7GoCnLR4f8q8rU7TrkE7wnzD2JSoUt0pLmAOLoMpg7Ry3Y17woNr4YoPlIBWKBQKxQWB2xcWCiGCgD+Bx6WUWU72cT9wP0B0dDTx8fGuM7AKMjIyMBqNVsdroW/B6mOriTfUji32kJOTU2vnpibUFzuh/tham3b6552i7aEfaXhuM3n+jTjc9XnONoyDvSnaqxrq0jnNyMgAsGlPXbL1QkRVLFQoFHUZt4poIYQ3moD+VUr5l5Ump4FmFu+bmreVQ0r5DfANQN++feWwYcNcb6wVwsLCyMjIwNp4Z/ae4e1Nb9OubzuaBNUN53l8fLxVW+sa9cVOqD+21oqd+Rmw8j3Y/DV4+cOlrxHQ70G6efk61E1dOqdhYWEANu2pS7bWBkKIH4AxQIqUsquV/cOAOcBR86a/pJSvudwOVbFQoVDUA9wmooW2vPp7YK+U8iMbzeYCE4QQf6AtKMys6/HQJVjmi27Stm6IaIXCLZiMsPVnWPGGtoCw9+1wyUsQFOVpyxSu5yfgc+CXKtqsllKOqR1zFAqFou7iTk/0IOB2YKcQYpt52/NAcwAp5VfAQmA0cAjIA+5yoz0upW1YWxr4NiAhKYGr217taXMUCvdwdBX88xwk74LmA2HUO9Coh6etUrgJKeUq8xoWhUKhUFSDO7NzrIGq5+TMWTkecZcN7kQIQd8YLV+0lNIjeU0VCrdx7igsfQn2zoPQ5nDDT9D5alDfcwUMEEJsB84AT0kpd3vaIIVCofAEqmJhDYiLiWPp8aWcyjlFs+Bm1R+gUNR1CrNh9Yew/gvQecHFL8LACeDt72nLFHWDrUALKWWOEGI0MBstz385aroY/HDmYQBWr16Nr86xmPua4qqFpPV5UWp9th3qt/3Kds/hjP1KRNeAkrjozUmblYhW1G9MJtj+Oyx/FXKSofvNMOIVCGnsacsUdQjLDEtSyoVCiC+FEBFSyrQK7Wq0GPzYrmOwBYYMGUKAd0DNDbeHn7X/XLWQtD4vSq3PtkP9tl/Z7jmcsb9W8kSfr7QObU24XzibklS+aEU95sQG+O4SmPMwhDaDe5fDtV8rAa2ohBAixrxoHCFEHNo95KxnrVIoFArPoDzRNUAIQWxMrIqLVtRPMk7Csldg158Q3Aiu+Qa63QA69Wx9oSKE+B0YBkQIIU4BrwDeULoY/HrgISGEAcgHbpYqmbNCobhAUSK6hsTFxLH42GJOZp+keUhzT5ujUFRPUR6s/VR7IeGiZ2Dw4+AT6GnLFB5GSjmumv2fo6XAUygUigseJaJrSN+YvoCWL1qJaEWdRkrN67z0Zcg6DV2ugUtfgzD1vVXUTSTKya1QKOouat62hrQKaUWEf4SKi1bUbU5vgR8uhz/vgYCGcNciLW2dEtCKOogKjVMoFPUB5YmuIUIIYqNj2Zy0WcVFK+oe2Umw7FXY/hsERsKVn0HPW0Gn97RlCoVCoVDUa5SIdgF9Y/qy6Ngijmcdp2VoS0+bo1BAcQFs+AJWfwSGQhj0GAx5CvxCPG2ZQqFQKBTnBUpEu4C4mDgAEpITlIhWeBYpYe9cWPISZByHDlfAZa9DwzaetkyhUCgUivMKFRPtAlqEtCDSP5KExARPm1K3MRrJ3LuX5DVrMBYWetqa84+knfDzWJhxh5Zp4445MO43JaAV9RaVPc+9SCkp2LPH02YoFPUW5Yl2ASX5ojclbbqg46KNBQUk79vH6e3bST58mFMHD3J4/36OJCdzMiODo3l5lJQ1+3jtWh5ftMij9p435KbRfv+XsHIp+IXB6A+gz12gV3/eCoXCNpmz55D43HM0/eJzgocP97Q5CkW9Q91lXURsTCwLjy7kaNZRWoe29rQ5LkGaTOQkJpKyfz+nduwg+fRpslJTyUpPJy0lhaTUVJKys0nKzia5sJBkkwljhT58gVY+PrQIDqZn69bM2buXFKORKx55xBMf6fzCUASbvoGV79GoMBv6PQDDJoJ/A09bplAo6gGFhw5q/x85okS0QuEESkS7iJK46M1Jm+usiD66di3/zZoF3t5gNILBQGZ2NqeOHSPl7FnSMjNJzcwktaCAtKIi0kwmimz05QVE6fU08vGhUVAQPZs2pXF0NE2bNqVxy5ZEt2tHk65dadynDzp9WSaIvnfeyQO//IJ/REStfObzEinhwGJY8gKcPQRtR5DQ4GriRt3uacsUCkU9onTWVEXNKBROoUS0i2gW3IyogCgSkhK4scONnjanEukHD9Jz8GCybOxvIAQN9XqiAgJoFRpKbEgIEQ0aEBEZSWRMDE07dCC6eXNCmzUjtGlTgho1Quft7bAdoSFadoiMlBSa1uDzXLCk7IPFz8Ph5dCwHdwyE9pfRl58vKctUyhcjiq24maEeVmUij1XKJxCiWgXIYQgLiaO9WfW18m46DVffEEW8P2DD9L3kkvAywu8vAgMCaFJjx74hYXVih3N2rUD4OTu3XS98spaGfO8IO8cxL8DCd+BTxBc/hbE3gdePp62TKFwOYK6df08b9GViGiTZ+1QKOopSkS7kNiYWOYfmc/RzKO0DqtbIR0bV69GD9z8/vsEBAV5zI5mcVrYyym1Itw+jAbY8iP8+yYUZEKf8XDxCxCowmEUCkUNKYnmMJUX0fMz5rNw1ULeu+g9DxilUNQfVIo7FxIbEwtQJ0uAbzp4kG5BQR4V0ACNevbECzhy+LBH7agXHF4BXw2GhU9BdFd4YDWM+VgJaIVC4RKEzno4x+LMxSw6qrInuQJDejrSWHHJveJ8QYloF9I0qCkxgTEkJNWtfNGmnBw2ZWfTzxxK4Um8/Pxo5eXFoZMnPW1K3eXsYfh9HEy9Bgz5cNM0uHMexHT1tGUKheK8wuyKNqmYaHdgzMnh4ICBJL/7rqdNUbgJJaJdSElc9ObkzXWqSMDBuXPJBOIGD/a0KQA0DgjgxLlznjaj7lGQCUtehC/6wdFVMPwVeHgjdBoLdSzGXqGoDdTCQjdTmp3D4jwbVCEsV2HKzgYge8lSD1uicBdKRLuYvtF9OVdwjsMZdSdcYeO8eQD0u/56D1ui0TgsjGP5+Z42o+5gMsKWn+GzPrDuc+h+Ezy6BYY8Ad5+nrZOoah16trC7POB/J27KNi3r/zG0vNsIaL/uLXWbDrvsfaQojivUAsLXUxcI23h3KakTbRt0NbD1mhs2rGDICHoOGiQp00BoElMDCknTpCTmEhQo0aeNsezHFsL/0zUSnY36w+3zIAmvT1tlaKOIoQYANwGDAEaAfnALmABME1KmelB8xR1mGM33ABAp317yzbqNJFXbub00FJo1bw2TTt/USL6vEd5ol1Mk6AmNA5szObkzZ42pZSNx48TGxqK3qLoiSeJaapliD68erWHLfEg6cdhxh3w02jIS4frvoe7/1ECWmETIcQi4F5gMTASTUR3Bl4E/IA5QgiVN1JhN6UefxUTragFjCbJ6/P3kJxV4GlTXIbyRLuB2JhYVp5aiUma0AnPPqcU5uWxPTeX//Xp41E7LInu2hX++osD69fT40b7C9Mse+89pkyezLH0dIK8vYkJDqZpdDRR4eFExMQQ1bIlUY0bE9qkCQFhYQSEheEfE4N/eHi5qoluRUqKsrLIz8igIDubnKQkslNTyc/OJjc9nczE0+QeXEfe8V3kFkmy/ZuQ4+NPwfevkF/4LFmFhRQVFmI0mTAKgQEwAR2iorh09GiufPNN9D4qN/QFyu1SyrQK23KArebXh0KI8yZ1yw9vHyHghytAWPzt3ngjPPww5OXB6NGVDxo/XnulpYG18LWHHoKbboKTJ+H28hU+f0g6ws8jI+BOYP9+eOCByse/+CKMGAHbtsHjj1fe/9ZbMHAgrFtHz8cfh4r59z/5BHr2hGXL4I03Kh//9dfQoQPMmwcfflh5/9Sp0KwZTJ8OU6ZU3j9rFkREwE8/aS8Lmp84zsmmzbQ3X34JM2YQeuYM/qdO4f3F5zBnNpiLNt25KI2h27Lgx2FlHfj7wyJzxo7XX4fly8uP3bAh/Pmn9vNzz8H69eX3N20K06ZpPz/+uHYOLWnfHr75Rvv5/vvhwIHy+3v21M4fwG23walT5fcPGABvvw1Al5dfLsuBXcLw4fDSS9rPo0ZBxZDCMWPgqae0n4cNoxJOfPf0RcU0P3EckZio/c5sfPcAePJJGDsW/xMnrI/vwHeP55+vvL82vntg9bsHwMKFrDudS+Hkz0l/aiPRMcHl95cUDPvgA5g/v/w+V373XIwS0W4gNiaWOYfncCjjEO0btPeoLTvnz6cYiB0wwKN2WBLTuzd6YPu2bdxg5zFrPv+ckRMnEq3T0SM8nNyiIrYlJzP/1Cny7DjeDwgUgiCdDi8h8Cr5Xwj8vLwI8PJCAiYpkVJiMr+KjUaETlf6XgIFRiO5RiMGKTFKiQEwSokRKDK/7OcoARzFXwh8dTpC9Hp89Xr0Oh16QA8gJVP37GHKnj0M/fZbFh87hm8tFcdR1B0qCmghRAgW13Ap5TkrIrteErFsG51PSGR4MfjUjRm08xMVbuBWSkP71fmFsgmPupR4oaaI+vZh+vbtKzdvrp1QiWHDhpGRkcG2ik/M1XAm5wyX/3k5z8Y9y62dam+RRnx8PMMqPMF+ddddPPTTTxz95x9aXn55rdlSFfHx8Tx62WU0b9iQBYmJdh1zVaNGbExJ4cDRo4Q0Lx+vl5eZSeqePaQeOEDyqVNkJSeTl5tLfl4eeRkZ5Ofnk5efT3ZODrkFBRhNJgxGIwbz/wXFxeQWF6MTAh3aFKfO/DIaDPj6+KATonS7j7c3Qf7+eOl06PV6vEpEr06Ht7c3IWFhBPj74+fnR2BoKEH6AgLPrCag6AwhzdoQOOxhArsNJTAmhsCwMPRe1T/LGgoK+O7ee3no11/57KabmPDHH5XOacXffV2lLtlaYke8jbLp7rBVCLFFStm3Bsc/ALwKFFB2d5ZSyrpV4cmMM9fshKsuJWj/KRr++BVRA4a6ybLydPu5GwA779zpkv7q0vccYG/HTkD5mOi0b74l9aOPaHjfvUQ9+aS2cVIo3cwx0VWdizF/j6FXVC9eH/S6+4x2krpy7otTUjh00VD0kRG0tzN8sa7Y7gzV2b7yQCp3/rCJIe0imHpPv9ozzE5s2V/VNVt5ot1A46DGNAlqQkJSQq2KaGts3bKFcKDFJZd41I6K9GrcmGUVp+NsISUbk5MZ3bZtJQENEBAaSosBA2jhBm97jS5omadh2STYORtax8CIH7XMGxWnGe3Ay8+PB6dO5dtZs/ht0SImOGeR4vzgKaDr+eJ1topKzlG7OOFMO551nONZx+ukiK4rlMac1y9fpcIB1MJCN1GSL9okTdU3diP7TpygU1AQwtvbo3ZUpFfPniQajSTvrN7rc+7QIZKlpEunTrVgmQsozoeV78HnfWHPHBjypJayruc4pwR0KUIwpHNntmdlYTIYXGevor5xGOyKYqq3SJXVwG1cNuuysjfqYcW9qO/xeY8S0W4iNiaWzMJMDqYf9JgN0mhkR2Ym3ax4bz1Nz+HDAfivZDFAFSRt3w5A07Z1I2WgTaSEXX/C57Hw75vQ7lKYsAmGvwy+rim33q59e/KAlF27XNKfol7yHLBOCPG1EGJyycvTRrkUo7ngh9GzTojzkcTcyiF09S2s87xhUigseMrTVihqgBLRbiI2JhbQ8kV7ihOrV5MJ9Oxd99Km9br+egSwacWKatvmm6sb+jdo4GarasCZbfDjKJh1N/iFwfgFcOMv0KClS4fx8/UFoKjg/EkRpHCYr4EVwAZgi8Xr/MFg/n6b1IyLO1FFbWqJqh5SEr6tPTsULkfFRLuJmMAYmgU3IyEpgds7W0lnUwtsmzsXgB6XXVZNy9onrFEjuvv7s9IOj2p+bi4A/kGu8ea6lOxkWPEa/PcrBDSEsZ9Cr9tB556MAqLkYlxHcn4rPIK3lPIJTxvhVpS2q12UI9q9uMHTn1VQzNLdyVzXp6nL+1bYjxLRbiQ2Jpalx5diNBnRu0lUVcW2zZsRQLexY2t9bHsY2qYN3+7aRVFBAT5+tstb52dnA3VMRBsKYcMUWPWB5jUb8AgMfQb8Qt07rtEIgKxJbLWivrNICHE/MA8oLNkopTznOZPchAozcC8qZte9uPG8PvfnThbsTKRDTDBdm7j5vqOwiboTu5HYmFiyi7I5kH6g+sZuYMeRI7Tz9iawjuYUHjp0KPnA5pIk7TYoCecIiIysBauqQUrYOx++6AfLXoGWg+GRjXD5m+4X0IA0mWNE7UiLpzhvGYc5LpqyUI66UyLVlShx52aUy9+dlMaau+F7XFL1L7/Y6JL+9idl8+cWOzNmKUpRItqNxEZ7Ni56X2oqHRs29MjY9jDkjjsAWFmdiDZXlvK4Jzp5N/xyFUy/Fbx84ba/4JY/oGGbWjMh23wuQurog5HC/UgpW1l51ckc0c7iiewcPQ6baJV4gYr28/BhpaDYSHFdWZhq4/waCnQ4m8DL1X8il3+yiidnbndNZxcQbhPRQogfhBApQgirQa9CiGFCiEwhxDbz62V32eIpogOjaRHSgs1Jte8kKszI4EBREV3a1J7Ac5TI2Fg6e3mxcuPGKtvlmRfReUxE556F+U/AV4MhcTuMeh8eXAtth9e6KRnp6QCExMTU+tiKuosQ4vz6QpQKhNoTQS/MMPHuT67x6tUbzuOFhR1f+ofLP17lWSOqELimggIOzo4habNzM5h+52ZxT+rTGNXiW4/izjnhn4DPgV+qaLNaSjnGjTZ4nL7RfVlybEmtx0XvWboUA9CjT59aG9NhhGBoy5ZMPXwYQ3ExXjZyWecXamGf/oGBtWkdGItpcmoebLgDCnMg9l4Y9hwEhNeuHRZkZmcTBOjNWToUCjPfA1d42giXc/45SOsmTrgzR/xn4mDjui3Cj6TletgCafFveUzmWcXsU/40cqLnx6cvIqAQ0vKygSinLVTUDLd5oqWUq4Dzb6GLg8TFxJFdnM2+9H21Ou52cxnjHkOG1Oq4jjJ06FBypGSrOZOINUpFdG16og8uhSkDaXfoO2jSBx5aC6Pf96iABsjIySFMLSpUVEBKef4JaMDpue56SNHJk5hya1n01cATff8/Jt7/4QLz3DuLtYeU0m3OPSnq6uGfRt1+5HIOT69OGiCE2A6cAZ6SUu621si8Ev1+gOjoaOLNAtHdZGRkYDQaazResaEYgD/W/sHwEPdO/+fk5JTaumrFCvyBxIAAkmrpfNmLpZ0h3boBMHPyZPJsxG+fOnMGgK3btsGxY261LSD3FG0O/0DDc1vI82/MrrZPktdkCOxJ1l4eJik1lQbe3pW+k5bntK5Tl2zNyMgAsGlPXbL1gsIs7uQF5Io+fOll+PfsScs/frf7mLwiAwE+rriNXzjnuVaxx8PvpLKUJceZ6qGaPo/wpIjeCrSQUuYIIUYDs4F21hpKKb8BvgHo27evHDZsWK0YGBYWRkZGBjUd77u/v+Nc4Lka91Md8fHxpWO8c/YsHf38uHj0aLeO6QyWdjJ0KO2ffJK9hw/zvo3zs9TbGz0w7Mora1Y2uyry07VS3Vu+Ae8AuOwNAuIeIG/NOrf/3hzhBYOByMDASjaVO6d1nLpka5h5gaYte+qSrUKIbMrUTsktVKJdx32klDW+ngshfgDGAClSyq5W9gvgU2A0Wunx8VLKrTUdtyLS8tNdQORv22Z32xkJJ3nmzx2seHIorSOdnKUrjT2/wE50bVHVea3hOS/5G5Em9bvzJB6bF5ZSZkkpc8w/LwS8hRARnrLHncTGxLI1eSuGWlwAsO/cOTpER9faeE4jBEObNWPNmTMYjdanBvMLCggA9whoowESvofJvbW8z71ug0e3wsBHwcvH9ePVkKSCAqI9naVE4RGklMFSyhDzKxhoBLwJJKEJW1fwEzCyiv2j0Jwd7dBmB6e4aNzylIQZKIFgkyXmmbGDKTlO96EqFtYSbnhIKfU/qwcgj+IxES2EiDF7NRBCxJltOespe9xJXEwcOcU57DtXO3HRWYmJHDca6da+fa2MV1OGXXQRmVLy38KFVvfnFxXh746L/ZGV8PVFsOAJiOoMD6zSKg4G1YF81GZkcTHk5MC5c2AwkGYwEFmXy58r3I4QIkwIMQnYAQQDsVLKJ13Rtx1rWa4CfpEaG4AwIYQz66LstOfCmKp2xhMsBDQmzUUGuKYbRQWqyhPtMk/0hfE3UldxWziHEOJ3YBgQIYQ4BbwCeANIKb8CrgceEkIYgHzgZnmezin1jekLQEJSAl0jKs2QupxdixYB0K13b5f2W5yXx+MDBuCn0/HB5s0IF5WeHjF+PPzyC//8/DN9rVRXzC8ocK2IPncElrwE++ZDWHO48RfodGWdSve0/ttvue/RR9ltXlSpQ7vPSWDy9u38KAR6IFCnI9jLiwCdjrCAAML8/IgKCaFhZCThoaFEN2pEwxYt8A8NxS8iAv8GDfALCsI/JAS/6Gj8/P3x9/fHSxVvqfOYZ+qeBG4CfgB6SSkza9mMJsBJi/enzNsSLRvVdB2LrrgIgH1797Lfv3YeGkvm7VwVA+9QPL3R6PD4LRPX8q3fe/y9/jniU/tX295yXrJkDP9DhwkBTp86xT7ztmFW2lXVX11cM5CTk0NJrIon7dOdPUskYDAYKtmhy8ykxF1juc/e742/+Xa1Z/duZI7r1uvU5HxVZ/uuNG02/ty5c3X2e+OoXW67c0opx1Wz/3O0FHjnPRH+EbQObc2mpE3c1fUut4+32/wl6DpqlEv7/e2hh/hyxw4Arvn8cwY/9phL+o0aOpTeej0r1q3jRSv78/Ly8HeFyCvM1sp0b/gSdN5wyUswYAJ42y457imadOtGQz8/XoyLQ+fri1EIjh09yq+HDhEM3NOnD0ajkdy8PLJyc0nPyaGgqIh9ubmsSkzk3L59OOKf0AP+gJ8Q+AmBv15PsJcXzYKDadesGYMuvZQrX38dnRLbnuQ4kAr8iBaPfI/ldLyU8iMP2VWJmq5j2fCpD5BLxw4daVRLMel7zf+7KgZ+5YIFdN6xk4hHHq7W4WAqKmK/g+Nnb5sJ+dA1JId2dhyz1+LnkjHOnThBMtCkSRNiSvqIr9yuqv7qypoBSzQhpGU68aR9xadPcwjw0usr2WFITeUggChvo73rMDaY//Q7dejIsP4ucM79swCo2fmqznbdgVTYvIkGDcIZNqyf0+O4C2fWwKg7Yi0RGxPLvMPzMJgMeOnce9r3bt+OH9Bi8GDXdSolP/39N030erKNRr76+GOXiWh0OgY2a8aPx49jMBgqeUWziooIqYl4M5lg26+w/DXITYEet8DwlyHEbbPQTiGNRooyM/END6d5//6sNGeOKGH9l1/y6yOP8MfrrzP6xfKPGxX/+E1FRWQlJ5Oybx9nDx+mICuL/LNnKcjJIT8vj4K8PPKzsigoKqKgqIj84mIKiou1/4uKyMvLI6uggINZWSzesoUPtmzh/xYt4lMHFj4pXM77lE28B3vIhtNAM4v3Tc3b3EM9npwMnj6DtE2b8OvWleCLL66yrSwqKv3ZVFhIyocfEvnoo+iD3f1rrjuzb+cjZVnsKn+PazrxXhbO4do0g1JKz8TKF+bAlIFw7TfQvPqZlbqCEtG1RGxMLNP3T2fP2T10j+zu1rG2HT1K9+BgdC4KtwA4OXs28dnZvDZmDKknT/L19u1MPnKE8NauqTY8YOBAPj92jF3//kvPSy8tty+vqIhAZ0X08fXwz7OQuA2axsG4P6CpZwvQGIqKOLZmDbuXL2d3QgIHjx1jX2Iie3NyyAX2x8fTeujQSsedPngQgCadO1c7hs7Hh7BmzQhr1gwqnE+H7c3Pp3doKP/u3199Y4XbkFJO8rQNwFxgghDiD6AfkCmlTKzmGMcpEQj1OFhXmENSZGFRNS0hb1NC6c+Zf/9N+i9TEUJH9HPP2jzmjD6bbq2a874pw3paK0ewEHQzAwP54WMDPw+3vWTqPI28dANVZeeoWc+mkr8RGwvyne5Xgt5NGrqguApbz/wHGcdh+esU3zGPPWey6NEszD2GuBBVtaGW6BtdFhftTmRxMduys+nZqpVL+/39/fcBuOXNN7nj//6PImCueZsrGHDDDQCs/+OPSvvyiorw93Mw5CLjJMy8C34cCbmpcO13cM+SWhXQuampbJ46lZ/vu48XBg/m+qZN6ezrS4CvL+2GD+fqt97ihaVLWXL4MH56PQMiIjAAyYcOWe0vJSkJgJhaLuWevHkze4qLGRkXV6vjKsojhHhRCGEzQFgIcYkQokYVYM1rWdYDHYQQp4QQ9wghHhRCPGhushA4AhwCvgUersl4VRii/V+fF00J8+21Ck9h+vQZZP2zGO8mTUq3yWKD+f/iKrs/bEghdr+JBGNS6bb9ffqyr3cfUj79lLz//rPDxspq6UvfcIIK4JEFVZz781BEp3z6KXlb7ThnjiBtVywsoaZ6Vbj4d+HOB6T7p24BYM2hNJIyCwCY/d9pvl11hKnrjmiNhOD9xfu56ou1tHx2Acv2JJOUWUCxsW5eC5QnupZo6N+QtmFtSUhK4J5u97htnJPx8aQDPV25qNBoZFpCAgPCw2nTvTuyc2ca3XcfC+bPZ/wU12S4annFFcQIwZrVq3mowr58o5EAGyXBK1GUC2s/1V4AQyfCoMfAx30lw9P272fvkiUc3LaNPdu3s+vYMXZnZHDKwkOgB1p7e9O5YUOubNGC9h060HnIEDqPGkWI+Qa6+LXX+OeVV2wucEw+dQoBNOzY0W2fxRrfPPssJuDBd9+t1XEVldgJzBdCFKDl2U8F/NDSzfUElgFv1WQAO9aySOCRmoxhlx260gTG1myA4mKET91LQWmJ1GsiWlZx80965RUAmv/yMwBekWWZgXLi4+Hll2wee/n8ZBqfNrHWO5vUL79EFheXVjw8O+Urzk75Cu/mzWn+w/f4NG1qtY/U/FSzsWXnWdijoc5DEV1yzjrt21t9Y0exer5cFM7h4t9FbWWV7P/2co69cwWPT98GwGBdIrf7AELHjlMZpe0m/L6VgmIT4+Ka8/a13WrHOAdQIroW6RvdlzmH51BsKsZbZ6codJBtC7TFAT0vu8xlfe748Ud2Ggx8fvXVAAgvL0a2acPfhw5hKCzEy9e3xmMIb2+GRUfz79GjlWKy8kwmAqq7YUoJO2fC0lcg+wx0vQ5GvAphzao+zk5MBgPHVq/mwPr17EtIYPeePexJTORATg5pFhcxP6Czvz/DmjenfYsWdO7bly6XXEKboUPxDgio+iOYvW62FiElnztHhBAuOd/2UpSbyzcbNjAqKorW/etPnNr5iJRyDjBHCNEOGISWJzoLmAbcL6XM96R9bsFKirvUjz7m7Lff0vznn9H5+eLfo4fNw9/d9C6NAhtxR5c73GmldXQlIrpyfYCc1asp2FeW8vT0Y48DYEhLK32ILjZXai3hpvk3cWfnOxndWiugFZKpeaoHTT9BGp9ZNaH4xAkOj7iUMPNMX0W+3fkdmkun7BrW7ZgdKuo8FNFuwZ0VC83/m1wdE+2hECpdyVJ4oSsn5AsN2vYV+5IpMnTh4V+38vTlHegQ46llIeVRIroWiWsUxx/7/2B32m56RvV0yxjbNm5EAN1cWKnw1y++wAu48aUyr8jI0aP58dNP2fDddwx+xDWOqSFxcfwxdy7H//uPlhae9DyTCf+qhOPpLbDoWTi1CRr1hOt/gBYDnLKhODOTo2vWsGfVKvZs2cLW3bt5IiuLfXl5WCqUSCHoHBLCNe3a0aljRzrFxtJ+wABaDB6M3kmRW11sW3p2NuG1nB1j9qRJJJlMPPzAA7U6rsI2UsqDoC3sP28xC4vcX2dyLttI+O23AZrX7cyvP+ELnLjzTgBa/fUnxowMAgcOLD3caDKy5+wepu2dBsBFprY0iW6Ld1SU1eGSc8tShGUWZhLqG1qpzcH0gxzOPMzIllXVorH8DKWB3ZU4ed/95d4b09PNbWU54WXMyODotdfR+OOPiPx3F3NXPc2wGyJJnzmToBz7xVPGzJnl3u9K28W4BeO43GxiekEGGw/NYfJ/k3kmwY5p87oUZpN7FnR68A9zugtHci0XG0146YR9i+/cmCe6rGZp1f2k5RSyfG8yN8U2t6vb2nw+OptTWPqzzvyHUiwFJgsVrRcCg5TohGDn6QyW7U3m2cO386OpG2MmTiUiyMejRYOUiK5F+kRr8bibkze7TURvPnCADv7+BIVWvgk4g8lo5PedO7k8OprIli1Lt1/+1FN4ffop86ZOdZmIHnjNNTB3LuunTSsnovNNJgICrYRjZCXC8ldh++8QGAVXfaFl3rCjsmFWcjJ7//mHA2vXsmfnTnYfP86+tDSOFhdj6TdqptPRJSyMizt0oHP37nTo04f2F19MVJcubssrbcsTnZGXR1gtT2H/8PPPtNDrGfnCC7U6ruLCRpg1jWHnXpJ3vknaN18z/bZmdA3pQNuC8rHCR6+9DoBO+/byXsJ7ZBRkMO/IvHJt8m+4h0PAjc95MbjJYCYNmER0YFnm5Dv/uZOPzT8vOb6EG9pX9txeO/daAPtFdAkOqhJLz3Xm/AUUnznD8ZtuLo2hOT635l71ZceXlXu/+Og/fL92qf021tgCF/J+a0DApIxqm97w1TrOZBSw9tlLyu+wc3GeySRp98Iixg9syaQru9hvoztEdFlHVe59aNoWEo6lM7BNBM3Cq54NBTDVwC6TlCRm5hPi502gb3l5uet05ZT2g9/9t/RnYf4cqw+dI6dh2d+ATifApIlo79xEokinre4MbXVnGPHWT7SJCefrx6zPtNQGSkTXIuF+4bQNa8umxE3c2+1e1w9gMrE5PZ3h7Wq8VruU/6ZN46TRyBtXXllue2jTpgwJDmbJ7t24KlK26403EnTXXaxbsYKSwEwpJXlQ3hNdXADrP4fVH4GpGAY9DkOeBL+Qcv1JKTm9bRsHli1j36ZN7D14kP0nT7I/I4MTFp4HL6C9jw89oqK4vlUr2nfpQucBA+g0ciRb9u6ttTyjJvOFXNh4CDhXUECEowssa0DKtm0sT03lif79nfauKxROUeFGbkxN4/qP0wDbC7+2pWxj6p6p1Xa95vQaRswaAcDz/Z5nXMdxnM4py9L32vrXeG39a0T4R+Cj82FCrwmMbVO5CJQ1El+ZhG/btoTfflvpR8gtziXM8qNV4/VMeafsipr8xht2jesoJZ47a3LJmmsgrzgPozQS7GOeQq/Oc/vfr5C2Hy59rUZ22o99wi/hWHqlbRl5Rdw0OZ5P7DjeYPaQ/rz+mH0iugpBWuMUdyU/GKvuJzVb8/YWG00cSslmxEeryu3/6+GB9G5etl65JmbdvTgPFq8ot21U1xjeuqYbYz5bU6l9vkW2jpJwDhOCfUnZpduLzOEcQkD3GQPZZHELXOb7DKTDyvhQ+m19Gr9OI7X0tW5cA1URJaJrmbiYOP4+9DfFxmK89a6Ni87etYtEKYnt47oMFAunTkUAIx9/vNK+Qe3b8/aWLeRmZxPognymXgEBxIWFse7AgdJtxfn5GEHLziEl7JkDS1+CjBPQcQxc9jrG4GacXLeO3StWsGvLFvbt38/+xER25+aSZdF/CNAhIIAhLVvSpXVrOvfuTfuhQ2lz8cX4+PtbN2qvGxaZ2MBgFtG2qgemFhbSObL2SpJ/89RTGIDxkybV2pgKBZR5oh3h9kW3O3zMWxvfIsg7yOq+tHytpPbza56nXQP7HBMZ06cDEH77bRQlHycA+G/+jxTNmkuLX6dRfOo0R1wYaldThu00CxRg8C4T+5sKjFae4YfPHE5OcQ4779ypbahGaS1c9hRGAWNrKqJzUuCDdtD3HhjjulpC/51IZ+72M4T6e/PJsoN0T7WeEakiJV5ay49/4mweF73/L1/d1pvhnaIx7N2LPiwUn6ZNy4SytQWyNVzBVxoTTdVe9JIHpks+XGl1/7Vfriv33pYnushgIjO/mMhg6w6VTUfPWd2+aFcSi3YlWd1nSUk4h7SRNC4tPUNbdGSFofFmT/TGr7QXwLjp0MHBWSMnUCK6lomNieW3fb+x6+wuekX1cmnfR9doT3p9XbiocMGGDcQGBBBlJTdx/yFDMG7ZwpbZs7nodsdvYNYY0Lkzb65bxyc33MCOrVv599gxAMKDvSn4/DL2xa9hz7kw9uW15MD3q9h7Vxf2FxRQaNFHY52O9qGh3NayJV06daJDv350HDGCxt272/Ty1gWMBm0Ky8tGJpIck4mgWvJEmwwGvo2PZ0SDBnS6/PJaGVNhH0KI9sAUIFpK2VUI0R24UkrpHrelJ6itFAFoIrk6bpjn2HRx2pQphB3ThEObhDMUcIb93W0vgKxNHlpgZJb/HNqelrQ1Z/hukyi57D9JRiBMv0jHA4s0cW3MygKdjpzinHJ95BXlWu07rziPYTOGkR8VAYB9/nsLspPg64sgJxka9dAqygJs/l57vZAE3jYcHhZU5+W9poJwfHftV3aZZzRpMes/LH2bvbOfom38v+zencgXKz7kuYL7MISGM2P64wDls3xUsCd/z1zy/javMbIrHYoVSqYMqnngPJpm/XdVQvOsJPK8/EgLCANs+/T/N30bC3YmcvTt0VZjkG/8en3VhlSDpSfaGnfr/3Gsw99v0v6Pux9GvqPFzbsBJaJrGct80a4W0Qd27UIP9DRn0agpKbt2sSk3l1cuucTq/n7XXw+ffMKGefOcFtGGggK2zprFmr/+Yt2GDfyZqF3V/zdrFg2FoH9UJMeSU3hz7mIenVNyvUhCRxItvLzoHB7OZV260M4cgtF1xAjC2rZ17gN7GINZROttxETnm0wEBFn3mrmadZ9/zgmjkTdvvrlWxlM4xLfA08DXAFLKHUKI34DzRkQLJ0V029OSsyGQHuzZSnypn0726PhVcfEOyZQrUokpKjvHbcyOwrBcSgU0wIE4rTRz6/F6jjQqO6fbUrbRsEK/GQUZDJk+pNy2ETNHMPmSyXRuWE2BqF1/wqy7y29L3A5/3Vd+25sx8OwJ8Kt6zY/lt2fL8XMYTRDXKrxqG0pMOZ1J68hAAnwqyyODSdIz7RCN8jSv66qpcwlNPUODrEReW/8978TeVsEQ69/jaza+QGGw5Eu7LLJOdRULCw1Gnp65w+q+duknmbzy08p2jXnTWkIcABbs1O7NUpYtB0rLKaTvG8usH+AgJZ5oWyLam8pZbuxi0zfa6/+2Qbhr62eAEtG1TphfGO0btGdT0ibu735/9Qc4wH8nT9I7IICAsDCX9Lfg00+RwNjx463ujxgwgDZCsHHzZrv7zE1JYd/06ayaNIm1O3awPj2dkuinVl5ejI6MZGFqKuNbt+aHnx7lzIy3afo5eOv1vDSgP51j+9E5Lo52l12GbwObdSfqJaXhHFY80aWx4bXkif79q6/wB65SoRx1kQAp5aYK3iAn7zB1E2dF9Fu/GMnzhfFPuO/WVmgsxFdfeUrbkQwPnsan2LHze+8/Rp6/y+KcVliIdybnDJf/WXnGKjkvmZvmax7B0a1G88bgN8rSuxblwrkj8NVgq2Pu9/ZmcVAAj6ZnlpdV7zSHl86C3vbv2PLrc90UzUP6wQ3WZwLu3LOw3HvL2N37L2rNI8Pa4u+jw6uwABPe+BnK5j0bf/9x6c9tM0/z3bKyePbCw4dJm2L2cFeYAT2tg0g3rivMLzLS6eXKnlshTfy26FXCbMwkDEjcjcR6vaao3HOMPboWo/Fy/voviadmbq+R2ZVsq0JE6zHSTne60naH+Hks/G9XzfqwghLRHiAuJo5ZB2ZRZCzCR++abAvFRUX8l5PDA127uqQ/gHkLFtBEp6PXLbdYb6DT0T8qivhTp2z2cXrnTtZNm8b6JUvYcPAgm3NzKUabierq78+t3bpx8cUXM/iWW2jcrx9ISRcfH1JTTyCWv4KIiQOW8dyNN/HAr7+67LPVRYzmG5PeSkx0YYFW3cm/FrJzFJ87x4z9+xnbujXBNlKCKTxKmhCiDebbpxDiesD1pbc9iROrm376UHuOCCiEoDxJToB7vNF9p/Vl+Q3LiQoo+9so2LePo1df45bx3MG0D4z8OML+0La2SXDpVhPydhNCpyuXS/hcwTmrAroiC48uZOHRhbx30XuM8m0E3wytsv31TRsBcG9GFgEVvw+vN4RXMsg3FhDXqjlD8/L53GK3SWrCSyAxmGXOOz+v5JGDy5nS7WpMOj1BRXl8t+xdQm0IyqCiPOYv3MiIibcSXFyW4HRQM/vWHB2/9TaMGRnamyrCCJ36lpqMpb+BgMwDwLDSXUUGk1UBDbBwzjNVdtsvabfNSKpnN/9Kp/TjjHr0ew6FVSjeIyW371vMohb9S8NCKiIw8ZX3J/TQHea54nv511R+Jl5vnme2FhP9lNcMxug3VGl7tRRUzg7iCupugOh5TN+YvhQYC9iV5rqnou0LF5IPDOjXzyX9ZZ8+zZLERMZ27Ggz5RpAj86dOV1cTHpiItJk4vjatfz2yCM83LEj3Xx9adq9Oze+9x5Ttm1D7+XF/wYM4Ivx4zl39Cg78vKYsmMHN376qSagU/bC1GsYEGJkfbYB042/oxv7IeC6qkwZJ06w9fffmfvCC8yfNKna0rqOYMjLY8Pvv3Nk716n7M01X3ADIiIq7cvP0WISA2rBE738ww9JA265x32VNRU14hG0UI6OQojTwONQqdBnvcaZhYUBRWU///CpkT4HTU57tKtj+MzhnMo+xYmsE6T/Mb1eCegS7lrm2Em+b7GJnT27k5F6EvIKSrcPnV61GK7IM6ueoXDTN3wfGsy5mqxReTWMpceWALAywJ+p64+V7pISVvr+j+2+WjhI0+wUPo//mDFH19Mj7TChhdnMXPiyVQH90Pa/WTT7KWYufJnvl71bTkADjDi5xS7zTIUWK3V0OopNtu81hrQ0reqkPfeNpF3wWlloSpdNZTH9JpOk/YuLrB721tqvq+166OntGCrMqPyy/hgtn12AzhznobcSPtIyK4lb9i/j+QTb2XE+957M5frNxIh0fvR5v9L+Ek+00Yos7SEOV2t7dWQXmVh3KK3G/VSkWk+0ECIKrTpWYyAf2AVsltJW5IyiOvpG90Ug2JS0id7RrinPvf6PPwAYcNtt1bS0j58mTCAXuOHuu222MRYXU2y+CHY3l5U9Zf4DDBaCgRER3Nm3L0OvuooeN9+Mjzl3dXx8PGEWOafJOwfxb0PC9+AbxMDBA/h+7joOHCymYVtNwBdaXpAqIKUk+9QpTm/fzum9ezl7/Dhnz54lJSmJpORkkjMyOJGZyZH8fDIqXKRmZmdz/YcfOnp6rHJr+/bMOK1NOTXQ6egTGUmfVq3o3rMn7fv3p9MllxDYzHYFxTxzyd6AhhWjDSEvS8szUhue6N+mTSNMCKsZWRSeR0p5BBghhAgEdFLK7OqOqXe4QPxOnGViXpxg6nD3LCga9dcoYg+YePrPC+dW6F1kJHHIZVR+zHeML/OP8EN4Az4Jb8AD6ZmMy8qmoY1wmKPeXnQpsi5A5eoPS12BL83Zze0DWgLa2pmmQhNMIYW5fLv8vdJjYvLO8da6b2zaduXRtY5/IGu2FZQ9aOQYcnl42cN8e9m3pdtK1xMKOPPMRHLXrSPIomBQJYoL4Peb4ci/5TZLgC0/QZ/xtH5+obUjGbdvKb1S7avPFPfmciKCfFn37CV46wUvz9kNgNF8r9dbkX4mc2hZp/TjNvu9Qr+pynGtxUSHkMtTXjPQObv40oIik+C5v3ey8umLa9yXJTZFtBDiYuBZIBwtOWcKWoKRq4E2QohZwIdSyixbfSisE+obSofwDmxO2gwuWrC9ftMmmghBs6GOeQVssWXbNgBufPppljRqRO9bbiE3LY1Nv/3G088+y5b88k/np0wmbm7RgsFxcQy65hq6XXcd+uoEn9EAm3+Af9+EwizoezcMe56BvbfA3MtZ99df3PXDDzQUgsnz57O/Xz+Ki4oozssjPSODUzk5JBUUkGYyUWSlewE0FIIYHx+aBAfTv1kzWrVuTesuXcg8e5a7f/wRg8WFrqbsPXuWdno9T11+OZt372ZLYiIfJidj2LABvvoKAbTQ62kfHk7Ptm3pFRtLr4suou3IkegDAyk2e8V9rSwezE/Tbgj+1ZQOryl5SUn8feIEN3fpgq+bx1I4hxDi5QrvAZBS1lZSXrfjKg/ygL2SXy92jzf6ppVGrltXp8qO1BvSTGVOka8bhPJ1A83Bsv7YSYIqODrGNY5hx7GTVvuRZw9BZInTQdL6uQWlz1/HzJN2j26fVe6Y/9tW/r3bsPgcUsCGROvhCMl6PdGZGdobWwW8DIXwZnS5TdKy6bzHmJpnvUpvw/xM7ti32F6rAW3B4K4zmTRtUJYJxShsi+iSfY7ghaE01AbKsnP4UPbA9LDXXO7wsr8IUFWYEG6pxliVJ3o0cJ+U8kTFHUIIL2AMcCnwp+vNOv+JjYllxv4ZLouLXnfyJH3Cw11WRe+rrVu5/bvvuGXiRPrceiux99/P1txcqxkpr23cmD8OHsTbEdF1aDksfh5S90GroTDybYjWkte3Hz6ccCFYt349d+v1fHzvvTz97bfM3LQJb8BbpyPUx4dmQUH0bNqUyLAwIiIjadKqFY1btyayQwcaNGpERJs2eIeEWB3+m1tvBaDnVVc5eGZs06dpU+YdPsy9c+ZwvzmuuTAnh0MrVrBv7Vp2b9vGvgMH2JuUxMfr11O8fj1Mnkwg0D0ggPV5eQAkTJ1K9+uuI6xpWdxZ0jJtBXRgp04us9caC95+mxxg3P2uXfSqcCmWc9B+aNfi2ktoXgtk9Ygk4FRO9Q2rISIbnpnlek/xNWtNSkDXgLN5KeBTWXgNaNmMh9IzeTijLH5VVnFPs/wNHPO7lZYFv5W+z0vxwTe0mOG5WynEtTUZHMVk/gim3FxSPvmUT+cbyDTXAwnPEuxP20NrIKPgLJWibPMz4N0WNvsuOQe3L4/jO/ERx2VMuf3TFr/ukK2LZj8FwFjTOxh0XvRL3I2vsZimOakApWEdllx/KN6hMQA2+T5M78KyGQG9OYbrCv0mHimGKNLpJGx7tktYHuDP49GR/HviFBFG23/rkSKrXCy/q7ApoqWUT1exzwDMdrk1FxCx0bFM3TOVHak76BvTt0Z9Je7Zw3GDgdtcmNrNr0EDhj/9NP/3zz+8sWIFAV5e6AAjMDgkhCXHjuHvTHaMtEN03fkGxCdAg1Zw82/QYXQ58a/T6xkQGcm649of0O3ffMPtn38OxcVgrfy3E6xdv54onY4OI0a4pD+A4cOH89OhQ2z59Vdi77wT0LzKXa68ki5XXsl1Fm2LsrLY888/bFu5kv82bWLb4cNgFtFDH38cHn+cpl5edA4IQKfTsTkzk3AhGGIjU4qr+G3mTBrpdAx96LwKsT2vkFKWiz8SQnwAOOZqquMY/Vy35r334ZrdOH2LJD2PSDZ21MRN3wMmxq26cEI43MFaKwK6hCkNQpnSoHwKu19DgmhSbOTRmEir3uoSjvlpi+C/KhrD8RU1DTpxHVLA0B0m9r+t3esbAY0siie2Ttb+v2/l/3E8WjDj7AyKTEU0yskg6ucrK3dImXi2PBUrfZ+gTcFUjGghTI/9N8Npmwef3kHf265h+P+eKrf9HXNs9dt9b2NV054ADD21zWoffVs0ICLIl5xCA1TIPxAuyj8kiwoCd5Xv4/iJ6tcs/RGizdwe8PEhIr/qmeXexdsB6yl7ncWemOgw4A6gpWV7KeX/udSSC4w+MX0QCBKSEmosotf//DMA7aqKp3KSF5Yv5zmDgRcGD2blxo3c1LIlU/fswdtWhT9bFGTCyvdg49eECS8Y8Sr0fwi8rFc/Gti9OwuWLePswYM0bNcOfHy0l4vYeOoU/aKiXFp8ZfTTT6P/+mtmf/VVqYi2hU9ICD1vvJGeN97IePO2/+venc927mTBK6+wc+1adh04wL7MTHQmE/0iInht8mQimzatqtsakXH0KAsTE3m4Vy/0Ngq+KOokAYD7vhgewFBdBQknuTY7h51+Adw+9A22p27njuCORPz9IINa2F6rcN8/Ji7aLZkyGvrtk/Q+ojzQNaWtdxiHijPsbv9Ow3CaFGvZV457e9OlSAvgs/abyEn05TrDctKoOylQQ/PgkQXVf6cN5vD9G+ffWLrNXCOSb0NDmBwextajJzS/eqnfqbyn/nefN7ix6BWuOLqOkcerjkOuiolbfoMtv9nc/9zmaTy3eRpfdr8aL1k2R90l7QjvvnoHXZtUyOU9yfZYj+hn016eIGlriPZpOmOXgHaUZ4o+A/7n0j7tURAL0QT0TmCLxUtRA0J8QugY3pGE5IQa97V+2TJ8gMYu9KqWYCou5pHu3Xln40Ye6NKFXw8ccExAm4yw+UeY3BvWfwE9bmZT3BQY/LhNAQ0w9PrrAYj/yr5KUo6QfuQI+4uL6de9u0v7DW/ThqENGvD31q1OHZ+Vl0dzvZ7RkyYxcelSph4/TkJGBhszM5mfkkJvNxc+mfv66xQBNz32mFvHUdQMIcROIcQO82s3sB/4xMNmuZRUQ55b+n017Rx/nc3jmnbXMOnoHlr/+QAhJsnAvHybx0RkaVLtoYWmOiGg37uubiXVanPG8XMyoemlDh9z2lvz4Vl6LK2NfHJlQ9LWuk5Anx2qlY6e2XUkf7Z1zZojW7w6rXLAZLdWzdni68sPYVpoYkHF8JYKJyFOt592hpNM2P5XjWwRjZsQ+cQT1bZ7eMdsvC0ydnyw5svKAnrNJ6U/WkaDBJGHDhNPmGbS/q8TpB8I4tyBIHQ2CsjUFH9sJyhwFnv+Gv2klE9IKX+UUv5c8nK5JRcgcTFxbE/ZTqGxZr/Y9QcO0CckxLGYZDsozs3ltjZt+GrvXp4bOJApO3Y45qE8tga+HgrzH4eIdnB/PFz1OUW+1V/gYm+7jQBg5YoVzppvkwRzJpN+bnjouGboUPYWFbF/meNVnDJycwnzoAd49pIlNNHriXNRhheF2xiDVlF5LHAZ0FhK+XnVh9Qv3CtVJSx9GQ6WRcDkVTEjZdK5Zp3JqKs/YNTVH3DTqFet7n/mLutZRP4aKLjr8bJ9m9vruPG5ulPi4e2fHRc8+cVVl6KuCsvfhsk1v5pKRD3zDG2WLaPTvr0M/vpjOu3by8uzPqZVv57uGdBMSD7MeNtQaWHt+MbR5Ji/oyUfuSyco/JJmDy/cjVCR2k7/Xci7r+v+oZWyN1o4QHPSoRlrwCQl+bNvhmNyU3WZpV3+d3L1/qPOPB3o3LHL5g7kYyj9jnrHPkK+LihJpU9InqqEOI+IUQjIUR4ycvlllyAxMbEUmQqYkeq9dKc9lCcm8uWnBz6t2vnQssg/+xZrmnVit9PnuSdK67grbVr7Q99SD8G02+Hn66Aggy4/ke4axE07mn3+D6BgQwIC2PVgQPOmF8lG5cvRwCxbvDsXvW0tpRg7scfV9OyMpkFBYTWQgo7axRmZrLk9GnGtmuHroq84Io6QbbFKx8IOe+uzeZAzy+vKLvm7Gvior7z02FteZHxatrZcu9nnU7kpzNaoKorhNrHPW8o/TnLt/K6jhuf8+JYjPWB1nXSYTKfBneJxprS7rTtx56lJ04zKqdMNMflF1BoXkTuDEctHA3SuVIlVdJxz24a3n0XPk0rf+F6tyqffvTHzqO4b3jVBUycYfq7RoSNuO/BLZpioEJ2DgvSD9bMmdY2/l867duLV2QkAB22bKb1vLkO9XHCMpzxo46lP+Yla7PPuUlls9DDhfWZ28SNrg/Hcbp0eBXYo4qKgPeB9ZSFcthf51lhk97RvdEJHZuSnI9b2vn33xQA/YYMcZldWadOMbJNGxampvLVrbcycf58+w4szIHlr8HncXBoGVz8AkxIgK7XOpU15KKuXdmRl0f6mTMOH1sVm3btoqOPD6FV5Gx2lmYDB9LV359Fa9ZU37gCmUVFhPraDnFxJ6u/+opcYMxNN3lkfIVDbAVSgQPAQfPP59W1uUQ+bG0juPE5L255Ws+k2/T8PUDwfg3CGTKO+mOtwkHr4rKb67spaXQoKqZPYSEbjp2skXBNCYUb7o9lSUv7imDd9pSeW54u/xArpIV4ttBV+131UOEC3vzFaLWU+MW5ecQYjbyXepadR0+wU7bg+yt+Y1SH650ea2JUBJbRsq0TZangLMyquYe+KmdR4wZlAvWt2Nvo8sQjTqV3s4fp71j38BuFoFer5qXvLc+6yQhJW8JqNnCFe7UuMBBfCyddh/+2En6P7foRJRSdOgXrPrMxhs035XCkGokEPjFcW2UbX+EZEf0k0FZK2VJK2cr8au1ySy5Agn2C6RTeiYQk5+OiN5oFbr8bbqimpX2k7t3Lxe3bsy4zk98mTOCBadOqP8hkgm2/wWd9YPWH0OVqmLAZhj4D3g4uQLTgopEjkcDan10XPSRNJjamptKvefPqGzvJqJ49WZOVRba58Iq9nC0oIDw0tPqGbmDRzJn4AMNUVo76wFJgrJQyQkrZEC28Y8n5dW0uL8gMXgKTTvD7MD0J7XVMusU54ZK4sQHn9led4Wdkblk8dqCUNDc6v8Ap2x+KK8zs6ANtz64VeQsMXoKMCs7EEq+j5ac+1Fjb+PPw8uEdKZ65hPD8dE30fZWUUrotrKSAyqDH4aU0GD8fmsUS4B3Azjt3suOOHdzQ3vF712cNwgDwT9Tzzk9GxmyUFGZ5cWRhVNUH1pASgZ0e1ZTVTXri5+vNFZfXLDFAVfz0oQFbyY1LvhOpOj0b/DTny/6ZjV0watVPjUKvJ/rpp+m4exd+VawrOn777bDkxWpHyz9rO4Rx34zGmAxgMti2qaQOywzDUD4xXE/Lgt9oWfAbcQVfEG90USGOKrDnSnQIcM8qDwVxMXHsSN1BgcG5oh8bN28mUqejRf/+NbbldEICF/XsyZ78fOa8+io3f2bjKdKSk5vgu+Ew+yEIbQL3LINrv9F+riH97rkHb2DVvHk17quEY2vXkiol/fq678I36vrrKQb+/fJLh447azLRMCzMLTZVxz87d3JRw4YERkdX31jhafpLKUtLk0kpFwGuT83jQaqLiT7Qyvmwp9Sd1nPHl2B5u84/6034CdfGHwc0/6HaNo89WCa8swLsC+P44FodH16jY8LDXjwwQc/0IbW7ALHzSVhy7DSDLNKM6RAwKRMufRX0lcWSEIKXB7zMzjt38vog+/MZ/xgWwg5fH/Q52meMPWByiYBe0NJ6wZISTObP1iBFy9fWv3VDercM59or3qjx2NYIKIIZNjzSJUyKbMh9jaIpznfN71vIary1JbHZej2tZkyn7fJlbI1sX6mZITGp0raKzwP5Z7058W/VqQj3z2rM/lmNrO47J4NIlFoEW8Vy4Sk0YHzxRFoW/MaV+nvI1Ok46u26NMAl2HPWc4FtQoivhRCTS14ut+QCpW9MX4pNxWxP3e7U8ZtOnqRfZGSNU7UdXbmSoQMHcrqoiMWTJzP65ZerPiDzNPx5L3x/KWQnwjVfawK6WWyN7LDEPyaGuMBAVu3Z47I+N/2p1QaKGzPGZX1WZMC99+IH/LvQeglWa+RnZ5MLRHlARJ9Yt449RUWMGjy41sdWOMUZIcSLQoiW5tcLgGtjnuo4faOdfwiWJlHe+zUps9z+Er2aecyfY0sj7e73oPX7fKnL8MAbo7hvSKtyu9Z2Enx8VeVrd76v4NdhOt6/Tkd6sLAZ/2rJpg660lzW6cGCPwfreP6O2l3fILaYvfxS0iJZ4tXluqoPsODqtlez886d/DzSvpnHWxvHUGgOPejo2KSfTb7uZj0ncwlFR4+W/nzsnSto0TCQ0d0ake/tx5gr33WNEVaY8XZlYSsr/HBoTkylNtXR/Jef6bRvL+3WWZY6r+YRtoLW8G7ShBcGWS/OlXHE+kx0ScRI6o6qH2ir44aiV0r/Xk025KzwPsfR5kt5ODqKQz4drbapCfYor9nAm8A6VIo7l9M7qjd6oXcqLjrzxAn2FRUR17VrjWzYOWsWAy+5hHNGI0t++IGLHn3UduOiPIh/Vwvd2DMXhjylhW70uLnSH5cruKh9e7ZkZpKT5Zrq8nv/+w8BdLmy6otlTfALCWFAgwas3L/f7mPOHT4MQHhE7RcIWPHddwBcevvttT62winGAZHA38Bf5p/HedQiF1NdeV4pJboahD6ViuPHzIu6G7Sq1ObMBscWNr14Z2XBmhYqEDotHMTHS8cLV3Qut/+LMTrWd7Z+3ZwzQEdCe22fyUoTezKYHGoieOSh8nZFrphP+I9TKoWMWOPJe/W8cZP91/X0A0GYDDBkt+T9H4xE7XL8ut07ujc779xZfUNgWWDNM1I1fvcdWu/axairP6DYire8PNbPevxTwzDq9Iy+6r0a22OLGW8byi82LAnxkdD5uGN51aOenUinfXsJjIsDwCu8+vXIYTeb18s4sL4pcVOFvyGLp0FjkSA3uWZrgA7LJggp8TJIjFbCUPRBewlqq/1OLp+vJ233wRqNZ41q/zos09qpFHeuJ8gniM4NO7M5yfH1QBt//RUJ9Bs+3OnxN3z3HRfdeCN6YPXs2fS/6y7rDaWEnbPg81iIfwvaX64tGhz+EvgGOT1+dVx0ySUYgA0znK+8ZMn+Y8doodfjGxzskv5sMaxXL7bl55Nup5DOTEwEINSOi5mrWRkfT7gQdLn66lofW+EYQgg98JmU8jEpZS8pZW8p5eNSynOets2VlKbvEtAnug9/jPmj3P6ogCjaLltKuzWrS7d12re33AKo6igYMQ0aaOWU5Z1li6dP/BtOQYZjIRx3/k9fqTz1kl6CL6/Q4R26Da9Q634na+LYGlUJZlGNmk4NE9z3f2VCOqJxG6IHDOPs989VO+7A7mNoPuJKbp5ov0e7IMObpmmaUcUHU+0+zhkmzHe+KE+Tjz6k9by5hF51FV52OoC8m1ivadQyIhA/b51dMwY14YsvjVafMB+bY/95aLt8GQ2rqnxr4zPEvPwyHbZvQ1gR0Q0DbYdX5adZfzDJPO7YeqkzG8PIPObPe8XlF79ftr6Q3943oqvorNcVEtCsTKq2TpJ45bv+F2TzmyOEmCeEGCuEqHQGhBCthRCvCSGqX6KpqJa+MX3ZkbaDfIPthP/WWPLnn/gAg+65x6lxl3/wASPuu48ILy/WrFxJlyuvJC8tjecHDOCGRo24qUULxrdrx8SeXfisbwwLJtzKsWQvTLfPgxt/Lr0BuZNBt9yCDlg1Z45L+tubnEynhg2rb1hDhl5/PRJY80P18Y+gZUQBCKnlmGRpMrH4+HEuadpUpbarB0gpjUALIYRnciHWEtJCNvZv1J+2YVosY5/oPrw75F1e7P8i+uBgvCIiaDVnNo3ffQeA1vPmEvNKNaFoZo7e+wymPPNyn8CysI3cZD+St9rn5f72ch0PTNCT71f+5vzhNTq+G6kn31fb7t94JgBn88un0rM784cQrOwqeP3mslu2I4ItM7By49F/Vl90+InuE3h7yNuYdIJxz9h3fTi5smGpsO+/5rD9RpopKDbS8tkF1bZrmlqzbOIhsW1LH7rsTQUedtONNPv2WzruqBx+uWbiJfTrb2cmKyeJyIKXf9cEc8mnv2yriQZ2pt1ut34d3k2qXq9ky9EsdDp0NrJHzX3UdijgsWWRWrhnuc4kyQ5mEck8GsCZDQ2491qt+E2GDKRHszD67tHUs1dR+djx4A6vlHuvN4E+xPWrbqt6/LoPGALsFUIkCCEWCiH+FUIcBb4Gtkgp7VMIiiqJi4nDYDKwLWWbQ8dtOHCAPsHBBEY5vqBi3gsvcMXTT9PK15fVCQm0HDwYaTJxU9euvL1hA7vS09mRmMiKI4f5ZPse/m9rCmNWFdHqvR2EthvG0AYNeCo2lvXffuvw2I4Q3KsXvb28WLXJ+TSAJRjz8thfWEinVpWnbl1Nv1tvxReIX7rUrvZp5jR+DRu7YnW1/eybO5dEk4mRNZjNUNQ6R4C1QoiXhBBPlLw8bZQrMVlWpZMSX70v00ZP47NLPmN069EEeJdN4/t16EDoVVeVvg8ZNQr/Xr3sGmd/7z7mQSrssFOfHYsSpAeXqY6J4/Xc85i+NDbZkhn7ZzBsxrDyGx2YGv9irJ6drSr365Rv7Ug8ojoXNtrvId8sTox6QVibXHxCqs5WYirWEXdA6zsgr8ghszLziun40j/VtvM2SD76zvmqdscHFcAXZet3rHlXrSGEIGjIYISVfP6/HfiaPZnrSt+/Os66vDrieOhyOboel9yxrOyzD9pr35e17cK/8GpgR4iSE+lom4RV7VUuCtMWbJY40U3h3Rweo4TwAG+4aRqm+1cy/f7+GM2nOTrQjxVPahUl9UF7Kx2nM0EBzmfasYVNES2lTJJSPgNMBu4HXkcrOt5FSnmplNI1rkEFvaJ6oRd6h1LdFeflsSU7m35OFFn5/aGHuOatt+geGMjKXbuI6aGlgfn7mWeYn5zMR2OuYO/St9g7KZITr4RTsOh5krdsZM0XX/D1zTczvlMnioqL+WzzZobcfz/r3VCauxQhuKhtWzakpFBY4FwGkxKOb9hAAdCpUyfX2FYFfiEhDAgNZcW+fXa1TzWnw4to2dKNVlXm399+A+BiJ2czFB7hMDAf7fodbPE6bzBZTFn3itYEcY/IHgT7VP8x9WFhtPywhgUw/MKqbeIdaOBg0/KC42gjQXaAdRHy+oay7BN7XJ+ivkpMhRZrLQ4ug1+uQugh+fZ0fh2mY9Hl1jMypGYV0OnlMlHbKDaTNqNTaTmi6jCNxk4EF63Yl0yP15bY1fanj5wX0L8O07G7mzn84aTz6WUr8u3O8g6l3S0ry6u5/QTP3lXzbC9jEiQtHIiUaXV5Ct5/jq3xuM5y+PKR5d5nbk10uq+CrADoNJbwJu3w89aDt/b3ttJ/MxGhJtY+O6xcGEcJehMY3RBuY89vMwqYiZbg/wfAuTQSCpsEegfSJaKLQyK6tMiKgxkVvhk3jgf/+IOLwsKYt2cPwY3KlpS/O2UK7by9+L+hx2D5q9DhCrjsdUTDNkQBUb3jGPTww6XtM44do2Pr1rz7+uvMfvBBh+xwhIuGD+ejfftImDmTwTVY/Lb3338B6DSg6jRGrmLkgAE8+88/JG7fTqMeVeerzEjVrojhrWs3zW/8+vU00+tpNWhQrY6rcB4p5asAQogg8/scz1rkPl7s/RT9GzmRvnPffMI75nBun53rNSrEmeadrD60rtXIVP49Lri4hfU42ap4+0Y94dkOH2aV4fo23HbpNCYvP8g/uy3TipUITT2h5ACTAPhn7UZKJI3wkswZoGNAvuT//JMwFuo4sqhsZjM1I5vpPq9xb4Ux/SPs9+gZc3LRB1Wdm/uVObv4ef1xu/rzL5B4O6+hWdxbcHPJr/f7EaXZWS7vEk1kcM2LXR2JKZvIWNNZMHiPLN0+7ZLaD5kLbpaPXwMD5KZq3/PqPM3OCM2clGqbnA2fiAxbAiRjzHFshsKSgsRs/CzeF5mfVbyMMOj3QTYz9+hNkCtr5oizhj0LC18E2gHfA+OBg0KIt4QQbVxuzQVMbHQsu9J2kVdsX0ru0iIr19tZ+UlKPhw9mgf++INRUVEsOnKknIBOWT6LTXl53NVcovcPgttnw7jfoKHtX3NYy5aM69GDRWfOkJ3o/JNldQy+7z4AVv3+e4362btZW7zZ6fLLa2yTPVxirv636qefqm2bnpaGAELcWASmItJgIP70aYY1b273dKbC8wghugoh/gN2A7uFEFuEEM7XUa7DBDpbrEnoie5pX2aIvR07YcyxM6jUTJOB59B7SyJMJq7JdvwZptBHkNjQvr85abQu7PykJsgKjSZGT15dQUAD6M0vWOdblnFp+cGM0p9LLBASvPxM+IaW90hP//MP+un2MTTPsfU6lhzo25ekt96yuk9KSctnF9gtoAHGL3N+MSFYiSXfp8Vff317X964upowAylh/z9aaUAL1p4uSxH37F1ePGf2Nk++Ss+Nz3nx6i06No4ppHNhIStOnKrUbWhrx75/jtBkYHrZm/WfV3+AM7eC5a8S0yejyiYpX07l3LpkJzovT+Kzz1F45Cj/7P6bQ+mHyNNr3wcv869kc7L1JA16E6QXZ1rdVxPsWpIqpZRAkvllABoAs4QQNvO5CCF+EEKkCCF22dgvzDmnDwkhdgghejth/3lDXEwcBml/XPTGhAQihaClHR5EaTLxbL9+PLVoETc0b87fR47gXxIblZsG8x5n1TO3AnDxHePhwTXQ5mK77Ljm9tspAhZNdl/q8IY9etDD15fFGzbUqJ+9hw8TpdMR3sL9CyIBet10E0HAyiXVT1GmnjtHQyHQebm2sENV7J4zh1QpGXbJJbU2psIlfAM8IaVsIaVsgVZV1r2LE2qZkoWFOmfu6PMeg5XmhYajqveQARwcZP+Mnm9oMSHNyzxa92e4/sZcQt6JuyhMG1Fp+847d5aK6MT0qgWuDhOBorD0vUFW9obaOsvv+2gLwiYnp7Lt6Amrbew5x+m/TGVvx06YcsvEYkpWAS/OtioPylGYWrZeo0G25OKdNVtQqK+owVc6kJbuwD/w+02w5qNymx9cVvVM7ETvFF7NOcv0M8lEGis/BHj51uzBwBarb84r73i2o4KgUw6V/6bRoF3t1eQ7Mno0hY8+zzVzr6HY/HWubnbC2wheXjWfaahItSJaCPGYEGIL8B6wFugmpXwI6ANUlUn9J2BkFftHoXm426HFXE+x0+bzkp5RPfESXnbni9548iT9oqKqLbJiLCri/s6deTchgQe6dOH3Q4fwCQwEQxGs/wIm94atvxB/xp9AIejz7Jegt1/IDbrvPiKBuS7KnmGLK3v1Yk16OmkHnc/zuD85mY5B7kvHVxEvf38uiopihR02J6enE21lsYo7+Xf6dAAuqSrdkaIuEiil/LfkjZQyHqh6vryeURpd4egNvSgPtvxU+raiZ7WmBEYX0GJEWrltTQ1GbshyUWxGBYx5rTHkVC4Q8dfWU3afm5e8ppZ7b8D+kIKSEXRg8yjfUAPegfad5/19+lJ07BgnzuYR99Zyft1oXZhbUpR2KdIs/L/+vAZxHGYKK+YbS9wGx9exO213pewplcgxe1IzNLvzDfl8ssG6l70EbynpVVhU5eOgNLvH/+4yhD/bDq3aBjvZ3lLwWasQdvt482OoxVqC3Go+o3NLVWud9ubyUgbzF9Oriq/GxALtQbSLg/m07cEeT3Q4cK2U8nIp5UwpZTGAlNIE2Cz7JqVcBVS1vOAq4BepsQEIE0LYqvl03hPgHUDXiK4kJFcfF33u6FH2FRXRv5oiK0U5Odzarh3f7d/PC4MGMWXHDvTe3nBgCUwZAIufh6Z94eH1xKflMjgmBm8HhZw+OJhLIiOJP3QIWV2FhBpw9YQJmIA5bzhXXlVKyb7sbDo0qt2v2LC4OPYXF5O0bVuV7VJyc4n2d3Lq2kni166lpV5v12yGok5xxJyZo6Ri4YtoGTvOG0o1tKM39DmPuNyWEgq8vWl+sRbGUZGnz2XUqG9TceXKbaaicJDeyKKy9Ht5J+4ie//LPDFjOwYrHk1r3OW1uNz7piKtUhubZ9nO0z/qkvftawgcHjmKNYcq21CRvBPj8T0yGoCcfa8jTDW/v9z4nBdGveCnsPLnO3vPbG5ecHPl7CkVKbnHGTTP/hf/fcH3+6sOM0w4drLSNlPFzCjmt1IIvus6lqcGP1zpGEd5c5ymLm9u0oiPwi2yclhkJbGKMxq61UVOHOQaDHrNYB+D7e9Hn8lalqwmSbWYnaMEKeUrUkqrAUtSysp5ROynCWD57Tpl3nbBEhsTy+603dXGRa80Z8MYes01Ntvknz3Lta1bM/3ECd4dNYo31qxBnD0I066D327QGt0yA277k5QzBew2GBjWp49Tdg/t14/TxcUc2eK+Qpa9xo2jnbc30+bOder45P37OSslXTt0cLFlVTPsBu1cr6wmFWBSXl6tlvw2FRezKjGRoa1bO5XSSOFR7karUvgX8CcQAdiokuQYQoiRQoj95jC7Z63sHy+ESBVCbDO/Kq45cyk6R76bBZmw+69Km9tcUfM4TIDnLn2AUYVvW93nLyW+Jue9XCYLoVxG5c9uzO0ApoByu6vKF91FHKu0baL3H5Ub1oCxhW9QrPdmSrer7T7GW5i4+tAqfIy2Rc3AAwVMnT6XzmePMuOBQUx/1z4v9O9DdXx4TWVp8+UV5bdlWiSHNiTYFw2VZyziiagIUnfPxGAy8Nu+36o9psSDP7TwI2ijhaZkRZQXfOkm8wyp2aQWFw+Exavssskad/6vitmGvOo80c48rGiGt7y05sV19L5GGvdPr76hmao80UJKlqeNB6O2097iRo5QewGYNUAIcT9ayAfR0dHEx8fXyrgZGRkYjcZaG88n3wejNPLTsp/o7N/ZZrtFf/+NL5DfsmU523JycoiPj6cwLY03xo9nbW4uk0aMYOD/HubUt7fS5PRCjHp/jrW5m9NNRiPPeMOZlWw1i/JGHTs69Vkju3WD+fP59fXXueh//6u2fYmdjnJFly58sm0bf3/+OQ0cLHW+7w/txuEXE+PQ2M7aWoKxUSOCgUXz5xNtFtTWSDIYCPL2dnosR+1MXLmSNClp3759rX2/S6jpOXUlGRkZADbtqUu2WjBCSlmuUoYQ4ga0LEpOY66G+AVwKZpTI0EIMVdKuadC0+lSygk1Gas6SmKin5q5nc/DL6F38yry2ybvhqRd8Pf9Vnf7BBt5bZyutEiFsxzVNaJQ+vJ40cN84vNlpf2Fdla9q0hPXRNWn7qV4A6vaf2kXI5v1GKM+TX3KS3wfd6udjV5jN4ptYxCc9sM5qGds+06JnzTKh7YNZcHds1l1NUfWG3TM/UQAC2ykuiwYzVn7Oj33+6C+XGCYi8BaL/vex7T0+mkZFOH8r+fv4KDuCvTsTCchZn7WBoYQIEQ7J45nGKT7YeA3gUFfJ+oxYsnyQYclzFw+1+QeZqipZdTlj0FZGsvOGjg6YnXMijHl2HDNIeW3LObfT16QrH9HtTlPUSl4j+V2DsfOlkPJKguRNQqUjvX/g2d9/QGN80n+5Q/xkI9IS3yObOh+pzW3gZZKoy9rUQUTX/HyFm+K31vtLeqjgN4UkSfBiyzZTY1b6uElPIbtMU09O3bVw4bNsztxgGEhYWRkZFBbY3Xz9CPr3//msKoQob1sT3mc4mJxIaEcOkVV5TbHh8fT/dmzRg1ZgxbcnOZ+vBD3HpnL/j3USjIgD534nXJi7QNjKCtxXH/PPcc3sAtL7+MtxPlsOWAAUS+/TaH9u3jZTvOVXx8vFPntPnkyXxy0UXsmz2b5yY4dg/fY174OPqRR2javbvdxzlrqyWDIyPZmJTETzb6yUlJIRdo16aN02M5aufnU7QlCLdOnEiLIUOcGtNZXHFOXUWY2ftvy566ZKsFz1FZMFvb5ihxwCEp5REAIcQfaGF3FUV07SEFK/enVi2ipwystptdLXWUiCrn0W7As02DiTXs51av5eX2vp+SxtNREdYOrJJRKTtZbSorHmMqDgOgITmlN8SC5CvQ+Zb3qJdGBNQgyqHaQx3UHO/2uYWJW6r3znobyhTPotlPsT6mC6/1G19uVszbqLXp2qIhZyZWmhSpxAMT9OUK35SQHSDY1KHy9o/CG3BrZjY+2P/NKDlfqwP8oaDqZNjfJ6aUCiyBpFGoOTFbaBPyi8OAMo9wpy4R0GArxETAoTJhL3Q6Ou3cwZmJE8mcU/0s7JFo+Hq0HTHv028tTe1XGQd/6bv+gmOrS99GdssidWfl8KSqCGxUQMOuBrLNiUvsnYBqmlZW9VNX4cv8+yd6tDwYZZxvnui5wATzhbofkCmldF+etHqAv5c/3SO6V5kvOi81lS05OfyvX79K+zIPHmTYqFHsLyjgzwdv4aou/8HCX6HlEBj5DsRY997uPniQtn5+TgloAOHry0XR0aw84t6wzNZDhjA4LIzvV65kosHgUCaLbTt20ECno0k35yslOcvFvXuzaPFikvfvJ9pKOEnyXi0qKroW47VXr11LM72eFg7mGVd4DiHEKGA00EQIYZkOJ4SKdwvnsBZiV/lCA9cJIS4CDgD/k1JWCvqs6exhfqGWR1YKwYnjx/j339PMOlBMv0Z6moeUFwnDHOrZeaTFnf0Fwz1MMtzJQb87SreNzM2j68nTjGrmuAfZx6KSmrfQfh6q20mYfg5fGq+i+FzlB92qwjj0GPnXp3wRywcm6PEphmUW2kmaBZOr/HPxzXrbJaJPpyRhGcAyIGk3i+Y8zfWjXyfXR1sb4mPSvtLh+/+rtr/PxuoqCegn79FXElYVWR3gz/C8/HLnctXyJZj01tcGJSXbFxr0cXJqOXEV4g0v9NGV/h3oK6i59LwiGgBbtySQo29a+e9l1CiCsnMIXLHC5phHYuCVW20L6D+DArnOIpVjxTGizf+vW7eWYn87Khua6bP59XKVniK65Dgsohv1zaCwUHvI8G2g/e2nPXUvER98V9VhdD8qSx8mLbOu3LjRG31+5aw12zr4klfFtciZmUe3iWghxO9o17cIIcQp4BXAG0BK+RWwEO2GcAjIw0UxffWdvjF9+X7n9+QU5RDkUzmTxIrJkykGRlx7bbnt+5cvZ8JDD5FhNLLg5u6MiJ4PRS3gxqnQaazNRztpNLLh3DnGtG9fI7uH9u/Pn3PmcGzDBlr2d6I4gp08ctttjPv8c/55/31GP/ec3cdtOXWKPuHhHsmHPGj0aFi8mA2//cZVr75aaX/Szp0ARDtRfdIZjMXF/HvmDJe5Oh5aSlZ/+ilff/opgX5+fLpuHX72lJlV2MsZYDNwJWC5ACEbrZpsbTAP+F1KWSiEeAD4GaiUI7Gms4eb/9JEhgD+PlTMczdexF2LlxN/2sTTl7fmml5NCfU2gskA8fb1+fVIHQ/847w3uqIeaxYRChVSRDc1OJ49Qgo44Hcn72eGsdJ/Ekc5DGjey2e8pzPNOJwsyu4FA9s0ZN1hy7jWykqxtUikua58fGqpyLQU0eZNtq4CzlweDoU2oW2m1UnlUlq2amV1+6yFL3HnZc+TEhBOVJ4WF9tzf9WL7VNCYXXXMlF6U4ebCDkXwrd2ZH18PDqSnUdPYLI4AxflzIWrrOdTTlkyBxJ3VtvviAp5tf19vRlzWVna2M1++nLfnQZ3z4QNU+g94n6yVq2yOvslhw4lb+NGToy3LpVevF2Pwcv2L2xSZEPG5uRS8ngwbPBA8Cp7WChZ4DZw8CBEcFSl461yMgHiKzvPQlvlknnU/oRB3oEmDEXmB7omPVh12908svY5fvCDoCrqo9waX/b3rDP/2OW4ietXWE/7uPDKKO6v4lrkzMyjG5zbGlLKcVLKRlJKbyllUynl91LKr8wCGnNWjkeklG2klN2klNYzZF9gxMXEYZRGtqZstbp/zdKleAOD7i1bz7N99myGXHYZRUYjq64JZES3NBj+CjyyCTpfWeWV8PDSpaRJyYAaCt+h48YBEP/DDzXqpzque/NNooTgBwdKjRelp7OzsJDetbyosITe48bhDaxfutTq/pQzWrRfdC0VWtn+11+kSsmo4cOrb2wH0mRixRtvcEloKBf973/MOXaMb/bt40uVOs+lSCm3Syl/BtpKKX+2eP0lpbR/JY5tqg2xk1KelVKWJB3+Di3VqcspS8SgXbvi3tJCJ3KLjEyat4dn/9qhhXG8bb/Xd3M75x4Yi4V1797Ue/vBvZU9g3dm2lfkpSJPn8vghtwDvO6tXUNLbs5NRRoNArS8bHcOaMEnN/c079E+T0UJHUg+S33tK3temgXFjsxKS4zarzpp8BvEX7/NZrsnLnqUw6GNq+zr9422i6v8vOQtBp/eTlihffHKr96ix5CjOSC+HvE1L/Z/kXZ+9jskTnnpy4dzJG6ronX1D2HfJZq91d5lIToVK2KG+VSQXiGN4bLXoYp4ZCEEgf3703rRwkr7Yl59lXeHf2TlqPJ81iCs7M0HLnDafF85hzlAozjHc6fLWK2omtD7kJCxH4A3b7I/HePlW0wM/8/EK7/Z/h31ben6mVe3iWiFc/SI7IG38GLxhm/Zt/TLSq9VO7fRy9+XE1v+YN/SL/nrmTsYds01+EkTq2/2p9cdd8CjW2DIE+DtV+146//SVrMPuPrqGtnd9brriNHpWLCw8h+4K/EOCeHGLl1YcOIEuWfsWWqiFRUpBvp4KJWbX2QkvYKC2LDLemGBsye0nKMRNZwNsJf4X38FYJi5EqSzSIOBBRMnMig4mOEvvcT+vDw+vuEGks+do6efHwtWr66+E4XDlKQZdQMJQDshRCshhA9wM1rYXSkV0pBeSZkDy6UUFJs9ujZiFjLzi+HcYXcMXYmZ7TUPolGU3S6v692UJmH+0LQP9Ly1XPu4fMdKC0sLL+idGV+WxniWyIfnRnXiyh6aKO3ZPIyo4Kqv6w972Z+z35Fw6geK/weP7SBm+AQaBNq2oVjvxYSLn7C5H+ChNT9Xuf+FhKk0yqs65hggr0UUR9OeoeDMjdzf/X76N9acQToHpM2oZk3Kn4eknfBZHzBaiZCy8UBlSb8C8zOmr0WQgywv7KTR+XzXvq1a0d5cfXdJL0HhD0/T4KYbuazlZdUe+1NYSNljQEGG1TZ2z9busL0EQwjwtfN+1nrRQi0+u/PVpdv05vN8uLHg5+H2/S7bJlHlTJMID2Ni3ES7+nIEJaLrGH5efowJ7EaTosp/rHlp6WzOK6R/C+0+9t+fK7jj/ak01OmY/2AHCkfdA1d/CcExdo+3YfVqgoHOo0fXyG6dlxdXd+nCwtOnyTlRfQL9mnD1+PEUAKu+qzpeqoQt5oqBvcfYTGvudvq3b09CdjYGi4pdJZxN0VZwN6ilcI74zZtp6+VFk759nTo+Pz2d78aPp0dgIGPee48zRUVMGT+ewxkZPD5jBgENGjCobVs2padjLCpysfUKdyGlNAATgMVo4niGlHK3EOI1IcSV5mb/J4TYLYTYDvwfMN5N1pj/t35DLx/OUAXdboAxHztlwQe9b+aBS55iaqeRjLr6A0y6smvyy2Mssidd/WVp6jKAHoU1+86XhBaUeIcHt2vIvUNac0OfpozopEWuju3R2OqZ8aWIR7wcTwNanWyaZhiORAcNWoAQdG9SFvP650PVL+x0F70XrkAWN0Qag3m016PoREkYkGOzDuf0Fe63Zw/B6w3hcPmZhkXZh+zv9K5F0PV67WcXimgAfVAgEY8G4t0vhx4tepZu33BL9VV99/pYVJs5tNxKCzserUwm+MtGdssmfSGyE82++br6ftAeCgBM0nxOhCj9PQIsiHONTBVGibeuYqWdmqNEdB3klpD+XBIZR8dLHy73OrO3kGLgqnsncGhdKnd9/SctfXxYvWkTPtf8H7n+ji1MkyYT/x46RFxkJHoXVMu744knyAN+fPLJGvdVEWN+Pofmz+f3Rx7hy4+0aau8LPumTbf+9x+hQtDGg0VF+l90EXnAjlmzKu1LPnuWACAwNNTtdkgpWZOUxFAnQkdOJSTw/KBBNGvYkPt+/hmdTsePDz/MwZwcHvzxR/wsqkH2HzSIHGDP/PkutF4hhNALIaznBHMBUsqFUsr25jC7N83bXpZSzjX//JyUsouUsoeU8mIp5T532OFvNJfnNVY/m1YlvW6DvncDUGxeAaS34+9sRruLWd68LydCKjskHhzahtCACjfj28vyU4c6mC96dE75B+sSCVNycxZAs/AA3r+hB8F+2rif3NSzrECHxbGrfB+3a8wUGYbJwstfneQsoPz9QViInD4tGuDn7ZiUONKxmoIfdhD17ERERfFrxlKELb5uMXOurto7f3MTG46nqddAxkktBzmwOfuo/QY2bAMDS7JIlRemgQMH2N+PDSJN8HBGZrnfRaB3II/1fqzK425uYqETpl1buYE9RdNeq2Kty33L4ZENeMdU78yLePgh0vLTOJV9iql7tMqaWcXZ5BaX/5tIccWt0eCKtdeVUSK6HrFiwQL8gdOHDnHNpEl0Cwhg5Y4dNHKySMrWv/9mr8HA9Zde6hL7+t9xB0OCg3n377/JtnMVcwn5GRkcXLqUNVOm8Mejj/LmZZdxd/v2jAgPp523N/4BAbQbO5ZbvvySJWfO8GSfPlz1+ut29b3lxAl6hYU5l//SRQy8+WYA1s+bV2lfano6UXp9rSx6PLx2LelS0j/WzpuYyUTCxx8zrlkzWsbF8e66dVwUE0P8Rx/xX24u47/4Am9f30qH9bvuOgA2urkc/IWGlNIInPcpVVrkaQubRLH1lHGxwk7tbirz+OX5CVr89ittli+r/jBh+1rxv0ttzBh1s50H3hafJqcSXkF0l1hcejX4sfIsoV4n0Jtb6MxieKRuE9Eiw65xry58jVuLy3JIV1cKXF8pFrj8tWrzi47dQ0zeNfMINrhlHA2rWHNhGc7ROKgxrUNbMzHWuan8tV/3pdv0wXT72YnMTiXhHxV0aaM33iB8+hSn7CnD3GmF+8a93aqvf3SgyvNfjYhO3F5t/yW0ria8M2tkPy6ecTGj/hrFiuOaV7zYShGbN262PzbaFkFuSlVaL4qtKDTeXb2au5csYdxNN2EAsoqKeHD4cJpERRHqVYhXsD8HN57DPzwcv+Bg/P398Q8IwC8gAP+AAPyDg4ns0IHA8HAAvnnrLXyBm59+2iX2CZ2Od955hyGPPMJdffrw83//ERgZSVFODqe3bOHE7t0c37WLnVu2MKeoiJNpaRw/d44TBQWkWPHeNNLpaBEQQO/GjbmuSRPadetGn8suo8uoUXgHBFixoDLFOTlsz8tjgoPFWVxN87g4Gul0rN+8mYqFic/l5NDQihB1B5vNMfB9Ro2qsp0xI4N5Tz/Nh9OmsaaggBAheKxPHx79+GNa2pFXuu3w4YQLwYYNG3BrSbsLk/+EEHPR8kKXumyklJXL9dVTqopp1WNkpu9r9nVUwasW0Ls3AO3WruHgINvPInppe7rd18vGDf3ab2Fn1am6P0tK5YS3Fw1MRnKEjovzKmcR6F+gxVSPyjFXri3KgdUfwpDyM3wDvBsCpwks1lLCfeXzSZVjW5JMA1qQXCqXTspIwLbjI6xCNomKwi3I14unL+/A+4v322dADR0aUdXcs6yFc9zW+TbeTXjX7jGyhSBXp+PBGDszVVg1pORzlv8e6nx80DetYTEdaTvkae24tQz63fbM63VNG7HzqDns8sx/0LiXfWPmnYOvbZf43trrHXpbvPdtbT0LC2iidvT6yncHWbEkOpAUXjMHU/DIkTR6+60a9WELJaLrEd4BAXS9+moWLl/OjLffZunGjexISWHR6dNld9IV1ZfeDgNC9XqOG43c1bIlYT17uszGgQ8/zHsLF/LUggXMiYoiRAjSpbSyghyaeXvTPCSEnk2b0qJxY5q1a0dM8+Y07tqV1v37E2jHdFB17FuwgEKgtxvT7tmDEIIBMTGsO1059VNaXh7h/v61YseWNWvwBbpca2UaD8g9eZKfHnmETxYs4JDJRAsfHz6+/Xbu+eQTgs0PX/YgdDr6NWxIgpvj4y9Q/NAqNVimlpNoZcDPC6ryhQ3SWV+gW4k+46H1MKu7vBo2pPH773PGhhjT2TOlXZFqZpJ2HD2hyR3r2bdKaV1sKBM4JSx/rZKI9tfrKUYTjHoci7E1mj3PZZ+yCts7jWXsyA8YE1S1mHxoaBu++PcQeUWaLVUWXqnC018d4ffcjc7iennHgBZEBJV3QtRkVq8Y+CU0mE/Ca5CesyQ9bUkcvazsJBIO5GKuEiufNcQnhMFNBrPm9Bqbh2XodISZTPDTWHj+VNmOqr7779kWxbS6iKzQTpU2t125kkNDh1ba3ujtt2BumSAv0c7ZRTmV2gLMGKzjxjWOp6j0ad2aJh9/5LaZXiWi6yHNBg/myQULKLmkSinZ8sfbnN1/hDZdR5KfnExBbi75+fnkFxRQUFBAfkEBebm5pCQlcSo5mXOZmdzfoQNPzqxpkbPKPDl/Pv0+/5xFf/xBRmYmkWFhNG/XjuZt2tCiZ0+O5udz2fXXu3xca2xZvBiAPhWqO3qCgXFx/DV7NskbNxJtUSwnqaCADlE18HY4wM4jR+gSEIBPhcI6Z7Zu5fMHHuCrzZtJB/qFhvLW009zzcSJeDlQ1MaS1lFRbDhr5wIwhd1IKc/7nPqyihv5Lz72eRMNIz/AS2/7uxs6dgyhY8ewt2PlG/+WqJqlw1xx4hSXNG9abluNb+G7Z0PTvhCq9avXidISLQ/rHQubemhYG36KLyj3sHJt4SSe9f6dYCqklrtpmhWhUPnT6HSCd6/rzqO/a8VR4pv1Jr5Zb1pkJfLVig/LtQ0JdH7mLeqJ8pk/XrvKtbOMvVvVMNXo47vAx5wjWdgW0a7L0W+9n08u/oS+02wvHt/u68PQ/AIoqphK0Mbf3vyqM65w5eewvXLMuHd0FF7R0RgqhHh6VaghUFJ/JsmYDlbCi9Z2Ftxo+5nAKs1/+J7Age5d+KpE9HmAEIKgiDAMXq1pW0vitDoGT5jAYBuluU87WBGoJmzdvJkgoN0llepB1DoDrroKZs9m/YwZXG0hotONRsJDHKvw5CwZBQU0tPDiJMyaxSfPPsuMw4cxAde0aMETr7/OwNtvr/FYUdHRpO/ZQ1FWFj619PkuBIQQTYHPgJL52tXAY1LKU7aPqp+IGtS03nEmi94tGlJsNJF79BGk0b4QsNFXvYesgacUINJoYsmJ0xgEjHaigqFVZt4JwY3hSS2rYKCvFwVAOFnc7L3K4e7y8WOOcSBwECFhq2zPq8V38AFflDW608bCYBsCcGyPxqUiuoTjIY0YdfUHBBTn0z9xD0HF+TzbyMuy5ovdtPj9N5uLCT1NtD6Q9y+bAmEWqdZLzpOVh8Kae0atx0SX4Kv3ZeMtG+n3m7WiozAhJqpsxuOchfi19gC7Zy5s/r5qc3S2fy+tZs3k4JDyYSAfbi7/YLW/ieZtXtrL+udJbOjY+Wr+008E9rf+2V2JWlioOK/ZcuwYvUJCHCoR7i56X3stPsC6NWWP08bCQrKBsFrIzAHQKDiY/86d46tbbmFQeDhxN9zAvMOHmdC9Owf//ZdZx465READRDfWctum7rczTlJhLz+i5W9ubH7NM287b3AmmsKSP41DuHbKBk6czSOnwICpoBmyuKHVtoVXlnc8WBPQP98dx6Wdoyttr8TTR+C50zBgAo2MRpoZjHyXmMwraS6akcm2yI2fp/XZW+dA2rVqSJFh5Te0+v/27ju+rfJ6/PjnSPLelkcS24kdZwEJmZDEYQQIuxBmoAXK/EGhQNsUWmjLKKWUDWU2FCjjS9mU1ZSdMDIgjIQAYdghkySOnTixY8dDen5/XNl4SLYkS5Zkn/frpZcl3aur4+ur66PnPs95fI1/8J3QfP2XI7w+Xx+XxDtDJzPnht+S84tfBBVf8kQ/++72oeFN1vWACfmTmJjXOb7WKSG77q9Ay/D55ns7yXHdf3FckeCpuvJxN5OkrXgKnun5f8I7m5ayo8X7VyNHbi5Dbr21bVry7/PhkS8f6biSCM/tb2NHau/3S/Gzz/RJAg2aRKt+zNXUxPLaWiYNHx7pUABITE9nUkoKS779tu25nZ4JYzL6aHrsS+bOpdYYLnzySbbU1vL3ww5jQ3k5d6xYwfAQj17OK7JaZCrb/b4qJHKNMf8yxrR4bo8AuZEOKpR8JdH59DwBB8BlzRcAcNVLX/Dqyk1dlrvchqte/IL/fr6J42zTqHN0X0rvwFG5zDt9Mt/9tfsBuaQ4ISG1w+X7qbsbOam2a334XtuxPuiXDs6wfl/TqR73Vnp/HkqM894iuW9xNqtvOIr9RuYQX1jAmM9XUPra//zebvHTT/U6tmSHf1cjAjG20TO5SryXbbclz2Hoj2u6b4lulZmQ6XPZ6UM8444W39V+wx1X+s8FPYbiAn714Z+5a8tdPtfJ+MnRrBoqnHaZnT+cGdzVhF+db+fyc7p/bcGdd5I0LohKKkHSJFr1W2sWL6Ye2DuEAyd7q6ykhGU1NTR5Trw7Pf3E0vuou8PBl1/O5tWr+Xr+fL7dvZtLX3+d9NLSsLxXjqclusrLYErVK9UicrqnZrRdRE7HGmjYb9htPyYG02xf8UTcX7HjYpSt5x4rlzRdbE0MArz77VauerHjQMSNNQ2U/mE+jy9dyy///SkAFx3su7Z9TqrVWmezCXF2P/9lTg2upTWcFqzdwOvrrc/i6VOHMe+MycGndgF0Rdi3JJtvrj+CZ34xHVu7v6vExxNfXMyYVV+RcdKJ3W7Dlp5O0vjxwUbbZsGcBSz56RKOKPbeWh6ombvq+d227UzOncAlEy8J6LV90RINkJHQ/VXO2s5/yyAuAzV5tlHVUuV1eUNLAz/7788AaI4TXPbgfvdNTmFtfvevTT/i8KC2HSxNolW/9ZWn7/We/tZE7gPTp02jEVj+2msA7NxgJQUZfTSwECCzpITRRx6JLcx9C52eCV2qN/S7rrqRdg4wB9gMbAJOImwzB0bGuELrH/9025c8FX89M+xfMse+kGscj/X42rfck3wuK7nyv8y48Z0uz29N9t0Ce/nhQQwyzBrG1pwwXU7e9HlQL8txuxnSYlXOsNmEw/ca1FYSoSg7mT8fuxdPn+9vFSP/kqDnLyzjmQum+y4LiNU3eMj11zP6k4+x53asC17y0ksMuvZaRrzT9W8WjOS4ZFLjU7n5gJt5/5T3e729OyqryPjpczxy1OMMSx/me8WwVIbwryW6Jz8psho7lu9lXT2pe/cGuHcaNNT4vY33kqwrG+4u9cQtK7auYGXVyl7F6c2Dh0U+hY18BEqFyVcffQTAHr2c0jyUph9rzZ681DPpys6tWwECKh8XK5x7WFUPNIkOuUJjzLHGmFxjTJ4x5jiglyUFoktqvDWG4cb4Hwcz/S3uIUbYfvD1EgDOaLqCBnx3zQi0ke2N3xzAKfsEt2td9tB3HQBgXs912v1VkGntqySHnTPLipk63Hu/8S56SNxunzOem0/am8nD/O8eYktJYdT77zPqw6WkHXooRf98gMTRo8g69RTsqSl+b8cfItJjC61f2wGrC4/vN+o2htDofjtD07o/flunPL/naDtzrnTQsuIp2LoKlvsoT+hFVTdfkqD7uu/BKh8Eb0yOfAob+QiUCpNV33zDEJuNjOLiSIfSpmDWLLKAr5cvB2D7Jqu/Zma+H4OWYky2pyW6apt//ViV3+7287mYlRwf3EDg9917hzSOkXmpPa/kUy9HR3bn+x+rcfRmEOaEokzPvdC2lJ4wqZA5U4p6XtELe0YGhXffRaofkzr1hohw+8zbg379Yz9sxv7zl3qYqKTnPtEtvc3Ceqgkc9MBN3HvIffywKEP+Fzn9eSktmSw7XBy+z9N9s5uJs8xxnDuG+f6va3uXDT+IgBOv8zOVT+3c/fBP572ip99hsL77wvJ+wQi8iULlAqT6ro6BiV2P2CozyUlMSQ+nk2e5Hnz6tUADN47tP/8o0FcYiIZIlRpreiQEJHpQBmQKyLti7am462waixrDLwA2gmN14Y0hKt/smdoWgsn/Rx2bITTnoUtX3Q745vfHj0GSOtxtTZn/AceP77L060DCwP+NcM0cUVfmz54etCvndjYBMN8zwrYgY/9deNJNjbkCG8HE4CfAwvT4tM4oLD7Y+6y/FyyXFZXn7YOGQEk0fdlZfpcNuHxCX5vpzt5yXkcU3oM9624j9MmnMPcKdYpcJVneV8OJmxPW6JVv1Xf1ERSFNYUTbPZ2NFinaC2VFkDMfKipIJIqOU5HFTtCKYirPIiHkjFavxIa3fbidUvuv/YGnhZxE/NqJCGcM5+3czOFoih0+GMF6w6uoPHe285PPlR62fOKPj92tC8b3ul3uvk/9iK3T+S4kClxvfmSgP0uN966M7x6UgblVl9t+/vPeRen8taowimJdqXY188Fre3iWaCYaAwrZBXjnuFSydd2vZ02pFH4MiNXHEibYlW/VZiXByVDd3PsdtYU0PlV19RtX4929avZ3tlJbVVVbQ0NeFyudi0aRMrsrNxud24WlqwiZCWmUlicjIJiYkkp6SQnpdHQmoqCcnJJKWlkTZ4MHHJyTgSEqxbYiKu5mY2LF/Owocf5uPdu7lwtDVYqaa6miQgsR/2iQbITUyksrbzjFgqGMaYd4F3ReQRY8xaEUn1PO99nlwVtFOC7Irgl0s+gbs6dQFI90zIUr8NkjJhzuM91ub1uzH4mL/7XDQtMZ8kt5sz03o3Q2Mse+6Y5zjplSC+g866FrqZEdPSTRLtWRZ8lY7ABxZ21yLd2je6LZxeJtHzVszj+x1dZzAMVLIjmfqWegrTrJk6izOKOywvvOOOXr9Hb2gSrfqtWWVlzH3pJU4oKCA7K4smY2jatYttdXVsqK1lU3MzNb2d1SEI01JSuObJJwGoqasjw2brN5dHO8tJSWFtTU2kw+hv0kTkMyAbQESqgDONMV90/zLlr7+dEMpLw50+29nD4aKlsH0tPHmK9VyDZ9xAvadE2J7Hwm++gjv27N1bF0yGSWd2eOr25pNo7QuUY0/ko7UboPjHqhhZJx/P9mf/07v3jSGjswP/AmEXO+z3mwBe4fv8buvl7JiBXkVYMGcBBz1zkM/l7tbtuVug0b8GkBNz9+H5rcsoire+fC5cv5B7lt8TUFy+pMancuP+N3qZyCY6aBKt+q1LnnqK76dO5YUvvkA2bSLeGOIdDtITEtgjJ4dDnE7ycnPJLywkNy8PZ2EhWYMGkVZQgCMxEXtcHJ989hnTZ8zAHh+P3eHA5XJRt3kzjXV17K6ro37nTuq2bKGxvp7dDQ3U19aya/t2mpubaWlupqWlhZaWFkSEgqIixsyaxcQTTkA8AzGqd+7EGRcX4T0VPjlpaXxSWRnpMPqbB4C5xpgFACIy0/NcWQRjCikTQGKw68Br+HT5Z7A7NO999N6DO9QzDou8PazbyMMho9B7q19GAcx5DJ75efDvc9z9Xb6g3+U6gbmd12u3zqC/3MCOF5/H3Txwent+dNpHnPXaWXxV/VW36y1cu4GZwwr933B33TlaW6J724AS4OtzknK6Xb7TZiPP5YLKVfDPQ/zaZsmwA2HrMjLtmRhjuOSdwGpmd6fF3cJBQ30n/ZGmSbTqtxyJidy1YgW+51DqWer27ThHjuzwXObQ0FUTq6qvJzchIWTbizY5GRlUud0Yt7vti4PqtZTWBBrAGLNQREJbAyzSAsgLUg6ay/QD3PBH/2e/687vgqkL7YW09gW1dTMu47RnrJ/fvOZ9+fCZvQsip4d+4j6uxB1/2F+Jc7tY0cPmHYmu4OKKMkmOJMZkj/GaRL84+0WSqsoZ8t0C3Gf8FR6fwGl7nObnlqXDD2+CLv/Wi/7sp4w+hae/edrrsuMLB1PQ3ML8b+ZjS/BjErCfPoW7eXPbwz8t+lPA8XS2p3PPtr/F9TOu7/X2wkn/qykVQTubmkiPj490GGGTM2gQjUDdunWRDqU/WS0iV4lIsef2J2B1pIMKLd+JQVNC19rDDruND35/ELP2CKxUZF5aAm/N7dhP1OHvrIQ9aEui/blc76s1MTEDrvU+MDchq9laJdNH39U/bAqglbLjevVxSexI6H7Q3cjjN1N6dP+5yjQq68cvHM5EJ88d8xwrz1xJaWYpQ0YcDkfeiM1mZ8XPV3DZlMsC3HrXv0NrC3Rft0QD/HHqH7tdvjHOwUupKXDuGyxLTOCL+Hiava2YmAGjj8RlrC9TKxtW8nLFywHH09mY7DEAPPWTp9i/MLylDntLW6KViqDa5mbSUvpXI2J7OZ6ZGKvWriUtiup1x7hzgD8DL3gev+95rn9Lyoaz5xP/yNFeFxdmJfPgmVP4ZO12fvnEp2ze6bt/xxPnTWXGiB8va//j9EnwonXfEbKuHAEk0T2NzTjoj7Dgr9b94QfB6gWkF+4m8egtxKf5aA2O92eyF++D09Liobap+1c6EkJUdSFK/GzMz5iUN4k9nHt0u15AfZj9SHCD7xMd/HgefxL3mrJfQuZQzhnc8YvpmTt28pttNVZNzcsrAHC5Q3tF4sp9r+TAwgPZy7lXSLcbDtoSrVQE1ba0kJocppnNosAQT+m+DStDP+XrQGWM2W6MudQYM8lz+5UxZnuk4wq7I260+hG3Jpy/WgFXbuyy2uRhWSy58mDe/533fpRnlRV3SKABjhg7uO2+MyU0V4akNc7uunO08aybNtjHck/SUzAZjp/X9qzPBNrLfvH+tq2JWMek6tYDkvn82sP820Y/ISI9JtBBbNXnEuPZ90FX5/Dxt/PXB6d+0O3yFtxg69rO+mhGOhNKhjKuZCjbm63CQCErY+eR6Ejk4KHeyzJGG22JVipCWurqqDKG/H44W2Gr0mnTAKhYvpzovigX/USk2+ukxphj+yqW8POSGLQlo57kIT4NfHQ5EBGKspPJTI6jpt66EH3ufiUcPCaPfYq7LycZke4cKdYVG8b/tPv1hs+EtHyri8e13Uxb3U1XjLTEnv/tJziE9MT+O+C5z3lp+W2d6CbolmhPFwpvia4/MhIyGJ01mm+2e6/J3uxqBun+C+ABT1tdoX46pofj1oc3T3qTlVUr+W77d9y/4n4A4myxddxpEq1UhGytqMBAv06ih02YgB0o/7739UIV04H1wJPAhwy0GTLsnn+upzwBS+6FpK59oztb9sdZ1De5uO6Vr7jk4BFkJvfd+IPvS04jJ64BSg7seeXCyXD2/6BwX//fIH8cbPFyheeXy7p92fKr27cw932JzwGlLXnu+lFtbb0Nuk+0y9NLOcgkGuC8cedx+XuXe13W7G7x8yoKPPn1k0G9/6CUQQxKGcShww6lbEgZZ/zvjF7UzY4MTaKjkPn0O3ZnuPka/+aBNwb+83UDe6Q0YIwJzVS1KuyqyssByBvs6xJu7ItzOhlqs1GxNgyzsA08g4BDgZ8CPwP+CzxpjPkyolGFU/tTWYpnVrLiGdbND3F2GxlJNm6bMz70sfVgV+ow+MX7/r9gWDcVCr2d0i/8oGtr9DmvQ273FTns3vp899N/GY5eJJih4XvHtra4Bj31eGtZxB4nfPHtiJIjukmim8M6f8GkvEkdHo/IHAGEYKBlH4v0EaY6ad68GZ54h8TxJVDqX6mldbvieOCHYQD87Ya3KSt1UlaaQ9kIJ4VZ/be/bayrrrAGZWT30ym/W5WmplKxdWukw4h5xhgX8BrwmogkYCXTC0Xkz8aY0MxsEK0KJnefZPZ3rQO3err0P3RaYNuNwGRTfeWdk98h3h4llY+8JIbJccm8evyrDEoZFNw2W5PoXn5REKSta0l7j657jQUvdF83uzceOvyhDo9bu7X0fvKZvqVJdJTZesedCFB6473EFRT49ZoxwHsH1POv+Yuocjj5oLyKF5f/AMDQ7GRmjHAyvTSH6cOd5Kb135rEsaZ6yxYAnEOGRDiS8CrNy+PZ1f2sAluEeJLno7ES6GLgLqD/TS/n6lS27dy3IhNHtGhNohMzfa9z6WdBbLh3g9OiWW5ybqRD6LEld1j6sOC3PelMWHovOBKD3wbw4Wkfsu8TXbsR7ZM5mrS0AtbVhqc8aeerBN4S+VigSXQUafjyS3a89BLO/3ee3wl0q6HOZA4simPmzIkYY/h2Sx2LK6pYVF7Nqys28eRH6wEYnZ9G2QirpXrq8GwdPBJBW3+wvujk9PeW6NJStpWXU/PDD2T28y8M4SQijwFjgfnAn/v1NN/udlVpT3wIBvpEPTMuheZ62Oc878tzx1jTiQfKeC9xp0LFd5/oXjvseph1zY9jBYKU5Ejy2hp9eenJFIw6kneefKdX2291VMlRzP9+frdxzBo6K+hBipGiSXSUMMZQedPN2LOycJ5/fq+2JSKMHpTG6EFpnD2jhBaXmy9/2MmiiiqWVFTz5Efr+NeiNdgExhVmMsPT/WNKcRaJcf4NJFC9V+np4pDb35Po0aPh9depWLSIySefHOlwYtnpwC7gV8Cl7foOCmCMMX5MLxYjUvKAH6z6yONOinQ0kZeQBof/tevzKbmwayv8vwVdlwVEk+iwCseXFJsNbKG5svzEUU/ws/k/6/CcO2tYyCplXD39ak4edXJbEj1tcNduRzaxccdBd4Tk/fqSJtFRom7BAuo/+oj8q/6EPS0tpNt22G2ML8pkfFEmF80cQWOLi8/W1bC4vIrFFdU88N5q7ltYQbzdxqRhmZSV5jBjhJO9CzOJC1G5J9XVlspKskWIT+8/uY83pVOnAlCxdKkm0b1gjBlQH8ah69Ziu+IRSP3vj0/OmQMXXQT19XDUUV1fdNZZ1q2qCk7yknxfeCGccgqsXw9nnNHl/bZle8rfffMNXHBB19f/6U8waxYsXw6//nXX5TfcAGVlsHgxE379a8jM7Lj8zjthwgR46y243st0xvPmwejR8MorcNttXZc//jgUFcHTT8P994MrH9zZsNCzL557DnJy4JFHrFt7a3bBaZ4xMvfdB888A9vXwI5d8NJ9kPEyLFwIQNHTT8O113Z8fVIS/M8ztfpf/gKP7LL6aC+caT3ndMLzz1v3r7wSlizp+PrCQvi//7Pu//rX1j5sb9QoeOAB6/7558O333ZcPmGCtf8ATj8dNmzouHz6dPjb3wDY6+qru169OOQQuOoq6/6RR0JDQ8flP/kJXOaZiXDmTLoI5thzNcP6XWBvgqFP+zz2APjtb+GYY0hat877+wdw7PGHP3Rd3s2xNxZD8RGNrBmcwIGf7eTM16oY9sAlJMQl8/BmqyvelecXssUZz+Ef7uCUd6q7bH7uxUOpSXMw+/3tzP7gx7L1o7JHk/Gve2H+0dx24G18dPVZnLziVbih0+/oOfa49VZ49dWOyzofe2+/3XF5IMdeiGkSHQVMczOVt9xKfEkJWXPmhP39Ehx2pg13Mm24k7lAXWMLy9Zsa0uq73jrW25/E1Li7exbkt02SHGPQenYQjabl6rZsYMsh6PfX0odvt9+AFSsWhXhSJTybVO28N4EW2xN/WiP6/Xl/KDl7ennrIgDWWz08/VeVs70utzcXjl7kez48RhJjWutX95//ueJibHRuVOmTDEff/xxn7zXzJkzqampYXnnb8whtu2JJ9jyl+spvO8+0g72PsuWPxYuXMhMb99gA7R9VxNLV1ezqMJKqldv3QVAVnIc00utQYozSp2U5KQEVY4mVHH2hXDGelxeHt/X1bGivr7X24rqfWoM+XY7x4waxYNffx1VsbbGsbC1FaSTcMQqIp8YY6aEdKNRLJhzdvUlB1P55iZGPXk99oknhimyjsY9Og6AlWeGZnbNaDrOAascXmImXNGu3OQbV8Hiu2DWtbDfb9qejrrYAxQ18ddugdtGWd2TLv/Or5dEKvbW47/VY0c+xsS8iV2e9yUvKY/Khsq2xy/OfpHSzNIO6yzauIhfvPULyoaUMe/QeZ03EXG+9n1352xtiY4wV20tVffcS/LUqaQeNDPS4QCQlRLPkeMGc+Q4q37xph0NLKmoZlF5NYsrqpi/cjMAg9IT2wYplpU6GZKZFMmwY86u+npSkgbAPhOhJCWFNZWVPa+rVAf9p8Uq4v64xctVr/5bnSM6xFYjZau9c/ZmfK5VW/3ssWczf/V8ttRv6fY1Dx3+EMe8eAwAi3+6mLT40HZLjVZhTaJF5Ajg74AdeNAYc2On5WcBtwAbPU/dY4x5MJwxRZvqefNw1dSQ//vfRW2R8cEZSZwwqZATJhVijGFNdT2LK6pYXF7Nwm+28sKn1p+vJCeF6aVOZpTmMG14Ns5ULafXnXpga10dL1xzDXaHg5TkZOITEohPTCQpPZ3UnBxsdjtis7X9bH+//XP169axrbwcm92OIzGRuKwsHHFx2Gy2qDiushIT2da5D6JSvrRdIR1Q3cDDK85LKbSMIutnWv+d8CkqRME52F+vHPcKxRnFbY/nTp7L3Mlzu6y3tX4r1y29jo11Gzl7r7Mpzihm/9T9GVk80mcCPTFvIiMyR/CrSb8KV/h9LmxJtIjYgXuxZtjaACwTkZeNMZ2rdz9tjLk4XHFEs6YNG9n26GNkzJ5N4p57Rjocv4gIJTkplOSkcNrUYbjdhm+21LKo3Kr88fLyH/j3h1ZdyT0Gp1NW6mTGCCf7FGeTpuX0OphYUsK9X3zBidddF9b3SQJSRXAAdhEcNpt1EyHNbifRbifB4SA+Pp6UuDhS4uNJSE4mMSGBlMREMtPSyM7LI2fIEHKHDiW9sJDk/HxSBg0iJTub5ORkbD2UIHMbQ4vbHdbfU/WeHw0fCcBjwGSgGjjFGLMm5IFo6bW+sc//sxLp0UdGOpL+Kca6ywIdEuju5CbncvfBd3d4bo5zDjMnz/T5muS4ZP4zu3+VtQ9nS/S+QLkxZjWAiDwFzAbCNwVOjNl6++1gt5P769j9VmazCXsMTmePwemct/9wml1uVm7c0TZI8fGla3nog++x24TxhRmUleaQUudiWrNrwJfTu+vTTznv2WehqQl3czP19fU0NTbS1NhI/c6d7Nq5E7fbjTEG43a33Xe73V0eb6msJMfpxO1209LUREtDAy0tLbS4XOxqbGRXUxMutxuXy0XL7t20uFw0u1zUNjez2+WivrmZ7bt3U+9yUedy0eh202gMdYDLj98lSYRkm40Uu926xcWR7HCQIkJDczMLd+7kkr33DvcuVb3gZ8PHucB2Y8wIETkVuAk4JXwxaUt0WNlsMMZLpQkVYvplsL8KZxJdAKxv93gDMNXLeieKyAHAt8BvjDHrO68gIucD5wPk5+f7HAQUajU1NbhcrrC8n+P773HOn0/dUUey6Ouv4euve73Nurq6Pts3PRlrg7Ej4azhiZTXuPmq2sWq6p3ct7AGt4HbP3mNkVk29si2s4fTTkm6DXsUVv4I+z7tNPmIDUj03LID2ExxXR2pqak9rxgg43bTuGMH9Rs3smvTJnZu3kzT9u001tbSWFdHU309Dbt301hfT0NLCw0tLdS7XDQ0NdHQ3MxmY7DbbJxbWsrR11zDwoULo+o4rampAXwPLIymWPuAPw0fs4FrPfefA+4RETEhH6GuLdFKqegX6YGFrwBPGmMaReQC4FHg4M4rGWMeAB4Aa6R3X41czczMpKamJuQjZY0xrH3gnzTl5DDpur9gT00JyXajZkRyJ4e1u1+7u5mHXn6XnUlDWFxRxfPf1cJ3zaQmOJhakk3ZCGuQ4uj8tKgopxet+7SzWIkToivWTE8dX1/xRFOsfcCfho+2dYwxLSKyA3ACVaENRQe8qf5Avwz2d+FMojcCRe0eF/LjAEIAjDHtK3Y/CNwcxniiRu0bb9Lw6acMuu7PIUugY0VaYhwT8hzMnGn1Aa+ua2TJ6moWV1SzuLyKt7+2Kjg4U+KZ5hmkWFbqZJgzOSoGyCmletbbq4el9dYg1JVffsn2bX17jgzVlYdYvooRy7FD9MSfsLuK6UBjYxNL/Iwn0rH35r0jHXtvBRN/OJPoZcBIESnBSp5PBTrMKykig40xmzwPjwX6/WwMpqmJyttuI2HkSDJP7Jv6p9HMmZrAT/Yewk/2tro1bKxpYLFnkOKiiir++7l1eBRkJlmVPzwl9fLTvYw0V0r1Ro8NH+3W2SAiDiADa4BhB729elj9TCJ11DFu3FhsYwN7bdAetX6E6spDLF/FiOXYIYri37ERlkJCQoLf8UQs9hAc/1Gz34MUTPxhS6I9l/ouBl7HGun9sDHmSxG5DvjYGPMycKmIHAu0ANuAs8IVT7TY9u9/07xuHUX//CdiH9gD67wpyEzi5ClFnDylCGMMq6t2tQ1SfGvVFp77xJrqtTQ3pa0+9fRSJ5nJ8RGOXKmY12PDB/AycCawBDgJeCf0/aGJ1fK6SnWiB3J/F9Y+0caY+cD8Ts9d3e7+lcCV4Ywhmrhqaqi6/x+k7LcfqfvvF+lwop6IUJqbSmluKmdML8btNny1aadVo7qimuc/3cDjS9ciAnsOTmfGiBymlzrZtziblIRId/dXKrb42fDxEPC4iJRjNXycGqZorB82bWhQMSzJMzx82oWRjUOFjWYafajq/vtx19aSd/nlkQ4lJtlswtiCDMYWZHD+AaU0u9ysWF/D4opqFpVX8ciiNTzw3mocNmFCUWbbIMWJQzNJcOg/Y6V64kfDx27g5D6IxPNTx0GoGBafDNfuiHQUKow0ie4jTWvXsu3fT5J54gkkjh4V6XD6hTi7jSnF2UwpzubSQ0bS0OTi47Xb2gYp3vPOd9z19nckxtnYpzi7rfvH2IKMqCynp5TyaMuh9XOqlIpemkT3kcrbbkfi4si99NJIh9JvJcXb2X9kLvuPzAVgR0MzH7ZW/qio4qbXrFrcaYkOpg13emZTzGFkXqpW/lAqqmhLtFJ9ad6seSzZtCTSYcQcTaL7QP0nn1D7xhvkXHoJjtzcSIczYGQkxXHYXoM4bK9BAGytbWRxhVX5Y3FFNW9+tQWAnNQEykp/TKqLspMjGbZSSluilepTZQVllBWURTqMmKNJdJgZt5stN92MIz8f59lnRzqcAS03LYHZEwqYPaEAgPXb6ttK6S2uqOblFT8AUJiVZNWnHuHENLojGbJSA1TrJBU67bdSKnppEh1mO+f/j92ff87gv/0NW1JSpMNR7RRlJ1OUncycfaxyeuWVdW2DFP/3xSae/tiavO3eL99tq/wxbbiTjKS4CEeuVD9ntDuHUir6aRIdRu7GRrbefjsJe+xBxuxjIx2O6oaIMDI/jZH5aZxZVozLbfjyhx383xsfsckk8tSydTyyeA02gbEFGdbEL6U5TCnOIjleP0ZKhVTBJGABZJdEOhKllPJJ//uH0fbHH6f5hx8YesNfEZtelowldpuwd2EmRw2PZ+bMqTS2uFi+ziqnt6Simoc/+J55764mzi5MHJrV1p96fGEm8Q79WyvVK61JdGpepCNRSimfNIkOk5Zt26j6xzxSZ84kZdq0SIejeinBYWfqcCdThzv5zaFQ39TCsjXb22ZT/Pvb33HnW9+RHG/3lNOzkuo9BqdrOT2lYsDb6zZSq59VpVQANIkOk6p77sXd0EDe73Rilf4oOd7BgaNyOXCUVW2lpr6Jpau3tc2m+Lf/WeX0MpLimD7cSdkIJ2WlOZTmpmg5PaV6EI6ZxHuS53KR5+rzt1VKxTBNosOgcfVqtj/9NFmnzCFh+PBIh6P6QGZyPEeMHcQRY61yelt27rYqf3haql/7cjMAeWmecnqe2RQLs7ScnlI+6RdOpVQU0yQ6DCpvuRVbYiI5F18c6VBUhOSnJ3LcxAKOm1iAMYZ12+rbKn+8/10VLy63yukNcyZ7alRb1T9yUhMiHLlSSiml/KFJdIjtWvohdQsWkDt3Lo7s7EiHo6KAiDDMmcIwZwo/3Xcoxhi+2VLL4nJrJsVXV2ziyY+scnpjBqW1Vf7Yd3g26YlaTk8ppZSKRppEh5Bxu9ly8004hgwm+8yfRzocFaVEhDGD0hkzKJ1z9iuhxeVm5cYdbZU//v3hOv61yCqnt3dhZtsgxcnDskiMs0c6fKWUUkqhSXRI7Xj5ZRq/WsWQW27BlqCX5ZV/HHYbE4dmMXFoFr88aAS7m118tq6mbZDivPdWc9/CCuLtNiYNy2ybTXHvwkzi7FpOTymllIoETaJDxN3QwNY77iRx3DjSjz4q0uGoGJYYZ2d6qZPppU5+C9Q1trDs+21tgxRve/NbbnsTUuLt7FuSzYwROcTtdOF2G2xaokv1B31fnEMppQKmSXSIbHvkEVq2bKHg9tt0YhUVUqkJDg4ak8dBY6yJJ7btamLpamuQ4pKKahZ8swqAO5e/yXTPIMWyUiclOVpOT8U4PX6VUlFMk+gQaNm6lap/PkjaoYeSPHlypMNR/Vx2SjxHjRvMUeMGA7BpRwMPv/oB2+LyWFxRxfyVVjm9wRmJbYMUy0Y4GZyRFMmwlVJKqX5Fk+gQ2HrX3ZimJvIu+22kQ1ED0OCMJGYUxDFz5niMMayprv+xlfrrSl74dCMAw3NSrKR6RA7ThjvJTomPcORKRY9r4n9LRm4hcyMdiFIqZmgS3Uu7v/2WmuefJ+v004gfNizS4agBTkQoyUmhJCeF06cNw+02fL25tm2Q4oufbeSJD9cBsOfg9LbKH/uUZJOaoKcDNXD9+Q9XRzoEpVSM0f+avVR5y63YUlPJveiiSIeiVBc2m7DnkHT2HJLOefsPp9nl5vMNO1jsGaT42NK1PPjB99htwvjCDGaMsCZ9mTRUy+mpCIrAtN9KKRUoTaJ7oe6DRex6/33yfv977JmZkQ5HqR7F2W1MHpbF5GFZXHLISHY3u/hk7XYWV1SxqLyaexeUc/c75SQ4bEwpzmobpDiuIAOHltNTfU0HFiqlopgm0UEyLheVN99MXFERWaf9LNLhKBWUxDg7M0bkMGNEDpcfDjt3N/PR6m0srrBmU7zl9W8ASEtwMHV4tpVUj3AyOj9NK38opZQa0DSJDlLNCy/Q+O23FNx5J7Z4HaCl+of0xDhm7ZnPrD3zAaiqa2RJRXVbUv3WqkoAnCnxbYMUy0qdDM1O1qRaKaXUgKJJdBDcu3ax9a67SJo4kbTDD4t0OEqFTU5qAseMH8Ix44cAsGF7fdv05IvKq3j1800AFGQmUVbqpGyEVac6Pz0xkmErpZRSYadJdBCqH3oI19Yq8u++W1vf1IBSmJXMnCnJzJlShDGGiq27WOLpT/3GV1t49pMNAJTmprS1Uk8b7iQzWa/WKKWU6l80iQ5Q85YtVD/8L9KPOpKkCRMiHY5SESMijMhLZUReKmdML8blNqzatLNtkOKzH2/gsSVrEYG9hqQzo9Sq/NHYopUXVA+0OodSKgZoEh2grXf+HVwucudqSX6l2rPbhLEFGYwtyOD8A0ppanGzYkMNi8urWVRRxcOLvmfee6uxC0wqX9xW+WPC0EwSHFpOT3Wl1/mUUtFMk+gA7F61ih0vvkj2OWcTX1gY6XCUimrxDhv7FGezT3E2v5o1koYmF8vWbOOpBZ+xodnN3e98x9/f/o7EOGu9stIcZoxwsteQDOw2TZ+UUkpFN02i/WSMYctNN2PPyCDnggsiHY5SMScp3s4Bo3Jx/xDPzJn7saO+mQ+//7Hyx02vfQ1AeqKDacOdbbMpjshL1bEHSimloo4m0X6qW7iQ+qVLyf/Tn7Cnp0c6HKViXkZyHIftNYjD9hoEQGXtbqucnqf7xxtfbQEgNy3BqvxRalX+KMpOjmTYSimlFKBJtF9MczOVt9xKfHExWafMiXQ4SvVLeWmJzJ5QwOwJBQCs31bfNkhxUXk1Ly3/AYCi7KS2QYrTS53kpWk5PaWUUn1Pk2g/bH/2WZpWr6bw3nuQuLhIh6PUgFCUncwp2UM5ZZ+hGGP4rrKOxeVVLKqo5r8rN/HUsvUAjMpPbRukOHW4k4wk/YzGPq3OoZSKfppE9yDJGKruvofkffYh9eCDIx2OUgOSiDAqP41R+WmcNaMEl9vwxcYdbf2pn1q2jkcWr8EmMK4gg+meQYpThmWTFK+VP2KW9oVXSkWxsCbRInIE8HfADjxojLmx0/IE4DFgMlANnGKMWRPOmAI1u8WFa/t28n7/ex3cpFSUsNuE8UWZjC/K5MKZpTS2uPhsXY1nNsUqHnx/Nf94t4J4u42JQzPbKn+ML8okzm6LdPhRR0SygaeBYmANMMcYs93Lei5gpefhOmPMsX0Vo1JKRZuwJdEiYgfuBQ4FNgDLRORlY8xX7VY7F9hujBkhIqcCNwGnhCumQGW7XBzpcpEx+1iSxu4V6XCUUj4kOOxMG27Njsiho9jV2MKyNdvaWqrvfPtb7ngLkuPt7FuS3TZI0aC1iD2uAN42xtwoIld4Hv/ey3oNxpgJfRqZUkpFqXC2RO8LlBtjVgOIyFPAbKB9Ej0buNZz/zngHhERY6JjuqrZtXW4jZtfLl7M9pkzIx1Oj2pqasjMzIx0GD2KlTghdmKNlTghMrEWOhLZnVbE7oxhLNoxlIXfOAFwjb+AHUue6dNYotRsYKbn/qPAQrwn0UoppTzCmUQXAOvbPd4ATPW1jjGmRUR2AE6gqv1KInI+cD5Afn4+CxcuDFPI7RiDo6iQ/1Ss5vva2vC/Xwi4XC5qamoiHUaPYiVOiJ1YYyVOiGCsVZuxs4wUwJ2QTkt2CZWShau22uc5pa6urm/ON5GXb4zZ5Lm/Gcj3sV6iiHwMtAA3GmNe9LZSb8/ZCQ0NOPbem3ffew/ssdmnPZaPnViOHWI7fo09coKK3xgTlhtwElY/6NbHZwD3dFrnC6Cw3eMKIKe77U6ePNn0pQULFvTp+/VGrMQaK3EaEzuxxkqcxmiswMcmTOfd7m7AW55zbufbbKCm07rbfWyjwPNzOFbf6dKe3jfYc3YsHSfexHL8sRy7MbEdv8YeOb7i7+6cHc6W6I1AUbvHhZ7nvK2zQUQcQAbWAEOllFIhZIyZ5WuZiGwRkcHGmE0iMhio9LGNjZ6fq0VkITARq/FDKaUGnHAOU18GjBSREhGJB04FXu60zsvAmZ77JwHveLJ+pZRSfaf9ufhM4KXOK4hIlqeiEiKSA8yg4xgXpZQaUMKWRBtjWoCLgdeBVcAzxpgvReQ6EWkti/QQ4BSRcmAu1ohwpZRSfetG4FAR+Q6Y5XmMiEwRkQc96+wBfCwiK4AFWH2iNYlWSg1YYa0TbYyZD8zv9NzV7e7vBk4OZwxKKaW6Z4ypBg7x8vzHwHme+4uBcX0cmlJKRS2ddUAppZRSSqkAaRKtlFJKKaVUgDSJVkoppZRSKkCaRCullFJKKRUgibWKciKyFVjbh2+ZQ6cZFKNYrMQaK3FC7MQaK3GCxjrMGJMb4m1GrV6cs2PpOPEmluOP5dghtuPX2CPHV/w+z9kxl0T3NRH52BgzJdJx+CNWYo2VOCF2Yo2VOEFjVf6J9X0fy/HHcuwQ2/Fr7JETTPzanUMppZRSSqkAaRKtlFJKKaVUgDSJ7tkDkQ4gALESa6zECbETa6zECRqr8k+s7/tYjj+WY4fYjl9jj5yA49c+0UoppZRSSgVIW6KVUkoppZQKkCbRHiJyhIh8IyLlInKFl+UJIvK0Z/mHIlIcgTD9ifMsEdkqIss9t/MiFOfDIlIpIl/4WC4icpfn9/hcRCb1dYztYukp1pkisqPdPr26r2P0xFEkIgtE5CsR+VJEfuVlnajYr37GGvH9KiKJIvKRiKzwxPlnL+tExWd/IOnpPBfNRGSNiKz0HNMfRzqenng7/4lItoi8KSLfeX5mRTJGX3zEfq2IbGx3XjkqkjH64uscGUP73lf8Ub//fZ33RaTEc44v95zz43vcmDFmwN8AO1ABDAfigRXAnp3WuQj4h+f+qcDTURrnWcA9UbBPDwAmAV/4WH4U8D9AgGnAh1Ec60zg1SjYp4OBSZ77acC3Xv7+UbFf/Yw14vvVs59SPffjgA+BaZ3WifhnfyDd/DnPRfMNWAPkRDqOAOLtcv4Dbgau8Ny/Argp0nEGEPu1wGWRjs2P2L2eI2No3/uKP+r3v6/zPvAMcKrn+X8AF/a0LW2JtuwLlBtjVhtjmoCngNmd1pkNPOq5/xxwiIhIH8YI/sUZFYwx7wHbulllNvCYsSwFMkVkcN9E15EfsUYFY8wmY8ynnvu1wCqgoNNqUbFf/Yw14jz7qc7zMM5z6zxQJBo++wNJzJzn+gMf57/2x/yjwHF9GZO/YuXc7U0358hY2fcxcY73ppvz/sFY53jwc99rEm0pANa3e7yBrgdD2zrGmBZgB+Dsk+i8xODhLU6AEz2X8p8TkaK+CS1g/v4u0WK659LP/0Rkr0gH4+lSMBHrG3R7Ubdfu4kVomC/iohdRJYDlcCbxhif+zSCn/2BJOqO4QAZ4A0R+UREzo90MEHKN8Zs8tzfDORHMpggXOz5H/hwtHaHaK/TOTLm9r2Xc3zU7//O532sq181nnM8+Hne0SS6/3kFKDbG7I11YDzaw/qqZ59iTfs5HrgbeDGSwYhIKvA88GtjzM5IxtKTHmKNiv1qjHEZYyYAhcC+IjI2EnGofmM/Y8wk4EjglyJyQKQD6g1jXduOpTJe9wOlwARgE3BbRKPpQXfnyFjY917ij4n93/m8D4wJZjuaRFs2Au1bbAs9z3ldR0QcQAZQ3SfReYnBo0ucxphqY0yj5+GDwOQ+ii1Q/uzzqGCM2dl66ccYMx+IE5GcSMQiInFYJ6wnjDEveFklavZrT7FG0371xFADLACO6LQoGj77A0nUHMPBMMZs9PysBP6D9Q861mxp7Qbm+VkZ4Xj8ZozZ4kmQ3MA/ieL97+McGTP73lv8sbT/ocN5fzpW90eHZ5Ff5x1Noi3LgJGekZnxWIOHXu60zsvAmZ77JwHveL4l9qUe4+zU//VYrH5K0ehl4OdimQbsaHcJK6qIyKDWPrAisi/W56bPkyhPDA8Bq4wxt/tYLSr2qz+xRsN+FZFcEcn03E8CDgW+7rRaNHz2BxJ/zsdRSURSRCSt9T5wGOC16k+Ua3/Mnwm8FMFYAtLpf+DxROn+7+YcGRP73lf8sbD/fZz3V2El0yd5VvNr3zt6WmEgMMa0iMjFwOtYI8MfNsZ8KSLXAR8bY17GOlgeF5FyrIEMp0ZpnJeKyLFAiyfOs/o6TgAReRKr+kKOiGwArsHqvI8x5h/AfKxKEuVAPXB2JOIEv2I9CbhQRFqABqzRu5FIomYAZwArPX25AP4ADG0Xa7TsV39ijYb9Ohh4VETsWEn8M8aYV6Ptsz+Q+DrPRTgsf+UD//F8N3QA/zbGvBbZkLrn4/x3I/CMiJwLrAXmRC5C33zEPlNEJmB1g1gDXBCp+Hrg6xwZE/se3/H/NAb2v6/z/lfAUyJyPfAZ1rm/WzpjoVJKKaWUUgHS7hxKKaWUUkoFSJNopZRSSimlAqRJtFJKKaWUUgHSJFoppZRSSqkAaRKtlFJKKaVUgDSJVkoppZRSKkCaRKsBS0QGi8irAb7mVhE5OFwxKaVUfycimSJyUbvHQ0TkuTC913EicnU3y8eJyCPheG/V/2mdaDVgicgtwAfGGL9nhBKRYcA/jTGHhS8ypZTqv0SkGHjVGDO2D95rMXCsMaaqm3XeAs4xxqwLdzyqf9GWaNXvicg+IvK5iCR6puX9UkTGAicCr3nWOUtEXhSRN0VkjYhcLCJzReQzEVkqItkAxpi1gFNEBkXwV1JKqVh2I1AqIstF5BYRKRaRL8D/c7GIlIrIayLyiYi8LyJjOr+JiIwCGlsTaBE5WUS+EJEVIvJeu1VfQWciVUHQJFr1e8aYZcDLwPXAzcD/AbuA7caYxnarjgVOAPYB/grUG2MmAkuAn7db71OsKU+VUkoF7gqgwhgzwRhzuZfl/pyLHwAuMcZMBi4D7vOynRlY5+tWVwOHG2PGA8e2e/5jYP9e/D5qgHJEOgCl+sh1wDJgN3ApMBXY2mmdBcaYWqBWRHZgtU4ArAT2brdeJTAkvOEqpdSA1e25WERSgTLgWRFpfU2Cl+0MpuN5fhHwiIg8A7zQ7nk9p6ugaBKtBgonkArEAYlAg+dne+1bpd3tHrvp+Flpfb1SSqnQ6+lcbANqjDETethOA5DR+sAY8wsRmQocDXwiIpONMdXoOV0FSbtzqIFiHnAV8ARwE/AtUBzktkYBX4QmLKWUGnBqgbRgX2yM2Ql8LyInA4hlvJdVVwEjWh+ISKkx5kNjzNVYLdRFnkV6TldB0SRa9Xsi8nOg2Rjzb6wBLftgdeeoEJER3b6467bisE7KH4c8UKWUGgA8rb+LPIP8bglyM6cB54rICuBLYLaXdd4DJsqPfT5uEZGVnkGMi4EVnucPAv4bZBxqANMSd2rAEpHjgcnGmD8F+JpJxpirwheZUkqpUBCRvwOvGGPe8rE8AXgX2M8Y09KnwamYpy3RasAyxvwHWBPgyxzAbaGPRimlVBjcACR3s3wocIUm0CoY2hKtlFJKKaVUgLQlWimllFJKqQBpEq2UUkoppVSANIlWSimllFIqQJpEK6WUUkopFSBNopVSSimllArQ/wd+MwJL4IMdRwAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot Trajectory\n", "anim = plot_trajectory(cdpr, result, dt*N, dt, N, pdes, step=10);\n", @@ -65,11435 +52,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "daily-visiting", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Animate Trajectory\n", "import matplotlib\n", @@ -11504,23 +66,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "joint-batch", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot Controller Gains\n", "anim2 = draw_controller_anim(cdpr, controller, result, N, step=50);" @@ -11528,1257 +77,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "expired-inclusion", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Animate Controller Gains\n", "from IPython.display import HTML\n", @@ -12820,7 +122,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py index c217580b..6fa47e52 100644 --- a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py +++ b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py @@ -67,7 +67,7 @@ def xy2Pose3(traj): des_T.append(gtsam.Pose3(gtsam.Rot3(), (xy[0], 0, xy[1]))) return des_T -def main(fname='data/iros_logo_2.h', +def main(fname='data/ATL.h', debug=False, Q=np.ones(6) * 1e2, R=np.ones(1) * 1e-2, @@ -78,7 +78,7 @@ def main(fname='data/iros_logo_2.h', """Runs a simulation of the iLQR controller trying to execute a predefined trajectory. Args: - fname (str, optional): The trajectory filename. Defaults to 'data/iros_logo_2.h'. + fname (str, optional): The trajectory filename. Defaults to 'data/ATL.h'. debug (bool, optional): Whether to print debug information. Defaults to False. Q (np.ndarray, optional): Vector of weights to apply to the state objectives. The real weight matrix will be diag(Q). Defaults to np.ones(6)*1e2. diff --git a/gtdynamics/cablerobot/src/gerry03_stroke_tracking.ipynb b/gtdynamics/cablerobot/src/gerry03_stroke_tracking.ipynb index 2cfbf9bc..7a85e0d6 100644 --- a/gtdynamics/cablerobot/src/gerry03_stroke_tracking.ipynb +++ b/gtdynamics/cablerobot/src/gerry03_stroke_tracking.ipynb @@ -2,19 +2,10 @@ "cells": [ { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "08dd84c0", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", @@ -34,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "d191f422", "metadata": {}, "outputs": [], @@ -48,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "cb4afa56", "metadata": {}, "outputs": [], @@ -58,125 +49,51 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "72dcedc4", "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running per-stroke optimization...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|█████████████████████████████████████████████████████████████████████| 145/145 [03:05<00:00, 1.28s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Combining per-stroke results and running one final optimization...\n", - "Done.\n", - "Time elapsed: 363.07522892951965\n" - ] - } - ], + "outputs": [], "source": [ "cdpr, controller, result, des_poses, dt = timed(gerry03_stroke_tracking.main, fname=fname, Q=np.ones(6)*1e1, R=np.ones(1)*1e-3, speed_multiplier=1)\n" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "0151948c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time elapsed: 157.96959900856018\n" - ] - } - ], + "outputs": [], "source": [ "cdpr_2, controller_2, result_2, N, dt, pdes = timed(gerry02_traj_tracking.main, fname=fname, Q=np.ones(6)*1e1, R=np.ones(1)*1e-3, dN=1, debug=False, speed_multiplier=1)\n" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "095ea4e5", "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(2, 13602)\n", - "(2, 13601)\n" - ] - }, - { - "data": { 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\n", 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wp/V6bIexdI7t3MJS6+g0Mhkr4esrwRQON/8G7Qa2tET/JG4F5vlqEELcAdwB0LZtW9IdD8sg2V+yn0FAcUkJBk2Rl9zduzmcno7BbGZgcbHXdjk7d5KTnk5ISQn9fLRnb9tGXno6Ybm59PXRnrVlCwUxMURkZtLbR3vGpk0UmUxE791Lj+JibDYbxZp++9evp7SmhtitW+nmY/u9a9dSXlxMwqZNpPlo3/X331QdOULSli108tG+Y+VKqvfto822bXTw0b5t+XIscXGk7txJqtqulXHzkiXYw8Npv3s3KT6236h+T5327SPJo91WVcUWtT3twAESPNotdjvb1PaumZnEebRXh4SwQ23vcegQ0R7yVR4+zG61vdfhw0R6bF9+6BB71fa+R48S5tFekpnJAbW9X14eIaWlbu1FBw6QobYPKCzEWF3t1l6wbx9Zavtgz2O32dh9jF17nlRVVZGenn5MXXtaysvLSU9Pb/FrLxAOGeuNlLJV/Q0dOlQ2F+PGjZODBg1qtv01lMWLF0sppayyVMmlh5bKV/9+VZ436zzZ//P+sv/n/eW5P54rX1n1ilyStURWWapaXM7WQGuRtbXIKWUjyrrtFylfaCPle0OlLDxYryHGjRsnx40b57e9Kc4rsFYeA/dSf39AF2BrLX1OBXYASbWNV5979sebP5b9P+8vzVZznbdtTlrD7+5Yl/FYl09KXcbG4FiXT0r/MgZ7z9Yt1Mch4aZwp9sHQFZpljMt36w9s5i5cyZhxjCGpQ5zVm1Mi01rYal1dOrA6o/ht0eh4zC45juITGxpif4xCCEGAp8AE6SUBbX1bwjKs0xHR0fn2EdXqP8BdIrtxDWx13BN32uotlW7+V6/tvo1pU9MJ0a3H83YjmM5KfUkIkwRLSy1jo4PpIRFL8LSt6DXBLjsUwiNbGmp/jEIIToDs4DrpZS7a+tf7/2g56HW0dFpXegK9T+MMGMYozuMZnSH0TzGY2SVZTmV65/3/sw3u74h1BDKsNRhTit3l9gueqEFnZbHZoHZ98PGGTDkRjjvbTDqt7DGRAjxNTAeSBZCHAKeBUIApJQfAv8BkoAp6j3BKqUc1jLS6ujo6Bw76E+jfzidYjpxdZ+rubrP1VTbqlmXs87pHvLGmjd4Y80bdIju4AxsPCn1JCJDdIugTjNTXQ7f3wR7F8D4J2DcY6C/5DU6Usqra2m/DbitmcTR0dHRaTXoCrWOkzBjGKM6jGJUh1E8xmMcKjvE8uzlLMtexq/7fuXbXd8SaghlaNuhivW64xi6xnbVrdc6TUt5Hsy8HI5sgvMnw9CbWloinWZCz0Oto6PTWtAVah2/dIzpyJV9ruTKPldSY6th3dF1TveQN9e+yZtr33Rar8d0GMPw1OG69VqncSncr+SYLj0CV82E3hNaWiKdZkB/SdfR0Wlt6Aq1TlCEGkMZ2X4kI9uP5NGTHiW7PJvl2ctZmr3Uab0OMYQ4rddjO4yla5xuvdZpANnrYcblIO1w46/QaXhLS6Sjo6Ojo+MTXaHWqRcdojtwRe8ruKL3FdTYalifu55lhxTr9aS1k5i0dhLto9ozusNoxnQYw8ntTtat1zrBs/dP+PYGiEyC62dBcs+WlkhHR0dHR8cvukKt02BCjaGc3O5kTm53Mo+c9AiHyw87XUPm7p/L97u/x2QwMTRFsV6H1YQhpdSt1zq+2fg1/HoPtOmrlBKPSW1piXRaCD0PtY6OTmuhyRRqIUQn4EsgFbAD06SUkz36CGAycC5QCdwkpVzfVDLpNA/to9s7rdcWm0WxXqsK9lvr3gLgsx8/c7NeR4VEtbDUOi2OlLDsHVj4PHQdB1dOh/DYlpZKpwXQ81DrHM/sKtxFt/huhBhCWloUnUakKS3UVuBhKeV6IUQMsE4IsUBKuV3TZwLQU/0bAUxV/9c5TggxhjCi3QhGtBvBw8Me5kj5ET5d/Cm5Ubn8tv83ftj9AyaDiSEpQ5zBjT3ie+jW638adhvMfxxWT4P+l8FFU8EU2tJS6bQ05bkQlaIX79E5bsgszeSy2ZdxXd/reGz4YwCU1ZRx2a+X8db4t+if3L+FJdSpL02mUEspjwBH1M9lQogdQAdAq1BfCHyp1kpfJYSIF0K0U7fVOQ5pF92O0TGjGT9+PBabhQ25G1iWvYyl2Ut5e93bvL3ubVKjUpWqjR3GMqLdCKJDo1tabJ2mxGKGn+6A7b/AyHvgzBfBYGhpqXSOBd4bDB2GwW1/trQkOjqNQqG5EIDNeZud6zbkbuBwxWGmbJzClDOmtJRoOg2kWXyohRBdgBOBvz2aOgBZmuVD6jpdof4HEGIMYXi74QxvN5yHhj1ETkWO0zVk/sH5/LjnR0zCxIltT3Rar3vG99St18cTVUXwzbWQsRzOehlG3dPSEukcaxxa09ISHL9krITqMuh1VktLoqPT6mlyhVoIEQ38CDwgpSz1bPaxiVcUihDiDuAOgLZt25Kent7YYvqkuLgYm83WbPtrKOXl5a1C1kByJpPMReIiJrabyIHqA2yv2s72gu28k/MO76x7h3hjPH0j+nJCxAn0Du9NhCGixWQ9lmgtcoJL1jBzPgM3P09E1WF29n2Y3Jr+0MzHUFxcDOD33LWm83o8oftQNxOfnaP8/1xJy8qhA+iFjFo7TapQCyFCUJTpGVLKWT66HAI6aZY7Aoc9O0kppwHTAIYNGybHjx/f+ML6ID4+nuLiYpprfw0lPT29VchaHzlzKnJYcXgFy7KXsfLwSlaWr8QkTAxOGey0XvdK6NXo1uvj+Zy2FOnp6Yw/oS1M/zdYS+H6WZzQbRwntIAs8fHxAH7PXWs6r/8YVk6B35+A/xSCwdjS0ujo1Im6PKN2FOxgX8k+Jnab2IQS6TQWTZnlQwD/A3ZIKd/20+1X4B4hxDcowYgluv+0ji9So1K5pOclXNLzEix2C5tyNzndQ95d/y7vrn+XlMgUp3J9cruTiQmNaWmxdXwQV7wNPr0BTBFwyzxIHdDSIum0JhY+r/xvq4EmnqHS0WlOPGdmrphzBYCuULcSmtJCPRq4HtgihNiornsS6AwgpfwQ+A0lZd5elLR5NzehPDrHCSGGEIalDmNY6jAeGPoAuZW5zqqNCw4uYNaeWZiEiUEpg5xVG5vCeq1TD7b/yqBNz0JiF7juR0hIa2mJdHR0dHR0GkxTZvlYhm8faW0fCdzdVDLo/DNIiUzh4p4Xc3HPi7HYLWzO2+y0Xk9eP5nJ6yfTJqKNM+/1yPYjiQ3V8xs3O6s/ht8epSy2F3G3zofIxJaWSOcYJ6BHqbboS1UxrP8SRt0L+ouzTitA95c+/tArJeocV4QYQhjadihD2w7l/iH3k1eZ51SuF2Ys5Oe9P2MURga1GeR0D+mT2Ee3XjclUsLCF2DZ29BrApva3swpujKtE4A6/x5/ewS2fK+4D3U/tWmEOg7ZFhrCN7ExPC/tGISeqrKl0ZXsxmPGjhn0S+rH4JTBzbZPXaHWOa5pE9nGab222q1u1uv3NrzHexveIzkimdHtRzOm4xhGthtJXFhcS4t9/GCzwK/3waaZMORGOO9t7EuXtbRUOscbZjVLhbW6ZeWoJ5U1Vib9vptHz+5NRGjzBVr+OzWFQqOR+82FJEckN9t+dRrGe+vf45Mtn7D5xs21d/6H8trq1wDYcuOWZtunrlDr/GMwGUwMaTuEIW2HcN+Q+8ivyncq14uyFvHLvl8wCiMD2wx0Wq/t0t7SYrdeqsvh+xth758w/kkY93/6dLxOI+C4ho4fa95Hf+3n0+UHaBMTxl3ju7e0ODpNSGOkhPx4y8eNIEnzUzJ7Nocf/T96rliOKfH4m6XUFWqdfyzJEclc1OMiLupxEVa7lS35W1h6aCnLspfx3w3/5b8b/kuMIYZTl53K2A5jGdlet14HTXkezLwcjmyC89+DoTe2tEQ6rZAGqcy75sGWH+Cy/zWWOE2Gxaa8uNvs+gv8PwUpj58XwmApmvk1ADUHD+oKtY7O8YrJYOLElBM5MeVEp/V6efZyZm2YRXpWOr/u+xWDMDAwWbVedxxD38S+ut+hLwr3w1eXQFkOXDUTek9oaYl0jnt8WP2+vkr5vxUo1C2F+OfpdDotgK28Av4BL4u6Qq2j44PkiGQu7HEhcYfiGHPKGLbmb3W6h7y/8X3e3/g+ieGJjOkwhtHtRzOq/Sjiw+NbWuyWJ3s9zLgcpB1unA2dTmppiXSON3S3IR2dY4KCzz/HXl5Bm3vck7VV79+PMTYWU3Iy9poadg8bRsy4ce6ZeY5DdIVaR6cWTAalIuPglMHcc+I9FFQVsOLwCpZmL+WvQ385rdf9k/s7816fkHTCP896vedP+O4GiEyC62dBcs+WlkhHp1WhZxs6/mkMH+pjhdzXXgfwUqj3n3seIiyMPps2Is1mAML//hv69Gl2GZuTf9gTX0en4SRFJHF+9/N545Q3+OuKv5h+7nTuGHgHdrudqRuncvXcqzn1u1N5YukTzN0/l2JzcUuL3PRsnAlfXwmJ3eC2BboyrdMoBJ2HOrgtdHSOa6pt1Qz4YgCz981uaVGQ1YEz7hRUFTDgiwF8v/v7ZpKo6dEt1Do6DcBoUHJaD2oziLsH302huZDl2cud7iFz9s9BIBiQPMCZOaRfcr/jx3otpZJfeuEL0HUcXDkdwvWiOa0VIcSnwEQgV0rZ30e7ACajVLitBG6SUq5vdDmCsuLVQ3nO3QkxqRARX/dtj2P+iQFyLU1T5JwuqCoA4L8b/sv53c9v9PEbjPM6E2SVZQHw896fubzX5S0nUyOiK9Q6Oo1IYngi53c/n/O7n4/NbmNbwTancj1101SmbJpCQlgCozqMcvpfJ4QntLTY9cNug3mPwZqPof9lcNFUMIW2tFQ6DeNz4H3gSz/tE4Ce6t8IYKr6f+tgygho0xfuXtXSkhwTHD/OB62H1ujWI61WdvYfQNunniLx+uvqN0grPO66oivUOjpNhNGg5LQe2GYg/x78bwrNhaw4vIJl2ctYkb2CufvnIhBO3+sxHcbQL6kfRkPzFXaoNxYzzLoddvwKI++BM18Ew3Fidf8HI6VcIoToEqDLhcCXUjFprhJCxAsh2kkpjzSPhBrqa1XN29G4cujoHOfYVT/ovHfeqb9C3YxYi4paZL+6Qq2j00wkhicysdtEJnabiM1uY3vBdqf1+sNNHzJ101Tiw+IZ1V61XncYTWL4MZirs6oIvr4GMlfA2a/AyLtr30bneKEDkKVZPqSuc1OohRB3AHcAtG3blvT09DrtZG/pXgCkACRu24+12TACS5ctxWaKAmBAQQFJwJYtWyg4EgHAeLW/dltf6xpCeXl5o4yVkVEDwP79+0kXhxo8npZgZFyxYgVxppbJsd9Y57ApaUwZM6ozACgrK3OOua1qGwCFBYU+9+Nv39r15eXlrFqlzLyYzeZGPaeiqooUwGq3u43b1o982vWispIUFLeiA7klpAIbNm5gd55isS4tKW1UWUN37iTh3cmceLmBDT0MdRq7od+zrlDr6LQARoORAW0GMKDNAO4afBdF5iKX9frwCn478BsCQb+kfozpqFiv+yf1b3nrdckhmH4ZFOyFS/8HAy5rWXl0mhtf87ZepmIp5TRgGsCwYcPk+PHj67STrO1ZsMa17Lb9ciPYYeyYMRCuKoGHp0IhDBgwAHqrfdN9bOtrXQNIT09vlLH+Nu+EA/vo1q0b48f3aLhgGgLJ+Pw+5f+Ro0aSEpnSqPsNlsY6h01JY8q4NX8rzIXomGjnmIZDBlgICUkJ7vv5QvnPa98+1qenp9NrWC/4EcLCwxr1nNrKytgNmIxGt3Edcz2e+9Kut5WUsBuwSUGRWZIKnHjiiUR1EDAPYuNiG1XW/J07yQN6Z0s29Kjbb72h37OuUOvoHAMkhCdwXrfzOK/bedilne0F21mavZTl2cuZtnkaH276kPiweEa2H8nYDmMZ1X4USRFJzSvk0e0w/VKoLoPrfoRu45p3/zrHAoeATprljsDhFpGkmQPpzBYb1RY7cZEhzbrf5uB4SuX2T6bJv8d/gB90Q9AVah2dYwxHTuv+yf25a9BdFJuLWXlkpdM9ZN6BeQD0S+rH6A6jGdthLHbZxFWoDi6Hb64GUwTcMg9SBzTt/nSOVX4F7hFCfIMSjFjSIv7TAEjIWAFRbRptxJ05pVTW2BjS2TtQ+KIPlrMzp4yDr53XaPs7VmiKjBM6deeYfbFppJdXcZxfZ7pCrdMo2EtKWDtpEulTp3LZ44/T7ZFHWlqk44b48HgmdJ3AhK4TsEs7Owp3sGzfYpYdXcknWz5h2uZpRBoiOeWvU5y+18kRyY0nwPZf4MfbIb6zYplOSGu8sXWOKYQQX6O4GicLIQ4BzwIhAFLKD4HfUFLm7UVJm3dzU8rjfPyW58G0cXDtD+4dPlPL2vc6R92gYQ/sc95dCuBTad6ZUxZw2/155XRNjmpVWRyOdwXnWMShNDdlqkJfY0sp+XL7l1zQ/YL6Z5aqz7WtyuLvaI/Zl4h6oCvUOvWmPCODBe+8w5yffmJuZiZH1fXWl17iyToo1NJq5Ytbb+XrOXPIra4mOiSElMhI2sbHk9KmDSkdOpDUpg2JXbuSkJpKYtu2JHTpQkxKCqaIiGNqGspSU0NFTg6VxcVUFBZScfQo5QUFVJSUUJGfT0VZGRUVFZQXF1NRXk6F2UxFVRWVFRWYLRaqamqoqq7GbLdTJSVmKYkLCaFTbCxjR41CWix8PG8eWysqiBeC/rExdGmfjD21mj2Df+OPE37DHmKgb2JfpWpjx7EMSB6AyVDPn/rf02De/0HHk+CabyHyGAyS1Gk0pJRX19IugSaPQvV6yO6eB6XZsPIDrTBNLUbQrD1YyGUfruTFi/pz/cm+XzjNFhvhIb5jIFr6UNwUsCObITpFydet0/i00ONqU94mJq2dxJqcNbx/+vvNL4Cf5/TxNDuiK9Q6QSOlZOfChSz46CN+W7yYxQUF1ABxQjChZ0/OufBCbpo0iaJeveo07nNnnMELf/1F37AwukdFUW61sjM/n7+OHKFg+/aA24YAEUCkwUCEwUCk0UhESAiRRiORJhOhBgNSCKQQ2E0mJGCXEruUSPV/7WcpJXbAbrcjgZqaGoxC+N4GMNvtVNpsVNrtVALWOp7TcCBKCCINBsKNRiIMBsJDQogwGkkUgnAhKLZY+Dojg68zlOjwMODhk04iv7ycLZmZfLdjP5U7gMUQDwyLCieu/WHW9Eln7ohYyrumMCS2N4PtCYyOCDLgSUqSM/8g+VA69D5XCUAMjazj0enoNBIOl6b6Wsj8bbdrPhzeAKc+UX/ZgP35FQBszCz2qVDP3XyEu2euZ/4DY+mTeowXPvpoLBjD4JnclpbkuMRpofahSEokJdUlrM1Zy+lpp7u3qS899Z0BsdgtAJTVBJ5p8Ukjv/E15yzOwY/fp+qtD+izY3uT71dXqHUCknPwIAs//JAFc+bw565dZFsVlbFXSAj3DB/O+TffzOibbyYkLAyAD6ZO5e9du4IeP3/XLl7/6y+u7tSJGQcPIjxyGVsrKsjbuZPCzEyK9u6lKD+fwoICivLzKS8vp7KqiqrKSiorK6lUrbuV1dVUWq0UVldTbbcjpMQAGGw2BGBA+UEbAINQbm8GITD6WGeTktCwMAwGg7KNEM7PQgjCTSYiIyOJDA8nKiyMyPBwIuPiiIiMJDoqiqikJKLi44mKjSU6NZWohARluV07otq0wWiq/Sf4Zloaf5W5boL9IyJ4ZcUKDOq2douFWW+9ReXBgyxbsYJl+/fz555C2AMhs/MYYjxAQdJ6tvRLZtDEK4ju163WnNFGSzlxR/6mqO1wEq74Coz6rULnWEbzoNwwHZKDfKn/+krl/wYq1LU9phfuVObvtmaXHpMKtZeiYQtcNlqn/vhy+dDOyDyw+AHWHl3L4isWu2136x+3siZnDVtu3OK2fm3OWh756xEea/NYE0rtELTxFdKmdvmoePsDDAB2OxibNkuW/pTUcaOirIwFb7zBq1OmsLqw0K2tgxDclJbGmWefTdehQxUF02Bg04wZTmUzOSSEecXFlOblEdum9mChBVOmUA089MILXso0gCkqinZDh9Ju6NDGOsQ60dIpnfK3buX/MjMBSDEYiDcaWVdVxaoZMxh1440AGEJCSD75ZMY//jg3qNsV5Oay4rvvWDZnDss2bOCH3FwsudlMXvwOfQwGxqSlMWbMGMZcfTXdzj7b57nfK+1YQ2NJ0JVpnRZDTUTt66Hrz2r2y93e/ZrCMlVTgcGm5I9uLPtdS3mv6aXHmw/Hy4tPVweJsyS3xWZxa1qTs8a7P0qZ8QJzAdk12QxmsFvb/pL9lFaXMjhlsM9tg6YB10d9ri27tHPXn3dxY78bGdV+VL33rSW/Kp/kqJQmtVLrT8p/ODabjfVz57Jg+nQW/PUXK3JzqfHTN1tKPs/I4PNp02od99XzzuPV1atr7ffpRx8BcOEttxByyy2A74eT57rjtY8nRZqbUa7dTq5dmfoefdNNtLv5ZqRmDKn581w2Ao7b8067nZ0HDvDJgQPw1VcAnBwdzRWnn87JEydy4hVXEB4bizWsZQo96OgE99AL9kHdRMriK+0ZEZoApx9smvGbAcdZrrMf6+cTISIBrvyq0WVqTmzlFZT9/jtxl1zcbG4Ivlw+tEq2Y32w8vgax8GFP18I4GXVrjcNPkfBXWfllnJWHF7B5rzNrLxmZb32dM5ayQ+jpXOXp39/Og+e9DA392+6OGpdof4Hsn/1ahZ89BELFi1iUWYmRaqSNthk4v7+/bFUVPDugQNECsFPL7yA0WRCqj7DjrdNhz+x06dY9TkuKyjg+vff57U1a3g1CFn+rFamFs/s08dtva+frefNwmefWrapa5+KigqionxH7nuN40ueYGT2F6whJe9v3uyzDeC8E05AqNtXauQUmv04lqvLCwCIiFGyf9ilZPvhwyzLz6cG2F5ezkO//AK//ELo7bdzYlwcg9rGM3BAd8KjTyRtxAifVmydYwshxEjgOmAs0A6oArYCc4HpUsqSFhSvEdA8kI8Bq2pYjVLi+NgJi24mDi5taQkaRFZhJcnRYRS9+AIlv/xKaNcuRA4Z0iz7dirPflw+6vv+15SuE/WdwfhixUEu6BJV7/015Jgia+DCVdpzDMuyl+kKtU7DKDx4kEVTp7JgzhwW7NnDAYtiq+woBBd17syZ48dz+g03kDJunNPHqM/ll/OvH37gx2+/5cNNm+qkTF3/vhJBbK2uxqT6VtfG57UEH7YULe3y8b5G2Z57332cO3kygyIi6Bofz8dbtzrbapNz54IpAPQ5899++xxet46/v/mGVQsWsGrvXr7anUHV7gzu+XEUKQYDQ5KSGNy7NyeOHs3Qiy+m2/DhrSpF2PGOEGIeSpGVX4CXgVyUuNdewKnAL0KIt6WUv7aclI2By67qja9pdAmlRyC2XVMKpdPYHFoHmStg1L3u67f8AKHR0Puceg899o3FjO2ZzMt5eQDYq6oaImmd8KUkav2qnRbqIJRJraJb2yyD53gfbfqI9ze+z8brN2I0GCn47HOiRp5MuIdxqyE8++s2NnSN4s76DtDAx0u0WaNQN8P7t65QH4dUl5ay4uOPWTBrFgs2bmRdZSUSiAFObdOGh0aM4MybbqLXhRci/ATF3fndd2QMH86ra9eSdu65PDl/ftD7n5CczLz8fKZedRX3/vRT4xzUP5wI4Pf58zkXiDEYKLPWNZ9I7bQfOpSLhw7lYnV5689vcXD5JjIzzPy9bh2bsrP5c9kyrMuWweuvEy8EQ5KSGDNgAGdccAEjbr+d0Ki6WyN0Go3rpZT5HuvKgfXq31tCiEZMUN70eD8DtZa8IJ+QGcvhywuUTDVNTG1KzbHqq3xMyvXJacr/ngr1j7cq/z8X3GRLYUUNkaFGr5SFS/d4/lSaF7diXOplHch9wx/1teJ+uPlDAGzShhEjua+/DkDfnUrh8BXZK4gOjWZgm4H1Gt9BSWXdA1wbw0Lti6YOgNQV6uOAvB07WPPNNzz+8stssdm82sOAMxMSGH7CCZhCQigrL2fW++/D+4FzUUarytFTv//O9JAQrh8zJih52sbFQX4+9/38M/fW3h2ARS+95HQp0bqXeLqaOJZ9UZsrRyD3C399Dxw4QM2SJQ0aO6j+Pra3VFY6l6uA93bvJnHIEJZXVkJlJQtfeglQzsmBAwewLl3qOk+asaSU5O1TAloOLnelwpKadnx8ztu3FoBOvYfSqXdvLgOqy8rYumYN63btYl1hIYvy81m0eDEvLF4MDz5IO4OBpbNm0f3CC9FpXjyVaSFELJp7vJSy0IfC3crQBBgGWx00R3WbytLEdJQeadQIQJ/Kz7J3oPd50KZX4H60fC7eQPtfeXgldyy4gz8v+5O2UW2bUarGYciLCxjYMY6Zg6Dk55/p8NakFpXH17n25VcdzEtOQ64bxz79Vdm980/Frrzlxi1N4l4VSPa6+pH7H8dFl6Mg2zdtRWFdoW6lZMybx73XXsuK4mIKarnYq4E5RUXMWb683vvbYbXyZHp6nbeTdntQ7iKnP/NMPaT65/Hchg3Oz2fU65zNrcc284LuecRup8dFF1G6fTsxffvWY186DUUIcSfwAsp7mDZGtVuLCdVQ3B6sPlw+Aj14feWwfrth09o2u0Rr7/RSfixm+PM5WPYuPJ7hv18T8/f+Ap5aVsnC0f6LykBg5ebbXd8CsDl/M2dGndnoMjYHmw+VkPW+Umys/aQ3W1QWX9eAU3FspMujLop2UH0b7bqtW6ClAd+6g7TbKfziSxKuuBxDkLOir35h4zdrHtTfU6hWdIW6lXLf7bfzZ1ERV3fvzqf79gFwa48e/HfTJgyNnGuxOCODsQMGkFFTw5LPPmPw1QGLqQEw/dZbuW3GDNb+/DMnXXKJ337jTCaWW638+dFHztzOzsA67bJwTdY4lrV43qS8lj32688q67m8Y+dO+vTuHfRY9Vr2M97XU6fyyebNXNylC6bISL7fvp22BgNH1SDSJR9/7Nxm165d9FF937TnycGRbUpO0/b9T3Ou17b7stZnb12IADoMOMO9r4/thBBkrV/PBW+9BcD6b79l3HPPodMiPAL0a9XWaI+UYU7cp158tPtfl19eTWP5u1hsdgLdZf/ckcMZgK2m0l3x9tPf5UPbSAKqPPvrNrLLJdnbl9M92grdT/PZr0kU/f1/QU0F9Dm38cf2Q3FlDXM2H+HSuEoOXnwJnf73SbPt2xdSSqrWrydiyBCvdHn+FNm6uCXU5Xsz/bKQ796x8u5Lym9rwH47wmL3OYa9qorvXrUyY7wBbgx6F/6E9Lk60HE6rOb+LNRlCxeS+/rrWLIySf3Pf4IWJflIZe2dGoCuULdS9h49ypmpqZRVKBW6XjvnHEY89hgRkY1fza5tr14sWr6ckSefzEW33srK7t3pMnZswG0uvv9+/jVjBj9OmRJQoa42GDitfXvG3XFHY4vdKNSkpzO6hYIS53/2GaHAN5s3ExIdzd9hYQyNi+OnfEVPGnvbbc6+tvR0xgQMSjQD0OfM4COcYxaUq9tcH1T/xdOnOz8PuvbaoPej0+jsA5r2ydHUbP8F8KOACh8W6kBKiPpwziltPIXaWyT3/e/KKeMMwC4FRjTKQy36T2PH+Drk6v7T+cqKIP2OG4UvL2iyfVYLGPbFAB4Z9gg39nNpfI98v4k/d+RyYsfDGIDyhYsA9+wdjfnusP1wKYeKKjmrn+8y7SW//MKRx5+g/ZtvEnf+RGX/JYe8+vl0+QjCclxb/zPX28lMUcY2zVaMKnFFSmLcx79WfJvt//F2g7CVlgIwYa3a5vRpVjDv2MGBi13P9fxpH5N47TVBW4sBZHU1lyy3c2CifzcMf0q3NCvPM1tJaeB9NHPMvJ4Hq5VSaLXya04O3+fkMOmyy3hsXvDT8vWh47BhzP/pJ8xScs7pp1NQSzXExJNO4rToaH5cvjzgW3SF3U5kSEhji3tcsHr3bk6MiiI0JgYhBGf37cuf+ceu0fEh9Rr89LLLiO/Zs4Wl+UfzBLBCCPGREOI9x19LC1UXREWBnwbnP8H7UDv7Nd/T1Tlzoyo5NTZFhhfnKtmMyswWXp67nWqrEvNyLPtQOwhkUTxUdohRM0eRWZrZmGL5pUwoqsunWz91W19QoSiLVnsg/9zG49z3lnLHV+v8ttccPKj8n6Welz1/Yv/6Kq9+bgq1j9Ps7xkqkQxfdITHvvOOnQK4/Xc7L37lu82BHV+/Iw8hPPZf5uH+mff22+S+/Y7P8V0juc8Km2b8ylVL7AxZ6f1M82ehrsnKwlZe4S1fANyU3Ca+BegW6taIzUaOZvG7337j26gorDYbRoPBZ4EPLaK2/x0uAT7ai6Wk2GIhuU8fBoeGEhkS4necpar1fNnUqYz9t+90bVusVrZkZHB7t24+Za3VPaKJ+1eZzXwcHu6zf0PHduB0b9F8ttvt/FVcrLT7MVtdl5bmDNg0V1fzUViYewCnpq/FXI4QEBL2qtuYgb57i7kMAYSEv17rNodLXFaoG79q3cUejgM+AhYBW8Dn0/LYx1/chT+XD59Wa4+NGtH869dGoK43eOyq2qIoNWVmJTvPewv38PHSA3ROjOT6kV0aLM/U9H3klVXzn/NPqN8ADdQy5+yfQ5mljF/2/cK9JwYbiu5Nja2GQ2WH6BbfSO7+Pr5zu93HTyKAAt44YqhyHN1KIK1OSukzWFAi+fQdK6t7CXcXDAmnzVYqKy4DpNVG30xJQR1CBBzB/z7l9vvQ8nFey8v97UAjrmY7s2IhN1m8vw+HPJ4+1PvOPIuwPn1IuvVWr7GDoonfW3WFuhUiNSnTzm7TxvljtdTUEBYaCrgraI5l8Fa2PDM91Lb+zKgoFqi5OzfW1DAuOhqTqsR7btfTYmFPTQ2n3H030o9C7eC3rCznZ68MGXVdri3jRh2WpZQYAmTkaKis0vNPzWKyz+LHh1TDqiNHXGNKiTAYXEq5x/7sduWBLgyV3t+V43+P79plJSivdZtM9ZpcMmkSBs0LiE6LYJVSPtTSQjQIIdwfflqfauePqG4+1DKAIlNXPC263vegwMsOC2q1Vf2NeYyQV1bNin35XDi4Q1DyvD5/J4CXQu33iDP/Bqsr93LAjAulh5UP1WX+BXAoqQ1UWJ5d8Sxz9s9h2VXLiNOsv2buNewp2sOa63yX3/aLj+tBqzxacpWsR/lTphA9NrgsVnXbv/q/5vlbYPR+WdT6V186t5jYPBv2SzQKtZREm+G0zZ5GGvdl80ef8fwMG2/d7q/esQ8RpfQ+T8LtP2+F2+fLqT+lXHUXkb67eo60s3AnYcYw341A9c6dtbxAu2On+VwxdIW6FVKl+je9NmECj/32m3N9cxYhmXrFFfz7++/pmZrKtC1b/GbycNwo7FYrBj85rwGyg1AgW4KWKuxybrt2zMvJYfott3Dt/1z5c9uFhpJjsfDlDTdw/RdfBC1nMIVd6rvNqmnTGHXnnTw4YgRjH3446PF1mozFQog7gNkoSX4AkFIWtpxIdUW5nzgfl3O17wf1dfkIQHkuZK+jqzhCBLXnzfU0aHrPAnoq3O6agcGpYPke/9Yv1rD5UAlje7YhMSq0Vnn84VPvKc+FT89SPndqX/sgRQeUU16a7beLzFTLQ+ftqLOMWtbkKApzpaXSTaHeku+7dHa11caNz77Dfy8/C/PCPyFkWK37sBa4fgbWXMU4VKXJntSoOL9g5YuYWbqD11JTADhQcoBr5l7Dh2d+6LbJ+FUV6qa1+1N7pryz7c8AIKY8sJuH2xg+JrG8so54Xu++3FJqtfJLzXbS58VZUFXA5bMvd8nRCC/BzenXrPtQt0IqcxSHj8jY2BaT4a5vv+WpESP4ZPt2nj/jDL/9RqoyTr34Yq82WxMUJzlemKd+xxe/6l7AfXg7pdqbrbruyfKbAmtVFf9+4AHaGQw89/PPLS2OjsI1qH7UwDr1b22LSlRHhMFPDo2N010K8obpvvt4Esy08Bfnw9dXsTjsYX4LezKIIf2M6TLpOT747BYuzRwMv4YTMmf4bD9crARdWW1N4LEzyTu+oa4+3FVC4GYDrVGs18JiboBgdaemupovxHMcvuYqCqZ+SIhVMcwEOpoibeaPpq70ql4HuZW5PJz+MB8Uuvtbb8nfwuivR/Pu+nfV7i7JtYquv++nXr73nsqxTwu1u3tfUFMPtfzOhJS+Z4k0m9mk+4uAQdRdRdW69DR3UKJuoW6F1KhlUm1lZVTs3ev0gbJkZlKyc6dz2W63u9wIfPxp8ZlKrZZ193/0EVvOOIPnFy8m7JxzuFNNmabt/+HHHzPoyiu5Z84c7vY4jkrVRzgCwIeC6E/mgD5fgdwz6lpcRQiornZGFLdEme3I8HAodUUyn9SzJ79mZrJ2zRpu0p4zjZy+UgLaqtQHdEWF35SBnufUUlGFwSCwV1X5TFUI8PFVV7GhqopvH3qImFTfke46zYuUsmtLy9BgynLAX8KiavX3sPQtH43+fagD/noL9gUvG/5dbg8VKvdmITzvr+79oqxFAAw4NBPwzicfMC2xuRSOboO0kbXKWdsty++Uvs++rsGGd+lEmsXCnFq30mAugQXPwun/gchEx47r7gerwaAqndJR0MzrgH2MrVG4muuOPi9jPn8k+c9IsTlPKT60Pne9c11ZjcvFxvP7uedXG/lxIK/xc+58rfZzsJIACrWfmRjp48IqrirCp4NSsDNJwI97fnRbPlJxxHdHx+/Dx7Wzp3gviUHvsXFpMoVaCPEpMBHIlVL299E+HvgFOKCumiWlfKGp5DmeqFL9vh787Tce1Lh8tCRP/v47T/7+e8A+pbt3E9vLVTWsukh5qFQBxvBwt0DK1ojw87muy1rnFxEXhy8+2L+fD+rlq1wflwzXNgJlWksAjvmFzsDlk1q2+phOYIQQqVLKnNp7HiNEtwV7PcQNmJs60N2l9jvPhYZl9DFkAef57b76oOJO4Ony4YkzLsMrR72j3WezwpcXwOEN8NhBiEioVW43Sny7bUiN0jOga2euLinDaaeXOAI13LbJqGt2po0zYd1nENsexv2fsu7jUxl/eAOcqgQ1N4vRwmcwa5PtTNlNPba8eq6r3kOVxt8d4JRttViDg1wHqttIbT7SQbz0rDy8gn6BOmjH8GMUm7JxSq37UcTz/8Zptbu7jx4vPtSfA+8DXwbos1RKObEJZTgu2bPF5U/2+iWXODND5OXl0TYlBSEEBs8CKY7/PdaBd4CZ33WegYvqckVpKU+oKdOu6NyZUSef7Na+fts2vtq+na8efpi7Z892jlejltZuazBw2ymnuMkGeMuuOQZHuxbP31bA4i0B+mrbi4qKSEhIqFuhmCDH9te+cudO0o8epTaeGTfOWcSnqKiIxETXe7mbci8EFQVZgCA6qaNznbbdczsJlOVnIiVEJnbArgZL2h2zH1Ly6l9/AfDBs8+2iPVep078DzivpYUImvjOUJgT/Av2TtVWuvIDH42N85o+OdT1sK9tql3UMtdcW9Ci4+68cOdRLhzcgegwzaP6sOrvazGr03v+8Qyo5h3fWUDkxhkw/hnmH5gPwNdxMU6FWlYVQlTd6xu8Hx+HCcm/1OW8mlLO7tKJr0oPuhQvx7F4ylPb+Q1aCu+ebr6+db1v5e9RgjPb9geTu2+7tagIWWMhpG2KDzEadn8c880YvlM/V1gqnOs9M4E4gx/rcM37dPmoZbk+rhSiFqW8vMZ3lpA9RXvomeDhpuTnhRQC+137K7PeWDSZQi2lXCKE6NJU4/+TKVPT01yaksL//eiaImmpADqAW3fuZPSgQfyZlcVzU6bQ9zz3Z/fuuDjemz+fuzTBhxVqlopJd97JdVOCezNtblrinL533XWkz5hBdno67ceNc2sLFcJpwb7kuusYrBZ3ae6gxMnnuOq3TtQrIh7zSClbjzINiLCY+m14aLX3OmeWgQAP0zq6Hfhy+bjauJCtdsXbxuHy4bBU+x/en+ua8v9TP21l2Z58pl43VFlRlOHqtHE6nPJoQDmDftFd/i6Mf4bZmzwqC0qNalbHc/RRgjK75lCol1VkYRGCrysP8pI/eYNUlaWEpFJJuUcdEc+tbcXF4BnTqU2bF+D82Ow2Bn81GIBHUx9Vdvq+JujRo2DNnpGjAOi7UxOY6SyI0ngGh7HfjMXhee+tONf95VH6Ur/9KdT1cM/xpUjXZGRimrdE7aAo9SO/drkw3dzvZj7b9hkAH23+iEnjJiF9fG/2CtfLhatJYyDy2HVuZW6d5a8LLe1DPVIIsQk4DDwipdzmq5MasX4HQNu2bUn3SCreVBQXF2Oz2Zptf8FycP9+AKxWq5ts5eXlLSrrK2+8wT0PPsjZF17Ifz/8kLgePZxtl5x5Jo/9+CMf3nYbnS+/nPT0dLJXrQIgv7DwmDvHDlrinKYvWUIHYLfVym6PfWsns7754AOK1XNcm5zhqr96XY7F3zZVxcU8oLr3/PDxx3U+Py19nWopruW8HEuy/qNJ6AJFB+u5ceM7kvmarn41xJGN514vlw9PlcWO71m2Q0VVlJktbusPFmiKXk4e6Pq86KXaFepAjQOvhKKlrmWblSUlroJdZquZ8L2LSHdYpxe/DCfdy65CH0W9SrIhJDgFXtvjueRECg0G6lp1yLw/gqlf23juOvcgNol7oHvpb7/BRe7l1rXuLVqF+rz3lpJRUMnW588G4IVVLg/UN3Pe5IbPvg5KNqvNztT0fdw6VhvK0HgKtUXj0vD7wd+d1v4cS/08uny6fPhTnKXEkp1N5cJFXk1djkoq168ncojvypQmq8Xpe33kySddZ0QISmv8+5f/fvB3Jo2b5P4ipG5dsWwZWffcQ7sXX8SUkKAO5+HgIWg2X9KWVKjXA2lSynIhxLnAz4DP8mpSymnANIBhw4bJ5rIYxsfHU1xc3GJWX3/kzZ0LwKriYswLFzpv7lkZGaR17gy4Xz/aYL7a2rTtwbR5jnvjSSfxxurVXHT77Xx4ww3Eqm4IqW3bAvDejBl8ePPNjB8/npX7lECgLStX0l71FfZyj2gkV4r6unwcOXKEVI9gu4a4fzjw6dqiurX8pObkfv3SS93ySnvekl/fuJGBarnvw0eO0F7NAOKLomzFapLQYb/fPsFuc5Oaxu/ijh25VFP+PFhacibFk/j4eAC/8hxLstaGEKIM1yWn9d4xAaFSylrv90KIc4DJgBH4REr5mkd7HDAdxXXeBEySUn7WOEcQAIPqr3vFl/DdDXXbVg1eDOzX7LvtcHEVBeU1DOgYF7C3D8cC9yW/+op7w9erM1m5L7/Rnv+eBWbcMLpMtxKo/PFmt+ZFmYs495uroWtnt/V/ZPzhNZQsPwoJcV7ra+PHmOg6bwNgyVOuh/YFEuJcfrLFxoXAaAIpsL4snQDbDrsrdbP2zHLfLnOF+6iVvrNQztl8hLcW7Kao0sKdji/ahzgTu01kzn7FVenUTqeyOGuxV58T99opihYcTPV9PM+vfN7pCvJ94fdcg8vdsqDKT7VRD3wp1F4vjJrlA1dehc1Hxd4OhZBxzbXuFnplYx+f3Fvzq4KoAOzneyv/cyFFvXrR5r771Db/QzTmTIEvWkyhllKWaj7/JoSYIoRIllIeu7WVjxFSVMXpqN3OhJf8TZ61PP/60tt9fpfNxpaZMxk/fjzr1TRrn2Zm8um0ac0s3bHPfE31QX9oc1QHx4p6SOJ7m2/27q3HWDpNhZTSzU9CCBED/Bu4E/iptu2FEEbgA+BM4BCwRgjxq5Ryu6bb3cB2KeX5Qog2wC4hxAwpZfCVJIJC8/IfqlG6EpomgUnhrgjsNkFyX3c/zlGvKZa4g6+5e8zYg0gRBopiUlVjc1MkcsvMTF+Vyb/DodRsparUPdWcm0UaKKkMcGqlJJDbQhRmJoV86LvRGKp56xJcX7wawlxK9mNLH+Ncz21ydzB3/1z/8jQSwfoAC48XEzslmiW/gzMgby+5kXUL6FwZEc6oKs139enZwHNe/WyqP1CR5nvzspoCRmEkplJSFuHbt/fsLmdz66vKub7iieBUNZujgBfw2urXuMVHH89z6/Nc+wmWBXwq0/5oX55HmMb/fkDBAa8+7bIqvLJ7+JLJ/UXIva1q8xYq164lcph3HnLtSE0d6dNiCrUQIhU4KqWUQojhKC+Ywb1S/cOJVst0PzliBOffcw+gXCjbd+zghL59vdLd1ZoKz6PNV7+69p/x6qu8vHo1g8PDmTljBgaTie2LFnHJ5Mnc9/HH3DttGqt3KtW9Hj75ZG555RWvsXwt15rqDvy21ydt3rp16xg6dGjdtw3Q5qiG6Pys+Xv4mmv46eBBBoeH8/Irr3j1veCRR+gWHs6jN97IXR99xKTzzuP8xx5j/fr1DPGYatOSsUbRp9JOuthvn2C2mXzrrUzZs4dfJk8mNCws6LF0mg8hRDzwAHADMBM4SUoZzL11OLBXSrlfHecb4EJAq1BLIEYoF3U0UAg0ekJ5N0tSygnYKgsxKkK51rcdAEd9F/yoK0fXK9ZVT4XaLx7Pe61Yu4+W8d7CPdwUrigiff8znz6prned4S8vRFtOZfgrC7lsaEe/uzpcYqbL43M5+Pw478ayHIh1zUxlFVYSFWZiXUYRt3+5lv9EzuIy4xLfAxtdWToksDvM09kYzJ7KesYKsstdmUI25G7gRKvG+p+hvHxrA+fqgj8L4jWLbaSU4F5229f2AtJETkBfX6vVxhvLlZeMitgEgs2V9FibJJZmarKk5O8OLAs4rxNfRxVVWMX/JtuYOc5AfkdFETYZTDh+TvcMvocq6vbyUmUzE4aikM4/ON+pUK86sgp/UQk+gxLVNIRColZmDn7OZOfgE52f//fn67X2P2FjEc9t/wqA2wbcxidbPvHqs71gO33C07zkc1CxbBkVy5YBIG+c4FyflgvGZtSomzJt3tfAeCBZCHEIeBYIAZBSfghcBtwlhLCiZE67SgaTDFPHWRBl1JgxnHzddc71VenpjDhGpqdfuugiYs86i8cWLODjl1/m7XXr6H3BBTB5MgDZW7dSoAYUjBs9mhNOPbUlxfXLQbOZLmrWkubgl4MHAZjz5590GD3avVFKuj7yCKO6d+fmt9/m4Y8+Yv/+/fQaO5bDNhu9xo71O67drCgegfrUtk3+1q3M3LOHM9q14/x7763DUek0B0KIZJQch1cCnwInSilrn+Zw0QHI0iwfAkZ49Hkf+BUl7iUGuFJKb/NaQ+Nejmqy3BSXVVBeWkFHYMHytZyprt+ccjEDj26hNKYHsWW1z5b0yvo+6P075L3auJBIqklPj2K8pq3IbMfxmtnl8blcf0Iol6jLZ72zhCQPBWRnToCy3cAP6w65LcdWV9Ct9DAb27i8IHf9+DK9Pbbb8seXFCS7vqKxb7i7Ddxi/8HvPrOyc5wKRrkf35AtHkp2/rxHIM2l/D+84GEW7VmHjFfdPWoqSE9PZ7fZpWw6zqXcNgvaJFFR6R2X4Fg2q7n0V65cyWWOtsWLuWiVdPYbr9nOaW5Q/ws1F7Ig7CHW73S9fIRbq7EJV6Gg/YdynPmSfRXOSU9P9+kjX2x0LzZUEdkZzK5t2qrrd6guDzlHj5KVlUkUUFXtXfDGvEf5zofstfPDkTyG7rEzdNQtgBIMfmD9Aeqa2T+rKIseKPFVWp5d/CwvVZmJBmRuiZu8q/5eRYo9zrmcnp5OyO7dJAIxZtjZfwBFD9xPAmArL69VJ3XUQ6gT6kxLXlYeySWSzKxMt+aFfy8kT/TAkT8l+8GHvMdQiZ6V7vw88KC3pT3QvaihMTNNmeXj6lra30e5OevUEZs69WH0U+77WOHR+fPJPvFE3lm/ng4TJ/LwnDlMnjCB++fN4+xhw4iJUHI+JaX4SDP0D8Vxe/dSpgFKSsgHkhITCYuM5LS2bfl99+6gIq+l3Q6WuhkSrXbcitI+e9VVlALvfPqpnibv2CQDyAM+AyqBW7Xfk5Ty7Vq29/Wlel5cZwMbgdOA7sACIcRSrQufuq8Gxb0c/eMnUGs6xCcmU1ZaBMAJ/QeCapQeOHwsbIHYuAQIrK8CYKqDIX38+PHI9V8yXg00/MT4sFORi+k6kJumruRijWnzq+01vKhZbuiv49XlH9Kt9AjnXfgGdtVdoPdu70xIA6ybYfxjML8OlsyQSLBU0qlrDzioZEV5K8W36nZLu7Zuy6emuVvS86x5bssCGD/sBBZuWgjqO5Hju5+l1uqMjox2xSV84d4n6pswoo5ITr7kZFiuto0bh8Mrd/z48ZDuvj8tiVgozQwn1ljoDOD+ac5T7ItzzQlsz7M6FeoquzLNAnDyka0Mz9nBuFc/Iz0rHdx1OgC2tOnKgDzFbSGqMpPTDOtZZB/CiFFjOaj26d27D2zZTGpqWzqZO1MIlNvKiDALqsJdEicnJzs/X/p7Ln3X2ik5x3W+Tz31VOdxTzl9Crx6h7dAHhRYC+iBt8tE24S2RJUoE03X/pBDt0dP5LDaNrR3L7om9cQRatp35SoKv/jCbfuEdxVDWG2p7+rLk9/aeeUqIydWh3HtFBsb/mVmkcYjpyK5gjG9RxF4TkAhvKzKb5sQImBMTENjZlo6y4dOPbCVKU+PhyZNYs6cOdjVvMDllZVMj4jw+XZdq2tEXfsHOZ7D1/CRuXN5WQiu7tMHgG3V1Tgyot70+ON0e+01t+BGxx94uEg4/nwEWPqTpy7H7tlms9kwmUxB9Q3oblJbX6BGM40VSGGdvHQpuxITma8WxulpNJIWHc3rJt8/Z2m3K+mFBBBEWWVQfCorrYIQYSci/Gm2FhdzSEr+3bcv/TUp83SOKd7E9ZOoT965Q0AnzXJHcD53HdwMvKbOJu4VQhwA+gA+8tU1Ap2Gw8R34H1HuYLmeZH7dk0m5815lGh1dy/N3cFtqsJ86dSVDBSBKysKvC2fWp6/sB/8rnGVkJL+BfvZntQVuzDQpVTJ2DBQ7GUjvfwPtHMOxYF8rH0x7BZY+T4IAxiUe8Ym9dbR1mrlqJ/7iD+8HAJKDvHz3p+di2armXBTeFBOA1f8WsiI9Tbsl2k9lIJX4s5YfIjs3YlUdSx3S9HdvcR1GWuDU9tUuSZwnv37cwAO/99j3Nd/ntu4KVYruSYT10TbSIjowPysw0RKyQshn7Ooegh9/zMfxxbRq5cw7+eXlDQLNyuBntem27k2HW54yIg5THWPVPtHhkRiylOUQEOVd8VggJ4JPSlSP5/b9VyUSSJvHEdWba9GW85kQ+4GsCgW9nCzjedXPMftalvNWVdT+tKLzr6eynRzMPiA5KkRTxExR3EZStibBye52mfsmMEjPWp/oaiNnodstXdqALpC3QpZphZH2SElR3ftwoirep1ReHuh1Tk7RiMvRwtBuZQUAVNUv2mAKrV0dlh4OEVWq1uxGeH402bCQOO/bTC49fW3f1/H7q/d13FYpcQUINtJMMvBnu+1ZUGY2VQKrVYSDAaK7Hb2SkmszebzxywtFmSN+sANMfqvmey9JWYJ0i6Islo5pB7D8999V8t2Oi2FlPK5Bg6xBugphOgKZANXAdd49MkETgeWCiHaAr2B4FPHBInzNz3xHUjq7srn7ONFU2r6V8Z0JbLMO/Cprjz24xauDPc/df2Qyb8rBcCMW4fjSBb82U0ncfPna+hZlMWe+I7cNLorXZMVZccoBD/8ayR/33Yvpx1Syk5/0u88JY+1hPFiMxtlAIUaGPzCAlIqC6k2hlIS5grg9KvUm9Q3Ax/PiscLiniwbRufmxV3GgY1GV7rP4uLdV+x4r9ui8uyl3GGRWNcUNMfHi73fFeDrhmqQlmuCcwMwipqUA91+G5FQTbU+H+hOSNrXcCxSmfPhv7ud9Nvs3Oc1vkio5EDISH0q6nBhLeC1uU9V6KA0rnuMwcRNWBWQ0+iQ5R3XhNGHE8Bg59Z59SoVKdC/djwxzjqQ6HuGtYVUFyfPPMve3L7XfPdlo88/UzgDZqB0Wsr2RpA7vJF3qn62r32KkcefyLoffQ+1LRexbpC3QopVH2PXxsxgsfUXM5wbKf4Ks7IYGzv3mRUVzPl6ad57vXX2WdR8q1uLilB1LWMbTPRnOf05bPP5pk//iB/924Se3pnkHxv3DjuX7KEu0eP5v1ly5BS0iMsjH6xsTz0ww9uclqyszn81NNUrlpF1JgxtHvpRUJS6+aRt3hnLjd/vobzVv2X1X/9jglI7t+/gUep01QIIZ4GPpBSFvlpPw2IlFLO8dUupbQKIe4BfkdJm/eplHKbEOJfavuHwIvA50KILSh67GNNkZkp7HA5o3bYkee4K0ZSwjOWm0ignBvKzSQDeWXVTt/K/UOfpH/67dQQQigWr3HrS7yHT0mejHN+vmlUFz5fcdC5/OKF/QgPcbmXnNonhSFHd/Hyyo/5tudpPPf6B+zb45jMlwzrkkiUqkwD3LZtbp0r0X3xhxLUPeGiSUy+ajD3f7ORtvi8DBTLtPZ/DWEBlNflkRHgwxj+UXwsaRp3sj27Z0NHV6Dkg+kPsqXtRNcxqWW0y2p8GRDUFye3qnn1UIIaqDedlXaWW3rAZLungq7sIFUUca3xT2bYzvA5jjXXvZDImMiOzCabLsZoTkjuB/yiDqeMJ4Iokp0QloCvOroHqg+4Xzea7/K7V63I8OBdRCuTo4nMV76DtBnTybj2ulq2aDjlL70JtyoREr5m2T2V/qIHn6ZL9+5NLlddOLadcHV8YlStNLZWFMMZn5bGvCVLiDMaefTll7lgoFKgQMIxq0w3N4vXrWNgWJhPZRpgpxqw2KefkspfCMHZJ5zAooICbOpLlpSSou+/Z/8FF2LevJnUF56n08fT6qxMA4zv3YbeKVG88ZdSxCVz8+Z6HJVOM7IFmCOEWCiEeFMI8X9CiP8IIb5SFeDzgb8DDSCl/E1K2UtK2V1K+bK67kNVmUZKeVhKeZaUcoCUsr+UcnpTHEjsllwe+MXunFHRWqi/sp3Fe7ZLyFXTzRVVuhRnh0KyI2xQo8rT1+DuUDuxr8sq+5/z+ri1XT+yi1fO6xu7KcF9V+5ZpMrpOC5v7G5rZeBc0h4sti7hgkGKv/ALIZ97tf9uG4Yr9YT3418r9WCzu/vBAT+ZEasNBrcMIVe3b+vdaZXL/1vJfiF9WqhR076x/WeNUP6fcyfttrHjm/Ze68PyGvYytWHbwoDtosNQ5+eXQz4NetzuIfEAnB7uu2aAwcd34rVvP+6Ao4td3lpCwuPfu78ECHPwrkGznh7j/Bw5dGiAnt70XLa09k4aMjQTIomrVE/uIC76nAor4f37k/LIw851K+7wjKFuXnQLdSskXFVAq73emo9tOg4fzvxZsxhz4YV8u3Gjc33Rpk2AD9eIQIVZgm3zkCHY7RzLpdu2cSgsrG77xzcOFxZtikHHZ0tlJQsLCjjRZGKpJre49ub5v0zloW6vqGD7D8qUc6e2bakAlr30EonZ2RR/9x3m7TsI69GDuKuuolpKMr/91k1OXwV7tPvT/kW+qURTdzWaaDdggJ8j0zkWkFL+AvwihOiJUt2iHVCKUojlDiml/2idYxTv35br92BwrnPhKPktfaqqwROOuzJpk+6KToTZZZQ31JTSJibMrYxp2NGNAIQIRUEcmGxA60ASqMiMwSOk7MEzejF9wSr//TVJVsxzfoU3lVo82TLZq6+ZUHD2dz9H9wy+B+b/B4CxlVXcXVTMVR1cit/eEsWz58OcXP6V6j+QvNqH20Kh57qKPN5d/65zUUrprihqfJvdvuHcnWgZvL9pnoGPfV3NI7e71KPs9hP44PR/c/fCu5UVYR4hCnU0bgmE2+l3fON1DfYekjIER/jC/VMPsLKPsv3QvZIh+4KT6YvLE7nxe/ciNUeE6/xLKYm86jIqv1GeOR3efYe1n02i3aZsfBIWbCJCBbvm0ojdpjzjvCTXnN99se3pXnpYcVkSgqTbbiN3klK8aVrUWkbVae+Ni65Qt0Jiw5UL9oW1a/lSTeXjFsSn+b+2z564+SXb7aDxVfZqD/DZb7sQFAPFmgC8xMGD/Ujzz2OD1copzwT2Z7t/xgyYMcNt3XOrVvGcxv2HI4dhqZ/8s/XgxP987f3Q0zkmkVLuAfa0tByNia+rznEpanUZh+9oIFVCSjgwvw1JfcuJ6+L7HcMzd/MDfYpBdc3+4pbhME9T5rmykCuHdcL2l8BgUlT5pMX/57Z9ZN4Rp0ItpXRTqK1FflwzgKGd4xh7ek/WLvSf8i+5qthtOec//yHMOoSDoq3XE/5C4wqkHKmcT4+TetuA21imKtQAXfxkBepi8W/9lT3OgII1Xuv3h4a4fycZK9hf4nK9/zPzT85MO9PVvvd3cNQD0X7BX0ykOejs4cR0oOt1jOngstratMWGgMH5rrSN806+mAmrfvI7dptiycDZhxA3q+fRUkW72DRgH2mxadRWQFxqzv8t/W5GGw8cpV5kp2yrXZmuMUGoFTYlu+de7/rrL+Rtc1l9KywVmIYOAlWhjj3nHCZnvcRrm/yMG1K3+gQS+Pnarlw0w3/sw+jtruPJiUqke+lhrD7igSrDW/b5pCvUrZDBp58Os2bRUQjGdu/uVHKqKiuJilTuQp5lrZ2f1TGEj4AUp8ItJR87ggftdm5TM3No2z230a73G/Sntp9YWcn87Gy09cDevfpqN7kdBMyUEahoi59+tbZ5LOfk5NBOdZdoyDjO4i3aZc3nO9XI6k4hIaTFxjrXa1leqFgRPr33XiLDwhBCYK+q4ur3leyTH/fpQ/S4cZji4rwL83jI5bkeXNlTHPtdt2ABL69bx40DhpJeGcHKfQWM6uFt9dLRaTY0U+JGh0KtvYqd6wJTXRLC4VUJfhXq7nES7Q1q1AFXoN3ADnFKQRUHlYX8++Q+7L6/HeGJNXR9QbFaaymc6QpiLJo5E4a7XFL2jPS2qTleDES1otRGo8hZEZ5KlNld5bp25wK35eLvf+BnfsAaa8C71CHszSvH4VR2RGMdNBqMyGjF8mwAohK748tp2nG2Y2x2zAaBRXOv2yd9B3KuCg+nrU1V0CXw/Y3QtTOnbrJzMEXw896fOVNq83JozbcaK7S12qu5Mdia1JX+Pqr4OXdrinBzx7CEuFth++W7Xg6uHpFGsZ8JBYMQ3PqHndR9pZjXq370NguRIVGYgXBT7dZde4WraM6gNYWO7JIApOUGZ5VePFA4lVSL3UbkbTdS+YnyDArv1Yu8ta50iAXmAiJPH8cXpxvYeFIi84EKq+t38/5EA/fMcX1HxWYrd5z+KCNytnPrNt/pHB8ZezfX56czaMc2END93CtghqsIjOfzc8heRVazphhRUV2z2zQDukLdCjFERQHwxdNPc9oLLzjXN1YAnbWmhh/DwylUFasrb7uNMx5+uJatgic9PZ0fx493+9FccPnldL344kbbR2PRXEGJDoU6s8b/TeLRgQP5YOtWbn7vPQBK588n5/kXeDI6mlfKyzlv+nTa1dHfLRBfTJtGvBC8Pn8O5326lal/7dMVap3mQb01nP3LffxnzH8Z4fT79X7B9ghbbJTdXzryBNC60XY7FfYvBiAi1AhWjeJYdoSQ0M4AmAsVX2Jh95/z+uiLLxHx67fUlBsR0YHlTfhiGdXn7+PdsA9BwqrUawg5OoUv4mKZFjUQ474/vSzUDkyl3u4Qdin4fVsOPf08+WVYDFCm5Bs+7y1Y6l7A6fxuEyHrI+UYkVxXUs5n8S5/8qN23/evjxLieCbf5VaQZ1SU07t+U2S8ot0SsMareY49HGIsmpeeauVFpbHDh/bHtg+oUKMpDANgCYl0Wz4nQ7ESz+1yMjeZ/PtBa81Ybo49dTigktmumOIjTz3l1hZX6dlbwS7AoNnFt2MNjN5ucwhFxaBubu8o2kqXBVUFRMVGMXe4gXA1nYrZ7nKJWtPTtWX7SZMY9NoiiGlLkpvbjjvbkrryaUQ+k3dsQwIJyR3c2rXBlUZhZOx25fe0M6GzU86/9hRwp989tAx6UGIrxFHQxdZEPtSbZs2iUEo+v+kmuhuN3PfMM87qjE3FGZdcUnun4xRzif8bj5aCqiqSDAasRUVkP/Qw2Q88SEiHDlz4opJDdMHUqY0m04bPP+eXwkIemjCBtu1TuXVMV5buyWfLoeBk1dFpEA7XjbB8/rP+GldQouZJKzQW6tcsV7HdnuZ82Gqt1sUyyufYgZTv2IXuLhsOZRog1OZh1f7zWcU9zg/2au/cwrYFi9g3py2W/MCPYENVDfvPm0iIGhA4Y7eBu1NTWB0RzhFVuRmSF7x3zy7pKsyinTEf33Y4ADJWU7glMokHhz7otn3/5AFuKdnuLyp2az9arSjNdxT5v08IJLem+ghcXOmnztv6L7zX2RrHRD1pwgMAfHHChMAdVXrEKVklrKHuCnWFarH+ttfpAbeXAtdl55wl1PosuY6rbKHrjc6821XS5OjLLwclq5b3Lo9wWy6Mde1VArl9lJmJv3sLbHb3VID5VfnsylHcQiprXEq4S2bXx6gJmvNYi3ug1j0rMTyR1Oee9TnoRT0ucn5+58QrnTEDdj/jm1swx4FuoW6FmFSFeseqVaTNmgUoU/X5W7eys6DALejMKxBNHcNnm+qG8PkbbwAQGhnJFQMG8OrGjTx6yilcd7cSkOHL1cHzf7d1HstHt21je557ha39wBtjx2KIjPQq3OJTdh+f/eHvZ+3PH1i7trCoiBUJCUH1rW1sf33/t9j1sA7RVrZT/zwf1Q906oS02TC1aYNh317kPsV/78b//Y/169Y59+Xp9uPp7uO1rOnz8po1mID7pk0D4LqTOzMlfS9T/9rLlGsbzwqu0/gIIXoBU4G2Usr+QoiBwAVSypdq2fSYwe/vShP9b9CUnf7QdgEf2i5gPoplrajSoiT+w6WwvGK5midDvm6wbIZyDy9XYXBTqKXdPU+HzYePtH2noiBV7zd6tQWk59mAkgv+k6hQnqvb1vxkG0OsUMyYcw66Mpcsy1XuG3LQlbBKHbVtf7rUuN+n20a2dYuT8ZT+aJXS/5aSUqYlxLm1/S8uHgBzXBcOyAI88Xcfl2v/572yAfr0xqvvYfDXivJ+y92XMiGsI/07xHr1C6+WziIsDl4a8zJXzb2KQpu7a0tamZLILi8yAQgu9rdddDuKgXZW38VGqjVKdMWShsXDDL70dvhOndl0162RAnIrc7n3CUUdPNfs/t0UmAvILnJPcWh3C6h00eOpeT7XX37uC1ywfxnX73SlItS+SCSEJxB9yiloGp38uOdHrlQ/50YlahRq3y+jtvAQCODn35ToCnUr5OhapYbr/QsXwsLA6X0awjVTXKmO3lm5kndWrmyyfQE8tmxZk47fGghmHuCzigpFAc7Kgqwst2fLpxs3ojWCSM1fbcuOdY7bey+TibgOylRcTHgIN4xMY0r6PvblldO9jXtQjs4xxcfAo8BHAFLKzUKImUCrUagdBCpQ4dCttV18Zc9w/D6sdXncJXSFIncXgMN/x1NyIJI+N2S763Mdh1OTkeFctB45gtZIdujue7zHV4Oya3LqNkm8aFceMX2Vz1ssPoIZDYaA1vLV9r6cYVSU50WWTByCXtlFtSyqlROVoEVB93j3PL+DUwZj6X0uVG0KqNOGSEnHPElhDNSEgVUICqSg+2FJTXvfCuSyCJf/sNt3WnII8E6NV1+q4l1uazHhjuP1Ppp2RXDAI9uoI5DyycO/c76/HQTpvWFSzzX5IUo+Hg9K/3D5xjuyWNSF1BdeIOc/SpDpwoyFjFfXL+kviA+LB1yRl4uyXEVT8irdX6K+2PYFp8W8iD/85UzfntjF+bk8NJJKD/9w5zkXEBsaS0icxp3QZiHGZuf60lI29T8P+MvZ9EOP8Qw/upPcjkokwLqMQrTzBabEJPcYh2ZEV6hbITsPHQKgh8HAcw884LREZmVlkdZZ8eVzS82mbucrIM3XuoteecXnfkeGhfGYxpfaM0jRV7Cir7aMzEw6d+7Mlaol3MGpUVH8euiQMx2Or/Rynp8914HiA565fj0Zq1dTcOAA4yZOJPm009z25StlnK/1f/31F6do35w9+try8zmwYgUdRo8mIinJ79jaddWlpexeupStixezdcMG3l3jHRUP0C00lH6JifRtk0JaYSEvZB+ie0gIy8rLEaGhbn1fu+ACnpg9m79mzODEazyL29UNx7n8Vp39cHDz6K58svQA0/7az+uXDWzQPnSalEgp5WqPWZKm9dlqZDwf0r6e2Y7Dq7S4FEirqqi6p82rh8OthzINUHJAeWzLwkPu8lQVcXTSJOfiweuvp6fmlmHets1rLOsixeIoa+pvat1decRrXa/Vq9k9bJjfbWo0j3yz5pJ4vL9aiNpRabGNEoieFpvmtn1SRBI50SlQpX4ncZ397uvtT2wcSoLXboFck4kHfrEzdK/k88ciwVDM6Moq0Lx67AoNpbff0Tyox1e649p76DvjfUrbp9XeGUgplliTrGRp6iT0S1JqAPRN7Ms3JWYmFOcRa/UtzLJ2AxhzZIvHWs33Xab5/nw8M6r37vVaFwjzkCGEr3cVCIq/+CKnQn1ut3MB5TqUAoqri52/MSlgefZy53ZHK93LxmSXZzN1/X6iezlErX1WGMBiDM73QgJxoe6zGdSUMfuPImI6mPnmtJPRKtRb2vTgxYc/I9lqp6iihkunrmSeZtNOL7xE7o23BbXvxkZXqFshBjVV3oV9+nDtW64310YJoJMSVIX6nYkTEULwgFrqfG91NROffRajhzJXVxxyfvfll6zLy+OozUYVkF5RQc6CBfS4/HKf29nMZgozM8ndvp28AwfIzckhNyOD3Jwcjh49yoH8fPaXlpJRU+OmPXSfMoVdxcUY4+J8jhuIkPBwwiIjfTfW1DCuVy+W1NRgBE4IC2NQu3YMPOEEBo4eTZ8RI6isqGDbypVsW72arbt3sy03l901NT4K1sJzY8bQb9gw+p9xBj1OPx1htZH75psUf/stoYMGMbO8jDCr1UuZBuh3/vkweza/T5/eIIV608yZzs/9z3e3wSRHh3HlSZ34enUmD5zZk3ZxEZ6b6xwb5AshuqOqHUKIywBv7atV4XQ8xRi5F0P4Yaot49gWGorN7Hq8z950BLXskXOd8BhBM1T9JCk+5L7i6Daqt7vuONbDwZ9qQ7gdW7mRuMsupeSHH4PYwrd114ExOipg+z7psvQuD8n07uA4N0n+K9AJk/K7FwAR8YQYKrDYXVPsA9sMRBxQxu5YAJNy87mhfSrdDyuDrzXnQ6RSefF+zZ16cmI8U9Rk3rUpbPWJSTwy5mwequjCXTHxrnF8DLS6axTDD1QwYa2d88JzsAy5AUex8q5xXTEKI7FhsbwcF84qUxKvbHPP6OIY1GrwducRUnPZ+Xhpk1r3jyDdFipDIbIGKseNc1OotQXTrj/hem6/7C0e+8HO3vbuF77nKcitdK/u2CG6A7s0yzuOlLm98Na1qqdrQ9eeQzyU79CSSnI3xlG4M5pTTrR4OdEUVVrYcaSUE19cgCfhJw6up0ANR1eoWyFhJuVrq7YFvrnWhzzVmhIDPDB7Nht+/RVmz+YEk4ntVivf3H8/1zZS8FunlBTm5OS4PfB6XnEFT591Fnk5OeSXlJBXVkZeRQX5FgsFdruXPzEokbXJQtAlIoKTkpK4snNnuvfoQZcBA5jx/vt8eugQFTYb3l5yDaN43z6W1NRwftu2RIeH83VGBlsOHoSDB+G333xuEwJc2a0b555xBgNPO43snTuZ8NxzzH7mGSZqMrZU/L2aI08+ieXwYRJvuYU2999HTUoKMSbfP9mYnj0ZFB7OHytW8HgDjmnwtdcCMPcl394Bt4/txoy/M/nf0gM8PfGEBuxJpwm5G5gG9BFCZKNkUG762sFNgOPeoL3TRaZ9AsDO8q480SGVCYUWHLWY9+eVgcf7pjOgMQgN2lJpoLokhOh23oGEDmShh8Jckkl9XRJs5YrSZUpICNhPSsUiL4xVXuvrwle3j2H1Zz8B0M4WzRGjIwex+7nx5QLhbOtxKhz5VVkY+zBftu3B1XOvdrbf1O8mWO3KRDG42j3zR7W92mt/vrixXQoFRiNzDvl6QamnFmfw7d+sncz5YvAIhh9YxJYuBi6VkvCotpp+gsiQSArNSvBlscFA7ibfRV7K4pPBo/aJ1rYrtP5M6jaWwz6qR9ZCRgr0PUTA6oIhhhDW9TRw/x2CI0mCEe1GAIpV+sIeF/J9yWJnKfjMEnd3CaNHhpP7fv4GqVlV34Qrzue+RuyKbm2J2n+UEPXnZ7cJQsOivBRqU4Bj9RW/ZA6B8GZwq9YV6lZIuKpUTd21i45nnoldSuxSUlBUxJL4eK/+npdXoHzKPy1XfmRlwCtnnMFTqo/2ef36sX3TJq778EMyd+9GaCpf1WV8gKKiItYmJlJZUoKvx9ZLf/xBG6CNyURyWBgnJCSQHBdHckoKbZOTSenUiZS0NFLatyeld28S+/bFGO47f+cNjyvqZZzqjtEUzD56tPZOKhZg5v79zJw2DdSAP4DzX3wRXvTjp/bY/yl/Kl00xXycSEmmlGA2kyQEUT6qlXlt40G5xvfyXI90TA46JUZywaD2zFydyd2n9iAhqmGzFTqNj5RyP3CGECIKMEgpy2rb5ljD855RpQYG/L7dZR3OVVOoZYa5rtu4yBCwKtf5edUvY8XIj6HPAS6FOtBvYO+vitNs36sOk9l+Ap0Pz/PqYyvMxWSKAGvtwWeNld7NVm1gXWgvMLjfMS0VQQY13pEOkUkMj2/DIjXfcyd7G41CHTwidZBrofe5dPFIlacNXAT/qu97R/MAzxcJ9TsSsF69pxd73MuK9kZSkxE4X7MMMyCq7eTHQLJ69R8ybyKm9/Mcrn7a1c/HtgXRirX0yqV2uAo46VZY56rQWFZT5lQ+BZDQo5Kq/DCeGnm726iDOiU4vCw0gmn2mO8KOnTkls7/sPEyNfniSJJyfs9KOwuHQh0fnkBZnusWsTbLfeaiuLpYsyQ5GvVfIjQxmZ4W6sfO6cMbi5aCLfB35CveYf+d5zDgsS8obFtNWzVNd8kzynNx6rmu62Bsz2S2ZJd4bd8huoPXOggci9GY1KpQCyFSUMrYtkcJX90KrJVS+o980GlS0noqzvg24PE//2yy/TylCXh8Uy0PDvDkokW+ujca7YTgsM1Wa9qd2pB2u6eB4LhgfK9ezs+OM1RRWYn98GEOWa0YDQZO79UrYFVDXy2f7lDuYL/5UaYd/Gtcd37akM0XKw/ywBm9AvbVaX6EEP/xWAZASvmCzw2OQSrs7uakWbFG/q8YPv97D46qJDU+siOUVlqcFuptsqvyQUrFwqs+wNfYexNL4HcMKcFkVbJh1IgwQqVLkbVs+JMwjQXbUuH98irtavKPOvpIt3noIcr++APz1q1u6+1WQc/oQowGd2Wn/LC30tLu1Vc58sQT7itNERCv+Dv/z3YuVozsDS3wSuEWCG2lQNe4oURJ9+n6+LB4SOkLqApPVBuvzcLtdsZXVrHTQ6F2KBXvazKEPNUmCW1YZ87a+FplFYkhfNvDwvITBO99pFwnOdWKUpxncSnHjtgW7dFXhXmEG8SkAu4lz7WYi5XjPxTdxjEoABU13tenm2JXkQvqvGnNwYMAhPXsSc3efQGODEK7d6dmn+8+doPAYJdU9O8acIxyi+tFKjnSvbZAUXUR0SHRQDGA+vLgoZEGcPkY37sNUzKCCKKUQvsfAJZY1Z2oqhTPqaaMFOEQiemrMvBFdnl2nTJsNTZ+FWohxKnA40AisAHIBcKBi4DuQogfgLeklKX+xtBpGkJTlJyRvz35JOOffBKDwYDBYGDJkiWceuqpbn09A+RqWx4WF8fm6mru6NePaar7x/sXXEB0YiKVFRX8+/vv6SkEm8vL6zW+lJKlS5cyduxYt/bhbdtiMBjYXlXFESmprqwkLCqwP2BtbJ+jTDt+esMN3KwWTgkaKSnZt4+5X39NnN1Oxq5dZB44QOaRI2QUFpJZWclhm83LBaWNEKRFRNA5Lo60lBTSunShc8+epA0cSOchQ0jq29dp3d8xbx4nnKuUMvt03Dg2rV7NdquVLTYbOT4i9UdFRfHnvn1EtPXO4Zqens6XQ4aQGBfH1SecwOQtnsEwgbGazSyOiaFdZCQT/Lh7OOidGsMZfVP4fMVB7jilG5Gh+kTXMUaF5nM4MBHY0UKy1Itq6a7UbAw3YhYCaXIpwt8fXebl3oEP946SzZFk7UzCcLGi4JhlqNP9y59ZSNqhLLoL5P5Fblhnko+4/F2z/kqi71WuqfniA94xFrYaA6ZwO9Zq5bceNWo4FStWI0wSafXxeNfcJ+1V3pbv3I2xtBlbijHKXaE2RXorbTFnnumtUGuUDAsmPrGdR4wMzjns96xsJnRsz5PDn1SHcrf0eyowcWFxiGG3Am8rKyryuGvQqyBc1SbNBoO7kqP6tDi+ji1hrvLVKVYrEFyAmwODgB/H1CGDijbdqzH4CnwCKNypBHJWmxQZnc88Xy8pUrqUah9W0/I/a8/apVWmwwcMgCLXvX7FbScxZtpqatK8nxF9Ykews/RvAFIjXelLkiPcX3hyrGvdRUZ6uRrZA2iofdsF51zpcsVyYahRXqR7rvWe+TwYleZUqEvNvmOsz0o7y7e70jFgoT4XuF1K6RW5IIQwodykzwSCiaTQaUzUH2xkRAQRGqXTGBKCwY+PbbAItVLfNE1k+j2//urWZ4+UrP7kE06577567SMiKoroWPcf3bUjR/Kf9HTOjI1lQWkp/z7pJP63fXu9xnew/PvvARh7ww1ebbaaGo5s2EDGxo1kbt1K5t69ZGZkkJGfT2ZZGZlms8O+4iQE6GQy0TkqitPS0khr356o6Ggenz+fy9q358tNm4hIDr6S4Iu3K1OEz7dty8k5Rznn4UdIeez/MEZHc2TrVnb/8QcHdu6kuqSEbkOGcNp992GM8B8IGB4by7g2bfh91y6X02WQ/PT00xywWpn0r38F1f+u8d25dOpKvl6dxa1jAltDdJoXKaWbeUgIMQn41U/3Yxv1QbglwshJXTphrHQpHLmhrkeTCCnAFL0Lc2U1A7p25rbsGtT4Nsr2KFZck/qSatPUM9v5XXvC4rydK+1WAyVxfbFbBKVRSdg3e+RNNphArYZor/FW3Cpywji8KoHkfsoLQOwZ4xWF2gge7wok3X6bS6EWkHDlFRx95VW3PjXlJl6LehRbVRXgSl+avSzRa98ixMczQJOzNzk6lPxy30qjr/wN7a02Nh3MgthOnoNqNpQYpGIhjQuLw66WMHdgsQd2YH3qaBEvp3ofC8APsTFcoQYv1lbL7EhqJO1yKgkzGfEXwOkzw5NmnTD4958HuLD7hfyy7xcAyonAcaGVhUa5jdW3vXcQvNBmwvIlRhCBiCIyElmpzJ4k3nQj4p1HnG0HT+rApmwDZ95wltd2R0qqnV/ZhK4T2CCUjF3Jkd4zCJ6EJPpPaVvvoEQHmu1t4YoibfTx1Vlxzca8eskAnpjlbTS6f8j9x6aFWkr5aIA2K/BzUwikUzsB34AbSJfUVHJzcqgSgmK7nY0//USYxi975axZ3PLf/zLu/vuR9VSofXH+v/7FM+npXDhhAgu+/ZZPd+zARzr/OrFYdYd5+a67yMzPJ7O8nEyLhdrsDx0MBvpFR5OWlERieDj9+/UjrVcvUnv1wqhGTmtTDk6aP5+1R47USZmWNTV8na04pHQNDSXjlLEciQhn++TJSjvK99y+Uyfo1Al7dTUL3nxTaVOL8mjvxwcOHKAyPR27lOyyWJh25ZWk9e/vTCnoVeRFU8wFKbn5rbfobjI5qy7WxtC0RIZ3TeSTpfu5/uQ0QgOU29VpcSKBbi0tRF0otpt9rreH+si9DET3UH4bJTmDAVgXawaPudPBHWOhALq2icWMK9dudYm39bOm1IQ9v5pdP7aj+IwIIswe1/dpzygVEoGQaG9r2dH1isEgf5sasKb5sSbefDOFn33mXDbGxWErLnYux5x5ppdCHRpj5fKxg5mevtj74D3QZnfwRa+2MeSXexdWcRujlmfLmevtHOgT71y+YaGdiWskVz6m+nSnjXZ17nex+9g+xuuz3QgeOZ/f/dBK+yK44gmXmrKnwE/GJZW85HDa5VQGfDTW+tisRaF2KNMAu+ydMEXuxlppchYaiRiopBQNGTgQL/VYOxNhCf6eKcLDkWblN5H2xRccVDNhxZ13HmgUaoTgr4EGzojyZXixa7q5TkIbHy45o9qPAjTFZOyu5x64K9HBGn/3xbkH7morJTqwJHkEeGqwG11nMzbc9zUuEF4vTJ+eaeD6Rc3joRyMD3U8cAPQRdtfStl42pROnahRI4HPefppxqq5nCVgtVp5SWOh9soF7bneR79lqiuH44f/7+uv9xrHQb/wcKI9LOLC83/PaomA3WbDaDS6t6lj3/Ptt86xVrz2GqMer3/Oim9ylGjlz/cEX5oXINtuJ7u8nBWOc7FrV+ANgHwpWfPxx5x0++219jXv2s1hzXHdkJUFH39cJxlr487vvwfVQh8sV/TsWaeUiP8e352bPlvDzxuzuWKYp/VKp6UQQmzB9bM2Am2AVuM/DWDzE81nsMRjM/kv2iADRB8Z1La05Bhq+0VnL0+gvHc8cUBBTgJd4q2YizS/jVhX8NPR9fFe29tq3IMFjbGKoiCACo8CWQYPhdrgw9WtLCuCmDADIQm1F9cSBgN72nWgf9VBn+0hRkWRs1ujMZh8ByX6y9MPQJWZ23+3U7CqGO5SVk1Yq/Q3OGw9kZog8DEPcprJQDkfOle9MOoFOHCTczk6w1sVaa++O7UrcMlSsDrOK4xRS3aHKAZuLQxokqwtUFQYqzmUKumYU7vByh6VibVSkf2KYR3JKKgkeuxIei5bShYR3gq11aXYFe4KvjiWMS4Oq6pQh6b5z/3t8gn3lr2gwozJxy6jQrxXjus4Dq1CLe0u63BN0UmERK92dQ7SrrelTQ9Pab36nNftPAp4w2s9gDC6XrJX7S/AFLseY/hhqnMnOtd3jOlItdXdZGZpRo/EYHb1G7AK2IJ3FWSdFmD5AiX3ohko01gfbQYDJg/fNF8KreN/4aNdy7i4OEJMJq/tUCOSt1dXc06M640yGAVeSonFasWkjutLWe8eEsI+i4VPPvqo3gp1aa6SS7Mt8NK119Kue3eMISE+S7EHWnfg4EFnsRytrNq+f8yaxSc7d3LLvfeyJYBCLa1WCj75H3kffECxuv0lnTtz7xNP+C2248u67Ktt+44d9OndG2m3M/K22+hgNPLdRx95lZh3WLa160/7978BeOTXunkFjOvVhhPaxfLhX/u4bEhHDAFSGek0KxM1n63AUXVWsdVg9GNGtEUEroBWFVLhty0nXDXSp40GNgYcx2p2vfBLYaD8SJhbu8UcRghQIgNbTB2E93EF71bvdA9wM8bFuSleBh95702RVmRcR+zm9hjD8rzaPekQWgGVvtvCTGCK2epXmQ6WqEr/6oCbhTs8jgEJXZyOKhLomdDTTbG15LifX+0DqXOeq2NCme/r4kAKzDjVQDcR5rPdXbbAK40RGVSrBlBbtfAqr/7GKW/wf0uUrEtaF443LnNlPzElJ9MV78CF/l/sZbf6yAyLs/icHXFgiI7Grhp1rJpMUp7Xh3T7rCrUPg/S/fty+sD7yAhlqXZ3yRSOa0VIQhPWoL1g6+Oe3DMlGkOZWoRJI2pKZArauRO7RePbbnApytHhJiI6fAfgplArx+1+7JJjKMsHEC6lfKjJJdEJmhL1TfWdiROdRVeg4YVd8rKzSenYEYDpt97KtZ984rOftaqKEPVHPS+v9pu7J/7k/P255zjn+ed579ln+eXLL/ly927e2L+f5G51n61e+8MPAHz+1FOcU0uQXX1k1XLZiy/yidHI1mr/U4XV+/dz+PEnMG/eTMyEc/j76FHYtpVnJk9m8EUX1Vs+BxXp6YxQ5bzljTeYtXs3I667DmNY4AfMotdfd35O7OFpQQiMEIK7xnfn3q838Mf2HM7p76N+rk5L4JnCIlb7gJVSFjavOHVH41JcK+VG19PySKL/6nLZkX3h4V1QYwc+CDyoAQZ1jCMP6Nc+jpot7mqV1RoedJicKdKKCHX1Du3SxZnVAcAYF+/qLEH4iIOxVpqIS+5MSNwmrzZftLUfoVqawBgKNneL3RH+JKLj1+4beJhtA2YI8mGEMShqC91jfRSECfNQzoDkiGSq218M/O1b/iKXPJcur92Ol50s2NzNQLcD9dSc3HyoLXTPUo6uIvFSr/oFWuNPaC0uz3mx0EbjehSZXw0xytiGkMDH5VCmAdo++SRH1YJrvq4Pp2wOhdrHLyclNoxCO0ivMqTefXMKXc8NaYtAqOkVnT3drXZuEtRGWUgEr106gFc/edpne9zFF1M2exZ2q0s5/qt3PIZQl6qdGOmaLQpJXOq2fcDZlSYmGCeer4QQtwsh2gkhEh1/TS6Zjl+qrYqxKbyBFQs92bPY5Z/38OefMy46mlOioxkbFcXYqCjGqH/jNL7Cq99/v9H2P/6BB4gGfp05k3uffx4z8PH999drrFW//w7AcD9VFxsT7Rt+meZBCSBtNgo++5wDF1+CJSODDm+/Rcd33mHRihUkGwwM9KhG2Bicde65FANrPv+81r6nqzMAqzQ+nXVhQv9U0pIimZq+r0VvZDpurAfygN3AHvXzOvVvbYDtjhnsdTAp5YT6Vkxi+j5ORMcvnHY5KYGY1OCuU7vLNSLcRxYbaVPaJAJThI2INq6X6bAUd1XbGOoeINzuuWfc2+PjNHEx/kVKiQnHXhNfu+yAMEikXUCIty9tn44+tMA6xOPU2JQzqv2KHFtf1u0y7w3CFJOs9qzHh8VjMbi7LiSWuno8/a3rO+3iXrjPJ479x1iUd8VAh1OX25S0e3/3PRJchocw9VRGneBt7T9SfoSPz/GvYlXl125NdxA5YoTbcruXXiRNU9XWQaBr23FOqrJu9mqwlJyoGcNAtMkVUGm3xhAV6Zju8M7M4T6W70BQEVKIMBUx6bqXuO2MxxFCYPATD2GIilKVaRfjdhWrjYoxMS7C9RsLbzvXrW+DgyQbQDAW6hrgTeAp3GfvW1WQy/GEWY0EtlZVUZmpRLpLKbHk5DgVOuf0vud0v4d7g5b7773X+fmozcZR1bWje2gosR7BeJ1NJjKtVn6YOpXeauo38LZsaJcdnyw5OZQfPOjepn4eGx/P9zt28PaoUYwKC+O/v/3GIzU1hNTx5eHLucqP7LWbbnK5OKBcuHat64PaZtdaKDQWmIqKCmZFR7v5hHu6ymiP+K1LLuE5tfxrTWYmh594kqp164g+7TTaPf8cpjZtkDU1fJWRAcDtak7xhlJlNvOVWgihQLVsjPzXv7hFY4H2JFdjARlx00312q/JaODOU7rz5E9bWLGvgNE9gg/M1Gky5gO/Sil/AxBCTADOkFI+3LJiBU9dXs6qA5iFTDE7sAjhkV2v4U9cW4lrEsBaZSQq1UxVnqIgGUPdlYrqYncF2xDuLrAxzqW8BLIMSymR1lgILa5VPiFQZvhDIsHsnq8oKdqHIud0Yav9vFsch+ej69mdJ3ivNLoHtAEUV4B53jq3br22DETJ0Ft/wm3+3VgCVX/0R8m6TDxzdcSGumzWPdVy6uVHvc9pla2KA6n1v9YSL59I4fdK6tfQrl3c2uIv8/HiAtwx4A625O3glI6n+Gh1RAEavFYLq8tGKi2xhGsCW6U1BmN4qeo7UYvQfgI6o3softH55R9SWl7sNkysyT0jjK8YghpVUxWGKqQ9nEuHduRFf0nAPDTqAv9xjo1OMAr1Q0APKWV+UwujExwJauq0e+fN4960tCbf376aGqjxnRvjze3bebO7j2m+BhKlOa5nzjiD15YsCdDbm11qWfb/btzo8hcHDA6FWLtO89nN31vz57nOueyRbWPT3r1Iu52ib74h981JCJOJdq+9StyFFzofll/ddZez/x9ZWXU6Ln9IKX0+jAONf0id6Zj3UMM8ui4Z0oF3/tzNlPS9ukJ9bHCSlNKZ/1BKOU8IEVz6lmOE+s51JJVIpk6x8f25FjY4XFodgxnAVlyMvVz1sw7WlOVDubcWKdY1S6GinJQccCkBtrJqvPIma36bBo+cYMaY4J745m3bMUZ6ZbH1iTBI7HahuHw0Og63Am8O5leQmJzs9kJkN5sxeFSy3X20jDbb97ute2jJBnLi6yeRzZl+Qjm3liBfyIQQGMIPIfEdIGiv9ra4mjR9H/tBsaT7Sp1otpopiRJk3FpM2v/inevDLcHJFt69o/OzwRyc+rV6Twjb19zJ9sFWRno8losra0DV+1ftL3C+ZO7Nr8BqDXcqg3ZrHCv25TNKXZbWGKod2ZOFYszz99N547I+bstP3GikqhZDvEG4q6HWo96l5teeHgVUE9llKjUF4zAazvM7nvaJ/Nw1BranGWiu8L9gFOpt+A1v0GkJug8cCBs28PCAAbTp2tVpNc3Pz6dNmzb+rak+rKsOpsyezQ7VB/iJE04gtW9fpJSsXL2abw8dwh9PDB1KG4+gPQfam6q2LT8/nyRNKXBtW15ODpP+/ptw4Kmzz+aZ33/n9aVLea22k6IhV83qYQSqGuiGUBe/9LMSE9lTWUnmrbdSuXIVUaNH0+7llwhJdc8H9YjqirHm888ZduONDZLPn5zPjB3Lq8uWsWXfPuI7e0eFm0tLSUtIYGhcHOe8FURVqwCEhxi5bUxXXp23k01ZxQzqFN+g8XQaTL4Q4mlgOsrP6zogcJ40FSHEOcBklJ/PJ1JKr5+eEGI88C6K1pgvpRzXKFK7oSptdfz5dstRNui/08r3gzxmy6pL2X3yyKDHqtq0GQBbobfLec7Lb5JwFVjLvEt/Bwo0kxIMBnfjhBA1Qb1BCKNvU3xorIWaUvd9CpNE2oSSL7sO+LLiVpcaqSk34VD7A30nT/20hXlPpLm9hNhKSjGEh2PSbHfDp6vxLupe9+n69d0EQ/ZL53a7QuycCRz0zq/hk+yKg0R1fZ/CmtNJuvNOSg/uAZZgCZGEWAQRvb0n4st8aEPlA8xgMUOI68XBbFXdE2zuSnkf/49TAAqjIbEcjBGaa6vQ/eWD51S7+XPusw+Octyv/LaD2fe6V7asttowhQEI1mUU4fglvLVgN9IS7+wnLXEs2pmHIxmftEZjd5xPPy4dDrJqVgMDncv72gfwxffzbC752Ts4vkPCYSAJQ0gp4amzkfJl/0JoLiJFmW4+gvm12YCNQojFgNOer6fNazmqVJePFz79lMhhw5zr6xuUWFlYyCuzZjmXX92+nceBJ3//HdN997ExJ4ddqjUz1WTiv488wsnXXEPHAQPqJX9tcq6IjsYsJU/Pn887QlAIfPfss1zx/PNBjf+zGoR4RafmS+UmpWRMp048u3kzOav+ptfzzxN/xeU+rcZ5anWCxlKmfXHWpZfy0rJlLJ46lYtffdWrffr995Nrt/Pwo37TzdeJa0Z05oPFe5mavo8Prx/aKGPq1JurgWeBn1BUtSXquoAIIYwo0XpnAoeANUKIX6WU2zV94oEpwDlSykwhRIrPwRpIQ73xy0x28MjPUJz1re/OfnAEDvqqXAhQnhPmFDS+WwXF+31Xdk3o5Z55xCDcp8VFtbtS5I+SefNAk43OUBMGWH2eLINRVaiDxr/Lx/7flKp7fT3W+1KsKxwV7DQVWKw5R9hsdlc1omt82+iMdTAkXv+wkZN3SobsdwliEUpBF5vw75+gPcbSGmWmodqQQcqD7zJn66ewbglVJkGIBWwm73Cxggrv2driNCtYKt0U6txKxfk7tJ4Xs1Fo3Fe058vuX6l1ZMdxKNb+CNPUDai2SqxlJziXpdXdycVu9Z5BsVV1ALxnS4qqj3qtc9tOlBKSsAK79P9iGzlkMJXrN7qtWx7nPsNhtftPWlRbDvWmJBj1/WfgZWAFrsCWdYE20GlaKkuVsOHwOhQSCcSUm2/miN1O+tNPs27WLG7s14/Xtm8ntlMn7vnpJ2LDw/ny3nsxl5dzxGLhsldfrbcyHQznjx7N+spKsjdsYOmXXwJw5QvBp9H97Y8/AJjYBAF/vrAczSXrX/+i31HlBnpw7FgSrrzC5w+7UmvtsliC/pM1NciaGuzV1c4/m9mMzWzGWlWFrboaS2Wl82/INdcQBsz+/nsslZXYzGZkTY0yVnU178ycyeDwcE577LFGOQcx4SHcMLILv2/PYW9uw9Jx6dQfVSl+T0p5v5TyRCnlECnlA0Fm9hgO7JVS7pdS1gDfABd69LkGmOWooCulDCJkrO7UV6EO9Ci1iOBLSoPLl7PaTx76rPQkiv9Wsh3F93ApPPHd3BXosFgLVLomCES6x73MHJxCXfz9D27LMVWK9llT5m0RFwaQdm2Ri+CUDGGu5oUvrSRmB/gNO+InfXxJxWbVkqmxPh688irmbs4hUl1nsETz0HrfLzdRvuv5+KQ61PuYDNK/shnMGegUoxhhwuyKrPZK75ep4kpv6/fc2CiwuPd1FIDJM3nPYgSDcbUmH/MmTQBijf/UkP7yt3uNrUlxOq53G7Rnx251z2sifSjUIsLbLQPglwPfBNxvbtinhKf+yqEKl8XdYnN/i0q68Qqv7S43uycuWpjlXaL9r/5qmssWjI2v1UItpfyiOQTRCZ5y1X9v9vPPQ2ys86aZdegQRaql2VfwodP3V7OutKSER9X8wzkFBeQsWMA5o0axZ9s29gDXDB7MiJNPRtbU8OPDD7uPrZEp0DotQgiOHD5M1syZThcUx3rHcqV6fDecfjp3Xu0yrGVv3UqH/v1rPT9L1YIuN06ZwoNTp7rnj9aeB82fr2WnzJo/g8ey9s/xznzvx9O4ctpHPmVb9Oyzzs+m0FCPHKLu/zcGn+3bx2eaIA+tn/inV1zhMwdpfbl5dBc+Wbafj/7ax5uXD6p9A51GR0ppE0K0EUKEqkpxXegAaJ3uDwEjPPr0AkKEEOlADDBZSvllvQX2g72BT0Xt1o57jFUG5wbglEEN2rVXultTw9LaU3M4z61MtFGTAi0qtdrNWm0wSUTZYZc8KX1R7FMqVcVQoyqwFm8FzhRuw2o2EnLemYBrJrG6aCzwB+EDyjFvcfcBdmRJkFbl+GsOHUbYYglp62dCwaRYAEO37qdPNkT9uFNxFPJFgK/GoRt53vvbx4djlMqm4VmXMDLnM5/bRwUuUuiFp5JcblD8Zetro4wwKfFJoVKxcNsrvJXX8morWMO4dqnruvg7Mgys7m8D4zqOY1n2MoZVaetyBo+bPWbbT4BaabBG87LjYa3+Y5tvC7GUUvMGJPhmdRaD1cWKapvbvga2a0+BNjuM9H4hqCkYB/wZ1HFosQnlfNo0afFLq92tzZbMDK/toj1qzj/6l/vMqqOa5r9oWfwq1EKI2cA0YL6U7nciIUQ34CbgoJTy0yaVUMeLD1coN+OLgkiLVheumjrVa93kjRth48ZG3U+wLCoqYtGUKc7lD+64g1dWrAiwhcKA8HD+Mpu5VS0B6/AbdyrveGTp8FTsNe2lpaVEx8S4FUSxS4ndaqUmJwdbWRkiIgJTaiqEhvLxli0Bb56btiuz53cMGECbxETnPh1o5dAu17auqKiIxIQEt7GmLlpEtt3OjV270q1rV0VuKXkxPR2AK95+O4CkdScpOowrh3Vi5upMHjyzF+3jfZW/1WkGDgLLhRC/Ak6NQEpZ2xfuSw/xVJ9MwFDgdCACWCmEWCWl3O02kBB3AHcAtG3blnT1mguWpMpIIDjLbbAU2upWMbXCz72mPCkB06Gj7nmYNfP6hhD3U2YIsbNz6ybndPDBMveX2K3rVhC+8y8A8rcsZEv6ANqF2J2lqYWaZ7t6+negKcOdUqGcn5wwA/HqOsd5bqtW4Ss8YCY5GfZddScARz+cyqFCbyfe9PW7gd1kZWTQBrDU1LjG8hi7oKSAE1AuFs8+jnUhu3ajdZZ4ae4O5jvOh9XbNeapkbfz8sr6V4uV2ps7YLd7u3wUFhWBQbmnO9izW7lsrTYb6enpbKrcpAwTbocaA0VHjrA/PZ3y8nLnsa48YGHIHgMXrnJ9z2dXVrJ2xV+Ux7jeR7eUbAEgWso6KdSO+Ybq0ASE0e5M0ehg9dI/Ga5+3jfdFVBeVVVFlygr+aq+nZ6e7vxevv1tsWsACbuOuiy+2w5kEqWxopdWHaCwzDX7ra2S6Frp3+oe6Ldut9nACDt37nT7drTbpO1f5LZNbOdKDPbgXrDT09MxW6wEStUQSD7t91wfAlmob0fJ8PGOEKIIJZdpBEoJ8r3A+1LKX+q9Z516U6G+/a/79luEWmBFCMHmzZsZNMhlGfRUzrSfhRAUZmVxyu230ys0lJ9mz/bbz3M8v+0B1oErdd+GjRsZPGiQe0o/3K3qD1x9NX8UFLD622+JiInhXxdcwKdr1vBcdTWhtRQrOWKzcUnHjny4aVPAfsHgy9+7dP7v5Dz/PHZhoM3r95N4000Io3KD+cVgIFdKNv74I4MvvdR7vE2bGBgWxkebNzdYttrkvGrpUnqccgonDRnC3Wqhm2VTpjgV6qgOHWhsbj+lG1+tyuCdSZ8S+ffP/L5yJbsrKjg1Npavtmwh0keApE6jc1j9MwB1SRp1CNAGHnRUx/Hsky+lrAAqhBBLgEEoOa+dSCmnoRhkGDZsmKxrbMdvn04GfE8rtzQp3XpQtH6b2zqthdoQ4+6DajBJ+qSlOE9Ql7YJbhX0+nfvwNGYGAo5SnJSIr3HjyczuYaKI6oi4/GaYyntBzKE0DIl2LkyTjoVasd5zkippjI3jMhY9ywf4045hVVrV3mV8HNst7c6Hwu/kRKTwknquh0efbIOZVCOUmbcsW6Hqu9IBCeNHIO1otLtwkmuKnZ9VoPt1rfpyZA85SUnJ6p+pS281CyHO4qPqq2JCQlQArGxmrR3vXrBBjAZTYwfP56dm3ZCHoScUYxlVjLx8fEMGj/e7f76XfY6ImwesxbCzjDbOhh/i3Pd5vWbMRVJwuo425Jgs2HFSFh8W3pM3IG12gB9L8CRQn54v+6wRunb3XgEx1MuIiKCczrG8VHx5VxT8xTjx9/l/O5+PRypOVvu5+avQzbMJlfQad+ks9ibWeRsN5rdM3dA4ODR8ePHgx+/BmuoclX06dOHzepPSILbs8tWtYzdP7lefg0hkoggz+H48eMpq6gkUNxnoHtRQ4vj+VWopZQ5wP8JIbKAZUA4UAXsllLqWT+OAYZcfDFo8kUWR0UxqA4XwxMjRyKAH2bO5ISzzmp8Af2QBfSoRc7/e+QR/njiCQ5v2sSFL7/M0//6FxPef5/vXnmF6wIEJ1aXlbHXYuGKJkgnaC0q4uiLL1H622+E9+9P+9deJcyjuuATF1zAg7/8wpuPPMIMD4W6prKS5QUF3N6vX6PL5ovuY8fSzWTij6VLuVtdN/Zu5dPS//63UfdVkpXFso8+4rdffqF6+w4m2W0YgJOjo5nQowcz9+7ltEce4e7vvmvU/ep4I6V8HkAIEaMsymCd2tcAPYUQXYFs4CoUn2ktvwDvCyFMQCiKS8g7jSK4hmCm7I02yc0L7MwaZaAwVn2ZD/DcjayjO4GbPGFhSDULkiEuya3NEBWF0BgRDfHJQLFrOUS6+0l7+kxrl4UAm4XkE8pdCrUH9qrO1BSOI9H4MwCTO8fy2aBCrGaX1TChZwWVuWFeST6s+fkB8zHbrfEAVFf5TiMH/rMzACAluWXV2CZPdlvdodxlo7244gCAU5kGOBzdxv/+khKRBb5DAPwdifSRNDlgjm9V2fxgo1JBMyveSI8OVVjKvX86SVFhFHicgxqjAIO71ba8ppQoe93cT2I6VVGVr74E1ZRjirBjirCD1Lg8LNZkuOg6FtjiXLxoyz0kiTJuNc7DYrvTub640oJwvlsFlujXNVa0mexiI0KwG0Kw2LWOCg0L/AukHhtS3TOrlIYnkShrn62yVXVUx245J+pgsny0Bb5Hqb71KThfiHRamhD/6ZlqI2fzZt5btYqr0tIY4MOS2tKcct99xD35JL9+/z0XvvwyZ73yCr2nTGHyf//Ltc895/fmuHv+fOzA4cOHWfDcc65c0T6s4f4+OxBCkJGRQcnvv1OTmUnFkiXYzWaihw4lsmcPdnyhvIY73UaEoJNqgZ158CAzPGRb89FHVAHvbdvGmiBzzwaDBGw2G0aj9zTcfquV/bm5jIyOdsvNunPNGnb6yTLi73Zkqakh7+hRjhYUsDc7m4UFBQGze3Y3GpFCsE/1aV+3fPn/t3fe8VFUawN+zvb0Sgoh9N6RJk0QUQEVe++9XL161Wu71nst12vvXttVsX9WwF4IFkBApPcOARJKEtKT3T3fHzO7O1uzaSSB8/x+gZkzZ2benZ2dec973hLlJ1I0BiFEf2A6aLPuQoi9wEVSypWR9pNSOoUQ1wPfoqXIeENKuVIIcY2+/WUp5WohxDfAMrTkrq9JKVc0+Wfw/B/h3dh/i+S4PyXtStw8cnbwvd9rh8RtOMal30efQsLeozvV631lzE0JCbh0hbro40/8+hoLswCYU7PwU6gtblj3ra/D2q8Bg9uDwXoLQHUpse1q6HZCARu/zCQQZ7lWDGri+nzt+G5I6xOQSUR3E5GDLoblPgNE1cqViPjwytDeshpSgAOVEfzN69BXdpVUkrLNPwNE/72bvF9q/B7tebArNo3sirqzOdo65lIdRqEOh5YFou4c3OGuRI+aGkxWO9UhFOpal5vanacBviDRzxLj+edm/1oJ5VXFxEfpqhCSku0Qqy8bAxG3Gp6j392N17caSK/Q7tlOpgLKDb7JJwzM5vGfzsKWPhtXZa5fpcFg/F1M4u0WLI40Ciq0781Z1rNZVVbh9LfXVmwEWxQ5EKoLplJV60I25po3kmiCEu8WQtwDHAdcimad+Ah4XUq5sbkFVITn0i5d/IqLVFZV8brDEVQFEM+yYd+P9IIfawoLObtjx6AHS9B6CAW2oftUVVXxhp7oP9I+JVLyxvr1ODt3xiQEa91uKCrihxdf5Ni//IVQvHHvvdr/mzfzRpRp9urNt99qf/Xk+7vu8i7PC/Ggbk7mBwTXXPl2k8eRBbHe5WJ9qc9X7/OdO1EBFweFV4CbpZSzwZs3+lXw1moIi15d8auAtpcD1h9Dq57bbBifA86ynlji1wX3qeMY/5ruH7CVWo+fnCnO30Jrjo/HtVcrruEu8beWmQIUalNaNrDGt26VULAcyNYaqkvwU6gDLdbV/hkNAJKOG0PJd78h3BK3njN4U2o/jti6mZS4GkNCW0BKr9+1O2cMeQn/R+YHN2qyxMRGVzEwRJe9ZdWkx9uDBjnuAAVmY2EZw/y7cMHa7zWHUXzBdjcfdT3vf+N7RscMGkRlCDe9jHvvYfupoSsDhuOAKXiAFYpwqlc7l4tZpj70Ly0O2lZe4wqd3q/HsX6rZdUlxAUE0/1x6ZEM/d/8qGTzE27TbIyKs5EuyV1hh3+e6pGmNSzfX+lV8CpqnMjaNKp3adextMp/wHR0r3a6QwncelxPnvhpoXdbvMNCvCPDq1ALc/gsI9Ei3TL8xa+p0EbBul+Jo6oKAaQ7XeyNkDFFuu38vG4PI3NDp688GESV9V1KKYUQu4HdaMkMUoCPhRDfSylvC7WPEOIN4ESgUEoZlJpBaE/MZ4CpaIVjLpFSLm7Yxzi8sKHVg/9JL7jiDaJzuzHpWRuE8S+gTPY2Q9XDSpeLpQX+kcGB93mobB1BfepYN7ZJtxthMkV1HoC3t26lo8VCMprd57jrr0eGUaifXqO9yD594AHa5eb6BRn6FbgxZBXxW9fbJFC5Zi2F77yDqKwg/uijSTz2WDCbg/y9Ay3gT9xzD5/u2MHelStJN7h3zE1MJK2qihkv++knTcK6devo1asXLqeTvVu2ULhzJwW7d7N240be1fPpekg3mdjvdjeqdlRfm41JffsyZuJE4pOSMJnNmEwmTGYzZrOZ/JJq3pi7lckD2nNU7yymXXMNfQ2+i4pmJc6jTANIKfOEEC33lmkIBoVOysivqZBW7MbNSFMZEIgtDVPuaVddxb5XXvGum+Pjoe/JeBxbTe384wRMFqPyEEKwqmLDdunL4qB3NVvdVG/VlJmuu2GJW7O8OssrcQlYlGgDowFXSq+F2lmwG3OVL/uErKlGxNR9cUJd0ye/X8fDpw7AYXbgCetzV1ZyQJr9Ptc/P13KDJPJLxc1gEloRS06/LkAgGKHb5YuwW4hdsTwkAq1LadDUFsgob2D6+pN0OAiwZpAaW0pNglJ1oqQWT5mLt3JCQHZNeLdbljwCkz1jTPLa0qJlwHXoLAelSvDfZheU2Gtb8yb4kghVKb0G95fjCfNwAuz/W2fgUbcEV3SWLRVWx7dPZ0nDHGBK3ce4IQ+Ppece0b9g/u+/wyA9foYcfrRJtbkRv+jc0VIb4jbickicddqx1t/7An04VViTRYiTY9It51Xf9nEiHMPjktlKOpUqIUQfwUuBvYCrwF/l1LWCiFMwHogpEINvAk8D4QzhU0Beuh/I4GXCE7RpAhB/5QUsqurmRXwY4/Wof7Knj15e/161v/2Gx1H12m0anKilXP/hg1k9OjBHePG8aBeetyjFG9ctkyrGBmGU3VLdUNwlZVT+NhjFH/4IfE5OXR/9hliIpwrEOly8ekVV/DLf//Lqc8+C2juEr/t2cNlPXow+uqr6ziCPxUFBRSuWkXB1q0UrltHQX4+hbt3U7h7NwXFxRSWlrKrrIy9Tid7Zd0eZENTUshISiIzLY2MtDQyO3VCuN0s++MP/ty4kT9LSrzv564WC2O6dGH06NGMPu00+k2ditkSXfW1Wa/M49O9Fdx4+QSG3HUXJWHK1yuanE36rOJ0ff0CYHMLylNv/OxQ7vq7tjX1pG/tVp8LQ+Lk4/0UalNcHAw9C49CLZL9g31NPY/CvcNjsZYgfJ/OllgbYKEWUK0p1NY4FxmDS0jsWElR4hCq1m4kd69kiVXbP762gnIHdK4NKHIh3VQVa9ds5z/uhnvv8W6q+GMxYlwExSeCn3FVjcsjoZf9b73Fb0eeRE+v9JJTN84JUqZBS+EXjsEdk4npMiTkNnNsrHe5ygoO3bjavaYGhzu0xTJS0Jzx3kiJ1a6TxwVifO54luz4DcE21ppzyXHuwR3iuWUJUJSznMEfrqy2nJQAzTUxJnSxsXYDD7BnWZQGh1qfS4Q7PtglyMO2/cGhbhacOAPUPgkkOHxt8faA7RLSY3zpFqf2GsR933/GHZeY2Z2itc08UjPkJdn9Z2vCUeWKEIa39H1dodZWPylKZkqqmRhbIjgj+FK77SzcUhTWMHcwiObNmA6cJqXcamyUUrqFECeG20lK+bMQonOE454MvC21Tz9fCJEshMiWUrbO0O5WRGVNDTGO0AErdbH+m2/43/r1XDd4cIso0/UhtXt3xiYkMGPhQh7U2145+miumj2byWPG+LkTNBXlCxaw665/UJufT+qll7J6yGAG1EOZBhh+/vnEXHEFeT/+yKl626IPP6RCSiZMmoTL6WT/tm0ULltGwaZNFG7dSuHOnRTm51NYVERhcTEFpaUUVldT6HQSbqY6Aci0WslwOOiSkMBR2dlkpKSQ2b49GR06kJGTQ2bfvrxy99089ccfxAKTsrP5fPt2SpctY+4nnzDnhx/4YM4cFuvV4NKE4LjOnTl+yhSOvfxycoY2vOrhtRO6c/EbC/jiz51YzGacIV6yimbhMuABfEmLf0ZLc9rmiMbmVd9y1Y3FFOdv7DfFxuIXlZiY7bddxKZC5X6gne5757POmSxS96E2PM9rtOeaEJDWWzOauKs0pW7iUjdfHuHGLN30inFTFmdiiy0gl7x0k5BTxe5FkHbF5ewz5Ms2xcchCP/clCGWPHz6Zz5Pnj0Yafgdl3wxgxu35PK1HgQoJFy6yr+ouAuBGYm7JvwX1a99EgkTDfY0iwV0JVUY3DeE1aoVuwI+y9/N/e5sjHdJtKXq0/qW4qoyUR6vZYzqkKL5o1S7qrHr1ud9Fk3BDbRS98iIp98B/5SgG2w2yPC3jJbXlpMb8Mzb138w3T4OjK4BW3yI0Ua4z2LwuTeVGWaXA5T8S8d00Wql6kwx/c5Ltmc4rfp+Fsuefn2NCnWsLXiQkubwBeImObTrsik7+PssqS6huKo4qD3oIzgN1zTwMJXFmptUpSZH15KdWIWL2LICiKD3SGnT/2/FCrWUMqypT0q5Oty2KAhVRCCHELmSGpvTtKEUFxfj0vNTtibKq6sxOxxBckWTQ/G5Sy7BBky64YYW+1z1yfU4qn9//j1vHp898wwpgwbR/Y47YPZsNpSV8d1332Gz+abQXAYrwRkZ2oja+NMy+pt71z3LUkJlJVRXa24dMTHIx/4DUvKE0V3E+Cf8JwyNbZXAs6tWcers2SAEH9x5pybXSy9BiHzf0RILtDObaWe14vAEIbrdlFRXU7p1K6u2bvXmDffItlj3164AZuzahSmMhTkF6B8XR8Hevbw9fTrTp0/3215XSddQPvW5Tnj2WfitUnuJ5wXkdG1piouLgfC5SVuTrPVgkpTyr8YGIcSZaMHlbQNT4C8rPNEqUfUh49ZbKHz8Ce96/IQJlOn3gclgMYVgBZv4LP/1lZ+B2/MZ/IWVEt1CbVAUqoOH0AmTJlL08Qx674D7av/HiII1bOjYl1K7m6CCx9KNyaYpVyZHDKLa52C954knEe9eFeoja4TIdR+IUcG05uTQLsHu3aNdgp3dsSlkVfjSrs3P7seYXf5xqx1eeB6+97mi3DDRP1uS/fiJVH/5nS6ST5pODz7C7ptvjSBdBDyKlnSTMVB7HuUHdKlx1WAr13zlE0o1K2rNhg1+fSprXcQYBkWeoiLuwpV+38TummKGBih3tQlN4PYWprKmyenv+PHWr+sxep6/ZNMyr0x0rGFxZU995kcbnCQ6fLNAmoVa8MpkEyXWeE4bksNxHQfxyornfQeL8Jv75/y6qxpvOBAhjrn3VEzWL72r+3K1eyOmrmBDfbZCBgwsrC4zexOdZBVDTLtGpPqJgujmbpuHUL/ZkFessTlNG0pycjLFxcWNykvYHGxzOtm2bx9Lj/UFQXiURc/DJ5RP83qDwnlLCLeDunya6+s3Hc6P2iinB6Oialzf5NIeXKfddBM9LBa/m2bTxx9zjWHqde3vv3uX/ywpCTqWdzmwyIuUSKcTISXCbMYkBFRXI6uqcOn+3l7ZCa+YB7YDxK1ezfDrruMWPdNFKDKEIMNqJdXh0M4dgrquba3TicmQ5cMox4CEBOaFseYPio0lzmLx9vVMbjbVd+8UbqprfQ+4CRMmRO3yczBITk4GwucmbU2y1oM7CVaeQ7W1WhLtdQeVxVvjwevNq9FQ5XpRd8GwDb6dTfH+QYn2Pr3DKtTV69eDmORrSAhQqCMgXULzocYwdV8TrFDHjhwLwLIBCYxYrrmPdN+2KsxB3ZrB3GzCXVGBqSrwWRulP0QYyt/xDbTLf/2VPemneNezkuPJqihie3w7cvVUeY8NPY8xs+7yO4a9Z0/43peLPy7AzUDE+AYYQgjaj9pPyZZYUqaeEKBQRz890bFiJb8DuTVhvJ/ePJE5YivYNSNNXK2m8Fdv3AhZvu90R1ElVrf2LnUZTl8rwFMhoaK2gip3LZ8kxHP/Xp+DuzRMp9iTaqkuaUCmrjAWYFe1vxtFHL4Bi7HUeJzuX/+vkZdw2oY51JitARZqC9KZwJcdjqO2ZDC3ZSaQHqP7UNf4z76E4vut39fZ59sdHxG25vHGnzBbXHhK3GzqPgQO4C1dHx7tM6zedQBjYstbivfQzWGjHAfpfZo3GUBLKtTRFBFQRGBgB/9gjaqqKmJifFNRgY+a9Zu1B8lxGRmkxceHfBQFWRlDKL6N6Q9aRadYw0spqLALPuX0SOC9TVoE89DcXCQwuLaWj3bs4OUPP+Tq//7Xe85V32i1uBY8/zzDwwQtGnHX1LD3uefY9/obWLIyaf/QQ8SNGuXXp6EK1WtXXcWVr77KP++7j08vvZS1Lhd/6d+f55cvr3vnBlCXnGfn5vLrrl3sqKlp0nLjdeFyS455Io/Vt008aOc8XBFCTEEL8s4RQjxr2JSIFkzeZrAYBofumtA5il1N6EL02OkmLG549zFtAG+K8Z/SN9kNCl7AtsolS8Bl8LONCShScuLT8Onf/JoScqsp3W7HGkdAHmpCWqhFjGYFH7g89MDYhcHvXLoRAkx2K+6KCoTNp7TFjhjh94weuMnNxT+6kefWIGzRBcy5K8JneehXo1l3cw15p6stwce1ZmXx2BnA56GPIwKKdyV1qiKpU1VQv3IcQHC7UfW66SoziRUwyeGCWsiND6OYbfkFuvgCSj/rchTjdizD2iEXnJol15PR5IBJ+0yPnOV7ljpTu3sV6tKa0N+T8czC1MDRX6CFWlf8ZbX/4DJBVPL3sdeSWlWKy2DdjbVo9/jijF4szuil9TVYqG0WEyCo2asNEmOs2mcsXXs/KQH3fmPYl6Ddhyuy0jjb07jiUyjehsma4u23orACHFFYqHWmz9vCTYb1kZXVRJNCsSloSYV6BnC9EOIDtGDEEuU/XTdG/7UPN/uPtCMpVYtee40PrryS+ydO5L4ff2xOEeukvkpq+2HDeOaPP/jvr7+S2F5LHXTsMcdw5U8/8cvnn3PUqZqn8qp58wDofWJY134vlStWsuvOO6hev4HkM88g4/bbtWj9JuLCJ5/kyldfZdbevSz+6CPKgfETW06pPP7oo/lo+nRWfPEFA049te4dmgizSXD1+G7MOWhnPKzZiVZObRrwh6G9FPhbyD1aKR6Dmnvr+dS4+mNvF2z1qjAENp36m5vxK9x8cFTDBovSJKg17OrSXYG88iT4ng2BA1Jhs4HLMF4JHLBm9A06X8KR/SjdvgFTjBWcVaBbPJFAFLmZvXLqAYoHTCZSPO8G3R3B5LBq7hnCJ7u1fXs/C/WV37rJLIba3buxGaqYml2h81BX1DixdOzkl6Wvc8ku3AhMSLq6ootrEVYrHVJiw243d6w7s0e07EwT7EyDY4u3gLlu17VUfVa0Sh8IuCsrvDUfqpzattwEbX1FZ9+xfq3M53h92ZPF4h6DdRoCZv4a6vsf4NJQu1ufqdi1RPPb07nYNpuH0s8K2v3sqo+523w6tS6fLEYLdSCxNn2b24EgvEXdWd4FS1z0sc+70gTXX2Nme3Uv/uVp3KsV+zFZ/JXn3929iZH+2cgCufGYHjzz43rmbtjrVaiXb9aCibcdpDiLZlOohRDvAxOAdCHEDuA+dBu+ntf0KzRrygY0985Lm0uWQ4mq/doPNHLx7QCk5B+33UaaEPzt3eCAiNbOSRdcwON//MF3TzzBGU9ofo3nPf00tw8cyDN33ulTqFevpqPJREJ6OlL3GzamtZNSImtq2PP6G+x7803MqalkPvYf4kaNorqyEllR4d3HQ3VREeWFhUEp9+pathoe2nnvvw9Ah5wcNs6c6Xf8SK4VQdvC7CelpHDlSlbt2xf2GO17aMUgnrj5Zm7RXX/CHo/QeNxlQn72CNv61kQoEqFoMqSUS4GlQoj3pJSHxkWv6gjW0Eqy8R157s+akmE1WYFqbI20x1et9c97bY6Q8rH9vx8hoq9EXLo3L3Riru7natEsfcKiKyhOg4U7hEItpWRbO+i4x799eS87UMP3cbGcVapbtvUiICaHnZLPPsOYd8FdUaEXPQnA87zS/+uQvyG4D/Dzuj10/cY/6PCs9T95P31ivqZQ3Tj+rzwzxzdJknLRhRS97R+TkRZvIzB5mr0nVK+DpIljKXvkqZAyNJiSfEhNjtglw+lkXKXu6mHWvhtZWelTqHX3taz5mlHKbXClKDcMpKpd2pAj1mAAMztcLM8v4Sh9XdgdoCcvtcZrVyImrcZXKTFKjTu7z162F6RS5vD/yV/ELB7Co1D7358JDiv7y2sM67ofuDM4u2aMzRzi7g6WTTrr7x9emCKQBcHHMln9Bw3JlIUsP/7z2T9z1IfaFb1pkqZQi0YlhG0czaZQSynPrWO7BOqel1f4UbxCc+avBtbPmoXL7cat/+1asYLl+4IfxnnvvMN3RUVc3LMnW1tBtbpdK1eGlDMcCZmaf+HDL71ELz0ziZSS7hYLn65dy9bFi+l0xBG8p1fnEvWxNE+bFn3fBnKHXgRm9O23N/u56uKtLVt466xgq8XBYLjJzJLtxS1y7sOJQ0aZxpieWWKS/gpMmiMdKPTr79RzbR2xsXGRirGTJlLy6afe9UCfaiOmuDiIVN09Ng2TGXqcuhuzV1HQP4fHHcJlsPmGUKhLqkuClGmA7OoYoIYYo/uLR6GODc6I4K6sxC3DKxx1XTWnW+LausWvrVtxvqaQS0nXH7Rrti7ZPz1cKKuwzWwKyp/c8aoTsSx6BVLa+QUMDq16CRtO5tUhny1Cyhevv7M7UI33USMEFl1xqzLrFuqKStAHVPM2at+NozB4Mj3X6dQCH4XgrZVvAbDOZoPyCrqfvBuTWbIi/wB7HUmkV5UgzGY8CnVMai1dpxZiS3Cyf139UsbHZ1XT55xdlBnN08Bq2cm7HIN/MF6Cw+KnUMfZLFi3nkNRTRetv9VMZa3Lu+zBe3VliBiHOvLFR6KkslZLXahnywm0UH/sOJUkd3AG5hSH7zMbEwcE9eteTvkuB46U5n00tqTLh6IB1Oz3TSH1POmkeu371rp1vHVG/SpOtSb+rKxkYAj5/z1xIk+t9iWcuXfSJD+LKVLizM+ndtt2TFYL9u7dsaanBxe9MQYrCoGUkn379pGalubv320MSAxcNvT7acECfjZUVXv7xhu9y345DAL9zg3rkXzSjdu2b99OR8OUbah+Nz/1FNudTt6++mpi9epu4Y4XeN7AzxaqlLtx3bjPkiVLeDwvj26Dj+WlvA2cGzoVq0JhwJORQfv/zF/cnPmb5IJbzZTsvgiATikrCFSou+9smpQfrt5d/NZNseGVHGF3gIzg6uDQfmsWu0+RTRiUTdL8+WQc1wt2r4cKg2tACIV6U8kmQnmvtqvW5ipTjAp11QFd5mCXCndFhdcdwUwMBKbQC7ACrsgv8csJvmNPKd0CjtmxrBC3yRzgIOx7fky/fASx22PZ/5amEBV27UsftAp8gQq1ZcojMPYGiPf3m9/nZ2cPJtepeZGnuUwQZPfW2O3JbhShimKtENikZLZrENW6Qr37/vvhZS0z01/e02rPVXbpQczm9X771gihue9YY1hSuASAHVbtnNYY7fupqnLx4sBTuXfBmxCfgTHPiD0x8rRKWp9Sg/U6mCxLuV+kRJ57EKNNK+goCkkJSL4a6OJh2r2Exe7/MFMcCZynfTe6Qh0qjV5N0WiEpQxnWS+sSYuxJi8m1mYmWnW1fWwndlZs9a5X1bp0hVqXJ0ChPjFxM/fZo/PfFiGGhQk51fQ5p/lD9JRC3cZw6g/JYzMzufjsszGZTJhNJkxCBClVAF9+9hn/27iRY9PSuPrSxnnVSClxulzUOJ1U19YihMButeKwWr0VGiPhdrvZV1TEij//pKK2lvzCQvJLS8mvrqa4HrkjY4Aci4WcuDjmlJTwTkkJvfXPNiU7mwe+9/lbVm/axM477qRq+w4SrriCrHvvwZKaGubIwTQmy8PNu3Z5fb5PS0vjwqefbtBxoiEaOeNTU5ly551kZGdz/H33NZssgaQ//zyP5+UxaGBvXl5ZwPiUpgtsUfgjhDAD/5ZS/r2lZWkUAWamSUu050NcFewr0/L9WlLXBO7FSQuaRqE+YPIv5mFy+DvZOfr2pWqV5rtqinEAkoQOlcQf0Sv4YCGssyarmfYji0FoirTFXAwkYUl0hFSo1xatZXAIOU3bdgIWdhtLMn9zuy5XsPJV+ccf1Lp7A5p7jCcrSn5xJV0CBrpSSnaXVGEsU/P2Z/MYH0qOCFbfvtmJJPQ42rte0G0AAOnxdoKM7mYLJHcMbGX2rRMorw5WOD0mkB61LpZu3sZfkzoDsLJz8DWfEa8Pisw2ZsTH4cKYFUEyOzaGcpOJPxx2bjMv9fpQh8JcUY5jwADAZ8iZ73AwpngbtOtF77TebCzZyIDq4DRtlZ7jxqUTlLiv36kwI/QscsagyP7psS5/pfkmi2+GZbXb/8vNsNbQjiJGm1byteko2K3NfJ9k1sqix9st7CnVZI8JoVAjrVQXngCAsBZhTdYGGssuWsbAt+uu21Dr9v99VdV67h/tezNZ/X/H2wv2UN65bh3jX6f058nP59bZr7lQCnUbo1LPAXrlBRdw5uOP+20LVKrctbX867nn6Gq18uW2bVhDWCxCUbBiBe89/DBLFi1iy5497C4rY7fTSSmhpwTjgbO6deOWl19mb34++cuWkb9lC/n5+ZrSXFxMflkZu2prqQnYVwBZQtDd4SAnLo6czEztr2NHcrp1I6dPH9qPGAFSkt65MzcfeSSPzvNN/K394Qf6HXssN+luFRdefDGgBW/uf/tt9jz1NCaHg5wnnyBx6tSoPn9TkZDtSzH09zvuOKjnDsVRV1+N/c47+fbTTw+qQl2g53E97thRvLnaxFebaznvoJ398EJK6RJCDBVCCNmSFQ6aCKm/YC0mG56kjh9edSSfL8nHWth8r69Hlz7FjYZ1Y9YJt3QjDAUmhMMBLkmHsUXQO73ugw+/Eu+TtO/JsGspqT3Lsca6SBjZHeZ/F7TL2v1ricuGHgGeBu57/wq1L/JAehpnlOrZN3boJdAtod0f0mK0pGKDk6YAWkzNH1uL6TLAv1/Nxo1U1Pq/My5a/W2dHy/nuWfhR9+TPiW28RkWuqTX5QYhMQF70k1ce52ZM1wHIMCq7fT6iQv+0U67Bm8e0K2Wpbv5a6ZmFV9lt+O2xOCKYMm2FeykqmAnnOi7B99MTuQW/X4dmTWSLzd9yeSyCBUBjYy6HnYthY6jgWCF2mVPxlxdHPEQMTJ89pUl7u70MfnKfkyt/ZZbbd/Q17SVZabhUOs/VxBnSFsZYzMHTlyExGo2IYTg4bEPc9evd2EW5rAlxvdU+d/Iq3cdoFOa7zs2mQNO2PdkRu57nC8SIrtzXnhkJz7/4pO6hW0mlELdxqjQg+1io1COP/7b31haXc30G26IWple+fnnjD/1VPYB7YWga3w87VNTyZSSiooK9lRVsdflwviYKAPe2LiRNwx5sQHigByzmZyYGMZlZZGTkUFOZibSbmfkuHHkDB1K1pAhWBISopJtQkoKMxcv5lHdTw2g16RJXNqnD6/pLh/71q/n9dNPp2LxYpz79mPJysTRozvivffgvfdCuiQEPiuMbh+FhYWse/VVvza/5QCXkcD9PXz+/vvM+OgjP5cR7/8QUg7jsbzrRrcMQ3tpaSlfGa5juH7VwFPLlmEfMSJoW7TuJZFSJ4bq99h8zerRY3B/zomXTJ+3hfziSnKSlaW6mfgT+EII8X+A9y0rpfw0/C5th5Fd0xjZNY3/vtJ86R9/2z3XX6G2+rIbFFUV+VmsTQ4HlNdj7OKqAT3gDVsCWGMRtRUkdqwC3KEt1PvXUjLIRI9d/v7PHboNhjUwpiLQeQJESnsguIBGmZ7nelzGaXgU6nI9cNioODn37eevn63FGII4Pn+J37FKrTEkBChjttxcYKN33WQKfGI0xzjPd459SQLcHQhyZ/FgzMjiqTRY6XO5+deefVSf8Bx8GPmMjkEDAV8u8CMrKzWXD8Cp11kPqZIbnpHx7au0a97xSDj+IVj0Brnj9rN/bTyWWJ8y6rY4MNdRkyQ2RPpAD/nCPzd6vMlJltDus1irgGr/VHxxNp9q6OdDHXKMpjWO6a4NUk7qdhKdEzsjXTWc/+0lkYUGQOqp+vD6UBOQUjD7yLM5/qN/hlSoE22JXD7gcu/6E9aXqQx95ZsdpVC3MSp2aiPqx154gT90S61HidlfVMSvKZqTvtPp5IHZswHYvHo1D0+erPU1HCtQAXRLyV26pTdRCKwWC7+XllIbUBREAFlAjtVKTmIiOWlpvLROi4rPFIJTOnemfbt2JKSlgcnk52NbDezduZM5338P338f5IMbiLF1eUkJBW43l/boQfduPk++akNp3Rs+CRidlh6A9f6+bi3Bo4sXo9WfqvvPQ6TiKeGWo+3374UL/RIgNcW56iK+WzeubO9m+rwtvPbLJu47qV/dOykaQiqwDzDmaZT4SpG3fvQbK3B4ZjZ4EndN6grNlJBxcufJwCzvuq1rV+9ySXUJVnuAhbrMI3DAoFT3oaXrBNiUpy0vfguGX+HrFJsOJVpANVUlEMKqt7ZoLUW5wdqMRZhJtMaR6wxWHmt37w9qA3hv2VtgEcQa8k4XV7qC5K/880+08hAaI7sEu8rdOeYans/zz8ZhTknh8rFdwuaYbg48YnsUM5PZHt6vfXMeJIf3yY53u7HHJmES4T2CZWwcMYMG4VGoBYKBVTXg1LTe3/I1K7Ml4L0mA8wRuUfp39HGn6DPSbDsIxwpTtofWezXz2WOiZCwLjT7ZTypQhs8tbNU+D2sq2N9hYRsFjP89pxBSOlXaMeoUIdGO3CsQQkf0G4AK5e9E7WsXyzZycTemV7/ds/3ac/UvoMdJdV0CJOH+rdz/S36DlFDzkkluF0htf9mRSnUbYy8zz8HYM7+/cz54Yeo9rk3yn5Ghjoc5KSkkJOWRk779poLRo8etB8yhKw+fbC2b+/38P3P6tUk9O1LgZT8d/NmCMiR3ZS8uXEjbNwYctsjiUkcNX487a67Flu6Nv3qsSRHSnfnrTBpDK6TkoULFzJs2DC/tojLuhxSShbNmME5Dz8MQFebjY0h/Omaimh9vZd/8QUDTzmF1y+5hMv+979mk8c4QPL41wu7nRw7HJlt4YMF27lhYg9S4w5Owv3DCSll44IlWgPeiq++3ylAF6svx/yIrBHspHnu4W+2fMNlhvUDNb6iGfur9pNltFDb7fhGAP5Wc1mrW0OzB/kU6imPwd61vk5xBoW6PHT2o1p3ra96uQHnnj3EWWKpCGE6dB0oCd4BiKmBUguM65GBxwu9qFx7NpkLfBVd9zz9NJzicytMcWmW6M2JWXQ5oPXbmGz0sNawZmZyVM/g2YPCMceS8dv3zWKg9n765I5QsoHKpPZQvDdk32JjvI/3OeW7fg4pMa36lMSY08Kfr6Ick2FAYjdZqPUEJQI/btPS6gUmnKsKV2AkUYu1IbUrbNNdGs12bqy8nO0yg1fc7xOcswXKpIN4Edoy7TSodxfLL/y2peKrJOwGfwv16hnE2Hw+13armUSHhXOG53L+yE5ETYRsMoGM6aa7Sum/n4QOVSTkVhI7WLsvhRDERHm8FHM11riWSZ2nFOo2xpoCbYpq+qWXct5zz/kpLnN+/pmjxo2juqiIHp07k+lwMD8/31c6O0TeYWPbR7fcwiVvvsnC6dMZdsEF9ZIrvk8f33FuvplJt9wSVmn9be5cxo0dG3Z7IEIIXDU1zH/zTSbedpvftq5mM51tNn6q1B72r1eUc+M704mJkDe2PiQVFZEzeHCD9v38wQe9y5tqAr3HW4b+J51EtsnEtz/84KcwNDWRiidM7Wrlt52VvDl3Czcf27MZpTg8EUJ0AJ4DxqCpL78CN0opd7SoYI3ApN9PI7ukULl8BbKqMnIJ7TqYNVxQYRec9Wt0L957f7uXa/TlH7f9yIU2n0ItYmMNyoPPT9fPf8K43Gsy7PXkuZaQYCjnvCS8VW9XWvDntXfpQtyaWF8O5Mpi77akk6ZRqOftNxJTDaWxkBrrs/bv1QPQkv73fNjzTyvTYiE8ynQkQhUKqczVMqfUxkTnftgg9PzeXpeaEPxfYmQXQ5uUkDuSuLWh1aMUPYvKvtdehzu1PlXuWn6JdTBh/yqO6DKOKdlj+HrXbyTpVtX3bGfyYekASjC4LBifkQP0VKZj/wZL9FoR1hi+KNdKzm8+IEnXv+IyEUe87i+9VWbST/iyZRjJEMVhP+O4rS9QpMuybV85ftp6/mLslk4ss19BoUzGYV2BEIJ/nx462PDf08bxwKJP6JTor2zXZ9yUX+xxG9KDEi2SDmOK+Nw6BUphSG6yX7XHSNjdUfqtNwMHrwaxokkor9JGo0mZmZji4jDHx3v/TLGxWBISeP2GG9glJY89/DD2lBRsSUnYkpKwJyd7/xwpKThSUohJTfX+bV6/HgEMPC38yDwSC/WiMWc9+SQp7duTnJ1NcnY2SVlZJGZmkpCRQUJGBo7kZOLS04lNSyM2LY2Y1FQcKSl+8tmTk6kqK+OL++7j8iFDyM3M9FOm7xk7lpVvv83syy7jZL04whNTp7LB6eSxg1gJMBKzFyygp8H3MqrIjmZGmEwc17073+/Ygasy2O/yYJATb+K4vpm8NXcLZSEi9xWN5n9olWjbAznATL2tDeH/W4m3aUrYuf1OZsuZZ7L1wou8+oi5AcaotyeZ+Xhc9K+/X/N/9S6/s/od/6BEq5WfLW4GdOnI9p6TvG1hiQ0IXEzMDt0PNFeRS78Ju9nWrRsbSrfyY5yupJb50giK2NAxCnH6RJnZEHS3YEsRAKaAsuJWg79xSrXmQvFNpxHeNnOQf7RGu/jg0mO7J57E84NOY/2oyeE+Tr2p8ei8uuHXc08462EdBTBGsFgkMOgcg5IH6GkJJ/XJoLNNW0674nLjIdhgs3Hxiudh1RfErf2Gdk7ftasUdpbK7oEn9fLb1lI63/ElRU6fBVtafd9frSHH825890+yiJD/vKGkdMZuMZEoKuhu2onNrP9Onh4IP/4TAIvhez+j3zG8etyrXNbf30Qj61Fg5ZkfdbdM4/d2x3aW9r+LBIeF3NRYslzhM8m0FpSFuo1Rpmf5iMsNncy3fNcuHpo5k6NTUznGkPc4Grbt2kW22YwtygDGQIaddx6cfz4A6z79lJ4NUMzX/fgjs559llm//MIvRUU4gTQhmNqpEyedcgoJaWlMueceuiYmYn7+BSqqq/mwXHuozPjlFwDu++kn3jCb6Rwf7x+EaPwLcNHwEBhQ6HI6sej5S0Vgn4C+xrZal4vfyv1fTsJkoqPuUx5IY9pA/zwhckeH2r9Ab7fExpLpnVpv3PlBG50LNGuiMKyH4toJ3fhuVQHv/76NK4/qGqaXooG0k1IaFeg3hRA3tZQwjcGTU9aECTcQY/EpsrU7tHiSHgWCaOxhn40SnDqvYYPaGnf4NHpCCD7YocWrLIqLJxetemL+zbfogWv4y2cLeL4m+AeM+XHWdHCEn20TloBX+PRTfDKGyZ19xq9uHjvDjEkEDygqh48hZqHPJzXdYPFOfvc1AJ4bdDqTty4A4Ohe7YgbN45y/dl7oINmhc5NjTUklNMxm/myy2guNjddwNgvveKIrajm8txajCmk3Pr17m2OZ40rgtK5/CP9IeV7UhVazGAJcLDQn5m1LkkHvQxn7MiRsOmt4GMWrqFWCE25Mtu1oj113HZvL9CyXqzeW8tovc1p8inUo82+4MdSt80rbo6IrkCa2/sLCub0I3KMsZXwzR3YB/7kXRVCaD7exVvhlyfgmHuxmAVOg8X4yOwjg47ref/0r65mhT14gGXk6F563nFj/IAjkaJKF8mx2uDUISVDK6v4IyaU80vrQCnUbQxPAN4FN95Iv3/8A7fUxoFuKal1uZinK3El+/czMSXFT/nzlojGXxn0rH9dVNRo+T686y7Ofvhhep1+OuP14iHGbBagBUx6lFSny8X88vDpfuxAv4QE8ouKePl//8OtWwou/eorJlqtmGJimKu3zTEET251u+lk0tL4GBW7cNfAIyf4P/tqAKvV6ucbbezjF7Bn+Jy/HThAIMnAxJ6ai0MoRTNkWwjXiVD9yisqiI8LfoGG6rvvwAE+2aHN/p9icNWJ9lyh+nk+u9v4v35PTl+/nus6d/brP6RjCqO6pvHar5u4aHQn7JaWico+RNkrhLgAeF9fPxctSLHNEKR/hLjnqpYuBcBRFZ0lLCYzB2i810uXpC6Ivf4KwtI9miz3zr2XU3uciiUjAwCTLYwiYfw8CREs1BFcpwC92p6BA768xpXLlobcp2e+ZP5584Pay6qdfso0wNidy4L6uQ2W7SfOHEz8qY+zbsRIAHYcNZWRESVuOjpXvUei6UlmHmniikL8FOohse15b/8Sbo/pwaVlf4Y9hti1DNpn+lmou9TUghD0z0nk025HcdrGn0G3jq7cWcKIEu3Zbg6XnUoIXzYKezxUVGO8o4vs2jZH7x7eGjRSTwtZK3z3i9MSeoahzG0Pkz4kPG5hwmSw/tbYkkGfHRxQFpC32Vnly7rhYYv/fXFc3yxmLN1Jenz4GBjPu1KEGUx0SerC5pLNxNstZCXpSrJn9uZcLcVKSWUtyYZ86kapLu/vP0PQGlAKdRtjQrduzFu1imy7nQq3G5OuMJpMJq87CMDIpCRvvSivVdbt9lNsjenamsoZ4ayHHuJsPRCvyuXCYbH4KbUCKHE6WV4R3s+pk9lMp/h4r0xutGelu7YWaQjsK7XbkYZpoJGJiVQ6nSzTj33X7bdzfCPzPze0sItDCKqBtV98QccJE0hKSuLK4cP5z4IFjZInHPWVc1h8PA4heHnlymaRx0PBihVMHzCAvsOHB2277uhuXPj6Aj5bnM85I4KLOSgazGXA88BTaD/tuUCbClQMVe0sEOeeELW4I9A3rQ/1UaidFoHFGSzH5pLNCPvxfm3GoEWAmEGDiD/jVNKvuVZriOTuFcpCbYkBZ3QuWWf3PIsP130U1G7Vi0oB2Ht05+6hm7jnAzfpE44lzqoNvoUrHvQqeku2FpEWcIzLVn0V8dxJsVagedzadqRBhzqHgaHP184Sx/LN2+CIoyPu7bnPtBk+rc1TdTIzwcF+h6Y0C71tb1kN1fu0zBymCAq1F1s8VOzzk3JrYjZzb3mMy6b1gJee0Rp1n+8nZ2/1Fs6pNfkssRvc7elu0mZkKvEp3SvdnehnCu1DbcQi/V3rzK5qPFr56D3B+QG9bh4efvGveXHRqE5sXzaHxOQwMTBle5Dl2u8z3JBwXM44NpdspqzayfsLtvPIaQN9Lh9ZWlL04spar4Ua/BXqVEf0BdoOFsqHuq2RnIwZWFRSwm8lJfxSXMyc4mJmFxVxlJ7aaenHHzOnuJif9b9fiov5tbiY30pKmFtSwjz9b35JCb8fOMCCAwdYGMKi2lC+f+wxAK464wzyioqYvX8/z7/1FsePHEm1281KPUAvUwgu79mTz+66i9L8fG+2jC1Op5/8eVu28MHlV/BabkfePfkUPrrySgAe/de/+MfVVwNajur5JSVcdrTvAfrYf/7TZJ+pvnjU/p7TpuFITGRwXBy/r10bcZ+DyfFDhjC/rIySMNlSmopCPWVhRk5wNoCx3dPpn5PIf3/eFHXAiSIqcqWU06SU7aSUGVLKUzAWhWtDBKYZM1I+t3krorls/vYmox28iPCzaqD5UE/tMZMHt7wcusOgc9hhMVPbdTwktA/e7lXKwn/+mGFDAUiL0ax6fh6mf1lAguFZ6Bg8hLUdtGMl9/FVcBFun7Jy7cuzw38gncx77o64PZw1siHsePwvPHh95EI5pgALfn0DVV0hZgCs+qDgxzWFDNi7STtPcbF3+5idy7W2sK6Rggynk6GVVWDX3HVSYv196ks69UCk+1K/SpO2fekOX7aNNft83+gnrqO8y+U4uKTm75xc/U/mWXz+7AC1MZrrxIemyEXMzK5KbHqh8DJHwIBOmIIt1AEkOKx8Zr+Pxw7cHrrD492DlPCocOupCk0Wtu4rJ7+oUitJrsv1u8Hd49w+59b/+M2MslC3MUpLS0kQImiqr2DJEt5Ys4ZzunRh4Omnt5B0GsfcfDN9/vEPLn/zTRYvXMiX69axRXdVOSImhuv79+eCm29m6AUXYIoUvAOUzp7NrnvvxVVUTPoN15N+1VW027MH26uvMmv6dNZu1UbnU8eMYcOXX3LTl1/S3WrFIgQ/FxWxTfftC0xzF67Ng8etYd/SpWySMmzKPQh2HakxuM68r5dE31Rezl7gmwcewGy1+hd3CWPVCSq0EqHwyqbNm6kNUDAi9beYzbiAu086iVMvuqhe56qPbD/oecEzunQhECEE103oznXvLuabFbs5YWCEqW9FfXgOOCKKtlZPkKW6ERZQS5lvBu+dqe9QUVsBj4TPdVNuqsXosHH1DWbi9UNUmd0R8wJ7ftMzNs7gobEP+eQ+Tsv8U57Rmym5OUxbO52HjrilIR/Hq9DZzNqUeI0QxHjOk9QBk9NgJLFaqLFogwLX3n3sfvAhMm652btZIBm/Y0md57RmZsLy5kv/aeTSUddz6ajrg9ofONdEtVVw//i+fLg7hh0hXaSjU6x/1AM3jS4fHoX6xIHZjPxc8wRP/c9jvNVR8xPenJTNqN0rsbRrF/qgQhDnlqS7XGDW5DhhYHumW5NZsr1YPx9amXUdsylYgS2q9W2vNtxtldJOnnsIAKOrV/lPENjioXIPB4zZRMIQJ7TvsSi+J12KDO8O6aZdTYSZnOpSemVp1vmMmm11niccIQc/nhL2ZivjH8sD8CnUOcMAX4YZq6m+mbmbH6VQtzFKKyuJB5xFRb5Kf1Jy75lnUgHc8Z//UL57t9828CmCJt2vONRyYylYvZov//MfZn33Hat1K/QLK1dyfHIyN48dy9QbbyRn0CB+/fVX+o0eTeW+fSGVWwB3eTl7nnue0q+/wt61G9kPP4yjZ0+qioqwWK2MSkri0yVLOKBPxd02axa3zdIKMWwwFHrpdJRvZN8SnPfmm37rU+6/v0XkCMfzq1fz/J13Nvt5YmNC+wMe3y+LrulxvDRnA1MHZEVMt6eIjBBiFDAaaCeEuNmwKZEovS6FEJOBZ/T+r0kp/x2m33BgPnC2lPLjRgkeCqndBxes+o5vRpxSpy9xNKTmLfcuD2o3CCAocO77IYI0XQ+tCXg7lsQLSnQ9pdYivHrMmv1r/Pppg+VAxd8/33Gl7s7xa/6vMOZBoqXdjX9lzzPPAr6gw72VWr7lIrOJGKeukFgcmOJ8Fs59zhLQXe/2v6UF0nn8vD1S/WXZZ3WeP27cOK6q2RS+aMtB+Pmu7Ky9qy4Z04VPPqvLkh95APZNiLgTT8TM0E4p3jZTRQX3zdDc4+JrKkGI4IBQLwKX0Kf/92izkg6ziVOH5HgV6mADRfBRjDmrawyqWoVhmFcdOKyza4pujYg+v3+cuySorXNJsI+9l/kvwfjbwm+vCC4olOByUxroRqLTMTWWjZ5d3LprisFPP9GjUI+5ERY0/7uqMSiFuo3x1gYtF6g1NbT/0OAzz2zU8etTZkM6nSz58ENmvfUWs+bNY4FeFr2D2cwVPXrwmj7d/21xMd/OmsVfZ82KdLjwbNwI339Xr12Gxcdz1RlnYDKbw5YKD9dmDEDcuXMn2dnZ3vYgK7ehr+f/R95+m80uFxOzsmgXH48A5m/fzhbd//uXl1/2BYmGycEdaLWua33N2rX06unzZwt6nYfY/+S//IViKcl74QX/bVHsG+36sXqmGUt66Klbs0lw9fiu3P7Jcn5Zv5ejeoax+iiiwQbEoz3XjQ6eB4Az6tpZCGEGXgCORXM2XiiEmCGlXBWi36PAt00kd1gmb1nAtA523I2wTKdefhn7X3+DgpNH0umVyCK/Otn3Is8qDt+vlCo8E/7fbfF/NpXWlhJvDbAQeuQPyKwhEMHaVI/jYYsvTZ/n99Q7tTfWZJ97iLNQS5FnN2sK1h6zmfZOl+ZmYDJjMiiLvxbMA5+HgYbbjfQ8+0Jc30eGXcCdi/zzYpvsdjISggMtdw4eRfsl8yIOfOx6xT2HofJet++/43d9FjFaZpwyA4uIrLpEe7fsDREMbe2n/VTibBZuH3MNj/7mc9vpXryDaZt/C9rH79xSssds1tLvdR6jZcjAPzd3UHapEJfNFhOP7pVBrUFVKzckjW4fJ/2CMaVNu++2i2A3IrfJhsldw+/20Yys9lmkc/b/HtRXGAMi8xf7bzSFuPYvjoYBZ8C4m6E62H105o6dvJuUwKt6hcqbjriJoiptJnfbfi3mqarWhcOrUPvOEeu5X6xRZPdo4dS0SqFuo/zz+OO9GSvu/kbLU3pRt2707dXLl8lCCD8l0S8TQ8ByaVUVj82Zwz1Dh0Y8b8WePfz03HPM+vRTZq1eTb7bjQBGxMby4NFHc+IVVzDwnHMQJhMrExKYpyvZD02Z4pVp3759pKel+ckJgMtFzcaN1OTnY4qNxd6nD6aEBD+lVQLL163jPX1gYcQKzLjvPo67994msbhDw4ISP54xg9jycn7ctcvbJp1Or3vLWN3vuympyctjTD3lfOjnn/nLBx/QvmtXekxuutywRsw33ogL6BVBtlOG5PDk9+t4MW+DUqgbgZRyDjBHCPGmlHKrECJBa5bRJqsdAWyQUm4CEEJ8AJyMf1ItgBuAT4DgSNNmwFJb49UZpCHwOloy/vY30q64gkW7ZgUp1NdfYyb9gOTsXmfz4drg4KxwrC5Zh6d4c6LNP63dlpIt9Evr57+DJ9gqcOAcSvVb/y1YfcqwJ2Xf8Z2Pp/T1PG975WJN0RmSoU3/Oz1P+6ma76rRLXCvs5hQkxQelxpLqHLnKaHd7ntmBgfj1cZoipyMoFCfMbQDu0uquHq8L02mLTcXV/sQPuQR6JIU7D7WWMu4rC4DPRWiKVYzVsXazZTYfd+Dye3iwbmv1Hmsz8o2UGkyMTMhjofb9dYVakmiw2dN3l9ed6GvXRW+d5hT+r67Sukb0Jwr/YNGS0fdBh+dwRLRh3JrGnG1vqjOWkca9opd/OkY7qdQx1fuDDp3h3bJvpU9/jMw/PiApjgbKVwJP67U2l3+AZAAaW43nfSqoX2qa7h8wOU8vlC7Ty0mQS1QWuXE4fL5UHuIsemf3Wzj163bGdspl9yEMCEhNZFjG7wFdJoJpVC3Ue7Rlei1M2dy3zffcMPQoZz8+OMNykgBsGfnTh7LySE5KzjifMfChXz5xBPMysvjh4ICqtDMYMe3b8+JJ57IlOuuI3PQoKD9ftq+nZgUbdrs2uefJ0UPmgylpFYsXMjOu/5BrclM6t330O6mGzHpxRNqKiv5+fXXmfHmm8xcupQtTv8f7FkdOvDB1q3eipAtSW1tLb/u38+lPf2jn8NPD7Ycx119NXzwAd++9lqzKdSe13RsSkrYPnaLmSvHdeXBL1ezeFsRR3QM31cRFQlCiD+BVAAhxF7gYinlijr2ywG2G9Z3gH8WNCFEDnAqMJEICrUQ4irgKoDMzEzy8vLq9QGsFeXeie3i/fsx19RgBv74+GOS63UkmDNnDpjNbDiwgWF6m0eewhRBYYrg2qqxjO00lhu23gBAJ1snVnbcSL8wLqIf2ZYyQV+eu9Y/duHiry7m8Y6+gKy8vDxi8zdSHBtDlw0byK/K44BLs+LV1tSSl5fHeEwIPexxe4eTaL/ze8zAL7/8wgGhtW/dvJWC0lKvZbxs2jTy8vKYvnc6AI+kpfDxzt3M3W2hpkj7fB6lv9YcrHFu2rLZa/0+r33wQKUgzj/nR2HvARQEfI+e6/j94KPJ3lVGQUZn0vL8z2387odYYcFcfwWurKys3veHhyxXFlvYgjSnAUUcOKClTt22fTvDgJ27dhGyZncATsNAoHzF1yyMPYH1hU52xvlm1o7KX+oNknVmZvjJHG+Kp8ytjVt/2rPO2751ZwGdgM2bN7Nxn+/n9+7v2+ht2cOK2isZaVpNYWFwxppKw3yx0UJdY7J7H6wr7EMZWfu9d9ucbZJbqt8mBUGV1YLRoaXcZcEOFFbW7f2VPvef3uVVazfQN2B7Xl6e9/4PXE7bu4ABBOMZOvaoqSEvL4/tRdqj5ogMwW97Ie+X3+i3YzV9gXe++tm737bNG8lzbSOpeBVD3JJ3Ei6nKHVwyHvGVr3Pm8c7FDv2l7Mhwr3WmHsRlELd5rnv2mtxAHe++y6rDBbR+lKl54J22Gy4XS4WvfMOs155hZmLF7NEtwp1sVi4auBATjz/fI66+mrsep7pcDiSk7l92DAeXbSI1G7dQgbfuauq2PPUU+x/ezrW3Fw6TX+b2GHD2L9lC189+igzv/ySb7Zv5wDac3FSejp3TZrECTffzHWnnMIXO3fy3c6d3DJ6dNggwcasFxcX81Nysnfdz1UkxH579++nQkpeWLuWF8JYa84PyMncFFRVVfGqw9Gg9Ic3fPIJc5tIJhNa5L1Z1C/e/twRHXnupw28lLeRVy8aVvcOiki8AtwspZwNIISYoLdFetdAaDtf4C31NHC7lNIVyd9dSvmKfk6GDRsm6zvQX/LpS95l2/r1WDIzcQLpM2dR39qa4ydMQJjNbFvp04698rwVen1rzVZdCdU+vtVkpdbti83Ym+T77PPK5vmdz4mTo8YfBdN9xx6x9SYqM9uxvEdPeoycoPk9fwQ2m007d85H8K7mapB7xTtUPNyexxNSuPnIIXyV/zPsgE2WTeR07UfRb5rLQfcjhpAyYQLJhclc+PWFTC7Xps5HH+8LSvf4iLtC6FBdu3RlNZriMv5z7YNP730cF67RXFj+d8lwP19pcem1jNevk+e4nuu2sHoNz++ycWuPnkyY0CNkn3A0NDUpwGjXaDaXbKabPR3yF5G06X0o2Exux1zYAO2zs6Go7uxKOwwGj7iEZCZMmIB5/R5qF/vSnNrcTty6y46loFCTWb9f7hp9F3f9ehcAyQlJUFpIqstFp05dYBt06dKFcT2G88gCn3tLUsfefPhbJR+6joaC4BmCKmlUqH1foLAleF1Blueez8jVPoV61KhRMOcnHA47Jix4fixuk5WExCTYs53xQweA/y0LMSlQ6Qumd1T7LNt9O2cFBRtMmDAB8sIs339y0GcB/wfJhAkTWLRwEawCc2yytr1dD/ou0MIxtlk6AFqGlerYTCZMGAg7EmAJDOrfF3pO8D/4c0Ohz0kw5OzgzwYw4ExY/n90yO1Mhwj3WmPuRVAKdZvl87/9jcXz5vFhfj69rFbynn2W7du3s+vDD7V808agRMNyYLCip+86vdDHNZ99xt02GwVuNyZgTHIyjx59NCdefz19pkypd9DYI7//zqP61OP1/fsz6fjj2b59O8UzZ1JbWEj5L79SW1LMvsxMfl7wOz3PP5/5O3eysKwMN5BlMnFWjx6cNG0ak/76V2I7+vIVX3DmmXzxzDMUu928+rvmBxZYcKWp/28KPt66lVyrtUnjd6QxE0kD9p+7Ywe2Rlr4PfnMXZ5iQ/XYN85u4eLRnXn2x/WsLyilR4hpZUXUxHmUaQApZZ4QInTZPH924J9erwMQOB88DPhAv9fSgalCCKeU8vPGiexP0Nhbv7edBQX1P1gTBDQalWkAh9mBJ39zIMn25KAHRqVuUtzrrCCdEFVMux0D3SfBCM0d7MM4B/+XGEfCqrcY1l4bB53X+zzajR5D0XRNUzfpgb4pDm1GZ6/ZDEf9PaRMzjp+2qJUs5h/2WWUV6Hu1z4R6333svuBf0baFWg511Wb2Uav1F7aSs/jYZNeyyiCPHGWWMqd/nUQXMZbZLQ2S9EjQ3sGLcjszYiCNVy77DPKrP6p8r47/TsKKgp4f837vkY9U4VVSsjUbbvtepEY469uhcvu5MEYlOhwOLwPVJM9Dk/Wxm/WFXNFmP1Tqn1FfgQSqz5LMX5A12Clc9T18NO/Qh/o6xD3VCO+8MBfY3ayNoXw5/ZiQjlkrNylB016sqK4QrjL7NsAvz6lxR+EInswLP+/hohbL5RC3ZYw3MSnPv20d3ltbS1nv/hik5zCBRzdoQMnTp7M5FtvJa1Hj0YdT5hMrPz6a/pNmcILK1fyQrhCInqez69LShgVF8fdY8ZwwiWXMOySSzCFcZfI0K2q3z36KMfeFiHquBGEGrHWmYLP7Wb+W2/x0mOP8UEIX+8aYKOeiaSTEFwxahRn3n47PaZODftZGyJnNHx+xx2c+uijvPnUU0y44YYGnTsSp2Vns96QwzUSl4zuzKs/b+KlORt58qzBTS7LYcQmIcQ9eG2kXABsjmK/hUAPIUQXIB84BzjP2EFK6XVgFUK8CcxqamUaoDi2E1kEV+mrC1NsLO7AolG6Qj0sq34zH6YwOsOJXU/kz8I/CadQF1cX+/lGGxWnf+7O41n+ZhBNVy9MJrjgE2+7W/j2rdEViK5JXREOn/+CcGgKdZVTm0F8NymBOyaGzhNdretmCRecS+k773tOHtTvgM037spIdFA90ldSOppxSUtn6Yk0L2aSErcQ/HvU/dzwi//7osJoTNBLw8faNUPQKwNOZkTBGhyuWkxJVqiC2CO165Idn012fDYvLfXNqFTr35ddSuh/OqT3gqz+JFT6D8rqyr3vNFil7XY76HV+TAa/7kkDOsGSiIcBQLgN8zqhvqNYQ5KDFP0nXhTNIyN6/PPc+O6VA/p1eX/BNh5x+PcB+MdUfVDiSZMXMLj1o6q4KURtMEqhbkNU7tOmYc7t2pUxRx7J9e+9x3ndu3PnM89gMplYtnw5gwcPRnjS4ZlM3mXAt+75Ay39j8nEwq+/5vS772bGjTdykkFZrw9ul4vty5ax4eef2bBwIevXr2fD9u1s2B+cRicU4+Pi+GjmTDIMBQki0XW0ZrlZ+NNPYRXqqtJS9qxcyb5Nm9i/YQP7CwvZX1DAvv372X/gAKVlZdQ4ndS63d7/zYDdZMIhBC6nk5mpqThsNuw2Gw67HXtCAg6HA7vdjiM2FntystZut2Oz2bDabMRbrfz9llu4oqyMgo0b+X72bN4MUdhlq5TcM3cu95zsmyazApf07s1FN93EyIsuwhom5VxTMPHaazE/+ijfvf9+syjUZVVVxNeRa9xDapyNc0bkMn3eVm4+ticdUsIVTlDUwWXAA8CnaO+mn4miUqKU0imEuB4te4cZeENKuVIIcY2+PUyVkqanNCaHEPUDw2LNyaE2Px8R4V7rm9Y3KE1eOO4YcQdzVjzMwC3BSk+yPZni6mL+N8nE5izPrJDwU6KrXD6f5LJan+J9wO3vqxzOSmksaOPZP94aj7D7AtJMepGLnilavMbFfS8O+3nm9daOZw6RJs7vvAFZSOxdDQGAEaySra0sk/e7MMhsQjPyDs2oIx27boX2ZJfIj/cFStv2aykKK+b7p5U7qdtJzN2p+dJ7rN/xbk/Vv/7aut1f3dq4J3KssNFv2mZzeBVqm8P3XDymfwc/hbrB30OMIW4lZyjk/xGhs/Clt6sHgQr1Jf0uYVPJJm4YcCFf/7Yo7H5pnvLmer51/u8S6DQW4kMEsFcW11uupkQp1G2IIr2IyVEjR/LuzJlkmUy8Om8esXpKskKHg94N9P/ZvHQpAHG6v3A4XE4n25csYcOcOaxftowNa9awYft21hcVsamqCmPKfzvQzWaje0oKx2dlYS0tZcbmzZRLSf+UFKaMHcvxp59O97PP9rO8REuH4cM5LimJ+779lt+zs0lPSmJvWRmF5eUUVlSwp7aW0ggvAQeQaDJhEwKrEN7/3UCVlFRLSZXbTdXOnVRLWW/fzcBzXdWzJ0/9+iuxekGA2poa/pw5k+mPPsrrixZRqctaC7y6Zg2vXnMNXHON9xjHpKVx2UUXMe3OO3Ek+mcWcNXU4KwOX3DBaDkyLifk5HBkYiLf/fknD7uC/fgaS3ltLXH1+G6vHNeV6fO28tovm7l/Wr+6d1AEIaUsAv7awH2/Ar4KaAupSEspL2nIORqCc/fuiNuFTX/Z2oIV6oZYTWMtsazopO23tLP//sn2ZMpry/l6uE/5DMzW4ckNDXjTgwFUWOs3OBZC+BRqW7zfZ6nZvt3bxyzM3gIvoahw6Ap1oiHuJeCyxEVpyGjN+K5P8Hf+8J59/Dc5EUuEgiCnHyiD/ZuAY7CEyZscCmNWl/LKfWAVxAdYoM0mf5lemB26Su0BGUOiqCTPPdjXGJMEuueDPcY3KDIq10YEsH/A5aQuf93XmNEXdi/XclWb7eAyvC9iDBbq6tJg67Sfki2hrDDkeSOhp5b3fjNpMWm8cMwL1LoiOwdaPNfNUAiHx7vDGf+DZR/B2dN97cpCrYiW/foDdP6CBfxaWsoL557rVaYbS5murMe0b4/L6WTbkiVs+O031v/xBxvWrWPDjh2s37uXTdXVxrSXONCU5l5JSZzQpw89unen++DBdB8/ng4jR2KyWKhau46dd95B9arV3HDLrWyYMJ7xJ5zQeKGF4MM//uCuM87gpxUrWFhYSDuLhQy7na5pabRLSyMjPZ12ubmkt29PardupHboQGqnTqR26kRMHdYa8HelcLlcVFdVUbVvH9Xl5VSVllJdWkrV3r1UVVVRXVVFTXU1tTU11NbWIl0uUuLjSe/enR5HH40t4HxWm40Rp5/OiNNP5zm9TUrJlnnz+OThh3nz+++9ZdoBfty3jx+fegqeeqrx1y4EoTKRiAYue/6vASivI5WRgfbJMZwyJIcPFm7jhondSYsPznmrCI0QYkak7VLKaQdLlsZSX0ubpV07ajZvJnbYMEq//qbR569wVlCUIPjP6SZWdRSc3uN0PlmvuWQk2SMHYwPsLvMNAP69wFcbZ3WJvxIVVtkfeCZs/RJMVspqfBZqI3GjRnmXzcKMK0Tqu7Rrr2HHxx8CWvYLW7sMv+3pFT5lP/fZZ+CeCPn+6zkwMbdLx61n3TjYhLp/ppRXMKW8guoIn+OW/UXQte6BRewI/5LfMYa8zWXuWsCGo4F+xuOqnyFVlLJD+iyw+xP6eIsEOgwKtd2gUNcEqHPCMHhzDboA8wmPwZALIbUr9D8Nlhr8vo0W6vUhcrVn9ve3WpdFHuBC+FiewHZrHQMX77gkcMD4sT7pVmiYd1IWakW07N2mRam/tVF7KP8xfz7X9Ozpzc9cXlnJO3qmB2Peab/lMD/yd7dsAWDyNddQefXVGL2UYoDuVit9UlKYlptL927d6D50KD1GjaL9iBGY7KGVHul0sveVV9n73HOYEhLIee5ZEo89lvWNSEsTSHK3brz4559NdrxImM1mYuPiiI1CEW8oQgi6jB7NrbNmcauhvWLvXr5+8EFenz6dr+twoekfG8vwzp3pkpWFL0+BhvH79yzNX7WKbwoK6G21ctaYMRH7RtNubPt3PYs2AFwzviufLN7Bm3O3cMtxveq9/2HMKLS0d+8Dv3NQ6ta1DjwBekknTWsShXr1Pu0lvain9rLvl94vokLdJ7UPq/f7Xuw3z/Hl6f0lv/6/AdK6w1ZAt1DbzXasZn/Lqqf0OIDZZMblDlaoM268keuyfoEqzd3MYsjMJGv9fVGF1cqkPplBVRD3p2SSWlQQ0eVjcr8sXsrbyMTePoW9x+zZBz1aMarcQhEU6gQpweBHnuiwcKDKyaNDz+P2P97zttu6dfXbz6hQF+rFYhr64yshnhIZjxC+y5cU61MmY2LjQJ8LjjEo1NX4v4eFzbfNZI/VfMO7jNMaAoIr/XyoQxHY/9WJdX+QAGTQgj9mPXB3qdv/2nZrp38f4WYWjG5KdVqom/d+VAp1G+LD11/3W5+1bZu/RVBKTJ5iKZ6/wHUi/9AnpafTo2NHevToQfdBg+g+ZgztR470TalGSfWmzey88w6qli4j4bjjyLr/Pixhqjsq6iY2PZ3Tn36a0w3+7bVFRayZMYPF337Lz7/8wsq9e1lSVcWKigpWrFpF2po1DM/IYHi/fow4+miGn3UWmSGCTF21taTb7Yzu2JEHZs8O2t4Y/t2A6fbuGQkc1zeTt+Zu4aqjupLgiM4HW0EWWpXDc9GCCb8E3pdShokEbs00z1gg8957iB0ypM5+YzuM5YuNX3jXjdbhUAr1yOyRfgp1eW34WZmymrLQBV1CIBCU1oSovAh+iqFFWHDK0E5p+5y+FGjCGI/h9CngQndDs1uDrYVzxp/JqZ8/jzM7J6ycg3KT2fJv/1nHlsy977u+wdfZqHSfKpL5TBb7dzBU5Lt6fDce+3YteR2G+CnUWXf7B386LL59ig0FdRqDcSySFON7BsbExuNRqO12X3u1sPkZO0w233ctAhViW4BRyGihPvtd+PB8/+2Gyp0RKd8bdpMnLkAYv5Pi7ZCo3VdjTVqe7kGmTX4+Z95ZHHM4hdrwrAhroQ704G4elELdhvhdL+UN4KquxhSg5NaV6UFKyf7Nm8mfO5edGzawa8sW9hYUsHLdOjZu2sTfBgzgyWX1j6z3O4fbzf6332bPU09jcjho/8TjJE6d2uLR34ci1pQUBlx8MQMuvphO+ndfs2cPKz79lAXffsvCP/9kYX4+3/34I+4ff4S77ybXYmFEdjbDBw5k+KRJDDvrLBLbt+eYDh34bvNmpMvlV2GtpbhuQne+XVnA+wu2cdVRgTWTFaGQUrqAb4BvhBB2NMU6TwjxTynlc5H3bl1Eq3AGISD31Vew5eaycfKUoM2p550XYqdgRrf3T9kdZ6hcGEqh9rQdnXs0s7f7BqWBwYoAjyx4hBuPuDEqOUALSkywhUoj6Xummk1mnGECxY6MP5JZxbOC2qUhZsLSR8ukcNLA4KqF63oNZ8opj/O/xJSgba2Nnik9WbB7ASmW8AHNAt+gwR3qPjMox9+t0tM0Bry/Ap+RNlOwwakpbaGJBoU6Pj4e0AZJdovvs6TLInZ45BPCv0R4kEIdMEAzbk/qECxA7ggoWA5Bc54BVAe7+ARlwPQs7NsIzx0BE+8G+pJkzJoTSl0IVfIc/EcedVmom1kPUQp1G+LPSi3M94KePcOO/l0uF/lbtrBhwQI2Ll3Kxj//ZOOGDWwsKGBjRQUHQkzBJQjBtb1781AjrZM127ax8667qFz0B/ETJpD1zwewZmTUvaOiybC1a8cRV1/NEVdfjSecsXzbNhb/3/+x8McfWbBsGQt37uST7dvhyy8Rf/sbvWw21ui+2sdmZzNl7FgcsbHa7EZAeXjvrIehcItJXzcJgUnPHmNq5INrUG4yY7qn8dovm7l4dGfslpZX8tsCuiJ9Apoy3Rl4Fi3bx2FD/LhxUff9dNqnQaXDwX8KH2BnmS8dd7I9Oai/5xhx1jgGpg+krLaMTSWbQg4MZmycEaRQV9RWsGb/Go7IDM5AUVZT5mehNqel4dq3D2EIcgvnQw2wo2aHd9mWY7AyG0pEx996OwCT+2cFZUJpqRzTDeHmoTczqdMkeuVHmJQxhQ4mvX+Pbsk3+1wnlm4vjuq8oQxGTXnZjBbqWIOrT+B5MxMd9MpM4M6pvYn7xuA0aAtUqAPWjcexhxi8jbsZFr0O1hiorQje7mHZh2E3BdmIS/SirJt/pn/OkVTu0q77N67hoS+eKcw7wJiXWvlQK6Jl8d/+xhFPPcU769bxq81G3/h44iwWKqSkwu1mR3k5W2tr/YIGrUBnk4luSUmMzs2la7du5PbrR/uePcnu1Yt2PXoQn5HRKAuylJLiDz6g4LHHESYT2Q8/TNKppyirdCshrmNHxt1yC+NuucXbtnflShZ98gkL8/JYuGoVawsKkMCPe/bw42efNen5BzfwPrh2fHcueP13Pl2cz7kjOta9w2GOEOItoD/wNfBAFKXGWy0xe8MHPQm7HVldTdxR4yj/uQH+yQZ6pITOs28N8Nc0ZtBIsgVbqO26EiYQ7K7YTWFFdFkQPMPSG2ffyPxd87lt+G1c2PdCvz5ltWXEGyyKGbfcwq677sJs9IdGsq5oHYHkl+WzpGKJd91iMHBUb/JlcrC0D7ZMt0WsZitDM4eCJVlrGHg2/Pxz2P5Gve20Mt1Nx6Bw28wmalxuRmSZ6fbDD2ycNCnssa4ddK1fPuq7xgQXxLFbTFQ761PySiPR4PYW4widKWabOZeOZhPf/u0orWHZQCjSg2ADs8usMsQvmwNioBxJweve30Mdz/I9a4Lb4tsBBeEHGFJy87E9+fxtLfbhceeZHBPqNCJM8KLTkK0knIX6II0KlULdhhjy5JOU3HEHH991F19+/TWbS0uprKwk1mQixmSiZ0ICp2Rm0r1zZ7r16kW3fv3InTgRc5cuzTbVUbtzJ7vuvpvyufOIGz2a7IcexJqd3SznUjQd6f36MblfPybfey+gueps+uYbfnjrLeb9/js/b93qVwkk02JhXKdOjB09mjHTppGpW7qklLjdbm/Aq1uvvOl2uzmwZw8jzjmHc487rkEyjumexsAOSfx3zkbOGpYblHZKEcSFaDXUegJ/NQxoBSCllMGm2FZKxx8+D79R/1zhgqGbg2M6HsN9c+8D/N0/fCL57s1QyrTNZKPGHaLCm878XVpe4xeWvBCsUNeUkRHrU4STTzuV5NNO9euzv2o/+6v2s7dyL+kxvsxPl397ub+chjzdZT/9FFYev33a4s8urRvcr+eZC9CnjT7ULoOaJ8x2rUS1gemXj+CBmau4or8TW4fwPuTgfw/8unU7SacFB+7Z6qFQj+icyoItWgC6nw91GIW6Oi7AVePIv8BK3Tiy+WcYbqipWL7Ht2wPcP8ItFAbU+rVdTOsDGGMic8EQo3tfcfatKecJ6zaYKRQJoc+djiF2pj+T1moFfUhMSODy157jctCbGtsHfr6IKWk5NNPKXjk30i3m6z77yf57LOUVbqNIkwmuk2dSrepU7labytft475b7/NL199xS9r1vDVxo18vHEjTJ9O95gYxnXvzrgxYxh3xhl0mzAhyK9w2QcfANC+V8MydQghuHZ8N659dzFfr9jFiSH8OxU+pJSNqx3fiijp3IukLcGFkIwIa/0CpRuD0W868Bk3JmdMnWWkbWZ/hTpURg7wBTMaj1daWxpSiQ9FpbPSb72kuiSq/SLRllw+IqP5/xoVar+Pdk/wQGhk1zS+unEceXpmqm4/fB82QN9k8M1OcksQwS4KdouZ0igrGsTZffsnOCyMq36KDmIvj9hDB+fF2APkMlqlA/NGT/43vH+2tlyxz3+bJWCgGptmUKQF9JwC674OLXRGPygMdLcRhn9Dc3y/LKw/ar+JA8Tx3zmbgjs11EJ94lM+3+5m1k8OmQew4uBRW1jIjmuuZdc/7sbRuzddv/iclHPOVsr0IUZcz54c8+CD3L94MT9WVFC8YwfzH32Ux8aOpZ/Vyozly7ns5ZfpMWkSGVYrE5OT+euQIbx62WX8+t//8qgeCT/uzDPrOFN4ju+XRdd2cbw4e2OdSovi0MEZW7cCWd/MQ81FYAaOUAGHaTFpfuvP/emLEQ1Ugl9c8qJ3WQgR5EMdiXdXv+u3HlUaucMNw3vKE5T4jy6nRbWrrUOHsHFBzy953r8hhM+vux7PsFibz94ZYzOzXWYyz92PWFtoX2K3CLCPGvM2T7jTf1uqf2o6AI66Da74MYQgqfipwymdwgttUKbrc+flJBut7mH2jEahdlaBI9m7+pzzFBh2mWFUqBRqRStBSknJzFlsOmka5fPnk3nXnXR8+y1subktLZriIGDNyWHkbbdx6y+/8HlJCYWVlaz8+GNevvBCTundmwqXizeWLOGq//2Pcddcw3sbN3LX4MF0Gju2wec0mQTXHNWNVbsO8PP68CmZFIcYYSy4RiKVGW9ujIpqaY1/ZoO+aX2D+vdP7++3PnPTTAD2VO7xlqz2YPTDdUkXFc6KMFk+gglUqJU+HYzR8OMpGpIS5YClXoRQqCtrQt/Xk/tlAYacy/inMIyx+o7lCKNQV9kCirwZfaETA9xVAtPmAUz8B3QYpp/QkNElJtU3CBECfg9ZOFUjc0D4bUZK/WMkTNG484Uz2BkVaoCYZO9i6IoJzYdSqBVR4dy3j/wbb2Ln3/+OvXNnunz2GakXXYQwqVvocMXkcND39NO5+u23eXXVKuaXlnKgupqNX3/NrHvuYdn06Tz0xx91H6gOThmSQ3aSgxdnb2gCqRVtAeGu2880ZvDgiNszbr0FU0J0imgoTOEsYqAFvukYFWIhRMi0eqYIr9qbZt8U1ObJPlGhZ1SI1kLt4bY5t/HBmg/qtU84xvfUclR3SvNlhug++yd6/Nq4gNCWwpg2z+NDbW6CkccHJ2rX+7Mdu7SGwFR1wC3H9Qy5r0NXnuPsPiuzzVBB0Khc28JUFtyS6+9XT0Kmb9mQW1s7iEGhzh4UfDCj7HUVfTFSsDy6ft/cEf0x62Jznv+6wUL9m0tX8JP1oPbULk133hA0qzYkhJgshFgrhNgghAi6gkKICUKIEiHEEv3v3uaUR9EwDnz3HZtOmkbZ7Nm0u+VmOr33LvauzXtjKtomJpuNrpMnc8I//8mACy7wi5hvKDaLiSvGdeX3zfv5Y2tR3Tso2jzhFOo4QyXPhGPDZ1wASLviCnotXNBgGRxmR9htkXyaQ6XV81hF7x1Vv1dcWa2WmzdaC7WHr7d8zUO/P+R1k+qe3J1Hxj1Sr2N4uHRMZxbdPYmu7XxKvTU7G0t6eoS9Wg+dEjtxSb9LvOtGI9BAobka5DgaX3SsX1o/ll+8nO6374Tbt4QsRDK5f1bIfees04IEje4c36z0WXEdBgt1oEK9G82dyGSJMGNjiaBQ5wwL7m/cHujyEUhDAgGry/wtzjURUvHVxfaA37huoX7dOYXfZR+trd9pcPFMGHppw88TBc2mUAshzMALwBSgL3CuECJ4Lgx+kVIO1v+C88woWgxXcTH5t/6d/L/eiDUri86ffEz6lVe2isIfisOLc4bnkhxr5aW8jS0tiuIgkB4XWjlw9De4TghjcFnTT+k6ApUQA8Y81fePut/v/KHS6nkITMdXF4t2LwKgylVVr/08eBTycR3GcWLXE8P2i+TaK4QgPf7gZVRpamadOotbht0SctulIpUvduykT2CGjMZgsfm7TBjokBLLyxcE5xrvmKpZhI359o2pQuMN/tQmk6B9ku/erL7wK+b0fwiLOUKOicBAQ6OyH5hSD/wVaqPLRygeD7C6D9NSJvSsqaVnSk9uG3AlAC793peg+Tob+fXJ8Mevi94B97V+7V1G9VYI6HJUmw5KHAFskFJuklLWAB8AJzfj+RRNSGleHptOmsaBb74h/Ybr6fzhBzh6hp6uUiiamzi7hUtGd+aH1QWs3R1cjUtxaGGL5FNpeCkmTp1K5l13hdzWWCJZqI0KdU6Cv39qoPXaaB0N3DccHh9tT37rXinRZ8rZX7U/qC1U8RqFVoCqa62Tg+VjC9AxNXh244IjgwP9ju7lC34M9DGeccNYPrtOq+bZqVtvxp9xfeSTRhgchlSo9xkMF4F5qQPzVrsCfJh3LNR2k5JPpn3C4NR+wcffEuAutOwjADa5Q1vwIxJYaMbg8jEoN5mDSXOmzcsBthvWdwAjQ/QbJYRYCuwEbpVSBpU4EkJcBVwFkJmZ6U1h09wUFxfjcrkO2vkaS1lZWaNlFZWVJPzfx8TMnUtt+/YcuO3vFHTsCL/91jRC0jRyHizaiqxtRU5ouKzd3RK7GR74aC5XDWwai1lxcTFAWHna0nU9lJCRghI95lQhyHnyCQDKfvvVf1sTsLN8Z9htdoNSERtQ5jow29GQjCH8uO1HXTwZlNUjFJ5j1LprAUhxRF/2e1/lvqC2+vpgH5IMOheWvkdLR2qGyvRh9J32MKRjcthjpMfb6zdrEML9xEsohbr6gG85MC/1jUvgyT7hj7c7wI/amHIvFFJ6leIv3UeGPy5orjQmCyyeDt/qmUtqyv376C4fFx3ZiYuOr+N4TUxzKtShrl7gnbQY6CSlLBNCTAU+B4JKV0kpXwFeARg2bJg8WLmWk5OTKS4uPmi5nRtLY/NQl8+dy84H/omzoIC0q64i/fq/YGqG1FQHM192Y2krsrYVOaFxsv5Zs4o3527h0QtGkJsaHPRTX5KTkwHCytOWrushhStMUKKfMtJyipExYDFQoQ4k1ZFaZ/q6sTlj+TVfGxQc2+lY3l/zPgAHajTFJhqrtgeLKfi1bmyzde9GzYaNxI0/ivI54asIHnJMew4mP9IkcR1NQbd2cWzcoymDdkuwTOammG054Qk/i21I6rq3bPH+Mz+JddQDyBoQrFSHYtvvvmW90MzzzlMi7+NxpYkz+O8HWag1i7rDagLrwXVPbc47awdgzKfWAc0K7UVKeUBKWaYvfwVYhRBtI9LhEMJdXs6uBx5g22WXY3I46Pz+e2Tc/LdmUaYVisZwxbgumAS89kuIxP+KQ4bYof5+prHDhwMgq6siu3U0ocvHW5PfCrttxT5f5bfYENkcjAQGKT414amgPsa0et9v/d5bbdFT6CWUkmzkzJ6+XO//XfbfoO1Gl4/O771H+nXXkfucLxe2sRLfIYvZ4pdSDWiRMpAeC7XVEFxoCiFHoJvHI6cN4F+n9A/qF5HhV8CAMyL3CcwAAv55q+0JeAev0cwADbs8oCHMNZ79YFBTNVHqHEY5VnziW7Ynhiyoc7BoToV6IdBDCNFFCGEDzgFmGDsIIbKEPrclhBihyxM8X6VoNioWLWLTKadS/MGHpF58MV0++5SYQSHS6CgUrYDspBhOHZLDBwu3s7esuu4dFG2SjFtvZe99vowYrgOapfbA199425pbFxrYbmDYbUPaDfEux1piIxYdSnGk+LmB9EmLMF0ehlCZQ4zcO+pehmdpg44vN30ZtN1oUTcnJtLurzf4FcaxhbCQHlYcxKJRnlNZzKFv4GvGdwvZfu6IjlwYwte60YTKWJOQ7Vs2Wer3Y+s1xX+9ET/U+BCuMBGpyxrfzDTbr0hK6QSuB74FVgMfSSlXCiGuEUJco3c7A1ih+1A/C5wjVTm0g4K7qoqCR/7N1gsvAqDT9LfJvPMOTI4IwQsKRSvg6vHdqHG5efO3LS0tiqKZEFYrrmzfS73z++9hTk6m60yDTcbwok67+GIAYgZEWVgiCiwmC48d9RjPT3w+aNt1g6/zLseE8kE1kGBL8FO4I2UBCUWaI61OCzXAub3PDbvNZlazjaFpSbeh4HMLAbce15Ol9x538ATpOj64bcgFvmVjHupolOOwMzZh9tXjBEIRduYkO8xgN6Z+v62mpjl9qD1uHF8FtL1sWH4eCH5aKZqVyqVL2XnHndRs3kzKeeeSccstmOLqLvWrULQGurWLZ3K/LN6at4Wrx3clwXEYTFcf5phiY+k5f17Y7XGjR9NnzeomP+/kLpNDthvdPOxme1AgopHAAjGBWUBuGHIDNa6asPtHSt9nZEz7MQ3apmg9SAkWs4mk2IM4Y5AQIrOGscpjQhZUlWjL0dg7HYEZZYTh3xBU+QIgu6bHsWmvL8iw2hkmliKjD9y1E945A7YZKo0eqhZqRevDXVND4RNPsuXc83BXVdHxjdfJuvdepUwr2hzXTuhGaZWTd3/f1tKiKA42rXASM3BitUO8f15jo8IdqHx3iO8QMY92fll+VDJE8uU2hyiDHYmE44+vV/+2T8vcU5sensqGh6bU3bE5OON/MPZvLXNuI3v0gXB6Lz67bgxpcTa++us4AJJiIth8bXHQaZTmkuIhJrlF/OI9KIX6MKFy5Uq2nH4G+159laRTT6HrjC+IGz26pcVSKBrEwA7JjO2ezuu/bqaqNkKKNUW9iKK67flCiGX631whxCEVcJEZm1l3JwOxJk2J7ZKkVY4dnDEYgLuPvDtk/1CKs6f4CsBfBv+lXudvDvqsWU2HZ55uaTEODi2gfBnvAJNJYDEEJwaKkxzbjLNv/U+DSfc33/E96B9Kev4P16/vySTFWvnjnmPp2z6R/5wxkOmXh8q0bMBkBbfTt97CFupmdflQtDyytpa9/32FvS+/jCUlhQ4vv0SCSgOmOAS4bkI3znvtdz5ZvIPzRzZDsM5hhqG67bFoWZoWCiFmSClXGbptBsZLKYuEEFPQ0pnW8dZrO3x92te4CTPNHMDyi5d7c5QPzhjM16d9TU68VuQlUHGOlDavc2JndpfvDrv9qA5HRSWPInoGpA/grF5nwYL3W1qUiPxw8/hWEnwdcP9OvBt+Cs7SEdW+4ZjgP34/a1humI4GAvNrG7O4tMBMlrJQH8JUrVvHlrPPYe/zz5M4ZQpdZ85QyrTikGFUtzQGdUjiv3M24QyXt1hRH+qsbiulnCulLNJX56OlQ20ZmsG6aDVb/Yq2GPn4pI95esLTYfftkNDB686Rm+CvDHjaQ8Xce7JzQGjF++cd0eeKvn5wcMW8LGsDqs8d4rx3wnuc0v2UlhajTtLj7fTOaqEql7dthn8UBDTq92+HEU1/vnq6JQEQGGzrSKYlA02VhfoQRLpc7HvjDfY++xymhARynn2GxOMOYtSwQnEQEEJw7YTuXPPOH3y1YjfTBtVRcEBRF9FWt/VwOfB1qA1NUd22rKwMjwOGcf8MlwsB/PzLL3CQc+WbMZO32SdLNJU08/Ly2LVvFwBr1q4hb6d//zlz5rC1aCsAW7ZsoaOtI9tqtgUdIxr2lO4Jarss/rKw+4e6vgeblqxG2n/fPtKB5cuXsW9X+GwtTSnjpmLNRa20tNR7zGV7NLeF/fv2N/g8zXkdzc4KxgFOl5Nf8/JILlrK4DB98/LymGBYttYUM4bQg8nA/epLh+2b6W5YL1z+EwcSe9Ed2L5jOxvreczGXkOlUB9iVG/azK4776Ry6VISjjuOrPvuxZKW1tJiKRTNwnF9M+nWLo6X8jZy0sDsiNkWFHUSTXVbraMQR6Mp1GNDbW+K6rbGF5tx/zUmExI4atw4TDHRVxBsDiJV0nx669OkxqQyJGMIeXPzYD307NWTCT0ncMGCC3hn9TuA9tmWLl4Ky6FLly5clnsZp804ze9Y0V4/51YnH+Z96NeWlZAVdn9PXpSWrAbaotVId74M+2BA/wHQO7wMTSlj0rYimD+XxIQEJkzQfj7uNQXwxyJS01KZMKFh1t9mvY5VB+BXsJit2jm2x8LSEP2umsOE9oMhT1udMGEClO2BucHBuIE0SPZP3vVbzbBWkNGtG2yE3A655NbzmI29hsrl4xBBut3E/vgjm089leotW2j/+OPkPPO0UqYVhzQmk+Ca8d1YvesAeeuCrXOKelFndVsAIcRA4DXgZCmlKsQVhmM6HcOQjCF+bR4rnSeIMRCBCNoWmGYvEuNzg3MKq0GmounwuHwMD705K3wxpGa5CwPzs096wLCifKgVDaBm+3a2XXQxCf/3MXFHHknXGTNIOvEE9SBVHBacPDiH7CQHL+VtbGlR2jrRVLftCHwKXCilXNcCMmKKj/cI0xKnbxCBz+ITu57ot26cDg8s4hLOpzsUVpPKya5oBgJ/a0LAP0IE0ppCqJSefSO5fLTr3TC57PH+645DuLCLonmRUlL84YcU/OcxhMlEyUUX0vvOO5UirTissFlMXDmuK/+ctYo/tu5naKfUundSBCGldAohPNVtzcAbnuq2+vaXgXuBNOBF/TnjlFIOO5hydn73HUrz8tpUVdfAYMNYayzPHP2MN3jRkxUk1LO7XUy75hfwsKcF8lCHqpR48KVoOHVUCPWhfaoeenrToVUhspY0NH2fPcF/3VnVsOM0EcpC3Uap3bWL7Zdfwe77HyB28CC6zviCqtGjlTKtOCw5Z0QuKbFWXpytrNSNQUr5lZSyp5Sym5TyIb3tZU+FWynlFVLKFCnlYP3voCrTALbOnUm75JKDfdpG4bEym4Uvk8HEjhPpkdIjZP8HRvumrlMdDR8gTuo4qcH7Hhao92V0WGMhpQtMe7bBhxhU42K2M4MTyyvg5Bf9N3YJUf48GgKqkJI1ECz6jI4l+pmdpkJZqNsYUkpKPvucgocfRrrdZN13L8nnnKMp0utaZAZWoWhxYm0WLhndhad+WMea3QdaLtWUQhGCvwz+C1azlWndpkXV/5iOx3Df3PsAcMmGFy766xF/ZeufWxu8/yHP1Mc0N4HuauAREZMZblwSfvu4W6Bif52HSZe6ApzWzX+DLXyVz4gEKtTWGDjiYigraJEqkMpC3YaoLSxkx7XXseuuu3D07k3XLz4n5dxzlVVaoQAuHt2JWJuZl5UvtaKVEW+L5+ahN2MNLEQRBuMz3WmsBFdP6uN/fViS1AFOffmgWjNDOZckOLT7IiupZbPWNJhj7oWTno6io+fTCxh5TePPG6hQCwEWm1Z4xhZ9MG9ToRTqNoCUkpJZX7LppGmUz5tH5p130PHtt7DlRlFJSKE4TEiOtXHeiI7MXLaL7fsrWlochaLBuN2+QkVOWT+F+uqBV3uX28er3OytFaMZbHjnVJ49dwj3nti3xeQ5qAgBk//d+ONEOUA9WCiFupXj3L+f/BtvYuett2Lr3Ikun31G6sUXI0JF0yoUhzlXjOuKScArP29qaVEUigZjTJXnctfP5cNT/lzRtpg2qD0xtgZUC2zthJtBb4qZ9ZHXNI2lu4lQWlkr5sB337HpxJMomz2bdjffTOd338XeNXT+UoVCAVlJDk4b0oGPFm1nT2mIaHKFohUSmAXEarbywYkfAOCW7lC7hOXI7CMByIjNaBrhFIomQUZOndcQbHEw5VG4dh5clde0x24ASqFuhbhKSsj/+23k//VGLFmZdP7kY9KvuhJhUTGkCkVdXD2+KzUuN//7bXNLi6JQRIUM4VnbPk5z1zih6wn1Ola72HYMbjeYB8c82CSyKZqWptYp2yZNHPeV2RfaD6m7XzOjNLRWRtmcOey6+x6cRUWkX3896VdfhbC2Celt/gAAE7dJREFULj8hhaI107VdPFP7ZzN93laumdCNRIf6/SjaHimOFBZfsDio0EtdWEwWpk+d3kxSKRRRMPVxSA+dEtKPvqfAoHOaXZyDhVKoWwmusjIKHnmEkk8+xd6jOx1efomYfv1aWiyFok1y7YRufLl8F+/O38a1E7rVvYNC0YIEunx4iDYriELRqhhxZYhGSVCOk7PeOhjSHDSUy0croHzePDZNm0bJZ5+TduWVdP7kE6VMKxSNoH9OEuN6pPP6r5upqm14Hl+FQqFoDg6bbLeHzQdVFuoWxV1eTuETT1D03vvYOnem83vvEjN4cEuLpVAcElw7oRvnvfo7H/+xgwuO7NTS4ijqyeCbbsK2exeWdu1gwgSt8ayz4LrroKICpk4N3umSS7S/vXvhjDOCt197LZx9NmzfDhdeGLz9llvgpJNg7Vq4+urg7XffDZMmwZIlcNNNDC4uhuRk3/aHH4bRo2HuXLjrruD9n34aBg+GH36AB30+zmeU7mBM+S62/Gc3DABmzoQnngjef/p0yM2FDz+El14K3v7xx5CeDm++qf2Bv4xffQWxsfDii/DRR/SMjwOXy3d98/K0/x9/HGbN8j92TAx8/bW2/K9/wY8/+m9PS4NPPtGW77wT5s3z396hA7zzjrZ8003aNTTK17MnvPKKtv2qq4ILlQ0erF0/gAsugB07/LePGgWPPKItn3467Nvnv/2YY+Cee7TlKVOgstJ/+4knwq23asue6+E5dXGxJlMT3Hv2XTv44L07tNzTHxkKUNXz3gsk8YwzNLnree95+e9/oVev+t17W8q1//MmhLz3AHA7YVs5XBQLCFhYA99eE1w2/GDde82IUqhbiIpFi9h5513U7thB6sUX0e6mmzDFtNGk7gpFK2RU1zQG5ybz3583cs7wXCxmNSHX1og9zAwMoYITmxNzfPxBPZ8C+mYnsT05hqxER0uLomhqpJRt6m/o0KHyYDF+/Hg5aNCgJj2mq7JS7n7k33JV7z5y/TGTZPmCBU127NmzZzfZsZqTtiKnlG1H1rYip5QHV9ZvVuySnW6fJT//c0fI7ePHj5fjx48Pu39zyAoskq3gWXqw/hr6zG4L93RTyfjUoqdk/zf7y1eWvtIkxzPS2q9ja5dPSiVjWO5L1P4iUbFfyvsSZe0/s6V8ZaLWf1vT6T1NSbhrGO0zW5lsDiKVS5ey+dTT2P/mmySfczZdv/ic2OHDW1osheKQ5dg+mXTPiOelvI1Ila9KoVAoDjIeH+pD//mrFOqDgLumhsInn2LLuefhrqoi9/XXyL7vPkxxB7/WvEJxOGEyCa4Z3401u0vJW7unpcVRKEIiDqPALYXiUFWulULdzFStWsWWM85k3yuvkHTKKXSd8QXxY8a0tFgKxWHDyYPbk5Mcw4t5G1paFIVCoVAcoiiFupmQtbXseeEFNp91Ns6i/XR46UXaP/wQ5oSEundWKBRNhtVs4spxXVi4pYiFW/a3tDgKhUJx+GCNBWBHhxNbWJDmRynUzUD1+vVsOedc9j73PImTJ9N1xgwSjj66pcVSKA5bzh7ekdQ4Gy/lbWxpURSKsBzsLB8KRbNjscH9JWzpcv4hn5NaKdRNiHS52Pvqq2w+7XRqd+4k55lnyHn8MSwpKS0tmkJxWBNjM3Pp6M78tKaQ1bsOtLQ4CoVCoTjEUAp1E1G9eTNbz7+APU88SfyECXSdNZPE449rabEUCoXORaM6E2cz8/IcZaVWKBQKRdOiFOpGIt1u9r/9NptPPY3qzZtp/9hj5Dz7DJa0tJYWTaFQGEiKtXL+kZ2YuXQn2/ZVtLQ4CoUXwaE9Fa5Q+HGIpjBVCnUjqNmxg20XX0LBw48QO3IEXWfMIOmkE1UKJIWilXL52C5YTCZe+UVZqRUKhULRdCiFugFIKSn64EM2TTuZqlWryH7oQXJffhlrZkZLi6ZQKCKQmejg9KE5fLRoB4WlVS0tjkIBwMndTybGEsOULlNaWhSFQtFAlEJdT2p37WL7FVey+/77iR08iK4zZ5B8+unKKq1QtBGuOqobTpeb//22paVFaXUIISYLIdYKITYIIe4IsV0IIZ7Vty8TQhzREnIeanRK7MSC8xeQm5Db0qIoFNFzyVdw0Yx67HBo60mWlhagrSClpOSzzyl4+GGky0XWffeSfM45SpFWKNoYXdLjmDIgm3fmbSXFbMPkqmlpkVoFQggz8AJwLLADWCiEmCGlXGXoNgXoof+NBF7S/1coFIcbnVWROiPNaqE+VKwdtYWF7LjuL+y66y7svXvR9YvPSTn3XKVMKxRtlGvHd6O02smBzMEtLUprYgSwQUq5SUpZA3wAnBzQ52TgbakxH0gWQmQfbEEVCoWitdFsFupDwtohJaNcLjafNA13VRUZd9xO6kUXIUzKU0ahaMv0z0niqJ7t+LVmGIm7Fre0OK2FHGC7YX0Hwc/jUH1ygF3GTkKIq4CrADIzM8nLy6u3MGVlZQ3a72CiZGw8rV0+UDI2BWVlZZQcKCEJWPznYg5sqmxpkYJo7DVsTpcPr7UDQAjhsXYYFWqvtQOYL4RIFkJkSyl3BR/u4CKl5LKSEobXOtlQUc5bSUkUvPkmvPlmS4sWluLiYpKTk1tajDppK3JC25G1rcgJrUfWqoQOuPudy58l9pYWpbUQasotML9VNH2QUr4CvAIwbNgwOWHChHoLk5eXR0P2O5goGRtPa5cPlIxNQV5eHklpWXBgLUccMRRyR7S0SEE09ho2p0LdqqwdDcGcm8t7GzfxpcmEu6zsoJyzMbhcLoqLi1tajDppK3JC25G1rcgJrUdWWVxMucjDXVka9pnS2q0+TcwOwBgV1wHY2YA+CoVCEcxpr8DC16DD8JaWpFloToW6VVk7GsSECeTl5fGvVjzqM9LaR6ge2oqc0HZkbStygpK1FbMQ6CGE6ALkA+cA5wX0mQFcr884jgRKWsOMokKhaAMkZMHEu1taimajORVqZe1QKBSKNoKU0imEuB74FjADb0gpVwohrtG3vwx8BUwFNgAVwKUtJa9CoVC0JppToVbWDoVCoWhDSCm/QlOajW0vG5Yl8JeDLZdCoVC0dppNoVbWDoVCoVAoFArF4UCzFnZR1g6FQqFQKBQKxaGOSqisUCgUCoVCoVA0AqVQKxQKhUKhUCgUjUAp1AqFQqFQKBQKRSNQCrVCoVAoFAqFQtEIhBYX2HYQQuwBth7EU6YDew/i+RpDW5G1rcgJbUfWtiInKFk7SSnbNfExWy2NeGa3hftEydh4Wrt8oGRsClq7fBBexqie2W1OoT7YCCEWSSmHtbQc0dBWZG0rckLbkbWtyAlKVkV0tIVrr2RsPK1dPlAyNgWtXT5ovIzK5UOhUCgUCoVCoWgESqFWKBQKhUKhUCgagVKo6+aVlhagHrQVWduKnNB2ZG0rcoKSVREdbeHaKxkbT2uXD5SMTUFrlw8aKaPyoVYoFAqFQqFQKBqBslArFAqFQqFQKBSNQCnUCoVCoVAoFApFI1AKtY4QYrIQYq0QYoMQ4o4Q24UQ4ll9+zIhxBGtVM4JQogSIcQS/e/eFpLzDSFEoRBiRZjtreJ66rLUJWtruaa5QojZQojVQoiVQogbQ/RpFdc1Sllb/LoKIRxCiAVCiKW6nA+E6NMqrunhRF3PuRaQJ+T9LIRIFUJ8L4RYr/+f0gpkNQsh/hRCzGqNMgohkoUQHwsh1ujXc1RrklEI8Tf9O14hhHhff0a0qHyh3lGRZBJC3Kn/dtYKIY5vQRkf07/nZUKIz4QQya1NRsO2W4UQUgiR3mAZpZSH/R9gBjYCXQEbsBToG9BnKvA1IIAjgd9bqZwTgFmt4JoeBRwBrAizvcWvZz1kbS3XNBs4Ql9OANa1xvu0HrK2+HXVr1O8vmwFfgeObI3X9HD5i+Y51wIyhbyfgf8Ad+jtdwCPtoLrdzPwnue31dpkBN4CrtCXbUBya5ERyAE2AzH6+kfAJS0tX6h3VDiZ9PtyKWAHuui/JXMLyXgcYNGXH22NMurtucC3aAWo0hsqo7JQa4wANkgpN0kpa4APgJMD+pwMvC015gPJQojsVihnq0BK+TOwP0KX1nA9gahkbRVIKXdJKRfry6XAarQXgJFWcV2jlLXF0a9Tmb5q1f8CI7VbxTU9jGh1z7kI9/PJaAoi+v+ntIiAOkKIDsAJwGuG5lYjoxAiEU2peR1ASlkjpSymFckIWIAYIYQFiAV20sLyhXlHhZPpZOADKWW1lHIzsAHtN3XQZZRSfieldOqr84EOrU1GnaeA2/B/9tdbRqVQa+QA2w3rOwh++UfTp7mJVoZR+hT210KIfgdHtHrTGq5nfWhV11QI0RkYgmZRNdLqrmsEWaEVXFd9inwJUAh8L6Vs9df0EKdVX++A+zlTSrkLNKUbyGhB0QCeRlMM3Ia21iRjV2AP8D/dLeU1IURca5FRSpkPPA5sA3YBJVLK71qLfAGEk6m1/n4uQ5vpg1YkoxBiGpAvpVwasKneMiqFWkOEaAu0UkXTp7mJRobFaHXnBwHPAZ83t1ANpDVcz2hpVddUCBEPfALcJKU8ELg5xC4tdl3rkLVVXFcppUtKORjNejJCCNE/oEuruqaHAa32etdxP7coQogTgUIp5R8tLUsELGhT7i9JKYcA5WjuCq0C3Q/5ZLQp/vZAnBDigpaVqt60ut+PEOIfgBN419MUottBl1EIEQv8AwgVv1NvGZVCrbEDzYfGQwe0aZ769mlu6pRBSnnAM4UtpfwKsBqd7FsRreF6RkVruqZCCCvaC/1dKeWnIbq0mutal6yt6brqMhQDecDkgE2t5poeJrTK6x3mfi7wuP/o/xe2lHzAGGCaEGILmpvMRCHEO7QuGXcAOwyzQB+jKditRcZJwGYp5R4pZS3wKTC6FclnJJxMrer3I4S4GDgROF/qzsm0Hhm7oQ2eluq/mw7AYiFEFg2QUSnUGguBHkKILkIIG3AOMCOgzwzgIqFxJNpU0K7WJqcQIksIIfTlEWjf8b6DLGc0tIbrGRWt5ZrqMrwOrJZSPhmmW6u4rtHI2hquqxCinSfyXAgRg/ZCXRPQrVVc08OIaJ7HB5UI9/MM4GJ9+WLgi4Mtmwcp5Z1Syg5Sys5o1+wnKeUFtC4ZdwPbhRC99KZjgFW0Hhm3AUcKIWL17/wYNH/51iKfkXAyzQDOEULYhRBdgB7AghaQDyHEZOB2YJqUssKwqVXIKKVcLqXMkFJ21n83O9CCj3c3SMZIEYuH0x9aJP86tEjOf+ht1wDX6MsCeEHfvhwY1krlvB5YiRadOh8Y3UJyvo/mg1ar36SXt8brGaWsreWajkWbcloGLNH/prbG6xqlrC1+XYGBwJ+6nCuAe/X2VndND6e/UM+5FpYn3P2cBvwIrNf/T21pWXV5J+DL8tGqZAQGA4v0a/k5kNKaZAQeQBtUrwCmo2V5aFH5wryjwsqE5sawEVgLTGlBGTeg+SF7fjMvtzYZA7ZvQc/y0RAZVelxhUKhUCgUCoWiESiXD4VCoVAoFAqFohEohVqhUCgUCoVCoWgESqFWKBQKhUKhUCgagVKoFQqFQqFQKBSKRqAUaoVCoVAoFAqFohEohVpx2CKEyBZCzKrnPo8LISY2l0wKhUJxqCOESBZCXGdYby+E+LiZznWKECJUJTzP9gFCiDeb49yKwwuVNk9x2CKEeAz4VUoZdZJ+IUQn4FUp5XHNJ5lCoVAcugghOqPlye5/EM41F62wyN4IfX4ALpNSbmtueRSHLspCrTjkEUIMF0IsE0I4hBBxQoiVQoj+wOnAN3qfS4QQnwshZgohNgshrhdC3CyE+FMIMV8IkQogpdwKpOmlSRUKhUJRf/4NdBNCLBFCPCaE6CyEWAHRP4uFEN2EEN8IIf4QQvwihOgdeBIhRE+g2qNMCyHOFEKsEEIsFUL8bOg6E626pELRYJRCrTjkkVIuRCsj+iDwH+AdoBwoklJWG7r2B84DRgAPARVSyiHAPOAiQ7/FwJiDILpCoVAcitwBbJRSDpZS/j3E9miexa8AN0gphwK3Ai+GOM4YtOe1h3uB46WUg4BphvZFwLhGfB6FAktLC6BQHCT+CSwEqoC/AiOBPQF9ZkspS4FSIUQJmtUCtHLTAw39CoH2zSuuQqFQHLZEfBYLIeKB0cD/CSE8+9hDHCcb/+f8b8CbQoiPgE8N7eqZrmg0SqFWHC6kAvGAFXAAlfr/RozWardh3Y3/b8Wzv0KhUCianrqexSagWEo5uI7jVAJJnhUp5TVCiJHACcASIcRgKeU+1DNd0QQolw/F4cIrwD3Au8CjwDqgcwOP1RNY0TRiKRQKxWFHKZDQ0J2llAeAzUKIMwGExqAQXVcD3T0rQohuUsrfpZT3AnuBXH2TeqYrGo1SqBWHPEKIiwCnlPI9tGCY4WguHxuFEN0j7hx8LCvaA3pRkwuqUCgUhwG6Vfg3PUDwsQYe5nzgciHEUmAlcHKIPj8DQ4TPL+QxIcRyPQDyZ2Cp3n408GUD5VAoAJU2T3EYI4Q4FRgqpby7nvscIaW8p/kkUygUCkVTIIR4BpgppfwhzHY7MAcYK6V0HlThFIcUykKtOGyRUn4GbKnnbhbgiaaXRqFQKBTNwMNAbITtHYE7lDKtaCzKQq1QKBQKhUKhUDQCZaFWKBQKhUKhUCgagVKoFQqFQqFQKBSKRqAUaoVCoVAoFAqFohEohVqhUCgUCoVCoWgESqFWKBQKhUKhUCgawf8DzCnpU2YfYU0AAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "gerry02_traj_tracking.plot(cdpr_2, controller_2, result_2, len(pdes), dt, pdes)" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "6c0951da", "metadata": {}, "outputs": [], diff --git a/gtdynamics/cablerobot/src/gerry03_stroke_tracking.py b/gtdynamics/cablerobot/src/gerry03_stroke_tracking.py index 0b41cdb0..a3110cdd 100644 --- a/gtdynamics/cablerobot/src/gerry03_stroke_tracking.py +++ b/gtdynamics/cablerobot/src/gerry03_stroke_tracking.py @@ -140,7 +140,7 @@ def main(fname: str = 'data/ATL_filled.h', """Runs a simulation of the iLQR controller trying to execute a predefined trajectory. Args: - fname (str, optional): The trajectory filename. Defaults to 'data/iros_logo_2.h'. + fname (str, optional): The trajectory filename. Defaults to 'data/ATL_filled.h'. Q (np.ndarray, optional): Vector of weights to apply to the state objectives. The real weight matrix will be diag(Q). Defaults to np.ones(6)*1e2. R (np.ndarray, optional): Vector of weights to apply to the control costs. The real weight From e9ba1b5765ed3b5408fce3bedc6ac605bb394f56 Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Tue, 11 Jan 2022 22:00:56 -0500 Subject: [PATCH 71/73] fix controller docstrings --- gtdynamics/cablerobot/src/cdpr_controller.py | 3 +- .../cablerobot/src/cdpr_controller_ilqr.py | 3 +- .../src/cdpr_controller_tension_dist.py | 39 +++---------------- 3 files changed, 8 insertions(+), 37 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_controller.py b/gtdynamics/cablerobot/src/cdpr_controller.py index 631aec9e..b0d61b5d 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller.py +++ b/gtdynamics/cablerobot/src/cdpr_controller.py @@ -5,8 +5,7 @@ See LICENSE for the license information @file cdpr_controller.py -@brief Optimal controller for a cable robot. Solved by creating a factor graph and adding state -objectives and control costs, then optimizing +@brief Base class for a controller for a cable robot. @author Frank Dellaert @author Gerry Chen """ diff --git a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py index b9578f7b..a44db910 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py @@ -21,8 +21,7 @@ class CdprControllerIlqr(CdprControllerBase): - """Precomputes the open-loop trajectory - then just calls on that for each update. + """Precomputes the open-loop trajectory then just calls on that for each update. """ def __init__(self, cdpr, diff --git a/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py b/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py index 319a6800..b803bce6 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_tension_dist.py @@ -5,8 +5,9 @@ See LICENSE for the license information @file cdpr_controller_tension_dist.py -@brief Optimal controller for a cable robot. Solved by creating a factor graph and adding state -objectives and control costs, then optimizing +@brief Controller for a cable robot based on computing the wrench necessary to track the desired +trajectory exactly followed by applying a tension distribution algorithm to compute the necessary +torque to achieve said wrench. @author Frank Dellaert @author Gerry Chen """ @@ -19,8 +20,9 @@ from cdpr_planar import Cdpr class CdprControllerTensionDist(CdprControllerBase): - """Precomputes the open-loop trajectory - then just calls on that for each update. + """Does whatever wrench is needed to follow the desired trajectory exactly. Uses a simple + least-squares minimum effort tension distribution algorithm (using GTSAM) to find the tensions + necessary to enact such a wrench. """ def __init__(self, cdpr, x0, pdes=[], dt=0.01, Q=None, R=np.array([1.])): """constructor @@ -38,35 +40,6 @@ def __init__(self, cdpr, x0, pdes=[], dt=0.01, Q=None, R=np.array([1.])): self.R = R N = len(pdes) - # # initial guess - # lid = cdpr.ee_id() - # x_guess = gtsam.Values() - # x_guess.insert(0, dt) - # for k, T in enumerate(pdes): - # gtd.InsertPose(x_guess, lid, k, T) - # utils.InsertTwist(x_guess, lid, 0, x0) - # for k in range(N-1): - - # utils.InsertJointAngles(x_guess, k, xk) - # utils.InsertJointVels(x_guess, k, xk) - # utils.InsertTorques(x_guess, k, xk) - # utils.InsertWrenches(x_guess, lid, k, xk) - # utils.InsertTwist(x_guess, lid, k+1, xk) - # utils.InsertTwistAccel(x_guess, lid, k, xk) - # for ji in range(4): - # gtd.InsertJointAngleDouble(x_guess, ji, N-1, 0) - # gtd.InsertJointVelDouble(x_guess, ji, N-1, 0) - # gtd.InsertTorqueDouble(x_guess, ji, N-1, 0) - # gtd.InsertWrench(x_guess, lid, ji, N-1, np.zeros(6)) - # gtd.InsertTwistAccel(x_guess, lid, N-1, np.zeros(6)) - # # create iLQR graph - # fg = self.create_ilqr_fg(cdpr, x0, pdes, dt, Q, R) - # # optimize - # self.optimizer = gtsam.LevenbergMarquardtOptimizer(fg, x_guess) - # # self.result = self.optimizer.optimize() - # self.result = x_guess - # self.fg = fg - def update(self, values, t): """New control: returns the entire results vector, which contains the optimal open-loop control from the optimal trajectory. From 1d34bbaa4eb9fe03feda06d370efc137629bd7fe Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Wed, 12 Jan 2022 18:35:37 -0500 Subject: [PATCH 72/73] leftover merge fixes --- gtdynamics/cablerobot/src/utils.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/gtdynamics/cablerobot/src/utils.py b/gtdynamics/cablerobot/src/utils.py index 93e8b866..896b1770 100644 --- a/gtdynamics/cablerobot/src/utils.py +++ b/gtdynamics/cablerobot/src/utils.py @@ -120,13 +120,13 @@ def InsertTwistAccel(dest, link_id, k, source): gtd.InsertTwistAccel(dest, link_id, k, gtd.TwistAccel(source, link_id, k)) def InsertJointAngles(dest, k, source): for ji in range(4): - gtd.InsertJointAngleDouble(dest, ji, k, gtd.JointAngleDouble(source, ji, k)) + gtd.InsertJointAngle(dest, ji, k, gtd.JointAngle(source, ji, k)) def InsertJointVels(dest, k, source): for ji in range(4): - gtd.InsertJointVelDouble(dest, ji, k, gtd.JointVelDouble(source, ji, k)) + gtd.InsertJointVel(dest, ji, k, gtd.JointVel(source, ji, k)) def InsertTorques(dest, k, source): for ji in range(4): - gtd.InsertTorqueDouble(dest, ji, k, gtd.TorqueDouble(source, ji, k)) + gtd.InsertTorque(dest, ji, k, gtd.Torque(source, ji, k)) def InsertWrenches(dest, link_id, k, source): for ji in range(4): gtd.InsertWrench(dest, link_id, ji, k, gtd.Wrench(source, link_id, ji, k)) From 3353dc2d3bce7243f7a180d32a5928da97236fac Mon Sep 17 00:00:00 2001 From: Gerry Chen Date: Fri, 3 Jun 2022 04:51:33 -0400 Subject: [PATCH 73/73] minor syntax updates --- .../cablerobot/src/cdpr_controller_ilqr.py | 9 +++++++- gtdynamics/cablerobot/src/draw_cdpr.py | 2 +- .../src/gerry02_traj_tracking.ipynb | 17 ++++++++------ .../cablerobot/src/gerry02_traj_tracking.py | 15 ++++++++----- gtdynamics/cablerobot/src/paint_parse.py | 22 +++++++++++-------- 5 files changed, 42 insertions(+), 23 deletions(-) diff --git a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py index a44db910..e17521bf 100644 --- a/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py +++ b/gtdynamics/cablerobot/src/cdpr_controller_ilqr.py @@ -31,7 +31,8 @@ def __init__(self, Q=None, R=np.array([1.]), x_guess=None, - debug=False): + debug=False, + progress=None): """constructor Args: @@ -64,6 +65,12 @@ def __init__(self, params = utils.MyLMParams(None) # params.setRelativeErrorTol(1e-10) # params.setAbsoluteErrorTol(1e-10) + # params.setVerbosityLM('TRYLAMBDA') + if progress is not None: + def hook(iter, errorBefore, errorAfter): + progress.update() + progress.set_postfix(iter=iter, errorBefore=errorBefore, errorAfter=errorAfter) + params.iterationHook = hook self.optimizer = gtsam.LevenbergMarquardtOptimizer(fg, x_guess, params) def optimize(optimizer): # so this shows up in the profiler return optimizer.optimize() diff --git a/gtdynamics/cablerobot/src/draw_cdpr.py b/gtdynamics/cablerobot/src/draw_cdpr.py index 38a38185..58928d66 100644 --- a/gtdynamics/cablerobot/src/draw_cdpr.py +++ b/gtdynamics/cablerobot/src/draw_cdpr.py @@ -111,7 +111,7 @@ def plot_trajectory(cdpr, result, Tf, dt, N, x_des, step=1): act_T = [gtd.Pose(result, cdpr.ee_id(), k) for k in range(N+1)] act_xy = np.array([pose32xy(pose) for pose in act_T]).T des_xy = np.array([pose32xy(pose) for pose in x_des]).T - tensions = np.array([[gtd.TorqueDouble(result, ji, k) for ji in range(4)] for k in range(N)]) + tensions = np.array([[gtd.Torque(result, ji, k) for ji in range(4)] for k in range(N)]) # plot fig = plt.figure(1, figsize=(12,4)) diff --git a/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb b/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb index b8a2c8be..0701f6bb 100644 --- a/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb +++ b/gtdynamics/cablerobot/src/gerry02_traj_tracking.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "impressive-plumbing", "metadata": {}, "outputs": [], @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "fifteen-crawford", "metadata": { "scrolled": true @@ -33,7 +33,7 @@ "# cdpr, controller, result, N, dt, pdes = main(Q=np.ones(6)*1e3, R=np.ones(1)*1e-3, debug=False)\n", "# cdpr, controller, result, N, dt, pdes = main(Q=np.ones(6)*1e3, R=np.ones(1)*1e-3, N0=9700, dN=10, debug=False)\n", "# cdpr, controller, result, N, dt, pdes = main(Q=np.ones(6)*1e3, R=np.ones(1)*1e-3, N0=9700, debug=False)\n", - "cdpr, controller, result, N, dt, pdes = main(fname='data/ATL.h', Q=np.ones(6)*1e3, R=np.ones(1)*1e-3, dN=1, debug=False)" + "cdpr, controller, result, N, dt, pdes = main(fname='/Users/gerry/Downloads/104494979_svg_output_3mps2.h', Q=np.ones(6)*1e1, R=np.ones(1)*1e-3, dN=1, N=None, debug=False)" ] }, { @@ -47,7 +47,7 @@ "source": [ "# Plot Trajectory\n", "anim = plot_trajectory(cdpr, result, dt*N, dt, N, pdes, step=10);\n", - "plt.suptitle('Motor inertia: {:.1e} kg.m^2'.format(cdpr.params.winch_params.inertia_));" + "plt.suptitle('Motor inertia: {:.1e} kg.m$^2$'.format(cdpr.params.winch_params.inertia_));" ] }, { @@ -89,12 +89,12 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "other-calvin", "metadata": {}, "outputs": [], "source": [ - "save_controller('data/ATL_controller_1e6.h', controller)" + "save_controller('/Users/gerry/Downloads/104494979_svg_output_3mps2_controller_full.h', controller)" ] }, { @@ -107,8 +107,11 @@ } ], "metadata": { + "interpreter": { + "hash": "402f513bd64bb05ccdfd11315d0c88453571d1d1d73db48414a1b2a41f771ebc" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.6 ('base')", "language": "python", "name": "python3" }, diff --git a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py index 6fa47e52..e56ec281 100644 --- a/gtdynamics/cablerobot/src/gerry02_traj_tracking.py +++ b/gtdynamics/cablerobot/src/gerry02_traj_tracking.py @@ -74,7 +74,8 @@ def main(fname='data/ATL.h', N0=0, N=None, dN=1, - speed_multiplier=1): + speed_multiplier=1, + progress=None): """Runs a simulation of the iLQR controller trying to execute a predefined trajectory. Args: @@ -128,13 +129,13 @@ def main(fname='data/ATL.h', gtd.InsertTwist(x0, cdpr.ee_id(), 0, (0, 0, 0, 0, 0, 0)) # controller - controller = CdprControllerIlqr(cdpr, x0, des_T, dt, Q, R) + controller = CdprControllerIlqr(cdpr, x0, des_T, dt, Q, R, progress=progress) # feedforward control xff = np.zeros((N, 2)) uff = np.zeros((N, 4)) for t in range(N): xff[t, :] = gtd.Pose(controller.result, cdpr.ee_id(), t).translation()[[0, 2]] - uff[t, :] = [gtd.TorqueDouble(controller.result, ji, t) for ji in range(4)] + uff[t, :] = [gtd.Torque(controller.result, ji, t) for ji in range(4)] if debug: print(xff) print(uff) @@ -156,7 +157,11 @@ def save_controller(fname, controller): if __name__ == '__main__': # cProfile.run('results = main(N=100)', sort=SortKey.TIME) - results = main(fname='data/ATL_filled.h', Q=np.ones(6)*1e1, R=np.ones(1)*1e-3, dN=1, debug=False, speed_multiplier=1) + # results = main(fname='data/ATL_filled.h', Q=np.ones(6)*1e1, R=np.ones(1)*1e-3, dN=1, debug=False, speed_multiplier=1) + # plot(*results) + # plt.show() + # save_controller('data/tmp.h', results[1]) + results = main(fname='/Users/gerry/Downloads/105074147_svg_output_3mps2.h', Q=np.ones(6)*1e1, R=np.ones(1)*1e-3, dN=1, N=1000, debug=False, speed_multiplier=1) + save_controller('/Users/gerry/Downloads/105074147_svg_output_3mps2_controller_10s.h', results[1]) plot(*results) plt.show() - save_controller('data/tmp.h', results[1]) diff --git a/gtdynamics/cablerobot/src/paint_parse.py b/gtdynamics/cablerobot/src/paint_parse.py index 48cb61a6..83e41e43 100644 --- a/gtdynamics/cablerobot/src/paint_parse.py +++ b/gtdynamics/cablerobot/src/paint_parse.py @@ -17,19 +17,23 @@ FLOATEXPR = '(-?[0-9]*?\.?[0-9]*?)' def ParseFile(fname): - with open(fname) as f: + with open(fname) as file: + f = filter(lambda line: not line.startswith('//'), file) + # for _ in range(4): + # next(f) + # painton - assert next(f) == 'bool painton[] = {\n', "variable `painton` not found" + assert next(f) == 'const bool PROGMEM painton[] = {\n', "variable `painton` not found: \n{:}\n{:}".format() paintons = [bool(int(e)) for e in next(f).strip().split(',')] assert next(f) == '};\n', "parse error on variable `painton`" # colorinds - assert next(f) == 'uint8_t colorinds[] = {\n', "variable `colorinds` not found" + assert next(f) == 'const uint8_t PROGMEM colorinds[] = {\n', "variable `colorinds` not found" colorinds = [int(e) for e in next(f).strip().split(',')] assert next(f) == '};\n', "parse error on variable `colorinds`" # colorpalette - assert next(f) == 'uint8_t colorpalette[][3] = {\n', "variable `colorpalette` not found" + assert next(f) == 'const uint8_t PROGMEM colorpalette[][3] = {\n', "variable `colorpalette` not found" colors = [] while True: matches = re.search(f'\\{{{UINTEXPR}, {UINTEXPR}, {UINTEXPR}\\}},?', next(f)) @@ -39,7 +43,7 @@ def ParseFile(fname): colors = np.array(colors) # trajectory - assert next(f) == 'float traj[][2] = {\n', "variable `traj` not found" + assert next(f) == 'const float PROGMEM traj[][2] = {\n', "variable `traj` not found" traj = [] while True: matches = re.search(f'\{{{FLOATEXPR},{FLOATEXPR}}},?', @@ -60,20 +64,20 @@ def writeControls(fname, gains_ff): with open(fname, 'w') as f: Ks, uffs, vffs, xffs = zip(*gains_ff) f.write('// u = K * ([v;x]-[vff;xff]) + uff\n') - f.write('float xffs[][2] = {\n') + f.write('const float PROGMEM xffs[][2] = {\n') for xff in xffs: f.write('\t{{{:f}, {:f}}},\n'.format(*xff.translation()[[0, 2]])) f.write('};\n') - f.write('float vffs[][2] = {\n') + f.write('const float PROGMEM vffs[][2] = {\n') for vff in vffs: f.write('\t{{{:f}, {:f}}},\n'.format(*vff[[3, 5]])) f.write('};\n') - f.write('float uffs[][4] = {\n') + f.write('const float PROGMEM uffs[][4] = {\n') for uff in uffs: f.write('\t{{{:f}, {:f}, {:f}, {:f}}},\n'.format(*uff)) f.write('};\n') f.write('// vx, vy, x, y\n') - f.write('float Ks[][4][4] = {\n\t') + f.write('const float PROGMEM Ks[][4][4] = {\n\t') for K in Ks: f.write('{\n') for Krow in K: