{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from tango import DeviceProxy\n",
"import pandas as pd\n",
"import time\n",
" \n",
"ds_WC1 = DeviceProxy('er/ca/rac.05-elr.01-wac.01')\n",
"ds_phase = DeviceProxy('ER/HF/RAC.17-API.AO1')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(columns=('phaseRF', 'Ch0_Amp', 'Ch0_Q', 'Ch0_Q_integrate'))\n",
"\n",
"\n",
"Nstep = 200\n",
"phase_min = 0"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"Npoints = 10\n",
"for i in range(Nstep+1):\n",
" phase = phase_min + i*2\n",
" ds_phase.AO2 = phase\n",
" ch0_Amp_raw_s = 0\n",
" ch0_Charge_raw_s = 0\n",
" ch0_Charge_integre_s = 0\n",
" for j in range(Npoints):\n",
" time.sleep(1.1)\n",
" ch0_Amp_raw = ds_WC1.Ch0_Peak\n",
" ch0_Charge_raw = ds_WC1.Ch0_Charge\n",
" ch0_Charge_integre = 1e9 * (ds_WC1.Ch0[400:600] -ds_WC1.Ch0[:300].mean()).sum() *10 / (3200 * 1e6 * 50)\n",
" \n",
" ch0_Amp_raw_s += ch0_Amp_raw\n",
" ch0_Charge_raw_s += ch0_Charge_raw\n",
" ch0_Charge_integre_s += ch0_Charge_integre\n",
" \n",
" df.loc[i] = [phase, ch0_Amp_raw_s/Npoints, ch0_Charge_raw_s/Npoints, ch0_Charge_integre_s / Npoints]\n",
" \n",
" print(i, phase, ch0_Amp_raw_s / Npoints, ch0_Charge_raw_s / Npoints, ch0_Charge_integre_s / Npoints)\n",
" time.sleep(1.1)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='phaseRF'>"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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33P8K7t15FPm0gRXdaeRSBtKGVtNspDLnmkQ4BFb4JEydQiU0ojeOWPXLIbD7R4MQVd8nUfJ8EppGMHWqO5GXZ3FcA4Hz+rlDk3hq/wSmSjZeODKF4zNVzFRsDBipRr72rAxPlXHpZx/CVz/4BvzcaUMt7UNEM/lColzrjJQ1CuWTaJ2xmSoKVadu2ZeK7SBjeD6JuHsn0CRqhbSsYR+dalFINOCTEEJCNv+Ia6URTeIAL+0im6uSTEmys7rjjmtFPKWqA8theO7QJO5/4Rj2jRbxwpEp9Ga9C6kvZ4YcyUA4KqIVTeLoZBkHx4PJXKwmqo5XNkIcb21fNlT6I3rjBOam4OIS/oihfMrP8ZgoWujj3yelaw1GN9VqEgYPCxF+EiFIjhcqOD7tnYe5XA3tHplBxXbbKrZXY26SwmGrtue8rkqaRElFN7WM+O3rLkIsSZOoV7spTpMoBJUQWimJwxhDxXbQkzXqjnPUNzfVRsAVq/XNaYWK7S+WSs1qEos8BHbZIlbh9+48ildHvPhrxoAevtroy6ZqBEHYJ9F8rsRNdz6HP7ojqOoqr05myrYfstqXS4UuVDuqSQhzkyQk9o0VoGuEs0/qxVihimLVge0yX+ilTb2ljOvpso2V3WnkUrqv6ouxHZ+uYLRQu/pqFyGMxtvIR6lnbqrYQYiiRt5qUZjb5qufwVKmEad/xXaQNnR0Z4zYPIl6IbBCkzhjdb6lUGzbZXCle3s2x3W4nHyQP1NvkSX8gS4LWxmSFk+y9rDYk+mWLeLH+9bjXrmJc9f2AIC/2ujLmaFoIyAsJFrJlTgyWQ5dQLK5aLps+yGr3ZmwJTG6uirGRDftHythbV8WK7vTGC9U/bH35biQMLS6PgkxyUcFyUzFQnfGxJreDI5Oeas4sZ/RQtVffc2pkOAmhXb8PkIrCxzXYSEhvkM+bcBlwIxUwkPRHH6OSZ1M5orlIsM1iWilAEAKgY2ZiMeLVRgaYdPK7oaTOr/62B68/8uPe8fm++xu1CcR0SSEJl3PwSybe+Uw3aT7Qk7OW+yO62WL+GFKloPutIFPXHkWAETMTXU0iRYmsIliNaw9VBwMcSf5TMX2o5HEikfQiLlp/1gR6wdyGOhKYbRQ9YVYb9bbf8rQWtIkZio28hkDa3qz/ipOqODHZyqBHXcOzU2+JtGGkBgrVKFr5Netijquq/7E4Z3ryZLIelc+iWYJNIk6QsJ2kTZ05NMmilUnFEXmusyP9InTRsYKFvpyKZzUl8HRqXJiBNqtj7yKD3zlCQDAXc8cxva9XjdGEeoshERSMp2IbpKv5bLtoJ9HO+44OIGtf3l/rO9lv1S2RkQBZk090d8g/JF9OXPRh8AuW0pVx8/GveiUAbzp1EHceOVmXHXeGgCeuSnJJ0EUr0nsGy3gfbf9FJ/+txfwakzznvGiFbogilUbq3ni3lTZ8qORoprEbI7rsuVg17FpnLqiCwNdKVRs159ow5rE7MXGyjWahJcpu7o3gyMTEZ/EdCXQJOZFSLRubhotVNCfSyGb8nJEwiGwQXOpnqwQEt73UJpE8/g+iTrmJhEC25X2fg95gSO0CF2jULKjYKJYRX/OxJreLByXxYZiA8ALh6fwyCsj2D08jZ2HJlGynJCZKJ/m5qaEshxiweP1Hgl6kAzkPCHxo13HcXymgn3Ha8uDHJCEhLgnBrpSiVqC+P6DXSlVKnwhKVsObv/x3oZi3YtVG1s3DAAA3nTaIDSNcMMvnIZ1/TkAQF+XZ26SL1ix+hzKp2MjNH62dxyP7R7FbT/ag8/e+1LN2EqW4180rstQrDpYzRODPJ9EvLlJFhKW4/oTmYiKevjlYRSrDq44e5V/Qb98zCu1JXI+UrMICaFi10Y3WcjzsGDxncV+9owWfcE5Wz/fZhDmpqgm1wyjM1UMdgXhv/KkVLFkTcI712IF6yifRNOIRcvsmoTmn2/59xD3a46Xjzk4XsJvfeNJf+EyVqiin2sSQDg8XEb8hrfcv8u/R4pV279esynNi/JLGOfojKd9AqJLpfe9xD308lHvnoqb1GVNQgib/i4zMZRbzAOD+bQyNy0k971wDJ+8+3k8e3Bi1vcWqw5et64XX//QRfgvb9xQ83pfNoWqVB0UCITE6p5MrClkSqoiG/3hhVZSspxQtqXIHp2peD6Jvqzpm0CEpiOr4PJ+xY32/R1HMNiVwptOHfRV4wdePIasqeOUIa8OVdqo77j2NYlIUtRMxUZ32kDONFDlqytx4b98dMp/32I0Nw10pfxEQpmq4/oTRXcka15pEs3TSC+Oiu14ZTn4al72p4nrO8O1vsd2H8cPdx71J+WJooX+LhP9fAE0WYrXMMVv+m/PHvG3FavBZJ82dKQNPVaTKPCoJLFoK1uu/76BfHjhlSQkNgx6C0yh6fTnkjUJYXZekU8rc9NC8hqPUprtpAsVNJcycOmmFcjwFYyMMNOEin3xi3BVTzo2ukk4R4fy6ZoLUZ7wyrbjm4rERTlVsnyfhFhtiRWMfPOVIkKiULHxwEvHcOV5q2Homv+Zpw9M4IL1fX4ZDS8Eto7jml/4LgtPlDNlG/m0gWzK24/cB1ycb29cc2PLtxwXI3wlNtFGdNNooYrBfKBJAJ6ZEODmJitsbhLYyifRNMVZNAnGmBcCa2jI+5pEuFoAAOS4kBBJrGIRNF6shkyH5YT7O+74MxXb/63Thub55mKaDonV/7r+rHcMy/GT6oR2Lib/6MTvugwHxks4e01PaF/1zE3FildwsL/LVOamhUSUEp6tBadYNYuLMo7+GCEhVuKruCYRtZ1OlfiqO6XXTMiykChUHP8GEELi6FQFjsvQl035juuBLs/pKl/8YuwiSuThl0dQtly883Un8c94FzRjwNYNQQ+ptJnsuHZd7ybuEjehVDa7UHWQzwSVZEuWE5ud3GxC0JP7xvCpu5/HR7/1lO/f2T08jeHpChgDTurNYLpit5zJPTpTwWBXCoau+SYEoTVUbdefKKKmPVWWo3lmC4EVK/y0qSOfFkIiuD9EVJS4xsT1MF22wZhX3K+/K4WMEVyDcViO6y8E1vZ5k32xEixq0oaOlB5/HwintTA3l61gISHuKUHUhDQ8XUHVdnGWLyS8aKzujJE41kLVRlfKQC5lqGS6heS140KTqD+xiNV4to6QEFFBsl1cNjd5vR/CP+5kySsQGOckloVNqer4af793G4uTDdD3YHjepBfnPJFLVYmK7rTmC7beOnoFDQCLlzvCQT5gt66ccB/XC+ZTlzIwlRVtly8cmwa01wzyqcNX9sqVZ1YIdxsGY3/+b3n8Y2f7sO/PXsEP31tFC8fncblf/0Ibnv0NQDwb7iJUvMmp6rtYqps+0I2bXi3itAaKnZgSogKCZUn0TyzRTcFk7Tkk5DuHbHYEPej8H3NlG0UeNJrf84MNIkEx3PVdnH+uj783GmDeN8b1wPwJmPf3GRqSJvx94FY/a/1NQnXf58o0+N/34h2IKKdzlzd7Y27YiNj6nUFQLHiIJfWkTF1lC133uqfKSEhwRjDHh5RNNuEJSbaepqEb26KqfkjmvhEy3ZMlS10Z4xY+7+sSRQt29ckutJegtFjr44CALZuGPB9EmLCl1fs4oZc0Z1GxXbx2vEC1vRmkRITYcaArhE0Ai5Y3+d/rl4ynS8kuFp9cLyId3z+Edz+430AvIk0K2kZsimNyEtIa0Zlth0vm/rXtq4D4CUi7Rr27L0ib8UXEi1EOIlzLW5uX0hkgugWYTrsjoQbK02iecqzmJvE/RjWJILfVQhmcT+K8PKpsuVrFX25VLBQSbjWqo6XPPqt//pGXMLLuRSi5qYETUJEJK3jGkjZDjTmqCYRPb4QMCdJ92HG1JGtIwACTUL3jzcfKCEhMVao+iv72c1NswsJMWHKk3vVcaBr5K9Qow60Ka5JxEUSyZNdser4K4yulIF8xkDVdrG2L4t1/dlan4S0L7GKEfH/Lx6ZwskDWf91IkJ/LoUzV/eEJsC6mkQ1rEnsHyvCZcBjrx4H4IUOClNAserdPOJ5fy6FrpTRVITGXh4V9YaNA+jOGDg4XvKjQ8QYN6/xVmVJSYuuy/DBrz4R2+tb3PCDUmQXECRKVp3a6CaBclw3T1CWI/7cyZO0KK8vF6cUIbBx5iZx/w3kUv7rSYtAyw5K2YtQ20I1bG5KMrsKjXVNn3BcB5/rzZqQ+2pGNQkhJIa6U75JM2NqwcIqZj4qVj1NQsxB8xXhpISEhNzaMEkdFYg0+GwquUZinOO6artI6ZqfcBc1hfj9G2Kym0Op+lVH0iQMf3V18akDICJ/xdufS4Eo6pMQQsK7mPccL+BkbkcVXLvlJLzv4vWhbUlqNiBrEt5xh6e8i37HgQkAqPVJ2K7v4BvsSiGT0psyN73Co0TOWNWNk/tzODhewoGxou9kzpia3x0wKVeiZDl46OURbN83XvOaONdCyKa5LTuIkw8mgKgmoRzXzVPi99ts5qZMSJOoDYEV96PsuBa/f3+XCVP3NOSkwBTLcZEyvNk8x/dVrEjmJq5JxN0Hk0ULKUNDHzczl6qBJpE1dT881/u+4eMHi5K075jPmvUFQKFiI5cKm3Hng7arwC4l5EibuTA3ZUzdqwQbMTelDM0XIFFNYrpsoydjxobZhcxN1SBfQpibAOCNp3odXgfyKaQNDSf1ZWBqWihJSQg4UZOIMeDkgbCQ+JN3nl3zfepFN4mxCO1J5CkESUiGv5IqVR1P6+nPYtfwDAbzXvJeMxf5y0enoRFw+so81vVnsed4AVXbxdkn9fAqmo4/wSeFwYoxV2J+a1FPapCX5AhMAJonwOtpEson0TSz5Un45ibDCyLIpfRICKzQJLzfKdAkAnOTt2AiZE29ruM60CS837VQdfx2m2lTS6w8MFnyws8zfAxl2wXxiz5j6silDVRsFwNdqVhNopdbEPK+JqEHC6uYe6NY9aotiDloviKclCYhsed4AYZGIGpcSGRjQl9l+nOpkLmj6nhCojcbLyQ8c5MRu2qfKFqSycb2Q2A9TcLb35u4kMinDTzw338B77pgLUydQjVxZMe1YH1ESMRRL7pJXMRC+EXLMXdLmoTQnkT0yGDeK/5XT10enirjxn991v9dXjk2jY2DXciYOk4e8DSJfWMFnDyQw/959/n4q185L9bcJyP2FfdbR81NaclOnDY0zyfBBWaPim5qGz/aLUHAyo5rgEfmVWOS6VLBxA6EzU3iesjW0Voth/lCQky+nk8ikicRs1iaKHrh5xnJpCULt66UjpP6ssinayOWjs9UfP9XXjI3+dpMnCZRtZFLG8rctJDsOV7A+oEcMsbspo9GQmCB2iJ/lai5qRiO9Z6uCE1CQ9UJO6zGi1U/Y7Qom5tSBtb1Z3H6ynxII1jXn4OhazANLTZPYqUkJGSfRBLezVFb8gAItBOxeh+OCIl82pDsxbycOTc3DXV5DsV6K6GfvDaKb//sAJ7ePwHA0yTOWNXNv2cWJcvBgbES1g9kcdaaHlx86iCyKW9CT3Jcx/Xk/vHu4/jAV57A0akydI2CCrh8cvLi5L3zEK3dBHiLBuWTaJ6gLEeSuSmYpAHPfDkdl0wXWbTNVGyMF6ogCiLT6l1rVUmTMHVPayhIGdciTyLO3DRRqqI3GwgJ2SSZMXX0Zk1sGMwhm9JrNYnpql9IUmimGcncJI93umzhuYOTKFYcdKV0/3jzFQarzE0S+0a9jMfxYnVWn0Rgbqp/CnuzZiiCqcpLC4gVqVw0ToSL9mbNUDObjMZX4EUL6/qzeHWk4JubMqanft945eZEf4Gph81Nvk+iRxIS/Q1oEnyitBzm220FImS4j6/Wjk1VoBH8vsL5jOHfGH512WwK771oPa44ezVeOTZTVzAL09ve0QIuWN+HvaMFvPN8L69jnTT26PcY6ErVOK5HpishlV92Cj740jAeeWUErw7PoD+XgsbzI1IRTUIu8CcXU8xnDKVJtMBs5qZgsvV+h+5Id7poMp1gquz5JPqypp/r4oWMJpubxHUOeIsbbzKOJtPFmZtsrO3L+mMsWY5//aQNDZ9+13nImBpu/NfnajWJQgVnre7xjynGmU3VCoCP3bED//HCMWhEyPE8CaD9ToxJtKVJENEAEd1HRLv4//6E991LRBNEdE9k+zeJ6GUi2klEXyEik29/CxFNEtEz/O9P2xlno4wVPGlez2b5H88fxbce3+9f1Ll0A+amSJ6EmHB6s+GmRKKpjciTAMKFxMaLVd/ZW6p6IbDyBdUbyfwVpPSIJmE5SOmBgy1taCHTUxL+mGIjLWz+fQNz04rutK+tdEkONpE3kjY0/NWvnIef3zQ0q7lJTOR7jhewe3gGLgPO5JqErAVFzWZ9uVTIcV2s2viFzz2Eu545FFu1VkRIHZoo+aYmb6ze2H2fhC07roOFQnfaUI7rFpg1T8IKaxJdkcZD0WQ6wXTZwhjPthZkzdqVvMCLbgoWQLmU7pmbbC8q0dA139wYZbIY1iTKVtBhLm3oOHdtL05f2e1pElEhMV3xqznnM8k+iRcOT+Hfnz+GjKHDcRm6ToDophsBPMAY2wTgAST3of4cgPfHbP8mgM0AzgOQhdTuFMCjjLEt/O/P2xxnQ/hZmXVWGt96Yj++9J+7A01iFp9E1NwkfBLiNdknIUpy9GQMv16Q3LBksmRhRXcGhkaeJsGjG2bD0KnG3JQxg2qaJw/kfAdbPcS44/wS4nyJm7Fqu+jLpnDm6m50pXToGgU+CT5py6aBTMyNE7f/PccLeOGwlzQoQlxDmkRESPTnzJCQnixZKFYdHJksx5qb5CJrcgJUYG7SfXND1XahUXhiymcM5bhugCOTJb+uEiBrErP4JMzAJyFrEsLEl4loEjNl26sA2xUWEkmWAtknIY5TqNqYKdv+ZJxO1CQ8n4SpazA0ChX4k+t/ZSJCqmI7mCrbfpCE8C9mTa1GAHzhgV3ozhj4/u9egtet68Xr1vWFQsvng3aFxDUAbuePbwdwbdybGGMPAJiO2f4DxgHwBIB1bY6nZUo8FrovZyJd5yKaLFkYma6gWPVW44Ze/xT25VKYKFb9SU6EwALcFCUJCfHY0yS4XZPfHFMlC4zBzxotVr0LKxpZE4epa6GJq1j1hEsXFzAn98/ujwBkTaL23PjRTdLN2Jsz8UvnrcHbzlrFx0HQNfIn7Yx04+RMPVRP54sP78Y//mSv7/8Q5qw9xwvYcXAC3WkDp/AQ13zaQH/OMyes4WXTBVFNTrR8LFTsmp7cjDHsHyv62pqcABWKbuJJhaJTmqaR/5vm04byScxC1Xbxvtsexzv/5lF8f8dhALO3LxUrd7GwyGeiQqJWk0gZmue4Lli+hgskL0gYYyGfBABfwx2ZqfhacVwyneW4KFQdX5sXWdBlKb9D3qd8fBEkEeeTEOYmEfJ+7/NH8b6LN+D0ld24+3d+Hm8/Z3UoSXU+aFdIrGKMiXKJRwGsamUn3Mz0fgD3SpvfREQ7iOiHRHROnc9+hIi2E9H2kZHapKhGkSMgMmZyqOdUyULZcjE8Xa5bkkPw5k1DsByGO7YfABBjbpI1CSEkMmaNaUcen3fh2jwiYnYzkeeTCEc35VLe5DaUT+P0lflZ9wEEE2WckBAXfZ9k8urNmrj+ovX4m/deAMBL0suZuq9JCEEIeBEnReki/+JDr+JP73oe//2OHV5xN34e9o8W8cyBCZy3rte39wKeNrG2L1sjtIVNOfjutn8OfE2Cf59R3rL11y9ej3za8KOv5LGmDR1pHgos/5YpwyshnTF15ZOYha88tgevjXhBIr/37afx5L5x/7dIMtWVpTwFoNYnEc24BoA1vRmULG+C75PMTRlDi51QhXBPSRN6Fz/OyHTFN8mmzdroJrHAE9F9GVNDiWsSaUMLaepRc5efSFcT3RTkhBSqtn+MU4bC2nLHzU1EdD/3GUT/rpHfx7WBVu+OLwJ4hDH2KH/+FIANjLHzAfwNgO8lfZAxditjbCtjbOuKFStaPLw8CZt1bZYiI3vfaHHWyCbA6zNx4fo+fOnhV3lROFlIpDBVsjBVtvAv2w/4F0FvzqyZkIVdvS9n+tnJsh2zHqkYc5MQcHfc8Cb87mWbZt0HEEyUcau9UtW7GeSVXF+MjyST0v0QWFmTkM952fJWTUP5NL771CEcGCsFmpjj4vnDU3jdur7Qft/9hpNrkv/EMeRVm6xJiO3C3r2Pt4/cvLobd//OJfjtt57ufy6sSWj+bylHPWVMz6ymNIlkxgpVfOGBXbj8rFX4p//6RjDmJVyKayrR3BRZkecznk9CaJp+7SYzLCSAIFBBEOcT8I7tHUP2SXSlvEWGJyS8/QlNQo7yEwufIBpO96KbrLAjHKiNrvI1iW5hbgqEhMjVmCkHIe/CHOXvz5hfITGrrYIxdnnSa0R0jIjWMMaOENEaAMPNDoCIPglgBYDflI45JT3+ARF9kYiGGGPHm91/o0z4k7Dnk5D7GcuIiXzfaKGmRHQcRITfvWwTPvjVn+GuZw7FmpvufOoQPnn383jn67yOdj0Zo8a0I0oMD+XTfgjd8ZkqVjSgSRgRx3WxGpTEEL0iGkGMO07LKlmBdiKSjfpytecna+p+tJHsk8imvLBSV+oa9rp1vXjwpWHMVOyald/563pDz98f088DQE0md0iTiJibRGew9QNdOHVFWLuS8yRSuoYxXuAvJQkJXSMYGqn2pXV45sA4ilUHH3nzqRjMp6FrhAPjgR8orj81EBMCmzZh83L9GSnsWNbuT+oNNEH5Wswm+BwtXhIkZG5K6yhUbYwVgnstm9LhMvjHBoJ5QQiJbEpH2XaQtrWasNxonoYoaz/ES/UEjmsNJneUz1RsP+S3KxIso2kUMqfNNe2am+4GsI0/3gbgrmY+TEQfBvB2AO9ljLnS9tXE9TMiuoiPc7TNsdYlam6K80mULcdf8RyfqTakSQDAW85YgVxKx0tHp2vMTTMVGy9xB94jr4xAI2/14vskLCEkvLyDlT1e4tmx6TKqjuvbMeth6hRuOmQ5DZnKogjnW5wmIQseMaHKKr4ga+qY5isieYUll+wQ6reIVCpZNsqWG1olnn9yX0NjzhiB8AGCJKtCNRA84rfeN1oEUdAPIO67p42gCmjFCZubstxBrxzXyewe9gponrEqz2uYpXzhnEvpdfIkvBLeYpWf5xOlmDjjoptEDSUg6OcA1DqO/WPw0u+ykOhKGTg+4/kghblJLA7lhaRoXSuueTGHVCy3pmlVztRhOcxfuMl1m4CgHL34Lt0ZA9OVQJOI80M+/ae/iD+84oya7XNBu0LiMwCuIKJdAC7nz0FEW4noNvEmInoUwB0ALiOig0T0dv7S38PzY/wkEup6HYCdRLQDwBcAXM/iMrjmEL++S85ExghfRKJ39FQkOzpnNpZmQkS+1lCxw9FNAPD0/nF+HBvdGROaRjXRTcPTXt7BYFca2ZThm0bEhVUPM6JJlLlPolkCTSLe3CQiS8TKKS4kV44+iWoSgBAS3g0nIpVEQcB1/Vl0pXQM5dM1Duok/MQmPmbRPD7sk+D+jrEiVvdkYhtIpfl3T5tBPwEv5yXwVWRNHYYyN9Vl9/AMhvJpfzJdkU/jwJhXJrsnY9bNk5Bt+0HjISEk4nwSsiYRFhJxi0CxkJKbTHWlDf+9wnEtMuzl+SBqbhIJuWUe3CAjX+uAl0iXS+l+pKIcAgvwSK5yUPU5am6ab9pKpmOMjQK4LGb7dkjhrIyxSxM+H3t8xtjfAvjbdsbWLBNyOeFIw58/umMHilUHn/zlcD2jZlbjQkjIdmxxQYlidfK2qLlpeKqCIa6e50zdX0E1oklE8ySKVmOhs1HSZrJP4thU2VfHha8h3twU3ICxmkS1VpPwq8amdJyzthcru9MNhezKxxOfF5pEsWr7EVOyuSkaQhv97mlD88syyAI/bXoTmKFrynFdh13DMzh9ZWDiXNGdxhN7xgB41/7RqTIcl/mJbwJZIAPBRClW134IrCTgT5I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