1077 lines
245 KiB
Plaintext
1077 lines
245 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Jastrow factors"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## H2O"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-04-21T10:23:56.428148Z",
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"start_time": "2020-04-21T10:23:56.383715Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"onedet = \"Jastrows/H2O/H2O-cc-pcvTz.wfj-1det_J2.ud_exp.dat\"\n",
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"os = 1.-7.92535494e-01\n",
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"\n",
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"multidet = \"Jastrows/H2O/H2O-cc-pcvTz-multidet.wfj_J2.ud_exp.dat\"\n",
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"ms = 1.-8.59001137e-01\n",
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"\n",
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"onedet_H = \"Jastrows/H2O/H2O-cc-pcvTz.wfj-1det_J1.H_exp.dat\"\n",
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"ohs = 1.-1.03221118e+00\n",
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"onedet_O = \"Jastrows/H2O/H2O-cc-pcvTz.wfj-1det_J1.O_exp.dat\"\n",
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"oos = 1.-1.43887500e+00\n",
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"\n",
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"#onedet = \"~/Anouar/Jastrows/N2H4/N2H4-tr6.wfj_J2.ud_exp.dat\"\n",
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"#os = 1.-8.03464242e-01\n",
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"\n",
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"!pwd\n",
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"/home/scemama/TEX/RSDFT-CIPSI-QMC/Data\n",
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"unset output\n"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"onedet = \"Jastrows/H2O/H2O-cc-pcvTz.wfj-1det_J2.ud_exp.dat\"\n",
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"os = 1.-7.92535494e-01\n",
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"\n",
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"multidet = \"Jastrows/H2O/H2O-cc-pcvTz-multidet.wfj_J2.ud_exp.dat\"\n",
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"ms = 1.-8.59001137e-01\n",
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"\n",
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"onedet_H = \"Jastrows/H2O/H2O-cc-pcvTz.wfj-1det_J1.H_exp.dat\"\n",
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"ohs = 1.-1.03221118e+00\n",
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"onedet_O = \"Jastrows/H2O/H2O-cc-pcvTz.wfj-1det_J1.O_exp.dat\"\n",
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"oos = 1.-1.43887500e+00\n",
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"\n",
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"#onedet = \"~/Anouar/Jastrows/N2H4/N2H4-tr6.wfj_J2.ud_exp.dat\"\n",
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"#os = 1.-8.03464242e-01\n",
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"\n",
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"!pwd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-04-21T10:24:00.383722Z",
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"start_time": "2020-04-21T10:24:00.144428Z"
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}
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},
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"outputs": [
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{
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"data": {
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"image/png": 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X+moUFBSMGzcuLCwsLi5u7969Xc7KL8xbL/gvnstIV31lvbiu4WhyZUZ4ycFHfyr7p1mqESa+EZZSOnhhZdz0g/1G/zm8/wx1GwXogd16PgF9NX7Rhb4ao0ePnjlz5rp16xYtWnT16tUpU6aMHj26O/YJwLz1gm/jmUpddWftd87ZDecdzeVEaIK811hF3ALdpE8j1P27cgu6B2rr+Vx3/Lx0XOdq0MsvvzxmzJhp06Z5fdZrXw2r1SqXy933DBo5cuSMGTOys7PffvttzskDDeedYcQ5HkOxjVecd87Z6763135vQyyKeFQZMV4Zv1AfPlrB/aKn4N56gIvO1aAu9NU4ceLEkCFDWuaJi4srLCzsfE7gewzJGi446s7aak7b7pyzKyMkEROUfX+rGvvf4bo4WfuvB8AX/HJeRk1NTXJycnp6+qBBg2w2m0KhaHlKqVTabLbWXgi9ffy9hIv5l+6cs1/5a+MX0yu26YtOp1ZbKsn4JfqUG4Pj9zT/Zkfk0Ff0ujgZ5r/Fg7aEloc4/BbdoLdPQUFBdHT09u3b3Q8//vjj5OTklmfT09NffPFFry/sWpiAwby/StvxmoqdP3xy9/isyn/oivfEl+am1pRmm+wGEpN4vMM8HuY4fnL93ldj6NChLV+QIYQKCwsTExN9u9LAwPw82vvjOZvoqq+s1f+2Vn1lZUi27+Oq/k+rH9sUoYri4evzbrf1QMD4va/GpEmTGIbZsmVLamrqt99+e+rUqQ0bNnBfKfCKoVjDBUfVV9bbX1qailwRjyr7TlMNXxnaY5hcAFdfATx1fJeJoiiZTCaTyYRCoVgslslkaWlpLMuOGDHCfWi2b98+hJDMw+uvv86y7JUrV5KSknQ63bBhw3Jycvy0R/cgs9eTJTuNp+ZVbQ8r3jus9LvXa2+ftJA2mu9cwDdu3LjBd4S2cPzkYnRqMubnSWN4WWPjNeftE5byI5amImfICCphTmR0slozAMcWkhhuPU+Yxwvu86Qx+thjXoMwQRNszWlb5XFL+RELYtnoZHX/ZHXk5BCxEo61AD+gBj0QSAtTecJSfthSedyijZVGJ6sHPKvpmShHcEEo4BvUoGDmbKR/Otz8U05z3Xe28NGKmFnaAc+qefliC4DWQA0KkEAekzsaqFsHmn860GzIt0dNVcXM1PR/Wt32zZIxHzKAeFxgPlwFfTUCJACNK1xG+qdDzTezzIZ8R78nVEOX6qfn9OvgLU0fkL4afoJ5PJwLEHcY7Xpgvh/kP7STrTxuubHbVP2NLXKSctBLuv4z1HA3ZdBdwLFYN9Zw2XE901T2T7N+iCxugS5mlkbeA7rTgG4GalD3Q5jp0j3m4u1Nzrt03CJ9/CKddiCOJ/UATAT3eBDs8HeU56XDXdZw2XH61ZrPB5RWfW0d937vBT8NHvtuuE8KkE/i+Q/E4wLnAsQdRrseQbwfxNJs2f7mwk/uWirIh5b1GPKKXtkLvg0AQQK+F8MaaWWKtjX9sOmuqp9k+MrQgbM00NkGAE9Qg/zF2Uj/sPnuj39vipoS8uSBfuGjFe2/BgBvMD99iSMYD+qojrdecDRQeX+8syeu1G6gZufFPJHdNwAFCPPOEBCPi+Duq+Hj3j5eZ1CpVHIPmP9/t6YjrRdcJvr8GsPe+JuklXnph9jJf+8TsC+8MO8MAfG4gL4av2i7t4/XGUiStNlsBoMhPDy86zEx0PbOMGVnfvik6erfGgc8o55zOVYdHehLujDfV4d4oDW+7O3jdQaTyYQQ0ul0XY6IOxbd2G268Jah5yjFzG8H6OOhIwUAndC5Y7G2e/t4ncFoNIrF4oULF0ZHRyckJGzdurWNl3e7vhqGC459Y2+d33D78b19px/sp4+HjhSwBOir0UlduPdi23017pmhoqJiyZIleXl5DMPk5eXpdLpDhw55fVXXwgTMPa0X7Aby34urMyNLijOaWIavUL/AvDMExAtiHD+5fv9eLDo6OiMjIykpSSAQJCUlpaSkHDlyxN8r9YdfzlVlUfEOY1ZCmUwnnFcUO2SJHocbiWF+Ki3EA63x+/lBBoOhurq65f+YoiiptBtfG2W6SZxeWkPZmRknons+DKf8AMCVD/aDcnJyiouLW3u2rKxs8uTJ586dQwjl5+dnZWXNnj2b+0oDj2XQ1f9pPDD+1oBn1M+fi4ECBAKmm57O0lEdP2xrt7dPazPs2LEjNjZWq9XGx8dnZmb66ajSr8y3XDtHXDsw4ZbxhpPvLN5hPqIB8bhobm7mO0JbOH5yMbpMFNtrVm98bvr+jbqH/9hz5OowHIZ+AMAKXLPqR4SZzk2tbbzmfObUgJ4j5XzHASAIwfVirWq86vzX6FuSEOGcSwOhAAHgJ1CDvCv+h/Hw4+WPpIdP2R4pVggRQp6nb2EI4nGBeTzMb7HGERyL3Yt2st+m1dbl2Wd9F+N54QXmrRcgHheYxwvu05cwGgbGYUzaWk2emHVb3U8y9bMo6GwBQEfA/aR9pr7AcSDppwHPap7c39GuXgAAjuCT9rNb+5u/eKrisY8jRv//nnxnAeBXgns8iP/DnxY8Hotd/qCxcPPdpw73a+Ps57b7qzhostFpIxn6/qfEQpFGItNJ/XteNebtXyBeEIP+Ytyw6NsVtbXf2p8+Fq3q2/6Nx0yE40fjnevm+nLL3XJL022b6Y7DYnBYCIYOlSmVYi+XwhEMZSKcDooMlSlVElm4XNVLodJKFf1Ver1UGRWi7aPURCq1fZQaiRAaHAaCgyadNIUQspAu6r4/GzTLNpPOeyZSDGMhXR1fhUIskYu8f+ETIpZK2/uPloskCvGv3o1qsUwsxPSoBc5R7DraxZ56qcpppGefjZGoW/0PLrc0fV1383tDeUFjVYW1abCmZ4I+YoC6R3LfIX2U2qgQbagsJFSmbHtdLpq667JbKVe9w2pwWO667NV2c2lzQ+6dsiqbyeCwNjitvRXqviG6viG6/uoefZSaviG6fiH6CIU6Qqnx9a/ePRAM3Uw4m0kni5CJcBhdDgvpoljaRhI2irBSBELIRDispMtBk82ECyFEMJSNIqwkQTK0iXC4PxkUQ1tIl3shCCGJUKQSS9HPlcLLHx6dVC6474x4vawTe7IW0kUxjNenmkkn3d4n1kq6PPepW5L/OqTinohSkTjE219BHzITDsYjvNdgnfXg7geRVubErNtSrfDxPX1F0nvfcDTL5NVXHqr88Vh1cZPLMTUidkKvAY+E9R3eo09rf984ctBklc1UbTPX2s1lzXdr7eZaR3Ol1Vhnb7ZSRJRS20ep6aPU9laoeyvUYfIQnVTRS6EOl6tUEmlPuardP628cNJUM+m0/ufzb3Q5TISDRWwz4TIRDoplmgmnmXSaCaeLpmwUYSIczaTTTpEOmjQTDidNaSVyvUyJEHIfz6olMrlIIhOJQ8RSnVSOEFJJZCqxTCmWuGuEAAl0UoVcJFaIJSqxTPKffQf3QnDem2jD/X01aJZpvm+/zE4RLprya5IQsVT66/e/RiITC0VwLNZpLhN9NLlSHyeb8o8+9zT8um4yfH7r0p5blzUS+az+CclRQ8aE9RUKBKWlpXzdWtxMOOsczVU2U4PTVm0z3XXZ7zgsZsJR77TWO6xWimhwWlUiaZhCJReJ9VKFXqZUiCRykThEItVK5BqpXCQQIoTcE8VCoVrSiRvOmgknw7IU+8vBiHu3wklTDppsJpw0y7KINREO9w6IjSIQQmbCYSFdDGI1ErlOqiBJMlSp0kkVWqlcKhQrxRJ3pVCKpTqpQiORhUikMqFYJ1XopAqVRCoRirRSuUYSoNPTefzP7QjM4wV6PGjbtm1/+MMf/vKXv6SlpXVwhoKCghUrVpSVlYWGhr7zzjvz5s3zHiVQNcjRQH3x24o+E0Me3RjRssfNIvZEdcn//Pjtj6Y782JGLo4dM7zHr3ot4NzjiWHZirsGJJNYScJI2I2Eg2RoG0lYKcJGEUaX3T2blSJIhnbSlIMiO77wEMnP4xeeO/96mVIsEKolshCxVCoSIYQ0ErlSLFWJpVqpHCEUIpZqpHLFf450cN56CPt4mAvoeFAX+moQBDFz5sx169YtWrTo6tWrU6ZMGT16NI9F3dFAHXysPHaOduy7P/f5oFnmX+XX1l45pRRL3nho0pwBiV6Pa3B+jwoFgpiw3nynaAvOWw9hHy+4de7Y+OWXX96zZ49Kper4DLm5uXK5fPHixQKBYOTIkTNmzMjOzu56Xm7sBurgo+VxKT8XIIZl91VcG3Lwg/+9cf6TpJmXn/39/IGj8BxYASBY+b2vRklJyZAhQ1oexsXFFRUVtfZyv/bVsN+hDk0pj5uv1bxoslgsFxurHj3+yfprX78elpA7fdnjfQYLkACTPgqwBFiC58/QV+NeneqrsX79+hdeeKHlqQ8//DA5Odnrq7oWpoPs9WTW8JsX3jawLGtyOZbnHQjf+872GxdopqM9MTC/1R7E4wLzeJjj+Mn1+/lBKpXKbre3PLRarW0cyvkJYaaPJlf2+63qkfTwY1XXU8/tf6JP3PVZf+zR3kk9njA/jxbicYF5vODm9xo0dOjQjRs3tjwsLCxMTEz090o9UQ7m2DO3w0cpRrzX47W8/SdrbmQ++tK0PoMCmQEA0Bq/99WYNGkSwzBbtmxhGCY3N/fUqVNz587lvtIOYkj2yxeqVH0l2v9mxxzdZKfIH557AwoQ6F6gr8bPutxX48qVK0lJSTqdbtiwYTk5OX46qvTq6yXVR54s33mjoMeeP2eU5nNZFOZDBhCPC8zjQV+NAPH5OYrn1xhuf9Oc92Hx0brinKmLE/QR7b8GANBJcM2qdz/+venHnMaDH55zWqjzT68Mk4fwnQgA4EVw1qDKY5Z/b6zMXHd6jDbq70nPd8fLFAF4QAThh7PxmvOffyjZ/PaXswclbJ8wx1cFCPPWCxCPC8zjBfd9FIOtBtnrqB2vXNv0pxOrRj72duLjPlxyXV2dD5fmcxCPC8zjBffpS0E1Jk072Y9nXVw/+9hfJyYvHtTOZSUAAJ+Ae7n+IjP1h9WJBzY/8dy8mJG+SgUAaBv09vnZsY23Vg878OGkGVCAALZ0Ot2XX37pOSUqKmr//v3up0QikdjDli1beIoZUEFSgy6erFvM7n5nzOOLh4z20yowHxeEeFxgEu/YsWOUh+XLl/OdKBCCoQZVl5lnXs58OeaRleMe9d9aMB8XhHhcYB4vuHX7GmS3kdP/mfGYfsCG56bznQUA0Gnd+xxFFrGzPt2lFsl2vfYi31kA6JB58+ZJpb904GloaGj5efr0X/0d/eqrr6ZNm3b/EvLX1hek1/svoduYd8IfWRvu77Wg7l6D3th5/AZTf+l3q9x9I/wK894GEI+LQMb79NNPp0yZ0vLw4Ycfbvn5xIkTTz75ZLtLeGRtgKpDYHT6o7tt2zaVSvXJJ594fbagoGDcuHFhYWFxcXF79+51T1SpVHIPvroRwZ68qzub8g8/vqRHD/+2UXaLiMD6kleIx0Ug4+n1+t4ehA/8hUS+7KvhtYXGgAEDbDabwWAID/dl5f7xTt3yKwf+oX1p+KhePlxsGzBvvQDxuMA8XnDzZV8Nry00TCYTQkin0/kg7H9YSdf0AxlLKx+d8/IwHy4WABB4ndsParuvxv0tNAoLC41Go1gsXrhw4blz5zQazfLly5ctW9bFsP/xfPbuwTci/vIXX14OBkAAuP8ke6qurm7tqQeEL49FbTabQvHL0IxSqbTZbDKZbMGCBatWraqoqNi2bduaNWsOHz7c2hI60tvn/x0+WFJZv2vxHLFSiALYiWX37t0cl+DXTiye8TDpSOO5hJycHN4ztLGE7777zk8ZgsylS5fWrl3r294+XbnQY9q0ac8999z9vZ43b9588uTJo0ePuh++++67xcXF93Q0TEtLczgcO3bs8BKlA1edFNTfnnbgH3uci57+fWxnYwMA/AGj68WGDh3qeXP7wsLChIQEg8Hg+WeBoijPkyM6pZl0zlYJy8sAAAsiSURBVD6867WLk5JXQQECIEj4sq+G1xYaZWVlkydPPnfuHEIoPz8/Kytr9uzZXVtR6tGcAVfC306f5P+TgQAAgdLx29+321eDbaWFxo4dO2JjY7VabXx8fGZmZmvLbzvMv25c6/23d6/+s77jgX0L89YLEI8LzONhrlNlxMvLfZWDuzZ+k1qbWf+/f968vCCQeQDwCa1WKxQKRR4+/fRT91P79u1LSkoKCQlRq9Xjxo3717/+5Z4+atSorVu38he5czjWoO5xVDP/SPbEc/FL34MbA4FuyetNOT7++ONXXnll6dKl9fX1NTU1K1asePXVVzMzM/kOG2jd4Hqx7T/ml1XcPf3iSzKdiO8sAPiG1Wp96623PvrooyVLlrinzJs3r3fv3l3+xqb7wr0G1dqb/yvv6N/uzI55UsNvEovFgvMZ/RCPi8DHKygosFqt9/Q9/81vfhPIDJjAvQYtOfzPyXnxCz5O4DsIqqurw/lTBPG48He8+2/KYTab9Xq95zm9Hbf2yqn0q6d8FK1V7yQ+sXbkE/5eC8K8BmVdv/pjdf1389OkGv7HrXC+9QSCeNz4O979N+U4c+ZMU1NT1/a/1o4MUHUIDP4/262567Kv/PbQm9VPxEzT8p0FAB8bM2aMVqvdvn2758SjR49++OGHfEXiC7416A9Hv0i42u+1tQ+3PysA3Y1SqdywYcOaNWs2bdpkMpmam5t37do1f/78/v378x0t0DCtQedqKr+oKf7r1GSZHpfvwjC/EBHiccFLvNdee23Pnj3Z2dmRkZH9+vXLyMg4ePBgl68i6L5w7HFIs8ywbR/OuD7irx8Fz0EvAMEKo2tWfeXD3O9QlXDtf01pf1YAQDeHXQ2667S/V/LvDZEzQiIlfGf5lbVr1/IdoS0QjwuIxyPsjsVe2LHbXsQc/dtC3C6O57jD6W8QjwuIxwXHeHidH5T/U/Up+40rC/6AWwECAPhJIHr7eJ3o1e8O709tnhgzUt/ZVACAbqpzNWjZsmVnzpxpu7dPampqQ0NDdnb28uXLS0tLvU5sbfl3Xfb0FTAUDcADxO+9fbxOvP+1DieJEHqv/1NyDV6HhwAAv/J7bx+dTnf/xPtfe+ZCBUJo0dyRi+be/yQuuLcQ8CuIxwXE44svdzq89vbxOvH+1z45aRDOI/8AAD/x5fdPKpXKbre3PLRarSqVyutEH64UANCt+b23j9eJPlwpAKBb83tvH68Tua8UABAkOn77+y739vE6EQAAWJbF+hxwAEDQg2siAAB8ghoEAOAT1CAAAJ+gBgEA+AQ1CADAJx5qULu38uj4vT54iadSqeQe2rgNgJ904fYpgdR2PH63Xl5eXlJSkl6v79+//8aNG++fgd+t1248frfel19+mZiYqNfrY2Njt2zZcv8MXdx6AT4XwOVyRUZGZmZmMgxz+fJlrVZ748aNTs3AbzyCIBBCBoMhYJHukZqaOm/evLFjx7ack+WJ363Xbjx+t57RaNRqtZ999hnLspcuXQoJCTl79qznDPxuvXbj8bv1ampqQkJCjh8/zrJsQUGBQqHIz8/3nKHLWy/QNejkyZMDBw5seTh//vz09PROzcBvvPr6eoSQy+UKWKR7uP/jp06d6vVDzu/WY9uLx+/WMxgMmZmZLQ/Hjx+/fft2zxn43XrtxuN361VXV+/fv7/l4fDhwz///HPPGbq89QJ9LHb//T2Kioo6NQO/8YxGo1gsXrhwYXR0dEJCwtatWwOWza2zt08J5NZD7cXjd+uFh4cvXrzY/XNVVVVRUdHEiRM9Z+B367Ubj9+tFxkZ6e59RtP0gQMHamtrp0z51e0Gu7z1An3DsHZv5dHBe33wFU8mky1YsGDp0qVZWVnnz59/6qmn+vTp8+yzzwYsYdv43XrtwmTr1dTUJCcnp6enDxo0yHM6JluvtXg4bL2srKz58+f36NFj+/btUVFRnk91eesFej+o3Vt58Huvj3bXHh0dnZGRkZSUJBAIkpKSUlJSjhw5ErB47cL8Tik4bL2LFy9OmDBh1apVq1atuucpHLZeG/Fw2Hpz584lCOLw4cMrV648ePCg51Nd3nqBrkHt3sqD33t9tLt2g8Hg2ReYoiipVBqweO3C/E4pvG+98+fPP/fcc5999tkrr7xy/7O8b7224/G79YqLiw8fPowQEolE48ePT05OPn78uOcMXd96vhqy6iCSJKOjoz/99FOapk+fPq3RaG7dusWy7MGDB4uKitqYAZN433//vUqlysvLY1n2woULGo3mq6++Cli8FvcM+mKy9dqNx+/Ws9ls0dHR33zzzT3TMdl67cbjd+udPXs2JCTkzJkzLMuWl5fHxMRs2bKF9cXWC3QNYlu5lUe7NwDBJ96OHTtiY2O1Wm18fLznFxkB0OXbp+ATj8ett2/fPoSQzMPrr7/OYrP1OhKPx63HsmxmZmZMTIxKpYqMjFy9ejVFUawvth7cuwMAwCe4VgMAwCeoQQAAPkENAgDwCWoQAIBPUIMAAHyCGgQA4BPUIAAAn6AGAQD4BDUIAMAnqEEAAD5BDQIA8AlqEACAT1CDgB+dPn16xIgRnre26prVq1evXLnSJ5EAbuC6eeAvZrM5Pj7+5MmTw4cP57gokiQffvjhDRs2PPXUUz7JBvAB+0GgVRUVFSEhIR988EF4eHhdXV1nX75p06bJkye7C9CJEycSExMHDBgwcODAzMzMe+YsKSkRCAQtDxMTE/fv3+85g0Qi+fOf/7xmzZou/R4Aa1CDQKtkMpnD4SAIwmAwREREdPblWVlZL774IkLI6XTOmTNn/fr15eXlGRkZv/vd7xoaGjq7tGeeeaa0tLSkpKSzLwSYgxoEWiUQCFiWTUlJce+k1NXVTZs2bdq0aS0zfPDBBwkJCQ899NBrr73GMIzna41GY0lJyfjx4xFCcrm8urp6+vTpCKFJkyZJJJLy8vLOhpHL5aNGjTp37hzX3wpgBmoQaEdYWJj7h3nz5iUnJ7dM//7773ft2lVQUPDDDz/cvHnznh4P9fX1IpGoZ8+e7oeff/75hAkTxo4dO27cOIIg7ilYHdS7d+87d+509fcAmAp0fzHQ7bSM1Bw5cuTatWvHjh1zPxw9enRubq5cLkcIRUVFNTU1tbaEgwcPrlu3Lj8/Pzo6mqbp+1u+iEQihBDDMEKhECHU3Nzsj18E4An2g0BHqdVqz4dyuTw0NBQhdP369by8vJkzZ3o+Gx4eTtO0e9zHaDSGhob27duXZdkNGzYIBAJ397ucnBx3N5iIiAixWOwe6zl79mxVVZV7IS0zuN25c6d3797+/SVBwEENApxcvHhx1qxZ2dnZer3ec7per4+Pj3cP38yZMycyMjI2NvaRRx4ZNGhQSkrKwoULi4qK0tPTv/nmG4SQSqVav379zJkzf/vb337xxRfjx493nzLSMgNCyOVyXbp0KSkpKeC/IvAzP7QAAUHru+++mzp1asvDs2fPDhs2rKSkxOvM6enp8+bN89Wq9+3bN3z4cF8tDeAD9oNAF5lMpldeeeX48eNxcXFeZ1i5cuXXX39dVFTEfV00Ta9bt+69997jviiAGxiTBh1SXl4+Y8YMu91eX1//0EMPzZs3T6PR1NXVtZy4vGDBgjfffNPzJTqdbs+ePfPnzz979qxSqeSy9j/96U+PPfbY008/zWUhAE9wrQYAgE9wLAYA4BPUIAAAn6AGAQD4BDUIAMAnqEEAAD5BDQIA8AlqEACAT1CDAAB8ghoEAOAT1CAAAJ+gBgEA+AQ1CADAJ6hBAAA+QQ0CAPAJahAAgE9QgwAAfPo/wihzdjpd4JMAAAAASUVORK5CYII=\n",
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"text/plain": [
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"<IPython.core.display.Image object>"
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]
|
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},
|
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"metadata": {},
|
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<IPython.core.display.Image object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"set yrange [1:1.4]\n",
|
|
"set xrange [0:3]\n",
|
|
"set grid\n",
|
|
"set xlabel \"r_{12} (a.u.)\"\n",
|
|
"set format y \"%.2f\"\n",
|
|
"set format x \"%.1f\"\n",
|
|
"set key bottom right\n",
|
|
"\n",
|
|
"set output '/tmp/gnuplot-inline-1587464640.145517.692211090260.png'\n",
|
|
"plot onedet u 1:($2+os) w l title \"HF\" , multidet u 1:($2+ms) w l title \"FCI\"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"set yrange [0:1]\n",
|
|
"set output '/tmp/gnuplot-inline-1587464640.145606.7249194234.png'\n",
|
|
"plot onedet_O u 1:($2+oos) w l title \"O\" , onedet_H u 1:($2+ohs) w l title \"H\"\n",
|
|
"\n",
|
|
"\n"
|
|
]
|
|
},
|
|
"execution_count": 2,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"set yrange [1:1.4]\n",
|
|
"set xrange [0:3]\n",
|
|
"set grid\n",
|
|
"set xlabel \"r_{12} (a.u.)\"\n",
|
|
"set format y \"%.2f\"\n",
|
|
"set format x \"%.1f\"\n",
|
|
"set key bottom right\n",
|
|
"\n",
|
|
"plot onedet u 1:($2+os) w l title \"HF\"\\\n",
|
|
", multidet u 1:($2+ms) w l title \"FCI\"\n",
|
|
"\n",
|
|
"set yrange [0:1]\n",
|
|
"plot onedet_O u 1:($2+oos) w l title \"O\"\\\n",
|
|
", onedet_H u 1:($2+ohs) w l title \"H\"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2020-04-21T10:24:03.912512Z",
|
|
"start_time": "2020-04-21T10:24:03.529845Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"unset output\n",
|
|
" f1(x) = exp(a_1 * x/(1. + b_1*x))\n",
|
|
"f2(x) = exp(a_2 * x/(1. + b_2*x))\n",
|
|
"f3(x) = exp(a_3 * x**2+b_3)\n",
|
|
"f4(x) = exp(a_4 * x**2+b_4)\n",
|
|
"a_1 = 0.5\n",
|
|
"a_2 = 0.5\n",
|
|
"a_3 = 0.5\n",
|
|
"a_4 = 0.5\n",
|
|
"b_1 = 1.\n",
|
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"b_2 = 1.\n",
|
|
"b_3 = 1.\n",
|
|
"b_4 = 1.\n",
|
|
"\n",
|
|
"fit f1(x) onedet u 1:($2+os) via b_1\n",
|
|
"Max. number of data points scaled up to: 3072\n",
|
|
"iter chisq delta/lim lambda b_1 \n",
|
|
" 0 5.7947436972e+01 0.00e+00 2.58e-01 1.000000e+00\n",
|
|
"\n",
|
|
"\n",
|
|
" 1 5.1445429129e+00 -1.03e+06 2.58e-02 1.538628e+00\n",
|
|
" 2 1.4305276261e-01 -3.50e+06 2.58e-03 1.862386e+00\n",
|
|
" 3 3.1749513729e-02 -3.51e+05 2.58e-04 1.928474e+00\n",
|
|
" 4 3.1669788584e-02 -2.52e+02 2.58e-05 1.930350e+00\n",
|
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"\n",
|
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"\n",
|
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" * 3.1669788590e-02 1.98e-05 2.58e-04 1.930346e+00\n",
|
|
" * 3.1669788590e-02 1.98e-05 2.58e-03 1.930346e+00\n",
|
|
" * 3.1669788590e-02 1.98e-05 2.58e-02 1.930346e+00\n",
|
|
" * 3.1669788590e-02 1.98e-05 2.58e-01 1.930346e+00\n",
|
|
" * 3.1669788589e-02 1.67e-05 2.58e+00 1.930346e+00\n",
|
|
"\n",
|
|
"\n",
|
|
" 5 3.1669788530e-02 -1.69e-04 2.58e-01 1.930347e+00\n",
|
|
"iter chisq delta/lim lambda b_1 \n",
|
|
"\n",
|
|
"After 5 iterations the fit converged.\n",
|
|
"final sum of squares of residuals : 0.0316698\n",
|
|
"rel. change during last iteration : -1.69165e-09\n",
|
|
"\n",
|
|
"degrees of freedom (FIT_NDF) : 2999\n",
|
|
"rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.00324963\n",
|
|
"variance of residuals (reduced chisquare) = WSSR/ndf : 1.05601e-05\n",
|
|
"\n",
|
|
"Final set of parameters Asymptotic Standard Error\n",
|
|
"======================= ==========================\n",
|
|
"b_1 = 1.93035 +/- 0.0006837 (0.03542%)\n",
|
|
"fit f2(x) multidet u 1:($2+ms) via b_2\n",
|
|
"Max. number of data points scaled up to: 3072\n",
|
|
"iter chisq delta/lim lambda b_2 \n",
|
|
" 0 1.6351844388e+02 0.00e+00 2.58e-01 1.000000e+00\n",
|
|
"\n",
|
|
"\n",
|
|
" 1 2.8331475972e+01 -4.77e+05 2.58e-02 1.903108e+00\n",
|
|
" 2 3.2508405969e+00 -7.72e+05 2.58e-03 2.993388e+00\n",
|
|
" 3 4.4711074925e-01 -6.27e+05 2.58e-04 3.771719e+00\n",
|
|
" 4 3.5779184535e-01 -2.50e+04 2.58e-05 3.979382e+00\n",
|
|
" 5 3.5770183297e-01 -2.52e+01 2.58e-06 3.986616e+00\n",
|
|
"\n",
|
|
"\n",
|
|
" 6 3.5770182428e-01 -2.43e-03 2.58e-07 3.986505e+00\n",
|
|
"iter chisq delta/lim lambda b_2 \n",
|
|
"\n",
|
|
"After 6 iterations the fit converged.\n",
|
|
"final sum of squares of residuals : 0.357702\n",
|
|
"rel. change during last iteration : -2.42953e-08\n",
|
|
"\n",
|
|
"degrees of freedom (FIT_NDF) : 2999\n",
|
|
"rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.0109212\n",
|
|
"variance of residuals (reduced chisquare) = WSSR/ndf : 0.000119274\n",
|
|
"\n",
|
|
"Final set of parameters Asymptotic Standard Error\n",
|
|
"======================= ==========================\n",
|
|
"b_2 = 3.9865 +/- 0.008324 (0.2088%)\n",
|
|
"fit [0:1] f3(x) onedet_O u 1:($2+oos) via a_3, b_3\n",
|
|
"iter chisq delta/lim lambda a_3 b_3 \n",
|
|
" 0 5.9977169495e+03 0.00e+00 2.41e+00 5.000000e-01 1.000000e+00\n",
|
|
" 1 5.3596427313e+02 -1.02e+06 2.41e-01 2.883807e-01 3.494757e-01\n",
|
|
" 2 2.5513945129e+01 -2.00e+06 2.41e-02 -5.571542e-02 3.008984e-02\n",
|
|
" 3 4.3942069520e-01 -5.71e+06 2.41e-03 -3.233185e-01 -1.938040e-02\n",
|
|
" 4 1.3950695794e-01 -2.15e+05 2.41e-04 -3.784304e-01 -1.599114e-02\n",
|
|
" 5 1.3930187120e-01 -1.47e+02 2.41e-05 -3.801893e-01 -1.569610e-02\n",
|
|
" 6 1.3930184761e-01 -1.69e-02 2.41e-06 -3.802119e-01 -1.569005e-02\n",
|
|
"iter chisq delta/lim lambda a_3 b_3 \n",
|
|
"\n",
|
|
"After 6 iterations the fit converged.\n",
|
|
"final sum of squares of residuals : 0.139302\n",
|
|
"rel. change during last iteration : -1.69332e-07\n",
|
|
"\n",
|
|
"degrees of freedom (FIT_NDF) : 998\n",
|
|
"rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.0118144\n",
|
|
"variance of residuals (reduced chisquare) = WSSR/ndf : 0.000139581\n",
|
|
"\n",
|
|
"Final set of parameters Asymptotic Standard Error\n",
|
|
"======================= ==========================\n",
|
|
"a_3 = -0.380212 +/- 0.001559 (0.41%)\n",
|
|
"b_3 = -0.0156901 +/- 0.0005992 (3.819%)\n",
|
|
"\n",
|
|
"correlation matrix of the fit parameters:\n",
|
|
" a_3 b_3 \n",
|
|
"a_3 1.000 \n",
|
|
"b_3 -0.704 1.000 \n",
|
|
"fit [0:1] f4(x) onedet_H u 1:($2+ohs) via a_4, b_4\n",
|
|
"iter chisq delta/lim lambda a_4 b_4 \n",
|
|
" 0 5.3856009833e+03 0.00e+00 2.41e+00 5.000000e-01 1.000000e+00\n",
|
|
" 1 4.3790957890e+02 -1.13e+06 2.41e-01 3.455453e-01 3.625475e-01\n",
|
|
" 2 1.5298404788e+01 -2.76e+06 2.41e-02 1.187994e-01 5.316664e-02\n",
|
|
" 3 7.1643957948e-02 -2.13e+07 2.41e-03 -2.301630e-02 -5.474022e-04\n",
|
|
" 4 4.7733373771e-04 -1.49e+07 2.41e-04 -4.157329e-02 -9.283341e-04\n",
|
|
"\n",
|
|
"\n",
|
|
" 5 4.7373176545e-04 -7.60e+02 2.41e-05 -4.173964e-02 -9.101041e-04\n",
|
|
" 6 4.7373176473e-04 -1.53e-04 2.41e-06 -4.173975e-02 -9.100709e-04\n",
|
|
"iter chisq delta/lim lambda a_4 b_4 \n",
|
|
"\n",
|
|
"After 6 iterations the fit converged.\n",
|
|
"final sum of squares of residuals : 0.000473732\n",
|
|
"rel. change during last iteration : -1.52553e-09\n",
|
|
"\n",
|
|
"degrees of freedom (FIT_NDF) : 998\n",
|
|
"rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.000688971\n",
|
|
"variance of residuals (reduced chisquare) = WSSR/ndf : 4.74681e-07\n",
|
|
"\n",
|
|
"Final set of parameters Asymptotic Standard Error\n",
|
|
"======================= ==========================\n",
|
|
"a_4 = -0.0417397 +/- 7.482e-05 (0.1792%)\n",
|
|
"b_4 = -0.000910071 +/- 3.289e-05 (3.614%)\n",
|
|
"\n",
|
|
"correlation matrix of the fit parameters:\n",
|
|
" a_4 b_4 \n",
|
|
"a_4 1.000 \n",
|
|
"b_4 -0.740 1.000 \n",
|
|
"unset output\n"
|
|
]
|
|
},
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"f1(x) = exp(a_1 * x/(1. + b_1*x))\n",
|
|
"f2(x) = exp(a_2 * x/(1. + b_2*x))\n",
|
|
"f3(x) = exp(a_3 * x**2+b_3)\n",
|
|
"f4(x) = exp(a_4 * x**2+b_4)\n",
|
|
"a_1 = 0.5\n",
|
|
"a_2 = 0.5\n",
|
|
"a_3 = 0.5\n",
|
|
"a_4 = 0.5\n",
|
|
"b_1 = 1.\n",
|
|
"b_2 = 1.\n",
|
|
"b_3 = 1.\n",
|
|
"b_4 = 1.\n",
|
|
"\n",
|
|
"fit f1(x) onedet u 1:($2+os) via b_1\n",
|
|
"fit f2(x) multidet u 1:($2+ms) via b_2\n",
|
|
"fit [0:1] f3(x) onedet_O u 1:($2+oos) via a_3, b_3\n",
|
|
"fit [0:1] f4(x) onedet_H u 1:($2+ohs) via a_4, b_4\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2020-04-21T10:24:04.702021Z",
|
|
"start_time": "2020-04-21T10:24:04.625567Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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xSUlJQr4JuDHl5eVCvm+wrcKjbez9Y5Z7meaSg2YiAIuYqBy5MdQZJ87VU/tqyNu/GMIV+MBA8ZxYeV+tCG/xmazHpPXaicDD85Nvnajs7Ozk5OTGnvVcVVWVmZnpfnzYiBEjFi5c2KNHj2XLlhUVFblWzps3Lzo6+t133/USCjwW4w9pZO5lWu7sM94/ZgkaIo2YoAiZrCzRgKvV5PVqUo4j8UHigYHi/joxvGwHqsfPb65v/aDk5OQmtgYHB7sT0IMHD/Ly8kaNGnX48OF+/fq5y8TExOTm5voeJ9QunHqm+ICpaK+p/Iw1JEXW+0VV1CfdbjLMD1Vk8QNzP6t4SLB4Zl95ELwVC2o37XLO4uHDh5MmTVq7dm10dLTVapVKpe5NMpnMarU2tiPiYcWKFe71OTk57uXCwkLPw2PPTZ7LZrO5sLAQ1uC1BqeBKdiu/250/vYeBXczTH1mqROv985fyfwzmvv0jqXKxvRjyzaPC1yZrH4mQiqhbcL8FLAGXmpYvXo18t+AnzjfPf300xs3bmxsa3Z2dkRExJYtW1wvP/vss0mTJrm3rl27dtasWV53bF0wHeby5ct8h9CUloRHWZnCbw0/Ti75f8q8HyeX/PKV/swd+8arxleOVf/5bN33Rdb7JorH8HgEw/OHn9/cNnjCmacLFy5Mnz79m2++GTNmjGtN//793SfIAAC5ubkJCQlt+6YdQ8gXqoImw2Mp7sHPlsJvjCU/mroNlYWlqsQfB10xUXvryL41IDmEmNNPoW3nO0I7b+sJgcDD81Mb5KCMjIyYmJj+/fvbbLaXXnppx44d7gQEABg9ejTLsmlpaUuWLDl9+vSxY8c++ugj/98UaomKC7bb6cbbu4yqXqIe89Td/6S9YqMyDNQgKzUyjHh9sAoOMEO882FAm2EYuVwOAKAoCkVRDMMWL168cePGhISERYsWLV++fO/evTNmzCAIwr3L0qVL169ff+3atWXLlt28eTMsLOz999+fMmWK91DgebE2YiunC3YYbn2tZxxcxDyNdZLsKkvfNlAJQURKKBEfJIbXEEJtCF4n3UEE/myDwsLCPj2jS3403/xSX55ljZymBnOUN7XgWo2zX4B4RHcisRtB8Jd6hN96MLxWgzmogwj52QZ1NxzX0ipL9tm1sYT2VU3JAPx8tbObDBsZJknpTihE/N+xJeTWAzA8/8Ac9PiiLOzt74z5W/SW+2TPxVr9ROkFJ22nuNHhkpFhkhB4kzrUIWAOehzV/uK48f/qir4zdhshE72izuuO3Kgjk7oRY8Il/XRiONgDdSSYgx4jDMkVfWe8sanOfJ+KXKKtniA9ayYlGPJMhHREdwk8yQXxoqPvm39seV4q2vHMJeT5lRVfhd+6tcOgXhlgOBDy9RNIjYhbkaD6aGTAuJ7S/OtXeAyvWfy2XrNgeDwSUNcD9oO84EDpccsvG+vKs6xR8zXWl5QnHCTLgfER0pFhEhns+EACAI/FuibGyd362vDLxlqOA1Gva+8OJ46XO3qr8Qm9ZAMD4YgPJCAwB3U1jhrml421uV/UdhsmC3s9IKc7OFfmGBYqmdRLFqaAp7ogwYHjQR2kA47JDYXOE4seftO30FZJJ52MuP1XTRriVInR9aN1vxuobDoBCXzIAIbnD4GH5ycBdT0e535QzTXH5Q+qyk7bBi0PwGYrf6x1VlqZSb1lT/WQ8HhxMwS1BDwW69yqsu05f6uuOG8b8qcg6kXFwQc2M8lOiZI/GSaByQfqFGAO6qxqcx2X3q2qzLYn/imQnKLYf8/mYLhpUbKUUIn/00JBUIeBOaiDtOE9O8Yi8uI7lQ9PWoe8HQhmKveU2BwMNy1aPiyEaHX2EfgtRTA8fwg8PDgm3UHKy8v9r8ReTZ9eXrY35U5Af2JYTq+DKfj225aJvaQfjwxICW19Amqr8NoPDM8fAg/PTwLqegi8H+Qnxsld31B79ePqvqmaiLd1+yrtRQZ6erR8VDgc94E6N3gs1gncO2Q++z/l6r5E4sfd/g3ok6WOSb2kk3rJ4FxiUBcAc5Cgme9RZ14v0990jlgfemcgvrvQOiSYmBUj17Tz/M0Q1GHgeFAH8fU6MZbmrv5fze7EouAk6eDTkZtU1JmHjlVPaF4dpGyPBCTwy9hgeP4QeHh+ElDXoyv1g2quO46/8lCsQod80f0HpzOvjpwbq0jpLoGHXlDXA4/FhIWluCt/r76+oXbo30MePC3ZX2Qd20PyYpRcAu9xh7qojj4W27x5s0Kh+Pzzz1teQKFQSDx4PuaxizEUOveNuFt21vbk+V7p/cClSseaFG1qrAImIAhqjG85aOnSpadOnYqLi2t5AYqirFbr/fv3Hb8S8hMCmtBs6sz/l37f8LvRL6sdG4P+r8TyVA/Ju8O0HXanu8AzOwzPHwIPz0++PeNw4cKFycnJ48aNa3kBg8EAANBoNK0OUSBCQ0Mb2+TUM8cXPTTdJYf+FLHD4gg00etGBbT3k0tbHp4QwPD8IfDw/OTb9yQ5OdnXAnq9Hsfx+fPnR0REDBw4cNOmTb4FKBiNXSxfddm+O/GONAQnvgv9rMY2IVL2xyR1ByegJsITCBiePwQenp/a/atCEMS8efPeeOONkpKSzZs3r169+sCBA40VRjysWLHCvd7z3GRhYaHZbPa6yXPZbDZ79mDbqYYb/6z74bkS2WvOE3Ol1+uoD57UKqvyOjgGWAOsoSNrWL16NfLfgH9aM6A9bty4KVOmLF++vBUFli9fbrfbt27d6iWUTnVejCG506+VVZyzBX8VutvgmNhL9nxvGbzfHXoMCf0axcrKSs8kStO0WCxu7zdtD56fwlZJH3iq2GhgyrYFHbOTfxmqeaEPzwlI4JexwfD8IfDw/NQGOSgjIyM/P7+xrUVFRWPGjDl//jwA4NKlS+np6dOmTfP/TTteYmKia6E217Ev5S4+QXHiNblKir0/QttD6dvQfntwhydMMDx/CDw8f3EtRtM0QRAEQaAoiuM4QRDLly/nOC4+Pn7jxo1NFNi6dWtUVJRarY6Njd22bVtj9fsUDF/uHzVvDbr51e7axceqL5Q7+A4Hgvjn5zdXQEMwwh8PKtiuP/tuVd3WoFoJ+H2iupsMPuUCggQ/HtRlHHzt2okvavO/DJQF4X8drhVaAhL4kAEMzx8CD89PAup6CLkfdO6PFdl3HTdeVcyMkT8TIeU7HAgSEHjPavviWHBqadkljL4zRfZGoqq/rlOe1IOg9uPnN5f/EzpCxjHc0d8+PBmHgmGKD5PVOqmwjr8gqAuA40GNYmnux98+PPQkHjxW9tcRWjFt4zuipnhe4SpAMDx/CDw8P8Ec5B3HcAcXP8x8Gh88VPY/SRoxhgj82QYwPH/A8HgkoCEY4YwHcSzYs+LhkZH4C0MUL/SV8x0OBAkaHA9qe7v/VJY5Gl+YrBrdC54Cg6D2JZSuBxBMP+i7DyoORSMrhmmSexJ8xwJBnQC8RrEt7dpUfSgKedtbAhL4dWIwPH/A8FqNo61+1iCIrocL7/2gvel1P3DkqqHa2D6wBwQJGkeZOY7mKCtgSdcaljIDjuYoC8dSj8qQevd6AABH2znGAQDgSAMAHMdSHGXxqNDIcSxgaY42AwA42sYxzkebWNKVaDjaChgSAMBSRsCxrq0ILg+dZ4XXKLaBH44Y99Ta3x6oihsk4ysGqHPjGJYyAQA4p+vLbwIc40oKHGMDjNP9ZQYcy5JGAADH2AHj4BgnR9t+3Z3jSAMAgCUNgOM4ysRxzKPqSQPgONf3HxEpEESEiOQAfXTRLCpSAgRHcDmCPVqDiLXu9QAABJMguBQAgIjUAEERVISIFO7YEZEaQVCA4giuBAAguAzBHv0SI6gYweWulQAjAACoSA2Q/xxCwTHpNpB53ry3xv5mDzlMQI8RlmJpC2ApjrK4sgNLGjjaBhiH6yVH2znaxlFGVw/iUQZhaY42cxzLkUYAAEvqAUtylJUlDQBBUJEaAIAQri+/CiCYKykgmBRgEveXGSAoKlYDAAAmQcRaFBUjIjkAj1Y+yh1iNUBQRKRCkEdXxiJiDUAQV7W8NFg7gTkInMq37Sq2LpVIhoxuatbewsLCek8EYTnunkV/11JbbjOV28wW2lnrtNlo0kI96h4rRGIJJtKKpSqxRCUiAiXyEKkqVKoMk6ulmKhtP0XD8ASlzcPjKBNH2zja5kocHGPjSCNLWQBjZykzR5k4xs5RVo4ycrSNpa0caXyUdDiWI40sbQGAQ3ElQEWISEHRHCELQMRqBJchmBRBRQiuQDAJIpIjYi0q6464MwiCISIVQBBUrAEAoGINQAlEJK/XNWhbAv/j+ulxPxa7Wupcf84w+yE64X+Cmi5pNpuVSmWBsepsZXFObenlmtJ8Q4VGLI1QaCPk2lCZSiEidIRMgRMq8aNOrJF02BlK77TbaLLKYTGQ9nKbudxuKreZgiSKMLk6SqmLUgX2UgT0VuqiVYGhMlWrP4grvFbv3t4aDY+lWdLAUUaWNLBOPUcaWMrIUWaOtnGUiaXMgLGzlIUjje5iHG3jKDMiUiK4DMHlqEiN4DIElyFiDYLLEVyGipSISIlgUkSkQH7dioo1VpZlUJmJJjmRCmAyFhEZKQcAwEQ6zFarTCYzkHYOePkfaKMpJ0P7+pExBHX/T2iMFBNJfv01QhCgEf/nWhCliMB/TWpWq1Uuf3SdmgjFFKL/VCvHxWKUn26RgbS7GiuAkMFjsVa6UUNuuGCYeJ6esD6siWI0yx4vL/rhQd6PD246GGpMSJ/koB6zeiUM0oYGEK05dqNZttRmKLHoS8x1Reban8oKbxmr75hrMQTpqw6KUQf3UwdHqQKjVYHRqiAJ1qK/kXASEEeZWWcN66hlSQNH6lnSwJEGQBqMpOFRKiENj/6ljBxtR8VqRKxBxVpUrEHEalSseZRfxFqRvAfAZKhIgYhUDK4wAMKJyk0sUs1getJGsazeabPQpIOhjKTDSDrMlJOlOKPRbiQdFpp0MrUA1OqdNgdD2xnK9XVVi6Xor9PuasVSAIAMFxEYDh597b18n8UYJsd9vlfZydA2mmq6jJly0r8O91Asa6Gc7k160u51FxtNeiZEI+VgPb7/BIbLGnSxcRRTinw7zeJgKHuD4C00SbGM+6VCRIjaouv3+PaD7hrp/z1RNyzdvujrnhjhfS7oPEPF1sJLu4qvdZeqXug5YFKP/oN13RHQXhNHP7QZC4xVhcbqfENliUV/01D50GYMl2ti1EF9lLreSl2kIqCXIqCHQqMVd/jFkyzFOmtZRw3rrGWdtayj6tflGtZRyzprH3VSSANARSihQ4lAlNAiYg0q1jz6V6RGPV5yIrWeE1mAyEg6Kh1mM+W0UGS1w+Jg6GqHpc5ps9JkrdNW7bCQLFPtsNppSkfIFCJCjot1hExLSEUophXLNGIJjmJqkUQtlmgJKQBALZIqRYRCJHYlDqWIkGAiX7+EnZSVJkmPNOHioCk700w2rKdeb8tFguFexxDg3B2tUWlj/nKiLur/mVds7ikL8dLROPawcF3uiXxj5YKopHl9EvtpunVMYPXYGequubbQWHPLWFVi0T+wGu6aa+9Z9HKROFii6KUM0BHySIU2gJAFSxQasTRMplaIxEESha9fOY62sfZyxl7JOqpY20PGXsXaK1h7BeOo4pw1jL2KY2woofs1uehQSRAqCUIJHSoJRAkdJVJbgMSOyuyovI5m9KTd1TfRO+1Olq51WuucdjtN1Tlt1Q6LhSbNlLPWaVWLJFpCJsNFwRKFjpBLcVEgIZdgeLBUoSNkUkykk8iDJHIpJlKLpbpWdTmhDgBzkM/MJPvnk3UB2yxL/6dbtyfqdyiOPCz4c85hkmVWDhw7PTLefSiUk5MjkKnFOcCV2UyVdssDq6HSbi61GeuctltlD1iZuMJutlDOGqeVZlmdRC7FREoRoRZLlCIiAGXUtKEbYgtg6oI5k4KuUzAGJW2QUHopXYtwrF0U4BAFOHCtTVwliawAACAASURBVKRz4JoaVGnDtRZMVcMRZlRZST86SDGQdidLmymnkXQAACyU0zUuoBZLVCIJjqBaQhpAyBQ4IReJtWKpGMUCJXJzeXVcdF+liAgk5AG/5he+BjIaEs4f1yuBhwdzkG+cDLc2y4B/b3mpv6L/Iq3npmt1Zb+/dLDSbl47+NlpkQPb75ir/XC0lbGUMNZSu+WB1XSXs96nLSWI7QHuqGQRnCKCbbjGRgQbsQCbKNCIqgyo2iZSWzEdhUnqVaURS12Pr9OIJQhANGKpaxRFgRMSDFeJJWqRBEUQCSbSiKUyvI1P80GdSEdfH7R58+bf//73f//73xt7xmHDAtnZ2StWrCgqKtLpdO+9915qamqrw/UTx4EvrpnQX5yjjahnAjJTzr9cOZx+99qaweNfjRmGtdtJ1rbCsSRjLmbMRbT5LmO5x1hKGOs9xlLCUVZM2QuThaGy7ip5T6z7GEwRiSkiMHk4gsH7byEh8i0HLV261GQyxcXFtbwASZJTp059//33X3755WvXro0dOzYpKYmvix12FVrKSsghX1nGnOnjXvlTWeHirD1jQvrkT/1joER4M3VwDG0uZowFtLGANhcxpiLafIe1l2PynpiyD6bsjSsixUFDMUUEpohEJcF8hws1RaPR7Nq167nnnnOvCQ8P//TTT6dPn67RaMxms+ejkz/77LNly5bxEWaH8i0HLVy4MDk5edy4cS0vcPLkSYlEsmDBAgDA4MGDJ0+evGvXrnfffbe1Abfe6VLHmRLHwJV1Ew/3wiQIAMDJ0KtyMncXX9/y5IznwmI7PqSGOMpEG2/Rxpu0sYA23qKNBYy5GJWF4qoYXBMr0g6SRLyIK/tg8giAPtbXVXRJhw4d8kxPjwnf/h8nJyf7WqCgoKBfv37ulzExMbm5uT69aZu4pae+ybckfmB86oMQTbQYAFBiqZt5YkeoTPXLlD+07jKfNsCxtPkOXXeNqrtG1V2n9ddZ0oirY3BVDK6OlfZ6CVP1xdUxSIPBGgjqMtp94MNqtUql/xmJkMlkVmujN/sjHlasWOFe7zl3QWFhoef0up6bPJfNZnNhYaFr2eBkP75QnfJvsm+sJHqWurCwMPNu7hM/fDazV/z3Ty8ovnHTaw3tgWMcVPVFW+Fm4/mltYeGV3yrrjv2rP1uOoKKZX0X6SacCZljCvxNtmbUN4r4v0giZ4gC4mECgoQjJydnzZo1yH/zs852788rFAqb7T+zwVssFoVC0VjhxkbXPU9M1htL8tzkuaxUKl2XDjMc2HDVmGQlxAfqRl7pAwA4AwyrL2Z+O3rOuO7RTdTQVhjzHbL6IlV9kay+QBtu4OpYUUACHpAg7Z0q0g5CXPcuQo+N1NRUsfg/V11XV1e7lydMmOBZ8qeffvI67nFpTVX22qr2i9Al+b3gJ9bUH15MTExMTExcs2aN50o/01C756D+/fuvX7/e/TI3NzchIaG939TTzpsWYOdEy6ufOxaJy9D3rh799u7VkxOWtt9lhxzjoGqyycqzZFUWVXMRwaSioKHioGGqyBmiwER4fuox98UXX4wdO9b9csiQIe7lw4cPt2Q86Ik1XrJD59UGOSgjIyMmJqZ///5et44ePZpl2bS0tCVLlpw+ffrYsWMfffSR/2/aQhfKnZcrncP/19RvVVDAIGLp+X2Xa0rPTHwtRNrWd1dxDFV7xVn2s7P8Z6rmEq7uJw4eIYteKB7xL1Qa2sbvBXVmWq02JCTE/RJFhX4hSLvjWoymaYIgCIJAURTHcYIgli9fznFcfHz8xo0bmyhw9erVlJQUjUYzYMCAjIyMxur3KZiWqLTSi49VZ35alTH2Ls0wC87sGp2ZZiYdbfgWlLHQkr+x7t9TK3ZqqzIGGC++4bj/A0ua2vAtoK5ErVYfPnzYc01YWNiePXu8buos/Pzm+tAPwjDM4XA0XH/t2rWmCyQkJJw7d641CdIPDAc2XjM9IxeXf1j24qVer2Ttvm81HHrmlVbcAF0fx5CVZx0PfnA++IGjLUTYBEnkdHVKGioNaX5fCIL+W5e9V2PXLetdA9XrdzUJb+g2xB4vMFb9OG5hw1uBfcAxZMUpe8kex/0MTNpdEjGVCJ8k0g0GnfCWDghqQ3AuVy/ya6mTD+yzLrDWHqLtg85drXh4bPzvWpmAOJasOmsv3u24txeT95RETg+cmIUp+zS/IwRBLdAFc5CV4j6/bpyjlNzeWGo4WH3wft6Zia+pxT5fZUMb8ux3vrHf/RYltJJeLwVOPIcpe7dHwBD0OOuCx2IbrprUIlQ6r6L6zdq/SY6enbi8lzKg5btztM1e/J3t1j9Ze7m09xxpn7m4ZoD/UUFQVwWPxf7LpQrnPRM9M4u50E//ofjwoadfaXkCYiz3rDc32ou2i4JTlAnvEmET2m+WcgiCXLpUDtI72C03zEt10pP/urX+w6NfpExNDuzRkh2pmmzLjf8jy/8tjV4YODkHU0S0d6gQBLl0nd95DoBNv5jG95Te+X3Frj9nzeubOCMyvtm9yMrTdcee1Z+cIQ4eHjy9WJW0DiYgqG1pNBoMw3APaWlprk179+4dPny4QqFQqVQpKSl79uxxrU9KSvrnP//JX8gdquv0g86UOswk2+8cuTrhgq6n9K9Dnm26PFmVZc5ZzdrL5ANXyfrMAyicCRBqL14n5fjss8/eeeedTz/9dNasWQzD/PDDD4sWLbJYLL/97W95CZIvXSQHmUn22wLLm1HKL945eW1xSc6oN5uYC5HW55qurKb1N5SD10p7z+liT62EOgWLxfKXv/zl008/dWec1NTUkJAQz9tZHxNdJAftuGkZESa5+kHxztSsw88samw6RNZeYb7yF0fpIcWgVdqx+xD0sft7QwKRnZ1tsVhmz57tufKpp57iKx4edYUclF9L5ddS/yMmnu15cGXi2KFBPRuW4RiHNW+9Nf8fsqiFwS/eQkStf6IpBPmq4aQcRqNRq9V6Tq3VcmuuHlt77Vgbhdao9xLGrxk8vr3fBXSBHESx3OZc02/7yf/07vcho+RvDxnTsAxZfd54diGu7hc46SK8zhDqeA0n5Th16lRdXV3rntC9ZnAHZYeO0enPi/1419ZDiRfuvvPzkPw90+bVeyAPx9jNOX8ynJiuHPy/2qf2wwQECURycrJard6yZYvnyh9//PGTTz7hKyS+dO4cVOdgf7xr/02A6A+O7/9v0G9CZf91hEXVXas5OJixlQW+kCuJnM5XkBDUkEwm++ijj1avXr1hwwaDwWAymXbs2DF37tzIyEi+Q+tonftYLL3AMj5C+u43B2LVQa+MeMJzk/3O16bLb6uSP5H2nsNXeBDUhFdffTUoKOjjjz9evXq1SCQaPHjw/v37H8Nh6U58v9hdI70u2zAR08+/8XXe7D+GBT7qBHGUxXD2ZcZ6Xzt6F7zBHYLam5/3i3XWYzEOgO155pnRsjdy9v1Z+aw7ATHmuzWZKSgRqIMzbEBQZ9BZc1DWQwfDgcOnTgbY5H+YP8K1kqw8XZP5pLT3XPXw/wev/YGgTqFTjgfRLLe70PqbHuz0a+eOjlqCoggAwH53pyn7Le3oXeKQ0XwHCEFQS3XKftCREnsPJf6/P+2dVjYkZUw4AMB265/myyt144/BBARBnYvPOWjz5s0KheLzzz/3ujU7O3vYsGGBgYExMTHffvuta6VCoZB4cD/+tHVsNHfwji0AKSsy1X6yeCIAwJL7kSV3nW7CKVw70J+aIQjqeL7loKVLl546dSouLs7rVpIkp06dumTJkurq6l27di1btqywsJCiKKvVev/+fcev6j0o1VcHiqwJwaJ3sveucjwTECmx/PI3+50dgZPgCHRLnThxIj4+3vPht/V4/SHx1LY/Kg2tXLnS60PoPCNv+rewFZqu8PTp00888YRWq42JiTl8+LBr5ZEjRxISErRabVRUlHs6jlZ76623Xn/99YbLXZ9PTwK6dOkSx3FPP/2064Fi9Rw9erRPnz7ul3Pnzl27dm1VVRUAwOl0Nlt5S4KpszOvHKteefTnIX/4zGlkrAX/rNzbh7GV+fIhHmsGgyEkJOT69euNFXA6nWFhYdu2bWNZ9sqVK2q1+tatW54FSJIEAFRWVrZfkAMGDPjll1/qrfSMfMmSJampqUOHDvX6/9Cr06dPl5eXN7a16QqrqqrUavXu3btZlj148KBcLi8rK3v48KFcLs/MzOQ4Ljs7WyqVur4drUaSZFxc3KFDh+otC5+vaaQe3/pBycnJTWwtKCjo16+f+2VMTExeXp5er8dxfP78+REREQMHDty0aVMTNSAeVqxY4V6fk5PjWth72xorNW26e+J/e05kDQfM19aWd//Y/SBTdzEAgNls9vx99txUWFhoNpu9bvJcFqYLFy6kpKT07du3b9++X375JQDgX//618CBAymKAgBs3769X79+DoejqKhIIpH84x//eO6556Kjo5ctW0bTNABgw4YNY8aMGTRoUGP1nzx5UiKRLFiwAEGQwYMHT548edeuXZ4FDAYDAECj0TRWQ0lJiVwuX7duXXBwcHl5ua8f8P79+0ajceDA+ofVnpEvXLhw586dCoWi5dVu2LDhxo0bjW1tusKsrKzu3bvPmDEDQZDJkycnJiZ+//33HMd99dVXrptRk5KSoqOjCwoKPPfytR1EItE777yzevXqesuCkpOTs2bNGuS/+VlnW45JW61Wz/uAZTKZ1WolCGLevHlvvPFGSUnJ5s2bV69efeDAgcZq8MyOGzdudK9PTEwEANTYmYsVzpyqy0/m9B07tdh4fqlu/NH4lKn1irkolUrPgz7PTX379vW8UdBzk+eyANXU1IwfP/73v/99YWHhkSNH/vCHP+Tl5S1evLhHjx6ffPJJTU3NqlWrduzYIZFIcBx3Op0syx45cuT69evHjh1zzdGXnp4+a9asJt7C6w+JZ4Fmf1QIgrDb7SRJVlZWhob6/JzrzMxMr89c94y86d/CVmi2Qs7jGryAgIDCwsKwsLBp06YBABiG2bdvX1lZmedT5EGr2uH5558vLCx05TLPZeFITExcs2ZNw36QP9ry3LxCofAcZbBYLAqFIiIiwvVzDQBISUmZM2fOwYMHX3jhhVbUn1FkGxREv3f7xolRk0znXtCO2f24DUIfOXJEp9PNmDEDANC7d+/Jkyd/9913f/3rX7du3frEE0+cPn361VdfTUpKcpefO3cuAEAmk40fP/7kyZPPPfdcQUHB8OHDm3gLrz8kngVcPyqLFy9OT0+/cOHCxIkTu3fv7vkHdV01O2fOHNcvZHl5+bx58wAAP//8s6vAunXrduzYwXHciBEjNm3aVO+B65mZmQ0nEtTr9c1G7tW+fftee+01AIDBYDh+/LhYLJbL5Xfu3PGpkpEjR5aVle3YsSM1NfXUqVOnT592PzA+PT197ty5AQEBW7ZsCQ8P99yrFe0gkUgSExPPnz8fGxvruVwvnj2F1r23raCdTY+Wz+jrfR6uttWWOah///7r1693v8zNzU1ISKisrCwtLXX3L2iabt1McbV25kK5s7j68OycAWGTF8n7rRV3G9U2cXceer2+vLzcfVujw+Fw/RSHhoY+//zzmzdv/uqrrzzLu4+YVCrV3bt3q6qqMAwLCgoCAGzdunXlypUAgBEjRnj2TL3+kHjW2cIflcDAQNdCamrq888/f+jQIdfLs2fP7tixIzs7WywWjxs37uDBg1OmTHHv5XQ6z5w5s3Pnznq1eUbuk2nTprmaaPr06UuWLBk3bpyvNQAAdDrd999///bbb7/99tvjx49/5plntFqta9Ps2bNnzpx58eLF2bNnMwzz4osv1tvX13YICQmpqKhwlfFc9jSjbwdlh47RBsdiGRkZ+fn5AIDRo0ezLJuWlsay7MmTJ48dOzZ79uyioqIxY8acP38eAHDp0qX09HTX/wlfHbhj66O1FVQ9WPVMujhkpCzmVf8j73TCw8P79u1b8quKioovvvgCAJCbm3vw4MFFixa9+eabnuUrKytdCzU1NQEB//WMo9mzZ9+4cePGjRvbtm3zXN+/f3/XX9MlNze33tBMZWWl56hZYz8q7mGCgwcPeh7pJCUluYacUBQNDw+vq6vz3OvkyZMJCQmtmFKnvY0dOzY7O7u8vPyrr766efNmYmJifn6+K3djGDZ8+PBJkyZlZmY23LF17fB4afnwNU3TBEEQBIGiKI7jBEEsX76c47j4+Hj32YSrV6+mpKRoNJoBAwZkZGS4Vm7dujUqKkqtVsfGxm7btq2x+psIps7BvHKsetBXG/e+O78mcyTHkC0PuysxGAw6ne7w4cMcx1kslkWLFuXk5Didzvj4+PT0dLvdHh0dvWfPHo7jiouLAQDvvPMOx3E1NTXdu3ffvXu36z9606e0KIqKiIj44osvGIY5ceKESqW6c+cOx3H79+/Py8vjOO7s2bMKheLcuXMcx128eFGlUv3000+eNbjGX81ms3vNmTNnnn766XpvlJ+f36dPn7q6Os+Vr7/++rp16xpG5TXyeudn3RF6NW3atHpxNtRYhSaTKTIy8uLFiwzDpKWl9ejRw2azZWVlyeXyU6dOcRxXXFzcu3fvtLQ0z9pa1w5PPvnkl19+2XBZyHxKI152b6s4/NfEJ9meZ/7LubuTP3rt4dfdGFujZ1gfB+fPnx82bFifPn2ioqL+9Kc/0TS9evXqyZMnu7aePHkyKCiooqLClYPS0tISEhJ0Ot1rr73GMAzHcbGxsa5zOk3w+kPi+UvT9I9KS7572dnZsbGx2dnZ9faNiorKzc31GpU78pb8FvqkJRXu3LkzIiJCrVYPGzbMfd3Atm3bevfurVAowsLC3nrrLZqm/WwHh8MhlUpv3rxZb1ngun4OMjmZ3x6tGrR93c20Hvbi3R0cVSflykEURdVbv3bt2tTU1A4Opt53Lysra8CAAQUFBT5VwkvkbavZdtizZ8+gQYMaLgtc189Bewotfzh1N23D8LKMuR0cUufVWA7S6/XdunW7ceNGRwbj+d3T6/WxsbH37t3ztRJeIm9bTbcDTdPx8fE//PBDvWXh8zMHCf2+eYrlfr5nN5b+YwlR1G3ij3yH0+lpNJqdO3fOnTs3KytLJpO199sVFxdPnjzZZrNVVVXFxcWlpqaqVKry8vKJEye6CsybN891eq5ZHRx522pJO9TW1o4cOfI3v/kNAGDVqlXu5S5P6PMoHn9gP3i7+Ld3R4TGfhv85ASvO0IQxKOuPI8iB8Chu/YeD1ba6obCBARBXZKgj8WuVpGI/dZk5njA09f4jgWCoHYh6H7Qj3dtIyrfLHowLzAxmu9Yug44d0dDzc7C4XXuDq8rWw3O3cG/esHcM1F/PbD78hZdVY6er5C6Hjh3R0PNzsLhde4Oryt9/qgeHtu5O4Sbg7bm6i9+Fbl91V/5ikeYXNcoRkdHR0dHb926leO4zZs3x8XFkSTJcdy2bdtiY2Ptdvvt27cJgvjkk0+effbZqKiopUuXus7Tr1279qWXXmqifq+TQHkWaHZCqOLiYtcD/IKCglrxtbx37154eHjD9Z6RNz2PlVdNXCddWlq6d+9e98tBgwZ9/fXXngUyMjL69evnfjlq1Ki0tDSvKz33akU7fPfdd/Hx8Q2XBc7PHCTQYzE7zRmLvtWT6PgX/sB3LAIC5+5wLbft3B3NzsIBvM3d0dhKty45d0d7EOiY9JlS20jzP87dXvTsok52JUi7gnN3+NBYAABf5u5oYhYOr3N3NDGhR6vboYVzd5ivrbFcW+trU/hKkfCeMmFNe78LAEIdD/r8h3/+tLV38Qkjj/EI0GeffUYQRMSvunXrtmzZMtemZcuW4TheVVXleum6Ttput7terly5csaMGQUFBRiGsSzLcdyWLVt0Op1Op3v++efrvcWkSZPcL9euXTtr1qwmQnrttdcWLlzoucZ1n5TJZHK9HDNmzD/+8Q/39cFnzpyJi4uz2+0Mw4wdO9Z9P5qLw+HQaDTufd08I3drq2MxN5qms7KyevbsuW/fvnqbjh8/npSUFBISMn/+/FmzZq1ataqxlW6ta4fp06d/+OGHDZeFzM80IsRjsfxacph+ffadOZFjVHzHIixw7o720JJZOBrO3dHYynrg3B3NEmIOunp1p5Whpox4g+9ABOepp54qKys7cuQIAMBqtS5evPjKlSskSc6bN+/jjz9ev359dnb23r173eW3bt0KAKitrT18+PDTTz8dHBzMMEx1dTUAQCaThYSEhISE1MtNXieBAh6zRPk6IVS9hCKRSHQ6HQDg5s2b586dmzp1qufWzMxM9+0Lnjwjb4w7Ql8ZDIY5c+acPn0aAFBSUnL06FFXNnFXaDabe/XqdenSJZZlN23apNfrJ06c6HVlE+/SwnaoqKhwH9N5LndlbdUf858rGL2Dvrhj0DvvvM13OAIF5+5o87k7uEZm4Wh27g6vK/1pBzh3B89cn+TYhT1Z26PPpd/nO5zODc7dIUBw7g7vu7dVHP5zfZKfvh3/3idLWYbvaDo5OHeHAMG5O7zv3lZx+A8AcLv8ftF29f7Nl/mOxYv33nuP7xCaUi+8xnIQx3E///xzQkKC1WrtgKju3r07YMCAXr16iUSiAQMGfPDBBxs3blSr1QN+9fe//73ltbVf5O39x3W3g1wub6wd/vjHP7oOKjmO81zugPD85GcOEtbcHbv3/cFuuDlnzo8Y4e+D09qcnxMUtDcYnj9geP7oUnN3xFr3ORyzBJiAIAhqJz7noKbvV/Z6y3Wz92G72TnR7Jkv+RoSBEGdl285aOnSpadOnYqLi/O6lSTJqVOnLlmypLq6eteuXcuWLSssLPS6srH6r5OTlYGteQIiBEGdlG85aOHChTt37qz34E0313WfCxYsQBBk8ODBkydP3rVrl9eVDfctul8CAHhy6Gu+fwQIgjox3+5Zbfp+5Ya3XOfm5mo0moYrG+577fo5AED/QX18iqeDua+7FyYYnj9geHxpy/vmvd5y3ex92C7TJ6dyXGobBgNBUKfQlufFFAqF5wyhFotFoVB4XdmGbwpBUKfWljnI6y3Xzd6HDUHQ46wNcpD79mKvt1w3dh82BEEQAL5cZN2S+5W93nLtdSUEQRAnqHs1IAh6DAnrXg0Igh43MAdBEMQnmIMgCOITzEEQBPEJ5iAIgvjEQw5qdiqPls/1wUt4CoVC4qGJaQDaSSumT+lITYfHb+udO3cuJSVFq9VGRkauX7++YQF+W6/Z8PhtvSNHjiQkJGi12qioqLS0tIYFWtl6HXwtgNPpDAsL27ZtG8uyV65cUavVt27d8qkAv+GRJAkAqKys7LCQ6lmyZElqaurQoUO9PkOC39ZrNjx+W0+v16vV6u3bt3Mcl5OTI5fLs7KyPAvw23rNhsdv6z18+FAul2dmZnIcl52dLZVKL1265Fmg1a3X0Tno6NGjffr0cb+cO3fu2rVrfSrAb3hVVVUAAKfT2WEh1eP6wzf2iFF+W49rLjx+W6+ystLzSUTDhw/fsmWLZwF+W6/Z8PhtvdLS0r1797pfDho06Ouvv/Ys0OrW6+hjsYbze+Tl5flUgN/w9Ho9juPz58+PiIgYOHDgpk2bOiw2F1+nT+nI1gPNhcdv6wUHBy9YsMC1/ODBg7y8vFGjRnkW4Lf1mg2P39YLCwtzPcySYZh9+/aVlZWNHTvWs0CrW68t5+5oiWan8mjhXB98hUcQxLx58xYvXpyenn7hwoWJEyd27979hRde6LAIm8Zv6zVLIK338OHDSZMmrV27Njo62nO9QFqvsfCE0Hrp6elz584NCAjYsmVLeHi456ZWt15H94OancqD37k+mn33iIiIL7/8MiUlBUGQlJSUOXPmHDx4sMPCa5bAZ0oRQutdvnx5xIgRb7zxxhtv1H+YuBBar4nwhNB6s2fPJknywIEDr7/++v79+z03tbr1OjoHNTuVB79zfTT77pWVlTk5Oe6XNE2LxQKaAFvgM6Xw3noXLlyYMmXK9u3bX3nllYZbeW+9psPjt/Xy8/MPHDgAAMAwbPjw4ZMmTcrMzPQs0PrWa6shqxaiKCoiIuKLL75gGObEiRMqlerOnTscx+3fvz8vL6+JAgIJ7+zZswqF4ty5cxzHXbx4UaVS/fTTTx0Wnlu9QV+BtF6z4fHbelarNSIi4vjx4/XWC6T1mg2P39bLysqSy+WnTp3iOK64uLh3795paWlcW7QeD89Z9TqVR7MTgAgnvK1bt0ZFRanV6tjYWM8TGR2g1dOnCCc8Hltvz549AADCw5tvvskJpvVaEh6Prcdx3LZt23r37q1QKMLCwt566y2aprm2aD04dwcEQXyC92pAEMQnmIMgCOITzEEQBPEJ5iAIgvgEcxAEQXyCOQiCID7BHARBEJ9gDoIgiE8wB0EQxCeYgyAI4hPMQRAE8QnmIAiC+ARzENSOTpw4ER8f7zm1Veu89dZbr7/+epuEBAkNvG8eai9GozE2Nvbo0aODBg3ysyqKooYMGfLRRx9NnDixTWKDhAP2g6BGlZSUyOXydevWBQcHl5eX+7r7hg0bxowZ40pAhw8fTkhI6NWrV58+fbZt21avZEFBAYIg7pcJCQl79+71LCASid55553Vq1e36nNAggZzENQogiDsdjtJkpWVlaGhob7unp6ePmvWLACAw+GYOXPmhx9+WFxc/OWXX/7ud7+rrq72tbbnn3++sLCwoKDA1x0hgYM5CGoUgiAcx82ZM8fVSSkvLx83bty4cePcBdatWzdw4MC4uLhXX32VZVnPffV6fUFBwfDhwwEAEomktLR0woQJAIDRo0eLRKLi4mJfg5FIJImJiefPn/f3U0ECA3MQ1IzAwEDXQmpq6qRJk9zrz549u2PHjuzs7F9++eX27dv1nvFQVVWFYVhQUJDr5ddffz1ixIihQ4cOGzaMJMl6CauFQkJCKioqelQWpgAAAi9JREFUWvs5IIHq6OeLQZ2Oe6Tm4MGD169fP3TokOtlUlLSyZMnJRIJACA8PLyurq6xGvbv3//+++9funQpIiKCYZiGj3zBMAwAwLIsiqIAAJPJ1B4fBBIm2A+CWkqpVHq+lEgkOp0OAHDz5s1z585NnTrVc2twcDDDMK5xH71er9PpevTowXHcRx99hCCI6+l3GRkZrqfBhIaG4jjuGuvJysp68OCBqxJ3AZeKioqQkJD2/ZBQh4M5CPLL5cuXX3zxxV27dmm1Ws/1Wq02NjbWNXwzc+bMsLCwqKioJ554Ijo6es6cOfPnz8/Ly1u7du3x48cBAAqF4sMPP5w6deqzzz77ww8/DB8+3HXJiLsAAMDpdObk5KSkpHT4R4TaWTs8AgTqss6cOfP000+7X2ZlZQ0YMKCgoMBr4bVr16amprbVW+/Zs2fQoEFtVRskHLAfBLWSwWB45ZVXMjMzY2JivBZ4/fXX//3vf+fl5fn/XgzDvP/++x988IH/VUFCA8ekoRYpLi6ePHmyzWarqqqKi4tLTU1VqVTl5eXuC5fnzZu3cuVKz100Gs3OnTvnzp2blZUlk8n8efdVq1aNHDnyN7/5jT+VQMIE79WAIIhP8FgMgiA+wRwEQRCfYA6CIIhPMAdBEMQnmIMgCOITzEEQBPEJ5iAIgvgEcxAEQXyCOQiCID7BHARBEJ9gDoIgiE8wB0EQxCeYgyAI4hPMQRAE8QnmIAiC+ARzEARBfPr/s9GX0lVYh+AAAAAASUVORK5CYII=\n",
|
|
"text/plain": [
|
|
"<IPython.core.display.Image object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"set xrange [0:3]\n",
|
|
"set yrange [1.:1.3]\n",
|
|
"set grid\n",
|
|
"set xlabel \"r_{12} (a.u.)\"\n",
|
|
"set format y \"%.2f\"\n",
|
|
"set format x \"%.1f\"\n",
|
|
"set key bottom right\n",
|
|
"\n",
|
|
"set output '/tmp/gnuplot-inline-1587464644.6269653.290848243961.png'\n",
|
|
"plot onedet u 1:($2+os) w l title \"HF\" , multidet u 1:($2+ms) w l title \"FCI\" , f1(x) title \"exp(- 0.5 r_{12} / (1. + 1.93 r_{12}))\" , f2(x) title \"exp(- 0.5 r_{12} / (1. + 3.99 r_{12}))\"\n",
|
|
"\n",
|
|
"\n",
|
|
"unset output\n"
|
|
]
|
|
},
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"set xrange [0:3]\n",
|
|
"set yrange [1.:1.3]\n",
|
|
"set grid\n",
|
|
"set xlabel \"r_{12} (a.u.)\"\n",
|
|
"set format y \"%.2f\"\n",
|
|
"set format x \"%.1f\"\n",
|
|
"set key bottom right\n",
|
|
"\n",
|
|
"plot onedet u 1:($2+os) w l title \"HF\"\\\n",
|
|
", multidet u 1:($2+ms) w l title \"FCI\"\\\n",
|
|
", f1(x) title \"exp(- 0.5 r_{12} / (1. + 1.93 r_{12}))\"\\\n",
|
|
", f2(x) title \"exp(- 0.5 r_{12} / (1. + 3.99 r_{12}))\"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2020-04-21T10:24:05.565443Z",
|
|
"start_time": "2020-04-21T10:24:05.480752Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<IPython.core.display.Image object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"set xrange [0:1]\n",
|
|
"set yrange [0.6:1.]\n",
|
|
"set grid\n",
|
|
"set xlabel \"r_{12} (a.u.)\"\n",
|
|
"set format y \"%.2f\"\n",
|
|
"set format x \"%.1f\"\n",
|
|
"set key bottom right\n",
|
|
"\n",
|
|
"set output '/tmp/gnuplot-inline-1587464645.4823086.669140871663.png'\n",
|
|
"plot onedet_O u 1:($2+oos) w l title \"O\" , onedet_H u 1:($2+ohs) w l title \"H\" , f3(x) title \"exp(- 0.5 r_{12} / (1. + 1.93 r_{12}))\" , f4(x) title \"exp(- 0.5 r_{12} / (1. + 3.99 r_{12}))\"\n",
|
|
"\n",
|
|
"\n",
|
|
"unset output\n"
|
|
]
|
|
},
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"set xrange [0:1]\n",
|
|
"set yrange [0.6:1.]\n",
|
|
"set grid\n",
|
|
"set xlabel \"r_{12} (a.u.)\"\n",
|
|
"set format y \"%.2f\"\n",
|
|
"set format x \"%.1f\"\n",
|
|
"set key bottom right\n",
|
|
"\n",
|
|
"plot onedet_O u 1:($2+oos) w l title \"O\"\\\n",
|
|
", onedet_H u 1:($2+ohs) w l title \"H\"\\\n",
|
|
", f3(x) title \"exp(- 0.5 r_{12} / (1. + 1.93 r_{12}))\"\\\n",
|
|
", f4(x) title \"exp(- 0.5 r_{12} / (1. + 3.99 r_{12}))\"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2020-04-21T10:24:08.538856Z",
|
|
"start_time": "2020-04-21T10:24:08.535239Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"#set term pdf font \"Times,15pt\"\n",
|
|
"#set output \"jastrow_h2o.pdf\"\n",
|
|
"unset output\n"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"#set term pdf font \"Times,15pt\"\n",
|
|
"#set output \"jastrow_h2o.pdf\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Range-separated Coulomb operator"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2020-04-21T10:24:23.559508Z",
|
|
"start_time": "2020-04-21T10:24:23.551368Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"w(x) = 1./x\n",
|
|
"w_lr(mu,x) = erf(mu*x)/x\n",
|
|
"w_sr(mu,x) = w(x) - w_lr(mu,x)\n",
|
|
"unset output\n"
|
|
]
|
|
},
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"w(x) = 1./x\n",
|
|
"w_lr(mu,x) = erf(mu*x)/x\n",
|
|
"w_sr(mu,x) = w(x) - w_lr(mu,x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2020-04-21T10:24:24.194298Z",
|
|
"start_time": "2020-04-21T10:24:24.163407Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<IPython.core.display.Image object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"set xrange [0:4]\n",
|
|
"set yrange [0:3]\n",
|
|
"set key top right\n",
|
|
"set output '/tmp/gnuplot-inline-1587464664.164814.164661607519.png'\n",
|
|
"plot w(x) title '1/r_{12}', w_lr(0.5,x) title '{/Symbol m}=0.5' ls 2, w_sr(0.5,x) notitle ls 2, w_lr(1.0,x) title '{/Symbol m}=1.0' ls 4, w_sr(1.0,x) notitle ls 4\n",
|
|
"unset output\n"
|
|
]
|
|
},
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"set xrange [0:4]\n",
|
|
"set yrange [0:3]\n",
|
|
"set key top right\n",
|
|
"plot w(x) title '1/r_{12}',\\\n",
|
|
" w_lr(0.5,x) title '{/Symbol m}=0.5' ls 2,\\\n",
|
|
" w_sr(0.5,x) notitle ls 2, \\\n",
|
|
" w_lr(1.0,x) title '{/Symbol m}=1.0' ls 4,\\\n",
|
|
" w_sr(1.0,x) notitle ls 4"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## DMC Energies"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Single-determinant"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2020-04-21T10:24:32.345058Z",
|
|
"start_time": "2020-04-21T10:24:32.301229Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
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"text/plain": [
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"<IPython.core.display.Image object>"
|
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]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
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{
|
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"data": {
|
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"image/png": 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\n",
|
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"text/plain": [
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"<IPython.core.display.Image object>"
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]
|
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},
|
|
"metadata": {},
|
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"output_type": "display_data"
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},
|
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<IPython.core.display.Image object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"data = \"Jastrows/data_dmc\"\n",
|
|
"set key top right\n",
|
|
"set xrange [0:7.5]\n",
|
|
"set xlabel \"{/Symbol m} (a.u.)\"\n",
|
|
"set ylabel \"Energy (a.u.)\"\n",
|
|
"set format y \"%.4f\"\n",
|
|
"set yrange [-76.4115:-76.4095]\n",
|
|
"set output '/tmp/gnuplot-inline-1587464672.3021863.691199111999.png'\n",
|
|
"plot data index 0 u 1:5:6 w errorlines title \"1 det / cc-pVDZ\"\n",
|
|
"set yrange [-76.4215:-76.4195]\n",
|
|
"set output '/tmp/gnuplot-inline-1587464672.302258.883689911885.png'\n",
|
|
"plot data index 1 u 1:5:6 w errorlines title \"1 det / cc-pVTZ\"\n",
|
|
"set yrange [-76.4232:-76.4212]\n",
|
|
"set output '/tmp/gnuplot-inline-1587464672.3023045.89529610025.png'\n",
|
|
"plot data index 2 u 1:5:6 w errorlines title \"1 det / cc-pVQZ\"\n",
|
|
"unset output\n"
|
|
]
|
|
},
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"data = \"Jastrows/data_dmc\"\n",
|
|
"set key top right\n",
|
|
"set xrange [0:7.5]\n",
|
|
"set xlabel \"{/Symbol m} (a.u.)\"\n",
|
|
"set ylabel \"Energy (a.u.)\"\n",
|
|
"set format y \"%.4f\"\n",
|
|
"set yrange [-76.4115:-76.4095]\n",
|
|
"plot data index 0 u 1:5:6 w errorlines title \"1 det / cc-pVDZ\"\n",
|
|
"set yrange [-76.4215:-76.4195]\n",
|
|
"plot data index 1 u 1:5:6 w errorlines title \"1 det / cc-pVTZ\"\n",
|
|
"set yrange [-76.4232:-76.4212]\n",
|
|
"plot data index 2 u 1:5:6 w errorlines title \"1 det / cc-pVQZ\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Multi-determinant"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2020-04-21T10:24:44.333376Z",
|
|
"start_time": "2020-04-21T10:24:44.311082Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<IPython.core.display.Image object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"data = \"Jastrows/data_dmc\"\n",
|
|
"set key top right\n",
|
|
"set xrange [0:7.5]\n",
|
|
"set xlabel \"{/Symbol m} (a.u.)\"\n",
|
|
"set ylabel \"Energy (a.u.)\"\n",
|
|
"set format y \"%.3f\"\n",
|
|
"set yrange [-76.44:-76.405]\n",
|
|
"set output '/tmp/gnuplot-inline-1587464684.3126948.332194534957.png'\n",
|
|
"plot data index 0 u 1:7:8 w errorlines title \"CIPSI / cc-pVDZ\", data index 1 u 1:7:8 w errorlines title \"CIPSI / cc-pVTZ\", data index 2 u 1:7:8 w errorlines title \"CIPSI / cc-pVQZ\"\n",
|
|
"unset output\n"
|
|
]
|
|
},
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"data = \"Jastrows/data_dmc\"\n",
|
|
"set key top right\n",
|
|
"set xrange [0:7.5]\n",
|
|
"set xlabel \"{/Symbol m} (a.u.)\"\n",
|
|
"set ylabel \"Energy (a.u.)\"\n",
|
|
"set format y \"%.3f\"\n",
|
|
"set yrange [-76.44:-76.405]\n",
|
|
"plot data index 0 u 1:7:8 w errorlines title \"CIPSI / cc-pVDZ\", \\\n",
|
|
"data index 1 u 1:7:8 w errorlines title \"CIPSI / cc-pVTZ\", \\\n",
|
|
"data index 2 u 1:7:8 w errorlines title \"CIPSI / cc-pVQZ\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Fit $\\mu$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"-----\n",
|
|
"$\\Psi(r_1,\\dots,r_N)$ is a CI trial wave function:\n",
|
|
"$$\n",
|
|
"|\\Psi \\rangle = \\sum_{I \\in \\mathcal{B}} c_I |I\\rangle\n",
|
|
"$$\n",
|
|
"When running a FN-DMC calculation, the fixed-node wave function can be written as $\\Phi(r_1,\\dots,r_N) = \\Psi(r_1,\\dots,r_N)\\times w(r_1,\\dots,r_N)$, where $w$ is a positive function, such that\n",
|
|
"$$\n",
|
|
"E = \\min_w \\frac{ \\langle w \\Psi | H | \\Psi \\rangle } {\\langle w \\Psi | \\Psi \\rangle}\n",
|
|
"$$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We want to find the change in the potential that would model the effect of the FN-DMC. This corresponds to removing some short-range potential:\n",
|
|
"\n",
|
|
"$$\n",
|
|
"\\frac{ \\langle w \\Psi | V_{ee} | w \\Psi \\rangle } {\\langle w \\Psi | w \\Psi \\rangle} = \n",
|
|
"\\frac{ \\langle \\Psi | V_{ee} - \\delta V_{ee} | \\Psi \\rangle } {\\langle \\Psi | \\Psi \\rangle} \n",
|
|
"$$\n",
|
|
"where\n",
|
|
"\n",
|
|
"$$\n",
|
|
"V_{ee} = \\frac{1}{r_{12}} \n",
|
|
"$$\n",
|
|
"and\n",
|
|
"$$\n",
|
|
"\\delta V_{ee} = \n",
|
|
"\\alpha \\left( \\frac{1}{r_{12}} - \\frac{\\text{erf}( \\mu r_{12})}{r_{12}} \\right).\n",
|
|
"$$\n",
|
|
"\n",
|
|
"\n",
|
|
"$$\n",
|
|
"\\frac{ \\langle w \\Psi | V_{ee} | w \\Psi \\rangle } {\\langle w \\Psi | w \\Psi \\rangle} = \n",
|
|
"\\frac{ \\langle \\Psi | V_{ee} | \\Psi \\rangle } {\\langle \\Psi | \\Psi \\rangle} -\n",
|
|
"\\alpha \\frac{ \\langle \\Psi | V_{ee} | \\Psi \\rangle } {\\langle \\Psi | \\Psi \\rangle} +\n",
|
|
"\\alpha \\frac{ \\langle \\Psi | V_{ee}^{lr} | \\Psi \\rangle } {\\langle \\Psi | \\Psi \\rangle} \n",
|
|
"$$\n",
|
|
"\n",
|
|
"$$\n",
|
|
"\\frac{\\langle \\Psi | w^2 V_{ee} | \\Psi \\rangle}\n",
|
|
"{\\langle w \\Psi | w \\Psi \\rangle}\n",
|
|
"=\n",
|
|
"(1-\\alpha) \\langle \\Psi | V_{ee} | \\Psi \\rangle +\n",
|
|
"\\alpha \\langle \\Psi | V_{ee}^{lr} | \\Psi \\rangle \n",
|
|
"$$\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"$$\n",
|
|
"w^2 = \\left[ (1-\\alpha) + \\alpha\\, \\text{erf}(\\mu\\, r_{12}) \\right]\\langle w \\Psi | w \\Psi \\rangle\n",
|
|
"$$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"To find the parameter $\\mu$, we minimize\n",
|
|
"$$\n",
|
|
"\\int \\left|a\\, \\text{erf}(\\mu\\,r_{12}) + b - w(r_{12})^2 \\right|^2 \\text{d}r_{12}\n",
|
|
"$$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2020-04-21T10:24:58.433108Z",
|
|
"start_time": "2020-04-21T10:24:58.207652Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"set yrange [1:1.3]\n",
|
|
"set xrange [0:3]\n",
|
|
"set grid\n",
|
|
"set xlabel \"r_{12} (a.u.)\"\n",
|
|
"set format y \"%.2f\"\n",
|
|
"set format x \"%.1f\"\n",
|
|
"set key bottom right\n",
|
|
"\n",
|
|
"f(x) = (a * (erf(mu*x)) +b)\n",
|
|
"mu = 2.\n",
|
|
"b=1.\n",
|
|
"a = 1.\n",
|
|
"fit f(x) multidet u 1:( ($2+ms)**2 ) via a, b, mu\n",
|
|
"Max. number of data points scaled up to: 3072\n",
|
|
"iter chisq delta/lim lambda a b mu \n",
|
|
" 0 1.4844650639e+03 0.00e+00 7.96e-01 1.000000e+00 1.000000e+00 2.000000e+00\n",
|
|
"\n",
|
|
"\n",
|
|
" 1 6.1263778783e-01 -2.42e+08 7.96e-02 2.358053e-01 1.001813e+00 2.187483e+00\n",
|
|
" 2 1.0286041895e-01 -4.96e+05 7.96e-03 2.362049e-01 1.001768e+00 2.898819e+00\n",
|
|
" 3 8.8809271871e-02 -1.58e+04 7.96e-04 2.419347e-01 9.971019e-01 3.071483e+00\n",
|
|
"\n",
|
|
"\n",
|
|
" 4 8.8738434949e-02 -7.98e+01 7.96e-05 2.413214e-01 9.977999e-01 3.052213e+00\n",
|
|
" 5 8.8736855066e-02 -1.78e+00 7.96e-06 2.414138e-01 9.977008e-01 3.054867e+00\n",
|
|
" * 8.8736862980e-02 8.92e-03 7.96e-05 2.414014e-01 9.977142e-01 3.054509e+00\n",
|
|
"\n",
|
|
"\n",
|
|
" * 8.8736862980e-02 8.92e-03 7.96e-04 2.414014e-01 9.977142e-01 3.054509e+00\n",
|
|
" * 8.8736862980e-02 8.92e-03 7.96e-03 2.414014e-01 9.977142e-01 3.054509e+00\n",
|
|
" * 8.8736862977e-02 8.91e-03 7.96e-02 2.414014e-01 9.977142e-01 3.054509e+00\n",
|
|
" * 8.8736862692e-02 8.59e-03 7.96e-01 2.414015e-01 9.977141e-01 3.054511e+00\n",
|
|
"\n",
|
|
"\n",
|
|
" 6 8.8736851827e-02 -3.65e-03 7.96e-02 2.414067e-01 9.977084e-01 3.054660e+00\n",
|
|
"iter chisq delta/lim lambda a b mu \n",
|
|
"\n",
|
|
"After 6 iterations the fit converged.\n",
|
|
"final sum of squares of residuals : 0.0887369\n",
|
|
"rel. change during last iteration : -3.65015e-08\n",
|
|
"\n",
|
|
"degrees of freedom (FIT_NDF) : 2997\n",
|
|
"rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.00544137\n",
|
|
"variance of residuals (reduced chisquare) = WSSR/ndf : 2.96086e-05\n",
|
|
"\n",
|
|
"Final set of parameters Asymptotic Standard Error\n",
|
|
"======================= ==========================\n",
|
|
"a = 0.241407 +/- 0.0006833 (0.283%)\n",
|
|
"b = 0.997708 +/- 0.0006832 (0.06848%)\n",
|
|
"mu = 3.05466 +/- 0.01162 (0.3804%)\n",
|
|
"\n",
|
|
"correlation matrix of the fit parameters:\n",
|
|
" a b mu \n",
|
|
"a 1.000 \n",
|
|
"b -0.987 1.000 \n",
|
|
"mu 0.590 -0.636 1.000 \n",
|
|
"unset output\n"
|
|
]
|
|
},
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"set yrange [1:1.3]\n",
|
|
"set xrange [0:3]\n",
|
|
"set grid\n",
|
|
"set xlabel \"r_{12} (a.u.)\"\n",
|
|
"set format y \"%.2f\"\n",
|
|
"set format x \"%.1f\"\n",
|
|
"set key bottom right\n",
|
|
"\n",
|
|
"f(x) = (a * (erf(mu*x)) +b)\n",
|
|
"mu = 2.\n",
|
|
"b=1.\n",
|
|
"a = 1.\n",
|
|
"fit f(x) multidet u 1:( ($2+ms)**2 ) via a, b, mu\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2020-04-21T10:25:03.688870Z",
|
|
"start_time": "2020-04-21T10:25:03.627414Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<IPython.core.display.Image object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"set output '/tmp/gnuplot-inline-1587464703.6283643.751356038540.png'\n",
|
|
"plot f(x), multidet u 1:( ($2+ms)**2) title \"{/Symbol m}=3.05\" w l\n",
|
|
"\n",
|
|
"\n"
|
|
]
|
|
},
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"plot f(x), multidet u 1:( ($2+ms)**2) title \"{/Symbol m}=3.05\" w l"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2020-04-21T10:25:10.154538Z",
|
|
"start_time": "2020-04-21T10:25:09.898089Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"unset output\n",
|
|
" set yrange [1:1.7]\n",
|
|
"set xrange [0:2.5]\n",
|
|
"set grid\n",
|
|
"set xlabel \"r_{12} (a.u.)\"\n",
|
|
"set format y \"%.2f\"\n",
|
|
"set format x \"%.1f\"\n",
|
|
"set key bottom right\n",
|
|
"\n",
|
|
"f(x) = (a * (erf(mu*x)) +b)\n",
|
|
"mu = 2.\n",
|
|
"b=1.\n",
|
|
"a = 1.\n",
|
|
"fit f(x) onedet u 1:( ($2+os)**2) via a, b, mu\n",
|
|
"Max. number of data points scaled up to: 3072\n",
|
|
"iter chisq delta/lim lambda a b mu \n",
|
|
" 0 6.3210450983e+02 0.00e+00 7.92e-01 1.000000e+00 1.000000e+00 2.000000e+00\n",
|
|
"\n",
|
|
"\n",
|
|
" 1 3.7399726680e+00 -1.68e+07 7.92e-02 4.372151e-01 1.042051e+00 1.438663e+00\n",
|
|
" 2 1.0886112747e+00 -2.44e+05 7.92e-03 4.564069e-01 1.038516e+00 9.132499e-01\n",
|
|
" 3 4.1264008258e-01 -1.64e+05 7.92e-04 4.670442e-01 1.043923e+00 9.352905e-01\n",
|
|
"\n",
|
|
"\n",
|
|
" 4 4.1237511508e-01 -6.43e+01 7.92e-05 4.674657e-01 1.043147e+00 9.390785e-01\n",
|
|
" 5 4.1236761417e-01 -1.82e+00 7.92e-06 4.675227e-01 1.043020e+00 9.397870e-01\n",
|
|
" * 4.1236765338e-01 9.51e-03 7.92e-05 4.675328e-01 1.042997e+00 9.399192e-01\n",
|
|
"\n",
|
|
"\n",
|
|
" * 4.1236765338e-01 9.51e-03 7.92e-04 4.675328e-01 1.042997e+00 9.399192e-01\n",
|
|
" * 4.1236765338e-01 9.51e-03 7.92e-03 4.675328e-01 1.042997e+00 9.399192e-01\n",
|
|
"\n",
|
|
"\n",
|
|
" * 4.1236765338e-01 9.51e-03 7.92e-02 4.675328e-01 1.042997e+00 9.399192e-01\n",
|
|
" * 4.1236765335e-01 9.50e-03 7.92e-01 4.675328e-01 1.042997e+00 9.399192e-01\n",
|
|
" * 4.1236765043e-01 8.79e-03 7.92e+00 4.675326e-01 1.042997e+00 9.399178e-01\n",
|
|
" 6 4.1236757813e-01 -8.74e-03 7.92e-01 4.675248e-01 1.043011e+00 9.398501e-01\n",
|
|
"iter chisq delta/lim lambda a b mu \n",
|
|
"\n",
|
|
"After 6 iterations the fit converged.\n",
|
|
"final sum of squares of residuals : 0.412368\n",
|
|
"rel. change during last iteration : -8.74041e-08\n",
|
|
"\n",
|
|
"degrees of freedom (FIT_NDF) : 2497\n",
|
|
"rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.0128509\n",
|
|
"variance of residuals (reduced chisquare) = WSSR/ndf : 0.000165145\n",
|
|
"\n",
|
|
"Final set of parameters Asymptotic Standard Error\n",
|
|
"======================= ==========================\n",
|
|
"a = 0.467525 +/- 0.0009255 (0.198%)\n",
|
|
"b = 1.04301 +/- 0.0009232 (0.08851%)\n",
|
|
"mu = 0.93985 +/- 0.003224 (0.3431%)\n",
|
|
"\n",
|
|
"correlation matrix of the fit parameters:\n",
|
|
" a b mu \n",
|
|
"a 1.000 \n",
|
|
"b -0.873 1.000 \n",
|
|
"mu 0.269 -0.620 1.000 \n",
|
|
"unset output\n"
|
|
]
|
|
},
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"set yrange [1:1.7]\n",
|
|
"set xrange [0:2.5]\n",
|
|
"set grid\n",
|
|
"set xlabel \"r_{12} (a.u.)\"\n",
|
|
"set format y \"%.2f\"\n",
|
|
"set format x \"%.1f\"\n",
|
|
"set key bottom right\n",
|
|
"\n",
|
|
"f(x) = (a * (erf(mu*x)) +b)\n",
|
|
"mu = 2.\n",
|
|
"b=1.\n",
|
|
"a = 1.\n",
|
|
"fit f(x) onedet u 1:( ($2+os)**2) via a, b, mu\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2020-04-21T10:25:15.207225Z",
|
|
"start_time": "2020-04-21T10:25:15.165730Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<IPython.core.display.Image object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"set output '/tmp/gnuplot-inline-1587464715.1665385.128003803699.png'\n",
|
|
"plot f(x), onedet u 1:( ($2+os)**2 ) title \"{/Symbol m}=0.94\" w l\n",
|
|
"unset output\n"
|
|
]
|
|
},
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"plot f(x), onedet u 1:( ($2+os)**2 ) title \"{/Symbol m}=0.94\" w l"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "gnuplot",
|
|
"language": "gnuplot",
|
|
"name": "gnuplot"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": "Octave",
|
|
"file_extension": ".gp",
|
|
"help_links": [
|
|
{
|
|
"text": "MetaKernel Magics",
|
|
"url": "https://metakernel.readthedocs.io/en/latest/source/README.html"
|
|
}
|
|
],
|
|
"mimetype": "text/x-gnuplot",
|
|
"name": "gnuplot"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|