diff --git a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb index d3ce661..0d4efab 100644 --- a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb +++ b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb @@ -653,12 +653,12 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ8AAAD8CAYAAACo9anUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO2deXyc1XX3v2dmtEuWLFnyIsmWjGW82xhhCBDCjllNAiROQkqWt7Qp9H0b2rdAszSh4W1J05C0JSEkJKEkwRAgwQGHfQkJixdsvIGxLG+yjSVr32e77x/zzGg0mk2yNJvO9/PRxzN37nPuncfS/Oace+65YoxBURRFURKJLdkTUBRFUSYfKj6KoihKwlHxURRFURKOio+iKIqScFR8FEVRlISj4qMoiqIknLjER0RWi8geEWkQkTvCvJ4jIo9ar78tIjVBr91pte8Rkcti2RSRWsvGXstmdhxjLBORN0Vkl4jsEJHcsdwMRVEUJTHEFB8RsQP3AZcDi4BPi8iikG5fAtqNMfOAe4F7rGsXAWuBxcBq4IciYo9h8x7gXmNMHdBu2Y42hgP4JfDXxpjFwPmAa5T3QVEURUkg8Xg+q4AGY0yjMcYJrAPWhPRZAzxkPX4cuEhExGpfZ4wZNMbsBxose2FtWtdcaNnAsnltjDEuBbYbY94FMMa0GmM88d8CRVEUJdE44uhTCRwOet4EnBmpjzHGLSKdQJnV/lbItZXW43A2y4AOY4w7TP9IY8wHjIg8B5TjE7vvhL4JEbkZuBmgoKDg9AULFsTx1hVl8nKiZ5BjnQMsnjUFm8iI1z/sGqCle5CllcVJmN0QO450UlGUw/Qpvmh7Y0svAHPLC5I5rYxky5YtJ4wx5eNhKx7xGflbB6E1eSL1idQezuOK1j/aGA7gXOAMoA94SUS2GGNeGtbRmAeABwDq6+vN5s2bw5hTFMXPj17dxz3Pvs8b/7Ka3Cz7iNe/9/we/vPlBjb96xVIGHFKBB6v4ZR/2sBtl8znf19UB8DaB97Ea+Cxv/pIUuaUyYjIwfGyFU/YrQmoDnpeBRyN1MdagykG2qJcG6n9BFBi2QgdK9oYrxljThhj+oANwMo43peiKFHweL0A2G3hhcVmtXuTWB7Sa9WmDJ6iTQStWZn6xCM+m4A6KwstG18CwfqQPuuBm6zH1wMvG9///npgrZWpVgvUARsj2bSuecWygWXzqRhjPAcsE5F8S5Q+BuyO/xYoihIOt6Uq9ghejcP6xPckUX384hPsedlEkiqISnzEDLtZ6yu34vuQtwM/M8bsEpG7gM3GmPXAg8DDItKAzxtZa127S0QewycGbuAWfzJAOJvWkLcD60Tk28BWyzZRxmgXke/hEzQDbDDGPHNSd0VRFNweg02GPJxQbCkgPn4HJ3hNSmRIlJTUJZ41H4wxG/CFs4LbvhH0eAC4IcK1dwN3x2PTam/Elw0X2h5tjF/iS7dWFGWccHsNDlvk4IjfI/Ik8YM+UtgtGXrocrloampiYGAg8YOPM7m5uVRVVZGVlTVhY8QlPoqiTD48Xi8Oe+REAnsKeD7eMJ6PTUjKmk9TUxNFRUXU1NQkLQFjPDDG0NraSlNTE7W1tRM2jpbXURQlLG6viZhsAEPi402i+PiFT0I8n2QI4sDAAGVlZWktPOBbPysrK5twD07FR1GUsHi8JpBUEA6/+LiTuuZjhs0FfGtRyZpSuguPn0S8DxUfRVHC4vIY7FHWfPyhrmQu7qdS2E0ZHSo+Ch6vobk7/RdJlfHF4/VG9XxSKdV6ZMKBik8kXn31Va666ioABgcHufjii1mxYgWPPvpoQuehCQcK33n2fX78x0Y2f+1iphXmJHs6Sorg9pqoCQepkGodEBnd5zMmtm7disvlYtu2bQkfWz0fhRffOw5AR58zyTNRUgm3J0bCgaSA+HiHzwUm9z6fAwcOsGDBAm666SaWLVvG9ddfT19fH88++ywLFizg3HPP5cknnwSgubmZG2+8kW3btrFixQr27duX0Lmq56MoSljcXi/Z9sjfT/1eUTL3+bgt9QkOD/rK6yRrRj6+9ftd7D7aNa42F82awj9fvThmvz179vDggw9yzjnn8MUvfpHvfe97/PjHP+bll19m3rx5fOpTnwKgoqKCn/70p3z3u9/l6aefHte5xoN6PoqihMXpNjiiiE8g4SAFPB+bbXjCQTK9sWRTXV3NOeecA8CNN97I5s2bqa2tpa6uDhHhxhtvTPIMfajno4woUa4oAC6Pl+x4Npkm0c3wBFKth9p8qdbJ/a2Ox0OZKELTpDs7O1MyBVw9H0VRwuL2esmKw/Nxe5K/ydQmqRV2SyaHDh3izTffBOCRRx7h4osvZv/+/YE1nUceeSSZ0wug4qMoSlhcbhNVfBy2VNjnE2aT6SROOABYuHAhDz30EMuWLaOtrY2vfOUrPPDAA1x55ZWce+65zJkzJ9lTBDTspihKBJweL0VZkT8iUqG2myfMsQ+TfZ+PzWbj/vvvH9a2evVq3n///RF9zz//fM4///wEzWw46vkoihKWWNluthTwfAJhN1twqrXu80kHVHwURQlLvGG3ZK75BMJuWl4HgJqaGnbu3JnsacSFio+iKGFxebxkOWInHCQ1280bbs0neZ5PpoheIt6Hio8SROqlYyrJw+nxkhXXkQqJmtFIwotPctahcnNzaW1tTXsB8p/nk5ubO6HjaMKBoihhcXuih938L6Wc55OkfT5VVVU0NTXR0tKS8LHHG/9JphOJio+iKGHxhd2ieT4+9fEk0fXxmNTZ55OVlTWhJ39mGhp2UxQlLE6PF0eU83yGCosmakYjCRQW1X0+aYeKj6IoYXF5vGRHSzjwh92Suc8nXHmdSb7PJ11Q8VEUJSy+NZ9oh8n5w27JLCw6Muym+3zSAxUfRVFG4PUa3N40TTiYxPt80gkVH0VRRuCyFlPiKSyazCMVIiUcTOYjFdIFFR9FUUbgsqoWRAu7pUJtN2/EVOtkzUiJFxUfRQ/0UUbg9sT2fFJBfPyejyMk7AYaekt1VHwURRmBMw7x8b/mSuY+nzCFRQPhQNWelCYu8RGR1SKyR0QaROSOMK/niMij1utvi0hN0Gt3Wu17ROSyWDZFpNaysdeymR1tDBGpEZF+Edlm/QyvJa4oyqiJJ+yWCoVFwx+p4PtX061Tm5jiIyJ24D7gcmAR8GkRWRTS7UtAuzFmHnAvcI917SJgLbAYWA38UETsMWzeA9xrjKkD2i3bEcew2GeMWWH9/PWo7oCiKCNwuWN7Pg6/55PEXabhst0k4Pmo+KQy8Xg+q4AGY0yjMcYJrAPWhPRZAzxkPX4cuEh8vwFrgHXGmEFjzH6gwbIX1qZ1zYWWDSyb18YYQzlZ9C4qIbjjyHbze0XuZCYcmMhhN9We1CYe8akEDgc9b7LawvYxxriBTqAsyrWR2suADstG6FiRxgCoFZGtIvKaiHw0jvekKEoUnO54wm6+jw93Uj0f378adks/4iksGu63L/R/NVKfSO3hRC9a/2hjHANmG2NaReR04HcistgY0zVsgiI3AzcDzJ49O4wpRVH8uOJKOBCrb/Kz3Wwh5XUguVl4Smzi8XyagOqg51XA0Uh9RMQBFANtUa6N1H4CKLFshI4VdgwrpNcKYIzZAuwD5oe+CWPMA8aYemNMfXl5eRxvexKhf6NKCPGIj4hgt0kgRJcMvOESDmya7ZYOxCM+m4A6KwstG18CwfqQPuuBm6zH1wMvG1+S/XpgrZWpVgvUARsj2bSuecWygWXzqWhjiEi5lcCAiMy1xmiM/xYoihKK00o4yIlSWBR8GW9J9XwilNcB3eeT6sQMuxlj3CJyK/AcYAd+ZozZJSJ3AZuNMeuBB4GHRaQBn8ez1rp2l4g8BuwG3MAtxhgPQDib1pC3A+tE5NvAVss2kcYAzgPuEhE34AH+2hjTNvZboijKoF98suxR+2XbbUnNdouWcKCeT2oT12FyxpgNwIaQtm8EPR4Abohw7d3A3fHYtNob8WXDhbaHHcMY8wTwRMw3oShK3Ay6PUAcno9dkrrPx59pF67Cga75pDZa4UBRlBEMxht2s9uSuubjCXOkgv+EVc12S21UfBRFGcGgK76wW1aS13zCFRYNVF5QzyelUfFRFGUE8YfdbMnd52NGZrsFCp4mURSV2Kj4KAG0XoTiJ/6wm+BKZlVrr0FkeMKBPeD5JE8Uldio+CiKMoIh8YkVdkuu5+PyGLJswz/G7IF9Pur5pDIqPoqijGDQ5UEkenkdgCxHctd83B4vjpA5hq75GGOSmg6uhEfFR1GUEQy6veQ4bIEK0ZFw2JK7z8ftNcPSrCEo7GaJ4nee20PdV/+Q8Lkp0VHxURRlBD7xiR5yA59nlMx9Pi6Pd0QJoNATVn/06j5gKIliovnXP7zH9174ICFjpTMqPoqijGDQ7YmZbAA+zyeZC/tujxkRdrNHSLXuHZx48fF6DT9+rZH/fGmvlveJgYqPoigjGHR5ycmKQ3zsyV3zcXm9gaMd/DgibDLtHXQz0Rzp6A88bukZnPDx0hkVH0WLWisjiD/slnzPJzQpInTNx0+fc+I9n9ZeZ+Bxc5eKTzRUfBRFGUH8Ybdk13bzBo7z9hO65uOn1znxnk+wdxUsRMpIVHwURRmBP9stFllJrmrt8kTJdgvxyPoT4Pl0DwyJz4lu9XyioeKjBND1UcXPoCv+bLdk7/MJzXZzRNhkmgjxGe75qPhEQ8VHUZQRDLo9cSYcJLfCgdsbJdstRBT7XRMvPj1B4tPR55rw8dIZFR8lCHV9FB/xh92SW9vN5fGOKK/jFyP/mk+25RklUnwKcxx0Daj4REPFR1GUEQy4PGTHEXZzJLm2m9tjhh2nAEMVrv37fPzZcIkIu/UMusmyC9MKs+nqn/gEh3RGxUcJoGs+ip8+p4f8GGf5QPJPMnVFCbv513yyHAn0fAbcFOY4mJKXpZ5PDFR8FEUZQb/LQ152fPt8nMk8z8cbLuHA99wviv4EhEQlHBTmOpiSm0VXv4pPNFR8lADq+Ch++p0e8uMQnxyHL9U6WaVk3OFSrUPWfPxLUonwfLoH3RRkOyjOy6JTxScqKj6KogzD6fbi9pq4xcdrkndkddjCoiFrPv59SAlZ8xlwU5TrYEqeg64BXfOJhoqPoijD8H9I52U7YvbNttZT/IfPJZpoqdb+I7b9HlAiPJ9ep5uCHA27xYOKjxJAEw4UgD6X7xt7fJ6Pr89gAj7Yw+ELu4XfZOqxPB53AsUnOOFg0O1lIEn3JR1Q8VEUZRj+Apzxht0geZ6PL+wWfs3HLzoBzydBqdZFuQ6m5Pq8xm4NvUVExUcJYDTlQCEo7BZHqrW/CoIzlcJuMpRwYIxJuPgUZPs8H0DTraOg4qMoyjCGPJ/Yaz6BsFsSPZ/QsFvwmk9wIsREh908XkOf0+NLtbbERzPeIqPiowTQNR8FoM86eiCefT7+0jWJOqI6lEH3yEPvhtZ8zLBjFSZ6/cV/ZENhji/VGlR8oqHioyjKMPpHs+aTlbw1H2MMzjCH3gUfox3s+Uz0YXI9A2HER4uLRiQu8RGR1SKyR0QaROSOMK/niMij1utvi0hN0Gt3Wu17ROSyWDZFpNaysdeymR1rDOv12SLSIyL/MNqboPhQz0eB0SYc+LPdEi8+/soKoQVQRQSb+MJgHk/iwm7+4xQKcx2UqOcTk5jiIyJ24D7gcmAR8GkRWRTS7UtAuzFmHnAvcI917SJgLbAYWA38UETsMWzeA9xrjKkD2i3bEccI4l7gD/G+cUVRwtPn8u/ziT/bzelJfNjNn+QQrvq2w2az1nx8fbLswsAEez7dlvgU5OiaTzzE4/msAhqMMY3GGCewDlgT0mcN8JD1+HHgIhERq32dMWbQGLMfaLDshbVpXXOhZQPL5rUxxkBErgUagV3xv3XFT7JKoyipSb/Tv88njoQDf9gtCZ6PP9SXHUZ87DbBExR2K8rNos/lmdDfdb/nU5TjIMtuoyDbrmf6RCEe8akEDgc9b7LawvYxxriBTqAsyrWR2suADstG6FhhxxCRAuB24FvR3oSI3Cwim0Vkc0tLS4y3PDnRVGsFhsJu8aRaDyUcJCHsFtXzEVweb0B8CnMceLxmQk9d9a/5FOT4RLskP1s9nyjEIz4Spi30fzBSn/FqjzbGt/CF6XrCvD7U0ZgHjDH1xpj68vLyaF0VZVLT5/SQm2UbcU5OOHKy/KnWiQ+7DQbEZ6RIOuwybM2nyNr0OZHrPt1BB8kBTNHiolGJ7Vf7vI/qoOdVwNEIfZpExAEUA20xrg3XfgIoERGH5d0E9480xpnA9SLyHaAE8IrIgDHmv+N4b0oQGn1TALoHXBTlZsXVN7DmkwTPxy944cJuWXabVSDVNy+/IAy4PIFMtPEmEHazhK44z0Fnv3NCxsoE4vF8NgF1VhZaNr4EgvUhfdYDN1mPrwdeNr7g6npgrZWpVgvUARsj2bSuecWygWXzqWhjGGM+aoypMcbUAN8H/p8Kj6KMnS6rMnM8JLO8TrSwm/+cIY93uOczkenWoWE3PVYhOjF/w4wxbhG5FXgOsAM/M8bsEpG7gM3GmPXAg8DDItKAzxtZa127S0QeA3YDbuAWY4wHIJxNa8jbgXUi8m1gq2WbSGMoijK+dA+44/Z8klnVOlrYzXfO0NAaj//9TGSJnR6nm2yHLXDEQ0leNp39HRM2XroT19cbY8wGYENI2zeCHg8AN0S49m7g7nhsWu2N+LLhQtsjjhHU55vRXlcUJTbdA65AYcxYBBIOklC92Rkl2y3LbsPlHun5TOiaz4B72H0rzs/SbLcoaIUDJYCu+Sjg93ziEx8RIcdhS5Ln4xOSsGE3h+D0jFzzmVDPxzpOwU+xHqsQFRUfRVGG0T3gGvYhGotkiU9MzydozacwAZ5Pz6A7MA4QSGzQQ+XCo+KjKMowekax5gOQm2VPyHEFoQxGSTjIDmS7WWG3nASIz4Cbopyh++YXnw4Vn7Co+CgBdJOp4vEaep2euMNu4Mvu6ktCaMlfVSEnzGbYbIfP83GHJBxMZImd7giej2a8hUfFR1GUAP504dF4PvnZdvoGE39iZ9Q1HyvVOnTNx39cxETQM+gKeFgAJfmW56NJB2FR8VECaMKB4j95czSeT362fcKPKwhHtOrb2XYbLrcJs+YzcWtTPQPDPZ+p+dkAtPUOTtiY6YyKj6IoAbotzyfeVGvwFSCdSI8iEr1ODyKQG2afT5Y/7GaJT0H2xK75GGN8CQdBnk95UQ4AJ3q0ykE4VHyUAOr4KEOezyjDbknwfPqdbvKy7NjC1KDLsguDQft8shxCXpY9ULF7vBl0e3F5zDDPJzfLTlGOg5Zu9XzCoeKjKEqAjj7ft3R/yCgefJ5P4sWn1+mJeOBddkiqtcMm5E2gSPo9xqKQFPVpRTm09Kj4hEPFRwmg5/oobb0+z2dqwWg9n8SH3fqdnohnDvmz3fziY7fZJrTWWrflMRaGhCunFWZzQj2fsKj4KIoSoH0snk9OcsJuvYPuiJ6Pb5Pp0GFyDptQMoHlbtotu6H3rbwohxPq+YRFxUcJoH6P0t7rJC/LTm4cB8n5yc9yDFtfSRT9rshhN/+RCh4r1dpuE6bmZwfEdbxp7w0v2tMKczThIAIqPoqiBGjrc1JaEL/XA1CQ4xOARIfefJ5PhLCb3VfbzV/V2uEXn96JEYI2S9RC7920whw6+11JOWwv1VHxURQlQEefa1TrPQB52X7xSewHbF+0hIOQox58nk9WIDw23gQ8n4KRYTeAVvV+RqDiowTQfAOlrdc5qvUeGNpDk2jxiRV2AwKp1Q6bjakF2fS7PBNSZbq9z0W23UZByHwqLPE53jUw7mOmOyo+iqIEaO8bvfj4PZ/eBJfY6R30kBch7BYQH0to7HYJvK+JSDpo73VSkp+FyPA9R7NK8gA40tE/7mOmOyo+ShDq+kx22nudTM0fXdjNv6s/0eLTNeBiSl7kVGsY8sYcVtgNfN7deNPaOxh2raxyqiU+7So+oaj4KIoC+Ap1dg24KS3IGdV1U6xqCF0DiROfAZcHp9sbGDuUvKzh3pjdJoH1l+bu8Q+BHe0YYGZx7oj2KblZFOU61PMJg4qPEkDXfCY3/jIw06eMTnyScXSAfyz/2KH4Q4E9fvERYaYVAvuwc+zi09nvCrtm9GHXQMB+KJUleer5hEHFR1EUAJoD4jPyG3w0kiE+XbHEx/J8ugfc2ARsNqGiKAcRODpG8Wlo7mHV3S/yiR++gdszVB17wOWhrdfJrDCeD0DV1Dz1fMKg4qMEUMdnctNsZWT5w1Px4i8pk0qeT26Q+Dhsvo+5LLuN8sIcPuwcmxD8busRBt1edh/r4pkdxwLtRy1hmVmsns9oUPFRFAWA411j83zsNqEo1xHwRhLBaMJu9qCq1zNL8jg2Rs9n+5FOFs+aQk1ZPg+9cSDQvq+lF4Da8oKw11VNzad70D1hG1zTFRUfRT0eBfDtRbHbhLJRVjgAnwgkQ3ymxAi79Qy4cQSLz5TcgKcyWpra+5hTls+NZ83hnUMd7DzSCcAHx7sBqKsoDHvdKRU+UdrX0jOmcTMVFR8lgCYcTG6auwcpL8wJez5OLCayYnQ4Yno+WUGej33o/cwpy+dwW/+wNZt4MMZwpL2f6qn53HB6NblZNh5+8yAAu492UVmSF/EMpFPKfaKk4jMcFR9FUQCf5zPaTDc/U3ITKz7tvU5EIp+4mpvt+2jrGRzu+ZxSUYjT4+XwKNdgWnoGGXR7qZqaR3F+FteuqOSpd4/Q3uvkzcZWzqwtjXht1dR8sh22QHhO8aHiowTQ83wmN8c6B0a93uMn0Z5PS88gZQU5OOzhP8LygqpyB6/5BLyQ5tF5IYfbfGJVNTUfgM99ZA4DLi+f//lG2nqdXLiwIuK1dpswd1oBDaMcM9NR8VEUBWMMTe19VJfmj+n6hItP92DUrLzgIyFyHEOP51nrMg2jDIE1tfcBvrRpgMWzirl6+SzebepkwYwiLls8I+r1p1QUatgthLjER0RWi8geEWkQkTvCvJ4jIo9ar78tIjVBr91pte8Rkcti2RSRWsvGXstmdrQxRGSViGyzft4VkY+P9WZMdtTvmby09Awy4PJSPTV8unAsygqzaet14k3QmT6xxCfLbiPLWuvxl9oBn0hOn5LD+8e6RjVekxWmqwy6P9//1Aqe+PJHePzLZwdqyUWirqKQQ219CS9BlMrEFB8RsQP3AZcDi4BPi8iikG5fAtqNMfOAe4F7rGsXAWuBxcBq4IciYo9h8x7gXmNMHdBu2Y44BrATqDfGrLDG+LGIhA8EK4oSFn9YaayeT3lRDm6voSNB3s+JHiflhdHXp/zeT45j+Mfc8qoSth3uGNV4Te39lBVkDzs/yG4TTp9TGqhtF42llcUYA7tHKXqZTDyezyqgwRjTaIxxAuuANSF91gAPWY8fBy4SX3nXNcA6Y8ygMWY/0GDZC2vTuuZCywaWzWujjWGM6TPG+L9O5KJf4MeMLvlMXvxhpZMRHxgq0TORGGNiej4wtO6THSI+K+dM5UBr36gKjDa19wVCbmNhaWUxANubOsdsI9OIR3wqgcNBz5ustrB9LCHoBMqiXBupvQzoCBKT4LEijYGInCkiu4AdwF8HXR9ARG4Wkc0isrmlpSWOt60okwd/WGmsH7B+L+REz8SLT1uvE6fHGzgrJxJ+jyQ7JCR2WnUJAO8cbI97zKb2/kCywViomJLL9Ck5gb1BkegecHHFD17nF3/eP+ax0oV4xCdc0n/od+RIfcarPeo8jDFvG2MWA2cAd4rIiJQdY8wDxph6Y0x9eXl5GFOKMnk53NbHtMLsiMdSxyKRno8/TXp2DC/NvwE1J2v4AW/Lq0vIy7Lz+t74voR6vb49PlWlY/d8wOf9bG+KHu57fe8Jdh/r4pu/353x2afxiE8TUB30vAo4GqmPtd5SDLRFuTZS+wmgJGjNJnisSGMEMMa8B/QCS+J4X0oIRiOWk5Z9LT3MnRZ+h348JFJ8DrXFFyIssc7vCfV8crPsnDNvGi++1xzXB3xLzyBOj/ekPB+ApZUlNJ7opWsg8rpYcPWFpgyvBxeP+GwC6qwstGx8CQTrQ/qsB26yHl8PvGx8/6vrgbVWplotUAdsjGTTuuYVywaWzaeijWHZcACIyBzgVOBA3HdAUSY5xhg+ON7DvOljF5/CHAe5WTZaEhB2OxwQn+ieSEnA8xn5MXfRwgqOdPTz3rHumOOFplmPlTNqp2IMbD7QFrFPcN25PR/Gnls6E1N8rPWTW4HngPeAx4wxu0TkLhG5xur2IFAmIg3AbcAd1rW7gMeA3cCzwC3GGE8km5at24HbLFtllu2IYwDnAu+KyDbgt8DfGGNOjO12THLU8ZmUtPQM0tnvilibLB5EhIqi3JM6Kyde4g0RlljHZheFyUa7bPEMsuzC41ua4hjPygQ8SfFZOXsq2XYbbzVGE5/+wFrWnuOZLT5xBXiNMRuADSFt3wh6PADcEOHau4G747FptTfiy4YLbQ87hjHmYeDhmG9CUZSwNBz3bX6sqyg6KTvVpXkctryEiaShuYfaaeErSAfjr/tWEEZ8SguyuXTxDJ7c2sQ/rj512KbUUPxhvpMNu+Vm2VleXczbja0R+xzrHKBueiFZdlugYGmmohUOlADq+ExO9lplX+pOIuwGvgQAf0hsovB6De9/2M3CmVNi9i2y6r71OUeePArw2TNn09Hn4jebD4d93c+htj5mTMmNKlDxctbcMnYe7QqcsBrKsY4BZhbnMX96IXuPZ3ZFBBUfRff3THLe/7CbKbmOmKnLsaiams+JHueE7uI/0tFPz6CbBTNii88586YBcNbc8EU/PzK3jPo5U7nvlX1hj8b2c6itL2ZmXbycNbcMj9ewcf9I78ft8dLcPcCs4lzqphexr6UHT4IqRiQDFR8lgIrQ5GR7UwfLqkrw7fEeO/4P6IkMve066qsQsGBm7BDhwplTeOfrl7BmRei2RB8iwm2XzufDrgF+/FpjRDsHTvSOefNtKKfPmUpelp1X94xM827uHsRrYEZxHvMqChl0ewPJDpmIio+iKdaTmAGXhz0fdmRsk9IAACAASURBVLOsqvikbfk/oA+2TtwH5qYDbWQ7bCyKI+wGvrWdaJx9yjSuWT6L+15poKF55BrLiZ5BmrsHWRiH2MWDL827jJffH5nm7c90m1mSG0j+yOTQm4qPEkBFaPKx+1gXbq9hWVXJSduaax0jPZFHB7y9v5XTqkvGZf3Fz9evWkRBjp1bf72VPufwkOFuy9NaNCs+sYuHCxZU0NTeP6LK9bFOX1bdzOLcQPXtvRl8DIOKj6LhtknMdqvA5vLqk/d8puRmUTU1b8KKZ3b0Odl9tIsz55aNq93yohx+sPY09hzv5vYndgyrzL1xfxt2m7Ck8uTvj5/zT/Wd/fPK+8NDb/409ZnFvlNRZxbnsjeMN5YpqPgoKj6TmE0H25lZnMuMMR4iF8rCmVNGfVxBvDy/+zheAxdHObhtrJw3v5x/vGwBv3/3KN9YvxOv12CM4YXdx1k5u4QpEY7IHguVJXmcOr2Il94/Pqz9aMcA+dn2wOms8yoyO+NNjx5QAqgITS68XsNb+1r52KnlJ51s4GfhzCm89N5x+p0e8rLHLzQGsGHHMSpL8gIVosebv/7YXDr7Xdz/2j6OtPezonoqe45382+fWDruY126eDr3vdIwrDr3sc5+ZhbnBv4v6iqKeGTjIbxeg802Pv8/qYR6PooySfmguZvWXidnnzJt3GwuryrGaxj1eTmxONzWx2sftHDtabPGTShDERFuX30q37pmMW/sa+XeFz/gI3PLuO70qnEf68plM/EaeHbXh4G2Ix39VAZtZK2bXki/y8ORjsys8aaejxLIulHHZ3Lx5j7fXpOPnDJ+ayj1NaXYBN5sbB1Xuz//8wHsInzurJpxsxkOEeGms2u49rRKDrf1sXDmFOwT4HWcOr2IU8oLeGb7UT531hwAjrT3szgoscGf8dbQ3DNuqd6phHo+iorOJOXl95uZO62AypKTq1kWTHFeFotnFfPWvsglZEbLodY+fvnWQa49rZIZxeOzNhWL4rwsllQWT4jwgE/krlw2i43722juHqDf6aG11zns/2Io4y0zkw5UfJQAmX5+iDJEZ7+LN/e1csni6eNu+6N109hyqJ3Wcahw7fUavvbUTuw24f9eduo4zC51uNoKva3fdpQDrb3A8GMiSvKzKS/KydikAxUfRRMNJiGv7mnG7TVctnjGuNu+atksPF7Dhp0fxu4cgx+9to8/ftDCP125kOnjlJGXKtRNL+L0OVP55VsHAyecLg7ZT1RXUZixe31UfJQAqkGThw07jlFRlMOKcdhcGsrCmUXMn17Io5sOnZQ3/cjGQ/z7c3u4evksbjxz9jjOMHW46ewaDrT2cceTO5iS66A25EC/uopC9h7vHrb3KFNQ8VG0ssEko7VnkJfea2bNilkTksIrInzp3Fp2HunitQ/iO6o6GK/X8N3n9nDnkzv42PxyvnvDsgnLcEs2VyyZwfKqYjxew5oVlSPWmBbPKqbX6WG/FZbLJDTbTdGw2yTjt1uP4PYabqivjt15jHz8tCr++5UGvvX73ZxZWxb3np+G5m7ueGIHmw+286n6av7l2iVkOzL3O7LDbuOhL67ijX2tXLhg5ObZZVblie1NHZxSfnJHXqQamfu/qoweFaGMxxjDo5sOs6K6hPnTx6dYZjiyHTbu+cQy9p/o5e8e3YrT7Y3a/1BrH3c8sZ3V33+dvc09fPeG5fzbdUszWnj8lORnc8XSmWHr1c0rLyQ3y8b2ps4kzGxiUc9HUc2ZRLz6QUvgw32iOXveNP756kV86/e7+cSP/sxtl8zn7FOmkZtlx+3xcqC1j7caW3lm+zHebGwl22Hjs2fO5tYL6wK7/ic7DruNJbOKVXyUzEbXfjKfH7+2j5nFuVyzfFZCxvvCObXMLM7ln9fv4ou/2IxNoCDbQa/TjX8NfU5ZPrddMp9PnVGdcRlt48GyqhJ+vfEgbo8Xhz1zPEEVH0XXfCYJWw628VZjG1+7cmFCw1mrl8zkwgXTeX1vC9ubOukacFGU46CqNJ9VNaXMKcvP2ISC8WB5dTE/+7OXD473jOvRDslGxUfBH3hTEcpcjDHc/cx7VBTl8JkkpC1nO2xctHA6Fy0c/02tmY7/rKV3mzoySnwyx4dTxoyKTubz7M4PeedQB7ddMp/8bP3OmU7UlOVTWpDNloPtyZ7KuKLiowRQEcpMugdc/MvTuzl1etGEplcrE4OIcPqcqWw+0JbsqYwrKj6KphlkOPc8+z7Hugb41+uWTlihTGViOaNmKgda+2juHkj2VMYNFR9FC4pmMH/ae4JfvnWIL55Ty8rZU5M9HWWM1NeUArDlQOaE3lR8lAAqQZnFh50D/J91W6mrKOTvL52f7OkoJ8GSWcXkOGxszqB1HxUfRUUnA3G6vfztI+/Q7/LwoxtXapJBmpPtsLGiuiSj1n3iEh8RWS0ie0SkQUTuCPN6jog8ar3+tojUBL12p9W+R0Qui2VTRGotG3stm9nRxhCRS0Rki4jssP69cKw3Y7Kj4bfMwBjD7U9sZ9OBdv7tumXMq5i4MjpK4jijppSdR7voc7qTPZVxIab4iIgduA+4HFgEfFpEFoV0+xLQboyZB9wL3GNduwhYCywGVgM/FBF7DJv3APcaY+qAdst2xDGAE8DVxpilwE3Aw6O7BYpqTmbxnef28NutR/iHS+cnrJKBMvGcXjMVj9ew7VBHsqcyLsTj+awCGowxjcYYJ7AOWBPSZw3wkPX4ceAi8W1ZXgOsM8YMGmP2Aw2WvbA2rWsutGxg2bw22hjGmK3GmKNW+y4gV0S0MNQYUA1Kf/7rpb386NV9fObM2dxywbxkT0cZR1bOnooIbMyQ0Fs84lMJHA563mS1he1jjHEDnUBZlGsjtZcBHZaN0LEijRHMdcBWY8yI83tF5GYR2Swim1taRn/GSCaj4bb0xxjD957fw3+88AGfOK2Su65ZrCVrMozivCxOnV7E5gzJeItHfML9Bod+WkXqM17tMechIovxheL+Kkw/jDEPGGPqjTH15eXl4bpMelSD0hOv11c65z9fbuCT9VX8+w3LM6oApTLEqtpS3jnUjtsT/YiKdCCe39AmIHhbdBVwNFIfEXEAxUBblGsjtZ8ASiwboWNFGgMRqQJ+C/yFMWZfHO9JCUI1J33pd3r48q+28NM/7efzZ9fwb59YphtJM5gzakrpc3rYfawr2VM5aeIRn01AnZWFlo0vgWB9SJ/1+Bb7Aa4HXja+WM56YK2VqVYL1AEbI9m0rnnFsoFl86loY4hICfAMcKcx5s+jefOKjyGPR2UonTjeNcCnHniT53cf55+vXsQ3r1k8IcdiK6nDGdZm043703/dJ6b4WOsrtwLPAe8BjxljdonIXSJyjdXtQaBMRBqA24A7rGt3AY8Bu4FngVuMMZ5INi1btwO3WbbKLNsRx7DszAO+LiLbrJ+R59EqSgbxp70nuOIHr9PQ3MNPPlfPF86pTfaUlAQwoziX6tI8NmVA0kFcO8+MMRuADSFt3wh6PADcEOHau4G747FptTfiy4YLbQ87hjHm28C3Y74JJSKacJA+eLyG/3p5Lz94aS/zygv50Y0rdR/PJOOMmlJe29OCMSatk0p0VVIJoBqU2hzp6OdzD77N91/cy8dXVPLUreeo8ExCVtWU0trrpPFEb7KnclJozQ1FV3pSHGMMv9ncxF1P78YYwz3XLeWT9dVp/a1XGTv+IqOb9rdxSnlhkmczdlR8lAAqQqlHc9cAdz65g5feb+asuaX8+/XLqS7NT/a0lCRySnkBZQXZbDzQxtpViT+VdrxQ8VE03JaCeLyGX719kH9/bg9Ot5dvXLWIz59do9lsCiJCfc3UtN9squKj4LXUR0UoNdjR1MlXf7eD7U2dnDtvGnetWczcNA6vKOPPGTWlPLfrOMe7Bpg+JTfZ0xkTKj5KQHyU5NI14OJ7z3/A/7x5gNKCHH6wdgXXLJ+lazvKCIL3+1ydpsVjVXwUvJb2GF31SQpuj5dHNh3m3hc+oL3PyefOmsPfX3oqxXlZyZ6akqIsnjWF/Gw7mw6o+ChpjMeropMsXt3TzN3PvMfe5h5W1ZbyjasWsaSyONnTUlIch93GytlT2ZTG6z4qPoqSBD443s3dz7zHax+0MKcsn/tvPJ3LFk/XEJsSN/U1U/nBS3vp7HelpZes4qME0KWfiaepvY8fvLiXJ95poiDHwdeuXMjnPjKHHIc92VNT0oxVNaUYA+8cbOeCBelXUUzFR1ESQHP3APe93MCvNx5CEG46u4a/vbCO0oLsZE9NSVNOmz0Vh03YeKBNxUdJb9TxGX86+pzc/1ojv3hjPy6P4ZP1VfzthXXMKslL9tSUNCcv286SymI2p2mRURUfRZkAugZc/OLPB/jJHxvpcbq5ZvksvnLxfGqmFSR7akoGcUbNVB564yADLg+5WekVulXxUQJodeuTp73Xyc/+vJ9fvHGA7gE3lyyazt9fOp8FM6Yke2pKBnJGTSk/eX0/25s6WVVbmuzpjAoVH0UZB1q6B/np6408/NZB+pweLl8yg1sumKdp08qEEigyeqBNxUdRJhMfdg5w/2v7eGTjIVweL1cvn8UtF8xj/nQ96kCZeEoLsplXUZiWh8up+CjKGNh/opefvN7I45ub8BrDx0+r5Mvnn6I12JSEc0ZNKU9vP4rXa9Kq8KyKj6KMgi0H23ngj/t4fvdxsmw2rq+v4ssfO0WPOVCSxmmzS3hk4yEaT/Sk1eGCKj5KAM03CI/Xa3jhveP85I+NbD7YTnFeFrecP4+/OHsOFUXpWVFYyRxOqy4BYNvhThUfRckEBlwennznCD99vZHGE71UTc3jm1cv4ob6agpy9E9HSQ1OKS+kMMfBtsPtXH96VbKnEzf6F6QE0KrWPlp7BvnV24f4nzcPcKLHydLKYv7r06dx+ZIZOOy2ZE9PUYZhswnLqorZdrgj2VMZFSo+GcJYN5np3p4hdh7p5BdvHGD9u0dxur2cf2o5N583l4/MLdOCn0pKs6K6hAf+2JhWm01VfDKADTuO8Te/eofn/u48Tp0xuphv8HEKk1GH3B4vz+06zi/e2M+mA+3kZ9v5VH01N509J63i58rkZkV1CW6vYeeRzsDen1RHxScDePG94wDsONI5avFxerwTMaWUp63XySMbD/HLtw5yrHOA6tI8vnblQm6or07L8vTK5GbFbH/SQYeKj5IeuNyTy/PZfbSLh944wO+2HWHQ7eXcedP4lzVLuGBBBfY02iOhKMFUFOVSWZKXVus+Kj4ZgMvjU40s++g/PAc9nvGeTsox4PLwh53H+NVbh9h8sJ28LDvXn17FTWfXaCUCJWNYXp1eSQcqPhmA0+0TkBzH6DOx/MKViTS29PDIxkP8ZksTHX0uaqcV8NUrFvLJ+mqK8zW0pmQWK6pL2LDjQ070DDKtMCfZ04lJXJ9WIrJaRPaISIOI3BHm9RwRedR6/W0RqQl67U6rfY+IXBbLpojUWjb2Wjazo40hImUi8oqI9IjIf4/1RqQzg27fuo3DNnrxcbqH1nwyQYZcHi8bdhzjsz99iwv/4zV+/ucDnHPKNH79v87k5b//GH953lwVHiUjWVE9FYB308T7ien5iIgduA+4BGgCNonIemPM7qBuXwLajTHzRGQtcA/wKRFZBKwFFgOzgBdFZL51TSSb9wD3GmPWicj9lu0fRRoDGAC+DiyxfiYdLitpwDuGRRtXhiQcNLX3sW7jYR7dfJiW7kEqS/L4v5edyg31VVqFQJkULK0sxm4Tth3u4KKF05M9nZjEE3ZbBTQYYxoBRGQdsAYIFp81wDetx48D/y2+jRFrgHXGmEFgv4g0WPYIZ1NE3gMuBD5j9XnIsvujSGMYY3qBP4nIvFG874zC772MRXyGeT5plnHg9nh5dU8Lv954iFf2NCPAhQsq+OyZczhvfrkmECiTirxsO3UVhWxv6kz2VOIiHvGpBA4HPW8CzozUxxjjFpFOoMxqfyvk2krrcTibZUCHMcYdpn+kMU7E8R4yGr+AjMWJGXSnX8LB/hO9PLrpME+800RL9yDlRTncesE81q6aTaUeT61MYpZUFvPK+80YY1J+Y3Q84hPuHYR+RY7UJ1J7uMWJaP3jnUdERORm4GaA2bNnx3tZWjB4Ep5P94A78DiV/Z4+p5sNOz7ksU2H2XigDbtNuODUcj5ZX80FCyrI0rI3isLSymIe39LEh10DzCxO7S9i8YhPE1Ad9LwKOBqhT5OIOIBioC3GteHaTwAlIuKwvJ/g/pHGiAtjzAPAAwD19fWp/Dk7ak4m7NY7mLqejzGGd5s6eXTTYX7/7lF6Bt3UTivgH1efynUrq5g+RddyFCUY/8m5O5o6M0J8NgF1IlILHMGXQPCZkD7rgZuAN4HrgZeNMUZE1gO/FpHv4Us4qAM24vNiRti0rnnFsrHOsvlUtDHG9rYziz6nT0CCS+XES+/gkOeTKq5PW6+T3249wmObDrPneDe5WTauWDqTT9VXs6q2NOXDCYqSLBbNnIJNYOfRLi5dPCPZ04lKTPGx1lduBZ4D7MDPjDG7ROQuYLMxZj3wIPCwlVDQhk9MsPo9hi85wQ3cYozxAISzaQ15O7BORL4NbLVsE2kMy9YBYAqQLSLXApeGZONlNL1On4CMRXx6gsUniXi8htf3tvCbzU08v/tDXB7D8uoS7v74Eq5ePospuZoerSixyMu2M6+ikJ1HUj/pIK5NpsaYDcCGkLZvBD0eAG6IcO3dwN3x2LTaGxnKiAtujzZGTdQ3kOH4vZex+IHD13wS7/ocau3j8S2HeXxLE0c7B5ian8Xnzqrhk2dUsWDGlITPR1HSnSWzinm9IfXzsLTCQQbgd3g8Y1Cflp6BcZ5NbPzJA7/ZfJi397chAh+tK+erVy7i4kUV5DjSoyS8oqQiSyqLeXLrEZq7BqhI4XVRFZ80JzjU5h5D2K25a5CiHAfdExx+M8aw+WA7v9l8mGe2H6PX6aGmLJ9/uHQ+n1hZxSxNkVaUcWFplZV0cKSTi1R8lImiuXvIc/GORXy6B6mYkkN3i3tCqlof6+znyXeO8PiWJvaf6CU/286VS2dyQ301Z9RM1eQBRRlnFs2cggjsPNKV0pUOVHzSnKMdQ+IzWs/HGMOB1l5WVJewr6V33OY04PLwwu7j/GZLE3/a24LXwJm1pdxywTwuXzKDghz9tVOUiaIgx8HcaQXsSPGkA/0USHMOnBgSDY93dCUOWnoG6ehzceqMIl7d08IYHKdh7Gjq5LHNh3lq2xG6BtxUluRx6wXzuO70KuaUFZyccUVR4mZJZTFvN8a9DTIpqPikOTuOdCLiy3Qbrefz7mHfN6Ol1sa0sSQs9Ay6eWrbER7ZeIidR7rIcdhYvWQGN5xezdmnlGHT+mqKknCWVhbz1LajgfJTqYiKTxpjjOG1D1pYVVPK2/vb8IzybJ7XPmgmP9vOKuvYXfcoisPtaOrk1xsPsX7bEXqdHhbMKOKuNYtZs6JSj6FWlCSzeJbvC+XOo51ccGpFkmcTHhWfNOb1vSfYf6KXm8+by9v720bl+fQ7PazfdpQLFlSQl+1LbY61SdXjNTy360N+8nojWw91kJtl46pls/jMmbM5rbpEkwcUJUVYXOnbI7ezScVHGWc6+px87Xc7qZqaxydWVvLV3+4YVYWD+1/bR9eAmy+cXRM4hC7SqaaDbg+PbTrMT17fz6G2PmaX5vONqxZx3elV6uUoSgoyJTeL2hRPOlDxSUPae518/heb+LBzgHV/dRY5DjsOmy1uz+f1vS3c90oD1yyfRX1NadCRDMPDbsYYfrftCP/x/Ac0tfezcnYJd16+gEsXz9CzchQlxVlSWcw7B9uTPY2IqPikGY0tPfzVw1s42NbHf3/mNFbO9h2da7dJXNluG3Yc47bHtjGvopC7P+47+NVhCUmweB1u6+P2J7bzxr5WFs+awv/7+FI+WjdNQ2uKkiYsrZzC7989Sluvk9KC7GRPZwQqPmnE7989yp1P7sBhF37xhTM4+5RpgdccNonq+bR0D/KdZ9/nN1uaWDm7hAf+op4iq1inzSbYBNxW2G3zgTb+8n824/IY7v74Ej59xmzNWlOUNGPJrKFKBx+bX57k2YxExScNONrRzzfX7+L53cdZObuE//rMyhEndtrtErbCgdPt5aE3DvCfL+1lwO3hr86by1cumU9u1vD6af6w3XvHurjpZxupmJLLzz5/BrXTdH+OoqQji60tFDtVfJTR0jXg4qev7+fB1xvxGMPtqxfwvz5aG/bUznCez6t7mrnr6d00tvRy4YIKvnblQuaWF4Ydy2EXBlwe/vaRrRTmOvj1X56Z8odRKYoSmeK8LOaU5bOjKTWTDlR8UpD2Xie/evsgP3l9P539Li5fMoN/umIh1aX5Ea/xrfn4xOdQax93Pb2LF99rpnZaAT///BlcsCB6uqXdJvx64yGcbi8//Yt6FR5FyQCWVBaz7VBHsqcRFhWfFOK9Y1089MYBfrv1CINuLxctqOArl8wPHI0bDX/Y7Hdbj3DHk9uxi3Dn5Qv4wjm1ZDtGekojrxe63V4WzCjiooWpuS9AUZTRsbSymGe2H6O918nUFEs6UPFJMi3dgzyz/Sjr3z3KO9bGzU+srOKms+eM6jA1u014c18rT7zTxKqaUr6/dsWovJf2PhcAn6yv1ow2RckQ/KWzdhzp5LwUW/dR8UkCXQMunt35Ib9/9yh/bjiB18CCGUX80xUL+GR9NSX5o/+G4rAJh9r6qCzJ4+dfOIP87NH9184tL6CxpZc1K2aNemxFUVKT4Iw3FZ9JyoDLw0vvNbP+3SO8sqcFp9vL7NJ8/ub8eVyzYhbzpxedlP2WnkEAPr2qetTCA3DPdcto6R6krDA1ixAqijJ6ivOzmF2az84UrHSg4jOBuDxe/tRwgt9vO8pzuz6k1+mhvCiHz545m2uWz2LFONZD81ccuHLZ2DyXM6ziooqiZBZLK4t5tyn1kg5UfMYZYwxbD3fwu61HeHr7Mdp6nUzJdXDVslmsWTGLM+eWTUhpmq9fuYgth9p1X46iKMNYUlnMMztSL+lAxWecaO0Z5JGNh/jNliYOtvaR47Bx8aLpXLuikvPmTyPHYY9t5CS47vQqrju9akLHUBQl/fAnHew82slH61Jn3UfF5yRp7hrg3hf38sQ7TTjdXs4+pYxbL5jH6iUzAuVrFEVRksUS63iFHUdUfDICYwy/fOsg//qH93F5vNxQX80Xz6lhXsXJJQ4oiqKMJyX52VSX5rHrSFeypzIMFZ8x4PUa/um3O1i36TAfm1/OXWsWM6dM11oURUlNllYWp9zZPrG3visj+P6LH7Bu02FuueAUfv75M1R4FEVJaZZUFnOorY+2XmeypxJAxWeUfHC8m/te3ccnVlbyD5eeqkcNKIqS8pxZ69tK8XZja5JnMoSKzyh58PX9ZNttfP3KRVqGRlGUtGBZVQn52XbeTDfxEZHVIrJHRBpE5I4wr+eIyKPW62+LSE3Qa3da7XtE5LJYNkWk1rKx17KZPdYxxhtjDC+9f5xLFk1PqXx5RVGUaGTZbdTXlPLGvjQSHxGxA/cBlwOLgE+LyKKQbl8C2o0x84B7gXusaxcBa4HFwGrghyJij2HzHuBeY0wd0G7ZHvUYo70R8XC8a5ATPU7qa6ZOhHlFUZQJ49x5ZTQ097D/RG+ypwLE5/msAhqMMY3GGCewDlgT0mcN8JD1+HHgIvHFpNYA64wxg8aY/UCDZS+sTeuaCy0bWDavHeMY405rr69+WkWR1j9TFCW9uHZFJXab8B/P70n2VID4Uq0rgcNBz5uAMyP1Mca4RaQTKLPa3wq5ttJ6HM5mGdBhjHGH6T+WMQKIyM3AzdbTHhFpBU5EfNdRuPyesVyV0kxjjPciA9F74UPvwxAZdS/uA+777JgunQbMGa95xCM+4VbVTZx9IrWH87ii9R/LGMMbjHkAeMD/XEQ2G2Pqw1w76dB7MYTeCx96H4bQe+HDug8142UvnrBbE1Ad9LwKOBqpj4g4gGKgLcq1kdpPACWWjdCxRjuGoiiKkqLEIz6bgDorCy0b3+L++pA+64GbrMfXAy8bY4zVvtbKVKsF6oCNkWxa17xi2cCy+dQYx1AURVFSlJhhN2t95VbgOcAO/MwYs0tE7gI2G2PWAw8CD4tIAz5vZK117S4ReQzYDbiBW4wxHoBwNq0hbwfWici3ga2WbcYyRgweiN1l0qD3Ygi9Fz70Pgyh98LHuN4H8TkPiqIoipI4tMKBoiiKknBUfBRFUZSEMynFJ1a5oExARH4mIs0isjOorVREXrBKF70gIlOtdhGR/7Tux3YRWRl0zU1W/70iclO4sVIZEakWkVdE5D0R2SUi/8dqn1T3QkRyRWSjiLxr3YdvWe0pW85qorGqrWwVkaet55PyXojIARHZISLbRGSz1Tbxfx/GmEn1gy/BYR8wF8gG3gUWJXteE/A+zwNWAjuD2r4D3GE9vgO4x3p8BfAHfHumzgLettpLgUbr36nW46nJfm+jvA8zgZXW4yLgA3wlnSbVvbDeT6H1OAt423p/jwFrrfb7gS9bj/8GuN96vBZ41Hq8yPqbyQFqrb8le7Lf3xjvyW3Ar4GnreeT8l4AB4BpIW0T/vcxGT2feMoFpT3GmD/iywoMJrhEUWjpov8xPt7Ct9dqJnAZ8IIxps0Y0w68gK9+XtpgjDlmjHnHetwNvIevAsakuhfW++mxnmZZP4YULmc1kYhIFXAl8FPreUqX9koCE/73MRnFJ1y5oBHleDKU6caYY+D7UAYqrPZI9ySj7pUVLjkN37f+SXcvrDDTNqAZ34fDPuIsZwUEl7NK6/tg8X3gHwGv9Tzu0l5k3r0wwPMiskV8ZcggAX8fk/EY7bjK8UwyTqp0UTogIoXAE8DfGWO6JPJZTBl7L4xv/9sKESkBfgssDNfN+jdj3mdFeAAAAa1JREFU74OIXAU0G2O2iMj5/uYwXTP+XlicY4w5KiIVwAsi8n6UvuN2Lyaj5zOZy/Ect1xkrH+brfbRlkFKK0QkC5/w/MoY86TVPCnvBYAxpgN4FV/MfjKWszoHuEZEDuALu1+IzxOajPcCY8xR699mfF9KVpGAv4/JKD7xlAvKVIJLFIWWLvoLK5PlLKDTcrWfAy4VkalWtsulVlvaYMXmHwTeM8Z8L+ilSXUvRKTc8ngQkTzgYnzrX5OunJUx5k5jTJXxFclci++9fZZJeC9EpEBEivyP8f1e7yQRfx/JzrRIxg++jI0P8MW8v5rs+UzQe3wEOAa48H0r+RK+OPVLwF7r31Krr+CrtL4P2AHUB9n5Ir6F1AbgC8l+X2O4D+fic/+3A9usnysm270AluErV7Xd+nD5htU+F98HZgPwGyDHas+1njdYr88NsvVV6/7sAS5P9ns7yftyPkPZbpPuXljv+V3rZ5f/8zARfx9aXkdRFEVJOJMx7KYoiqIkGRUfRVEUJeGo+CiKoigJR8VHURRFSTgqPoqiKErCUfFRFEVREo6Kj6IoipJw/j+gX7eOJq2lrAAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -732,7 +732,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -785,7 +785,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -814,7 +814,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -831,7 +831,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -861,7 +861,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -870,7 +870,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -892,7 +892,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -929,7 +929,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -951,7 +951,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -979,7 +979,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -1012,37 +1012,37 @@ "# mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=_p[:(splits-1)]),\n", "# components_distribution=tfd.Uniform(low=list_of_borders[:(splits-1)], \n", "# high=list_of_borders[-(splits-1):]))\n", - "# mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.05, dtype=dtype),\n", - "# tf.constant(0.93, dtype=dtype),\n", - "# tf.constant(0.05, dtype=dtype),\n", - "# tf.constant(0.065, dtype=dtype),\n", - "# tf.constant(0.05, dtype=dtype)]),\n", - "# components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), \n", - "# tf.constant(3090, dtype=dtype),\n", - "# tf.constant(3681, dtype=dtype), \n", - "# tf.constant(3070, dtype=dtype),\n", - "# tf.constant(3660, dtype=dtype)], \n", - "# high=[tf.constant(x_max, dtype=dtype),\n", - "# tf.constant(3102, dtype=dtype), \n", - "# tf.constant(3691, dtype=dtype),\n", - "# tf.constant(3110, dtype=dtype), \n", - "# tf.constant(3710, dtype=dtype)]))\n", - " dtype = tf.float64\n", - " mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.04, dtype=dtype),\n", - " tf.constant(0.90, dtype=dtype),\n", - " tf.constant(0.02, dtype=dtype),\n", - " tf.constant(0.07, dtype=dtype),\n", - " tf.constant(0.02, dtype=dtype)]),\n", + " mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.05, dtype=dtype),\n", + " tf.constant(0.93, dtype=dtype),\n", + " tf.constant(0.05, dtype=dtype),\n", + " tf.constant(0.065, dtype=dtype),\n", + " tf.constant(0.05, dtype=dtype)]),\n", " components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), \n", - " tf.constant(3089, dtype=dtype),\n", - " tf.constant(3103, dtype=dtype), \n", - " tf.constant(3681, dtype=dtype),\n", - " tf.constant(3691, dtype=dtype)], \n", - " high=[tf.constant(3089, dtype=dtype),\n", - " tf.constant(3103, dtype=dtype), \n", - " tf.constant(3681, dtype=dtype),\n", - " tf.constant(3691, dtype=dtype), \n", - " tf.constant(x_max, dtype=dtype)]))\n", + " tf.constant(3090, dtype=dtype),\n", + " tf.constant(3681, dtype=dtype), \n", + " tf.constant(3070, dtype=dtype),\n", + " tf.constant(3660, dtype=dtype)], \n", + " high=[tf.constant(x_max, dtype=dtype),\n", + " tf.constant(3102, dtype=dtype), \n", + " tf.constant(3691, dtype=dtype),\n", + " tf.constant(3110, dtype=dtype), \n", + " tf.constant(3710, dtype=dtype)]))\n", + "# dtype = tf.float64\n", + "# mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.04, dtype=dtype),\n", + "# tf.constant(0.90, dtype=dtype),\n", + "# tf.constant(0.02, dtype=dtype),\n", + "# tf.constant(0.07, dtype=dtype),\n", + "# tf.constant(0.02, dtype=dtype)]),\n", + "# components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), \n", + "# tf.constant(3089, dtype=dtype),\n", + "# tf.constant(3103, dtype=dtype), \n", + "# tf.constant(3681, dtype=dtype),\n", + "# tf.constant(3691, dtype=dtype)], \n", + "# high=[tf.constant(3089, dtype=dtype),\n", + "# tf.constant(3103, dtype=dtype), \n", + "# tf.constant(3681, dtype=dtype),\n", + "# tf.constant(3691, dtype=dtype), \n", + "# tf.constant(x_max, dtype=dtype)]))\n", "# mixture = tfd.Uniform(tf.constant(x_min, dtype=dtype), tf.constant(x_max, dtype=dtype))\n", "# sample = tf.random.uniform((n_to_produce, 1), dtype=dtype)\n", " sample = mixture.sample((n_to_produce, 1))\n", @@ -1060,7 +1060,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -1069,16 +1069,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.001309082138940001" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "0.00133/(0.00133+0.213+0.015)*(x_max-3750)/(x_max-x_min)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -1087,11 +1098,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6/6 of Toy 1/1\n", + "Time taken: 1 min, 33 s\n", + "Projected time left: \n" + ] + } + ], "source": [ "# zfit.run.numeric_checks = False \n", "\n", @@ -1135,7 +1156,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -1152,9 +1173,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time to generate full toy: 93 s\n", + "(5404696,)\n" + ] + } + ], "source": [ "print(\"Time to generate full toy: {} s\".format(int(time.time()-start)))\n", "\n", @@ -1176,9 +1206,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(5404696,)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.clf()\n", "\n", @@ -1203,7 +1253,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -1226,7 +1276,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -1235,7 +1285,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -1251,9 +1301,194 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
FCN = -852457.5735961825TOTAL NCALL = 43NCALLS = 43
EDM = 1.41554916767651e-06GOAL EDM = 5e-06\n", + " UP = 0.5
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ValidValid ParamAccurate CovarPosDefMade PosDef
TrueTrueTrueTrueFalse
Hesse FailHasCovAbove EDMReach calllim
FalseTrueFalseFalse
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
+NameValueHesse ErrorMinos Error-Minos Error+Limit-Limit+Fixed?
0omega_s5.202940.0355895No
1psi2s_s1256.640.734138No
2rho_s1.758890.235982No
3jpsi_s10341.62.34196No
4p4415_s1.049730.163853No
5phi_s17.08260.0113619No
\n", + "
\n",
+       "\n",
+       "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Function minimum: -852457.5735961825\n" + ] + } + ], "source": [ "start = time.time()\n", "\n", @@ -1263,19 +1498,27 @@ "# minimizer._use_tfgrad = False\n", "result = minimizer.minimize(nll)\n", "\n", - "param_errors = result.error()\n", + "# param_errors = result.error()\n", "\n", - "for var, errors in param_errors.items():\n", - " print('{}: ^{{+{}}}_{{{}}}'.format(var.name, errors['upper'], errors['lower']))\n", + "# for var, errors in param_errors.items():\n", + "# print('{}: ^{{+{}}}_{{{}}}'.format(var.name, errors['upper'], errors['lower']))\n", "\n", "print(\"Function minimum:\", result.fmin)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time taken for fitting: 22 s\n" + ] + } + ], "source": [ "print(\"Time taken for fitting: {}\".format(display_time(int(time.time()-start))))\n", "\n", @@ -1287,9 +1530,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.clf()\n", "# plt.plot(x_part, calcs, '.')\n", @@ -1305,7 +1561,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -1319,7 +1575,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -1340,9 +1596,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.522535170724314\n", + "1.522535170724314\n" + ] + } + ], "source": [ "print((zfit.run(jpsi_p)%(2*np.pi))/np.pi)\n", "print((zfit.run(psi2s_p)%(2*np.pi))/np.pi)" diff --git a/__pycache__/pdg_const.cpython-37.pyc b/__pycache__/pdg_const.cpython-37.pyc index 62b0ac1..0bc95ea 100644 --- a/__pycache__/pdg_const.cpython-37.pyc +++ b/__pycache__/pdg_const.cpython-37.pyc Binary files differ diff --git a/data/zfit_toys/toy_0/0.pkl b/data/zfit_toys/toy_0/0.pkl index 8da98c7..d46c993 100644 --- a/data/zfit_toys/toy_0/0.pkl +++ b/data/zfit_toys/toy_0/0.pkl Binary files differ diff --git a/data/zfit_toys/toy_0/1.pkl b/data/zfit_toys/toy_0/1.pkl index b79899a..ae410b7 100644 --- a/data/zfit_toys/toy_0/1.pkl +++ b/data/zfit_toys/toy_0/1.pkl Binary files differ diff --git a/data/zfit_toys/toy_0/2.pkl b/data/zfit_toys/toy_0/2.pkl index e58c433..ad66105 100644 --- a/data/zfit_toys/toy_0/2.pkl +++ b/data/zfit_toys/toy_0/2.pkl Binary files differ diff --git a/data/zfit_toys/toy_0/3.pkl b/data/zfit_toys/toy_0/3.pkl index b1faa49..45b9f9d 100644 --- a/data/zfit_toys/toy_0/3.pkl +++ b/data/zfit_toys/toy_0/3.pkl Binary files differ diff --git a/data/zfit_toys/toy_0/4.pkl b/data/zfit_toys/toy_0/4.pkl index 1beb224..5c804b0 100644 --- a/data/zfit_toys/toy_0/4.pkl +++ b/data/zfit_toys/toy_0/4.pkl Binary files differ diff --git a/data/zfit_toys/toy_0/5.pkl b/data/zfit_toys/toy_0/5.pkl index aa81eb6..f00da81 100644 --- a/data/zfit_toys/toy_0/5.pkl +++ b/data/zfit_toys/toy_0/5.pkl Binary files differ diff --git a/raremodel-nb.ipynb b/raremodel-nb.ipynb index d3ce661..bd78d08 100644 --- a/raremodel-nb.ipynb +++ b/raremodel-nb.ipynb @@ -32,6 +32,20 @@ "If you depend on functionality not listed there, please file an issue.\n", "\n" ] + }, + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'iminuit'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mitertools\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcompress\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mzfit\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 21\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mzfit\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mztf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mclear_output\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\__init__.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_variable_scope\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mztypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 31\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 32\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mconstraint\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mminimize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 33\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameter\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mParameter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mComposedParameter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mComplexParameter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert_to_parameter\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 34\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlimits\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSpace\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert_to_space\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msupports\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimize.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# from .minimizers.optimizers_tf import RMSPropMinimizer, GradientDescentMinimizer, AdagradMinimizer, AdadeltaMinimizer,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mminimizers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptimizers_tf\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mAdamMinimizer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mWrapOptimizer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mminimizers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminimizer_minuit\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mMinuitMinimizer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mminimizers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminimizers_scipy\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mScipyMinimizer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\minimizer_minuit.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtyping\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mList\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0miminuit\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 8\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtexttable\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'iminuit'" + ] } ], "source": [ @@ -64,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -83,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -284,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -339,7 +353,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -370,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -447,7 +461,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -475,19 +489,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Colocations handled automatically by placer.\n" - ] - } - ], + "outputs": [], "source": [ "#rho\n", "\n", @@ -495,8 +499,8 @@ "\n", "rho_m = zfit.Parameter(\"rho_m\", ztf.constant(rho_mass), floating = False)\n", "rho_w = zfit.Parameter(\"rho_w\", ztf.constant(rho_width), floating = False)\n", - "rho_p = zfit.Parameter(\"rho_p\", ztf.constant(rho_phase), floating = False)\n", - "rho_s = zfit.Parameter(\"rho_s\", ztf.constant(rho_scale))\n", + "rho_p = zfit.Parameter(\"rho_p\", ztf.constant(rho_phase))\n", + "rho_s = zfit.Parameter(\"rho_s\", ztf.constant(rho_scale), floating = False)\n", "\n", "#omega\n", "\n", @@ -504,8 +508,8 @@ "\n", "omega_m = zfit.Parameter(\"omega_m\", ztf.constant(omega_mass), floating = False)\n", "omega_w = zfit.Parameter(\"omega_w\", ztf.constant(omega_width), floating = False)\n", - "omega_p = zfit.Parameter(\"omega_p\", ztf.constant(omega_phase), floating = False)\n", - "omega_s = zfit.Parameter(\"omega_s\", ztf.constant(omega_scale))\n", + "omega_p = zfit.Parameter(\"omega_p\", ztf.constant(omega_phase))\n", + "omega_s = zfit.Parameter(\"omega_s\", ztf.constant(omega_scale), floating = False)\n", "\n", "\n", "#phi\n", @@ -514,8 +518,8 @@ "\n", "phi_m = zfit.Parameter(\"phi_m\", ztf.constant(phi_mass), floating = False)\n", "phi_w = zfit.Parameter(\"phi_w\", ztf.constant(phi_width), floating = False)\n", - "phi_p = zfit.Parameter(\"phi_p\", ztf.constant(phi_phase), floating = False)\n", - "phi_s = zfit.Parameter(\"phi_s\", ztf.constant(phi_scale))\n", + "phi_p = zfit.Parameter(\"phi_p\", ztf.constant(phi_phase))\n", + "phi_s = zfit.Parameter(\"phi_s\", ztf.constant(phi_scale), floating = False)\n", "\n", "#jpsi\n", "\n", @@ -524,8 +528,8 @@ "\n", "jpsi_m = zfit.Parameter(\"jpsi_m\", ztf.constant(jpsi_mass), floating = False)\n", "jpsi_w = zfit.Parameter(\"jpsi_w\", ztf.constant(jpsi_width), floating = False)\n", - "jpsi_p = zfit.Parameter(\"jpsi_p\", ztf.constant(jpsi_phase), floating = False)\n", - "jpsi_s = zfit.Parameter(\"jpsi_s\", ztf.constant(jpsi_scale))\n", + "jpsi_p = zfit.Parameter(\"jpsi_p\", ztf.constant(jpsi_phase))\n", + "jpsi_s = zfit.Parameter(\"jpsi_s\", ztf.constant(jpsi_scale), floating = False)\n", "\n", "#psi2s\n", "\n", @@ -533,8 +537,8 @@ "\n", "psi2s_m = zfit.Parameter(\"psi2s_m\", ztf.constant(psi2s_mass), floating = False)\n", "psi2s_w = zfit.Parameter(\"psi2s_w\", ztf.constant(psi2s_width), floating = False)\n", - "psi2s_p = zfit.Parameter(\"psi2s_p\", ztf.constant(psi2s_phase), floating = False)\n", - "psi2s_s = zfit.Parameter(\"psi2s_s\", ztf.constant(psi2s_scale))\n", + "psi2s_p = zfit.Parameter(\"psi2s_p\", ztf.constant(psi2s_phase))\n", + "psi2s_s = zfit.Parameter(\"psi2s_s\", ztf.constant(psi2s_scale), floating = False)\n", "\n", "#psi(3770)\n", "\n", @@ -542,7 +546,7 @@ "\n", "p3770_m = zfit.Parameter(\"p3770_m\", ztf.constant(p3770_mass), floating = False)\n", "p3770_w = zfit.Parameter(\"p3770_w\", ztf.constant(p3770_width), floating = False)\n", - "p3770_p = zfit.Parameter(\"p3770_p\", ztf.constant(p3770_phase), floating = False)\n", + "p3770_p = zfit.Parameter(\"p3770_p\", ztf.constant(p3770_phase))\n", "p3770_s = zfit.Parameter(\"p3770_s\", ztf.constant(p3770_scale), floating = False)\n", "\n", "#psi(4040)\n", @@ -551,7 +555,7 @@ "\n", "p4040_m = zfit.Parameter(\"p4040_m\", ztf.constant(p4040_mass), floating = False)\n", "p4040_w = zfit.Parameter(\"p4040_w\", ztf.constant(p4040_width), floating = False)\n", - "p4040_p = zfit.Parameter(\"p4040_p\", ztf.constant(p4040_phase), floating = False)\n", + "p4040_p = zfit.Parameter(\"p4040_p\", ztf.constant(p4040_phase))\n", "p4040_s = zfit.Parameter(\"p4040_s\", ztf.constant(p4040_scale), floating = False)\n", "\n", "#psi(4160)\n", @@ -560,7 +564,7 @@ "\n", "p4160_m = zfit.Parameter(\"p4160_m\", ztf.constant(p4160_mass), floating = False)\n", "p4160_w = zfit.Parameter(\"p4160_w\", ztf.constant(p4160_width), floating = False)\n", - "p4160_p = zfit.Parameter(\"p4160_p\", ztf.constant(p4160_phase), floating = False)\n", + "p4160_p = zfit.Parameter(\"p4160_p\", ztf.constant(p4160_phase))\n", "p4160_s = zfit.Parameter(\"p4160_s\", ztf.constant(p4160_scale), floating = False)\n", "\n", "#psi(4415)\n", @@ -569,8 +573,8 @@ "\n", "p4415_m = zfit.Parameter(\"p4415_m\", ztf.constant(p4415_mass), floating = False)\n", "p4415_w = zfit.Parameter(\"p4415_w\", ztf.constant(p4415_width), floating = False)\n", - "p4415_p = zfit.Parameter(\"p4415_p\", ztf.constant(p4415_phase), floating = False)\n", - "p4415_s = zfit.Parameter(\"p4415_s\", ztf.constant(p4415_scale))" + "p4415_p = zfit.Parameter(\"p4415_p\", ztf.constant(p4415_phase))\n", + "p4415_s = zfit.Parameter(\"p4415_s\", ztf.constant(p4415_scale), floating = False)" ] }, { @@ -582,7 +586,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -610,7 +614,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -653,22 +657,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.clf()\n", "# plt.plot(x_part, calcs, '.')\n", @@ -687,7 +678,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -703,7 +694,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -719,7 +710,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1012,37 +1003,37 @@ "# mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=_p[:(splits-1)]),\n", "# components_distribution=tfd.Uniform(low=list_of_borders[:(splits-1)], \n", "# high=list_of_borders[-(splits-1):]))\n", - "# mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.05, dtype=dtype),\n", - "# tf.constant(0.93, dtype=dtype),\n", - "# tf.constant(0.05, dtype=dtype),\n", - "# tf.constant(0.065, dtype=dtype),\n", - "# tf.constant(0.05, dtype=dtype)]),\n", - "# components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), \n", - "# tf.constant(3090, dtype=dtype),\n", - "# tf.constant(3681, dtype=dtype), \n", - "# tf.constant(3070, dtype=dtype),\n", - "# tf.constant(3660, dtype=dtype)], \n", - "# high=[tf.constant(x_max, dtype=dtype),\n", - "# tf.constant(3102, dtype=dtype), \n", - "# tf.constant(3691, dtype=dtype),\n", - "# tf.constant(3110, dtype=dtype), \n", - "# tf.constant(3710, dtype=dtype)]))\n", - " dtype = tf.float64\n", - " mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.04, dtype=dtype),\n", - " tf.constant(0.90, dtype=dtype),\n", - " tf.constant(0.02, dtype=dtype),\n", - " tf.constant(0.07, dtype=dtype),\n", - " tf.constant(0.02, dtype=dtype)]),\n", + " mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.05, dtype=dtype),\n", + " tf.constant(0.93, dtype=dtype),\n", + " tf.constant(0.05, dtype=dtype),\n", + " tf.constant(0.065, dtype=dtype),\n", + " tf.constant(0.05, dtype=dtype)]),\n", " components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), \n", - " tf.constant(3089, dtype=dtype),\n", - " tf.constant(3103, dtype=dtype), \n", - " tf.constant(3681, dtype=dtype),\n", - " tf.constant(3691, dtype=dtype)], \n", - " high=[tf.constant(3089, dtype=dtype),\n", - " tf.constant(3103, dtype=dtype), \n", - " tf.constant(3681, dtype=dtype),\n", - " tf.constant(3691, dtype=dtype), \n", - " tf.constant(x_max, dtype=dtype)]))\n", + " tf.constant(3090, dtype=dtype),\n", + " tf.constant(3681, dtype=dtype), \n", + " tf.constant(3070, dtype=dtype),\n", + " tf.constant(3660, dtype=dtype)], \n", + " high=[tf.constant(x_max, dtype=dtype),\n", + " tf.constant(3102, dtype=dtype), \n", + " tf.constant(3691, dtype=dtype),\n", + " tf.constant(3110, dtype=dtype), \n", + " tf.constant(3710, dtype=dtype)]))\n", + "# dtype = tf.float64\n", + "# mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.04, dtype=dtype),\n", + "# tf.constant(0.90, dtype=dtype),\n", + "# tf.constant(0.02, dtype=dtype),\n", + "# tf.constant(0.07, dtype=dtype),\n", + "# tf.constant(0.02, dtype=dtype)]),\n", + "# components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), \n", + "# tf.constant(3089, dtype=dtype),\n", + "# tf.constant(3103, dtype=dtype), \n", + "# tf.constant(3681, dtype=dtype),\n", + "# tf.constant(3691, dtype=dtype)], \n", + "# high=[tf.constant(3089, dtype=dtype),\n", + "# tf.constant(3103, dtype=dtype), \n", + "# tf.constant(3681, dtype=dtype),\n", + "# tf.constant(3691, dtype=dtype), \n", + "# tf.constant(x_max, dtype=dtype)]))\n", "# mixture = tfd.Uniform(tf.constant(x_min, dtype=dtype), tf.constant(x_max, dtype=dtype))\n", "# sample = tf.random.uniform((n_to_produce, 1), dtype=dtype)\n", " sample = mixture.sample((n_to_produce, 1))\n", @@ -1263,10 +1254,10 @@ "# minimizer._use_tfgrad = False\n", "result = minimizer.minimize(nll)\n", "\n", - "param_errors = result.error()\n", + "# param_errors = result.error()\n", "\n", - "for var, errors in param_errors.items():\n", - " print('{}: ^{{+{}}}_{{{}}}'.format(var.name, errors['upper'], errors['lower']))\n", + "# for var, errors in param_errors.items():\n", + "# print('{}: ^{{+{}}}_{{{}}}'.format(var.name, errors['upper'], errors['lower']))\n", "\n", "print(\"Function minimum:\", result.fmin)" ] diff --git a/test.png b/test.png index ab5aa70..b85c7d2 100644 --- a/test.png +++ b/test.png Binary files differ diff --git a/test2.png b/test2.png index ab10d3b..7418856 100644 --- a/test2.png +++ b/test2.png Binary files differ diff --git a/test3.png b/test3.png index 5e94ef0..dcb51be 100644 --- a/test3.png +++ b/test3.png Binary files differ