diff --git a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb index 1767cf4..994958a 100644 --- a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb +++ b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb @@ -21,35 +21,16 @@ ] }, { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\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---> 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"\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow_probability\\__init__.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 76\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 77\u001b[0m \u001b[1;31m# from tensorflow_probability.google import staging # DisableOnExport\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 78\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow_probability\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;31m# pylint: disable=wildcard-import\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 79\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow_probability\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mversion\u001b[0m \u001b[1;32mimport\u001b[0m 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namespace\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m \u001b[0mmodule\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimportlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimport_module\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 45\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parent_module_globals\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_local_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 46\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\__init__.py\u001b[0m in \u001b[0;36mimport_module\u001b[1;34m(name, package)\u001b[0m\n\u001b[0;32m 125\u001b[0m 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"\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load_unlocked\u001b[1;34m(name, import_)\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_load_unlocked\u001b[1;34m(spec)\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36mexec_module\u001b[1;34m(self, module)\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36mget_code\u001b[1;34m(self, fullname)\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36mpath_stats\u001b[1;34m(self, path)\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36m_path_stat\u001b[1;34m(path)\u001b[0m\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n" ] } ], @@ -75,7 +56,7 @@ "import tensorflow as tf\n", "import zfit\n", "from zfit import ztf\n", - "from IPython.display import clear_output\n", + "# from IPython.display import clear_output\n", "import os\n", "import tensorflow_probability as tfp\n", "tfd = tfp.distributions" @@ -83,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -105,15 +86,11 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# t = np.array([1,2,3,6,8,4,-2,4])\n", - "\n", - "# np.where((t >= 6) & (t <=10))" - ] + "source": [] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -301,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -356,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -462,9 +439,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "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" + ] + } + ], "source": [ "# formfactors\n", "\n", @@ -573,7 +560,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -603,7 +590,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -620,7 +607,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -660,7 +647,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -711,7 +698,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -757,9 +744,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:12: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", + " if sys.path[0] == '':\n", + "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\pylabtools.py:128: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", + " fig.canvas.print_figure(bytes_io, **kw)\n" + ] + }, + { + "data": { + "image/png": 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UlKj0DPi48OvP8I2ndqd6Kkqa4YTv4mffpXKvUqTzlHKz3MCQKOVmuVSU0gwVJSUqwRDHU++EH5+lTHac8F2M7DtXOqwpRQjfWU/pdJ8jSgXZHhWlNENFSVGUpPH6TdrvUxoshRTBU+q2nlJ+jlsTHdIMFSVFUZLGFwjEzL4b9JRSuE8p0nlKHpfgkqHwXUG2hwF/IKWVJ5ThqCgpipI0Pr+JGb4LHhGRytBYMHznDpmniJCb5R4UpbxsN8akdu1LGY6KkqIoSeONs3k2Jy1EyfnpCjsdN8fjoitkTQk0LTydUFFSFCVpvP7Y4bugKPWnQaJD+IntOZ4hTyk/21lj0mSH9EFFSVGUpPEFTMzNs9lBUfKmck3pzEQHcNLAg6I0JcfxlFSU0gcVJUVRksbrj71PKcfjeCD9Pn/UPmNN9PDd8DUlgH4VpbQhIVESkTUiUici9SJyV4TXc0TkMfv6VhGpCnntbtteJyLXxbMpItXWxn5rMzvWGCJSKiJbRKRLRL4fNq+LRWSnveZ7IuGOvKIoI8GXYPguHRIdwvMxQjfMFuTomlK6EVeURMQN3A9cD9QCN4tIbVi324A2Y8wC4D7gXnttLbAOWAysAX4gIu44Nu8F7jPG1ABt1nbUMYA+4CvA30SY/g+B24Ea+29NvPerKEp8fIHY+5SCNeZS6YEEs9HDv4sGvTgIWVNSUUobEvGUlgP1xpiDxpgBYD2wNqzPWuBh+/gJYLX1StYC640x/caYQ0C9tRfRpr1mlbWBtXlDrDGMMd3GmJdwxGkQEZkJFBpjXjVOcPnnIbYURTkLvP5AzPOUctypD4tF85SCggma6JCOJCJKs4FjIc8bbFvEPsYYH9ABlMa4Nlp7KdBubYSPFW2MWPNuiDNvAETkdhHZLiLbm5qaYphUFAXiF2Qd8pRSt6YUqfYdDPeUpuZmARq+SycSEaVIv3nhO82i9Rmt9kTnkciczmw05gFjzDJjzLLy8vIYJhVFAadSQ6zwXbY7jdaUwqYZTG4AKLSiNB4e3Yv7mvj0T7fRO5A6oZ4IJCJKDcCckOeVQHiFzsE+IuIBioDWGNdGa28Giq2N8LGijRFr3pVx5q0oygjwxjkO3eUSstySFuG78DWlqbmewceFeeOXEv7tTXVsqWti2+FYH1tKIqL0OlBjs+KycRIXNoT12QDcah/fCGy26zgbgHU2c64aJ9lgWzSb9pot1gbW5pNxxoiIMeYEcFpELrFrVX8aYktRlLMg3nHo4ITJUrlPKVpKeNA7gqHw3XiI0omOXgAONnWN+VgTGU+8DsYYn4jcAWwC3MBPjDG7ROTrwHZjzAbgIeAXIlKP472ss9fuEpHHgd2AD/icMcYPEMmmHfJOYL2IfBPYYW0TbQxr6zBQCGSLyA3AtcaY3cBfAj8D8oDf2X+Kopwl3kDsNSVw0sIH/KlcU4qc6BDqKeXZquFjXc3cGDN4XMbx9t4xHWuiE1eUAIwxG4GNYW1fDXncB9wU5dp7gHsSsWnbD+Jk54W3xxqjKkr7duD8SK8p8TExl+yUyYzPHyArRkUHcEQpLT2lvCFPKVh5YqzFs9frHwxlHm/vi9N7cqMVHZTo2D9qreqvhOLzBwiYoQ/0aGR7XGmypjS8vTDEUxqvckgdvd7Bxy3d/WM61kRHRUmJi9bBUEIJCk1OHFHK8bhTW2YowiF/MDx8l2/Dd73esZ1nZ69v8HFbtzdGT0VFSYmKOkhKJIJJAfE8pZyQcj6pIBi+c4ctKpUU5Aw+zs9xRKlnjNO0g55S5bQ8WnsGxnSsiY6KkhIVDdspkQiW5AndhBqJbHd6hO/CEx3mlxcAcE5pPtluFx6XDB6PPlYERam6rIC27gE96TYGCSU6KJMTTXRQIhFcf0nEU+pLaaJDtH1KWXxj7WKWzpmGiJCf7R43T6mqtIA/7G/mdL9vWGq6MoSKkqIoSRHMVEtkTSl0gX+8iVZmCOCTl1YNPs7P9tAzMLaeUmdQlMocL621a0BFKQoavlOiohEGJRL9ia4ppTwlPHL4Lpz8HDfd4+Yp5QPoulIMVJSUqAxpkqbfKUMkKkq5WW760vCQv3AKsj1jXo+uo9fL1BwPZVOcJIt2FaWoqCgpURlajFWXSRliIMGU8Lxsd0qLj0bbpxROfrZ7zBMdOvu8FOZlUVKQDUCrpoVHRUVJUZSkSHSfUn6Wm+7+dCgzFMdTyvGMeaJDZ6+XorwspllRautWTykaKkpKVIbWlDR8pwwx5CnFTgnPz/HQ6/UPbmIdbxIN3+Vlu+ke40SHDitKBdlustyia0oxUFFSFCUpEt08GzzVNVXrSokmOhSMQ5ixs9dHYZ4HEWFafrZ6SjFQUVIUJSmCpYOy4xxdERSlVIXwgp5S+D6lcPKzPXSNx5qSTQEvKcimVUUpKipKSlSGwnea6KAMMRi+y4onSs42yFQlO0Q7uiKcorwsuvp9+McwzHi6zzd4dtO0/GzaNHwXFRUlJSom5kn0ymQlWGYoUU+pxzu2Xkg0/FEKsoZTnJ+FMUMbXMdiHl39vsFTbksKsmnr0ey7aKgoKYqSFMENsTlZsRMd8lIcvguKUnhB1nCK8x0Ppn2MRKnLHu436CkVZOmaUgxUlJSoaEUHJRIJe0rBYyFSFL5LWJTybJr2GIXUOvscsQue4xQM36UqKzHdUVFSoqJ/Mkok+u3ZQ1lxjkMvyHE+hMe6rlw0/PZblSdBT6ljjEJqQVEKXVMKmKF2ZTgqSoqiJEW/P0COxxU3qy0YvhvrA/SikXj4zvGU2nvHyFOyB/yFrikBmoEXBRUlJSp65osSib4B/6DgxCLVKeE+f6LhO8eDGasTYYfCd8E1pbENF050EhIlEVkjInUiUi8id0V4PUdEHrOvbxWRqpDX7rbtdSJyXTybIlJtbey3NrPPYowvisguEXlXRB4Vkdzkbs/kZjD3TpPvlBB6BvyD60WxyM9KbfguuHk2nigV5mUhMnaJDqdtosPgPqX8YKkhDd9FIq4oiYgbuB+4HqgFbhaR2rButwFtxpgFwH3AvfbaWmAdsBhYA/xARNxxbN4L3GeMqQHarO2RjDEb+DywzBhzPuC2/ZQkUYdJCaXH6yc3AU9pMHyXokQHXyC4phT7Y87tEorzsmju6h+TeQRTzYPhu+AalpYaikwintJyoN4Yc9AYMwCsB9aG9VkLPGwfPwGsFifgvBZYb4zpN8YcAuqtvYg27TWrrA2szRtGOAY4hxjmiYgHyAeOJ/B+FYuKkRKJvgH/YGguFtkeF9ke15hXS4jG4D6lBD7lphfm0tjZNybzCHpKU3KGrylpWnhkEhGl2cCxkOcNti1iH2OMD+gASmNcG629FGi3NsLHSmoMY8x7wHeAo8AJoMMY80ykNygit4vIdhHZ3tTUFPVGTD4SK/2vTC56BvzkJRC+AydklaosM3+CnhLAjKJcTo6RKHX0einIduOxKfT52W6yPS71lKKQiChF+kgK/w4drc9otSc9hohMw/GiqoFZQIGI3BKhL8aYB4wxy4wxy8rLyyN1mdSox6SE0uv1k2dLCMWjMM8zmH023vgCiZUZAphRmMvJjrEJ37X1DAwmN4BTi69Ei7JGJRFRagDmhDyv5Mww2GAfGyorAlpjXButvRkotjbCx0p2jGuAQ8aYJmOMF/gNcFkC71exqBgpkegd8JMXp+5dkNR6SgHcLombug5O+K6lux+vf/SPb2/pHqA0RJTACeE1d6koRSKR36zXgRqbFZeNkyywIazPBuBW+/hGYLNx8ok3AOts5lw1UANsi2bTXrPF2sDafHKEYxwFLhGRfLv2tBrYk9htUUCz75TI9Hh9g8VW41GUlzVmNeXi4Q/Ez7wLMqMoF2Og8fToe0ut3f3DPCWAmUW5nOwYm3DhRCeuKNn1mzuATTgf6o8bY3aJyNdF5GO220NAqYjUA18C7rLX7gIeB3YDTwOfM8b4o9m0tu4EvmRtlVrbIxljK05CxJvATvteHxjBPVIUJYTegQC5ia4p5WXR2ZeqRIcA7gS/Uc0scnaLHG/vHfV5tHYNDCY3BBnLNayJTkJfd4wxG4GNYW1fDXncB9wU5dp7gHsSsWnbDzKUPRfaPpIxvgZ8LdI1Snw0fKdEonfAl1D2HTj13lLlKfkCJm6JoSDVZQUAHGru5v1VJaM2B2NMxPDdzKJcWrsH6PP6Exb4yYJWdFCiYrT6nRKGMcZJdEjKU/KmpDpIIGBwx6nPF2R2cR5ZbuFgU/eIx4pEz4Cffl+AkoKcYe0zivIAOKXe0hmoKClRUU9JCaffFyBgSKjMEDiJDl6/oc87+gkE8fAFTMLhO4/bxdySfA41dyU9zv5Tp1n69Wf4p41nLlm32GSG0ilnekoAJ3Rd6QxUlBRFSZg+W1w1cU/JWSFIRQaeP2ASTnQAmFc+hUPNyXtKv3jtCJ19Pn784kFawqpCHO9w1qhmWc8oyAwrSprscCYqSkpU1FNSwumxJYOS8ZRg7E51jYU/iTUlgAUVjij1+5Iri7TreCdT7VlJT+86Oey1YOLErOLhZTdnFNrEio7RT6yY6KgoKVEJrilpRrgSJFgyKFgyJx7BOm+pOP7bHzC4khClC2YX4fUb6k6eTvgaYwz1jV189MJZVJXm8/S70URpuKdUkOOhpCCbY609CY81WVBRUuKiDpMS5PTggXWJiVKpXeAPD2uNB8lk34EjSgBvN3QkfE1z1wAdvV4WlE/husUzePVACx0hXuF77b2UFGRHzLCrLisYUbgw01FRUqKi4TslnOCeo+ApqvEoswv8zSkoqeM3ya0pVU7Lo6Qgm50N7QlfU9/oJEYsqJjCtYtn4AsYnq9rHPb6/PKCiNdWlaooRUJFSYmLhu+UIF2DZwMl5ikFN402j0GlhHj4/cmJkoiwdE4x2w61JnxNfdOQKF00p5iKqTlssutKxjihwHOnT4147bzyAk519qfsvKl0RUVJiYs6TEqQwWMYEhQlj9vFtPwsWrpTE75zJ3JuRQiXLyjjcEtPwms9Bxq7KMh2M7MoF5dL+GDtdJ6va6LP66ehrZfOPh+LZkQWpapSx4M63KzrSqGoKClR0fCdEs7QmlJi4TuAsik5NJ9OQfguEEhqTQnginPLAPjD/uaE+tc3djG/Yspg0ddrF8+gZ8DPH/Y388oBx8aKeaURrw1WkTjQlPzeqExGRUmJimbfKeF09ftwCRQkmBIOzsbRsTrVNRZevyHbk9xH3PzyKcwqyuW5PacS6l/f2MWC8imDzy+dV8r0why+99x+Htl6lNnFedRUTIl47fyKAjwuYc+JzqTmmOmoKCmKkjCn+3xMyfEkdBxEkLIpObSkINFhwBcgK8EyQ0FEhA8vmckL+5rinnd0us/Lyc4+5oeITrbHxZc/XMvO9zp4p6GDv7hqftR7leNxUzN9KruOqyiFklhgWJmUaPhOCaezz5tU6A6C4bvx95QG/AEKs5ObK8ANF83mP/5wiN/uPMEtl5wTtV9o5l0oH7twFmVTsjnd5+Pa2ukxxzp/ViGb9zZijElK6DMZ9ZSUqKgmKeF09fkS3qMUZGZRLqf7fYPrUeOF1x8gO0lPCaB2ZiGLZkzlP187ErOQ7P5TjihFyq67bH4Z1y2eEVdoFs8qpKV7gFOdsUW7u9/Hd5+pmxRliVSUFEVJGMdTSk6UZk9zqhm8NwZnFcXCCd8l/xEnIvzZB+ax9+TpmAkP+06dJsfjFHIdKRdUFgPw1rG2mP1+/WYD39tczzd/u3vEY00UVJSUqKTiuAElvWnr9jItPzt+xxBm2xI7Da3jK0pe/8hECZwQ3PTCHP7tuf1R/w72NXYxv3xKUnuhwrlgdhF5WW5eOxh7b9SOo86G3nffS7zaxERFRUmJikqSEk5rz8AZxzDEI1We0kiy74Jke1x88ZpzeeNIG0+9c+KM1wMBw86GdhbPKjyrOWZ7XCyrmsZrB1ti9jtoKz8cbe0ZrNSeqagoKYqSEMYY2roHkvaUygpyyPa4xj98dxaeEsBNy+ZQO7OQbzy1m9awTLwDTV209Xh5f/XZn1J7ybxS9p48fcYYQYwxHGrqoigvi4CBwy2ZXZpIRUmJikbvlFA6+3z4AmawdFCiuFzC7OI83msb/zWlkcwu7xIAACAASURBVCQ6BHG7hG/ftIT2Hi9/+6u38YecLvt8XRMAl1RH3hibDJfNd2y8uK8p4uut3QN09vn4oM3kO9CooqRMWlSVlCGC+3aSFSWAOSX54/4N/2zWlIIsnlXElz98Hs/tbeTv//td/AGDP2D41RvHuGB2EXNLR57kEOTCyuE188IJFm29emEFoJ4SACKyRkTqRKReRO6K8HqOiDxmX98qIlUhr91t2+tE5Lp4NkWk2trYb21mn8UYxSLyhIjsFZE9InJpcrdHUZQgrT2OKE0bgSjVVEzhQFMXgcD4fdHx+gMjXlMK5dbLqvirq+bz6LajfPzHr/L59TvYd6qLP79i3ijMksGaeS/sa4q4XnS4xamNVzurkOmFORxsmuSiJCJu4H7geqAWuFlEasO63Qa0GWMWAPcB99pra4F1wGJgDfADEXHHsXkvcJ8xpgZos7aTHsNe82/A08aYRcCFwJ5Eb4yi4TtlOK1d1lNKck0JHFHq8wbGbV3JGIPXb87aUwryt9ct5Ns3LqGhrZdN757ks1fM46NLZo6KbYDrbM28YFgwlCMt3bhtCLSqtEA9JWA5UG+MOWiMGQDWA2vD+qwFHraPnwBWi7NrbC2w3hjTb4w5BNRbexFt2mtWWRtYmzeMZAwRKQSuAB4CMMYMGGMSPyhF0eCdMozWswjf1Ux3qh7sb0z8VNezYcAfABgVTwmcvUs3LZvDq3evYu831nD3h84b1QoMl80vpWJqDr/afuyM14609DCrOJdsj4vqsgIOZ/gZTIn8j80GQu9Ug22L2McY4wM6gNIY10ZrLwXarY3wsZIdYx7QBPxURHaIyIMiEvG0LRG5XUS2i8j2pqbIi42KMtlpPO1UEyifmpP0tQvKnaoHwSoIY82AzxGlZGvfxUNE8IyS9xWKx+3ixosr2VLXeEbVhiMt3ZxT4nx0VZcV0NI9MOx020wjkbsb6X81/Et0tD6j1T6SMTzA+4AfGmMuArqBM9bDAIwxDxhjlhljlpWXl0fqMinR8J0SyomOPqblZ0U82jseRflZzCzKHbfio31eR5TyRjDXVPHx988hYODRbUcH24wxHGzu5hybUFFVFjyDKXO9pUREqQGYE/K8EjgerY+IeIAioDXGtdHam4FiayN8rJGM0WCM2Wrbn8ARKSVBgjvZtU6kAnCqs48ZRXkjvn7pnGJ2xCmnM1r0DjgJA3nZE6fm9DmlBVxz3nQefvUwXf1OsKihrZfTfT4WzyoChs5gyuR1pURE6XWgxmbFZeMkFWwI67MBuNU+vhHYbJxPtA3AOps5Vw3UANui2bTXbLE2sDafHMkYxpiTwDERWWivWQ1kfuGoMUA9JgUcT2lGYfKhuyAXzS3mWGsvTeNQMbzXZrFNJE8J4I5VC2jv8fLzVw8D8E6DU1YoWDlibkk+IkNp4plIXFGy6zd3AJtwstceN8bsEpGvi8jHbLeHgFIRqQe+hA2TGWN2AY/jiMHTwOeMMf5oNq2tO4EvWVul1nbSY9hr/j/gERF5B1gK/GOyN2gyo1qkhHK2ntL75k4DYMfRsfeWegYcTyM/icMI04Glc4pZvaiC72+u51hrDy/sa2RqrodaK0q5WW5mFeVltCgl5NsaYzYCG8PavhryuA+4Kcq19wD3JGLTth/Eyc4Lbx/JGG8ByyJdo8Qn6CFp+E7p9/lp7hpgRmHuiG2cP7uIHI+LVw+2cO3iGaM4uzMZ9JQmmCgB/MPaxVx334t88qGtnOjo46MXzhqW2p7pGXha0UGJi4bvlBPtTkbYzOKRi1JulptL55eyZW/jaE0rKoNrShMsfAdQOS2fH39yGZ19PqYX5vKFa2qGvV5dVsCh5u6MreI/cVYBlXHHaABPsRyyC+vBhfaRcvXCCr5Wt4tDzd1nbSsWQU9pooXvglxeU8Ybf38NwBn7oarKCujs89HaPUDplJGv8aUr6ikp0dHwnWI5YsNFVaVnJySrFjn12zbuPPM4iNGkx3pKI0lfTxdEJOIG3eoyJz08UzPwVJQURYnL4ZYeCrLdlCV5llI4c0ryWVFdwq+2HxvT8FMwfDdRPaVYVJc51TEONfekeCZjg4qSEhUN3ilBDjV3U1VWMCqldW5aNofDLT28Gudgu7Oh01Y8mJqbNWZjpIrKaXlkuWXcSjaNNypKSlQydB1VGQEHmrpGbQ3oI0tmUjYlh/u31I+KvUh09HrJy3KPWu27dCLL7eK8mYW8cywzj0bPvP8xRVFGlY5eLw1tvZw38+yO/g6Sm+Xms1fM4+X6Fl49MDbeUkevl+L8zPOSgiypLOLd9zrG9SiQ8UJFSYmKZt8pAHtOOPXqglUFRoNbLjmHyml5fPm/d0Y8Q+hsae/1UpSXyaJUzOl+HwczcL+SipISFQ3fKQC7bRHV2lEUpbxsN//4vy7gYFM333hq9Kt/dfR6KcxgUbqwshiAt49l3mk8KkqKosTk3fc6KJ+aQ8XUkW+cjcQV55bz2Svn8cjWozzw4oFRtd3eM0BxBovSgoopFGS7x63A7Xiim2eVqKijpABsPdTKsnOmjYntv7tuEcdae/jHjXs53efjC9eci9t19hl+pzr7uWRe6SjMMD1xu4RlVSW8drA11VMZddRTUqKSqWVMlMQ51trDe+29rKguGRP7bpfwvXUXcdPFlfz75nr+9CdbOdZ6dvtvegZ8dPR6mVE0up5dunHJvFLqG7sGD1/MFFSUFEWJytZDzjfxFWPodXjcLv75xiV8648u4M0j7az+7gt8e9PewePXkyV4cuvMDBelS+c7/ydbM8xbUlFSoqJ+kvLcnlOUT81h4fSpYzqOiLBu+Vye++srWbN4BvdvOcBl33qOrz75LgeakjtC/UiL42nNLs4fi6mmDefPKmRKjmdMNyGnAhUlJTqqSpOaPq+fF/Y1cW3tdFyjsM6TCLOK8/jezRfx7Bev4KNLZvHotqOs/pcX+MR/vMbGnSfw+gNxbdSdciodjLWQphqP28Xy6hJeqW9O9VRGFRUlJSrBfUqCVmSdjLywr4meAT/XjfHZR5GomT6Vb990IS/ftYq/ufZcjrT08FePvMnKb23mu8/Ucby9N+q1777XwcyiXIoyePNskCtqyjjc0pNR5yupKClRCdgvpeP0JVlJMx57/RgVU3MG1y5SQcXUXO5YVcOLf3c1D926jMWzCvn3LfVcfu9m/vzn23lhX9OwqgY+f4CX65u5bH5ZyuY8nly10Km6/nzd2J9RNV5oSrgSlYDNvhuNIpzKxOK99l6er2vkc1cvGHbqaapwu4TV501n9XnTOdbawy+3HeXx14/x7O5TzC3J5xMr5vIny+awaddJ2nq8XH/++Ht3qaCqrIB5ZQVsqWviUyurUz2dUUFFSYlKBpbVUhLkxy8cwGWTD9KNOSX53LlmEV+4poZNu07xn68d4Vu/28s/P72XgIHlVSWD5zZNBq5eVMEvXjtC74B/Qh7/Ho6KkhKV4D4lV+q/KCvjyHvtvazfdoybls1hdnFeqqcTlRyPm49dOIuPXTiL/adO85sd7zElx8Otl1WNW2JGOnD1wgoeeukQrx5sZtWi6amezlmT0MeNiKwRkToRqReRuyK8niMij9nXt4pIVchrd9v2OhG5Lp5NEam2NvZbm9kjHcO+5haRHSLyVOK3RYEhT0kTHSYX3/if3YjAHasWpHoqCVMzfSp3rlnE565ewJScyfVd+/3V08jPdrN5b2asK8UVJRFxA/cD1wO1wM0iUhvW7TagzRizALgPuNdeWwusAxYDa4AfWJGIZfNe4D5jTA3QZm0nPUbI3P4PsCex26GEMrSmlOKJKOPGxp0neHrXSb5wzblp7SUpQ+R43KxcUMaWvU0ZUYUlEU9pOVBvjDlojBkA1gNrw/qsBR62j58AVouzOr4WWG+M6TfGHALqrb2INu01q6wNrM0bRjgGIlIJfBh4MLHboYQSFCWXqtKk4EBTF3c+8Q5LKov4sw9kxqL5ZGHVogrea+9l36nkNhqnI4mI0mzgWMjzBtsWsY8xxgd0AKUxro3WXgq0WxvhYyU7BsC/An8HxNxxJyK3i8h2Edne1NQUq+ukwgyG75RMp+l0P3/+8HayPC5+eMvFaZFxpyTO1TY1PBNCeIn85kX6TAr3EaP1Ga32pMcQkY8AjcaYNyK8PryzMQ8YY5YZY5aVl5fH6z5pGDzkT1Upo2nu6ud/P/gaJzr6+PEnL9aw3QRkRlEui2cVsnnvqVRP5axJRJQagDkhzyuB49H6iIgHKAJaY1wbrb0ZKLY2wsdKdoyVwMdE5DBOeHCViPxnAu9XsQQ3z6omZS57T3ay9vsvc7S1h4c+tYz3V41NNXBl7Fm9qII3jrTR3jOyQrbpQiKi9DpQY7PisnGSCjaE9dkA3Gof3whsNs6K2wZgnc2cqwZqgG3RbNprtlgbWJtPjmQMY8zdxphKY0yVtb/ZGHNLgvdFQTfPZjLGGJ54o4E//sEr+AIBHv/spZOmCkKmcvWiCgLGKQ81kYmbO2mM8YnIHcAmwA38xBizS0S+Dmw3xmwAHgJ+ISL1ON7LOnvtLhF5HNgN+IDPGWP8AJFs2iHvBNaLyDeBHdY2IxlDOTuCa0qTaMvHpKCxs4+//+93eWb3KZZXl/C9dRdl/NlDk4ELK4spLchm895G1i4NX/afOCSU0G+M2QhsDGv7asjjPuCmKNfeA9yTiE3bfhCbPRfWnvQYIa8/Dzwf7XUlMoOekgbwMoI+r5+HXjrED7bU4/Ubvvyh87jt8upJtdE0k3G5hKsWVvD7Pafw+QN4JmiyyuTaZaYkxeDmWf3MmtB4/QH+a8d7fO+5/TS09XJt7XT+74fOo6qsINVTU0aZ1edV8Os3G9hxrH3Crg+qKClR0TWliU2/z88TbzTww+cP0NDWy/mzC/nnP17CZQt07ShTubymDI9LeG5Po4qSknmYwfCdMpFoOt3Po9uO8sjWI5zq7GfpnGK+sfZ8rlpYrl8wMpzC3CyWV5ewZW8jd12/KNXTGREqSkpUNHw3sXj7WDsPv3KYp945wYA/wAdqyvj2jRfygZoyFaNJxKpFFXzzt3toaOuhctrEOxJeRUmJitHad2nP6T4vv33nBOtfP8Zbx9opyHZz8/I5/OllVcwvn5Lq6Skp4GorSlv2NvLJS6tSPZ2kUVFSoqJVwtMTYwzbDrXy+PYGNu48Qa/Xz4KKKfy/j9byxxdXMjU3848BV6Izr6yAqtJ8nlNRUjKNoYKsKZ6IAsDJjj5+/WYDv9p+jMMtPUzJ8XDDRbP5k2WVLJ1TrCE6BXASk65eVMEjW4/SM+AjP3tifcxPrNkq48pgFXz9sEsZp/u8PLPrFE++fZyX9jcRMLCiuoTPr65hzfkzJtwHjjI+rF40nZ++fJhX6lu4pnZiHfynv9FKVAKafZcS+n1+Xqhr4sm3j/P73afo9wWonJbHX121gBsvrtT9RUpclleXUJDtZnNdo4qSkjn47KKSR+N3Y04gYNh6qJUNb7/Hxp0n6ej1UlKQzcffP4e1S2fzvrkanlMSJ9vj4gM15WzZ24gxZkL97qgoKVHx+R1RcqsojQnGGHYd72TD28fZ8NZxTnb2kZ/t5rrFM/jY0llcvqBMzzVSRsyqRRU8veske06cpnZWYaqnkzAqSkpUfPbsCj15dvQICtHGnSfYuPMEh1t68LiEK88t5+4PLeKDtdN1nUgZFa5c6JwN91J9k4qSkhkEw3fmjDMdlWQICtFvd57gd1aI3C7h0nml/PkV87j+/JmUFGSneppKhjG9MJcFFVN4qb6F26+Yn+rpJIyKkhIVn9/xlIxqUtKECtHGnSc4YoXosvmlfPbK+VxbO53SKTmpnqaS4Vy+oIz1rx+l3+cnx+NO9XQSQkVJiYrXr2qUDMYY3n1vSIiOtg4J0V9eOZ9rF89Qj0gZV1YuKONnrxxmx9F2LplXmurpJISKkhIV/2D4TomGP2DYcbSNTbtOsmnXKY62OmtEly0o43NXz+fa2hlMUyFSUsSKeSW4XcLL9c0qSsrEJ5jooAyn3+fnlfoWntl9kmd3n6K5a4Bst4tL55dyx9UL+GDtdBUiJS0ozM3iwsoiXqpv5q+vXZjq6SSEipISlcHwnbpKnO7zsqWuiWd2neT5uia6+n1MyfFw1cJyrls8g6sWlmvNOSUtWbmgjPu31NPZ56VwAvyOqihlODuOtlE5LZ/yqckvqvsnefZd4+k+fr+7kU27TvLKgWa8fkPZlGw+euFMrl08g8vml06YxWNl8rJyQRn/vrme1w60cO3iGameTlxUlDKc//WDV6iYmsO2L1+T9LVe/+QL3x1q7ubZ3c760JtH2zAG5pbk86nLqrhu8QwumjtNNxMrE4qL5haTl+Xm5frmzBElEVkD/BvgBh40xnwr7PUc4OfAxUAL8HFjzGH72t3AbYAf+LwxZlMsmyJSDawHSoA3gU8aYwaSHUNE5tj+M4AA8IAx5t+SvUETmYD1dBpP94/o+n5f5qeE9/v8bDvUypa9TWypa+RQczcAi2cV8sVrzuXaxdNZOH3qhCrToiih5HjcLK8u4eUDLameSkLEFSURcQP3Ax8EGoDXRWSDMWZ3SLfbgDZjzAIRWQfcC3xcRGqBdcBiYBbwexE5114Tzea9wH3GmPUi8iNr+4cjGMMH/LUx5k0RmQq8ISLPhs07o+nx+s/q+t6Bs7s+XTnV2ceWvY1s3tvIy/XNdA/4yfa4uHReKZ+6rIpViyqYUzLxTuxUlGhcOr+Ub/1uL81d/ZSl+f64RDyl5UC9MeYggIisB9YCoR/ua4H/Zx8/AXxfnK+Wa4H1xph+4JCI1Ft7RLIpInuAVcAnbJ+Hrd0fJjuGMeZV4ASAMea0tT07bN4ZTXe/76yu77WiNtEdJX/A8HZD+6AQ7TreCcCsolxuuGg2qxZVcNn8MvKydX1IyUyWV5cAsO1QKx+6YGaKZxObRERpNnAs5HkDsCJaH2OMT0Q6gFLb/lrYtbPt40g2S4F2Y4wvQv+RjAGAiFQBFwFbI71BEbkduB1g7ty5kbpMSLrOUpR6rKdkJmD8rqPXy4v7mtiyt5Hn9zXR2j2AS+Dic6bxd2sWsmpRhYbllEnDBbOLyMtys/VgS0aIUqS/2vBPqWh9orVHKn0cq/9IxnAuEpkC/Br4gjGmM0JfjDEPAA8ALFu2bOJ9AkfhbD2lvrMM/40nxhj2nepi895GttQ18saRNvwBw7T8LK5aWMFVC8u58txyivN1/5Ay+chyu7j4nGlsPdSa6qnEJRFRagDmhDyvBI5H6dMgIh6gCGiNc22k9magWEQ81lsK7Z/0GCKShSNIjxhjfpPAe80oTvc5opTtGdnxBz0DzvXpqtK9A35ePdjsCNHeJt5r7wWgdmYhf3nlfK5eVMHSOcWaLacoOCcWf/f3+2jvGUjrL2eJiNLrQI3NinsPJ6ngE2F9NgC3Aq8CNwKbjTFGRDYAvxSR7+IkIdQA23C8mzNs2mu2WBvrrc0nRzKGXW96CNhjjPlusjcmE+js9QKQP8K1kg57fTpF7xraegbXhl450EK/L0B+tpuVC8q4Y9UCrl5YwYyi3FRPU1HSjuXVJRgDrx9u44NpfBptXFGy6zd3AJtw0rd/YozZJSJfB7YbYzbgfPj/wiYZtOKIDLbf4zjJBT7gc8YYP0Akm3bIO4H1IvJNYIe1TbJjiMjlwCeBnSLylrXxf40xG0d2qyYeQU8pPyt5UeoZ8NHnTf0+JZ8/wBtH2thc18iWvY3sO9UFwDml+dy8fC6rFlWwYl6JbmJVlDhcOKeYbI+LrQdbJrYoAdgP8o1hbV8NedwH3BTl2nuAexKxadsPMpShF9qe1BjGmJeIvN40aejsczyd3BF4Si1dA4OPx9tRaunq5/m6JjbXNfLiviZO9/nIcgvLq0v4k2VzuHpRBfPKCjRJQVGSIDfLzdI5xWw7nN7rSlrRIYNp7XaEZSRexEg33I6E4NlDm21Y7u2GdoyB8qk5XH/+DFYtqmDlgjKtLacoZ8kl1SV8f0s9p/u8afv3pKKUwRy3C//Byg7JcNhWNphbkj8mi0pd/T5e2t/MFpst13i6HxFYUlnMF1afy6pFFSyeVYhLkxQUZdRYXl1KYHM9bxxp46qFFameTkRUlDKY4+19AHhHcARFfVMXbpcwtyR/MAx4thxr7eH3e06xeW8jrx1swes3TM3xcMW55Vy9yEnbTvfd5ooykXnfOcV4XMLWQ60qSsr4EggY9pxwtmX5R+ApbT/cyvmzi8hyj9xT8fkD7DjWznN7Gnluzyn2NzpJCvPLC/j0ymquXljBsqppZLlHlrKuKEpy5Gd7WFJZxNaD6VsHT0UpQ9l1vJPT/T5cAr4kjzVv7OzjzaPt/MWV89h9vDOp6F2f18/zdY1s2nWKLXWNtPd48biEFfNKWLd8LqsXVVBVVpDku1EUZbRYXl3Kg384SO+APy1La6koZSg/feUQ2W4X19RWsP1wW1LXPvDiQfwBw40Xz+EbJ3bHPU+pd8ARot/uPMHmvY30DPiZlp/FqkUVrF40nQ+cWzYhDhdTlMnAinkl/OiFA7x5tI2VC8pSPZ0zUFHKQB7ddpTfvPkef3HlfLr7ffiSCN89u/sUP3n5EDcvn0N1WQFul0T1tHY2dLD+9aNseOs4p/t9lBZkc8NFs/nwBTNZUV2CR8NyipJ2LDtnGi6BrQdbVJSUsaXf5+efNu7lZ68c5spzy/niB2v41u/2JnRYnz9gePAPB/n2pjouqCzmyx+uBSDH42LAN/z6l+ub+bfn9rPtUCs5HhcfXjKTG99XyYp5pVrSR1HSnKm5WSyeVcRraVoHT0UpQ3i+rpF/+J/dHGru5jMrq7nr+kVke1x4Yng64OwRemFfE/+0cS91p05z/fkzuPfGJUzJcX41cjzuwcP+WrsH+Mp/v8tvd55gemEOX/lILTdeXElRnobmFGUisaK6hJ+/doQ+r5/cEVR8GUtUlCY42w+38u+b63lhXxPzygr4+WeWc8W55YOve9wufBFSwvu8fv7n7eP89OXD7D7RydySfO7/xPv40AUzhlVKyPa46PcFONnRxyf+4zUa2nr5m2vP5c8+MC/tfpkVRUmMFfNKefClQ7x9rJ0V80pTPZ1hqChNQLz+AM/taeSnLx9i66FWSgqyufv6RXx6ZfUZFcGzXILXbzDGICI0nu7jkdeO8sjWIzR3DXDu9Cl8648u4I/eVxmxmniOx0XvgI+/fOQNTnX28cs/X8GyqpLxequKoowBy6tKEIGth1pVlJSRU9/Yxa/eOMav32iguWuAmUW5fOUjtdy8fA752ZH/K4PJBq3dA/zLs/v41fZjeP2GVYsq+MzKalYuKI1ZQy4ny0X3gJ8dR9v5148vVUFSlAygKD+LhdOnsvVQC87BCumDilIaY4zhnYYONu06yTO7T1Hf6FRZWLWogpuXz+GKmvK4GW4eu/n1j3/4Csfaerl5+Rw+s7KaeeVTEppDsG5eTcUU1i6ddXZvSFGUtOGSeaWsf/0oA77AiM9cGwtUlNIMrz/A1oOtPLP7JM/sOsXJzj7cLmFFdQmfvOQcrj9/BhWFiZ8XlOVyftkOt/Scsd6UCO+bWwzA51fXaFVuRckgVlSX8LNXDrPzvQ4uPmdaqqcziIpSGtAz4OPFfU1s2nWK5/acorPPR26WiyvPLedvaxey+ryKEZ8UGdyxfWFlUdKCBHDVwgq2fXk1FVP14DxFySSWVzuh+K2HWlSUFGjrHuD3e06xadcp/rC/iX5fgOL8LD5YO4PrFk/nAzXlo1ICZOWCMiqn5fG1jy0esQ0VJEXJPEqn5LCgYgpbD7byV1elejZDqCiNI42dfWzadZKnd53ktYOt+AOGWUW53Lx8Ltcuns7yqtGvglBdVsBLd64aVZuKomQGK6pLePKt4/j8gbSpwKKiNMb0ef1s2nWSX21v4OUDzRgD88oL+Isr53Hd4hlcMLtI12oURUkJK+aV8sjWo+w+0cmSyuJUTwdQURoz2nsG+Nkrh/nZK4dp7/FSOS2Pz6+q4SNLZlIzfWqqp6coisIldl3p1QMtKkqZij9g+OXWI/zzpjpO9/m45rzpfHplFZfOK9VTVBVFSSsqCnOpnVnIs7tP8dkr56d6OgAkFEQUkTUiUici9SJyV4TXc0TkMfv6VhGpCnntbtteJyLXxbMpItXWxn5rM3u0xxgr2nsG+NRPt/GVJ3dxYWUxT3/hAzx46zJWLihTQVIUJS1Zc/4M3jjaRuPpvlRPBUhAlETEDdwPXA/UAjeLSG1Yt9uANmPMAuA+4F57bS2wDlgMrAF+ICLuODbvBe4zxtQAbdb2aI8x6rR1D3DTj15l68FW/umPLuAXty1n0YzCsRpOURRlVLhu8QyMgafePpHqqQCJeUrLgXpjzEFjzACwHlgb1mct8LB9/ASwWpzV+7XAemNMvzHmEFBv7UW0aa9ZZW1gbd4wmmMkdluSY8AX4FM/e50jrT387NPv5+blczV5QVGUCcG506ewvKqE723ez/H23lRPJ6E1pdnAsZDnDcCKaH2MMT4R6QBKbftrYdfOto8j2SwF2o0xvgj9R2uMMxCR24Hb7dMuEWkBmiP1jcfKe0ZyVdpSxgjvQwai92IIvRcOGXcfZn9txJeWAeeMxhwSEaVIX/nDD+iJ1idaeyQPLVb/0RzjzEZjHgAeCD4Xke3GmGWR+k4m9D4MofdiCL0XDnofhrD3omo0bCUSvmsA5oQ8rwSOR+sjIh6gCGiNcW209mag2NoIH2u0xlAURVHSlERE6XWgxmbFZeMkFWwI67MBuNU+vhHYbIwxtn2dzZyrxqmRvi2aTXvNFmsDa/PJ0RwjsduiKIqipIK44Tu7fnMHsAlwAz8xxuwSka8D240xG4CHgF+ISD2O97LOXrtLRB4HdgM+4HPGGD9AJJt2yDuB9SLyTWCHtc0ojxGPTCcqJAAAA8RJREFUB+J3mRTofRhC78UQei8c9D4MMWr3QhxnQ1EURVFST3pU4FMURVEUVJQURVGUNEJFKYTxLkuUCkTkJyLSKCLvhrSViMiztrTTsyIyzbaLiHzP3o93ROR9IdfcavvvF5FbI42VzojIHBHZIiJ7RGSXiPwf2z4Z70WuiGwTkbftvfgH2z5qJb8mErYizA4Reco+n6z34bCI7BSRt0Rku20b+78PY4z+c9bV3MABYB6QDbwN1KZ6XmPwPq8A3ge8G9L2z8Bd9vFdwL328YeA3+HsBbsE2GrbS4CD9uc0+3haqt9bkvdhJvA++3gqsA+nHNVkvBcCTLGPs4Ct9j0+Dqyz7T8C/tI+/ivgR/bxOuAx+7jW/t3kANX278md6vc3gvvxJeCXwFP2+WS9D4eBsrC2Mf/7UE9piHErS5RKjDEv4mQvhhJawim8tNPPjcNrOHvIZgLXAc8aY1qNMW3Aszh1BycMxpgTxpg37ePTwB6cSiCT8V4YY0yXfZpl/xlGr+TXhEFEKoEPAw/a56NZ+iwTGPO/DxWlISKVU5odpW+mMd0YcwKcD2ugwrZHuycZda9s2OUiHA9hUt4LG7J6C2jE+eA4QIIlv4DQkl8T/V78K/B3QMA+T7j0GZl1H8D5YvKMiLwhTik2GIe/Dz1PaYhEyilNNpIt7TThEJEpwK+BLxhjOiV6Id2MvhfG2du3VESKgf8CzovUzf7MyHshIh8BGo0xb4jIVcHmCF0z+j6EsNIYc1xEKoBnRWRvjL6jdi/UUxpiMpclOmVdbezPRtue0SWcRCQLR5AeMcb8xjZPynsRxBjTDjyPsy4wWiW/JgorgY+JyGGc8P0qHM9pst0HAIwxx+3PRpwvKssZh78PFaUhJnNZotASTuGlnf7UZtZcAnRYl30TcK2ITLPZN9fatgmDjf0/BOwxxnw35KXJeC/KrYeEiOQB1+CssY1Wya8JgTHmbmNMpXEKi67DeV//m0l2HwBEpEBEpgYf4/xev8t4/H2kOsMjnf7hZJDsw4mnfznV8xmj9/gocALw4nyLuQ0nDv4csN/+LLF9BeegxAPATmBZiJ3P4Czg1gOfTvX7GsF9uBwnjPAO8Jb996FJei+W4JT0esd+8HzVts/D+TCtB34F5Nj2XPu83r4+L8TWl+09qgOuT/V7O4t7chVD2XeT7j7Y9/y2/bcr+Hk4Hn8fWmZIURRFSRs0fKcoiqKkDSpKiqIoStqgoqQoiqKkDSpKiqIoStqgoqQoiqKkDSpKiqIoStqgoqQoiqKkDf8/Z+T2JUHicG0AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.clf()\n", "# plt.plot(x_part, calcs, '.')\n", @@ -778,7 +788,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -794,7 +804,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -810,7 +820,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -823,7 +833,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -880,7 +890,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -964,7 +974,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -973,7 +983,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -982,7 +992,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -991,7 +1001,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "scrolled": false }, @@ -1039,7 +1049,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -1056,7 +1066,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1080,7 +1090,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -1107,7 +1117,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -1130,7 +1140,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -1139,7 +1149,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -1155,7 +1165,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1185,7 +1195,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -1199,7 +1209,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -1217,7 +1227,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -1231,7 +1241,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -1252,7 +1262,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -1262,7 +1272,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -1293,7 +1303,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1310,7 +1320,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1419,16 +1429,242 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "## Reset params" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "def reset_param_values(): \n", + " jpsi_m.set_value(jpsi_mass)\n", + " jpsi_s.set_value(jpsi_scale)\n", + " jpsi_p.set_value(jpsi_phase)\n", + " jpsi_w.set_value(jpsi_width)\n", + " psi2s_m.set_value(psi2s_mass)\n", + " psi2s_s.set_value(psi2s_scale)\n", + " psi2s_p.set_value(psi2s_phase)\n", + " psi2s_w.set_value(psi2s_width)\n", + " p3770_m.set_value(p3770_mass)\n", + " p3770_s.set_value(p3770_scale)\n", + " p3770_p.set_value(p3770_phase)\n", + " p3770_w.set_value(p3770_width)\n", + " p4040_m.set_value(p4040_mass)\n", + " p4040_s.set_value(p4040_scale)\n", + " p4040_p.set_value(p4040_phase)\n", + " p4040_w.set_value(p4040_width)\n", + " p4160_m.set_value(p4160_mass)\n", + " p4160_s.set_value(p4160_scale)\n", + " p4160_p.set_value(p4160_phase)\n", + " p4160_w.set_value(p4160_width)\n", + " p4415_m.set_value(p4415_mass)\n", + " p4415_s.set_value(p4415_scale)\n", + " p4415_p.set_value(p4415_phase)\n", + " p4415_w.set_value(p4415_width)\n", + " rho_m.set_value(rho_mass)\n", + " rho_s.set_value(rho_scale)\n", + " rho_p.set_value(rho_phase)\n", + " rho_w.set_value(rho_width)\n", + " omega_m.set_value(omega_mass)\n", + " omega_s.set_value(omega_scale)\n", + " omega_p.set_value(omega_phase)\n", + " omega_w.set_value(omega_width)\n", + " phi_m.set_value(phi_mass)\n", + " phi_s.set_value(phi_scale)\n", + " phi_p.set_value(phi_phase)\n", + " phi_w.set_value(phi_width)\n", + " Dstar_m.set_value(Dstar_mass)\n", + " DDstar_s.set_value(0.0)\n", + " DDstar_p.set_value(0.0)\n", + " D_m.set_value(D_mass)\n", + " Dbar_m.set_value(Dbar_mass)\n", + " Dbar_s.set_value(0.0)\n", + " Dbar_p.set_value(0.0)\n", + " tau_m.set_value(pdg['tau_M'])\n", + " Ctt.set_value(0.0)\n", + " b0_0.set_value(0.292)\n", + " b0_1.set_value(0.281)\n", + " b0_2.set_value(0.150)\n", + " bplus_0.set_value(0.466)\n", + " bplus_1.set_value(-0.885)\n", + " bplus_2.set_value(-0.213)\n", + " bT_0.set_value(0.460)\n", + " bT_1.set_value(-1.089)\n", + " bT_2.set_value(-1.114)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "# Analysis" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Toy 0: Generating data...\n", + "Toy 0: Data generation finished\n", + "Toy 0: Loading data...\n", + "Toy 0: Loading data finished\n", + "Toy 0: Fitting pdf...\n", + "------------------------------------------------------------------\n", + "| FCN = 241.5 | Ncalls=33 (33 total) |\n", + "| EDM = 2.47E-06 (Goal: 5E-06) | up = 0.5 |\n", + "------------------------------------------------------------------\n", + "| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n", + "------------------------------------------------------------------\n", + "| True | True | False | False |\n", + "------------------------------------------------------------------\n", + "| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n", + "------------------------------------------------------------------\n", + "| False | True | True | True | False |\n", + "------------------------------------------------------------------\n", + "Function minimum: 241.45590864280427\n", + "----------------------------------------------------------------------------------------------\n", + "| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n", + "----------------------------------------------------------------------------------------------\n", + "| 0 | p4040_s | 1.1 | 0.5 | | |0.00501244| 2.01499 | |\n", + "| 1 | p4160_s | 2.7 | 0.6 | | | 0.71676 | 3.68324 | |\n", + "| 2 | p4415_p | 4.2 | 2.2 | | |-6.28319 | 6.28319 | |\n", + "| 3 | bplus_1 | -0.885 | 0.000 | | | -2 | 2 | |\n", + "| 4 | Ctt | 0.16 | 0.21 | | | -0.5 | 0.5 | |\n", + "| 5 | DDstar_p | -4.8 | 1.9 | | |-6.28319 | 6.28319 | |\n", + "| 6 | Dbar_p | 0.11 | 9.47 | | |-6.28319 | 6.28319 | |\n", + "| 7 | p4415_s | 0.18 | 0.16 | | |0.126447 | 2.35355 | |\n", + "| 8 | p3770_p | 2.7 | 2.7 | | |-6.28319 | 6.28319 | |\n", + "| 9 | DDstar_s | 0.020 | 0.138 | | | -0.3 | 0.3 | |\n", + "| 10| bplus_2 | -2.130E-1 | 0.019E-1 | | | -2 | 2 | |\n", + "| 11| p3770_s | 1.6 | 0.6 | | |0.918861 | 4.08114 | |\n", + "| 12| Dbar_s | 0.07 | 0.13 | | | -0.3 | 0.3 | |\n", + "| 13| bplus_0 | 0.466 | 0.000 | | | -2 | 2 | |\n", + "| 14| p4160_p | 6.0 | 0.6 | | |-6.28319 | 6.28319 | |\n", + "| 15| p4040_p | -0.11 | 3.88 | | |-6.28319 | 6.28319 | |\n", + "----------------------------------------------------------------------------------------------\n", + "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "| | p4040_s p4160_s p4415_p bplus_1 Ctt DDstar_p Dbar_p p4415_s p3770_p DDstar_s bplus_2 p3770_s Dbar_s bplus_0 p4160_p p4040_p |\n", + "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "| p4040_s | 1.000 0.045 0.009 -0.001 -0.002 -0.003 -0.082 -0.005 -0.013 -0.009 -0.001 0.011 -0.032 -0.000 -0.065 0.621 |\n", + "| p4160_s | 0.045 1.000 0.003 -0.000 0.000 -0.001 -0.040 0.000 -0.009 -0.008 -0.000 0.002 -0.020 -0.000 0.093 0.075 |\n", + "| p4415_p | 0.009 0.003 1.000 0.000 -0.000 0.001 -0.008 -0.093 -0.002 -0.002 0.000 0.000 -0.008 0.000 0.008 0.013 |\n", + "| bplus_1 | -0.001 -0.000 0.000 1.000 0.000 -0.000 0.000 0.000 0.000 -0.000 0.677 -0.000 0.000 0.450 -0.000 -0.001 |\n", + "| Ctt | -0.002 0.000 -0.000 0.000 1.000 0.001 0.019 0.000 -0.001 0.002 0.000 -0.000 0.004 0.000 -0.002 -0.002 |\n", + "| DDstar_p | -0.003 -0.001 0.001 -0.000 0.001 1.000 -0.030 0.001 -0.003 0.298 -0.000 0.000 0.012 -0.000 -0.002 -0.004 |\n", + "| Dbar_p | -0.082 -0.040 -0.008 0.000 0.019 -0.030 1.000 0.011 0.059 -0.091 0.000 0.004 0.364 0.000 -0.008 -0.098 |\n", + "| p4415_s | -0.005 0.000 -0.093 0.000 0.000 0.001 0.011 1.000 0.002 0.002 0.000 -0.000 0.005 0.000 -0.003 -0.009 |\n", + "| p3770_p | -0.013 -0.009 -0.002 0.000 -0.001 -0.003 0.059 0.002 1.000 -0.000 0.000 -0.128 0.029 0.000 -0.003 -0.003 |\n", + "| DDstar_s | -0.009 -0.008 -0.002 -0.000 0.002 0.298 -0.091 0.002 -0.000 1.000 -0.000 0.001 -0.037 -0.000 -0.002 -0.008 |\n", + "| bplus_2 | -0.001 -0.000 0.000 0.677 0.000 -0.000 0.000 0.000 0.000 -0.000 1.000 -0.000 0.000 0.190 -0.000 -0.001 |\n", + "| p3770_s | 0.011 0.002 0.000 -0.000 -0.000 0.000 0.004 -0.000 -0.128 0.001 -0.000 1.000 0.002 -0.000 0.002 0.015 |\n", + "| Dbar_s | -0.032 -0.020 -0.008 0.000 0.004 0.012 0.364 0.005 0.029 -0.037 0.000 0.002 1.000 0.000 -0.001 -0.031 |\n", + "| bplus_0 | -0.000 -0.000 0.000 0.450 0.000 -0.000 0.000 0.000 0.000 -0.000 0.190 -0.000 0.000 1.000 -0.000 -0.001 |\n", + "| p4160_p | -0.065 0.093 0.008 -0.000 -0.002 -0.002 -0.008 -0.003 -0.003 -0.002 -0.000 0.002 -0.001 -0.000 1.000 -0.074 |\n", + "| p4040_p | 0.621 0.075 0.013 -0.001 -0.002 -0.004 -0.098 -0.009 -0.003 -0.008 -0.001 0.015 -0.031 -0.001 -0.074 1.000 |\n", + "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "Hesse errors: OrderedDict([(, {'error': 0.5475039449324027}), (, {'error': 0.6200845337212617}), (, {'error': 2.1784786478303917}), (, {'error': 0.00045254373012648674}), (, {'error': 0.2132717560841134}), (, {'error': 1.8581258189915308}), (, {'error': 9.473191034944215}), (, {'error': 0.1559314560994186}), (, {'error': 2.6685592600641765}), (, {'error': 0.13789484579224884}), (, {'error': 0.0019374513205349109}), (, {'error': 0.5908195162116194}), (, {'error': 0.13394604673222416}), (, {'error': 4.94973454072678e-05}), (, {'error': 0.631080637656769}), (, {'error': 3.8768081157581706})])\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:160: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Toy 1/2\n", + "Time taken: 2 min, 59 s\n", + "Projected time left: 2 min, 59 s\n", + "Toy 1: Generating data...\n", + "Toy 1: Data generation finished\n", + "Toy 1: Loading data...\n", + "Toy 1: Loading data finished\n", + "Toy 1: Fitting pdf...\n", + "------------------------------------------------------------------\n", + "| FCN = 241.4 | Ncalls=39 (39 total) |\n", + "| EDM = 1.08E-06 (Goal: 5E-06) | up = 0.5 |\n", + "------------------------------------------------------------------\n", + "| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n", + "------------------------------------------------------------------\n", + "| True | True | False | False |\n", + "------------------------------------------------------------------\n", + "| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n", + "------------------------------------------------------------------\n", + "| False | True | True | True | False |\n", + "------------------------------------------------------------------\n", + "Function minimum: 241.3777412895305\n", + "----------------------------------------------------------------------------------------------\n", + "| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n", + "----------------------------------------------------------------------------------------------\n", + "| 0 | p4040_s | 0.45 | 0.18 | | |0.00501244| 2.01499 | |\n", + "| 1 | p4160_s | 2.91 | 0.28 | | | 0.71676 | 3.68324 | |\n", + "| 2 | p4415_p | 3.1 | 1.4 | | |-6.28319 | 6.28319 | |\n", + "| 3 | bplus_1 | -0.885 | 0.000 | | | -2 | 2 | |\n", + "| 4 | Ctt | -0.022 | 0.107 | | | -0.5 | 0.5 | |\n", + "| 5 | DDstar_p | -1.8 | 1.3 | | |-6.28319 | 6.28319 | |\n", + "| 6 | Dbar_p | 4.3 | 1.0 | | |-6.28319 | 6.28319 | |\n", + "| 7 | p4415_s | 1.59 | 0.23 | | |0.126447 | 2.35355 | |\n", + "| 8 | p3770_p | -0.8 | 1.1 | | |-6.28319 | 6.28319 | |\n", + "| 9 | DDstar_s | -0.03 | 0.07 | | | -0.3 | 0.3 | |\n", + "| 10| bplus_2 | -2.130E-1 | 0.019E-1 | | | -2 | 2 | |\n", + "| 11| p3770_s | 3.59 | 0.25 | | |0.918861 | 4.08114 | |\n", + "| 12| Dbar_s | -0.08 | 0.06 | | | -0.3 | 0.3 | |\n", + "| 13| bplus_0 | 0.466 | 0.000 | | | -2 | 2 | |\n", + "| 14| p4160_p | -2.0 | 6.8 | | |-6.28319 | 6.28319 | |\n", + "| 15| p4040_p | -5.0 | 0.8 | | |-6.28319 | 6.28319 | |\n", + "----------------------------------------------------------------------------------------------\n", + "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "| | p4040_s p4160_s p4415_p bplus_1 Ctt DDstar_p Dbar_p p4415_s p3770_p DDstar_s bplus_2 p3770_s Dbar_s bplus_0 p4160_p p4040_p |\n", + "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "| p4040_s | 1.000 -0.001 0.131 0.000 -0.006 0.021 0.032 0.001 0.075 0.045 0.000 -0.003 0.041 -0.000 0.178 0.017 |\n", + "| p4160_s | -0.001 1.000 -0.007 0.000 0.001 -0.001 -0.002 0.001 -0.006 -0.003 0.000 0.000 -0.003 0.000 -0.011 -0.004 |\n", + "| p4415_p | 0.131 -0.007 1.000 0.001 -0.023 0.089 0.137 -0.025 0.305 0.180 0.001 -0.010 0.165 -0.000 0.738 0.079 |\n", + "| bplus_1 | 0.000 0.000 0.001 1.000 0.000 -0.000 -0.000 -0.000 -0.000 0.000 0.677 -0.000 0.000 0.450 0.001 0.000 |\n", + "| Ctt | -0.006 0.001 -0.023 0.000 1.000 -0.003 -0.004 -0.000 -0.012 -0.009 0.000 0.000 -0.008 0.000 -0.032 -0.003 |\n", + "| DDstar_p | 0.021 -0.001 0.089 -0.000 -0.003 1.000 0.005 0.000 0.052 -0.034 0.000 -0.001 0.022 -0.000 0.116 0.012 |\n", + "| Dbar_p | 0.032 -0.002 0.137 -0.000 -0.004 0.005 1.000 0.001 0.078 0.043 0.000 -0.002 -0.030 -0.000 0.180 0.019 |\n", + "| p4415_s | 0.001 0.001 -0.025 -0.000 -0.000 0.000 0.001 1.000 0.003 0.001 -0.000 -0.000 0.001 -0.000 0.009 0.001 |\n", + "| p3770_p | 0.075 -0.006 0.305 -0.000 -0.012 0.052 0.078 0.003 1.000 0.103 0.000 0.002 0.092 -0.000 0.420 0.039 |\n", + "| DDstar_s | 0.045 -0.003 0.180 0.000 -0.009 -0.034 0.043 0.001 0.103 1.000 0.000 -0.003 0.052 -0.000 0.252 0.026 |\n", + "| bplus_2 | 0.000 0.000 0.001 0.677 0.000 0.000 0.000 -0.000 0.000 0.000 1.000 -0.000 0.000 0.190 0.002 0.000 |\n", + "| p3770_s | -0.003 0.000 -0.010 -0.000 0.000 -0.001 -0.002 -0.000 0.002 -0.003 -0.000 1.000 -0.003 -0.000 -0.014 -0.001 |\n", + "| Dbar_s | 0.041 -0.003 0.165 0.000 -0.008 0.022 -0.030 0.001 0.092 0.052 0.000 -0.003 1.000 -0.000 0.228 0.023 |\n", + "| bplus_0 | -0.000 0.000 -0.000 0.450 0.000 -0.000 -0.000 -0.000 -0.000 -0.000 0.190 -0.000 -0.000 1.000 -0.000 0.000 |\n", + "| p4160_p | 0.178 -0.011 0.738 0.001 -0.032 0.116 0.180 0.009 0.420 0.252 0.002 -0.014 0.228 -0.000 1.000 0.114 |\n", + "| p4040_p | 0.017 -0.004 0.079 0.000 -0.003 0.012 0.019 0.001 0.039 0.026 0.000 -0.001 0.023 0.000 0.114 1.000 |\n", + "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "Hesse errors: OrderedDict([(, {'error': 0.1817980073330014}), (, {'error': 0.28105296332603724}), (, {'error': 1.4460585352975572}), (, {'error': 0.00045254152319595953}), (, {'error': 0.1070128591631859}), (, {'error': 1.274894582621453}), (, {'error': 0.9863367274899124}), (, {'error': 0.22635340269660909}), (, {'error': 1.0686131922004622}), (, {'error': 0.06595112952400507}), (, {'error': 0.0019374367701227024}), (, {'error': 0.24635197699053668}), (, {'error': 0.06369342157651345}), (, {'error': 4.949731518788525e-05}), (, {'error': 6.846065392563649}), (, {'error': 0.7735769115033309})])\n", + "Toy 2/2\n", + "Time taken: 5 min, 55 s\n", + "Projected time left: \n" + ] + }, + { + "data": { + "image/png": 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AdzPGFCBzMbl4OuEgOrd5dbNuxyRa9TLhCmn5bAYWuiy0BF4CwYasfTYA17nXVwHPqjfqtwFY7TLV5gMLgRfCynTHPOfKwJX5o1znUNWPqeo8VZ0H/B3wPyzwGDO2RiwmF8VuNwDxugQt9pSGvC0fVU2KyC3A00AceEhVt4nIHcAWVd0ArAMeEZEWvNbIanfsNhF5AngdSAI3q+oQQFCZ7pS3AutF5E5gqyubsHMYY8ZfSjNnOHDbIhZ9BK+O1vIpDYV0u6GqTwFPZW37asbrPuDqkGPvAu4qpEy3fSdeNlz29tBzZOzz17k+N8YcH80Y84lktxuKiFjLp4TYDAfGmLwyl9GOZKq1a/nYmE/psOBjjMkrpRowsWgRK5RFXbdgTCRSQdGEs+BjjMkrc2JRf12fKM0koHgzHIhIpIKiCWfBxxiTV8qlMkNGt1uUgo9r+cRj0QqKJpwFH2NMXqrDE4pGstvN/YyJ2JhPibDgY4zJa+SYj9sWoejjLyYXs263kmHBxxiTlwZku0WrhaH2nE+JseBjjMkrc2LReBTX88nIdrPYUxos+Bhj8kplrJcjUex2ww8+1vIpFRZ8jDF5KQS0fKJzk/cXk7PnfEqHBR9jTE6qmh7Qh+hmu4l4dYxQTDQ5WPAxxuTk38z9oON3u0XuOR+853yi1CIz4Sz4GGNy8m/m6W43F32i9DCnt6SC2HM+JcSCjzEmJ797LXtutyiNrfiLydn0OqXDgo8xJid18wekx3wimGoNw9luEK1WmQlmwccYk1P2mE8Ub/DDSypEMzCa0Sz4GGNyyh7ziWS3W3oxOe+9jftEnwUfY0xOqayWT2RnOGC4a9CCT/RZ8DHG5OTfyP2EA4lg6yJzeh2AVKrIFTJ5WfAxxuSk7kbutyri6Rt8hIKPW0wu7u5oUQqMJpgFH2NMTn62W/aYT4Rij5cUkdnyseATeRZ8jDE5+YkFkZ7hgOwxn6JWxxTAgo8xJic/yPjP9/hZZVFKtUbtOZ9SY8HHGJOTP3jvj/VANJer9me1Bmv5lAILPsaYnPyWTzzjbuEtXVCkCgXIHpeKWmA0o1nwMcbklMoa8wGIxaLVteWnWttzPqXDgo8xJic/4aAsPrLbLVozHNhzPqWmoOAjIitFZLuItIjIbQGfV4jI4+7zTSIyL+Oz29327SJyeb4yRWS+K2OHKzOR6xwiskJEXnJ/XhaRPzrei2GMGS0Z0PKJR2z2aH8lU3vOp3TkDT4iEgfuAz4JLAbWiMjirN1uADpVdQFwL3C3O3YxsBo4E1gJ3C8i8Txl3g3cq6oLgU5Xdug5gNeA5aq61J3jAREpO7bLYIwJk0qP+QwHH5Fo3eAzVzKFaNXNBCuk5bMCaFHVnao6AKwHVmXtswp42L1+ErhUvH8Fq4D1qtqvqu8ALa68wDLdMZe4MnBlXpnrHKraq6pJt70St66UMWZs+N1rI7LdYtHKdsueeTtCVTMhCgk+s4DdGe9b3bbAfVwgOAQ05Tg2bHsT0JURTDLPFXYOROR8EdkGvArclHF8mojcKCJbRGRLW1tbAV/bGAMZD5nGsrvdonOH91o+Nqt1KSkk+EjAtuy/2bB9xmp7znqo6iZVPRM4D7hdRCpH7ai6VlWXq+ry5ubmgKKMMUHS3W6S2e0WrVRr3Eqm9pxP6Sgk+LQCczLezwb2hO3jxlvqgY4cx4ZtbwcaMsZsMs8Vdo40VX0D6AHOKuB7GWMKkO52y2z5RC3VGn/Mx3tvLZ/oKyT4bAYWuiy0BF4CwYasfTYA17nXVwHPqvcvcwOw2mWqzQcWAi+ElemOec6VgSvzR7nO4cooAxCRU4APAbsKvgLGmJxSOrrbLWozHGSvZBqlwGiC5c0KU9WkiNwCPA3EgYdUdZuI3AFsUdUNwDrgERFpwWuNrHbHbhORJ4DXgSRws6oOAQSV6U55K7BeRO4EtrqyCTsHcBFwm4gMAingS6rafvyXxBiTaShkep0odbsNr2Tqr7Ja5AqZvApKSVbVp4CnsrZ9NeN1H3B1yLF3AXcVUqbbvhMvGy57e+A5VPUR4JG8X8IYc1yCu92i2fKx53xKh81wYIzJKSj4lMWEwQg1L2x6ndJjwccYk1PQxKLxWPSm1yFjVmuLPdFnwccYk1PQxKJl8Vh62p0oUNUR6/lYyyf6LPgYY3IK63aLUssHXLabq2OUAqMJZsHHGJNTeiVTGZlwEMUxn7KYP6u1BZ+os+BjjMkpFdDyKY9Hq+Wj+LNae3UcHIpO3UwwCz7GmJyGAma1jsckUl1bfsun3GVFRCkwmmAWfIwxOQ0FJRzEYiSj1O2GF3zi6TGf6NTNBLPgY4zJKb2SaWbCQdS63dxicn4dk9btFnkWfIwxOYVlu0Wq2w1AvBYZWLZbKbDgY4zJKWhi0XhMotW6UK9bsCxu3W6lwoKPMSanoIlFvYdMo3ODT6kSyxjziVKXoAlmwccYk1P6OZ+Mu0XUHjIdUiUmQrnf7RalVpkJZMHHGJNT+jmfUQ+ZRucGn0p53W5x63YrGRZ8jDE5lcL0Ouq63cpsep2SYcHHGJNTUMJB1CYWTfkJBzbmUzIs+BhjcvK718ozBn28VOvodG2lVInFhlOto9QlaIJZ8DHG5OTPZOCnMYNbzydCN/iUegvJ+WM+QxEKjCaYBR9jTE6DATMclEes283GfEqPBR9jTE6DQynKYpJeohqit5JpyqVa2/Q6pcOCjzEmp+RQKj1btK8sJgxGqGvLTziIW8unZFjwMcbkNDikI8Z7wBvYV43Oom0pt4y2uNaPjflEnwUfY0xOyVRAyycerRaG6vCSD5Gbd84EsuBjjMkpOaQjkg0geuvm+HO7QfSSIUwwCz7GmJwGhzRwzAei0/LxEw4geskQJpgFH2NMTslUKmDMxz1PE5HurVSKdDZeWUwYjNAqqyaYBR9jTE5+qnWmsngs/VkUpFTxG2dlcRvzKQUFBR8RWSki20WkRURuC/i8QkQed59vEpF5GZ/d7rZvF5HL85UpIvNdGTtcmYlc5xCR/yAiL4rIq+7nJcd7MYwxowV1uyXKvPf9yegEH7/bLVEWYyAiQdGEyxt8RCQO3Ad8ElgMrBGRxVm73QB0quoC4F7gbnfsYmA1cCawErhfROJ5yrwbuFdVFwKdruzQcwDtwH9U1Q8D1wGPHNslMMbkEvScT4ULPlG5yfvT6wBUlsXpTw4VuUYmn0JaPiuAFlXdqaoDwHpgVdY+q4CH3esngUvF+5ewClivqv2q+g7Q4soLLNMdc4krA1fmlbnOoapbVXWP274NqBSRikIvgDEmt2Rq9HM+fvDpHyx+8FF/1m1XxYryWCTqZXIrJPjMAnZnvG912wL3UdUkcAhoynFs2PYmoMuVkX2usHNk+gywVVX7s7+EiNwoIltEZEtbW1uer2yM8Q0OpUbMaA3D3W5RaPn4iW1+t1tFWZw+a/lEXiHBRwK2ZY/mhe0zVtvz1kNEzsTrivvPAfuhqmtVdbmqLm9ubg7axRgTIBkww0EiHgdgIAJjPqnslk+ZtXxKQSHBpxWYk/F+NrAnbB8RKQPqgY4cx4ZtbwcaXBnZ5wo7ByIyG/gX4POq+nYB38kYU6DBlKaz23wV5X7CQfFbGH7wkXTLJxaZRAgTrpDgsxlY6LLQEngJBBuy9tmAN9gPcBXwrHodsRuA1S5TbT6wEHghrEx3zHOuDFyZP8p1DhFpAH4C3K6qvz2WL2+MyW8wmaI8lt3ycd1uEbjJa1a3W2W5JRyUgrzBx42v3AI8DbwBPKGq20TkDhH5Q7fbOqBJRFqALwO3uWO3AU8ArwM/A25W1aGwMl1ZtwJfdmU1ubJDz+HKWQD8lYi85P5MP87rYYzJEjS3W3rMJwLBJ7DbLQL1MrmV5d8FVPUp4KmsbV/NeN0HXB1y7F3AXYWU6bbvxMuGy94eeA5VvRO4M++XMMYcl6Axn4oIPecTmHAwaC2fqLMZDowxOQ0MpdItHV8UWz6SmWodgXqZ3Cz4GGNy6k+mqCiLj9iWnuEgAqnW6qoQy0w4sGy3yLPgY4zJqW9wKN3N5vODUX8EureGssZ8/IQD/+FTE00WfIwxOfUnU+nUal+Uptfxu938NYYqymKk1JuTzkSXBR9jTChVZSCo2y0enel1/OBDRqo1wNGB4rfKTDgLPsaYUP7AfXa3WywmlMclGi0fVwV/2Ye6Si+J90j/YLGqZApgwccYE8oPPn5rIlMiHotEtpu/lHc8HXzKAejuT4YeY4rPgo8xJpQ/U0B2ywegojwaz9P4S2b7LZ/aCtfy6bPgE2UWfIwxofwxnaDgU1Ue52gEgo+fWBDP7nbrs263KLPgY4wJlR7zCeh2q6mI09tf/OAz3PLxbmd+t5u1fKLNgo8xJpTfrRbU8qlOlNEzUPwbvD/m408BNNzyKX7dTDgLPsaYUGHZbuBaPhFIZ84e87HgUxos+BhjQg0nHIzudqtOlNETgYyyZGrkmE9VeZxEPEbX0YFiVsvkYcHHGBNqeMwnoOWTiFrLx6ujiNBcV0Hb4f5iVsvkYcHHGBPKTyioTgS0fCrK6I3CmE9WthvAjCkV7D/SV6wqmQJY8DHGhPITCmoSo5f+qknE6YlAtlt2wgHAjCmV7DtkwSfKLPgYY0L5Yzo1FaODT3WijKODQ+lur2LJHvMBOKneCz5DKSUZgSmAzGgWfIwxofwxnZqKoIQDN4FnkR80HXLdbuWx4dvZwul19AwM8dkHnmfVfb8tVtVMDgUto22MmZy6+5OUxSQ9i3Wmatca6h1Ipqe0KYagls+ZJ08BYMu7nQAcOjpIfVX5xFfOhLKWjzEmVG9/kpqKMkRk1Gd1EZlDLZ3tFh8ZfOY1VaffH+y2zLeoseBjjAnV3T9ETUCmG0B9tdeS6Oot7vM02bNaA5TFYzz5Zx/lb65aAkBHjz3zEzUWfIwxoXoHkoHJBgCN1QkAOnuKO4Fn9gwHvmm1FZw2vRaAIxF4GNaMZMHHGBOquz+ZHtvJ1uhaPp3FbvkEPOfjq7JVTSPLgo8xJlR3f5LagEw3gAbX8jl0tLgtn/4hf/658Iy8KMzEYEay4GOMCXWodzAdZLJNqSwjHpOit3z6/Zm3A6YAqvLTwSMwE4MZyYKPMSZUZ+9Aunstm4jQUFVOZ2+RWz5u/rnAdPCEnw5uLZ+oseBjjAmUSimHjg7SUBXc8gEv463Y2W4DOZZ98Md8LPhET0HBR0RWish2EWkRkdsCPq8Qkcfd55tEZF7GZ7e77dtF5PJ8ZYrIfFfGDldmItc5RKRJRJ4TkW4R+ebxXojJ7InNu5l320/otowgk+Fw3yAphYaQlg94GWXtR4rc7ZZMkSiLBT6LFI8JFWWxos/CYEbLG3xEJA7cB3wSWAysEZHFWbvdAHSq6gLgXuBud+xiYDVwJrASuF9E4nnKvBu4V1UXAp2u7NBzAH3AXwH/7Ri/u3G+9W9vA7Dv0NEi18REid+d1hgy5gMws76SvYeL++9mIJmiIqDLzVediE/Y7NuqyrNv7i96EkYpKKTlswJoUdWdqjoArAdWZe2zCnjYvX4SuFS8X0NWAetVtV9V3wFaXHmBZbpjLnFl4Mq8Mtc5VLVHVX+DF4TM8UjPCzn6N0czefndaY014S2fmfVV7DvUR6qIk4v2J4cCkw181YmyCet2e+rVfXzhn7bwXx7bOiHnK2WFBJ9ZwO6M961uW+A+qpoEDgFNOY4N294EdLkyss8Vdg5jzDjwZwXI1/IZHFIOFnEGgf5kKjDN2leViE/Ycz7PbT8AwG92tHG4z1o/uRQSfIJ+Hc7+NSdsn7HaXmg9QonIjSKyRUS2tLW1FXqYMZPWvsNeZ8LM+qrQfWbWV3r7FnHtnAE35hOmegJXXN36XieV5TFSCi+91zUh5yxVhQSfVmBOxvvZwJ6wfUSkDKgHOnIcG7a9HWhwZWSfK+wcBVHVtaq6XFWXNzc3F3rYpBIwXmsmsX2H+ogJTKsNb/mc3OAFpve7eieqWqP0J4cC06x9VeUT0/I5dHSQt9t6+JOPzkcEXnQzaptghQSfzcBCl4WWwEsg2JC1zwbgOvf6KuBZVVW3fbXLVJsPLAReCCvTHfOcKwNX5o/ynMN8QHYRTZB9h/qYXldJWfqpuUcAABU1SURBVI4b+7xpNQC83dYzUdUapXdgKHC9IV91Ik7v4PgnHLzS6rV0LlowjQ/NqGPrbmv55JJ3EQ5VTYrILcDTQBx4SFW3icgdwBZV3QCsAx4RkRa81shqd+w2EXkCeB1IAjer6hBAUJnulLcC60XkTmCrK5uwc7iydgFTgISIXAl8QlVfP96LMtn4MdwaPibTvsN9zHDdamFqK8o4ub6SHfuPTFCtRjvSl6SuMvxW5iUcjH/LbOt7XYjAkjn1nDO3kZ+8sodUSokFzDlnClxMTlWfAp7K2vbVjNd9wNUhx94F3FVImW77TrxsuOztuc4xL+cXMMYcs/e7jvKhGXV591swo44dB7onoEbBevqTnNwQHiSrJyjh4KXdXSxormVKZTnnzG3gsRfeY2d7DwvczNpmJJvhwKQVMVvWRMzgUIr3DvYy33Wr5XL69FpaDnQz6Cb4nGjd/UlqErlaPuOfcKCqbH2vk6VzGgBYNtf7ufU9G/cJY8HHpMd8bAjN+HZ39JJMKac15/+t/ew5DfQnU7yx9/AE1Gy07r4ktTm63aoSZePe8nmnvYfO3kGWndIIwKnTaplSWcbvLOMtlAUfkzZkwcc4fgLBqc35Wz7nzZsKwAvvFJx8OmZUle6BZHpJ7yDViTgDQymS49gy84PMsrle8InFhKVzG63lk4MFH5OWKk6viYmgN/ceRoSCxitOqq9kztQqNu48OAE1G+nQ0UFUYUpV+CwM6TV9xnF+t9/saKOxunzE9Tp3biNv7T/Cwe7+cTtvKbPgY9JS1vIxztbdXZzWXEtdZfhNPdOli2bwbzvaOTLBT/W3HfFu7NOnhCccDK/pMz7BJzmU4ldvtfH7i6aPWE310jOmk1L4xRv7x+W8pc6Cj0mz4GNgePD8HDd4XogrlsxkIJni59sm9kbb5loVzbUVofuM92qmz755gK7eQS4/86QR2888eQqzG6v48St7x+W8pc6Cj8GPOZbtZgC27TlMZ+9geiynEMvmNnJacw3rfvPOhCau+C2f5rrwWRjqKrzW23jMNK2q/OOvdzKzvpJLF00f8ZmI8Jlls/n1jnbebiteKnpUWfAxaUMWfQzwyzcOIAK/n3UzzSUWE/7zx0/j9b2H+cmrE/eb/rsHvYdHZzVUh+5z0jjOP/fElt1s3tXJLZcsCJwJ4nMfOYVEWYxvPtsy5ucudRZ8TJqlWhtVZcPL77NsbiPNdeFdWUE+fc4sPjyrnr/esI29E7Q21Ntt3cxqqEqP6wQZnvx07Oqkqnxv07v85Q9f48IFTaw+b27gftNqK/jix+bzL1vf57ct7WN2/hNBQTMcmBObuid9rOFjnn/7IG+39fC3Vy845mPL4jH+1zVn8+n7/53rHnqBf7p+RXri0fGybc9hTp+ROyNvak2CmkSctw50092fZNPOg+w40M3B7n6S7h99oixGQ1WChupymmoSnDa9llOmVge2Zjp6Brj1+6/wzOv7+djCaXzzj5eNSDTIdsvvL+Rnr+3jlkd/xw9vvpBTmnKnrz+xeTcvtXbxZx8/jTlTw1t0pc6Cj0mzbrfJTVX5m59vp7mugiuWzDyuMk6fUcfaz5/Ljd95kSv+4TfcuvJDfGbZ7JyTkx6vvYeO0nKgm6vPnZ1zPxHhwgXTeHTTezyxeXc64FSWxyh39eofTDGQ9RxQZXmMj5/ezB98eCafWHwSVYk4v21p58tPvERHzwB/+akz+MKF8/PO3VaViPPgdefxR/f/lmseeJ7v3nA+C0OmLdqyq4OvfP8VAH6+bR+PfvECTi9giqNSZMHHpFm32+T2yMZ32fpeF3d/5sNUlod3Y+Xz0dOm8cObL+QrT77Mrd9/lXueeYs/PPtkLlwwjXPmNlKf45mcY/G9je8B8ImsLLMgf3XFYmoryzhpSiUXLZzGktkN1GY8mKqq9A2m6Do6wIHD/bQc6Obl1i5+vm0/T2/bT00izsIZdby0u8tLrLjuPM6aVV9wXedPq2H9jRfwuXUv8On7/52/uXoJK88aHeC/u/Fd6irLePRPL+CGhzezZu1GHv3iBXzopBMvAMlkvOEsX75ct2zZUuxqRMZFdz9La+dRHrlhBR9baGsdTUbPvXmAGx/ZwscWNvPg55ePyUzMqsov3zjAoy+8x693tDE45N1rptVWcOq0GprrKphWm6CptoK6yjJqEmVUV8S9n4k4NRVlVJbHKY8L8ZhQHo8RjwkdPQM88/p+7n3mLf7gwzP532vO+cB1DZNKKS/s6uCHW9/n1fcPcdkZM7jp46flHGPKpbWzl5u/9ztebj3E9RfO49aVi9KBvrNngPO//kvWnDeHr606i51t3az5x40MDimPfvF8Fp00ZSy/2nERkRdVdflYlGUtH0MqZWM+k1Uqpaz7zTt84+k3+dBJddx7zdIxWwJARLhs8QwuWzyDowNDbHm3g9f3HKblQDfvdvTyxr7DHOweOO4U6EsXTefOPzprTOoaJhYTLji1iQtObRqT8mY3VvPETR/h60+9ybd/u4tf72jnnmvOZsnsBr7/u1YGkilWr/CSF05trmX9jR9hzdqN/PE/buJ7f3o+Z8wsfgAaK9byMay46xccONLPt68/j9//UOHptaZ0DSRTPPP6fv7h2R28ue8Il585g29cdfaYdYkda116B5L0DAzR25/1cyDJUEpJDinJlJJMpZhSWc5Zs6awYHppd0X961tt3PrkK7R19/PHK+by09f2Ma+pmif/7KMj9tvV3sOaf9xI3+AQj9xw/jF19401a/mYMeUnGqSs6XNC6+wZYOPOg/y6pZ2fvrqXzt5BTp1Wwz+sOYcrlsxEirSOeqIsRqIsQY5HdU5IHz+9maf//Pf42o+38b1N7zK1JsH/e+Xoltw8N160Zu1GVq/dyP+5dtkJ0T1uwceks38s9pwYhlLK3kNHeWv/Ed7Ye4Q39x3hjb1edxd4081csmg6n1k2m48tnDYumWimMPXV5dxzzVLuWHUWiXiMRFnw38UpTTV8/0sf5fpvb+b6b2/mf3z6w1yzfM4E13ZsWfAx6ZaPpVqXhqGUcrC7n32H+9jdcZTdnb2819HLbvfn/a6j6cF9gFkNVZwxs45VZ5/MRxc0sWR2QzrF2ERDbY4lIXwz66v455s+wpe+9zu+8uQrvN95lD+/bGHRWqwflAUfkw46k3H8L0pUlY6eAfYf7mf/kT4OHO7zXrufB470sf9wH21H+ke1Uhury5k7tZozZ9Wz8qyZzJlaxekz6jh9Rl1RxnHM+KirLOehPzmP23/wKn//yx20dh7l65/+cGiLKcos+Bj6k95sv9bwGXtDKaWrd4D27gEOdvfT3jNA+5F+Dvb0c7B7gPbuftq7B2g74gWXzBaLb2pNgul1FcyYUsmik+o4aUol06dUMr2ugtmN1cyZWlXw0gem9JXHY/zNVUuY3VjF3/1iB/sP93H/tcuYUmL/Biz4mHTQsZVMC9M3OJQOGge7vSDS5n4e7OmnvdsPLAN09IxupQDEY0JTjfeMy7TaBKdOq2FGfSUzXJCZPqWSGVMqaK6roKLs+B/4NCcmEeHPLzudWQ1V3P6DV7nmW8/z7evPY2b9+E5nNJYs+Ji0ydjt5rdMOnsH6OgZpLN3gM6eATr8n25bR4+3T/uRfnpC1oWprSijqTbBtNoK5k6t5py5jTS7hyj97dNqEzTVVFBfVT5mz9OYyevq5XOYWV/FTd99kSvv+y3f/pMVLD65NJ4FsuBzAvjNjnauXbeJX3z54wUte5zJ73KD0k84SKWUw32D6UDR0TM4HEiygom/3V+GOUh1Ik5jdYLGmnIaqxOc0lTNND+Q1FQwrc4LJH5g+SBT0hhzvC5aOI1/vukjXP/tzVzzwPOsu24554/RQ7HjyYLPCWDdb3YC8Mbew8ccfHZ39KZfRyn2DA6l6OoddK0SL2Accj8709sH6OwZTLdSOnsHQr9DoizG1OoEjTUJptaUs/jkKUytSdBYnfB+1iTc5+Xp7RZMTKk4Y+YU/uXmj3Ltg5u4/p82850vrGD5MSwGWAwWfE4AO9zzG8ezDPbmXZ3p1+PxkKnfGulygaOrd5Cuo17Q8ANL19HhYOIFnEG6+5OhZZbHhfqqBE01XrA4fUbtcBAJCSZV5fGSTUk1phAz66t47IsXsHrtRq576AW+c8P5nHtKY7GrFcqCTwlTVf797YO0dnqLZHX1HtscWdv2HOKbz7ZQW1FGd38yb8KBqnK4L+kNsvcMpAfYO7r9IDIwMsi4bq2wmCYC9VXlNFSV01CdoLm2gtOn11Ff7XVzNVaXU+9+NlZ7a600VHtrs1ggMWa06VMqefSLF7B67fP8yUMv8Mifns/SOQ3FrlYgCz4Rp6p09ydpd2m5bUf62bG/mzf2Hmbb3kPs7jjKrIYq3u86SkfPQODxbd393sOI7iHEdw728Lt3O9l1sJeG6nIe+Ny5/KcHN9Hdl6Q/OcRr7x/m9T2HePdgL62dR3m/6ygHjvTR0TMQmAoMUJOI0+ACRGN1glkNVenXDdUJGqrKaazxgkejez+lqjznIlzGmGN3Un0lj914AZ99YCOfW7eJR26IZgCyiUWLQFU5dHTQBRP/WY/MFN1+2rq9zKr27n76kyMXuRKBeU01nDGzjo+eNo1VS0/mY994jqVzGjh/fhNv7jvM3q4+9h32/gxkHd9cV8HZs+v5yGnTuOrc2UypLOP0v/wpg0NKY3U5na4FVVkeY1ZDFbMaq5lRV8G0ugqXHuwNtE+t8QbaG2vKLR3YmIh5v+soa9ZupLN3YMwC0IRPLCoiK4G/B+LAg6r6P7M+rwC+A5wLHAQ+q6q73Ge3AzcAQ8B/VdWnc5UpIvOB9cBU4HfA51R14HjOMZH8LqkDh/s4cMRroXhBpJ/2I8PPf/ivg1oQMYGpNV46bnOdt+bJtHSKrnfzn1abYF5TDTVZ03FMq63gV9vb+NX2Nhcwqlg6p4GZ9ZXMrK9kblM1c6dWM7uxOnAgfcnsBl58t5Pz5k3l08tmc/acek6aUmndW8aUqFkNVTzmJiSNYgsob8tHROLAW8B/AFqBzcAaVX09Y58vAUtU9SYRWQ38kap+VkQWA48BK4CTgV8Ap7vDAssUkSeAH6jqehH5FvCyqv6fYz2HqgY/jMEHa/kkh1K8tucwW9/r5O22bna29dDa6XVL9Q2mRu1fHheaXFpuOojUDgeYzPeN1Ynjfvbj2Tf3s2VXJ5/7yCnH9aDZkb5BegeGmDGl8rjOb4yJpnQLqGeAv1+zlEsWzTjussay5VNI8PkI8Neqerl7fzuAqn49Y5+n3T7Pi0gZsA9oBm7L3Nffzx02qkzgfwJtwEmqmsw897GeQ1WfD/tOxxt8Xny3k8+v25R+yHBKZRmnNtcyd2o1M6ZUML2ukunuZ3NdBc21FUypKrPWgzGmqN7vOsqfPryFN/Ye5jPLZvO/rjn7uMqZ6G63WcDujPetwPlh+7igcQhocts3Zh07y70OKrMJ6FLVZMD+x3OONBG5EbjRve0WkYNAe+i3LtCrH7SAaJjGGFyLE4RdC49dh2En1LW4B7jns8d16DTglLGqRyHBJ+jX9uzmUtg+YduDpmDNtf/xnGPkBtW1wFr/vYhsGasIXursWgyza+Gx6zDMroXHXYd5Y1VeIfNwtwKZqxbNBvaE7eO6xOqBjhzHhm1vBxpcGdnnOtZzGGOMiahCgs9mYKGIzBeRBLAa2JC1zwbgOvf6KuBZ9QaTNgCrRaTCZbEtBF4IK9Md85wrA1fmj47zHMYYYyIqb7ebG1+5BXgaLy36IVXdJiJ3AFtUdQOwDnhERFrwWiOr3bHbXPba60ASuNnPQgsq053yVmC9iNwJbHVlczznyGNt/l0mDbsWw+xaeOw6DLNr4RnT6zApHzI1xhhTXKW39qoxxpiSZ8HHGGPMhJuUwUdEVorIdhFpEZHbil2f8SAiD4nIARF5LWPbVBF5RkR2uJ+NbruIyP921+MVEVmWccx1bv8dInJd0LmiTETmiMhzIvKGiGwTkf/LbZ9U10JEKkXkBRF52V2Hr7nt80Vkk/tOj7sEIFwCz+PuOmwSkXkZZd3utm8XkcuL840+OBGJi8hWEfmxez8pr4WI7BKRV0XkJRHZ4raN//8PVZ1Uf/ASHN4GTgUSwMvA4mLXaxy+5+8By4DXMrZ9A7jNvb4NuNu9/gPgp3jPTF0AbHLbpwI73c9G97qx2N/tGK/DTGCZe12HN63T4sl2Ldz3qXWvy4FN7vs9Aax2278F/Jl7/SXgW+71auBx93qx+z9TAcx3/5fixf5+x3lNvgw8CvzYvZ+U1wLYBUzL2jbu/z8mY8tnBdCiqjtVdQBvEtNVRa7TmFPVf8PLCsy0CnjYvX4YuDJj+3fUsxHvWauZwOXAM6raoaqdwDPAyvGv/dhR1b2q+jv3+gjwBt4MGJPqWrjv0+3elrs/ClwCPOm2Z18H//o8CVwqIuK2r1fVflV9B2jB+z9VUkRkNvAp4EH3Xpik1yLEuP//mIzBJ2i6oFHT8ZygZqjqXvBuysB0tz3smpxQ18p1l5yD91v/pLsWrpvpJeAA3s3hbQqczgrInM6qpK+D83fAVwB/NuCCp/bixLsWCvxcRF4UbxoymID/H5NxMbmCpuOZZD7Q1EWlQERqge8Df66qhyV8stcT9lqo9/zbUhFpAP4FOCNoN/fzhL0OInIFcEBVXxSRi/3NAbue8NfCuVBV94jIdOAZEXkzx75jdi0mY8tnMk/Hs981kXE/D7jtxzoNUkkRkXK8wPM9Vf2B2zwprwWAqnYBv8Lrs5+M01ldCPyhiOzC63a/BK8lNBmvBaq6x/08gPdLyQom4P/HZAw+hUwXdKLKnKIoe+qiz7tMlguAQ66p/TTwCRFpdNkun3DbSobrm18HvKGq92R8NKmuhYg0uxYPIlIFXIY3/jXpprNS1dtVdbZ6k2Suxvtu/4lJeC1EpEZE6vzXeP+uX2Mi/n8UO9OiGH/wMjbewuvz/u/Frs84fcfHgL3AIN5vJTfg9VP/Etjhfk51+wpwn7serwLLM8r5At5AagtwfbG/13Fch4vwmv+vAC+5P38w2a4FsARvuqpX3M3lq277qXg3zBbgn4EKt73SvW9xn5+aUdZ/d9dnO/DJYn+3D3hdLmY4223SXQv3nV92f7b598OJ+P9h0+sYY4yZcJOx280YY0yRWfAxxhgz4Sz4GGOMmXAWfIwxxkw4Cz7GGGMmnAUfY4wxE86CjzHGmAn3/wO9s+T7BvTaWAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# zfit.run.numeric_checks = False \n", "\n", @@ -1463,7 +1699,9 @@ " if not os.path.exists(dirName):\n", " os.mkdir(dirName)\n", " print(\"Directory \" , dirName , \" Created \")\n", - "\n", + " \n", + " reset_param_values()\n", + " \n", " for call in range(calls):\n", "\n", " sampler.resample(n=event_stack)\n", @@ -1604,9 +1842,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2/2 fits converged\n", + "Mean Ctt value = 0.07076623985541536\n", + "Mean Ctt error = 0.16014230762364964\n" + ] + } + ], "source": [ "print('{0}/{1} fits converged'.format(len(Ctt_list), nr_of_toys))\n", "print('Mean Ctt value = {}'.format(np.mean(Ctt_list)))\n", diff --git a/data/plots/toy_fit_cut_region0.png b/data/plots/toy_fit_cut_region0.png index 5106864..74f88d8 100644 --- a/data/plots/toy_fit_cut_region0.png +++ b/data/plots/toy_fit_cut_region0.png Binary files differ diff --git a/data/plots/toy_fit_cut_region1.png b/data/plots/toy_fit_cut_region1.png index 30b838d..5d3b712 100644 --- a/data/plots/toy_fit_cut_region1.png +++ b/data/plots/toy_fit_cut_region1.png Binary files differ diff --git a/data/zfit_toys/toy_0/0.pkl b/data/zfit_toys/toy_0/0.pkl index 961db38..9b29e72 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 09599a3..3451e1e 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 a80fede..b659247 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 240e3db..236642e 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 cd61d6b..2694e65 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 fe64c9f..bd59c4f 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 1767cf4..994958a 100644 --- a/raremodel-nb.ipynb +++ b/raremodel-nb.ipynb @@ -21,35 +21,16 @@ ] }, { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\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 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disable=wildcard-import\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 79\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow_probability\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mversion\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0m__version__\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 80\u001b[0m \u001b[1;31m# pylint: enable=g-import-not-at-top\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow_probability\\python\\__init__.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mprint_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m 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globals.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 43\u001b[0m \u001b[1;31m# Import the target module and insert it into the parent's namespace\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m \u001b[0mmodule\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimportlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimport_module\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 45\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parent_module_globals\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_local_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 46\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\__init__.py\u001b[0m in \u001b[0;36mimport_module\u001b[1;34m(name, package)\u001b[0m\n\u001b[0;32m 125\u001b[0m \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 126\u001b[0m \u001b[0mlevel\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 127\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_bootstrap\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_gcd_import\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpackage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 128\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\contrib\\__init__.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 39\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontrib\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdeprecated\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 40\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontrib\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdistribute\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 41\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontrib\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdistributions\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 42\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontrib\u001b[0m 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"\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load_unlocked\u001b[1;34m(name, import_)\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_load_unlocked\u001b[1;34m(spec)\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36mexec_module\u001b[1;34m(self, module)\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36mget_code\u001b[1;34m(self, fullname)\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36mpath_stats\u001b[1;34m(self, path)\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36m_path_stat\u001b[1;34m(path)\u001b[0m\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n" ] } ], @@ -75,7 +56,7 @@ "import tensorflow as tf\n", "import zfit\n", "from zfit import ztf\n", - "from IPython.display import clear_output\n", + "# from IPython.display import clear_output\n", "import os\n", "import tensorflow_probability as tfp\n", "tfd = tfp.distributions" @@ -83,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -105,15 +86,11 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# t = np.array([1,2,3,6,8,4,-2,4])\n", - "\n", - "# np.where((t >= 6) & (t <=10))" - ] + "source": [] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -301,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -356,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -462,9 +439,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "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" + ] + } + ], "source": [ "# formfactors\n", "\n", @@ -573,7 +560,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -603,7 +590,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -620,7 +607,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -660,7 +647,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -711,7 +698,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -757,9 +744,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:12: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", + " if sys.path[0] == '':\n", + "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\pylabtools.py:128: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", + " fig.canvas.print_figure(bytes_io, **kw)\n" + ] + }, + { + "data": { + "image/png": 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UlKj0DPi48OvP8I2ndqd6Kkqa4YTv4mffpXKvUqTzlHKz3MCQKOVmuVSU0gwVJSUqwRDHU++EH5+lTHac8F2M7DtXOqwpRQjfWU/pdJ8jSgXZHhWlNENFSVGUpPH6TdrvUxoshRTBU+q2nlJ+jlsTHdIMFSVFUZLGFwjEzL4b9JRSuE8p0nlKHpfgkqHwXUG2hwF/IKWVJ5ThqCgpipI0Pr+JGb4LHhGRytBYMHznDpmniJCb5R4UpbxsN8akdu1LGY6KkqIoSeONs3k2Jy1EyfnpCjsdN8fjoitkTQk0LTydUFFSFCVpvP7Y4bugKPWnQaJD+IntOZ4hTyk/21lj0mSH9EFFSVGUpPEFTMzNs9lBUfKmck3pzEQHcNLAg6I0JcfxlFSU0gcVJUVRksbrj71PKcfjeCD9Pn/UPmNN9PDd8DUlgH4VpbQhIVESkTUiUici9SJyV4TXc0TkMfv6VhGpCnntbtteJyLXxbMpItXWxn5rMzvWGCJSKiJbRKRLRL4fNq+LRWSnveZ7IuGOvKIoI8GXYPguHRIdwvMxQjfMFuTomlK6EVeURMQN3A9cD9QCN4tIbVi324A2Y8wC4D7gXnttLbAOWAysAX4gIu44Nu8F7jPG1ABt1nbUMYA+4CvA30SY/g+B24Ea+29NvPerKEp8fIHY+5SCNeZS6YEEs9HDv4sGvTgIWVNSUUobEvGUlgP1xpiDxpgBYD2wNqzPWuBh+/gJYLX1StYC640x/caYQ0C9tRfRpr1mlbWBtXlDrDGMMd3GmJdwxGkQEZkJFBpjXjVOcPnnIbYURTkLvP5AzPOUctypD4tF85SCggma6JCOJCJKs4FjIc8bbFvEPsYYH9ABlMa4Nlp7KdBubYSPFW2MWPNuiDNvAETkdhHZLiLbm5qaYphUFAXiF2Qd8pRSt6YUqfYdDPeUpuZmARq+SycSEaVIv3nhO82i9Rmt9kTnkciczmw05gFjzDJjzLLy8vIYJhVFAadSQ6zwXbY7jdaUwqYZTG4AKLSiNB4e3Yv7mvj0T7fRO5A6oZ4IJCJKDcCckOeVQHiFzsE+IuIBioDWGNdGa28Giq2N8LGijRFr3pVx5q0oygjwxjkO3eUSstySFuG78DWlqbmewceFeeOXEv7tTXVsqWti2+FYH1tKIqL0OlBjs+KycRIXNoT12QDcah/fCGy26zgbgHU2c64aJ9lgWzSb9pot1gbW5pNxxoiIMeYEcFpELrFrVX8aYktRlLMg3nHo4ITJUrlPKVpKeNA7gqHw3XiI0omOXgAONnWN+VgTGU+8DsYYn4jcAWwC3MBPjDG7ROTrwHZjzAbgIeAXIlKP472ss9fuEpHHgd2AD/icMcYPEMmmHfJOYL2IfBPYYW0TbQxr6zBQCGSLyA3AtcaY3cBfAj8D8oDf2X+Kopwl3kDsNSVw0sIH/KlcU4qc6BDqKeXZquFjXc3cGDN4XMbx9t4xHWuiE1eUAIwxG4GNYW1fDXncB9wU5dp7gHsSsWnbD+Jk54W3xxqjKkr7duD8SK8p8TExl+yUyYzPHyArRkUHcEQpLT2lvCFPKVh5YqzFs9frHwxlHm/vi9N7cqMVHZTo2D9qreqvhOLzBwiYoQ/0aGR7XGmypjS8vTDEUxqvckgdvd7Bxy3d/WM61kRHRUmJi9bBUEIJCk1OHFHK8bhTW2YowiF/MDx8l2/Dd73esZ1nZ69v8HFbtzdGT0VFSYmKOkhKJIJJAfE8pZyQcj6pIBi+c4ctKpUU5Aw+zs9xRKlnjNO0g55S5bQ8WnsGxnSsiY6KkhIVDdspkQiW5AndhBqJbHd6hO/CEx3mlxcAcE5pPtluFx6XDB6PPlYERam6rIC27gE96TYGCSU6KJMTTXRQIhFcf0nEU+pLaaJDtH1KWXxj7WKWzpmGiJCf7R43T6mqtIA/7G/mdL9vWGq6MoSKkqIoSRHMVEtkTSl0gX+8iVZmCOCTl1YNPs7P9tAzMLaeUmdQlMocL621a0BFKQoavlOiohEGJRL9ia4ppTwlPHL4Lpz8HDfd4+Yp5QPoulIMVJSUqAxpkqbfKUMkKkq5WW760vCQv3AKsj1jXo+uo9fL1BwPZVOcJIt2FaWoqCgpURlajFWXSRliIMGU8Lxsd0qLj0bbpxROfrZ7zBMdOvu8FOZlUVKQDUCrpoVHRUVJUZSkSHSfUn6Wm+7+dCgzFMdTyvGMeaJDZ6+XorwspllRautWTykaKkpKVIbWlDR8pwwx5CnFTgnPz/HQ6/UPbmIdbxIN3+Vlu+ke40SHDitKBdlustyia0oxUFFSFCUpEt08GzzVNVXrSokmOhSMQ5ixs9dHYZ4HEWFafrZ6SjFQUVIUJSmCpYOy4xxdERSlVIXwgp5S+D6lcPKzPXSNx5qSTQEvKcimVUUpKipKSlSGwnea6KAMMRi+y4onSs42yFQlO0Q7uiKcorwsuvp9+McwzHi6zzd4dtO0/GzaNHwXFRUlJSom5kn0ymQlWGYoUU+pxzu2Xkg0/FEKsoZTnJ+FMUMbXMdiHl39vsFTbksKsmnr0ey7aKgoKYqSFMENsTlZsRMd8lIcvguKUnhB1nCK8x0Ppn2MRKnLHu436CkVZOmaUgxUlJSoaEUHJRIJe0rBYyFSFL5LWJTybJr2GIXUOvscsQue4xQM36UqKzHdUVFSoqJ/Mkok+u3ZQ1lxjkMvyHE+hMe6rlw0/PZblSdBT6ljjEJqQVEKXVMKmKF2ZTgqSoqiJEW/P0COxxU3qy0YvhvrA/SikXj4zvGU2nvHyFOyB/yFrikBmoEXBRUlJSp65osSib4B/6DgxCLVKeE+f6LhO8eDGasTYYfCd8E1pbENF050EhIlEVkjInUiUi8id0V4PUdEHrOvbxWRqpDX7rbtdSJyXTybIlJtbey3NrPPYowvisguEXlXRB4Vkdzkbs/kZjD3TpPvlBB6BvyD60WxyM9KbfguuHk2nigV5mUhMnaJDqdtosPgPqX8YKkhDd9FIq4oiYgbuB+4HqgFbhaR2rButwFtxpgFwH3AvfbaWmAdsBhYA/xARNxxbN4L3GeMqQHarO2RjDEb+DywzBhzPuC2/ZQkUYdJCaXH6yc3AU9pMHyXokQHXyC4phT7Y87tEorzsmju6h+TeQRTzYPhu+AalpYaikwintJyoN4Yc9AYMwCsB9aG9VkLPGwfPwGsFifgvBZYb4zpN8YcAuqtvYg27TWrrA2szRtGOAY4hxjmiYgHyAeOJ/B+FYuKkRKJvgH/YGguFtkeF9ke15hXS4jG4D6lBD7lphfm0tjZNybzCHpKU3KGrylpWnhkEhGl2cCxkOcNti1iH2OMD+gASmNcG629FGi3NsLHSmoMY8x7wHeAo8AJoMMY80ykNygit4vIdhHZ3tTUFPVGTD4SK/2vTC56BvzkJRC+AydklaosM3+CnhLAjKJcTo6RKHX0einIduOxKfT52W6yPS71lKKQiChF+kgK/w4drc9otSc9hohMw/GiqoFZQIGI3BKhL8aYB4wxy4wxy8rLyyN1mdSox6SE0uv1k2dLCMWjMM8zmH023vgCiZUZAphRmMvJjrEJ37X1DAwmN4BTi69Ei7JGJRFRagDmhDyv5Mww2GAfGyorAlpjXButvRkotjbCx0p2jGuAQ8aYJmOMF/gNcFkC71exqBgpkegd8JMXp+5dkNR6SgHcLombug5O+K6lux+vf/SPb2/pHqA0RJTACeE1d6koRSKR36zXgRqbFZeNkyywIazPBuBW+/hGYLNx8ok3AOts5lw1UANsi2bTXrPF2sDafHKEYxwFLhGRfLv2tBrYk9htUUCz75TI9Hh9g8VW41GUlzVmNeXi4Q/Ez7wLMqMoF2Og8fToe0ut3f3DPCWAmUW5nOwYm3DhRCeuKNn1mzuATTgf6o8bY3aJyNdF5GO220NAqYjUA18C7rLX7gIeB3YDTwOfM8b4o9m0tu4EvmRtlVrbIxljK05CxJvATvteHxjBPVIUJYTegQC5ia4p5WXR2ZeqRIcA7gS/Uc0scnaLHG/vHfV5tHYNDCY3BBnLNayJTkJfd4wxG4GNYW1fDXncB9wU5dp7gHsSsWnbDzKUPRfaPpIxvgZ8LdI1Snw0fKdEonfAl1D2HTj13lLlKfkCJm6JoSDVZQUAHGru5v1VJaM2B2NMxPDdzKJcWrsH6PP6Exb4yYJWdFCiYrT6nRKGMcZJdEjKU/KmpDpIIGBwx6nPF2R2cR5ZbuFgU/eIx4pEz4Cffl+AkoKcYe0zivIAOKXe0hmoKClRUU9JCaffFyBgSKjMEDiJDl6/oc87+gkE8fAFTMLhO4/bxdySfA41dyU9zv5Tp1n69Wf4p41nLlm32GSG0ilnekoAJ3Rd6QxUlBRFSZg+W1w1cU/JWSFIRQaeP2ASTnQAmFc+hUPNyXtKv3jtCJ19Pn784kFawqpCHO9w1qhmWc8oyAwrSprscCYqSkpU1FNSwumxJYOS8ZRg7E51jYU/iTUlgAUVjij1+5Iri7TreCdT7VlJT+86Oey1YOLErOLhZTdnFNrEio7RT6yY6KgoKVEJrilpRrgSJFgyKFgyJx7BOm+pOP7bHzC4khClC2YX4fUb6k6eTvgaYwz1jV189MJZVJXm8/S70URpuKdUkOOhpCCbY609CY81WVBRUuKiDpMS5PTggXWJiVKpXeAPD2uNB8lk34EjSgBvN3QkfE1z1wAdvV4WlE/husUzePVACx0hXuF77b2UFGRHzLCrLisYUbgw01FRUqKi4TslnOCeo+ApqvEoswv8zSkoqeM3ya0pVU7Lo6Qgm50N7QlfU9/oJEYsqJjCtYtn4AsYnq9rHPb6/PKCiNdWlaooRUJFSYmLhu+UIF2DZwMl5ikFN402j0GlhHj4/cmJkoiwdE4x2w61JnxNfdOQKF00p5iKqTlssutKxjihwHOnT4147bzyAk519qfsvKl0RUVJiYs6TEqQwWMYEhQlj9vFtPwsWrpTE75zJ3JuRQiXLyjjcEtPwms9Bxq7KMh2M7MoF5dL+GDtdJ6va6LP66ehrZfOPh+LZkQWpapSx4M63KzrSqGoKClR0fCdEs7QmlJi4TuAsik5NJ9OQfguEEhqTQnginPLAPjD/uaE+tc3djG/Yspg0ddrF8+gZ8DPH/Y388oBx8aKeaURrw1WkTjQlPzeqExGRUmJimbfKeF09ftwCRQkmBIOzsbRsTrVNRZevyHbk9xH3PzyKcwqyuW5PacS6l/f2MWC8imDzy+dV8r0why+99x+Htl6lNnFedRUTIl47fyKAjwuYc+JzqTmmOmoKCmKkjCn+3xMyfEkdBxEkLIpObSkINFhwBcgK8EyQ0FEhA8vmckL+5rinnd0us/Lyc4+5oeITrbHxZc/XMvO9zp4p6GDv7hqftR7leNxUzN9KruOqyiFklhgWJmUaPhOCaezz5tU6A6C4bvx95QG/AEKs5ObK8ANF83mP/5wiN/uPMEtl5wTtV9o5l0oH7twFmVTsjnd5+Pa2ukxxzp/ViGb9zZijElK6DMZ9ZSUqKgmKeF09fkS3qMUZGZRLqf7fYPrUeOF1x8gO0lPCaB2ZiGLZkzlP187ErOQ7P5TjihFyq67bH4Z1y2eEVdoFs8qpKV7gFOdsUW7u9/Hd5+pmxRliVSUFEVJGMdTSk6UZk9zqhm8NwZnFcXCCd8l/xEnIvzZB+ax9+TpmAkP+06dJsfjFHIdKRdUFgPw1rG2mP1+/WYD39tczzd/u3vEY00UVJSUqKTiuAElvWnr9jItPzt+xxBm2xI7Da3jK0pe/8hECZwQ3PTCHP7tuf1R/w72NXYxv3xKUnuhwrlgdhF5WW5eOxh7b9SOo86G3nffS7zaxERFRUmJikqSEk5rz8AZxzDEI1We0kiy74Jke1x88ZpzeeNIG0+9c+KM1wMBw86GdhbPKjyrOWZ7XCyrmsZrB1ti9jtoKz8cbe0ZrNSeqagoKYqSEMYY2roHkvaUygpyyPa4xj98dxaeEsBNy+ZQO7OQbzy1m9awTLwDTV209Xh5f/XZn1J7ybxS9p48fcYYQYwxHGrqoigvi4CBwy2ZXZpIRUmJikbvlFA6+3z4AmawdFCiuFzC7OI83msb/zWlkcwu7xIAACAASURBVCQ6BHG7hG/ftIT2Hi9/+6u38YecLvt8XRMAl1RH3hibDJfNd2y8uK8p4uut3QN09vn4oM3kO9CooqRMWlSVlCGC+3aSFSWAOSX54/4N/2zWlIIsnlXElz98Hs/tbeTv//td/AGDP2D41RvHuGB2EXNLR57kEOTCyuE188IJFm29emEFoJ4SACKyRkTqRKReRO6K8HqOiDxmX98qIlUhr91t2+tE5Lp4NkWk2trYb21mn8UYxSLyhIjsFZE9InJpcrdHUZQgrT2OKE0bgSjVVEzhQFMXgcD4fdHx+gMjXlMK5dbLqvirq+bz6LajfPzHr/L59TvYd6qLP79i3ijMksGaeS/sa4q4XnS4xamNVzurkOmFORxsmuSiJCJu4H7geqAWuFlEasO63Qa0GWMWAPcB99pra4F1wGJgDfADEXHHsXkvcJ8xpgZos7aTHsNe82/A08aYRcCFwJ5Eb4yi4TtlOK1d1lNKck0JHFHq8wbGbV3JGIPXb87aUwryt9ct5Ns3LqGhrZdN757ks1fM46NLZo6KbYDrbM28YFgwlCMt3bhtCLSqtEA9JWA5UG+MOWiMGQDWA2vD+qwFHraPnwBWi7NrbC2w3hjTb4w5BNRbexFt2mtWWRtYmzeMZAwRKQSuAB4CMMYMGGMSPyhF0eCdMozWswjf1Ux3qh7sb0z8VNezYcAfABgVTwmcvUs3LZvDq3evYu831nD3h84b1QoMl80vpWJqDr/afuyM14609DCrOJdsj4vqsgIOZ/gZTIn8j80GQu9Ug22L2McY4wM6gNIY10ZrLwXarY3wsZIdYx7QBPxURHaIyIMiEvG0LRG5XUS2i8j2pqbIi42KMtlpPO1UEyifmpP0tQvKnaoHwSoIY82AzxGlZGvfxUNE8IyS9xWKx+3ixosr2VLXeEbVhiMt3ZxT4nx0VZcV0NI9MOx020wjkbsb6X81/Et0tD6j1T6SMTzA+4AfGmMuArqBM9bDAIwxDxhjlhljlpWXl0fqMinR8J0SyomOPqblZ0U82jseRflZzCzKHbfio31eR5TyRjDXVPHx988hYODRbUcH24wxHGzu5hybUFFVFjyDKXO9pUREqQGYE/K8EjgerY+IeIAioDXGtdHam4FiayN8rJGM0WCM2Wrbn8ARKSVBgjvZtU6kAnCqs48ZRXkjvn7pnGJ2xCmnM1r0DjgJA3nZE6fm9DmlBVxz3nQefvUwXf1OsKihrZfTfT4WzyoChs5gyuR1pURE6XWgxmbFZeMkFWwI67MBuNU+vhHYbJxPtA3AOps5Vw3UANui2bTXbLE2sDafHMkYxpiTwDERWWivWQ1kfuGoMUA9JgUcT2lGYfKhuyAXzS3mWGsvTeNQMbzXZrFNJE8J4I5VC2jv8fLzVw8D8E6DU1YoWDlibkk+IkNp4plIXFGy6zd3AJtwstceN8bsEpGvi8jHbLeHgFIRqQe+hA2TGWN2AY/jiMHTwOeMMf5oNq2tO4EvWVul1nbSY9hr/j/gERF5B1gK/GOyN2gyo1qkhHK2ntL75k4DYMfRsfeWegYcTyM/icMI04Glc4pZvaiC72+u51hrDy/sa2RqrodaK0q5WW5mFeVltCgl5NsaYzYCG8PavhryuA+4Kcq19wD3JGLTth/Eyc4Lbx/JGG8ByyJdo8Qn6CFp+E7p9/lp7hpgRmHuiG2cP7uIHI+LVw+2cO3iGaM4uzMZ9JQmmCgB/MPaxVx334t88qGtnOjo46MXzhqW2p7pGXha0UGJi4bvlBPtTkbYzOKRi1JulptL55eyZW/jaE0rKoNrShMsfAdQOS2fH39yGZ19PqYX5vKFa2qGvV5dVsCh5u6MreI/cVYBlXHHaABPsRyyC+vBhfaRcvXCCr5Wt4tDzd1nbSsWQU9pooXvglxeU8Ybf38NwBn7oarKCujs89HaPUDplJGv8aUr6ikp0dHwnWI5YsNFVaVnJySrFjn12zbuPPM4iNGkx3pKI0lfTxdEJOIG3eoyJz08UzPwVJQURYnL4ZYeCrLdlCV5llI4c0ryWVFdwq+2HxvT8FMwfDdRPaVYVJc51TEONfekeCZjg4qSEhUN3ilBDjV3U1VWMCqldW5aNofDLT28Gudgu7Oh01Y8mJqbNWZjpIrKaXlkuWXcSjaNNypKSlQydB1VGQEHmrpGbQ3oI0tmUjYlh/u31I+KvUh09HrJy3KPWu27dCLL7eK8mYW8cywzj0bPvP8xRVFGlY5eLw1tvZw38+yO/g6Sm+Xms1fM4+X6Fl49MDbeUkevl+L8zPOSgiypLOLd9zrG9SiQ8UJFSYmKZt8pAHtOOPXqglUFRoNbLjmHyml5fPm/d0Y8Q+hsae/1UpSXyaJUzOl+HwczcL+SipISFQ3fKQC7bRHV2lEUpbxsN//4vy7gYFM333hq9Kt/dfR6KcxgUbqwshiAt49l3mk8KkqKosTk3fc6KJ+aQ8XUkW+cjcQV55bz2Svn8cjWozzw4oFRtd3eM0BxBovSgoopFGS7x63A7Xiim2eVqKijpABsPdTKsnOmjYntv7tuEcdae/jHjXs53efjC9eci9t19hl+pzr7uWRe6SjMMD1xu4RlVSW8drA11VMZddRTUqKSqWVMlMQ51trDe+29rKguGRP7bpfwvXUXcdPFlfz75nr+9CdbOdZ6dvtvegZ8dPR6mVE0up5dunHJvFLqG7sGD1/MFFSUFEWJytZDzjfxFWPodXjcLv75xiV8648u4M0j7az+7gt8e9PewePXkyV4cuvMDBelS+c7/ydbM8xbUlFSoqJ+kvLcnlOUT81h4fSpYzqOiLBu+Vye++srWbN4BvdvOcBl33qOrz75LgeakjtC/UiL42nNLs4fi6mmDefPKmRKjmdMNyGnAhUlJTqqSpOaPq+fF/Y1cW3tdFyjsM6TCLOK8/jezRfx7Bev4KNLZvHotqOs/pcX+MR/vMbGnSfw+gNxbdSdciodjLWQphqP28Xy6hJeqW9O9VRGFRUlJSrBfUqCVmSdjLywr4meAT/XjfHZR5GomT6Vb990IS/ftYq/ufZcjrT08FePvMnKb23mu8/Ucby9N+q1777XwcyiXIoyePNskCtqyjjc0pNR5yupKClRCdgvpeP0JVlJMx57/RgVU3MG1y5SQcXUXO5YVcOLf3c1D926jMWzCvn3LfVcfu9m/vzn23lhX9OwqgY+f4CX65u5bH5ZyuY8nly10Km6/nzd2J9RNV5oSrgSlYDNvhuNIpzKxOK99l6er2vkc1cvGHbqaapwu4TV501n9XnTOdbawy+3HeXx14/x7O5TzC3J5xMr5vIny+awaddJ2nq8XH/++Ht3qaCqrIB5ZQVsqWviUyurUz2dUUFFSYlKBpbVUhLkxy8cwGWTD9KNOSX53LlmEV+4poZNu07xn68d4Vu/28s/P72XgIHlVSWD5zZNBq5eVMEvXjtC74B/Qh7/Ho6KkhKV4D4lV+q/KCvjyHvtvazfdoybls1hdnFeqqcTlRyPm49dOIuPXTiL/adO85sd7zElx8Otl1WNW2JGOnD1wgoeeukQrx5sZtWi6amezlmT0MeNiKwRkToRqReRuyK8niMij9nXt4pIVchrd9v2OhG5Lp5NEam2NvZbm9kjHcO+5haRHSLyVOK3RYEhT0kTHSYX3/if3YjAHasWpHoqCVMzfSp3rlnE565ewJScyfVd+/3V08jPdrN5b2asK8UVJRFxA/cD1wO1wM0iUhvW7TagzRizALgPuNdeWwusAxYDa4AfWJGIZfNe4D5jTA3QZm0nPUbI3P4PsCex26GEMrSmlOKJKOPGxp0neHrXSb5wzblp7SUpQ+R43KxcUMaWvU0ZUYUlEU9pOVBvjDlojBkA1gNrw/qsBR62j58AVouzOr4WWG+M6TfGHALqrb2INu01q6wNrM0bRjgGIlIJfBh4MLHboYQSFCWXqtKk4EBTF3c+8Q5LKov4sw9kxqL5ZGHVogrea+9l36nkNhqnI4mI0mzgWMjzBtsWsY8xxgd0AKUxro3WXgq0WxvhYyU7BsC/An8HxNxxJyK3i8h2Edne1NQUq+ukwgyG75RMp+l0P3/+8HayPC5+eMvFaZFxpyTO1TY1PBNCeIn85kX6TAr3EaP1Ga32pMcQkY8AjcaYNyK8PryzMQ8YY5YZY5aVl5fH6z5pGDzkT1Upo2nu6ud/P/gaJzr6+PEnL9aw3QRkRlEui2cVsnnvqVRP5axJRJQagDkhzyuB49H6iIgHKAJaY1wbrb0ZKLY2wsdKdoyVwMdE5DBOeHCViPxnAu9XsQQ3z6omZS57T3ay9vsvc7S1h4c+tYz3V41NNXBl7Fm9qII3jrTR3jOyQrbpQiKi9DpQY7PisnGSCjaE9dkA3Gof3whsNs6K2wZgnc2cqwZqgG3RbNprtlgbWJtPjmQMY8zdxphKY0yVtb/ZGHNLgvdFQTfPZjLGGJ54o4E//sEr+AIBHv/spZOmCkKmcvWiCgLGKQ81kYmbO2mM8YnIHcAmwA38xBizS0S+Dmw3xmwAHgJ+ISL1ON7LOnvtLhF5HNgN+IDPGWP8AJFs2iHvBNaLyDeBHdY2IxlDOTuCa0qTaMvHpKCxs4+//+93eWb3KZZXl/C9dRdl/NlDk4ELK4spLchm895G1i4NX/afOCSU0G+M2QhsDGv7asjjPuCmKNfeA9yTiE3bfhCbPRfWnvQYIa8/Dzwf7XUlMoOekgbwMoI+r5+HXjrED7bU4/Ubvvyh87jt8upJtdE0k3G5hKsWVvD7Pafw+QN4JmiyyuTaZaYkxeDmWf3MmtB4/QH+a8d7fO+5/TS09XJt7XT+74fOo6qsINVTU0aZ1edV8Os3G9hxrH3Crg+qKClR0TWliU2/z88TbzTww+cP0NDWy/mzC/nnP17CZQt07ShTubymDI9LeG5Po4qSknmYwfCdMpFoOt3Po9uO8sjWI5zq7GfpnGK+sfZ8rlpYrl8wMpzC3CyWV5ewZW8jd12/KNXTGREqSkpUNHw3sXj7WDsPv3KYp945wYA/wAdqyvj2jRfygZoyFaNJxKpFFXzzt3toaOuhctrEOxJeRUmJitHad2nP6T4vv33nBOtfP8Zbx9opyHZz8/I5/OllVcwvn5Lq6Skp4GorSlv2NvLJS6tSPZ2kUVFSoqJVwtMTYwzbDrXy+PYGNu48Qa/Xz4KKKfy/j9byxxdXMjU3848BV6Izr6yAqtJ8nlNRUjKNoYKsKZ6IAsDJjj5+/WYDv9p+jMMtPUzJ8XDDRbP5k2WVLJ1TrCE6BXASk65eVMEjW4/SM+AjP3tifcxPrNkq48pgFXz9sEsZp/u8PLPrFE++fZyX9jcRMLCiuoTPr65hzfkzJtwHjjI+rF40nZ++fJhX6lu4pnZiHfynv9FKVAKafZcS+n1+Xqhr4sm3j/P73afo9wWonJbHX121gBsvrtT9RUpclleXUJDtZnNdo4qSkjn47KKSR+N3Y04gYNh6qJUNb7/Hxp0n6ej1UlKQzcffP4e1S2fzvrkanlMSJ9vj4gM15WzZ24gxZkL97qgoKVHx+R1RcqsojQnGGHYd72TD28fZ8NZxTnb2kZ/t5rrFM/jY0llcvqBMzzVSRsyqRRU8veske06cpnZWYaqnkzAqSkpUfPbsCj15dvQICtHGnSfYuPMEh1t68LiEK88t5+4PLeKDtdN1nUgZFa5c6JwN91J9k4qSkhkEw3fmjDMdlWQICtFvd57gd1aI3C7h0nml/PkV87j+/JmUFGSneppKhjG9MJcFFVN4qb6F26+Yn+rpJIyKkhIVn9/xlIxqUtKECtHGnSc4YoXosvmlfPbK+VxbO53SKTmpnqaS4Vy+oIz1rx+l3+cnx+NO9XQSQkVJiYrXr2qUDMYY3n1vSIiOtg4J0V9eOZ9rF89Qj0gZV1YuKONnrxxmx9F2LplXmurpJISKkhIV/2D4TomGP2DYcbSNTbtOsmnXKY62OmtEly0o43NXz+fa2hlMUyFSUsSKeSW4XcLL9c0qSsrEJ5jooAyn3+fnlfoWntl9kmd3n6K5a4Bst4tL55dyx9UL+GDtdBUiJS0ozM3iwsoiXqpv5q+vXZjq6SSEipISlcHwnbpKnO7zsqWuiWd2neT5uia6+n1MyfFw1cJyrls8g6sWlmvNOSUtWbmgjPu31NPZ56VwAvyOqihlODuOtlE5LZ/yqckvqvsnefZd4+k+fr+7kU27TvLKgWa8fkPZlGw+euFMrl08g8vml06YxWNl8rJyQRn/vrme1w60cO3iGameTlxUlDKc//WDV6iYmsO2L1+T9LVe/+QL3x1q7ubZ3c760JtH2zAG5pbk86nLqrhu8QwumjtNNxMrE4qL5haTl+Xm5frmzBElEVkD/BvgBh40xnwr7PUc4OfAxUAL8HFjzGH72t3AbYAf+LwxZlMsmyJSDawHSoA3gU8aYwaSHUNE5tj+M4AA8IAx5t+SvUETmYD1dBpP94/o+n5f5qeE9/v8bDvUypa9TWypa+RQczcAi2cV8sVrzuXaxdNZOH3qhCrToiih5HjcLK8u4eUDLameSkLEFSURcQP3Ax8EGoDXRWSDMWZ3SLfbgDZjzAIRWQfcC3xcRGqBdcBiYBbwexE5114Tzea9wH3GmPUi8iNr+4cjGMMH/LUx5k0RmQq8ISLPhs07o+nx+s/q+t6Bs7s+XTnV2ceWvY1s3tvIy/XNdA/4yfa4uHReKZ+6rIpViyqYUzLxTuxUlGhcOr+Ub/1uL81d/ZSl+f64RDyl5UC9MeYggIisB9YCoR/ua4H/Zx8/AXxfnK+Wa4H1xph+4JCI1Ft7RLIpInuAVcAnbJ+Hrd0fJjuGMeZV4ASAMea0tT07bN4ZTXe/76yu77WiNtEdJX/A8HZD+6AQ7TreCcCsolxuuGg2qxZVcNn8MvKydX1IyUyWV5cAsO1QKx+6YGaKZxObRERpNnAs5HkDsCJaH2OMT0Q6gFLb/lrYtbPt40g2S4F2Y4wvQv+RjAGAiFQBFwFbI71BEbkduB1g7ty5kbpMSLrOUpR6rKdkJmD8rqPXy4v7mtiyt5Hn9zXR2j2AS+Dic6bxd2sWsmpRhYbllEnDBbOLyMtys/VgS0aIUqS/2vBPqWh9orVHKn0cq/9IxnAuEpkC/Br4gjGmM0JfjDEPAA8ALFu2bOJ9AkfhbD2lvrMM/40nxhj2nepi895GttQ18saRNvwBw7T8LK5aWMFVC8u58txyivN1/5Ay+chyu7j4nGlsPdSa6qnEJRFRagDmhDyvBI5H6dMgIh6gCGiNc22k9magWEQ81lsK7Z/0GCKShSNIjxhjfpPAe80oTvc5opTtGdnxBz0DzvXpqtK9A35ePdjsCNHeJt5r7wWgdmYhf3nlfK5eVMHSOcWaLacoOCcWf/f3+2jvGUjrL2eJiNLrQI3NinsPJ6ngE2F9NgC3Aq8CNwKbjTFGRDYAvxSR7+IkIdQA23C8mzNs2mu2WBvrrc0nRzKGXW96CNhjjPlusjcmE+js9QKQP8K1kg57fTpF7xraegbXhl450EK/L0B+tpuVC8q4Y9UCrl5YwYyi3FRPU1HSjuXVJRgDrx9u44NpfBptXFGy6zd3AJtw0rd/YozZJSJfB7YbYzbgfPj/wiYZtOKIDLbf4zjJBT7gc8YYP0Akm3bIO4H1IvJNYIe1TbJjiMjlwCeBnSLylrXxf40xG0d2qyYeQU8pPyt5UeoZ8NHnTf0+JZ8/wBtH2thc18iWvY3sO9UFwDml+dy8fC6rFlWwYl6JbmJVlDhcOKeYbI+LrQdbJrYoAdgP8o1hbV8NedwH3BTl2nuAexKxadsPMpShF9qe1BjGmJeIvN40aejsczyd3BF4Si1dA4OPx9tRaunq5/m6JjbXNfLiviZO9/nIcgvLq0v4k2VzuHpRBfPKCjRJQVGSIDfLzdI5xWw7nN7rSlrRIYNp7XaEZSRexEg33I6E4NlDm21Y7u2GdoyB8qk5XH/+DFYtqmDlgjKtLacoZ8kl1SV8f0s9p/u8afv3pKKUwRy3C//Byg7JcNhWNphbkj8mi0pd/T5e2t/MFpst13i6HxFYUlnMF1afy6pFFSyeVYhLkxQUZdRYXl1KYHM9bxxp46qFFameTkRUlDKY4+19AHhHcARFfVMXbpcwtyR/MAx4thxr7eH3e06xeW8jrx1swes3TM3xcMW55Vy9yEnbTvfd5ooykXnfOcV4XMLWQ60qSsr4EggY9pxwtmX5R+ApbT/cyvmzi8hyj9xT8fkD7DjWznN7Gnluzyn2NzpJCvPLC/j0ymquXljBsqppZLlHlrKuKEpy5Gd7WFJZxNaD6VsHT0UpQ9l1vJPT/T5cAr4kjzVv7OzjzaPt/MWV89h9vDOp6F2f18/zdY1s2nWKLXWNtPd48biEFfNKWLd8LqsXVVBVVpDku1EUZbRYXl3Kg384SO+APy1La6koZSg/feUQ2W4X19RWsP1wW1LXPvDiQfwBw40Xz+EbJ3bHPU+pd8ARot/uPMHmvY30DPiZlp/FqkUVrF40nQ+cWzYhDhdTlMnAinkl/OiFA7x5tI2VC8pSPZ0zUFHKQB7ddpTfvPkef3HlfLr7ffiSCN89u/sUP3n5EDcvn0N1WQFul0T1tHY2dLD+9aNseOs4p/t9lBZkc8NFs/nwBTNZUV2CR8NyipJ2LDtnGi6BrQdbVJSUsaXf5+efNu7lZ68c5spzy/niB2v41u/2JnRYnz9gePAPB/n2pjouqCzmyx+uBSDH42LAN/z6l+ub+bfn9rPtUCs5HhcfXjKTG99XyYp5pVrSR1HSnKm5WSyeVcRraVoHT0UpQ3i+rpF/+J/dHGru5jMrq7nr+kVke1x4Yng64OwRemFfE/+0cS91p05z/fkzuPfGJUzJcX41cjzuwcP+WrsH+Mp/v8tvd55gemEOX/lILTdeXElRnobmFGUisaK6hJ+/doQ+r5/cEVR8GUtUlCY42w+38u+b63lhXxPzygr4+WeWc8W55YOve9wufBFSwvu8fv7n7eP89OXD7D7RydySfO7/xPv40AUzhlVKyPa46PcFONnRxyf+4zUa2nr5m2vP5c8+MC/tfpkVRUmMFfNKefClQ7x9rJ0V80pTPZ1hqChNQLz+AM/taeSnLx9i66FWSgqyufv6RXx6ZfUZFcGzXILXbzDGICI0nu7jkdeO8sjWIzR3DXDu9Cl8648u4I/eVxmxmniOx0XvgI+/fOQNTnX28cs/X8GyqpLxequKoowBy6tKEIGth1pVlJSRU9/Yxa/eOMav32iguWuAmUW5fOUjtdy8fA752ZH/K4PJBq3dA/zLs/v41fZjeP2GVYsq+MzKalYuKI1ZQy4ny0X3gJ8dR9v5148vVUFSlAygKD+LhdOnsvVQC87BCumDilIaY4zhnYYONu06yTO7T1Hf6FRZWLWogpuXz+GKmvK4GW4eu/n1j3/4Csfaerl5+Rw+s7KaeeVTEppDsG5eTcUU1i6ddXZvSFGUtOGSeaWsf/0oA77AiM9cGwtUlNIMrz/A1oOtPLP7JM/sOsXJzj7cLmFFdQmfvOQcrj9/BhWFiZ8XlOVyftkOt/Scsd6UCO+bWwzA51fXaFVuRckgVlSX8LNXDrPzvQ4uPmdaqqcziIpSGtAz4OPFfU1s2nWK5/acorPPR26WiyvPLedvaxey+ryKEZ8UGdyxfWFlUdKCBHDVwgq2fXk1FVP14DxFySSWVzuh+K2HWlSUFGjrHuD3e06xadcp/rC/iX5fgOL8LD5YO4PrFk/nAzXlo1ICZOWCMiqn5fG1jy0esQ0VJEXJPEqn5LCgYgpbD7byV1elejZDqCiNI42dfWzadZKnd53ktYOt+AOGWUW53Lx8Ltcuns7yqtGvglBdVsBLd64aVZuKomQGK6pLePKt4/j8gbSpwKKiNMb0ef1s2nWSX21v4OUDzRgD88oL+Isr53Hd4hlcMLtI12oURUkJK+aV8sjWo+w+0cmSyuJUTwdQURoz2nsG+Nkrh/nZK4dp7/FSOS2Pz6+q4SNLZlIzfWqqp6coisIldl3p1QMtKkqZij9g+OXWI/zzpjpO9/m45rzpfHplFZfOK9VTVBVFSSsqCnOpnVnIs7tP8dkr56d6OgAkFEQUkTUiUici9SJyV4TXc0TkMfv6VhGpCnntbtteJyLXxbMpItXWxn5rM3u0xxgr2nsG+NRPt/GVJ3dxYWUxT3/hAzx46zJWLihTQVIUJS1Zc/4M3jjaRuPpvlRPBUhAlETEDdwPXA/UAjeLSG1Yt9uANmPMAuA+4F57bS2wDlgMrAF+ICLuODbvBe4zxtQAbdb2aI8x6rR1D3DTj15l68FW/umPLuAXty1n0YzCsRpOURRlVLhu8QyMgafePpHqqQCJeUrLgXpjzEFjzACwHlgb1mct8LB9/ASwWpzV+7XAemNMvzHmEFBv7UW0aa9ZZW1gbd4wmmMkdluSY8AX4FM/e50jrT387NPv5+blczV5QVGUCcG506ewvKqE723ez/H23lRPJ6E1pdnAsZDnDcCKaH2MMT4R6QBKbftrYdfOto8j2SwF2o0xvgj9R2uMMxCR24Hb7dMuEWkBmiP1jcfKe0ZyVdpSxgjvQwai92IIvRcOGXcfZn9txJeWAeeMxhwSEaVIX/nDD+iJ1idaeyQPLVb/0RzjzEZjHgAeCD4Xke3GmGWR+k4m9D4MofdiCL0XDnofhrD3omo0bCUSvmsA5oQ8rwSOR+sjIh6gCGiNcW209mag2NoIH2u0xlAURVHSlERE6XWgxmbFZeMkFWwI67MBuNU+vhHYbIwxtn2dzZyrxqmRvi2aTXvNFmsDa/PJ0RwjsduiKIqipIK44Tu7fnMHsAlwAz8xxuwSka8D240xG4CHgF+ISD2O97LOXrtLRB4HdgM+4HPGGD9AJJt2yDuB9SLyTWCHtc0ojxGPTCcqJAAAA8RJREFUB+J3mRTofRhC78UQei8c9D4MMWr3QhxnQ1EURVFST3pU4FMURVEUVJQURVGUNEJFKYTxLkuUCkTkJyLSKCLvhrSViMiztrTTsyIyzbaLiHzP3o93ROR9IdfcavvvF5FbI42VzojIHBHZIiJ7RGSXiPwf2z4Z70WuiGwTkbftvfgH2z5qJb8mErYizA4Reco+n6z34bCI7BSRt0Rku20b+78PY4z+c9bV3MABYB6QDbwN1KZ6XmPwPq8A3ge8G9L2z8Bd9vFdwL328YeA3+HsBbsE2GrbS4CD9uc0+3haqt9bkvdhJvA++3gqsA+nHNVkvBcCTLGPs4Ct9j0+Dqyz7T8C/tI+/ivgR/bxOuAx+7jW/t3kANX278md6vc3gvvxJeCXwFP2+WS9D4eBsrC2Mf/7UE9piHErS5RKjDEv4mQvhhJawim8tNPPjcNrOHvIZgLXAc8aY1qNMW3Aszh1BycMxpgTxpg37ePTwB6cSiCT8V4YY0yXfZpl/xlGr+TXhEFEKoEPAw/a56NZ+iwTGPO/DxWlISKVU5odpW+mMd0YcwKcD2ugwrZHuycZda9s2OUiHA9hUt4LG7J6C2jE+eA4QIIlv4DQkl8T/V78K/B3QMA+T7j0GZl1H8D5YvKMiLwhTik2GIe/Dz1PaYhEyilNNpIt7TThEJEpwK+BLxhjOiV6Id2MvhfG2du3VESKgf8CzovUzf7MyHshIh8BGo0xb4jIVcHmCF0z+j6EsNIYc1xEKoBnRWRvjL6jdi/UUxpiMpclOmVdbezPRtue0SWcRCQLR5AeMcb8xjZPynsRxBjTDjyPsy4wWiW/JgorgY+JyGGc8P0qHM9pst0HAIwxx+3PRpwvKssZh78PFaUhJnNZotASTuGlnf7UZtZcAnRYl30TcK2ITLPZN9fatgmDjf0/BOwxxnw35KXJeC/KrYeEiOQB1+CssY1Wya8JgTHmbmNMpXEKi67DeV//m0l2HwBEpEBEpgYf4/xev8t4/H2kOsMjnf7hZJDsw4mnfznV8xmj9/gocALw4nyLuQ0nDv4csN/+LLF9BeegxAPATmBZiJ3P4Czg1gOfTvX7GsF9uBwnjPAO8Jb996FJei+W4JT0esd+8HzVts/D+TCtB34F5Nj2XPu83r4+L8TWl+09qgOuT/V7O4t7chVD2XeT7j7Y9/y2/bcr+Hk4Hn8fWmZIURRFSRs0fKcoiqKkDSpKiqIoStqgoqQoiqKkDSpKiqIoStqgoqQoiqKkDSpKiqIoStqgoqQoiqKkDf8/Z+T2JUHicG0AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.clf()\n", "# plt.plot(x_part, calcs, '.')\n", @@ -778,7 +788,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -794,7 +804,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -810,7 +820,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -823,7 +833,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -880,7 +890,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -964,7 +974,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -973,7 +983,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -982,7 +992,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -991,7 +1001,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "scrolled": false }, @@ -1039,7 +1049,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -1056,7 +1066,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1080,7 +1090,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -1107,7 +1117,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -1130,7 +1140,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -1139,7 +1149,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -1155,7 +1165,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1185,7 +1195,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -1199,7 +1209,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -1217,7 +1227,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -1231,7 +1241,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -1252,7 +1262,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -1262,7 +1272,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -1293,7 +1303,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1310,7 +1320,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1419,16 +1429,242 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "## Reset params" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "def reset_param_values(): \n", + " jpsi_m.set_value(jpsi_mass)\n", + " jpsi_s.set_value(jpsi_scale)\n", + " jpsi_p.set_value(jpsi_phase)\n", + " jpsi_w.set_value(jpsi_width)\n", + " psi2s_m.set_value(psi2s_mass)\n", + " psi2s_s.set_value(psi2s_scale)\n", + " psi2s_p.set_value(psi2s_phase)\n", + " psi2s_w.set_value(psi2s_width)\n", + " p3770_m.set_value(p3770_mass)\n", + " p3770_s.set_value(p3770_scale)\n", + " p3770_p.set_value(p3770_phase)\n", + " p3770_w.set_value(p3770_width)\n", + " p4040_m.set_value(p4040_mass)\n", + " p4040_s.set_value(p4040_scale)\n", + " p4040_p.set_value(p4040_phase)\n", + " p4040_w.set_value(p4040_width)\n", + " p4160_m.set_value(p4160_mass)\n", + " p4160_s.set_value(p4160_scale)\n", + " p4160_p.set_value(p4160_phase)\n", + " p4160_w.set_value(p4160_width)\n", + " p4415_m.set_value(p4415_mass)\n", + " p4415_s.set_value(p4415_scale)\n", + " p4415_p.set_value(p4415_phase)\n", + " p4415_w.set_value(p4415_width)\n", + " rho_m.set_value(rho_mass)\n", + " rho_s.set_value(rho_scale)\n", + " rho_p.set_value(rho_phase)\n", + " rho_w.set_value(rho_width)\n", + " omega_m.set_value(omega_mass)\n", + " omega_s.set_value(omega_scale)\n", + " omega_p.set_value(omega_phase)\n", + " omega_w.set_value(omega_width)\n", + " phi_m.set_value(phi_mass)\n", + " phi_s.set_value(phi_scale)\n", + " phi_p.set_value(phi_phase)\n", + " phi_w.set_value(phi_width)\n", + " Dstar_m.set_value(Dstar_mass)\n", + " DDstar_s.set_value(0.0)\n", + " DDstar_p.set_value(0.0)\n", + " D_m.set_value(D_mass)\n", + " Dbar_m.set_value(Dbar_mass)\n", + " Dbar_s.set_value(0.0)\n", + " Dbar_p.set_value(0.0)\n", + " tau_m.set_value(pdg['tau_M'])\n", + " Ctt.set_value(0.0)\n", + " b0_0.set_value(0.292)\n", + " b0_1.set_value(0.281)\n", + " b0_2.set_value(0.150)\n", + " bplus_0.set_value(0.466)\n", + " bplus_1.set_value(-0.885)\n", + " bplus_2.set_value(-0.213)\n", + " bT_0.set_value(0.460)\n", + " bT_1.set_value(-1.089)\n", + " bT_2.set_value(-1.114)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "# Analysis" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Toy 0: Generating data...\n", + "Toy 0: Data generation finished\n", + "Toy 0: Loading data...\n", + "Toy 0: Loading data finished\n", + "Toy 0: Fitting pdf...\n", + "------------------------------------------------------------------\n", + "| FCN = 241.5 | Ncalls=33 (33 total) |\n", + "| EDM = 2.47E-06 (Goal: 5E-06) | up = 0.5 |\n", + "------------------------------------------------------------------\n", + "| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n", + "------------------------------------------------------------------\n", + "| True | True | False | False |\n", + "------------------------------------------------------------------\n", + "| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n", + "------------------------------------------------------------------\n", + "| False | True | True | True | False |\n", + "------------------------------------------------------------------\n", + "Function minimum: 241.45590864280427\n", + "----------------------------------------------------------------------------------------------\n", + "| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n", + "----------------------------------------------------------------------------------------------\n", + "| 0 | p4040_s | 1.1 | 0.5 | | |0.00501244| 2.01499 | |\n", + "| 1 | p4160_s | 2.7 | 0.6 | | | 0.71676 | 3.68324 | |\n", + "| 2 | p4415_p | 4.2 | 2.2 | | |-6.28319 | 6.28319 | |\n", + "| 3 | bplus_1 | -0.885 | 0.000 | | | -2 | 2 | |\n", + "| 4 | Ctt | 0.16 | 0.21 | | | -0.5 | 0.5 | |\n", + "| 5 | DDstar_p | -4.8 | 1.9 | | |-6.28319 | 6.28319 | |\n", + "| 6 | Dbar_p | 0.11 | 9.47 | | |-6.28319 | 6.28319 | |\n", + "| 7 | p4415_s | 0.18 | 0.16 | | |0.126447 | 2.35355 | |\n", + "| 8 | p3770_p | 2.7 | 2.7 | | |-6.28319 | 6.28319 | |\n", + "| 9 | DDstar_s | 0.020 | 0.138 | | | -0.3 | 0.3 | |\n", + "| 10| bplus_2 | -2.130E-1 | 0.019E-1 | | | -2 | 2 | |\n", + "| 11| p3770_s | 1.6 | 0.6 | | |0.918861 | 4.08114 | |\n", + "| 12| Dbar_s | 0.07 | 0.13 | | | -0.3 | 0.3 | |\n", + "| 13| bplus_0 | 0.466 | 0.000 | | | -2 | 2 | |\n", + "| 14| p4160_p | 6.0 | 0.6 | | |-6.28319 | 6.28319 | |\n", + "| 15| p4040_p | -0.11 | 3.88 | | |-6.28319 | 6.28319 | |\n", + "----------------------------------------------------------------------------------------------\n", + "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "| | p4040_s p4160_s p4415_p bplus_1 Ctt DDstar_p Dbar_p p4415_s p3770_p DDstar_s bplus_2 p3770_s Dbar_s bplus_0 p4160_p p4040_p |\n", + "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "| p4040_s | 1.000 0.045 0.009 -0.001 -0.002 -0.003 -0.082 -0.005 -0.013 -0.009 -0.001 0.011 -0.032 -0.000 -0.065 0.621 |\n", + "| p4160_s | 0.045 1.000 0.003 -0.000 0.000 -0.001 -0.040 0.000 -0.009 -0.008 -0.000 0.002 -0.020 -0.000 0.093 0.075 |\n", + "| p4415_p | 0.009 0.003 1.000 0.000 -0.000 0.001 -0.008 -0.093 -0.002 -0.002 0.000 0.000 -0.008 0.000 0.008 0.013 |\n", + "| bplus_1 | -0.001 -0.000 0.000 1.000 0.000 -0.000 0.000 0.000 0.000 -0.000 0.677 -0.000 0.000 0.450 -0.000 -0.001 |\n", + "| Ctt | -0.002 0.000 -0.000 0.000 1.000 0.001 0.019 0.000 -0.001 0.002 0.000 -0.000 0.004 0.000 -0.002 -0.002 |\n", + "| DDstar_p | -0.003 -0.001 0.001 -0.000 0.001 1.000 -0.030 0.001 -0.003 0.298 -0.000 0.000 0.012 -0.000 -0.002 -0.004 |\n", + "| Dbar_p | -0.082 -0.040 -0.008 0.000 0.019 -0.030 1.000 0.011 0.059 -0.091 0.000 0.004 0.364 0.000 -0.008 -0.098 |\n", + "| p4415_s | -0.005 0.000 -0.093 0.000 0.000 0.001 0.011 1.000 0.002 0.002 0.000 -0.000 0.005 0.000 -0.003 -0.009 |\n", + "| p3770_p | -0.013 -0.009 -0.002 0.000 -0.001 -0.003 0.059 0.002 1.000 -0.000 0.000 -0.128 0.029 0.000 -0.003 -0.003 |\n", + "| DDstar_s | -0.009 -0.008 -0.002 -0.000 0.002 0.298 -0.091 0.002 -0.000 1.000 -0.000 0.001 -0.037 -0.000 -0.002 -0.008 |\n", + "| bplus_2 | -0.001 -0.000 0.000 0.677 0.000 -0.000 0.000 0.000 0.000 -0.000 1.000 -0.000 0.000 0.190 -0.000 -0.001 |\n", + "| p3770_s | 0.011 0.002 0.000 -0.000 -0.000 0.000 0.004 -0.000 -0.128 0.001 -0.000 1.000 0.002 -0.000 0.002 0.015 |\n", + "| Dbar_s | -0.032 -0.020 -0.008 0.000 0.004 0.012 0.364 0.005 0.029 -0.037 0.000 0.002 1.000 0.000 -0.001 -0.031 |\n", + "| bplus_0 | -0.000 -0.000 0.000 0.450 0.000 -0.000 0.000 0.000 0.000 -0.000 0.190 -0.000 0.000 1.000 -0.000 -0.001 |\n", + "| p4160_p | -0.065 0.093 0.008 -0.000 -0.002 -0.002 -0.008 -0.003 -0.003 -0.002 -0.000 0.002 -0.001 -0.000 1.000 -0.074 |\n", + "| p4040_p | 0.621 0.075 0.013 -0.001 -0.002 -0.004 -0.098 -0.009 -0.003 -0.008 -0.001 0.015 -0.031 -0.001 -0.074 1.000 |\n", + "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "Hesse errors: OrderedDict([(, {'error': 0.5475039449324027}), (, {'error': 0.6200845337212617}), (, {'error': 2.1784786478303917}), (, {'error': 0.00045254373012648674}), (, {'error': 0.2132717560841134}), (, {'error': 1.8581258189915308}), (, {'error': 9.473191034944215}), (, {'error': 0.1559314560994186}), (, {'error': 2.6685592600641765}), (, {'error': 0.13789484579224884}), (, {'error': 0.0019374513205349109}), (, {'error': 0.5908195162116194}), (, {'error': 0.13394604673222416}), (, {'error': 4.94973454072678e-05}), (, {'error': 0.631080637656769}), (, {'error': 3.8768081157581706})])\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:160: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Toy 1/2\n", + "Time taken: 2 min, 59 s\n", + "Projected time left: 2 min, 59 s\n", + "Toy 1: Generating data...\n", + "Toy 1: Data generation finished\n", + "Toy 1: Loading data...\n", + "Toy 1: Loading data finished\n", + "Toy 1: Fitting pdf...\n", + "------------------------------------------------------------------\n", + "| FCN = 241.4 | Ncalls=39 (39 total) |\n", + "| EDM = 1.08E-06 (Goal: 5E-06) | up = 0.5 |\n", + "------------------------------------------------------------------\n", + "| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n", + "------------------------------------------------------------------\n", + "| True | True | False | False |\n", + "------------------------------------------------------------------\n", + "| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n", + "------------------------------------------------------------------\n", + "| False | True | True | True | False |\n", + "------------------------------------------------------------------\n", + "Function minimum: 241.3777412895305\n", + "----------------------------------------------------------------------------------------------\n", + "| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n", + "----------------------------------------------------------------------------------------------\n", + "| 0 | p4040_s | 0.45 | 0.18 | | |0.00501244| 2.01499 | |\n", + "| 1 | p4160_s | 2.91 | 0.28 | | | 0.71676 | 3.68324 | |\n", + "| 2 | p4415_p | 3.1 | 1.4 | | |-6.28319 | 6.28319 | |\n", + "| 3 | bplus_1 | -0.885 | 0.000 | | | -2 | 2 | |\n", + "| 4 | Ctt | -0.022 | 0.107 | | | -0.5 | 0.5 | |\n", + "| 5 | DDstar_p | -1.8 | 1.3 | | |-6.28319 | 6.28319 | |\n", + "| 6 | Dbar_p | 4.3 | 1.0 | | |-6.28319 | 6.28319 | |\n", + "| 7 | p4415_s | 1.59 | 0.23 | | |0.126447 | 2.35355 | |\n", + "| 8 | p3770_p | -0.8 | 1.1 | | |-6.28319 | 6.28319 | |\n", + "| 9 | DDstar_s | -0.03 | 0.07 | | | -0.3 | 0.3 | |\n", + "| 10| bplus_2 | -2.130E-1 | 0.019E-1 | | | -2 | 2 | |\n", + "| 11| p3770_s | 3.59 | 0.25 | | |0.918861 | 4.08114 | |\n", + "| 12| Dbar_s | -0.08 | 0.06 | | | -0.3 | 0.3 | |\n", + "| 13| bplus_0 | 0.466 | 0.000 | | | -2 | 2 | |\n", + "| 14| p4160_p | -2.0 | 6.8 | | |-6.28319 | 6.28319 | |\n", + "| 15| p4040_p | -5.0 | 0.8 | | |-6.28319 | 6.28319 | |\n", + "----------------------------------------------------------------------------------------------\n", + "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "| | p4040_s p4160_s p4415_p bplus_1 Ctt DDstar_p Dbar_p p4415_s p3770_p DDstar_s bplus_2 p3770_s Dbar_s bplus_0 p4160_p p4040_p |\n", + "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "| p4040_s | 1.000 -0.001 0.131 0.000 -0.006 0.021 0.032 0.001 0.075 0.045 0.000 -0.003 0.041 -0.000 0.178 0.017 |\n", + "| p4160_s | -0.001 1.000 -0.007 0.000 0.001 -0.001 -0.002 0.001 -0.006 -0.003 0.000 0.000 -0.003 0.000 -0.011 -0.004 |\n", + "| p4415_p | 0.131 -0.007 1.000 0.001 -0.023 0.089 0.137 -0.025 0.305 0.180 0.001 -0.010 0.165 -0.000 0.738 0.079 |\n", + "| bplus_1 | 0.000 0.000 0.001 1.000 0.000 -0.000 -0.000 -0.000 -0.000 0.000 0.677 -0.000 0.000 0.450 0.001 0.000 |\n", + "| Ctt | -0.006 0.001 -0.023 0.000 1.000 -0.003 -0.004 -0.000 -0.012 -0.009 0.000 0.000 -0.008 0.000 -0.032 -0.003 |\n", + "| DDstar_p | 0.021 -0.001 0.089 -0.000 -0.003 1.000 0.005 0.000 0.052 -0.034 0.000 -0.001 0.022 -0.000 0.116 0.012 |\n", + "| Dbar_p | 0.032 -0.002 0.137 -0.000 -0.004 0.005 1.000 0.001 0.078 0.043 0.000 -0.002 -0.030 -0.000 0.180 0.019 |\n", + "| p4415_s | 0.001 0.001 -0.025 -0.000 -0.000 0.000 0.001 1.000 0.003 0.001 -0.000 -0.000 0.001 -0.000 0.009 0.001 |\n", + "| p3770_p | 0.075 -0.006 0.305 -0.000 -0.012 0.052 0.078 0.003 1.000 0.103 0.000 0.002 0.092 -0.000 0.420 0.039 |\n", + "| DDstar_s | 0.045 -0.003 0.180 0.000 -0.009 -0.034 0.043 0.001 0.103 1.000 0.000 -0.003 0.052 -0.000 0.252 0.026 |\n", + "| bplus_2 | 0.000 0.000 0.001 0.677 0.000 0.000 0.000 -0.000 0.000 0.000 1.000 -0.000 0.000 0.190 0.002 0.000 |\n", + "| p3770_s | -0.003 0.000 -0.010 -0.000 0.000 -0.001 -0.002 -0.000 0.002 -0.003 -0.000 1.000 -0.003 -0.000 -0.014 -0.001 |\n", + "| Dbar_s | 0.041 -0.003 0.165 0.000 -0.008 0.022 -0.030 0.001 0.092 0.052 0.000 -0.003 1.000 -0.000 0.228 0.023 |\n", + "| bplus_0 | -0.000 0.000 -0.000 0.450 0.000 -0.000 -0.000 -0.000 -0.000 -0.000 0.190 -0.000 -0.000 1.000 -0.000 0.000 |\n", + "| p4160_p | 0.178 -0.011 0.738 0.001 -0.032 0.116 0.180 0.009 0.420 0.252 0.002 -0.014 0.228 -0.000 1.000 0.114 |\n", + "| p4040_p | 0.017 -0.004 0.079 0.000 -0.003 0.012 0.019 0.001 0.039 0.026 0.000 -0.001 0.023 0.000 0.114 1.000 |\n", + "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "Hesse errors: OrderedDict([(, {'error': 0.1817980073330014}), (, {'error': 0.28105296332603724}), (, {'error': 1.4460585352975572}), (, {'error': 0.00045254152319595953}), (, {'error': 0.1070128591631859}), (, {'error': 1.274894582621453}), (, {'error': 0.9863367274899124}), (, {'error': 0.22635340269660909}), (, {'error': 1.0686131922004622}), (, {'error': 0.06595112952400507}), (, {'error': 0.0019374367701227024}), (, {'error': 0.24635197699053668}), (, {'error': 0.06369342157651345}), (, {'error': 4.949731518788525e-05}), (, {'error': 6.846065392563649}), (, {'error': 0.7735769115033309})])\n", + "Toy 2/2\n", + "Time taken: 5 min, 55 s\n", + "Projected time left: \n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# zfit.run.numeric_checks = False \n", "\n", @@ -1463,7 +1699,9 @@ " if not os.path.exists(dirName):\n", " os.mkdir(dirName)\n", " print(\"Directory \" , dirName , \" Created \")\n", - "\n", + " \n", + " reset_param_values()\n", + " \n", " for call in range(calls):\n", "\n", " sampler.resample(n=event_stack)\n", @@ -1604,9 +1842,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2/2 fits converged\n", + "Mean Ctt value = 0.07076623985541536\n", + "Mean Ctt error = 0.16014230762364964\n" + ] + } + ], "source": [ "print('{0}/{1} fits converged'.format(len(Ctt_list), nr_of_toys))\n", "print('Mean Ctt value = {}'.format(np.mean(Ctt_list)))\n", diff --git a/raremodel-nb.py b/raremodel-nb.py index 6353aec..540691f 100644 --- a/raremodel-nb.py +++ b/raremodel-nb.py @@ -432,7 +432,7 @@ jpsi_m = zfit.Parameter("jpsi_m", ztf.constant(jpsi_mass), floating = False) jpsi_w = zfit.Parameter("jpsi_w", ztf.constant(jpsi_width), floating = False) -jpsi_p = zfit.Parameter("jpsi_p", ztf.constant(jpsi_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi) +jpsi_p = zfit.Parameter("jpsi_p", ztf.constant(jpsi_phase), floating = False) #, lower_limit=-2*np.pi, upper_limit=2*np.pi) jpsi_s = zfit.Parameter("jpsi_s", ztf.constant(jpsi_scale), floating = False) #, lower_limit=jpsi_scale-np.sqrt(jpsi_scale), upper_limit=jpsi_scale+np.sqrt(jpsi_scale)) #psi2s @@ -441,7 +441,7 @@ psi2s_m = zfit.Parameter("psi2s_m", ztf.constant(psi2s_mass), floating = False) psi2s_w = zfit.Parameter("psi2s_w", ztf.constant(psi2s_width), floating = False) -psi2s_p = zfit.Parameter("psi2s_p", ztf.constant(psi2s_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi) +psi2s_p = zfit.Parameter("psi2s_p", ztf.constant(psi2s_phase), floating = False) #, lower_limit=-2*np.pi, upper_limit=2*np.pi) psi2s_s = zfit.Parameter("psi2s_s", ztf.constant(psi2s_scale), floating = False) #, lower_limit=psi2s_scale-np.sqrt(psi2s_scale), upper_limit=psi2s_scale+np.sqrt(psi2s_scale)) #psi(3770) @@ -520,17 +520,19 @@ x_min = 2*pdg['muon_M'] x_max = (pdg["Bplus_M"]-pdg["Ks_M"]-0.1) +epsilon = 0.3 + # # Full spectrum -# obs = zfit.Space('q', limits = (x_min, x_max)) +obs_toy = zfit.Space('q', limits = (x_min, x_max)) # Jpsi and Psi2s cut out -obs1 = zfit.Space('q', limits = (x_min, jpsi_mass - 50.)) -obs2 = zfit.Space('q', limits = (jpsi_mass + 50., psi2s_mass - 50.)) -obs3 = zfit.Space('q', limits = (psi2s_mass + 50., x_max)) +obs1 = zfit.Space('q', limits = (x_min + epsilon, jpsi_mass - 50. - epsilon)) +obs2 = zfit.Space('q', limits = (jpsi_mass + 50. + epsilon, psi2s_mass - 50. - epsilon)) +obs3 = zfit.Space('q', limits = (psi2s_mass + 50. + epsilon, x_max - epsilon)) -obs = obs1 + obs2 + obs3 +obs_fit = obs1 + obs2 + obs3 # with open(r"./data/slim_points/slim_points_toy_0_range({0}-{1}).pkl".format(int(x_min), int(x_max)), "rb") as input_file: # part_set = pkl.load(input_file) @@ -547,7 +549,7 @@ # In[10]: -total_f = total_pdf(obs=obs, jpsi_mass = jpsi_m, jpsi_scale = jpsi_s, jpsi_phase = jpsi_p, jpsi_width = jpsi_w, +total_f = total_pdf(obs=obs_toy, jpsi_mass = jpsi_m, jpsi_scale = jpsi_s, jpsi_phase = jpsi_p, jpsi_width = jpsi_w, psi2s_mass = psi2s_m, psi2s_scale = psi2s_s, psi2s_phase = psi2s_p, psi2s_width = psi2s_w, p3770_mass = p3770_m, p3770_scale = p3770_s, p3770_phase = p3770_p, p3770_width = p3770_w, p4040_mass = p4040_m, p4040_scale = p4040_s, p4040_phase = p4040_p, p4040_width = p4040_w, @@ -561,7 +563,21 @@ tau_mass = tau_m, C_tt = Ctt, b0_0 = b0_0, b0_1 = b0_1, b0_2 = b0_2, bplus_0 = bplus_0, bplus_1 = bplus_1, bplus_2 = bplus_2, bT_0 = bT_0, bT_1 = bT_1, bT_2 = bT_2) - + +total_f_fit = total_pdf(obs=obs_fit, jpsi_mass = jpsi_m, jpsi_scale = jpsi_s, jpsi_phase = jpsi_p, jpsi_width = jpsi_w, + psi2s_mass = psi2s_m, psi2s_scale = psi2s_s, psi2s_phase = psi2s_p, psi2s_width = psi2s_w, + p3770_mass = p3770_m, p3770_scale = p3770_s, p3770_phase = p3770_p, p3770_width = p3770_w, + p4040_mass = p4040_m, p4040_scale = p4040_s, p4040_phase = p4040_p, p4040_width = p4040_w, + p4160_mass = p4160_m, p4160_scale = p4160_s, p4160_phase = p4160_p, p4160_width = p4160_w, + p4415_mass = p4415_m, p4415_scale = p4415_s, p4415_phase = p4415_p, p4415_width = p4415_w, + rho_mass = rho_m, rho_scale = rho_s, rho_phase = rho_p, rho_width = rho_w, + omega_mass = omega_m, omega_scale = omega_s, omega_phase = omega_p, omega_width = omega_w, + phi_mass = phi_m, phi_scale = phi_s, phi_phase = phi_p, phi_width = phi_w, + Dstar_mass = Dstar_m, DDstar_scale = DDstar_s, DDstar_phase = DDstar_p, D_mass = D_m, + Dbar_mass = Dbar_m, Dbar_scale = Dbar_s, Dbar_phase = Dbar_p, + tau_mass = tau_m, C_tt = Ctt, b0_0 = b0_0, b0_1 = b0_1, b0_2 = b0_2, + bplus_0 = bplus_0, bplus_1 = bplus_1, bplus_2 = bplus_2, + bT_0 = bT_0, bT_1 = bT_1, bT_2 = bT_2) # print(total_pdf.obs) @@ -607,7 +623,7 @@ probs = total_f.pdf(test_q, norm_range=False) calcs_test = zfit.run(probs) -res_y = zfit.run(jpsi_res(test_q)) +# res_y = zfit.run(jpsi_res(test_q)) # b0 = [b0_0, b0_1, b0_2] # bplus = [bplus_0, bplus_1, bplus_2] # bT = [bT_0, bT_1, bT_2] @@ -798,7 +814,7 @@ # In[18]: -# total_f._sample_and_weights = UniformSampleAndWeights +total_f._sample_and_weights = UniformSampleAndWeights # In[19]: @@ -1164,28 +1180,96 @@ # 6. Constraint on phases of Jpsi and Psi2s for cut out fit + constraint6_0 = zfit.constraint.GaussianConstraint(params = jpsi_p, mu = ztf.constant(pdg["jpsi_phase_unc"]), sigma = ztf.constant(jpsi_phase)) +constraint6_1 = zfit.constraint.GaussianConstraint(params = psi2s_p, mu = ztf.constant(pdg["psi2s_phase_unc"]), + sigma = ztf.constant(psi2s_phase)) -constraint6_1 = zfit.constraint.GaussianConstraint(params = psi2s_p, mu = ztf.constant(pdg["psi2s_phase_unc"]), - sigma = ztf.constant(psi2s_phase)) +# zfit.run(constraint6_0) + +# ztf.convert_to_tensor(constraint6_0) #List of all constraints -constraints = [constraint1, constraint2, constraint3_0, constraint3_1, constraint4, constraint6_0, constraint6_1] +constraints = [constraint1, constraint2, constraint3_0, constraint3_1, constraint4]#, ztf.convert_to_tensor(constraint6_0)]#, ztf.convert_to_tensor(constraint6_1)] + + +# ## Reset params + +# In[39]: + + +def reset_param_values(): + jpsi_m.set_value(jpsi_mass) + jpsi_s.set_value(jpsi_scale) + jpsi_p.set_value(jpsi_phase) + jpsi_w.set_value(jpsi_width) + psi2s_m.set_value(psi2s_mass) + psi2s_s.set_value(psi2s_scale) + psi2s_p.set_value(psi2s_phase) + psi2s_w.set_value(psi2s_width) + p3770_m.set_value(p3770_mass) + p3770_s.set_value(p3770_scale) + p3770_p.set_value(p3770_phase) + p3770_w.set_value(p3770_width) + p4040_m.set_value(p4040_mass) + p4040_s.set_value(p4040_scale) + p4040_p.set_value(p4040_phase) + p4040_w.set_value(p4040_width) + p4160_m.set_value(p4160_mass) + p4160_s.set_value(p4160_scale) + p4160_p.set_value(p4160_phase) + p4160_w.set_value(p4160_width) + p4415_m.set_value(p4415_mass) + p4415_s.set_value(p4415_scale) + p4415_p.set_value(p4415_phase) + p4415_w.set_value(p4415_width) + rho_m.set_value(rho_mass) + rho_s.set_value(rho_scale) + rho_p.set_value(rho_phase) + rho_w.set_value(rho_width) + omega_m.set_value(omega_mass) + omega_s.set_value(omega_scale) + omega_p.set_value(omega_phase) + omega_w.set_value(omega_width) + phi_m.set_value(phi_mass) + phi_s.set_value(phi_scale) + phi_p.set_value(phi_phase) + phi_w.set_value(phi_width) + Dstar_m.set_value(Dstar_mass) + DDstar_s.set_value(0.0) + DDstar_p.set_value(0.0) + D_m.set_value(D_mass) + Dbar_m.set_value(Dbar_mass) + Dbar_s.set_value(0.0) + Dbar_p.set_value(0.0) + tau_m.set_value(pdg['tau_M']) + Ctt.set_value(0.0) + b0_0.set_value(0.292) + b0_1.set_value(0.281) + b0_2.set_value(0.150) + bplus_0.set_value(0.466) + bplus_1.set_value(-0.885) + bplus_2.set_value(-0.213) + bT_0.set_value(0.460) + bT_1.set_value(-1.089) + bT_2.set_value(-1.114) # # Analysis -# In[37]: +# In[40]: # zfit.run.numeric_checks = False +fitting_range = 'cut' + Ctt_list = [] Ctt_error_list = [] -nr_of_toys = 2 +nr_of_toys = 100 nevents = int(pdg["number_of_decays"]) nevents = pdg["number_of_decays"] event_stack = 1000000 @@ -1202,6 +1286,8 @@ ### Generate data +# clear_output(wait=True) + print("Toy {}: Generating data...".format(toy)) dirName = 'data/zfit_toys/toy_{0}'.format(toy) @@ -1209,13 +1295,14 @@ if not os.path.exists(dirName): os.mkdir(dirName) print("Directory " , dirName , " Created ") - + + reset_param_values() + for call in range(calls): sampler.resample(n=event_stack) s = sampler.unstack_x() sam = zfit.run(s) -# clear_output(wait=True) c = call + 1 @@ -1234,51 +1321,112 @@ total_samp = np.append(total_samp, sam) total_samp = total_samp.astype('float64') - - data = zfit.data.Data.from_numpy(array=total_samp[:int(nevents)], obs=obs) - print("Toy {}: Loading data finished".format(toy)) + if fitting_range == 'full': - ### Fit data + data = zfit.data.Data.from_numpy(array=total_samp[:int(nevents)], obs=obs) - print("Toy {}: Fitting pdf...".format(toy)) + print("Toy {}: Loading data finished".format(toy)) - for param in total_f.get_dependents(): - param.randomize() + ### Fit data - nll = zfit.loss.UnbinnedNLL(model=total_f, data=data, fit_range = (x_min, x_max), constraints = constraints) + print("Toy {}: Fitting pdf...".format(toy)) - minimizer = zfit.minimize.MinuitMinimizer(verbosity = 5) - # minimizer._use_tfgrad = False - result = minimizer.minimize(nll) - - print("Toy {}: Fitting finished".format(toy)) + for param in total_f.get_dependents(): + param.randomize() - print("Function minimum:", result.fmin) - print("Hesse errors:", result.hesse()) - - params = result.params - Ctt_list.append(params[Ctt]['value']) - Ctt_error_list.append(params[Ctt]['minuit_hesse']['error']) - - #plotting the result - - plotdirName = 'data/plots'.format(toy) - - if not os.path.exists(plotdirName): - os.mkdir(plotdirName) -# print("Directory " , dirName , " Created ") - calcs_test = zfit.run(probs) - res_y = zfit.run(jpsi_res(test_q)) - plt.clf() - plt.plot(test_q, calcs_test, label = 'pdf') - plt.legend() - plt.ylim(0.0, 6e-6) - plt.savefig(plotdirName + '/toy_fit{}.png'.format(toy)) + nll = zfit.loss.UnbinnedNLL(model=total_f, data=data, fit_range = (x_min, x_max), constraints = constraints) - print("Toy {0}/{1}".format(toy+1, nr_of_toys)) - print("Time taken: {}".format(display_time(int(time.time() - start)))) - print("Projected time left: {}".format(display_time(int((time.time() - start)/(c+calls*(toy))*((nr_of_toys-toy)*calls-c))))) + minimizer = zfit.minimize.MinuitMinimizer(verbosity = 5) + # minimizer._use_tfgrad = False + result = minimizer.minimize(nll) + + print("Toy {}: Fitting finished".format(toy)) + + print("Function minimum:", result.fmin) + print("Hesse errors:", result.hesse()) + + params = result.params + Ctt_list.append(params[Ctt]['value']) + Ctt_error_list.append(params[Ctt]['minuit_hesse']['error']) + + #plotting the result + + plotdirName = 'data/plots'.format(toy) + + if not os.path.exists(plotdirName): + os.mkdir(plotdirName) +# print("Directory " , dirName , " Created ") + + probs = total_f.pdf(test_q, norm_range=False) + calcs_test = zfit.run(probs) + plt.clf() + plt.plot(test_q, calcs_test, label = 'pdf') + plt.legend() + plt.ylim(0.0, 6e-6) + plt.savefig(plotdirName + '/toy_fit_full_range{}.png'.format(toy)) + + print("Toy {0}/{1}".format(toy+1, nr_of_toys)) + print("Time taken: {}".format(display_time(int(time.time() - start)))) + print("Projected time left: {}".format(display_time(int((time.time() - start)/(c+calls*(toy))*((nr_of_toys-toy)*calls-c))))) + + if fitting_range == 'cut': + + tot_sam_1 = np.where((total_samp >= x_min) & (total_samp <= (jpsi_mass - 50.))) + + tot_sam_2 = np.where((total_samp >= (jpsi_mass + 50.)) & (total_samp <= (psi2s_mass - 50.))) + + tot_sam_3 = np.where((total_samp >= (psi2s_mass + 50.)) & (total_samp <= x_max)) + + tot_sam = np.append(tot_sam_1, tot_sam_2) + tot_sam = np.append(tot_sam, tot_sam_3) + + data = zfit.data.Data.from_numpy(array=tot_sam[:int(nevents)], obs=obs_fit) + + print("Toy {}: Loading data finished".format(toy)) + + ### Fit data + + print("Toy {}: Fitting pdf...".format(toy)) + + for param in total_f_fit.get_dependents(): + param.randomize() + + nll = zfit.loss.UnbinnedNLL(model=total_f_fit, data=data, constraints = constraints) + + minimizer = zfit.minimize.MinuitMinimizer(verbosity = 5) + # minimizer._use_tfgrad = False + result = minimizer.minimize(nll) + + print("Function minimum:", result.fmin) + print("Hesse errors:", result.hesse()) + + params = result.params + + if result.converged: + Ctt_list.append(params[Ctt]['value']) + Ctt_error_list.append(params[Ctt]['minuit_hesse']['error']) + + #plotting the result + + plotdirName = 'data/plots'.format(toy) + + if not os.path.exists(plotdirName): + os.mkdir(plotdirName) + # print("Directory " , dirName , " Created ") + + probs = total_f_fit.pdf(test_q, norm_range=False) + calcs_test = zfit.run(probs) + plt.clf() + plt.plot(test_q, calcs_test, label = 'pdf') + plt.legend() + plt.ylim(0.0, 6e-6) + plt.savefig(plotdirName + '/toy_fit_cut_region{}.png'.format(toy)) + + print("Toy {0}/{1}".format(toy+1, nr_of_toys)) + print("Time taken: {}".format(display_time(int(time.time() - start)))) + print("Projected time left: {}".format(display_time(int((time.time() - start)/(c+calls*(toy))*((nr_of_toys-toy)*calls-c))))) + # In[ ]: @@ -1287,9 +1435,10 @@ -# In[ ]: +# In[41]: +print('{0}/{1} fits converged'.format(len(Ctt_list), nr_of_toys)) print('Mean Ctt value = {}'.format(np.mean(Ctt_list))) print('Mean Ctt error = {}'.format(np.mean(Ctt_error_list))) @@ -1303,5 +1452,5 @@ # In[ ]: - +# sample from original values