diff --git a/docs/tutorials/reproducing_pober_2015.ipynb b/docs/tutorials/reproducing_pober_2015.ipynb index 6776e28..ea13da1 100644 --- a/docs/tutorials/reproducing_pober_2015.ipynb +++ b/docs/tutorials/reproducing_pober_2015.ipynb @@ -88,7 +88,7 @@ "outputs": [ { "data": { - "image/png": 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GgZ+fH99//z1xcXE3tM/27dtTuXJlRo4cycSJE3F1deWzzz7L8yVbGHfddRezZ89m5MiR7N27l27dumGz2di4cSMNGjRg8ODBdOjQgX/9618MHz6cTZs20blzZ7y9vTl58iRr166lSZMmPPzwwzf02v7u3nvvJTY2lmHDhnH48GGaNGnC2rVree211+jduzc33XRTse2rqHr27EmvXr14+umnSUpKokOHDjl3kzVv3pyhQ4fmWr9q1ar069ePF154gZCQED799FPi4uKYPHkyXl5eAIwePZp58+bRuXNnnnzySZo2bYrNZiM+Pp5ly5YxduxY2rRpc93adu/eze7du4Hs3qeUlBS++eYbABo2bJgzgOmQIUMIDw8nKiqKgIAA9u3bx1tvvcXp06evGY7FcSgMicN77LHHaNWqFW+99RZjx47l3Llz+Pn50bFjR9auXUu7du1uaPuzZ88mJCSEDz/8kLfffpvIyEi++uorbr75ZipVqlQsr6F79+7MmjWLyZMn07dvX6pVq8aDDz5IYGDgDY8S3bx5c5o3b87WrVvzXHzs6urK999/zxNPPMFDDz2Ei4sLN910Ez/99FPOLf5F4e/vzw8//MDYsWO555578Pb25tZbb2Xu3Lk5vQuF5eLiwuLFi5k0aRJffPEF06ZNw8fHh2bNmuUa/fr999+nbdu2vP/++8yYMQObzUbVqlXp0KEDrVu3LvJruhoPDw9WrFjBc889xxtvvMGZM2eoVq0aTz31FBMnTizWfRWVxWJh/vz5vPDCC8yePZtXX32VgIAAhg4dymuvvZYncEdGRjJ8+HAmTpzIvn37qFq1KlOnTuXJJ5/MWcfb25s1a9bw+uuvM3PmTA4dOoSnpyfh4eHcdNNNBe4Z+uqrr3jxxRdztQ0aNAiAiRMn8sILLwDZF9PPnTuX9957j+Tk5JzP9yeffJLnNJ84JothFGFENhG5IevWraNDhw589tlnDBkyxOxyREQcmsKQSAmLi4tj/fr1tGzZEk9PT7Zv387rr7+Or68vO3bs0PUKIiIm02kykRJWsWJFli1bxrRp07h06RIBAQHExMQwadIkBSERkTJAPUMiIiLi0HRrvYiIiDg0hSERERFxaApDIiIi4tB0AfV12Gw2Tpw4gY+Pj4ZsFxERsROGYXDp0qXrzkkICkPXdeLECcLCwswuQ0RERIrg6NGjV5178u8Uhq7Dx8cHyH4z/znjtIiIiJRNSUlJhIWF5XyPX4vC0HVcOTVWsWJFhSERERE7U5BLXHQBtYiIiDg0hSERERFxaApDIiIi4tAUhkRERMShKQyJiIiIQ1MYEhEREYemMCQiIiIOTWFIREREHJrCkIiIiDg0hSERERFxaApDIiIi4tAUhkRERMShKQyJiIiIOQyDP1Z/g2GzmlpGuQ9Dly5dolWrVkRGRtKkSRM++OADs0sSERERYOdPn1B/+Qj+mNwdm9W8QORi2p5LiZeXF6tWrcLLy4uUlBQaN27MgAED8Pf3N7s0ERERh5V66SJBv7wAwHn/5jg5O5tWS7nvGXJ2dsbLywuAtLQ0rFYrhmGYXJWIiIhj+/2zcQRyjmOWYJrf/bKptZT5MLR69Wr69u1L1apVsVgszJ8/P886M2bMoGbNmnh4eNCyZUvWrFmTa/nFixdp1qwZoaGhjBs3joCAgFKqXkRERP7p8M51tDj5FQAJnV7Dy9vH1HrKfBi6fPkyzZo1Y/r06VddPnfuXEaPHs1zzz3H1q1b6dSpEzExMcTHx+esU6lSJbZv386hQ4f4/PPPOX36dGmVLyIiIn9jy8oia8HjOFsMfq3QjRbdbze7JCyGHZ0zslgsfPfdd/Tv3z+nrU2bNrRo0YJ33303p61Bgwb079+fSZMm5dnGww8/TPfu3Rk0aNBV95Genk56enrO46SkJMLCwkhMTKRixYrF92JEREQc0KavJhO1+zWSDC9S/rWB4GrVS2Q/SUlJ+Pr6Fuj7u8z3DF1LRkYGmzdvJjo6Old7dHQ069atA+D06dMkJSUB2W/M6tWriYiIyHebkyZNwtfXN+cnLCys5F6AiIiIAzl/6ggRu98GYGf9J0osCBWWXYehs2fPYrVaCQoKytUeFBTEqVOnADh27BidO3emWbNmdOzYkUcffZSmTZvmu83x48eTmJiY83P06NESfQ0iIiKO4sjnT+BDKnud69Fm4Fizy8lRLm6tt1gsuR4bhpHT1rJlS7Zt21bgbbm7u+Pu7l6c5YmIiDi83avn0TxpBVbDAn2n4eLqanZJOey6ZyggIABnZ+ecXqArEhIS8vQWiYiIiDnSU5OptOJZADYE3kFEZAeTK8rNrsOQm5sbLVu2JC4uLld7XFwc7du3N6kqERER+bvtn0+gqnGK0/jT+O7XzS4njzJ/miw5OZn9+/fnPD506BDbtm3Dz8+P8PBwxowZw9ChQ4mKiqJdu3bMnDmT+Ph4Ro4ceUP7jY2NJTY2FquJw4OLiIjYu+N/biMy/iOwwJE2E2ldyc/skvIo87fWr1y5km7duuVpHzZsGHPmzAGyB12cMmUKJ0+epHHjxrz99tt07ty5WPZfmFvzRERE5P8YNht7J3emfvpOtni0pfm4H7E4lc5JqcJ8f5f5MGQ2hSEREZGi2bowluZbniXFcOfcsDWE1cp/aJvi5jDjDImIiEjZlHTuFDW3ZA9+vKXWQ6UahApLYUhERESK3b7PnqQSlzjgVJ1Wg58zu5xrUhjKR2xsLA0bNqRVq1ZmlyIiImJX/vx1CS3PLwYgpeebuLt7mFzRtemaoevQNUMiIiIFl5WRxonXowi3HWV9pb60G/2pKXXomiERERExxda5LxNuO8p5KhJxz1tml1MgZX6cIRERESnbUi4mcGD9Qqx7l9DswgqwwJ/NnqFtgH3MBqEwJCIiIoVjGJzY+ysnNy3EJ345tdP30MTy11U3Ftjs2Y7W/R42t8ZCUBgSERGR68pISWL/hkWk7f6R8HNrqWqcp+qVhRbYb6nOySqd8G7Sm6Zto3Fytp8rcRSGRERE5KrOxf/BkfXz8Dj8E3VSdtDQkpWzLMVwZ49Hc1JqdCes9a3UrhVBHYvFxGqLTmEoH5qbTEREHI1hzeTQ1hWc37qQoFMrCLMew//KQgscJZjDfh3waHAzEe1iaFnBx8xyi41urb8O3VovIiLlWVrSOfavm0/WHz9S8+J6fEnOWZZpOLPbrTGJod0JirqVuvWb2c3pr8J8f6tnSERExMGcOfQ7Rzd+h9ehZdRJ+53GFlvOsguGD3/4tMWoG03dDv1pFhBoYqWlQ2FIRESknDOsWRzevpLzm+cTdHI5obbjVLmy0AIHLWEcr9IF76Z9aNiqB+3c3cwst9QpDImIiJRDmamX2L9+Iem/L6L6+bXUJImafy3LMJzZ7daUpOo3Ua31rdSq25hadnrxc3FQGBIRESknLp09ysG183De9yN1L2+mAZk5yy4a3uzxaYdRrzcRHW4l0j/AxErLFoUhERERe2UYnD64jePr51HxyDLqZO6l2d8WHyOIQ/5d8GrSl4Zte9HOw920UssyhaF86NZ6EREpiwyblaM713Dmt28IPvEz1Wwn+PukF3uc65EQ0oOAqNto0KQVoXZy95eZdGv9dejWehERMZstM50Dvy7m0rYFVD+7En/jQs6ydMOVXR6RJNfoRY32txNevZaJlZYdurVeRETEzmVcTmT/uu/I3PU9tS+uoy4pOcsuGZ7sqtAWa8Qt1O84gBZ+/tfYklyPwpCIiEgZkXLxNPtXf4Xz3kXUTd6Ua/qLM0Yl9lbqhGvjfjTq0Ie2Xl4mVlq+KAyJiIiY6NLZoxxcPRf3fYuok7KdplcGQLTAEUI4XKUbPpH9adS6Ox1dXc0ttpxSGBIRESlliScOcGjNl3gd/IE6abtpZvnr8l0L/GmpycmqPQlodTsNmrSmui6ALnEKQyIiIqXgXPxujqz9Et9DP1I7808iryywwG6nepwJ7UVwuzuoV78J9Rx4AEQzKAyJiIiUkDMHd3B07ef4x/9I9azDOTPA2wwLu1wbcT78ZsI73EHD2hGm1unoFIbyoXGGRESkKP4ZgK7MAZZpOPO7W1OSasZQq9OdNAmrYWaZ8jcaZ+g6NM6QiIhczz8D0BUZhjM73VtwuU4f6na+g5DgquYV6WA0zpCIiEgJuxKAAuJ/JPxvPUB/D0D1utxJy6AQU+uU61MYEhERKaDz8Xs4svoT/A//oABUjigMiYiIXEPiqYMcXPkpvgcWUitzH35/tec+BXYnLYMVgOyVwpCIiMg/XD53nP0rP8XrzwXUTd9F87/aswwndrg151KdvtTrMlgBqJxQGBIREQHSEs+wb9XnuO6ZT92UrTkDIdoMC7+7NOJ8rb7U6TKEFqHhJlcqxU1hSEREHFZmahL7Vs3F2PkN9ZJ/o4nlr+FULLDLKYIz1W+heuchNK1Z19xCpUQpDImIiEMxsjLYv2EhqZu/pO6F1TQkPXvBX1NhHA/tTbUOd9EwojEWjQTtEBSGRESk/LPZOLpjJWfXf0rN08uoy6WcRfEEczC4NwHthtCoaZSmwnBACkP50AjUIiL278yBLRxb9TFVj/1AmC2BsL/azxq+7PbvSYVWQ2jaqivhLs6m1inm0gjU16ERqEVE7EvS6SMcXD6bygfmUz3rUE57suHJdp9OODW7g8hO/fD0cDexSilpGoFaREQcSmZKIn+u/BznnV9RL2UrkX/dCZZhOLPNow3pDQbQuOsddKjka3KlUhYpDImIiF0yrFkc+m0xyRs/oe6FVTT624XQO5wbcb72bdTrfg+tNRaQXIfCkIiI2JWEfZs5vmo2Ycd/oJZxPqf9CCEcrNqXqp3upUl93QkmBacwJCIiZd7lc8fY/9NsfPd/S43MgwT+1X7BqMDOyjfh3epumrXpQXVdCC1FoDAkIiJlki0jjX1rv8a6+VMikjfmjAidbriwzbMNmY3upGn3gXT29ja5UrF3CkMiIlJ2GAan9m7g5KpZ1Dq5mAiSs9v/GhE6odZt1Ot+L22qVjO3TilXFIZERMR0KRdOsj/uQ3z//JrqWYcJ/qv9tOHH7sBbCOw8nIaNW9BI1wFJCVAYEhERUxhZ6ez/5TsyNn1MvaQNNP1rXrB0w5UtXh0wIofQvMttdPNwM7lSKe8UhkREpFSdPbiNYz/PpMbx76lLUnajBXY71eV0rduJuOk+2ul2eClFCkMiIlLiMlOT+PPnj/DY+Rm10/cQ8Ff7GaMSuwJi8Os4nCaRrWmo02BiAoUhEREpGYbBiV1rOLPqA+qeWUYj0gDINJzZ4tGGzKZDaN59EF09PUwuVBydwlA+NFGriEjRpCUmsC/uf1T64wvCsuKp+lf7EapyIOw2avZ4gDY1aplao8jfaaLW69BErSIiBWCzcXjTD1xaN4v6F1fhSvZ/JFMNN7ZU6IJL1H206BSDqwZFlFKiiVpFRKRUpJw/zv4l7xG0fy41bKdz2v+w1OZE7UE06Hk/HYKCTKxQ5PoUhkREpHBsNo5s/pFLaz8g4uLqnFviEw1vtlWOxqfdcCJbdaa+ky6GFvugMCQiIgWSdvE0fy59n4A/v6C69UR2owV+d4rgTMQQmvW6jy6VKplao0hRKAyJiEj+DINj2+K4sHomERdW0JQsAC4ZnmypFE3Fjv+iWcsOOKkXSOyYwpCIiOSRfukcfy6dSeU9nxFqPUroX+17LHU4WfcuGt88nC5+/qbWKFJcFIZERCRHwp8bORX3DvXOLKUJGQBcNtzZXPEmvDs8SPPWXWmgXiApZxSGREQcnC0jjT9XfoLr5g+pnb6HwL/a/7TU4FjtwTSKfoDOgVVMrVGkJCkMiYg4qKTThzj04/8j/Mg86huJAGQYzmzy6oRTm38R1fFm6mlcIHEACkMiIo7EZiN+82IurXmP+olraWbJHnf3lOHPrqoDqNXrEdprdGhxMApDIiIOIPPyBfYueR+/3R8Tbj2e3WiBrc5NSWxyH1G97qaH5ggTB6UwJCJSjl2I383RJW9T+8RCGv81Ueolw5PNlW6mUpeHiWzeGotmihcHpzAkIlLeGAbxvy3i8urpNEjeQOW/mg8QyqFad9Mk5l90rRJgaokiZYnCkIhIOWFNS2bvsg+ouGMW4VnxANgMC5vcWpER9S9adx9AbVddEC3yTwpDIiJ27tLpgxz64W1qxs+jIZez2wxPNlWOIaD7Y7Rq0lynwkSuQWFIRMQeGQYndq7gwvL/Uv/CKpr+dVdYPEHsrX43jXo/TLegwOtsRERAYUhExK4Y1kz2r/wMl40zqJmxl6oAFtjs3IxLkQ/SJnow4e6uZpcpYlcUhkRE7EBWykX2Lp5Bld2zqGs7A0Ca4coGn2h8uoyiRVR7nQoTKSKFoXzExsYSGxuL1Wo1uxQRcWCXzxzhwKI3qXXkGxqRAsBZw5etwYOo3fsJulYPN7lCEftnMQzDMLuIsiwpKQlfX18SExOpWLGi2eWIiIM4++evnF76BvXO/Ywr2f8pO0goB2oPo3nfhwio5GtyhSJlW2G+v9UzJCJSVthsxP+6gPTV/6VuylaujAS0xbkpiZEP0a7XndRy0/VAIsVNYUhExGRGVjr7f56N12+xOeMDZRrObPDsjGunJ2jdritOTroeSKSkKAyJiJjEmnaJPxfHEvD7B9S1nQWyxwfa4NePatGj6dSgockVijgGhSERkVKWnpTAvu/fInzfpzQgGYAEozLbQu+mYZ/H6RkSZHKFIo5FYUhEpJRcTjjEoYWTqXNsHo3JAOAwIfxZezgt+z5MdCXdpCFiBoUhEZESdvHQNk4sfp26Z5bR+K87w/ZYanOs0Uja3TKMGp7uJlco4tgUhkRESsiZ3as5v3QyEYlrqfRX2ybnZlyKepT2Nw2ggav+CRYpC/RJFBEpTobBqR1xJC+bRJ3LW6hC9szx69zbY+k4mrYde+KsO8NEyhSFIRGR4mAYnNjyA2k/vU6t1J0AZBjO/OLdE58eT9GhRZSmyxApoxSGRERuhGFw/NfvyFwxhRppewBIN1xY6xNDlZhn6NaosckFisj1KAyJiBSFzUb8urlYVr9JWMZ+AFINN9b69qXaLU/TIyLC5AJFpKAUhkRECsNm5cjqT3H5ZSrhmYcBuGy4s7bybVTv82961qljbn0iUmgKQyIiBWHN4tDKOXisf5vqWccASDI8WRcwkDr9xtFLs8eL2C2FIRGRa7FZiV/zKS5r3qBm1lEALhrebAi8g4hb/83NodVMLlBEbpTCkIjI1dhsHF03F6dVr+ecDrtgVGBD8BAa9R/LzSHB5tYnIsVGYUhE5O8Mg+Mb52Fb/hphGQcASDS8+CXwLhrdNo6YqgpBIuWNwpCICIBhcHLTQjJ/foXwtD+B7Bnk1wbcQUT/p+kdptNhIuWVwpCIODbDIGHbElKWvUyN1F1A9t1hqyvfTp3+44mpoQujRco7hSERcVhnf1/OpR9foObl7UD2OEGrfG+ler/xxNSpbXJ1IlJaFIZExOEkHdpCwnfPUidpPQFAuuHKyop9qHrLeG6ur8ESRRyNwpCIOIzU0/uJ/+Y5Is4soSKQaTizwvtmAm95jl6NGpldnoiYRGFIRMq9zMRTHJg3kTrxXxOBFYCVrp3x7PU8PVtqAlURR6cwJCLlli3lIvsXTCJs7xzqkwbARqfmpHZ+js6db8LJSSFIRBwgDB09epShQ4eSkJCAi4sLEyZMYNCgQWaXJSIlKTONgz9Ow39rLPWMJAB+pw7Ho56m280DcXNxMrlAESlLLIZhGGYXUZJOnjzJ6dOniYyMJCEhgRYtWrB37168vb0L9PykpCR8fX1JTEykYsWKJVytiNwQm5X4FR/itW4KAdYzABw0qrG7wWi69R+Ot4eryQWKSGkpzPd3ue8ZCgkJISQkBIDAwED8/Pw4f/58gcOQiNiHM1sWkbnkOcIzDgNw0vDjtxoj6XD7o/SpqM+7iOSvzPcVr169mr59+1K1alUsFgvz58/Ps86MGTOoWbMmHh4etGzZkjVr1lx1W5s2bcJmsxEWFlbCVYtIaUmO387Bt6OpsvBuqmYc5qLhzcLAh7GO2kS/4U/jryAkItdR5sPQ5cuXadasGdOnT7/q8rlz5zJ69Giee+45tm7dSqdOnYiJiSE+Pj7XeufOnePee+9l5syZpVG2iJSwrMST7PtgOJ6zulIrcSMZhjOLKwzg1H0b6PfI64QG+ptdoojYCbu6ZshisfDdd9/Rv3//nLY2bdrQokUL3n333Zy2Bg0a0L9/fyZNmgRAeno6PXv25MEHH2To0KHX3Ed6ejrp6ek5j5OSkggLC9M1QyJlhJFxmUPfTyFk53t4/nWH2CqX9rj1eom2UbpNXkSyOcw1QxkZGWzevJlnnnkmV3t0dDTr1q0DwDAM7rvvPrp3737dIAQwadIkXnzxxRKpV0RugM3GiTVz8Fj9KrWsZwHYSV2Ot36OHr1uxdW5zHd0i0gZZdf/epw9exar1UpQUFCu9qCgIE6dOgXAL7/8wty5c5k/fz6RkZFERkayc+fOfLc5fvx4EhMTc36OHj1aoq9BRK7vwq6fOTalDVVXPImf9SzHjADm136Z8HHruLn3bQpCInJD7Lpn6Ip/dosbhpHT1rFjR2w2W4G35e7ujru7e7HWJyJFk3bqT058NZZa51dTGUgyPPm5ylCi7hhP/0A/s8sTkXLCrsNQQEAAzs7OOb1AVyQkJOTpLRIR+2GkJXHw25cI/3M2tcgiy3AiziuGqv1f5LaIumaXJyLljF2HITc3N1q2bElcXBy33XZbTntcXBy33nqriZWJSJEYBqfWfoT7ihepbTsPwHpLc9J6vMzNHTvq4mgRKRFlPgwlJyezf//+nMeHDh1i27Zt+Pn5ER4ezpgxYxg6dChRUVG0a9eOmTNnEh8fz8iRI29ov7GxscTGxmK1Wm/0JYhIASQf3sSFr0cTdjn7mr4jRhBbGozj5tvuw9O9zP9TJSJ2rMzfWr9y5Uq6deuWp33YsGHMmTMHyB50ccqUKZw8eZLGjRvz9ttv07lz52LZv6bjEClZtktnOPT1eGrGf4MTBimGO0v87iHqrgmEB1Y2uzwRsVOF+f4u82HIbApDIiXEmsXxuHfw3fgmFYxkAH5y6Yx3n1dpF9nU5OJExN45zDhDImKfEnf/TMqCp6iWfhCAP4zq7G0xgZhbbteM8iJS6hSGRKTUZF04ytEvn6Tm6Th8gQtGBX4K+RedBz/FrZU0h5iImENhKB+6gFqkGFmzOL5sGpU3vklNUrEaFhZ79CZ0wCsMiqhldnUi4uB0zdB16JohkRtz+eAGkr4eRUhq9l2hW4ngePtXiLmpJ85OulVeREqGrhkSEdMZqRc48tUzhB+aizcGFw1vloQ8wk13j6W5j6fZ5YmI5FAYEpHiZRicXf8ZLj/9hxq2CwAsdelGpVtfZ3CT+iYXJyKSl8KQiBSbjIR9nP58FGEXNwJw0KjK5sYT6Nv/TjxcnU2uTkTk6hSGROTGZaVzfNFrVNkWSxiZpBuuzK94F1F3v8CgYH+zqxMRuSaFoXzobjKRgkne8xOp342mWsZRANbTjMQek7ijU3vNJSYidkF3k12H7iYTuToj5QJHv3yS8PjvAEgwKhEXPpredz5C5QruJlcnIo5Od5OJSIm6uPlbLIvHEm49j82w8L1bDKEDJ3F3RA2zSxMRKTSFIREpMCM5gWOfP0bYiSUAHDCqsqnpS9x2q6bREBH7pTAkItdnGFzY+Bkuy8YTZksiy3DiO88BNB4yiTvDA82uTkTkhigMicg1GYnHOPHpw1Q7sxqAPUZ1dka9yoDet+DirN4gEbF/CkMicnWGwbnVM/Fc+QLVjBTSDRe+qXAXbe55iTtC/MyuTkSk2CgM5UO31osjs509yOnP/kXIhd8A2GbU4UC7yQyO7qH5xESk3NGt9dehW+vFodhsnF3+X3zWTsKddFINN76qeB9dhk6gRqD+/kXEfujWehEpNNv5wyR8cj/BFzYDsNFoxIkuUxjatQNO6g0SkXKsSGFo4cKFhX5Oz5498fTUTNUiZY5hkLj+I9zixhNspHDZcGeu30NED32GNn7eZlcnIlLiihSG+vfvX6j1LRYL+/bto1atWkXZnYiUlOQznPrsIYJP/gzAZiOCI53fYnj3jppKQ0QcRpFPk506dYrAwIKNL+Lj41PU3YhICbm8YyG2BY8TbL1AhuHMF9730OHelxkQ7Gt2aSIipapIYWjYsGGFOuV1zz336OJjkbIiLYnTX48h6MDXAOy1hfFr80kM6XcLrho3SEQckO4muw7dTSblSfqBtaTOfYBKGSexGRbmut1K/SGTaV4z2OzSRESKVWG+vwv938DU1FSOHz+ep33Xrl2F3ZSIlJasdM5++zSun/ShUsZJjtqq8EHtd+j31IcKQiLi8AoVhr755hvq1atH7969adq0KRs3bsxZNnTo0GIvzkyxsbE0bNiQVq1amV2KyA2xntjBubfbE7DjPZwwmG/pwYGBS3no3qF4u2t0DRGRQp0mi4yMJC4ujipVqrBp0yaGDRvGc889x5AhQ2jevDlbt24tyVpNodNkYrcMg6RV0/Fc+SKuZHLGqMjc4H9z19CH8K/gbnZ1IiIlqsQGXczMzKRKlSoAREVFsXr1agYMGMD+/ft1G65IWZJynjOfjqDKieUALDeiuNRzKqM6NNVnVUTkHwp1miwwMJAdO3bkPPb39ycuLo49e/bkahcR82QeWEPStDZUObGcdMOFd71HUuvRBdzasZmCkIjIVRTqNNmxY8dwcXEhODjvBZe//PILHTp0KNbiygKdJhO7YbOSuHQSFTa+hTM2DthCiGs4ieED++Hu4mx2dSIiparETpOFhobmu+xqQSgpKUkBQqQ0JJ3g3Mf34n82e5b5BXTB+/a3GdmstsmFiYiUfUUeYe3NN9+85vKkpCSio6OLunkRKaCM3Yu5/N+2+J/9jWTDg2kVn6LlE19yk4KQiEiBFPm+2gkTJuDv78/w4cPzLEtOTqZXr14kJSXdUHEicg1Z6SR+/yy+2/+HG7DTVoN1zd9gVL+bNJK0iEghFDkMffLJJwwdOpTKlSvnmrg1OTmZ6Ohozp8/z+rVq4ujRhH5p3MHuPDxUConZg92+pnlFkIHT+ahhmEmFyYiYn+KHIYGDhzIxYsXGTJkCD/88APdunUjOTmZm2++mbNnz7Jq1SqCgoKKs1YRAdK3fonx/Wgq21I5b1Tgf/5PMey+hwmq6GF2aSIidumGhp994IEHOH/+PP3792fBggVMmDCBU6dOsWrVKkJCQoqrRlPExsYSGxuL1Wo1uxSRbFkZJC4Yh+/O2QBstNVnZ5s3GRvTEWcn3TIvIlJUxTJR6/jx45kyZQo1atRg1apV17zrzN7o1nopE5JOcvGjIVQ6twWAD50G0uCu12hfV72vIiJXU2K31v/dgAEDcj12dXUlICCAxx9/PFf7t99+W9RdiAhgO7iG1C/upVLmeZIML2Irj2PE/Q8TqNNiIiLFoshhyNfXN9fju+6664aLEZG/MQxS17yD2/KJeGNjjy2cpY3fYOyAaNxcdLeYiEhxKXIYmj17dnHWISJ/l57Mpa9G4nPgewAW2jpivWUao9vUNbkwEZHy54YuoL4iLS2NHTt2kJCQgM1my2m3WCz07du3OHYh4jjO7uPSR4PxubSfTMOZd1zvJ/q+/9A4tJLZlYmIlEs3HIaWLFnC0KFDOXfuXJ5lFotFd2OJFIJ110Ky5j2Ejy2F00Yl3g18nsfvG4qft5vZpYmIlFs3fOHBo48+yh133MHJkyex2Wy5fhSERArImkXqjxNw/noo7rYUNtrqM7fFZ/xn5HAFIRGREnbDPUMJCQmMGTNGAyyKFNXlsyR/di8VTvwCwBzjFgIHvM7jkeEmFyYi4hhuuGdo4MCBrFy5shhKEXFAp3dxeXonKpz4hRTDnZfcx9Lu4ffprSAkIlJqbnjQxZSUFAYNGkSVKlVo0qQJrq6uuZb/c9whe6NBF6Wk2P5YQtZXw3GzpXDYFsSHoa/y1ND++Hq6Xv/JIiJyTaUy6OIVn3/+OUuXLsXT05OVK1disfzftAAWi8Xuw5BIsTMMMn95B+efnscNg/XWhvzWehov3tIaJ02rISJS6m64Zyg4OJjHH3+cZ555Bien8jcQnHqGpFhlZZC64Ek8d34KwFxbN5z7TGVg61omFyYiUr6Uas9QRkYGd955Z7kLQpqoVYpdynlSPh2C14n1WA0LUy330mnY87StHWB2ZSIiDu2Ge4aefPJJqlSpwrPPPltcNZUp6hmSYnHmT1I+GohX8hEuGZ685vkUD44YSa0qFcyuTESkXCrVniGr1cqUKVNYunQpTZs2zXMB9dSpU290FyL27cByMr64F6+sSxy1VWFa4Mv8Z/jtVNb4QSIiZcINh6GdO3fSvHlzAH7//fdcy/5+MbWII7Jt/AB+fBo3rPxmq8ei+m8w6Y7OmmhVRKQMueEwtGLFiuKoQ6R8sWaRufhpXDf/D4B51k4kdJ3CC90b6D8JIiJlTLFM1Coif5N6kfQvh+F+ZCUAb1rvov7ACTzcrJq5dYmIyFUVqa9+x44duWanv55du3aRlZVVlF2J2JekE6TNjMb9yEpSDHeecvo3PR6cRB8FIRGRMqtIYah58+ZXnaU+P+3atSM+Pr4ouxKxH2f2kv5edzwu7OW0UYmxPq/zxKNjaB5e2ezKRETkGop0mswwDCZMmICXl1eB1s/IyCjKbkTsx9FfyfhkIO4ZiRywhTAteDKT7+9NRQ9NrSEiUtYVKQx17tyZvXv3Fnj9du3a4enpWZRdiZR9e5eQNXcYbrY0ttrq8GmtN3jj7i54uDqbXZmIiBRAkcKQZqkXyWZs+Rhj4WhcsLLcGsmKplOYcntrnDXHmIiI3dDdZCJFYRjYVr+J04pXsABfZ3XmSPtJvBTTSLfOi4jYGYUhkcKyWbEuHofzpuwxhGKz+uEe/QJPda5tcmEiIlIUCkMihZGZhnXegzj/sRCbYeFl6700vm0ct7cMNbsyEREpIoUhkYJKSyTrs8G4HF1HuuHC07ZR9BkyipsaBpldmYiI3ACFIZGCSDpJ5scDcD27m0uGJ6Mt/2bkiOG0quFndmUiInKDSnW2yM2bN5fm7kSKx9n9ZH1wE65nd5NgVGKk68s89dCDCkIiIuVEqYah2267rTR3J3LjEv4g68Obcbl0jIO2YB7znMykh++mQUhFsysTEZFiUuynye64446rthuGwfnz54t7dyUmNjaW2NhYrFar2aWIWU7tJGtOP1zSzrPHFs6LlV/jnQd6EujjYXZlIiJSjCyGYRjFuUE/Pz8++eQTKlSokKvdMAzuvPNOTp8+XZy7K3FJSUn4+vqSmJhIxYrqDXAYJ7Zi/ehWnNMT2WGryeSAScx48CZ8PTW9hoiIPSjM93ex9wx17dqVChUq0KVLlzzLmjdvXty7Eyl+R3/D+skAnDOS2GKrw5tVXuPdB7orCImIlFPF3jNU3qhnyMEcWY/109txzrzMr7YIplZ5lfcf6KogJCJiZwrz/V3oC6hTU1M5fvx4nvZdu3YVah2RMufgKmyf3IZz5mXWWRvyZuAkZj6oICQiUt4VKgx988031KtXj969e9O0aVM2btyYs2zo0KEFXkekzNn/E7bPBuGUlcoqa1PeDnqV/z3QmYoeCkIiIuVdocLQK6+8wpYtW9i+fTuzZs3i/vvv5/PPPy/wOjojJ2XS3iXYPr8LJ2s6P1mbMz3oJT58oJOCkIiIgyjUBdSZmZlUqVIFgKioKFavXs2AAQPYv39/gdbRbN5S5uz5HtvXw3GyZfKjtRWzgv/DhyM6KAiJiDiQQvUMBQYGsmPHjpzH/v7+xMXFsWfPnpz2gqwjUib8Pg/jq2E42TJZaG3Hh8ETFIRERBxQoe4mO3bsGC4uLgQHB+dZ9ssvv9ChQweOHTuGq6srQUFBbNu2jcjIyDzr2BPdTVZObfsCY8EjWAwb86wd+Tz4aeaMaIePgpCISLlQYneThYaGXjUIATkhx8fHh3nz5tGyZUuioqKuuo6IqbZ/iTH/YSyGjS+yuikIiYg4uGKbm2z58uXcc889hISE8OKLL1KjRg1dMC1lz+6F2UEIg0+ybuKbkLEKQiIiDu6GwtCxY8d45ZVXqF27Nv369cMwDL755htOnDjBiy++WFw1ihSP/T9hfHM/FsPG11mdmR8ymjkj2ioIiYg4uCJPx9G7d29WrFhB9+7deemll+jfvz/e3t45y3XnmJQpR9ZhfHkPFlsmi6xtmBMwls/vVxASEZEbCENLlixhyJAhjB49Os+1QSJlyvEtGJ8NwpKVynJrJFMrPMWXI9pqZGkREQFu4DTZL7/8gqenJ927dyciIoKXXnop13hDImVCwh6MT2/HkpHMBlsDJriNY/YDHQj08TC7MhERKSOKHIbatWvHBx98wKlTp3j66adZtmwZERERtG3blnfeeYfTp08XZ50ihXfuAMbHt2JJPc82W22esDzDzBEdqe7vff3nioiIwyjWWev37t3Lhx9+yCeffMLp06exWCxYrdbi2rwpNM6QnUo8jjGrF5bEo+yxhXGvbSLT7+9Om1r+ZlcmIiKloERnrb+WiIgIpkyZwrFjx/j222+55ZZbinPzIgWTfAY+vhVL4lEO2oIZljmeV+/qpCAkIiJXVaw9Q+WReobsTOoFmNMXTu/kmBHAHenPM3pgd+6ICjO7MhERKUWm9QyJmCr9Enw2CE7v5Izhyz0Z47k3pqOCkIiIXJPCkJQPmWnwxV1w7DcuGBW4J2M80Z06MLJLbbMrExGRMk5hSOyfNRO+HgaH13DZ8GBYxtM0adGe8TH1za5MRETsgMKQ2DfDgB/GwJ9LSMOV+zP+TWD9drw+oIlGQRcRkQJRGBL7tnYqbPkYK06MyngcW/X2TB/SAhdn/WmLiEjBFHk6DhHT7fgafn4JgImZwzge2JW5w1rh4epscmEiImJPHOK/z7fddhuVK1dm4MCBZpcixeXwLxgLHgFgZtYtLPHsw4f3tdJ8YyIiUmgOEYYef/xxPv74Y7PLkOJydh98OQSLNYPF1ta8ZdzN+0NbUq2Sp9mViYiIHXKIMNStWzd8fHzMLkOKQ/IZ+GwgpF1ki60OT2Y+wqTbm9GyemWzKxMRETtV5sPQ6tWr6du3L1WrVsVisTB//vw868yYMYOaNWvi4eFBy5YtWbNmTekXKiUvMxW+GAwXDhNvBPJgxliGd2nAgBahZlcmIiJ2rMyHocuXL9OsWTOmT59+1eVz585l9OjRPPfcc2zdupVOnToRExNDfHx8KVcqJcpmhW8fhOObuEgF7ssYR/MGdfl3rwizKxMRETtX5u8mi4mJISYmJt/lU6dOZcSIETzwwAMATJs2jaVLl/Luu+8yadKkQu8vPT2d9PT0nMdJSUmFL1qKX9zzsOd7MnHhwfQxuAZGMG1wc5ydNJaQiIjcmDLfM3QtGRkZbN68mejo6Fzt0dHRrFu3rkjbnDRpEr6+vjk/YWGa18p0G2fC+uyewbEZIzng1ZT/DYuignuZz/IiImIH7DoMnT17FqvVSlBQUK72oKAgTp06lfO4V69eDBo0iMWLFxMaGspvv/2W7zbHjx9PYmJizs/Ro0dLrH4pgL0/wpKnAZiSeSc/Wjrw7t0tCPPzMrkwEREpL8rFf63/Oe2CYRi52pYuXVrgbbm7u+Pu7l5stckNOL4FvrkfDBtfZHVjhrUfk29vTJta/mZXJiIi5Yhd9wwFBATg7OycqxcIICEhIU9vkdiZi/Hw+Z2QmcIaoxkTsoYzomMt7mwVbnZlIiJSzth1GHJzc6Nly5bExcXlao+Li6N9+/YmVSU3LC0JPhsElxPYZ6nOw+mP0aFeiGahFxGRElHmT5MlJyezf//+nMeHDh1i27Zt+Pn5ER4ezpgxYxg6dChRUVG0a9eOmTNnEh8fz8iRI29ov7GxscTGxmK1Wm/0JUhh2Gww/2E48wfnnPwZmvIUQVWq8M6Q5pp8VURESoTFMAzD7CKuZeXKlXTr1i1P+7Bhw5gzZw6QPejilClTOHnyJI0bN+btt9+mc+fOxbL/pKQkfH19SUxMpGLFisWyTbmG1W/C8pfJsrgyMG0ChzwasGBUB2oEeJtdmYiI2JHCfH+X+TBkNoWhUrTvp+ypNjB4JvMBvjZ68Mn9rWlfJ8DsykRExM4U5vtb5x2kbDh/COaNAAzm2rrzpbU742PqKwiJiEiJUxgS82Vchrn3QNpFdjvVY0LGMHrUD2REx5pmVyYiIg5AYSgfsbGxNGzYkFatWpldSvlmGLDwcTj9O5ecK3N/ymME+Prw5qBmecaPEhERKQm6Zug6dM1QCVs/A5aOx2ZxYXDaeDZbGjL3X22JquFndmUiImLHdM2Q2IdDa2DZfwCYZL2bX40GjI2upyAkIiKlSmFIzJF4DL6+Dwwrca5d+SAjmi71qjCyc22zKxMREQejMCSlLzMN5g6FlLMc96jLY5eGEVTRg6l3NMPJSdcJiYhI6VIYktJlGLD4KTixhXRXX+5MHEWGxZ3/Dm6OfwVNkCsiIqVPYSgfupushGyeDVs/wbA48UjaKI4ZgYy+qR5tNRO9iIiYRHeTXYfuJitGR3+F2b3Blsksj3t56eLNtK/tzycj2uCs02MiIlKMdDeZlD2XTsNX94Itk52+XXnpYi8CKrgxbXCkgpCIiJhKYUhKnjUr+86xSydJ8qnN4NNDsVgsTLuzOYE+HmZXJyIiDk5hSEreykkQvw6bawUGJz7KZTx5tFsdOtbVvGMiImI+hSEpWQdWwJq3AJji9gi7M4JoXcOPJ3rUNbkwERGRbApDUnKSE+DbfwEGm/z78d65SCp7ufL/7mqOi7P+9EREpGzQN1I+dGv9DbLZ4LuH4HICl33rcs/x2wCYekckwb66TkhERMoOhaF8jBo1it27d/Pbb7+ZXYp9+mUaHFiO4eLJgymPkoY7w9pVp1v9QLMrExERyUVhSIpf/EZY/goA8wIfZ92lKoT7efF0TH2TCxMREclLYUiKV+oFmDcCDCunq/fhqYNNsVjgzUHN8HJzMbs6ERGRPBSGpPgYBix4FBKPYq1Uk7tODAYs3Ne+Bq1r+pldnYiIyFUpDEnx+e1/8McicHLlHb/xHLzkRA1/L8b10ukxEREpuxSGpHic3AFLnwXgz2b/ZtruCjmnxzzdnE0uTkREJH8KQ3Lj0pPhm+FgzSCzdi/u2dkCgBEdahJVQ6fHRESkbFMYkhu3+Ck4tx8qVuNFp1EkJGdQK8Cbp3pFmF2ZiIjIdSkM5UODLhbQti9g+xdgceLXFlP4dGcyThZ4845meLjq9JiIiJR9FsMwDLOLKMuSkpLw9fUlMTGRihUrml1O2XJ2H7zfBTIvk9rxGTptaMXZ5HQe6lKL8TENzK5OREQcWGG+v9UzJEWTmQZf3weZl6FmZ8af6cnZ5HTqBFbgyZvqmV2diIhIgSkMSdEs+w+c/h28AljR8BXmbz+dfXpskE6PiYiIfVEYksLbswh++wCAS72n8++lCQCM7FKbyLBKJhYmIiJSeApDUjiJx2DBqOzf2z/O+B1BnE3OoF5QBZ64qa65tYmIiBSBwpAUnM0K3/4L0i5C1eYsDnyARTtO4uxk4c1BzXB30ekxERGxPwpDUnCr34Qjv4BbBc7HvMt/vv8TgEe61qZpaCVzaxMRESkihSEpmCPrYdXr2b/3eZsJq1M5fzmD+sE+PNZdp8dERMR+KQzJ9aWch3kPgGGDZnexwq0rP+z8v9Njbi76MxIREfulb7F8aATqvxgGfP84JB0Dv1qk3vQ6zy/8HYD7O9SgcTVfkwsUERG5MRqB+jocfgTqTbNg0ZPg5AoPxPHGTk9iVxwgxNeDn8Z0wdvdxewKRURE8tAI1FI8Tu+GJeOzf7/pBfa71GHm6oMATOzbSEFIRETKBYUhubrMVPjmfshKgzo3YbR9mP/M/51Mq0GP+oH0ahRkdoUiIiLFQmFIrm7pc3BmD3gHQv/3+G7bSTYcPI+HqxMv9GuExWIxu0IREZFioTAkee1eCJs+zP59wPtcdPLl1R/2APB4j7qE+XmZWJyIiEjxUhiS3C4ehYWPZv/e4Qmo3Z3JS/Zy7nIGdQMr8EDHWubWJyIiUswUhuT/WLP+mm4jEaq1hO4T2HzkAl/8Gg/AK/0ba0whEREpd/TNJv9n9RsQvw7cfOD2/5GFM/+Znz2m0MCWobSp5W9ygSIiIsVPYUiyHf4FVk/J/r3P2+BXiznrDrPnZBK+nq6Mj6lvbn0iIiIlRGFI4PxB+Gpo9nQbkXdD00GcuJjK1LjsiVjHx9THv4K7yUWKiIiUDIUhR5dyHj4dCCnnIKQZxGT3Dr30/W5SMqy0rF6ZO6LCTC5SRESk5CgMObLMNPjiLjh/AHzDYMhX4F6B5X+cZsmuUzg7WXilf2OcnDSmkIiIlF8KQ/ko9xO12mzw3UNwdAO4+8LdX4NPMKkZVp5fsAuAER1r0iDEAedjExERh6IwlI9Ro0axe/dufvvtN7NLKRk/TYTd87MnYB38KQQ2AGD6in0cu5BKVV8PnuhR19waRURESoHCkCP67X+w7v9l/35rLNTsDMD+hEv/NxFrP03EKiIijkFhyNHsXQKL/539e7f/QLM7ATAMg+e+y56I9aYGgUQ31ESsIiLiGBSGHMnxLfDN8Oxb6JsPhc5P5Sz6dstxNh7Knoh1Yl9NxCoiIo5DYchRXDgCn98JmSlQu0f2wIp/BZ7ElExeW6yJWEVExDEpDDmC1Avw2SC4nABBjWHQHHB2zVn85rLsiVjraCJWERFxQApD5V1WOnx5D5zdCz5Vs8cS8vi/2+V3Hkvk041HAHj5Vk3EKiIijkfffOWZYcCCR+HI2uzJV+/+Gnyr5Sy22Qz+s+B3DANujaxKu9qaiFVERByPwlB5tvwV2PkVOLnAnR9DcONci7/87Sjbj17Ex92F53o3MKlIERERcykMlVfrpsOaN7N/7/tfqN091+JzyelMXvIHAE/2rEdgRY/SrlBERKRM0Kh65Y3NCkufhY3vZT/u8jQ0vyfPapOX/EFiaiYNQipyb7vqpVykiIhI2aEwVJ5kXIZ5D8DexdmPe74E7R/Ps9rmI+f5atMxAF7p3wgXZ3UQioiI41IYKi8unYYv7oQTW8HZHQa8D41uy7NaltXGf+ZnT8Q6qGUoLav7lXalIiIiZYrCUHmQ8Ef2OEKJ8eDpB3d9CeFtrrrqJxuOsOdkEr6erjwTU7+UCxURESl7dH7ELBkp8PV98Oey7Ot8iurgKvgwOjsI+dWCB37KNwglJKUxddmfAIy7OQL/Cu5F36+IiEg5oZ4hs/w+D3Z9l/1TqTpEDc+eL8w7oODb2PYFLHwMbJkQ1hYGfw7e+Y8V9NriPVxKz6JZqC+DW4UXw4sQERGxf+oZMkuNDtB2FHj4wsUj8NMLMLVB9gXQR9ZnD5iYH8OAla/D/JHZQajRbXDvgmsGofUHzjF/2wksFni5f2OcnTQRq4iICCgM5Ss2NpaGDRvSqlWrktmBXy24+TUY8wfcGgtVW4A1A3Z+DbNvhnc7wG//g/RLuZ+XlQHzH4aVk7IfdxgNt88C1/zHCcq02nh+we8A3N0mnKahlUrmNYmIiNghi2FcqwtCkpKS8PX1JTExkYoVK17/CTfixFb47UPY+Q1kpWa3uVWApndA1AjwDYW598DhNWBxhlvehKj7r7vZ91cdYNKPf+Dv7cbysV3x9XK97nNERETsWWG+vxWGrqNUw9AVqRdg+5fZwejcvv9r9/CFtMTsgDRoDtTted1NnUxMpcdbq0jJsPLGwKYMigorubpFRETKiMJ8f+s0WVnkWRnaPgyP/gbDvoeG/bPnF0tLBJ8QGP5jgYIQwMuLdpOSYSWqemVubxFasnWLiIjYId1NVpZZLFCzc/bPpVOwLy47BPkEF+jpq/88w+Kdp3B2svBy/8Y46aJpERGRPBSG7IVPMLQYWuDV07OsTFyYPdL0sHY1aBBSSqf4RERE7IxOk5VT/1tziENnLxPo486TPeuaXY6IiEiZpTBUDl1MyeC9VQcAGN+7Pj4euntMREQkPwpD5dC7qw5wKS2L+sE+3NqsmtnliIiIlGkKQ+XMqcQ05vxyGMief0wXTYuIiFybwlA58/+W7yM9y0ZU9cp0iwg0uxwREZEyT2GoHDl89jJf/XYUgHE318diUa+QiIjI9SgMlSNT4/4ky2bQNaIKrWv6mV2OiIiIXVAYKid2n0hi4fYTADwVHWFyNSIiIvZDYaiceHPZXgD6NA2hcTVfk6sRERGxHwpD5cCmw+dZ/kcCzk4WxqpXSEREpFAUhuycYRhMXvIHAHdEhVEzwNvkikREROyLwpCdW7n3DL8dvoC7ixNP9NC0GyIiIoWlMGTHbDaDKUuzrxUa1r4Gwb4eJlckIiJifxSG7NiinSfZczIJH3cXHu5S2+xyRERE7JLCkJ3KtNqY+tcdZA92rkVlbzeTKxIREbFPCkN26utNxzh8LgV/bzfu71jT7HJERETslsKQHUrLtPLfn/8EYFS3OlRwdzG5IhEREfulMGSHPl5/mNNJ6VSr5MndbcPNLkdERMSuKQzZmaS0TGasPADA6Jvq4u7ibHJFIiIi9k1hyM58sPogF1MyqRNYgQEtQs0uR0RExO45RBhatGgRERER1K1bl//9739ml1NkZy6l8+HaQwA8FV0PZyeLyRWJiIjYv3J/5W1WVhZjxoxhxYoVVKxYkRYtWjBgwAD8/PzMLq3QYlfsJyXDSrNQX3o1Cja7HBERkXKh3PcM/frrrzRq1Ihq1arh4+ND7969Wbp0qdllFdqxCyl8vjEegH/3qo/Fol4hERGR4lDmw9Dq1avp27cvVatWxWKxMH/+/DzrzJgxg5o1a+Lh4UHLli1Zs2ZNzrITJ05QrVq1nMehoaEcP368NEovNoZh8MLC3WRYbbSv7U/HugFmlyQiIlJulPkwdPnyZZo1a8b06dOvunzu3LmMHj2a5557jq1bt9KpUydiYmKIj8/uRTEMI89z7K1XZfHOU/y05zSuzhZe6NfI7HJERETKlTJ/zVBMTAwxMTH5Lp86dSojRozggQceAGDatGksXbqUd999l0mTJlGtWrVcPUHHjh2jTZs2+W4vPT2d9PT0nMdJSUnF8CqK7mJKBhMX/g7Aw13rUC/Ix9R6REREypsy3zN0LRkZGWzevJno6Ohc7dHR0axbtw6A1q1b8/vvv3P8+HEuXbrE4sWL6dWrV77bnDRpEr6+vjk/YWFhJfoarue1xXs4m5xBncAKjOqmyVhFRESKm12HobNnz2K1WgkKCsrVHhQUxKlTpwBwcXHhrbfeolu3bjRv3px///vf+Pv757vN8ePHk5iYmPNz9OjREn0N1/LL/rN8tekYFgtMvr2JBlgUEREpAWX+NFlB/PMaIMMwcrX169ePfv36FWhb7u7uuLu7F2t9RZGaYeXZ73YCMLRtdVpWt7+hAEREROyBXfcMBQQE4OzsnNMLdEVCQkKe3iJ7M+2nPzlyLoUQXw/+3SvC7HJERETKLbsOQ25ubrRs2ZK4uLhc7XFxcbRv396kqm7c78cT+WDNQQBe6d8YHw9XkysSEREpv8r8abLk5GT279+f8/jQoUNs27YNPz8/wsPDGTNmDEOHDiUqKop27doxc+ZM4uPjGTly5A3tNzY2ltjYWKxW642+hELJtNoY980ObAb0aRpCjwb23cMlIiJS1lmMqw3EU4asXLmSbt265WkfNmwYc+bMAbIHXZwyZQonT56kcePGvP3223Tu3LlY9p+UlISvry+JiYlUrFixWLZ5Le+tOsDrP/6Br6crP43pQhUf869fEhERsTeF+f4u82HIbKUZhg6fvUyvaatJz7LxxsCmDIoy97Z+ERERe1WY72+7vmaoPDEMg/Hf7iQ9y0bHOgEMbBlqdkkiIiIOQWGojPh60zHWHzyHh6sTr93WxO6mDBEREbFXCkP5iI2NpWHDhrRq1arE95WQlMYrP+wGYGzPCML9vUp8nyIiIpJN1wxdR2lcM/TIZ5tZvPMUTar58t0j7XFxVkYVERG5EbpmyI4s3XWKxTtP4exk4fXbmygIiYiIlDJ985ooKS2T5xdkz0j/r861aFTV1+SKREREHI/CkIkm//gHp5PSqRngzRM96ppdjoiIiENSGDLJr4fO89nGeAAmDWiCh6tmpBcRETGDwlA+SvpusospGVTycuWu1mG0reVfIvsQERGR69PdZNdRkneTnU1Ox9XZCV9PTcQqIiJSnArz/V3mJ2otzwIqaN4xERERs+k0mYiIiDg0hSERERFxaApDIiIi4tAUhkRERMShKQzlozQnahURERHz6Nb66yiNiVpFRESkeGmiVhEREZECUhgSERERh6YwJCIiIg5NYUhEREQcmsKQiIiIODSFIREREXFoCkP50DhDIiIijkHjDF1HYmIilSpV4ujRoxpnSERExE4kJSURFhbGxYsX8fX1vea6LqVUk926dOkSAGFhYSZXIiIiIoV16dKl64Yh9Qxdh81m48SJE/j4+GCxWMwup9y4ktjV42Y+HYuyQ8ei7NCxKDuKeiwMw+DSpUtUrVoVJ6drXxWknqHrcHJyIjQ01Owyyq2KFSvqH5oyQsei7NCxKDt0LMqOohyL6/UIXaELqEVERMShKQyJiIiIQ1MYElO4u7szceJE3N3dzS7F4elYlB06FmWHjkXZURrHQhdQi4iIiENTz5CIiIg4NIUhERERcWgKQyIiIuLQFIZERETEoSkMSYmZMWMGNWvWxMPDg5YtW7JmzZp81125ciUWiyXPzx9//FGKFZc/q1evpm/fvlStWhWLxcL8+fOv+5xVq1bRsmVLPDw8qFWrFu+9917JF+oACnss9JkoOZMmTaJVq1b4+PgQGBhI//792bt373Wfp89G8SvKsSiJz4bCkJSIuXPnMnr0aJ577jm2bt1Kp06diImJIT4+/prP27t3LydPnsz5qVu3bilVXD5dvnyZZs2aMX369AKtf+jQIXr37k2nTp3YunUrzz77LI8//jjz5s0r4UrLv8Ieiyv0mSh+q1atYtSoUWzYsIG4uDiysrKIjo7m8uXL+T5Hn42SUZRjcUWxfjYMkRLQunVrY+TIkbna6tevbzzzzDNXXX/FihUGYFy4cKEUqnNMgPHdd99dc51x48YZ9evXz9X20EMPGW3bti3ByhxPQY6FPhOlJyEhwQCMVatW5buOPhuloyDHoiQ+G+oZkmKXkZHB5s2biY6OztUeHR3NunXrrvnc5s2bExISQo8ePVixYkVJlilXsX79+jzHrVevXmzatInMzEyTqnJs+kyUvMTERAD8/PzyXUefjdJRkGNxRXF+NhSGpNidPXsWq9VKUFBQrvagoCBOnTp11eeEhIQwc+ZM5s2bx7fffktERAQ9evRg9erVpVGy/OXUqVNXPW5ZWVmcPXvWpKockz4TpcMwDMaMGUPHjh1p3Lhxvuvps1HyCnosSuKzoVnrpcRYLJZcjw3DyNN2RUREBBERETmP27Vrx9GjR3nzzTfp3LlzidYpuV3tuF2tXUqWPhOl49FHH2XHjh2sXbv2uuvqs1GyCnosSuKzoZ4hKXYBAQE4Ozvn6QVKSEjI8z+ra2nbti379u0r7vLkGoKDg6963FxcXPD39zepKrlCn4ni9dhjj7Fw4UJWrFhBaGjoNdfVZ6NkFeZYXM2NfjYUhqTYubm50bJlS+Li4nK1x8XF0b59+wJvZ+vWrYSEhBR3eXIN7dq1y3Pcli1bRlRUFK6uriZVJVfoM1E8DMPg0Ucf5dtvv2X58uXUrFnzus/RZ6NkFOVYXM2NfjZ0mkxKxJgxYxg6dChRUVG0a9eOmTNnEh8fz8iRIwEYP348x48f5+OPPwZg2rRp1KhRg0aNGpGRkcGnn37KvHnzdNvqDUpOTmb//v05jw8dOsS2bdvw8/MjPDw8z3EYOXIk06dPZ8yYMTz44IOsX7+eDz/8kC+++MKsl1BuFPZY6DNRckaNGsXnn3/OggUL8PHxyenx8fX1xdPTE8j7b5Q+GyWjKMeiRD4bxXZfmsg/xMbGGtWrVzfc3NyMFi1a5LpVctiwYUaXLl1yHk+ePNmoXbu24eHhYVSuXNno2LGj8cMPP5hQdfly5RbUf/4MGzbMMIy8x8EwDGPlypVG8+bNDTc3N6NGjRrGu+++W/qFl0OFPRb6TJScqx0HwJg9e3bOOvpslI6iHIuS+GxY/ipGRERExCHpmiERERFxaApDIiIi4tAUhkRERMShKQyJiIiIQ1MYEhEREYemMCQiIiIOTWFIREREHJrCkIiYrmvXrowePbrA669cuRKLxYLFYqF///5F3o5ZunbtmlP/tm3bzC5HxOEpDImI3dq7dy9z5swp0nNr1qzJkiVLcoJV5cqVSUtLy7XOr7/+mhNaitO3337Lr7/+WqzbFJGiUxgSEbsVGBhIpUqVCv28HTt2cO7cObp165bT5uPjw3fffZdrvVmzZhEeHn6jZebh5+dHlSpVin27IlI0CkMiUuYsWbIEX1/fnIkZC8NmszFu3Dj8/PwIDg7mhRdeyLPOggUL6NWrF+7u7jltw4YNY9asWTmPU1NT+fLLLxk2bFiu586ZM4dKlSoxf/586tWrh4eHBz179uTo0aO51lu4cCFRUVF4eHgQEBDAgAEDCv1aRKR0KAyJSJny5Zdfcscdd/Dxxx9z7733Fvr5H330Ed7e3mzcuJEpU6bw0ksvERcXl2udhQsXcuutt+ZqGzp0KGvWrCE+Ph6AefPmUaNGDVq0aJFnHykpKbz66qt89NFH/PLLLyQlJTF48OCc5T/88AMDBgzglltuYevWrfz8889ERUUV+rWISOlwMbsAEZErZsyYwbPPPsuCBQtyncIqjKZNmzJx4kQA6taty/Tp0/n555/p2bMnAMePH2f79u307t071/MCAwOJiYlhzpw5PP/888yaNYv777//qvvIzMxk+vTptGnTBsgOYA0aNODXX3+ldevWvPrqqwwePJgXX3wx5znNmjUr0usRkZKnniERKRPmzZvH6NGjWbZsWZGDEGSHob8LCQkhISEh5/HChQvp0KEDfn5+eZ57//33M2fOHA4ePMj69eu5++67r7oPFxeXXD099evXp1KlSuzZsweAbdu20aNHjyK/BhEpXQpDIlImREZGUqVKFWbPno1hGEXejqura67HFosFm82W8/hqp8iu6N27N2lpaYwYMYK+ffvi7++f736udofZlTZPT8+ilC4iJlEYEpEyoXbt2qxYsYIFCxbw2GOPlcg+kpOTWbFiBf369bvqcmdnZ4YOHcrKlSvzPUUGkJWVxaZNm3Ie7927l4sXL1K/fn0gu3fq559/Lt7iRaTEKAyJSJlRr149VqxYkXPKrLgtWbKEunXrUqtWrXzXefnllzlz5gy9evXKdx1XV1cee+wxNm7cyJYtWxg+fDht27aldevWAEycOJEvvviCiRMnsmfPHnbu3MmUKVOK/fWISPFQGBKRMiUiIoLly5fzxRdfMHbs2GLd9oIFC/I9RXaFm5sbAQEB1xxo0cvLi6effpohQ4bQrl07PD09+fLLL3OWd+3ala+//pqFCxcSGRlJ9+7d2bhxY7G9DhEpXhbjRk7Oi4iYYOXKlXTr1o0LFy4UeNBFq9VKYGAgP/74Y04PTlHMmTOH0aNHc/HixSJvA+Dw4cPUrFmTrVu3EhkZeUPbEpEbo54hEbFboaGh3HXXXQVa99y5czz55JO0atWqhKu6vpiYGBo1amR2GSLyF/UMiYjdSU1N5fjx4wBUqFCB4ODgUtt3cfQMHT9+nNTUVADCw8Nxc3MrpupEpCgUhkRERMSh6TSZiIiIODSFIREREXFoCkMiIiLi0BSGRERExKEpDImIiIhDUxgSERERh6YwJCIiIg5NYUhEREQcmsKQiIiIOLT/DwcJ0Varc2JGAAAAAElFTkSuQmCC", + "image/png": 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", "text/plain": [ "
" ] @@ -188,7 +188,7 @@ "source": [ "Now, let's create the HERA Observatory model. Here, we set the receiver temperature to 100K,\n", "as per Table 1 of P14. We also set the latitude of the instrument to that of Green Bank (38:25:59.24),\n", - "which was the assumed location of HERA in P14 & P15:" + "which was the assumed location of HERA in P14 & P15. We also set the keyword `beam_crossing_time_incl_latitude` to `False`, which means that the beam crossing time is solely determined by the beam's size, and not the latitude of the telescope (this setting is purely available for backwards compatibility, and its default is `True`):" ] }, { @@ -202,6 +202,7 @@ " beam=beam,\n", " latitude=0.6707845*un.rad,\n", " Trcv=100*un.K,\n", + " beam_crossing_time_incl_latitude=False,\n", ")" ] }, @@ -291,8 +292,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "finding redundancies: 100%|██████████| 330/330 [00:01<00:00, 303.03ants/s]\n", - "gridding baselines: 100%|██████████| 1260/1260 [00:00<00:00, 6204.30baselines/s]\n" + "finding redundancies: 100%|██████████| 330/330 [00:00<00:00, 1139.98ants/s]\n", + "gridding baselines: 100%|██████████| 1260/1260 [00:00<00:00, 7322.08baselines/s]\n" ] }, { @@ -342,16 +343,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "calculating 2D sensitivity: 0%| | 0/569 [00:00" ] @@ -455,7 +449,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.12.4" }, "orig_nbformat": 4, "vscode": { diff --git a/src/py21cmsense/observation.py b/src/py21cmsense/observation.py index 1bef077..f012873 100644 --- a/src/py21cmsense/observation.py +++ b/src/py21cmsense/observation.py @@ -29,7 +29,7 @@ class Observation: A class defining an interferometric Observation. Note that to achieve the same behaviour as the original 21cmSense's ``--track`` - option (i.e. track-scan instead of drift-scan) merely requires setting ``obs_duration`` + option (i.e. track-scan instead of drift-scan) merely requires setting ``lst_bin_size`` to the length of the tracked scan. Parameters @@ -43,9 +43,9 @@ class Observation: If simulating a tracked scan, `hours_per_day` should be a multiple of the length of the track (i.e. for two three-hour tracks per day, `hours_per_day` should be 6). track - Tracked scan observation duration. This is an alias for ``obs_duration``, and + Tracked scan observation duration. This is an alias for ``lst_bin_size``, and is provided only for ease of use for those familiar with 21cmSense v1. - obs_duration : float or Quantity, optional + lst_bin_size : float or Quantity, optional The time assigned to a single LST bin, by default the time it takes for a source to travel through the beam's FWHM. If a float, assumed to be in minutes. integration_time : float or Quantity, optional @@ -175,7 +175,7 @@ def _integration_time_vld(self, att, val): raise ValueError("integration_time must be <= lst_bin_size") @lst_bin_size.default - def _obstime_default(self): + def _lst_bin_size_default(self): # time it takes the sky to drift through beam FWHM if self.track is not None: return self.track @@ -195,7 +195,7 @@ def beam_crossing_time(self): @cached_property def baseline_groups( self, - ) -> dict[tuple(float, float, float), list[tuple(int, int)]]: + ) -> dict[tuple[float, float, float], list[tuple[int, int]]]: """A dictionary of redundant baseline groups. Keys are tuples of floats (X,Y,LENGTH), and @@ -272,8 +272,8 @@ def n_lst_bins(self) -> float: Number of LST bins in the complete observation. An LST bin is considered a chunk of time that may be averaged - coherently, so this is given by `hours_per_day/obs_duration`, - where `obs_duration` is the time it takes for a source to travel + coherently, so this is given by `hours_per_day/lst_bin_size`, + where `lst_bin_size` is the time it takes for a source to travel through the beam FWHM. """ return (self.time_per_day / self.lst_bin_size).to("").value diff --git a/src/py21cmsense/observatory.py b/src/py21cmsense/observatory.py index ee6dc95..f1bba59 100644 --- a/src/py21cmsense/observatory.py +++ b/src/py21cmsense/observatory.py @@ -63,6 +63,11 @@ class Observatory: of the array). Assumed to be in units of meters if no units are supplied. Can be used to limit antennas in arrays like HERA and SKA that have a "core" and "outriggers". The minimum is inclusive, and maximum exclusive. + beam_crossing_time_incl_latitude + Whether the beam-crossing time is dependent on the latitude of the telescope. + By default it is, so that the beam-crossing time is + ``tday * FWHM / (2pi cos(lat))``. This affects both the thermal and sample + variance calculations. """ _antpos: tp.Length = attr.ib(eq=attr.cmp_using(eq=np.array_equal)) @@ -78,6 +83,7 @@ class Observatory: min_antpos: tp.Length = attr.ib( default=0.0 * un.m, validator=(tp.vld_physical_type("length"), ut.nonnegative) ) + beam_crossing_time_incl_latitude: bool = attr.ib(default=True, converter=bool) @_antpos.validator def _antpos_validator(self, att, val): @@ -287,7 +293,8 @@ def longest_baseline(self) -> float: @cached_property def observation_duration(self) -> un.Quantity[un.day]: """The time it takes for the sky to drift through the FWHM.""" - return un.day * self.beam.fwhm / (2 * np.pi * un.rad) + latfac = np.cos(self.latitude) if self.beam_crossing_time_incl_latitude else 1 + return un.day * self.beam.fwhm / (2 * np.pi * un.rad * latfac) def get_redundant_baselines( self,