diff --git a/USAGE.ipynb b/USAGE.ipynb index 3f39017c..46d26916 100644 --- a/USAGE.ipynb +++ b/USAGE.ipynb @@ -28,18 +28,21 @@ "outputs": [], "source": [ "from epymorph import *\n", + "from epymorph.adrio import acs5, commuting_flows, us_tiger\n", + "from epymorph.data.pei import pei_humidity\n", + "from epymorph.geography.us_census import StateScope\n", "\n", - "# Select a geo (a bundle of geographic data)\n", - "# NOTE: this system is under construction, pardon our dust...\n", - "geo = geo_library['pei']()\n", + "# Describe the geographic scope of our simulation:\n", + "scope = StateScope.in_states_by_code(['FL', 'GA', 'MD', 'NC', 'SC', 'VA'])\n", "\n", - "rume = Rume.single_strata(\n", + "\n", + "rume = SingleStrataRume.build(\n", " # Load an IPM from the library\n", " ipm=ipm_library['pei'](),\n", " # Load an MM from the library\n", " mm=mm_library['pei'](),\n", - " # Use the geo's scope\n", - " scope=geo.spec.scope,\n", + " # Use our scope\n", + " scope=scope,\n", " # Create a SingleLocation initializer\n", " init=init.SingleLocation(location=0, seed_size=10_000),\n", " # Set the time-frame to simulate\n", @@ -50,11 +53,15 @@ " 'move_control': 0.9,\n", " 'infection_duration': 4,\n", " 'immunity_duration': 90,\n", - " 'centroid': geo['centroid'],\n", - " 'population': geo['population'],\n", - " 'commuters': geo['commuters'],\n", - " 'humidity': geo['humidity'],\n", - " 'meta::geo::label': geo['label'],\n", + " # Geographic data can be loaded using ADRIOs\n", + " 'centroid': us_tiger.InternalPoint(),\n", + " 'population': acs5.Population(),\n", + " 'commuters': commuting_flows.Commuters(),\n", + " 'meta::geo::label': us_tiger.PostalCode(),\n", + " # Except this one...\n", + " # We don't have a humidity ADRIO yet, so we need to load it another way.\n", + " # For now, we have the Pei data files in the project as a convenience.\n", + " 'humidity': pei_humidity,\n", " },\n", ")" ] @@ -110,7 +117,7 @@ "• 2015-01-01 to 2015-05-31 (150 days)\n", "• 6 geo nodes\n", "|####################| 100% \n", - "Runtime: 0.284s\n" + "Runtime: 0.299s\n" ] } ], @@ -119,7 +126,7 @@ "sim = BasicSimulator(rume)\n", "\n", "# Run inside a sim_messaging context to display a nice progress bar\n", - "with sim_messaging(sim):\n", + "with sim_messaging():\n", " # Run and save the simulation Output object for later\n", " out = sim.run(\n", " # Use a seeded RNG (just for the sake of keeping this notebook's results consistent)\n", @@ -152,24 +159,24 @@ "That's (T,N,E) -- simulation time steps, number of geo nodes, and number of IPM events.\n", "\n", "Here are the initial conditions (SIR) for all six geo nodes:\n", - "[[18801310 10000 0]\n", - " [ 9687653 0 0]\n", - " [ 5773552 0 0]\n", - " [ 9535483 0 0]\n", - " [ 4625364 0 0]\n", - " [ 8001024 0 0]]\n", + "[[21206924 10000 0]\n", + " [10516579 0 0]\n", + " [ 6037624 0 0]\n", + " [10386227 0 0]\n", + " [ 5091517 0 0]\n", + " [ 8509358 0 0]]\n", "\n", "And here are the SIR compartment values for the first geo node (Florida) after the first ten timesteps:\n", - "[[18797786 10319 844]\n", - " [18797696 11119 2495]\n", - " [18793768 11470 3400]\n", - " [18793718 12244 5348]\n", - " [18789820 12649 6311]\n", - " [18789453 13499 8358]\n", - " [18785974 13921 9412]\n", - " [18784778 14808 11724]\n", - " [18781056 15376 12840]\n", - " [18779554 16468 15288]]\n" + "[[21203512 10273 844]\n", + " [21203395 10963 2566]\n", + " [21199930 11273 3454]\n", + " [21199636 11865 5423]\n", + " [21196452 12277 6346]\n", + " [21195490 13056 8378]\n", + " [21192151 13523 9418]\n", + " [21190919 14414 11591]\n", + " [21186568 14925 12727]\n", + " [21185848 16079 14997]]\n" ] } ], @@ -214,7 +221,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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uvvtuZGRk4Ouvv8a+fftw99132y2vatG4sAWjuLgYCxYsqFMtZ8+exVdffVVtWUVFxRXfYqBQKDBq1Cj88ccfSExMrLa8qsa77roLCQkJ+Pfff6utU1RUBJPJdNl9ZWRkYPny5bb3Op0O3333HeLi4hAUFGSbr1KpcO+992LJkiVYuHAhunXrVq1VqCajR49GXl4eZs+efcnjuOmmm2A2m6utM3PmTEiSVOsfkvVhMpkwb94823uDwYB58+bB398fvXr1AlDz98OOHTuQkJBQ5/1UBceLvxevllKpxOjRo7Fs2bIaw39ubm69PtfV1fWS95KyuxNydK26xa42aWlpWLBgAdLS0mytIc899xxWrlyJBQsW4L333rNbv7KyEosWLcJLL70kR7nUgg0ePBiPPfYYpk+fjqSkJNxwww1Qq9VISUnB0qVL8emnn+LOO++0rV/VB95zzz1n+8V4oRtuuAEajQYjR47EY489htLSUnz11VcICAhAZmZmrbU88MADWLJkCR5//HGsX78eAwcOhNlsxtGjR7FkyRL8+++/l73cfLH33nsPq1atwuDBg/Hoo4+ic+fOyMzMxNKlS7FlyxZ4eXnh+eefx++//45bbrkF48ePR69evVBWVoYDBw7gl19+walTp+Dn51frfjp06ICHHnoIu3btQmBgIL755htkZ2fXGGjHjh2Lzz77DOvXr8eMGTPqdBxjx47Fd999h6lTp2Lnzp0YNGgQysrKsGbNGjzxxBO47bbbMHLkSFx33XV45ZVXcOrUKXTv3h2rVq3Cb7/9hilTptg9aNIQQkJCMGPGDJw6dQodOnTAzz//jKSkJMyfP9/WqnjLLbfg119/xe23346bb74ZqampmDt3LmJiYlBaWlqn/bRv3x5eXl6YO3cu3N3d4erqivj4+Hrdj3ex999/H+vXr0d8fDweeeQRxMTEoKCgAHv27MGaNWtQUFBwxZ/Zq1cv/Pzzz5g6dSr69OkDNzc3jBw5EoD133Hjxo0NeunWaDTinXfeqTbfx8cHTzzxRIPth6hOZHgSt1kCYNfX0Z9//ikACFdXV7tJpVKJu+66q9r2ixcvFiqVSmRlZTVh1dQcXKofu4tdrluE+fPni169eglnZ2fh7u4uunXrJl544QWRkZFRbd37779fABDDhg2r8bN+//13ERsbK5ycnETbtm3FjBkzbN11XNxlxoXdnQhh7f5jxowZokuXLkKr1Qpvb2/Rq1cv8eabb4ri4mLbegDEpEmTqu07IiKiWtchp0+fFmPHjhX+/v5Cq9WKdu3aiUmTJtl1W1FSUiKmTZsmoqKihEajEX5+fmLAgAHio48+sus3rCYRERHi5ptvFv/++6+IjY0VWq1WdOrUSSxduvSS23Tp0kUoFApbdzR1UV5eLl555RURGRkp1Gq1CAoKEnfeeaetT7eq43jmmWdESEiIUKvVIjo6Wnz44Ye2LlGq1HT+qrrYuLBPQiHOd/Vx4fEMHjxYdOnSRSQmJor+/fsLJycnERERUa2vQovFIt577z0REREhtFqt6NGjh/jzzz/FuHHj7LoEudS+q/z2228iJiZGqFQquy5Lquq4WNW/ycVqOu7s7GwxadIkERYWZjuvQ4cOFfPnz6/1HFxY94VdqJSWlor77rtPeHl5CQB2x3ml3Z1c6nxUqerGqKapffv2l90PUUOThOAdp4D1nowLn4r9+eefcf/99+PQoUPVbtZ1c3Ozu7QDWB+a8PDwsLsURETNV48ePeDj44O1a9fKXUq9DBkyBHl5eZe9f5GIWhdeir2EHj16wGw2Iycn57L3zKWmpmL9+vX4/fffm6g6IroaiYmJSEpKsg2LRUTkKFp1sCstLbUbYzA1NRVJSUnw8fFBhw4dcP/992Ps2LH4+OOP0aNHD+Tm5mLt2rWIjY3FzTffbNvum2++QXBwcIPfGE1EDevgwYPYvXs3Pv74YwQHB1d78ISIqKVr1U/FJiYmokePHujRowcAYOrUqejRowdee+01ANaB1MeOHYtnn30WHTt2xKhRo7Br1y67LiQsFgsWLlyI8ePHX7J/JSJqHn755RdMmDABRqMRP/74I5ycnOQuiYioQfEeOyIiIiIH0apb7IiIiIgcCYMdERERkYNodQ9PWCwWZGRkwN3dncPOEBERUbMnhEBJSQlCQkIuO651qwt2GRkZ1QYyJyIiImru0tPTERoaWus6rS7YVQ3MnZ6eDg8PD5mrISIiIqqdTqdDWFiYLcPUptUFu6rLrx4eHgx2RERE1GLU5RYyPjxBRERE5CAY7IiIiIgcBIMdERERkYNodffYERERUctiNpthNBrlLqPRqNXqBhuWlMGOiIiImiUhBLKyslBUVCR3KY3Oy8sLQUFBV93HLoMdERERNUtVoS4gIAAuLi4OObCAEALl5eXIyckBAAQHB1/V5zHYERERUbNjNpttoc7X11fuchqVs7MzACAnJwcBAQFXdVmWD08QERFRs1N1T52Li4vMlTSNquO82nsJGeyIiIio2XLEy681aajjZLAjIiIichAMdkREREQOgsGOiIiIqIHl5uZi4sSJCA8Ph1arRVBQEIYPH46tW7c26n75VCwRERFRAxs9ejQMBgO+/fZbtGvXDtnZ2Vi7di3y8/Mbdb8Mdo0gt0SPk7mlUCklqBQKKBWS7WuVQoJSIUGtPDdfIUGplKBWKKBRWecRERFRy1VUVITNmzdjw4YNGDx4MAAgIiICffv2bfR9M9g1gk3JuXh26b56beusVkKlPB/81AoJKqUCWpUCzholnFRKOGmUcFIpoFUroVVZA6FWpYBWpYSTWgFXjQquWhVctUq4aFRw0SjhrFFaX9XWr921ajhrGmb4EiIioqYghECF0SzLvp3Vyjo/uerm5gY3NzesWLEC/fr1g1arbeTqzmOwawSuWhXa+bvCbBEwmQVMFsu5VwGT2QKjRcBisb6/WIXRDDTRcHhOagXctGq4aZXngqAKbloVPJxU8HbVwMdFA69zr94uani6qOHlooGXsxoumrp/gxMRETWECqMZMa/9K8u+D781HC6ausUmlUqFhQsX4pFHHsHcuXPRs2dPDB48GPfccw9iY2MbtU4Gu0ZwY9cg3Ng16LLrCSFgEYDJYoHZIlBhMKPcYIbRbIHJImA0W+cbzQJ6kxl6owUVRjMqDGZUGM0wmCzQmyznXs3Qm6zLy/UmlBnMKDeYUKo3o8Jgsm1Xfm5bIYBKowWVRj3ySq/8GDVKBbxc1Ajw0CLA3QmBHlr4n3sNuODVz00DlZLP6BARUesyevRo3Hzzzdi8eTO2b9+Of/75Bx988AG+/vprjB8/vtH2KwkhqjcbOTCdTgdPT08UFxfDw8ND7nJkIYRAmcGMglIDSvUmlBlMKNWbUFppQpneBF2lEYXlRhSWGVBQZkBhuQFF5UYUVRhRXG6EwWyp874kCfBz0yLU2xlh3i4I93FBmI8zwnxcEOHrimAPJyh4XyEREV2ksrISqampiIyMhJOTE4CWcyn2Uh5++GGsXr0ap0+frraspuOtciXZhS12rZAkSXA7d9n1SlX9pyoqN6KgzICckkpk6/TI0emRXVKJHJ0eOedec0v1MFsEckv0yC3RY29aUbXP06gUCPN2RoSvKyJ8XdApyB0xwZ6IDnSDk5r3ABIR0XmSJNX5cmhzFBMTgxUrVjTqPlru2SFZVP2nctGoEOLlDMDzkuuaLQIFZQZkFVcivbAc6QXlSC8sR1pBhfXrgnIYTBacyC3Didwyu22VCglR/m6ICfFATLCH7dXbVdPIR0hERHR18vPzMWbMGDz44IOIjY2Fu7s7EhMT8cEHH+C2225r1H0z2FGjUSok+Ltr4e+uRbfQ6gHQZLYgs7gSp/LLcDq/HKl5ZTiapcOhDB2Kyo04ll2CY9klWL73rG2bYE8nu6AXE+KBMG8XXs4lIqJmw83NDfHx8Zg5cyZOnDgBo9GIsLAwPPLII3j55Zcbdd+8x46aHSEEsnSVOJyhs06Z1ul0fnmN63s4qdCvnS8GtPfFwCg/RAW48YldIqIWrrZ7zhwR77EjhyVJEoI9nRHs6YyhnQNt80sqjTiaVWIX+I5llUBXacKqw9lYdTgbgPVhjQHtzwe9MB8XuQ6FiIioSTHYUYvh7qRGn7Y+6NPWxzbPaLbgUIYOW4/nIeFEPnadKkBeqR6/78vA7/syAACh3s62kDekYwA8ndVyHQIREVGjYrCjFk2tVCAuzAtxYV6YdF0U9CYz9qYVYdvxPGw7kY+k9CKcKazAksQzWJJ4BmqlhEHR/hjRNQg3xATB04Uhj4iIHAeDHTkUrUqJfu180a+dL6YCKNObsOtUARJO5GPd0Ryk5JRi3dEcrDuag5eVBzAwyg83dQ3GDV0C4eXCJ26JiKhl48MT1KqkZJfg7wNZ+PtAJo5ll9jmqxQSBkT5YXTPNhjeJYh96BERyYwPT5zHhyeILiE60B1PB7rj6WHROJ5Tin8OZOLvg1k4kqnDpuRcbErOhbuTCrd2D8G9fcPRtc2l++kjIiJqbthiRwTgZG4pViRlYNnuMzhbVGGbHxvqifvjwzGye0iL7u2ciKilYYvdeVeSXRjsiC5gsQgknMzHz7vSsfJglm1cXA8nFe6ND8f4AW0R7Oksc5VERI6Pwe68K8kuisYs8nKmT5+OPn36wN3dHQEBARg1ahSOHTt22e2WLl2KTp06wcnJCd26dcPff//dBNVSa6BQSBgY5YfP7u2BhGnXY9qITojwdYGu0oR5G09i0Iz1mPLTXhzN0sldKhERUTWyBruNGzdi0qRJ2L59O1avXg2j0YgbbrgBZWVll9xm27ZtuPfee/HQQw9h7969GDVqFEaNGoWDBw82YeXUGvi6afHY4PZY/+wQfDW2N+IjfWCyCKxIysCNszbj4W8TkZReJHeZRERENs3qUmxubi4CAgKwceNGXHvttTWuc/fdd6OsrAx//vmnbV6/fv0QFxeHuXPnXnYfvBRLV+PAmWLM3XQCfx/IRNX/nGs7+OO5GzogNtRL1tqIiBxJS74UO378eBQVFWHFihV13sYhLsVerLi4GADg4+NzyXUSEhIwbNgwu3nDhw9HQkJCo9ZGBADdQj3xxX09sfqZwRjdMxRKhYRNybm4dfZWPPZ9IpIv6EKFiIioqTWbYGexWDBlyhQMHDgQXbt2veR6WVlZCAwMtJsXGBiIrKysGtfX6/XQ6XR2E9HVigpww8d3dce6Zwfjjh5tIEnAv4eyceOsTXhp2X7klFTKXSIREbVCzab/hkmTJuHgwYPYsmVLg37u9OnT8eabbzboZxJVifB1xSd3x2HikPb4eFUyVh7Kwk+70vHHvgw8cV0UHromkp0dExE1FCEAY7k8+1a7AJIkz76vQLMIdpMnT8aff/6JTZs2ITQ0tNZ1g4KCkJ2dbTcvOzsbQUFBNa4/bdo0TJ061fZep9MhLCzs6osmukB0oDvmPtALiacK8Pafh7HvTDE+/PcYliSm463bumJwB3+5SyQiavmM5cB7IfLs++UMQOMqz76vgKyXYoUQmDx5MpYvX45169YhMjLystv0798fa9eutZu3evVq9O/fv8b1tVotPDw87CaixtK7rQ+WPzEQs+6OQ6CHFqfzyzHum52YvHgPcnS8PEtERI1L1ha7SZMmYfHixfjtt9/g7u5uu0/O09MTzs7WTmDHjh2LNm3aYPr06QCAp59+GoMHD8bHH3+Mm2++GT/99BMSExMxf/582Y6D6EIKhYRRPdpgaOcAzFydgoXbUvHn/kxsTM7F6yO7YHTPNpBaQHM+EVGzo3axtpzJte8WQNZgN2fOHADAkCFD7OYvWLAA48ePBwCkpaVBoTjfsDhgwAAsXrwY//vf//Dyyy8jOjoaK1asqPWBCyI5uDup8drIGNzRsw1eWX4A+84U47ml+7DyYCbeu70bAjxa1uP7RESyk6QWcTlUTrIGu7p0obdhw4Zq88aMGYMxY8Y0QkVEDa9rG08smzgA8zefxKzVKVhzJAe7Tm3CW7d1wa3dQ9h6R0REDabZdHdC5MhUSgWeGBKFP568Bl3beKC4woinf0rCxB/2IK9UL3d5RETkIBjsiJpQxyB3LH9iIKb+pwNUCgkrD2VhxKebkXAiX+7SiIiogSxcuPCKRp1oSAx2RE1MrVTgqaHR+G3yQEQHuCG3RI/7v96Oz9amwGxpNiP8ERFRC8RgRySTLiGe+G3yQIzpFQqLAD5ZnYxx3+xEbgkvzRIRUf0w2BHJyEWjwodjuuOjMd3hrFZiy/E83PzZZuw+XSh3aURE1AIx2BE1A3f2CsXvkweiQ6Abckr0uHf+dixJTJe7LCIiamEY7IiaiehA64MVN3YJgsFswQu/7MebfxyCyWyRuzQiImohGOyImhFXrQpf3t8TU4ZFAwAWbD2F8Qt2oajcIHNlRETUEjDYETUzCoWEKcM6YO5/e8JFY73v7rYvtiI5u0Tu0oiIqJljsCNqpm7sGoxfnxiAUG9nnM4vx+gvt7G/OyIiqhWDHVEz1inIA79PvgZ92/qgRG/CuG924q/9mXKXRUREzRSDHVEz5+OqwXcP9cWIrtaHKib/uAcLt6bKXRYRETVDDHZELYCTWonZ9/XE2P4REAJ444/DeP+foxCCI1UQETU348ePhyRJkCQJarUakZGReOGFF1BZWdno+1Y1+h6IqEEoFRLevLULAj2c8OG/xzB34wkUlRvw3u3doFBIcpdHREQXuPHGG7FgwQIYjUbs3r0b48aNgyRJmDFjRqPuly12RC2IJEmYdF0UPrwzFgoJ+GlXOp5buo993RERNTNarRZBQUEICwvDqFGjMGzYMKxevbrR98sWO6IWaEzvMDiplZjycxJ+3XsWBrMFM++Og1rJv9WIyHEJIVBhqpBl384qZ0hS/a6OHDx4ENu2bUNEREQDV1Udgx1RCzWyewjUSgWe/HEP/tyfCYPJgs/v6wGtSil3aUREjaLCVIH4xfGy7HvHfTvgonap8/p//vkn3NzcYDKZoNfroVAoMHv27Eas0Ip/3hO1YDd2DcK8B3pBo1Jg1eFsPP79blQazXKXRUTU6l133XVISkrCjh07MG7cOEyYMAGjR49u9P2yxY6ohbu+UyD+b1xvPPJdItYfy8XD3ybi63G94aRmyx0RORZnlTN23LdDtn1fCVdXV0RFRQEAvvnmG3Tv3h3/93//h4ceeqgxyrNhsCNyAIOi/bFwQl88uHAXthzPwyPfJeKrsQx3RORYJEm6osuhzYVCocDLL7+MqVOn4r777oOz85WFxCvaV6N9MhE1qX7tfLFwQl84q5XYnJKHJxbtgcHEp2WJiJqDMWPGQKlU4osvvmjU/TDYETmQvpE++L/xvaFVKbDuaA4mL94DI7tCISKSnUqlwuTJk/HBBx+grKys0fYjiVbWdb1Op4OnpyeKi4vh4eEhdzlEjWJTci4e/i4RBpMFN3cLxqf3xEHFrlCIqAWprKxEamoqIiMj4eTkJHc5ja62472S7MKf9EQO6NoO/pj3315QKyX8dSATLyzbD4ulVf0NR0TUKjHYETmo6zoF4Iv7ekKpkPDrnrN49+8jHFuWiMjBMdgRObAbugThg9GxAID/25KKLzeckLkiIiJqTAx2RA5udK9Q/O/mzgCAD/89hh93pslcERERNRYGO6JW4OFB7TDpuvYAgFeWH8A/BzJlroiIiBoDgx1RK/HcDR1xb99wWATw9E9J2HY8T+6SiIguy2JpHV02NdRxcuQJolZCkiS8M6orisoN+OdgFh75LhE/PtoPsaFecpdGRFSNRqOBQqFARkYG/P39odFoIEmS3GU1OCEEDAYDcnNzoVAooNForurz2I8dUSujN5nx4MJd2Ho8Hz6uGvzyeH+083eTuywiomoMBgMyMzNRXl4udymNzsXFBcHBwTUGuyvJLgx2RK1Qqd6E+77ajv1nihHh64JfJw6Ar5tW7rKIiKoRQsBkMsFsNstdSqNRKpVQqVSXbJFksKsFgx2RVV6pHrd/uRXpBRXoGe6FxY/0g5NaKXdZRER0EY48QUSX5eemxYLxfeHprMaetCJMXZLE0SmIiFo4BjuiViwqwA3zH+gFjVKBvw9kYcbKo3KXREREV4HBjqiVi2/niw/HWEenmLfpJL7fflrmioiIqL4Y7IgIt8W1wXM3dAAAvP7bQaw7mi1zRUREVB8MdkQEAJh0XRTu6h0KiwAmL96Lg2eL5S6JiIiuEIMdEQGwdmD87u3dcE2UH8oN1r7uMosr5C6LiIiuAIMdEdmolQp8+d+e6BjojpwSPR75LhHlBpPcZRERUR0x2BGRHQ8nNb4e1xu+rhocPKvDc0v3sRsUIqIWgsGOiKoJ83HB3Ad6Qa2U8PeBLMxamyJ3SUREVAcMdkRUoz5tffDe7d0AAJ+tTcEf+zJkroiIiC6HwY6ILmlM7zA8MigSAPDc0n3Yf6ZI3oKIiKhWDHZEVKuXRnTG9Z0CoDdZ8Mh3icgqrpS7JCIiugQGOyKqlVIh4dN74tAh0A3ZOj0e/T4RFQaz3GUREVENGOyI6LLcndT4emwfeLuosf9MMZ7/ZR+E4JOyRETNDYMdEdVJuK8L5vy3F1QKCX/uz8Tn647LXRIREV2EwY6I6qxfO1+8M6orAOCT1cn450CmzBUREdGFGOyI6Irc0zccDw60Pin7zJIkjilLRNSMMNgR0RV7+aZOuLaDPyqN1idlc3R8UpaIqDlgsCOiK6ZSKjD7vh5o7++KzOJKPPr9blQa+aQsEZHcGOyIqF48nNT4v3F94OWiRlJ6EV5ctp9PyhIRyYzBjojqra2fK768vydUCgm/JWXgyw0n5C6JiKhVY7AjoqsyoL0f3ri1CwDgw3+P4d9DWTJXRETUejHYEdFV+2+/CIzrHwEAeObnJBzO0MlcERFR68RgR0QN4tVbYnBNlB/KDWY8/O0u5Jbo5S6JiKjVYbAjogahUirwxX090c7PFRnFlXjs+0ToTXxSloioKTHYEVGD8XRR4+txveHhpMKetCJM+/UAn5QlImpCDHZE1KDa+bvhy/t7QamQ8Oues5i36aTcJRERtRoMdkTU4K6J9sPrI2MAADNWHsXqw9kyV0RE1Dow2BFRo3igXwTujw+HEMCUn/biaBaflCUiamwMdkTUKCRJwhu3dsGA9r4oM5jx8LeJyC/lk7JERI2JwY6IGo1aqcCX9/dEhK8LzhRW4PEfdvNJWSKiRsRgR0SNystFg/8b1xvuWhV2nSrE/5Yf5JOyRESNhMGOiBpdVIA7Pr+vBxQSsHT3GfzfllS5SyIickgMdkTUJIZ0DMD/brY+Kfve30ew/miOzBURETkeBjsiajITBrbFvX3DYBHAkz/uRXJ2idwlERE5FAY7ImoykiThzVu7Ij7SB6V6Ex76dhcKygxyl0VE5DAY7IioSWlUCsz5by+E+7ggvYBPyhIRNSQGOyJqcj6uGnw9rjfctCrsTC3A1J/3wWzhk7JERFeLwY6IZNEh0B1z/tsTaqWEvw5k4q0/DrEbFCJqMQwmC3aczMdH/x7DqkNZcpdjI2uw27RpE0aOHImQkBBIkoQVK1bUuv6GDRsgSVK1KSur+ZxQIqq7QdH+mHl3HADg24TTmLfppLwFERFdQlWQ+2TVMdz31Xb0eGsV7p6/HbPXH8fyvWflLs9GJefOy8rK0L17dzz44IO444476rzdsWPH4OHhYXsfEBDQGOURURO4JTYEWcWVeOevI3j/n6MI9nTCbXFt5C6LiFo5k9mCY9kl2HY8H1uO52FnagEqjPb3A/u4anBNlB9u6BIoU5XVyRrsRowYgREjRlzxdgEBAfDy8mr4gohIFg8Paoes4kp8vSUVzy3dBz83LQZG+cldFhG1IhaLwOFMHbYcz8OWlDwkni5ApdFit46fmwYD2vuhf3tfxIZ6onOQBxQKSaaKayZrsKuvuLg46PV6dO3aFW+88QYGDhx4yXX1ej30+vMDj+t0uqYokYiu0Ms3dUaWrhJ/7s/EY9/vxk+P9kPXNp5yl0VEDspiETiaVYK96YXYdiIf247nobDcaLeOq0aJvpE+GBjlh2ui/dAx0B2S1LyC3MVaVLALDg7G3Llz0bt3b+j1enz99dcYMmQIduzYgZ49e9a4zfTp0/Hmm282caVEdKUUCgkf39UdeaV6bD9ZgPELduHXiQMQ7usid2lE5CAyiiqwJSUPm4/nYevxvGr9aLppVejXzhrkBkb5Icrfrdm1yF2OJJrJY2iSJGH58uUYNWrUFW03ePBghIeH4/vvv69xeU0tdmFhYSguLra7T4+ImgddpRF3z9uOI5k6tPV1wS8TB8DPTSt3WUTUAmXrKrElJQ970wuRcCIfJ3LL7Ja7apSIC/dC7wgfDIr2Q/cwL6iVza/DEJ1OB09PzzpllxbVYleTvn37YsuWLZdcrtVqodXylwJRS+HhpMa3E/rgjjnbcCq/HBMW7MKPj/aDm7bF/7giokZWaTRj16kCbE7Jw6bkXBzNsh+2UCEBsaFeuDbaD9dE+6NHePMMclejxf+kTEpKQnBwsNxlEFEDCvBwwncP9sWdcxNw4GwxHv0uEd+M7wMntVLu0oioGTGZLdibXoRNybnYdaoASelFdg88SBIQ28YTfSN90DPcGwPa+8HTRS1jxY1P1mBXWlqK48eP296npqYiKSkJPj4+CA8Px7Rp03D27Fl89913AIBZs2YhMjISXbp0QWVlJb7++musW7cOq1atkusQiKiRtPN3w8IJfXDv/O3YdiIfT/24F1/e3xMqB/vrmoiuzNmiCmxKzsXGY7nYeiIPJZUmu+WBHlpcG+2PQR38cU2UH3xcNTJVKg9Zg11iYiKuu+462/upU6cCAMaNG4eFCxciMzMTaWlptuUGgwHPPvsszp49CxcXF8TGxmLNmjV2n0FEjiM21AtfjeuN8d/swqrD2Zj26wF8cGdss38qjYgaTlG5AZtT8rAxORcJJ/JxtqjCbrm3ixrXRPtjYHtf9IrwRlSAW6v+GdFsHp5oKldyAyIRNQ//HsrCxB92wyKABwdG4tVbOrfqH9xEjsxotuDg2WJsSs7DxuQcJKUX4cKhpBUS0CPcG4M7+OPaDv7o1sYTyhb25OqValUPTxCR4xveJQgzRsfi+V/245utqXBSK/D88I4Md0QO4nR+GdYfzcGG5FxsP5lfrWPgDoFuGNzBH4POPfDg7uTY98ldDQY7ImoRxvQOQ6XRjFd/O4QvN5yAk1qJp4ZGy10WEdWDrtKI3acLsSk5FxuO5SI1z74bEg8nFQa098Pgjv4Y3MEfIV7OMlXa8jDYEVGL8UD/ttCbLHjnryP4ZHUytCoFHhvcXu6yiOgyhLAO17XhWC7WH83BnrRCu8urKoWE3m29MaRjAAZ38EfHQPcW1zFwc8FgR0QtysOD2kFvsuDDf49h+j9H4aJR4oH+beUui4gukq2rxM7UAmxJycOG5Bxk6/R2yyN8XdAv0hfXdfLHwCg/Xl5tIAx2RNTiTLouChUGM2avP45XfzsEJ7USY3qHyV0WUatmtgjsTSvEuqM5WH8sF0cy7cdmd1YrMTDKD9d18seQjgFow8urjYLBjohapGdv6IAygwkLtp7CC8v2o9JoZssdURM7nV+G7SfzsfV4Pjal5KKo3GhbJklAlxAP9G1rbZXrG+kDrYqdjDc2BjsiapEkScJrt8TAaLbgh+1pePW3QyjRm/DEkCi5SyNyWHqTGbtSC7H+WA7WH83ByYseevB0VuPaDv64vpM/BncIaHWdAzcHDHZE1GJJkoS3b+sKH1ctPlubgg9WHgMAhjuiBiKEwPGcUmxOyUPCyXxsPZ6HcoPZtlylkNAz3Bt9Iq0PPvQI8+LoMDJjsCOiFk2SJEz9TweoFBI+WZ2MD1YeQ4XBjGeGdeBTdUT1UGk0Y/vJfKw/moO1R3NwptB+pAd/dy2u6+iP6zsF8KGHZojBjogcwlNDoyEB+Hh1Mj5fdxzpBeX4cEx3qNl6QFQrIQSOZZdgU3IudqYWYOvxfFQYz7fKaZQKxLfzQb92vhjcwR8xwR78o6kZY7AjIofx5NBoBHo64eVfD2BFUgYqjGZ8dm8P3rBNdJFKoxkJJ/Kx9mg21h/NrTb+aqCHFtd3CsB1Ha2tcq5axoWWgv9SRORQ7uodBh8XDZ5YtAf/HsrGw98mYva9PeHpwstF1HpVtcptOGYdsuviYbu0KgX6t/dFv3a+uCbKD11CPDhkXwslCSHE5VdzHFcykC4RtVxbUvLwyHeJqDCaEeHrgm8n9EVbP1e5yyJqMpVGM7adyMO6ozlYdyQHGcWVdsuDPZ1wfacAXN8pAAPa+8FZw5bt5upKsguDHRE5rEMZxXjs+904U1gBX1cNFkzog9hQL7nLImoUQggcytBhw7Ec7EgtwM7UAuhN9q1yA6P8MDDKDwPa+6JTkDtb5VoIBrtaMNgRtS65JXpMWLgTB8/q4KJRYsboWIzsHiJ3WUQNokxvwpbjeVh/NAfrj1UftquNlzOu6+SPoZ0C0b+9L5zUbJVriRjsasFgR9T6lOpNmPjDbmxOyQMAPHxNJF6+qTOf7KMWp6pVbt3RHGw5noektCIYzOdb5azDdvliULQ/+rXzRYdAN7bKOQAGu1ow2BG1TiazBTPXJOOL9ScAAMO7BGLW3T14XxE1e2V6E7Yezzs3Bmv1VrkwH2dc3zEA13UKQL92bJVzRAx2tWCwI2rdfks6i+eX7ofBbEGHQDfMursHYkL4s4CaD4tF4MDZYqw7moOtx/Ow/0xxDa1yfhjS0R8D2vsi0s+VrXIOjsGuFgx2RLTrVAEm/rAbeaUGaFUKfHBnLG6LayN3WdSKFVcYsTklF+uO5mDjsVzklxnslof5OGNop0Bc1ykA8ZE+bJVrZRjsasFgR0QAkF+qx7NL92HDsVwAwCODIvHc8I7szJiahMlswd70Imw4loNNyXk4lFEMywW/jd20KgyKtrbKxUf6IsLXha1yrRiDXS0Y7Iioitki8PGqY/hyg/W+u+gAN3x+Xw90CuLPBmp4OSWV2JSch/XHcrA5ORe6SpPd8qgAN1zfKQBDOvqjd4QPNCoOh0dWDHa1YLAjooutPJiJV5YfRH6ZAU5qBd6+rSvG9A6Tuyxq4coNJuxILcCWlDxsPZ6Ho1kldss9ndW4toM/hnTwx8AoPwR5OslUKTV3DHa1YLAjoprkl+rxzJJ92JRsvTT7n5hAvD4yBqHeLjJXRi2FyWzBgbPF2JKShy3H87AnrRBG8/lfsZIEdA3xxJCO/hjSMQBxYV5QsssdqgMGu1ow2BHRpVgsAl+sP45P16bAZBFw16rwzu1d+WAF1UgIgVP55diSkostx/Ow7UQ+Si66vNrGyxmDov1wTbQfBrT3g4+rRqZqqSVjsKsFgx0RXU5KdgleXLYfe9KKAAAjugbhhRs7IZJjzbZ6+aV6bD2Rj63nWuXOFlXYLfdwUmFAe2uQuybKjw89UINgsKsFgx0R1YXJbMEX60/gs3UpMFsENEoFnh4WjceubQeVkje1txaVRjN2nSqwXV49lKGzW65WSugV4Y1rovxwTbQ/urXx5OVVanAMdrVgsCOiK3EkU4f3/j5iG46sWxtPPD+8IwZF+7ElxgGZLQKHM3TYfDwXW4/nYdepQhhMFrt1OgW5nwtyfugb6QMXjUqmaqm1YLCrBYMdEV0pIQR+3XMWb/5xyNZFxaBoP7x5axe083eTuTq6WukF5dh87snVrSfyUFRutFse5OGEa6L9MOjcfXL+7lqZKqXWisGuFgx2RFRfOSWVmLvhJH7YfhoGswVqpYS7eofhoWsiGfBaCCEEzhRWYPvJfOxMLcCO1AKkFZTbreOmVaFfO18MivbDwCg/tPfnkF0kLwa7WjDYEdHVOpVXhjf+OGQbtUKpkHBPnzBMGdaBrTnNjBACJ/PKrCHuXJjLKK60W0elkNAj3AsDo6ytcrGhXlDzPkpqRhjsasFgR0QNQQiBhJP5+L/NqVh7NAcAoFEqMCwmAI8Pbo/YUC95C2ylLBaBlJxS7EjNx47UAuxMLUBuid5uHZVCQmyoJ/pG+iK+nQ/6tPWBm5b3yVHzJUuwKyoqgpeXV0N8VKNisCOihrbjZD7eX3kUe891jwIAQzr6485eoRjWOZADtjeicoMJB8/qkJReiMRThdh1qgCFF90jp1EpEBfmhX6RPugb6YueEV584IFalEYPdjNmzEDbtm1x9913AwDuuusuLFu2DEFBQfj777/RvXv3+lXeBBjsiKixHM7Q4evNJ7Ei6axtQHdvFzX+2y8Cd/UOQ5gPR7G4GmaLwPGcUiSlFyIpvQhJ6cVIzi6B2WL/a8xZrUSvCG/0jfRBfKQPuod5MVxTi9bowS4yMhKLFi3CgAEDsHr1atx11134+eefsWTJEqSlpWHVqlX1Lr6xMdgRUWM7lVeGxTvT8Nf+TLsObDsFuePOXqG4o2coRyCog6ziSiSlF2JvehH2pRfhwJlilBnM1dYL9NAiLswLcWHWMNetjSc0Kt4jR46j0YOds7MzkpOTERYWhqeffhqVlZWYN28ekpOTER8fj8LCwnoX39gY7IioqZgtAqsOZWHhtlNIPF1oa1mSJKBTkAeGdwnErd1DWv0TtUII5JTocSRTh8OZOuxLL0JSehGydfpq67pqlOgW6om4MG/EhVlfgzydZKiaqOlcSXap100G3t7eSE9PR1hYGFauXIl33nkHgPU/p9lc/a8pIqLWSKmQMKJbMEZ0C0ZxuRG/78/Az7vScPCsDkcyrdOsNSkI9NCid1sfDOscgIFRfvB30zps9xplehNSckpxLEuHo1klOJpZgqNZumr3xQHW89ch0B1xYV7oEeaF7mFeiApw48gORLWoV7C74447cN999yE6Ohr5+fkYMWIEAGDv3r2Iiopq0AKJiByBp4saD/SLwAP9IpCjq8TmlDz8vi8DW47nIVunx1/7M/HX/kwAgJ+bBv3b+2FAe1+09XVFTIgHPJ3VMh9B3ZktApnFFTidX45T+WVIyy/HidwyJGeXVOszropCAtr5u6FTkDu6h1pDXNc2HnzIgegK1etSrNFoxKeffor09HSMHz8ePXr0AADMnDkT7u7uePjhhxu80IbCS7FE1JyU6k04nKHDxuQcrD2Sg2PZJbj4p7IkAR0C3NHO3xVtvJzRpY0H2vu7wdNZjTZezk06dq3FIlBYbkBOiR7Zukpk6yqRVaxHRlEFMoorcLawAmcKK2AwWy75GX5uWnQMckPHQA90DnZH52APRAW48QEHoktgP3a1YLAjouas0mjG/jPF2JKSiz1pRThdUIb0gopLrq9WSgj3cYG7kxp+bhqEervAw1kNtUKCr5sWXi5qmC0CLhol3LQqmC0CxoueIq26sllQZoCuwogSvQllehN0FSYUlhtQVG5EYbkBeaV65JUaqj2FWhONUoFQH2e09XVFhK8L2vq6IjrQDR0D3eHrxk6cia5Eo9xj9/vvv9e5gFtvvbXO6xIR0XlOaiX6Rvqgb6SPbV5uiR570wqRWVyJ1LwyHM7Q4XRBGYrKjdCbLDiRW9bkdfq4ahDo4YRADy2CPJwQ4uVsnTydEO7rgmBPZ94LRySDOrfYKRR1a+qXJKlZP0DBFjsichQWi0DGuXvZyvQm5JTocaawAmV6EwwmC/JK9SiuMEKpkFBhNKO00gSVUrIbLssirA++AYC3iwbermq4alRw1arg4aSC17l5Xs4a+Ltr4eemha+bhkNuETWhRmmxs1gufb8EERE1PYVCQqi3C0K92fExEVnxTy4iIiIiB1Hv58jLysqwceNGpKWlwWAw2C176qmnrrowIiIiIroy9Qp2e/fuxU033YTy8nKUlZXBx8cHeXl5cHFxQUBAAIMdERERkQzqdSn2mWeewciRI1FYWAhnZ2ds374dp0+fRq9evfDRRx81dI1EREREVAf1CnZJSUl49tlnoVAooFQqodfrERYWhg8++AAvv/xyQ9dIRERERHVQr2CnVqtt3Z8EBAQgLS0NAODp6Yn09PSGq46IiIiI6qxe99j16NEDu3btQnR0NAYPHozXXnsNeXl5+P7779G1a9eGrpGIiIiI6qBeLXbvvfcegoODAQDvvvsuvL29MXHiROTm5mL+/PkNWiARERER1Q3HiiUiIiJqxq4ku7CDYiIiIiIHUa977CIjIyFJlx7c+eTJk/UuiIiIiIjqp17BbsqUKXbvjUYj9u7di5UrV+L5559viLqIiIiI6ArVK9g9/fTTNc7/4osvkJiYeFUFEREREVH9NOg9diNGjMCyZcsa8iOJiIiIqI4aNNj98ssv8PHxaciPJCIiIqI6qncHxRc+PCGEQFZWFnJzc/Hll182WHFEREREVHf1CnajRo2ye69QKODv748hQ4agU6dODVEXEREREV0hdlBMRERE1IxdSXapc4udTqercwEMTERERERNr87BzsvLq9ZOiS9kNpvrXRARERER1U+dg9369ettX586dQovvfQSxo8fj/79+wMAEhIS8O2332L69OkNXyURERERXVa97rEbOnQoHn74Ydx777128xcvXoz58+djw4YNDVVfg+M9dkRERNSSXEl2qVc/dgkJCejdu3e1+b1798bOnTvr85FEREREdJXqFezCwsLw1VdfVZv/9ddfIyws7KqLIiIiIqIrV69+7GbOnInRo0fjn3/+QXx8PABg586dSElJ4ZBiRERERDKpV4vdTTfdhOTkZIwcORIFBQUoKCjAyJEjkZycjJtuuqmhayQiIiKiOmAHxURERETNWKN0ULx//3507doVCoUC+/fvr3Xd2NjYun4sERERETWQOge7uLg4ZGVlISAgAHFxcZAkCTU19kmSxA6KiYiIiGRQ53vsUlNT4e/vb/v65MmTSE1NrTadPHmyzjvftGkTRo4ciZCQEEiShBUrVlx2mw0bNqBnz57QarWIiorCwoUL67w/IiIiIkdW5xa7iIgI29eBgYFwcnK66p2XlZWhe/fuePDBB3HHHXdcdv3U1FTcfPPNePzxx7Fo0SKsXbsWDz/8MIKDgzF8+PCrroeIiIioJatXdycBAQG4/fbb8d///hdDhw6FQlGvh2sxYsQIjBgxos7rz507F5GRkfj4448BAJ07d8aWLVswc+ZMBjsiIiJq9eqVyL799luUl5fjtttuQ5s2bTBlyhQkJiY2dG3VJCQkYNiwYXbzhg8fjoSEhEtuo9frodPp7CYiIiIiR1SvYHf77bdj6dKlyM7OxnvvvYfDhw+jX79+6NChA956662GrtEmKysLgYGBdvMCAwOh0+lQUVFR4zbTp0+Hp6enbeLIGEREROSo6ncN9Rx3d3dMmDABq1atwv79++Hq6oo333yzoWprENOmTUNxcbFtSk9Pl7skIiIiokZRr3vsqlRWVuL333/H4sWLsXLlSgQGBuL5559vqNqqCQoKQnZ2tt287OxseHh4wNnZucZttFottFpto9VERERE1FzUK9j9+++/WLx4MVasWAGVSoU777wTq1atwrXXXtvQ9dnp378//v77b7t5q1evRv/+/Rt1v0REREQtQb2C3e23345bbrkF3333HW666Sao1ep67by0tBTHjx+3vU9NTUVSUhJ8fHwQHh6OadOm4ezZs/juu+8AAI8//jhmz56NF154AQ8++CDWrVuHJUuW4K+//qrX/omIiIgcSb2CXXZ2Ntzd3a9654mJibjuuuts76dOnQoAGDduHBYuXIjMzEykpaXZlkdGRuKvv/7CM888g08//RShoaH4+uuv2dUJEREREQBJ1DQuWA10Op1t4NnLdRlyuQFq5XQlA+kSERERye1KskudW+y8vb2RmZmJgIAAeHl5QZKkausIIThWLBEREZFM6hzs1q1bBx8fHwDA+vXrG60gIiIiIqqfOl+KdRS8FEtEREQtSaNcit2/f3+dC4iNja3zukRERETUMOoc7OLi4iBJku0+utrwHjsiIiKiplfnIcVSU1Nx8uRJpKamYtmyZYiMjMSXX36JvXv3Yu/evfjyyy/Rvn17LFu2rDHrJSIiIqJLqHOLXUREhO3rMWPG4LPPPsNNN91kmxcbG4uwsDC8+uqrGDVqVIMWSURERESXV+cWuwsdOHAAkZGR1eZHRkbi8OHDV10UEREREV25egW7zp07Y/r06TAYDLZ5BoMB06dPR+fOnRusOCIiIiKqu3oNKTZ37lyMHDkSoaGhtidg9+/fD0mS8McffzRogURERERUN/Xux66srAyLFi3C0aNHAVhb8e677z64uro2aIENjf3YERERUUvSKP3YXczV1RWPPvpofTcnIiIiogZW72CXkpKC9evXIycnBxaLxW7Za6+9dtWFEREREdGVqVew++qrrzBx4kT4+fkhKCjIrsNiSZIY7IiIiIhkUK9g98477+Ddd9/Fiy++2ND1EBFRMyKEgMFigEahgUmYUGYog6vaFSqFCiZhggQJKkW9L/4QUQOr1//GwsJCjBkzpqFrISKiRiSEgFmYkVmWiZTCFOSW56LCVIG0kjRklGYAElBcWYyc8hyYhRmSJKGosggmYYJKsga5KgpJAYuwQIIET60nJEiQJAl+zn5wUblAqVDCx8kHgS6BcFY5w03jhnD3cHhqPeGmdkOoeyjcNe4yng0ix1SvYDdmzBisWrUKjz/+eEPXQ0REDaBYX4yknCQcKzyG/Ip8HC04iqMFR1FuKq/X510Y6gDAIqz3VgsIFOmLbPMLKgvq/JmeWk94ajwR4BKAKK8ohLqHoo1bG4S6hyLMPQyu6ubdywJRc1SvYBcVFYVXX30V27dvR7du3aBWq+2WP/XUUw1SHBERXV5eRR5OFZ/Cruxd2J6xHad1p1FQWQCBmnuz0ig0aOfVDiGuIdCqtAhzD0OoWygkSYKHxgOBLoFQKVSwCAu8nbzhonZBubEczipnuKhdUGIogdlihkapgdFiRFFlEQDALMzIq8hDpakSRmFEbnku8iryoDfrUVhZiDMlZ6Az6KAz6FBQWYBifTGK9cVIK0lDYnZitTp9nXwR7hGOCI8ItPVoa5082yLcIxxqhbra+kRUz37sahpOzPaBkoSTJ09eVVGNif3YEVFLV6wvxr7cfdifux/r09cjuTC5xvXaerRFrH8sfJ19EeUVhRifGPg5+8FN4yb7fXHF+mLkVeShSF+Es6VncbLoJM6WnrVNtbX8qSQVIjwi0N6rPaK8o9DBqwOivaMR6h4KhVSvAZWImrUryS717qC4pWKwI6KW6GTxSSTlJGFd2jpszdgKk8X+frcglyB08euCASED0MW3CwJdA+Hj5CNjxVenxFCCtJI0pOnScEp3CqeKT9leL3U52VnljGivaER7R6OjT0d09O6IaO9o3stHLV6jBLupU6fi7bffhqurK6ZOnXrpD5QkfPzxx1dWcRNisCOiluJMyRlsy9iGFcdX4EDeAbtlbT3aorNPZ1wTeg2ubXMtvJy85CmyiQkhkFWWheNFx3Gi6ARSilKQUpiCE0UnYLAYatymrUdbxAXEoatvV3Tx64Jo72holdomrpyo/hpl5Im9e/fCaDTavr6UC/u0IyKiK2O0GLE2bS1+OPwD9uXus81XKVSI849Dr8BeGBE5Au292stYpXwkSUKwWzCC3YIxKHSQbb7JYkJaSRqSC5KRXJiMY4XHkFyYjKyyLGtLn+4UVhxfAcB6KTfaOxoxvjHo7t8dcQFxaOvRlr+/yCHwUiwRUTOQUpiCBQcXYH36epQaSwFYA0iMXwxuiLgBt7S7Bb7OvjJX2fIUVRZhX+4+HMg7gEP5h3Ao7xAK9YXV1vPSeiHOPw5xAXHoEdADXfy6sFWPmg3eY1cLBjsiai6MFiPWnF6Dv07+hY1nNtrm+zr54q6Od+GujnfBz9lPxgodT9Wl3EP5h3Ag7wCScpJwKP8Q9Ga93XpqhRoxvjHoEdDDFvZa8j2L1LIx2NWCwY6I5GYRFqxMXYnZSbORXpIOAJAgYVjEMIyNGYtuft2gVChlrrL1MJqNOFxwGEk5Sdibsxd7c/bW+FRuW4+26BXYyzaFuIXIUC21Rgx2tWCwIyK5GMwGLDqyCIuOLEJ2eTYAwMfJB3dE34GR7UeinWc7mSskwNqql16Sbgt5STlJOFF8otp6wa7B6BXYCz0De6JXYC9EekTyPj1qFAx2tWCwI6KmZraYsS59HWbtnoW0kjQAgLvGHeNixuGBmAfgonaRuUK6nKqRPHZn78bu7N04nH+42mgcPk4+6BXYC/FB8egb3JcPZFCDYbCrBYMdETWlTWc2YfqO6ThTegYA4Ofsh6d6PIWb2t3Em/NbsHJjOfbn7bcFvf25+6vdpxfgEmALefFB8Qh2C5apWmrpGOxqwWBHRE3hVPEpfLb3M6w+vRqA9anLMR3G4KFuD3EMVAdkMBtwKP8QdmXtwo7MHUjKSarWr164e7gt5PUJ6sOnnKnOGOxqwWBHRI2p3FiOWXtm4aejP0FAQCkp8UDMA5jYfSIvubYilaZK7Mvdhx2ZO7AjawcO5R2CWZjt1on2jra26AX1Re+g3hwhgy6Jwa4WDHZE1Fh2ZO7Amwlv2p50HRI6BJN7TEZHn44yV0ZyKzWUYk/OHmzP3I6dmTtxrPCY3XKFpEAX3y7oG9QX8cHxiAuIg7PKWaZqqblhsKsFgx0RNbS8ijy8vu11bDqzCQAQ5BqEtwa8hf4h/WWujJqrwspC7MzaiZ2ZO7EzaydO6U7ZLVcr1Oju3x3xwfGID45HV7+uUCvU8hRLsmOwqwWDHRE1pM1nNuN/W/+HgsoCqBQqjOkwBk/2eJKX1eiKZJVlYWfWTuul28wdtu5wqjirnNEzsCf6BfVD3+C+6OjdkX0dtiIMdrVgsCOihpBXkYf3drxnezgi2jsaH177Yasdw5UajhACaSVp2JG5w9aqd/EwaB4aD/QN6ou+wX3RP7g/Ijwi2LWKA2OwqwWDHRFdrb05e/HshmeRW5ELhaTAfZ3uw5ReU9h9CTUKi7AgpTDFFvJ2Ze9CmbHMbp1g12D0D+mP/iH90S+oH7ycvOQplhoFg10tGOyIqL70Zj2+OfAN5u+fD5Mwob1ne8y4dgYfjqAmZbKYcDj/MHZm7cT2jO3Yk7MHRovRtlyChM6+ndE/uD8GhAxAXEAcNEqNjBXT1WKwqwWDHRHVR2ZpJp5e/zSOFBwBAIxoOwJvDHiDXZiQ7CpMFdidvRsJGQlIyExASmGK3fKq+/MGBA9A/5D+iPKK4mXbFobBrhYMdkR0pfbm7MWU9VNQUFkAHycfTOs7DcPbDucvR2qWcstzsT1zuy3o5VXk2S33d/ZHv+B+tku3fs5+MlVKdcVgVwsGOyKqK7PFjC+SvsA3B7+BWZjR0bsjPr/+cw4NRS2GEAIpRSm2kLc7azcqzZV260R7R9su2/YM7Mn+85ohBrtaMNgRUV2UG8vxwqYXsPHMRgDATZE34fX+r/PSK7VoerMeSTlJtqB3JP8IBM7HALVCjZ4BPdEvpB8GhAxAJ59OUEgKGSsmgMGuVgx2RHQ5GaUZmLJ+Co4UHIFWqcXbA9/GiMgRcpdF1OAKKwuxI3MHEjITsC1jG7LKsuyWe2u9ER8cb71sG9yfrdUyYbCrBYMdEdVmY/pGTNs8DSXGEvg4+eCz6z9Dd//ucpdF1OiEEDilO2VrzduZuRPlpnK7ddp6tEX/EOtl2z5BfeCqdpWp2taFwa4WDHZEdCnLU5bjzYQ3YRZmxPrHYsagGQh1D5W7LCJZGC1GHMg9gITMBCRkJOBA3gFYhMW2XCWpEOsfa7ts28W3C1QKlYwVOy4Gu1ow2BHRxcwWM+btn4c5++YAAG5tfyveGPAGx+YkuoDOoMOuzF22y7bpJel2y93V7raRMAaEDECYR5hMlToeBrtaMNgR0YXKjeWYunEqtp7dCgB4qOtDeLrn0+zKhOgyzpScsbXmbc/cjhJDid3yNm5tbPfmxQfHw1PrKVOlLR+DXS0Y7Iiois6gw6Q1k5CUmwRnlTNeiX8Ft0XdJndZRC2O2WLG4fzDtta8fbn7YLKYbMsVkgJdfLvY+s+L84+DWskW8bpisKsFgx0RAUB2WTaeXPckjhQcgbvGHXOGzeFDEkQNpNxYjsTsRCRkWIPeyeKTdsudVc7oE9QH/YL7oV9wP46GcRkMdrVgsCOiowVH8fjqx5FfmQ8fJx/M/898jvdK1IiyyrJso2Fsz9yOgsoCu+V+zn6ID463Bb0g1yCZKm2eGOxqwWBH1Lodyj+ER1c9Cp1Bh2jvaHx63acIc+dN3kRNxSIsSC5MxvaM7dieuR27s6uPhtHWo63tsm2foD5w17jLVG3zwGBXCwY7otZrX+4+TFwzESWGEsT6x2LusLmt/hcGkdwMZgP25e6zteYdyj9k162KQlKgq19XW2ted//u0Cg1Mlbc9BjsasFgR9Q6rTq1CtM2T4PBYkCPgB74cuiXcNO4yV0WEV2kWF+MXVm7sD1zO3Zk7sAp3Sm75c4qZ/QM7In+wf3RL7gfor2jHX7YMwa7WjDYEbU+q06twgubXoBZmDEkdAhmXDuDY74StRCZpZnYnrndNl18f56Pkw/ig+LRL6Qf+gb1dchOxRnsasFgR9S6rE1bi+c2PAeTMOHW9rfirQFvQalQyl0WEdWDEAIpRSm2+/MSsxNRYaqwW6eNWxv0DeqLvsF90TeoLwJcAmSqtuEw2NWCwY6o9ViWvAxvb38bZmHGLe1uwTsD32GoI3IgRrMR+/P2IyEjATuzduJA7gGYhMlunUjPSPQN6ov44Hj0CewDLycveYq9Cgx2tWCwI2odliYvxVsJbwGwDhH25oA3OY4lkYMrN5ZjT84e7Mzcie2Z23G04CgE7GNOR++O6BvcF/FB8egV2KtF3GvLYFcLBjsix/fnyT/x8uaXISAwvst4TO01lZ2fErVCxfpiJGYnYmfmTuzM2onjRcftlislJbr4drFdto0LiIOzylmmai+Nwa4WDHZEju2vk3/hlS2vwCzMuKfjPXg5/mWGOiICAORV5GFX1i7szNqJnZk7kVaSZrdcpVChm1839A7sjd5BvRHnH9csHrRisKsFgx2R41qeshyvbXsNAHBb+9vw1sC3HL4bBCKqv8zSTGvIy9qJHZk7kF2ebbdcJanQxa8L+gT1QZ/APogLkCfoMdjVgsGOyDGtPb0WUzdOhUVYcG+ne/FS35cY6oiozoQQOFNyBonZidYpKxEZZRl266gkFbr6dUWfoD5N2qLHYFcLBjsix7MzcycmrpkIg8WAO6LvwBv93+DlVyK6amdLz2JX1i7blFmWabe86tLtsPBhGNtlbKPVcSXZhY+IEVGLtu3sNjy9/mkYLAZcH3Y9Xu33KkMdETWINm5t0CaqDUZFjYIQwhb0ErMTsTNrJ7LKsrA3Zy9C3ZpPp8gMdkTUYu3N2Yun1j8FvVmPa9pcgw8Gf8AuTYioUUiShFD3UIS6h+L26Nutl25LzyAxK7FZjXbBn4BE1CIdLzyOSWsnQW/W49rQazFryCyolWq5yyKiVkKSJIS5hyHMPUzuUuzwzmIianHSdGl4bM1jKDGUoLt/d3w0+COGOiIisMWOiFqY9JJ0TFg5ATkVOWjn2Q6zr5/dLDsUJSKSA1vsiKjFKKwsxMQ1E5FTkYMoryj83/D/a5HjPhIRNRYGOyJqESpMFZi8bjJO604jxDUE8/8zH37OfnKXRUTUrDDYEVGzZ7KY8MLGF7A/dz88NB6YM2wO/F385S6LiKjZYbAjomZNCIE3E97EhjMboFVq8fn1n6OdVzu5yyIiapYY7IioWZu1ZxZWHF8BpaTEh9d+iJ6BPeUuiYio2WKwI6Jm69tD3+Kbg98AAF7v/zquC79O5oqIiJq3ZhHsvvjiC7Rt2xZOTk6Ij4/Hzp07L7nuwoULIUmS3eTk5NSE1RJRU1hybAk+SvwIAPB0z6dxe/TtMldERNT8yR7sfv75Z0ydOhWvv/469uzZg+7du2P48OHIycm55DYeHh7IzMy0TadPn27Ciomosf176l+8vf1tAMCELhPwUNeHZK6IiKhlkD3YffLJJ3jkkUcwYcIExMTEYO7cuXBxccE333xzyW0kSUJQUJBtCgwMbMKKiagx7c7ejZc3vwwAuKfjPXim1zOQJEnmqoiIWgZZg53BYMDu3bsxbNgw2zyFQoFhw4YhISHhktuVlpYiIiICYWFhuO2223Do0KGmKJeIGtnJopN4at1TMFgMuD7serzU9yWGOiKiKyBrsMvLy4PZbK7W4hYYGIisrKwat+nYsSO++eYb/Pbbb/jhhx9gsVgwYMAAnDlzpsb19Xo9dDqd3UREzU9eRR4mrpkInUGHWP9YvH/t+1AqlHKXRUTUosh+KfZK9e/fH2PHjkVcXBwGDx6MX3/9Ff7+/pg3b16N60+fPh2enp62KSwsrIkrJqLLKTeW44k1TyCjLAPh7uH4/PrPOf4rEVE9yBrs/Pz8oFQqkZ2dbTc/OzsbQUFBdfoMtVqNHj164Pjx4zUunzZtGoqLi21Tenr6VddNRA3HZDHh2Y3P4kjBEXhrvTFn2Bz4OPnIXRYRUYska7DTaDTo1asX1q5da5tnsViwdu1a9O/fv06fYTabceDAAQQHB9e4XKvVwsPDw24iouZBCIF3tr+DLWe3wEnphNlDZyPcI1zusoiIWiyV3AVMnToV48aNQ+/evdG3b1/MmjULZWVlmDBhAgBg7NixaNOmDaZPnw4AeOutt9CvXz9ERUWhqKgIH374IU6fPo2HH35YzsMgonr4ct+XWJayDApJgRnXzkCsf6zcJRERtWiyB7u7774bubm5eO2115CVlYW4uDisXLnS9kBFWloaFIrzDYuFhYV45JFHkJWVBW9vb/Tq1Qvbtm1DTEyMXIdARPWw5NgSzN03FwDwSvwruD78epkrIiJq+SQhhJC7iKak0+ng6emJ4uJiXpYlksna02sxdeNUWIQFj3d/HJPiJsldEhFRs3Ul2aXFPRVLRC3b7uzdeGHTC7AIC0ZHj8YT3Z+QuyQiIofBYEdETSalMAVPrnsSBosBQ8KG4H/9/scOiImIGhCDHRE1iayyLDy+5nGUGEoQ5x+HD679ACqF7Lf5EhE5FAY7Imp0xfpiPLb6MeSU56CdZzvMHjqbHRATETUCBjsialSVpko8ue5JnCw+iQCXAMwdNheeWk+5yyIickgMdkTUaEwWE57f9Dz25uyFu9odc4fNRbBbzZ2JExHR1WOwI6JGIYTAuzvexYb0DdAoNPjs+s8Q7R0td1lERA6NwY6IGsXcfXPxS/IvkCBhxrUz0Duot9wlERE5PAY7ImpwS5OX4st9XwKwjioxLGKYzBUREbUODHZE1KDWpq3FO9vfAQA8FvsY7u50t8wVERG1Hgx2RNRg9mTvwYubXoRFWHBH9B0cKoyIqIkx2BFRgzheeByT102G3qzH4NDBeLXfqxxVgoioiTHYEdFVu3BUiVj/WHw4+EOOKkFEJAMGOyK6KsX6YkxcMxHZ5dmI9IzEF9d/wVEliIhkwmBHRPVWaarEU+uewvGi4whwto4q4eXkJXdZREStFoMdEdWL2WLGS5tfwp6cPXBXu2POf+YgxC1E7rKIiFo1BjsiumJVo0qsTVsLtUKNT6//FB28O8hdFhFRq8dgR0RXbN7+eViavBQSJLw/6H30Ceojd0lERAQGOyK6QsuSl+GLpC8AAC/Hv4wb2t4gc0VERFSFwY6I6mzlqZV4a/tbAIBHuj2CezrdI3NFRER0IQY7IqqTdWnr8NKml2ARFoyOHo0nezwpd0lERHQRBjsiuqzNZzbj2Y3PwizMuKXdLRxVgoiomWKwI6Ja7cjcgWc2PAOTxYQbIm7A2wPfhlKhlLssIiKqAYMdEV3Snuw9eHLdk9Cb9RgSOgTvX/s+hwojImrGGOyIqEYHcg/gibVPoMJUgYEhA/HxkI+hVqjlLouIiGrBYEdE1RzJP4LH1jyGMmMZ+gT1wczrZkKj1MhdFhERXQaDHRHZOZR/CI+sfgQlhhLE+cdh9vWz4axylrssIiKqAwY7IrJJyknCI/8+gmJ9Mbr5dcOXw76Ei9pF7rKIiKiOGOyICACQmJWIx1Y/hhJjCXoG9MT8/8yHu8Zd7rKIiOgKMNgREbZlbMPENRNRbipHfHA85gybAzeNm9xlERHRFWKwI2rlNqZvxJNrn0SluRKD2gzC7Otn8/IrEVELxWBH1IqtOb0GUzZMgcFiwPVh12PWdbPgpHKSuywiIqonBjuiVuqPE3/guY3PwWQx4ca2N+KjIR+xSxMiohaOXcgTtTJCCCw4tAAzd88EANza/la8NeAtDhNGROQAGOyIWhGzxYwPdn2AxUcXAwAeiHkAz/V+DgqJjfdERI6AwY6oldCb9Zi2eRpWn14NAHiu93MY12WczFUREVFDYrAjagWK9cV4at1T2JOzB2qFGu9e8y5GRI6QuywiImpgDHZEDi6zNBOPr3kcJ4tPwl3tjk+v/xR9gvrIXRYRETUCBjsiB3Y4/zCeXPskcipyEOASgDnD5qCDdwe5yyIiokbCO6aJHNSfJ//E2H/GIqciB1FeUVh00yKGOiIiB8cWOyIHY7KYMHP3THx3+DsAwKA2g/D+te/DQ+Mhc2VERNTYGOyIHEhhZSGe3/Q8dmTuAAA80u0RTIqbxD7qiIhaCQY7IgdxrOAYnl7/NM6WnoWzyhnvXvMu/hPxH7nLIiKiJsRgR+QA/kn9B69tfQ2V5kqEuYfh0+s+RbR3tNxlERFRE2OwI2rByo3l+DDxQ/yS/AsAYGCbgZgxaAY8tZ4yV0ZERHJgsCNqoQ7nH8aLm17EKd0pSJDwULeHMDluMu+nIyJqxRjsiFoYi7Bg4aGF+Hzv5zBZTAhwCcB717yH+OB4uUsjIiKZMdgRtSBZZVn435b/YUeW9anXYeHD8Hr/1+Hl5CVvYURE1Cww2BG1EKtOrcKbCW9CZ9DBWeWMF/u8iDui74AkSXKXRkREzQSDHVEzl1WWhfd2vIf16esBADG+MZgxaAbaeraVtzAiImp2GOyImimzxYwfj/6Iz/d+jnJTOVSSChO6TsDE7hOhVqrlLo+IiJohBjuiZuhw/mG8mfAmDucfBgDE+cfhtf6vsW86IiKqFYMdUTNSbizHF0lf4IcjP8AiLHBXu2NKrym4s8OdUEgKucsjIqJmjsGOqBkwWUxYfnw55iTNQW5FLgDgxrY34sW+L8LP2U/m6oiIqKVgsCOSkRAC69LX4dM9nyK1OBUAEOoWipfjX8ag0EEyV0dERC0Ngx2RTPbm7MUniZ8gKTcJAOCl9cJjsY/hro53QaPUyFscERG1SAx2RE3sWMExfJn0JdalrwMAOCmd8EDMA5jQdQLcNe4yV9fAzEagLA8oywXKcs5/bawEhBmwmC6YzOemC94rlIDaBVA7X/DqVMM8F0DjBrj6A87egIL3IxJR68RgR9QEhBDYlbUL3xz6BlvPbgUAKCQFbo+6HU/EPYEAlwCZK6wHfSlQdBooPHVuOg2UZp8LbznWAFdR2PR1SUrA1Q9wDQDc/K2v7oGARyjgGQp4trF+7eIDsHNnInIwDHZEjchsMWNt2losOLgAB/MPArAGuhsibsDE7hPRzqudzBVehsUCFKYCOUeAvGQg/ziQl2KdV5Zbt8+QFICLH+AWcC5w+Vtb2BQqa4vcha9S1XuVtdXNYgGM5YCx4vyrqeLc+wr7ZZU6oLLI2hJYmm2dsmupS+UMeIQAXuGAd9vqk7PX1Z07IiIZMNgRNYJyYzn+PPknvjv8HU7rTgMAtEotRkWNwrgu4xDmHiZzhTUoLwCyD1mnnKrXI9bQdCnO3ueDkFc44B5sDW6u/ueCnD/g7NN0l0ZNBqA8Dyg912JYmmNtPSzJAorPALqzQPFZ6zxTBVBwwjrVxMnz/LH5tAf8OwJ+HayT1q1pjoeI6Aox2BE1oEP5h7AseRn+Tv0bZcYyAICHxgP3dLoH93W6D77OvjJXCEAIQJcBZO0HMvcDmfusXxen17y+UmsNNf4dAd9owC/KGnSaY6uWSmNthfMIqX09k/58yCtKu+By8rmpLAeoLLaem8x91bf3DLMGvKqw59/J+rWLT8MfExHRFWCwI7pKJYYS/H3ybyxLWYYjBUds88Pcw3Bvp3sxOno0XNQu8hWoywTO7rZOmUnWMFeeV/O6XhFAYFcgMAYI7AIEdAF82gFKB/tRodJaj8vnEpfCDWXWewaLTgMFqUB+CpCbDOQetZ674nTrdGKt/XYufudDsF9HwL+D9dUjhPfzEVGTcLCf1kRNw2g2Ynvmdqw8tRKrT69GhakCAKBWqDEsfBhGdxiNPkF9mn60CH0JkJEEnE20Brkzu4GSjOrrSUprK1NwLBDcHQiKBYK6Wi8/EqBxPRduY6ovKy8Aco8BecfOh728ZGvQK88DTucBp7de9Hnu50Oe7bWjtdVToWySQyKi1kESQgi5i2hKOp0Onp6eKC4uhoeHh9zlUAtiNBuRkJmAVadWYV36OpQYSmzL2nm2w+jo0RjZfiS8nbybpiCzCcg5fD7End1jvScOF/2XlhRAQAzQphcQEmcNcgEx1m5CqOHoS+1b9vKSrQGw4KT1gY6aKLWAb5R96PPvZJ2n0jZt/UTUbF1JdmGLHVEtyo3l2JG5A2vT1lYLc75OvhgWMQw3t7sZcf5xkBrzUpsQ1nu/Mvaev6yakWR9AOBinmFAm55Am97nw5zGtfFqIyutGxDSwzpdyGSwPqCRe+x82Ms7Zn262FRpfVAl55D9NpLC2ppX1bJXdWnXLxpw4h+kRHRpDHZEFxBCIKUoBVvPbsXWs1uxO2c3TBaTbbmfsx+GhQ/DDW1vQM+AnlA2xmU0s9H6yz9rP5B1wHpPXNYBQF9cfV2tx7kQ1+t8kHMPbPiaqP5UGiCgs3W6kMVsfXCjKuxdeHlXX2xt6Ss4CST/Y7+de8gFYS/63BPJbQGvMLbyEREvxVLrJoRAZlkm9uTswc7Mndh6dityKnLs1mnj1gbXhl6LGyJuQI+AHg0b5vSl1m5FsqqeTj1gvZxq1ldfV6mxXkIN7X0+yPlGcZQFRyOEtQ8+u7B3rrWvtLaO+aRz/fJFAN4R517bnv/aPZjfK0QtFC/FEl2C2WLG8aLj2JOzB3uz92JPzh5kl9v/snRSOqFPUB8MbDMQ17S5BuHu4Q1zmbU0F8jad0Er3H4g/wSq3RMHWFvigrpZH2oIjrV+7dfR2vpDjk2SAPcg69RusP2yikJri15V2Ms/fv7pXWO5tQsX3VkgbVv1z1VqrJfpPUOtIc89qIbXILb6EbVwDHbksCzCgtO60ziSfwSH8w/jSIH1tdRYareeSlKhk08n9AzsiYEhA9EzsCecVE7126kQ1o5xc4+eb3GpupH+Uq0t7sEXhbhYawsLW1foYs7eQHi8dbqQENah3ApPnR/mrej0+dBXlA6YDbV3yGzbh4/1e9LjUuEv2DpMm6N1gUPkIPg/kxxCsb4YJ4pO4ETxCZwoOoEj+UdwtOAoyk3VR01wUbkgLiAOPQJ6oGdAT3T163pl/cxZLEBJ5rnObFOtrwXnXvOPW4e1qpEE+LY/17VIt/Mhzq0FjhNLzYskWcfFdfMHwvpUX242WVvyik5b+zUsybSOxnHxq1kPVBRYp4sf6LDfofX7tirouQVah4tz8b1g8rH26+fia314h/34ETUJBjtqMSpMFUgvSUeaLg2ndaeRXpKO07rTOKU7hbyKmjvcdVI6oaNPR3T26YwY3xjE+MagvVd7qBS1fOubjdZfcroMax9wugzrTe4FqeeC3Oma74Gzkaz3Nvl3Ot99RdVTjRyKiuSgVFnvtfOOuPQ6Qlgv9ZbUFPwu+vrC8XhrGpmj2v61FwU+X2vro5PnRZMHoL3waw9rtzwMhUR1xmBHzYIQAoX6QmSWZSKrLAtZZVnILsu2vc8oy0BOeU6tnxHkGoT2Xu3R3rM9Ovp0RIxPDNp6tj0f4sxG6+Wq7MPWy6VledZfVrqMc/cmnQtxpdmo8b63C0lK61OI3pHnxhKtem1nfaCBfcRRSyNJ50KXj3XUkUuxmIHy/IvCX7Z1nt1UYO2w2VRp/UOoJKPmzrIvR6E+H/KcPKyhT3vuVe0CqJ2sryon6/87ldP5+SrnSyx3Pv81QyM5mGbxVOwXX3yBDz/8EFlZWejevTs+//xz9O3b95LrL126FK+++ipOnTqF6OhozJgxAzfddFOd9sWnYpuORVhQYiiBTq9DfmW+dao4/1pQWYD8inzkVeQhuzwb+lpbwazcNe6IcI9AuHsowp38Ea7xRITSFe0UTnAzlFt/mVQUWi8llZ+bynKt0yUvkdZAoT53n9G5cUc9Q8+HN+9I63ulut7nhqhVEML6UMfFga8szzoWb2XRuddioKII0OuASp21u5dKHS77B1ZDsAW+OoRA29fOFwTHC5dXba+1Pqyi1Fh/Tii1F3x9br5CyVBJddainor9+eefMXXqVMydOxfx8fGYNWsWhg8fjmPHjiEgoPq9R9u2bcO9996L6dOn45ZbbsHixYsxatQo7NmzB127dpXhCByTRVhQaapEuakcFcYK66upAuXGcpQYS6Az6KDT66yvNXxdbChGqaEU4gp/MPspnBCkdEIQ1AgSCgRZgCCTGcFGPcL1FfDS5wH6U4CxrH4HJimtl4Fc/QFX3wvCW5vzIc6jjfXeID68QHR1JMl6f53GFfAKv7JtLRbAUHo+7FUWVw9+xnLAWGFtFTRW1PB1BWCsPPd67mtjuf1IIKZK64TCBj30y5MuCn8Xfq22/qxSVE2qc+9V1p9Ltq+rlikueq+0rme33bnPuvB9jfuo6f2l9nGJbWurT1Ja30sK6/eH3eu5CRe9lySG4Csge4tdfHw8+vTpg9mzZwMALBYLwsLC8OSTT+Kll16qtv7dd9+NsrIy/Pnnn7Z5/fr1Q1xcHObOnXvZ/TWLFjshAGGxXtIQFttksZhgNhthtBhgtphgNhtgsphgshhgNptgthhhsk0mGEx6GM0G6M16GCwGGMx6GMzGc++tn6M36c/NN8Bgsa5rNBthsBhRaTGgwmxAhcWAcrMBFRajdRJGVFxqCKR6cLZY4GO2wNdsho/ZDF+zBT4WM3zPfe1rNiPIZEKgyYwr6sxDUgBOXtZLR87e1qf5XHysr87egIv3BSHu3OTkxcBG1NqZjZcOgcby8/MvFxhrW9dstD6JXPVqMcp91C1fXcKf3XKpjq+4wvVr2D76BmDoa4126C2mxc5gMGD37t2YNm2abZ5CocCwYcOQkJBQ4zYJCQmYOnWq3bzhw4djxYoVNa6v1+uh15+/xKfT6a6+8MvYuPldzD36A0wATADM0kWvkGCSALMkwQzAJEkwARDN8C8SSQg4CwFni4CLsMDZIuAmLPAwW+BhuWgyW+BZw3u7C5ZKjfXyhcYF0LgBbm7WV637udc6vnfyZEgjovqpahVDE/5xL8QFYc9wia+N1vsRLaZzf/ibra8Ws3XeJd+bzjUWmGpeV5gvWHap97Xtw2RtQbXb7lL7tFTfzu69GfW+xF7VENIc+XeSuwIbWYNdXl4ezGYzAgPth0AKDAzE0aNHa9wmKyurxvWzsrJqXH/69Ol48803G6bgOio2luGgpuFOrUoIqASghIASgEpY/+HUAtAC0AhAc+ErJGiEZF0mKaCBwvoqKaGRlNBKKmgUSmgkFZwVargo1HBWquGi0MJZqbG+qrRwUTrBSamFVHV5QKE696o+/4NRoT53r4nTBfelaK3hTaU9f+9J1cQgRkStkSRZOxhnJ+NWVVeu7KYa5gFXtx5ELa+wf19VV63bXOLVLaipztxlyX6PXWObNm2aXQufTqdDWFhYo+6zb9yD+CI4DkqFCkqFCiqFBiqFCkqF2vpeqYFKoTz3Xn3uvRpKpQZKpRqqc/OUChUUkqJxB5cnIiJqapJkvd8OjTDedisna7Dz8/ODUqlEdrZ9j/zZ2dkICqo5/QYFBV3R+lqtFlpt0w6RE+QThSCfqCbdJxEREZGs18U0Gg169eqFtWvX2uZZLBasXbsW/fv3r3Gb/v37260PAKtXr77k+kREREStheyXYqdOnYpx48ahd+/e6Nu3L2bNmoWysjJMmDABADB27Fi0adMG06dPBwA8/fTTGDx4MD7++GPcfPPN+Omnn5CYmIj58+fLeRhEREREspM92N19993Izc3Fa6+9hqysLMTFxWHlypW2ByTS0tKguOCG+wEDBmDx4sX43//+h5dffhnR0dFYsWIF+7AjIiKiVk/2fuyaWrPox46IiIiojq4ku7DvCSIiIiIHwWBHRERE5CAY7IiIiIgcBIMdERERkYNgsCMiIiJyEAx2RERERA6CwY6IiIjIQTDYERERETkIBjsiIiIiB8FgR0REROQgZB8rtqlVjaCm0+lkroSIiIjo8qoyS11GgW11wa6kpAQAEBYWJnMlRERERHVXUlICT0/PWteRRF3inwOxWCzIyMiAu7s7JElqlH3odDqEhYUhPT39soP1OjqeC3s8H/Z4PuzxfNjj+bDH82GvNZ0PIQRKSkoQEhIChaL2u+haXYudQqFAaGhok+zLw8PD4b/Z6ornwh7Phz2eD3s8H/Z4PuzxfNhrLefjci11VfjwBBEREZGDYLAjIiIichAMdo1Aq9Xi9ddfh1arlbsU2fFc2OP5sMfzYY/nwx7Phz2eD3s8HzVrdQ9PEBERETkqttgREREROQgGOyIiIiIHwWBHRERE5CAY7BrYF198gbZt28LJyQnx8fHYuXOn3CU1ienTp6NPnz5wd3dHQEAARo0ahWPHjtmtU1lZiUmTJsHX1xdubm4YPXo0srOzZaq46bz//vuQJAlTpkyxzWtt5+Ls2bP473//C19fXzg7O6Nbt25ITEy0LRdC4LXXXkNwcDCcnZ0xbNgwpKSkyFhx4zGbzXj11VcRGRkJZ2dntG/fHm+//bbdUEGOfD42bdqEkSNHIiQkBJIkYcWKFXbL63LsBQUFuP/+++Hh4QEvLy889NBDKC0tbcKjaDi1nQ+j0YgXX3wR3bp1g6urK0JCQjB27FhkZGTYfUZrOR8Xe/zxxyFJEmbNmmU335HOR30w2DWgn3/+GVOnTsXrr7+OPXv2oHv37hg+fDhycnLkLq3Rbdy4EZMmTcL27duxevVqGI1G3HDDDSgrK7Ot88wzz+CPP/7A0qVLsXHjRmRkZOCOO+6QserGt2vXLsybNw+xsbF281vTuSgsLMTAgQOhVqvxzz//4PDhw/j444/h7e1tW+eDDz7AZ599hrlz52LHjh1wdXXF8OHDUVlZKWPljWPGjBmYM2cOZs+ejSNHjmDGjBn44IMP8Pnnn9vWceTzUVZWhu7du+OLL76ocXldjv3+++/HoUOHsHr1avz555/YtGkTHn300aY6hAZV2/koLy/Hnj178Oqrr2LPnj349ddfcezYMdx6661267WW83Gh5cuXY/v27QgJCam2zJHOR70IajB9+/YVkyZNsr03m80iJCRETJ8+Xcaq5JGTkyMAiI0bNwohhCgqKhJqtVosXbrUts6RI0cEAJGQkCBXmY2qpKREREdHi9WrV4vBgweLp59+WgjR+s7Fiy++KK655ppLLrdYLCIoKEh8+OGHtnlFRUVCq9WKH3/8sSlKbFI333yzePDBB+3m3XHHHeL+++8XQrSu8wFALF++3Pa+Lsd++PBhAUDs2rXLts4///wjJEkSZ8+ebbLaG8PF56MmO3fuFADE6dOnhRCt83ycOXNGtGnTRhw8eFBERESImTNn2pY58vmoK7bYNRCDwYDdu3dj2LBhtnkKhQLDhg1DQkKCjJXJo7i4GADg4+MDANi9ezeMRqPd+enUqRPCw8Md9vxMmjQJN998s90xA63vXPz+++/o3bs3xowZg4CAAPTo0QNfffWVbXlqaiqysrLszoenpyfi4+Md8nwMGDAAa9euRXJyMgBg37592LJlC0aMGAGg9Z2PC9Xl2BMSEuDl5YXevXvb1hk2bBgUCgV27NjR5DU3teLiYkiSBC8vLwCt73xYLBY88MADeP7559GlS5dqy1vb+ahJqxsrtrHk5eXBbDYjMDDQbn5gYCCOHj0qU1XysFgsmDJlCgYOHIiuXbsCALKysqDRaGw/jKoEBgYiKytLhiob108//YQ9e/Zg165d1Za1tnNx8uRJzJkzB1OnTsXLL7+MXbt24amnnoJGo8G4ceNsx1zT/x1HPB8vvfQSdDodOnXqBKVSCbPZjHfffRf3338/ALS683Ghuhx7VlYWAgIC7JarVCr4+Pg4/PmprKzEiy++iHvvvdc2NmprOx8zZsyASqXCU089VePy1nY+asJgRw1u0qRJOHjwILZs2SJ3KbJIT0/H008/jdWrV8PJyUnucmRnsVjQu3dvvPfeewCAHj164ODBg5g7dy7GjRsnc3VNb8mSJVi0aBEWL16MLl26ICkpCVOmTEFISEirPB9UN0ajEXfddReEEJgzZ47c5chi9+7d+PTTT7Fnzx5IkiR3Oc0WL8U2ED8/PyiVympPNmZnZyMoKEimqpre5MmT8eeff2L9+vUIDQ21zQ8KCoLBYEBRUZHd+o54fnbv3o2cnBz07NkTKpUKKpUKGzduxGeffQaVSoXAwMBWcy4AIDg4GDExMXbzOnfujLS0NACwHXNr+b/z/PPP46WXXsI999yDbt264YEHHsAzzzyD6dOnA2h95+NCdTn2oKCgag+kmUwmFBQUOOz5qQp1p0+fxurVq22tdUDrOh+bN29GTk4OwsPDbT9bT58+jWeffRZt27YF0LrOx6Uw2DUQjUaDXr16Ye3atbZ5FosFa9euRf/+/WWsrGkIITB58mQsX74c69atQ2RkpN3yXr16Qa1W252fY8eOIS0tzeHOz9ChQ3HgwAEkJSXZpt69e+P++++3fd1azgUADBw4sFrXN8nJyYiIiAAAREZGIigoyO586HQ67NixwyHPR3l5ORQK+x+9SqUSFosFQOs7Hxeqy7H3798fRUVF2L17t22ddevWwWKxID4+vslrbmxVoS4lJQVr1qyBr6+v3fLWdD4eeOAB7N+/3+5na0hICJ5//nn8+++/AFrX+bgkuZ/ecCQ//fST0Gq1YuHCheLw4cPi0UcfFV5eXiIrK0vu0hrdxIkThaenp9iwYYPIzMy0TeXl5bZ1Hn/8cREeHi7WrVsnEhMTRf/+/UX//v1lrLrpXPhUrBCt61zs3LlTqFQq8e6774qUlBSxaNEi4eLiIn744QfbOu+//77w8vISv/32m9i/f7+47bbbRGRkpKioqJCx8sYxbtw40aZNG/Hnn3+K1NRU8euvvwo/Pz/xwgsv2NZx5PNRUlIi9u7dK/bu3SsAiE8++UTs3bvX9pRnXY79xhtvFD169BA7duwQW7ZsEdHR0eLee++V65CuSm3nw2AwiFtvvVWEhoaKpKQku5+ter3e9hmt5XzU5OKnYoVwrPNRHwx2Dezzzz8X4eHhQqPRiL59+4rt27fLXVKTAFDjtGDBAts6FRUV4oknnhDe3t7CxcVF3H777SIzM1O+opvQxcGutZ2LP/74Q3Tt2lVotVrRqVMnMX/+fLvlFotFvPrqqyIwMFBotVoxdOhQcezYMZmqbVw6nU48/fTTIjw8XDg5OYl27dqJV155xe4XtSOfj/Xr19f4s2LcuHFCiLode35+vrj33nuFm5ub8PDwEBMmTBAlJSUyHM3Vq+18pKamXvJn6/r1622f0VrOR01qCnaOdD7qQxLigu7OiYiIiKjF4j12RERERA6CwY6IiIjIQTDYERERETkIBjsiIiIiB8FgR0REROQgGOyIiIiIHASDHREREZGDYLAjIiIichAMdkREdTBkyBBMmTJF7jKIiGrFYEdERETkIBjsiIiIiBwEgx0R0UXKysowduxYuLm5ITg4GB9//LHd8u+//x69e/eGu7s7goKCcN999yEnJwcAIIRAVFQUPvroI7ttkpKSIEkSjh8/DiEE3njjDYSHh0Or1SIkJARPPfVUkx0fETkuBjsioos8//zz2LhxI3777TesWrUKGzZswJ49e2zLjUYj3n77bezbtw8rVqzAqVOnMH78eACAJEl48MEHsWDBArvPXLBgAa699lpERUVh2bJlmDlzJubNm4eUlBSsWLEC3bp1a8pDJCIHJQkhhNxFEBE1F6WlpfD19cUPP/yAMWPGAAAKCgoQGhqKRx99FLNmzaq2TWJiIvr06YOSkhK4ubkhIyMD4eHh2LZtG/r27Quj0YiQkBB89NFHGDduHD755BPMmzcPBw8ehFqtbuIjJCJHxhY7IqILnDhxAgaDAfHx8bZ5Pj4+6Nixo+397t27MXLkSISHh8Pd3R2DBw8GAKSlpQEAQkJCcPPNN+Obb74BAPzxxx/Q6/W2oDhmzBhUVFSgXbt2eOSRR7B8+XKYTKamOkQicmAMdkREV6CsrAzDhw+Hh4cHFi1ahF27dmH58uUAAIPBYFvv4Ycfxk8//YSKigosWLAAd999N1xcXAAAYWFhOHbsGL788ks4OzvjiSeewLXXXguj0SjLMRGR42CwIyK6QPv27aFWq7Fjxw7bvMLCQiQnJwMAjh49ivz8fLz//vsYNGgQOnXqZHtw4kI33XQTXF1dMWfOHKxcuRIPPvig3XJnZ2eMHDkSn332GTZs2ICEhAQcOHCgcQ+OiByeSu4CiIiaEzc3Nzz00EN4/vnn4evri4CAALzyyitQKKx/B4eHh0Oj0eDzzz/H448/joMHD+Ltt9+u9jlKpRLjx4/HtGnTEB0djf79+9uWLVy4EGazGfHx8XBxccEPP/wAZ2dnRERENNlxEpFjYosdEdFFPvzwQwwaNAgjR47EsGHDcM0116BXr14AAH9/fyxcuBBLly5FTEwM3n///Wpdm1R56KGHYDAYMGHCBLv5Xl5e+OqrrzBw4EDExsZizZo1+OOPP+Dr69vox0ZEjo1PxRIRNZLNmzdj6NChSE9PR2BgoNzlEFErwGBHRNTA9Ho9cnNzMW7cOAQFBWHRokVyl0RErQQvxRIRNbAff/wRERERKCoqwgcffCB3OUTUirDFjoiIiMhBsMWOiIiIyEEw2BERERE5CAY7IiIiIgfBYEdERETkIBjsiIiIiBwEgx0RERGRg2CwIyIiInIQDHZEREREDoLBjoiIiMhB/D8ZsSzI5osBLQAAAABJRU5ErkJggg==", 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", 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", 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" ] diff --git a/doc/demo/01-SIRH-IPM.ipynb b/doc/demo/01-SIRH-IPM.ipynb index 729a1c3f..8e03e5e9 100644 --- a/doc/demo/01-SIRH-IPM.ipynb +++ b/doc/demo/01-SIRH-IPM.ipynb @@ -30,40 +30,42 @@ "metadata": {}, "outputs": [], "source": [ + "from typing import Sequence\n", + "\n", "from sympy import Max\n", "\n", "from epymorph import *\n", "from epymorph.compartment_model import *\n", + "from epymorph.compartment_model import ModelSymbols\n", "from epymorph.simulation import AttributeDef\n", "\n", "\n", - "def construct_ipm():\n", - " symbols = create_symbols(\n", - " compartments=[\n", - " compartment('S'),\n", - " compartment('I'),\n", - " compartment('R'),\n", - " compartment('H', tags=['immobile']),\n", - " ],\n", - " attributes=[\n", - " AttributeDef('beta', type=float, shape=Shapes.TxN),\n", - " AttributeDef('gamma', type=float, shape=Shapes.TxN),\n", - " AttributeDef('xi', type=float, shape=Shapes.TxN),\n", - " AttributeDef('hospitalization_prob', type=float, shape=Shapes.TxN),\n", - " AttributeDef('hospitalization_duration', type=float, shape=Shapes.TxN),\n", - " ])\n", - "\n", - " [S, I, R, H] = symbols.compartment_symbols\n", - " [β, γ, ξ, h_prob, h_dur] = symbols.attribute_symbols\n", - "\n", - " # formulate N so as to avoid dividing by zero;\n", - " # this is safe in this instance because if the denominator is zero,\n", - " # the numerator must also be zero\n", - " N = Max(1, S + I + R + H)\n", - "\n", - " return create_model(\n", - " symbols=symbols,\n", - " transitions=[\n", + "class Sirh(CompartmentModel):\n", + " compartments = [\n", + " compartment('S'),\n", + " compartment('I'),\n", + " compartment('R'),\n", + " compartment('H', tags=['immobile']),\n", + " ]\n", + "\n", + " requirements = [\n", + " AttributeDef('beta', type=float, shape=Shapes.TxN),\n", + " AttributeDef('gamma', type=float, shape=Shapes.TxN),\n", + " AttributeDef('xi', type=float, shape=Shapes.TxN),\n", + " AttributeDef('hospitalization_prob', type=float, shape=Shapes.TxN),\n", + " AttributeDef('hospitalization_duration', type=float, shape=Shapes.TxN),\n", + " ]\n", + "\n", + " def edges(self, symbols: ModelSymbols) -> Sequence[TransitionDef]:\n", + " [S, I, R, H] = symbols.all_compartments\n", + " [β, γ, ξ, h_prob, h_dur] = symbols.all_requirements\n", + "\n", + " # formulate N so as to avoid dividing by zero;\n", + " # this is safe in this instance because if the denominator is zero,\n", + " # the numerator must also be zero\n", + " N = Max(1, S + I + R + H)\n", + "\n", + " return [\n", " edge(S, I, rate=β * S * I / N),\n", " fork(\n", " edge(I, H, rate=γ * I * h_prob),\n", @@ -71,11 +73,10 @@ " ),\n", " edge(H, R, rate=H / h_dur),\n", " edge(R, S, rate=ξ * R),\n", - " ],\n", - " )\n", + " ]\n", "\n", "\n", - "sirh_ipm = construct_ipm()" + "sirh_ipm = Sirh()" ] }, { @@ -98,12 +99,12 @@ "• 2015-01-01 to 2015-05-31 (150 days)\n", "• 1 geo nodes\n", "|####################| 100% \n", - "Runtime: 0.018s\n" + "Runtime: 0.041s\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -117,10 +118,10 @@ "\n", "from epymorph.geography.scope import CustomScope\n", "\n", - "rume = Rume.single_strata(\n", + "rume = SingleStrataRume.build(\n", " ipm=sirh_ipm,\n", " mm=mm_library['no'](),\n", - " scope=CustomScope(np.array(['AZ'])),\n", + " scope=CustomScope(['Somewhere']),\n", " params={\n", " 'beta': 0.45,\n", " 'gamma': 0.25,\n", @@ -134,7 +135,7 @@ ")\n", "\n", "sim = BasicSimulator(rume)\n", - "with sim_messaging(sim):\n", + "with sim_messaging():\n", " output = sim.run()\n", "\n", "plot_pop(output, pop_idx=0)" diff --git a/doc/demo/02-states-GEO.ipynb b/doc/demo/02-states-GEO.ipynb index c4bb6df0..5df9ae22 100644 --- a/doc/demo/02-states-GEO.ipynb +++ b/doc/demo/02-states-GEO.ipynb @@ -4,11 +4,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 2. Modeling with a GEO at the US State granularity\n", + "# 2. Modeling with US State granularity\n", "\n", - "We can use epymorph's GEO system to dynamically fetch data from external data sources to suit our modeling experiment.\n", + "We can use epymorph's ADRIO system to dynamically fetch data from external data sources to suit our modeling experiment.\n", "\n", - "First we construct the geo, then we will run simulations with two movement models and inspect the difference.\n", + "First we describe the scope of our geography, then we will run simulations with two movement models and inspect the difference.\n", "\n", "We constructed the SIRH model ourselves in the previous part, but epymorph's IPM library already includes it, so we can reference it from there as well." ] @@ -23,53 +23,17 @@ "output_type": "stream", "text": [ "nodes: 4\n", - "name: ['Arizona' 'Colorado' 'New Mexico' 'Utah']\n", - "population: [7174064 5684926 2097021 3151239]\n" + "geoid: ['04', '08', '35', '49']\n" ] } ], "source": [ - "from epymorph import *\n", - "from epymorph.geo import *\n", - "from epymorph.geo.adrio import adrio_maker_library\n", - "from epymorph.geo.cache import save_to_cache\n", - "from epymorph.geo.dynamic import DynamicGeo\n", - "from epymorph.geo.util import convert_to_static_geo\n", "from epymorph.geography.us_census import StateScope\n", "\n", - "spec = DynamicGeoSpec(\n", - " attributes=[\n", - " AttributeDef('label', str, Shapes.N),\n", - " AttributeDef('geoid', str, Shapes.N),\n", - " AttributeDef('centroid', CentroidType, Shapes.N),\n", - " AttributeDef('population', int, Shapes.N),\n", - " AttributeDef('median_income', int, Shapes.N),\n", - " AttributeDef('commuters', int, Shapes.NxN),\n", - " ],\n", - " time_period=Year(2020),\n", - " scope=StateScope.in_states_by_code([\"AZ\", \"NM\", \"CO\", \"UT\"], year=2020),\n", - " source={\n", - " 'label': 'Census:name',\n", - " 'geoid': 'Census',\n", - " 'centroid': 'Census',\n", - " 'population': 'Census',\n", - " 'median_income': 'Census',\n", - " 'commuters': 'Census',\n", - " },\n", - ")\n", - "\n", - "geo = DynamicGeo.from_library(spec, adrio_maker_library)\n", + "scope = StateScope.in_states_by_code([\"AZ\", \"NM\", \"CO\", \"UT\"], year=2020)\n", "\n", - "# It's convenient to pre-fetch the data but this isn't mandatory.\n", - "geo = convert_to_static_geo(geo)\n", - "\n", - "# Let's inspect a few values...\n", - "print(f\"nodes: {geo.nodes}\")\n", - "print(f\"name: {geo['label']}\")\n", - "print(f\"population: {geo['population']}\")\n", - "\n", - "# Then save it to a cache so we don't bother the Census API too much.\n", - "save_to_cache(geo, 'demo-four-states')" + "print(f\"nodes: {scope.nodes}\")\n", + "print(f\"geoid: {scope.get_node_ids().tolist()}\")" ] }, { @@ -91,15 +55,15 @@ "output_type": "stream", "text": [ "Running simulation (BasicSimulator):\n", - "• 2015-01-01 to 2015-05-31 (150 days)\n", + "• 2020-01-01 to 2020-05-30 (150 days)\n", "• 4 geo nodes\n", "|####################| 100% \n", - "Runtime: 0.093s\n" + "Runtime: 0.240s\n" ] }, { "data": { - "image/png": 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", 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", 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", 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", "text/plain": [ "
" ] @@ -122,17 +86,13 @@ "import numpy as np\n", "\n", "from epymorph import *\n", - "from epymorph.geo.cache import load_from_cache\n", + "from epymorph.adrio import acs5, us_tiger\n", "from epymorph.plots import map_data_by_state\n", "\n", - "geo = load_from_cache('demo-four-states')\n", - "if geo is None:\n", - " raise Exception(\"Oops, we need to cache the demo geo first (see above cell).\")\n", - "\n", - "rume = Rume.single_strata(\n", + "rume = SingleStrataRume.build(\n", " ipm=ipm_library['sirh'](),\n", " mm=mm_library['centroids'](),\n", - " scope=geo.spec.scope,\n", + " scope=scope,\n", " params={\n", " 'beta': 0.45,\n", " 'gamma': 0.25,\n", @@ -140,19 +100,22 @@ " 'hospitalization_prob': 0.1,\n", " 'hospitalization_duration': 7.0,\n", " 'phi': 40.0,\n", - " 'population': geo['population'],\n", - " 'centroid': geo['centroid'],\n", + " 'population': acs5.Population(),\n", + " 'centroid': us_tiger.GeometricCentroid(),\n", + " 'meta::geo::label': us_tiger.Name(),\n", " },\n", - " time_frame=TimeFrame.of(\"2015-01-01\", 150),\n", + " time_frame=TimeFrame.of(\"2020-01-01\", 150),\n", " # Initialize the infection in Arizona with 10k individuals.\n", + " # Arizona is the node at index 0 because it has the lowest\n", + " # FIPS code of the states we selected.\n", " init=init.SingleLocation(location=0, seed_size=10_000),\n", ")\n", "\n", "sim = BasicSimulator(rume)\n", - "with sim_messaging(sim):\n", + "with sim_messaging():\n", " output = sim.run()\n", "\n", - "EVENT_S_TO_I = rume.ipm.events_by_dst(\"I\")[0]\n", + "EVENT_S_TO_I = rume.ipm.event_by_name(\"S->I\")\n", "\n", "plot_event(output, event_idx=EVENT_S_TO_I)\n", "\n", @@ -160,11 +123,11 @@ " # argmax gives us an index, but the index is equal to the tau step index\n", " # so just need to floor-div by number of tau steps to get day\n", " float(np.argmax(output.incidence[:, n, EVENT_S_TO_I])) // output.dim.tau_steps\n", - " for n in range(geo.nodes)\n", + " for n in range(scope.nodes)\n", "])\n", "\n", "map_data_by_state(\n", - " geo=geo,\n", + " scope=scope,\n", " data=day_of_peak_infection,\n", " title='Day of Peak Infection by State',\n", " vmin=0,\n", @@ -192,15 +155,15 @@ "output_type": "stream", "text": [ "Running simulation (BasicSimulator):\n", - "• 2015-01-01 to 2015-05-31 (150 days)\n", + "• 2020-01-01 to 2020-05-30 (150 days)\n", "• 4 geo nodes\n", "|####################| 100% \n", - "Runtime: 0.110s\n" + "Runtime: 0.277s\n" ] }, { "data": { - "image/png": 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+iKurK6tXr65Qh66q5Ka6P8flf4bLj2YWFxdz6tQpm/0c1FRERATNmjXjwoULl23j5OR01VM+YmNjWbZsGSNHjmTx4sUOVepFOBZZYyeEnWm1Wu68805+/fVXvv76a0pLSytMw4J5R+a5c+f49NNPK72+oKCAvLy8Gvfr4eEBUK2TJ3r27ElgYCALFiyoMC22cuVKDh8+bN2lGBAQwMCBA1m0aBHx8fEV7mEZ6dBqtYwcOZJff/2VHTt2VOrL0s6SBG3cuNH6nKW0RV316NGDNm3a8Pbbb1dZ8T8lJaXG9xw5ciRarZZXXnml0vo8y/cUExODwWDg9ddfp6Sk5LL95ufnU1hYWOG5Nm3a4OXlVam8zeXMnTu3Qv9z587F2dmZQYMGVWh33333cejQIZ544gl0Op11J3ZNDR8+nG3btrFlyxbrtby8PD755BNat25tnaIePXo0qampFeIrHyeYR8s0Gk2F0dnTp09XecKEh4dHtX6GBw8ejF6v58MPP6ww6rZw4UKysrKsP8M1Vd1yJ1u3bq3y7+m2bdtIS0ur8yjpa6+9xuDBg1myZMlVp7lF0yY/HULUwcqVK6v8R79fv34VRg3Gjh3Lf/7zH1588UWio6Ot65Is7rvvPr7//nsefvhh/vjjD6677jqMRiNHjhzh+++/t9Zvq4kePXoA8NxzzzFu3DicnZ259dZbrQlfec7Ozrz55ptMmjSJ66+/nrvvvtta7qR169bMmDHD2vbDDz+kf//+dO/enYceeojw8HBOnz7NihUr2LNnD2A+LeD333/n+uuvt5ZvuXDhAkuXLuWvv/7Cx8eHIUOG0KpVKyZPnmxNOhYtWkRAQEClpLGmtFotn332GcOGDaNTp05MmjSJFi1acO7cOf744w8MBgO//vprje7Ztm1bnnvuOV599VUGDBjAqFGjcHFxYfv27TRv3pzZs2djMBiYP38+9913H927d2fcuHHW72fFihVcd911zJ07l2PHjjFo0CDGjBlDx44dcXJyYtmyZSQlJVUr8XJ1dWXVqlVMnDiRPn36sHLlSlasWMGzzz5baYpuxIgR+Pn5sXTpUoYNG0ZgYGCNvm+Lp59+mv/+978MGzaMf/7zn/j6+vLll19y6tQpfvzxR+uGhQkTJvDVV18xc+ZMtm3bxoABA8jLy2PNmjU8+uij3H777YwYMYJ3332XoUOHcs8995CcnMy8efNo27ZtpTVuPXr0YM2aNbz77rs0b96c8PDwKtf6BQQE8Mwzz/Dyyy8zdOhQbrvtNo4ePcpHH31Er169alzexaK65U6+/vprvv32W+644w569OiBXq/n8OHDLFq0CFdXV5599tla9W/xv//9z7rpRogrUmczrhAN25XKnQCVShKYTCYlNDRUAZTXXnutynsWFxcrb775ptKpUyfFxcVFadasmdKjRw/l5ZdfVrKysqztAGXq1KmVXn9peQpFUZRXX31VadGihaLVaqtV+mTJkiXKNddco7i4uCi+vr7K+PHjlYSEhErtDhw4oNxxxx2Kj4+P4urqqkRGRiovvPBChTZnzpxRJkyYoAQEBCguLi5KRESEMnXqVKWoqMjaZufOnUqfPn0UvV6vtGrVSnn33XcvW+5kxIgRleK4WlmH3bt3K6NGjVL8/PwUFxcXJSwsTBkzZoyydu1aaxtLuRNLmRCLquJQFHM5D8t71KxZM+X6669XYmNjK8UVExOjeHt7K66urkqbNm2U+++/X9mxY4eiKIqSmpqqTJ06VYmKilI8PDwUb29vpU+fPsr3339f5fdR3sSJExUPDw8lLi5OGTJkiOLu7q4EBQUpL774YqUyLBaPPvqoAiiLFy++6v0tqvp5iouLU+68807rn3vv3r2V5cuXV3ptfn6+8txzzynh4eGKs7OzEhwcrNx5551KXFyctc3ChQuVdu3aKS4uLkpUVJTy+eefV1kC58iRI8rAgQMVNzc3BbDGdLk/n7lz5ypRUVGKs7OzEhQUpDzyyCNKRkZGhTbXX3+90qlTp0pxT5w4UQkLC6vUtjq/Kvft26c88cQTSvfu3RVfX1/FyclJCQkJUe666y5l165dV329LUm5k6ZNoygNaLW1EEKIGpsxYwYLFy4kMTERd3d3tcMRQtiRrLETQohGrLCwkG+++YbRo0dLUidEEyBr7IQQohFKTk5mzZo1/PDDD6SlpfH444+rHZIQoh5IYieEEI3QoUOHGD9+PIGBgXz44YeXLdEihGhcZI2dEEIIIUQjIWvshBBCCCEaCUnshBBCCCEaCVljV49MJhPnz5/Hy8vLZsdGCSGEEKJxUxSFnJwcmjdvbi0GfjmS2NWj8+fPVzrMXQghhBCiOs6ePUvLli2v2EYSu3rk5eUFmP9gDAaDytEIIYQQoiHIzs4mNDTUmkdciSR29cgy/WowGCSxE0IIIUSNVGcZl2yeEEIIIYRoJCSxE0IIIYRoJCSxE0IIIYRoJGSNnRBCCCFUZTQaKSkpUTsM1Tg7O6PT6WxyL0nshBBCCKEKRVFITEwkMzNT7VBU5+PjQ3BwcJ3r3EpiJ4QQQghVWJK6wMBA3N3dm2TxfkVRyM/PJzk5GYCQkJA63U/VNXazZ8+mV69eeHl5ERgYyMiRIzl69GiFNjfccAMajabCx8MPP1yhTXx8PCNGjMDd3Z3AwECeeOIJSktLK7RZv3493bt3x8XFhbZt2/LFF19UimfevHm0bt0aV1dX+vTpw7Zt2yo8X1hYyNSpU/Hz88PT05PRo0eTlJRkmzdDCCGEaEKMRqM1qfPz88PNzQ1XV9cm9+Hm5oafnx+BgYFkZmZiNBrr9L6qmtht2LCBqVOn8vfffxMbG0tJSQlDhgwhLy+vQrspU6Zw4cIF68ecOXOszxmNRkaMGEFxcTGbN2/myy+/5IsvvmDWrFnWNqdOnWLEiBHceOON7Nmzh+nTp/Pggw+yevVqa5slS5Ywc+ZMXnzxRXbt2kXXrl2JiYmxZtAAM2bM4Ndff2Xp0qVs2LCB8+fPM2rUKDu+Q0IIIUTjZFlT5+7urnIkjsHyPtR5raHiQJKTkxVA2bBhg/Xa9ddfrzz++OOXfc1vv/2maLVaJTEx0Xpt/vz5isFgUIqKihRFUZQnn3xS6dSpU4XXjR07VomJibF+3bt3b2Xq1KnWr41Go9K8eXNl9uzZiqIoSmZmpuLs7KwsXbrU2ubw4cMKoGzZsqVa319WVpYCKFlZWdVqL4QQQjRWBQUFyqFDh5SCggK1Q3EIV3o/apI/OFS5k6ysLAB8fX0rXP/222/x9/enc+fOPPPMM+Tn51uf27JlC9HR0QQFBVmvxcTEkJ2dzcGDB61tBg8eXOGeMTExbNmyBYDi4mJ27txZoY1Wq2Xw4MHWNjt37qSkpKRCm6ioKFq1amVtI4QQQgihJofZPGEymZg+fTrXXXcdnTt3tl6/5557CAsLo3nz5uzbt4+nnnqKo0eP8tNPPwHmhZflkzrA+nViYuIV22RnZ1NQUEBGRgZGo7HKNkeOHLHeQ6/X4+PjU6mNpZ9LFRUVUVRUZP06Ozu7um+HEEIIIUSNOcyI3dSpUzlw4ADfffddhesPPfQQMTExREdHM378eL766iuWLVtGXFycSpFW3+zZs/H29rZ+hIaGqh2SEEIIIWzkapsuwbzrddiwYWg0Gn7++We7x+QQid20adNYvnw5f/zxBy1btrxi2z59+gBw4sQJAIKDgyvtTLV8HRwcfMU2BoMBNzc3/P390el0VbYpf4/i4uJKtXbKt7nUM888Q1ZWlvXj7NmzV/zehBBCCNEwVGfTJcD7779fr2VcVE3sFEVh2rRpLFu2jHXr1hEeHn7V1+zZswe4WOelb9++7N+/v8IbGRsbi8FgoGPHjtY2a9eurXCf2NhY+vbtC4Ber6dHjx4V2phMJtauXWtt06NHD5ydnSu0OXr0KPHx8dY2l3JxccFgMFT4EA1fZn4xJ1NyURRF7VCEEEKo5N1332XKlClMmjSJjh07smDBAtzd3Vm0aJG1zZ49e3jnnXcqXLM3VdfYTZ06lcWLF/O///0PLy8v61o1b29v3NzciIuLY/HixQwfPhw/Pz/27dvHjBkzGDhwIF26dAFgyJAhdOzYkfvuu485c+aQmJjI888/z9SpU3FxcQHg4YcfZu7cuTz55JM88MADrFu3ju+//54VK1ZYY5k5cyYTJ06kZ8+e9O7dm/fff5+8vDwmTZpkjWny5MnMnDkTX19fDAYDjz32GH379uXaa6+t53dOqCX2UBJP/LCXzPwSwv09GNIpiFu7NKdzC2+1QxNCiAZPURQKSupWx6223Jx11R5Zs2y6fOaZZ6zXLt10mZ+fzz333MO8efMuO7NnD6omdvPnzwfMRYjL+/zzz7n//vvR6/WsWbPGmmSFhoYyevRonn/+eWtbnU7H8uXLeeSRR+jbty8eHh5MnDiRV155xdomPDycFStWMGPGDD744ANatmzJZ599RkxMjLXN2LFjSUlJYdasWSQmJtKtWzdWrVpVYUPFe++9h1arZfTo0RQVFRETE8NHH31kp3dHOJKiUiOzfzvCF5tPW6+dSs3j4w0n+XTjST6f1Jvr2weoF6AQQjQCBSVGOs5affWGdnDolRjc9dVLi1JTU6+66XLGjBn069eP22+/3eaxXomqid3VprJCQ0PZsGHDVe8TFhbGb7/9dsU2N9xwA7t3775im2nTpjFt2rTLPu/q6sq8efOYN2/eVWMSjUdxqYlxn/zN7vhMAB7sH87UG9uyKS6Vr7acYdupdL7YdEoSOyGEEAD88ssvrFu37qp5hz04TLkTIRzVhmMp7I7PxMvViQ/GdeOmKPP/0G7p0pxOzb258e31bDiWQnJ2IYEGV5WjFUKIhsvNWcehV2Ku3tBOfVfX1TZdrlu3jri4uEol0kaPHs2AAQNYv369DSKumiR2QlzFygMXALizR0trUmcR7u9Bz7Bm7DiTwU+7z/Hw9W3UCFEIIRoFjUZT7elQNZXfdDly5Ejg4qbLadOmcf/99/Pggw9WeE10dDTvvfcet956q11jc/x3TwgVFZeaiD1k/h/Z8OiQKtvc1bMlO85k8MPOBP4xMKJet7ULIYRQx5U2XQYFBVW5YaJVq1bVqgBSF5LYCXEFm+NSySksJcDLhR6tmlXZZnh0CC/+cpATybnsTciiW6hP/QYphBCi3lVn06UaJLET4gpW7jeX4BnaKRittuqROC9XZ4Z1DmHZ7nP8sPOsJHZCCNFEXG3TZXn1VfvUIU6eEMIRlRpN/H7InNgN63zlGkR39jCfmPLLnvMUqlSDSQghhJDETojL2HoqnYz8Enw99PQO971i274RfrTwcSO7sNS6Jk8IIYSob5LYCXEZv+0374aN6RSEk+7Kf1W0Wg2jurcAYMW+C3aPTQghhKiKJHZCVMFoUlh9sGx9Xeeqd8Ne6qaoQMC84aLUaLJbbEIIIcTlSGInRBV2nskgNbcYbzdn+rXxq9ZrurT0weDqRHZhKfvOZdk5QiGEEKIySeyEqMK2U2kADGjnj/NVpmEtdFoN/dv5A/DnsVS7xSaEEEJcjiR2QlRhb4J5xK2mpUv6tzWfF/vXiRRbhySEEEJclSR2QlRhf1li16WlT41eN6BsxG5XfCY5hSW2DksIIYS4IknshLhEcnYhidmFaDXQqbmhRq8N9XUn3N8Do0lhS1yanSIUQgghqiaJnRCX2Fc2Wtc20BMPl5ofztK/rXnU7q8Tss5OCCFE/ZLETohLWHa0RrfwqdXrLdOxfx6XxE4IIRqzefPm0bp1a1xdXenTpw/btm2zPpeYmMh9991HcHAwHh4edO/enR9//NHuMUliJ8Ql9idkAtClpXetXt+3jR86rYZTqXmcTc+3YWRCCCEcxZIlS5g5cyYvvvgiu3btomvXrsTExJCcnAzAhAkTOHr0KL/88gv79+9n1KhRjBkzht27d9s1LknshChHURTrVGxtEzsvV2euKdtNK6N2QgjROL377rtMmTKFSZMm0bFjRxYsWIC7uzuLFi0CYPPmzTz22GP07t2biIgInn/+eXx8fNi5c6dd45LETohyzmcVkpZXjJNWQ4eQmm2cKG9AO3PZkz+PS9kTIYSoNkWB4jx1PhSl2mEWFxezc+dOBg8ebL2m1WoZPHgwW7ZsAaBfv34sWbKE9PR0TCYT3333HYWFhdxwww22ftcqqPnKcCEaMcs0bGSwF67Oulrfp19bP95bYz7BQgghRDWV5MPrzdXp+9nzoPeoVtPU1FSMRiNBQUEVrgcFBXHkyBEAvv/+e8aOHYufnx9OTk64u7uzbNky2rZta/PQy5MROyHK2VvHaVgLy2hfck4R6XnFdY5LCCFEw/LCCy+QmZnJmjVr2LFjBzNnzmTMmDHs37/frv3KiJ0Q5dS2MPGlPF2caOXrTnx6PkcSs+nXxt8G0QkhRCPn7G4eOVOr72ry9/dHp9ORlJRU4XpSUhLBwcHExcUxd+5cDhw4QKdOnQDo2rUrf/75J/PmzWPBggU2Db08GbEToox540QmANEt6jZiBxAV7AXAkQs5db6XEEI0CRqNeTpUjQ+Nptph6vV6evTowdq1a63XTCYTa9eupW/fvuTnmysiaLUV0yydTofJZLLNe3UZktgJUeZMWj7ZhaXonbREliVldRFVNh17JDG7zvcSQgjhWGbOnMmnn37Kl19+yeHDh3nkkUfIy8tj0qRJREVF0bZtW/7xj3+wbds24uLieOedd4iNjWXkyJF2jUumYoUoYylM3DHEgLOu7v/n6VCWHB5NlBE7IYRobMaOHUtKSgqzZs0iMTGRbt26sWrVKuuGit9++42nn36aW2+9ldzcXNq2bcuXX37J8OHD7RqXJHZClKlrYeJLWUbsjiblYDQp6LTVH+YXQgjh+KZNm8a0adOqfK5du3b1ctLEpWQqVogyR8pG1jo1r339uvJa+brj5qyjsMTEmbQ8m9xTCCGEuBJJ7IQoc7hsk0NksG0SO51WQ/sgT+Bi0iiEEELYkyR2QgBpuUWk5hah0WBNxmwhqixJPHJBNlAIIYSwP0nshODiBocwX3fc9bZbehoVYt5AcVhG7IQQQtQDSeyE4OJUqS3KnJRnHbGTkidCCCHqgSR2QnAx8bLV+joLS5His+kF5BSW2PTeQgghxKUksROCi1OxUTYesWvmoSfY4ArAsSSZjhVCCGFfktiJJs9oUjiWlAvYPrGDi+vsZGesEEIIe5PETjR58en5FJQYcXXWEubnYfP7X9wZK4mdEEII+5LETjR5R8vW17UL9LLL6RAdrCN2soFCCCGEfUliJ5o8e+2ItSg/Yqcoil36EEIIUX/mz59Ply5dMBgMGAwG+vbty8qVK63P33DDDWg0mgofDz/8cL3EJmfFiibPMkVqj/V1ABEBHuh1WnKKSolPz7fLdK8QQoj607JlS9544w3atWuHoih8+eWX3H777ezevZtOnToBMGXKFF555RXra9zd3eslNknsRJN3NMmS2Nm21ImFs05LhxAv9iZksf9cliR2QgjRwN16660Vvv6///s/5s+fz99//21N7Nzd3QkODq732CSxE01aQbGR02l5gP2mYgG6tPRhb0IW+xKyuKVLc7v1I4QQDZmiKBSUFqjSt5uTGxpNzddZG41Gli5dSl5eHn379rVe//bbb/nmm28IDg7m1ltv5YUXXqiXUTtJ7ESTdiwpB0UBPw89AV4udusnuqU3APsSMu3WhxBCNHQFpQX0WdxHlb633rMVd+fqJ1779++nb9++FBYW4unpybJly+jYsSMA99xzD2FhYTRv3px9+/bx1FNPcfToUX766Sd7hW8liZ1o0qyFiUPsN1oH0KUssTtwLhuTSUFrh923Qggh6k9kZCR79uwhKyuLH374gYkTJ7JhwwY6duzIQw89ZG0XHR1NSEgIgwYNIi4ujjZt2tg1LknsRJNm3REbZJ/1dRZtAzxxddaSW1TKqbQ82gR42rU/IYRoiNyc3Nh6z1bV+q4JvV5P27ZtAejRowfbt2/ngw8+4OOPP67Utk8f8yjkiRMnJLETwp4steXstSPWwkmnpVNzb3aeyWBfQqYkdkIIUQWNRlOj6VBHYjKZKCoqqvK5PXv2ABASEmL3OCSxE03aiWTzUWLt7ZzYgXk61pzYZXHHNS3t3p8QQgj7eOaZZxg2bBitWrUiJyeHxYsXs379elavXk1cXByLFy9m+PDh+Pn5sW/fPmbMmMHAgQPp0qWL3WOTxE40WdmFJSTnmP931SbA/iVILOvs9idk2b0vIYQQ9pOcnMyECRO4cOEC3t7edOnShdWrV3PzzTdz9uxZ1qxZw/vvv09eXh6hoaGMHj2a559/vl5ik8RONFlxZaN1QQYXvFyd7d5fdAsfAA6ez6bUaMJJJwe/CCFEQ7Rw4cLLPhcaGsqGDRvqMZqK5DeLaLIs07BtA+tnvVuEvweeLk4UlBiJS8mrlz6FEEI0LZLYiSbrRIo5sauvjQxarYbOLcy7b/dKPTshhBB2IImdaLLiks2jZvW5Q7VLSx9A1tkJIYSwD0nsRJMVl1K/U7EA0S3KTqA4J4mdEEII25PETjRJRaVG4tPzgfoesTMndocvZFNcaqq3foUQQjQNktiJJulMWj5Gk4KnixNBBvudEXupVr7ueLs5U1xq4lhSTr31K4QQommQxE40SZZSJ20CPNBo6u/cVo1GQ4eyc2klsRNCCGFrktiJJslS6qRNPa6vs4gom/q1rPETQgghbEUSO9EkxdVzqZPyLH2elFp2QgghbEwSO9EknVBhR6xFRNnxZZLYCSGEsDVJ7ESTYzIpqtSws2jjb+7zVFoeRpNS7/0LIYSwrTfeeAONRsP06dOt1+Li4rjjjjsICAjAYDAwZswYkpKS7B6LJHaiybmQXUhBiREnrYYwP/d6779FMzf0TlqKS02cyyio9/6FEELYzvbt2/n444/p0qWL9VpeXh5DhgxBo9Gwbt06Nm3aRHFxMbfeeismk31LXUliJ5ocy47YMD93nHX1/1dAp9UQ7meejo1LlQ0UQgjRUOXm5jJ+/Hg+/fRTmjVrZr2+adMmTp8+zRdffEF0dDTR0dF8+eWX7Nixg3Xr1tk1JlUTu9mzZ9OrVy+8vLwIDAxk5MiRHD16tEKbwsJCpk6dip+fH56enowePbrSUGZ8fDwjRozA3d2dwMBAnnjiCUpLSyu0Wb9+Pd27d8fFxYW2bdvyxRdfVIpn3rx5tG7dGldXV/r06cO2bdtqHItwfJYdsWqsr7OQdXZCCFGZoiiY8vNV+VCUmi+NmTp1KiNGjGDw4MEVrhcVFaHRaHBxuVgn1dXVFa1Wy19//VXn9+lKnOx696vYsGEDU6dOpVevXpSWlvLss88yZMgQDh06hIeH+RffjBkzWLFiBUuXLsXb25tp06YxatQoNm3aBIDRaGTEiBEEBwezefNmLly4wIQJE3B2dub1118H4NSpU4wYMYKHH36Yb7/9lrVr1/Lggw8SEhJCTEwMAEuWLGHmzJksWLCAPn368P777xMTE8PRo0cJDAysViyiYVBzR6zFxcRORuyEEMJCKSjgaPceqvQduWsnGvfqL8/57rvv2LVrF9u3b6/03LXXXouHhwdPPfUUr7/+Ooqi8PTTT2M0Grlw4YItw65E1RG7VatWcf/999OpUye6du3KF198QXx8PDt37gQgKyuLhQsX8u6773LTTTfRo0cPPv/8czZv3szff/8NwO+//86hQ4f45ptv6NatG8OGDePVV19l3rx5FBcXA7BgwQLCw8N555136NChA9OmTePOO+/kvffes8by7rvvMmXKFCZNmkTHjh1ZsGAB7u7uLFq0qNqxiIbBIUbs/KWWnRBCNFRnz57l8ccf59tvv8XV1bXS8wEBASxdupRff/0VT09PvL29yczMpHv37mi19k29VB2xu1RWlvlgdF9fXwB27txJSUlJhSHOqKgoWrVqxZYtW7j22mvZsmUL0dHRBAUFWdvExMTwyCOPcPDgQa655hq2bNlSaZg0JibGunuluLiYnTt38swzz1if12q1DB48mC1btlQ7lksVFRVRVFRk/To7O7u2b42wobgU9XbEWlgKI8tUrBBCXKRxcyNy107V+q6unTt3kpycTPfu3a3XjEYjGzduZO7cuRQVFTFkyBDi4uJITU3FyckJHx8fgoODiYiIsEf4Vg6T2JlMJqZPn851111H586dAUhMTESv1+Pj41OhbVBQEImJidY25ZM6y/OW567UJjs7m4KCAjIyMjAajVW2OXLkSLVjudTs2bN5+eWXq/kOiPqQXVhCaq452bZMh6rB0ndyThE5hSV4uTqrFosQQjgKjUZTo+lQtQwaNIj9+/dXuDZp0iSioqJ46qmn0Ol01uv+/v4ArFu3juTkZG677Ta7xuYwid3UqVM5cOCA3RcV1qdnnnmGmTNnWr/Ozs4mNDRUxYhEfFo+AP6eelWTKYOrM/6eLqTmFnEqNY8uLX1Ui0UIIUTNeHl5WQehLDw8PPDz87Ne//zzz+nQoQMBAQFs2bKFxx9/nBkzZhAZGWnX2BwisZs2bRrLly9n48aNtGzZ0no9ODiY4uJiMjMzK4yUJSUlERwcbG1z6e5Vy07V8m0u3b2alJSEwWDAzc0NnU6HTqersk35e1wtlku5uLhU2BEj1Befbk7sQn3V/x9hRIAHqblFxKXkSmInhBCNzNGjR3nmmWdIT0+ndevWPPfcc8yYMcPu/aq6eUJRFKZNm8ayZctYt24d4eHhFZ7v0aMHzs7OrF271nrt6NGjxMfH07dvXwD69u3L/v37SU5OtraJjY3FYDDQsWNHa5vy97C0sdxDr9fTo0ePCm1MJhNr1661tqlOLMLxnSkbsQtzgMROzowVQojGY/369bz//vvWr9944w0SExMpLi7m2LFjzJw5E41GY/c4VB2xmzp1KosXL+Z///sfXl5e1rVq3t7euLm54e3tzeTJk5k5cya+vr4YDAYee+wx+vbta92sMGTIEDp27Mh9993HnDlzSExM5Pnnn2fq1KnW0bKHH36YuXPn8uSTT/LAAw+wbt06vv/+e1asWGGNZebMmUycOJGePXvSu3dv3n//ffLy8pg0aZI1pqvFIhyfZcSulUMkdlLLTgghhG2pmtjNnz8fgBtuuKHC9c8//5z7778fgPfeew+tVsvo0aMpKioiJiaGjz76yNpWp9OxfPlyHnnkEfr27YuHhwcTJ07klVdesbYJDw9nxYoVzJgxgw8++ICWLVvy2WefWWvYAYwdO5aUlBRmzZpFYmIi3bp1Y9WqVRU2VFwtFuH44tPNSVQrP/U2TlhYNlBIyZN6cHID/Pk2RN8F3SeoHY0QQtiNRqlNqWVRK9nZ2Xh7e5OVlYXBYFA7nCZpwJx1nE0v4Pt/9KV3uK+qsZxOzeOGt9fj4qTl8CtD0WrtP0Tf5BTnw9qXYeuCi9dGzodu96gXkxACMJ/mdOrUKcLDw6usBdfUXOn9qEn+IGfFiiajxGjifGYhYD4nVm0tm7nhrNNQVGriXGaB2uE0PilH4eMBF5O65mX1pv43DY6suPzrhBCiAZPETjQZ5zMLMJoUXJy0BHiqv1vZSaelddmU8MlUWWdnU6VF8P0ESDsBXiEw/keYsg663QuKEZZOglMb1Y5SCAG1OqO1MbLV+yCJnWgyLDtiW/m6O8y0p5wZayfrZ0PKEfAIhH/8Ce0Gg0YDt34AUbeAscic3BXnqx2pEE2Ws7O5lmh+vvw9hIvvg+V9qS2HqGMnRH1wpB2xFhEBnkCS7Iy1pYQdsOkD8+e3vg+eARef0znB6IUwrzdknoG9i6HXg6qEKURTp9Pp8PHxsZYrc3d3r5dyII5GURTy8/NJTk7Gx8enwqkVtSGJnWgyrImdA6yvs4jwl52xNlVSAD8/AooJuoyFqBGV2zi7Qt+psPJJ2DIPekwCbd3+IRVC1I6lwH/5WrRNleUs2bqSxE40GfFpjjdi1yZQihTb1Po3IPUYeAbD0Dcu367bePjjdUg/CUd/gw631l+MQggrjUZDSEgIgYGBlJSUqB2Oapydnes8UmchiZ1oMs6Ujdg5wo5Yizb+5sQuMbuQvKJSPFzkr2StGUtg5+fmz0e8De5XKGfj4gm9JsOf78Dm/0hiJ4TKLEd7irqTzROiSVAUhfi0suLEvuoXJ7bwdnfGz0MPwCnZGVs3ZzZBYRZ4BEDk8Ku37/0Q6PRwdivEb7V/fEIIUQ8ksRNNQnpeMXnFRjQac/04RyInUNiIpTZd5LDqrZnzCoYuY8yfb/mP/eISQoh6JImdaBIs07DBBldcnR1ruL9NgHk6Nk7W2dWeolxM7KJuqf7r+k4zPx5eDlkJto9LCCHqmSR2okk4W5bYhTrQxgkLqWVnAxf2QPY5cPaA8Our/7rADhB6LaDA8d/tFZ0QQtQbSexEk2ApThzmiImdv+yMrTPLaF3bQeZyJjXRbrD58fga28YkhBAqkMRONAmOWJzYwjpil5qLySRH69RKbaZhLdrebH48tQFKi20XkxBCqEASO9EkWGvYOVCpE4tQX3ectBoKS0xcyC5UO5yGJy0Okg+BRgfth9T89cFdzEePFedC/BbbxyeEEPVIEjvRJDjyiJ2zTmutrSfr7Grh6G/mx9b9wa1ZzV+v1ULbsunYE7G2i0sIIVQgiZ1o9ApLjCSWjYSF+TlODbvyIgJknV2t1WUa1kLW2QkhGglJ7ESjl5BhHq3zdHGimbuzytFUTWrZ1VJBBsT/bf48cljt7xNxI2i0kHJYyp4IIRo0SexEoxdfrtSJRqNROZqqtZGdsbVzdhuggG8b8Amt/X3cfaFFT/Pnx2U6VgjRcEliJxq9hIwCAEId7MSJ8toESi27Wjmz2fwY1rfu92pXtjv2hEzHCiEaLknsRKNnSexaNnO8jRMWllp257MKyS8uVTmaBsQyDdvKBomdZQPFyfVS9kQI0WBJYicavXPWxM5xR+yaeeit6/9kOraaSgrh/C7z57ZI7EK6gUeAuezJ2b/rfj8hhFCBJHai0bNsnnDkxA7K7YxNlcSuWs7vAmMxeAaBb0Td76fVQsQN5s9P/1X3+wkhhAoksRONXkOYigVoI2fG1oxlfV2ra8FWm2IsI3/xMmInhGiYJLETjVp+cSlpeeb1Ui0ayoidTMVWj3V9XT/b3bPVtebHhB1glLWOQoiGRxI70ahZ1tcZXJ3wdnPMGnYWEf5Sy67aTEY4u9X8uSUZs4WADuDiDSV5kLTfdvcVQoh6IomdaNQayjQsXByxO5Wah6IoKkfj4JIPQVE26L0gqLPt7qvVQmhv8+fxW213XyGEqCeS2IlGzbJxwtGnYQHC/Nxx0mrIL754BJq4jDNbzI+hvUDnZNt7t+pjfozfYtv7CiFEPZDETjRqCQ2g1ImFs05LK1/zyKKss7sKS9Jly/V1FqFlU7tnt4KMnAohGhhJ7ESj1pCmYkHOjK0WRSmX2NlwfZ1Fix6gdYKcC5AZb/v7CyGEHUliJxq1hlLDzkJ2xlZD5hlz0qV1hpY9bX9/vTuEdDV/LmVPhBANjCR2olFrSFOxIDtjq+XcTvNjSBdwttOfq6WenZxAIYRoYCSxE41W+Rp2DWUqtk2gjNhd1YW95seQbvbrI9SygUJ2xgohGhZJ7ESjZalh59UAathZWEbszmcVUFBsVDkaB2VN7Lrarw/L2r3kQ1CQab9+hBDCxiSxE41WQ9s4AeDrocfbzRlFMdezE5dQlPpJ7DwDy86fVSBhu/36EUIIG5PETjRaDW3jBIBGo7HujD2ZKuvsKsk6CwUZ5o0TgR3s25el7IlsoBBCNCCS2IlGq6FtnLBoIztjL88yWhfYAZxc7NuX5QSKczvs248QQtiQJHai0WqIU7FwsZbdSdkZW1l9TMNatOhufjy3G0wm+/cnhBA2IImdaLQSMhvmiF2Ev3nELk5G7Cqrz8QusCM4uUJRFqSftH9/QghhA5LYiUbrXANcYwfQptyInSJHWlVUH6VOLHTOFxNIS+08IYRwcJLYiUapoNhIam7DqmFn0crPHZ1WQ16xkeScIrXDcRw5iZCbBBotBHWqnz6bl03Hnt9VP/0JIUQdSWInGqVzmebRuoZUw87CxUlHaNkoo5xAUY5ltM4/0nzsV31o0cP8KCN2QogGQhI70SidbaAbJywsZ8bKOrty6nN9nYVlA8WFfWAsqb9+hRCiliSxE41SQy11YmE5gUJ2xpajRmLnGwGu3mAsgqSD9devEELUkiR2olFKSDdPxbbwaZiJnZwZWwU1EjuNRtbZCSEaFEnsRKMUX5bYhfk10KlYfzl9ooK8NPOpEwDB0fXbt6yzE0I0IJLYiUbJktiFNvA1dgkZBRSWGFWOxgEklo3W+UaAq6F++7Ymdrvrt18hhKgFSexEo3S2LLFr1UBH7Pw99Xi5OqEocDpNpmO5sM/8GNyl/vu2bKBIOQzF8mchhHBsktiJRicrv4TswlKg4Y7YaTQa66idrLPj4saFEBUSO69gMLQAxXRxnZ8QQjgoSexEo3O27MQJf08X3PQ6laOpvTZyZuxFlsQuqLM6/Te/xvwo6+yEEA5OEjvR6FjW17XybZg7Yi3aSC07s9JiSD1q/ry+Tpy4lGygEEI0EJLYiUbHunHCt2FOw1pILbsyqcfAVGquJ2dooU4MlnV256TkiRDCsUliJxod68aJhp7YlVtjpyiKytGoqPw0rEajTgyW2nmZZ6AgQ50YhBCiGiSxE41OYxmxC/NzR6uBnKJSUnKL1A5HPUkHzI+BHdWLwa0ZNGtt/lw2UAghHJgkdqLROdvAa9hZuDrrrGfdxiU34XV21hE7ldbXWYR0Mz+e36NmFEIIcUWS2IlGxWhSOJdpPie2odawKy8iQE6gUH1HrEXzbubHC3vUjEIIIa5IEjvRqCRmF1JiVHDWaQg2uKodTp1F+DfxWnZ5qZCbaP48sIO6sVhG7GQqVgjhwCSxE41KfJp5GraFjxs6rUoL7W2oTWAT3xlrGa1rFg4unurGYtlAkX4SCrPUjUUIIS5DEjvRqFiKEzf0jRMWlhG7JlvLzlHW1wG4+4JPK/PnMmonhHBQktiJRuVsI9kRa2E5fSIhI5+iUqPK0ajAUdbXWcgGCiGEg5PETjQq8Y2khp1FgJcLni5OmBQ4UzbN3KRYSp04wogdyAYKIYTDUzWx27hxI7feeivNmzdHo9Hw888/V3j+/vvvR6PRVPgYOnRohTbp6emMHz8eg8GAj48PkydPJje34nqkffv2MWDAAFxdXQkNDWXOnDmVYlm6dClRUVG4uroSHR3Nb7/9VuF5RVGYNWsWISEhuLm5MXjwYI4fP26bN0LYTGMpTmyh0Wia7pmxxlJIOWL+3FESO9lAIYRwcKomdnl5eXTt2pV58+Zdts3QoUO5cOGC9eO///1vhefHjx/PwYMHiY2NZfny5WzcuJGHHnrI+nx2djZDhgwhLCyMnTt38tZbb/HSSy/xySefWNts3ryZu+++m8mTJ7N7925GjhzJyJEjOXDggLXNnDlz+PDDD1mwYAFbt27Fw8ODmJgYCgsLbfiOiLqKTzeXOmnoNezKi2iqZ8amn4TSQnB2N2+ecASWxC7tBBRmqxqKEEJUxckWN8nOzmbdunVERkbSoUP1SxIMGzaMYcOGXbGNi4sLwcHBVT53+PBhVq1axfbt2+nZsycA//nPfxg+fDhvv/02zZs359tvv6W4uJhFixah1+vp1KkTe/bs4d1337UmgB988AFDhw7liSeeAODVV18lNjaWuXPnsmDBAhRF4f333+f555/n9ttvB+Crr74iKCiIn3/+mXHjxlX7exb2U1BsJLXshIbGMmIHF8+MjWtqI3blT5zQOsiqEQ8/8A6FrLOQuA9a91c7IiGEqKBW/1qOGTOGuXPnAlBQUEDPnj0ZM2YMXbp04ccff7RpgOvXrycwMJDIyEgeeeQR0tLSrM9t2bIFHx8fa1IHMHjwYLRaLVu3brW2GThwIHq93tomJiaGo0ePkpGRYW0zePDgCv3GxMSwZcsWAE6dOkViYmKFNt7e3vTp08fapipFRUVkZ2dX+BD2Y9kRa3B1wtvdWeVobKf8mbFNiiPtiC3PUvZENlAIIRxQrRK7jRs3MmDAAACWLVuGoihkZmby4Ycf8tprr9ksuKFDh/LVV1+xdu1a3nzzTTZs2MCwYcMwGs27AxMTEwkMDKzwGicnJ3x9fUlMTLS2CQoKqtDG8vXV2pR/vvzrqmpTldmzZ+Pt7W39CA0NrdH3L2rGUsOuseyItYgot8ZOURSVo6lHyYfMj46W2MkGCiGEA6tVYpeVlYWvry8Aq1atYvTo0bi7uzNixAibbigYN24ct912G9HR0YwcOZLly5ezfft21q9fb7M+7OmZZ54hKyvL+nH27Fm1Q2rULCN2jWkaFiDc3wONBrILS0nNLVY7nPqTctT8GBClbhyXCrnG/CgjdkIIB1SrxC40NJQtW7aQl5fHqlWrGDJkCAAZGRm4utrvGKeIiAj8/f05ceIEAMHBwSQnJ1doU1paSnp6unVdXnBwMElJSRXaWL6+Wpvyz5d/XVVtquLi4oLBYKjwIeynsZU6sXB11lk3g5xIbiLr7EqLIOO0+XP/9qqGUollKjbtBBTlqBuLEEJcolaJ3fTp0xk/fjwtW7YkJCSEG264ATBP0UZHR9syvgoSEhJIS0sjJCQEgL59+5KZmcnOnTutbdatW4fJZKJPnz7WNhs3bqSkpMTaJjY2lsjISJo1a2Zts3bt2gp9xcbG0rdvXwDCw8MJDg6u0CY7O5utW7da2wj1WUqdtGxkiR1A+yAvAI4lNZFEIv0UKEbQe4HX5f/zpArPADC0ABRI3K92NEIIUUGtErtHH32ULVu2sGjRIjZt2oS2bMdaREREjdbY5ebmsmfPHvbs2QOYNyns2bOH+Ph4cnNzeeKJJ/j77785ffo0a9eu5fbbb6dt27bExMQA0KFDB4YOHcqUKVPYtm0bmzZtYtq0aYwbN47mzZsDcM8996DX65k8eTIHDx5kyZIlfPDBB8ycOdMax+OPP86qVat45513OHLkCC+99BI7duxg2rRpgLmW2PTp03nttdf45Zdf2L9/PxMmTKB58+aMHDmyNm+hsIOEDEupEzeVI7G9yGDzBoqjTSWxSy2bhvVvBxoHPPNXTqAQQjgqpQ6KioqUI0eOKCUlJbV6/R9//KEAlT4mTpyo5OfnK0OGDFECAgIUZ2dnJSwsTJkyZYqSmJhY4R5paWnK3XffrXh6eioGg0GZNGmSkpOTU6HN3r17lf79+ysuLi5KixYtlDfeeKNSLN9//73Svn17Ra/XK506dVJWrFhR4XmTyaS88MILSlBQkOLi4qIMGjRIOXr0aI2+36ysLAVQsrKyavQ6cXUmk0np+MJKJeyp5crxpJyrv6CB+Xl3ghL21HJl9Eeb1A6lfmyYoygvGhTlp3+oHUnV1r9pju/HKWpHIoRoAmqSP2gUpebb7PLz83nsscf48ssvATh27BgRERE89thjtGjRgqefftp2mWcjkp2djbe3N1lZWbLezsYy8oq55tVYAI68OhRXZ53KEdnWkcRshr7/J16uTux7cQgaRxzFsqWfHoJ9S2DQLBjwL7WjqezY77D4LvCPhGnb1I5GCNHI1SR/qNVU7DPPPMPevXtZv359hc0SgwcPZsmSJbW5pRB1YpmGDfByaXRJHUCEvydOWg05haUkZjeB005Sj5kfHW3jhIVlA0XqMShuYvUFhRAOrVaJ3c8//8zcuXPp379/hZGDTp06ERcXZ7PghKguS6mTxri+DkDvpCW87ASKI4mNfJ2dokBqWdkkR03svILAKwTZQCGEcDS1SuxSUlIqFQYG89mvjX6KSDikhLLErmUjOiP2Uu2Dy3bGNvbELvs8FOeC1gl8I9SO5vJkA4UQwgHVKrHr2bMnK1assH5tSeY+++wzKf8hVHE2vWxHrG/jHLEDiCwredLod8ZapmGbhYPOgY+GkxMohBAOyKk2L3r99dcZNmwYhw4dorS0lA8++IBDhw6xefNmNmzYYOsYhbiqJjFi11Rq2Tn6NKyFjNgJIRxQrUbs+vfvz549eygtLSU6Oprff/+dwMBAtmzZQo8ePWwdoxBXdbZs80TLRrrGDiCybCr2eFIuRlMjPjPWunGinbpxXI11A8VRKM5XNxYhhChTqxE7gDZt2vDpp5/aMhYhakVRFOuIXWgjHrFr5euOi5OWolIT8en51s0UjY6lOHFApLpxXI0hBDyDIDcJkg5AaG+1IxJCiNqN2P3222+sXr260vXVq1ezcuXKOgclRE2k5hZTWGJCo4EQH/udVaw2nVZDu6CyEyga8waKhjIVCzIdK4RwOLVK7J5++mmMRmOl64qiSHFiUe8so3XBBldcnBpfDbvyGv06u8JsyLlg/tyvrbqxVIdsoBBCOJhaJXbHjx+nY8eOla5HRUVx4sSJOgclRE00hfV1Fo1+Z2xa2WidZxC4+agaSrXIiJ0QwsHUKrHz9vbm5MmTla6fOHECD49Guu5HOKymsL7OIrKx17JrSNOwcHHELuUIlBSoGooQQkAtE7vbb7+d6dOnVzhl4sSJE/zrX//itttus1lwQlRHQlMasStL7E6l5lFUWnk5RIOXUrZxoqEkdl4h4BEAihGSDqodjRBC1C6xmzNnDh4eHkRFRREeHk54eDgdOnTAz8+Pt99+29YxCnFFZ9PLatj5Nv4Ru2CDK16uTpSaFE6lNsIzSh39jNhLaTTlpmN3qxqKEEJALcudeHt7s3nzZmJjY9m7dy9ubm506dKFgQMH2jo+Ia7qXBMasdNoNEQGebHjTAZHE3OICjaoHZJtWadiHbyGXXnNu8GJWNlAIYRwCLWuY6fRaBgyZAhDhgyxZTxC1IjJpFinYpvCGjswnxm740xG49sZW1oM6WXLOxy9hl151hG7vaqGIYQQUIfEbu3ataxdu5bk5GRMJlOF5xYtWlTnwISojpTcIoqNJnRaDSHejbeGXXnWnbGJuSpHYmPpcWAqBRcDGFqoHU31WTdQHIaSQnBuGj+HQgjHVKs1di+//DJDhgxh7dq1pKamkpGRUeFDiPpiWV8XbHDFSVerH+cGp9HWsks+ZH4MiDSvXWsoDC3A3c+clCbLBgohhLpqNWK3YMECvvjiC+677z5bxyNEjVinYX0b//o6i/Zlp0/Ep+eTX1yKu77WA++OJfmI+TEgSt04asqygSJurbmeXQs5L1sIoZ5aDXEUFxfTr18/W8ciRI1Zd8Q2kfV1AH6eLvh7ugBwPKkRTcemHDY/BlYufu7w5AQKIYSDqFVi9+CDD7J48WJbxyJEjTW1jRMWkcGN8MxYy4hdYAMbsQM5gUII4TBqNYdTWFjIJ598wpo1a+jSpQvOzs4Vnn/33XdtEpwQV3M2wzJi13SmYsG8zm7TibTGc7RYaRGkl51mE9BB3VhqwzJil3zY/L04uagajhCi6apVYrdv3z66desGwIEDByo8p2lIi55Fg3dxjV0TG7FrbBsoUo+bT29w9QavYLWjqTnvUHBrBgUZ5k0gza9ROyIhRBNVq8Tujz/+sHUcQtRYidHEucymt3kCzLXsoBFNxaZYNk50aFg7Yi0sGyhO/mGejpXETgihkjrVhzhx4gSrV6+moMD8y1VRFJsEJUR1nMsowGhScHXWEuTVtGqHWUqeJOcUkZFXrHI0NmApddIQ19dZyAYKIYQDqFVil5aWxqBBg2jfvj3Dhw/nwoULAEyePJl//etfNg1QiMs5nWY+KzXM1wOttgGO8tSBp4uTdV1ho5iOTS43YtdQyQYKIYQDqFViN2PGDJydnYmPj8fd/eLaprFjx7Jq1SqbBSfElZxJM2+cCPNrWuvrLBrVOjtrqZMGnNhZN1AcMh+PJoQQKqhVYvf777/z5ptv0rJlywrX27Vrx5kzZ2wSmBBXYxmxa+3voXIk6rCus2voiV1JAaSfMn/ekBM7nzBw9QFj8cVEVQgh6lmtEru8vLwKI3UW6enpuLjINn9RP2TErmzErqGfGZt6DFDAzRc8AtSOpvY0Ggjpav5cpmOFECqpVWI3YMAAvvrqK+vXGo0Gk8nEnDlzuPHGG20WnBBXYh2x82uiI3ZBF0fsGvTGJWth4ga6I7Y86waKvaqGIYRoumpV7mTOnDkMGjSIHTt2UFxczJNPPsnBgwdJT09n06ZNto5RiEqMJsV6nFhTHbGLCPBAp9WQVVBCck4RQYYGujPYsiO2oZ0RW5XgLubHxH3qxiGEaLJqNWLXuXNnjh07Rv/+/bn99tvJy8tj1KhR7N69mzZt2tg6RiEqOZ9ZQIlRQa/TEuLdtGrYWbg662hdltQ26Hp2KeVG7Bo6y1Rs4gEwGdWNRQjRJNV4xK6kpIShQ4eyYMECnnvuOXvEJMRVWdbXhfq6oWtipU7Kiwz2Ii4lj2NJOQxs30DXpyWXbTRoDCN2vm1A7wnFuebTNBpyXT4hRINU4xE7Z2dn9u2TaQahLsv6uvAmuiPWwrLO7tCFbJUjqaXiPMgs20nfGEbstFoI6mz+XKZjhRAqqNVU7L333svChQttHYsQ1XbGUpy4iW6csIhu4Q3AgXNZKkdSS6nHzI/u/uDhr24sthJSts5ONlAIIVRQq80TpaWlLFq0iDVr1tCjRw88PCr+cn333XdtEpwQl3O6bCq2dRPdOGFhSexOJOeSV1SKh0ut/kqrJ+Wo+bExTMNaWNbZSWInhFBBrX4LHDhwgO7duwNw7NixCs9pGnq5AtEgyIidWaDBlWCDK4nZhRw8n03vcF+1Q6oZa2LXXt04bKn8zlhFafglXIQQDUqNEzuj0cjLL79MdHQ0zZo1s0dMQlyRyaRYN0801Rp25XVp6U3ioUL2JWQ2vMTOMhXrH6luHLYUEAVaZyjMMq8fbNZa7YiEEE1IjdfY6XQ6hgwZQmZmph3CEeLqErMLKSo14aTV0NyngdZus6EuLc3TsfsSGuA6u8Y4Yuekv7gR5IJsoBBC1K9a17E7efKkrWMRolosO2JDfd1x0tXqR7hR6dLSB4D9DW0DRWkxpJf9O9KYRuygXD07SeyEEPWrVr8VX3vtNf7973+zfPlyLly4QHZ2doUPIeypqZ8ReynLBopTqXlkFZSoHE0NpJ8ExQh6LzA0Vzsa25INFEIIldRq88Tw4cMBuO222ypsllAUBY1Gg9EoFdeF/TT1M2Iv1cxDTytfd+LT8zlwLovr2jaQsiGpZdOw/u0a3wYDywYKmYoVQtSzWiV2f/zxh63jEKLazqRKqZNLRbf0Jj49n70JmQ0nsUsp2zgR0MimYQGCOwMayE2EnCTwClI7IiFEE1GrxO7666+3dRxCVJtlxC6siZ86UV6XFt6s2HeB/Q1pA4V1xK4RbZyw0HuYRyJTj5nX2XndrHZEQogmolaJ3caNG6/4/MCBA2sVjBBXoyhS6qQqlg0UDWpnrHVHbCMcsQPzdGzqMfM6u3aS2Akh6ketErsbbrih0rXya+1kjZ2wl5ScIgpKjOi0Glr4uKkdjsPo3MKARgPnMgtIyy3Cz9NF7ZCuzGSC1OPmzxvbjliLkC5w4AfZQCGEqFe12hWbkZFR4SM5OZlVq1bRq1cvfv/9d1vHKIRVXIp5GrZlMzf0TlLqxMLL1ZmIsqnpfQ2h7EnWWSgtAJ2+8RbwbdHD/Jiw3XwChRBC1INajdh5e3tXunbzzTej1+uZOXMmO3furHNgQlQlLiUXgDYBnipH4ni6tPQhLiWPfWezuDEyUO1wrsxy4oRvG9A1sPNtq6t5d9DoIOcCZCWAT6jaEQkhmgCbDnkEBQVx9OhRW95SiApOlo3YtQmQ9XWXspxAsf9cprqBVEdjPHHiUnp383QswNmt6sYihGgyavVf5X37KtZmUhSFCxcu8MYbb9CtWzdbxCVElSwjdhEyYldJgzpaLOWI+TEgSt047C20D5zfDWe3QfSdakcjhGgCapXYdevWDY1Gg3LJupFrr72WRYsW2SQwIapyMlWmYi+nY4g3Oq2G5JwiErMKCfZ24HN0LVOxjbHUSXkte8HWBZCwTe1IhBBNRK0Su1OnTlX4WqvVEhAQgKurA/8iEQ1eYYmRhIwCQKZiq+Km19Eu0JMjiTnsS8gk2DtY7ZCqpiiNv9SJRWgf8+OFfVCcZ65vJ4QQdlSrxC4sLMzWcQhxVadS81AU8HZzxtdDr3Y4DqlLS++yxC6LIZ0cNLHLS4HCTEADfm0rPa0oCoV795KxdClFR47iN+VBvGJiKpRUajC8W4JXc8g5b56Sbd1f7YiEEI1crTZP/POf/+TDDz+sdH3u3LlMnz69rjEJUaXyGyca5C/5emAtVOzIJU8so3XNwsC5Yi3CrF+Xc+r2kZwedzdZP/5E4cGDnJs+g4SHH6Hk3DkVgq0jjQZCe5s/lw0UQoh6UKvE7scff+S6666rdL1fv3788MMPdQ5KiKrIxomrs+6MTcistAbWYVg2TpQrTGwqKuLCCy9w/oknKDp2DI2LC96334bfg5PB2ZncDRuIu+VWctauVSnoOrBMx56VdXZCCPur1VRsWlpalbXsDAYDqampdQ5KiKpIDburiwz2wlmnISO/hISMAkJ93dUOqbLkw+bHoI4AlJw7R8I/H6fw4EHQaPB/5BF8J05AV/ZvjPcdd5D44kvk79jB+aeeJvznZehbtlQr+pqzJnZbzesLZbRZCGFHtRqxa9u2LatWrap0feXKlURERNQ5KCGqIjXsrs7FSUeHEAPgwGVPkg+ZHwM7UpKUzKm7xlB48CA6Hx9CP/2UgH8+Zk3qAFzatKHVF5/jds01mHJzOf/EkyilpSoFXwvB0eDkCgUZkHZC7WiEEI1crRK7mTNn8uSTT/Liiy+yYcMGNmzYwKxZs3j66aeZMWOGrWMUAkVRZCq2mqJbWOrZZaobSFUUBZIuJnZJr72GMT0dl3ZtCf/xBzz7V17iAaBxcqL5W2+h9fSkYPduUucvqMeg68hJbz6FAmSdnRDC7mqV2D3wwAO88847LFy4kBtvvJEbb7yRb775hvnz5zNlyhRbxygEidmF5BcbcdJqCPNzwOlFB9LVsoHCEUfsss9DURZodOTsPUtObCw4OdH87bdxbtHiii/Vt2xB8EsvAZA6fz75DenoQtlAIYSoJ7U+UuyRRx4hISGBpKQksrOzOXnyJBMmTLBlbEJYWaZhW/m646yz6Ul4jU502QaKA+eyMJkcbANF2TSs0astif/3BgB+kybhGlm9enbet4zA+/bbwWTi/LPPopSU2C1Um5INFEKIelKr35CnTp3i+PHjAAQEBODpaZ4aO378OKdPn672fTZu3Mitt95K8+bN0Wg0/PzzzxWeVxSFWbNmERISgpubG4MHD7b2a5Gens748eMxGAz4+PgwefJkcnNzK7TZt28fAwYMwNXVldDQUObMmVMplqVLlxIVFYWrqyvR0dH89ttvNY5F2I9Mw1Zfu0BPXJ215BSVciotT+1wKipL7FL2uFGanIxzWCv8pz5ao1sEvfA8Oj8/Ss7Ek/H99/aI0vZa9jI/phwxr7UTQgg7qVVid//997N58+ZK17du3cr9999f7fvk5eXRtWtX5s2bV+Xzc+bM4cMPP2TBggVs3boVDw8PYmJiKCwstLYZP348Bw8eJDY2luXLl7Nx40Yeeugh6/PZ2dkMGTKEsLAwdu7cyVtvvcVLL73EJ598Ym2zefNm7r77biZPnszu3bsZOXIkI0eO5MCBAzWKRdiPdeNEoGycuBonnZZOzS1lTxxsOjbpEAXpzmRsTwYg5OWX0dbwxBqdp6c1GUyd9xHGXAdLXqviGQC+ZRvLEhrQFLIQouFRasHLy0s5fvx4pevHjx9XvL29a3NLBVCWLVtm/dpkMinBwcHKW2+9Zb2WmZmpuLi4KP/9738VRVGUQ4cOKYCyfft2a5uVK1cqGo1GOXfunKIoivLRRx8pzZo1U4qKiqxtnnrqKSUyMtL69ZgxY5QRI0ZUiKdPnz7KP/7xj2rHUh1ZWVkKoGRlZVX7NcLs3s/+VsKeWq4s2RavdigNwov/O6CEPbVceemXA2qHUtH8/krCkFbKocgoJeFf/671bUzFxcqJITHKocgoJfmDD20YoB399A9FedGgKGtfUzsSIUQDU5P8oVYjdhqNhpycnErXs7KyMBqNdUo0LU6dOkViYiKDBw+2XvP29qZPnz5s2bIFgC1btuDj40PPnj2tbQYPHoxWq2Xr1q3WNgMHDkSvv3gEVUxMDEePHiUjI8Papnw/ljaWfqoTi7CvuOSyGnYyYlctXUMdcMTOWErp2WPknDWfNuE7cWKtb6VxdiagbAd+2hdfUJKcbJMQ7Uo2UAgh6kGtEruBAwcye/bsCkmc0Whk9uzZ9O9vm7MQExMTAQgKCqpwPSgoyPpcYmIigYGBFZ53cnLC19e3Qpuq7lG+j8u1Kf/81WKpSlFREdnZ2RU+RM3lF5dyPss85R3hL2vsqiO6hQ8AB85nUWo0qRuMRcYpsk44oZg0uHbuhFt05zrdzitmCK5du6Dk55M67yMbBWlHlg0U53aCsQHV4RNCNCi1SuzefPNN1q1bR2RkJJMmTWLSpElERkayceNG3nrrLVvH2GDNnj0bb29v60doaKjaITVIlvV1vh56mnnor9JaAET4e2BwdaKwxMThC5VH19WgXNhPxglzqZpmd99d5/tpNBqC/v1vADJ/+IGSJAcftQuIAhcDFOdeLNIshBA2VqvErmPHjuzbt4+xY8eSnJxMTk4OEyZM4MiRI3TuXLf/hVsEBwcDkJSUVOF6UlKS9bng4GCSL5mCKS0tJT09vUKbqu5Rvo/LtSn//NViqcozzzxDVlaW9ePs2bNX+a5FVS4eJSbTsNWl1Wro1doXgK2n0lSOxixvwzpK8pzQujlhGD7cJvd079ULt+7dwWgk65Jd9Q5Hq4OWZctGZDpWCGEntS4I5u7ujq+vLyEhIfj4+ODp6YlOp7NZYOHh4QQHB7O23KHf2dnZbN26lb59+wLQt29fMjMz2VmuUOm6deswmUz06dPH2mbjxo2UlKt3FRsbS2RkJM2aNbO2WXvJ4eKxsbHWfqoTS1VcXFwwGAwVPkTNWUbsZBq2ZnqFmxO77afTVY7ELCN2BwDe/TuhdXOz2X197roLgMwff0QxOci08+VIPTshhJ3VKrHbsWMHbdq04b333iM9PZ309HTee+892rRpw65du6p9n9zcXPbs2cOePXsA8yaFPXv2EB8fj0ajYfr06bz22mv88ssv7N+/nwkTJtC8eXNGjhwJQIcOHRg6dChTpkxh27ZtbNq0iWnTpjFu3DiaN28OwD333INer2fy5MkcPHiQJUuW8MEHHzBz5kxrHI8//jirVq3inXfe4ciRI7z00kvs2LGDadOmAVQrFmE/1hE72ThRI5YRu22n0lEUdQsVl5w/T+4x80aOZqNvt+m9DTFD0Hp6UhIfT/627Ta9t83JBgohhL3VZttt//79lfvvv18pKSmxXispKVEmTpyoDBgwoNr3+eOPPxSg0sfEiRMVRTGXGXnhhReUoKAgxcXFRRk0aJBy9OjRCvdIS0tT7r77bsXT01MxGAzKpEmTlJycnApt9u7dq/Tv319xcXFRWrRoobzxxhuVYvn++++V9u3bK3q9XunUqZOyYsWKCs9XJ5arkXIntTPs/Y1K2FPLldiDiWqH0qAUlRiVyOd/U8KeWq4cS8xWNZbk995RDkVGKacHhCtKtu3/HM+/+KK5hMrMf9n83jZVkKUoL3qby55kX1A7GiFEA1GT/EGjKDX/r7ybmxu7d+8mKiqqwvVDhw7Rs2dP8vPz655xNkLZ2dl4e3uTlZUl07LVZDIpdHxxFYUlJv749w2E+8uoXU3c/cnfbDmZxv/d0ZnxfcJUiUExmYi76QZKElNofn0J3guOg0Zj0z4KDhzk9J13onF2pu3GDTiVLbNwSPOvg6QDMOZr6Hib2tEIIRqAmuQPtZqKNRgMxMfHV7p+9uxZvLy8anNLIap0PquAwhITzjoNoc1sty6rqegdfnE6Vi0FO3dSkpiC1tmEV8/2Nk/qAFw7dcSlQweUkhKyf11u8/vblOV4MZmOFULYQa0Su7FjxzJ58mSWLFnC2bNnOXv2LN999x0PPvggd9ugjIEQFpaNE2F+Hjjpar3Xp8kqn9jVYnDeJjL/9z8AvEIL0Lawza75S2k0GnzuHG3ub+lS1dcUXpFlA0WCg68HFEI0SE61edHbb7+NRqNhwoQJlJaaC206OzvzyCOP8MYbb9g0QNG0SamTurmmlQ9OWg0XsgpJyCgg1Ne9Xvs3FRaSs3IVAD6tCyCoo9368r7lFpLnvEXR8eMU7t2LW7duduurTiwbKM7vhtIicHJRNx4hRKNSqyEQvV7PBx98QEZGhnVXq2VnrIuL/CMlbMeS2EUESKmT2nDXO9G5hfl4MTXKnuSsXYspLw9nTwW3gGIIirZbXzpvbwxDhwKQ8d0Su/VTZ74R4BEAxmLzKRRCCGFDdZrbcnd3Jzo6mujoaNzd63ckQDQNlqnYNpLY1VofFdfZZZVNw3qH5aLRaCCwg1378xk3FoDslSsxZmbata9a02gg7Drz56c3qRuLEKLRkUVLwqHJVGzdWevZ1fOIXWlKCnmbNgPg3Tof/NqA3r7/AXTr1g2XqCiUoiIyl/1s177qpHXZmdqn/1Q3DiFEoyOJnXBYOYUlJGUXATIVWxe9Wvui0ZhHP1Nyiuqt36wVK8BoxK1NEHovIwTZZ+NEeRqNxnoObeZ33znuSRStB5gfz24zr7MTQggbkcROOKxTqeZpWH9PF7zdnFWOpuHydncmMshchmhHPY7aZf/yq7n/TmVJebD9EzsA71tGoPXwoPjMGfL//rte+qyxgEhw94fSAjhX/dN6hBDiaiSxEw7r4sYJmYatK0vZk631tM6uOD6ewkOHQKfDKyjVfNGOGyfK03p44H27+diyjP9+Vy991phGU2469i91YxFCNCqS2AmHJRsnbMeyzq6+dsZmr14NgEevnjjlHTdfDOpUL30DNLt7HAA569ZRkpRUb/3WiKyzE0LYgSR2wmHJxgnbsYzYHbqQTXZhid37y1llTuy8+nUBUym4eoN3S7v3a+HSrh1uPXuA0UjWsmX11m+NyDo7IYQdSGInHFZcsozY2UqQwZUwP3cUBXaeybBrX8Vnz1J48CBotXhFlZ1pGBRtl6PErsTnjlEAZK/4rV77rbaASHM9O1lnJ4SwIUnshEMymhROpUliZ0u9W9dPPbuc338HwL13b5wKTpkv1tPGifK8Bg8CZ2eKjh+n6Pjxeu//qmSdnRDCDiSxEw7pXEYBxaUm9E5aWjRzUzucRqFX2XTsdjsndtll07CGoTGQuN98sR7X11novL3xvM5cCDh75cp6779aZJ2dEMLGJLETDsmyvi7czwOdtn6n8BorywkUexMyKSwx2qWP4oRzFO7fb56GHTQIkg6Yn6iHGnZVMQwfBkD2bytRFEWVGK5I1tkJIWxMEjvhkKwbJwJl44SttPJ1J9DLhRKjwp6zmXbpI6dsN6x7z544uRohPw00WrsfJXY5njfdhEavp/j0aYqOHFElhivyb19unZ2cGyuEqDtJ7IRDOpFcVsPOX9bX2YpGo7HujrXXOrvs38t2ww6NgcSy0Tq/tuCsznS6ztMTz+sHmmP7zQGnY8uvszu1Ud1YhBCNgiR2wiEdPJ8NQMfmBpUjaVwsiZ096tmVJCZSuHcfaDR4DR4MSZb1depMw1oYhg8HzOvsHHI6NuIG8+PJDaqGIYRoHCSxEw6nxGjiaGIOAJ0ksbMpS2K380wGpUbbnqOa+8cfALh17YpzYCAkHTQ/ocKO2PI8r78ejZsbJQkJFB44oGosVQq/3vyYsA2KctWNRQjR4EliJxzO8aRcio0mvFydaOXrrnY4jUr7QC+83ZzJLzZaR0VtJWftOgA8B91kvmDdEVs/R4ldjtbdHa8bbwAcdDrWNxx8wsyFnM9sVjsaIUQDJ4mdcDgHz2cB0DHEgKaei9o2dlqthp5hzQD4+2Saze5rzM0lb+tWAPNu2KIcSDlqfrJ5N5v1U1tew8p2x65ahWKy7UilTVinY9erGYUQohGQxE44HMtIUqfm3ipH0jj1b+cPwNojyTa7Z95ff0FJCfqwMPTh4XB+D6CAoSV4Btqsn9ryHDgQrYcHpRcuULBnr9rhVBZRNh17StbZCSHqRhI74XAsI3adW8j6OnsY3CEIgB2n08nIK7bJPS9Oww4yj7KeLzsiq0V3m9y/rrQuLtYp4uzfHPCIMcs6u6QDkGu7hFsI0fRIYiccismkcEhG7Owq1NedqGAvTAqss8GonVJSQu4G80iTl2V93TnHSuwADJbp2NWrUIz2KdBcax7+F9ciStkTIUQdSGInHMrptDzyio24OGlpEyDFie1lSEfzqF3soaQ63yt/5y5M2dnomjXDrVs380XLiF1zx0nsPK+7Dq3BgDEllfwdDlgM2DIdK+vshBB1IImdcCiW9XVRIQacdPLjaS+DyxK7jcdT6ny8WM66tQB43nADGp0O8lIhM978pANsnLDQ6PV43TwYgOyVDjgdG3Gj+fHkenDEentCiAZBfnMKh3KgbH2d1K+zr+gW3gQbXMkvNrIlrva7YxVFIXeduX6d501liYllGtavHbg61nS6YZi5WHHO77EopaUqR3OJsL6gdYass5B+Uu1ohBANlCR2wqFY1td1lvV1dqXRaBjc0bxbNfZw7adji44fpyQhAY2LC57XXWe+6GAbJ8rzuLYPumbNMKanW8uzOAy9B4T2Nn8uu2OFELUkiZ1wGIqicOCcjNjVF8vu2DWHkjCZajf1l7fRvNDfvU9vtO5lxaTPOd76OguNkxNeQ4YA5iPGHI6lnt2x1aqGIYRouCSxEw7jQlYhGfkl6LQaIoO91A6n0evbxg8PvY7knCL2lSXUNZW78U8APAcMNF9QFIcesYOLu2Nzfo/FVGybci82E3WL+TFuHRTW7s9ECNG0SWInHIZltK5doCeuzjqVo2n8XJx0XB8ZAJhH7WrKmJtH/i5zEuc5cID5YlYC5KWA1gmC1T1K7HLce/XEKTAQU3Y2uevXqx1ORYEdwL89GIvhqAOOKAohHJ4kdsJhWHbEdpRp2Hpzcx3KnuT/vQVKS3EOa4U+LMx80TJaF9gRnN1sFaZNaXQ6vG+7FYCsX35ROZpLaDTQcaT584M/qxmJEKKBclI7ACEsDsrGiXp3Y2QgOq2Go0k5xKfl08rPvdqvtU7D9h9w8eI5c324hOAO/LLnIw6lHUKr0eKkdcLT2ZMJHSfQtllbm34PtWG47TbSPltI7oaNlGZk4NSsmdohXdRpJGycA3FrzdOxDrazWAjh2GTETjiM/ecyAdk4UZ983PX0am1OamqyO1ZRFHL/KkvsBl5M7Dac+4sHgwMZlrWF+XvnsyFhA3+c/YPYM7EsO7GMu1fczS9x6o+SubZvj0uHDlBSQs6qVWqHU1FgR3OpGGMxHHWw2IQQDk8SO+EQkrILScouQquBzi1khKI+3dwxGIDYQ4nVfk1xXByl5y+g0etx720u0fH5/kVM06Sw1c0VDRr6hvTluT7P8WLfF3m+z/P0DelLobGQ5/56jpc2v0RhaaFdvp/q8r7tNgCy/qd+olmBRmMetQM49LOakQghGiBJ7IRD2Jdg3jjRNtATDxdZIVCfbi4re7L9dAaZ+dXbJZr7518AuPfqhcbVlfd3vs+7u94DYGxuAatGLueTIZ8wLmocd7a/k7FRY5k/eD6PdnsUDRp+PP4jD6x+gPTCdPt8U9VgGDEctFoK9uyh+PRp1eKokmWd3Ym1UJitaihCiIZFEjvhEPYlZALQpaWPqnE0Ra383IkM8sJoUvjjaHK1XpP3Z1n9ugH9efXvV1l4YCEA09MzeN69Pc29W1V6jU6r45Guj/DJkE/wdvFmf+p+7vvtPs5mn7XdN1MDzoGBePTrB0DWL7+qEsNlBXUqm44tgmMyHSuEqD5J7IRDsIzYdWkp07BqqMnuWFNeHvnbdwCwoUU2S48tRYOGF90jmZyVAy17XfH114Zcy9fDvqaFZwvic+K5d+W97E/ZX/dvoha8by+bjv31VxRHOp+1/HSs7I4VQtSAJHZCdYqiyIidygaXJXYbjqZQVGq8Ytu8bdtQSkrQNA/mzaSvAXiq91PcmXLO3OAqiR1AuHc43wz/hg6+HUgvTOfhNQ+TnF+90UJb8ho0CK27OyVnz1Kwe0+9939F1unYNTIdK4SoNknshOoSMgrIyC/BWaehQ4icOKGGLi28CfRyIa/YyJa4tCu2zfvTvBt2b4SGAmMh3QO7c3frEZB82NygZc9q9env5s/nQz+ng28HsouzmbVpVr2Pmmnd3fEcPAiA7N9+q9e+ryqoE/i1LZuOlSPGhBDVI4mdUN3estG6qGADLk5y4oQatFqNddRuzRXKniiKYq1ftzI4Cb1Wz0v9XkJ7YQ+ggHcr8Aqudr8ezh68MeAN9Fo9m85vYumxpXX5NmrFMHw4ANmrVqEYrzxaWa/KFyuW3bFCiGqSxE6obr+sr3MIlt2xvx9MwmiqeuSs+PRpShISKNXCgTANj3Z7lHDvcEjYbm4QevVp2EtF+EQwvcd0AN7e8TZnss/UKv7a8uzXD523N8bUVPK3bavXvq/Kss7ueCwU5agaihCiYZDETqhur3V9nSR2aurX1g9vN2eSc4r460RqlW1yy6ZhD4dqaBPSiYmdJpqfSDBvpqjO+rqqjO8wnt7BvSkoLeC5v56j1FRaq/vUhkavx2vIEMARp2M7g28b83SsFCsWQlSDJHZCVSaTwoFz5oXhsnFCXS5OOkZ2aw7A0h1VlyA58/vPAOxro+OVfq/gpHUCRbk4YlfLxE6r0fLada/h6ezJ3pS9fHHwi1rdp7YMI0YAkP17LEpx9Wr51QspViyEqCFJ7ISqTqbmkVtUiquzlnaBnmqH0+Td1TMUME/HXlqsOCMzEd0e8waJNkPvJNI30vxE+knITwOdHoKja913iGcIz/R5BoB5e+ZxJP1Ire9VU+69euIUEIApK4vcTZvqrd9qsayzk+lYIUQ1SGInVGUpc9K5uTdOOvlxVFun5gaigr0oNpr4Ze/5Cs99+9/n0JdCprcT9wx/+uITlmnYkG7g5FKn/m+NuJVBrQZRairlmT+fochYVKf7VZdGp8Nr2FAAslc42HRscDT4RsjuWCFEtchvUqGqi4WJfdQNRACg0WgYUzZqt3RHgvX6nwl/UrRpCwBeAwfiUj6Bq+M07KX9z+o7C19XX05knmDu7rl1vmd1eZdNx+asW4epoKDe+r2q8rtjDy5TNRQhhOOTxE6oSjZOOJ6R17TAWadh/7ksDl/IxqSYeHvH23Q7ad4p2+rm2yu+wJrYVa9+3dX4uvrycr+XAfjy4JfsTdlrk/tejWuXLji3bImSn0/OmjX10me1dbrD/CjFioUQVyGJnVBNcamJQ+ctGycksXMUvh56BkWZS58s3ZHAmjNryD0dR/N0wEmHR9++FxsX50PSAfPnNhixs7gh9AZujbgVBYWP935ss/teiUajwXuUOYFK+/xzxzpiLDjafHZsaSEc/kXtaIQQDkwSO6Ga/ecyKSo14euhp7Wfh9rhiHLG9GoJwLI9CXy87xOuKRutc+92DTqvcqeDnN8NplLwDAbvljaN4eGuD6PVaPnz3J8cTT9q03tfTrO770bj5kbRocPkb9lSL31Wi0YDXceaP9/7nbqxCCEcmiR2QjV/n0wHoHdrX7RajcrRiPIGtgsg0MuFbM1ejmUcpedJ8z8VHgMHVmx4+i/zY1g/c/JhQ60MrRgSZq4vt/DAQpve+3KcmjXD5847AUj7rH76rLboMebH039BVsKV2wohmixJ7IRq/j5pPpP02ghflSMRl3LSabmrR0tc/P/AvVAh+pQJAK9BN1VseNpcsJjW/e0SxwOdHwBg9enVnM2puraerflOnAg6HXmbN1N46FC99FktzcIg7DpAgX3fqx2NEMJBSWInVFFiNLHzTAYA17bxUzkaUZX24efRuZ2l51EtWqMJl3ZtcWnT5mKDkkI4W3YEV/jAqm9SRx38OnBd8+swKSa+PPilXfq4lL5lCwzDhgGQtnBRvfRZbV3HmR/3LTEXhhZCiEtIYidUsf9cFvnFRnzcnWkf6HX1F4h699NJcyLV54D5z8crZmjFBud2mGureQaBX1u7xTE5ejIAy44vI7Wg6qPObM1vsnmkMHvVKooTHGjas+Pt4OQKKUfgwh61oxFCOCBJ7IQqtpatr+sTLuvrHNH2xO3sSt6FV5ET3c5mAuB6880VG52yTMMOsPn6uvJ6BvWkS0AXik3FfH7gc7v1U55rhw54XHcdGI2kf/VVvfRZLa7eEGkeTWTvEnVjEUI4JEnshCos6+v6hMs0rCP6ZN8nADyU3Q1nxcQZryDWFVyyc9myccJO6+ssNBoND3d5GIDFRxZzOuu0Xfuz8L1/IgBZP//PsQoWdymbjj3wAxhL1Y1FCOFwJLET9a7UaGLHafOI3bURktg5mr0pe/n7wt84aZy47qh5JO6v5l34dmv8xUYlBZBg3/V15Q1oOYABLQZQaiplzvY5du8PwOO663Bu2RJTdjbZv62slz6rpe0gcPeHvBQ45kBxCSEcgiR2ot4dOJ9NXrERbzdnooJlfZ2jsYzW3RESg/Fv8zmwm1p2YdupdE4klx1Cn7AdjMXgFWI+x7QePNnrSZy0Tvx57k82Jmy0e38arRafseYSIxlLHGjaU+cM3e8zf75lnrqxCCEcjiR2ot5ZpmF7y/o6h3M47TAbEzai1WgZn94epaQEfUQEbXt3BWDhX6fNDetpfV15rb1bc19Hc0IzZ/scio3Fdu/TZ9QocHamcN8+Cg4etHt/1db7H6B1hvgtkLBD7WiEEA5EEjtR77Za19dJ/TpH8+n+TwEY2noo+g07ATAMjWHKQHOZkx93JZCcU1hv6+su9Y8u/8DfzZ8z2Wf4+tDXdu/Pyc8PQ9mmkcwlDlQ7zhAC0XeZP9/8H3VjEUI4FIdO7F566SU0Gk2Fj6ioKOvzhYWFTJ06FT8/Pzw9PRk9ejRJSUkV7hEfH8+IESNwd3cnMDCQJ554gtLSiguO169fT/fu3XFxcaFt27Z88cUXlWKZN28erVu3xtXVlT59+rBt2za7fM+NXanRxPbTZfXrZH2dQzmZdZLYM7EAPBgxnry/zMmbV8xQerVuxjWtfCguNfHNxiPmqViA8AH1GqOHswcze8wEzEloWkGa3ftsdrd5s0LW8uUYc3Pt3l+19Z1qfjz8C2ScVjUUIYTjcOjEDqBTp05cuHDB+vFX2S8bgBkzZvDrr7+ydOlSNmzYwPnz5xk1apT1eaPRyIgRIyguLmbz5s18+eWXfPHFF8yaNcva5tSpU4wYMYIbb7yRPXv2MH36dB588EFWr15tbbNkyRJmzpzJiy++yK5du+jatSsxMTEkJyfXz5vQiBw8n01uUSkGVyc6hBjUDkeUs/jwYgBuaHkDgbvPoBQXow8Px6V9O/PO1OvNo3aHtq8BUwkYWkCz8HqPc0TECDr6dSSvJI8FexfYvT+3nj3Rt2mDkp9P1i+/2L2/agvuDBE3gmKCv+3/PgghGgaHT+ycnJwIDg62fvj7+wOQlZXFwoULeffdd7npppvo0aMHn3/+OZs3b+bvv/8G4Pfff+fQoUN88803dOvWjWHDhvHqq68yb948iovN63MWLFhAeHg477zzDh06dGDatGnceeedvPfee9YY3n33XaZMmcKkSZPo2LEjCxYswN3dnUWLHKwqfQPw1wlzgdk+EX7oZH2dw8gqyuKXOHPScl/H+8gu+4+N19AYNGVr6G7uEEREgAfdS/eaX1SP6+vK02q0/LvnvwFYemwpp7JO2bU/jUZDs7FjAcj8739RHOnEh37TzI+7v4aCTFVDEUI4BodP7I4fP07z5s2JiIhg/PjxxMebSy7s3LmTkpISBg8ebG0bFRVFq1at2LJlCwBbtmwhOjqaoKAga5uYmBiys7M5WLYQesuWLRXuYWljuUdxcTE7d+6s0Ear1TJ48GBrG1F9G4+lADCwfYDKkYjyfjr+EwWlBbRv1p7uXh3J22jeHGGIibG20Wo1/GNgBDdpdwNQGjFIlVgBegX34vqW12NUjLy/83279+c98nY07u4UHT9B/lYHWobRZhAEdoTiXNj5hdrRCCEcgEMndn369OGLL75g1apVzJ8/n1OnTjFgwABycnJITExEr9fj4+NT4TVBQUEkJiYCkJiYWCGpszxvee5KbbKzsykoKCA1NRWj0VhlG8s9LqeoqIjs7OwKH01ZblEpu+LN6+sGtvNXORphUWoqZfER8zTsvR3uJW/9BvM0bFgYLpGRFdqODDcSpT1LqaJleX5HNcK1mtFjBlqNlnVn17Ezaadd+9IZDHjfdisAGd9+Y9e+akSjgb5lo3ZbP4ZS++8UFkI4NodO7IYNG8Zdd91Fly5diImJ4bfffiMzM5Pvv3eg3WlXMHv2bLy9va0foaGhaoekqr/j0igxKrTydSfMz+PqLxD1Ym38WhLzEvF19WV4xHByVq8CwGvoUOs0rIVLnHlzxU6lPR9uTsVoUm9aso1PG0a1M6+pfWfHO3afIvUdPx6AnLXrKDl3zq591Uj0nebzenPOw8Gf1I5GCKEyh07sLuXj40P79u05ceIEwcHBFBcXk5mZWaFNUlISwcHBAAQHB1faJWv5+mptDAYDbm5u+Pv7o9PpqmxjucflPPPMM2RlZVk/zp49W+PvuTH587h5GnaAjNY5lG8OmUegxkSOwbmwlFzLNOzQmMqNj5mTvr+0vTiZmseK/RfqLc6qTO02FTcnN/an7ufPc3/atS+Xdu1w73stmExkfPedXfuqEScX6P2Q+fPNc8GR1gAKIepdg0rscnNziYuLIyQkhB49euDs7MzatWutzx89epT4+Hj69u0LQN++fdm/f3+F3auxsbEYDAY6duxobVP+HpY2lnvo9Xp69OhRoY3JZGLt2rXWNpfj4uKCwWCo8NGU/XncvHFC1tc5jv0p+9mTsgcnrRNjI8eSs349SlERzmGtcClXWgiAolw4bU6eAnvcDsDcdccxqThq5+/mz7hIczmSj/d+bP9Ru3vvBSDz+6WYCgvt2leN9HwAnN0haT+c2qB2NEIIFTl0Yvfvf/+bDRs2cPr0aTZv3swdd9yBTqfj7rvvxtvbm8mTJzNz5kz++OMPdu7cyaRJk+jbty/XXnstAEOGDKFjx47cd9997N27l9WrV/P8888zdepUXFxcAHj44Yc5efIkTz75JEeOHOGjjz7i+++/Z8aMGdY4Zs6cyaeffsqXX37J4cOHeeSRR8jLy2PSpEmqvC8N0dn0fE6m5qHTaujbRurXOYpvDptH64a1Hoa/mz85q8y7YQ0xladhOfmH+RixZuHcNuh6vFycOJaUy++HrrzW1N4mdJqAq86Vfan72HLevhuaPG+4AecWLTBmZZG9fLld+6oRd1+4xpx0snmuurEIIVTl0IldQkICd999N5GRkYwZMwY/Pz/+/vtvAgLMIz7vvfcet9xyC6NHj2bgwIEEBwfz008X15jodDqWL1+OTqejb9++3HvvvUyYMIFXXnnF2iY8PJwVK1YQGxtL165deeedd/jss8+IKbcbcOzYsbz99tvMmjWLbt26sWfPHlatWlVpQ4W4PEuZk2tCfTC4OqscjQBIykvi99O/AzC+43hMeXnk/nn1aVjaD8XbXc/917UG4D/rTqhaAsTfzZ+7Is2nMCzYt8CusWh0OprdczcA6V9/41ilT659BNDAiVhIPqJ2NEIIlWgUh/qXqXHLzs7G29ubrKysJjct+8g3O1l5IJEZg9vz+OB2aocjgA93fcin+z+le2B3vhz2JZnLfubCM8/gHNaKNqtWVRyxM5ngnUjIS4YJ/4OIG8jIK6b/m+vIKzaycGJPBnVQ7z86KfkpDP1xKMWmYhYOWUjvkN5268uYmcnxG29CKSig5by5eA1Sr+xLJUvuhcO/QrfxMPIjtaMRQthITfIHhx6xE41DqdHEprIRuwHtZeOEIygsLWTpsaWAuSAxQNayZQD43HFH5WnYC7vNSZ3eC1r1A6CZh557+4YB8N6aY6qutQtwD2B0+9GAedTOnnQ+PvjeZ37Pkt97D8VotGt/NXLddPPj3v9CyjFVQxFCqEMSO2F3+85lkV1oPkasSwtvtcMRwIqTK8gsyqSFZwtuDL2R4rNnyd+2DTQavG+/vfILjpZNw7a9CZz01ssPDYjAy8WJA+eyWbpT3V3fD3R+AGetM9sTt9t9rZ3fg5PRentTfCKOrF9/tWtfNdKyJ0SOMB8z9sdrakcjhFCBJHbC7v44Yt6VfF1bf5x08iOnNkVRrJsm7o66G51WR9bP/wPAo29fnENCKr/oyArzY/uhFS77ebpYp9bnrDpKVkGJ/QK/imCPYMZEjgHg9a2vU2K0Xyw6gwG/BycDkPqfuSjFDlQY+KbnAQ0c+h+c26V2NEKIeia/ZYVdKYrC8n3mWmdDOslmE0fw94W/OZF5Ancnd0a1G4ViMlmnYb3vuKPyC1JPQPJB0DpVSuwAJvRtTZsAD9Lyivlw7XF7h39Fj3Z7FD9XP05nn+bLQ1/atS/fe+/FKSCAknPnyPh+qV37qpGgjtDFfLYta1+5clshRKMjiZ2wq4PnszmVmofeSctgFRfXi4sso3Uj247ES+9F/rbtlJw/j9bTE6+bB1d+wWHzaB7h15vLalxC76Rl1q2dAPhy82lOJOfYLfarMegN/KvnvwBzXbvzueft1pfWzQ3/qY8CkDp/Pqa8PLv1VWM3PgNaZ3OJmpNS106IpkQSO2FXlpMJbowMwEvKnKjudNZpNiZsRIOGezrcA0DWMnOJIMPw4WhdXSu/6FBZYtfxtsve9/r2AQzuEEipSeHlXw+pWgbklohb6BnUk0JjIW9ue9OuffmMHo1zq1YY09JI+/wLu/ZVI81aQ8+yOptrXgSTA23wEELYlSR2wm7M07DmEZNbujRXORoB8O3hbwEY2HIgYYYwjLm5ZK8217LzGVXFNGzGabiwFzRaiLrlivd+fkRH9Dotfx5PZc3h5Cu2tSeNRsNzfZ7DSePEurPr2Jiw0X59OTsTONNczDxt4UJKktX7visZ+AS4GOD8btgyT+1ohBD1RBI7YTf7ErI4m16Am7OOQR0C1Q6nycsuzuZ/cebRt3s7mk8pyFm1CqWwEH14OK5du1Z+0aFfzI9h14HHlUvVtPb3YPKAcABeXX6IwhL1RonaNmtrLePy+tbXKSy13/FfXjExuHXtilJQQOp//mO3fmrMMxBiXjd/vu41SDmqbjxCiHohiZ2wG8to3U0dAnHXO6kcjVh2fBkFpQW09WlLn+A+AGQu+xkA71FV1K6DctOwVZRAqcK0G9sSZHAhPj2fhX+dskXYtfZw14cJcg/iXO45Ptv/md360Wg0BD71FACZP/5E4VEHqh93zb3Q9mYwFsHPj4CxVO2IhBB2JomdsAuTSWFF2W7YW7tUUT5D1KtSUymLDy8GzAWJNRoNxWfOULBzJ2i1eN9WReKWlQDndgAa6HBrtfrxcHHi6WFRAMz74wSJWfYbKbsad2d3nuptTrgWHVjEmewz9uur+zV4xcSAyUTy22/brZ8a02jgtg/BxRvO7YTNH6odkRDCziSxE3ax+2wG57MK8dDruCFSpmHV9sfZPzifd55mLs0YHj4cgMyyEice/a/DOaiKP6PDZYV3W10LXsHV7mtktxZ0b+VDfrGRN1epe2bp4FaDua75dZSYSpi9dbZdN3UEzpwBzs7k/fknuX/+Zbd+aszQHIaVbSJZPxuSDqkbjxDCriSxE3bx617zaN3NHYNwddapHE3TpigKXx4013S7K/IuXJ1cUYxGa1Fin6pq10GNp2EtNBoNL93WCY0Glu0+x4ZjKbWOva40Gg3P9nkWvVbPpvObiD0Ta7e+9GFh+N5j3mmc+PLLjlX+pOs4aD8MjMVlU7LqFZIWorFS81jF8iSxEzZXYjRZ19fd2lV2w6ptW+I29qbsRa/VMy5yHAB5f/9NaWIiWm9vPG+6qfKLUo5C/BbzbtgOly9zcjldWvpw37Xmc2Snf7ebc5kFdfoe6qKVoRUPRD8AwJvb3iS3ONduffk/9hhOzUMoSUgg+f0P7NZPjWk0cOv74OoDF/bAX++rG48QjUiJ0cTrvx3miR/2qR0KIImdsIM/j6eQmluMv6eege0D1A6nyft438cAjGo3igB3859HlmXTxIjhaF1cKr9o2yfmx/bDwLtFrfp9dngHolt4k5FfwqPf7qKoVL1dspM7TybUK5TkgmTm7plrt350nh6EvPIqABnffEP+Lgc60ssrGIaXrf/b8CYk7lc3HiEagYSMfMZ8vIVPNp7kx10J7I7PUDskSeyE7f248xwAt3VtgbOcDauqXUm72J64HSetE5OjzWebGrOzyYk1T0l63zGq8osKs2DPf82f93mo1n27Ouv4aHx3vN2c2Xs2k/9bcbjW96orVydXnr/2eQD+e+S/HEw7aLe+PPtfh/foUaAoXHj2OUyF6m0gqST6TnM9QlOJeUq21IHOuBWigYk9lMSID/9id3wmXq5OLLi3B9e0aqZ2WJLYCdvKyi8h9nASAKO6126kR9iOZbTu9ja3E+xh3gCR/dtKlKIiXNq1xbVzp8ov2vNfKMkD/0jzMWJ1EOrrzvtjuwHw1ZYz/LQroU73q4t+zfsxLHwYJsXEK1tewWjH0xiCnnoKp4AAik+fJsWRattpNHDLe+Dmax6xW/W02hEJ0eDEpeQy5asdTPlqB1kFJXRt6c1v/xzA0M7V32RmT5LYCZtasf8CxaUmooK96NTcoHY4Tdr+lP1sPr8ZnUZnHa1TTCbSv/4aAO9RoyvXrjOZLk7D9p5iTgTq6MaoQP55U1sAnv5pP/sSMut8z9p6steTeDl7cSjtEN8d/c5u/egMBoJffhmA9EWfk7dli936qjHPQBj5EaCBHQth6ydqRyREg5CVX8Ks/x1gyHsbiT2UhE6rYcqAcJY+3I9QX3e1w7OSxE7YlGVEZlT3FlUXvBX1xjJaNyJiBKFeoQDkrl9PcVwcWk9PfO4cXflFcesgPc58FFXXu20Wy/TB7RncIZDiUhMPfbWT5Gx1pif93fx5vPvjAHyw6wO71rbzuulGfMaOBUXh/JNPUZqh/tobq8hhMPgl8+ernoITa1UNRwhHF3soicHvbeCrLWcwmhQGdwhk9fQBPDeiI3onx0qlHCsa0aCdSctjx5kMtBq4vZtMw6rpcNphNiRsQKvRMiV6ivV62qfmExia3T0OnZdX5RdaRuu6jQcXT5vFo9VqeG9sN9oGepKYXcjD3+xUbTPFXZF30TOoJwWlBTy98WlKTPYr/RH09FPo27ShNCWFC88+Z9c6ejV23ePQ9R5QTLD0fjlyTIgqZOQVM/273Uz5agcpOUVEBHiw+ME+fDaxF20Dq/g31AFIYids5sdd5k0T/dsFEGRwVTmapu2TfeYELaZ1DK29WwOQv3MnBbt3o3F2ptl991V+UfpJOP67+fPeUyo/X0ders58OqEnBlcndsVn8uQP+zCqUPdJq9Eye8BsDHoDB9IO8NGej+zXl5sbLd55G42zM7l//EHG4sV266vGLCVQQq+Fomz4bjwUZqsdlRAOY9WBC9z83kZ+3nMerQb+cX0Ev/1zAP3aXvncbLVJYidsotRo4sed5mnY0bJpQlXHM46zJn4NAA9FX9zVahmt8x45EufAKk6a2PYZoJjPFvVrY5fYwv09mHtPd3RaDf/bc55nf9qvSlHPYI9gXuz7IgAL9y9ke+J2u/XlGhVF4BNPAJD85hzHOkvWyQXGfgOGFpB2HP73KDjSqKIQKkjLLWLa4l08/M0uUnOLaBfoyU+PXsczwzo0iIL7ktgJm1i+7wLnMgvw9dAzpKNj7Axqqj7d9ykAN4fdTNtm5k0LhceOkbt+PWg0+D4wqfKLivNg9zfmz3vXvsRJdQxsH8AH47qh1cCSHWeZ9csBVaYoh7Qewh1t70BB4Zk/nyGtIM1ufTW77148rh+IUlzM+X//y7FKoHgGwJivQKc3HyO3yYEKKwtRj4pLTXz250lufHs9y/ddQKfVMPXGNiz/Z3+6hfqoHV61SWIn6sxkUvho/QkAJvcPx03v+P+jaaxOZZ1i1elVADzU5WKClr5wEQBeN9+MS3h45RfuWwJFWeAbAW0H2z3OW7o0550xXdFo4Ju/43ltxWFVkrunez9Na0NrkvKTePyPxykyFtmlH41GQ/PXX0fn70/R8RMkz5ljl35qrWXPi+fJrn0ZTq5XNRwh6pOiKKw6kMjN723gtRWHyS4spUOIgZ8fvY4nYqJwcWpYv9MksRN1tuZwEseScvFyceLesmOkhDo+2/8ZCgo3hN5AlG8UAMVnzpC1fDkAflMerPwiRblY8qLXFNDWzz8Ld1zTkjdHdQFg4V+n+HjjyXrptzx3Z3c+vOlDvPRe7E3Zywt/vWC3BNPJz4/mb7wBQMbi/5Kz1sF2ovaYZN40Y9lMkXpC7YiEsLvErEKmfLWTh7/ZyZm0fAK8XHhzdDTLH+tPdEtvtcOrFUnsRJ0oisK89XEA3Nc3DG83Z5Ujarris+NZcXIFAA93edh6PXX+AjAa8bh+IG7R0ZVfePpPSDkMzh5wzfj6CheAMb1CeX5EBwDeWHlElQLG4d7hvH/D+zhpnFh5eiUf7bXfZgrP/tfhO8k8FX7h2ecoPOZA6+00GhjxDrToCQUZ8O2dkJeqdlRC2IXJpLB4azw3v7uBNYeTcNZpmHZjW9b/+wbG9mqFTttwy3VJYifqZHNcGnvPZuLipOWB/lVM8Yl68+7OdzEqRga0GEAnf/OJEsVnzpD1668ABEydWvULLSVOuo4D1/r/H+qDAyJ4sOxn58kf9rHxWEq9x9A7pDcv9H0BgAV7F7Dq1Cq79RU4Yzqu0dEYs7KIn3g/hYfVO2qtEmc3uPs78AmDjFPw3T1Q4kDrAYWwgb+Op3LLf/7i2WX7ySkqpVuoDyv+OYB/x0Ti4eKkdnh1JomdqBPL2rpxvULx96ziMHlRL7Ynbmdt/Fp0Gh0ze8y0Xk/9aD4YjXhefz1uXbpUfmHmWThiHuWzR4mT6np2eAdu69qcUpPCw9/sZHNc/Y8UjWo3ikmdzKNpszbP4njGcbv0o9HrafXpJ7h27owxI4Mz90+iYP8Bu/RVK54BMH6pOck/uxWWPQRG+9X6E6K+xKXkMnHRNu5duJVDF7LxcnHihVs68uMj/Wgf5Jg16WpDEjtRaztOp7PpRBpOWg1TBkaoHU6TZTQZeWv7WwDc2f5O607Y4tOnraN1/tMuM1q3cY55TVX4QAjsUC/xVkWr1fDWXV0Y0M6f/GIj93++ndhDSfUexz+7/5NrQ66loLSAGetnkFOcY5d+dD4+tPp8EW7dumHKyiJ+0iQK9u+3S1+1EhAJY78FrTMc+h8suQ9KCtSOSohaMZoUPt14kuEf/MmGYyk4aTXc3681G568kcn9wxv0tGtVJLETtaIoCm+tNleqv7NHS1o2c5xz8pqaX+J+4XD6YbycvXi026PW66nzF4DJhOcNN1S9ti7p0MUSJzc+X0/RXp6Lk45PJ/RkSMcgiktNPPzNTpbtrt81d05aJ+YMnEOIRwhnss/w7F/PYlJMdulL5+VF6Gef4d6rF6bcXM4+OIWi4/YZJayV8AHmGndOrnBsJXw9Cgqz1I5KiBo5dD6bMR9v4f9+O0xRqYkB7fxZM/N6XrqtE74eerXDswtJ7ESt/HUila2n0tHrtPxzUDu1w2my8kvy+XD3h4C5vImvqy8ABfv3Xxytu9zauthZ5tG6DrdBqz71Eu/VuDrr+Gh8d0Z1b4HRpDBjyV4+WHO8XosYN3Ntxns3vIdeq2f92fW8t/M9u+2U1Xl60HL+fFy7dDGvuZv8IMUJ9b+B5LIih8J9y8xnB8dvhs9HQE79j6QKURM5hSUs3hrP7fM2MfzDP9l5JgNPFydmj4rmqwd609rfQ+0Q7UoSO1FjiqLwdtlo3fhrW9Hcx03liJqu/+z+D6kFqYR6hXJPh3sAUEpLuTDrRTCZMNx6K27RnSu/MO4POBELWqeLh8E7CCedlrfv7Mrksg0V7605xoNf7SArv/7WeXXy72TdTPHFwS94fevr9hu58/Sg1Scf49KuLaXJycRPeoCS5GS79FUrYf3g/hXgEQhJ+2FRDGScVjsqISpQFIUdp9P599K99P6/tTy7bD97z2bipNUwIjqEVdMHcHfvVmg0jWvatSqS2Ikaiz2UxN6ELNycdTx6Q1u1w2myNp3bxDeHzVOpT/d+Gr3OPK2Q/uVXFB0+jM7bm6Cnn6r8QpMJYs1JC70etNvxYXWh1Wp44ZaOvHVnF/ROWtYdSea2eX+xKz6j3mIY2XYkL1z7Aho0fHf0O2ZtmkWpqdQufel8fAj9bCHOoaGUnD1L/ISJlCQ50MhYSBd4YBX4tDLvll0YA0kH1Y5KCBRFYfXBRIa8t5E7F2zhh50JFJQYaRPgwbPDo9jyzCDmje/epJYLSWInasRoUnjnd3PtrUnXtSbAS3bCqiG9MJ3nN5nXxY2LHMfAlgMBKE44R8rcuQAEPvkkTn5+lV+87ztI/P/27jtOivp+/PhrZmf77e71O467owvSRGkilhgw2LAltqBgSfyqYI1Yf8Ykfo29ROwmwd74KhYSjUgTFSlHERAOkHZwXC+7t313Pr8/5m7hOFQgV5fP08c8ZndmdubzGbnd93zqWqN67eTb2y3Nh+PCEQV8cN0JdE+1s6M6wAXPfcNts9ZQ4WufITgu6n8RD5z4ACbFxEc/fMQdX95BtI16iJpzsin85z/Q8roR2b6dHZdPJlpa2ibXOiwZfeCqzyF7IDSUwcwzoPjTjk6VdARbu6ueS176lv95vYjNFQ04LCYuHJ7P+9eN4YtbT+Gak/sckb9RiuiIeXyOUF6vF4/HQ319PW63u6OTc1jeW1HC7f/3HS6bxle3/xKPQw5I3N6EENy44EYWliykt6c37579LjbNhhCCkmv+B//ixThGjaLw1VdaVjsEa+GZkeCvhPF/hhNv7ogsHLJaf4T//dcG3m8cwDjFqjF9Qn8mj+nRLlUr83bM47YvbyOmxzg5/2QeP+VxbJqtTa4V2bWbnVOmEN29G3P37hS++gqW/Pw2udZhCdbCWxcbQ6EADDofzngEUrI7Nl3SEUHXBYu3VPHK19tYUGyMeWnVVH5/Um/+55TeuGzJ+Zt0KPGDLLGTDlqtP8KD/zYGU512al8Z1HWQWZtmsbBkIWbVzMMnP5wIMOo//Aj/4sUoZjO5f/rTgQOeeX8xgrrM/nD89S33d1JpTguPX3QMs68/gWPyPTSEY9z38Xp+/9oKavyRNr/+uB7jmPHLGVhNVr7c9SVT500lEA20ybUs+d3p8cbrmAsLie7ezfYLL6Jh0aI2udZhsafB5I/ghBtBUWH9bONhYd37HZ0yKYk1hGO8+s12xj+xiCn/XMaC4koUBc4blsf8237BbRP6J21Qd6hkiV076uoldrf/3xreW7GL/jku5tx4ImaTfC5ob1vrt3LxJxcTioe4bcRtTBk0BYBISQnbzj0PPRAg65ZbyPyfa1p+eFcR/H0cIGDKHGM4iy5I1wWvLdnOXz/dSCSmk+O28uTFwzihT2abX3t52XKmzZtGIBZgaNZQnv3ls6TaUtvkWtHyckquvY5w48wU6VdcQfatt6BYOtEQDaWr4eNpRtU+GA8Lp/0FTPIHVmodO6sDzPxmG7NW7KIhbLRxdVk1LhxRwOQxPZK+h2uTQ4kfZGDXjrpyYLdsWw0XvbgEgPevG8PwHukdnKIjTzQeZdK/J7GhZgNjuo3hhdNeQFVURCzGjssnE1y1Cvvw4fR47VUUk6n5h+MxePlUKPsOhl4CF7zYMZloRd+Xepn29kq2VvoBYzzFO88Y0OYzoKytXMu1X1yLN+Klp7snz49/nnxX21SV6uEwFY8+Ru0bRicZ29ChFDz7DFpWVptc77DEo7Dgr/DVE8b7HmPhNzPBldOx6ZK6tNK6IE/P28ysol3EG4c76p3l5MoTenL+cfmkJMHUX4dCBnadVFcN7CIxnbOeXszmigYuHVXAgxccYGoqqc09UfQEM9fNJNWayvvnvE+2w2jTVPX881T+7WlUp5NeH32EJb97yw9/+zx8dqcxTdS0ImPaqCQQiMT4339t4K2lOwFw2zSmnz6A345q20m8t9Ru4bp511HmLyPDlsFz459jYMbANrueb948Su++B72+Hi2vGwUvvIDtqKPa7HqHZcMnMPs6iPjAmQUT/wYDzuroVEldhBCCnTUBVpfUseSHaj5YuZtI3Bhi6OSjsvjdib04sW8mapLNEnGwZGDXSXXVwO6JuZt4et5mMpwW5v3hFFIdnagq6AixdM9Sfv/57xEInjr1KcYVjgMgsGoVOy67HOJx8h5+CM+557b88J418I9fQSwEZz0BI69u59S3vZU7a7n3w3WsL/UCMKS7h/vPG8ywgtQ2u2a5v5zr5l3H5trNODQH94+9n1/1/FWbXS+yYwcl/3Mtke3bUVNS6P63p0gZO7bNrndYqjbDe5Oh4nvj/TGXwukPgT21Q5MldV4VvhBvLNnB28tLqPSFm+0b3Sud6RP6M6KnrCGSgV0n1RUDu/kby7n61RUIAX+7ZBjnDjtAaZDUpkq8JVz+6eVUh6r5zVG/4b4x9wFGu7rtF19CvKYG1xmn0/2JJw7cC/bFU6BuB/T7FVz6LqjJ2TYyrgve+HYHj31ejC8UQ1HgkpGFXP+LPhSkt80YVr6Ij1sW3MLSMqOH6OUDL+eW4bdgVtumjVmstpZdN9xAcEURqCqeiRPJnHo9lsLCNrneYYmFjarZb542ZjZJyYGxN8HwK8ByZLSHkn5aXDcGE55VtIuPV5cmSuYsJpWBeW6GFaRy2sAcTuiTcUQMKHwwZGDXSXW1wG57lZ+Jz3yFLxRj8pge/OXcA8xgILWpikAFkz+dzO6G3RyVdhSvn/E6DrODWG0tO347ici2bVgHHk3P119Hde73o6nr8PYlsPk/xsCy1ywCR/I/+Vb6wjz47w18sGp3YtvYvhlcNKKACYNysZlNP/HpQxfTYzy98mlmrp8JwLCsYfz1xL9S4C5o1es00SMRyv70Z+o/+MDYoGmknn8+mdOmYc7pREOOlCyD2ddCzQ/Ge0cGjJkKo64Bq6tj0ya1u1hcZ9n2Gj5dW8Zn68ualc4N75HG1Sf2YtzR2Vi11v37TBYysOukulJgF4jEOP/Zbygu9zG8Rxpv//54LFpylvR0VvXheq747Aq21G2hwFXAa2e8RqY9Ez0cZudVVxMsKkLr1o2e77xz4B/0Lx+F+f8LJitc/TnkDWv3PHSkpVurmTF/C1//UEXTt5zbpnHusO5cNKKAwd3drVoaMG/nPO796l58UR8W1cKUQVP43ZDf4TC3TWlhcO1aKp+egX/xYgAUh4PMa68l/YopqJ2l52wsYgyIvfgJY8YKMAK8k/4AI64Gc9uMBSh1DrX+CKtKavliQwX/WVdG9T5DE7ltGr8alMtvRxdyXGFaB6aya5CBXSfVVQI7XRfc8M4q/vXdHrJcVubccCI5bvkF3J4C0QDXzL2GNZVryLJn8doZr5HvykfoOqW33Yb335+ipqTQ4603D9yI/vuPjbZOCDjnGTju8nbPQ2dRUhPg/ZW7mLViF7vrgontR3dzc+moAs49pnurjclY4i3h/m/vZ8keowd5jiOHO0fdyfge41vl/AcSKCqi4pFHCa5ZA4C5sJDc+/7YudrfxWPGOHeLHt5bgufKg1G/M9rhufM6Nn1Sq6j0hfl6SxVfb6miaEctW6v8zfanOsxMGJjLGUNyOaFPpiwsOAQysOukukpg19RZwmxSeOv3xzNSNlxtV8FYkKnzprK8bDkui4tXT3+Vfmn9AKh4/HGqX/47aBqFL7+Ec8yYlifYtQJeOcvoLDHyd3DW4+2cg85J1wXf/FDNuytK+M/6MiIxo12PVVM5c0g3LhlZwKhe6f91KZ4Qgvkl83l0+aPsbjCqg0/rcRp3j76bTHvbjLUndJ36jz+m4vHHiVdWAZB22WVk3/YHVFsneiiLx2DNW7DwYfAas4igqNBnnPFv9agJINtUdSk/VDYwZ80ePltfxoY93hb7e2c5Ob53BmcMzuX43hly/NPDJAO7TqorBHYfrd7NTe+sBuCRXw/lopFt005IOrBwPMwN825gyZ4lOM1OXj7tZYZkDQGg9p13KfvTnwDo9uCDpJ5/XssT1GyDv4+HQBX0mwCXvAWmI2u8p4NRF4jw4ardvLO8hI1lvsT2XplOLhpRwOmDc+n1Xw58GoqFeOm7l/jnun8SF3E8Vg83HXcT5/U5D3MbDeAbb/BT+cQT1L71FgCWPn3odv/9OI47tk2ud9iiIVj3f7DqDdi5ZO/2nCFw0q0w8FxQZVurziauC7ZV+VlfWs+63fV8vaWa7/cL5gbluTmxXybH98rg2MJUOYpCK5GBXSfV2QO7lTtrueSlb4nEdK45uTd3n3l0RyfpiBKJR7hl4S18uetL7Jqdl057iWHZwwBoWLSIkuuuB10nc9o0sqZNbXkCf5UxMXvVJsgdCld+CtaU9s1EFyOEYM2uet5dvpOPV5fij8QT+/pkORl/dA4n9M3k2MJU3Ic5XdHGmo388es/sqHGmEEi25HN5IGT+c1Rv8Fpbpteog2LF1N6992J0jvbwIGkXnQR7rPPxpTSyXqmVv8ARTNhxUyINBjbXN2g50nQcyz0OgXSe3VsGo9AsbjOlsoG1u32sm63Ech9v8dLYJ+/EQBNVTixXyZnD83j1P5ZZLTxAOFHKhnYdVKdObArLvPx25e/pdofYfzRObx4+fA2HeBVai4YC3LLglv4uvRrbCYbz41/jpG5IwHwfvYZpdNvR0SjeM4/n25/faBldWFDBbx6DlRuAHc+/O4LcHfrgJx0Xf5wjDnflfLxmlKWbq0hpu/9alQUGJDr5tT+WZx/bHf65Rxar86oHuWdje/wyrpXqAhWAOCyuLh0wKVMOnoS6bbWb+4Qq62l4pFH8c6Zg4hGAVAdDtxnn03qRRdhHzyo1a/5XwnUwNIXYekLEKprvq/3L4zetEedLkvy2tDWygY+XVfGvA3lrC/1Em5srrAvm1llYDc3Q7p7GJKfyrgB2aQ5ZalcW5OBXSfVWQO79aX1XPb3pdQGogzu7ubda8bgPMKma+lIDZEGps2fRlF5EXbNzoxfzmB0t9EAVL/yChUPPwJCkDJ+HPlPPNFyrlBfmRHUVRUbJR1TPoHMfh2Qk+ThDUVZVFzJwuJKVuyoYUd1oNn+QXluThuYw9Hd3AzIdVGQ5jioEfEj8Qhzts5h5rqZbPduB8BqsnJe3/P4db9fMyB9QKuP2xWrraV+9ofUvfceke3bE9ttgwaRevFFeM46q+VQOR0pGoSSpbD9a9i+2HgtGgMMTyEcPRH6nQY9TgBNlg4dqrhuzPCwqdzHjmo/ZfVhyn0hNpf72FTe0OzYFKvGoDw3g7t7GNzdzeA8D72zUuRDfweQgV0n1RkDu7W76rnsH0upD0Y5Jt/Da1eNbrUegtLPqw3Vcv0X17Oueh0us4vnxj/HsOxhiFiMikcfpebV1wBI++1vybnn7pZzwNbvgtfOheot4O5uBHUZfTogJ8mtwhtiydZqPlmzh4XFFc1K8wBcNo1Tjspi/NE5/KJ/1s+2K4rrcRaULODva//O+ur1ie29Pb05u/fZnNf3PLIcrTvtmxCCwLLl1L33Hr7PP29eijdxIqm/+Q22wYM634CwtTtgxT9g5WvGgNtNzE6jJK/facbiaZv5eruy+kCUVSW1bCzzsanMx6YKH5vLGw5YEgdGteqYPhmcMbgbY/pk0CP94B5YpLYnA7tOqrMFdvM2lHPzu6vxhWIcW5jKq1eNOux2RNKhW1m+ktu/vJ3yQDmp1lRePO1FBmYMJFpezu4//MGYXQDInn4b6Vdd1fIHd/vXxpAmgSrwFBhBnWyL1OZq/BH+vXYPK3cYP5hbKhoSI+c3SXWYyUqxkuWy0ivTyYDGkr3+ua5mf2NCCJaVLeO94vdYWLKQiG6M86WpGmf2OpPJAyfTP71/q+chVlOztxRvx47Edmv//qT++te4JkzoXIMdg1GSt+k/sHkubJkLDeXN92cPMgK8oyZA/qgjqtOQLxRlZ02AkpoAO2sCbK30s3JnbYsSuCZWTaVfTgq9M1Po5rGR7baR57FxQp9M+WDfScnArpPqLIFdNK7z2OfFvLhoKwCjeqbzjytG4JJBXbuI63H+vvbvPLfmOXSh09Pdk6dOfYo+qX1o+OprSm+/nXhNDarTSbcHHsB9+oTmJxAClr0M/7kL9BjkDoFL3oZU2YO5I8TiOmt21fPFhnLmbSj/0R/TJt1T7Ykgr3+ui6O7uemV6SQU9/PFji/4YPMHrK5cnTh+cMZgTs4/mZPyT2JgxkBUpfWGixBCEFi6zCjF++ILRGTvALLm/Hwcw4djHzEcx/DhWHr16jyleboO5Wth8+dGoLdr+d7qWjBK83KHQLdjIO9Y6HUyeLrudIiBSIzdtUF21QbZVRtgV13T6yAlNQFq9hn4d3+9Mp0M7u6hf04K/XJc9M9xUZDukNWpXYwM7DqpzhDY7aoNcMu7q1m+3ajSuOKEntx95tFyoMh28kPdD/xlyV9YWbESgIm9J/L/jv9/2GIKFU8+Se3rb4AQWI8+mvynnsTSo0fzE/ir4dPpxmCvAIN/A+fMAEvbzG4gHbq6QIQKX5hKX5iy+hCbKxooLvOysczHnvrQAT+jqQr5aXYK0h30yHBgcexiU+hffFe7GMHegCXLnsWEnhM4s9eZDM4c3KqBVryujvo5/6L+o48IrV9vBE/7MKWl4Rg1CvcZZ5By6i9QrZ2ofVugBrbMM6bP2/JF8yrbJlkDoM8vjUAvqz9kHgVme/undT/RuE4oGqfcG2JNST1rdtWxscyHNxilIRzDF4pRH4z+7HkynBYK0h0UNi5D8j0M75FGpuylmhRkYNdJdWRgF9cFr36zncc+LyYQieOyajzym6GcMUT2nGwP4Xg4MaZZTI9h1+zcM/oezu17LoGiIkrvvpvojp0ApF56CTl33tnyh3P9bPjXbUbVq6LC+D/BCTfKAV27kPpAlI1lXorLfWzY46O4zEtxma/ZMCv7UjQvmrMYU8pGNOcWFNPe+TXdWg5He0ZxXNbxjM4bRZ7bQ5rD0ipz4cZ9PoKr1xAoWkFwRRHB775rVpqnpqTgOu00HCNHYh86xCjN27/9Z0fR41C1Gcq+gz1rYOe3ULqyeYkeGH9DaT2NgC9rAKT3Bleusbi7H9a8yg3hGDurA5T7QkRjOjFdEInpeENR6gLGsqc+yO7GErf6YJS4fnA/wW6bRn6ag+5pdrqn2slPs5OfZgRxBel2WeOS5GRg10l1VGC3cmctf/nke1aX1AEwqlc6j/x6KD3/ywFYpZ8nhGBByQIeW/EYJb4SAE7JP8WYhaAmRtVzz1P/0UcgBFpODt3+935STjqp+UlKlhnzvm7+3HifdTSc9yx0H97OuZHagq4L9nhD7KwOsLPGz45qo51U01IfjBpz3SoxTM5NmN1r0Fzfo6h7S3GEMKGHs9DDuZhiebiUXmSZ+5LhdJOVYiXXYyPHbSMzxYrLppFi1UixabisGk6rhsNi+snSPz0SIbRuPQ0L5lM/51/E9uxptl91OLANHoxtyGDsQ4ZgHzIELS+v81TdBmpg60Kjl23FRmNYoAOV6u3LnobI6IdI703cnkncmkrE4qGUbIqjOazxuShviFAXiFDjj1LuDf1klejPsZlVhnT3cEx+KoO7e0h3WnDZNFw2jWy3TbZ/7kx0HfwVULez+RKogovfaJNLysCuk2rPwC4W1/nP+nL+8dVWVu6sA8Bl1bjzzAFcOrJQ9nRqB1tqt/DI8kcS84Zm27O5a/RdnKQcRfULLxoBXdwoqfGcfz45d92JqenfhRBG1dJXT8KOr4xtqgYn3gon3yaHeTiC6LrAHzGq5PbUB/mh0k9xRRXrq4vYE11DPWuJm6pbfE4IBT2cjR7qjh7JRg9nEQ/lIWItJ1xXFXBajYDPadVQgJguiMZ17GYT3VLtdHPb8DjM+AIRnJvXU/D9cvLKtpFTth1zNNzinBGXB3/Powj37IOenQs5OajdumPKL8Bms2A2KYkSrUhcx2pScdnMpNg0rJqKqiioCoRjOmXeEGX1IWoDESwmFbvFhFUzIYQgEteJxHQEYFIUTKpCvPGeNYRjhKI6JkVBMykoijExfaU3RNRXQbfIdnrEd1IQ3UFatIyUWDVp8RrSqf/Z/y9hYaZUpFNJKlXCQ5XwUCk8BCzpCEc2DZZ0/KZ0/NZ07DYnqU4LbruZXLetscTNQUaKBZtmwmpWsWpq5wmEjxS6DhEfBOuMsRObresPsK3OeEjw7ob4jwTxd+4Em6fVkyoDu06qPQI7byjKe8tLmPn19sSE5xaTyrnD8vjDr/qT6+lE80YmoZgeY9GuRcwqnsU3pd8gEJhVM1MGTeGK9DPx//1V6j/8CGIxAJwnn0TWtGnYhw41TqDH4fsPjYCubK2xTTXDMZfA2Jshs2+H5EvqvIQQ7PHvobimmHWVxXxfvZGNteupCpUd8HhTPAM11I+ovzcBfxbxcCaIwy8NUoVOga+co2pL6F9bQr+6EnrXl6LtX/XZKKxqbHd3Y6snjx88eWz1dGe7O5egufN8N9kI00spo7eyhx5KOalKA6k0kK546aNVUiDK0Igd/AlVDSwpxmJPBXuaUdXryAB7uvHa5gGTFUxmMDsgrYdRVXwkP8QJAWGvMQWd2W4simoEV4EqCFRDxG/MWBI1fu8wWYx7GIsYpbI/tYTqWlbRHyxFNarsPQWQWrh3GXwBWFq/NkwGdp1UWwZ2ZfUhXvpyK++tKKEhbHzhpDstXDa6kMvG9CDb1Xm+NJPRnoY9vL/5fWZvnp2YWQDgtO6/5AZOxTr3W+r/9a+9Ad2JJ5I1bSr2YcOMA33lxuToRa9C7TZjm9kJI66E46/v0j36pI5RFaziu8rv2Fy7ma31W9lav5UttVuIieYBiaqo5DrySLdmkaKl4TClkWsvoKf7KHq6+xKLaeypD7GnLoQvFMVtN+O2aTisGrG4IByLE44ZHQDCMZ1wVCcaDOIs2UZayWbcFbtx1lfjqq8irb4S6wFK9wCiJg0ECMBrdbIlNZ9NqQWUpOVhSkvDmZGONT0Nn81BMCYIRuJoqopZU9BUFUUBXRglnKqqkGI14bBo2MwqcR3iuk5chzSHmSyXlcwUK5pJIRYXxHQdi6aS5rCQ7rTgtpmxaCpmk4rFpKKZFMwmFbNJMUrV4jGo3wnePUaVXEPjsu/rpvc/VrJzMBTVGJ+vaeo5RQGUvWtVBc0OZttPrG3G8QKMGywagxkBiskIQqwpxjUOtcRQ6EZAFQs1BlaN5997QONqn22xkDFUja8cgjVGHlXNWOtx437FI0YJmb/COL6taTawpRrBtT3VeN20PtA2T3cjqGujOZ8PRAZ2nVRbBnbrdtdz9gyjyq5vdgpXn9iL84/t3ioNqaUDqwnV8E3pN3y27TMW716M3vjkl2FJY4oYw0kbQJ//NfHqvdVkzhNOIHPaNGNSdl8Z/DAfNnxijM8lGhvQ29Ng9LXGFEqH0YBbkn6MP+qnqLyIpXuWsqZyDVvrt+KL+H70eFVRKXQVUugupNBVSL4rn0x7Jpn2TLLsWWTaM3GYD75HttB1ojt3Etq4kdCGjYQ2biC8sZhYefnPf7iJ2Yw5KwstJwdTWhomtxuTx43q8WByezB53JjcblS3G5PHY+x3u1vO2NIemkqcwg2NJUuN1X7BWqO0KVBjBDeBagh5G4OaKIR9xgNe5KeHzjlyKY2lnhl7g9KmHs561CitM5mN70972o8vtlRjvU9psRACEY2iNzQQr6tH99YTr29c6hrXPi+6r8FYe33EfT70xqXfkm9Q1NYfZUIGdp1UW1fFPvjpBsb0zuDkflmyDV0b8Ea8rK5YzaqKVXxb+i3rq9cjGp9IzVHBuYH+TCjPIePrDcTK9laDmVJTcZ0+Ac/ZZ+JID8IP82DLfGMcrn3lj4LjLodBFxhfVpLUxoQQVIeq2Va/japgFVXBKioDlWyu28yG6g1Uh1q23dufXbMngr1MeyYZtgxSLCmYVTNm1UyKJYVCVyEFrgK6pXTDrLYs5YjV1iJCoaZEEd2zh9C6dQTXriOyfbvxY+r1onu9+5UIHTzFbsfkcqFYrShmM4rZjCk1FS07G3NONqbUVBSrDcVqwZSSgpadjZaTg5aZiaJpRumY2o7t4IQwSrZqt0MszN7SsH1KwfRYY2lZCGLBxnVobwnaviVpKEap2L6lfnoMIgEjgIz49577oClGQKU1lgwm5vFVmq32vleMqtKUHEjJNgIzMNKhx42Su6aqVJvHOMaZbVwjFjbyqMcRVg8iriPCYfRQCBEOI0Ih9HAEEQ413xYMofv9xGpriNfUEvfWN8um7vUSq6slXltnBGfBYKLt8+Hot2wpWhv8vsvArpPqDOPYSQcvEo+wqmIVX+/+mm9Kv2FT7SYEAkUIcmugV7lgeH0ag8ospP9QCdG9VVyq04lr3Km4R/XFmV6PUroMdnwDUf8+V1Agbxj0GQdDLoTsAe2eR0n6KRWBCrbVb2OHdwc7vTsp9ZdSHaxOBIGBWODnT7IfBQVN1TCrZlwWFx6rB4/VQ6o1FbfFjcfqwWVxYdfsODQHds1uvDY7cGDB6YtirwlgqWmAel9jwNdUquIl7vUSr69H9xqvdd+Pl0geDtXpxOTxoKZ6UK02I9hrCvpMqhE8mVQURQWTCVQFRTU1Hqeg2O3GOVJSUJ1OVKexVsxmdL8f3d+AHgqjNh6nOp0ommmfalLFiJMUZW+QqexTPZt4j7F/32OaArrEJmXvZ/c9PyBCQeJ+P7rfD3HdyJNJM/Komhrfm0AIhK6DLoygKhBEDzUGR0IghDDqyJuC0v23Cd34fFxH9zcY/w99XvRAABGONAZsRpAmwmHjdTh82AH+oQhYocEGDXZosCk02MFvM7YFrAoBq/Heb218b4P3r/8Gp10Gdl3Ks88+y6OPPkpZWRnHHHMMM2bMYNSoUQf1WRnYdU6BaIASXwklvhJ2eHfwQ90PbKnbQmnZFjKrIuRVC/JqBHk10LPOTFZ1DC3assGtluHBeVQuKb1MpKTuRq3dtLd6tYkzG/qOM4K5PqeCM7OdcilJrS8QDRiBXqiqWYlfMBYkqkeJ6TFqQjWU+ErY5dtFKN667aWcZicuiwu3xY3NZENVVFRFxaSajNeomDGREjGREgZXRMUSV7EJE1ZdxeGP46gNYa8NYAlEMUXjaNE4Jn8YU3U9SlUtSuTnBweWOo4ORMwQ0fYu0cZ12KwYawt4HcbSYFPQ98a2+K3gc4DPbgRqIQuEzRCxqpjNNmwmGzbNRpotjTRbGqnWVMyqGQUjsHaanYkHEo/Fw7ge47CaWr/Diwzs2si7777L5MmTeeGFFxg9ejRPPfUUs2bNori4mOzsn59XUQZ2HSOmx6gL17G7YbcRwHl3sqdqO1XlO/BW7kLU1pHeAOk+yPA2BnHVkOb/8XMqZhVbpgmrJ4TN5cOZHcacEm/Z9jglBwqPh8Ix0GMs5Aw2qnQk6QijCx1v2EtMxIjpMSLxCL6Ij/pwPfWRemMdrqcuXIc/6icQCxCMBQlEG9exAP6on4ZIw2GVFB4WIbBHQNVBEaAKsIchJQSuoMAcb75PEcZ7EwomoWASKiYUVKGgCWNtjYE9YpzHHhbYIgJ7GExxnaAFGsw6YZOOI27CEVFxREHRMZp9iH1KvBp/ulUjxDAK8Rr/U2lsK6br6Ogogr3HiKZzGflTIFE12bQfjGApaIGgxQiE1MY8mnSBqu/NL4CugFAgoimJICvWWCsrGveJxtdNx9O4remzumpcz29T8Nsg1BSsmSGqKc0Ct8RihrgKFpMVq8mK2WRONAFwmp1k2DNIt6XjNDuJ6TGiepS4iJNhyyDLnkWGPQOH5kg8CLgsLtJt6WTYMnCanZ1q+BkZ2LWR0aNHM3LkSJ555hkAdF2noKCAG264gTvvvPNnPy8Du+aEEMRF3Fh0Y60LnWg8iq7HicUiBANegoFaQoF6ggEv4ZCPULCBSMBHNNBANOgnFvSjh4LEg01F9hEIR1DCEVR/BEsgSkpQkBJs+kIG80E2oTDZ4ljdMSyuGBZ3DGvj2uyI02zKTnsapPYwhijIHgTdhkLuUHDnyZkhJKmVRfUoDZEGvBEvvogPb9hLOB5GF3rie6Rp3RREhuNhInqEUCyUeH/AJWasI/EIoXio2TocD6MpWiKAiIs4gWgg0da2q2vKm6ZqWFSL8VrRUBUVXegIjKBSR090Ftu3utxu3lt9rqkaQGM4aVT77g1B9763mqxYNSs2kw2LyYLNZMOqGYGa1bTPds2W2GY1WbFpxnarydqq8yd3VocSP2jtlKYuLxKJUFRUxF133ZXYpqoq48ePZ8mSJQf8TDgcJhze27Xf6/W2WfpKv5vHhhtvaNH2Vdn/++ZH94vE+8Zmts2e4g50DuVn3jedT9X3Pgkq+z/d7vO66dj9/0RNgLNxaU1CFWDVMVt0rPY4mj2O2aEbQZwrhiXdjMmTBvaslr2pnFlGENcUzLXBgJSSJB2YWTUnqsbakxCiRSmOEIJgLEgkHkkEPE1L08NrUzDU9DqxbZ9jFUVJVPuZVTPheJhALEAgGkBVVDRVw6SY0FTNWBQNFKNGIq7HEyWhTYtNs+HQHNg0GwqKsb1xqBuLyZIo2TKr5kSgeiQESEcCGdgdpKqqKuLxODk5Oc225+TksHHjxgN+5sEHH+TPf/5zeySPaNBLXllyPDX+mJhJEDcZRfZxDXQNhCYQGqCBoikoGqiagmpW0DQFm03D6bCS4rRhTrFjcjkwuZyYPG6U1CwUR0bLoM2RbnSD70QDpkqS1PEOVDWnKIrRseMQhn2RpLYkA7s2dNddd3Hrrbcm3nu9XgoKCtrkWhl9R7Ln1jONN2qisNso9m56CFNUY7u6tzVGU8+pphYaid5UqI1vGz/c1ANL2aclR6J3lfFaUdRm7wFUk9lov6CZjLVqwmTSMJk01Ma1yWTCZLZjMlkxmS2omhXFbEExmVGsdhSrFcxWFJO5cSBLk2ynJkmSJEkHIAO7g5SZmYnJZKJ8v4E0y8vLyc3NPeBnrFYrVmv7TAeTkpHP8dc83i7XkiRJkiSpc5LFHgfJYrEwfPhw5s2bl9im6zrz5s1jzJgxHZgySZIkSZIkgyyxOwS33norU6ZMYcSIEYwaNYqnnnoKv9/PlVde2dFJkyRJkiRJkoHdobj44ouprKzkj3/8I2VlZQwbNozPPvusRYcKSZIkSZKkjiDHsWtHchw7SZIkSZIO1aHED7KNnSRJkiRJUpKQgZ0kSZIkSVKSkIGdJEmSJElSkpCBnSRJkiRJUpKQgZ0kSZIkSVKSkIGdJEmSJElSkpCBnSRJkiRJUpKQgZ0kSZIkSVKSkIGdJEmSJElSkpCBnSRJkiRJUpKQc8W2o6bZ27xebwenRJIkSZKkrqIpbjiYWWBlYNeOfD4fAAUFBR2cEkmSJEmSuhqfz4fH4/nJYxRxMOGf1Cp0Xae0tBSXy4WiKK1+fq/XS0FBASUlJT87SXAyOtLzD/IeHOn5B3kPjvT8g7wHyZh/IQQ+n4+8vDxU9adb0ckSu3akqir5+fltfh232500/5gPx5Gef5D34EjPP8h7cKTnH+Q9SLb8/1xJXRPZeUKSJEmSJClJyMBOkiRJkiQpScjALolYrVbuu+8+rFZrRyelQxzp+Qd5D470/IO8B0d6/kHegyM9/7LzhCRJkiRJUpKQJXaSJEmSJElJQgZ2kiRJkiRJSUIGdpIkSZIkSUlCBnZJ4tlnn6Vnz57YbDZGjx7NsmXLOjpJbebBBx9k5MiRuFwusrOzOe+88yguLm52TCgUYurUqWRkZJCSksKvf/1rysvLOyjFbeuhhx5CURRuvvnmxLZkz//u3bu57LLLyMjIwG63M2TIEFasWJHYL4Tgj3/8I926dcNutzN+/Hg2b97cgSluXfF4nHvvvZdevXpht9vp06cP999/f7PphpLpHnz55ZdMnDiRvLw8FEXhww8/bLb/YPJaU1PDpEmTcLvdpKamcvXVV9PQ0NCOufjv/NQ9iEaj3HHHHQwZMgSn00leXh6TJ0+mtLS02Tm68j34uX8D+7r22mtRFIWnnnqq2faunP9DIQO7JPDuu+9y6623ct9997Fy5UqOOeYYJkyYQEVFRUcnrU0sWrSIqVOn8u233zJ37lyi0Si/+tWv8Pv9iWNuueUWPvnkE2bNmsWiRYsoLS3lggsu6MBUt43ly5fz4osvMnTo0Gbbkzn/tbW1jB07FrPZzKeffsr333/P448/TlpaWuKYRx55hKeffpoXXniBpUuX4nQ6mTBhAqFQqANT3noefvhhnn/+eZ555hk2bNjAww8/zCOPPMKMGTMSxyTTPfD7/RxzzDE8++yzB9x/MHmdNGkS69evZ+7cucyZM4cvv/ySa665pr2y8F/7qXsQCARYuXIl9957LytXruSDDz6guLiYc845p9lxXfke/Ny/gSazZ8/m22+/JS8vr8W+rpz/QyKkLm/UqFFi6tSpiffxeFzk5eWJBx98sANT1X4qKioEIBYtWiSEEKKurk6YzWYxa9asxDEbNmwQgFiyZElHJbPV+Xw+0a9fPzF37lxxyimniJtuukkIkfz5v+OOO8SJJ574o/t1XRe5ubni0UcfTWyrq6sTVqtVvP322+2RxDZ31llniauuuqrZtgsuuEBMmjRJCJHc9wAQs2fPTrw/mLx+//33AhDLly9PHPPpp58KRVHE7t272y3trWX/e3Agy5YtE4DYsWOHECK57sGP5X/Xrl2ie/fuYt26daJHjx7iySefTOxLpvz/HFli18VFIhGKiooYP358YpuqqowfP54lS5Z0YMraT319PQDp6ekAFBUVEY1Gm92TAQMGUFhYmFT3ZOrUqZx11lnN8gnJn/+PP/6YESNGcOGFF5Kdnc2xxx7Lyy+/nNi/bds2ysrKmuXf4/EwevTopMg/wAknnMC8efPYtGkTAGvWrOGrr77ijDPOAI6Me9DkYPK6ZMkSUlNTGTFiROKY8ePHo6oqS5cubfc0t4f6+noURSE1NRVI/nug6zqXX34506dPZ9CgQS32J3v+9yXniu3iqqqqiMfj5OTkNNuek5PDxo0bOyhV7UfXdW6++WbGjh3L4MGDASgrK8NisSS+0Jrk5ORQVlbWAalsfe+88w4rV65k+fLlLfYle/63bt3K888/z6233srdd9/N8uXLufHGG7FYLEyZMiWRxwP9TSRD/gHuvPNOvF4vAwYMwGQyEY/HeeCBB5g0aRLAEXEPmhxMXsvKysjOzm62X9M00tPTk+5+gNHG9o477uDSSy9NzJWa7Pfg4YcfRtM0brzxxgPuT/b870sGdlKXNnXqVNatW8dXX33V0UlpNyUlJdx0003MnTsXm83W0clpd7quM2LECP76178CcOyxx7Ju3TpeeOEFpkyZ0sGpax/vvfceb775Jm+99RaDBg1i9erV3HzzzeTl5R0x90A6sGg0ykUXXYQQgueff76jk9MuioqK+Nvf/sbKlStRFKWjk9PhZFVsF5eZmYnJZGrR47G8vJzc3NwOSlX7mDZtGnPmzGHBggXk5+cntufm5hKJRKirq2t2fLLck6KiIioqKjjuuOPQNA1N01i0aBFPP/00mqaRk5OT1Pnv1q0bAwcObLbt6KOPZufOnQCJPCbz38T06dO58847ueSSSxgyZAiXX345t9xyCw8++CBwZNyDJgeT19zc3BadyWKxGDU1NUl1P5qCuh07djB37txEaR0k9z1YvHgxFRUVFBYWJr4Td+zYwR/+8Ad69uwJJHf+9ycDuy7OYrEwfPhw5s2bl9im6zrz5s1jzJgxHZiytiOEYNq0acyePZv58+fTq1evZvuHDx+O2Wxudk+Ki4vZuXNnUtyTcePGsXbtWlavXp1YRowYwaRJkxKvkzn/Y8eObTG8zaZNm+jRowcAvXr1Ijc3t1n+vV4vS5cuTYr8g9ELUlWbf32bTCZ0XQeOjHvQ5GDyOmbMGOrq6igqKkocM3/+fHRdZ/To0e2e5rbQFNRt3ryZL774goyMjGb7k/keXH755Xz33XfNvhPz8vKYPn06//nPf4Dkzn8LHd17Q/rvvfPOO8JqtYpXXnlFfP/99+Kaa64RqampoqysrKOT1iauu+464fF4xMKFC8WePXsSSyAQSBxz7bXXisLCQjF//nyxYsUKMWbMGDFmzJgOTHXb2rdXrBDJnf9ly5YJTdPEAw88IDZv3izefPNN4XA4xBtvvJE45qGHHhKpqanio48+Et99950499xzRa9evUQwGOzAlLeeKVOmiO7du4s5c+aIbdu2iQ8++EBkZmaK22+/PXFMMt0Dn88nVq1aJVatWiUA8cQTT4hVq1YlenweTF5PP/10ceyxx4qlS5eKr776SvTr109ceumlHZWlQ/ZT9yASiYhzzjlH5Ofni9WrVzf7XgyHw4lzdOV78HP/Bva3f69YIbp2/g+FDOySxIwZM0RhYaGwWCxi1KhR4ttvv+3oJLUZ4IDLzJkzE8cEg0Fx/fXXi7S0NOFwOMT5558v9uzZ03GJbmP7B3bJnv9PPvlEDB48WFitVjFgwADx0ksvNduv67q49957RU5OjrBarWLcuHGiuLi4g1Lb+rxer7jppptEYWGhsNlsonfv3uKee+5p9iOeTPdgwYIFB/ybnzJlihDi4PJaXV0tLr30UpGSkiLcbre48sorhc/n64DcHJ6fugfbtm370e/FBQsWJM7Rle/Bz/0b2N+BAruunP9DoQixz1DlkiRJkiRJUpcl29hJkiRJkiQlCRnYSZIkSZIkJQkZ2EmSJEmSJCUJGdhJkiRJkiQlCRnYSZIkSZIkJQkZ2EmSJEmSJCUJGdhJkiRJkiQlCRnYSZIkSZIkJQkZ2EmSJLWxX/ziF9x8880dnQxJko4AMrCTJEmSJElKEjKwkyRJkiRJShIysJMkSWpFfr+fyZMnk5KSQrdu3Xj88ceb7X/99dcZMWIELpeL3Nxcfvvb31JRUQGAEIK+ffvy2GOPNfvM6tWrURSFLVu2IITgT3/6E4WFhVitVvLy8rjxxhvbLX+SJHVuMrCTJElqRdOnT2fRokV89NFHfP755yxcuJCVK1cm9kejUe6//37WrFnDhx9+yPbt27niiisAUBSFq666ipkzZzY758yZMzn55JPp27cv77//Pk8++SQvvvgimzdv5sMPP2TIkCHtmUVJkjoxRQghOjoRkiRJyaChoYGMjAzeeOMNLrzwQgBqamrIz8/nmmuu4amnnmrxmRUrVjBy5Eh8Ph8pKSmUlpZSWFjIN998w6hRo4hGo+Tl5fHYY48xZcoUnnjiCV588UXWrVuH2Wxu5xxKktTZyRI7SZKkVvLDDz8QiUQYPXp0Ylt6ejr9+/dPvC8qKmLixIkUFhbicrk45ZRTANi5cycAeXl5nHXWWfzzn/8E4JNPPiEcDicCxQsvvJBgMEjv3r35/e9/z+zZs4nFYu2VRUmSOjkZ2EmSJLUTv9/PhAkTcLvdvPnmmyxfvpzZs2cDEIlEEsf97ne/45133iEYDDJz5kwuvvhiHA4HAAUFBRQXF/Pcc89ht9u5/vrrOfnkk4lGox2SJ0mSOhcZ2EmSJLWSPn36YDabWbp0aWJbbW0tmzZtAmDjxo1UV1fz0EMPcdJJJzFgwIBEx4l9nXnmmTidTp5//nk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+ "image/png": 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", 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", 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", "text/plain": [ "
" ] @@ -223,17 +186,13 @@ "import numpy as np\n", "\n", "from epymorph import *\n", - "from epymorph.geo.cache import load_from_cache\n", + "from epymorph.adrio import acs5, commuting_flows, us_tiger\n", "from epymorph.plots import map_data_by_state\n", "\n", - "geo = load_from_cache('demo-four-states')\n", - "if geo is None:\n", - " raise Exception(\"Oops, we need to cache the demo geo first (see above cell).\")\n", - "\n", - "rume = Rume.single_strata(\n", + "rume = SingleStrataRume.build(\n", " ipm=ipm_library['sirh'](),\n", " mm=mm_library['pei'](),\n", - " scope=geo.spec.scope,\n", + " scope=scope,\n", " params={\n", " 'beta': 0.45,\n", " 'gamma': 0.25,\n", @@ -242,20 +201,21 @@ " 'hospitalization_duration': 7.0,\n", " 'move_control': 0.9,\n", " 'theta': 0.1,\n", - " 'population': geo['population'],\n", - " 'centroid': geo['centroid'],\n", - " 'commuters': geo['commuters'],\n", + " 'population': acs5.Population(),\n", + " 'centroid': us_tiger.GeometricCentroid(),\n", + " 'commuters': commuting_flows.Commuters(),\n", + " 'meta::geo::label': us_tiger.Name(),\n", " },\n", - " time_frame=TimeFrame.of(\"2015-01-01\", 150),\n", + " time_frame=TimeFrame.of(\"2020-01-01\", 150),\n", " # Initialize the infection in Arizona with 10k individuals.\n", " init=init.SingleLocation(location=0, seed_size=10_000),\n", ")\n", "\n", "sim = BasicSimulator(rume)\n", - "with sim_messaging(sim):\n", + "with sim_messaging():\n", " output = sim.run()\n", "\n", - "EVENT_S_TO_I = rume.ipm.events_by_dst(\"I\")[0]\n", + "EVENT_S_TO_I = rume.ipm.event_by_name(\"S->I\")\n", "\n", "plot_event(output, event_idx=EVENT_S_TO_I)\n", "\n", @@ -263,11 +223,11 @@ " # argmax gives us an index, but the index is equal to the tau step index\n", " # so just need to floor-div by number of tau steps to get day\n", " float(np.argmax(output.incidence[:, n, EVENT_S_TO_I])) // output.dim.tau_steps\n", - " for n in range(geo.nodes)\n", + " for n in range(scope.nodes)\n", "])\n", "\n", "map_data_by_state(\n", - " geo=geo,\n", + " scope=scope,\n", " data=day_of_peak_infection,\n", " title='Day of Peak Infection by State',\n", " vmin=0,\n", diff --git a/doc/demo/03-counties-GEO.ipynb b/doc/demo/03-counties-GEO.ipynb index 4094d83b..6f7a8b3b 100644 --- a/doc/demo/03-counties-GEO.ipynb +++ b/doc/demo/03-counties-GEO.ipynb @@ -4,9 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 3. Modeling with a GEO at the US County granularity\n", + "# 3. Modeling with US County granularity\n", "\n", - "Perhaps modeling at the state level doesn't produce insights at a useful granularity. We can easily construct a similar GEO at the county level and run the same simulations." + "Perhaps modeling at the state level doesn't produce insights at a useful granularity. We can easily construct a similar RUME at the county level and run the same simulations." ] }, { @@ -19,57 +19,19 @@ "output_type": "stream", "text": [ "nodes: 141\n", - "label: ['Apache County, Arizona' 'Cochise County, Arizona'\n", - " 'Coconino County, Arizona' 'Gila County, Arizona'\n", - " 'Graham County, Arizona']\n", - "population: [ 71714 126442 142254 53846 38304]\n" + "geoid (first 8): ['04001', '04003', '04005', '04007', '04009', '04011', '04012', '04013']\n", + "geoid (last 8): ['49043', '49045', '49047', '49049', '49051', '49053', '49055', '49057']\n" ] } ], "source": [ - "from epymorph import *\n", - "from epymorph.geo import *\n", - "from epymorph.geo.adrio import adrio_maker_library\n", - "from epymorph.geo.cache import save_to_cache\n", - "from epymorph.geo.dynamic import DynamicGeo\n", - "from epymorph.geo.util import convert_to_static_geo\n", "from epymorph.geography.us_census import CountyScope\n", "\n", - "geo_scope = CountyScope.in_states_by_code([\"AZ\", \"NM\", \"CO\", \"UT\"], year=2020)\n", - "\n", - "spec = DynamicGeoSpec(\n", - " attributes=[\n", - " AttributeDef('label', str, Shapes.N),\n", - " AttributeDef('geoid', str, Shapes.N),\n", - " AttributeDef('centroid', CentroidType, Shapes.N),\n", - " AttributeDef('population', int, Shapes.N),\n", - " AttributeDef('median_income', int, Shapes.N),\n", - " AttributeDef('commuters', int, Shapes.NxN),\n", - " ],\n", - " time_period=Year(2020),\n", - " scope=geo_scope,\n", - " source={\n", - " 'label': 'Census:name',\n", - " 'geoid': 'Census',\n", - " 'centroid': 'Census',\n", - " 'population': 'Census',\n", - " 'median_income': 'Census',\n", - " 'commuters': 'Census',\n", - " },\n", - ")\n", - "\n", - "geo = DynamicGeo.from_library(spec, adrio_maker_library)\n", + "scope = CountyScope.in_states_by_code([\"AZ\", \"NM\", \"CO\", \"UT\"], year=2020)\n", "\n", - "# It's convenient to pre-fetch the data but this isn't mandatory.\n", - "geo = convert_to_static_geo(geo)\n", - "\n", - "# Let's inspect a few values...\n", - "print(f\"nodes: {geo.nodes}\")\n", - "print(f\"label: {geo['label'][0:5]}\")\n", - "print(f\"population: {geo['population'][0:5]}\")\n", - "\n", - "# Then save it to a cache so we don't bother the Census API too much.\n", - "save_to_cache(geo, 'demo-four-states-by-county')" + "print(f\"nodes: {scope.nodes}\")\n", + "print(f\"geoid (first 8): {scope.get_node_ids().tolist()[0:8]}\")\n", + "print(f\"geoid (last 8): {scope.get_node_ids().tolist()[-8:]}\")" ] }, { @@ -91,15 +53,15 @@ "output_type": "stream", "text": [ "Running simulation (BasicSimulator):\n", - "• 2015-01-01 to 2015-05-31 (150 days)\n", + "• 2020-01-01 to 2020-05-30 (150 days)\n", "• 141 geo nodes\n", "|####################| 100% \n", - "Runtime: 7.330s\n" + "Runtime: 10.502s\n" ] }, { "data": { - "image/png": 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", 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", 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", 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", "text/plain": [ "
" ] @@ -122,17 +84,13 @@ "import numpy as np\n", "\n", "from epymorph import *\n", - "from epymorph.geo.cache import load_from_cache\n", + "from epymorph.adrio import acs5, us_tiger\n", "from epymorph.plots import map_data_by_county\n", "\n", - "geo = load_from_cache('demo-four-states-by-county')\n", - "if geo is None:\n", - " raise Exception(\"Oops, we need to cache the demo geo first (see above cell).\")\n", - "\n", - "rume = Rume.single_strata(\n", + "rume = SingleStrataRume.build(\n", " ipm=ipm_library['sirh'](),\n", " mm=mm_library['centroids'](),\n", - " scope=geo_scope,\n", + " scope=scope,\n", " params={\n", " 'beta': 0.45,\n", " 'gamma': 0.25,\n", @@ -140,19 +98,20 @@ " 'hospitalization_prob': 0.1,\n", " 'hospitalization_duration': 7.0,\n", " 'phi': 40.0,\n", - " 'population': geo['population'],\n", - " 'centroid': geo['centroid'],\n", + " 'population': acs5.Population(),\n", + " 'centroid': us_tiger.GeometricCentroid(),\n", " },\n", - " time_frame=TimeFrame.of(\"2015-01-01\", 150),\n", + " time_frame=TimeFrame.of(\"2020-01-01\", 150),\n", " # Initialize the infection in Maricopa County, Arizona with 10k individuals.\n", + " # It's at index 7 because counties are ordered by FIPS code.\n", " init=init.SingleLocation(location=7, seed_size=10_000),\n", ")\n", "\n", "sim = BasicSimulator(rume)\n", - "with sim_messaging(sim):\n", + "with sim_messaging():\n", " output = sim.run()\n", "\n", - "EVENT_S_TO_I = rume.ipm.events_by_dst(\"I\")[0]\n", + "EVENT_S_TO_I = rume.ipm.event_by_name(\"S->I\")\n", "\n", "plot_event(output, event_idx=EVENT_S_TO_I)\n", "\n", @@ -160,11 +119,11 @@ " # argmax gives us an index, but the index is equal to the tau step index\n", " # so just need to floor-div by number of tau steps to get day\n", " float(np.argmax(output.incidence[:, n, EVENT_S_TO_I])) // output.dim.tau_steps\n", - " for n in range(geo.nodes)\n", + " for n in range(scope.nodes)\n", "])\n", "\n", "map_data_by_county(\n", - " geo=geo,\n", + " scope=scope,\n", " data=day_of_peak_infection,\n", " title='Day of Peak Infection by County',\n", " vmin=0,\n", @@ -193,15 +152,15 @@ "output_type": "stream", "text": [ "Running simulation (BasicSimulator):\n", - "• 2015-01-01 to 2015-05-31 (150 days)\n", + "• 2020-01-01 to 2020-05-30 (150 days)\n", "• 141 geo nodes\n", "|####################| 100% \n", - "Runtime: 5.370s\n" + "Runtime: 9.398s\n" ] }, { "data": { - "image/png": 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", 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", 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", 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", "text/plain": [ "
" ] @@ -224,17 +183,13 @@ "import numpy as np\n", "\n", "from epymorph import *\n", - "from epymorph.geo.cache import load_from_cache\n", + "from epymorph.adrio import acs5, commuting_flows, us_tiger\n", "from epymorph.plots import map_data_by_county\n", "\n", - "geo = load_from_cache('demo-four-states-by-county')\n", - "if geo is None:\n", - " raise Exception(\"Oops, we need to cache the demo geo first (see above cell).\")\n", - "\n", - "rume = Rume.single_strata(\n", + "rume = SingleStrataRume.build(\n", " ipm=ipm_library['sirh'](),\n", " mm=mm_library['pei'](),\n", - " scope=geo_scope,\n", + " scope=scope,\n", " params={\n", " 'beta': 0.45,\n", " 'gamma': 0.25,\n", @@ -243,20 +198,20 @@ " 'hospitalization_duration': 7.0,\n", " 'move_control': 0.9,\n", " 'theta': 0.1,\n", - " 'population': geo['population'],\n", - " 'centroid': geo['centroid'],\n", - " 'commuters': geo['commuters'],\n", + " 'population': acs5.Population(),\n", + " 'centroid': us_tiger.GeometricCentroid(),\n", + " 'commuters': commuting_flows.Commuters(),\n", " },\n", - " time_frame=TimeFrame.of(\"2015-01-01\", 150),\n", + " time_frame=TimeFrame.of(\"2020-01-01\", 150),\n", " # Initialize the infection in Maricopa County, Arizona with 10k individuals.\n", " init=init.SingleLocation(location=7, seed_size=10_000),\n", ")\n", "\n", "sim = BasicSimulator(rume)\n", - "with sim_messaging(sim):\n", + "with sim_messaging():\n", " output = sim.run()\n", "\n", - "EVENT_S_TO_I = rume.ipm.events_by_dst(\"I\")[0]\n", + "EVENT_S_TO_I = rume.ipm.event_by_name(\"S->I\")\n", "\n", "plot_event(output, event_idx=EVENT_S_TO_I)\n", "\n", @@ -264,11 +219,11 @@ " # argmax gives us an index, but the index is equal to the tau step index\n", " # so just need to floor-div by number of tau steps to get day\n", " float(np.argmax(output.incidence[:, n, EVENT_S_TO_I])) // output.dim.tau_steps\n", - " for n in range(geo.nodes)\n", + " for n in range(scope.nodes)\n", "])\n", "\n", "map_data_by_county(\n", - " geo=geo,\n", + " scope=scope,\n", " data=day_of_peak_infection,\n", " title='Day of Peak Infection by County',\n", " vmin=0,\n", diff --git a/doc/demo/04-time-varying-beta.ipynb b/doc/demo/04-time-varying-beta.ipynb index eb49157d..24b01648 100644 --- a/doc/demo/04-time-varying-beta.ipynb +++ b/doc/demo/04-time-varying-beta.ipynb @@ -6,7 +6,7 @@ "source": [ "# 4. Time-varying beta functions\n", "\n", - "Far from being limited to static beta values, we can also simulate the affects of a time-and-location-varying beta function. For this demo, we'll use the State-level GEO we defined in Part 2." + "Far from being limited to static beta values, we can also simulate the affects of a time-and-location-varying beta function. For this demo, we'll use the State-level geo scope we defined in Part 2." ] }, { @@ -31,7 +31,8 @@ "import matplotlib.pyplot as plt\n", "\n", "from epymorph import *\n", - "from epymorph.geo.cache import load_from_cache\n", + "from epymorph.adrio import acs5, commuting_flows, us_tiger\n", + "from epymorph.geography.us_census import StateScope\n", "from epymorph.params import ParamFunctionTimeAndNode\n", "from epymorph.simulator.data import evaluate_param\n", "\n", @@ -53,16 +54,14 @@ " return value\n", "\n", "\n", - "geo = load_from_cache('demo-four-states')\n", - "if geo is None:\n", - " raise Exception(\n", - " \"Can't load the demo-four-states geo from cache; see demo part 2 for that.\")\n", + "scope = StateScope.in_states_by_code([\"AZ\", \"NM\", \"CO\", \"UT\"], year=2020)\n", + "\n", "\n", "# Now we create a simulation, passing in our beta function.\n", - "rume = Rume.single_strata(\n", + "rume = SingleStrataRume.build(\n", " ipm=ipm_library['sirh'](),\n", " mm=mm_library['pei'](),\n", - " scope=geo.spec.scope,\n", + " scope=scope,\n", " params={\n", " 'beta': Beta(),\n", " 'gamma': 0.25,\n", @@ -71,8 +70,9 @@ " 'hospitalization_duration': 7.0,\n", " 'move_control': 0.9,\n", " 'theta': 0.1,\n", - " 'population': geo['population'],\n", - " 'commuters': geo['commuters'],\n", + " 'population': acs5.Population(),\n", + " 'commuters': commuting_flows.Commuters(),\n", + " 'meta::geo::label': us_tiger.Name(),\n", " },\n", " time_frame=TimeFrame.of(\"2015-01-01\", 150),\n", " # Initialize the infection in Arizona with 10k individuals.\n", @@ -82,9 +82,10 @@ "# Now plot the beta function. We can evaluate the data series for beta in this RUME.\n", "# This way we can be sure we're plotting beta exactly as the simulation will see it.\n", "beta_values = evaluate_param(rume, 'beta')\n", + "state_names = evaluate_param(rume, 'meta::geo::label')\n", "fig, ax = plt.subplots(figsize=(8, 4))\n", "ax.set(title='beta function', ylabel='beta by time and location', xlabel='days')\n", - "ax.plot(beta_values, label=geo['label'])\n", + "ax.plot(beta_values, label=state_names)\n", "ax.legend()\n", "fig.tight_layout()\n", "plt.show()" @@ -103,12 +104,12 @@ "• 2015-01-01 to 2015-05-31 (150 days)\n", "• 4 geo nodes\n", "|####################| 100% \n", - "Runtime: 0.116s\n" + "Runtime: 0.247s\n" ] }, { "data": { - "image/png": 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", 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Lfg8X7c3My8vj7NmzFvs+qKgmTZrg4+PD5cuXS23j4OBQ5i4fa9eu5bfffmPQoEH88MMPdrXUi7AvMsdOCCtTq9Xcf//9/Pnnn3z77bcUFBSYDcOCoSLz4sWLfPXVV8Wuz87OJjMzs8LPdXNzAyjXzhMdO3YkMDCQzz//3GxYbOXKlRw7dsxUpRgQEED37t1ZsGABMTExZvcw9nSo1WoGDRrEn3/+ye7du4s9y9jOmARt2rTJdM64tEVVdejQgaZNm/Lhhx+WuOL/lStXKnzPQYMGoVareeutt4rNzzN+TlFRUXh6evLuu++Sn59f6nOzsrLIyckxO9e0aVM8PDyKLW9Tmrlz55o9f+7cuTg6OtKrVy+zdiNHjuTo0aM8//zzaDQaUyV2Rd19993s3LmT7du3m45lZmby5Zdf0rhxY9MQ9ZAhQ7h69apZfEXjBENvmUqlMuudPXfuXIk7TLi5uZXre7h37944OTkxZ84cs163+fPnk5qaavoerqjyLneyY8eOEv+f7ty5k8TExCr3kr7zzjv07t2bJUuWlDnMLeo2+e4QogpWrlxZ4g/9W2+91azXYNiwYfzf//0fb7zxBpGRkaZ5SUYjR45k6dKlPPHEE6xfv57bbrsNnU7H8ePHWbp0qWn9toro0KEDAK+++irDhw/H0dGRe++915TwFeXo6MiMGTMYO3YsPXr04MEHHzQtd9K4cWOeffZZU9s5c+Zw++23c/PNNzN+/HjCwsI4d+4cf/31F/v37wcMuwWsWbOGHj16mJZvuXz5MsuWLWPLli14e3vTp08fGjZsyLhx40xJx4IFCwgICCiWNFaUWq3m66+/pl+/frRu3ZqxY8dSv359Ll68yPr16/H09OTPP/+s0D2bNWvGq6++yttvv023bt0YPHgwWq2WXbt2ERISwnvvvYenpyfz5s1j5MiR3HzzzQwfPtz0+fz111/cdtttzJ07lxMnTtCrVy+GDh1Kq1atcHBw4LfffiM+Pr5ciZezszOrVq1i9OjRdOnShZUrV/LXX3/xyiuvFBui69+/P35+fixbtox+/foRGBhYoc/b6KWXXuLHH3+kX79+PP300/j6+rJ48WLOnj3LL7/8YipYGDVqFN988w1Tpkxh586ddOvWjczMTP755x+efPJJBg4cSP/+/Zk1axZ9+/bloYceIiEhgU8//ZRmzZoVm+PWoUMH/vnnH2bNmkVISAhhYWElzvULCAjg5Zdf5s0336Rv374MGDCA6OhoPvvsMzp16lTh5V2Myrvcybfffsv333/PfffdR4cOHXBycuLYsWMsWLAAZ2dnXnnllUo93+j33383Fd0IcUO2KcYVoma70XInQLElCfR6vRIaGqoAyjvvvFPiPfPy8pQZM2YorVu3VrRareLj46N06NBBefPNN5XU1FRTO0CZOHFiseuvX55CURTl7bffVurXr6+o1epyLX2yZMkS5aabblK0Wq3i6+urjBgxQrlw4UKxdocPH1buu+8+xdvbW3F2dlbCw8OV119/3azN+fPnlVGjRikBAQGKVqtVmjRpokycOFHJzc01tdmzZ4/SpUsXxcnJSWnYsKEya9asUpc76d+/f7E4ylrWYd++fcrgwYMVPz8/RavVKo0aNVKGDh2qrFu3ztTGuNyJcZkQo5LiUBTDch7Gr5GPj4/So0cPZe3atcXiioqKUry8vBRnZ2eladOmypgxY5Tdu3criqIoV69eVSZOnKhEREQobm5uipeXl9KlSxdl6dKlJX4eRY0ePVpxc3NTTp8+rfTp00dxdXVV6tWrp7zxxhvFlmExevLJJxVA+eGHH8q8v1FJ30+nT59W7r//ftO/e+fOnZUVK1YUuzYrK0t59dVXlbCwMMXR0VEJCgpS7r//fuX06dOmNvPnz1eaN2+uaLVaJSIiQlm4cGGJS+AcP35c6d69u+Li4qIApphK+/eZO3euEhERoTg6Oir16tVTJkyYoCQnJ5u16dGjh9K6deticY8ePVpp1KhRsbbl+VV58OBB5fnnn1duvvlmxdfXV3FwcFCCg4OVBx54QNm7d2+Z11uSLHdSt6kUpQbNthZCCFFhzz77LPPnzycuLg5XV1dbhyOEsCKZYyeEELVYTk4O3333HUOGDJGkTog6QObYCSFELZSQkMA///zDzz//TGJiIs8884ytQxJCVANJ7IQQohY6evQoI0aMIDAwkDlz5pS6RIsQonaROXZCCCGEELWEzLETQgghhKglJLETQgghhKglZI5dNdLr9Vy6dAkPDw+LbRslhBBCiNpNURTS09MJCQkxLQZeGknsqtGlS5eKbeYuhBBCCFEesbGxNGjQ4IZtJLGrRh4eHoDhH8bT09PG0QghhBCiJkhLSyM0NNSUR9yIJHbVyDj86unpKYmdEEIIISqkPNO4pHhCCCGEEKKWkMROCCGEEKKWkMROCCGEEKKWkDl2QgghhA3pdDry8/NtHYawIUdHRzQajUXuJYmdEEIIYQOKohAXF0dKSoqtQxF2wNvbm6CgoCqvcyuJnRBCCGEDxqQuMDAQV1dXWbi+jlIUhaysLBISEgAIDg6u0v0ksRNCCCGqmU6nMyV1fn5+tg5H2JiLiwsACQkJBAYGVmlYVoonhBBCiGpmnFPn6upq40iEvTB+L1R1vqUkdkIIIYSNyPCrMLLU94IkdkIIIYQQtYQkdkIIIYSwqunTp9O+fXtbh1EnSGInhBBCiArZvn07Go2G/v37l6v9c889x7p166wclQBJ7IQQQghRQfPnz+epp55i06ZNXLp0qdR2iqJQUFCAu7u7VP9WE0nshKgGqVn5pGbLyvJCiJovIyODJUuWMGHCBPr378+iRYtM5zZs2IBKpWLlypV06NABrVbLli1big3FqlSqYq/GjRubzm/cuJHOnTuj1WoJDg7mpZdeoqCgwHS+Z8+ePP3007zwwgv4+voSFBTE9OnTzeKcNWsWkZGRuLm5ERoaypNPPklGRoaVvir2QxI7IazsQnIWd3y0ga7vrWPzySu2DkcIYYcURSErr8AmL0VRKhTr0qVLiYiIIDw8nIcffpgFCxYUu8dLL73E+++/z7Fjx2jbtm2xe1y+fNn0OnXqFM2aNaN79+4AXLx4kbvvvptOnTpx4MAB5s2bx/z583nnnXfM7rF48WLc3NzYsWMHM2fO5K233mLt2rWm82q1mjlz5nDkyBEWL17Mv//+ywsvvFChz7UmkgWKhbCiAp2eZ37aT1JmHgCPLNrFx8Pac0/bEBtHJoSwJ9n5OlpNW22TZx99KwpXp/KnA/Pnz+fhhx8GoG/fvqSmprJx40Z69uxpavPWW29x1113lXqPoKAgwJDQDhkyBC8vL7744gsAPvvsM0JDQ5k7dy4qlYqIiAguXbrEiy++yLRp01CrDX1Sbdu25Y033gCgefPmzJ07l3Xr1pmeO3nyZNPzGjduzDvvvMMTTzzBZ599Vu7PtSaSHjshrGj2upPsOZ+Mh9aB3i0DydcpPPXjPr7dfs7WoQkhRIVFR0ezc+dOHnzwQQAcHBwYNmwY8+fPN2vXsWPHct3vlVdeYfv27fz++++m3ReOHTtG165dzdZ1u+2228jIyODChQumY9f3BAYHB5u25QL4559/6NWrF/Xr18fDw4ORI0eSmJhIVlZWxT7pGkZ67ISwku2nE5m7/hQA7w6O5O7IYN744zDf/RfD678foUU9D7o0kcnEQghwcdRw9K0omz27vObPn09BQQEhIddGHRRFQavVMnfuXNMxNze3Mu/13Xff8fHHH7Nhwwbq169fsaABR0dHs49VKhV6vR6Ac+fOcc899zBhwgT+97//4evry5YtWxg3bhx5eXm1escPSeyEsIK0nHyeXbIfRYGhHRtwbzvDD8G3B7YhM1fHb/susnj7OUnshBCAISmpyHCoLRQUFPDNN9/w0Ucf0adPH7NzgwYN4scffyQiIqJc99q+fTuPPvooX3zxBbfccovZuZYtW/LLL7+gKIqp127r1q14eHjQoEGDct1/z5496PV6PvroI9PQ7dKlS8t1bU0nQ7FCWMHv+y8Rl5ZDIz9Xpg9obTquUqkY370JAGuOxJOQlmOrEIUQokJWrFhBcnIy48aNo02bNmavIUOGFBuOLU1cXBz33Xcfw4cPJyoqiri4OOLi4rhyxVBc9uSTTxIbG8tTTz3F8ePH+f3333njjTeYMmWKKUkrS7NmzcjPz+f//u//OHPmDN9++y2ff/55pT/3mkQSOyGs4Pd9FwF4uEujYn+Ftwz2pEMjHwr0Cj/tirVFeEIIUWHz58+nd+/eeHl5FTs3ZMgQdu/ezcGDB8u8z/Hjx4mPj2fx4sUEBwebXp06dQKgfv36/P333+zcuZN27drxxBNPMG7cOF577bVyx9quXTtmzZrFjBkzaNOmDd9//z3vvfde+T/ZGkylVLTOWVRaWloaXl5epKam4unpaetwhJXEJmXRbeZ6VCrY/lIvgryci7VZvu8ik5fsJ9jLmc0v3IGDRv7GEqIuycnJ4ezZs4SFheHsXPxnhKh7bvQ9UZH8QX6bCGFhfxwwrMJ+S5hfiUkdQL/IIHzdnLicmsO64wklthFCCCEqShI7ISzsj/2GxG7QTaWvVad10DC0YygA3/13vlriEkIIUftJYieEBR27nEZ0fDpOGjV92wTfsO2ILg1RqWDzyaucu5pZTREKIYSozSSxE8KCfi/srbsjIgAvF8cbtg31daVHiwAAft5z4YZthRBCiPKQxE4IC9HrFf7Yb6iGHdi+fItt3neTod3qI3FWi0sIIUTdIYmdEBay+3wyl1Jz8NA6cGdEYLmu6RkeiINaxcmEDM5cybByhEIIIWo7SeyEsJBVhw29bn1aB+Fczi16vFwc6drUsPvE2qPxVotNCCFE3SCJnRAWsvGEYdmSXi3L11tn1KdVPQDWSGInhBCiiiSxE8ICLiRncfpKJhq1itua+Vfo2t6Fid3emGQS0mWLMSGEEJUniZ0QFrDpxFUAbgr1LrMa9nrBXi60a+CFosC6Y7JYsRCi9ps+fTrt27e32fN79uzJ5MmTbfZ8a5LETggLMA7DGpcvqag+rYMAWCPVsUKIGiAuLo6nnnqKJk2aoNVqCQ0N5d5772XdunW2Dq3Ok8ROiCrK1+nZdioRgO6VTewKh2O3nkokI7fAYrEJIYSlnTt3jg4dOvDvv//ywQcfcOjQIVatWsUdd9zBxIkTqy2O/Pz8antWTSKJnRBVtC8mhfTcAnzdnIis71WpezQLdCfM3408nZ6N0VcsHKEQQljOk08+iUqlYufOnQwZMoQWLVrQunVrpkyZwn///QdATEwMAwcOxN3dHU9PT4YOHUp8fOkFYnq9nrfeeosGDRqg1Wpp3749q1atMp0/d+4cKpWKJUuW0KNHD5ydnfn+++9JTEzkwQcfpH79+ri6uhIZGcmPP/5odu/MzExGjRqFu7s7wcHBfPTRR8Wen5yczKhRo/Dx8cHV1ZV+/fpx8uRJC33FqpckdkJUkXEYtltzf9RqVaXuoVKpTL12a4/KcKwQdY6iQF6mbV6KUu4wk5KSWLVqFRMnTsTNza3YeW9vb/R6PQMHDiQpKYmNGzeydu1azpw5w7Bhw0q97+zZs/noo4/48MMPOXjwIFFRUQwYMKBYcvXSSy/xzDPPcOzYMaKiosjJyaFDhw789ddfHD58mPHjxzNy5Eh27txpuub5559n48aN/P7776xZs4YNGzawd+9es/uOGTOG3bt388cff7B9+3YUReHuu++ukb2CDrYOQIiabuMJQw9b9+aVG4Y16t4igC82nWH3+WRLhCWEqEnys+DdENs8+5VL4FQ8SSvJqVOnUBSFiIiIUtusW7eOQ4cOcfbsWUJDQwH45ptvaN26Nbt27aJTp07Frvnwww958cUXGT58OAAzZsxg/fr1fPLJJ3z66aemdpMnT2bw4MFm1z733HOm95966ilWr17N0qVL6dy5MxkZGcyfP5/vvvuOXr16AbB48WIaNGhguubkyZP88ccfbN26lVtvvRWA77//ntDQUJYvX84DDzxQrq+NvZAeOyGq4GpGLocvpgHQrUXFljm5XmQDL1QquJCczdWMXEuEJ4QQFqWUo3fv2LFjhIaGmpI6gFatWuHt7c2xY8eKtU9LS+PSpUvcdtttZsdvu+22Yu07duxo9rFOp+Ptt98mMjISX19f3N3dWb16NTExMQCcPn2avLw8unTpYrrG19eX8PBws3gdHBzM2vj5+REeHl5ivPZOeuyEqILNJw29da1DPAn0cK7SvTydHWka4M6phAwOXkjhzoh6lghRCFETOLoaes5s9exyat68OSqViuPHj1sxoNJdP/z7wQcfMHv2bD755BMiIyNxc3Nj8uTJ5OXl2SQ+eyA9dkJUwebC9esqu8zJ9do18AZgf2yqRe4nhKghVCrDcKgtXqryzw329fUlKiqKTz/9lMzMzGLnU1JSaNmyJbGxscTGxpqOHz16lJSUFFq1alXsGk9PT0JCQti6davZ8a1bt5bY/vo2AwcO5OGHH6Zdu3Y0adKEEydOmM43bdoUR0dHduzYYTqWnJxs1qZly5YUFBSYtUlMTCQ6OrrM59sjSeyEqILDlwwJWKfGvha5X/tQQ1Xt/tgUi9xPCCEs7dNPP0Wn09G5c2d++eUXTp48ybFjx5gzZw5du3ald+/eREZGMmLECPbu3cvOnTsZNWoUPXr0KDaUavT8888zY8YMlixZQnR0NC+99BL79+/nmWeeuWEszZs3Z+3atWzbto1jx47x+OOPm1Xfuru7M27cOJ5//nn+/fdfDh8+zJgxY1Cr1Wb3GDhwII899hhbtmzhwIEDPPzww9SvX5+BAwda5otWjWQoVohKyi3QceaK4S/W8CAPi9yzXag3AAdiU1AUBVUF/pIWQojq0KRJE/bu3cv//vc/pk6dyuXLlwkICKBDhw7MmzcPlUrF77//zlNPPUX37t1Rq9X07duX//u//yv1nk8//TSpqalMnTqVhIQEWrVqxR9//EHz5s1vGMtrr73GmTNniIqKwtXVlfHjxzNo0CBSU6+NenzwwQdkZGRw77334uHhwdSpU83OAyxcuJBnnnmGe+65h7y8PLp3787ff/+No2PFdhKyByqlPDMhhUWkpaXh5eVFamoqnp6etg5HVNHRS2ncPWczHs4OHHyjj0WSsLwCPW2mryavQM+G53rS2L98lWpCiJolJyeHs2fPEhYWhrNz1ebnitrhRt8TFckfZChWiEqKjjdUw0YEeVisZ83JQU3rEMN/2gMXUixyTyGEEHWHJHZCVNLxuHTAcsOwRsYCin0xKRa9rxBCiNpPEjshKinalNhZdli9vXGenfTYCSGEqCBJ7ISoJGNiF2HhHjtjYnfkUhp5BXqL3lsIIUTtJomdEJWQmpXP5dQcAFrUs2xi18jPFS8XR/IK9KbkUQghhCgPSeyEqIToeEPCFeLljJeLZcvhVSqVadmT/TIcK4QQogIksROiEqLjDBWxli6cMGrfoHChYimgEEIIUQGS2AlRCcetVDhhZOqxi022yv2FEELUTpLYCVEJ1iqcMIos7LE7czWTnHydVZ4hhBCi9pHETogKUhTFNMfOWkOxAe5afN2cUBQ4lZBhlWcIIYQovzFjxjBo0CBbh1EmSeyEqKBLqTmk5xTgoFbRNMDdKs9QqVS0qGe493GpjBVC2IkxY8agUql4//33zY4vX7682ve2VqlUqFQq/vvvP7Pjubm5+Pn5oVKp2LBhg8WeN3v2bBYtWmSx+1mLJHZCVJCxcKJJgBtODtb7LxReuIzKiXhJ7IQQ9sPZ2ZkZM2aQnGz7OcChoaEsXLjQ7Nhvv/2Gu7vl/+j28vLC29vb4ve1NEnshKggaxdOGLUoHOaVteyEEPakd+/eBAUF8d57792w3ZYtW+jWrRsuLi6Ehoby9NNPk5mZCcDcuXNp06aNqa2xx+/zzz83e85rr712w2eMHj2an376iezsbNOxBQsWMHr06GJtY2NjGTp0KN7e3vj6+jJw4EDOnTsHwPHjx3F1deWHH34wtV+6dCkuLi4cPXoUKD4Uq9frmTlzJs2aNUOr1dKwYUP+97//mc4fOnSIO++8ExcXF/z8/Bg/fjwZGdafWiOJnRAVZO3CCSPj/aXHTojaT1EUsvKzbPJSFKVCsWo0Gt59913+7//+jwsXLpTY5vTp0/Tt25chQ4Zw8OBBlixZwpYtW5g0aRIAPXr04OjRo1y5cgWAjRs34u/vbxo6zc/PZ/v27fTs2fOGsXTo0IHGjRvzyy+/ABATE8OmTZsYOXKkWbv8/HyioqLw8PBg8+bNbN26FXd3d/r27UteXh4RERF8+OGHPPnkk8TExHDhwgWeeOIJZsyYQatWrUp89ssvv8z777/P66+/ztGjR/nhhx+oV68eAJmZmURFReHj48OuXbtYtmwZ//zzj+nztyYHqz9BiFrGtEeshXecuF7zwvtfTs0hNTvf4gshCyHsR3ZBNl1+6GKTZ+94aAeujq4Vuua+++6jffv2vPHGG8yfP7/Y+ffee48RI0YwefJkAJo3b86cOXPo0aMH8+bNo02bNvj6+rJx40buv/9+NmzYwNSpU5k9ezYAO3fuJD8/n1tvvbXMWB555BEWLFjAww8/zKJFi7j77rsJCAgwa7NkyRL0ej1ff/21aS7gwoUL8fb2ZsOGDfTp04cnn3ySv//+m4cffhgnJyc6derEU089VeIz09PTmT17NnPnzjX1DjZt2pTbb78dgB9++IGcnBy++eYb3NzcAEMv5b333suMGTNMCaA1SI+dEBVQoNNz+oqhK91aFbFGns6OhHg5A9JrJ4SwPzNmzGDx4sUcO3as2LkDBw6waNEi3N3dTa+oqCj0ej1nz55FpVLRvXt3NmzYQEpKCkePHuXJJ58kNzeX48ePs3HjRjp16oSra9kJ58MPP8z27ds5c+YMixYt4pFHHikxnlOnTuHh4WGKx9fXl5ycHE6fPm1qt2DBAg4ePMjevXtZtGhRqQUhx44dIzc3l169epV6vl27dqakDuC2225Dr9cTHR1d5udUFdJjJ0QFnEvMIl+n4Oqkob63i9Wf1yLIg0upOUTHpdOpsa/VnyeEsA0XBxd2PLTDZs+ujO7duxMVFcXLL7/MmDFjzM5lZGTw+OOP8/TTTxe7rmHDhgD07NmTL7/8ks2bN3PTTTfh6elpSvY2btxIjx49yhWHn58f99xzD+PGjSMnJ4d+/fqRnm7+x3BGRgYdOnTg+++/L3Z90d69AwcOkJmZiVqt5vLlywQHB5f4TBcX6//8ryxJ7ISogFMJhh8WzQLdUautX9ofXs+DDdFXpMdOiFpOpVJVeDjUHrz//vu0b9+e8PBws+M333wzR48epVmzZqVe26NHDyZPnsyyZctMc+l69uzJP//8w9atW5k6dWq543jkkUe4++67efHFF9FoNMXO33zzzSxZsoTAwEA8PUsufEtKSmLMmDG8+uqrXL58mREjRrB3794Sk7jmzZvj4uLCunXrePTRR4udb9myJYsWLSIzM9PUa7d161bUanWxr5WlyVCsEBVwMt4wDNss0Drr112vRT2pjBVC2K/IyEhGjBjBnDlzzI6/+OKLbNu2jUmTJrF//35OnjzJ77//blY80LZtW3x8fPjhhx/MErvly5eTm5vLbbfdVu44+vbty5UrV3jrrbdKPD9ixAj8/f0ZOHAgmzdv5uzZs2zYsIGnn37aVADyxBNPEBoaymuvvcasWbPQ6XQ899xzJd7P2dmZF198kRdeeIFvvvmG06dP899//5nmG44YMQJnZ2dGjx7N4cOHWb9+PU899RQjR4606vw6kMROiAo5UbgLRAsrF04YhRepjK1o5ZoQQlSHt956C71eb3asbdu2bNy4kRMnTtCtWzduuukmpk2bRkhIiKmNSqWiW7duqFQqU9FB27Zt8fT0pGPHjmbz08qiUqnw9/fHycmpxPOurq5s2rSJhg0bMnjwYFq2bGkauvX09OSbb77h77//5ttvv8XBwQE3Nze+++47vvrqK1auXFniPV9//XWmTp3KtGnTaNmyJcOGDSMhIcH0vNWrV5OUlESnTp24//776dWrF3Pnzi3351RZKkV+W1SbtLQ0vLy8SE1NLbUrWNi3vp9s4nhcOvNHd6RXS+v+1QWQk6+j1bRV6BXY+WovAj2crf5MIYT15eTkcPbsWcLCwnB2lv/X4sbfExXJH6THTohyKtDpOXPVsLhm88Dq6bFzdtTQ2M/wV6sMxwohhCiLJHZClFNMUhZ5BXqcHdU08Km+iiiZZyeEEKK8bJrYvffee3Tq1AkPDw8CAwMZNGhQsfVdcnJymDhxIn5+fri7uzNkyBDi4+PN2sTExNC/f39cXV0JDAzk+eefp6CgwKzNhg0buPnmm9FqtTRr1qzEjXw//fRTGjdujLOzM126dGHnzp0VjkXUXicTrhVOVEdFrFEL2YFCCCFEOdk0sdu4cSMTJ07kv//+Y+3ateTn59OnTx/TXnIAzz77LH/++SfLli1j48aNXLp0icGDB5vO63Q6+vfvT15eHtu2bWPx4sUsWrSIadOmmdqcPXuW/v37c8cdd7B//34mT57Mo48+yurVq01tlixZwpQpU3jjjTfYu3cv7dq1IyoqyjQRsjyxiNrtVGFiV13DsEbGHS6i462/x6AQQogaTrEjCQkJCqBs3LhRURRFSUlJURwdHZVly5aZ2hw7dkwBlO3btyuKoih///23olarlbi4OFObefPmKZ6enkpubq6iKIrywgsvKK1btzZ71rBhw5SoqCjTx507d1YmTpxo+lin0ykhISHKe++9V+5YypKamqoASmpqarnaC/vy9I97lUYvrlA+XX+yWp97Mj5dafTiCqXl6ysVnU5frc8WQlhHdna2cvToUSU7O9vWoQg7caPviYrkD3Y1xy41NRUAX1/DCvt79uwhPz+f3r17m9pERETQsGFDtm/fDsD27duJjIw0WxcmKiqKtLQ0jhw5YmpT9B7GNsZ75OXlsWfPHrM2arWa3r17m9qUJxZRuxnXsKvuHrvGfq44adRk5em4kJxdrc8WQghRs9hNYqfX65k8eTK33XYbbdq0ASAuLg4nJye8vb3N2tarV4+4uDhTm+sX+zN+XFabtLQ0srOzuXr1KjqdrsQ2Re9RVizXy83NJS0tzewlaiadXjHtEdu8mhYnNnLQqGla+MxomWcnhBDiBuwmsZs4cSKHDx/mp59+snUoFvPee+/h5eVleoWGhto6JFFJsUlZ5Bbo0TqoCfWt/m1/wusZEjspoBBCCHEjdpHYTZo0iRUrVrB+/XoaNGhgOh4UFEReXh4pKSlm7ePj4wkKCjK1ub4y1fhxWW08PT1xcXHB398fjUZTYpui9ygrluu9/PLLpKamml6xsbHl+GoIe2SsiG0a4I6mGitijYyVsbLkiRBCiBuxaWKnKAqTJk3it99+499//yUsLMzsfIcOHXB0dGTdunWmY9HR0cTExNC1a1cAunbtyqFDh8yqV9euXYunpyetWrUytSl6D2Mb4z2cnJzo0KGDWRu9Xs+6detMbcoTy/W0Wi2enp5mL1EznUwwJFQt6lXvMKyRsTJWeuyEELXZ9OnTad++va3DqNFsmthNnDiR7777jh9++AEPDw/i4uKIi4sjO9swQdzLy4tx48YxZcoU1q9fz549exg7dixdu3bllltuAaBPnz60atWKkSNHcuDAAVavXs1rr73GxIkT0Wq1gGFj3zNnzvDCCy9w/PhxPvvsM5YuXcqzzz5rimXKlCl89dVXLF68mGPHjjFhwgQyMzMZO3ZsuWMRtZepcKKa9oi9nnHP2NNXMsjX6ctoLYQQ1tOzZ08mT55c7PiiRYtM89DHjBnDoEGDqjUuYeBgy4fPmzcPMHyTFLVw4ULGjBkDwMcff4xarWbIkCHk5uYSFRXFZ599Zmqr0WhYsWIFEyZMoGvXrri5uTF69GjeeustU5uwsDD++usvnn32WWbPnk2DBg34+uuviYqKMrUZNmwYV65cYdq0acTFxdG+fXtWrVplVlBRViyi9jL22DWr5sIJo/reLrg5acjM03H2aqZpNwohhBCiKJsmdoqilNnG2dmZTz/9lE8//bTUNo0aNeLvv/++4X169uzJvn37bthm0qRJTJo0qUqxiNpHr1dMixPbKqFSqVS0CPJgX0wK0XHpktgJIezW9OnTWbx4MWD42QWwfv16evbsyYsvvshvv/3GhQsXCAoKYsSIEUybNg1HR0eze3z77be8/vrrJCcn069fP7766is8POTnXnnYNLEToia4mJJNTr4eJ42a0GrcI/Z64fUMiZ3MsxOi9lEUBSXbNutUqlxcTAmYJTz33HMcO3aMtLQ0Fi5cCFxbn9bDw4NFixYREhLCoUOHeOyxx/Dw8OCFF14wXX/69GmWL1/OihUrSE5OZujQobz//vv873//s1iMtZkkdkKU4VyiYYu7Rn6uOGhsNy3V2EsnlbFC1D5KdjbRN3ewybPD9+5B5Wq5ZZzc3d1xcXEhNze32KoRr732mun9xo0b89xzz/HTTz+ZJXZ6vZ5FixaZeuhGjhzJunXrJLErJ0nshCjDucQsABr5udk0joggqYwVQtRsS5YsYc6cOZw+fZqMjAwKCgqKrRjRuHFjs2HX4OBgs5UvxI1JYidEGc5fvdZjZ0vGtezOJ2WRnafDxUlj03iEEJajcnEhfO8emz27Ijw9PU1bgBaVkpKCl5dXqddt376dESNG8OabbxIVFYWXlxc//fQTH330kVm76+fbqVQq9HpZDaC8JLETogzGHrvGNk7s/N21+Lk5kZiZx8mEdNo28LZpPEIIy1GpVBYdDrWm8PBw1qxZU+z43r17adGiBWBYH1an05md37ZtG40aNeLVV181HTt//rx1g62D7GLnCSHsWUySscfOtkOxIPPshBC2N2HCBE6cOMHTTz/NwYMHiY6OZtasWfz4449MnToVMAynGs9dvXqV/Px8mjdvTkxMDD/99BOnT59mzpw5/Pbbbzb+bGofSeyEuAG9XuG8qcfO9olduMyzE0LYWJMmTdi0aRPHjx+nd+/edOnShaVLl7Js2TL69u0LwGOPPUZ4eDgdO3YkICCArVu3MmDAAJ599lkmTZpE+/bt2bZtG6+//rqNP5vaR6WUZzE5YRFpaWl4eXmRmpoq24vVEJdTs+n63r84qFUcf7uvTatiAX7YEcMrvx2ie4sAvnmks01jEUJUXk5ODmfPniUsLAxnZ2dbhyPswI2+JyqSP0iPnRA3cO6qobcu1Ne2S50YmXrsZChWCCFECWz/m0oIO3a+cA27hr72Mam5RT3DlmZxaTmkZuXbOBohhBD2RhI7IW7AXipijTycHanvbViaIFrm2QkhhLiOJHZC3MD5RPupiDVqXthrJwUUQgghrieJnRA3YKqI9bePHjuApgGGxO5s4cLJQoiaS+oXhZGlvhcksROiFIqi2GWPXZMAQyxnrmTYOBIhRGUZd1fIysqycSTCXhi/F67feaOiZOcJIUpxNSOPzDwdKhU08KnYljvWFOZfmNhJj50QNZZGo8Hb29u0B6qrqysqlcrGUQlbUBSFrKwsEhIS8Pb2RqOp2naRktgJUQpjb12IlwtaB/vZl9U4FBublEVegR4nB+l4F6ImCgoKApAN7gUA3t7epu+JqpDETohSnLPD+XUAgR5a3Jw0ZObpiEnKpFmgh61DEkJUgkqlIjg4mMDAQPLzZfmiuszR0bHKPXVGktgJUQp7nF8Hhl8GYQFuHL6YxpkrktgJUdNpNBqL/VIXQsZwhCjFeTtbw66oMH/DcKzMsxNCCFGUJHZClMJee+wAmhQWUJy9IomdEEKIaySxE6IUxjl2jeywx8605MlVWfJECCHENZLYCVGClKw8UrMNk5ntZZ/Yopr4yyLFQgghipPETogSGHvr6nlqcXWyvxqjsMIeu6sZ1xJQIYQQQhI7IUpgz/PrANy1DgR6aAHptRNCCHGNJHZClMCeK2KNZGsxIYQQ15PETogSnLPzHjsosuSJVMYKIYQoJImdECU4b8cVsUZNC3vsZChWCCGEkSR2QpTAOMeusR332BmHYk/LUKwQQohCktgJcZ30nHyuZuQB0NCOe+yMQ7HnEjPR6xUbRyOEEMIeSGInxHWMw7B+bk54OjvaOJrShfq44KBWkZOv53Jajq3DEUIIYQcksRPiOjFJ9j+/DsBBozb1KMrWYkIIIUASOyGKqQkVsUbGHShkazEhhBAgiZ0QxZy/WjN67KDoWnbSYyeEEEISOyGKOVcDKmKNmgUYeuxOJUiPnRBCCEnshCimJqxhZ9S8niGxOxGfbuNIhBBC2ANJ7IQoIjtPR1xhhWmN6LELNCR2Cem5pGbl2zgaIYQQtiaJnRBFGCtiPZ0d8Ha136VOjDycHQnxcgbgZIL02pUq8TR8OxiOrbB1JEIIYVWS2AlRxPkiFbEqlcrG0ZRP83oeAJyIl3l2JcrPgaWj4fQ62L3A1tEIIYRVSWInRBE1aX6dUfNAmWd3Q2unQfwhw/u6PNvGIoQQViaJnRBF1KSKWKMWhT12UhlbguN/wc4vrn2s19kuFiGEqAaS2AlRRI3ssZPK2JKlXoDlTxreD2xleKuXAhMhRO0miZ0QRZh67PxrTo+dVMaW4t93ICcFgtvDHa8ajunk6yOEqN0ksROiUF6Bnksp2UDN6rGTytgSZCfDkd8M79/9ATi6GN6XoVghRC0niZ0QhS4kZ6FXwNVJQ4C71tbhVIhUxl7n4DIoyDEMwTboBJrCpWtkKFYIUctJYidEIeP8uoa+rjVmqRMjY2Ws9NgBigJ7Fxvev3k0qFSgdjB8LEOxQohaThI7IQrVxIpYI2Nl7EnpsYNLeyH+MGi00Hao4Zja2GNXYLu4hBCiGkhiJ0ShmlgRaySVsUXsKeytazUQXH0N72sKe+wksRNC1HKS2AlRKLZwO7FQ35qX2EllbKHcDDj8i+H9DqOvHVdLYieEqBsksROiUGxyzU3spDK20OFfIC8D/JpBo9uuHTcOxcocOyFELSeJnRCAoijEJhmWOgn1cbFxNJUjlbHA/u8Nb28eZSiaMNLIHDshRN0giZ0QQFJmHtn5hjXOQrxraGJX1ytj0y5D7A7D+5FDzc+pNYa3ktgJIWo5SeyEAGKTDb119Ty1ODtqbBxN5dT5ytjovw1vG3QCz2DzczIUK4SoIySxE4IihRM+NW9+nVGzenW8x+74X4a3Ef2Ln5PiCSFEHSGJnRDAhcIeu5pYOGFkrIyNT8slLaeO9UzlpMLZTYb3I+4pft44xw5FthUTQtRqktgJwbWK2AY1tHACwNPZkUAPw1ZoZ65k2jiaanZyrWG7MP9w8G9e/Lyxxw5kOFYIUatJYicEtWMoFq712p1KqGPz7I79aXhb0jAsmCd2MhwrhKjFJLETgmtDsQ18a26PHdTRxC4/B079Y3i/pGFYKDIUi6FnTwghailJ7ESdp9crXDTOsavhPXZNA+pgYnd2k2FRYo8QCLmp5DZmPXYyx04IUXtJYifqvIT0XPJ0ejRqFcGFuzfUVMYeu9NX6lBid9w4DHs3qEv5kaZSgapwGRuZYyeEqMUksRN1nrFwItjLGQdNzf4vYUzszidmkltQB3qm9HqIXml4v7T5dUam3ScksRNC1F41+7eYEBZQWwonAAI9tHhoHdArcO5qlq3Dsb4rxyDzCji6QqPbb9xW1rITQtQBktiJOu/aGnY1u3ACQKVS0bQuDcfGbDe8bdAJHJxu3NaY2OkksRNC1F6S2Ik6z9hj16AW9NhBHauMPV+Y2DW6tey20mMnhKgDbJrYbdq0iXvvvZeQkBBUKhXLly83Oz9mzBhUKpXZq2/fvmZtkpKSGDFiBJ6ennh7ezNu3DgyMsx/oR08eJBu3brh7OxMaGgoM2fOLBbLsmXLiIiIwNnZmcjISP7++2+z84qiMG3aNIKDg3FxcaF3796cPHnSMl8IYVPGOXa1occO6lhiF/Of4W3DW8puK3PshBB1gE0Tu8zMTNq1a8enn35aapu+ffty+fJl0+vHH380Oz9ixAiOHDnC2rVrWbFiBZs2bWL8+PGm82lpafTp04dGjRqxZ88ePvjgA6ZPn86XX35parNt2zYefPBBxo0bx759+xg0aBCDBg3i8OHDpjYzZ85kzpw5fP755+zYsQM3NzeioqLIycmx4FdE2EJsUu1Y6sSozix5khILaRcM1a4NOpXdXl2Y2MlQrBCiFnMou4n19OvXj379+t2wjVarJSgoqMRzx44dY9WqVezatYuOHTsC8H//93/cfffdfPjhh4SEhPD999+Tl5fHggULcHJyonXr1uzfv59Zs2aZEsDZs2fTt29fnn/+eQDefvtt1q5dy9y5c/n8889RFIVPPvmE1157jYEDBwLwzTffUK9ePZYvX87w4cMt9SUR1axApycuzZCc17ah2DNXM9DrFdRqlY0jshLj/LrgduDkVnZ7deFyJzIUK4Soxex+jt2GDRsIDAwkPDycCRMmkJiYaDq3fft2vL29TUkdQO/evVGr1ezYscPUpnv37jg5XZtYHRUVRXR0NMnJyaY2vXv3NntuVFQU27cbfnGcPXuWuLg4szZeXl506dLF1KYkubm5pKWlmb2EfbmcmoNOr+DkoDbts1rThfq44KRRk5Ov52JKtq3DsZ6YCsyvAxmKFULUCXad2PXt25dvvvmGdevWMWPGDDZu3Ei/fv3Q6Qzrc8XFxREYGGh2jYODA76+vsTFxZna1KtXz6yN8eOy2hQ9X/S6ktqU5L333sPLy8v0Cg0NrdDnL6zPVDjh7VJrerYcNGrC/A09WLV6ONZYOFGe+XUgxRNCiDrBrhO74cOHM2DAACIjIxk0aBArVqxg165dbNiwwdahlcvLL79Mamqq6RUbG2vrkMR1ru0RWzuGYY1qfQFFVpJhDTuA0AomdjLHTghRi9l1Yne9Jk2a4O/vz6lTpwAICgoiISHBrE1BQQFJSUmmeXlBQUHEx8ebtTF+XFaboueLXldSm5JotVo8PT3NXsK+GCtiG/jUjopYo1q/ll3sTsNbv+bgHlC+a2QoVghRB9SoxO7ChQskJiYSHBwMQNeuXUlJSWHPnj2mNv/++y96vZ4uXbqY2mzatIn8/Gs/zNeuXUt4eDg+Pj6mNuvWrTN71tq1a+natSsAYWFhBAUFmbVJS0tjx44dpjaiZqpNu04UVet77GK2Gd6WdxgWZChWCFEn2DSxy8jIYP/+/ezfvx8wFCns37+fmJgYMjIyeP755/nvv/84d+4c69atY+DAgTRr1oyoqCgAWrZsSd++fXnsscfYuXMnW7duZdKkSQwfPpyQkBAAHnroIZycnBg3bhxHjhxhyZIlzJ49mylTppjieOaZZ1i1ahUfffQRx48fZ/r06ezevZtJkyYBhtX8J0+ezDvvvMMff/zBoUOHGDVqFCEhIQwaNKhav2bCsmJr0a4TRTUNKJxjdyUDRVFsHI0VGNevK2/hBBRZ7kR67IQQtZdNlzvZvXs3d9xxh+ljY7I1evRo5s2bx8GDB1m8eDEpKSmEhITQp08f3n77bbTaa9WL33//PZMmTaJXr16o1WqGDBnCnDlzTOe9vLxYs2YNEydOpEOHDvj7+zNt2jSzte5uvfVWfvjhB1577TVeeeUVmjdvzvLly2nTpo2pzQsvvEBmZibjx48nJSWF22+/nVWrVuHs7GzNL5GwsvOJhh67hrVsjl3TAHdUKkjJyicxMw9/99pR8QtAfjZc3Gt4v0I9dsblTnSWj0kIIeyESrHAn/NpaWn8+++/hIeH07JlS0vEVSulpaXh5eVFamqqzLezAxm5BbR5YzUAB97og5eLo40jsqxuM/8lNimbn8bfwi1N/GwdjuWc3wYL+4F7PZgaDapyVjN/NwRO/QOD5kH7h6wboxBCWFBF8odKDcUOHTqUuXPnApCdnU3Hjh0ZOnQobdu25ZdffqnMLYWoducTMwHwdXOqdUkdQLPaugPFpX2Gtw06lT+pAxmKFULUCZVK7DZt2kS3bt0A+O2331AUhZSUFObMmcM777xj0QCFsBbjMGwjv9o1DGtUawsojIldSPuKXSc7Twgh6oBKJXapqan4+voCsGrVKoYMGYKrqyv9+/fn5MmTFg1QCGs5V9hj19ivHNtR1UDNauuSJ8bELvimil1nWu5EEjshRO1VqcQuNDSU7du3k5mZyapVq+jTpw8AycnJUkwgaozzV+tGj93p2tRjl5MGiYZ1LCveY2dcoFiGYoUQtVelqmInT57MiBEjcHd3p2HDhvTs2RMwDNFGRkZaMj4hrKa299g1LZxjdyk1h8zcAty0Ni2Ct4zLBwxvvULBzb9i16qlx04IUftV6if9k08+SefOnYmNjeWuu+5CrTZ0/DVp0kTm2Ikao7bPsfN2dcLf3YmrGXmcvpJB2wbetg6p6io7vw5AY1ygWHrshBC1V6X/hO/YsSNt27bl7NmzNG3aFAcHB/r372/J2ISwmuw8HXFpOUDt7bEDQ6/d1YwkTiXUksTu8n7D25AKzq+DIjtPyDp2Qojaq1Jz7LKyshg3bhyurq60bt2amJgYAJ566inef/99iwYohDWcTzIMw3o6O+DtWvuWOjGqdZWxpsKJ9hW/VpY7EULUAZVK7F5++WUOHDjAhg0bzIolevfuzZIlSywWnBDWcq6wcCLM3w1VRdZCq2FqVWKXnQJJZwzvV6nHThI7IUTtVamh2OXLl7NkyRJuueUWs1+KrVu35vTp0xYLTghrMS5O3KgWD8NCLVvyxFg44d0QXH0rfr1GhmKFELVfpXrsrly5QmBgYLHjmZmZtbr3Q9Qe5woLJxrX0sIJI2Nl7PnELPJ1ehtHU0WmwolK9NaBDMUKIeqESiV2HTt25K+//jJ9bEzmvv76a7p27WqZyISworrSYxfs5Yybk4YCvWL6nGusqhROQJGhWFnuRAhRe1VqKPbdd9+lX79+HD16lIKCAmbPns3Ro0fZtm0bGzdutHSMQliccamTxv61u8dOpVLRNNCdgxdSOZWQQbNAD1uHVHlVKZwAWe5ECFEnVKrH7vbbb2f//v0UFBQQGRnJmjVrCAwMZPv27XTo0MHSMQphUTn5Oi6lZgO1v8cOoFlALSigyEqC5HOG9yuzhh0U2XlCeuyEELVXpdexa9q0KV999ZUlYxGiWlxIzkJRwF3rgJ+bk63DsbqmtaEy1lg44RMGLj6Vu4fsPCGEqAMq1WP3999/s3r16mLHV69ezcqVK6sclBDWdK7IHrF1odjHtORJTa6MNc2va1/5e2iMiZ0MxQohaq9KJXYvvfQSOl3xJQMUReGll16qclBCWFNt3yP2eqYlTxIy0esVG0dTSXGHDG+D2lb+HmqN4a302AkharFKJXYnT56kVatWxY5HRERw6tSpKgclhDWZErtaXjhh1NDXFQe1iux8HZcLt1GrceIOG94GRVb+HqblTiSxE0LUXpVK7Ly8vDhz5kyx46dOncLNrW70goiay1gRWxcKJwAcNWoa+xs+15Px6TaOphLysyHxpOH9em0qfx/ZeUIIUQdUKrEbOHAgkydPNttl4tSpU0ydOpUBAwZYLDghrKGuDcUChAcZljmJjquBiV3CUVD04OoPHkGVv49GiieEELVfpRK7mTNn4ubmRkREBGFhYYSFhdGyZUv8/Pz48MMPLR2jEBaTV6DnYrJhqZPavutEURH1anBiZxqGbQNVKXaR5U6EEHVApZY78fLyYtu2baxdu5YDBw7g4uJC27Zt6d69u6XjE8KiLiRnoVfAxVFDgIfW1uFUG2OP3fGamNjFFyZ2VRmGBdl5QghRJ1R6HTuVSkWfPn3o06ePJeMRwqquza+rG0udGEUEeQKGtezydXocNZXqrLcNU0VsFQonQJY7EULUCZVO7NatW8e6detISEhArzffXHzBggVVDkwIa6iL8+sAGvi44OakITNPx7mrmTSvV0O2FlMUiD9ieL+qiZ2pKlYSOyFE7VWpP9vffPNN+vTpw7p167h69SrJyclmLyHslanHro4sdWKkVqtoUROHY1POQ24aaJzAv0XV7mVax674GpxCCFFbVKrH7vPPP2fRokWMHDnS0vEIYVV1tccOICLIg30xKUTHpXNvO1tHU07GwomA8GtDqZUlQ7FCiDqgUj12eXl53HrrrZaORQirKzrHrq4Jr2fssUuzcSQVYJxfV6+Kw7AgxRNCiDqhUondo48+yg8//GDpWISwqgKdntgkQ2IX5l8He+yCDQUUNWooNt4CO04Yyc4TQog6oFJDsTk5OXz55Zf8888/tG3bFkdH8yGSWbNmWSQ4ISzpYko2BXoFrYOaeh7Otg6n2kUUzrG7kJxNRm4B7tpK105VH1NFbBWXOgHQyM4TQojar1I/2Q8ePEj79u0BOHz4sNm5urSEhKhZzhUZhlWr6973qberE/U8tcSn5RIdl06HRj62DunGclINxRNQ9TXsQIZihRB1QqUSu/Xr11s6DiGs7nxh4URd2SO2JOFBnsSnXeF4XJr9J3bGZU4864Orb9XvJ8udCCHqgCqtUnrq1ClWr15NdrZhiyZFUSwSlBDWcO6qoceuLm0ldr2WNWnP2DgLzq8DWe5ECFEnVCqxS0xMpFevXrRo0YK7776by5cvAzBu3DimTp1q0QCFsBTpsathW4vFGytiLTAMC7LciRCiTqhUYvfss8/i6OhITEwMrq7Xej+GDRvGqlWrLBacEJZUl9ewMwov0mNn9z3sph47CyV2MhQrhKgDKjXHbs2aNaxevZoGDRqYHW/evDnnz5+3SGBCWJJOrxCbZJgyUBfXsDNqFuiORq0iNTuf+LRcgrzstDpYVwAJRw3vW2INO7hWPKHoDFuVSaGXEKIWqlSPXWZmpllPnVFSUhJarbbKQQlhaZdTs8nT6XHSqAnxdrF1ODajddDQpHANv2P2vFBx0mkoyAFHN/ANs8w9NUX+jpXKWCFELVWpxK5bt2588803po9VKhV6vZ6ZM2dyxx13WCw4ISzFWDgR6uuCpg4udVKUaZ7dZTueZ2facaLVtaKHqlJLYieEqP0qNRQ7c+ZMevXqxe7du8nLy+OFF17gyJEjJCUlsXXrVkvHKESVyfy6a1qFeLLi4GWOXrbjHjvjjhOWKpyAa3PswDDPzrHu9twKIWqvSvXYtWnThhMnTnD77bczcOBAMjMzGTx4MPv27aNp06aWjlGIKpOK2Gtah3gBcORSqo0juQFLF07AtapYkB47IUStVeEeu/z8fPr27cvnn3/Oq6++ao2YhLA4464Tjf3rbuGEUesQw56xZ69m2u/WYqatxNpa7p6qIn/HSmInhKilKtxj5+joyMGDB60RixBWIz121/i7awnydEZR4Jg9DsdmXoWMOEAFga0sd1+VSpY8EULUepUain344YeZP3++pWMRwir0eoXzibLrRFFt6ht67Y5ctMPhWGNvnW8YaN0te2/TfrGS2AkhaqdKjcEUFBSwYMEC/vnnHzp06ICbm3kvyKxZsywSnBCWEJ+eQ26BHge1ivp1eKmTolqFePHPsQQOX7LDHjtrFE4YaRyhIFu2FRNC1FqVSuwOHz7MzTffDMCJEyfMzqlk0U9hZ85eNQzDNvBxwUFTpe2Ra402hfPsDttzj50l59cZGXvsZChWCFFLVTix0+l0vPnmm0RGRuLj42ONmISwqDNXDIldmL/MrzNqU99QGXsqIYOcfB3OjhZaK84SrFERa2QaipXiCSFE7VTh7guNRkOfPn1ISUmxQjhCWJ6xx65JgIXna9VgwV7O+Lg6UqBXOBFvRwsVF+TC1WjD+9YaigWZYyeEqLUqvY7dmTNnLB2LEFZx5koGAE0CpMfOSKVSmXrtjtjTPLsr0YbeNGcv8GpQdvuKMu5ioZMeOyFE7VSpxO6dd97hueeeY8WKFVy+fJm0tDSzlxD25MxVGYotSSt7nGdXdH6dNebrGpc7kaFYIUQtVaniibvvvhuAAQMGmBVLKIqCSqVCp5OKM2Efcgt0xCYZljppKkOxZtoU7kBhV5Wx1qyIBRmKFULUepVK7NavX2/pOISwitikLPQKuDlpCPTQ2jocu2LcgeL45TQKdHr7qBg29dhZKbGT4gkhRC1XqcSuR48elo5DCKs4feVa4YQsxWOusZ8bbk4aMvN0nL6SSXiQh20DUhTr99iZljuRxE4IUTtVKrHbtGnTDc937969UsEIYWlnTImdzK+7nlqtolWIJ7vOJXPkUqrtE7vUWMhONsyDC2xpnWfIzhNCiFquUoldz549ix0r2hsic+yEvTBWxErhRMlah3ix61wyhy+mMfhmGwdz+YDhbWAEOFhp2FwjxRNCiNqtUpNqkpOTzV4JCQmsWrWKTp06sWbNGkvHKESlyRp2N2Zc8uTQxRTbBgJw+aDhbXA76z1Ddp4QQtRyleqx8/LyKnbsrrvuwsnJiSlTprBnz54qByaEJRiXOmkiPXYlah/qDcChi6nk6/Q42rKAwthjF1QNiZ3sFSuEqKUs+lO8Xr16REdHW/KWQlRaSlYeSZl5gMyxK00Tfzc8nB3IydcTHWfjHSjiqqHHTpY7EULUcpXqsTt48KDZx4qicPnyZd5//33at29vibiEqDJjRWywlzOuTpX6Vq/11GoV7UO92XzyKvtikk1Ds9UuPR7SLwMq6y11AjIUK4So9Sr12659+/aoVCoURTE7fsstt7BgwQKLBCZEVZ2VHSfK5aaGPobELjaFkV1tFISxt86/OThZ8d9L1rETQtRylUrszp49a/axWq0mICAAZ2dniwQlhCXIHrHlc1PhPLv9MSm2C8I4v86aw7AgVbFCiFqvUoldo0aNLB2HEBZnWsPOXypib8RYQHHmaibJmXn4uDlVfxCmwom21n2O9NgJIWq5ShVPPP3008yZM6fY8blz5zJ58uSqxiSERZy5Kj125eHj5mQart5/IcU2QVRH4QQYFj8GmWMnhKi1KpXY/fLLL9x2223Fjt966638/PPPVQ5KiKrS6RXOJWYB0mNXHjYdjs1OhuRzhveDrd1jpzG8lR47IUQtVanELjExscS17Dw9Pbl69Wq577Np0ybuvfdeQkJCUKlULF++3Oy8oihMmzaN4OBgXFxc6N27NydPnjRrk5SUxIgRI/D09MTb25tx48aRkZFh1ubgwYN069YNZ2dnQkNDmTlzZrFYli1bRkREBM7OzkRGRvL3339XOBZhPy6lZJNXoMfJQU19Hxdbh2P32jf0BmBfbEr1PzzukOGtd0Nw8bHus2SOnRCilqtUYtesWTNWrVpV7PjKlStp0qRJue+TmZlJu3bt+PTTT0s8P3PmTObMmcPnn3/Ojh07cHNzIyoqipycHFObESNGcOTIEdauXcuKFSvYtGkT48ePN51PS0ujT58+NGrUiD179vDBBx8wffp0vvzyS1Obbdu28eCDDzJu3Dj27dvHoEGDGDRoEIcPH65QLMJ+nC4snGjs54pGrSqjtbgp1JBQ7Y9JRq9XymhtYdWx44SRDMUKIWo7pRLmz5+vuLi4KNOmTVM2bNigbNiwQXn99dcVV1dX5csvv6zMLRVA+e2330wf6/V6JSgoSPnggw9Mx1JSUhStVqv8+OOPiqIoytGjRxVA2bVrl6nNypUrFZVKpVy8eFFRFEX57LPPFB8fHyU3N9fU5sUXX1TCw8NNHw8dOlTp37+/WTxdunRRHn/88XLHUh6pqakKoKSmppb7GlE58zefURq9uEJ5/Jvdtg6lRsgr0CktXv1bafTiCuVkfHr1PvznRxXlDU9F2TDT+s9a9YrhWatfs/6zhBDCQiqSP1Sqx+6RRx7ho48+Yv78+dxxxx3ccccdfPfdd8ybN4/HHnvMIgnn2bNniYuLo3fv3qZjXl5edOnShe3btwOwfft2vL296dixo6lN7969UavV7Nixw9Sme/fuODldq/SLiooiOjqa5ORkU5uizzG2MT6nPLGUJDc3l7S0NLOXqB4n4g27KDQLlPl15eGoUdO2gWF6xf7qHo6trsIJkKFYIUStV+ktxSZMmMCFCxeIj48nLS2NM2fOMGrUKIsFFhcXBxi2KSuqXr16pnNxcXEEBgaanXdwcMDX19esTUn3KPqM0toUPV9WLCV577338PLyMr1CQ0PL+KyFpRwv3B4rItjDxpHUHMZlT/bFJFffQ/My4eoJw/vVMhQrO08IIWq3SiV2Z8+eNRUOBAQE4O5u6BU5efIk586ds1hwNd3LL79Mamqq6RUbG2vrkOoEvV4x9dhFBHnaOJqa46aGhnl2+6qzMjbhGCh6cAsEj3plt68qtfTYCSFqt0oldmPGjGHbtm3Fju/YsYMxY8ZUNSYAgoKCAIiPjzc7Hh8fbzoXFBREQkKC2fmCggKSkpLM2pR0j6LPKK1N0fNlxVISrVaLp6en2UtYX2xyFll5OrQOahr7udo6nBrjpsLK2ONxaWTlVVPiE3/E8NZC+8Pmx8WRfeQIik5XcgONcYFi6bETQtROlUrs9u3bV+I6drfccgv79++vakwAhIWFERQUxLp160zH0tLS2LFjB127Gja07Nq1KykpKezZs8fU5t9//0Wv19OlSxdTm02bNpGff+0H+dq1awkPD8fHx8fUpuhzjG2MzylPLMJ+HLts6K1rXs8dB02lZxvUOcFeLgR5OqNX4NCF1Op5qDGxC2xV6VvkRJ8g4aNZnBk4iFM97+DckPs52a07l157jYxNm8z3tDbtPFFK4ieEEDVcpX7rqVQq0tPTix1PTU1FV9pfyiXIyMhg//79pmTw7Nmz7N+/n5iYGFQqFZMnT+add97hjz/+4NChQ4waNYqQkBAGDRoEQMuWLenbty+PPfYYO3fuZOvWrUyaNInhw4cTEhICwEMPPYSTkxPjxo3jyJEjLFmyhNmzZzNlyhRTHM888wyrVq3io48+4vjx40yfPp3du3czadIk0+dbVizCfhyPMxSpyDBsxZnm2VVXAYUxsatXuR67tLVrOXv//SR+9RW50dGgUqF2c0OXlETqz78QO/5xLr/8yrUePFnuRAhR21Wm7Paee+5RHnjgAaWgoMB0rKCgQBkyZIjSt2/fct9n/fr1ClDsNXr0aEVRDMuMvP7660q9evUUrVar9OrVS4mOjja7R2JiovLggw8q7u7uiqenpzJ27FglPd18uYYDBw4ot99+u6LVapX69esr77//frFYli5dqrRo0UJxcnJSWrdurfz1119m58sTS1lkuZPq8cS3u5VGL65Qvtp02tah1DifbzilNHpxhTL+m11lN64qvV5R3mtoWH7k0oEKX57y5wrlaKvWytHwCOX82LFKyh9/KvlJSYo+L0/J2LpVuTR9uun8xRdeVPQFBYqy40vD85aMtMInJIQQ1lGR/EGlKEqFVyM9evQo3bt3x9vbm27dugGwefNm0tLS+Pfff2nTxjLzZWqbtLQ0vLy8SE1Nlfl2VnTHhxs4ezWT78Z14fbm/rYOp0bZeTaJoV9sJ9BDy45XeqFSWXFx57RLMKslqDTw6mVw0Jb70pRffuHya6+DouA1cCDB/3sHlYND8UesWs3FqVNBpzO0G9IM1d9TIeIeGP69JT8bIYSwmorkD5Uaim3VqhUHDx5k2LBhJCQkkJ6ezqhRozh+/LgkdcKmsvN0nEvMBCA8SJY6qajI+l5o1CoS0nO5nGrlXVWMw7D+zSuU1GX+t4PLr74GioL38GEEv/duiUkdgGffKOp/9BFoNKT+/jvx320wnJChWCFELVXpmeWurq74+voSHByMt7c37u7uaDQaS8YmRIWdiE9HUcDf3YkAj/InC8LAxUlDRGFCbPVlT+ILt+yr17rclyj5+cS98zYAXvfdR9Abb6BS3/jHmCG5+xCA5DV7yLislapYIUStVanEbvfu3TRt2pSPP/6YpKQkkpKS+Pjjj2natCl79+61dIxClFt04cLE0ltXecZlT6y+ULGpcKL8iV3St9+Rd+o0Gl9f6r30YrmHij379sXn4YcBuLzTG11WboXDFUKImqBSid2zzz7LgAEDOHfuHL/++iu//vorZ8+e5Z577mHy5MkWDlGI8jsmFbFVdlOoYRkgq28tFn/U8LacFbH58QlcnTsXgMCpU9B4eVXocYFTp+AU7EdBtob4tQllXyCEEDVQpXvsXnzxRRyKzGtxcHDghRdeYPfu3RYLToiKOn5Zeuyqythjd+hiKnkFeus8pCAPrkYb3i/nGnYJM2eiz8rCuV1bvO67r8KPVLu4EPz0cFAppB7OJP26tSuFEKI2qFRi5+npSUxMTLHjsbGxeHjIL1RhG4qimNawayk9dpUW5u+Gl4sjuQV609fT4q6eMGzrpfUCrwZlNs/atYu0v/4ClYqgadPKnFdXGtfWzfELzwDg8vTp6DMzK3UfIYSwV5X66Ths2DDGjRvHkiVLiI2NJTY2lp9++olHH32UBx980NIxClEuV9JzSc7KR60y7DohKkelUl1bqNhaBRRF59eVY57clU8/A8B76FBcWpd/Tl4xag3+kek4eqnRXblK4vz5lb+XEELYoZLXCCjDhx9+iEqlYtSoURQUGPaUdHR0ZMKECbz//vsWDVCI8jpeWDjR2N8NZ0ep0K6Kmxp6s/HEFfbHpjDaGg9IKH/hRPbBg2T99x84OOD/+PiqPVftiFoDgbc6c3FlFokLFuI9dCiON9jzWQghapJK9dg5OTkxe/ZskpOTTVuCGStjtVpZYkLYxrWtxGQ6QFXd1NBQQGG1ylhTj13Z8+sSv/oaAK977sGxcKvAStMYthTzCFNw6dgBJSeHKx9/UrV7CiGEHanSDumurq5ERkYSGRmJq6urpWISolKMPXZSEVt17Rt4A3AuMYvEDCssDVLOPWJzz5wh/Z9/APB7dFzVn6s2DFKoFB31XnwRgNTffyf7yJGq31sIIexAlRI7IezJ0UvSY2cpXq6ONA80zFPceTbJsjfPSoL0y4b3A1vesGni/PmgKLj36oW2WbOqP7swsUNXgEtkJJ733gtAwoyZVGJ3RSGEsDuS2IlaIbdAx6kEQ7Vj6/oVW99MlOy2ZoZ9drecumrZGxt763wag7b0JDw/Lo7UP/4EwP+xRy3zbGNiV7jzROCzk1FptWTt3EnWjh2WeYYQQtiQJHaiVjgZn0GBXsHLxZEQL2dbh1Mr3F6Y2G21dGJ3eb/hbRnDsEmLv4H8fFw7dcKlfXvLPLtwjh36wqKvkBC8hww2PG/hIss8QwghbEgSO1ErGIdhW4d4lnubKXFjXZr4olGrOJeYxYXkLMvd+ELhIub1O5TaRMnLI3X5cgB8HxlruWerCxM7XYHpkO+oUaBSkbFxI7mnT1vuWUIIYQOS2Ila4cilVABaBUvhhKV4ODvSroFhWHvbqUTL3fjiHsPbBh1LbZKxeTO65GQ0Af64d+tmuWerC5fB0V9L7JwaN8b9zjuBwl5CIYSowSSxE7XC0cuFPXb1JbGzpNstPc8uPQ5SYwEVhNxUarPU5b8D4HXPvagcKrXcZslMQ7H5Zof9xo4xPPf33ylIsnCxiBBCVCNJ7ESNp9crpqHYVsFSOGFJtxWZZ6fXW6Bq1DgMG9iy1MKJguRk0jdsAMBr0KCqP7MoU1WseWLn0qEDzm3aoOTmkvzjj5Z9phBCVCNJ7ESNF5OURWaeDicHNU0D3GwdTq1yU0MfXBw1JGbmER2fXvUbXixM7G4wDJv299+Qn4+2VUucw1tU/ZlFGefYoYBebzqsUqnwLey1S/7hR/S5Vli7TwghqoEkdqLGO1Jk/ToHjXxLW5KTg5rOYb6AhapjTYUTpSd2xmFY74EDq/6862mKDOteNxzr2acPDsHB6BITSf39d8s/WwghqoH8FhQ13tHLhsKJ1iEyv84aLLbsiV4Hl/YZ3i+lxy739GlyDh0CBwc877mnas8ribpoYldgdkrl6Ijv6FGAYWFkRaez/POFEMLKJLETNd4R0/w6SeyswTjPbsfZJPIK9GW0voEr0ZCXAU7uEBBRYhNjb517t244+PlV/lmlMQ3FUmyeHYDPAw+g8fIi/3wM6WvWWP75QghhZZLYiRrPVDgRIoUT1hAR5IGvmxNZeTr2x6ZU/kYXdhnehtx0bdmRIhSdjtQ//gCsUDRhdIMeOwC1mxs+I0cCcPXLr2SbMSFEjSOJnajRrqTnkpCei0ole8Rai1qt4tamht6zKg3HllE4kfnffxTEx6P28sL9jp6Vf86NqNWgKvyxV0JiB+D78AhUrq7kHjtG5pYt1olDCCGsRBI7UaMZ168L83fDTWvB9c6EGYvMs7tQuDBxKYUTxoIFz7v7oXZyqvxzymLafaL4UCyAxtsbn6FDAUj84kvrxSGEEFYgiZ2o0Y7K/LpqYZxntz82hYzcknu6big3Ha4cM7xfQo+dLiOT9LX/AOBtrWFYI+NwbCk9doBh6RNHR7J27yZr7z7rxiOEEBYkiZ2o0YxbibWW+XVWFerrSkNfVwr0CjvPVmJ7sUv7QNGDZwPwCCp2On31apTsbJwaN8a5bVsLRHwDmrITO8d69fAeZFhuJfn7760bjxBCWJAkdqJGMw7FtpKlTqzO2Gu35WQlEjvj+nUNOpR4OnX5csBQNKFSqSoTXvmVsvvE9bwfeACA9PXr0efkWDcmIYSwEEnsRI2VlpPP2auZgKxhVx2qNM/uYunz6/IuXCBr1y5QqfAaOKAqIZaPcY7dDXrsAJwjI3EMCUHJyiJj0ybrxyWEEBYgiZ2osQ5fSEVRoIGPC/7uWluHU+t1beqHSgXR8ekkpFewB+vSfsPb+jcXO2Vc4sT1li44BgdXMcpy0BgTuxv32KlUKjz69QUgfdUqa0clhBAWIYmdqLEOXDDMr2vXwNu2gdQRvm5OpiKV7acrMBybkQBpFwAVBLczO6Uoiqka1ssaW4iVxLiGnr7snSU8+/YDIH39BvRZWdaMSgghLEISO1FjHShcLLdtAymcqC63m+bZVWA49uJew1v/FqA1X2sw59Ah8s/HoHJ1xfOuuywV5o2VsdxJUc5tWuPYoAFKdrYMxwohagRJ7ESNdfBCCgDtQr1tGkddcluReXbl3pXBuD9sCcOwaatXA+DRswdqNzeLxFgm03InZSd2KpUKz8Lh2LSVMhwrhLB/ktiJGikhPYdLqTmoVNCmvvTYVZdOjX1x0qi5lJpjKlwp06XCHrsQ88ROURTS16wFwKNPH0uGeWPlWO6kKI++hsQuY+NG9Jnl/JyFEMJGJLETNdLBWMP8uuaB7rjLjhPVxsVJQ4dGPgBsLc88O0W5NhQbcpPZqdzjx8mPjUWl1eLerZulQy2daSi2fImdc6tWODZsiJKTQ8bGjVYMTAghqk4SO1EjHSgchm0rhRPV7vbmhuHY9ccTym6cegGyrhqGP4MizU4Zh2Hdu3ervmFYKNfOE0WpVCo8+xqHY1daKyohhLAISexEjWSqiJX5ddXurlb1AEMBRZnbixmHYQNbgaOz2SmbDMNCuZc7Kcqz/90ApG/YSMHVKuyXK4QQViaJnahxFEW5VjghFbHVrnmgO0383cjT6cvutSttGPbUKfLOnAFHR9x79rROoKUxLndSzqFYAOfwcFzatYP8fFJ+/sVKgQkhRNVJYidqnJikLFKy8nHSqIkIkh0nqptKpSKqjWG/11WH427cuJSK2LQ1awBwv/VWNB4e119lXeXceeJ63g8OByB56RIUXdlr4AkhhC1IYidqHOMwbMsQT5wc5FvYFvq2NiR266MTyMkvJcnR66/tOHFdRWz6akNiV+3DsFCpoVgAz3790Hh5UXDpsqxpJ4SwW/JbUdQ4xoWJZRjWdto28CLYy5msPB2bS1usOOkM5KaCgzMEtjQdzjt/ntzoaNBocL/zDrNLcgpyyMy38pIixuKJcixQbHaZVovX4MEAJP/0k6WjEkIIi5B1IkSNc21+nbdN46jLVCoVUa2DWLTtHKsOx5kKKswYh2GDIq/1knFtGNatSxey3Rz46eBXHLhygNMpp7mYcREFhVCPUFr6tqRtQFvub3E/bo4WrJo1VcVWfDjVZ9hQkhYuJHPTZvIuXMCpQQPLxSWEEBYgPXaiRinQ6Tl00VgRKz12ttS3cJ7dP8fiydfpizcoZWFi4zDskUhP7v71bubsm8PGCxu5kHEBBcNuFrHpsaw5v4YPd3/IoN8HsemCBYc+K7DzxPWcGjfG7dZbQVFIWbLUcjEJIYSFSI+dqFGi49PJydfjrnWgib+7rcOp0zo19sXPzYnEzDx2nEkyrW9ncnGP4W2Ritj8ixfJOXwYvQr+p11Laq6KMK8whocPp7lPc8K8wnBQOXA8+ThHE4+yNHopFzMuMnHdRO4Ou5tXuryCl7aKCb2mcsUTRt4PDidz2zZSfvmFgMnPoNJoqhaPEEJYkPTYiRplz/lkAG5q6I1arbJxNHWbRq2iT2vDEOyqI5fNT+ZlXVvqJLSz6XDi6r8BOBYKWv96TO86nV8H/MpDLR+iU1An/F388Xb25pbgW3ikzSP8OuBXRrUahVql5u+zfzN29ViScpKqFngl59gZefToYbg8KQl9enrVYhFCCAuTxE7UKLvPGRK7jo18bRyJAIhqfW3Zk4Kiw7Gx/xmGOj0bgG8T0+ETv34DwOHWHiy5dwlDWgzBQV36wIGroyvPd3qe7/p9h7+LPyeTT/LomkerltxVcOeJ66mcnFA5ORlukZ1d+TiEEMIKJLETNYqxx65jYx8bRyIAbmvmj4+rI1cz8thWdO/Ys4Vz4sK6gcrQs7pq1w8EnDJU0PYb+yb+Lv7X365UkQGRLIhaQIBLQNWTuyoOxQKoXVwMt8jKqvQ9hBDCGiSxEzXGpZRsLqZko1GraC9bidkFR42a/m2DAfh9/6VrJ85uNrwN6w7AudRzbPr+AwBSmwfRpW2/Cj8rzCuM+VHzTcndxH8mkl+JAoiqDsUCqFxdAdBnSY+dEMK+SGInaozdhb11LYM9cNNK3Y+9GNi+PgCrj8QZFivOSbu21EnjbugVPdO2TeOmY7kANB30cKWfZUzuPJw8OJx4mC8Pflnxm1RxKBaK9NhlS4+dEMK+SGInaow95wxDbzK/zr50aOhDfW8XMnIL+Pd4AsRsB0UHPmHgHcrvp37n1Lm9tIoxLGXiFRVVpeeFeYXx+i2vA/DVwa84eOVgxW5gwaFYRebYCSHsjCR2osYw9th1aCTz6+yJWq1iQPsQAJbvu2g2vy4lJ4VZe2bR+YSCWgHn1q0tsqhvv7B+9Avrh07R8cqWV8jKr0DPmQWGYtWmoVjpsRNC2BdJ7ESNkJFbwLHLaYAUTtijgYWJ3YboK+hObzQcbNyd2ftmk5KbQo+zhh4uj7vustgzX+3yKoGugZxPO8+sPbPKf6G66j12Kldj8YT02Akh7IskdqJG2B+Tgl6B+t4uBHu52DoccZ2IIE/C63ngoktDnXAYgANefvx84mec8hXCz+QBFNsbtiq8tF68fdvbACyJXsKWi1vKd6HGEnPsCnvsZChWCGFnJLETNcLu84Xz66S3zm4NvCmELupjqFDQ+zfn3UNfAPBoTmdUefk4hoSgbd7cos+8NeRWRrQcAcC0rdNIyUkp+yIpnhBC1GKS2IkawbR+ncyvs1sD2oXQVX0UgD8DmnE08SiuDq70vRwAgPsdd6BSWX63kMk3TybMK4wr2Vd4+7+3URTlxhcYh2KrMsdOiieEEHZKEjth93R6hX0xKQB0kIpYu9XAx5VeztHkA5/kXgBgTKvRFGz+DwD3nj2t8lxnB2feu/09HFQOrDm/hr/O/nXjC9SFe7tWZg084y3cCodiM6XHTghhXySxE3bveFwaGbkFeGgdCA/ysHU4ojQZV2hYcI7fPNy5qqTi4+zDcHUXChISULm64tq5k9Ue3dq/NU+0ewKAd/97l/jM+NIbm5Y70VX6eSrTUKz02Akh7IskdsLu7TxrmF/XvqE3GrXlh/KEhZzbTLZKxVxvQ6/q3Q1GUrDF0FvndmtX1FqtVR8/LnIckf6RpOen89mBz0pvaJGhWCmeEELYJ0nshN0z7kF6a9Py7y0qbODcZr739CDZAfR5PiTFdSBj/QYAPO6wXDVsaRzUDrzQ6QUAlp9azumU0yU3lOIJIUQtJomdsGs6vcJ/Z4yJnZ+NoxE3kn52Iwu8PAHIvXIX23ecIuewYekT9+7dqyWG9oHt6dWwF3pFzyd7Pim5kSWWOylcx06RdeyEEHZGEjth1w5fTCU9pwAPZwfa1PeydTiiNGmX+angCukaNU08GhGo7kqrGENS5xwZiUNAQLWF8szNz6BRadhwYQN74vcUbyA7TwghajFJ7IRdMw7D3tLET+bX2bGs0+v41stQ2PJou8e5v0NDOscZlj5x79mjWmMJ8wpjcPPBAMzaM6v48ieW2HlCiieEEHZKEjth17advgrIMKy9++XEMpI1GhpoXOkX1o/B4d50SIgGIKfTbdUez4R2E3BxcOHglYP8E/OP+UlTVawUTwghah+7TuymT5+OSqUye0VERJjO5+TkMHHiRPz8/HB3d2fIkCHEx5svcxATE0P//v1xdXUlMDCQ559/noIC87/UN2zYwM0334xWq6VZs2YsWrSoWCyffvopjRs3xtnZmS5durBz506rfM7imtwCHbvOGSpib2smhRP2Kk+Xx6KsMwA80qg/DmoHfA7sQKsv4IKbP99dcar2mAJcAxjVahQAs/fOJr9oEmdax67yy50Y59hJ8YQQwt7YdWIH0Lp1ay5fvmx6bdlybT/IZ599lj///JNly5axceNGLl26xODBg03ndTod/fv3Jy8vj23btrF48WIWLVrEtGnTTG3Onj1L//79ueOOO9i/fz+TJ0/m0UcfZfXq1aY2S5YsYcqUKbzxxhvs3buXdu3aERUVRUJCQvV8Eeqo/TEp5OTr8Xd3onmgu63DEaVYfnABCWoILNAxsMNEANJWrgJgU/32fL8zlrScyveOVdbYNmPxdfblfNp5fj3x67UTltx5QhYoFkLYGbtP7BwcHAgKCjK9/P0NPTepqanMnz+fWbNmceedd9KhQwcWLlzItm3b+O8/w9pZa9as4ejRo3z33Xe0b9+efv368fbbb/Ppp5+Sl2fYlPzzzz8nLCyMjz76iJYtWzJp0iTuv/9+Pv74Y1MMs2bN4rHHHmPs2LG0atWKzz//HFdXVxYsWFD9X5A6xDi/rmtTf6tsRSWqrkBfwILj3wEwVuWNk6sfurQ0MjdvBuB8265k5Bbww46Yao/NzdGNx9s+DsC8A/PIyi9MwkzLnVigeCI7u+wtzIQQohrZfWJ38uRJQkJCaNKkCSNGjCAmxvALYs+ePeTn59O7d29T24iICBo2bMj27dsB2L59O5GRkdSrV8/UJioqirS0NI4cOWJqU/QexjbGe+Tl5bFnzx6zNmq1mt69e5vaCOswzq+7TebX2a0159ZwMS8VX52OIaGG/yPp//6Lkp+PU9Om3DvwdgAWbDlLbkHlhz4r64EWDxDqEUpiTiKLjyw2HNRYonjCkNihKCi5uVWMUgghLMeuE7suXbqwaNEiVq1axbx58zh79izdunUjPT2duLg4nJyc8Pb2NrumXr16xMXFARAXF2eW1BnPG8/dqE1aWhrZ2dlcvXoVnU5XYhvjPUqTm5tLWlqa2UuUT1ZegWl/WFmY2D4pisI3R78B4MG0dFya3glA2sqVAHj268fAmxoQ5OlMQnouy/ddrPYYHTWOPH3z0wAsPLKQq9lXiyx3UpUFip1N70sBhRDCnth1YtevXz8eeOAB2rZtS1RUFH///TcpKSksXbrU1qGVy3vvvYeXl5fpFRoaauuQaoxd55Ip0Cs08HGhoZ+rrcMRJdibsJcjiUfQ6vUMzcyF0C7oUlLI3LoNAM9+fXFyUDPu9jAAvth0Br2++octoxpF0cavDdkF2Xx+4HOL7Dyh0mhQFW6RpshadkIIO2LXid31vL29adGiBadOnSIoKIi8vDxSUlLM2sTHxxMUFARAUFBQsSpZ48dltfH09MTFxQV/f380Gk2JbYz3KM3LL79Mamqq6RUbG1vhz7mu2npKljmxd98cMfTW3ZuRiW/9zuDoQvq6dVBQgLZFC7RNmwLwYJeGeDg7cOZKJmuPxd/ollahUqmY0nEKAL+c+IXzOYbvrarMsYMi24pJYieEsCM1KrHLyMjg9OnTBAcH06FDBxwdHVm3bp3pfHR0NDExMXTt2hWArl27cujQIbPq1bVr1+Lp6UmrVq1MbYrew9jGeA8nJyc6dOhg1kav17Nu3TpTm9JotVo8PT3NXqJsiqKw5ohhmLtHi0AbRyNKEpMWw/rY9QCMTEuHFn0BSPu7cBj27n6mtu5aB0be0giAzzeetkmxQaegTnSr340CpYDZp5YZDuakQk7lp0cULaAQQgh7YdeJ3XPPPcfGjRs5d+4c27Zt47777kOj0fDggw/i5eXFuHHjmDJlCuvXr2fPnj2MHTuWrl27cssttwDQp08fWrVqxciRIzlw4ACrV6/mtddeY+LEiWgLh1GeeOIJzpw5wwsvvMDx48f57LPPWLp0Kc8++6wpjilTpvDVV1+xePFijh07xoQJE8jMzGTs2LE2+brUdtHx6ZxLzMLJQU3P8OrbikqU3/fHvkdBoVtWDk3yCyC8HwXJyWQWVqR79u1r1n7MbY1xclCzLyaF3eeTbREykztMRoWKtRc3cdC/ESh6uFD59ShVxrXsZL9YIYQdsevE7sKFCzz44IOEh4czdOhQ/Pz8+O+//wgo3Hfy448/5p577mHIkCF0796doKAgfv312npVGo2GFStWoNFo6Nq1Kw8//DCjRo3irbfeMrUJCwvjr7/+Yu3atbRr146PPvqIr7/+mqioKFObYcOG8eGHHzJt2jTat2/P/v37WbVqVbGCCmEZqw4beuu6Nw/ATetg42jE9VJzU/nt1G8AjEpNBb9m4NeU9DVrQadD26olTo0bm10T6OHMkJsbAPDFxtPVHTIALXxaMKDpAABmeXugAJzbWun7Xdt9QoZihRD2w65/a/700083PO/s7Mynn37Kp59+WmqbRo0a8ffff9/wPj179mTfvn03bDNp0iQmTZp0wzbCMlYfMczDimotibM9WnZiGdkF2bRQu9IlJxduKhyGLVINW5LHuoXx064Y/jmWwIn4dFrU86i2mI0m3TSJVedWsUeXxiYXZ3qc31bpe8kcOyGEPbLrHjtR95xPzOTY5TQ0ahW9W0piZ2+yC7L59ui3AIxJSkIFhmHYq1fJKtxm7/phWKMmAe5EtTIUHH256Ux1hFtMkFsQD7V8CIBPfL0puLgH8is3lGrafULm2Akh7IgkdsKurC4smriliS8+btW/x6i4sV9P/kpSThL1nf3ol5wAzl4Q2oW0NWtAr8c5MhKnGyzr83iPJgD8vv8il1NtkxCNazMOTydPTjk58burE1zYXan7qN0Kh2Jljp0Qwo5IYifsyrVh2BsvJSOqX74un4WHFwLwiDbUMI+j2V2gcSS9cG/Y0oZhjW5q6EOXMF/ydQrzN5+1csQl89J6mbYa+9THi6yzmyp1H5VxKFZ67IQQdkQSO2E3EtJy2FNYMdmnlSR29uaP038QnxVPgEsAAy9GGw6G9yM/PoGs3YZeL8++UTe4g8GEnob17b7bcZ6EtByrxXsjwyOGU9/RkysODiyOXVWpe0jxhBDCHkliJ+zG6qOG3rr2od4EeTmX0VpUpwJ9AfMPzwdgdNi9aK9Eg0oDzXqRvno1KAou7dvjGBJS5r16tAjg5obe5OTr+XT9KWuHXiInjROTW48DYKE+mavplyp8DymeEELYI0nshN1YdfgyAH3bSG+dvVlzbg2x6bF4a715IK/wx0bDruDiQ9qqwmHYu288DGukUql4LiocgB92xnAh2TaJUVTkGNrm6chWq/j0v3crfL3aVYonhBD2RxI7YRcS0nPYfjoRgH6S2NkVvaLnq0NfATCi5Qhcj/5hONHyHvIvXyZ7715QqfCIKnsY1ujWpv7c2tSPfJ3CnHUnrRF2mVRqNVPdWwDw66VNnEyuWBymnSekeEIIYUcksRN24c8Dl9ErcFNDbxr5udk6HFHEhtgNnEo5hZujGw/WuxUu7gaVGloPJuUXw4Lgrh074ljBBbun9jH02v2y9yJnrmRYOuxyuTksirsys9CjMGPnjAptdybFE0IIeySJnbALv++/CMDAdmXP0RLVR1EUvj70NQDDw4fjFb3acCKsB4qLHynLDPuueg8bVuF7d2jkw50Rgej0Ch//Y5teOxrdxpSkZJwUhR1xO1gXs67sawpJ8YQQwh5JYids7syVDA5eSEWjVnGPJHZ25b/L/3Ho6iG0Gi0jWz4Mh5YaTkQ+QPr69RTEx6Px9cWjz12Vuv/UPi1QqeDPA5dYdyzegpGXU73WNHBwZ0xKGgAf7v6QXF1uuS5Vu0rxhBDC/khiJ2zu9/2GisTbm/nj7661cTSiKOPcuiHNh+CXegmungCNFlreQ8qPhi3/vIcMQe1UucWkW4d48ejtYQC8+MtBEjPKl1RZjFoDzfswLjWNQLUzFzMusvjI4vJdatx5QubYCSHsiCR2wqYURTENww66SXrr7Mn+hP3situFg9qBsW3GwiHDsCvhfcmLSyZz2zZQqSo1DFvU1D7hhNfz4GpGHi//eqhC89wsouW9uCoKU9INSeXXh74mPrPs3kNT8YTMsRNC2BFJ7IRNHbiQyrnELJwd1bIosZ358uCXAAxoOoAgl0A4/IvhROQDJC8xDMm6de+GU4P6VXqOs6OGj4e1x1GjYs3ReJbtvlCl+1VYs97g4MzdCedp5RlGdkE2Wy5uKfMylYskdkII+yOJnbCp5fsMvXV3tQrCTetg42iE0fGk42y+uBm1Ss0jbR6BmG2QdhG0XugbdCP1V0M1rM/w4RZ5XqsQT1OV7Jt/HiEmsRrnrTm5QbPeqIAWOsOh5NzkMi+TOXZCCHskiZ2wmXydnhUHDYsSD2ovw7D25KuDhrl1UY2iaOTZCA4WFk20upe0dRvQpabiGBKCe/fuFnvmY92a0LmxL5l5OqYs3Y9OX41Dsi0HAOCTbOgtTM4pR2Lncm2BYkWvt15sQghRAZLYCZv5+9Blrmbk4u+upXuLAFuHIwqdST3D2vNrAXi07aOQlwmHDT10tB12rWhi6FBUGo3FnqtRq/hoaDvctQ7sPp/MF5tOW+zeZWoRBWoHvDMSAEjJTSnzEmNiB6Dk2GbPWyGEuJ4kdsImFEXh681nARjdtRGOGvlWtBfzD81HQaFnaE9a+LSAI8shLx18wsjJ8iP7wAFwdMT7/iEWf3aorytv3NsKgI/XnuDwxVSLP6NELt4Q1gMfnaHnrTw9dqoiiZ3MsxNC2Av5bSpsYte5ZA5dTEXroGbELY1sHY4odDHjIn+d+QuAxyIfMxzc+43h7c0jTUUTnnf1xsHf3yox3N+hAVGt65GvU3h2yX5y8nVWeU4xLe/Fu3BItTw9diq1WnafEELYHUnshE18vfkMAINvboCvW+XWQBOWt/DwQnSKji7BXWgb0BauREPsf6DSoGs6kNQVKwDwHmaZoomSqFQq3r0vEn93LScTMvhgdbTVnmUmov+1HrusK+W6xDgcq8+UAgohhH2QxE5Uu/OJmawt3GVg3O2NbRuMMEnISuC3k78BMD5yvOGgsbeuRRSp63egZGXh1LQprp07WTUWP3ctM++PBGD+lrNsPXXVqs8DwD0Q73ptAUjJSSzXJdcKKCSxE0LYB0nsRLVbuPUcigI9wwNoFuhh63BEofmH5pOnz6N9QHs6BXWCglw48CMAyk2jSPnJUDThM2wYKpXK6vHcGVGPEV0aAvDcsgOkZudb/Zk+bR4AIEOfT35+2btgmJY8kaFYIYSdkMROVKvU7HyW7o4F4NHbm9g4GmEUlxnHshOGnSUm3jTRkLhF/w1ZieARTHaaL7knT6FyccFr0MBqi+vV/i1p7OfK5dQcpv1+2OrP82j3EOrCnS9STq0ss71Kdp8QQtgZSexEtfp2+zmy8nREBHlwWzM/W4cjCn118Cvy9fl0rNeRLkFdDAf3FO6Z2n4EST8Yeus8+9+NxtOz2uJydXLg42Ht0ahV/L7/kmlBa2tRaz3wVhv2K04+8H3Z7Y27T8gcOyGEnZDETlSbrLwC5m8xLHEyoWfTahnOE2W7mHGRX08Z1ql7sv2Thn+X+KNwZj2gIsfjNtJXrQLAd+TIao/vpoY+TLyjGQAv/XqQgxdSrPo8b1fDmoop57dA+o33jDUVT8gcOyGEnZDETlSbH3fGkpyVTyM/V/pHBts6HFHoq4NfUaAvoEtwF8PcOoBt/2d42/JernxjSPo8+vXFOTzcJjE+06s5d4QHkJOv57FvdhOXar0Fgb3d6gGQrFJg37c3bFt09wkhhLAHktiJapFboOPLwp0EJvRoioMsSGwXYtNiWX5qOQAT2080HEy9CIcM69Vl+w8g4591oFYTMGmSjaI07Eox58GbaB7oTnxaLuO/3U12nnXWt/Nx9gEgRaOBvYtBX/pz1G4yx04IYV/kt6uoFr/suUh8Wi5Bns7cd3N9W4cjCn205yN0io7bQm7jpsCbDAf/+wz0BdC4G1d+Mmwt5nXvvWibNrVhpODh7Mj80Z3wcXXk4IVUq+0n6631BiBZ6wopMXD631LbmhYozpLETghhHySxE1ZXoNPz+UZDb9347k3QOlhuf1FRedsvbWddzDo0Kg1TO041HMxOhj2LAMjyuZfMzZvBwQH/iU/aLtAiGvq58vnDHXDUqFh5OI7Xlh9GUSyb3Jl67AIjDAd2fV1qW1PxRJbMsRNC2AdJ7ITV/XHgEjFJWfi6OfFg54a2DkcA+fp8ZuycAcDwiOE092luOLF7AeRloAS04srPWwHwvu8+nBraz79blyZ+fDLsJlQq+HFnjMV3pjD12PkUfs4nVkPi6RLbSvGEEMLeSGInrConX8eHhb94H+vWBBcn6a2zB0uOL+F06mm8td5MaDfBcDA/G/77HIB0xz5k7dqFyskJ/wlP2DDSkvVvG8y79xl2pvhsw2nTFnWWYOqxowCaRwEK7PiixLbGBYqleEIIYS8ksRNWNX/LWS6l5lDf24WxtzW2dTgCSMpJ4rP9nwHw9M1P46X1MpzY8QVkJqB3a0DCki0A+I57BMeQEFuFekMPdm7IC30NVbozVh0nM7fAIvc19djlJMMthUnt/u8hJ7VYW7VxgWKZYyeEsBOS2AmruZKey2frTwHwfFQ4zo7SW2cPPt7zMen56bT0bcngZoMNB7OSYMssAJLSu5N/8RIO9erh/9hjNoy0bBN6NMXLxZF8ncKFZMskVz7awh673BRocgcEREBeBuz7rlhbU/GE9NgJIeyEJHbCaj7+5wSZeTraNfBiQDv77PWpa3Ze3mla3uSVLq+gURcm25s/gpxU8l1bcfXPHQAEPvecqUfKXqlUKup7G5KriymWmefm7ewNFCZ2KhV0Key12/FFsaVPpHhCCGFvJLETVnEiPp2fdsYA8No9rVCrZZcJW8vV5fLWf28BMCx8GO0D2xtOpMTAzi8BSDjdDCU7G5ebb8bznv42irRi6vsUJnYW7rHLLsgmuyAb2g4DFx9IOQ8nVpm1Nc6xk+IJIYS9kMROWJxer/D68sPoFejbOohOjX1tHZIAvjz4JefTzhPgEsAzNz9z7cS//wNdHpnqjqRt3A0qFfVefaXGbPlm7LG7kGKZxM7N0Q0HtQMAqbmp4OQKHcYYTm6dA0WWVzHtPCFz7IQQdkISO2FxX285w46zSbg6aXjl7pa2DkcAJ5NPsuDQAsAwBOvh5GE4cWk/HFyCXgdxmwwJi/fQobi0bm2jSCuugYV77FQqlanXLjkn2XCw83hwcIbY/wzLnxQyFU/IHDshhJ2QxE5Y1LHLaXy4+gQA0+5pRUM/+56jVRfk6/OZvm06BUoBd4TeQa+GvQwnCnJh+ZOAQmJiF/IuXEYT4E/g1Ck2jbeirs2xs1xyZZxnl5xbmNh5hkCXxw3v//MG6AwVuCqZYyeEsDOS2AmLycnX8eyS/eTp9PRuWY9hnUJtHZLAMAR78OpBPBw9eKVLkSHWDe9DwhFy8/xJ3BwHQNArr6Dx9LRhtBVn6Tl2UKQyNifl2sHbpxjm2l05Dgd+AIqsY5ebi6Kzzt61QghREZLYCYv5aE00x+PS8XNz4v0hkTVmjlZtti9hH18eNBRGTOs6jSC3IMOJ2F2w9RMUBeKiI1Dy83Hr3g2Pvn1tGG3lGHvsEtJzyS2wTHJlWsvO2GMH4OIN3Z4zvL/+XcjLMs2xA9Bn51jk2UIIURWS2AmL+OPAJb7afBaA94e0xd9da+OIRHpeOi9vfhm9omdA0wH0DStM2vKyYPkToOhJzrydrCNnUDk7EzTtjRqZjPu6OeHsaPhRdjnFMsmVafeJ3BTzE50fA++GkH4ZdsxD5exsWBIFUKQyVghhBySxE1V28EIKzy87AMD47k24q1U9G0ckFEXhfzv+x8WMi9R3r8/LnV++dnLdW5B4iuzsYOJXGZakCZwyBacG9W0UbdWYr2VnmeFYs90ninLQwp2vG97f/DGq9MtF9ouVAgohhO1JYieqJCEth/Hf7CG3QM8d4QG82DfC1iEJYNmJZfx15i/UKjXvd3sfdyd3w4mzm2DHPApyVVzY6gsFBXj06YPPyIdtG3AV1fcxFDFYbC270nrsANrcD/U7Ql46rJiCyriWnRRQCCHsgCR2otKy8goY/+0e4tJyaBbozuwHb0IjCxHb3MErB3lv53sAPH3T09cWIs5Nh+UTURS4dCySgqvJODVqRPC7/6uRQ7BF1fd2Biy3lp2xx86seMJIrYaBc0HtCCdWotboAdkvVghhHySxE5WSnadj3KLd7I9NwcvFka9HdcTT2dHWYdV5idmJPLvhWQr0BfRu2JtH2jxy7eTqVyE1hsQzDcg8fhWVVkv9ObPRuLvbLmALMQ3FWnj3CbPiiaICW0L35wHQFCQCkL1vr0WeLYQQVSGJnaiwnHwd47/dzfYzibhrHVg4thON/d1sHVadl6/L54VNL5CQlUCYVxhv3/b2tZ64k2th72Iy45y4ssewEHHQtGk4h4fbMGLLMS15Yun9YkvqsTO6/VkIbI13kzQArvzfXPJiYy3yfCGEqCxJ7ESF5BbomPDdHjafvIqrk4aFYztxc0MfW4dV5+Xqcnl2w7PsjNuJq4Mrn/T85Nq8urjD8Ms48rPVXNxdH/QKXoMH4z1ksG2DtqD63oVz7Cw0FFu0x04psoWYGQcnGDgX76Y5uAbmouTkcPn1aaW3F0KIaiCJnSi3vAI9E7/fx/roKzg7qlkwppPsA2sHsvKzmLhuIhsvbESr0TKr5yyaeDcxnEw8Dd/eh5KVysXdDdFl5KINDyfo9ddsG7SFGXvsLqfkoNNXPbEy9tjl6/PJKrhBL2D9m1H1fJHgTimoNApZ//1H6i+/VPn5QghRWZLYiXLJ1+l55qd9/HMsHq2DmvmjO3FLEz9bh1XnZeRl8MQ/T7Dj8g5cHFyY13set9W/zXAyJRa+GYiSkUD8iSZkX8xD7eZGg9mfmC2sWxvU89CiUaso0CskpFd9LTsXBxecNYaCjGJLnlyv+ws43TKAgEjDkGz8+++TH59Q5RiEEKIyJLETZSrQ6Zmy9AArD8fhpFHzxcgO3NbM39Zh1Xl5ujyeXv80+xL24eHowVd9vqJTUCfDyYwEQ1KXEktCdEOS9xuSneD//Q+nxo1tF7SVOGjUBHkaEjFLFVCY5tmVtORJUWo1DJqH7x0ROPvmoc/I5OKkJ9HnyE4UQojqJ4mduKGkzDzGLd7Nnwcu4ahRMe/hm+kZHmjrsOo8nV7Hy5tfZlfcLtwc3fgq6ivaBbQznMxONgy/Jp4m/kh9kvYbNqyvN+11PPtG2TBq67pWQGHheXZl9dgBOLqgGvETIXc5o3bSk33oCJdfeA5Fr7dILEIIUV6S2IlS7T6XxN2zN7PxxBW0DmrmPnQzvVrKrhK2pigKM3bNYM35NTioHfjkjk9o7dfacDI3A75/AOXSYeIOBJF8uLAC9s038X3oIRtGbX0NrLT7RJk9dkbugWgn/kKD3npQKaStWceVj2ZaJBYhhCgvB1sHIOyPTq/wxabTfLTmBDq9QpMANz4bcTMRQZ62Dq3Oy9fl8/Hej/nx+I8AvHv7u9wSfIvhZHYKLB1J7tF9XNoRRE6iGlQqgt95G+8hQ2wXdDUx9dhZeCi2XD12RoERuL28guCMAVzepCJx/mKcguvh/fBYi8QkhBBlkcROmIlNymLq0gPsPJcEwMD2Ibx7XyRuWvlWsbUzqWd4adNLHEs6BsALnV6gX1g/w8lzW1B+fZyUvUnE7wtA0anQeHsT/L938OjVy4ZRVx9L7xdrHIotd4+dUUA43u+uIG/SvSTuV3H5nZkomcn4PD7FInEJIcSNyFCsAAy9dEt3x3L37M3sPJeEm5OGmfe35ZNh7SWpszFFUVhyfAnD/hzGsaRjeGm9mNVzFiNbjYSCPFj7BgWfD+DCX5nE7fZG0alwu/VWwn7/vc4kdWD5HrsA1wAA/jj9B8eTjlfsYv/mBPzfn/hEGn7Exn38FYnvP2eRuMT/t3fn0VHV5+PH33fWzGRfSEKAhLAosooEaETFFn6lwsGqLbUYJQKWg6KA+KNoPWr7VQviRkEL1J7Cr4WqpQoI/aqFsGmBEBJWgUAVCBASlpB9Mtv9/P4YGAhECJBkksnzOtyTmc/n3pvn+TCZeeauQoirkU/sVk4pxZffFPPOmnwOFlcC0C8lmnd/cTvJsfYARyfOOM7wyuZX2HR8EwDpbdN57a7XiLfHw6kD8OkTVO44SGF2HN4aI5rZTJvnphEzZgyaoXV9b7t0i51S6qbvf/tAlwdY+d+VHCk/wpjPx/DaoNf4cccf13t5rU1XEhZvwvD0SM5uOcepxf/CW3SENrOXoFlCbio2IYT4Pq3rnV/4KaXYePA0P33/P0xcksvB4koiQkw8f183Pp7wAynqAkwpxbqCdfzss5+x6fgmLAYLM/rPYMH/WUC8rQ1kL8Qz914KPyvg2MZYvDVGLF0603HZP4h9/PFWV9QBJJ0v7KpdXkqr3Te9vjhbHEuGL+HOpDtxeBw8t/E53sl9B6fXWe91aKGxxP95E22G3wbA2S++4dj9A/Dm/+em4xNCiLpoSu5/02TKy8uJjIykrKyMiIjAnYiQc6SEN7/MZ9th33F0douRcYNS+dU9nYi0mQMWl/AVdF+d+IqFuxay+8xuALpGd2XW3bO4JfoWOLoZ9e+XKd30Dad3ReB1+Qq46EdGE//rX2MIad1bgtJeW8uZSicLHu3HT3omNsg6PbqHd3Pf5a/7/gpAamQq/3Pn/3B7/O3XtZ7SuS9StPATlFfDHOql3aTh2B79PVjkS5QQ4uqup36Qwq4JBbKwc7i8rNpVyNLso+w6XgaAxWTgsR+k8OS9nYkLszZpPKI2XelkFWTxp91/8h/PZTVaybgtg6dufwpr8X70L1+jbO1/KDkUiqvcV4Bbb7mFxN++gv2OOwIZfrPx1NJc/ndPEZoG4wel8n+H3UqI2dgg6157dC2vbX2NszVn0dAY3W00T/Z50n/2bH3U5G7m+DNP4S5xgqaI7q4RN20GpvQxvgsdCyFEHaSwa6aaurBze3W2fHuW/91zkn/tOUlFje9CtWajxs/7dWDykC60jQyuW0u1NB7dwxdHvuDPu//Mt2XfAmA32Xm428OMufUR4o5sxr32j5RsyKf0Wzu62/fhbwgNJe6Zp4l59FE0kxwqe0GZw81rq/exLPc4AJ3iQpk4uDPDe7clrAFOAipzljE7ZzafffsZAOHmcJ7o/QSPdHuEEFP9tpZ6Kyo4OWU8FZv3AGAw68TdGUX01Ncx3Db0pmMUQgQfKeyaqaYo7Cpq3Hx96AxZB06xdn9xrWONUmLtjB6QzM/7tZctdAFWUlPCJwc/4eP8jymuLgZ8RUJG9wwyOgwjcvenOP61iJJdDiqOh4DynQhgbteWmMfHEfnggxjDQgOZQrO2/sApnv90N8XlvuPhQswG7uvZlkcGJpOWEn3TJ1ZsLtzM29vf5uC5gwAk2BMY23MsD3V9CJupfl+Wqr7aSPHvfoPzuO+QCKNFJyotjugpv8PcRwo8IcRFUtg1U41Z2B0oKufV1fvYdrgEt/fif2lsqIWf9ExkRO+2/CA1FoPh5j7QxI1ze91sLtzMv777F1kFWbh0FwAxITE82nUUD5viMW9cQfm6/1B+1IKn+uIWJnv/O4gZ9wRhgwe3yhMjbkSZw82SrUf5JO84352u8rf3bh/J+LtSGd6rLWbjjY+lV/ey+rvVzNsxz1+cx4TEkHFbBvd3vp/E0Gsf46e8Xso+/H+c+eN7uEvOX6bFoIi8zUbcL4ZjGTwaEnrATRaiQoiWTQq7ZqoxC7uTZQ7SZ64DIDUulB91i2fIbfEM6BiD6SY+vMTNKaoqIqcoh+yT2Ww4voEyZ5m/r2d4Co9Y2/PD/O9wbD5A+VErroqLxZzBZiVi+HCixzxOyK23BCL8oKCUYuexUj7adozlO0/g8vju3xobamF4r7aM7JNEWkr0DX/pcXqdrDi0gkXfLOJE5Ql/e4/YHgxNGcqPkn9Ep8hOV4/R66Xis48pWTgXx5HzrxFNEdHBQVSfMGyDR2DoMRKS08Eou96FaG2ksGumGntX7D9zj3NHchSd2oQ1+LpF/ZxxnGF70Xayi7LJKcrhaPnRWv3xysKDZ+CHB08Rc1yjqtiKu+riB7VmNhCW3o+Inz9K2ODBGKyyy7whna10sjS7gL9uOcqZyouXLYkLs9AvJZo7kqO5IyWaXu0ir/ukC4/u4fPDn7Ps4DJ2ntqJ4uJba2pkKkOSh3BP+3voGdsTs/H7zz53bNnAmblvU7njv/42zaiwx7mwt9MI7debkEHD0br8EGJSwdAwJ4cIIZovKewa0fvvv8+bb75JUVERffr0Yd68eQwYMKBeyzaXy52Im6MrncLKQg6XHfZN5Yf5rvQ7jpQfoaTm/PFSXkWbMkg5reh/UnHrSTdxJRrGCoP/eDk/o4HQAXcQ+eAown40RI6dawJur87mb8/y2c5C/v1NERVOT61+k0GjR1IEfZOj6RwfRnKMneQYO+2ibFhM194CfsZxhvXH1pNVkEX2yWw8+sX1hxhD6BPfh9vb3E7HyI6kRqSSHJFMuCW81jpq9u+nZNFfqPxqI95zFbX6DCYdWxsX1miFtV0c1k6dMHXqgalTH7TE7r6C7yrFoxCiZZHCrpF8/PHHjBkzhgULFjBw4EDmzJnDsmXLyM/PJz4+/prLS2HX/FW7qyl1lnKu5hznnOd8P88/Pl5+jO/OHaL47DFsFS6iqiCqUvl+VimiKiG6EpJLdKLLwaDXvWtPs5qxdkzBnj6I0PR0bP3SpJgLoBq3lz0nysg7eo68gnPkFZRyuqLuixAbNGgbaaNDjI2UmFCSY+10iLHTPtpGfLiVuDDrFVv6KlwVbDq+iXUF68gpyuGc81yd644NiSUlIoWUiBTa2NsQExLjm6zRRBVVYt9xCLX5Pzh27EGvdtWdjKYwhehYIrxY48OwJLfFktIRc0pnzKm3YYjvCLYoCIkCS6gcuydECyGFXSMZOHAg/fv357333gNA13U6dOjAM888w/PPP3/N5aWwuzFKKbzK65t0Lx6vC4/bgdfjwOtx4vY48Hpq8Lhr8LiqcbqqcLmqcbqqcTkqcNdU466pxlVTjdtZg6fGQY3TQXVNNTWuGhxOJ26XC5fbg8GtY3Mp7E6wOcHmAnsN2FyKiGqIqoKQet7UQDNpWNtGY+12CyF978TavReW1FRM8fE3fVamaDxKKY6fc5BXcI5dx8ooKKmioKSagpJqatz6NZcPDzERbjVht5qwW4zYLUZCLb7nNpOGx1REuTpAuTpKheckpZ5CKj11F3uX09CIMIXTvSSUWws12p1yEV9cTeRpB7ZyN9q13s1NOlgUhguT1YjRZsZkt2IMtWEMC8UYHoHRHobBHoZmCwerHc1iQbOEoFms56cQ32QNQQuxoVlsvsdWG1qIHaw2NKMFjBbflkN5vQtxU6SwawQulwu73c4///lPHnjgAX97ZmYmpaWlrFy58oplnE4nTufFb/7l5eV06NChUQq7wt1Z7J/8jO9JHf+jdb7hqytn/v75QKuj7YaWvcr8mqq9ak35JoMOBnVxutCmXWjTm/b+eJoZTOEWTNHhmGLjMLWJwxSfiCmhHeZbemHp1MVXwMkZrEFDKcXpSifHzhd5BWcdFJRUc6ykmhOlDk5XOHF5r1341clQg8Fyxj9pxko0U9UlP6vQjNVoV6ncDLoisgriyiHprKLdWUW7sxBfqogrh9D63wmtQXgNCq8BvOePPlCadv7nJRNc2VZH+/e5Wt+NuHR9l676e0f9arE1QDzNmxTrden/aRZRsW0bfL3XU9jJ6VX1dObMGbxeLwkJCbXaExISOHDgQJ3LzJw5k9/97ndNER5uRzlJRcH/VnIjvAbwGkE3aXhNoEwaymRAMxkwmI2YTEZMZjNmixmrxYIlLAxjeDiG8EgMkdEYI2MwRMVibNMOU1IypoS2GEJl12lro2ka8eEhxIeH0C8l5op+pRRlDjdnKp1UOb1Uu7xUuzxUubxUOz21njtcXqoua6t2xVN9fjmvUui68v/UFejKg1erRhkqUMZKlFaFZnSgGR1gcKAZq3EaHZyOqmF/tButqxc036RpXuwuN5HVHkKdHuwuL6FOL6EuHbsTQmt8W6l9j8HsAZOuMHrB5PVt6DNd8th42WOL98rxMuoaxhusc0VLIJ83ddE9nmvP1MiksGtEL7zwAtOmTfM/v7DFrjHEdknj5LT7fE80Dd9OG9D8l3DQ/O2+fxqaZqg9v8bFtku3NGkG37o0w4XFURjOL2Y4P4t2/rdoF9v881/83b75tPO7Imu3XWg3GowYNaPvp8mM0WDCaDJhMtswmEIwWe0YLTYMZhua2QIms2/LmNEImoZmNILBgGYw+HYhyZ0ZRBPQNI0ou4Uou6XJfqev6PMVgEqB93wxqHTwKoVXV+cPZThfHOq+Nl35Jo9X4dE91Hhd6LrCrXvw6LrvkIfzj3Wl41E6LnyPdaWjo6OUQuE9H4MX3etG83jQ3U40twuD24nB7cLgcYLXg0H3onndoBSa7gVdr/VYU+p8mxdNV6D08+3nq0NFrTONr3RJn6przkt2B1y2o0pdvnytzrrbr7q9qkF2hDVC4RTQHXT1/9313xbY/IrLThGxgQ5BCrv6iouLw2g0UlxcXKu9uLiYxMS6L0RqtVqxNtHlKsJiO/CDCe80ye8SQjQPBoOGAU3eyIUQfnIAUD1ZLBb69etHVlaWv03XdbKyskhPTw9gZEIIIYQQPvJF7zpMmzaNzMxM0tLSGDBgAHPmzKGqqoqxY8cGOjQhhBBCCCnsrsfDDz/M6dOnefnllykqKuL222/niy++uOKECiGEEEKIQJDLnTQhuY6dEEIIIa7X9dQPcoydEEIIIUSQkMJOCCGEECJISGEnhBBCCBEkpLATQgghhAgSUtgJIYQQQgQJKeyEEEIIIYKEFHZCCCGEEEFCCjshhBBCiCAhhZ0QQgghRJCQwk4IIYQQIkjIvWKb0IW7t5WXlwc4EiGEEEK0FBfqhvrcBVYKuyZUUVEBQIcOHQIciRBCCCFamoqKCiIjI686j6bqU/6JBqHrOoWFhYSHh6NpWoOvv7y8nA4dOnDs2LFr3iQ4GLX2/EHGoLXnDzIGrT1/kDEIxvyVUlRUVJCUlITBcPWj6GSLXRMyGAy0b9++0X9PRERE0LyYb0Rrzx9kDFp7/iBj0NrzBxmDYMv/WlvqLpCTJ4QQQgghgoQUdkIIIYQQQUIKuyBitVp55ZVXsFqtgQ4lIFp7/iBj0NrzBxmD1p4/yBi09vzl5AkhhBBCiCAhW+yEEEIIIYKEFHZCCCGEEEFCCjshhBBCiCAhhV2QeP/99+nYsSMhISEMHDiQbdu2BTqkRjNz5kz69+9PeHg48fHxPPDAA+Tn59eap6amhkmTJhEbG0tYWBg/+9nPKC4uDlDEjWvWrFlomsbUqVP9bcGe/4kTJ3j00UeJjY3FZrPRq1cvtm/f7u9XSvHyyy/Ttm1bbDYbQ4cO5dChQwGMuGF5vV5eeuklUlNTsdlsdO7cmVdffbXW7YaCaQw2bdrEyJEjSUpKQtM0VqxYUau/PrmWlJSQkZFBREQEUVFRjB8/nsrKyibM4uZcbQzcbjczZsygV69ehIaGkpSUxJgxYygsLKy1jpY8Btd6DVxq4sSJaJrGnDlzarW35PyvhxR2QeDjjz9m2rRpvPLKK+Tl5dGnTx+GDRvGqVOnAh1ao9i4cSOTJk1i69atrFmzBrfbzY9//GOqqqr88zz77LOsWrWKZcuWsXHjRgoLC3nooYcCGHXjyMnJYeHChfTu3btWezDnf+7cOQYNGoTZbObzzz9n3759vP3220RHR/vnmT17NnPnzmXBggVkZ2cTGhrKsGHDqKmpCWDkDeeNN95g/vz5vPfee+zfv5833niD2bNnM2/ePP88wTQGVVVV9OnTh/fff7/O/vrkmpGRwTfffMOaNWtYvXo1mzZtYsKECU2Vwk272hhUV1eTl5fHSy+9RF5eHp9++in5+fncf//9teZryWNwrdfABcuXL2fr1q0kJSVd0deS878uSrR4AwYMUJMmTfI/93q9KikpSc2cOTOAUTWdU6dOKUBt3LhRKaVUaWmpMpvNatmyZf559u/frwC1ZcuWQIXZ4CoqKlTXrl3VmjVr1ODBg9WUKVOUUsGf/4wZM9Rdd931vf26rqvExET15ptv+ttKS0uV1WpVH374YVOE2OhGjBihxo0bV6vtoYceUhkZGUqp4B4DQC1fvtz/vD657tu3TwEqJyfHP8/nn3+uNE1TJ06caLLYG8rlY1CXbdu2KUAdPXpUKRVcY/B9+R8/fly1a9dO7d27V6WkpKh3333X3xdM+V+LbLFr4VwuF7m5uQwdOtTfZjAYGDp0KFu2bAlgZE2nrKwMgJiYGAByc3Nxu921xqRbt24kJycH1ZhMmjSJESNG1MoTgj//zz77jLS0NEaNGkV8fDx9+/blgw8+8PcfPnyYoqKiWvlHRkYycODAoMgf4M477yQrK4uDBw8CsGvXLr7++mvuu+8+oHWMwQX1yXXLli1ERUWRlpbmn2fo0KEYDAays7ObPOamUFZWhqZpREVFAcE/Brqu89hjjzF9+nR69OhxRX+w538puVdsC3fmzBm8Xi8JCQm12hMSEjhw4ECAomo6uq4zdepUBg0aRM+ePQEoKirCYrH439AuSEhIoKioKABRNryPPvqIvLw8cnJyrugL9vy/++475s+fz7Rp0/jNb35DTk4OkydPxmKxkJmZ6c+xrr+JYMgf4Pnnn6e8vJxu3bphNBrxer28/vrrZGRkALSKMbigPrkWFRURHx9fq99kMhETExN04wG+Y2xnzJjB6NGj/fdKDfYxeOONNzCZTEyePLnO/mDP/1JS2IkWbdKkSezdu5evv/460KE0mWPHjjFlyhTWrFlDSEhIoMNpcrquk5aWxu9//3sA+vbty969e1mwYAGZmZkBjq5p/OMf/2Dp0qX8/e9/p0ePHuzcuZOpU6eSlJTUasZA1M3tdvOLX/wCpRTz588PdDhNIjc3lz/84Q/k5eWhaVqgwwk42RXbwsXFxWE0Gq8447G4uJjExMQARdU0nn76aVavXs369etp3769vz0xMRGXy0VpaWmt+YNlTHJzczl16hR33HEHJpMJk8nExo0bmTt3LiaTiYSEhKDOv23btnTv3r1W22233UZBQQGAP8dg/puYPn06zz//PL/85S/p1asXjz32GM8++ywzZ84EWscYXFCfXBMTE684mczj8VBSUhJU43GhqDt69Chr1qzxb62D4B6Dr776ilOnTpGcnOx/Tzx69CjPPfccHTt2BII7/8tJYdfCWSwW+vXrR1ZWlr9N13WysrJIT08PYGSNRynF008/zfLly1m3bh2pqam1+vv164fZbK41Jvn5+RQUFATFmAwZMoQ9e/awc+dO/5SWlkZGRob/cTDnP2jQoCsub3Pw4EFSUlIASE1NJTExsVb+5eXlZGdnB0X+4DsL0mCo/fZtNBrRdR1oHWNwQX1yTU9Pp7S0lNzcXP8869atQ9d1Bg4c2OQxN4YLRd2hQ4dYu3YtsbGxtfqDeQwee+wxdu/eXes9MSkpienTp/Pll18CwZ3/FQJ99oa4eR999JGyWq1q8eLFat++fWrChAkqKipKFRUVBTq0RvHkk0+qyMhItWHDBnXy5En/VF1d7Z9n4sSJKjk5Wa1bt05t375dpaenq/T09ABG3bguPStWqeDOf9u2bcpkMqnXX39dHTp0SC1dulTZ7Xa1ZMkS/zyzZs1SUVFRauXKlWr37t3qpz/9qUpNTVUOhyOAkTeczMxM1a5dO7V69Wp1+PBh9emnn6q4uDj161//2j9PMI1BRUWF2rFjh9qxY4cC1DvvvKN27NjhP+OzPrn+5Cc/UX379lXZ2dnq66+/Vl27dlWjR48OVErX7Wpj4HK51P3336/at2+vdu7cWet90el0+tfRksfgWq+By11+VqxSLTv/6yGFXZCYN2+eSk5OVhaLRQ0YMEBt3bo10CE1GqDOadGiRf55HA6Heuqpp1R0dLSy2+3qwQcfVCdPngxc0I3s8sIu2PNftWqV6tmzp7Jarapbt27qT3/6U61+XdfVSy+9pBISEpTValVDhgxR+fn5AYq24ZWXl6spU6ao5ORkFRISojp16qRefPHFWh/iwTQG69evr/NvPjMzUylVv1zPnj2rRo8ercLCwlRERIQaO3asqqioCEA2N+ZqY3D48OHvfV9cv369fx0teQyu9Rq4XF2FXUvO/3poSl1yqXIhhBBCCNFiyTF2QgghhBBBQgo7IYQQQoggIYWdEEIIIUSQkMJOCCGEECJISGEnhBBCCBEkpLATQgghhAgSUtgJIYQQQgQJKeyEEEIIIYKEFHZCCNHI7r33XqZOnRroMIQQrYAUdkIIIYQQQUIKOyGEEEKIICGFnRBCNKCqqirGjBlDWFgYbdu25e23367V/7e//Y20tDTCw8NJTEzkkUce4dSpUwAopejSpQtvvfVWrWV27tyJpmn897//RSnFb3/7W5KTk7FarSQlJTF58uQmy08I0bxJYSeEEA1o+vTpbNy4kZUrV/Lvf/+bDRs2kJeX5+93u928+uqr7Nq1ixUrVnDkyBEef/xxADRNY9y4cSxatKjWOhctWsQ999xDly5d+OSTT3j33XdZuHAhhw4dYsWKFfTq1aspUxRCNGOaUkoFOgghhAgGlZWVxMbGsmTJEkaNGgVASUkJ7du3Z8KECcyZM+eKZbZv307//v2pqKggLCyMwsJCkpOT2bx5MwMGDMDtdpOUlMRbb71FZmYm77zzDgsXLmTv3r2YzeYmzlAI0dzJFjshhGgg3377LS6Xi4EDB/rbYmJiuPXWW/3Pc3NzGTlyJMnJyYSHhzN48GAACgoKAEhKSmLEiBH85S9/AWDVqlU4nU5/oThq1CgcDgedOnXiV7/6FcuXL8fj8TRVikKIZk4KOyGEaCJVVVUMGzaMiIgIli5dSk5ODsuXLwfA5XL553viiSf46KOPcDgcLFq0iIcffhi73Q5Ahw4dyM/P549//CM2m42nnnqKe+65B7fbHZCchBDNixR2QgjRQDp37ozZbCY7O9vfdu7cOQ4ePAjAgQMHOHv2LLNmzeLuu++mW7du/hMnLjV8+HBCQ0OZP38+X3zxBePGjavVb7PZGDlyJHPnzmXDhg1s2bKFPXv2NG5yQogWwRToAIQQIliEhYUxfvx4pk+fTmxsLPHx8bz44osYDL7v0MnJyVgsFubNm8fEiRPZu3cvr7766hXrMRqNPP7447zwwgt07dqV9PR0f9/ixYvxer0MHDgQu93OkiVLsNlspKSkNFmeQojmS7bYCSFEA3rzzTe5++67GTlyJEOHDuWuu+6iX79+ALRp04bFixezbNkyunfvzqxZs664tMkF48ePx+VyMXbs2FrtUVFRfPDBBwwaNIjevXuzdu1aVq1aRWxsbKPnJoRo/uSsWCGEaIa++uorhgwZwrFjx0hISAh0OEKIFkIKOyGEaEacTienT58mMzOTxMREli5dGuiQhBAtiOyKFUKIZuTDDz8kJSWF0tJSZs+eHehwhBAtjGyxE0IIIYQIErLFTgghhBAiSEhhJ4QQQggRJKSwE0IIIYQIElLYCSGEEEIECSnshBBCCCGChBR2QgghhBBBQgo7IYQQQoggIYWdEEIIIUSQkMJOCCGEECJI/H+3Bhg82hmGRQAAAABJRU5ErkJggg==", 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" ] @@ -122,10 +123,10 @@ "\n", "# Nothing changed from the simulation we declared above, so let's use that to run it.\n", "sim = BasicSimulator(rume)\n", - "with sim_messaging(sim):\n", + "with sim_messaging():\n", " output = sim.run()\n", "\n", - "EVENT_S_TO_I = rume.ipm.events_by_dst(\"I\")[0]\n", + "EVENT_S_TO_I = rume.ipm.event_by_name(\"S->I\")\n", "\n", "plot_event(output, event_idx=EVENT_S_TO_I)" ] diff --git a/doc/demo/05-visualizing-mm.ipynb b/doc/demo/05-visualizing-mm.ipynb index 222252fd..581e0c5f 100644 --- a/doc/demo/05-visualizing-mm.ipynb +++ b/doc/demo/05-visualizing-mm.ipynb @@ -8,7 +8,7 @@ "\n", "epymorph expresses a model's movement dynamics as Movement Models: modular components which support rapid experimentation and comparison. To help visualize the differences between movements models, we can plot their concepts of movement probability between geographic nodes.\n", "\n", - "We'll use the counties geo we created (and cached) in part 3." + "We'll use the same counties scope we created in part 3." ] }, { @@ -20,25 +20,34 @@ "import numpy as np\n", "\n", "from epymorph import *\n", - "from epymorph.geo.cache import load_from_cache\n", - "from epymorph.geo.static import StaticGeo\n", + "from epymorph.adrio import commuting_flows, us_tiger\n", + "from epymorph.geography.us_census import STATE, CountyScope\n", "\n", - "\n", - "def load_example_geo() -> StaticGeo:\n", - " geo = load_from_cache('demo-four-states-by-county')\n", - " if geo is None:\n", - " msg = \"Can't load the demo-four-states-by-county geo from cache; see demo part 3 for that.\"\n", - " raise Exception(msg)\n", - " return geo\n", - "\n", - "\n", - "geo = load_example_geo()\n", + "# Create our scope: the counties in our four states.\n", + "scope = CountyScope.in_states_by_code([\"AZ\", \"NM\", \"CO\", \"UT\"], year=2020)\n", "\n", "# We can extract the state fips codes from the county fips codes.\n", - "state_fips = {s[:2] for s in geo['geoid']}\n", + "state_fips = {STATE.truncate(x) for x in scope.get_node_ids()}\n", "\n", "# Find Maricopa County's index in the geo\n", - "MARICOPA_CO_IDX = np.where(geo['geoid'] == '04013')[0][0]" + "MARICOPA_CO_IDX = np.where(scope.get_node_ids() == '04013')[0][0]\n", + "\n", + "# We need a placeholder RUME to fetch data from ADRIOs...\n", + "rume = SingleStrataRume.build(\n", + " # We're not going to use the RUME to run a simulation, so\n", + " # the choice of IPM, MM, and Initializer are immaterial.\n", + " ipm=ipm_library['no'](),\n", + " mm=mm_library['no'](),\n", + " init=init.NoInfection(),\n", + " # But we do set our scope and \"install\" some ADRIOs\n", + " # whose data we're interested in.\n", + " scope=scope,\n", + " params={\n", + " 'centroid': us_tiger.GeometricCentroid(),\n", + " 'commuters': commuting_flows.Commuters(),\n", + " },\n", + " time_frame=TimeFrame.of(\"2020-01-01\", 150),\n", + ")" ] }, { @@ -59,20 +68,13 @@ "import numpy as np\n", "\n", "from epymorph.data_type import CentroidDType\n", + "from epymorph.simulator.data import evaluate_param\n", "from epymorph.util import pairwise_haversine, row_normalize\n", "\n", - "params = {\n", - " 'phi': 40.0,\n", - "}\n", - "\n", - "\n", - "def calc_centroids_kernel():\n", - " centroid = geo['centroid'].astype(CentroidDType)\n", - " distance = pairwise_haversine(centroid['longitude'], centroid['latitude'])\n", - " return row_normalize(1 / np.exp(distance / params['phi']))\n", - "\n", - "\n", - "centroids_kernel = np.log(calc_centroids_kernel())" + "phi = 40.0\n", + "centroid = evaluate_param(rume, 'centroid').astype(CentroidDType)\n", + "distance = pairwise_haversine(centroid['longitude'], centroid['latitude'])\n", + "centroids_kernel = np.log(row_normalize(1 / np.exp(distance / phi)))" ] }, { @@ -82,7 +84,7 @@ "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -94,21 +96,19 @@ "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", - "import pygris\n", "\n", - "df_states = pygris.states(cb=True, resolution='5m', cache=True, year=2020)\n", - "df_states = df_states.loc[df_states['GEOID'].isin(state_fips)]\n", + "state_fips = tuple({STATE.truncate(x) for x in scope.get_node_ids()})\n", + "gdf_counties = us_tiger.get_counties_geo(scope.year) # type:ignore\n", + "gdf_counties = gdf_counties[gdf_counties[\"GEOID\"].str.startswith(state_fips)]\n", "\n", - "df_counties = pd.concat([\n", - " pygris.counties(state=s, cb=True, resolution='5m', cache=True, year=2020)\n", - " for s in state_fips\n", - "])\n", + "gdf_states = us_tiger.get_states_geo(scope.year) # type:ignore\n", + "gdf_states = gdf_states[gdf_states[\"GEOID\"].str.startswith(state_fips)]\n", "\n", - "df_merged = pd.merge(\n", + "gdf_merged = pd.merge(\n", " on=\"GEOID\",\n", - " left=df_counties,\n", + " left=gdf_counties,\n", " right=pd.DataFrame({\n", - " 'GEOID': geo['geoid'],\n", + " 'GEOID': scope.get_node_ids(),\n", " 'data': centroids_kernel[MARICOPA_CO_IDX],\n", " }),\n", ")\n", @@ -116,10 +116,10 @@ "fig, ax = plt.subplots(figsize=(8, 6))\n", "ax.axis('off')\n", "ax.set_title(\"Movement probability by county (origin: Maricopa County, AZ, logscale)\")\n", - "df_merged.plot(ax=ax, column='data', cmap='Purples', legend=True)\n", - "df_states.plot(ax=ax, linewidth=1, edgecolor='black', color='none', alpha=0.8)\n", + "gdf_merged.plot(ax=ax, column='data', cmap='Purples', legend=True)\n", + "gdf_states.plot(ax=ax, linewidth=1, edgecolor='black', color='none', alpha=0.8)\n", "# Get Maricopa County's centroid from the geo so we can mark it.\n", - "origin = geo['centroid'][MARICOPA_CO_IDX]\n", + "origin = centroid[MARICOPA_CO_IDX]\n", "ax.plot(origin[0], origin[1], marker='*', color='yellow', markersize=10)\n", "fig.tight_layout()\n", "plt.show()" @@ -142,10 +142,11 @@ "source": [ "import numpy as np\n", "\n", + "from epymorph.simulator.data import evaluate_param\n", "from epymorph.util import row_normalize\n", "\n", "# Commuters as a ratio to the total commuters living in that county.\n", - "commuters = geo['commuters'].astype(np.int64)\n", + "commuters = evaluate_param(rume, 'commuters').astype(np.int64)\n", "pei_kernel = np.log(row_normalize(commuters) + 0.0000000001)" ] }, @@ -156,7 +157,7 @@ "outputs": [ { "data": { - "image/png": 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D4RDnnXdezHyRC7SewLXnfh0IBERpaWmv7zCUwHXjxo0CgHjnnXeEEEL8/ve/F1lZWaKjo6PPwLWvY++aa64RGRkZMeepSFn++Mc/9pq/57H297//XQAQ99xzT695I/uikeeRvpx//vkCQK9jpz92PI76s2bNGgFAvP7660KIrt+wvLxcXHfddQN+bsWKFUJRFLFw4cKYBM5Q9Axc9R7b3/72t4UkSWLt2rXRaQ0NDaKgoCBmGz733HPReKQ/b775pgAgrr322l7vdT/H9rUfLl68WIwbNy5mWs/j5pFHHhGyLEeP3Yg//vGPAoBYuXJlzPQDBw4IAOLOO+/st8w0uCF3h3XxxRfD7/fj3//+N9ra2vDvf/+732oCL7/8MhRFwbXXXhsz/bvf/S6EEHjllVcAAJMmTcJRRx2Fp556KjqPqqp45plnsGTJEni9XgDA008/jdzcXJx++umor6+PvmbPno2srCy89dZbMeuZNm1aTD2eSAvLBQsWYPTo0b2m79y5EwDQ2NiIN998ExdffDHa2tqi62loaMDixYuxbds27N+/P2ZdX/3qV2MeJ51yyilQVRV79uzRsVX7d95552HUqFHRv4877jgcf/zxePnll3vN+/Wvfz3mb73bP2LOnDmYPXt29O/Ro0fj3HPPxWuvvRZ9HBn5LYCurj4aGhowYcIE5OXl4eOPP+5Vpq9+9atwOp0xZXQ4HH2WP1GWLl2KQCCAZ555JjrtqaeeQjgcxhe+8AVdyxis3LIs44orrsCLL76Itra26HyPPfYYTjzxRIwdO7bfZa9duxa7du3Cd77zHeTl5cW8F9mHqqursW7dOixbtizmsfGRRx6J008/fVjb77TTTsP48eNjlpmTkxPd/weSm5uLc889F0888UT0MbGqqnjqqadw3nnnDVqvuqGhodcjSFVVsXz5cpx33nkYN25cdHpZWRkuv/xyvPvuu2htbY35zNVXXz1ofdb//ve/CIfD+MY3vhEz/dvf/vag3zMiKysrZp9xuVw47rjjem2rFStW9Fv9pj9HHHEEjjzyyGgjrccffxznnnsuMjIy+py/+7EXOS+dcsop8Pl8vaopuN1ufPGLXxy0DP/6179QVFTU5zaJ7ItmOI90F9kXsrOzB/1+dj2O+vPYY49hxIgR0ap3kiThkksuwZNPPtmrSklEfX09Lr/8chQWFuLRRx+FLCeut8x4ju1XX30Vc+bMwVFHHRWdr6CgIFrtLSJyzvz3v//dZ1UtoGu/liSpV91eADHX6e77YUtLC+rr6zFv3jzs3LkTLS0t/X6vp59+GlOnTsWUKVNiYpEFCxYAQK9YJHLOYzug4RnynllcXIzTTjsNjz/+OJ599lmoqhpTB7W7PXv2YOTIkb1OMFOnTo2+H3HJJZdg5cqV0YBwxYoVOHjwIC655JLoPNu2bUNLSwtKSkpQXFwc82pvb8fBgwdj1tM9OAUQbcVcUVHR5/RIHcbt27dDCIGf/vSnvdYTORAGW1dkR+1ZnzNeEydO7DVt0qRJveqCORyOaF2/iHi2/0Dr8vl80fq6fr8fP/vZz6J13YqKilBcXIzm5uY+D/Sey8zKykJZWdmQ+trUa8qUKTj22GNj6nY99thjOOGEEzBhwgRdy9BT7qVLl8Lv90fr027ZsgUfffQRrrzyygGXHak7OFDL58hv01dd2alTp6K+vh4dHR26vktPPfdVoGt/1buvLl26FHv37sU777wDAHjjjTdQW1s76PeO6Bng1dXVwefz9ftdNU3Dvn37YqYPdGMQEdmGPX/zgoIC3fX3ysvLe9VvjGdbDebyyy/H008/je3bt+O9997rNwkAAJs2bcL555+P3Nxc5OTkoLi4OBpU9zz2Ro0apash1o4dOzB58uQBGz6a4TzSXU5ODgDE3DAOVHbAnsdRT6qq4sknn8T8+fOxa9cubN++Hdu3b8fxxx+P2tpa/Pe//+31GSEEli5diurqavzjH/9AaWnpkNbdn3iO7T179vR5fu45bd68ebjgggtwyy23oKioCOeeey4efPDBmLqlO3bswMiRI/usK97dypUrcdppp0XrPhcXF0frgw+0H27btg2bNm3qFR9MmjQJQO/4IHLOS0Vf5HY2rObZl19+Oa6++mrU1NTgzDPP7JU1GopLLrkEP/rRj/D000/jO9/5Dv75z38iNzcXZ5xxRnQeTdNQUlLSb2Xz4uLimL/7y8j0Nz2yc2maBgD43ve+128l+Z4H02DLTDa3253QO+X+fPvb38aDDz6I73znO5gzZw5yc3MhSRIuvfTS6HYzg6VLl+K6665DVVUVAoEAVq9ejfvvvz+h65g2bRpmz56NRx99FEuXLsWjjz4Kl8uFiy++OKHrGUx/J8P+MizD3VcXL16MESNG4NFHH8XcuXPx6KOPorS0FKeddtqgny0sLExI0Nc9U5JMyT6uL7vsMvzoRz/C1VdfjcLCQixatKjP+ZqbmzFv3jzk5OTg1ltvxfjx4+HxePDxxx/jBz/4Qa9jL1XbZ6iGcx6ZMmUKAOCTTz7BKaeckrAyWek46subb76J6upqPPnkk3jyySd7vf/YY4/12r/uvvtuvPLKK/j+978/rAZhqSRJEp555hmsXr0aL730El577TV86Utfwq9//WusXr1ad//HO3bswMKFCzFlyhTcc889qKiogMvlwssvv4x77713wP1Q0zTMmDED99xzT5/v90yORc55RUVFOr8l9WVYgev555+Pa665BqtXr455vN9TZWUl3njjDbS1tcXcrUcea3Xvb3Hs2LE47rjj8NRTT+Fb3/oWnn32WZx33nkxfcOOHz8eb7zxBk466aSknpgjjzScTueQTyJ9Gcrd1rZt23pN27p1q67OoePZ/gOtKyMjI3pT8Mwzz+Cqq66K6c6os7Oz334Kt23bFtNjRHt7O6qrq3HWWWcNWv7BDLQ9L730Utxwww144okn4Pf74XQ6Y7L3g9Fb7qVLl+KGG25AdXU1Hn/8cZx99tmDZvMijxc3btzY7/4V+W22bNnS673NmzejqKgo+jgxPz+/z+0/nGoqA21bRVFw+eWX46GHHsKdd96J559/Xteje6Ar6Ni1a1fMtOLiYmRkZPT7XWVZ7nUh0COyDbdv3x6ToW1oaEhYxnS4Ro8ejZNOOgkrVqyIVkfpy4oVK9DQ0IBnn302pkeGntsyXuPHj8f777+PUCgUUzWmOzOcR7pbsmQJfvnLX+LRRx8dNHC163HUl8ceewwlJSX4/e9/3+u9Z599Fs899xz++Mc/Rq+d77//Pn7yk5/g+OOPT/godhHxHNuVlZV99jnb1zSgq5/ZE044AT//+c/x+OOP44orrsCTTz6Jr3zlKxg/fjxee+01NDY29pt1femllxAIBPDiiy/GZM97Pubvy/jx47F+/XosXLhQ13U9cpxGnlLQ0AwrNZeVlYUHHngAN998M5YsWdLvfGeddRZUVe2V6br33nshSRLOPPPMmOmXXHIJVq9ejb///e+or6/vFWhcfPHFUFUVt912W691hcPhhHXyXFJSglNPPRV/+tOfUF1d3ev97t1cxSMzMzPuMj7//PMx9Wk/+OADvP/++722XV/i3f6rVq2KqV+2b98+vPDCC1i0aFH0ZKooSq+Mwu9+97t+sxJ//vOfY+ohPfDAAwiHw7rKP5iBtmdRURHOPPNMPProo9EuhuK529Vb7ssuuwySJOG6667Dzp07ddWhnTVrFsaOHYv77ruvV/kj27asrAxHHXUUHn744Zh5Nm7ciOXLl8cE0OPHj0dLSws2bNgQnVZdXd1nl2B6RS7m/W3fK6+8Ek1NTbjmmmvQ3t6uu+7wnDlzsHHjxpjHeoqiYNGiRXjhhRdiqmLU1tbi8ccfx8knnxx9PByPhQsXwuFw9OreJtGZdyD+7rC6u/3223HTTTcNWPc2cvx1P/aCweCwh5K84IILUF9f3+c2iazLDOeR7ubMmYMzzjgDf/3rX/scgCQYDOJ73/seAPseRz35/X48++yzOOecc3DhhRf2en3rW99CW1tbtDvH5uZmXHrppcjIyMATTzzR703LcMVzbC9evBirVq3CunXrovM1Njb2esLa1NTUa9+J1IuNnFcuuOACCCFwyy239CpT5LN9HVMtLS148MEHB/1eF198Mfbv34+//OUvvd7z+/29qp989NFHkCSpz75zSb9h9+R+1VVXDTrPkiVLMH/+fPzkJz/B7t27MXPmTCxfvhwvvPACvvOd78RUbAe6dobvfe97+N73voeCgoJe2ah58+bhmmuuwS9/+UusW7cOixYtgtPpxLZt2/D000/jN7/5Tb/1beP1+9//HieffDJmzJiBq6++GuPGjUNtbS1WrVqFqqoqrF+/Pu5lzp49Gw888ABuv/12TJgwASUlJdHK3P2ZMGECTj75ZHz9619HIBDAfffdh8LCQtx4442Dri/e7T99+nQsXrwY1157Ldxud/Si2P3gP+ecc/DII48gNzcX06ZNw6pVq/DGG2+gsLCwzzIEg0EsXLgQF198MbZs2YI//OEPOPnkk/G5z31u0PIPZvbs2XjjjTdwzz33YOTIkRg7dmzMEIdLly6N7g993ewMRG+5I30DPv3008jLy8PZZ5896LJlWcYDDzyAJUuW4KijjsIXv/hFlJWVYfPmzdi0aRNee+01AF0jTJ155pmYM2cOvvzlL8Pv9+N3v/sdcnNzY8YCv/TSS/GDH/wA559/Pq699lr4fD488MADmDRp0qANXfoTaVzzk5/8BJdeeimcTieWLFkSvRAfffTRmD59erSRwqxZs3Qt99xzz8Vtt92Gt99+O+ax5e23347XX38dJ598Mr7xjW/A4XDgT3/6EwKBQJ/9puoxYsQIXHfddfj1r3+Nz33uczjjjDOwfv16vPLKKygqKkpofbOlS5fi7bffHlIVgnnz5mHevHkDznPiiSciPz8fV111Fa699lpIkoRHHnlk2FUWli5din/84x+44YYb8MEHH+CUU05BR0cH3njjDXzjG9/Aueeea4rzSE//+Mc/sGjRInz+85/HkiVLsHDhQmRmZmLbtm148sknUV1dHe3L1erH0bJly/Dwww9j165d/T5pizQS7e+8esIJJ6C4uBiPPfYYLrnkEnzta1/D7t27o21LVq5c2efnIoH0ihUrMH/+fNx0000x20wPvcf2jTfeiEcffRSnn346vv3tbyMzMxN//etfMXr0aDQ2NkaP14cffhh/+MMfcP7552P8+PFoa2vDX/7yF+Tk5ERvRObPn48rr7wSv/3tb7Ft2zacccYZ0DQN77zzDubPn49vfetbWLRoEVwuF5YsWRK9cfjLX/6CkpKSPhNW3V155ZX45z//ia997Wt46623cNJJJ0FVVWzevBn//Oc/8dprr+GYY46Jzv/666/jpJNO0r1/Uz/i6YKge3dYA+nZHZYQQrS1tYnrr79ejBw5UjidTjFx4kRx1113xXRJ0d1JJ50kAIivfOUr/a7nz3/+s5g9e7bwer0iOztbzJgxQ9x4443iwIEDA5ZFiK6uOr75zW/GTOveRU13O3bsEEuXLhWlpaXC6XSKUaNGiXPOOUc888wz0Xn62zZ9da9TU1Mjzj77bJGdnS0ADNg1Vvcy/frXvxYVFRXRfkvXr18fM29fXedE6N3+ke3y6KOPiokTJwq32y2OPvromPILIURTU5P44he/KIqKikRWVpZYvHix2Lx5s6isrIzpXiiyXd5++23x1a9+VeTn54usrCxxxRVXxHRLI8TQu8PavHmzmDt3rvB6vb26NxKiq+ui/Px8kZubK/x+f5/bp6d4yh3xz3/+UwAQX/3qV3WtI+Ldd98Vp59+usjOzhaZmZniyCOP7NUl3BtvvCFOOukk4fV6RU5OjliyZIn49NNPey1r+fLlYvr06cLlconJkyeLRx99tN9ufHru/0KIXr+fEELcdtttYtSoUUKW5T679Il0EfaLX/wiru995JFHRvuZ7O7jjz8WixcvFllZWSIjI0PMnz8/pjs7IQY+F/XVj2s4HBY//elPRWlpqfB6vWLBggXis88+E4WFheJrX/tadL7+usM64ogjeq2nr26ThtId1kD6OqZXrlwpTjjhBOH1esXIkSPFjTfeGO3iT0+5I+/1PO/4fD7xk5/8RIwdO1Y4nU5RWloqLrzwwpjui4w6jwzE5/OJu+++Wxx77LEiKytLuFwuMXHiRPHtb387ppsqIax9HF1wwQXC6/UO2P3XkiVLhMfjGbAP3GXLlgmn0ynq6+uj3UUO9op46aWX+u1irSf06A5LCH3HthBCrF27VpxyyinC7XaL8vJy8ctf/lL89re/FQCifZd//PHH4rLLLhOjR48WbrdblJSUiHPOOSemCzYhuo79u+66S0yZMkW4XC5RXFwszjzzTPHRRx9F53nxxRfFkUceKTweT7S/9EgXcd1/p76Om2AwKO68805xxBFHCLfbLfLz88Xs2bPFLbfcIlpaWqLzNTc3C5fLJf76178Ouu1oYHEFrpR6ei9w1L9QKCSKi4vFl770paSuJ9LPpd4O0e3ivvvuE5IkxfSJrMc//vEPkZ2drbsfzkRramrqs19SGr7+Ajrq32DHUUlJSa9BJlLt+9//vigvL4/pLzhVrrvuOuHxeHr122wV9957rygrK+uzz1iKT/KbnxMZ7Pnnn0ddXR2WLl2a1PX85S9/wbhx43DyyScndT1mIoTA3/72N8ybN6/PboEGcsUVV2D06NF9NiJJNL/f32vafffdBwCDDjNMlGyDHUebNm2C3+/HD37wAwNKd9hbb72Fn/70pzGNpZOh5/Ha0NCARx55BCeffPKQG60ZKRQK4Z577sH//d//mb6nDysYdh1XIrN6//33sWHDBtx22204+uijB60/OFRPPvkkNmzYgP/85z/4zW9+kxZ99HV0dODFF1/EW2+9hU8++QQvvPBC3MuQZRkbN25MQul6e+qpp/DQQw/hrLPOQlZWFt5991088cQTWLRoEU466aSUlIGoJ73H0RFHHNFr8A0jfPjhhylZz5w5c3Dqqadi6tSpqK2txd/+9je0trbipz/9aUrWn2hOpxN79+41uhi2wcCVbOuBBx7Ao48+iqOOOgoPPfRQ0tZz2WWXISsrC1/+8pd7jc5kV3V1dbj88suRl5eHH//4xwlpZJdMRx55JBwOB371q1+htbU12mDr9ttvN7polMasdhylyllnnYVnnnkGf/7znyFJEmbNmoW//e1vMV3AUfqShEhRz/hERERERMPAOq5EREREZAkMXImIiIjIEljHlYiIiMggnZ2dCAaDKVufy+WCx+NJ2foSjYErERERkQE6OzuR6y1GEO0pW2dpaSl27dpl2eCVgSsRERGRAYLBIIJoxwm4DgqS2z8uAKgIYHXNbxAMBhm4EhEREVH8HPDAISU/cJWE9fsZZ+MsIiIiIrIEBq5EREREZAmsKkBERERkJOnQKxUsPuwUM65EREREZAnMuBIREREZSJIlSFLyU66SkAA16atJKmZciYiIiMgSmHElIiIiMpAkdb2Svp7kryLpmHElIiIiIktgxpWIiIjISBJSk3K1AWZciYiIiMgSmHElIiIiMhDruOrHjCsRERERWQIzrkREREQGSmk/rhbHjCsRERERWQIzrkRERERGSlUlVxvUcmXGlYiIiIgsgYErEREREVkCqwoQERERGYjdYenHjCsRERERWQIzrkREREQGkqQUdYdlg5wrM65EREREZAnMuJpEMBhEe3s7nE4nMjIyoChK3MsQQkBV1egrEAhAVVVomgZVVSGEgCRJCAQC0WnBYBDhcDj6fmT+ni8hBIQQMX/3N2/38gghYv7d/TXYdwGAoqIiFBUVIRwOx3yPyH97fiZSxu7/7Wu+vj4XDocRCoWinxvoe/T13oknnohJkybp+q2IiIiiJNijAmoKMHA1iWuvvRZr1qyJ/u31epGVlQWv1xudFgmsVFWNBpvdX92DRiN0PeaIfdwhy3L078ijkO6v7roHhJH3fL4OBAJhhMM9vpvo789+3+iXosjwZjgBAE6nq98ydv+757/b29uxefNm3HHHHYOvkIiIiIaEgatJtLa2Ys4Jc3D2Oeeg0+9He3s72tvb4fP5o0GSw+GAw6FAURQ4HA4oihJ9Rf6Wu01zuVxd0yQZiqJAkiQIIeDxeCDLcvRzLpcLsizHvCLzR/4bCUAjfwOAw+GImZaM+jmNjY04eLAJK97aCVnu+i7RYLjH+iRIkGS568ZVkiBJcq8y9QxEI6bPKMP0GWVD/g7XXXet4TcORERkTRzyVT8GriahKApGlJbi9NNON7ooplJQUICCggKo4Uxs/KQmaevZvr0B02eUJW35RERENHxsnGUSiqJAVVWji2FaEyYWJbWPu2BA7V0dgYiIKAUiDxFT8bI6Bq4mIcsyA9cBuN0OLDn3CDicid9lM7NcOPa4Cjid8TeIi0jFIx4iIqJ0x8DVJJhxHZzX68TC0yYmfLm+jiBGlGYPezmD9ZRARETUJ6ZcdWPgahI7duxg1k6HvDwvJk4qSugynS4FXq8zocskIiKixGPgahJOpxO1NclrfGQn48YXJnyZLS2dw14GM65ERDQkqUq22iA/xsDVJEaMGIHKMWOMLoYl5OZ6Evq0IxhQ8eZ/t6Gx0Ze4hRIREVHCMXA1CTbO0k+SJCw8PbF1XcMhDZ99Wjvkz7OaBxERUfKxH1eTiAwOQPooSuLvuXwdQWiagCwPLQjl70dEREMhSRKkIV574lqPZv0kCzOuJsGMa3zWfFiV8GU2Nvrh6wgO6bPMuBIRESUfM64mwYyrfqGQiob6jqQsu609gKxs95A+y9+PiIiGJFVdVdkgycKMK1lOMke4WrVy99A+aIOTARERkdkx40qW09kZStqy3W4eEkRElFpMuOrHjCtZTn5+BsZPSHxfrkBXNtfvT15gTEREREPHwJUs6YjppUlZbmdnGHt2N8b9OTbOIiKioZIkKWUvq2PgahJs2BMfj8cBlys5u2/JiOwhfU7Tklf3loiIiFjH1TQ0TYPDwZ9DL0mSDvXlmthgsWJ0HgoKMhK6TCIiogGlajhW6ydcmXE1CyGELVL4qZSd60n4MjVt6JlvZs2JiIiSiyk+k2DgGr+JE4twsKY9oct0uZQhfa6rH96EFoWIiNKEJKdo5CwbpFyZcTUJBq7xKy7OwvQZpTjrnKlwJagbq7HjCob8Wf5+REREycXA1UQY+MTH7XbgiOmlyM52Y8HCCcNe3pixBUOu38pqAkRENGRSCl8Wx8DVRBj8DF1WlgslJZlD/rzLpWD2MaMONfgaGt54EBERJRcDV5NQFAWqqhpdDMtSFBlzTx0/5M+XjMiCwzG0+q1ERESUGmycRbbQ0RHAf176bEifdToVHD1r1LDWL4SALPM+kIiI4peqwQHs8GSQV1qTYDWB4cnMdGPRGZOH9Nn8Ai8yMlwJLhERERElGjOuJsFeBZLH4ZAx86gylIzIxtqP92PMmHxoQqC0NAerVu7GtCNGDH8lvPEgIqIhYsZVPwauJsHAdfjy8rw45rhy1FS3obMzjKZGH1RVwON1YnRlPlwuB+b1qAc7f+GEhG13/n5ERETJxcDVRBj4DN/48UUYP74IANDY6MOWzQcxbnwhXK6+d/VEbXNNS+zQs0RElEZksPKmTgxcTYIZ18QrKMjAnBPHpGx9isJeCYiIiJKJgSsRERGRgVjHVT8mpk2CvQoQERERDYwZV5NgVQEiIqL0JEldr1Ssx+qYcTUJBq5EREREA2PG1UQYuFoXq3oQEdGQMeWqGzOuJqFpGgNXIiIiogEw42oSzNgRERGlJyZc9WPG1SRS1RUGERERkVUx42oiDFx7U9WBR6SSZQb8RERE6YKBq0mwqkAXIQT8/hB2727C2o+q0NTUOeD8xcWZOOmUMcjO9iAUUgEARUWZqSgqERFRQkiSBElOwQAEwvqJHgauJpHO3WEJIdDS0okd2+ux9uMD6OwM6/5sXV0Hnn92U/TvEaXZuOjiI5NRzEGl6+9HRESUKgxcyRCaJlBf14FPP6vFpk9qkKiEc3l5TmIWRERElCpsnaUbA1cTkWD9Hao/QggcrG3Hnj1NOFjbjt27m5KynpbmQFKWS0RERMZj4GoSdqzjqqoa9h9oxe6djdi1sxEdHUEAwMiRycuK7thRD1WdCEVhhxlERGQNTLjqx8DVJOwSuIaCKvbsbcKuXY3Ys6sJwaDaa57a2jZkZ7vQ1hZM+PqFABob/SguZgMtIiIiu2HgahKapkEIgXB44O6fzCgQCGPvnibs3NmIqn3NUNWBg3BVFcjITE7gCgAr39mFJedOS3nWddOmTfjZz34GSZIgy3K0sVZffyuKAkVR+m3Q5XA44PV6IUlSzE2NEAKqqkLTNKiqCiEEHA5HdJqmaQiFQgiFQtF9StM0yLIMVVWj64+8IvNElt397+4i5QiFQgiHw9F19/ffyPodDgcURYlZzoYNGzBmzBjk5eX12kZ6Rcof+Uzkuw1ECBHzHbtvx8i/+/pMPCLLjnyu59/d/6uqhxtkdp8e/bcmIBC7flmWo99ZURQ4nUp0e3TXfbv29d/IshwOBxwOB9xuNzweD1wuV3Tf7L6P9tzeQ2mI2PM7dv+u/U3vOU+8um9fn8+HUCh06PfWovuxpmrQhAZNi+wfXfuw0AQkOXa/7FnevtYVO02Gpqkx70eOu4jIemMq+vf4vbp+q8PliOzvkf3Y4XDEnCv62mZ97YOR3zQyref+GBnRsfuyVVWN2R/6+o267zOHv2fsuaVneR0OB26//XZMmjSpV9nTQar6crdDI2IGriYRCmlYtWovPK7VRhclJeoOdqC0NBs1NW0JX3ZVVQuWv7oFZ549NeHL7s+pp56KV199Ffur9ve68PYMDoUQ0ETXhbM/oVAIfr8/+nfPAE+W5WhQEQ6HoShKdJrD4YDT4YwGy5ELhKIovcrRX1Admafnf91uNxSHA45Dy+/5inw+EhBFAoXuy3nqqaeQmZGJKVOm9hvE6NE9KHQ49J3Kum+TSFAd2Xbdt0N38dQ9737B7u/GBQB8viA+3VQDoFuPIt3elyB1/RlZBiQIiGgwKzQN2dluTJhYFLP+vn6z7v+NzoeugCccDiMcDiMYDMLv96PT3xkN5MPhcDTA6r6t+wrwB9I9KOq5XfoKcPrbhv1dcPvbbw7WtaOl+fAx5HC44FCch34PBZIsQ5YUyLLz0LJj1ytBglAFwmEBoOeNQc8yxE6LFEkILRr4atFiikPr6vpLRtdv37MrJHF4IcjN8WL8hMO/dfcbCFmSEVYPn0titlmvcsduZ03TICD63Me7B7F9/T6RfaGvmyIAMftJZB09g+DI54KhIJ555mls3749bQNX0o+Bq1nYpKpAPNrbA3A4pEMXhkQvO4iWlk7k5noSvuy+nH/+53H++Z9Pybqs7te//jVmzZqFW26+xeiiGKapyYfXXtkyrGXMmz8eZWXsRaM/H7y/B5s/PWh0MRKiYnQe5i+caHQxkiYY7Apc05qM1IxlaoNQgy1YTOLQPbjRxUgpny+E0tLkXHhPOLEyZUErxUeSAE1Yr0pMIiXiad3evc3DXwgRkcUwcDWJdB2AoLq6Fbl58QeYbpeCggIv8vO9fb7/4vOboGk2uLW0IUmSoA0ylK/9Df9Y372zAevW7kdTky8B5SEzS8drQ7rpWUUmmS+rY+BqEnbpVSBeQgAulzL4jNH5BQoKvGhs8GHblnp0+kP9Lre2NvH1Z2n4JEnS1ZiKBiYEsPmzg1i39gA+2VCNlpbOtD2PEFH6YOBqEul8wWmo9+nq29XtVpDhdXYFrIeGhfV4nf3Ov+7j/QkrIyVWOu/vQGIzaLU1bdi0sQav/OcztLUFsG1rHerrOxK2fCJKvq72manIuBr9TYePgatJpGtVgYjmZj+crr53x2iWtd6Hqn0tMe+pAzxy3r69ASve3I6DB9uhqn1380SpJ0kSVC29M67JOtRf/vdn+GhNFVat3I1AoP9eK4jMJJ2vfRQ/9ipgJml88HZ2hlE2Mgf7q2IDU1kGMrxubNtS3+fnamvakJnlQqTnFSEEcnI9gBCorW7H22/txNtv7QQAjJ9QiC9cNTup34MGJ0kSRJrXP072kd7REUQopMLt5imerCOdkwuS3PVK+npssImZcTUJIfruSy+dVB9ojWls5XTKUGQZe3Y39fsZX0cIEiS4XQoUWYIa1rB7RyN272yCv0f91x3bG/DpptqklZ/0S+cLFICU3KR+sHovGygSke3wdtwkRJp3DxQhH+qE25vhRFtLJ1qaOwf9zMHadt3L376tHtOOGDHk8tHwdXXWnt43aalQX99x6AaB29rq7H648HxA8WDgahJCCF5fADQ1+VExOhebNx2MNsBKpMgQmWQk0WuUoHSTiuv08SeMTvmwx0Q0RD1Gz0vqeiyOZzWTCIfCUGQGVQBQV9eRtMDmg9V7sX1b3/VlKTWYcU0+RZFQXpFndDGI4pL2VYhIFwauJiHLSjqO+tqnYEDFuPGFSVv+gf2tSVs26SEgp6IVgoklO24vKMhgtpUsJ51vaCMJ11S8rI5nNpMQ0GyxQyXKwYPtmDY9OXVR33l7J4NXMliSD3aeS2xF5k0IURTruJqEqmr2uBVKpCRtjnBYQ3V1K0aOGnzQA0o89uOa/EM9J6fvoZDJmoqKMowuQlKxigAgyVJK6v5LwvpxBm/jTERK88enPanh5J3Mioszk7ZsGpgkSdAGGDiChm/cuAKji2A4O8VCdu8qMRK4pnNVAdKPkZJJaJpm81NT/ILB5I38887bO9O+E3yjSJKEcDjdR3VK7tEuKzybwE6HN39O+2MlV90YuJqEpmm82+zB5wuhoCA5j8i2b2vAu+/sSsqyaWDcz5N77Rg7rgD5+fZ+tKyHsFHkyiOG6DAGribCqgKxNE2gNIn1UD/77GDSlk390zQNDkd6Vq9XVQ1tbQHU1+kfNCNeoVB61x+OsFNVATtkyWhgTLjql55XDxPSNNb5S6WZR4/EojMmGV2MtKRpGpxOp9HFMERzsx+vv7Y1qetIZt1wS7HRZrBDsDEQNs6ieDBwNQlWFUiN8RMLIQC0tnbC40nP4MlomqbB5XYZXQxDpOIIP3iwDYFAGG53ep/eWVXAetL6GpiiXgVgg14F0vvMZjKsKtDbcA8xh0NGOKxhZHkOsrPdqK/3QTvUKKulpRP5+ew2KNU0TaRdVYGWFj8OHGhFfX1H0telqgKfbKjGMcdWJH1dpmafuNX+KddDmHklPdLr6mFirCrQN3UY3SZNOaIEtTXtcDhl+P1h+P2xLdmzs9Mz62ekrv1cpN3wxg31PqxfeyBl69u7pwlTppYgK8udsnWajVEh0Nz545GX540phDj8jyjR4x8DzZOVZe9zVVpnWqNSVQHV+tuagatJaJoGWWbGtaehduszbkIhamu6GsCEQ30Hv9zexknJIzET0VKcSQoGVbS2dqZ14GpU66ycbM/hwJWIEo6BK5maosjIynIhEAgj1E8AGlFalo3cPC8kCaitHbjV9mmLJkJOs+DJTNIt42pEn8H5eendJZZRGVcmD4mSi4GrSUiSxPo9PZSVZWPfvha4vU6MKMtG/cEOZGW7kZHpQkdHAAeqWqPzjh1fgNa2AA4eHLyboeNPGI1p00Yks+jUj8jAA+mXcU39Ouvq2jG6Mj/1KzYLg06n6bZvU2KkqqsqO9xY8VmpSTBo7S2yRTRNoLHRD4dLgc8fQn19BxwOBV6vE4oiYeLkIrS2BaCp+rZhVVULOjvTfeQmY0TqcqdbQ0RhQB32jo5gytdJgGyHyIDIxNLr6mFi4XAYDge7Z+quvi62BbbWLW3V1hZAboEXJWXZXT0F6AxaAWB/VQvefHN7wspJNBgjbkzTPX5iVQHriBwf6dxISzrUHVYqXlbHwNUkmHHtzeUauB5koDOMTv/QMqfbt9ajal/zkD5LFC92GpJ6Rp1T0zn4Gi5eB0kPBq5kWj5fCJmZyesGZvlrW1Ff34HW1k4EAqw6QMljTMY1zQMo1nG1nLTeZ6UUviyOjbPI1PILvEmrq9feHsTjj66N/n3lVbOQn5/eLbEpOTQDWmc1t3QiGAzD5UqP0/yaD/ehoz3Q9YckoU5HQ81kSOfYa6giASv7Myc9mHEl03I4ZDQ3+VO2viceW2dIgEH2JoTAp5tqU77eXTsa0NGePg20aqtbsWd3U9drVyN8bJxGFiJJUspeVsfAlUxLkiX4U9j6v3JMPmS5q1uy4YzYRWQW6dRXMR/RW58dgipKvvR4hkSWFAqqKCjwou5g8sd3B4Ad2xvw5z+tjjb4qqzMQ3aOByeeVAmPhz0+kPWkVeDKoMey2CgLKWvxb4cbPAauZGqp7hOxey8Fe/Y0AwB272rEVV88BorCBxRkLbLJ9llV03Bgfys0TUAIAaEBktw1Qp7DIUNRZCiKBFUVaG8PQA1rUDUBTdUgyRJkWYIiS5BkuasuqegKeoRg/UgriwSuHIab9GDgSqbmchu/i7a3B/Hxx/tx7LEVRheFLMjITKDZMq7r1x7A+6v3JmXZo0blJGW5lHzsx5UjZ8XD+KiAAEQOXBvsUQlWX9+BEaVZqK0xpoVwhMcEATRZlcCiMyZ3Hd0SIHX9Tz9/S9ELS+Qi/p+XPh1yo0GzBa61NW1GF4FMKJ0DVoofr8YmwmO3t1BQHXQgglT439s7MXVaCRwO48tCViOhoMCYbtbMFLhqmsDevc1GF4PInJhy1Y0VSkzF+jtUMpih6pqqCjz91AY2IiBLMVMmS5Yl5OV5jS5G8vEUQZRUDFxNgMFQ/1wuxTSPF+vqOjjC1jCx8UX8hhN7mihuBQCccFIl8vPTIHiluPAaSPHgVcQEWDG9f263A+GwCVKuhwQCqtFFILKsytH5OHnuWKOLQSaVztfASHdYqXhZHQNXMrW2tgDKR+caXQwAXdmr7Gy30cUgsrSmxtSNhkfWwswr6cHGWSYQucsUwjyZRbOQJKC9NWB0MQAAR84sM1VjF0oPDqcMVbVPpj8UStJ3YcxDFsa2Wfox42oCh6sK8OfoaURpNpqbO40uBgDA6WSPAonCmzT9vF77jNomhMA+9ixAZEtjxoyBJEkxrzvuuCPh62HG1QT4eKR/qmqeAMfjcSAQCMPpUlI+ohelL4/bCcAcN2/DJUlSSp5a5OR60Npij22WDg4/dUzja6FNUq633norrr766ujf2dnZCV8HA1cyNTNV1n/3nd14953dOHX+OBw5c6TRxaE04fLYI9Pv94ewe3cjDhxoTep6ph4xAiNH5eC/y7cldT1E1Ft2djZKS0uTug4GriZgpuDMTMpG5mDeqeNQd7Adr7261ejiRK14ayfGjitkQy1KCbdz6KfpluZOFBQaM/hBT3t2N2LFmzuStvyS0iwUl2ShrCwH4ZCGEaVdmZ6egxIeyu0hWf1m797d0Gugkv4SiaKPGbwZLlRW5ielbGaXzhnXyKP1VKwHAFpbY28g3W433O7hX9PuuOMO3HbbbRg9ejQuv/xyXH/99XA4EhtqMnAlUyoZkYXzzj8CktQ16lBnZxir3tuDYNAcjVSe+ed6LD5zMkaONEePB1bBflzjN9ThXgGgrr7dNIGroiT3tz/q6FFwuRzw+0N47l+fJHVdA6mr6xjW50eUZqdd4MrkTepVVFTE/H3TTTfh5ptvHtYyr732WsyaNQsFBQV477338KMf/QjV1dW45557hrXcnhi4msDhu8z0PXhlWYpeoGVZwtGzRsWczGYeNRKqKvDuO7uMKmKMtrYgXnx+E675+hyedIdADCMYSzfqMLaVmfbM5ubEd4NVXp6LlpZOTD+yFA6HgmBQxb9f2pTw9RAlmyR3vVKxHgDYt28fcnJyotP7y7b+8Ic/xJ133jngMj/77DNMmTIFN9xwQ3TakUceCZfLhWuuuQa//OUvE5LNjWDgagLaoTFN0zn+GT+xEEcfPQrbttZjzNh8lJXl9JonN8+ja1knnzIGhUWZeOG55F7ANA1453+7MGVqCUpKspK6Lkpf2jAaKJrppio3z4spU0u6ommByP90/yfy8r1oavSjsdGH+kEylx6PA2edMxW7djZi7LgCKIoMVdUQCpqnQedQmOcXIzvLycmJCVz7893vfhfLli0bcJ5x48b1Of34449HOBzG7t27MXny5KEUs08MXE1ACAEIQErnU5YAiooyUVSU2e8s48cX4siZZdiwvrrP9yUJOHrWKMw8aiQURca0aSPw6ae1ySoxwmEN69YeQEFhBgNXSqjamja0twUACejsDCEn53C2QgCQBCCiAWD/Nm2swZoP9wHoFhBJXWeaseMLcNzxlUkofd8mTSrGpEnFuubdvasRaz6s6soO9fNA6uhZ5di2tR5lI3MgyxIaG33Yu6cprjK53AqEJhAOa/3WQ025NL4MpDWT9ipQXFyM4mJ9x21P69atgyzLKCkpGdLn+8PA1STch7IHZ545p9d7eiusx84m+pne80N9/jOuZfS33sGXf5jDoe8ZSaSxVnV1W8z0qdNG4NT542L6Wp07fxwam3yo6TFvon2yoRrt7YHojUfkvCBJgBASJAk45thyU2W/yNw2bazBzh0NSV1HQ50vqcsfjmBQHbT3genTw/jooyoEV6nIznLD6ZLRFsdgJfn5XjQ1d2LvnmbIsoRx4wvgdMjo6AgOt/gUJ54brW/VqlV4//33MX/+fGRnZ2PVqlW4/vrr8YUvfAH5+Ymts83A1UQkub8+DnlQR/j9YdTVxz5CVBQJCxaO79X4w+VUcNrpE/HoPz5OapnqDnag7uDAjzWPObY8qWUgipfDae2Gct17GmlrDWDMmPgujsGQir17mgF0NYDr6AgiI8M+gz2QtUhIUcI1Sct1u9148skncfPNNyMQCGDs2LG4/vrrY+q9JgoDV7KUHdvrEQ7F1mFbcu60flss5+d7Ictd9VGJ6LDGBh+EECnLdjU1+eBwKMjKciVnnZK+J1OSBLhcCkI9ziOFBRlobTV+0IJ0TlOkc3dYVjdr1iysXr06Jeti4EqWMm58Id7q1hfk+AmFGD26/0xLc3Mng1aiPrS1BbBzRwPGTyhK+ro2bDiALZ8dhKoKlJZm45RTx0FJdNdoQl/I19oahKpqkGUJubluCNEVMKViRC/qW6SbPI0na9KBgasJRA5WRbHHCDnJ1L3+2fwF4zFufOGA88uyhMu/cDTkQ9UwAoEwmpu6uuURIr5HMzk5HmRmuQ7V6RWR/4ckSXAeeux6OGEgYur+sg5XLIlBgils21qXtMC1szMEt9uBxgYfPttUGz0eamra8K9/rseRM0diytQRSVl3d7m5HmhCxNR/rajIRVtbbH3YZHTXNSQ8V6QlSZZScl60w7mXgasJHO4Oy/o7VLJ99mktHE4Z539+ep9dZvWUm9u7C60RIxI/djKRFVXta0FrSydy+jhO4qWqGl7+92coLcuG1+vEpo018Hic8PtDvebVNGDd2gOQJAmTpySmxbGqCTgcSrTursupQFYkbPykq2cRRek6v4ZCGlpbOzF1WglaW4yvGkCHr32sKkB6MHA1gchjElU1x6hQZjbv1PGYd+p4o4tBQ8RHgeazdu1+nDJ33KHeeIZ+86woMtxuB3ZsP9wbQl9Ba8y6P94Pj8eB8oo8yIo8rPqd7e1B7Nvb3Od7mZlOTJhYFO0TV5Yl+HwDl41Sh4ErTNsdlhkxcDUBZlop3UipGCKGdNm2pQ579zRhzomVmDBxaP01Rjhd8Vd3WvXeHgB7AHRdUydNLkZnIBz/yvsJesaMyUdenudQYqDrXKtpAh6PAx6v49BHD3eMK4Q4VO8VXX+jrwZTUnTiQO8Bosf7Ut+zdXvP40nfy3JaB66kW/oeIUSUcsy4mlOgM4x3/tc1nPJQg9fVq/agtmZ4fSYLAWzZXIfsnPiHh+wv5gmHVBzY3zKscqWSewjBv9Ux48qEazyY9jCByMHKzCulC7bgNh81rOGdt3di3dr9cQcQjY0+7N7VmKSS6dMziV9e3lUHPhhWLd9nrd0xcKV4MONKREQAAFUVWPPBPrhcCqYdUar7cz1b6A/bEOKXxgY/sjJdaD/U80hVVSsyvE7k53rQ1GSSHgP0SON7unQOXNmrgH4MXE2AGVdKF6wqYA2rVu5Ge1sAU6aNQE7O4D0OlJfn4uhZoyDLErZuqRt2IBtv+FJalo3du5owcWJX93hCCLS1B9HeFkSHP4yi4kzU1w08uh0ZS5YVNlAmXRi4ElHKsXHWIAy+hxUC2LC+Gps21uD8C45EXr53wPkVRY52a1U2MgcH9rdg//7WYdd51aO4JAv7q1rh8Tigqlq0n2YAKC3Ngt8XQkODL+nloOHpGgQijc8LrOSqWxrvJebDjCsRAYbHrVGqKhCIs4V/VpYbkyaXYO68cfB6nUNbcRwpV19HEKPKczB6dG5M0AoAHR0BSBIgNOs8gk7Xy4AkSWldVYD0Y8bVBHiwUrpgVQHrkGUJF148c0gt/IGuEbI6O4fYV2ocwVtHRxAdHUGMHp3b671gQAXAx89WIMtyWp8fmHDVj4ErERH1IssSsrJccT8JEkJgzYf7sG9vM5xOBZLUNdyyJB1qgCJJkCV0XUEjfaYe+pzo+seQbuZlRUZGpqtnYXrPGPk+3d6TpF6dqsZ+ZKAVD7B9Bv5c35M9Q81SW5wkSWkduJJ+DFyJiCiG06kgHFZRW9uOspGDD63cnSRJGD+hCGs/2j/k9RcWZcY3f2EG6mrboKpDe3p19ddOYFUtg6V7VQFJTk2Lfzs0L2DgagLsw47SDYMEczv2+ApkZbtRWpYd92c7O0PYX9Wc+EINICvLhY72ofdkwP3ReOkeuJJ+DFxNQFG6RkphVyBEZAYdHcG4+nGNEELgxec2orm5Mwml6l8wOIQhYsl0eANBejBwJSKiGK4hDDsqhMC+vc0pDVolCRg9Og8HD7anbJ2UPOmccZUkKSWBux1uDhi4ElHKpfMFygpaWuILPoUQ2LunGa+9sjkxBdB5bR09Om/YfcXa4DpuC3YIqCg1GLiaAEfOIiIz2ba1HjNnjkRu3sADDwBAOKzh8Uc+Qmdn6h/X8/aHbENCajpwtkGYYYP2ZdbHwJXSRWRkHCHY7Y2ZCU3oHm1qf1WzIUFrZpYToeDw2wXwvGsObJxFejHjagIMXCldRAJXzUIjGaWr91ftwYgRWcjM6nsAAiEEduyox+qVe5Ow9oHPhZIEKLKEpkYO5WoX6R64SrKUou6wrB9nMHA1AQaulC6YcTWXgsIMuFwKaqp71xMtKs6E7Oj6vUIhFT5fEJmZbjgOTft4TRU+WlOVlHINdiZ0uRSEQ9yH7CTdR84i/Ri4ElFKCQHs3N6A/y7f2vvNviIWqe83+pk8QNAj9f1eCuqWeTxOtMXRz2hzox9Z2e5D37FrVKfI95UgHf4vBCBJ3d7rNk9EP/NmZrgwd944AMDKd3chO9sDAYED+1tx5MwyjBlbEL2Z3rD+AD76sAqSBMw9dTwyM134ZMOBYW+XoVJVgUQljpgvMIe0T9ykqFcBO+zwDFyJKGUiGZWWlk7s3NlocGlSw5vhhL8zjIAB9UAHcuqC8XC5uy4B8xdOHHDekSNz8RGqIATw9ls7UlG8AZWWZqO+LjFdYKV9wGQS6V5VgPRj4GoCrCpA6Sad9nWPx5nyDvkHUlycifx8L8rK9A3lunNnAz5YnYx6rAPpP4BRHDL8vmAKy0KpIElSelcVkCUk7DHCYOuxOAauJsDAldJFOl6Ymho7UF6Ri2BANUVH+RddMhOyzouXEAJvvNZHlQ4DjRyZjbraBG5HnnZNQZaZcSV92B2WCTBwpXRxOHBNp1OPhLradrS2+OHxGJ8r2L0rvioassl+qmCCq1zwrGse6Ry4SlLqXlZnslNSemLgSukm3Xb108+YhEsuPxoul/GBa2dnSPe8kiTBZYJgG4gM75qLtjb9jdx0LjnBy6OhkGU5rQNX0s8cZyQiIptxOmV87vzp0DSBwsIMSJKEmUeV4Z3/7TK0XMUl2XHNP3tWBVa+a2yZAaCiIg8Ha4c3vGtf0u0myqzSvY6rhNT0KtBP3yqWwsDVBHiXSenF/vt7Xr4XZ5w5Gdk5npjpZjjURZyDP6xftz9JJelfzxIqitTHVLITPnEkvVhVwARYVYDSRTpkVPLyvTjz7Cm9glYAmDS5yIASxcov8MY1v6yk/jLR81xYNjIHBxPZICtmZclZLMUn3TOupB8zribCwJXsLnJhsuO+7vE4cPK8cRg7tqDfeTIyXPjKV4/Drp2NePO/21OagZ0wqQgF+V44nUpcnyspyUJri3HdeeXketCR8Hqth9nh0akdpH3gyu6wdGPgagLMuFK6kSR7PewZOTIH8xdOQEama9B5PR4npk4bgaxsN154blMKSgdMmFCEM86YPKTPFhRkJLg08cnJdqG+rsPQMlDysXEW6cXA1QQYuFK6sGNGJb/Ai9PPmAyXK75MZllZDmYeNbL/GYTApk21CIeHv806fEPLWIbDKjasT/3Qrv6OIEpLsyArEk6YU9nP0L69J360Zh9UVeu/Omyvj3RNePXlz/qZXYr5TOQU3ff0biU69I+cXA+OPW50P4Wh7tJ95KxUdVVlhzCDgSsRpUwkcJVtknF1OhUsGkLQCgAOh4xT5o4dcJ7Zx5bjsUfWIhAYXt+lCxZOGNLn/P4QioozUbWvZVjrj1dHRxDo6Nq+I0flxvU5v09/d1/JVlScaXQRLCPdA1fSzx5XD4vjwUrpwooZV7fbgbHjCnoFp/n5Xpxx1hTk9NEIK1EidWLLy/UHbz0pioS8/KE97s/O9qCg0DrBV7z1d5ONT9H0S/eqApIspexldcy4mgCrClC6iDbOMttwTN2UjczGmDEF8HgdACSMrsyHy6VAVTXs3NGA997djclTSzB9eimyst1JL48kSVh0xiT8/a8f6pq/sDgDDXW+mM+HwyqcjviDOp8viA3rUl9V4LD4AhmHw1z7FU/p8UnnwJX0Y+BqAtHAla1byebC4a5H3rJsrsxYxMyjR2L2MeVQ+ugCSlFkTJxUjPKKPHg8jpTeaKqqQE6OG62t/ddVLS/PxTmfmwpFkaFpArt3NeGVlzdj3qnjhhS0AoDX6xxqkRMi3jDGbIGrzMhVt7SvKsBKrrqZ6yhPc8y4kt2Z9cLkcMg46ZQxOO740X0Grd15vc6UH6vZ2W584arZOPHkMdFpmVmxPRjMmz8ODocCSZKgKDLGjS/A6Mq8rsZKQ6QJgYwMY4PXeJgtcOU5Xb+0D1xJN2ZcTYQHLdnd4YyreQIMWZZw6oLxGDuu0OiiDEiWJMyaNQqTJhWhqqoFkyYX4+EH16CjPYhzPjcV+T3qsUqShHOWTBtWwy5FlrHkvOnw+0PY/Gkttm6pG+7XiI/FT4k2aYOYEukeuEpSioZ8tcHNFANXE4hcxIXVz9JEgzBj4Dp+QqHpg9busrLcmDKlBABw1bJj0NkZQkZG3/3HyrI07Mf9ubke5OZ6UFKSlfrANU5mi3vsECSkSroHrqQfA1cTiJzceNCS3YVCXV0VKbJ5Tj3jJlgnaO1JlqV+g9ZE8/sPdzPlcMgJ6V820cx2DmUd1/iY7fdLJUlOTYbeDk8BzHP1SGORwNWKXQURDYUZehVwOGTMmFmG0aPzjS6KJWRmuvCVa06AqmpwOhVs+qQGK9/dldR1Wj2OsUOQkCrMuJJePKxMIFpVgAct2VxnoGvMe6OrCjgcMqZMLcExx1YYWg6rkWUp2lfqETNKceWyY0zVIMpsp1BWFdAv3QPXSB3XVLysjhlXE2DGldKFGlYBAIpizKnH43Fg0pRiTDuiFNkp6IPV7pxOBUfNGoUd2+vR1OhPwhqsHcjYIUhIFW4r0ouBqwn4/V0nfLeLF1KyN6PquI4ozca0I0owZmyhqTKEVudwyJg1uxyFhRl47ZUtCV++3gRcW1sAGzdUo7WlM+FlGBbGYrqle8aV/bjqx8DVRBTFnJ2yEyVKe3sbAMDt8iZ9XU5n14ABU6aVoNBCw5Za0aqVuw1df1tbJ7ZsPmhoGWj40jpwJd2YeiCilGlv7wAAuN2epK4nK9uNy6+cjZNOGcugNQU+d/50Q9cfDKqGrr9fjMN0k5DmGVfSjRlXE2B3WJQufH4fhACc7uRmXJ0OGS4Xn2CkSkaGC+MmFGLn9gZD1u92OzC6Mj86SpgsS2hp6TRf1QHqlyzLaX0NZHdY+jFwJaKU8fsi9bmTm3Gl1GpvDxgWtAJAWVkOyspyYqZ9urEGH7y/16ASRaRvIDYUbKBFejBwJaKUiXSH5XIycLWTZNVxHU4GzgwxUBonEIckrTOuHPJVNxskja0v0g2W0X1bEiVbMBgEADgcwxuGdDDpe/kzxr69zUYXoTfrX5+JqA/MuJoAA1dKF6FQV+BqVD+ulHjhsGbK4V811fjblzROIFK8ZKnrlYr1WByvHkSUMprWdSWXGbjaRmNjBxacPhGyLB3qilKChEOPJKWu1uLR6V1vHHr/0L8lRN9HZHrkM8Mol6oxarSStO/HlXTj1cMEeLBSulDVrm6L5CTXs1IU62cVrKKkJBslJdlGF6MX1QQZV1ZaiU86XwtZx1U/Pps2gcjBaocdimgg0QtTkvf1jAxXUpdP5uc2QXdoaRyHDQmvgaQHM64mwoOW0oVsh84EydRy8thzhdWk8zUwWn0mBeuxOl49TCCdH49QehHCfI14yJ5MEQTx1E6UcMy4mogpTrREKcAeNCgdMG4l3dirgG68epgIM6+ULiJdwBEliznyADynx4PXQNKDGVcTiGSfeDEnu5NYt5VSxvjIlXFYfNL5qSN7FdCPgasJRLoIUhTjW8ESEdmBwyGhZERWr+lCHM7G9hdY9ry2SxIgIMWEwr2v/1Kv6fkF3rjKnO6YcSU9GLiaALvDomTy+4MIhxKYzZcw5CegwUAIADDn5NHIyspJXJl6yM5id1jpToKE9vagoWUYVZ5r6PqthtdA0oOBK5HNaRrw3L82mOKx5aebDgIApkwpRVZW72wYEVE6iowgl4r1WB0DVxPgXSYlS0uLH+3tAVMErURph6d2ooRj4GoirN9DiZaT48GmjTVGF4Mo9UwQNJqgCJaS1tdAdoelG5v4mgAzrpQskiRh+vQyo4tBlHLmOKuaoxRWoCgKwuGw0cUgC2DG1QQigWta321SUggh8NGafZBlCZrG/YsolZiTiE86J3HYHZZ+zLgS2VzF6DycdMpYOBw83CmNWP/6nFaEELYIqij5mHE1gcjBygEIKNEkScKEicVobe1Ebp4HnZ1hdBjcRRBRajAIshJJktL6qaMkA1IK6p/aYQwYG3wF6+NdJiVbTo4HM48eBV8Hg1ZKD2Y4rUoMnnXjdZD0YsbVRNL5bpOSLz8vA06ngmBQNbooROmBsVhc0voaKCE1+4sN9klmXE2AjbMoFbKy3SivyDO6GEREvTDjSnoxcCVKI+MnFmHsuELD1p+R0TVa1v79+w0rA9mTECLmpYZN0GaAsVhc0jl5E+lVIBUvq2NVARNQ1a5Htw4Hfw5KrpEjc6GpArt3NRgymlZF+UQAwLp16zB58uTUF4Bsq7amDf97e6fRxYgh26Cz91RJ98ZZpB8jJROxw50QmV99fTsURUbYgIxU+ajxEEJgy9YtKV832ZwJT5+KwoeaeqX79U+SpRT1KmD97cyjykR4t0mpMGXqCDicxhz6WVm5cDnd2LFjuyHrJ/syYwt+Bq7x4TWQ9OBRZQLpfqdJqRUOaVBk4w59b0Y2qqqqDFs/2ZQJT6OKDbJbRGbDqgJEaaSjI4h/v7QJgU7jxgT3erPQ2Nhk2PrJnswYIsqKGUtFppSqhlM2SJQxcDUJny+Ifz6xFqtX+lO63l6tDKWu/VrqNkGSuz2EO9TXXNd/pMPzSLHHgxTtk07qmk86/PHzLpiB3FxPcr4Q9UtVNWzbWmdo0BrBpwyUaKbcp/jkWzdWEyC9GLiaQOSAFQJI/aivAqk/u/IElUqhkApFkfHRmn34bFOt0cUBOCY5JUEwZPwNWU/MuJJuHIBANwauJiCE6IrleDGnBNM0Da/85zOEgmG0t5tjuFchuKtT4qmq+W6IzdhgjMjqGLiaCE9ylGifbapFU6PP6GL0wowrpQXu5rqJNH8Sk6rBAeywjdmrgAmwbg8lgxrWUFfXbnQxehFCs8XJk2gw3Mv1S/fAlfRjxpXIpjZtrMae3eZsvc8LFCWaKXcpM5aJTKlnA+dkrsfqmHE1EzvsUWQoTRPYsb0Onf4QGhrMV0UAAERXhW6ji0G2w33KymRZ5tNH0oUZVzPhQUvD9Mn6A1i3dj8cThnhUOqHdNWFjwQpTbDdgn6SJKV14CohRRnX5K8i6ZhxpZRb9e5uo4tgSw0NHVi3dj8AmDdoBfhkgZLClLuVGctkUukeuJJ+zLiaQCT7JNKkf1OJfRsmXNW+Zqx4c5vRxdBFlhSoqvn63CRKNJ7p9Ev3wJW9CujHjKsJyLIMSOnTu4Asc7dLtI6OgCn7seyLoigIBkNGF4NsxpSnT+vHCCkjyzK01I/AQxbEjKsJRDOupjzzJh4TrokhhMCBAy3IzHSjsTG1QwUPhyzL0FTV6GKQzZSVZUOSBg5gZRnIyHQhI8OFDK8L3gwH3G4HZEXGti11aGsLJLRMrOOqnx0ygcPBXgX0Y+BqKvYMXBefORmTJhcbXQzLe+u/23BgfwskCXC6FMw+pgLvvL3T6GLFTdNUKA6eeiixHA4F554/HR0dQTgcChwOGQ6HDFmWIMtdV+uBgqMJE4oQCIRRVdWCTZ9Uo7OT1VlSiRlX0otXD1Oxwa1QH4IBNe3vpodL0zQcONCCcLjrxB4KaVj13m5jCzUM3B0oGVwuB1yuoV/W3G4Hxo8vxLjxhfD7gtizpwmfbaqNHndx436umyRJDFxJFwaulHw8eQ+Lpmn47+vbevUUYOqeAwaRJrViyKIkABkZLkydOgJTppSgvT2InTsasHXLwbj2XZ769Ev35AYbZ+nHwNUEHA4HJEhQVXs1WBmsvhkNLhRSUVvThgP7W4wuSgJZ/8RJ6UOSJGRnuzHzqJE4cmYZGhp82Lm9HrtNOiqdlaVLOw8aHgauJmC3Vvafv3AGyivyAByqtcuT0ZCt/bgKn22qNboYCcdHgmRFkiShqCgTRUWZOOa40aivb8enG2tw8GBH3/PLvEnTS5ZlqGncaJONs/Rj4GoCwWAQAgKKYo+fo/ujCKlrgmFlsbqxYwshBLD5U/sEr0IIKIpidDGIhkWWJZSUZKNkQTbCYQ01Na3YuKEara2J7ZkgXciKktaBK+lnj0jJ4trb2yE0wOXyGF0UMpnikixUVTUbXYyEEkKDJNnrKQOlN4dDRnl5HsrL8xAIhLFvXzM2bqixRX3CVFEUBeGwvarLxYN1XPVj4GoC4XD4UMbVaXRREkLTWDUgUVRVw87tDUYXI6HY7Q3ZmdvtwIQJRRg/vpDnwjiwVwHSi4GrCditQnpra6fRRbCN91buQns7Hz0SWY0kSVA42opuHPKVdVz14vM6E4gcrHYZZUVL45NPIoXDKnbtsFe2FejaP+zWIJGIhkeW5bQOXEk/ZlxNIHqw2iNuheDjsYTYX9Viyw4ZhCaiIxkREQH2e/IYL+nQ/6ViPVbHwNUEohlXO+TwwRPQcLW2dqKmuhUfrdlndFHSVntHAO5uIzAd3qXjO1aFEMMayYkoXdjl+kfJxzOqWYjh3wnJioT8/IyYaU2NvpQ3EGD9+qELBMJ47pkNRhcjqcw8MIUQAls+O4gPP9ibkDLOObESEyeXDH9BRGRrrOOqHwNXExBCQJIllJRmY0RJ7kBzDrwcTaC6ui1mWkaGEz5farsYYcZ1aOrq2rHynZ1GFyPpJEmGppmzv8bt2+rxwft7jS4GUdrhdYP0YuBqAqqqQtME6g/6oIasP7Qn67gOzY5t9Whptn+PDF13/Oa87d9pw8ZwRFYghEjr6gLMuOrHpr12Z8BOyl4F4le1rxm7dzUaXQwiIsOkc+BK+jHjahISADFIVYChcDkVqO7Dy+25jnBIi7sO7MmnjMURM0r7fZ99F8anqcmHFW9ug6qmR8Bv1vsany+Ihvq+x5wnouRiVQHSixlXE4jeZSbhwG1u7kQgEI6+ggE15lValh33MnPzPHC7Hf2+HA6OQx+PTzZUp03QCgCqGjblPtLU5Ec4zJaFRJR6kSFfU/FKlp///Oc48cQTkZGRgby8vD7n2bt3L84++2xkZGSgpKQE3//+9xEOh+NaDzOuJmDmxyNlI3OgqV0X80hopTh4v5NIRUWZ2LenKW2CJlULw+l0GV2MXtQ02f5EZpTuI2fZQTAYxEUXXYQ5c+bgb3/7W6/3VVXF2WefjdLSUrz33nuorq7G0qVL4XQ68Ytf/EL3ehi4prmGhg5kZbkgyRJkSYI3wwlF6QpMJQnw+0JoaPDFfCZdAqxkEkJg+aubITQgEAyn1TYVmmbKjKvDyRsyIjKGHRpn3XLLLQCAhx56qM/3ly9fjk8//RRvvPEGRowYgaOOOgq33XYbfvCDH+Dmm2+Gy6UvocHA1QQiGVcj7jUDnSoCONw1UUuL/Vu1m8Haj/ejpkfXZelCExocDvOdehpZv5XIUMy4pk5ra2vM3263G263O6nrXLVqFWbMmIERI0ZEpy1evBhf//rXsWnTJhx99NG6lsMUgwlIkgRIgBDWyLqZt2KDdcw4sgynzB1ndDEMIYSAopgn49reHsBLL2zC2o/3J3zZvAwT6WPmKnMpEUm5puIFoKKiArm5udHXL3/5y6R/xZqampigFUD075qaGt3LYeBqApHsk6aas1P2XtL8/DJcQggcPNiOltb0zG53VRVwGl2MqLff2o6mRl9SejvgoUKkT9oHrim2b98+tLS0RF8/+tGP+pzvhz/84aCNvTZv3pzSspvveV0644GbFiRJws7t9Wnb2b0QmmkyrsFgGI096nATEaVaquu45uTkICcnZ9D5v/vd72LZsmUDzjNunL6nh6Wlpfjggw9iptXW1kbf04uBKw0BA+zhmnVMRdoGrprBVQXCYQ179zSipaUT27bUmbZfWaJ0omnWqCqXboqLi1FcXJyQZc2ZMwc///nPcfDgQZSUlAAAXn/9deTk5GDatGm6l8PAlSjJqqqaEegMY9z4QkiShGAwjLf+u9XoYhnKyMeCH63Zhy2fHTRs/UTUm6ZpkOX0rb2Y7D5Wu68nWfbu3YvGxkbs3bsXqqpi3bp1AIAJEyYgKysLixYtwrRp03DllVfiV7/6FWpqavB///d/+OY3vxlXwzAGrkRJlpfrxQeb92D7tnqMrszDtq31aGrk42mjNDakrvcAJnOJ9GEdV+v72c9+hocffjj6d6SXgLfeegunnnoqFEXBv//9b3z961/HnDlzkJmZiauuugq33nprXOth4EqUZFnZbowozcaaD/ahprp18A9Q0gSDYdQdZLdXRGYjSVJaVxewQz+uDz30UL99uEZUVlbi5ZdfHtZ60jcvT0MWCsY3PFu627OnER+vqTK6GATgs021RheBiPqRzlUFSD9mXE2g6xGJBKu0EgmF0veueChCQRWaZo3fNlWM6Gjc7wth4yf6+wokotQRQqR3dYEU1XG1Q+9FvL0xAVmWIaFrRCGyn61b6owugqlIkgQ1xX0WB4Mq3lu5C6rKY4yIyMqYcTURid1M2U5Lix9NTWyI1V2q93JNE3jxuU/g84VSvGYiIn3sUMc1VZhxNYHoY1Mb7FAUq60tgDCrVsRIdT+uQgj4/QxaiYjsgBlXExBCQIAZV7vZvauBdSpNIBTSrFJ9nIiIBsHA1QS6ugARkCQmwO2irq4dG9YfQFOj3+iimI6E1I6S43IpkCTJkAZhRER6HGqinZL1WB0jJRMR7K7cFpoafdj8aS2D1n6kOojc/Fktg1Yik1NVFfX19UYXgyyAGVcTCIVCgAAUhT+HVbS2diIz04X6ug44nTJCIQ11de2or2tHQ30H2tuDRhfRvFLcq0BtTVvK1tUT42UifdpaW1FYWGh0MQxjhyFfU4WRkgm4XC5AAlSVHftbwe7djXhnxQ6UjczB/qoWo4tjOV0Z19RUFejsDKFqX3NK1kVEQ5eXnw+/nz2w0OAYuJqAoiiQIEGk8XB3ZlZf146cXC9aWvwIhzWsencXNE0waB0qkbr63NUHWpn1JCLTk5Ci7rCSv4qkYx1XE5Bl+dDAWQxczaip0YeX/70Jfl8Iy1/ZjGAwtZ3n21EqHlcFAmF8/BGH2iWyCtZFJz2YcTUBWZahaRqammvhdHqi0/oz3AB3qOeGyMhe+/Y54PG2d007lCWOvCcGGdpU07Toq/vnhmqg9Q3Ucn3A94QGh+KA4lDg96nYvasJe3Y3oqFhN6r2N0OWZEiSDEmSEAwFEAx0IhwOdnVrJjRoAkC37yWE6PqeQkDrtvGFpnb9LQRC4a46sU6HC6oahiY0yJKMadOOQ35ese7tEd2uPb6fLMsx+1Rf7w+0zK5XuM/tdnhaZN3i0DJ7Bqdd7WY1TYOqqmhvb9f1XQb6d19/A13b/J23d6CmOrZ+a1c9cg2aJrr9Xl3/FUIk/KlHdbUDGZmBpPSiMGrUKDgcPIXbWcy5stu5U88L6GrwNNC+N2LECHg8HqiqCiFE9L+apkGSJDgcDrhcrpTcaKZ70Mo6rvrxrGcCfr8fe/fuxMOP3mx0UXSRftNXUGJD4nAQpmPWuAy09UTPeXqdaET8K5Ui/2P8xSEYDOCtt95E2ciyfuZITBn1/nbJ1HUxAgAp4Y8BvV4vysoOb8OuQLwr6Ij8u7W1FZ2dAZQUF0MTh6dHggQhBCAO92giy3LM+xKk6M1l9wCq53J6ill+D5ELZ/f3uv+7+4U18u/u/+3ZK4WiKHA6nHC6nFAUBVq3YX2799QyWFlCoVDMOiRJOty3ttRjfk1A1dRokNh9e+h7RcrT+/fo+mdq9t2srGxUVJQPOI8kycjOzkZ2dlb0Brfn79F92uHPSb2CMVmSoShKV/W4Q9NluSsJsG7dOsw5cU7CvhvZFwNXE8jJyUFp6ShUjJqFrOxC+H1t8HqzErPwfq6Wwx3soHsdxeiJ6dB/5P5qoGgagkHtUF0eGRjGHWZ/5e/3e/WTUexrbgEAmgYNGoSmRS/csnxotKdIhg6AU3HB4XRDUZyQIEGSu1/sIif5Q39Kctc8koysfA/CYQ2y3FW/ORQOQpEdhzKjCiRZRjgcxLZtHyJ8KBvb128Zu/16XOQP/d3Z2Y6CwuxoErjrYiL32vZ9XdiFOFRGSY5eYPracvIgv6PotvzX//skMjJycPyxiw+to+/d9PAFMXZfE0LEBjc692VNaNA09dD3kaK/V1dQGdkmuhYVt0mTS1BYlDmsZXTPilcfOICV761EKBTuNU/Xq+s7dXZ2YtSocmRkeKPBQs+AIrIvdAW9KiRJiu7r4tAoZ13TuuaTu/1bkuRD27B3kBktU7fpottTB1nq+3M9g97uTynUsBotY0QoHEZnpx+BQADhsAqHQ+lVhr7K1Z0QAg6HAw6H83Am/lCg3l8QHpk/ss0VOXKMyJCVru2jyIf/7VAckGQJyqH9T1ZkKHJXECcr8qEnOV37ZH//7joODx+/cp/T+v5vz++w4q234PP7cO2110bn6b6PCCEQDofh9/vR2tqKtra2PoLs2G3S872e72uahnA4HLNtIzdbI0eNxPz58/v9jeyOQ77qx8DVBGRZhsfjxRHT5mLUqMlGFydpRpXlYPvmOqOLYQp5JZm66spWjp6ekPWNHZOHQMAcvVasfO8llBSV45yzvmx0UVKickw+Tl88yRaP6Mg+WlpasGfPbixZssToohDFhYGrCUTvSm18YXO7Hdi3u8noYpBZ2Hhf76moOJNBKxENiHVc9WOvAiYQqWc23Mf3Zpab60Gg0xwZP6JU8vtDRheBqE/p3iCKrIkZV0o6p1NG48GBW5BTerHDXb8eZSOzMWVqidHFIOol0hCPzIF1XPVj4GoCdq8qkJvrwb4drCZA6SMry4XZx5Zj3PgiOBx8sEVElCgMXE1AiK5OW+wZtgIOhRduSh9HzCjFpEnFKCoeXi8CRETUGwNXM7FhxtXlUlCzv9XoYhClxGmLJ2HMmPy0qQpB1sb91DxYVUA/psIoqfLyPPB3sHEK2V9OjhsV5bkMBsgSWL+VrIoZV0qqsI6+Sons4Lg5lXA4FaOLQUQWxO6w9GPGlZIqFEz8GO1EZpQWwyCTrdghiKH0w4wrJZXTxXsjSg+MAYhoqFjHVT9GFZQ0DoeMhtoOo4tBlBLMXpGVsI4rWRUzrpQ0eXle7GvwG10MopRwsX4rEQ0R67jqx4wrJY3HzQs5pQ+Xm3kAsg5mXMmqeKY1E5udSOz1bYgG5lCsn8kgIoNISM0oRDY4TTHjagKSJEGC/e6AfT7230rpQ2KvAkRESceMqwk4nU5AAlQtbHRREqqlpRMOh4xwmF1ikf2xOyyyEjvUdbSTrl4FUlHHNemrSDpmXE1AURRbZlxdLgWqyqCV0oMs83RKRJRszLiaQPQuy2aBa1FhBprYHRalCWZciWio2KuAfkwRmIAsy4Bkr4xrdrYb2z6tM7oYRCnDuJWsRNP4NIysiYGrCXQ9YpQghH1OJFlZLqOLQJRaNshkUHqxQ/aN0g8DVxOInDzslHFtbu6EN9NpdDHIhOyzl8diDEBWEA6HceMPbsR776201TXH6iJDvqbiZXUMXE3A7XZDkoCwGjS6KAnj94cwqjLP6GKQCdngvElkWc3NzXj77RWYOHEiLrvsMqOLQxQ3Ns4yAa/XC0mSEAoFjC4KERGlgSuuuAInn3yy0cWgQ9g4Sz9mXE1AkiQoimK7yvK+NvtkkClx+HCSiIiGihlXk7DDXVBPGdlsoEXpg9UFiWioUlX/1A6hBjOulDTBoGp0EciEbNsgxK7fi4jIRJhxNQlN02yXdQ0HGLhS3+y2rxNZBY89k0pRHVc7pFwZuJqApmnQNA2yrBhdlGFzuhR43A5kZDixe2uD0cUhShnmW4mIko+BqwmEw2FACCg2CFwz3QpaWwJoPsihXql/EjvFIiKKYq8C+jFwNQEhhOWzNTk5bjhkCU0Nfvja2ZsApR9WcSUriAQuduvFhtIHA1cTCAQCgAAUh/VGmsrKdsHrcaKuug3+jpDRxSGrsMFdf2+MXIloaNirgH4MXE3ESo9PR43Mgd8XxIG9Lcw0UXzsusPY9GsREZkJA1cT8Hg8gASEw+Z/xD5qZA5CQRU7NtfZNv4gGgoeDmQFkaoCtu2WzqIkpKiOq4USZP1h4GoCiqJAkiRownx1jsrKstHREkBmjhutzX5s/6zO6CKRxVm/RnffGAiQFdihcQ6lNwauJuDz+YwuQp/KynKwd3sDwmENqG4zujhkI7a8djJuJQvhjRZZFQNXE/jZz34Gny+EnJxio4sCAMjP9yLkD2PnZmZXifSSZDtG42Q3rCpgTpIspeQcYofzFAPXIdA0De+++y42b94MAKioqMAZZ5wBSZJQXV2NAwcOYOzYsSgoKBh0WVu3bsVrr/4XJ8+5HCPLJiS76P2SpK76q8GAioa6DrS3BgwrC9mfHa+Ztswik+2wqgBZHQPXOKxduxY/uPFHaGisQ1tbEE5nJgIBH1wuCXfddTe8Xi8a6hvR2NSKzEwP/va3P2PWrFkDLvOxxx6DLGdj/PhjUvQt+jaiJAvbPmWGlZJPkmSji5AUDAjISphxNRd2h6UfA9dB7N27F++//z7Wr1+Pf7/0CpqaO1GQX4pT556L0RXT4PO14rkX7kBNdQc6A7UoHTEJZy76Cv737uO4/vrv4YEH7se0adP6XPZvfvMb/POfz2HKpEVQFGNHzfJ3mL9HA7IPYcKGiMNlg+sBEZHpMXAdwIYNG7Bs2VfQ2upHTnYpRlecjHOXLIbT6YrOk5GRgysu+wUAIBwOwXFoEIGzz/w2/vPKb/DFL34Fzz33DEaOHNlr+cuXv4FgUMHMIxem5gsdIkkSioszEQiE0dLSidLSbOzaUp/SMlB6s0OXLL3YIZVBtscnA+bEIV/1s+czuwRZvXo1mpva8bmzv4/Pn/cDHHfskpigtSdHt5GvPJ5MLDn7etTWtuChhx7qNa/P50NLSwvGjZ0V87lUKCvLxp5tDajZ24KwL4Q92xpSun4iO7LB9YDSCKsKkFUxcB3ApZdeivyCLGzbvmZIn5ckGZIEPPPMv/D+++/HvPfmm2+ipqYBs45anIii6uZ2O9BU1xH9OxTSoGk8gRENlx0yGWR/7FXAnCJ1XFPxsjpWFRhATk4OMjK8aGzaP6TPO51unLX423j/wxdw3XU34K9//RNkWcYLL7yAt99+G25XNrKzB+95IJHy8jzYvZUZVjKYHc6ePdjwK5GN8UaLrIqB6wCCwSA0TYPT4R7yMsrKJmDx6dfgP6/8BhddeDmEEJBlL7yeXBx/7AUJLK0+nZ3hlK+TiIjMgZlWc2IdV/0YuA6gtbUVTU0tOPH484e1HK83C+ef+0McOLAFTqcHxcWVhvUi0NLSiawcN/tpJUONHJWDCy+ZGe3QVaBbq/whnFiHeioWOHwhF0IA4tAAWOLQ0LSiq4jd/w2IrmlCANKh9zVhiwsCEZHZMXAdQE5ODnJysrGv6jOMGXPksJalKAoqKvruFivVisuyGbiSYSQATqeC/Hyv0UWhVJHAIXFNInKjxhstc2HGVT82zhqAy+XC2WefgZ271iAUYqBHRDQkDFqJKEEYuA5i3rx58GZI+HDNf4wuSsIoivXvuIiIiOyCvQrox8B1EHPnzsUpp5yIqv2bEAz6jS5OQtiy83ciIiKyPQauOlx33bXwZoTw8qu/h6ZZf6jK/QdakV/I+oVkDMHnxkSGYa8CZHVsnKVDZmYmgsEAmpv3Ihj0w+PJNLpIwyKEgMPBn56MI9vheRWRhdmhkY6tpOo5vg1+d2Zcdfjf//6HlhY/zlx8reWDVgAoLMpAXW270cUgIqIUY8aVrI5pNx2qq6vhUJwoKa40uigJ4XXzZyciSkfhcNcgNHzqZi7sDks/Zlx1+PDDj+BwZMLhcBpdlIRQw9avp0vWxYwPkfF4HJJVMXDVYcGCUyFE0OhiJAzPV0RE6cnlcgE4nHklc2B3WPoxcNUhPz8foXAnByEgIiJL48hZZHWs5KKDx+M5dLDzQCciIutiFQFzkmQJkpyCOq4pWEeyMeOqw/r165HhzYfT6TK6KES2wGwPkbF4DJJVMXDVYdKkSQiGWvDJxhVGF4WIiIhshnVc9WPgqsN5552HzEwP2tubjC4KERHRkLGOK1kd67jqEAqFoKoqgqFOo4tCNCR79rb0eaddMToPwc5Q6gtERERR7MdVP2ZcdcjIyMB3v/sd7N7zPvbv32J0cYjipmkCqtr7xXYaROmFGVeyOgauOn3hC1/A9OmTsXnLKqOLQmRtDJaJiGJEMq6peFkdA1edWlpasG/ffuTkFBtdFCLLs8PJk4iIUo+Bq06dnZ3o6PChpLjS6KIQWR4DVyIiGgo2ztKpoKAAiiKjrb3B6KIQWRzrChAZjTeP5pKqrqrs8LMz46rTa6+9hnBYhaI4jS7KsCkA8nJcyMtxY/ToXDgcNtiTaUicTp4CiNIJR84iq2PGVadVq1Yhw1uGKZPnGF2UYQsEwqg/2AEAqD/YjswsF/IKvcjIdGHPnmbeiacRX3vA6CIQkQF4njcXdoelH9MtOo0dOxYdHXUIh4NGF2XYeu63He1B1Oxvxc6t9Rhdmcc7ckoqwaoCRIbh+Z2sjoGrTnPnzoUkhdHQsN/ooiTVrq31KCrwomREptFFIRuTJJ56iIxkh8ybvaSqKyzr/+68eug0ZswYFBblY+Omt6FpmtHFSara6jYc2NOMigrWfyUishNmXMnqGLjq5PF48JOf/BB1DRvx0cevGF2clNi1rR4el4LiEmZfiYjshBlXc4n0KpCKl9UxcI3DWWedhaVLL8eOnavT5q61udGPmn3NGD06N22+MyWfJNvg7ElkQTyPk9UxcI3TwoULEQq142DdHqOLkjJCADu31mPUqByji0JERGQ7HPJVP3aHFadZs2ahtKwYO3euxYiSMUYXJ6WCnWGUV+SgoyOEpkYf7FDJm4whNGtnfTRNYP/+ZkSSV7Isobw8z9AyERGlAwaucZJlGSeeeDz++8YGo4uScjUHWqP/9ngdKCnNguJQ0NDgg88XMrBkZCUSJAhh7QaOmqbhzde3Rf92exy49PJZBpaIKD52yLzZSVf901T045r0VSQdA9chyMrKgtDCRhfDUJ3+MPbuagIAuNwKRo7MBWQJDqXrqKipaUcwGAazstRbcvaJhoYOtDT7Y9cUXZXUx7TYCdKh/5G6/idm3u7TAEBTYzPGQhOorm4FICAEDmViRTQjG6lXKAQAAcgyUFCYBUkCXC4FisJaW0REejBwHYLMzEx0BtqMLoZpBAMq9u5qjJmWneuBCKnwZLoRCKoGlYzMKhkNRHbvasTGDdUJX64ewaCK5a9sHtJnK8cW4NT5ExJcIqK+sXGWOaWqxb8dMq68zR+Co446Cp2BVrS3NxldFNNqa+mEqgoUFWVA7nMv48kzbUmwfV/I8dizqxHhMLcHpRarCpBVMeM6BOPGjYPDIaO5uRZZWflGF8fU9uxsxOhxBQiGVKiqQIbXCVUTcCgSqqpaB18A2ZLGrE+MfXubMHZcodHFoDSgql1PwBi4moskSynpJtAOXREycB2CvLw8yJIEn6/F6KJYwt6djb2mjazINaAkZAa8YPa25oN9KC2L7W6ue/3c3vVye9YU7r1NnU4ZkiQhFFLR132CFFOPV+r2twQhBOvd2hyPQ7IqBq5DkJ2djclTJmLn7vWYNOl4o4tDRICla5/4fEG8+spm7NzR+yZvqEaPzoPH68DWLfVxf/bY4yowd964hJWFzCMSsLKuK1kVb6mHQJIkHHfcMfD7WceVrI4XL7NIdP5r797mIQWtZG8MXM2JQ77qx8B1iEKhEGSJm2/Iot0F8eRpJCMeF0pJ6g7L6ntSS7MfRUUZRhcDAIMaO4v8tqwqQFbFqgJx2r17N9asWYMPP1wDjzfP6OJY1oGqw/WDJQkYNToPNbUdBpaIyHiFRRmor/cZXQyyMQau5iQd+r9UrMfqGLjGYfv27bjookvR0uJDZmYh5s9bZnSRbEEI6w8BalVMrJlLa3MnHA6Z3WNR0kS6omPgSlbFwDUOVVVVCHQGkZ9XjrPO+BbcbnM81rMDxk80fNbfi0IhFaMr8xLaSIuoO/lQx9qsDmIyElIz0KQN7ldYSTMO8+bNw5//8geEwnX4ZOMKo4tDlAC8eJmNGU7K3CuIyKyYcY2DJEl499134feHUFRYbnRxbIV3/zRsNtmFmpv9KCrMQH0D67oSpQtJklJSfcMOVUTMcHNvGS0tLXj00Scxc8Y5GDPmSKOLYy82CTqshvcL5lRocO8CrHNORGbFjGscVFWF0AQyMzjqU6LxMknDZad9qLXF4EZa1k/KEFlKqvpYtUHClRnXeBQUFGD06HLUHtxtdFHsJ8FRhxACbpeS2IUSpUgopGL06Dyji0FEZDrMuMYhFAqhav8BTJsyy+ii2E5DfQfyC7yAiI5NEPmfw4+zBSAOTdNUgc7OMLoGMei2oEP/VlUNxSWZqKpqTUn5ST8BIBgMo6mxWx3OPrIA0f4GpR6zSH3MAyAYCCeymCnncind9mUBh8MGqREi0sUOdVx//vOf4z//+Q/WrVsHl8uF5uZmXet/4okncOmll+peDwPXODgcDhQXFaKjg0O9JlooqKKupt3oYqQdoyrq19a04cXnNxqybjPKynZDVWMfO/j9YRQZNCDBxk9qUVPdFv37wouOhNPJJxh2wgaxlGjBYBAXXXQR5syZg7/97W/9zvfggw/ijDPOiP6dl5cX13oYuMZBkiT4fH7k5XqMLgrpYIe6PJTeSsuyDQlcg4EwDuw//LRCVTUGrjZhh1bldmSHOq633HILAOChhx4acL68vDyUlpYOeT0MXOMQDocRVlVIEk/gZH2GXb+Y6emlv5/C7wtBliVocbTyd7lkTJ5cErtgcejf4vCfEALbdzSg02/tKhYUH2ZaCQBaW2Or0bndbrjd7pSs+5vf/Ca+8pWvYNy4cfja176GL37xi3HdUDFwjYPP54Pf70dmRo7RRSEd7DAmczIx82Ie/YUSwaCKsWMLsGNHg67lSBIweXIJmpv9uuYfN7YAn356UGcpyU54/Ke3ioqKmL9vuukm3HzzzUlf76233ooFCxYgIyMDy5cvxze+8Q20t7fj2muv1b0MBq5xyM7OxvHHH4P3Vj4LSZIxceKxRheJBsLzMlnEQLtqMBjG6NH6u+DTE7ROO2IEjpje9ajuzLOn6l42ESVHqhtn7du3Dzk5h5Nw/WVbf/jDH+LOO+8ccJmfffYZpkyZomv9P/3pT6P/Pvroo9HR0YG77rqLgWuySJKE++67F3feeSf++dRjKCgoQ6EVR9DikyIC6wBbSaKf7qbqIknmxd8/veXk5MQErv357ne/i2XLlg04z7hx44ZcjuOPPx633XYbAoGA7qoKDFzjlJOTg1tvvRUrV67G1m0fYo4VA1ciAEVFmcasWJIghEEd65tVimMIxixE5mLWxlnFxcUoLi5OTmEArFu3Dvn5+XHVr2XgOgSKouD444/B8tfWQAhhuTvXdEm4Vu1pQobHGTNNkgBJliBBgiQDvoCKcCg9gyiHU4ZQ0/O7pzurnbMo8dhIixJt7969aGxsxN69e6GqKtatWwcAmDBhArKysvDSSy+htrYWJ5xwAjweD15//XX84he/wPe+97241sPAdYguvvhivPjCK/hk41s4csYCo4tDfQgGVAQD6oDzeLNT04rSlAy8bjFwMpYsc/sTmYkdBiD42c9+hocffjj699FHHw0AeOutt3DqqafC6XTi97//Pa6//noIITBhwgTcc889uPrqq+NaDwPXIZo9ezau/uoy/OmPDyIcDuGomadDljmCrtUw62AAISBJPFa6k7r3VZWK9TFuTVs851GyPPTQQwP24XrGGWfEDDwwVAxch+GGG26AJEn4858eRG3tTsw/dSk8HoPqDcaD562odD6Hp/N3N50UB5LMuBKfepiLWeu4mhHTHsN0/fXX4/7f34v2jl346ONXjC6OToxYItI5eBPcD9IWgxYisioGrgmwcOFCLPncWdh/4FNomvkbuzBcOYyPzSgdcb8nMpdIxjUVL6tj4Jog5557LgKBJtTX7zO6KES6MHYxj1RfS+IZQpbsJZJcYdadrIqBa4JMnjwZBYW52Lb9Q6OLMjhes6LSOfPEao4mkuLfIo13+7QXOeexMbG5SCn8P6vjnpsgOTk5mD37aOyr+gTBYKfRxSGd0vkC7nazbWa6SucbNurCfYCsioFrAp199lkIhVpR31BldFFIp3R+XOZw8PA3C8YQlGrpfO4zK9Zv1YcplwSaPHkynC4Zn372DspKx/PEYHLp/vMYtX8KCGRluzFl2ohDnW73MU+/gVw/b8Q3eYDCHeprQRz67AB/d3aGuuqK9tkFq3T4PwNEpZF3FFk+tB0kQOo+f/+/UUd7AJ2dYX3fqwfetBCRVTFwTaBx48bhjjt+ju9+90c4cGArRo2abHSRaADpfmNhZA8YBfkZOP6ESsPWnwj/W7EDVVUthq1fliUUF2eita0ToWB8v6XDoSSpVGR2rCJAVsfb7gQ7++yzMXPmEVi3frnRRemXxFY5AJhxZSf01qZpArW17QgFNRQWZcLp1H86V5hxTXvpfuNuNpEhX1PxsjqevRJMkiScc85ZaGzea3RRaFDWP4CHwwJdDpuaWfJW4bCG2po2BAIqcvM8KCjIwGANxjd/Vou3V+xAY6MvNYUk02DGlayOVQWS4ODBg/C4s40uBg0icuM5oiwbtdVtxhbGAIHg0OpHJoId7vrNRtMEGuq7AlGHQ0Z+gRdCAM1NPvS8SWtq9AMAJk8pSXUxyWCRwJXHoLlwyFf9mHFNgtbWViiKE+3tTZYYSSttSUBRSSY83vS8f/N1hIwugrWZOHEVDmuoO9iB+roOyLKMgsIM5OS6mW2jKAauZFUMXJNAkiS0ttbgX8/fgpdf/QP7dTUpoQGdgTAgAWMmFAAAcvLcyMh09ZpXkiUUFmWkuohJpRkVxNgkdhIW+SLBoIqDte1obPDD5XagsChz0KoERJRarOOqX3qmmpLsxhtvxIwZM9Da2orf/e4BvPjvu3HukhvhdPYOiMg4o8flo6U9CKEBsgKUjsxGhy+E4hEZUNUMOBwyQiEVLpeCuroOSIqE/AJv9DErDZ0dTp5W5PeF4PeF4HY74PHIlgm+iYgiGLgmQWZmJi688EIAwLHHHotLL/0CnnvhDiyc/yUUFpYbXDoqKMpAUAOqDrShZEQW2tsDaGsNIDfPA6dTRjis4cD+1l6f62gPIr/AizETCtBY14HWloABpU8cho7pKxAIIxCAbbLfRFbHOq768YFRks2YMQO/+c2vMWFiCV5Zfj+amqqNLlJaGzupCG3+EAKBroZJB2vb0dToRzisoaHeh0BnGK3NnXC5++7nsqnRjz27muDrDKN8dC7GTihAxZg8S1YjYMwyTDbYgKzzmn7YOIusjhnXFDjttNNw3HHH4ZJLLsML/74TJcWTMPfky5CVlW900dJOXX3vFtbd+Xz6GiypYQ37u2VlPV4nMjKdACT4OoLDLGVqGHnZ4kWTyBiRBsMyKzqbSqrqn9rh3Ms9N0VycnLw6KP/wG23/QQZGe1Y8b9HjC5S2ikekRXNtCaaokgoHpENT4YD+QXepKwj4WxwAqPhYb6ViKyGgWsKFRYW4rLLLsMPf3QjGpt2Yteu9YaUI13DFU1L3mW6oz2IvXua0NToRyCoWid4NQTDJdPgT0FkDlIKXxbHqgIGWLBgARYsOBmrVz2DE088GS2tnQntU3N0ZR40VcDplCHQFbB1+sNQNQ0ZGS4EWq3dqGiocgoy0OpL/tjyfn8Ifn8IJWXZcLsV7NvdDLdHQWFxFtxuBVV7m5NeBj1scP4ioiFi/WayKgauBpBlGUceeSTeeOMdNDW2w+l0oGxkNqoPDH/0pvwCL1qb/QiF+h74oNMXQqY7PX/2VAdqdQfbIUldI3PVH2yH3xdE9YFOVIzOQ6tF6sHSAGwQ+TN4ST+Ruq387c2FdVz1S88IxmBPPfUUHvjDnyFLDqhqGIrigK89gDFj8hEIhOF0KQiHNFRXt8HplJGb60FdXYeuZbtdCtpajRvK06yycz1oaNC3DRNJCODgwXZkZrrQ3Nw1EEUwqKa8HGZil75D8/K8fXabZiW7dzWhocE37OXk5XlQWVmQgBIREQ2MgWuKvfbaa7j99jtQXDwN8+deApfLAwAIhwXqDrbHzOv1dHXJ5OsIorAwA06ngrCqISPDCU0TqNp3+LG3osgor8hFfY9lUJfi0izsqzIuyOjolmGtrWlDbqEJus8y8MZbkqxfvT4312N0EYZt756mhCynckw+A1eLsEPGjdIbA9cUevPNN/GDH/wEZSOOxOLTlw3aHUn3E4ymaWhtCUKSJPg7gnA4JFRU5CIYUuHxONBY38GgdUA8WffELUJEZA4cgEA/Bq4pEAwGsXv3bvzqV3dBRi4WnbY07j70ggE1JpANhwUaDz3ia0/TxlbxaGvhMK1ERERWx8A1ybZv345lV30JTU0tUBxZWHzaV6Ao3OyplpXjQXMbG0TFMPDWW5JtcNtPRJQgbJylHyOoJPH5fHj33XfxwAN/gt/vxKlzv4jRFVOjdVopddweByRJwuiK3Oi0rha13TLYaldjuHRi/dOXwbgBo9hAnYhShYFrgrW0tGDNmjV45JFH8d57HyIzoxDnnPl1FBSUGV20tBXoDGPnlroB5xkzvjBFpTEPl1uBUPvuNi2ZGOQQEcVK1dgAdrjfZuCaQK2trbjkksuwa9c+uF3ZWLTwqxg39khbpObtrra69dARnT6/VWaGC+1tnYasm8cEkbHYjytZFQPXBHrqqaewa9cBnLfkeyguKmddVgvJynajpSPIbCDRkPDAIRoO1nHVz/qdKZrI1q1bUVxYidIRYxi0WkxdbTvKSrONLgZZiPVP/5SOIplWOwQwlJ4YXSWQx+NBKMyuqRJtRGEmRFiD5FIQDKtoaw8g3M+QtkOVX5iBmtr0apxlJF40bYYJV6JhYT+u+jFwTZCtW7di06bP4FBcRhfFVhRFwuY3d2Hre1Ux04src1E2qQAF5bnILPRC8TqgQqDDF4LfF4p7PZm5brTXcqhcIrK3yE0j67iSVTFwTQAhBO68806s/fhTXHLhD40uzuACYYwozEz8cmWgpT2ATn/iAkC3X+sVtAJA3Z4W1O1p6eMTQGa+B+VTi1A0Jg85JZlwZjoHvc1s3dGMcG0HPONyEU7BLWlxcRK2/wAOHmxHKMFZaqIIhkDWwaoC5sQ6rvoxcB0mVVXx8MMPY+XKD3DCcZ/DiBGVRhdpUDtWVuH9F7YkZdmKQ8bMxeNRPnMEfGEVbW1Drzrh9jiw8qGP4/5cR1MntrxXhS19BLyDOe2bxyLsVeL+XLy2bx64e65EKxmZg1DIPNVY7JHtsf4FgNIPA1eyOgauw6BpGpYt+yI+/GAtpk45Fccde6bRRdIniTGDGtbw8X+24eP/bAMATDmpAuPnlENzK2hs9OleTnFRJja9tBXBBGZvBzNiTB78HpnhSArYI3Alsp5QqKsqldPpNLgk1B3ruOrHwHUY1q1bhw8/XIsjpy/GCcefY3RxTGnzyn3YvHIfAKBiWjGmLRwDZ54H9Q0dA3Y9FWzsxK51tSkqZZf2Jj+zECkgSYDQGLjaCn9OywiHu5IBDgcv/2RN3HOHoaKiAhXlZdiybTWOO/ZMdoE1iH2f1mHfp12PyAtGZeOoMycga2QWGpr8CIdj618aET+qYQ2tH9WieFIBAtnMRiSTEKxvS2SESOCqKMmvEkWUDOzHdRiKi4sx58QT0NrWiFAoaHRxdDPDY9rG/W14869r8eKt72Dtw5/A0RxEaVEm3B4HMsPA6w+sSXmZOjtC2PDWLlZdTDLJLhvYJl8jEQRTrpahaV03jQxczSVSVSAVL6tjinCYJEmC2+1A7cE9qBw91eji6GOya4yvNYCVT2wE0NW4Sw0bm43b/N9dGP+5idDscISbkmSKmyeidMSqAmR13HOHwefz4bnnXoDLmYMRJaONLo5uZo4ZjA5aAaB6ZxMmMJ2WRIJ1ie3GxOcUisXA1ZzYHZZ+3HOHwev1oqioCB7XGHg8qe2Xc3h4lRmIpgpIDX6gyGt0UWzJLo+VrX/6HxpZllDUox/i3DweK1YRVlnHlayNgeswSJKEcFhFEJ1GF4USTHbypJ5MkmT96vUeT3o24HO5FZx2+iSji0FDpKoqAAauZsPusPSz/tXDYAsWzENT835L1dmzUFENI8k2OLoH0HXySuSOIPqp/C+iLyEiL3tsXzt8B0o/rCpAVsc9d5gWLVqEJ574FxoaD6CocJTRxdGHgeugpEx7Z9PGjytA+zBGNevJ5XLhwIFWXfMKTbNFPSsiK4pkXBm4mgvruOrHjOswRR63hMMhg0tCiVS/4SCcDPB1q67WF7RG2OHkSWRFkZGzGLiSVTFwHaa8vDw4nTKqqrYaXRTdrFStwSjr3tiJrS9uhcRtNSCHQ0Z2jgder70z1ER2obKqAFkc99xh2r9/PzIzMtDcctDoolCC7d/WiKlCQieTg73k5HpRfaANmqahpqYjvg8z20pkGFVVIcvMWZF1ce8dhsbGRlx//ffgUEpwzKxFRheHkmDP23vh6CPrqgBp28otJ9eLnTsa4feHEAio8S9AMOtPZBRN09ijgClJ0XquyXzZoSM/ZlyHweFwQFM1jBs7E3l5JUYXRzcGDfpt++gAOjuCGH32+Gi9TJcAPvnnp5i5ZDI6M9LtAiCgqsMcJELiPmgmTqeM6TPKYqYFgip8HUF0dATR0RFAOHT4N3exqzhLC4VCrCZAlsa9dxhkWYYA4HK5jS5KfBgzxGXf5npMPKkCwXw3JCHw6bNbULevFbtXV6F0QaXRxUsZIQScLgeq9rUYXRRKIIdDwdRpI4wuBqWIqqoMXE2I/bjqx6oCw6CqKiQJCAStNQABk13xe+fRDejYUAe3BtTsagYAbP+4Gh7V3htTCIHcXC8UhwJZUVBb056ApUoQmr23G5FZqarKqgJkabztGobly5cjEFAxtvIIo4sSJwYN8QoFVKxdvgPKm7tipjdvbYRnaqFBpUq+nFwPdu5sTPBSBTvvNxG7DMFL+miaxsZZZGkMXIdBkiRIkOB0eYwuCqWIGo6t37n+9Z04aUohwiaPw3Jz3cjJccPpVCAg4HTLgK//+bNzPNhf1YKmpuQ8TWA/ribCuNVUqg+0IhgMR38W0eMfff1cQnRvciNipvdUU9MCOzTQofTFwHUY2tvboTiAHTvW4aiZ840uDhkgFFQhDvqAERlGF2VAfn8I7W3BmGljx+ajtbV3YCqEQHtbAKHQMBth9YeBElG/3lu5G40NA9xVDtOnG2sgSawqQNbF5wVD9Mknn+Cuu+7FmNHHYfoRJxtdHDLQJ6/vtGTF4X37WpCT642Z5nY74HI7UV+fvAsnJGZczcR6ey4NhyZYx9WMIo2zUvGyOgauQ7Bx40Z89atfB4Qbc0+5EA4HRw1KZ4017XD7h9CfaYq4XAr8vnCv6eGwhprqNrg9XQ9e3G4HamvbUVPdluoikpEYuZpLkm+CNVWFk70KkIUxcI2Tpmm49977EAy4cclFP4bb5R38QxR1xJx9+P2qv+OIOfuMLkpC7X6vyugi9KtiTH6/fa/6/aFDDTUE3B4n1FT0kmDB7PRwmS3j4fE4kJnpQmamCxkZvPE2k2QfHaqmQnEw42o2XUMDpOL/rI+3XXF64YUX8M47q3HW4m8hOytf9+dy8zxwOPTfJ9TXddjycernv/0hjjhhP87/1ofYtKrC0LIcv2Ryr2kCYuADu9/fRCDH40BrZ+/MZqq43AoqxxZAUSQI4NApSiAQHDgbXLWvBU6njIYGfQ2xEjF4gGyDfbu4OBOXXHZUzHHa/WvFc/x236Y9N29f2zt2koj9W8Q0z4n+0X0Wp1OBzJ4d0pLQNLhcvPSTdXHvjdNHH32EvNyRqBw9Nb4PCmD7lgbdsxcUeW2XmMot9GHehZ8BAE696DP87lofWhqMadQkSUBjZ3DwGeNQFExSY6Z+ZGS6oCgS8gu6tqHTraCurmNIy0paQ6w+2GW3liQJipKY4K+/4PfQlISsgywiyQdIV8aVl37TSdVorDY4nXDvHYTP58MzzzyDp5/+FyAE9u7bj8kT2IPAUCy+aj1kueusLMsCi5ZuwNP3nmBIWZKRzd75SS1GHlWKQCi59V2nTB+Bzs4wOv0hdHaG0doW6HrDQlVT7fg0gSgRkn1jp2ka67iSpXHvHcSKFStwxy/vRjAkY8K4WZg4bjyOO/ZMo4tlekUjW5E/Ijb7d9431gDSodOyJHD+Nz/EuhWxQ6Y21Wai/kBOqoqZUOGwhhyXA3VJCFwLCjNQUpYNoQnUHWxPTV3UJJEAqFpqs9NEZufzBdHZGe63PnqiaFoYDicv/WbDIV/14947iPnz56NyTAU6fdlYfPpVKVtvQUFG0u6896WgbttPn3gWM+fGNsASGiAdquYry0DZuGb85eO/xsyz7u3R+M6pqdvOibZvcx08FdlI5POY8tG5EJDs09pfkqCGzdsLA5ERNm2sxbqP9yd9PZqmwcGMK1kY995BeL1eXHDB+bjrV/cPc0nxBTLbt+qvDxsvWUl+ZxL/+evRmHLsATjdKiKjC0o9Vtv9zk/TgFBAwct/OyrpZQMAxZmcbdDS6Ef5ESWo76Nj/6HKyvHYJ2gFAAEO+UpkECE45KsZparNvx36FeDeq0NLSwvc7uF1e2X9XSU+yx+Zia/OvhpV2wqgqgN/e1WVULW1EF+dfTWWPzIzJeVzOJPXHYwviaPe2AXruBIZQxMaHOwOiyyMgesgli9fjr//7R+YNHHO8BaUhtfpPZ8V46uzrsaKfw7cA8OKp6bh6llXY89nxTHTk9ldjxJH12TxOrCrCQltYmHd6qz9EAxciQwiNI0jZ5mRlMKXxTFwHcS2bdvg7wzB4XCis3NoXQ0BsGHwoU+nz4X1/6tEf21xNA1Y/79KBPy9O0GfdMzIpJUrWVUFACDgDyM3y52w5QWDxvUNmwwC9ujHlciKNE2Fy+UyuhhEQ8bAdRCXXXYZLrjgHGzd/ib+88qfhr6gNL5OT55dDU3t2tUiAWz0v6qMSbOr+/ycqgq4vcmphp3sOl6uBP7gycwOG0EIZlyJjCIEG2eZEROu+tnripgEBQUF+OUvf4GMDC9a25LXYMrOpp6wHw6nhnBIRiig4J/3HI9QQEE4JMHh1DDthL6HS1UcMibMr8SJF02DJ9Naw1J2NPoTtqyggaNxJYME1nElMoqqheB0Wut8StQdA1cdFEVBZeVouF2eoS8kTasKuNxhVE6pBwAc2JGPr86+Gn/47iJ8dfbVOLCza8jcyqn1cLn7Ds6CIQ11zX4ceeYEzJhb2ec8Q5GIYUsHsntzHbIzEvM4bu/uJuTnD69xoJkIJH/7E1HfhBCs40qWxucFOkiShGnTpmLL5r4faetbSOLKM2wpjBlc3hB2bSzB1o9L8ZtvnRmtyxppuHXd/a9iwlE1cHnCCAb63x2bWjohuWUccVIF1O7Dk3bL3IWCYUDr6mpJViRIstQrsyfJXb+ny+tERxI3hBCA5AslbFmaZp9AT5IkBq5EBtE0lYGrCUlS7+tVstZjdQxcdRruj22q63QK99v2Zi+unnU1hOi90k6fC3d+6XOQJNHn+z0JAMGM/ndZGc6YeQfa5KEURO/bN9Rg/AnlaBtCACtLEgqzXRAAikbmQMgSWloSX0YiSi+s40pWx6oCOlVVVcHryR3GEswUuabWYEGpnqDVioQAJH/8I0TlZrkgGv3Y+M5ebHpnL1RVoLa2PQklJKJ0o7E7LHNi6yzdGLjqUF9fj48+Woey0nFGF4UsZvuGGmRlDN4QIj/bjfwsN4oyXDi4uQE1e+2dXtVM9QiCKH1ogoErWRufF+jw/PPPo7W1E0fNPNXoopDFCE1A6Rw465rldWDHB/sRCsafnbUiG9zwE1mW0FhVwIxSlQy1w/mXGVcd1q5dB0X2wuWyT8tuSp3t62uQ6ek761qY7cbO99MnaCUiYwnBxllkbQxcdZg4cQLCais2b/nA6KKQBWmagDPUOzAtzvHA1+AfvOGeyW6RHc7hXfRYSYDIOJqmMuNqQpFeBVLxsjoGrjpcd911OOGEY/D6fx/Gf996HKFQAMFQAIGAz+iikUVsW1eDDM/hi4UQAnW7mrD7szoDSzU0I0ZkDW8BQsDBjI/pqGo/4zKTrTBwJavj3quDJEk4++yz8P7qj7B77/uof74Kbe31CIY64HR4MGXySZh78ue7dykKSZLgdCiQ5K6Jqso8U7xscGMYpWkC7rBA5FZHkSW0tgR0fdZs7ZhqqtuQkeGEbxj91EaOCzJee3sAK97cgbmnjkNOzjAGWSFLUFU16UNeEyUTA1edPv/5zyMjIwOtra14e8X/4PZMxMKFC7B9+3b88YG/ozB/IspGTDC6mPZip8gVwNZ11ag8ZiR8nSHkOZ2obtMXuJptM4TDGkpGZA0rcCXj+f0hvPvOTmzdUm90USiF2KsAWR0DV50URcHZZ58NALjsssui04UQ+Ne/XsCmT99GSVElFMUCY0CbLRLql8lSjcOkqV1Z14wMNzat2md0cYblwP5W5OV70NzUOaTPCxuNBGY1wWAYq1ftwScbaowuChlBCGZcTUiSUnNptszlfwAMXIdJkiRMnDgO7777AT5a+zKOO+Zco4tkG5LZWiUlwOaPDhhdhIRxu4d2+mDIaoxwWMNHH1VhzQfWvmlKBtHtf1O9g4bDGkJBFaE+GnAmhWSPYT8pfTFwTYD/+7+f4IILLoaq8tFpQvHcamoHa9sxYkRW3KN68aKZWqqq4ZMN1Vj57u4+3y8oyMCpC8YjM9OV2oKl2Afv78W2reZrDJmf58X+/a1GF4MMlqoW/3Y4/zJwTYBAIIDOzhCKikYbXRSilAoGw5Ck+BuQCbO1OLMhTRPYsvkgVry1HdoAHQa4XArKynJSVzAiomFg4JoAU6dOxbHHzsRnm97DxPHHGV0copRpbu7EyFE52F/FjJFZCCGwc2cD3li+DeEwu7iiHgRvHMnaGLgmgCzLKC8vx5bPmo0uiq3Y4IlGWmhu8sPhkBAO82JoJCEE9u1rxuuvbUVnZ9jo4pCJ2eFxMaUvBq4J0tzcAqeTQ8ImFk+ugPn6ce3J5wuhvCIX+/a2GF2UtCSEQG1tG157dQva24JGF8e8zH4gpYgQgoErWRoD1wTYu3cv1q5dj7ysGUYXxVZ4bu1ihe1QU92GsrJsVFe36ZqfjyqHTwBoqO/A68u3orGBo/gNhnvcYTz+zIfdYenHwHWYVq9ejeuuvQF+nxPz5pxudHGIDBEOa6ipaUN+vgdNQ+zblfQLh1W88Pwm1Oi8URiQDS5kVpbqEJLZVrI6Bq7D4PP58MMf/gRqKA9nnv5luN2ZRheJyFBOF0fkSQWfL5SYoJXSD+NWU5IO/V8q1mN1DFyHobq6GjXVB3HS8V9h0JoMzAxYTt3BDpSOzEbNgYGDKo2PKtHWFoDf37vv59jdXup1GEgAamsZtNIQ8dAji2PgqsP27duxZs0afO5zn0NGRgYOHjyIl19+GU899QxUVcDfabGLiGWCBquUM8ksthl87cFB+3aVeVOCj9ZUYdNGDruaKsJqB1ISsbqACUlITTbcBj89A9dBqKqKb3/7Omzbuhu33fZLOBQZmibg86sYWTodJxz7BYwuP8LoYtqSHR5ppKO2tgBGleeial/fvQywVTMREQ0VA9dB/Pe//8X27Xtw3OzLoGkqWtvq0NnZjmNnfQ5ud4bRxbM3xjaW1dzk7zfrKgGQFTnlZaK+8TAjMh57FdCPgesANE3D3//+EHIyyzFh3DFGFyftMCtnXR0dQYwoze6zAREf2BIZRwjB7rDI0pj2GMCrr76Kj9ZswOyjlxhdFCLLcTh4eiETYaxGJial8GV1vLL0o7m5Gb/+9X0oKZqK0hHjjS4OkeVommZ0EYjML8WRhCTbIXShdMbAtQ+ffvopzj//QlTta8AJx15gdHESj5kHSoFQkIErkenw/G9OkUquqXhZHOu49mHNmjXYvWs/zl78fWRm5hldnMSz/n5LFuDxWvv0IgCEgmq3vw79b7cLv6Ydri8oSb37XB1IIBBORDGHzwYXMiJKH9a+siSJEAKKIiMnu8joohBZltXbfwhN4N8vfZq05YdC6uAzUeJYfH+k/2/vzoOiOPs8gH+7B2YYkPsa7lOMR6EJETQqK+irvFqpVN5aKtFEJRrNYZVViUZNpSrZ/SdJvWslmzW7UffN8WY3eUs5hktBENSoGBWP9USDibcYFCJCVGC694+BkeGIgw7T08P3Y03N0NPTz9ODzPz6179+HiIzBq79OH36NAAdTKYOuLlple4OERERuTDOP2A71rj2Y/r06RA17Whqvq50V4hUq7W1XekuODVX+AIhInI0Zlz7ceTIEejcA2AIjVe6K+QiRFGA5iHDQ/Weo76bWs9wenq647fmu0p3w4osy5Ak295Rnsp3HbIso7XNOQ+kOtpNiIj0sfx8o6EVnZ28sHG44QQEtmPg2o9bt26hqbkBl6+cRmTEaIiiRuku2VVUShhGhHpBEATI6PqPLMuQhQeTrMqyebkMmGMo2XwvwPzY/LquJ7pmSBK6VjBfrCJYYi/zWl3LBkHr6Q69v8dj7+8f6b6gRhB6TDEry5Bl9DtIt+WPXhBw8+bvNrdz726HzQFTHyo9L/LrjTsICvbCzcY2pbtice1aCw78eEnpbpCDyTJwo6HvZBjOoK2tHW09/kTctZqhDVxdIHCh4Y2Baz9efPFFXLp0BT8e/juEWj2io55CUmIa/P3ClO6aXWj9PNB6rQVWubzuiNTq5y5yr3vLcrnXcnmAx703ZKPW++YbqZLJJKP1zn34+OrQcpu/xz5cIfVBRPbBlKvNGLj2IzU1FXl5W3Du3DkUFRWhqKgU5Tv2wntEGOJiJiIxPgU6nZfS3SRyevfvd8Jdq4Fe74a7d51k+CciIlItlZ6EdIykpCS888472LlzBzb993qkTx+Hcz+XoaDkX1G1+ytcvnIKksQ6OBpaah9WqvXOfXh6aaHRqP9In4hoqKh5utcLFy5gyZIliIuLg16vR0JCAj744AO0t1vXlh8/fhzTpk2Dh4cHoqKi8Ne//nXQbTHjagN3d3dkZGQgIyMDTU1NKCsrQ0FBIfbXfgMBesREpmBkYqrLlBKQc/HwcINe7wZR7K7HNX/8PKjK6BvZPizY7a9+91EDZFEUzH0TBYgCuu4FdHRKkLvqejvaO+Hl5Q5A2TNV/e23UhjGE5GrqKurgyRJ2LhxIxITE3Hy5EksXboUbW1tWLduHQCgpaUFs2bNwsyZM7FhwwacOHECixcvhp+fH5YtW2ZzWwxcBykgIAAvvfQS5s+fj7Nnz6K4uLirlGAPvEeEIyEuDQlxT0Gr1SvdVXIxDy7ucp7gCzD3a7AXngkuUGf12PgWEFEXtZe4ZmVlISsry/JzfHw8zp49iy+++MISuH733Xdob2/HV199Ba1Wi7Fjx+LYsWP45JNPBhW4slTgEQmCgCeeeAKrV6/Gzp07sGHjZ5gy7QnU/VSK/OIPsHPP33H12lnIMoc1IbLiRFlPIiIaGrdv30ZAQIDl5/379yM9PR1a7YOJnWbPno2zZ8+iubnZ5u0y42oHWq0WM2bMwIwZM9DY2Iht27YhL68Aew/8DW4ab8RGP42khDR4ewcq3VUiJyBAFHjMTET0gGPnzmppabFaqtPpoNPp7NZKfX091q9fb8m2AkBDQwPi4uKs1gsNDbU85+/vb9O2+e1hZ8HBwVi0aBGKiwuRm/sdXpz3ZzQ21aKk/EOUVX6On84fRGencw6ETeQIspOVOiiFlQKkCP75EYCoqCj4+vpabh999FG/661du7ZrvPOBb3V1dVavuXr1KrKyspCdnY2lS5fave/MuA4RQRCQnJyM5ORkrFq1Cjt37kRBgRE1+/Jw5P8KERE2HqNGTkJQYDTr/YiIiIYxAQ6qce26v3z5Mnx8HszYNlC2deXKlcjJyfnDbcbHP5hl9Nq1a8jIyMAzzzyDTZs2Wa1nMBhw48YNq2XdPxsMBhv3gIGrQ+j1esyZMwdz5szB5cuXUVJSgoKCIlTtXg9PfQjiY1ORmDARHhwbloYJHqw5D/4qiIYfHx8fq8B1IMHBwQgODrZpm1evXkVGRgZSUlLw9ddfQxStT+pPnjwZ7733Hjo6OuDubh5lprKyEqNGjbK5TABgqYDDRUVF4c0330RFRRm+/mYjZs5KwfmLlTB2jQ175eoZXtBFNFSc6TQpI0ZSiDMNC0eu4erVq5g+fTqio6Oxbt06NDY2oqGhAQ0NDZZ15s+fD61WiyVLluDUqVPYvHkzPvvsM7z99tuDaosZV4WIoogpU6ZgypQpaG5uRllZGXJz81Fz8CtoxBHmC7oSJ/GCLnLJo0tmXMHRFRxIFPn/rRv/9mgoVFZWor6+HvX19YiMjLR6rvtAydfXFxUVFVi+fDlSUlIQFBSE999/f1BDYQEMXJ2Cv78/5s+fj3nz5uH06dMoLCxEUdFWnC2vRoBfPBIT0hAbnQw3N+3DN0auh98zrom/VyJyETk5OQ+thQWA5ORk7Nmz57HaYuDqRARBwNixYzF27Fi8/fbbqK6uRkGBEftrtuDwMSMiw8cjKXESggKjeNQ8jDAvZz9O9V46VWeISElqn4DAkRi4Oim9Xo+5c+di7ty5uHTpEkpKSpCfb0TV7oPw8jQgMX4SEuJSOEPXMOACnzPWeIrczBW+QYiIHIyBqwpER0dj+fLleP3111FTU4O8vDxUV23FsRMlCAsdh1EjJ8MQmsAsrItimOeiGMATkYVjJyBQMwauKqLRaDBt2jRMmzYNv/76K0pLS5GfZ8QPNRuhdfdHfOxEjExIhaenr9JdJRoQwzUiZXFUAVIzBq4qFRISgsWLFyMnJwdHjx5FYWEhtpaW41TddgQHJSEpcTKiIsZAFDVKd5WoD4FXeTOAJ2UIAgNXJ8QaV9sxcFU5URSRkpKClJQUrF69GuXl5cjPN+LA4f/BwVoPxEY/jVEjJ8PHx7YBhMkJ8TuGiIgIAANXl+Lt7Y3s7GxkZ2fj3LlzMBqNKCwsRen23fD3i0NSwmTExiQr3U0aLBc4QnYWYWEPnynGUVwh80HqxOshSM1ccWxzApCUlIQ1a9Zg164d+I/1/4bkCQYcPbEZeUX/gsqd/8BvLQ0P3wjRkJDhplHmmFmjcZ4vbI3Ij18iosFixtXF6XQ6zJkzB3PmzMHFixdRVFSEvFwjjp/aCS+PcMRFPI2YiPFwd9Mp3VUaiIuVCrC8zkxg3EpE3TiogM0YuA4jMTExWLFiBd544w3s2bMHBflG7Kwuw8nzZQgLGIfE2DQE+kU+fEPkWC7wQUOO19FxH+UV36CttQlyj6MfWZYhQ4YAQJYl6PVa5Bt9IEkSJEmyrNN9bzKZYDKZrLbdfaq5v/ue6wqCAI1GAzc3NwiCYLkBgACh77Iej/vzKKe4u19z4ULTgOv0OZaSH94Xm2s9eryX5k3L1kdvgmBpr7vtTpP0h9sauE9AR/t9uLlpB+ze/ft3bes3kZNi4DoMubu7IzMzE5mZmWhoaEBxcTG2bM7DnqMb4ekegrjIiYgJH8/JDZyFi2UoWV7nGFXV/0DDjZMYP34cRo8ebRUciqJoCcx6XmHec3k3Nzc3iKIIsau0Qe4diMmy1ePubXQv6+zshMlk6rNez1vvZbbovV5/QWbPdeLi42zaLgCrIN6eer6/Pd+j3vv+qMF793NtbW3w8vLqdx2NZjLS09MfdRdoiAhd/xzRjtoxcB3mDAYDli1bhiVLlmDfvn3Iy8tHdVUFTp4vQ7BvEuKiUhAekgSB5zWJVMXPLxi/XJDw7LPP4oUXXlC6O0REdsHAlQCYJzdIT09Heno6GhsbUVZWBqOxCEdOfo8jZ/SICE5GfFQK/HwMSnd12HGxhCs5yITx03GwtgS3b99WuitERHbDwJX6CA4OxsKFC7FgwQLU1dWhpKQERUVbsat2Pzx1BsSEPYnYyAnQafs/FUX2pf4TO9SvIT4iaWi4AABoahq4tpOISG0YuNKABEHA6NGjMXr0aLz11lvYu3cviouLsaOyGqd/LkegbxISoieylGCIMePqqob2kGRH9bfw9fXA888/P6TtEBE5EgNXsom7uzsyMjKQkZGBpqYmbN++Hfn5Rhw5/j2OnvFEZMgExEc/DZ8RQUp3lZwch8NyDE9Pf5ikmxg1apTSXSGih+CUr7ZjmowGLSAgAPPmzUNu7mYUFuUi59W/oNV0ClUH/h1V+zfi/KVadHTeV7qb5LRkCKIyn57SMIqaExMm4O7v9/Dtt98q3RUiIrth4EqPrLuUYO3atdj9QzX+a8OnSJ0ah7pLpdj6w8f48WgeGpsu2Dy8DQ3Exd4/GdCIGkWaFl0h3WCjp1P+BJ0uGOvWfYr6+nqlu0NEZBcMXMkudDodZs+ejY0bN6Cquhyr1y6HV2Azao5/ifK9n+L0T7tw9/4dpbupSkMwnKSyBECSFdopQYC///AYn1gUNZj9p0WQZT3+8vw/Iyvrz7h165bS3SIieiyscSW7Cw8Px7Jly/Dqq6+itrYWhYVFKNtWgbo9OxDok4iEqIkID30CokJZN1KWLMuKZVx/OncO//v957j9m/KzBwkA7tzpp6SmV1a4o/0eZFmGVusBAFazYPUsGJZkCffu/Q5PT2+r5zz1frh+vQmtrRfx+utvIDd3i133g4jsgEWuNmPgSkNGFEWkpqYiNTUVa9euwfbt21GQb8TRI1twpE6LyJAJSIieCF/vEKW7SsPEvn17ceZMLZ588slBv/ZxPu7lHq/vru+VZSA0TNf1eOByEEnyRFNTEwIDR1hmr7L0qceXUGPjrwgMjLBaZn7sDyAObW1tmDlzxmPsBRGR8hi4kkP4+PggOzsb2dnZOHfuHIqLi2E0lqD64H546yMQE/4UYiMnwN1Np3RXycX5+vpgw4YvlO4GEZGFAMeM2a3+fCsDV1JAUlISVq1ahRUrVmDXrl0wGgvxw65ynDpfhtCAMUiMSUWQf8wfzsmtNG9fD/gFmGslH3RTgCD0yK4JlqXw9NGZ1+t5lrdHHk6wLHvwrCVD57xvAxERkUMxcCXFaLVazJo1C7NmzUJDQwNKSkqQl1uAmuNfQiv6IyY8BfFRKfDQjVC6q32ER/vi3gCnd3vHmTKAtvbOR27Lg5GrXXGUCyJyOky52oyBKzkFg8GApUuXYsmSJTh06BCMxkKUb6vE2YtVCPIZiYToVISFjOQMXURERMMYA1dyKqIoIi0tDWlpaVi7dg22bduG3Nx8HD75HYQzXog2PImE6Inw8vRXtJ/M2akXM65E5GyYcLUdA1dyWn5+fpg/fz7mzZuHU6dOwWg0orhoKypqfoC/dzzioyYiwjAaGtHx/41d4Y9/OHLmumkiIno4Bq7k9ARBwLhx4zBu3DisXLkSlZWVyMstwKFDeThWp0Vk6AQkxqTCZ0Sww/rkyJydzPyuXTHjSkROh+O42oyBK6mKp6cnnnvuOTz33HM4f/48jEYjCvKLUHVgP3w8IxEXORHR4clw07gr3VW78NC7oblJ+cHy7a17LFNF2naBD24iouGKV7qQaiUkJGDVqlXYtbsK//nFJ0iZHIMzF4uxbffHOHS8EM0t14eucUcl7VwxOSgDokIX2THbSkSkbsy4kupptVpkZWUhKysLly5dQmFhIfLzCrG7thZeujDERU5ETMR4VU5u4IplAjJkRbOezLgSEakXM67kUqKjo7FixQpUVVdg098+x7TMMfjpShm2/vAxDhzLx63my+rKuqmoq4OhVKmALCsbNBMR0eNhxpVckpubGzIzM5GZmYnr16+juLgYuVvysffYJni4ByM2PAVxkU9Bq9X3+3pBAMY8HTHg9jslCbhvGqruExHRMMLhsGzHwJVcXlhYGF577TUsXboUNTU1yM8vQGVFFU7/UoFQ/zEYGZvW7xSzrXc7FOrxA2pKDttKlmVoRI1ibTPjSkSkXgxcadgQRRFTp07F1KlTcfPmTZSUlGDz5jzUHP8SOk0gYiMmIi7ySei0nk4TMKqqrGEQRJFVSkREFky52oyBKw1LQUFBeOWVV7Bo0SIcPHgQeXn52F6+A2d+qUCI/2iMjJmEKCmIAdYQYdaTiIgeBQNXGtZEUcSkSZMwadIkvPvuLUsWdv+JL3Hm8jaMHTMVY8ZMht7DS+muEhGRixK6/jmiHbVj4ErUJTAwEDk5OVi4cCEOHTqEvLx8VFSU4MeDhYiNmYAJydMRHp7AbCEREZFCGLgS9SKKItLS0pCWloZ3322yZGGLSj7BiBEhzMISEZF9scbVZgxcif5AQEAAFi1ahAULFqC2thb5+QXYvr3UkoUdn/xPiAhPZBaWiIjIARi4EtlAFEWkpqYiNTUVa9Y0obS0FFs256K45FN4e4ciNCTOPPhr1ygAkixBluWumwTIgCRbj/tqqTUSumqbhF7PCQI0GhGSSQIgW40wYNkuHow8YL6Xza8WzDcIgmV61d6v6X48FARB7NO+IIgQBQGAjMNHDuPDjz7ssX7fwF+WZZhMJkiSBEmSeu0n+jy2xc8/n3+EvSEiGlpMuNqOgSvRIAUEBGDhwoV4+eWXLbWwV65cAWAOpERRA0EQoNFoIIqC5WdR1EGjES3BliSZg8f+ArHuoFeSOiGK3YGoaAnwun/ufg4ANKIIQRQgSzKkrtebTBIkyRwwi6JoeY0oitBohmbEBMkk9WnfvC/mAHTUqFHw8fFBff1PVvvcH1EULbeewe1Ajx/Gz88PU6ZMeYS9IiIiZ8DAlegR9ayFJSIiemRMudqMg1QSERERkSowcCUiIiIiVWCpABEREZGiWCtgK2ZciYiIiEgVmHElIiIiUhDzrbZjxpWIiIiIVIEZVyIiIiIlMeVqM2ZciYiIiEgVmHElIiIiUhATrrZjxpWIiIiIVIEZVyIiIiIlCYL55oh2VI4ZVyIiIiJSBQauRERERKQKDFyJiIiISBVY40pERESkIJa42o4ZVyIiIiJSBQauRERERKQKDFyJiIiISBUYuBIRERGRKvDiLCIiIiIFCYIAwQFXTjmijaHGjCsRERERqQIDVyIiIiJSBZYKEBERESmopaXFpdoZSgxciYiIiBSg1WphMBgQGxfjsDYNBgO0Wq3D2rM3QZZlWelOEBEREQ1H9+7dQ3t7u8Pa02q18PDwcFh79sbAlYiIiIhUgRdnEREREZEqMHAlIiIiIlVg4EpEREREqsDAlYiIiIhUgYErEREREakCA1ciIiIiUgUGrkRERESkCv8P1o2qxTdL3gAAAAAASUVORK5CYII=", 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", 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" ] @@ -168,21 +169,19 @@ "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", - "import pygris\n", "\n", - "df_states = pygris.states(cb=True, resolution='5m', cache=True, year=2020)\n", - "df_states = df_states.loc[df_states['GEOID'].isin(state_fips)]\n", + "state_fips = tuple({STATE.truncate(x) for x in scope.get_node_ids()})\n", + "gdf_counties = us_tiger.get_counties_geo(scope.year) # type:ignore\n", + "gdf_counties = gdf_counties[gdf_counties[\"GEOID\"].str.startswith(state_fips)]\n", "\n", - "df_counties = pd.concat([\n", - " pygris.counties(state=s, cb=True, resolution='5m', cache=True, year=2020)\n", - " for s in state_fips\n", - "])\n", + "gdf_states = us_tiger.get_states_geo(scope.year) # type:ignore\n", + "gdf_states = gdf_states[gdf_states[\"GEOID\"].str.startswith(state_fips)]\n", "\n", - "df_merged = pd.merge(\n", + "gdf_merged = pd.merge(\n", " on=\"GEOID\",\n", - " left=df_counties,\n", + " left=gdf_counties,\n", " right=pd.DataFrame({\n", - " 'GEOID': geo['geoid'],\n", + " 'GEOID': scope.get_node_ids(),\n", " 'data': pei_kernel[MARICOPA_CO_IDX],\n", " }),\n", ")\n", @@ -191,10 +190,10 @@ "fig, ax = plt.subplots(figsize=(8, 6))\n", "ax.axis('off')\n", "ax.set_title(\"Movement probability by county (origin: Maricopa County, AZ, logscale)\")\n", - "df_merged.plot(ax=ax, column='data', cmap='Purples', legend=True)\n", - "df_states.plot(ax=ax, linewidth=1, edgecolor='black', color='none', alpha=0.8)\n", + "gdf_merged.plot(ax=ax, column='data', cmap='Purples', legend=True)\n", + "gdf_states.plot(ax=ax, linewidth=1, edgecolor='black', color='none', alpha=0.8)\n", "# Get Maricopa County's centroid from the geo so we can mark it.\n", - "origin = geo['centroid'][MARICOPA_CO_IDX]\n", + "origin = centroid[MARICOPA_CO_IDX]\n", "ax.plot(origin[0], origin[1], marker='*', color='yellow', markersize=10)\n", "fig.tight_layout()\n", "plt.show()" diff --git a/doc/devlog/2023-06-30.ipynb b/doc/devlog/2023-06-30.ipynb index 7e420db8..ebfa275e 100644 --- a/doc/devlog/2023-06-30.ipynb +++ b/doc/devlog/2023-06-30.ipynb @@ -9,9 +9,19 @@ "\n", "_author: Tyler Coles_\n", "\n", - "Introducing epymorph's CompartmentModel system for specifying IPMs. In its current form, this is a declarative system using Python object syntax. This represents a step improvement over our current system of defining IPMs as procedural implementations of a base class, which leaves to the developer most of the responsibility for encoding the model computation. It does not represent our ultimate goal. However it is a step on that path.\n", + "_updated: 2024-08-12_\n", "\n", - "Let's start by seeing it in action with a couple examples.\n", + "Introducing epymorph's CompartmentModel system for specifying custom models (or IPMs).\n", + "\n", + "You will define your model as a Python class, extending the base CompartmentModel class, and overriding class attributes and methods which together define the model's:\n", + "\n", + "1. compartments (or states),\n", + "2. required data (or model parameters), and\n", + "3. transitions between compartments (or events).\n", + "\n", + "CopmartmentModels are designed to be declarative, rather than procedural, in nature. That is -- these lines of code describe the structure of the IPM, without having to write complicated and repetitive logic that performs the simulation calculations. epymorph will use the structure of the model to perform the calculations for you.\n", + "\n", + "Let's see it in action with a couple examples.\n", "\n", "## The Pei Model\n", "\n", @@ -45,10 +55,7 @@ "source": [ "### Specifying the IPM\n", "\n", - "Now we get into epymorph specifics. There are two steps in using the CompartmentModel system.\n", - "\n", - "1. Declare and extract symbols representing the model compartments and required geo/parameter attributes.\n", - "2. Use the symbols to define the model transitions and the rate equations for each transition." + "Now we get into epymorph specifics. We will first declare the model's compartments and parameters. Then we can define the transitions between compartments with rate expressions -- leveraging symbolic math library `sympy` -- using symbols which represent the compartments and parameters we declared." ] }, { @@ -57,53 +64,73 @@ "metadata": {}, "outputs": [], "source": [ + "from typing import Sequence\n", + "\n", "from sympy import exp\n", "\n", "from epymorph import *\n", "from epymorph.compartment_model import *\n", + "from epymorph.compartment_model import ModelSymbols\n", + "\n", "\n", - "# We have compartments S, I, and R;\n", - "# and attributes D, L,(from the simulation parameters) and H (from the geo).\n", - "symbols = create_symbols(\n", - " compartments=quick_compartments('S I R'),\n", - " attributes=[\n", - " # Attribute constructor functions take arguments for:\n", - " # 1. its name (which will be matched against the parameters dictionary or geo)\n", - " # 3. a data type\n", - " # 2. a shape description\n", - " # 4. an optional symbol name (if you want it to be different from #1)\n", - " # 5. an optional comment to describe the attribute\n", - " AttributeDef('infection_duration', type=float, shape=Shapes.TxN,\n", - " comment=\"Mean duration of infection.\"),\n", - " AttributeDef('immunity_duration', type=float, shape=Shapes.TxN,\n", + "# Declare a new class (you can name it what you like) and extend CompartmentModel\n", + "class PeiIpm(CompartmentModel):\n", + "\n", + " # 1. Declare compartments\n", + " # Each of our S, I, and R compartments needs a name...\n", + " compartments = [\n", + " compartment(\n", + " name='S',\n", + " description=\"Susceptible\" # but we can also provide a description\n", + " ),\n", + " compartment(\"I\", description=\"Infectious\"),\n", + " compartment(\"R\", description=\"Recovered\"),\n", + " ]\n", + "\n", + " # 2. Declare requirements\n", + " # Each needs a name, type, and the expected shape of the data (think, the shape of the numpy array of values)\n", + " requirements = [\n", + " AttributeDef(\n", + " name='infection_duration', # this name is important; it's how we provide values later\n", + " type=float, # the values are floating point numbers\n", + " shape=Shapes.TxN, # and we'll allow time-and-node-varying data\n", + " comment=\"Mean duration of infection.\" # a comment helps users know what this value means\n", + " # default_value=... (optional; if it makes sense to have a default)\n", + " ),\n", + " AttributeDef('immunity_duration', float, Shapes.TxN,\n", " comment=\"Mean duration of immunity after recovery.\"),\n", - " AttributeDef('humidity', type=float, shape=Shapes.TxN,\n", + " AttributeDef('humidity', float, Shapes.TxN,\n", " comment=\"Relative humidity.\"),\n", - " ])\n", - "\n", - "# Extract the symbols so we can use sympy math operators to build equations.\n", - "[S, I, R] = symbols.compartment_symbols\n", - "[D, L, H] = symbols.attribute_symbols\n", - "\n", - "# For the sake of readability and re-use, we can define expressions as needed.\n", - "# Here's our beta function, filling in known constants.\n", - "# (Although we certainly could have decided to make these constants parameters as well.)\n", - "beta = (0.7 * exp(-180 * H) + 1.3) / D\n", - "\n", - "# And now we can define our model by:\n", - "# 1. providing the symbols we just declared, and\n", - "# 2. specifying the transition edges and their associated rate equations.\n", - "pei: CompartmentModel = create_model(\n", - " symbols=symbols,\n", - " transitions=[\n", - " # The edge constructor takes arguments for:\n", - " # 1. the source compartment,\n", - " # 2. the destination compartment,\n", - " # 3. the rate equation as a sympy Expression\n", - " edge(S, I, rate=beta * S * I / (S + I + R)),\n", - " edge(I, R, rate=I / D),\n", - " edge(R, S, rate=R / L)\n", - " ])" + " ]\n", + "\n", + " # 3. Describe the transitions between compartments (aka, edges; the arrows from our diagram)\n", + " def edges(self, symbols: ModelSymbols) -> Sequence[TransitionDef]:\n", + " # This is a method because working with edges is a little more complicated.\n", + " # We need to refer to the compartments and requirements we just defined,\n", + " # and this method allows us to defer the calculation of these edges\n", + " # until those references are ready.\n", + "\n", + " # Plus now that we're in a method body we can write more-involved Python expressions,\n", + " # with variables and stuff.\n", + "\n", + " # First, extract symbols for compartments and requirements.\n", + " # Then we'll use sympy math operators to build the rate expressions.\n", + " [S, I, R] = symbols.all_compartments\n", + " [D, L, H] = symbols.all_requirements\n", + "\n", + " # For example, here we build our beta function as a standalone expression.\n", + " beta = (0.7 * exp(-180 * H) + 1.3) / D\n", + "\n", + " # But the job of the `edges()` method is to return the edge definitions for the model.\n", + " # Each edge has a source and destination compartment, and a rate expression (using sympy expressions).\n", + " return [\n", + " # The S -> I edge...\n", + " edge(S, I, rate=beta * S * I / (S + I + R)),\n", + " # The I -> R edge...\n", + " edge(I, R, rate=I / D),\n", + " # The R -> S edge...\n", + " edge(R, S, rate=R / L)\n", + " ]" ] }, { @@ -111,11 +138,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note that this code is fully declarative; there are no necessarily-procedural elements. This makes it imminently suitable to translation between other formats, such as text files or as the data model of a graphical builder GUI.\n", - "\n", "### Running the simulation\n", "\n", - "Now that we have a CompartmentModel defined, we can wrap that with a CompartmentModelIpmBuilder and pass it to the Simulation constructor. This part is basically the same as running a simulation with any of the previously developed IPMs.\n" + "Now that we have a custom CompartmentModel class, we can create an instance and pass that into our RUME, then run a simulation with the RUME.\n" ] }, { @@ -125,7 +150,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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" ] @@ -145,27 +170,29 @@ } ], "source": [ - "from typing import cast\n", + "import epymorph.data.pei as pei\n", + "from epymorph.adrio import acs5, commuting_flows, us_tiger\n", "\n", - "from epymorph.geo.static import StaticGeo\n", - "\n", - "geo = cast(StaticGeo, geo_library['pei']())\n", - "\n", - "rume = Rume.single_strata(\n", - " ipm=pei,\n", + "rume = SingleStrataRume.build(\n", + " ipm=PeiIpm(),\n", " mm=mm_library['pei'](),\n", - " scope=geo.spec.scope,\n", + " scope=pei.pei_scope,\n", " params={\n", " # movement model parameters\n", " 'theta': 0.1,\n", " 'move_control': 0.9,\n", "\n", " # IPM parameters\n", + " # NOTE: these names match the names we declared in our IPM!\n", " 'infection_duration': 4.0,\n", " 'immunity_duration': 90.0,\n", "\n", - " # geo params\n", - " **geo.values,\n", + " # geographic params\n", + " \"population\": acs5.Population(),\n", + " \"centroids\": us_tiger.GeometricCentroid(),\n", + " \"commuters\": commuting_flows.Commuters(),\n", + " # TODO: replace this with ADRIO when we have one for humidity\n", + " \"humidity\": pei.pei_humidity,\n", " },\n", " time_frame=TimeFrame.of(\"2015-01-01\", duration_days=150),\n", " init=init.SingleLocation(location=0, seed_size=10_000),\n", @@ -183,12 +210,6 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note: these plots aren't currently smart-enough to know what to call the compartments or events, so they just use `c0`, `e0`, etc. But one advantage of having the CompartmentModel object is that it does have name info and could be used to improve plotting, etc.\n", - "\n", - "`c0`, `c1`, and `c2` are S, I, and R respectively.\n", - "\n", - "`e0` is the infection event (S-to-I).\n", - "\n", "## The SPARSEMOD COVID-19 model\n", "\n", "Now we look at constructing [a more involved model, this one for COVID-19](https://www.medrxiv.org/content/10.1101/2021.05.13.21256216v1). The most significant difference (besides that there are more states and more parameters) is that this model contains forked transitions -- that is, a set of edges which go from a common starting compartment to more than one destination compartment, sharing a base occurrence rate which is split between destinations by a simple ratio.\n", @@ -207,7 +228,7 @@ "\n", "CompartmentModel will use this form to _define_ forked transitions, however the epymorph engine does not treat them as wholly independent calcuations. In brief: a simple edge is resolved by a poisson draw; a fork is a poisson draw (using the base rate) followed by a multinomial draw (using the proportions).\n", "\n", - "Let's define our symbols, using a more verbose syntax than we saw earlier. This will allow us to provide more descriptive names for the compartments." + "Now let's build the compartment model. The process is the same as above, there's just more of each kind of thing to declare!" ] }, { @@ -216,10 +237,13 @@ "metadata": {}, "outputs": [], "source": [ - "symbols = create_symbols(\n", - " # So I have a symbol name and a descriptive name for each.\n", - " # (The descriptive names aren't currently being used, but could be useful for debugging, plotting, etc.)\n", - " compartments=[\n", + "from typing import Sequence\n", + "\n", + "from epymorph.compartment_model import ModelSymbols\n", + "\n", + "\n", + "class SparsemodIpm(CompartmentModel):\n", + " compartments = [\n", " compartment('S', description='susceptible'),\n", " compartment('E', description='exposed'),\n", " compartment('Ia', description='infected asymptomatic'),\n", @@ -231,75 +255,71 @@ " compartment('Ic2', description='infected in ICU Step-Down'),\n", " compartment('D', description='deceased'),\n", " compartment('R', description='recovered')\n", - " ],\n", - " # For these attributes, the symbol name and the params file name are the same,\n", - " # so I don't have to repeat myself.\n", - " # But this time I'm going to choose to be explicit about the fact that I expect these to be scalar floats.\n", - " attributes=[\n", - " AttributeDef('beta_1', shape=Shapes.TxN, type=float),\n", - " AttributeDef('omega_1', shape=Shapes.TxN, type=float),\n", - " AttributeDef('omega_2', shape=Shapes.TxN, type=float),\n", - " AttributeDef('delta_1', shape=Shapes.TxN, type=float),\n", - " AttributeDef('delta_2', shape=Shapes.TxN, type=float),\n", - " AttributeDef('delta_3', shape=Shapes.TxN, type=float),\n", - " AttributeDef('delta_4', shape=Shapes.TxN, type=float),\n", - " AttributeDef('delta_5', shape=Shapes.TxN, type=float),\n", - " AttributeDef('gamma_a', shape=Shapes.TxN, type=float),\n", - " AttributeDef('gamma_b', shape=Shapes.TxN, type=float),\n", - " AttributeDef('gamma_c', shape=Shapes.TxN, type=float),\n", - " AttributeDef('rho_1', shape=Shapes.TxN, type=float),\n", - " AttributeDef('rho_2', shape=Shapes.TxN, type=float),\n", - " AttributeDef('rho_3', shape=Shapes.TxN, type=float),\n", - " AttributeDef('rho_4', shape=Shapes.TxN, type=float),\n", - " AttributeDef('rho_5', shape=Shapes.TxN, type=float)\n", - " ])\n", - "\n", - "# Again extract the symbols, there's just a lot more this time.\n", - "[S, E, Ia, Ip, Is, Ib, Ih, Ic1, Ic2, D, R] = symbols.compartment_symbols\n", - "[beta_1, omega_1, omega_2, delta_1, delta_2, delta_3, delta_4, delta_5,\n", - " gamma_a, gamma_b, gamma_c, rho_1, rho_2, rho_3, rho_4, rho_5] = symbols.attribute_symbols\n", - "\n", - "# It's handy to specify some intermediate expressions for the total number of people and our \\lambda_1\n", - "N = S + E + Ia + Ip + Is + Ib + Ih + Ic1 + Ic2 + D + R\n", - "\n", - "lambda_1 = (omega_1 * Ia + Ip + Is + Ib + omega_2 * (Ih + Ic1 + Ic2)) / (N - D)\n", - "\n", - "# Now define the transitions.\n", - "sparsemod = create_model(\n", - " symbols=symbols,\n", - " transitions=[\n", - " edge(S, E, rate=beta_1 * lambda_1 * S),\n", - "\n", - " # This is the fork we talked about earlier! E -> {Ia,Ip}\n", - " # Wrapping `edge`s with a `fork` alerts epymorph that these\n", - " # should not be treated as statistically independent events,\n", - " # but that they share a common base rate and a proportional split.\n", - " fork(\n", - " edge(E, Ia, rate=E * delta_1 * rho_1),\n", - " edge(E, Ip, rate=E * delta_1 * (1 - rho_1))\n", - " ),\n", - "\n", - " edge(Ip, Is, rate=Ip * delta_2),\n", - "\n", - " # And here's a fork with three destinations and two proportional parameters.\n", - " fork(\n", - " edge(Is, Ib, rate=Is * delta_3 * (1 - rho_2 - rho_3)),\n", - " edge(Is, Ih, rate=Is * delta_3 * rho_2),\n", - " edge(Is, Ic1, rate=Is * delta_3 * rho_3)\n", - " ),\n", - "\n", - " fork(\n", - " edge(Ih, Ic1, rate=Ih * delta_4 * rho_4),\n", - " edge(Ih, R, rate=Ih * delta_4 * (1 - rho_4))\n", - " ),\n", - " fork(\n", - " edge(Ic1, D, rate=Ic1 * delta_5 * rho_5),\n", - " edge(Ic1, Ic2, rate=Ic1 * delta_5 * (1 - rho_5))\n", - " ),\n", - " edge(Ia, R, rate=Ia * gamma_a),\n", - " edge(Ib, R, rate=Ib * gamma_b),\n", - " edge(Ic2, R, rate=Ic2 * gamma_c)\n", - " ])" + " ]\n", + "\n", + " requirements = [\n", + " AttributeDef('beta_1', float, Shapes.TxN),\n", + " AttributeDef('omega_1', float, Shapes.TxN),\n", + " AttributeDef('omega_2', float, Shapes.TxN),\n", + " AttributeDef('delta_1', float, Shapes.TxN),\n", + " AttributeDef('delta_2', float, Shapes.TxN),\n", + " AttributeDef('delta_3', float, Shapes.TxN),\n", + " AttributeDef('delta_4', float, Shapes.TxN),\n", + " AttributeDef('delta_5', float, Shapes.TxN),\n", + " AttributeDef('gamma_a', float, Shapes.TxN),\n", + " AttributeDef('gamma_b', float, Shapes.TxN),\n", + " AttributeDef('gamma_c', float, Shapes.TxN),\n", + " AttributeDef('rho_1', float, Shapes.TxN),\n", + " AttributeDef('rho_2', float, Shapes.TxN),\n", + " AttributeDef('rho_3', float, Shapes.TxN),\n", + " AttributeDef('rho_4', float, Shapes.TxN),\n", + " AttributeDef('rho_5', float, Shapes.TxN)\n", + " ]\n", + "\n", + " def edges(self, symbols: ModelSymbols) -> Sequence[TransitionDef]:\n", + " [S, E, Ia, Ip, Is, Ib, Ih, Ic1, Ic2, D, R] = symbols.all_compartments\n", + " [beta_1, omega_1, omega_2, delta_1, delta_2, delta_3, delta_4, delta_5,\n", + " gamma_a, gamma_b, gamma_c, rho_1, rho_2, rho_3, rho_4, rho_5] = symbols.all_requirements\n", + "\n", + " # It's handy to specify some intermediate expressions for the total number of people and our \\lambda_1\n", + " N = S + E + Ia + Ip + Is + Ib + Ih + Ic1 + Ic2 + D + R\n", + "\n", + " lambda_1 = (omega_1 * Ia + Ip + Is + Ib + omega_2 * (Ih + Ic1 + Ic2)) / (N - D)\n", + "\n", + " # Now define the transitions.\n", + " return [\n", + " edge(S, E, rate=beta_1 * lambda_1 * S),\n", + "\n", + " # This is the fork we talked about earlier! E -> {Ia,Ip}\n", + " # Wrapping `edge`s with a `fork` alerts epymorph that these\n", + " # should not be treated as statistically independent events,\n", + " # but that they share a common base rate and a proportional split.\n", + " fork(\n", + " edge(E, Ia, rate=E * delta_1 * rho_1),\n", + " edge(E, Ip, rate=E * delta_1 * (1 - rho_1))\n", + " ),\n", + "\n", + " edge(Ip, Is, rate=Ip * delta_2),\n", + "\n", + " # And here's a fork with three destinations and two proportional parameters.\n", + " fork(\n", + " edge(Is, Ib, rate=Is * delta_3 * (1 - rho_2 - rho_3)),\n", + " edge(Is, Ih, rate=Is * delta_3 * rho_2),\n", + " edge(Is, Ic1, rate=Is * delta_3 * rho_3)\n", + " ),\n", + "\n", + " fork(\n", + " edge(Ih, Ic1, rate=Ih * delta_4 * rho_4),\n", + " edge(Ih, R, rate=Ih * delta_4 * (1 - rho_4))\n", + " ),\n", + " fork(\n", + " edge(Ic1, D, rate=Ic1 * delta_5 * rho_5),\n", + " edge(Ic1, Ic2, rate=Ic1 * delta_5 * (1 - rho_5))\n", + " ),\n", + " edge(Ia, R, rate=Ia * gamma_a),\n", + " edge(Ib, R, rate=Ib * gamma_b),\n", + " edge(Ic2, R, rate=Ic2 * gamma_c)\n", + " ]" ] }, { @@ -307,7 +327,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Speaking generally, the method here is the same as for the Pei model, just more. The same could be said for running the simulation." + "Now run it." ] }, { @@ -317,7 +337,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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" ] @@ -347,10 +367,12 @@ } ], "source": [ - "rume = Rume.single_strata(\n", - " ipm=sparsemod,\n", + "ipm = SparsemodIpm()\n", + "\n", + "rume = SingleStrataRume.build(\n", + " ipm=ipm,\n", " mm=mm_library['pei'](),\n", - " scope=geo.spec.scope,\n", + " scope=pei.pei_scope,\n", " params={\n", " # movement model params\n", " 'move_control': 1,\n", @@ -374,8 +396,12 @@ " 'rho_4': 0.2,\n", " 'rho_5': 0.6,\n", "\n", - " # geo params\n", - " **geo.values,\n", + " # geographic params\n", + " \"population\": acs5.Population(),\n", + " \"centroids\": us_tiger.GeometricCentroid(),\n", + " \"commuters\": commuting_flows.Commuters(),\n", + " # TODO: replace this with ADRIO when we have one for humidity\n", + " \"humidity\": pei.pei_humidity,\n", " },\n", " time_frame=TimeFrame.of(\"2015-01-01\", 200),\n", " init=init.SingleLocation(location=0, seed_size=10_000),\n", @@ -386,8 +412,8 @@ "\n", "plot_pop(out, 0) # Florida prevalence\n", "\n", - "plot_event(out, 5) # 5: Is->Ih: hospitalizations, non-ICU\n", - "plot_event(out, 9) # 9: Ic1->D: deaths" + "plot_event(out, ipm.event_by_name(\"Is->Ih\")) # 5: Is->Ih: hospitalizations, non-ICU\n", + "plot_event(out, ipm.event_by_name(\"Ic1->D\")) # 9: Ic1->D: deaths" ] }, { diff --git a/doc/devlog/2023-07-13.ipynb b/doc/devlog/2023-07-13.ipynb index 20377ad5..925a02ac 100644 --- a/doc/devlog/2023-07-13.ipynb +++ b/doc/devlog/2023-07-13.ipynb @@ -22,156 +22,156 @@ "name": "stdout", "output_type": "stream", "text": [ - "failed: ('single_pop', 'centroids', 'pei') in 90.128 ms\n", - "failed: ('single_pop', 'sparsemod', 'pei') in 91.624 ms\n", - "succeeded: ('pei', 'no', 'pei') in 95.319 ms\n", - "succeeded: ('pei', 'icecube', 'pei') in 104.071 ms\n", - "failed: ('single_pop', 'pei', 'pei') in 105.966 ms\n", - "succeeded: ('single_pop', 'centroids', 'sirs') in 30.628 ms\n", - "succeeded: ('pei', 'sparsemod', 'pei') in 115.188 ms\n", - "succeeded: ('pei', 'no', 'sirs') in 12.014 ms\n", - "succeeded: ('pei', 'pei', 'pei') in 122.721 ms\n", - "succeeded: ('pei', 'centroids', 'pei') in 110.173 ms\n", - "succeeded: ('single_pop', 'sparsemod', 'sirs') in 30.124 ms\n", - "succeeded: ('pei', 'icecube', 'sirs') in 20.113 ms\n", - "succeeded: ('pei', 'sparsemod', 'sirs') in 28.557 ms\n", - "succeeded: ('single_pop', 'pei', 'sirs') in 38.468 ms\n", - "succeeded: ('pei', 'centroids', 'sirs') in 27.103 ms\n", - "succeeded: ('pei', 'pei', 'sirs') in 43.795 ms\n", - "succeeded: ('single_pop', 'centroids', 'sirh') in 155.287 ms\n", - "succeeded: ('pei', 'no', 'sirh') in 143.413 ms\n", - "succeeded: ('pei', 'icecube', 'sirh') in 159.838 ms\n", - "succeeded: ('single_pop', 'sparsemod', 'sirh') in 185.492 ms\n", - "succeeded: ('pei', 'centroids', 'sirh') in 150.153 ms\n", - "succeeded: ('pei', 'sparsemod', 'sirh') in 186.480 ms\n", - "succeeded: ('single_pop', 'pei', 'sirh') in 187.597 ms\n", - "succeeded: ('pei', 'pei', 'sirh') in 163.045 ms\n", - "succeeded: ('pei', 'no', 'sparsemod') in 105.149 ms\n", - "succeeded: ('single_pop', 'centroids', 'sparsemod') in 124.281 ms\n", - "succeeded: ('pei', 'no', 'no') in 4.672 ms\n", - "succeeded: ('single_pop', 'centroids', 'no') in 15.301 ms\n", - "succeeded: ('single_pop', 'sparsemod', 'sparsemod') in 147.577 ms\n", - "succeeded: ('pei', 'centroids', 'sparsemod') in 121.376 ms\n", - "succeeded: ('pei', 'icecube', 'sparsemod') in 169.113 ms\n", - "succeeded: ('single_pop', 'pei', 'sparsemod') in 122.848 ms\n", - "failed: ('single_pop', 'icecube', 'pei') in 12.893 ms\n", - "failed: ('single_pop', 'no', 'pei') in 15.661 ms\n", - "succeeded: ('single_pop', 'sparsemod', 'no') in 16.513 ms\n", - "succeeded: ('pei', 'icecube', 'no') in 12.872 ms\n", - "succeeded: ('pei', 'centroids', 'no') in 18.222 ms\n", - "succeeded: ('pei', 'pei', 'sparsemod') in 138.901 ms\n", - "succeeded: ('single_pop', 'no', 'sirs') in 10.216 ms\n", - "succeeded: ('single_pop', 'icecube', 'sirs') in 17.030 ms\n", - "succeeded: ('single_pop', 'pei', 'no') in 27.795 ms\n", - "succeeded: ('single_pop', 'no', 'sirh') in 18.618 ms\n", - "succeeded: ('pei', 'sparsemod', 'sparsemod') in 197.853 ms\n", - "succeeded: ('pei', 'pei', 'no') in 29.467 ms\n", - "succeeded: ('single_pop', 'icecube', 'sirh') in 31.142 ms\n", - "succeeded: ('pei', 'sparsemod', 'no') in 21.967 ms\n", - "succeeded: ('single_pop', 'no', 'sparsemod') in 81.969 ms\n", - "succeeded: ('single_pop', 'no', 'no') in 4.732 ms\n", - "failed: ('us_states_2015', 'pei', 'pei') in 39.196 ms\n", - "succeeded: ('single_pop', 'icecube', 'sparsemod') in 85.290 ms\n", - "failed: ('us_states_2015', 'sparsemod', 'pei') in 20.517 ms\n", - "succeeded: ('single_pop', 'icecube', 'no') in 9.987 ms\n", - "failed: ('us_counties_2015', 'centroids', 'pei') in 208.560 ms\n", - "failed: ('us_states_2015', 'centroids', 'pei') in 21.789 ms\n", - "failed: ('us_counties_2015', 'pei', 'pei') in 237.922 ms\n", - "failed: ('us_counties_2015', 'icecube', 'pei') in 208.011 ms\n", - "failed: ('us_counties_2015', 'sparsemod', 'pei') in 254.638 ms\n", - "succeeded: ('us_states_2015', 'sparsemod', 'sirs') in 88.394 ms\n", - "failed: ('us_counties_2015', 'no', 'pei') in 223.349 ms\n", - "succeeded: ('us_states_2015', 'centroids', 'sirs') in 88.574 ms\n", - "succeeded: ('us_states_2015', 'sparsemod', 'sirh') in 103.904 ms\n", - "succeeded: ('us_states_2015', 'centroids', 'sirh') in 93.256 ms\n", - "succeeded: ('us_states_2015', 'pei', 'sirs') in 273.819 ms\n", - "succeeded: ('us_states_2015', 'sparsemod', 'sparsemod') in 204.523 ms\n", - "succeeded: ('us_states_2015', 'sparsemod', 'no') in 54.183 ms\n", - "succeeded: ('us_states_2015', 'pei', 'sirh') in 230.444 ms\n", - "succeeded: ('us_states_2015', 'centroids', 'sparsemod') in 246.938 ms\n", - "failed: ('us_states_2015', 'icecube', 'pei') in 10.917 ms\n", - "succeeded: ('us_states_2015', 'centroids', 'no') in 37.827 ms\n", - "failed: ('us_states_2015', 'no', 'pei') in 6.779 ms\n", - "succeeded: ('us_states_2015', 'icecube', 'sirs') in 87.567 ms\n", - "succeeded: ('us_states_2015', 'no', 'sirs') in 36.959 ms\n", - "succeeded: ('us_states_2015', 'no', 'sirh') in 52.920 ms\n", - "succeeded: ('us_states_2015', 'icecube', 'sirh') in 122.258 ms\n", - "succeeded: ('us_states_2015', 'pei', 'sparsemod') in 278.064 ms\n", - "succeeded: ('us_states_2015', 'pei', 'no') in 90.055 ms\n", - "succeeded: ('us_states_2015', 'no', 'sparsemod') in 232.261 ms\n", - "succeeded: ('us_states_2015', 'no', 'no') in 13.063 ms\n", - "succeeded: ('us_states_2015', 'icecube', 'sparsemod') in 251.001 ms\n", - "succeeded: ('us_states_2015', 'icecube', 'no') in 23.206 ms\n", - "succeeded: ('us_counties_2015', 'icecube', 'sirs') in 9093.230 ms\n", - "succeeded: ('us_counties_2015', 'no', 'sirs') in 17242.632 ms\n", - "succeeded: ('us_counties_2015', 'icecube', 'sirh') in 11844.981 ms\n", - "succeeded: ('us_counties_2015', 'pei', 'sirs') in 21091.584 ms\n", - "succeeded: ('us_counties_2015', 'centroids', 'sirs') in 21200.505 ms\n", - "succeeded: ('us_counties_2015', 'sparsemod', 'sirs') in 26194.347 ms\n", - "failed: ('us_sw_counties_2015', 'centroids', 'pei') in 30173.048 ms\n", - "failed: ('us_sw_counties_2015', 'sparsemod', 'pei') in 30400.724 ms\n", - "succeeded: ('us_sw_counties_2015', 'centroids', 'sirs') in 258.124 ms\n", - "failed: ('us_sw_counties_2015', 'pei', 'pei') in 30596.406 ms\n", - "succeeded: ('us_sw_counties_2015', 'sparsemod', 'sirs') in 288.825 ms\n", - "succeeded: ('us_sw_counties_2015', 'pei', 'sirs') in 280.905 ms\n", - "succeeded: ('us_sw_counties_2015', 'centroids', 'sirh') in 383.822 ms\n", - "succeeded: ('us_sw_counties_2015', 'sparsemod', 'sirh') in 386.625 ms\n", - "succeeded: ('us_sw_counties_2015', 'pei', 'sirh') in 377.187 ms\n", - "succeeded: ('us_sw_counties_2015', 'centroids', 'sparsemod') in 555.140 ms\n", - "succeeded: ('us_sw_counties_2015', 'centroids', 'no') in 194.305 ms\n", - "failed: ('us_sw_counties_2015', 'icecube', 'pei') in 15.464 ms\n", - "succeeded: ('us_sw_counties_2015', 'sparsemod', 'sparsemod') in 680.414 ms\n", - "succeeded: ('us_sw_counties_2015', 'pei', 'sparsemod') in 587.340 ms\n", - "succeeded: ('us_sw_counties_2015', 'icecube', 'sirs') in 158.274 ms\n", - "succeeded: ('us_sw_counties_2015', 'pei', 'no') in 170.590 ms\n", - "failed: ('us_sw_counties_2015', 'no', 'pei') in 13.256 ms\n", - "succeeded: ('us_sw_counties_2015', 'sparsemod', 'no') in 293.685 ms\n", - "succeeded: ('us_sw_counties_2015', 'icecube', 'sirh') in 215.620 ms\n", - "succeeded: ('us_sw_counties_2015', 'no', 'sirs') in 129.221 ms\n", - "failed: ('maricopa_cbg_2019', 'pei', 'pei') in 132.992 ms\n", - "failed: ('maricopa_cbg_2019', 'pei', 'sirs') in 41.735 ms\n", - "succeeded: ('us_sw_counties_2015', 'no', 'sirh') in 144.879 ms\n", - "failed: ('maricopa_cbg_2019', 'pei', 'sirh') in 45.310 ms\n", - "failed: ('maricopa_cbg_2019', 'pei', 'sparsemod') in 81.237 ms\n", - "failed: ('maricopa_cbg_2019', 'pei', 'no') in 32.732 ms\n", - "failed: ('maricopa_cbg_2019', 'sparsemod', 'pei') in 22.393 ms\n", - "failed: ('maricopa_cbg_2019', 'sparsemod', 'sirs') in 20.837 ms\n", - "failed: ('maricopa_cbg_2019', 'sparsemod', 'sirh') in 29.872 ms\n", - "failed: ('maricopa_cbg_2019', 'sparsemod', 'sparsemod') in 60.595 ms\n", - "failed: ('maricopa_cbg_2019', 'sparsemod', 'no') in 21.958 ms\n", - "succeeded: ('us_sw_counties_2015', 'no', 'sparsemod') in 321.312 ms\n", - "failed: ('maricopa_cbg_2019', 'centroids', 'pei') in 17.808 ms\n", - "succeeded: ('us_sw_counties_2015', 'icecube', 'sparsemod') in 608.904 ms\n", - "succeeded: ('us_sw_counties_2015', 'no', 'no') in 70.458 ms\n", - "failed: ('maricopa_cbg_2019', 'icecube', 'pei') in 22.315 ms\n", - "succeeded: ('us_sw_counties_2015', 'icecube', 'no') in 80.540 ms\n", - "failed: ('maricopa_cbg_2019', 'no', 'pei') in 14.077 ms\n", - "succeeded: ('us_counties_2015', 'no', 'sirh') in 19059.882 ms\n", - "succeeded: ('us_counties_2015', 'icecube', 'sparsemod') in 17432.596 ms\n", - "succeeded: ('maricopa_cbg_2019', 'icecube', 'sirs') in 7961.806 ms\n", - "succeeded: ('us_counties_2015', 'centroids', 'sirh') in 22641.202 ms\n", - "succeeded: ('us_counties_2015', 'pei', 'sirh') in 22787.629 ms\n", - "succeeded: ('maricopa_cbg_2019', 'no', 'sirs') in 11499.279 ms\n", - "succeeded: ('maricopa_cbg_2019', 'centroids', 'sirs') in 12121.145 ms\n", - "succeeded: ('us_counties_2015', 'icecube', 'no') in 7404.676 ms\n", - "succeeded: ('maricopa_cbg_2019', 'icecube', 'sirh') in 8726.150 ms\n", - "succeeded: ('us_counties_2015', 'sparsemod', 'sirh') in 26853.101 ms\n", - "succeeded: ('maricopa_cbg_2019', 'no', 'sirh') in 10824.716 ms\n", - "succeeded: ('us_counties_2015', 'no', 'sparsemod') in 21032.420 ms\n", - "succeeded: ('maricopa_cbg_2019', 'centroids', 'sirh') in 12090.043 ms\n", - "succeeded: ('maricopa_cbg_2019', 'icecube', 'sparsemod') in 12200.088 ms\n", - "succeeded: ('maricopa_cbg_2019', 'icecube', 'no') in 5542.299 ms\n", - "succeeded: ('maricopa_cbg_2019', 'no', 'sparsemod') in 12827.652 ms\n", - "succeeded: ('us_counties_2015', 'centroids', 'sparsemod') in 26676.122 ms\n", - "succeeded: ('us_counties_2015', 'pei', 'sparsemod') in 27776.683 ms\n", - "succeeded: ('us_counties_2015', 'no', 'no') in 15377.958 ms\n", - "succeeded: ('maricopa_cbg_2019', 'centroids', 'sparsemod') in 16258.650 ms\n", - "succeeded: ('maricopa_cbg_2019', 'no', 'no') in 8699.601 ms\n", - "succeeded: ('maricopa_cbg_2019', 'centroids', 'no') in 8844.106 ms\n", - "succeeded: ('us_counties_2015', 'sparsemod', 'sparsemod') in 30364.625 ms\n", - "succeeded: ('us_counties_2015', 'pei', 'no') in 14841.990 ms\n", - "succeeded: ('us_counties_2015', 'centroids', 'no') in 17091.046 ms\n", - "succeeded: ('us_counties_2015', 'sparsemod', 'no') in 19587.574 ms\n" + "succeeded: ('pei', 'no', 'no') in 20.893 ms\n", + "failed: ('single_pop', 'no', 'pei') in 19.147 ms\n", + "failed: ('single_pop', 'sparsemod', 'pei') in 37.559 ms\n", + "succeeded: ('single_pop', 'centroids', 'no') in 37.431 ms\n", + "succeeded: ('pei', 'no', 'sirs') in 39.067 ms\n", + "succeeded: ('pei', 'sparsemod', 'no') in 42.339 ms\n", + "succeeded: ('single_pop', 'no', 'sirs') in 21.220 ms\n", + "succeeded: ('pei', 'sparsemod', 'sirs') in 60.810 ms\n", + "succeeded: ('pei', 'icecube', 'pei') in 52.544 ms\n", + "failed: ('single_pop', 'icecube', 'pei') in 16.803 ms\n", + "succeeded: ('single_pop', 'icecube', 'sirh') in 59.832 ms\n", + "succeeded: ('single_pop', 'centroids', 'sirs') in 62.856 ms\n", + "failed: ('single_pop', 'pei', 'pei') in 40.880 ms\n", + "succeeded: ('pei', 'pei', 'pei') in 70.390 ms\n", + "succeeded: ('pei', 'centroids', 'sirh') in 70.753 ms\n", + "succeeded: ('single_pop', 'pei', 'sirh') in 80.036 ms\n", + "succeeded: ('single_pop', 'sparsemod', 'sirs') in 39.784 ms\n", + "succeeded: ('pei', 'no', 'sirh') in 25.946 ms\n", + "succeeded: ('single_pop', 'no', 'sirh') in 23.186 ms\n", + "succeeded: ('pei', 'icecube', 'sirs') in 21.722 ms\n", + "succeeded: ('pei', 'centroids', 'pei') in 36.229 ms\n", + "succeeded: ('single_pop', 'icecube', 'sirs') in 28.506 ms\n", + "succeeded: ('pei', 'sparsemod', 'sirh') in 40.941 ms\n", + "succeeded: ('pei', 'icecube', 'sparsemod') in 113.975 ms\n", + "succeeded: ('single_pop', 'centroids', 'sirh') in 38.457 ms\n", + "succeeded: ('single_pop', 'sparsemod', 'sparsemod') in 117.901 ms\n", + "succeeded: ('single_pop', 'pei', 'sirs') in 47.992 ms\n", + "succeeded: ('pei', 'pei', 'sirs') in 45.421 ms\n", + "succeeded: ('single_pop', 'sparsemod', 'sirh') in 41.594 ms\n", + "succeeded: ('pei', 'pei', 'sparsemod') in 143.017 ms\n", + "succeeded: ('pei', 'icecube', 'no') in 15.911 ms\n", + "succeeded: ('pei', 'centroids', 'sirs') in 26.565 ms\n", + "succeeded: ('pei', 'icecube', 'sirh') in 40.928 ms\n", + "succeeded: ('single_pop', 'icecube', 'sparsemod') in 78.960 ms\n", + "succeeded: ('single_pop', 'sparsemod', 'no') in 20.370 ms\n", + "succeeded: ('pei', 'no', 'pei') in 15.476 ms\n", + "succeeded: ('pei', 'pei', 'no') in 38.467 ms\n", + "\n", + "succeeded: ('single_pop', 'icecube', 'no') in 15.643 ms\n", + "succeeded: ('pei', 'no', 'sparsemod') in 79.280 mssucceeded: ('single_pop', 'no', 'sparsemod') in 77.510 ms\n", + "succeeded: ('pei', 'centroids', 'sparsemod') in 109.144 ms\n", + "succeeded: ('single_pop', 'pei', 'sparsemod') in 111.914 ms\n", + "failed: ('single_pop', 'centroids', 'pei') in 24.595 ms\n", + "succeeded: ('pei', 'pei', 'sirh') in 80.554 ms\n", + "succeeded: ('single_pop', 'centroids', 'sparsemod') in 112.381 ms\n", + "succeeded: ('pei', 'sparsemod', 'sparsemod') in 122.869 ms\n", + "succeeded: ('single_pop', 'no', 'no') in 13.991 ms\n", + "succeeded: ('pei', 'sparsemod', 'pei') in 53.625 ms\n", + "succeeded: ('pei', 'centroids', 'no') in 34.597 ms\n", + "succeeded: ('single_pop', 'pei', 'no') in 52.220 ms\n", + "failed: ('us_states_2015', 'pei', 'pei') in 57.599 ms\n", + "succeeded: ('us_states_2015', 'sparsemod', 'no') in 97.504 ms\n", + "failed: ('us_states_2015', 'icecube', 'pei') in 80.801 ms\n", + "succeeded: ('us_states_2015', 'sparsemod', 'sirs') in 150.765 ms\n", + "failed: ('us_states_2015', 'centroids', 'pei') in 24.566 ms\n", + "succeeded: ('us_states_2015', 'centroids', 'sirh') in 178.085 ms\n", + "failed: ('us_counties_2015', 'centroids', 'pei') in 376.932 ms\n", + "succeeded: ('us_states_2015', 'icecube', 'sirs') in 102.603 ms\n", + "succeeded: ('us_states_2015', 'pei', 'sirs') in 214.125 ms\n", + "succeeded: ('us_states_2015', 'centroids', 'sirs') in 121.126 ms\n", + "succeeded: ('us_states_2015', 'sparsemod', 'sirh') in 259.750 ms\n", + "failed: ('us_counties_2015', 'pei', 'pei') in 535.273 ms\n", + "succeeded: ('us_states_2015', 'icecube', 'sirh') in 210.090 ms\n", + "succeeded: ('us_states_2015', 'icecube', 'sparsemod') in 467.440 ms\n", + "succeeded: ('us_states_2015', 'pei', 'sparsemod') in 646.660 ms\n", + "succeeded: ('us_states_2015', 'no', 'no') in 57.350 ms\n", + "succeeded: ('us_states_2015', 'no', 'sirs') in 238.119 ms\n", + "succeeded: ('us_states_2015', 'icecube', 'no') in 79.431 ms\n", + "succeeded: ('us_states_2015', 'centroids', 'sparsemod') in 526.739 ms\n", + "failed: ('us_states_2015', 'no', 'pei') in 39.635 ms\n", + "succeeded: ('us_states_2015', 'no', 'sirh') in 169.381 ms\n", + "succeeded: ('us_states_2015', 'centroids', 'no') in 190.882 ms\n", + "succeeded: ('us_states_2015', 'sparsemod', 'sparsemod') in 724.984 ms\n", + "succeeded: ('us_states_2015', 'pei', 'sirh') in 986.059 ms\n", + "succeeded: ('us_states_2015', 'no', 'sparsemod') in 364.870 ms\n", + "succeeded: ('us_states_2015', 'pei', 'no') in 691.799 ms\n", + "failed: ('us_states_2015', 'sparsemod', 'pei') in 25.273 ms\n", + "succeeded: ('us_counties_2015', 'icecube', 'no') in 9332.789 ms\n", + "failed: ('us_counties_2015', 'no', 'pei') in 18.036 ms\n", + "succeeded: ('us_counties_2015', 'icecube', 'sirs') in 12671.465 ms\n", + "succeeded: ('us_counties_2015', 'no', 'sirh') in 20739.261 ms\n", + "succeeded: ('us_counties_2015', 'pei', 'no') in 25841.281 ms\n", + "failed: ('us_counties_2015', 'sparsemod', 'pei') in 56.943 ms\n", + "succeeded: ('us_counties_2015', 'icecube', 'sirh') in 13882.659 ms\n", + "succeeded: ('us_counties_2015', 'pei', 'sirs') in 29788.308 ms\n", + "succeeded: ('us_counties_2015', 'no', 'sirs') in 21457.598 ms\n", + "succeeded: ('us_counties_2015', 'centroids', 'sirs') in 33628.194 ms\n", + "succeeded: ('us_counties_2015', 'centroids', 'sparsemod') in 42686.374 ms\n", + "succeeded: ('us_counties_2015', 'sparsemod', 'sirh') in 46597.776 ms\n", + "succeeded: ('us_counties_2015', 'no', 'sparsemod') in 26361.952 ms\n", + "succeeded: ('us_counties_2015', 'icecube', 'sparsemod') in 22939.093 ms\n", + "failed: ('maricopa_cbg_2019', 'pei', 'sirs') in 87.478 ms\n", + "failed: ('maricopa_cbg_2019', 'pei', 'sirh') in 129.370 ms\n", + "failed: ('maricopa_cbg_2019', 'pei', 'sparsemod') in 156.881 ms\n", + "failed: ('maricopa_cbg_2019', 'pei', 'no') in 51.239 ms\n", + "failed: ('maricopa_cbg_2019', 'sparsemod', 'pei') in 25.833 ms\n", + "failed: ('maricopa_cbg_2019', 'sparsemod', 'sirs') in 22.067 ms\n", + "failed: ('maricopa_cbg_2019', 'sparsemod', 'sirh') in 69.197 ms\n", + "failed: ('maricopa_cbg_2019', 'sparsemod', 'sparsemod') in 143.134 ms\n", + "failed: ('maricopa_cbg_2019', 'sparsemod', 'no') in 22.891 ms\n", + "failed: ('maricopa_cbg_2019', 'centroids', 'pei') in 25.511 ms\n", + "succeeded: ('us_counties_2015', 'pei', 'sirh') in 32565.839 ms\n", + "succeeded: ('us_sw_counties_2015', 'icecube', 'sirh') in 62556.598 ms\n", + "failed: ('us_sw_counties_2015', 'pei', 'pei') in 63301.605 ms\n", + "succeeded: ('us_sw_counties_2015', 'pei', 'sirh') in 63466.665 ms\n", + "succeeded: ('us_sw_counties_2015', 'pei', 'sirs') in 472.709 ms\n", + "succeeded: ('us_sw_counties_2015', 'icecube', 'sparsemod') in 717.402 ms\n", + "succeeded: ('us_sw_counties_2015', 'icecube', 'no') in 91.399 ms\n", + "succeeded: ('us_sw_counties_2015', 'pei', 'sparsemod') in 805.029 ms\n", + "succeeded: ('us_sw_counties_2015', 'centroids', 'sirs') in 63725.315 ms\n", + "failed: ('us_sw_counties_2015', 'no', 'pei') in 63747.398 ms\n", + "succeeded: ('us_sw_counties_2015', 'pei', 'no') in 362.749 ms\n", + "succeeded: ('us_sw_counties_2015', 'no', 'sirs') in 216.250 ms\n", + "failed: ('us_sw_counties_2015', 'sparsemod', 'pei') in 64506.859 ms\n", + "succeeded: ('us_sw_counties_2015', 'no', 'sirh') in 171.447 ms\n", + "succeeded: ('us_sw_counties_2015', 'centroids', 'sirh') in 685.291 ms\n", + "succeeded: ('us_sw_counties_2015', 'sparsemod', 'sirs') in 482.207 ms\n", + "succeeded: ('us_sw_counties_2015', 'centroids', 'no') in 64851.230 ms\n", + "failed: ('us_sw_counties_2015', 'icecube', 'pei') in 54.440 ms\n", + "succeeded: ('us_sw_counties_2015', 'sparsemod', 'sparsemod') in 65283.764 ms\n", + "succeeded: ('us_sw_counties_2015', 'centroids', 'sparsemod') in 671.066 ms\n", + "succeeded: ('us_sw_counties_2015', 'icecube', 'sirs') in 180.546 ms\n", + "succeeded: ('us_sw_counties_2015', 'sparsemod', 'sirh') in 607.624 ms\n", + "succeeded: ('us_sw_counties_2015', 'sparsemod', 'no') in 388.132 ms\n", + "failed: ('us_sw_counties_2015', 'centroids', 'pei') in 32.409 ms\n", + "succeeded: ('us_counties_2015', 'no', 'no') in 20360.240 ms\n", + "succeeded: ('maricopa_cbg_2019', 'centroids', 'sirs') in 17487.389 ms\n", + "succeeded: ('us_counties_2015', 'centroids', 'sirh') in 35238.972 ms\n", + "succeeded: ('us_counties_2015', 'sparsemod', 'sirs') in 43420.789 ms\n", + "succeeded: ('maricopa_cbg_2019', 'icecube', 'no') in 6121.286 ms\n", + "failed: ('maricopa_cbg_2019', 'no', 'pei') in 5.184 ms\n", + "succeeded: ('us_counties_2015', 'centroids', 'no') in 30250.661 ms\n", + "failed: ('us_counties_2015', 'icecube', 'pei') in 18.617 ms\n", + "succeeded: ('maricopa_cbg_2019', 'icecube', 'sirs') in 8950.455 ms\n", + "succeeded: ('maricopa_cbg_2019', 'no', 'sirh') in 10244.139 ms\n", + "succeeded: ('maricopa_cbg_2019', 'no', 'sirs') in 7832.043 ms\n", + "succeeded: ('maricopa_cbg_2019', 'icecube', 'sirh') in 6811.509 ms\n", + "succeeded: ('maricopa_cbg_2019', 'centroids', 'sirh') in 12521.056 ms\n", + "succeeded: ('maricopa_cbg_2019', 'centroids', 'sparsemod') in 17269.748 ms\n", + "succeeded: ('maricopa_cbg_2019', 'no', 'sparsemod') in 8531.297 ms\n", + "succeeded: ('us_counties_2015', 'sparsemod', 'sparsemod') in 40020.356 ms\n", + "succeeded: ('us_sw_counties_2015', 'no', 'sparsemod') in 55830.141 ms\n", + "succeeded: ('us_sw_counties_2015', 'no', 'no') in 43.231 ms\n", + "failed: ('maricopa_cbg_2019', 'pei', 'pei') in 29.795 ms\n", + "succeeded: ('maricopa_cbg_2019', 'icecube', 'sparsemod') in 9099.661 ms\n", + "succeeded: ('us_counties_2015', 'pei', 'sparsemod') in 27555.321 ms\n", + "succeeded: ('maricopa_cbg_2019', 'no', 'no') in 5825.926 ms\n", + "succeeded: ('maricopa_cbg_2019', 'centroids', 'no') in 9315.077 ms\n", + "failed: ('maricopa_cbg_2019', 'icecube', 'pei') in 6.478 ms\n", + "succeeded: ('us_counties_2015', 'sparsemod', 'no') in 19277.509 ms\n" ] } ], @@ -268,7 +268,7 @@ " else:\n", " initializer = init.SingleLocation(location=0, seed_size=100)\n", "\n", - " rume = Rume.single_strata(\n", + " rume = SingleStrataRume.build(\n", " ipm=ipm_library[ipm](),\n", " mm=mm_library[mm](),\n", " scope=geo_loaded.spec.scope,\n", @@ -417,47 +417,47 @@ " \n", " \n", " 0\n", - " us_counties_2015\n", + " us_sw_counties_2015\n", " sparsemod\n", " sparsemod\n", " True\n", - " 30.364625\n", + " 65.283764\n", " None\n", " \n", " \n", " 1\n", - " us_counties_2015\n", - " pei\n", - " sparsemod\n", + " us_sw_counties_2015\n", + " centroids\n", + " no\n", " True\n", - " 27.776683\n", + " 64.851230\n", " None\n", " \n", " \n", " 2\n", - " us_counties_2015\n", - " sparsemod\n", - " sirh\n", + " us_sw_counties_2015\n", + " centroids\n", + " sirs\n", " True\n", - " 26.853101\n", + " 63.725315\n", " None\n", " \n", " \n", " 3\n", - " us_counties_2015\n", - " centroids\n", - " sparsemod\n", + " us_sw_counties_2015\n", + " pei\n", + " sirh\n", " True\n", - " 26.676122\n", + " 63.466665\n", " None\n", " \n", " \n", " 4\n", - " us_counties_2015\n", - " sparsemod\n", - " sirs\n", + " us_sw_counties_2015\n", + " icecube\n", + " sirh\n", " True\n", - " 26.194347\n", + " 62.556598\n", " None\n", " \n", " \n", @@ -471,20 +471,20 @@ " \n", " \n", " 112\n", - " pei\n", + " single_pop\n", + " sparsemod\n", " no\n", - " sirs\n", " True\n", - " 0.012014\n", + " 0.020370\n", " None\n", " \n", " \n", " 113\n", - " single_pop\n", + " pei\n", + " icecube\n", " no\n", - " sirs\n", " True\n", - " 0.010216\n", + " 0.015911\n", " None\n", " \n", " \n", @@ -493,25 +493,25 @@ " icecube\n", " no\n", " True\n", - " 0.009987\n", + " 0.015643\n", " None\n", " \n", " \n", " 115\n", - " single_pop\n", - " no\n", + " pei\n", " no\n", + " pei\n", " True\n", - " 0.004732\n", + " 0.015476\n", " None\n", " \n", " \n", " 116\n", - " pei\n", + " single_pop\n", " no\n", " no\n", " True\n", - " 0.004672\n", + " 0.013991\n", " None\n", " \n", " \n", @@ -520,18 +520,18 @@ "" ], "text/plain": [ - " geo mm ipm runs runtime error\n", - "0 us_counties_2015 sparsemod sparsemod True 30.364625 None\n", - "1 us_counties_2015 pei sparsemod True 27.776683 None\n", - "2 us_counties_2015 sparsemod sirh True 26.853101 None\n", - "3 us_counties_2015 centroids sparsemod True 26.676122 None\n", - "4 us_counties_2015 sparsemod sirs True 26.194347 None\n", - ".. ... ... ... ... ... ...\n", - "112 pei no sirs True 0.012014 None\n", - "113 single_pop no sirs True 0.010216 None\n", - "114 single_pop icecube no True 0.009987 None\n", - "115 single_pop no no True 0.004732 None\n", - "116 pei no no True 0.004672 None\n", + " geo mm ipm runs runtime error\n", + "0 us_sw_counties_2015 sparsemod sparsemod True 65.283764 None\n", + "1 us_sw_counties_2015 centroids no True 64.851230 None\n", + "2 us_sw_counties_2015 centroids sirs True 63.725315 None\n", + "3 us_sw_counties_2015 pei sirh True 63.466665 None\n", + "4 us_sw_counties_2015 icecube sirh True 62.556598 None\n", + ".. ... ... ... ... ... ...\n", + "112 single_pop sparsemod no True 0.020370 None\n", + "113 pei icecube no True 0.015911 None\n", + "114 single_pop icecube no True 0.015643 None\n", + "115 pei no pei True 0.015476 None\n", + "116 single_pop no no True 0.013991 None\n", "\n", "[117 rows x 6 columns]" ] diff --git a/doc/devlog/2024-04-04-draw-demo.ipynb b/doc/devlog/2024-04-04-draw-demo.ipynb index b827ba8b..6e8f2a17 100644 --- a/doc/devlog/2024-04-04-draw-demo.ipynb +++ b/doc/devlog/2024-04-04-draw-demo.ipynb @@ -42,7 +42,7 @@ "outputs": [ { "data": { - "image/png": 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fazP5kpOTFy9e/NgLdGdnZ//++++nTp2KioqKiIhYuXLl+D9u1Gp1cXHxyZMnz58///TTT/v4+IhEovb2dk9Pz7feemucT659iaampsuXL+N32Wx2UFBQX1+fi4vLjRs3oqOjtR0+AHhCVCpVUVHR+++/P87uNUBPwN+iYWOxWOHh4WfOnFmwYMGqVavIZDKGYRKJZPfu3b29vcuXL5/8kE5JSQkKCmIwGNqDbGKxODU19dKlS59++imXy33gM4SEhGAY9tVXX7355ptubm4T8llDJpN9fHxiYmKOHDmyc+dOCoWi0WjEYvHHH3+8cePGQ4cOjfP51Wp1VVXV2bNn3333XQRBlErljRs3/vKXv3z++edcLnfGjBm//vrrG2+8Mf43AsAYGhsbNRrNrFmzYAw9NcBRbsNmZGR07949Go1mb2/PZDLpdDqDweDxeE899VReXl5paekk19Pa2pqXl8fn87Urb1dVVf3zn/9sbGysr68f2X96VCYmJkVFRXZ2dk5OTubm5mOfTrt79+7DPCeJRCKTySUlJf7+/mw2m06nM5lMKysrFxeX33//fYwHqlSqh1lkramp6cCBAytWrKDT6XQ63dLS0sPDw9nZGW/MxePxOBzOb7/99jClAvDYcnNzra2t4ST0lAEJbfDKy8uZTKbuxDESicTn8xsaGiahDcYw58+ff+qpp6ysrLQfEO7u7s8//7ynp+ewrpFjS09PnzNnDpVKfeAHzd69ex/yOdVqdWZmZlRUlO6W+vp6Dw+PMR41MDDw/fffP/DJBwYGcnJy3N3d8bskEsnGxiY6Oho/AMBms11cXFJTUx+yVAAeT05OjouLC94oFkwBkNAGr7GxkU6nD1u+ikwmazQaiUSiVCoRBCkrK0tKSoqMjPzxxx//+OOP48eP//3vfy8tLb169eovv/yyefPmc+fOicVi/LESieTnn3/+6aeftm3b9vvvv+OznP75z38uXbqUx+N98cUXfX19e/bsCQwMXLNmTUVFBYqi2tfNysricDi6rasoFIqJickjxTOCIOnp6REREQ9zfFuhUDzkc6IompGRoU1opVKZlpZWVFT07bffjvEoDMPw7tdjMzY2VqvVulc5czgcbd9JMplsZmZGo9Fqa2sfstppbmBgYNgN8EBKpbKkpCQgIACatUwZkNCGrbu7u6Ojw83NbdjFV93d3RKJxMzMDD8d5evru2HDBmtr687OzsDAwBUrVnR2dn755Zd8Pv/555//05/+dPr06YqKCvyxv/zyC4/HW7hw4Ycffnjq1Knc3FwURePj40+cOOHn58fhcKhUqrOz8xdffLFv3z5vb29t+ra0tBgbG1MolHEeYbt7965SqYyKiprA2S74TDGVSnX37t1ff/318OHD33zzTXNz882bN728vMb//A4ODuvXr9+6dautre3q1asvXrxIJpN1zwWyWCwXF5f8/Pzxv9aURyKRtPMnWCwWHLB9SE1NTSKRKCQkZDp0d50mIKENW2VlpVKptLW1HfZ/sqysjEajWVpa4p9uZDKZxWL19/e7ublxuVwKhcLlcvFjyBQKxdPTUywWS6VS/LGtra3Z2dlisZhKpXI4nLKyMpFIRKFQaDTazp07T5w4cePGDRqNFhISwmKxyGSy7owwHo83/iNsN27csLOzs7a2nsCPZhRFb968OXPmzPXr1z/77LPx8fEFBQV0On2iWmEyGIykpKTKysodO3ZIJJJNmzYdOHBAdwdjY2MzMzOiRoRNTU3av1/9h41AdEX6Tq1Wd3d3p6WlKRQKEomkVqvhlzY1wFxuw1ZRUcFkMvl8/rAwKy0t9fX1dXNz093I5XItLCzwPS0sLExMTPDOS/gW7X/pv/3tbwiCSCSS8vLytrY2Op2uPY4dHh4eHR29e/fu/fv3a+Nfq6WlhUajPeoB7ZHS09N9fX3vNxbftGmT7nH11NTUpKQk7d3Y2NilS5eOfBR+iDs6OtrIyAjDMFtb28WLF2/atGnt2rXD9uzr69u6dav2rlwuLygo6Onpwe+amZnFx8fHx8cPexSJRDIyMuJwOImJiS+88MKJEyd+++231157TbsDjUZjs9nFxcUP+1uYUO+88865c+dMTU15PJ6Dg4OjoyOfz7ezs7O3t7e1tXVwcOByuTD713AplcrS0tKysjIPD4/Kyko/Pz/o2jI1QEIbMAzDKioqhvW6wDAsLS1NLBa//PLL2olLj/ScNTU1hw8ftra2fu6551xdXWk0Gj6OwfPS29v7X//6V1lZmbOz82Nf8Tx2Aenp6X/+85/vd4h72GnjpKSkgwcPPvA58aPceJaTSCQMw7Tn3YextLTUfcLu7u59+/Z9/PHH93tykUh0+/ZtgUCgvcDMwsIiICDg+vXruruhKCqXyxkMxtilPiFCofDChQtyubyhoaGhoYFMJuPfVLTfdUgkEpvNtrW1dXR0dHJyIupKevB4TE1N58+fP3/+fKILARMMjnIbMKlUKhKJ3N3d7ezs8C0Yhg0ODp48eTIqKkogEIzx2PsdBJNIJO+++66Pj88bb7zh5eWlUChQFG1tbcUnZLW0tFhbW2/btu3IkSM1NTXDnsTe3r6rqwufm/ZAeGqOLKOxsVEkEs2bN28CT0JjGFZfXy+VSrXTxFAUzczMdHJywjBMo9GM55CgRCK5ffu2RqPRvpZEIuno6IiNjdXdDUVRFEW1f1OTLCIiQlshgiAajUalUukeisAwrK+vr6Ki4sqVK+bm5kKhkIgyAQD/AxLaUKEoWlpa2tfXZ2tra2pqqlKplEplW1vbgQMHEhISVq9erdt4TqPRoCiq++fg4KBCoVCr1Wq1WqVSaX/U2dnZ39/v6OhoamqqVqvlcrlKpaqpqRkYGFAoFL/99pujo2NsbOz8+fP3799fV1enm7L29vbd3d3DEhoPg97eXhRF8VTA929sbDx69KhUKtU+HMMwlUqVlpZmZmZmb2+Pr74yzt8SPvqXy+UpKSlubm7alhUYhkmlUiaTOTQ0VFhYeO/evcd+Cblcnp2dfeHCBZVKpVKpFApFXV1deXm57iFufLfu7m6ixtDBwcEP7IZpZGTk4+OTnZ29Z88emGoEgD6Ao9wGSSaTpaen//DDD7W1tZmZmSKRCEEQqVSqVCqfe+65oKCgYfvfu3fvzJkzpaWlFy9e9Pb2rqioyM7OxjDM1dV1xowZ+IoiP//8M4/H8/X1TUxMzMzMHBwcZLPZb7755k8//SSXy8+dO3fu3Lmurq7o6GgEQfAoVSqV69atmzNnDn4+m8Fg2NjY4MeQtaeQ29raLl++fO7cuerq6n379s2dO3fhwoVmZmZ5eXl79+6NiIjw9PTEdxaLxT/99NMPP/xgYmJy9OjR9evXT8i5tN7e3mPHjvX19UVHRx85cgQPThqN9s4773z//feXL182MTEJDQ197Odns9nLly93dXU9ePCgqampRCLhcrk7duwYtptEIsGPDYzrzTwWtVpdWlrK5/Pr6+tH/dKDn4HeunXr1q1b4VJaAPQHCab8gQl06tQpExOTp5566mFaxzc0NJBIJAcHh/Ec0H7nnXe++uqrx374A/X09Bw8eBCfPffY1Gp1WVnZpUuXPvzww4kqbGwKhSInJycjIyMzM/PWrVtisRg/0KJ7ZBtHIpHCw8OPHj3q7e2t3Xj16tWnnnqqr6+PzWZPTsF6yNXVNTEx8YMPPiC6EDB9wVFuMJFWrlyZl5fX3d2te9bzfpqbm3k83jjPN09U34v7YTKZ69atG88zYBjW0tKSmZm5cOHCCSpqdIODg6mpqdu3b4+Li2Oz2fPmzdu/fz+dTv/HP/6Rl5eXkpIyLJ6NjY3Nzc2/+uqrzMxM3XgGAOgJOMoNJtiCBQsKCgpYLNbYiwNrNBqZTDb+QzjOzs7jfIaxGRsb29vbj+cZlEplT08Pg8EIDAycqKq0Ojo6cnNzs7KyUlNTCwsLNRqNq6urQCDYt2+fQCDw8/PT7imTySgUCh7S+JJzsbGxycnJ43x3U1tzc/PAwACTySS6kPuqqKioqal54G4MBgNmehsiSGgwwebNm3fjxo2enh46nT7GJbYSicTJyWk69Mjr6+traWlZvXr1RD1hQ0NDZmZmVlZWZmZmRUWFkZGRl5eXUCjcsmVLVFTU/U7em5mZBQYG5ufnUygUNpuNdwidqJKmqp9//vmHH35Yvnz5q6++OvbFEUQpKCh4mI4szs7OkNCGCM5DA6Dv8NaWeCSnp6c3NzebmZkFBwcLhUKBQBAZGfmQg7zNmzfv2bPn1Vdf/fzzz8d+CJyHRhDE1dV17dq1PB7v0KFDBQUFXl5eL7/88kRNYATgYUBCA6CPZDJZQUEBnsqZmZn9/f0MBiMsLCw2NlYgEMyaNesxLoi6desWiqKRkZEP3BMSGvnfmWL5+fnHjh07ceKERCJ59tlnExMTY2JiYMFw8KRN/WOMABiKwcHBO3fu4EewMzIyhoaGeDyeUCjcvn27UCgMDg4e5xLic+fOnahSp5vQ0NDQ0NDPPvssJSUlOTk5Li7OwcFh5cqVr7/+uqOjI9HVIQiCyOXyxsbGmpoaFotlbW1tZGSkVCqlUqmPj890/ppl6CChASBSe3s7PkrOysrSner19ddfx8XFubi4EF0g+C8ajbZs2bJly5ZVVVX98MMP33333RdffBEdHZ2YmPjcc88RO6kCX3tn69atK1euDAsLo1AoUqm0paWluLh4xYoVsIyrgYKEBmCyDZvqRaFQAgMDBQLBli1boqOjra2tiS4QPIC3t/euXbt27Nhx8eLF5OTkF154gcfjrV69euPGjUR9qWKxWHjnujVr1vj4+OAb+/v7X331VTab/eKLLxJSFRgnSGgAnjgURYuLi/FUvn79ek9Pj7m5eVBQUEJCAn5Z1AOX5AR6yMTEBB9S19bWfvfddz/88MPnn38+f/78xMTExYsXT36vsMLCQkdHR3yBPxydTl+wYMH3338PCW2gIKEBeCKkUmlhYSE+UM7IyBgYGOBwOGFhYe+9955AIAgLC4P1NacMDw+PXbt2ffLJJ9evX09OTl6xYoWNjc3y5csTExN1L0l/0kpKStzc3HQTGkEQFotVW1urVCrh35shgoQGYMKIxeKcnJzU1NTMzMzc3FylUolP9dqxY4dQKAwJCYHZv1OYkZFRbGxsbGxsa2vriRMnDhw48PXXX4eGhiYmJq5atWpYcE44mUx29+7dpUuXatvD4AYHB/E+ck/01cETAgkNwLi0tbXhA+WsrKyCggK8H0lsbGxiYmJkZOSTXvIM6CE+n79ly5bNmzenpaUlJydv2rRpy5Yty5cvf/3115/EunK4xsZGmUzm6elJo9G0GzUaTUVFhZWVFZxGMVCQ0AA8soaGBnygnJmZ2djYqDvVKyYmxtLSkugCAfHIZDI+pG5vbz927Njhw4eTk5PxIfXKlSstLCwm9uWKi4vZbLalpaXuJXkajebWrVsPcwU80E+Q0AA8mO5Ur7S0tN7eXgsLi/Dw8DVr1uALe8EYBdwPj8fDh9S3bt06fvz422+/vXnz5hdffDExMXE8bU+HKSkp4fF4wxqQl5eXDwwMrFy5cqJeBUwySGgARqed6pWampqVlSWXy7lc7qxZszZv3iwQCGbPnj35k3WB4SKTyUKhUCgU7tq168yZM99++21ycrKvr++aNWs2bNgwzuMuKpWqoaEhODhYd2iuUCj27t370ksvzZw5c9zlA2JAQgPwX52dnTk5Ofh5Zd2pXnv37hUIBL6+vjDVC4wTm81OTExMTEzMz89PTk7euXPn9u3bExISxrOSaHt7u1gs9vLy0ib0wMDA1atX2Wz2xo0bJ7R8MKkgocF0h0/1ws8rV1ZWkslkvFVUYmJiVFSUnqzpCKae0NDQQ4cO7d69+/Tp0wcPHoyLi/P29l63bt0jNedAUbSpqenixYttbW2dnZ2FhYUIgigUir6+PplM9tFHH8GsCIMGCQ2mHd1WUTdv3mxqajI2Ng4ICEhISOSDyoQAABY0SURBVNi1a5dQKIR1jMGkYTKZukPqTz75ZNu2bYsWLXrIITWGYUqlksFgLF26lMvlymQyBEHkcjmVSl21atU4F3IHhIOEBtOC7lSva9eu9fX10en02bNnr1u3Dj87qHuNCgCTDx9S79u3T9ucw93d/aWXXnrllVccHBzu9yhjY2MfHx/tMp9gioGEBlOWRCK5ffu2dgVshUJha2s7c+bM999/PzY2dvytogCYcNrmHJWVlT/++OP+/fs//vhjPWnOASYf/H2DKaWjoyM3Nxc/r6zbKgpf/noyl2AEYDx8fHxGbc6RlJQEy+BMH5DQwODptorSneq1ZcuWqKioh590A4C+0TbnqKmpOXr06Pfff09scw4wySChgeHRneqVnp7e3NxsZmYWHByMT/WKiIiAbrhgivH09Ny1a9c//vGPP/744/jx43hzjrVr17766qvu7u5EVweeGEzH6dOniS5HLxw8eBADekapVObl5e3atWvhwoX4XGsGgxEbG7tt27arV68qFAqiC5xSrly5giBIX18f0YUQycXF5dNPPyW6itG1tLTs2rXLyckJ+c8UM5lMRnRR45Kbm0vw575+eP/993V/LaOMoad5Tq9fv57oEsC/DQ4O3rlzR3eqF75+yLZt24RCIUz1AtPW/ZpzvPHGGwEBAURX9/i++eYbDodDdBWE+eCDD4ZtGSWhly9fPinF6ClYgodY7e3teEeKrKysYVO9YmNjXV1diS4QAH0xrDlHcnKytjnHSy+9NKwNpUF45plnpvP/8c8++2zYFjgPDYinO9WroqJCt1VUdHS0tbU10QUCoNe0zTnS0tKOHTumbc6xcePGkJAQoqsDjw8SGhBAO9UrNTU1PT29u7vb3Nw8KCgIn+oVGRnJZDKJrhEAA6MdUu/bt2/Cm3MAQkBCg0kik8kKCgrwgXJGRsbAwACHwwkLC/vLX/4iEAjCwsKoVCrRNYL/4eLiMp07hYjFYqJLeExjNOeIjY0lujrwCCChwRMkFotzcnLwphR5eXlDQ0P4VK8dO3YIhcKQkJDpHAD6zNfX9+DBg0RXQbzZs2cTXcK43K85xyuvvAInjwwCJDSYYHirqGFTvWJjYxMTEyMjI2E5JIPA5/NhyuSUMc7mHIBAkNBgAminel29erWxsVF3qtf8+fOtrKyILhAA8O8h9d69ey9duoQ35/Dw8HjllVfWrVvH5XKJrg6MAhIaPA7dVlFpaWm9vb0WFhaBgYHLly+PjY0VCASmpqZE1wgAGIWpqaluc47PP//8ww8/hOYc+gn+MsDDkkqlhYWF+ATsrKwsuVzO5XJnzZq1efNmgUAwe/ZsWCUYAAMysjmHnZ3dqlWroDmH/oCEBmPp6uq6c+cOfl45NzdXqVTiU7327t0rEAh8fX3hJBYABg2ac+gzSGgwnO5Ur4KCAm2rqMTExHnz5uFLAQMAphhtc44LFy4cO3ZM25xjw4YNbm5uRFc3TUFCA0Sj0VRWVuKpfPPmzaamJt2pXjExMbDWAQDTBJVKxYfULS0tJ0+e/H//7//t3r177ty5a9asWb16NcwvmWSQ0NOU7lSva9eu9fX1WVhYhIeHr1u3TigUCoVCGo1GdI0AAMLY29vrNud44403pkZzDsMCCT2NSCSS27dv46mMT/WytbWdOXPm+++/HxsbC62iAADDTL3mHIYFPpGnuM7OzpSUlA8++EAoFFpaWsbFxR0/fpzH4+3du7esrKy9vT0lJWXLli2hoaEQzwCA+8Gbc9TW1l69etXV1fXNN9+0s7PbuHFjQUHB2A/EMOyvf/3r0NDQ5NQ5xcAYegrSbRVVWVmpO9UrOjrawcGB6AIBAAZJO6QWiURnzpz55ptv8CH16tWr16xZw2azRz7k+vXru3btSk9Pv3TpEixe9Khg2DQVqNXq8vLy5OTkNWvWODk5ubm5vfbaa+Xl5QkJCRcuXOjp6SkvLz906NCaNWsgngEA44c35ygtLc3LywsNDd26daudnd3y5ctTU1OH7Xno0CEKhZKfnx8aGlpbW0tItYYLxtCGSqVSlZSU4E0psrKyRCIRnU6fPXv2yy+/LBQKIyIiTExMiK4RADDF6TbnOHDgQFxcnI+Pz9q1a/HmHL29vefOnUNRFEGQ1tbWmTNnXrx4cd68eURXbTAgoQ3J4ODgnTt3tEewFQoFvn7Itm3bhEIhTPUCABBiWHOOjz/+GG/OYWVlpdFo8H1QFJVIJHFxcUePHl21ahWxBRsKSGh9197enpeXh6+1qW0VJRAI9u3bJxAI/Pz8iC4QAAD+DR9Sf/755z/99NORI0fOnDmju+ygRqPRaDSrV6+uq6vbvn07cWUaDEhofaQ71auiosLIyCgoKAhfPyQ6OhoauwIA9BmDwdi4caO/v79QKMQwbOQOO3fubGxsPHLkCKwqOjZIaL2gVqurqqrwgXJ6enp3d7eZmVlwcHBCQsKuXbsiIyOZTCbRNQIAwCNITk42NjZWqVQjf6TRaE6dOtXU1HT+/HkWizX5tRkKSGjCyGSygoICfKCcmZnZ39/PYDDCwsL+8pe/CASCWbNmwVQvAICBGhgYOH369KjxjENR9NatW2FhYVeuXIFWWvcDCT2pxGJxTk4OfgQ7IyNjaGgIn+q1fft2oVAYEhICraIAAFPAyZMnH7hKiUqlqqurmzlz5u+//w4ffaOChH7idFtFaad6xcbGrl69OiIiwsXFhegCAQBggimVyrVr18pksoGBgaGhob6+PpVKNTg4qFAohoaGZDIZPscbw7De3t6oqKgdO3YQXbI+goR+IrRTvVJTUxsaGnRbRc2fPx8W1gEATG1vv/322DtgGNbf369UKqVSqVQqLSkpmZzCDAsk9MTQbRV1/fr1np4ec3PzoKCgZcuWxcbGCgQC6NoGAABaJBJJd5VQWLh7VJDQj08qlRYWFuJHsDMyMgYGBjgcTlhY2HvvvScQCMLCwqhUKtE1AgAAMFSQ0I+mq6vrzp07eCrn5uYqlUp8qteOHTtgqhcAAIAJBAn9YLpTvQoKCnRbRc2bN8/JyYnoAgEAAExBkNCja2howJtSZGRk3L17V3eqV0xMjKWlJdEFAgAAmOIgoUexc+fOpKQkJpMpEAgSExMjIiJg/RAAAACTDBJ6FEuWLFm/fr2/v7+RkRHRtQAwvQwMDFRUVOTn5/N4PD6fr1AoBgcH7e3tg4ODiS4NgMkGzQpH4evrGxgYCPEMwORTq9Wtra27d+9ua2uTy+Uymayrq+vs2bMZGRlElwbAZIMxNABAj1haWnK5XC6Xu2TJEjs7OwRBent7P/rooxMnTkRERBBdHQCTCsbQAAD9UlhY6Obmpp35QSKRNBqNQqEgtioAJh8kNABAvxQWFvr4+OAJrdFoampqurq6EhISiK4LgMmmLwl9+/bt1157jUQivfbaa2lpaQMDA7D0BwDTkEgkunv3romJSV1dXW5ubkZGRmVl5dKlSxcsWEB0aWBKIY2waNGiPXv21NTUEF3af+lFQt++fXvOnDnz5s3DMOzAgQNWVlarV68muigAAAGqq6vJZLKbmxuVSlWpVL/99huJRFqxYoWFhQXRpYEpBcOwzs5O7W0Mw44cOdLf3+/l5VVcXExsbVp6kdA//vgjgiAvvvgifjcwMHDnzp2EVgQAIEZZWRmXy505c6avr29ISIitre3NmzeJLgpMTRwOZ9jd9957D0GQgwcPElTRcHqR0K2trQiC6H5tCQwMJK4cAABhampq+Hy+ubk5giA0Gs3FxQUSGkwaJpOJQEIPg4+Yg4KCDh8+PDAwgG/EMIzQogAAk00qlba0tLi6umqPaVdWVrJYLGKrAtPHvXv3EAT54osviC7k3/QioQMDA6urq5OSkhITE1ks1s8//6zNaQDA9FFfXz8wMODi4kKj0fAttbW1NjY2Q0NDbW1tEomE2PLA1FZcXLxp06aEhAT9mQilFwmNIIinp+eBAweys7OTkpJWrFjBYrFSUlKILgoAMElQFK2trc3IyGAwGM3Nzdownjt3LovFKi4ubmlpgWX+wBOCz+UOCgp6++23L168OOz8NIH0JaFx4eHheE4nJCQsWrQIQhqAaYJEIpmamgYFBSUmJgYEBFAo/17ucMmSJa+88oqlpaWDg4OpqSmxRYKpCp/LnZCQcP36daJr+R96kdAkEkn3sHZ4ePi3336LIMiiRYuIKwoAMHmMjIzs7e0FAkF0dPTs2bPNzMzw7TweLzY21t3dncfjEVshmPKOHDlSXFz897//nehC/ksvEhpBkPz8fN27jo6OCILAKkIAAAAmB4fD0beQ1pfOGTExMdeuXQsNDWUymQMDA7///jvynzneAAAAwITr6urS3sDPPXM4nJ07dwYFBTk6Oj777LOEn5DWlzE0hmH29va//PILiURisVjl5eXV1dVwVTQAAIAngUQicblc/DaXy9WuMx0YGFhUVJSYmMjlcvfs2UNcgQiiJ2No/NJnT09PT0/PDRs2EF0OAACAKW6MJTcCAwP1ZEEOfRlDAwAAAECXXoyhAQDTXHp6enJy8tj7REdHwzE2MK1AQj8ChULR3t7e1tbW0dHR2tra0dHR1tbW3NxsZmYGl24DMB6Ojo4PvLrS1dV1cooBQE9AQo+iubn5zJkzeBi3t7e3trbeu3evs7NTLBZr96FSqWQyWa1Woyi6Zs0aAqsFYApwdXWFAAZgGEjoUZiamiYnJ6empiIIYmRkpFarR+6jVCrxG0ZGRpGRkZNaHwAAgGkAEnoU1tbWV69ePXPmzIYNG2Qy2agJraVWq+fNmzdptQEwtXV2dt64cePSpUuenp4+Pj7x8fHaPlcATDcwl/u+li1bVl1dvWzZMgRByOT7/qLYbLabm9sk1gXAVEan0zkcTmZmppeX14wZM0xMTIiuCADCQEKPhcvlnjx58uLFizY2NsbGxiN3MDIyEolETCYzLi5u+/btKSkp/f39k18nAFOGmZmZQqFgsVizZ8/28PAY9f8dANMEJPSDJSQkVFVVrV27lkQiDet/RyKRNm/evH37djab/d133y1atMjGxiY4OPiNN944fvx4XV0dUTUDYLgKCwtdXV3Nzc21yzwBMD3BeeiHwmKxDh8+vHr16nXr1jU3N6Moim9HUXTt2rV+fn7vvPMOgiAtLS3Z/3HkyBGlUmljYzNnzpzw8PCwsLDQ0FAWi0Xo+wDAAFRWVvr7+8PxbQBgDP0IIiMjS0tLN23aRCaT8f61DAbD19dXu4O9vf2yZcu+/PLL7OxssViclZX1wQcfGBsb79+/PzY21tLS0tPTc+XKlV9++eXNmze1PeoBAFoikaitrS0kJIRGoxFdC3jiNBrNjRs3Xn/9dehkOCpI6Edjbm7+1VdfZWVl4dduzps3734H4kxMTObOnfvuu++ePXu2paWlra3twoULK1asGBgY+Oyzz+bNm8dkMv38/NauXfvNN99kZ2fL5fLJfSsA6KOamhqNRuPg4DDsjBKYSjAMy8rKeuutt+zt7aOiorKysp577jmii9JHcJT7cYSHh5eWlv7f//2ftbX1Qz6Ex+MlJCRovyfeu3cv7z+2bdsmEokoFIqfn19ISEhgYGBAQEBgYKClpeUTewcA6KmysjIGg0Gn07XffVEUFYlENjY2xBYGxg9F0Zs3b164cOHcuXPNzc2+vr4bN25cvny5j49PXl7egQMHiC5Q70BCPyYqlbp9+/bHfrijo6Ojo+Pzzz+P362rq8PTuqio6OLFi729vQiCODg4aNM6MDDQ3d0dRhVgyisrK+Pz+dproDUaTVtb29DQECS04RocHLx8+fKFCxf+9a9/iUQif3//tWvXLl++fMaMGUSXpu8gofWCu7u7u7v7iy++iN9tbW0tKSkpLi4uLi4+f/787t27URQ1MzPz9/fH0zogIMDf35/NZhNbNgATq7+/v6WlJSoqisFg4FtUKtWVK1dWrlxJbGHgMdTV1V25ciUlJeX69esoigqFwr/97W/PPvssLCDx8CCh9RGfz+fz+U8//TR+V6FQlJeX44FdUlJy9uxZkUiEIAiXy/X19fXy8vL19fX29vb29nZwcCC0cADGpa2tTSqVuri4aKeJpaSk9PX1mZmZEVsYeEj9/f1paWlXrly5evVqQ0MDnU6Pi4s7dOjQwoULraysiK7O8EBCGwAajRYaGhoaGqrdcu/evcrKyoqKiqqqqvLy8rNnz/b09CAIQqfTvb29fXx8fHx8vLy8/Pz8XF1d8WnnAOgzlUpVXFz8/fffV1ZWZmRk9PT0dHR0lJeX5+bmnjx5kujqwFhkMtnt27dv3rx59erVnJwcDMNCQ0NXrlwZFxc3Z84cWHNmPOCz2yDhp7Hj4+O1W3p6eiorKysrK6uqqioqKm7evNnU1IRhGJVKdXV1dXNzc3V1dXFxwTsIubi4wFrHQK8YGRl5eXm9++67iYmJLBbLxMREqVRKpVKFQuHn50d0dWC4np6erKysjIyMzMzMgoIClUrl7OwcGxv71ltv4VeWEl3gFAEJPUVYW1tHRERERERot0il0urq6qqqqqqqqsbGxvz8/DNnznR0dOA/5XA42szWJre9vT1MRgOEIJPJdDqdTqcTXQgYnUQiKS4uLiwsLCgouHPnTmVlJYlE8vPzi4iIeOuttyIiIuzt7YmucQqChJ6yzM3NQ0JCQkJCdDfK5fKG/2hsbGxoaDh//nxjY6NMJkMQxNjYGD8FzuPx7HTweDw+n89kMgl6KwCAydba2lpVVYVHcmFhIX6dOpvNDg4OTkhI+OyzzwQCAUxWfdIgoacXU1NTPz+/kYcNOzo68Mxubm5ub29vbW3Ny8trbW3t6OgYGhrC9zEzM+Pz+ba2tvb29viflpaWLBaLzWaz2Wz8BoyBADAsGIZ1dXU1NjZWV1fX1tbW1tbW1NTU1tZKpVIEQXg8XkhIyNKlS4ODg4ODg11cXIiud3qBhAYIgiC2tra2trZz584d+aPu7u6Ojo6WlpbOzs6Wlhb8dnZ2dktLi0gkwv8ba1EolGGZjd9gsVimpqY0Gs3MzMzExMTc3JxKpdLpdN1ZbCwWCzolADBR5HK5QqFQKBT4DfxPkUjU1dXV1tbW1dXV3t7e0dHR1dWFNxqg0Wju7u6enp7x8fGbNm3y8PDw9vbmcDhEv49pDRIaPICNjY2Njc391hZQKpX9/f0ikQj/c+SN2tpa/Db+GSGTybSDcgDAZDI1NWUymRwOh8/nczgcf39/W1tb/K6zs7ODgwOZDOtA6xdIaDAuVCqVw+E86hdtiUSiUqnEYrFarca3qNVqsVj8BAoEYJqi0Wj4gStTU1P8BtEVgUcGCQ0IgF/rBdNMAABgDHBMAwAAANBHkNAAAACAPoKEBgAAAPQRJDQAAACgjyChAQAAAH0ECQ0AAADoI0hoAAAAQB9BQgMAAAD6CBIaAAAA0EejrCmWmJg4+XXoD7wPIwAAgEn24YcfTuf+ePfu3Ru25X8SmsvlxsTENDQ0TGJJegdakQMAwCRjMBgxMTHd3d3d3d1E10KYwMBADw8P3S0kDMOIqgYAAAAA9wPnoQEAAAB9BAkNAAAA6CNIaAAAAEAf/X+mR5cjd9YQzAAAAABJRU5ErkJggg==" + "image/png": 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" }, "metadata": {}, "output_type": "display_data" @@ -59,12 +59,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" + "image/png": 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" }, "metadata": {}, "output_type": "display_data" @@ -73,7 +73,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Model saved successfully at scratch\\sirh\n" + "Model saved successfully at scratch/sirh\n" ] } ], @@ -82,17 +82,17 @@ "\n", "# Assuming you have a \"scratch\" directory in the main Epymorph directory\n", "# If not saving, provide an absolute file path\n", - "render_and_save(ipm, r\"scratch\\sirh\")" + "render_and_save(ipm, \"scratch/sirh\")" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" + "image/png": 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" }, "metadata": {}, "output_type": "display_data" @@ -101,7 +101,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Model saved successfully at scratch\\seirs\n" + "Model saved successfully at scratch/seirs\n" ] } ], @@ -110,17 +110,17 @@ "\n", "# Assuming you have a \"scratch\" directory in the main Epymorph directory\n", "# If not saving, provide an absolute file path\n", - "render_and_save(ipm, r\"scratch\\seirs\")" + "render_and_save(ipm, \"scratch/seirs\")" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" + "image/png": 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}, "metadata": {}, "output_type": "display_data" @@ -134,12 +134,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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}, "metadata": {}, "output_type": "display_data" @@ -160,62 +160,60 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ + "from typing import Sequence\n", + "\n", "from sympy import Max\n", "\n", "from epymorph import *\n", "from epymorph.compartment_model import *\n", - "\n", - "\n", - "def construct_ipm():\n", - " symbols = create_symbols(\n", - " compartments=[\n", - " compartment('S'),\n", - " compartment('I'),\n", - " compartment('R'),\n", - " compartment('V'),\n", - " ],\n", - " attributes=[\n", - " param('beta', shape=Shapes.TxN),\n", - " param('gamma', shape=Shapes.TxN),\n", - " param('theta', shape=Shapes.TxN),\n", - " # add a parameter to simulate the rate at which vaccinated become susceptible\n", - " param('phi', shape=Shapes.TxN),\n", - " ])\n", - "\n", - " [S, I, R, V] = symbols.compartment_symbols\n", - " [β, γ, θ, φ] = symbols.attribute_symbols\n", - "\n", - " # formulate N so as to avoid dividing by zero;\n", - " # this is safe in this instance because if the denominator is zero,\n", - " # the numerator must also be zero\n", - " N = Max(1, S + I + R + V)\n", - "\n", - " return create_model(\n", - " symbols=symbols,\n", - " transitions=[\n", + "from epymorph.compartment_model import ModelSymbols\n", + "\n", + "\n", + "class Sirv(CompartmentModel):\n", + " compartments = [\n", + " compartment('S'),\n", + " compartment('I'),\n", + " compartment('R'),\n", + " compartment('V'),\n", + " ]\n", + " requirements = [\n", + " AttributeDef('beta', float, Shapes.TxN),\n", + " AttributeDef('gamma', float, Shapes.TxN),\n", + " AttributeDef('theta', float, Shapes.TxN),\n", + " # add a AttributeDef to simulate the rate at which vaccinated become susceptible\n", + " AttributeDef('phi', float, Shapes.TxN),\n", + " ]\n", + "\n", + " def edges(self, symbols: ModelSymbols) -> Sequence[TransitionDef]:\n", + " [S, I, R, V] = symbols.all_compartments\n", + " [β, γ, θ, φ] = symbols.all_requirements\n", + "\n", + " # formulate N so as to avoid dividing by zero;\n", + " # this is safe in this instance because if the denominator is zero,\n", + " # the numerator must also be zero\n", + " N = Max(1, S + I + R + V)\n", + "\n", + " return [\n", " edge(S, I, rate=3.1415926535897932384626433832795028841971693993751058209749445923078164062862089986280348253421170679821480865132823066470938446095505822317253594081284811174502841027019385211055596446229489549303819644288109756659334461284756482337867831652712019091456485669234603486104543266482133936072602491412737245870066063155881748815209209628292540917153643678925903600113305305488204665213841469519415116094330572703657595919530921861173819326117931051185480744623799627495673518857527248912279381830119491298336733624406566430860213949463952247371907021798609437027705392171762931767523846748184676694051320005681271452635608277857713427577896091736371787214684409012249534301465495853710507922796892589235420199561121290219608640344181598136297747713099605187072113499999983729780499510597317328160963185950244594553469083026425223082533446850352619311881710100031378387528865875332083814206171776691473035982534904287554687311595628638823537875937519577818577805321712268066130019278766111959092164201989380952572010654858632788659361533818279682303019520353018529689957736225994138912497217752834791315155748572424541506959508295331168617278558890750983817546374649393192550604009277016711390098488240128583616035637076601047101819429555961989467678374494482553797747268471040475346462080466842590694912933136770289891521047521620569660240580381501935112533824300355876402474964732639141992726042699227967823547816360093417216412199245863150302861829745557067498385054945885869269956909272107975093029553211653449872027559602364806654991198818347977535663698074265425278625518184175746728909777727938000816470600161452491921732172147723501414419735685481613611573525521334757418494684385233239073941433345477624168625189835694855620992192221842725502542568876717904946016534668049886272327917860857843838279679766814541009538837863609506800642251252051173929848960841284886269456042419652850222106611863067442786220391949450471237137869609563643719172874677646575739624138908658326459958133904780275900994657640789512694683983525957098258226205224894077267194782684826014769909026401363944374553050682034962524517493996514314298091906592509372216964615157098583874105978859597729754989301617539284681382686838689427741559918559252459539594310499725246808459872736446958486538367362226260991246080512438843904512441365497627807977156914359977001296160894416948685558484063534220722258284886481584560285060168427394522674676788952521385225499546667278239864565961163548862305774564980355936345681743241125150760694794510965960940252288797108931456691368672287489405601015033086179286809208747609178249385890097149096759852613655497818931297848216829989487226588048575640142704775551323796414515237462343645428584447952658678210511413547357395231134271661021359695362314429524849371871101457654035902799344037420073105785390621983874478084784896833214457138687519435064302184531910484810053706146806749192781911979399520614196634287544406437451237181921799983910159195618146751426912397489409071864942319615679452080951465502252316038819301420937621378559566389377870830390697920773467221825625996615014215030680384477345492026054146659252014974428507325186660021324340881907104863317346496514539057962685610055081066587969981635747363840525714591028970641401109712062804390397595156771577004203378699360072305587631763594218731251471205329281918261861258673215791984148488291644706095752706957220917567116722910981690915280173506712748583222871835209353965725121083579151369882091444210067510334671103141267111369908658516398315019701651511685171437657618351556508849099898599823873455283316355076479185358932261854896321329330898570642046752590709154814165498594616371802709819943099244889575712828905923233260972997120844335732654893823911932597463667305836041428138830320382490375898524374417029132765618093773444030707469211201913020330380197621101100449293215160842444859637669838952286847831235526582131449576857262433441893039686426243410773226978028073189154411010446823252716201052652272111660396665573092547110557853763466820653109896526918620564769312570586356620185581007293606598764861179104533488503461136576867532494416680396265797877185560845529654126654085306143444318586769751456614068007002378776591344017127494704205622305389945613140711270004078547332699390814546646458807972708266830634328587856983052358089330657574067954571637752542021149557615814002501262285941302164715509792592309907965473761255176567513575178296664547791745011299614890304639947132962107340437518957359614589019389713111790429782856475032031986915140287080859904801094121472213179476477726224142548545403321571853061422881375850430633217518297986622371721591607716692547487389866549494501146540628433663937900397692656721463853067360965712091807638327166416274888800786925602902284721040317211860820419000422966171196377921337575114959501566049631862947265473642523081770367515906735023507283540567040386743513622224771589150495309844489333096340878076932599397805419341447377441842631298608099888687413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* β * S * I / N),\n", " edge(I, R, rate=γ * I),\n", " edge(S, V, rate=θ * S),\n", " edge(V, S, rate=φ * V),\n", " ]\n", - " )\n", - "\n", "\n", - "debug_ipm = construct_ipm()\n", "\n", - "render(debug_ipm)" + "render(Sirv())" ] }, { @@ -227,62 +225,61 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ + "from typing import Sequence\n", + "\n", "from sympy import Max\n", "\n", "from epymorph import *\n", "from epymorph.compartment_model import *\n", - "\n", - "\n", - "def construct_ipm():\n", - " symbols = create_symbols(\n", - " compartments=[\n", - " compartment('S'),\n", - " compartment('I'),\n", - " compartment('R'),\n", - " compartment('V'),\n", - " ],\n", - " attributes=[\n", - " param('beta', shape=Shapes.TxN),\n", - " param('gamma', shape=Shapes.TxN),\n", - " param('theta', shape=Shapes.TxN),\n", - " param('es9Lw5E8pMZjfAuN1bgr0OuTGky7xQHdaFc6VoP3X2B4WnSIYzDqCtRKmJelhivU',\n", - " shape=Shapes.TxN)\n", - " ])\n", - "\n", - " [S, I, R, V] = symbols.compartment_symbols\n", - " [β, γ, θ, lorem] = symbols.attribute_symbols\n", - "\n", - " # formulate N so as to avoid dividing by zero;\n", - " # this is safe in this instance because if the denominator is zero,\n", - " # the numerator must also be zero\n", - " N = Max(1, S + I + R + V)\n", - "\n", - " return create_model(\n", - " symbols=symbols,\n", - " transitions=[\n", + "from epymorph.compartment_model import ModelSymbols\n", + "\n", + "\n", + "class Sirv(CompartmentModel):\n", + " compartments = [\n", + " compartment('S'),\n", + " compartment('I'),\n", + " compartment('R'),\n", + " compartment('V'),\n", + " ]\n", + "\n", + " requirements = [\n", + " AttributeDef('beta', float, Shapes.TxN),\n", + " AttributeDef('gamma', float, Shapes.TxN),\n", + " AttributeDef('theta', float, Shapes.TxN),\n", + " AttributeDef('es9Lw5E8pMZjfAuN1bgr0OuTGky7xQHdaFc6VoP3X2B4WnSIYzDqCtRKmJelhivU',\n", + " float, Shapes.TxN)\n", + " ]\n", + "\n", + " def edges(self, symbols: ModelSymbols) -> Sequence[TransitionDef]:\n", + " [S, I, R, V] = symbols.all_compartments\n", + " [β, γ, θ, lorem] = symbols.all_requirements\n", + "\n", + " # formulate N so as to avoid dividing by zero;\n", + " # this is safe in this instance because if the denominator is zero,\n", + " # the numerator must also be zero\n", + " N = Max(1, S + I + R + V)\n", + "\n", + " return [\n", " edge(S, I, rate=β * S * I / N),\n", " edge(I, R, rate=γ * I),\n", " edge(S, V, rate=θ * S),\n", " edge(V, S, rate=lorem * V),\n", " ]\n", - " )\n", "\n", "\n", - "debug_ipm = construct_ipm()\n", - "\n", - "render(debug_ipm)" + "render(Sirv())" ] }, { @@ -294,62 +291,61 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ + "from typing import Sequence\n", + "\n", "from sympy import Max\n", "\n", "from epymorph import *\n", "from epymorph.compartment_model import *\n", - "\n", - "\n", - "def construct_ipm():\n", - " symbols = create_symbols(\n", - " compartments=[\n", - " compartment('S'),\n", - " compartment('I'),\n", - " compartment('R'),\n", - " compartment('V'),\n", - " ],\n", - " attributes=[\n", - " param('beta', shape=Shapes.TxN),\n", - " param('gamma', shape=Shapes.TxN),\n", - " param('theta', shape=Shapes.TxN),\n", - " param('phi', shape=Shapes.TxN)\n", - " ])\n", - "\n", - " [S, I, R, V] = symbols.compartment_symbols\n", - " [β, γ, θ, φ] = symbols.attribute_symbols\n", - "\n", - " # formulate N so as to avoid dividing by zero;\n", - " # this is safe in this instance because if the denominator is zero,\n", - " # the numerator must also be zero\n", - " N = Max(1, S + I + R + V)\n", - "\n", - " return create_model(\n", - " symbols=symbols,\n", - " transitions=[\n", + "from epymorph.compartment_model import ModelSymbols\n", + "\n", + "\n", + "class Sirv(CompartmentModel):\n", + " compartments = [\n", + " compartment('S'),\n", + " compartment('I'),\n", + " compartment('R'),\n", + " compartment('V'),\n", + " ]\n", + "\n", + " requirements = [\n", + " AttributeDef('beta', float, Shapes.TxN),\n", + " AttributeDef('gamma', float, Shapes.TxN),\n", + " AttributeDef('theta', float, Shapes.TxN),\n", + " AttributeDef('phi', float, Shapes.TxN),\n", + " ]\n", + "\n", + " def edges(self, symbols: ModelSymbols) -> Sequence[TransitionDef]:\n", + " [S, I, R, V] = symbols.all_compartments\n", + " [β, γ, θ, φ] = symbols.all_requirements\n", + "\n", + " # formulate N so as to avoid dividing by zero;\n", + " # this is safe in this instance because if the denominator is zero,\n", + " # the numerator must also be zero\n", + " N = Max(1, S + I + R + V)\n", + "\n", + " return [\n", " edge(S, I, rate=β * S * I / N),\n", " edge(I, R, rate=γ * I),\n", " edge(S, V, rate=θ * S),\n", " edge(V, S, rate=φ * V),\n", " edge(V, S, rate=θ * V) # notice the repeat edge\n", " ]\n", - " )\n", - "\n", "\n", - "debug_ipm = construct_ipm()\n", "\n", - "render(debug_ipm)" + "render(Sirv())" ] } ], diff --git a/doc/devlog/2024-06-03.ipynb b/doc/devlog/2024-06-03.ipynb index d251eaee..b5ce5541 100644 --- a/doc/devlog/2024-06-03.ipynb +++ b/doc/devlog/2024-06-03.ipynb @@ -8,9 +8,7 @@ "\n", "_author: Trevor Johnson_\n", "\n", - "ADRIOMakerCensus has been refactored to utilize the recently added GeoScope class heirarchy. This notebook tests the correct functionality of Census-based ADRIOs post-refactor by creating a DynamicGeo for each granularity and populating them with every attribute that is valid for their granularity.\n", - "\n", - "Additional cases can be tested by changing the type of the scope objects, changing the year in time period or scope, or changing the includes attribute of the scope object." + "Tests ACS5 ADRIOs at a variety of granularities." ] }, { @@ -19,185 +17,233 @@ "metadata": {}, "outputs": [], "source": [ - "from epymorph.data_shape import Shapes\n", - "from epymorph.data_type import CentroidType\n", - "from epymorph.geo.adrio import adrio_maker_library\n", - "from epymorph.geo.dynamic import DynamicGeo\n", - "from epymorph.geo.spec import DynamicGeoSpec, Year\n", - "from epymorph.geography.us_census import DEFAULT_YEAR, StateScopeAll\n", - "from epymorph.simulation import AttributeDef\n", + "from epymorph import *\n", + "from epymorph.adrio import acs5, adrio, commuting_flows, us_tiger\n", + "from epymorph.data_shape import DataShapeMatcher\n", + "from epymorph.geography.us_census import (BlockGroupScope, CountyScope,\n", + " StateScope, TractScope)\n", + "from epymorph.params import ParamValue\n", + "from epymorph.simulator.data import evaluate_param\n", + "from epymorph.util import NumpyTypeError, check_ndarray, match\n", + "\n", + "# This is the expected type and shape for every attribute we're going to test.\n", + "expected: list[AttributeDef] = [\n", + " AttributeDef(\"label\", str, Shapes.N),\n", + " AttributeDef(\"population\", int, Shapes.N),\n", + " AttributeDef(\"population_by_age_table\", int, Shapes.NxA),\n", + " AttributeDef(\"population_by_age\", int, Shapes.N),\n", + " AttributeDef(\"average_household_size\", float, Shapes.N),\n", + " AttributeDef(\"dissimilarity_index\", float, Shapes.N),\n", + " AttributeDef(\"commuters\", int, Shapes.NxN),\n", + " AttributeDef(\"gini_index\", float, Shapes.N),\n", + " AttributeDef(\"median_age\", float, Shapes.N),\n", + " AttributeDef(\"median_income\", float, Shapes.N),\n", + " AttributeDef(\"pop_density_km2\", float, Shapes.N),\n", + "]\n", + "\n", + "# And here are the ADRIOs for each of those attributes.\n", + "params: dict[str, ParamValue] = {\n", + " \"label\": us_tiger.Name(),\n", + " \"population\": acs5.Population(),\n", + " \"population_by_age_table\": acs5.PopulationByAgeTable(),\n", + " \"population_by_age\": acs5.PopulationByAge(18, 24),\n", + " \"average_household_size\": acs5.AverageHouseholdSize(),\n", + " \"dissimilarity_index\": acs5.DissimilarityIndex(\"White\", \"Black\"),\n", + " \"commuters\": commuting_flows.Commuters(),\n", + " \"gini_index\": acs5.GiniIndex(),\n", + " \"median_age\": acs5.MedianAge(),\n", + " \"median_income\": acs5.MedianIncome(),\n", + " \"land_area_km2\": adrio.Scale(us_tiger.LandAreaM2(), 1e-6),\n", + " \"pop_density_km2\": adrio.PopulationPerKm2(),\n", + "}\n", "\n", - "spec = DynamicGeoSpec(\n", - " attributes=[\n", - " AttributeDef('label', str, Shapes.N),\n", - " AttributeDef('population', int, Shapes.N),\n", - " # AttributeDef('population_by_age', int, Shapes.NxA(3)),\n", - " # AttributeDef('population_by_age_x6', int, Shapes.NxA(6)),\n", - " AttributeDef('centroid', CentroidType, Shapes.N),\n", - " AttributeDef('geoid', str, Shapes.N),\n", - " AttributeDef('average_household_size', int, Shapes.N),\n", - " AttributeDef('dissimilarity_index', float, Shapes.N),\n", - " AttributeDef('commuters', int, Shapes.NxN),\n", - " AttributeDef('gini_index', float, Shapes.N),\n", - " AttributeDef('median_age', int, Shapes.N),\n", - " AttributeDef('median_income', int, Shapes.N),\n", - " AttributeDef('pop_density_km2', float, Shapes.N)\n", - " ],\n", - " time_period=Year(2020),\n", - " scope=StateScopeAll(DEFAULT_YEAR),\n", - " source={\n", - " 'label': 'Census:name',\n", - " 'population': 'Census',\n", - " # 'population_by_age': 'Census',\n", - " # 'population_by_age_x6': 'Census',\n", - " 'centroid': 'Census',\n", - " 'geoid': 'Census',\n", - " 'average_household_size': 'Census',\n", - " 'dissimilarity_index': 'Census',\n", - " 'commuters': 'Census',\n", - " 'gini_index': 'Census',\n", - " 'median_age': 'Census',\n", - " 'median_income': 'Census',\n", - " 'pop_density_km2': 'Census',\n", - " 'tract_median_income': 'Census'\n", - " }\n", - ")\n", "\n", - "geo = DynamicGeo.from_library(spec, adrio_maker_library)" + "def run_test(rume: Rume, skip: tuple[str, ...] = ()):\n", + " for attr in (a for a in expected if a.name not in skip):\n", + " actual = evaluate_param(rume, attr.name)\n", + " try:\n", + " check_ndarray(\n", + " actual,\n", + " dtype=match.dtype(attr.dtype),\n", + " shape=DataShapeMatcher(attr.shape, rume.dim, True),\n", + " )\n", + " print(f\"{attr.name}: good\")\n", + " except NumpyTypeError as e:\n", + " print(f\"{attr.name}: FAILED\")\n", + " print(e)\n", + "\n", + "\n", + "def placeholder_rume(scope, time_frame):\n", + " return SingleStrataRume.build(\n", + " ipm=ipm_library['no'](),\n", + " mm=mm_library['no'](),\n", + " init=init.NoInfection(),\n", + " scope=scope,\n", + " time_frame=time_frame,\n", + " params=params\n", + " )" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "label: good\n", + "population: good\n", + "population_by_age_table: good\n", + "population_by_age: good\n", + "average_household_size: good\n", + "dissimilarity_index: good\n", + "commuters: good\n", + "gini_index: good\n", + "median_age: good\n", + "median_income: good\n", + "pop_density_km2: good\n" + ] + } + ], "source": [ - "geo.validate()" + "rume = placeholder_rume(\n", + " scope=StateScope.all(year=2020),\n", + " time_frame=TimeFrame.year(2020),\n", + ")\n", + "\n", + "run_test(rume)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "label: good\n", + "population: good\n", + "population_by_age_table: good\n", + "population_by_age: good\n", + "average_household_size: good\n", + "dissimilarity_index: good\n", + "commuters: good\n", + "gini_index: good\n", + "median_age: good\n", + "median_income: good\n", + "pop_density_km2: good\n" + ] + } + ], "source": [ - "from dataclasses import replace\n", - "\n", - "from epymorph.geography.us_census import (BlockGroupScope, CountyScope,\n", - " StateScope, TractScope)\n", + "rume = placeholder_rume(\n", + " scope=StateScope.in_states(['04', '08'], year=2020),\n", + " time_frame=TimeFrame.year(2020),\n", + ")\n", "\n", - "spec = replace(spec, scope=StateScope.in_states(['04', '08']))\n", - "geo = DynamicGeo.from_library(spec, adrio_maker_library)" + "run_test(rume)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [], - "source": [ - "geo.validate()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "spec = replace(spec, scope=CountyScope.in_counties(['35001', '04013', '04017']))\n", - "geo = DynamicGeo.from_library(spec, adrio_maker_library)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "geo.validate()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "label: good\n", + "population: good\n", + "population_by_age_table: good\n", + "population_by_age: good\n", + "average_household_size: good\n", + "dissimilarity_index: good\n", + "commuters: good\n", + "gini_index: good\n", + "median_age: good\n", + "median_income: good\n", + "pop_density_km2: good\n" + ] + } + ], "source": [ - "spec = replace(spec, scope=TractScope.in_tracts(['35001000720', '35001000904', '35001000906',\n", - " '04027011405', '04027011407']), attributes=[\n", - " AttributeDef('label', str, Shapes.N),\n", - " AttributeDef('population', int, Shapes.N),\n", - " # AttributeDef('population_by_age', int, Shapes.NxA(3)),\n", - " # AttributeDef('population_by_age_x6', int, Shapes.NxA(6)),\n", - " AttributeDef('centroid', CentroidType, Shapes.N),\n", - " AttributeDef('geoid', str, Shapes.N),\n", - " AttributeDef('average_household_size', int, Shapes.N),\n", - " AttributeDef('dissimilarity_index', float, Shapes.N),\n", - " AttributeDef('gini_index', float, Shapes.N),\n", - " AttributeDef('median_age', int, Shapes.N),\n", - " AttributeDef('median_income', int, Shapes.N),\n", - " AttributeDef('pop_density_km2', float, Shapes.N)\n", - "])\n", + "rume = placeholder_rume(\n", + " scope=CountyScope.in_counties(['35001', '04013', '04017'], year=2020),\n", + " time_frame=TimeFrame.year(2020),\n", + ")\n", "\n", - "geo = DynamicGeo.from_library(spec, adrio_maker_library)" + "run_test(rume)" ] }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# tract and block group geos fetch shape file attributes prior to validating so that the kernel\n", - "# is not overwhelmed by several large shapefile requests in parallel\n", - "geo['centroid']\n", - "geo['pop_density_km2']\n", - "geo.validate()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "label: good\n", + "population: good\n", + "population_by_age_table: good\n", + "population_by_age: good\n", + "average_household_size: good\n", + "dissimilarity_index: good\n", + "gini_index: good\n", + "median_age: good\n", + "median_income: good\n", + "pop_density_km2: good\n" + ] + } + ], "source": [ - "spec = replace(spec, scope=BlockGroupScope.in_block_groups(['350010007201', '350010009041', '350010009061',\n", - " '040270114053', '040270114072']), attributes=[\n", - " AttributeDef('label', str, Shapes.N),\n", - " AttributeDef('population', int, Shapes.N),\n", - " # AttributeDef('population_by_age', int, Shapes.NxA(3)),\n", - " # AttributeDef('population_by_age_x6', int, Shapes.NxA(6)),\n", - " AttributeDef('centroid', CentroidType, Shapes.N),\n", - " AttributeDef('geoid', str, Shapes.N),\n", - " AttributeDef('average_household_size', int, Shapes.N),\n", - " AttributeDef('gini_index', float, Shapes.N),\n", - " AttributeDef('median_age', int, Shapes.N),\n", - " AttributeDef('median_income', int, Shapes.N),\n", - " AttributeDef('pop_density_km2', float, Shapes.N),\n", - " AttributeDef('tract_median_income', int, Shapes.N)\n", - "])\n", + "rume = placeholder_rume(\n", + " scope=TractScope.in_tracts([\n", + " '35001000720', '35001000904', '35001000906', '04027011405', '04027011407'\n", + " ], year=2020),\n", + " time_frame=TimeFrame.year(2020),\n", + ")\n", "\n", - "geo = DynamicGeo.from_library(spec, adrio_maker_library)" + "run_test(rume, skip=(\"commuters\",))" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Gini Index cannot be retrieved for block group level, fetching tract level data instead.\n" + "label: good\n", + "population: good\n", + "population_by_age_table: good\n", + "population_by_age: good\n", + "average_household_size: good\n", + "Gini Index cannot be retrieved for block group level, fetching tract level data instead.\n", + "gini_index: good\n", + "median_age: good\n", + "median_income: good\n", + "pop_density_km2: good\n" ] } ], "source": [ - "geo['centroid']\n", - "geo['pop_density_km2']\n", - "geo.validate()" + "rume = placeholder_rume(\n", + " scope=BlockGroupScope.in_block_groups([\n", + " '350010007201', '350010009041', '350010009061', '040270114053', '040270114072'\n", + " ], year=2020),\n", + " time_frame=TimeFrame.year(2020),\n", + ")\n", + "\n", + "run_test(rume, skip=(\"commuters\", \"dissimilarity_index\"))" ] } ], diff --git a/doc/devlog/2024-06-05.ipynb b/doc/devlog/2024-06-05.ipynb index 5eee6db3..98e9e4c3 100644 --- a/doc/devlog/2024-06-05.ipynb +++ b/doc/devlog/2024-06-05.ipynb @@ -44,55 +44,70 @@ "source": [ "### **Basic Queries**\n", "\n", - "The 'label' and 'commuters' are two simple yet imperative queries that show the basic functionality of the LODES ADRIO maker. The 'label' query represents the GEOIDs that are involved with the commuter matrices. The input given by the user for the scope, in this case being states, is translated into a list of GEOIDs. The 'commuters' query shows the total number of workers moving from a home GEOID to a work GEOID as a matrix. The matrix is read so that the rows represent the residence GEOID and the columns are the work location GEOID." + "The Commuters and Geoid calls are two simple yet imperative queries that show the basic functionality of the LODES ADRIO maker. The Geoid query represents the GEOIDs that are involved with the commuter matrices. The input given by the user for the scope, in this case being states, is translated into a list of GEOIDs. The Commuters query shows the total number of workers moving from a home GEOID to a work GEOID as a matrix. The matrix is read so that the rows represent the residence GEOID and the columns are the work location GEOID." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, + "outputs": [], + "source": [ + "from unittest.mock import Mock\n", + "\n", + "import numpy as np\n", + "\n", + "from epymorph.data_shape import SimDimensions\n", + "from epymorph.geography.us_census import StateScope\n", + "from epymorph.simulation import NamespacedAttributeResolver\n", + "\n", + "state_scope = StateScope.in_states_by_code([\"AZ\", \"CO\", \"NV\", \"NM\"])\n", + "\n", + "data = Mock(spec=NamespacedAttributeResolver)\n", + "dim = Mock(spec=SimDimensions)\n", + "rng = Mock(spec=np.random.Generator)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from epymorph.adrio import adrio, lodes\n", + "\n", + "time_period = 2015\n", + "commuters = lodes.Commuters(time_period)\n", + "geoids = adrio.NodeId()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Home/Work GEOIDs: ['04' '08' '32' '35']\n", + "Home/Work GEOIDs:\n", + " ['04' '08' '32' '35']\n", "\n", "Commuters Matrix:\n", " [[2550132 2582 13263 8100]\n", " [ 1202 2405258 382 5557]\n", " [ 3552 535 1179411 361]\n", - " [ 6813 4824 409 764244]]\n" + " [ 6813 4824 409 764244]]\n", + "\n" ] } ], "source": [ - "from epymorph.data_shape import Shapes\n", - "from epymorph.geo.adrio import adrio_maker_library\n", - "from epymorph.geo.dynamic import DynamicGeo\n", - "from epymorph.geo.spec import DynamicGeoSpec, Year\n", - "from epymorph.geography.us_census import StateScope\n", - "from epymorph.simulation import AttributeDef\n", - "\n", - "spec = DynamicGeoSpec(\n", - " attributes=[\n", - " AttributeDef('label', str, Shapes.N),\n", - " AttributeDef('commuters', int, Shapes.NxN),\n", - " ],\n", - " time_period=Year(2015),\n", - " scope=StateScope.in_states_by_code([\"AZ\", \"CO\", \"NV\", \"NM\"]),\n", - " source={\n", - " 'label': 'LODES:geoid',\n", - " 'commuters': 'LODES'\n", - " }\n", - ")\n", - "\n", - "geo = DynamicGeo.from_library(spec, adrio_maker_library)\n", - "\n", + "print(\n", + " f\"Home/Work GEOIDs:\\n {geoids.evaluate_in_context(data, dim, state_scope, rng)}\\n\")\n", "\n", - "print(f\"Home/Work GEOIDs: {geo['label']}\\n\")\n", - "\n", - "print(f\"Commuters Matrix:\\n {geo['commuters']}\")" + "print(\n", + " f\"Commuters Matrix:\\n {commuters.evaluate_in_context(data, dim, state_scope, rng)}\\n\")" ] }, { @@ -101,30 +116,30 @@ "source": [ "# Attributes\n", "\n", - "The 'commuters' attribute outputs the total number of workers commuting, but LODES provides three categories for attributes specifying the type of workers: Age, Monthly Income, and Industry Sectors. Within each category, there are three ranges within them, and the sum of the ranges equals the total number of workers. All of these categories and the total commuters are displayed as NxN matrices of integers, excluding the label query.\n", + "The Commuters class outputs the total number of workers commuting, but LODES provides three categories for attributes specifying the type of workers: Age, Monthly Income, and Industry Sectors. Within each category, there are three ranges within them, and the sum of the ranges equals the total number of workers. All of these categories and the total commuters are displayed as NxN matrices of integers, excluding the Geoid query.\n", "\n", "## Age\n", - "- 'commuters_29_under'\n", + "- '29 and Under'\n", " - Commuters that are ages 29 and under.\n", - "- 'commuters_30_to_54\n", + "- ''30_54'\n", " - Commuters that are between the ages of 30 and 54.\n", - "- 'commuters_55_over'\n", + "- '55 and Over'\n", " - Commuters that are ages 55 and over.\n", "\n", "## Monthly Income\n", - "- 'commuters_1250_under_earnings'\n", + "- '$1250 and Under'\n", " - Commuters that earn $1250 and under per month.\n", - "- 'commuters_1251_to_3333_earnings'\n", + "- '$1251_$3333'\n", " - Commuters that earn between $1251 and $3333 per month.\n", - "- 'commuters_3333_over_earnings'\n", + "- '$3333 and Over'\n", " - Commuters that earn over $3333 per month.\n", "\n", "## Industry Sector\n", - "- 'commuters_goods_producing_industry'\n", + "- 'Goods Producing'\n", " - Commuters that work in Goods Producing industry sectors.\n", - "- 'commuters_trade_transport_utility_industry'\n", + "- 'Trade Transport Utility'\n", " - Commuters that work in Trade, Transportation, and Utility industry sectors.\n", - "- 'commuters_other_industry'\n", + "- 'Other'\n", " - Commuters that work under all other service industry sectors other than the above claimed industries.\n" ] }, @@ -133,32 +148,53 @@ "metadata": {}, "source": [ "## Job Type\n", - "Along with the above categories, LODES provides files detailing different job types and the total number of jobs under that type. However, unlike the attributes, these matrices do not sum to be the total number of workers. \n", + "Along with the above categories, LODES provides files detailing different job types and the total number of jobs under that type. However, unlike the attributes, these matrices do not sum to be the total number of workers.\n", "\n", - "- 'all_jobs'\n", + "- 'All Jobs'\n", " - All jobs regardless of job type. Allows for multiple jobs per person and is the default when calling the above attributes.\n", - "- 'primary_jobs'\n", + "- 'Primary Jobs'\n", " - Primary jobs, which a primary job is the highest paying job for an individual worker for the year. Limits to one job per worker.\n", - "- 'all_private_jobs'\n", + "- 'All Private Jobs'\n", " - All private jobs, which are privately owned businesses and organizations excluding federal government jobs.\n", - "- 'private_primary_jobs'\n", + "- 'Private Primary Jobs'\n", " - Primary jobs within the private sector.\n", - "- 'all_federal_jobs'\n", + "- 'All Federal Jobs'\n", " - All jobs within the federal government sector.\n", - "- 'federal_primary_jobs\n", - " - Jobs under the federal government sector that are defined as primary jobs." + "- 'Federal Primary Jobs\n", + " - Jobs under the federal government sector that are defined as primary jobs.\n", + "\n", + "Job type is an additional variable that can be combined with the previously explained attributes. For example, a user may retrieve a matrix of commuters work for the Goods Producing industry sector for only Primary jobs. This variable is not required for all calls and the default value will be 'All Jobs'." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "### Age Range Attribute Example\n", + "\n", "Below is an example of calling the three different age ranges provided by LODES in a geo spec. The example here loads four counties into the matrices rather than the four states that was used previously." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from epymorph.adrio.lodes import CommutersByAge\n", + "from epymorph.geography.us_census import CountyScope\n", + "\n", + "time_period = 2015\n", + "county_scope = CountyScope.in_counties([\"04013\", \"08041\", \"32003\", \"35001\"])\n", + "\n", + "commuters_29_under = CommutersByAge(time_period, '29 and Under')\n", + "commuters_30_54 = CommutersByAge(time_period, '30_54')\n", + "commuters_55_over = CommutersByAge(time_period, '55 and Over')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -187,37 +223,12 @@ } ], "source": [ - "from epymorph.data_shape import Shapes\n", - "from epymorph.geo.adrio import adrio_maker_library\n", - "from epymorph.geo.dynamic import DynamicGeo\n", - "from epymorph.geo.spec import DynamicGeoSpec, Year\n", - "from epymorph.geography.us_census import CountyScope\n", - "from epymorph.simulation import AttributeDef\n", - "\n", - "spec = DynamicGeoSpec(\n", - " attributes=[\n", - " AttributeDef('label', str, Shapes.N),\n", - " AttributeDef('commuters_29_under', int, Shapes.NxN),\n", - " AttributeDef('commuters_30_to_54', int, Shapes.NxN),\n", - " AttributeDef('commuters_55_over', int, Shapes.NxN),\n", - " ],\n", - " time_period=Year(2015),\n", - " scope=CountyScope.in_counties([\"04013\", \"08041\", \"32003\", \"35001\"]),\n", - " source={\n", - " 'label': 'LODES:geoid',\n", - " 'commuters_29_under': 'LODES',\n", - " 'commuters_30_to_54': 'LODES',\n", - " 'commuters_55_over': 'LODES',\n", - " }\n", - ")\n", - "\n", - "geo = DynamicGeo.from_library(spec, adrio_maker_library)\n", - "\n", - "print(f\"Commuters ages 29 and under:\\n {geo['commuters_29_under']}\\n\")\n", - "\n", - "print(f\"Commuters between ages 30 and 54:\\n {geo['commuters_30_to_54']}\\n\")\n", - "\n", - "print(f\"Commuters ages 55 and over:\\n {geo['commuters_55_over']}\\n\")" + "print(\n", + " f\"Commuters ages 29 and under:\\n {commuters_29_under.evaluate_in_context(data, dim, county_scope, rng)}\\n\")\n", + "print(\n", + " f\"Commuters between ages 30 and 54:\\n {commuters_30_54.evaluate_in_context(data, dim, county_scope, rng)}\\n\")\n", + "print(\n", + " f\"Commuters ages 55 and over:\\n {commuters_55_over.evaluate_in_context(data, dim, county_scope, rng)}\\n\")" ] } ], diff --git a/doc/devlog/2024-06-12.ipynb b/doc/devlog/2024-06-12.ipynb index f7a4c03c..32bd33b3 100644 --- a/doc/devlog/2024-06-12.ipynb +++ b/doc/devlog/2024-06-12.ipynb @@ -10,7 +10,9 @@ "\n", "We have a new class of ADRIO capable of loading in data from CSV files with shapes N, TxN, and NxN. CSV files used must have a column (or two in the NxN case) to identify geographic location and a column containing the relevant data. A time column in YYYY-MM-DD format must also be included if loading in time-series data. Available formats for geographic identifiers are state_abbrev (AZ), county_state, (Maricopa, Arizona), and geoid (04013).\n", "\n", - "The following notebook creates a series of incorrectly sorted CSV files with various data formats and geographic identifiers, then creates geos with CSV ADRIOs to load the data into NDArrays. These geos also contain Census ADRIOs that fetch identical data and are used as a source of truth as to whether the CSV ADRIOs fetched, filtered, and sorted their data correctly." + "The following notebook creates a series of incorrectly sorted CSV files with various data formats and geographic identifiers, then creates CSV ADRIOs to load the data into NDArrays. Census ADRIOs are also created that fetch identical data and are used as a source of truth as to whether the CSV ADRIOs fetched, filtered, and sorted their data correctly.\n", + "\n", + "## Creating sample .csv files" ] }, { @@ -22,280 +24,358 @@ "from datetime import date, datetime\n", "from pathlib import Path\n", "\n", - "from numpy import array_equal\n", - "from pandas import DataFrame, concat, read_csv\n", + "import numpy as np\n", + "from pandas import DataFrame, read_csv\n", "\n", - "from epymorph.data_shape import Shapes\n", - "from epymorph.geo.adrio import adrio_maker_library\n", - "from epymorph.geo.adrio.census.adrio_census import ADRIOMakerCensus\n", - "from epymorph.geo.adrio.file.adrio_csv import (CSVSpec, CSVSpecMatrix,\n", - " CSVSpecTime)\n", - "from epymorph.geo.dynamic import DynamicGeo\n", - "from epymorph.geo.spec import DynamicGeoSpec, Year\n", + "from epymorph import *\n", + "from epymorph.adrio import acs5, commuting_flows, csv\n", "from epymorph.geography.us_census import (STATE, CountyScope, StateScope,\n", " get_us_counties, get_us_states)\n", - "from epymorph.simulation import AttributeDef\n", - "\n", - "# create and store 'pei_population.csv'\n", - "census_maker = ADRIOMakerCensus()\n", - "states_list = ['AZ', 'FL', 'GA', 'MD', 'NY', 'NC', 'SC', 'VA']\n", - "population = census_maker.make_adrio(\n", - " AttributeDef('population', int, Shapes.N),\n", - " StateScope.in_states_by_code(states_list),\n", - " Year(2015),\n", - ")\n", - "df = DataFrame({'label': states_list, 'population': population.get_value()})\n", - "df.sort_values(by='population', inplace=True)\n", - "df.to_csv(\"./scratch/pei_population.csv\", header=False, index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "spec = DynamicGeoSpec(\n", - " attributes=[\n", - " AttributeDef('label', str, Shapes.N),\n", - " AttributeDef('population', int, Shapes.N),\n", - " AttributeDef('population_census', int, Shapes.N)\n", - " ],\n", - " time_period=Year(2015),\n", - " scope=StateScope.in_states(['12', '13', '24', '37', '45', '51']),\n", - " source={\n", - " 'label': 'Census:name',\n", - " 'population': CSVSpec(file_path=Path(\"./scratch/pei_population.csv\"),\n", - " key_col=0, data_col=1, key_type=\"state_abbrev\", skiprows=None),\n", - " 'population_census': 'Census:population'\n", - " }\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "geo = DynamicGeo.from_library(spec, adrio_maker_library)\n", + "from epymorph.simulation import TimeFrame\n", + "from epymorph.simulator.data import evaluate_param\n", "\n", - "# validate geo and ensure both ADRIOs fetched identical data\n", - "geo.validate()\n", - "if not array_equal(geo['population'], geo['population_census']):\n", - " raise Exception(\"Data not equal.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: resurrect this test once we have a replacement for population_by_age\n", - "# # create and store 'us_sw_counties_population.csv'\n", - "\n", - "# # get commuters data from asc5\n", - "# states_list = ['04', '08', '49', '35', '32']\n", - "# population_2015 = census_maker.make_adrio(\n", - "# AttributeDef('population_by_age', int, Shapes.NxA(3)),\n", - "# CountyScope.in_states(states_list),\n", - "# Year(2015),\n", - "# ).get_value()\n", - "\n", - "# # get county and state info from shapefiles and convert to dataframes\n", - "# counties_info = get_us_counties(2010)\n", - "# states_info = get_us_states(2010)\n", - "# counties_info_df = DataFrame({\n", - "# 'state_geoid': [STATE.extract(county_id) for county_id in counties_info.geoid],\n", - "# 'geoid': counties_info.geoid,\n", - "# 'name': counties_info.name,\n", - "# })\n", - "# states_info_df = DataFrame({\n", - "# 'state_geoid': states_info.geoid,\n", - "# 'state_name': states_info.name,\n", - "# })\n", - "\n", - "# # merge dataframes and create \"County, State\" name column\n", - "# merged_df = counties_info_df.merge(states_info_df, on='state_geoid')\n", - "# merged_df['county_name'] = merged_df['name'] + \", \" + merged_df['state_name']\n", - "# merged_df = merged_df.loc[merged_df['state_geoid'].isin(states_list)]\n", - "\n", - "# # create and merge dataframes to be converted to csvs\n", - "# df = DataFrame({\n", - "# 'Date': [date(2015, 1, 1) for i in merged_df.index],\n", - "# 'County': merged_df['county_name'],\n", - "# 'Young': [pop[0] for pop in population_2015],\n", - "# 'Adult': [pop[1] for pop in population_2015],\n", - "# 'Elderly': [pop[2] for pop in population_2015],\n", - "# })\n", - "\n", - "# # sort incorrectly and store as csv\n", - "# df.sort_values('Young', inplace=True)\n", - "# df.to_csv(\"./scratch/us_sw_counties_population.csv\", index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# spec = DynamicGeoSpec(\n", - "# attributes=[\n", - "# AttributeDef('label', str, Shapes.N),\n", - "# AttributeDef('population', int, Shapes.N),\n", - "# AttributeDef('population_0-19', int, Shapes.N),\n", - "# AttributeDef('population_20-64', int, Shapes.N),\n", - "# AttributeDef('population_65+', int, Shapes.N),\n", - "# AttributeDef('population_by_age', int, Shapes.NxA(3))\n", - "# ],\n", - "# time_period=Year(2015),\n", - "# scope=CountyScope.in_states(['04', '08', '49', '35', '32']),\n", - "# source={\n", - "# 'label': 'Census:name',\n", - "# 'population': 'Census',\n", - "# 'population_0-19': CSVSpec(file_path=Path(\"./scratch/us_sw_counties_population.csv\"),\n", - "# key_col=1, data_col=2, key_type=\"county_state\", skiprows=1),\n", - "# 'population_20-64': CSVSpec(file_path=Path(\"./scratch/us_sw_counties_population.csv\"),\n", - "# key_col=1, data_col=3, key_type=\"county_state\", skiprows=1),\n", - "# 'population_65+': CSVSpec(file_path=Path(\"./scratch/us_sw_counties_population.csv\"),\n", - "# key_col=1, data_col=4, key_type=\"county_state\", skiprows=1),\n", - "# 'population_by_age': 'Census'\n", - "# }\n", - "# )" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# geo = DynamicGeo.from_library(spec, adrio_maker_library)\n", - "\n", - "# geo.validate()\n", - "\n", - "# census_df = DataFrame({\n", - "# 'Young': [pop[0] for pop in geo['population_by_age']],\n", - "# 'Adult': [pop[1] for pop in geo['population_by_age']],\n", - "# 'Elderly': [pop[2] for pop in geo['population_by_age']],\n", - "# })\n", - "# if not array_equal(geo['population_0-19'], census_df['Young']):\n", - "# raise Exception(\"Young data not equal.\")\n", - "# if not array_equal(geo['population_20-64'], census_df['Adult']):\n", - "# raise Exception(\"Adult data not equal.\")\n", - "# if not array_equal(geo['population_65+'], census_df['Elderly']):\n", - "# raise Exception(\"Elderly data not equal.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# create and store 'vaccination_time_series.csv'\n", - "fips = '\\'' + '\\',\\''.join(['08001', '35001', '04013', '04017']) + '\\''\n", - "url = f\"https://data.cdc.gov/resource/8xkx-amqh.csv?$select=date,fips,series_complete_yes&$where=fips%20in({fips})&$limit=1962781\"\n", - "df = read_csv(url, dtype={'fips': str})\n", "\n", - "df['date'] = [datetime.fromisoformat(\n", - " week.replace('/', '-')).date() for week in df['date']]\n", + "def placeholder_rume(scope, time_frame, params):\n", + " return SingleStrataRume.build(\n", + " ipm=ipm_library['no'](),\n", + " mm=mm_library['no'](),\n", + " init=init.NoInfection(),\n", + " scope=scope,\n", + " time_frame=time_frame,\n", + " params=params\n", + " )\n", + "\n", + "\n", + "def create_pei_population() -> None:\n", + " # create 'pei_population.csv' if it doesn't exist\n", + " if Path(\"./scratch/pei_population.csv\").exists():\n", + " return\n", + "\n", + " states_list = ['AZ', 'FL', 'GA', 'MD', 'NY', 'NC', 'SC', 'VA']\n", + " scope = StateScope.in_states_by_code(states_list, year=2015)\n", + "\n", + " rume = placeholder_rume(scope, TimeFrame.year(2015), {\n", + " \"population\": acs5.Population()\n", + " })\n", + " result = evaluate_param(rume, \"population\")\n", + "\n", + " df = DataFrame({'label': states_list, 'population': result})\n", + " df.sort_values(by='population', inplace=True)\n", + " df.to_csv(\"./scratch/pei_population.csv\", header=False, index=False)\n", + "\n", + "\n", + "def create_us_sw_counties_population() -> None:\n", + " # create 'us_sw_counties_population.csv' if it doesn't exist\n", + " if Path(\"./scratch/us_sw_counties_population.csv\").exists():\n", + " return\n", + "\n", + " # get commuters data from asc5\n", + " states_list = ['04', '08', '49', '35', '32']\n", + " scope = CountyScope.in_states(states_list, year=2015)\n", + " rume = placeholder_rume(scope, TimeFrame.year(2015), {\n", + " 'population_by_age_table': acs5.PopulationByAgeTable(),\n", + " 'population_00-19': acs5.PopulationByAge(0, 19),\n", + " 'population_20-64': acs5.PopulationByAge(20, 64),\n", + " 'population_65-79': acs5.PopulationByAge(65, 79),\n", + " })\n", + "\n", + " young = evaluate_param(rume, 'population_00-19')\n", + " adult = evaluate_param(rume, 'population_20-64')\n", + " elderly = evaluate_param(rume, 'population_65-79')\n", + "\n", + " # get county and state info from shapefiles and convert to dataframes\n", + " counties_info = get_us_counties(2010)\n", + " states_info = get_us_states(2010)\n", + " counties_info_df = DataFrame({\n", + " 'state_geoid': [STATE.extract(county_id) for county_id in counties_info.geoid],\n", + " 'geoid': counties_info.geoid,\n", + " 'name': counties_info.name,\n", + " })\n", + " states_info_df = DataFrame({\n", + " 'state_geoid': states_info.geoid,\n", + " 'state_name': states_info.name,\n", + " })\n", + "\n", + " # merge dataframes and create \"County, State\" name column\n", + " merged_df = counties_info_df.merge(states_info_df, on='state_geoid')\n", + " merged_df['county_name'] = merged_df['name'] + \", \" + merged_df['state_name']\n", + " merged_df = merged_df.loc[merged_df['state_geoid'].isin(states_list)]\n", + "\n", + " # create and merge dataframes to be converted to csvs\n", + " df = DataFrame({\n", + " 'Date': [date(2015, 1, 1) for i in merged_df.index],\n", + " 'County': merged_df['county_name'],\n", + " 'Young': young,\n", + " 'Adult': adult,\n", + " 'Elderly': elderly,\n", + " })\n", + "\n", + " # sort incorrectly and store as csv\n", + " df.sort_values('Young', inplace=True)\n", + " df.to_csv(\"./scratch/us_sw_counties_population.csv\", index=False)\n", + "\n", + "\n", + "def create_vaccination_time_series() -> None:\n", + " # create 'vaccination_time_series.csv' if it doesn't exist\n", + " if Path(\"./scratch/vaccination_time_series.csv\").exists():\n", + " return\n", + "\n", + " fips = \",\".join(f\"'{node}'\" for node in ['08001', '35001', '04013', '04017'])\n", + " url = f\"https://data.cdc.gov/resource/8xkx-amqh.csv?$select=date,fips,series_complete_yes&$where=fips%20in({fips})&$limit=1962781\"\n", + " df = read_csv(url, dtype={'fips': str})\n", + "\n", + " df['date'] = [\n", + " datetime.fromisoformat(week.replace('/', '-')).date()\n", + " for week in df['date']\n", + " ]\n", + "\n", + " df = df[\n", + " (df['date'] >= date(2021, 1, 1)) &\n", + " (df['date'] <= date(2021, 12, 31))\n", + " ]\n", + "\n", + " df.to_csv(\"./scratch/vaccination_time_series.csv\", index=False)\n", + "\n", + "\n", + "def create_counties_commuters() -> None:\n", + " # create 'counties_commuters_2020.csv' if it doesn't exist\n", + " if Path(\"./scratch/counties_commuters_2020.csv\").exists():\n", + " return None\n", + "\n", + " scope = CountyScope.in_counties(['08001', '35001', '04013', '04017'], year=2020)\n", + " rume = placeholder_rume(\n", + " scope=scope,\n", + " time_frame=TimeFrame.year(2020),\n", + " params={\n", + " \"commuters\": commuting_flows.Commuters(),\n", + " }\n", + " )\n", "\n", - "df = df[df['date'] >= date(2021, 1, 1)]\n", - "df = df[df['date'] <= date(2021, 12, 31)]\n", + " commuters = evaluate_param(rume, \"commuters\")\n", "\n", - "df.to_csv('./scratch/vaccination_time_series.csv', index=False)" + " # Convert square numpy array to DataFrame:\n", + " geoids = scope.get_node_ids()\n", + " home, work = np.meshgrid(geoids, geoids, indexing='ij')\n", + " df = DataFrame({\n", + " \"res_geoid\": home.flatten(),\n", + " \"wrk_geoid\": work.flatten(),\n", + " \"workers\": commuters.flatten()\n", + " })\n", + " df.sort_values(by='workers', inplace=True)\n", + " df.to_csv(\n", + " './scratch/counties_commuters_2020.csv',\n", + " columns=['res_geoid', 'wrk_geoid', 'workers'],\n", + " index=False,\n", + " )\n", + "\n", + "\n", + "create_pei_population()\n", + "create_us_sw_counties_population()\n", + "create_vaccination_time_series()\n", + "create_counties_commuters()" ] }, { - "cell_type": "code", - "execution_count": 8, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "spec = DynamicGeoSpec(\n", - " attributes=[\n", - " AttributeDef('label', str, Shapes.N),\n", - " AttributeDef('population', int, Shapes.N),\n", - " AttributeDef('vaccinations', int, Shapes.TxN),\n", - " ],\n", - " time_period=Year(2021),\n", - " scope=CountyScope.in_counties(['08001', '04013', '35001']),\n", - " source={\n", - " 'label': 'Census:name',\n", - " 'population': 'Census',\n", - " 'vaccinations': CSVSpecTime(file_path=Path(\"./scratch/vaccination_time_series.csv\"),\n", - " time_col=0, key_col=1, data_col=2, key_type=\"geoid\", skiprows=1),\n", - " }\n", - ")" + "## Load .csvs with ADRIOs and compare with known values" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓\n" + ] + } + ], "source": [ - "geo = DynamicGeo.from_library(spec, adrio_maker_library)\n", + "# Check pei_population.csv\n", + "rume = placeholder_rume(\n", + " scope=StateScope.in_states(['12', '13', '24', '37', '45', '51'], year=2015),\n", + " time_frame=TimeFrame.year(2015),\n", + " params={\n", + " \"csv_result\": csv.CSV(\n", + " file_path=Path(\"./scratch/pei_population.csv\"),\n", + " key_col=0,\n", + " data_col=1,\n", + " data_type=np.int64,\n", + " key_type=\"state_abbrev\",\n", + " skiprows=None\n", + " ),\n", + " \"census_result\": acs5.Population(),\n", + " }\n", + ")\n", "\n", - "geo.validate()" + "if np.array_equal(\n", + " evaluate_param(rume, \"csv_result\"),\n", + " evaluate_param(rume, \"census_result\")\n", + "):\n", + " print(\"✓\")\n", + "else:\n", + " raise Exception(\"Data not equal.\")" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓\n", + "✓\n", + "✓\n" + ] + } + ], "source": [ - "# create and store 'counties_commuters_2020.csv'\n", - "counties_list = ['08001', '35001', '04013', '04017']\n", - "df = census_maker.fetch_commuters(CountyScope.in_counties(counties_list), 2020)\n", - "df['res_geoid'] = df['res_state_code'] + df['res_county_code']\n", - "df['wrk_geoid'] = df['wrk_state_code'].apply(lambda x: x[1:]) + df['wrk_county_code']\n", + "# Check us_sw_counties_population.csv\n", + "rume = placeholder_rume(\n", + " scope=CountyScope.in_states(['04', '08', '49', '35', '32'], year=2015),\n", + " time_frame=TimeFrame.year(2015),\n", + " params={\n", + " \"young_csv\": csv.CSV(\n", + " file_path=Path(\"./scratch/us_sw_counties_population.csv\"),\n", + " key_col=1,\n", + " data_col=2,\n", + " data_type=np.int64,\n", + " key_type=\"county_state\",\n", + " skiprows=1\n", + " ),\n", + " \"adult_csv\": csv.CSV(\n", + " file_path=Path(\"./scratch/us_sw_counties_population.csv\"),\n", + " key_col=1,\n", + " data_col=3,\n", + " data_type=np.int64,\n", + " key_type=\"county_state\",\n", + " skiprows=1\n", + " ),\n", + " \"elderly_csv\": csv.CSV(\n", + " file_path=Path(\"./scratch/us_sw_counties_population.csv\"),\n", + " key_col=1,\n", + " data_col=4,\n", + " data_type=np.int64,\n", + " key_type=\"county_state\",\n", + " skiprows=1\n", + " ),\n", + " \"population_by_age_table\": acs5.PopulationByAgeTable(),\n", + " \"young_census\": acs5.PopulationByAge(0, 19),\n", + " \"adult_census\": acs5.PopulationByAge(20, 64),\n", + " \"elderly_census\": acs5.PopulationByAge(65, 79),\n", + " }\n", + ")\n", "\n", - "df.sort_values(by='workers', inplace=True)\n", + "if np.array_equal(\n", + " evaluate_param(rume, \"young_csv\"),\n", + " evaluate_param(rume, \"young_census\")\n", + "):\n", + " print(\"✓\")\n", + "else:\n", + " raise Exception(\"Young data not equal.\")\n", "\n", - "df.to_csv('./scratch/counties_commuters_2020.csv',\n", - " columns=['res_geoid', 'wrk_geoid', 'workers'], index=False)" + "if np.array_equal(\n", + " evaluate_param(rume, \"adult_csv\"),\n", + " evaluate_param(rume, \"adult_census\")\n", + "):\n", + " print(\"✓\")\n", + "else:\n", + " raise Exception(\"Adult data not equal.\")\n", + "\n", + "if np.array_equal(\n", + " evaluate_param(rume, \"elderly_csv\"),\n", + " evaluate_param(rume, \"elderly_census\")\n", + "):\n", + " print(\"✓\")\n", + "else:\n", + " raise Exception(\"Elderly data not equal.\")" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓\n" + ] + } + ], "source": [ - "spec = DynamicGeoSpec(\n", - " attributes=[\n", - " AttributeDef('label', str, Shapes.N),\n", - " AttributeDef('population', int, Shapes.N),\n", - " AttributeDef('commuters', int, Shapes.NxN),\n", - " AttributeDef('commuters_census', int, Shapes.NxN)\n", - " ],\n", - " time_period=Year(2020),\n", - " scope=CountyScope.in_counties(['35001', '04013', '04017']),\n", - " source={\n", - " 'label': 'Census:name',\n", - " 'population': 'Census',\n", - " 'commuters': CSVSpecMatrix(file_path=Path(\"./scratch/counties_commuters_2020.csv\"),\n", - " from_key_col=0, to_key_col=1, data_col=2, key_type=\"geoid\", skiprows=1),\n", - " 'commuters_census': 'Census:commuters'\n", + "# Check vaccination_time_series.csv\n", + "rume = placeholder_rume(\n", + " scope=CountyScope.in_counties(['08001', '04013', '35001'], year=2021),\n", + " time_frame=TimeFrame.year(2021),\n", + " params={\n", + " \"vax_csv\": csv.CSVTimeSeries(\n", + " file_path=Path(\"./scratch/vaccination_time_series.csv\"),\n", + " time_col=0,\n", + " time_frame=TimeFrame.year(2021),\n", + " key_col=1,\n", + " data_col=2,\n", + " data_type=np.float64,\n", + " key_type=\"geoid\",\n", + " skiprows=1\n", + " ),\n", " }\n", - ")" + ")\n", + "\n", + "result = evaluate_param(rume, \"vax_csv\")\n", + "if result.shape == (365, 3):\n", + " print(\"✓\")\n", + "else:\n", + " raise Exception(\"Vaccination data is an invalid shape.\")" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓\n" + ] + } + ], "source": [ - "geo = DynamicGeo.from_library(spec, adrio_maker_library)\n", + "# Check counties_commuters_2020.csv\n", + "rume = placeholder_rume(\n", + " scope=CountyScope.in_counties(['35001', '04013', '04017'], year=2020),\n", + " time_frame=TimeFrame.year(2020),\n", + " params={\n", + " \"commuters_csv\": csv.CSVMatrix(\n", + " file_path=Path(\"./scratch/counties_commuters_2020.csv\"),\n", + " from_key_col=0,\n", + " to_key_col=1,\n", + " data_col=2,\n", + " data_type=np.int64,\n", + " key_type=\"geoid\",\n", + " skiprows=1\n", + " ),\n", + " \"commuters_census\": commuting_flows.Commuters(),\n", + " }\n", + ")\n", "\n", - "geo.validate()\n", - "if not array_equal(geo['commuters'], geo['commuters_census']):\n", + "if np.array_equal(\n", + " evaluate_param(rume, \"commuters_csv\"),\n", + " evaluate_param(rume, \"commuters_census\")\n", + "):\n", + " print(\"✓\")\n", + "else:\n", " raise Exception(\"Data not equal.\")" ] } diff --git a/doc/devlog/2024-07-03-cdc-adrio-demo.ipynb b/doc/devlog/2024-07-03-cdc-adrio-demo.ipynb index 2577b736..7944681b 100644 --- a/doc/devlog/2024-07-03-cdc-adrio-demo.ipynb +++ b/doc/devlog/2024-07-03-cdc-adrio-demo.ipynb @@ -49,12 +49,20 @@ "metadata": {}, "outputs": [], "source": [ - "from epymorph.geo.adrio.cdc.adrio_cdc import ADRIOMakerCDC\n", + "from unittest.mock import Mock\n", + "\n", + "import numpy as np\n", + "\n", + "from epymorph.data_shape import SimDimensions\n", "from epymorph.geography.us_census import CountyScope, StateScope\n", + "from epymorph.simulation import NamespacedAttributeResolver\n", "\n", - "maker = ADRIOMakerCDC()\n", "county_scope = CountyScope.in_states(['04', '08'])\n", - "state_scope = StateScope.in_states(['04', '08'])" + "state_scope = StateScope.in_states(['04', '08'])\n", + "\n", + "data = Mock(spec=NamespacedAttributeResolver)\n", + "dim = Mock(spec=SimDimensions)\n", + "rng = Mock(spec=np.random.Generator)" ] }, { @@ -77,18 +85,13 @@ "metadata": {}, "outputs": [], "source": [ - "from datetime import date\n", + "from epymorph.adrio.cdc import CovidCasesPer100k, CovidHospitalizationsPer100k\n", + "from epymorph.simulation import TimeFrame\n", "\n", - "from epymorph.data_shape import Shapes\n", - "from epymorph.geo.spec import DateRange\n", - "from epymorph.simulation import AttributeDef\n", + "time_period = TimeFrame.range(\"2022-02-24\", \"2023-05-04\")\n", "\n", - "time_period = DateRange(date(2022, 2, 24), date(2023, 5, 4))\n", - "\n", - "cases = maker.make_adrio(AttributeDef(\"covid_cases_per_100k\", float,\n", - " Shapes.TxN), state_scope, time_period)\n", - "hospitalizations = maker.make_adrio(AttributeDef(\n", - " \"covid_hospitalizations_per_100k\", float, Shapes.TxN), state_scope, time_period)" + "cases = CovidCasesPer100k(time_period)\n", + "hospitalizations = CovidHospitalizationsPer100k(time_period)" ] }, { @@ -113,8 +116,10 @@ } ], "source": [ - "print(f\"COVID cases per 100k:\\n {cases.get_value()[:3]}\\n\")\n", - "print(f\"COVID hospitalizations per 100k:\\n {hospitalizations.get_value()[:3]}\")" + "print(\n", + " f\"COVID cases per 100k:\\n {cases.evaluate_in_context(data, dim, state_scope, rng)[:3]}\\n\")\n", + "print(\n", + " f\"COVID hospitalizations per 100k:\\n {hospitalizations.evaluate_in_context(data, dim, state_scope, rng)[:3]}\")" ] }, { @@ -137,16 +142,17 @@ "metadata": {}, "outputs": [], "source": [ - "time_period = DateRange(date(2020, 12, 13), date(2023, 5, 10))\n", + "from epymorph.adrio.cdc import (CovidHospitalizationAvgFacility,\n", + " CovidHospitalizationSumFacility,\n", + " InfluenzaHospitalizationSumFacility,\n", + " InfluenzaHosptializationAvgFacility)\n", + "\n", + "time_period = TimeFrame.range(\"2020-12-13\", \"2023-05-10\")\n", "\n", - "covid_avg = maker.make_adrio(AttributeDef(\n", - " \"covid_hospitalization_avg_facility\", float, Shapes.TxN), state_scope, time_period)\n", - "covid_sum = maker.make_adrio(AttributeDef(\n", - " \"covid_hospitalization_sum_facility\", float, Shapes.TxN), state_scope, time_period)\n", - "flu_avg = maker.make_adrio(AttributeDef(\n", - " \"influenza_hospitalization_avg_facility\", float, Shapes.TxN), state_scope, time_period)\n", - "flu_sum = maker.make_adrio(AttributeDef(\n", - " \"influenza_hospitalization_sum_facility\", float, Shapes.TxN), state_scope, time_period)" + "covid_avg = CovidHospitalizationAvgFacility(time_period)\n", + "covid_sum = CovidHospitalizationSumFacility(time_period)\n", + "flu_avg = InfluenzaHosptializationAvgFacility(time_period)\n", + "flu_sum = InfluenzaHospitalizationSumFacility(time_period)" ] }, { @@ -159,32 +165,396 @@ "output_type": "stream", "text": [ "COVID hospitalization average:\n", - " [[('2020-12-13', -999999.) ('2020-12-13', -999999.)]\n", - " [('2020-12-20', -999999.) ('2020-12-20', -999999.)]\n", - " [('2020-12-27', -999999.) ('2020-12-27', -999999.)]]\n", + " [[('2020-12-13', 6.40000e+00) ('2020-12-13', -9.99999e+05)\n", + " ('2020-12-13', -9.99999e+05) ('2020-12-13', 2.88000e+01)\n", + " ('2020-12-13', 2.48000e+01) ('2020-12-13', 1.96000e+01)\n", + " ('2020-12-13', -9.99999e+05) ('2020-12-13', -9.99999e+05)\n", + " ('2020-12-13', 7.29000e+01) ('2020-12-13', -9.99999e+05)\n", + " ('2020-12-13', 1.65600e+02) ('2020-12-13', 8.80000e+00)\n", + " ('2020-12-13', 2.21000e+02) ('2020-12-13', 3.23200e+02)\n", + " ('2020-12-13', 1.60600e+02) ('2020-12-13', 8.90000e+00)\n", + " ('2020-12-13', 1.44300e+02) ('2020-12-13', -9.99999e+05)\n", + " ('2020-12-13', -9.99999e+05) ('2020-12-13', 7.07000e+01)\n", + " ('2020-12-13', -9.99999e+05) ('2020-12-13', nan)\n", + " ('2020-12-13', nan) ('2020-12-13', nan)\n", + " ('2020-12-13', nan) ('2020-12-13', 7.89000e+01)\n", + " ('2020-12-13', 2.74000e+01) ('2020-12-13', -9.99999e+05)\n", + " ('2020-12-13', -9.99999e+05) ('2020-12-13', 8.10000e+00)\n", + " ('2020-12-13', nan) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', -9.99999e+05) ('2020-12-13', nan)\n", + " ('2020-12-13', 3.33000e+01) ('2020-12-13', -9.99999e+05)\n", + " ('2020-12-13', nan) ('2020-12-13', nan)\n", + " ('2020-12-13', 1.60000e+01) ('2020-12-13', -9.99999e+05)\n", + " ('2020-12-13', nan) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', 6.30000e+00) ('2020-12-13', 5.26000e+01)\n", + " ('2020-12-13', -9.99999e+05) ('2020-12-13', nan)\n", + " ('2020-12-13', 1.04000e+01) ('2020-12-13', 4.90000e+00)\n", + " ('2020-12-13', 8.40000e+00) ('2020-12-13', 6.10000e+00)\n", + " ('2020-12-13', nan) ('2020-12-13', -9.99999e+05)\n", + " ('2020-12-13', 1.14000e+01) ('2020-12-13', -9.99999e+05)\n", + " ('2020-12-13', 0.00000e+00) ('2020-12-13', nan)\n", + " ('2020-12-13', 0.00000e+00) ('2020-12-13', -9.99999e+05)\n", + " ('2020-12-13', nan) ('2020-12-13', 4.27000e+01)\n", + " ('2020-12-13', nan)]\n", + " [('2020-12-20', 1.28000e+01) ('2020-12-20', -9.99999e+05)\n", + " ('2020-12-20', 1.03300e+02) ('2020-12-20', 1.17000e+01)\n", + " ('2020-12-20', 1.11000e+01) ('2020-12-20', 6.70000e+00)\n", + " ('2020-12-20', -9.99999e+05) ('2020-12-20', 1.54200e+02)\n", + " ('2020-12-20', 6.50000e+01) ('2020-12-20', -9.99999e+05)\n", + " ('2020-12-20', 1.66000e+02) ('2020-12-20', 8.00000e+00)\n", + " ('2020-12-20', 2.48400e+02) ('2020-12-20', 3.08000e+02)\n", + " ('2020-12-20', 1.31800e+02) ('2020-12-20', nan)\n", + " ('2020-12-20', 4.74000e+01) ('2020-12-20', nan)\n", + " ('2020-12-20', -9.99999e+05) ('2020-12-20', 5.50000e+01)\n", + " ('2020-12-20', nan) ('2020-12-20', -9.99999e+05)\n", + " ('2020-12-20', 0.00000e+00) ('2020-12-20', nan)\n", + " ('2020-12-20', 1.19000e+01) ('2020-12-20', 6.20000e+01)\n", + " ('2020-12-20', 3.66000e+01) ('2020-12-20', nan)\n", + " ('2020-12-20', 1.01100e+02) ('2020-12-20', 4.40000e+00)\n", + " ('2020-12-20', -9.99999e+05) ('2020-12-20', 0.00000e+00)\n", + " ('2020-12-20', -9.99999e+05) ('2020-12-20', nan)\n", + " ('2020-12-20', 3.23000e+01) ('2020-12-20', nan)\n", + " ('2020-12-20', nan) ('2020-12-20', -9.99999e+05)\n", + " ('2020-12-20', 1.27000e+01) ('2020-12-20', -9.99999e+05)\n", + " ('2020-12-20', nan) ('2020-12-20', nan)\n", + " ('2020-12-20', nan) ('2020-12-20', 1.18000e+01)\n", + " ('2020-12-20', -9.99999e+05) ('2020-12-20', nan)\n", + " ('2020-12-20', nan) ('2020-12-20', -9.99999e+05)\n", + " ('2020-12-20', nan) ('2020-12-20', -9.99999e+05)\n", + " ('2020-12-20', nan) ('2020-12-20', nan)\n", + " ('2020-12-20', 1.13000e+01) ('2020-12-20', -9.99999e+05)\n", + " ('2020-12-20', 0.00000e+00) ('2020-12-20', 0.00000e+00)\n", + " ('2020-12-20', nan) ('2020-12-20', nan)\n", + " ('2020-12-20', nan) ('2020-12-20', 5.72000e+01)\n", + " ('2020-12-20', -9.99999e+05)]\n", + " [('2020-12-27', 1.14000e+01) ('2020-12-27', -9.99999e+05)\n", + " ('2020-12-27', -9.99999e+05) ('2020-12-27', 3.48000e+01)\n", + " ('2020-12-27', 3.26000e+01) ('2020-12-27', 5.80000e+00)\n", + " ('2020-12-27', -9.99999e+05) ('2020-12-27', 2.31700e+02)\n", + " ('2020-12-27', 6.18000e+01) ('2020-12-27', -9.99999e+05)\n", + " ('2020-12-27', 1.64900e+02) ('2020-12-27', 9.20000e+00)\n", + " ('2020-12-27', 1.39300e+02) ('2020-12-27', 2.70600e+02)\n", + " ('2020-12-27', -9.99999e+05) ('2020-12-27', 6.70000e+00)\n", + " ('2020-12-27', 1.02500e+02) ('2020-12-27', -9.99999e+05)\n", + " ('2020-12-27', -9.99999e+05) ('2020-12-27', 4.29000e+01)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', -9.99999e+05)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', nan)\n", + " ('2020-12-27', nan) ('2020-12-27', 7.56000e+01)\n", + " ('2020-12-27', 5.39000e+01) ('2020-12-27', -9.99999e+05)\n", + " ('2020-12-27', nan) ('2020-12-27', nan)\n", + " ('2020-12-27', -9.99999e+05) ('2020-12-27', 0.00000e+00)\n", + " ('2020-12-27', nan) ('2020-12-27', -9.99999e+05)\n", + " ('2020-12-27', 2.56000e+01) ('2020-12-27', nan)\n", + " ('2020-12-27', -9.99999e+05) ('2020-12-27', nan)\n", + " ('2020-12-27', 1.36000e+01) ('2020-12-27', -9.99999e+05)\n", + " ('2020-12-27', -9.99999e+05) ('2020-12-27', -9.99999e+05)\n", + " ('2020-12-27', 6.30000e+00) ('2020-12-27', 3.31000e+01)\n", + " ('2020-12-27', -9.99999e+05) ('2020-12-27', 4.60000e+00)\n", + " ('2020-12-27', nan) ('2020-12-27', nan)\n", + " ('2020-12-27', 5.40000e+00) ('2020-12-27', -9.99999e+05)\n", + " ('2020-12-27', nan) ('2020-12-27', nan)\n", + " ('2020-12-27', 6.30000e+00) ('2020-12-27', -9.99999e+05)\n", + " ('2020-12-27', nan) ('2020-12-27', nan)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', -9.99999e+05)\n", + " ('2020-12-27', -9.99999e+05) ('2020-12-27', 3.72000e+01)\n", + " ('2020-12-27', -9.99999e+05)]]\n", "\n", "COVID hospitalization sum:\n", - " [[('2020-12-13', -999999.) ('2020-12-13', -999999.)]\n", - " [('2020-12-20', 24524.) ('2020-12-20', -999999.)]\n", - " [('2020-12-27', 26680.) ('2020-12-27', -999999.)]]\n", + " [[('2020-12-13', 4.50000e+01) ('2020-12-13', 4.48000e+02)\n", + " ('2020-12-13', 6.78000e+02) ('2020-12-13', 2.01000e+02)\n", + " ('2020-12-13', 1.74000e+02) ('2020-12-13', 9.80000e+01)\n", + " ('2020-12-13', -9.99999e+05) ('2020-12-13', -9.99999e+05)\n", + " ('2020-12-13', 5.10000e+02) ('2020-12-13', 4.68500e+03)\n", + " ('2020-12-13', 1.15800e+03) ('2020-12-13', 6.20000e+01)\n", + " ('2020-12-13', 1.54600e+03) ('2020-12-13', 2.26200e+03)\n", + " ('2020-12-13', 1.12400e+03) ('2020-12-13', 6.20000e+01)\n", + " ('2020-12-13', 1.01000e+03) ('2020-12-13', 6.00000e+00)\n", + " ('2020-12-13', 6.00000e+00) ('2020-12-13', 4.94000e+02)\n", + " ('2020-12-13', -9.99999e+05) ('2020-12-13', nan)\n", + " ('2020-12-13', nan) ('2020-12-13', nan)\n", + " ('2020-12-13', nan) ('2020-12-13', 5.52000e+02)\n", + " ('2020-12-13', 1.92000e+02) ('2020-12-13', 8.00000e+00)\n", + " ('2020-12-13', 1.47900e+03) ('2020-12-13', 5.70000e+01)\n", + " ('2020-12-13', nan) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', 5.00000e+00) ('2020-12-13', nan)\n", + " ('2020-12-13', 2.33000e+02) ('2020-12-13', -9.99999e+05)\n", + " ('2020-12-13', nan) ('2020-12-13', nan)\n", + " ('2020-12-13', 1.12000e+02) ('2020-12-13', 8.90000e+01)\n", + " ('2020-12-13', nan) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', 4.40000e+01) ('2020-12-13', 3.68000e+02)\n", + " ('2020-12-13', 2.60000e+01) ('2020-12-13', nan)\n", + " ('2020-12-13', 7.30000e+01) ('2020-12-13', 3.40000e+01)\n", + " ('2020-12-13', 5.90000e+01) ('2020-12-13', 4.30000e+01)\n", + " ('2020-12-13', nan) ('2020-12-13', 1.10000e+01)\n", + " ('2020-12-13', 8.00000e+01) ('2020-12-13', -9.99999e+05)\n", + " ('2020-12-13', 0.00000e+00) ('2020-12-13', nan)\n", + " ('2020-12-13', 0.00000e+00) ('2020-12-13', 6.00000e+00)\n", + " ('2020-12-13', nan) ('2020-12-13', 2.99000e+02)\n", + " ('2020-12-13', nan)]\n", + " [('2020-12-20', 9.00000e+01) ('2020-12-20', 4.45000e+02)\n", + " ('2020-12-20', 7.23000e+02) ('2020-12-20', 8.20000e+01)\n", + " ('2020-12-20', 7.80000e+01) ('2020-12-20', 4.00000e+01)\n", + " ('2020-12-20', 2.47390e+04) ('2020-12-20', 1.07900e+03)\n", + " ('2020-12-20', 3.74000e+02) ('2020-12-20', 5.14800e+03)\n", + " ('2020-12-20', 1.16200e+03) ('2020-12-20', 5.60000e+01)\n", + " ('2020-12-20', 1.73800e+03) ('2020-12-20', 2.15600e+03)\n", + " ('2020-12-20', 9.22000e+02) ('2020-12-20', nan)\n", + " ('2020-12-20', 3.32000e+02) ('2020-12-20', nan)\n", + " ('2020-12-20', 5.00000e+00) ('2020-12-20', 3.85000e+02)\n", + " ('2020-12-20', nan) ('2020-12-20', 2.10000e+01)\n", + " ('2020-12-20', 0.00000e+00) ('2020-12-20', nan)\n", + " ('2020-12-20', 8.30000e+01) ('2020-12-20', 4.34000e+02)\n", + " ('2020-12-20', 2.56000e+02) ('2020-12-20', nan)\n", + " ('2020-12-20', 7.08000e+02) ('2020-12-20', 3.10000e+01)\n", + " ('2020-12-20', 9.00000e+00) ('2020-12-20', 0.00000e+00)\n", + " ('2020-12-20', 9.00000e+00) ('2020-12-20', nan)\n", + " ('2020-12-20', 2.26000e+02) ('2020-12-20', nan)\n", + " ('2020-12-20', nan) ('2020-12-20', 4.00000e+00)\n", + " ('2020-12-20', 8.90000e+01) ('2020-12-20', 3.73000e+02)\n", + " ('2020-12-20', nan) ('2020-12-20', nan)\n", + " ('2020-12-20', nan) ('2020-12-20', 8.30000e+01)\n", + " ('2020-12-20', 1.70000e+01) ('2020-12-20', nan)\n", + " ('2020-12-20', nan) ('2020-12-20', -9.99999e+05)\n", + " ('2020-12-20', nan) ('2020-12-20', 1.20000e+01)\n", + " ('2020-12-20', nan) ('2020-12-20', nan)\n", + " ('2020-12-20', 7.90000e+01) ('2020-12-20', 4.00000e+00)\n", + " ('2020-12-20', 0.00000e+00) ('2020-12-20', 0.00000e+00)\n", + " ('2020-12-20', nan) ('2020-12-20', nan)\n", + " ('2020-12-20', nan) ('2020-12-20', 4.00000e+02)\n", + " ('2020-12-20', 1.50000e+01)]\n", + " [('2020-12-27', 8.00000e+01) ('2020-12-27', 2.72000e+02)\n", + " ('2020-12-27', 7.51000e+02) ('2020-12-27', 2.44000e+02)\n", + " ('2020-12-27', 2.28000e+02) ('2020-12-27', 2.30000e+01)\n", + " ('2020-12-27', 3.38780e+04) ('2020-12-27', 1.62200e+03)\n", + " ('2020-12-27', 4.32000e+02) ('2020-12-27', 6.18000e+03)\n", + " ('2020-12-27', 1.15400e+03) ('2020-12-27', 6.40000e+01)\n", + " ('2020-12-27', 9.75000e+02) ('2020-12-27', 1.89400e+03)\n", + " ('2020-12-27', 9.45000e+02) ('2020-12-27', 4.70000e+01)\n", + " ('2020-12-27', 7.18000e+02) ('2020-12-27', 1.40000e+01)\n", + " ('2020-12-27', 1.30000e+01) ('2020-12-27', 3.00000e+02)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', -9.99999e+05)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', nan)\n", + " ('2020-12-27', nan) ('2020-12-27', 5.29000e+02)\n", + " ('2020-12-27', 3.77000e+02) ('2020-12-27', 1.20000e+01)\n", + " ('2020-12-27', nan) ('2020-12-27', nan)\n", + " ('2020-12-27', 5.40000e+01) ('2020-12-27', 0.00000e+00)\n", + " ('2020-12-27', nan) ('2020-12-27', 1.30000e+01)\n", + " ('2020-12-27', 1.79000e+02) ('2020-12-27', nan)\n", + " ('2020-12-27', -9.99999e+05) ('2020-12-27', nan)\n", + " ('2020-12-27', 9.50000e+01) ('2020-12-27', -9.99999e+05)\n", + " ('2020-12-27', 8.00000e+00) ('2020-12-27', -9.99999e+05)\n", + " ('2020-12-27', 4.40000e+01) ('2020-12-27', 2.32000e+02)\n", + " ('2020-12-27', 1.90000e+01) ('2020-12-27', 3.20000e+01)\n", + " ('2020-12-27', nan) ('2020-12-27', nan)\n", + " ('2020-12-27', 3.80000e+01) ('2020-12-27', 5.40000e+01)\n", + " ('2020-12-27', nan) ('2020-12-27', nan)\n", + " ('2020-12-27', 4.40000e+01) ('2020-12-27', -9.99999e+05)\n", + " ('2020-12-27', nan) ('2020-12-27', nan)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', 1.10000e+01)\n", + " ('2020-12-27', 9.00000e+00) ('2020-12-27', 2.60000e+02)\n", + " ('2020-12-27', -9.99999e+05)]]\n", "\n", "Influenza hospitalization average:\n", - " [[('2020-12-13', -999999.) ('2020-12-13', -999999.)]\n", - " [('2020-12-20', -999999.) ('2020-12-20', -999999.)]\n", - " [('2020-12-27', -999999.) ('2020-12-27', -999999.)]]\n", + " [[('2020-12-13', 0.) ('2020-12-13', 0.)\n", + " ('2020-12-13', 0.) ('2020-12-13', 0.)\n", + " ('2020-12-13', -999999.) ('2020-12-13', 0.)\n", + " ('2020-12-13', -999999.) ('2020-12-13', -999999.)\n", + " ('2020-12-13', 0.) ('2020-12-13', -999999.)\n", + " ('2020-12-13', 0.) ('2020-12-13', 0.)\n", + " ('2020-12-13', 0.) ('2020-12-13', 0.)\n", + " ('2020-12-13', 0.) ('2020-12-13', 0.)\n", + " ('2020-12-13', 0.) ('2020-12-13', 0.)\n", + " ('2020-12-13', 0.) ('2020-12-13', 0.)\n", + " ('2020-12-13', -999999.) ('2020-12-13', nan)\n", + " ('2020-12-13', nan) ('2020-12-13', nan)\n", + " ('2020-12-13', nan) ('2020-12-13', 0.)\n", + " ('2020-12-13', 0.) ('2020-12-13', 0.)\n", + " ('2020-12-13', 0.) ('2020-12-13', 0.)\n", + " ('2020-12-13', nan) ('2020-12-13', 0.)\n", + " ('2020-12-13', 0.) ('2020-12-13', nan)\n", + " ('2020-12-13', 0.) ('2020-12-13', 0.)\n", + " ('2020-12-13', nan) ('2020-12-13', nan)\n", + " ('2020-12-13', -999999.) ('2020-12-13', 0.)\n", + " ('2020-12-13', nan) ('2020-12-13', 0.)\n", + " ('2020-12-13', 0.) ('2020-12-13', 0.)\n", + " ('2020-12-13', 0.) ('2020-12-13', nan)\n", + " ('2020-12-13', 0.) ('2020-12-13', 0.)\n", + " ('2020-12-13', 0.) ('2020-12-13', 0.)\n", + " ('2020-12-13', nan) ('2020-12-13', 0.)\n", + " ('2020-12-13', 0.) ('2020-12-13', 0.)\n", + " ('2020-12-13', 0.) ('2020-12-13', nan)\n", + " ('2020-12-13', 0.) ('2020-12-13', 0.)\n", + " ('2020-12-13', nan) ('2020-12-13', -999999.)\n", + " ('2020-12-13', nan)]\n", + " [('2020-12-20', -999999.) ('2020-12-20', 0.)\n", + " ('2020-12-20', 0.) ('2020-12-20', 0.)\n", + " ('2020-12-20', 0.) ('2020-12-20', 0.)\n", + " ('2020-12-20', -999999.) ('2020-12-20', 0.)\n", + " ('2020-12-20', 0.) ('2020-12-20', -999999.)\n", + " ('2020-12-20', 0.) ('2020-12-20', 0.)\n", + " ('2020-12-20', 0.) ('2020-12-20', 0.)\n", + " ('2020-12-20', -999999.) ('2020-12-20', nan)\n", + " ('2020-12-20', 0.) ('2020-12-20', nan)\n", + " ('2020-12-20', 0.) ('2020-12-20', 0.)\n", + " ('2020-12-20', nan) ('2020-12-20', 0.)\n", + " ('2020-12-20', 0.) ('2020-12-20', nan)\n", + " ('2020-12-20', 0.) ('2020-12-20', 0.)\n", + " ('2020-12-20', 0.) ('2020-12-20', nan)\n", + " ('2020-12-20', 0.) ('2020-12-20', 0.)\n", + " ('2020-12-20', 0.) ('2020-12-20', 0.)\n", + " ('2020-12-20', 0.) ('2020-12-20', nan)\n", + " ('2020-12-20', 0.) ('2020-12-20', nan)\n", + " ('2020-12-20', nan) ('2020-12-20', 0.)\n", + " ('2020-12-20', -999999.) ('2020-12-20', 0.)\n", + " ('2020-12-20', nan) ('2020-12-20', nan)\n", + " ('2020-12-20', nan) ('2020-12-20', 0.)\n", + " ('2020-12-20', 0.) ('2020-12-20', nan)\n", + " ('2020-12-20', nan) ('2020-12-20', 0.)\n", + " ('2020-12-20', nan) ('2020-12-20', 0.)\n", + " ('2020-12-20', nan) ('2020-12-20', nan)\n", + " ('2020-12-20', 0.) ('2020-12-20', 0.)\n", + " ('2020-12-20', 0.) ('2020-12-20', 0.)\n", + " ('2020-12-20', nan) ('2020-12-20', nan)\n", + " ('2020-12-20', nan) ('2020-12-20', -999999.)\n", + " ('2020-12-20', 0.)]\n", + " [('2020-12-27', -999999.) ('2020-12-27', 0.)\n", + " ('2020-12-27', 0.) ('2020-12-27', 0.)\n", + " ('2020-12-27', 0.) ('2020-12-27', 0.)\n", + " ('2020-12-27', -999999.) ('2020-12-27', 0.)\n", + " ('2020-12-27', 0.) ('2020-12-27', -999999.)\n", + " ('2020-12-27', -999999.) ('2020-12-27', 0.)\n", + " ('2020-12-27', 0.) ('2020-12-27', 0.)\n", + " ('2020-12-27', 0.) ('2020-12-27', 0.)\n", + " ('2020-12-27', 0.) ('2020-12-27', 0.)\n", + " ('2020-12-27', 0.) ('2020-12-27', 0.)\n", + " ('2020-12-27', 0.) ('2020-12-27', 0.)\n", + " ('2020-12-27', 0.) ('2020-12-27', nan)\n", + " ('2020-12-27', nan) ('2020-12-27', 0.)\n", + " ('2020-12-27', 0.) ('2020-12-27', 0.)\n", + " ('2020-12-27', nan) ('2020-12-27', nan)\n", + " ('2020-12-27', 0.) ('2020-12-27', 0.)\n", + " ('2020-12-27', nan) ('2020-12-27', 0.)\n", + " ('2020-12-27', 0.) ('2020-12-27', nan)\n", + " ('2020-12-27', 0.) ('2020-12-27', nan)\n", + " ('2020-12-27', -999999.) ('2020-12-27', 0.)\n", + " ('2020-12-27', 0.) ('2020-12-27', 0.)\n", + " ('2020-12-27', 0.) ('2020-12-27', 0.)\n", + " ('2020-12-27', 0.) ('2020-12-27', 0.)\n", + " ('2020-12-27', nan) ('2020-12-27', nan)\n", + " ('2020-12-27', 0.) ('2020-12-27', 0.)\n", + " ('2020-12-27', nan) ('2020-12-27', nan)\n", + " ('2020-12-27', 0.) ('2020-12-27', 0.)\n", + " ('2020-12-27', nan) ('2020-12-27', nan)\n", + " ('2020-12-27', 0.) ('2020-12-27', 0.)\n", + " ('2020-12-27', 0.) ('2020-12-27', -999999.)\n", + " ('2020-12-27', 0.)]]\n", "\n", "Influenza hospitalization sum:\n", - " [[('2020-12-13', -9.99999e+05) ('2020-12-13', -9.99999e+05)]\n", - " [('2020-12-20', -9.99999e+05) ('2020-12-20', -9.99999e+05)]\n", - " [('2020-12-27', -9.99999e+05) ('2020-12-27', 2.50000e+01)]]\n" + " [[('2020-12-13', 0.00000e+00) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', 0.00000e+00) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', -9.99999e+05) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', -9.99999e+05) ('2020-12-13', -9.99999e+05)\n", + " ('2020-12-13', 0.00000e+00) ('2020-12-13', -9.99999e+05)\n", + " ('2020-12-13', 0.00000e+00) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', 0.00000e+00) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', 0.00000e+00) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', 0.00000e+00) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', 0.00000e+00) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', 6.00000e+00) ('2020-12-13', nan)\n", + " ('2020-12-13', nan) ('2020-12-13', nan)\n", + " ('2020-12-13', nan) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', 0.00000e+00) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', 0.00000e+00) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', nan) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', 0.00000e+00) ('2020-12-13', nan)\n", + " ('2020-12-13', 0.00000e+00) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', nan) ('2020-12-13', nan)\n", + " ('2020-12-13', 7.00000e+00) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', nan) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', 0.00000e+00) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', 0.00000e+00) ('2020-12-13', nan)\n", + " ('2020-12-13', 0.00000e+00) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', 0.00000e+00) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', nan) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', 0.00000e+00) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', 0.00000e+00) ('2020-12-13', nan)\n", + " ('2020-12-13', 0.00000e+00) ('2020-12-13', 0.00000e+00)\n", + " ('2020-12-13', nan) ('2020-12-13', 1.40000e+01)\n", + " ('2020-12-13', nan)]\n", + " [('2020-12-20', -9.99999e+05) ('2020-12-20', 0.00000e+00)\n", + " ('2020-12-20', 0.00000e+00) ('2020-12-20', 0.00000e+00)\n", + " ('2020-12-20', 0.00000e+00) ('2020-12-20', 0.00000e+00)\n", + " ('2020-12-20', -9.99999e+05) ('2020-12-20', 0.00000e+00)\n", + " ('2020-12-20', 0.00000e+00) ('2020-12-20', 1.00000e+01)\n", + " ('2020-12-20', 0.00000e+00) ('2020-12-20', 0.00000e+00)\n", + " ('2020-12-20', 0.00000e+00) ('2020-12-20', 0.00000e+00)\n", + " ('2020-12-20', -9.99999e+05) ('2020-12-20', nan)\n", + " ('2020-12-20', 0.00000e+00) ('2020-12-20', nan)\n", + " ('2020-12-20', 0.00000e+00) ('2020-12-20', 0.00000e+00)\n", + " ('2020-12-20', nan) ('2020-12-20', 0.00000e+00)\n", + " ('2020-12-20', 0.00000e+00) ('2020-12-20', nan)\n", + " ('2020-12-20', 0.00000e+00) ('2020-12-20', 0.00000e+00)\n", + " ('2020-12-20', 0.00000e+00) ('2020-12-20', nan)\n", + " ('2020-12-20', 0.00000e+00) ('2020-12-20', 0.00000e+00)\n", + " ('2020-12-20', 0.00000e+00) ('2020-12-20', 0.00000e+00)\n", + " ('2020-12-20', 0.00000e+00) ('2020-12-20', nan)\n", + " ('2020-12-20', 0.00000e+00) ('2020-12-20', nan)\n", + " ('2020-12-20', nan) ('2020-12-20', 0.00000e+00)\n", + " ('2020-12-20', 7.00000e+00) ('2020-12-20', 0.00000e+00)\n", + " ('2020-12-20', nan) ('2020-12-20', nan)\n", + " ('2020-12-20', nan) ('2020-12-20', 0.00000e+00)\n", + " ('2020-12-20', 0.00000e+00) ('2020-12-20', nan)\n", + " ('2020-12-20', nan) ('2020-12-20', 0.00000e+00)\n", + " ('2020-12-20', nan) ('2020-12-20', 0.00000e+00)\n", + " ('2020-12-20', nan) ('2020-12-20', nan)\n", + " ('2020-12-20', 0.00000e+00) ('2020-12-20', 0.00000e+00)\n", + " ('2020-12-20', 0.00000e+00) ('2020-12-20', 0.00000e+00)\n", + " ('2020-12-20', nan) ('2020-12-20', nan)\n", + " ('2020-12-20', nan) ('2020-12-20', 1.40000e+01)\n", + " ('2020-12-20', 0.00000e+00)]\n", + " [('2020-12-27', -9.99999e+05) ('2020-12-27', 0.00000e+00)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', 0.00000e+00)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', 0.00000e+00)\n", + " ('2020-12-27', -9.99999e+05) ('2020-12-27', 0.00000e+00)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', -9.99999e+05)\n", + " ('2020-12-27', -9.99999e+05) ('2020-12-27', 0.00000e+00)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', 0.00000e+00)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', 0.00000e+00)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', 0.00000e+00)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', 0.00000e+00)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', 0.00000e+00)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', nan)\n", + " ('2020-12-27', nan) ('2020-12-27', 0.00000e+00)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', 0.00000e+00)\n", + " ('2020-12-27', nan) ('2020-12-27', nan)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', 0.00000e+00)\n", + " ('2020-12-27', nan) ('2020-12-27', 0.00000e+00)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', nan)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', nan)\n", + " ('2020-12-27', 7.00000e+00) ('2020-12-27', 0.00000e+00)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', 0.00000e+00)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', 0.00000e+00)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', 0.00000e+00)\n", + " ('2020-12-27', nan) ('2020-12-27', nan)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', 0.00000e+00)\n", + " ('2020-12-27', nan) ('2020-12-27', nan)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', 0.00000e+00)\n", + " ('2020-12-27', nan) ('2020-12-27', nan)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', 0.00000e+00)\n", + " ('2020-12-27', 0.00000e+00) ('2020-12-27', 1.40000e+01)\n", + " ('2020-12-27', 0.00000e+00)]]\n" ] } ], "source": [ - "print(f\"COVID hospitalization average:\\n {covid_avg.get_value()[:3]}\\n\")\n", - "print(f\"COVID hospitalization sum:\\n {covid_sum.get_value()[:3]}\\n\")\n", - "print(f\"Influenza hospitalization average:\\n {flu_avg.get_value()[:3]}\\n\")\n", - "print(f\"Influenza hospitalization sum:\\n {flu_sum.get_value()[:3]}\")" + "print(\n", + " f\"COVID hospitalization average:\\n {covid_avg.evaluate_in_context(data, dim, county_scope, rng)[:3]}\\n\")\n", + "print(\n", + " f\"COVID hospitalization sum:\\n {covid_sum.evaluate_in_context(data, dim, county_scope, rng)[:3]}\\n\")\n", + "print(\n", + " f\"Influenza hospitalization average:\\n {flu_avg.evaluate_in_context(data, dim, county_scope, rng)[:3]}\\n\")\n", + "print(\n", + " f\"Influenza hospitalization sum:\\n {flu_sum.evaluate_in_context(data, dim, county_scope, rng)[:3]}\")" ] }, { @@ -207,16 +577,17 @@ "metadata": {}, "outputs": [], "source": [ - "time_period = DateRange(date(2020, 12, 13), date(2024, 6, 28))\n", + "from epymorph.adrio.cdc import (CovidHospitalizationAvgState,\n", + " CovidHospitalizationSumState,\n", + " InfluenzaHospitalizationAvgState,\n", + " InfluenzaHospitalizationSumState)\n", "\n", - "covid_avg = maker.make_adrio(AttributeDef(\n", - " \"covid_hospitalization_avg_state\", float, Shapes.TxN), state_scope, time_period)\n", - "covid_sum = maker.make_adrio(AttributeDef(\n", - " \"covid_hospitalization_sum_state\", float, Shapes.TxN), state_scope, time_period)\n", - "flu_avg = maker.make_adrio(AttributeDef(\n", - " \"influenza_hospitalization_avg_state\", float, Shapes.TxN), state_scope, time_period)\n", - "flu_sum = maker.make_adrio(AttributeDef(\n", - " \"influenza_hospitalization_sum_state\", float, Shapes.TxN), state_scope, time_period)" + "time_period = TimeFrame.range(\"2020-12-13\", \"2024-06-28\")\n", + "\n", + "covid_avg = CovidHospitalizationAvgState(time_period)\n", + "covid_sum = CovidHospitalizationSumState(time_period)\n", + "flu_avg = InfluenzaHospitalizationAvgState(time_period)\n", + "flu_sum = InfluenzaHospitalizationSumState(time_period)" ] }, { @@ -228,7 +599,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/waff/Desktop/CCL/Epymorph/epymorph/geo/adrio/cdc/adrio_cdc.py:213: UserWarning: State level hospitalization data is voluntary past 5/1/2024.\n", + "/home/tcoles/Workspaces/Epymorph/epymorph/adrio/cdc.py:115: UserWarning: State level hospitalization data is voluntary past 5/1/2024.\n", " warn(\"State level hospitalization data is voluntary past 5/1/2024.\")\n" ] }, @@ -248,7 +619,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/waff/Desktop/CCL/Epymorph/epymorph/geo/adrio/cdc/adrio_cdc.py:213: UserWarning: State level hospitalization data is voluntary past 5/1/2024.\n", + "/home/tcoles/Workspaces/Epymorph/epymorph/adrio/cdc.py:115: UserWarning: State level hospitalization data is voluntary past 5/1/2024.\n", " warn(\"State level hospitalization data is voluntary past 5/1/2024.\")\n" ] }, @@ -268,7 +639,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/waff/Desktop/CCL/Epymorph/epymorph/geo/adrio/cdc/adrio_cdc.py:213: UserWarning: State level hospitalization data is voluntary past 5/1/2024.\n", + "/home/tcoles/Workspaces/Epymorph/epymorph/adrio/cdc.py:115: UserWarning: State level hospitalization data is voluntary past 5/1/2024.\n", " warn(\"State level hospitalization data is voluntary past 5/1/2024.\")\n" ] }, @@ -288,7 +659,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/waff/Desktop/CCL/Epymorph/epymorph/geo/adrio/cdc/adrio_cdc.py:213: UserWarning: State level hospitalization data is voluntary past 5/1/2024.\n", + "/home/tcoles/Workspaces/Epymorph/epymorph/adrio/cdc.py:115: UserWarning: State level hospitalization data is voluntary past 5/1/2024.\n", " warn(\"State level hospitalization data is voluntary past 5/1/2024.\")\n" ] }, @@ -305,10 +676,14 @@ } ], "source": [ - "print(f\"COVID hospitalization average:\\n {covid_avg.get_value()[:3]}\\n...\\n\")\n", - "print(f\"COVID hospitalization sum:\\n {covid_sum.get_value()[:3]}\\n...\\n\")\n", - "print(f\"Influenza hospitalization average:\\n {flu_avg.get_value()[:3]}\\n...\\n\")\n", - "print(f\"Influenza hospitalization sum:\\n {flu_sum.get_value()[:3]}\\n...\")" + "print(\n", + " f\"COVID hospitalization average:\\n {covid_avg.evaluate_in_context(data, dim, state_scope, rng)[:3]}\\n...\\n\")\n", + "print(\n", + " f\"COVID hospitalization sum:\\n {covid_sum.evaluate_in_context(data, dim, state_scope, rng)[:3]}\\n...\\n\")\n", + "print(\n", + " f\"Influenza hospitalization average:\\n {flu_avg.evaluate_in_context(data, dim, state_scope, rng)[:3]}\\n...\\n\")\n", + "print(\n", + " f\"Influenza hospitalization sum:\\n {flu_sum.evaluate_in_context(data, dim, state_scope, rng)[:3]}\\n...\")" ] }, { @@ -331,14 +706,14 @@ "metadata": {}, "outputs": [], "source": [ - "time_period = DateRange(date(2020, 12, 13), date(2024, 5, 10))\n", + "from epymorph.adrio.cdc import (CovidBoosterDoses, FullCovidVaccinations,\n", + " OneDoseCovidVaccinations)\n", + "\n", + "time_period = TimeFrame.range(\"2021-12-13\", \"2024-05-10\")\n", "\n", - "full = maker.make_adrio(AttributeDef(\"full_covid_vaccinations\",\n", - " float, Shapes.TxN), state_scope, time_period)\n", - "one = maker.make_adrio(AttributeDef(\"one_dose_covid_vaccinations\",\n", - " float, Shapes.TxN), state_scope, time_period)\n", - "booster = maker.make_adrio(AttributeDef(\"covid_booster_doses\",\n", - " float, Shapes.TxN), state_scope, time_period)" + "full = FullCovidVaccinations(time_period)\n", + "one = OneDoseCovidVaccinations(time_period)\n", + "booster = CovidBoosterDoses(time_period)" ] }, { @@ -351,260 +726,380 @@ "output_type": "stream", "text": [ "Full COVID vaccinations:\n", - " [[('2020-12-13', 0.) ('2020-12-13', 0.) ('2020-12-13', 0.)\n", - " ('2020-12-13', 0.) ('2020-12-13', 0.) ('2020-12-13', 0.)\n", - " ('2020-12-13', 0.) ('2020-12-13', 0.) ('2020-12-13', 0.)\n", - " ('2020-12-13', 0.) ('2020-12-13', 0.) ('2020-12-13', 0.)\n", - " ('2020-12-13', 0.) ('2020-12-13', 0.) ('2020-12-13', 0.)\n", - " ('2020-12-13', 0.) ('2020-12-13', 0.) ('2020-12-13', 0.)\n", - " ('2020-12-13', 0.) ('2020-12-13', 0.) ('2020-12-13', 0.)\n", - " ('2020-12-13', 0.) ('2020-12-13', 0.) ('2020-12-13', 0.)\n", - " ('2020-12-13', 0.) ('2020-12-13', 0.) ('2020-12-13', 0.)\n", - " ('2020-12-13', 0.) ('2020-12-13', 0.) ('2020-12-13', 0.)\n", - " ('2020-12-13', 0.) ('2020-12-13', 0.) ('2020-12-13', 0.)\n", - " ('2020-12-13', 0.) ('2020-12-13', 0.) ('2020-12-13', 0.)\n", - " ('2020-12-13', 0.) ('2020-12-13', 0.) ('2020-12-13', 0.)\n", - " ('2020-12-13', 0.) ('2020-12-13', 0.) ('2020-12-13', 0.)\n", - " ('2020-12-13', 0.) ('2020-12-13', 0.) ('2020-12-13', 0.)\n", - " ('2020-12-13', 0.) ('2020-12-13', 0.) 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" ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan) ('2021-12-14', nan)\n", + " ('2021-12-14', nan)]\n", + " [('2021-12-15', 2.00940e+04) ('2021-12-15', 1.80370e+04)\n", + " ('2021-12-15', 3.03170e+04) ('2021-12-15', 9.47000e+03)\n", + " ('2021-12-15', 5.44100e+03) ('2021-12-15', 1.11300e+03)\n", + " ('2021-12-15', 1.77000e+03) ('2021-12-15', 5.76163e+05)\n", + " ('2021-12-15', 2.07320e+04) ('2021-12-15', 2.11070e+04)\n", + " ('2021-12-15', 1.83761e+05) ('2021-12-15', 5.08790e+04)\n", + " ('2021-12-15', 1.00320e+04) ('2021-12-15', 3.02740e+04)\n", + " ('2021-12-15', 2.82930e+04) ('2021-12-15', 9.19710e+04)\n", + " ('2021-12-15', 3.09400e+03) ('2021-12-15', 1.41957e+05)\n", + " ('2021-12-15', 3.60300e+03) ('2021-12-15', 5.06000e+02)\n", + " ('2021-12-15', 6.18000e+02) ('2021-12-15', 9.96980e+04)\n", + " ('2021-12-15', 2.17360e+04) ('2021-12-15', 5.97900e+03)\n", + " ('2021-12-15', 1.79000e+02) ('2021-12-15', 1.78800e+03)\n", + " ('2021-12-15', 1.32100e+03) ('2021-12-15', 1.00600e+03)\n", + " ('2021-12-15', 4.41000e+02) ('2021-12-15', 1.00500e+03)\n", + " ('2021-12-15', 5.43200e+03) ('2021-12-15', 1.85086e+05)\n", + " ('2021-12-15', 3.21000e+02) ('2021-12-15', 9.04910e+04)\n", + " ('2021-12-15', 1.28900e+04) ('2021-12-15', 3.17700e+03)\n", + " ('2021-12-15', 1.21638e+05) ('2021-12-15', 6.09700e+03)\n", + " ('2021-12-15', 1.22030e+04) ('2021-12-15', 1.44300e+03)\n", + " ('2021-12-15', 3.68900e+03) ('2021-12-15', 4.98000e+03)\n", + " ('2021-12-15', 2.33000e+02) ('2021-12-15', 1.37900e+03)\n", + " ('2021-12-15', 2.62000e+02) ('2021-12-15', 1.65523e+05)\n", + " ('2021-12-15', 2.39000e+02) ('2021-12-15', 7.66000e+02)\n", + " ('2021-12-15', 1.44800e+03) ('2021-12-15', 1.67160e+04)\n", + " ('2021-12-15', 8.90960e+04) ('2021-12-15', 2.97500e+03)\n", + " ('2021-12-15', 7.40000e+02) ('2021-12-15', 2.67300e+03)\n", + " ('2021-12-15', 2.73840e+04) ('2021-12-15', 3.52000e+02)\n", + " ('2021-12-15', 1.59900e+03) ('2021-12-15', 6.07400e+03)\n", + " ('2021-12-15', 7.83700e+03) ('2021-12-15', 3.51400e+03)\n", + " ('2021-12-15', 3.21800e+03) ('2021-12-15', 1.57300e+03)\n", + " ('2021-12-15', 3.31300e+03) ('2021-12-15', 5.49000e+02)\n", + " ('2021-12-15', 4.55200e+03) ('2021-12-15', 1.51500e+03)\n", + " ('2021-12-15', 3.29190e+04) ('2021-12-15', 7.46000e+02)\n", + " ('2021-12-15', 2.38500e+03) ('2021-12-15', 8.38500e+03)\n", + " ('2021-12-15', 9.05000e+02) ('2021-12-15', 2.47000e+02)\n", + " ('2021-12-15', 2.61600e+03) ('2021-12-15', 5.05000e+02)\n", + " ('2021-12-15', 7.57000e+03) ('2021-12-15', 5.43800e+03)\n", + " ('2021-12-15', 5.07000e+02) ('2021-12-15', 5.51140e+04)\n", + " ('2021-12-15', 1.30600e+03)]]\n" ] } ], "source": [ - "print(f\"Full COVID vaccinations:\\n {full.get_value()[:3]}\\n\")\n", - "print(f\"One dose COVID vaccinations:\\n {one.get_value()[:3]}\\n\")\n", - "print(f\"COVID booster doses:\\n {booster.get_value()[:3]}\")" + "print(\n", + " f\"Full COVID vaccinations:\\n {full.evaluate_in_context(data, dim, state_scope, rng)[:3]}\\n\")\n", + "print(\n", + " f\"One dose COVID vaccinations:\\n {one.evaluate_in_context(data, dim, state_scope, rng)[:3]}\\n\")\n", + "print(\n", + " f\"COVID booster doses:\\n {booster.evaluate_in_context(data, dim, state_scope, rng)[:3]}\")" ] }, { @@ -627,8 +1122,9 @@ "metadata": {}, "outputs": [], "source": [ - "deaths = maker.make_adrio(AttributeDef(\"covid_deaths_county\", float, Shapes.TxN),\n", - " state_scope, DateRange(date(2020, 1, 4), date(2024, 4, 5)))" + "from epymorph.adrio.cdc import CovidDeathsCounty\n", + "\n", + "deaths = CovidDeathsCounty(TimeFrame.range(\"2021-01-04\", \"2024-04-05\"))" ] }, { @@ -641,15 +1137,16 @@ "output_type": "stream", "text": [ "COVID deaths:\n", - " [[('2020-01-04', 0.) ('2020-01-04', 0.)]\n", - " [('2020-01-11', 0.) ('2020-01-11', 0.)]\n", - " [('2020-01-18', 0.) ('2020-01-18', 0.)]]\n", + " [[('2021-01-09', 921.) ('2021-01-09', 180.)]\n", + " [('2021-01-16', 960.) ('2021-01-16', 125.)]\n", + " [('2021-01-23', 926.) ('2021-01-23', 97.)]]\n", "\n" ] } ], "source": [ - "print(f\"COVID deaths:\\n {deaths.get_value()[:3]}\\n\")" + "print(\n", + " f\"COVID deaths:\\n {deaths.evaluate_in_context(data, dim, state_scope, rng)[:3]}\\n\")" ] }, { @@ -672,12 +1169,12 @@ "metadata": {}, "outputs": [], "source": [ - "time_period = DateRange(date(2020, 1, 4), date(2024, 4, 5))\n", + "from epymorph.adrio.cdc import CovidDeathsState, InfluenzaDeathsState\n", + "\n", + "time_period = TimeFrame.range(\"2021-01-04\", \"2024-04-05\")\n", "\n", - "covid_deaths = maker.make_adrio(AttributeDef(\n", - " \"covid_deaths_state\", float, Shapes.TxN), state_scope, time_period)\n", - "flu_deaths = maker.make_adrio(AttributeDef(\n", - " \"influenza_deaths\", float, Shapes.TxN), state_scope, time_period)" + "covid_deaths = CovidDeathsState(time_period)\n", + "flu_deaths = InfluenzaDeathsState(time_period)" ] }, { @@ -690,22 +1187,24 @@ "output_type": "stream", "text": [ "COVID deaths:\n", - " [[('2020-01-04', 0.) ('2020-01-04', 0.)]\n", - " [('2020-01-11', 0.) ('2020-01-11', 0.)]\n", - " [('2020-01-18', 0.) ('2020-01-18', 0.)]]\n", + " [[('2021-01-09', 942.) ('2021-01-09', 211.)]\n", + " [('2021-01-16', 996.) ('2021-01-16', 165.)]\n", + " [('2021-01-23', 959.) ('2021-01-23', 158.)]]\n", "...\n", "\n", "Influenza deaths:\n", - " [[('2020-01-04', 0.) ('2020-01-04', 0.)]\n", - " [('2020-01-11', 0.) ('2020-01-11', 0.)]\n", - " [('2020-01-18', 11.) ('2020-01-18', 0.)]]\n", + " [[('2021-01-09', 0.) ('2021-01-09', 0.)]\n", + " [('2021-01-16', 0.) ('2021-01-16', 0.)]\n", + " [('2021-01-23', 0.) ('2021-01-23', 0.)]]\n", "...\n" ] } ], "source": [ - "print(f\"COVID deaths:\\n {covid_deaths.get_value()[:3]}\\n...\\n\")\n", - "print(f\"Influenza deaths:\\n {flu_deaths.get_value()[:3]}\\n...\")" + "print(\n", + " f\"COVID deaths:\\n {covid_deaths.evaluate_in_context(data, dim, state_scope, rng)[:3]}\\n...\\n\")\n", + "print(\n", + " f\"Influenza deaths:\\n {flu_deaths.evaluate_in_context(data, dim, state_scope, rng)[:3]}\\n...\")" ] } ], diff --git a/doc/devlog/2024-07-08.ipynb b/doc/devlog/2024-07-08.ipynb index 1ce578c0..a6493e8a 100644 --- a/doc/devlog/2024-07-08.ipynb +++ b/doc/devlog/2024-07-08.ipynb @@ -28,11 +28,10 @@ "import numpy as np\n", "\n", "from epymorph import *\n", + "from epymorph.geography.us_census import StateScope\n", "\n", - "# We'll use the Pei geo to source geo data.\n", - "# This will eventually be replaced using a geo scope and ADRIOs directly.\n", - "pei_geo = geo_library['pei']()\n", - "# scope = StateScope.in_states_by_code(['FL', 'GA', 'MD', 'NC', 'SC', 'VA'])\n", + "# We'll use the same geographic scope: six southern states (aka, the Pei states).\n", + "scope = StateScope.in_states_by_code(['FL', 'GA', 'MD', 'NC', 'SC', 'VA'])\n", "\n", "# And we will use an SIRS model for our demo. Might as well load it now.\n", "sirs_ipm = ipm_library['sirs']()" @@ -70,8 +69,8 @@ "single_loc_initializer = init.SingleLocation(\n", " location=0,\n", " seed_size=10_000,\n", - " initial_compartment=sirs_ipm.compartments_by(\"S\")[0],\n", - " infection_compartment=sirs_ipm.compartments_by(\"I\")[0],\n", + " initial_compartment=sirs_ipm.compartment_by_name(\"S\"),\n", + " infection_compartment=sirs_ipm.compartment_by_name(\"I\"),\n", ")" ] }, @@ -99,7 +98,7 @@ } ], "source": [ - "single_loc_initializer.attributes" + "single_loc_initializer.requirements" ] }, { @@ -124,12 +123,12 @@ "• 2015-01-01 to 2015-05-31 (150 days)\n", "• 6 geo nodes\n", "|####################| 100% \n", - "Runtime: 0.231s\n" + "Runtime: 0.242s\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -139,12 +138,14 @@ } ], "source": [ - "rume = Rume.single_strata(\n", + "from epymorph.adrio import acs5, us_tiger\n", + "\n", + "rume = SingleStrataRume.build(\n", " ipm=sirs_ipm,\n", " # And we haven't mentioned it so far, but we'll also use the centroids movement model.\n", " mm=mm_library['centroids'](),\n", " init=single_loc_initializer,\n", - " scope=pei_geo.spec.scope,\n", + " scope=scope,\n", " time_frame=TimeFrame.of(\"2015-01-01\", duration_days=150),\n", " params={\n", " # IPM params\n", @@ -153,18 +154,18 @@ " 'xi': 1 / 90,\n", " # movement params\n", " 'phi': 60.0,\n", - " 'centroid': pei_geo['centroid'],\n", + " 'centroid': us_tiger.InternalPoint(),\n", " # population is needed by both the MM and our initializer\n", - " 'population': pei_geo['population'],\n", + " 'population': acs5.Population(),\n", " # geo labels\n", - " 'meta::geo::label': pei_geo['label'],\n", + " 'meta::geo::label': us_tiger.PostalCode(),\n", " },\n", ")\n", "\n", "sim = BasicSimulator(rume)\n", - "with sim_messaging(sim):\n", + "with sim_messaging():\n", " out = sim.run()\n", - " plot_event(out, rume.ipm.events_by_dst(\"I\")[0]) # plot of daily new infections" + " plot_event(out, rume.ipm.event_by_name(\"S->I\")) # plot of daily new infections" ] }, { @@ -207,7 +208,7 @@ ")\n", "\n", "# And notice the Explicit initializer doesn't use any other data attribute; it doesn't need any!\n", - "explicit_initializer.attributes" + "explicit_initializer.requirements" ] }, { @@ -223,12 +224,12 @@ "• 2015-01-01 to 2015-05-31 (150 days)\n", "• 6 geo nodes\n", "|####################| 100% \n", - "Runtime: 0.241s\n" + "Runtime: 0.249s\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -238,11 +239,11 @@ } ], "source": [ - "rume = Rume.single_strata(\n", + "rume = SingleStrataRume.build(\n", " ipm=sirs_ipm,\n", " mm=mm_library['centroids'](),\n", " init=explicit_initializer,\n", - " scope=pei_geo.spec.scope,\n", + " scope=scope,\n", " time_frame=TimeFrame.of(\"2015-01-01\", duration_days=150),\n", " params={\n", " # IPM params\n", @@ -251,17 +252,16 @@ " 'xi': 1 / 90,\n", " # movement params\n", " 'phi': 60.0,\n", - " 'centroid': pei_geo['centroid'],\n", - " 'population': pei_geo['population'],\n", - " # geo labels\n", - " 'meta::geo::label': pei_geo['label'],\n", + " 'centroid': us_tiger.InternalPoint(),\n", + " 'population': acs5.Population(),\n", + " 'meta::geo::label': us_tiger.PostalCode(),\n", " },\n", ")\n", "\n", "sim = BasicSimulator(rume)\n", - "with sim_messaging(sim):\n", + "with sim_messaging():\n", " out = sim.run()\n", - " plot_event(out, rume.ipm.events_by_dst(\"I\")[0]) # plot of daily new infections" + " plot_event(out, rume.ipm.event_by_name(\"S->I\")) # plot of daily new infections" ] }, { @@ -284,12 +284,12 @@ "• 2015-01-01 to 2015-05-31 (150 days)\n", "• 6 geo nodes\n", "|####################| 100% \n", - "Runtime: 0.231s\n" + "Runtime: 0.251s\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -314,11 +314,11 @@ "# NOTE: Florida and South Carolina start out with a sizable infected population.\n", "# We'll see that in the graph.\n", "\n", - "rume = Rume.single_strata(\n", + "rume = SingleStrataRume.build(\n", " ipm=sirs_ipm,\n", " mm=mm_library['centroids'](),\n", " init=proportional_initializer,\n", - " scope=pei_geo.spec.scope,\n", + " scope=scope,\n", " time_frame=TimeFrame.of(\"2015-01-01\", duration_days=150),\n", " params={\n", " # IPM params\n", @@ -327,18 +327,16 @@ " 'xi': 1 / 90,\n", " # movement params\n", " 'phi': 60.0,\n", - " 'centroid': pei_geo['centroid'],\n", - " # population is needed by both the MM and our initializer\n", - " 'population': pei_geo['population'],\n", - " # geo labels\n", - " 'meta::geo::label': pei_geo['label'],\n", + " 'centroid': us_tiger.InternalPoint(),\n", + " 'population': acs5.Population(),\n", + " 'meta::geo::label': us_tiger.PostalCode(),\n", " },\n", ")\n", "\n", "sim = BasicSimulator(rume)\n", - "with sim_messaging(sim):\n", + "with sim_messaging():\n", " out = sim.run()\n", - " plot_event(out, rume.ipm.events_by_dst(\"I\")[0]) # plot of daily new infections" + " plot_event(out, rume.ipm.event_by_name(\"S->I\")) # plot of daily new infections" ] }, { @@ -361,14 +359,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "FL @ t=0, gamma=1 : [18680920 6520 6520]\n", - "FL @ t=0, gamma=1/4: [18474257 16853 16853]\n", - "FL @ t=0, gamma=1/8: [18397983 20666 20666]\n" + "FL @ t=0, gamma=1 : [21069859 7353 7353]\n", + "FL @ t=0, gamma=1/4: [20836768 19008 19008]\n", + "FL @ t=0, gamma=1/8: [20750741 23309 23309]\n" ] } ], "source": [ - "from epymorph.data_type import SimArray\n", "from epymorph.initializer import Initializer\n", "\n", "# It's convenient to define these attributes into variables.\n", @@ -380,10 +377,10 @@ "class MyInitializer(Initializer):\n", "\n", " # First we declare the attributes we require.\n", - " attributes = [POPULATION, GAMMA]\n", + " requirements = [POPULATION, GAMMA]\n", "\n", " # Now implement `evaluate()`...\n", - " def evaluate(self) -> SimArray:\n", + " def evaluate(self):\n", " # This function needs to return an (N,C) array of integers:\n", " # - N: the number of geo nodes\n", " # - C: the number of disease compartments\n", @@ -406,11 +403,11 @@ "\n", "\n", "# Create a RUME with an instance of the MyInitializer class.\n", - "rume = Rume.single_strata(\n", + "rume = SingleStrataRume.build(\n", " ipm=sirs_ipm,\n", " mm=mm_library['centroids'](),\n", " init=MyInitializer(),\n", - " scope=pei_geo.spec.scope,\n", + " scope=scope,\n", " time_frame=TimeFrame.of(\"2015-01-01\", duration_days=150),\n", " params={\n", " # IPM params\n", @@ -419,11 +416,9 @@ " 'xi': 1 / 90,\n", " # movement params\n", " 'phi': 60.0,\n", - " 'centroid': pei_geo['centroid'],\n", - " # population is needed by both the MM and our initializer\n", - " 'population': pei_geo['population'],\n", - " # geo labels\n", - " 'meta::geo::label': pei_geo['label'],\n", + " 'centroid': us_tiger.InternalPoint(),\n", + " 'population': acs5.Population(),\n", + " 'meta::geo::label': us_tiger.PostalCode(),\n", " },\n", ")\n", "\n", diff --git a/doc/devlog/2024-07-10.ipynb b/doc/devlog/2024-07-10.ipynb index 37272cf3..8b4f969b 100644 --- a/doc/devlog/2024-07-10.ipynb +++ b/doc/devlog/2024-07-10.ipynb @@ -21,27 +21,26 @@ "metadata": {}, "outputs": [], "source": [ - "from epymorph.data_shape import Shapes\n", - "from epymorph.data_type import CentroidDType, CentroidType\n", - "from epymorph.geo.adrio.census.adrio_census import ADRIOMakerCensus\n", - "from epymorph.geo.spec import Year\n", + "from epymorph import *\n", + "from epymorph.adrio import acs5, commuting_flows, us_tiger\n", + "from epymorph.data_type import CentroidDType\n", "from epymorph.geography.us_census import CountyScope\n", - "from epymorph.simulation import AttributeDef\n", - "\n", - "# make adrios for one attribute from each fetch method\n", - "maker = ADRIOMakerCensus()\n", - "geoids = ['04001', '04003', '04005', '04013', '04017']\n", - "scope = CountyScope.in_counties(geoids)\n", - "time_period = Year(2020)\n", - "attribs = [\n", - " AttributeDef('population', int, Shapes.N),\n", - " AttributeDef('centroid', CentroidType, Shapes.N),\n", - " AttributeDef('commuters', int, Shapes.NxN),\n", - "]\n", - "\n", - "population = maker.make_adrio(attribs[0], scope, time_period)\n", - "centroid = maker.make_adrio(attribs[1], scope, time_period)\n", - "commuters = maker.make_adrio(attribs[2], scope, time_period)" + "from epymorph.rume import SingleStrataRume\n", + "\n", + "# make a placeholder rume for testing ADRIOs\n", + "rume = SingleStrataRume.build(\n", + " ipm=ipm_library[\"no\"](),\n", + " mm=mm_library[\"no\"](),\n", + " init=init.NoInfection(),\n", + " scope=CountyScope.in_counties(\n", + " ['04001', '04003', '04005', '04013', '04017'], year=2020),\n", + " time_frame=TimeFrame.year(2020),\n", + " params={\n", + " \"population\": acs5.Population(),\n", + " \"centroid\": us_tiger.GeometricCentroid(),\n", + " \"commuters\": commuting_flows.Commuters(),\n", + " }\n", + ")" ] }, { @@ -62,24 +61,29 @@ "source": [ "import numpy as np\n", "\n", + "from epymorph.simulator.data import evaluate_param\n", "from epymorph.util import check_ndarray, match\n", "\n", - "T = time_period.days\n", - "N = len(population.get_value())\n", + "population = evaluate_param(rume, \"population\")\n", + "centroid = evaluate_param(rume, \"centroid\")\n", + "commuters = evaluate_param(rume, \"commuters\")\n", + "\n", + "T = rume.dim.days\n", + "N = rume.dim.nodes\n", "\n", "# validate datatype and shape\n", "check_ndarray(\n", - " population.get_value(),\n", + " population,\n", " dtype=match.dtype(int),\n", " shape=match.shape_literal((N,))\n", ")\n", "check_ndarray(\n", - " centroid.get_value(),\n", + " centroid,\n", " dtype=match.dtype(CentroidDType),\n", " shape=match.shape_literal((N,))\n", ")\n", "check_ndarray(\n", - " commuters.get_value(),\n", + " commuters,\n", " dtype=match.dtype(int),\n", " shape=match.shape_literal((N, N))\n", ")\n", @@ -102,11 +106,11 @@ " [706, 14, 1347, 592, 30520]]\n", "\n", "# validate values and sort order\n", - "if np.array_equal(population_array, population.get_value()):\n", + "if np.array_equal(population_array, population):\n", " print('AC5 attribute validation passed.')\n", - "if np.allclose(centroid_array.tolist(), centroid.get_value().tolist()):\n", + "if np.allclose(centroid_array.tolist(), centroid.tolist()):\n", " print('Shapefile attribute validation passed.')\n", - "if np.array_equal(commuters_matrix, commuters.get_value()):\n", + "if np.array_equal(commuters_matrix, commuters):\n", " print('Commuting flows attribute validation passed.')" ] }, @@ -123,21 +127,14 @@ "metadata": {}, "outputs": [], "source": [ - "from io import BytesIO\n", - "from urllib.request import urlopen\n", - "\n", "from geopandas import read_file\n", "\n", "# load in shapefile data for use in centroid caclulations\n", - "with urlopen(\"https://www2.census.gov/geo/tiger/TIGER2020/COUNTY/tl_2020_us_county.zip\") as f:\n", - " file_buffer = BytesIO()\n", - " file_buffer.write(f.read())\n", - " file_buffer.seek(0)\n", - " gdf = read_file(file_buffer, engine=\"fiona\", ignore_geometry=False,\n", - " include_fields=[\"GEOID\", \"STUSPS\"])\n", - " gdf = gdf[gdf['GEOID'].isin(geoids)]\n", - " gdf.sort_values(by='GEOID', inplace=True)\n", - " geometry = gdf['geometry'].to_list()" + "url = \"https://www2.census.gov/geo/tiger/TIGER2020/COUNTY/tl_2020_us_county.zip\"\n", + "gdf = read_file(url, engine=\"fiona\", ignore_geometry=False,\n", + " include_fields=[\"GEOID\", \"STUSPS\"])\n", + "gdf = gdf[gdf['GEOID'].isin(rume.scope.get_node_ids())]\n", + "gdf.sort_values(by='GEOID', inplace=True)" ] }, { @@ -175,7 +172,7 @@ "source": [ "# calculate centroids manually using polygon centroid formula https://en.wikipedia.org/wiki/Centroid#Of_a_polygon\n", "centroids = []\n", - "for county in geometry:\n", + "for county in gdf['geometry']:\n", " sum = 0.0\n", " coords = list(county.exterior.coords)\n", " for point in range(0, len(coords) - 1):\n", diff --git a/doc/devlog/2024-07-12-v0.6.ipynb b/doc/devlog/2024-07-12-v0.6.ipynb new file mode 100644 index 00000000..61de3014 --- /dev/null +++ b/doc/devlog/2024-07-12-v0.6.ipynb @@ -0,0 +1,680 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# epymorph: migrating from v0.5 to v0.6\n", + "\n", + "This one was actually written well after the original interactive workshop, but it's still a good comparison: this is the v0.6 version.\n", + "\n", + "_Updated 2024-08-21_" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running simulation (BasicSimulator):\n", + "• 2015-01-01 to 2016-08-23 (600 days)\n", + "• 1 geo nodes\n", + "|####################| 100% \n", + "Runtime: 0.137s\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Example 1: basic simulation\n", + "# watch for changes -- SingleStrataRume class, no more geo_library, hard-coded \"geo\" values\n", + "\n", + "from epymorph import *\n", + "from epymorph.geography.scope import CustomScope\n", + "\n", + "rume = SingleStrataRume.build(\n", + " ipm=ipm_library['sirh'](),\n", + " mm=mm_library['no'](),\n", + " init=init.SingleLocation(location=0, seed_size=100),\n", + " scope=CustomScope([\"AZ\"]),\n", + " time_frame=TimeFrame.of(\"2015-01-01\", 600),\n", + " params={\n", + " 'beta': 0.4,\n", + " 'gamma': 1 / 4,\n", + " 'xi': 1 / 90,\n", + " 'hospitalization_prob': 0.1,\n", + " 'hospitalization_duration': 7,\n", + " 'population': [100_000],\n", + " },\n", + ")\n", + "\n", + "sim = BasicSimulator(rume)\n", + "with sim_messaging():\n", + " output = sim.run(\n", + " # params={'beta': 0.9},\n", + " rng_factory=default_rng(42),\n", + " )\n", + "\n", + "plot_pop(output, pop_idx=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running simulation (BasicSimulator):\n", + "• 2015-01-01 to 2015-05-31 (150 days)\n", + "• 6 geo nodes\n", + "|####################| 100% \n", + "Runtime: 0.280s\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Example 2: custom parameter functions\n", + "# watch for changes -- can still load the old pei geo data directly (this is a temporary convenience)\n", + "\n", + "from math import pi, sin\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from epymorph import *\n", + "from epymorph.adrio import us_tiger\n", + "from epymorph.data import pei\n", + "from epymorph.params import ParamFunctionTimeAndNode\n", + "from epymorph.simulator.data import evaluate_param\n", + "\n", + "POPULATION = AttributeDef('population', int, Shapes.N)\n", + "\n", + "\n", + "class Beta(ParamFunctionTimeAndNode):\n", + "\n", + " requirements = [POPULATION]\n", + "\n", + " def evaluate1(self, day: int, node_index: int) -> float:\n", + " x = 0.35 + 0.05 * sin(2 * pi * (day / self.dim.days))\n", + " cutoff = 50 + (node_index * 3)\n", + " if day > cutoff:\n", + " pop = self.data(POPULATION)[node_index]\n", + " cut = 0.3 if pop < 9_000_000 else 0.25\n", + " x -= cut\n", + " return x\n", + "\n", + "\n", + "rume = SingleStrataRume.build(\n", + " scope=pei.pei_scope,\n", + " ipm=ipm_library['sirs'](),\n", + " mm=mm_library['pei'](),\n", + " init=init.SingleLocation(location=0, seed_size=10_000),\n", + " time_frame=TimeFrame.of(\"2015-01-01\", 150),\n", + " params={\n", + " 'beta': Beta(),\n", + " 'gamma': 1 / 6,\n", + " 'xi': 1 / 90,\n", + " 'theta': 0.1,\n", + " 'move_control': 0.9,\n", + " 'population': pei.pei_population,\n", + " 'commuters': pei.pei_commuters,\n", + " 'meta::geo::label': us_tiger.PostalCode(),\n", + " },\n", + ")\n", + "\n", + "sim = BasicSimulator(rume)\n", + "with sim_messaging():\n", + " out = sim.run()\n", + "\n", + "# For the sake of graphing beta, I need to evaluate beta in the context of the RUME.\n", + "beta_values = evaluate_param(rume, 'beta')\n", + "\n", + "\n", + "### GRAPHS ###\n", + "fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=(8, 8))\n", + "x_axis = np.arange(out.dim.days)\n", + "ax1.set(title='New infections', ylabel='infections')\n", + "ax1.plot(x_axis, out.incidence_per_day[:, :, 0], label=out.geo_labels)\n", + "ax1.legend()\n", + "\n", + "ax2.set(title='beta function', ylabel='beta', xlabel='days')\n", + "ax2.plot(x_axis, beta_values, label=out.geo_labels)\n", + "ax2.legend()\n", + "\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running simulation (BasicSimulator):\n", + "• 2015-01-01 to 2015-02-20 (50 days)\n", + "• 6 geo nodes\n", + "|####################| 100% \n", + "Runtime: 0.033s\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Example 3: custom initialization\n", + "# watch for changes -- defining initializers, class parameterization\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from numpy.typing import NDArray\n", + "\n", + "from epymorph import *\n", + "from epymorph.adrio import us_tiger\n", + "from epymorph.data import pei\n", + "from epymorph.initializer import Initializer\n", + "\n", + "POPULATION = AttributeDef('population', int, Shapes.N)\n", + "\n", + "\n", + "class MyInitializer(Initializer):\n", + "\n", + " requirements = [POPULATION]\n", + "\n", + " infected_multiplier: int\n", + "\n", + " def __init__(self, infected_multiplier: int = 100_000):\n", + " self.infected_multiplier = infected_multiplier\n", + "\n", + " def evaluate(self) -> NDArray[SimDType]:\n", + " _, N, C, _ = self.dim.TNCE\n", + " initial = np.zeros(shape=(N, C), dtype=SimDType)\n", + " initial[:, 0] = self.data(POPULATION)\n", + " for n in range(N):\n", + " initial[n, 0] -= self.infected_multiplier * (n + 1)\n", + " initial[n, 1] += self.infected_multiplier * (n + 1)\n", + " return initial\n", + "\n", + "\n", + "rume = SingleStrataRume.build(\n", + " scope=pei.pei_scope,\n", + " ipm=ipm_library['pei'](),\n", + " mm=mm_library['no'](),\n", + " init=MyInitializer(),\n", + " # init=MyInitializer(10000),\n", + " time_frame=TimeFrame.of(\"2015-01-01\", duration_days=50),\n", + " params={\n", + " 'infection_duration': 4,\n", + " 'immunity_duration': 90,\n", + " 'population': pei.pei_population,\n", + " 'humidity': pei.pei_humidity,\n", + " 'meta::geo::label': us_tiger.PostalCode(),\n", + " },\n", + ")\n", + "\n", + "sim = BasicSimulator(rume)\n", + "with sim_messaging():\n", + " out = sim.run()\n", + "\n", + "\n", + "### GRAPHS ###\n", + "fig, ax = plt.subplots()\n", + "ax.set_title(f\"Infected individuals\")\n", + "ax.set_xlabel('days')\n", + "ax.set_ylabel('individuals')\n", + "ax.plot(out.ticks_in_days, out.prevalence[:, :, 1], label=out.geo_labels)\n", + "ax.legend()\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The census tracts we'll model:\n", + "['04013010102' '04013050603' '04013061027' '04013061061' '04013071913'\n", + " '04013092305' '04013093104' '04013103612' '04013104502' '04013106502'\n", + " '04013108601' '04013110502' '04013112507' '04013114000' '04013116607'\n", + " '04013116732' '04013216838' '04013217502' '04013319710' '04013420210'\n", + " '04013421102']\n" + ] + } + ], + "source": [ + "# Example 4: construct a multistrata RUME\n", + "# changes -- MultistrataRumeBuilder class-based syntax\n", + "# notice we no longer need to do the awkward \"cheater\" step of creating a geo!\n", + "\n", + "from functools import reduce\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sympy import Max\n", + "\n", + "from epymorph import *\n", + "from epymorph.adrio import acs5, us_tiger\n", + "from epymorph.compartment_model import (MultistrataModelSymbols, TransitionDef,\n", + " edge)\n", + "from epymorph.geography.us_census import TractScope\n", + "from epymorph.rume import MultistrataRumeBuilder\n", + "from epymorph.simulator.data import evaluate_param\n", + "\n", + "# Select 21 census tracts out of Maricopa County, AZ\n", + "maricopa_tracts = TractScope.in_counties(['04013'], year=2020)\n", + "\n", + "subset_tracts = maricopa_tracts.get_node_ids()[::33][0:21]\n", + "\n", + "scope = TractScope.in_tracts(subset_tracts.tolist())\n", + "\n", + "print(f\"The census tracts we'll model:\\n{scope.get_node_ids()}\")\n", + "\n", + "\n", + "class MyRume(MultistrataRumeBuilder):\n", + " strata = [\n", + " Gpm(\n", + " name=\"age_00-19\",\n", + " ipm=ipm_library['sirs'](),\n", + " mm=mm_library['centroids'](),\n", + " init=init.NoInfection(),\n", + " ),\n", + " Gpm(\n", + " name=\"age_20-59\",\n", + " ipm=ipm_library['sirs'](),\n", + " mm=mm_library['centroids'](),\n", + " init=init.SingleLocation(location=0, seed_size=100),\n", + " ),\n", + " Gpm(\n", + " name=\"age_60-79\",\n", + " ipm=ipm_library['sirs'](),\n", + " mm=mm_library['no'](),\n", + " init=init.NoInfection(),\n", + " ),\n", + " ]\n", + "\n", + " meta_requirements = [\n", + " AttributeDef(\"beta_12\", float, Shapes.TxN),\n", + " AttributeDef(\"beta_13\", float, Shapes.TxN),\n", + " AttributeDef(\"beta_21\", float, Shapes.TxN),\n", + " AttributeDef(\"beta_23\", float, Shapes.TxN),\n", + " AttributeDef(\"beta_31\", float, Shapes.TxN),\n", + " AttributeDef(\"beta_32\", float, Shapes.TxN),\n", + " ]\n", + "\n", + " def meta_edges(self, symbols: MultistrataModelSymbols) -> list[TransitionDef]:\n", + " # extract compartment symbols by strata\n", + " S_1, I_1, R_1 = symbols.strata_compartments(\"age_00-19\")\n", + " S_2, I_2, R_2 = symbols.strata_compartments(\"age_20-59\")\n", + " S_3, I_3, R_3 = symbols.strata_compartments(\"age_60-79\")\n", + "\n", + " # extract compartment totals by strata\n", + " N_1 = Max(1, S_1 + I_1 + R_1)\n", + " N_2 = Max(1, S_2 + I_2 + R_2)\n", + " N_3 = Max(1, S_3 + I_3 + R_3)\n", + "\n", + " # extract meta attributes\n", + " beta_12, beta_13, beta_21, beta_23, beta_31, beta_32 = symbols.all_meta_requirements\n", + "\n", + " return [\n", + " edge(S_1, I_1, rate=S_1 * beta_12 * I_2 / N_2), # 2 infects 1\n", + " edge(S_1, I_1, rate=S_1 * beta_13 * I_3 / N_3), # 3 infects 1\n", + " edge(S_2, I_2, rate=S_2 * beta_21 * I_1 / N_1), # 1 infects 2\n", + " edge(S_2, I_2, rate=S_2 * beta_23 * I_3 / N_3), # 3 infects 2\n", + " edge(S_3, I_3, rate=S_3 * beta_31 * I_1 / N_1), # 1 infects 3\n", + " edge(S_3, I_3, rate=S_3 * beta_32 * I_2 / N_2), # 2 infects 3\n", + " ]\n", + "\n", + "\n", + "rume = MyRume().build(\n", + " scope=scope,\n", + " time_frame=TimeFrame.of(\"2020-01-01\", 180),\n", + " params={\n", + " # IPM params\n", + " \"gpm:age_00-19::ipm::beta\": 0.05,\n", + " \"gpm:age_20-59::ipm::beta\": 0.20,\n", + " \"gpm:age_60-79::ipm::beta\": 0.35,\n", + " \"*::ipm::gamma\": 1 / 10,\n", + " \"*::ipm::xi\": 1 / 90,\n", + " \"meta::ipm::beta_12\": 0.05,\n", + " \"meta::ipm::beta_13\": 0.05,\n", + " \"meta::ipm::beta_21\": 0.20,\n", + " \"meta::ipm::beta_23\": 0.20,\n", + " \"meta::ipm::beta_31\": 0.35,\n", + " \"meta::ipm::beta_32\": 0.35,\n", + "\n", + " # MM params\n", + " \"gpm:age_00-19::mm::phi\": 20.0,\n", + " \"gpm:age_20-59::mm::phi\": 40.0,\n", + " \"gpm:age_60-79::mm::phi\": 30.0,\n", + "\n", + " # ADRIO things!\n", + " \"*::*::centroid\": us_tiger.InternalPoint(),\n", + " \"*::*::population_by_age_table\": acs5.PopulationByAgeTable(),\n", + " \"gpm:age_00-19::*::population\": acs5.PopulationByAge(0, 19),\n", + " \"gpm:age_20-59::*::population\": acs5.PopulationByAge(20, 59),\n", + " \"gpm:age_60-79::*::population\": acs5.PopulationByAge(60, 79),\n", + " },\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running simulation (BasicSimulator):\n", + "• 2020-01-01 to 2020-06-29 (180 days)\n", + "• 21 geo nodes\n", + "|####################| 100% \n", + "Runtime: 3.141s\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Example 4: now run it\n", + "\n", + "sim = BasicSimulator(rume)\n", + "with sim_messaging():\n", + " out = sim.run()\n", + "\n", + "\n", + "# calc total new infections (depending on the IPM this may represent this as separate events)\n", + "infection_events = [\n", + " rume.ipm.events_by_dst(\"I_age_00-19\"),\n", + " rume.ipm.events_by_dst(\"I_age_20-59\"),\n", + " rume.ipm.events_by_dst(\"I_age_60-79\"),\n", + "]\n", + "\n", + "infections = np.array([\n", + " reduce(lambda a, b: a + b,\n", + " (out.incidence_per_day[:, :, j].sum(axis=1) for j in infection_events[i]))\n", + " for i in [0, 1, 2]\n", + "])\n", + "\n", + "\n", + "### GRAPHS ###\n", + "\n", + "pop_00_19 = evaluate_param(rume, 'gpm:age_00-19::_::population')\n", + "pop_20_59 = evaluate_param(rume, 'gpm:age_20-59::_::population')\n", + "pop_60_79 = evaluate_param(rume, 'gpm:age_60-79::_::population')\n", + "\n", + "# Plot infections by age class\n", + "age_label = ['age [0,20)', 'age [20,60)', 'age [60,80)']\n", + "age_total_thousands = np.array([pop_00_19, pop_20_59, pop_60_79])\\\n", + " .sum(axis=1) / 1000\n", + "t_window = slice(0, None)\n", + "\n", + "# Day of Peak Infection by age class\n", + "dpi = [\n", + " int(np.argmax(infections[i]))\n", + " for i in [0, 1, 2]\n", + "]\n", + "max_y_value = infections.max()\n", + "dpi_x_pos = 80 # an absolute x offset (to keep them horizontally aligned)\n", + "dpi_y_pos = -0.025 * max_y_value # an offset from the peak's y position\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True, figsize=(8, 6))\n", + "x_axis = np.arange(out.dim.days)[t_window]\n", + "\n", + "ax1.set_title('New infections by age class')\n", + "ax1.set_ylabel('occurrences')\n", + "for i in [0, 1, 2]:\n", + " color = ax1._get_lines.get_next_color()\n", + " y_axis = infections[i][t_window]\n", + " ax1.plot(x_axis, y_axis, color=color, label=age_label[i])\n", + " # Mark day of peak infection\n", + " d = dpi[i]\n", + " ax1.text(dpi_x_pos, y_axis[d] + dpi_y_pos, f\"day {d}\", color=color)\n", + " ax1.hlines(y=y_axis[d], xmin=d, xmax=dpi_x_pos - 1,\n", + " color=color, linewidth=0.5, linestyle='dashed')\n", + "ax1.legend()\n", + "\n", + "ax2.set_title('New infections by age class (per thousand)')\n", + "ax2.set_xlabel('days')\n", + "ax2.set_ylabel('occurrences per thousand')\n", + "for i in [0, 1, 2]:\n", + " y_axis = infections[i][t_window] / age_total_thousands[i]\n", + " ax2.plot(x_axis, y_axis, label=age_label[i])\n", + "ax2.legend()\n", + "\n", + "fig.tight_layout()\n", + "plt.show()\n", + "\n", + "\n", + "# Plot infections by location\n", + "pop_total_thousands = (pop_00_19 + pop_20_59 + pop_60_79) / 1000\n", + "\n", + "t_window = slice(0, 50)\n", + "\n", + "infections_by_loc = out.incidence_per_day[:, :, rume.ipm.events_by_dst(\"I_age_*\")]\\\n", + " .sum(axis=2, dtype=np.int64)\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "x_axis = np.arange(out.dim.days)[t_window]\n", + "ax.set_title('New infections by location (per thousand)')\n", + "ax.set_xlabel('days')\n", + "ax.set_ylabel('occurrences per thousand')\n", + "for n in range(rume.dim.nodes):\n", + " y_axis = infections_by_loc[t_window, n] / pop_total_thousands[n]\n", + " ax.plot(x_axis, y_axis, linewidth=0.8)\n", + "\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gpm:age_00-19::ipm::beta (type: float, shape: TxN)\n", + " infectivity\n", + "\n", + "gpm:age_00-19::ipm::gamma (type: float, shape: TxN)\n", + " progression from infected to recovered\n", + "\n", + "gpm:age_00-19::ipm::xi (type: float, shape: TxN)\n", + " progression from recovered to susceptible\n", + "\n", + "gpm:age_20-59::ipm::beta (type: float, shape: TxN)\n", + " infectivity\n", + "\n", + "gpm:age_20-59::ipm::gamma (type: float, shape: TxN)\n", + " progression from infected to recovered\n", + "\n", + "gpm:age_20-59::ipm::xi (type: float, shape: TxN)\n", + " progression from recovered to susceptible\n", + "\n", + "gpm:age_60-79::ipm::beta (type: float, shape: TxN)\n", + " infectivity\n", + "\n", + "gpm:age_60-79::ipm::gamma (type: float, shape: TxN)\n", + " progression from infected to recovered\n", + "\n", + "gpm:age_60-79::ipm::xi (type: float, shape: TxN)\n", + " progression from recovered to susceptible\n", + "\n", + "meta::ipm::beta_12 (type: float, shape: TxN)\n", + "\n", + "meta::ipm::beta_13 (type: float, shape: TxN)\n", + "\n", + "meta::ipm::beta_21 (type: float, shape: TxN)\n", + "\n", + "meta::ipm::beta_23 (type: float, shape: TxN)\n", + "\n", + "meta::ipm::beta_31 (type: float, shape: TxN)\n", + "\n", + "meta::ipm::beta_32 (type: float, shape: TxN)\n", + "\n", + "gpm:age_00-19::mm::population (type: int, shape: N)\n", + " The total population at each node.\n", + "\n", + "gpm:age_00-19::mm::centroid (type: [(longitude, float), (latitude, float)], shape: N)\n", + " The centroids for each node as (longitude, latitude) tuples.\n", + "\n", + "gpm:age_00-19::mm::phi (type: float, shape: S, default: 40.0)\n", + " Influences the distance that movers tend to travel.\n", + "\n", + "gpm:age_00-19::mm::commuter_proportion (type: float, shape: S, default: 0.1)\n", + " Decides what proportion of the total population should be\n", + " commuting normally.\n", + "\n", + "gpm:age_00-19::init::population (type: int, shape: N)\n", + " The population at each geo node.\n", + "\n", + "gpm:age_20-59::mm::population (type: int, shape: N)\n", + " The total population at each node.\n", + "\n", + "gpm:age_20-59::mm::centroid (type: [(longitude, float), (latitude, float)], shape: N)\n", + " The centroids for each node as (longitude, latitude) tuples.\n", + "\n", + "gpm:age_20-59::mm::phi (type: float, shape: S, default: 40.0)\n", + " Influences the distance that movers tend to travel.\n", + "\n", + "gpm:age_20-59::mm::commuter_proportion (type: float, shape: S, default: 0.1)\n", + " Decides what proportion of the total population should be\n", + " commuting normally.\n", + "\n", + "gpm:age_20-59::init::population (type: int, shape: N)\n", + " The population at each geo node.\n", + "\n", + "gpm:age_60-79::init::population (type: int, shape: N)\n", + " The population at each geo node.\n", + "\n" + ] + } + ], + "source": [ + "# Example 4...\n", + "print(rume.params_description())" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Example 4...\n", + "render(rume.strata[0].ipm)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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FN99809XVFRMTcwM3ZTQajUYjk8m8gXPh9igpKcnJyRk5cuSwYcNu7ApFRUXkzhoXLlwQi8U8Hq+wsDA/P7+oqKipqYnP5/N4vH5O/+CDDxoaGsxm865duzgcjkgkOnz4cEFBQVZWVk9Pj4+Pj5ubm6OxWCxeuHDhxIkTKRTKt99+W1VVlZCQ4Pwjee2ysrKOHTu2bdu2zs7OgIAADodz8uTJ4uLigoKCtrY2oVDo/LqXWrVqlUql0mq1//3vfwMCAvh8/i+//FJWVnbs2DGdTicUCh1v+6KiotDQUB6PFxYWJpFImEzm0aNHo6Ojb2BK/6FDh06cOPHNN9+Qv8eYTObBgwfLysry8vLkcnlAQED/P2tLly5ls9m9vb2bN28ePHiwm5vbd999V11dffDgQbvdLhaLaTTa9YYEN+CDDz4oKCi4K7rY7h0//vhjenp6fHy8qwMBgAEIHfwAf2A0GgsKChISEm7sdLPZXFdXV15e/vbbb7e3t7/++uujRo2qra2trKxcs2ZNW1vbP/7xj5SUlH6uoNFocnNzlUqlj48PQRA6na6oqKimpmb9+vXnz59///33P//884tOEQgETz311OzZs7///vvt27cvW7bsesPWaDR1dXV+fn6ZmZnHjx//5z//uWvXrrNnz7a0tLz88stk1p2UlNTPFXQ63d69e3fs2DF79uwyG5KsAAAgAElEQVQ333xTqVQWFBTIZLKVK1cWFhZu3br17bffvtK5Wq22o6MjLCwsJCQkMzPz559/Xrdu3XfffdfZ2Xn69Gm1Wn29t+Nw7NixvLy8t95664avALdabW0tjUYj3+034MKFC93d3RMmTJgzZ84LL7zg7u4+YcKEgoKC0aNHjxo16t133+Xz+f2vse/o6Pjggw9EItFbb70VHR3d0NBw6tSpqVOnLliw4M033+TxeI7qkg6pqampqamZmZmvvPJKc3PzDewjeO7cObVaPW3atDlz5sybNy8wMDAqKio3N3fWrFkxMTGvvvpqcHCwl5dXP1eQSqWbN28OCQn58MMPAwMDa2trT548+dxzz0VFRb3wwgt+fn79VO5sb283Go3XG/PZs2f1ev3cuXPnzJmTlpYWHx/P4XCysrKWL18uFouffPLJIUOGOFc3uFRra+u8efNiY2M3b97s6elZVVV14sSJtWvX+vr6zp8//8MPP/T19b3eqODGjB49eufOna6OAn5HoVBcHQIADFgo2gfwB0ajsbCw8Ibn8+v1+vz8fHLojEql0un0I0eOFBQUOB/Jzc3t5wqenp7Tpk1zjL1oNJrS0lLHU51OV11dTRBEb2/vwYMHVSqV40Qul5uQkHBjhbjMZnNLS0tJSQmDwRAIBFQqtbCwsKKigpxmT6fTzWbz6dOn+7lCaGjoF1980dzcvH79eh6P193dXVtb69joW6lU1tfXX/bEvr6+rq6u4ODgxsbGiooKJpMpEAisVmtDQ0NgYODkyZNFItEN3BFBED09PQqFAinEHe5PLuDXaDRlZWUymYzL5Xp5eUml0uzsbJVKxWAwCILgcDh1dXUymayfKzz00EP5+fklJSVTpkxhMplVVVUEQTgWmDQ0NPT29hIEkZubW1dXZzKZHCdGRERIJBLyha6XWq0uLCzs6enh8XheXl5NTU0nT540mUzkNHt3d/fy8vKenp5+rrBgwYIzZ86QXRt0Or24uJjL5bLZbPKztbW1Go3msidWV1cHBwdzOJzrjVmpVObm5mo0Gk9PTy8vr7q6uhMnTtBoNCqVShAEj8fLz8/XarX9XGHJkiVNTU2nTp2Kjo6m0Wg5OTlCodCxsqCqqsr5ywsAAAA3BUb4AX5nsViampqMRqMj4bdYLDU1NQcOHHBzc2tpaVm5ciWfz1epVJs2bfL395fJZDExMWlpacXFxYWFhQEBAV1dXe3t7cHBwQRB0Gg0MvWVSqWhoaGOI9c1ZM1kMn19fWtrawmCMJlMjY2NbW1tcXFxZMIwZswYR0utViuXy8kS/QaDoaqqKisri0ql6nS6ZcuWeXh4tLe379y509vbu7u7e/DgwSNHjiwsLKyqqvL19eVyuQ8//LDjlm02W1BQ0JEjR8jphXQ6ncPhXCl/IKlUquzs7La2NrVaHRgYmJiY6OnpKZfLCYLQ6/WNjY1yuTwyMlKr1XZ3dztPsZZKpU1NTUuXLnUMw5rNZhqNFh4eftFLaDSakpKSkpISgUDg7u6u0Wi0Wq1IJJLL5Ww2e9GiRT09Pfn5+eQsbhaLpdfrDx8+HBcXV1VVFRISUlxcXF5ebjAYvLy8Fi5c2P9QJNweCoVCLpfHxsY6SvR3dnaePHlSLpfL5fL77rtv/PjxLBbrwIEDUqnUaDSyWCxyOv2pU6csFguFQhkzZszrr79OnqvRaPz9/a1Wq91uJ3f443K5Op3ObDb3E0NjY2NlZSWTyTSZTJMnT/b19dVqteS7vaOjw263a7VagUBQUVExePBg8geZdP78+bi4OHKpi0wmO3z4sFqtbm5unjlzZlJSEoPB2Llzp06nUyqVnp6e999/v1qtPnnyJJPJtFgs6enpjkKVarU6NDT03LlzDAaD7D7g8XhGo9FisfQTdl1d3YULF+x2u8VimTFjRkBAQG5ursFgIAhCKpU2NjYajUZ3d/e2tjYWi+W8XP/YsWNLliwRCAStra379++3WCz19fWLFi2Kjo6m0+lbtmxhMBgKhUIoFE6bNk0qlR4/flwgEOj1+pkzZ65bt468iFKpjI6O3rdvH4fDIefhe3p6arVam83WT8xVVVU9PT16vd5ms82fPz8oKKigoIC8zZaWlsbGxuTkZCzAAQAAuLkwwg/wO5PJlJub6+XlRRbistlsjY2Nr7322muvvTZ79uy8vDw+n69QKP7617+mp6dnZGT09fWRbd59991ly5bFxsY6D7mTV1Aqlc5HrFbrRUf6x+fz09PTrVZre3t7QUEB2WVAEIRWqz116tRPP/1ENrNYLI2NjUVFRcuWLbNYLGVlZZ988skrr7wyadKksrIyDw+Pc+fOvffeexkZGWPHjlWpVBaLpby8/L///e/ixYvDw8MdfRDnzp379ddfX3311YvCMJvN/fdTREVFrVy5cv78+XFxcd9//z2Xyx07dqzZbG5qaiorK6PT6WSCrdPpyOSfPKuiouLw4cMLFy50ZPvl5eX5+fnPPffcpS+h1WpNJhOTyWxra8vMzOTz+fn5+VOmTImKimpsbDSZTCdPnszLy3vssccCAgJYLFZoaGhcXNzTTz8dFRV18ODB48ePv/TSSzExMefPn0e2f4eora3t6ekJCAjw9PQkCEKr1X7yySd0On3ZsmUUCqWvr4/FYn322WcymWzGjBlCodBkMmm12k2bNgkEgmnTprm5uTnGhLds2RIXFzd69OiLXkKj0fQ/fX3BggWLFy9+4oknKisrS0tLyfKBWq22uLi4s7OTy+WSSXhDQwNZJsBx2T179owfPz4uLk6j0axZsyYsLOz555/v7e01Go0MBmPt2rV2u/2hhx7icrnkj8/7778/aNCgyZMnU6lURzL/73//OyMj49Lp9+QPaT9hP/vss08++eRTTz119OjR2tracePGeXt7q9XqvLw8pVLp7u5O5uHt7e2NjY1kRwBBEGvXrr3vvvuSkpKMRuMrr7wybty4pUuXNjY2UigUOp3+6quvikSimTNnEgRhtVpVKtUbb7yRkZExbtw4s9lstVrJi7z55puLFy++tKiHUql0tLmsFStWPPLII4sWLfruu+9aWloyMjK4XK5SqczKyjIYDB4eHpjVDAAAcNNhhB+AIAjCZrPJZLJNmzZ98803Fovlk08+WbhwIYPBOHLkSGpqqsViaW9vJ4vtGQyGoqKiwYMHS6XSkpKSZ5999vDhw0KhMCcnh/yP/7///S95TavVqtVqw8LCyCmv5BGdThcYGNjd3f3FF184Z/5UKjU8PPzpp5++NLbg4OC1a9c2NDQkJCSIRCJy1F0gEMyaNSs4OPj99993c3PTaDRisfj//u//CILo7e0tKioaMWKEwWBQKBTk/+VKpbK6ujomJqaioqK+vn7hwoUHDx7kcrnFxcXh4eFknT+FQlFRUZGRkTFhwgSZTOb4h95isZjNZj8/v+bm5m+//dY586fT6QkJCePHj9+7dy9BEE8//TSdTqdSqXK5fNiwYWFhYe3t7bGxsefOnSO3ATObzW5ubjwe7+eff6ZQKAaD4ZlnnomIiCCv1t7eXl9fP2XKlMuWORCJRBKJpK6uLi0traenx2w233fffY6p13K5nNxyrKqqKi4uTiQS7d69u7u7WyQSnTt3rrm5edCgQWq1Wq/X31iJNbi5dDrdsWPHNm3aVF5ezufzo6Ojk5KSSkpKWCwWWTCSyWSSfVs1NTVJSUk+Pj7V1dVRUVFKpdJsNjc3N0skkgceeIBcw1JUVESu3g8JCWlsbGxvbydfRavV8vl8d3f33bt3FxcXO88Y53K5mZmZfX19WVlZU6ZMIdef9/T0aLXal19+uaGhgcViBQYGxsTECAQCgiBSUlLc3NxycnIOHz5MDr/PmzePfFdnZ2eLRCIfHx+ZTObh4UEW2ysvL09KSvL09CwrK3vwwQdbW1uZTCa5fmHu3LnkjPqsrKzAwMBx48aJRCI/Pz/HT5ZarQ4JCWGz2du2bTt79qxz5s/j8R5//PFz587l5uY+8sgjoaGh7u7unZ2dBoPh7bffrq2t9ff3F4vFgwYNIn801Gr18OHDT58+zWAw5HJ5ampqYmIil8vdtWtXVFSUh4dHa2urj48POa5eUFDw6KOPcjicgoKC11577ezZs3w+v6SkZPDgwU8++SQ5aeLgwYPx8fH333+/h4dHYGBgS0sLGZhSqUxISGAwGJs2bWpqanIe6vf09Hz22Wfz8/Pz8/NffPFFb29vDw+Ptra2iIiIjRs3kj+wQqFwyJAh5EsAAADATYSEH4AgCIJKpQYEBKxevXr16tWOg1qtlk6nBwcHGwyG4uLiESNGZGVl+fv7JyQk2Gy23t5ejUYjEAh8fHxGjhw5YcIEciV8UlLS119/TRCExWLp6uqaPHlySUnJtm3bCIIwm83d3d1kNuuYh9w/ck3B1q1b165dW1JS4lilLxaLyWHwCRMmXHoWm80WCoUqlaquri4qKiovL49Go8XExFgslt7eXoPBIBAIxGKxu7v7uHHj9Hp9e3s7h8MpLy8PCQmJiYkpLy8nS+jn5OQQBKHX6w0GA1m07x//+MelL6dSqQYNGkQOhFosFhqNFhISkp2dferUqRdffJFcg+CYxu/p6XlpFTSCIDo7O6urqyMjI0NCQiorK319fR0lAJzbyOXyhISEtra2zs7OxMTE1tZWcsVEXV1dYGBgZGTkmDFj1Go1WXc9JCREJpOdOXOGx+P5+vrKZLKenh4vL68zZ84MGjToWr7+cItwOJzp06dPnz79ouNCoZDH49XW1np4eCiVynPnzvn5+fn4+Gi12r6+PjqdTvYOjBo1KiEhobOzkyCIlpYWpVKZkpJiNBpramqCg4Pz8/O7u7sJgujs7ExJSfHz83vwwQcffPDBS8Mgp5GTdSK0Wi25Jv+dd94ZP358aGioRCIJDAwkk2Fy3PtKxGIx+RMUEBDQ2trq6+sbEhLi6empVqvJeQo8Hi8uLo68rFQqJQiipqbGZDJNnDixu7u7t7c3Li4uNzeXnCIklUqnTp3q5eU1f/78y76cRqNJTU3l8/nk4/DwcJvNtmLFiqeeespqtUZERPj5+Tmm8cfGxl62/h95a6dOnYqLizt79qybm1t0dLSbm5tKperr6+NyuXw+PyEhYeLEiUKhkLypkpISGo02ZcqU5uZmBoORlJREDs4TBNHW1jZy5Egul/vMM89cNmaJRMJiscjeEJVKFR8fbzAYli9f/q9//Uun08XHx/v6+jr6RgEAAOBmQcIPcEVubm4PPPDAzp07s7OzbTZbX18flUoViUTDhw//7rvvcnJyUlNT3dzcxowZ8+9//3vnzp3kZ4cOHTp+/PgffvhBrVYvXryYwWDEx8ePHTuWPHKl/4YdNBrNkSNHjh49SqfTJRJJenq6v79/bGzszp07zWbzqlWrrhq2QCAYP3784cOHCwsLLRaLRqOh0+kBAQHh4eFbt24tLi5OSkri8/nDhg375ptvdu7cSdZIVygU5PwCi8UybNiwU6dO8Xi8vr6+bdu26fX6KyUeJA8PDzLDLysr0+l0b7/9NoPBCAkJqa+v//HHHzkczgsvvNB/zFqtds+ePa+88gpBEGazOS0t7Zdffuno6CC/FCKRyNfXlyxYwGQyyQX8BEEkJCSQd9fb2ysSiYRC4aFDh8gJ1eHh4RKJhFy3P2HChPr6+vLy8r6+vqsujb7r9Pb22mw2x66Hd7XExMTe3t6Kigpy5bzRaORyuUOHDpVKpe+//75Op/P394+Pj6+urs7JyamtreVwOAEBAevXrycnmGi12i+//PLRRx9NS0traWnZsmVLTExM/9tchYWFdXR0ZGdnNzQ0PPTQQ8nJye7u7omJiY2NjadPn54+fXp0dPRVwx47duyePXuKi4v1er1GozGbzTweb8SIEbW1tb/++iuXy/X29h40aBC5Z56npyeXy5VIJK+//npRURFBEBqN5sCBA2lpaRkZGbW1tUVFRffdd1//m3fGxMRIpVJyMv+LL74YHx/PZrOTk5NLSko6OjoWLVrkqIlwJZMnT96xY0deXp5KpSKn4nt4eKSkpBQUFJSVlYWEhLi7u8fExBQUFJAVTHg8nkajWbZsWUNDA0EQKpWqqqoqKirqwQcfzMvL6+vry8zM7H9bgYSEhP379+/bt6+ysvLjjz8WiUQMBmPs2LHHjx9va2t74403bqzg6J2goaEhLCwM6xEAAODORLHb7a6OAeDuo1KpvvjiC39//wULFrg6luvQ3t7+/fffR0REPPTQQ7f/1ZVK5YULF3x9ffvfJg2uy759+8iyba4O5NY6cOBAfX19enp6TEyMq2O5Djt27Ojs7JwxY4ZEIrn9r15TU0OlUoODgx3V+6/Fli1byO33BkYv0m3w7LPPbtiw4S6anpCZmUkQBLblu6NQKJTt27fPmTPH1YEAwAB01/x9ArgTqNXqrVu3Njc3GwyGsrKyadOm/T979x3Q5LU2APwkJARIAgl7J2wQoSIIDkBRsYjWWalVUeuoWhT11lpbW1etdXZYadXW1jo/RaWOqkBFlL2nbBJGQgIhe+/vj/feXIoVEaSI9/z+8j2840mIwHPGc4Y7on7p7Oy8fPkyg8EQiUR0On3q1KnDEoZMJhOLxTDbh/rv0aNHjx8/FolEtbW1Tk5OPYvkv8ru3r1bUFAglUrLysr8/f2fXpzyz2AymTY2Nv3M9q9du1ZeXo7sSzpx4kSkjCIEQRAEQSMdnNIPQS9ArVaXlpbicDgmk7l06dKR8jexVCotLS3VaDRcLnfhwoXDNXXWwcEBZvvQC2ltbeXxeNXV1RYWFmPHjh0pRd2am5tbW1sLCgp8fX39/PyQ8hb/vOnTp/f/5NraWpFI9PDhw/DwcCqV2nMnPwiCIAiCRi74Gx2CXoClpeXx48eHO4oX5u7ubthAG4JGkOXLlw93CAPx3KIVr6D+1AeBIAiCIGjEgQk/NIRgESMIeok+/vjjgwcP3rt37/fffzc0IpsR3r17Fzl0c3ObPXv26NGj9+zZ03PLCQiCBkmpVCJbNnzwwQdardbQ/uDBg/Xr1xt+302bNg2uxIYgCIJeHbBoHwRB0Aj2P1K0D4JeWbBoHzR4sGgfBEFDZ8T8foIgCIIgCIIgCIIgqP9gwg+9PnQ6nUAgGO4oIOjlUCgUMplsuKN4MTKZTKFQDHcUEPRy8Pl8OAsSgiAIGungGn7oNaFQKL766quBLVpWKpVpaWlXr15NTk5euHBhTExMfHz8S4/wFSGRSB49enT69OmcnJzZs2d7enpyOBwulztp0qQNGzYMd3RDS6lUlpeXZ2ZmIocUCsXd3R0AEBoaOpxhPUNFRUVHR0dkZORI2Q791q1blpaW4eHhA7i2trb21q1bZ8+eNTMzi42Nfffdd0eNGvXSI3wVNDQ0/N///d/p06fd3d0jIiIAAO3t7RYWFps3b/b09Bzu6IacUCi8ePGiWCxGDmfOnEmn06Ojo83MzIY3sL915syZOXPmeHp6jqDp+hAEQRDUC0z4odeBSqW6cOHCypUrkUOdTsfhcK5evfrw4cMbN24893IcDhcTE2NsbJyWlpaUlEQikYY23GFFIBAmT57c2dnJ4XDOnj2LNDY1NX3xxReNjY1ff/31sEY3hJRK5YMHD5qbmz/++GMAgEgk+uWXX1JSUg4dOjTcof29sLCw1NTUR48eLViwYLhjeb7c3FwikTh69GjkUCaT5eXlXbt2bcaMGfPnz3/u5X5+fmZmZgUFBZGRkRs2bBgp2+8NgLe39+LFi+/evbtr1y7DtnnffPPN1q1b9+3bFxQUNLzhDSmhULhjx449e/bY2dkBAFpbW2fOnHnkyJFXdgvAbdu2JSQkfPvttzDhhyAIgkYu+DsMGvF0Ol1TU1NVVZWbmxsAQKPRFBUVpaSk8Pl8Pp/fz5solcrc3FwqlTpCs32FQsFgMPp5slAorK2t9fHxMbTg8XhbW9vi4uKhiW5oicXizs7O556WlZWVm5trKIlkbm7u5+cXEhJCpVKHNr5B8PHxkUqlhikJf2vixIkDG1R/ibq7u6urq3U6HfLfp729PTU1tbKyksfj9X+Gf11dnVKptLOzG6HZPovFkkql/TmzrKzM0tKSSCQaWry8vBobG/v/8+pV09bWplKpnnvaxo0bV6xYYWlpiRxSKJSxY8cGBwcjpe9fTQsWLPjmm280Gk0f52zbtg1uSQNBEAS9smDCD414KpXq0qVLmzdvRg4xGExYWNiqVateKJFTqVTI6OKQhDj02Gz2uXPn+nmyQCBobGwcN26coUUqlTKZTG9v76GJbmjV1dXdunXruaex2ey2tjZkaBFBpVJDQkKGMrTBolKpGo2mqqqqj3PIZPKwz/nPz8+3tbUNDg5GDl1cXObPnz9lyhQLC4v+36S2ttbc3NzZ2XloYhxy169fb25u7s+ZZWVlFArFkPcCABobG+3t7Xt2AYwsP//8M4/He+5pOTk5tra2WCzW0DJz5kw8Hj+UoQ3WtGnTUlJSem7C9zQPDw+Y8EMQBEGvLJjwQ0MoPT09ODg4ODi4pKQkOTn55MmT69ev1+v1p06dQrafKSkpQc7U6/Xd3d2HDh26cuXKhg0bSktLAQAajSYpKSkoKAiHw924cUOlUq1evdrGxmbTpk09h8I0Gs3jx4+RxdgDplKp8vPzkSW1rz2hUNjY2GjIdfl8fnp6ukQieb23bScQCO3t7VeuXDG0+Pj4REVFDWNI/UEikVQqVf+nbxggixeCg4P37Nnz559//vbbb7t3775z505RUdG5c+d27dp1/PhxFouFnCyXy1NTU0+fPn3gwIEzZ86w2WwAQEFBwapVq+zt7ZctW1ZXV3f9+vWwsLApU6bcvHmz17h9Q0ODTCYb5OyYuro6c3NzJyenwdxkRCgtLe2Z8JeUlPz555+bNm3y9/cf3sCGmqur67Fjx7hcrqFl6dKlr343h5ubW3l5uU6nG+5AIAiCIGggXtGFc9DrITo6GgCwfv16Ho+3aNEinU7n5eX1ySefHDx4EPnjacOGDYWFhQAArVZ77NixNWvWeHh4zJ8//4033sjNzSWTyQkJCe+99567uzuVSkWhUOPHj9+4cWPPZa46na6lpYVMJg8mTo1G09LSgsPhRu4If/9JJJK6ujqFQlFVVdXU1MTn8yUSiZOTU38GyUe0sLCwSZMmrVq1asuWLbGxscuWLXv1s30AAJVKbWtra2pqetGhbw8Pj/fff1+hUPD5fDc3t+nTp3/zzTenTp3avHnz8uXLq6urT5069ccff6xZswYA8OjRI4lEsmjRIjKZfPjw4cuXL2/YsCEsLCwsLGz37t1yuZxEIjk5Oe3atSs4ONje3r7ng7q7u1UqlYmJyWBeJofDEYlEoaGhr33C39DQIBKJWCxWamqqXC4XCARIz+arvLTkZdm/f//q1atPnjw5YcKEBQsWrF279tXP9gEAU6dOzczMDA4Ohiv5IQiCoJEI/vaChpalpSWNRkMyfwCAYRwehUKNGTOGRqMhh3q9vrm5OSMjA/kShUJB/g0AwOFwZ86cWbZsWUtLi5WVVa+iVnq9XiwWD/JvZZVKlZOTM3IX8L8QZAF/ZGTk6tWr4+Lixo0b19jYOCL+7B4kBweH/fv3P3ny5JNPPqHRaImJiVevXh3uoJ7P1NQUhULJ5fKBXatQKMhksoeHBwDA0tLS0DXm4OBgbGxs2MaSx+MVFxfT6XTktO7u7paWFuRLS5cuFYvFv/76q0ql8vT07JXtAwDkcjmBQHih2ftPq62tVSgUI3cBf/8hC/gXL168ZMmSuLg4gUCg1Wpf2ap1L1d4eHh9ff3t27eDg4N379790Ucf9WchwLAjk8lwfz4IgiBo5Pqf+CMDGl49J9uTSCSktF4vWCz26tWrer2ex+M1NjbyeDzDpH0jI6Po6OiJEyeuXLkyJyen14U6na61tXWQyYZSqczPzw8MDBzMTSQSyb1797RarVQq9fHxCQ8PN7TIZDIfH59Jkyb1cblWq21tbU1OTra2tl69ejUAgMfj3bhxw8TERCgURkVF9dqlrK2t7csvvzQcikSi2tra1tZW5NDc3PzNN9801ADvCVnAP2PGDAAABoPx8vLy9/c/cuTI3LlzB/bCOzs7Hz9+rFarNRqNv79/cHAwm83OyspSqVRarXb06NFjx47t43KVSvXkyZO0tDQvLy+kHD2dTn/48CEajdbpdJMnT0aS1Z6Ki4t/+uknwyGbzeZwOIaKg3Z2drGxsePHj//bx1Gp1MTExLfffjspKSkzM9NQw++FNDc3l5aWKhQKIyOjoKAgPz+/pqamsrIyhUKBwWCCgoJ8fX37uFyhUBQUFOTn54eEhEybNg0AUFFRUV5erlKpCARCREREz8F8EomERqMHnBeRyWRDkm9iYuLg4PC302GWLFmyZMkSuVze1NREp9N5PJ6hi8Hb23v69OlnzpxxdHT82ykwXC5XpVINMlGvq6szMjKytrYezE0KCwufPHnS0NDg4eExa9YsBweHgoKCtrY2iURCIpHGjx/v4ODQx+XHjh2zt7f39fXNzc2dMmVKQEBAampqd3c3j8fz9PScMGFCrw7BpKSkyspKw2F5eXlGRoaNjQ1yOGbMmHnz5j39RGQBP1J2gUgkzps3b+3ataGhoYMpXpCRkUGn0588eRIaGhoTE0MikR48eMDj8YRCoZ2dXWRkZN8/Ibdv3z5lyhQ7O7u0tLQVK1Y4OjomJydrNBo2mx0UFBQWFmZqatrrkj179hjWgwAAcnNzaTSaYTX+hAkTFi5c+KxuxNjY2NjY2Lfffnv16tUSiaRnLYP+u3XrFpvNrqioiImJefPNN42NjW/evKlSqfh8vqur69SpU/suBLh8+fL169cji8V27dqFdCsTicSWlpYpU6aEhIT0HMx3c3NLTk6GCT8EQRA0QsGEH3ol6HS64uLio2SaZUAAACAASURBVEePRkdHz5s3r1enADIdICcnp7S0tO/scWCQBfw98+cXpVar6+vry8vLv/zySxaLtX379rCwsLq6uoqKiv379zMYjM8++6zvhF8qlWZnZwuFQiTnkcvlhYWFtbW1x44do9FoBw8ePH36dM/zXV1dT506ZThsaWm5dOnSp59++txQJRJJe3u7ISPVarUSiWQgrxkAAIBMJquurmYymVu2bCkuLk5OTvbz86uqqmKxWImJiUVFRdevX+/7W9bd3V1cXGzYl1sgEOTn53d3d2/fvj07O/v8+fN79uzpdUlISEjPYntFRUXl5eVr16591iNaW1vb2trGjh1rSEisrKx8fHz6Lob3LEKhEEnO4+Pj79y5k56e7ujoWF5erlar4+Pjb968mZ6e3nfC39bWVllZKZPJkMPOzs6cnBwCgbB27drk5OQ//vhj3bp1hpPlcrler38643q5mEzmlStXurq65syZ4+7u3mu5spOTk4WFRXNzM5vNfnqE/6Woq6sjEAiDmc9Pp9O5XG5UVFRcXFxCQoK5ufnkyZPz8/MnTJgQGhp64MABCwuLvhP+zs7Ob775xs7Obv/+/T4+Ps3NzY8fP54zZ05YWNiePXvMzc17/RdOSEjoeXjixInIyMjn9hs2NDQEBgaam5sjhxKJpO8K8M9VV1cnkUjmzp27aNGiuXPnenh4ODg4ZGVlLV++3N3dfePGjV5eXn0n/Ewm87333vP09Dxx4oStrW1NTc3Dhw8//vhjCoXy/vvvUyiUp3tpe/2v3LVr1wcffNDHZ+Pu3btvvPGGvb29kZER0hIYGGhrazuwSfIVFRUajWb58uUKhSIkJCQvL08qlT58+HD//v0EAmHhwoUTJkzoO+FvaWmZPXv2mDFjzp8/b2xsXFZW9uDBgzNnzpiams6ePfvatWs9l6jw+XwymQzL8kEQBEEjFJzSDw0/vV7P4XBmzZq1Y8eOtWvXkkgkgUCg1+uRCf/IKn0fH5+jR4/Gx8f3uhaNRru6uhqmHw+ARqOh0+kKhWIwC/gVCkVeXh7yRzwajcZisenp6fn5+YYWDAbz9PSEnszNzd966y3DKJ9EIikpKTEcyuXympqaAYfXM862tjYjIyNDwiwQCKqrqx0dHQd2Qx6PV1lZSSAQAABYLFahUKSnp1dXVyOpNQaDkcvlSAnGZ3F0dIyOjjakCl1dXU1NTYZa+gKBoJ+Vz/vAZrOrq6t7tnC5XL1e33Ofgv5jMpk0Gg3JwI2Njblcbnp6eq+Wurq6Pu7g7e09efJkw8BmW1tbV1eXodJ+Z2dnzxJ9SqXSxMRkYKOg/SSXy5OSkphM5ubNmydOnKjX6xUKhWGWDZfLRaPRq1at4nA4t2/ffvpyKysrjUYjFAoHHACHw2Gz2e7u7oMZ5ZZKpaWlpSwWC4/HW1paMpnMx48fi0QipCY8Ho+vr6/vOSj9tHnz5uXn55eUlMycOdPY2LiyshKFQhk6iZqbmw0rIAaso6ODw+EEBAQYvt2lpaU4HG4wHToikSg/P18oFJqbm1taWjY1NWVmZup0OmSZAJFILCkp6fu7s3Llyvr6+pycnKCgIAwGU1BQQCKRDAlzTU3NwFaU9JSbm9vrJpWVldOmTTMzMxvA3QQCQWZmplKpJJFIZDK5qqrqwYMHyOIXAICFhQUy56iPO2zdurWzszMjI8PJyQmFQj18+NDFxcXQ+1BWVtazz0sgELi5ucGEH4IgCBqh4Ag/9I961l/MdDpdKBQahoL5fL5OpysrK3N3d9doNL/99tv+/ftVKlV4eHhiYuLx48cNFz4r4dfr9b02tdZqtQ0NDYWFhStWrOjZrlars7OzLS0tDYUANBpNXV3d3bt3TU1N29radu7cSSKRRCLR6dOnnZycmEymr6/v7NmzlUplUVFRQUGBk5NTd3c3k8mkUChISObm5lwul8lkIvc0MjIiEokikaj/b5SxsbGdnV1tbS0AQKVS0Wg0BoPRa1b/AHC53NzcXAqFYhhgVKvVMpnM1tZWIpE0NTURiUQOh5OdnY28Yx988AGRSGxra0tJSUEWsr7xxhtjx47Nz8+vq6uztrbu7Ozs7u5G+guwWKypqSmHw+FyuchQLRaLNTEx6efm5Ag8Hk8kEjs7OwEAUqmURqN1dnY+Pav/RV91Wlqas7PzW2+9BQBQKpXl5eUSiQT5JIhEoqKiImTI3dbWdsmSJXg8vqKiIicnB4fDKZXK0NBQCoWSm5vLZDKJRCKbzRaLxUgeiMPhMBhMZ2enRCJBWkxMTIyMjAyj9/1hYWGBwWCQ0uUCgYBGo/F4PEPqKxAIpFLpSxnhVygUvarrI3g8HpvN9vT0RAbA5XK5QqFgMplOTk5kMrmgoMDMzCw6OlokEqWlpVEoFGQ9iMGzEn6ZTNbrcRwOp7CwkEql9ipHX1tby+PxnJycDB/Lrq6uzMxMNpvNZrMnT548ZcoUHA539+5dFoulVCpxONy0adOoVGpLS8vjx481Gg0KhZo0adLOnTuRy5E6lFqtVq/XI2sN8Hi8XC7vOwlsbm6uqqrCYrFqtTo6OtrOzk4ikSCfXsMHcpBlPrKyslAoVM+t6cRiMfIpamho0Ol0WCz20aNHMpmMTqcvWbIkMDAQi8VevHhRq9Ui48wzZ860sbGpq6vLzMzE4/EqlWrWrFkHDhxA7iYSiTw9PQsKCnA4HDKWbm5uLpfL+95VrqampqOjQ61Wa7XahQsXOjk55eTkqFQqAACDwaDRaOHh4YP8BFZVVf32228ffvgh8gYKhcKLFy/u27cP6clqbW29efMmFot98uTJhx9+SKFQ0Gj0Dz/8QCaTkc/hvHnzGhsbHzx4YG9vLxaLly1bNnnyZOTOAoEgICAgOzvb3NwcycmRH9d9z8AvKytTKpXIhxbZw7WoqAhJ8ul0Oo1GGzdunCH/p9Pphk8mBEEQBI04cIQfGkIlJSUHDx6k0WiHDh0CABw+fLi4uPjatWvp6enFxcWffvopj8fbsWOHQCAICQnZunXrjh070tPTMzMzz507l5GRodFovvzySz8/v5SUFACAXq/v6ur68ccfExISysrKkEegUKieq5QBAFqttra29vDhw7/88kt+fv6nn376+++/AwBUKtW9e/f27t1r6HTQ6XQdHR2HDh1KSkqSSqXHjx8XCAQ6nY5Op3/44Yfbt2+Pi4vLzc0lkUhcLnfLli0zZsyIiYlB/kbU6XTITPvExMRRo0b1Sua1Wm2v/Ofplr5ZWFjMmDFDq9Uymcy8vDyky2AA3wIDpBfj559/lslkDg4O58+fR9pdXFxWr15tbGx87949DofDYDB+/vnnbdu2RUZGVlRUEInEJ0+efPfddzExMRMnThSLxRqNpri4OCUlZc2aNVQqtVcyr1arDZPzn9XSNycnp4kTJ6rVahqNVlFRgcViB79NN4VCmTdvHgqF+vrrr7///vtvv/1Wp9Mh87GVSuXdu3cfP368detWb2/vpqYmPB6fkZFx69at2NjYgIAAqVSqUqlSU1OrqqqWLVtmb2/fK5lXKBS93gSlUvlCCb+3t3dgYKBCoaitrW1sbMThcD2nE7NYLCKRGBAQ8KKvuqOjIzk5+fbt23fu3MnOzs7KykpOTv7zzz9v376dnZ197ty5u3fv3rx5886dO5aWlnFxcUKh8PTp0+np6RMnTvT3929pacnMzFywYMGmTZsaGhoAADKZLDMzc9euXb/88guybx/CzMzM0tKyZ8zd3d3Xr18/fvz4/fv3z5w5k5SU1NTUBACor69PSkrKyMgwdATIZLJbt24dPny4vLw8LS0tNzdXqVRKpdLvv/8eg8EkJibq9XqBQIDD4X788cfOzs65c+fa2NgguahUKj116hSZTJ49e7aJiQnSCAD49ddfR48eHRYW1usNkUgkSqWyj3csPj5+3bp1q1atqqysLC0tDQkJcXJykkgkRUVFXV1deDy+5wbyL0osFl+8eDEvL8/Pz+/PP/9E6iMCABYvXjx+/PiioiLk037gwIGAgICNGzeyWCy1Wo3FYnfv3m1mZjZ//nwcDodM/heJRF999VVYWNjUqVP1er0hmT906NA777zz9AalIpGo71UDmzdvXrFixZo1a27fvt3U1DR16lQLCwuRSJSdnS2RSIhEomEe/oDFxsbOmDHj3LlzP/zww5EjR06ePHno0CFbW1sAgFAoXL9+/aJFizZs2FBeXo7D4dBo9Lp16/z8/BYuXKhQKHQ6nVAo3LZt27JlyyZMmCCTyQzD79u2bduzZ8/TPxuRn+R9xLNv377FixevW7fuhx9+6Orqmj9/PgaDEQgEDx480Gq1JBKp53h+aWnpggULBv8mQBAEQdCwgCP80BAKDg5OTk42HO7YsWPHjh2Gw+vXr/c8GekUMDDsl24YtcPhcEjm3wsGgwkPD8/IyJg6dSoAwMjIyM/P7/PPP//88897nmZqarp58+a5c+fy+XxklAmNRjs6Ou7Zs6fnelSpVJqWlhYREaHRaBgMhqurK/hPlbXAwEAWi1VcXPzhhx8iLTY2NtnZ2e7u7ps2bTKk0DqdTiqVuru7GwaIkLp9Li4uPB7v5MmTPTN/NBrt4eGBbI3Wi6ur64EDB5qbm5HFroPcoxuDwfj6+u7evbtXO4FAmDdv3rx58wAALBbrypUrISEhcrmcx+Mh4718Pr++vt7Hx6eoqIhOp7/77rv37t3D4XClpaXu7u4rV65E+lMAAEjdPnd3d0NehIwZ2tvbNzc3X7p0qWexACwWGxQUtHDhwqdDDQ0N9fb2ZrFYvr6+TCZzkMP7AAB/f/9nvXstLS0MBsPf318kEsnlcqTiGjLvmkqllpWVCYVCIpHIYDCUSmVNTc0bb7zh5OT0559/IpcrlUoMBuPu7t7R0YG0IHX7rK2tS0pK7ty50zPzx+FwkydPRqr09TJnzhw2my2RSDw8PPh8fq+Z7SYmJgOY+ezo6Lh58+bNmzcbWiIiIgz/Dg8P37p1q+EwJiYmJibGcBgcHIz8Y/369YbG+Pj4p9fUILy8vNhsdmtrKzLJxdraeuHChU9/c8PDw3/77TeRSISsUwAAmJmZzZkzZ86cOT1PQwaonZycurq6cDgcks7V1taGhoZaW1vX1NR4e3s7ODggC7lbW1tdXFxmz56NnFZYWEggEKZOnUqhUJqbmw3fF2RwnkAg3Lx5s7i42NA7AADA4/FxcXF8Pv/Ro0ezZs0KCAjA4/E8Hk8qlX700UfNzc04HM7Z2dnPz28ww/tEInHp0qVLly7t1e7t7X306FHk37dv33Z2diaTyR0dHWQyGRlULykpefPNN4lEYnFxcXx8PIlEysrKMjMzq66uHjt27NKlS5HTMjIy3N3dp06damVl5eTkZOhSEYlEo0ePxuFw586dq6ur6znUb25uvnr16qqqqtzc3LVr1zo6OiITWFQq1bFjx2pqajw9PW1tbQMDAwc/wQQpS9HzE2hw586doKAgExOT1tZWR0dHZCVCdnb2rl27jI2Ns7Ky3nnnnYKCAjs7u5ycHD8/v40bNyLn3Lp1Kyws7K233jIxMaFQKMgsDACAQCBwdXU1MjL67rvvWCxWz6F+Eom0devWe/fuFRYW7t6928TExMLCoqWlxd7e/vz582VlZWPGjLG0tBw7dmyv4gK9ugAgCIIgaASBCT804uFwuISEhPXr1yMJfx+0Wm17e/uUKVP6Pg2DwVAoFKVSWVhYOG7cuMzMTGdn58DAQJ1OJxAIxGIxiUSSy+XW1tahoaFRUVFqtbqtrS0kJOS3334DAKjV6u7u7ujo6NLS0gsXLiAtXC4XqfjVn7p64D/zFH799dcDBw6Ulpb2rPL1t+zt7ZcvX96fO/fN1NTU3t4eWTnv7u6en58PAPDx8VGr1Xw+H9njzdHR0draOjw8XCaTsVgsT0/PoqIiAIBMJkPmQmdmZiJTMORyuUajQVZq9Op/eRaVSpWdnV1cXPzBBx+0tLT4+fkhBQL64Ovri/TLDAyRSLSxseno6BAIBCQSqbS01NTUlEqlKpVKZMs0MplMoVDIZPL48eNFIhEOh7OyskI2RBCJRCYmJtHR0bdv3za0mJqaIkX7DGlz3xQKxc2bN5VK5Zw5c8zNza2srAzpfUFBgVAo7Lvc46tg2rRpv/76a3V1NZLw94HL5eLx+Oduq2FjY0MkEmtray0sLIRCYUNDg4ODg7W1tVQqFQgEGAwGh8OZmZl5e3uPHz8+ICAASfZaW1uRt0upVNbW1lIolLy8PGStRFdXV3h4uIODw9y5c/92QwoajTZ+/HhkzFkqlbq4uCDj7dOmTaNQKC4uLs7Ozn3XgVu4cOHgJ347OjqampqWlJS4u7s3NzcjWyoSCASxWCwQCExNTbFYLJFI9Pf3j4qKcnZ2ZjKZVlZWlZWVWq02OjqazWZ3d3cHBgZmZWUhE0+YTObSpUstLCye9fOBx+NFREQg3SVisdjb21uj0WzZsmXr1q1qtdrX19fOzu65g9tr1qwZTJkJCoWCwWCys7NDQkKKiopCQkJGjx6NbBspEAjMzc3JZHJQUNC0adOQQvrOzs45OTmmpqYxMTG1tbVmZmbh4eH79u1DJjK0traGh4djsdievV09UalUAoGA9BoIhcIxY8aIRKJNmzb99NNPLBYrKCjIysrKkN4fPXp0xYoVcHgfgiAIGrmMnq6ADUEjkV6vb2xs7Ls6ukajefLkSd/nYDAYBweHoqIimUzG4/G4XK6Hh4eHhweLxaLRaFevXvXx8Zk+fToWi7W2tk5PT+dyucji28DAQI1GU1VV9eTJk6lTp/r6+pJIJLVaXVVVVVtbGxUV5eXl1cdzJRJJamrqrVu3kPWiXl5eGAymu7u7oaGhs7Nzy5Ytfb98DAYz+GTDxMSEQCBUVlZKJBIulyuTyTw9PR0dHVksVnV19YMHDwICAqZOnYrFYh8/ftzZ2Umn03E4nI+Pj1QqraioYLFYkZGRnp6eRCJRKpWWl5d3dnZGRET0nQR2dXXdvXs3NTUVSV2QeuBdXV21tbUYDOZZQ8o94XC453YKPAuRSNTr9Uid8+7ubp1O5+3tjQyxFhQUlJeXjx07NiIiQiaTFRUVdXR0tLe3k0gkCoUiEAhKS0uVSuWkSZPc3NzMzMyQFpVKNXHixL6rwbe2tt65cycjI4PL5drZ2Tk7O2u1WjabXVVV5eLiMmvWLOQ0ZJI/gUB4enb6KwiFQnV2dmKxWEM5ur/V1taGwWD6Tg6tra27uroYDIZIJELm0vv6+qJQqI6Ojvv37/N4vPDwcFdXV1tb2+bm5vr6+paWFg6Ho9FoDh06dODAgR9++OHrr78ODQ1FVv4zmczCwkI7O7tx48b10dGAfNOrq6tv3749derUSZMmEQgEmUzGYDBKSkpiY2NHjx7d9ztAJBL77hF4LkdHx+bm5q6uLqFQyGazra2tfX19FQpFR0fH9evX0Wi0YQC/pKSktbW1qakJKXeyZcuWEydOfPfdd99++y2y8h+DwbS0tOTl5Y0aNSooKKiPIXobG5umpqbq6urk5OTVq1ePGjUKj8eLxeLW1tbCwsK1a9f2/WFGWFhYDDglplAoZWVlIpGIz+ezWCxnZ+dRo0Yh/z5//ryNjU1MTIyPj8+DBw+6urrq6+slEoler3/nnXfOnTt39OjR77777rPPPkNK7jU0NOTk5EyaNCkgIADJ5/+Wg4NDZWVlTU3NhQsXvvjiC3t7ezMzMz6f39LSkpubu3fvXsP3kc/nP3jwYNWqVX3cbSRCZt4NbFNSaIjs3bt30aJFg5zKB0EQ9LdQcGtZ6PWgVqtv374dFBT09A5SCJ1Ox+PxGAzGmDFjBvYIkUh08uRJJyenp+flvvba2tqSk5O9vb2Runf/CyoqKjIyMsaOHWsoD/YPKygokMlkUVFRw/L0ASgvL5fL5X1MfZfJZEwm08LCAhlIH4C7d+82NTXNmDGj726718/FixcFAsGCBQv6k36/Nn744QcUCrV8+fLBF/IYmKNHj27ZsuU1y/YBAIsWLQL/SfuhVwQKhbpy5QrshYEgaCi8br/GoP9ZWCx2wYIFfZyARqOtra2RXe5fiEQiuXbt2pQpU/B4fGlp6fvvvz+IMEeYjo6O7OzsSZMm8fn89vb2/4XXXldX19jYGBYWxmQykV2+hyuSETGw39Nzu9LMzMz6nufyLI8ePUKhUGPGjKmpqXFzc3tWp97r59atW46Ojv7+/iUlJQsXLhzAj6+R6MKFC0FBQT4+PllZWfv3738pW1QMzLZt24br0RAEQRD0ssCEH4KeQ61WI3tlM5nMZcuWDXJfrpFFLBaXlpYqFAo+nz9v3rxB7hQwInC53NLS0q6uLqVSGR0dPVxDi1BPra2tPB6vurqaTCaPHTsW2Wnvf0FjYyObzc7JyRkzZoy3t/dgtgkYQaqqqvR6/b179+bMmePo6Nirfh4EQRAEQS8ETumHoL9QKBRpaWlXr169fv36ggULZs6cuWzZsuEOqi8ajYZOp9+4cQM5RGrLdXV1TZ8+fXgDey5knTwOh+vq6iISiX9bsR+CIAh6ueCU/lcQnNIPQdDQgSP8EPQXJiYmMTExxsbGDx48SEpKesXH8zUaTXV19aVLlw4fPgwAUCgU6enpa9euPXv27HCH9hytra21tbXz588HANTU1Pzxxx/l5eUDLq8AQRAEQRAEQdDT4Ew5COpNqVTm5uZSqdRXPNsHANDp9OPHj2/atAk5NDEx8fHxCQ4O9vPzG97A+sbhcDo7O8PDw5FDoVDY3Nw8vCFBEARBEARB0OsHJvwQ1JtKpSosLIyIiBjuQJ5PLBaXl5e7uLgYWshk8rRp04YxpP6QyWRcLhfZS6+rq6u8vNzGxgYO70MQBEEQBEHQywUTfgjqTalUjpSE39jYGIVCfffdd4YWGxubV38xPBaLVSgUP/3007x585YuXdrR0fHFF18Md1AQBEEQBEEQ9LqBa/gh6C80Gk1LSwsOhxsRCb+rq+v777+/bdu2zz//PCYm5p133nn1s30Oh8NgMKKjo0UikaOjo1gsbmxslEgkyIA/BEEQBEEQBEEvC0z4IegvlEplXl7eIBfwCwSCM2fOuLq64vH42NhYsVh8//59rVYrk8l8fHwmTZrUx7VtbW3Hjh3bt2+fhYUF0sLlclNSUkxMTIRCYVRU1KhRowwnm5ubr1u3Li4u7saNG3/88UdCQgKTyUxMTBxAzN3d3WlpaR0dHcgOfOPGjevq6nr06JFarVar1f7+/n3vSF9RUXHhwoXFixez2Ww2m7169er29vb09HQjIyO1Wj158mTDBuyG+fwEAsHR0bGjo0MsFl+7di06OjozM5PBYCgUirlz544ZM4bBYFRUVOBwuObmZjc3txkzZrzoi1IqlRqNBm6tB0EQBEEQBP1vglP6IegvlEplfn5+QEDAgO/AYrESEhLWrFkzfvz4U6dOqdXqurq6ysrKxYsXz5gx46effur76U+ePDlx4gSVSjU3N3dwcEhKSiosLKytrV22bNmbb755/Pjxp68ik8mrV6/+9ddf9+zZk5KSMoCYJRJJfX29g4PD5s2bIyMjd+/eLZfLnzx50tbWtmTJkjFjxly+fLnvO8hksjt37ixYsCAjIyMuLk4oFObn57NYrBUrVgQEBPS9a4BUKq2vr29ubqZSqVu3bvX19T1y5IhKpSotLX348GFkZCQOhzt9+vQAXteDBw+++uqrAVwIQRAEQRAEQa8BmPBD0F8olcqCgoIBz+dXKpU1NTXm5uZKpVIoFN68eVMmk+Xn55ubmwMA0Gg0BoPJycl51uVqtZpAIPB4PD6f39LScvz48UWLFpWWljo7OyMnyGSympoaAACfz79//75IJDJci8fjAwICkAe9KLVa3dbWVlJSgsViyWQyGo0uKCioqKhAptljMBi1Wl1cXNzHHdzc3E6dOtXa2nrs2DEikcjlcuvq6uzt7ZGvCoXCxsbGv71QIBB0d3dTKBQajVZRUWFsbEwmk7VabXt7+5w5c44ePSqVSgEAgYGBL/qieDweh8OxtbV90QshCIIgCIIg6PUAp/RD0H9pNBo6na5UKg0Jv0ajqa2tvXfvnqmpaVtb286dO0kkkkgkOn36tKOjI4vF8vX1nT59elFRUUFBgZOTk1AoFIlENBqtvb1dKBR+9tlnW7Zs6ejocHNzAwAYGRkRiUSxWPysAAgEAvJoHo+Xmpo6a9YsnU5na2tbV1cHAFCpVDQajcFgjBo1SiwWFxQUTJw40XCtVCpls9lIiX6FQlFVVfXo0SM0Gi2TyRITE83NzZlMZnJyspWVFZfLDQwMDA0NLSgoqKqqsrW1xePx7777ruEl63Q6V1fX9PR0f39/AAAGgzEzM5NIJH28dSKRKCsri8FgiMViZ2fn4OBgCwsLNpsNAJDL5TQajc1me3l5SaVSLpdLJBINF3Z0dNDp9ISEBAcHB6RFrVYbGRl5eHgAAOh0+oULFzAYzL/+9S+JRFJSUlJSUkImkwkEgkQikUqldnZ2bDbbxMRk7dq1PB4vLy+PTqeTSCQcDieXy9PS0kaNGlVVVUWlUouKisrLyxUKhaWlZXx8PJznD0EQBEEQBL324Ag/BP2XSqXKycmxtLSkUqkAAJ1OR6PRtm/fvn379rfffjs3N5dEInE4nK1bt86YMSMmJkYgECDnHDx4MDEx0c/PTyAQSCQSW1vb4OBgLy+v4uLi6urqno/QarVCobDvMJRKZUtLi0wmMzc3J5FIM2bM0Gq1TCYzPz8f6TIAAEil0sePH9+4cQO5RKPR0Gi0wsLCxMREjUZTVlb2/fffb9u2bdq0aWVlZebm5g0NDYcPH46JiYmIiBCJRBqNpry8/MKFC+vWrfPw8DD0QTQ0NNy8efOjjz7qFZJare6jnwIA4O3tvXPnziVLlowaNery5ct4PD4iIkKtVtPp9LKyr8xk5QAAIABJREFUMgwGgyTYMpkMSf6RqyoqKtLS0uLj4w3Zfnl5eV5e3gcffIAcurm5vf/++/b29hcvXpRKpSqVytjYmMFgLFq0iEQi5eXlzZw509vbm0ajqVSqzMzM3NzcFStWODk54XA4Nze3UaNGrVmzxtvb+/79+xkZGf/61798fX2bmppgtg9BEARBEAT9L4Aj/BAEAAA6nY7FYp0+ffrcuXMajeb777+Pj4/HYrHp6enh4eEajYbJZLq6ugIAFApFYWFhYGBgR0dHSUnJhg0b0tLSbGxssrOz3d3dExISrl+/rtFoAABoNBqPx7PZbBcXF+QpSN0+Z2dnLpd76tSpnpk/Go328PBYs2YNAIDP59+5c2f69OnIlygUyoEDB5qbmwMCAuzs7JBRdzKZvHDhQgqFcvToUVNTU4lEYm9vf+jQIeTywsLCcePGKRQKDofj6OgIABAKhTU1Nb6+vhUVFY2NjfHx8ffv38fj8UVFRR4eHmFhYQAADodTUVERExMTFRXFYrGQCwEAGo1GrVY7ODi0traeP3++Z+aPwWACAgKmTJly584dAMCaNWswGAwajWaz2UFBQe7u7kwm08/Pr6GhASnap1arTU1NiURiSkoKCoVSKBTvv/++p6cncjcmk9nY2Dhz5kykrqFKpVKpVHZ2dpaWlj/++OP48eNdXFzq6+unT5/O4/GQWoCG2v5sNptOp2u12qqqqlGjRtnZ2d26dYvL5drZ2TU0NLS2to4ePVosFsvlchsbm6H5EEEQBEEQBEHQqwUm/BAEAABoNNrJyWnv3r179+41NEqlUgwGQ6FQFApFUVHRuHHjHj165OjoGBAQoNPp+Hy+RCIhk8nW1tahoaFRUVFqtZrBYISFhf35558AALVa3d3dPX369KampkuXLiEtXC4XyWY//fTTZwUjEokyMzPfe+898J81BWfPnj1w4EBJSYlhlb69vT0yDB4VFfX0HUxMTGxsbEQiUX19vbe3d25urpGRka+vr0aj4fP5CoWCTCbb29sTCITIyEi5XM5kMs3MzMrLy6lUqq+vb3l5OZFI9Pb2zs7OBgDI5XKFQoFU6f/ss8/+NuDRo0djsVgkYCMjIyqVmpWV9fjx4y1btiBrEAzT+C0sLMLDw5++SVdXV01NjZeXF5VKrayslEqleXl51tbWy5cvNzY2trS0JBAITCaTzWYHBAQwGIyurq7g4OD29nZkxUR9fb2zs7OXl9fEiRPFYnFzc7NaraZSqSwWq7q6mkgk2traslgsHo9naWlZXV09evTo/n44IAiCIAiCIGhkggk/BD2TqalpbGxscnJyVlaWTqcTCARoNNrOzm7s2LEXL17Mzs4ODw83NTWdOHHiN998k5ycjHx1zJgxs2bN+uGHH+Ry+ccff2xtbY3D4SIiIv7v//5PLBa///77z30uGo0mk8lIYo/BYBwdHf38/JKTk9Vq9eeff/7cy8lk8pQpU9LS0goKCjQajUQiwWAwTk5OHh4eZ8+eLSoqCgkJIZFIQUFB586dS05ONjIysra25nA4yPwCjUYTFBT0+PFjIpEoEAguXbokl8uXLFnSxxPNzc2RDL+srEwmk3355ZdYLJZKpTY2Nl67ds3MzGzTpk19xyyVSm/fvr1t2zYAgFqtnj59+tWrVy0tLWtray9evFhbWxsXF+fh4cFkMo2NjZEF/ACAgIAA5NXx+Xw7OzsbG5vU1FSlUonFYj08PFxcXJB1+1FRUY2NjeXl5QKBANmo77nvIQRBEARBEAS9BlB6vX64Y4CgkUckEp06dcrR0XHp0qXDHcsLYDKZly9f9vT0nDdv3j//dKFQ2NLSYmtra1ixD0EQBP3DFi1aBABITk4e7kCg/0KhUFeuXImLixvuQCAIeg3Bon0Q9ALEYvHZs2dbW1sVCkVZWdns2bOHO6J+6ezsvHz5MoPBEIlEdDp96tSpwxKGTCYTi8Uw24cgCIIgCIKgfwac0g9BL0CtVpeWluJwOCaTuXTpUgsLi+GOqF+kUmlpaalGo+FyuQsXLkQWC/zzHBwcYLYPQRAEQRAEQf8YmPBD0AuwtLQ8fvz4cEfxwtzd3Y8cOTLcUUAQBEEQBEEQ9I+CCT80hFAo1HCHAEGvj48//vjgwYPDHQUEQRAEQRA0YsCEHxpCsCQkBEEQBEEQBEHQcIFF+yAIgiAIgiAIgiDoNQQTfuj1odPpBALBcEcBQS+HQqGQyWTDHQUEQRAEQRA0gsEp/dBrQqFQfPXVV3v37h3AtUqlMi0t7erVq8nJyQsXLoyJiYmPj3/pEb4iJBLJo0ePTp8+nZOTM3v2bE9PTw6Hw+VyJ02atGHDhuGObmgplcry8vLMzEzkkEKhuLu7AwBCQ0OHM6xnqKio6OjoiIyMtLKyGu5YIAiCIAiCoBEJJvzQ60ClUl24cGHlypXIoU6n43A4V69effjw4Y0bN557OQ6Hi4mJMTY2TktLS0pKIpFIQxvusCIQCJMnT+7s7ORwOGfPnkUam5qavvjii8bGxq+//npYoxtCSqXywYMHzc3NH3/8MQBAJBL98ssvKSkphw4dGu7Q/l5YWFhqauqjR48WLFgw3LFAEARBEARBIxKc0g+NeDqdrqmpqaqqys3NDQCg0WiKiopSUlL4fD6fz+/nTZRKZW5uLpVKHaHZvkKhYDAY/TxZKBTW1tb6+PgYWvB4vK2tbXFx8dBEN7TEYnFnZ+dzT8vKysrNzY2Li0MOzc3N/fz8QkJCqFTq0MY3CD4+PlKp1DAlAYIg6B+zytoI9V+4iceadcMdUt80/Nr7pz5dNu0NihUeZ2xq4eA76e1tP+d3aXufqBeU/bp1dpAr2czMwnF09Lrvs58+5690bccn41C9Ydy2Zqt7nqZmPPzug9ggihUeZ0py9J+2+mh6u+rlvkgIgqAXBxN+aMRTqVSXLl3avHkzcojBYMLCwlatWvVCiZxKpSooKIiMjBySEIcem80+d+5cP08WCASNjY3jxo0ztEilUiaT6e3tPTTRDa26urpbt2499zQ2m93W1mZnZ2dooVKpISEhQxnaYFGpVI1GU1VVNdyBQBD0P+eXbq26+NNRGGAy76JYr8z90OPV/pNR9WD75Fn/uol++7s/G7ql0q7q3z8JpJ9eN3nCmhRWj64KvSh314zJG24T1yU/4XAa733invdJ9JRN9zmD3VVI3fjzorA3P31st/FqRaeIW/fHZ6MqvpgVHn+x5Tm9CRAEQUPs1f7pDY1w6enpwcHBwcHBJSUlycnJJ0+eXL9+vV6vP3Xq1JUrV+Li4kpKSpAz9Xp9d3f3oUOHrly5smHDhtLSUgCARqNJSkoKCgrC4XA3btxQqVSrV6+2sbHZtGlTz6F7jUbz+PFjZDH2gKlUqvz8/IiIiMHcZKQQCoWNjY2GXJfP56enp0skkoFVQBgpCARCe3v7lStXDC0+Pj5RUVHDGFJ/kEgklUrV/+kbEARB/6uMKKuTTm+Y4mVlisESXcJWfH/+4yA97cJnJ8o0/zlFVX7sg8Ol5JU/nF4f5ojH276x9Pgvmz0bTyfsfSjp+97e2wvU+r/Q0L8Jx/77y3rWpQ8/utXltemXk6vDnAk4M/ugd785/0kQ+9q/dlwfdGcCBEHQYMA1/NAQio6OBgCsX7+ex+MtWrRIp9N5eXl98sknBw8e1Ol0AIANGzYUFhYCALRa7bFjx9asWePh4TF//vw33ngjNzeXTCYnJCS899577u7uVCoVhUKNHz9+48aNQUFBhkfodLqWlhYymTyYODUaTUtLCw6HG7kj/P0nkUjq6uoUCkVVVVVTUxOfz5dIJE5OTv0ZJB/RwsLCJk2atGrVqi1btsTGxi5btuzVz/YBAFQqta2trampydnZebhjgSAIemUZv/kTi/bXJoxnSBAJXUpvaFaBYAwAAChzzpyt1jp/8O4U4n9OwgUtXjT6yN7LP93/curbFgN9uujhzYdCtMOymUG4/z7ea+ZM38/33f7ld3bcWoeB3hmCIGiw4Ag/NLQsLS1pNBqS+QMADOPwKBRqzJgxNNq/fzvr9frm5uaMjAzkSxQKBfk3AACHw505c2bZsmUtLS1WVlY9s33kQrFYPMhl2CqVKicnZ+Qu4H8hyAL+yMjI1atXx8XFjRs3rrGxkUgkPv/KEc7BwWH//v1Pnjz55JNPaDRaYmLi1atXhzuo5zM1NUWhUHK5fLgDgSAIGmlk3G6Z3shzlLcxcqypffioQ4cNCAk0/u9JRt7BY8yBICujZODr7XW8To5KjyJbkVA9WtFWNlZovaI4p3TAN4YgCBo8OMIPDbmek+1JJBJSWq8XLBZ79epVvV7P4/EaGxt5PJ5h0r6RkVF0dPTEiRNXrlyZk5PT60KdTtfa2mphMeBeeQAAUCqV+fn5gYGBg7kJAEAgEJw5c8bV1RWPx8fGxkokknv37mm1WplM5uPjM2nSpD6ubW9vP3LkyBdffGF4LTwe78aNGyYmJkKhMCoqatSoUT3Pb2tr+/LLLw2HIpGotra2tbUVOTQ3N3/zzTenT5/+t0E2NjbOmDEDAIDBYLy8vPz9/Y8cOTJ37twBv3A6nX779m0HBwc7O7vIyEg2m52VlaVSqbRa7ejRo8eOHdvHtUVFRffv39+6dSuBQDDc7eHDh2g0WqfTTZ482cPDo9clxcXFP/30k+GQzWZzOBxDxUE7O7vY2Njx48f/7eOoVGpiYuLbb7+dlJSUmZlpqOH3osrKyvLy8mxsbDw8PMaOHdvU1FRWVqZQKDAYTFBQkK+vbx/X/vHHHwwGIz4+3szMDGmpqKgoLy9XqVQEAiEiIqLnYD6JREKj0Tweb2BxQhAE/UPUXSU3fkr65UZ2ZWObwMjGKzR2zc69CZH2Rv89RVZ7dd/OQ5cynrAUJo6jp6/ctc36xJTEdCUAaJs1dxk/vWkMgI6T//OXX/10M6emQ4K2cPaPmL/hs53Lg/6SSfePvuuPa48VNnO2vx/47792VU31dC2K7OSI73mekYOTHVrX3NjI00+1f9Zj9OKyn9ZFr7pX0NitNrV2C5yyYMOOjxYHWiDno0jWlhiUns/l95y9rxcJhHqgE7e1vHDsEARBLw8c4YdeCTqdrrCw8J133rl+/bq7u3uvTgFkOgCPx0PW9r90L2UBP4vFSkhIWLNmzYQJE06ePKlWq+vq6ioqKhYvXjxjxoyeCerfBvDkyZOkpCQqlWpubm5vb3/ixInCwsLa2tply5bNnDnz22+/7XWJq6vrqR6++uqruLg4w+GRI0f+NtsHAEgkkvb2dkNGqtVqJZK+Vy4+R2lp6YkTJ9577z1nZ+crV67IZLKqqioWi7V06VI/P7/r16/3ca1UKi0tLd23b5+LiwuBQPD39//ll1/y8/O7u7tXrlzp7e19/vz5p68KCQnp+do/++yz9957z3C4b9++Xtl+a2trVlaWVCo1tFhZWfn4+ODx+Kfu3S937txJT09fsWIFCoVKTU0VCoXl5eVqtRrJ4dPT0/u4ViAQ5OXlbd682cnJydTUNCIi4vLlyzk5OSgUau3atRgM5o8//uh5vlwu1+v1pqamAwsVgiDon6FM/Sh62SnujGNpDd2izvLLCbaPP35z+o7Hhl8w6vofF0S++02Fx0e363mizvLL68x+TThcpkZZxN9SaLuQbJ95fdXEKQnXdXE/F3aIhG05SXNkF9dETtuZLX7RePTs3z/67B55+amkJU7//mNXLxWKNHqUGcHsL2k9yoyARwGdSCDsYx8CvZitemNbcjlbImZXXN/u33Bi2YSIrWndSIKPsoiYMd5Ux3pwv1RpuETX9md6rQYAvXRQv2QhCIIGCSb80PDT6/UcDmfWrFk7duxYu3YtiUQSCAR6vR6Z8I+s0vfx8Tl69Gh8fHyva9FotKura0tLy4CfrtFo6HS6QqEYzAJ+pVJZU1Njbm6uVCr5fP6tW7dkMll+fr65uTkSJAaDeXp6goFKpcLj8ci8hpaWluPHj7/zzjslJSWGkV65XF5TUzPg8AwUCkVbW5uRkZGhYp9AIKiurnZ0dBzYDUUiEZ1OJxAIIpHIysoqKSmJy+VWV1cjuTQGg5HL5X1000il0qCgIGQDxaqqqq+//jo8PLypqclQS18gEDQ3Nw8sNgM2m11dXd2zhcvl6vX6nvsU9B+Hw2Gz2aampt3d3ePGjfvkk08YDAaNRkNycmNjYy6XW1dX96zLhULhokWLkO91QUHBoUOHPD09u7q6rKyskBM6Ozt7luhTKpUmJiaWlpYDCBWCIOgfhDKZ/vmpf02hEo2NzSnh605+G29Vd/Lov0vW6Xm/796VzrNf+t3PmyIoRGMc2St6+88fjf9Lji2693nieZr529/8tm2auwUOR/Z685OzB2ONyr/+8Ie6Fyl2r+c+2jn//azQH+/+OM/++X/q6p9TVQ/tmpjJrfwt8U0/WzMszoIyYWXStQNR6OoTGw/lI+sA0NSVX30cZlb//eoNvxR2yNQKTtX1HfGHGkgkNEAZG/d9ewiCoCEFp/RD/yiBQPC37XQ6XSgUGuZ+8/l8nU5XVlbm7u6u0Wh+++23/fv3q1Sq8PDwxMTE48ePGy58VsKv1+t7VvIHAGi12oaGhsLCwhUrVvRsV6vV2dnZlpaWhkIAGo2mrq7u7t27pqambW1tO3fuJJFIIpHo9OnTTk5OTCbT19d39uzZSqWyqKiooKDAyclJKBSKRCIajdbe3i4SiXbu3Ll161Ymk4nc08jIiEgkikSiZ70tyERuAACPx0tNTY2NjQUA2NnZ1dbWAgBUKhWNRmMwGL1m9Q8Al8vNzc2lUChITwTy8mUyma2trUQiaWpqIhKJHA4nOzsbecc++OADIpHY1taWkpJCJpP5fP4bb7wxduzY/Pz8uro6a2trsVjMYDBaWlrYbDaDwbh7925sbCyXy3VycgIAYLFYExOTnkPrvdja2tra2gIAWlpaqquro6KihEIhkUjs7OwEAEilUhqN1tnZ+fSs/hd91Wlpac7Ozm+99RYAQKlUlpeXSyQS5JMgEomKiooqKyuR92HJkiV4PL6ioiInJweHwymVytDQUAqFkpuby2QyiUSiTCarqalBo9Hd3d01NTVGRkZubm4SiQTp4zAxMTEyMpLJZM8KhkKhUCgUAEBlZSWXyw0LC2MwGBgMhsvlAgAEAgGNRuPxeIa+HoFAIJVK4Qg/BEGvONzssx2zezaYevu5Gcmry+s1K2ywQF2YmsHTm8yNjjQ3nIEijw/3x1yv/M+xKv/mXbbeZM5b0ZaGIXiUTWRUAOZW4e17rR/5uvdrmEovyt0za2Gy14mMXxZTsT2+gMJbmGNQeplU9pcEH2lAm5Ms0EBTsXtsyL6qf1f1N3LZ+Gfz91N63uPf0K5vzQvZ+iD77h/VX00YiwEA4EM/T81y37/n+P6ZnuslRtbeE+d/cOVi56Y39zXaDbBLHYIg6KWAI/zQECopKTl48CCNRjt06BAA4PDhw8XFxdeuXUtPTy8uLv700095PN6OHTsEAkFISMjWrVt37NiRnp6emZl57ty5jIwMjUbz5Zdf+vn5paSkAAD0en1XV9ePP/6YkJBQVlaGPAKFQpHJ5J5V+rVabW1t7eHDh5HJ4Z9++unvv/8OAFCpVPfu3du7d6+h00Gn03V0dBw6dCgpKUkqlR4/flwgEOh0Ojqd/uGHH27fvj0uLi43N5dEInG53C1btsyYMSMmJkYoFCLX0mi0gwcPJiYmjho1SiAQSCQSW1vb4OBgLy+v4uLiXkPKWq0WubAPKpWqpaVFJpNZWFhYWFjMmDFDq9Uymcy8vDyky2Aw3wukF+Pnn3+WyWQODg6GqfIuLi6rV682Nja+d+8eh8NhMBg///zztm3bIiMjKyoqiETikydPvvvuu5iYmIkTJ4rFYo1GU1xcnJKSsmbNGiqVKhAIlEqlk5NTcHCwra1tSUlJY2Njz+eq1Wqx+DlTMaVSaXt7u1qtJhAITk5OEydOVKvVNBqtoqICi8UOeOK9AYVCmTdvHgqF+vrrr7///vtvv/1Wp9MlJCQAAJRK5d27dx8/frx161Zvb++mpiY8Hp+RkXHr1q3Y2NiAgACpVKpSqVJTU6uqqpYtW2Zvb498SCgUSnBwsKmpaXFxcVtbW8/HKZXKPhJ+hEAgYDKZOp3OzMzM29s7MDBQoVDU1tY2NjbicDgTExPDmSwWi0gkBgQEDPJNgCAIGlJ6fulvH78TMcqJbIo1QqNQKCP3f2Wr9HK5XA8AAIrubrEOZUYmmfS4BmVqatpjdr2S0yXU6WW/LyWh/svIbWu2CmhaGls0oD9U9T8tjfvV4dAfZ/6a7QMAgLGnj5uRns/s+MuPaC2L2alDW3t5WaIA5o29lf/dfU/T9rfZPgD/LsgHdNyubsMcBZR54LLDNwppXJlKKe6oSk3aEKpkMLUYnyD4AxyCoOEER/ihIRQcHJycnGw43LFjx44dOwyHvVZ3I50CBob90nfu3In8A4fDIZl/LxgMJjw8PCMjY+rUqQAAIyMjPz+/zz///PPPP+95mqmp6ebNm+fOncvn85Fq/Gg02tHRcc+ePXv27DGcJpVK09LSIiIiNBoNg8FwdXUFACgUioKCgsDAQBaLVVxc/OGHHyItNjY22dnZ7u7uGzduvHbtmkajQQLA4/FsNtvFxQW5J1K3z8XFhcfjnTx5smfmj0ajPTw81qxZAwDg8/l37twxrL13dXU9cOBAc3NzYGCgra2tv7//89/xZ8NgML6+vrt37+7VTiAQ5s2bN2/ePAAAi8W6cuVKSEiIXC7n8XgODg5IVPX19T4+PkVFRXQ6/d1337137x4OhystLXV3d/fw8EhJSVEoFAAALBZrZGQkFArt7e2Rm6vVaq1Wa29v39zcfOnSpZ7FArBYbFBQ0MKFCwEAra2t+fn5hq0cQkNDvb29WSyWr68vk8kc5PA+AMDf3/9Z715LSwuDwfD39xeJRHK53MbGBgDQ0dHB4XCoVGpZ2f+zd99xTZ37H8BP9gYCYYcNCoICoqIyioqjtdi6W6u21uto62jt0Fu9WmuX1Y5rtbWtq8NWUdur1Z9aJ4qCA0QsArJ3ZCWEDLJ/fxwbU5AhjjA+7z/6CscnJ0+QSj7Peb7fc43ccVBeXk5WbYSGhvr6+h46dIgcyWazDQaDVqsViUTkCcm+fSKRKC0t7fDhw5bJn8ViPfHEE6NGjSIIIjMzs7Cw0Px3PWHCBIlEolAo/Pz8pFJpszvwsdlsc3s/AICuyJC/ZfKIpecdp27a88Nzg72FbCphLP5vXN9leXcuprNFIgHVpJTKmgjCvMPdpGhUWFxsZzk521Ep6hn/k/w0gd38FTrEKPlj0bPva946snfunc78+qvvhr2g+e6vz4YzCIIeGBfr+lH2jbRM7ezYv2dhyEvPkBO28SMj7mfnPRn1qSInUeuXzvQ3L16S0oJefbpPp94MAMDDgcAP3R6LxXrttdcWLlxIBv42GAyGsrKyuLi4tofR6XQvLy+NRnP58uXBgwefPXtWLBYPGDDAaDTKZLLGxkY7Ozu1Wi0SiYYMGTJixAidTldeXh4ZGXny5EmCIHQ6XW1tbXx8fEFBwc8//0weqaurI7v0v/vuu629rlwuP3v27Jw5c4i/9yns3Lnzo48+Sk9PHzBggHkT/j25uLjMnj277ffVERwOh7yIXVBQ4Ovrm5qaShBE3759dTqdVCptamoSCoVubm4ikSg6OlqlUtXU1AQFBR09epQgCLVardVq4+Pjr169Sm7BUKvVer2erNRotv5iqbKyMiMjY+bMmQRBaLXa5OTkq1evvvrqq8XFxUFBQebu/a0JDAwk12U6RyAQODo6VlZWymQyOzu79PR0Dofj7e2t0WhkMpnBYBAKhV5eXkKhcOjQoXK5XKvVurm5VVVVEQQhl8vZbHZ8fPypU6fIWyTI5XIOh0P2RIyIiGjtRfPy8sjbTBIE0dTUdPDgQY1GM2HCBBsbGwcHB3O8v3TpUkNDQ9v3dwAAsD71ldMpcor3/Nf/FeN755q4SafV3U3zjCHjRtnvTEw9cU7+/NN3fp2Zai+ez9LdPQkz8pnxrtt/uHz+mmbCMPP97A35X8SFfhb0263vxra98mlqvLRu8qLs5/cdWRraykh29Nw5Ids+PLQn6cPY0eTvFu31vfv+MnrNnzeutdv9mGq2P+W7Y/T188vulhQYSw7+flVH8xn3VAj5SdpUuXV00L7JN0+8Ym4RWH/8qx9y7J/5YcEAfNgGAGvCln7oCRgMRkJCArl1vw0mk6ndffUcDmf8+PE1NTXnzp0jEz6VSiX36u/evfvLL7+Mjo4mhw0fPryysnLfvn2HDh0qLy8Xi8Xjx4//+uuv9+3bt2LFCpFIFBwcHBsbu2fPnj///HP+/PntvgsajSYUCslgT6PR3NzcgoKC9u3bl52dvXr16rafy2azm10W7gSRSBQTE1NeXn7lyhVyKz6dTvf09PT09Ny5c+eBAwciIiLs7e0HDBiQn5+/b9++48eP19bWBgYGDhgwYPPmzdeuXVu4cKGTk9PAgQP79Omze/funJycqVOntvu6DAZDKBSSNQtMJtPHx0ckEu3fv7+pqWnhwoXtPl0gEJib/N0vb2/v0NDQ/Pz8rKwsjUajVCpZLFZAQACbzf7mm29SU1ODgoLEYrGvr++NGzf27dt35syZxsbGQYMGOTg4kDUg06dPd3V1HTp0qJ2d3U8//aRWq81bFdrAYrGEQiEZ7NlsdmBgoMlk2rdvn7Oz85QpU8gxTU1N9fX1zs7OAQEBnXt3ANB76HS69gc9OpyB0RE8Y3Hi+i1nCmVao66h6NzW1d+l392FT7F/Zu26sQ7Vu1+ftyW5VKHTyvL//HTRjnxbyw+iNuPWbX7Jt2zLy3O3nMmv1xh08pKLO1+b/n5B/Lp/x7ezz0lfsGvWxHUppclrhttS71YEMAYj4r55AAAgAElEQVR/bNnujxn+5tdvh9fueG3Bd1ckKnVt5q9LX/4iz3/+lvdGtrm2rEv9aPbbuy+VNGgNOnlpys5Xp6xMMoW89tWKYRbbAjRJH736ZVKJQqeVl17++a0Jc/aLluz++nnX+7+jIADAw2QC6BG0Wu2BAwcKCwtbG2AwGGpqaq5du9bpl2hoaFi/fv3PP//c6TN0XyUlJRs3bjx06JC1J/L4ZGRkfP7552fPnrXWBFJTU0+fPm2tVweA7uX111/39/d/+eWXt2/ffvPmTaPR2NrIKVOmTJkypd0TznGwjOLMYRvzDW2O11ee2fBSXKAzj0Fj8p37xs3ZsHP5nTTMiFyfqzeZTCZlTuKKiQM9bJh0lp135IxPT13bEs+iCF883HT3PIbay9vfnBTpK+Iy6EyBS1Dc7A8OF6jb/wZIf0hopQyA5rfsgtZipFGatn3p+FCxLZvFdw4aNe+/5yT6ts/dVHll/2dLJsWE+rvashgcO3Fw3Av/+TWzwfJ7bFQUntiy9JnIvq4CNlfoHjLqpQ9+z1W0P28SQRB79+7t6GgAgPtBMbV3LxKAXk6hUOzfvz8uLo7H4y1evHjr1q1kC4DeoLKyMjk5OSoqqra2dufOnevWrXvA3oFdX05OTl5eXmRk5NWrV69fv75kyZIH7xoIAPCoffbZZ++88w6FQiEIwmAwCASCYcOGxcbGRkVFDR482PLfMXLjlWWHHasxZH88NPQ/siXnb24c1kpzvF6CQqHs3bt32rRp1p4IAPRAKCsCaIdOp0tPT2exWBUVFTNnzuw9aZ8giMbGxvT09KamJqlU+uyzz/b4tE8QRF1dXXp6enV1tUajGT16NNI+AHQLHh4eJpPJaLzTM76xsfHEiRNJSUkajYZKpfr4+IwaNSoqKqqN3iKPmrFsc3zsjRU3vx3z921GjVVJZ7KNoqlx/Xt32u92TCbT9evXU1JSzp49O3To0LCwsBEjRlh7UgDQKlzhBwAAAOjeUlJShg8f3sYAJpOp1+uNRiOLxXJ0dPz222+feuqpxzY9giCMZZtHBayQzvl2x+pnBzhSam8e+2rpwk9vDtqcfGhBQG+//tS9rvCbTKaampozZ87MmTPn+PHj3t7e5tsSAUAXhKZ9AAAAAN2YWq1ut2mfVqslH7BYLLFYPG7cuPt7Dc3+aRxKKxhhazP17Z2AKp6z48jGUTVbX4zysePZ9xm7Mlm8aP+FAx1N+w88AXhYKBSKg4NDXV2dm5vb0KFDkfYBurjevqQKAAAA0C1IpdLCwsLCwsLKysqqqirzg+LiYqPRSKG0um2TTqfr9fqgoKA1a9YkJiYSBEGl3uclH9aURPWD7Qml8HxGLfxsVPv3XnlUE4CHx2g0nj59OioqisFAPQZAV4fADwAAANBVSCSSsrKy8vLy0tLS0tLS8vLy8vLykpISiURiMBgIgqDT6a6uruQ9U4cPH+7h4eHl5SUWi5988snq6upmZyOjfkRExMqVKxMSEgiCIAM/wIMwmUynTp3atGmTtSfSFo1Gc+jQod27d3M4nHnz5o0cOZIgiJycHC6X6+npae3ZATw+CPwAAAAAj5Varba8RG++bl9aWqpQKMgxQqHQ1dXVzc0tJCRkwoQJ5GNfX19PT086/R6f37y8vCwDP4PB0Ol0ERERH3744ahRox7TG4NewGQylZaWqtXqLv5zdeTIkdzc3NmzZ+fl5V2+fNnV1dXFxUWn0zGZTGtPDeCxQuAHAAAAePg0Gk1FRUWz7feFhYUFBQUymYwcw2azyRjv6uoaERHh6+tLPvby8uLz+ff1cv7+/mlpaUajkU6nGwyGsWPHrl69evDgwY/gnUGvZjKZzp496+np6eLiYu25tCozM5PL5b744otOTk56vf7ChQuNjY2NjY0cDsfR0dHaswN4rBD4AQAAADqvtdL6oqIisqiexWK5u7uTl+jj4+NnzZpFhnw/P7+HeKtXDw8PspJ/4sSJq1atGjBgwMM6M4Alcj9/eHj4fXeC+JtUKj116pREIpFKpePHjx84cGBNTc2FCxd0Op3BYOjXr1/bP71JSUlXrlyJj48vLCxksVjjx4/Pysq6fv26TqcTCARDhw4l///y8fHh8XhUKpXFYjk7OxuNRq1Wy2KxaDRaJ+Z88eLFgoKC3Nzc0NDQ0aNH29nZnTt3rr6+Xi6XOzs7Dx8+vO1bFy9ZsuS5557jcrlHjhxZtmwZh8P56aef+Hx+aWlpbGxsWFgYhULpxKwAOgKBHwAAAKAdZKpvebm+tLRUr7/TIV4oFJKX6CMiIszX7d3c3Ly9vTsdjTrO19d31qxZ7777bmBg4KN+LejNTCbTyZMnP/zww849XaVS5eTkCIXChISEs2fPko0k//rrr+Li4sWLF6elpe3fv7/twC+VSr///vsdO3a8/PLLCxcurK6uTk5OZrPZM2fO3Ldv3/Hjx+fMmcPj8QiCMKdod3f38+fPu7u7BwQEdGLO+fn59fX18fHxCQkJM2bMEIvFnp6eSUlJM2bM8Pb2XrJkib+/f9uBPycnZ9KkSSEhIdu2bWOxWBkZGceOHdu+fTuDwZgwYcIff/yBwA+PDgI/AAAAAEFYlNY3y/YlJSVKpZIcYy6t9/X1jY+Pb7e0/rFZsGDBggULrDgB6A1MJlN+fn5DQ0N8fLz5iOV/qVQqecMIy3tGkGmWPKLT6crLy4uKikaNGuXg4GAymS5dupSRkcHn82k0GoPBUCqV165dCw8Pb20OAwYM+PnnnyMiIsgz5+bmSiSSgQMHkpfuKysrKyoq3N3dLZ/C5XJVKpVcLrecm8lkMt/a0Wg0Ws622VuQy+VXr1718/NzdXW1s7MrKCgoKCjQ6XQMBoNGo/F4vMuXLzs5ObWR+RctWjR69GgWi0We/+zZs2KxmEaj0Wg0vV6flpaG6ht4dBD4AQAAoBdpVlpvzvZkjCHHPMTSeoCOWL58eVJS0hNPPPHEE084Oztbezr3ZjKZNBrNsWPH7Ozs3N3dyTxsMBgyMzPPnTvH4XCys7PXrl0rEAhqa2t//fVXV1fXsrKy4ODgmJiYixcv5ubmOjo60mi0qVOnEgRhNBrVajWDwfDw8Dhx4oSrqytBEAwGg8VimdfX7kkqlaakpBQUFDQ0NPTt21csFjOZzLq6OoPBUF9fX1RUJJVKmwV+pVLp4eHh6upqMpmqqqouXrxYW1tbVlYWHR09cuRICoVy/PhxmUwml8s5HM7o0aPFYnFOTs6lS5fodLpWq42Pj3/vvfcIgtBoNCqVyt/f/9q1a0wmk1xisLGxUalU5E00WpOTk6PRaBQKhUKhmDdvnp+fX2pqqtFo1Ov1RUVFRUVFCPzw6CDwAwAAQE+j0+lqamru2QlfIpE8ztJ6gI7o37//lStXvvvuO71eHxQUFBsbS4Z/Nzc3a0/troaGhp9++unXX391cHDYsWPHjBkzBAJBcXHxv//972PHjtXU1Pzwww+2tra1tbVvvvnmu+++KxKJvvjiCyqVevPmzW3btm3fvj0vL+/WrVsEQRiNxlu3bv35559vvfUWhUKxrKvX6XQqlUqv1zc1NVluEyAIgsFg0On0oKCg4OBgNpu9e/fuY8eOrVq1avDgwRUVFbdu3SopKeFyuVwut9nMKyoq+Hy+p6enXC7fvn17UFDQ/Pnz16xZI5fLWSzWpk2bbG1tJ0yYcPjw4aamJiqV2tDQsHXr1oSEhKCgoOTkZHIaWq1269atEydODAgIuH79unkTvslkUigURqOxZeynUChcLler1b7yyitkQ4GIiIinn356/Pjxx44dq6+vz83NJQhCJBI9gr+uhyklJaWsrMzas4COGj58uFgsNn+JwA8AAADdVWul9SUlJeQnbwaDIRKJyCQfFRVlWVrv4+ODulnoImbOnDlt2jSlUpmSkpKcnHzhwoVdu3ZpNBry5zY6OnrMmDHe3t7WnaSdnd3ixYsXL15sPqLVak+cOBETE2MwGAoLC/38/NRqdVNT082bNwMDA0tKSm7cuLFo0aLjx487OTllZWV5eHj079/faDRKJJLs7Oxx48YNGjSotrbW3d1dp9ORJzQYDGKxuKys7PDhwyqVyvxaDAYjLCzMz8/vxIkTzs7OCQkJDAbDYDA0NDQMHTq0pqaGIAgfHx+NRtNylaSyspLL5bJYrLS0NAaDIRaLJRIJi8Xicrk6na6goCAqKsrW1jYrKysyMtLJySktLY3JZFZVVYnF4qeeeorH42k0mpSUFF9f38GDBwsEAg8PD5lMRi4ESKXS4OBgFot1+PDhkpISy+oAgUAwe/bs8+fPp6WlvfHGGzwej8/nV1dXi8XiNWvWVFdXh4SEODk5tVG/0EV8/vnn+/fvt/YsoKP27t07bdo085cI/AAAANCldbC0nkzyzUrrvby8OteUG+Dx4/F48fHxZHm8SqVKT0+/cOHCyZMnlyxZotFoXF1do6OjyQG+vr7WnixBEASFQrG1tWUwGA0NDdeuXevfv/+lS5e8vb3DwsLI3fUEQQiFQg8PD41GExgYqNPpyIvwR48eFQgELBbr9OnTISEhQUFBKSkpMpmsoaGBz+f369ePIAjLlQUzqVQ6YMAABoNRV1en0Wg8PDxEItHx48elUmlcXJy9vb2DgwObzbZ8irk/P0EQbDbb0dGRTqfn5ORwOBy5XF5SUkKW6pSWlpJb+hkMhlAoDAgICAkJcXV1rampMRgMFy9erKqqCg4OTktL8/b27t+//+XLl+vr68nuAKGhoTwezzJiWfLz86PT6Wq1WqFQ2NraBgcHq9XqVatWffTRR2VlZWPGjOkWW4qmTJmyb98+a88C2tdyIRuBHwAAAKyvqampsrKyg6X1zS7Xe3t7k025AXoMLpcbHR0dHR29fPlyy/C/dOnSpqYmc/iPiooKDg621iTpdPrYsWP/7//+Lzc3l9yXbmNj4+joOGLEiOTk5FOnTg0bNozD4QwcOLCgoODKlStUKtXOzq6ysvLnn38mCMJgMAQGBj755JM8Hk+r1aampmo0mkmTJrXxikKh0N3dPTs7Oz8/XygUPv300ywWq1+/fjk5Oenp6X5+fmQzP0tardbZ2dnGxoYgiH79+qlUqtraWgqFYmdnZ2NjY2NjExkZWVdXl5iYyGQynZycCILo27dvfn5+cXFxXV0dh8MxGo1Hjx7966+/CIJQqVTffPONh4fH+PHjq6urCwsLJ06cSD6rNX369JFIJBkZGbdu3fryyy+5XC6VSh0/fvy1a9cqKytXrVqFrUbwSFGa1cYAAAAAPCL3W1pv7pbn5ubm7+9va2tr7XfQ7ZH90nClrkuhUCjNtuC2Qa/XX79+/eTJkydPnrxw4YJarXZxcYmJiSF3/g8cONC66dFgMNTU1NjZ2alUqk2bNg0fPnzMmDFWnE+7ZDIZhULh8XgHDhyQy+Xjxo3z8PCw9qS6HPy70Y20/PcEV/gBAADgIetcaT2Z7V1dXXG9C6A1dDo9IiIiIiJi+fLl5vCfnJy8evVquVzu7OwcGxtrxfCvVqsTExNjY2MNBoNKpYqKinrME7hfqamper3e39+/oqJi4MCBLi4u1p4RwEOGwA8AAACd0VppfXFxsbnVFkrrAR6d1sL/e++9J5PJnJychgwZQu78f2zhn7xa3tjYKJVKn3vuua5fa8NmsxUKRVFRUVhYWEhICIPBsPaMAB4yBH4AAABoVWul9Xl5eXK5nByD0noAq7MM/waDISMjg+z2v379+hUrVjg6OkZGRpLhPzw8nEqlPqJp8Hi8uXPnPqKTPwpxcXHWngLAo/WPwJ+YmDh9+nRrTQXuS8drvQAAANqm1Wpra2vbLq0nU735rvXz589HaT1A10Sj0cjwv3TpUoPBkJOTQzb8+/TTT1esWCEQCCIjI8lu/480/ANAV3CPK/x79+59/POA+4J1GQAA6ASU1gP0NjQaLTg4ODg4eP78+Zbhf8OGDStWrODz+UOHDiW7/UdGRmJDO0DPc4/Aj+vGXR8CPwAAtEYqlbaM9IWFhWVlZTqdjhyD0nqAXsgy/BMEUVhYSHb737hxozn8kw3/YmNjmUymtecLAA8BavgBAAC6H7K0vuXl+o6U1vv4+HC5XOvOHwCsztfXd/78+ZbhPzk5efv27WvXruXxeMOGDSPDf0xMDIvFsvZkAaCTEPgBAAC6KMvS+mbZvt3S+oCAABsbG2u/AwDoHu4Z/nfu3Ll27VoulxseHk42/IuOjmaz2daeLADcBwR+AAAAK2tWWm9+bC6tZzKZDg4OKK0HgMegWfgnu/3v2bNn/fr1HA5n4MCBCP8A3QgCPwAAwONwX6X1wcHBCQkJ5GM3NzeU1gOAVZBri7NnzyYIorKykmz4l5iYuH79ejqdHhoaSnb7j4qK4nA41p4sANwDAj8AAMBD01pp/a1btxobG8kxQqHQ3CTP8nK9h4cHWmQDQJfl5uY2derUqVOnEhbh/48//rAM/1FRUbGxsbhVJ0DXgcAPAABwf9oora+qqiLHmBvmkZfrUVoPAD2JZfivqqpKTk5uFv7Jhn+jR4+2s7Oz9mQBejUEfgAAgHtrrbS+uLjYaDQS/yytt7y5HfnA2tMHAHgcXF1dzeFfIpGcP3+eLPv/6quvqFRqWFgYGf7j4+OFQqG1JwvQ6yDwAwBAr9ZaaX1paaleryfHoLQeAKAjXFxczOH/9u3b586dswz/ffv2JZP/qFGj7O3trT1ZgF4BgR8AAHo+lNYDADxmzs7O5vBfXV196dIlsux/27ZtBEEEBgaS4X/kyJEODg7WnixAj4XADwAAPcSDlNb36dNHIBBYd/4AAD2Vk5NTQkJCQkICQRByufzy5csnT54kw7/RaCSrouLj4+Pi4hwdHa09WYAeBYEfAAC6GbK0vmWkR2k9AEDXZ2NjQ8Z7giAaGxsvXbp08uTJ5OTknTt36nQ68t/tqKioESNGeHh4WHuyAN0eAj8AAHRFlqX1ltm+I6X13t7eVCrVuvMHAIB2CQQCc/hXKBSpqalk+N+1a5dWqyULrKKjo8eNG+fp6WntyQJ0Swj8AABgNWq1umW3vMrKyrKysnZL6z09Pel0/BYDAOgh+Hy+OfwrlcqUlBSy4d/ixYstw/+YMWO8vb2tPVmAbgMflQAA4NHSaDQVFRX37IQvlUrJMZal9REREeZr9SitBwDohXg83j3D/5IlSzQajaurK9nwb/To0T4+PtaeLECXhsAPAAAPR0dK68ViMZnkUVoPAAAdYRn+VSpVeno62e1/6dKlTU1N5vAfFRUVHBxs7ckCdDkI/AAAcB86Xlrv6+sbERFhGelRWg8AAA+Cy+VGR0dHR0cvX75cp9NlZmaS3f5ff/11tVptGf779etHoVCsPV8A60PgBwCA5lorrS8tLVUoFOQYy9J6y8v1KK0HAIDHgMFgRERERERELF++XK/XX79+nWz49/bbb8vlchcXl5iYGLLsf+DAgQj/0GvhMxkAQC/VWml9QUGBTCYjx7QsrScfe3l58fl8684fAACARKfT7xn+16xZ09DQ4OzsPHjwYPLiP8I/9DYI/AAAPVy7pfUsFsvd3d1cWj9r1ixzJ3yhUGjt6QMAANwHy/BvMBgyMjLIhn+ffPLJihUrHB0dIyMjyfAfHh6OQjPo8RD4AQB6ApTWAwAANEOj0cjwv3TpUsvwv379+hUrVggEgsjISLIjIMI/9FQI/AAA3QZK6wEAADqnWfjPyckhu/1v2LDBMvxHRUVFRkYyGAxrzxfg4cCHPwCArgWl9QAAAI8UjUYLDg4ODg6eP38+QRCFhYVkt/+NGzeuWLGCz+cPHTqUDP9DhgxhMpnWni9A5yHwAwBYgU6nq6mpaRbpyQdFRUUmk4lovbTez8/Pzs7O2u8AAACgh/D19Z0/f75l+E9OTt68efOKFSt4PN6wYcPIbv8xMTEsFsvakwW4Pwj8AACPENkwr+Xl+pKSEoPBQI5BaT0AAEAXcc/wv2PHjrVr13K53PDwcLLhH8I/dBcI/AAAD8pcWt8s25eUlCiVSnIMSusBAAC6l2bhn2z4t2fPnvXr11uG/+joaDabbe3JAtwbPmUCAHRIs9J6c7bPz89vaGggx7RWWu/t7c3j8aw7fwAAAOg08nf67NmzCYvwv3fv3vXr19Pp9NDQULLbf1RUFIfDsfZkAe5C4AcAuOuepfXkY4lEgtJ6AAAAsAz/lZWVZLf/P/74wzL8R0VFPfHEEzY2NtaeLPR2CPwA0Bu1W1rPYDBEIhGZ5KOiosgH5OV6V1dXCoVi7XcAAAAA1ufm5jZ16tSpU6cSbYb/mJgYXBUAq0DgB4Aeq4Ol9WSMb1Za7+XlRaPRrDt/AAAA6EYsw79EIjl//nxycvLJkyc//fRTKpUaFhZGdvuPj48XCoXWniz0Fgj8ANC9NTU1VVZWdrC03vJyPUrrAQAA4BFxcXExh//bt2+fO3eOLPv/6quvqFRq3759yeQ/atQoe3t7a08WejIEfgDoBjpRWj9//nzysb+/v62trbXfAQAAAPRSzs7O5vBfXV196dIlcuf/tm3bKBSKOfyPHDnSwcHB2pOFngaBHwC6EJTWAwAAQA/m5OSUkJCQkJBAEERNTU1qaqo5/BuNRrLAMD4+fsSIESKRyNqThZ4AgR8AHrfWSuuLi4tVKhU5BqX1AAAA0LM5Ojqaw39jY+OlS5dOnjzZLPxHRUWNHDlSLBZbe7LQXSHwA8Aj0VppfV5enlwuJ8egtB4AAACAIAiBQEBe2ycIQqFQpKamnjx5Mjk5eefOnTqdzhz+4+LiPD09rT1Z6E4Q+AGg87RabW1tbdul9WSqR2k9AAAAQEfw+fxm4Z9s+Ldr1y6tVkteJomOjh47dqyXl5e1JwtdHQI/ALSvWWm9+bG5tJ7JZDo4OKC0HgAAAOAhsgz/SqUyJSWFDP9LlizRaDSurq5kw7/Ro0f7+Ph04vw3btzw9/fncDgPe+LQVSDwA8AdUqm0Zbe8wsLCsrIynU5HjjGX1gcHByckJKC0HgAAAODx4PF45vCvUqnS09PJhn9Lly5tamoyh/+oqKjg4OAOnvP999+/evXqjz/+GBMT8yjnDlaDwA/Qu5Cl9S074XektN7Hx4fL5Vp3/gAAAADA5XKjo6Ojo6OXL1+uVqvT0tI6Ef5NJtOZM2fq6+ufeOKJV155Zf369Xw+/3G+C3gMEPgBeiDL0vqW2Z4c01ppfUBAgI2NjXXnDwAAAAAdxOFwzOFfr9dfv36d7Pb/+uuvq9VqFxeXmJgYsux/4MCBloWWubm5dXV15OPvvvvuf//7386dO8eMGWOl9wGPBAI/QDd2X6X1lje3Q2k9AECvtX//fvz7D9BT0en0iIiIiIgIy/CfnJy8Zs2ahoYGZ2fn2NhYc/hPSkqi0Wjkh0a9Xi+RSMaOHTt58uRvv/3WwcHB2m8FHg4EfoCurhOl9eRjNzc3lNYDAIClZcuWTZ061dqzgOaGDx9u7SlAD2QZ/nU63ZUrV5KSkpKSklatWqVQKJydndlstuXyn9FoJAji0KFDZ8+e/fbbbydPnmy9ucNDg8AP0CW0Vlp/69atxsZGcoxQKDRforfshO/h4cFgMKw7fwAA6BaGDRs2bNgwa88CAB43BoMxfPjw4cOH//vf/9br9WlpaUlJSe+9955er282UqfTSaXSKVOmTJ48+ZtvvnF0dLTKhOFhQeAHeHw6XlpvvlyP0noAAAAAeIjodHpkZKS9vf3y5cvvOcB8qf/PP//cuHHj450dPGQI/AAPX2ul9cXFxeQ/oG2U1ru5uVl7+gAAAADQw1kW8N+TTqfT6/ULFixwcXGJiIh4nHODhwiBH6CTWiutLy0tNW+Oaq203tvbm0qlWnf+AAAAANBrnT179p7HmUwmhUIxGAx6vd5kMhEEIZFIkpKSLly4EBUV9VinCA8DAj9AW1BaDwAAAAA9z5kzZ8yX95lMpkgkcnNzc3d3F4vFTk5Orq6uLi4uzs7Obm5uS5cupVKpSPvdFAI/AKHVasvLy1teru9IaX2fPn0EAoF15w8AAAAA0HFarXbjxo3u7u5ktre1tW1jMPaldmsI/NCLkKX1LSM9SusBAAAAoPdgMpnPP/+8tWcBjwMCP/Q0lqX1ltkepfUAAAAAANCrIPBDt6RWq1t2y6usrCwtLVUoFOSY1krrPT096XT85AMAAAAAQA+H2ANdl0ajqaiouGcnfKlUSo6xLK2PiIgwX6tHaT0AAAAAAPRyCPxgfR0prReLxWSStyyt9/X1FQqF1p4+AAAAAABAV4TAD49Jx0vrfX19IyIiLLvlobQeAAAAAADgfiHww8N0v6X1lpfrUVoPAAAAANBNvSyi7awz/uMQhUJj2br1Hfr0vFXvvRLl1B2u3+ml2ScTf/o58cj5jHxJo5Ht4BUS9fSct96ZM9SJ9o+BJtm1XWv/s+nAhdxao53vkIQFa9a9Gt1szD8ZSzeNCFh6TvvPozTv18/e+iKaYT6gKz/z9Ucbdh25dEuiYjj4Rjw5Z8V7S0Z7MDv7jpCv4L61VlpfUFAgk8nIMS1L68nHXl5efD7fuvMHAAAAAICHa0et4bu0laFDPyp8enfN7zP4BEEY1DW5p795/V9rF8efLzl+8dPYLt9hS3vqnSfG/+L4wsYtJxMjfWz0Vem/ffTq4gVP/H5hT/L2ia5/L1mY5BdXjxm3oX78l/uyZoXQ8//3n1kLRsdl/560ZZwj5UFeX5e3bWrcqyeEL2xKvD49zF5x8+CH8xaOj76yM+mXF7zbWk1oHQI/tKrd0noWi+Xu7m4urZ81axYZ8v38/Ozs7Kw9fQAAAAAAsB4ax7Hf+FU71pzv8+qprz9OXBYz1+WB4vBjQfOau+W7V+LYBEEQhEfki1/9VH4lbNXPqzYvSvgwgkzP2ozPXv00XTjn/75bGCkgCCL0hU07bl4ctv61tVOubx7ZxsVNWg7q0ygAACAASURBVJ93Ll1bP6S1DG6q+uXNtw9VB7zz+9a5Q1gEQfDDn//ip5LLYSuXrZg85tdpnVpMQODv7VBaDwAAAAAAjwjVJWKQF+3krcy0m7q5Lp3emf54MMd+X1X4z0N0/0HhdtT0olsFWoIM/JoL23f9ZRC/+nyceccCK/y5qSEb1v76/bEPR06x7eyry88cPNNAdZ35ZDjr7ssHPPlk4H/e/2PH/yTT5rl24qQI/L1Ca6X1JSUlSqWSHIPSegAAAAAAeERMJmvPoHNUdbUqE82/X587ixX67DNJlUbGuEEDLFYvaH0iwmyIzPOn07RTRnZyVcNYf7tGa6IIHewsr+RTHRwdqKa/rl5IJ+aN78RZEeR6DpTWAwAAAABAl2KUpF0tMVD4g4b1Z7Q/Wled9tv3W3b8lpyZVyqjOQYMeepfK9e+FutiUb+uyk58f+X6X05nVTWx3ULiX1r9lmhz3JITGoKgOv7r/8q/H8skCGNN6rYPP/7+4IWblQqqrTg4ZuIrq1bODre7/z3xpuoj+881OU54Z/6AO9FZm59bZKAI3d14luNoru7OVGNBXl69aWSrhQumxmvfLxj98tFLebU6jshnQNykV1a8/dwAW3I8xU5kT6eYpHVSy6URk1zWYCKMjaXF9z13giA6H/j1ty/s+PTzH46kZJXWqgiuo2fggMFRI8clTHw6xt+2c+0EHqce0H0xOTm5qKiovLy8pKSkrKysvLy8traW/CMOh+Pp6SkWi8Vi8fjx4z08PMRisaenp6enJ1I9AAAAAAA8Dsam2tzT37y+9oze/enPP37Oqf24rTn+9uiZZ2I++fHP/cPdTFWXf1017/Wx8WXHUzfEkjFGl/vNpNhFZ2wmb/zjt5ciXfQl575757VPr+kotrMO3v4xgUUQBGGsOPBy3Au7m0Z/tOvy/Gh3Y+nZb5bM+Vfs6dyjZz+Kvr++gSbJ/95edVQ4+9ctM9zvVDKblA1yvYnC5XP/8XYoXD6PQhjlsgYj4dJaXDQ1SrShG/Z9HONvq6tM//2Txa/PHHbo0u+nvxgjohAExTZmzFDOH+dOHUvXxEXe2dVvLD15IltPEHSl4r5mbvGiFvbu3dvsyL0ZyhNnejM5gc9/eTzrtkKna5KWXv39gwRvJoXq9sopTfsnsDbNsX85Urn9Zn195latSqeVl6buWjhQQGX6vvRbpeHuMGPDhVWDBSy/575JrVAobmf8PL8/jx30ytFqYxvnNpT8N7ZlbKd5v35eax6jvfX9M24MbvBL21LLGpuUVem/LIqwYXhO+7lI37E3QJ5UKBRGRERMnTp1yZIln3zySWJi4vnz5wsKCgwGQ/unAAAAAAAAaM+UKVOmTJnSkZG6q+/2a3ZBmcJ0G7F8301ZB/NJ0x8vuo77rupu3FIcm+dO4yfsIhOYsS5xuohKdZvzR4N5hLH21yn2VIrtrENN5IGGw3PcqFTH5/bVmU9jlPwwwZbKGvJJdgfj1p0zn/33UJHPtJ25TZZHq7eNYxE0vzcvaC0H67M/GsQgGEM+yen4SxiKtoziU2gBb138O0IrLq0dakPlBs/ZfqlCqVVXZ+5/O8rN1VVIJZjRn3fklARB7N279x9HLL/oYODXpb4dQKP5LklS/+OwKuXtIGa3CfwuPovPWM5fl/VBBIOg93v3qs48Kn11KJPmueC4/O8jTVfeDaHTfF871dj6uQ0l/x0R8s4lXesjjJW7Euwo9H7LL939ydHd/HgIk+o0fW+biwlmBEHs2bOnIyMBAAAAAAA67X4DP/vZ3Y0mk8lkUFVd+fZ5PyaVF/bOWVmHUk5LhsLPo5k0nzsXTzVH5zpSKdyJvzRYDin6MoZ5N/Brjs9zplK4z+6W/WPMF9FMghn1eUFHr4waGy6sjnTwn/lrkbbZnyj3TeNRqK4LT/wj9+rSVvajE6z4rVVGky5jdf+7Cx80j0Vnmp/j72mVfhXHJOj9VqaZ06Ox4fpPb08c7GPPYTD5riFjXv36/Ok1YQwKb3KH0l/LwN+ZFuvy7KxSA83D3+efF7I5EZPGd/LmgI8bc+z3VYWb7txrgUR2XzQU3Sr4ezP+ne6LzzTvvkgt/vX7Yw0P8Op3ui+ObNF9kVrzx47/STp4Fgql69/TAgAAAAAAeicqx2XQ/O92vBagvf7Fqx9fburIc0zS9B+WT4/p5y7kMGhUCoVC812WrDWp1WoTQRBEU21to5HCFdpZBjkKh8OxSEaamuoGo0n1vxfsKHfRfN5I1hL64rxifYfmrs39/oVpO13XH9n+nHfzzgNM/74+NJO0olJledRQVXHbSBUFBNhTCHro2sy713/1pV/FtdK9gOrg6EAljHXVtUbzu7EZMPPT3y4X1qm0msbKG8e3vDJEU15hoPcN79+hmbd8jU48h21jy6Lob5w6IzH+4zhj6Ibc0q8725TQ2lrpvtj/Ht0XZedPp2nvfZYOaKv7YtPVC+mdPjEAAAAAAEAXwo/599qJIkP2N//ZVWxsb7Ahf8vkES9/nua+eE9aZaPOaLpz+Z4g7pQ0s0UiAdWkksosVw9MikaFRZc7lpOzHZViO/OguvnVb5OhvENh1Sj5Y9Gz72ve+mP33DvZUH/13ZC+b17UEQRBEPTAuFhXqu5GWqZFJDTkpWfICdvokRH3k4bJqE8VOYlaj+X6mxcvSWlBCU/3uY8TW+hM4OfEThrvRNQfWhjz9PIdZwsb2/2ba0lXnbb3w5fHRvRxtWFzhB4Dxi7YdE5i+McQVXbiikkRnnZsBtvOa9CUNYdSvxrDJtdnnOYdJ7+5xprU715/ZrCPiMdiC5z8h05++4drsk7d7uH+ui/W5uXVt/EyZPfFEDcbFpNj59YvdsaqXzMbzOMfTfdFAAAAAACALobiOPk/r0ewGk9//NGf8nbGqq+cTpFTvCe+/q8YXyGbShAEYdJpdXdTE2PIuFH2lKbUE+funspUe/F8lu7uSZiRz4x3pSovn7+msTi1If+LGJ54/vF/XJW/F1PjpXWTF2U/v2//0lDuvYewo+fOCaGVHdqTZO6jp72+d99fRq/n542zbe28NdufFER9XmiZnY0lB3+/qqP5jHsqhMygpsqt8bajvqm4O8hUf/yrH3Lsn1m+YEAn2+13JvBTRJO/2rfuSU9T0dFP5470d3YfMHrWW5/tuVCi6mjW1hx/e/TMb+vGfPbnrVr57YxfX3M6t3xs/Ipz5u+YLvebSbHPf3Hd7+0/cuvltzN+XcDdeaf74qEmQzV5r4WKAy8Pj3vtgHHatsuV8obSC1smqHb/K3bUyuTG+31Hf3df/Pa+ui+2frpGiTb0rX0ZEkWj5PqBd4JvbZ45LOaNP2vJbw/ZfdFYdepY+t2fwb+7L5o63X0RAAAAAACgq6EHv7pmhjtR9tOaLVltb6jnDIyO4BmLE9dvOVMo0xp1DUXntq7+Lv3ukyj2z6xdN9ahevfr87Yklyp0Wln+n58u2pFva5lqbcat2/ySb9mWl+duOZNfrzHo5CUXd742/f2C+HX/jm8lw/9NX7Br1sR1KaXJa4bbUu9WBDAGf5xjcXWaGf7m12+H1+54bcF3VyQqdW3mr0tf/iLPf/6W90a2eUs0XepHs9/efamkQWvQyUtTdr46ZWWSKeS1r1YMs9gWoEn66NUvk0oUOq289PLPb02Ys1+0ZPfXz7t2up7bco9DR7v0k9Sl53/84JVnIr1t6BSCIAgKwyny1V9yWm6daAndFx9690UAAAAAAICHroNN++Y4WKZuCu+5A3fTlSF/UxyfTKzM2P+WtN44T195ZsNLcYHOPAaNyXfuGzdnw87ld9IwI3J9rt5kMpmUOYkrJg70sGHSWXbekTM+PXVtSzyLInzxsEWaM9Re3v7mpEhfEZdBZwpcguJmf3C4oANBVfpDApu4J5rfsn8EQ6M0bfvS8aFiWzaL7xw0at5/z0naCYhNlVf2f7ZkUkyov6sti8GxEwfHvfCfXzMbLNsZGhWFJ7YsfSayr6uAzRW6h4x66YPfcxXtz9usZU58gMBvpq3LPrVz9XNh9jQKhdH3zfOq+z4Dui8+cPdFAAAAAACAh67jXfqtQ3/zo0EMmv+bF1sJY71Ky5zYmS39zTHsA0e+tPbXiyfeDaXr83//PV3X3jPQffGhd18EAAAAAADo2Yxlm0f6LPhTbXGkKulMtlEUHde/lTDWy3Um8OvOv+4f+HZK81jPCXlimCPF1KRuaqeUH90X7+FBuy+2Qa/v2PIHAAAA9HpKpTI3N/fYsWPnzp27efPmzZs3r169mpmZqVK12+rK+rRabU1NjbVnAQCPlqlq9zvLdqdXKfV6lSTzt//MWn2GN2btu+PaLJ/vvTp1hd9k0hcfP5Kp+edRY/mNv+pN7P7hQe2sraD74iPovthMeXn5Dz/8MGvWLBcXl6tXrz6UcwIAAECPp1Qq09PTly1blpiYmJ2dnZOTc+3atT179hw8eFAqlVp7dm0xGAyFhYU//vjjxYsXs7KylEqltWcEAC1o9k/jUFrBCFub2e6FSqp4zo4jG0fVbH0xyseOZ99n7Mpk8aL9Fw4sCOhYjHrgCXQ/lpfGO1jDr01a4kWjsHyf+eDAlRJpk96gkVfeOPbVnFABzWbImpR2mwrocz6L4lHons9+cbpAqjFoZYVJm5/zZ1KoLvP/vFMwr8395klHGtNv2ubzJY1ajTTv+CdT44I9aRZN+0yGyt/n9uFwAl/YfDqvrkmvbSi+sGPBQDvXCTsK22uop8vf8Ywr7V6NDv/RjMHYcGHlID4rYMa3l6uUqprrvywM5bEDX/m/amOrZzZWbxvHojpELfs5tVim0WsbSi7uWDDQhsrtv+RYzd9PM1Z8M4pFE0/47Gxxo1bTUHLpp2VRjjYDl51o48T/RNyrhr++vv7AgQOvvvqqr68vQRA0Go1GoxEEkZ+f38HTAgAAAFy8eHHUqFGnTp0yH9m2bdvw4cOvXr1qxVm1rampKT09/cMPP1y5cuVnn322d+9emUzW/tMAoAO6eg0/WGiZEztzhZ8R/cHZc3s/mSw4v2HuqBA3GzbfKTB+0Y+3h/x736VTa4by2ns+re/SfYc/nemb80lCoEhg6xH58i712MkRDKPkuzEs5tBPbxkIRp+F+8//umxA/vrxAUKBc/jMH6gLv3i1D52gUs0zpro++93FpM3j1T/9a5iHgM0XD/3Xj+pJPyfvneNDa2cGigu/H68ytHsTQYrN8HUnkr5+quHrSYEOQp8x6/KGfPTn2a+edGz9nggUx5k7khPfjSj+dkF8kBOP5xI67bPcoKW7U5O/HCv6+2kU11nfH940mZK4YJibjdB7+Nxd9U9+d+XcZ/FtnLgVarU6OTl5/fr1TzzxhKOj45QpU7Zt21ZYWEgQhMFgMBgMBEE4Ojre72kBAACg10pPT3dycrK3tzcfoVKparWa/FzRNUkkkq+//tpgMCxcuPDZZ5+Njo62tW1tPyYAQC9CIZcBSImJidOnT7c80oUYsj8eGvof2ZLzNzcO6939GAwGA51Of+6556qqqlJSUrRaLZvNbmpquudgOp2u1WoplE7ftxEAAAB6l7lz54pEosWLF4vFYoIgSktLP/jgA1dX1wULFri5uVl7dvfQ1NR09OjRdevWHTx40MPDw9rTAehppk6dShDEvn37rD0RaB+FQtm7d++0adPMRx5OxfhDZyzbHB97Y8XNb8dw/j5Cdl+c2tu7LxqNxk2bNhEEsWfPHvPB1tI+QRA2NjbFxcUODg42NjaPY34AAADQnd2+fbukpEQoFObl5RUUFKhUqurq6lGjRg0bNszJycnas7s3CoVC7gEViUTWngsAQNfSRQM/caf7Yqxo9bMDHCm1N499tXT1Gd6Yzb2++yKVSn3jjTeWLVv22muvnT59Ojs7m06nt9GHn06nkyX9BEGw2Ww3NzdXV1fhP1kedHJyotO77k8FAAAAPFI3btyg0+mDBg0KCAhQKpV79+5lMBizZ892d3fv9DkvXbokk8kKCwsHDx4cHh5Oo9EuXrxoNBoVCoWLi0tYWFgbz1UoFNu3bx8/frxGo8nOzp4yZYpSqbxw4QKLxVIoFP379/f09GQymcHBwcHBwW+++eaYMWOMRuOkSZM6N1WJRJKdnV1dXc1gMIYMGSIWiysqKoqKinQ6HZ1O9/f3d3V1bePpJ0+eNBgMfn5++fn5vr6+ffr0uXHjRl1dnVKpdHJyCgwMFAgEnZsYAEDnPJpop9k/zW7qvlb2mIe+l3Z1TTvN6KniOTuO0Dd/s/XFqDeKKuRUe5/w+EX7v3/7Gf8Od198sAl0fbGxsZs3b05OTv78888PHjxIbt1vOczf3//w4cN1LVRVVZG/gerq6ixvtEOhUBxaEIlEIpGo2UEGo3fvtQAAAOiJ0tPTXV1d+/XrJxaLDQbD0KFDN2zYMHLkyE4H/vz8/IqKikGDBjk5Ob355puHDx+WyWSHDx9eunSp0Wh89913d+7c2cbTNRpNYmLiwYMHw8LC5s2bp9Vq//rrrzNnzrzxxhvl5eU7dux477339Hq9QqEICAjw8vLSaDSd3tVPrkrQ6fSIiIg//vijpKRk3rx5165da2pqiomJOX36dE1NTdtLCTdu3Pjtt99cXFwmTpw4ZMgQiURy6tSpfv36hYeHk9uhBw8e3Lm5AQB0zqOJvawpieoHawRA4fmMWvjZqIVWm0A3ER0dHR0dnZ+fv2nTpu+//16v11te7adQKGKxuN1fLWq12rwQUFNTY7kucPv27Zs3b5LH5XK55bNsbGwsFwLs/yYUCu0tCIVC8mYBAAAA0PXl5OQ4OzsLhUKCIGg0GofDkUgkarW60yesqqq6cuXKuHHj3N3db9++nZ2dnZubq9PpRCIRjUZraGhISUkZNmxYa0/ncDgvvfRSVFQUj8cTi8UKhSIlJUUgEDg5OanV6oKCgoKCAi6Xm5SUtHDhQh6PZzAYmExm56aqUqlu3brF5/OjoqLs7OyuXbt29erVvLw8f39/Z2dnJpOZn59fVlbWxoLCoEGDgoODvb29nZycbGxszp49W19fT+6mVCqVubm5ffv2RZUldC8KhUKhUPD5vXybdTfW3a9zA0EQhL+//6ZNm9atW7dr165PPvnk9u3bFArFaDQyGIyOlNtxOByxWEw25mmDTqdrtk2gtra2traWfFxcXFz/t2Z7DWxtbe3vpeXSAJvNfqBvBAAAADyA6urqyspK8tY/5JHMzEwmk8liscgvJRLJmTNndDpdUVHRc8895+/vT6PR9u7dy2KxampqHB0dx4wZw+Vyc3JyLly4IBKJlErlU089FRwczGazqVSqUqn09fU9ePCgUCgkOwrb29tXVla2MSWDwVBVVVVQUCCXy3k8Xnx8PJ/PLykpIQhCp9OVlJTcuHGDQqH4+/s3+8zT1NSUlZWVlpZmMpkoFMoLL7zA4/GKioqSkpJ4PJ5SqRwwYMDAgQOlUmlaWlplZSWfz3d2dp48eTKVSqVSqQaDgZxzQ0MDl8slCILD4RgMhmbXP5qRy+USiaShoUGr1UZGRvJ4vKamJnK5pK6uTqvVNjY2IvBDF1dVVZWRkXH9+vVr165lZGTk5+fz+fwxY8ZYe17QSQj8PYetre3SpUtfeeWVxMTETz/99MaNGzqdzsHB4WGdn8FguLi4uLi4tDtSoVBIpdJ6C1KptK6ujnyck5NjPq5UKi2fyOVyW1sOaLY0gBI4AACAhy41NVWn07m5uZkTfn19PZvNZjAYeXl5KpXqs88+W7ZsWb9+/aZPn05m45UrV0ZFRcXExGzdupXNZhuNxoaGhhUrVmzevFmj0Rw+fJjBYPB4PIIgVqxYsWbNGj6fbzAYzPdZptFodXV1bUyJz+e/8847NBqtsrJyzpw5I0aMGDt27LVr1yQSyaVLl4xGo06nu379+vPPP2/5LL1ef+XKlcTExI0bN6akpPz000/z5s3LyMj47bffZsyYodFojh49GhgYqNVqz549m5mZuWjRovT09MbGRvIDRnJyskQimTJlilarJdcLCIKgUCjm9N6aMWPGkLe7ev/99w0Gw5QpUy5fvqxUKjMyMiorK/v3749OSdDVGAyG/Pz8jIwMMt5nZGTcvn2bIAgvL6+wsLDnn38+NDT0+++/t/Y0ofPwj05Pw2QyZ86cOXPmzHPnzn3++edWaajL5/P5fH5HKug0Go3l0kCzZYLy8vLMzEzzH1k+kcFg2FkQCoXm/94T9g4AAAC0QalU/t///V9qampAQEBycrKPj4+/vz9BENOnT5fL5ZcvX3Z0dJTL5d7e3vb29rdv33ZwcCAXBZKSkl544QU+n3/x4sV169ZxOJxjx47Z2tpevXo1KCjotddeo9FoJpPp0KFDERERzz77LJ1O9/Hxqa2tJV+3rq7O19fXaDRu2LDB8nc9hUIhbwR44MCBoqKilStXMplMOp1eVlYWHBy8cePGvLy84OBgZ2fnQYMG5eTkLF269L///W9QUBCFQqFQKDU1NWlpaf379zcYDI2NjeTHodu3b+fn5/ft2/fMmTMSiSQkJEQikRQUFGg0mry8vNDQUEdHR5PJVFhYWFVVNX78+LCwsL/++sve3p6cklqtZrFYQqEwOTn55MmTlvdI4nA4Tz31FIPBOHXq1LBhw4YPH85ms6VSqUqlWrRoUXFxMY1GE4vFgYGBZK0EgBXdvn37xo0bf/31V1ZWVmZmZlZWllKppNPpQUFBYWFh77zzTlhYWFhYmPknnyCIX375xYoThgeEwN9jxcbGxsbGGo1Ga0+kLSwWq4O7BkwmU7MtAzILUqm0sLDQ8qDBYLB8OpvNbmM5oOV6ARbgAQCgV+HxeFOnTiXvtm2pT58+GzZsIB8fOXJEr9fTaLTU1NS+fftmZWXxeLzg4GAGg3H79m25XC4QCGg0mr29fWhoaExMjEAgKCkp8fDwOHHihNFojIuLu3jxolgsHjVq1Jo1azQaDYVCqaqqio+PJwhi+fLlLWel1+sDAwPd3Nw0Gk1TUxOZSfLz87/++ut169ZlZmZGRkb6+PgsXLhQqVTGx8cPHDhwxowZzzzzDIVCEQgEIpGopqaGnMOVK1cYDEZAQIBCoaiurjYajXw+X6lUenl5BQQEDBw4sLGx8fbt2yqVKjMz09PTk8fjpaWlkY2KKioqNBqNTCYTiUR+fn5+fn7R0dEtZ1tRUTFkyBAXFxe1Wq3T6Tw8PLhc7ldffdW/f39/f39vb28fH59O9xcA6By5XJ6VlWWZ8MnlNkdHx/79+0dGRs6bNy88PDwkJMS8rwd6GAq574iUmJg4ffp0yyPQNVEolL17906bNs3aE+m6Ghsbm60IyFrXbPsAQRB8Pv+eiwIkgUAgFApt/iYQCFBiAAAAPZ5CoTh27JiNjY1cLi8rKwsKCoqOjj569CiTybx48aJKpVq9ejVZ/P/NN9+4ubkxGAxyx9+LL75Ilr43NjZmZmbyeLxjx44ZjUaZTGZnZ/fUU0+18aIajebUqVM6na60tHTSpEnu7u6NjY1HjhzhcDhNTU3Tp08nhxkMhtLS0p9++mnfvn1BQUG//PJLUVFRamqqq6traWmpTqeLjo7m8/lnz541GAzp6enh4eFz584lCCIrKys5OdnDw4PBYIhEoqtXr27ZsoUgCK1WO3369DVr1hQVFWVnZ5PX84ODg/v27dvGbLOyskpKSioqKvz8/IYMGcLn81NTUyUSSWNj49ChQwMCAh7SXwXAvdXX19+6dYtsjUkm/OLiYoIgBAJBcHBw//79g4ODQ0JC+vfvf1+7gMmlQPJOE9DFtcyJCPzdEgL/Q9fuooB5QENDQ8uGPRQKxc7OztbWlsz/5EKAra0tuTpgXhcgj5i/JGsaAQAAuimpVMpisdhs9qZNm+zt7SdOnGitFXCTyaTValUqVXZ29o8//rh169ZmA7RarVKp5PP5eXl5hw8fjo6OHj58uFWmCvBQ6PX6oqKi3NzcnJyc3Nxc8kFNTQ1BEBwOp2/fvv369evfv39ISAh55wiyFUXnIPB3Iy1zIvYtAxAEQZCX7js+XiqVyuVyuVze2NhIPiDXAsxfNjY2FhYWkqsD5JcKhaLZSWg0mo2NDblZwLwuYGNjY14UMC8NmFcTbGxs0I8AAAC6iEOHDvn6+rq7u5eVlSUkJFhxIZvcL5CdnW0wGGbNmtVyQHl5+blz5wYPHlxWVkan08PDwx//JAE6x2QyVVRUFBYWFhQU3Lp1i8z2BQUF5I2x3N3dAwMDQ0JCpkyZEhgY2KdPHy8vrweJ99DDIPADdIZQKLzfvjtk42KZTGa5TCCXy6VSqeWXNTU15tUEuVzeshswuUNSKBRyOBwul2tra8vn87lcLp/Pt7W1JQ8KhUIul8vlcsn1Ai6Xy+PxbG1tuVwuh8N5eN8GAADo1bhcrk6ny8rKeuaZZ9zc3My99x8/Go3m7Ozs7Ozc2gA6nc5kMsnq/fHjx+O3IXRNarW6sLCQzPaFFjQaDUEQHA6nT58+ffr0mTx5clBQUJ8+ffr27YvCUmgbAj/AY0KlUjuxTKDX61tuH1AoFDKZTKVSqVSqhoYGhUKhUqmqq6vlcjl5kOwMTP5uaIasPuDxeFwul+w+QC4H2NnZkWsEtra25j+1sbEhD1quLzyk7wcAAHR7LZv8dVmenp4zZsyw9iwA7iAbYZSXl1dUVJSVlZkTflVVFTnAxcXF19fX19d36tSpfn5+5GM3NzfrThu6IwR+gC6NTqfb29tb3hml4wwGA7mbQKVSKZVKco1ArVbLZDKlUqlSqcgVBHKNoKioiHxAPkWtVresQSCZNxTY2NjQaDShUEin0wUCAYvFIlcHWCyWQCCg0+l2dnY0Gs3W1pbclcBmszkcDo/HYzKZ5uc+2LcHAAAAoIuSSqUVFRWlpaUVFRXmB+Xl5WVlZY2NjeQYsrGlj49PeHj45MmTfX19yXjPSnr6wgAAIABJREFU5XKtO3noMRD4AXosMlE/SKhuaGggVwHuuUYglUrJNQWyE5JMJpNIJAqFQqfTNTQ0GAwGmUym1+vNv9Jam6SNjQ2TyeTxeBwOh81m8/l8BoNha2tLo9HIWyS2vZpAPpcgCDs7OwqFQi4rdPotAwAAALRLp9PV1NTU1tZKJJKamhrycVVVVW1tLfllZWWlSqUiBwsEAg8PDw8PD7FYPGzYMLFY7O7u7unpKRaLsXcSHjUEfgBola2t7UP5PaTRaMg1Ao1G09jYqNfrZTKZwWBoaGjQ6XQKhaKpqUmtViuVSq1WK5fLDQaDVCrV6/WFhYXkaoJarW5qajKvJhiNxrZfkaxcIAiCXCMglxUIgiBXE8gdB+QbpFKp5GoCQRDkigNBEOZFBCqVav4OkAsK5pMQBGFeXDCfsNlTAAAAoFtQKBSNfyP7K5nJZDLz49ra2tra2urq6vr6evNzGQyGo6OjSCRycXFxdHT09fUViURubm5ubm5isdjDw4P8EAJgFQj8APDIsVgsFov1EDfwm0wm8/YBcjWBPEIQBLmsQG49IAiCXCMglxUIgiAXC8inEARB3pyWXE0wDyYIglyAIAjC/MQH0eyNk6sM5i/JDQuWA1oeaY156QEAAABaMv9mJ68fkAelUin5gLzSQBCETCZreWNyNptNdjsSCoWCv4WHhzs6Ojo6Ojo7O5MPnJycUKIIXRkCPwB0PxQKhfzl6ujo+Hhe0bygQPy9akBYfFAwLxAQ//xIQRBEs6IG8mYNlmc2f+wgdXyJ4aEsRgAAAPRgTk5OLBaL+LuEkDxINhIiLHbqmW+QTP7Xzs5OIBBgSR16BgR+AID2mZcYiBZX7AEAAAAAuiar3S4VAAAAAAAAAB4dBH4AAAAAAACAHgiBHwAAAAAAAKAHQuAHAAAAAAAA6IEQ+AEAAAAAAAB6IAR+AAAAAAAAgB4IgR8AAAAAAACgB0LgBwAAAAAAAOiBEPgBAAAAAAAAeiAEfgAAAAAAAIAeCIEfAAAAAAAAoAdC4AcAAAAAAADogRD4AQAAAADg/9m78+g2qzNx/K/2fd83W/K+xHHiJbaTOHFIQkwWEiChUFIKA7SHmQ6nMG0Pp9+eQjtl2nLKtKXtmbaUdjrtDIWklCULkI0sxHHsOIktW7JkWYu1L6/27dWrV78/7m90PAlRgmmwE+7nr763Wu4ry8HPvc/zXAiCbkMw4IcgCIIgCIIgCIKg2xAM+CEIgiAIgiAIgiDoNgQDfgiCIAiCIAiCIAi6DcGAH4IgCIIgCIIgCIJuQzDghyAIgiAIgiAIgqDbEAz4IQiCIAiCIAiCIOg2BAN+CIIgCIIgCIIgCLoNwYAfgiAIgiAIgiAIgm5DMOCHIAiCIAiCIAiCoNsQDPghCIIgCIIgCIIg6DYEA34IgiAIgiAIgiAIug3BgB+CIAiCIAiCIAiCbkMw4IcgCIIgCIIgCIKg2xAM+CEIgiAIgiAIgiDoNgQDfgiCIAiCIAiCIAi6DcGAH4IgCIIgCIIgCIJuQzDghyAIgiAIgiAIgqDbEAz4IQiCIAiCIAiCIOg2BAN+CIIgCIIgCIIgCLoNwYAfgiAIgiAIgiAIgm5DMOCHIAiCIAiCIAiCoNsQDPghCIIgCIIgCIIg6DYEA34IgiAIgiAIgiAIug3BgB+CIAiCIAiCIAiCbkMw4IcgCIIgCIIgCIKg2xAM+CEIgiAIgiAIgiDoNkRd7AlAEARBEARBEARBS9e5c+f27Nmz2LOAFgIG/NDnGkEQgUDA7Xa73W6NRqNUKquqqhZ7UpWUSqV0Ou3xeMAlnU4Xi8X5fF4uly/uxK4rl8uhKEomk/P5PJVK1Wg0iz0jaIlKJpNer9fpdFIoFJVKpdPpeDzeYk+qEhzHfT5fJpMBl1KpFMdxgUDAZDIXd2LXFQwGS6USjuOZTKaqqorBYCz2jCAIgqClqK+vb7GnAN2o3bt363S6+SMw4Ic+1wiCcLvdb7311ssvv/zcc891d3cv5YCfIIhYLDY6OgpCi2KxmE6ni8VifX39Eg/4M5mM0+lMJBI0Gs3tdkej0Z07dwqFwsWeF7QUxePx0dHRn//851qtdvfu3Xw+fykH/DiOj4+PezyeYrGIIEg2m81kMiKRaO3atUs84He73T6fj0ql5vP5oaGhLVu21NfX02i0xZ4XBEEQtOQ888wziz0FaOFgwA99rlGp1OXLl1utVqlU+uSTT3I4nMWeUSWpVOrEiRPhcPirX/0qgiCFQuHs2bMvvPDC73//+8WeWiX5fN7tdieTyZ6eHgRBqFTqhQsX7Hb7ypUrF3tq0FKk1WqXL1+uVqu3bNmyZ8+eJR6CXr58+cyZM3fddVdDQwOCINFo9Gtf+9r27du5XO5iT62SSCRitVo7OjoEAgGCIEeOHJmcnNTpdEv804YgCIIg6JOCTfugzzsMw4aGhlpaWpZ4tI8giNfr/fOf/3zfffeBSxqNVl1d3dfXp9VqF3dilUWj0UAg0NbWhiBIPp/3er3RaBRu70MVmEwmBEFUKtXSjz//+Mc/6vX66upqcCkSiTo7O1taWths9uJOrLLx8fGmpiawKhGJRBwOB5fLpVLhHgAEQRAE3W7gf92hz7tCoTA8PFyOopcyDMMcDkc0GpVKpWBELBbfddddizur6yoUCvF43O/3+3w+FEUtFktfX5/BYFjseUFLl9ls5vF4arV6sSdyfeCLnc/nywXwAwMDS3/miUQimUz6fL50Om2xWGpqalauXMlisRZ7XhAEQRAE/Z19TMAPGzBCnx+lUikej9tstnXr1i32XK6Px+PV1ta++OKL27ZtUyqVWq1Wq9X29vYu9rwqyefzGIaJxeKpqamjR4+mUimVSvXUU08t9rygpSufzzscDoVCcUt0duzs7Dx27BiGYS0tLWq1urq6uqOjY7EndR2RSESpVKZSqUOHDoHt/R/96EdLvA8IBEEQBEELQ3n++efLF4lEIh6PL95koBvV0tIyODh4RQNGaAFwHB8eHj527Nj3vvc9Op2+sFfwer3xeBxF0WKxyGazrx6p8HTQdX92djaZTIpEIgRBMAxzuVzJZDIUClGp1Pl9s5lMpkKhOHfu3Pvvvz8+Pu5wOHQ6nUQiWcC08/m83++PxWLRaJQgCBaLdfVIhacTBJFIJBwORzabBTXAmUzG7XYnEoloNDp/2uFw2Ol0dnZ2gnbrdXV1kUhEJpOVkxQ+kUwmEwgEYrFYLBZDEITJZJZHwL9dldukFYvFWCzmdDoLhQLoAwc2OaPRKGgouLDvAPT35XQ6Dx8+3NHRsXbtWgqFsoBXQFE0EAjMzc1lMhkWi0WlUiORCIqiKIqm02k6nV45d91isaTTaYIgPB4PhUJhMpmBQABF0VAohGEYnU6fPyuNRuPz+Y4cOTI8PHz58mUWi6VWqxfW7j4UCkWj0Ugkks1mGQwGhUK5eqTC0wuFwtzcnN/v53A4oBTC4/HEYrFgMFgsFhkMBpn8/xfxnT9/vra2tra2ViwW9/b2xuNxJpOp0WhIJJLL5QoEAuX0/kAgEAqFnE4njuNsNrv8CjeuUCiQyWQSibSADwSCIAiCoE/v//zR09fXt2/fvsWaCgR99jAM++ijj+rr6xdWwE8QRDgcPnToUGtraywWi0Qie/fuDYVChw8fbmlpicViKIp+6UtfqjyBqampDz74QKlUPv7448Vi0eVyvfPOO11dXSDg37lzZ/nBLBZr06ZNfX19w8PDw8PDR44cwTDsxRdf/KTTLhaLHo/n1KlTIPzGcXzXrl1gpLa2FqxT3HvvvRVeIZvNXrp06fTp03V1dQ888EChULBarWfOnGltbQ2FQkKhcPPmzeUbTCaTLBaLxWJ1dnZ6vV6r1XrixImmpqZPOm0cx+12+6VLl3Q6XSgU4nA4mzZtmp2dvXz5Mhjhcrlbtmyp8AqpVGpkZGR0dLS9vX379u35fH5iYmJqaqqmpiYcDqvV6rVr137SWUF/dyaTCcdxhUKxsAL+dDo9OTmZSCTC4bDf79+2bVtdXd3w8HCpVGKz2S6Xq6+vDzTYu5Z9+/Ylk8mamhoajbZhwwYKhXL06FEul0un01EU7e/vn3+Wh8FgeO6550wm08WLF999991XXnlFo9EsYJM/m82eOnUKRNpOp3PLli1isfjkyZN8Pp9CoTgcjsHBwcopD16v9+DBg4lE4uGHH9ZoNIlE4sCBA9XV1TiOR6PRwcFBmUwGHhmJRKhUKoVCWb58OYIg3d3dR48ebWtrw3H8wIEDqVTqscceYzKZiURibGwMrAKEw+HHH398AVkAJ0+e7O3tXeItDCEIgiDoNgab9kGfa6BjX2dn54KfPjEx4Xa716xZ09HRcezYMa/XWx5ZuXLlsWPHwuFwhVcgCCIej5dKJXCZyWRGRkYikci6des6OjreeeedRCIB3sjv9xcKBQRBOBzOHXfc8dRTT33lK18ZGxtbwLQzmYzRaIxEImvXrq2rqzt16pTb7Z6YmIjFYv39/TU1NadOnUJRtMIrFAqFZDJZnnYsFhsbG8tkMgMDAwaD4f33308mk9d6YiKRwHF8AdOOx+NGozGbza5bt04mkw0NDTmdTqPRiGHYunXrpFLp0NBQ5RwlDMNSqVR52qFQ6NKlSwiC3HHHHXw+/9SpU+l0egETg/6+TCYTg8FYcIa51+udm5urq6v7whe+YLPZLl68ODMzMzU1pVAoNmzY4PP5zGZzLper8AocDsfj8dhsthUrVmi1WovFMjU1pdPp7rrrLovFYjabMQxDECQcDieTSYIgEARpbm7+4he/+P3vf58giMq/O9cyOzs7NTWl1+s3btxotVpnZmbMZvPU1JTBYNi4ceP09LTNZgPvey1X/GYZjcbp6emGhobt27cPDw87HI5r/d4lEolcLgfqm+Y/xm63e73e7u7uvXv3Hj582OFwgKMHb1w8Hn/llVdAPg4EQRAEQYsCNu2DPr/AH7gmk+kb3/gGGCEIIplM+v1+CoWSyWSamprodHqhULDb7UwmM5fL8Xg8hUIBcoNZLFY8Hr906RLIaadQKAwGY3R01Gq1ghEymcxgMIxG48DAwLXmwGKxurq6rFYruMQwzOv1gioAgiCi0ajVau3s7EwkEkePHr377rvLe55UKlUul+v1egRBisViPB4HKws4joPDtLPZrNvtptPpGIYJBAKxWIyiaDweZzAYgUDAbDaDPvlUKpVEIo2MjNhsNlAdAN5ienq6r6/vWtMWCoUrVqxwOBzgMpvNBoNBkCVRLBbD4bDD4QAbhvl8fn76NIqiPp/v7rvvxnEcZECUSiUymWwwGKhUajKZDAQCVCoVx3GRSCSRSDAMQ1E0lUrR6XSn02m320E7dHBfIyMjDocD9P8DI7OzsxVO+5PJZG1tbV6vF1wmk8loNAp2a3Ec9/l8bre7sbHxWk+HPgP5fN5ms8lksvJudj6fDwaD+Xw+l8uBYhAKheLz+XK5HEEQZDIZLA2Ew2GwlFMoFED6OpPJZDKZ2Wx2cnIyk8mALzaHw3G73SiKVuir19raumbNGplMRhAEKHUpFosgnT6Xy9lsts7OTolEMjw8XFVV1djYWK4E4fF4BoMB7GZns9lAIIDjeCaT0Wg0IpGITCbPzc0RBFEoFGg0mkKhKBQKoVCIQqEQBHHhwgWCIMAkuVzu7OwsCMLBrw+Xy7XZbMuWLROLxdeadltb28zMjM1mA5cej4dMJoMk/Gw2a7FYmpqaeDxeJpOhUCjzk/PHxsYaGxs5HI5Op7NareXfayaTSafTSSQSm81mMBigz18kEkEQBIxnMhkmk0kmkyORSGNjI5PJjMfjoVCITqcXi0Uej3f69Gk+nx+Px5VKJfjHjUQiJRIJkFQF8/whCIIg6DMAA37o8wvH8ampKQRBurq6EAQplUqpVOrgwYMcDqdUKr399tu//OUvS6XS+fPnHQ6HVqs9d+5cW1vbmjVr3nrrLZlMRqPRLBYLiqIgYiSRSCwWC6wFgKAUhByfqC8Gi8VqaGg4fvw4CKGDwSB4ejqdHhoa6ujoaGlpQRAE7CLabLZ77rkHlBUcP36cw+Ekk8nR0dEXXnghn8+PjIz4/X6RSDQxMbFy5crm5uYPPvgAhPRWqzUej6tUKgRBKBQKjUZDUTSRSIDj/chkMp1OT6VSNz5tPp9fVVVltVrT6TSYNtjhz2azfr+//Gc9WF5hMBjLly/3eDxDQ0McDsfn8/n9/m9961uJROLChQuxWIxOp8/Ozvb09PT29jqdzrNnz4rF4kKhMDMzk0qlQHMBKpUKwox0Og3q9sFIJpO58WlLJBKFQhGLxVKpVCAQCIfDcId/0bnd7mAwuHbtWoVCgSAIjuMXLlzweDwcDuf06dNr16698847HQ7H5cuX2Wy22+1GEGRgYMDpdIZCIYFAEAqFVq1aBQ7dsNvtDAajtrY2GAwi/7uSxWQyU6lU5a1yBoMRi8WSyeTIyMjmzZsNBsPp06fT6TRoDcDj8cDTjUZjKpXS6XQg4MdxfGxsrLOzU6fT4Th+9uzZaDTK4XAOHz78wAMPrFq1anp6emJiQiwWT09PcziczZs3j42NZbNZPp/v9XodDke5OwCLxcpkMpFIhEKhgBE2m53NZkGOzw1qamo6depUNpsNh8OBQAAU7yAI4vF45t++yWTy+/07d+4EXS3ma2xsBOtfID+itrY2EAiAZAGQpb9///6BgQGpVPrHP/7xu9/9rlwuP3z4MJVK5fF4Dodj9erV77//vl6vTyaTuVzu6NGjBEFwOJz//u///v73v89ms2HAD0EQBEGfARjwQ59HpVIpl8tNT0+/++67LBYrkUgIhUISiTQxMXHw4ME//elPLpfrF7/4BYfDCQaDL7300s9//nMej7d///7+/n6TyXTo0KFf/epXIFAs566XSqVCoQB2w+aPIAgCdp6v+GOdy+VefTQdm83u6+sLBAJ+v9/v97NYLBD2lEqlRCJhNBopFAqVSgV7nkwmc9u2bdlsdmxsbGho6Kc//SmoImaz2SaT6Y9//OMPfvADDMOOHTvGZrMvX7585syZf/3XfwV5yOXAGOw3grWJ+dMulUqZTAY06ypPj0QiCQSCq7tFCoXCvr6+YrHo9/vD4TCXywU9+XK5XDgcplKpHo+HRCLZ7fZ4PP6FL3yBRqOdO3fObDZ/5zvfOXr0qNFoZDKZIyMjBw8efPbZZ71e74ULFwQCQTqdHh0dnZiYePrppwOBQCKRKAcqBEHgOA6yKuaPgA/K7XbPzz0mk8kikejqHV25XN7b2zs9Pe31emOxGI/HA30ToUWB43gwGDx+/LjH4ykUCqBNnd/vf//99/v6+jZs2HDmzBkMw2g02r59+9Rq9ebNm19//XUSiZRMJt9///2urq6Wlha73Q5+AVEUPX36dE9Pz/Lly0+fPl1OM8FxHCThe71e0J+yPAEqlarVajEMk8vlKpVKIBD87W9/m56e3rlzZ29vL5lMBhOTSqVg1QnHcZvNZjKZQC5AJpMJBoP9/f0ajcZutx84cOCLX/zismXL3nzzTYIgqFTq73//+/7+/tWrV1+6dEkkEkUikQMHDuzevbu+vp5CoUSj0fLXG8dxGo3GZDLLX+NCoQD+t8vlSiQS5bIUMG29Xn91l822trbe3l4MwzweD0EQCoUCLEwEAoF8Ph8IBAqFQi6XO3Xq1I4dO5qbm6/VMcHv97/33ntf/vKXFQrF3Nwcn8+PRqPLli3jcrk//OEPH3vssebm5pdeegnDsLm5ub/+9a/f/va3BQJBsVg0GAyZTGb79u2tra02m23fvn0/+clPBALBT3/6UxqNtoD+f9Dfy7//+78PDQ0t9iygKz3zzDMVEusgCIIWDAb80OcUyHElkUibNm2yWCwqlYpEIl2+fLmzsxPHcavV2tDQEAqFcrlcMBisqqryeDxms7mpqenIkSN6vR60mt+wYQNBECADFuTVL1++nMlkzs3NgZFEItHY2Ag6883fPSaTyQqF4uqAv1Qq0Wi09vZ2BEF0Ol1VVRV4jEgkeuihh3Q63fT0NJ1Oz+VyAoEAtANMpVI2m62trQ3k8Ov1erBTHYvF1Gr15cuX3W53dXW1xWJRq9WxWEylUonF4mKxCJYqCoVCPp9va2uj0+lgBMOwfD5fU1OTyWSmpqbm75lTqVSdTnd1wE8QBJfLbW5uplAoWq22uroaPAYcUlBVVeV2u8lkcqlU2rVrl0qlcjqdgUCgqakpkUiAE8JQFE0mk9lsVi6Xj4+PRyIR0D4wHo9LpdJUKlVVVSUQCD788EOQegBqsJcvX17+YMGITqeLx+OTk5Pzi7RpNFpdXd3VAX+xWJRIJLW1tQwGA0R6IOsBWhSgl2QoFALfxnA4zOfzbTYbl8uVy+Vut5vNZlMolGQyGQ6H6+rqWCzW7OzsihUrMAwD5SSlUmnFihUcDicej09NTWm12pqaGgaDoVKpygXw8XhcrVYLhUKr1Wqz2eYvw7FYLD6fHwgERkdHBwYGBAIBlUoFK2KNjY0EQQgEAq1WW19fD5L2N27cyGAwUBS1Wq04jheLxcHBQaVSiSCIyWRSKBRCodBut0ul0mKxmE6nA4GASCSi0WjT09MdHR2xWEwsFmMYRiaTV61aRafTP/roIzAfFEV7enqYTObY2Fh5pLm5mcPhWCyWubm5+etZbDZbLpdfHfAXCoW2tjYGg8Hj8bRabUNDA1jUy+VyK1asSKfT+XwetO0AvwIf+0OJRCLj4+NdXV0NDQ3FYrG+vv6tt97auHGjQCC4cOFCX1+fXC4PBoMSiSSRSPh8Pq1Wm0gk5HI56H8J7jEWi42MjLS0tDCZTLPZbDAYEomEUqlc2BEM0Kc3NDR07ty5JX6k6+fN/v379+zZAwN+CIJuBhjwQ59HJBJJLBbff//9999/f3kwl8tptdpgMOjz+axWa3V19eTkZG1t7YoVK5xO58TEBJfL5fP5BoPB5/MJhcJcLpdMJleuXGkymebm5gKBgEwma29v53A409PT4HwsmUwG2tFfq+k9juOBQCAYDNJoNL/fL5fLQ6HQwYMHH374YRRFBwcHwXalQCAYHBxEEKS1tfWKV6DT6Wq1OpvNejwet9stl8stFgufz29qagJtwDgcjlAo1Ov1uVyOz+enUqlsNtva2nr8+HGXyxUMBpVK5cqVKykUysmTJ10uVygUUiqVdXV1CIKA1OirgRSDYDCI43goFBKJRHNzcydOnLj77rsLhcKGDRvK4QebzW5ubr7i6UwmU6VSkclkl8sViUT4fP7s7Cyfz6+urgZhGJfLZTKZHA5Hr9eD7gCgIVldXd3k5KTL5YpGo6AXOoZhZrO5PAKKKa51YiUoqw4EAqD4gsfjWSwWk8m0fv16FovV19dX+VS/pQnEsbfBgYIMBqO7u7u7u3v+oEgkkslk+XzearUyGAxwXCXo+DAxMZFIJDgcjlqtrqqqksvlVCrV7/cLBIIzZ86YzWadTmexWNrb2w0Gg9FoDIVCDoejVCrV19cLhcKr3wugUChNTU3geyWRSBobGxkMxokTJxobGzOZzPLly2tqakC+QIV4SSqVymSydDptt9uFQmEwGIxGo01NTYVC4dKlS/l8nsPhSKXSqqoqmUwGcgcMBsPY2FgwGARZPI2NjUql0mw2B4NBMplMo9EaGxu5XG5/f/+13hRFUfBb6fV6pVIpQRCHDh264447otHomjVrNBpNOcDWaDQf2wsgEomAXxCPxyMUCnEcf+ONN8LhcENDw9jY2ODgYG1trcViufvuu1ks1uXLl8HyytmzZ+vq6pxOp1ar1el0Uqm0VCq5XC6RSKTX60FLFLBsh6Lo5OSkTqez2Wwfm5Vwi0qlUrdcS4Le3l54KtOScmt9fyAIurVQnn/++cWeAwQtCaD7F4qi4Dx2JpNZU1Oj0WjIZHIqlfrggw8aGhoGBgZEIpHP50MQJJ1Os1gsvV4PdhRTqVRvb69Go+FyuWw2uzxSedMYZPv7/X4ajQYSiel0OplMTqfTpVJp+/bt1502jUYDe2h0Op1Op3M4nJqaGqVSSSKRIpHI2bNnW1pa1qxZA8oTSqVSNpvl8XjV1dWgUBnH8e7ubpVKxeVyGQxGNBotFovd3d2Ve6Rns1m73R4OhxkMhkKhANnC4INis9kbN25EEKRQKICU6as/ASaTyePxUqkUjUaj0WhgGUUul5NIJLfbPT4+3tbW1t7eDnqGRSKRYrEIkhp0Oh3oOECj0To6OhQKBTjGrDwCmhRcSzKZdDgc0WiUxWIplUqZTAbi5GQyKZfLb9GtFbvdjqIoqKG4/QiFQlCsAb4qSqWyurqax+OVSqWRkZFsNtvT09Pc3JzJZHK5XDabJQiCTqePjIxMTU05HA6bzdbV1dXa2srj8XAcD4fD1dXVTU1N5bqbq3G53FwuF4vFHA5HX19fc3Mzk8mk0WigzWRfX9+1lpPmE4vF+XweTIZGo4ElCTabTRDEmTNnyGTymjVr6uvr4/E4hmEgiUar1Uql0nw+HwqFGhsb6+vrRSIRn8/P5XKhUKipqam+vv5a+/CAx+NxuVyFQkEoFGo0Gg6HQ6PRCoUCiqKbN28G35BEIoGiqEql+tiXcrvdLperWCyKRCKNRpPNZg8fPuxyuaxWq91u37Ztm1gs9ng83d3dXC7X6XR2d3eLRKJCoYBhmFarbWtrQ1GUIIhUKkWhUKqrq1OpVLFYbGlpqampSSaTYOWCRqNVVVWpVKrbZof/2LFjdXV1t1DABkL9+evd0KL73ve+t2fPnqvX9CEIgj490vxSQAiC5sNx3O12y2SyQqHw4x//eNOmTSCUXeJAga5UKg2Hw7///e83b968Zs2az34aKIrOzs7W1tbeSGE8qEGQSCRWq/XAgQObN28GnRSh6zp48CCO4zt37lzsiXxGQqEQgiBCoXD//v3pdHrLli2sVXI3AAAgAElEQVQ3EoEvOp/Px2Aw+Hz+q6++ymazBwcHZTLZZz+NsbExsVis0WiuVbEPLcCTTz75q1/96hbqSrBnzx7kf8N+aIkgkUivv/46XIWBIOhmgCn9EHRN2Wz2rbfe6u/vL5VKxWLxVql4RFH0wIEDfX198XicSqV2dHQsyjRA/8IbbIPn9XpPnTrV2dkJ+rEvW7bsZk8PukWNjIzgOF5TUxMMBpcvXw5q5pe+06dPgxYA4XB4cHBwsdpDUqlUqVQKo30IgiAI+vyAAT8EXROZTObz+dlsFkXRL3zhC6Ax2NJHpVK5XG4ymUylUqDadlGmIRKJbjyqodFobDY7Go2SyeQ777zzVqylhz4bbDY7nU673e62trbW1tZbJXblcrkkEsnpdK5Zs6a2trZ8asBnbPny5YvyvhAEQRAELRYY8EM30f79+xd7Cp8Wn8/3+/0IgtjtdrvdvtjTuVFsNhucPW6xWCwWy2JP54bQaLRIJIIgiNFoNBqNiz2dpaixsbGtrc3v93s8nvLgzMwMOKweXHI4HHCk3NTU1NTU1CLN9DNy6tSpxZ7CJ5bJZI4ePbrYs4AW4t577wV5+2NjY/PLIQOBwNjYWLmGXy6X3xJlJhAEQdDnBAz4oZvoL3/5y2JPAYJuH7t27Wpra/P5fKOjo+XB6enpYrEIDopDEEQul7PZbIFAYDQa33jjjUWaKQTdhnbu3AkC/gsXLhAEUR73+/2jo6PlgL+trQ0G/BAEQdDSAZv2QRAE3cI+b037IGipgU37oE8PNu2DIOjmuWX++wRB11UqlcCZ5BB0GygWiziOL/YsPhkcx4vF4mLPAoL+PjAMg5siEARB0K0OpvRDt4lisWg2m/V6PThZ/ZM+NxAIuN1ut9ut1WoVCkV1dfXNmORSgON4KBSy2+3hcFilUnG53Hw+j2GYRCKpra1d7NndXMViMR6Pg+4GCIJwOBzQiFEsFi/qvD5eKBTKZrNqtbry6etLh9frLZVKcrl8AeerJxIJr9frdDopFIpKpdLpdHw+/2ZMctElk8m5uTm73c7hcKRSKYIgmUyGTqfX1dWV6zJuY4VCweVyFQoFcKlSqdLptFwuX6wuhpVNTk6Cn0s5XR+CIAiCbjlL8T+xEPRJEQRhs9mGh4dbW1sRBCmVSvl83u12B4PB1atX38jT3W7322+//fLLLz/33HPd3d23d8A/Nzf3pz/96fDhw9/85jeVSmU8Hvf7/QRB7Nixo729fbEneLMUi0Wv12s0GvP5PIIghUIhnU5TqdS2tralGfAnk0mTyZTP55uamhZ7LtcXDocvXbqk1Wo1Gg2CIDiOoyjqdrsVCgUYqSyRSIyNjf3sZz/TaDR79uwRCAS3a8CfSqWGh4d/+MMfbtiwYf369SQSKRwOe73erq6ujRs3CoXCxZ7gTVQoFIaHhyORCNg2T6fTmUxGoVBs3rx5aQb8c3NzPp9vy5YtC1jDgiAIgqAlYin+JxaCPpFSqRSLxd599929e/ciCEIQRDQadTqdJ06cOHHixIEDB677CjQarb29fWZmRiqVPvnkk7fK8XvzFYvFfD7PZrOv+0gmk1lfX9/U1DQ7O/tP//RPYNBut//whz/82c9+9oc//OEmz/TvD8dxHMeve5Kf3+//8MMPeTzerl27EATJZrOvvfbamTNn7rzzzs9kmp9YfX19KBSamJiQSCQymexaD5NIJIueRZ/P50dGRphMJkgSyWQygUDg4sWLBw8e3Lp163333XfdV9BqtcuXL1er1XfeeeeePXtulcP25stmszQa7bqBq0qlam1tVSqVDz744MDAABh85ZVXfvnLX+p0ulWrVt30id4EmUyGyWRet4j9o48+Onfu3Fe/+lVwYGcoFNq5c+dPfvKTpRntIwhy9913/9u//Zter29qaqpwdw0NDTAFAIIgCFqyYA0/dMsrFosjIyN0Ol2hUCAIUiqVUBQFadvpdPoGXySfzw8NDbW0tNyK0T6CIKlU6uLFizf44FgsZjabQTYEQKPR6HR6Odf91hIMBk0m03UfdvHixYsXL27YsAFcslishoaGrq4uuVx+kye4cHV1ddls9uzZsxUes2zZskU/XN3pdAaDQaFQCH59MplMMBjMZDKgWuQGX8RsNiMIolKpbsVoH0GQyclJFEVv5JEXL16Uy+UCgaA8wuPxotFoLpe7abO7uUZGRjKZzHUf9uKLL27ZsoXH44FLmUy2evXqhoaGpfwT37Fjx29+85vKa2oPPvjgZzYfCIIgCPqkluiyOnR7CAQCExMTCIJ0dnam02kMw2Kx2MqVK+12O41G83g8jY2NYKsH9NtzOBxsNjsUChkMBpFIRBCE3W632+2JRKKnp0elUo2NjXm9Xp1O19raWq7VLxQKf/vb3773ve+BSwqFUl9fX11d/YnCVwzDhoeHb2QrcmmKRqMnT55cs2bNjTw4Ho9brdbHHnsMXGIYBgK2jRs33sw53iwej+fSpUsrV66s/LBUKjU3N5dIJMqBVk1NzRLPoAb18A6HI5vNslisj33MtQq/U6mU3W4PBAJqtVoikeRyuVQqJRaLGQxGMplMpVIMBkOj0YCXLRaLoVAok8lgGMZgMFQqFZPJRFF0dnbW5/MpFIrm5uZ4PG6xWMDvl0KhmJ/kfOHCBS6X29jYCC6lUqlUKqXT6adOnbrxmzWZTDwe70by/5emc+fO0en0G1k/Ghsbq66uLheSRKPRy5cvd3V1gSXLW9GxY8caGxuv24PA5XIFAoH5S407d+5c4musbW1tYClHLpdfaxtfqVR+xrOCIAiCoBsHA37oJvL7/X/605/GxsZ++9vflkqlZDL561//+nvf+57f7+dwOPv3729tbX388ccRBCmVShcuXLDb7Xq93mg0nj9//h/+4R8oFIrVan3//fd/97vf7d+/XyaTvf766zabbdu2bQ0NDSDgBy9rtVo/zd/KBEHE43GbzbZu3bq/280vVTiOgw6FUqnU5XJhGJZIJKampjo6Oh555JHFnt1NpFarEQR5+eWX169fr1Qqq6qq1Go1GFzKZDJZIBBwuVzlcPoGpVKpjz766M033+zq6tq+fTuGYWfOnOHz+V1dXYVCYXp6OhwODw4OdnR0IAji8XgmJiZIJFIulwsEAp2dnZ2dnZFIZHh4+M9//vOyZcueffbZ6enpffv2yeXy3bt3S6XScsCfz+c9Ho9Wq/00kVs+n3c4HAqFQqvVLvhFbgng36t169al02m73V4oFGZmZigUyuOPP67X6xd7djfX+vXrX331Vb/fX11drdFoDAZDf3//Yk/q+lpbW0dGRu666y5YyQ9BEATdimDAD91E7e3tjz766Hvvvcdms9vb2wmC+OY3v/k///M/P/rRjwiCSKVSzzzzDAj4CYJ47bXXHnrood7e3lWrVvX29m7YsKGxsXFwcHBgYMBisdjt9t7e3mXLlv3jP/6jwWAovwVBEF6v91NmZeM4bjab+Xz+oqdGfwaSyaTZbBYIBGQy2eFwuN1uk8nU2tr6xBNPLPbUbq62trZ77733jTfeGB0dVSgUPT09u3fv1ul0iz2v61AqlSaTye12f9KAX6lUDg4OGo1GDMNqamqUSuXk5OTx48dbWlo2bdqk0Wh+8YtfDA8Pg4D/0qVLgUDgrrvu0mq1P/vZz44fP15bW1tfX19fX89kMo1GYygUEgqFjzzySHt7+xVpEbFYDEGQT7lP63a7U6lUV1cXaFx/GzOZTDiOc7ncSCSSyWRAJc4TTzxxGzcKLfva1772H//xH6+++qpQKFQoFI8++mh3d/cCzlX5jHV0dFy8eHFwcHCxJwJBEARBCwEDfujm4vF46XS63Pu9vA1IIpH0en0gEADjJBLJYDCkUikMw+LxuEAgGBsbAxEOnU5//vnnn3nmGT6f39TUND/aRxCkVCpFIpFPmQaMYdjZs2fr6+s/TdCC47jf7y+VSjiO83g8qVR69UiFp5dKpXQ67Xa7GQwGuEcMwzweD4VCwTBMLpdf0bQ8k8nML1z3+Xxut/vChQvgkkajKRSKj816AAX8a9as2bhxY7FYtFgso6OjLpdrwTeey+XC4TBBEKVSic/ni0Siq0cqPJ0giEQi4ff7y+nc6XQ6FAqB/1cqlV6dJxyNRmdnZ8uXJpPJ4XCU753JZCqVSolEcsWzRCLRgw8+uGnTppGRkZMnTx4+fJhOp5fbFn4iqVQqFosVi0USiSQUCvl8/tUjFZ5eLBZRFI1EImKxGKxVxWKxWCxWKpWoVKpEIpnffJHD4ZDJ5FQqtYB5glBKLBaDlGM+ny+RSMCgSCSiUqnlJhcikSiZTIK0fxKJFIlE/H4/+Ay3bt3qcrn27du3adOmq6N98GnQ6fRPeaSc2WzGcVyhUHyacm4URROJRDKZ5HK5SqWSxWKhKJrJZHAcp9FoEomkcmdHi8XCZDJ5PF4kEpHJZAKBIBAIgDYEXC5XLBZfEZrabDaw2AE4nc6pqanygXNCoVCtVl9diAEK+O+4446Ojo5sNjs7O2s0Gm+kAL6CYDCYTqcTiQT4WdNotGAwiGFYoVBgMplSqbTypzo+Pi6TyZhMZiAQqK6uZrFYbre7VCpls1mRSCQWi6/e2Z6amspms+VLr9c7Pj7u8XjApUQi0Wg0V79pc3Pzyy+/PDY2dunSpX379r300ku//vWvF7Zc6/V6c7lcPB5XKBRKpZJMJnu9XoIgMAxjs9lyubxyB8Hz58/X1taSyWSPx9Pc3EyhUOx2O5VKzWQyMplMJBLNz95XKBQXLlwAJwtAEARB0C0HBvzQTTc/7ORwOB/75x2ZTH7kkUcsFovZbCaTyQRBlEMRMpnc3t7e09Pz05/+9IMPPrjiiaVSKRQK3Uh3+gpAx76enp4FvwJBEKFQ6PDhwy0tLbFYLBKJ7N27d/5INBoFhwhcC4ZhU1NTH3zwgUqleuyxx4rFosvleuedd7q6ukKhEJVK3blz5/zHp9Pp0dHR8mU4HHa5XOURFou1YsWKjw34E4mEzWb70pe+hCAIhUJRKBQqlertt9/+1re+tYAbB4f8DQ0NGQyGSCRCIpG2bdvmdDqHh4cNBkM4HKZSqTt27KjwCplMZmxs7OzZsy0tLffeey+GYWazeWRkpLGxMRwOS6XScpu9MhRF59+73W6ff+9CoRBEd+UHpNPpbDYrFAqpVKpMJtu6deuqVat+97vfTU9PL+CWQT78zMyMUqkMBoMymWzNmjVms9lmsymVykAgoFAo1q9fX+EV4vH40NDQxMREb2/vxo0bc7nc2NjY3NycRqMJh8O1tbXd3d3lBzOZTBKJND+4+kS4XG55GYtOp4tEoo9d1QIF5PF4PJ/Pp9Pp+c32VCpVT0/Pb37zm9ra2qt/FgiCJJNJEFEvbIaA2Wy+wQL4a0mn01NTU7FYDEVRn8+3devW+vr64eFhgiDYbLbL5err62toaKjwCvv27UsmkzU1NTQabWBggEKhHD16lMvl0ul0FEX7+/urqqrmP95qtTqdzvKlw+FgMBjxeBxc6vV6sVh8dcA/Pj6u0+nAKhiLxero6Pjtb397zz33NDc3L+zGk8nkpUuXisWix+MJBoMPP/ywRCI5ceKEWCwmk8l2u33Hjh2VK55eeeUVmUym0WhIJJJMJisUCu+8805dXR2GYdFodPv27Vev2RmNxmg0Wr50u92XLl0qd8dobGy8Yu3G7/cLBALwZe7o6Ojo6Ojq6nrooYcW1qcwFouBUyFsNhuKok8//TSCIIcOHWpoaMjn8zMzM4888si1el4AP/7xj3t7e6VSaalUamxsTCaTf/nLX9auXRuJRIaGhvbu3Tv/4AChUAjW4xYwVQiCIAhadDDghxZfqVTK5XL/+Z//GY1G169f39XVJRaLS6VSKpXicrlgo6mtrW1ubu6NN9746le/esXT2Wz2jXfj/9h3j8fj09PT3/jGNxb8IhiGTUxMuN3uJ554wufzPfvss3fcccfk5KTX633iiSc8Hs93vvOdwcHBCpv8xWIxHo+X/6bMZDIjIyMoiq5bt252dvaFF17YsGHD/H1jmUw2/6NwOBwkEunqD+fqd0FRFEXRZcuWgRFQs135j+MKUqnUxMREIpHo7+8fGxt77bXXli9fbjQak8lkf3//6Ojo/v37165dW2GTH8OwZDJZvoxGo2NjYxiGbdiw4cyZM4cPH+7u7r5i97i2thYc/waMjIxcunSpQkmC2+2emZkZGBgo/xHPYDDUavXC/oKPRCJGo5HBYKxfv/7QoUOjo6MajWZycpLJZK5fv/7dd98dHR1duXJlhU3+fD4/f0fX5/NdvnxZpVJt2rRp3759Q0NDra2t5TWsXC5HJpM/5f55ZeCci9OnTyuVyrVr1yoUilgshmEYhmF0Oj2fz0skkpaWFqfTaTKZrm6OyGAwCIIo72wvQD6ft9lsYFt4wS/i9Xrn5uZWrlyp1+ufeuqpsbExMpk8OTk5MDDQ1dX14osvmkymqqqqCpv8HA7HbDYTBPHggw/qdLrx8fGpqal77rmnq6vr+eefl8lkSqVy/ib/FTnehUJh3bp1lcuCstmsw+HYtGlTOTYGSUCfpjjc4XB4vd4tW7YMDAzs2rWrr68vFAqZTKYvf/nLBoPh61//emtrq1gsrrAiw+PxZmZm8vn8448/LhQKh4aGpqend+zYodPpvvKVr7S3t/P5/CtmeP/998+/9Hg8Dz/8cIXedUeOHBkYGFCr1eXXEQqF9fX1C1snmpmZ8fv9e/fuXbt2bVdX1wMPPICiqMlkeuCBB7hc7quvvrp58+aampoKm/xCodBoNBoMhqeeeopKpZ4/f95msz399NNMJnP79u2Dg4MKhaK8yR+LxSp07IMgCIKgJQ4G/NBn6lpRgc/n+/73vz82NlZTU1MoFKLRaKlUslgsHR0dBEGMjIysWLGipaXlG9/4Rk9Pz4oVK8pPJJPJVVVVDofjuu8F2vuhKHpFZywcx6emphAE6erqAiMEQSSTSb/fT6FQMplMc3MzjUYrFAp2u53JZOZyOR6Pp1KpisViNBpFUZTFYsXj8cuXL4M/4ikUCoPBGB0dtVqtIOojk8kMBsNoNJaP3b4am83u6uqyWq3gEsMwr9cLoj6CIKLRqNVq7ezsvM7nez3pdHp6eprL5TY1NYER0MtdJpPhOJ5KpahUaj6fj0Qi4H1ra2tpNFomk/F4PHQ6HcMwoVAoFApB7jSDwfB6veWOiVQqFfyw7HY7+NOfRqMVi8WZmZn5W9ZXEIvF7e3tc3Nz5RmGw2GwP4/jeDAYdDqd89t6L0AgEBgZGWlpaQG1EsVicW5urlAogH148H0DCfkMBqOqqopKpYI0DZBsIhaL2Wx2JBLJZrNUKnVmZsbj8YAVEzqdnkqlzp8/7/F42traEARhMBjgOIAKc1apVC0tLeV6llgslkgkQAl3oVDweDx+v7+mpqb8gZBIpPlHuC1YsVj82NPFUqnUW2+9xWAw7r33XpFI9OGHH+ZyuVAoFIlEVCqVy+UiCOKBBx548803jxw5olarr9guFovFYLnq6rcjCGL+SD6fR1GUw+FcsRridruDwWBvb2/5lfP5fDAYzOfzuVxOKpXKZDIKheLz+XK5HEEQZDJZLpdzOBzwbSEIgkQiFQoFOp1OJpOZTCaTycxms5OTk5lMBoSUbDbb7XZXLv9pbW1ds2aNTCYjCCKXy/n9/mKxCBaJwJJER0fHp2wxYLVaE4lEQ0ND+ROwWCwMBgMcnYAgCIlECofDOI6n0+mqqirQa8PlcoFzTOh0ulKpBA8OhUIUCgXcO41GI5FILBYLfP3Av4dg5jwez2q1Njc3VziQorOzc+/evSwWC9Qfud1uKpUKouVsNms2m2traz9lj4bz58+r1Wpw6gSCIIVC4fTp07t27QJH9KXTaZ/PRyKRwIfDZrNJJJLNZqPRaLlcjslkajQacMtMJhP8oMEts9lskFVx/vx5DocDYnKBQDA+Pl5VVVWhO8DatWs3bNhAEEQsFhMKhXa7ncVigaen02mj0Qi+cuDBwWBQrVbDgB+CIAi6RcGAH7qJotGo2WxOpVJms7mpqWl6etrtdns8nkAgQKPRQDuxiYmJhoaGQqGg0WjS6XQgECgWiyAcmp2dZbFYMzMzb7755h/+8Id8Pm8wGP7f//t/P/jBDwwGA/j7lUwmq9Xq+ZW0ILB3OBxGoxFFUaPRKBaL1Wo1juMXLlx47733/vVf/7Xc4T+Xy01PTx88eJDJZCYSCZANnkwmDx48yOFwSqXSO++884tf/IIgiPPnzzscDo1GMzw83NbWtm3btng8/vbbb4P6WIvFgqIoiNnAX95gIx0sLoAgJJFI3PhHB06JP3HiRDabDQQCwWDw6oDqEwFVEhMTE1NTUzqdLhgMgtmCve5cLudwOCKRCJvNNhqNHA4H/Oy++93vZjKZ0dHRcDjM5XLNZnNHR0dNTc2JEyckEgkI5hOJBLhNKpVKpVJRFE0mkyC0plAoFArlE9WfC4VCrVbr8XhSqVQgEAiFQgsrX7/i3sPh8OTkZKFQIJPJuVzO6XRqNJrVq1cXi0W73X7x4kUOhzM7O1ssFp988kkURcfGxjKZDGgJ2dvby2azL168KBKJstmszWbL5XIMBgNBEBBMRiKRK0Y+UUm2QqEQi8XxeDyRSAQCgXA4PD+BH0XRYrEok8k+6V1ns1m32+31ehEECYfDCILY7XaPx+Pz+UABCNhb9vl8VCqVy+VSKJRoNIphGI/HA1XcAoHAbrefOHGivb29r69venr6L3/5C51O37Vrl1KpLG+VC4VCCoUyPzc7n8+HQqHJycm5uTmHwwHKH7hcrsfjOXDgQFtb27p160A0BdZ0jh8/7na7C4VCNptlMBilUmlsbMztdnM4nDNnzqxevXrLli0Oh+Py5cssFguUam/evJnBYAwPDwcCAaFQGAqFVq1aBY7VBKn1dXV14K7BT4TJZCaTycppCAwGIxaLJZPJ0dHRjRs3GgyG06dPp9NpFEUDgQCHw/k0WQyFQsHn8505c0YgEORyuXw+DwJyqVSqUCgSiYTJZBIKhVarNZfLcTicd99995FHHuno6JicnDQajVKpdGpqis/nb926VSgUHjt2DMMwPp/v8Xi2bt360EMPIQgyPT0tkUhqa2udTieNRgMRO0iAwnG8wtyYTGYwGCwWi6dOnXr00UebmppOnjyZzWbD4XAgEIhEIpVPob/B2zeZTKAlB1jOiMVie/fu5XK5hULhgw8+KJVKHA7nz3/+8w9+8AOdTnfx4sXJycnq6uqhoSGdTrd9+/YDBw6wWCwej2ez2R5++GGQSTE+Pl5dXV1TU3Pw4MFyxM7hcBKJROX8HfANz2azR44c+e53v7tixYqTJ0/m8/l4PB4IBFAUnf90i8XS09MDA34IgiDoFlWpqw0EfUooivr9/v7+frPZjCCI2WxubW2lUql+vx9FUbvdvmXLlvHx8UKhUFdX9+yzz46Pj/v9/nA4/O1vf5vD4ajV6qmpqffee68c27e0tHC53AsXLpTLR0kkEpfL1ev1wWAQjJRKpUQiYbFYyGRya2vr+Pi42+1G/nchwO/3z18dyGazFouFRCJt3LjRYrEUCgUcxycmJg4ePLhjx44VK1bMzc1xOJx4PP7SSy+tW7euvb3d4/GAhnwmk+nQoUOrVq0yGAxqtbqcg10qlQqFAth9mj8C9ujMZvPE/2W326/+6Nhsdl9fX0tLi9/vByn3n/KMbvCxeL3e1tbWDRs2lGuP5XL5l770pe7ubvABzs7Ojo2Nbdu2rba2Frzv7Ozs66+/3t/fD2rymUzmxYsXR0ZGOjs7DQaDQqEoB34EQeA4DlY3ym+K4ziozjCZTPPv2mg0gp/LFcRicV9fn16vB18SLpd7dfu9T6q2tnb37t0qlWp6etpisYD4E+RjJxKJjz76yO12b9myRS6XR6NRJpM5MjJy5syZ/v5+mUwGlhuGhobsdvuKFSv0er1MJivvHIJbplKp5ZFisQhuORqNTk1Nzb/lycnJ8rd0PrVa3dvbK5PJfD5fIpEQCATz9/Oj0SiHw7miV+WNyGazoVAIpCeEw+FQKEQmk5VKZSaTCYVCXq9XJpNxOByv1ysQCO65556amhqbzeb3+3t6egYGBiQSSTab/eijj7xeL1jLUCqVWq02GAzOzs7OD+8ZDIZSqQTpMGAkn8+73W4URbVaLYZhMzMzYPsaJI+Ew+F8Pl/+uDweTzgcXrZsGZPJBNv1fr///fff53K5GzduJJFIGIbRaLS//vWviURizZo1CIKQSCQmkxkKhT744APwz0JVVRX4dUNR9PTp0z09Pe3t7QwGo1zEgeN4sVgslUper3dycnL+z2VqaiqRSEQiEblcvmrVqjvuuMPj8UxPT1dXV/f29lIoFI/Hg2GYVCqt3POvMhzHnU4ni8XasWMHiHjB+JYtW3bv3g1+U9Lp9OHDh2tra9evXw++eBQK5be//S2fz1+9enUqlWIymTQazev1vvPOOwqForGxUafTge9eMBg8cuTInj17dDodi8UqZ7ODf3kQBHE6nUajcf6Nm0wmUNFjMBh6eno2btx4+fJlh8PR0tLS29uby+VA6z7QBXDBNw7cd999Gzdu9Hg8MzMzZrPZ5XI9+uij4Htus9neeOONtWvXrl271u/3gzSNF154obm5efXq1T6fTyQSud3u119/vaWlpa6uTqfTgdUir9d74MCBp59+msvlgqSA8i2D1BKr1XrFP7Zmsxl85VpbW/v6+jZv3nz8+HGfz9fd3d3b2xuJRFwuF5VKBe0MypOfnZ1du3Zt5S6AEARBELRkwR1+6Caqra39l3/5l/LlPffcc88995Qvr6iZB23kyp555hnwP8CuHYIgNBrtYxvL0Wi0bdu2HTx48NFHH0UQhEwma7Xa3bt37969e/7D6HT61q1b5wdOJBIMuiAAACAASURBVBJJLBbff//98+tR0+n0+Ph4Z2cnjuNWq7W+vj4UCuVyuWAwWFVVNTc3Nz093dbWVigUnE6nXq8HDb03bNgAEsWR/63Gb2trYzKZIKbFcTyZTDY2NoLOfPM7DoAY7OpwrlQq0Wg0cLqBTqerqqqqHPIxmcwrOopdgUKhaDSaPXv2XDHOYrFWrVq1atUqBEF8Pt/58+dbW1vT6bTf79dqtZFIJJ1OJ5NJpVJpt9v9fr9Go5mamgIN3tRqtUgkKhaLIDgBXcGXL19uMpnASD6fLxaL4PyFycnJ+RvXVCrVYDBcfeg6QRB8Pr+hoYFGo+l0Or1ef92D2Xk8nlqtrvAAnU4Hjt+7uiYCpE7U1dVFo9F0Oi2RSKLRaCKRwHFcLBYHAoF0Oi0QCLLZLJ/Pz2QyNTU1PB7vww8/BMFtJpOh0+ltbW0gexxBkGw2S6fTNRpNNBo1Go3lyBZBEDqd3tLScnVfumKxCIrDWSyWTqeTSCTl/XwURQuFgkKhACH3JyIWi7dt27Zt27byyPy2cM3Nzdu3by9frly5cn5xfrlFwp133lkeXLdu3bp16z72vdrb2y9dumSz2UBdA5/P7+3t7e3tveJhzc3NX/nKV8rtABEEYTAY3d3dV1R8zM7Ogu6ebrebzWZTKJRkMhkOhw0GA5vNttvty5Ytk0gkExMToPcEQRArVqwAC3Mmk0mj0dTU1DAYDJVKNTMzA94uHo8rlUqhUDgzM2Oz2ebv1TOZTNCQf3R0dGBgQCAQUCiUYrFYKBQaGxsJghAIBFqttr6+HuSfX4tGo6nQaoHFYvX391997LxarX744YfB/3733Xe1Wq1AIJidnVUoFBiGZTKZQCAgkUioVKrZbO7v7+fxeKCpPigw6enp4XK5KIpOTEy0tbXV19cjCGIwGHw+H9iWR1G0o6MDZO54vd75e/VsNlulUlmt1suXL0ulUhaLRaPRcBwvFArt7e2g86VWq21sbLzu189gMFQ+XW/z5s3I//0Gll24cKG1tZXBYJjN5pqamng8LhKJvF6vRqMplUrj4+Nf//rXTSaTXq+PRqN6vX79+vV0Oj0UCl28eHHdunWNjY35fL65uXloaAgsbYRCIdB432KxgOqw8ntxudza2trJycnJycmGhgYKhUKj0cA/XL29vcViUavVarXapqamcnhvsViqq6u5XC7c4YcgCIJuUTDgh255NBpt69atzz33nMfjqdz0C2wegpjkWkBgHAqFfD6f1Wqtrq6empqqqalZsWKF0+kcHx/ncDgcDgfDMIPBANKJc7lcMplcuXLl9PT03NxcIBCQyWQgArFYLHNzcz6fTyqVgrL5e++992PfF8fxQCAQCAQYDIbf75fL5aFQ6NChQ3v37o1Go4ODg5VPIpBIJHfdddcNfFqV0Ol0tVpdKBTAnCUSyczMDJvNrqurs9lsFouFw+GIxWK9Xl8qlXg8XiqVyuVyTU1N586dc7lc4XBYpVKtXLkSVPK7XC5QBA6WKq5YfykD1dqBQIBMJofDYVBPe+7cObADv27duutuq1ZXV1foFlYZm80GZ3o5HI5kMslkMl0uFzjbbHp62uVycTgcqVSq1+uz2SybzY5GoxQKRa/X+3w+p9OZTCa1Wm1nZycoQAAjOp0OfA/LdfhXAFFcMBgE+SZsNntiYiIcDnd0dIhEoubmZhBfFYvFycnJYrF4dZ+8paatrW1mZmZ2dlav11eOimOxGNiPrfAYkUgkl8sxDLNaraBLQigUMhgMVCp1YmIiHo9zOBwajSaTyaqrq+VyOY1GA03gP/roI5PJpNPpLBbL8uXLa2pqjEZjKBRyOBylUqmurk4kEl29vgBQqVRwLKjZbJZIJE1NTQwG48SJEw0NDZlMBrza/M7tVxsYGPiUx4XIZDKpVJpKpSwWi1gsBl+P5ubmXC538eLFQqHA4XAoFIpara6qqpLJZCQSyePxyOXyt956y+fz1dXVXb58ecOGDS0tLRcuXAA9COh0enNzM5vNvlYDEb1eD1LZw+GwXq9vaGggkUjvvvvutm3bUBRdv349+AWpPPMdO3ZUPoqyApVKlUgkUBSdnJysqqqamZmRy+WdnZ0gC4xGo3E4nOrq6qqqKqlUShCEw+Goqqr6r//6r0wm09LSMjw8fN9993V2doJsFAqFwuVyly1bRqVS5692zVdfX18sFv1+fyKRaGtrq62txXH8tdde++d//meLxbJr1y4+nw/CewzD9u/ff//993+arooQBEEQtLgozz///GLPAYL+DrhcrsViqdCoqVQq5fP5ubm5+Q3erwb6gUWjURqNRqfTmUxmTU2NRqMBZ6EfOXKkoaGhv7+fQqGIRCKfz4cgSDqdZrFYYPsxHo+nUqne3l6w3QdG0ul0b2+vSqWq8L4Yhs3OzgYCATqdLpPJVCoVSG0F+8bzN2M/FoVC+ZTBBoIgDAaDz+cnk0k6nQ5OVgdJ+yQSCfS9A3mwbDY7FAoRBJHNZnk8HvjMQZFFV1eXSqUCJ5mBkc7Ozsr15+l02m63RyIRJpOpUCjkcjmdTieRSKlUSigUVj7fDqDRaAvOtWaz2SwWC7R2o9PpQqFQr9eDIoLZ2dmZmZn29vauri4ymRyNRnEcxzBMJBKBZuOg/9yKFSvkcjmPxyOTyaAGob29vcKpBAiCxGIxh8MRj8fBcoNEIqHRaKDmorq6upyGEAwGPR5PdXU12LZd4phMJthNBSX9H/sYULHPZDIrNJBDEEQoFIK2/zQajUajKRSK6upqHo9XKpVGR0czmUxPT49GoxEIBJlMJpvNgk5+dDp9ZGRkcnLS4XDYbLbOzs7W1lYej1coFMLhcHV1dVNTU4XOc1wuN5fLgR9NT09PS0sLyJ8vFouxWKy3t7dyBg2CICwWq/KKwHVJJJLyvdBoNI1GA0oVCII4efIknU4HB16IxeJoNFooFEBDRzqd/v+xd+eBTZVZw8BvbpZma5qkTZvubbov0JaWUpAWKDuICLKMI77uIjjjjKgzzuCGzriOy+e4zKu+ijqKiIwgsijIIpalG9B9SZumbZqk2ff13vv98YwxlFJKWbp4fn+R2+fePDeN4LnPOec5cuSIQqGQy+UKhWL+/PmJiYkCgQB1XszJyUlJSRliiV4gENjtdrPZLJfLb7zxxvj4eDabjTp0mkymhQsXDv1lRrhc7ogz3lELAxzH0V0nJibGx8czmUySJPft2yeRSMrLy+Pi4jQaDYZhNpsNVdDs2bNHo9G0trYqFIq1a9dGRESgv7u0Wm1xcXFiYuIQIbpIJDKZTGhzljvuuEMoFIaEhLBYLLfbrdfr165dGyhhQBVnc+fOnWAB/44dO7ALtloAo2vLli2rV6++wg61AAAwKBpsLQsmBr/fX11djQLUQQdcrEv/cK7c29uLtqd++eWX586dO3fu3Ksw4/EANe6KiIhQqVTbtm2bP3/+hXnaE4zNZrPZbOHh4WfOnPnpp58WLFgw9EZr1057e7vH4wlsoDj2KRQKr9cbFxd3sbgadenncrmXu+mATqfDMEwoFO7cudNmsy1atAjVaEx4fX19qOLg3XffFYlEixYtuvKWFmOfUqkUiUR8Pv9vf/tbbm7ukiVLrqR7wpX45ptvli5dOsGifQzDUG0XCvvBGEGj0bZv3w5PYQAA1wKk9IMJgsFgDB2L0mg0gUAwgrxTp9O5a9eu0tJSiqIIgpjwEW8wnU63f//+4uJitAKPegpMbAqF4uzZs3l5eb29vSKRKCMjY7RmMi4W9oNdsrMgqqsfwZWrq6t9Pp9MJuvv78/NzR1xBce4c/ToUYlEEhMTYzQay8rKhs6MmDC+/fbb/Px8sViMciuG7g5wTd10002j9dYAAADA1QIBPwDnIQhCq9Wi7QNjY2OjoqLCw8MFAoHL5TIajWvWrLnC/aivOrTfnkqlQi9ZLBZKDL7Crv4Ig8FAe1x5PJ4lS5agHmlXC/pIcRxHW5Rdsjnf9YHqONA2hAUFBSPolgeuOi6Xa7fbe3t7c3Nzc3Nzr7xp/HiBugB2dXXNmjUrKSlp4i01DyosLAz1TF25cmVERAS0xwcAAACuBKT0A3Aen8935syZXbt2vfXWW0899VRxcfHFOpOPBSRJms1mVNiMYRhBEA6HgyCI9PR0tIHZmOVwOLq7u1FFbm9vr9lsvummm34lC5gAADCKIKV/DIKUfgDAtQMr/ACch8lkTp48ub29XSKRbNiwYayt5w9gs9kOHz5sNBrvv/9+DMN8Pl9FRcWLL774wQcfjPbUhoL2aXc4HGg7QDqdXltb29XVlZ+fP9pTAwAAAAAAYOKATDkABvJ4PCdPnszOzh7j0T6GYX19fZ9//vktt9yCXqIe1yUlJWMkPf5iTCaTTqdD7eg8Hk9fX5/JZILlfQAAAAAAAK4uWOEHYCCPx1NZWXmxfePHFI/Ho1QqjUZjoHe3WCxGO9iPZT6fz2KxqNVqtVptNBrb2tqmT59+ubsnAAAAAAAAAIYGAT8A5yFJ0mKxdHZ2lpaWjvZcLk0gEMhksldeeWXJkiVSqTQ+Pj42NnaM7yPg8XjQbvaNjY0//PCD3W6PiYl56KGHRnteAAAAAAAATDQQ8ANwHr/f39LSEhYWdiW7r3u93q6uLi6Xy2AwpFKpz+fTarUURfn9/tDQ0IiIiCHOdTqdbW1tOTk5gVbkKOmdTqf7fD6JRBK8s6BUKr3//vs/+OCDV199NSoqKisr64477khNTR3BnD0ej06nI0mSJEmBQCAWi4OPhIWFiUSiIU4nSdJms6nVah6Ph/ZIdzqdWq2WRqNRFCWRSFC/cQzDTCaTWq0uLCx0uVzR0dEmk+nMmTNyuTw9Pd1isWi12tDQ0NjYWAzDXC6X2WzGcdzhcPB4vBHsO0AQBEVRDAb8RQcAAAAAAH6N4P+DATgPKuBPS0sbcQG/y+U6deqUyWQKCwtraWnZsGGDTqc7cOBAVlaW2Ww2mUzr1q272LkkSXZ2dj7xxBPr1q1js9kMBiMzM5OiqD179hQVFel0OiaTGbw1NJfLnT9//vTp00+fPn369OlDhw75fL4XX3zxcudMEIRKpfrxxx9TUlKMRiNBEMuXL+/p6amoqJDJZEajkSTJFStWDH3XZ8+ePX78eFpa2tq1a30+X1tbW0VFRU5Ojk6nE4lE8+bNQyM9Ho/NZuNwOBwORywW9/X1tbe3Hz16NDY2tra2FnVPWLlyJUmSHR0d1dXV+fn5VVVVJEmuX7/+cu+rr6+vv7+/sLDwck8EAAAAAABgAoCmfQCcx+PxnD59esqUKSM7nSCI7u7ujz/+eM6cOcnJyXa73ePx1NfX9/X13XDDDfn5+T/88INer7/Y6V6vV61WM5nM/fv3b9u27e23325sbKyurjYajWVlZXl5ed98843VakUjNRqNz+fDMIzP58+dO/cPf/jDfffdV1tbO4JpOxyO+vp6s9lcWloqk8l+/PHHnp6ehoYGdCQ5Ofn48eMmk2mIK/h8PpvNFnhpNptra2vdbvfs2bMTEhK+//57u91+sVu22Wx+v9/n8wWPIUnS7XbjOJ6ens5kMisrKy/3prxeb3V19b59+y73RAAAAAAAACYGWOEH4BdoW/vW1tZHH300cMRms2k0Gjqd7nQ6s7KymEymz+dTKBRsNtvtdoeGhkZGRppMJqPRyOFwnE5nfX09agRgMpmWLl3qdrvr6upQHj6O4yEhIY2NjbNmzbrYHOLi4j777DMGg9HR0aHT6bKzs7du3crlctFkTCZTe3t7YWGhxWI5fPjwjTfeGMj8ZzAYkZGRiYmJGIYRBGGxWNCTBYIgUlNTmUymy+Xq7e1lsVherzcsLEwsFhuNRovFEhISotVqW1paUOc/dMHq6uqOjg5UfcBgMCiKam1tHaI7gFAozMvL6+rqQi+dTmd/f39oaCiagE6nUygUkyZN8vv9Xq83OMfeYDCo1erly5eLxeLJkyf39PQEbqeoqCgrK6upqcntds+bN8/v95tMJpPJxGKxGAyG3+/3+/3ot0Cn05OTk71er8FgcDgcLBYLx3GLxdLe3s7j8SwWC5fLNZlMZrOZJEkWi5WQkAB5/gAAAAAAYMKD/+UF4Bd+v7+5uZlGo6EkcIqibDbb3r17eTweSZJ79uz55z//SZJkZWVlV1dXbGxsZWVlbm7ujBkzdu/eHRERwWQyOzo67Ha72WwmCIIgiHffffeRRx4xGo3JyckYhuE4zmaz0RL9oNhsdlZWFkmSKpWqpqZm5cqVFEWlp6cfPXrU5XL19/f39/ej020228mTJwsKCtCjBJIkDQZDZ2fnihUrSJLU6XRHjhzh8XhWq/Xs2bPPPfec2+2urq7WaDRCobCxsTE/Pz8zM/PgwYMoyG9vb7darWgzPxzHWSyWwWCwWq2oGp9OpzOZzIst0Q9KIBAkJCR0dHQ4HA40bXS60+nUaDQ0Gg0NM5vNLS0tHA4nLy9v0OvY7faWlhaXy1VWVma32xsaGs6dO8disfLz8zs7Ozs6OsrLyzs6Osxm80MPPaRQKE6fPo26D6DTFQpFaWmpyWTq7++vra3lcrldXV0ej+d3v/sdBPwAAAAAAGDCg5R+ADAMwyiKcrlcjY2Ne/fuRTG51+v1+/11dXX79+9ftmxZXl5eT08Pj8czm82vvfZaWVnZ5MmTe3t7hUJhc3Pzvn37iouLk5OTo6OjGQxGdHR0SkpKTEyMUqlsa2tD6/PoXXw+H0VRHo+npaWlPkhDQ4NCoUDDnE5nXV1dSEgIl8vl8XjTp0/Pzs7WaDRarZbD4URGRqJLWSyW+vr61tbWjo6OlpaWtrY2DoezZMkSt9tdU1Nz6tSppUuXpqenazQaDofT3d39ySeflJWVpaam9vf3c7ncc+fOVVRUFBUVJSUlSaVSNpsdPEn0bGLAtB0OR3Nz84BpB9bkg4lEounTp8tkMrVardfr+Xw+ShZwuVx6vd7lcqlUKpVK1dDQYLVaV69ePWhHQJIkw8PDly1bFhkZ+cUXX1gsFiaTyWAw0OI/WrSfOnVqdHS0Vqu12+2VlZUtLS0FBQVJSUmJiYkikSgyMnLJkiVhYWEnTpxQKpWLFi2SSqUmkylwawAAAAAAAExgsMYFAIb9HPC3tbXhOF5eXt7e3h4dHY1hWH19/ZQpU3w+n1wuT0tL0+l0brdbp9MlJCSgYD4zM/P7779PSkoyGAyRkZFz5swJCQnp6+sLXJnFYqWmpqpUKgzD/H6/zWZLT0/3er1NTU0OhyMwDMdxqVSKEgFMJtOBAwfuueceDMNQCjpaAI+Pj09MTERjwsPDf/vb38bFxbW2trJYLLfbLRQKUTtAh8OB8uedTmdfX19CQoLBYHA6nRaLJTo6ura2tre3NzExsbW1NSYmxmw2R0dHi8VigiBQEb7X6/V4PJMnT2axWIEjXq9XJpM5nc7GxkaXyxWYNoPBSEhIQIkAwUiSDA0NRRUQ8fHxSUlJKH2AIAgejxcXF4c+EBqNtmLFCqlUeuFvxOFwKJVKOp2ekZEhEAjOnj1rt9tDQ0Pdbnd5eTlK3Z8+fTpJki6Xi8fj9fT02Gw2kUhkt9sTExPZbPahQ4cIgkA1FCaTKS0tzWQy2e32iIgIi8USFhZ2Vb45AAAAAAAAjFkQ8AOAYRiG47hYLF67du3atWsDB91ud2xsrF6vV6vVcrk8MTGxqakpOTk5Ly9PqVSeO3eOz+cLBILk5GStVisUCl0ul8PhmDx58vHjxzs6OjQaTUxMTFFRUW9vb3t7e09Pj1qtjoiIyMzMxDBs5cqVF5uMy+Vqb29Hu9BRFNXf379v375169aZTKaFCxeifAGhULho0SIMw3JzcweczmQyY2Ji0Cp6b2+vRCJpa2vj8/kZGRmdnZ3Nzc18Pl8oFCYlJXm9XoFAYLfb3W53dnb2sWPHuru7dTqdVCotKCig0WjHjx8PHElJScEwbNWqVYPO2ePxoNR9VLEvEomUSuXRo0eXLVvm9/tnzZrF4XDQSC6Xm52dfeEV3G53f3+/VqvFcVyv1/v9/rNnz9JoNA6HY7FYMjMzw8PD29rarFarTCaTy+UWi6W8vBxV5ovFYoPBkJSUZDKZUNG+wWBgsVgCgaCrqwvt/0en05VKpcViYbPZPT09EPADAAAAAIAJj/7MM8+M9hwAGKNwHI+MjDQajSwWi8VisdlslKiP47jNZjt06FB6evqsWbNEIpFarcYwzOFwsNnsuLg4gUCg0Wjsdnt5eXlSUhKfz+dyuRaLxeFwlJSUoNyBIaCO96WlpUwmE1XU4zjudDpxHF+6dOklp81kMkUikdVqZTKZTCaTx+MlJydHRUWhQPrUqVPZ2dk33HADn8/XarUotYHP5yckJISEhJhMJoIgUJ48n88PPoJKCS7G5XJ1dnbq9Xo2mx0ZGRkVFYWmjZbly8vL0X2ZTCaSJAf9BFBigsFgYLPZUVFRcXFxERERDAbDYDDY7faZM2dmZGQ4HA4ajZaXl2ez2SiKmjJlCo7jHo+HzWZnZGRIJBKj0ej3+z0ej1AoFAqFaM0/JycnNDTU6XSi36NQKExISICAHwDw67Rjxw4Mw9asWTPaEwG/2LJly+rVq3NyckZ7IgCACYhGUdRozwGAccPv96M1c6/X+49//GPu3LkolB3jUBlCREREf3//1q1b58+fP2PGjOs/DaPRqFAoZDLZoBX7AAAAroPVq1djP4f9YIyg0Wjbt2+HpzAAgGsBUvoBuAxOp3PXrl1lZWWoCf8Q29SNKQaD4dtvvy0pKTGbzUwms6CgYFSmQaPRuFwuRPsAAAAAAOPIyZMnB23SDMamGTNmoOZZCAT8AFwGGo0mEAicTqfRaFyzZk2g/f4Yx2Aw+Hy+1Wp1Op3Lli0LlNNfZyKRCKJ9AAAAAIDx5bXXXvvqq69GexZguAZkDEHAD66hCflXA6rPxzCss7Ozs7NztKczXBwOR6fTYRjW2tra2to62tMBI5GRkTFp0qTRngUAAAAAfnVWrVoFpUDjAo1GG3AEAn5wDX3xxRejPQUAJo6bb74ZAn4AAAAAADB8EPCDa2hCrvADAAAAAAAAwLiAj/YEALhqKIryer2jPQsArg6CIPx+/2jPAgAAAAAAjGOwwg8mCIIgWlpakpKSWCzWCM7VarW9vb29vb1xcXFRUVGJiYnXYpJjgd/v1+l0CoVCr9dHR0fz+XyPx+P1esPDw1NSUkZ7dtcWQRAWi6W/vx+95PF4PB4PwzCxWDyq8xqcTqdzuVwxMTEhISGjPRcAAAAAADAuQcAPJgKSJDs6Ok6fPp2Tk4NhGEVRHo+nt7e3v79/OBvOkyTZ29u7e/fuN9988+mnn546derEDvh7eno+/fTT/fv3P/bYY1Kp1GKxaDQakiSXLVuWl5c32hO8VgiC6Ovra2ho8Hg8GIb5fD6Hw8FgMCZNmjQ2A36bzdbc3OzxeDIzM0d7LgAAAAAAYFw6L+D/8ssv165dO1pTAZdlwHYLv2YURZnN5j179qxbtw7DMJIkTSaTUqk8cuTIkSNHvv3220tegclk5uXlyeXyiIiIDRs2oFXf8YUgCI/HM5xtAtlsdlpaWmZmZmdn54MPPogOKhSKF1544Y033vjoo4+u8UyvPr/f7/f72Wz20MM0Gs3Ro0dDQ0NvvvlmDMNcLte2bdt++umnBQsWXJdpXra0tDSdTldfXx8eHi6RSEZ7OgAAAAAAYPwZZIV/+/bt138e4LLAc5lgBEFUVVWxWKyoqCgMwyiKMhqNKG3b4XAM8yIej+fkyZPZ2dnjMdrHMMxutzc0NNxwww3DGWw2m1taWlA2BMJkMlkslkqlumYTvIb6+/u1Wm1BQcHQw86cOXPmzJmnn34aveRwOOnp6SRJRkZGXvs5jlBqaqpcLj9x4sTy5ctHey4AAAAAAGD8GSTgh3XjsW+8BPxarba+vh7DsMLCQofD4fV6zWZzQUGBQqFgMpkqlSojI0MkEmE/99vr6uricrk6nS45OVkkEpEkqVAoFAqF1WqdNm1adHR0bW1tX19ffHx8Tk5OoFbf5/N9/fXXW7ZsQS/pdHpaWlpiYmKgVHs4vF7v6dOnb7nllqv9GVwnJpPp2LFjwwz4LRZLe3v7Pffcg156vV6lUtnf3z937txrOcdrRaVSnT179pIBv91u7+npsVqtYWFh6IhMJhMKhdd+giMXGRlJp9O7urpcLheHwxnt6QAAAAAAgHEGuvSDa0ij0Xz66acPP/xwS0tLd3d3e3v7c889V19fL5fLu7u7t2/fvnPnTjSSoqiamprq6uru7u6qqqrt27d7vV6Kotrb2/fu3XvHHXc0NDT4/f7t27dv3bq1trbW5/MFTrTZbO3t7Wh5f2RIkrRYLB0dHWVlZVfhtsc2v9+POhRGRER0d3fL5fKGhoampqYpU6bceeedoz27aygmJgbDsDfffPPbb7+trq7u7++PiYnJzc0d7XldgkQiIQiiu7t7tCcCAAAAAADGH2jaB66hvLy8u+6668CBA1wuNy8vjyTJxx577PPPP3/xxRdJkrTb7Zs2bbr33nsxDCNJctu2bbfddltJSUlxcXFJScmcOXMyMjIWLVo0e/bstrY2hUJRUlKSm5u7cePG5OTkwFuQJNnX13eFWdl+v7+lpUUgEEyePPlK73nMs9lsLS0tYWFhOI53dXX19vY2Nzfn5OTcd999oz21a2vSpEkrV6788ssvq6uro6Kipk2btmrVqvj4+NGe1yVIpdLm5ube3t6MjIzRngsAAAAAABhnIOAH11ZoaKjD4Qj0fo+Li0NF8jQaLSkpSavVouM0Gi05Odlut3u9XovFEhYWVltbiyIcFov1zDPPbNq0SSAQZGZmBkf7GIZRFGUwGGJjY69kkl6v98SJE2lpaVdYwB+oSqDT6dHR0X6/X6PRUBTl9/tDQ0MjIiKGONfpdLa1teXk5DCZzMDVVCoVymtkHwAAIABJREFUnU73er2RkZECgWDA+Obm5sBLtVrd29tbU1ODXjKZzKioqEGzHlAB/w033DB37lyCINra2lBixZXcuMPhUKvVbDY7JCREIpG43W69Xk+SJEVRAoEAVW1cjNFo1Gq1aWlpDAYjcDWdTof+HBERwefzB5xiMpk6OzsDL5ubm7u6ugL3zmazpVJpeHj4gLNEItGtt946b968qqqqY8eO7d+/n8ViBdoWXi6z2WwwGEJCQng8nkgkstvtZrOZIAgajSYUCgf8sgZQq9UulyshISFwy2az2Ww2UxTFYDDCw8ODmy/yeDwcx+12+8jmCQAAAAAAfs0g4AfXXHDYyePxBl2Nx3H8zjvvbGtra2lpwXGcJMlAvz0cx/Py8qZNm/b6669///33A06kKEqn0w2nO/0QUMe+adOmXclFXC7XqVOnTCZTWFhYc3Pzxo0bdTrd/v37s7OzzWazyWRCmwgMCnUr2Lx58+23385ms+l0elZWFoZh33zzTVFRkU6nYzAYA9q2ORyO6urqwEu9Xt/d3R04wuFw8vPzBw34rVZrR0fH7bffjmEYnU6PioqKjo7evXv3n/70p5HduMlkqqmpcTqdOI5bLJa1a9cqlcrTp08nJyfr9XoGg7Fs2bKLnev3+2tqaj766KPly5ezWCwej5eWlmY0GquqqjIyMvR6fURExJw5cwacZTQag+9doVAE37tQKGQymcEBv8PhcLlcQqGQwWBIJJIlS5YUFxd/8MEHra2tI7tltVp97tw59C3l8/nz5s1raWnp6OiQSqVarTYqKmrWrFkXO9fr9R48ePDkyZPl5eUMBkMkEslkMrlc3tPTExsbq9frU1JSpk6dGhjPZrNpNJrL5RrZVAEAYGTujqB/ZCB/fsWa/o+mnx5JGfOFoJT5zNYtT765s6JVTwplxcvWP/3cxpmR9MseMyhX575XH9v00i7Vsq/0n68IueDnvt4j7zz/yta9p9s0Tma4rHDxXY8/89D8eNbVujkAABgRCPjB6KMoyu12b9261WQyzZo1q6ioSCwWUxRlt9v5fD5FUS6Xa9KkST09PV9++eX69esHnM7lcoffjX/Qd7dYLK2trY8++uiIL4KqrD/++OPXX3/dbDZXVVV5PJ66urq+vr777rtPpVI98cQTixYtutgiv9frVavVLBZr//79brfbYrFs2LDB6XQajcaysrLOzs6///3vc+bMCV43lkgkwR9FV1cXjUa78MO5cJ5Go9FoNAZq191ut1arHXFDOJ/P19DQcPDgwccff1yhUKjVarRfgM1mKy0tra6u/uqrr2bOnHmxRX6Hw2Eymbxe7759+6xWK5vNvvXWW7VardfrnTNnzk8//bR///6pU6cOWORPSUlJSUkJvKyqqjp79uwQJQm9vb1yuXz27NmBFfWQkJCYmBiKokZwyx6Pp6KiQqlU3n333WfOnLFYLAaDobGxkc1mz5o1a8+ePdXV1QUFBRdb5LdYLE6n02AwfPvtt3q9PjExcfHixXK5PDo6et68eTt27Dh58mROTk7gGZbb7cZx/MI0BwAAuKY+1BPv1WzOK3m+88bPdF//djz8HURZTzy1YNErxqVv7Gi8PZch3/Xk7evnz27++tjbiyS0yxgzCKf8m5cfeex9RbhY6xz8Xw5f+werZ288KLrtzS/Prc0X25t2//2+B5bOrPro2Oe3JQ3jaQIAAFwrEPCD6yrQbG8AtVr97LPP1tbWymQyn89nMpkoimpra5syZQpJklVVVfn5+dnZ2Y8++ui0adPy8/MDJ+I4npCQ0NXVdcn3Qu39jEZjUlJS8HG/39/U1IRhWFFRETpCkqTNZtNoNHQ63el0ZmVlMZlMn8+nUCjYbLbb7Q4NDY2OjiYIwmQyGY1GDofjdDrr6+tR8z+j0XjjjTe63e66ujoU9eE4HhIS0tDQMHv27It9MrGxsZ999hmDwejo6NDpdDk5OVu3bkVRH0mSJpOpvb29sLBwOB/yEBwOR2trK5/Pz8zMREfsdrtCoZBIJH6/3263MxgMj8djMBjQ+6akpDCZTKfTqVKpWCyW1+sVCoVCodBoNFqt1pCQEJvN1tbW5vf7jUYjRVGzZ882m81yuVwqlWIYxmQyCYKQy+XBS9YDTJky5aabbkLDfD6fWCxubGxE6/N+v7+/v1+pVAbvIDgCWq22qqoqOzsb1YMQBNHT0+Pz+dA6PPq+oYT8kJAQlGmPMvZRsolYLOZyuQaDweVyMRgMk8mkVCp9Pp9OpwsLC0tNTdVoNCqVatKkSRiGhYSEoO0AhpjzokWL7rrrLpfL1d7ezuPxPB5PbW1tYmIimoxKpdJoNDKZLPAro9FogZ0FAAAADM579tWNL9eK7tr33gPTQjEMy7vtzQ+bTkx/6cEtq869Vc4f9phB2Hc/82T99LdPf85+bdLsRt2FAyj154889k1/2p++/tc9xSEYhvELbn39U2Vl/uZNj9+yYNuaoR4mjD8kSR46dGjHjh1bt25ds2bN/PnzJ3bTXwDGuzGfnAXGM5PJ1NLSYrfbW1paMAxrbW3t7e1VqVRardZkMjU0NHi93vr6eq/X6/P5YmNjHQ6HVqvV6XTZ2dlarbazs7O5uXnfvn2fffZZXl7e5MmTk5OTN2/efObMGbPZjN4Cx/GYmJjASwzDKIqyWq0tLS0NDQ1Go7GhoaGvrw/7OXv83Xff9Xq9gZEul6uxsXHv3r1sNttqtaKtAWw22969e9EV3njjDa/X6/F4Tp06dfr0ablcvnPnzpqaGpQXsHv37ubm5vr6+u+//769vR0FjSRJvvPOO+hZAIrYcRxH17/YB8Vms7OysthstlarrampKSoqQrvE6/V6l8ul1Wr7+/stFsuV/C7QU4z6+vqmpqb4+PjAnoVorZvH43V1dbW2tioUiu+//76lpeXkyZMff/yx3++3WCwnT56sra1taGj45ptv0Oe5f//+lpaW2traw4cPd3Z2olvT6XT//ve/jUajzWZDKQN0Op1Opw9Rf44CZgaD0d3drVQqc3NzhUJhXFyc0Wi02+3oy3Dl5eskSer1+sbGxra2Nrlc3tzcrFAoYmNjZ8yYQRCEQqE4cuRIW1vbwYMHd+/ejZ4ynDp16ty5czU1NXv37m1vb29vb//hhx+am5srKyuPHz+uVCptNhudTm9tbd2zZ4/BYHC73SEhIRiGoRYMTqfzYpORSCRJSUk0Gq21tdVms6WkpERFRYnFYovFYrVatVot+qUHxhuNRoIgJBLJFX4IAAAwsXkq/m9rAxG3/NbZoT8fCin4zepcvGvb+wcswx8zGN6K9yt3Pj4vjnWxuN16ZPcRCx5dvrjgl0x/RtrixZm4bs+HuzRXdmdjDo7j8+bN+5//+R82m/32229DtA/AGAcBP7iGjEajRqMpLS1FAX9LS0tOTg6DwdBoNEajUaFQLFy4sK6uzufzpaamPv7443V1dRqNRq/X//Wvf+XxeDExMU1NTQcOHECbpeM4np2dzefza2pqTCYTegsajcbn85OSkgIRLAr429racBzPycmpq6vr7e3Ffl7h12g0wU8HXC5XW1sbjUabO3duW1ubz+fz+/319fV79+5dtmxZfn5+T08Pj8ezWCyvvvpqWVlZXl6eSqWKiIjw+/3oYURxcXFycnJ0dDSDwYiOjk5JSYmJiVEqlW1tbYGsbIqifD4fRVFer7elpaX+fAqFAg1zOp11dXUhISFcLpfL5U6fPj07O1uj0aCU+yvZdzDwsfT19eXk5MyZM0epVKLjkZGRt99++9SpU9EH2NnZWVtbu3Tp0pSUFPS+nZ2d27dvLy0tRTX5bDb7zJkzVVVVhYWFycnJEokkJCQkNjY2JSVFJBKhcno2mx14U7/fj6oz0JORgIaGBvR7wTDMZDK1tbWFhISw2WyxWDx9+vSkpCT0JeHz+Re237tcKSkpq1atio6Obm1tRTG/VCpdtGgRhmFWq7WioqK3t3fhwoWRkZEmk4nNZldVVf3000+lpaUSiQQ9bjh58qRCocjPz09KShKJRFwuNy4uLiUlhcPhyOXy/v5+Fuu/RZoEQaBbNplMTU1Nwbfc2NgY+Jaq1eqenh4Oh4MeuJSUlEgkErVabbVaw8LCgtfzTSYTj8cb0KsSAADA+fzNR471kcxJRZODaubp6YX5Asx8/HCNd7hjBkVjcy4s2Q9CGrU6L0UThQuDnwjg4ZJwnHJXV9SO7JbGMoqiDh06lJ6ejv4PDQAwlkFKP7iGUlJSHnnkkcDLFStWrFixIvByQM08aiMXsGnTJvSHW265Bf2ByWQO2liOyWQuXbp07969d911F4ZhOI7HxcWtWrVq1apVwcNYLNaSJUuCAycajSYWi9esWbNmzZrAQYfDUVdXV1hY6Pf729vb09LSdDqd2+3u7+9PSEjo6elpbW2dNGmSz+dTKpVJSUkGg0EikZSXl1dUVKBUgsDbpaamopjW7/fbbLaMjAyv19vU1BTccQDHcalUimZlsVgOHDhwzz33YBhGURSTyUS7G8THxyckJAwd8rHZ7ISEhCEG0On02NjY1atXDzjO4XCKi4uLi4sxDFOr1ZWVlTk5OQ6HQ6PRxMXFGQwGh8Nhs9mkUqlCodBoNLGxsU1NTVFRURaLJSYmRiKRkCQZ3OSfz+fHxsaiONnj8RAEgfZfaGxsDF64ZjAYycnJcXFxGIZ1d3dXVlaiLwBJkgKBID09nclkxsfHJyUloTFDCA0NjYmJGWJAfHw82n7vwpoIlDqRmppqMpkcDkd4eLjJZLJarX6/XywWa7Vah8MRFhbmcrkEAoHT6ZTJZBEREXa7PRDhYxiG1ufRqr7L5WKxWLGxsSiHxePxBIaxWKzs7GzUtLKxsbGzsxPNhyAIqVTKYrE4HE58fHx4eHhgPd9oNPp8vqioKJQ+AAAAY5evv+Y/77/94X9+qmvvNtMlacVL7t285cEyaVD9urP5y2c3v/T54Ua1mx2TO+/Opx6NeGv2Qwc9GIZL7t3X+/5CFoaRulMf/P2F93dXNPXZ8bC4nNIVG57Y/D8FwkvlxHvlrQqCJoqNOW+3HXp0bBROdrS3G6lyKW04Y0Z07zRhhJhBo0wGU3CBP2U1WyiMtHV3jeiiYxpJkocPH547d+5oT2QoaAdoi8VCo9FEIhHaicnj8eA4HtgRCYBfAwj4wbjHZDKXLFny9NNPq1SqoffnI0lSo9GgWuuLQYGxTqdTq9Xt7e2JiYlNTU0ymSw/P1+pVNbV1fF4PB6P5/V6k5OTtVqtUCh0u90Gg2Hy5MnHjx/v6OjQarUxMTFFRUUqlaqtra2np0etVkdERKCy+ZUrV17srVFRN1rJR7sP7Nu3b926dSaTadGiRUPvRBAeHr548eJLfFKXwmKxYmJifD4fmnN4eLhcLudyuampqR0dHW1tbTweTywWJyUlURQVGhpqt9v9fn9mZqZSqUQL3QkJCVOnTuVyuVVVVd3d3QaDITo6Gj2qGPD8JZjFYunr60Mr+WjDglOnTqEV+LKyskC+wMUkJiailgEjwOVypVIpjuNdXV02m43NZnd3d4vFYqlU2tra2t3dzePxIiIikpKSXC4Xl8s1mUx0Ol0mk3V0dMjlcqvVKpPJiouLSZLUarUo1T8+Ph59DwN1+BfSarUWiwUtjBAEUV9fr9frp0yZIhKJsrKyUHhPEERjYyNBEAUFBSO7OwAAuG483z02f92R0hc/+f6rGTGUunLbE/f9ceG8nu9OvVKGSuN9re+uLPvdEcEt/9jznzunSf3KH9/704Mvn/HRwm7frf1kWQiGYRip2nn37Ns+c89/fmvl/TNjye6j7z50171lh1v3H31+ZuhQb085LFY/RePyueeF7DQun0fDSKvZQmJSfBhjRtZejxZWuqCEs+fHHw7UemZP++8DWrL70MFmP4YxHBNtX1WKojwez+nTp5944onRnstQdDrdjh07vv76axqNdu+99958880MBqO5uTk0NDS49S8AEx6k9IOJgMFg3HTTTZWVlUO060d59Ze8FIvFmjlzZlRUlNFolMlkiYmJEokkIiJi0aJFHR0dR48eRbv3sVisSZMmRUdHd3d3q9VqGo0WFRW1fPlyuVxuMBg2bNggEAiSkpLmz5/f3d1ttVoH5C8MKjQ0dO7cuSidG63833DDDSqVisFgXLgyP8CAjehGRigUFhcXC4VCl8slk8kSEhIiIiISEhJKSkqampoaGhomT57M5/NzcnJEIlF3d3d/fz+O4zKZbObMme3t7XQ6/be//W1YWFhWVtbUqVOVSiWTyRxiT74AdKehoaHYzyv/kyZN6uvrk0gkCxYsuOTpHA5nxCmFUql0ypQpHA6HJEmZTBYfHy8WizMzMzMzM8+cOaPRaNLT08PDw1GHhe7ubpPJxGQyJ0+enJ2d3dbWJpVKly5dKhKJCgoK0tPTu7q6oqOjy8rKLvm+KSkphYWF6CEOWvlPSEjQaDRpaWnTp09HY3Q6ncvlyszMjI6OHtndAQDAdURjz3vyfzfNTgplsQSJM9f/643bw1v+9Y+dOgrDMIwy7nr6qYNG6W3/74PflyaGskJEafP/9MFjJWTwFaz7n3zo007Bqtc/fnSuLCwkRJS28C9bX1xCP/vaI++0ECOb1XC2YxnRli3nwZPufOHP07it/7xnw4eVfU6fW1e/8/HbX2oTCnGMxpqAG/M1NTVxOJwZM2aM9kQuyuFwHD58mE6nv/rqqw888EBfX19zczPqx3yxnYMAmKhghR9MBAwGY9q0adXV1Xa7HaVsDYogiLS0tKEvheN4eHh4cJjq9/t7e3vnzp3r8/l++OEHlP2OYRifz//Nb34TfO4NN9wQ/PJy/y2Miop67LHHAi/RBu/DP/3K0en0QPY74nK59Hp9eXm5SqWqq6ubPHkyhmFSqTRQZ4GUlpYGvxSJRJd149nZ2dnZ2YGXkZGR5eXlI7yHy8RkMtPT09PT0wNHbDabzWabM2fOmTNn1Go1+lFaWtqAb86AnAKpVHpZWQYDHgokJSUN2DwCzSQmJiawgSIAAIxlITdu7bsx+AAnPSuZ7mo42+q/Q8LEfJXfHTZS7OXzy37ZspQmKpmZw9hZ9/Nr76nd+zQU+6Zl88WBJXiapGzOJMY3lXv2Kx/LlF18mYrGCxMwaJTTcf6ueegALhCG4cMa4z/39JSiZ+v96Cf0+N8d6vjn7GElf/OKn/zuuOxvz7z5t8WpD9jpEekzVmzc/pn29wufbY8aquhsPAoU8KMn9SPg8/nMZjNFUSRJ8ni80NBQr9drsVgoiqIoisfjDb0ZbSBXn8/no+gdlR9SFEWn08PCwkJCQrq7u2UyWVZWlkAgyM/Pr6io8Pv9PT09oaGhYrF4BHO2WCxer5cgCCaTKRAImEym2WxGvZ9YLFZYWFhg699B+f1+rVZLkmRMTAydTscwDD19cLvdQqGQz+fTaBNqKwcwpkDADyYIBoNRUlIyxAAajSYQCC62O/oQnE7nrl27SktLKYoiCGLod5lgdDrd/v37i4uLDQYDm81GPQUmNoVCcfbs2by8vN7eXpFIlJGRMVozueTDKQAACPjiiy/+85//LFiwYOHChcHPba8bylT7yYsvfbDnpwZFv9XjJ1FMjUtdLgrDMMyt19tIGl8kDC7SonE4nKAgx6Prt5CUe9dtQtptA66Od7V3+THZEEvlrNSMZDolV/U5MeyXYYRapSXxiLQ0MW1YYxh5W+p8W0b2CdAEk9e9/J91L/9yxHvgXhXByCgYqpBwPEIBP0p4HAFUX3n48OGEhASz2Uyn05cuXdrT03PixIm4uDiTycTlclFZ38XYbLaTJ0/W1NRMnTp1wYIFHo+npqZGoVBERkZaLJasrKy8vDxUVRfogCORSPR6/YiX991u99GjR1ksFo7jarV64cKFIpHohx9+4PP5FEWp1eply5ZFREQMcYWenp6vvvqKIIg//OEPHA7HarV+8sknxcXFOp3O5/PdeuutI5gVAMMEKf0AXAKdThcIBC6Xq6+vb82aNUNkEEw8DAaDx+OhDQuXLFmCNtub2FDzPIPBEBoaOnv2bOiWBwAYF+h0+o4dO9avX5+QkCCTyR5++OEDBw4MsUXoVUbI375lzt2v1cT+/ouaPpuPpCiKULxRysKw/y6msyMiQnHKaTK7g86i7DZ70GJ7SGSUEKeFrdvtogYiet8pHzoxnpE5uywa99XX1AU12yfaa89asbCZ5YWs4Y65ivxNJ06b6FnLbky/9NjxA+28U1NTM3/+/JFdwel0njt3TqfTzZkzJz09/fDhw729vXV1dWazec6cOQkJCceOHQveUOlCbrfbarVSP5diqNXqM2fOsFgs1MyooqLC5XKFhIQE/wseGRmpUqkIghi62dPFKBSKhoaG5OTkhQsXNjc3oy1+6+vr09PTFy1aVFtbq1Ao/H7/EFfQ6/XBA06fPt3a2jpjxozVq1d//PHHgc2nALgWIOAH4BJ4PN7dd989c+bMm2666cI27xNbTEzMunXrZs2atWTJkpycnNGezvWQmZm5evXq8vLyhQsXQlMfAMB4gdYtSZLEMEyhULz99tuLFy8WCAQlJSUvvfRSTU0NdeWF6kNwVR0+aaUlrfjjvaUyERvHMAyjfF7fL2/JLF40V0xznzr4ozVwjNKfON4Y1FuHNW350mjcUXn8zC/7m2AYIX+9lBd3/3eXenbBnnnPXbn0nm++OBbokec9t31HA5l4632LwoY/ZmSovn/NC5v7ruqXngSU8bt/ftwiXv7n9ZMnWjrt2bNnCYIIdJyhKMpisTQ2Nra2ttbU1Hi9XgzD0LZECoWiubm5r68PJbQ3Nzd3dXU1Nzc3NjaijEsGg0Gj0SorK1tbW1GBAJPJJAhCLpcPMYGoqKjc3NxAzqbVarVYLGj3HK/X29PTo9FoBpxCp9MDKfdut1upVLa0tJw9e7avr48gCAzDent7Ozo6WltbOzo6UEMom80ml8tRj96qqiq/3496+4eGhsrl8pMnT6L5YxgmEAhaW1uHfr42derUmJgYHP9v5NXV1cXhcFAav91uP3fu3LA/fgAuGwT8AAAAAADj24BEZdSkliCIysrKJ598sqioSCwWr169+r333gveIfWq4UyZWcgju7586e0jnWYv6bMofvzXU+/V/rKgSRMv3/LcwvD+z/5439s/ddt9XrP8+5d/96E8LPh/RAWLnnvrTlnP23ff8/YRudFD+KzKEx89uPbZjnnP/WXeUDvVYBiGYayCR955rED/4YPr36vSOF36um1/uPv19tT7336mnH85Y0bMc+z5jW8cU9p9Xmt35b8fvemuryIe+uydW6MnUGm2x+M5e/bstm3bOByOWq32eDxowf/LL7/s7e3t7Ox87bXX/H6/2+0+cuRIfX19T0/Prl270Hr+zp07u7u7m5qafvzxR4vFgnIGURxuMBjQRjnoCJ1Ot9lsw5+VVCpFyfwmk0mtVhsMBrfbPWCMyWSSSqVJSUl+v7+ysrKysrKvr2/Hjh3V1dUURTU2Np46dUqpVB4/fvzIkSNoT9zDhw+fOXNGoVAcP35cLpejiWEYxuFwnE6nTqdjMBgogOdwOHa7HT04GKaioiKDweDxeHp7e3U6ncViGf65AFwuCPgBAAAAAMa3i1UmB3aoMZvNu3btWr9+/d69e3/44Yc9e/YMfcG7I+jMoueb/Jh7122htJAZr3aQQ4ymZ/xhx7cvr5O1vLgsMyI0LH7a3VtdC28pZJKa9xaEsEpebiMwZvoDXx3ftmmy/KWlaaLQqIJ1H+MPvL4xnYEFFj0xDI+++b0Tx95a6vr03unxoWx+XMm9n7hW/vun7XclD2O/PJpgxnMHj72zxPLOysxwUfKC59qLn//+6D8XS2iXN2YQnm/vEOM0Gmvm6wqCsm9byabRaOzF/6cLpDDQom9//9s3b6F9uX56jECUNOOercbF71X9+Oq8S1x4nCEIwmAwxMbG3n///QaDgSAIkiRra2t//PHH+fPnZ2Vl9ff3c7lcq9X65ptvlpWVpaend3d3h4eH19XVnTx5MiMjIzk5OS0tLdCTjyRJn88XEhISqJdER+h0uslkOn369E9BTpw4oVAoLpxVZGRkaWlpQkJCf3+/1+sNDw+/8D+H3t5eHMclEolKpfrxxx95PN706dMZDIbP52MwGF9//bXb7Z42bZrL5WKxWFwuV6VSHT58mMFgyGSyjIyMqKioQIKA1+ul0Wh8Pj/4CI7jNBqtoaGhoqIieM5VVVUo5WGAgoKCJUuW9Pf3G41GFot1YeNeAK6iiZZlBAAAAAAwMbhcLtPwGI3G4VyQwWDExMSkpaVdcs/UD/XEh5czVXr07Ec/mv3oeccevfPF815zM1a/8J/VLwReE80v/JnEwyPEQctPePjUu/+x8+5/XM57/4ImnHL3G9/e/caVjhko5MaPjeTHQ16Vlzxv4xvzNl7OVccfLpc7b9684C2EUOr+lClT/H5/Y2NjTk6OUqnEMMzlckVFRXV0dHR1dclksoMHD6akpFAUFRkZGRoa6na7VSoVOt3j8RQVFdXX16PvsMfjIQgiNTWVIAiHwxEcLdPpdI/Hc8GkML/fj+M4l8vl8/kxMTEYhl24UbHBYODxeEwmU6VScbncqKgopVKJkuqNRqPdbg8NDWUymV1dXWVlZUKhUKFQREREcLlcLpebmZmJ4/ihQ4fQu+v1+vz8fC6Xe+TIEfQ0DfUj4HA4brfbbrcHl8/4/f5Bq2ncbjdq9R8RESGTyaBNL7imIOAHAAAAALhOnE7nMGN4k8k0ILzhcrmiIOHh4ampqejPQqHwjjvuGDS0wHGcoiixWHzvvff+/ve//+Mf/3i97vU8ZM9b88rqH2/63wU/t38l1ceONJMRq2dPGtbGd2AswnE8JyenoaGho6NDqVRKJJKurq6MjIwpU6acPXu2urpaKpUKBIIlyR79AAAgAElEQVTMzEy1Wo02z8MwbPLkyVqttq6urqenJzs7OzMzk6KoioqKc+fO9fX1ZWdno7j9Ynv0Wq3Wjo6Ojo4OGo2Wm5sbGRmp0Wi6u7tJkoyMjExPT0fF9gEoLEcL8tHR0dHR0Sj5n0ajORwOq9WakZGBcvgdDkdYWBjapRi199Pr9R6PB71UqVR2uz0iIiI9PT0yMrK1tbW7u1uv10dHR6enp4eEhBQVFV3sg1IoFB0dHVqttrGxsaCgAMOw6urqpKQkpVK5fv16LveSJSsAjBwE/AAAAAAAIzf8dXiTyTSgupjNZgfH8GKxOCUlRTQYsViMipwvZuPGjXa7PfgIk8n0+XyTJk3atGnTrbfeOiAKuv4o9Wd/2lQW8dTNkyU0fdOBf/7hqSO8BW/9ddGVl8+D0UKn0wsLC7lcLofDmTFjhsPhSExMFIvFv/nNbyiKUiqVRUVFLBYrPT0dZcujZHiRSLRw4UKSJBMTEydPnsxkMlNTU9lsNkEQKSkpQ4TNCI1Gi4uLmzNnTlhYGIPBYDAYKOxHF7xwW0oajTZp0iTUtD8uLm7atGkej4dGo0kkEj6fHx4ePnv2bIfDcerUqaioKLS7XlRUVElJid/vZ7FYPB5PLBbPnz/f6XR6vd4VK1ZER0ejvQPdbrfH41mzZs2FOQUDMJnM6dOnO51OFNuz2ew1a9awWKzo6OiioiLUvQ+AawQCfgAAAACA81zFGF4kEslkspHF8JdFIBAEAn60mLl8+fJNmzYFuqlfEc9Xa4Srdwxshfbfd8t7pqb66Us0o8fj7vpwL+Otd/91xw0PK1RWXJxcMO93X73/2PLU4f2/6BVPYIyTy+V6vX7ovdzHIBqNxuPxgkN0j8dTU1OTnp5OEITdbi8rK8MwjMfjzZgxI/jE9PTzdivkcrkDjgwhNDS0oKAArZMjEolEIpFcbDyDwQgUyTOZTJlMFvzTtrY2Go2WmppaUVGRkZGBkgswDMvOzg4eFhcXN+CyCQkJw5wwOn3AFSZNmjT80wG4EuP670YAAAAAgGG5rHr4Abn0oxXDXxaxWKxWqzEMi4qKeuihh+67776rGT2GrPrSdWUb+9F4yXMfeHXuA6M2gbFt8+bNmzdvZrPZMTEx2dnZOTk5sp8lJiai/vDjgsfj+e6771CqfHp6ekZGxmjP6BLq6+udTqdcLicIIjs7OyzsijZoBGAMgoAfAAAAAOPShI/hL0tkZGRYWNjDDz+8fPnyQP9wMF68+eabWVlZHT87cOBAZ2cnqnhnsVjJyckymSwliEwmG5tfSw6Hs3z58vDwcLfbPWPGDBaLNdozuoSioiLUeD8rKysyMnIcPVsBYJjg3wMAAAAAjBXXJ4YPDw9HBb0TyRdffDFEVjMY46KiogY0wMcwzGQydQZpaWnZt29fV1cXSZIYhqFv+IVG6Q7+i8lkBifbj32JiYmjPQUAri0I+AEAAABwDUEMf31AtD/xiESiwsLCwsLC4IMej0elUgU/CDh06FBTU5PL5cIwjM1my2Sy4IqAcVcUAAC4uiDgBwAAAMDlgRgegNESEhIy6Ep+X19fU1NT8FOA9vZ2q9WKYRiTyYyPjx+QCJCRkcHnww4JAEx8EPADAAAAAGJ4AMa3mJiYQIf5gAFFAehBgEKhoCgKG6tFAQCAqwsCfgAAAGBighgegF+54RcFNDY2og0mB30KAEUBAIxfEPADAAAA4wbE8ACAKzRoUYDf7+/u7h7wFCBQFMBiseLi4gY8BcjMzOTxeKN0EwCA4YKAHwAAABhNEMMDAEYdg8EYNJ//cosCcnJyoqOjR+MOAACDg4AfAAAAuMoghgcATAzDKQpobGzcsWNHd3e33+/HoCgAgDEGAn4AAADg0oYfwxsMBq/XG3wuxPAAgIlk0KIAn8/X09MzIBegra3NZrNhUBQAwOiBgB8AAMCvFMTwAABwtTCZzCsvCsjOzs7NzRUKhaNxBwBMTBDwAwAAmDiuTwwfERHBYrFG6x4BAGAcGbQowO129/X1BSoCmpqaLlkUkJSUhOP4KN0EAOMYBPwAAADGNIjhAQBggmGz2VelKCArK4vL5Y7STQAwPkDADwAA4HqDGB4AAMAAV14UkJ2dnZOTk5KSAkUBAARAwA8AAOAqgBgeAADAtTB0UQCqCOjs7ISiAAAGBQE/AACAwUEMDwAAYGwKFAXMmzcvcBCKAgC4EAT8AADwKwIxPAAAgInqCosCUEWATCaDogAwkUDADwAA4xvE8AAAAMAQhigKCFQEdHZ2fvrpp0qlkiAIDIoCwAQCAT8AAIw5EMMDAAAA11SgKGDZsmWBg4MWBbS2ttrtdgyKAsD4BAE/AABcD8OP4fV6vc/nCz4XYngAAADgOrisooDOzk70U/TvcqAiQCaTpaamhoWFXffpAzAICPgBAGCEIIYHAAAAfg0uVhTQ0dERqAjo7OysqKiAogAw1kDADwAAv7g+MbxEImEymaN1jwAAAAC4cmw2OycnJycnJ/jg0EUBISEhsbGxA54CZGdnczicUboJMPFBwA8AmOAghgcAAADA9THiooDgioDrVhSg1Wp5PB6fz7/WbwRGEQT8AIDxB2J4AAAAAIwjgxYFDPoU4HoWBezevfsvf/nLX//61w0bNkDrwYkKAn4AwJgAMTwAAAAAflUGfQrg9Xp7e3sHPAVoaWlxOBzY1S4K6OzsNJvNf/7zn1944YWnnnrq/vvvZ7PZV+fewJgBAT8A4FqBGB4AAAAA4LKwWKxhFgXs2bNHrVajn0ZHRwdXBMhksrS0NIFAMPR7tbe3YxhGEITBYHj44Yeff/75v/zlL+vXr4ewfyKBgB8AcBkghgcAAAAAuP6uVlFAcnIyjUZDpzc3N5Mkif5MkqRWq920adOLL764ZcuWu+++m8GAUHEigN8iAL92EMMDAAAAAIxHgz4FcLvdCoWiI8ju3bsVCoXH48EwjM/np6SkyGSylJQUhUIx4IIo7N+wYcOzzz771FNPQdg/AcDvD4AJCGJ4AAAAAIBfJzabnZWVlZWVFXyQJMne3l4U/3d2dnZ0dBw8eNDtdl94OkVRFEX19fU98MADL7300tNPP01RVCApAIw7EPADMD4MP4bX6XR+vz/4XIjhAQAAAAB+zXAcT0hISEhImDNnDjpy+vTpkpKSi42nKArDMIVCcccdd4SGhubm5kLYP05BwA/AqIEYHgAAAAAAjIqOjg4cxwM1/APQ6XQcx30+H47jaMH/+PHjZWVl13mS4MpBwA/A1XR9YvjIyEiopwIAAAAAACMml8uZTCYq7McwDMfxwMvw8PBp06ZNnTq1sLBw5syZ999/P4Zhl4z2746gf2Q4//EBjUYPCYvJKLnxviee2XBDJH5NbuQaoMxntm558s2dFa16UigrXrb+6ec2zoykX/aYQbk697362KaXdqmWfaX/fEXIBT/39R555/lXtu493aZxMsNlhYvvevyZh+bHs0Z6MxAzAHAJEMMDAAAAAIAJprOz0+v1oj/HxsaWlJQUFxcXFRUVFhaGhYWN4IIf6on3ajbnlTzfeeNnuq9/y8cwjHDpWg+/+8d7t/x+3nHldydeLgu9mndwjVDWE08tWPSKcekbOxpvz2XIdz15+/r5s5u/Pvb2IgntMsYMwin/5uVHHntfES7WOqlBR/jaP1g9e+NB0W1vfnlubb7Y3rT77/c9sHRm1UfHPr8taRhPEwYBAQb4NYIYHgAAAAAA/JqlpKQ899xzRUVFRUVF4eHh1+Q96BxJ9tInPnz6ePrGH9554ctNpfdIx3wXAO/ZVze+XCu6a997D0wLxTAs77Y3P2w6Mf2lB7esOvdWOX/YYwZh3/3Mk/XT3z79Ofu1SbMbdRcOoNSfP/LYN/1pf/r6X/cUh2AYxi+49fVPlZX5mzc9fsuCbWuGephwURCNgAkCYngAAAAAAACG6cknn7w+b4RLC4sS6Yfa6mqafPdIR5yZfp14Kv5vawMRt/HW2YFshJCC36zOfWXLtvcP/L18VdgwxwyGt+L9yls5IZiv4iIDrEd2H7Hg0esWF/yS6c9IW7w488ln93y4S7PmvugR3BGELmDsghgeAAAAAACAiYEaPIt9TPE3HznWRzIXFU0OejJBTy/MF2B1xw/XeFeVs4Y1ZlA0NufCkv0gpFGr81I0UbgweCUfD5eE41RDdUUtdt/SEdwSxDnguoIYHgAAAAAAgF8PUlNTrSRo/KLpk4axb5Svv+Y/77/94X9+qmvvNtMlacVL7t285cEyaVD9urP5y2c3v/T54Ua1mx2TO+/Opx6NeGv2Qwc9GIZL7t3X+/5CFoaRulMf/P2F93dXNPXZ8bC4nNIVG57Y/D8FwkvlxHvlrQqCJoqN4QUfpUfHRuFkR3u7kSqX0oYzZtifTjCaMELMoFEmgyn40QhlNVsojLR1d43ooiMO+P3aig9ffu3jvScbu/VOjCtJyJw89YbyRctW3FiaGjaydgLX3YTqvjiKIIYHAAAAAAAADES69a2H3/3jliP+2Btfe+E3kZeOgz3fPTZ/3ZHSFz/5/qsZMZS6ctsT9/1x4bye7069UoZK432t764s+90RwS3/2POfO6dJ/cof3/vTgy+f8dHCbt+t/WRZCIZhGKnaeffs2z5zz39+a+X9M2PJ7qPvPnTXvWWHW/cffX7mkH0DKYfF6qdoXD73vKnSuHweDSOtZguJSfFhjBlZPEwLK11Qwtnz4w8Haj2zp/03giS7Dx1s9mMYw2Ef0UVHFvCTqh13zVy3k33LC28euvWGdDHDrm44+smWh5+4/Y03HjioeOdiSQxjyUTrvnh1QQwPAAAAAAAAGAH3rttCabf99wWNFTP74S/O/WVlVtjwduWjsec9+b+bZktpGIYlzlz/rzdqjiz91z92/qn0DgkNo4y7nn7qoFF6x0cf/L5UgGEYljb/Tx8YqtNv2/nLFaz7n3zo007Bmu0fPzpXTMMwLG3hX7a+eDrjrtceeed/Tvw5cyTh1nDKEa68ZAFPuvOFP/97/pZ/3rMh/cO//aZA7GjZ+8qGl9qEQtzlYI0wxh5JuOWv+n+bt/XEPXj4wz+UsTEMwzBhfOHNm7dLLYWzPhvZNK67Cdd98ZKGH8P39/cTBBF8LsTwAAAAAAAAgOFg34y25SNdmtpPN/3m99vf+vvWxfNfnBU2jCgn5MatfTcGH+CkZyXTXQ1nW/13SJiYr/K7w0aKvXx+mSAwgiYqmZnD2Fn382vvqd37NBT7pmXzxYE3pEnK5kxifFO5Z7/ysUzZxZ880HhhAgaNcjrOX7dFB3CBMAwf1hj/uaenFD1b/99VUXr87w51/HP2MOoZMIxX/OR3x2V/e+bNvy1OfcBOj0ifsWLj9s+0v1/4bHtUzHAucKGRxGbW5sZugl6Smnz+QwZO4cqlSV84RjaP62xidF+EGB4A8P/bu/O4qMr9D+DnzM4M27AzDNsAsgyLLC4omCmpZWqiRlqalpq3urd7rW7b63oz7zVv2k3LLX+WlSWBiuWSWpihiAgyKnuyb8MwrAMMs8/8/jjXEVGRcBnAz/sPXnOeec7he4wYvs95nu8DAAAAMCTRrNxiVu76slSSsPmTlz9MzNswjnPHc0ztkm82/Gf3kczCKnmnRm+kcmqam0plIgiCULe0dBlJa7597yuRVlZWvcYSNM1yhdGk/uFZe/M0A3NE1WXVekLUz6Nyln+gL91U3iDtIYjr3QyNDU1GmlNAgAM5oD6MiLX5urV3vNlbIm3Dn/so7bmPrrdoTyxvMDACI8MGd8HBJHIcWzs2qS84dVr2yhJBr/ER5viNv9cOLowHbCRUX0xKSup9SKPR+iTqQqEwLCzsljm8nd3tRis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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Example 4...\n", + "render(rume.ipm)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/devlog/2024-07-18.ipynb b/doc/devlog/2024-07-18.ipynb new file mode 100644 index 00000000..1d95d865 --- /dev/null +++ b/doc/devlog/2024-07-18.ipynb @@ -0,0 +1,185 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# devlog 2024-07-18\n", + "\n", + "A simple demo of ADRIOs \"version 2\"." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from epymorph import *\n", + "from epymorph.adrio import acs5, us_tiger\n", + "from epymorph.geography.us_census import StateScope\n", + "from epymorph.rume import SingleStrataRume\n", + "from epymorph.simulator.data import evaluate_param\n", + "\n", + "# Look ma, no geo!\n", + "\n", + "rume = SingleStrataRume.build(\n", + " ipm=ipm_library['sirs'](),\n", + " mm=mm_library['centroids'](),\n", + " init=init.SingleLocation(location=0, seed_size=10_000),\n", + " scope=StateScope.in_states_by_code([\n", + " 'AZ', 'CO', 'NM', 'UT', 'NV', 'CA', 'OR', 'WA',\n", + " ], year=2020),\n", + " time_frame=TimeFrame.of(\"2020-01-01\", 300),\n", + " params={\n", + " 'ipm::beta': 0.4,\n", + " 'ipm::gamma': 1 / 5,\n", + " 'ipm::xi': 1 / 90,\n", + " 'mm::phi': 40.0,\n", + " 'population': acs5.Population(),\n", + " 'centroid': us_tiger.InternalPoint(),\n", + "\n", + " # Realistically, if I needed populations by age group, I would have a multistrata RUME,\n", + " # but this is just for demonstrating these ADRIOs work...\n", + " 'population_by_age_table': acs5.PopulationByAgeTable(),\n", + " 'population_00-19': acs5.PopulationByAge(0, 19),\n", + " 'population_20-59': acs5.PopulationByAge(20, 59),\n", + " 'population_60-79': acs5.PopulationByAge(60, 79),\n", + " 'geo::label': us_tiger.Name(),\n", + "\n", + " # Example: I can use a different definition of centroid!\n", + " # 'centroid': tiger.GeometricCentroid(),\n", + "\n", + " # Example: I can calculate pop density (persons per km^2) by combining ADRIOs...\n", + " # 1. get land area in m^2 from TIGER\n", + " # 2. scale that to be in km^2\n", + " # 3. use a generic pop density ADRIO which combines 'population' and 'land_area_km2'\n", + " # 'land_area_km2': adrio.Scale(tiger.LandAreaM2(), factor=1e-6),\n", + " # 'population_km2': adrio.PopulationPerKm2(),\n", + "\n", + " # Additional ACS5 attributes.\n", + " 'average_household_size': acs5.AverageHouseholdSize(),\n", + " 'dissimilarity_index': acs5.DissimilarityIndex('White', 'Native'),\n", + " 'gini_index': acs5.GiniIndex(),\n", + " 'median_age': acs5.MedianAge(),\n", + " 'median_income': acs5.MedianIncome(),\n", + " },\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ADRIO Population fetching `gpm:all::mm::population`... done (1.266 seconds)\n", + "ADRIO InternalPoint fetching `gpm:all::mm::centroid`... done (0.179 seconds)\n", + "ADRIO Population fetching `gpm:all::init::population`... done (0.000 seconds)\n", + "ADRIO Name fetching `meta::geo::label`... done (0.097 seconds)\n", + "Running simulation (BasicSimulator):\n", + "• 2020-01-01 to 2020-10-27 (300 days)\n", + "• 8 geo nodes\n", + "|####################| 100% \n", + "Runtime: 0.556s\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sim = BasicSimulator(rume)\n", + "with sim_messaging():\n", + " out = sim.run()\n", + "\n", + "plot_event(out, event_idx=rume.ipm.event_by_name(\"S->I\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " >>> population_00-19 [1836857 9986244 1405688 753880 539036 965716 1022625 1830822] \n", + "\n", + "\n", + " >>> population_20-59 [1836857 9986244 1405688 753880 539036 965716 1022625 1830822] \n", + "\n", + "\n", + " >>> population_60-79 [1836857 9986244 1405688 753880 539036 965716 1022625 1830822] \n", + "\n", + "\n", + " >>> average_household_size [2.65 2.94 2.6 2.65 2.59 2.49 3.09 2.53] \n", + "\n", + "\n", + " >>> dissimilarity_index [0.45477918 0.13515806 0.24425009 0.16504944 0.57190601 0.21115552\n", + " 0.33199027 0.24426182] \n", + "\n", + "\n", + " >>> gini_index [0.4661 0.4874 0.4565 0.4638 0.4742 0.4579 0.4245 0.4574] \n", + "\n", + "\n", + " >>> median_age [37.9 36.7 36.9 38.2 38.1 39.5 31.1 37.8] \n", + "\n", + "\n", + " >>> median_income [61529. 78672. 75231. 62043. 51243. 65667. 74197. 77006.] \n", + "\n" + ] + } + ], + "source": [ + "# Let's check out the values of some of the attributes we didn't use in the simulation...\n", + "\n", + "def show(name):\n", + " print(\"\\n\", \">>>\", name, evaluate_param(rume, name), \"\\n\")\n", + "\n", + "\n", + "show('population_00-19')\n", + "show('population_20-59')\n", + "show('population_60-79')\n", + "show('average_household_size')\n", + "show('dissimilarity_index')\n", + "show('gini_index')\n", + "show('median_age')\n", + "show('median_income')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/devlog/2024-08-13.ipynb b/doc/devlog/2024-08-13.ipynb new file mode 100644 index 00000000..4c76e012 --- /dev/null +++ b/doc/devlog/2024-08-13.ipynb @@ -0,0 +1,282 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# devlog 2024-08-13\n", + "\n", + "Testing the `@adrio_cache` method for adding cache behavior to ADRIOs." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from numpy.typing import NDArray\n", + "\n", + "from epymorph import *\n", + "from epymorph.adrio.adrio import Adrio, adrio_cache\n", + "from epymorph.geography.us_census import StateScope\n", + "from epymorph.simulator.data import evaluate_param" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test 1:\n", + "\n", + "A non-caching ADRIO will be evaluated every time." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We should see '!!! calculating cosine !!!' three times...\n", + "!!! calculating cosine !!!\n", + "!!! calculating cosine !!!\n", + "!!! calculating cosine !!!\n" + ] + } + ], + "source": [ + "class Cosine(Adrio[np.float64]):\n", + " \"\"\"Trivial ADRIO -- calculate a cosine curve.\"\"\"\n", + " requirements = [AttributeDef('gamma', float, Shapes.S)]\n", + "\n", + " def evaluate(self) -> NDArray[np.float64]:\n", + " print(\"!!! calculating cosine !!!\")\n", + " T = self.dim.days\n", + " t = np.arange(0, T)\n", + " gamma = self.data('gamma')\n", + " return gamma * np.cos(2 * np.pi * t / T)\n", + "\n", + "\n", + "rume1 = SingleStrataRume.build(\n", + " ipm_library['sirs'](), mm_library['no'](), init.NoInfection(),\n", + " StateScope.in_states(['04']),\n", + " TimeFrame.of(\"2020-01-01\", 20),\n", + " {\n", + " 'population': 100,\n", + " 'beta': Cosine(),\n", + " 'gamma': 2.0,\n", + " 'xi': 1 / 10,\n", + " }\n", + ")\n", + "\n", + "print(\"We should see '!!! calculating cosine !!!' three times...\")\n", + "sim = BasicSimulator(rume1)\n", + "out = sim.run()\n", + "out = sim.run()\n", + "out = sim.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test 2:\n", + "\n", + "A caching ADRIO will be re-evaluated if its requirements change." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We should see '!!! calculating cosine !!!' only twice...\n", + "!!! calculating cosine !!!\n", + "!!! calculating cosine !!!\n" + ] + } + ], + "source": [ + "@adrio_cache\n", + "class CachedCosine(Adrio[np.float64]):\n", + " \"\"\"Trivial ADRIO -- calculate a cosine curve.\"\"\"\n", + " requirements = [AttributeDef('gamma', float, Shapes.S)]\n", + "\n", + " def evaluate(self) -> NDArray[np.float64]:\n", + " print(\"!!! calculating cosine !!!\")\n", + " T = self.dim.days\n", + " t = np.arange(0, T)\n", + " gamma = self.data('gamma')\n", + " return gamma * np.cos(2 * np.pi * t / T)\n", + "\n", + "\n", + "rume2 = SingleStrataRume.build(\n", + " ipm_library['sirs'](), mm_library['no'](), init.NoInfection(),\n", + " StateScope.in_states(['04']),\n", + " TimeFrame.of(\"2020-01-01\", 20),\n", + " {\n", + " 'population': 100,\n", + " 'beta': CachedCosine(),\n", + " 'gamma': 2.0,\n", + " 'xi': 1 / 10,\n", + " }\n", + ")\n", + "\n", + "print(\"We should see '!!! calculating cosine !!!' only twice...\")\n", + "sim = BasicSimulator(rume2)\n", + "out = sim.run()\n", + "out = sim.run()\n", + "out = sim.run()\n", + "out = sim.run({\"gamma\": 22.0})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test 3:\n", + "\n", + "If the scope or any SimDimension changes, the value should recalculate. (This is kind of an odd use-case, to share ADRIOs between RUMEs, but I wouldn't want this to cause problems if the user did it.)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "should only see '!!! calculating cosine !!!' once here...\n", + "!!! calculating cosine !!!\n", + "\n", + "and then we should see it three more times...\n", + "!!! calculating cosine !!!\n", + "!!! calculating cosine !!!\n", + "!!! calculating cosine !!!\n" + ] + } + ], + "source": [ + "@adrio_cache\n", + "class CachedCosine2(Adrio[np.float64]):\n", + " \"\"\"Trivial ADRIO -- calculate a cosine curve.\"\"\"\n", + "\n", + " def evaluate(self) -> NDArray[np.float64]:\n", + " print(\"!!! calculating cosine !!!\")\n", + " T = self.dim.days\n", + " t = np.arange(0, T)\n", + " return 2.0 * np.cos(2 * np.pi * t / T)\n", + "\n", + "\n", + "cosine = CachedCosine2()\n", + "\n", + "# Base RUME\n", + "rume3 = SingleStrataRume.build(\n", + " ipm_library['sirs'](), mm_library['no'](), init.NoInfection(),\n", + " StateScope.in_states(['04']),\n", + " TimeFrame.of(\"2020-01-01\", 20),\n", + " {\n", + " 'population': 100,\n", + " 'beta': cosine,\n", + " 'gamma': 2.0,\n", + " 'xi': 1 / 10,\n", + " }\n", + ")\n", + "\n", + "# Nothing changed...\n", + "rume3b = SingleStrataRume.build(\n", + " ipm_library['sirs'](), mm_library['no'](), init.NoInfection(),\n", + " StateScope.in_states(['04']),\n", + " TimeFrame.of(\"2020-01-01\", 20),\n", + " {\n", + " 'population': 100,\n", + " 'beta': cosine,\n", + " 'gamma': 2.0,\n", + " 'xi': 1 / 10,\n", + " }\n", + ")\n", + "\n", + "# Change scope\n", + "rume4 = SingleStrataRume.build(\n", + " ipm_library['sirs'](), mm_library['no'](), init.NoInfection(),\n", + " StateScope.in_states(['35']),\n", + " TimeFrame.of(\"2020-01-01\", 20),\n", + " {\n", + " 'population': 100,\n", + " 'beta': cosine,\n", + " 'gamma': 2.0,\n", + " 'xi': 1 / 10,\n", + " }\n", + ")\n", + "\n", + "# Change start date\n", + "rume5 = SingleStrataRume.build(\n", + " ipm_library['sirs'](), mm_library['no'](), init.NoInfection(),\n", + " StateScope.in_states(['04']),\n", + " TimeFrame.of(\"2019-01-01\", 20),\n", + " {\n", + " 'population': 100,\n", + " 'beta': cosine,\n", + " 'gamma': 2.0,\n", + " 'xi': 1 / 10,\n", + " }\n", + ")\n", + "\n", + "# Change duration\n", + "rume6 = SingleStrataRume.build(\n", + " ipm_library['sirs'](), mm_library['no'](), init.NoInfection(),\n", + " StateScope.in_states(['04']),\n", + " TimeFrame.of(\"2020-01-01\", 99),\n", + " {\n", + " 'population': 100,\n", + " 'beta': cosine,\n", + " 'gamma': 2.0,\n", + " 'xi': 1 / 10,\n", + " }\n", + ")\n", + "\n", + "print(\"should only see '!!! calculating cosine !!!' once here...\")\n", + "res1 = evaluate_param(rume3, 'beta')\n", + "res1 = evaluate_param(rume3, 'beta')\n", + "res1 = evaluate_param(rume3, 'beta')\n", + "res1 = evaluate_param(rume3b, 'beta')\n", + "print(\"\\nand then we should see it three more times...\")\n", + "res1 = evaluate_param(rume4, 'beta')\n", + "res1 = evaluate_param(rume5, 'beta')\n", + "res1 = evaluate_param(rume6, 'beta')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/devlog/README.md b/doc/devlog/README.md index 3a565cdd..8b6da79f 100644 --- a/doc/devlog/README.md +++ b/doc/devlog/README.md @@ -18,10 +18,10 @@ This folder is a handy place to put Jupyter notebooks or other documents which h | 2023-06-01-sparsemod-example.ipynb | Frank | | Demonstration of the 'sparsemod' movement model. | | 2023-06-28.ipynb | Tyler | | Proving validity of the newly-added declarative compartment model IPM implementation. | | 2023-06-30.ipynb | Tyler | ✓ | Demonstrating the newly-added declarative compartment model IPM system. (This is a good reference for building custom IPMs, so we're keeping it current.) | -| 2023-07-06.ipynb | Tyler | ✓ | Creates the Pei Geo. (Maintained until such a time as the ADRIO system can replace it.) | -| 2023-07-07.ipynb | Tyler | ✓ | Creates the 2015 US States and US Counties Geos. (Maintained until such a time as the ADRIO system can replace them.) | -| 2023-07-12.ipynb | Tyler | ✓ | Creates the 2019 Maricopa County CBGs Geo. (Maintained until such a time as the ADRIO system can replace it.) | -| 2023-07-13.ipynb | Tyler | ✓ | Implements a compatibility matrix test: are all possible combinations of IPM/MM/GEO valid? | +| 2023-07-06.ipynb | Tyler | | Creates the Pei Geo. (Maintained until such a time as the ADRIO system can replace it.) | +| 2023-07-07.ipynb | Tyler | | Creates the 2015 US States and US Counties Geos. (Maintained until such a time as the ADRIO system can replace them.) | +| 2023-07-12.ipynb | Tyler | | Creates the 2019 Maricopa County CBGs Geo. (Maintained until such a time as the ADRIO system can replace it.) | +| 2023-07-13.ipynb | Tyler | | Implements a compatibility matrix test: are all possible combinations of IPM/MM/GEO valid? | | 2023-07-14.ipynb | Tyler | | Demonstrates filtering a geo down to a subset of its nodes. (While the motivation to do this still exists, recent changes have made this exact approach obsolete.) | | 2023-07-20-movement-probs.ipynb | Tyler | | Analyzing statistical correctness of our movement processing algorithms. | | 2023-07-24.ipynb | Tyler | | Experiments with adapting an IPM by "attaching" a function to an IPM parameter. This approach has been superseded by a design for direct support for functional parameters. | @@ -47,19 +47,24 @@ This folder is a handy place to put Jupyter notebooks or other documents which h | 2024-02-14.ipynb | Tyler | | Prep work related to the "Z-virus" workshop. (Not very organized.) | | 2024-03-01.ipynb | Tyler | | Getting the indices of IPM events and compartments by name with wildcard support. | | 2024-03-13.ipynb | Tyler | | Showing off movement data collection (NEW!) | -| 2024-03-19.ipynb | Tyler | ✓ | Create and save the `us_sw_counties_2015.geo` spec file. | +| 2024-03-19.ipynb | Tyler | | Create and save the `us_sw_counties_2015.geo` spec file. | | 2024-04-04-draw-demo.ipynb | Izaac | | Showing the new draw module for visualising IPMs (NEW!) | | 2024-04-16.ipynb | Izaac | | Showing error handling for common ipm errors (NEW!)| -| 2024-04-25.ipynb | Tyler | ✓ | Integration test: epymorph cache utilities | +| 2024-04-25.ipynb | Tyler | | Integration test: epymorph cache utilities | | 2024-05-03.ipynb | Tyler | ✓ | Integration test: loading US Census geography from TIGER | | 2024-05-09-lodes-adrio-demo.ipynb | Meaghan | | A full geo spec for testing LODES ADRIOs | | 2024-05-22.ipynb | Sachin | | Integrating particle filter with epymorph. Propagating the particles using epymorph simulation and plot the infection rates | | 2024-06-03.ipynb | Trevor | ✓ | Integration test: using dynamic geos to fetch Census data | | 2024-06-05.ipynb | Meaghan | ✓ | A user manual and basic demonstrations of calling LODES ADRIOs | | 2024-06-12.ipynb | Trevor | ✓ | Integration test: CSV file ADRIOs | -| 2024-07-03.ipynb | Trevor | | Demonstration of CDC ADRIO functionality and attributes. | +| 2024-07-03.ipynb | Trevor | ✓ | Demonstration of CDC ADRIO functionality and attributes. | | 2024-07-08.ipynb | Tyler | ✓ | Demonstrates the updated Initializers system, including library examples and custom initializers. | | 2024-07-10.ipynb | Trevor | ✓ | Integration test: Census ADRIOs | +| 2024-07-12-v0.4.ipynb | Tyler | | Comparing v0.4 to v0.5 via example. (See next also.) | +| 2024-07-12-v0.5.ipynb | Tyler | | Comparing v0.4 to v0.5 via example. (See previous also.) | +| 2024-07-12-v0.6.ipynb | Tyler | | Comparing v0.5 to v0.6 via example. (See previous also.) | +| 2024-07-18.ipynb | Tyler | | A simple demo of ADRIOs "version 2". | +| 2024-08-13.ipynb | Tyler | | Demo @adrio_cache. | ## Contributing diff --git a/epymorph/__init__.py b/epymorph/__init__.py index cb737814..0195199e 100644 --- a/epymorph/__init__.py +++ b/epymorph/__init__.py @@ -4,13 +4,13 @@ import epymorph.compartment_model as IPM import epymorph.initializer as init -from epymorph.data import geo_library, ipm_library, mm_library +from epymorph.data import ipm_library, mm_library from epymorph.data_shape import Shapes from epymorph.data_type import CentroidType, SimDType from epymorph.draw import render, render_and_save from epymorph.log.messaging import sim_messaging from epymorph.plots import plot_event, plot_pop -from epymorph.rume import Gpm, Rume, RumeSymbols +from epymorph.rume import Gpm, MultistrataRume, Rume, SingleStrataRume from epymorph.simulation import AttributeDef, TimeFrame, default_rng from epymorph.simulator.basic.basic_simulator import BasicSimulator @@ -22,7 +22,6 @@ 'IPM', 'ipm_library', 'mm_library', - 'geo_library', 'SimDType', 'CentroidType', 'Shapes', @@ -31,7 +30,8 @@ 'init', 'Rume', 'Gpm', - 'RumeSymbols', + 'SingleStrataRume', + 'MultistrataRume', 'BasicSimulator', 'sim_messaging', 'plot_event', diff --git a/epymorph/movement/__init__.py b/epymorph/adrio/__init__.py similarity index 100% rename from epymorph/movement/__init__.py rename to epymorph/adrio/__init__.py diff --git a/epymorph/adrio/acs5.py b/epymorph/adrio/acs5.py new file mode 100644 index 00000000..8f9c7545 --- /dev/null +++ b/epymorph/adrio/acs5.py @@ -0,0 +1,479 @@ +"""ADRIOs that access the US Census ACS 5-year data.""" +import re +from collections import defaultdict +from functools import cache +from json import load as load_json +from os import environ +from typing import Literal, NamedTuple, Sequence, TypeGuard + +import numpy as np +import pandas as pd +from census import Census +from numpy.typing import NDArray +from pandas import DataFrame +from typing_extensions import override + +from epymorph.adrio.adrio import Adrio, adrio_cache +from epymorph.cache import load_or_fetch_url, module_cache_path +from epymorph.data_shape import Shapes +from epymorph.error import DataResourceException +from epymorph.geography.scope import GeoScope +from epymorph.geography.us_census import (BLOCK_GROUP, COUNTY, STATE, TRACT, + BlockGroupScope, CensusScope, + CountyScope, StateScope, + StateScopeAll, TractScope, + get_census_granularity) +from epymorph.simulation import AttributeDef +from epymorph.util import filter_with_mask + +_ACS5_CACHE_PATH = module_cache_path(__name__) + +Acs5Year = Literal[2009, 2010, 2011, 2012, 2013, 2014, + 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022] +"""A supported ACS5 data year.""" + +ACS5_YEARS: Sequence[Acs5Year] = (2009, 2010, 2011, 2012, 2013, 2014, + 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022) +"""All supported ACS5 data years.""" + + +@cache +def _get_api() -> Census: + api_key = environ.get('CENSUS_API_KEY') + if api_key is None: + msg = "Census API key not found. " \ + "Please ensure you have set the environment variable 'CENSUS_API_KEY'" + raise DataResourceException(msg) + return Census(api_key) + + +@cache +def _get_vars(year: int) -> dict[str, dict]: + try: + vars_url = f"https://api.census.gov/data/{year}/acs/acs5/variables.json" + cache_path = _ACS5_CACHE_PATH / f"variables-{year}.json" + file = load_or_fetch_url(vars_url, cache_path) + return load_json(file)['variables'] + except Exception as e: + raise DataResourceException("Unable to load ACS5 variables.") from e + + +@cache +def _get_group_vars(year: int, group: str) -> list[tuple[str, dict]]: + return sorted(( + (name, attrs) + for name, attrs in _get_vars(year).items() + if attrs['group'] == group + ), key=lambda x: x[0]) + + +def _validate_scope(scope: GeoScope) -> CensusScope: + if not isinstance(scope, CensusScope): + raise DataResourceException( + "Census scope is required for acs5 attributes." + ) + if not is_acs5_year(scope.year): + raise DataResourceException( + f"{scope.year} is not a supported year for acs5 attributes." + ) + return scope + + +def is_acs5_year(year: int) -> TypeGuard[Acs5Year]: + """A type-guard function to ensure a year is a supported ACS5 year.""" + return year in ACS5_YEARS + + +def _make_acs5_queries(scope: CensusScope) -> list[dict[str, str]]: + """Formats scope geography information into dictionaries usable in census queries.""" + match scope: + case StateScopeAll(): + return [{"for": "state:*"}] + + case StateScope(includes_granularity='state', includes=includes): + return [{"for": f"state:{','.join(includes)}"}] + + case CountyScope(includes_granularity='state', includes=includes): + return [{ + "for": "county:*", + "in": f"state:{','.join(includes)}", + }] + + case CountyScope(includes_granularity='county', includes=includes): + counties_by_state: dict[str, list[str]] = defaultdict(list) + for state, county in map(COUNTY.decompose, includes): + counties_by_state[state].append(county) + return [ + {"for": f"county:{','.join(cs)}", "in": f"state:{s}"} + for s, cs in counties_by_state.items() + ] + + case TractScope(includes_granularity='state', includes=includes): + return [{ + "for": "tract:*", + "in": f"state:{','.join(includes)} county:*", + }] + + case TractScope(includes_granularity='county', includes=includes): + counties_by_state: dict[str, list[str]] = defaultdict(list) + for state, county in map(COUNTY.decompose, includes): + counties_by_state[state].append(county) + return [ + {"for": "tract:*", + "in": f"state:{s} county:{','.join(cs)}"} + for s, cs in counties_by_state.items() + ] + + case TractScope(includes_granularity='tract', includes=includes): + tracts_by_county: dict[str, list[str]] = defaultdict(list) + + for state, county, tract in map(TRACT.decompose, includes): + tracts_by_county[state + county].append(tract) + + return [ + {"for": f"tract:{','.join(tracts_by_county[state + county])}", + "in": f"state:{state} county:{county}"} + for state, county in [COUNTY.decompose(c) for c in tracts_by_county.keys()] + ] + + case BlockGroupScope(includes_granularity='state', includes=includes): + # This wouldn't normally need to be multiple queries, + # but Census API won't let you fetch CBGs for multiple states. + states = {STATE.extract(x) for x in includes} + return [ + {"for": "block group:*", "in": f"state:{s} county:* tract:*"} + for s in states + ] + + case BlockGroupScope(includes_granularity='county', includes=includes): + counties_by_state: dict[str, list[str]] = defaultdict(list) + for state, county in map(COUNTY.decompose, includes): + counties_by_state[state].append(county) + return [ + {"for": "block group:*", + "in": f"state:{s} county:{','.join(cs)} tract:*"} + for s, cs in counties_by_state.items() + ] + + case BlockGroupScope(includes_granularity='tract', includes=includes): + tracts_by_county: dict[str, list[str]] = defaultdict(list) + + for state, county, tract in map(TRACT.decompose, includes): + tracts_by_county[state + county].append(tract) + + return [ + {"for": "block group:*", + "in": f"state:{state} county:{county} tract:{','.join(tracts_by_county[state + county])}"} + for state, county in [COUNTY.decompose(c) for c in tracts_by_county.keys()] + ] + + case BlockGroupScope(includes_granularity='block group', includes=includes): + block_groups_by_tract: dict[str, list[str]] = defaultdict(list) + + for state, county, tract, block_group in map(BLOCK_GROUP.decompose, includes): + block_groups_by_tract[state + county + tract].append(block_group) + + return [ + {"for": f"block group:{'.'.join(block_groups_by_tract[state + county + tract])}", + "in": f"state:{state} county:{county} tract:{tract}"} + for state, county, tract in [TRACT.decompose(t) for t in block_groups_by_tract.keys()] + ] + + case _: + raise DataResourceException("Unsupported query.") + + +def _fetch_acs5(variables: list[str], scope: CensusScope) -> DataFrame: + census = _get_api() + queries = _make_acs5_queries(scope) + + # fetch all queries and combine results + df = pd.concat( + pd.DataFrame.from_records( + census.acs5.get(variables, geo=query, year=scope.year) + ) for query in queries + ) + if df.empty: + msg = "ACS5 query returned empty. Ensure all geographies included in your scope are supported and try again." + raise DataResourceException(msg) + + # concatenate geoid components to create 'geoid' column + columns: list[str] = { + 'state': ['state'], + 'county': ['state', 'county'], + 'tract': ['state', 'county', 'tract'], + 'block group': ['state', 'county', 'tract', 'block group'], + }[scope.granularity] + df['geoid'] = df[columns].apply(''.join, axis=1) + + # check and sort results for 1:1 match with scope + try: + return pd.DataFrame({'geoid': scope.get_node_ids()})\ + .merge(df, on='geoid', how='left', validate="1:1") + except pd.errors.MergeError: + msg = "Fetched data was not an exact match for the scope's geographies." + raise DataResourceException(msg) from None + + +@adrio_cache +class Population(Adrio[np.int64]): + """ + Retrieves an N-shaped array of integers representing the total population of each geographic node. + Data is retrieved from Census table variable B01001_001 using ACS5 5-year estimates. + """ + + @override + def evaluate(self) -> NDArray[np.int64]: + scope = _validate_scope(self.scope) + df = _fetch_acs5(['B01001_001E'], scope) + return df['B01001_001E'].to_numpy(dtype=np.int64) + + +@adrio_cache +class PopulationByAgeTable(Adrio[np.int64]): + """ + Creates a table of population data for each geographic node split into various age categories. + Data is retrieved from Census table B01001 using ACS5 5-year estimates. + """ + + @override + def evaluate(self) -> NDArray[np.int64]: + scope = _validate_scope(self.scope) + # NOTE: asking acs5 explicitly for the [B01001_001E, ...] vars + # seems to be about twice as fast as asking for group(B01001) + age_vars = [var for var, _ + in _get_group_vars(scope.year, 'B01001')] + age_vars.sort() + df = _fetch_acs5(age_vars, scope) + return df[age_vars].to_numpy(dtype=np.int64) + + +_exact_pattern = re.compile(r"^(\d+) years$") +_under_pattern = re.compile(r"^Under (\d+) years$") +_range_pattern = re.compile(r"^(\d+) (?:to|and) (\d+) years") +_over_pattern = re.compile(r"^(\d+) years and over") + + +class AgeRange(NamedTuple): + """ + Models an age range for use with ACS age-categorized data. + Unlike Python integer ranges, the `end` of the this range is inclusive. + `end` can also be None which models the "and over" part of ranges like "85 years and over". + """ + start: int + end: int | None + + def contains(self, other: 'AgeRange') -> bool: + """Is the `other` range fully contained in (or coincident with) this range?""" + if self.start > other.start: + return False + if self.end is None: + return True + if other.end is None: + return False + return self.end >= other.end + + @staticmethod + def parse(label: str) -> 'AgeRange | None': + """Parse the age range of an ACS field label; e.g.: `Estimate!!Total:!!Male:!!22 to 24 years`""" + parts = label.split("!!") + if len(parts) != 4: + return None + bracket = parts[-1] + if (m := _exact_pattern.match(bracket)) is not None: + start = int(m.group(1)) + end = start + elif (m := _under_pattern.match(bracket)) is not None: + start = 0 + end = int(m.group(1)) + elif (m := _range_pattern.match(bracket)) is not None: + start = int(m.group(1)) + end = int(m.group(2)) + elif (m := _over_pattern.match(bracket)) is not None: + start = int(m.group(1)) + end = None + else: + raise DataResourceException(f"No match for {label}") + return AgeRange(start, end) + + +@adrio_cache +class PopulationByAge(Adrio[np.int64]): + """ + Retrieves an N-shaped array of integers representing the total population + within a specified age range for each geographic node. + Data is retrieved from a population by age table constructed from Census table B01001. + """ + + POP_BY_AGE_TABLE = AttributeDef('population_by_age_table', int, Shapes.NxA) + + requirements = [POP_BY_AGE_TABLE] + + _age_range: AgeRange + + def __init__(self, age_range_start: int, age_range_end: int | None): + self._age_range = AgeRange(age_range_start, age_range_end) + + @override + def evaluate(self) -> NDArray[np.int64]: + scope = _validate_scope(self.scope) + + age_ranges = [ + AgeRange.parse(attrs['label']) + for var, attrs + in _get_group_vars(scope.year, 'B01001') + ] + + adrio_range = self._age_range + + def is_included(x: AgeRange | None) -> TypeGuard[AgeRange]: + return x is not None and adrio_range.contains(x) + + included, col_mask = filter_with_mask(age_ranges, is_included) + + # At least one var must have its start equal to the ADRIO range + if not any((x.start == adrio_range.start for x in included)): + raise DataResourceException(f"bad start {adrio_range}") + # At least one var must have its end equal to the ADRIO range + if not any((x.end == adrio_range.end for x in included)): + raise DataResourceException(f"bad end {adrio_range}") + + table = self.data(self.POP_BY_AGE_TABLE) + return table[:, col_mask].sum(axis=1) + + +@adrio_cache +class AverageHouseholdSize(Adrio[np.float64]): + """ + Retrieves an N-shaped array of floats representing the average number of people + living in each household for every geographic node. + Data is retrieved from Census table variable B25010_001 using ACS5 5-year estimates. + """ + + @override + def evaluate(self) -> NDArray[np.float64]: + scope = _validate_scope(self.scope) + df = _fetch_acs5(['B25010_001E'], scope) + return df['B25010_001E'].to_numpy(dtype=np.float64) + + +@adrio_cache +class DissimilarityIndex(Adrio[np.float64]): + """ + Calculates an N-shaped array of floats representing the amount of racial segregation between a specified + racial majority and minority groups on a scale of 0 (complete integration) to 1 (complete segregation). + Data is calculated using population data from Census table B02001 using ACS5 5-year estimates. + """ + + RaceCategory = Literal[ + 'White', 'Black', + 'Native', 'Asian', + 'Pacific Islander', 'Other' + ] + + race_variables: dict[RaceCategory, str] = { + 'White': 'B02001_002E', + 'Black': 'B02001_003E', + 'Native': 'B02001_004E', + 'Asian': 'B02001_005E', + 'Pacific Islander': 'B02001_006E', + 'Other': 'B02001_007E' + } + + majority_pop: RaceCategory + minority_pop: RaceCategory + + def __init__(self, majority_pop: RaceCategory, minority_pop: RaceCategory): + self.majority_pop = majority_pop + """The race category of the majority population""" + self.minority_pop = minority_pop + """The race category of the minority population of interest""" + + @override + def evaluate(self) -> NDArray[np.float64]: + scope = _validate_scope(self.scope) + if isinstance(scope, BlockGroupScope): + msg = "Dissimilarity index cannot be retreived for block group scope." + raise DataResourceException(msg) + + majority_var = self.race_variables[self.majority_pop] + minority_var = self.race_variables[self.minority_pop] + + df = _fetch_acs5([majority_var, minority_var], scope) + df2 = _fetch_acs5([majority_var, minority_var], + scope.lower_granularity()) + df2['geoid'] = df2['geoid'].apply( + get_census_granularity(scope.granularity).truncate) + + df.rename(columns={majority_var: 'high_majority', + minority_var: 'high_minority'}, inplace=True) + df2.rename(columns={majority_var: 'low_majority', + minority_var: 'low_minority'}, inplace=True) + + df3 = df.merge(df2, on='geoid') + + df3['score'] = abs(df3['low_minority'] / df3['high_minority'] - + df3['low_majority'] / df3['high_majority']) + df3 = df3.groupby('geoid').sum() + df3['score'] *= .5 + df3['score'] = df3['score'].replace(0., 0.5) + df3 = df3.reset_index() + + return df3['score'].to_numpy(dtype=np.float64) + + +@adrio_cache +class GiniIndex(Adrio[np.float64]): + """ + Retrieves an N-shaped array of floats representing the amount of income inequality on a scale of + 0 (perfect equality) to 1 (perfect inequality) for each geographic node. + Data is retrieved from Census table variable B19083_001 using ACS 5-year estimates. + """ + + @override + def evaluate(self) -> NDArray[np.float64]: + scope = _validate_scope(self.scope) + df = _fetch_acs5(['B19083_001E'], scope) + df['B19083_001E'] = df['B19083_001E'].astype( + np.float64).fillna(0.5).replace(-666666666, 0.5) + + # set cbg data to that of the parent tract if geo granularity = cbg + if isinstance(scope, BlockGroupScope): + print( + "Gini Index cannot be retrieved for block group level, fetching tract level data instead.") + df2 = _fetch_acs5(['B19083_001E'], scope.raise_granularity()) + df['merge_geoid'] = df['geoid'].apply(lambda x: x[:-1]) + df = df.drop(columns='B19083_001E') + + df = df.merge(df2, left_on='merge_geoid', + right_on='geoid', suffixes=(None, '_y')) + + return df['B19083_001E'].to_numpy(dtype=np.float64) + + +@adrio_cache +class MedianAge(Adrio[np.float64]): + """ + Retrieves an N-shaped array of floats representing the median age in each geographic node. + Data is retrieved from Census table variable B01002_001 using ACS 5-year estimates. + """ + + @override + def evaluate(self) -> NDArray[np.float64]: + scope = _validate_scope(self.scope) + df = _fetch_acs5(['B01002_001E'], scope) + return df['B01002_001E'].to_numpy(dtype=np.float64) + + +@adrio_cache +class MedianIncome(Adrio[np.float64]): + """ + Retrieves an N-shaped array of floats representing the median yearly income in each geographic node. + Data is retrieved from Census table variable B19013_001 using ACS 5-year estimates. + """ + + @override + def evaluate(self) -> NDArray[np.float64]: + scope = _validate_scope(self.scope) + df = _fetch_acs5(['B19013_001E'], scope) + return df['B19013_001E'].to_numpy(dtype=np.float64) diff --git a/epymorph/adrio/adrio.py b/epymorph/adrio/adrio.py new file mode 100644 index 00000000..e1977dac --- /dev/null +++ b/epymorph/adrio/adrio.py @@ -0,0 +1,83 @@ +"""Implements the base class for all ADRIOs, as well as some general-purpose ADRIO implementations.""" +import functools +from typing import TypeVar + +import numpy as np +from numpy.typing import NDArray +from typing_extensions import override + +from epymorph.data_shape import Shapes +from epymorph.simulation import AttributeDef, SimulationFunction + +T_co = TypeVar('T_co', bound=np.generic, covariant=True) +"""The result type of an Adrio.""" + + +class Adrio(SimulationFunction[NDArray[T_co]]): + """ + ADRIO (or Abstract Data Resource Interface Object) are functions which are intended to + load data from external sources for epymorph simulations. This may be from web APIs, + local files or database, or anything imaginable. + """ + + +AdrioClassT = TypeVar('AdrioClassT', bound=type[Adrio]) + + +def adrio_cache(cls: AdrioClassT) -> AdrioClassT: + """Adrio class decorator to add result-caching behavior.""" + + original_eval = cls.evaluate_in_context + cached_value: NDArray | None = None + cached_hash: int | None = None + + @functools.wraps(original_eval) + def evaluate_in_context(self, data, dim, scope, rng): + req_hashes = (data.resolve(r).data.tobytes() for r in self.requirements) + curr_hash = hash(tuple([dim, scope, *req_hashes])) + nonlocal cached_value, cached_hash + if cached_value is None or cached_hash != curr_hash: + cached_value = original_eval(self, data, dim, scope, rng) + cached_hash = curr_hash + return cached_value + + cls.evaluate_in_context = evaluate_in_context + return cls + + +class NodeId(Adrio[np.str_]): + """An ADRIO which simply gives access to the node IDs as they exist in the geo scope.""" + + @override + def evaluate(self) -> NDArray: + return self.scope.get_node_ids() + + +class Scale(Adrio[np.float64]): + """Scales the result of another ADRIO by multiplying its values by the configured factor.""" + + _parent: Adrio[np.int64 | np.float64] + _factor: float + + def __init__(self, parent: Adrio[np.int64 | np.float64], factor: float): + self._parent = parent + self._factor = factor + + @override + def evaluate(self) -> NDArray[np.float64]: + return self.defer(self._parent).astype(dtype=np.float64) * self._factor + + +class PopulationPerKm2(Adrio[np.float64]): + """Calculates population density by combining the values from `population` and `land_area_km2`.""" + + POPULATION = AttributeDef('population', int, Shapes.N) + LAND_AREA_KM2 = AttributeDef('land_area_km2', float, Shapes.N) + + requirements = [POPULATION, LAND_AREA_KM2] + + @override + def evaluate(self) -> NDArray[np.float64]: + pop = self.data(self.POPULATION) + area = self.data(self.LAND_AREA_KM2) + return (pop / area).astype(dtype=np.float64) diff --git a/epymorph/adrio/cdc.py b/epymorph/adrio/cdc.py new file mode 100644 index 00000000..8ad68a6a --- /dev/null +++ b/epymorph/adrio/cdc.py @@ -0,0 +1,532 @@ +"""ADRIOs that access data.cdc.gov website for various health data.""" +from datetime import date, timedelta +from typing import NamedTuple +from urllib.parse import quote, urlencode +from warnings import warn + +import numpy as np +from numpy.typing import NDArray +from pandas import DataFrame, concat, read_csv +from typing_extensions import override + +from epymorph.adrio.adrio import Adrio +from epymorph.error import DataResourceException +from epymorph.geography.scope import GeoScope +from epymorph.geography.us_census import (STATE, CensusGranularityName, + CensusScope, get_us_states, + state_fips_to_code) +from epymorph.simulation import TimeFrame + + +class QueryInfo(NamedTuple): + url_base: str + date_col: str + fips_col: str + data_col: str + state_level: bool = False + """Whether we are querying a dataset reporting state-level data.""" + + +def _fetch_cases(attrib_name: str, scope: CensusScope, time_frame: TimeFrame) -> NDArray[np.float64]: + """ + Fetches data from HealthData dataset reporting COVID-19 cases per 100k population. + Available between 2/24/2022 and 5/4/2023 at state and county granularities. + https://healthdata.gov/dataset/United-States-COVID-19-Community-Levels-by-County/nn5b-j5u9/about_data + """ + if time_frame.start_date < date(2022, 2, 24) or time_frame.end_date > date(2023, 5, 4): + msg = "COVID cases data is only available between 2/24/2022 and 5/4/2023." + raise DataResourceException(msg) + + info = QueryInfo("https://data.cdc.gov/resource/3nnm-4jni.csv?", + "date_updated", "county_fips", attrib_name) + + df = _api_query(info, scope.get_node_ids(), + time_frame, scope.granularity) + + df.rename(columns={'county_fips': 'fips'}, inplace=True) + + if scope.granularity == 'state': + df['fips'] = [STATE.extract(x) for x in df['fips']] + + df = df.groupby(['date_updated', 'fips']).sum() + df.reset_index(inplace=True) + + df = df.pivot(index='date_updated', columns='fips', values=info.data_col) + + dates = df.index.to_numpy(dtype='datetime64[D]') + + return np.array([ + list(zip(dates, df[col])) + for col in df.columns + ], dtype=[('date', 'datetime64[D]'), ('data', np.float64)]).T + + +def _fetch_facility_hospitalization(attrib_name: str, scope: CensusScope, time_frame: TimeFrame) -> NDArray[np.float64]: + """ + Fetches data from HealthData dataset reporting number of people hospitalized for COVID-19 + and other respiratory illnesses at facility level during manditory reporting period. + Available between 12/13/2020 and 5/10/2023 at state and county granularities. + https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/anag-cw7u/about_data + """ + if time_frame.start_date < date(2020, 12, 13) or time_frame.end_date > date(2023, 5, 10): + msg = "Facility level hospitalization data is only available between 12/13/2020 and 5/10/2023." + raise DataResourceException(msg) + + info = QueryInfo("https://healthdata.gov/resource/anag-cw7u.csv?", + "collection_week", "fips_code", attrib_name) + + df = _api_query(info, scope.get_node_ids(), time_frame, scope.granularity) + + if scope.granularity == 'state': + df['fips_code'] = [STATE.extract(x) for x in df['fips_code']] + + # if the sentinel value '-999999' appears in the data, ensure aggregated value is also -999999 + df['is_sentinel'] = df[info.data_col] == -999999 + df = df.groupby(['collection_week', 'fips_code']).agg( + {info.data_col: 'sum', 'is_sentinel': any}) + df.loc[df['is_sentinel'], info.data_col] = -999999 + + df.reset_index(inplace=True) + df = df.pivot(index='collection_week', + columns='fips_code', values=info.data_col) + + dates = df.index.to_numpy(dtype='datetime64[D]') + + return np.array([ + list(zip(dates, df[col])) + for col in df.columns + ], dtype=[('date', 'datetime64[D]'), ('data', np.float64)]).T + + +def _fetch_state_hospitalization(attrib_name: str, scope: CensusScope, time_frame: TimeFrame) -> NDArray[np.float64]: + """ + Fetches data from CDC dataset reporting number of people hospitalized for COVID-19 + and other respiratory illnesses at state level during manditory and voluntary reporting periods. + Available from 1/4/2020 to present at state granularity. Data reported voluntarily past 5/1/2024. + https://data.cdc.gov/Public-Health-Surveillance/Weekly-United-States-Hospitalization-Metrics-by-Ju/aemt-mg7g/about_data + """ + if scope.granularity != 'state': + msg = "State level hospitalization data can only be retrieved for state granularity." + raise DataResourceException(msg) + if time_frame.start_date < date(2020, 1, 4): + msg = "State level hospitalization data is only available starting 1/4/2020." + raise DataResourceException(msg) + if time_frame.end_date > date(2024, 5, 1): + warn("State level hospitalization data is voluntary past 5/1/2024.") + + info = QueryInfo("https://data.cdc.gov/resource/aemt-mg7g.csv?", + "week_end_date", "jurisdiction", attrib_name, True) + + state_mapping = state_fips_to_code(scope.year) + fips = scope.get_node_ids() + state_codes = np.array([state_mapping[x] for x in fips]) + + df = _api_query(info, state_codes, time_frame, scope.granularity) + + df = df.groupby(['week_end_date', 'jurisdiction']).sum() + df.reset_index(inplace=True) + df = df.pivot(index='week_end_date', + columns='jurisdiction', values=info.data_col) + + dates = df.index.to_numpy(dtype='datetime64[D]') + + return np.array([ + list(zip(dates, df[col])) + for col in df.columns + ], dtype=[('date', 'datetime64[D]'), ('data', np.float64)]).T + + +def _fetch_vaccination(attrib_name: str, scope: CensusScope, time_frame: TimeFrame) -> NDArray[np.float64]: + """ + Fetches data from CDC dataset reporting total COVID-19 vaccination numbers. + Available between 12/13/2020 and 5/10/2024 at state and county granularities. + https://data.cdc.gov/Vaccinations/COVID-19-Vaccinations-in-the-United-States-County/8xkx-amqh/about_data + """ + if time_frame.start_date < date(2020, 12, 13) or time_frame.end_date > date(2024, 5, 10): + msg = "Vaccination data is only available between 12/13/2020 and 5/10/2024." + raise DataResourceException(msg) + + info = QueryInfo("https://data.cdc.gov/resource/8xkx-amqh.csv?", + "date", "fips", attrib_name) + + df = _api_query(info, scope.get_node_ids(), + time_frame, scope.granularity) + + df = df.pivot(index='date', columns='fips', values=info.data_col) + + dates = df.index.to_numpy(dtype='datetime64[D]') + + return np.array([ + list(zip(dates, df[col])) + for col in df.columns + ], dtype=[('date', 'datetime64[D]'), ('data', np.float64)]).T + + +def _fetch_deaths_county(attrib_name: str, scope: CensusScope, time_frame: TimeFrame) -> NDArray[np.float64]: + """ + Fetches data from CDC dataset reporting number of deaths from COVID-19. + Available between 1/4/2020 and 4/5/2024 at state and county granularities. + https://data.cdc.gov/NCHS/AH-COVID-19-Death-Counts-by-County-and-Week-2020-p/ite7-j2w7/about_data + """ + if time_frame.start_date < date(2020, 1, 4) or time_frame.end_date > date(2024, 4, 5): + msg = "County level deaths data is only available between 1/4/2020 and 4/5/2024." + raise DataResourceException(msg) + + if scope.granularity == 'state': + info = QueryInfo("https://data.cdc.gov/resource/ite7-j2w7.csv?", + "week_ending_date", "stfips", attrib_name, True) + else: + info = QueryInfo("https://data.cdc.gov/resource/ite7-j2w7.csv?", + "week_ending_date", "fips_code", attrib_name) + + df = _api_query(info, scope.get_node_ids(), + time_frame, scope.granularity) + + if scope.granularity == 'state': + df = df.groupby(['week_ending_date', info.fips_col]).sum() + df.reset_index(inplace=True) + + df = df.pivot(index='week_ending_date', + columns=info.fips_col, values=info.data_col) + + dates = df.index.to_numpy(dtype='datetime64[D]') + + return np.array([ + list(zip(dates, df[col])) + for col in df.columns + ], dtype=[('date', 'datetime64[D]'), ('data', np.float64)]).T + + +def _fetch_deaths_state(attrib_name: str, scope: CensusScope, time_frame: TimeFrame) -> NDArray[np.float64]: + """ + Fetches data from CDC dataset reporting number of deaths from COVID-19 and other respiratory illnesses. + Available from 1/4/2020 to present at state granularity. + https://data.cdc.gov/NCHS/Provisional-COVID-19-Death-Counts-by-Week-Ending-D/r8kw-7aab/about_data + """ + if time_frame.start_date < date(2020, 1, 4): + msg = "State level deaths data is only available starting 1/4/2020." + raise DataResourceException(msg) + + fips = scope.get_node_ids() + states = get_us_states(scope.year) + state_mapping = dict(zip(states.geoid, states.name)) + state_names = np.array([state_mapping[x] for x in fips]) + + info = QueryInfo("https://data.cdc.gov/resource/r8kw-7aab.csv?", + "end_date", "state", attrib_name, True) + + df = _api_query(info, state_names, time_frame, scope.granularity) + + df = df.groupby(['end_date', 'state']).sum() + df.reset_index(inplace=True) + df = df.pivot(index='end_date', columns='state', values=info.data_col) + + dates = df.index.to_numpy(dtype='datetime64[D]') + + return np.array([ + list(zip(dates, df[col])) + for col in df.columns + ], dtype=[('date', 'datetime64[D]'), ('data', np.float64)]).T + + +def _api_query(info: QueryInfo, fips: NDArray, time_frame: TimeFrame, granularity: CensusGranularityName) -> DataFrame: + """ + Composes URLs to query API and sends query requests. + Limits each query to 10000 rows, combining several query results if this number is exceeded. + Returns pandas Dataframe containing requested data sorted by date and location fips. + """ + # query county level data with state fips codes + if granularity == 'state' and not info.state_level: + location_clauses = [ + f"starts_with({info.fips_col}, '{state}')" + for state in fips + ] + # query county or state level data with full fips codes for the respective granularity + else: + formatted_fips = ",".join(f"'{node}'" for node in fips) + location_clauses = [ + f"{info.fips_col} in ({formatted_fips})" + ] + + date_clause = f"{info.date_col} " \ + + f"between '{time_frame.start_date}T00:00:00' " \ + + f"and '{time_frame.end_date + timedelta(days=1)}T00:00:00'" + + df = concat([_query_location(info, loc_clause, date_clause) + for loc_clause in location_clauses]) + + df = df.sort_values(by=[info.date_col, info.fips_col]) + return df + + +def _query_location(info: QueryInfo, loc_clause: str, date_clause: str) -> DataFrame: + """ + Helper function for _api_query() that builds and sends queries for individual locations. + """ + current_return = 10000 + total_returned = 0 + df = DataFrame() + while current_return == 10000: + url = info.url_base + urlencode( + quote_via=quote, + safe=",()'$:", + query={ + '$select': f'{info.date_col},{info.fips_col},{info.data_col}', + '$where': f"{loc_clause} AND {date_clause}", + '$limit': 10000, + '$offset': total_returned + }) + + df = concat([df, read_csv(url, dtype={info.fips_col: str})]) + + current_return = len(df.index) - total_returned + total_returned += current_return + + return df + + +def _validate_scope(scope: GeoScope) -> CensusScope: + if not isinstance(scope, CensusScope): + msg = 'Census scope is required for CDC attributes.' + raise DataResourceException(msg) + return scope + + +class CovidCasesPer100k(Adrio[np.float64]): + """Number of COVID-19 cases per 100k population.""" + + time_frame: TimeFrame + """The time period the data encompasses.""" + + def __init__(self, time_frame: TimeFrame): + self.time_frame = time_frame + + @override + def evaluate(self) -> NDArray[np.float64]: + scope = _validate_scope(self.scope) + return _fetch_cases('covid_cases_per_100k', scope, self.time_frame) + + +class CovidHospitalizationsPer100k(Adrio[np.float64]): + """Number of COVID-19 hospitalizations per 100k population.""" + + time_frame: TimeFrame + """The time period the data encompasses.""" + + def __init__(self, time_frame: TimeFrame): + self.time_frame = time_frame + + @override + def evaluate(self) -> NDArray[np.float64]: + scope = _validate_scope(self.scope) + return _fetch_cases('covid_hospital_admissions_per_100k', scope, self.time_frame) + + +class CovidHospitalizationAvgFacility(Adrio[np.float64]): + """Weekly averages of COVID-19 hospitalizations from facility level dataset.""" + + time_frame: TimeFrame + """The time period the data encompasses.""" + + def __init__(self, time_frame: TimeFrame): + self.time_frame = time_frame + + @override + def evaluate(self) -> NDArray[np.float64]: + scope = _validate_scope(self.scope) + return _fetch_facility_hospitalization('total_adult_patients_hospitalized_confirmed_covid_7_day_avg', scope, self.time_frame) + + +class CovidHospitalizationSumFacility(Adrio[np.float64]): + """Weekly sums of all COVID-19 hospitalizations from facility level dataset.""" + + time_frame: TimeFrame + """The time period the data encompasses.""" + + def __init__(self, time_frame: TimeFrame): + self.time_frame = time_frame + + @override + def evaluate(self) -> NDArray[np.float64]: + scope = _validate_scope(self.scope) + return _fetch_facility_hospitalization('total_adult_patients_hospitalized_confirmed_covid_7_day_sum', scope, self.time_frame) + + +class InfluenzaHosptializationAvgFacility(Adrio[np.float64]): + """Weekly averages of influenza hospitalizations from facility level dataset.""" + + time_frame: TimeFrame + """The time period the data encompasses.""" + + def __init__(self, time_frame: TimeFrame): + self.time_frame = time_frame + + @override + def evaluate(self) -> NDArray[np.float64]: + scope = _validate_scope(self.scope) + return _fetch_facility_hospitalization('total_patients_hospitalized_confirmed_influenza_7_day_avg', scope, self.time_frame) + + +class InfluenzaHospitalizationSumFacility(Adrio[np.float64]): + """Weekly sums of influenza hospitalizations from facility level dataset.""" + + time_frame: TimeFrame + """The time period the data encompasses.""" + + def __init__(self, time_frame: TimeFrame): + self.time_frame = time_frame + + @override + def evaluate(self) -> NDArray[np.float64]: + scope = _validate_scope(self.scope) + return _fetch_facility_hospitalization('total_patients_hospitalized_confirmed_influenza_7_day_sum', scope, self.time_frame) + + +class CovidHospitalizationAvgState(Adrio[np.float64]): + """Weekly averages of COVID-19 hospitalizations from state level dataset.""" + + time_frame: TimeFrame + """The time period the data encompasses.""" + + def __init__(self, time_frame: TimeFrame): + self.time_frame = time_frame + + @override + def evaluate(self) -> NDArray[np.float64]: + scope = _validate_scope(self.scope) + return _fetch_state_hospitalization('avg_admissions_all_covid_confirmed', scope, self.time_frame) + + +class CovidHospitalizationSumState(Adrio[np.float64]): + """Weekly sums of COVID-19 hospitalizations from state level dataset.""" + + time_frame: TimeFrame + """The time period the data encompasses.""" + + def __init__(self, time_frame: TimeFrame): + self.time_frame = time_frame + + @override + def evaluate(self) -> NDArray[np.float64]: + scope = _validate_scope(self.scope) + return _fetch_state_hospitalization('total_admissions_all_covid_confirmed', scope, self.time_frame) + + +class InfluenzaHospitalizationAvgState(Adrio[np.float64]): + """Weekly averages of influenza hospitalizations from state level dataset.""" + + time_frame: TimeFrame + """The time period the data encompasses.""" + + def __init__(self, time_frame: TimeFrame): + self.time_frame = time_frame + + @override + def evaluate(self) -> NDArray[np.float64]: + scope = _validate_scope(self.scope) + return _fetch_state_hospitalization('avg_admissions_all_influenza_confirmed', scope, self.time_frame) + + +class InfluenzaHospitalizationSumState(Adrio[np.float64]): + """Weekly sums of influenza hospitalizations from state level dataset.""" + + time_frame: TimeFrame + """The time period the data encompasses.""" + + def __init__(self, time_frame: TimeFrame): + self.time_frame = time_frame + + @override + def evaluate(self) -> NDArray[np.float64]: + scope = _validate_scope(self.scope) + return _fetch_state_hospitalization('total_admissions_all_influenza_confirmed', scope, self.time_frame) + + +class FullCovidVaccinations(Adrio[np.float64]): + """Cumulative total number of individuals fully vaccinated for COVID-19.""" + + time_frame: TimeFrame + """The time period the data encompasses.""" + + def __init__(self, time_frame: TimeFrame): + self.time_frame = time_frame + + @override + def evaluate(self) -> NDArray[np.float64]: + scope = _validate_scope(self.scope) + return _fetch_vaccination('series_complete_yes', scope, self.time_frame) + + +class OneDoseCovidVaccinations(Adrio[np.float64]): + """Cumulative total number of individuals with at least one dose of COVID-19 vaccination.""" + + time_frame: TimeFrame + """The time period the data encompasses.""" + + def __init__(self, time_frame: TimeFrame): + self.time_frame = time_frame + + @override + def evaluate(self) -> NDArray[np.float64]: + scope = _validate_scope(self.scope) + return _fetch_vaccination('administered_dose1_recip', scope, self.time_frame) + + +class CovidBoosterDoses(Adrio[np.float64]): + """Cumulative total number of COVID-19 booster doses administered.""" + + time_frame: TimeFrame + """The time period the data encompasses.""" + + def __init__(self, time_frame: TimeFrame): + self.time_frame = time_frame + + @override + def evaluate(self) -> NDArray[np.float64]: + scope = _validate_scope(self.scope) + return _fetch_vaccination('booster_doses', scope, self.time_frame) + + +class CovidDeathsCounty(Adrio[np.float64]): + """Weekly total COVID-19 deaths from county level dataset.""" + + time_frame: TimeFrame + """The time period the data encompasses.""" + + def __init__(self, time_frame: TimeFrame): + self.time_frame = time_frame + + @override + def evaluate(self) -> NDArray[np.float64]: + scope = _validate_scope(self.scope) + return _fetch_deaths_county('covid_19_deaths', scope, self.time_frame) + + +class CovidDeathsState(Adrio[np.float64]): + """Weekly total COVID-19 deaths from state level dataset.""" + + time_frame: TimeFrame + """The time period the data encompasses.""" + + def __init__(self, time_frame: TimeFrame): + self.time_frame = time_frame + + @override + def evaluate(self) -> NDArray[np.float64]: + scope = _validate_scope(self.scope) + return _fetch_deaths_state('covid_19_deaths', scope, self.time_frame) + + +class InfluenzaDeathsState(Adrio[np.float64]): + """Weekly total influenza deaths from state level dataset.""" + + time_frame: TimeFrame + """The time period the data encompasses.""" + + def __init__(self, time_frame: TimeFrame): + self.time_frame = time_frame + + @override + def evaluate(self) -> NDArray[np.float64]: + scope = _validate_scope(self.scope) + return _fetch_deaths_state('influenza_deaths', scope, self.time_frame) diff --git a/epymorph/adrio/commuting_flows.py b/epymorph/adrio/commuting_flows.py new file mode 100644 index 00000000..095f3a8c --- /dev/null +++ b/epymorph/adrio/commuting_flows.py @@ -0,0 +1,118 @@ +"""ADRIOs that access the US Census ACS Commuting Flows files.""" +import numpy as np +from numpy.typing import NDArray +from pandas import read_excel +from typing_extensions import override + +from epymorph.adrio.adrio import Adrio +from epymorph.cache import load_or_fetch_url, module_cache_path +from epymorph.error import DataResourceException +from epymorph.geography.us_census import (BlockGroupScope, CensusScope, + StateScope, StateScopeAll, + TractScope) + +_COMMFLOWS_CACHE_PATH = module_cache_path(__name__) + + +class Commuters(Adrio[np.int64]): + """Makes an ADRIO to retrieve ACS commuting flow data.""" + + @override + def evaluate(self) -> NDArray[np.int64]: + scope = self.scope + + if not isinstance(scope, CensusScope): + msg = "Census scope is required for commuting flows data." + raise DataResourceException(msg) + + # check for invalid granularity + if isinstance(scope, TractScope | BlockGroupScope): + msg = "Commuting data cannot be retrieved for tract or block group granularities" + raise DataResourceException(msg) + + # check for valid year + year = scope.year + if year not in [2010, 2015, 2020]: + # if invalid year is close to a valid year, fetch valid data and notify user + passed_year = year + if year in range(2010, 2015): + year = 2010 + elif year in range(2015, 2020): + year = 2015 + elif year in range(2020, 2024): + year = 2020 + else: + msg = "Invalid year. Commuting data is only available for 2010-2023" + raise DataResourceException(msg) + + print( + f"Commuting data cannot be retrieved for {passed_year}, fetching {year} data instead.") + + if year != 2010: + url = f'https://www2.census.gov/programs-surveys/demo/tables/metro-micro/{year}/commuting-flows-{year}/table1.xlsx' + + # organize dataframe column names + group_fields = ['state_code', + 'county_code', + 'state', + 'county'] + + all_fields = ['res_' + field for field in group_fields] + \ + ['wrk_' + field for field in group_fields] + \ + ['workers', 'moe'] + + header_num = 7 + + else: + url = 'https://www2.census.gov/programs-surveys/demo/tables/metro-micro/2010/commuting-employment-2010/table1.xlsx' + + all_fields = ['res_state_code', 'res_county_code', 'wrk_state_code', 'wrk_county_code', + 'workers', 'moe', 'res_state', 'res_county', 'wrk_state', 'wrk_county'] + + header_num = 4 + + node_ids = scope.get_node_ids() + + # a discrepancy exists in data for Connecticut counties in 2020 and 2021 + # raise an exception if this data is requested for these years. + if year in [2020, 2021] and any(connecticut_county in node_ids for connecticut_county in ['09001', '09003', '09005', '09007', '09009', '09011', '09013', '09015']): + msg = "Commuting flows data cannot be retrieved for connecticut counties for years 2020 or 2021." + raise DataResourceException(msg) + + try: + cache_path = _COMMFLOWS_CACHE_PATH / f"{year}.xlsx" + commuter_file = load_or_fetch_url(url, cache_path) + except Exception as e: + raise DataResourceException("Unable to fetch commuting flows data.") from e + + # download communter data spreadsheet as a pandas dataframe + df = read_excel(commuter_file, header=header_num, names=all_fields, dtype={ + 'res_state_code': str, 'wrk_state_code': str, 'res_county_code': str, 'wrk_county_code': str}) + + match scope.granularity: + case 'state': + df.rename(columns={'res_state_code': 'res_geoid', + 'wrk_state_code': 'wrk_geoid'}, inplace=True) + + case 'county': + df['res_geoid'] = df['res_state_code'] + \ + df['res_county_code'] + df['wrk_geoid'] = df['wrk_state_code'] + \ + df['wrk_county_code'] + + case _: + raise DataResourceException("Unsupported query.") + + df = df[df['res_geoid'].isin(node_ids)] + df = df[df['wrk_geoid'].isin(['0' + x for x in node_ids])] + + if isinstance(scope, StateScope | StateScopeAll): + # group and aggregate data + data_group = df.groupby(['res_geoid', 'wrk_geoid']) + df = data_group.agg({'workers': 'sum'}) + df.reset_index(inplace=True) + + df = df.pivot(index='res_geoid', columns='wrk_geoid', values='workers') + df.fillna(0, inplace=True) + + return df.to_numpy(dtype=np.int64) diff --git a/epymorph/adrio/csv.py b/epymorph/adrio/csv.py new file mode 100644 index 00000000..547aaff2 --- /dev/null +++ b/epymorph/adrio/csv.py @@ -0,0 +1,310 @@ +"""ADRIOs that load data from locally available CSV files.""" +from datetime import date +from os import PathLike +from pathlib import Path +from typing import Any, Literal + +from numpy.typing import DTypeLike, NDArray +from pandas import DataFrame, Series, read_csv +from typing_extensions import override + +from epymorph.adrio.adrio import Adrio +from epymorph.error import DataResourceException, GeoValidationException +from epymorph.geography.scope import GeoScope +from epymorph.geography.us_census import (STATE, CensusScope, CountyScope, + StateScope, get_census_granularity, + get_us_counties, get_us_states, + state_code_to_fips) +from epymorph.simulation import TimeFrame + +KeySpecifier = Literal['state_abbrev', 'county_state', 'geoid'] + + +def _parse_label(key_type: KeySpecifier, scope: GeoScope, df: DataFrame, key_col: int, key_col2: int | None = None) -> DataFrame: + """ + Reads labels from a dataframe according to key type specified and replaces them + with a uniform value to sort by. + Returns dataframe with values replaced in the label column. + """ + match (key_type): + case "state_abbrev": + result = _parse_abbrev(scope, df, key_col, key_col2) + + case "county_state": + result = _parse_county_state(scope, df, key_col, key_col2) + + case "geoid": + result = _parse_geoid(scope, df, key_col, key_col2) + + _validate_result(scope, result[key_col]) + + if key_col2 is not None: + _validate_result(scope, result[key_col2]) + + return result + + +def _parse_abbrev(scope: GeoScope, df: DataFrame, key_col: int, key_col2: int | None = None) -> DataFrame: + """ + Replaces values in label column containing state abreviations (i.e. AZ) with state + fips codes and filters out any not in the specified geographic scope. + """ + if isinstance(scope, StateScope): + state_mapping = state_code_to_fips(scope.year) + df[key_col] = [state_mapping.get(x) for x in df[key_col]] + if df[key_col].isnull().any(): + raise DataResourceException("Invalid state code in key column.") + df = df[df[key_col].isin(scope.get_node_ids())] + + if key_col2 is not None: + df[key_col2] = [state_mapping.get(x) for x in df[key_col2]] + if df[key_col2].isnull().any(): + raise DataResourceException("Invalid state code in second key column.") + df = df[df[key_col2].isin(scope.get_node_ids())] + + return df + + else: + msg = "State scope is required to use state abbreviation key format." + raise DataResourceException(msg) + + +def _parse_county_state(scope: GeoScope, df: DataFrame, key_col: int, key_col2: int | None = None) -> DataFrame: + """ + Replaces values in label column containing county and state names (i.e. Maricopa, Arizona) + with state county fips codes and filters out any not in the specified geographic scope. + """ + if not isinstance(scope, CountyScope): + msg = "County scope is required to use county, state key format." + raise DataResourceException(msg) + + # make counties info dataframe + counties_info = get_us_counties(scope.year) + counties_info_df = DataFrame( + {'geoid': counties_info.geoid, 'name': counties_info.name}) + + # make states info dataframe + states_info = get_us_states(scope.year) + states_info_df = DataFrame( + {'state_geoid': states_info.geoid, 'state_name': states_info.name}) + + # merge dataframes on state geoid + counties_info_df['state_geoid'] = counties_info_df['geoid'].apply( + STATE.truncate) + counties_info_df = counties_info_df.merge( + states_info_df, how='left', on='state_geoid') + + # concatenate county, state names + counties_info_df['name'] = counties_info_df['name'] + \ + ", " + counties_info_df['state_name'] + + # merge with csv dataframe and set key column to geoid + df = df.merge(counties_info_df, how='left', left_on=key_col, right_on='name') + df[key_col] = df['geoid'] + + if key_col2 is not None: + df = df.merge(counties_info_df, how='left', left_on=key_col2, right_on='name') + df[key_col2] = df['geoid_y'] + + return df + + +def _parse_geoid(scope: GeoScope, df: DataFrame, key_col: int, key_col2: int | None = None) -> DataFrame: + """ + Replaces values in label column containing state abreviations (i.e. AZ) + with state fips codes and filters out any not in the specified geographic scope. + """ + if not isinstance(scope, CensusScope): + msg = "Census scope is required to use geoid key format." + raise DataResourceException(msg) + + granularity = get_census_granularity(scope.granularity) + if not all(granularity.matches(x) for x in df[key_col]): + raise DataResourceException("Invalid geoid in key column.") + + df = df[df[key_col].isin(scope.get_node_ids())] + if key_col2 is not None: + df = df[df[key_col2].isin(scope.get_node_ids())] + + return df + + +def _validate_result(scope: GeoScope, data: Series) -> None: + """Ensures that the key column for an attribute contains at least one entry for every node in the scope.""" + if set(data) != set(scope.get_node_ids()): + msg = "Key column missing keys for geographies in scope or contains unrecognized keys." + raise DataResourceException(msg) + + +class CSV(Adrio[Any]): + """Retrieves an N-shaped array of any type from a user-provided CSV file.""" + + file_path: PathLike + """The path to the CSV file containing data.""" + key_col: int + """Numerical index of the column containing information to identify geographies.""" + data_col: int + """Numerical index of the column containing the data of interest.""" + data_type: DTypeLike + """The data type of values in the data column.""" + key_type: KeySpecifier + """The type of geographic identifier in the key column.""" + skiprows: int | None + """Number of header rows in the file to be skipped.""" + + def __init__(self, file_path: PathLike, key_col: int, data_col: int, data_type: DTypeLike, key_type: KeySpecifier, skiprows: int | None): + self.file_path = file_path + self.key_col = key_col + self.data_col = data_col + self.data_type = data_type + self.key_type = key_type + self.skiprows = skiprows + + @override + def evaluate(self) -> NDArray[Any]: + + if self.key_col == self.data_col: + msg = "Key column and data column must not be the same." + raise GeoValidationException(msg) + + path = Path(self.file_path) + # workaround for bad pandas type definitions + skiprows: int = self.skiprows # type: ignore + if path.exists(): + df = read_csv(path, skiprows=skiprows, + header=None, dtype={self.key_col: str}) + df = _parse_label(self.key_type, self.scope, df, self.key_col) + + if df[self.data_col].isnull().any(): + msg = "Data for required geographies missing from CSV file or could not be found." + raise DataResourceException(msg) + + df.rename(columns={self.key_col: 'key'}, inplace=True) + df.sort_values(by='key', inplace=True) + return df[self.data_col].to_numpy(dtype=self.data_type) + + else: + msg = f"File {self.file_path} not found" + raise DataResourceException(msg) + + +class CSVTimeSeries(Adrio[Any]): + """Retrieves a TxN-shaped array of any type from a user-provided CSV file.""" + + file_path: PathLike + """The path to the CSV file containing data.""" + key_col: int + """Numerical index of the column containing information to identify geographies.""" + data_col: int + """Numerical index of the column containing the data of interest.""" + data_type: DTypeLike + """The data type of values in the data column.""" + key_type: KeySpecifier + """The type of geographic identifier in the key column.""" + skiprows: int | None + """Number of header rows in the file to be skipped.""" + time_frame: TimeFrame + """The time period encompassed by data in the file.""" + time_col: int + """The numerical index of the column containing time information.""" + + def __init__(self, file_path: PathLike, key_col: int, data_col: int, data_type: DTypeLike, key_type: KeySpecifier, skiprows: int | None, time_frame: TimeFrame, time_col: int): + self.file_path = file_path + self.key_col = key_col + self.data_col = data_col + self.data_type = data_type + self.key_type = key_type + self.skiprows = skiprows + self.time_frame = time_frame + self.time_col = time_col + + @override + def evaluate(self) -> NDArray[Any]: + + if self.key_col == self.data_col: + msg = "Key column and data column must not be the same." + raise GeoValidationException(msg) + + path = Path(self.file_path) + skiprows: int = self.skiprows # type: ignore + if path.exists(): + df = read_csv(path, skiprows=skiprows, + header=None, dtype={self.key_col: str}) + df = _parse_label(self.key_type, self.scope, df, self.key_col) + + if df[self.data_col].isnull().any(): + msg = "Data for required geographies missing from CSV file or could not be found." + raise DataResourceException(msg) + + df[self.time_col] = df[self.time_col].apply(date.fromisoformat) + + if any(df[self.time_col] < self.time_frame.start_date) or any(df[self.time_col] > self.time_frame.end_date): + msg = "Found time column value(s) outside of provided date range." + raise DataResourceException(msg) + + df.rename(columns={self.key_col: 'key', self.data_col: 'data', + self.time_col: 'time'}, inplace=True) + df.sort_values(by=['time', 'key'], inplace=True) + df = df.pivot(index='time', columns='key', values='data') + return df.to_numpy(dtype=self.data_type) + + else: + msg = f"File {self.file_path} not found" + raise DataResourceException(msg) + + +class CSVMatrix(Adrio[Any]): + """Recieves an NxN-shaped array of any type from a user-provided CSV file.""" + + file_path: PathLike + """The path to the CSV file containing data.""" + from_key_col: int + """Numerical index of the column containing information to identify source geographies.""" + to_key_col: int + """Numerical index of the column containing information to identify destination geographies.""" + data_col: int + """Numerical index of the column containing the data of interest.""" + data_type: DTypeLike + """The data type of values in the data column.""" + key_type: KeySpecifier + """The type of geographic identifier in the key columns.""" + skiprows: int | None + """Number of header rows in the file to be skipped.""" + + def __init__(self, file_path: PathLike, from_key_col: int, to_key_col: int, data_col: int, data_type: DTypeLike, key_type: KeySpecifier, skiprows: int | None): + self.file_path = file_path + self.from_key_col = from_key_col + self.to_key_col = to_key_col + self.data_col = data_col + self.data_type = data_type + self.key_type = key_type + self.skiprows = skiprows + + @override + def evaluate(self) -> NDArray[Any]: + + if len({self.from_key_col, self.to_key_col, self.data_col}) != 3: + msg = "From key column, to key column, and data column must all be unique." + raise GeoValidationException(msg) + + path = Path(self.file_path) + skiprows: int = self.skiprows # type: ignore + if path.exists(): + df = read_csv(path, skiprows=skiprows, header=None, dtype={ + self.from_key_col: str, self.to_key_col: str}) + df = _parse_label(self.key_type, self.scope, df, + self.from_key_col, self.to_key_col) + + df = df.pivot(index=self.from_key_col, + columns=self.to_key_col, values=self.data_col) + + df.sort_index(axis=0, inplace=True) + df.sort_index(axis=1, inplace=True) + + df.fillna(0, inplace=True) + + return df.to_numpy(dtype=self.data_type) + + else: + msg = f"File {self.file_path} not found" + raise DataResourceException(msg) diff --git a/epymorph/adrio/lodes.py b/epymorph/adrio/lodes.py new file mode 100644 index 00000000..1c2c032f --- /dev/null +++ b/epymorph/adrio/lodes.py @@ -0,0 +1,318 @@ +"""ADRIOs thta access the US Census LODES files for commuting data.""" +from pathlib import Path +from typing import Literal + +import numpy as np +import pandas as pd +from numpy.typing import NDArray +from typing_extensions import override + +from epymorph.adrio.adrio import Adrio +from epymorph.cache import load_or_fetch_url, module_cache_path +from epymorph.error import DataResourceException +from epymorph.geography.scope import GeoScope +from epymorph.geography.us_census import STATE, CensusScope, state_fips_to_code + +_LODES_CACHE_PATH = module_cache_path(__name__) + +# job type variables for use among all commuters classes +JobType = Literal[ + 'All Jobs', 'Primary Jobs', + 'All Private Jobs', 'Private Primary Jobs', + 'All Federal Jobs', 'Federal Primary Jobs' +] + +job_variables: dict[JobType, str] = { + 'All Jobs': 'JT00', + 'Primary Jobs': 'JT01', + 'All Private Jobs': 'JT02', + 'Private Primary Jobs': 'JT03', + 'All Federal Jobs': 'JT04', + 'Federal Primary Jobs': 'JT05' +} + + +def _fetch_lodes(scope: CensusScope, worker_type: str, job_type: str, year: int) -> NDArray[np.int64]: + """Fetches data from LODES commuting flow data for a given year""" + + # check for valid year input + if year not in range(2002, 2022): + msg = "Invalid year. LODES data is only available for 2002-2021" + raise DataResourceException(msg) + + # file type is main (residence in state only) by default + file_type = "main" + + # initialize variables + aggregated_data = None + geoid = scope.get_node_ids() + n_geocode = len(geoid) + geocode_to_index = {geocode: i for i, geocode in enumerate(geoid)} + geocode_len = len(geoid[0]) + data_frames = [] + # can change the lodes version, default is the most recent LODES8 + lodes_ver = "LODES8" + + if scope.granularity != 'state': + states = STATE.truncate_list(geoid) + else: + states = geoid + + # check for multiple states + if (len(states) > 1): + file_type = "aux" + + # no federal jobs in given years + if year in range(2002, 2010) and (job_type == "JT04" or job_type == "JT05"): + + msg = "Invalid year for job type, no federal jobs can be found between 2002 to 2009" + raise DataResourceException(msg) + + # LODES year and state exceptions + # exceptions can be found in this document for LODES8.1: https://lehd.ces.census.gov/data/lodes/LODES8/LODESTechDoc8.1.pdf + invalid_conditions = [ + (year in range(2002, 2010) and (job_type == "JT04" or job_type == "JT05"), + "Invalid year for job type, no federal jobs can be found between 2002 to 2009"), + + (('05' in states) and (year == 2002 or year in range(2019, 2022)), + "Invalid year for state, no commuters can be found for Arkansas in 2002 or between 2019-2021"), + + (('04' in states) and (year == 2002 or year == 2003), + "Invalid year for state, no commuters can be found for Arizona in 2002 or 2003"), + + (('11' in states) and (year in range(2002, 2010)), + "Invalid year for state, no commuters can be found for DC in 2002 or between 2002-2009"), + + (('25' in states) and (year in range(2002, 2011)), + "Invalid year for state, no commuters can be found for Massachusetts between 2002-2010"), + + (('28' in states) and (year in range(2002, 2004) or year in range(2019, 2022)), + "Invalid year for state, no commuters can be found for Mississippi in 2002, 2003, or between 2019-2021"), + + (('33' in states) and year == 2002, + "Invalid year for state, no commuters can be found for New Hampshire in 2002"), + + (('02' in states) and year in range(2017, 2022), + "Invalid year for state, no commuters can be found for Alaska in between 2017-2021") + ] + for condition, message in invalid_conditions: + if condition: + raise DataResourceException(message) + + # translate state FIPS code to state to use in URL + state_codes = state_fips_to_code(scope.year) + state_abbreviations = [state_codes.get( + fips, "").lower() for fips in states] + + for state in state_abbreviations: + + # construct the URL to fetch LODES data, reset to empty each time + url_list = [] + + # always get main file (in state residency) + url_main = f'https://lehd.ces.census.gov/data/lodes/{lodes_ver}/{state}/od/{state}_od_main_{job_type}_{year}.csv.gz' + url_list.append(url_main) + + # if there are more than one state in the input, get the aux files (out of state residence) + if file_type == "aux": + url_aux = f'https://lehd.ces.census.gov/data/lodes/{lodes_ver}/{state}/od/{state}_od_aux_{job_type}_{year}.csv.gz' + url_list.append(url_aux) + + try: + files = [ + load_or_fetch_url(u, _LODES_CACHE_PATH / Path(u).name) + for u in url_list + ] + except Exception as e: + raise DataResourceException("Unable to fetch LODES data.") from e + + unfiltered_df = [pd.read_csv(file, compression="gzip", converters={ + 'w_geocode': str, 'h_geocode': str}) for file in files] + + # go through dataframes, multiple if there are main and aux files + for df in unfiltered_df: + + # filter the rows on if they start with the prefix + filtered_rows = [df[df['h_geocode'].str.startswith( + tuple(geoid)) & df['w_geocode'].str.startswith(tuple(geoid))]] + + # add the filtered dataframe to the list of dataframes + data_frames.append(pd.concat(filtered_rows)) + + for data_df in data_frames: + # convert w_geocode and h_geocode to strings + data_df['w_geocode'] = data_df['w_geocode'].astype(str) + data_df['h_geocode'] = data_df['h_geocode'].astype(str) + + # group by w_geocode and h_geocode and sum the worker values + grouped_data = data_df.groupby( + [data_df['w_geocode'].str[:geocode_len], data_df['h_geocode'].str[:geocode_len]])[worker_type].sum() + + if aggregated_data is None: + aggregated_data = grouped_data + else: + aggregated_data = aggregated_data.add(grouped_data, fill_value=0) + + # create an empty array to store worker type values + output = np.zeros((n_geocode, n_geocode), dtype=np.int64) + + # loop through all of the grouped values and add to output + for (w_geocode, h_geocode), value in aggregated_data.items(): # type: ignore + w_index = geocode_to_index.get(w_geocode) + h_index = geocode_to_index.get(h_geocode) + output[h_index, w_index] += value + + return output + + +def _validate_scope(scope: GeoScope) -> CensusScope: + if not isinstance(scope, CensusScope): + msg = 'Census scope is required for LODES attributes.' + raise DataResourceException(msg) + + # check if the CensusScope year is the current LODES geography: 2020 + if scope.year != 2020: + msg = "GeoScope year does not match the LODES geography year." + raise DataResourceException(msg) + + return scope + + +class Commuters(Adrio[np.int64]): + """ + Creates an NxN matrix of integers representing the number of workers moving from a home GEOID to a work GEOID. + """ + + year: int + """The year the data encompasses.""" + + job_type: JobType + + def __init__(self, year: int, job_type: JobType = 'All Jobs'): + self.year = year + self.job_type = job_type + + @override + def evaluate(self) -> NDArray[np.int64]: + scope = self.scope + scope = _validate_scope(scope) + job_var = job_variables[self.job_type] + df = _fetch_lodes(scope, "S000", job_var, self.year) + return df + + +class CommutersByAge(Adrio[np.int64]): + """ + Creates an NxN matrix of integers representing the number of workers moving from a + home GEOID to a work GEOID that fall under a certain age range. + """ + + year: int + """The year the data encompasses.""" + + job_type: JobType + + AgeRange = Literal[ + '29 and Under', '30_54', + '55 and Over' + ] + + age_variables: dict[AgeRange, str] = { + '29 and Under': 'SA01', + '30_54': 'SA02', + '55 and Over': 'SA03' + } + + age_range: AgeRange + + def __init__(self, year: int, age_range: AgeRange, job_type: JobType = 'All Jobs'): + self.year = year + self.age_range = age_range + self.job_type = job_type + + @override + def evaluate(self) -> NDArray[np.int64]: + scope = self.scope + scope = _validate_scope(scope) + age_var = self.age_variables[self.age_range] + job_var = job_variables[self.job_type] + df = _fetch_lodes(scope, age_var, job_var, self.year) + return df + + +class CommutersByEarnings(Adrio[np.int64]): + """ + Creates an NxN matrix of integers representing the number of workers moving from a + home GEOID to a work GEOID that earn a certain income range monthly. + """ + + year: int + """The year the data encompasses.""" + + job_type: JobType + + EarningRange = Literal[ + '$1250 and Under', '$1251_$3333', + '$3333 and Over' + ] + + earnings_variables: dict[EarningRange, str] = { + '$1250 and Under': 'SE01', + '$1251_$3333': 'SE02', + '$3333 and Over': 'SE03' + } + + earning_range: EarningRange + + def __init__(self, year: int, earning_range: EarningRange, job_type: JobType = 'All Jobs'): + self.year = year + self.earning_range = earning_range + self.job_type = job_type + + @override + def evaluate(self) -> NDArray[np.int64]: + scope = self.scope + scope = _validate_scope(scope) + earning_var = self.earnings_variables[self.earning_range] + job_var = job_variables[self.job_type] + df = _fetch_lodes(scope, earning_var, job_var, self.year) + return df + + +class CommutersByIndustry(Adrio[np.int64]): + """ + Creates an NxN matrix of integers representing the number of workers moving from a + home GEOID to a work GEOID that work under specified industry sector. + """ + + year: int + """The year the data encompasses.""" + + job_type: JobType + + Industries = Literal[ + 'Goods Producing', 'Trade Transport Utility', + 'Other' + ] + + industry_variables: dict[Industries, str] = { + 'Goods Producing': 'SI01', + 'Trade Transport Utility': 'SI02', + 'Other': 'SI03' + } + + industry: Industries + + def __init__(self, year: int, industry: Industries, job_type: JobType = 'All Jobs'): + self.year = year + self.industry = industry + self.job_type = job_type + + @override + def evaluate(self) -> NDArray[np.int64]: + scope = self.scope + scope = _validate_scope(scope) + industry_var = self.industry_variables[self.industry] + job_var = job_variables[self.job_type] + df = _fetch_lodes(scope, industry_var, job_var, self.year) + return df diff --git a/epymorph/adrio/us_tiger.py b/epymorph/adrio/us_tiger.py new file mode 100644 index 00000000..02c57cc2 --- /dev/null +++ b/epymorph/adrio/us_tiger.py @@ -0,0 +1,138 @@ +"""ADRIOs that access the US Census TIGER geography files.""" +import numpy as np +from geopandas import GeoDataFrame +from pandas import DataFrame, to_numeric +from typing_extensions import override + +from epymorph.adrio.adrio import Adrio +from epymorph.data_type import CentroidDType, StructDType +from epymorph.error import DataResourceException +from epymorph.geography.scope import GeoScope +from epymorph.geography.us_census import CensusScope +from epymorph.geography.us_tiger import (TigerYear, get_block_groups_geo, + get_block_groups_info, + get_counties_geo, get_counties_info, + get_states_geo, get_states_info, + get_tracts_geo, get_tracts_info, + is_tiger_year) + + +def _validate_scope(scope: GeoScope) -> CensusScope: + if not isinstance(scope, CensusScope): + raise DataResourceException( + "Census scope is required for us_tiger attributes." + ) + return scope + + +def _validate_year(scope: CensusScope) -> TigerYear: + year = scope.year + if not is_tiger_year(year): + raise DataResourceException( + f"{year} is not a supported year for us_tiger attributes." + ) + return year + + +def _get_geo(scope: CensusScope) -> GeoDataFrame: + year = _validate_year(scope) + match scope.granularity: + case 'state': + gdf = get_states_geo(year) + case 'county': + gdf = get_counties_geo(year) + case 'tract': + gdf = get_tracts_geo(year) + case 'block group': + gdf = get_block_groups_geo(year) + case x: + raise DataResourceException( + f"{x} is not a supported granularity for us_tiger attributes." + ) + df = DataFrame({'GEOID': scope.get_node_ids()}) + return GeoDataFrame(df.merge(gdf, on='GEOID', how='left', sort=True)) + + +def _get_info(scope: CensusScope) -> DataFrame: + year = _validate_year(scope) + match scope.granularity: + case 'state': + gdf = get_states_info(year) + case 'county': + gdf = get_counties_info(year) + case 'tract': + gdf = get_tracts_info(year) + case 'block group': + gdf = get_block_groups_info(year) + case x: + raise DataResourceException( + f"{x} is not a supported granularity for us_tiger attributes." + ) + df = DataFrame({'GEOID': scope.get_node_ids()}) + return df.merge(gdf, on='GEOID', how='left', sort=True) + + +class GeometricCentroid(Adrio[StructDType]): + """The centroid of the geographic polygons.""" + + @override + def evaluate(self): + scope = _validate_scope(self.scope) + return _get_geo(scope)['geometry']\ + .apply(lambda x: x.centroid.coords[0])\ + .to_numpy(dtype=CentroidDType) + + +class InternalPoint(Adrio[StructDType]): + """ + The internal point provided by TIGER data. These points are selected by + Census workers so as to be guaranteed to be within the geographic polygons, + while geometric centroids have no such guarantee. + """ + + @override + def evaluate(self): + scope = _validate_scope(self.scope) + df = _get_info(scope) + return np.array([x for x in zip( + to_numeric(df['INTPTLON']), + to_numeric(df['INTPTLAT']) + )], dtype=CentroidDType) + + +class Name(Adrio[np.str_]): + """For states and counties, the proper name of the location; otherwise its GEOID.""" + + @override + def evaluate(self): + scope = _validate_scope(self.scope) + if scope.granularity in ('state', 'county'): + return _get_info(scope)['NAME'].to_numpy(dtype=np.str_) + else: + # There aren't good names for Tracts or CBGs, just use GEOID + return scope.get_node_ids() + + +class PostalCode(Adrio[np.str_]): + """For states only, the postal code abbreviation for the state ("AZ" for Arizona, and so on).""" + + @override + def evaluate(self): + scope = _validate_scope(self.scope) + if scope.granularity != 'state': + raise DataResourceException( + "PostalCode is only available at state granularity." + ) + return _get_info(scope)['STUSPS'].to_numpy(dtype=np.str_) + + +class LandAreaM2(Adrio[np.float64]): + """ + The land area of the geo node in meters-squared. This is the 'ALAND' attribute + from the TIGER data files. + """ + + @override + def evaluate(self): + scope = _validate_scope(self.scope) + return _get_info(scope)['ALAND'].to_numpy(dtype=np.float64) diff --git a/epymorph/cache.py b/epymorph/cache.py index e77dfa19..9422ba02 100644 --- a/epymorph/cache.py +++ b/epymorph/cache.py @@ -1,10 +1,16 @@ """epymorph's file caching utilities.""" from hashlib import sha256 from io import BytesIO +from math import log from os import PathLike, getenv from pathlib import Path +from shutil import rmtree +from sys import modules from tarfile import TarInfo, is_tarfile from tarfile import open as open_tarfile +from typing import Callable, NamedTuple, Sequence +from urllib.request import urlopen +from warnings import warn from platformdirs import user_cache_path @@ -24,6 +30,30 @@ def _cache_path() -> Path: CACHE_PATH = _cache_path() +def module_cache_path(name: str) -> Path: + """ + When epymorph modules need to store files in the cache, + they should use a subdirectory tree within the application's + cache path. This tree should correspond to the module's path + within epymorph. e.g.: module epymorph.adrio.acs5 will store + files at $CACHE_PATH/adrio/acs5. + (The returned value is a relative path since the cache functions + require that.) + + Usage example: + + `_TIGER_CACHE_PATH = module_cache_path(__name__)` + """ + file_name = modules[name].__file__ + if file_name is None: + return CACHE_PATH + file_path = Path(file_name).with_suffix("") + root = file_path.parent + while root.name != "epymorph": + root = root.parent + return file_path.relative_to(root) + + class FileError(Exception): """Error during a file operation.""" @@ -60,7 +90,7 @@ class CacheWarning(Warning): def save_file(to_path: str | PathLike[str], file: BytesIO) -> None: """ - Save a single file. `to_path` can be absolute or relative; relative paths will be reoslved + Save a single file. `to_path` can be absolute or relative; relative paths will be resolved against the current working directory. Folders in the path which do not exist will be created automatically. """ @@ -222,14 +252,22 @@ def load_bundle(from_path: str | PathLike[str], version_at_least: int = -1) -> d def _resolve_cache_path(path: str | PathLike[str]) -> Path: cache_path = Path(path) if cache_path.is_absolute(): - msg = "When saving to or loading from the cache, please supply a relative path." - raise ValueError(msg) - return CACHE_PATH.joinpath(cache_path).resolve() + raise ValueError( + "When saving to or loading from the cache, please supply a relative path." + ) + resolved = CACHE_PATH.joinpath(cache_path).resolve() + if not resolved.is_relative_to(CACHE_PATH): + # Ensure the resolved path is still inside CACHE_PATH. + raise ValueError( + "When saving to or loading from the cache, please supply a relative path." + ) + return resolved def save_file_to_cache(to_path: str | PathLike[str], file: BytesIO) -> None: """ Save a single file to the cache (overwriting the existing file, if any). + This is a low-level building block. """ save_file(_resolve_cache_path(to_path), file) @@ -237,6 +275,7 @@ def save_file_to_cache(to_path: str | PathLike[str], file: BytesIO) -> None: def load_file_from_cache(from_path: str | PathLike[str]) -> BytesIO: """ Load a single file from the cache. + This is a low-level building block. """ try: return load_file(_resolve_cache_path(from_path)) @@ -244,6 +283,44 @@ def load_file_from_cache(from_path: str | PathLike[str]) -> BytesIO: raise CacheMiss() from e +def load_or_fetch(cache_path: Path, fetch: Callable[[], BytesIO]) -> BytesIO: + """ + Attempts to load a file from the cache. If it doesn't exist, uses the provided + fetch method to load the file, then attempts to save the file to the cache for + next time. (This is a higher-level but still generic building block.) + Any exceptions raised by `fetch` will not be caught in this method. + """ + try: + # Try to load from cache. + return load_file_from_cache(cache_path) + except CacheMiss: + # On cache miss, fetch file contents. + file = fetch() + # And attempt to save the file to the cache for next time. + try: + save_file_to_cache(cache_path, file) + except Exception as e: + # Failure to save to the cache is not worth stopping the program: + # raise a warning. + warn( + f"Unable to save file to the cache ({cache_path}). Cause:\n{e}", + CacheWarning + ) + return file + + +def load_or_fetch_url(url: str, cache_path: Path) -> BytesIO: + """ + Attempts to load a file from the cache. If it doesn't exist, fetches + the file contents from the given URL, then attempts to save the file to the cache + for next time. + """ + def fetch_url(): + with urlopen(url) as f: + return BytesIO(f.read()) + return load_or_fetch(cache_path, fetch_url) + + def save_bundle_to_cache(to_path: str | PathLike[str], version: int, files: dict[str, BytesIO]) -> None: """ Save a tar bundle of files to the cache (overwriting the existing file, if any). @@ -267,3 +344,114 @@ def load_bundle_from_cache(from_path: str | PathLike[str], version_at_least: int return load_bundle(_resolve_cache_path(from_path), version_at_least) except FileError as e: raise CacheMiss() from e + + +#################### +# Cache Management # +#################### + + +# https://en.wikipedia.org/wiki/Metric_prefix +_suffixes = ('B', 'kiB', 'MiB', 'GiB', 'TiB', 'PiB', 'EiB', 'ZiB', 'YiB', 'RiB', 'QiB') + + +def format_file_size(size: int) -> str: + """Format a file size given in bytes in 1024-based-unit representation.""" + if size < 0: + raise ValueError("size cannot be less than zero.") + if size < 1024: + return f"{size} {_suffixes[0]}" + magnitude = int(log(size, 1024)) + if magnitude >= len(_suffixes): + raise ValueError("size is too large to format.") + fsize = size / pow(1024, magnitude) + return f"{fsize:.1f} {_suffixes[magnitude]}" + + +class Directory(NamedTuple): + """A directory.""" + name: str + """The directory name.""" + size: int + """The combined size of all of this directory's children.""" + children: "Sequence[FileTree]" + """The directory's children, which may be files or nested directories.""" + + +class File(NamedTuple): + """A file.""" + name: str + """The file name.""" + size: int + """The file size.""" + + +FileTree = Directory | File +"""Nodes in a file tree are either directories or files.""" + + +def cache_inventory() -> Directory: + """Lists the contents of epymorph's cache as a FileTree.""" + def recurse(directory: Path) -> Directory: + children = [] + size = 0 + for path in directory.iterdir(): + if path.is_symlink(): + # Ignore symlinks. + continue + if path.is_file(): + file_size = path.stat().st_size + children.append(File(path.name, file_size)) + size += file_size + elif path.is_dir(): + d = recurse(path) + children.append(d) + size += d.size + return Directory(directory.name, size, children) + + if not CACHE_PATH.exists(): + return Directory(CACHE_PATH.name, 0, []) + return recurse(CACHE_PATH) + + +def cache_remove_confirmation(path: str | PathLike[str]) -> tuple[Path, Callable[[], None]]: + """ + Creates a function which removes a directory or file from the cache. + Also returns the resolved path to the thing that will be removed; + this allows the application to confirm the removal. + """ + try: + # This makes sure we don't delete things outside of the cache path. + to_remove = _resolve_cache_path(path) + except ValueError as e: + raise FileError(str(e)) from None + if not to_remove.exists(): + raise FileError(f"Given path is not in the cache: {to_remove}") + + def confirm_remove() -> None: + # Remove the target file/dir + if to_remove.is_file(): + to_remove.unlink() + else: + rmtree(to_remove) + + # Remove any newly-empty parent directories, up to the cache dir + parents = [p for p in to_remove.parents + if p.is_relative_to(CACHE_PATH) + and p != CACHE_PATH] + for p in parents: + if any(p.iterdir()): + break # parent not empty, we can stop + p.rmdir() # parent is empty + + # We may need to replace the cache dir if we just deleted it. + CACHE_PATH.mkdir(parents=True, exist_ok=True) + + return to_remove, confirm_remove + + +def cache_remove(path: str | PathLike[str]) -> None: + """Removes a directory or file from the cache.""" + # This is the "no confirmation" version of `cache_remove_confirmation` + _, confirm_remove = cache_remove_confirmation(path) + confirm_remove() diff --git a/epymorph/cli/cache.py b/epymorph/cli/cache.py index f37db95f..12ff9df3 100644 --- a/epymorph/cli/cache.py +++ b/epymorph/cli/cache.py @@ -1,197 +1,64 @@ """ Implements the `cache` subcommands executed from __main__. """ -import os from argparse import _SubParsersAction -from pathlib import Path -from epymorph.cache import CACHE_PATH -from epymorph.data import adrio_maker_library, geo_library, geo_library_dynamic -from epymorph.geo import cache -from epymorph.geo.static import StaticGeoFileOps as F +from epymorph.cache import (CACHE_PATH, Directory, FileError, cache_inventory, + cache_remove_confirmation, format_file_size) def define_argparser(command_parser: _SubParsersAction): - """ - Define `cache` subcommand. - ex: `epymorph cache ` - """ + """Define `cache` subcommand.""" p = command_parser.add_parser( 'cache', - help='cache geos and access geo cache information') + help="manage epymorph's file cache") sp = p.add_subparsers( title='cache_commands', dest='cache_commands', required=True) - fetch_command = sp.add_parser( - 'fetch', - help='fetch and cache data for a geo') - fetch_command.add_argument( - 'geo', - type=str, - help='the name of the geo to fetch; must include a geo path if not already in the library') - fetch_command.add_argument( - '-p', '--path', - help='(optional) the path to a geo spec file not in the library' - ) - fetch_command.add_argument( - '-f', '--force', - action='store_true', - help='(optional) include this flag to force an override of previously cached data') - fetch_command.set_defaults(handler=lambda args: fetch( - geo_name_or_path=args.geo, - force=args.force - )) + list_command = sp.add_parser( + "list", help="list the contents of the cache") + list_command.set_defaults(handler=lambda args: handle_list()) remove_command = sp.add_parser( - 'remove', - help="remove a geo's data from the cache") + "remove", help="remove a file or folder from the cache") remove_command.add_argument( - 'geo', + "path", type=str, - help='the name of a geo from the library') - remove_command.set_defaults(handler=lambda args: remove( - geo_name=args.geo + help="the relative path to a file or folder in the cache") + remove_command.set_defaults(handler=lambda args: handle_remove( + path=args.path )) - list_command = sp.add_parser( - 'list', - help='list the names of all currently cached geos') - list_command.set_defaults(handler=lambda args: print_geos()) - - clear_command = sp.add_parser( - 'clear', - help='clear the cache') - clear_command.set_defaults(handler=lambda args: clear()) - - export_command = sp.add_parser( - 'export', - help='export geo as a .geo.tar file') - export_command.add_argument( - 'geo', - type=str, - help='the name of a geo or the path to a geo spec file') - export_command.add_argument( - '-o', '--out', - type=str, - help='(optional) the directory in which to write the file') - export_command.add_argument( - '-r', '--rename', - type=str, - help='(optional) an override for the name of the file') - export_command.add_argument( - '-i', '--ignore_cache', - action='store_true', - help='(optional) do not add this geo to the local cache') - export_command.set_defaults(handler=lambda args: export( - geo_name_or_path=args.geo, - out=args.out, - rename=args.rename, - ignore_cache=args.ignore_cache - )) - -# Exit codes: -# - 0 success -# - 1 geo not found -# - 2 empty cache - - -def fetch(geo_name_or_path: str, force: bool) -> int: - """CLI command handler: cache dynamic geo data.""" - # split geo name and path - if geo_name_or_path in geo_library_dynamic: - geo_name = geo_name_or_path - geo_path = None - elif os.path.exists(Path(geo_name_or_path).expanduser()): - geo_path = Path(geo_name_or_path).expanduser() - geo_name = geo_path.stem - else: - raise cache.GeoCacheException("Specified geo not found.") +def handle_list() -> int: + """CLI command handler: cache list.""" + def print_folders_in(directory: Directory, indent: str = " "): + child_dirs = (d for d in directory.children if isinstance(d, Directory)) + for x in sorted(child_dirs, key=lambda x: x.name): + print(f"{indent}- {x.name} ({format_file_size(x.size)})") + print_folders_in(x, indent + " ") - # cache geo according to information passed - file_path = CACHE_PATH / F.to_archive_filename(geo_name) - if geo_path is not None and geo_name in geo_library: - msg = f"A geo named {geo_name} is already present in the library. Please use the existing geo or change the file name." - raise cache.GeoCacheException(msg) - choice = 'y' - if os.path.exists(file_path) and not force: - choice = input(f'{geo_name} is already cached, overwrite? [y/n] ') - if force or choice == 'y': - try: - cache.fetch(geo_name_or_path, geo_library_dynamic, adrio_maker_library) - print("geo sucessfully cached.") - except cache.GeoCacheException as e: - print(e) - return 1 # exit code: geo not found + cache = cache_inventory() + print(f"epymorph cache is using {format_file_size(cache.size)} ({CACHE_PATH})") + print_folders_in(cache) return 0 # exit code: success -def export(geo_name_or_path: str, out: str | None, rename: str | None, ignore_cache: bool) -> int: - """CLI command handler: export compressed geo to a location outside the cache.""" - # split geo name and path - if geo_name_or_path in geo_library_dynamic or os.path.exists(CACHE_PATH / F.to_archive_filename(geo_name_or_path)): - geo_name = geo_name_or_path - geo_path = CACHE_PATH / F.to_archive_filename(geo_name) - elif os.path.exists(Path(geo_name_or_path).expanduser()): - geo_path = Path(geo_name_or_path).expanduser() - geo_name = geo_path.stem - else: - raise cache.GeoCacheException("Specified geo not found.") - - cache.export(geo_name, geo_path, out, rename, ignore_cache, - geo_library_dynamic, adrio_maker_library) - - print("Geo successfully exported.") - - return 0 # exit code: success - - -def remove(geo_name: str) -> int: - """CLI command handler: remove geo from cache""" +def handle_remove(path: str) -> int: + """CLI command handler: remove a file or folder from the cache.""" try: - cache.remove(geo_name) - print(f'{geo_name} removed from cache.') - return 0 # exit code: success - except cache.GeoCacheException as e: - print(e) - return 1 # exit code: not found - - -def print_geos() -> int: - """CLI command handler: print geo cache information""" - geos = cache.list_geos() - n = len(geos) - if n > 0: - print( - f"epymorph geo cache contains {n} geo{('s' if n > 1 else '')} " - f"totaling {cache.get_total_size()} ({CACHE_PATH})" - ) - for (name, file_size) in geos: - print(f"* {name} ({cache.format_size(file_size)})") - else: - print(f'epymorph geo cache ({CACHE_PATH}) is empty') - return 0 # exit code: success - - -def clear() -> int: - """CLI command handler: clear geo cache""" - geos = cache.list_geos() - if len(geos) > 0: - print( - f'The following geos will be removed from the cache ({CACHE_PATH}) and free {cache.get_total_size()} of space:') - for (name, file_size) in geos: - print(f"* {name} ({cache.format_size(file_size)})") - choice = input('proceed? [y/n] ') - if choice == 'y': - cache.clear() - print('cleared geo cache.') + to_remove, confirm_remove = cache_remove_confirmation(path) + if to_remove.is_dir(): + print(f"This will delete all cache entries at {to_remove}") else: - print('cache clear aborted.') - + print(f"This will delete the cached file {to_remove}") + response = input("Are you sure? [y/N]: ") + if response.lower() in ("y", "yes"): + confirm_remove() return 0 # exit code: success - else: - print(f'epymorph geo cache ({CACHE_PATH}) is empty, nothing to clear.') - return 2 # exit code: empty cache + except FileError as e: + print(f"Error: {e}") + return 1 # exit code: failed diff --git a/epymorph/compartment_model.py b/epymorph/compartment_model.py index 95e00c6f..61c34871 100644 --- a/epymorph/compartment_model.py +++ b/epymorph/compartment_model.py @@ -3,18 +3,23 @@ This represents disease mechanics using a compartmental model for tracking populations as groupings of integer-numbered individuals. """ +import dataclasses import re +from abc import ABC, ABCMeta, abstractmethod from dataclasses import dataclass, field from functools import cached_property -from typing import Iterable, Iterator, OrderedDict, Sequence +from typing import (Any, Callable, Iterable, Iterator, OrderedDict, Sequence, + Type) from sympy import Expr, Float, Integer, Symbol from epymorph.database import AbsoluteName from epymorph.error import IpmValidationException -from epymorph.simulation import AttributeDef +from epymorph.simulation import (DEFAULT_STRATA, META_STRATA, AttributeDef, + gpm_strata) from epymorph.sympy_shim import simplify, simplify_sum, substitute, to_symbol -from epymorph.util import acceptable_name, iterator_length +from epymorph.util import (acceptable_name, are_instances, are_unique, + iterator_length) ############################################################ # Model Transitions @@ -121,7 +126,7 @@ def _remap_fork(f: ForkDef, symbol_mapping: dict[Symbol, Symbol]) -> ForkDef: ) -def remap_transition(t: TransitionDef, symbol_mapping: dict[Symbol, Symbol]) -> TransitionDef: +def _remap_transition(t: TransitionDef, symbol_mapping: dict[Symbol, Symbol]) -> TransitionDef: """Replaces all symbols used in the transition using substitution from `symbol_mapping`.""" match t: case EdgeDef(): @@ -160,65 +165,80 @@ def quick_compartments(symbol_names: str) -> list[CompartmentDef]: ############################################################ -# Compartment Symbols +# Compartment Models ############################################################ -@dataclass(frozen=True) class ModelSymbols: - """ - Keeps track of the symbols used in constructing an IPM. - These symbols are necessary for defining the model's transition rate expressions. - """ - compartments: Sequence[CompartmentDef] - """The compartments of a model.""" - attributes: OrderedDict[AbsoluteName, AttributeDef] - """The attributes of a model.""" - compartment_symbols: Sequence[Symbol] - """Compartment symbols in definition order.""" - attribute_symbols: Sequence[Symbol] - """Attribute symbols in definition order.""" + """IPM symbols needed in defining the model's transition rate expressions.""" + all_compartments: Sequence[Symbol] + """Compartment symbols in definition order.""" + all_requirements: Sequence[Symbol] + """Requirements symbols in definition order.""" + + _csymbols: dict[str, Symbol] + """Mapping of compartment name to symbol.""" + _rsymbols: dict[str, Symbol] + """Mapping of requirement name to symbol.""" + + def __init__(self, + compartments: Sequence[tuple[str, str]], + requirements: Sequence[tuple[str, str]]): + # NOTE: the arguments here are tuples of name and symbolic name; this is redundant for + # single-strata models, but allows multistrata models to keep fine-grained control over + # symbol substitution while allowing the user to refer to the names they already know. + cs = [(n, to_symbol(s)) for n, s in compartments] + rs = [(n, to_symbol(s)) for n, s in requirements] + self.all_compartments = [s for _, s in cs] + self.all_requirements = [s for _, s in rs] + self._csymbols = dict(cs) + self._rsymbols = dict(rs) + + def compartments(self, *names: str) -> Sequence[Symbol]: + """Select compartment symbols by name.""" + return [self._csymbols[n] for n in names] + + def requirements(self, *names: str) -> Sequence[Symbol]: + """Select requirement symbols by name.""" + return [self._rsymbols[n] for n in names] + + +class BaseCompartmentModel(ABC): + """Shared base-class for compartment models.""" + + compartments: Sequence[CompartmentDef] = () + """The compartments of the model.""" + + requirements: Sequence[AttributeDef] = () + """The attributes required by the model.""" + + # NOTE: these two attributes are coded as such so that overriding + # this class is simpler for users. Normally I'd make them properties, + # -- since they really should not be modified after creation -- + # but this would increase the implementation complexity. + # And to avoid requiring users to call the initializer, the rest + # of the attributes are cached_properties which initialize lazily. -class CompartmentModel: - """ - A compartment model definition and its corresponding metadata. - Effectively, a collection of compartments, transitions between compartments, - and the data parameters which are required to compute the transitions. - """ - - _symbols: ModelSymbols - _transitions: list[TransitionDef] - - def __init__(self, symbols: ModelSymbols, transitions: list[TransitionDef]): - self._symbols = symbols - self._transitions = transitions - self._validate() - - @property + @cached_property + @abstractmethod def symbols(self) -> ModelSymbols: - """The symbols used in the model.""" - return self._symbols + """The symbols which represent parts of this model.""" - @property + @cached_property + @abstractmethod def transitions(self) -> Sequence[TransitionDef]: """The transitions in the model.""" - return self._transitions - @property - def compartments(self) -> Sequence[CompartmentDef]: - """The compartments in the model.""" - return self.symbols.compartments + @cached_property + @abstractmethod + def requirements_dict(self) -> OrderedDict[AbsoluteName, AttributeDef]: + """The attributes required by this model.""" @cached_property def num_compartments(self) -> int: """The number of compartments in this model.""" - return len(self.symbols.compartments) - - @property - def attributes(self) -> OrderedDict[AbsoluteName, AttributeDef]: - """The attributes required by this model.""" - return self.symbols.attributes + return len(self.compartments) @cached_property def events(self) -> Sequence[EdgeDef]: @@ -308,73 +328,307 @@ def compartment_by_name(self, name: str) -> int: msg = f"No matching compartment found for name: {name}" raise ValueError(msg) from None - def _validate(self) -> None: - if len(self.symbols.compartments) == 0: - msg = "CompartmentModel must contain at least one compartment." - raise IpmValidationException(msg) - # Extract the set of compartments used by any transition. +############################################################ +# Single-strata Compartment Models +############################################################ + + +class CompartmentModelClass(ABCMeta): + """ + The metaclass for user-defined CompartmentModel classes. + Used to verify proper class implementation. + """ + def __new__( + mcs: Type['CompartmentModelClass'], + name: str, + bases: tuple[type, ...], + dct: dict[str, Any], + ) -> 'CompartmentModelClass': + # Skip these checks for known base classes: + if name in ("BaseCompartmentModel", "CompartmentModel"): + return super().__new__(mcs, name, bases, dct) + + # Check model compartments. + cmps = dct.get("compartments") + if cmps is None or not isinstance(cmps, (list, tuple)): + raise TypeError( + f"Invalid compartments in {name}: please specify as a list or tuple." + ) + if len(cmps) == 0: + raise TypeError( + f"Invalid compartments in {name}: please specify at least one compartment." + ) + if not are_instances(cmps, CompartmentDef): + raise TypeError( + f"Invalid compartments in {name}: must be instances of CompartmentDef." + ) + if not are_unique(c.name for c in cmps): + raise TypeError( + f"Invalid compartments in {name}: compartment names must be unique." + ) + # Make compartments immutable. + dct["compartments"] = tuple(cmps) + + # Check transitions... we have to instantiate the class. + cls = super().__new__(mcs, name, bases, dct) + instance = cls() + + trxs = instance.transitions + + # transitions cannot have the source and destination both be exogenous; this would be madness. + if any(edge.compartment_from in exogenous_states and edge.compartment_to in exogenous_states + for edge in _as_events(trxs)): + raise TypeError( + f"Invalid transitions in {name}: " + "transitions cannot use exogenous states (BIRTH/DEATH) as both source and destination." + ) + + # Extract the set of compartments used by transitions. trx_comps = set( compartment - for e in _as_events(self.transitions) + for e in _as_events(trxs) for compartment in [e.compartment_from, e.compartment_to] # don't include exogenous states in the compartment set if compartment not in exogenous_states ) - # Extract the set of symbols referenced by any transition rate expression. - # This includes compartment symbols. - trx_attrs = set( + # Extract the set of requirements used by transition rate expressions + # by taking all used symbols and subtracting compartment symbols. + trx_reqs = set( symbol - for e in _as_events(self.transitions) + for e in _as_events(trxs) for symbol in e.rate.free_symbols if isinstance(symbol, Symbol) ).difference(trx_comps) - # transitions cannot have the source and destination both be exogenous; this would be madness. - if any((edge.compartment_from in exogenous_states and edge.compartment_to in exogenous_states - for edge in _as_events(self.transitions))): - msg = "Transitions cannot use exogenous states (BIRTH/DEATH) as both source and destination." - raise IpmValidationException(msg) - - # transitions_compartments minus symbols_compartments should be empty - missing_comps = trx_comps.difference(self.symbols.compartment_symbols) + # transition compartments minus declared compartments should be empty + missing_comps = trx_comps.difference(instance.symbols.all_compartments) if len(missing_comps) > 0: - msg = "Transitions reference compartments which were not declared as symbols.\n" \ - f"Missing states: {', '.join(map(str, missing_comps))}" - raise IpmValidationException(msg) + raise TypeError( + f"Invalid transitions in {name}: " + "transitions reference compartments which were not declared.\n" + f"Missing compartments: {', '.join(map(str, missing_comps))}" + ) + + # transition requirements minus declared requirements should be empty + missing_reqs = trx_reqs.difference(instance.symbols.all_requirements) + if len(missing_reqs) > 0: + raise TypeError( + f"Invalid transitions in {name}: " + "transitions reference requirements which were not declared.\n" + f"Missing requirements: {', '.join(map(str, missing_reqs))}" + ) - # transitions_attributes minus symbols_attributes should be empty - missing_attrs = trx_attrs.difference(self.symbols.attribute_symbols) - if len(missing_attrs) > 0: - msg = "Transitions reference attributes which were not declared as symbols.\n" \ - f"Missing attributes: {', '.join(map(str, missing_attrs))}" - raise IpmValidationException(msg) + return cls + + +class CompartmentModel(BaseCompartmentModel, ABC, metaclass=CompartmentModelClass): + """ + A compartment model definition and its corresponding metadata. + Effectively, a collection of compartments, transitions between compartments, + and the data parameters which are required to compute the transitions. + """ + + @cached_property + def symbols(self) -> ModelSymbols: + """The symbols which represent parts of this model.""" + return ModelSymbols( + [(c.name, c.name) for c in self.compartments], + [(r.name, r.name) for r in self.requirements]) + + @cached_property + def requirements_dict(self) -> OrderedDict[AbsoluteName, AttributeDef]: + """The attributes required by this model.""" + return OrderedDict([ + (AbsoluteName(gpm_strata(DEFAULT_STRATA), "ipm", r.name), r) + for r in self.requirements + ]) + + @cached_property + def transitions(self) -> Sequence[TransitionDef]: + """The transitions in the model.""" + return self.edges(self.symbols) + + @abstractmethod + def edges(self, symbols: ModelSymbols) -> Sequence[TransitionDef]: + """ + When implementing a CompartmentModel, override this method + to build the transition edges between compartments. You are + given a reference to this model's symbols library so you can + build expressions for the transition rates. + """ ############################################################ -# Function-based creation API +# Multi-strata Compartment Models ############################################################ -def create_symbols(compartments: Sequence[CompartmentDef], attributes: Sequence[AttributeDef]) -> ModelSymbols: - """Create a symbols object by combining compartment and attribute definitions.""" - csym = [to_symbol(c.name) for c in compartments] - asym = [to_symbol(a.name) for a in attributes] - return ModelSymbols( - compartments=list(compartments), - attributes=OrderedDict([ - (AbsoluteName("gpm:all", "ipm", a.name), a) - for a in attributes - ]), - compartment_symbols=csym, - attribute_symbols=asym, - ) +class MultistrataModelSymbols(ModelSymbols): + """IPM symbols needed in defining the model's transition rate expressions.""" + + all_meta_requirements: Sequence[Symbol] + """Meta-requirement symbols in definition order.""" + _msymbols: dict[str, Symbol] + """Mapping of meta requirements name to symbol.""" -def create_model(symbols: ModelSymbols, transitions: Sequence[TransitionDef]) -> CompartmentModel: + strata: Sequence[str] + """The strata names used in this model.""" + + _strata_symbols: dict[str, ModelSymbols] """ - Construct a compartment model with the given set of symbols and the given transitions. - `symbols` must include all of the symbols used in the transition definitions: all compartments and all attributes. - Raises an IpmValidationException if a valid IPM cannot be constructed from the arguments. + Mapping of strata name to the symbols of that strata. + The symbols within use their original names. """ - return CompartmentModel(symbols, list(transitions)) + + def __init__(self, + strata: Sequence[tuple[str, CompartmentModel]], + meta_requirements: Sequence[AttributeDef]): + # These are all tuples of: + # (original name, strata name, symbolic name) + # where the symbolic name is disambiguated by appending + # the strata it belongs to. + cs = [ + (c.name, strata_name, f"{c.name}_{strata_name}") + for strata_name, ipm in strata + for c in ipm.compartments + ] + rs = [ + (r.name, strata_name, f"{r.name}_{strata_name}") + for strata_name, ipm in strata + for r in ipm.requirements + ] + ms = [ + (r.name, "meta", f"{r.name}_meta") + for r in meta_requirements + ] + + super().__init__( + compartments=[(sym, sym) for _, _, sym in cs], + requirements=[ + *((sym, sym) for _, _, sym in rs), + *((orig, sym) for orig, _, sym in ms), + ] + ) + + self.strata = [strata_name for strata_name, _ in strata] + self._strata_symbols = { + strata_name: ModelSymbols( + compartments=[ + (orig, sym) for orig, strt, sym in cs + if strt == strata_name + ], + requirements=[ + (orig, sym) for orig, strt, sym in rs + if strt == strata_name + ] + ) + for strata_name, _ in strata + } + + self.all_meta_requirements = [ + to_symbol(sym) + for _, _, sym in ms + ] + self._msymbols = { + orig: to_symbol(sym) + for orig, _, sym in ms + } + + def strata_compartments(self, strata: str, *names: str) -> Sequence[Symbol]: + """ + Select compartment symbols by name in a particular strata. + If `names` is non-empty, select those symbols by their original name. + If `names` is empty, return all symbols. + """ + sym = self._strata_symbols[strata] + return sym.all_compartments if len(names) == 0 else sym.compartments(*names) + + def strata_requirements(self, strata: str, *names: str) -> Sequence[Symbol]: + """ + Select requirement symbols by name in a particular strata. + If `names` is non-empty, select those symbols by their original name. + If `names` is empty, return all symbols. + """ + sym = self._strata_symbols[strata] + return sym.all_requirements if len(names) == 0 else sym.requirements(*names) + + +MetaEdgeBuilder = Callable[[MultistrataModelSymbols], Sequence[TransitionDef]] +"""A function for creating meta edges in a multistrata RUME.""" + + +class CombinedCompartmentModel(BaseCompartmentModel): + """A CompartmentModel constructed by combining others.""" + + compartments: Sequence[CompartmentDef] + """All compartments; renamed with strata.""" + requirements: Sequence[AttributeDef] + """All requirements, including meta-requirements.""" + + _strata: Sequence[tuple[str, CompartmentModel]] + _meta_requirements: Sequence[AttributeDef] + _meta_edges: MetaEdgeBuilder + + def __init__(self, + strata: Sequence[tuple[str, CompartmentModel]], + meta_requirements: Sequence[AttributeDef], + meta_edges: MetaEdgeBuilder): + + self._strata = strata + self._meta_requirements = meta_requirements + self._meta_edges = meta_edges + + self.compartments = [ + dataclasses.replace(comp, name=f"{comp.name}_{strata_name}") + for strata_name, ipm in strata + for comp in ipm.compartments + ] + + self.requirements = [ + *(r for _, ipm in strata for r in ipm.requirements), + *self._meta_requirements, + ] + + @cached_property + def symbols(self) -> MultistrataModelSymbols: + """The symbols which represent parts of this model.""" + return MultistrataModelSymbols( + strata=self._strata, + meta_requirements=self._meta_requirements) + + @cached_property + def transitions(self) -> Sequence[TransitionDef]: + symbols = self.symbols + + # Figure out the per-strata mapping from old symbol to new symbol + # by matching everything up in-order. + strata_mapping = list[dict[Symbol, Symbol]]() + all_cs = iter(symbols.all_compartments) + all_rs = iter(symbols.all_requirements) + for _, ipm in self._strata: + mapping = {} + old = ipm.symbols + for old_symbol in old.all_compartments: + mapping[old_symbol] = next(all_cs) + for old_symbol in old.all_requirements: + mapping[old_symbol] = next(all_rs) + strata_mapping.append(mapping) + + return [ + *(_remap_transition(trx, mapping) + for (_, ipm), mapping in zip(self._strata, strata_mapping) + for trx in ipm.transitions), + *self._meta_edges(symbols), + ] + + @cached_property + def requirements_dict(self) -> OrderedDict[AbsoluteName, AttributeDef]: + return OrderedDict([ + *((AbsoluteName(gpm_strata(strata_name), "ipm", r.name), r) + for strata_name, ipm in self._strata + for r in ipm.requirements), + *((AbsoluteName(META_STRATA, "ipm", r.name), r) + for r in self._meta_requirements), + ]) diff --git a/epymorph/data/__init__.py b/epymorph/data/__init__.py index 6b4a3e5b..a4d6e95a 100644 --- a/epymorph/data/__init__.py +++ b/epymorph/data/__init__.py @@ -2,10 +2,6 @@ from epymorph.data.registry import * __all__ = [ - 'geo_library', - 'geo_library_dynamic', - 'geo_library_static', 'ipm_library', 'mm_library', - 'mm_library_parsed', ] diff --git a/epymorph/data/geo/maricopa_cbg_2019.geo.tgz b/epymorph/data/geo/maricopa_cbg_2019.geo.tgz deleted file mode 100644 index 059107e8..00000000 Binary files a/epymorph/data/geo/maricopa_cbg_2019.geo.tgz and /dev/null differ diff --git a/epymorph/data/geo/pei.geo.tgz b/epymorph/data/geo/pei.geo.tgz deleted file mode 100644 index 1209e10e..00000000 Binary files a/epymorph/data/geo/pei.geo.tgz and /dev/null differ diff --git a/epymorph/data/geo/single_pop.py b/epymorph/data/geo/single_pop.py deleted file mode 100644 index e9e617fa..00000000 --- a/epymorph/data/geo/single_pop.py +++ /dev/null @@ -1,33 +0,0 @@ -"""Implement a simple geo with a single population and some basic data. Handy for testing.""" -import numpy as np - -from epymorph.data import registry -from epymorph.data_shape import Shapes -from epymorph.data_type import CentroidDType, CentroidType -from epymorph.geo.spec import LABEL, NO_DURATION, StaticGeoSpec -from epymorph.geo.static import StaticGeo -from epymorph.geography.us_census import StateScope -from epymorph.simulation import AttributeDef - - -@registry.geo('single_pop') -def load() -> StaticGeo: - """Load the single_pop geo.""" - spec = StaticGeoSpec( - attributes=[ - LABEL, - AttributeDef('geoid', type=str, shape=Shapes.N), - AttributeDef('centroid', type=CentroidType, shape=Shapes.N), - AttributeDef('population', type=int, shape=Shapes.N), - AttributeDef('commuters', type=int, shape=Shapes.NxN), - ], - scope=StateScope.in_states_by_code(['AZ'], year=2020), - time_period=NO_DURATION - ) - return StaticGeo(spec, { - 'label': np.array(['AZ'], dtype=np.str_), - 'geoid': np.array(['04'], dtype=np.str_), - 'centroid': np.array([(-111.856111, 34.566667)], dtype=CentroidDType), - 'population': np.array([100_000], dtype=np.int64), - 'commuters': np.array([[0]], dtype=np.int64) - }) diff --git a/epymorph/data/geo/us_counties_2015.geo.tgz b/epymorph/data/geo/us_counties_2015.geo.tgz deleted file mode 100644 index 7f96705f..00000000 Binary files a/epymorph/data/geo/us_counties_2015.geo.tgz and /dev/null differ diff --git a/epymorph/data/geo/us_states_2015.geo.tgz b/epymorph/data/geo/us_states_2015.geo.tgz deleted file mode 100644 index 6ddffa04..00000000 Binary files a/epymorph/data/geo/us_states_2015.geo.tgz and /dev/null differ diff --git a/epymorph/data/geo/us_sw_counties_2015.geo b/epymorph/data/geo/us_sw_counties_2015.geo deleted file mode 100644 index 1b28263f..00000000 --- a/epymorph/data/geo/us_sw_counties_2015.geo +++ /dev/null @@ -1 +0,0 @@ -{"py/object": "epymorph.geo.spec.DynamicGeoSpec", "py/state": {"attributes": [{"py/object": "epymorph.simulation.AttributeDef", "name": "label", "type": {"py/type": "builtins.str"}, "shape": {"py/object": "epymorph.data_shape.Node"}, "default_value": null, "comment": null}, {"py/object": "epymorph.simulation.AttributeDef", "name": "population", "type": {"py/type": "builtins.int"}, "shape": {"py/id": 4}, "default_value": null, "comment": null}, {"py/object": "epymorph.simulation.AttributeDef", "name": "centroid", "type": [{"py/tuple": ["longitude", {"py/type": "builtins.float"}]}, {"py/tuple": ["latitude", {"py/type": "builtins.float"}]}], "shape": {"py/id": 4}, "default_value": null, "comment": null}, {"py/object": "epymorph.simulation.AttributeDef", "name": "geoid", "type": {"py/type": "builtins.str"}, "shape": {"py/id": 4}, "default_value": null, "comment": null}, {"py/object": "epymorph.simulation.AttributeDef", "name": "dissimilarity_index", "type": {"py/type": "builtins.float"}, "shape": {"py/id": 4}, "default_value": null, "comment": null}, {"py/object": "epymorph.simulation.AttributeDef", "name": "median_income", "type": {"py/type": "builtins.int"}, "shape": {"py/id": 4}, "default_value": null, "comment": null}, {"py/object": "epymorph.simulation.AttributeDef", "name": "pop_density_km2", "type": {"py/type": "builtins.float"}, "shape": {"py/id": 4}, "default_value": null, "comment": null}, {"py/object": "epymorph.simulation.AttributeDef", "name": "commuters", "type": {"py/type": "builtins.int"}, "shape": {"py/object": "epymorph.data_shape.NodeAndNode"}, "default_value": null, "comment": null}], "scope": {"py/object": "epymorph.geography.us_census.CountyScope", "year": 2010, "includes_granularity": "state", "includes": ["04", "08", "49", "35", "32"]}, "time_period": {"py/object": "epymorph.geo.spec.Year", "year": 2015, "days": 365, "start_date": {"py/object": "datetime.date", "__reduce__": [{"py/type": "datetime.date"}, ["B98BAQ=="]]}, "end_date": {"py/object": "datetime.date", "__reduce__": [{"py/type": "datetime.date"}, ["B+ABAQ=="]]}}, "source": {"label": "Census:name", "population": "Census", "centroid": "Census", "geoid": "Census", "dissimilarity_index": "Census", "median_income": "Census", "pop_density_km2": "Census", "commuters": "Census"}}} \ No newline at end of file diff --git a/epymorph/data/ipm/no.py b/epymorph/data/ipm/no.py index 1271cd79..1d8254ba 100644 --- a/epymorph/data/ipm/no.py +++ b/epymorph/data/ipm/no.py @@ -1,16 +1,12 @@ """Defines a compartmental IPM with one compartment and no transitions.""" -from epymorph.compartment_model import (CompartmentModel, compartment, - create_model, create_symbols) +from epymorph.compartment_model import CompartmentModel, compartment from epymorph.data import registry @registry.ipm('no') -def load() -> CompartmentModel: - """Load the 'no' IPM.""" - return create_model( - symbols=create_symbols( - compartments=[compartment('P')], - attributes=[] - ), - transitions=[] - ) +class No(CompartmentModel): + """The 'no' IPM: a single compartment with no transitions.""" + compartments = (compartment('P'),) + + def edges(self, symbols): + return [] diff --git a/epymorph/data/ipm/pei.py b/epymorph/data/ipm/pei.py index b49c58c5..f134c4b0 100644 --- a/epymorph/data/ipm/pei.py +++ b/epymorph/data/ipm/pei.py @@ -1,42 +1,40 @@ """Defines a compartmental IPM mirroring the Pei paper's beta treatment.""" from sympy import Max, exp, log -from epymorph.compartment_model import (CompartmentModel, compartment, - create_model, create_symbols, edge) +from epymorph.compartment_model import CompartmentModel, compartment, edge from epymorph.data import registry from epymorph.data_shape import Shapes from epymorph.simulation import AttributeDef @registry.ipm('pei') -def load() -> CompartmentModel: - """Load the 'pei' IPM.""" - symbols = create_symbols( - compartments=[ - compartment('S'), - compartment('I'), - compartment('R'), - ], - attributes=[ - AttributeDef('infection_duration', float, Shapes.TxN), - AttributeDef('immunity_duration', float, Shapes.TxN), - AttributeDef('humidity', float, Shapes.TxN), - ]) - - [S, I, R] = symbols.compartment_symbols - [D, L, H] = symbols.attribute_symbols - - beta = (exp(-180 * H + log(2.0 - 1.3)) + 1.3) / D - - # formulate N so as to avoid dividing by zero; - # this is safe in this instance because if the denominator is zero, - # the numerator must also be zero - N = Max(1, S + I + R) - - return create_model( - symbols=symbols, - transitions=[ +class Pei(CompartmentModel): + """The 'pei' IPM: an SIRS model driven by humidity.""" + compartments = [ + compartment('S'), + compartment('I'), + compartment('R'), + ] + + requirements = [ + AttributeDef('infection_duration', float, Shapes.TxN), + AttributeDef('immunity_duration', float, Shapes.TxN), + AttributeDef('humidity', float, Shapes.TxN), + ] + + def edges(self, symbols): + [S, I, R] = symbols.all_compartments + [D, L, H] = symbols.all_requirements + + beta = (exp(-180 * H + log(2.0 - 1.3)) + 1.3) / D + + # formulate N so as to avoid dividing by zero; + # this is safe in this instance because if the denominator is zero, + # the numerator must also be zero + N = Max(1, S + I + R) + + return [ edge(S, I, rate=beta * S * I / N), edge(I, R, rate=I / D), - edge(R, S, rate=R / L) - ]) + edge(R, S, rate=R / L), + ] diff --git a/epymorph/data/ipm/seirs.py b/epymorph/data/ipm/seirs.py index 531b34c1..3c68f8ba 100644 --- a/epymorph/data/ipm/seirs.py +++ b/epymorph/data/ipm/seirs.py @@ -1,42 +1,40 @@ from sympy import Max -from epymorph.compartment_model import (CompartmentModel, compartment, - create_model, create_symbols, edge) +from epymorph.compartment_model import CompartmentModel, compartment, edge from epymorph.data import registry from epymorph.data_shape import Shapes from epymorph.simulation import AttributeDef @registry.ipm('seirs') -def load() -> CompartmentModel: - symbols = create_symbols( - compartments=[ - compartment('S'), - compartment('E'), - compartment('I'), - compartment('R'), - ], - attributes=[ - AttributeDef('beta', type=float, shape=Shapes.TxN, - comment='infectivity'), - AttributeDef('sigma', type=float, shape=Shapes.TxN, - comment='progression from exposed to infected'), - AttributeDef('gamma', type=float, shape=Shapes.TxN, - comment='progression from infected to recovered'), - AttributeDef('xi', type=float, shape=Shapes.TxN, - comment='progression from recovered to susceptible'), - ]) +class Seirs(CompartmentModel): + """A basic SEIRS model.""" + compartments = [ + compartment('S'), + compartment('E'), + compartment('I'), + compartment('R'), + ] + requirements = [ + AttributeDef('beta', type=float, shape=Shapes.TxN, + comment='infectivity'), + AttributeDef('sigma', type=float, shape=Shapes.TxN, + comment='progression from exposed to infected'), + AttributeDef('gamma', type=float, shape=Shapes.TxN, + comment='progression from infected to recovered'), + AttributeDef('xi', type=float, shape=Shapes.TxN, + comment='progression from recovered to susceptible'), + ] - [S, E, I, R] = symbols.compartment_symbols - [β, σ, γ, ξ] = symbols.attribute_symbols + def edges(self, symbols): + [S, E, I, R] = symbols.all_compartments + [β, σ, γ, ξ] = symbols.all_requirements - N = Max(1, S + E + I + R) + N = Max(1, S + E + I + R) - return create_model( - symbols=symbols, - transitions=[ + return [ edge(S, E, rate=β * S * I / N), edge(E, I, rate=σ * E), edge(I, R, rate=γ * I), - edge(R, S, rate=ξ * R) - ]) + edge(R, S, rate=ξ * R), + ] diff --git a/epymorph/data/ipm/sirh.py b/epymorph/data/ipm/sirh.py index 0ef5dc62..ff7fffc6 100644 --- a/epymorph/data/ipm/sirh.py +++ b/epymorph/data/ipm/sirh.py @@ -1,8 +1,7 @@ """Defines a compartmental IPM for a generic SIRH model.""" from sympy import Max -from epymorph.compartment_model import (CompartmentModel, compartment, - create_model, create_symbols, edge, +from epymorph.compartment_model import (CompartmentModel, compartment, edge, fork) from epymorph.data import registry from epymorph.data_shape import Shapes @@ -10,39 +9,39 @@ @registry.ipm('sirh') -def load() -> CompartmentModel: - """Load the 'sirh' IPM.""" - symbols = create_symbols( - compartments=[ - compartment('S'), - compartment('I'), - compartment('R'), - compartment('H', tags=['immobile']) - ], - attributes=[ - AttributeDef('beta', type=float, shape=Shapes.TxN, - comment='infectivity'), - AttributeDef('gamma', type=float, shape=Shapes.TxN, - comment='recovery rate'), - AttributeDef('xi', type=float, shape=Shapes.TxN, - comment='immune waning rate'), - AttributeDef('hospitalization_prob', type=float, shape=Shapes.TxN, - comment='a ratio of cases which are expected to require hospitalization'), - AttributeDef('hospitalization_duration', type=float, shape=Shapes.TxN, - comment='the mean duration of hospitalization, in days') - ]) - - [S, I, R, H] = symbols.compartment_symbols - [β, γ, ξ, h_prob, h_dur] = symbols.attribute_symbols - - # formulate N so as to avoid dividing by zero; - # this is safe in this instance because if the denominator is zero, - # the numerator must also be zero - N = Max(1, S + I + R + H) - - return create_model( - symbols=symbols, - transitions=[ +class Sirh(CompartmentModel): + """A basic SIRH model.""" + + compartments = [ + compartment('S'), + compartment('I'), + compartment('R'), + compartment('H', tags=['immobile']), + ] + + requirements = [ + AttributeDef('beta', type=float, shape=Shapes.TxN, + comment='infectivity'), + AttributeDef('gamma', type=float, shape=Shapes.TxN, + comment='recovery rate'), + AttributeDef('xi', type=float, shape=Shapes.TxN, + comment='immune waning rate'), + AttributeDef('hospitalization_prob', type=float, shape=Shapes.TxN, + comment='a ratio of cases which are expected to require hospitalization'), + AttributeDef('hospitalization_duration', type=float, shape=Shapes.TxN, + comment='the mean duration of hospitalization, in days'), + ] + + def edges(self, symbols): + [S, I, R, H] = symbols.all_compartments + [β, γ, ξ, h_prob, h_dur] = symbols.all_requirements + + # formulate N so as to avoid dividing by zero; + # this is safe in this instance because if the denominator is zero, + # the numerator must also be zero + N = Max(1, S + I + R + H) + + return [ edge(S, I, rate=β * S * I / N), fork( edge(I, H, rate=γ * I * h_prob), @@ -50,4 +49,4 @@ def load() -> CompartmentModel: ), edge(H, R, rate=H / h_dur), edge(R, S, rate=ξ * R), - ]) + ] diff --git a/epymorph/data/ipm/sirs.py b/epymorph/data/ipm/sirs.py index 69bf3eea..0c5802dd 100644 --- a/epymorph/data/ipm/sirs.py +++ b/epymorph/data/ipm/sirs.py @@ -1,43 +1,41 @@ """Defines a compartmental IPM for a generic SIRS model.""" from sympy import Max -from epymorph.compartment_model import (CompartmentModel, compartment, - create_model, create_symbols, edge) +from epymorph.compartment_model import CompartmentModel, compartment, edge from epymorph.data import registry from epymorph.data_shape import Shapes from epymorph.simulation import AttributeDef @registry.ipm('sirs') -def load() -> CompartmentModel: - """Load the 'sirs' IPM.""" - symbols = create_symbols( - compartments=[ - compartment('S'), - compartment('I'), - compartment('R'), - ], - attributes=[ - AttributeDef('beta', type=float, shape=Shapes.TxN, - comment='infectivity'), - AttributeDef('gamma', type=float, shape=Shapes.TxN, - comment='progression from infected to recovered'), - AttributeDef('xi', type=float, shape=Shapes.TxN, - comment='progression from recovered to susceptible'), - ]) +class Sirs(CompartmentModel): + """A basic SIRS model.""" + compartments = [ + compartment('S'), + compartment('I'), + compartment('R'), + ] - [S, I, R] = symbols.compartment_symbols - [β, γ, ξ] = symbols.attribute_symbols + requirements = [ + AttributeDef('beta', type=float, shape=Shapes.TxN, + comment='infectivity'), + AttributeDef('gamma', type=float, shape=Shapes.TxN, + comment='progression from infected to recovered'), + AttributeDef('xi', type=float, shape=Shapes.TxN, + comment='progression from recovered to susceptible'), + ] - # formulate N so as to avoid dividing by zero; - # this is safe in this instance because if the denominator is zero, - # the numerator must also be zero - N = Max(1, S + I + R) + def edges(self, symbols): + [S, I, R] = symbols.all_compartments + [β, γ, ξ] = symbols.all_requirements - return create_model( - symbols=symbols, - transitions=[ + # formulate N so as to avoid dividing by zero; + # this is safe in this instance because if the denominator is zero, + # the numerator must also be zero + N = Max(1, S + I + R) + + return [ edge(S, I, rate=β * S * I / N), edge(I, R, rate=γ * I), - edge(R, S, rate=ξ * R) - ]) + edge(R, S, rate=ξ * R), + ] diff --git a/epymorph/data/ipm/sparsemod.py b/epymorph/data/ipm/sparsemod.py index db86b8c1..38647a01 100644 --- a/epymorph/data/ipm/sparsemod.py +++ b/epymorph/data/ipm/sparsemod.py @@ -1,8 +1,7 @@ """Defines a copmartmental IPM mirroring the SPARSEMOD COVID model.""" from sympy import Max -from epymorph.compartment_model import (CompartmentModel, compartment, - create_model, create_symbols, edge, +from epymorph.compartment_model import (CompartmentModel, compartment, edge, fork) from epymorph.data import registry from epymorph.data_shape import Shapes @@ -10,54 +9,53 @@ @registry.ipm('sparsemod') -def load() -> CompartmentModel: - """Load the 'sparsemod' IPM.""" - symbols = create_symbols( - compartments=[ - compartment('S', description='susceptible'), - compartment('E', description='exposed'), - compartment('Ia', description='infected asymptomatic'), - compartment('Ip', description='infected presymptomatic'), - compartment('Is', description='infected symptomatic'), - compartment('Ib', description='infected bed-rest'), - compartment('Ih', description='infected hospitalized'), - compartment('Ic1', description='infected in ICU'), - compartment('Ic2', description='infected in ICU Step-Down'), - compartment('D', description='deceased'), - compartment('R', description='recovered') - ], - attributes=[ - AttributeDef('beta', type=float, shape=Shapes.TxN), - AttributeDef('omega_1', type=float, shape=Shapes.TxN), - AttributeDef('omega_2', type=float, shape=Shapes.TxN), - AttributeDef('delta_1', type=float, shape=Shapes.TxN), - AttributeDef('delta_2', type=float, shape=Shapes.TxN), - AttributeDef('delta_3', type=float, shape=Shapes.TxN), - AttributeDef('delta_4', type=float, shape=Shapes.TxN), - AttributeDef('delta_5', type=float, shape=Shapes.TxN), - AttributeDef('gamma_a', type=float, shape=Shapes.TxN), - AttributeDef('gamma_b', type=float, shape=Shapes.TxN), - AttributeDef('gamma_c', type=float, shape=Shapes.TxN), - AttributeDef('rho_1', type=float, shape=Shapes.TxN), - AttributeDef('rho_2', type=float, shape=Shapes.TxN), - AttributeDef('rho_3', type=float, shape=Shapes.TxN), - AttributeDef('rho_4', type=float, shape=Shapes.TxN), - AttributeDef('rho_5', type=float, shape=Shapes.TxN), - ]) +class Sparsemod(CompartmentModel): + """A model similar to one used in sparsemod.""" + compartments = [ + compartment('S', description='susceptible'), + compartment('E', description='exposed'), + compartment('Ia', description='infected asymptomatic'), + compartment('Ip', description='infected presymptomatic'), + compartment('Is', description='infected symptomatic'), + compartment('Ib', description='infected bed-rest'), + compartment('Ih', description='infected hospitalized'), + compartment('Ic1', description='infected in ICU'), + compartment('Ic2', description='infected in ICU Step-Down'), + compartment('D', description='deceased'), + compartment('R', description='recovered'), + ] - [S, E, Ia, Ip, Is, Ib, Ih, Ic1, Ic2, D, R] = symbols.compartment_symbols - [beta, omega_1, omega_2, delta_1, delta_2, delta_3, delta_4, delta_5, - gamma_a, gamma_b, gamma_c, rho_1, rho_2, rho_3, rho_4, rho_5] = symbols.attribute_symbols + requirements = [ + AttributeDef('beta', type=float, shape=Shapes.TxN), + AttributeDef('omega_1', type=float, shape=Shapes.TxN), + AttributeDef('omega_2', type=float, shape=Shapes.TxN), + AttributeDef('delta_1', type=float, shape=Shapes.TxN), + AttributeDef('delta_2', type=float, shape=Shapes.TxN), + AttributeDef('delta_3', type=float, shape=Shapes.TxN), + AttributeDef('delta_4', type=float, shape=Shapes.TxN), + AttributeDef('delta_5', type=float, shape=Shapes.TxN), + AttributeDef('gamma_a', type=float, shape=Shapes.TxN), + AttributeDef('gamma_b', type=float, shape=Shapes.TxN), + AttributeDef('gamma_c', type=float, shape=Shapes.TxN), + AttributeDef('rho_1', type=float, shape=Shapes.TxN), + AttributeDef('rho_2', type=float, shape=Shapes.TxN), + AttributeDef('rho_3', type=float, shape=Shapes.TxN), + AttributeDef('rho_4', type=float, shape=Shapes.TxN), + AttributeDef('rho_5', type=float, shape=Shapes.TxN), + ] - # formulate the divisor so as to avoid dividing by zero; - # this is safe in this instance becase if the denominator is zero, - # the numerator must also be zero - N = Max(1, S + E + Ia + Ip + Is + Ib + Ih + Ic1 + Ic2 + R) - lambda_1 = (omega_1 * Ia + Ip + Is + Ib + omega_2 * (Ih + Ic1 + Ic2)) / N + def edges(self, symbols): + [S, E, Ia, Ip, Is, Ib, Ih, Ic1, Ic2, D, R] = symbols.all_compartments + [beta, omega_1, omega_2, delta_1, delta_2, delta_3, delta_4, delta_5, + gamma_a, gamma_b, gamma_c, rho_1, rho_2, rho_3, rho_4, rho_5] = symbols.all_requirements - return create_model( - symbols=symbols, - transitions=[ + # formulate the divisor so as to avoid dividing by zero; + # this is safe in this instance becase if the denominator is zero, + # the numerator must also be zero + N = Max(1, S + E + Ia + Ip + Is + Ib + Ih + Ic1 + Ic2 + R) + lambda_1 = (omega_1 * Ia + Ip + Is + Ib + omega_2 * (Ih + Ic1 + Ic2)) / N + + return [ edge(S, E, rate=beta * lambda_1 * S), fork( edge(E, Ia, rate=E * delta_1 * rho_1), @@ -80,4 +78,4 @@ def load() -> CompartmentModel: edge(Ia, R, rate=Ia * gamma_a), edge(Ib, R, rate=Ib * gamma_b), edge(Ic2, R, rate=Ic2 * gamma_c) - ]) + ] diff --git a/epymorph/data/mm/centroids.movement b/epymorph/data/mm/centroids.movement deleted file mode 100644 index a9715d2e..00000000 --- a/epymorph/data/mm/centroids.movement +++ /dev/null @@ -1,25 +0,0 @@ -[move-steps: per-day=2; duration=[1/3, 2/3]] - -[attrib: name=population; type=int; shape=N; default_value=None; - comment="The total population at each node."] - -[attrib: name=centroid; type=[(longitude, float), (latitude, float)]; shape=N; default_value=None; - comment="The centroids for each node as (longitude, latitude) tuples."] - -[attrib: name=phi; type=float; shape=S; default_value=40.0; - comment="Influences the distance that movers tend to travel."] - -[predef: function = -def centroids_movement(): - centroid = data['centroid'] - distance = pairwise_haversine(centroid['longitude'], centroid['latitude']) - dispersal_kernel = row_normalize(1 / np.exp(distance / data['phi'])) - return { 'dispersal_kernel': dispersal_kernel } -] - -# Commuter movement: assume 10% of the population are commuters -[mtype: days=all; leave=1; duration=0d; return=2; function= -def centroids_commuters(t): - n_commuters = np.floor(data['population'] * 0.1).astype(SimDType) - return np.multinomial(n_commuters, predef['dispersal_kernel']) -] diff --git a/epymorph/data/mm/centroids.py b/epymorph/data/mm/centroids.py new file mode 100644 index 00000000..a48d3348 --- /dev/null +++ b/epymorph/data/mm/centroids.py @@ -0,0 +1,60 @@ +from functools import cached_property + +import numpy as np +from numpy.typing import NDArray + +from epymorph.data import registry +from epymorph.data_shape import Shapes +from epymorph.data_type import CentroidType, SimDType +from epymorph.movement_model import EveryDay, MovementClause, MovementModel +from epymorph.simulation import AttributeDef, Tick, TickDelta, TickIndex +from epymorph.util import pairwise_haversine, row_normalize + + +class CentroidsClause(MovementClause): + """The clause of the centroids model.""" + requirements = ( + AttributeDef('population', int, Shapes.N, + comment="The total population at each node."), + AttributeDef('centroid', CentroidType, Shapes.N, + comment="The centroids for each node as (longitude, latitude) tuples."), + AttributeDef('phi', float, Shapes.S, default_value=40.0, + comment="Influences the distance that movers tend to travel."), + AttributeDef('commuter_proportion', float, Shapes.S, default_value=0.1, + comment="Decides what proportion of the total population should be commuting normally.") + ) + + predicate = EveryDay() + leaves = TickIndex(step=0) + returns = TickDelta(step=1, days=0) + + @cached_property + def dispersal_kernel(self) -> NDArray[np.float64]: + """ + The NxN matrix or dispersal kernel describing the tendency for movers to move to a particular location. + In this model, the kernel is: + 1 / e ^ (distance / phi) + which is then row-normalized. + """ + centroid = self.data('centroid') + phi = self.data('phi') + distance = pairwise_haversine(centroid['longitude'], centroid['latitude']) + return row_normalize(1 / np.exp(distance / phi)) + + def evaluate(self, tick: Tick) -> NDArray[np.int64]: + pop = self.data('population') + comm_prop = self.data('commuter_proportion') + n_commuters = np.floor(pop * comm_prop).astype(SimDType) + return self.rng.multinomial(n_commuters, self.dispersal_kernel) + + +@registry.mm('centroids') +class Centroids(MovementModel): + """ + The centroids MM describes a basic commuter movement where a fixed proportion + of the population commutes every day, travels to another location for 1/3 of a day + (with a location likelihood that decreases with distance), and then returns home for + the remaining 2/3 of the day. + """ + steps = (1 / 3, 2 / 3) + clauses = (CentroidsClause(),) diff --git a/epymorph/data/mm/flat.movement b/epymorph/data/mm/flat.movement deleted file mode 100644 index 5bcdea2a..00000000 --- a/epymorph/data/mm/flat.movement +++ /dev/null @@ -1,27 +0,0 @@ -# This model evenly weights the probability of movement to all other nodes. -# It uses parameter 'commuter_proportion' to determine how many people should -# be moving, based on the total normal population of each node. - -[move-steps: per-day=2; duration=[1/3, 2/3]] - -[attrib: name=population; type=int; shape=N; default_value=None; - comment="The total population at each node."] - -[attrib: name=commuter_proportion; type=float; shape=S; default_value=0.2; - comment="The ratio of the total population that is assumed to be commuters."] - -[predef: function= -def flat_predef(): - ones = np.ones((dim.nodes, dim.nodes)) - np.fill_diagonal(ones, 0) - dispersal_kernel = row_normalize(ones) - return { 'dispersal_kernel': dispersal_kernel } -] - -# Assume a percentage of the population move around, -# evenly weighted to all other locations. -[mtype: days=all; leave=1; duration=0d; return=2; function= -def flat_movement(t): - n_commuters = np.floor(data['population'] * data['commuter_proportion']).astype(SimDType) - return np.multinomial(n_commuters, predef['dispersal_kernel']) -] diff --git a/epymorph/data/mm/flat.py b/epymorph/data/mm/flat.py new file mode 100644 index 00000000..73aed334 --- /dev/null +++ b/epymorph/data/mm/flat.py @@ -0,0 +1,53 @@ +from functools import cached_property + +import numpy as np +from numpy.typing import NDArray + +from epymorph.data import registry +from epymorph.data_shape import Shapes +from epymorph.data_type import SimDType +from epymorph.movement_model import EveryDay, MovementClause, MovementModel +from epymorph.simulation import AttributeDef, Tick, TickDelta, TickIndex +from epymorph.util import row_normalize + + +class FlatClause(MovementClause): + """The clause of the flat model.""" + requirements = ( + AttributeDef('population', int, Shapes.N, + comment="The total population at each node."), + AttributeDef('commuter_proportion', float, Shapes.S, default_value=0.1, + comment="Decides what proportion of the total population should be commuting normally.") + ) + + predicate = EveryDay() + leaves = TickIndex(step=0) + returns = TickDelta(step=1, days=0) + + @cached_property + def dispersal_kernel(self) -> NDArray[np.float64]: + """ + The NxN matrix or dispersal kernel describing the tendency for movers to move to a particular location. + In this model, the kernel is full of 1s except for 0s on the diagonal, which is then row-normalized. + Effectively: every destination is equally likely. + """ + ones = np.ones((self.dim.nodes, self.dim.nodes)) + np.fill_diagonal(ones, 0) + return row_normalize(ones) + + def evaluate(self, tick: Tick) -> NDArray[SimDType]: + pop = self.data('population') + comm_prop = self.data('commuter_proportion') + n_commuters = np.floor(pop * comm_prop).astype(SimDType) + return self.rng.multinomial(n_commuters, self.dispersal_kernel) + + +@registry.mm('flat') +class Flat(MovementModel): + """ + This model evenly weights the probability of movement to all other nodes. + It uses parameter 'commuter_proportion' to determine how many people should + be moving, based on the total normal population of each node. + """ + steps = (1 / 3, 2 / 3) + clauses = (FlatClause(),) diff --git a/epymorph/data/mm/icecube.movement b/epymorph/data/mm/icecube.movement deleted file mode 100644 index 70d90411..00000000 --- a/epymorph/data/mm/icecube.movement +++ /dev/null @@ -1,18 +0,0 @@ -# A toy example: ice cube tray movement movement model -# Each state sends a fixed number of commuters to the next -# state in the line (without wraparound). - -[move-steps: per-day=2; duration=[2/3, 1/3]] - -[attrib: name=population; type=int; shape=N; default_value=None; - comment="The total population at each node."] - -[mtype: days=all; leave=1; duration=0d; return=2; function= -def icecube(t, src): - # using `zeros_like` here is just a convenient way - # to make an empty integer array of the correct length - commuters = np.zeros_like(data['population']) - if (src + 1) < commuters.size: - commuters[src + 1] = 5000 - return commuters -] diff --git a/epymorph/data/mm/icecube.py b/epymorph/data/mm/icecube.py new file mode 100644 index 00000000..bff28c4e --- /dev/null +++ b/epymorph/data/mm/icecube.py @@ -0,0 +1,43 @@ +import numpy as np +from numpy.typing import NDArray + +from epymorph.data import registry +from epymorph.data_shape import Shapes +from epymorph.data_type import SimDType +from epymorph.movement_model import EveryDay, MovementClause, MovementModel +from epymorph.simulation import AttributeDef, Tick, TickDelta, TickIndex + + +class IcecubeClause(MovementClause): + """The clause of the icecube model.""" + requirements = ( + AttributeDef('population', int, Shapes.N, + comment="The total population at each node."), + AttributeDef('commuter_proportion', float, Shapes.S, default_value=0.1, + comment="Decides what proportion of the total population should be commuting normally.") + ) + + predicate = EveryDay() + leaves = TickIndex(step=0) + returns = TickDelta(step=1, days=0) + + def evaluate(self, tick: Tick) -> NDArray[np.int64]: + N = self.dim.nodes + pop = self.data('population') + comm_prop = self.data('commuter_proportion') + commuters = np.zeros((N, N), dtype=SimDType) + for src in range(N): + if (src + 1) < N: + commuters[src, src + 1] = pop[src] * comm_prop + return commuters + + +@registry.mm('icecube') +class Icecube(MovementModel): + """ + A toy example: ice cube tray movement movement model + Each state sends a fixed number of commuters to the next + state in the line (without wraparound). + """ + steps = (1 / 2, 1 / 2) + clauses = (IcecubeClause(),) diff --git a/epymorph/data/mm/no.movement b/epymorph/data/mm/no.movement deleted file mode 100644 index 7c0ec44e..00000000 --- a/epymorph/data/mm/no.movement +++ /dev/null @@ -1,10 +0,0 @@ -# No movement at all. -# Obviously this isn't the most-efficient-imaginable way to accomplish this, -# but this way we don't have to write any new code, so here we are. - -[move-steps: per-day=1; duration=[1]] - -[mtype: days=all; leave=1; duration=0d; return=1; function= -def no(t, src, dst): - return 0 -] diff --git a/epymorph/data/mm/no.py b/epymorph/data/mm/no.py new file mode 100644 index 00000000..8940fd69 --- /dev/null +++ b/epymorph/data/mm/no.py @@ -0,0 +1,29 @@ +import numpy as np +from numpy.typing import NDArray + +from epymorph.data import registry +from epymorph.data_type import SimDType +from epymorph.movement_model import EveryDay, MovementClause, MovementModel +from epymorph.simulation import Tick, TickDelta, TickIndex + + +class NoClause(MovementClause): + """The clause of the "no" model.""" + requirements = () + predicate = EveryDay() + leaves = TickIndex(step=0) + returns = TickDelta(step=0, days=0) + + def evaluate(self, tick: Tick) -> NDArray[np.int64]: + N = self.dim.nodes + return np.zeros((N, N), dtype=SimDType) + + +@registry.mm('no') +class No(MovementModel): + """ + No movement at all. This is handy for cases when you want to disable movement + in an experiment, or for testing. + """ + steps = (1.0,) + clauses = (NoClause(),) diff --git a/epymorph/data/mm/pei.movement b/epymorph/data/mm/pei.movement deleted file mode 100644 index 79ce9c80..00000000 --- a/epymorph/data/mm/pei.movement +++ /dev/null @@ -1,53 +0,0 @@ -# Modeled after the Pei influenza paper, this model simulates -# two types of movers -- regular commuters and more-randomized dispersers. -# Each is somewhat stochastic but adhere to the general shape dictated -# by the commuters array. Both kinds of movement can be "tuned" through -# their respective parameters: move_control and theta. - -[move-steps: per-day=2; duration=[1/3, 2/3]] - -[attrib: name=commuters; type=int; shape=NxN; default_value=None; - comment="A node-to-node commuters matrix."] - -[attrib: name=move_control; type=float; shape=S; default_value=0.9; - comment="A factor which modulates the number of commuters by conducting a binomial draw with this probability and the expected commuters from the commuters matrix."] - -[attrib: name=theta; type=float; shape=S; default_value=0.1; - comment="A factor which allows for randomized movement by conducting a poisson draw with this factor times the average number of commuters between two nodes from the commuters matrix."] - -[predef: function = -def pei_movement(): - """Pei style movement pre definition""" - commuters = data['commuters'] - - # Average commuters between locations. - commuters_average = (commuters + commuters.T) // 2 - - # Total commuters living in each state. - commuters_by_node = np.sum(commuters, axis=1) - - # Commuters as a ratio to the total commuters living in that state. - # For cases where there are no commuters, avoid div-by-0 errors - commuting_probability = row_normalize(commuters) - - return { - 'commuters_average': commuters_average, - 'commuters_by_node': commuters_by_node, - 'commuting_probability': commuting_probability - } -] - -# Commuter movement -[mtype: days=all; leave=1; duration=0d; return=2; function= -def commuters(t): - typical = predef['commuters_by_node'] - actual = np.binomial(typical, data['move_control']) - return np.multinomial(actual, predef['commuting_probability']) -] - -# Random dispersers movement -[mtype: days=all; leave=1; duration=0d; return=2; function= -def dispersers(t): - avg = predef['commuters_average'] - return np.poisson(avg * data['theta']) -] diff --git a/epymorph/data/mm/pei.py b/epymorph/data/mm/pei.py new file mode 100644 index 00000000..d7fce5d6 --- /dev/null +++ b/epymorph/data/mm/pei.py @@ -0,0 +1,88 @@ +from functools import cached_property + +import numpy as np +from numpy.typing import NDArray + +from epymorph.data import registry +from epymorph.data_shape import Shapes +from epymorph.data_type import SimDType +from epymorph.movement_model import EveryDay, MovementClause, MovementModel +from epymorph.simulation import AttributeDef, Tick, TickDelta, TickIndex +from epymorph.util import row_normalize + +_COMMUTERS_ATTRIB = AttributeDef('commuters', int, Shapes.NxN, + comment="A node-to-node commuters marix.") + + +class Commuters(MovementClause): + """The commuter clause of the pei model.""" + + requirements = ( + _COMMUTERS_ATTRIB, + AttributeDef('move_control', float, Shapes.S, default_value=0.9, + comment="A factor which modulates the number of commuters by conducting a binomial draw " + "with this probability and the expected commuters from the commuters matrix."), + ) + + predicate = EveryDay() + leaves = TickIndex(step=0) + returns = TickDelta(step=1, days=0) + + @cached_property + def commuters_by_node(self) -> NDArray[SimDType]: + """Total commuters living in each state.""" + commuters = self.data('commuters') + return np.sum(commuters, axis=1) + + @cached_property + def commuting_probability(self) -> NDArray[np.float64]: + """ + Commuters as a ratio to the total commuters living in that state. + For cases where there are no commuters, avoid div-by-0 errors + """ + commuters = self.data('commuters') + return row_normalize(commuters) + + def evaluate(self, tick: Tick) -> NDArray[np.int64]: + move_control = self.data('move_control') + actual = self.rng.binomial(self.commuters_by_node, move_control) + return self.rng.multinomial(actual, self.commuting_probability) + + +class Dispersers(MovementClause): + """The dispersers clause of the pei model.""" + + requirements = ( + _COMMUTERS_ATTRIB, + AttributeDef('theta', float, Shapes.S, default_value=0.1, + comment="A factor which allows for randomized movement by conducting a poisson draw " + "with this factor times the average number of commuters between two nodes " + "from the commuters matrix."), + ) + + predicate = EveryDay() + leaves = TickIndex(step=0) + returns = TickDelta(step=1, days=0) + + @cached_property + def commuters_average(self) -> NDArray[SimDType]: + """Average commuters between locations.""" + commuters = self.data('commuters') + return (commuters + commuters.T) // 2 + + def evaluate(self, tick: Tick) -> NDArray[SimDType]: + theta = self.data('theta') + return self.rng.poisson(theta * self.commuters_average) + + +@registry.mm('pei') +class Pei(MovementModel): + """ + Modeled after the Pei influenza paper, this model simulates + two types of movers -- regular commuters and more-randomized dispersers. + Each is somewhat stochastic but adhere to the general shape dictated + by the commuters array. Both kinds of movement can be "tuned" through + their respective parameters: move_control and theta. + """ + steps = (1 / 3, 2 / 3) + clauses = (Commuters(), Dispersers()) diff --git a/epymorph/data/mm/sparsemod.movement b/epymorph/data/mm/sparsemod.movement deleted file mode 100644 index b0ebeae7..00000000 --- a/epymorph/data/mm/sparsemod.movement +++ /dev/null @@ -1,32 +0,0 @@ -# Modeled after the SPARSEMOD COVID-19 paper, this model simulates -# movement using a distance kernel parameterized by phi, and using a commuters -# matrix to determine the total expected number of commuters. - -[move-steps: per-day=2; duration=[1/3, 2/3]] - -[attrib: name=centroid; type=[(longitude, float), (latitude, float)]; shape=N; default_value=None; - comment="The centroids for each node as (longitude, latitude) tuples."] - -[attrib: name=commuters; type=int; shape=NxN; default_value=None; - comment="A node-to-node commuters matrix."] - -[attrib: name=phi; type=float; shape=S; default_value=40.0; - comment="Influences the distance that movers tend to travel."] - -[predef: function = -def sparsemod_predef(): - centroid = data['centroid'] - distance = pairwise_haversine(centroid['longitude'], centroid['latitude']) - dispersal_kernel = row_normalize(1 / np.exp(distance / data['phi'])) - return { - 'commuters_by_node': np.sum(data['commuters'], axis=1), - 'dispersal_kernel': dispersal_kernel - } -] - -# Commuter movement -[mtype: days=all; leave=1; duration=0d; return=2; function= -def sparsemod_commuters(t): - n_commuters = predef['commuters_by_node'] - return np.multinomial(n_commuters, predef['dispersal_kernel']) -] diff --git a/epymorph/data/mm/sparsemod.py b/epymorph/data/mm/sparsemod.py new file mode 100644 index 00000000..05ba2989 --- /dev/null +++ b/epymorph/data/mm/sparsemod.py @@ -0,0 +1,65 @@ +from functools import cached_property + +import numpy as np +from numpy.typing import NDArray + +from epymorph.data import registry +from epymorph.data_shape import Shapes +from epymorph.data_type import CentroidType, SimDType +from epymorph.movement_model import EveryDay, MovementClause, MovementModel +from epymorph.simulation import AttributeDef, Tick, TickDelta, TickIndex +from epymorph.util import pairwise_haversine, row_normalize + + +class SparsemodClause(MovementClause): + """The clause of the sparsemod model.""" + requirements = ( + AttributeDef('commuters', int, Shapes.NxN, + comment="A node-to-node commuters marix."), + AttributeDef('centroid', CentroidType, Shapes.N, + comment="The centroids for each node as (longitude, latitude) tuples."), + AttributeDef('phi', float, Shapes.S, default_value=40.0, + comment="Influences the distance that movers tend to travel."), + ) + + predicate = EveryDay() + leaves = TickIndex(step=0) + returns = TickDelta(step=1, days=0) + + @cached_property + def commuters_by_node(self) -> NDArray[SimDType]: + """ + The number of commuters that live in any particular node + (regardless of typical commuting destination). + """ + return np.sum(self.data('commuters'), axis=1) + + @cached_property + def dispersal_kernel(self) -> NDArray[np.float64]: + """ + The NxN matrix or dispersal kernel describing the tendency for movers to move to a particular location. + In this model, the kernel is: + 1 / e ^ (distance / phi) + which is then row-normalized. + """ + centroid = self.data('centroid') + phi = self.data('phi') + distance = pairwise_haversine(centroid['longitude'], centroid['latitude']) + return row_normalize(1 / np.exp(distance / phi)) + + def evaluate(self, tick: Tick) -> NDArray[np.int64]: + return self.rng.multinomial( + self.commuters_by_node, + self.dispersal_kernel + ) + + +@registry.mm('sparsemod') +class Sparsemod(MovementModel): + """ + Modeled after the SPARSEMOD COVID-19 paper, this model simulates + movement using a distance kernel parameterized by phi, and using a commuters + matrix to determine the total expected number of commuters. + """ + steps = (1 / 3, 2 / 3) + clauses = (SparsemodClause(),) diff --git a/epymorph/data/pei.py b/epymorph/data/pei.py new file mode 100644 index 00000000..842fe75c --- /dev/null +++ b/epymorph/data/pei.py @@ -0,0 +1,409 @@ +""" +This is data drawn from Pei's data files. +We're including it as a temporary measure, since we don't have +an ADRIO for humidity yet, and because the ADRIOs we do have +produce data values that differ from these. +It would also be feasible to use CSV ADRIOs to load this data +directly. But since a lot of epymorph uses Pei as an example +this is a convenience, and expected to be temporary. +""" +import numpy as np + +from epymorph.data_type import CentroidDType +from epymorph.geography.us_census import StateScope + +pei_scope = StateScope.in_states_by_code( + states_code=['FL', 'GA', 'MD', 'NC', 'SC', 'VA'], + year=2015 +) + +pei_centroids = np.array([ + (-81.5158, 27.6648), + (-82.9071, 32.1574), + (-76.6413, 39.0458), + (-79.0193, 35.7596), + (-81.1637, 33.8361), + (-78.6569, 37.4316), +], dtype=CentroidDType) + +pei_population = np.array([ + 18811310, 9687653, 5773552, 9535483, 4625364, 8001024 +], dtype=np.int64) + +pei_commuters = np.array([ + [7993452, 13805, 2410, 2938, 1783, 3879], + [15066, 4091461, 966, 6057, 20318, 2147], + [949, 516, 2390255, 947, 91, 122688], + [3005, 5730, 1872, 4121984, 38081, 29487], + [1709, 23513, 630, 64872, 1890853, 1620], + [1368, 1175, 68542, 16869, 577, 3567788] +], dtype=np.int64) + +# humidity in 2015 (365 days) +pei_humidity = np.array([ + [0.01003, 0.008144, 0.004738, 0.00681, 0.007467, 0.004909], + [0.0105, 0.008211, 0.004495, 0.006562, 0.007302, 0.004716], + [0.010631, 0.008169, 0.0053, 0.007162, 0.007736, 0.005407], + [0.010797, 0.007956, 0.0054, 0.00712, 0.00776, 0.005611], + [0.010727, 0.008105, 0.005233, 0.00726, 0.007819, 0.005504], + [0.01009, 0.008425, 0.004814, 0.007089, 0.007881, 0.005119], + [0.009815, 0.008386, 0.005, 0.007195, 0.007867, 0.00521], + [0.010363, 0.007858, 0.004857, 0.006825, 0.007343, 0.00491], + [0.010242, 0.007508, 0.0047, 0.006633, 0.007217, 0.00473], + [0.009609, 0.007048, 0.004052, 0.006199, 0.006764, 0.004276], + [0.009782, 0.00739, 0.00429, 0.006251, 0.006876, 0.004457], + [0.009573, 0.007775, 0.004914, 0.00681, 0.007402, 0.005012], + [0.009151, 0.008051, 0.005257, 0.00729, 0.007793, 0.005478], + [0.009114, 0.007194, 0.004824, 0.006514, 0.007036, 0.004898], + [0.009586, 0.00701, 0.004419, 0.006087, 0.006702, 0.004422], + [0.009771, 0.006976, 0.004324, 0.006073, 0.006602, 0.004267], + [0.009916, 0.007182, 0.003924, 0.005774, 0.006512, 0.00395], + [0.00951, 0.007415, 0.004476, 0.006351, 0.006874, 0.004434], + [0.009165, 0.007085, 0.003914, 0.005829, 0.006433, 0.004002], + [0.009451, 0.006524, 0.003748, 0.005594, 0.006214, 0.003876], + [0.00939, 0.007217, 0.003633, 0.005592, 0.006452, 0.003741], + [0.009205, 0.00759, 0.00381, 0.005946, 0.006779, 0.004008], + [0.008819, 0.007765, 0.004548, 0.006529, 0.007188, 0.00471], + [0.009298, 0.007635, 0.004833, 0.006829, 0.007386, 0.004962], + [0.010034, 0.006387, 0.004024, 0.005414, 0.005981, 0.003903], + [0.009865, 0.006424, 0.003424, 0.004979, 0.005731, 0.003404], + [0.009244, 0.006912, 0.003743, 0.005405, 0.006252, 0.00379], + [0.009452, 0.007364, 0.004395, 0.006227, 0.006857, 0.004595], + [0.009922, 0.008088, 0.003971, 0.006337, 0.007169, 0.00427], + [0.010102, 0.008568, 0.004552, 0.006879, 0.00766, 0.004733], + [0.00967, 0.00694, 0.003957, 0.005821, 0.006364, 0.00393], + [0.009465, 0.00713, 0.004038, 0.005765, 0.006412, 0.004019], + [0.009473, 0.007817, 0.00411, 0.006108, 0.0069, 0.004237], + [0.009869, 0.007693, 0.004205, 0.006364, 0.006993, 0.004406], + [0.009859, 0.007357, 0.004557, 0.006596, 0.007105, 0.0048], + [0.009472, 0.006649, 0.003981, 0.005577, 0.00616, 0.004025], + [0.00972, 0.007214, 0.003967, 0.005705, 0.006469, 0.004114], + [0.009781, 0.007535, 0.004129, 0.006332, 0.006967, 0.0044], + [0.008998, 0.006993, 0.003995, 0.005981, 0.006588, 0.004349], + [0.008965, 0.007864, 0.0043, 0.00638, 0.007248, 0.004713], + [0.009688, 0.008125, 0.0045, 0.006562, 0.00735, 0.004655], + [0.009386, 0.008112, 0.004081, 0.006412, 0.007248, 0.004393], + [0.009492, 0.007551, 0.003886, 0.006048, 0.006869, 0.004161], + [0.009803, 0.007173, 0.003852, 0.00592, 0.006648, 0.004171], + [0.009468, 0.007719, 0.004324, 0.006504, 0.007162, 0.004708], + [0.00919, 0.00809, 0.004238, 0.0067, 0.007405, 0.00477], + [0.009572, 0.008445, 0.004719, 0.007239, 0.007883, 0.005179], + [0.010095, 0.008001, 0.004143, 0.006375, 0.007126, 0.004413], + [0.01012, 0.00777, 0.0044, 0.00623, 0.006993, 0.004546], + [0.010347, 0.007843, 0.00461, 0.0065, 0.007074, 0.004821], + [0.010309, 0.008111, 0.004719, 0.006764, 0.007474, 0.005031], + [0.010009, 0.008877, 0.00489, 0.007371, 0.008162, 0.005248], + [0.010024, 0.009679, 0.005, 0.007724, 0.008545, 0.00542], + [0.010101, 0.008848, 0.004862, 0.007165, 0.007981, 0.005105], + [0.010214, 0.00791, 0.004452, 0.006431, 0.007071, 0.004664], + [0.010278, 0.008018, 0.004086, 0.006123, 0.00694, 0.004479], + [0.010087, 0.008654, 0.004448, 0.006748, 0.007605, 0.004894], + [0.010355, 0.008181, 0.004571, 0.006644, 0.007498, 0.005017], + [0.010427, 0.008333, 0.004762, 0.007206, 0.007821, 0.005261], + [0.011015, 0.008748, 0.004338, 0.00699, 0.007924, 0.00494], + [0.010848, 0.009258, 0.005414, 0.007926, 0.008529, 0.00593], + [0.010208, 0.008746, 0.00509, 0.007583, 0.008217, 0.005597], + [0.009757, 0.008039, 0.004595, 0.006887, 0.007631, 0.005057], + [0.010093, 0.00801, 0.004838, 0.006923, 0.007574, 0.005283], + [0.010081, 0.008942, 0.005186, 0.007589, 0.008407, 0.005759], + [0.010327, 0.009295, 0.005267, 0.007867, 0.008633, 0.005875], + [0.010199, 0.008657, 0.005767, 0.007563, 0.008121, 0.005987], + [0.010143, 0.008652, 0.00511, 0.007189, 0.007998, 0.00551], + [0.009692, 0.008573, 0.005038, 0.007301, 0.008052, 0.005444], + [0.009769, 0.00821, 0.00491, 0.007044, 0.007712, 0.00533], + [0.009577, 0.008525, 0.004795, 0.007027, 0.00786, 0.005278], + [0.009547, 0.009267, 0.005524, 0.007917, 0.008676, 0.006165], + [0.009643, 0.009277, 0.005695, 0.008032, 0.008738, 0.006319], + [0.009895, 0.00928, 0.005729, 0.007868, 0.008574, 0.006176], + [0.010718, 0.009733, 0.00549, 0.008096, 0.008912, 0.00613], + [0.010884, 0.009432, 0.005295, 0.007907, 0.00864, 0.005974], + [0.011186, 0.009346, 0.005052, 0.007533, 0.008398, 0.005693], + [0.011291, 0.009375, 0.005414, 0.007588, 0.008374, 0.005939], + [0.011956, 0.009446, 0.005329, 0.007596, 0.008531, 0.005845], + [0.011348, 0.009345, 0.0056, 0.007965, 0.008719, 0.006239], + [0.010864, 0.008571, 0.005567, 0.007408, 0.008069, 0.005916], + [0.010934, 0.008793, 0.005129, 0.007307, 0.008095, 0.005749], + [0.011081, 0.009018, 0.005414, 0.007615, 0.0084, 0.006056], + [0.010876, 0.009925, 0.005381, 0.008106, 0.009105, 0.006285], + [0.010844, 0.010088, 0.006033, 0.008535, 0.009357, 0.006739], + [0.011068, 0.010262, 0.006343, 0.008819, 0.009569, 0.006995], + [0.011229, 0.010521, 0.006876, 0.009365, 0.01, 0.007534], + [0.011388, 0.010819, 0.006719, 0.009286, 0.010055, 0.007323], + [0.0113, 0.010336, 0.006514, 0.008746, 0.009414, 0.006969], + [0.011776, 0.010332, 0.006338, 0.008826, 0.00966, 0.007092], + [0.011904, 0.01025, 0.007038, 0.009333, 0.009902, 0.007741], + [0.01186, 0.009836, 0.006876, 0.008806, 0.009355, 0.007369], + [0.011244, 0.009998, 0.006767, 0.008824, 0.009552, 0.007385], + [0.010541, 0.010074, 0.006686, 0.008862, 0.009548, 0.007277], + [0.010939, 0.009864, 0.005638, 0.008251, 0.009198, 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0.007915, 0.008457, 0.006019], + [0.011703, 0.009302, 0.006295, 0.007898, 0.00845, 0.005938], + [0.01181, 0.009289, 0.006948, 0.008345, 0.008729, 0.006593], + [0.012207, 0.009271, 0.006995, 0.00816, 0.008607, 0.006561], + [0.012137, 0.009202, 0.006443, 0.008363, 0.008776, 0.00652], + [0.011707, 0.008574, 0.005976, 0.007802, 0.008229, 0.005899], + [0.011552, 0.0084, 0.005457, 0.0072, 0.007814, 0.00548], + [0.012004, 0.008992, 0.00609, 0.007875, 0.008471, 0.006175], + [0.011993, 0.009167, 0.006248, 0.008014, 0.00865, 0.006176], + [0.01153, 0.009773, 0.006367, 0.008455, 0.009145, 0.006374], + [0.011896, 0.009225, 0.006814, 0.008319, 0.008764, 0.006564], + [0.011666, 0.00897, 0.006267, 0.00803, 0.0085, 0.00615], + [0.011368, 0.009163, 0.005957, 0.00803, 0.008588, 0.005959], + [0.011085, 0.009443, 0.005976, 0.008062, 0.008743, 0.006027], + [0.011072, 0.009193, 0.006638, 0.008548, 0.008836, 0.0066], + [0.011602, 0.00905, 0.005833, 0.00799, 0.008619, 0.005856], + [0.01244, 0.009117, 0.006814, 0.008386, 0.008833, 0.006632], + [0.011874, 0.009242, 0.005843, 0.007852, 0.008502, 0.005869], + [0.011349, 0.009207, 0.00621, 0.008037, 0.008669, 0.006128], + [0.011422, 0.008271, 0.006067, 0.007585, 0.008062, 0.005868], + [0.011062, 0.00796, 0.004752, 0.006706, 0.007405, 0.004725], + [0.011107, 0.008276, 0.005295, 0.007098, 0.007683, 0.005263], + [0.01091, 0.008195, 0.00541, 0.0072, 0.007717, 0.005366], + [0.01112, 0.008152, 0.0054, 0.00717, 0.00769, 0.005263], + [0.010993, 0.007307, 0.00501, 0.006412, 0.006886, 0.004797], + [0.010937, 0.007689, 0.004933, 0.006685, 0.007224, 0.004825], + [0.010624, 0.007639, 0.00451, 0.006338, 0.006933, 0.004482], + [0.010169, 0.008821, 0.00479, 0.007113, 0.007929, 0.004914], + [0.010332, 0.009127, 0.005543, 0.007785, 0.008443, 0.005641], + [0.010528, 0.007977, 0.005586, 0.007189, 0.007598, 0.005354], + [0.010656, 0.007851, 0.005152, 0.006639, 0.007274, 0.004995], + [0.010928, 0.009048, 0.005333, 0.007562, 0.008338, 0.005418], + [0.011414, 0.008946, 0.005629, 0.007704, 0.008281, 0.005572], + [0.011492, 0.008282, 0.005048, 0.007038, 0.007724, 0.005001], + [0.01091, 0.008123, 0.005086, 0.007113, 0.007712, 0.005007], + [0.01069, 0.008071, 0.00519, 0.006805, 0.007502, 0.005049], + [0.010311, 0.007923, 0.004786, 0.006554, 0.007293, 0.004736], + [0.010774, 0.007794, 0.0048, 0.007061, 0.007612, 0.004977], + [0.010538, 0.007211, 0.004543, 0.006189, 0.006793, 0.004352], + [0.010193, 0.007412, 0.004252, 0.006079, 0.006833, 0.004265], + [0.010178, 0.008175, 0.0045, 0.006533, 0.007424, 0.00459], + [0.010176, 0.008385, 0.005262, 0.007142, 0.007857, 0.005239], + [0.009689, 0.00764, 0.004762, 0.006556, 0.007105, 0.004494], + [0.009405, 0.007095, 0.004105, 0.005948, 0.006612, 0.003906], + [0.010011, 0.006248, 0.004152, 0.005548, 0.005948, 0.003723], + [0.010304, 0.006649, 0.003848, 0.005436, 0.006088, 0.0036], + [0.009848, 0.007485, 0.004376, 0.006337, 0.006964, 0.004437], + [0.010184, 0.008096, 0.005119, 0.007088, 0.007724, 0.005085], + [0.009652, 0.00765, 0.004686, 0.006511, 0.007274, 0.004692], + [0.009625, 0.008175, 0.004614, 0.006756, 0.007564, 0.004848], +], dtype=np.float64) diff --git a/epymorph/data/registry.py b/epymorph/data/registry.py index 3d59ec94..0e20f7b3 100644 --- a/epymorph/data/registry.py +++ b/epymorph/data/registry.py @@ -1,24 +1,19 @@ """ -Library creation and registration for built-in IPMs, MMs, and GEOs. +Library registration for built-in IPMs and MMs. """ from importlib import import_module -from importlib.abc import Traversable -from importlib.resources import as_file, files -from inspect import signature -from typing import Callable, NamedTuple, TypeGuard, TypeVar, cast +from importlib.resources import files +from inspect import isclass +from typing import Callable, Mapping, NamedTuple, TypeVar from epymorph.compartment_model import CompartmentModel -from epymorph.error import ModelRegistryException -from epymorph.geo.adrio import adrio_maker_library -from epymorph.geo.dynamic import DynamicGeo, DynamicGeoFileOps -from epymorph.geo.geo import Geo -from epymorph.geo.static import StaticGeo, StaticGeoFileOps -from epymorph.movement.parser import MovementSpec, parse_movement_spec +from epymorph.movement_model import MovementModel from epymorph.util import as_sorted_dict ModelT = TypeVar('ModelT') LoaderFunc = Callable[[], ModelT] -Library = dict[str, LoaderFunc[ModelT]] +Library = Mapping[str, LoaderFunc[ModelT]] +ClassLibrary = Mapping[str, type[ModelT]] class _ModelRegistryInfo(NamedTuple): @@ -35,17 +30,19 @@ def annotation(self) -> str: """Get the ID annotation name for this model type.""" return f'__epymorph_{self.name}_id__' - def get_model_id(self, func: Callable) -> str | None: + def get_model_id(self, obj: object) -> str: """Retrieves the tagged model ID.""" - value = getattr(func, self.annotation, None) - return value if isinstance(value, str) else None + value = getattr(obj, self.annotation, None) + if isinstance(value, str): + return value + else: + raise ValueError("Unable to load model ID during model registry.") - def set_model_id(self, func: Callable, model_id: str) -> None: + def set_model_id(self, obj: object, model_id: str) -> None: """Sets a model ID as a tag.""" - setattr(func, self.annotation, model_id) + setattr(obj, self.annotation, model_id) -GEO_REGISTRY = _ModelRegistryInfo('geo') IPM_REGISTRY = _ModelRegistryInfo('ipm') MM_REGISTRY = _ModelRegistryInfo('mm') @@ -54,36 +51,28 @@ def set_model_id(self, func: Callable, model_id: str) -> None: def ipm(ipm_id: str): - """Decorates an IPM loader so we can register it with the system.""" - def make_decorator(func: LoaderFunc[CompartmentModel]) -> LoaderFunc[CompartmentModel]: - IPM_REGISTRY.set_model_id(func, ipm_id) - return func + """Decorates an IPM class so we can register it with the system.""" + def make_decorator(model: type[CompartmentModel]) -> type[CompartmentModel]: + IPM_REGISTRY.set_model_id(model, ipm_id) + return model return make_decorator def mm(mm_id: str): - """Decorates an IPM loader so we can register it with the system.""" - def make_decorator(func: LoaderFunc[MovementSpec]) -> LoaderFunc[MovementSpec]: - MM_REGISTRY.set_model_id(func, mm_id) - return func - return make_decorator - - -def geo(geo_id: str): - """Decorates an IPM loader so we can register it with the system.""" - def make_decorator(func: LoaderFunc[Geo]) -> LoaderFunc[Geo]: - GEO_REGISTRY.set_model_id(func, geo_id) - return func + """Decorates an MM class so we can register it with the system.""" + def make_decorator(model: type[MovementModel]) -> type[MovementModel]: + MM_REGISTRY.set_model_id(model, mm_id) + return model return make_decorator # Discovery and loading utilities -DiscoverT = TypeVar('DiscoverT', bound=CompartmentModel | MovementSpec | StaticGeo) +DiscoverT = TypeVar('DiscoverT', bound=CompartmentModel | MovementModel) -def _discover(model: _ModelRegistryInfo, library_type: type[DiscoverT]) -> Library[DiscoverT]: +def _discover_classes(model: _ModelRegistryInfo, library_type: type[DiscoverT]) -> ClassLibrary[DiscoverT]: """ Search for the specified type of model, implemented by the specified Python class. """ @@ -91,21 +80,6 @@ def _discover(model: _ModelRegistryInfo, library_type: type[DiscoverT]) -> Libra # you'll just probably come up empty in that scenario. But this is an internal method, # so no need to be over-careful. - def is_loader(func: Callable) -> TypeGuard[LoaderFunc[DiscoverT]]: - """Is `func` an acceptable model loader?""" - if not callable(func): - return False - if model.get_model_id(func) is None: - return False - sig = signature(func, eval_str=True) - if len(sig.parameters) > 0 or sig.return_annotation != library_type: - msg = f"""\ -Attempted to register model of type '{model.name}' with an invalid method signature. -See function '{func.__name__}' in {func.__module__} -The function must take zero parameters and its return-type must be correctly annotated ({library_type.__name__}).""" - raise ModelRegistryException(msg) - return True - in_path = model.path modules = [ import_module(f"{in_path}.{f.name.removesuffix('.py')}") @@ -114,83 +88,23 @@ def is_loader(func: Callable) -> TypeGuard[LoaderFunc[DiscoverT]]: ] return { - cast(str, model.get_model_id(x)): x + model.get_model_id(x): x for mod in modules for x in mod.__dict__.values() - if callable(x) and x.__module__ == mod.__name__ and is_loader(x) + if isclass(x) and issubclass(x, library_type) and x.__module__ == mod.__name__ } -def _mm_spec_loader(mm_spec_file: Traversable) -> Callable[[], MovementSpec]: - """Returns a function to load the identified movement model.""" - def load() -> MovementSpec: - with as_file(mm_spec_file) as file: - spec_string = file.read_text(encoding="utf-8") - return parse_movement_spec(spec_string) - return load - - -def _geo_spec_loader(geo_spec_file: Traversable) -> Callable[[], DynamicGeo]: - """Returns a function to load the identified GEO (from spec).""" - def load() -> DynamicGeo: - with as_file(geo_spec_file) as file: - return DynamicGeoFileOps.load_from_spec(file, adrio_maker_library) - return load - - -def _geo_archive_loader(geo_archive_file: Traversable) -> Callable[[], StaticGeo]: - """Returns a function to load a static geo from its archive file.""" - def load() -> StaticGeo: - with as_file(geo_archive_file) as file: - return StaticGeoFileOps.load_from_archive(file) - return load - - -_MM_DIR = files(MM_REGISTRY.path) -_GEO_DIR = files(GEO_REGISTRY.path) +# The model libraries -# The model libraries (and useful library subsets) - - -ipm_library: Library[CompartmentModel] = as_sorted_dict({ - **_discover(IPM_REGISTRY, CompartmentModel), +ipm_library: ClassLibrary[CompartmentModel] = as_sorted_dict({ + **_discover_classes(IPM_REGISTRY, CompartmentModel), }) """All epymorph intra-population models (by id).""" -mm_library_parsed: Library[MovementSpec] = as_sorted_dict({ - # Auto-discover all .movement files in the data/mm path. - f.name.removesuffix('.movement'): _mm_spec_loader(f) - for f in _MM_DIR.iterdir() - if f.name.endswith('.movement') -}) -"""The subset of MMs that are parsed from movement files.""" -mm_library: Library[MovementSpec] = as_sorted_dict({ - **_discover(MM_REGISTRY, MovementSpec), - **mm_library_parsed, +mm_library: ClassLibrary[MovementModel] = as_sorted_dict({ + **_discover_classes(MM_REGISTRY, MovementModel), }) """All epymorph movement models (by id).""" - -geo_library_static: Library[StaticGeo] = as_sorted_dict({ - # Auto-discover all .geo.tgz files in the data/geo path. - name: _geo_archive_loader(file) - for file, name in StaticGeoFileOps.iterate_dir(_GEO_DIR) -}) -"""The subset of GEOs that are saved as archive files.""" - -geo_library_dynamic: Library[DynamicGeo] = as_sorted_dict({ - # Auto-discover all .geo (spec) files in the data/geo path. - f.name.removesuffix('.geo'): _geo_spec_loader(f) - for f in _GEO_DIR.iterdir() - if f.name.endswith('.geo') -}) -"""The subset of GEOs that are assembled through geospecs.""" - -geo_library: Library[Geo] = as_sorted_dict({ - # Combine static, dynamic, and Python geos. - **_discover(GEO_REGISTRY, StaticGeo), - **geo_library_static, - **geo_library_dynamic, -}) -"""All epymorph geo models (by id).""" diff --git a/epymorph/data_shape.py b/epymorph/data_shape.py index 4a5d57c9..b591a567 100644 --- a/epymorph/data_shape.py +++ b/epymorph/data_shape.py @@ -33,11 +33,11 @@ def build( tau_steps = len(tau_step_lengths) ticks = tau_steps * days return cls( - tau_step_lengths, tau_steps, start_date, days, ticks, + tuple(tau_step_lengths), tau_steps, start_date, days, ticks, nodes, compartments, events, (ticks, nodes, compartments, events)) - tau_step_lengths: Sequence[float] + tau_step_lengths: tuple[float, ...] """The lengths of each tau step in the MM.""" tau_steps: int """How many tau steps are in the MM?""" diff --git a/epymorph/data_type.py b/epymorph/data_type.py index 37dc2f3f..201f123b 100644 --- a/epymorph/data_type.py +++ b/epymorph/data_type.py @@ -2,7 +2,7 @@ Types for source data and attributes in epymorph. """ from datetime import date -from typing import Any, Sequence +from typing import Any import numpy as np from numpy.typing import DTypeLike, NDArray @@ -10,8 +10,13 @@ # Types for attribute declarations: # these are expressed as Python types for simplicity. +# NOTE: In epymorph, we express structured types as tuples-of-tuples; +# this way they're hashable, which is important for AttributeDef. +# However numpy expresses them as lists-of-tuples, so we have to convert; +# thankfully we had an infrastructure for this sort of thing already. + ScalarType = type[int | float | str | date] -StructType = Sequence[tuple[str, ScalarType]] +StructType = tuple[tuple[str, ScalarType], ...] AttributeType = ScalarType | StructType """The allowed type declarations for epymorph attributes.""" @@ -38,16 +43,16 @@ def dtype_as_np(dtype: AttributeType) -> np.dtype: return np.dtype(np.str_) if dtype == date: return np.dtype(np.datetime64) - if isinstance(dtype, Sequence): - dtype = list(dtype) - if len(dtype) == 0: + if isinstance(dtype, tuple): + fields = list(dtype) + if len(fields) == 0: raise ValueError(f"Unsupported dtype: {dtype}") try: return np.dtype([ (field_name, dtype_as_np(field_dtype)) - for field_name, field_dtype in dtype + for field_name, field_dtype in fields ]) - except TypeError: + except (TypeError, ValueError): raise ValueError(f"Unsupported dtype: {dtype}") from None raise ValueError(f"Unsupported dtype: {dtype}") @@ -62,17 +67,17 @@ def dtype_str(dtype: AttributeType) -> str: return "str" if dtype == date: return "date" - if isinstance(dtype, Sequence): - dtype = list(dtype) - if len(dtype) == 0: + if isinstance(dtype, tuple): + fields = list(dtype) + if len(fields) == 0: raise ValueError(f"Unsupported dtype: {dtype}") try: values = [ f"({field_name}, {dtype_str(field_dtype)})" - for field_name, field_dtype in dtype + for field_name, field_dtype in fields ] return f"[{', '.join(values)}]" - except TypeError: + except (TypeError, ValueError): raise ValueError(f"Unsupported dtype: {dtype}") from None raise ValueError(f"Unsupported dtype: {dtype}") @@ -81,22 +86,22 @@ def dtype_check(dtype: AttributeType, value: Any) -> bool: """Checks that a value conforms to a given dtype. (Python types only.)""" if dtype in (int, float, str, date): return isinstance(value, dtype) - if isinstance(dtype, Sequence): - dtype = list(dtype) + if isinstance(dtype, tuple): + fields = list(dtype) if not isinstance(value, tuple): return False - if len(value) != len(dtype): + if len(value) != len(fields): return False return all(( dtype_check(field_dtype, field_value) - for ((_, field_dtype), field_value) in zip(dtype, value) + for ((_, field_dtype), field_value) in zip(fields, value) )) raise ValueError(f"Unsupported dtype: {dtype}") -CentroidType: AttributeType = [('longitude', float), ('latitude', float)] +CentroidType: AttributeType = (('longitude', float), ('latitude', float)) """Structured epymorph type declaration for long/lat coordinates.""" -CentroidDType: DTypeLike = [('longitude', np.float64), ('latitude', np.float64)] +CentroidDType: DTypeLike = dtype_as_np(CentroidType) """The numpy equivalent of `CentroidType` (structured dtype for long/lat coordinates).""" # SimDType being centrally-located means we can change it reliably. diff --git a/epymorph/database.py b/epymorph/database.py index 0768b56d..f55e56d6 100644 --- a/epymorph/database.py +++ b/epymorph/database.py @@ -86,14 +86,17 @@ def parse_with_defaults(cls, name: str, strata: str, module: str) -> Self: Parse a module name from a ::-delimited string, where strata and module can be omitted to be filled from defaults. """ + def replace_star(string: str, default_value: str) -> str: + return default_value if string == "*" else string + parts = name.split("::") - match len(parts): - case 1: - return cls(strata, module, *parts) - case 2: - return cls(strata, *parts) - case 3: - return cls(*parts) + match parts: + case [i]: + return cls(strata, module, i) + case [m, i]: + return cls(strata, replace_star(m, module), i) + case [s, m, i]: + return cls(replace_star(s, strata), replace_star(m, module), i) case _: raise ValueError("Invalid number of parts for absolute name.") diff --git a/epymorph/draw.py b/epymorph/draw.py index 3a8dd478..73b38b37 100644 --- a/epymorph/draw.py +++ b/epymorph/draw.py @@ -9,7 +9,7 @@ from matplotlib.image import imread from sympy import Expr, preview -from epymorph.compartment_model import CompartmentModel +from epymorph.compartment_model import BaseCompartmentModel class EdgeTracker: @@ -66,7 +66,7 @@ def check_draw_requirements() -> bool: return latex_check is not None and graphviz_check is not None -def build_ipm_edge_set(ipm: CompartmentModel) -> EdgeTracker: +def build_ipm_edge_set(ipm: BaseCompartmentModel) -> EdgeTracker: """ given an ipm, creates an edge tracker object that converts the transitions of the ipm into a set of adjacencies. @@ -166,7 +166,7 @@ def draw_console(graph: Digraph): plt.show() -def draw_and_return(ipm: CompartmentModel, console: bool) -> Digraph | None: +def draw_and_return(ipm: BaseCompartmentModel, console: bool) -> Digraph | None: """ main function for converting an ipm into a visual model to be displayed by default in jupyter notebook, but optionally to console. @@ -196,14 +196,14 @@ def draw_and_return(ipm: CompartmentModel, console: bool) -> Digraph | None: return ipm_graph -def render(ipm: CompartmentModel, console: bool = False) -> None: +def render(ipm: BaseCompartmentModel, console: bool = False) -> None: """ default render function, draws to jupyter by default """ draw_and_return(ipm, console) -def render_and_save(ipm: CompartmentModel, file_path: str, +def render_and_save(ipm: BaseCompartmentModel, file_path: str, console: bool = False) -> None: """ render function that saves to file system, draws jupyter by default diff --git a/epymorph/event.py b/epymorph/event.py index 5ddb6480..7ebfe55f 100644 --- a/epymorph/event.py +++ b/epymorph/event.py @@ -3,22 +3,29 @@ The idea is to have a set of classes which define event protocols for logical components of epymorph. """ -from typing import NamedTuple, Protocol, runtime_checkable +from typing import NamedTuple from numpy.typing import NDArray from epymorph.data_shape import SimDimensions from epymorph.data_type import SimDType +from epymorph.database import AbsoluteName from epymorph.simulation import TimeFrame -from epymorph.util import Event +from epymorph.util import Event, Singleton -# Simulation Events +##################### +# Simulation Events # +##################### class OnStart(NamedTuple): - """The payload of a Simulation on_start event.""" + """The payload of a simulation on_start event.""" + simulator: str + """Name of the simulator class.""" dim: SimDimensions + """The dimensions of the simulation.""" time_frame: TimeFrame + """The timeframe for the simulation.""" class OnTick(NamedTuple): @@ -27,48 +34,9 @@ class OnTick(NamedTuple): percent_complete: float -@runtime_checkable -class SimulationEvents(Protocol): - """ - Protocol for Simulations that support lifecycle events. - For correct operation, ensure that `on_start` is fired first, - then `on_tick` any number of times, then finally `on_finish`. - """ - - on_start: Event[OnStart] - """ - Event fires at the start of a simulation run. Payload is a subset of the context for this run. - """ - - on_tick: Event[OnTick] - """ - Event which fires after each tick has been processed. - Event payload is a tuple containing the tick index just completed (an integer), - and the percentage complete (a float). - """ - - on_finish: Event[None] - """ - Event fires after a simulation run is complete. - """ - - -class SimulationEventsMixin(SimulationEvents): - """A mixin implementation of the SimulationEvents protocol which initializes the events.""" - - def __init__(self): - self.on_start = Event() - self.on_tick = Event() - self.on_finish = Event() - - def has_subscribers(self) -> bool: - """True if there is at least one subscriber on any simulation event.""" - return self.on_start.has_subscribers \ - or self.on_tick.has_subscribers \ - or self.on_finish.has_subscribers - - -# Movement Events +################### +# Movement Events # +################### class OnMovementStart(NamedTuple): @@ -116,85 +84,76 @@ class OnMovementFinish(NamedTuple): """The total number of individuals moved during this tick.""" -@runtime_checkable -class MovementEvents(Protocol): - """ - Mixin for Simulations that support movement events. - For correct operation, ensure that `on_movement_start` is fired first, - then `on_movement_clause` any number of times, then finally `on_movement_finish`. - """ - - on_movement_start: Event[OnMovementStart] - """ - Event fires at the start of the movement processing phase for every simulation tick. - """ - - on_movement_clause: Event[OnMovementClause] - """ - Event fires after every movement clause has been processed, excluding clauses that are not triggered in this tick. - """ +################ +# ADRIO Events # +################ - on_movement_finish: Event[OnMovementFinish] - """ - Event fires at the end of the movement processing phase for every simulation tick. - """ +class AdrioStart(NamedTuple): + """The payload of AdrioEvents.on_adrio_start""" + adrio_name: str + """The name of the ADRIO.""" + attribute: AbsoluteName + """The name of the attribute.""" -class MovementEventsMixin(MovementEvents): - """A mixin implementation of the MovementEvents protocol which initializes the events.""" - def __init__(self): - self.on_movement_start = Event() - self.on_movement_clause = Event() - self.on_movement_finish = Event() +class AdrioFinish(NamedTuple): + """The payload of AdrioEvents.on_adrio_finish""" + adrio_name: str + """The name of the ADRIO.""" + attribute: AbsoluteName + """The name of the attribute.""" + duration: float + """The number of seconds spent fetching.""" - def has_subscribers(self) -> bool: - """True if there is at least one subscriber on any movement event.""" - return self.on_movement_start.has_subscribers \ - or self.on_movement_clause.has_subscribers \ - or self.on_movement_finish.has_subscribers +############ +# EventBus # +############ -class SimWithEvents(SimulationEvents, MovementEvents, Protocol): - """Intersection type of SimulationEvents and MovementEvents""" +class EventBus(metaclass=Singleton): + """The one-stop for epymorph events. This class uses the singleton pattern.""" -# Geo/ADRIO Events + # Simulation Events + on_start: Event[OnStart] + """Event fires at the start of a simulation run.""" + on_tick: Event[OnTick] + """Event fires after each tick has been processed.""" -class FetchStart(NamedTuple): - """The payload of a DynamicGeo fetch_start event.""" - adrio_len: int + on_finish: Event[None] + """Event fires after a simulation run is complete.""" + # Movement Events + on_movement_start: Event[OnMovementStart] + """Event fires at the start of the movement processing phase for every simulation tick.""" -class AdrioStart(NamedTuple): - """The payload of a DynamicGeo adrio_start event.""" - attribute: str - adrio_index: int | None - """An index assigned to this ADRIO if fetching ADRIOs as a batch.""" - adrio_len: int | None - """The total number of ADRIOs being fetched if fetching ADRIOs as a batch.""" + on_movement_clause: Event[OnMovementClause] + """Event fires after processing each active movement clause.""" + on_movement_finish: Event[OnMovementFinish] + """Event fires at the end of the movement processing phase for every simulation tick.""" -@runtime_checkable -class DynamicGeoEvents(Protocol): - """ - Protocol for DynamicGeos that support lifecycle events. - For correct operation, ensure that `fetch_start` is fired first, - then `adrio_start` any number of times, then finally `fetch_end`. - """ + # ADRIO Events + on_adrio_start: Event[AdrioStart] + """Event fires when an ADRIO is fetching data.""" - fetch_start: Event[FetchStart] - """ - Event that fires when geo begins fetching attributes. Payload is the number of ADRIOs. - """ + # on_adrio_progress: Event[AdrioProgress] + # """Event that fires when...""" - adrio_start: Event[AdrioStart] - """ - Event that fires when an individual ADRIO begins data retreival. Payload is the attribute name and index. - """ + on_adrio_finish: Event[AdrioFinish] + """Event fires when an ADRIO has finished fetching data.""" - fetch_end: Event[None] - """ - Event that fires when data retreival is complete. - """ + def __init__(self): + # SimulationEvents + self.on_start = Event() + self.on_tick = Event() + self.on_finish = Event() + # MovementEvents + self.on_movement_start = Event() + self.on_movement_clause = Event() + self.on_movement_finish = Event() + # AdrioEvents + self.on_adrio_start = Event() + self.on_adrio_finish = Event() diff --git a/epymorph/geo/__init__.py b/epymorph/geo/__init__.py deleted file mode 100644 index 929f5c76..00000000 --- a/epymorph/geo/__init__.py +++ /dev/null @@ -1,25 +0,0 @@ -"""epymorph's geo package and exports""" -from epymorph.geo.cache import load_from_cache, save_to_cache -from epymorph.geo.dynamic import DynamicGeo -from epymorph.geo.spec import (DateAndDuration, DateRange, DynamicGeoSpec, - Geography, GeoSpec, NonspecificDuration, - SpecificTimePeriod, StaticGeoSpec, TimePeriod, - Year) -from epymorph.geo.static import StaticGeo - -__all__ = [ - 'DateAndDuration', - 'DateRange', - 'DynamicGeoSpec', - 'Geography', - 'GeoSpec', - 'NonspecificDuration', - 'SpecificTimePeriod', - 'StaticGeoSpec', - 'TimePeriod', - 'Year', - 'DynamicGeo', - 'StaticGeo', - 'save_to_cache', - 'load_from_cache', -] diff --git a/epymorph/geo/adrio/__init__.py b/epymorph/geo/adrio/__init__.py deleted file mode 100644 index 6c010271..00000000 --- a/epymorph/geo/adrio/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -"""AdrioMaker library.""" -from epymorph.geo.adrio.cdc.adrio_cdc import ADRIOMakerCDC -from epymorph.geo.adrio.census.adrio_census import ADRIOMakerCensus -from epymorph.geo.adrio.census.lodes import ADRIOMakerLODES -from epymorph.geo.adrio.file.adrio_csv import ADRIOMakerCSV -from epymorph.geo.dynamic import ADRIOMaker - -adrio_maker_library: dict[str, type[ADRIOMaker]] = { - 'Census': ADRIOMakerCensus, - 'CSV': ADRIOMakerCSV, - 'LODES': ADRIOMakerLODES, - 'CDC': ADRIOMakerCDC -} diff --git a/epymorph/geo/adrio/adrio.py b/epymorph/geo/adrio/adrio.py deleted file mode 100644 index b831eb01..00000000 --- a/epymorph/geo/adrio/adrio.py +++ /dev/null @@ -1,54 +0,0 @@ -""" -ADRIOs enable dynamic geos to fetch data from varied external data sources, -and ADRIOMakers create ADRIOs for a data soruce and specialized for a geo's purposes. -""" -from abc import ABC, abstractmethod -from typing import Any, Callable - -from numpy.typing import NDArray - -from epymorph.geo.spec import TimePeriod -from epymorph.geography.scope import GeoScope -from epymorph.simulation import AttributeDef - - -class ADRIO: - """Data retrieval class that fetches and stores a data value on demand.""" - - attrib: str - """The name of the attribute to fetch.""" - - _fetch: Callable[[], NDArray] - """The function that carries out data retrieval.""" - - _cached_value: NDArray | None - """The stored value of the attribute once retrieved.""" - - def __init__(self, attrib: str, fetch_data: Callable[[], NDArray]) -> None: - self.attrib = attrib - self._fetch = fetch_data - self._cached_value = None - - def get_value(self) -> NDArray: - """Returns cached data value or retrieves it using callable fetch function if not yet cached.""" - if self._cached_value is None: - self._cached_value = self._fetch() - return self._cached_value - - -class ADRIOMaker(ABC): - """Abstract class to serve as an outline for ADRIO makers for specific data sources.""" - attributes: list[AttributeDef] - - @staticmethod - @abstractmethod - def accepts_source(source: Any) -> bool: - """Checks whether the ADRIOMaker accepts a given source type and returns the result as a boolean.""" - - @abstractmethod - def make_adrio(self, attrib: AttributeDef, scope: GeoScope, time_period: TimePeriod, source: Any | None = None) -> ADRIO: - """Creates an ADRIO to fetch the specified attribute for the specified time and place.""" - - -ADRIOMakerLibrary = dict[str, type[ADRIOMaker]] -"""ADRIOMaker objects for all supported data sources.""" diff --git a/epymorph/geo/adrio/cdc/adrio_cdc.py b/epymorph/geo/adrio/cdc/adrio_cdc.py deleted file mode 100644 index 37e14540..00000000 --- a/epymorph/geo/adrio/cdc/adrio_cdc.py +++ /dev/null @@ -1,389 +0,0 @@ -from datetime import date -from typing import Any, NamedTuple -from urllib.parse import quote, urlencode -from warnings import warn - -import numpy as np -from numpy.typing import NDArray -from pandas import DataFrame, concat, read_csv - -from epymorph.data_shape import Shapes -from epymorph.error import DataResourceException, GeoValidationException -from epymorph.geo.adrio.adrio import ADRIO, ADRIOMaker -from epymorph.geo.spec import SpecificTimePeriod, TimePeriod -from epymorph.geography.scope import GeoScope -from epymorph.geography.us_census import (STATE, CensusGranularityName, - CensusScope, CountyScope, StateScope, - StateScopeAll, get_us_states, - state_fips_to_code) -from epymorph.simulation import AttributeDef - - -class QueryInfo(NamedTuple): - url_base: str - date_col: str - fips_col: str - data_col: str - state_level: bool = False - """Whether we are querying a dataset reporting state-level data.""" - - -class ADRIOMakerCDC(ADRIOMaker): - """ - CDC ADRIO template to serve as a parent class for ADRIOs that fetch data from various - HealthData and CDC datasets. - """ - - attributes = [ - AttributeDef("covid_cases_per_100k", float, Shapes.TxN, - comment='Number of COVID-19 cases per 100k population.'), - AttributeDef("covid_hospitalizations_per_100k", float, Shapes.TxN, - comment='Number of COVID-19 hospitalizations per 100k population.'), - AttributeDef("covid_hospitalization_avg_facility", float, Shapes.TxN, - comment='Weekly averages of COVID-19 hospitalizations from facility level dataset.'), - AttributeDef("covid_hospitalization_sum_facility", float, Shapes.TxN, - comment='Weekly sums of all COVID-19 hospitalizations from facility level dataset.'), - AttributeDef("influenza_hospitalization_avg_facility", float, Shapes.TxN, - comment='Weekly averages of influenza hospitalizations from facility level dataset.'), - AttributeDef("influenza_hospitalization_sum_facility", float, Shapes.TxN, - comment='Weekly sums of influenza hospitalizations from facility level dataset.'), - AttributeDef("covid_hospitalization_avg_state", float, Shapes.TxN, - comment='Weekly averages of COVID-19 hospitalizations from state level dataset.'), - AttributeDef("covid_hospitalization_sum_state", float, Shapes.TxN, - comment='Weekly sums of COVID-19 hospitalizations from state level dataset.'), - AttributeDef("influenza_hospitalization_avg_state", float, Shapes.TxN, - comment='Weekly averages of influenza hospitalizations from state level dataset.'), - AttributeDef("influenza_hospitalization_sum_state", float, Shapes.TxN, - comment='Weekly sums of influenza hospitalizations from state level dataset.'), - AttributeDef("full_covid_vaccinations", float, Shapes.TxN, - comment='Cumulative total number of individuals fully vaccinated for COVID-19.'), - AttributeDef("one_dose_covid_vaccinations", float, Shapes.TxN, - comment='Cumulative total number of individuals with at least one dose of COVID-19 vaccination.'), - AttributeDef("covid_booster_doses", float, Shapes.TxN, - comment='Cumulative total number of COVID-19 booster doses administered.'), - AttributeDef("covid_deaths_county", float, Shapes.TxN, - comment='Weekly total COVID-19 deaths from county level dataset.'), - AttributeDef("covid_deaths_state", float, Shapes.TxN, - comment='Weekly total COVID-19 deaths from state level dataset.'), - AttributeDef("influenza_deaths", float, Shapes.TxN, - comment='Weekly total influenza deaths from state level dataset.') - ] - - attribute_cols = { - "covid_cases_per_100k": "covid_cases_per_100k", - "covid_hospitalizations_per_100k": "covid_hospital_admissions_per_100k", - "covid_hospitalization_avg_facility": "total_adult_patients_hospitalized_confirmed_covid_7_day_avg", - "covid_hospitalization_sum_facility": "total_adult_patients_hospitalized_confirmed_covid_7_day_sum", - "influenza_hospitalization_avg_facility": "total_patients_hospitalized_confirmed_influenza_7_day_avg", - "influenza_hospitalization_sum_facility": "total_patients_hospitalized_confirmed_influenza_7_day_sum", - "covid_hospitalization_avg_state": "avg_admissions_all_covid_confirmed", - "covid_hospitalization_sum_state": "total_admissions_all_covid_confirmed", - "influenza_hospitalization_avg_state": "avg_admissions_all_influenza_confirmed", - "influenza_hospitalization_sum_state": "total_admissions_all_influenza_confirmed", - "full_covid_vaccinations": "series_complete_yes", - "one_dose_covid_vaccinations": "administered_dose1_recip", - "covid_booster_doses": "booster_doses", - "covid_deaths_county": "covid_19_deaths", - "covid_deaths_state": "covid_19_deaths", - "influenza_deaths": "influenza_deaths" - } - - @staticmethod - def accepts_source(source: Any) -> bool: - return False - - def make_adrio(self, attrib: AttributeDef, scope: GeoScope, time_period: TimePeriod, source: Any | None = None) -> ADRIO: - if attrib not in self.attributes: - msg = f"{attrib.name} is not supported for the CDC data source." - raise GeoValidationException(msg) - if not isinstance(scope, StateScope | StateScopeAll | CountyScope): - msg = "CDC data requires a CensusScope object and can only be retrieved for state and county granularities." - raise GeoValidationException(msg) - if not isinstance(time_period, SpecificTimePeriod): - msg = "CDC data requires a specific time period." - raise GeoValidationException(msg) - - if attrib.name in ["covid_cases_per_100k", "covid_hospitalizations_per_100k"]: - return self._make_cases_adrio(attrib, scope, time_period) - elif attrib.name in ["covid_hospitalization_avg_facility", "covid_hospitalization_sum_facility", "influenza_hospitalization_avg_facility", "influenza_hospitalization_sum_facility"]: - return self._make_facility_hospitalization_adrio(attrib, scope, time_period) - elif attrib.name in ["covid_hospitalization_avg_state", "covid_hospitalization_sum_state", "influenza_hospitalization_avg_state", "influenza_hospitalization_sum_state"]: - return self._make_state_hospitalization_adrio(attrib, scope, time_period) - elif attrib.name in ["full_covid_vaccinations", "one_dose_covid_vaccinations", "covid_booster_doses"]: - return self._make_vaccination_adrio(attrib, scope, time_period) - elif attrib.name == "covid_deaths_county": - return self._make_deaths_adrio_county(attrib, scope, time_period) - elif attrib.name in ["covid_deaths_state", "influenza_deaths"]: - return self._make_deaths_adrio_state(attrib, scope, time_period) - else: - raise GeoValidationException(f"Invalid attribute: {attrib.name}.") - - def _make_cases_adrio(self, attrib: AttributeDef, scope: CensusScope, time_period: SpecificTimePeriod) -> ADRIO: - """ - Makes ADRIOs for HealthData dataset reporting COVID-19 cases per 100k population. - Available between 2/24/2022 and 5/4/2023 at state and county granularities. - https://healthdata.gov/dataset/United-States-COVID-19-Community-Levels-by-County/nn5b-j5u9/about_data - """ - if time_period.start_date <= date(2022, 2, 17) or time_period.end_date >= date(2023, 5, 11): - msg = "COVID cases data is only available between 2/24/2022 and 5/4/2023." - raise DataResourceException(msg) - - def fetch() -> NDArray: - info = QueryInfo("https://data.cdc.gov/resource/3nnm-4jni.csv?", - "date_updated", "county_fips", self.attribute_cols[attrib.name]) - - df = self._api_query(info, scope.get_node_ids(), - time_period, scope.granularity) - - df.rename(columns={'county_fips': 'fips'}, inplace=True) - - if scope.granularity == 'state': - df['fips'] = [STATE.extract(x) for x in df['fips']] - - df = df.groupby(['date_updated', 'fips']).sum() - df.reset_index(inplace=True) - - df = df.pivot(index='date_updated', columns='fips', values=info.data_col) - - dates = df.index.to_numpy(dtype='datetime64[D]') - - return np.array([ - list(zip(dates, df[col])) - for col in df.columns - ], dtype=[('date', 'datetime64[D]'), ('data', attrib.dtype)]).T - - return ADRIO(attrib.name, fetch) - - def _make_facility_hospitalization_adrio(self, attrib: AttributeDef, scope: CensusScope, time_period: SpecificTimePeriod) -> ADRIO: - """ - Makes ADRIOs for HealthData dataset reporting number of people hospitalized for COVID-19 - and other respiratory illnesses at facility level during manditory reporting period. - Available between 12/13/2020 and 5/10/2023 at state and county granularities. - https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/anag-cw7u/about_data - """ - if time_period.start_date <= date(2020, 12, 6) or time_period.end_date >= date(2023, 5, 17): - msg = "Facility level hospitalization data is only available between 12/13/2020 and 5/10/2023." - raise DataResourceException(msg) - - def fetch() -> NDArray: - info = QueryInfo("https://healthdata.gov/resource/anag-cw7u.csv?", - "collection_week", "fips_code", self.attribute_cols[attrib.name]) - - df = self._api_query(info, scope.get_node_ids(), - time_period, scope.granularity) - - if scope.granularity == 'state': - df['fips_code'] = [STATE.extract(x) for x in df['fips_code']] - - # if the sentinel value '-999999' appears in the data, ensure aggregated value is also -999999 - df['is_sentinel'] = df[info.data_col] == -999999 - df = df.groupby(['collection_week', 'fips_code']).agg( - {info.data_col: 'sum', 'is_sentinel': any}) - df.loc[df['is_sentinel'], info.data_col] = -999999 - - df.reset_index(inplace=True) - df = df.pivot(index='collection_week', - columns='fips_code', values=info.data_col) - - dates = df.index.to_numpy(dtype='datetime64[D]') - - return np.array([ - list(zip(dates, df[col])) - for col in df.columns - ], dtype=[('date', 'datetime64[D]'), ('data', attrib.dtype)]).T - - return ADRIO(attrib.name, fetch) - - def _make_state_hospitalization_adrio(self, attrib: AttributeDef, scope: CensusScope, time_period: SpecificTimePeriod) -> ADRIO: - """ - Makes ADRIOs for CDC dataset reporting number of people hospitalized for COVID-19 - and other respiratory illnesses at state level during manditory and voluntary reporting periods. - Available from 1/4/2020 to present at state granularity. Data reported voluntarily past 5/1/2024. - https://data.cdc.gov/Public-Health-Surveillance/Weekly-United-States-Hospitalization-Metrics-by-Ju/aemt-mg7g/about_data - """ - if scope.granularity != 'state': - msg = "State level hospitalization data can only be retrieved for state granularity." - raise DataResourceException(msg) - if time_period.start_date <= date(2019, 12, 29): - msg = "State level hospitalization data is only available starting 1/4/2020." - raise DataResourceException(msg) - - def fetch() -> NDArray: - if time_period.end_date >= date(2024, 5, 1): - warn("State level hospitalization data is voluntary past 5/1/2024.") - - info = QueryInfo("https://data.cdc.gov/resource/aemt-mg7g.csv?", - "week_end_date", "jurisdiction", self.attribute_cols[attrib.name], True) - - state_mapping = state_fips_to_code(scope.year) - fips = scope.get_node_ids() - state_codes = np.array([state_mapping[x] for x in fips]) - - df = self._api_query(info, state_codes, time_period, scope.granularity) - - df = df.groupby(['week_end_date', 'jurisdiction']).sum() - df.reset_index(inplace=True) - df = df.pivot(index='week_end_date', - columns='jurisdiction', values=info.data_col) - - dates = df.index.to_numpy(dtype='datetime64[D]') - - return np.array([ - list(zip(dates, df[col])) - for col in df.columns - ], dtype=[('date', 'datetime64[D]'), ('data', attrib.dtype)]).T - - return ADRIO(attrib.name, fetch) - - def _make_vaccination_adrio(self, attrib: AttributeDef, scope: CensusScope, time_period: SpecificTimePeriod) -> ADRIO: - """ - Makes ADRIOs for CDC dataset reporting total COVID-19 vaccination numbers. - Available between 12/13/2020 and 5/10/2024 at state and county granularities. - https://data.cdc.gov/Vaccinations/COVID-19-Vaccinations-in-the-United-States-County/8xkx-amqh/about_data - """ - if time_period.start_date <= date(2020, 12, 6) or time_period.end_date >= date(2024, 5, 17): - msg = "Vaccination data is only available between 12/13/2020 and 5/10/2024." - raise DataResourceException(msg) - - def fetch() -> NDArray: - info = QueryInfo("https://data.cdc.gov/resource/8xkx-amqh.csv?", - "date", "fips", self.attribute_cols[attrib.name]) - - df = self._api_query(info, scope.get_node_ids(), - time_period, scope.granularity) - - df = df.pivot(index='date', columns='fips', values=info.data_col) - - dates = df.index.to_numpy(dtype='datetime64[D]') - - return np.array([ - list(zip(dates, df[col])) - for col in df.columns - ], dtype=[('date', 'datetime64[D]'), ('data', attrib.dtype)]).T - - return ADRIO(attrib.name, fetch) - - def _make_deaths_adrio_county(self, attrib: AttributeDef, scope: CensusScope, time_period: SpecificTimePeriod) -> ADRIO: - """ - Makes ADRIOs for CDC dataset reporting number of deaths from COVID-19. - Available between 1/4/2020 and 4/5/2024 at state and county granularities. - https://data.cdc.gov/NCHS/AH-COVID-19-Death-Counts-by-County-and-Week-2020-p/ite7-j2w7/about_data - """ - if time_period.start_date <= date(2019, 12, 28) or time_period.end_date >= date(2024, 4, 12): - msg = "County level deaths data is only available between 1/4/2020 and 4/5/2024." - raise DataResourceException(msg) - - def fetch() -> NDArray: - if scope.granularity == 'state': - info = QueryInfo("https://data.cdc.gov/resource/ite7-j2w7.csv?", - "week_ending_date", "stfips", self.attribute_cols[attrib.name], True) - else: - info = QueryInfo("https://data.cdc.gov/resource/ite7-j2w7.csv?", - "week_ending_date", "fips_code", self.attribute_cols[attrib.name]) - - df = self._api_query(info, scope.get_node_ids(), - time_period, scope.granularity) - - if scope.granularity == 'state': - df = df.groupby(['week_ending_date', info.fips_col]).sum() - df.reset_index(inplace=True) - - df = df.pivot(index='week_ending_date', - columns=info.fips_col, values=info.data_col) - - dates = df.index.to_numpy(dtype='datetime64[D]') - - return np.array([ - list(zip(dates, df[col])) - for col in df.columns - ], dtype=[('date', 'datetime64[D]'), ('data', attrib.dtype)]).T - - return ADRIO(attrib.name, fetch) - - def _make_deaths_adrio_state(self, attrib: AttributeDef, scope: CensusScope, time_period: SpecificTimePeriod) -> ADRIO: - """ - Makes ADRIOs for CDC dataset reporting number of deaths from COVID-19 and other respiratory illnesses. - Available from 1/4/2020 to present at state granularity. - https://data.cdc.gov/NCHS/Provisional-COVID-19-Death-Counts-by-Week-Ending-D/r8kw-7aab/about_data - """ - if time_period.start_date <= date(2019, 12, 29): - msg = "State level deaths data is only available starting 1/4/2020." - raise DataResourceException(msg) - - def fetch() -> NDArray: - fips = scope.get_node_ids() - states = get_us_states(scope.year) - state_mapping = dict(zip(states.geoid, states.name)) - state_names = np.array([state_mapping[x] for x in fips]) - - info = QueryInfo("https://data.cdc.gov/resource/r8kw-7aab.csv?", - "end_date", "state", self.attribute_cols[attrib.name], True) - - df = self._api_query(info, state_names, time_period, scope.granularity) - - df = df.groupby(['end_date', 'state']).sum() - df.reset_index(inplace=True) - df = df.pivot(index='end_date', columns='state', values=info.data_col) - - dates = df.index.to_numpy(dtype='datetime64[D]') - - return np.array([ - list(zip(dates, df[col])) - for col in df.columns - ], dtype=[('date', 'datetime64[D]'), ('data', attrib.dtype)]).T - - return ADRIO(attrib.name, fetch) - - def _api_query(self, info: QueryInfo, fips: NDArray, time_period: SpecificTimePeriod, granularity: CensusGranularityName) -> DataFrame: - """ - Composes URLs to query API and sends query requests. - Limits each query to 10000 rows, combining several query results if this number is exceeded. - Returns pandas Dataframe containing requested data sorted by date and location fips. - """ - # query county level data with state fips codes - if granularity == 'state' and not info.state_level: - location_clauses = [ - f"starts_with({info.fips_col}, '{state}')" - for state in fips - ] - # query county or state level data with full fips codes for the respective granularity - else: - formatted_fips = ",".join(f"'{node}'" for node in fips) - location_clauses = [ - f"{info.fips_col} in ({formatted_fips})" - ] - - date_clause = f"{info.date_col} " \ - + f"between '{time_period.start_date}T00:00:00' " \ - + f"and '{time_period.end_date}T00:00:00'" - - df = concat([self._query_location(info, loc_clause, date_clause) - for loc_clause in location_clauses]) - - df = df.sort_values(by=[info.date_col, info.fips_col]) - return df - - def _query_location(self, info: QueryInfo, loc_clause: str, date_clause: str) -> DataFrame: - """ - Helper function for _api_query() that builds and sends queries for individual locations. - """ - current_return = 10000 - total_returned = 0 - df = DataFrame() - while current_return == 10000: - url = info.url_base + urlencode( - quote_via=quote, - safe=",()'$:", - query={ - '$select': f'{info.date_col},{info.fips_col},{info.data_col}', - '$where': f"{loc_clause} AND {date_clause}", - '$limit': 10000, - '$offset': total_returned - }) - - df = concat([df, read_csv(url, dtype={info.fips_col: str})]) - - current_return = len(df.index) - total_returned - total_returned += current_return - - return df diff --git a/epymorph/geo/adrio/census/adrio_census.py b/epymorph/geo/adrio/census/adrio_census.py deleted file mode 100644 index 1c264b6a..00000000 --- a/epymorph/geo/adrio/census/adrio_census.py +++ /dev/null @@ -1,603 +0,0 @@ -import os -from collections import defaultdict -from functools import partial -from typing import Any - -import numpy as np -from census import Census -from geopandas import GeoDataFrame -from numpy.typing import NDArray -from pandas import DataFrame, Series, concat, read_excel -from shapely import area - -from epymorph.data_shape import Shapes -from epymorph.data_type import CentroidDType, CentroidType -from epymorph.error import (DataResourceException, GeographyError, - GeoValidationException) -from epymorph.geo.adrio.adrio import ADRIO, ADRIOMaker -from epymorph.geo.spec import TimePeriod, Year -from epymorph.geography.us_census import (BLOCK_GROUP, COUNTY, STATE, TRACT, - BlockGroupScope, - CensusGranularityName, CensusScope, - CountyScope, StateScope, - StateScopeAll, TractScope) -from epymorph.geography.us_tiger import (get_block_groups_geo, - get_counties_geo, get_states_geo, - get_tracts_geo, is_tiger_year) -from epymorph.simulation import AttributeDef - - -class ADRIOMakerCensus(ADRIOMaker): - """ - Census ADRIO template to serve as parent class and provide utility functions for Census-based ADRIOS. - """ - - population_query = ['B01001_003E', # population 0-19 - 'B01001_004E', - 'B01001_005E', - 'B01001_006E', - 'B01001_007E', - 'B01001_027E', # women - 'B01001_028E', - 'B01001_029E', - 'B01001_030E', - 'B01001_031E', - 'B01001_008E', # population 20-34 - 'B01001_009E', - 'B01001_010E', - 'B01001_011E', - 'B01001_012E', - 'B01001_032E', # women - 'B01001_033E', - 'B01001_034E', - 'B01001_035E', - 'B01001_036E', - 'B01001_013E', # population 35-54 - 'B01001_014E', - 'B01001_015E', - 'B01001_016E', - 'B01001_037E', # women - 'B01001_038E', - 'B01001_039E', - 'B01001_040E', - 'B01001_017E', # population 55-64 - 'B01001_018E', - 'B01001_019E', - 'B01001_041E', # women - 'B01001_042E', - 'B01001_043E', - 'B01001_020E', # population 65-74 - 'B01001_021E', - 'B01001_022E', - 'B01001_044E', # women - 'B01001_045E', - 'B01001_046E', - 'B01001_023E', # population 75+ - 'B01001_024E', - 'B01001_025E', - 'B01001_047E', # women - 'B01001_048E', - 'B01001_049E'] - - attributes = [ - AttributeDef('name', type=str, shape=Shapes.N, - comment='The proper name of the place.'), - AttributeDef('population', type=int, shape=Shapes.N, - comment='The number of residents of the place.'), - # TODO: arbitrary dimensions are no longer supported; have to figure out what to do with these - # AttributeDef('population_by_age', type=int, shape=Shapes.NxA(3), - # comment='The number of residents, divided into three age categories: 0-19, 20-64, 65+'), - # AttributeDef('population_by_age_x6', type=int, shape=Shapes.NxA(6), - # comment='The number of residents, divided into six age categories: 0-19, 20-34, 35-54, 55-64, 65-75, 75+'), - AttributeDef('centroid', type=CentroidType, shape=Shapes.N, - comment='A geographic centroid for the place, in longitude/latitude.'), - AttributeDef('geoid', type=str, shape=Shapes.N, - comment='The GEOID (in many cases synonymous with FIPS code) for the place.'), - AttributeDef('average_household_size', type=int, shape=Shapes.N, - comment='Average household size within the place.'), - AttributeDef('dissimilarity_index', type=float, shape=Shapes.N, - comment='An index describing the amount of racial segregation in the place, from 0 to 1.'), - AttributeDef('commuters', type=int, shape=Shapes.NxN, - comment='The number of commuters between places, as reported by the ACS Commuting Flows data.'), - AttributeDef('gini_index', type=float, shape=Shapes.N, - comment='An index describing wealth inequality in the place, from 0 to 1.'), - AttributeDef('median_age', type=int, shape=Shapes.N, - comment='The median age of residents in the place.'), - AttributeDef('median_income', type=int, shape=Shapes.N, - comment='The median income of residents in the place.'), - AttributeDef('tract_median_income', type=int, shape=Shapes.N, - comment='The median income according to the Census Tract which encloses this place.' - 'This attribute is only valid if the geo granularity is below tract.'), - AttributeDef('pop_density_km2', type=float, shape=Shapes.N, - comment='The population density of this place by square kilometer.'), - ] - - attrib_vars = { - 'name': ['NAME'], - 'geoid': ['NAME'], - 'population': ['B01001_001E'], - 'population_by_age': population_query, - 'population_by_age_x6': population_query, - 'median_income': ['B19013_001E'], - 'median_age': ['B01002_001E'], - 'tract_median_income': ['B19013_001E'], - 'dissimilarity_index': ['B03002_003E', # white population - 'B03002_013E', - 'B03002_004E', # minority population - 'B03002_014E'], - 'average_household_size': ['B25010_001E'], - 'gini_index': ['B19083_001E'], - 'pop_density_km2': ['B01003_001E'], - } - - census: Census - """Census API interface object.""" - - @staticmethod - def accepts_source(source: Any): - return False - - def __init__(self) -> None: - """Initializer to create Census object.""" - api_key = os.environ.get('CENSUS_API_KEY') - if api_key is None: - msg = "Census API key not found. Please ensure you have an API key and have assigned it to an environment variable named 'CENSUS_API_KEY'" - raise Exception(msg) - self.census = Census(api_key) - - def make_adrio(self, attrib: AttributeDef, scope: CensusScope, time_period: TimePeriod) -> ADRIO: - if attrib not in self.attributes: - msg = f"{attrib.name} is not supported for the Census data source." - raise GeoValidationException(msg) - if not isinstance(time_period, Year): - msg = f"Census ADRIO requires Year (TimePeriod), given {type(time_period)}." - raise GeoValidationException(msg) - - year = time_period.year - - if attrib.name == 'geoid': - return self._make_geoid_adrio(scope, year) - elif attrib.name == 'population_by_age': - return self._make_population_adrio(scope, year, 3) - elif attrib.name == 'population_by_age_x6': - return self._make_population_adrio(scope, year, 6) - elif attrib.name == 'dissimilarity_index': - return self._make_dissimilarity_index_adrio(scope, year) - elif attrib.name == 'gini_index': - return self._make_gini_index_adrio(scope, year) - elif attrib.name == 'pop_density_km2': - return self._make_pop_density_adrio(scope, year) - elif attrib.name == 'centroid': - return self._make_centroid_adrio(scope) - elif attrib.name == 'tract_median_income': - return self._make_tract_med_income_adrio(scope, year) - elif attrib.name == 'commuters': - return self._make_commuter_adrio(scope, year) - else: - return self._make_simple_adrios(attrib, scope, year) - - def fetch_acs5(self, variables: list[str], scope: CensusScope, year: int) -> DataFrame: - """Utility function to fetch Census data by building queries from ADRIO data and sorting the result.""" - queries = self.make_acs5_queries(scope) - - # fetch and combine all queries - df = concat([ - DataFrame.from_records( - self.census.acs5.get(variables, geo=query, year=year) - ) - for query in queries - ]) - - if df.empty: - msg = "ACS5 query returned empty. Ensure all geographies included in your scope are supported and try again." - raise DataResourceException(msg) - - df = self.concatenate_fips(df, scope.granularity) - - return df - - def fetch_sf(self, scope: CensusScope) -> GeoDataFrame: - """Utility function to fetch shape files from Census for specified regions.""" - # call appropriate us_tiger function based on granularity and sort result - scope_year = scope.year - if not is_tiger_year(scope_year): - raise GeographyError(f"Unsupported year: {scope_year}") - - match scope: - case StateScopeAll() | StateScope(): - df = get_states_geo(year=scope_year) - - case CountyScope(): - df = get_counties_geo(year=scope_year) - - case TractScope(): - df = get_tracts_geo(year=scope_year) - - case BlockGroupScope(): - df = get_block_groups_geo(year=scope_year) - - case _: - raise DataResourceException("Unsupported query.") - - df = df.rename(columns={'GEOID': 'geoid'}) - - df = df[df['geoid'].isin(scope.get_node_ids())] - df = df.sort_values(by='geoid') - - return GeoDataFrame(df) - - def fetch_commuters(self, scope: CensusScope, year: int) -> DataFrame: - """Utility function to fetch commuting data from .xslx format filtered down to requested regions.""" - # check for invalid granularity - if isinstance(scope, TractScope | BlockGroupScope): - msg = "Commuting data cannot be retrieved for tract or block group granularities" - raise DataResourceException(msg) - - # check for valid year - if year not in [2010, 2015, 2020]: - # if invalid year is close to a valid year, fetch valid data and notify user - passed_year = year - if year in range(2008, 2012): - year = 2010 - elif year in range(2013, 2017): - year = 2015 - elif year in range(2018, 2022): - year = 2020 - else: - msg = "Invalid year. Communting data is only available for 2008-2022" - raise DataResourceException(msg) - - print( - f"Commuting data cannot be retrieved for {passed_year}, fetching {year} data instead.") - - if year != 2010: - url = f'https://www2.census.gov/programs-surveys/demo/tables/metro-micro/{year}/commuting-flows-{year}/table1.xlsx' - - # organize dataframe column names - group_fields = ['state_code', - 'county_code', - 'state', - 'county'] - - all_fields = ['res_' + field for field in group_fields] + \ - ['wrk_' + field for field in group_fields] + \ - ['workers', 'moe'] - - header_num = 7 - - else: - url = 'https://www2.census.gov/programs-surveys/demo/tables/metro-micro/2010/commuting-employment-2010/table1.xlsx' - - all_fields = ['res_state_code', 'res_county_code', 'wrk_state_code', 'wrk_county_code', - 'workers', 'moe', 'res_state', 'res_county', 'wrk_state', 'wrk_county'] - - header_num = 4 - - # download communter data spreadsheet as a pandas dataframe - data = read_excel(url, header=header_num, names=all_fields, dtype={ - 'res_state_code': str, 'wrk_state_code': str, 'res_county_code': str, 'wrk_county_code': str}) - - node_ids = scope.get_node_ids() - match scope.granularity: - case 'state': - data.rename(columns={'res_state_code': 'res_geoid', - 'wrk_state_code': 'wrk_geoid'}, inplace=True) - - case 'county': - data['res_geoid'] = data['res_state_code'] + \ - data['res_county_code'] - data['wrk_geoid'] = data['wrk_state_code'] + \ - data['wrk_county_code'] - - case _: - raise DataResourceException("Unsupported query.") - - data = data[data['res_geoid'].isin(node_ids)] - data = data[data['wrk_geoid'].isin(['0' + x for x in node_ids])] - - return data - - def make_acs5_queries(self, scope: CensusScope) -> list[dict[str, str]]: - """Formats scope geography information into dictionaries usable in census queries.""" - match scope: - case StateScopeAll(): - queries = [{"for": "state:*"}] - - case StateScope(includes_granularity='state', includes=includes): - queries = [{"for": f"state:{','.join(includes)}"}] - - case CountyScope(includes_granularity='state', includes=includes): - queries = [{ - "for": "county:*", - "in": f"state:{','.join(includes)}", - }] - - case CountyScope(includes_granularity='county', includes=includes): - counties_by_state: dict[str, list[str]] = defaultdict(list) - for state, county in map(COUNTY.decompose, includes): - counties_by_state[state].append(county) - queries = [ - {"for": f"county:{','.join(cs)}", "in": f"state:{s}"} - for s, cs in counties_by_state.items() - ] - - case TractScope(includes_granularity='state', includes=includes): - queries = [{ - "for": "tract:*", - "in": f"state:{','.join(includes)} county:*", - }] - - case TractScope(includes_granularity='county', includes=includes): - counties_by_state: dict[str, list[str]] = defaultdict(list) - for state, county in map(COUNTY.decompose, includes): - counties_by_state[state].append(county) - queries = [ - {"for": "tract:*", - "in": f"state:{s} county:{','.join(cs)}"} - for s, cs in counties_by_state.items() - ] - - case TractScope(includes_granularity='tract', includes=includes): - tracts_by_county: dict[str, list[str]] = defaultdict(list) - - for state, county, tract in map(TRACT.decompose, includes): - tracts_by_county[state + county].append(tract) - - queries = [ - {"for": f"tract:{','.join(tracts_by_county[state + county])}", - "in": f"state:{state} county:{county}"} - for state, county in [COUNTY.decompose(c) for c in tracts_by_county.keys()] - ] - - case BlockGroupScope(includes_granularity='state', includes=includes): - # This wouldn't normally need to be multiple queries, - # but Census API won't let you fetch CBGs for multiple states. - states = {STATE.extract(x) for x in includes} - queries = [ - {"for": "block group:*", "in": f"state:{s} county:* tract:*"} - for s in states - ] - - case BlockGroupScope(includes_granularity='county', includes=includes): - counties_by_state: dict[str, list[str]] = defaultdict(list) - for state, county in map(COUNTY.decompose, includes): - counties_by_state[state].append(county) - queries = [ - {"for": "block group:*", - "in": f"state:{s} county:{','.join(cs)} tract:*"} - for s, cs in counties_by_state.items() - ] - - case BlockGroupScope(includes_granularity='tract', includes=includes): - tracts_by_county: dict[str, list[str]] = defaultdict(list) - - for state, county, tract in map(TRACT.decompose, includes): - tracts_by_county[state + county].append(tract) - - queries = [ - {"for": f"block group:*", - "in": f"state:{state} county:{county} tract:{','.join(tracts_by_county[state + county])}"} - for state, county in [COUNTY.decompose(c) for c in tracts_by_county.keys()] - ] - - case BlockGroupScope(includes_granularity='block group', includes=includes): - block_groups_by_tract: dict[str, list[str]] = defaultdict(list) - - for state, county, tract, block_group in map(BLOCK_GROUP.decompose, includes): - block_groups_by_tract[state + county + tract].append(block_group) - - queries = [ - {"for": f"block group:{'.'.join(block_groups_by_tract[state + county + tract])}", - "in": f"state:{state} county:{county} tract:{tract}"} - for state, county, tract in [TRACT.decompose(t) for t in block_groups_by_tract.keys()] - ] - - case _: - raise DataResourceException("Unsupported query.") - - return queries - - def concatenate_fips(self, df: DataFrame, granularity: CensusGranularityName) -> DataFrame: - """ - Adds column to dataframe resulting from an acs5 query that is a concatination - of all component fips codes up to the specified granularity. - Returns dataframe sorted by new column named 'geoid'. - """ - columns: list[str] = { - 'state': ['state'], - 'county': ['state', 'county'], - 'tract': ['state', 'county', 'tract'], - 'block group': ['state', 'county', 'tract', 'block group'], - }[granularity] - df['geoid'] = df[columns].apply(lambda xs: ''.join(xs), axis=1) - df = df.sort_values(by='geoid') - - return df - - def _validate_result(self, scope: CensusScope, data: Series): - """Ensures that data produced for an attribute contains exactly one entry for every node in the scope.""" - if set(data) != set(scope.get_node_ids()): - msg = "Attribute result missing data for geographies in scope or contains data for geographies not supported by ACS5." - raise DataResourceException(msg) - - def _make_geoid_adrio(self, scope: CensusScope, year: int) -> ADRIO: - """Makes an ADRIO to retrieve GEOID.""" - def fetch() -> NDArray: - df = self.fetch_acs5(self.attrib_vars['geoid'], scope, year) - - self._validate_result(scope, df['geoid']) - - return df['geoid'].to_numpy(dtype=str) - return ADRIO('geoid', fetch) - - def _make_population_adrio(self, scope: CensusScope, year: int, num_groups: int) -> ADRIO: - """Makes an ADRIO to retrieve population data split into 3 or 6 age groups.""" - def fetch() -> NDArray: - def group_cols(first: int, last: int, source: DataFrame) -> Series: - result = source[f"B01001_{first:03d}E"] - for line in range(first + 1, last + 1): - result = result + source[f"B01001_{line:03d}E"] - return result - - df = self.fetch_acs5(self.population_query, scope, year) - - self._validate_result(scope, df['geoid']) - - group = partial(group_cols, source=df) - - if num_groups == 3: - output = DataFrame({'pop_0-19': group(3, 7) + group(27, 31), - 'pop_20-64': group(8, 19) + group(32, 43), - 'pop_65+': group(20, 25) + group(44, 49)}) - else: - output = DataFrame({'pop_0-19': group(3, 7) + group(27, 31), - 'pop_20-34': group(8, 12) + group(32, 36), - 'pop_35-54': group(13, 16) + group(37, 40), - 'pop_55-64': group(17, 19) + group(41, 43), - 'pop_65-75': group(20, 22) + group(44, 46), - 'pop_75+': group(23, 25) + group(47, 49)}) - - return output.to_numpy(dtype=int) - - if num_groups == 3: - return ADRIO('population_by_age', fetch) - else: - return ADRIO('population_by_age_x6', fetch) - - def _make_dissimilarity_index_adrio(self, scope: CensusScope, year: int) -> ADRIO: - """Makes an ADRIO to retrieve dissimilarity index.""" - if isinstance(scope, BlockGroupScope): - msg = "Dissimilarity index cannot be retreived for block group scope." - raise DataResourceException(msg) - - def fetch() -> NDArray: - vars = self.attrib_vars['dissimilarity_index'] - df = self.fetch_acs5(vars, scope, year) - df2 = self.fetch_acs5(vars, scope.lower_granularity(), year) - df2 = self.concatenate_fips(df2, scope.granularity) - - df['high_majority'] = df[vars[0]] + df[vars[1]] - df2['low_majority'] = df2[vars[0]] + df2[vars[1]] - df['high_minority'] = df[vars[2]] + df[vars[3]] - df2['low_minority'] = df2[vars[2]] + df2[vars[3]] - - df3 = df.merge(df2, on='geoid') - - df3['score'] = abs(df3['low_minority'] / df3['high_minority'] - - df3['low_majority'] / df3['high_majority']) - df3 = df3.groupby('geoid').sum() - df3['score'] *= .5 - df3['score'] = df3['score'].replace(0., 0.5) - df3 = df3.reset_index() - - self._validate_result(scope, df3['geoid']) - - return df3['score'].to_numpy(dtype=float) - return ADRIO('dissimilarity_index', fetch) - - def _make_gini_index_adrio(self, scope: CensusScope, year: int) -> ADRIO: - """Makes an ADRIO to retrieve gini index.""" - def fetch() -> NDArray: - var = self.attrib_vars['gini_index'] - df = self.fetch_acs5(var, scope, year) - df[var] = df[var].astype(np.float64).fillna(0.5).replace(-666666666, 0.5) - - # set cbg data to that of the parent tract if geo granularity = cbg - if isinstance(scope, BlockGroupScope): - print( - "Gini Index cannot be retrieved for block group level, fetching tract level data instead.") - df2 = self.fetch_acs5(var, scope.raise_granularity(), scope.year) - df['merge_geoid'] = df['geoid'].apply(lambda x: x[:-1]) - df = df.drop(columns=var) - - df = df.merge(df2, left_on='merge_geoid', - right_on='geoid', suffixes=(None, '_y')) - - self._validate_result(scope, df['geoid']) - - return df[var].to_numpy(dtype=float).squeeze() - return ADRIO('gini_index', fetch) - - def _make_pop_density_adrio(self, scope: CensusScope, year: int) -> ADRIO: - """Makes an ADRIO to retrieve population density per km2.""" - def fetch() -> NDArray: - df = self.fetch_acs5( - self.attrib_vars['pop_density_km2'], scope, year) - geo_df = self.fetch_sf(scope) - - geo_df = geo_df.merge(df, on='geoid') - - self._validate_result(scope, geo_df['geoid']) - - # calculate population density - output = geo_df['B01003_001E'] / (area(geo_df['geometry']) / 1e6) - - return output.to_numpy(dtype=float) - return ADRIO('pop_density_km2', fetch) - - def _make_centroid_adrio(self, scope: CensusScope) -> ADRIO: - """Makes an ADRIO to retrieve geographic centroid coordinates.""" - def fetch() -> NDArray: - df = self.fetch_sf(scope) - - output = DataFrame( - {'geoid': df['geoid'], 'centroid': df['geometry'].apply(lambda x: x.centroid.coords[0])}) - - self._validate_result(scope, output['geoid']) - - return output['centroid'].to_numpy(dtype=CentroidDType) - return ADRIO('centroid', fetch) - - def _make_tract_med_income_adrio(self, scope: CensusScope, year: int) -> ADRIO: - """Makes an ADRIO to retrieve median income at the Census tract level.""" - def fetch() -> NDArray: - if isinstance(scope, BlockGroupScope): - var = self.attrib_vars['tract_median_income'] - # query median income at cbg and tract level - df = self.fetch_acs5(['NAME'], scope, year) - df2 = self.fetch_acs5(var, scope.raise_granularity(), year) - df2 = df2.fillna(0).replace(-666666666, 0) - - df['tract_geoid'] = df['geoid'].apply(lambda x: x[:-1]) - df = df.merge(df2, left_on='tract_geoid', - right_on='geoid', suffixes=(None, '_y')) - - self._validate_result(scope, df['geoid']) - - return df[var].to_numpy(dtype=int).squeeze() - - else: - msg = "Tract median income can only be retrieved for block group scope." - raise DataResourceException(msg) - - return ADRIO('tract_median_income', fetch) - - def _make_commuter_adrio(self, scope: CensusScope, year: int) -> ADRIO: - """Makes an ADRIO to retrieve ACS commuting flow data.""" - def fetch() -> NDArray: - df = self.fetch_commuters(scope, year) - - if isinstance(scope, StateScope | StateScopeAll): - # group and aggregate data - data_group = df.groupby(['res_geoid', 'wrk_geoid']) - df = data_group.agg({'workers': 'sum'}) - df.reset_index(inplace=True) - - df = df.pivot(index='res_geoid', columns='wrk_geoid', values='workers') - df.fillna(0, inplace=True) - - return df.to_numpy(dtype=int) - - return ADRIO('commuters', fetch) - - def _make_simple_adrios(self, attrib: AttributeDef, scope: CensusScope, year: int) -> ADRIO: - """Makes ADRIOs for simple attributes that require no additional postprocessing.""" - def fetch() -> NDArray: - df = self.fetch_acs5( - self.attrib_vars[attrib.name], scope, year) - df = df.fillna(0).replace(-666666666, 0) - - self._validate_result(scope, df['geoid']) - - return df[self.attrib_vars[attrib.name]].to_numpy(dtype=attrib.type).squeeze() - return ADRIO(attrib.name, fetch) diff --git a/epymorph/geo/adrio/census/lodes.py b/epymorph/geo/adrio/census/lodes.py deleted file mode 100644 index 96c74e26..00000000 --- a/epymorph/geo/adrio/census/lodes.py +++ /dev/null @@ -1,284 +0,0 @@ -from pathlib import Path -from typing import Any -from warnings import warn - -import numpy as np -import pandas as pd -from numpy.typing import NDArray - -from epymorph.cache import (CacheMiss, CacheWarning, load_file_from_cache, - save_file_to_cache) -from epymorph.data_shape import Shapes -from epymorph.error import DataResourceException -from epymorph.geo.adrio.adrio import ADRIO, ADRIOMaker -from epymorph.geo.spec import TimePeriod, Year -from epymorph.geography.us_census import STATE, CensusScope, state_fips_to_code -from epymorph.geography.us_tiger import _fetch_url -from epymorph.simulation import AttributeDef - -_LODES_CACHE_PATH = Path("geo/adrio/census/lodes") - - -class ADRIOMakerLODES(ADRIOMaker): - """ - LODES8 ADRIO template to serve as parent class and provide utility functions for LODES8-based ADRIOS. - """ - - @staticmethod - def accepts_source(source: Any): - return False - - attributes = [ - AttributeDef('geoid', type=str, shape=Shapes.N, - comment='The matrix of geoids from states or the input.'), - AttributeDef('commuters', type=int, shape=Shapes.NxN, - comment='The number of total commuters from the work geoid to the home geoid'), - AttributeDef('commuters_29_under', type=int, shape=Shapes.NxN, - comment='The number of total commuters ages 29 and under from the work geoid to the home geoid'), - AttributeDef('commuters_30_to_54', type=int, shape=Shapes.NxN, - comment='The number of total commuters between ages 30 to 54 from the work geoid to the home geoid'), - AttributeDef('commuters_55_over', type=int, shape=Shapes.NxN, - comment='The number of total commuters ages 55 and over from the work geoid to the home geoid'), - AttributeDef('commuters_1250_under_earnings', type=int, shape=Shapes.NxN, - comment='The number of total commuters that earn $1250 or under monthly from the work geoid to the home geoid'), - AttributeDef('commuters_1251_to_3333_earnings', type=int, shape=Shapes.NxN, - comment='The number of total commuters that earn between $1251 to $3333 monthly from the work geoid to the home geoid'), - AttributeDef('commuters_3333_over_earnings', type=int, shape=Shapes.NxN, - comment='The number of total commuters that earn more than $3333 monthly from the work geoid to the home geoid'), - AttributeDef('commuters_goods_producing_industry', type=int, shape=Shapes.NxN, - comment='The number of total commuters that work in the goods producing industry from the work geoid to the home geoid'), - AttributeDef('commuters_trade_transport_utility_industry', type=int, shape=Shapes.NxN, - comment='The number of total commuters that work in trade, transport, or utility industries from the work geoid to the home geoid'), - AttributeDef('commuters_other_industry', type=int, shape=Shapes.NxN, - comment='The number of total commuters that work in any other industry than goods and producing, or trade, transport, or utilities from the work geoid to the home geoid'), - AttributeDef('all_jobs', type=int, shape=Shapes.NxN, - comment='The number of total commuters that work in any type of job from the work geoid to the home geoid'), - AttributeDef('primary_jobs', type=int, shape=Shapes.NxN, - comment='The number of total commuters that work only in primary jobs from the work geoid to the home geoid'), - AttributeDef('all_private_jobs', type=int, shape=Shapes.NxN, - comment='The number of total commuters, all from private jobs, from the work geoid to the home geoid'), - AttributeDef('private_primary_jobs', type=int, shape=Shapes.NxN, - comment='The number of total commuters that work in private primary jobs from the work geoid to the home geoid'), - AttributeDef('all_federal_jobs', type=int, shape=Shapes.NxN, - comment='The number of total commuters, all from federal jobs, from the work geoid to the home geoid'), - AttributeDef('federal_primary_jobs', type=int, shape=Shapes.NxN, - comment='The number of total commuters that work in federal primary jobs from the work geoid to the home geoid') - ] - - attrib_vars = { - 'geoid': ["h_geocode"], - 'commuters': ["S000"], - 'commuters_29_under': ["SA01"], - 'commuters_30_to_54': ["SA02"], - 'commuters_55_over': ["SA03"], - 'commuters_1250_under_earnings': ["SE01"], - 'commuters_1251_to_3333_earnings': ["SE02"], - 'commuters_3333_over_earnings': ["SE03"], - 'commuters_goods_producing_industry': ["SI01"], - 'commuters_trade_transport_utility_industry': ["SI02"], - 'commuters_other_industry': ["SI03"], - 'all_jobs': ["JT00"], - 'primary_jobs': ["JT01"], - 'all_private_jobs': ["JT02"], - 'private_primary_jobs': ["JT03"], - 'all_federal_jobs': ["JT04"], - 'federal_primary_jobs': ["JT05"] - } - - def make_adrio(self, attrib: AttributeDef, scope: CensusScope, time_period: TimePeriod) -> ADRIO: - if attrib not in self.attributes: - msg = f"{attrib.name} is not supported for the LODES data source." - raise DataResourceException(msg) - if not isinstance(time_period, Year): - msg = f"LODES ADRIO requires Year (TimePeriod), given {type(time_period)}." - raise DataResourceException(msg) - - year = time_period.year - - if attrib.name.startswith("commuters"): - sorting_type = self.attrib_vars[attrib.name][0] - return self._make_commuter_adrio(scope, sorting_type, "JT00", year) - elif attrib.name.endswith("jobs"): - job_type = self.attrib_vars[attrib.name][0] - return self._make_commuter_adrio(scope, "S000", job_type, year) - elif attrib.name == 'geoid': - return self._make_geoid_adrio(scope) - else: - raise DataResourceException("Unsupported attribute.") - - def _make_geoid_adrio(self, scope: CensusScope) -> ADRIO: - """Makes an ADRIO to retrieve home and work geocodes geoids.""" - def fetch() -> NDArray: - - geoid = scope.get_node_ids() - output = np.array(geoid, dtype=str) - - return output - - return ADRIO('geoid', fetch) - - def _make_commuter_adrio(self, scope: CensusScope, worker_type: str, job_type: str, year: int) -> ADRIO: - """Makes an ADRIO to retrieve LODES commuting flow data.""" - - # check for valid year input - if year not in range(2002, 2022): - passed_year = year - # adjust to closest year - if year in range(1999, 2002): - year = 2002 - elif year in range(2022, 2025): - year = 2021 - else: - msg = "Invalid year. LODES data is only available for 2002-2021" - raise DataResourceException(msg) - - print( - f"Commuting data cannot be retrieved for {passed_year}, fetching {year} data instead.") - - def fetch() -> NDArray: - - # file type is main (residence in state only) by default - file_type = "main" - - # initialize variables - aggregated_data = None - geoid = scope.get_node_ids() - n_geocode = len(geoid) - geocode_to_index = {geocode: i for i, geocode in enumerate(geoid)} - geocode_len = len(geoid[0]) - data_frames = [] - # can change the lodes version, default is the most recent LODES8 - lodes_ver = "LODES8" - - if scope.granularity != 'state': - states = STATE.truncate_list(geoid) - else: - states = geoid - - # check for multiple states - if (len(states) > 1): - file_type = "aux" - - # no federal jobs in given years - if year in range(2002, 2010) and (job_type == "JT04" or job_type == "JT05"): - - msg = "Invalid year for job type, no federal jobs can be found between 2002 to 2009" - raise DataResourceException(msg) - - # LODES year and state exceptions - # exceptions can be found in this document for LODES8.1: https://lehd.ces.census.gov/data/lodes/LODES8/LODESTechDoc8.1.pdf - invalid_conditions = [ - (year in range(2002, 2010) and (job_type == "JT04" or job_type == "JT05"), - "Invalid year for job type, no federal jobs can be found between 2002 to 2009"), - - (('05' in states) and (year == 2002 or year in range(2019, 2022)), - "Invalid year for state, no commuters can be found for Arkansas in 2002 or between 2019-2021"), - - (('04' in states) and (year == 2002 or year == 2003), - "Invalid year for state, no commuters can be found for Arizona in 2002 or 2003"), - - (('11' in states) and (year in range(2002, 2010)), - "Invalid year for state, no commuters can be found for DC in 2002 or between 2002-2009"), - - (('25' in states) and (year in range(2002, 2011)), - "Invalid year for state, no commuters can be found for Massachusetts between 2002-2010"), - - (('28' in states) and (year in range(2002, 2004) or year in range(2019, 2022)), - "Invalid year for state, no commuters can be found for Mississippi in 2002, 2003, or between 2019-2021"), - - (('33' in states) and year == 2002, - "Invalid year for state, no commuters can be found for New Hampshire in 2002"), - - (('02' in states) and year in range(2017, 2022), - "Invalid year for state, no commuters can be found for Alaska in between 2017-2021") - ] - for condition, message in invalid_conditions: - if condition: - raise DataResourceException(message) - - # check if the CensusScope year is the current LODES geography: 2020 - if scope.year != 2020: - msg = "GeoScope year does not match the LODES geography year." - raise DataResourceException(msg) - - # translate state FIPS code to state to use in URL - state_codes = state_fips_to_code(scope.year) - state_abbreviations = [state_codes.get( - fips, "").lower() for fips in states] - - for state in state_abbreviations: - - # construct the URL to fetch LODES data, reset to empty each time - url_list = [] - cache_list = [] - files = [] - - # always get main file (in state residency) - url_main = f'https://lehd.ces.census.gov/data/lodes/{lodes_ver}/{state}/od/{state}_od_main_{job_type}_{year}.csv.gz' - url_list.append(url_main) - - # if there are more than one state in the input, get the aux files (out of state residence) - if file_type == "aux": - url_aux = f'https://lehd.ces.census.gov/data/lodes/{lodes_ver}/{state}/od/{state}_od_aux_{job_type}_{year}.csv.gz' - url_list.append(url_aux) - cache_list = [_LODES_CACHE_PATH / Path(u).name for u in url_list] - - # try to load the urls from the cache - try: - files = [load_file_from_cache(path) for path in cache_list] - - # on except CacheMiss - except CacheMiss: - - # fetch info from the urls - files = [_fetch_url(u) for u in url_list] - - # try to save the files - try: - for f, path in zip(files, cache_list): - save_file_to_cache(path, f) - - except Exception as e: - msg = "We were unable to save LODES files to the cache.\n" \ - f"Cause: {e}" - warn(msg, CacheWarning) - - unfiltered_df = [pd.read_csv(file, compression="gzip", converters={ - 'w_geocode': str, 'h_geocode': str}) for file in files] - - # go through dataframes, multiple if there are main and aux files - for df in unfiltered_df: - - # filter the rows on if they start with the prefix - filtered_rows = [df[df['h_geocode'].str.startswith( - tuple(geoid)) & df['w_geocode'].str.startswith(tuple(geoid))]] - - # add the filtered dataframe to the list of dataframes - data_frames.append(pd.concat(filtered_rows)) - - for data_df in data_frames: - # convert w_geocode and h_geocode to strings - data_df['w_geocode'] = data_df['w_geocode'].astype(str) - data_df['h_geocode'] = data_df['h_geocode'].astype(str) - - # group by w_geocode and h_geocode and sum the worker values - grouped_data = data_df.groupby( - [data_df['w_geocode'].str[:geocode_len], data_df['h_geocode'].str[:geocode_len]])[worker_type].sum() - - if aggregated_data is None: - aggregated_data = grouped_data - else: - aggregated_data = aggregated_data.add(grouped_data, fill_value=0) - - # create an empty array to store worker type values - output = np.zeros((n_geocode, n_geocode), dtype=np.int64) - - # loop through all of the grouped values and add to output - for (w_geocode, h_geocode), value in aggregated_data.items(): # type: ignore - w_index = geocode_to_index.get(w_geocode) - h_index = geocode_to_index.get(h_geocode) - output[h_index, w_index] += np.int64(value) - - return output - - return ADRIO('commuters', fetch) diff --git a/epymorph/geo/adrio/file/adrio_csv.py b/epymorph/geo/adrio/file/adrio_csv.py deleted file mode 100644 index 117cb43a..00000000 --- a/epymorph/geo/adrio/file/adrio_csv.py +++ /dev/null @@ -1,258 +0,0 @@ -import os -from dataclasses import dataclass -from datetime import date -from typing import Any, Literal - -from numpy.typing import NDArray -from pandas import DataFrame, Series, read_csv - -from epymorph.error import DataResourceException, GeoValidationException -from epymorph.geo.adrio.adrio import ADRIO, ADRIOMaker -from epymorph.geo.spec import AttributeDef, SpecificTimePeriod, TimePeriod -from epymorph.geography.scope import GeoScope -from epymorph.geography.us_census import (STATE, CensusScope, CountyScope, - StateScope, get_census_granularity, - get_us_counties, get_us_states, - state_code_to_fips) - -KeySpecifier = Literal['state_abbrev', 'county_state', 'geoid'] - - -@dataclass -class _BaseCSVSpec(): - file_path: os.PathLike - key_col: int - data_col: int - key_type: KeySpecifier - skiprows: int | None - - -@dataclass -class CSVSpec(_BaseCSVSpec): - """Dataclass to store parameters for CSV ADRIO with data shape N.""" - - -@dataclass -class CSVSpecTime(_BaseCSVSpec): - """Dataclass to store parameters for time-series CSV ADRIO with data shape TxN.""" - time_col: int - - -@dataclass -class _BaseCSVSpecMatrix(): - file_path: os.PathLike - from_key_col: int - to_key_col: int - data_col: int - key_type: KeySpecifier - skiprows: int | None - - -@dataclass -class CSVSpecMatrix(_BaseCSVSpecMatrix): - """Dataclass to store parameters for CSV ADRIO with data shape NxN.""" - - -class ADRIOMakerCSV(ADRIOMaker): - @staticmethod - def accepts_source(source: Any) -> bool: - if isinstance(source, CSVSpec | CSVSpecTime | CSVSpecMatrix): - return True - else: - return False - - def make_adrio(self, attrib: AttributeDef, scope: GeoScope, time_period: TimePeriod, spec: CSVSpec | CSVSpecTime | CSVSpecMatrix) -> ADRIO: - if isinstance(spec, CSVSpec | CSVSpecTime): - return self._make_single_column_adrio(attrib, scope, time_period, spec) - else: - return self._make_matrix_adrio(attrib, scope, spec) - - def _make_single_column_adrio(self, attrib: AttributeDef, scope: GeoScope, time_period: TimePeriod, spec: CSVSpec | CSVSpecTime) -> ADRIO: - """Makes an ADRIO to fetch data from a single relevant column in a .csv file.""" - if spec.key_col == spec.data_col: - msg = "Key column and data column must not be the same." - raise GeoValidationException(msg) - - def fetch() -> NDArray: - df = self._load_from_file(spec, scope) - - # check for null values (missing data in file) - if df[spec.data_col].isnull().any(): - msg = f"Data for required geographies missing from {attrib.name} attribute file or could not be found." - raise DataResourceException(msg) - - if isinstance(spec, CSVSpec): - df.rename(columns={spec.key_col: 'key'}, inplace=True) - df.sort_values(by='key', inplace=True) - return df[spec.data_col].to_numpy(dtype=attrib.dtype) - else: - if not isinstance(time_period, SpecificTimePeriod): - raise GeoValidationException("Unsupported time period.") - - df[spec.time_col] = df[spec.time_col].apply(date.fromisoformat) - - if any(df[spec.time_col] < time_period.start_date) or any(df[spec.time_col] > time_period.end_date): - msg = "Found time column value(s) outside of geo's date range." - raise DataResourceException(msg) - - df.rename(columns={spec.key_col: 'key', spec.data_col: 'data', - spec.time_col: 'time'}, inplace=True) - df.sort_values(by=['time', 'key'], inplace=True) - df = df.pivot(index='time', columns='key', values='data') - return df.to_numpy(dtype=attrib.dtype) - - return ADRIO(attrib.name, fetch) - - def _make_matrix_adrio(self, attrib: AttributeDef, scope: GeoScope, spec: CSVSpecMatrix) -> ADRIO: - """Makes an ADRIO to fetch data from a single column within a .csv file and converts it to matrix format.""" - if len({spec.from_key_col, spec.to_key_col, spec.data_col}) != 3: - msg = "From key column, to key column, and data column must all be unique." - raise GeoValidationException(msg) - - def fetch() -> NDArray: - df = self._load_from_file(spec, scope) - - df = df.pivot(index=spec.from_key_col, columns=spec.to_key_col, - values=spec.data_col) - - df.sort_index(axis=0, inplace=True) - df.sort_index(axis=1, inplace=True) - - df.fillna(0, inplace=True) - - return df.to_numpy(dtype=attrib.dtype) - - return ADRIO(attrib.name, fetch) - - def _load_from_file(self, spec: CSVSpec | CSVSpecTime | CSVSpecMatrix, scope: GeoScope) -> DataFrame: - """ - Loads .csv at path location into a pandas DataFrame, filtering out data outside of the specified - geographic scope and time period. - Returns a DataFrame with the resulting data. - """ - path = spec.file_path - if os.path.exists(path): - if isinstance(spec, CSVSpec | CSVSpecTime): - if spec.skiprows is not None: - df = read_csv(path, skiprows=spec.skiprows, - header=None, dtype={spec.key_col: str}) - else: - df = read_csv(path, header=None, dtype={spec.key_col: str}) - else: - if spec.skiprows is not None: - df = read_csv(path, skiprows=spec.skiprows, header=None, dtype={ - spec.from_key_col: str, spec.to_key_col: str}) - else: - df = read_csv(path, header=None, dtype={ - spec.from_key_col: str, spec.to_key_col: str}) - - if isinstance(spec, CSVSpec | CSVSpecTime): - df = self._parse_label(spec.key_type, scope, df, spec.key_col) - else: - df = self._parse_label(spec.key_type, scope, df, - spec.from_key_col, spec.to_key_col) - - return df - - else: - msg = f"File {spec.file_path} not found" - raise DataResourceException(msg) - - def _parse_label(self, key_type: KeySpecifier, scope: GeoScope, df: DataFrame, key_col: int, key_col2: int | None = None) -> DataFrame: - """ - Reads labels from a dataframe according to key type specified and replaces them - with a uniform value to sort by. - Returns dataframe with values replaced in the label column. - """ - match (key_type): - case "state_abbrev": - result = self._parse_abbrev(scope, df, key_col, key_col2) - - case "county_state": - result = self._parse_county_state(scope, df, key_col) - - case "geoid": - result = self._parse_geoid(scope, df, key_col, key_col2) - - self._validate_result(scope, result[key_col]) - - return result - - def _parse_abbrev(self, scope: GeoScope, df: DataFrame, key_col: int, key_col2: int | None = None) -> DataFrame: - """ - Replaces values in label column containing state abreviations (i.e. AZ) with state - fips codes and filters out any not in the specified geographic scope. - """ - if isinstance(scope, StateScope): - state_mapping = state_code_to_fips(scope.year) - df[key_col] = [state_mapping.get(x) for x in df[key_col]] - if df[key_col].isnull().any(): - raise DataResourceException("Invalid state code in key column.") - df = df[df[key_col].isin(scope.get_node_ids())] - if key_col2 is not None: - df = df[df[key_col2].isin(scope.get_node_ids())] - return df - - else: - msg = "State scope is required to use state abbreviation key format." - raise DataResourceException(msg) - - def _parse_county_state(self, scope: GeoScope, df: DataFrame, key_col: int) -> DataFrame: - """ - Replaces values in label column containing county and state names (i.e. Maricopa, Arizona) - with state county fips codes and filters out any not in the specified geographic scope. - """ - if not isinstance(scope, CountyScope): - msg = "County scope is required to use county, state key format." - raise DataResourceException(msg) - - # make counties info dataframe - counties_info = get_us_counties(scope.year) - counties_info_df = DataFrame( - {'geoid': counties_info.geoid, 'name': counties_info.name}) - - # make states info dataframe - states_info = get_us_states(scope.year) - states_info_df = DataFrame( - {'state_geoid': states_info.geoid, 'state_name': states_info.name}) - - # merge dataframes on state geoid - counties_info_df['state_geoid'] = counties_info_df['geoid'].apply( - STATE.truncate) - counties_info_df = counties_info_df.merge( - states_info_df, how='left', on='state_geoid') - - # concatenate county, state names - counties_info_df['name'] = counties_info_df['name'] + \ - ", " + counties_info_df['state_name'] - - # merge with csv dataframe and set key column to geoid - df = df.merge(counties_info_df, how='left', left_on=key_col, right_on='name') - df[key_col] = df['geoid'] - - return df - - def _parse_geoid(self, scope: GeoScope, df: DataFrame, key_col: int, key_col2: int | None = None) -> DataFrame: - """ - Replaces values in label column containing state abreviations (i.e. AZ) - with state fips codes and filters out any not in the specified geographic scope. - """ - if not isinstance(scope, CensusScope): - msg = "Census scope is required to use geoid key format." - raise DataResourceException(msg) - - granularity = get_census_granularity(scope.granularity) - if not all(granularity.matches(x) for x in df[key_col]): - raise DataResourceException("Invalid geoid in key column.") - - df = df[df[key_col].isin(scope.get_node_ids())] - if key_col2 is not None: - df = df[df[key_col2].isin(scope.get_node_ids())] - - return df - - def _validate_result(self, scope: GeoScope, data: Series): - """Ensures that key column for an attribute contains exactly one entry for every node in the scope.""" - if set(data) != set(scope.get_node_ids()): - msg = "Key column missing keys for geographies in scope or contains unrecognized keys." - raise DataResourceException(msg) diff --git a/epymorph/geo/cache.py b/epymorph/geo/cache.py deleted file mode 100644 index 7f584b0a..00000000 --- a/epymorph/geo/cache.py +++ /dev/null @@ -1,200 +0,0 @@ -"""Logic for saving to, loading from, and managing a cache of geos on the user's hard disk.""" -import os -from pathlib import Path -from typing import Callable, overload - -from epymorph.cache import CACHE_PATH -from epymorph.geo.adrio.adrio import ADRIOMaker -from epymorph.geo.dynamic import DynamicGeo -from epymorph.geo.dynamic import DynamicGeoFileOps as DF -from epymorph.geo.geo import Geo -from epymorph.geo.static import StaticGeo -from epymorph.geo.static import StaticGeoFileOps as F -from epymorph.geo.util import convert_to_static_geo -from epymorph.log.messaging import dynamic_geo_messaging - -AdrioMakerLibrary = dict[str, type[ADRIOMaker]] -DynamicGeoLibrary = dict[str, Callable[[], DynamicGeo]] - - -class GeoCacheException(Exception): - """An exception raised when a geo cache operation fails.""" - - -def fetch(geo_name_or_path: str, - geo_library_dynamic: DynamicGeoLibrary, - adrio_maker_library: AdrioMakerLibrary) -> None: - """ - Caches all attribute data for a dynamic geo from the library or spec file at a given path. - Raises GeoCacheException if spec not found. - """ - - # checks for geo in the library (name passed) - if geo_name_or_path in geo_library_dynamic: - file_path = CACHE_PATH / F.to_archive_filename(geo_name_or_path) - geo_load = geo_library_dynamic.get(geo_name_or_path) - if geo_load is not None: - geo = geo_load() - with dynamic_geo_messaging(geo): - static_geo = convert_to_static_geo(geo) - static_geo.save(file_path) - - # checks for geo spec at given path (path passed) - else: - geo_path = Path(geo_name_or_path).expanduser() - if os.path.exists(geo_path): - geo_name = geo_path.stem - file_path = CACHE_PATH / F.to_archive_filename(geo_name) - geo = DF.load_from_spec(geo_path, adrio_maker_library) - with dynamic_geo_messaging(geo): - static_geo = convert_to_static_geo(geo) - static_geo.save(file_path) - else: - raise GeoCacheException(f'spec file at {geo_name_or_path} not found.') - - -def export(geo_name: str, - geo_path: Path, - out: str | None, - rename: str | None, - ignore_cache: bool, - geo_library_dynamic: DynamicGeoLibrary, - adrio_maker_library: AdrioMakerLibrary) -> None: - """ - Exports a geo as a .geo.tar file to a location outside the cache. - If uncached, geo to export is also cached. - User can specify a destination path and new name for exported geo. - Raises a GeoCacheException if geo not found. - """ - # check for out path specified - if out is not None: - if not os.path.exists(out): - raise GeoCacheException(f'specified output directory {out} not found.') - else: - out_dir = Path(out) - else: - out_dir = Path(os.getcwd()) - - # check for geo name specified - if rename is not None: - geo_exp_name = rename - else: - geo_exp_name = geo_name - - out_path = out_dir / F.to_archive_filename(geo_exp_name) - cache_file_path = CACHE_PATH / F.to_archive_filename(geo_name) - cache_out_file_path = CACHE_PATH / F.to_archive_filename(geo_exp_name) - - # if cached, copy cached file - if os.path.exists(cache_file_path): - geo = load_from_cache(geo_name) - if geo is not None: - geo.save(out_path) - - # if geo uncached or spec file passed, fetch and export - elif geo_name in geo_library_dynamic or os.path.exists(geo_path): - geo_loader = geo_library_dynamic.get(geo_name) - if geo_loader is not None: - geo = geo_loader() - else: - geo = DF.load_from_spec(geo_path, adrio_maker_library) - with dynamic_geo_messaging(geo): - static_geo = convert_to_static_geo(geo) - if not ignore_cache: - static_geo.save(cache_out_file_path) - static_geo.save(out_path) - - else: - raise GeoCacheException("Geo to export not found.") - - -def remove(geo_name: str) -> None: - """ - Removes a geo's data from the cache. - Raises GeoCacheException if geo not found in cache. - """ - file_path = CACHE_PATH / F.to_archive_filename(geo_name) - if os.path.exists(file_path): - os.remove(file_path) - else: - msg = f'{geo_name} not found in cache, check your spelling or use the list subcommand to view all currently cached geos' - raise GeoCacheException(msg) - - -def list_geos() -> list[tuple[str, int]]: - """Return a list of all cached geos, including name and file size.""" - return [(name, os.path.getsize(CACHE_PATH / F.to_archive_filename(name))) - for file, name in F.iterate_dir_path(CACHE_PATH)] - - -def clear(): - """Clears the cache of all geo data.""" - for file in F.iterate_dir_path(CACHE_PATH): - os.remove(CACHE_PATH / file[0]) - - -def save_to_cache(geo: Geo, geo_name: str) -> None: - """Save a Geo to the cache (if you happen to already have it as a Geo object).""" - match geo: - case DynamicGeo(): - static_geo = convert_to_static_geo(geo) - case StaticGeo(): - static_geo = geo - case _: - raise GeoCacheException('Unable to cache given geo.') - file_path = CACHE_PATH / F.to_archive_filename(geo_name) - F.save_as_archive(static_geo, file_path) - - -@overload -def load_from_cache(geo_name: str, or_else: Callable[[], StaticGeo]) -> StaticGeo: - ... - - -@overload -def load_from_cache(geo_name: str) -> StaticGeo | None: - ... - - -def load_from_cache(geo_name: str, or_else: Callable[[], StaticGeo] | None = None) -> StaticGeo | None: - """ - If a geo has already been cached, load and return it. - Otherwise, if you provide a fall-back function (`or_else`), use that to fetch a geo. - If there is no fall-back function, `None` is returned. - If the fallback function is used, the result will be saved to the cache. - """ - file_path = CACHE_PATH / F.to_archive_filename(geo_name) - - if os.path.exists(file_path): - return F.load_from_archive(file_path) - - if or_else is not None: - geo = or_else() - F.save_as_archive(geo, file_path) - return geo - - return None - - -def format_size(size: int) -> str: - """ - Given a file size in bytes, produce a 1024-based unit representation - with the decimal in a consistent position, and padded with spaces as necessary. - """ - if abs(size) < 1024: - return f"{size:3d}. " - - fnum = float(size) - magnitude = 0 - while abs(fnum) > 1024: - magnitude += 1 - fnum = int(fnum / 100.0) / 10.0 - suffix = [' B', ' kiB', ' MiB', ' GiB'][magnitude] - return f"{fnum:.1f}{suffix}" - - -def get_total_size() -> str: - """Returns the total size of all files in the geo cache using 1024-based unit representation.""" - total_size = sum((os.path.getsize(CACHE_PATH / file) - for file, _ in F.iterate_dir_path(CACHE_PATH))) - return format_size(total_size) diff --git a/epymorph/geo/dynamic.py b/epymorph/geo/dynamic.py deleted file mode 100644 index 3155c313..00000000 --- a/epymorph/geo/dynamic.py +++ /dev/null @@ -1,183 +0,0 @@ -""" -A dynamic geo is capable of fetching data from arbitrary external data sources -via the use of ADRIO implementations. It may fetch this data lazily, loading -only the attributes needed by the simulation. -""" -import dataclasses -import os -from concurrent.futures import ThreadPoolExecutor, wait -from typing import Any, Self - -import numpy as np -from numpy.typing import NDArray - -from epymorph.error import AttributeException, GeoValidationException -from epymorph.event import AdrioStart, DynamicGeoEvents, FetchStart -from epymorph.geo.adrio.adrio import ADRIO, ADRIOMaker, ADRIOMakerLibrary -from epymorph.geo.geo import Geo -from epymorph.geo.spec import LABEL, DynamicGeoSpec, validate_geo_values -from epymorph.simulation import AttributeArray -from epymorph.util import Event, MemoDict - - -def _memoized_adrio_maker_library(lib: ADRIOMakerLibrary) -> MemoDict[str, ADRIOMaker]: - """ - Memoizes an adrio maker library to avoid constructing the same adrio maker twice. - Will raise GeoValidationException if asked for an adrio maker that doesn't exist. - """ - def load_maker(name: str) -> ADRIOMaker: - maker_cls = lib.get(name) - if maker_cls is None: - msg = f"Unknown attribute source: {name}." - raise GeoValidationException(msg) - return maker_cls() - return MemoDict[str, ADRIOMaker](load_maker) - - -class DynamicGeo(Geo[DynamicGeoSpec], DynamicGeoEvents): - """A Geo implementation which uses ADRIOs to dynamically fetch data from third-party data sources.""" - - @classmethod - def from_library(cls, spec: DynamicGeoSpec, adrio_maker_library: ADRIOMakerLibrary) -> Self: - """Given an ADRIOMaker library, construct a DynamicGeo for the given spec.""" - def get_maker_by_source(source: Any, makers: dict[str, type[ADRIOMaker]]) -> str | None: - for maker_name, maker_type in makers.items(): - if maker_type.accepts_source(source): - return maker_name - - return None - - makers = _memoized_adrio_maker_library(adrio_maker_library) - - # loop through attributes and make adrios for each - adrios = dict[str, ADRIO]() - for attr in spec.attributes: - source = spec.source.get(attr.name) - if source is None: - msg = f"Missing source for attribute: {attr.name}." - raise GeoValidationException(msg) - - if isinstance(source, str): - maker_name = source - adrio_attrib = attr - - # If source is formatted like ":" then - # the geo wants to use a different name than the one the maker uses; - # no problem, just provide a modified AttribDef to the maker. - if ":" in source: - maker_name, adrio_attrib_name = source.split(":")[0:2] - adrio_attrib = dataclasses.replace(attr, name=adrio_attrib_name) - - # Make and store adrio. - adrio = makers[maker_name].make_adrio( - adrio_attrib, - spec.scope, - spec.time_period - ) - adrios[attr.name] = adrio - - else: - maker_name = get_maker_by_source(source, adrio_maker_library) - if maker_name is None: - msg = f"Unknown source for attribute: {attr.name}" - raise GeoValidationException(msg) - maker = makers[maker_name] - - adrio = maker.make_adrio( - attr, - spec.scope, - spec.time_period, - source - ) - adrios[attr.name] = adrio - - return cls(spec, adrios) - - spec: DynamicGeoSpec - _adrios: dict[str, ADRIO] - - def __init__(self, spec: DynamicGeoSpec, adrios: dict[str, ADRIO]): - if not LABEL.name in adrios: - raise ValueError("Geo must contain an attribute called 'label'.") - self._adrios = adrios - labels = self._adrios[LABEL.name].get_value() - super().__init__(spec, labels.size) - - # events - self.fetch_start = Event() - self.adrio_start = Event() - self.fetch_end = Event() - - def __getitem__(self, name: str, /) -> AttributeArray: - if name not in self._adrios: - raise AttributeException(f"Attribute not found in geo: '{name}'") - if self._adrios[name]._cached_value is None: - self.adrio_start.publish(AdrioStart(name, None, None)) - return self._adrios[name].get_value() - - def __contains__(self, name: str, /) -> bool: - return name in self._adrios - - @property - def labels(self) -> NDArray[np.str_]: - """The labels for every node in this geo.""" - # Since we've already accessed this adrio during construction, - # the adrio should have already cached this value. - return self._adrios[LABEL.name].get_value() - - def validate(self) -> None: - """ - Validate this geo against its specification. - Raises GeoValidationException for any errors. - WARNING: this will fetch all data! - """ - if self.spec.attribute_map.keys() != self._adrios.keys(): - raise GeoValidationException('Geo values do not match the given spec.') - validate_geo_values(self.spec, self._fetch_all()) - - def _fetch_all(self) -> dict[str, NDArray]: - """For internal purposes: retrieves all Geo attributes using ADRIOs and returns a dict of the values.""" - def fetch(key: str, adrio: ADRIO) -> tuple[str, NDArray]: - return (key, adrio.get_value()) - - with ThreadPoolExecutor(max_workers=5) as executor: - futures = [executor.submit(fetch, key, adrio) - for key, adrio in self._adrios.items()] - return dict(result.result() for result in wait(futures).done) - - def fetch_all(self) -> None: - """Retrieves all Geo attributes from geospec object using ADRIOs""" - num_adrios = len(self._adrios) - self.fetch_start.publish(FetchStart(num_adrios)) - - def fetch_attribute(adrio: ADRIO, index: int) -> NDArray: - self.adrio_start.publish(AdrioStart(adrio.attrib, index, num_adrios)) - return adrio.get_value() - - # initialize threads - with ThreadPoolExecutor(max_workers=5) as executor: - for index, adrio in enumerate(self._adrios.values()): - executor.submit(fetch_attribute, adrio, index) - - self.fetch_end.publish(None) - - -class DynamicGeoFileOps: - """Helper functions for saving and loading dynamic geos and specs.""" - - @staticmethod - def get_spec_filename(geo_id: str) -> str: - """Returns the standard filename for a geo spec file.""" - return f"{geo_id}.geo" - - @staticmethod - def load_from_spec(file: os.PathLike, adrio_maker_library: ADRIOMakerLibrary) -> DynamicGeo: - """Load a DynamicGeo from its spec file.""" - try: - with open(file, mode='r', encoding='utf-8') as f: - spec_json = f.read() - spec = DynamicGeoSpec.deserialize(spec_json) - return DynamicGeo.from_library(spec, adrio_maker_library) - except Exception as e: - msg = f"Unable to load '{file}' as a geo: {e}" - raise GeoValidationException(msg) from e diff --git a/epymorph/geo/geo.py b/epymorph/geo/geo.py deleted file mode 100644 index c6acc9c2..00000000 --- a/epymorph/geo/geo.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -A geo represents a simulation's metapopulation model -with all of its attached data attributes. -""" -from abc import ABC, abstractmethod -from typing import Generic, TypeVar - -import numpy as np -from numpy.typing import NDArray - -from epymorph.geo.spec import GeoSpec -from epymorph.simulation import AttributeArray - -SpecT_co = TypeVar('SpecT_co', bound=GeoSpec, covariant=True) - - -class Geo(Generic[SpecT_co], ABC): - """ - Abstract class representing the GEO model. - Implementations are thus free to vary how they provide the requested data. - """ - - spec: SpecT_co - """The specification for this Geo.""" - - nodes: int - """The number of nodes in this Geo.""" - - @property - @abstractmethod - def labels(self) -> NDArray[np.str_]: - """The labels for every node in this geo.""" - - def __init__(self, spec: SpecT_co, nodes: int): - self.spec = spec - self.nodes = nodes - - # Implement DataSource protocol - - @abstractmethod - def __getitem__(self, name: str, /) -> AttributeArray: - pass - - @abstractmethod - def __contains__(self, name: str, /) -> bool: - pass diff --git a/epymorph/geo/spec.py b/epymorph/geo/spec.py deleted file mode 100644 index 18d3a15c..00000000 --- a/epymorph/geo/spec.py +++ /dev/null @@ -1,220 +0,0 @@ -""" -A geo specification contains metadata about a geo: -its attributes and specific dimensions in time and space. -""" -import calendar -from abc import ABC -from dataclasses import dataclass, field -from datetime import date, timedelta -from functools import cached_property -from types import MappingProxyType -from typing import Any, Self, cast - -import jsonpickle -from numpy.typing import NDArray - -import epymorph.data_shape as shape -from epymorph.data_shape import Shapes -from epymorph.error import GeoValidationException -from epymorph.geography.scope import GeoScope -from epymorph.simulation import AttributeDef -from epymorph.util import NumpyTypeError, check_ndarray, match - -LABEL = AttributeDef('label', type=str, shape=Shapes.N, - comment='The label associated with each node.') -""" -Label is a required attribute of every geo. -It is the source of truth for how many nodes are in the geo. -""" - - -class Geography(ABC): - """ - Describes the geographic extent of a dynamic geo. - Exactly how this extent is specified depends strongly on the data source. - """ - - -@dataclass(frozen=True) -class TimePeriod(ABC): - """Expresses the time period covered by a GeoSpec.""" - - days: int - """The time period as a number of days.""" - - -@dataclass(frozen=True) -class SpecificTimePeriod(TimePeriod, ABC): - """Expresses a real time period, with a determinable start and end date.""" - - start_date: date - """The start date of the date range. [start_date, end_date)""" - end_date: date - """The non-inclusive end date of the date range. [start_date, end_date)""" - - -@dataclass(frozen=True) -class DateRange(SpecificTimePeriod): - """TimePeriod representing the time between two dates, exclusive of the end date.""" - start_date: date - end_date: date - days: int = field(init=False) - - def __post_init__(self): - days = (self.end_date - self.start_date).days - object.__setattr__(self, 'days', days) - - -@dataclass(frozen=True) -class DateAndDuration(SpecificTimePeriod): - """TimePeriod representing a number of days starting on the given date.""" - days: int - start_date: date - end_date: date = field(init=False) - - def __post_init__(self): - end_date = self.start_date + timedelta(days=self.days) - object.__setattr__(self, 'end_date', end_date) - - -@dataclass(frozen=True) -class Year(SpecificTimePeriod): - """TimePeriod representing a specific year.""" - year: int - days: int = field(init=False) - start_date: date = field(init=False) - end_date: date = field(init=False) - - def __post_init__(self): - days = 366 if calendar.isleap(self.year) else 365 - start_date = date(self.year, 1, 1) - end_date = date(self.year + 1, 1, 1) - object.__setattr__(self, 'days', days) - object.__setattr__(self, 'start_date', start_date) - object.__setattr__(self, 'end_date', end_date) - - -@dataclass(frozen=True) -class NonspecificDuration(TimePeriod): - """ - TimePeriod representing a number of days not otherwise fixed in real time. - This may be useful for testing purposes. - """ - - def __post_init__(self): - if self.days < 1: - raise ValueError("duration_days must be at least 1.") - - -NO_DURATION = NonspecificDuration(1) - - -@dataclass -class GeoSpec(ABC): - """ - Abstract class describing a Geo. - Subclasses will add fields and behavior specific to different types of Geos. - """ - - @classmethod - def deserialize(cls, spec_string: str) -> Self: - """deserializes a GEOSpec object from a pickled text""" - spec = jsonpickle.decode(spec_string) - if not isinstance(spec, cls): - raise GeoValidationException('Invalid geo spec.') - return spec - - attributes: list[AttributeDef] - """The attributes in the spec.""" - - scope: GeoScope - """ - The physical bounds of this geo: how many nodes are included? - Under some geographic systems (like the US Census delineations), - this may include the hierarchical granularity of the nodes, and - which delineation year we're using. - """ - - time_period: TimePeriod - """ - The time period covered by the spec. By defining the time period, - we can make reasonable assertions about whether any time-series data - is well-formed. - """ - - @cached_property - def attribute_map(self) -> MappingProxyType[str, AttributeDef]: - """The attributes in the spec, mapped by attribute name.""" - return MappingProxyType({a.name: a for a in self.attributes}) - - def __getstate__(self): - state = self.__dict__.copy() - if 'attribute_map' in state: - del state['attribute_map'] # don't pickle properties! - return state - - def serialize(self) -> str: - """Serializes this spec to string.""" - return cast(str, jsonpickle.encode(self, unpicklable=True)) - - -@dataclass -class StaticGeoSpec(GeoSpec): - """The spec for a StaticGeo.""" - # Nothing but the default stuff here. - - -@dataclass -class DynamicGeoSpec(GeoSpec): - """The spec for a DynamicGeo.""" - source: dict[str, Any] - - -def validate_geo_values(spec: GeoSpec, values: dict[str, NDArray]) -> None: - """ - Validate a set of geo values against the given GeoSpec. - All spec'd attributes should be present and have the correct type and shape. - Raises GeoValidationException for any errors. - """ - # TODO: this isn't being called anymore by the BasicSimulator (aka StandardSim). - # But I'll leave it here until we rip out Geos entirely. - - if LABEL not in spec.attributes or LABEL.name not in values: - msg = "Geo spec and values must both include the 'label' attribute." - raise GeoValidationException(msg) - - N = len(values[LABEL.name]) - T = spec.time_period.days - - attribute_errors = list[str]() - for a in spec.attributes: - try: - value = values[a.name] - check_ndarray(value, dtype=match.dtype_cast(a.dtype)) - # check_ndarray's shape matching requires SimDimensions (which we don't have) - # So fake its logic for the time being. - match a.shape: - case shape.Time(): - shape_matches = value.shape == (T,) - case shape.Node(): - shape_matches = value.shape == (N,) - case shape.TimeAndNode(): - shape_matches = value.shape == (T, N) - case shape.NodeAndNode(): - shape_matches = value.shape == (N, N) - case _: - msg = f"Geo attribute is using an unsupported shape: {a.name}; {a.shape}" - raise GeoValidationException(msg) - if not shape_matches: - msg = f"Not a numpy shape match: got {value.shape}, expected {a.shape}" - raise NumpyTypeError(msg) - except KeyError: - msg = f"Geo is missing values for attribute '{a.name}'." - attribute_errors.append(msg) - except NumpyTypeError as e: - msg = f"Geo attribute '{a.name}' is invalid. {e}" - attribute_errors.append(msg) - - if len(attribute_errors) > 0: - msg = "Geo contained invalid attributes." - raise GeoValidationException(msg, attribute_errors) diff --git a/epymorph/geo/static.py b/epymorph/geo/static.py deleted file mode 100644 index 93a72aac..00000000 --- a/epymorph/geo/static.py +++ /dev/null @@ -1,179 +0,0 @@ -""" -A static geo is one that is pre-packaged with all of its data; it doesn't need to fetch any data from outside itself, -and all of its data is resident in memory when loaded. -""" -from importlib.abc import Traversable -from io import BytesIO -from os import PathLike -from pathlib import Path -from typing import Iterator, Self, cast - -import numpy as np -from jsonpickle import encode as json_encode -from numpy.typing import NDArray - -import epymorph.data_shape as shape -from epymorph.cache import load_bundle, save_bundle -from epymorph.error import AttributeException, GeoValidationException -from epymorph.geo.geo import Geo -from epymorph.geo.spec import LABEL, StaticGeoSpec, validate_geo_values -from epymorph.simulation import AttributeArray, AttributeDef -from epymorph.util import NDIndices, as_sorted_dict - -_STATIC_GEO_CACHE_VERSION = 2 - - -class StaticGeo(Geo[StaticGeoSpec]): - """A Geo implementation which contains all of data pre-fetched and in-memory.""" - - values: dict[str, AttributeArray] - - def __init__(self, spec: StaticGeoSpec, values: dict[str, NDArray]): - if not LABEL.name in values or not np.issubdtype(values[LABEL.name].dtype, np.str_): - msg = "Geo must contain an attribute called 'label' of type string." - raise ValueError(msg) - self.values = values - super().__init__(spec, len(values[LABEL.name])) - - def __getitem__(self, name: str, /) -> AttributeArray: - if name not in self.values: - raise AttributeException(f"Attribute not found in geo: '{name}'") - return self.values[name] - - def __contains__(self, name: str, /) -> bool: - return name in self.values - - @property - def labels(self) -> NDArray[np.str_]: - """The labels for every node in this geo.""" - return self.values[LABEL.name] # type: ignore (constructor check should be sufficient) - - def validate(self) -> None: - """ - Validate this geo against its specification. - Raises GeoValidationException for any errors. - """ - if self.spec.attribute_map.keys() != self.values.keys(): - raise GeoValidationException('Geo values do not match the given spec.') - validate_geo_values(self.spec, self.values) - - def filter(self, selection: NDIndices) -> Self: - """ - Create a new geo by selecting only certain nodes from another geo. - Does not alter the original geo. - """ - - def select(attrib: AttributeDef) -> NDArray: - """Perform selections on attribute arrays.""" - arr = self.values[attrib.name] - match attrib.shape: - # it's possible not all of these shapes really make sense in a geo, - # but not too painful to support them anyway - case shape.Node(): - return arr[selection] - case shape.NodeAndNode(): - return arr[selection[:, np.newaxis], selection] - case shape.NodeAndCompartment(): - return arr[selection, :] - case shape.Time(): - return arr - case shape.TimeAndNode(): - return arr[:, selection] - case x: - raise ValueError(f"Unsupported shape {x}") - - filtered_values = { - attrib.name: select(attrib) - for attrib in self.spec.attributes - } - return self.__class__(self.spec, filtered_values) - - def save(self, file: PathLike) -> None: - """Saves this geo to tar format.""" - StaticGeoFileOps.save_as_archive(self, file) - - -class StaticGeoFileOps: - """Helper functions for saving and loading static geos as files.""" - - @staticmethod - def to_archive_filename(geo_id: str) -> str: - """Returns the standard filename for a geo archive.""" - return f"{geo_id}.geo.tgz" - - @staticmethod - def to_geo_name(filename: str) -> str: - """Returns the geo ID from a standard geo archive filename.""" - return filename.removesuffix('.geo.tgz') - - @staticmethod - def iterate_dir(directory: Traversable) -> Iterator[tuple[Traversable, str]]: - """ - Iterates through the given directory non-recursively, returning all archived geos. - Each item in the returned iterator is a tuple containing: - 1. the Traversable instance for the file itself, and - 2. the geo's ID. - """ - return ((f, StaticGeoFileOps.to_geo_name(f.name)) - for f in directory.iterdir() - if f.is_file() and f.name.endswith('.geo.tgz')) - - @staticmethod - def iterate_dir_path(directory: Path) -> Iterator[tuple[Path, str]]: - """ - Iterates through the given directory non-recursively, returning all archived geos. - Each item in the returned iterator is a tuple containing: - 1. the Path for the file itself, and - 2. the geo's ID. - """ - return ((f, StaticGeoFileOps.to_geo_name(f.name)) - for f in directory.iterdir() - if f.is_file() and f.name.endswith('.geo.tgz')) - - @staticmethod - def save_as_archive(geo: StaticGeo, file: PathLike) -> None: - """Save a StaticGeo to its tar format.""" - - # Write the data file - # (sorting the geo values makes the sha256 a little more stable) - npz_file = BytesIO() - np.savez_compressed(npz_file, **as_sorted_dict(geo.values)) - - # Write the spec file - geo_file = BytesIO() - geo_json = cast(str, json_encode(geo.spec, unpicklable=True)) - geo_file.write(geo_json.encode('utf-8')) - - save_bundle( - to_path=file, - version=_STATIC_GEO_CACHE_VERSION, - files={ - "data.npz": npz_file, - "spec.geo": geo_file, - }, - ) - - @staticmethod - def load_from_archive(file: PathLike) -> StaticGeo: - """Load a StaticGeo from its tar format.""" - try: - files = load_bundle(file, version_at_least=_STATIC_GEO_CACHE_VERSION) - if "data.npz" not in files or "spec.geo" not in files: - msg = 'Archive is incomplete: missing data, spec, and/or checksum files.' - raise GeoValidationException(msg) - - # Read the spec file - geo_file = files["spec.geo"] - geo_file.seek(0) - spec_json = geo_file.read().decode('utf8') - spec = StaticGeoSpec.deserialize(spec_json) - - # Read the data file - npz_file = files["data.npz"] - npz_file.seek(0) - with np.load(npz_file) as data: - values = dict(data) - - return StaticGeo(spec, values) - except Exception as e: - raise GeoValidationException(f"Unable to load '{file}' as a geo.") from e diff --git a/epymorph/geo/util.py b/epymorph/geo/util.py deleted file mode 100644 index 1560f654..00000000 --- a/epymorph/geo/util.py +++ /dev/null @@ -1,21 +0,0 @@ -"""Utility functions for interacting with geos of various types.""" -from epymorph.geo.dynamic import DynamicGeo -from epymorph.geo.spec import StaticGeoSpec -from epymorph.geo.static import StaticGeo - - -def convert_to_static_geo(geo: DynamicGeo) -> StaticGeo: - """ - Convert a DynamicGeo to a StaticGeo, proactively fetching all of its values. - """ - spec = StaticGeoSpec( - attributes=geo.spec.attributes, - scope=geo.spec.scope, - time_period=geo.spec.time_period, - ) - geo.fetch_all() - values = { - attr.name: geo[attr.name] - for attr in geo.spec.attributes - } - return StaticGeo(spec, values) diff --git a/epymorph/geography/us_census.py b/epymorph/geography/us_census.py index 7452ba1c..9353a4dc 100644 --- a/epymorph/geography/us_census.py +++ b/epymorph/geography/us_census.py @@ -10,7 +10,6 @@ from dataclasses import dataclass, field from functools import cache from io import BytesIO -from pathlib import Path from typing import (Callable, Iterable, Literal, Mapping, NamedTuple, ParamSpec, Sequence, TypeVar) @@ -19,7 +18,7 @@ import epymorph.geography.us_tiger as us_tiger from epymorph.cache import (CacheMiss, load_bundle_from_cache, - save_bundle_to_cache) + module_cache_path, save_bundle_to_cache) from epymorph.error import GeographyError from epymorph.geography.scope import GeoScope from epymorph.util import filter_unique, prefix @@ -252,7 +251,7 @@ def get_census_granularity(name: CensusGranularityName) -> CensusGranularity: DEFAULT_YEAR = 2020 -_GEOGRAPHY_CACHE_PATH = Path("geography") +_USCENSUS_CACHE_PATH = module_cache_path(__name__) _CACHE_VERSION = 2 @@ -262,7 +261,7 @@ def get_census_granularity(name: CensusGranularityName) -> CensusGranularity: def _load_cached(relpath: str, on_miss: Callable[[], ModelT], on_hit: Callable[..., ModelT]) -> ModelT: # NOTE: this would be more natural as a decorator, # but Pylance seems to have problems tracking the return type properly with that implementation - path = _GEOGRAPHY_CACHE_PATH.joinpath(relpath) + path = _USCENSUS_CACHE_PATH.joinpath(relpath) try: content = load_bundle_from_cache(path, _CACHE_VERSION) with np.load(content['data.npz']) as data_npz: @@ -371,12 +370,12 @@ def _get_us_cbgs() -> BlockGroupsInfo: P = ParamSpec('P') -def verify_fips(granularity: CensusGranularityName, year: int, fips: Sequence[str]) -> None: +def validate_fips(granularity: CensusGranularityName, year: int, fips: Sequence[str]) -> Sequence[str]: """ - Validates a list of FIPS codes are valid for the given granularity and year. + Validates a list of FIPS codes are valid for the given granularity and year and + returns them as a sorted list of FIPS codes. If any FIPS code is found to be invalid, raises GeographyError. """ - fips = sorted(fips) match granularity: case 'state': valid_nodes = get_us_states(year).geoid @@ -392,6 +391,7 @@ def verify_fips(granularity: CensusGranularityName, year: int, fips: Sequence[st if not all((curr := x) in valid_nodes for x in fips): msg = f"Not all given {granularity} fips codes are valid for {year} (for example: {curr})." raise GeographyError(msg) + return tuple(sorted(fips)) # We use the set of 56 two-letter abbreviations and FIPS codes returned by TIGRIS. @@ -428,7 +428,7 @@ def validate_state_codes_as_fips(year: int, codes: Sequence[str]) -> Sequence[st except KeyError: msg = f"Unknown state postal code abbreviation: {curr}" raise GeographyError(msg) from None - return sorted(fips) + return tuple(sorted(fips)) # Census GeoScopes @@ -507,7 +507,7 @@ def in_states(states_fips: Sequence[str], year: int = DEFAULT_YEAR) -> 'StateSco Create a scope including a set of US states/state-equivalents, by FIPS code. Raise GeographyError if any FIPS code is invalid. """ - verify_fips('state', year, states_fips) + states_fips = validate_fips('state', year, states_fips) return StateScope(includes_granularity='state', includes=states_fips, year=year) @staticmethod @@ -546,7 +546,7 @@ def in_states(states_fips: Sequence[str], year: int = DEFAULT_YEAR) -> 'CountySc Create a scope including all counties in a set of US states/state-equivalents. Raise GeographyError if any FIPS code is invalid. """ - verify_fips('state', year, states_fips) + states_fips = validate_fips('state', year, states_fips) return CountyScope(includes_granularity='state', includes=states_fips, year=year) @staticmethod @@ -565,7 +565,7 @@ def in_counties(counties_fips: Sequence[str], year: int = DEFAULT_YEAR) -> 'Coun Create a scope including a set of US counties, by FIPS code. Raise GeographyError if any FIPS code is invalid. """ - verify_fips('county', year, counties_fips) + counties_fips = validate_fips('county', year, counties_fips) return CountyScope(includes_granularity='county', includes=counties_fips, year=year) def get_node_ids(self) -> NDArray[np.str_]: @@ -609,7 +609,7 @@ def in_states(states_fips: Sequence[str], year: int = DEFAULT_YEAR) -> 'TractSco Create a scope including all tracts in a set of US states/state-equivalents. Raise GeographyError if any FIPS code is invalid. """ - verify_fips('state', year, states_fips) + states_fips = validate_fips('state', year, states_fips) return TractScope(includes_granularity='state', includes=states_fips, year=year) @staticmethod @@ -628,7 +628,7 @@ def in_counties(counties_fips: Sequence[str], year: int = DEFAULT_YEAR) -> 'Trac Create a scope including all tracts in a set of US counties, by FIPS code. Raise GeographyError if any FIPS code is invalid. """ - verify_fips('county', year, counties_fips) + counties_fips = validate_fips('county', year, counties_fips) return TractScope(includes_granularity='county', includes=counties_fips, year=year) @staticmethod @@ -637,7 +637,7 @@ def in_tracts(tract_fips: Sequence[str], year: int = DEFAULT_YEAR) -> 'TractScop Create a scope including a set of US tracts, by FIPS code. Raise GeographyError if any FIPS code is invalid. """ - verify_fips('tract', year, tract_fips) + tract_fips = validate_fips('tract', year, tract_fips) return TractScope(includes_granularity='tract', includes=tract_fips, year=year) def get_node_ids(self) -> NDArray[np.str_]: @@ -688,7 +688,7 @@ def in_states(states_fips: Sequence[str], year: int = DEFAULT_YEAR) -> 'BlockGro Create a scope including all block groups in a set of US states/state-equivalents. Raise GeographyError if any FIPS code is invalid. """ - verify_fips('state', year, states_fips) + states_fips = validate_fips('state', year, states_fips) return BlockGroupScope(includes_granularity='state', includes=states_fips, year=year) @staticmethod @@ -707,7 +707,7 @@ def in_counties(counties_fips: Sequence[str], year: int = DEFAULT_YEAR) -> 'Bloc Create a scope including all block groups in a set of US counties, by FIPS code. Raise GeographyError if any FIPS code is invalid. """ - verify_fips('county', year, counties_fips) + counties_fips = validate_fips('county', year, counties_fips) return BlockGroupScope(includes_granularity='county', includes=counties_fips, year=year) @staticmethod @@ -716,7 +716,7 @@ def in_tracts(tract_fips: Sequence[str], year: int = DEFAULT_YEAR) -> 'BlockGrou Create a scope including all block gropus in a set of US tracts, by FIPS code. Raise GeographyError if any FIPS code is invalid. """ - verify_fips('tract', year, tract_fips) + tract_fips = validate_fips('tract', year, tract_fips) return BlockGroupScope(includes_granularity='tract', includes=tract_fips, year=year) @staticmethod @@ -725,7 +725,7 @@ def in_block_groups(block_group_fips: Sequence[str], year: int = DEFAULT_YEAR) - Create a scope including a set of US block groups, by FIPS code. Raise GeographyError if any FIPS code is invalid. """ - verify_fips('block group', year, block_group_fips) + block_group_fips = validate_fips('block group', year, block_group_fips) return BlockGroupScope(includes_granularity='block group', includes=block_group_fips, year=year) def get_node_ids(self) -> NDArray[np.str_]: diff --git a/epymorph/geography/us_tiger.py b/epymorph/geography/us_tiger.py index b8503ecf..7a0d5a27 100644 --- a/epymorph/geography/us_tiger.py +++ b/epymorph/geography/us_tiger.py @@ -6,16 +6,13 @@ from io import BytesIO from pathlib import Path from typing import Literal, Sequence, TypeGuard -from urllib.request import urlopen -from warnings import warn from geopandas import GeoDataFrame from geopandas import read_file as gp_read_file from pandas import DataFrame from pandas import concat as pd_concat -from epymorph.cache import (CacheMiss, CacheWarning, load_file_from_cache, - save_file_to_cache) +from epymorph.cache import load_or_fetch_url, module_cache_path from epymorph.error import GeographyError # A fair question is why did we implement our own TIGER files loader instead of using pygris? @@ -52,7 +49,7 @@ _TIGER_URL = "https://www2.census.gov/geo/tiger" -_TIGER_CACHE_PATH = Path("geography/tiger") +_TIGER_CACHE_PATH = module_cache_path(__name__) # _SUPPORTED_STATE_FILES = ['us', '60', '66', '69', '78'] _SUPPORTED_STATE_FILES = ['us'] @@ -73,50 +70,19 @@ """ -def _fetch_url(url: str) -> BytesIO: - """Reads a file from a URL as a BytesIO.""" - with urlopen(url) as f: - file_buffer = BytesIO() - file_buffer.write(f.read()) - file_buffer.seek(0) - return file_buffer - - def _load_urls(urls: list[str]) -> list[BytesIO]: """ Attempt to load the list of URLs from disk cache, or failing that, from the network. If the files are not cached, they will be saved to our TIGER cache path. """ try: - cached_paths = [ - _TIGER_CACHE_PATH / Path(u).name + return [ + load_or_fetch_url(u, _TIGER_CACHE_PATH / Path(u).name) for u in urls ] - try: - # Try to load files from cache. - files = [ - load_file_from_cache(path) - for path in cached_paths - ] - except CacheMiss: - # On cache miss, fetch info from the URLs. - files = [_fetch_url(u) for u in urls] - - # Attempt to save the files to the cache for next time. - try: - for f, path in zip(files, cached_paths): - save_file_to_cache(path, f) - except Exception as e: - # Failure to save the files to the cache is not worth stopping the program. - # Issue a warning. - msg = "We were unable to save TIGER files to the cache.\n" \ - f"Cause: {e}" - warn(msg, CacheWarning) - except Exception as e: msg = "Unable to retrieve TIGER files for US Census geography." raise GeographyError(msg) from e - return files def _get_geo(cols: list[str], urls: list[str], result_cols: list[str]) -> GeoDataFrame: diff --git a/epymorph/initializer.py b/epymorph/initializer.py index 8b3440aa..d637850e 100644 --- a/epymorph/initializer.py +++ b/epymorph/initializer.py @@ -14,19 +14,20 @@ from epymorph.data_shape import DataShapeMatcher, Shapes, SimDimensions from epymorph.data_type import SimArray, SimDType from epymorph.error import InitException +from epymorph.geography.scope import GeoScope from epymorph.simulation import (AttributeDef, NamespacedAttributeResolver, SimulationFunction) -from epymorph.util import NumpyTypeError, check_ndarray, match, not_none +from epymorph.util import NumpyTypeError, check_ndarray, match -class Initializer(SimulationFunction[np.int64], ABC): +class Initializer(SimulationFunction[SimArray], ABC): """ Represents an initialization routine responsible for determining the initial values of populations by IPM compartment for every simulation node. """ - def __call__(self, data: NamespacedAttributeResolver, dim: SimDimensions, rng: np.random.Generator) -> SimArray: - result = super().__call__(data, dim, rng) + def evaluate_in_context(self, data: NamespacedAttributeResolver, dim: SimDimensions, scope: GeoScope, rng: np.random.Generator) -> SimArray: + result = super().evaluate_in_context(data, dim, scope, rng) # Result validation: it must be an NxC array of integers where no value is less than zero. try: @@ -91,7 +92,7 @@ class NoInfection(Initializer): (default: the first compartment). """ - attributes = (_POPULATION_ATTR,) + requirements = (_POPULATION_ATTR,) initial_compartment: int """The IPM compartment index where people should start.""" @@ -129,7 +130,7 @@ class Proportional(Initializer): - `ratios` a (C,) or (N,C) numpy array describing the ratios for each compartment """ - attributes = (_POPULATION_ATTR,) + requirements = (_POPULATION_ATTR,) ratios: NDArray[np.int64 | np.float64] """The initialization ratios to use.""" @@ -145,11 +146,16 @@ def evaluate(self) -> SimArray: shape=DataShapeMatcher(Shapes.NxC, self.dim, True) ) except NumpyTypeError as e: - msg = f"Initializer argument 'ratios' is not properly specified. {e}" - raise InitException(msg) from None - - ratios = not_none(Shapes.NxC.adapt(self.dim, self.ratios, True))\ - .astype(np.float64, copy=False) + raise InitException( + f"Initializer argument 'ratios' is not properly specified. {e}" + ) from None + + ratios = Shapes.NxC.adapt(self.dim, self.ratios, True) + if ratios is None: + raise InitException( + "Initializer argument 'ratios' is not properly specified." + ) + ratios = ratios.astype(np.float64, copy=False) row_sums = cast(NDArray[np.float64], np.sum(ratios, axis=1, dtype=np.float64)) if np.any(row_sums <= 0): @@ -210,7 +216,7 @@ class IndexedLocations(SeededInfection): - `seed_size` the number of individuals to infect in total """ - attributes = (_POPULATION_ATTR,) + requirements = (_POPULATION_ATTR,) selection: NDArray[np.intp] """Which locations to infect.""" @@ -277,7 +283,7 @@ class SingleLocation(IndexedLocations): - `seed_size` the number of individuals to infect in total """ - attributes = (_POPULATION_ATTR,) + requirements = (_POPULATION_ATTR,) def __init__( self, @@ -308,7 +314,7 @@ class LabeledLocations(SeededInfection): - `seed_size` the number of individuals to infect in total """ - attributes = (_POPULATION_ATTR, _LABEL_ATTR) + requirements = (_POPULATION_ATTR, _LABEL_ATTR) labels: NDArray[np.str_] """Which locations to infect.""" @@ -349,7 +355,7 @@ class RandomLocations(SeededInfection): - `seed_size` the number of individuals to infect in total """ - attributes = (_POPULATION_ATTR,) + requirements = (_POPULATION_ATTR,) num_locations: int """The number of locations to choose (randomly).""" @@ -415,7 +421,7 @@ def __init__( raise InitException(msg) self.top_attribute = top_attribute - self.attributes = (_POPULATION_ATTR, top_attribute) + self.requirements = (_POPULATION_ATTR, top_attribute) self.num_locations = num_locations self.seed_size = seed_size @@ -474,7 +480,7 @@ def __init__( raise InitException(msg) self.bottom_attribute = bottom_attribute - self.attributes = (_POPULATION_ATTR, bottom_attribute) + self.requirements = (_POPULATION_ATTR, bottom_attribute) self.num_locations = num_locations self.seed_size = seed_size diff --git a/epymorph/log/file.py b/epymorph/log/file.py index daf782d3..bc0452c2 100644 --- a/epymorph/log/file.py +++ b/epymorph/log/file.py @@ -5,19 +5,19 @@ from time import perf_counter from typing import Generator -from epymorph.event import (AdrioStart, DynamicGeoEvents, OnMovementClause, - OnMovementFinish, OnMovementStart, OnStart, OnTick, - SimWithEvents) +from epymorph.event import (AdrioFinish, EventBus, OnMovementClause, + OnMovementFinish, OnMovementStart, OnStart, OnTick) from epymorph.util import subscriptions +_events = EventBus() + @contextmanager def file_log( - sim: SimWithEvents, log_file: str = 'debug.log', log_level: str | int = DEBUG, ) -> Generator[None, None, None]: - """Attach file logging to a simulation.""" + """Enable detailed file logging during a simulation.""" # Initialize the logging system and create some Loggers for epymorph subsystems. log_handler = FileHandler(log_file, "w", "utf8") @@ -28,21 +28,21 @@ def file_log( epy_log.setLevel(log_level) sim_log = epy_log.getChild('sim') - geo_log = epy_log.getChild('geo') + adrio_log = epy_log.getChild('adrio') mm_log = epy_log.getChild('movement') # Define handlers for each of the events we're interested in. start_time: float | None = None - def on_start(ctx: OnStart) -> None: - start_date = ctx.dim.start_date - end_date = ctx.dim.end_date - duration_days = ctx.dim.days + def on_start(e: OnStart) -> None: + start_date = e.dim.start_date + end_date = e.dim.end_date + duration_days = e.dim.days - sim_log.info(f"Running simulation ({sim.__class__.__name__}):") + sim_log.info(f"Running simulation ({e.simulator}):") sim_log.info(f"- {start_date} to {end_date} ({duration_days} days)") - sim_log.info(f"- {ctx.dim.nodes} geo nodes") + sim_log.info(f"- {e.dim.nodes} geo nodes") nonlocal start_time start_time = perf_counter() @@ -56,9 +56,10 @@ def on_finish(_: None) -> None: if start_time is not None: sim_log.info(f"Runtime: {(end_time - start_time):.3f}s") - def adrio_start(adrio: AdrioStart) -> None: - geo_log.debug( - "Uncached geo attribute requested: %s. Retreiving now.", adrio.attribute) + def on_adrio_finish(e: AdrioFinish) -> None: + adrio_log.info( + f"ADRIO {e.adrio_name} fetched `{e.attribute}` in ({e.duration:.3f} seconds)" + ) def on_movement_start(e: OnMovementStart) -> None: mm_log.info("Processing movement for day %d, step %d.", e.day, e.step) @@ -70,33 +71,27 @@ def on_movement_clause(e: OnMovementClause) -> None: if e.is_throttled: cl_log.debug( "WARNING: movement is throttled due to insufficient population") - cl_log.debug("moved:\n%s", e.actual) + cl_log.debug("moved:\n%s", e.actual.sum(axis=2)) cl_log.info("moved %d individuals", e.total) def on_movement_finish(e: OnMovementFinish) -> None: mm_log.info(f"Moved a total of {e.total} individuals.") - # Set up a subscriptions context, subscribe our handlers, - # then yield to the outer context (where the sim should be run). with subscriptions() as subs: - # Simulation logging - subs.subscribe(sim.on_start, on_start) - subs.subscribe(sim.on_tick, on_tick) - subs.subscribe(sim.on_finish, on_finish) - - # Geo logging will be attached if it makes sense. - sim_geo = getattr(sim, 'geo', None) - if isinstance(sim_geo, DynamicGeoEvents): - geo_log.info("Geo not loaded from cache; " - "attributes will be lazily loaded during simulation run.") - subs.subscribe(sim_geo.adrio_start, adrio_start) - - # Movement logging - subs.subscribe(sim.on_movement_start, on_movement_start) - subs.subscribe(sim.on_movement_clause, on_movement_clause) - subs.subscribe(sim.on_movement_finish, on_movement_finish) + # Set up a subscriptions context, subscribe our handlers, + # then yield to the outer context (where the sim should be run). + subs.subscribe(_events.on_start, on_start) + subs.subscribe(_events.on_tick, on_tick) + subs.subscribe(_events.on_finish, on_finish) + + subs.subscribe(_events.on_adrio_finish, on_adrio_finish) + + subs.subscribe(_events.on_movement_start, on_movement_start) + subs.subscribe(_events.on_movement_clause, on_movement_clause) + subs.subscribe(_events.on_movement_finish, on_movement_finish) yield # to outer context + # And now our event handlers will be unsubscribed. # Close out the log file. # This isn't necessary if we're running on the CLI, but if we're in a Jupyter context, diff --git a/epymorph/log/messaging.py b/epymorph/log/messaging.py index 1b607b10..42357e45 100644 --- a/epymorph/log/messaging.py +++ b/epymorph/log/messaging.py @@ -6,46 +6,36 @@ from time import perf_counter from typing import Generator -from epymorph.event import (AdrioStart, DynamicGeoEvents, FetchStart, OnStart, - OnTick, SimulationEvents) +from epymorph.event import AdrioFinish, AdrioStart, EventBus, OnStart, OnTick from epymorph.util import progress, subscriptions +_events = EventBus() + @contextmanager -def sim_messaging(sim: SimulationEvents, geo_messaging=False) -> Generator[None, None, None]: +def sim_messaging(adrio=True) -> Generator[None, None, None]: """ - Attach fancy console messaging to a Simulation such as a progress bar. - If `geo_messaging` is true, provide verbose messaging about geo operations - (if applicable, e.g., when fetching external data). + Produce console messaging during simulation runs, like a progress bar. + If `adrio` is True: display when ADRIOs are fetching data. """ start_time: float | None = None - # If geo_messaging is true, the user has requested verbose messaging re: geo operations. - # However we don't want to make a strong assertion that a sim has a geo, nor what type that geo is. - # So we'll do this dynamically! - # - if we have a geo, and - # - if it's an instance of DynamicGeoEvents, and - # - if the user has enabled geo messaging, then and only then will we subscribe to its adrio_start event - sim_geo = None - if hasattr(sim, 'geo'): - sim_geo = getattr(sim, 'geo') - - def on_start(ctx: OnStart) -> None: - start_date = ctx.dim.start_date - end_date = ctx.dim.end_date - duration_days = ctx.dim.days + def on_start(e: OnStart) -> None: + start_date = e.dim.start_date + end_date = e.dim.end_date + duration_days = e.dim.days - print(f"Running simulation ({sim.__class__.__name__}):") + print(f"Running simulation ({e.simulator}):") print(f"• {start_date} to {end_date} ({duration_days} days)") - print(f"• {ctx.dim.nodes} geo nodes") - print(progress(0.0), end='\r') + print(f"• {e.dim.nodes} geo nodes") + print(progress(0.0), end="\r") nonlocal start_time start_time = perf_counter() def on_tick(tick: OnTick) -> None: - print(progress(tick.percent_complete), end='\r') + print(progress(tick.percent_complete), end="\r") def on_finish(_: None) -> None: end_time = perf_counter() @@ -53,52 +43,20 @@ def on_finish(_: None) -> None: if start_time is not None: print(f"Runtime: {(end_time - start_time):.3f}s") - def adrio_start(adrio: AdrioStart) -> None: - print(f"Uncached geo attribute requested: {adrio.attribute}. Retreiving now...") - - # Set up a subscriptions context, subscribe our handlers, - # then yield to the outer context (ostensibly where the sim will be run). - with subscriptions() as subs: - subs.subscribe(sim.on_start, on_start) - subs.subscribe(sim.on_tick, on_tick) - subs.subscribe(sim.on_finish, on_finish) - if geo_messaging and isinstance(sim_geo, DynamicGeoEvents): - print("Geo not loaded from cache; " - "attributes will be lazily loaded during simulation run.") - subs.subscribe(sim_geo.adrio_start, adrio_start) - yield # to outer context - - -@contextmanager -def dynamic_geo_messaging(dyn: DynamicGeoEvents) -> Generator[None, None, None]: - """ - Attach progress messaging to a DynamicGeo for verbose printing of data retreival progress. - Creates subscriptions on the Geo's events. - """ + def on_adrio_start(e: AdrioStart) -> None: + print(f"ADRIO {e.adrio_name} fetching `{e.attribute}`...", end="") - start_time: float | None = None - - def fetch_start(event: FetchStart) -> None: - print("Fetching dynamic geo data") - print(f"• {event.adrio_len} attributes") - - nonlocal start_time - start_time = perf_counter() - - def adrio_start(event: AdrioStart) -> None: - msg = f"Fetching {event.attribute}..." - if event.adrio_index is not None and event.adrio_len is not None: - msg = f"{msg} [{event.adrio_index + 1}/{event.adrio_len}]" - print(msg) - - def fetch_end(_: None) -> None: - print("Complete.") - end_time = perf_counter() - if start_time is not None: - print(f"Total fetch time: {(end_time - start_time):.3f}s") + def on_adrio_finish(e: AdrioFinish) -> None: + print(f" done ({e.duration:.3f} seconds)") with subscriptions() as subs: - subs.subscribe(dyn.fetch_start, fetch_start) - subs.subscribe(dyn.adrio_start, adrio_start) - subs.subscribe(dyn.fetch_end, fetch_end) + # Set up a subscriptions context, subscribe our handlers, + # then yield to the outer context (ostensibly where the sim will be run). + subs.subscribe(_events.on_start, on_start) + subs.subscribe(_events.on_tick, on_tick) + subs.subscribe(_events.on_finish, on_finish) + if adrio: + subs.subscribe(_events.on_adrio_start, on_adrio_start) + subs.subscribe(_events.on_adrio_finish, on_adrio_finish) yield # to outer context + # And now our event handlers will be unsubscribed. diff --git a/epymorph/log/movement.py b/epymorph/log/movement.py index f0735527..7ab7e322 100644 --- a/epymorph/log/movement.py +++ b/epymorph/log/movement.py @@ -8,9 +8,11 @@ from epymorph.data_shape import SimDimensions from epymorph.data_type import SimDType -from epymorph.event import OnMovementClause, OnStart, SimWithEvents +from epymorph.event import EventBus, OnMovementClause, OnStart from epymorph.util import subscriptions +_events = EventBus() + class MovementData(Protocol): """ @@ -112,7 +114,7 @@ def actual_all(self) -> NDArray[SimDType]: @contextmanager -def movement_data(sim: SimWithEvents) -> Generator[MovementData, None, None]: +def movement_data() -> Generator[MovementData, None, None]: """ Run a simulation in this context in order to collect detailed movement data throughout the simulation run. This returns a MovementData object which @@ -130,7 +132,7 @@ def on_clause(e: OnMovementClause): md.actual.append(_Entry(e.clause_name, e.tick, e.actual)) with subscriptions() as sub: - sub.subscribe(sim.on_start, on_start) - sub.subscribe(sim.on_movement_clause, on_clause) + sub.subscribe(_events.on_start, on_start) + sub.subscribe(_events.on_movement_clause, on_clause) yield md md.ready = True diff --git a/epymorph/movement/compile.py b/epymorph/movement/compile.py deleted file mode 100644 index 5264b572..00000000 --- a/epymorph/movement/compile.py +++ /dev/null @@ -1,336 +0,0 @@ -""" -Compilation of movement models. -""" -import ast -from functools import wraps -from typing import Any, Callable, Mapping, Protocol, Sequence - -import numpy as np -from numpy.typing import NDArray - -from epymorph.code import (ImmutableNamespace, compile_function, - epymorph_namespace, parse_function) -from epymorph.data_type import SimDType -from epymorph.error import AttributeException, MmCompileException, error_gate -from epymorph.movement.movement_model import (DynamicTravelClause, - MovementContext, - MovementFunction, MovementModel, - PredefData, TravelClause) -from epymorph.movement.parser import (ALL_DAYS, DailyClause, MovementClause, - MovementSpec) -from epymorph.simulation import AttributeDef, Tick, TickDelta -from epymorph.util import identity - - -def _empty_predef(_ctx: MovementContext) -> PredefData: - """A placeholder predef function for when none is given by the movement spec.""" - return {} - - -def compile_spec( - spec: MovementSpec, - rng: np.random.Generator, - name_override: Callable[[str], str] = identity, -) -> MovementModel: - """ - Compile a movement model from a spec. Requires a reference to the random number generator - that will be used to execute the movement model. - By default, clauses will be given a name from the spec file, but you can override - that naming behavior by providing the `name_override` function. - """ - with error_gate("compiling the movement model", MmCompileException, AttributeException): - # Prepare a namespace within which to execute our movement functions. - global_namespace = _movement_global_namespace(rng) - - # Compile predef (if any). - if spec.predef is None: - predef_f = _empty_predef - else: - orig_ast = parse_function(spec.predef.function) - transformer = PredefFunctionTransformer(spec.attributes) - trns_ast = transformer.visit_and_fix(orig_ast) - predef_f = compile_function(trns_ast, global_namespace) - - return MovementModel( - tau_steps=spec.steps.step_lengths, - attributes=spec.attributes, - predef=predef_f, - clauses=[_compile_clause(c, spec.attributes, global_namespace, name_override) - for c in spec.clauses] - ) - - -def _movement_global_namespace(rng: np.random.Generator) -> dict[str, Any]: - """Make a safe namespace for user-defined movement functions.""" - def as_simdtype(func): - @wraps(func) - def wrapped_func(*args, **kwargs): - result = func(*args, **kwargs) - if np.isscalar(result): - return SimDType(result) # type: ignore - else: - return result.astype(SimDType) - return wrapped_func - - global_namespace = epymorph_namespace(SimDType) - # Add rng functions to np namespace. - np_ns = ImmutableNamespace({ - **global_namespace['np'].to_dict_shallow(), - 'poisson': as_simdtype(rng.poisson), - 'binomial': as_simdtype(rng.binomial), - 'multinomial': as_simdtype(rng.multinomial) - }) - # Add simulation details. - global_namespace |= { - 'MovementContext': MovementContext, - 'PredefData': PredefData, - 'np': np_ns, - } - return global_namespace - - -def _compile_clause( - clause: MovementClause, - model_attributes: Sequence[AttributeDef], - global_namespace: dict[str, Any], - name_override: Callable[[str], str] = identity, -) -> TravelClause: - """Compiles a movement clause in a given namespace.""" - # Parse AST for the function. - try: - orig_ast = parse_function(clause.function) - transformer = ClauseFunctionTransformer(model_attributes) - fn_ast = transformer.visit_and_fix(orig_ast) - fn = compile_function(fn_ast, global_namespace) - except MmCompileException as e: - raise e - except Exception as e: - msg = "Unable to parse and compile movement clause function." - raise MmCompileException(msg) from e - - # Handle different types of MovementClause. - match clause: - case DailyClause(): - clause_weekdays = set( - i for (i, d) in enumerate(ALL_DAYS) - if d in clause.days - ) - - def move_predicate(_ctx: MovementContext, tick: Tick) -> bool: - return clause.leave_step == tick.step and \ - tick.date.weekday() in clause_weekdays - - def returns(_ctx: MovementContext, _tick: Tick) -> TickDelta: - return TickDelta( - days=clause.duration.to_days(), - step=clause.return_step - ) - - return DynamicTravelClause( - name=name_override(fn_ast.name), - move_predicate=move_predicate, - requested=_adapt_move_function(fn, fn_ast), - returns=returns - ) - - -def _adapt_move_function(fn: Callable, fn_ast: ast.FunctionDef) -> MovementFunction: - """ - Wrap the user-provided function in order to handle functions of different arity. - Movement functions as specified by the user can have signature: - f(tick); f(tick, src); or f(tick, src, dst). - """ - match len(fn_ast.args.args): - # Remember `fn` has been transformed, so if the user gave 1 arg we added 1 for a total of 2. - case 2: - @wraps(fn) - def fn_arity1(ctx: MovementContext, tick: Tick) -> NDArray[SimDType]: - requested = fn(ctx, tick) - np.fill_diagonal(requested, 0) - return requested - return fn_arity1 - - case 3: - @wraps(fn) - def fn_arity2(ctx: MovementContext, tick: Tick) -> NDArray[SimDType]: - N = ctx.dim.nodes - requested = np.zeros((N, N), dtype=SimDType) - for n in range(N): - requested[n, :] = fn(ctx, tick, n) - np.fill_diagonal(requested, 0) - return requested - return fn_arity2 - - case 4: - @wraps(fn) - def fn_arity3(ctx: MovementContext, tick: Tick) -> NDArray[SimDType]: - N = ctx.dim.nodes - requested = np.zeros((N, N), dtype=SimDType) - for i, j in np.ndindex(N, N): - requested[i, j] = fn(ctx, tick, i, j) - np.fill_diagonal(requested, 0) - return requested - return fn_arity3 - - case invalid_num_args: - msg = f"Movement clause '{fn_ast.name}' has an invalid number of arguments ({invalid_num_args})" - raise MmCompileException(msg) - - -# Code transformers - -class HasLineNo(Protocol): - lineno: int - - -class _MovementCodeTransformer(ast.NodeTransformer): - """ - This class defines the logic that can be shared between Predef and Clause function - transformers. Some functionality might be more than is technically necessary for either - case, but only if that extra functionality is effectively harmless. - """ - - check_attributes: bool - attributes: Mapping[str, AttributeDef] - - def __init__(self, attributes: Sequence[AttributeDef]): - # NOTE: for the sake of backwards compatibility, MovementModel attribute declarations - # are optional; so our approach will be that attributes will only be checked if at least - # one attribute declaration is provided. - if len(attributes) == 0: - self.check_attributes = False - self.attributes = {} - else: - self.check_attributes = True - self.attributes = {a.name: a for a in attributes} - - def _report_line(self, node: HasLineNo): - return f"Line: {node.lineno}" - - def visit_Subscript(self, node: ast.Subscript) -> Any: - """Modify references to data and predef pseudo-dictionaries.""" - - if isinstance(node.value, ast.Name) and isinstance(node.slice, ast.Constant) and node.value.id in ['data', 'predef']: - source = node.value.id - attr_name = node.slice.value - - # Check data attributes against declarations (but ignore predefs). - if self.check_attributes and source == 'data' and attr_name not in self.attributes: - msg = f"Movement model is using an undeclared attribute: `data[{attr_name}]`. "\ - f"Please add a suitable attribute declaration. ({self._report_line(node)})" - raise MmCompileException(msg) - - # NOTE: what we are *NOT* doing is checking if usage of predef attributes are - # actually provided by the predef function. Doing this at compile time would be - # exceedingly difficult, as we'd have to scrape and analyze all code that contributes to - # the returned dictionary's keys. In simple cases this might be straight-forward, but not - # in the general case. For the time being, this will remain a simulation-time error. - - # Rewrite to access via context resolver. - return ast.Call( - func=ast.Attribute( - value=ast.Attribute( - value=ast.Name(id='ctx', ctx=ast.Load()), - attr='data', - ctx=ast.Load(), - ), - attr='resolve_name', - ctx=ast.Load(), - ), - args=[node.slice], - keywords=[], - ) - - return self.generic_visit(node) - - def visit_Attribute(self, node: ast.Attribute) -> Any: - """Modify references to objects that should be in context.""" - if isinstance(node.value, ast.Name) and node.value.id in ['dim']: - node.value = ast.Attribute( - value=ast.Name(id='ctx', ctx=ast.Load()), - attr=node.value.id, - ctx=ast.Load(), - ) - return node - return self.generic_visit(node) - - def visit_and_fix(self, node: ast.AST) -> Any: - """ - Shortcut for visiting the node and then running - ast.fix_missing_locations() on the result before returning it. - """ - transformed = self.visit(node) - ast.fix_missing_locations(transformed) - return transformed - - -class PredefFunctionTransformer(_MovementCodeTransformer): - """ - Transforms movement model predef code. This is the dual of - ClauseFunctionTransformer (below; see that for additional description), - but specialized for predef which is similar but slightly different. - Most importantly, this transforms the function signature to have the context - as the first parameter. - """ - - def _report_line(self, node: HasLineNo): - return f"predef line: {node.lineno}" - - def visit_FunctionDef(self, node: ast.FunctionDef) -> Any: - """Modify function parameters.""" - new_node = self.generic_visit(node) - if isinstance(new_node, ast.FunctionDef): - ctx_arg = ast.arg( - arg='ctx', - annotation=ast.Name(id='MovementContext', ctx=ast.Load()), - ) - new_node.args.args = [ctx_arg, *new_node.args.args] - return new_node - - -class ClauseFunctionTransformer(_MovementCodeTransformer): - """ - Transforms movement clause code so that we can pass context, etc., - via function arguments instead of the namespace. The goal is to - simplify the function interface for end users while still maintaining - good performance characteristics when parameters change during - a simulation run (i.e., not have to recompile the functions every time - the params change). - - A function like: - - def commuters(t): - typical = np.minimum( - data['population'][:], - data['commuters_by_node'], - ) - actual = np.binomial(typical, data['move_control']) - return np.multinomial(actual, predef['commuting_probability']) - - Will be rewritten as: - - def commuters(ctx, t): - typical = np.minimum( - ctx.data.resolve_name('population')[:], - ctx.data.resolve_name('commuters_by_node'), - ) - actual = np.binomial(typical, ctx.data.resolve_name('move_control')) - return np.multinomial(actual, ctx.data.resolve_name('commuting_probability')) - """ - - clause_name: str = "" - - def _report_line(self, node: HasLineNo): - return f"{self.clause_name} line: {node.lineno}" - - def visit_FunctionDef(self, node: ast.FunctionDef) -> Any: - """Modify function parameters.""" - self.clause_name = f"`{node.name}`" - new_node = self.generic_visit(node) - if isinstance(new_node, ast.FunctionDef): - ctx_arg = ast.arg( - arg='ctx', - annotation=ast.Name(id='MovementContext', ctx=ast.Load()), - ) - new_node.args.args = [ctx_arg, *new_node.args.args] - return new_node diff --git a/epymorph/movement/movement_model.py b/epymorph/movement/movement_model.py deleted file mode 100644 index b2228ce0..00000000 --- a/epymorph/movement/movement_model.py +++ /dev/null @@ -1,134 +0,0 @@ -""" -The basis of the movement model system in epymorph. -This module contains all of the elements needed to define a -movement model, but Rume of it is left to the mm_exec module. -""" -from abc import ABC, abstractmethod -from dataclasses import dataclass -from typing import Callable, Protocol - -import numpy as np -from numpy.typing import NDArray - -from epymorph.data_type import AttributeArray, SimDType -from epymorph.error import AttributeException, MmSimException -from epymorph.simulation import (AttributeDef, NamespacedAttributeResolver, - SimDimensions, Tick, TickDelta) - - -class MovementContext(Protocol): - """The subset of the RumeContext that the movement model clauses need.""" - - @property - @abstractmethod - def dim(self) -> SimDimensions: - """The simulation dimensions.""" - - @property - @abstractmethod - def rng(self) -> np.random.Generator: - """The simulation's random number generator.""" - - @property - @abstractmethod - def data(self) -> NamespacedAttributeResolver: - """The resolver for simulation data.""" - - -PredefData = dict[str, AttributeArray] -PredefClause = Callable[[MovementContext], PredefData] - - -class TravelClause(ABC): - """A clause moving individuals from their home location to another.""" - - name: str - - @abstractmethod - def predicate(self, ctx: MovementContext, tick: Tick) -> bool: - """Should this clause apply this tick?""" - - @abstractmethod - def requested(self, ctx: MovementContext, tick: Tick) -> NDArray[SimDType]: - """Evaluate this clause for the given tick, returning a requested movers array (N,N).""" - - @abstractmethod - def returns(self, ctx: MovementContext, tick: Tick) -> TickDelta: - """Calculate when this clause's movers should return (which may vary from tick-to-tick).""" - - -MovementPredicate = Callable[[MovementContext, Tick], bool] -"""A predicate which decides if a clause should fire this tick.""" - -MovementFunction = Callable[[MovementContext, Tick], NDArray[SimDType]] -""" -A function which calculates the requested number of individuals to move due to this clause this tick. -Returns an (N,N) array of integers. -""" - -ReturnsFunction = Callable[[MovementContext, Tick], TickDelta] -"""A function which decides when this clause's movers should return.""" - - -class DynamicTravelClause(TravelClause): - """ - A travel clause implementation where each method proxies to a lambda. - This allows us to build travel clauses dynamically at runtime. - """ - - name: str - - _move: MovementPredicate - _requested: MovementFunction - _returns: ReturnsFunction - - def __init__(self, - name: str, - move_predicate: MovementPredicate, - requested: MovementFunction, - returns: ReturnsFunction): - self.name = name - self._move = move_predicate - self._requested = requested - self._returns = returns - - def predicate(self, ctx: MovementContext, tick: Tick) -> bool: - return self._move(ctx, tick) - - def requested(self, ctx: MovementContext, tick: Tick) -> NDArray[SimDType]: - try: - return self._requested(ctx, tick) - except KeyError as e: - # NOTE: catching KeyError here will be necessary (to get nice error messages) - # until we can properly validate the MM clauses. - msg = f"Missing attribute {e} required by movement model clause '{self.name}'." - raise AttributeException(msg) from None - except Exception as e: - # NOTE: catching exceptions here is necessary to get nice error messages - # for some value error cause by incorrect parameter and/or clause definition - msg = f"Error from applying clause '{self.name}': see exception trace" - raise MmSimException(msg) from e - - def returns(self, ctx: MovementContext, tick: Tick) -> TickDelta: - return self._returns(ctx, tick) - - -@dataclass(frozen=True) -class MovementModel: - """ - The movement model divides a day into simulation parts (tau steps) under the assumption - that each day part will have movement characteristics relevant to the simulation. - That is: there is no reason to have tau steps smaller than 1 day unless it's relevant - to movement. - """ - - tau_steps: list[float] - """The tau steps for the simulation.""" - - attributes: list[AttributeDef] - - predef: PredefClause - """The predef clause for this movement model.""" - - clauses: list[TravelClause] - """The clauses which express the movement model""" diff --git a/epymorph/movement/parser.py b/epymorph/movement/parser.py deleted file mode 100644 index f63ab45b..00000000 --- a/epymorph/movement/parser.py +++ /dev/null @@ -1,226 +0,0 @@ -"""Parsing of MovementSpecs.""" -from typing import Literal, NamedTuple, cast - -import pyparsing as P -from pyparsing import pyparsing_common as PC - -import epymorph.movement.parser_util as p -from epymorph.error import MmParseException -from epymorph.simulation import AttributeDef - -# A MovementSpec has the following object structure: -# -# - MovementSpec -# - MoveSteps (1) -# - AttributeDef (0 or more) -# - Predef (0 or 1) -# - MovementClause (1 or more) - -############################################################ -# MoveSteps -############################################################ - - -class MoveSteps(NamedTuple): - """The data model for a MoveSteps clause.""" - step_lengths: list[float] - """The lengths of each tau step. This should sum to 1.""" - - -move_steps: P.ParserElement = p.tag('move-steps', [ - p.field('per-day', PC.integer)('num_steps'), - p.field('duration', p.num_list)('steps') -]) - - -@move_steps.set_parse_action -def marshal_move_steps(results: P.ParseResults): - """Convert a pyparsing result to a MoveSteps.""" - fields = results.as_dict() - return MoveSteps(fields['steps']) - - -############################################################ -# Attributes -############################################################ - - -attribute: P.ParserElement = p.tag('attrib', [ - p.field('name', p.name), - p.field('type', p.dtype), - p.field('shape', p.shape), - p.field('default_value', p.scalar_value | p.none), - p.field('comment', p.quoted), -]) - - -@attribute.set_parse_action -def marshal_attribute(results: P.ParseResults): - """Convert a pyparsing result to an Attribute.""" - fields = results.as_dict() - field_type = fields['type'][0] - default_value = fields['default_value'][0] - - # We can coerce integers to floats for convenience. - if field_type == float and isinstance(default_value, int): - default_value = float(default_value) - - return AttributeDef( - name=fields['name'], - type=field_type, - shape=fields['shape'], - default_value=default_value, - comment=fields['comment'], - ) - - -############################################################ -# Predef -############################################################ - - -class Predef(NamedTuple): - """The data model of a predef clause.""" - function: str - - -predef: P.ParserElement = p.tag('predef', [p.field('function', p.fn_body('function'))]) -"""The parser for a predef clause.""" - - -@predef.set_parse_action -def marshal_predef(results: P.ParseResults): - """Convert a pyparsing result into a Predef.""" - fields = results.as_dict() - return Predef(fields['function']) - - -############################################################ -# MovementClause -############################################################ - - -day_list: P.ParserElement = p.bracketed( - P.delimited_list(P.one_of('M T W Th F Sa Su')) -) -"""Parser for a square-bracketed list of days-of-the-week.""" - - -DayOfWeek = Literal['M', 'T', 'W', 'Th', 'F', 'Sa', 'Su'] -"""Type for days of the week values.""" - -ALL_DAYS: list[DayOfWeek] = ['M', 'T', 'W', 'Th', 'F', 'Sa', 'Su'] -"""A list of all days of the week values.""" - - -class DailyClause(NamedTuple): - """ - The data model for a daily movement clause. - Note: leave_step and return_step should be 0-indexed in this form. - """ - # Conversion from 1-indexed steps happens during parsing. - days: list[DayOfWeek] - leave_step: int - duration: p.Duration - return_step: int - function: str - - -daily: P.ParserElement = p.tag('mtype', [ - p.field('days', ('all' | day_list)), - p.field('leave', PC.integer), - p.field('duration', p.duration), - p.field('return', PC.integer), - p.field('function', p.fn_body) -]) -""" -Parser for a DailyClause. e.g.: -``` -[mtype: days=[all]; leave=0; duration=7d; return=0; function= -def simple_movement(t, src, dst): - return 10 -] -``` -""" - - -@daily.set_parse_action -def marshal_daily(instring: str, loc: int, results: P.ParseResults): - """Convert a pyparsing result to a Daily.""" - fields = results.as_dict() - days = fields['days'] - leave_step = fields['leave'] - duration = fields['duration'][0] - return_step = fields['return'] - function = fields['function'] - if not isinstance(days, list): - msg = f"Unsupported value for movement clause daily: days ({days})" - raise P.ParseException(instring, loc, msg) - elif days == ['all']: - days = ALL_DAYS.copy() - else: - days = cast(list[DayOfWeek], days) - if leave_step < 1: - msg = f"movement clause daily: leave step must be at least 1 (value was: {leave_step})" - raise P.ParseException(instring, loc, msg) - if return_step < 1: - msg = f"movement clause daily: return step must be at least 1 (value was: {leave_step})" - raise P.ParseException(instring, loc, msg) - return DailyClause(days, leave_step - 1, duration, return_step - 1, function) - - -MovementClause = DailyClause -"""Data classes representing all possible movement clauses.""" - - -############################################################ -# MovementSpec -############################################################ - - -class MovementSpec(NamedTuple): - """The data model for a movement model spec.""" - steps: MoveSteps - attributes: list[AttributeDef] - predef: Predef | None - clauses: list[MovementClause] - - -def parse_movement_spec(string: str) -> MovementSpec: - """Parse a MovementSpec from the given string.""" - try: - result = movement_spec.parse_string(string, parse_all=True) - return cast(MovementSpec, result[0]) - except Exception as e: - msg = "Unable to parse MovementModel." - raise MmParseException(msg) from e - - -movement_spec = P.OneOrMore( - move_steps | - attribute | - predef | - daily -).ignore(p.code_comment) -"""The parser for MovementSpec.""" - - -@movement_spec.set_parse_action -def marshal_movement(instring: str, loc: int, results: P.ParseResults): - """Convert a pyparsing result to a MovementSpec.""" - s = [x for x in results if isinstance(x, MoveSteps)] - a = [x for x in results if isinstance(x, AttributeDef)] - c = [x for x in results if isinstance(x, MovementClause)] - d = [x for x in results if isinstance(x, Predef)] - if len(s) < 1 or len(s) > 1: - msg = f"Invalid movement specification: expected 1 steps clause, but found {len(s)}." - raise P.ParseException(instring, loc, msg) - if len(c) < 1: - msg = "Invalid movement specification: expected more than one movement clause, but found 0." - raise P.ParseException(instring, loc, msg) - if len(d) > 1: - msg = f"Invalid movement specification: expected 0 or 1 predef clause, but found {len(d)}" - raise P.ParseException(instring, loc, msg) - - predef_clause = d[0] if len(d) == 1 else None - return P.ParseResults(MovementSpec(steps=s[0], attributes=a, predef=predef_clause, clauses=c)) diff --git a/epymorph/movement/parser_util.py b/epymorph/movement/parser_util.py deleted file mode 100644 index 09d4c0fc..00000000 --- a/epymorph/movement/parser_util.py +++ /dev/null @@ -1,186 +0,0 @@ -""" -Common parsing utilities. -""" -from functools import reduce -from typing import NamedTuple - -import pyparsing as P -from pyparsing import pyparsing_common as PC - -from epymorph.data_shape import parse_shape - -# It's likely this will need to move to a different package (i.e., not `movement`) -# but it's fine here for now since movement is the only thing with a spec parser. - -E = P.ParserElement -_ = P.Suppress -l = P.Literal -none = l('None') - - -@none.set_parse_action -def marshal_none(_results: P.ParseResults): - """Marshal a None value.""" - return P.ParseResults([None]) - - -quoted = P.QuotedString(quote_char='"', unquote_results=True) |\ - P.QuotedString(quote_char="'", unquote_results=True) -"""Allow both single- or double-quote-delimited strings.""" - -name: E = P.Word( - init_chars=P.srange("[a-zA-Z]"), - body_chars=P.srange("[a-zA-Z0-9_]"), -) -"""A name string, suitable for use as a Python variable name, for instance.""" - -code_comment: E = P.AtLineStart('#') + ... + P.LineEnd() -"""Parser for Python-style code comments.""" - - -def field(field_name: str, value_parser: E) -> E: - """Parser for a clause field, like `=`""" - return _(l(field_name) + l('=')) + value_parser.set_results_name(field_name) - - -def bracketed(value_parser: E) -> E: - """Wrap another parser to surround it with square brackets.""" - return _('[') + value_parser + _(']') - - -def tag(tag_name: str, fields: list[E]) -> E: - """ - Parser for a spec tag: this is a top-level item, surrounded by square brackets, - identified by a tag name and containing one-or-more fields. e.g.: - `[: =; =]` - """ - def combine(p1, p2): - return p1 + _(';') + p2 - field_list = reduce(combine, fields[1:], fields[0]) - return bracketed(_(l(tag_name) + l(":")) - field_list) - - -num_list: E = bracketed(P.delimited_list(PC.fraction | PC.fnumber)) -"""Parser for a list of numbers in brackets, like: `[1,2,3]`""" - - -class Duration(NamedTuple): - """Data class for a duration expression.""" - value: int - unit: str - - def to_days(self) -> int: - """Return the equivalent number of days.""" - if self.unit == 'd': - return self.value - elif self.unit == 'w': - return self.value * 7 - else: - raise ValueError(f"unsupported unit {self.unit}") - - -duration = P.Group(PC.integer + P.one_of('d w')) -"""Parser for a duration expression.""" - - -@duration.set_parse_action -def marshal_duration(results: P.ParseResults): - """Convert a pyparsing result to a Duration object.""" - [value, unit] = results[0] - return P.ParseResults(Duration(value, unit)) - - -fn_body = P.SkipTo(P.AtLineStart(']'))\ - .set_parse_action(lambda toks: toks.as_list()[0].strip()) -""" -Parser for a function body, which runs until the end of a clause ends. -For example, if a tag value should be a function, you can define it like this: -`p.field('function', p.fn_body('function'))` which can be used to parse things like: -``` -[: function= -def my_function(): - return 'hello world' -] -``` -""" - - -# Shapes - - -shape = P.one_of("S T N TxN NxN") -""" -Describes the dimensions of an array in terms of the simulation. -For example "TxN" describes a two-dimensional array which is the -number of simulation days in the first dimensions and the number -of geo nodes in the second dimension. See `epymorph.data_shape` for more info. -(Excludes Shapes with arbitrary dimensions for simplicity.) -""" - - -@shape.set_parse_action -def marshal_shape(results: P.ParseResults): - """Convert a pyparsing result to a Shape object.""" - value = str(results[0]) - return P.ParseResults(parse_shape(value)) - - -# dtypes - - -base_dtype = P.one_of('int float str') -"""One of epymorph's base permitted data types.""" - - -@base_dtype.set_parse_action -def marshal_base_dtype(results: P.ParseResults): - """Convert a pyparsing result to dtype object.""" - match results[0]: - case 'int': - return int - case 'float': - return float - case 'str': - return str - case x: - return P.ParseException(f"Unable to parse '{x}' as a dtype.") - - -struct_dtype_field = _('(') + name('name') + _(',') + base_dtype('dtype') + _(')') -"""A single named field in a structured dtype.""" - - -@struct_dtype_field.set_parse_action -def marshal_struct_dtype_field(results: P.ParseResults): - """Convert a pyparsing result to a tuple representing a single named field in a structured dtype.""" - return (results['name'], results['dtype']) - - -struct_dtype = bracketed(P.delimited_list(struct_dtype_field)) -"""A complete structured dtype.""" - - -@struct_dtype.set_parse_action -def marshal_struct_dtype(results: P.ParseResults): - """Convert a pyparsing results to a structured dtype object.""" - return [results.as_list()] - - -dtype = base_dtype | struct_dtype - - -# Scalar Values - - -base_scalar = quoted | PC.number - -tuple_scalar = _('(') + P.delimited_list(base_scalar, ',') + _(')') - - -@tuple_scalar.set_parse_action -def marshal_tuple_scalar(results: P.ParseResults): - """Convert a pyparsing result to a tuple of values.""" - return tuple(results.as_list()) - - -scalar_value = tuple_scalar | base_scalar diff --git a/epymorph/movement/test/__init__.py b/epymorph/movement/test/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/epymorph/movement/test/compile_test.py b/epymorph/movement/test/compile_test.py deleted file mode 100644 index 328680c8..00000000 --- a/epymorph/movement/test/compile_test.py +++ /dev/null @@ -1,57 +0,0 @@ -# pylint: disable=missing-docstring -import unittest -from unittest.mock import MagicMock - -from epymorph.code import compile_function, parse_function -from epymorph.movement.compile import (ClauseFunctionTransformer, - PredefFunctionTransformer) -from epymorph.movement.movement_model import MovementContext - - -class TestMovementClauseTransformer(unittest.TestCase): - - def test_transform_clause(self): - source = """ - def foo(t, src, dst): - return t * src * dst - """ - - ast1 = parse_function(source) - f1 = compile_function(ast1, {}) - - transformer = ClauseFunctionTransformer([]) - ast2 = transformer.visit_and_fix(ast1) - - f2 = compile_function(ast2, { - 'MovementContext': MovementContext, - }) - - ctx = MagicMock(spec=MovementContext) - - self.assertEqual(f1(3, 5, 7), 105) - self.assertEqual(f2(ctx, 3, 5, 7), 105) - with self.assertRaises(TypeError): - f2(3, 5, 7) - - def test_transform_predef(self): - source = """ - def my_predef(): - return {'foo': 42} - """ - - ast1 = parse_function(source) - f1 = compile_function(ast1, {}) - - transformer = PredefFunctionTransformer([]) - ast2 = transformer.visit_and_fix(ast1) - - f2 = compile_function(ast2, { - 'MovementContext': MovementContext, - }) - - ctx = MagicMock(spec=MovementContext) - - self.assertEqual(f1(), {'foo': 42}) - self.assertEqual(f2(ctx), {'foo': 42}) - with self.assertRaises(TypeError): - f2() diff --git a/epymorph/movement/test/parser_test.py b/epymorph/movement/test/parser_test.py deleted file mode 100644 index a7a80e39..00000000 --- a/epymorph/movement/test/parser_test.py +++ /dev/null @@ -1,111 +0,0 @@ -# pylint: disable=missing-docstring -import unittest - -from pyparsing import ParseBaseException - -from epymorph.data_shape import Shapes -from epymorph.data_type import CentroidType -from epymorph.movement.parser import (DailyClause, MoveSteps, attribute, daily, - move_steps) -from epymorph.movement.parser_util import Duration -from epymorph.simulation import AttributeDef - - -class TestMoveSteps(unittest.TestCase): - def test_successful(self): - cases = [ - '[move-steps: per-day=2; duration=[2/3, 1/3]]', - '[move-steps: per-day=1; duration=[2/3]]', - '[ move-steps : per-day = 3 ; duration = [0.25, 0.25, 0.5] ]', - '[move-steps: per-day=3; duration=[0.25, 0.25, 0.5]]', - '[move-steps:per-day=3;duration=[0.25, 0.25, 0.5]]' - ] - exps = [ - MoveSteps([2 / 3, 1 / 3]), - MoveSteps([2 / 3]), - MoveSteps([0.25, 0.25, 0.5]), - MoveSteps([0.25, 0.25, 0.5]), - MoveSteps([0.25, 0.25, 0.5]) - ] - for c, e in zip(cases, exps): - a = move_steps.parse_string(c)[0] - self.assertEqual(a, e, f"{str(a)} did not match {str(e)}") - - def test_failures(self): - cases = [ - '[move-steps: duration=[2/3, 1/3]; per-day=2]', - '[move-steps: per-day=2; duration=[]]', - '[move-steps-2: per-day=2; duration=[]]' - ] - for c in cases: - with self.assertRaises(ParseBaseException): - move_steps.parse_string(c) - - -class TestAttribute(unittest.TestCase): - def test_successful(self): - cases = [ - '[attrib: name=commuters; type=int; shape=NxN; default_value=None; comment="hey1"]', - '[attrib: name=move_control; type=float; shape=TxN; default_value=42; comment="hey2"]', - '[attrib: name=move_control; type=float; shape=TxN; default_value=-32.7;\n comment="hey3"]', - '[attrib:\nname=theta;\ntype=str;\nshape=S;\ndefault_value="hi";\ncomment="hey4"]', - '[attrib: name=centroids; type=[(longitude, float), (latitude, float)]; shape=N; default_value=(1.0, 2.0); comment="hey5"]', - ] - exps = [ - AttributeDef('commuters', int, Shapes.NxN, None, 'hey1'), - AttributeDef('move_control', float, Shapes.TxN, 42.0, 'hey2'), - AttributeDef('move_control', float, Shapes.TxN, -32.7, 'hey3'), - AttributeDef('theta', str, Shapes.S, 'hi', 'hey4'), - AttributeDef('centroids', CentroidType, Shapes.N, (1.0, 2.0), 'hey5'), - ] - for c, e in zip(cases, exps): - a = attribute.parse_string(c)[0] - self.assertEqual(a, e, f"{str(a)} did not match {str(e)}") - - def test_failures(self): - cases = [ - '[attrib: name=commuters; type=int; shape=NxN; default_value=23; comment="hey1"]', - '[attrib: name=move_control; type=uint8; shape=TxN; default_value=1; comment="hey2"]', - '[attrib: name=move_control; type=float; shape=TxA; default_value=27.3; comment="hey3"]', - ] - for c in cases: - with self.assertRaises(ParseBaseException): - move_steps.parse_string(c) - - -class TestDailyMoveClause(unittest.TestCase): - def test_successful_01(self): - case = '[mtype: days=[M,Th,Sa]; leave=2; duration=1d; return=4; function=\ndef(t):\n return 1\n]' - exp = DailyClause( - days=['M', 'Th', 'Sa'], - leave_step=1, - duration=Duration(1, 'd'), - return_step=3, - function='def(t):\n return 1' - ) - act = daily.parse_string(case)[0] - self.assertEqual(act, exp) - - def test_successful_02(self): - case = '[mtype: days=all; leave=1; duration=2w; return=2; function=\ndef(t):\n return 1\n]' - exp = DailyClause( - days=['M', 'T', 'W', 'Th', 'F', 'Sa', 'Su'], - leave_step=0, - duration=Duration(2, 'w'), - return_step=1, - function='def(t):\n return 1' - ) - act = daily.parse_string(case)[0] - self.assertEqual(act, exp) - - def test_failed_01(self): - # Invalid leave step - with self.assertRaises(ParseBaseException): - case = '[mtype: days=all; leave=0; duration=2w; return=2; function=\ndef(t):\n return 1\n]' - daily.parse_string(case) - - def test_failed_02(self): - # Invalid days value - with self.assertRaises(ParseBaseException): - case = '[mtype: days=[X]; leave=0; duration=2w; return=2; function=\ndef(t):\n return 1\n]' - daily.parse_string(case) diff --git a/epymorph/movement_model.py b/epymorph/movement_model.py new file mode 100644 index 00000000..443c071d --- /dev/null +++ b/epymorph/movement_model.py @@ -0,0 +1,276 @@ +""" +The basis of the movement model system in epymorph. +Movement models are responsible for dividing up the day +into one or more parts, in accordance with their desired +tempo of movement. (For example, commuting patterns of work day +versus night.) Movement mechanics are expressed using a set of +clauses which calculate a requested number of individuals move +between geospatial nodes at a particular time step of the simulation. +""" +import re +from abc import ABC, ABCMeta, abstractmethod +from functools import cached_property +from math import isclose +from typing import Any, Literal, Sequence, Type, TypeVar, cast + +import numpy as np +from numpy.typing import NDArray + +from epymorph.data_shape import SimDimensions +from epymorph.data_type import SimDType +from epymorph.geography.scope import GeoScope +from epymorph.simulation import (AttributeDef, NamespacedAttributeResolver, + SimulationFunctionClass, + SimulationTickFunction, Tick, TickDelta, + TickIndex) +from epymorph.util import are_instances + +DayOfWeek = Literal['M', 'T', 'W', 'Th', 'F', 'Sa', 'Su'] +"""Type for days of the week values.""" + +ALL_DAYS: tuple[DayOfWeek, ...] = ('M', 'T', 'W', 'Th', 'F', 'Sa', 'Su') +"""All days of the week values.""" + +_day_of_week_pattern = r"\b(M|T|W|Th|F|Sa|Su)\b" + + +def parse_days_of_week(dow: str) -> tuple[DayOfWeek, ...]: + """ + Parses the string as a list of days of the week using our standard abbreviations: M,T,W,Th,F,Sa,Su. + This parser is pretty permissive, simply ignoring invalid parts of the input while keeping the valid parts. + Any separator is allowed between the day of the week themselves. Returns an empty tuple if there are no matches. + """ + ds = re.findall(_day_of_week_pattern, dow) + return tuple(set(ds)) + + +class MovementPredicate(ABC): + """Checks the current tick and responds with True or False.""" + + @abstractmethod + def evaluate(self, tick: Tick) -> bool: + """Check the given tick.""" + + +class EveryDay(MovementPredicate): + """Return True for every day.""" + + def evaluate(self, tick: Tick) -> bool: + return True + + +class DayIs(MovementPredicate): + """Checks that the day is in the given set of days of the week.""" + + week_days: tuple[DayOfWeek, ...] + + def __init__(self, week_days: Sequence[DayOfWeek] | str): + if isinstance(week_days, str): + self.week_days = parse_days_of_week(week_days) + else: + self.week_days = tuple(week_days) + + def evaluate(self, tick: Tick) -> bool: + return tick.date.weekday() in self.week_days + + +################## +# MovementClause # +################## + + +_TypeT = TypeVar("_TypeT") + + +class MovementClauseClass(SimulationFunctionClass): + """ + The metaclass for user-defined MovementClause classes. + Used to verify proper class implementation. + """ + def __new__( + mcs: Type[_TypeT], + name: str, + bases: tuple[type, ...], + dct: dict[str, Any], + ) -> _TypeT: + # Skip these checks for known base classes: + if name in ("MovementClause",): + return super().__new__(mcs, name, bases, dct) + + # Check predicate. + predicate = dct.get("predicate") + if predicate is None or not isinstance(predicate, MovementPredicate): + raise TypeError( + f"Invalid predicate in {name}: please specify a MovementPredicate instance." + ) + # Check leaves. + leaves = dct.get("leaves") + if leaves is None or not isinstance(leaves, TickIndex): + raise TypeError( + f"Invalid leaves in {name}: please specify a TickIndex instance." + ) + if leaves.step < 0: + raise TypeError( + f"Invalid leaves in {name}: step indices cannot be less than zero." + ) + # Check returns. + returns = dct.get("returns") + if returns is None or not isinstance(returns, TickDelta): + raise TypeError( + f"Invalid returns in {name}: please specify a TickDelta instance." + ) + if returns.step < 0: + raise TypeError( + f"Invalid returns in {name}: step indices cannot be less than zero." + ) + if returns.days < 0: + raise TypeError( + f"Invalid returns in {name}: days cannot be less than zero." + ) + + return super().__new__(mcs, name, bases, dct) + + +class MovementClause(SimulationTickFunction[NDArray[SimDType]], ABC, metaclass=MovementClauseClass): + """ + A movement clause is basically a function which calculates _how many_ individuals + should move between all of the geo nodes, and then epymorph decides by random draw + _which_ individuals move (as identified by their disease status, or IPM compartment). + It also has various settings which determine when the clause is active + (for example, only move people Monday-Friday at the start of the day) + and when the individuals that were moved by the clause should return home + (for example, stay for two days and then return at the end of the day). + """ + + # in addition to requirements (from super), movement clauses must also specify: + + predicate: MovementPredicate + """When does this movement clause apply?""" + + leaves: TickIndex + """On which tau step does this movement clause apply?""" + + returns: TickDelta + """When do the movers from this clause return home?""" + + def is_active(self, tick: Tick) -> bool: + """Should this movement clause be applied this tick?""" + return self.leaves.step == tick.step and self.predicate.evaluate(tick) + + @abstractmethod + def evaluate(self, tick: Tick) -> NDArray[SimDType]: + """ + Implement this method to provide logic for the clause. + Your implementation is free to use `data`, `dim`, and `rng` in this function body. + You can also use `defer` to utilize another MovementClause instance. + """ + + def evaluate_in_context( + self, + data: NamespacedAttributeResolver, + dim: SimDimensions, + scope: GeoScope, + rng: np.random.Generator, + tick: Tick + ) -> NDArray[SimDType]: + """ + Evaluate this function within a context. + epymorph calls this function; you generally don't need to. + """ + requested = super().evaluate_in_context(data, dim, scope, rng, tick) + np.fill_diagonal(requested, 0) + return requested + + +################# +# MovementModel # +################# + + +class MovementModelClass(ABCMeta): + """ + The metaclass for user-defined MovementModel classes. + Used to verify proper class implementation. + """ + def __new__( + mcs: Type[_TypeT], + name: str, + bases: tuple[type, ...], + dct: dict[str, Any], + ) -> _TypeT: + # Skip these checks for known base classes: + if name in ("MovementModel",): + return super().__new__(mcs, name, bases, dct) + + # Check tau steps. + steps = dct.get("steps") + if steps is None or not isinstance(steps, (list, tuple)): + raise TypeError( + f"Invalid steps in {name}: please specify as a list or tuple." + ) + if not are_instances(steps, float): + raise TypeError( + f"Invalid steps in {name}: must be floating point numbers." + ) + if len(steps) == 0: + raise TypeError( + f"Invalid steps in {name}: please specify at least one tau step length." + ) + if not isclose(sum(steps), 1.0, abs_tol=1e-6): + raise TypeError( + f"Invalid steps in {name}: steps must sum to 1." + ) + if any(x <= 0 for x in steps): + raise TypeError( + f"Invalid steps in {name}: steps must all be greater than 0." + ) + dct["steps"] = tuple(steps) + + # Check clauses. + clauses = dct.get("clauses") + if clauses is None or not isinstance(clauses, (list, tuple)): + raise TypeError( + f"Invalid clauses in {name}: please specify as a list or tuple." + ) + if not are_instances(clauses, MovementClause): + raise TypeError( + f"Invalid clauses in {name}: must be instances of MovementClause." + ) + if len(clauses) == 0: + raise TypeError( + f"Invalid clauses in {name}: please specify at least one clause." + ) + for c in cast(Sequence[MovementClause], clauses): + # Check that clause steps are valid. + num_steps = len(steps) + if c.leaves.step >= num_steps: + raise TypeError( + f"Invalid clauses in {name}: {c.__class__.__name__} uses a leave step ({c.leaves.step}) " + f"which is not a valid index. (steps: {steps})" + ) + if c.returns.step >= num_steps: + raise TypeError( + f"Invalid clauses in {name}: {c.__class__.__name__} uses a return step ({c.returns.step}) " + f"which is not a valid index. (steps: {steps})" + ) + dct["clauses"] = tuple(clauses) + + return super().__new__(mcs, name, bases, dct) + + +class MovementModel(ABC, metaclass=MovementModelClass): + """ + A MovementModel (MM) describes a pattern of geospatial movement for individuals in the model. + The MM chops the day up into one or more parts (tau steps), and then describes movement clauses + which trigger for certain parts of the day. + """ + + steps: Sequence[float] + clauses: Sequence[MovementClause] + + @cached_property + def requirements(self) -> Sequence[AttributeDef]: + """The combined requirements of all of the clauses in this model.""" + return [req + for clause in self.clauses + for req in clause.requirements] diff --git a/epymorph/params.py b/epymorph/params.py index 7dd8f653..e5d53b55 100644 --- a/epymorph/params.py +++ b/epymorph/params.py @@ -10,6 +10,7 @@ from numpy.typing import NDArray from sympy import Expr, Symbol +from epymorph.adrio.adrio import Adrio from epymorph.data_type import (AttributeValue, ScalarDType, ScalarValue, StructDType, StructValue) from epymorph.simulation import SimulationFunction @@ -19,7 +20,7 @@ """The result type of a ParamFunction.""" -class ParamFunction(SimulationFunction[T_co], ABC): +class ParamFunction(SimulationFunction[NDArray[T_co]], ABC): """Parameter functions can be specified in a variety of forms; this class describe the common elements.""" @@ -152,7 +153,7 @@ def simulation_symbols(*symbols: ParamSymbol) -> tuple[Symbol, ...]: class ParamExpressionTimeAndNode(ParamFunction[np.float64]): """A param function based on a sympy expression for a time-by-node matrix of data.""" - attributes = () + requirements = () _expr: Expr @@ -184,6 +185,6 @@ def evaluate(self) -> NDArray[np.float64]: ListValue = Sequence[Union[ScalarValue, StructValue, 'ListValue']] -ParamValue = ScalarValue | StructValue | ListValue | ParamFunction | Expr \ +ParamValue = ScalarValue | StructValue | ListValue | ParamFunction | Adrio | Expr \ | NDArray[ScalarDType | StructDType] """All acceptable input forms for parameter values.""" diff --git a/epymorph/plots.py b/epymorph/plots.py index 938d7333..fa9b709f 100644 --- a/epymorph/plots.py +++ b/epymorph/plots.py @@ -8,9 +8,9 @@ from pandas import DataFrame from pandas import merge as pd_merge -from epymorph.geo.geo import Geo from epymorph.geography import us_tiger -from epymorph.geography.us_census import STATE +from epymorph.geography.us_census import (STATE, CensusScope, CountyScope, + StateScope) from epymorph.simulator.basic.output import Output @@ -59,7 +59,7 @@ def _subset_states(gdf: GeoDataFrame, state_fips: tuple[str, ...]) -> GeoDataFra def map_data_by_county( - geo: Geo, + scope: CountyScope, data: NDArray, *, title: str, @@ -71,22 +71,21 @@ def map_data_by_county( ) -> None: """ Draw a county-level choropleth map using the given `data`. This must be a numpy array whose - ordering is the same as the nodes in the geo. - Assumes that the geo contains an attribute (`geoid`) containing the geoids of its nodes. - (This information is needed to fetch the map shapes.) + ordering is the same as the nodes in the geo scope. """ - state_fips = tuple(STATE.truncate_list(geo["geoid"])) + state_fips = tuple(STATE.truncate_list(scope.get_node_ids())) gdf_counties = us_tiger.get_counties_geo(year) gdf_counties = _subset_states(gdf_counties, state_fips) gdf_borders = gdf_counties if outline == 'states': gdf_states = us_tiger.get_states_geo(2020) gdf_borders = _subset_states(gdf_states, state_fips) - return _map_data_by_geo(geo, data, gdf_counties, gdf_borders=gdf_borders, title=title, cmap=cmap, vmin=vmin, vmax=vmax) + return _map_data_by_geo(scope, data, gdf_counties, gdf_borders=gdf_borders, + title=title, cmap=cmap, vmin=vmin, vmax=vmax) def map_data_by_state( - geo: Geo, + scope: StateScope, data: NDArray, *, title: str, @@ -97,18 +96,17 @@ def map_data_by_state( ) -> None: """ Draw a state-level choropleth map using the given `data`. This must be a numpy array whose - ordering is the same as the nodes in the geo. - Assumes that the geo contains an attribute (`geoid`) containing the geoids of its nodes. - (This information is needed to fetch the map shapes.) + ordering is the same as the nodes in the geo scope. """ - state_fips = tuple(STATE.truncate_list(geo["geoid"])) + state_fips = tuple(STATE.truncate_list(scope.get_node_ids())) gdf_states = us_tiger.get_states_geo(year) gdf_states = _subset_states(gdf_states, state_fips) - return _map_data_by_geo(geo, data, gdf_states, title=title, cmap=cmap, vmin=vmin, vmax=vmax) + return _map_data_by_geo(scope, data, gdf_states, + title=title, cmap=cmap, vmin=vmin, vmax=vmax) def _map_data_by_geo( - geo: Geo, + scope: CensusScope, data: NDArray, gdf_nodes: GeoDataFrame, *, @@ -125,7 +123,7 @@ def _map_data_by_geo( df_merged = pd_merge( on="GEOID", left=gdf_nodes, - right=DataFrame({'GEOID': geo['geoid'], 'data': data}), + right=DataFrame({'GEOID': scope.get_node_ids(), 'data': data}), ) fig, ax = plt.subplots(figsize=(8, 6)) diff --git a/epymorph/rume.py b/epymorph/rume.py index b3e36dcb..515bf449 100644 --- a/epymorph/rume.py +++ b/epymorph/rume.py @@ -6,28 +6,31 @@ """ import dataclasses import textwrap -from dataclasses import dataclass +from abc import ABC, abstractmethod +from copy import deepcopy +from dataclasses import dataclass, field from functools import cached_property -from itertools import accumulate -from typing import Callable, Mapping, OrderedDict, Self, Sequence +from itertools import accumulate, pairwise, starmap +from typing import (Callable, Mapping, NamedTuple, OrderedDict, Self, Sequence, + final) import numpy as np from numpy.typing import NDArray -from sympy import Add, Expr, Max, Symbol +from sympy import Symbol -from epymorph.compartment_model import (CompartmentDef, CompartmentModel, - ModelSymbols, TransitionDef, - remap_transition) +from epymorph.compartment_model import (BaseCompartmentModel, + CombinedCompartmentModel, + CompartmentModel, MetaEdgeBuilder, + MultistrataModelSymbols, TransitionDef) from epymorph.data_shape import SimDimensions from epymorph.data_type import dtype_str from epymorph.database import AbsoluteName, ModuleNamePattern, NamePattern from epymorph.geography.scope import GeoScope from epymorph.initializer import Initializer -from epymorph.movement.parser import (DailyClause, MovementClause, - MovementSpec, MoveSteps) +from epymorph.movement_model import MovementClause, MovementModel from epymorph.params import ParamSymbol, ParamValue, simulation_symbols -from epymorph.simulation import AttributeDef, TimeFrame -from epymorph.sympy_shim import to_symbol +from epymorph.simulation import (DEFAULT_STRATA, META_STRATA, AttributeDef, + TickDelta, TickIndex, TimeFrame, gpm_strata) from epymorph.util import are_unique, map_values ####### @@ -42,18 +45,21 @@ class Gpm: that make up a RUME. """ + name: str ipm: CompartmentModel - mm: MovementSpec + mm: MovementModel init: Initializer params: Mapping[ModuleNamePattern, ParamValue] def __init__( self, + name: str, ipm: CompartmentModel, - mm: MovementSpec, + mm: MovementModel, init: Initializer, params: Mapping[str, ParamValue] | None = None, ): + self.name = name self.ipm = ipm self.mm = mm self.init = init @@ -63,71 +69,11 @@ def __init__( } -##################################### -# Utilities for building meta edges # -##################################### - - -class RumeSymbols: - """ - A symbol dictionary for the symbols in a RUME. This information is made available during - the meta-edge builder function so that you can reference the RUME symbols to create the - appropriate transition rates. - """ - _compartments: dict[str, tuple[Symbol, ...]] - _attr: dict[str, tuple[Symbol, ...]] - _meta: tuple[Symbol, ...] - - def __init__( - self, - compartments: dict[str, tuple[Symbol, ...]], - attr: dict[str, tuple[Symbol, ...]], - meta: tuple[Symbol, ...], - ): - self._compartments = compartments - self._attr = attr - self._meta = meta - - def compartments(self, strata: str) -> tuple[Symbol, ...]: - """A tuple of symbols for the compartments in a strata.""" - return self._compartments[strata] - - def total(self, strata: str) -> Expr: - """A sympy expression for the total of all compartments in a strata.""" - return Add(*self._compartments[strata]) - - def total_nonzero(self, strata: str) -> Expr: - """ - A sympy expression for the total of all compartments in a strata, - but clamped so that it's never less than one. (This is useful as - a divisor, if you can guarantee the numerator is zero when the - sum otherwise would be zero.) - """ - return Max(1, self.total(strata)) - - def attributes(self, strata: str) -> tuple[Symbol, ...]: - """A tuple of symbols for the (non-meta) IPM attributes in a strata.""" - return self._attr[strata] - - def meta_attributes(self) -> tuple[Symbol, ...]: - """A tuple of symbols for the meta attributes.""" - return self._meta - - -MetaEdgeBuilder = Callable[[RumeSymbols], Sequence[TransitionDef]] -"""A function for creating meta edges in a multistrata RUME.""" - - ######## # RUME # ######## -DEFAULT_STRATA = "all" -"""The strata name used as the default, primarily for single-strata simulations.""" -META_STRATA = "meta" -"""A strata for meta-strata information.""" - GEO_LABELS = AbsoluteName(META_STRATA, "geo", "label") """ If this attribute is provided to a RUME, it will be used as labels for the geo node. @@ -135,105 +81,13 @@ def meta_attributes(self) -> tuple[Symbol, ...]: """ -@dataclass(frozen=True) -class _StrataSymbols(ModelSymbols): - """The remapping of an IPM's symbols to use in a multi-strata IPM.""" +class _CombineTauStepsResult(NamedTuple): + new_tau_steps: tuple[float, ...] + start_mapping: dict[str, dict[int, int]] + stop_mapping: dict[str, dict[int, int]] - mapping: dict[Symbol, Symbol] - @classmethod - def map_to_strata(cls, symbols: ModelSymbols, strata: str) -> Self: - """Remap an IPM's ModelSymbols to be in the given strata.""" - compartments = list[CompartmentDef]() - attributes = OrderedDict[AbsoluteName, AttributeDef]() - compartment_symbols = list[Symbol]() - attribute_symbols = list[Symbol]() - mapping = dict[Symbol, Symbol]() - - for comp, old_symbol in zip(symbols.compartments, symbols.compartment_symbols): - new_name = f"{comp.name}_{strata}" - new_symbol = to_symbol(new_name) - mapping[old_symbol] = new_symbol - compartments.append(dataclasses.replace(comp, name=new_name)) - compartment_symbols.append(new_symbol) - - for (name, attr), old_symbol in zip(symbols.attributes.items(), symbols.attribute_symbols): - new_name = name.in_strata(f"gpm:{strata}") - new_symbol = to_symbol(f"{attr.name}_{strata}") - mapping[old_symbol] = new_symbol - attributes[new_name] = attr - attribute_symbols.append(new_symbol) - - return cls(compartments, attributes, compartment_symbols, attribute_symbols, mapping) - - -def combine_ipms( - strata: list[tuple[str, CompartmentModel]], - meta_attributes: list[AttributeDef], - meta_edges: MetaEdgeBuilder, -) -> CompartmentModel: - """ - Combine IPMs for different strata, remapping symbols as appropriate and using the - `meta_edges` function to construct edges connecting the strata compartments. - """ - - strata_symbols = [ - _StrataSymbols.map_to_strata(ipm.symbols, strata) - for strata, ipm in strata - ] - - meta_attributes_symbols = [ - to_symbol(f"{a.name}_{META_STRATA}") - for a in meta_attributes - ] - - rume_symbols = RumeSymbols( - compartments={ - strata: tuple(symbols.compartment_symbols) - for (strata, _), symbols in zip(strata, strata_symbols) - }, - attr={ - strata: tuple(symbols.attribute_symbols) - for (strata, _), symbols in zip(strata, strata_symbols) - }, - meta=tuple(meta_attributes_symbols), - ) - - ipm_symbols = ModelSymbols( - compartments=[ - compartment - for symbols in strata_symbols - for compartment in symbols.compartments - ], - attributes=OrderedDict([ - *((name, attr) - for symbols in strata_symbols - for name, attr in symbols.attributes.items()), - *((AbsoluteName(META_STRATA, "ipm", attr.name), attr) - for attr in meta_attributes), - ]), - compartment_symbols=[ - compartment - for symbols in strata_symbols - for compartment in symbols.compartment_symbols - ], - attribute_symbols=[ - *(a for s in strata_symbols for a in s.attribute_symbols), - *meta_attributes_symbols, - ], - ) - - ipm_transitions = [ - *(remap_transition(trx, symbols.mapping) - for (_, ipm), symbols in zip(strata, strata_symbols) - for trx in ipm.transitions), - *meta_edges(rume_symbols), - ] - - return CompartmentModel(ipm_symbols, ipm_transitions) - - -def combine_tau_steps(strata_tau_lengths: dict[str, list[float]]) -> tuple[list[float], dict[str, dict[int, int]], dict[str, dict[int, int]]]: +def combine_tau_steps(strata_tau_lengths: dict[str, Sequence[float]]) -> _CombineTauStepsResult: """ When combining movement models with different tau steps, it is necessary to create a new tau step scheme which can accomodate them all. This function performs that calculation, @@ -244,10 +98,10 @@ def combine_tau_steps(strata_tau_lengths: dict[str, list[float]]) -> tuple[list[ """ # Convert the tau lengths into the starting point and stopping point for each tau step. # Starts and stops are expressed as fractions of one day. - def tau_starts(taus: list[float]) -> list[float]: + def tau_starts(taus: Sequence[float]) -> Sequence[float]: return [0.0, *accumulate(taus)][:-1] - def tau_stops(taus: list[float]) -> list[float]: + def tau_stops(taus: Sequence[float]) -> Sequence[float]: return [*accumulate(taus)] strata_tau_starts = map_values(tau_starts, strata_tau_lengths) @@ -261,10 +115,10 @@ def tau_stops(taus: list[float]) -> list[float]: combined_tau_stops.sort() # Now calculate the combined tau lengths. - combined_tau_lengths = [ + combined_tau_lengths = tuple( stop - start for start, stop in zip(combined_tau_starts, combined_tau_stops) - ] + ) # But the individual strata MMs are indexed by their original tau steps, # so we need to calculate the appropriate re-indexing to the new tau steps @@ -278,48 +132,45 @@ def tau_stops(taus: list[float]) -> list[float]: for name, curr in strata_tau_stops.items() } - return combined_tau_lengths, tau_start_mapping, tau_stop_mapping + return _CombineTauStepsResult(combined_tau_lengths, tau_start_mapping, tau_stop_mapping) -def remap_taus(strata_mms: list[tuple[str, MovementSpec]]) -> tuple[list[float], OrderedDict[str, MovementSpec]]: +def remap_taus(strata_mms: list[tuple[str, MovementModel]]) -> OrderedDict[str, MovementModel]: """ When combining movement models with different tau steps, it is necessary to create a new tau step scheme which can accomodate them all. """ new_tau_steps, start_mapping, stop_mapping = combine_tau_steps({ - strata: mm.steps.step_lengths + strata: mm.steps for strata, mm in strata_mms }) def clause_remap_tau(clause: MovementClause, strata: str) -> MovementClause: - match clause: - case DailyClause(): - return DailyClause( - days=clause.days, - leave_step=start_mapping[strata][clause.leave_step], - duration=clause.duration, - return_step=stop_mapping[strata][clause.return_step], - function=clause.function, - ) - - def spec_remap_tau(orig_spec: MovementSpec, strata: str) -> MovementSpec: - return MovementSpec( - steps=MoveSteps(new_tau_steps), - attributes=orig_spec.attributes, - predef=orig_spec.predef, - clauses=[ - clause_remap_tau(c, strata) - for c in orig_spec.clauses - ], + leave_step = start_mapping[strata][clause.leaves.step] + return_step = stop_mapping[strata][clause.returns.step] + + clone = deepcopy(clause) + clone.leaves = TickIndex(leave_step) + clone.returns = TickDelta(clause.returns.days, return_step) + return clone + + def model_remap_tau(orig_model: MovementModel, strata: str) -> MovementModel: + clone = deepcopy(orig_model) + clone.steps = new_tau_steps + clone.clauses = tuple( + clause_remap_tau(c, strata) + for c in orig_model.clauses ) + return clone - return new_tau_steps, OrderedDict([ - (strata_name, spec_remap_tau(spec, strata_name)) - for strata_name, spec in strata_mms + return OrderedDict([ + (strata_name, model_remap_tau(model, strata_name)) + for strata_name, model in strata_mms ]) -class Rume: +@dataclass(frozen=True) +class Rume(ABC): """ A RUME (or Runnable Modeling Experiment) contains the configuration of an epymorph-style simulation. It brings together one or more IPMs, MMs, initialization routines, @@ -329,132 +180,100 @@ class Rume: running a disease simulation and providing time-series results of the disease model. """ - original_gpms: OrderedDict[str, Gpm] - ipm: CompartmentModel - mms: OrderedDict[str, MovementSpec] + strata: Sequence[Gpm] + ipm: BaseCompartmentModel + mms: OrderedDict[str, MovementModel] scope: GeoScope time_frame: TimeFrame params: Mapping[NamePattern, ParamValue] - dim: SimDimensions - is_single_strata: bool + dim: SimDimensions = field(init=False) - def __init__( - self, - strata: OrderedDict[str, Gpm], - ipm: CompartmentModel, - mms: OrderedDict[str, MovementSpec], - tau_step_lengths: list[float], - scope: GeoScope, - time_frame: TimeFrame, - params: Mapping[NamePattern, ParamValue], - is_single_strata: bool, - ): - """ - This is the 'internal' constructor for Rume; you probably want to use the - `single_strata` or `multistrata` static methods instead. - """ - if not are_unique(strata): + def __post_init__(self): + if not are_unique(g.name for g in self.strata): msg = "Strata names must be unique; duplicate found." raise ValueError(msg) - # Create dimensions + # We can get the tau step lengths from a movement model. + # In a multistrata model, there will be multiple remapped MMs, + # but they all have the same set of tau steps so it doesn't matter + # which we use. (Using the first one is safe.) + first_strata = self.strata[0].name + tau_step_lengths = self.mms[first_strata].steps + dim = SimDimensions.build( tau_step_lengths=tau_step_lengths, - start_date=time_frame.start_date, - days=time_frame.duration_days, - nodes=len(scope.get_node_ids()), - compartments=ipm.num_compartments, - events=ipm.num_events, + start_date=self.time_frame.start_date, + days=self.time_frame.duration_days, + nodes=len(self.scope.get_node_ids()), + compartments=self.ipm.num_compartments, + events=self.ipm.num_events, ) - - self.original_gpms = strata - self.ipm = ipm - self.mms = mms - self.scope = scope - self.time_frame = time_frame - self.params = params - self.dim = dim - self.is_single_strata = is_single_strata + object.__setattr__(self, 'dim', dim) @cached_property - def attributes(self) -> Mapping[AbsoluteName, AttributeDef]: + def requirements(self) -> Mapping[AbsoluteName, AttributeDef]: """Returns the attributes required by the RUME.""" def generate_items(): # IPM attributes are already fully named. - yield from self.ipm.attributes.items() + yield from self.ipm.requirements_dict.items() # Name the MM and Init attributes. - for strata, gpm in self.original_gpms.items(): - strata_name = f"gpm:{strata}" - for a in gpm.mm.attributes: + for gpm in self.strata: + strata_name = gpm_strata(gpm.name) + for a in gpm.mm.requirements: yield AbsoluteName(strata_name, "mm", a.name), a - for a in gpm.init.attributes: + for a in gpm.init.requirements: yield AbsoluteName(strata_name, "init", a.name), a - return dict(generate_items()) + return OrderedDict(generate_items()) - def compartment_mask(self, strata_name: str) -> NDArray[np.bool_]: + @cached_property + def compartment_mask(self) -> Mapping[str, NDArray[np.bool_]]: """ - Returns a mask which describes which compartments belong in the given strata. - For example: if the model has three strata ('1', '2', and '3') with three compartments each, - `strata_compartment_mask('2')` returns `[0 0 0 1 1 1 0 0 0]` + Masks that describe which compartments belong in the given strata. + For example: if the model has three strata ('a', 'b', and 'c') with three compartments each, + `strata_compartment_mask('b')` returns `[0 0 0 1 1 1 0 0 0]` (where 0 stands for False and 1 stands for True). - Raises ValueError if no strata matches the given name. """ - result = np.full(shape=self.ipm.num_compartments, - fill_value=False, dtype=np.bool_) - ci, cf = 0, 0 - found = False - for strata, gpm in self.original_gpms.items(): - # Iterate through the strata IPMs: - ipm = gpm.ipm - if strata_name != strata: - # keep count of how many compartments precede our target strata - ci += ipm.num_compartments - else: - # when we find our target, we now have the target's compartment index range - cf = ci + ipm.num_compartments - # set those to True and break - result[ci:cf] = True - found = True - break - if not found: - raise ValueError(f"Not a valid strata name in this model: {strata_name}") - return result - - def compartment_mobility(self, strata_name: str) -> NDArray[np.bool_]: - """Calculates which compartments should be considered subject to movement in a particular strata.""" - compartment_mobility = np.array( + def mask(length: int, true_slice: slice) -> NDArray[np.bool_]: + # A boolean array with the given slice set to True, all others False + m = np.zeros(shape=length, dtype=np.bool_) + m[true_slice] = True + return m + + # num of compartments in the combined IPM + C = self.ipm.num_compartments + # num of compartments in each strata + strata_cs = [gpm.ipm.num_compartments for gpm in self.strata] + # start and stop index for each strata + strata_ranges = pairwise([0, *accumulate(strata_cs)]) + # map stata name to the mask for each strata + return dict(zip( + [g.name for g in self.strata], + [mask(C, s) for s in starmap(slice, strata_ranges)] + )) + + @cached_property + def compartment_mobility(self) -> Mapping[str, NDArray[np.bool_]]: + """Masks that describe which compartments should be considered subject to movement in a particular strata.""" + # The mobility mask for all strata. + all_mobility = np.array( ['immobile' not in c.tags for c in self.ipm.compartments], dtype=np.bool_ ) - return self.compartment_mask(strata_name) * compartment_mobility - - def with_time_frame(self, time_frame: TimeFrame) -> 'Rume': - """Create a RUME with a new time frame.""" - # TODO: do we need to go through all of the params and subset any that are time-based? - # How would that work? Or maybe reconciling to time frame happens at param evaluation time... - return Rume( - strata=self.original_gpms, - ipm=self.ipm, - mms=self.mms, - tau_step_lengths=list(self.dim.tau_step_lengths), - scope=self.scope, - time_frame=time_frame, - params=self.params, - is_single_strata=self.is_single_strata, - ) + # Mobility for a single strata is all_mobility boolean-and whether the compartment is in that strata. + return { + strata.name: all_mobility & self.compartment_mask[strata.name] + for strata in self.strata + } + @abstractmethod def name_display_formatter(self) -> Callable[[AbsoluteName | NamePattern], str]: """Returns a function for formatting attribute/parameter names.""" - if self.is_single_strata: - return lambda n: f"{n.module}::{n.id}" - else: - return str def params_description(self) -> str: """Provide a description of all attributes required by the RUME.""" format_name = self.name_display_formatter() lines = [] - for name, attr in self.attributes.items(): + for name, attr in self.requirements.items(): properties = [ f"type: {dtype_str(attr.type)}", f"shape: {attr.shape}", @@ -476,7 +295,7 @@ def generate_params_dict(self) -> str: """Generate a skeleton dictionary you can use to provide parameter values to the room.""" format_name = self.name_display_formatter() lines = ["{"] - for name, attr in self.attributes.items(): + for name, attr in self.requirements.items(): value = 'PLACEHOLDER' if attr.default_value is not None: value = str(attr.default_value) @@ -489,63 +308,120 @@ def symbols(*symbols: ParamSymbol) -> tuple[Symbol, ...]: """Convenient function to retrieve the symbols used to represent simulation quantities.""" return simulation_symbols(*symbols) + def with_time_frame(self, time_frame: TimeFrame) -> Self: + """Create a RUME with a new time frame.""" + # TODO: do we need to go through all of the params and subset any that are time-based? + # How would that work? Or maybe reconciling to time frame happens at param evaluation time... + return dataclasses.replace(self, time_frame=time_frame) + + +@dataclass(frozen=True) +class SingleStrataRume(Rume): + """A RUME with a single strata.""" + + ipm: CompartmentModel + @classmethod - def single_strata( + def build( cls, ipm: CompartmentModel, - mm: MovementSpec, + mm: MovementModel, init: Initializer, scope: GeoScope, time_frame: TimeFrame, params: Mapping[str, ParamValue], ) -> Self: """Create a RUME with only a single strata.""" - gpm = Gpm(ipm, mm, init, {}) - return cls( - strata=OrderedDict([(DEFAULT_STRATA, gpm)]), + strata=[Gpm(DEFAULT_STRATA, ipm, mm, init, {})], ipm=ipm, mms=OrderedDict([(DEFAULT_STRATA, mm)]), - tau_step_lengths=mm.steps.step_lengths, scope=scope, time_frame=time_frame, params={ NamePattern.parse(k): v for k, v in params.items() - }, - is_single_strata=True, + } ) + def name_display_formatter(self) -> Callable[[AbsoluteName | NamePattern], str]: + """Returns a function for formatting attribute/parameter names.""" + return lambda n: f"{n.module}::{n.id}" + + +@dataclass(frozen=True) +class MultistrataRume(Rume): + """A RUME with a multiple strata.""" + + ipm: CombinedCompartmentModel + @classmethod - def multistrata( + def build( cls, - strata: list[tuple[str, Gpm]], - meta_attributes: list[AttributeDef], + strata: Sequence[Gpm], + meta_requirements: Sequence[AttributeDef], meta_edges: MetaEdgeBuilder, scope: GeoScope, time_frame: TimeFrame, params: Mapping[str, ParamValue], ) -> Self: - """Create a multi-strata RUME by combining GPMs, one for each strata.""" - # Combine IPMs - ipm = combine_ipms( - [(strata_name, gpm.ipm) for strata_name, gpm in strata], - meta_attributes, meta_edges) - - # Combine MMs - tau_step_lengths, mms = remap_taus( - [(strata_name, gpm.mm) for strata_name, gpm in strata]) - + """Create a multistrata RUME by combining one GPM per strata.""" return cls( - strata=OrderedDict(strata), - ipm=ipm, - mms=mms, - tau_step_lengths=tau_step_lengths, + strata=strata, + # Combine IPMs + ipm=CombinedCompartmentModel( + strata=[(gpm.name, gpm.ipm) for gpm in strata], + meta_requirements=meta_requirements, + meta_edges=meta_edges), + # Combine MMs + mms=remap_taus([(gpm.name, gpm.mm) for gpm in strata]), scope=scope, time_frame=time_frame, params={ NamePattern.parse(k): v for k, v in params.items() - }, - is_single_strata=False, + } + ) + + def name_display_formatter(self) -> Callable[[AbsoluteName | NamePattern], str]: + """Returns a function for formatting attribute/parameter names.""" + return str + + +class MultistrataRumeBuilder(ABC): + """Create a multi-strata RUME by combining GPMs, one for each strata.""" + + strata: Sequence[Gpm] + """The strata that are part of this RUME.""" + + meta_requirements: Sequence[AttributeDef] + """ + A set of additional requirements which are needed by the meta-edges + in our combined compartment model. + """ + + @abstractmethod + def meta_edges(self, symbols: MultistrataModelSymbols) -> list[TransitionDef]: + """ + When implementing a MultistrataRumeBuilder, override this method + to build the meta-transition-edges -- the edges which represent + cross-strata interactions. You are given a reference to this model's symbols library + so you can build expressions for the transition rates. + """ + + @final + def build( + self, + scope: GeoScope, + time_frame: TimeFrame, + params: Mapping[str, ParamValue], + ) -> MultistrataRume: + """Build the RUME.""" + return MultistrataRume.build( + self.strata, + self.meta_requirements, + self.meta_edges, + scope, + time_frame, + params ) diff --git a/epymorph/simulation.py b/epymorph/simulation.py index b4be0af9..fa577c68 100644 --- a/epymorph/simulation.py +++ b/epymorph/simulation.py @@ -1,14 +1,14 @@ """General simulation requisites and utility functions.""" -import logging -from abc import ABC, abstractmethod +from abc import ABC, ABCMeta, abstractmethod +from copy import deepcopy from dataclasses import dataclass, field from datetime import date, timedelta -from functools import cached_property -from importlib import reload +from functools import cache, cached_property from typing import (Any, Callable, Generator, Generic, Iterable, NamedTuple, - Self, Sequence, TypeVar, final, overload) + Self, Sequence, Type, TypeVar, final, overload) import numpy as np +from jsonpickle.util import is_picklable from numpy.random import SeedSequence from numpy.typing import NDArray @@ -18,7 +18,8 @@ from epymorph.database import (AbsoluteName, AttributeName, Database, ModuleNamespace) from epymorph.error import AttributeException -from epymorph.util import acceptable_name +from epymorph.geography.scope import GeoScope +from epymorph.util import acceptable_name, are_instances, are_unique def default_rng(seed: int | SeedSequence | None = None) -> Callable[[], np.random.Generator]: @@ -29,14 +30,6 @@ def default_rng(seed: int | SeedSequence | None = None) -> Callable[[], np.rando return lambda: np.random.default_rng(seed) -def enable_logging(filename: str = 'debug.log', movement: bool = True) -> None: - """Enable simulation logging to file.""" - reload(logging) - logging.basicConfig(filename=filename, filemode='w') - if movement: - logging.getLogger('movement').setLevel(logging.DEBUG) - - ######## # Time # ######## @@ -51,6 +44,27 @@ def of(cls, start_date_iso8601: str, duration_days: int) -> Self: """Alternate constructor for TimeFrame, parsing start date from an ISO-8601 string.""" return cls(date.fromisoformat(start_date_iso8601), duration_days) + @classmethod + def year(cls, year: int) -> Self: + """Alternate constructor for TimeFrame, comprising one full calendar year.""" + start = date(year, 1, 1) + end = date(year + 1, 1, 1) + duration = (end - start).days + return cls(start, duration) + + @classmethod + def range(cls, start_date: date | str, end_date: date | str) -> Self: + """ + Alternate constructor for TimeFrame, comprising the (endpoint inclusive) date range. + If a date is passed as a string, it will be parsed using ISO-8601 format. + """ + if isinstance(start_date, str): + start_date = date.fromisoformat(start_date) + if isinstance(end_date, str): + end_date = date.fromisoformat(end_date) + duration = (end_date - start_date).days + return cls(start_date, duration) + start_date: date """The first date in the simulation.""" duration_days: int @@ -83,6 +97,11 @@ class Tick(NamedTuple): """What's the tau length of the current step? (0.666,0.333,0.666,0.333,...)""" +class TickIndex(NamedTuple): + """A zero-based index of the simulation tau steps.""" + step: int # which tau step within that day (zero-indexed) + + class TickDelta(NamedTuple): """ An offset relative to a Tick expressed as a number of days which should elapse, @@ -331,133 +350,301 @@ def resolve_name(self, attr_name: str) -> NDArray: ######################## -T_co = TypeVar('T_co', bound=np.generic, covariant=True) +T_co = TypeVar('T_co', covariant=True) """The result type of a SimulationFunction.""" -_DeferredT = TypeVar('_DeferredT', bound=np.generic) +_DeferredT = TypeVar('_DeferredT') """The result type of a SimulationFunction during deference.""" -class _Context: - def data(self, attribute: AttributeKey) -> NDArray: - """Retrieve the value of a specific attribute.""" - raise ValueError("Invalid access of function context.") +class _Context(ABC): + """ + The evaluation context of a SimulationFunction. We want SimulationFunction + instances to be able to access properties of the simulation by using + various methods on `self`. But we also want to instantiate SimulationFunctions + before the simulation context exists! Hence this object starts out "empty" + and will be swapped for a "real" context when the function is evaluated in + a simulation context object. + """ + + @abstractmethod + def data(self, attribute: AttributeDef) -> NDArray: + """Retrieve the value of an attribute.""" @property + @abstractmethod def dim(self) -> SimDimensions: """The simulation dimensions.""" - raise ValueError("Invalid access of function context.") @property + @abstractmethod + def scope(self) -> GeoScope: + """The simulation GeoScope.""" + + @property + @abstractmethod def rng(self) -> np.random.Generator: """The simulation's random number generator.""" - raise ValueError("Invalid access of function context.") - def defer(self, other: 'SimulationFunction[T_co]') -> NDArray[T_co]: - """Defer processing to another similarly-typed instance of a SimulationFunction.""" - raise ValueError("Invalid access of function context.") + @abstractmethod + def export(self) -> tuple[NamespacedAttributeResolver, SimDimensions, GeoScope, np.random.Generator]: + """Tuples the contents of this context so it can be re-used (see: defer()).""" + + +class _EmptyContext(_Context): + def data(self, attribute: AttributeDef) -> NDArray: + raise TypeError("Invalid access of function context.") + + @property + def dim(self) -> SimDimensions: + raise TypeError("Invalid access of function context.") + + @property + def scope(self) -> GeoScope: + raise TypeError("Invalid access of function context.") + + @property + def rng(self) -> np.random.Generator: + raise TypeError("Invalid access of function context.") + + def export(self) -> tuple[NamespacedAttributeResolver, SimDimensions, GeoScope, np.random.Generator]: + raise TypeError("Invalid access of function context.") -_EMPTY_CONTEXT = _Context() +_EMPTY_CONTEXT = _EmptyContext() class _RealContext(_Context): - # The following attributes make up the evaluation context. - # They are set for the duration of `__call__()` and cleared afterwards. - # This allows implementations to use `self` to access the context during - # evaluation. It also allows us to cache attribute resolution results - # as implementations may be doing that within a hot loop. - _cache: dict[AttributeKey, AttributeArray] + _cached_data: Callable[[AttributeDef], AttributeArray] _data: NamespacedAttributeResolver _dim: SimDimensions + _scope: GeoScope _rng: np.random.Generator - def __init__(self, data: NamespacedAttributeResolver, dim: SimDimensions, rng: np.random.Generator): - self._cache = {} + def __init__(self, data: NamespacedAttributeResolver, dim: SimDimensions, scope: GeoScope, rng: np.random.Generator): + self._cached_data = cache(data.resolve) self._data = data self._dim = dim + self._scope = scope self._rng = rng - def data(self, attribute: AttributeKey) -> NDArray: - """Retrieve the value of a specific attribute.""" - if (result := self._cache.get(attribute)) is None: - result = self._data.resolve(attribute) - self._cache[attribute] = result - return result + def data(self, attribute: AttributeDef) -> NDArray: + # attribute resolutions are cached because implementations may be + # calling this function within a hot loop + # (and we can't just throw @cache on this method because it interferes + # with abstract method overriding) + return self._cached_data(attribute) @property def dim(self) -> SimDimensions: - """The simulation dimensions.""" return self._dim + @property + def scope(self) -> GeoScope: + return self._scope + @property def rng(self) -> np.random.Generator: - """The simulation's random number generator.""" return self._rng - def defer(self, other: 'SimulationFunction[_DeferredT]') -> NDArray[_DeferredT]: - """Defer processing to another similarly-typed instance of a SimulationFunction.""" - return other(self._data, self._dim, self._rng) + def export(self) -> tuple[NamespacedAttributeResolver, SimDimensions, GeoScope, np.random.Generator]: + return (self._data, self._dim, self._scope, self._rng) + +_TypeT = TypeVar("_TypeT") -class SimulationFunction(ABC, Generic[T_co]): + +class SimulationFunctionClass(ABCMeta): + """ + The metaclass for SimulationFunctions. + Used to verify proper class implementation. + """ + def __new__( + mcs: Type[_TypeT], + name: str, + bases: tuple[type, ...], + dct: dict[str, Any], + ) -> _TypeT: + # Check requirements if this class overrides it. + # (Otherwise class will inherit from parent.) + if (reqs := dct.get("requirements")) is not None: + if not isinstance(reqs, (list, tuple)): + raise TypeError( + f"Invalid requirements in {name}: please specify as a list or tuple." + ) + if not are_instances(reqs, AttributeDef): + raise TypeError( + f"Invalid requirements in {name}: must be instances of AttributeDef." + ) + if not are_unique(r.name for r in reqs): + raise TypeError( + f"Invalid requirements in {name}: requirement names must be unique." + ) + # Make requirements list immutable + dct["requirements"] = tuple(reqs) + + # Check serializable + if not is_picklable(name, mcs): + raise TypeError( + f"Invalid simulation function {name}: classes must be serializable (using jsonpickle)." + ) + + # NOTE: is_picklable() is misleading here; it does not guarantee that instances of a class are picklable, + # nor (if you called it against an instance) that all of the instance's attributes are picklable. + # jsonpickle simply ignores unpicklable fields, decoding objects into attribute swiss cheese. + # It will be more effective to check that all of the attributes of an object are picklable before we try to + # serialize it... Thus I don't think we can guarantee picklability at class definition time. + # Something like: + # [(n, is_picklable(n, x)) for n, x in obj.__dict__.items()] + # Why worry? Lambda functions are probably the most likely problem; they're not picklable by default. + # But a simple workaround is to use a def function and, if needed, partial function application. + + return super().__new__(mcs, name, bases, dct) + + +class BaseSimulationFunction(ABC, Generic[T_co], metaclass=SimulationFunctionClass): """ A function which runs in the context of a simulation to produce a value (as a numpy array). - Implement a SimulationFunction by extending this class and overriding the `evaluate()` method. + This base class exists to share functionality without limiting the function signature + of evaluate(). """ - attributes: Sequence[AttributeDef] = () - """The attribute definitions which describe the data requirements for this function.""" + requirements: Sequence[AttributeDef] = () + """The attribute definitions describing the data requirements for this function.""" _ctx: _Context = _EMPTY_CONTEXT - def __call__( + def with_context( self, data: NamespacedAttributeResolver, dim: SimDimensions, + scope: GeoScope, rng: np.random.Generator, - ) -> NDArray[T_co]: - try: - self._ctx = _RealContext(data, dim, rng) - return self.evaluate() - finally: - self._ctx = _EMPTY_CONTEXT - - @abstractmethod - def evaluate(self) -> NDArray[T_co]: + ) -> Self: """ - Implement this method to provide logic for the function. - Your implementation is free to use `data`, `dim`, and `rng` in this function body. - You can also use `defer` to utilize another SimulationFunction instance. + Constructs a clone of this instance which has access to the given context. + epymorph calls this function; you generally don't need to. """ - - @overload - def data(self, attribute: AttributeKey[type[int]]) -> NDArray[np.int64]: ... - @overload - def data(self, attribute: AttributeKey[type[float]]) -> NDArray[np.float64]: ... - @overload - def data(self, attribute: AttributeKey[type[str]]) -> NDArray[np.str_]: ... - @overload - def data(self, attribute: AttributeKey[Any]) -> NDArray[Any]: ... - - def data(self, attribute: AttributeKey) -> NDArray: + # clone this instance, then run evaluate on that; accomplishes two things: + # 1. don't have to worry about cleaning up _ctx + # 2. instances can use @cached_property without surprising results + clone = deepcopy(self) + setattr(clone, "_ctx", _RealContext(data, dim, scope, rng)) + return clone + + def data(self, attribute: AttributeDef | str) -> NDArray: """Retrieve the value of a specific attribute.""" - if attribute not in self.attributes: - msg = "You've accessed an attribute which you did not declare as a dependency!" - raise ValueError(msg) - return self._ctx.data(attribute) + if isinstance(attribute, str): + name = attribute + req = next((r for r in self.requirements if r.name == attribute), None) + else: + name = attribute.name + req = attribute + if req is None or req not in self.requirements: + raise ValueError( + f"Simulation function {self.__class__.__name__} accessed an attribute ({name}) " + "which you did not declare as a requirement." + ) + return self._ctx.data(req) @property def dim(self) -> SimDimensions: """The simulation dimensions.""" return self._ctx.dim + @property + def scope(self) -> GeoScope: + """The simulation GeoScope.""" + return self._ctx.scope + @property def rng(self) -> np.random.Generator: """The simulation's random number generator.""" return self._ctx.rng + +class SimulationFunction(BaseSimulationFunction[T_co]): + """ + A function which runs in the context of a simulation to produce a value (as a numpy array). + Implement a SimulationFunction by extending this class and overriding the `evaluate()` method. + """ + + def evaluate_in_context( + self, + data: NamespacedAttributeResolver, + dim: SimDimensions, + scope: GeoScope, + rng: np.random.Generator, + ) -> T_co: + """ + Evaluate this function within a context. + epymorph calls this function; you generally don't need to. + """ + return super()\ + .with_context(data, dim, scope, rng)\ + .evaluate() + + @abstractmethod + def evaluate(self) -> T_co: + """ + Implement this method to provide logic for the function. + Your implementation is free to use `data`, `dim`, and `rng` in this function body. + You can also use `defer` to utilize another SimulationFunction instance. + """ + + @final + def defer(self, other: 'SimulationFunction[_DeferredT]') -> _DeferredT: + """Defer processing to another instance of a SimulationFunction.""" + return other.evaluate_in_context(*self._ctx.export()) + + +class SimulationTickFunction(BaseSimulationFunction[T_co]): + """ + A function which runs in the context of a simulation to produce a sim-time-specific value (as a numpy array). + Implement a SimulationTickFunction by extending this class and overriding the `evaluate()` method. + """ + + def evaluate_in_context( + self, + data: NamespacedAttributeResolver, + dim: SimDimensions, + scope: GeoScope, + rng: np.random.Generator, + tick: Tick + ) -> T_co: + """ + Evaluate this function within a context. + epymorph calls this function; you generally don't need to. + """ + return super()\ + .with_context(data, dim, scope, rng)\ + .evaluate(tick) + + @abstractmethod + def evaluate(self, tick: Tick) -> T_co: + """ + Implement this method to provide logic for the function. + Your implementation is free to use `data`, `dim`, and `rng` in this function body. + You can also use `defer` to utilize another SimulationTickFunction instance. + """ + @final - def defer(self, other: 'SimulationFunction[_DeferredT]') -> NDArray[_DeferredT]: - """Defer processing to another similarly-typed instance of a SimulationFunction.""" - return self._ctx.defer(other) + def defer(self, other: 'SimulationTickFunction[_DeferredT]', tick: Tick) -> _DeferredT: + """Defer processing to another instance of a SimulationTickFunction.""" + return other.evaluate_in_context(*self._ctx.export(), tick) + + +############### +# Multistrata # +############### + + +DEFAULT_STRATA = "all" +"""The strata name used as the default, primarily for single-strata simulations.""" +META_STRATA = "meta" +"""A strata for information that concerns the other strata.""" + + +def gpm_strata(strata_name: str) -> str: + """The strata name for a GPM in a multistrata RUME.""" + return f"gpm:{strata_name}" diff --git a/epymorph/simulator/basic/basic_simulator.py b/epymorph/simulator/basic/basic_simulator.py index 67d67741..13e9931c 100644 --- a/epymorph/simulator/basic/basic_simulator.py +++ b/epymorph/simulator/basic/basic_simulator.py @@ -9,8 +9,7 @@ InitException, IpmSimException, MmSimException, SimValidationException, ValidationException, error_gate) -from epymorph.event import (MovementEventsMixin, OnStart, OnTick, - SimulationEventsMixin) +from epymorph.event import EventBus, OnStart, OnTick from epymorph.params import ParamValue from epymorph.rume import GEO_LABELS, Rume from epymorph.simulation import TimeFrame, simulation_clock @@ -18,11 +17,13 @@ from epymorph.simulator.basic.mm_exec import MovementExecutor from epymorph.simulator.basic.output import Output from epymorph.simulator.data import (evaluate_params, initialize_rume, - validate_attributes) + validate_requirements) from epymorph.simulator.world_list import ListWorld +_events = EventBus() -class BasicSimulator(SimulationEventsMixin, MovementEventsMixin): + +class BasicSimulator(): """ A simulator for running singular simulation passes and producing time-series output. The most basic simulator! @@ -33,8 +34,6 @@ class BasicSimulator(SimulationEventsMixin, MovementEventsMixin): mm_exec: MovementExecutor def __init__(self, rume: Rume): - SimulationEventsMixin.__init__(self) - MovementEventsMixin.__init__(self) self.rume = rume def run( @@ -62,7 +61,7 @@ def run( }, rng=rng, ) - validate_attributes(rume, db) + validate_requirements(rume, db) except AttributeException as e: msg = f"RUME attribute requirements were not met. See errors:\n- {e}" raise SimValidationException(msg) from None @@ -92,9 +91,10 @@ def run( with error_gate("compiling the simulation", CompilationException): ipm_exec = IpmExecutor(rume, world, db, rng) - movement_exec = MovementExecutor(rume, world, db, rng, self) + movement_exec = MovementExecutor(rume, world, db, rng) - self.on_start.publish(OnStart(dim, rume.time_frame)) + _events.on_start.publish( + OnStart(self.__class__.__name__, dim, rume.time_frame)) # Run the simulation! for tick in simulation_clock(dim): @@ -109,8 +109,8 @@ def run( out.prevalence[tick.sim_index] = tick_prevalence t = tick.sim_index - self.on_tick.publish(OnTick(t, (t + 1) / dim.ticks)) + _events.on_tick.publish(OnTick(t, (t + 1) / dim.ticks)) - self.on_finish.publish(None) + _events.on_finish.publish(None) return out diff --git a/epymorph/simulator/basic/ipm_exec.py b/epymorph/simulator/basic/ipm_exec.py index fb43286f..e40c4a46 100644 --- a/epymorph/simulator/basic/ipm_exec.py +++ b/epymorph/simulator/basic/ipm_exec.py @@ -7,7 +7,7 @@ import numpy as np from numpy.typing import NDArray -from epymorph.compartment_model import (CompartmentModel, EdgeDef, ForkDef, +from epymorph.compartment_model import (BaseCompartmentModel, EdgeDef, ForkDef, TransitionDef, exogenous_states) from epymorph.data_type import (AttributeArray, AttributeValue, SimArray, SimDType) @@ -53,9 +53,9 @@ class CompiledFork: CompiledTransition = CompiledEdge | CompiledFork -def _compile_transitions(model: CompartmentModel) -> list[CompiledTransition]: +def _compile_transitions(model: BaseCompartmentModel) -> list[CompiledTransition]: # The parameters to pass to all rate lambdas - rate_params = [*model.symbols.compartment_symbols, *model.symbols.attribute_symbols] + rate_params = [*model.symbols.all_compartments, *model.symbols.all_requirements] def f(transition: TransitionDef) -> CompiledTransition: match transition: @@ -71,7 +71,7 @@ def f(transition: TransitionDef) -> CompiledTransition: return [f(t) for t in model.transitions] -def _make_apply_matrix(ipm: CompartmentModel) -> SimArray: +def _make_apply_matrix(ipm: BaseCompartmentModel) -> SimArray: """ Calc apply matrix; this matrix is used to apply a set of events to the compartments they impact. In general, an event indicates @@ -80,7 +80,7 @@ def _make_apply_matrix(ipm: CompartmentModel) -> SimArray: either add or subtract from the model but not both. By nature, they alter the number of individuals in the model. Matrix values are {+1, 0, -1}. """ - csymbols = ipm.symbols.compartment_symbols + csymbols = ipm.symbols.all_compartments matrix_size = (ipm.num_events, ipm.num_compartments) apply_matrix = np.zeros(matrix_size, dtype=SimDType) for eidx, e in enumerate(ipm.events): @@ -116,7 +116,7 @@ class IpmExecutor: def __init__(self, rume: Rume, world: World, data: Database[AttributeArray], rng: np.random.Generator): ipm = rume.ipm - csymbols = ipm.symbols.compartment_symbols + csymbols = ipm.symbols.all_compartments # Calc list of events leaving each compartment (each may have 0, 1, or more) events_leaving_compartment = [[eidx @@ -138,7 +138,7 @@ def __init__(self, rume: Rume, world: World, data: Database[AttributeArray], rng self._events_leaving_compartment = events_leaving_compartment self._source_compartment_for_event = source_compartment_for_event self._attribute_values_txn = AttributeResolver(data, rume.dim)\ - .resolve_txn_series(list(ipm.attributes.items())) + .resolve_txn_series(list(ipm.requirements_dict.items())) def apply(self, tick: Tick) -> Result: """ @@ -256,7 +256,7 @@ def _get_default_error_args(self, rate_attrs: list, node: int, tick: Tick) -> li arg_list.append(("ipm params", { attribute.name: value for attribute, value - in zip(self._rume.ipm.attributes.values(), + in zip(self._rume.ipm.requirements, rate_attrs[self._rume.dim.compartments:]) })) diff --git a/epymorph/simulator/basic/mm_exec.py b/epymorph/simulator/basic/mm_exec.py index 8c7327ba..f43ca4bc 100644 --- a/epymorph/simulator/basic/mm_exec.py +++ b/epymorph/simulator/basic/mm_exec.py @@ -1,33 +1,28 @@ -from typing import NamedTuple +from copy import deepcopy import numpy as np from numpy.typing import NDArray -from epymorph.data_shape import SimDimensions from epymorph.data_type import AttributeArray, SimDType -from epymorph.database import (Database, DatabaseWithFallback, ModuleNamespace, - NamePattern) -from epymorph.error import MmCompileException -from epymorph.event import (MovementEventsMixin, OnMovementClause, - OnMovementFinish, OnMovementStart) -from epymorph.movement.compile import compile_spec -from epymorph.movement.movement_model import (MovementContext, MovementModel, - TravelClause) +from epymorph.database import Database, ModuleNamespace +from epymorph.error import MmSimException +from epymorph.event import (EventBus, OnMovementClause, OnMovementFinish, + OnMovementStart) +from epymorph.movement_model import MovementClause from epymorph.rume import Rume -from epymorph.simulation import (NamespacedAttributeResolver, Tick, +from epymorph.simulation import (NamespacedAttributeResolver, Tick, gpm_strata, resolve_tick_delta) from epymorph.simulator.world import World from epymorph.util import row_normalize def calculate_travelers( - # General movement model info. - ctx: MovementContext, - # Clause info. - clause: TravelClause, + clause_name: str, clause_mobility: NDArray[np.bool_], + requested_movers: NDArray[SimDType], + available_movers: NDArray[SimDType], tick: Tick, - local_cohorts: NDArray[SimDType], + rng: np.random.Generator ) -> OnMovementClause: """ Calculate the number of travelers resulting from this movement clause for this tick. @@ -35,27 +30,26 @@ def calculate_travelers( then selects exactly which individuals (by compartment) should move. Returns an (N,N,C) array; from-source-to-destination-by-compartment. """ - _, N, C, _ = ctx.dim.TNCE + # Extract number of nodes and cohorts from the provided array. + (N, C) = available_movers.shape - clause_movers = clause.requested(ctx, tick) - np.fill_diagonal(clause_movers, 0) - clause_sum = clause_movers.sum(axis=1, dtype=SimDType) + initial_requested_movers = requested_movers + np.fill_diagonal(requested_movers, 0) + requested_sum = requested_movers.sum(axis=1, dtype=SimDType) - available_movers = local_cohorts * clause_mobility + available_movers = available_movers * clause_mobility available_sum = available_movers.sum(axis=1, dtype=SimDType) # If clause requested total is greater than the total available, # use mvhg to select as many as possible. - if not np.any(clause_sum > available_sum): + if not np.any(requested_sum > available_sum): throttled = False - requested_movers = clause_movers - requested_sum = clause_sum else: throttled = True - requested_movers = clause_movers.copy() + requested_movers = requested_movers.copy() for src in range(N): - if clause_sum[src] > available_sum[src]: - requested_movers[src, :] = ctx.rng.multivariate_hypergeometric( + if requested_sum[src] > available_sum[src]: + requested_movers[src, :] = rng.multivariate_hypergeometric( colors=requested_movers[src, :], nsample=available_sum[src] ) @@ -70,14 +64,14 @@ def calculate_travelers( continue # Select which individuals will be leaving this node. - mover_cs = ctx.rng.multivariate_hypergeometric( + mover_cs = rng.multivariate_hypergeometric( available_movers[src, :], requested_sum[src] ).astype(SimDType) # Select which location they are each going to. # (Each row contains the compartments for a destination.) - travelers_cs[src, :, :] = ctx.rng.multinomial( + travelers_cs[src, :, :] = rng.multinomial( mover_cs, requested_prb[src, :] ).T.astype(SimDType) @@ -86,24 +80,15 @@ def calculate_travelers( tick.sim_index, tick.day, tick.step, - clause.name, - clause_movers, + clause_name, + initial_requested_movers, travelers_cs, requested_sum.sum(), throttled, ) -class _Ctx(NamedTuple): - dim: SimDimensions - rng: np.random.Generator - data: NamespacedAttributeResolver - - -class _StrataInfo(NamedTuple): - model: MovementModel - mobility: NDArray[np.bool_] - ctx: _Ctx +_events = EventBus() class MovementExecutor: @@ -115,10 +100,8 @@ class MovementExecutor: """the world state""" _rng: np.random.Generator """the simulation RNG""" - _event_target: MovementEventsMixin - _data: Database[AttributeArray] - _strata: dict[str, _StrataInfo] + _clauses: list[tuple[str, MovementClause]] def __init__( self, @@ -126,91 +109,77 @@ def __init__( world: World, db: Database[AttributeArray], rng: np.random.Generator, - event_target: MovementEventsMixin, ): - # Introduce a new data layer so we have a place to store predefs - data = DatabaseWithFallback({}, db) - self._rume = rume self._world = world - self._data = data self._rng = rng - self._event_target = event_target - self._strata = { - strata: _StrataInfo( - # Compile movement model - model=compile_spec(mm, rng), - # Get compartment mobility for this strata - mobility=rume.compartment_mobility(strata), - # Assemble a context with a resolver for this strata - ctx=_Ctx( - dim=rume.dim, - rng=rng, - data=NamespacedAttributeResolver( - data=data, - dim=rume.dim, - namespace=ModuleNamespace(f"gpm:{strata}", "mm"), - ), - ), + + # Clone and set context on clauses. + self._clauses = [] + for strata, model in self._rume.mms.items(): + data = NamespacedAttributeResolver( + data=db, + dim=rume.dim, + namespace=ModuleNamespace(gpm_strata(strata), "mm"), ) - for strata, mm in rume.mms.items() - } - self._compute_predefs() - - def _compute_predefs(self) -> None: - """Compute predefs and store results to our database.""" - for strata, (model, _, ctx) in self._strata.items(): - result = model.predef(ctx) - if not isinstance(result, dict): - msg = f"Movement predef: did not return a dictionary result (got: {type(result)})" - raise MmCompileException(msg) - for key, value in result.items(): - if not isinstance(value, np.ndarray): - msg = f"Movement predef: key '{key}' invalid; it is not a numpy array." - pattern = NamePattern(f"gpm:{strata}", "mm", key) - self._data.update(pattern, value.copy()) + for clause in model.clauses: + c = deepcopy(clause).with_context(data, rume.dim, rume.scope, rng) + self._clauses.append((strata, c)) def apply(self, tick: Tick) -> None: """Applies movement for this tick, mutating the world state.""" - self._event_target.on_movement_start.publish( + _events.on_movement_start.publish( OnMovementStart(tick.sim_index, tick.day, tick.step)) # Process travel clauses. total = 0 - for model, mobility, ctx in self._strata.values(): - for clause in model.clauses: - if not clause.predicate(ctx, tick): - continue - local_array = self._world.get_local_array() - - clause_event = calculate_travelers( - ctx, clause, mobility, tick, local_array) - self._event_target.on_movement_clause.publish(clause_event) - travelers = clause_event.actual + for strata, clause in self._clauses: + if not clause.is_active(tick): + continue + + try: + requested_movers = clause.evaluate(tick) + except Exception as e: + # NOTE: catching exceptions here is necessary to get nice error messages + # for some value error cause by incorrect parameter and/or clause definition + msg = f"Error from applying clause '{clause.__class__.__name__}': see exception trace" + raise MmSimException(msg) from e + + available_movers = self._world.get_local_array() + clause_event = calculate_travelers( + clause.__class__.__name__, + self._rume.compartment_mobility[strata], + requested_movers, + available_movers, + tick, + self._rng + ) + _events.on_movement_clause.publish(clause_event) + travelers = clause_event.actual - returns = clause.returns(ctx, tick) - return_tick = resolve_tick_delta(ctx.dim, tick, returns) - self._world.apply_travel(travelers, return_tick) - total += travelers.sum() + return_tick = resolve_tick_delta(self._rume.dim, tick, clause.returns) + self._world.apply_travel(travelers, return_tick) + total += travelers.sum() # Process return clause. - return_movers = self._world.apply_return(tick, return_stats=True) - return_total = return_movers.sum() + return_movers_nnc = self._world.apply_return(tick, return_stats=True) + return_movers_nn = return_movers_nnc.sum(axis=2) + return_total = return_movers_nn.sum() total += return_total - self._event_target.on_movement_clause.publish( + _events.on_movement_clause.publish( OnMovementClause( tick.sim_index, tick.day, tick.step, "return", - return_movers, - return_movers, + return_movers_nn, + return_movers_nnc, return_total, False, ) ) - self._event_target.on_movement_finish.publish( + _events.on_movement_finish.publish( OnMovementFinish(tick.sim_index, tick.day, tick.step, total)) diff --git a/epymorph/simulator/basic/test/basic_simulator_test.py b/epymorph/simulator/basic/test/basic_simulator_test.py index 33d8de27..ab2b4d05 100644 --- a/epymorph/simulator/basic/test/basic_simulator_test.py +++ b/epymorph/simulator/basic/test/basic_simulator_test.py @@ -1,19 +1,19 @@ # pylint: disable=missing-docstring import unittest from math import inf -from typing import cast +from typing import Mapping import numpy as np +from numpy.typing import NDArray from epymorph import * -from epymorph.compartment_model import (CompartmentModel, compartment, - create_model, create_symbols, edge) +from epymorph.compartment_model import CompartmentModel, compartment, edge from epymorph.error import (IpmSimInvalidProbsException, IpmSimLessThanZeroException, IpmSimNaNException, MmSimException) -from epymorph.geo.static import StaticGeo from epymorph.geography.scope import CustomScope from epymorph.geography.us_census import StateScope +from epymorph.rume import SingleStrataRume from epymorph.simulation import AttributeDef @@ -28,13 +28,28 @@ def _pei_scope(self) -> StateScope: pei_states = ["FL", "GA", "MD", "NC", "SC", "VA"] return StateScope.in_states_by_code(pei_states, 2010) - def _pei_geo(self) -> StaticGeo: - return cast(StaticGeo, geo_library['pei']()) + def _pei_geo(self) -> Mapping[str, NDArray]: + # We don't want to use real ADRIOs here because they could fail + # and cause these tests to spuriously fail. + # So instead, hard-code some values. They don't need to be real. + t = np.arange(start=0, stop=2 * np.pi, step=2 * np.pi / 365) + return { + "*::population": np.array([18811310, 9687653, 5773552, 9535483, 4625364, 8001024]), + "*::humidity": np.array([ + 0.005 + 0.005 * np.sin(t) for _ in range(6) + ]).T, + "*::commuters": np.array([ + [7993452, 13805, 2410, 2938, 1783, 3879], + [15066, 4091461, 966, 6057, 20318, 2147], + [949, 516, 2390255, 947, 91, 122688], + [3005, 5730, 1872, 4121984, 38081, 29487], + [1709, 23513, 630, 64872, 1890853, 1620], + [1368, 1175, 68542, 16869, 577, 3567788], + ]), + } def test_pei(self): - - geo = self._pei_geo() - rume = Rume.single_strata( + rume = SingleStrataRume.build( ipm=ipm_library['pei'](), mm=mm_library['pei'](), init=init.SingleLocation(location=0, seed_size=10_000), @@ -45,17 +60,13 @@ def test_pei(self): 'ipm::immunity_duration': 90, 'mm::move_control': 0.9, 'mm::theta': 0.1, - '*::population': geo.values['population'], - '*::humidity': geo.values['humidity'], - '*::commuters': geo.values['commuters'], + **self._pei_geo(), }, ) sim = BasicSimulator(rume) - out1 = sim.run( - rng_factory=default_rng(42), - ) + out1 = sim.run(rng_factory=default_rng(42)) np.testing.assert_array_equal( out1.initial[:, 1], @@ -98,8 +109,7 @@ def test_pei(self): ) def test_override_params(self): - geo = self._pei_geo() - rume = Rume.single_strata( + rume = SingleStrataRume.build( ipm=ipm_library['pei'](), mm=mm_library['pei'](), init=init.SingleLocation(location=0, seed_size=10_000), @@ -110,9 +120,7 @@ def test_override_params(self): 'ipm::immunity_duration': 90, 'mm::move_control': 0.9, 'mm::theta': 0.1, - '*::population': geo.values['population'], - '*::humidity': geo.values['humidity'], - '*::commuters': geo.values['commuters'], + **self._pei_geo(), }, ) @@ -136,9 +144,7 @@ def test_override_params(self): def test_less_than_zero_err(self): """Test exception handling for a negative rate value due to a negative parameter""" - - geo = self._pei_geo() - rume = Rume.single_strata( + rume = SingleStrataRume.build( ipm=ipm_library['pei'](), mm=mm_library['pei'](), init=init.SingleLocation(location=0, seed_size=10_000), @@ -149,63 +155,49 @@ def test_less_than_zero_err(self): 'ipm::immunity_duration': -100, # notice the negative parameter 'mm::move_control': 0.9, 'mm::theta': 0.1, - '*::population': geo.values['population'], - '*::humidity': geo.values['humidity'], - '*::commuters': geo.values['commuters'], + **self._pei_geo(), }, ) - # geo = geo_library['pei']() sim = BasicSimulator(rume) with self.assertRaises(IpmSimLessThanZeroException) as e: - sim.run( - rng_factory=default_rng(42), - ) + sim.run(rng_factory=default_rng(42)) err_msg = str(e.exception) - self.assertIn("Less than zero rate detected", err_msg) - self.assertIn("Showing current Node : Timestep", err_msg) - self.assertIn("S: ", err_msg) - self.assertIn("I: ", err_msg) - self.assertIn("R: ", err_msg) - self.assertIn("infection_duration: 4.0", err_msg) self.assertIn("immunity_duration: -100.0", err_msg) - self.assertIn("humidity: 0.01003", err_msg) def test_divide_by_zero_err(self): """Test exception handling for a divide by zero (NaN) error""" - def load_ipm() -> CompartmentModel: - """Load the 'sirs' IPM.""" - symbols = create_symbols( - compartments=[ - compartment('S'), - compartment('I'), - compartment('R'), - ], - attributes=[ - AttributeDef('beta', type=float, shape=Shapes.TxN), - AttributeDef('gamma', type=float, shape=Shapes.TxN), - AttributeDef('xi', type=float, shape=Shapes.TxN) - ]) - - [S, I, R] = symbols.compartment_symbols - [β, γ, ξ] = symbols.attribute_symbols - - # N is NOT protected by Max(1, ...) here - N = S + I + R # type: ignore - - return create_model( - symbols=symbols, - transitions=[ - edge(S, I, rate=β * S * I / N), # type: ignore - edge(I, R, rate=γ * I), # type: ignore - edge(R, S, rate=ξ * R), # type: ignore - ]) - - rume = Rume.single_strata( - ipm=load_ipm(), + class Sirs(CompartmentModel): + compartments = [ + compartment('S'), + compartment('I'), + compartment('R'), + ] + + requirements = [ + AttributeDef('beta', type=float, shape=Shapes.TxN), + AttributeDef('gamma', type=float, shape=Shapes.TxN), + AttributeDef('xi', type=float, shape=Shapes.TxN), + ] + + def edges(self, symbols): + [S, I, R] = symbols.all_compartments + [β, γ, ξ] = symbols.all_requirements + + # N is NOT protected by Max(1, ...) here + N = S + I + R # type: ignore + + return [ + edge(S, I, rate=β * S * I / N), + edge(I, R, rate=γ * I), + edge(R, S, rate=ξ * R), + ] + + rume = SingleStrataRume.build( + ipm=Sirs(), mm=mm_library['no'](), init=init.SingleLocation(location=1, seed_size=5), scope=CustomScope(np.array(['a', 'b', 'c'])), @@ -221,26 +213,18 @@ def load_ipm() -> CompartmentModel: sim = BasicSimulator(rume) with self.assertRaises(IpmSimNaNException) as e: - sim.run( - rng_factory=default_rng(1), - ) + sim.run(rng_factory=default_rng(1)) err_msg = str(e.exception) - self.assertIn("NaN (not a number) rate detected", err_msg) - self.assertIn("Showing current Node : Timestep", err_msg) self.assertIn("S: 0", err_msg) self.assertIn("I: 0", err_msg) self.assertIn("R: 0", err_msg) - self.assertIn("beta: 0.4", err_msg) - self.assertIn("gamma: 0.2", err_msg) - self.assertIn("xi: 0.01", err_msg) self.assertIn("S->I: I*S*beta/(I + R + S)", err_msg) def test_negative_probs_error(self): """Test for handling negative probability error""" - geo = self._pei_geo() - rume = Rume.single_strata( + rume = SingleStrataRume.build( ipm=ipm_library['sirh'](), mm=mm_library['no'](), init=init.SingleLocation(location=1, seed_size=5), @@ -252,38 +236,25 @@ def test_negative_probs_error(self): 'xi': 1 / 100, 'hospitalization_prob': -1 / 5, 'hospitalization_duration': 15, - 'population': geo.values['population'], + **self._pei_geo(), }, ) sim = BasicSimulator(rume) with self.assertRaises(IpmSimInvalidProbsException) as e: - sim.run( - rng_factory=default_rng(1), - ) + sim.run(rng_factory=default_rng(1)) err_msg = str(e.exception) - self.assertIn("Invalid probabilities for fork definition detected.", err_msg) - self.assertIn("Showing current Node : Timestep", err_msg) - self.assertIn("S: ", err_msg) - self.assertIn("I: ", err_msg) - self.assertIn("R: ", err_msg) - self.assertIn("beta: 0.4", err_msg) - self.assertIn("gamma: 0.2", err_msg) - self.assertIn("xi: 0.01", err_msg) self.assertIn("hospitalization_prob: -0.2", err_msg) self.assertIn("hospitalization_duration: 15", err_msg) - self.assertIn("I->(H, R): I*gamma", err_msg) self.assertIn( "Probabilities: hospitalization_prob, 1 - hospitalization_prob", err_msg) def test_mm_clause_error(self): """Test for handling invalid movement model clause application""" - - geo = self._pei_geo() - rume = Rume.single_strata( + rume = SingleStrataRume.build( ipm=ipm_library['pei'](), mm=mm_library['pei'](), init=init.SingleLocation(location=1, seed_size=5), @@ -295,18 +266,17 @@ def test_mm_clause_error(self): 'humidity': 20.2, 'move_control': 0.4, 'theta': -5.0, - 'population': geo.values['population'], - 'commuters': geo.values['commuters'], + **self._pei_geo(), }, ) sim = BasicSimulator(rume) with self.assertRaises(MmSimException) as e: - sim.run( - rng_factory=default_rng(1), - ) + sim.run(rng_factory=default_rng(1)) err_msg = str(e.exception) self.assertIn( - "Error from applying clause 'dispersers': see exception trace", err_msg) + "Error from applying clause 'Dispersers': see exception trace", + err_msg + ) diff --git a/epymorph/simulator/basic/test/ipm_exec_test.py b/epymorph/simulator/basic/test/ipm_exec_test.py index 0470c0ec..40c435da 100644 --- a/epymorph/simulator/basic/test/ipm_exec_test.py +++ b/epymorph/simulator/basic/test/ipm_exec_test.py @@ -7,8 +7,7 @@ import numpy.testing as npt from epymorph.compartment_model import (BIRTH, DEATH, CompartmentModel, - compartment, create_model, - create_symbols, edge) + compartment, edge) from epymorph.data_shape import Shapes, SimDimensions from epymorph.data_type import AttributeArray, SimDType from epymorph.database import Database @@ -18,66 +17,58 @@ from epymorph.simulator.world_list import ListWorld -def _model1() -> CompartmentModel: - symbols = create_symbols( - compartments=[ - compartment('S', tags=['test_tag']), - compartment('I'), - compartment('R'), - ], - attributes=[ - AttributeDef('beta', float, Shapes.TxN), - AttributeDef('gamma', float, Shapes.TxN), - ] - ) - - [S, I, R] = symbols.compartment_symbols - [beta, gamma] = symbols.attribute_symbols - - return create_model( - symbols=symbols, - transitions=[ +class Sir(CompartmentModel): + compartments = [ + compartment('S', tags=['test_tag']), + compartment('I'), + compartment('R'), + ] + requirements = [ + AttributeDef('beta', float, Shapes.TxN), + AttributeDef('gamma', float, Shapes.TxN), + ] + + def edges(self, symbols): + [S, I, R] = symbols.all_compartments + [beta, gamma] = symbols.all_requirements + return [ edge(S, I, rate=beta * S * I), edge(I, R, rate=gamma * I), - ], - ) - - -def _model2() -> CompartmentModel: - symbols = create_symbols( - compartments=[ - compartment('S'), - compartment('I'), - compartment('R'), - ], - attributes=[ - AttributeDef('beta', float, Shapes.TxN), - AttributeDef('gamma', float, Shapes.TxN), - AttributeDef('b', float, Shapes.TxN), # birth rate - AttributeDef('d', float, Shapes.TxN), # death rate ] - ) - [S, I, R] = symbols.compartment_symbols - [beta, gamma, b, d] = symbols.attribute_symbols - return create_model( - symbols=symbols, - transitions=[ +class Sirbd(CompartmentModel): + compartments = [ + compartment('S'), + compartment('I'), + compartment('R'), + ] + + requirements = [ + AttributeDef('beta', float, Shapes.TxN), + AttributeDef('gamma', float, Shapes.TxN), + AttributeDef('b', float, Shapes.TxN), # birth rate + AttributeDef('d', float, Shapes.TxN), # death rate + ] + + def edges(self, symbols): + [S, I, R] = symbols.all_compartments + [beta, gamma, b, d] = symbols.all_requirements + + return [ edge(S, I, rate=beta * S * I), edge(BIRTH, S, rate=b), edge(I, R, rate=gamma * I), edge(S, DEATH, rate=d * S), edge(I, DEATH, rate=d * I), edge(R, DEATH, rate=d * R), - ], - ) + ] class StandardIpmExecutorTest(unittest.TestCase): def test_init_01(self): - ipm = _model1() + ipm = Sir() rume = MagicMock(spec=Rume) rume.ipm = ipm @@ -106,7 +97,7 @@ def test_init_01(self): ) def test_init_02(self): - ipm = _model2() + ipm = Sirbd() rume = MagicMock(spec=Rume) rume.ipm = ipm diff --git a/epymorph/simulator/data.py b/epymorph/simulator/data.py index 4944d346..49e8392b 100644 --- a/epymorph/simulator/data.py +++ b/epymorph/simulator/data.py @@ -1,21 +1,26 @@ """Functions for managing simulation data.""" +from time import perf_counter from typing import Callable, Generator, Mapping, Sequence, TypeVar import numpy as np from sympy import Expr +from epymorph.adrio.adrio import Adrio from epymorph.data_shape import DataShapeMatcher, SimDimensions from epymorph.data_type import AttributeArray, SimArray from epymorph.database import (AbsoluteName, Database, DatabaseWithFallback, DatabaseWithStrataFallback, ModuleNamespace, NamePattern) from epymorph.error import AttributeException, InitException +from epymorph.event import AdrioFinish, AdrioStart, EventBus from epymorph.params import (ParamExpressionTimeAndNode, ParamFunction, ParamValue) from epymorph.rume import GEO_LABELS, Gpm, Rume -from epymorph.simulation import NamespacedAttributeResolver +from epymorph.simulation import NamespacedAttributeResolver, gpm_strata from epymorph.util import NumpyTypeError, check_ndarray, match +_events = EventBus() + _EvalFunction = Callable[ [AbsoluteName, list[AbsoluteName], ParamValue | None], AttributeArray @@ -33,22 +38,20 @@ def _evaluation_context( format_name = rume.name_display_formatter() # vals_db stores the raw values as provided by the user. - vals_db = DatabaseWithFallback( - # Simulation overrides... - dict(override_params.items()), - # falls back to RUME params... - DatabaseWithStrataFallback( - data=dict(rume.params.items()), - children={ - # which falls back to GPM params, as scoped to that GPM - f"gpm:{strata}": Database[ParamValue]({ - k.to_absolute(f"gpm:{strata}"): v - for k, v in gpm.params.items() - }) - for strata, gpm in rume.original_gpms.items() - }, - ), + vals_db = DatabaseWithStrataFallback( + data={**rume.params}, + children={ + # which falls back to GPM params, as scoped to that GPM + gpm_strata(gpm.name): Database[ParamValue]({ + k.to_absolute(gpm_strata(gpm.name)): v + for k, v in gpm.params.items() + }) + for gpm in rume.strata + }, ) + # If override_params is not empty, wrap vals_db in another fallback layer. + if len(override_params) > 0: + vals_db = DatabaseWithFallback({**override_params}, vals_db) # This is the database of parameter evaluation results. # It is mutable so that we can add values as we go. @@ -123,22 +126,30 @@ def evaluate(name: AbsoluteName, chain: list[AbsoluteName], default_value: Param raise AttributeException(msg) from None # Otherwise, evaluate and store the parameter based on its type. - if isinstance(raw_value, ParamFunction): + if isinstance(raw_value, ParamFunction | Adrio): # ParamFunction: first evaluate all dependencies of this function (recursively), # then evaluate the function itself. namespace = name.to_namespace() - for dependency in raw_value.attributes: + for dependency in raw_value.requirements: dep_name = namespace.to_absolute(dependency.name) evaluate(dep_name, [*chain, name], dependency.default_value) data = NamespacedAttributeResolver(attr_db, rume.dim, namespace) - value = raw_value(data, rume.dim, rng) + + if not isinstance(raw_value, Adrio): + value = raw_value.evaluate_in_context(data, rume.dim, rume.scope, rng) + else: + adrio_name = raw_value.__class__.__name__ + _events.on_adrio_start.publish(AdrioStart(adrio_name, name)) + t0 = perf_counter() + value = raw_value.evaluate_in_context(data, rume.dim, rume.scope, rng) + t1 = perf_counter() + _events.on_adrio_finish.publish(AdrioFinish(adrio_name, name, t1 - t0)) + elif isinstance(raw_value, type) and issubclass(raw_value, ParamFunction): msg = f"Invalid parameter: '{format_name(match_pattern)}' "\ "is a ParamFunction class instead of an instance." raise AttributeException(msg) - # elif isinstance(param, Adrio): # TODO: adrios as param values! - elif isinstance(raw_value, np.ndarray): # numpy array: make a copy so we don't risk unexpected mutations value = use_eval_cache(match_pattern, raw_value, @@ -178,13 +189,13 @@ def evaluate_params( # Evaluate every attribute required by the RUME. attr_db, evaluate = _evaluation_context(rume, override_params, rng) - rume_attributes: list[tuple[AbsoluteName, ParamValue | None]] = [ - *((name, attr.default_value) for name, attr in rume.attributes.items()), + rume_reqs: list[tuple[AbsoluteName, ParamValue | None]] = [ + *((name, attr.default_value) for name, attr in rume.requirements.items()), # Artificially require the special geo labels attribute. (GEO_LABELS, rume.scope.get_node_ids()), ] - for name, default_value in rume_attributes: + for name, default_value in rume_reqs: try: evaluate(name, [], default_value) except AttributeException as e: @@ -217,15 +228,15 @@ def evaluate_param( return evaluate(param, [], None) -def validate_attributes( +def validate_requirements( rume: Rume, data: Database[AttributeArray], ) -> None: """ - Validate all attributes in a RUME; raises an ExceptionGroup containing all errors. + Validate all attributes requirements in a RUME; raises an ExceptionGroup containing all errors. """ def validate() -> Generator[AttributeException, None, None]: - for name, attr in rume.attributes.items(): + for name, attr in rume.requirements.items(): attr_match = data.query(name) if attr_match is None: msg = f"Missing required parameter: '{name}'" @@ -257,8 +268,8 @@ def initialize_rume( Executes Initializers for a multi-strata simulation by running each strata's Initializer and combining the results. Raises InitException if anything goes wrong. """ - def init_strata(strata: str, gpm: Gpm) -> SimArray: - namespace = ModuleNamespace(f"gpm:{strata}", "init") + def init_strata(gpm: Gpm) -> SimArray: + namespace = ModuleNamespace(gpm_strata(gpm.name), "init") strata_dim = SimDimensions.build( rume.dim.tau_step_lengths, rume.dim.start_date, @@ -268,12 +279,12 @@ def init_strata(strata: str, gpm: Gpm) -> SimArray: gpm.ipm.num_events, ) strata_data = NamespacedAttributeResolver(data, strata_dim, namespace) - return gpm.init(strata_data, strata_dim, rng) + return gpm.init.evaluate_in_context(strata_data, strata_dim, rume.scope, rng) try: return np.column_stack([ - init_strata(strata, gpm) - for strata, gpm in rume.original_gpms.items() + init_strata(gpm) + for gpm in rume.strata ]) except InitException as e: raise e diff --git a/epymorph/simulator/test/data_test.py b/epymorph/simulator/test/data_test.py index d59cdfdf..946bcf06 100644 --- a/epymorph/simulator/test/data_test.py +++ b/epymorph/simulator/test/data_test.py @@ -8,7 +8,7 @@ from numpy.typing import NDArray from epymorph import * -from epymorph.compartment_model import edge +from epymorph.compartment_model import MultistrataModelSymbols, edge from epymorph.data_type import AttributeArray from epymorph.database import Database, NamePattern from epymorph.error import AttributeException @@ -16,7 +16,7 @@ from epymorph.params import (ParamFunctionNode, ParamFunctionNumpy, ParamFunctionScalar, ParamFunctionTimeAndNode, ParamValue, simulation_symbols) -from epymorph.rume import Gpm, Rume +from epymorph.rume import Gpm, MultistrataRume, Rume from epymorph.simulator.data import evaluate_params @@ -54,30 +54,32 @@ def _default_params(self) -> dict[str, ParamValue]: } def _create_rume(self, rume_params: dict[str, ParamValue] | None = None) -> Rume: - meta_attributes = [ + meta_requirements = [ AttributeDef("beta_bbb_aaa", float, Shapes.TxN), ] - def meta_edges(s: RumeSymbols): - [S_aaa, I_aaa, R_aaa] = s.compartments("aaa") - [S_bbb, I_bbb, R_bbb] = s.compartments("bbb") - [beta_bbb_aaa] = s.meta_attributes() - N_aaa = s.total_nonzero("aaa") + def meta_edges(s: MultistrataModelSymbols): + [S_aaa, I_aaa, R_aaa] = s.strata_compartments("aaa") + [S_bbb, I_bbb, R_bbb] = s.strata_compartments("bbb") + [beta_bbb_aaa] = s.all_meta_requirements + N_aaa = sympy.Max(1, S_aaa + I_aaa + R_aaa) return [ edge(S_bbb, I_bbb, beta_bbb_aaa * S_bbb * I_aaa / N_aaa), ] - return Rume.multistrata( + return MultistrataRume.build( strata=[ - ('aaa', Gpm( + Gpm( + name='aaa', ipm=ipm_library['sirs'](), mm=mm_library['centroids'](), init=init.SingleLocation(location=0, seed_size=100), params={ # leave phi unspecified to test default value resolution }, - )), - ('bbb', Gpm( + ), + Gpm( + name='bbb', ipm=ipm_library['sirs'](), mm=mm_library['centroids'](), init=init.SingleLocation(location=0, seed_size=100), @@ -85,9 +87,9 @@ def meta_edges(s: RumeSymbols): "beta": 99.0, # we'll override this value to test value shadowing "phi": 33.0, # test GPM value resolution }, - )), + ), ], - meta_attributes=meta_attributes, + meta_requirements=meta_requirements, meta_edges=meta_edges, scope=StateScope.in_states(['04', '35']), time_frame=TimeFrame.of("2021-01-01", 180), @@ -101,7 +103,7 @@ def test_eval_1(self): # We should have as many entries in our DB as we have attributes in the RUME, # plus 1 (for geo labels). - self.assertEqual(len(db._data), len(rume.attributes) + 1) + self.assertEqual(len(db._data), len(rume.requirements) + 1) self.assert_db(db, "gpm:aaa::ipm::beta", np.array(0.4, dtype=np.float64)) self.assert_db(db, "gpm:bbb::ipm::beta", np.array(0.3, dtype=np.float64)) @@ -195,7 +197,7 @@ def test_eval_param_function_1(self): class Beta(ParamFunctionTimeAndNode): GAMMA = AttributeDef('gamma', float, Shapes.TxN) - attributes = [GAMMA] + requirements = [GAMMA] r_0: float @@ -227,7 +229,7 @@ def test_eval_param_function_2(self): class Xi(ParamFunctionNode): BETA = AttributeDef('beta', float, Shapes.TxN) - attributes = [BETA] + requirements = [BETA] def evaluate1(self, node_index: int) -> float: beta = self.data(self.BETA)[0, node_index] @@ -249,7 +251,7 @@ def test_eval_param_function_chained(self): class Gamma(ParamFunctionScalar): BETA = AttributeDef('beta', float, Shapes.S) - attributes = [BETA] + requirements = [BETA] def evaluate1(self) -> float: return float(self.data(self.BETA)) / 4.0 @@ -258,7 +260,7 @@ class Xi(ParamFunctionNumpy): ALPHA = AttributeDef('alpha', float, Shapes.S) GAMMA = AttributeDef('gamma', float, Shapes.S) - attributes = [ALPHA, GAMMA] + requirements = [ALPHA, GAMMA] def evaluate(self) -> NDArray[np.float64]: alpha = self.data(self.ALPHA) @@ -283,7 +285,7 @@ def test_eval_param_function_circular(self): class Gamma(ParamFunctionNumpy): XI = AttributeDef('xi', float, Shapes.S) - attributes = [XI] + requirements = [XI] def evaluate(self) -> NDArray[np.float64]: return np.array(0) @@ -291,7 +293,7 @@ def evaluate(self) -> NDArray[np.float64]: class Xi(ParamFunctionNumpy): GAMMA = AttributeDef('gamma', float, Shapes.S) - attributes = [GAMMA] + requirements = [GAMMA] def evaluate(self) -> NDArray[np.float64]: return np.array(0) diff --git a/epymorph/test/compartment_model_test.py b/epymorph/test/compartment_model_test.py index 86239581..85ae02f0 100644 --- a/epymorph/test/compartment_model_test.py +++ b/epymorph/test/compartment_model_test.py @@ -1,12 +1,15 @@ -# pylint: disable=missing-docstring +# pylint: disable=missing-docstring,unused-variable import unittest -from epymorph.compartment_model import (BIRTH, DEATH, CompartmentDef, - compartment, create_model, - create_symbols, edge) +from sympy import Max +from sympy import symbols as sympy_symbols + +from epymorph.compartment_model import (BIRTH, DEATH, CombinedCompartmentModel, + CompartmentDef, CompartmentModel, + MultistrataModelSymbols, compartment, + edge) from epymorph.data_shape import Shapes from epymorph.database import AbsoluteName -from epymorph.error import IpmValidationException from epymorph.simulation import AttributeDef from epymorph.sympy_shim import to_symbol @@ -14,191 +17,299 @@ class CompartmentModelTest(unittest.TestCase): def test_create_01(self): - symbols = create_symbols( - compartments=[ + class MyIpm(CompartmentModel): + compartments = [ compartment('S', tags=['test_tag']), compartment('I'), compartment('R'), - ], - attributes=[ + ] + + requirements = [ AttributeDef('beta', float, Shapes.N), AttributeDef('gamma', float, Shapes.N), - ], - ) + ] - [S, I, R] = symbols.compartment_symbols - [beta, gamma] = symbols.attribute_symbols + def edges(self, symbols): + S, I, R = symbols.compartments('S', 'I', 'R') + beta, gamma = symbols.requirements('beta', 'gamma') + return [ + edge(S, I, rate=beta * S * I), + edge(I, R, rate=gamma * I), + ] - model = create_model( - symbols=symbols, - transitions=[ - edge(S, I, rate=beta * S * I), - edge(I, R, rate=gamma * I), - ], - ) + model = MyIpm() self.assertEqual(model.num_compartments, 3) self.assertEqual(model.num_events, 2) - self.assertEqual(model.transitions, [ - edge(S, I, rate=beta * S * I), - edge(I, R, rate=gamma * I), - ]) - self.assertEqual(model.compartments, [ + self.assertEqual(list(model.compartments), [ CompartmentDef('S', ['test_tag']), CompartmentDef('I', []), CompartmentDef('R', []), ]) - self.assertEqual(list(model.attributes.keys()), [ + self.assertEqual(list(model.requirements_dict.keys()), [ AbsoluteName("gpm:all", "ipm", "beta"), AbsoluteName("gpm:all", "ipm", "gamma"), ]) - self.assertEqual(list(model.attributes.values()), [ + self.assertEqual(list(model.requirements_dict.values()), [ AttributeDef('beta', type=float, shape=Shapes.N), AttributeDef('gamma', type=float, shape=Shapes.N), ]) - [S, I, R] = model.symbols.compartment_symbols - [beta, gamma] = model.symbols.attribute_symbols - - self.assertEqual(model.transitions, [ + S, I, R = model.symbols.all_compartments + beta, gamma = model.symbols.all_requirements + self.assertEqual(list(model.transitions), [ edge(S, I, rate=beta * S * I), edge(I, R, rate=gamma * I), ]) def test_create_02(self): - symbols = create_symbols( - compartments=[ + + class MyIpm(CompartmentModel): + compartments = [ compartment('S'), compartment('I'), compartment('R'), - ], - attributes=[ + ] + requirements = [ AttributeDef('beta', float, Shapes.N), AttributeDef('gamma', float, Shapes.N), AttributeDef('b', float, Shapes.N), # birth rate AttributeDef('d', float, Shapes.N), # death rate - ], - ) + ] - [S, I, R] = symbols.compartment_symbols - [beta, gamma, b, d] = symbols.attribute_symbols - - model = create_model( - symbols=symbols, - transitions=[ - edge(S, I, rate=beta * S * I), - edge(BIRTH, S, rate=b), - edge(I, R, rate=gamma * I), - edge(S, DEATH, rate=d * S), - edge(I, DEATH, rate=d * I), - edge(R, DEATH, rate=d * R), - ], - ) + def edges(self, symbols): + S, I, R = symbols.all_compartments + beta, gamma, b, d = symbols.all_requirements + return [ + edge(S, I, rate=beta * S * I), + edge(BIRTH, S, rate=b), + edge(I, R, rate=gamma * I), + edge(S, DEATH, rate=d * S), + edge(I, DEATH, rate=d * I), + edge(R, DEATH, rate=d * R), + ] + + model = MyIpm() self.assertEqual(model.num_compartments, 3) self.assertEqual(model.num_events, 6) def test_create_03(self): # Test for error: Attempt to reference an undeclared compartment in a transition. - symbols = create_symbols( - compartments=[ - compartment('S', tags=['test_tag']), - compartment('I'), - compartment('R'), - ], - attributes=[ - AttributeDef('beta', float, Shapes.N), - AttributeDef('gamma', float, Shapes.N), - ], - ) + with self.assertRaises(TypeError) as e: + class MyIpm(CompartmentModel): + compartments = [ + compartment('S', tags=['test_tag']), + compartment('I'), + compartment('R'), + ] - [S, I, R] = symbols.compartment_symbols - [beta, gamma] = symbols.attribute_symbols + requirements = [ + AttributeDef('beta', float, Shapes.N), + AttributeDef('gamma', float, Shapes.N), + ] - with self.assertRaises(IpmValidationException): - create_model( - symbols=symbols, - transitions=[ - edge(S, I, rate=beta * S * I), - edge(I, R, rate=gamma * I), - edge(I, to_symbol('bad_compartment'), rate=gamma * I), - ], - ) + def edges(self, symbols): + S, I, R = symbols.all_compartments + beta, gamma = symbols.all_requirements + return [ + edge(S, I, rate=beta * S * I), + edge(I, R, rate=gamma * I), + edge(I, to_symbol('bad_compartment'), rate=gamma * I), + ] + self.assertIn("missing compartments: bad_compartment", str(e.exception).lower()) def test_create_04(self): - # Test for error: Attempt to reference an undeclared attribute in a transition. - symbols = create_symbols( - compartments=[ - compartment('S', tags=['test_tag']), - compartment('I'), - compartment('R'), - ], - attributes=[ - AttributeDef('beta', float, Shapes.N), - AttributeDef('gamma', float, Shapes.N), - ], - ) + # Test for error: Attempt to reference an undeclared requirement in a transition. + with self.assertRaises(TypeError) as e: + class MyIpm(CompartmentModel): + compartments = [ + compartment('S', tags=['test_tag']), + compartment('I'), + compartment('R'), + ] - [S, I, R] = symbols.compartment_symbols - [beta, gamma] = symbols.attribute_symbols + requirements = [ + AttributeDef('beta', float, Shapes.N), + AttributeDef('gamma', float, Shapes.N), + ] - with self.assertRaises(IpmValidationException): - create_model( - symbols=symbols, - transitions=[ - edge(S, I, rate=beta * S * I), - edge(I, R, rate=gamma * to_symbol('bad_symbol') * I), - ], - ) + def edges(self, symbols): + S, I, R = symbols.all_compartments + beta, gamma = symbols.all_requirements + + return [ + edge(S, I, rate=beta * S * I), + edge(I, R, rate=gamma * to_symbol('bad_symbol') * I), + ] + self.assertIn("missing requirements: bad_symbol", str(e.exception).lower()) def test_create_05(self): # Test for error: Source and destination are both exogenous! - symbols = create_symbols( - compartments=[ - compartment('S', tags=['test_tag']), - compartment('I'), - compartment('R'), - ], - attributes=[ - AttributeDef('beta', float, Shapes.N), - AttributeDef('gamma', float, Shapes.N), - ], - ) + with self.assertRaises(TypeError) as e: + class MyIpm(CompartmentModel): + compartments = [ + compartment('S', tags=['test_tag']), + compartment('I'), + compartment('R'), + ] - [S, I, R] = symbols.compartment_symbols - [beta, gamma] = symbols.attribute_symbols + requirements = [ + AttributeDef('beta', float, Shapes.N), + AttributeDef('gamma', float, Shapes.N), + ] - with self.assertRaises(IpmValidationException): - create_model( - symbols=symbols, - transitions=[ - edge(S, I, rate=beta * S * I), - edge(I, R, rate=gamma * I), - edge(BIRTH, DEATH, rate=100), - ], - ) + def edges(self, symbols): + S, I, R = symbols.all_compartments + beta, gamma = symbols.all_requirements + return [ + edge(S, I, rate=beta * S * I), + edge(I, R, rate=gamma * I), + edge(BIRTH, DEATH, rate=100), + ] + self.assertIn("both source and destination", str(e.exception).lower()) def test_create_06(self): # Test for error: model with no compartments. - symbols = create_symbols( - compartments=[], - attributes=[ - AttributeDef('beta', float, Shapes.N), - AttributeDef('gamma', float, Shapes.N), - ], - ) + with self.assertRaises(TypeError) as e: + class MyIpm(CompartmentModel): + compartments = [] + requirements = [ + AttributeDef('beta', float, Shapes.N), + AttributeDef('gamma', float, Shapes.N), + ] - with self.assertRaises(IpmValidationException): - create_model( - symbols=symbols, - transitions=[], - ) + def edges(self, symbols): + return [] + self.assertIn("invalid compartments", str(e.exception).lower()) - def test_create_07(self): - # Test for attribute/compartment names that include spaces. + def test_compartment_name(self): + # Test for compartment names that include spaces. with self.assertRaises(ValueError): compartment("some people") + def test_attribute_name(self): + # Test for attribute names that include spaces. with self.assertRaises(ValueError): AttributeDef('some attribute', float, Shapes.N) + + def test_combined_01(self): + class Sir(CompartmentModel): + compartments = [ + compartment('S'), + compartment('I'), + compartment('R'), + ] + + requirements = [ + AttributeDef('beta', float, Shapes.TxN), + AttributeDef('gamma', float, Shapes.TxN), + ] + + def edges(self, symbols): + S, I, R = symbols.all_compartments + beta, gamma = symbols.all_requirements + return [ + edge(S, I, rate=beta * S * I), + edge(I, R, rate=gamma * I), + ] + + sir = Sir() + + def meta_edges(sym: MultistrataModelSymbols): + [S_aaa, I_aaa, R_aaa] = sym.strata_compartments("aaa") + [S_bbb, I_bbb, R_bbb] = sym.strata_compartments("bbb") + [beta_bbb_aaa] = sym.all_meta_requirements + N_aaa = Max(1, S_aaa + I_aaa + R_aaa) + return [ + edge(S_bbb, I_bbb, beta_bbb_aaa * S_bbb * I_aaa / N_aaa), + ] + + model = CombinedCompartmentModel( + strata=[('aaa', sir), ('bbb', sir)], + meta_requirements=[ + AttributeDef("beta_bbb_aaa", float, Shapes.TxN), + ], + meta_edges=meta_edges, + ) + + self.assertEqual(model.num_compartments, 6) + self.assertEqual(model.num_events, 5) + + # Check compartment mapping + self.assertEqual( + [c.name for c in model.compartments], + ['S_aaa', 'I_aaa', 'R_aaa', 'S_bbb', 'I_bbb', 'R_bbb'], + ) + + self.assertEqual( + model.symbols.all_compartments, + list(sympy_symbols("S_aaa I_aaa R_aaa S_bbb I_bbb R_bbb")), + ) + + self.assertEqual( + model.symbols.strata_compartments("aaa"), + list(sympy_symbols("S_aaa I_aaa R_aaa")) + ) + + self.assertEqual( + model.symbols.strata_compartments("bbb"), + list(sympy_symbols("S_bbb I_bbb R_bbb")) + ) + + # Check requirement mapping + self.assertEqual( + model.symbols.all_requirements, + list(sympy_symbols("beta_aaa gamma_aaa beta_bbb gamma_bbb beta_bbb_aaa_meta")), + ) + + self.assertEqual( + model.symbols.strata_requirements("aaa"), + list(sympy_symbols("beta_aaa gamma_aaa")), + ) + + self.assertEqual( + model.symbols.strata_requirements("bbb"), + list(sympy_symbols("beta_bbb gamma_bbb")), + ) + + self.assertEqual( + model.symbols.all_meta_requirements, + [sympy_symbols("beta_bbb_aaa_meta")], + ) + + self.assertEqual( + list(model.requirements_dict.keys()), + [ + AbsoluteName("gpm:aaa", "ipm", "beta"), + AbsoluteName("gpm:aaa", "ipm", "gamma"), + AbsoluteName("gpm:bbb", "ipm", "beta"), + AbsoluteName("gpm:bbb", "ipm", "gamma"), + AbsoluteName("meta", "ipm", "beta_bbb_aaa"), + ], + ) + + self.assertEqual( + list(model.requirements_dict.values()), + [ + AttributeDef('beta', float, Shapes.TxN), + AttributeDef('gamma', float, Shapes.TxN), + AttributeDef('beta', float, Shapes.TxN), + AttributeDef('gamma', float, Shapes.TxN), + AttributeDef('beta_bbb_aaa', float, Shapes.TxN), + ], + ) + + [S_aaa, I_aaa, R_aaa, S_bbb, I_bbb, R_bbb] = model.symbols.all_compartments + [beta_aaa, gamma_aaa, beta_bbb, gamma_bbb, + beta_bbb_aaa] = model.symbols.all_requirements + + self.assertEqual(model.transitions, [ + edge(S_aaa, I_aaa, rate=beta_aaa * S_aaa * I_aaa), + edge(I_aaa, R_aaa, rate=gamma_aaa * I_aaa), + edge(S_bbb, I_bbb, rate=beta_bbb * S_bbb * I_bbb), + edge(I_bbb, R_bbb, rate=gamma_bbb * I_bbb), + edge(S_bbb, I_bbb, beta_bbb_aaa * S_bbb * + I_aaa / Max(1, S_aaa + I_aaa + R_aaa)), + ]) diff --git a/epymorph/test/data_type_test.py b/epymorph/test/data_type_test.py index 85b97ff3..6a1d0033 100644 --- a/epymorph/test/data_type_test.py +++ b/epymorph/test/data_type_test.py @@ -15,7 +15,7 @@ def test_dtype_as_np(self): self.assertEqual(dtype_as_np(str), np.str_) self.assertEqual(dtype_as_np(date), np.datetime64) - struct = [('foo', float), ('bar', int), ('baz', str), ('bux', date)] + struct = (('foo', float), ('bar', int), ('baz', str), ('bux', date)) self.assertEqual( dtype_as_np(struct), [('foo', np.float64), ('bar', np.int64), @@ -28,7 +28,7 @@ def test_dtype_str(self): self.assertEqual(dtype_str(str), "str") self.assertEqual(dtype_str(date), "date") - struct = [('foo', float), ('bar', int), ('baz', str), ('bux', date)] + struct = (('foo', float), ('bar', int), ('baz', str), ('bux', date)) self.assertEqual( dtype_str(struct), "[(foo, float), (bar, int), (baz, str), (bux, date)]" @@ -53,8 +53,8 @@ def test_dtype_check(self): self.assertTrue(dtype_check(str, "")) self.assertTrue(dtype_check(date, date(2024, 1, 1))) self.assertTrue(dtype_check(date, date(1066, 10, 14))) - self.assertTrue(dtype_check([('x', int), ('y', int)], (1, 2))) - self.assertTrue(dtype_check([('a', str), ('b', float)], ("hi", 9273.3))) + self.assertTrue(dtype_check((('x', int), ('y', int)), (1, 2))) + self.assertTrue(dtype_check((('a', str), ('b', float)), ("hi", 9273.3))) self.assertFalse(dtype_check(int, "hi")) self.assertFalse(dtype_check(int, 42.42)) @@ -68,18 +68,10 @@ def test_dtype_check(self): self.assertFalse(dtype_check(date, '2024-01-01')) self.assertFalse(dtype_check(date, 123)) - dt1 = [('x', int), ('y', int)] + dt1 = (('x', int), ('y', int)) self.assertFalse(dtype_check(dt1, 1)) self.assertFalse(dtype_check(dt1, 78923.1)) self.assertFalse(dtype_check(dt1, "hi")) self.assertFalse(dtype_check(dt1, ())) self.assertFalse(dtype_check(dt1, (1, 237.8))) self.assertFalse(dtype_check(dt1, (1, 2, 3))) - - dt2 = (('x', int), ('y', int)) - self.assertFalse(dtype_check(dt2, 1)) - self.assertFalse(dtype_check(dt2, 78923.1)) - self.assertFalse(dtype_check(dt2, "hi")) - self.assertFalse(dtype_check(dt2, ())) - self.assertFalse(dtype_check(dt2, (1, 237.8))) - self.assertFalse(dtype_check(dt2, (1, 2, 3))) diff --git a/epymorph/test/database_test.py b/epymorph/test/database_test.py index e8b94a20..03a1f651 100644 --- a/epymorph/test/database_test.py +++ b/epymorph/test/database_test.py @@ -104,6 +104,31 @@ def test_parse_with_defaults_two_parts(self): self.assertEqual(name.module, "module") self.assertEqual(name.id, "id") + def test_parse_with_defaults_wildcards(self): + name = AbsoluteName.parse_with_defaults( + "gpm:alpha::*::id", "default_strata", "default_module") + self.assertEqual(name.strata, "gpm:alpha") + self.assertEqual(name.module, "default_module") + self.assertEqual(name.id, "id") + + name = AbsoluteName.parse_with_defaults( + "*::mm::id", "default_strata", "default_module") + self.assertEqual(name.strata, "default_strata") + self.assertEqual(name.module, "mm") + self.assertEqual(name.id, "id") + + name = AbsoluteName.parse_with_defaults( + "*::*::id", "default_strata", "default_module") + self.assertEqual(name.strata, "default_strata") + self.assertEqual(name.module, "default_module") + self.assertEqual(name.id, "id") + + name = AbsoluteName.parse_with_defaults( + "*::id", "default_strata", "default_module") + self.assertEqual(name.strata, "default_strata") + self.assertEqual(name.module, "default_module") + self.assertEqual(name.id, "id") + def test_str_representation(self): name = AbsoluteName("strata", "module", "id") self.assertEqual(str(name), "strata::module::id") diff --git a/epymorph/test/initializer_test.py b/epymorph/test/initializer_test.py index 1f9f830c..7610f0e3 100644 --- a/epymorph/test/initializer_test.py +++ b/epymorph/test/initializer_test.py @@ -10,20 +10,22 @@ from epymorph.data_type import SimDType from epymorph.database import Database, ModuleNamespace, NamePattern from epymorph.error import AttributeException, InitException +from epymorph.geography.scope import CustomScope from epymorph.simulation import AttributeDef, NamespacedAttributeResolver def test_context(additional_data: dict[str, NDArray] | None = None): + scope = CustomScope(list("ABCDE")) dim = SimDimensions.build( tau_step_lengths=[1 / 3, 2 / 3], start_date=date(2020, 1, 1), days=100, - nodes=5, + nodes=scope.nodes, compartments=3, events=3, ) params = { - 'label': np.array(list('ABCDE'), dtype=np.str_), + 'label': scope.get_node_ids(), 'population': np.array([100, 200, 300, 400, 500], dtype=SimDType), 'foosball_championships': np.array([2, 4, 1, 9, 6]), **(additional_data or {}), @@ -36,7 +38,7 @@ def test_context(additional_data: dict[str, NDArray] | None = None): dim=dim, namespace=ModuleNamespace("gpm:all", "init"), ) - return (data, dim, np.random.default_rng(1)) + return (data, dim, scope, np.random.default_rng(1)) _FOOSBALL_CHAMPIONSHIPS = AttributeDef("foosball_championships", int, Shapes.N) @@ -53,7 +55,7 @@ def test_explicit(self): [0, 0, 500], ]) exp = initials.copy() - act = init.Explicit(initials)(*test_context()) + act = init.Explicit(initials).evaluate_in_context(*test_context()) np.testing.assert_array_equal(act, exp) @@ -88,7 +90,7 @@ def test_proportional(self): exp = ratios1.copy() for ratios in [ratios1, ratios2, ratios3]: - act = init.Proportional(ratios)(*test_context()) + act = init.Proportional(ratios).evaluate_in_context(*test_context()) np.testing.assert_array_equal(act, exp) def test_bad_args(self): @@ -101,7 +103,7 @@ def test_bad_args(self): [300, 100, 0], [0, 0, 500], ]) - init.Proportional(ratios)(*test_context()) + init.Proportional(ratios).evaluate_in_context(*test_context()) with self.assertRaises(InitException): # row sums to negative! @@ -112,7 +114,7 @@ def test_bad_args(self): [300, 100, 0], [0, 0, 500], ]) - init.Proportional(ratios)(*test_context()) + init.Proportional(ratios).evaluate_in_context(*test_context()) class TestIndexedInitializer(unittest.TestCase): @@ -121,7 +123,7 @@ def test_indexed_locations(self): out = init.IndexedLocations( selection=np.array([1, -2], dtype=np.intp), # Negative indices work, too. seed_size=100, - )(*test_context()) + ).evaluate_in_context(*test_context()) # Make sure only the selected locations get infected. act = out[:, 1] > 0 exp = np.array([False, True, False, True, False]) @@ -135,19 +137,19 @@ def test_indexed_locations_bad(self): init.IndexedLocations( selection=np.array([[1], [3]], dtype=np.intp), seed_size=100, - )(*test_context()) + ).evaluate_in_context(*test_context()) with self.assertRaises(InitException): # indices must be in range (positive) init.IndexedLocations( selection=np.array([1, 8], dtype=np.intp), seed_size=100, - )(*test_context()) + ).evaluate_in_context(*test_context()) with self.assertRaises(InitException): # indices must be in range (negative) init.IndexedLocations( selection=np.array([1, -8], dtype=np.intp), seed_size=100, - )(*test_context()) + ).evaluate_in_context(*test_context()) class TestLabeledInitializer(unittest.TestCase): @@ -156,7 +158,7 @@ def test_labeled_locations(self): out = init.LabeledLocations( labels=np.array(["B", "D"]), seed_size=100, - )(*test_context()) + ).evaluate_in_context(*test_context()) # Make sure only the selected locations get infected. act = out[:, 1] > 0 exp = np.array([False, True, False, True, False]) @@ -169,7 +171,7 @@ def test_labeled_locations_bad(self): init.LabeledLocations( labels=np.array(["A", "B", "Z"]), seed_size=100, - )(*test_context()) + ).evaluate_in_context(*test_context()) class TestSingleInitializer(unittest.TestCase): @@ -185,7 +187,7 @@ def test_single_loc(self): act = init.SingleLocation( location=2, seed_size=99, - )(*test_context()) + ).evaluate_in_context(*test_context()) np.testing.assert_array_equal(act, exp) @@ -196,7 +198,7 @@ def test_top(self): top_attribute=_FOOSBALL_CHAMPIONSHIPS, num_locations=3, seed_size=100, - )(*test_context()) + ).evaluate_in_context(*test_context()) act = out[:, 1] > 0 exp = np.array([False, True, False, True, True]) np.testing.assert_array_equal(act, exp) @@ -209,7 +211,7 @@ def test_missing_attribute(self): num_locations=3, seed_size=100, ) - i(*test_context()) + i.evaluate_in_context(*test_context()) def test_wrong_type_attribute(self): with self.assertRaises(AttributeException): @@ -219,7 +221,7 @@ def test_wrong_type_attribute(self): num_locations=3, seed_size=100, ) - i(*test_context({ + i.evaluate_in_context(*test_context({ "quidditch_championships": np.array([1.0, 2.0, 3.0, 4.0, 5.0], dtype=np.float64), })) @@ -231,7 +233,7 @@ def test_invalid_size_attribute(self): num_locations=3, seed_size=100, ) - i(*test_context({ + i.evaluate_in_context(*test_context({ "quidditch_relative_rank": np.arange(25, dtype=np.float64).reshape((5, 5)), })) @@ -243,7 +245,7 @@ def test_bottom(self): bottom_attribute=_FOOSBALL_CHAMPIONSHIPS, num_locations=3, seed_size=100, - )(*test_context()) + ).evaluate_in_context(*test_context()) act = out[:, 1] > 0 exp = np.array([True, True, True, False, False]) np.testing.assert_array_equal(act, exp) @@ -256,7 +258,7 @@ def test_missing_attribute(self): num_locations=3, seed_size=100, ) - i(*test_context()) + i.evaluate_in_context(*test_context()) def test_invalid_size_attribute(self): with self.assertRaises(InitException): @@ -267,6 +269,6 @@ def test_invalid_size_attribute(self): num_locations=3, seed_size=100, ) - i(*test_context({ + i.evaluate_in_context(*test_context({ "quidditch_relative_rank": np.arange(25, dtype=np.float64).reshape((5, 5)), })) diff --git a/epymorph/test/movement_model_test.py b/epymorph/test/movement_model_test.py new file mode 100644 index 00000000..d86b45ee --- /dev/null +++ b/epymorph/test/movement_model_test.py @@ -0,0 +1,163 @@ +# pylint: disable=missing-docstring,unused-variable +import unittest + +import numpy as np +from numpy.typing import NDArray + +from epymorph.data_type import SimDType +from epymorph.movement_model import EveryDay, MovementClause, MovementModel +from epymorph.simulation import Tick, TickDelta, TickIndex + + +class MovementClauseTest(unittest.TestCase): + + def test_create_01(self): + # Successful clause! + class MyClause(MovementClause): + leaves = TickIndex(step=0) + returns = TickDelta(days=0, step=1) + predicate = EveryDay() + + def evaluate(self, tick: Tick) -> NDArray[SimDType]: + return np.array([0]) + + clause = MyClause() + + self.assertEqual(clause.leaves, TickIndex(step=0)) + self.assertEqual(clause.returns, TickDelta(days=0, step=1)) + + def test_create_02(self): + # Test for error: forgot 'leaves' + with self.assertRaises(TypeError) as e: + class MyClause(MovementClause): + # leaves = TickIndex(step=0) + returns = TickDelta(days=0, step=1) + predicate = EveryDay() + + def evaluate(self, tick: Tick) -> NDArray[SimDType]: + return np.array([0]) + self.assertIn("invalid leaves in myclause", str(e.exception).lower()) + + def test_create_03(self): + # Test for error: forgot 'returns' + with self.assertRaises(TypeError) as e: + class MyClause(MovementClause): + leaves = TickIndex(step=0) + # returns = TickDelta(days=0, step=1) + predicate = EveryDay() + + def evaluate(self, tick: Tick) -> NDArray[SimDType]: + return np.array([0]) + self.assertIn("invalid returns in myclause", str(e.exception).lower()) + + def test_create_04(self): + # Test for error: forgot 'predicate' + with self.assertRaises(TypeError) as e: + class MyClause(MovementClause): + leaves = TickIndex(step=0) + returns = TickDelta(days=0, step=1) + # predicate = EveryDay() + + def evaluate(self, tick: Tick) -> NDArray[SimDType]: + return np.array([0]) + self.assertIn("invalid predicate in myclause", str(e.exception).lower()) + + def test_create_05(self): + # Test for error: invalid 'leaves' index + with self.assertRaises(TypeError) as e: + class MyClause(MovementClause): + leaves = TickIndex(step=-23) + returns = TickDelta(days=0, step=1) + predicate = EveryDay() + + def evaluate(self, tick: Tick) -> NDArray[SimDType]: + return np.array([0]) + self.assertIn("step indices cannot be less than zero", str(e.exception).lower()) + + def test_create_06(self): + # Test for error: invalid 'returns' index + with self.assertRaises(TypeError) as e: + class MyClause(MovementClause): + leaves = TickIndex(step=0) + returns = TickDelta(days=0, step=-23) + predicate = EveryDay() + + def evaluate(self, tick: Tick) -> NDArray[SimDType]: + return np.array([0]) + self.assertIn("step indices cannot be less than zero", str(e.exception).lower()) + + +class MovementModelTest(unittest.TestCase): + + class MyClause(MovementClause): + leaves = TickIndex(step=0) + returns = TickDelta(days=0, step=1) + predicate = EveryDay() + + def evaluate(self, tick: Tick) -> NDArray[SimDType]: + return np.array([0]) + + def test_create_01(self): + class MyModel(MovementModel): + steps = [1 / 3, 2 / 3] + clauses = [MovementModelTest.MyClause()] + + model = MyModel() + self.assertEqual(model.steps, (1 / 3, 2 / 3)) + self.assertEqual(len(model.clauses), 1) + self.assertEqual(model.clauses[0].__class__.__name__, "MyClause") + + def test_create_02(self): + # Test for error: forgot 'steps' + with self.assertRaises(TypeError) as e: + class MyModel(MovementModel): + # steps = [1 / 3, 2 / 3] + clauses = [MovementModelTest.MyClause()] + self.assertIn("invalid steps in mymodel", str(e.exception).lower()) + + def test_create_03(self): + # Test for error: 'steps' don't sum to 1 + with self.assertRaises(TypeError) as e: + class MyModel1(MovementModel): + steps = [1 / 3, 1 / 3] + clauses = [MovementModelTest.MyClause()] + self.assertIn("steps must sum to 1", str(e.exception).lower()) + + with self.assertRaises(TypeError) as e: + class MyModel2(MovementModel): + steps = [0.1, 0.75, 0.3, 0.2] + clauses = [MovementModelTest.MyClause()] + self.assertIn("steps must sum to 1", str(e.exception).lower()) + + def test_create_04(self): + # Test for error: 'steps' aren't all greater than zero + with self.assertRaises(TypeError) as e: + class MyModel(MovementModel): + steps = [1 / 3, -1 / 3, 1 / 3, 2 / 3] + clauses = [MovementModelTest.MyClause()] + self.assertIn("steps must all be greater than 0", str(e.exception).lower()) + + def test_create_05(self): + # Test for error: forgot 'clauses' + with self.assertRaises(TypeError) as e: + class MyModel(MovementModel): + steps = [1 / 3, 2 / 3] + # clauses = [MovementModelTest.MyClause()] + self.assertIn("invalid clauses", str(e.exception).lower()) + + def test_create_06(self): + # Test for error: clauses reference steps which don't exist + with self.assertRaises(TypeError) as e: + class MyClause(MovementClause): + leaves = TickIndex(0) + returns = TickDelta(days=0, step=9) + predicate = EveryDay() + + def evaluate(self, tick: Tick) -> NDArray[SimDType]: + return np.array([0]) + + class MyModel(MovementModel): + steps = (1 / 3, 2 / 3) + clauses = (MyClause(),) + self.assertIn("return step (9)", str(e.exception).lower()) + self.assertIn("not a valid index", str(e.exception).lower()) diff --git a/epymorph/test/params_test.py b/epymorph/test/params_test.py index 46bec171..bdb16114 100644 --- a/epymorph/test/params_test.py +++ b/epymorph/test/params_test.py @@ -11,6 +11,7 @@ from epymorph.data_shape import SimDimensions from epymorph.data_type import AttributeArray, AttributeValue from epymorph.database import Database, ModuleNamespace +from epymorph.geography.scope import CustomScope, GeoScope from epymorph.params import (ParamExpressionTimeAndNode, ParamFunctionNode, ParamFunctionNodeAndCompartment, ParamFunctionNodeAndNode, ParamFunctionNumpy, @@ -21,7 +22,7 @@ class ParamFunctionsTest(unittest.TestCase): - def _create_dim_and_data(self) -> tuple[SimDimensions, NamespacedAttributeResolver]: + def _dim_data_scope(self) -> tuple[SimDimensions, NamespacedAttributeResolver, GeoScope]: dim = SimDimensions.build( tau_step_lengths=[1 / 3, 2 / 3], start_date=date(2021, 1, 1), @@ -35,10 +36,11 @@ def _create_dim_and_data(self) -> tuple[SimDimensions, NamespacedAttributeResolv dim=dim, namespace=ModuleNamespace("gpm:all", "ipm"), ) - return dim, data + scope = CustomScope(["a", "b", "c", "d"]) + return dim, data, scope def test_numpy_1(self): - dim, data = self._create_dim_and_data() + dim, data, scope = self._dim_data_scope() class TestFunc(ParamFunctionNumpy): def evaluate(self) -> NDArray[np.int64]: @@ -47,12 +49,12 @@ def evaluate(self) -> NDArray[np.int64]: f = TestFunc() npt.assert_array_equal( - f(data, dim, np.random.default_rng(1)), + f.evaluate_in_context(data, dim, scope, np.random.default_rng(1)), np.arange(400).reshape((4, 100)), ) def test_scalar_1(self): - dim, data = self._create_dim_and_data() + dim, data, scope = self._dim_data_scope() class TestFunc(ParamFunctionScalar): dtype = np.float64 @@ -63,12 +65,12 @@ def evaluate1(self) -> AttributeValue: f = TestFunc() npt.assert_array_equal( - f(data, dim, np.random.default_rng(1)), + f.evaluate_in_context(data, dim, scope, np.random.default_rng(1)), np.array(42.0, dtype=np.float64), ) def test_time_1(self): - dim, data = self._create_dim_and_data() + dim, data, scope = self._dim_data_scope() class TestFunc(ParamFunctionTime): dtype = np.float64 @@ -79,12 +81,12 @@ def evaluate1(self, day: int) -> AttributeValue: f = TestFunc() npt.assert_array_equal( - f(data, dim, np.random.default_rng(1)), + f.evaluate_in_context(data, dim, scope, np.random.default_rng(1)), 2 * np.arange(100, dtype=np.float64), ) def test_node_1(self): - dim, data = self._create_dim_and_data() + dim, data, scope = self._dim_data_scope() class TestFunc(ParamFunctionNode): dtype = np.float64 @@ -95,12 +97,12 @@ def evaluate1(self, node_index: int) -> AttributeValue: f = TestFunc() npt.assert_array_equal( - f(data, dim, np.random.default_rng(1)), + f.evaluate_in_context(data, dim, scope, np.random.default_rng(1)), 3 * np.arange(4, dtype=np.float64), ) def test_node_and_node_1(self): - dim, data = self._create_dim_and_data() + dim, data, scope = self._dim_data_scope() class TestFunc(ParamFunctionNodeAndNode): dtype = np.int64 @@ -111,7 +113,7 @@ def evaluate1(self, node_from: int, node_to: int) -> AttributeValue: f = TestFunc() npt.assert_array_equal( - f(data, dim, np.random.default_rng(1)), + f.evaluate_in_context(data, dim, scope, np.random.default_rng(1)), np.array([ [0, 1, 2, 3], [10, 11, 12, 13], @@ -121,7 +123,7 @@ def evaluate1(self, node_from: int, node_to: int) -> AttributeValue: ) def test_node_and_compartment_1(self): - dim, data = self._create_dim_and_data() + dim, data, scope = self._dim_data_scope() class TestFunc(ParamFunctionNodeAndCompartment): dtype = np.int64 @@ -132,7 +134,7 @@ def evaluate1(self, node_index: int, compartment_index: int) -> AttributeValue: f = TestFunc() npt.assert_array_equal( - f(data, dim, np.random.default_rng(1)), + f.evaluate_in_context(data, dim, scope, np.random.default_rng(1)), np.array([ [0, 1, 2], [10, 11, 12], @@ -142,7 +144,7 @@ def evaluate1(self, node_index: int, compartment_index: int) -> AttributeValue: ) def test_time_and_node_1(self): - dim, data = self._create_dim_and_data() + dim, data, scope = self._dim_data_scope() class TestFunc(ParamFunctionTimeAndNode): dtype = np.float64 @@ -154,7 +156,7 @@ def evaluate1(self, day: int, node_index: int) -> AttributeValue: cosine = np.cos(np.arange(100) / 100, dtype=np.float64) npt.assert_array_equal( - f(data, dim, np.random.default_rng(1)), + f.evaluate_in_context(data, dim, scope, np.random.default_rng(1)), np.stack([ 1.0 * cosine, 2.0 * cosine, @@ -164,14 +166,14 @@ def evaluate1(self, day: int, node_index: int) -> AttributeValue: ) def test_expr_time_and_node_1(self): - dim, data = self._create_dim_and_data() + dim, data, scope = self._dim_data_scope() d, n, days = simulation_symbols('day', 'node_index', 'duration_days') f = ParamExpressionTimeAndNode((1.0 + n) * sympy.cos(d / days)) cosine = np.cos(np.arange(100) / 100, dtype=np.float64) npt.assert_array_equal( - f(data, dim, np.random.default_rng(1)), + f.evaluate_in_context(data, dim, scope, np.random.default_rng(1)), np.stack([ 1.0 * cosine, 2.0 * cosine, diff --git a/epymorph/test/rume_test.py b/epymorph/test/rume_test.py index 8551850a..c8fcac1c 100644 --- a/epymorph/test/rume_test.py +++ b/epymorph/test/rume_test.py @@ -1,125 +1,39 @@ # pylint: disable=missing-docstring -import unittest - +import numpy as np from numpy.testing import assert_array_equal -from sympy import Max, symbols +from numpy.typing import NDArray from epymorph import AttributeDef, Shapes, TimeFrame, init, mm_library -from epymorph.compartment_model import (compartment, create_model, - create_symbols, edge) +from epymorph.compartment_model import CompartmentModel, compartment, edge from epymorph.database import AbsoluteName from epymorph.geography.us_census import StateScope -from epymorph.movement.parser import (ALL_DAYS, DailyClause, MovementSpec, - MoveSteps) -from epymorph.movement.parser_util import Duration -from epymorph.rume import (DEFAULT_STRATA, Gpm, Rume, RumeSymbols, - combine_ipms, combine_tau_steps, remap_taus) +from epymorph.movement_model import EveryDay, MovementClause, MovementModel +from epymorph.rume import (DEFAULT_STRATA, Gpm, MultistrataRume, + SingleStrataRume, combine_tau_steps, remap_taus) +from epymorph.simulation import Tick, TickDelta, TickIndex from epymorph.test import EpymorphTestCase -def _create_sir(): - sym = create_symbols( - compartments=[ - compartment('S'), - compartment('I'), - compartment('R'), - ], - attributes=[ - AttributeDef('beta', float, Shapes.TxN), - AttributeDef('gamma', float, Shapes.TxN), - ], - ) - - [S, I, R] = sym.compartment_symbols - [beta, gamma] = sym.attribute_symbols - - return create_model( - symbols=sym, - transitions=[ - edge(S, I, rate=beta * S * I), - edge(I, R, rate=gamma * I), - ], - ) - +class Sir(CompartmentModel): + compartments = [ + compartment('S'), + compartment('I'), + compartment('R'), + ] -class CombineIpmTest(unittest.TestCase): - def test_combine_1(self): - sir = _create_sir() + requirements = [ + AttributeDef('beta', float, Shapes.TxN), + AttributeDef('gamma', float, Shapes.TxN), + ] - meta_attributes = [ - AttributeDef("beta_bbb_aaa", float, Shapes.TxN), + def edges(self, symbols): + [S, I, R] = symbols.all_compartments + [beta, gamma] = symbols.all_requirements + return [ + edge(S, I, rate=beta * S * I), + edge(I, R, rate=gamma * I), ] - def meta_edges(s: RumeSymbols): - [S_aaa, I_aaa, R_aaa] = s.compartments("aaa") - [S_bbb, I_bbb, R_bbb] = s.compartments("bbb") - [beta_bbb_aaa] = s.meta_attributes() - N_aaa = s.total_nonzero("aaa") - return [ - edge(S_bbb, I_bbb, beta_bbb_aaa * S_bbb * I_aaa / N_aaa), - ] - - model = combine_ipms( - strata=[('aaa', sir), ('bbb', sir)], - meta_attributes=meta_attributes, - meta_edges=meta_edges, - ) - - self.assertEqual(model.num_compartments, 6) - self.assertEqual(model.num_events, 5) - - # Check compartment mapping - self.assertEqual( - [c.name for c in model.compartments], - ['S_aaa', 'I_aaa', 'R_aaa', 'S_bbb', 'I_bbb', 'R_bbb'], - ) - - self.assertEqual( - model.symbols.compartment_symbols, - list(symbols("S_aaa I_aaa R_aaa S_bbb I_bbb R_bbb")), - ) - - # Check attribute mapping - self.assertEqual( - model.symbols.attribute_symbols, - list(symbols("beta_aaa gamma_aaa beta_bbb gamma_bbb beta_bbb_aaa_meta")), - ) - - self.assertEqual( - list(model.attributes.keys()), - [ - AbsoluteName("gpm:aaa", "ipm", "beta"), - AbsoluteName("gpm:aaa", "ipm", "gamma"), - AbsoluteName("gpm:bbb", "ipm", "beta"), - AbsoluteName("gpm:bbb", "ipm", "gamma"), - AbsoluteName("meta", "ipm", "beta_bbb_aaa"), - ], - ) - - self.assertEqual( - list(model.attributes.values()), - [ - AttributeDef('beta', float, Shapes.TxN), - AttributeDef('gamma', float, Shapes.TxN), - AttributeDef('beta', float, Shapes.TxN), - AttributeDef('gamma', float, Shapes.TxN), - AttributeDef('beta_bbb_aaa', float, Shapes.TxN), - ], - ) - - [S_aaa, I_aaa, R_aaa, S_bbb, I_bbb, R_bbb] = model.symbols.compartment_symbols - [beta_aaa, gamma_aaa, beta_bbb, gamma_bbb, - beta_bbb_aaa] = model.symbols.attribute_symbols - - self.assertEqual(model.transitions, [ - edge(S_aaa, I_aaa, rate=beta_aaa * S_aaa * I_aaa), - edge(I_aaa, R_aaa, rate=gamma_aaa * I_aaa), - edge(S_bbb, I_bbb, rate=beta_bbb * S_bbb * I_bbb), - edge(I_bbb, R_bbb, rate=gamma_bbb * I_bbb), - edge(S_bbb, I_bbb, beta_bbb_aaa * S_bbb * - I_aaa / Max(1, S_aaa + I_aaa + R_aaa)), - ]) - class CombineMmTest(EpymorphTestCase): @@ -187,59 +101,55 @@ def test_combine_tau_steps_4(self): }) def test_remap_taus_1(self): - mm1 = MovementSpec( - steps=MoveSteps([1 / 3, 2 / 3]), - attributes=[], - predef=None, - clauses=[ - DailyClause( - days=ALL_DAYS, - leave_step=0, - duration=Duration(0, 'd'), - return_step=1, - function='place-hodor', - ), - ], - ) + class Clause1(MovementClause): + leaves = TickIndex(0) + returns = TickDelta(days=0, step=1) + predicate = EveryDay() - mm2 = MovementSpec( - steps=MoveSteps([1 / 2, 1 / 2]), - attributes=[], - predef=None, - clauses=[ - DailyClause( - days=ALL_DAYS, - leave_step=1, - duration=Duration(0, 'd'), - return_step=1, - function='place-hodor', - ), - ], - ) + def evaluate(self, tick: Tick) -> NDArray[np.int64]: + return np.array([]) + + class Model1(MovementModel): + steps = (1 / 3, 2 / 3) + clauses = (Clause1(),) - new_taus, new_mms = remap_taus([('a', mm1), ('b', mm2)]) + class Clause2(MovementClause): + leaves = TickIndex(1) + returns = TickDelta(days=0, step=1) + predicate = EveryDay() + + def evaluate(self, tick: Tick) -> NDArray[np.int64]: + return np.array([]) + + class Model2(MovementModel): + steps = (1 / 2, 1 / 2) + clauses = (Clause2(),) + + new_mms = remap_taus([('a', Model1()), ('b', Model2())]) + + new_taus = new_mms["a"].steps self.assertListAlmostEqual(new_taus, [1 / 3, 1 / 6, 1 / 2]) self.assertEqual(len(new_mms), 2) new_mm1 = new_mms['a'] - self.assertEqual(new_mm1.clauses[0].leave_step, 0) - self.assertEqual(new_mm1.clauses[0].return_step, 2) + self.assertEqual(new_mm1.clauses[0].leaves.step, 0) + self.assertEqual(new_mm1.clauses[0].returns.step, 2) new_mm2 = new_mms['b'] - self.assertEqual(new_mm2.clauses[0].leave_step, 2) - self.assertEqual(new_mm2.clauses[0].return_step, 2) + self.assertEqual(new_mm2.clauses[0].leaves.step, 2) + self.assertEqual(new_mm2.clauses[0].returns.step, 2) class RumeTest(EpymorphTestCase): def test_create_monostrata_1(self): # A single-strata RUME uses the IPM without modification. - sir = _create_sir() + sir = Sir() centroids = mm_library['centroids']() # Make sure centroids has the tau steps we will expect later... - self.assertListAlmostEqual(centroids.steps.step_lengths, [1 / 3, 2 / 3]) + self.assertListAlmostEqual(centroids.steps, [1 / 3, 2 / 3]) - rume = Rume.single_strata( + rume = SingleStrataRume.build( ipm=sir, mm=centroids, init=init.NoInfection(), @@ -249,7 +159,6 @@ def test_create_monostrata_1(self): ) self.assertIs(sir, rume.ipm) - self.assertTrue(rume.is_single_strata) self.assertEqual(rume.dim.compartments, 3) self.assertEqual(rume.dim.events, 2) self.assertEqual(rume.dim.days, 180) @@ -258,43 +167,44 @@ def test_create_monostrata_1(self): self.assertEqual(rume.dim.nodes, 2) assert_array_equal( - rume.compartment_mask(DEFAULT_STRATA), + rume.compartment_mask[DEFAULT_STRATA], [True, True, True], ) assert_array_equal( - rume.compartment_mobility(DEFAULT_STRATA), + rume.compartment_mobility[DEFAULT_STRATA], [True, True, True], ) def test_create_multistrata_1(self): # Test a multi-strata model. - sir = _create_sir() + sir = Sir() no = mm_library['no']() # Make sure 'no' has the tau steps we will expect later... - self.assertListAlmostEqual(no.steps.step_lengths, [1.0]) + self.assertListAlmostEqual(no.steps, [1.0]) - rume = Rume.multistrata( + rume = MultistrataRume.build( strata=[ - ('aaa', Gpm( + Gpm( + name="aaa", ipm=sir, mm=no, init=init.SingleLocation(location=0, seed_size=100), - )), - ('bbb', Gpm( + ), + Gpm( + name="bbb", ipm=sir, mm=no, init=init.SingleLocation(location=0, seed_size=100), - )), + ), ], - meta_attributes=[], + meta_requirements=[], meta_edges=lambda _: [], scope=StateScope.in_states(['04', '35']), time_frame=TimeFrame.of("2021-01-01", 180), params={}, ) - self.assertFalse(rume.is_single_strata) self.assertEqual(rume.dim.compartments, 6) self.assertEqual(rume.dim.events, 4) self.assertEqual(rume.dim.days, 180) @@ -303,24 +213,24 @@ def test_create_multistrata_1(self): self.assertEqual(rume.dim.nodes, 2) assert_array_equal( - rume.compartment_mask("aaa"), + rume.compartment_mask["aaa"], [True, True, True, False, False, False], ) assert_array_equal( - rume.compartment_mask("bbb"), + rume.compartment_mask["bbb"], [False, False, False, True, True, True], ) assert_array_equal( - rume.compartment_mobility("aaa"), + rume.compartment_mobility["aaa"], [True, True, True, False, False, False], ) assert_array_equal( - rume.compartment_mobility("bbb"), + rume.compartment_mobility["bbb"], [False, False, False, True, True, True], ) # NOTE: these tests will break if someone alters the MM or Init definition; even just the comments - self.assertDictEqual(rume.attributes, { + self.assertDictEqual(rume.requirements, { AbsoluteName("gpm:aaa", "ipm", "beta"): AttributeDef("beta", float, Shapes.TxN), AbsoluteName("gpm:aaa", "ipm", "gamma"): AttributeDef("gamma", float, Shapes.TxN), AbsoluteName("gpm:bbb", "ipm", "beta"): AttributeDef("beta", float, Shapes.TxN), @@ -334,27 +244,27 @@ def test_create_multistrata_1(self): def test_create_multistrata_2(self): # Test special case: a multi-strata model but with only one strata. - sir = _create_sir() + sir = Sir() centroids = mm_library['centroids']() # Make sure centroids has the tau steps we will expect later... - self.assertListAlmostEqual(centroids.steps.step_lengths, [1 / 3, 2 / 3]) + self.assertListAlmostEqual(centroids.steps, [1 / 3, 2 / 3]) - rume = Rume.multistrata( + rume = MultistrataRume.build( strata=[ - ('aaa', Gpm( + Gpm( + name="aaa", ipm=sir, mm=centroids, init=init.NoInfection(), - )), + ), ], - meta_attributes=[], + meta_requirements=[], meta_edges=lambda _: [], scope=StateScope.in_states(['04', '35']), time_frame=TimeFrame.of("2021-01-01", 180), params={}, ) - self.assertFalse(rume.is_single_strata) self.assertEqual(rume.dim.compartments, 3) self.assertEqual(rume.dim.events, 2) self.assertEqual(rume.dim.days, 180) @@ -363,16 +273,31 @@ def test_create_multistrata_2(self): self.assertEqual(rume.dim.nodes, 2) # NOTE: these tests will break if someone alters the MM or Init definition; even just the comments - self.assertDictEqual(rume.attributes, { - AbsoluteName("gpm:aaa", "ipm", "beta"): AttributeDef("beta", float, Shapes.TxN), - AbsoluteName("gpm:aaa", "ipm", "gamma"): AttributeDef("gamma", float, Shapes.TxN), - AbsoluteName("gpm:aaa", "mm", "population"): AttributeDef("population", int, Shapes.N, - comment="The total population at each node."), - AbsoluteName("gpm:aaa", "mm", "centroid"): AttributeDef("centroid", [('longitude', float), ('latitude', float)], Shapes.N, - comment="The centroids for each node as (longitude, latitude) tuples."), - AbsoluteName("gpm:aaa", "mm", "phi"): AttributeDef("phi", float, Shapes.S, - comment="Influences the distance that movers tend to travel.", - default_value=40.0), - AbsoluteName("gpm:aaa", "init", "population"): AttributeDef("population", int, Shapes.N, - comment="The population at each geo node."), + self.assertDictEqual(rume.requirements, { + AbsoluteName("gpm:aaa", "ipm", "beta"): + AttributeDef("beta", float, Shapes.TxN), + + AbsoluteName("gpm:aaa", "ipm", "gamma"): + AttributeDef("gamma", float, Shapes.TxN), + + AbsoluteName("gpm:aaa", "mm", "population"): + AttributeDef("population", int, Shapes.N, + comment="The total population at each node."), + + AbsoluteName("gpm:aaa", "mm", "centroid"): + AttributeDef("centroid", (('longitude', float), ('latitude', float)), Shapes.N, + comment="The centroids for each node as (longitude, latitude) tuples."), + + AbsoluteName("gpm:aaa", "mm", "phi"): + AttributeDef("phi", float, Shapes.S, + comment="Influences the distance that movers tend to travel.", + default_value=40.0), + + AbsoluteName("gpm:aaa", "mm", "commuter_proportion"): + AttributeDef("commuter_proportion", float, Shapes.S, default_value=0.1, + comment="Decides what proportion of the total population should be commuting normally."), + + AbsoluteName("gpm:aaa", "init", "population"): + AttributeDef("population", int, Shapes.N, + comment="The population at each geo node."), }) diff --git a/epymorph/test/simulation_test.py b/epymorph/test/simulation_test.py index b2e958d1..a48382ab 100644 --- a/epymorph/test/simulation_test.py +++ b/epymorph/test/simulation_test.py @@ -1,8 +1,16 @@ -# pylint: disable=missing-docstring +# pylint: disable=missing-docstring,unused-variable import unittest from datetime import date +from functools import cached_property +from unittest.mock import MagicMock -from epymorph.simulation import SimDimensions, Tick, simulation_clock +import numpy as np + +from epymorph.data_shape import Shapes +from epymorph.geography.scope import GeoScope +from epymorph.simulation import (AttributeDef, NamespacedAttributeResolver, + SimDimensions, SimulationFunction, Tick, + simulation_clock) class TestClock(unittest.TestCase): @@ -31,3 +39,101 @@ def test_clock(self): Tick(11, 5, date(2023, 1, 6), 1, 1 / 3), ] self.assertEqual(act, exp) + + +class TestSimulationFunction(unittest.TestCase): + + def context(self, bar: int): + data = MagicMock(spec=NamespacedAttributeResolver) + data.resolve.return_value = np.array([bar]) + dim = MagicMock(spec=SimDimensions) + scope = MagicMock(spec=GeoScope) + rng = MagicMock(spec=np.random.Generator) + return (data, dim, scope, rng) + + def test_basic_usage(self): + class Foo(SimulationFunction[int]): + requirements = [AttributeDef('bar', int, Shapes.S)] + + baz: int + + def __init__(self, baz: int): + self.baz = baz + + def evaluate(self) -> int: + return 7 * self.baz * self.data('bar')[0] + + f = Foo(3) + + self.assertIsInstance(Foo.requirements, tuple) + + self.assertEqual(42, f.evaluate_in_context(*self.context(bar=2))) + + with self.assertRaises(TypeError) as e: + f.evaluate() + self.assertIn("invalid access of function context", str(e.exception).lower()) + + def test_immutable_requirements(self): + class Foo(SimulationFunction[int]): + requirements = [AttributeDef('bar', int, Shapes.S)] + + def evaluate(self) -> int: + return 7 * self.data('bar')[0] + + f = Foo() + self.assertEqual(Foo.requirements, f.requirements) + self.assertIsInstance(Foo.requirements, tuple) + self.assertIsInstance(f.requirements, tuple) + + def test_undefined_requirement(self): + class Foo(SimulationFunction[int]): + requirements = [AttributeDef('bar', int, Shapes.S)] + + def evaluate(self) -> int: + return 7 * self.data('quux')[0] + + with self.assertRaises(ValueError) as e: + Foo().evaluate_in_context(*self.context(bar=2)) + self.assertIn("did not declare as a requirement", str(e.exception).lower()) + + def test_bad_definition(self): + with self.assertRaises(TypeError) as e: + class Foo1(SimulationFunction[int]): + requirements = "hey" # type: ignore + + def evaluate(self) -> int: + return 42 + self.assertIn("invalid requirements", str(e.exception).lower()) + + with self.assertRaises(TypeError) as e: + class Foo2(SimulationFunction[int]): + requirements = ["hey"] # type: ignore + + def evaluate(self) -> int: + return 42 + self.assertIn("invalid requirements", str(e.exception).lower()) + + with self.assertRaises(TypeError) as e: + class Foo3(SimulationFunction[int]): + requirements = [AttributeDef("foo", int, Shapes.S), + AttributeDef("foo", int, Shapes.S)] + + def evaluate(self) -> int: + return 42 + self.assertIn("invalid requirements", str(e.exception).lower()) + + def test_cached_properties(self): + class Foo(SimulationFunction[int]): + requirements = [AttributeDef('bar', int, Shapes.S)] + + @cached_property + def baz(self): + return self.data('bar')[0] * 2 + + def evaluate(self) -> int: + return 7 * self.baz + + f = Foo() + + self.assertEqual(42, f.evaluate_in_context(*self.context(bar=3))) + self.assertEqual(84, f.evaluate_in_context(*self.context(bar=6))) diff --git a/epymorph/util.py b/epymorph/util.py index 7bfa267b..3757bec9 100644 --- a/epymorph/util.py +++ b/epymorph/util.py @@ -4,7 +4,7 @@ from dataclasses import dataclass from re import compile as re_compile from typing import (Any, Callable, Generator, Generic, Iterable, Literal, - Mapping, OrderedDict, Self, TypeVar) + Mapping, OrderedDict, Self, TypeGuard, TypeVar, overload) import numpy as np from numpy.typing import DTypeLike, NDArray @@ -17,6 +17,8 @@ T = TypeVar('T') +A = TypeVar('A') +B = TypeVar('B') def identity(x: T) -> T: @@ -39,23 +41,6 @@ def call_all(*fs: Callable[[], Any]) -> None: f() -def or_raise(value: T | None, message: str) -> T: - """Enforce that the given value is not None, or else raise an exception.""" - if value is None: - raise Exception(message) - return value - - -def not_none(value: T | None) -> T: - """ - Assert that a value could never be None (or else raise a generic exception). - Be very careful using this! - """ - if value is None: - raise Exception("You asserted a value would never be None, but it was!") - return value - - # collection utilities @@ -101,6 +86,19 @@ def are_unique(xs: Iterable[T]) -> bool: return True +@overload +def are_instances(xs: list[Any], of_type: type[T]) -> TypeGuard[list[T]]: ... +@overload +def are_instances(xs: tuple[Any], of_type: type[T]) -> TypeGuard[tuple[T]]: ... + + +def are_instances(xs: list[Any] | tuple[Any], of_type: type[T]) -> TypeGuard[list[T] | tuple[T]]: + """TypeGuards a collection to check that all items are instances of the given type (`of_type`).""" + # NOTE: TypeVars can't be generic so we can't do TypeGuard[C[T]] :( + # Thus this only supports the types of collections we specify explicitly. + return all(isinstance(x, of_type) for x in xs) + + def filter_unique(xs: Iterable[T]) -> list[T]: """Convert an iterable to a list, keeping only the unique values and maintaining the order as first-seen.""" xset = set[T]() @@ -112,6 +110,21 @@ def filter_unique(xs: Iterable[T]) -> list[T]: return ys +def filter_with_mask(xs: Iterable[A], predicate: Callable[[A], TypeGuard[B]]) -> tuple[list[B], list[bool]]: + """ + Filters the given iterable for items which match `predicate`, and also + returns a boolean mask the same length as the iterable with the results of `predicate` for each item. + """ + matched = list[B]() + mask = list[bool]() + for x in xs: + is_match = predicate(x) + mask.append(is_match) + if is_match: + matched.append(x) + return matched, mask + + def as_list(x: T | list[T]) -> list[T]: """If `x` is a list, return it unchanged. If it's a single value, wrap it in a list.""" return x if isinstance(x, list) else [x] @@ -126,10 +139,6 @@ def as_sorted_dict(x: dict[K, V]) -> OrderedDict[K, V]: return OrderedDict(sorted(x.items())) -A = TypeVar('A') -B = TypeVar('B') - - def map_values(f: Callable[[A], B], xs: Mapping[K, A]) -> dict[K, B]: """Maps the values of a Mapping into a dict by applying the given function.""" return {k: f(v) for k, v in xs.items()} @@ -529,6 +538,20 @@ def subscriptions() -> Generator[Subscriber, None, None]: sub.unsubscribe() +# singletons + + +class Singleton(type): + """A metaclass for classes you want to treat as singletons.""" + + _instances: dict[type['Singleton'], 'Singleton'] = {} + + def __call__(cls: type['Singleton'], *args: Any, **kwargs: Any) -> 'Singleton': + if cls not in cls._instances: + cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) + return cls._instances[cls] + + # string builders diff --git a/pyproject.toml b/pyproject.toml index 86969745..5742e528 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,8 +42,6 @@ dependencies = [ "geopandas~=0.14.4", "census~=0.8.22", "jsonpickle~=3.2.1", - "pygris~=0.1.6", - "shapely~=2.0.4", "platformdirs~=4.2.2", "graphviz~=0.20.3", "typing_extensions~=4.12.2",