From fbb230224b8d248e3760f9bcf6304dcba46ce529 Mon Sep 17 00:00:00 2001 From: Julian Pollinger Date: Wed, 24 Jan 2024 07:10:38 +0100 Subject: [PATCH] benchmarks --- benchmark.ipynb | 33 ++++++++---------------------- benchmark_files/benchmark_4_0.png | Bin 46419 -> 46364 bytes 2 files changed, 9 insertions(+), 24 deletions(-) diff --git a/benchmark.ipynb b/benchmark.ipynb index f2fa7f3..b03fa23 100644 --- a/benchmark.ipynb +++ b/benchmark.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": { "is_executing": true }, @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -49,27 +49,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "metadata": {}, "outputs": [ - { - "ename": "ValueError", - "evalue": "shape mismatch: objects cannot be broadcast to a single shape. Mismatch is between arg 0 with shape (6,) and arg 1 with shape (5,).", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 17\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m attribute, measurement \u001b[38;5;129;01min\u001b[39;00m means\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 16\u001b[0m offset \u001b[38;5;241m=\u001b[39m width \u001b[38;5;241m*\u001b[39m multiplier\n\u001b[0;32m---> 17\u001b[0m rects \u001b[38;5;241m=\u001b[39m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbar\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43moffset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeasurement\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwidth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattribute\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 18\u001b[0m ax\u001b[38;5;241m.\u001b[39mbar_label(rects, padding\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m)\n\u001b[1;32m 19\u001b[0m multiplier \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n", - "File \u001b[0;32m/usr/lib/python3.11/site-packages/matplotlib/__init__.py:1465\u001b[0m, in \u001b[0;36m_preprocess_data..inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1462\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 1463\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1467\u001b[0m bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1468\u001b[0m auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m 1469\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n", - "File \u001b[0;32m/usr/lib/python3.11/site-packages/matplotlib/axes/_axes.py:2457\u001b[0m, in \u001b[0;36mAxes.bar\u001b[0;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[1;32m 2454\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m yerr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 2455\u001b[0m yerr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_convert_dx(yerr, y0, y, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits)\n\u001b[0;32m-> 2457\u001b[0m x, height, width, y, linewidth, hatch \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbroadcast_arrays\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2458\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Make args iterable too.\u001b[39;49;00m\n\u001b[1;32m 2459\u001b[0m \u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43matleast_1d\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwidth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlinewidth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhatch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2461\u001b[0m \u001b[38;5;66;03m# Now that units have been converted, set the tick locations.\u001b[39;00m\n\u001b[1;32m 2462\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m orientation \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvertical\u001b[39m\u001b[38;5;124m'\u001b[39m:\n", - "File \u001b[0;32m/usr/lib/python3.11/site-packages/numpy/lib/stride_tricks.py:540\u001b[0m, in \u001b[0;36mbroadcast_arrays\u001b[0;34m(subok, *args)\u001b[0m\n\u001b[1;32m 533\u001b[0m \u001b[38;5;66;03m# nditer is not used here to avoid the limit of 32 arrays.\u001b[39;00m\n\u001b[1;32m 534\u001b[0m \u001b[38;5;66;03m# Otherwise, something like the following one-liner would suffice:\u001b[39;00m\n\u001b[1;32m 535\u001b[0m \u001b[38;5;66;03m# return np.nditer(args, flags=['multi_index', 'zerosize_ok'],\u001b[39;00m\n\u001b[1;32m 536\u001b[0m \u001b[38;5;66;03m# order='C').itviews\u001b[39;00m\n\u001b[1;32m 538\u001b[0m args \u001b[38;5;241m=\u001b[39m [np\u001b[38;5;241m.\u001b[39marray(_m, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, subok\u001b[38;5;241m=\u001b[39msubok) \u001b[38;5;28;01mfor\u001b[39;00m _m \u001b[38;5;129;01min\u001b[39;00m args]\n\u001b[0;32m--> 540\u001b[0m shape \u001b[38;5;241m=\u001b[39m \u001b[43m_broadcast_shape\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 542\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m(array\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m==\u001b[39m shape \u001b[38;5;28;01mfor\u001b[39;00m array \u001b[38;5;129;01min\u001b[39;00m args):\n\u001b[1;32m 543\u001b[0m \u001b[38;5;66;03m# Common case where nothing needs to be broadcasted.\u001b[39;00m\n\u001b[1;32m 544\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m args\n", - "File \u001b[0;32m/usr/lib/python3.11/site-packages/numpy/lib/stride_tricks.py:422\u001b[0m, in \u001b[0;36m_broadcast_shape\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m 417\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Returns the shape of the arrays that would result from broadcasting the\u001b[39;00m\n\u001b[1;32m 418\u001b[0m \u001b[38;5;124;03msupplied arrays against each other.\u001b[39;00m\n\u001b[1;32m 419\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 420\u001b[0m \u001b[38;5;66;03m# use the old-iterator because np.nditer does not handle size 0 arrays\u001b[39;00m\n\u001b[1;32m 421\u001b[0m \u001b[38;5;66;03m# consistently\u001b[39;00m\n\u001b[0;32m--> 422\u001b[0m b \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbroadcast\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m32\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 423\u001b[0m \u001b[38;5;66;03m# unfortunately, it cannot handle 32 or more arguments directly\u001b[39;00m\n\u001b[1;32m 424\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m pos \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m32\u001b[39m, \u001b[38;5;28mlen\u001b[39m(args), \u001b[38;5;241m31\u001b[39m):\n\u001b[1;32m 425\u001b[0m \u001b[38;5;66;03m# ironically, np.broadcast does not properly handle np.broadcast\u001b[39;00m\n\u001b[1;32m 426\u001b[0m \u001b[38;5;66;03m# objects (it treats them as scalars)\u001b[39;00m\n\u001b[1;32m 427\u001b[0m \u001b[38;5;66;03m# use broadcasting to avoid allocating the full array\u001b[39;00m\n", - "\u001b[0;31mValueError\u001b[0m: shape mismatch: objects cannot be broadcast to a single shape. Mismatch is between arg 0 with shape (6,) and arg 1 with shape (5,)." - ] - }, { "data": { - "image/png": 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3NV/hAAAQwvkmlZeXR3l5eezatSvTpQAAAP+iZ8+ekZ2d3WiX/MaNGxvtpo+IeOutt2Lt2rVRVVUVF198cURE7N69O9LpdHTu3Dkefvjh+PjHP97ovk6dOsWIESPsnAcAoEV4rU0Tpk6dGjU1NbFmzZpMlwIAAPyLnJycKCkpiYqKigbtFRUVMWrUqEb9u3XrFk8//XRUV1fXH1OmTIkBAwZEdXV1fOQjH2nyc9LpdFRXV8eRRx7ZIvMAAKBjs3MeAABoc6ZPnx4TJkyI4cOHx8iRI+P222+P2tramDJlSkREzJgxIzZs2BB33nlndOrUKYqLixvc/6EPfShyc3MbtF911VVxyimnxHHHHRdbtmyJm266Kaqrq6O8vDzRuQEA0DEI5wEAgDZn/PjxsXnz5pg9e3akUqkoLi6OpUuXRkFBQUREpFKpqK2t3a8x33jjjfj6178edXV1kZeXF8OGDYvHH388Tj755JaYAgAAHZxwHgAAaJPKysqirKysyWuLFi3a672zZs2KWbNmNWi78cYb48Ybb2ym6gAAYO+8cx4AAAAAABImnAcAAAAAgIQJ5wEAAAAAIGHCeQAAAAAASJhwHgAAAAAAEtY50wUAAAD8s8LLfpvpEvbb+txMVwAAQFtj5zwAAAAAACRMOA8AAAAAAAkTzgMAAAAAQMKE8wAAAAAAkDDhPAAAAAAAJEw4DwAAAAAACRPOAwAAAABAwoTzAAAAAACQMOE8AAAAAAAkTDgPAAAAAAAJE843oby8PAYPHhwjRozIdCkAAAAAALRDwvkmTJ06NWpqamLNmjWZLgUAAAAAgHZIOA8AAAAAAAkTzgMAAAAAQMKE8wAAAAAAkDDhPAAAAAAAJEw4DwAAAAAACRPOAwAAAABAwoTzAAAAAACQMOE8AAAAAAAkTDgPAAAAAAAJE84DAAAAAEDChPMAAAAAAJAw4TwAAAAAACRMOA8AAAAAAAkTzgMAAAAAQMKE8wAAAAAAkDDhPAAAAAAAJEw4DwAAAAAACRPOAwAAAABAwoTzAAAAAACQMOE8AAAAAAAkTDgPAAAAAAAJE84DAAAAAEDChPMAAAAAAJAw4TwAAAAAACRMOA8AAAAAAAkTzjehvLw8Bg8eHCNGjMh0KQAAAAAAtEPC+SZMnTo1ampqYs2aNZkuBQAAAACAdkg4DwAAAAAACRPOAwAAAABAwoTzAAAAAACQMOE8AAAAAAAkTDgPAAAAAAAJE84DAAAAAEDChPMAAAAAAJAw4TwAAAAAACRMOA8AAAAAAAkTzgMAAAAAQMKE8wAAAAAAkDDhPAAAAAAAJEw4DwAAAAAACRPOAwAAAABAwoTzAAAAAACQMOE8AAAAAAAkTDgPAAAAAAAJE84DAAAAAEDChPMAAAAAAJAw4TwAAAAAACRMOA8AAAAAAAkTzgMAAAAAQMKE8wAAAAAAkDDhPAAAAAAAJEw4DwAAAAAACRPOAwAAAABAwoTzAAAAAACQMOE8AAAAAAAkTDgPAAAAAAAJE84DAAAAAEDChPMAAAAAAJAw4TwAAAAAACRMOA8AAAAAAAkTzgMAAAAAQMKE8wAAAAAAkDDhPAAArd78+fOjqKgocnNzo6SkJFasWLHHvqlUKi688MIYMGBAdOrUKaZNm9aozxlnnBFZWVmNjrPPPrsFZwEAAPD/E843oby8PAYPHhwjRozIdCkAAB3e4sWLY9q0aTFz5syoqqqK0aNHx9ixY6O2trbJ/tu3b49evXrFzJkzY8iQIU32uf/++yOVStUfzzzzTGRnZ8d5553XklMBAACoJ5xvwtSpU6OmpibWrFmT6VIAADq8OXPmxKRJk2Ly5MkxaNCgmDt3bvTr1y8WLFjQZP/CwsKYN29eTJw4MfLy8prs06NHj+jdu3f9UVFREYcccohwHgAASIxwHgCAVmvHjh1RWVkZpaWlDdpLS0tj1apVzfY5CxcujPPPPz8OPfTQZhsTAABgbzpnugAAANiTTZs2xa5duyI/P79Be35+ftTV1TXLZzz55JPxzDPPxMKFC5tlPAAAgH1h5zwAAK1eVlZWg/N0Ot2o7YNauHBhFBcXx8knn9ws4wEAAOwL4TwAAK1Wz549Izs7u9Eu+Y0bNzbaTf9BbNu2Le67776YPHnyAY8FAACwP4TzAAC0Wjk5OVFSUhIVFRUN2isqKmLUqFEHPP4vf/nL2L59e3zpS1864LEAAAD2h3fOAwDQqk2fPj0mTJgQw4cPj5EjR8btt98etbW1MWXKlIiImDFjRmzYsCHuvPPO+nuqq6sjImLr1q3x2muvRXV1deTk5MTgwYMbjL1w4cI499xz44gjjkhsPgAAABHCeQAAWrnx48fH5s2bY/bs2ZFKpaK4uDiWLl0aBQUFERGRSqWitra2wT3Dhg2r/7mysjLuueeeKCgoiPXr19e3//nPf46VK1fGww8/nMg8AAAA/plwHgCAVq+srCzKysqavLZo0aJGbel0+n3HPP744/epHwAAQEvwznkAAAAAAEiYcB4A4ADMnz8/ioqKIjc3N0pKSmLFihV77JtKpeLCCy+MAQMGRKdOnWLatGmN+ixatCiysrIaHe+8806HrhkAAKC9Ec4DAHxAixcvjmnTpsXMmTOjqqoqRo8eHWPHjm30/vP3bN++PXr16hUzZ86MIUOG7HHcbt26RSqVanDk5uZ22JoBAADaI+E8AMAHNGfOnJg0aVJMnjw5Bg0aFHPnzo1+/frFggULmuxfWFgY8+bNi4kTJ0ZeXt4ex83KyorevXs3ODpyzQAAAO2RcB4A4APYsWNHVFZWRmlpaYP20tLSWLVq1QGNvXXr1igoKIijjjoqPvnJT0ZVVdUBjfeetlgzAABAeyWcBwD4ADZt2hS7du2K/Pz8Bu35+flRV1f3gccdOHBgLFq0KH7961/HvffeG7m5uXHqqafG888/f6Alt8maAQAA2qvOmS4AAKAty8rKanCeTqcbte2PU045JU455ZT681NPPTU+/OEPx8033xw33XTTBx73n7Wmmk/8+Ykf+HMz5ZeZLgAAAGgX7JwHAPgAevbsGdnZ2Y12nG/cuLHRzvQD0alTpxgxYkSz7EJvizUDAAC0V8J5AIAPICcnJ0pKSqKioqJBe0VFRYwaNarZPiedTkd1dXUceeSRBzxWW6wZAACgvfJaGwCAD2j69OkxYcKEGD58eIwcOTJuv/32qK2tjSlTpkRExIwZM2LDhg1x55131t9TXV0dEf/4AtXXXnstqqurIycnJwYPHhwREVdddVWccsopcdxxx8WWLVvipptuiurq6igvL++wNQMAALRHwnkAgA9o/PjxsXnz5pg9e3akUqkoLi6OpUuXRkFBQUREpFKpqK2tbXDPsGHD6n+urKyMe+65JwoKCmL9+vUREfHGG2/E17/+9airq4u8vLwYNmxYPP7443HyySd32JoBAADaI+E8AMABKCsri7KysiavLVq0qFFbOp3e63g33nhj3Hjjjc1R2h61xZoBAADaG++cBwAAAACAhAnnAQAAAAAgYcJ5AGjH5s+fH0VFRZGbmxslJSWxYsWKPfZNpVJx4YUXxoABA6JTp04xbdq0Rn3uv//+GD58eHTv3j0OPfTQGDp0aNx1110tOAMAAABon4TzANBOLV68OKZNmxYzZ86MqqqqGD16dIwdO7bRl32+Z/v27dGrV6+YOXNmDBkypMk+PXr0iJkzZ8bq1avjj3/8Y1x00UVx0UUXxUMPPdSSUwEAAIB2RzgPAO3UnDlzYtKkSTF58uQYNGhQzJ07N/r16xcLFixosn9hYWHMmzcvJk6cGHl5eU32OeOMM+Izn/lMDBo0KI455pj49re/HSeddFKsXLmyJacCAAAA7U7nTBcAADS/HTt2RGVlZVx22WUN2ktLS2PVqlXN8hnpdDr++7//O/70pz/Ftdde2yxjtiaFl/020yXst/W5F2a6hP1X1D/TFQAAAGSEcB4A2qFNmzbFrl27Ij8/v0F7fn5+1NXVHdDYb775ZvTt2ze2b98e2dnZMX/+/DjrrLMOaEwAAADoaITzANCOZWVlNThPp9ON2vbXYYcdFtXV1bF169Z45JFHYvr06XH00UfHGWeccUDjAgAAQEcinAeAdqhnz56RnZ3daJf8xo0bG+2m31+dOnWKY489NiIihg4dGuvWrYurr75aOA8AAAD7wRfCAkA7lJOTEyUlJVFRUdGgvaKiIkaNGtWsn5VOp2P79u3NOiYAAAC0d8J5gHZg/vz5UVRUFLm5uVFSUhIrVqzYY99UKhUXXnhhDBgwIDp16hTTpk1r1OenP/1pjB49Og4//PA4/PDD48wzz4wnn3yyBWdAS5g+fXr87Gc/izvuuCPWrVsXl156adTW1saUKVMiImLGjBkxceLEBvdUV1fXv7Lmtddei+rq6qipqam/fvXVV0dFRUW88MIL8dxzz8WcOXPizjvvjC996UuJzg0AAADaOq+1AWjjFi9eHNOmTYv58+fHqaeeGrfddluMHTs2ampqon///o36b9++PXr16hUzZ86MG2+8sckxly9fHhdccEGMGjUqcnNz47rrrovS0tJ49tlno2/fvi09JZrJ+PHjY/PmzTF79uxIpVJRXFwcS5cujYKCgoj4xx9qamtrG9wzbNiw+p8rKyvjnnvuiYKCgli/fn1ERLz99ttRVlYWf/vb3+Lggw+OgQMHxi9+8YsYP358YvMCAACA9kA4D9DGzZkzJyZNmhSTJ0+OiIi5c+fGQw89FAsWLIirr766Uf/CwsKYN29eRETccccdTY559913Nzj/6U9/Gr/61a/ikUceabTTmtatrKwsysrKmry2aNGiRm3pdHqv4/3oRz+KH/3oR81RGgAAAHRoXmsD0Ibt2LEjKisro7S0tEF7aWlprFq1qtk+Z9u2bfHuu+9Gjx49mm1MAAAAgI5MOA/Qhm3atCl27doV+fn5Ddrz8/Ojrq6u2T7nsssui759+8aZZ57ZbGMCAAAAdGReawPQDmRlZTU4T6fTjdo+qOuuuy7uvffeWL58eeTm5jbLmAAAAAAdnXAeoA3r2bNnZGdnN9olv3Hjxka76T+I66+/Pn7yk5/E7373uzjppJMOeDwAAAAA/sFrbQDasJycnCgpKYmKiooG7RUVFTFq1KgDGvvf//3f44c//GEsW7Yshg8ffkBjAQAAANCQcJ5mM3/+/CgqKorc3NwoKSmJFStW7LFvKpWKCy+8MAYMGBCdOnWKadOmNerz7LPPxuc+97koLCyMrKysmDt3bssVD23Y9OnT42c/+1nccccdsW7durj00kujtrY2pkyZEhERM2bMiIkTJza4p7q6Oqqrq2Pr1q3x2muvRXV1ddTU1NRfv+666+KKK66IO+64IwoLC6Ouri7q6upi69atic4NAAAAoL3yWhuaxeLFi2PatGkxf/78OPXUU+O2226LsWPHRk1NTfTv379R/+3bt0evXr1i5syZceONNzY55rZt2+Loo4+O8847Ly699NKWngK0WePHj4/NmzfH7NmzI5VKRXFxcSxdujQKCgoi4h9/DKutrW1wz7Bhw+p/rqysjHvuuScKCgpi/fr1EfGPP7bt2LEjPv/5zze478orr4xZs2a16HzYN+sGDsp0Cftt0HPrMl0CAAAAtBrCeZrFnDlzYtKkSTF58uSIiJg7d2489NBDsWDBgrj66qsb9S8sLIx58+ZFRMQdd9zR5JgjRoyIESNGRETEZZdd1kKVQ/tQVlYWZWVlTV5btGhRo7Z0Or3X8d4L6QEAAABoGV5rwwHbsWNHVFZWRmlpaYP20tLSWLVqVYaqAgAAAABovYTzHLBNmzbFrl27Ij8/v0F7fn5+1NXVZagqAAAAAIDWSzhPs8nKympwnk6nG7UBAAAAACCcpxn07NkzsrOzG+2S37hxY6Pd9AAAAAAACOdpBjk5OVFSUhIVFRUN2isqKmLUqFEZqgoAAAAAoPXqnOkCaB+mT58eEyZMiOHDh8fIkSPj9ttvj9ra2pgyZUpERMyYMSM2bNgQd955Z/091dXVERGxdevWeO2116K6ujpycnJi8ODBEfGPL5qtqamp/3nDhg1RXV0dXbt2jWOPPTbZCUKGFF7220yXsN/WX3N2pksAAAAAaPXsnKdZjB8/PubOnRuzZ8+OoUOHxuOPPx5Lly6NgoKCiIhIpVJRW1vb4J5hw4bFsGHDorKyMu65554YNmxYjBs3rv76K6+8Ut8nlUrF9ddfH8OGDYvJkycnOrfWbv78+VFUVBS5ublRUlISK1as2GPfVCoVF154YQwYMCA6deoU06ZNS65QaIesPwAAAOCDEs7TbMrKymL9+vWxffv2qKysjNNOO63+2qJFi2L58uUN+qfT6UbH+vXr668XFhY22edfx+nIFi9eHNOmTYuZM2dGVVVVjB49OsaOHdvoDyHv2b59e/Tq1StmzpwZQ4YMSbhaaF+sPwAAAOBACOehDZszZ05MmjQpJk+eHIMGDYq5c+dGv379YsGCBU32LywsjHnz5sXEiRMjLy8v4WqhfbH+AAAAgAMhnIc2aseOHVFZWRmlpaUN2ktLS2PVqlUZqgo6BusPAAAAOFDCeWijNm3aFLt27Yr8/PwG7fn5+VFXV5ehqqBjsP4AAACAAyWchzYuKyurwXk6nW7UBrQM6w8AAAD4oITz0Eb17NkzsrOzG+3S3bhxY6PdvEDzsv4AAACAAyWchzYqJycnSkpKoqKiokF7RUVFjBo1KkNVQcdg/QEAAAAHqnOmC6D1K7zst5kuYb+tv+bsTJeQiOnTp8eECRNi+PDhMXLkyLj99tujtrY2pkyZEhERM2bMiA0bNsSdd95Zf091dXVERGzdujVee+21qK6ujpycnBg8eHAmpgBtlvUHAAAAHAjhPLRh48ePj82bN8fs2bMjlUpFcXFxLF26NAoKCiIiIpVKRW1tbYN7hg0bVv9zZWVl3HPPPVFQUBDr169PsnRo86w/AAAA4EB4rQ20cWVlZbF+/frYvn17VFZWxmmnnVZ/bdGiRbF8+fIG/dPpdKNDMEgmzJ8/P4qKiiI3NzdKSkpixYoVe+3/2GOPRUlJSeTm5sbRRx8dt956a4Pr7777bsyePTuOOeaYyM3NjSFDhsSyZctacgrWHwAAAPCBdYhw/jOf+Uwcfvjh8fnPfz7TpQAQEYsXL45p06bFzJkzo6qqKkaPHh1jx45ttNP8PS+++GKMGzcuRo8eHVVVVXH55ZfHJZdcEkuWLKnvc8UVV8Rtt90WN998c9TU1MSUKVPiM5/5TFRVVSU1LQAAAIB91iHC+UsuuaTBO38ByKw5c+bEpEmTYvLkyTFo0KCYO3du9OvXLxYsWNBk/1tvvTX69+8fc+fOjUGDBsXkyZPjq1/9alx//fX1fe666664/PLLY9y4cXH00UfHN7/5zRgzZkzccMMNSU0LAAAAYJ91iHD+Yx/7WBx22GGZLgOAiNixY0dUVlZGaWlpg/bS0tJYtWpVk/esXr26Uf8xY8bE2rVr4913342IiO3bt0dubm6DPgcffHCsXLmyGasHAAAAaB4ZD+cff/zxOOecc6JPnz6RlZUVDz74YKM++/teYgBar02bNsWuXbsiPz+/QXt+fn7U1dU1eU9dXV2T/Xfu3BmbNm2KiH+E9XPmzInnn38+du/eHRUVFfFf//VfkUqlWmYiAAAAAAcg4+H822+/HUOGDIlbbrmlyev78l7ikpKSKC4ubnS88sor+1XL9u3bY8uWLQ0OAFpGVlZWg/N0Ot2o7f36/3P7vHnz4rjjjouBAwdGTk5OXHzxxXHRRRdFdnZ2M1cOAAAAcOA6Z7qAsWPHxtixY/d4/Z/fSxwRMXfu3HjooYdiwYIFcfXVV0dERGVlZbPUcvXVV8dVV13VLGPB/jrx5ydmuoT99vSXn850CbRBPXv2jOzs7Ea75Ddu3Nhod/x7evfu3WT/zp07xxFHHBEREb169YoHH3ww3nnnndi8eXP06dMnLrvssigqKnrfmtri+vtlpgsAAAAADkjGd87vzQd5L/GBmDFjRrz55pv1x8svv9zsnwHQ0eXk5ERJSUlUVFQ0aK+oqIhRo0Y1ec/IkSMb9X/44Ydj+PDhcdBBBzVoz83Njb59+8bOnTtjyZIl8elPf7p5JwAAAADQDDK+c35vPsh7iZsyZsyYeOqpp+Ltt9+Oo446Kh544IEYMWJEo35dunSJLl26HHDdAOzd9OnTY8KECTF8+PAYOXJk3H777VFbWxtTpkyJiH/8sXTDhg1x5513RkTElClT4pZbbonp06fH1772tVi9enUsXLgw7r333voxn3jiidiwYUMMHTo0NmzYELNmzYrdu3fH97///YzMEQAAAGBvWnU4/579fS/xv3rooYeauyQADsD48eNj8+bNMXv27EilUlFcXBxLly6NgoKCiIhIpVINvlukqKgoli5dGpdeemmUl5dHnz594qabborPfe5z9X3eeeeduOKKK+KFF16Irl27xrhx4+Kuu+6K7t27Jz09AAAAgPfVqsP5D/JeYgDahrKysigrK2vy2qJFixq1nX766fHUU0/tcbzTTz89ampqmqs8AAAAgBbVqt85/0HeSwwAAAAAAK1dxnfOb926Nf7yl7/Un7/44otRXV0dPXr0iP79+7/ve4kBAAAAAKCtyXg4v3bt2vjYxz5Wfz59+vSIiPjyl78cixYtet/3EgMAAAAAQFuT8XD+jDPOiHQ6vdc+e3svMQAAAAAAtDWt+p3zAAAAAADQHmV853xrVF5eHuXl5bFr165MlwLQ9szKy3QF+6+of6YrAAAAADoYO+ebMHXq1KipqYk1a9ZkuhQAAAAAANoh4TwAAAAAACRMOA8AAAAAAAkTzgMAAAAAQMKE8wAAAAAAkDDhPAAAAAAAJEw4T4c2f/78KCoqitzc3CgpKYkVK1bstf9jjz0WJSUlkZubG0cffXTceuutDa6fccYZkZWV1eg4++yzW3IarZ7nDAAAAAANCefpsBYvXhzTpk2LmTNnRlVVVYwePTrGjh0btbW1TfZ/8cUXY9y4cTF69OioqqqKyy+/PC655JJYsmRJfZ/7778/UqlU/fHMM89EdnZ2nHfeeUlNq9XxnAEAAACgMeE8HdacOXNi0qRJMXny5Bg0aFDMnTs3+vXrFwsWLGiy/6233hr9+/ePuXPnxqBBg2Ly5Mnx1a9+Na6//vr6Pj169IjevXvXHxUVFXHIIYd06NDYcwYAAACAxoTzdEg7duyIysrKKC0tbdBeWloaq1atavKe1atXN+o/ZsyYWLt2bbz77rtN3rNw4cI4//zz49BDD22ewtsYzxkAAAAAmiacb0J5eXkMHjw4RowYkelSaCGbNm2KXbt2RX5+foP2/Pz8qKura/Keurq6Jvvv3LkzNm3a1Kj/k08+Gc8880xMnjy5+QpvYzxnAAAAAGiacL4JU6dOjZqamlizZk2mS6GFZWVlNThPp9ON2t6vf1PtEf/YzV1cXBwnn3xyM1TatnnOAAAAANCQcJ4OqWfPnpGdnd1o9/bGjRsb7dp+T+/evZvs37lz5zjiiCMatG/bti3uu+++Dr+b23MGAAAAgKYJ5+mQcnJyoqSkJCoqKhq0V1RUxKhRo5q8Z+TIkY36P/zwwzF8+PA46KCDGrT/8pe/jO3bt8eXvvSl5i28jfGcAQAAAKBpwnk6rOnTp8fPfvazuOOOO2LdunVx6aWXRm1tbUyZMiUiImbMmBETJ06s7z9lypR46aWXYvr06bFu3bq44447YuHChfHd73630dgLFy6Mc889t9FO747IcwYAAACAxjpnugDIlPHjx8fmzZtj9uzZkUqlori4OJYuXRoFBQUREZFKpaK2tra+f1FRUSxdujQuvfTSKC8vjz59+sRNN90Un/vc5xqM++c//zlWrlwZDz/8cKLzaa08ZwAAAABoTDhPh1ZWVhZlZWVNXlu0aFGjttNPPz2eeuqpvY55/PHH13+BKf/gOQMAAABAQ15rAwAAAAAACRPOAwAAAABAwoTzAAAAAACQMOE8AAAAAAAkTDgPAAAAAAAJE84DAAAAAEDCOme6gNaovLw8ysvLY9euXZkuhQ9qVl6mK9h/Rf0zXcF+WzdwUKZL2G+DnluX6RIAAAAAwM75pkydOjVqampizZo1mS4FAAAAAIB2SDgPAAAAAAAJE84DAAAAAEDChPMAAAAAAJAw4TwAAAAAACRMOA8AAAAAAAkTzgMAAAAAQMKE8wAAAAAAkDDhPAAAAAAAJEw4DwAAAAAACRPOAwAAAABAwoTzAAAAAACQMOE8AAAAAAAkTDjfhPLy8hg8eHCMGDEi06UAAAAAANAOCeebMHXq1KipqYk1a9ZkuhQAAAAAANoh4TwAAAAAACRMOA8AAAAAAAkTzgMAAAAAQMKE8wAAAAAAkDDhPAAAAAAAJEw4DwAAAAAACRPOAwAAAABAwoTzAAAAAACQMOE8AAAAAAAkTDgPAAAAAAAJE84DAAAAAEDChPMAAAAAAJAw4TwAAAAAACRMOA8AAAAAAAkTzgMAAAAAQMKE8wAAAAAAkDDhfBPKy8tj8ODBMWLEiEyXAgAAAABAOyScb8LUqVOjpqYm1qxZk+lSAAAAAABoh4TzAAAAAACQMOE8AAAAAAAkTDgPAAAAAAAJE84DAAAAAEDChPMAAAAAAJAw4TwAAAAAACRMOA8AAAAAAAkTzgMAAAAAQMI6Z7qA1iydTkdExJYtWzJcSWbt3r4t0yXsty1Z6UyXsN92/b9dmS5hv23d1fZqbmvr2fpLhvWXDOuv5Vl/ybD+Wp71lwzrLxnWX8uz/pJh/bU86y8Z1l8y2tr6a27vzf+9fHlPstLv16MD+9vf/hb9+vXLdBkAAAAAALQxL7/8chx11FF7vC6c34vdu3fHK6+8EocddlhkZWVluhxagS1btkS/fv3i5Zdfjm7dumW6HOhQrD/IHOsPMsf6g8yx/iBzrD/aunQ6HW+99Vb06dMnOnXa85vlvdZmLzp16rTXv2zQcXXr1s1/HCBDrD/IHOsPMsf6g8yx/iBzrD/asry8vPft4wthAQAAAAAgYcJ5AAAAAABImHAe9kOXLl3iyiuvjC5dumS6FOhwrD/IHOsPMsf6g8yx/iBzrD86Cl8ICwAAAAAACbNzHgAAAAAAEiacBwAAAACAhAnnAQAAAAAgYcJ5AAAAAABImHCeDu/xxx+Pc845J/r06RNZWVnx4IMPNrieTqdj1qxZ0adPnzj44IPjjDPOiGeffbZBn+3bt8e3vvWt6NmzZxx66KHxqU99Kv72t78lOAto/ZJaa6+//npMmDAh8vLyIi8vLyZMmBBvvPFGC88OWpfWtN5qa2vjnHPOiUMPPTR69uwZl1xySezYsaMlpg0Z0dbW29NPPx2nn356HHzwwdG3b9+YPXt2pNPpZnse0FLa41p77LHHoqSkJHJzc+Poo4+OW2+99cAeEjSTjrrelixZEoMHD44uXbrE4MGD44EHHtjPJwf7TzhPh/f222/HkCFD4pZbbmny+nXXXRdz5syJW265JdasWRO9e/eOs846K9566636PtOmTYsHHngg7rvvvli5cmVs3bo1PvnJT8auXbuSmga0ekmttQsvvDCqq6tj2bJlsWzZsqiuro4JEya0+PygNWkt623Xrl1x9tlnx9tvvx0rV66M++67L5YsWRLf+c53Wm7ykLC2tN62bNkSZ511VvTp0yfWrFkTN998c1x//fUxZ86cFngy0Lza21p78cUXY9y4cTF69OioqqqKyy+/PC655JJYsmRJcz42+EA64npbvXp1jB8/PiZMmBD/9//+35gwYUJ84QtfiCeeeKJZninsURqoFxHpBx54oP589+7d6d69e6evueaa+rZ33nknnZeXl7711lvT6XQ6/cYbb6QPOuig9H333VffZ8OGDelOnTqlly1blljt0Ja01FqrqalJR0T6D3/4Q32f1atXpyMi/dxzz7XwrKB1yuR6W7p0abpTp07pDRs21Pe599570126dEm/+eabLTJfyKTWvt7mz5+fzsvLS7/zzjv1fa6++up0nz590rt3727GJwEtqz2ste9///vpgQMHNpjXN77xjfQpp5xyQM8GmltHWW9f+MIX0v/2b//WoM+YMWPS559//n48Ldh/ds7DXrz44otRV1cXpaWl9W1dunSJ008/PVatWhUREZWVlfHuu+826NOnT58oLi6u7wPsXXOttdWrV0deXl585CMfqe9zyimnRF5envUI/yPJ9bZ69eooLi6OPn361PcZM2ZMbN++PSorK1t0ntAatLb1tnr16jj99NOjS5cuDfq88sorsX79+uZ/AJCQtrjWVq9e3aCW9/qsXbs23n333eZ4LNAi2ut621Mf/46kpQnnYS/q6uoiIiI/P79Be35+fv21urq6yMnJicMPP3yPfYC9a661VldXFx/60Icajf+hD33IeoT/keR6q6ura/Q5hx9+eOTk5FiTdAitbb011ee9c2uStqwtrrU99dm5c2ds2rRpH2cOyWuv621Pffz3kZYmnId9kJWV1eA8nU43avtX+9IHaKg51lpT/a1HaCyp9WZNQutab03Vsqd7oa1pa2vNeqQta4/r7YPMCQ6UcB72onfv3hHReCfRxo0b6/+i2rt379ixY0e8/vrre+wD7F1zrbXevXvHq6++2mj81157zXqE/5Hkeuvdu3ejz3n99dfj3XfftSbpEFrbemuqz8aNGyOi8Q5IaEva4lrbU5/OnTvHEUccsY8zh+S11/W2pz7++0hLE87DXhQVFUXv3r2joqKivm3Hjh3x2GOPxahRoyIioqSkJA466KAGfVKpVDzzzDP1fYC9a661NnLkyHjzzTfjySefrO/zxBNPxJtvvmk9wv9Icr2NHDkynnnmmUilUvV9Hn744ejSpUuUlJS06DyhNWht623kyJHx+OOPx44dOxr06dOnTxQWFjb/A4CEtMW1NnLkyAa1vNdn+PDhcdBBBzXHY4EW0V7X2576+HckLS6hL56FVuutt95KV1VVpauqqtIRkZ4zZ066qqoq/dJLL6XT6XT6mmuuSefl5aXvv//+9NNPP52+4IIL0kceeWR6y5Yt9WNMmTIlfdRRR6V/97vfpZ966qn0xz/+8fSQIUPSO3fuzNS0oNVJaq3927/9W/qkk05Kr169Or169er0iSeemP7kJz+Z+Hwhk1rLetu5c2e6uLg4/YlPfCL91FNPpX/3u9+ljzrqqPTFF1+c3MOAFtaW1tsbb7yRzs/PT19wwQXpp59+On3//fenu3Xrlr7++usTeFJwYNrbWnvhhRfShxxySPrSSy9N19TUpBcuXJg+6KCD0r/61a9a8jHCPumI6+33v/99Ojs7O33NNdek161bl77mmmvSnTt3Tv/hD39okWcM7xHO0+E9+uij6YhodHz5y19Op9Pp9O7du9NXXnllunfv3ukuXbqkTzvttPTTTz/dYIz/9//+X/riiy9O9+jRI33wwQenP/nJT6Zra2szMBtovZJaa5s3b05/8YtfTB922GHpww47LP3FL34x/frrryc0S2gdWtN6e+mll9Jnn312+uCDD0736NEjffHFF6ffeeedlpw+JKqtrbc//vGP6dGjR6e7dOmS7t27d3rWrFnp3bt3N/tzgebWHtfa8uXL08OGDUvn5OSkCwsL0wsWLGiehwUHqKOut//8z/9MDxgwIH3QQQelBw4cmF6yZMkHfIKw77LS6f/5BgQAAAAAACAR3jkPAAAAAAAJE84DAAAAAEDChPMAAAAAAJAw4TwAAAAAACRMOA8AAAAAAAkTzgMAAAAAQMKE8wAAAAAAkDDhPAAAAAAAJEw4DwAA7dRzzz0Xp5xySuTm5sbQoUMzXc5+KSwsjLlz52a6DAAAaDHCeQAAyLDXXnstDjrooNi2bVvs3LkzDj300KitrT3gca+88so49NBD409/+lM88sgjTfb5yle+Eueee279+RlnnBHTpk074M/eV4sWLYru3bs3al+zZk18/etfT6wOAABIWudMFwAAAB3d6tWrY+jQoXHIIYfEE088ET169Ij+/fsf8Lh//etf4+yzz46CgoJmqHL/7NixI3Jycj7w/b169WrGagAAoPWxcx4AADJs1apVceqpp0ZExMqVK+t/3pvdu3fH7Nmz46ijjoouXbrE0KFDY9myZfXXs7KyorKyMmbPnh1ZWVkxa9as9x3zK1/5Sjz22GMxb968yMrKiqysrFi/fn1ERNTU1MS4ceOia9eukZ+fHxMmTIhNmzbV33vGGWfExRdfHNOnT4+ePXvGWWedFRERc+bMiRNPPDEOPfTQ6NevX5SVlcXWrVsjImL58uVx0UUXxZtvvln/ee/V+a+vtamtrY1Pf/rT0bVr1+jWrVt84QtfiFdffbX++qxZs2Lo0KFx1113RWFhYeTl5cX5558fb731Vn2fX/3qV3HiiSfGwQcfHEcccUSceeaZ8fbbb7/vcwEAgJYgnAcAgAyora2N7t27R/fu3WPOnDlx2223Rffu3ePyyy+PBx98MLp37x5lZWV7vH/evHlxww03xPXXXx9//OMfY8yYMfGpT30qnn/++YiISKVSccIJJ8R3vvOdSKVS8d3vfvd9a5o3b16MHDkyvva1r0UqlYpUKhX9+vWLVCoVp59+egwdOjTWrl0by5Yti1dffTW+8IUvNLj/5z//eXTu3Dl+//vfx2233RYREZ06dYqbbropnnnmmfj5z38e//3f/x3f//73IyJi1KhRMXfu3OjWrVv95zVVZzqdjnPPPTf+/ve/x2OPPRYVFRXx17/+NcaPH9+g31//+td48MEH4ze/+U385je/icceeyyuueaa+udxwQUXxFe/+tVYt25dLF++PD772c9GOp1+3+cCAAAtwWttAAAgA/r06RPV1dWxZcuWGD58ePzhD3+Irl27xtChQ+O3v/1t9O/fP7p27brH+6+//vr4X//rf8X5558fERHXXnttPProozF37twoLy+P3r17R+fOnaNr167Ru3fvfaopLy8vcnJy4pBDDmlwz4IFC+LDH/5w/OQnP6lvu+OOO6Jfv37x5z//OY4//viIiDj22GPjuuuuazDmP7+/vqioKH74wx/GN7/5zZg/f37k5OREXl5eZGVl7bXG3/3ud/HHP/4xXnzxxejXr19ERNx1111xwgknxJo1a2LEiBER8Y//m2DRokVx2GGHRUTEhAkT4pFHHokf//jHkUqlYufOnfHZz362/jU/J5544j49FwAAaAl2zgMAQAZ07tw5CgsL47nnnosRI0bEkCFDoq6uLvLz8+O0006LwsLC6NmzZ5P3btmyJV555ZVGr7859dRTY926dc1ea2VlZTz66KPRtWvX+mPgwIER8Y/d6u8ZPnx4o3sfffTROOuss6Jv375x2GGHxcSJE2Pz5s379TqZdevWRb9+/eqD+YiIwYMHR/fu3RvMt7CwsD6Yj4g48sgjY+PGjRERMWTIkPjEJz4RJ554Ypx33nnx05/+NF5//fV9fwgAANDM7JwHAIAMOOGEE+Kll16Kd999N3bv3h1du3aNnTt3xs6dO6Nr165RUFAQzz777F7HyMrKanCeTqcbtTWH3bt3xznnnBPXXntto2tHHnlk/c+HHnpog2svvfRSjBs3LqZMmRI//OEPo0ePHrFy5cqYNGlSvPvuu/v8+Xua17+2H3TQQQ2uZ2Vlxe7duyMiIjs7OyoqKmLVqlXx8MMPx8033xwzZ86MJ554IoqKiva5FgAAaC52zgMAQAYsXbo0qquro3fv3vGLX/wiqquro7i4OObOnRvV1dWxdOnSPd7brVu36NOnT6xcubJB+6pVq2LQoEEHVFdOTk7s2rWrQduHP/zhePbZZ6OwsDCOPfbYBse/BvL/bO3atbFz58644YYb4pRTTonjjz8+Xnnllff9vH81ePDgqK2tjZdffrm+raamJt588839mm9WVlaceuqpcdVVV0VVVVXk5OTEAw88sM/3AwBAcxLOAwBABhQUFETXrl3j1VdfjU9/+tPRv3//qKmpic9+9rNx7LHH1r8XfU++973vxbXXXhuLFy+OP/3pT3HZZZdFdXV1fPvb3z6gugoLC+OJJ56I9evXx6ZNm2L37t0xderU+Pvf/x4XXHBBPPnkk/HCCy/Eww8/HF/96lf3Gqwfc8wxsXPnzrj55pvjhRdeiLvuuituvfXWRp+3devWeOSRR2LTpk2xbdu2RuOceeaZcdJJJ8UXv/jFeOqpp+LJJ5+MiRMnxumnn97kq3Sa8sQTT8RPfvKTWLt2bdTW1sb9998fr7322gH/MQMAAD4o4TwAAGTI8uXLY8SIEZGbmxtPPPFE9O3bN/r06bNP915yySXxne98J77zne/EiSeeGMuWLYtf//rXcdxxxx1QTd/97ncjOzs7Bg8eHL169Yra2tro06dP/P73v49du3bFmDFjori4OL797W9HXl5edOq0539SDB06NObMmRPXXnttFBcXx9133x1XX311gz6jRo2KKVOmxPjx46NXr16NvlA24h873h988ME4/PDD47TTToszzzwzjj766Fi8ePE+z6tbt27x+OOPx7hx4+L444+PK664Im644YYYO3bsvj8cAABoRlnpdDqd6SIAAAAAAKAjsXMeAAAAAAASJpwHAAAAAICECecBAAAAACBhwnkAAAAAAEiYcB4AAAAAABImnAcAAAAAgIQJ5wEAAAAAIGHCeQAAAAAASJhwHgAAAAAAEiacBwAAAACAhAnnAQAAAAAgYf8fIMBRZO342BwAAAAASUVORK5CYII=", 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" ] @@ -88,7 +73,7 @@ "}\n", "\n", "x = np.arange(len(runs)) # the label locations\n", - "width = 0.25 # the width of the bars\n", + "width = 0.20 # the width of the bars\n", "multiplier = 0\n", "\n", "fig, ax = plt.subplots(layout=\"constrained\", figsize=(15, 10))\n", @@ -127,12 +112,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -163,12 +148,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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