From 3aa9cd0524980f097bdcba5473ffe8b254c15283 Mon Sep 17 00:00:00 2001 From: "David W.H. Swenson" Date: Fri, 31 Mar 2023 18:28:03 -0400 Subject: [PATCH 1/2] Update ligand_networks_for_developers - [x] Removed a bunch of debug stuff (which caused problems on GitHub for some browsers). - [x] Added something about writing ligand networks as GraphML. The reason to write to GraphML is that right now, that's the best way to use the visualization tool (via the horrible incantation `python -m openfe.utils.atommapping_network_plotting network.graphml` -- we should probably put that somewhere more easily accessible.) --- networks/ligand_networks_for_developers.ipynb | 818 ++---------------- 1 file changed, 54 insertions(+), 764 deletions(-) diff --git a/networks/ligand_networks_for_developers.ipynb b/networks/ligand_networks_for_developers.ipynb index c022ed8..067d609 100644 --- a/networks/ligand_networks_for_developers.ipynb +++ b/networks/ligand_networks_for_developers.ipynb @@ -2,14 +2,19 @@ "cells": [ { "cell_type": "markdown", - "id": "bfa59498", + "id": "85baea30", "metadata": {}, "source": [ "# Creating custom tools to generate alchemical perturbation networks\n", "\n", - "OpenFE has built-in atom mappers and network planners to handle common use cases. However, it is relatively easy to create new mappers and planners. This document is aimed at people who are interested in customizing these tools.\n", - "\n", - "\n", + "OpenFE has built-in atom mappers and network planners to handle common use cases. However, it is relatively easy to create new mappers and planners. This document is aimed at people who are interested in customizing these tools." + ] + }, + { + "cell_type": "markdown", + "id": "bfa59498", + "metadata": {}, + "source": [ "## Overview of network creation" ] }, @@ -26,15 +31,7 @@ "execution_count": 1, "id": "3bc50f7a", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:root:Warning: importing 'simtk.openmm' is deprecated. Import 'openmm' instead.\n" - ] - } - ], + "outputs": [], "source": [ "from openfe import SmallMoleculeComponent" ] @@ -47,7 +44,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "" ] @@ -202,7 +199,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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t9qICw85tJU/9K2fSyOqffxC6Uv+Vmpp6++2333DDDY8//nhFRYXQ5bSjpj1Ci8Vy+vTp4uJi4YqCtkIQNsNqtU6ZMmXy5MmHDh1KTk6ePXu2UJW8vydv5hsn/vqK8XDmwH//tuOk1gclNaXT6e67776n5t75deegWCmzLKfU/bk8EZW9+mL5B68T7ynX25W9pDj/rsnl727gagxcTbWLM3ieq6l26LTa55YXLbqPM9b4vEZ/l5ubO2PGjLlz527cuDEvL++hhx4SuqJ21CgIjxw5kpSU9OCDDw4YMGDJkiWClgZXr72C8OLFi+np6TU1rt819Hp9enp6Xl5eO313L9q5c2dQUNDixYs7der04osvpqWlnT171vdlPP1VxtLPLpitXEtONpjtd7xx8r/7L7d3VU3FxMT8uXlj94/+E1xdNSc65M8ai/uIY4iIt1krN3+gXblEkCy05efm3zXJdulibkVVttlm4lzXUOXgss22wsoq07HU/LuncYYqH9fp5xITE9PS0qZNm9arV69ly5bt27dP6IraUaOh0SVLlixZsuTIkSOnT5/evHnzsWPHBK0OrlJ7BeHo0aN79eq1d+9el49+/vnnvXr1uv3229vpu3tRZmbmwIEDnccqlapHjx6ZmZk+ruG93blv/HypxtLMMGN9RqvjX5+c+/mMhw5Zu7Ckp1U88zhvNhFRrsWWoJB66roSERFnMtbs+7nsrfU+KK/B9zVUFc6fxVVV8g77jPTCv50r+KPa7PLMLWXVfztXsDinlLda7AWXCxfO4x2t+F2IQWRkpPOgsLAwPj5e2GLaVf0eYXFxcWpq6oIFC4hIo9FMnTp169atAtf3F6PVUVFjs9hcf3Tev3//Bx98oNfrt2/f/s033/i4Nj+EyTKu1Vgc2VojERXoKu0kdR4TESNVZBeW133pA9la05LPzpta1hesz2hxzHztxL6VI8LVvvot26z8ogfIbCYiC8e/XqR/sFNYS57HmYyVX2xS9Oqr6NO/nUu8ovTlVQ59Oc+17gfL26zWixkVm96JnL+onQrruKxW67p16xYtatFP5syZMwzj4mNSbq7wl43dMZvNlZWVcrk8PDyciAoKCsLDw8PCav/IExMTL168KGB51WbHV4cLNx8sOJFTZbZxEobsHB+mlo5MiZg3uvOtQzvJpbU9n9LS0uzsbOfU3MuXBRg68jcIQtd2ny3tvngfEVFWFeVd2OY8JqLzOSe2lDxxaJ+b5/kXg9k+ZMVvPvt288t+eaxMr+J5E8f/65J2UJBiZlRwC5/LW8wl/360XcvzFt5sqtz0XtiMuySR0ULX4kcsFsudd945YMCA++67ryXnL1y4sNlzdDpdVlaWRqPp1KmTWq1uc41t5RwXtUpD1fftio9QhprzDCbrQx+djQtXRATJzuRVmmq4tPzqhEhlmM8+fRIRkd3Br/8he+33WcRTtcVR20hEROXVth2ntAculD2ySbJhTq97xiQQ0W233Xbbbbf5skI/17rfltFoLC8vj4uLk0gk7VSQ3+k0gE58QGY9KcOp4iJZqii6p9A1+SMJOf5Z/rOKt+RYbAuzdRPCVI/HRzQ7LtpB8RxX+cWmyIXLGrVbrVatVhsbGyuXywUpTCh5eXkzZ868/vrrN2zY4LKf19TUqVNd3sd26tSpulsMd+7cee+99zqPlUplREREfHx8XFxc/YO6Y41Gw7bzLS7OICRlmNnGZWuNZFGQxfjBztOkDCci+v0EhSbsXH6AiJQyNiJIFh+hdGZkvQNFXLgyIkiqCVewLftBNaugwnzLy8cuaY01f0VgUwazg8ix8ONznx8q/ObxwSFKdIEaaMWPY+XKle+//358fLxOp/v4449vuumm9ivLj4TEU7cJ9Msy0lxL+anU/y6SqoSuyR8NM2axxGWYrLMyiiOkbLmdW5VXRkSL4sJjZYH2sYm3Wqq+/7pREH700UerV692XkVes2bN/fffL1R5Pnb69OkJEyZER0crlcoVK1YQ0dNPPx0aGur5WU888cTo0aObtq9bt64uCIODg4cPH15UVKTT6UwmU1FRUVFRkbsXlMvlMTExnTp10mg0MTExsbGxGo0mNjbWeeBsaeMneOcFQlJG1H6tCCHNtXR+C117P1XlU+ExGjDH+YjZxhXpLUV6tzcyKWRsZJDMVUY6GxVdo1RSSfNJmVtqGrby9/Jqm73RVC+bkaw1pAghqbKurcZiP3ChYsSqQ4dWX+/jPqufa+nPori4uLi4OD8/Xy6Xf/TRR0uXLj19+nS7VuZHrltIujQyFFHKLRSRLHQ1fmpUzQW1wxoulaztGlW/Xc0GZreQMxjsJcXSTrV9GrvdvmjRov379w8dOvTYsWNjx46dN2+eVCqK9xq1Wv3iiy/Wb/HWiFH9ETyTyVRRUVFUVFRYWOjyoKSkpKCgoKCgwMMLKpXKpn3KuoOEhIS6C34u/RWE9c4Z+k/6bR1tmUOcnQbOo5DOLfxfs/yVlOcKXN2046xWxrrvU8q6RCklLDP2hcNl1TZHXQqWX6Tz31LxCbIYalvU0RR/HfWZScEaIjLbHNla45QNf+x7Zri3uqQBoKX/UDUazYcfflj3pUrVol7R6dOnXY7sZ2Rk1B07HA6TydT0HKGYzWYiIt5B9npVRSTXRqDdj0olu3OWI+8PVV1TnW3h7MES5sawxn8bRo4jIo7nicjM88ZWzk9pV8p6o2nnTVapq7eGHIutaSMjl1mzM+qCkGEYlmUzMjKGDh1qNBrVanULRwgDQEpKSkpKSnt/F5VKpVKp4uPjhwwZ4vIEi8VSVlbmISx1Op3ZbM7Ozs7Oznb3XdwNwDoPsrKyiOr1CIkoJJ7+/ibZjCRVEOPlkQ/nAKyHqXkSluF44utuPTr7BZ3+jIgnVkZR15BMTZZK0udQ1o+UvZtGPE5JY50ve+JS1Ws/5iyZ1M27BXdcrfvE+tVXX23ZsuXkyZNbtmxpyfnOcRLPtm3bNnPmzFaV0d5YluUKjtH/86+q3LLW+EOpC1pwDkN02wW3Q1uC2N47vreq9nre/xW0YkkU3sFz+vK6LyUSyYcffjhnzpxPP/00Kytrx44dIrqO7h8UCkV8fHx8fHzfvn3dnVNRUeGMRpd5efnyZYPB4HkAlogocycVHCVVJKkiSR5MqihSRfx1EEnKMK8nojuO+sOhmTvo9KdEDPWZSX1nkeyvHoipnI6/T3m/UeorpAwnzSAiqrHYn/02474bEyKCZL4p1c+1LggHDRqkUqmqqqreeuut9957r9nzJ0+e7HJp8zNnzuzfv7+2AqnUrxa+czgcZrOZGJYk/j/fgSe7hYghqYtV/H1MydkY8tTVs3A8R6RgGb9azah+MRPC1PFyF/8iLpisx5rcYsgwzv9qmc3mV1999bnnnktKSnrrrbcee+yxvXv3KpVKAn/inFzj4QS9Xl9cXKzT6bRarfOgpKSkruXSpUsOh4O3GakyjyrdrwciD67NSOdBo7xUx5LM2/MMLFV08iMiogFzqN+dDR5SRdIN/6aD6+jy73T0DZqykRiWiBwcvb8n799Tu3u5ko6pdUHYs2fPnj17Dhw4sFu3bq+++mqzE5ofeuihyZMnN21/55136oJw2rRp1dVuR8l9b9u2bdOnT6fOw2jMSqFraY7VQN/MJnkQzfxK6FLo3ctvja457+GESecLM0zWLT3jeqr89BPG3JiQ0aEu3qE+1lY1DUJi2Pq3T/zyyy9ardY5BDJ79uzExMRdu3ZNmzatPesF7wsPDw8PD+/Vq5fLRydOnLhnzx4atZzCE8lUQaYyslaTqZxM5VcOzHqyVpPV43uaRN4wIxuFZQipIjwvo9hY9i9kt5A6ivre4ephhoY+TPmHqbqECo5RwnAiMlkdH/x6GUHo1NIg1Gq1//3vf5ctWyaRSM6cORMWFia2CeLCs1SS7hx1Hk7+1aeqdUrVbYQxQ8a7ncAdaKwWecqVG2liY2OrqqpKS0ujo6MrKyuNRmNMTIyA1UF7qL19IrQLhSVSWKLb86zVf2Wkq7A0lpLDWtvijjMpXXYoawdgwxu8DxSfIiJKuN7tqKwqkjr1p+JTVHzSGYREVKg366qsMaF4J29xEKrV6uPHj3ft2jU+Pj43N3fjxo0imRHnF6wGOvUx5R8hcwXdscUfBkKb+i2o133le9oWhHzrPgULio2Mqt8jHD58+MMPPzxw4MB+/fqlpaUtWLDg+uuvF7A8f5abm8vzvLshyscee+yBBx5wuWev4GpnjarCmznPmWEeWAxk0ZO5kswVZK47qCBzJZn1ZNaT3VyblO4GYBkJKcNIEUZjV5M6miouEVEzc9ojkqn4FOlz6hpUMvZsvmFcnyj3zxGLloZZcHDw119/XV5ertfru3TpIpPhEqsPSRSUOIaufYC+9t/VWU8qk82sIphryw5QHSYFGaUqdMZdjRrXrl27atWqwsLCmJiYkJAQQQrrEDz3ldVqtT8sItOUw+EoKysjhiFFM7dINk8RQooQCu3i/ptZyVpNVoOnAVjngfM2QauBiEjhcUVD513/1itLxjt48nCno6i0rlcXGRlZt7ou+I5ETp0Gkt2v/2Q5hnkjetK/tVvVbcrCjoFh2bDb5zZtVyqVycm40zQwlZaWOhwOUvhkUqhEXjso6m4AlrOTpYrMFSQPIuKJ54iomb2jnUOp9W5e4nmyOwTbAc2vYHjTtUmDYj79wC+Wzikot4x49ncPiyd5EBOiOLF2VJDCR5O5ece4qnuOcpdzeL7VdwoyCmXcW58oUny3fF3pK2uqd//IW1zvOOEBq1JHPLqMDW3ReuIQMJwXCFMS4zc+M6K40qKttOgM1iK9RVtp1RmsxXpLSaXFaPXVNXJWWpuUTrIgshqamaHjfLTemK2EpfAgRAARgtAdmYT1kztsIoJkmx4aeO/7fxpbufNPkELy/RNDEiJ9OYNfFvLaxvy5U/hW7l7LqFSR/1yqGjysncpyKWbFWvOZU/aCfN7h4n55dxiFQjloaPgd89qvMPBPzguEXRPibuztdlTMZHVU1NiK9JbCCktFje2vY3PdQUmllWuPrTdD4qksnaryPZ3jvNwYmlDX4OD4Hp386NY1AbVXEKanp3Mc5+4Gwfnz58+ZMwfTbVro9uGaIr35qc9Ot3zzJ7Vc8vnCa0ekNHdV39tkid3i3vqk6NF7OJOxhXvtsip16O13h895oL1ra4RRKDtv/Krgnun2Ut3+vgmc+9Xg5sSE3BYVLGUYRqmS9+it2fA+iWbVGKjTaG96l1RyiUouiY9QDnG/ZktFjc0Zk0V6818HV8LycpnJdhXDlTF9qCydik7QgLtdn+CwUslpIqKY3vVamZ7xCEKi9gtCz5MF5HI57r5olRl9pTQ5/LH/EdUUkzLc81XxmFD5d0uGjOzh6cbh9qMcOKTzJ1uLHrvfXlTQbBYycnnkkmfCZtzp+bR2IomKSfh8R/GTjzBppzij248ZMoaRSRhGpQq5aUr00y8wmCkmSo32pr9qzmW1PZxgsjrq+pRNw7KgwlzZdHQoeSJd2EplGVR8yrl2TGOZO8laTVIVdb3B2SBhaNqQWCw36oQ+Wcfw5JNPZmRkXNN3YEbqq9R5GPWf4+Hksy+Pjg0Vcva5PLlH12/3XBo7iLc0swhqzNNrQ6YIuS8aGxoW/95n1bu2l762lqup5k1GarQUqkTKyuXSzl2in1yjGjJcoDJBeC3pEXqFSi5JjlUnx7qdOmsw24sqLF8eLnrpuyyTcw/68CRKGkc5e+nQehr3HEU0vE0+/wj9+QkRUb9ZJKvtAirkkqWTMLGrFoKwY/j888+J6Mxlw4B/H2z25GA/2GyMkcsZhaLZIJRE+cXetsE3Twn+22TTiSM1v/5kPn7Epi3mrRZWFSSNi1cPuyHopkmKnm6XrwSR8FaPsO1ClNKQOOkzt3b/38GCi9qa2mGXof8kQwGVZdBPi6nrKOo0gOQhZCqjwuNUdJyIqOsN1PvKh84hSWHXJrX5PpBAIfWhEcYAABGcSURBVPw7JoBfYBjVkBGqISOErgP8lM96hC3EMsxXiwaNXnO4dqqqPIgmrKUzn1PGD5R7gHIPXDlVHkL9ZlHPW+tf2+4c4Y9LFggFQQgA0DxnEPpDj7DO4KSwt+/ru3BTWm0WSlV07QPU/y4qPk3VRWQ3kTyYwrpSTF9iG7/Vf3us+HKZuUsU1oUnQhACALSEc2jUf3qETveOSeCJf/Tjcyaro3aMVKqqW03UA5uDf2NXzvq7XC8vLjb+uHwzAIC/0el05Gc9Qqf7xnTZ98yIxGi1p8kBZem0dxVl767f9v6ePL2xFTfRBjAEIQBAM/R6vdlsDg0NVam8vZWgN1yXHJa54ca37unbNyFEKWNDVdLGC/caCqnoOJ37uv4dTQaz/d3d7ndVFBMEIQBAM/xnyqg7Uglzz5jOZ18enfP6uM8WDrqpX8P52IljKKgTVeVTwZH6za/9eMnks2Xh/BiCEACgGf42ZdSDTmGKydfGfvrIIJW83iLDjIR6TyciSmuwibe2yrr5YIFvC/RHCEIAgGZ0oCB0ig2VzxvduUFT8t9IEUZlGaRNq9/8fz9kOzix70GBIAQAaIb/D4029eTkZEn95XOlCrpmMhHRua/rn5atNW45Vuzb0vwOghAAoBkdrkdIRMmx6hnXaRo09ZxCUhUV/kEV2fWb1353sT22xOhAEIQAAM3oiD1CInp6WvcGq2rLQ6j7TUQ8Xdha/7RTuVW/nivzcW1+BUEIANCMjtgjJKJBiaHj+0Q1aOo9g1gp5R6gGm395pe3X/RpZX4GQQgA0IwOGoREtHxKw50o1DHUdTRxdrqwrX7zL2dKj1+q9Gll/gRBCADQjA46NEpEN/WPHtKt4falfWYSMXTxJ7JU1W9e/0ODC4eigiAEAGhGx+0REtGyyQ33HQxPovihZLdQxg/1m785WpxZXOPTyvwGghAAwBOTyWQwGBQKRWhoh9zAb+YwTUqnhtv89plJRJT+Pdmv7Bjq4PhXd17ybWn+AkEIAOBJXXeQYZhmT/ZDEpZZ0mgz+th+FN2brIZGy3BvOpBfpLf4tDj/gCAEAPCk414grHPfjQlx4Q134u1zGxHRuW+Js9e1WWzcWz/n+LQy/4AgBADwpENfIHRSythH/5bUoClhBIV1JaOO8g7Wb377l9xKo51EBkEIAOCJH+5NfxUW3pQYpq6/YSFDvaYTEZ37hujKujKVRvvGfZd9XZzQEIQAAJ745970rRWmls4f26VBU7fxFBRD+hwqPF6/+T87L1ntnE+LExqCEADAkwC4Rui0eFI3ubTeez4rpWumEjk7hVcUVJg/+73Qt6UJDEEIAOBJAFwjdOocoZwzKr5BU8rfSR5M2jNUeqF+88vbL3JiWocbQQgA4EnABCERLZ/Sna1/E4hMRT3+QUR0vkGnML2oZvsJLYkGghAAwJOAGRolop5xQVMGN/wf6TmVJHK6fJgq8+o3r/1ORMtwIwgBADwJpB4hET09reEy3MpwSp7YdG+moxf1B9PLfVqZcBCEAABu2e328vJyiUQSFRXV/NkdwbDu4aN7RjZo6n0bMRK69CsZdfWbX94ulmW4EYQAAG7pdDqO46KjoyUSidC1eM3yKQ1XXAvWUJfribNT+vb6zTtPaU/lNtihIlAhCAEA3AqkC4R1Jg2KHZTYcAHxvrOIGMraSdYrG1DwPG0QxzLcCEIAALcC7AKhE8PQkkndGjRFdCPNQLKZKHNH/eYvUwtzS00U6BCEAABuBWQQEtGdI+MTo1UNmvrcTkSU/h05rHVtdoco9mZCEAIAuBWQQ6NEJJUwjTuFmkEUmUJmPV3aU795497LpQYrBTQEIQCAW4HaIySi+eO6RIfIGzQ5N+w99w3xV9YaNVodb/+S69vSfA1BCADgVqD2CIlILZcsvCmxQVOXURQST9XFdPlQ/eY3duVUmx0+Lc63EITQbmy2Zk/hLWYfFAJw1XQ6HQVoEBLRopuTgpX1bgthWOo1nYihsvT6p5VX2zbtD+S9mRheTCurtoRer8/KyoqIiOjevXvzZ/vWmcuGAf+ut4smz1HFRWIkFNHgrqCaTTer5ULe8+QoLytZ/oj55LH6f11ZZpuJ43oo5Ur2ylKHbFCQZsMHquuuF6JMgBYpLS0NDg5WKpVCF9IuFm0+9+aunCtfO6xkKKTwpEandYlSXvzPOJmEoUCEHmFj4eHhQ4cO9cMUdIFhKbJHoxQUnO1ybv7sW8ynT/J8gy3NUpSy/mpF/RQkIq6mpujx+VXbvvJtjQCuVVZW6vV6h6PBMGB0dLQzBS0Wi16vr6mpcfPsDmnZP5IbxJu1mqRKshkbnXa5zPz/DheZTKbs7Oy8vDwKLAhCevjhhzUazfPPP+/y0YyMDI1Go9FoLBaLjwtr6vtvPqctc2jfs27P+OFh2jLn4IEDPiyqAUd5WeEDM+0V5dk1xhFn8kecuWx1M+TwVG7piDOX3yuu5M2m0vWra/b86ONSAZqKioqKiIj4448/XD760ksvRUREzJo1y8dVtasuUco7RsRd+frgi/T9A40mjjq9vP3ir7/u7d69+3XXXee7+nwCQUh6vb6kpKSqyvVKQjabraSkpKSkxB/GkM0mI5n1ZHG/6JFZT2a91SpYZpc8+U9HlZ44zs5Tqc1RanO4+7FVOrhSm6OG44iIN5u1q5+wFxX4tFYAICKi5VO6My0Y8jxz2XA8p7L9yxEAghC8pubXnyzpaXwL5sg0xVks2rUrvF4SADSrf5eQWwbEtOTMb44UtXcxgkAQgteUvfEyZzQSXdXldIfDcuKoNTvT20UBQPOWT2nRrIgzlw3tXYkgEITgHdbMC46yNm1pzdvtmDUDIIgbe0eO7BEhdBWCQRCCdxh/38fb7G15Bd5uM+7/xVv1AECrLJvcrdlzmJZcS+yApEIX4C94nrfbXbyPN5pI7S94v6vK9OcfvM3FgoQckcsJMy7n0NiKCnm7nZHizxKEdOnSpeDg4KbtzpvrA9WtQzR9Ogefc35RXUTas/UflUnYcX0j+yexG/YKUVw7wztOrQ0bNmzYsEHoKlqmLIO+mCp0EY05il1fRe9/qhWrFDIymaOiTBoTgOs6Qgdy5513Cl2CABiGlv4j+YFNRER04Tu68F39R21EP++inwWprP0hCGvFxMTExcU1bbdYLOnp6U3bBRETIicipUrVJdH1TfQXMy9wDodciNUfeFf9aSLqqZK7rKbAajc4uMatDEP+2QUHMZkwYUJkZGTT9nPnzqWlpfm+Hp+5+4bO/5SyViKK6UvhiSoZ2zchpH/XEIW09iJaXl7ezp07Ba2xXSAIa91zzz3r169v2p6WltavXz/f1+OSTMoS0YD+/Y8cOeLyhKioqPLyckHG8SXh4S7bt/SMU7Au6nkkW/uzvvHqFWS3sqFhXq8NoFVefPHF4cOHN21fs2ZNYAehTMLEhStyiym634Rnnly8YHwXVcPFGnfu3BmQQYjJMuAd8mv6UJsDmFWqWXWQV+oBgKsQGyonolXTUx67JUkl6JLFvoQgBO9QDb+BDXIxv6BVFNcG2tJNAB2LhGUocGeHuoMgBO9Qj7iB7G26vMcGBYVOmemtegAAWghBCN7ByBUht93FKBRX/wpKtfrGiV4sCQCgJRCE4DWRD/6LkV1lELIqdczyNYwEs7cAwNcQhOA1bEhop7WvsapW71/KKJXq628MmvD39qgKAMAz7FBPx48fLygoSElJ6dOnT9NHDQbD3r17iWjy5MksK/Dnhtzc3D///DMiImL06NEuT/jpp5+sVuvIkSNjYlq0lnx70H+xqeLt9YbqmsPVZiIaF6Z2+VM7bbTobI4khSwlLER+TZ/OH3zOyK9+WBXAKzIzM3me79q1q8v96MvLy5271cfHx/u+Nt+orKy02WzBwcEufwI2m62yspJlWZf3WXZcCELwPsOOLaXrnuGtFr7pLfMNsSqVasSYTmtfRwoCgFAQhNAubLnZ2ueWW9LP8VYzuYpDNiiIkSmin1wdfPMU35cHAFAHQQjtyHLuTNWWz2p+2+soL2MVCuKJ5xzEsMr+14ZOnx00/hZGJhO6RgAQOwQh+AJvNtnLdETEqoIkkVFClwMAcAWCEAAAWuGnn3565plnnMe9evX69NNPha2n7XDbFgAAtML58+d79+69Zs0aIlKpVEKX4wW4j7CB1NTUoX+ZMsXfJ3GcOXNm7ty5Q4cOnT59ekZGhtDluHD48OG5c+du2rSprmXr1q3jx48fOXLkunXrOK6ZOaUAAkpNTVWpVAMGDBC6EL9TWFjYs2fP5OTk5ORkl7vXdTjoETaQmZkZHx//2muvEZFcLhe6HE8yMzPHjh377LPPrlq1qri4OCEhQeiKGlu9evWxY8dqamrqdq45efLkvffeu3nz5s6dO997771KpXLx4sXCFgngktVqffDBBxcvXvzDDz8IXYvfKS4uPnv27G233RYUFLR06dKBAwcKXVFb4RphA+vWrdPpdK+++qrQhTTv0UcfNZvNGzduFLqQZqxYscJisbzyyitE9PDDD8vl8jfeeIOItm3btnz5cv/Z9BigvtWrV2dlZT3wwAOPPfbY6dOnhS7Hvxw/frywsLBHjx47dux48cUXMzIyoqOjhS6qTTA02kBxcfGpU6dmzZo1Z86co0ePCl2OJ2fPnr3mmmtWrFgxZ86c//3vf0KX0yLnz58fMmSI83jw4MFZWVk2m03YkgCaunDhwrvvvutyp24goiFDhkyZMqVXr15Lly6NjY09ePCg0BW1FYKwgfnz5y9cuPDZZ58dM2bMxIkTc3JyhK7IraKioi+//LJ3794zZsxYuXKl/3cNichgMKjVaudxcHAwx3HV1dXClgTQCMdx8+fPf/755wPj6ld7uHDhgvPAYDDodLoA+EHhGmED/fv379+/PxH16dNn8+bNu3fvnj9/vtBFuRYZGfn444/PmjWLiLKzs7/77ju/LbWORqPRarXO45KSEqVSGR4eLmxJAI188cUXhw8f1mg0u3fv1mq1eXl5Tz311Lp164Suy1/Y7fa7775boVAMGzZsz549kyZNGjFihNBFtRWCsIGMjIyUlBSWZa1Wa0FBgUajEboitwYPHvzbb785g7CjjNHfeOONP/zww8KFC4lox44dY8eOFdtG2OD/Ro8e/dNPPzmP//zzz7y8vOnTpwtbkl+RSqWpqamHDx/OyMiYOXPm9ddfL3RFXoDJMlfwPD9x4sTq6upRo0b9/vvv0dHR33//vUQiEbou1zIzM0eMGDFz5ky73b5169YDBw7069dP6KIauHDhwscff3zgwAG73T5+/PgnnniCZdlrr712xIgRCQkJH3300Y4dO0aNGiV0mQBu7d27F5NlxABB2ADHcUeOHDl//ny3bt38v79SWFi4e/duk8k0depUPxymz8vLq/tkTUSzZs0KCwurqKjYunVrTU3N3//+95SUFAHLA2iWyWQqKyvzw3uTwLsQhAAAIGqYNQoAAKKGIAQAAFFDEAIAgKghCAEAQNQQhAAAIGoIQgAAEDUEIQAAiBqCEAAARA1BCAAAooYgBAAAUUMQAgCAqCEIAQBA1BCEAAAgaghCAAAQNQQhAACIGoIQAABEDUEIAACihiAEAABRQxACAICoIQgBAEDUEIQAACBqCEIAABA1BCEAAIgaghAAAEQNQQgAAKKGIAQAAFFDEAIAgKghCAEAQNQQhAAAIGoIQgAAEDUEIQAAiBqCEAAARA1BCAAAooYgBAAAUUMQAgCAqCEIAQBA1BCEAAAgaghCAAAQNQQhAACIGoIQAABEDUEIAACihiAEAABRQxACAICoIQgBAEDUEIQAACBqCEIAABA1BCEAAIgaghAAAEQNQQgAAKKGIAQAAFFDEAIAgKghCAEAQNQQhAAAIGoIQgAAEDUEIQAAiBqCEAAARA1BCAAAooYgBAAAUUMQAgCAqCEIAQBA1BCEAAAgaghCAAAQNQQhAACIGoIQAABEDUEIAACihiAEAABRQxACAICoIQgBAEDU/j+khBhIrQGqZwAAAKB6VFh0cmRraXRQS0wgcmRraXQgMjAyMi4wOS41AAB4nHu/b+09BiDgZYAAJiDmBmIuIG5gZFNIANIszFCaCcZnZNAAKWYkl+ZmYGRgBFrFDDSMgYGVgZGNgZGdgYmDgYmTgYmLQQSkSLwPpBLqLJCbDuzv0du1GCaAYB+wR7AdDvxstlGFiu8HySGx9zPAAYzdoIpQ4+CAbCaSXnuYejEAt/cjTPbQ4E8AAAD0elRYdE1PTCByZGtpdCAyMDIyLjA5LjUAAHicjZJJDoMwDEX3OcW/ACghTFkyqVQVILW0d+i+91dtEDgIKcJhYTsvlu2PAtuzfXx/2C1plQJ04HPO4WO11moAO6i7231EM1f1lmmm9zi/YAyMxnKObDVPw5YxaBCZONNsiHSsV0+cjUyI3LMh0BJ4qWKKHpE935/AjMG95O6cwfwARgGyIFKmCYClDwY4R5y9sh7SpMeVYUg/Dwz12I3tQdRV5noaW5GZTyJaUgArivFtKrpwmMn2Ocxlx/y2kEVyWMq6DJVzshUOjT/8kjB+636jHG//MvnqD1TClKwLOZCQAAAAuXpUWHRTTUlMRVMgcmRraXQgMjAyMi4wOS41AAB4nG2PMQ+CMBCF/4ojJKXptbTQY3TBxbgTBmMcSCQl2JEfL62mZ43D3b2X773hhn48FkM/lnH9l/sctqJSTLCuAq7TDfNWcXUiEJFMPJ98LKdOqpTs6t18Wd2Cgk/P07w8ptvkOew2kLPzd64QyAiUeVASq1HlTBHTWJMB1HlQEzNocmaINdiQkdjmwZZYizZnlphF+PkVvp4Fsb0Aoyh329TuSDUAAACcelRYdHJka2l0UEtMMSByZGtpdCAyMDIyLjA5LjUAAHice79v7T0GIOBlgAAmIGYDYlYgbmBkU0gA0izMHGCaiZGRQQOkCCfNzcAINoIZqImBkZVBBCQqHgSSgprPwPY37uz+u59X7AJxWh8/3Ku3jmMfiP3Q3+1AHBebHYhdUrxnf4fYfnsQu83r3n6m7P37QewnW1ntz+tA2GIAMTEgmCqUGdIAAADyelRYdE1PTDEgcmRraXQgMjAyMi4wOS41AAB4nH2RS27DMAxE9z7FXMACRepjLmM7SIsiNtC6vUP2vT8qxnCUAEJJLYbSIyVSHcw+54/bLx7Gc9cB9M9SVfwIEXVXmMB4vrwvmLbTeOxM6/eyfSEhlozir+RpW6/HjseE3rtEISSPnhyR50FhwqzmMlY7955Y812JhJwbpOANvTjSIXF5QAFp0FbJYKB3ITNnQRHKQ44NMO5glpSC3lM0higNMhWSXGQWyjsoMbZKnpf5ZRD7aMZ1metozLn2b6HUJi0MtRULY31wyUR6vuy5tMXHfxXd/QHiCmLmZYPTjQAAAMB6VFh0U01JTEVTMSByZGtpdCAyMDIyLjA5LjUAAHicVY09D4IwFEX/iiMmpXmf/XJ0wUXdCYMxDiQSiDLy4y04NAxtes+577Vtutu5apvuuF35HJYKrBIxeFOjlciq5lSDRQSKmeUXs3ifIVoHIm5lAEghAmbKFmJwbNYihBi3ongib9Z9FMJGPDun/y9U1BzNYx6H+2ecEtj+exmmd//sZ4s5ruY6zi+rCUvARCVA4v0UF0dJ9k6K46R7p8XJ8gO5j07fLyWurAAAAABJRU5ErkJggg==", "text/plain": [ "" ] @@ -233,7 +230,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "" ] @@ -338,7 +335,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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"text/plain": [ "" ] @@ -359,7 +356,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "" ] @@ -442,7 +439,7 @@ "outputs": [ { "data": { - "image/png": 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dtqNCEQKAQDn+04S5Zfqw/6hXH7iuNZgNpgYXUKzUmTOLtHcvP/7Jrqv2jNdp4NIoAAhUTEwMwzCJiYkGg0Emk/Ed53aFFYaIt+Pzy/VGM3dza3U+lV4hfSWJpaTyJq9+JK5JrjGYF/2WbjSzb03uzU/iDgtFCAAC5e3t3a9fv0uXLp05cyYiIoLvOLdgOe6+FScKKgw3WzDnBJ3dQKUZtxwnkVPQ3RQ6g+QuRFStNy3ZdnlwgOs9g33sHrkDw6VRABAuh71NuPLPzAs5lTcvh178jQ4tptIMcu5K/R+goc/SoH9S91HEmiltB/01m6oLLAdq9ObHvzlTpTPzFr0DQhECgHA5ZhFqDOa3f0urri2z3JN0eh0RR6GP0f3f0dBZ1P9BGjidRi+ke74i5y5UmUPxHxJXc7zOyH66GzcLmwFFCADCZSnC+Ph4juOaPNhuforPMd+8L8jRqbVEHAXFUugMYm59atCtJ41ZTCIpFV2irJpRPxq9+fM9V82sA/2NHByKEACEKzAwsEePHkVFRRcvXuQ7C+WV6XecKnhrU+rrGy9U6/8+HSxOo/JMIoYGTrf+MreeFBBDRJTxV+02M8slppe1d+BOA4NlAEDQoqKiNm3apFarQ0JC7P/ZrxRo1Kml8Wml6tSSizlVVs5LC84TEbl2I+euDb5Ltwi6eoCKLhFntpwyag3mI5eKo/t5tFvwTgVFCACCFhMTs2nTpri4uFmzZtnh05lZ7lJOdXxaiTq19NDF4uvFuiZeUJlDROTavbFjXHsSEZl0pC0hlQ8RGc3cqWuVbZNYAFCEACBodhgvYzRzyVkV+84VWU7+SquNzXlxNRGRzKmxY2R/z5VqqLIUIREVlOtbklWQUIQAIGgDBw709PTMzMzMysrq2bNn3V1Xr141GAzBwcEM0+xpPCt1pmOXy2ove+qMDc4L0wSRhIiIbfTlrOnvg6U3X9f8zIKFIgQAQWMYJjIycufOnWq1+rHHHrNs1Gq1kydPvnz5skqlkkqlf/75Z5cuXZp8q9wyvTq1xFJ+p69VsC0Yiaoro+JUKrxAhRfojkfIf3jN2Z6+vLFX1e6Vu9Ru6+4lb/ZnFyoUYdNOnTo1d+7ca9euhYaGfv7554GBgXwlMeXm2HKYuaRI4t+jvcM0ZP/+/UuXLs26fr3c6EdDniGnxn58ZORr7BYMoCExMTE7d+6Mi4urLcL//ve/FRUVqampUql02rRpH3zwwWeffWb1tXVHu1y4UdWST68tocILlHeaCi9Q+XWiv+szP5n8h5N7LyKi0qtEHFEDJ3mlV4iIlJ4kd7NsEIkoLMCtJWEECY9PNMFgMNx///2TJk1KSEgICgqaPr2BEcztr/y3n/LmvWDLkden3aNRH2jvPFYVFhY+/fTT/3hmdlnUCrPCkxI/afz4OT9dfHdLuiM9wQVCVH/27S1btjz99NMymYxhmFmzZm3durV2l5nlzmdXrT6Q9ciXp31f2Nf79UNPrjq7+kBWM1qQY6k8ky7vocRPaPvTtPWfpF5Ol/dQeRZJ5OQTQiFTafxSGvRPIqIug4lhSFdKBRcafMOsOCIivyG1G1iW/jhbaDLjW8sm7XVGmJGRYTKZunfv7uRk5R5vWVlZfn6+Uqm87Yq8A9q9e7eTk9Prr79OREuXLvX19T137tzAgQPtHKN45Yryn9dxepvufrOa6vx5L3nPW+wy+ZH2DnYbHx+fr/4vccaqc9VGEfW9l3a/3NivsUR6I7ti55XL+ZoNzw/m8Y6G5cu1R48eKpWq/l7Ll6tKperRg7fzbGhX4eHhKpXq/PnzxcXFXl5eRJSdnR0QEGDZGxAQkJOTc+RiceLlMnVqqTq1tEzTnNEuFpyZSq9S4XkqvEB5Z8lQZ0inwp28gsnnDvIZQF79am4K1nLuQl2HUc4JOrOOYj+4fS8R5SRR3hkior731d28/3zRzO9T1j03CPcKm9ReRRgTE5Obm7tjx45JkybV3/vTTz/9+9//joiIOHbsWDsFaCvp6emDBw+2fKxUKvv27Zuenm7nIiz/v/9VbPqB02ltfwmr0xZ9uFjs21U1Mqb9gtV3+lrFjFXnap4Frswl5y6NtKBFtd68NSnvP57K5dOC7RHRmhEjRhQXF+/Zs2fChAn1965fv/61116Ljo52tIm4oK3IZLLhw4cfPnw4ISHh/vvvJyKJRFKpNVjGef6lPm1kmTHvH23yi/l2Ji0VpdaUX+EFMhtu7lJ6kk8I+YSQzx3k2buJdx42iwpSqOgiHXmfRrxCSs+buzIP07EviYh6303e/W973fq4bFeV5MsneHg+smPBPULrWJ3WeCOLiCrzcmWs2fIxESnEovLrmbV/tAPTjevFny6x8VywLlanzX3z38wPf5KznW4VGEzsvSuSNJYWNBso5Uca8A9bXlitN3/xx9WwAJfwIH7ualgmo8ot018pqLln6e0ic1Xiu0NAYmJiDh8+vOuvgxWew9SpJVk6l+nLdnP9pERENy6QysfWFtSVUnFaTfMVp90cz0lEzn7kN4R8Qsg3lJx8mxHOpRuNfofi3qecJNr+NPmEkJMvmfRUnFoz13aPSAq3ft/kqz+veThJ3/1H32Z8OuHBt7p12qPqrMljiEheVJlZqrF8TETZ57PFK29krf+C13S2Mmh0m2e9tajLNHt/YpOO1B+QV3/qfZeNr9AazdO+Ot2uoRqjMRLR098l0+813xFfPXnHS3cH8JYH7M7yNOF3m/74riSWiKhLFF34PwqKJbGcUrdT4NjGXlyVd/O0r+5oF0ZMnn1qTvv8BpPMpbE3aZzfELrvWzq3ia4nUH7y3+/PkFc/6v8ABYxupKff25LuJBe/OSmo5Z+9s2teEWo0mpKSkq5du4rF4qaP7hRGuSiWZpcUGc3eUvEFjaHUxA5xcrgFPBsi44wPlR392GdypUhpv89amUNxS6nbCBr0z2ZfSurgDAZDQUGBr6+vA67yCo2LjIyUSCSmkstk0pNEToHjqOAcbXuaGBH5htCAKbcczbFUcZ0KL1LheSpIoerCm7skSvLuV1N+PiG1q+baTiUThwW6RvfzjAhy/3BnRsr1Sq3BTESk8qGIl2n4S6QpJH0FiaWk9G7iQfu/vbXpkptS8tydjj4mgy/NKMK33377u+++8/f3Lyws/OGHH+66y9Zf9ju0ALl0iqfz9LS8KFfF3jLNK13dVaKONNSWZZg7K1O2udlr0dGyTNo7l+SupC+npJVERKEzbrml0ZjGhtU4vrVr1y5evNhyF/ndd9995pln+E4EzeDs7Dx48OCTJ09ScSp1GUSMiEa8SuEvEMeSREFExJqo7Brlnak5+TPUGSPa+GgXG7gqJRG93WMHekUFew4PcpNLa37I3Bvm8+jK03vPFd1ckolhyMm3sSurrJHiV1DPaAoYXbuN4+jFdeddlJLHIv2bm00IbP0Py8vLy8vLy87Olslka9eunTNnTnJyctMv6xTe6+l1okqXqTdN93YZoOxgv+k7sfpxVefsV4RyFxrxyi1bLD9EbNKBW9BkMr3yyiuHDx8ODw9PSkoaO3bsE088IZHg1kNHEh0dffLkSSo8T10G1WxizVTc6GgXvzDyGUBuPVvw1dvVXR7dzzMq2CO6n0dYoKvViWAUUtHW14f9qM55beN5vYm1abnda0foejzdOEYyJ+o6rHYzy3FPfHtWJhFNjfBrbtROz9ZvVD8/v++//772j0qlTZfakpOTrY5HT0tLq/3YbDZrtc0YD9nedDodEZmJ09SZ0yhEJQtRyYhI0/hER/al5Vgi4ppKFai5RiZ7/QtLleQ/7PaNls9ueVrQrLdfGFuIFVT7A6j0ColqrvmnnSk/IMmwfJyent7k2zAMIxKJ0tLSwsPDNRqNSqVqwaRcwK+YmJgvvviC8s6QRxAVXqC8M1SaQbVPuTIicutZM9TTdxA5+bTgUwT5qizNd1eody8fKz8brXo82v/hEX6/HM39/mBWUka5WMzIxCKW47QGs1wiqtKbbqnhoDupIosu/B8deZ/GvUe+obV7zCw34+szLophEwa1JHwnxjRrOcpffvlly5Ytp0+f3rJlS+OPEPj7++fm5jb+bpbHJ3777bepU6fansEORCIR60iF1wjm5n35joBhyNEenr/nK/IIot+mk76JqfqbfHxi06ZNM2bMmDBhwuXLl3/88ceICHudhUOrXbp0Sa1W792799dff71lh1hGXn3JZ2BN/0ltra5aEjEzuKerpfzGhXh5u7T2khLHUU6ZrqjSoJCKu3konOTiWWtT1hy8fttRdPxruvwHSVU0fil53fJgkkom/vOtCKzQVFfzLt0MGTJEqVRWVFSsXLly1apVTR4/adIkqxOSpaSkHD58uCaBRGL1oXu+mM1mnU4nZkjm8L/Rc0Q6lmOIFKLGozJakQNc0TXrieNILCeH+odl6tzx7T6CVDW3Xkb39xzUs2aM39mzZ5t8glCn03366afvvfdeYGDgypUrX3311YMHDyoUtl8WBrsym82XLl2Kj4/ft2/foUOHCgsLb9ntE0L+w8n3DvIKrjuNtY2c5OIhAa6Wy55jBni27XM4DEPdPBTdPG5+aa16ZmCl1vTL0bonHgwN/zeZtHTtEB16h2I/JLebQ6A1BvOkj5MOLBgxNBBzsNVo3hmhRWZmZq9evaqqqqxe9rSwnBE29ED9N99847AP1G/btm3KlCmxbqpVvZvzoA8fykxseHKWm1h0cnBjg8Euy7tO7LXQbqkatPtFKsuke78m90C+o9RjOSMct4S6DrVsqPv4xBdffNHkA/U7dux4+eWXr127RkRmszkgIODrr79+4IEH2j862Eqj0Zw6dSo+Pl6tVqvV6rKymwu4+/n5hYeHR0dHJyYmbt++nYY+S/0fbNab+7rKInq7R/fziAr2jOjtJpPYdUid0cxN+ezkrtMFt2zlWIr/kLLUpHCn2A9vW9HQx1V2eOHIAd2cCWw/IywoKPjvf/87d+5csVickpLi5uaGAeJ2VmIyn6zSj3dTiZtzQsURc06BicHana+vb0VFRVFRkbe3d3l5uUaj8fHBbRj+VVZWHjt2TK1Wx8fHx8XF6etMTBEUFBQVFRUdHR0VFRUSEmK5p7t+/frt27dT4TlbirDuaJehgW48XumQipnNr4Td+9GJQxeLb25lRBQ5l4xayj1JBxbSXR/VHWtaWGG4+4PjR94Zafutyk7M1iJUqVQnT57s2bOnv79/ZmbmmjVrMCLObspM7Iqc0gPlmkKjOWVIgLI533DVYvkB59Cmj4PWGTFixPPPPz948OCBAweeP39+1qxZkZGRfIcSqNzcXMs5X3x8/OnTp2vv94vF4pCQEEvzjRs3zurMsZbH6qngfEMP8wT5qmIHekcFe4zu7xnoY8fHc5uilIm3zxkWu+xY0pU6CzaJJBSzgA69TQXn6cACuusjUty8NZhdortr+fG4d0Z1dRf6gk22lpmzs/PmzZtLSkrKysp69OghlTb7ujm0mFLETPJwmt/NY8jZZk/tJubYg072niJcmJYtW/bOO+/k5OT4+Pi4uLRiDhFovitXrliaT61WX7hwc5UGqVQaFhYWGxtrOfnz8GhihEhQUFD37t2zs7Op4oblWmLd0S7j7/DycnbcK2GuSslf8yPGvX/sTGbFza0SOY1ZRPvnU0kGHXibYpfXneAmI19z9/Ljh94e4ch/Lzto3lmdp6enp6eND0dDm5GLmFEuCi3b7Lu5OpF0vfsYhxgpIwwKhSIoCBNZERENGTJEp9P9/PPPYWFh9fd+9913n332WUxMTN2HspqlkdEuLi4uI0aMsDRfdHR0c4csRUZG/vrrr3fIMqc/PH50f8/hQW5KWYeZSMtdJf3zrYjR7yWm5lbf3Cp1onFLaO88KrtKhxbT+PdJcvNc9lx2Zeyy4wcXjnBXCff0Bpc3rVNFj+310098pyAiMhXm33jyQbZFj1qq3F3n/7ziP0o73QMwmbmodxMzCjRs8ztbKRPvmTc8tNeOh60AABZOSURBVAc/J1J99shK9PR/r4aNj62ZL0nVcX72OabU1FSdTqfRWF94uaioKDU1tblrXNcd7RIXF1defvMaYNeuXYcNGxYdHR0bGxsWFiZqxfRPMTExv/766zDn7IUP9mnxm/DI11W2d/6ImPcSM4vq/NCQu9H492nfm1R0iQ4vobGL6879diaz4r4VJ/56K8JJLtAvexShdYxEKnJ1iLHFMlc3n0UfFyx+gxr4mdIQRqHs+un3Cm+7/i12vhEe/nZ8pdbU9KF1OMnFSx4OHt2ft4sNlruuzgqJh5Nwfyl2TBUVFcePH9+3b59arT5x4kRDo13uuOOOtvqMltuEHXrJrR5eir3zI0YvOZpXVmfVGpU3jV9Ke9+k/LMU/wFF/6fuVHAJaaVTPju5Y0547exugtJeRZiamsqybEMPCM6cOXPGjBkYbmMj57vuNRUXaL/4wPaXMEpll6VfKkKtXJVqV8Fdnfa8OXziR0lVOpOND+Y4KcQvxga8fk+vdo7WmKtXrzby5frCCy889dRT+HK1m5ycHMtpX0OjXWJjY8eOHdtO43JDQ0Pd3d2vXr16/fr1jrsUc18/pz/nRYxberSkqs4aws5dadz7tH8eZR+jo5/TqDl1H+rdm1L06Mozv74SJmnWwPROob2+txsfLCCTyfD0RbNURcdqtUY6OivbYPKSiDwljV3BEHl4dv30e8WgoXaLV1dksMfRdyPvW5GUWahtsgrlUtFnMwY8O57nSfHx5cq72tEue/fuvXr1au12lUoVFhZmOe2LiYlxd3dv7yQikSgyMnL37t3x8fHTp09v70/Xfgb1dNk9d3js8mO3zFDqHkDjl9H+t+jaQRJJaOSrdQfHbj2R96/vk9c9N8jqxKedGH7J7RjefPPNtLS0sAH95167Mt5N9UrXxn4c9Pz1T7Gnt92y1RfSzfnSijGes/ZqDE3MEfzdM6FPju5mn1TgmFatWrVgwYKSkpLaLV5eXpbai46OHjZsmP3HqMfExOzevTsuLq5DFyERjejjvn12+H0rknTGOnNGegTR2HfpwEK6spekKho2q+5LNsTdcFVKvnqyzS41dwgowo7hp59+IiLD5dTr0yY2ebBIxf+UdXKpSCETNVmEXdxwpiV07u7ullVOLad90dHRrRzt0nqd4DZhrfF3eP3yStjUz08ZzXUu0HgPoNEL6dC7lLqd5K408Ja+X/lXpoeT9L2pwbe/V+eFIgSA9nL33XdbrTSD4eZ6Rvfdd19mZmbPng60Zmx4eLhCoTh37lxJSUkneGBs8tAuP78UNu2r0+a6w7n9wihqHqmXU/JGEstowEN1X7Jk62UnuXje/b3tnZUnQhwgBAAtc+nSpR9++GHbtm2W1cqa5OLi4mFN3XXcXFxcHKoFiUgul0dERHAcl5CQwHeWtvGPCL/vZ4befuOvxyga+SoxDJ3+L13ec9tL5v+Sump/s2fw6KBQhABgk1WrVt1zzz0XL15cvXp1RESELcuI/vbbb1nWzJ071w6BWyM6OpqI1Go130HazNNjun/2eMjtW3vdScOeI+Io6WvKPFJ3D8fRi+vOrTucbb+I/MGlUQCwiYuLS1xcXPfu3VmW7du3719//dWJl9foTLcJa706MbC4yrBk6+VbtgbfT4YqSv6REj8hqZL8h9fu4Th6dk2Ki1LS6Re1xxkhANhkxowZ3bt3JyKRSOTh4WEyNW/ahI4lMjJSLBafOHGioclxOqj3pga/cV+9WQAHPkohU4k1Udwyyk+pu8eyqP2es4W3v6RzQRECQPNcvHgxIyNj9OjRfAdpR66uroMGDTIYDElJSXxnaWMfPdr/2XH1JgoY8hT1vZfMBjryLhWn1d1jMLH/+PxUXGrJ7S/pRFCEANAMxcXF06ZN++ijjzr9goud8uooETEMffvMwOmj/G/bTMNfpN53k1FLBxdS6ZW6+zQG8/0fnzh5tZw6KRQhANgqNTV1zJgxTz311LPPPst3lnbXWYuQiMQiZsMLg+8L8711M0MRL1PPaJKqSHL7CoXlGtM9HyVduFFlt5D2hCIEAJts2bIlMjLy2WefffDBB69cuVJ3LphOyVKECQkJnfJuqGVR+7EDvG7Zyogo8g266xNy6UalGXRoEalvTnFcWGG4e/nxq4U1N01nz55977337tq1y56x2wmKEABs8u233/bq1Wvjxo2PPPLII488snHjxkYOXrt27fr164ODrc9OMnny5PXr1zv4QxRdunTp27dvVVXV2bNn+c7SLpQy8c654VHBty5WLJKSyouISF9BOSco73TdnTdKdXctP55TqiOihISEP/74IzMz036J2w0enwAAm+zdu9f2gx977LFG9oaGhoaGhrY6UbuLjo5OT09Xq9XDhg3jO0u7cJKLd84NH7/02OlrFU0fTUREGfma8UuPHXlnVLsGszOcEQIAWNeJbxPWcldJ98yL6O/vbPtLUnOrJ3xw3Nz89bcdFooQAMA6SxEeOXKEs3F1zY7J11W2Z97wHl4K219ypUBzy4oWHRyKEADAuj59+vj7+xcWFqanp/OdpX0FeCsPLBjp5377YFGrPJykf74V4SRvbFXUjgVFCADQoKioKOrsV0ct+nRR/fVWhKdzE6s/dnGTH1o4cmSfdl8h2Z4wWAYAoEExMTGbN2+Oi4v717/+xXeWdhfaw2X33OF3LT9eqfv7iRGjhnb/u/YAqVjk5qt8XC0iooyMDF5CtgcUIQBAg4QwXqauEX3ct80edt+KpJp1tjiWyq7V7jUSpRXzE6xd4dIoAECDBg0a5ObmduXKlexsQSxIRETj7/DaNnuYRMwQEcmc6dEd9OiOkLkHrxdVm+oYMWIE30nbDIoQAKBBIpFo1KhRRBQfH893FvuZMMhn/uS/l6dnRMN7exx5J7K7l0pcB68B2xiKEACgMZaro51pkV5bxPTztHwwdoDX/gUjvJxl/OZpV7hHCADQGKHdJqzLWSH+Y95whbSTnzJ18r8eAEArRUREKBSKlJSUsrIyvrPYm1Qs6vQtSChCAIDGyeXy8PBwlmUTEhL4zgLtAkUIANAEIV8dFQLcIwQAaEJMTMzy5csFVYS9e/detGiRUqls6ICZM2dOnDhx+PDh9kzVTlCEAABNiIqKEovFJ06c0Gq1jXRDZxIUFLR48eJGDpg5c6a9srQ7XBoFAGiCq6traGioXq9PSkriO4tDe+KJJ8L/1oEWr8cZIQBA06Kjo8+cOaNWq0ePHs13Fsd1+PDhdevWBQYGEpGfnx/fcWyFM0IAgKZhvEyTOI7Ly8uLiIgICgoKCgpSqVR8J7IVihAAoGmWE8GEhASz2cx3FgdVXFzMsuxbb711zz33LFmyRKfT8Z3IVrg0CgDQND8/v969e2dkZCQnJ4eFhfEdxxG5ubn99NNPgYGBYrH41Vdfzc/PX7lyJd+hbIIzQgAAm+DqaOOkUunDDz88fPjwoUOHLliwYOfOnXwnshWKEADAJijCxul0utrVei9fvty1a1d+89gOl0Y7DY6I4TuDFf31N2KqLw7TZCwzFGURLc/7X4VhQIKqX6JTPwODLz/oSGJiYkJDQwcMGMB3EAd1+PDhGTNm3HvvvTKZbMeOHb/88gvfiWyFn0QdCWc0NLyzTguybPtnadq40lMvZG3pYi4Xk1nGmj7ljEQ0SHstuCR3Wlk8Q9wG9zEi3R18xwSwVd++fZOTk/lO4bgmTJhw4cKFxMREjUazbNkyX19fvhPZiuE4ju8MjqWsrOzy5cseHh69e/du+mg70l9MyX3lGXNJUe0WM0cXtHoJwwxQ3rJUmCwo2O/zNdJuPeye8e9gJcX5814sOXtWadbXbrysM2pZtq9CphDVdLaOkcmcFN0//lY5PJKnpABNM5vN69atI6KpU6e6u7vXP+D48ePJycm9e/ceN26c3dNBG0ARdgyaI/vz/vMyp9ORDf9fjEjEqJz8v/2fPCTUDtluY7yemfOvqebycs5ksOVqLaNQes9d5PrgNDtkA2gBrVZreSTu/PnzISEh9Q944403Pvnkk+nTp//88892TwdtAINl6Pnnn/fz81uyZInVvWlpaX5+fn5+fnq93uoBdqA7cyJv/sucVrulqHJkyvV/Xc5v6MgJF26MTLl+rELDVlXlPP+YMeuaHWMSEZlLinP+NdVUWnKlWjMyJXtkynVDA809P7NoZMr1VXnlnE5btGJx9f4/7BwVAMACRUhlZWX5+fkVFRVW9xqNxvz8/Pz8fL5OndmK8rzZMzmdloi0LFdkNJeZG7wFWGwyFxnNBpYj4jiNJvfVZziTyY5hKf/NF8wVZcSyJo6KjOYio7mhf7ZyM1tkNFezLBFxOl3B4jdMuTfsGRUAwAJF6OiKv/qQ07ZkggaOY82F+eW/bmjzSA2pPrBHn3qeMxpb8FpWry9YtqDNIwEANAlF6NDMpSWVu7ayhhZelWW1mrLvv7TbSWHxlx+yGk0Ln+Iwm/WnjhuupLd1KACAJqAIHVrVnt+JadXTgZzZrD1mj+d/DemXzMUFrXkHzmSq2NZhHjwCgE4DzxE6tKr9uy13B1uM1WiqD+1TRbX7qG5N/CHO2KpTT85k1BzeS7MXtlUkgLb19ddf+/j41N+ekJBg/zDQhlCENTiOM1m7hMjvTPPGjDSr2xscglIfx+rPnWqzQA3Tnj1h9Xl/toG0Vv8CxtwczmRiJPiyBEf0zTff8B0B2gV+4tT45JNPPvnkE75T3Irj2MqK+nOnna3W9zudafvbmIqLmj6o1cx5uVa3h55pRlRGKjWXFkt8urRRKIC29Omnn3bv3r3+9o0bN+7YscP+eaCtoAhr+Pj4WJ0iVq/Xp6am2j8PERHHcsTUH3uiFDEBcqnVV6TrDOZ6p1pcw49btKGGhuT0U8qs3uS8YTBV1g/GMITF3sBRTZgwweoD9ceOHbN/GGhDKMIaTz755IoVK+pvP3/+/MCBA+2fh4hIJGakUq7ekNFgpey3ftandQ9Pzioz3d4uYifndol322exNvUUEW3p11UuslKFL14p+KtMc/tWk0Hk6tbm2QAAGoFRow5N0tW/9W8iDerT+jdpkiw4pJUDXIlIpFCJVE5tkgcAwEYoQoemGjmGEYtb8w4ipdIpamwbxWmMckS0qNWnnvKw4W0SBgDAdihCh+Zy34OMXN6ad+DMrNP4iW2VpxGqkdFkatXtPZGTk+v9U9sqDwCAjVCEDk1+x2BJt54tXnCXkUicYsaJvaw8+dTmGJnc5R+Ptaa2GYVKNSa2DSMBANgCRejofOa/L5IrW/ZaRiL1es1+E3h6PvsyI21hEYqUKp957zJijN4ChyMSiUJDQ0NDQxUKhdUD/P39Q0NDAwIC7BwM2gqK0NEpBg9znTpDpFA194UipcpzztsSfyuPPbUTkYtrl2Wfi5TWf1g0glEoVJFjnO68pz1SAbSSXC5PTk5OTk4OCgqyesDs2bOTk5M/+OADOweDtoKFeenkyZM3btzo06eP1SeEKisrDx48SESTJk0SiXj6vYFl8954XnNMnV1ReUlrcBWLhjtbL5sjFVojx4U5yb1cnN3+McPrdR7Wcyj7eV3p1ysqq6qPVumIaJybyuq/WrJGX2g0B8qlfdxcZMEh3Vb/xMhadTcUAKBlUIQdBGsu+mhx5c4trLbes3e3YxiFwmPWqx5PPmePYNZU7tpStHwhZ9A3+Sy/SKlUjhzdZdkXaEEA4AuKsCPRJB4pfH8+W1nOVldb2c0wIqVK4t/dd9EKeUio3dPdwph5peC9efrUC5xeR6yVOhQ5OTFSufebi50n3G//eAAAtVCEHQ3LVqsPVmz9WXfyOGfUM2IJEcOZ9IyTi9PIGJeHHlMOG8F3xJv0F1IqtvyvWn3QXFIsksuJI441EyNShIa5TpnuNH4iI7U+VxwAgN2gCDswtqLMXFlBRGJ3z9Y/zN6uOJ3WVFxIRCKlk9jTi+84AAA3oQgBAFolLy/voYce8vHx2b59O99ZoCVQhAAArfLwww+LxeKkpKSMjAy+s0BL4DnCWyQmJob/7f77HX0QR0pKyuOPPx4eHj5lypS0NOtL+PLr6NGjjz/++Lp162q3bN26dfz48aNGjVq+fDlrbRANQMeya9eu1NTUV155he8g0HKYyOMW6enp/v7+n3/+ORHJZDK+4zQmPT197NixixYteuedd/Ly8qyuF8qvxYsXJyUlVVdXnz9/3rLl9OnTTz311IYNG7p16/bUU08pFIrXX3+d35AArVFRUfHiiy9u2rRJIsHP0g4Ml0ZvsXz58sLCwk8//ZTvIE176aWXdDrdmjVr+A7ShAULFuj1+o8//piInn/+eZlM9uWXXxLRtm3b5s2bx9uixwBt4bnnnpNKpStXrkxKSpo+fToujXZQuDR6i7y8vDNnzkybNm3GjBnHjx/nO05jzp07FxwcvGDBghkzZmzcuJHvODa5ePHisGHDLB8PHTr08uXLRqOR30gALZaYmLhmzRpXV9cPP/xww4YNZWVljv+LKViF0/lbzJw5My0tbcCAAXFxcbGxscnJyYGBgXyHsi43N3fTpk2zZ88eOnTonDlz9Hr9zJkz+Q7VhMrKSpWqZtJUZ2dnlmWrqqo8PDz4TQXQMiqVatmyZXyngDaAIryFZY55IgoJCdmwYcO+ffsctl08PT1fe+21adOmEdGVK1e2b9/usFFr+fn5FRQUWD7Oz89XKBTu7u78RgJoscGDBw8ePNjycVJS0u7dux3/exCswqXRW6SlpVmGMhoMhhs3bvj5+fGdqEFDhw5Vq9WWj9PS0ry9vfnNY4sxY8bs3LnT8vGuXbvGjh3LMC1daxHAkfTu3furr77iOwW0EAbL3MRxXGxsbFVVVVRUVHx8vLe39++//y4Wi/nOZV16evrIkSOnTp1qMpm2bt165MiRgQMH8h3qFpcuXfrhhx+OHDliMpnGjx//xhtviESisLCwkSNHdu/efe3atbt27YqKiuI7JgAIHYrwFizLHjt27OLFi7169XL885WcnJx9+/ZptdrJkyd37dqV7zi3y8rK2rNnT+0fp02b5ubmVlpaunXr1urq6nvuuadPnz48xgMAsEARAgCAoOEeIQAACBqKEAAABA1FCAAAgoYiBAAAQUMRAgCAoKEIAQBA0FCEAAAgaChCAAAQNBQhAAAIGooQAAAEDUUIAACChiIEAABBQxECAICgoQgBAEDQUIQAACBoKEIAABA0FCEAAAgaihAAAAQNRQgAAIKGIgQAAEFDEQIAgKChCAEAQNBQhAAAIGgoQgAAEDQUIQAACBqKEAAABA1FCAAAgoYiBAAAQUMRAgCAoKEIAQBA0FCEAAAgaChCAAAQNBQhAAAIGooQAAAEDUUIAACChiIEAABBQxECAICgoQgBAEDQUIQAACBoKEIAABA0FCEAAAgaihAAAAQNRQgAAIKGIgQAAEFDEQIAgKChCAEAQNBQhAAAIGgoQgAAEDQUIQAACBqKEAAABA1FCAAAgoYiBAAAQUMRAgCAoKEIAQBA0FCEAAAgaChCAAAQNBQhAAAIGooQAAAEDUUIAACChiIEAABBQxECAICgoQgBAEDQUIQAACBoKEIAABA0FCEAAAgaihAAAAQNRQgAAIKGIgQAAEH7f43zWL1GXS9+AAAAnnpUWHRyZGtpdFBLTCByZGtpdCAyMDIyLjA5LjMAAHice79v7T0GIOBlgABGIOYGYi4gbmBkU0gA0izMUJoJxmdk0AArJpfmBtrDyMTAwAw0jIGBlYGRjYGRnYGJg4GJk4GJi0GEQbwP6hYwADrowP4evV2LYQII9gF7BNvhwM9mG1Wo+H6QHBJ7PwMcwNgNqgg1Dg7IZiLptYepFwMAoUMjS8r6gX0AAAD0elRYdE1PTCByZGtpdCAyMDIyLjA5LjMAAHicjZJJDoMwDEX3OcW/ACghTFkyqVQVILW0d+i+91dtEDgIKcJhYTsvlu2PAtuzfXx/2C1plQJ04HPO4WO11moAO6i7231EM1f1lmmm9zi/YAyMxnKObDVPw5YxaBCZONNsiHSsV0+cjUyI3LMh0BJ4qWKKHpE935/AjMG95O6cwfwARgGyIFKmCYClDwY4R5y9sh7SpMeVYUg/Dwz12I3tQdRV5noaW5GZTyJaUgArivFtKrpwmMn2Ocxlx/y2kEVyWMq6DJVzshUOjT/8kjB+636jHG//MvnqD1TClKy3U8ITAAAAuXpUWHRTTUlMRVMgcmRraXQgMjAyMi4wOS4zAAB4nG2PMQ+CMBCF/4ojJKXptbTQY3TBxbgTBmMcSCQl2JEfL62mZ43D3b2X773hhn48FkM/lnH9l/sctqJSTLCuAq7TDfNWcXUiEJFMPJ98LKdOqpTs6t18Wd2Cgk/P07w8ptvkOew2kLPzd64QyAiUeVASq1HlTBHTWJMB1HlQEzNocmaINdiQkdjmwZZYizZnlphF+PkVvp4Fsb0Aoyh324GRCu8AAACbelRYdHJka2l0UEtMMSByZGtpdCAyMDIyLjA5LjMAAHice79v7T0GIOBlgABGIGYDYlYgbmBkU0gA0izMHGCaiZGRQQOsCBfNDdLPxMDADNTEwMjKIMIgHgQ1FAzYos2z9sse/GsL4hjHc9vzCX60A7GrPosfYEhM2Qdi329euD9y+Sl7EPt7muB+0/er9oPYSV0n7TfNkAOzxQAzyh2sXD1kHAAAAPJ6VFh0TU9MMSByZGtpdCAyMDIyLjA5LjMAAHicfZFdbsMgDMffOYUvUOQvIDw2SbVWVRNpy3aHve/+mr0qo5VQDQ+29fvb2ARwe5+v3z/wbzyHAIAvbq0VvgQRww3cgfH0dllg2o7jnpnWz2X7gAzJFHaeyeO23vYMwQQHjJW0YgGMxFJRzME/a1KG1bJJVZnM0YKD9DiBMxw4SlGW7KWZBTl3SHWSImcapADFNAgLd8DkoPXOpZbiEpGknDpkNtIKFU333plqotIBT8v8tIb7YsZ1mdti/HAb30NpU3qobRQPU3uwKSE/Nnss7fH+W+aHXxL7YnmqJ99XAAAAwHpUWHRTTUlMRVMxIHJka2l0IDIwMjIuMDkuMwAAeJxVzL0OgjAUBeBXccSk3txfSsvogou6EwZjHEgkEGXk4S04NAy9yTlfTtumu52LtumO20nvsBQE5tXUnRBKCkbe1QimqkwOQT1WIq5OGkgD+tQRS8C1YxCvvE2ZBblMHQGXVKlL31bC/6WVPnjvkomYiju6xzwO9884RYT+exmmd//sZ6AUV7mO8wssUg4UOQeMsl9JNo66N80m0fZm2XT5AUm7TnelWD3FAAAAAElFTkSuQmCC\n", + "image/png": 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"text/plain": [ "" ] @@ -463,7 +460,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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t9qICw85tJU/9K2fSyOqffxC6Uv+Vmpp6++2333DDDY8//nhFRYXQ5bSjpj1Ci8Vy+vTp4uJi4YqCtkIQNsNqtU6ZMmXy5MmHDh1KTk6ePXu2UJW8vydv5hsn/vqK8XDmwH//tuOk1gclNaXT6e67776n5t75deegWCmzLKfU/bk8EZW9+mL5B68T7ynX25W9pDj/rsnl727gagxcTbWLM3ieq6l26LTa55YXLbqPM9b4vEZ/l5ubO2PGjLlz527cuDEvL++hhx4SuqJ21CgIjxw5kpSU9OCDDw4YMGDJkiWClgZXr72C8OLFi+np6TU1rt819Hp9enp6Xl5eO313L9q5c2dQUNDixYs7der04osvpqWlnT171vdlPP1VxtLPLpitXEtONpjtd7xx8r/7L7d3VU3FxMT8uXlj94/+E1xdNSc65M8ai/uIY4iIt1krN3+gXblEkCy05efm3zXJdulibkVVttlm4lzXUOXgss22wsoq07HU/LuncYYqH9fp5xITE9PS0qZNm9arV69ly5bt27dP6IraUaOh0SVLlixZsuTIkSOnT5/evHnzsWPHBK0OrlJ7BeHo0aN79eq1d+9el49+/vnnvXr1uv3229vpu3tRZmbmwIEDnccqlapHjx6ZmZk+ruG93blv/HypxtLMMGN9RqvjX5+c+/mMhw5Zu7Ckp1U88zhvNhFRrsWWoJB66roSERFnMtbs+7nsrfU+KK/B9zVUFc6fxVVV8g77jPTCv50r+KPa7PLMLWXVfztXsDinlLda7AWXCxfO4x2t+F2IQWRkpPOgsLAwPj5e2GLaVf0eYXFxcWpq6oIFC4hIo9FMnTp169atAtf3F6PVUVFjs9hcf3Tev3//Bx98oNfrt2/f/s033/i4Nj+EyTKu1Vgc2VojERXoKu0kdR4TESNVZBeW133pA9la05LPzpta1hesz2hxzHztxL6VI8LVvvot26z8ogfIbCYiC8e/XqR/sFNYS57HmYyVX2xS9Oqr6NO/nUu8ovTlVQ59Oc+17gfL26zWixkVm96JnL+onQrruKxW67p16xYtatFP5syZMwzj4mNSbq7wl43dMZvNlZWVcrk8PDyciAoKCsLDw8PCav/IExMTL168KGB51WbHV4cLNx8sOJFTZbZxEobsHB+mlo5MiZg3uvOtQzvJpbU9n9LS0uzsbOfU3MuXBRg68jcIQtd2ny3tvngfEVFWFeVd2OY8JqLzOSe2lDxxaJ+b5/kXg9k+ZMVvPvt288t+eaxMr+J5E8f/65J2UJBiZlRwC5/LW8wl/360XcvzFt5sqtz0XtiMuySR0ULX4kcsFsudd945YMCA++67ryXnL1y4sNlzdDpdVlaWRqPp1KmTWq1uc41t5RwXtUpD1fftio9QhprzDCbrQx+djQtXRATJzuRVmmq4tPzqhEhlmM8+fRIRkd3Br/8he+33WcRTtcVR20hEROXVth2ntAculD2ySbJhTq97xiQQ0W233Xbbbbf5skI/17rfltFoLC8vj4uLk0gk7VSQ3+k0gE58QGY9KcOp4iJZqii6p9A1+SMJOf5Z/rOKt+RYbAuzdRPCVI/HRzQ7LtpB8RxX+cWmyIXLGrVbrVatVhsbGyuXywUpTCh5eXkzZ868/vrrN2zY4LKf19TUqVNd3sd26tSpulsMd+7cee+99zqPlUplREREfHx8XFxc/YO6Y41Gw7bzLS7OICRlmNnGZWuNZFGQxfjBztOkDCci+v0EhSbsXH6AiJQyNiJIFh+hdGZkvQNFXLgyIkiqCVewLftBNaugwnzLy8cuaY01f0VgUwazg8ix8ONznx8q/ObxwSFKdIEaaMWPY+XKle+//358fLxOp/v4449vuumm9ivLj4TEU7cJ9Msy0lxL+anU/y6SqoSuyR8NM2axxGWYrLMyiiOkbLmdW5VXRkSL4sJjZYH2sYm3Wqq+/7pREH700UerV692XkVes2bN/fffL1R5Pnb69OkJEyZER0crlcoVK1YQ0dNPPx0aGur5WU888cTo0aObtq9bt64uCIODg4cPH15UVKTT6UwmU1FRUVFRkbsXlMvlMTExnTp10mg0MTExsbGxGo0mNjbWeeBsaeMneOcFQlJG1H6tCCHNtXR+C117P1XlU+ExGjDH+YjZxhXpLUV6tzcyKWRsZJDMVUY6GxVdo1RSSfNJmVtqGrby9/Jqm73RVC+bkaw1pAghqbKurcZiP3ChYsSqQ4dWX+/jPqufa+nPori4uLi4OD8/Xy6Xf/TRR0uXLj19+nS7VuZHrltIujQyFFHKLRSRLHQ1fmpUzQW1wxoulaztGlW/Xc0GZreQMxjsJcXSTrV9GrvdvmjRov379w8dOvTYsWNjx46dN2+eVCqK9xq1Wv3iiy/Wb/HWiFH9ETyTyVRRUVFUVFRYWOjyoKSkpKCgoKCgwMMLKpXKpn3KuoOEhIS6C34u/RWE9c4Z+k/6bR1tmUOcnQbOo5DOLfxfs/yVlOcKXN2046xWxrrvU8q6RCklLDP2hcNl1TZHXQqWX6Tz31LxCbIYalvU0RR/HfWZScEaIjLbHNla45QNf+x7Zri3uqQBoKX/UDUazYcfflj3pUrVol7R6dOnXY7sZ2Rk1B07HA6TydT0HKGYzWYiIt5B9npVRSTXRqDdj0olu3OWI+8PVV1TnW3h7MES5sawxn8bRo4jIo7nicjM88ZWzk9pV8p6o2nnTVapq7eGHIutaSMjl1mzM+qCkGEYlmUzMjKGDh1qNBrVanULRwgDQEpKSkpKSnt/F5VKpVKp4uPjhwwZ4vIEi8VSVlbmISx1Op3ZbM7Ozs7Oznb3XdwNwDoPsrKyiOr1CIkoJJ7+/ibZjCRVEOPlkQ/nAKyHqXkSluF44utuPTr7BZ3+jIgnVkZR15BMTZZK0udQ1o+UvZtGPE5JY50ve+JS1Ws/5iyZ1M27BXdcrfvE+tVXX23ZsuXkyZNbtmxpyfnOcRLPtm3bNnPmzFaV0d5YluUKjtH/86+q3LLW+EOpC1pwDkN02wW3Q1uC2N47vreq9nre/xW0YkkU3sFz+vK6LyUSyYcffjhnzpxPP/00Kytrx44dIrqO7h8UCkV8fHx8fHzfvn3dnVNRUeGMRpd5efnyZYPB4HkAlogocycVHCVVJKkiSR5MqihSRfx1EEnKMK8nojuO+sOhmTvo9KdEDPWZSX1nkeyvHoipnI6/T3m/UeorpAwnzSAiqrHYn/02474bEyKCZL4p1c+1LggHDRqkUqmqqqreeuut9957r9nzJ0+e7HJp8zNnzuzfv7+2AqnUrxa+czgcZrOZGJYk/j/fgSe7hYghqYtV/H1MydkY8tTVs3A8R6RgGb9azah+MRPC1PFyF/8iLpisx5rcYsgwzv9qmc3mV1999bnnnktKSnrrrbcee+yxvXv3KpVKAn/inFzj4QS9Xl9cXKzT6bRarfOgpKSkruXSpUsOh4O3GakyjyrdrwciD67NSOdBo7xUx5LM2/MMLFV08iMiogFzqN+dDR5SRdIN/6aD6+jy73T0DZqykRiWiBwcvb8n799Tu3u5ko6pdUHYs2fPnj17Dhw4sFu3bq+++mqzE5ofeuihyZMnN21/55136oJw2rRp1dVuR8l9b9u2bdOnT6fOw2jMSqFraY7VQN/MJnkQzfxK6FLo3ctvja457+GESecLM0zWLT3jeqr89BPG3JiQ0aEu3qE+1lY1DUJi2Pq3T/zyyy9ardY5BDJ79uzExMRdu3ZNmzatPesF7wsPDw8PD+/Vq5fLRydOnLhnzx4atZzCE8lUQaYyslaTqZxM5VcOzHqyVpPV43uaRN4wIxuFZQipIjwvo9hY9i9kt5A6ivre4ephhoY+TPmHqbqECo5RwnAiMlkdH/x6GUHo1NIg1Gq1//3vf5ctWyaRSM6cORMWFia2CeLCs1SS7hx1Hk7+1aeqdUrVbYQxQ8a7ncAdaKwWecqVG2liY2OrqqpKS0ujo6MrKyuNRmNMTIyA1UF7qL19IrQLhSVSWKLb86zVf2Wkq7A0lpLDWtvijjMpXXYoawdgwxu8DxSfIiJKuN7tqKwqkjr1p+JTVHzSGYREVKg366qsMaF4J29xEKrV6uPHj3ft2jU+Pj43N3fjxo0imRHnF6wGOvUx5R8hcwXdscUfBkKb+i2o133le9oWhHzrPgULio2Mqt8jHD58+MMPPzxw4MB+/fqlpaUtWLDg+uuvF7A8f5abm8vzvLshyscee+yBBx5wuWev4GpnjarCmznPmWEeWAxk0ZO5kswVZK47qCBzJZn1ZNaT3VyblO4GYBkJKcNIEUZjV5M6miouEVEzc9ojkqn4FOlz6hpUMvZsvmFcnyj3zxGLloZZcHDw119/XV5ertfru3TpIpPhEqsPSRSUOIaufYC+9t/VWU8qk82sIphryw5QHSYFGaUqdMZdjRrXrl27atWqwsLCmJiYkJAQQQrrEDz3ldVqtT8sItOUw+EoKysjhiFFM7dINk8RQooQCu3i/ptZyVpNVoOnAVjngfM2QauBiEjhcUVD513/1itLxjt48nCno6i0rlcXGRlZt7ou+I5ETp0Gkt2v/2Q5hnkjetK/tVvVbcrCjoFh2bDb5zZtVyqVycm40zQwlZaWOhwOUvhkUqhEXjso6m4AlrOTpYrMFSQPIuKJ54iomb2jnUOp9W5e4nmyOwTbAc2vYHjTtUmDYj79wC+Wzikot4x49ncPiyd5EBOiOLF2VJDCR5O5ece4qnuOcpdzeL7VdwoyCmXcW58oUny3fF3pK2uqd//IW1zvOOEBq1JHPLqMDW3ReuIQMJwXCFMS4zc+M6K40qKttOgM1iK9RVtp1RmsxXpLSaXFaPXVNXJWWpuUTrIgshqamaHjfLTemK2EpfAgRAARgtAdmYT1kztsIoJkmx4aeO/7fxpbufNPkELy/RNDEiJ9OYNfFvLaxvy5U/hW7l7LqFSR/1yqGjysncpyKWbFWvOZU/aCfN7h4n55dxiFQjloaPgd89qvMPBPzguEXRPibuztdlTMZHVU1NiK9JbCCktFje2vY3PdQUmllWuPrTdD4qksnaryPZ3jvNwYmlDX4OD4Hp386NY1AbVXEKanp3Mc5+4Gwfnz58+ZMwfTbVro9uGaIr35qc9Ot3zzJ7Vc8vnCa0ekNHdV39tkid3i3vqk6NF7OJOxhXvtsip16O13h895oL1ra4RRKDtv/Krgnun2Ut3+vgmc+9Xg5sSE3BYVLGUYRqmS9+it2fA+iWbVGKjTaG96l1RyiUouiY9QDnG/ZktFjc0Zk0V6818HV8LycpnJdhXDlTF9qCydik7QgLtdn+CwUslpIqKY3vVamZ7xCEKi9gtCz5MF5HI57r5olRl9pTQ5/LH/EdUUkzLc81XxmFD5d0uGjOzh6cbh9qMcOKTzJ1uLHrvfXlTQbBYycnnkkmfCZtzp+bR2IomKSfh8R/GTjzBppzij248ZMoaRSRhGpQq5aUr00y8wmCkmSo32pr9qzmW1PZxgsjrq+pRNw7KgwlzZdHQoeSJd2EplGVR8yrl2TGOZO8laTVIVdb3B2SBhaNqQWCw36oQ+Wcfw5JNPZmRkXNN3YEbqq9R5GPWf4+Hksy+Pjg0Vcva5PLlH12/3XBo7iLc0swhqzNNrQ6YIuS8aGxoW/95n1bu2l762lqup5k1GarQUqkTKyuXSzl2in1yjGjJcoDJBeC3pEXqFSi5JjlUnx7qdOmsw24sqLF8eLnrpuyyTcw/68CRKGkc5e+nQehr3HEU0vE0+/wj9+QkRUb9ZJKvtAirkkqWTMLGrFoKwY/j888+J6Mxlw4B/H2z25GA/2GyMkcsZhaLZIJRE+cXetsE3Twn+22TTiSM1v/5kPn7Epi3mrRZWFSSNi1cPuyHopkmKnm6XrwSR8FaPsO1ClNKQOOkzt3b/38GCi9qa2mGXof8kQwGVZdBPi6nrKOo0gOQhZCqjwuNUdJyIqOsN1PvKh84hSWHXJrX5PpBAIfWhEcYAABGcSURBVPw7JoBfYBjVkBGqISOErgP8lM96hC3EMsxXiwaNXnO4dqqqPIgmrKUzn1PGD5R7gHIPXDlVHkL9ZlHPW+tf2+4c4Y9LFggFQQgA0DxnEPpDj7DO4KSwt+/ru3BTWm0WSlV07QPU/y4qPk3VRWQ3kTyYwrpSTF9iG7/Vf3us+HKZuUsU1oUnQhACALSEc2jUf3qETveOSeCJf/Tjcyaro3aMVKqqW03UA5uDf2NXzvq7XC8vLjb+uHwzAIC/0el05Gc9Qqf7xnTZ98yIxGi1p8kBZem0dxVl767f9v6ePL2xFTfRBjAEIQBAM/R6vdlsDg0NVam8vZWgN1yXHJa54ca37unbNyFEKWNDVdLGC/caCqnoOJ37uv4dTQaz/d3d7ndVFBMEIQBAM/xnyqg7Uglzz5jOZ18enfP6uM8WDrqpX8P52IljKKgTVeVTwZH6za/9eMnks2Xh/BiCEACgGf42ZdSDTmGKydfGfvrIIJW83iLDjIR6TyciSmuwibe2yrr5YIFvC/RHCEIAgGZ0oCB0ig2VzxvduUFT8t9IEUZlGaRNq9/8fz9kOzix70GBIAQAaIb/D4029eTkZEn95XOlCrpmMhHRua/rn5atNW45Vuzb0vwOghAAoBkdrkdIRMmx6hnXaRo09ZxCUhUV/kEV2fWb1353sT22xOhAEIQAAM3oiD1CInp6WvcGq2rLQ6j7TUQ8Xdha/7RTuVW/nivzcW1+BUEIANCMjtgjJKJBiaHj+0Q1aOo9g1gp5R6gGm395pe3X/RpZX4GQQgA0IwOGoREtHxKw50o1DHUdTRxdrqwrX7zL2dKj1+q9Gll/gRBCADQjA46NEpEN/WPHtKt4falfWYSMXTxJ7JU1W9e/0ODC4eigiAEAGhGx+0REtGyyQ33HQxPovihZLdQxg/1m785WpxZXOPTyvwGghAAwBOTyWQwGBQKRWhoh9zAb+YwTUqnhtv89plJRJT+Pdmv7Bjq4PhXd17ybWn+AkEIAOBJXXeQYZhmT/ZDEpZZ0mgz+th+FN2brIZGy3BvOpBfpLf4tDj/gCAEAPCk414grHPfjQlx4Q134u1zGxHRuW+Js9e1WWzcWz/n+LQy/4AgBADwpENfIHRSythH/5bUoClhBIV1JaOO8g7Wb377l9xKo51EBkEIAOCJH+5NfxUW3pQYpq6/YSFDvaYTEZ37hujKujKVRvvGfZd9XZzQEIQAAJ745970rRWmls4f26VBU7fxFBRD+hwqPF6/+T87L1ntnE+LExqCEADAkwC4Rui0eFI3ubTeez4rpWumEjk7hVcUVJg/+73Qt6UJDEEIAOBJAFwjdOocoZwzKr5BU8rfSR5M2jNUeqF+88vbL3JiWocbQQgA4EnABCERLZ/Sna1/E4hMRT3+QUR0vkGnML2oZvsJLYkGghAAwJOAGRolop5xQVMGN/wf6TmVJHK6fJgq8+o3r/1ORMtwIwgBADwJpB4hET09reEy3MpwSp7YdG+moxf1B9PLfVqZcBCEAABu2e328vJyiUQSFRXV/NkdwbDu4aN7RjZo6n0bMRK69CsZdfWbX94ulmW4EYQAAG7pdDqO46KjoyUSidC1eM3yKQ1XXAvWUJfribNT+vb6zTtPaU/lNtihIlAhCAEA3AqkC4R1Jg2KHZTYcAHxvrOIGMraSdYrG1DwPG0QxzLcCEIAALcC7AKhE8PQkkndGjRFdCPNQLKZKHNH/eYvUwtzS00U6BCEAABuBWQQEtGdI+MTo1UNmvrcTkSU/h05rHVtdoco9mZCEAIAuBWQQ6NEJJUwjTuFmkEUmUJmPV3aU795497LpQYrBTQEIQCAW4HaIySi+eO6RIfIGzQ5N+w99w3xV9YaNVodb/+S69vSfA1BCADgVqD2CIlILZcsvCmxQVOXURQST9XFdPlQ/eY3duVUmx0+Lc63EITQbmy2Zk/hLWYfFAJw1XQ6HQVoEBLRopuTgpX1bgthWOo1nYihsvT6p5VX2zbtD+S9mRheTCurtoRer8/KyoqIiOjevXvzZ/vWmcuGAf+ut4smz1HFRWIkFNHgrqCaTTer5ULe8+QoLytZ/oj55LH6f11ZZpuJ43oo5Ur2ylKHbFCQZsMHquuuF6JMgBYpLS0NDg5WKpVCF9IuFm0+9+aunCtfO6xkKKTwpEandYlSXvzPOJmEoUCEHmFj4eHhQ4cO9cMUdIFhKbJHoxQUnO1ybv7sW8ynT/J8gy3NUpSy/mpF/RQkIq6mpujx+VXbvvJtjQCuVVZW6vV6h6PBMGB0dLQzBS0Wi16vr6mpcfPsDmnZP5IbxJu1mqRKshkbnXa5zPz/DheZTKbs7Oy8vDwKLAhCevjhhzUazfPPP+/y0YyMDI1Go9FoLBaLjwtr6vtvPqctc2jfs27P+OFh2jLn4IEDPiyqAUd5WeEDM+0V5dk1xhFn8kecuWx1M+TwVG7piDOX3yuu5M2m0vWra/b86ONSAZqKioqKiIj4448/XD760ksvRUREzJo1y8dVtasuUco7RsRd+frgi/T9A40mjjq9vP3ir7/u7d69+3XXXee7+nwCQUh6vb6kpKSqyvVKQjabraSkpKSkxB/GkM0mI5n1ZHG/6JFZT2a91SpYZpc8+U9HlZ44zs5Tqc1RanO4+7FVOrhSm6OG44iIN5u1q5+wFxX4tFYAICKi5VO6My0Y8jxz2XA8p7L9yxEAghC8pubXnyzpaXwL5sg0xVks2rUrvF4SADSrf5eQWwbEtOTMb44UtXcxgkAQgteUvfEyZzQSXdXldIfDcuKoNTvT20UBQPOWT2nRrIgzlw3tXYkgEITgHdbMC46yNm1pzdvtmDUDIIgbe0eO7BEhdBWCQRCCdxh/38fb7G15Bd5uM+7/xVv1AECrLJvcrdlzmJZcS+yApEIX4C94nrfbXbyPN5pI7S94v6vK9OcfvM3FgoQckcsJMy7n0NiKCnm7nZHizxKEdOnSpeDg4KbtzpvrA9WtQzR9Ogefc35RXUTas/UflUnYcX0j+yexG/YKUVw7wztOrQ0bNmzYsEHoKlqmLIO+mCp0EY05il1fRe9/qhWrFDIymaOiTBoTgOs6Qgdy5513Cl2CABiGlv4j+YFNRER04Tu68F39R21EP++inwWprP0hCGvFxMTExcU1bbdYLOnp6U3bBRETIicipUrVJdH1TfQXMy9wDodciNUfeFf9aSLqqZK7rKbAajc4uMatDEP+2QUHMZkwYUJkZGTT9nPnzqWlpfm+Hp+5+4bO/5SyViKK6UvhiSoZ2zchpH/XEIW09iJaXl7ezp07Ba2xXSAIa91zzz3r169v2p6WltavXz/f1+OSTMoS0YD+/Y8cOeLyhKioqPLyckHG8SXh4S7bt/SMU7Au6nkkW/uzvvHqFWS3sqFhXq8NoFVefPHF4cOHN21fs2ZNYAehTMLEhStyiym634Rnnly8YHwXVcPFGnfu3BmQQYjJMuAd8mv6UJsDmFWqWXWQV+oBgKsQGyonolXTUx67JUkl6JLFvoQgBO9QDb+BDXIxv6BVFNcG2tJNAB2LhGUocGeHuoMgBO9Qj7iB7G26vMcGBYVOmemtegAAWghBCN7ByBUht93FKBRX/wpKtfrGiV4sCQCgJRCE4DWRD/6LkV1lELIqdczyNYwEs7cAwNcQhOA1bEhop7WvsapW71/KKJXq628MmvD39qgKAMAz7FBPx48fLygoSElJ6dOnT9NHDQbD3r17iWjy5MksK/Dnhtzc3D///DMiImL06NEuT/jpp5+sVuvIkSNjYlq0lnx70H+xqeLt9YbqmsPVZiIaF6Z2+VM7bbTobI4khSwlLER+TZ/OH3zOyK9+WBXAKzIzM3me79q1q8v96MvLy5271cfHx/u+Nt+orKy02WzBwcEufwI2m62yspJlWZf3WXZcCELwPsOOLaXrnuGtFr7pLfMNsSqVasSYTmtfRwoCgFAQhNAubLnZ2ueWW9LP8VYzuYpDNiiIkSmin1wdfPMU35cHAFAHQQjtyHLuTNWWz2p+2+soL2MVCuKJ5xzEsMr+14ZOnx00/hZGJhO6RgAQOwQh+AJvNtnLdETEqoIkkVFClwMAcAWCEAAAWuGnn3565plnnMe9evX69NNPha2n7XDbFgAAtML58+d79+69Zs0aIlKpVEKX4wW4j7CB1NTUoX+ZMsXfJ3GcOXNm7ty5Q4cOnT59ekZGhtDluHD48OG5c+du2rSprmXr1q3jx48fOXLkunXrOK6ZOaUAAkpNTVWpVAMGDBC6EL9TWFjYs2fP5OTk5ORkl7vXdTjoETaQmZkZHx//2muvEZFcLhe6HE8yMzPHjh377LPPrlq1qri4OCEhQeiKGlu9evWxY8dqamrqdq45efLkvffeu3nz5s6dO997771KpXLx4sXCFgngktVqffDBBxcvXvzDDz8IXYvfKS4uPnv27G233RYUFLR06dKBAwcKXVFb4RphA+vWrdPpdK+++qrQhTTv0UcfNZvNGzduFLqQZqxYscJisbzyyitE9PDDD8vl8jfeeIOItm3btnz5cv/Z9BigvtWrV2dlZT3wwAOPPfbY6dOnhS7Hvxw/frywsLBHjx47dux48cUXMzIyoqOjhS6qTTA02kBxcfGpU6dmzZo1Z86co0ePCl2OJ2fPnr3mmmtWrFgxZ86c//3vf0KX0yLnz58fMmSI83jw4MFZWVk2m03YkgCaunDhwrvvvutyp24goiFDhkyZMqVXr15Lly6NjY09ePCg0BW1FYKwgfnz5y9cuPDZZ58dM2bMxIkTc3JyhK7IraKioi+//LJ3794zZsxYuXKl/3cNichgMKjVaudxcHAwx3HV1dXClgTQCMdx8+fPf/755wPj6ld7uHDhgvPAYDDodLoA+EHhGmED/fv379+/PxH16dNn8+bNu3fvnj9/vtBFuRYZGfn444/PmjWLiLKzs7/77ju/LbWORqPRarXO45KSEqVSGR4eLmxJAI188cUXhw8f1mg0u3fv1mq1eXl5Tz311Lp164Suy1/Y7fa7775boVAMGzZsz549kyZNGjFihNBFtRWCsIGMjIyUlBSWZa1Wa0FBgUajEboitwYPHvzbb785g7CjjNHfeOONP/zww8KFC4lox44dY8eOFdtG2OD/Ro8e/dNPPzmP//zzz7y8vOnTpwtbkl+RSqWpqamHDx/OyMiYOXPm9ddfL3RFXoDJMlfwPD9x4sTq6upRo0b9/vvv0dHR33//vUQiEbou1zIzM0eMGDFz5ky73b5169YDBw7069dP6KIauHDhwscff3zgwAG73T5+/PgnnniCZdlrr712xIgRCQkJH3300Y4dO0aNGiV0mQBu7d27F5NlxABB2ADHcUeOHDl//ny3bt38v79SWFi4e/duk8k0depUPxymz8vLq/tkTUSzZs0KCwurqKjYunVrTU3N3//+95SUFAHLA2iWyWQqKyvzw3uTwLsQhAAAIGqYNQoAAKKGIAQAAFFDEAIAgKghCAEAQNQQhAAAIGoIQgAAEDUEIQAAiBqCEAAARA1BCAAAooYgBAAAUUMQAgCAqCEIAQBA1BCEAAAgaghCAAAQNQQhAACIGoIQAABEDUEIAACihiAEAABRQxACAICoIQgBAEDUEIQAACBqCEIAABA1BCEAAIgaghAAAEQNQQgAAKKGIAQAAFFDEAIAgKghCAEAQNQQhAAAIGoIQgAAEDUEIQAAiBqCEAAARA1BCAAAooYgBAAAUUMQAgCAqCEIAQBA1BCEAAAgaghCAAAQNQQhAACIGoIQAABEDUEIAACihiAEAABRQxACAICoIQgBAEDUEIQAACBqCEIAABA1BCEAAIgaghAAAEQNQQgAAKKGIAQAAFFDEAIAgKghCAEAQNQQhAAAIGoIQgAAEDUEIQAAiBqCEAAARA1BCAAAooYgBAAAUUMQAgCAqCEIAQBA1BCEAAAgaghCAAAQNQQhAACIGoIQAABEDUEIAACihiAEAABRQxACAICoIQgBAEDU/j+khBhIrQGqZwAAAKB6VFh0cmRraXRQS0wgcmRraXQgMjAyMi4wOS41AAB4nHu/b+09BiDgZYAAJiDmBmIuIG5gZFNIANIszFCaCcZnZNAAKWYkl+ZmYGRgBFrFDDSMgYGVgZGNgZGdgYmDgYmTgYmLQQSkSLwPpBLqLJCbDuzv0du1GCaAYB+wR7AdDvxstlGFiu8HySGx9zPAAYzdoIpQ4+CAbCaSXnuYejEAt/cjTPbQ4E8AAAD0elRYdE1PTCByZGtpdCAyMDIyLjA5LjUAAHicjZJJDoMwDEX3OcW/ACghTFkyqVQVILW0d+i+91dtEDgIKcJhYTsvlu2PAtuzfXx/2C1plQJ04HPO4WO11moAO6i7231EM1f1lmmm9zi/YAyMxnKObDVPw5YxaBCZONNsiHSsV0+cjUyI3LMh0BJ4qWKKHpE935/AjMG95O6cwfwARgGyIFKmCYClDwY4R5y9sh7SpMeVYUg/Dwz12I3tQdRV5noaW5GZTyJaUgArivFtKrpwmMn2Ocxlx/y2kEVyWMq6DJVzshUOjT/8kjB+636jHG//MvnqD1TClKwLOZCQAAAAuXpUWHRTTUlMRVMgcmRraXQgMjAyMi4wOS41AAB4nG2PMQ+CMBCF/4ojJKXptbTQY3TBxbgTBmMcSCQl2JEfL62mZ43D3b2X773hhn48FkM/lnH9l/sctqJSTLCuAq7TDfNWcXUiEJFMPJ98LKdOqpTs6t18Wd2Cgk/P07w8ptvkOew2kLPzd64QyAiUeVASq1HlTBHTWJMB1HlQEzNocmaINdiQkdjmwZZYizZnlphF+PkVvp4Fsb0Aoyh329TuSDUAAACcelRYdHJka2l0UEtMMSByZGtpdCAyMDIyLjA5LjUAAHice79v7T0GIOBlgAAmIGYDYlYgbmBkU0gA0izMHGCaiZGRQQOkCCfNzcAINoIZqImBkZVBBCQqHgSSgprPwPY37uz+u59X7AJxWh8/3Ku3jmMfiP3Q3+1AHBebHYhdUrxnf4fYfnsQu83r3n6m7P37QewnW1ntz+tA2GIAMTEgmCqUGdIAAADyelRYdE1PTDEgcmRraXQgMjAyMi4wOS41AAB4nH2RS27DMAxE9z7FXMACRepjLmM7SIsiNtC6vUP2vT8qxnCUAEJJLYbSIyVSHcw+54/bLx7Gc9cB9M9SVfwIEXVXmMB4vrwvmLbTeOxM6/eyfSEhlozir+RpW6/HjseE3rtEISSPnhyR50FhwqzmMlY7955Y812JhJwbpOANvTjSIXF5QAFp0FbJYKB3ITNnQRHKQ44NMO5glpSC3lM0higNMhWSXGQWyjsoMbZKnpf5ZRD7aMZ1metozLn2b6HUJi0MtRULY31wyUR6vuy5tMXHfxXd/QHiCmLmZYPTjQAAAMB6VFh0U01JTEVTMSByZGtpdCAyMDIyLjA5LjUAAHicVY09D4IwFEX/iiMmpXmf/XJ0wUXdCYMxDiQSiDLy4y04NAxtes+577Vtutu5apvuuF35HJYKrBIxeFOjlciq5lSDRQSKmeUXs3ifIVoHIm5lAEghAmbKFmJwbNYihBi3ongib9Z9FMJGPDun/y9U1BzNYx6H+2ecEtj+exmmd//sZ4s5ruY6zi+rCUvARCVA4v0UF0dJ9k6K46R7p8XJ8gO5j07fLyWurAAAAABJRU5ErkJggg==", "text/plain": [ "" ] @@ -506,7 +503,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "" ] @@ -572,746 +569,26 @@ "id": "f3bce367", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWGothic-Demi.otf', name='URW Gothic', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSansBoldOblique.ttf', name='FreeSans', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstQurn.ttf', name='KacstQurn', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/C059-Italic.otf', name='C059', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Sawasdee.ttf', name='Sawasdee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgMono-Bold.ttf', name='Tlwg Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Laksaman-Italic.ttf', name='Laksaman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypewriter-Bold.ttf', name='Tlwg Typewriter', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst-one/KacstOne.ttf', name='KacstOne', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgMono.ttf', name='Tlwg Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWGothic-BookOblique.otf', name='URW Gothic', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Purisa-BoldOblique.ttf', name='Purisa', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-tamil-classical/Lohit-Tamil-Classical.ttf', name='Lohit Tamil Classical', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Suruma.ttf', name='Suruma', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSansNarrow-Oblique.otf', name='Nimbus Sans Narrow', style='oblique', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi-Bold.ttf', name='Norasi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi-BoldOblique.ttf', name='Norasi', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='condensed', size='scalable')) = 1.535\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-deva-extra/chandas1-2.ttf', name='Chandas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypist-Oblique.ttf', name='Tlwg Typist', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-gujarati/Lohit-Gujarati.ttf', name='Lohit Gujarati', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWGothic-DemiOblique.otf', name='URW Gothic', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/LikhanNormal.ttf', name='Likhan', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSerifBold.ttf', name='FreeSerif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush-BoldOblique.ttf', name='Umpush', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSans-BoldItalic.ttf', name='Liberation Sans', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-oriya/Lohit-Odia.ttf', name='Lohit Odia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Loma-BoldOblique.ttf', name='Loma', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWBookman-Demi.otf', name='URW Bookman', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-LI.ttf', name='Ubuntu', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari-Italic.ttf', name='Kinnari', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 0.5349999999999999\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSansNarrow-BoldItalic.ttf', name='Liberation Sans Narrow', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-assamese/Lohit-Assamese.ttf', name='Lohit Assamese', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSans-Regular.otf', name='Nimbus Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari.ttf', name='Kinnari', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/padauk/Padauk-Bold.ttf', name='Padauk', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Garuda-Oblique.ttf', name='Garuda', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWBookman-Light.otf', name='URW Bookman', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/pagul/Pagul.ttf', name='Pagul', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypo-Bold.ttf', name='Tlwg Typo', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeMonoBold.ttf', name='FreeMono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSerifItalic.ttf', name='FreeSerif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationMono-Italic.ttf', name='Liberation Mono', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Sahadeva/sahadeva.ttf', name='Sahadeva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-malayalam/Lohit-Malayalam.ttf', name='Lohit Malayalam', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/RaghuMalayalamSans-Regular.ttf', name='RaghuMalayalamSans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-deva-extra/kalimati.ttf', name='Kalimati', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Waree-BoldOblique.ttf', name='Waree', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWGothic-Book.otf', name='URW Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypo-Oblique.ttf', name='Tlwg Typo', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypewriter-BoldOblique.ttf', name='Tlwg Typewriter', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-ExtraLight.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=200, stretch='normal', size='scalable')) = 0.24\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-gujr-extra/aakar-medium.ttf', name='aakar', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstPoster.ttf', name='KacstPoster', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Waree.ttf', name='Waree', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstBook.ttf', name='KacstBook', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/P052-Bold.otf', name='P052', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Karumbi-Regular.ttf', name='Karumbi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgMono-BoldOblique.ttf', name='Tlwg Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-B.ttf', name='Ubuntu', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSerif-Regular.ttf', name='Liberation Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/D050000L.otf', name='D050000L', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-devanagari/Lohit-Devanagari.ttf', name='Lohit Devanagari', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi-Oblique.ttf', name='Norasi', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Keraleeyam-Regular.ttf', name='Keraleeyam', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ttf-khmeros-core/KhmerOS.ttf', name='Khmer OS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/C059-BdIta.otf', name='C059', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/noto/NotoSansCJK-Bold.ttc', name='Noto Sans CJK JP', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/C059-Bold.otf', name='C059', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi-BoldItalic.ttf', name='Norasi', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSans-BoldItalic.ttf', name='Liberation Sans', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Loma-Oblique.ttf', name='Loma', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeMonoBoldOblique.ttf', name='FreeMono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-gujr-extra/Rekha.ttf', name='Rekha', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Gubbi/Gubbi.ttf', name='Gubbi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Purisa-Oblique.ttf', name='Purisa', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-BI.ttf', name='Ubuntu', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi.ttf', name='Norasi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Nakula/nakula.ttf', name='Nakula', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/mitra.ttf', name='Mitra Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/samyak-fonts/Samyak-Gujarati.ttf', name='Samyak Gujarati', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-Th.ttf', name='Ubuntu', style='normal', variant='normal', weight=250, stretch='normal', size='scalable')) = 10.1925\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst-one/KacstOne-Bold.ttf', name='KacstOne', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSans-Italic.ttf', name='Liberation Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Rasa-Medium.ttf', name='Rasa', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/noto/NotoMono-Regular.ttf', name='Noto Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSansOblique.ttf', name='FreeSans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSans-Regular.ttf', name='Liberation Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Sawasdee-BoldOblique.ttf', name='Sawasdee', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/MuktiNarrow.ttf', name='Mukti Narrow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstNaskh.ttf', name='KacstNaskh', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Manjari-Thin.otf', name='Manjari', style='normal', variant='normal', weight=100, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc', name='Noto Sans CJK JP', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Uroob-Regular.ttf', name='Uroob', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-BI.ttf', name='Ubuntu Mono', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Yrsa-Regular.ttf', name='Yrsa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-RI.ttf', name='Ubuntu', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypo.ttf', name='Tlwg Typo', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationMono-BoldItalic.ttf', name='Liberation Mono', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypo-BoldOblique.ttf', name='Tlwg Typo', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Garuda.ttf', name='Garuda', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeMono.ttf', name='FreeMono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Gayathri-Regular.otf', name='Gayathri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypewriter.ttf', name='Tlwg Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSerif-BoldItalic.ttf', name='Liberation Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusRoman-Regular.otf', name='Nimbus Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSerif-Bold.ttf', name='Liberation Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/openoffice/opens___.ttf', name='OpenSymbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSerif-Bold.ttf', name='Liberation Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Laksaman-Bold.ttf', name='Laksaman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/abyssinica/AbyssinicaSIL-Regular.ttf', name='Abyssinica SIL', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-telu-extra/vemana2000.ttf', name='Vemana2000', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-punjabi/Lohit-Gurmukhi.ttf', name='Lohit Gurmukhi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSansNarrow-Bold.otf', name='Nimbus Sans Narrow', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Laksaman-BoldItalic.ttf', name='Laksaman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusMonoPS-Bold.otf', name='Nimbus Mono PS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/ani.ttf', name='Ani', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/AnjaliOldLipi-Regular.ttf', name='AnjaliOldLipi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-kannada/Lohit-Kannada.ttf', name='Lohit Kannada', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/droid/DroidSansFallbackFull.ttf', name='Droid Sans Fallback', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/StandardSymbolsPS.otf', name='Standard Symbols PS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationMono-Bold.ttf', name='Liberation Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSerif-BoldItalic.ttf', name='Liberation Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/Z003-MediumItalic.otf', name='Z003', style='italic', variant='normal', weight=500, stretch='normal', size='scalable')) = 11.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusMonoPS-BoldItalic.otf', name='Nimbus Mono PS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusRoman-BoldItalic.otf', name='Nimbus Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSerif.ttf', name='FreeSerif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSans-Italic.otf', name='Nimbus Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Yrsa-Light.ttf', name='Yrsa', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/P052-Roman.otf', name='P052', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Manjari-Bold.otf', name='Manjari', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-bengali/Lohit-Bengali.ttf', name='Lohit Bengali', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-deva-extra/samanata.ttf', name='Samanata', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/P052-Italic.otf', name='P052', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Gargi/Gargi.ttf', name='Gargi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari-Bold.ttf', name='Kinnari', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/JamrulNormal.ttf', name='Jamrul', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-gujr-extra/padmaa.ttf', name='padmaa', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi-Italic.ttf', name='Norasi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/padauk/PadaukBook-Bold.ttf', name='Padauk Book', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-gujr-extra/padmaa-Medium-0.5.ttf', name='padmaa', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgMono-Oblique.ttf', name='Tlwg Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypewriter-Oblique.ttf', name='Tlwg Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationMono-Regular.ttf', name='Liberation Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/padauk/Padauk-Regular.ttf', name='Padauk', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Rasa-Light.ttf', name='Rasa', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-guru-extra/Saab.ttf', name='Saab', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstDigital.ttf', name='KacstDigital', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeMonoOblique.ttf', name='FreeMono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Rasa-Bold.ttf', name='Rasa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Rachana-Regular.ttf', name='Rachana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Waree-Bold.ttf', name='Waree', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSansNarrow-Regular.otf', name='Nimbus Sans Narrow', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSansNarrow-Italic.ttf', name='Liberation Sans Narrow', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWBookman-DemiItalic.otf', name='URW Bookman', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypist-BoldOblique.ttf', name='Tlwg Typist', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Navilu/Navilu.ttf', name='Navilu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-R.ttf', name='Ubuntu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSerifBoldItalic.ttf', name='FreeSerif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusMonoPS-Regular.otf', name='Nimbus Mono PS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationMono-BoldItalic.ttf', name='Liberation Mono', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationMono-Bold.ttf', name='Liberation Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-R.ttf', name='Ubuntu Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Sawasdee-Oblique.ttf', name='Sawasdee', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuMathTeXGyre.ttf', name='DejaVu Math TeX Gyre', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-kalapi/Kalapi.ttf', name='Kalapi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Yrsa-Medium.ttf', name='Yrsa', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush-Bold.ttf', name='Umpush', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush-Oblique.ttf', name='Umpush', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 0.25\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-MI.ttf', name='Ubuntu', style='italic', variant='normal', weight=500, stretch='normal', size='scalable')) = 11.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ttf-khmeros-core/KhmerOSsys.ttf', name='Khmer OS System', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari-Oblique.ttf', name='Kinnari', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Yrsa-Bold.ttf', name='Yrsa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Gayathri-Bold.otf', name='Gayathri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSans-Regular.ttf', name='Liberation Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWBookman-LightItalic.otf', name='URW Bookman', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstPen.ttf', name='KacstPen', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Waree-Oblique.ttf', name='Waree', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSansNarrow-Regular.ttf', name='Liberation Sans Narrow', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Gayathri-Thin.otf', name='Gayathri', style='normal', variant='normal', weight=100, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari-BoldOblique.ttf', name='Kinnari', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSerif-Italic.ttf', name='Liberation Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Meera-Regular.ttf', name='Meera', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Dyuthi-Regular.ttf', name='Dyuthi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationMono-Italic.ttf', name='Liberation Mono', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstArt.ttf', name='KacstArt', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tibetan-machine/TibetanMachineUni.ttf', name='Tibetan Machine Uni', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstFarsi.ttf', name='KacstFarsi', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Chilanka-Regular.otf', name='Chilanka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSerif-Regular.ttf', name='Liberation Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-orya-extra/utkal.ttf', name='ori1Uni', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lao/Phetsarath_OT.ttf', name='Phetsarath OT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSans-Bold.ttf', name='Liberation Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSerif-Italic.ttf', name='Liberation Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Rasa-Regular.ttf', name='Rasa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-gujr-extra/padmaa-Bold.1.1.ttf', name='padmaa-Bold.1.1', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush-Light.ttf', name='Umpush', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/P052-BoldItalic.otf', name='P052', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypist-Bold.ttf', name='Tlwg Typist', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstOffice.ttf', name='KacstOffice', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSans-BoldItalic.otf', name='Nimbus Sans', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/mry_KacstQurn.ttf', name='mry_KacstQurn', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSans-Italic.ttf', name='Liberation Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-telugu/Lohit-Telugu.ttf', name='Lohit Telugu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstTitle.ttf', name='KacstTitle', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Rachana-Bold.ttf', name='Rachana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/samyak-fonts/Samyak-Malayalam.ttf', name='Samyak Malayalam', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSans.ttf', name='FreeSans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSansNarrow-BoldOblique.otf', name='Nimbus Sans Narrow', style='oblique', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Laksaman.ttf', name='Laksaman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Purisa-Bold.ttf', name='Purisa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/sinhala/lklug.ttf', name='LKLUG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstScreen.ttf', name='KacstScreen', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSans-Bold.ttf', name='Liberation Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/samyak/Samyak-Devanagari.ttf', name='Samyak Devanagari', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Rasa-SemiBold.ttf', name='Rasa', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Loma.ttf', name='Loma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/C059-Roman.otf', name='C059', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/padauk/PadaukBook-Regular.ttf', name='Padauk Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='condensed', size='scalable')) = 1.25\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-tamil/Lohit-Tamil.ttf', name='Lohit Tamil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Loma-Bold.ttf', name='Loma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSansNarrow-Bold.ttf', name='Liberation Sans Narrow', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush-LightOblique.ttf', name='Umpush', style='oblique', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Manjari-Regular.otf', name='Manjari', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusRoman-Bold.otf', name='Nimbus Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Sawasdee-Bold.ttf', name='Sawasdee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Yrsa-SemiBold.ttf', name='Yrsa', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari-BoldItalic.ttf', name='Kinnari', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusMonoPS-Italic.otf', name='Nimbus Mono PS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Garuda-Bold.ttf', name='Garuda', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/MuktiNarrowBold.ttf', name='Mukti Narrow', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusRoman-Italic.otf', name='Nimbus Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-B.ttf', name='Ubuntu Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/noto/NotoSerifCJK-Bold.ttc', name='Noto Serif CJK JP', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSans-Bold.otf', name='Nimbus Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush.ttf', name='Umpush', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-M.ttf', name='Ubuntu', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-C.ttf', name='Ubuntu Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-telu-extra/Pothana2000.ttf', name='Pothana2000', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Purisa.ttf', name='Purisa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSansBold.ttf', name='FreeSans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Sarai/Sarai.ttf', name='Sarai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/noto/NotoSerifCJK-Regular.ttc', name='Noto Serif CJK JP', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-RI.ttf', name='Ubuntu Mono', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstDecorative.ttf', name='KacstDecorative', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstTitleL.ttf', name='KacstTitleL', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypist.ttf', name='Tlwg Typist', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/samyak-fonts/Samyak-Tamil.ttf', name='Samyak Tamil', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Garuda-BoldOblique.ttf', name='Garuda', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-L.ttf', name='Ubuntu', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstLetter.ttf', name='KacstLetter', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationMono-Regular.ttf', name='Liberation Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n", - "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWGothic-Demi.otf', name='URW Gothic', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSansBoldOblique.ttf', name='FreeSans', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstQurn.ttf', name='KacstQurn', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/C059-Italic.otf', name='C059', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Sawasdee.ttf', name='Sawasdee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgMono-Bold.ttf', name='Tlwg Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Laksaman-Italic.ttf', name='Laksaman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypewriter-Bold.ttf', name='Tlwg Typewriter', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst-one/KacstOne.ttf', name='KacstOne', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgMono.ttf', name='Tlwg Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWGothic-BookOblique.otf', name='URW Gothic', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Purisa-BoldOblique.ttf', name='Purisa', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-tamil-classical/Lohit-Tamil-Classical.ttf', name='Lohit Tamil Classical', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Suruma.ttf', name='Suruma', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSansNarrow-Oblique.otf', name='Nimbus Sans Narrow', style='oblique', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi-Bold.ttf', name='Norasi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi-BoldOblique.ttf', name='Norasi', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='condensed', size='scalable')) = 1.535\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-deva-extra/chandas1-2.ttf', name='Chandas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypist-Oblique.ttf', name='Tlwg Typist', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-gujarati/Lohit-Gujarati.ttf', name='Lohit Gujarati', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWGothic-DemiOblique.otf', name='URW Gothic', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/LikhanNormal.ttf', name='Likhan', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSerifBold.ttf', name='FreeSerif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush-BoldOblique.ttf', name='Umpush', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSans-BoldItalic.ttf', name='Liberation Sans', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-oriya/Lohit-Odia.ttf', name='Lohit Odia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Loma-BoldOblique.ttf', name='Loma', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWBookman-Demi.otf', name='URW Bookman', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-LI.ttf', name='Ubuntu', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari-Italic.ttf', name='Kinnari', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 0.5349999999999999\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSansNarrow-BoldItalic.ttf', name='Liberation Sans Narrow', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-assamese/Lohit-Assamese.ttf', name='Lohit Assamese', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSans-Regular.otf', name='Nimbus Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari.ttf', name='Kinnari', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/padauk/Padauk-Bold.ttf', name='Padauk', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Garuda-Oblique.ttf', name='Garuda', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWBookman-Light.otf', name='URW Bookman', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/pagul/Pagul.ttf', name='Pagul', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypo-Bold.ttf', name='Tlwg Typo', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeMonoBold.ttf', name='FreeMono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSerifItalic.ttf', name='FreeSerif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationMono-Italic.ttf', name='Liberation Mono', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Sahadeva/sahadeva.ttf', name='Sahadeva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-malayalam/Lohit-Malayalam.ttf', name='Lohit Malayalam', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/RaghuMalayalamSans-Regular.ttf', name='RaghuMalayalamSans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-deva-extra/kalimati.ttf', name='Kalimati', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Waree-BoldOblique.ttf', name='Waree', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWGothic-Book.otf', name='URW Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypo-Oblique.ttf', name='Tlwg Typo', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypewriter-BoldOblique.ttf', name='Tlwg Typewriter', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-ExtraLight.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=200, stretch='normal', size='scalable')) = 0.24\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-gujr-extra/aakar-medium.ttf', name='aakar', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstPoster.ttf', name='KacstPoster', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Waree.ttf', name='Waree', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstBook.ttf', name='KacstBook', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/P052-Bold.otf', name='P052', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Karumbi-Regular.ttf', name='Karumbi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgMono-BoldOblique.ttf', name='Tlwg Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-B.ttf', name='Ubuntu', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSerif-Regular.ttf', name='Liberation Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/D050000L.otf', name='D050000L', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-devanagari/Lohit-Devanagari.ttf', name='Lohit Devanagari', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi-Oblique.ttf', name='Norasi', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Keraleeyam-Regular.ttf', name='Keraleeyam', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ttf-khmeros-core/KhmerOS.ttf', name='Khmer OS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/C059-BdIta.otf', name='C059', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/noto/NotoSansCJK-Bold.ttc', name='Noto Sans CJK JP', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/C059-Bold.otf', name='C059', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi-BoldItalic.ttf', name='Norasi', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSans-BoldItalic.ttf', name='Liberation Sans', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Loma-Oblique.ttf', name='Loma', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeMonoBoldOblique.ttf', name='FreeMono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-gujr-extra/Rekha.ttf', name='Rekha', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Gubbi/Gubbi.ttf', name='Gubbi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Purisa-Oblique.ttf', name='Purisa', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-BI.ttf', name='Ubuntu', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi.ttf', name='Norasi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Nakula/nakula.ttf', name='Nakula', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/mitra.ttf', name='Mitra Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/samyak-fonts/Samyak-Gujarati.ttf', name='Samyak Gujarati', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-Th.ttf', name='Ubuntu', style='normal', variant='normal', weight=250, stretch='normal', size='scalable')) = 10.1925\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst-one/KacstOne-Bold.ttf', name='KacstOne', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSans-Italic.ttf', name='Liberation Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Rasa-Medium.ttf', name='Rasa', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/noto/NotoMono-Regular.ttf', name='Noto Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSansOblique.ttf', name='FreeSans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSans-Regular.ttf', name='Liberation Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Sawasdee-BoldOblique.ttf', name='Sawasdee', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/MuktiNarrow.ttf', name='Mukti Narrow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstNaskh.ttf', name='KacstNaskh', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Manjari-Thin.otf', name='Manjari', style='normal', variant='normal', weight=100, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc', name='Noto Sans CJK JP', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Uroob-Regular.ttf', name='Uroob', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-BI.ttf', name='Ubuntu Mono', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Yrsa-Regular.ttf', name='Yrsa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-RI.ttf', name='Ubuntu', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypo.ttf', name='Tlwg Typo', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationMono-BoldItalic.ttf', name='Liberation Mono', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypo-BoldOblique.ttf', name='Tlwg Typo', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Garuda.ttf', name='Garuda', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeMono.ttf', name='FreeMono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Gayathri-Regular.otf', name='Gayathri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypewriter.ttf', name='Tlwg Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSerif-BoldItalic.ttf', name='Liberation Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusRoman-Regular.otf', name='Nimbus Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSerif-Bold.ttf', name='Liberation Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/openoffice/opens___.ttf', name='OpenSymbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSerif-Bold.ttf', name='Liberation Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Laksaman-Bold.ttf', name='Laksaman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/abyssinica/AbyssinicaSIL-Regular.ttf', name='Abyssinica SIL', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-telu-extra/vemana2000.ttf', name='Vemana2000', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-punjabi/Lohit-Gurmukhi.ttf', name='Lohit Gurmukhi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSansNarrow-Bold.otf', name='Nimbus Sans Narrow', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Laksaman-BoldItalic.ttf', name='Laksaman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusMonoPS-Bold.otf', name='Nimbus Mono PS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/ani.ttf', name='Ani', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/AnjaliOldLipi-Regular.ttf', name='AnjaliOldLipi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-kannada/Lohit-Kannada.ttf', name='Lohit Kannada', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/droid/DroidSansFallbackFull.ttf', name='Droid Sans Fallback', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/StandardSymbolsPS.otf', name='Standard Symbols PS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationMono-Bold.ttf', name='Liberation Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSerif-BoldItalic.ttf', name='Liberation Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/Z003-MediumItalic.otf', name='Z003', style='italic', variant='normal', weight=500, stretch='normal', size='scalable')) = 11.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusMonoPS-BoldItalic.otf', name='Nimbus Mono PS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusRoman-BoldItalic.otf', name='Nimbus Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSerif.ttf', name='FreeSerif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSans-Italic.otf', name='Nimbus Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Yrsa-Light.ttf', name='Yrsa', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/P052-Roman.otf', name='P052', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Manjari-Bold.otf', name='Manjari', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-bengali/Lohit-Bengali.ttf', name='Lohit Bengali', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-deva-extra/samanata.ttf', name='Samanata', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/P052-Italic.otf', name='P052', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Gargi/Gargi.ttf', name='Gargi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari-Bold.ttf', name='Kinnari', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/JamrulNormal.ttf', name='Jamrul', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-gujr-extra/padmaa.ttf', name='padmaa', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Norasi-Italic.ttf', name='Norasi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/padauk/PadaukBook-Bold.ttf', name='Padauk Book', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-gujr-extra/padmaa-Medium-0.5.ttf', name='padmaa', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgMono-Oblique.ttf', name='Tlwg Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypewriter-Oblique.ttf', name='Tlwg Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationMono-Regular.ttf', name='Liberation Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/padauk/Padauk-Regular.ttf', name='Padauk', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Rasa-Light.ttf', name='Rasa', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-guru-extra/Saab.ttf', name='Saab', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstDigital.ttf', name='KacstDigital', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeMonoOblique.ttf', name='FreeMono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Rasa-Bold.ttf', name='Rasa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Rachana-Regular.ttf', name='Rachana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Waree-Bold.ttf', name='Waree', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSansNarrow-Regular.otf', name='Nimbus Sans Narrow', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSansNarrow-Italic.ttf', name='Liberation Sans Narrow', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWBookman-DemiItalic.otf', name='URW Bookman', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypist-BoldOblique.ttf', name='Tlwg Typist', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Navilu/Navilu.ttf', name='Navilu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-R.ttf', name='Ubuntu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSerifBoldItalic.ttf', name='FreeSerif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusMonoPS-Regular.otf', name='Nimbus Mono PS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationMono-BoldItalic.ttf', name='Liberation Mono', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationMono-Bold.ttf', name='Liberation Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-R.ttf', name='Ubuntu Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Sawasdee-Oblique.ttf', name='Sawasdee', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuMathTeXGyre.ttf', name='DejaVu Math TeX Gyre', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-kalapi/Kalapi.ttf', name='Kalapi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Yrsa-Medium.ttf', name='Yrsa', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush-Bold.ttf', name='Umpush', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush-Oblique.ttf', name='Umpush', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 0.25\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-MI.ttf', name='Ubuntu', style='italic', variant='normal', weight=500, stretch='normal', size='scalable')) = 11.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ttf-khmeros-core/KhmerOSsys.ttf', name='Khmer OS System', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari-Oblique.ttf', name='Kinnari', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Yrsa-Bold.ttf', name='Yrsa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Gayathri-Bold.otf', name='Gayathri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSans-Regular.ttf', name='Liberation Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/URWBookman-LightItalic.otf', name='URW Bookman', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstPen.ttf', name='KacstPen', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Waree-Oblique.ttf', name='Waree', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSansNarrow-Regular.ttf', name='Liberation Sans Narrow', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Gayathri-Thin.otf', name='Gayathri', style='normal', variant='normal', weight=100, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari-BoldOblique.ttf', name='Kinnari', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSerif-Italic.ttf', name='Liberation Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Meera-Regular.ttf', name='Meera', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Dyuthi-Regular.ttf', name='Dyuthi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationMono-Italic.ttf', name='Liberation Mono', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstArt.ttf', name='KacstArt', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tibetan-machine/TibetanMachineUni.ttf', name='Tibetan Machine Uni', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstFarsi.ttf', name='KacstFarsi', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Chilanka-Regular.otf', name='Chilanka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSerif-Regular.ttf', name='Liberation Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-orya-extra/utkal.ttf', name='ori1Uni', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lao/Phetsarath_OT.ttf', name='Phetsarath OT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSans-Bold.ttf', name='Liberation Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSerif-Italic.ttf', name='Liberation Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Rasa-Regular.ttf', name='Rasa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-gujr-extra/padmaa-Bold.1.1.ttf', name='padmaa-Bold.1.1', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush-Light.ttf', name='Umpush', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/P052-BoldItalic.otf', name='P052', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypist-Bold.ttf', name='Tlwg Typist', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstOffice.ttf', name='KacstOffice', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSans-BoldItalic.otf', name='Nimbus Sans', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/mry_KacstQurn.ttf', name='mry_KacstQurn', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSans-Italic.ttf', name='Liberation Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-telugu/Lohit-Telugu.ttf', name='Lohit Telugu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstTitle.ttf', name='KacstTitle', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/malayalam/Rachana-Bold.ttf', name='Rachana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/samyak-fonts/Samyak-Malayalam.ttf', name='Samyak Malayalam', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSans.ttf', name='FreeSans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSansNarrow-BoldOblique.otf', name='Nimbus Sans Narrow', style='oblique', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Laksaman.ttf', name='Laksaman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Purisa-Bold.ttf', name='Purisa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/sinhala/lklug.ttf', name='LKLUG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstScreen.ttf', name='KacstScreen', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationSans-Bold.ttf', name='Liberation Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/samyak/Samyak-Devanagari.ttf', name='Samyak Devanagari', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Rasa-SemiBold.ttf', name='Rasa', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Loma.ttf', name='Loma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/C059-Roman.otf', name='C059', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/padauk/PadaukBook-Regular.ttf', name='Padauk Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSansCondensed-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='condensed', size='scalable')) = 1.25\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/lohit-tamil/Lohit-Tamil.ttf', name='Lohit Tamil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Loma-Bold.ttf', name='Loma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation/LiberationSansNarrow-Bold.ttf', name='Liberation Sans Narrow', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush-LightOblique.ttf', name='Umpush', style='oblique', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/malayalam/Manjari-Regular.otf', name='Manjari', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusRoman-Bold.otf', name='Nimbus Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Sawasdee-Bold.ttf', name='Sawasdee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-yrsa-rasa/Yrsa-SemiBold.ttf', name='Yrsa', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Kinnari-BoldItalic.ttf', name='Kinnari', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusMonoPS-Italic.otf', name='Nimbus Mono PS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Garuda-Bold.ttf', name='Garuda', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-beng-extra/MuktiNarrowBold.ttf', name='Mukti Narrow', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusRoman-Italic.otf', name='Nimbus Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-B.ttf', name='Ubuntu Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/noto/NotoSerifCJK-Bold.ttc', name='Noto Serif CJK JP', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/urw-base35/NimbusSans-Bold.otf', name='Nimbus Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Umpush.ttf', name='Umpush', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-M.ttf', name='Ubuntu', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-C.ttf', name='Ubuntu Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/fonts-telu-extra/Pothana2000.ttf', name='Pothana2000', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Purisa.ttf', name='Purisa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/freefont/FreeSansBold.ttf', name='FreeSans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/Sarai/Sarai.ttf', name='Sarai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/opentype/noto/NotoSerifCJK-Regular.ttc', name='Noto Serif CJK JP', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/UbuntuMono-RI.ttf', name='Ubuntu Mono', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstDecorative.ttf', name='KacstDecorative', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstTitleL.ttf', name='KacstTitleL', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/TlwgTypist.ttf', name='Tlwg Typist', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/samyak-fonts/Samyak-Tamil.ttf', name='Samyak Tamil', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/tlwg/Garuda-BoldOblique.ttf', name='Garuda', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/dejavu/DejaVuSerifCondensed-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/ubuntu/Ubuntu-L.ttf', name='Ubuntu', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/kacst/KacstLetter.ttf', name='KacstLetter', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/share/fonts/truetype/liberation2/LiberationMono-Regular.ttf', name='Liberation Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/home/richard/miniconda3/envs/openfe/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n" - ] - }, { "data": { - "image/png": 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\n", 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", 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" + "
" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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\n", + "image/png": 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", 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" + "
" ] }, "execution_count": 24, @@ -1594,9 +871,9 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" + "
" ] }, "execution_count": 30, @@ -1635,7 +912,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "" ] @@ -1662,9 +939,9 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] }, "execution_count": 32, @@ -1678,13 +955,26 @@ "plot_atommapping_network(new_network)" ] }, + { + "cell_type": "markdown", + "id": "fe507fdb", + "metadata": {}, + "source": [ + "## Writing a ligand network to disk\n", + "\n", + "If you want to save a ligand network to disk in order to transfer it elsewhere, you can do some with the `to_graphml()` method." + ] + }, { "cell_type": "code", - "execution_count": null, - "id": "0915a377", + "execution_count": 33, + "id": "63da9af9", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "with open(\"network.graphml\", mode='w') as f:\n", + " f.write(new_network.to_graphml())" + ] } ], "metadata": { @@ -1703,7 +993,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.9.16" } }, "nbformat": 4, From 0b3526061ca57e0368356f5a52541d1bc0e53675 Mon Sep 17 00:00:00 2001 From: "David W.H. Swenson" Date: Fri, 31 Mar 2023 18:36:35 -0400 Subject: [PATCH 2/2] Update networks/ligand_networks_for_developers.ipynb Co-authored-by: Irfan Alibay --- networks/ligand_networks_for_developers.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/networks/ligand_networks_for_developers.ipynb b/networks/ligand_networks_for_developers.ipynb index 067d609..36d5f73 100644 --- a/networks/ligand_networks_for_developers.ipynb +++ b/networks/ligand_networks_for_developers.ipynb @@ -962,7 +962,7 @@ "source": [ "## Writing a ligand network to disk\n", "\n", - "If you want to save a ligand network to disk in order to transfer it elsewhere, you can do some with the `to_graphml()` method." + "If you want to save a ligand network to disk in order to transfer it elsewhere, you can do so with the `to_graphml()` method." ] }, {