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@PeridexisErrant
Created October 21, 2016 10:54
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The /r/dwarffortress survey, October 2016
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Welcome to PeridexisErrant's Survey Analysis\n",
"\n",
"I'm going to analyse the results from a survey Jecowa posted on [/r/dwarffortress](https://www.reddit.com/r/dwarffortress) last week ([here](https://www.reddit.com/r/dwarffortress/comments/579g8f/dwarf_fortress_user_preferences_survey_october/)).\n",
"\n",
"While it's always tricky to interpret surveys and our sample is very biased indeed... I don't have to tell my survey design professor what I'm doing over the weekend ;) Oh, and even biased results can be useful if you're careful!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Start by importing some Python packages I'll use\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import numpy as np\n",
"import matplotlib as mpl\n",
"%matplotlib inline\n",
"\n",
"mpl.pyplot.rcParams['figure.figsize'] = (3, 3)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def condense_columns(df, to_colname, col_start, other=None):\n",
" \"\"\"The downloaded file has multiple-choice answers in seperate columns.\n",
" This is silly, so I'll combine them into a new column and delete the old ones.\n",
" Note that this only keeps the first selection though!\n",
" \"\"\"\n",
" col_list = [n for n in df.columns if n.startswith(col_start)]\n",
" if other is not None:\n",
" col_list += other\n",
" df[to_colname] = np.nan\n",
" for name in col_list:\n",
" df[to_colname].fillna(inplace=True, value=df[name])\n",
" df.drop(col_list, inplace=True, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Load up the data. Download it from http://peridexiserrant.neocities.org/survey_results_20161016.csv\n",
"df = pd.DataFrame.from_csv('~/Dropbox/survey_results_20161016.csv', header=1)\n",
"\n",
"# Fix mangled \"smart quotes\" in some column names\n",
"df.columns = [n.replace(''', \"'\") for n in df.columns]\n",
"\n",
"# Now let's condense things a bit...\n",
"condense_columns(df, 'Operating System', 'Which operating system(s) do you use?', ['Other:.1'])\n",
"condense_columns(df, 'OS Bitness', 'What is the bitness of your operating system?', ['Other:.2'])\n",
"condense_columns(df, 'Linux DE/WM', 'Linux users, which desktop environment(s)', ['Other:.3'])\n",
"df['OS Type'] = df['Operating System'].replace(['Linux.*', 'Mac OS X.*', 'Windows.*'],\n",
" ['Linux', 'Mac OS X', 'Windows'], regex=True)\n",
"\n",
"# Set non-answers to our nodata value for better handling\n",
"df.replace([\"I don't know.\", \"I don't know what that is.\", 'I have no idea.', \"I don't use Linux.\",\n",
" \"I don't know what an operating system is.\"], np.nan, inplace=True)\n",
"\n",
"# Split out 'Other' answers and 'Which other utilities do you use?' cols...\n",
"others = df[[n for n in df.columns if 'Other' in n]]\n",
"utils = df[[n for n in df.columns if 'Which of these other utilities / apps do you use?' in n] + \\\n",
" [n for n in df.columns if n.startswith('Do you use ') and ' pack' not in n.lower()]]\n",
"\n",
"# Simplify the Therapist column\n",
"_cname = \"I'm sorry to inform you that Dwarf Therapist is only compatible with 32-bit Dwarf Fortress.\\r\\nWhich of these would you choose to play with?\"\n",
"df['Prefer 32bit with Therapist to 64bit without'] = df[_cname].str.contains('32-bit').fillna(False)\n",
"df.drop(_cname, axis=1, inplace=True)\n",
"\n",
"# Drop the columns we condensed, plus the blank and uninteresting metadata\n",
"discard_cols = ['Email', 'First Name', 'Last Name', 'CustomA', 'CustomB', \n",
" 'Start Time', 'End Time', 'Duration',\n",
" 'The next 8 questions are about different tools and utilities for Dwarf Fortress.']\n",
"df.drop(discard_cols + list(others.columns) + list(utils.columns), inplace=True, axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Just get on with it!\n",
"\n",
"Alright, that's a lot of setup - the data was in a *really* strange format. But now we're ready to get some answers! Just remember:\n",
"- This dataset is **not** a representative sample, so our conclusions won't apply to DF players in general\n",
"- We don't have a large enough sample to do robust subgroup analysis, so I'm not going into many correlations. More on this later...\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
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FY0w/xZK4s3Cra41YnuflFU1e+I0xk+blhB2LGZkiSSnMPuGS0wsmzP5C2LEYYxJLLGOb\nfgb8VUR+iVsys/sELD2t1Z1Qxk49/JZZx140L+w4zMiWM6Y0bdz0o69Iy8q/v6Vhb3nY8RhjEkMs\nibtzBbCf9/CcDyT0ikj542ZcOPekj74jKSUt7FDMKDDt8HOm1ezUWz3Pe4ctRmKM6YtYEveI7WHt\neV7etCPe+80xk+fnhh2LGR28SBKzjr3o5KZ9ey4Hbg07HmPM8BfLzGlbAEQkB5gNtAEbVHVfnGMb\ncsVli26adexF88OOw4wuucVl6WMmzbnC87w/+L7ffX4EY4x5i1hmTouIyE9wq4S9BLwKVIrITSKS\nsD1kM3OL502QE96RnJIedihmFJpx1AWHjJm84Hthx2GMGf5i6VX+NeAy4CvAItxCI18FPgx8KX6h\nDa28cTN/PGnuKSVhx2FGp9SMHMbNOOZ9aZl5U8KOxRgzvMXSxn058BlVvafLtqUiUgVcD9wYl8iG\nUN7Yqe+ee8rHTrIppE2Yphxy1uTdW5ffBJwbdizGmOErlkxVArzcw/aXgckDC2foeZ4XyRs7/atF\nkxdkhx2LGd0ikSTKDn3HqTlFpWeFHYsxZviKJXGvBd7Ww/YzgM0DiiYE+eNnfXbGUecfGXYcxgAU\nTzk0L79kxjU2o5oxpjexVJX/BLhFRKYBzwfbTgCuJMHauD3PS512+Hs+mlUwISXsWIzpVLrgzKP2\nVqw/G3gw7FiMMcNPv0vcqnonroPah4G/BT8fAq5V1V/FN7zBlT9evjB10TkLwo7DmK4KJ87JzBs7\n7XNhx2GMGZ76vTpYVyJSDHiqWhm/kIaG53nJUw9718vzTr18UdixGNNd1ZZldSuf+O259dXbngg7\nFmPM8NKnEreI3Cci+80mpqpViZi0AfJKZnyy7LB3HRJ2HMb0pKh0YW7e2GkJ1fRkjBkafa0qPx94\ny8wkIrJCRBKuFzmA53le3thpF2flj0voedXNyOV5HhNnn3RsZl7JMWHHYowZXvqauHvq4VoGJGSn\nruzCie+cPO/0w8KOw5gDGTvtiPz8khlfCTsOY8zwMipnHMkdO/3TBRMkM+w4jDkQz/MonDT3CM/z\nisKOxRgzfIy6xO153viiyQsODzsOY/pi0tzTJhdOnPvFsOMwxgwffU3cfvDT0/aEMmby/C9OmH2i\nzUluEkJKWia5xWWn2YQsxphOfZ2AxQMeEJHWLtsygHtE5C3LEKrqafEKLt48z/PKDn3nSbYCmEkk\nE+TEBZWblpwOPBZ2LMaY8PU1cd/Rw7a74hnIUMjMKzlp/KzjbMIVk1AKJszOzB077RNY4jbG0MfE\nraofHexAhkJOUdknCyfOzQg7DmP6w/M8CsbPPtLzvDzf92vDjscYE65R0znN87yM/JLph1tToUlE\nk+acXJZXMv1jYcdhjAnfqEncOUVTLp445+SZYcdhTCzSsvLJGVN6athxGGPCN2oSd3bhxDMz80qs\nuG0SVk7RlPme59m68caMcn2dq3yRiCTs9KCe50Uy88bPDTsOYwZighxflltcdnHYcRhjwtXXEvdT\nwHgAEXlCRPIHLaJBkJZVcNTYssNmhB2HMQORkVNMduGkt4UdhzEmXH0dDtYGXC4iTwKnACeLSE1P\nO6rqM3GKLW5yikovKZg4x3qTm4SXmTdunud5Ed/3o2HHYowJR18T90+AG4Bv4WZLe6CX/Xxg2FWp\nZxdMXBCJDLuwjOm3sVMXzShf+dixwPNhx2KMCUefqspV9XtAATAVN4vaUcHj7j/TBifM2HmeNza3\neKqEHYcx8ZA/bmZaVuHE94YdhzEmPH0tcaOqdUCdiKwEalV1y+CFFT+5xWXvKS5bZHOTmxEhkpRC\nZl6JDWs0ZhSLZTjYFFybd0JIzyk+OiNnTNhhGBM36VkF02zREWNGr1gS97+Bq0QkJ97BDIaMnKIp\nYcdgTDzllcwsxTVNGWNGoT5XlXcxHng/8HkRqQS6rw42bNq5Pc9LmnXcxZa4zYgyZtLcvLySGe/F\ndRo1xowysSTuJ4OfYS+SlDq/cMKcSWHHYUw8pWbkkplXsijsOIwx4eh34lbV6wcjkMGQW1x2Tv64\nmTZ+24w46dmFVpNkzCgVS4kbEVkILODNMdsekAYcqaofj1NsA5aRWzw7OdXythl5UtKySmwiFmNG\np34nbhH5IvA/wX99XNLufDysZk1Ly8ofF3YMxgyG7MJJY4AJwLawYzHGDK1YepVfAfwIyAR2A5OA\nQ4DVwN/jF9rApaRm2fhtMyLlFE0pzMgde2jYcRhjhl4siXsScJuqNgPLcNXjK4AvApfHM7iB8Dyv\nID2nyAZwmxEpM28c6dmFx4QdhzFm6MWSuBt4s217PTAveLwaKItDTHGRlJI2M7eotDjsOIwZDEnJ\nKaRlFdiICWNGoVgS9/PAV0UkE1gKnCMiEeAEoC6ewQ1EVsHERRl5JbayiBmx0jJyx4YdgzFm6MWS\nuL8GnIVr6/4jMA6oBu4Ebo9bZAOUkpo5My0zoZYNN6ZfktOyisKOwRgz9PqduFV1JTAd+L2q1gNH\nA9cDF6vq1+McX8xSMnLG2HTOZiSLJKVkhR2DMWboxTIc7BHgb8A/AVS1AvhpnOMasOSUDPtSMyNa\nUnJqlud5Sb7vd4QdizFm6MRSVb4CuArYLCJLRORbInJInOMasEhyis28Yka0tKz8DMDag4wZZWKp\nKr9aVecAs4A7gOOAl0Rkk4jcFO8AY5WUZInbjGxpmQXZgLVzGzPKxFLiBkBVNwD34jqkPQBMxpXE\nh4WIJW4zwqVl5mUmp2VZz3JjRplY2rjPA04NfuYAO4DHgY8Cj8U1ugHwkpItcZsRLTUjl4ycoqnA\ns2HHYowZOrEsMvJnIIqb3vQjqrokviHFRySSZInbjGjJaZkkJafatL7GjDKxJO5zgbcBZwAviMh/\ncSXux4EXVbU9jvHFxPO8iBx/SXrYcRgzmDwvApASdhzGmKEVy3rcfydYTEREJuOS+OnAV4AOICee\nAcao66plxoxIXiQJ8GJamtcYk7hi/qMXkYm4hH0GLnl3AI/GKa4B8X3fn33CJTa2dQhFo1EW3/+l\nxhy8aFpSWjQlJdXWiR5kvu+T2tZQEHYcxpihFUvntJtwyXo2rmPaP4GPAI+ramt8wxsA37fEMYQi\nkQi587Nbx8rY3Ial2xqS99RG8zu8jnw/KTI2JS1lYX5h5vTcvOSsFKvZjZfWjg6ueqG8Nuw4jDFD\nK5YS9wnAfcA/VHVpnOOJGx9L3EPNI6l90nEzIhw34y3NJdv3NvLqa1s7Gl9bU5dR19ae1xHpyPeT\nkiakZaYsyC/InJqTm5SebDW+/RX1fXz84XOzbIwZErG0cR8RrAyWJyKZqto4CHENnJW4h1y01fd7\n2p6en8mUU2Ynccrs3K7b1++u44Ul5W0tK15vyGhob82PJvn5flJSaUZWyrz8guwp2TleapIt8Nab\nDt/H92nry74iEgXuUdVLum3/CHCdqk4daDwi8nvAV9XLYjz+ECBTVV/s5fkocIqqPiMim3DrJdzQ\nw369vqeuz4nIycATqpokIlOATUCZqm6NJf4ur5GCG3Fz20DO08u5TwV2qKrGePxUQFT1P/GNzAyl\nPiduEckBvgxcDEzrsn0d8AfgJ8MsiVsb9xDraOnf/llFuUx9+7wU3j7vjfpzH1ixvYYnlmxtaVuz\nuTm7yW/L64j4+SQlT8vKSZmXX5g9MSuL5EjMcweNGO1+FN/vV4n7YhG5TVWf6ra9xxuuEDwAXAf0\nmLi78Tlw3H157nlgfB+P6Y+Lga8DcU/cuNE7pwAxJW7g/4CnAEvcCaxPiVtExgDP4GZHewC4BdgL\n5AGH45b6vFBETlTVYdHm5vtRS9xDzG+Lz3JsuRMLyJ1YkMY5pHVua4tG+e/WPTy0pLy5fe2G5twW\nvz0vGvHzveTkWdm5qXPyC7LGZ2YRGUUrwtW3tdPU0VHRj0M2A78UkUOGw7DNHvTnl1cJ7BrIiwXX\noHIg5+jFcL6rHD1/ICNYX0vc38F9GOepann3J0VkEvAQcDXwrfiFF7toR3tD2DGMNh1tkWTf9xmM\n5VQjkQgFZcUUlBWnA2+M0W+JRnl6fQV/e6W8KbppXUteG2150QgFXkrKnNz8NMnPzxibnjEoMYWt\nrq3V39XU2J9q3WuBX+Nqzn7Q0w7BaJGf4kaMRIF7gC+pap+q5HFNaA8AZwFrgc91lvCD6u1vq+qd\nwf9PBp5U1YiIPAlMAX4vIqf0obpdgTXBecYDvwNODLb9uy+BisgpuKry/RKtiFyFW674FFVdLiLz\ngZuBY4AtwM2q+usejjs5iAUR6QCmAuXAl4BP4Ur4LwbXZWUvcX0W+CJQAqwEPq+qzwfXD+BJEble\nVW8Qkctx37vTgDrgT8BVquoHTRcAhwHjcDNbngycFFzj0/pynczw09fE/U7gip6SNoCqbhORa4Eb\nGSaJu6OteV/YMYw2Kcm5kbaGVlKz0w6+c5xEIhGKZo2naNb4DOCN2fIa29v5z+pd/GlJeYO3qa41\nt532/GiSVxhJSZmfX5A+Mzc/rTAtLaETem1LS2NLR0dVPw7ZjquK/p6I3KOqW7o+GbTNPolLiicC\nY3HVvVHgC318jXOBG4BrgE8AD4hIqar29vfYWT19HrAM+DFu8aIDUtWPdPnvX3BJ6whgAa46eHcf\nYu2xul1E3gd8DzgrSNrpuJuB3wGX46Z6vlVE6lT17m6HPw98HpdMjwD2AN8GPhkcux74KvAfEZmp\nqk3dXvtQ3DV4L/B6cK77gInAkbgagvOAR0XkJOBnwAeApcHr3Y1L0H8LTnkJ8B6gAncjNTWI8ft9\nuD5mmOpr4i7BLed5IMuA0oGFEz/tbc11Yccw2mRkTUxr3tswpIm7N5HkZEoWTKJkwaQs4I212Wub\n23hg1Xb2vbq1PmljfVt+e6Q9z494RclpKQvyCjJm5uWn5qamhhh53+1paWkAqvt52M3ApcG/7+n2\n3DtwJcIjVLUOeF1ErgD+ISLf6GMflsWqeh2AiHwZl4AuBn57oINUtSYoodYdIMnvR0TmAkcDpaq6\nHVgjIkcAF/T1HN2cBPwKuFBVXwi2fQCo6HxfwEYR+T7uZuYtiVtV20WkFuhQ1aogxiuBa1T1weD/\nHwc24JLqrd1evwx3o7RVVbcGBaJ/ikhEVXeLCECNqjaKSD1wWTApFsBWEVkKzOPNxL1YVd+ogRCR\nVqBeVffGeH3MMNDXxJ0KNB1knyaG0fSL7a1NVuIeYln5U9Iadi9tyZ1UGH7m7kVyegoTDi+Dw8uy\nu26vqm/m3mXl0YbX1u9L2drSmtfhRfP9pEhJSnrKgvyCYTkGfU9zUz39TNyqGhWRTwPPisg53Z6e\nDawNknanF3B/1zOA5X14if92eS1fRF7DlVAHy1ygOkjanRYTW+L2cDcYSbjq7U5zgENFpOt3ShJw\n0I6BIjIWKOSt16VdRF6h5+vyMK6QtDJIwn8HblXV/UbJqOqrItIkItfhkvUC3O+pa8ezzQeL0SSe\nETt4tr2lwUrcQyy3qJSaimdagWGbuHuTmp3OpONnRjh+5lvGoG/b28iS17Z2NC7Tuoza1va8jqSO\nfD8yLMagN3d01Pl+z0PwDkRVXwzaP2/GVcu+ccoedk/q9u/BdO8UGuHNBNc91nhduO7tHQMZ2/41\n4DhcqfvkYFsyrvr5Mz281sH0dE3BXc/9rmlQdX500Fb+blztyKdFZJGq7uy6r4i8HddZ+A5cVf51\nuD4MfXl9k8D684dztYgcqMNX9gGeG3LtrU1VHW0tJKUkXA5JWOk5xTRtbhpRvfl7HYNeVccLS7a2\ntax6vSGzoaM1vz3i55OUNDkjK2V+fkH2lJxcL2WQh6y1dHQMZATHNbhq7C912abALBHJ71KVehzQ\nhqva7YsFnQ9EJAlYBPwj2NTKW9cymN7t2FiGY60ECkRkmqpuDLYtiuE8na//QPCzWkQuUdU/4K7L\nOcBmVfUBROQSXJvy53s5DwCqWiciFbhObSuCY5Nxo3Ee6X6giBwDnKaq3weeFpGv49qnTwDu77b7\n5cD/qepVXc47HTdk7EDv0SS4viburcCFfdxvWGhraVzb3FBNVv74g+9s4iISiRBtGR3D8LKKc5l6\n1vwUzpodg3OTAAAgAElEQVT/Rv15lC5j0Fe7Mej50YifR1Ly9KyclLlxHoPe0N4ec+JW1WoRuQbX\n+WxzsPlRYCNwl4h8DSjGlcrv7qw+F5ESoFZVeyvJnRQc+1fgc7hq9nuD5xYDHxORp4Jzf7H7WwJm\ni0iBqtb08X2sEZHHgd8FPcGnAVfiOqv1lxecc6uI/Bi4UUT+jpun4tvAb0Xkf3DJ8We4zrg9acDd\nTMzATeryE+AGEdnJm53T0nA9wLtrAr4dJPvHcGO2s3B9iDrPPT9ogtgDHBf0ePdxtQXjOHCNVwMw\nU0SKVbVKRApw7fFWQ5lA+pS4VbVskOOIu4aa7Uvrq7fVZ+WPH1Y1ASNdR+voSNy96W0M+stb9/Dv\nV7Y2d6zb0JzT6rfndyT5eV5SMAa9MGt8Zma/x6DXtLT0ZwzyfiUtVf2diFxGMAlJ0P59DvAL4CVg\nHy5pfaPLYTtx1bd39vIad+B6pH8LV8I8u0uSvxb4PfAKbtjWtbw1ef0K+BEwC3hfX95D4P24Tl4v\n4IZq3QTEMntb1/P/CPc+v6OqnxeRdwTnXYpLmDer6g97Oc8TuBqK5biS8v/iahp+C+QGcZ6iqnu6\nH6iqy0Tko7jr9/Pg/XxQVdcGu9yMu2GYjruZuAM3vKwWV13+a9zwr97chut1PwdXY/BX3M1FTLPd\nmXB4MTSRJQTP85Jnn/ChtTOOOn/AUzmavlv53+sqj7zymLFhx5EIotEo1esqqFlS3hTdVNOS10Zb\nfjSJgkhyypyc/LRZBxiD3tjexudfeO4bq2qqh3RYj4hcCjSq6n1D+brGmDeN2M5pvu+3zzjyvErc\nuEUzRKKt3nCeNWpYiUQiFMl4iuStY9Ab2tt5aPUu7n3FjUHPe3MMevK8/IKMWbn5aTsbGzq2NzY8\nM5TxioiHWwmwL81mxphBMmITN0BLU92ApkQ0/dfR6tmqIAPU2xj0vc1tPLByG/uWbq1v2lzZWtva\numoo4wqGd71NVUd1c4gxYRvRpaPm+j07RmpTwHAV8TKT2pv7Ojum6Y/k9BQmHDEV+fgJ2UmHT9ji\n+36fOnDFkyVtY8LX78QtIp8WkbzBCCbeWhpqFjfX92XmQxMvGVkTkptqbJr4wdayr2Vb2DEYY8IR\nS4n768BOEblXRM4M2r2GpX27tzy5d+c6m9pvCGXnl2U27WmwIvcg8n2fpur67Qff0xgzEsWSuEtx\nEze04yYq2Coi3xeRWXGNLD627K1Yv+Xgu5l4yRkzmfrKOputaRA1VO7zG3fXPxd2HMaYcPS7c1ow\nc9AjwCMikg2cjxtz+Vowt+5twL3dV70Jg+/7/qS5p6wHDgk7ltEiq2A8lVsb95tX2cTPrte2bq/b\nVvOvsOMwxoRjoJ3TsnAT6OfjZkiK4iZV2CQipw/w3HHRWFuxor019HuIUSMSSaajpaM97DhGsn07\n9270fX8g050aYxJYLJ3T0kXkAyLyEG4FnS8CzwBzVPVEVZ2OWx/39rhGGqO6yk337dm2ylYKG0Id\nLVErcQ8S3/epr6hbe/A9jTEjVSzjuCtxpet/4dbzfbiHJeceZ/+1fkPR0d6ypmbHms0l045YcPC9\nTTxEW20hg8Gyb8feaP2u2ofCjsMYE55YEve1uEUH9ptnt4t/qOpfY4wprnzf9yfPO20DXVYtMoPL\nZk8bPLte21pev7P24bDjMMaEp99fsKp6c09JW0RSReT4YJ9h1cbZULPz1bbm+rDDGDU6LHEPmvqK\nuo2+79tAeWNGsX6XuEVkEa7n+AJ6TvzDbsrLmp1rfrtz3YtXlC44oyTsWEYDL5qa1NHWQVLKsPso\nJLRoewf1u2pXhx2HMSZcsZSMbsKN4b4KaMWtfXsT0AZcFL/Q4sf3/Yq9u9atDDuO0SI9c1xy897G\nsMMYcSqWb6vbu2XPLWHHYYwJVyyJexFwpar+Brfe7ApVvRq3iPsn4hlcPNVXl79kw8KGRmZOaWZT\nTYPNaR1nu5aXr2re27g87DiMMeGKJXFHgJ3B43W82enr7wzjiU6qt6/+9a71L1WFHcdokD2m1Guo\nqLO7pDjqaG2nrrzm5bDjMMaEL5bEvQ44IXi8BjgyeJwHpMUjqMHg+/72mh06pMsgjlY5RaU0VNZb\niTuOdizZXF29ofIXYcdhjAlfLMPBfg78n4gA/BlYLiJNwPHAS3GMLe72VZf/t72t+ZTklPSwQxnR\nkpNTaWtqt8QdR1Wv71zZ1tS6Iew4jDHhi2U42G3AB4BtqroGuBRXAt8GfDKu0cVZ9bZVN21f/fTO\ng+9pBira4lvijpP25jZqt1W/EHYcxpjhIZYSN6r6ty6P7wHuiVtEg8j3/Z0TZ5+4eMrCt58Tdiwj\nXUerTZ4WL+UvbajYvXqnVZMbY4A+Jm4R+VZfT6iqN8QezuCrrdx0695da8/MHzfL6ssHUUdL2BGM\nHBXLt/3X931bf9sYA/S9xP3RPu7nA8M6cTfUbH9w2+tPL88fN+uosGMZyfy2iM2+Ege7dVdjzaYq\nK20bY97Qp8StqlMHO5Ch4vu+P2bS3Edam+uPSk3PDjucEcvvSI5EO6JEkmz204HY8oy+Wr+z9tGw\n4zDGDB8xtXGLSAQ4HTeGuwNYoqrPxTOwwVS9ffX/lq987JLpR7y3LOxYRqq09KLkltomMgqzwg4l\nYTXs3het2bT7ft/3rcOAMeYNsazHPR54DXgY+AZwPfCMiDwrInlxjm9Q+L6/t3r76y/6Uev4PFgy\nskszmmoaLOEMwMZHX3+9ZmPVb8KOwxgzvMRSj/lLoAWYo6pjVDUfV/LOxM1ZnhD27lz77W2rn6kM\nO46RKqdwclJD1T6bPS1G7c1tVG+ofNz3/dawYzHGDC+xJO63AZ9SVe3coKqrgCuA98QrsMHW3FCz\nbtf6l571/WjYoYxIOUVTaKjYN6yWd00kGx9fvaVy5fbvhR2HMWb4iSVx7wNSe9jeGvwkjNrKDdft\n0Odt/vJBkJqeTVtDmyXuGLQ1tbLrta0P+b5vn01jzH5iSdzfBn4rIod2bhCRMuBm4Lr4hDU0muqq\nVu7Q5562tu7B0dESteqMGKz79/K1FSu2fTXsOIwxw1Msvcq/DYwFlojIPtw63IWABxwrIr/s3FFV\nh/1Y3r079evbVj91wuR5p48LO5aRpqPF2iH6q3lvo1+xYvuffN+vDTsWY8zwFEvivjbuUYSouaFm\nXcn0Ix+fICd+MCm5pxYAE6uOVrywY0g0+s/Xlu9es9Pato0xvep34lbVOwYjkDBVbnzlCxuX/P3o\nmUdfMCPsWEaSaKtns6/0w76dtW27ddfvfN+3CWONMb2yL1bA9/2qyk1Lft9YW2GdqeIo2paU5Edt\nKHdf6T9fW1K93tbcNsYcmCXuQM2ONT9a99J9/w07jpEkNbUgqbW+OewwEkLlyu21NRurfuTb+ERj\nzEFY4g74vt9Rs2vttRUbFleHHctIkZE9Ob2ppjHsMIa99pY21j647D81m6r+dvC9jTGjnSXuLvbt\n3vrkluUPP9LRnlDD0Yet7ILSlMaqfVbkPojVD7y6eufSrZ8KOw5jTGLod+c0ETnpQM+r6jOxhxO+\nyk2vXLVh8V8Pn3XsRTPDjiXR5RZNoari0VbA1j7vxZ71FQ0VK7b/2Pf9vWHHYoxJDLEMB3sKt+52\n16E+fvATpedZ1RKG7/u7CybID4unHPqzggmzbd3PAUjPLqClotlmt+lFtL2DNQ8sfaJ6fcXtYcdi\njEkcsVSVTwWmBf9OBWYC78KtGHZ2/EILT80O/d3al+57sL3V1sgYqI5W3xJ3L/Sfy9ZXrCj/eNhx\nGGMSSyzjuLf0sHmDiNQBv8GtFJbwqja/evma5+6S+ad94tCD7216Y9Oe9qxm0+6mnUu3/Ly9pb0i\n7FiMMYklnp3TdgMjZgIT3/fr92xbdfWOtc/b0p8DELWpRPbT2tDCij++/PfduuvmsGMxxiSeeHVO\nywU+D6wccETDSF3V5ieKShfcVThh9hfSs8dYD/wYRNts9rSufN/ntTue/2/F8vLLwo7FGJOY4tU5\nDWAzcMkA4xl29pSv/OqqJ//v8MPO/sIpkaSUsMNJONHWSLLv+3ieTVsOsPZfyzbuXrPzw77vWwcK\nY0xMYkncU3vY1qqqOwcazHDk+357cmr6BaufueOJuad8bIEloP5JSsqJtDe1kpKZFnYooatcub12\n28sbr6+vqNOwYzHGJK5+V2Oq6pagg1o1bnnPAqA+3oENJ+2tzburtiy9bPNr/+6pY545gPSsCak2\nexo01TREVz/w6j171lXcGXYsxpjE1u/ELSIREfkJUAW8BLwKVIrITSIyYouj+/Zse2X7mqe/Wbl5\nqU2J2g85+VPSG3fXj+qp6Dpa21n6u+eerFy1/bNhx2KMSXyxdBz6GnAZ8BVgEXA48FXgw8CX4hfa\n8FOzQ+/a8N+/3FJfvX1UJ6L+yBlTSkPFvlHbtzzaEWXJbc/8d/viTef7vm+rzxljBiyWNu7Lgc+o\n6j1dti0VkSrgeuDGuEQ2TO3ZtvIbq566bcZh7/jCBakZuWGHM+xl5JWwc3PDqJyExfd9lt31wood\nS7a81/f92rDjMcaMDLGUuEuAl3vY/jIweWDhDH++7/tVm5desuzhnz9iM6sdXCQSIdoSHZWJ+/U/\nv6I7lmy5sLW+eUR23DTGhCOWxL0WeFsP28/ADQkb8Xzfb63YuPjcZQ///JmOtlFbC9xnHS2MutnT\n1j20YvO2lzZe1lBZtybsWIwxI0ssVeU/AW4RkWnA88G2E4ArGeFt3F35vt/oed45XlLyQ4ecedWx\nSck2xrs3Ha1+2CEMqS3Prd255Rn9XO226hfCjsUYM/LEMlf5nSJSCFwDfDnYXAFcq6q/imdww53v\n+7We573T87yHFp5x5dGWvHsWbRs9g9+3Pr9+14ZHVn29emPVP8KOxRgzMsUy5Wm+qt4E3CQixYCn\nqqN2Pm/f92s8zzsbvIcWnvGZo5KSE3pV00ERbfWSwo5hKGx6as32jY+9fvWedRV/CjsWY8zIFUtV\n+U4R+RtwO/CIqo6uetAe+L5f7Xne2zvaW/628IwrTk5Nt2W8u/LISGpvaSM5beTWSKx/eOWWzU/r\nVdUbKv8ZdizGmJEtls5p7wWiwF+BchH5gYjMim9Yicf3/b271r141msP/fTvTXVVo7IXdW8yMscn\nN4/Q2dN83+f1v7yybuNjr3/kQElbRJ4UkW8NZWy9xPFtEXnyAM9/epBe9yMismkwzt3tdZ4Vka8P\n4PjTRWRmL899R0QejT26wRG8598Gj8eKyPn9OHZYvqdEJSLTReTMLo+jIjIh3q8Ty5SnD6vqB4Fx\nwHXAMcAqEXleRC4XkZw4x5gwfN9vrty05Lxlj/z89rqqzTZWLJCVNyWzsbphxE0+4kejLLvzhZWb\nn1577t6te54OO55+6LGWLFj575dD/brDhYhEgEeB4gPsNhzfg8+bcf0P8PYYjjfx8XvgiC7/H5Rr\nG/OSi6q6T1VvAy4Evg0cCvwW2CEiN4vIqJydxPf96O6tyz++6snbfrK7fIVNukEwe1rlvuaw44in\n1oYW/vurJ1/c/Mzat9dX1K4KO544iTC6v8QTdQnaSmBX8HjUdAQdpobk+sfSxo2IpOGqzD+MG7+9\nEzdM7HbcJCw/Bf4MnBmXKBOM7/s+cO2YSfN2Ns6r+Frp/LdNDDumMGWPmcju8voR03ywd+uepuV3\nv/SPimXll/q+P+AbEhH5NnCKqp7aZdsm4NvBKI6FwK9xN8fVwG9V9TvBfqm42Qo/EBz6H+CzqloT\nPD8Hd0O9CHgRWN1LDFOAJ4LHHcCpqvqMiFyKm964DFgJXK2qz/ZyjihuZsWv4xYg+gfwCVXdr51E\nRM7B1djNAZqBh4Jjo7hRKh9R1b8F+ybjEtMFqtprNX8XpSLyDHAksAT4pKquEpEkoA04QVVfCM79\nMeCrqjoT6KzKf1ZEvqmq3+/h3Gki8ivgg0Aj8ANVvTk4Vx7wM+BsIA/YAHxZVR8UkRuBw1T1jTkw\nROTHwHxVPVtE8nG1He8C6nDfn9eoaquInA78BrgpuLa5wfMfV9V2QIE1IvKdIC5E5BRV3a8JU0Tm\nB+c6DDecd323548HfoT7rO0CfhgU0BCRu4JtU4B3AruDa/fH4Pk04H+Bi3C/x4eAz6nq3h6uIyLy\nQ+AjwbV6EbhCVdccLI4ezvMs7vd8TrBpHlAE/Ao4LTj+98D3OvtkicjZwHcBCa7fF1T16eC59wHf\nAUqB5cCXVPX5Lq/1L+B04ERgC24m0SeC63M8cJyInAK80ewUNJO9R1UP77LtGuBMVT29p/d1ILEs\nMnIb7g/rDtyqYO8GylT1m6q6QVWfAr4PHNffc480e7at+uXGV/52/orHb1k2midqiUSSaW/uGBFV\n5VufX79r6e+e+07FsvKL45G0uzhQSfdO3GI+c4CPAV8RkbOC536AWy/gLOAU3Jf6/fBGUn8Q9+V8\nGPAX4JO9vMZW4PwgjnHAC0HS/jnwPeAQ4HHg3yIy/gCxfgc3p8MpwELglu47BHNA3A/8AvfFeQFu\nUqdPqGoz8DfgfV0OOQNoBZ46wOt2dRlwN+5Lvxz3vg+k89ofGfx7Dq7w0ZMTgX3Buf8H+KmITA+e\n+znuBuc0XPJ4AbgtqIK/FzhRRAq6nOs84I/B4zuAdFzT43nAsbibgE6lQVxvw12v9xMkaVX9RpA8\nfxi817uBo7sHHiTWB4E1uM/D34GPd3l+PvAYrrngUNzv/WYReWeX01wVvK95uBuzW0QkK3jux7jf\n+ZnBNRgTvO/9iMgFwEeBc4Nz7QY6bxD6Ekd3lwbX5DzcjeDfcZ/pQ3B/Mx/C3YAiIofgPmP3BvH+\nGfi7iIwRkcODOL4NLMD9fv4T3Nh2uhZXSJ2Lu5m9Ndh+BfBf3A3HBd3i+yNwqIiUddl2AW/+/vsl\nlhL3IuCbwN2q2ttKWctxd12j3r495S97nndy077dd889+dIzswsmjtyu1QfQ0eon9Oxp0Y4oq+5b\nvHrn0i2f3btlz2ND/PJluC+aclXdKiJvAzaJSAbuy+JwVV0FrhMYsFtE5gXHFQKfDhLi2qAksF8b\nrqr6IlIdPK4KznUVcJOq3h3s9jURORmXmL/RS6w/UNX/BMd/FnhERD7TbZ8IcKWq/i74/1YReQz3\nBQ7uy+xeEUlV1VbcF9z9/RjBcr+q3hLE8Elc891pwMH6IVQF/9aoam99VLao6jXB4/8NaksW4krX\nTwAvdSk1/hSXnIpVdYmIbAHeA9wuIocBE3EJQwhK6Z21E0EnwcUicnXwWsm4Eul6YHXQoexIXMIH\nQFUbRKQZaOqscenmLCAHd+07Pw+nBdsAPgG8rKrXB/9fLyJzcQnvwWDbkmA4cGcp8gpgjoisAT4F\nLFRVDZ7/CG7lSOnc1sUUoAX3md4uIlcCM4LnPt6HOLr7h6ouDl73TGCsql7R5fhrcDUNP8Il8qdU\n9cfB898XkXTcEtVXA79W1fuC524OrtGncAtsdb7WPcFrfQ94RUSKVHW3iLQB9apaKyJFncGp6joR\nWYr7LN8Y3OzN5+A3lT2KZQKWRX3YR3HVD4Y3Jmp5d3tr4w1lh579iQmzjh8bdkxDLdriJ2zbaXNt\no7/0988/s/PVzRe1t7TvOvgRcfc9XGnqUyLyL+AuVa0MknMq8KLsv6TuLGA6sC74ku60GJck+mIO\nrjq7qxeD7b3pOlvcK7jvmLdU2arqehFpEdf7ez4uYc8F7gp2eRRXwj5LRP6Na5bra8zgSj2dr1Un\nIuuDmOPRgXBjt//X4krK4JLoecGNymxcTQhA5zwGf8KVCG8P/v1PEN+pwT4VLoe/RWdp3u/22nVA\nfwsBc3Bfz90/D6cFj2ez/zoUL+Cqszut6xYDQRzTg38Xd/ss+rjff/d8cDeuKnmziLwIPAB03sjN\n6UMc3W3u8ngOME5E9nXZFsE1c+QE8SzperCqfgveaFo6N7iR6JQCdK0x7O0aHMwfcbUCN+L6hj3e\nyw3WQcXUxm36L2j3/mZeyfTn9u5a9xM59uK5SSlpYYc1ZDpaE7PTTPmL6ys3Prb6vspV27/o+37b\nIL1MTzc1b/xtquqNInIfrlrx3cDjIvIJ3vzyOR5o6HZ8Je7LtPt178+StD01BSTxZiLqSddr1Lnf\nW2pbgqrKZ3HVmU/j2kW/0Pm8qnaIyJ9xVfetQK2qvtSPuLv3p4gE5zngdY7x3PDmNf4jrkbyD7hm\ngN2499npXuDqIHmcj1tNsTOGPcBR7P/72obrLxBV1e61VrH8TR3o89CX33dPnx+PN6/j0biSdFf7\n3eyq6s6gpuFMXLv+NcDlIrKoj3F01/WYZGAF7uao+/tt4K2f0e6ScU29d3fb3rWfRm/X4GDuBX4Y\nVLufz1ubQvolUXtRJqzaig0Pb3zlb8cv+deP/7R76/JR0+s82hpJqNnTmuua/MW3PPXyqvtfOb9i\n5barBjFpg/sieGMYpYhk476sEZE0EbkJaFPVm4KOLLfi/vA34BJJkapuVNWNuH4nNwXHrwRmyVuH\naB52gDi6JzbFtbl2dQwHrk07tMvjI3Ff4t33vwR4WlU/pKq3qOoSYCZv/fK7B9cB6hzgPvpnQecD\ncdMzTwdWB4kvSpdrzZslWhhAj/qgc9mFwPtU9QZV/QeugxQE7ytozliPq14uBTrH/SuuSaOjy+8x\nF9eG3t+pGA/0HlYCs7u0ScNbPw89/b6Po2+1p+txpdKun8VG3Gdxv6YZEXkXcJmq/ltVP4OrnZiD\nq3kZSByd76MMqOwSyyzguuAzsA7X9t01npdE5LzOYzuPC479DH0fYtfr9VfV7cBzuKaAObhahphY\niTsEvu/vBS7KK5l+/phJ874lx31gYXJqRthhDa5oalK0vYNI8vDP31tfWF+56fHVf6pctf1Lvu/3\np4Qaq8XADUFv1mW46ul2AFVtEZETgMki8jXcF/pJwF9VtV5EbgV+E5TAq3CjOybjekhvDX7+L2iP\nPAZXVddb6bUBICj1rArO9X8ishpXdfkxXHvuhw/wXm4I2nJbcCWK21W1sVsV8B5goYgciatq/iRu\n7OuGzh1U9TkRacBVj57QuT3o6FUM7Al6VPfkQyLyUhDzD4BVqvpc8Nxi4LMisgFXTf9hgupOVY2K\nSAswX0RWqmpdD+fuTRPu+l0oIrW4L+abgue6Vq39Cdcz/F+d7dmqujJo479HRD4X7HcrsCP4Hfcj\nDBqA6SIyXlW7Lyf7MG4E0G0icj2upuZ9vFkr8EvgyuC5P+Cu+yfp0oGtN0Gb7u24zmqfwv2ObwJK\nVHVLD4ck4/oI7MJ95j+E6/S3fiBxBB4CdgB3i8i1uJui3+B6g4MbobEi6IPxIHAx7sbxWVztwJMi\n8mpwnnNxHfJOOsDrdb3hbABmipsOvPtz4ErdPwX+rar1nRvFDZ+O9NYDvzsrcYeotmLDXza9+s/j\nl/zzx7dXbHylt45+I0J6xtjk5r3De/a05r2N/uLfPPny6r8sObdi5bbPxjlpH+hO/HGCVfdwQ3RW\n8NY2vguBTFzb7X9wvau/Gzx3Na5N+M+4dsAW4GxV9YPE9k7cF9cS3JffLw4Q4wpcb97ngXeo6v24\nJHMD7sv1JOAMVV17gHPcEfw8BDwJfLaHfW7GtZU/CjyDu9G4gf1rA+4Htqnq0i7bpuKSz1G9vL4f\nnP9TuDb2dN7aQ/1KoATXgfYLuI62Xf0Ml3B663zX0+uhqi245PN+3E3Pj3E3YFXd3te9QBb797b+\nIO4m63Fcgl2Bq5norztxNQ6vdn9CVdtwfQXG4j4Pl9Flwh1V3YxrijkHd32uAa7q7IjVi66f68/j\nPpt/wX2GGnCfv/2oG+p3Pe53tQbXj+Hd6uYH6W8cb/nbUtWO4PgU3E3qfbjOnV8Mnl+H6yT2Sdx1\nPgd4l6pWqRsmeCkuWa8KHr9fVTv/Hnv6O+667dbgfP/s4Tlwf6ep7P/7vwVXy9Qnnh9Dn6Ggy/yX\ncR+QNuB14KedvfpM/+UWl51VMF5umHnMhYdn5BSNuBuq8pWPRQtP2OYXzRo37Irc0fYO1j+8csuO\nV7f8uWrVjq8PUSl7xBE3jvsUVX0mTuf7A65z3fXdtn8XV+OwX3IyZjgLOr+9hKuJiHk4aSyrg50M\nPIJrL3kEV2o/AXhORE7TYKC66Z+6qs3/8Tzvsfrq8q8WlR76oWlHvGdWckr6wQ9MENljSiONlasb\nmDUu6+B7Dw3f9yl/YX3V1ufXP1q5ctvVIfUYN92IyNG4qvP38OYQsc7n8nAl/+uGPjJjYhP0M3k7\nrif9HwaStCG2Nu7vAb9T1bcsRiAiv8RV353a41HmoHzfbwe+63neL2p2rLmxZPpRZ5UuOGNSJCnx\nuyLkjCllW+W+YTN7WtXqHfXrH175XPX6yq82VO1bFnY8I0S8hvydhavW/Jqqbu36RNCWempQHWpM\nokjCVaMrMODFhvpdVS4ijcCizkkGumyfDSxW1VG7yEi8paRlTR4zad6N4+X4UyfKiWO9xOqYvZ91\n675TvfCDRxaGGUPd9po2/deyxTUbKn+0d8uef4QZizHGxCKWotxu3hzm0NVY9h+/ZwagraWhHLgo\nLTNfdurz1xaVHnJS6YIzShN1/HdHSzSU2dN836dy1Y76rc+vW7x30+47927efafvJ/ZMbsaY0SuW\nxP1P4BcicrGqrgYIpqO7mTd70pk4amncq8CHPM8rrti4+FsFE2a/rezQs2enZeaFHVq/DPW0p9GO\nKOXPr6/csXTLS3s37f7pvp17nxrK1zfGmMEQS+K+FjeMY2UwXhHc6i7LgC/FKzCzP9/3q4CrPM/L\n3L11+dX5JTPeM3HuyQvyxk5P9bzhPzFZtGVoZk9ra2xh4+OrN1Wt3vnsnrW7bmjZ17zh4EcZY0xi\niHU4WATXQ24+boD5cuCRHqbkM4PI87xIVsHEs3OLpnw4r2T6oklzT52enh1qE/IBLXvm+j3HfOno\nMarkrREAAAPrSURBVINx7o62Dna8sqm6ctWOFbXl1U/v0V0/831/RI+NN8aMTrF2V04HXsNNWzi8\nZ9UYwYJ22n8B//I8L2vnuhc/nls05R2Fk+YdOmHW8WOHW1u435ac7EejeJH4DFP3oz5Vr+9o2LFk\n8+u15dUv7VlXcXN7c9v6gx9pjDGJq88l7mAc2pdx08NN6/LUOty0dD+xJD48JCWnTsofN+vK7MKJ\nh2UVTJBxM44uzcofH3pd+uvP/aZ+3mVjs9PzM2M+R3tzGxUrt9ftXrNzw77tNcv3btnz26bqhhf9\nWKqOjDEmAfUpcYvIGN6cmvAB3DRxe3Ft24fjJkrYAJyoqqNm4YxE4Hleds6Y0nOzCia+PTO/ZM6Y\nSfNnFk1ekBNGaXzzsgc7Sk6vjhROK+7zTUS0I0r1hsqWimXlmxp216+t31W7rHp95V1+R3S9JWtj\nzGjU16ry7+BmSJunquXdnxSRSbi5ia8mDoPLTfz4vl+PW+f4Ls/zvK3LH5mbM2by+zPySiQ9u7As\nK3/c5IIJc8ZlF07yIoM8Tjy7oDSpsWpLU+G04h5XVPGjPg2VdVRvqNpTt726oqWueVv9rlqt215z\nf3NN44vBBDXGGDOq9bXEvQW4QlX/dYB93gPcqKqz4hifGWSe5xVk5BQfk5lXcnp6TtGU1Iycscmp\nGWPSMvMLssdMHpOVNy4tPbuQeEz+0tq0j521v6qbcebc3MY99TRW7Wus3Vazu3lv446WuqYdTXsa\nyptqGl7Yt2Pv88AOK1EbY8z++lriLsFVjx/IMtwasyaB+L5fg6steajrds/zcj0vqSy7cOJhKenZ\n81Mz84qTU9KzIkkpaZGk5PRIUkq6F0lKi0SSM7xIJM0NLvA7fD/a4ft+O+7fDj/a0R7taGvsaG+t\nb29t2tfcvjl1+8sbVrTsa1rRULlPgS2+7w9o3l5jjBlN+pq4U3HrzR5IE24ZNTMC+L5fhxvmtzzs\nWIwxxrxpxC0faYwxxoxk/RnHfbWINBzg+eyBBmOMMcaYA+tr4t4KXNjH/YwxxhgzSGKa8tQYY4wx\n4bA2bmOMMSaBWOI2xhhjEoglbmOMMSaBWOI2xhhjEoglbmOMMSaBWOI2xhhjEoglbmOMMSaBWOI2\nxhhjEoglbmOMMSaBWOI2xhhjEoglbmOMMSaBWOI2xhhjEoglbmOMMSaBWOI2xhhjEoglbmOMMSaB\nWOI2xhhjEoglbmOMMSaBWOI2xhhjEoglbmOMMSaBWOI2xhhjEoglbmOMMSaBWOI2xhhjEoglbmOM\nMSaBWOI2xhhjEoglbmOMMSaBWOI2xhhjEoglbmOMMSaBWOI2xhhjEsj/A4nKk4Q43kwIAAAAAElF\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x8681f7278>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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VRKfkTbxGUZSA/DnyRXoK3nnQ9re4E0AjES32LETONzSZSeJTco/OK5t3enhkjNlRRBAY\nO33B+LSCKQG3lMtXDalbChGVApgP4BQAMwBsYeZJBmUbcYqihOeUHPdl2QlXzjA7iwgeDZVrWrd/\n9eI5bU1Vn5qdJdDpvkeKiFIBjAeQD239XSgAv57hTx5VcifNPn+62TlEcEkvnJpkySi6Q1GUcLOz\nBDo9t5b9g4g2AagH8AC0LRzvBJDHzH57lTYmIbU4e/wR50bFWmUNihhxNPu8ack5JdJVZZjpubXs\nCs/HdwA8CWAZMzuNizTyFEVRMovmPJxTcrzsSSFMERVrVbKK5pwfHZ/yrLO9udzsPIFKzyltMoCF\nAJoAPArARkRLieiPRDTb0HQjJDF99CVFM8+eLQuMhZlyJ54wyppFcgFjGA25xTsRFQK4GsAiABHM\n7Fd7cyqKEppXdtKK0mMXTTM7ixCttdu6Nn322LltzdX/MTtLINJzSgsiSgNw/IA/KdDuvvivcdFG\nhiVj7BWFU06fbHYOIQAgKXt8TGL66GsVRfmv3IFhvEEXPCLaAKAEgB1as4DrAHzEzG0GZxt2iqKE\nF0w+5WexlgxdhV+I4ZA3cd50R2PF6dDmyYWB9PygfwrtFPYrZvbrHdatmXRt4ZTT/XbdoAhM1iyK\nTUwfc42iKO/KKM9YuubwiCgawAJozQLuBVAKYDMztxgbb/goihKaP/mUVSVHXzLF7CxC7M9WV961\n4ZOHzmtvrnnX7CyBRM86vHQAmwE8BuC30BqC3gBgMxH5zTq8xLTRvyyYdHLZoZ8pxMizZhbFJKaN\nPlDfSTEEepal3AdgC4BUAPvW310ErQjeY1CuYaUoipKYPvqiWGuWzN0Jn5VVNHtGTGLaVLNzBBI9\nBe8YALcwc9e+A8xsgzbKm2NUsOEUn5x7Zm7pCXIqK3xaWuFUS2La6N+bnSOQ6Cl48QA6DnBchc5l\nLiMtMX30pZaMMX59368IfIqiIK1gyuzwyNhcs7MECj0Fbxm+v6eFSkTh0HYs+9qQVMNIUZTMpFHF\ncmVW+IVRE47KsGYWySjPIHpGZDcAWEZER0Hb3+IxaF1TEgEcaVy04ZE0qvjqrKLZ6WbnEMIbIaHh\niEvOma4oiiJLVIZu0CM8Zt4GYCKA9wF8AsAN4DUAk5h5g7HxjJeQkjcnLEL2Qhb+I2PM4ROi4lP8\nYn7c1+mac2PmOgB/NDjLsIuIji8tO/HqUrNzCDEYSdnjoxNSC34Jbe9nMQTebtP4Jy9fT2Vmn+3p\nZUkfe1VawZREs3MIMRiKEoL45JzDFEUJVVXVr+9uMpu3I7yfH+LxeABWz+c+WfAURQkpnHL61JAQ\nv2rmIgQAIIvmUN2OFSfDDxt0+BJvt2ncf1/a7xDR+QD+AaABWosonxSTmHFcZtHsCWbnEEKPhNSC\niISU/PMgBW9IdK+b87SIehzA6QBeAnCNZwGyT0pIzbvYkjE20uwcQuihKAriU3InKYoSqapqj9l5\n/JWuTXyI6Bxot5fNBHAmM1/ky8VOUZTIhNSCw6SjsfBn2eOPHBuXnLPQ7Bz+bFAFj4hSiehNAC9D\nW5JSzMw+380h1po1P7No9lizcwgxFHHWrNCElLwzzM7hz7w+pSWihQAehrbu7kxm9pvmhDGJGSfE\nJY3SvSWlEL4i1po9TlGUEFVV3WZn8UfeLkt5A8CZACoB/A5AKxEdcaDnMvMy4+IZIyYxfYyczopA\nkJQ1LjckLGI8tCklMUjejvDO8nwcDeANAD9WPVRoG3L7DEVRIsfNufBHrzIL4U+sWeNiE1MLzoIU\nPF28LXh+WzAiYywzU3In5pidQwgjhEVEIzohrdjsHP7K23V41cMdZLjEJeeclpBWGG52DiGMEhWf\n4rcDELMF/ER+rCWjSO6uEIEkLik7T1GUVLNz+KOALniKooRExiYVmp1DCCOl5JSmxSXnzDM7hz8K\n6IIXFhFdkpQ9Ps/sHEIYKTohDTEJ6T7fe9IX6dm1bPxwBBkO8Sn5ZyVljY8xO4cQRlIUBdEJqTKP\np4Oee2m3ENFqAM8BeJWZHQZnMkxMYhqFhsvtsyLwRMen5CuKEqGqaq/ZWfyJnlPa8QAWA7gJQB0R\nvUJEJxCRz63sDY+MSzY7gxDDwZIxNhsAmZ3D3+hp8c7MfCOAPACnAegG8CqAGiK6g4hGG5xRt7CI\n6CSzMwgxHKIT0iLjkkbJerxB0n3RgplVZv4MWi+8JwEkAfg1gO1E9B8iMnWxr6IoYWER0dZDP1MI\n/xMVl4ywyBjp7zhIuvrhEVE2gAsAXAjtFHcFgKuhbeaTCuApAP8GMM2YmLpkxVoypeCJgBQaFo6I\nqPg0s3P4m0EXPCL6HNp2jI0AXgRwFjPzgKd0ENFj0C5qmCYiJnFsjCVT9q8QASs8Kk5+oQ+SnhGe\nA8AZAD5g5h/bUGQVgAN2Uxkp0fGpk2MSUn3uQooQRgmLiJGCN0h6Ct4DAL5m5u/14yKiWADXM/Nt\nzLwHwB4jAuoVHhWXFx4VZ2YEIYZVWES0xewM/kbPRYulAD4gooT9jscBuGXIiQwSEZ0gV2hFQAsN\ni7Qq0uhxUPQUPAXakpQVRJRvbBzjhEfESMETAS0qPjkBgKw1HQQ9BU8FcDIABrCaiOYYG8kYsgZP\nBLpYS6ZVUUKk1+Mg6B3hdTLzmQCeB/AZEf0MgMvIYEMVEhoeYXYGIYZTdHxKeKw1e5zZOfyJ3hEe\nAICZfwvgSmgLj28yKpQRlJAQafopAlpIaDhCQsPizc7hT/SO8L7DzE8DmA/g54YkMo7uTcaF8AdK\nSCiUkFDpjjEIegre0QBaBx7w3GI2C8BtRoQygqKESMETAc3TyVsK3iAMuigw8xdEFOa5vWxf73QF\nQD+AnUaG00tRFIVmXyAFTwQ0JTQMiqJIwRsEPbeWnQDgBWj3zO7PCeCloYYygCLrk0ZWfcWKPvuG\n/zhjQiNlg+gR4lZVRPR1pZidw5/oGQXdCWAttC4pbwA4H9q6vNvgO/N4qqq65QdvBFRv/qDbbv+6\nK/vwrIiEbZ3994wZa40Kk8H1SOjp78dVy2tsZufwJ3rm8IoB/J6ZPwKwHtoSlYegtYa6wchweqmq\nqmLA1WRhLLfbjfJvXurcuOqmFuv0veqM6+Yk5cwaHZd59Vzrkzu2yQ/gCHG53YCKbrNz+BM9v4r7\noTUQALQ5uxIAn0PrgnyfQbmGTit6wkAuVy/KVz3T5o6sdY05ieKshXNjBz4ekxKPHQXRoeUOe09R\nokXmloZZn9uNflWVgjcIekZ4m6F1OgaAbQD23WkxypBExpFTWoP0dndg49J723ZsvaO16NykmGm/\nmpNkLUw94MLuwosPT3iyZkeXW37fDDuX6oZbCt6g6Bnh3QXgTSLqBfAKgFuJ6H0AE6GN9HyC293f\nY3YGf9fV1oTyb5+0x2T1q2WXlVijLIfeAC4kJARR50+Kf/3dCsc5BWOkH+EwcrlVuFTVaXYOf6Jn\nT4t3AEwHsJKZdwOYB+22sncBXGZsPP36XT0+u5uar3M07VLXLfmzrd7+lH3qVSWWsgune1Xs9kkt\nyQ5bFtapNjmdMswbRr3ufvT293eancOf6LqcxsxrB3z+BYAviMgKoBTAMoOyDUl/X7cUvEFq2r3B\ntafizbaU4oTQGddNtYZG6L/aOvqaoyyP3va57ZaSw6RJ5TBpdDq7a7s6y83O4U+MXD8wHcAH+N9i\nZFO5epx2szP4i1pe2tPU+FlH5tTUiJlnzExSQnTv7fSdsKhwtB+RE7ls696OIzKypBPrMKjuaG8F\nUGV2Dn8SsAum+no6ZIR3EG63G9Ub33W2Odd05czOiT78gtnJRq/VzplXHPPm8g9ap6ekQdbmGc/W\n02NTVbXD7Bz+JGD/L+zr6Wx197sQEhqwf0Vd3G4Xyle/0NEXUtlTcNzo2Anj5g5rA8mMq+YkPfXQ\nGttV40rk1NZgna6+FrMz+JuArQauXif3dNoQnXCgO+CCj6u3C9tXPd2mxDW5xp4xPj4xZ86InGbG\npiagPC8ytNxh7y1KtEiPQgN19PW1HvpZYqCALXidtr3bujta+qMTUn1iTtEs3V128Oon7JGpXeqE\ni0oSY1OLhj5BN0iFP5+Z8NRNH9ruLpkaESK3OBumrbev2ewM/sargkdEu3DoW7W8X7cwAtz9fXu6\n2hrt1qxxQdnzv8NWi53rn7HF5SjK5MtLLZHxUaZl2bc27813KxwLZW2eIXr7++Ho7WkwO4e/8XaE\n90/4372pjc62pnYE2SYntr3b3VX8ssNKMSHTrplsDYvyjcbPKSXZYUvf36oe43SqKdHRMswbogan\nE/benvVm5/A3SiDfcjpm+k/Xjp974WSzc4yE+spVfXW7/9OeVpYcVngMJYSE+d6ZvKu7Dx2yNs8Q\nKxrqO3+zavkUVVXZ7Cz+JGDn8ACgp8u2G0BAF7zqzR9122zLurIPz4qcuXBOkhLiu4OnsKhwtM8d\nFfnltr0dc2Vt3pB41uBVm53D3wR0wXO2NVa43f37WmEHDLfbjYpvX+ly9m/tzjsqP5pK5yb5S7/T\nnJNKYt5Y/oFtmqzNG5K23l6bKo0DBm3Er9iNpE5b3QftzdU+tX3kULhdvdjy9WPtW9f+qXXUSe6w\nGVfPTcqYmBPtL8Vun4yr51if2rld+uYNQVtfr1yw0CGgf8V2d7Qsb9m9aW9iWmGu2VmGore7A9tX\nPeEItzr66ZwJCfGZBX69NV9sagI4JyJ0h8PeM1b65g1av6qitrNzh9k5/JFhIzwiiiCi2Ua9nhFU\nVe1ytjfXmJ1Dr662Jqxf8hd7VdV9tomX5CccdsmspPhMS0D8khr9y1nSN0+nHQ57X3V7+1tm5/BH\nejbxmQLgKWidUQ5UMH1qwqy7o6UK/2tS6hccTbtQufmftsTCcGXqVSWW8JjAGwSFhIQg8ryyuDf+\nU+E4W9bmDcrqxobqxm7nV2bn8Ed6RngPQOt/dxWAXgBXAngQQB+Ac4yLZgxnW9OGXme72TG80lSz\noX/dkptbHeobbTOum2otXjAlIIvdPqmlo8KXhXaoLd3SN28w6p1dO1VV7TU7hz/SU/AOA3AlMz8O\nYCOATcx8PYA/wIcagO7jaKx811a33acrXm35Fz3rl93U6kpe0nX4DbOS6JSJCUPpRedPCq892vLI\nzm3SystLfW439nR0bDM7h7/SU/BCANR5Pt8B7dQW0DoelxkRykiqu3+nvX7nbrNz7M/tdqNy/TvO\nDctvao2ira7Dr5+TVHj0uPiQ0IC+cP4DYVHhsM8eFflV/V5pc+SFLbbW7sr29tfMzuGv9Px07cD/\n5sS2A5jm+TwRgM+df6mqqnZ3tOwyO8c+brcL21c+17Hlmz+2ps5pVQ6/dm7SqGkFsf62tMRIefNL\nYl637e3r6e83O4rP+7apcZe9t2eN2Tn8lZ7zpocAPENEAPAmgI1E5AQwG8BKA7MZpqNl9/JeZ/v8\niGjzVnO4eruxfdVTbUpso2vMT8bFW3Lnyp0GA6RfOcf69CPf2q4YVyy3nR1Eg9O5Q1VV+c2gk55N\nfJ4GcB6A3cy8HcDF0EZ8ewAsMjSdQWx1/MTe8q9qzXjv7i47Niy5x7Gj/K+2CRelx039v9lJltxk\n37ij34fEpSdg26jw0J0Ou+w29yO6XS7UdnZsNjuHPwvo5gED5ZYc92HZiVfNG6n309ozPWuPzYFK\np5RYIxOiR+qt/Zbb7cbeGz+031061SJ9835oZUN9x03frDq8u9+1xews/io4LgUCaG+pWdLT5ZgX\nGTO8S75sdeyu2v6Sw1oUrUy7ZpLFV9oz+QNtbd7E2Lf+W+FYIGvzfmBdS3Nlj7t/q9k5/FnQXBK0\n1+94ai9/OWxXa+srV/WtW3pza1f0ex2HXz/DOv6MyVLsdEidmBO+RNbm/YDL7UZFm2ONGiynZMMk\naAqeqqo2R2PlJqNft2bLx93rv7yxNSRndffMG2YnjTmh2Cd70fmT0dceZXlU1uZ9z5f1da3b7bZ7\nzc7h74LmlBYA2ptrPuvutJ0cFTu0C4FutxsVa1/rcvZt7s47Kj+qaKL/tGfyB2FREbDPzo78uryu\nY3Z6plzNBrCysX6dradHFhwPka6CR0SZAC4FMB7ANQCOgHbHhU93X3U07Hxm7/Yvry2ccpqu7ilu\nVy+2r3rWd/txAAAWlUlEQVSuwx2xu3f0vKLYpNFzk4zOKDS580tjXlv5gW1qShoiQ4N7xFzf1aXu\ncDjeNjtHIBj0KS0RjQGwGdpylLMAxAE4G8AaIpphaDqDqara1ta0a+Ngv6+3uwMbl97n4C13tI49\nJzFq2hVzkpJGp/ncIutAk37lHOvTO6Rv3ns1VVzusD9tdo5AoGeEdx+At6GN8No8x84F8AKAuwAc\nbUy04dHeXP1+l6Ph5JjE9EMWe2d7M/ibJ+zRWS619JJiS0xSnJy3jqD/rc1z9I5JTAzKPW1dbjfY\nbl+hqqqsTzSAnosWswHcz8zfXS1iZheA26A1FvBpjsbKZ6o3fnTQxZttTdVYt/jP9rqWJ+xTriqx\nTLpohlWKnTlGXzIr4Ynq8qDtm/dVfZ2t3GG/z+wcgULPCC8UBy6UCQB8/pYXVVX7UnJLP+7r6ZoY\nHvn9rXSbazb276543ZE8IT50xq+nWoKlY4kv+25t3nuVjgX5o4Nubd7KxoZ1zd1OWWhsED0jvI8B\n/IGI9n2vSkRJAO4G8LlhyYZRy+7Nd9Zs+vi7hgK15V/0rvvixta+pM+19kynliVKsfMdqRNzwpcq\n7e7WIFub1+DsUnc67O+YnSOQDPrWMiLKArAUWneUZGgdU/IAtAI4gpn9Yuu4tIIp/0wdXbqgvWON\nc9Ts7MjsIO9Y4utc3b3oum2x7Y9BtKfts7yNn+Vtk2R3MuMMehjDzHuJaBK0CxWToY0SNwP4FzO3\nHfSbfUhr7dZ7rcX98w+/dG6y2VnEof1vbV595+z0jFiz8wy3Prcb22yty6XYGUvXnRbM3MXMzzDz\nldDW4a0G4DY02TBz9To3dTQ4Fqtuv4od1HLnl8a83rqnNxj65r1TVVnzbXPTTWbnCDR61uHlENEn\nRDSdiKIBrAOwCkCVZ+TnN1orGm/ZvaKiyewcwntpV8y2PhPge9o6XS6saKj/sKe/v+7QzxaDoXcT\nn0QAjQAWQpu/mwttbd49xkUbfs7Wzm17VlV+qbqDai7cr8VlJGJLVlhoRZsjYDexeXNXxc7VTY1/\nMDtHINJT8I4BsIiZqwCcDOBDZl4O4G8AZhqYbUS07my8dc9KGeX5kzGXzk54vCow1+a19fbim8aG\nd1RVPegolojCiOjPRFRBRN1EVEVE9xHRiN97TESpRPTTAV+7ieiIkc7hDT0FLxxAKxEpAI4F8NmA\n13IZFWykdLV0bKz6gj9w9fSZHUV4KSQkBBHnTIx5u3qXw+wsRnu1YsfWtS3Nf/biqfcAOAPALwEU\nAfg5gBMAvDx86X7U3dAGP/tkAFhuQo5D0lPw1kH7R14EwArgAyKKAPB7AOsNzDZi6jfsvnb7u+ul\nE4UfSZuUE7FYaQuotXl7Oztda5ub/qmqaqcXT/8ZgD8y81JmrmHmJdB+JucTUfrwJv2B763nYuZG\nz91XPkfP6trrAbwHIAXA3cy8h4geBXA6gBFroW4kVVXtyWPTH8udPebehGxrUN6z6Y8KrzvK+kgA\nrc17vnz76s22Vm973rkBHENE/x1wm+cKAMUAmokoEtrtnucCSIJ2U8AVnp/XPAC7AJwC4BFoP8vP\nAHgKwPPQuiAtAXAOM3cCABEtAvA7AKkAvgFwNTNvJqJboBVfENFRzFxIRG4ARzHzMiLa5clxOYAS\nAMug7V99P4CTAJQDOI+Zt3leY67nsWJoOyTeysz/9jyWA+BpALMAdAF4DcCvB1Nc9Wzi8w2ATADJ\nzHyj5/CDAEYz86rBvp6vaN3Z+PDWt75dLg1l/UdYVARss7IilzfUezMi8mmrGxsc2+22W1VV9Xad\n1N8BXA1tdcSjRHQmgBhm3s7M/QCeAPATABcAOBzaVNS7+73G7wCcCuASz2v923PseGjz8ZcAABGd\nCuBPAK4AMAnAlwAWE1EigHsBvA6t+Ez9kay3e153NrT77ddBu2NrKrTCdafnfTIA/BfAs9CK490A\nniOi2Z7XeRhAO4CJ0AZYZ+3L6C296/DczGwf8HU5M7fqeS1foaqqaqtsumH38p0NZmcR3ss7ZWLM\nqy27/Xptnsvtxr+rKhdXtDk+8fZ7mPkOAOcDqIHWuehNAHuJ6GdEZIFW6H7FzMuYebPnuURExw94\nmduYeTMzvwZt1cXLzLyYmVdAm5sf53nebwDcycwfMnMFM9/ied8LPCNAJwDnQWrAc8y8hJnXAVgM\nrXfmU55R3YsD3udXAD5l5seYuZKZX4Y26rzW83geAAe0HRNXQps3/MDbfzNAxymtZ7j6o8MgZvbb\nbo3tdfZvM8py/pN5WN6l4dFyZusv0q+cY33msbW2X5F/7mn7dlVl9frm5qsH+33M/AqAV4jICuBE\nAFdBOzXdBm1ebfWA59qIiKGdrpZ7Dg/coN4JoHq/r/f1fBwP4B4iumvA45EAxnoZdf/3qTrI+5xG\nRO0DHg8DsK+x8D0AngNwJhF9COA1Zt7gZQYA+kZ4v9jvz2XQhrVN8JzL+7OGjXuu3/b2WulO4Ufi\nMhKxNTMspNIP1+ZVtbf1LN1b+1R7X+8eb7+HiEqJ6Lu5Pma2MfOrAI6Ctj/0j82lh3r+7LP/3NeP\nnU6HQbujqmzAn3EA7vAy8mDe50Vop6z73qcY2mk3PCO+HGinx3EA3iCi27zM8N0bDAozP3+g40S0\nBtrQ+l+DfU1foqpqe9KYtIfs1S0PWvKSo8zOI7wz+rLZiY/f+KH9rtKpEf6yp21Pfz8e27r50w2t\nLXcO8lvDAPyaiF4cOMJh5j4icgJohlZkDgfwKQAQUTK0Edl2z9MHM1nNAHKYuXLfASJ6Ftqc33uD\nfK1Dvc9MZv5uREhE10Obf7yLiO4A8DozPwngSSL6HYCLoM0vesXIHkirAfzTwNczja2i6cnNr60+\nZea1x58ibaL8Q0hICMLPKY19+4NdjrPyC/2ib95T27ds/rqh/qLBbr3IzOuI6D0A7xLRH6CtecuA\ntu1CJLSfw2IADxPRZQBs0C4AVEObm8vCfktJDuF+AE8R0Q7Pey0CsADAXzyPdwIoJqIsZt47mL/L\nfh4FcBUR3e75O0z3vMfFnsfHef5OV0AbJZ4MYO1g3sCQbRo9q7uvAlBvxOuZTVVVtW5t9YUbX1o5\nqPkBYa60Sbnh2tq8bp+/1L50b23TmqamGw51R8VBLIR2+ncLtDm79wDEQ2vR1gngBmijuzehXVXt\nBHA8M+9bYb//v9HB5uVfB3ATtOUlm6Bt43AKM1d4nrLvwsO+dbjqgNfz+r8FM9dAO309yfM+twG4\nznO6DgD/B63GLIVWePdAO9X2mp5+eD920UIF8H/MHDCbjSSMss4ed9rk1/OPpCyzswjv9Hb1oueO\nJbabSyb77AWM+q4u1x3r1jywrrnpt2ZnCTZ6ztd+gR8WvF4AKweeeweCtj22r1PHZT5sKUj5kyVX\n5vP8QURMBOoOz4hcUVHfOdMH++a53G48tGXjsvUtzdIcwASDHuEFG0VRlIxJuW/OvPb4M8Oiws2O\nI7xUefP7tnvoMKuv7Wn7zPat/OauimPaenuHMtcldDJkDi+Qqaqq1q+v+dn6F5avlV8O/iPVB/vm\nfdPUaF/Z2HCLFDvzSMHzgqq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"text/plain": [
"<matplotlib.figure.Figure at 0x868294400>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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4dswWGk5EoVALix67rG5QVn6d89mud9ONXFwnq6kmJ9pxdCTSTYgbgQNUdVO4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\nRmFKn4yclD7pOckp6dlJySl9cJJSHCc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"text/plain": [
"<matplotlib.figure.Figure at 0x86845a278>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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brHF5atsiEPQGwnFYrwP4daQNiRZik/J+nj/24ikarZhTLeh+ckefnxOXPvhZ0SRXIOiY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\nPwzzwQTtnZHCAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x868b7b438>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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dvUfEHrVzkPBky5yrq1p1hzXVdJll5wN7e/c8s613uHcwaP9At26v7+45cOSh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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x868bdd550>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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42XLUZXc0yV0yPjdalbg4V62JGn3PQcL0lIjS97/sIrMv1o76k49xpHKViTXh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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x868c48d30>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x868cae9e8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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DvYMY7Ojfa3YOQF8RmQ1goafNgUxmdKjf9MdfRrDZRlG17fWB3r7t/dE54YFz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\nOHBf+qLcFVnLCpIDgrg+u6q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"text/plain": [
"<matplotlib.figure.Figure at 0x868d105c0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Do you play Dwarf Fortress?</th>\n",
" <th>Do you use a Lazy Newb Pack?</th>\n",
" <th>Do you use Masterwork Dwarf Fortress (Meph's mod pack)?</th>\n",
" <th>Operating System</th>\n",
" <th>OS Bitness</th>\n",
" <th>Linux DE/WM</th>\n",
" <th>OS Type</th>\n",
" <th>Prefer 32bit with Therapist to 64bit without</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>708</td>\n",
" <td>695</td>\n",
" <td>644</td>\n",
" <td>710</td>\n",
" <td>690</td>\n",
" <td>161</td>\n",
" <td>710</td>\n",
" <td>711</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>26</td>\n",
" <td>5</td>\n",
" <td>21</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Windows 10</td>\n",
" <td>64-bit</td>\n",
" <td>GNOME</td>\n",
" <td>Windows</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>542</td>\n",
" <td>396</td>\n",
" <td>482</td>\n",
" <td>357</td>\n",
" <td>638</td>\n",
" <td>40</td>\n",
" <td>631</td>\n",
" <td>396</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Do you play Dwarf Fortress? Do you use a Lazy Newb Pack? \\\n",
"count 708 695 \n",
"unique 3 3 \n",
"top Yes Yes \n",
"freq 542 396 \n",
"\n",
" Do you use Masterwork Dwarf Fortress (Meph's mod pack)? \\\n",
"count 644 \n",
"unique 3 \n",
"top No \n",
"freq 482 \n",
"\n",
" Operating System OS Bitness Linux DE/WM OS Type \\\n",
"count 710 690 161 710 \n",
"unique 26 5 21 3 \n",
"top Windows 10 64-bit GNOME Windows \n",
"freq 357 638 40 631 \n",
"\n",
" Prefer 32bit with Therapist to 64bit without \n",
"count 711 \n",
"unique 2 \n",
"top False \n",
"freq 396 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Let's get an overview of the data (skipping items with less than ten people)\n",
"\n",
"for name in df.columns:\n",
" # Make pie chanrts!\n",
" vals = df[name].value_counts()\n",
" vals[vals > 5].plot.pie()\n",
" mpl.pyplot.show()\n",
" \n",
"# Alright, I guess a table could be useful too...\n",
"df.describe(include='all')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Wow, that's a lot of scrolling past pie charts! But what did we learn?\n",
"\n",
"- Most people who answer DF surveys have played DF. About a quarter have stopped, but keep hanging around.\n",
"- A majority always use some kind of pack, and only a quarter never do.\n",
"- Meph's Masterwork Mod has a lot of occasional users as well as regulars.\n",
"- Windows 10 is more popular than I expected. Scrolling down to the general OS chart, Windows has about 80% share and Linux a bit more than OSX.\n",
"- Some people are still on 32bit platforms, but it's a shrinking minority (less than 10%).\n",
"- More people would choose Dwarf Therapist than 64-bit DF, if forced. More on this later...\n",
"\n",
"Note that only answers with more than 5 responses were graphed, just to keep the text legible. \n",
"\n",
"Next, I'm going to select responses just from people who might use my pack - it could help make some decisions about it!"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Now we come to the bit I'm interested in - people who might use my pack\n",
"criteria = df['Do you play Dwarf Fortress?'].isin(['Yes', \"No, but I'd like to start.\"]) & \\\n",
" df['Do you use a Lazy Newb Pack?'].isin(['Yes', 'Sometimes']) & df['OS Type'].isin(['Windows'])\n",
"starter_pack = pd.DataFrame(df[criteria][[n for n in df.columns if n not in ('OS Type', 'Linux DE/WM', 'OS Bitness')]])\n",
"starter_pack['64bit'] = df['OS Bitness'].isin(['64-bit'])\n",
"for colname in [\"Do you use Masterwork Dwarf Fortress (Meph's mod pack)?\", 'Do you play Dwarf Fortress?']:\n",
" starter_pack.drop(colname, axis=1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"368 Starter Pack users responded to this survey\n"
]
},
{
"data": {
"image/png": 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MWnZ2ycDjBA11D9BZ0/6K2zlC0bjPwJRSZcBmrPaJ84BU4CLgfaXU4Y6mc5hp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\n/6eGTX60mddsAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x868d10c88>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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GPY/Qvs8giGg5gG+FEC7/AymlrMmad341AK8916G9qVKWFj7fOvP62eEh0eGu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\nH/zrHpGrFgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x868dd0a58>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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L0/banto3Kre0VTavb93TcLdlWX1u54oEfk/xaq0vxx5Q/aIx5tVx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\n+T/JbnVLAQzdRQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x868e34198>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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IsaU8TsWWW37b8ktu+a90y4LE35iKJJRIJIS6nY97Rkcae/uPdVRyzlPqIiWd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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x868e71128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Now that we have a table with just the responses of interest, let's graph them again\n",
"print(len(starter_pack), 'Starter Pack users responded to this survey')\n",
"for name in starter_pack.columns:\n",
" # Make pie chanrts!\n",
" vals = starter_pack[name].value_counts()\n",
" vals[vals > 1].plot.pie()\n",
" mpl.pyplot.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Well, this is certainly interesting - I didn't expect more than half the respondents in this group! But somewhere around 300 is the sample size where I'm willing to work with even a self-selecting sample, so let's jump in:\n",
"\n",
"- Most Starter Pack users use it all the time, but not all.\n",
"- Only *one person* uses Windows Vista or earlier.\n",
"- There's a slight reversal of preference here; more prefer Therapist than 64-bit.\n",
"- A similar proportion (10%) do not have 64-bit computers.\n",
"\n",
"But what to do with this information? I only have time to manage one pack, so should I upgrade to 64bit at the expense of 10% of the users? And if so, should I wait for Dwarf Therapist first?\n",
"\n",
"My current plan is to wait for Therapist, then upgrade to 32-bit DF 43.05 - and consider going to 64-bit even without Therapist once DFHack is stable. After all, the [manipulator](http://dfhack.readthedocs.io/en/latest/docs/Plugins.html#manipulator) plugin is an in-game version of Therpist, and the [autolabor](http://dfhack.readthedocs.io/en/latest/docs/Plugins.html#autolabor) plugin makes it even easier for new players!\n",
"\n",
"*I plan to do further analysis covering the detailed utilities questions eventually - if nobody else beats me to it!*"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
@Easykill
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People who like outdated software can just use an outdated pack.

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