458 lines
1.4 MiB
Text
458 lines
1.4 MiB
Text
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "black-funeral",
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import sys\n",
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"import glob\n",
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"import matplotlib as mpl\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "68c2f54a-f838-4d41-af1e-d63f13f3d894",
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"metadata": {},
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"outputs": [],
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"source": [
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"DIR = \"/home/claudio/hdd/git/bachelorThesis/figure_9/\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "87bfcdfc-6fee-4cb5-8054-d278700bbbee",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "fe6f1eb5-bf36-49b7-854f-6b9dfa51f766",
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"metadata": {},
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"outputs": [],
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"source": [
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"def categorical_plot(df, key, title, sorted_xs = None):\n",
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" if sorted_xs == None:\n",
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" xs = set()\n",
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" lines = []\n",
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" for t in [4,5,6,7,8]:\n",
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" xs = xs.union(np.array(df[df.term == t][key]).reshape(-1))\n",
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" xs = [str(x) for x in sorted(xs)]\n",
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" else:\n",
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" xs = sorted_xs\n",
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" \n",
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" plt.figure(figsize=(25,8))\n",
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" ax = plt.gca()\n",
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" lines = []\n",
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" for t in [4,5,6,7,8]:\n",
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" ys = []\n",
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" for x in xs:\n",
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" partsum = df[(df.term != -1) & (df[key] == x)][\"count\"].sum().astype(int)\n",
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" y = df[(df.term == t) & (df[key] == x)][\"count\"]\n",
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" ys.append((y.values.astype(int)[0] / partsum if partsum > 0 else y.values.astype(int)[0]) if y.any() else 0)\n",
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" lines.append(ax.plot(xs,ys, marker='.')[0])\n",
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"\n",
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" plt.xticks(xs)\n",
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" plt.legend(lines, [\"EVICT\", \"FAIL\", \"FINISH\", \"KILL\", \"LOST\"],\n",
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" bbox_to_anchor=(1,1))\n",
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" plt.title(title)\n",
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" plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"id": "a9a09b9c-4281-41f4-a069-eb954e0a018f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Figure size 1800x576 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAABeIAAAHiCAYAAACEMLwhAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAACUoUlEQVR4nOzdeXyU5bn/8e89S5bJRkIWsrApIHtYImrdtVq1Lq1Wa6tV27r0qG1PT4+AtuLSAnraX4/LsbVVce+q1K2uRdu6VUkQEBBQ2ZMQQoBsk2WW+/fHTIYkJBBIwpPl83698mIycz8z1zMkkHznmus21loBAAAAAAAAAIDe4XK6AAAAAAAAAAAABjKCeAAAAAAAAAAAehFBPAAAAAAAAAAAvYggHgAAAAAAAACAXkQQDwAAAAAAAABALyKIBwAAAAAAAACgFxHEAwCAQc8YY40xY5yuoycYYy4zxrx+mB7rFmPMw4fjsaKP964xZvrhfmxjzGpjzCndvI/vG2Pu7pmKesfBPKfGmE3GmC/2dk3dYYxZaIz5zy6s+4cx5urDUFKPMsZ8aIyZ5HQdAAAA6BqCeAAAMGD114Ctq4wxo6IvInharrPWPm2tPbMXHusUY8y21tdZaxdYaw/L82uMOU9SrbX2o8P92NbaSdbaf3Tzbh6SdJkxJrsHSuqSgw3LD+dzeiDdDfqNMVmSrpD0256rar+Pd5Ux5p3D8Vit/FLSnYf5MQEAAHCICOIBAADQH3xP0pNOF3GorLWNkl5RJBzuFa1fkOnLDlOdV0l62VrbcBgeq9sO8Tl5QdKpxphhPV0PAAAAeh5BPAAAGBSMMdcYYz4zxuwyxrxgjMlrt+QcY8wGY8xOY8wvjDEd/pxkjHEZY+YaYz43xlQZY/5sjMmI3vYbY8yzrdbebYxZYiLijTG/NMZsMcZUGGMeNMYktlp7gTFmuTGmJnrfZ0Wvb9MZbIy53RjzVPTTf0X/3GOMqTPGHNe+M9cY8wVjzFJjTHX0zy+0uu0fxpifRUe+1BpjXjfGZHZwzkmKhMh50cepM8bkta6lVXf+t40xW40xu40x3zPGHG2MWWmM2WOM+b929/sdY8wn0bWvGWNGdvKcx0k6TdI/O3oeWj32ldHnd6cx5icd3Vd0/WPGmF8bY16Jnsu7xphhxph7orWsNdEROO3/DqKP+2djzBPR52y1Maao1do5xpjS6G3rjDGnt3rof0j6cic13WGMuT962WuMqTfG/CL6eaIxprHl66zdc/CMMeYpY0yNIuFzy21PShoh6cXoOc7u7Pno6DmNfn5+9Pz2RL9WJrQ75GhjzJroc/aoMSahk/u9Kvoc/68xpkrS7caYI40xb5rI99BOY8zTxpgh+6vdGHOsMea9aD0rzP7HBZ2tVl8v0eM7/B47wHPQ5l0n0XPZEP373Wgio6AmSHpQ0nHRevdE13b6PW+i7zCJfr1sl/RoB7V0+hxJsRd3SiR9aT/PAwAAAPoIgngAADDgGWNOk7RQ0iWSciVtlvTHdsu+KqlI0gxJF0j6Tid3931JX5F0sqQ8SbslPRC97ceSpkTDuhMlfVfSldZaK+kuSeMkTZM0RlK+pHnR+mZJekLSTZKGSDpJ0qYunNpJ0T+HWGuTrbXvtzvvDEl/k3SfpKGSfiXpb8aYoa2WfVPStyVlS4qT9N/tH8RaW69IsFkWfZxka21ZJzUdI2mspK9LukfSTyR9UdIkSZcYY06O1naBpFskXSgpS9Lbkv7QyX2OlRS21m7r5PYWJ0g6StLpkuZ1EBy3domkn0rKlNQk6X1Jy6KfP6PIc9WZ8xX5+hmiSFfy/0XP6ShJN0o62lqbokhAuqnVcZ9IKuzkPv8p6ZTo5aMlbdfev9/jJK2z1u7q4LgLovUOkfR0y5XW2m9J2iLpvOjf1//s53z2YYwZp8jfx38q8vfzsiLBeFyrZZcpco5HKvK1/dP93OUxkjZIypE0X5JR5HsyT9IEScMl3d5Z7caYfEW+ln8uKUORr9NnTWQETUemSFrX6nwO9XssxkRekLpP0tnRv98vSFpurf1EkXdsvB+td0j0kE6/56OGRc9lpKRrO3pIdfIctbK/rykAAAD0IQTxAABgMLhM0iJr7TJrbZOkmxXpXh3Vas3d1tpd1totigTI3+jkvr4n6SfW2m3R+7pd0teMMR5rrV/StxQJcZ+S9H1r7TZjjFEkaPtR9DFqJS2QdGn0Pr8bre8Na23YWltqrV3bA+f9ZUmfWmuftNYGrbV/kLRW0nmt1jxqrV0fHeHxZ0VCw+74mbW20Vr7uqR6SX+w1u6w1pYqEra3dJp/T9JCa+0n1tqgIs/HNNNxV/wQSbVdeOw7rLUN1toVklZo/wHlX621JdGu4r9KarTWPmGtDUn6U6s6O/KOtfbl6NonWz1OSFK8pInGGK+1dpO19vNWx9VKSuvkPt+XNDb6IslJkh6RlG+MSVbkRZ9/dnactfa56NdNT45h+bqkv0W/JgOKzCNPVCR8bvF/1tqt0RcI5qvz7xkp8iLO/dGvwwZr7WfR+26y1lYq8j1z8n6Ov1yRUTMvR8/1DUnFks7pZP0Qtf2a6anvsbCkycaYRGttubV2dUeLuvA933Jft0Wfg33+7rr4HNVGzxUAAAB9HEE8AAAYDPIU6YKXJFlr6yRVKdKh2mJrq8ubo8d0ZKSkv0bHY+xRpCM1pEinr6y1HyjS+WsUCbalSEexT1JJq+NejV4vRTpdWwe2PaXNeUdtVtvz3t7qsl9Scjcfs6LV5YYOPm+5/5GS7m31fOxS5DlrXVuL3ZJSuvDYB3MuXa2zK4+TEH0h5jNFOshvl7TDGPNH03YEUoqk6o7uMBrEFisStJ6kSPD+nqTjtf8gfmsn13dX+++ZcPSxDuV7pv1aGWNyos9PaXSszlOKvBuhMyMlXdzy9RL9mjlBkXe4dKT910y3v8ei7wz5uiIvIpUbY/5mjBnfyfIDfc9LUmX0haAOdfE5SpG055BOCAAAAIcVQTwAABgMyhQJ8iTFRkwMlVTaas3wVpdHRI/pyFZFRlMMafWREO34ljHmBkW6ossktczl3qlIuDup1TFp1trkVvd5ZCePV69IoNei9caMtpNjWrQ571bnVtrB2gM50GMdrK2Srmv3PCZaa9/rYO1nijQZdxTS9ynW2t9ba09Q5Hm3ku5udfMERTr1O/NPRWbhT5e0NPr5lyTN0t79APZ5yP2V08WyO9L+e8Yo8j1yKN8zHdWyIHrdFGttqiId72Y/67dKerLd10uStfauTh5vpSJjYVof39n3WGv7+36TtfY1a+0ZirwAsFbSQ53Ue6Dv+Y6Oae9Az5F04K8pAAAA9BEE8QAAYDD4g6RvG2OmGWPiFQm4PrDWbmq15iZjTLoxZrikHyoynqQjD0qa3zJCxRiTFZ133jJX++eKBGbfkjTbGDMt2k38kKT/NcZkR9fmG2NaNll8JFrf6SayGWx+q07b5ZIuNZENPIskfa1VLZWKjLc4opNaX5Y0zhjzTWOMxxjzdUkTJb10oCesAxWShhpjOhutcrAelHSzMWaSJBlj0owxF3e00FrbLOnv2v/oEscZY44yxpwW/RprVCSIDbdacrIim9525p+SrpC0JnrO/5B0taSN0dEkB6tCnX9tHMifJX05+jXpVWT/gyZFuvRb3GCMKYjuRfATdf4905EUSXWSqqMvsNx0gNqfknSeMeZLxhi3MSYhuuFpQSf3/7Lafr3s73usteWSTjLGjIh+rd/cckO0Q/2C6At5TdH6W/5+KyQVtMzQ78L3fFfs9zkykc1xZ0p64yDuEwAAAA4hiAcAAAOdtdb+XdKtkp6VVK5IZ+yl7dY9L6lEkSDub4oEdx25V5ENOl83xtRK+rekY4wxHkXCwruttSustZ8qshnpk9Fgdo4ind3/jo6Z+LsiG4vKWvuhIhum/q8io0v+qb3dyLdG690t6Q5Jv291Yn5FZnO/Gx1/cWy7E6+SdK4iIWqVIh3651prdx74aWsrOk/7D5I2RB9rf2NIunJ/f1WkW/yP0edjlSIbwnbmt4q8uNGXxSuyQedORcbXZCsa5EZ
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"text/plain": [
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"<Figure size 1800x576 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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"text/plain": [
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"<Figure size 1800x576 with 1 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1800x576 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"text/plain": [
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"<Figure size 1800x576 with 1 Axes>"
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{
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"data": {
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"<Figure size 1800x576 with 1 Axes>"
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"data": {
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"text/plain": [
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"text/plain": [
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"<Figure size 1800x576 with 1 Axes>"
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{
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"data": {
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"text/plain": [
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"<Figure size 1800x576 with 1 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1800x576 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"text/plain": [
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"<Figure size 1800x576 with 1 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1800x576 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1800x576 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"def to_9a_bin(col):\n",
|
||
|
|
" # 1 [2,10] [11,50] [51,100] 101+\n",
|
||
|
|
" if col <= 0:\n",
|
||
|
|
" return \"None known\"\n",
|
||
|
|
" elif col == 1:\n",
|
||
|
|
" return \"1\"\n",
|
||
|
|
" elif col <= 10:\n",
|
||
|
|
" return \"2-10\"\n",
|
||
|
|
" elif col <= 50:\n",
|
||
|
|
" return \"11-50\"\n",
|
||
|
|
" elif col <= 100:\n",
|
||
|
|
" return \"51-100\"\n",
|
||
|
|
" else:\n",
|
||
|
|
" return \"101+\"\n",
|
||
|
|
" \n",
|
||
|
|
"def to_9c_bin(col):\n",
|
||
|
|
" # 1 [2,10] [11,50] [51,100] 101+\n",
|
||
|
|
" if col <= 0:\n",
|
||
|
|
" return \"Unknown\"\n",
|
||
|
|
" elif col < 0.5:\n",
|
||
|
|
" return \"(0,0.5)\"\n",
|
||
|
|
" elif col < 0.9:\n",
|
||
|
|
" return \"[0.5,0.9)\"\n",
|
||
|
|
" elif col < 1.1:\n",
|
||
|
|
" return \"[0.9,1.1)\"\n",
|
||
|
|
" elif col < 1.5:\n",
|
||
|
|
" return \"[1.1,1.5)\"\n",
|
||
|
|
" else:\n",
|
||
|
|
" return \">=1.5\"\n",
|
||
|
|
"\n",
|
||
|
|
"for cluster in \"abcdefgh\":\n",
|
||
|
|
" dfa = pd.read_csv(DIR + cluster + \"_term_task_count.csv\") \\\n",
|
||
|
|
" .sort_values([\"term\", \"task_count\"])\n",
|
||
|
|
" dfa[\"task_count\"] = dfa[\"task_count\"].apply(to_9a_bin)\n",
|
||
|
|
" dfa = dfa.groupby([\"term\", \"task_count\"]).sum().reset_index()\n",
|
||
|
|
" \n",
|
||
|
|
" categorical_plot(dfa, \"task_count\", \"# of tasks w.r.t. job rate (cluster \" + \n",
|
||
|
|
" cluster + \")\", [to_9a_bin(x) for x in [-1,1,10,50,100,101]])\n",
|
||
|
|
" \n",
|
||
|
|
" # column should be called machine_count here, typo in analysis scripts\n",
|
||
|
|
" dfb = pd.read_csv(DIR + cluster + \"_priority_exectime/part-00000\", header=None,\n",
|
||
|
|
" names=[\"term\", \"priority\", \"interval\", \"count\"]).sort_values([\"term\", \"interval\"])\n",
|
||
|
|
" del dfb[\"priority\"]\n",
|
||
|
|
" dfb = dfb.groupby([\"term\", \"interval\"]).sum().reset_index()\n",
|
||
|
|
" \n",
|
||
|
|
" xs = [\"<1\", \"1-2\", \"2-4\", \"4-10\", \"10-60\", \"60-1d\", \">=1d\"]\n",
|
||
|
|
" categorical_plot(dfb, \"interval\", \"Job execution time (in mins) w.r.t. job rate (cluster \" + \n",
|
||
|
|
" cluster + \")\", xs)\n",
|
||
|
|
" \n",
|
||
|
|
" dfc = pd.read_csv(DIR + cluster + \"_term_machine_locality.csv\") \\\n",
|
||
|
|
" .sort_values([\"term\", \"machine_locality\"])\n",
|
||
|
|
" dfc[\"machine_locality\"] = dfc[\"machine_locality\"].apply(to_9c_bin)\n",
|
||
|
|
" dfc = dfc.groupby([\"term\", \"machine_locality\"]).sum().reset_index()\n",
|
||
|
|
" \n",
|
||
|
|
" categorical_plot(dfc, \"machine_locality\", \"Machine locality w.r.t. job rate (cluster \" + \n",
|
||
|
|
" cluster + \")\", [to_9c_bin(x) for x in [-1,0.49,0.99,1.09,1.49,1.5]])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "a81da964-f539-49df-8fa3-7b358ea642e1",
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"metadata": {},
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"outputs": [],
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|
"source": []
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||
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|
}
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||
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|
],
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"metadata": {
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"kernelspec": {
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"display_name": "venv",
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"language": "python",
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"name": "venv"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"version": "3.9.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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||
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|
}
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