2021-04-17 09:37:58 +00:00
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{
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"cells": [
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{
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"cell_type": "code",
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2021-04-20 07:15:19 +00:00
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"execution_count": 1,
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2021-04-17 09:37:58 +00:00
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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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2021-04-20 07:15:19 +00:00
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"execution_count": 2,
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2021-04-17 09:37:58 +00:00
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"id": "hawaiian-cabin",
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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_7/\""
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]
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},
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{
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"cell_type": "code",
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2021-04-20 07:15:19 +00:00
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"execution_count": 3,
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2021-04-17 09:37:58 +00:00
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"id": "c5d455be-278f-43e7-b98a-0d460be1328a",
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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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2021-04-18 20:15:26 +00:00
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" plt.figure(figsize=(10 if key != \"n_exec\" else 22,8))\n",
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2021-04-17 09:37:58 +00:00
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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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2021-04-18 20:15:26 +00:00
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" partsum = df[(df.term != -1) & (df[key] == (int(x) if key != \"exectime\" else x))][\"count\"].sum().astype(int)\n",
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" y = df[(df.term == t) & (df[key] == (int(x) if key != \"exectime\" else x))][\"count\"]\n",
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2021-04-17 09:37:58 +00:00
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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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2021-04-20 07:15:19 +00:00
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"execution_count": 4,
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2021-04-17 09:37:58 +00:00
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"id": "d79f5580-3b46-40e5-863f-a40a3b325e72",
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"metadata": {},
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"outputs": [
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2021-04-17 10:50:40 +00:00
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAp0AAAHiCAYAAACqSXe3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAADs60lEQVR4nOzdd3gc5bU/8O87s72pW7YlG4ObJGzZxoVqOsSATRokcEOoCekkJJdLkl8q6QlpJNybG2og5NJDbAIGDAYbbGPLuEtyweAiWVbX9jbz/v6YnfVa2pV2V1tmd8/neXiwVrszr8RiHZ33PecwzjkIIYQQQgjJJiHfCyCEEEIIIcWPgk5CCCGEEJJ1FHQSQgghhJCso6CTEEIIIYRkHQWdhBBCCCEk6yjoJIQQQgghWUdBJylojDE3Y+y0fK9DSxhj32WMPTiO1+9hjF04yudfZozdlO71ifYxxj7CGHshief9iDH29xwsKaMYY79ljH0p3+sgpNRQ0EkKAmPsQ8aYLxJkqv9M5pzbOOcHNbC+7w5bm48xJjPGqjNw7ZsZY1Lkuk7G2HbG2PJEz+ec/5xz/rl078c5P51z/mbk3iOCCs75FZzzv6V7/VxgjD3KGPtpvtcxFsbYm4yxtP9bJbhmJr72nwH4ZSbWMxbG2DTGGGeM6XJxv4h7AXyXMWbI4T0JKXkUdJJCsiISZKr/dGbrRqn+AIwEetG1AfgVgDc5570ZWtLGyHXLATwE4GnGWMXwJ43nB3eOf+hnVCGvPRW5+DoZY4sBlHHON2X7XpmQzveEc34MQDuAqzO/IkJIIhR0koIWyZDMiPy5ijG2KpIN3MIY+ylj7O3I50ZkU2KzTJFs4juMsd8zxvoA/IgxZmSM3csYO8wYO84Y+wtjzJzEmhiAGwH8LeaxWxhjbYwxF2PsIGPsC+l8vZxzGcDDAMwApkcykc8yxv7OGHMCuHl4dpIxdnVky3ww8jU3xnzuQ8bY3YyxnQA8jDFd5LFLGWPLAHwXwKcjWdYdw79vkY9vjXxtA4yxVxhjp6jfh8j3szvy32QXY2xOnO/XRYyxXTEfv8YY2xLz8XrG2MeGveZCxtjRyNq7ADwS87nbAXwGwH9F1r0qme8tY2x5JIs8yBjbwBhrjjx+N2Ps2WHP/SNj7L7In8sYYw8xxo4xxjoi7zsx8rmbGWNvR95HA4yxDxhjV0Q+9zMASwH8ObLOP8dZk/q+vY0xdhjAG5HHn2GMdTHGhhhj6xhjp4/2tTPGJjPGnmOM9UTWcMco34orALw1bB2nR/679Ef+X/hunLVeyBg7OuyxDxljl0b+vIQx1hJ5LxxnjP0u8rR1kX8PRtZ8duT5cd9Xkc9xxthXGGP7AeyP90Uk+h7FeBPAVaN8HwghGUZBJykm9wPwAJgI4KbIP6k4E8BBALU4sb04C8B8ADMA1AH4QRLXWQpgAoDnYh7rBrAcgAPALQB+zxg7Q/1kJNA5b6wLMyVo/hwAN078sP0ogGehZEGfGPb8WQD+D8A3ANQAeAnAKnbytuL1UH74lnPOw+qDnPPVAH4O4KlIBndenPV8FEpg+onI9ddH7gcAlwM4H8r3sAzApwD0xfmyNgGYyRirZozpATQDmMwYszMlyF8Uue5wEwFUAjgFwO0x6/5r5Pvw68i6V8R57fCvYwGUYP4LAKoA/C+AlYwxI4AnAVzJGLNHnitGvpZ/RF7+KIAwlPfIgsjXHbtlfiaAvQCqAfwawEOMMcY5/3+Rr+urkXV+dZQlXgCgEcBHIh+/DGAmlPfZe5GvN+7XzhgTAKwCsAPKe/gSAN9gjH0E8c2NrFf93tgBrAGwGsDkyNf5+ihrTeSPAP7IOXcAmA7g6cjj50f+XR5Z88Yx3leqj0H53jYluF/c71GMNgAj3tOEkOyhoJMUkhciwdkgG1bkEAkEPgngh5xzL+e8FTGZxiR1cs7/FAm8/FACmTs55/2ccxeUAOy6JK5zE4BnOedu9QHO+b855+9zxVsAXoUSnKqfL+ecvz3KNc9ijA0C6IISJH6ccz4U+dxGzvkLnHOZc+4b9rpPA/g35/w1znkIylk2M4BzYp5zH+f8SJzXJuOLAH7BOW+LfN9+DmB+JCsVAmAH0ACARZ5zbPgFIvfdAiX4WAglOHoHwLkAzgKwn3MeL1iVofz3DqS59li3A/hfzvm7nHMpcmY1AOAszvkhKEHLxyPPvRiAl3O+iTFWC+BKAN/gnHs4590Afo+T3yeHOOcPcM4lKO/JSVB+sUnFjyLX9wEA5/xhzrmLcx4A8CMA8xhjZQleuxhADef8Hs55MHIG+gEkfi+XA3DFfLwcQBfn/Lecc3/kvu+muH5AeT/MYIxVc87dY2zfj/a+Uv0i8v9m3P/2SXyPXFC+VkJIjpTEOShSND7GOV+T4HM1UN7PR2IeO5LguYnEPr8GgAXAVsaY+hgDII52AcaYBcC1ULKPsY9fAeCHULJ+QuTau0ZcILFNnPNEmdDRvs7JAA6pH3DOZcbYESgZr2ReP5ZTAPyRMfbbmMcYgDrO+RuRLeP7AZzCGHsewH9yzp1xrvMWgAsBHI38eQBKdi+AYVu9MXo45/5xrD3WKQBuYox9LeYxA5TvH6BkNa8H8BiA/8CJLOcpAPQAjsW8TwSc/D3tUv/AOfdGnmdLcX3R60V+wfoZlPdZDZTgG1AyqUMjX4pToGSOB2MeExE/ewwo33t7zMdTALyf4nrjuQ3APQDaGWMfAPgx5/zFBM9N+L7Cifdzwvdtkt8jO4DB1L8MQki6KNNJikUPlC3O+pjHpsT82RP5tyXmsYnDrsFj/twLwAfg9EgWspxzXhYp5hnNxwH0QzkvBgCIbNE+ByXLWMs5L4eyzc3ivD4dfJTPdUL5Aa6uhUH5vnQk+frRPgcoP/i/EPM9KuecmznnGwCAc34f53whlC3QWQDuSnAdNeg8P/Lnt6AEnRcgcdA5nnXH+zp+NuzrsHDO1S3dZwBcyBirh/Lf+B8xrwsAqI55nYNzPvz84HjXGfu8/4DyS82lUI4tTIs8zuI8V13jB8O+Njvn/MoE99oJ5b9V7OuTaUvmQcz/X5HAryb6BXC+n3N+PZTt7l8BeJYxZo2zXvWeCd9XCb7OWGN9jwDluMKOJL4uQkiGUNBJikJk6/J5KAVAFsZYA5RiHvXzPVACrRsYYyJj7FYo58oSXU+GsgX5e8bYBABgjNWNcg5OdROAxzjnsT8QDQCMiATGkazn5Sl/kel5GsBVjLFLIuclvwUlSNow+suijgOYFjkXGM9fAHwnppCljDF2beTPixljZ0bu64FyZEFOcJ0NAGYDWAJgM+d8D5Rg+UycKDRJxXEkFyipHgDwxch6GWPMyhi7Sj3HGXn/vAmlYOkDznlb5PFjUI5K/JYx5mCMCYyx6YyxC7K0TkDJ0AWgnI+1QNl6Hu2amwG4mFIQZY68/+cwpUo9npegBPuqFwFMYox9gynFdXbG2JlxXrcPgCnyfdMD+B6U9z0AgDF2A2OsJvL/1mDkYRnK/xfysDUnfF8laazvESJf48spXJMQMk4UdJJi8lUoWY0uAI9DKTwIxHz+81AybX0ATsfYgdfdAA4A2MSUyvA1UAKjuBhjdVDO+z0W+3jkPOgdUALAAShZmJXDXutmjC1FhnHO9wK4AcCfoGRvV0BpPRVM8hLPRP7dxxh7L871/wkla/Vk5Hu0G0r1M6AUTT0A5Ws+BOX7/psE6/RAOTe5J2ZtG6Gch+wGok3rPxPv9YyxpYwxd8xDDwFoij3/y5Sm9iOqriP3b4Hy/vhzZL0HANw87Gn/gJI5+8ewx2+E8otFa+S1z0I5t5mMPwK4JlKhfV+Sr3kMyvezI3LP4WcjT/raI7+QLYdSEPcBlPfBg1D+XxmBc/4egCE1sIy8fy+D8t7pglLAdlGc1w0B+HLk2h1QftGIrWZfBmBP5L/THwFcxzn3cc69ULbC34ms+awx3lfJGPV7xBibBCX7/kIK1ySEjBM7OSFDSPFgjP0KwETOOU3PISQFjLHLAXyZc/6xfK8lGyJnRd/nnP93vtdCSCmhoJMUjciWugFKgc5iKNu
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"text/plain": [
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"<Figure size 720x576 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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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
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]
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},
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"metadata": {
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2021-04-18 20:15:26 +00:00
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{
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"text/plain": [
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"<Figure size 1584x576 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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2021-04-17 09:37:58 +00:00
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{
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"data": {
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"<Figure size 720x576 with 1 Axes>"
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2021-04-20 07:15:19 +00:00
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"<Figure size 1584x576 with 1 Axes>"
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2021-04-17 09:37:58 +00:00
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
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]
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},
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"metadata": {
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2021-04-20 07:15:19 +00:00
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{
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"text/plain": [
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"<Figure size 1584x576 with 1 Axes>"
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]
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},
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"metadata": {
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},
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2021-04-17 09:37:58 +00:00
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{
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"data": {
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"text/plain": [
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2021-04-20 07:15:19 +00:00
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"text/plain": [
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"<Figure size 1584x576 with 1 Axes>"
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2021-04-17 09:37:58 +00:00
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
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]
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},
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"metadata": {
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2021-04-20 07:15:19 +00:00
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{
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"data": {
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"text/plain": [
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"<Figure size 1584x576 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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2021-04-17 09:37:58 +00:00
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{
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"data": {
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
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},
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2021-04-20 07:15:19 +00:00
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{
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"data": {
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"text/plain": [
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"<Figure size 1584x576 with 1 Axes>"
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2021-04-17 09:37:58 +00:00
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
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]
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},
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"metadata": {
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2021-04-20 07:15:19 +00:00
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{
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"data": {
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"text/plain": [
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"<Figure size 1584x576 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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2021-04-17 09:37:58 +00:00
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{
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"data": {
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"<Figure size 720x576 with 1 Axes>"
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},
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"metadata": {
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2021-04-20 07:15:19 +00:00
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1584x576 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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2021-04-17 09:37:58 +00:00
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}
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],
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"source": [
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2021-04-17 10:50:40 +00:00
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"for cluster in \"abcdefgh\":\n",
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2021-04-17 09:37:58 +00:00
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" # Processing figure 7a\n",
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" \n",
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" dfa = pd.read_csv(glob.glob(DIR + cluster + \"_priority/part-*\")[0], header=None,\n",
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" names=[\"term\", \"priority\", \"count\"])\n",
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" dfa = dfa.sort_values([\"term\", \"priority\"])\n",
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" \n",
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" categorical_plot(dfa, \"priority\", \"Figure 7a: Priorities w.r.t. event rate (cluster \" + cluster + \")\")\n",
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" \n",
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" # Processing figure 7b\n",
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" \n",
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" dfb = pd.read_csv(glob.glob(DIR + cluster + \"_exectime/part-*\")[0], header=None,\n",
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" names=[\"term\", \"ignore\", \"exectime\", \"count\"])\n",
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" del dfb[\"ignore\"]\n",
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" dfb = dfb.sort_values([\"term\", \"exectime\"])\n",
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" \n",
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" xs = [\"<1\", \"1-2\", \"2-4\", \"4-10\", \"10-60\", \"60-1d\", \">=1d\"]\n",
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|
|
" categorical_plot(dfb, \"exectime\", \"Figure 7b: Execution times (mins) w.r.t. event rate (cluster \" + cluster + \")\", xs)\n",
|
2021-04-18 20:15:26 +00:00
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"\n",
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" # Processing figure 7c\n",
|
2021-04-17 09:37:58 +00:00
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" \n",
|
2021-04-20 07:15:19 +00:00
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|
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" dfc = pd.read_csv(DIR + cluster + \"_figure7c.csv\", header=0)\n",
|
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|
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" dfc = dfc.sort_values([\"term\", \"n_exec\"])\n",
|
2021-04-18 20:15:26 +00:00
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"\n",
|
2021-04-20 07:15:19 +00:00
|
|
|
" categorical_plot(dfc, \"n_exec\", \"Figure 7c: Priorities w.r.t. machine concurrency (cluster \" + cluster + \")\")\n",
|
2021-04-17 09:37:58 +00:00
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" "
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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": null,
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|
"id": "a9a09b9c-4281-41f4-a069-eb954e0a018f",
|
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"metadata": {},
|
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|
"outputs": [],
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"source": []
|
2021-04-18 20:15:26 +00:00
|
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},
|
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{
|
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|
"cell_type": "code",
|
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|
|
"execution_count": null,
|
|
|
|
|
"id": "717dc811-1a7d-468c-9038-e405914b20aa",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
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|
"source": []
|
2021-04-17 09:37:58 +00:00
|
|
|
}
|
|
|
|
|
],
|
|
|
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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",
|
|
|
|
|
"nbconvert_exporter": "python",
|
|
|
|
|
"pygments_lexer": "ipython3",
|
2021-04-27 14:37:09 +00:00
|
|
|
"version": "3.9.4"
|
2021-04-17 09:37:58 +00:00
|
|
|
}
|
|
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|
|
},
|
|
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|
"nbformat": 4,
|
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|
"nbformat_minor": 5
|
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|
|
}
|
