bachelorThesis/spatial_resource_waste/spatial_resource_waste.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import sys\n",
"import glob\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib as mpl\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"DIR = \"/Users/maggicl/Git/bachelorThesis/spatial_resource_waste/\""
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {},
"outputs": [],
"source": [
"NAMES = {4: 'EVICT', 5: 'FAIL', 6: 'FINISH', 7: 'KILL'}\n",
"\n",
"def plot_df(df, cluster, type_of_data):\n",
" \n",
" df = df[df[\"term\"].isin(range(4,8))].sort_values(\"term\")\n",
" \n",
" s = df.sum()\n",
" #print(\"Cluster \" + cluster + \":\")\n",
" df[\"cpu\"] = df[\"cpu\"] / s[\"cpu\"]\n",
" df[\"ram\"] = df[\"ram\"] / s[\"ram\"]\n",
" \n",
" latex = df.copy()\n",
" latex[\"Task termination\"] = latex[\"term\"].apply(lambda x: NAMES[x])\n",
" del latex[\"term\"]\n",
" latex[\"% CPU\"] = (latex[\"cpu\"] * 100).round(2).apply(lambda x: \"%2.02f\" % x) + \"%\"\n",
" del latex[\"cpu\"]\n",
" latex[\"% Memory\"] = (latex[\"ram\"] * 100).round(2).apply(lambda x: \"%2.02f\" % x) + \"%\"\n",
" del latex[\"ram\"]\n",
" #print(latex.to_latex(index=False)) \n",
"\n",
" df2 = df.copy()\n",
" df[\"kind\"] = \"CPU\"\n",
" df[\"percent\"] = df[\"cpu\"] * 100\n",
" del df[\"cpu\"]\n",
" del df[\"ram\"]\n",
" \n",
" df2[\"kind\"] = \"Memory\"\n",
" df2[\"percent\"] = df2[\"ram\"] * 100\n",
" del df2[\"cpu\"]\n",
" del df2[\"ram\"]\n",
" \n",
" df = pd.concat([df, df2])\n",
" \n",
" fig, ax = plt.subplots()\n",
" fig.set_size_inches(4, 4)\n",
" \n",
" colors = plt.cm.Spectral([0.2, 0.4, 0.8, 0.9])\n",
" \n",
" a = pd.plotting.table(ax, latex.set_index(\"Task termination\"), rowColours=colors,\n",
" bbox=[0,-0,1,0.3])\n",
" ax.set_ylim(bottom=-50, top=105)\n",
" print(a)\n",
" \n",
" bottom = [0, 0]\n",
" lines = []\n",
" for t in [4,5,6,7]:\n",
" dft = df[df.term==t]\n",
" line = plt.bar(x=dft[\"kind\"], bottom=bottom, height=dft[\"percent\"], \n",
" color=colors[t-4], width=0.85)\n",
" #ax.bar_label(line, label_type='center', fmt=\"%.02f%%\")\n",
" lines.append(line)\n",
" bottom += df[df.term==t][\"percent\"].values\n",
" #plt.legend(lines, [\"EVICT\", \"FAIL\", \"FINISH\", \"KILL\"],\n",
" # bbox_to_anchor=(1,1))\n",
" if cluster == \"2011\":\n",
" plt.title(\"2011 data (%s resources)\" % type_of_data)\n",
" elif cluster == \"all\":\n",
" plt.title(\"2019 data (%s resources)\" % type_of_data)\n",
" else:\n",
" plt.title(\"Cluster %s (%s resources)\" % (cluster.upper(), type_of_data))\n",
" \n",
" ax.set_xticks([])\n",
" ax.set_yticks([0,20,40,60,80,100])\n",
" fig.savefig('../report/figures/spatial_resource_waste/%s-%s.pgf' % \n",
" (type_of_data, cluster))\n",
" plt.show()\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<matplotlib.table.Table object at 0x7fd57ebc5820>\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<matplotlib.table.Table object at 0x7fd57f9d65e0>\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<matplotlib.table.Table object at 0x7fd57ecb8f40>\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<matplotlib.table.Table object at 0x7fd577b8ef10>\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<matplotlib.table.Table object at 0x7fd57edc4fd0>\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<matplotlib.table.Table object at 0x7fd57f9a31c0>\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<matplotlib.table.Table object at 0x7fd57eb56130>\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<matplotlib.table.Table object at 0x7fd57ee57910>\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<matplotlib.table.Table object at 0x7fd57f9b1040>\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<matplotlib.table.Table object at 0x7fd57f9c6d60>\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<matplotlib.table.Table object at 0x7fd57869c6d0>\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<matplotlib.table.Table object at 0x7fd57f9a3fd0>\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<matplotlib.table.Table object at 0x7fd57ea812e0>\n"
]
},
{
"data": {
"image/png": 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jTrvPM8YYwBSynI5trFQ55ZWAIiKBWMHkn8aYhfbkVBFpYM9vABwpaFkd21ip8ssbv/II1lCjPxlj3nKb9SXWuMag4xsr5ZO8MRRpR+BBYKuIJNrTJgCxwGciMhL4BbjPC2VTSpVCmQcUY8x/ASlkdreyLItSyll6p6xSyjEaUJRSjtGAopRyjAYUpZRjNKAopRyjAUUp5RgNKEopx2hAUUo5RgOKUsoxGlCUUo7RgKKUcowGFKWUYzSgKKUcowFFKeUYDShKKcdoQFFKOUYDilLKMRpQlFKO0YCilHJMuQooItJTRP4nIrtERIciVcrHlJuAIiL+wHSgF9ASiLbHPFZK+YhyE1CAdsAuY8zPxpiLQDzWeMdKKR9RngJKI2C/2/sD9jSllI/wxkBfpSIio4HRAE2aNCnSMhue0+F+fI0eM99UnmooB4HGbu9D7Gm56NjGSpVf5SmgrAeai0gzEakEDMYa71gp5SPKTZPHGJMpIk8CywB/4ENjzDYvF0spVQzlJqAAGGMWA4u9XQ6lVMmIMcbbZSixoKAgc/HiRW8XQ5VAlSpVSE9P93YxVAmJSGZ2dnZgvum+HFBExPhy+SsyEUGPne+yj5/knV6eLspeUY4ePUqnTp0IDw/niy++cE3v27cvhw4dKnS52bNnEx4eTkREBK1ateKNN94AYPjw4TRr1ozIyEhat27NmjVrAOjatSsbNmxwLb93717Cw8M9s1E+riTH5MUXX0RE2LVrl2va1KlTEZFc+11ZNKB4yJw5c3j00UdZt24dU6dOBWDRokW0atWKhg0bFrjMkiVLmDp1KsuXL2fr1q2sXbuWGjVquOa//vrrJCYmEhsbyyOPPFIWm3FFKckxAYiIiCA+Pt71ft68eYSFhXm6uC6ZmZlltq7S0oDiIYGBgaSnp5ORkYG/vz+ZmZlMnTqVZ599ttBlpkyZwhtvvOE6uYOCghg1alS+dF26dMn1jamKpiTHBKBfv34kJCQAsHv3bmrUqEHdunVd85cvX06HDh1o3bo19957L2fPngWgadOmjB8/nsjISNq2bcumTZvo0aMH119/Pe+++y4AxhjGjh3rqpXOnTsXgO+++47OnTvTp08fWrZsyQsvvOAKggATJ05k2rRpTu4eR2hA8ZAhQ4aQkJBAVFQUEyZMYMaMGTz44INUqVKl0GWSkpJo06bNb+a9aNEiIiIinCxuhVCSYwJQvXp1GjduTFJSEvHx8QwaNMg179ixY8TExPDNN9+wadMm2rZty1tvveWa36RJExITE+ncuTPDhw9n/vz5rF27lkmTJgGwcOFCEhMT2bJlC9988w1jx44lJSUFgE2bNjFt2jR27tzJiBEjmD17NgDZ2dnEx8fzwAMPOL2LSq1c/Wx8JalRowb/+te/AEhLSyM2NpbPP/+cUaNGkZaWxjPPPEOHDh2KlefYsWOJiYmhXr16zJw5E7AujuVV0DRVumMyePBg4uPjWbZsGStWrOCjjz4CYO3atWzfvp2OHTsCcPHixVx59OnTB7CaTWfPnqVatWpUq1aNoKAgTp48yX//+1+io6Px9/enfv363Hbbbaxfv57q1avTrl07mjVrBli1nTp16rB582ZSU1Np1aoVderU8di+KikNKGVg8uTJTJw4kTlz5tCpUycGDhzIgAEDWLZsWa50YWFhbNy4kTvuuKPAfF5//XUGDhyYa1qdOnVIS0tzvT9x4kSu6rgqWFGPSY677rqLsWPH0rZtW6pXr+6abowhKiqKOXPmFLhcUFAQAH5+fq7XOe9/69pI1apVc71/+OGHmTVrFocPH2bEiBFF2s6ypk0eD0tOTubAgQN07dqV9PR0/Pz8EBHOnz+fL+348eMZO3Yshw8fBqxvuw8++OCy+Xft2pVPPvnE9RNsXFwct99+u/MbcgUpzjHJUaVKFV599VUmTpyYa3r79u1ZvXq165rWuXPn2LlzZ5HL0rlzZ+bOnUtWVhZHjx5l5cqVtGvXrsC0/fv3Z+nSpaxfv54ePXoUeR1lSWsoHjZx4kReeeUVAKKjo+nXrx+xsbG8/PLL+dL27t2b1NRUunfvjjEGEfnNb6LRo0ezY8cObr75ZkSEtm3bMmXKFI9sy5WiOMfE3eDBg/NNq1evHrNmzSI6OpqMjAwAYmJiaNGiRZHK0r9/f9asWeM6fq+99hrXXHMNO3bsyJe2UqVK3H777dSsWRN/f/8i5V/W9MY25RV6Y1vxZWdn07p1a+bNm0fz5s29Wha9sU0pH7Z9+3ZCQ0Pp1q2b14PJ5WgNRXmF1lB8W2E1lMteQ6kc4H/4QlZ2fc8Vq3RERH8i9VHBwcF67HxYYcfusjUUETFnnyq/j+K76u8r9FvOR2kNxbc5dg2l+jsr6DDnB9ffmxv28tcffmbS97lvBf/x6Blaf2J1YGs5azXHzluPGUg9l8GwpVuJiPueTvHrGPBlIluPnnHl1/i9/xAWt5oOc37grs83lWhjK7qlS5dyww03EBoaSmxsbL75GRkZDBo0iNDQUG699Vb27t0LwPHjx7n99tu56qqrePLJJ3Ol79mzJ+Hh4cyYMcM1ffTo0WzapMfIaSU9fmB13wgNDeWGG25w3VNT0o6qJVHsn40rB/izJvrWXNOS09Lp/+VmXvp9qGva/J2p3Nsid2vJGEP04h8ZcmMD4npat45vPXqG0xczXXk+8vV2ejarQ//QctvSKteysrJ44okn+PrrrwkJCeGWW25x9QfJMXPmTGrVqsWuXbuIj49n3LhxzJ07l+DgYCZPnkxSUhJJSUmu9MuWLaNTp05MmDCBjh078vjjj7NlyxaysrJo3bq1NzbzilWa47d9+3bi4+PZtm0bhw4donv37uzcudPVKXLAgAH07t2bfv36FalTZEk48itP81pVqBkUyPrDp1zTFu5K5d4W1+RK958DaQT4+fFwRIhrWkS9anRsVMuJYihg3bp1hIaGct1111GpUiUGDx7s6tiWIyEhgWHDhgEwcOBAVqywmo5Vq1alU6dOBAcH50qf06nu0qVLrmbK888/z+TJk8tmoyqQ0hy/hIQEBg8eTFBQEM2aNSM0NJR169aVuFNkSRQ7oJzPzMrV5Jm/MxWAe1vUZ36y9Xrd4VPUCg4ktGbuTlfbT5yj1dXVHCi2KszBgwdp3PjXwQNCQkI4ePBgoWkCAgKoUaMGx48fLzTPqKgo9u7dS/v27RkzZgxffvklrVu3dvzbTZXu+BW2bEk7RZaEI00egHua16fb/A1M6dTcau401ybLlSIgIIBPP/0UgEuXLtGjRw8SEhJ4+umn2bdvH0OHDnV1glPljyc6qhbGsRvbQqoFc231yqw6mEbC7iPc0yJ/QPld7apsPnLGqVWqAjRq1Ij9+38dgPHAgQM0atSo0DSZmZmcOnWqyD1XZ8yYwdChQ10Pf5o7dy5vvvmmcxtQwZXm+BVl2bydIuPi4njxxRcdK7+jd8re26I+z61Kpmn1yjS6Kjjf/K4htbiYlc2HSb9W4ZKOnWH1wbR8aVXJ3HLLLSQnJ7Nnzx4uXrxIfHx8vtpDnz59iIuLA2D+/PnccccdRbonJC0tja+++oqhQ4cWuVOdKp7SHL8+ffoQHx9PRkYGe/bsITk5OVdHw5J0iiyuYjd5cq6h5IhqUoeXO1q/7vQPvZqxK3fyRpeCO0aJCHN638Szq3byt42/EBTgx7XVgnm1kPSq+AICAnjnnXfo0aMHWVlZjBgxgrCwMF544QXatm1Lnz59GDlyJA8++CChoaHUrl071+MNmzZtyunTp7l48SJffPEFy5cvd/3C8PLLLzNx4kT8/Pzo0aMH06dPJyIigkcffdRbm3vFKc3xCwsL47777qNly5YEBAQwffr0XJ0IS9opsjj0xjblFXpjm2/TzoFKKY/TgKKUcsxlmzyVgwOzLmRkltugo9Vm3xUcHMyFCxe8XQxVQiJCdnZ2vibPb15Dyd77ukcLVhp+TcdqQPFR+mXg2xy7hhJw3bO06vWW62/v/hMATJ25isotxnPq9K8/QX23Zjd3j/gQgFnz1vPkC5+XeANU0fly5zLl28ev2AGlcnAgm5c87fpr2rg2APFfbuaWmxuzcGnSb+SgPCmnc9mSJUvYvn07c+bMYfv27bnSuHcu+/Of/8y4ceMAcnUuW7p0KY8//jhZWVklHnFPFZ+vHz9Hro/s/uUYZ9MvMvmZHsQv2uxElqqEfL1zWUXn68ev+J0DL1xyNXcGjJ4FQPyiLQy6+2Y6t2vG/34+SupRvb3eW3y9c1lF5+vHr/idA+0mj7v4Lzez8B/D8PPzY0DPCOYt/pEnh3V0rJDKu8qyc5lyXlkev1KPy7N1RwrJe49x5wPvA3DxUhbNGtfSgOIlxelcFhISUurOZb814p4qHl8/fqW+hjLny0Qm/fFO9qyewJ7VEzi47nkOpZ7mlwPa4c8bfL1zWUXn68ev1DWUuYsS+ddHI3NN69cjnPhFidwa2STX9Lj5G0hYvs31fs3nTxLSoGZpi6Dc+HrnsorO14+f3timvEJvbPNt2jlQKeVxGlCUUo65bJMnIKhSVtbFS+U26Gi12Xdp50DfVuLOgaNX/tOjBSuN97rcrwHFR+mXgW9z7BrK+10fYMGI8a6/MylHObR5O0vHWRdv/7fkP7x32wMc373Ptcy8YeM4k3IUgE/v+yMXTlp30m6a/QXzhj7L/OHPsWDEeI5st0YfXDQmhqM7fnYtfyblKPOGjStuUSusknYu+/rrr2nTpg0RERG0adOGf//73670OnJg2fHlzoHF/tnYP6gS93w4Jde0M4eP5npftV5tNs/+gu4vjSk0n9SkZPat2cyAD17Bv1IgF06eISszs7jFUXmUZuS5unXrsmjRIho2bEhSUhI9evTg4MGDOnJgGdKRAwtw7e9bkbb3ICf3FR790o+fJLhGNfwrBQIQXLMaVevqCIKlVZrOZe4nWFhYGOfPnycjI0NHDixDvt45sNg1lKyMiywYMR6Aag2u5s5X/pw/kQg3R9/F5o+/5PaJBT8RPeSWCDbFLWTukGdo1Dac6+5oT8PI37nm/3vyDAKCrGCTdSkT8Su314bLlYI6iP3www+FpnHvXFa3bl1XmgULFtC6dWuCgoKIiori448/pn379owdO1ZHDvSg0hy/gwcP0r59+1zL5nQOHDJkCO+99x6vvvpq+eocWFCTpyCh3X/P5o8TOH3oSIHzA6sE0//9Vzj84w4Obd7Oihf/TrtHBnFDr9sAuOP5x6l343WAdQ1l6XNvFLeoqoS2bdvGuHHjWL58OaAjB/o6nxw5MF/GAf7cNKg3Wz5dVHgafz8atmpJ2xED6finYez5z3pPFafCKO3IgQcOHKB///7Mnj2b66+/Pl/+OnKgZ+nIgZfRolcXDm7cxoWTp/PNO7nvEKf2H3a9P578C1fVr5svnSqe0nQuO3nyJH/4wx+IjY2lY8f8vcV15EDPq/CdAy/HPzCA8Hvu5Pu3P84379L5DL6fGkfG2XP4+ftTvVF9uox92JPFqRBK07nsnXfeYdeuXbz88suuDmPLly/n6quvBnTkwLJwxXcO1BvblCfojW2+TTsHKqU8TgOKUsoxl23y+AUGZZnMi+U26Gi12Xdp50DfVuLOgW2mfOPRgpXGxvHdNaD4KP0y8G2OXUPZPOku1+tTO34g6Y1hZKSlcuibOA6v/AyAvfNeI23rylzLZaQdZttU/RXHKdOmTSM8PJywsDDX4E1btmyhQ4cOREREcPfdd3P6dP6f6wtbFmDQoEFERkYSGRlJ06ZNiYyMBGD16tXcdNNNtG3bluTkZABOnjzJnXfeSXZ2tic30+eNGDGCq6++mvDwcNe0EydOEBUVRfPmzYmKiiItzXr+sjGGMWPGEBoayk033VRox8uNGzcSERFBaGgoY8aMcQXmwvJdsGABYWFhdO7cmePHjwOwe/duBg0a5Pj2lrg5c3rXJvYvmk7zh6YQVKu+k2VSvyEpKYn333+fdevWsWXLFr766it27drFww8/TGxsLFu3bqV///68/nr+x3cWtizA3LlzSUxMJDExkXvuuYcBAwYA8Oabb7J48WKmTp3Ku+++C0BMTAwTJkzAT7tEXNbw4cNZunRprmmxsbF069aN5ORkunXr5upRvGTJEpKTk0lOTua9997jscceKzDPxx57jPfff9+VNif/wvL9+9//zvr163nkkUdcdzz/5S9/ISYmxvHtLdHZcGbPj/yy8C1Ch8UQVEf7c5S1n376iVtvvZUqVaoQEBDAbbfdxsKFC9m5cyddunQBICoqigULFhR5WXfGGD777DOio6MBXJ3L0tPTCQwMZPfu3ezfv5+uXbt6fFt9XZcuXahdu3auae6d+4YNG+Z6pEBCQgJDhw5FRGjfvj0nT54kJSUl17IpKSmcPn2a9u3bIyIMHTo01/IF5evn50dGRobr+K1atYprrrmG5s2bO769xQ4o2ZmX2P3xJK5/8GWCr27y2wsox4WHh7Nq1SqOHz9Oeno6ixcvZv/+/YSFhbl6ps6bNy/Xbdi/tay7VatWUb9+fdcJN378eIYOHcqUKVN48sknmThxoke+3SqK1NRUGjRoAMA111xDamoqUPRRA0NCQgpMU1i+48ePp3v37ixatIjo6GgmT57M888/75FtK3ZAEf8ArmrSkuMblniiPKoIfve73zFu3DjuvPNOevbsSWRkJP7+/nz44YfMmDGDNm3acObMGSpVqlTkZd3NmTPHVTsBiIyMZO3atXz77bf8/PPPNGjQAGMMgwYN4oEHHnCduKr4RASRfNc2Hc03KiqKjRs3smjRIhISEujduzc7d+5k4MCBjBo1ivT0dMfWW/yAIsJ1Q57n3P4dpHz7qWMFUcUzcuRINm7cyMqVK6lVqxYtWrTgxhtvZPny5WzcuJHo6OgCO/cVtmyOzMxMFi5cWOAFO2MMMTExPP/887z00ku89tprjBo1irfffttj23klql+/vqspk5KS4uraUNSOgQcOHCgwTWH55khPT2fWrFk88cQTTJo0ibi4ODp16sQ//+nc3fAluobiVymY0OGvcCJxBcfWa03FG44csR4LsW/fPhYuXMiQIUNc07Kzs4mJiSm0j01By+b45ptvuPHGG3NVq3PMnj2b3r17U7t2bVfnMj8/P0e/4SoC9859cXFx9O3b1zV99uzZGGNcvblzmjA5GjRoQPXq1Vm7di3GGGbPnp1r+YLyzfH6668zZswYAgMDOX/+PCLi+PErcefAgCrVaf7QFP733tMEVK2Rb/4vn/+N/V9Zzx+tVKMezaIncuHofn6cMtiVpvFdj1Er4raSFqFCu+eeezh+/DiBgYFMnz6dmjVrMm3aNKZPnw7AgAEDeOihhwA4dOgQDz/8MIsXLy502Rzx8fG5mjs5cr7dcp6R8vTTT9O7d28qVark+uVA5RcdHc13333HsWPHCAkJ4aWXXuK5557jvvvuY+bMmVx77bV89pl1u0Xv3r1ZvHgxoaGhVKlShY8++siVT2RkJImJiYD1CInhw4dz/vx5evXqRa9evQAKzResc2DdunVMmjQJgKeeeopbbrmFmjVr5nrObGnpjW3KK/TGNt+mnQOVUh6nAUUp5Zjf6hx42GRe1NtgleO0yePzjDEmX4XksgFFKaWKQ5s8SinHaEBRSjlGA4pSyjEaUJRSjtGAopRyjAYUpZRjNKAopRyjAUUp5RgNKEopx/w/kdwMHKydyOEAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<matplotlib.table.Table object at 0x7fd57f588190>\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<matplotlib.table.Table object at 0x7fd57ee64b50>\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<matplotlib.table.Table object at 0x7fd57f0c5b50>\n"
]
},
{
"data": {
"image/png": 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RYCACWAtUNsYk2rMOApU9lS7leafHL/Z0Eq5Kpf/9fwVa32MXZUXkWmAu8Kwx5rj7PGOMAUwO6+nYxkp5KY+UUESkGFYw+cwYM8+enCQiVY0xiSJSFTiU3brGmI+AjwAiIyOzDToX07Nd0SvomU75Jk/c5RGsoUZ/McZMcJu1AGtcY9DxjZXySZ4oobQEHga2iUisPe2fwDhglogMAn4HenkgbUqpAijygGKM+RGQHGa3L8q0KKWcpS1llVKO0YCilHKMBhSllGM0oCilHKMBRSnlGA0oSinHaEBRSjlGA4pSyjEaUJRSjtGAopRyjAYUpZRjNKAopRyjAUUp5RgNKEopx2hAUUo5RgOKUsoxGlCUUo7RgKKUcowGFKWUY7wqoIjIXSLyq4jsEREdilQpH+M1AUVE/IH/AJ2ABkAfe8xjpZSP8JqAAjQD9hhjfjPGpADRWOMdK6V8hDcFlOpAvNv7BHuaUspHeHSw9PwQkceBxwFq1qyZq3VK//ubwkySKgSaZ77Jm0oo+4Eabu+D7GmZGGM+MsZEGmMiK1asWGSJU0pdnjcFlPVAbRGpJSLFgd5Y4x0rpXyE11R5jDHnReQpYAngD3xqjPnZw8lSSuWB1wQUAGPM18DXnk6HUip/xBjj6TTkW4kSJUxKSoqnk6HyoVSpUpw+fdrTyVD5JCLn09PTi2WZ7ssBRUSML6f/aiYiaN75Ljv/5OLp3nRR9oqSnJzMbbfdRqNGjZg/f75r+n333ceBAwdyXG/atGk0atSI0NBQIiIiePvttwHo378/tWrVIjw8nCZNmrB69WoA2rRpw4YNG1zrx8XF0ahRo8I5KB+Xnzx55ZVXEBH27NnjmjZx4kREJNPnriwaUApJVFQUTz75JOvWrWPixIkALFy4kIiICKpVq5btOosWLWLixIksXbqUbdu2sWbNGsqVK+ea/9ZbbxEbG8u4ceN44okniuIwrij5yROA0NBQoqOjXe9nz55Nw4YNCzu5LufPny+yfRWUBpRCUqxYMU6fPs25c+fw9/fn/PnzTJw4kWHDhuW4ztixY3n77bddX+4SJUrw2GOPZVmudevWmc6YKnfykycAXbt2JSYmBoC9e/dSrlw5rr/+etf8pUuX0qJFC5o0aULPnj05efIkAMHBwYwYMYLw8HAiIyPZtGkTHTt25KabbuLDDz8EwBjD0KFDXaXSmTNnArBy5UpatWrFvffeS4MGDXj55ZddQRBg5MiRvPvuu05+PI7QgFJIHnzwQWJiYujQoQP//Oc/+eCDD3j44YcpVapUjuts376dpk2bXnbbCxcuJDQ01MnkXhXykycAZcuWpUaNGmzfvp3o6GgeeOAB17w///yT0aNH880337Bp0yYiIyOZMGGCa37NmjWJjY2lVatW9O/fnzlz5rBmzRpGjRoFwLx584iNjWXLli188803DB06lMTERAA2bdrEu+++y65duxg4cCDTpk0DID09nejoaB566CGnP6IC86rbxleScuXK8dVXXwFw9OhRxo0bxxdffMFjjz3G0aNHef7552nRokWetjl06FBGjx5NxYoV+eSTTwDr4tjFspumCpYnvXv3Jjo6miVLlrB8+XImT54MwJo1a9ixYwctW7YEICUlJdM27r33XsCqNp08eZIyZcpQpkwZSpQowbFjx/jxxx/p06cP/v7+VK5cmdtvv53169dTtmxZmjVrRq1atQCrtFOhQgU2b95MUlISERERVKhQodA+q/zSgFIEXnvtNUaOHElUVBS33XYbPXr04P7772fJkiWZlmvYsCEbN26kXbt22W7nrbfeokePHpmmVahQgaNHj7reHzlyJFNxXGUvt3mSoUuXLgwdOpTIyEjKli3rmm6MoUOHDkRFRWW7XokSJQDw8/Nz/Z/x/nLXRkqXLp3p/aOPPsqUKVM4ePAgAwcOzNVxFjWt8hSy3bt3k5CQQJs2bTh9+jR+fn6ICGfOnMmy7IgRIxg6dCgHDx4ErLPdpEmTLrn9Nm3aMGPGDNct2KlTp9K2bVvnD+QKkpc8yVCqVCneeOMNRo4cmWl68+bNWbVqleua1qlTp9i1a1eu09KqVStmzpxJWloaycnJfP/99zRr1izbZbt168bixYtZv349HTt2zPU+ipKWUArZyJEjGTNmDAB9+vSha9eujBs3jldffTXLsp07dyYpKYk77rgDYwwictkz0eOPP87OnTsJCwtDRIiMjGTs2LGFcixXirzkibvevXtnmVaxYkWmTJlCnz59OHfuHACjR4+mTp06uUpLt27dWL16tSv/3nzzTapUqcLOnTuzLFu8eHHatm1LYGAg/v7+udp+UdOGbcojtGFb3qWnp9OkSRNmz55N7dq1PZoWbdimlA/bsWMHISEhtG/f3uPB5FK0hKI8Qksovi2nEsolr6FcE+B/8GxaeuXCS1bBiIjeIvVRJUuW1LzzYTnl3SVLKCJiTj7dvrDSVGDXvrdcz3I+Sksovs2xayhl319Oi6i1rtf4DXG8vvY3Rv2UuSn41uQTNJlhdWBrMGUVf56xHjOQdOoc/RZvI3TqT9wWvY77F8SyLfmEa3s1PvqOhlNX0SJqLV2+2JSvg70aDBw4kEqVKmXqCLhlyxZatGhBaGgo99xzD8ePH8+yXnx8PG3btqVBgwY0bNgwU/PtI0eO0KFDB2rXrk2HDh1c7Vvmzp1Lw4YNadWqFYcPHwasJujuLUZV7uWUBxl9hPz8/C7Z8XDx4sXUrVuXkJAQxo0b55q+b98+brnlFkJCQnjggQfIeLTHe++9R6NGjejcubNr2o8//siQIUMcP7Y8B5RrAvxZ3ecW1+v5yGB61qnC3N1JmZabsyuJnnUy15aMMfT5eiutqpdnW79b+bF3M/7V4iaOp5x3ba9zrYqMbhnC6j638GW3JgU7uitY//79Wbx4caZpjz76KOPGjWPbtm1069aNt956K8t6AQEBjB8/nh07drBmzRr+85//sGPHDgDGjRtH+/bt2b17N+3bt3d9Wd977z3Wr1/PE088weeffw7Aiy++yOjRowv5KK9MOeVBo0aNmDdvHq1bt85x3bS0NAYPHsyiRYvYsWMHUVFRrvx74YUXGDJkCHv27KF8+fKu1tSfffYZW7du5dZbb2XJkiUYY3jttdd46aWXHD82R+7y1C5fisASxVh/8C/XtHl7kuhZp0qm5b5LOEqAnx+Phga5poVWLEPL6uWdSMZVpXXr1lx33XWZpu3atcv1ZezQoQNz587Nsl7VqlVp0sQK1GXKlKF+/frs3289CzwmJoZ+/foB0K9fP1cXfz8/P86dO8fp06cpVqwYP/zwA1WqVPHquw3eLKc8qF+/PnXr1r3kuuvWrSMkJIQbb7yR4sWL07t3b2JiYjDGsGLFCldLavf8M8aQmprqyr8ZM2bQqVOnLN8fJ+S5YduZ82m0iFrrev9802B61KlMzzqVmbM7iZurlGPdwb8oX7IYIYGZO13tOHKKiEplCp5qla2GDRsSExND165dmT17NvHx8ZdcPi4ujs2bN3PLLbcAkJSURNWqVQGoUqUKSUlWqXPEiBHccccdVKtWjRkzZtCzZ89M3flV/l2cB5ezf/9+atS4MDhEUFAQa9eu5fDhwwQGBhIQEOCannGieOqpp2jevDkNGzakZcuW3HfffTl2MSioAld5etjVmu61KzN/zyHSjbGqO7W99ubQFevTTz/lgw8+oGnTppw4cYLixYvnuOzJkyfp3r07EydOzNQ3JYP7HbQOHTqwceNGFi5cSExMDJ07d2bXrl306NGDxx57TB/lmE+XywOnPPzww2zevJkZM2bwzjvv8Mwzz7Bo0SJ69OjBkCFDSE9Pd2xfjjVsCypTkhvKXsMP+48Ss/cQ3etkDSj1ryvN5kMnnNqluki9evVYunQpGzdupE+fPtx0003ZLpeamkr37t3p27cv999/v2t65cqVXV3nExMTqVSpUqb1Tp8+zZQpUxg8eDCjRo1i6tSp3HbbbXz22WeFd1BXqJzy4HKqV6+eqeSZkJBA9erVqVChAseOHXN1OMyY7u7AgQOsW7eOrl27Mn78eGbOnElgYCDLly935qBwuKVszzqVGf7DboLLXkP1a0tmmd8mqDwpael8uv3C+F3b/zzBqv1Hsyyr8u7QoUOA1UR79OjRPPnkk1mWMcYwaNAg6tevz3PPPZdp3r333svUqVMBq5PhffdlHlr6rbfe4plnnqFYsWKcOXMGEcHPz09LKHl0qTy4nJtvvpndu3ezb98+UlJSiI6O5t5770VEaNu2LXPmzAGyz7+XXnrJ1V+psPIvzwEl4xpKxuvlVRduF3cLqcQvR05lubuTQUSI6tyYb+OPEDr1JyI/W8Oon/ZSuXSJbJdXOevTpw8tWrTg119/JSgoiE8++YSoqCjq1KlDvXr1qFatGgMGDACsM1Pnzp0BWLVqFdOnT2fFihWEh4cTHh7O119bI5cMHz6cZcuWUbt2bb755huGDx/u2p/72Q3g6aef5uabb+bDDz/kwQcfLNqD93E55cEXX3xBUFAQq1ev5u6773b1KHbPv4CAAN5//306duxI/fr16dWrl+txlG+88QYTJkwgJCSEw4cPM2jQINc+N2/eDOC6GPzggw8SGhrKqlWruOuuuxw7Nm3YpjxCG7b5Nu0cqJQqdBpQlFKOuWSV55qSxdLOnjvvtUFHi82+q2TJkpw9e9bTyVD5JCKkp6dnqfJc9hpKelzW5tvewi94qAYUH6UnA9/m2DWUgBuHEdFpgusVF38EgImf/MA1dUbw1/ELz+VcuXov9wz8FIAps9fz1Mtf5PsAVGbZdQ586aWXaNy4MeHh4dx5553Zjob37bffuu4shIeHU7JkSVcTbW/oXHa1yC7/MowfPx4R4c8//8wyLzY2lhYtWtCwYUMaN27sGscHvCP/8t5StmQxNi96zvUKrmH1B4hesJmbw2owb/F2xxOpssquc+DQoUPZunUrsbGxdOnSJdtnpLZt25bY2FhiY2NZsWIFpUqV4s477wS8o3PZ1SK7/AOrJ/LSpUupWbNmtuuVKlWKadOm8fPPP7N48WKeffZZjh07BnhH/jlyfWTv739y8nQKrz3fkeiFm53YpLqM7DoHujffPnXq1GUfYDRnzhw6depEqVKlvKZz2dUiu/wDGDJkCG+++WaOeVenTh1Xp8xq1apRqVIlkpOTvSb/8t458GwqEZ2skdFq1biOeR/1J3rhFh64J4xWzWrx6/PJJCWfoHJF7QToCSNHjmTatGmUK1eOb7/99pLLRkdHu1pqekvnsqtZTEwM1atXJywsLFfLr1u3jpSUFG666Savyb8CVXnmfdQfsKo7ve8Jx8/Pj/vvCmX211udTqfKpTFjxhAfH0/fvn15//33c1wuMTGRbdu25Wp8l6LsXHa1On36NK+//vplh/LIkJiYyMMPP8zkyZPx87v0z9inOgdu25nI7rg/ufOhj6nV8nVmLtxC9AKt9nha3759s30eSoZZs2bRrVs3ihUrBuA1ncuuVnv37mXfvn2EhYURHBxMQkICTZo0cQ365u748ePcfffdjBkzhubNmwPek38FDihRC2IZ9Y872bfqn+xb9U/2r3uJA0nH+T1BO/wVtd27d7v+j4mJoV69ejkuGxUVRZ8+fVzvvaVz2dUqNDSUQ4cOERcXR1xcHEFBQWzatIkqVTI/pCwlJYVu3brxyCOPZBqW1lvyr8ABZebCWLp1zHzrq2vHRkQvjM2y7NQ5G6jRfLTrlZB4rKC7v2pl1zlw+PDhNGrUiMaNG7N06VLXs0o3bNjAo48+6lo3Li6O+Ph4br/99kzb9IbOZVeL7PIvJ+75N2vWLL7//numTJniuvUfGxsLeEf+acM25RHasM23aedApVSh04CilHLMJas8ASWKp6WlpHpt0NFis+/SzoG+Ld+dAx//3nufF/pR674aUHyUngx8m2PXUD5u8xBzB45wvU4kJnNg8w4Wv2BdvP110Xd8dPtDHN77h2ud2f1e4ERiMgCf9/oHZ49ZD6reNG0+sx8Zxpz+w5k7cASHdliPk1z4zGiSd/7mWv9EYjKz+72Q16Re0XIafS43HQS9vYPZle7s2bM0a9aMsLAwGjZsyKhRowCr7VDdunVp1KgRAwcOJDU1Ndv177rrLgIDA+nSpUum6d6Qd3kOKP4litP907GuV5mqFbMsU7ridWyeNv+S20navps/Vm/m/klj6DFlHHdP+CelK1XIa3KuWjmNPpebDoLe3sHsSleiRAlWrFjBli1biI2NZfHixaxZs4a+ffuyc+dOtm3bxpkzZ5g0aVK26w8dOpTp06dnme4NeVco10duuDWCo3H7OfZH1rNjhtOHj1GyXBn8i1stNUsGlqH09TqCYG7lNPpcbjoIensHsyudiHDttdcC1nAaqampiAidO3d2jYfUrFkzEhISsl2/ffv2lCmTua+ct+RdnjsHpp1LYe7AEQCUqVqJO8dkU2wSIaxPFzZPX0DbkVmHcgAIujmUTVPnMfPB56ke2Ygb2zWnWnh91/wVr31AQAkr2KSlnkcu01/hanbx6HN56SDojR3MrgZpaWk0bdqUPXv2MHjw4EwjB6ampjJ9+vRMA9lfjrfkXYGqPNkGE1vIHbdyaMcejh84lO38YqVK0u3jMbQaOoiSgWVY/sp7/LroO9f8di/93bWfTm8Oy2syrxrZjT6Xlw6C3tjB7Grg7+9PbGwsCQkJrFu3ju3bLzxH6O9//zutW7emVatWjuzLpzoH5rjhAH8aP9CZLZ8vzHkZfz+qRTQgcmAPWj7bj33frS+s5FyRLjf63KU6CHpzB7OrSWBgIG3btnU9bOlf//oXycnJTJgwIU/b8Za8K9R6RJ1Ordm/8WfOHjueZd6xPw7wV/yFnpSHd//OtZWvL8zkXFFyGn0uNx0Evb2D2ZUuOTnZdRH8zJkzLFu2jHr16jFp0iSWLFlCVFTUZUuMF/OWvCvUgOJfLIBG3e/kzNGsASX1zDlWvv4hsx4eypz+wzkat5/IAd0LMzlXlJxGn8tNB0Fv72B2pUtMTKRt27Y0btyYm2++mQ4dOtClSxeefPJJkpKSaNGiBeHh4a4f/8WdO1u1akXPnj1Zvnw5QUFBrush3pB32rBNeYQ2bPNt2jlQKVXoNKAopRxzySqPX7ESaeZ8itcGHS02+y7tHOjb8t05sOnYbwo1YQWxccQdGlB8lJ4MfJtj11A2j7rQIemvnWvZ/nY/zh1N4sA3Uzn4/SwA4ma/ydFt32da79zRg/w88VGUs9LS0oiIiHB1FBs0aBBhYWE0btyYHj16cPLkySzrpKSkMGDAAEJDQwkLC2PlypWueRs3biQ0NJSQkBCeeeYZ14/+hRdeoHHjxjzyyCOuZWfMmMHEiRML9fiuRAXpHOjtIz/muzpzfM8m4hf+h9oDxlKifGUn06Ty4N1336V+/QtdFt555x22bNnC1q1bqVmzZrYtZT/++GMAtm3bxrJly3j++eddrSX/9re/8fHHH7N79252797N4sWL+euvv9i0aRNbt26lePHirs5rkydPZvDgwUVzoFeQgnQO9PaRH/MVUE7s28rv8yYQ0m80JSpUczpNKpcSEhL46quvMrVRyGh+b4xxNV662I4dO2jXrh0AlSpVIjAwkA0bNpCYmMjx48dp3rw5IsIjjzzC/Pnz8fPzIzU1FWOMq4PZ22+/zdNPP+0ahkPlXkE7B2bwxpEf8xxQ0s+nsnf6KG56+FVKVsp+/FVVNJ599lnefPPNLK0qBwwYQJUqVdi5cydPP/10lvXCwsJYsGAB58+fZ9++fWzcuJH4+Hj2799PUFCQa7mMDmZlypShc+fOREREULVqVcqVK8fatWvp2rVrYR/iFSstLY3w8HAqVapEhw4dsu0ceLkGZ9HR0a6hUHLTOfCPP/6gZcuWhVqyzHNAEf8Arq3ZgMMbFhVGelQuffnll1SqVImmTZtmmTd58mQOHDhA/fr1Mz08KcPAgQMJCgoiMjKSZ599lltvvRV/f/9L7m/YsGHExsYyfvx4VxPuSZMm0atXL0aPHu3YcV0tCto50FtHfsx7QBHhxgdf4lT8ThK//dyxhKi8WbVqFQsWLCA4OJjevXuzYsUKHnroIdd8f39/evfunW3nwICAAN555x1iY2OJiYnh2LFj1KlTh+rVq2cqZmfXwWzz5s0YY6hbty6zZ89m1qxZ7N27N1MfIpV7+e0c6K0jP+brGopf8ZKE9B/Dkdjl/LleSyqeMHbsWBISEoiLiyM6Opp27doxffp09uyxHqNpjGHBggXZdg48ffo0p06dAmDZsmUEBATQoEEDqlatStmyZVmzZg3GGKZNm5ZtB7PXXnuN1NRU0tLSALRzYB450TnQW0d+zPMDllwrlipL7QFj+fWj5wgoXS7L/N+/eIf4Lz8AoHi5itTqM5KzyfFsHdvbtUyNLn+jfOjtWdZV+WOMoV+/fhw/fhxjDGFhYfz3v/8FYMGCBWzYsIFXX32VQ4cO0bFjR/z8/KhevXqmxwl+8MEH9O/fnzNnztCpUyc6derkmjd//nwiIyOpVs26EB8eHk5oaCiNGzcmLCysaA/WhyUmJtKvXz/S0tJIT0+nV69edOnShYCAAG644QZatGgBwP3338/LL7/Mhg0b+PDDD113fS418mPv3r158cUXiYiIyFXnwBo1ajBsmHPPG9KGbcojtGGbb9POgUqpQqcBRSnlmMt1DjxozqdoM1jlOK3y+DxjjMlSILlkQFFKqbzQKo9SyjEaUJRSjtGAopRyjAYUpZRjNKAopRyjAUUp5RgNKEopx2hAUUo5RgOKUsox/w+Zwo+2vPcpuAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"dft = None\n",
"for cluster in \"abcd\":\n",
" df = pd.read_csv(glob.glob(DIR + cluster + \"_actual/part-*\")[0], header=None,\n",
" names=[\"term\", \"cpu\", \"ram\"])\n",
" plot_df(df, cluster, \"used\")\n",
" if dft is None:\n",
" dft = df\n",
" else:\n",
" dft = dft.append(df)\n",
"\n",
"dft = dft.groupby(\"term\").sum().reset_index()\n",
"plot_df(dft, \"all\", \"used\")\n",
"\n",
"dft = None\n",
"for cluster in \"abcdefgh\":\n",
" data = None\n",
" with open(DIR + cluster + \"_res_micros_requested.json\", \"r\") as f:\n",
" data = json.loads(f.read())\n",
" dfd = {'term': [], 'cpu': [], 'ram': []}\n",
" for term in [4,5,6,7]:\n",
" dfd['term'].append(term)\n",
" dfd['cpu'].append(float(data[\"cpu-\" + (\"None\" if term == -1 else str(term))]))\n",
" dfd['ram'].append(float(data[\"ram-\" + (\"None\" if term == -1 else str(term))]))\n",
" df = pd.DataFrame(dfd, columns=['term', 'cpu', 'ram'])\n",
" if dft is None:\n",
" dft = df\n",
" else:\n",
" dft = dft.append(df)\n",
" plot_df(df, cluster, \"requested\")\n",
" \n",
"dft = dft.groupby(\"term\").sum().reset_index()\n",
"plot_df(dft, \"all\", \"requested\")\n",
"\n",
"dfr2011 = {'term': [4,5,6,7], 'cpu': [28.2, 31.7, 13.9, 26.2], 'ram': [30.4,23.5, 17.3, 28.8]}\n",
"dfr2011 = pd.DataFrame(dfr2011, columns=dfr2011.keys())\n",
"dfa2011 = {'term': [4,5,6,7],'cpu': [19.2, 13.7, 23.2, 43.9], 'ram': [21.0,14.2,32.1,32.7]}\n",
"dfa2011 = pd.DataFrame(dfa2011, columns=dfa2011.keys())\n",
"\n",
"plot_df(dfr2011, \"2011\", \"requested\")\n",
"plot_df(dfa2011, \"2011\", \"used\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}