2021-04-27 14:37:09 +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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2021-05-22 14:18:14 +00:00
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"execution_count": 21,
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"id": "3f392515",
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2021-04-27 14:37:09 +00:00
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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\n",
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"import itertools"
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]
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},
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{
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"cell_type": "code",
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2021-05-22 14:18:14 +00:00
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"execution_count": 16,
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"id": "c6d18e78",
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2021-04-27 14:37:09 +00:00
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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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2021-04-27 14:37:09 +00:00
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]
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},
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{
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"cell_type": "code",
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2021-05-22 14:18:14 +00:00
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"execution_count": 17,
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"id": "8330c5bd",
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2021-04-27 14:37:09 +00:00
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"metadata": {},
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"outputs": [],
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"source": [
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2021-05-22 14:18:14 +00:00
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"LABELS = {\"task_count\": \"Number of tasks\", \n",
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" \"interval\": \"Job execution time\",\n",
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" \"machine_locality\": \"Machine locality\"}\n",
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"\n",
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2021-04-27 14:37:09 +00:00
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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]:\n",
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2021-04-27 14:37:09 +00:00
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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=(4,3))\n",
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" ax = plt.gca()\n",
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" plt.xlabel(LABELS[key])\n",
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" plt.ylabel(\"Event rate [%]\")\n",
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" lines = []\n",
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" for t in [4,5,6,7]:\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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2021-05-22 14:18:14 +00:00
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" ys[-1] *= 100\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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" lgd = plt.legend(lines, [\"EVICT\", \"FAIL\", \"FINISH\", \"KILL\"],\n",
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" bbox_to_anchor=(1,1))\n",
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" if cluster == \"all\":\n",
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" plt.title(\"2019 data\")\n",
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" elif cluster == \"2011\":\n",
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" plt.title(\"2011 data\")\n",
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" else:\n",
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" plt.title(\"Cluster \" + cluster.upper())\n",
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" \n",
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" plt.savefig('../report/figures/figure_9/%s-%s.pgf' % \n",
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" (key.replace(\"_\", \"\"), cluster), bbox_extra_artists=(lgd,), bbox_inches='tight')\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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2021-05-22 14:18:14 +00:00
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"execution_count": 26,
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"id": "35d8f9f0-5233-4437-ab97-061d720f6f0a",
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2021-04-27 14:37:09 +00:00
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"metadata": {},
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"outputs": [
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{
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"data": {
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2021-05-22 14:18:14 +00:00
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"image/png": "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2021-04-27 14:37:09 +00:00
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"text/plain": [
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"<Figure size 288x216 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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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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2021-05-22 14:18:14 +00:00
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2021-04-27 14:37:09 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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2021-05-22 14:18:14 +00:00
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2021-04-27 14:37:09 +00:00
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"text/plain": [
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2021-05-22 14:18:14 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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]
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},
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{
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"data": {
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2021-05-22 14:18:14 +00:00
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2021-04-27 14:37:09 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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2021-05-22 14:18:14 +00:00
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2021-04-27 14:37:09 +00:00
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"text/plain": [
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2021-05-22 14:18:14 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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]
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},
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{
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"data": {
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2021-05-22 14:18:14 +00:00
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2021-04-27 14:37:09 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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]
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2021-05-22 14:18:14 +00:00
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2021-04-27 14:37:09 +00:00
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"text/plain": [
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2021-05-22 14:18:14 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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]
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},
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{
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"data": {
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2021-05-22 14:18:14 +00:00
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2021-04-27 14:37:09 +00:00
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"text/plain": [
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"<Figure size 288x216 with 1 Axes>"
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},
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2021-05-22 14:18:14 +00:00
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2021-04-27 14:37:09 +00:00
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"text/plain": [
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2021-05-22 14:18:14 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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]
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},
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{
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"data": {
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2021-05-22 14:18:14 +00:00
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2021-04-27 14:37:09 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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2021-05-22 14:18:14 +00:00
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2021-04-27 14:37:09 +00:00
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2021-05-22 14:18:14 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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]
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},
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2021-05-22 14:18:14 +00:00
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2021-04-27 14:37:09 +00:00
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"text/plain": [
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2021-05-22 14:18:14 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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},
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2021-05-22 14:18:14 +00:00
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2021-04-27 14:37:09 +00:00
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"text/plain": [
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2021-05-22 14:18:14 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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]
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},
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{
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"data": {
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2021-05-22 14:18:14 +00:00
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2021-04-27 14:37:09 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-05-22 14:18:14 +00:00
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2021-04-27 14:37:09 +00:00
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2021-05-22 14:18:14 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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]
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},
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{
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"data": {
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2021-05-22 14:18:14 +00:00
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2021-04-27 14:37:09 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-05-22 14:18:14 +00:00
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2021-04-27 14:37:09 +00:00
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"text/plain": [
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2021-05-22 14:18:14 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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]
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},
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{
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"data": {
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2021-05-22 14:18:14 +00:00
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2021-04-27 14:37:09 +00:00
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2021-05-22 14:18:14 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-05-22 14:18:14 +00:00
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2021-04-27 14:37:09 +00:00
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"text/plain": [
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2021-05-22 14:18:14 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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]
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},
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{
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"data": {
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2021-05-22 14:18:14 +00:00
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2021-04-27 14:37:09 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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2021-05-22 14:18:14 +00:00
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2021-04-27 14:37:09 +00:00
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"text/plain": [
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2021-05-22 14:18:14 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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]
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},
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{
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"data": {
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2021-05-22 14:18:14 +00:00
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2021-04-27 14:37:09 +00:00
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"text/plain": [
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2021-05-22 14:18:14 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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]
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},
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2021-05-22 14:18:14 +00:00
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"image/png": "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2021-04-27 14:37:09 +00:00
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"text/plain": [
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2021-05-22 14:18:14 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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]
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},
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{
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"data": {
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2021-05-22 14:18:14 +00:00
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAV8AAADgCAYAAACkXmWVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAA1p0lEQVR4nO3deXxU1dnA8d8zM9kTwhIIyGJAwAiEICCCLFIsFnEX7evSF9S6Va3W3dpWLW9d6msX+9bWqsWV1qqlijaA1gXcFWQLO7IjERL2BJLMzPP+cW/CJGYZkplMluf7+cxn7tx77rnPjePDmXPPPVdUFWOMMU3LE+sAjDGmLbLka4wxMWDJ1xhjYsCSrzHGxIAlX2OMiQFLvsYYEwOWfM1REZH7ReTFWMdhTEtnydd8i4hcKiILReSgiOwQkTkiMiaC9WeJiIqIL9p1isizIvKrSB3HmEix5GuqEJFbgd8DDwKZQC/gT8C5MQyrikgmbWNixZKvqSQi6cB04AZVnaWqxaparqpvqOodNZQfLyLbqq3bJCLfdZdHuC3o/SLyjYj81i22wH3f67auR7nlrxSRVSKyR0TmicixIfWqiNwgIuuAdVE4fWOalCVfE2oUkAj8K0L1PQY8pqrtgOOAl93149z39qqaqqqfiMi5wD3ABUBn4APg79XqOw84GRgQofiMiRlLviZUJ6BQVf0Rqq8c6CsiGap6UFU/raPsdcBDqrrKPf6DwJDQ1q+7fbeqHqqjnkIR2VvxAi5t9FkYEwWWfE2oIiAjgn2qPwT6A6tF5AsROauOsscCj4Ukzd2AAN1DymwN45gZqtq+4gX8rWGhGxNdlnxNqE+AUpyf9+EoBpIrPoiIF6fLAABVXaeqlwBdgF8Dr4pIClDTVHpbgWtDE6eqJqnqxyFlbAo+02pY8jWVVHUfcC/wuIicJyLJIhInImeIyCM17LIWSBSRM0UkDvg5kFCxUUR+ICKdVTUI7HVXB4Fd7nufkLqeAH4qIgPdfdNF5KJIn6MxzYUN2TFVqOpvRKQAJ5HOBA4Ai4AHaii7T0SuB54GvMAjQOjoh0nAb0UkGdgMXFzRXysiDwAfuUl7kqr+S0RSgZfcft59wNvAK1E6VWNiSmwydWOMaXrW7WCMMTFgydcYY2LAkq8xxsSAJV9jjIkBS77GGBMDLXqoWUZGhmZlZcU6DGPanEWLFhWqauf6S5raRC35isgM4Cxgp6oOctd1BP4BZAGbgO+r6h4REZxJWCYDJcDlqvplfcfIyspi4cKF0TkBY0ytRGRzrGNo6aLZ7fAsziD7UHcD76hqP+Ad9zPAGUA/93UN8OcoxmWMMTEXteSrqgtwJkcJdS7wnLv8HEfmEDgXeF4dnwLtRaRbtGIzxphYa+oLbpmqusNdLsB5UgI4M1eFzli1jaqzWVUSkWvcCboX7tq1K3qRGmNMFMXsgpuqqogc9b3Nqvok8CTA8OHD692/ZPFiSj7/guQRJ5F84okNiNQYE45FixZ18fl8TwODsJFUQSDf7/dfNWzYsJ01FWjq5PuNiHRT1R1ut0JFUNuBniHlerjrGqVk8WK2TJ2GlpeD10u7c84moU8fPMnJeJJT3PdkPCnJR5bdlyQl4VwHNMaEw+fzPd21a9cTOnfuvMfj8bTpSWOCwaDs2rVrQEFBwdPAOTWVaerkOxuYBjzsvr8esv5GEXkJ5zEx+0K6Jxqs5PMvUL/7UIZAgP2vvQ7hTiQkUjUZV0nQKd9K1p6UlNoTeeU+SYinrTcITCs2yBKvw+PxaOfOnfcVFBQMqq1MNIea/R0Yj/NkhG3AfThJ92UR+SHOFIPfd4vn4QwzW48z1OyKSMSQPOIkJCEBLS9H4uLoNWMGiSdkEywpOfIqrlgurlyn39pWQrDY2R7YvYfybdurrCcQCP/vkpRUNVnX9Eqpnrid8uXbv8b/zTekfme8daGY5shjifcI929Ra2srasnXfYJBTU6roawCN0Q6huQTT6TXMzO+1efrSUqCTp0icgxVRcvLCRYXH0naJd9O2lUSeZVXMYED+/F/U1CljJaV1XrMoqeeosPll5Nx5RX4Ots4d2MqeL3eYf369at8xt8FF1ywu7S0VA4fPux5/PHHK7syP/7446Qf/OAHfTZs2LCie/fuOQsXLlzVrVs3/5YtW3zXX399r6VLlya3a9cukJGRUf7www9vv+KKK3oD7NixIz41NTWQlpYW6Nixo//jjz9e29BYW/QdbuFIPvHEqLYSRQSJj8cTHw8dOkSsXi0vr5Kkd8+cyd6/v+R0m6iy55ln2PPssySfdBLtzphE2sSJ+DIyInZ8Y1qihISE4OrVq1eGrlu2bFnC5MmT+4cm3xdffLHjBRdcUGUobDAY5Jxzzul76aWXFr355psbAD755JOkffv2eSvqnDJlStZZZ52174orrtjT2FitA7KZkrg4vOnpxHXrRsJxx5F+9tlIQgJ4vUhiIt0e+TUZP/oR/sJCCn45nXXjTmXztMvZ89JL+IuKYh2+MWH5cH1hyq/nru764frClGgdY/DgwaXp6en+d999t/IYs2fP7jht2rQqyffNN99M8/l8euedd1aOYR01atShSZMmHYxGXK2+5dta1NaFkvHjGyldu44D8+ayf85cCu7/JQXT/4fkESNoN2kSaadPxNexY4yjN23NHa8u7bm24EByXWWKS/2erwqLk1XhiflfcVxGSklKgi9YW/n+XdNK/vfC3DqfYF1aWurJzs4eUPH5tttu23H11VfvmTJlyu6ZM2d2nDBhQvE777yT0r59e39OTk5p6L7Lli1Lys3NLQn3HBvLkm8LUlMXioiQeHx/Eo/vT8aPf0zp2rXsnzuXA3PmUnD//RRMn07yySNoN+kM0iZ+1xKxaTYOlvp9FYOPVJ3PKQm+2i92hKGmbgeAqVOn7h4zZswJgUBg68yZMztOmTKl+t23Tc6SbyviJOLjSTz+eDrfdBOla9YcScT33UfB9OmknHwyaZO+5/QRR7CP2phQ9bVQwelyuPLZL/r7A0GPz+sJPvr9IRvG9M0ojkY8ffv2Le/Ro0dpXl5eWl5eXoePPvpoVfUyOTk5h1577bUm+5/C+nxbKREhMTubLj/5CX3mzqH3v2bR6aqrKNu+jYJ772PdmLFs+eFV7HnlFfx7Gn3twJijNqZvRvGMy09ae+2px22fcflJa6OVeCtcdNFFu++4446ePXv2LD3uuOPKq28/++yzD5SVlcmjjz5aeeX6s88+S5o7d25qNOKx5NsGiAiJJ5xAl1t+wnFz59J71j/p9MMfUrZ1KwW/uNdJxFddzd5//pPA3r2xDte0IWP6ZhTfNSm7IFKJt6LPt+J1/fXXV84RM3Xq1D3r169PvOiii2rscvB4PMyePfurd999t13Pnj0H9e3bd+Bdd93VvXv37t9K1JHQoh8dP3z4cLX5fBtOVTm8ciUH5s5l/9x5lG/dCj4fKaNGORfrvnsa3vT0WIdpmiERWaSqw0PXLV26dFNubm5hrGJqjpYuXZqRm5ubVdM26/Ntw0SEpIEDSRo4kM633srhFSs5MHcO++fOY8fPfsaO++4j5ZRRzsW60yZYIjYmgiz5GsBNxIMGkjRoIJ1vu43D+SvYP3cOB+bOY8c997AjLq5qIm7XLtYhG9OiWfI13yIiJOUMIilnEF1uv53D+fnsnzOXA3PnsmP+T9kRF0fqKaeQdsYk0iZYIjamISz5mjo5iTiHpJwcutxxO4eXL2f/nLnsnzeXg/PnUxAXR8ro0bQ7YxKpEybgTUuLdcjGtAiWfE3YRISkwYNJGjyYLnfeweFly9xEPI+D77+PxMWRMmbMkUScGpUROsa0CpZ8TYOICEm5uSTl5n47Eb/3HhIffyQRf+c7loiNqcbG+ZpGE4+HpCFDyPzp3fR99x2O/fvf6HDJxRxesYKv77iTdaeMZuuNN7LvjTcJHIzqOHrTxnm93mGh43zXrFkTDzB9+vQuCQkJQ4uKirwVZd98882073znO30B/vCHP3SaOnVqr6aM1Vq+JqLE46mcg6LLXXdxaMnSylETB//zDhIfT+qp40g4Phs
|
2021-04-27 14:37:09 +00:00
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"text/plain": [
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2021-05-22 14:18:14 +00:00
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"<Figure size 288x216 with 1 Axes>"
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2021-04-27 14:37:09 +00:00
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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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"source": [
|
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|
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"def to_9a_bin(col):\n",
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" # 1 [2,10] [11,50] [51,100] 101+\n",
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" if col <= 0:\n",
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" return \"None known\"\n",
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" elif col == 1:\n",
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" return \"1\"\n",
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" elif col <= 10:\n",
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" return \"2-10\"\n",
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" elif col <= 50:\n",
|
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" return \"11-50\"\n",
|
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" elif col <= 100:\n",
|
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|
|
|
" return \"51-100\"\n",
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" else:\n",
|
|
|
|
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" return \"101+\"\n",
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" \n",
|
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|
|
"def to_9c_bin(col):\n",
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|
|
" # 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",
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|
|
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" else:\n",
|
|
|
|
|
" return \">=1.5\"\n",
|
2021-05-22 14:18:14 +00:00
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" \n",
|
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|
|
|
"dfatot = None\n",
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|
|
|
"dfbtot = None\n",
|
|
|
|
|
"dfctot = None\n",
|
2021-04-27 14:37:09 +00:00
|
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|
"\n",
|
|
|
|
|
"for cluster in \"abcdefgh\":\n",
|
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|
|
|
" dfa = pd.read_csv(DIR + cluster + \"_term_task_count.csv\") \\\n",
|
|
|
|
|
" .sort_values([\"term\", \"task_count\"])\n",
|
2021-05-22 14:18:14 +00:00
|
|
|
" dfa = dfa[dfa[\"task_count\"] > 0]\n",
|
|
|
|
|
" dfa = dfa[dfa[\"term\"].isin(range(4,8))]\n",
|
2021-04-27 14:37:09 +00:00
|
|
|
" dfa[\"task_count\"] = dfa[\"task_count\"].apply(to_9a_bin)\n",
|
|
|
|
|
" dfa = dfa.groupby([\"term\", \"task_count\"]).sum().reset_index()\n",
|
2021-05-22 14:18:14 +00:00
|
|
|
" if dfatot is None:\n",
|
|
|
|
|
" dfatot = dfa\n",
|
|
|
|
|
" else:\n",
|
|
|
|
|
" dfatot = dfatot.append(dfa)\n",
|
2021-04-27 14:37:09 +00:00
|
|
|
" \n",
|
|
|
|
|
" categorical_plot(dfa, \"task_count\", \"# of tasks w.r.t. job rate (cluster \" + \n",
|
2021-05-22 14:18:14 +00:00
|
|
|
" cluster + \")\", [to_9a_bin(x) for x in [1,10,50,100,101]])\n",
|
2021-04-27 14:37:09 +00:00
|
|
|
" \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",
|
2021-05-22 14:18:14 +00:00
|
|
|
" dfb = dfb[dfb[\"term\"].isin(range(4,8))]\n",
|
|
|
|
|
" if dfbtot is None:\n",
|
|
|
|
|
" dfbtot = dfb\n",
|
|
|
|
|
" else:\n",
|
|
|
|
|
" dfbtot = dfbtot.append(dfb)\n",
|
2021-04-27 14:37:09 +00:00
|
|
|
" \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",
|
2021-05-22 14:18:14 +00:00
|
|
|
" dfc= dfc[dfc[\"machine_locality\"] > 0]\n",
|
2021-04-27 14:37:09 +00:00
|
|
|
" dfc[\"machine_locality\"] = dfc[\"machine_locality\"].apply(to_9c_bin)\n",
|
|
|
|
|
" dfc = dfc.groupby([\"term\", \"machine_locality\"]).sum().reset_index()\n",
|
2021-05-22 14:18:14 +00:00
|
|
|
" dfc = dfc[dfc[\"term\"].isin(range(4,8))]\n",
|
|
|
|
|
" if dfctot is None:\n",
|
|
|
|
|
" dfctot = dfc\n",
|
|
|
|
|
" else:\n",
|
|
|
|
|
" dfctot = dfctot.append(dfc)\n",
|
2021-04-27 14:37:09 +00:00
|
|
|
" \n",
|
|
|
|
|
" categorical_plot(dfc, \"machine_locality\", \"Machine locality w.r.t. job rate (cluster \" + \n",
|
2021-05-22 14:18:14 +00:00
|
|
|
" cluster + \")\", [to_9c_bin(x) for x in [0.49,0.89,1.09,1.49,1.5]]) "
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 27,
|
|
|
|
|
"id": "cc72331b-f5e6-4519-85ea-03c149b2abb9",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
" term task_count count\n",
|
|
|
|
|
"0 4 1 2686\n",
|
|
|
|
|
"1 5 1 375924\n",
|
|
|
|
|
"2 5 101+ 30652\n",
|
|
|
|
|
"3 5 11-50 25790\n",
|
|
|
|
|
"4 5 2-10 32493\n",
|
|
|
|
|
"5 5 51-100 5781\n",
|
|
|
|
|
"6 6 1 8746326\n",
|
|
|
|
|
"7 6 101+ 41178\n",
|
|
|
|
|
"8 6 11-50 121019\n",
|
|
|
|
|
"9 6 2-10 493786\n",
|
|
|
|
|
"10 6 51-100 45858\n",
|
|
|
|
|
"11 7 1 7004985\n",
|
|
|
|
|
"12 7 101+ 865662\n",
|
|
|
|
|
"13 7 11-50 677346\n",
|
|
|
|
|
"14 7 2-10 1824913\n",
|
|
|
|
|
"15 7 51-100 272201\n",
|
|
|
|
|
" term interval count\n",
|
|
|
|
|
"0 4 1-2 1272\n",
|
|
|
|
|
"1 4 10-60 3849\n",
|
|
|
|
|
"2 4 2-4 1012\n",
|
|
|
|
|
"3 4 4-10 1381\n",
|
|
|
|
|
"4 4 60-1d 670\n",
|
|
|
|
|
"5 4 <1 3401\n",
|
|
|
|
|
"6 4 >=1d 16\n",
|
|
|
|
|
"7 5 1-2 212983\n",
|
|
|
|
|
"8 5 10-60 556418\n",
|
|
|
|
|
"9 5 2-4 139020\n",
|
|
|
|
|
"10 5 4-10 156903\n",
|
|
|
|
|
"11 5 60-1d 678146\n",
|
|
|
|
|
"12 5 <1 303335\n",
|
|
|
|
|
"13 5 >=1d 36972\n",
|
|
|
|
|
"14 6 1-2 6186670\n",
|
|
|
|
|
"15 6 10-60 7480361\n",
|
|
|
|
|
"16 6 2-4 4988230\n",
|
|
|
|
|
"17 6 4-10 7181336\n",
|
|
|
|
|
"18 6 60-1d 1916021\n",
|
|
|
|
|
"19 6 <1 15420868\n",
|
|
|
|
|
"20 6 >=1d 168715\n",
|
|
|
|
|
"21 7 1-2 6915345\n",
|
|
|
|
|
"22 7 10-60 9405950\n",
|
|
|
|
|
"23 7 2-4 5424771\n",
|
|
|
|
|
"24 7 4-10 5125738\n",
|
|
|
|
|
"25 7 60-1d 3462416\n",
|
|
|
|
|
"26 7 <1 21817580\n",
|
|
|
|
|
"27 7 >=1d 522971\n",
|
|
|
|
|
" term machine_locality count\n",
|
|
|
|
|
"0 4 >=1.5 1\n",
|
|
|
|
|
"1 4 [0.9,1.1) 2685\n",
|
|
|
|
|
"2 5 (0,0.5) 3575\n",
|
|
|
|
|
"3 5 >=1.5 72202\n",
|
|
|
|
|
"4 5 [0.5,0.9) 3783\n",
|
|
|
|
|
"5 5 [0.9,1.1) 376132\n",
|
|
|
|
|
"6 5 [1.1,1.5) 13165\n",
|
|
|
|
|
"7 6 (0,0.5) 841\n",
|
|
|
|
|
"8 6 >=1.5 295788\n",
|
|
|
|
|
"9 6 [0.5,0.9) 5993\n",
|
|
|
|
|
"10 6 [0.9,1.1) 9118378\n",
|
|
|
|
|
"11 6 [1.1,1.5) 26942\n",
|
|
|
|
|
"12 7 (0,0.5) 35176\n",
|
|
|
|
|
"13 7 >=1.5 988339\n",
|
|
|
|
|
"14 7 [0.5,0.9) 110383\n",
|
|
|
|
|
"15 7 [0.9,1.1) 8649436\n",
|
|
|
|
|
"16 7 [1.1,1.5) 271514\n"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
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"image/png": "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"text/plain": [
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"<Figure size 288x216 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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"source": [
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"cluster = \"all\"\n",
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"\n",
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"dfatot = dfatot.groupby([\"term\", \"task_count\"]).sum().reset_index()\n",
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"dfbtot = dfbtot.groupby([\"term\", \"interval\"]).sum().reset_index()\n",
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"dfctot = dfctot.groupby([\"term\", \"machine_locality\"]).sum().reset_index()\n",
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"\n",
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"print(dfatot)\n",
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"print(dfbtot)\n",
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"print(dfctot)\n",
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"\n",
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|
"categorical_plot(dfatot, \"task_count\", None, [to_9a_bin(x) for x in [1,10,50,100,101]])\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(dfbtot, \"interval\", None, xs)\n",
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"\n",
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|
|
"categorical_plot(dfctot, \"machine_locality\", None, [to_9c_bin(x) for x in [0.49,0.89,1.09,1.49,1.5]]) "
|
2021-04-27 14:37:09 +00:00
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|
]
|
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|
|
},
|
2021-05-22 14:18:14 +00:00
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|
{
|
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|
|
"cell_type": "code",
|
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|
|
"execution_count": 31,
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|
|
"id": "68db43ac-04d1-4b0b-a6e1-39c7799cd074",
|
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|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
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|
|
|
{
|
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|
|
"data": {
|
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|
|
"image/png": "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
|
|
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|
|
"text/plain": [
|
|
|
|
|
"<Figure size 288x216 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
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|
"image/png": "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"text/plain": [
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"<Figure size 288x216 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": "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"text/plain": [
|
|
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|
"<Figure size 288x216 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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"source": [
|
|
|
|
|
"def malusa(l, r):\n",
|
|
|
|
|
" return [a for a in itertools.chain(*[[b for x in range(r)] for b in l])]\n",
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"\n",
|
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"dfa11 = {'term': [x for x in range(4,8)] * 5,\n",
|
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|
" 'task_count': malusa([\"1\", \"2-10\", \"11-50\", \"51-100\", \"101+\"], 4),\n",
|
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" 'count': [0,1,22,77,\n",
|
|
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|
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" 0,0,78,21,\n",
|
|
|
|
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" 0,0,87,13,\n",
|
|
|
|
|
" 0,0,95,6,\n",
|
|
|
|
|
" 0,1,94,2]}\n",
|
|
|
|
|
"dfa11 = pd.DataFrame(data=dfa11, columns=dfa11.keys())\n",
|
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|
"\n",
|
|
|
|
|
"dfc11 = {'term': [x for x in range(4,8)] * 5,\n",
|
|
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|
|
" 'machine_locality': malusa([to_9c_bin(x) for x in [0.49,0.89,1.09,1.49,1.5]], 4),\n",
|
|
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|
|
" 'count': [0,0,91,9,\n",
|
|
|
|
|
" 0,1,90,9,\n",
|
|
|
|
|
" 0,0,40,60,\n",
|
|
|
|
|
" 0,8,91,3,\n",
|
|
|
|
|
" 0,18,77,13]}\n",
|
|
|
|
|
"dfc11 = pd.DataFrame(data=dfc11, columns=dfc11.keys())\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"dfb11 = {'term': [x for x in range(4,8)] * 7,\n",
|
|
|
|
|
" 'interval': malusa([\"<1\", \"1-2\", \"2-4\", \"4-10\", \"10-60\", \"60-1d\", \">=1d\"], 4),\n",
|
|
|
|
|
" 'count': [0,0,10,90,\n",
|
|
|
|
|
" 0,0,44,56,\n",
|
|
|
|
|
" 0,0,49,51,\n",
|
|
|
|
|
" 0,0,50,50,\n",
|
|
|
|
|
" 0,1,53,47,\n",
|
|
|
|
|
" 0,3,60,38,\n",
|
|
|
|
|
" 0,6,91,3]}\n",
|
|
|
|
|
"dfb11 = pd.DataFrame(data=dfb11, columns=dfb11.keys())\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"cluster = \"2011\"\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"categorical_plot(dfa11,\"task_count\", None, [to_9a_bin(x) for x in [1,10,50,100,101]])\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"xs = [\"<1\", \"1-2\", \"2-4\", \"4-10\", \"10-60\", \"60-1d\", \">=1d\"]\n",
|
|
|
|
|
"categorical_plot(dfb11, \"interval\", None, xs)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"categorical_plot(dfc11, \"machine_locality\", None, [to_9c_bin(x) for x in [0.49,0.89,1.09,1.49,1.5]]) "
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"id": "a8d25894",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": []
|
|
|
|
|
},
|
2021-05-16 10:22:27 +00:00
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
2021-05-22 14:18:14 +00:00
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"id": "7a337122",
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2021-05-16 10:22:27 +00:00
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"metadata": {},
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"outputs": [],
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"source": []
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},
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2021-04-27 14:37:09 +00:00
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{
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"cell_type": "code",
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"execution_count": null,
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2021-05-22 14:18:14 +00:00
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"id": "11314301-be1c-43ce-a2c0-63798ea8ebe8",
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2021-04-27 14:37:09 +00:00
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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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"metadata": {
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"kernelspec": {
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2021-05-22 14:18:14 +00:00
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"display_name": "venv",
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2021-04-27 14:37:09 +00:00
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"language": "python",
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2021-05-22 14:18:14 +00:00
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"name": "venv"
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2021-04-27 14:37:09 +00:00
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},
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},
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"name": "python",
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2021-05-22 14:18:14 +00:00
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2021-04-27 14:37:09 +00:00
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