1 files changed, 107 insertions, 27 deletions
diff --git a/examples/compute-qpi-report/report.ipynb b/examples/compute-qpi-report/report.ipynb
index b4068a37..34bacf5c 100644
--- a/examples/compute-qpi-report/report.ipynb
+++ b/examples/compute-qpi-report/report.ipynb
@@ -2,49 +2,129 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 100,
    "metadata": {
-    "scrolled": true
+    "collapsed": true,
+    "scrolled": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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0bWN9vBvYEvgfB47o9G0d5cXqHZRTwPS2t43ALbVv1wLPZOp9s9u3v+5sN0cBHxqQVwuB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\nEQa+JDXi/wPL3cFfQ6ukQgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5982b410d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "from asq.initiators import query\n",
     "import json\n",
     "\n",
-    "workload_name = []\n",
-    "workload_score = []\n",
     "project_name = 'workspace'\n",
     "\n",
-    "with open(\"qpi.json\".format(project_name)) as result:\n",
+    "with open(\"qpi.json\") as result:\n",
     "    final = json.load(result)\n",
     "\n",
-    "qpi = query(final['nodes']).where(lambda child: child['name'] == 'node-9') \\\n",
-    "        .select_many(lambda child: child['sections']) \\\n",
-    "        .where(lambda child: child['name'] == 'SSL') \\\n",
-    "        .select_many(lambda child: child['metrics']).to_list()\n",
+    "def extract_results(node_name, section_name):\n",
+    "    workload_name = []\n",
+    "    workload_score = []\n",
+    "    qpi = query(final['nodes']).where(lambda child: child['name'] == node_name) \\\n",
+    "            .select_many(lambda child: child['sections']) \\\n",
+    "            .where(lambda child: child['name'] == section_name) \\\n",
+    "            .select_many(lambda child: child['metrics']).to_list()\n",
     "\n",
-    "for wl in qpi[0]['workloads']:\n",
-    "    workload_name.append(wl['name'])\n",
-    "    workload_score.append(wl['score'])\n",
+    "    for wl in qpi[0]['workloads']:\n",
+    "        workload_name.append(wl['name'])\n",
+    "        workload_score.append(wl['score'])\n",
     "\n",
-    "x_axis = range(len(workload_name))\n",
+    "    x_axis = range(len(workload_name))\n",
     "\n",
-    "plt.bar(x_axis, workload_score)\n",
-    "plt.xticks(x_axis, workload_name)\n",
+    "    plt.bar(x_axis, workload_score)\n",
+    "    plt.xticks(x_axis, workload_name, rotation=45)\n",
+    "    plt.xlabel('Workloads')\n",
+    "    plt.ylabel('Score')\n",
+    "    plt.title('Metric Result')\n",
+    "    return plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Ivnq7UuvPMLZvp0zI+ASl63SGpJ2B44DVO1oWPwGOsH1pl0pf6mTQewSo91ks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\nAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffa5549c150>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "arith = extract_results('node-9', 'arithmetic')\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7Frg1Ik5QXrT0LbLcTirl9mZyGGXT5bYx8I6I+FbZXoQ8+90xIp4o+zYgf/hq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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffa552dcdd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ssl = extract_results('node-9', 'SSL')\n",
+    "ssl.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kykEqXnyWs97vejGLx6kPRPbREfezjz5unEZDhsImU1UnACdMuo7NTZI1VbVy0nVIG2If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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffa552e4fd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dpi = extract_results('node-9', 'DPI')\n",
+    "dpi.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SbDdit0OAW6tqa+A44MP9qkeSNLZ+XinsACyqqiurailwKrD3iH32Br7cvv4m8Pwk6WNN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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffa55476310>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "memory = extract_results('node-9', 'memory')\n",
+    "memory.show()"
+   ]
   }
  ],
  "metadata": {