reactive-usage-stats.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "import re\n",
    "import json\n",
    "import operator\n",
    "from collections import defaultdict\n",
    "\n",
    "import yaml\n",
    "import requests\n",
    "import pandas\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "API_URL = \"https://api.jujucharms.com/charmstore/v5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filename(charmname, type):\n",
    "    return \"cached/{}/{}.json\".format(type, charmname.replace('/',':'))\n",
    "\n",
    "\n",
    "def cache_charm_list():\n",
    "    resp = requests.get(API_URL + '/list?type=charm')\n",
    "    charms = resp.json()['Results']\n",
    "    with open('cached/charm-list-cached.json', 'w') as f:\n",
    "        json.dump(charms, f, indent=4)\n",
    "\n",
    "\n",
    "def get_charms():\n",
    "    with open('cached/charm-list-cached.json', 'r') as f:\n",
    "        charms = json.load(f)\n",
    "    return [charm['Id'][3:] for charm in charms]\n",
    "\n",
    "\n",
    "def cache_layer_files():\n",
    "    charms = get_charms()\n",
    "    numreactive = 0\n",
    "    for name in charms:\n",
    "        \n",
    "        if os.path.isfile(filename(name, 'layer-yaml')):\n",
    "            with open(filename(name, 'layer-yaml'), 'r') as f:\n",
    "                if not \"ArchiveDownloadCount\" in f.read():\n",
    "                    if \"ceph\" in name:\n",
    "                        print(name)\n",
    "                    continue\n",
    "        else:\n",
    "            continue\n",
    "        print(\"getting doing charm {}\".format(name))\n",
    "        resp = requests.get(\"{}/{}/archive/layer.yaml\".format(API_URL, name))\n",
    "        if resp.status_code == 200:\n",
    "            numreactive += 1\n",
    "            layer = yaml.load(resp.text)\n",
    "            with open(filename(name, 'layer-yaml'), 'w') as f:\n",
    "                json.dump(layer, f, indent=4)\n",
    "        else:\n",
    "            os.remove(filename(name, 'layer-yaml'))\n",
    "    print(\"Total charms: {}\\nReactive charms: {}\".format(len(charms), numreactive))\n",
    "\n",
    "def get_layer_yaml(name):\n",
    "    path = filename(name, 'layer-yaml')\n",
    "    try:\n",
    "        with open(path, 'r') as f:\n",
    "            layer_yaml = json.load(f)\n",
    "            return layer_yaml\n",
    "    except:\n",
    "        return None\n",
    "    \n",
    "def cache_metrics():\n",
    "    charms = get_charms()\n",
    "    for name in charms:\n",
    "        if not os.path.isfile(filename(name, 'metrics')):\n",
    "            resp = requests.get(\"{}/{}/meta/stats\".format(API_URL, name))\n",
    "            if resp.status_code == 200:\n",
    "                layer = yaml.load(resp.text)\n",
    "                with open(filename(name, 'metrics'), 'w') as f:\n",
    "                    json.dump(layer, f, indent=4)\n",
    "\n",
    "    \n",
    "def get_metrics(name):\n",
    "    path = filename(name, 'metrics')\n",
    "    try:\n",
    "        with open(path, 'r') as f:\n",
    "            metrics = json.load(f)\n",
    "            return metrics\n",
    "    except:\n",
    "        return None "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cache_charm_list()\n",
    "#cache_layer_files()\n",
    "#cache_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_reactive_charms():\n",
    "    reactive_charms = []\n",
    "    for name in get_charms():\n",
    "        if os.path.isfile(filename(name, 'layer-yaml')):\n",
    "            reactive_charms.append(name)\n",
    "    return reactive_charms\n",
    "  \n",
    "\n",
    "def remove_version(name):\n",
    "    name = re.sub('(-[0-9]+$)', '', name)\n",
    "    return name\n",
    "    \n",
    "    \n",
    "def normalize(name):\n",
    "    name = name.split('/')[-1]\n",
    "    name = remove_version(name)\n",
    "    return name\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reactive</th>\n",
       "      <th>include-layers</th>\n",
       "      <th>include-interfaces</th>\n",
       "      <th>downloads-day</th>\n",
       "      <th>downloads-week</th>\n",
       "      <th>downloads-month</th>\n",
       "      <th>downloads-total</th>\n",
       "      <th>name-normalized</th>\n",
       "      <th>name-versionless</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>keystone-273</th>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>68</td>\n",
       "      <td>590</td>\n",
       "      <td>3233</td>\n",
       "      <td>113113</td>\n",
       "      <td>keystone</td>\n",
       "      <td>keystone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rabbitmq-server-70</th>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>68</td>\n",
       "      <td>592</td>\n",
       "      <td>3208</td>\n",
       "      <td>154702</td>\n",
       "      <td>rabbitmq-server</td>\n",
       "      <td>rabbitmq-server</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percona-cluster-257</th>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>70</td>\n",
       "      <td>565</td>\n",
       "      <td>3125</td>\n",
       "      <td>78465</td>\n",
       "      <td>percona-cluster</td>\n",
       "      <td>percona-cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glance-261</th>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>68</td>\n",
       "      <td>580</td>\n",
       "      <td>3113</td>\n",
       "      <td>100037</td>\n",
       "      <td>glance</td>\n",
       "      <td>glance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nova-cloud-controller-304</th>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>71</td>\n",
       "      <td>585</td>\n",
       "      <td>3072</td>\n",
       "      <td>105079</td>\n",
       "      <td>nova-cloud-controller</td>\n",
       "      <td>nova-cloud-controller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           reactive include-layers include-interfaces  \\\n",
       "keystone-273                  False             []                 []   \n",
       "rabbitmq-server-70            False             []                 []   \n",
       "percona-cluster-257           False             []                 []   \n",
       "glance-261                    False             []                 []   \n",
       "nova-cloud-controller-304     False             []                 []   \n",
       "\n",
       "                           downloads-day  downloads-week  downloads-month  \\\n",
       "keystone-273                          68             590             3233   \n",
       "rabbitmq-server-70                    68             592             3208   \n",
       "percona-cluster-257                   70             565             3125   \n",
       "glance-261                            68             580             3113   \n",
       "nova-cloud-controller-304             71             585             3072   \n",
       "\n",
       "                           downloads-total        name-normalized  \\\n",
       "keystone-273                        113113               keystone   \n",
       "rabbitmq-server-70                  154702        rabbitmq-server   \n",
       "percona-cluster-257                  78465        percona-cluster   \n",
       "glance-261                          100037                 glance   \n",
       "nova-cloud-controller-304           105079  nova-cloud-controller   \n",
       "\n",
       "                                name-versionless  \n",
       "keystone-273                            keystone  \n",
       "rabbitmq-server-70               rabbitmq-server  \n",
       "percona-cluster-257              percona-cluster  \n",
       "glance-261                                glance  \n",
       "nova-cloud-controller-304  nova-cloud-controller  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_grouped_includes(dependencies):\n",
    "    '''converts a list of includes into two lists of includes: (layers, interfaces)\n",
    "    '''\n",
    "    layers = []\n",
    "    interfaces = []\n",
    "    for incl in dependencies or []:\n",
    "        type, l_name = incl.split(':')\n",
    "        if type in ('layer', 'cs'):\n",
    "            layers.append(l_name)\n",
    "        elif type == 'interface':\n",
    "            interfaces.append(l_name)\n",
    "        else:\n",
    "            print(\"type {} not known\".format(type))\n",
    "    return (layers, interfaces)\n",
    "\n",
    "\n",
    "charms = {k:{} for k in get_charms()}\n",
    "\n",
    "for charm_name in charms:\n",
    "    # If a charm has a layer.yaml file, it's a reactive charm\n",
    "    # Add dependencies of the reactive charm\n",
    "    layer_yaml = get_layer_yaml(charm_name)\n",
    "    if layer_yaml:\n",
    "        charms[charm_name]['reactive'] = True\n",
    "        (\n",
    "            charms[charm_name]['include-layers'],\n",
    "            charms[charm_name]['include-interfaces'],\n",
    "        ) = get_grouped_includes(layer_yaml.get('includes'))\n",
    "    else:\n",
    "        charms[charm_name]['reactive'] = False\n",
    "        charms[charm_name]['include-layers'] = []\n",
    "        charms[charm_name]['include-interfaces'] = []\n",
    "        \n",
    "    # Add the metrics for each charm\n",
    "    charms[charm_name]['downloads-day'] = get_metrics(charm_name).get('ArchiveDownloadAllRevisions')['Day']\n",
    "    charms[charm_name]['downloads-week'] = get_metrics(charm_name).get('ArchiveDownloadAllRevisions')['Week']\n",
    "    charms[charm_name]['downloads-month'] = get_metrics(charm_name).get('ArchiveDownloadAllRevisions')['Month']\n",
    "    charms[charm_name]['downloads-total'] = get_metrics(charm_name).get('ArchiveDownloadAllRevisions')['Total']\n",
    "    \n",
    "    # Add the normalized name for each charm\n",
    "    charms[charm_name]['name-normalized'] = normalize(charm_name)\n",
    "    \n",
    "    # Add the versionless name for each charm\n",
    "    charms[charm_name]['name-versionless'] = remove_version(charm_name)\n",
    "\n",
    "\n",
    "# Get a table where rows, the keys from the dict are the index of the table    \n",
    "charmstable = pandas.DataFrame.from_dict(charms, orient='index')\n",
    "charmstable.sort_values('downloads-month', inplace=True, ascending=False)\n",
    "\n",
    "charmstable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reactive</th>\n",
       "      <th>include-layers</th>\n",
       "      <th>include-interfaces</th>\n",
       "      <th>downloads-day</th>\n",
       "      <th>downloads-week</th>\n",
       "      <th>downloads-month</th>\n",
       "      <th>downloads-total</th>\n",
       "      <th>name-normalized</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name-versionless</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>keystone</th>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>68</td>\n",
       "      <td>590</td>\n",
       "      <td>3233</td>\n",
       "      <td>113113</td>\n",
       "      <td>keystone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rabbitmq-server</th>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
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       "      <td>68</td>\n",
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       "      <td>3208</td>\n",
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       "      <td>rabbitmq-server</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percona-cluster</th>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>70</td>\n",
       "      <td>565</td>\n",
       "      <td>3125</td>\n",
       "      <td>78465</td>\n",
       "      <td>percona-cluster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glance</th>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>68</td>\n",
       "      <td>580</td>\n",
       "      <td>3113</td>\n",
       "      <td>100037</td>\n",
       "      <td>glance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nova-cloud-controller</th>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>71</td>\n",
       "      <td>585</td>\n",
       "      <td>3072</td>\n",
       "      <td>105079</td>\n",
       "      <td>nova-cloud-controller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       reactive include-layers include-interfaces  \\\n",
       "name-versionless                                                    \n",
       "keystone                  False             []                 []   \n",
       "rabbitmq-server           False             []                 []   \n",
       "percona-cluster           False             []                 []   \n",
       "glance                    False             []                 []   \n",
       "nova-cloud-controller     False             []                 []   \n",
       "\n",
       "                       downloads-day  downloads-week  downloads-month  \\\n",
       "name-versionless                                                        \n",
       "keystone                          68             590             3233   \n",
       "rabbitmq-server                   68             592             3208   \n",
       "percona-cluster                   70             565             3125   \n",
       "glance                            68             580             3113   \n",
       "nova-cloud-controller             71             585             3072   \n",
       "\n",
       "                       downloads-total        name-normalized  \n",
       "name-versionless                                               \n",
       "keystone                        113113               keystone  \n",
       "rabbitmq-server                 154702        rabbitmq-server  \n",
       "percona-cluster                  78465        percona-cluster  \n",
       "glance                          100037                 glance  \n",
       "nova-cloud-controller           105079  nova-cloud-controller  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "versionless_charmstable = charmstable.groupby(\n",
    "    'name-versionless'\n",
    ").agg({\n",
    "    'reactive': lambda x: x.max(),\n",
    "    'include-layers': lambda x: list(set(x.sum())),\n",
    "    'include-interfaces': lambda x: list(set(x.sum())),\n",
    "    'downloads-day': lambda x: x[0],\n",
    "    'downloads-week': lambda x: x[0],\n",
    "    'downloads-month': lambda x: x[0],\n",
    "    'downloads-total': lambda x: x[0],\n",
    "    'name-normalized': lambda x: x[0],\n",
    "})\n",
    "\n",
    "versionless_charmstable.head()\n",
    "\n",
    "versionless_charmstable.sort_values('downloads-month', inplace=True, ascending=False)\n",
    "versionless_charmstable.head()\n",
    "\n",
    "#versionless_charmstable.loc[versionless_charmstable['name-normalized'] == \"mongodb\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reactive</th>\n",
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       "      <th>downloads-week</th>\n",
       "      <th>downloads-month</th>\n",
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       "      <th>percona-cluster</th>\n",
       "      <td>False</td>\n",
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       "      <td>115</td>\n",
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       "      <th>rabbitmq-server</th>\n",
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       "      <th>glance</th>\n",
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       "      <td>4499</td>\n",
       "      <td>192381</td>\n",
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       "    <tr>\n",
       "      <th>keystone</th>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>99</td>\n",
       "      <td>944</td>\n",
       "      <td>4431</td>\n",
       "      <td>205312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                       reactive include-layers include-interfaces  \\\n",
       "name-normalized                                                     \n",
       "percona-cluster           False             []                 []   \n",
       "rabbitmq-server           False             []                 []   \n",
       "glance                    False             []                 []   \n",
       "nova-cloud-controller     False             []                 []   \n",
       "keystone                  False             []                 []   \n",
       "\n",
       "                       downloads-day  downloads-week  downloads-month  \\\n",
       "name-normalized                                                         \n",
       "percona-cluster                  115            1030             4953   \n",
       "rabbitmq-server                  102             999             4894   \n",
       "glance                           107             988             4726   \n",
       "nova-cloud-controller            112             956             4499   \n",
       "keystone                          99             944             4431   \n",
       "\n",
       "                       downloads-total  \n",
       "name-normalized                         \n",
       "percona-cluster                 130100  \n",
       "rabbitmq-server                 305092  \n",
       "glance                          176272  \n",
       "nova-cloud-controller           192381  \n",
       "keystone                        205312  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \n",
    "\n",
    "normalized_charmstable = versionless_charmstable.loc[\n",
    "    versionless_charmstable['downloads-month'] >0        # remove all charms that aren't used\n",
    "].groupby(     \n",
    "    'name-normalized'                                    # Group by their charmname (without namespace)\n",
    ").agg({\n",
    "    'reactive': lambda x: x.max(),                       # If one of them is reactive, count them all as reactive\n",
    "    'include-layers': lambda x: list(set(x.sum())),      # Merge all layer dependencies\n",
    "    'include-interfaces': lambda x: list(set(x.sum())),  # Merge all interface dependencies\n",
    "    'downloads-day': lambda x: x.sum(),                  # sum all downloads\n",
    "    'downloads-week': lambda x: x.sum(),\n",
    "    'downloads-month': lambda x: x.sum(),\n",
    "    'downloads-total': lambda x: x.sum(),\n",
    "})\n",
    "\n",
    "normalized_charmstable.head()\n",
    "\n",
    "normalized_charmstable.sort_values('downloads-month', inplace=True, ascending=False)\n",
    "normalized_charmstable.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Active Charms: 529\n",
      "Regular:             353\n",
      "Reactive:            176\t(33.2703213610586%)\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Charms which are active (>0 downloads in last month)\n",
    "#\n",
    "\n",
    "activecharms = normalized_charmstable\n",
    "\n",
    "print(\"Total Active Charms: {}\"\n",
    "      \"\\nRegular:             {}\"\n",
    "      \"\\nReactive:            {}\"\n",
    "      \"\\t({}%)\".format(\n",
    "          len(activecharms),\n",
    "          len(activecharms.loc[activecharms['reactive'] == False]),\n",
    "          len(activecharms.loc[activecharms['reactive'] == True]),\n",
    "          activecharms['reactive'].mean()*100,\n",
    "      )\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>charms</th>\n",
       "      <th>num-charms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6wind-common</th>\n",
       "      <td>[virtual-accelerator-network, virtual-accelera...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apache-bigtop-base</th>\n",
       "      <td>[hadoop-plugin, hadoop-namenode, hadoop-slave,...</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apache-flume-base</th>\n",
       "      <td>[apache-flume-hdfs, apache-flume-kafka, apache...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apache-hadoop-datanode</th>\n",
       "      <td>[apache-hadoop-slave]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apache-hadoop-nodemanager</th>\n",
       "      <td>[apache-hadoop-slave]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                      charms  \\\n",
       "6wind-common               [virtual-accelerator-network, virtual-accelera...   \n",
       "apache-bigtop-base         [hadoop-plugin, hadoop-namenode, hadoop-slave,...   \n",
       "apache-flume-base          [apache-flume-hdfs, apache-flume-kafka, apache...   \n",
       "apache-hadoop-datanode                                 [apache-hadoop-slave]   \n",
       "apache-hadoop-nodemanager                              [apache-hadoop-slave]   \n",
       "\n",
       "                           num-charms  \n",
       "6wind-common                        2  \n",
       "apache-bigtop-base                 14  \n",
       "apache-flume-base                   3  \n",
       "apache-hadoop-datanode              1  \n",
       "apache-hadoop-nodemanager           1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reactive_charm_names = normalized_charmstable.loc[normalized_charmstable['reactive'] == True].index.values\n",
    "\n",
    "included_layers = defaultdict(lambda: defaultdict(list))\n",
    "included_interfaces = defaultdict(lambda: defaultdict(list))\n",
    "\n",
    "for charm_name in reactive_charm_names:\n",
    "    charm = normalized_charmstable.loc[charm_name]\n",
    "    for include_layer in charm['include-layers']:\n",
    "        included_layers[include_layer]['charms'].append(charm_name)\n",
    "    for include_interface in charm['include-interfaces']:\n",
    "        included_interfaces[include_interface]['charms'].append(charm_name) \n",
    "        \n",
    "included_layers = pandas.DataFrame.from_dict(included_layers, orient='index')\n",
    "included_layers['num-charms'] = included_layers['charms'].apply(len)\n",
    "\n",
    "\n",
    "included_interfaces = pandas.DataFrame.from_dict(included_interfaces, orient='index')\n",
    "included_interfaces['num-charms'] = included_interfaces['charms'].apply(len)\n",
    "\n",
    "included_layers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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VtPLwoFJpLheIhiPRnWwHURmuILQ38DIQSPRDn+WdPPYoz/ufJfpxlbLoynAk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\nB+U/y8PyS4rD6jU72D6y6HnSAeYF723pqV9/HBdbiBDBdoe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X1TF8wQ3uqzPT592bljIJ\nIeyq84S6mgRqVGcIBaO3fjp1ypK7W6La6pb1f7Ryv1cdQPMBh6sYeEt1jEjxr8SExfVm8yGqc2g9\nKxkh8q++zmxriGGV6iw+tg54V3UIFUKrkDY8DTSoDjEcZWZz2YyRuasXxsbMQAihOk84KE0Trf0f\npQFEt9VlTV1816TRmz+Yj5T1qvP04n+z58zUGxuEjwdVB4gErUK0/DElSXfpCHKuOJF2xfXmg4vz\n+Fp1Fh96Ir+k2FebZoWU0CukHS4Xxq5ZIemz2JiVJ43IMdebzYeqzhJO1ucIq+oMoWbs5g+nT156\nT6OtvT4Ydw7Vo9HhxOFaCXyoOka4uys99RtPlxZeWvDymoTl3ostha9PE/Nlz9tTh5IqInjjvNAr\npA1/AobSeFsZCfK29NR5N2akHSqFGMo2llof1uWQpDpDKIpprcmZtuiOiaO2fDIfKRtV5+m0aPac\nmV+pDqH53AOqA4Sz7Rbzjk9jY45SnUMbnNenm6fff4FpvUfQZ6/iIPd0fklxi+oQqoRmIe1wbQNe\nUR1joGpNpprjR+Ss+DA+boZeAOIfmzNFT9t7agM0btO704/5xlFnbW9YqToL8JDqAJofOFyLgS9V\nxwhXV2dmbEaIGNU5tMH7YbTpoFmzzaamKJyqswxBI/BX1SFUCuWiLiRWgi+Njlp93Mjc1kqL5UjV\nWcJZY4xI8qIXHA5HbEtV3rRFtx82YtsX85BS1da2382eM/N9RdfW/E+PSvvB/Jjo7zfbrFNU59CG\nrjZBZFxxg3n/9dnMV51lkB7NLyke1u6CoS60+kh357C/CFykOkZvHkpJnvdyYvwUhJ6/GwjP/9Ht\njGujYDjn+Ed1NWtaW1nT1kppRwc5Fgufjduvx2P/WFnB8uYWtna00+D1kmo2s39UFJempHBUbNxe\nx1a73TxYUc6ipiZiTCZ+lmhndloalm5rTV+oqeH5mhreHzOGBLN5OL/KsDTFZG5ZccTNLrc1LtCr\n/y+YPWdmyNxt0obAYf8COE51jHDhBe8xo/J+bNEtVMPGhV965p+xRB4jINhrh+3AhPySYlUDL0Eh\nlEekAe4Cgq5bQ6MQDT/Ny178sj1hhi6iA6fSzrD7I/+pqpKlzU2MsFpJNPX9z+O7lhYmREXxq5QU\n7snM5PykJLZ1dHDJtm2869o7yl1lO1nW3Mys1DTOSLTzXE01L9buPYC+vaOdJ6squTszU2kRDRDX\nUj5q+sLbDs7ZPn8eUgbq31gJ8HqArqWpczMQkav7/eHpJPsiXUSHl38fZ57+0HmmYq+gUnWWftwd\n6UU0hPqINIDD/gjwG9UxdvnBZlv3fzmZlg4hxqjOEmlmv+eZN+OH4W3Buq29nRE2GwBnbNpIs9fb\n64h0T5q8Xk7ZuAG72cz7Y8YC0Or1cuS6tdyflcVZdmNN5B07d7C1vYN/j9rTqerK0m1ECcGTuXnD\n+RV8rjEuZ9OKw29q9lhiDvLzpU6bPWfmB36+hhYMHPYXgCLVMUJdgxD1U0fltekF7OEp1SV3/vFZ\nT21MOweqztKDVcDESG1511Woj0iD0SarWnUIgKeTEhdckJOZp4toNdbliKjhnmNXET1UcSYTdrOZ\neo9n92PtUuIF7F1Gme1mM81yz+vPe/UuVrW0cHdG5rCu7w/xTTvGFC649YDsnYu/Qkp/tWn6RBfR\nEeVOIOJHsobr1oy0b3URHb6q7SL7VzeYx23OYIHqLD24WRfRhtAvpI2+0verjNAqRMu5OVkLnkpO\nmqZXTauzPkekqrhurdtNtdtNSWsrD5SXsbG9ncL4+N0/TzSbGWuz8XxNDZva21jR3MwH9fUcHmP8\nVanzeHikooKb0tLJtAbnTCCBNOf/+NKxk1Y8ss3sbvX1tt0e4CYfn1MLZg7XDuAPqmOEso1Wy5aF\nMdF6gWGYc1tE1G9+ZZn24ZHiaxk8bX8/yC8p/kJ1iGAR+oW04Slgg4oLb7Ratkwfmbu1JMo2TcX1\ntT22pTNCBnjuZZPXy9QN65m+YT1nb9nMWy4X59qTuLPbyPL9WVlsaW/n1E2buHjbVnKsVmanpgHw\nSEU5o202zk8K/lbYCY3b9pu+8NbxmeXL5iFlh49O+/fZc2au8dG5tNDxKLBTdYhQdVVWRjlCDO8W\nmhYyXjjRXPjYz00/eNXfgfcAtyrOEFRCf470Lg77ecCrgbzkKwnxSx5MTT4QIRIDed3Bqny/kpbN\nLbRsaaGjsgNrqpX9H99/n+PaK9tZe+vaPs+Vd2UeSVOMgs9d72bHSztoWt2EsAmSpyWT8bMMhHnv\nThRVH1dR9XEV438/HnOsfxfR/ecR93azF5/0lB7IHGmPlCxtbsaDZEdHB+/X15NlsXJHRgYpFste\nx7Z5vaxvbydaCMbYbJiEYFFTE7O2l/LGqNGMsdmYU13N+/UuJHBqYiKzUtMwB+ku8q6E0T+uPOx6\nvOaoff8yDVwdsN/sOTNVvzloKjjsvwKeVR0j1HwUF7viNxlpE1Xn0AIvs1aWPvacpzG6gwMURZiT\nX1J8jaJrByVL/4eECIfrNRz2m4Cj/X2pDui4Kitj8bKY6EJ/X8sXyt8oxxxnJnp0NN7m3gdsLYkW\n8q7seaHbjhd3IDsk8QV7piyUPltK6+ZW0s9Ix93gpvLDSsyxZtJ+krb7mPbKdireriD3ily/F9EA\nDTGUJzX5ppAeCLMQTInb0+ruHHsSRdu2cum2bbwxejTWLkVwlMnEQdHRu/+71evld+VlXJ6Swn5R\nUTxbXc2/62p5KCsbAdxetpNYYeJXqUpmrPTL3rB5/8IFt3SsPvCyeZVph01FiKG8ntyni+iI9jxw\nDaCLwgFyg/vutNTgv32l+UV5ssi7/AZzy8PPexbmVTM1wJdvAO4N8DWDXrhM7djlJvy8Sct2i3lH\n4ci8klApogEmPDqB/L/lM+bWMViSeq91TFEmkqYk7fNly7DhbfGScFgClgTj+d52L43ORjLPzSTt\n5DSyzski6Zgk6lfU73XOHf/aQdxBcdiPtPv1d9xlZzJKt7k2C8FpiYmsa29jeXPfa6n+WlWFVQiu\n7Jzi8aarjvPtScyIj6cwPp7z7Um86Rp2Rz+/MkmvtWD1szOOWPXEOpOnff0gn76WCN8RK+I5XF7g\nKozbxdoA/CElaWG7SYxTnUNTp90qYm660jL1s8PEPBnYfzsPRvrmKz0Jr0La4VoEzPHX6T+Mi13+\nk7ycqEazaVibfgSaLWN40+hq5hn9jpNnJO9+THZIkGCO3zPKbI4z423bM+Jdt6iO5vXN5FycM6zr\nD8amLKF8rlKb14jg8vb++ramtZV/1dbwu8wsbJ2j1uVuN1nWPR90sqwWyty+mobsX0muDfmFC24d\nkVrlnIeUA31hv2X2nJmh8Qtq/uNwrQCeVB0jFNSaTDUvJyYcqjqHFhz+8RPzjD/9zLRKQm0ALrcc\nvUC4R+EztWOP24BTgZG+OqEXvDdlpH39eWxMIUKE14ePfnhaPdQvq8eaaiX+oD3TOsxxZqKyo6j6\nqIqorCjcDW7qltTtHnl2N7rZ+Z+dZJ6biTU5cJ0o1uaK+J8u938t7fJ4iDGZdhfBuzR7vbzpqsME\nFET33MDFIyX3lpXxc3sSE2Njdz+ebrGwrm1Pd7l1bW2kW0Lnn6hJuqMO/WHOjJqkCau/O2RWjDRZ\nx/Zx+Eez58x8L2DhtGD3W+DnwAjVQYLZ9ZnpP0ghgvZuaFtZG3WL6mj8oZH2ynZkh8SWbiNxUiJp\nJ6dhitrz9ln1URX1q+ppL2vH0+TZ/Z6SemIqiRP3XnbkafVQ9kqZccdTgH2inaxfZO11PgDXchel\n/yhl/IPjsaVHxjrMxfmmiZszxNZH/+mpinIz3k+XaQOK8kuK9Z2jHoTOu/RAOVwNOOxXAR/54nTV\nJlPV2XnZW2vM5mN9cb5Q41rqwtvqJe2UNIRp76Ix97Jctv51K+vuWAdAzJgYMn6WAUDZf8qIyooi\n5biUgObdmCWG1Yj5XZeLHZ2jwLUeDx1SMqe6CoAci5Uz7MYHhWXNzfyuvIwTExIYabURZzJR2tHB\ne/UuytxuZqWmkttLK7t/1dZQ6XFzU/re7V9PT0zkmZoa7GYzAnjd5eLKlOCcH92XlLq1BxUuuKX1\n+4OvnlebfMD0Hj58NmDcztc0g8PV2Pm6/aHqKMFqtc22blWULdBzYgel9utaar6oIeGwBJImJyHM\ngsaSRireqqB+WT1jfzsWk814OWje1IwtzUbCoQlY4i14mjy4lrnY+uRWMs7KIOPMjN3nLX+tHNcS\nF2mnGtPgqj6oAhN73e30NHvY+dJOMs/OjJgiepedqWLk5TeYmx57zrM4q47JfrjEPfklxbqzUi/C\np2tHdw77v4CLh3OK+THR31+bmZ7hFSLLR6mUW3fXOryt3h67dvRkw/0baNnYwoQ/TMCWuu+Lk7fd\nS9uONoRNEJUVhTAJGlc3suWJLYz73TiisqOofLeSusV1ANiPsZNxZsY+RbmvCCm9rzzs6RAwpM1Z\nirZuYVlLS48/mxQTw9yRxk6EW9vbeaammpUtLVS43bR6vdjNZgqiozk/KZkZXfpId1Xa3s6Zmzfx\nSHYOJyQk7PWzdil5orKC9+uNeeanJSZyY3rGPqPeoaQ6Of/77wuutkuTZVSXh6+ZPWem36ZgaSHM\nB6/b4apwZO7KWrP5cNU5+tKyqQVbpm2fheXlb5ZT+V4l2Rdlk3pC74MD0iPZ4NhAe2U7+U/l736f\nKLmhhORjk8k8yxgnKX+7nNp5tRzwpz2NK7a/sJ2WzS2Mu2ec395fQsHs9zzzCn+Q04Xvpu4uBqbp\nzVd6F34j0nv8GjgJGNIIpSM1Zd6bCXFD7UQQFlq3t9KyoYX4g+N7LKIBTDYTMaP3TGHwtnvZ/sJ2\n0k5NIzo3msoPKqn+rJq8K/JAQOk/SjFFmUj/qX8245JCmDrMlNo8DGkxzq5CuT8jbTbuz8oe9Pnz\nbDZWTOj5Q4xNCG7LyOS2INzdcKhSa4sPKVxwa/N3h8yaV2ffrxAhvgL+rjqXFrRuAE4Ewmbwwhde\nT4hbWms2+70j1XDFjOl5Opv9KDuV71XSWtra5/OFWWBJttBa2op0S4TNKIi97V4s8Xveiruvx2la\n20Td/DrG3jM2ootogL+dbp7hHO1dNvt97wQBw13l3wJcoovovoXvfF+Hqwa4drBPqzcJ18l5OUvf\nTIyfEclFNBi36WDvRYb9qXi7ApPVRPrp6bvPkXJcCgmHJpBwSAIpx6XsPq+/1MZT6dcLaINi9rbH\nHrHqTzMKVj+zBLh89pyZYXobTBs2h6sWuFJ1jGDSDm2/T00J6Q8WHbXGdDmLfd+3VHejG3e9m9Yd\nrVT8t4JGZyNxB8TtngICELtfLDVf1tCyxdgPoeaLGmLHG+tLvB1edjy/g9STU4kZpTcWBvi6wDTp\n5svNNe3mYW9Ud1d+SXHfm0toYVxIAzhcbwBvDfTwb6NsxTNG5rl2WC1B/8nf37xuL3WL6jAnmEk8\nfGD7zbRsaaH602pyLsnBZDH+anXUdmBN2TNX2JpipaPGv40atqeJtv6P0gItveq7F2bPmblRdQ4t\nyDlc76HbIu52X1rKErcQA7tVFoSkV1LxbgWYIemYfdtfr7t9HSXXl7D+zvVUvFtB4sRERlyz95rT\n7F9mG9M+7t3Ahns3gDQeA2PDMemRu9fnaIbSdDHmihvMGZWJLB3iKeYDf/ZlpnAVCSOus4HjgD6H\nVf+UbJ//nD3xKIQY0tzacNOwsgFPg4fUk1MRlv5vlUmvZPvz20kuTCZuwp4NSqxJ1r1u57WWtmJN\n8m8Xjw3ZmI9QsmG81of38kuK/6E6hBYybgGmAxHd6q3cbC7/b3xcSG9Ws/PfO2lZ30LmOZlEZe/7\n9jryupF4O7y4a924lrmQHRJvqxe6jN9EZUcx/sHxtO1sQ0pJdE40wiJo3d5K1QdVjLpxFCabierP\nq6n5ogZvq7HvQdb5WXuNbEealiiRMHuW+agb3/HOO6ZEFgoY6LyXJuBSPaVjYML/b5jDVQZc3duP\nW4RoPis3a+FzSfbp4VpE1y6speLdCirercDd4MbT4tn937ULe55mMdhpHdWfVOOuc5N57t7ze+2T\n7dTOq6X87fLdC0R2bTHuL+tyRGB2f9EGqgK4XHUILYQ4XG3AL4C+dzUKc7My09cjRM8rl0NA+Zvl\n1HxeQ/KxyaSf1vO6mLj940g4OIHk6cmMvmk0pmgTGx/ciKdp705rwiKIHhFNzMgYhEUgpWTH8zuw\nH20n/qB4XEtdlL1SRvpp6eRdmUfD9w2UvVIWiF8zuAkhnjjLPOPvPzF9I42OSQNxe35JsR6OGqDw\nL6TB2D68hwVOP1qtG6ePzN2x3hbcLYWGq/brWireqqDirQo89R68zd7d/93TfOX26nYaf2gkdr9Y\nonOiezhjt+Mr2yl/u5yci3P2Wa2dfno6KcenUPtlLbVf1pJyfAppp6X1cibf2JQpBr8KUPOny/Vu\nWNqgOVwlwPWqY6iyLDpqzVqbdYrqHENV/rbRqSNpehI5RQPflCtpahJulxvX8r53da35ooa28jay\nLjCmj9d8XUPikYkkTU4ibv840k9Lp3Z+LdKrl2QAfHGY6ejfXGau6DCxuZ9DPwT+FoBIYSMSpnbs\n8mtgMnAIwAuJCYseT0k6JJQ/7Q/U2Dv62hdjX7ZUGwc/f/DAj0+3cdA/DurxZyaLiewLssm+IHC1\nrStepElw+WDFsjZ8z+SXFOuNV7Shcbiew2E/EThfdZRAuz4j3YMIzd6X5W+XU/nfSpKmJpF7ae6g\nfg3ZYRS+3Ueku+qo7aD8jXJy/i9ndzcPd617rw5S1hQrskPiafRgSYykUqd3WzLFuCtuMLsef8az\nLLWRST0cshG4KL+kWH/6GITIGJEGcLhagfPaofri7MyvH09NnhIJRXSkarFRqjqDRjFwo+oQWsi7\nCtikOkQg/dOesKjRbCpQnWMoKv5bYRTRU5LI/VVuj+3ovG1ePK37FsrSK6n+vBqA2HGx+/x8lx3/\n2kHsfrEkTd4zTdCSZKGtdM8687bSNoRFYI4393SKiNUcLeyzrjVPXL6fmNftRy3Az/NLigOx3XhY\nCd8NWXpx/HP551ZYLK+pzqH51x+ecS8cWUVYT9kJctXA0XqeneYTDvuhwEIgrr9DQ12zEE2TR+XV\ne0XoTVGr/qyanS/txJpqJePsjH1Goi2JFuIPjqdlSwubHt5E4pGJRGVHYY4z4651U7ekjvaydpKm\nJhl7D/TAtczF9me3s98D++21g2Ht/Fq2/3M7qSekYk2x7u4Aknd5z+fR4JTl3sWX/s97iDD+XRXl\nlxT/S3WmUBRxhTRAwdyCvwDXqc6h+c/1//XMm7ZGzlCdI0J1ACfmlxR3H/HQtKFz2M8G3mDgnQdC\n0q8z0uZ9Hhcbkq9dpc+UUrewrtefx+4fy9g7xuJucFPxTgXNa5vpqOnA0+rBHGMmelQ0yVOTsU+2\n9zgdxNPsYd2d60j7SRppJ++91kZKSdUHVUbXjjaja0f2RdmYY/SIdF/G7ZTrbnvN8/bkb4tvU50l\nVEVqIW0FvgQ9YhmufrLMu/jSz7yTVeeIUJfnlxQ/pzqEFoYc9nuA36mO4S/bLJbSn+ZlpyFE/6u8\nNc03vgJOchY5/bvBQxiLnDnSXXT+hTkX0L1xwtT6HJGqOkOE+qMuojU/uh94XXUIf7k6K32bLqK1\nANoInKOL6OGJyEIawFnk3AmcQYT3KQ1XW9PJlRB5t1vU+gC4VXUILYw5XBK4BPhWcRKfmxcT/d1W\nq1XfRdMCpQE43VnkrFYdJNRFbCEN4CxyLsNo+t97nx0tJLXZRJxXUK46RwT5AbhA74Sl+Z3D1Qyc\nSRjdUfSC95aMND0SrQWKF7jAWeRcozpIOIjoQhrAWeR8jwhu+h/OmqLD5402yFUCp+eXFA901yxN\nGx6HqxTjjmKj6ii+8GSyfWGrybS/6hxaxLjFWeT8QHWIcBHxhTSAs8j5FPAH1Tk03ypLpl51hgjQ\nDpyVX1K8WXUQLcI4XMuAnwFt/R0azOpNwvWcPTFfdQ4tYjicRc4nVIcIJ7qQ3uM3wKuqQ2i+sylL\n6DnS/ndlfknxQtUhtAjlcH0OXEAIT8+7OSNtlRQirf8jNW3YHnEWOcO2640qupDu5CxySqAImK86\ni+Yb63JE71tjab7waH5J8VzVIbQI53C9DVxBCC4uXm+1bloSHT1FdQ4tIvzFWeS8XXWIcKQL6S6c\nRc42jEUsxaqzaMO3MVtkqM4Qxl4F7lAdQtMAcLieB25WHWOwrspKr0IIq+ocWth7Bvi16hDhShfS\n3TiLnLXA8cBa1Vm04dmRQq40dtnTfOsl4ELdoUMLKg7XE8CDqmMM1PtxscsrLJZJqnNoYe9F4OrO\nu+6aH+hCugedPaaPRRfTIc1rEha3mVLVOcLMP4Gi/JLikJ2TqoUxh+tuQmDhuBvc96anpqjOoYW9\n14FLnUVOPejhR7qQ7oUupsNDXRyVqjOEkacxtv/WL8pa8HK4biXItxF/JDV5UbsQY1Xn0MLau8CF\nziKnHvTwM11I90EX06FvR6poUZ0hTPwpv6R4Vn5Jsb49qAU/h8uB0Ykp6NSYTNWvJMQfqjqHFtY+\nBc7TW38Hhi6k+6GL6dC2MUv/HfeBh/NLim9UHULTBsXhegyYTZB187g2M30NQthV59DC1kfAzzqb\nJ2gBoIuMAdDFdOhamysSVGcIcb/LLynW3Tm00ORwPQVcSpD0mf4+yvajM8o2VXUOLWz9HTjdWeTU\nd2IDSBfSA9RZTM8AVqnOog3cpiyRozpDCLszv6TYoTqEpg2LwzUXY9MW5be5Z2emtyCEft/VfE0C\ntzmLnFfrOdGBp/9BD4KzyFkGFAIfq86iDUxNgsiQ0KA6Rwi6Kb+k+CHVITTNJxyu14GTgVpVEV5J\niF9SZzYfpur6WthqA37hLHI+qjpIpNKF9CA5i5wNwOnAs6qzaAPTamW76gwhRAKz80uKn1AdRNN8\nyuH6EjgGWBfoS7cJWh9JTc4N9HW1sFcNHO8scr6mOkgk04X0EDiLnG5nkfMK4Leqs2j9q0mgRnWG\nENGK0SP6KdVBNM0vHK61GMX0vEBe9t601KVuIUYE8ppa2NsATHEWOReqDhLpdCE9DM4i5wPA/xEE\nc++03m3NEO2qM4SALcDU/JLiF1UH0TS/crhqgBOB5wNxuTKzueyDuNgjA3EtLWIsBo5xFjl1A4Qg\noAvpYXIWOV8ETgFcqrNoPVufLWyqMwS5/wET80uKv1UdRNMCwuHqwOG6DLgdP7fHuyYrfQNCxPnz\nGlpEeROY6SxyVqkOohl0Ie0DziLnF8AU4EfVWbR9rc8RyaozBLGHgVPyS4qrVQfRtIBzuB4BzgD/\nTP9aGh21er3VOsUf59Yijhe4HzjXWeRsVR1G20NIGVS96kNawdyCBOAZ4HzVWbQ9Ylpl/dwnPImq\ncwSZBoz50G+rDqJpyjnsI4FXMeZP+4QEOXlU3pomk+kgX51Ti1jbgYucRc6vVAfR9qVHpH3IWeRs\ncBY5fwFcB+h5uUGiJVokegWVqnMEkWLgKF1Ea1onh2srRmvTP+CjqR7P2hMX6SJa84F3gEN0ER28\n9Ii0nxTMLTgKeA0YpTqLBs894f4uoZVDVecIAm8Cl+SXFDeqDqJpQclhPw2YC6QM9RRNQjROGZXX\n6BUiy3fBtAjTCtzkLHI+rTqI1jc9Iu0nziLnN8ARwAeqs2hQkUS96gyKeYDb8kuKz9F566p0AAAP\nJElEQVRFtKb1weF6HzgcozPCkNyenrpcF9HaMKwGJukiOjToQtqPnEXOGozNW+4A3IrjRLTNmcKr\nOoNCVcDJ+SXFeucrTRuIPVM9fssgp+ltsVi2fRUb47O51lrEeQo40lnk/EF1EG1g9NSOACmYWzAR\no29pgeoskWjmKu83V3/kPUp1DgU+Bq7ILykuVR1E00KSw34wxmv3gHpB/yQve0mp1aoLaW2waoDL\nnEXO/6oOog2OHpEOEGeRcwXGC/ED6NHpgFufI9JUZwiwWoy50D/RRbSmDYPD9QNGN4/bgba+Dv08\nNmalLqK1IfgQOFQX0aFJj0grUDC34HCMEQ69+C1AzB7Z8fKjHpMAs+osAfAOcE1+SXGZ6iCaFlYc\n9gOAfwKTu//IA55jRuVtaDWZJgQ+mBaiNgE3OIuc76kOog2dHpFWwFnkXAlMAn6H3l48IDxmYfWY\n2K46h59VAufnlxSfpYtoTfMDh6sEmAbcDOy1aPfPyUkLdRGtDVAr4AAO1EV06NMj0ooVzC04FGN0\n+nDVWcLdnCfdy1MaBzbPMcRIjFGy2/QOhZoWIA57DvAY8EuXSbimj8xzSyFSVcfSgt67wK+dRc5N\nqoNovqEL6SBQMLfADFwJ3AdE2lzegLnn356vD94qC1Xn8LFVwKz8kuIht+rSNG0YHPbpV2emX7Mw\nNuYC1VG0oLYBuN5Z5PxQdRDNt/TUjiDgLHJ6OvtFjgeeQE/38IuN2aoT+FQ9cANwpC6iNU0hh2v+\nwtiYi4DLgB2q42hBpwW4BzhIF9HhSY9IB6GCuQXjgccxelBrPnJ0iXflzW97Q30KjQReBm7R86A1\nLbgUzC2IA24BbgXiFMfR1JIYO8ne6ixyblacRfMjXUgHsYK5BScCfwQOVp0lHKTXyZ1/e9oTquPS\nEngDuD+/pNg5nBMJIe7A2HVzIjAG2CKlHN3LsScCP+88tgCIAo6TUn41iOslA/8HnArkY0xf2grM\nA+6XUm7r4Tl2jFaRZwOpGLdF/wrMkfpFSwtyBXMLsjGm6hUBVsVxtMDyAq8BD+pNVSKDLqSDXOf8\n6cuBO4GRiuOENinlqw97WgTEqo4yCLtelB/ILyle7YsTCiEkRvP/bzEK5Po+CukXgF8CPwACOIzB\nF9KnAO8DnwNfYOy0eDBwFcaucVOklGu6HG8DFmAswH0SKAZ+ApwF/E5K6RjotTVNpYK5BSOAG4Er\ngHjFcTT/cgMvAQ85i5xrVYfRAkcX0iGiYG6BFWN043ZgnOI4IevFx9w/RrnZX3WOAfAAr2AU0CW+\nPLEQYqyUcmPnn38A4vsopHOBKillmxDiFowuBYMtpEcDZinlhm6PnwD8D3hTSnlOl8dnAX8DrpdS\nPtnl8TcxpjuNl1JuGej1NU21grkFycBs4HogXXEczbfaMLomPeIscurXpQikC+kQ0zlC/UuMEeoD\nFMcJOX+e416cXbvvZgpBxIMxB/qB/JJiv49q9FdIdzt2SIV0P+esBiqllAd0eWzXaHSqlLK1y+PT\nga+B26SUj/ri+poWSAVzC2KASzDmUY9Vm0Ybpmbg78AfnEVOvcg0gumuHSGms8PHi8BBwHnA94oj\nhZRtaaJddYZeuIEXgAPyS4r/LxBFtGqd86ATgPIuj5kw5m+v7FpEd/oGY674pICF1DQfchY5Wzo7\nNE0AfoExvUoLLfXAw8BoZ5HzJl1EaxbVAbShcRY5vcDrBXML3sC43X0jcKzSUCFgQ46wHLUuqO7C\ndAD/An6fX1K8UXWYALsLYyHW3C6PJQMxsO8ulJ3TS6qA3MDE0zT/cBY5PcCrwKsFcwtOAH4DnICx\nDkELTvMxNk973VnkbOzvYC1y6EI6xDmLnBJjp6R3C+YWHIQxD+9i9MKWHq3LIUl1hk7rMQrIufkl\nxft0rQh3QohzMG5vf4zx5rTLroWgbb08tZXQWiyqaX1yFjk/Az4rmFswGrgQuAg9bS9YlGK8Tr/g\nLHKuVx1GC066kA4jziLnamBWwdyC2zFekK9Abz2+l82ZQuVopgtjFGpufknxIoU5fE4IEQPYuz3s\nklK29HDsT4F/AyuA87u1s2vu/B7Vy6WiuxyjaWGjs9fwg8CDBXMLJmIU1BcAmSpzRaBW4B2MD/if\ndd791bRe6UI6DDmLnPXA08DTBXMLjsAoqC9g30In4jTGiCQvVJuM3sSB4MHoTDEXeCe/pLj7vN9w\ncT57jywDXIox73u3zlZ4bwGrgZOklPXdnlOLsRPYPh94hBBRGD2o5/kmsqYFJ2eRcwWwomBuwS3A\niRhF9c/Qm7z403KM17D/OIuctarDaKFDF9Jhzlnk/Ba4pmBuwa+Bk4FzgDOI4KK6JYodcW1+L6RX\nYxTPL+WXFO/087WCwScYb/hd7dX3urOIfgcoAU6QUu7zZiWl9AohvgUOF0JESSm7TvE4CmMO6XKf\nJte0INU5l/pj4OOCuQXxGL3ULwKOB8wqs4UBN7AE47XrHb15ijZUupCOEM4iZxt75lLbMIqec4Az\nMRZ4RYxKO664Cr+cuhr4D/BCfknxCr9cIUhJKXcCvX5gEEKcBLwN/AgcL6Ws6eN0/wGmAldibMiy\ny68x3vxeHXZgTQsxnQvcXgRe7OxLfRxGQX08hERv/GCwDaNw/hhj2oZLcR4tDOg+0hGuc6OX4zGK\n6tOBDLWJ/G/2e555M36QM3xwqg5gGcZufV8AC/NLioO1vd5uQoiLgVGd/3kdYAMe7/zvLVLKF7sc\newjGHQwwittTMDYf2NT52JNSyj7fjIQQR2KseBcYGwpVdT9GSvlSl+NtwCLgUOAvGDsb/hRjNO4B\nKeVvB/q7alokKJhbkIfxOn5C5/dstYmCRhvGa8/HwMed64g0zad0IT1AQoj9gXswetzmYLTt2gp8\nCDzWOSI3kPPc0XmOicAYjMJldB/HH42xAOVojB66i4DbpZSrhvzL9KFgbsHBwMzOrxkQNF0ufObE\nb71LrvjEe8wQnuoFVrKncJ6fX1Lc5NNwASCE+Arj/9uezJNSHtvl2EvYd+5zV2OklJv7uV5/50BK\nuVfbLyFEEvAAcDbGfPYNwFPA36R+0dK0PhXMLTiQPYX1sUCi0kCB0wp8BywFPgW+dBY59eJkza90\nIT1AQojjMfreLsFoieMGCjAWVNUDh0kp+50wIISQQA1GI/6JQH0f2zMfA3yF0VP3r50PX4sxajxF\nSukc+m/Uv4K5Bbs2x5iJcRtxOmGw2GXsTrn+4Rc8+w3w8NXsKZy/yi8prvNfMk3TNN/q3A33AODg\nzq+DOr+PI7Q3ZWvBeH1egbFuYjnwg7PI6VaaSos4upAeJiHEucBrDHDbYiHEWCnlxs4/97k9sxDi\nG4wXwHwp5fbOx3IxbnUvkVKe5JvfYmA6p4EcAhzW+XVo51dIjXZY3bL1pcc8NrHvm0gTsAZj1PlL\n4Iv8kmL/zKbWNE1TqHO78nz2FNa7vkaqzNWDOozBq9XAD12+NurWdFow0IsNh29L5/cBLdjbVUT3\nRwixH8ZWyP/cVUR3Pn+7EOJ14FIhRJaUsmywgYfKWeTswPj0v3shXcHcAoExReWwLl/7Y7wYRwcq\n22B0WERbm5V50R2UYbw47/raml9SrD9ZapoW9pxFzhaMO6N7bVNeMLcgARgBpHf7SuvlMesQLu8G\nKjAWKJd1fu/6593fnUXOcG0ZqoUJPSI9SEKIaIxdA6OBA4FHMIrHQinl/EGeq9cRaSHEBcDLwBVS\nyme7/ewK4B/AaVLKD4bye/hbZ4GdgVFQj+ryfdefszBGsn05VaQNYyFbZbevCmAzxm6CG5xFzmof\nXlPTNC1iFcwtSMLYbdTb5Uv28d8SaO/clVfTQp4ekR68y9m7Jddm4KLBFtEDkNP5fXsPP9v1mMpd\n+vrU+SJZ3vm1rLfjOudhx2MU1QmdX7v+HIOxoYm721fXx9ow2s5VOoucDX76dTRN07QeOIucdRjT\nLzQtIulCevB2bSgRj7H99hkYt7d8Lbbze1sPP2vtdkzI6pzjVt/5pWmapmmaFjJ0IT1IUspSjIUP\nAO8IId4ElgkhYqWUDwkh7BgjqV1VSik9g7zUrpY9UT38LLrbMZqmaZoW0SKlTa0WXHQhPUxSyu+F\nECuBWcBDwJ+Bom6HjcGYAjIYOzq/9zR9Y9djPU370DRN07RIlIexGc3b7N2m9krgF0KIAbWpBX7P\nnja1fe6l0K1N7T2dD18LzBdC+L1NraaeLqR9IwZI6fzzo8BL3X4+lM4au+YVTwae7fazYzA+9UbU\nNtSapmma1hsp5efA590fF0J8jdGm9hKM9+j+jOvepraPY/8CtGM0HNjVpvY1jDa1jwMBbVOrBZ4u\npAeot1ZzQojjMHpvfgUgpVyD0Yt4WKSU64UQy4FzhRC/lVLu6LxeDnAu8EUgW99pmqZpWoiKmDa1\nWuDpQnrgnhZCZGPscLcFY57yROAXQANw80BOIoS4GKMFHBh9OG1CiLs7/3uLlPLFLoffgLExyHwh\nxK5OIddhbCQyoOtpmqZpWiTppU0tGHOlfWlS5/fFPfxsCXAZRp3w/+3dMYoTcRjG4fdv4RWsrLWz\nFLUwoFfQThvrPYC1XsITCDYeQIvdRkFB0MpaBG9g+1nMxsVRMfniZk18HgiBECaZ7kfIvPNPztTy\ndwjp1T1Ncj/JvUwBXJmC+kmmixg+rXicB0luzl57dPx8lOR7SFfVqzHGIsnj48fyIoY7VfW+dxoA\nsNfM1LI1QnpFVfUs03+sNj3OYs33v05ya9PPBYD/hJlatkZIAwB7w0wt23TurL8AAMBpqaoPSZYz\ntck0U/tl9rjYOLSZWvwiDQDsPTO1nIpRVWf9HQAANvKHmdqXSQ6raq1rjpY70r+7s+EY422SS0ku\nz2ZqPyZ5U1W31zsLdo2QBgB23hjjeaY7G/5qpvZrksUqt+2ezdQeJDmf6eYqyWymdoxxPdNM7eec\nLIUcJLmQ5IaFrf0npAGAnTfGuJtppvZKfpypfZE1ZmrHGIf5eaZ26Wi+vjXGuJZpovZqTmZqH1bV\nu/XPgl0jpAEAoMFqBwAANAhpAABoENIAANAgpAEAoEFIAwBAg5AGAIAGIQ0AAA1CGgAAGoQ0AAA0\nCGkAAGgQ0gAA0CCkAQCgQUgDAECDkAYAgAYhDQAADUIaAAAahDQAADQIaQAAaBDSAADQIKQBAKDh\nG9YIGVkhhFK4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8eb7dc7588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "included_layer_buckets = pandas.cut(\n",
    "    included_layers['num-charms'],\n",
    "    [0,1,2,10,20,1000]\n",
    ").value_counts(normalize=True)*100\n",
    "\n",
    "included_interfaces_buckets = pandas.cut(\n",
    "    included_interfaces['num-charms'],\n",
    "    [0,1,2,10,50]\n",
    ").value_counts(normalize=True)*100\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(12, 6))  # a new figure window\n",
    "ax = fig.add_subplot(1, 2, 1) \n",
    "patches, texts, autotexts = ax.pie(\n",
    "    included_layer_buckets,\n",
    "    labels=['1','2','3-10','11-20','>20'],\n",
    "    autopct='%1.f%%'\n",
    ")\n",
    "for t in texts + autotexts:\n",
    "    t.set_size(18)\n",
    "ax.axis('equal') \n",
    "ax.set_title(\"Charms per layer\", fontsize=22)\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 2) \n",
    "patches, texts, autotexts = ax.pie(\n",
    "    included_interfaces_buckets,\n",
    "    labels=['1','2','3-10','>10'],\n",
    "    autopct='%1.f%%'\n",
    ")\n",
    "for t in texts + autotexts:\n",
    "    t.set_size(18)\n",
    "ax.axis('equal') \n",
    "ax.set_title(\"Charms per interface layer\", fontsize=22)\n",
    "\n",
    "fig.savefig(\"layer-interface-usage.pdf\", bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reactive</th>\n",
       "      <th>include-layers</th>\n",
       "      <th>include-interfaces</th>\n",
       "      <th>downloads-day</th>\n",
       "      <th>downloads-week</th>\n",
       "      <th>downloads-month</th>\n",
       "      <th>downloads-total</th>\n",
       "      <th>shared-interfaces</th>\n",
       "      <th>shared-layers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name-normalized</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>percona-cluster</th>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>115</td>\n",
       "      <td>1030</td>\n",
       "      <td>4953</td>\n",
       "      <td>130100</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rabbitmq-server</th>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>102</td>\n",
       "      <td>999</td>\n",
       "      <td>4894</td>\n",
       "      <td>305092</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glance</th>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>107</td>\n",
       "      <td>988</td>\n",
       "      <td>4726</td>\n",
       "      <td>176272</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nova-cloud-controller</th>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>112</td>\n",
       "      <td>956</td>\n",
       "      <td>4499</td>\n",
       "      <td>192381</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keystone</th>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>99</td>\n",
       "      <td>944</td>\n",
       "      <td>4431</td>\n",
       "      <td>205312</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       reactive include-layers include-interfaces  \\\n",
       "name-normalized                                                     \n",
       "percona-cluster           False             []                 []   \n",
       "rabbitmq-server           False             []                 []   \n",
       "glance                    False             []                 []   \n",
       "nova-cloud-controller     False             []                 []   \n",
       "keystone                  False             []                 []   \n",
       "\n",
       "                       downloads-day  downloads-week  downloads-month  \\\n",
       "name-normalized                                                         \n",
       "percona-cluster                  115            1030             4953   \n",
       "rabbitmq-server                  102             999             4894   \n",
       "glance                           107             988             4726   \n",
       "nova-cloud-controller            112             956             4499   \n",
       "keystone                          99             944             4431   \n",
       "\n",
       "                       downloads-total shared-interfaces shared-layers  \n",
       "name-normalized                                                         \n",
       "percona-cluster                 130100                []            []  \n",
       "rabbitmq-server                 305092                []            []  \n",
       "glance                          176272                []            []  \n",
       "nova-cloud-controller           192381                []            []  \n",
       "keystone                        205312                []            []  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_charmstable['shared-interfaces'] = normalized_charmstable['include-interfaces'].apply(\n",
    "    lambda x: [\n",
    "        i_name for i_name in x\n",
    "        if included_interfaces.loc[i_name]['num-charms'] > 1\n",
    "    ])\n",
    "normalized_charmstable['shared-layers'] = normalized_charmstable['include-layers'].apply(\n",
    "    lambda x: [\n",
    "        i_name for i_name in x\n",
    "        if included_layers.loc[i_name]['num-charms'] > 1\n",
    "    ])\n",
    "\n",
    "normalized_charmstable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3ybJ0+nj5TKeLVu2tB/ByOBLrkD/qlPIkZ8KO58nB/sSpTPF9VGs7g2pWH+DV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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8eb56dbf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer_percents = pandas.cut(\n",
    "    normalized_charmstable.loc[\n",
    "        activecharms['reactive'] == True\n",
    "    ]['shared-layers'].apply(\n",
    "        lambda x: len(x)\n",
    "    ),\n",
    "    [-1,1,2,3,100],\n",
    ").value_counts(normalize=True)*100\n",
    "\n",
    "\n",
    "interface_percents = pandas.cut(\n",
    "    normalized_charmstable.loc[\n",
    "        activecharms['reactive'] == True\n",
    "    ]['shared-interfaces'].apply(\n",
    "        lambda x: len(x)\n",
    "    ),\n",
    "    [-1,0,1,2,3,100],\n",
    ").value_counts(normalize=True)*100\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(12, 6))  # a new figure window\n",
    "ax = fig.add_subplot(1, 2, 1) \n",
    "patches, texts, autotexts = ax.pie(\n",
    "    layer_percents,\n",
    "    labels=['0','1','2','>2'],\n",
    "    autopct='%1.f%%',\n",
    ")\n",
    "for t in texts + autotexts:\n",
    "    t.set_size(18)\n",
    "ax.axis('equal') \n",
    "ax.set_title(\"Reused layers per charm\", fontsize=22)\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 2) \n",
    "patches, texts, autotexts = ax.pie(\n",
    "    interface_percents,\n",
    "    labels=['0', '1','2','3','>3'],\n",
    "    autopct='%1.f%%',\n",
    ")\n",
    "for t in texts + autotexts:\n",
    "    t.set_size(18)\n",
    "ax.axis('equal') \n",
    "ax.set_title(\"Reused interfaces per charm\", fontsize=22)\n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "\n",
    "#ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "#             ax.get_xticklabels() + ax.get_yticklabels()\n",
    "\n",
    "\n",
    "fig.savefig(\"charm-layer-usage.pdf\", bbox_inches=\"tight\")"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}