bulge_classifications_gz4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>subject_id</th>\n",
       "      <th>user</th>\n",
       "      <th>created_at</th>\n",
       "      <th>lang</th>\n",
       "      <th>user_agent</th>\n",
       "      <th>sloan-0</th>\n",
       "      <th>sloan-1</th>\n",
       "      <th>sloan-2</th>\n",
       "      <th>sloan-3</th>\n",
       "      <th>sloan-4</th>\n",
       "      <th>sloan-5</th>\n",
       "      <th>sloan-6</th>\n",
       "      <th>sloan-7</th>\n",
       "      <th>sloan-8</th>\n",
       "      <th>sloan-9</th>\n",
       "      <th>sloan-10</th>\n",
       "      <th>sloan-11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>504e52a80454e21d0b00000b</td>\n",
       "      <td>504e468dc499611ea600cb73</td>\n",
       "      <td>Freethesouls</td>\n",
       "      <td>2012-09-10 20:50:48 UTC</td>\n",
       "      <td>en</td>\n",
       "      <td>unknown</td>\n",
       "      <td>a-0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>a-0</td>\n",
       "      <td>x-0</td>\n",
       "      <td>a-0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>504e52c10454e21d0b00000f</td>\n",
       "      <td>504e468dc499611ea600cb74</td>\n",
       "      <td>Freethesouls</td>\n",
       "      <td>2012-09-10 20:51:13 UTC</td>\n",
       "      <td>en</td>\n",
       "      <td>unknown</td>\n",
       "      <td>a-0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>a-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>a-2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>504e52c70454e21d0b000010</td>\n",
       "      <td>504e468ec499611ea600cb79</td>\n",
       "      <td>Freethesouls</td>\n",
       "      <td>2012-09-10 20:51:19 UTC</td>\n",
       "      <td>en</td>\n",
       "      <td>unknown</td>\n",
       "      <td>a-0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>a-0</td>\n",
       "      <td>x-0</td>\n",
       "      <td>a-0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>504e52dc0454e21d12000009</td>\n",
       "      <td>504e468ec499611ea600cb82</td>\n",
       "      <td>Freethesouls</td>\n",
       "      <td>2012-09-10 20:51:40 UTC</td>\n",
       "      <td>en</td>\n",
       "      <td>unknown</td>\n",
       "      <td>a-0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>a-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>a-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>504e5305ba40af6cc200000b</td>\n",
       "      <td>504e468fc499611ea600cb8d</td>\n",
       "      <td>Freethesouls</td>\n",
       "      <td>2012-09-10 20:52:21 UTC</td>\n",
       "      <td>en</td>\n",
       "      <td>unknown</td>\n",
       "      <td>a-0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>a-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>a-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id                subject_id          user  \\\n",
       "0  504e52a80454e21d0b00000b  504e468dc499611ea600cb73  Freethesouls   \n",
       "1  504e52c10454e21d0b00000f  504e468dc499611ea600cb74  Freethesouls   \n",
       "2  504e52c70454e21d0b000010  504e468ec499611ea600cb79  Freethesouls   \n",
       "3  504e52dc0454e21d12000009  504e468ec499611ea600cb82  Freethesouls   \n",
       "4  504e5305ba40af6cc200000b  504e468fc499611ea600cb8d  Freethesouls   \n",
       "\n",
       "                created_at lang user_agent sloan-0 sloan-1 sloan-2 sloan-3  \\\n",
       "0  2012-09-10 20:50:48 UTC   en    unknown     a-0     NaN     NaN     NaN   \n",
       "1  2012-09-10 20:51:13 UTC   en    unknown     a-0     NaN     NaN     NaN   \n",
       "2  2012-09-10 20:51:19 UTC   en    unknown     a-0     NaN     NaN     NaN   \n",
       "3  2012-09-10 20:51:40 UTC   en    unknown     a-0     NaN     NaN     NaN   \n",
       "4  2012-09-10 20:52:21 UTC   en    unknown     a-0     NaN     NaN     NaN   \n",
       "\n",
       "  sloan-4 sloan-5 sloan-6 sloan-7 sloan-8 sloan-9 sloan-10 sloan-11  \n",
       "0     NaN     a-0     x-0     a-0     NaN     NaN      NaN      NaN  \n",
       "1     NaN     a-1     NaN     a-2     NaN     NaN      NaN      NaN  \n",
       "2     NaN     a-0     x-0     a-0     NaN     NaN      NaN      NaN  \n",
       "3     NaN     a-1     NaN     a-1     NaN     NaN      NaN      NaN  \n",
       "4     NaN     a-1     NaN     a-1     NaN     NaN      NaN      NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifications_loc = '/data/galaxy_zoo/weekly_dumps/2017-10-15_galaxy_zoo_sloan_classifications.csv'\n",
    "df = pd.read_csv(classifications_loc)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a-2    1132394\n",
      "a-1     889576\n",
      "a-3     401078\n",
      "a-0     218635\n",
      "Name: sloan-4, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if sys.path[0] == '':\n"
     ]
    }
   ],
   "source": [
    "bar_answers = df['sloan-4']\n",
    "print(bar_answers.value_counts())\n",
    "bar_df = df[['subject_id', 'sloan-4']]\n",
    "mapping = {\n",
    "    'a-0': 'no_bulge',\n",
    "    'a-1': 'noticeable',\n",
    "    'a-2': 'obvious',\n",
    "    'a-3': 'dominant',\n",
    "    'NaN': np.nan,\n",
    "    'nan': np.nan\n",
    "}\n",
    "bar_df['bar_response'] = bar_df['sloan-4'].apply(lambda x: mapping[str(x)])\n",
    "bar_df = bar_df.dropna(subset=['bar_response'])\n",
    "del bar_df['sloan-4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>bar_response</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>8860322</th>\n",
       "      <td>504e4d58c499611ea60134cb</td>\n",
       "      <td>noticeable</td>\n",
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       "    <tr>\n",
       "      <th>4541345</th>\n",
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       "      <td>obvious</td>\n",
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       "    <tr>\n",
       "      <th>1560801</th>\n",
       "      <td>504eb3c1c499611ea6030e23</td>\n",
       "      <td>obvious</td>\n",
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       "    <tr>\n",
       "      <th>11030751</th>\n",
       "      <td>504ecbc5c499611ea6035089</td>\n",
       "      <td>no_bulge</td>\n",
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       "    <tr>\n",
       "      <th>7335656</th>\n",
       "      <td>504ebf94c499611ea6032fef</td>\n",
       "      <td>noticeable</td>\n",
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       "    <tr>\n",
       "      <th>6624490</th>\n",
       "      <td>504e4bcbc499611ea60121ec</td>\n",
       "      <td>obvious</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3041092</th>\n",
       "      <td>504e9232c499611ea602a582</td>\n",
       "      <td>obvious</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12618419</th>\n",
       "      <td>504f06e9c499611ea603d906</td>\n",
       "      <td>obvious</td>\n",
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       "    <tr>\n",
       "      <th>2963596</th>\n",
       "      <td>504eef66c499611ea603a59f</td>\n",
       "      <td>noticeable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10984328</th>\n",
       "      <td>504e57c9c499611ea6019315</td>\n",
       "      <td>no_bulge</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        subject_id bar_response\n",
       "8860322   504e4d58c499611ea60134cb   noticeable\n",
       "4541345   504f1e38c499611ea6040a3c      obvious\n",
       "1560801   504eb3c1c499611ea6030e23      obvious\n",
       "11030751  504ecbc5c499611ea6035089     no_bulge\n",
       "7335656   504ebf94c499611ea6032fef   noticeable\n",
       "6624490   504e4bcbc499611ea60121ec      obvious\n",
       "3041092   504e9232c499611ea602a582      obvious\n",
       "12618419  504f06e9c499611ea603d906      obvious\n",
       "2963596   504eef66c499611ea603a59f   noticeable\n",
       "10984328  504e57c9c499611ea6019315     no_bulge"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bar_df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th colspan=\"4\" halign=\"left\">bar_response</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count_if_no_bulge</th>\n",
       "      <th>count_if_noticeable</th>\n",
       "      <th>count_if_obvious</th>\n",
       "      <th>count_if_dominant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>504e468dc499611ea600cb6a</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>504e468dc499611ea600cb6b</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>504e468dc499611ea600cb6c</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>504e468dc499611ea600cb6d</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>504e468dc499611ea600cb6e</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "                 subject_id      bar_response                      \\\n",
       "                            count_if_no_bulge count_if_noticeable   \n",
       "0  504e468dc499611ea600cb6a                 0                   2   \n",
       "1  504e468dc499611ea600cb6b                 2                  18   \n",
       "2  504e468dc499611ea600cb6c                 1                   3   \n",
       "3  504e468dc499611ea600cb6d                 1                   0   \n",
       "4  504e468dc499611ea600cb6e                 0                   0   \n",
       "\n",
       "                                      \n",
       "  count_if_obvious count_if_dominant  \n",
       "0                3                 2  \n",
       "1                5                 0  \n",
       "2                4                 1  \n",
       "3                1                 0  \n",
       "4                3                 1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def count_responses(series):\n",
    "    counts = {\n",
    "        'no_bulge': 0,\n",
    "        'noticeable': 0,\n",
    "        'obvious': 0,\n",
    "        'dominant': 0\n",
    "    }\n",
    "    for value in list(series):\n",
    "        print(value)\n",
    "        counts[value] = counts[value] + 1\n",
    "        print(counts[value])\n",
    "        raise Exception\n",
    "    return counts\n",
    "\n",
    "def count_if_no_bulge(series):\n",
    "    return len(series[series == 'no_bulge'])\n",
    "def count_if_noticeable(series):\n",
    "    return len(series[series == 'noticeable'])\n",
    "def count_if_obvious(series):\n",
    "    return len(series[series == 'obvious'])\n",
    "def count_if_dominant(series):\n",
    "    return len(series[series == 'dominant'])\n",
    "\n",
    "response_df = bar_df.groupby('subject_id').agg([count_if_no_bulge, count_if_noticeable, count_if_obvious, count_if_dominant]).reset_index()\n",
    "display(response_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "response_df = response_df['bar_response']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113d33358>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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26tcfxtffoH79wczMZgm/Q9rMzDIOBzMzy/iX/ZxDE/0SIDOz2cZHDmZmlnE4mJlZxuFg\nZmYZh4OZmWUcDmZmlnE4mJlZxuFgZmYZh4OZmWUcDmZmlnE4mJlZxuFgZmYZh4OZmWUcDmZmlnE4\nmJlZxuFgZmYZh4OZmWUcDmZmlnE4mJlZpmE4SNos6YSkVwu1SyXtlHQ4PS9IdUl6RNKwpFckXVPY\nZm0af1jS2kL9WkkH0jaPSFKrmzQzs8lp5sjhCaB/XG0TsCsieoBdaRngZqAnPQaAx6AeJsC9wHXA\nCuDeM4GSxgwUthv/tczM7BxrGA4R8dfAyXHlNcCW9HoLcGuh/mTUvQjMl7QQWA3sjIiTEXEK2An0\np3WfiIgfRUQATxb2ZWZmbTLVaw5dEXEcID1/OtUXAUcL40ZS7Wz1kZK6mZm10dwW76/sekFMoV6+\nc2mA+ikourq6qNVqDSc0Ojqajdu4bKzhdrPF+LmX9dPpqtZT1fqB6vVUtX6g9T1NNRzelrQwIo6n\nU0MnUn0EWFIYtxg4lup94+q1VF9cMr5URAwCgwC9vb3R19c30dD/p1arMX7cuk3PN9xutjhyZ99H\nlsv66XRV66lq/UD1eqpaP9D6nqZ6WmkrcOaOo7XAc4X6XemupZXA6XTaaQewStKCdCF6FbAjrXtP\n0sp0l9JdhX2ZmVmbNDxykPQd6v/rv0zSCPW7jh4Cnpa0HvgZcFsavh24BRgGfgF8CSAiTkq6H9iT\nxt0XEWcucn+Z+h1RFwPfTw8zM2ujhuEQEXdMsOqmkrEBbJhgP5uBzSX1vcBVjeZhZmbnjt8hbWZm\nGYeDmZllHA5mZpZxOJiZWcbhYGZmGYeDmZllHA5mZpZxOJiZWcbhYGZmGYeDmZllHA5mZpZxOJiZ\nWcbhYGZmmVb/Jjhrse5xv5ho47Ix1m16niMP/UabZmRm5wMfOZiZWcbhYGZmGYeDmZllHA5mZpZx\nOJiZWcbhYGZmGYeDmZllpvU+B0lHgPeAD4GxiOiVdCnwFNANHAH+RUSckiTgT4FbgF8A6yLix2k/\na4F/m3b7QERsmc68zgfj3/9wRqve/zDT+zez2a0VRw43RMTyiOhNy5uAXRHRA+xKywA3Az3pMQA8\nBpDC5F7gOmAFcK+kBS2Yl5mZTdFMnFZaA5z5n/8W4NZC/cmoexGYL2khsBrYGREnI+IUsBPon4F5\nmZlZkxQRU99YehM4BQTwZxExKOndiJhfGHMqIhZI2gY8FBE/TPVdwNeAPuDjEfFAqv874O8j4o9K\nvt4A9aMOurq6rh0aGmo4x9HRUebNm/eR2oG3Tk+l3Vmh62J4++8nXr9s0Sdb8nUm+jNq1f6Lyv6O\nOlnV+oHq9VS1fqD5nm644YZ9hTM9E5ruZytdHxHHJH0a2Cnpp2cZq5JanKWeFyMGgUGA3t7e6Ovr\nazjBWq3G+HHrJjif3gk2Lhvjjw9M/Nd25M6+lnydif6MWrX/orK/o05WtX6gej1VrR9ofU/TOq0U\nEcfS8wngWerXDN5Op4tIzyfS8BFgSWHzxcCxs9TNzKxNpnzkIOkS4GMR8V56vQq4D9gKrAUeSs/P\npU22AndLGqJ+8fl0RByXtAP4D4WL0KuAe6Y6r/Od7zIys1aYzmmlLuDZ+h2qzAX+MiL+StIe4GlJ\n64GfAbel8dup38Y6TP1W1i8BRMRJSfcDe9K4+yLi5DTmZWZm0zTlcIiIN4DPldT/J3BTST2ADRPs\nazOweapzMTOz1vI7pM3MLONwMDOzjH9N6HnCF6rNbDJ85GBmZhkfOVhHmOjIB3z0YzYTHA7nubP9\n0DWz85dPK5mZWcbhYGZmGYeDmZllfM3BrMUOvHW69FNtfeH8//Ot1bOfw8Emxf+oO5P/3myyHA7W\nEv7hY1YtDgebUQ4Ns87kcLC2KIbGxmVjHf3b+cyqyOFgHc9HJ2at53CwymrVu78dMnY+cjiYNTDZ\nkNm4rDX7mci5CKvxcz1z6m+yX3uyPU92fCv/LHwE+lEOB7MO08rPw5rpH94zbSrzOV9/2E+Ww8HM\nzivdm573TRBN8MdnmJlZZtaEg6R+Sa9LGpa0qd3zMTM7n82KcJA0B3gUuBlYCtwhaWl7Z2Vmdv6a\nLdccVgDDEfEGgKQhYA3wWltnZWbnvfP1LqbZEg6LgKOF5RHgujbNxcysoU66NXkqFBHtngOSbgNW\nR8TvpOUvAisi4vfGjRsABtLirwGvN7H7y4B3WjjddqtaP1C9nqrWD1Svp6r1A8339A8j4vJGg2bL\nkcMIsKSwvBg4Nn5QRAwCg5PZsaS9EdE7venNHlXrB6rXU9X6ger1VLV+oPU9zYoL0sAeoEfSFZIu\nBG4HtrZ5TmZm561ZceQQEWOS7gZ2AHOAzRFxsM3TMjM7b82KcACIiO3A9hnY9aROQ3WAqvUD1eup\nav1A9XqqWj/Q4p5mxQVpMzObXWbLNQczM5tFKhsOVfg4DkmbJZ2Q9GqhdqmknZIOp+cF7ZzjZEha\nIukFSYckHZT0lVTv5J4+LuklST9JPX091a+QtDv19FS60aJjSJoj6WVJ29Jyp/dzRNIBSfsl7U21\nTv6+my/pGUk/Tf+efr3V/VQyHCr0cRxPAP3japuAXRHRA+xKy51iDNgYEVcCK4EN6e+lk3v6ALgx\nIj4HLAf6Ja0EvgE8nHo6Baxv4xyn4ivAocJyp/cDcENELC/c7tnJ33d/CvxVRPwj4HPU/65a209E\nVO4B/Dqwo7B8D3BPu+c1xV66gVcLy68DC9PrhcDr7Z7jNHp7DvhnVekJ+BXgx9Tf3f8OMDfVP/L9\nONsf1N9ntAu4EdgGqJP7SXM+Alw2rtaR33fAJ4A3SdeMZ6qfSh45UP5xHIvaNJdW64qI4wDp+dNt\nns+USOoGrgZ20+E9pVMw+4ETwE7gb4B3I2IsDem0778/Af418H/S8qfo7H4AAvhvkvalT1qAzv2+\n+1XgfwB/kU79/UdJl9DifqoaDiqp+basWULSPOC7wFcj4uftns90RcSHEbGc+v+4VwBXlg07t7Oa\nGkn/HDgREfuK5ZKhHdFPwfURcQ31U80bJP3Tdk9oGuYC1wCPRcTVwPvMwCmxqoZDUx/H0aHelrQQ\nID2faPN8JkXSBdSD4dsR8b1U7uiezoiId4Ea9esp8yWdeR9RJ33/XQ/8pqQjwBD1U0t/Quf2A0BE\nHEvPJ4BnqYd4p37fjQAjEbE7LT9DPSxa2k9Vw6HKH8exFVibXq+lft6+I0gS8DhwKCK+WVjVyT1d\nLml+en0x8HnqFwdfAL6QhnVMTxFxT0Qsjohu6v9ufhARd9Kh/QBIukTSPzjzGlgFvEqHft9FxN8B\nRyX9WirdRP3XG7S2n3ZfXJnBiza3AP+d+vnff9Pu+Uyxh+8Ax4H/Tf1/C+upn//dBRxOz5e2e56T\n6OefUD8d8QqwPz1u6fCe/jHwcurpVeDfp/qvAi8Bw8B/AS5q91yn0FsfsK3T+0lz/0l6HDzz86DD\nv++WA3vT991/BRa0uh+/Q9rMzDJVPa1kZmbT4HAwM7OMw8HMzDIOBzMzyzgczMws43AwM7OMw8HM\nzDIOBzMzy/xfczCwcMcq0b4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11251f048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "response_df['total_responses'] = response_df['count_if_no_bulge'] + response_df['count_if_noticeable'] + response_df['count_if_obvious'] + response_df['count_if_dominant']\n",
    "response_df['total_responses'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "response_df['no_bulge_frac'] = response_df['count_if_no_bulge'] / response_df['total_responses']\n",
    "response_df['noticeable_frac'] = response_df['count_if_noticeable'] / response_df['total_responses']\n",
    "response_df['obvious_frac'] = response_df['count_if_obvious'] / response_df['total_responses']\n",
    "response_df['dominant_frac'] = response_df['count_if_dominant'] / response_df['total_responses']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "responses = response_df[response_df['total_responses'] > 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119280f28>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1195432e8>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119676da0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11974e0f0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119d77048>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x114385b38>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114349dd8>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x114344128>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x115466080>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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89nuBk8pDkiRJkiSpNZpe7iRJkiRJkqQBanQmTUT8Fsjx2jNz674lkiRJkiRJ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\nzMzMzMysA/4HVa0E2DWfIb8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118c6a978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=4, figsize=(16, 4), sharex=True, sharey=True)\n",
    "responses['no_bulge_frac'].hist(ax=axes[0])\n",
    "axes[0].set_xlabel('no bulge vote fraction')\n",
    "responses['noticeable_frac'].hist(ax=axes[1])\n",
    "axes[1].set_xlabel('noticeable vote fraction')\n",
    "responses['obvious_frac'].hist(ax=axes[2])\n",
    "axes[2].set_xlabel('obvious bulge vote fraction')\n",
    "responses['dominant_frac'].hist(ax=axes[3])\n",
    "axes[3].set_xlabel('dominant bulge vote fraction')\n",
    "axes[0].set_ylabel('num of galaxies')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def compare_remaining_votes(df, col_baseline, col_a, col_b, col_c, ax):\n",
    "    \n",
    "    color={\n",
    "        'count_if_no_bulge': 'b',\n",
    "        'count_if_noticeable': 'k',\n",
    "        'count_if_obvious': 'r', \n",
    "        'count_if_dominant': 'g'\n",
    "    }\n",
    "    vote_frac_bins = np.linspace(start=0, stop=30, num=30)\n",
    "    mean_col_a = []\n",
    "    mean_col_b = []\n",
    "    mean_col_c = []\n",
    "    df['bin_index'] = np.digitize(df[col_baseline], bins=vote_frac_bins)\n",
    "    for bin_index in range(len(vote_frac_bins)):\n",
    "        selected = df[df['bin_index'] == bin_index]\n",
    "        mean_col_a.append(selected[col_a].mean())\n",
    "        mean_col_b.append(selected[col_b].mean())\n",
    "        mean_col_c.append(selected[col_c].mean())\n",
    "    ax.plot(vote_frac_bins, mean_col_a, color[col_a])\n",
    "    ax.plot(vote_frac_bins, mean_col_b, color[col_b])\n",
    "    ax.plot(vote_frac_bins, mean_col_c, color[col_c])\n",
    "    ax.set_xlabel('{} votes'.format(col_baseline.replace('count_if_', '')))\n",
    "    ax.set_ylabel('other votes')\n",
    "    ax.legend([col_a.replace('count_if_', ''), col_b.replace('count_if_', ''), col_c.replace('count_if_', '')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  del sys.path[0]\n",
      "/data/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:2909: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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5xm1ZuVLE01ME9LMvD8VDcXuuXBF57DGxrUUqVBDZti33/fboofuzWvH4++eZ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\nZmYVxgU2ZmZmZmZmZmYV5v8BDUbIdGMx9iQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119532a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_context('talk')\n",
    "fig, axes = plt.subplots(ncols=4, figsize=(16, 4))\n",
    "compare_remaining_votes(responses, 'count_if_no_bulge', 'count_if_noticeable', 'count_if_obvious', 'count_if_dominant', axes[0])\n",
    "compare_remaining_votes(responses, 'count_if_noticeable', 'count_if_no_bulge', 'count_if_obvious', 'count_if_dominant', axes[1])\n",
    "compare_remaining_votes(responses, 'count_if_obvious', 'count_if_no_bulge', 'count_if_noticeable', 'count_if_dominant', axes[2])\n",
    "compare_remaining_votes(responses, 'count_if_dominant', 'count_if_no_bulge', 'count_if_noticeable', 'count_if_obvious', axes[3])\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def compare_remaining_fractions(df, col_baseline, col_a, col_b, col_c, ax):\n",
    "    \n",
    "    # define line colors for plot\n",
    "    color={\n",
    "        'count_if_no_bulge': 'b',\n",
    "        'count_if_noticeable': 'k',\n",
    "        'count_if_obvious': 'r', \n",
    "        'count_if_dominant': 'g'\n",
    "    }\n",
    "    \n",
    "    # 20 vote fraction bins from 0 to 1\n",
    "    vote_frac_bins = np.linspace(start=0., stop=.8, num=20)\n",
    "    baseline_vote_fraction = df[col_baseline] / (df[col_baseline] + df[col_a] + df[col_b])\n",
    "\n",
    "    # for every row, work out which vote fraction bin it should be in\n",
    "    bin_indices = np.digitize(baseline_vote_fraction, bins=vote_frac_bins)\n",
    "    bin_indices = bin_indices - 1 # should be bin 0 through bin 19, not bin 1 through 20\n",
    "\n",
    "    mean_col_a_votes = np.zeros_like(vote_frac_bins)\n",
    "    mean_col_b_votes = np.zeros_like(vote_frac_bins)\n",
    "    mean_col_c_votes = np.zeros_like(vote_frac_bins)\n",
    "    # for all the rows in each vote fraction bin, what's the mean number of votes?\n",
    "    for bin_index in range(20):\n",
    "        selected = df[bin_indices == bin_index]  # values with that baseline vote fraction\n",
    "        mean_col_a_votes[bin_index] = selected[col_a].mean()\n",
    "        mean_col_b_votes[bin_index] = selected[col_b].mean() \n",
    "        mean_col_c_votes[bin_index] = selected[col_c].mean() \n",
    "\n",
    "    total_other_votes = mean_col_a_votes + mean_col_b_votes + mean_col_c_votes\n",
    "    ax.plot(vote_frac_bins, mean_col_a_votes/total_other_votes, color[col_a])\n",
    "    ax.plot(vote_frac_bins, mean_col_b_votes/total_other_votes, color[col_b])\n",
    "    ax.plot(vote_frac_bins, mean_col_c_votes/total_other_votes, color[col_c])\n",
    "    ax.set_xlabel('{} vote fraction'.format(col_baseline.replace('count_if_', '')))\n",
    "    ax.set_ylabel('fraction of other votes')\n",
    "    ax.legend([col_a.replace('count_if_', ''), col_b.replace('count_if_', ''), col_c.replace('count_if_', '')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Rw2b8+PHuz/PcuXNL69atZerUqXL69GmnQ8syiYmJ8t5777nn1itQoIDMnDnT6bCClq/n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R0o0Rv7M3Om+XkEM9JaZvR1DxItibSb3Y5dF3ZRpUQV9j6xl5JFHRjB66GI3mSPu+mNkHdWrlzJ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\nASMjdS7xwsZ+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11422c6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_context('talk')\n",
    "fig, axes = plt.subplots(ncols=4, figsize=(16, 4))\n",
    "compare_remaining_fractions(responses, 'count_if_no_bulge', 'count_if_noticeable', 'count_if_obvious', 'count_if_dominant', axes[0])\n",
    "compare_remaining_fractions(responses, 'count_if_noticeable', 'count_if_no_bulge', 'count_if_obvious', 'count_if_dominant', axes[1])\n",
    "compare_remaining_fractions(responses, 'count_if_obvious', 'count_if_no_bulge', 'count_if_noticeable', 'count_if_dominant', axes[2])\n",
    "compare_remaining_fractions(responses, 'count_if_dominant', 'count_if_no_bulge', 'count_if_noticeable', 'count_if_obvious', axes[3])\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(ncols=3, figsize=(12, 4), sharex=True)\n",
    "_ = axes[0].hist2d(responses['no_bulge_frac'], responses['obvious_frac'])\n",
    "_ = axes[1].hist2d(responses['no_bulge_frac'], responses['dominant_frac'])\n",
    "_ = axes[2].hist2d(responses['obvious_frac'], responses['dominant_frac'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}