03-choose-data-ids.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we will process the `overlap` results and choose the data IDs that we will be using in our experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the `overlap` results.\n",
    "\n",
    "See: https://github.com/kchristidis/overlap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_overlap = pd.read_csv('02-segments-overlap.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>overlap_length</th>\n",
       "      <th>overlap_start</th>\n",
       "      <th>overlap_end</th>\n",
       "      <th>segment_count</th>\n",
       "      <th>segment_list</th>\n",
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       "      <th>0</th>\n",
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       "      <td>139</td>\n",
       "      <td>1103,1153,1169,1185,1192,1283,1464,1508,1601,1...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25466400</td>\n",
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       "      <td>1384560000</td>\n",
       "      <td>139</td>\n",
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       "      <th>2</th>\n",
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       "      <td>139</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>153120180</td>\n",
       "      <td>1359093600</td>\n",
       "      <td>1512213780</td>\n",
       "      <td>36</td>\n",
       "      <td>1103,1185,1642,1714,2129,2470,2980,3036,3527,3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>134503200</td>\n",
       "      <td>1359093600</td>\n",
       "      <td>1493596800</td>\n",
       "      <td>113</td>\n",
       "      <td>1103,1169,1185,1192,1642,1697,171,1714,1718,17...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   overlap_length  overlap_start  overlap_end  segment_count  \\\n",
       "0        26956800     1359093600   1386050400            139   \n",
       "1        25466400     1359093600   1384560000            139   \n",
       "2        22788000     1359093600   1381881600            139   \n",
       "3       153120180     1359093600   1512213780             36   \n",
       "4       134503200     1359093600   1493596800            113   \n",
       "\n",
       "                                        segment_list  \n",
       "0  1103,1153,1169,1185,1192,1283,1464,1508,1601,1...  \n",
       "1  1103,1153,1169,1185,1192,1283,1464,1508,1601,1...  \n",
       "2  1103,1153,1169,1185,1192,1283,1464,1508,1601,1...  \n",
       "3  1103,1185,1642,1714,2129,2470,2980,3036,3527,3...  \n",
       "4  1103,1169,1185,1192,1642,1697,171,1714,1718,17...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_overlap.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to identify *significant* overlaps.\n",
    "\n",
    "We define significance in terms of `overlap_length` and `segment_count`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert `overlap_length` from seconds to years.\n",
    "\n",
    "See:\n",
    "https://stackoverflow.com/a/12605055/2363529\n",
    "https://stackoverflow.com/a/38900352/2363529"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_overlap['overlap_length'] = df_overlap['overlap_length'].apply(lambda x: x/(60*60*8760))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_overlap.rename(columns={'overlap_length': 'overlap_length_yrs'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>overlap_length_yrs</th>\n",
       "      <th>overlap_start</th>\n",
       "      <th>overlap_end</th>\n",
       "      <th>segment_count</th>\n",
       "      <th>segment_list</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.854795</td>\n",
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       "      <td>139</td>\n",
       "      <td>1103,1153,1169,1185,1192,1283,1464,1508,1601,1...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.807534</td>\n",
       "      <td>1359093600</td>\n",
       "      <td>1384560000</td>\n",
       "      <td>139</td>\n",
       "      <td>1103,1153,1169,1185,1192,1283,1464,1508,1601,1...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.722603</td>\n",
       "      <td>1359093600</td>\n",
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       "      <td>139</td>\n",
       "      <td>1103,1153,1169,1185,1192,1283,1464,1508,1601,1...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.855409</td>\n",
       "      <td>1359093600</td>\n",
       "      <td>1512213780</td>\n",
       "      <td>36</td>\n",
       "      <td>1103,1185,1642,1714,2129,2470,2980,3036,3527,3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.265068</td>\n",
       "      <td>1359093600</td>\n",
       "      <td>1493596800</td>\n",
       "      <td>113</td>\n",
       "      <td>1103,1169,1185,1192,1642,1697,171,1714,1718,17...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   overlap_length_yrs  overlap_start  overlap_end  segment_count  \\\n",
       "0            0.854795     1359093600   1386050400            139   \n",
       "1            0.807534     1359093600   1384560000            139   \n",
       "2            0.722603     1359093600   1381881600            139   \n",
       "3            4.855409     1359093600   1512213780             36   \n",
       "4            4.265068     1359093600   1493596800            113   \n",
       "\n",
       "                                        segment_list  \n",
       "0  1103,1153,1169,1185,1192,1283,1464,1508,1601,1...  \n",
       "1  1103,1153,1169,1185,1192,1283,1464,1508,1601,1...  \n",
       "2  1103,1153,1169,1185,1192,1283,1464,1508,1601,1...  \n",
       "3  1103,1185,1642,1714,2129,2470,2980,3036,3527,3...  \n",
       "4  1103,1169,1185,1192,1642,1697,171,1714,1718,17...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_overlap.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can we identify the significant overlaps?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>overlap_length_yrs</th>\n",
       "      <th>overlap_start</th>\n",
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       "      <th>11210</th>\n",
       "      <td>0.040980</td>\n",
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       "      <td>1427000340</td>\n",
       "      <td>207</td>\n",
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       "      <th>25852</th>\n",
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       "      <td>206</td>\n",
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       "      <th>21408</th>\n",
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       "      <td>1425708000</td>\n",
       "      <td>206</td>\n",
       "      <td>1103,114,1153,1169,1185,1192,1283,1415,1508,16...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       overlap_length_yrs  overlap_start  overlap_end  segment_count  \\\n",
       "11210            0.040980     1425708000   1427000340            207   \n",
       "11188            0.066779     1425708000   1427813940            206   \n",
       "21465            0.046459     1425535200   1427000340            206   \n",
       "25852            0.025799     1427000340   1427813940            206   \n",
       "21408            0.005479     1425535200   1425708000            206   \n",
       "\n",
       "                                            segment_list  \n",
       "11210  1103,114,1153,1169,1185,1192,1283,1415,1508,16...  \n",
       "11188  1103,114,1169,1185,1192,1283,1415,1508,1642,16...  \n",
       "21465  1103,114,1153,1169,1185,1192,1283,1415,1508,16...  \n",
       "25852  1103,114,1169,1185,1192,1283,1415,1508,1642,16...  \n",
       "21408  1103,114,1153,1169,1185,1192,1283,1415,1508,16...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_overlap.sort_values(by=['segment_count', 'overlap_length_yrs'], ascending=[False, False]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This doesn't get us anywhere useful.\n",
    "\n",
    "We need to filter.\n",
    "\n",
    "At a minimum, we would like a 2-year overlap consisting of at least 100 segments. (This is an arbitrary choice.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "choose_duration_min = df_overlap['overlap_length_yrs'] >= 2\n",
    "choose_duration_max = df_overlap['overlap_length_yrs'] <= 2.1\n",
    "choose_count_min = df_overlap['segment_count'] >= 100\n",
    "choose_count_max = df_overlap['segment_count'] <= 110"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filtered = df_overlap[choose_duration_min & choose_duration_max & choose_count_min & choose_count_max]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>overlap_length_yrs</th>\n",
       "      <th>overlap_start</th>\n",
       "      <th>overlap_end</th>\n",
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       "      <th>1431</th>\n",
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       "      <td>1414994400</td>\n",
       "      <td>100</td>\n",
       "      <td>1103,1169,1185,1192,1283,1642,1697,171,1714,17...</td>\n",
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       "      <th>1537</th>\n",
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       "      <td>1348790400</td>\n",
       "      <td>1413935940</td>\n",
       "      <td>101</td>\n",
       "      <td>1103,1169,1185,1192,1283,1601,1642,1697,171,17...</td>\n",
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       "      <th>1615</th>\n",
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       "      <td>1348790400</td>\n",
       "      <td>1414454340</td>\n",
       "      <td>101</td>\n",
       "      <td>1103,1169,1185,1192,1283,1601,1642,1697,171,17...</td>\n",
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       "      <th>4146</th>\n",
       "      <td>2.019863</td>\n",
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       "      <td>1414994400</td>\n",
       "      <td>108</td>\n",
       "      <td>1103,1169,1185,1192,1283,1642,1697,171,1714,17...</td>\n",
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       "    <tr>\n",
       "      <th>4315</th>\n",
       "      <td>2.002738</td>\n",
       "      <td>1351296000</td>\n",
       "      <td>1414454340</td>\n",
       "      <td>109</td>\n",
       "      <td>1103,1169,1185,1192,1283,1601,1642,1697,171,17...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      overlap_length_yrs  overlap_start  overlap_end  segment_count  \\\n",
       "1431            2.099315     1348790400   1414994400            100   \n",
       "1537            2.065752     1348790400   1413935940            101   \n",
       "1615            2.082190     1348790400   1414454340            101   \n",
       "4146            2.019863     1351296000   1414994400            108   \n",
       "4315            2.002738     1351296000   1414454340            109   \n",
       "\n",
       "                                           segment_list  \n",
       "1431  1103,1169,1185,1192,1283,1642,1697,171,1714,17...  \n",
       "1537  1103,1169,1185,1192,1283,1601,1642,1697,171,17...  \n",
       "1615  1103,1169,1185,1192,1283,1601,1642,1697,171,17...  \n",
       "4146  1103,1169,1185,1192,1283,1642,1697,171,1714,17...  \n",
       "4315  1103,1169,1185,1192,1283,1601,1642,1697,171,17...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we're getting somewhere.\n",
    "\n",
    "Sort."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2.005478</td>\n",
       "      <td>1350691200</td>\n",
       "      <td>1413935940</td>\n",
       "      <td>104</td>\n",
       "      <td>1103,1169,1185,1192,1283,1601,1642,1697,171,17...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24053</th>\n",
       "      <td>2.005478</td>\n",
       "      <td>1351209600</td>\n",
       "      <td>1414454340</td>\n",
       "      <td>108</td>\n",
       "      <td>1103,1169,1185,1192,1283,1601,1642,1697,171,17...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27012</th>\n",
       "      <td>2.006579</td>\n",
       "      <td>1448927940</td>\n",
       "      <td>1512207420</td>\n",
       "      <td>110</td>\n",
       "      <td>1103,1169,1185,1415,1642,1697,1714,1718,1792,2...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       overlap_length_yrs  overlap_start  overlap_end  segment_count  \\\n",
       "26851            2.002738     1350777600   1413935940            105   \n",
       "4315             2.002738     1351296000   1414454340            109   \n",
       "10368            2.005478     1350691200   1413935940            104   \n",
       "24053            2.005478     1351209600   1414454340            108   \n",
       "27012            2.006579     1448927940   1512207420            110   \n",
       "\n",
       "                                            segment_list  \n",
       "26851  1103,1169,1185,1192,1283,1601,1642,1697,171,17...  \n",
       "4315   1103,1169,1185,1192,1283,1601,1642,1697,171,17...  \n",
       "10368  1103,1169,1185,1192,1283,1601,1642,1697,171,17...  \n",
       "24053  1103,1169,1185,1192,1283,1601,1642,1697,171,17...  \n",
       "27012  1103,1169,1185,1415,1642,1697,1714,1718,1792,2...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.sort_values(by=['overlap_length_yrs', 'segment_count'], ascending=[True, True]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Any row in here will do for our work."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Persist the dataframe to disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filtered.to_csv('03-segments-overlap-filtered.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before proceeding, we need to confirm graphically that there is indeed overlap.\n",
    "\n",
    "Pick the last row of the dataframe.\n",
    "\n",
    "See: https://stackoverflow.com/a/36542308/2363529"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "chosen_record = df_filtered.iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "overlap_length_yrs                                              2.05595\n",
       "overlap_start                                                1351086720\n",
       "overlap_end                                                  1415923140\n",
       "segment_count                                                       106\n",
       "segment_list          1103,1169,1185,1192,1283,1642,1697,171,1714,17...\n",
       "Name: 32545, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chosen_record"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each of the data IDs in the `segment_list`, extract their `egauge_min_time` and `egauge_max_time` from the dataframe of candidate records (refer to the first notebook in the series)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1103,1169,1185,1192,1283,1642,1697,171,1714,1718,1792,187,2072,2094,2129,2199,2233,2335,2337,2470,2755,2814,2818,2864,2925,2945,2965,2980,2986,3036,3134,3192,3224,3456,3527,3544,3635,370,3719,3723,3918,3935,3967,4154,4193,4302,4357,4373,4447,4526,4767,4957,5129,5218,5246,5275,5357,5403,5439,545,5615,5738,5785,5796,5817,585,5892,5972,6121,6139,6266,6423,6643,6871,6990,7016,7108,7429,744,7510,7627,7731,7739,7767,7800,7850,7863,7989,8084,8155,8419,861,8626,8669,8829,8890,890,8967,9019,9052,9121,9160,9631,9729,9737,9982'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chosen_record.segment_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(chosen_record.segment_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "chosen_list = chosen_record.segment_list.split(\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(chosen_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_candidate = pd.read_csv('01-segments-candidate.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataid</th>\n",
       "      <th>city</th>\n",
       "      <th>building_type</th>\n",
       "      <th>total_square_footage</th>\n",
       "      <th>house_construction_year</th>\n",
       "      <th>egauge_min_time</th>\n",
       "      <th>egauge_max_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26</td>\n",
       "      <td>Austin</td>\n",
       "      <td>Single-Family Home</td>\n",
       "      <td>2075.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2012-11-03 00:00:00</td>\n",
       "      <td>2017-12-02 09:59:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>77</td>\n",
       "      <td>Austin</td>\n",
       "      <td>Single-Family Home</td>\n",
       "      <td>2669.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2014-06-06 05:00:00</td>\n",
       "      <td>2017-07-23 08:34:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>93</td>\n",
       "      <td>Austin</td>\n",
       "      <td>Single-Family Home</td>\n",
       "      <td>2934.0</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>2012-12-09 00:00:00</td>\n",
       "      <td>2017-12-02 08:59:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>114</td>\n",
       "      <td>Austin</td>\n",
       "      <td>Single-Family Home</td>\n",
       "      <td>1842.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2013-10-16 00:00:00</td>\n",
       "      <td>2017-12-02 08:31:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>171</td>\n",
       "      <td>Austin</td>\n",
       "      <td>Single-Family Home</td>\n",
       "      <td>2376.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2012-05-03 00:00:00</td>\n",
       "      <td>2017-12-02 08:59:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dataid    city       building_type  total_square_footage  \\\n",
       "0      26  Austin  Single-Family Home                2075.0   \n",
       "1      77  Austin  Single-Family Home                2669.0   \n",
       "2      93  Austin  Single-Family Home                2934.0   \n",
       "3     114  Austin  Single-Family Home                1842.0   \n",
       "4     171  Austin  Single-Family Home                2376.0   \n",
       "\n",
       "   house_construction_year      egauge_min_time      egauge_max_time  \n",
       "0                   2008.0  2012-11-03 00:00:00  2017-12-02 09:59:00  \n",
       "1                   2009.0  2014-06-06 05:00:00  2017-07-23 08:34:00  \n",
       "2                   1993.0  2012-12-09 00:00:00  2017-12-02 08:59:00  \n",
       "3                   2008.0  2013-10-16 00:00:00  2017-12-02 08:31:00  \n",
       "4                   2008.0  2012-05-03 00:00:00  2017-12-02 08:59:00  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_candidate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_candidate_trimmed = df_candidate[['dataid', 'egauge_min_time', 'egauge_max_time']].set_index('dataid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>egauge_min_time</th>\n",
       "      <th>egauge_max_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2012-11-03 00:00:00</td>\n",
       "      <td>2017-12-02 09:59:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>2014-06-06 05:00:00</td>\n",
       "      <td>2017-07-23 08:34:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>2012-12-09 00:00:00</td>\n",
       "      <td>2017-12-02 08:59:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>2013-10-16 00:00:00</td>\n",
       "      <td>2017-12-02 08:31:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>2012-05-03 00:00:00</td>\n",
       "      <td>2017-12-02 08:59:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            egauge_min_time      egauge_max_time\n",
       "dataid                                          \n",
       "26      2012-11-03 00:00:00  2017-12-02 09:59:00\n",
       "77      2014-06-06 05:00:00  2017-07-23 08:34:00\n",
       "93      2012-12-09 00:00:00  2017-12-02 08:59:00\n",
       "114     2013-10-16 00:00:00  2017-12-02 08:31:00\n",
       "171     2012-05-03 00:00:00  2017-12-02 08:59:00"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_candidate_trimmed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df_chosen = df_candidate_trimmed[df_candidate_trimmed.index.isin(chosen_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>egauge_min_time</th>\n",
       "      <th>egauge_max_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>2012-05-03 00:00:00</td>\n",
       "      <td>2017-12-02 08:59:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>2012-05-16 00:00:00</td>\n",
       "      <td>2017-11-13 12:32:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>2012-07-11 05:00:00</td>\n",
       "      <td>2017-12-02 11:59:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>2012-08-13 00:00:00</td>\n",
       "      <td>2017-12-02 10:31:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>2012-01-01 06:00:00</td>\n",
       "      <td>2017-12-01 09:59:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            egauge_min_time      egauge_max_time\n",
       "dataid                                          \n",
       "171     2012-05-03 00:00:00  2017-12-02 08:59:00\n",
       "187     2012-05-16 00:00:00  2017-11-13 12:32:00\n",
       "370     2012-07-11 05:00:00  2017-12-02 11:59:00\n",
       "545     2012-08-13 00:00:00  2017-12-02 10:31:00\n",
       "585     2012-01-01 06:00:00  2017-12-01 09:59:00"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_chosen.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_chosen.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time to plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "egauge_min_time    object\n",
       "egauge_max_time    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_chosen.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.datetime(2012, 10, 24, 9, 52),\n",
       " datetime.datetime(2014, 11, 13, 18, 59)]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "x_min = datetime.fromtimestamp(chosen_record.overlap_start)\n",
    "x_max = datetime.fromtimestamp(chosen_record.overlap_end)\n",
    "[x_min, x_max]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "xs = zip(pd.to_datetime(df_chosen['egauge_min_time']), pd.to_datetime(df_chosen['egauge_max_time']))\n",
    "ys = zip(df_chosen.index.values, df_chosen.index.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel='date', ylabel='data_id', title='eGauge traces for the chosen houses')\n",
    "y_padding = 50\n",
    "ax.set(ylim=[max([min(df_chosen.index.values-y_padding),0]), max(df_chosen.index.values+y_padding)])\n",
    "ax.set(xlim=[x_min, x_max])\n",
    "\n",
    "for x, y in zip(xs, ys):\n",
    "    ax.plot(x, y, linewidth=1)\n",
    "fig.autofmt_xdate() # from: https://pythonspot.com/en/plot-time-with-matplotlib/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('03-segments-chosen-plot.eps')"
   ]
  }
 ],
 "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": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}