series_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import shapely\n",
    "from shapely.geometry import Point\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "from datetime import datetime, date, time\n",
    "\n",
    "from fbprophet import Prophet\n",
    "\n",
    "import matplotlib.pylab as plt\n",
    "plt.style.use('ggplot')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ll_crs = {'init': 'epsg:4326'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EIA region shapefile preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# power region geo boundaries (data provided by EIA)\n",
    "region_shp = gpd.read_file('data/ElectricSytemOperatingRegions_EIA-shape files/EIA930_Regions_20160506_WGS84.shx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "region_shp.drop(['Shape_Area', 'Shape_Leng'], axis=1, inplace=True)\n",
    "region_shp = region_shp.to_crs(ll_crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def union_parts(multi):\n",
    "    \"\"\"\n",
    "    - adds a buffer to all sub-polygons to eliminate faulty edges\n",
    "    - reduces number of sub-polygons by performing a cascading union, where possible\n",
    "    \"\"\"\n",
    "    if type(multi) == shapely.geometry.multipolygon.MultiPolygon:\n",
    "        allparts = [part.buffer(0) for part in multi]\n",
    "        unioned = shapely.ops.cascaded_union(allparts)\n",
    "        return unioned\n",
    "    else:\n",
    "        return multi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# touch up invalid polygons (bad self intersections)\n",
    "region_shp.loc[:, 'geometry'] = map(union_parts, region_shp.loc[:, 'geometry'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EIA_Region</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California</td>\n",
       "      <td>(POLYGON ((-114.2997240000745 34.1588979998338...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Carolinas</td>\n",
       "      <td>(POLYGON ((-77.94237900003289 36.5445129996888...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Central</td>\n",
       "      <td>(POLYGON ((-102.5477049998308 46.2827879998399...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Florida</td>\n",
       "      <td>(POLYGON ((-82.11678699982296 24.5491439996071...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mid Atlantic</td>\n",
       "      <td>(POLYGON ((-84.82944799969027 38.8971309999546...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     EIA_Region                                           geometry\n",
       "0    California  (POLYGON ((-114.2997240000745 34.1588979998338...\n",
       "1     Carolinas  (POLYGON ((-77.94237900003289 36.5445129996888...\n",
       "2       Central  (POLYGON ((-102.5477049998308 46.2827879998399...\n",
       "3       Florida  (POLYGON ((-82.11678699982296 24.5491439996071...\n",
       "4  Mid Atlantic  (POLYGON ((-84.82944799969027 38.8971309999546..."
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region_shp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# write region shapes to file\n",
    "with open('./data/region_shapes.pickle', 'wb') as handle:\n",
    "    pickle.dump(region_shp, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cities preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('./data/cities.pickle', 'rb') as handle:\n",
    "    cities = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def switch_latlng(point):\n",
    "    \"\"\"\n",
    "    swaps lat of for long\n",
    "    googlemaps API and shapely API coordinate sysems are opposite\n",
    "    \"\"\"\n",
    "    x = point.y\n",
    "    y = point.x\n",
    "    return Point(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# set geom with geopandas, switch lat/lng\n",
    "cities  = gpd.GeoDataFrame(cities, crs=ll_crs, geometry='location')\n",
    "cities['fg'] = map(switch_latlng, cities.loc[:, 'location'])\n",
    "cities.drop('location', axis=1, inplace=True)\n",
    "# rename geom column\n",
    "cities.columns = [u'city', u'state', u'state_abb', u'population', u'USAF',\n",
    "       u'WBAN', u'geometry']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>state_abb</th>\n",
       "      <th>population</th>\n",
       "      <th>USAF</th>\n",
       "      <th>WBAN</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>8550405</td>\n",
       "      <td>997271</td>\n",
       "      <td>99999</td>\n",
       "      <td>POINT (-74.0059413 40.7127837)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>CA</td>\n",
       "      <td>3971883</td>\n",
       "      <td>722874</td>\n",
       "      <td>93134</td>\n",
       "      <td>POINT (-118.2436849 34.0522342)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>IL</td>\n",
       "      <td>2720546</td>\n",
       "      <td>998499</td>\n",
       "      <td>99999</td>\n",
       "      <td>POINT (-87.6297982 41.8781136)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "      <td>TX</td>\n",
       "      <td>2296224</td>\n",
       "      <td>720594</td>\n",
       "      <td>188</td>\n",
       "      <td>POINT (-95.3698028 29.7604267)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>PA</td>\n",
       "      <td>1567442</td>\n",
       "      <td>724080</td>\n",
       "      <td>13739</td>\n",
       "      <td>POINT (-75.1652215 39.9525839)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           city         state state_abb  population    USAF   WBAN  \\\n",
       "0      New York      New York        NY     8550405  997271  99999   \n",
       "1   Los Angeles    California        CA     3971883  722874  93134   \n",
       "2       Chicago      Illinois        IL     2720546  998499  99999   \n",
       "3       Houston         Texas        TX     2296224  720594    188   \n",
       "4  Philadelphia  Pennsylvania        PA     1567442  724080  13739   \n",
       "\n",
       "                          geometry  \n",
       "0   POINT (-74.0059413 40.7127837)  \n",
       "1  POINT (-118.2436849 34.0522342)  \n",
       "2   POINT (-87.6297982 41.8781136)  \n",
       "3   POINT (-95.3698028 29.7604267)  \n",
       "4   POINT (-75.1652215 39.9525839)  "
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### aggregating weather to EIA regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# locate cities within regional boundaries with a spatial join\n",
    "cities_located = gpd.sjoin(cities, region_shp, how=\"inner\", op='within')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>state_abb</th>\n",
       "      <th>population</th>\n",
       "      <th>USAF</th>\n",
       "      <th>WBAN</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "      <th>EIA_Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>Thousand Oaks</td>\n",
       "      <td>California</td>\n",
       "      <td>CA</td>\n",
       "      <td>129339</td>\n",
       "      <td>723926</td>\n",
       "      <td>23136</td>\n",
       "      <td>POINT (-118.8375937 34.1705609)</td>\n",
       "      <td>0</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>Burbank</td>\n",
       "      <td>California</td>\n",
       "      <td>CA</td>\n",
       "      <td>105319</td>\n",
       "      <td>722880</td>\n",
       "      <td>23152</td>\n",
       "      <td>POINT (-118.3089661 34.1808392)</td>\n",
       "      <td>0</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Oxnard</td>\n",
       "      <td>California</td>\n",
       "      <td>CA</td>\n",
       "      <td>207254</td>\n",
       "      <td>723927</td>\n",
       "      <td>93110</td>\n",
       "      <td>POINT (-119.1770516 34.1975048)</td>\n",
       "      <td>0</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>Camarillo</td>\n",
       "      <td>California</td>\n",
       "      <td>CA</td>\n",
       "      <td>67608</td>\n",
       "      <td>723926</td>\n",
       "      <td>23136</td>\n",
       "      <td>POINT (-119.0376023 34.2163937)</td>\n",
       "      <td>0</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>Simi Valley</td>\n",
       "      <td>California</td>\n",
       "      <td>CA</td>\n",
       "      <td>126788</td>\n",
       "      <td>722886</td>\n",
       "      <td>23130</td>\n",
       "      <td>POINT (-118.781482 34.2694474)</td>\n",
       "      <td>0</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              city       state state_abb  population    USAF   WBAN  \\\n",
       "208  Thousand Oaks  California        CA      129339  723926  23136   \n",
       "281        Burbank  California        CA      105319  722880  23152   \n",
       "107         Oxnard  California        CA      207254  723927  93110   \n",
       "519      Camarillo  California        CA       67608  723926  23136   \n",
       "216    Simi Valley  California        CA      126788  722886  23130   \n",
       "\n",
       "                            geometry  index_right  EIA_Region  \n",
       "208  POINT (-118.8375937 34.1705609)            0  California  \n",
       "281  POINT (-118.3089661 34.1808392)            0  California  \n",
       "107  POINT (-119.1770516 34.1975048)            0  California  \n",
       "519  POINT (-119.0376023 34.2163937)            0  California  \n",
       "216   POINT (-118.781482 34.2694474)            0  California  "
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities_located.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "California 177\n",
      "Carolinas 24\n",
      "Central 39\n",
      "Florida 55\n",
      "Mid Atlantic 88\n",
      "Midwest 110\n",
      "New England 41\n",
      "New York 12\n",
      "Northwest 82\n",
      "Southern 25\n",
      "Southwest 25\n",
      "TVA 15\n",
      "Texas 58\n"
     ]
    }
   ],
   "source": [
    "# how many cities in each region?\n",
    "for i, group in cities_located.groupby('EIA_Region'):\n",
    "    print i, len(group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# write region-located city information to file\n",
    "with open('./data/region_cities.pickle', 'wb') as handle:\n",
    "    pickle.dump(cities_located.loc[:, ['city', 'state', 'state_abb', 'EIA_Region']],\\\n",
    "                handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read in the weather time series data for all US cities\n",
    "with open('./data/weather_series.pickle', 'rb') as handle:\n",
    "    weather = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def weighted_avg(row, city_pops):\n",
    "\"\"\"\n",
    "computes the population-weighted avg of temperature for a set of cities\n",
    "(given that all cities are in single df row for a given time stamp)\n",
    "ignores NaN values and NaN-equivalent values in calculation\n",
    "\"\"\"\n",
    "    def exists(num):\n",
    "        # the 'missing value' representation from NOAA is (+/-) 9999 \n",
    "        if (num > 100) or (num < -100) or (np.isnan(num)):\n",
    "            return False\n",
    "        else:\n",
    "            return True\n",
    "    \n",
    "    indices = np.where(map(exists, row))[0]\n",
    "    # weighted average, using a mask array that filters out missing values\n",
    "    return np.average(row[indices], weights=np.array(city_pops)[indices])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def date_maker(row):\n",
    "    \"\"\"\n",
    "    generates datetime object from a dataframe row\n",
    "    \"\"\"\n",
    "    return datetime(row['year'], row['month'], row['day'], row['hour'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# use a consistent index object for all regional weather time series\n",
    "master_index = pd.date_range('2015-01-01 01:00:00', '2017-05-03 05:00:00', freq=\"1H\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### weighted average algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REGION: California\n",
      "0 of 177: Thousand Oaks, CA\n",
      "1 of 177: Burbank, CA\n",
      "2 of 177: Oxnard, CA\n",
      "3 of 177: Camarillo, CA\n",
      "4 of 177: Simi Valley, CA\n",
      "5 of 177: San Buenaventura, CA\n",
      "6 of 177: Santa Clarita, CA\n",
      "7 of 177: Santa Barbara, CA\n",
      "8 of 177: Palmdale, CA\n",
      "9 of 177: Lancaster, CA\n",
      "10 of 177: Santa Maria, CA\n",
      "11 of 177: Bakersfield, CA\n",
      "12 of 177: Porterville, CA\n",
      "13 of 177: Tulare, CA\n",
      "14 of 177: Hanford, CA\n",
      "15 of 177: Visalia, CA\n",
      "16 of 177: Fresno, CA\n",
      "17 of 177: Clovis, CA\n",
      "18 of 177: Madera, CA\n",
      "19 of 177: Merced, CA\n",
      "20 of 177: Turlock, CA\n",
      "21 of 177: Modesto, CA\n",
      "22 of 177: Tracy, CA\n",
      "23 of 177: Manteca, CA\n",
      "24 of 177: Stockton, CA\n",
      "25 of 177: Lodi, CA\n",
      "26 of 177: Elk Grove, CA\n",
      "27 of 177: Sacramento, CA\n",
      "28 of 177: Rancho Cordova, CA\n",
      "29 of 177: Folsom, CA\n",
      "30 of 177: Citrus Heights, CA\n",
      "31 of 177: Roseville, CA\n",
      "32 of 177: Rocklin, CA\n",
      "33 of 177: Gilroy, CA\n",
      "34 of 177: Delano, CA\n",
      "35 of 177: West Sacramento, CA\n",
      "36 of 177: San Clemente, CA\n",
      "37 of 177: Laguna Niguel, CA\n",
      "38 of 177: Mission Viejo, CA\n",
      "39 of 177: Newport Beach, CA\n",
      "40 of 177: Costa Mesa, CA\n",
      "41 of 177: Lake Forest, CA\n",
      "42 of 177: Huntington Beach, CA\n",
      "43 of 177: Irvine, CA\n",
      "44 of 177: Fountain Valley, CA\n",
      "45 of 177: Santa Ana, CA\n",
      "46 of 177: Tustin, CA\n",
      "47 of 177: Westminster, CA\n",
      "48 of 177: Long Beach, CA\n",
      "49 of 177: Garden Grove, CA\n",
      "50 of 177: Orange, CA\n",
      "51 of 177: Carson, CA\n",
      "52 of 177: Anaheim, CA\n",
      "53 of 177: Torrance, CA\n",
      "54 of 177: Redondo Beach, CA\n",
      "55 of 177: Lakewood, CA\n",
      "56 of 177: Buena Park, CA\n",
      "57 of 177: Fullerton, CA\n",
      "58 of 177: Corona, CA\n",
      "59 of 177: Bellflower, CA\n",
      "60 of 177: Gardena, CA\n",
      "61 of 177: Yorba Linda, CA\n",
      "62 of 177: Paramount, CA\n",
      "63 of 177: Compton, CA\n",
      "64 of 177: Norwalk, CA\n",
      "65 of 177: Hawthorne, CA\n",
      "66 of 177: Lynwood, CA\n",
      "67 of 177: La Habra, CA\n",
      "68 of 177: Downey, CA\n",
      "69 of 177: Eastvale, CA\n",
      "70 of 177: South Gate, CA\n",
      "71 of 177: Inglewood, CA\n",
      "72 of 177: Whittier, CA\n",
      "73 of 177: Huntington Park, CA\n",
      "74 of 177: Pico Rivera, CA\n",
      "75 of 177: Chino Hills, CA\n",
      "76 of 177: Jurupa Valley, CA\n",
      "77 of 177: Chino, CA\n",
      "78 of 177: Montebello, CA\n",
      "79 of 177: Santa Monica, CA\n",
      "80 of 177: Diamond Bar, CA\n",
      "81 of 177: Los Angeles, CA\n",
      "82 of 177: Pomona, CA\n",
      "83 of 177: Monterey Park, CA\n",
      "84 of 177: Ontario, CA\n",
      "85 of 177: El Monte, CA\n",
      "86 of 177: West Covina, CA\n",
      "87 of 177: Rosemead, CA\n",
      "88 of 177: Baldwin Park, CA\n",
      "89 of 177: Fontana, CA\n",
      "90 of 177: Alhambra, CA\n",
      "91 of 177: Upland, CA\n",
      "92 of 177: Rancho Cucamonga, CA\n",
      "93 of 177: Arcadia, CA\n",
      "94 of 177: Glendale, CA\n",
      "95 of 177: Pasadena, CA\n",
      "96 of 177: Placentia, CA\n",
      "97 of 177: Glendora, CA\n",
      "98 of 177: Aliso Viejo, CA\n",
      "99 of 177: Riverside, CA\n",
      "100 of 177: Oceanside, CA\n",
      "101 of 177: Rialto, CA\n",
      "102 of 177: Carlsbad, CA\n",
      "103 of 177: Lake Elsinore, CA\n",
      "104 of 177: Colton, CA\n",
      "105 of 177: Hesperia, CA\n",
      "106 of 177: Victorville, CA\n",
      "107 of 177: Encinitas, CA\n",
      "108 of 177: San Bernardino, CA\n",
      "109 of 177: Vista, CA\n",
      "110 of 177: Moreno Valley, CA\n",
      "111 of 177: Perris, CA\n",
      "112 of 177: Murrieta, CA\n",
      "113 of 177: Highland, CA\n",
      "114 of 177: Apple Valley, CA\n",
      "115 of 177: Menifee, CA\n",
      "116 of 177: Redlands, CA\n",
      "117 of 177: San Marcos, CA\n",
      "118 of 177: San Diego, CA\n",
      "119 of 177: Temecula, CA\n",
      "120 of 177: National City, CA\n",
      "121 of 177: Escondido, CA\n",
      "122 of 177: Chula Vista, CA\n",
      "123 of 177: La Mesa, CA\n",
      "124 of 177: Santee, CA\n",
      "125 of 177: Hemet, CA\n",
      "126 of 177: El Cajon, CA\n",
      "127 of 177: Cathedral City, CA\n",
      "128 of 177: Indio, CA\n",
      "129 of 177: Yucaipa, CA\n",
      "130 of 177: Palm Desert, CA\n",
      "131 of 177: Poway, CA\n",
      "132 of 177: Santa Rosa, CA\n",
      "133 of 177: Daly City, CA\n",
      "134 of 177: San Francisco, CA\n",
      "135 of 177: Redding, CA\n",
      "136 of 177: Richmond, CA\n",
      "137 of 177: San Mateo, CA\n",
      "138 of 177: Berkeley, CA\n",
      "139 of 177: Oakland, CA\n",
      "140 of 177: Vallejo, CA\n",
      "141 of 177: Redwood City, CA\n",
      "142 of 177: San Leandro, CA\n",
      "143 of 177: Hayward, CA\n",
      "144 of 177: Fairfield, CA\n",
      "145 of 177: Sunnyvale, CA\n",
      "146 of 177: Concord, CA\n",
      "147 of 177: Fremont, CA\n",
      "148 of 177: Vacaville, CA\n",
      "149 of 177: Santa Clara, CA\n",
      "150 of 177: San Jose, CA\n",
      "151 of 177: Chico, CA\n",
      "152 of 177: Antioch, CA\n",
      "153 of 177: Livermore, CA\n",
      "154 of 177: Salinas, CA\n",
      "155 of 177: Mountain View, CA\n",
      "156 of 177: Napa, CA\n",
      "157 of 177: Pleasanton, CA\n",
      "158 of 177: Alameda, CA\n",
      "159 of 177: Milpitas, CA\n",
      "160 of 177: San Ramon, CA\n",
      "161 of 177: Union City, CA\n",
      "162 of 177: Pittsburg, CA\n",
      "163 of 177: Walnut Creek, CA\n",
      "164 of 177: Davis, CA\n",
      "165 of 177: South San Francisco, CA\n",
      "166 of 177: Yuba City, CA\n",
      "167 of 177: Palo Alto, CA\n",
      "168 of 177: Santa Cruz, CA\n",
      "169 of 177: Cupertino, CA\n",
      "170 of 177: Petaluma, CA\n",
      "171 of 177: San Rafael, CA\n",
      "172 of 177: Brentwood, CA\n",
      "173 of 177: Woodland, CA\n",
      "174 of 177: Dublin, CA\n",
      "175 of 177: Novato, CA\n",
      "176 of 177: Watsonville, CA\n",
      "REGION: Carolinas\n",
      "0 of 24: Charleston, SC\n",
      "1 of 24: Mount Pleasant, SC\n",
      "2 of 24: North Charleston, SC\n",
      "3 of 24: Columbia, SC\n",
      "4 of 24: Wilmington, NC\n",
      "5 of 24: Jacksonville, NC\n",
      "6 of 24: Greenville, SC\n",
      "7 of 24: Rock Hill, SC\n",
      "8 of 24: Fayetteville, NC\n",
      "9 of 24: Charlotte, NC\n",
      "10 of 24: Gastonia, NC\n",
      "11 of 24: Concord, NC\n",
      "12 of 24: Asheville, NC\n",
      "13 of 24: Greenville, NC\n",
      "14 of 24: Raleigh, NC\n",
      "15 of 24: Cary, NC\n",
      "16 of 24: Chapel Hill, NC\n",
      "17 of 24: Rocky Mount, NC\n",
      "18 of 24: High Point, NC\n",
      "19 of 24: Durham, NC\n",
      "20 of 24: Greensboro, NC\n",
      "21 of 24: Winston-Salem, NC\n",
      "22 of 24: Huntersville, NC\n",
      "23 of 24: Burlington, NC\n",
      "REGION: Central\n",
      "0 of 39: Lubbock, TX\n",
      "1 of 39: Amarillo, TX\n",
      "2 of 39: Omaha, NE\n",
      "3 of 39: Bellevue, NE\n",
      "4 of 39: Norman, OK\n",
      "5 of 39: Moore, OK\n",
      "6 of 39: Fort Smith, AR\n",
      "7 of 39: Midwest City, OK\n",
      "8 of 39: Oklahoma City, OK\n",
      "9 of 39: Edmond, OK\n",
      "10 of 39: Jonesboro, AR\n",
      "11 of 39: Broken Arrow, OK\n",
      "12 of 39: Fayetteville, AR\n",
      "13 of 39: Tulsa, OK\n",
      "14 of 39: Springdale, AR\n",
      "15 of 39: Rogers, AR\n",
      "16 of 39: Springfield, MO\n",
      "17 of 39: Wichita, KS\n",
      "18 of 39: Olathe, KS\n",
      "19 of 39: Lee's Summit, MO\n",
      "20 of 39: Lawrence, KS\n",
      "21 of 39: Overland Park, KS\n",
      "22 of 39: Shawnee, KS\n",
      "23 of 39: Topeka, KS\n",
      "24 of 39: Independence, MO\n",
      "25 of 39: Kansas City, MO\n",
      "26 of 39: Kansas City, KS\n",
      "27 of 39: St. Joseph, MO\n",
      "28 of 39: Lincoln, NE\n",
      "29 of 39: Manhattan, KS\n",
      "30 of 39: Blue Springs, MO\n",
      "31 of 39: Lenexa, KS\n",
      "32 of 39: Joplin, MO\n",
      "33 of 39: Enid, OK\n",
      "34 of 39: Grand Island, NE\n",
      "35 of 39: Longview, TX\n",
      "36 of 39: Shreveport, LA\n",
      "37 of 39: Lawton, OK\n",
      "38 of 39: Bossier City, LA\n",
      "REGION: Florida\n",
      "0 of 55: Jacksonville, FL\n",
      "1 of 55: Tallahassee, FL\n",
      "2 of 55: Homestead, FL\n",
      "3 of 55: Miami, FL\n",
      "4 of 55: Miami Beach, FL\n",
      "5 of 55: Doral, FL\n",
      "6 of 55: Hialeah, FL\n",
      "7 of 55: North Miami, FL\n",
      "8 of 55: Miami Gardens, FL\n",
      "9 of 55: Miramar, FL\n",
      "10 of 55: Pembroke Pines, FL\n",
      "11 of 55: Hollywood, FL\n",
      "12 of 55: Davie, FL\n",
      "13 of 55: Weston, FL\n",
      "14 of 55: Fort Lauderdale, FL\n",
      "15 of 55: Plantation, FL\n",
      "16 of 55: Lauderhill, FL\n",
      "17 of 55: Sunrise, FL\n",
      "18 of 55: Tamarac, FL\n",
      "19 of 55: Pompano Beach, FL\n",
      "20 of 55: Margate, FL\n",
      "21 of 55: Coconut Creek, FL\n",
      "22 of 55: Coral Springs, FL\n",
      "23 of 55: Deerfield Beach, FL\n",
      "24 of 55: Boca Raton, FL\n",
      "25 of 55: Delray Beach, FL\n",
      "26 of 55: Boynton Beach, FL\n",
      "27 of 55: Cape Coral, FL\n",
      "28 of 55: Fort Myers, FL\n",
      "29 of 55: Wellington, FL\n",
      "30 of 55: West Palm Beach, FL\n",
      "31 of 55: Jupiter, FL\n",
      "32 of 55: North Port, FL\n",
      "33 of 55: Port St. Lucie, FL\n",
      "34 of 55: Largo, FL\n",
      "35 of 55: Tampa, FL\n",
      "36 of 55: Clearwater, FL\n",
      "37 of 55: Palm Bay, FL\n",
      "38 of 55: Lakeland, FL\n",
      "39 of 55: Melbourne, FL\n",
      "40 of 55: Kissimmee, FL\n",
      "41 of 55: Orlando, FL\n",
      "42 of 55: Sanford, FL\n",
      "43 of 55: Deltona, FL\n",
      "44 of 55: Port Orange, FL\n",
      "45 of 55: Ocala, FL\n",
      "46 of 55: Daytona Beach, FL\n",
      "47 of 55: Palm Coast, FL\n",
      "48 of 55: Gainesville, FL\n",
      "49 of 55: Sarasota, FL\n",
      "50 of 55: Bradenton, FL\n",
      "51 of 55: Palm Beach Gardens, FL\n",
      "52 of 55: Bonita Springs, FL\n",
      "53 of 55: Pinellas Park, FL\n",
      "54 of 55: Coral Gables, FL\n",
      "REGION: Mid Atlantic\n",
      "0 of 88: Joliet, IL\n",
      "1 of 88: Tinley Park, IL\n",
      "2 of 88: Orland Park, IL\n",
      "3 of 88: Bolingbrook, IL\n",
      "4 of 88: Naperville, IL\n",
      "5 of 88: Aurora, IL\n",
      "6 of 88: Cicero, IL\n",
      "7 of 88: Chicago, IL\n",
      "8 of 88: Skokie, IL\n",
      "9 of 88: Schaumburg, IL\n",
      "10 of 88: Des Plaines, IL\n",
      "11 of 88: Elgin, IL\n",
      "12 of 88: Evanston, IL\n",
      "13 of 88: Arlington Heights, IL\n",
      "14 of 88: Palatine, IL\n",
      "15 of 88: Rockford, IL\n",
      "16 of 88: Waukegan, IL\n",
      "17 of 88: Oak Lawn, IL\n",
      "18 of 88: Berwyn, IL\n",
      "19 of 88: Mount Prospect, IL\n",
      "20 of 88: Wheaton, IL\n",
      "21 of 88: Oak Park, IL\n",
      "22 of 88: Hoffman Estates, IL\n",
      "23 of 88: Elizabeth, NJ\n",
      "24 of 88: Newark, NJ\n",
      "25 of 88: Paterson, NJ\n",
      "26 of 88: Clifton, NJ\n",
      "27 of 88: Passaic, NJ\n",
      "28 of 88: Jersey City, NJ\n",
      "29 of 88: Union City, NJ\n",
      "30 of 88: Bayonne, NJ\n",
      "31 of 88: East Orange, NJ\n",
      "32 of 88: New Brunswick, NJ\n",
      "33 of 88: Hoboken, NJ\n",
      "34 of 88: West New York, NJ\n",
      "35 of 88: Perth Amboy, NJ\n",
      "36 of 88: Plainfield, NJ\n",
      "37 of 88: South Bend, IN\n",
      "38 of 88: Muncie, IN\n",
      "39 of 88: Fort Wayne, IN\n",
      "40 of 88: Cincinnati, OH\n",
      "41 of 88: Dayton, OH\n",
      "42 of 88: Toledo, OH\n",
      "43 of 88: Columbus, OH\n",
      "44 of 88: Parma, OH\n",
      "45 of 88: Cleveland, OH\n",
      "46 of 88: Akron, OH\n",
      "47 of 88: Canton, OH\n",
      "48 of 88: Erie, PA\n",
      "49 of 88: Pittsburgh, PA\n",
      "50 of 88: Roanoke, VA\n",
      "51 of 88: Lynchburg, VA\n",
      "52 of 88: Richmond, VA\n",
      "53 of 88: Frederick, MD\n",
      "54 of 88: Gaithersburg, MD\n",
      "55 of 88: Alexandria, VA\n",
      "56 of 88: Washington, DC\n",
      "57 of 88: Baltimore, MD\n",
      "58 of 88: Suffolk, VA\n",
      "59 of 88: Newport News, VA\n",
      "60 of 88: Hampton, VA\n",
      "61 of 88: Portsmouth, VA\n",
      "62 of 88: Chesapeake, VA\n",
      "63 of 88: Norfolk, VA\n",
      "64 of 88: Virginia Beach, VA\n",
      "65 of 88: Reading, PA\n",
      "66 of 88: Scranton, PA\n",
      "67 of 88: Wilmington, DE\n",
      "68 of 88: Allentown, PA\n",
      "69 of 88: Bethlehem, PA\n",
      "70 of 88: Philadelphia, PA\n",
      "71 of 88: Camden, NJ\n",
      "72 of 88: Trenton, NJ\n",
      "73 of 88: Rockville, MD\n",
      "74 of 88: Youngstown, OH\n",
      "75 of 88: Lorain, OH\n",
      "76 of 88: Hamilton, OH\n",
      "77 of 88: Vineland, NJ\n",
      "78 of 88: Springfield, OH\n",
      "79 of 88: Lancaster, PA\n",
      "80 of 88: Bowie, MD\n",
      "81 of 88: Kettering, OH\n",
      "82 of 88: Anderson, IN\n",
      "83 of 88: Elyria, OH\n",
      "84 of 88: Harrisonburg, VA\n",
      "85 of 88: Elkhart, IN\n",
      "86 of 88: Leesburg, VA\n",
      "87 of 88: Lakewood, OH\n",
      "REGION: Midwest\n",
      "0 of 110: Bismarck, ND\n",
      "1 of 110: Council Bluffs, IA\n",
      "2 of 110: Davenport, IA\n",
      "3 of 110: West Des Moines, IA\n",
      "4 of 110: Hammond, IN\n",
      "5 of 110: Gary, IN\n",
      "6 of 110: Des Moines, IA\n",
      "7 of 110: Iowa City, IA\n",
      "8 of 110: Cedar Rapids, IA\n",
      "9 of 110: Ames, IA\n",
      "10 of 110: Waterloo, IA\n",
      "11 of 110: Sioux City, IA\n",
      "12 of 110: Dubuque, IA\n",
      "13 of 110: Kenosha, WI\n",
      "14 of 110: Janesville, WI\n",
      "15 of 110: Racine, WI\n",
      "16 of 110: Waukesha, WI\n",
      "17 of 110: West Allis, WI\n",
      "18 of 110: Milwaukee, WI\n",
      "19 of 110: Madison, WI\n",
      "20 of 110: Sioux Falls, SD\n",
      "21 of 110: Rochester, MN\n",
      "22 of 110: Oshkosh, WI\n",
      "23 of 110: Appleton, WI\n",
      "24 of 110: Green Bay, WI\n",
      "25 of 110: Lakeville, MN\n",
      "26 of 110: Burnsville, MN\n",
      "27 of 110: Eagan, MN\n",
      "28 of 110: Eau Claire, WI\n",
      "29 of 110: Bloomington, MN\n",
      "30 of 110: Eden Prairie, MN\n",
      "31 of 110: Woodbury, MN\n",
      "32 of 110: St. Paul, MN\n",
      "33 of 110: Minneapolis, MN\n",
      "34 of 110: Plymouth, MN\n",
      "35 of 110: Maple Grove, MN\n",
      "36 of 110: Brooklyn Park, MN\n",
      "37 of 110: Blaine, MN\n",
      "38 of 110: Coon Rapids, MN\n",
      "39 of 110: St. Cloud, MN\n",
      "40 of 110: Duluth, MN\n",
      "41 of 110: Fargo, ND\n",
      "42 of 110: Grand Forks, ND\n",
      "43 of 110: Ankeny, IA\n",
      "44 of 110: La Crosse, WI\n",
      "45 of 110: Minnetonka, MN\n",
      "46 of 110: Apple Valley, MN\n",
      "47 of 110: Edina, MN\n",
      "48 of 110: Conway, AR\n",
      "49 of 110: Evansville, IN\n",
      "50 of 110: St. Louis, MO\n",
      "51 of 110: St. Charles, MO\n",
      "52 of 110: O'Fallon, MO\n",
      "53 of 110: Columbia, MO\n",
      "54 of 110: Terre Haute, IN\n",
      "55 of 110: Springfield, IL\n",
      "56 of 110: Decatur, IL\n",
      "57 of 110: Champaign, IL\n",
      "58 of 110: Bloomington, IL\n",
      "59 of 110: Peoria, IL\n",
      "60 of 110: St. Peters, MO\n",
      "61 of 110: Normal, IL\n",
      "62 of 110: Southaven, MS\n",
      "63 of 110: Florissant, MO\n",
      "64 of 110: Lafayette, IN\n",
      "65 of 110: Bloomington, IN\n",
      "66 of 110: Indianapolis, IN\n",
      "67 of 110: Carmel, IN\n",
      "68 of 110: Fishers, IN\n",
      "69 of 110: Louisville, KY\n",
      "70 of 110: Wyoming, MI\n",
      "71 of 110: Grand Rapids, MI\n",
      "72 of 110: Kalamazoo, MI\n",
      "73 of 110: Lansing, MI\n",
      "74 of 110: Lexington, KY\n",
      "75 of 110: Ann Arbor, MI\n",
      "76 of 110: Flint, MI\n",
      "77 of 110: Westland, MI\n",
      "78 of 110: Farmington Hills, MI\n",
      "79 of 110: Livonia, MI\n",
      "80 of 110: Southfield, MI\n",
      "81 of 110: Dearborn, MI\n",
      "82 of 110: Rochester Hills, MI\n",
      "83 of 110: Troy, MI\n",
      "84 of 110: Detroit, MI\n",
      "85 of 110: Sterling Heights, MI\n",
      "86 of 110: Warren, MI\n",
      "87 of 110: Taylor, MI\n",
      "88 of 110: Pontiac, MI\n",
      "89 of 110: St. Clair Shores, MI\n",
      "90 of 110: Noblesville, IN\n",
      "91 of 110: Owensboro, KY\n",
      "92 of 110: Royal Oak, MI\n",
      "93 of 110: Novi, MI\n",
      "94 of 110: Kokomo, IN\n",
      "95 of 110: Dearborn Heights, MI\n",
      "96 of 110: Greenwood, IN\n",
      "97 of 110: Battle Creek, MI\n",
      "98 of 110: Kentwood, MI\n",
      "99 of 110: New Orleans, LA\n",
      "100 of 110: Beaumont, TX\n",
      "101 of 110: Lafayette, LA\n",
      "102 of 110: Baton Rouge, LA\n",
      "103 of 110: Jackson, MS\n",
      "104 of 110: Little Rock, AR\n",
      "105 of 110: Lake Charles, LA\n",
      "106 of 110: Conroe, TX\n",
      "107 of 110: Kenner, LA\n",
      "108 of 110: North Little Rock, AR\n",
      "109 of 110: Port Arthur, TX\n",
      "REGION: New England\n",
      "0 of 41: Stamford, CT\n",
      "1 of 41: Danbury, CT\n",
      "2 of 41: Norwalk, CT\n",
      "3 of 41: Bridgeport, CT\n",
      "4 of 41: Waterbury, CT\n",
      "5 of 41: New Haven, CT\n",
      "6 of 41: New Britain, CT\n",
      "7 of 41: Hartford, CT\n",
      "8 of 41: Springfield, MA\n",
      "9 of 41: Worcester, MA\n",
      "10 of 41: Nashua, NH\n",
      "11 of 41: Manchester, NH\n",
      "12 of 41: Cranston, RI\n",
      "13 of 41: Warwick, RI\n",
      "14 of 41: Providence, RI\n",
      "15 of 41: Pawtucket, RI\n",
      "16 of 41: Lowell, MA\n",
      "17 of 41: Newton, MA\n",
      "18 of 41: Lawrence, MA\n",
      "19 of 41: Fall River, MA\n",
      "20 of 41: Cambridge, MA\n",
      "21 of 41: Somerville, MA\n",
      "22 of 41: Boston, MA\n",
      "23 of 41: Brockton, MA\n",
      "24 of 41: Quincy, MA\n",
      "25 of 41: Lynn, MA\n",
      "26 of 41: New Bedford, MA\n",
      "27 of 41: Portland, ME\n",
      "28 of 41: Waltham, MA\n",
      "29 of 41: Haverhill, MA\n",
      "30 of 41: Malden, MA\n",
      "31 of 41: Bristol, CT\n",
      "32 of 41: Meriden, CT\n",
      "33 of 41: Medford, MA\n",
      "34 of 41: Taunton, MA\n",
      "35 of 41: Chicopee, MA\n",
      "36 of 41: Weymouth Town, MA\n",
      "37 of 41: West Haven, CT\n",
      "38 of 41: Revere, MA\n",
      "39 of 41: Peabody, MA\n",
      "40 of 41: Milford, CT\n",
      "REGION: New York\n",
      "0 of 12: New York, NY\n",
      "1 of 12: Yonkers, NY\n",
      "2 of 12: Mount Vernon, NY\n",
      "3 of 12: New Rochelle, NY\n",
      "4 of 12: Albany, NY\n",
      "5 of 12: Schenectady, NY\n",
      "6 of 12: White Plains, NY\n",
      "7 of 12: Hempstead, NY\n",
      "8 of 12: Buffalo, NY\n",
      "9 of 12: Rochester, NY\n",
      "10 of 12: Syracuse, NY\n",
      "11 of 12: Utica, NY\n",
      "REGION: Northwest\n",
      "0 of 82: Pocatello, ID\n",
      "1 of 82: Idaho Falls, ID\n",
      "2 of 82: West Valley City, UT\n",
      "3 of 82: Ogden, UT\n",
      "4 of 82: Layton, UT\n",
      "5 of 82: West Jordan, UT\n",
      "6 of 82: Taylorsville, UT\n",
      "7 of 82: South Jordan, UT\n",
      "8 of 82: Salt Lake City, UT\n",
      "9 of 82: Lehi, UT\n",
      "10 of 82: Sandy, UT\n",
      "11 of 82: Orem, UT\n",
      "12 of 82: Provo, UT\n",
      "13 of 82: Great Falls, MT\n",
      "14 of 82: Grand Junction, CO\n",
      "15 of 82: Billings, MT\n",
      "16 of 82: Casper, WY\n",
      "17 of 82: Boulder, CO\n",
      "18 of 82: Longmont, CO\n",
      "19 of 82: Arvada, CO\n",
      "20 of 82: Broomfield, CO\n",
      "21 of 82: Fort Collins, CO\n",
      "22 of 82: Lakewood, CO\n",
      "23 of 82: Loveland, CO\n",
      "24 of 82: Westminster, CO\n",
      "25 of 82: Denver, CO\n",
      "26 of 82: Thornton, CO\n",
      "27 of 82: Commerce City, CO\n",
      "28 of 82: Centennial, CO\n",
      "29 of 82: Castle Rock, CO\n",
      "30 of 82: Aurora, CO\n",
      "31 of 82: Colorado Springs, CO\n",
      "32 of 82: Cheyenne, WY\n",
      "33 of 82: Greeley, CO\n",
      "34 of 82: Pueblo, CO\n",
      "35 of 82: Rapid City, SD\n",
      "36 of 82: Logan, UT\n",
      "37 of 82: Carson City, NV\n",
      "38 of 82: Reno, NV\n",
      "39 of 82: Sparks, NV\n",
      "40 of 82: Bend, OR\n",
      "41 of 82: Kennewick, WA\n",
      "42 of 82: Pasco, WA\n",
      "43 of 82: Yakima, WA\n",
      "44 of 82: Spokane, WA\n",
      "45 of 82: Richland, WA\n",
      "46 of 82: Spokane Valley, WA\n",
      "47 of 82: Nampa, ID\n",
      "48 of 82: Meridian, ID\n",
      "49 of 82: Boise City, ID\n",
      "50 of 82: Missoula, MT\n",
      "51 of 82: St. George, UT\n",
      "52 of 82: Caldwell, ID\n",
      "53 of 82: Eugene, OR\n",
      "54 of 82: Salem, OR\n",
      "55 of 82: Hillsboro, OR\n",
      "56 of 82: Beaverton, OR\n",
      "57 of 82: Portland, OR\n",
      "58 of 82: Vancouver, WA\n",
      "59 of 82: Tacoma, WA\n",
      "60 of 82: Gresham, OR\n",
      "61 of 82: Seattle, WA\n",
      "62 of 82: Federal Way, WA\n",
      "63 of 82: Kent, WA\n",
      "64 of 82: Renton, WA\n",
      "65 of 82: Kirkland, WA\n",
      "66 of 82: Everett, WA\n",
      "67 of 82: Bellevue, WA\n",
      "68 of 82: Bellingham, WA\n",
      "69 of 82: Medford, OR\n",
      "70 of 82: Auburn, WA\n",
      "71 of 82: Marysville, WA\n",
      "72 of 82: Springfield, OR\n",
      "73 of 82: Redmond, WA\n",
      "74 of 82: Lakewood, WA\n",
      "75 of 82: Corvallis, OR\n",
      "76 of 82: Shoreline, WA\n",
      "77 of 82: Sammamish, WA\n",
      "78 of 82: Albany, OR\n",
      "79 of 82: Tigard, OR\n",
      "80 of 82: Burien, WA\n",
      "81 of 82: Olympia, WA\n",
      "REGION: Southern\n",
      "0 of 25: Valdosta, GA\n",
      "1 of 25: Dothan, AL\n",
      "2 of 25: Albany, GA\n",
      "3 of 25: Savannah, GA\n",
      "4 of 25: Montgomery, AL\n",
      "5 of 25: Columbus, GA\n",
      "6 of 25: Auburn, AL\n",
      "7 of 25: Warner Robins, GA\n",
      "8 of 25: Macon, GA\n",
      "9 of 25: Hoover, AL\n",
      "10 of 25: Augusta-Richmond County, GA\n",
      "11 of 25: Birmingham, AL\n",
      "12 of 25: Atlanta, GA\n",
      "13 of 25: Smyrna, GA\n",
      "14 of 25: Sandy Springs, GA\n",
      "15 of 25: Athens-Clarke County, GA\n",
      "16 of 25: Marietta, GA\n",
      "17 of 25: Roswell, GA\n",
      "18 of 25: Johns Creek, GA\n",
      "19 of 25: Alpharetta, GA\n",
      "20 of 25: Pensacola, FL\n",
      "21 of 25: Brookhaven, GA\n",
      "22 of 25: Mobile, AL\n",
      "23 of 25: Tuscaloosa, AL\n",
      "24 of 25: Gulfport, MS\n",
      "REGION: Southwest\n",
      "0 of 25: Buckeye, AZ\n",
      "1 of 25: Surprise, AZ\n",
      "2 of 25: Goodyear, AZ\n",
      "3 of 25: Avondale, AZ\n",
      "4 of 25: Peoria, AZ\n",
      "5 of 25: Glendale, AZ\n",
      "6 of 25: Phoenix, AZ\n",
      "7 of 25: Tempe, AZ\n",
      "8 of 25: Scottsdale, AZ\n",
      "9 of 25: Chandler, AZ\n",
      "10 of 25: Mesa, AZ\n",
      "11 of 25: Gilbert, AZ\n",
      "12 of 25: Flagstaff, AZ\n",
      "13 of 25: Tucson, AZ\n",
      "14 of 25: Las Cruces, NM\n",
      "15 of 25: Rio Rancho, NM\n",
      "16 of 25: Albuquerque, NM\n",
      "17 of 25: El Paso, TX\n",
      "18 of 25: Santa Fe, NM\n",
      "19 of 25: Casa Grande, AZ\n",
      "20 of 25: Las Vegas, NV\n",
      "21 of 25: North Las Vegas, NV\n",
      "22 of 25: Henderson, NV\n",
      "23 of 25: Yuma, AZ\n",
      "24 of 25: Lake Havasu City, AZ\n",
      "REGION: TVA\n",
      "0 of 15: Decatur, AL\n",
      "1 of 15: Huntsville, AL\n",
      "2 of 15: Chattanooga, TN\n",
      "3 of 15: Murfreesboro, TN\n",
      "4 of 15: Franklin, TN\n",
      "5 of 15: Knoxville, TN\n",
      "6 of 15: Nashville, TN\n",
      "7 of 15: Hendersonville, TN\n",
      "8 of 15: Johnson City, TN\n",
      "9 of 15: Memphis, TN\n",
      "10 of 15: Bartlett, TN\n",
      "11 of 15: Jackson, TN\n",
      "12 of 15: Clarksville, TN\n",
      "13 of 15: Bowling Green, KY\n",
      "14 of 15: Kingsport, TN\n",
      "REGION: Texas\n",
      "0 of 58: Odessa, TX\n",
      "1 of 58: Midland, TX\n",
      "2 of 58: Brownsville, TX\n",
      "3 of 58: McAllen, TX\n",
      "4 of 58: Mission, TX\n",
      "5 of 58: Edinburg, TX\n",
      "6 of 58: Laredo, TX\n",
      "7 of 58: Corpus Christi, TX\n",
      "8 of 58: San Antonio, TX\n",
      "9 of 58: League City, TX\n",
      "10 of 58: Pearland, TX\n",
      "11 of 58: Sugar Land, TX\n",
      "12 of 58: Pasadena, TX\n",
      "13 of 58: Houston, TX\n",
      "14 of 58: Austin, TX\n",
      "15 of 58: Round Rock, TX\n",
      "16 of 58: College Station, TX\n",
      "17 of 58: Bryan, TX\n",
      "18 of 58: Killeen, TX\n",
      "19 of 58: San Angelo, TX\n",
      "20 of 58: Waco, TX\n",
      "21 of 58: Tyler, TX\n",
      "22 of 58: Abilene, TX\n",
      "23 of 58: Arlington, TX\n",
      "24 of 58: Grand Prairie, TX\n",
      "25 of 58: Fort Worth, TX\n",
      "26 of 58: Mesquite, TX\n",
      "27 of 58: Dallas, TX\n",
      "28 of 58: Irving, TX\n",
      "29 of 58: Garland, TX\n",
      "30 of 58: Richardson, TX\n",
      "31 of 58: Carrollton, TX\n",
      "32 of 58: Plano, TX\n",
      "33 of 58: Lewisville, TX\n",
      "34 of 58: Allen, TX\n",
      "35 of 58: Frisco, TX\n",
      "36 of 58: McKinney, TX\n",
      "37 of 58: Denton, TX\n",
      "38 of 58: Wichita Falls, TX\n",
      "39 of 58: Pharr, TX\n",
      "40 of 58: Baytown, TX\n",
      "41 of 58: Missouri City, TX\n",
      "42 of 58: Temple, TX\n",
      "43 of 58: Flower Mound, TX\n",
      "44 of 58: New Braunfels, TX\n",
      "45 of 58: North Richland Hills, TX\n",
      "46 of 58: Victoria, TX\n",
      "47 of 58: Cedar Park, TX\n",
      "48 of 58: Harlingen, TX\n",
      "49 of 58: Mansfield, TX\n",
      "50 of 58: Georgetown, TX\n",
      "51 of 58: San Marcos, TX\n",
      "52 of 58: Rowlett, TX\n",
      "53 of 58: Pflugerville, TX\n",
      "54 of 58: Euless, TX\n",
      "55 of 58: DeSoto, TX\n",
      "56 of 58: Grapevine, TX\n",
      "57 of 58: Galveston, TX\n"
     ]
    }
   ],
   "source": [
    "# finally, compute population weighted averages for each EIA power region\n",
    "region_weather_dict = {}\n",
    "\n",
    "# iterate through each EIA region of member-cities\n",
    "for region, cities in cities_located.groupby('EIA_Region'):\n",
    "    print 'REGION: {}'.format(region)\n",
    "    cities.reset_index(drop=True, inplace=True)\n",
    "    # get population for entire region (all member cities)\n",
    "    region_pop = cities.population.sum()\n",
    "    # also need an array of city populations to use in the weighted avg\n",
    "    city_pops = []\n",
    "    # initiate a dataframe at the region level\n",
    "    region_weather = pd.DataFrame(index=master_index)\n",
    "    # iterate through each city in the region\n",
    "    for i, city in cities.iterrows():\n",
    "        city_pops.append(city['population'])\n",
    "        city_index = city['city'] + ', ' + city['state_abb']\n",
    "        print '{} of {}: {}'.format(i, len(cities), city_index)\n",
    "        # pull in the weather series for the given city\n",
    "        city_weather = weather[city_index]\n",
    "        city_weather['date_index'] = city_weather.apply(date_maker, axis=1)\n",
    "        city_weather.set_index('date_index', drop=True, inplace=True)\n",
    "        city_weather.drop(['year', 'month', 'day', 'hour'], axis=1, inplace=True)\n",
    "        # note: NOAA provides temperatures at 10X scale; remove this\n",
    "        city_weather.loc[:, 'temp'] = city_weather.loc[:, 'temp'] / 10.0\n",
    "        city_weather.columns = [city['city'] + '_temp']\n",
    "        # merge with region-level dataframe on datetime index\n",
    "        region_weather = region_weather.merge(city_weather, how='left', left_index=True, right_index=True)\n",
    "    # population-weighted mean for temperature, across rows for each timestamp\n",
    "    avg_weather = region_weather.apply(lambda row: weighted_avg(row, city_pops), axis=1)\n",
    "    region_weather_dict[region] = avg_weather\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('./data/region_weather.pickle', 'wb') as handle:\n",
    "    pickle.dump(region_weather_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# aggregate regional weather to the daily level,\n",
    "# capturing both daily highs (for cooling demand in summer)\n",
    "# and daily lows (for heating demand in winter)\n",
    "regional_highs = {}\n",
    "regional_lows = {}\n",
    "\n",
    "for key in region_weather_dict.keys():\n",
    "    regional_highs[key] = region_weather_dict[key].resample('1D').max()\n",
    "    regional_lows[key] = region_weather_dict[key].resample('1D').min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# write highs and lows to file\n",
    "with open('./data/region_highs.pickle', 'wb') as handle:\n",
    "    pickle.dump(regional_highs, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "    \n",
    "with open('./data/region_lows.pickle', 'wb') as handle:\n",
    "    pickle.dump(regional_lows, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('./data/region_highs.pickle', 'rb') as handle:\n",
    "    regional_highs = pickle.load(handle)\n",
    "\n",
    "with open('./data/region_lows.pickle', 'rb') as handle:\n",
    "    regional_lows = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cleaning up the power demand data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('./data/power_series.pickle', 'rb') as handle:\n",
    "    demand = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# rename demand dict keys\n",
    "demand['Mid Atlantic'] = demand.pop('Demand for Mid-Atlantic (region), Hourly')\n",
    "demand['TVA'] = demand.pop('Demand for Tennessee Valley Authority (TVA), Hourly')\n",
    "demand['Southwest'] = demand.pop('Demand for Southwest (region), Hourly')\n",
    "demand['California'] = demand.pop('Demand for California (region), Hourly')\n",
    "demand['Texas'] = demand.pop('Demand for Electric Reliability Council of Texas, Inc. (ERCO), Hourly')\n",
    "demand['Florida'] = demand.pop('Demand for Florida (region), Hourly')\n",
    "demand['New York'] = demand.pop('Demand for New York Independent System Operator (NYIS), Hourly')\n",
    "demand['Northwest'] = demand.pop('Demand for Northwest (region), Hourly')\n",
    "demand['Midwest'] = demand.pop('Demand for Midwest (region), Hourly')\n",
    "demand['Central'] = demand.pop('Demand for Central (region), Hourly')\n",
    "demand['Southern'] = demand.pop('Demand for Southeast (region), Hourly')\n",
    "demand['New England'] = demand.pop('Demand for New England ISO (ISNE), Hourly')\n",
    "demand['Carolinas'] = demand.pop('Demand for Carolinas (region), Hourly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# convert demand time series timestamps to real timestamps for use in axis\n",
    "def str_to_time(stamp):\n",
    "    \"\"\"\n",
    "    converts a timestamp string into proper datetime object\n",
    "    \"\"\"\n",
    "    yr = int(stamp[:4])\n",
    "    mth = int(stamp[4:6])\n",
    "    day = int(stamp[6:8])\n",
    "    hr = int(stamp[9:11])\n",
    "    return datetime(yr, mth, day, hr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def list_to_series(demand_list):\n",
    "    \"\"\"\n",
    "    converts the 'list of list' format given by EIA API into a regular indexed time series\n",
    "    \"\"\"\n",
    "    ser =  pd.Series(map(lambda datapoint: datapoint[1], demand_list),\n",
    "                     index=map(lambda datapoint: str_to_time(datapoint[0]), demand_list))\n",
    "    # resample to daily level\n",
    "    return ser.resample('1D').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for key in demand.keys():\n",
    "    # we have an extra two days of data on the demand series; truncate it\n",
    "    demand[key] = list_to_series(demand[key])[:-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-07-01    796723\n",
       "2015-07-02    774333\n",
       "2015-07-03    691318\n",
       "2015-07-04    669451\n",
       "2015-07-05    693343\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demand['Central'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# rewrite the clean demand data\n",
    "with open('./data/demand.pickle', 'wb') as handle:\n",
    "    pickle.dump(demand, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## weather & periodicity normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('./data/region_highs.pickle', 'rb') as handle:\n",
    "    regional_highs = pickle.load(handle)\n",
    "\n",
    "with open('./data/region_lows.pickle', 'rb') as handle:\n",
    "    regional_lows = pickle.load(handle)\n",
    "    \n",
    "with open('./data/demand.pickle', 'rb') as handle:\n",
    "    demand = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### problemo:\n",
    "note that for more robust weather normalization (that would be more sensitive to long stints of hot or cold), we'd want to compare observed temps against historical averages for each region. as it is, we're just comparing against itself looking for \"local extremes\" on moving averages. we're sensitive to initial changes in weather, but if the heat wave lastsa for a few weeks, the sensititivty gets worse... maybe that's ok? dunno. the perfect method would be to first normalize weather against historical averages, and then to remove seasonality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### weather normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for region in demand.keys():\n",
    "    \n",
    "    highs = regional_highs[region]\n",
    "    lows = regional_highs[region]\n",
    "    rolling_highs = regional_highs[region].rolling(window=14).mean()\n",
    "    rolling_lows = regional_highs[region].rolling(window=14).mean()\n",
    "    \n",
    "    for day in demand[region].index:\n",
    "        # adjustment for cooling loads (summer months)\n",
    "        if day.month in [5, 6, 7, 8, 9]:\n",
    "            # check if day's temp is higher than the rolling avg\n",
    "            percent_over = (highs[day] - rolling_highs[day]) / abs(rolling_highs[day])\n",
    "            if percent_over > 0:\n",
    "                # normalize heating demand downwards; 50% weight\n",
    "                print 'log: summer', region, day.date(), round(highs[day], 1), round(percent_over * 100, 1)\n",
    "                demand[region][day] = demand[region][day] - \\\n",
    "                (percent_over * 0.6 * demand[region][day]) # 60% weight\n",
    "        # adjustment for heating loads\n",
    "        elif day.month in [11, 12, 1, 2, 3]:\n",
    "            percent_under = (rolling_lows[day] - lows[day]) / abs(rolling_lows[day])\n",
    "            # check if day's temp is lower than the rolling avg\n",
    "            if percent_under > 0:\n",
    "                # normalize cooling demand downwards; 25% weight\n",
    "                print 'log: winter', region, day.date(), round(lows[day], 1), round(percent_under * 100, 1)\n",
    "                demand[region][day] = demand[region][day] - \\\n",
    "                (percent_under * 0.4 * demand[region][day]) # 40% weight\n",
    "        # this leaves two shoulder months that we dont adjust for\n",
    "        else:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### removing periodicity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ian/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:4: RuntimeWarning: invalid value encountered in log\n"
     ]
    }
   ],
   "source": [
    "for region in demand.keys():\n",
    "\n",
    "    df = pd.DataFrame(demand[region], columns=['actual'])\n",
    "    df['y'] = np.log(df.loc[:, 'actual'])\n",
    "    df['ds'] = df.index\n",
    "    \n",
    "    m = Prophet(yearly_seasonality=True, weekly_seasonality=True)\n",
    "    m.fit(df)\n",
    "    modelled = m.predict(df)\n",
    "    modelled['norm'] = modelled.actual - np.exp(modelled.yhat)\n",
    "    modelled.set_index('ds', drop=True, inplace=True)\n",
    "    \n",
    "    demand[region] = modelled.loc[:, ['actual', 'norm']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### isolating anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# build a list of anomalies\n",
    "anoms = []\n",
    "for region in demand.keys():\n",
    "    df = demand[region]\n",
    "    std = np.std(demand[region]['norm'])\n",
    "    mean = np.mean(demand[region]['norm'])\n",
    "    \n",
    "    # we're specifically interested in demand PEAKS, not unusual dips\n",
    "    # so we'll only isolate the abnormally high \n",
    "    regional_anoms = df[df.norm > (mean + 2*std)].index\n",
    "    for day in regional_anoms:\n",
    "        anoms.append((region, day))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# rewrite the clean demand data\n",
    "with open('./data/anoms.pickle', 'wb') as handle:\n",
    "    pickle.dump(anoms, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#anoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# write anomalies to file\n",
    "with open('./data/demand.pickle', 'wb') as handle:\n",
    "    pickle.dump(demand, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PLOTS FOR PAPER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12b9e2f10>"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZoAAAEiCAYAAADAnxR7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b96fbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "demand['Texas'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('./data/region_weather.pickle', 'rb') as handle:\n",
    "    regional_weather = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0YAAAINCAYAAAAazG8DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/slAy2gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127c3b9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2)\n",
    "fig.set_size_inches(14,8)\n",
    "fig.subplots_adjust(hspace=0.3)\n",
    "axs[0].plot(regional_weather['California'])\n",
    "axs[1].plot(regional_weather['California'][2880:3216])\n",
    "\n",
    "axs[0].set_ylabel('Temp (C)', size=15)\n",
    "axs[1].set_ylabel('Temp (C)', size=15)\n",
    "axs[0].tick_params(axis='both', which='major', labelsize=12)\n",
    "axs[1].tick_params(axis='both', which='major', labelsize=12)\n",
    "\n",
    "for label in axs[0].get_xmajorticklabels() + axs[1].get_xmajorticklabels():\n",
    "    label.set_rotation(30)\n",
    "    label.set_horizontalalignment(\"right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "std = np.std(demand['Texas']['norm'])\n",
    "mean = np.mean(demand['Texas']['norm'])\n",
    "std_line  = mean + 2*std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA28AAAIDCAYAAACTjiZpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a2ddd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2)\n",
    "fig.set_size_inches(14,8)\n",
    "fig.subplots_adjust(hspace=0.3)\n",
    "axs[0].plot(demand['Texas']['actual'])\n",
    "axs[1].plot(demand['Texas']['norm'])\n",
    "axs[1].axhline(y=std_line, color='k', linestyle='--')\n",
    "\n",
    "axs[0].set_ylabel('Real Demand (MWh)', size=15)\n",
    "axs[1].set_ylabel('Norm. Demand (MWh)', size=15)\n",
    "axs[1].set_ylim([-300000,300000])\n",
    "axs[0].tick_params(axis='both', which='major', labelsize=12)\n",
    "axs[1].tick_params(axis='both', which='major', labelsize=12)\n",
    "\n",
    "\n",
    "for label in axs[0].get_xmajorticklabels() + axs[1].get_xmajorticklabels():\n",
    "    label.set_rotation(30)\n",
    "    label.set_horizontalalignment(\"right\")"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}