13_timeseries.ipynb

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    "# Time Series Data\n",
    "* We've already seen pandas can handle date/time formats\n",
    "* Time series data adds new manipulation options to our data, and pandas was actually developed with time series data in mind."
   ]
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    "## Resampling \n",
    "* the process of converting a time series from one frequency to another.\n",
    "  * downsampling: going from a high frequency (e.g. daily) to a lower frequency (e.g. weekly)\n",
    "  * upsampling: going from a lower frequency to higher frequency\n",
    "  * remapping: aligning data to a set frequency (e.g. mapping weekly data to sundays)"
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    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# We can create date ranges with\n",
    "pd.date_range?"
   ]
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       "2018-01-01    0.0\n",
       "2018-01-02    0.0\n",
       "2018-01-03    0.0\n",
       "2018-01-04    0.0\n",
       "2018-01-05    0.0\n",
       "Freq: D, dtype: float64"
      ]
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   "source": [
    "# Some sample data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "dates = pd.date_range(start='1/1/2018', end='05/31/2018')\n",
    "ts=pd.Series(np.zeros(len(dates)), index=dates)\n",
    "ts.head()\n",
    "#ts = pd.Series(np.random.randn(len(dates)), index=dates)\n",
    "#ts.head()"
   ]
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    "# You need a date or time index on your dataframe to do some resampling\n",
    "# When we resample we need to determine the new frequency we want and how we want \n",
    "# to resample\n",
    "# Let's change our daily data down to weekly data\n",
    "resampler=ts.resample('W')\n",
    "resampler"
   ]
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   "source": [
    "# Just like groupby, this is an object which will do the resampling for us\n",
    "# Since we are downsampling (D->W) we need to decide how to aggregate our datapoints\n",
    "# We are now very used to this!\n",
    "resampler.apply(np.mean).head()"
   ]
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    "* Notice the frequency is now W-SUN (weekly beginning on sunday)\n",
    "* When we downsample we are \"binning\" our values and need to determine which end of the bin is open/closed\n",
    "* By default the right side is **closed** for weekly binning, which we did here\n",
    "  * Closed vs. open can be confusing! For example, is an observation at midnight on October 13, 2020 a Tuesday observation, or a Monday observation?\n",
    "  * If you have defined thing as left closed, then it's Monday. If you defined them as right closed, then it's Tuesday."
   ]
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    "# Here's an example\n",
    "* if you have a bunch of time sampled data in seconds and you are downsampling to minutes then:\n",
    "  * if you are **left closed** you are saying \"downsample to minutes where all of the values are **<** the next minute whole number\"\n",
    "  * if you are **right closed** you are saying \"downsample to minutes where all of the values are **<=** the next minute whole number\"\n",
    "* clear as a No.3 Dark Maple Syrup, eh?"
   ]
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   "source": [
    "# Let's look at 9 seconds which cross the minute boundry\n",
    "index = pd.date_range('10/13/2020 12:59:55', periods=9, freq='S')\n",
    "series = pd.Series(range(9), index=index)\n",
    "series"
   ]
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    "# if we resample this to 1 minute intervals closed on the left \n",
    "# then the first five seconds will be binned to the left value (<)\n",
    "series.resample('1T',closed=\"left\").apply(np.max)"
   ]
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    "# if we resample this to 1 minute intervals closed on the right \n",
    "# then the first six seconds will be binned to the left value (<=)\n",
    "series.resample('1T',closed=\"right\").apply(np.max)"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "<a href=\"https://stackoverflow.com/questions/48340463/how-to-understand-closed-and-label-arguments-in-pandas-resample-method\">https://stackoverflow.com/questions/48340463/how-to-understand-closed-and-label-arguments-in-pandas-resample-method</a>\n",
    "<img src=\"https://i.stack.imgur.com/nX6yv.png\"></img>"
   ]
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   "source": [
    "# another example, with 12 periods in minute chunks\n",
    "ts = pd.Series(np.arange(12), index=pd.date_range(start='1/1/2018', periods=12,freq='T'))\n",
    "ts"
   ]
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   "source": [
    "# what do you think will happen if we resample to the nearest whole 5 minute mark but \n",
    "# close left?\n",
    "# look at the data, write down in your mind\n",
    "ts.resample(\"5min\", closed='left').apply(np.sum)"
   ]
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   "source": [
    "# what do you think will happen if we resample to the nearest whole 5 minute mark but close right?\n",
    "# look at the data, write down in your mind\n",
    "ts.resample(\"5min\", closed='right').apply(np.sum)"
   ]
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   "execution_count": null,
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   "source": [
    "# Also, downresampling really is an aggregation exercise, so you can do all \n",
    "# sorts of things\n",
    "# What do you think this does in real language?\n",
    "ts.resample('2min').apply(\n",
    "    lambda x: pd.Series({\"mean\":np.mean(x),\"max\":np.max(x)})).unstack()"
   ]
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   "source": [
    "# Inline activity!\n",
    "df=pd.read_csv('datasets/si330_dstat.csv',skiprows=5)\n",
    "df.head()\n",
    "# How do we generate a dataframe which shows the 30 second averages and the \n",
    "# standard deviations of the idl (CPU idle) time?\n",
    "\n",
    "df=df[[\"epoch\",\"idl\"]]\n",
    "\n",
    "# convert our epoch time into a date time stamp\n",
    "df=df.set_index(pd.DatetimeIndex(pd.to_datetime(df[\"epoch\"],unit='s')))\n",
    "#delete the old epoch column\n",
    "del df[\"epoch\"]\n",
    "#df\n",
    "\n",
    "# let's spot check the mean value\n",
    "df.resample(\"30S\").apply(np.mean)\n",
    "\n",
    "def calculate(frame):\n",
    "    print(type(frame))\n",
    "    #retval=pd.Series({\"average\":np.average(frame), \n",
    "                      #\"std\":np.std(frame)[0], \n",
    "                      #\"number of observations\":len(frame)})\n",
    "    return 1#retval\n",
    "df.resample(\"30S\").apply(calculate)"
   ]
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   "source": [
    "# With upsampling there is no need to aggregate. \n",
    "\n",
    "# let's create a dataframe, with two weekly indices, and four columns. First the \n",
    "# indicies\n",
    "dates = pd.date_range(start='1/1/2018', periods=2, freq='W')\n",
    "# now let's fill in the DataFrame\n",
    "df = pd.DataFrame(np.random.randn(2,4), index=dates, \n",
    "                  columns=['col1','col2','col3','col4'])\n",
    "df.head()"
   ]
  },
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   "source": [
    "# Now we upsample from weekly frequency to daily frequency,\n",
    "df_daily = df.resample('D').asfreq()\n",
    "df_daily.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# As you notice, there will be NaN values, let's engage in interpolation\n",
    "# Foprward fill or backward fill\n",
    "df.resample('D').ffill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# We can also choose to only fill a certain number of periods, by using the limit \n",
    "# parameter in the ffill() function. For instance, here, we are limiting to \n",
    "# interpolating three observations\n",
    "df.resample('D').bfill(limit=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "# Working with time series data\n",
    "* we've now seen downsampling and upsampling, and have a better sense of how date ranges are handled in pandas\n",
    "* lets go back to a favorite dataset of ours which has lots of interesting time series data in it and try and explore a bit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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      "Index(['Ticket #', 'Badge', 'Issue Date ', 'IssueTime', 'Plate', 'State',\n",
      "       'Make', 'Model', 'Violation', ' Description', 'Location', 'Meter',\n",
      "       ' Fine ', 'Penalty'],\n",
      "      dtype='object')\n"
     ]
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2060009984</td>\n",
       "      <td>827</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1155</td>\n",
       "      <td>DGS5220</td>\n",
       "      <td>MI</td>\n",
       "      <td>HOND</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A15</td>\n",
       "      <td>15' FIRE HYDRAN</td>\n",
       "      <td>402 PAULINE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2060009983</td>\n",
       "      <td>827</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1153</td>\n",
       "      <td>CGT0482</td>\n",
       "      <td>MI</td>\n",
       "      <td>CHEV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A20</td>\n",
       "      <td>BLOCKING D'WAY</td>\n",
       "      <td>312 PAULINE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2060009982</td>\n",
       "      <td>827</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1140</td>\n",
       "      <td>CLL1347</td>\n",
       "      <td>MI</td>\n",
       "      <td>JAGU</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A01</td>\n",
       "      <td>EXPIRED METER</td>\n",
       "      <td>300 BLK OF ASHLEY NO</td>\n",
       "      <td>1165</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2060009981</td>\n",
       "      <td>827</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1137</td>\n",
       "      <td>BGU3003</td>\n",
       "      <td>MI</td>\n",
       "      <td>PORS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A01</td>\n",
       "      <td>EXPIRED METER</td>\n",
       "      <td>200 BLK OF MAIN ST N</td>\n",
       "      <td>1026A</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2060009980</td>\n",
       "      <td>827</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1135</td>\n",
       "      <td>CMUDAD</td>\n",
       "      <td>MI</td>\n",
       "      <td>FORD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A01</td>\n",
       "      <td>EXPIRED METER</td>\n",
       "      <td>200 BLK OF MAIN ST N</td>\n",
       "      <td>1023A</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2060010000</td>\n",
       "      <td>827</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1339</td>\n",
       "      <td>0</td>\n",
       "      <td>NE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2060009978</td>\n",
       "      <td>827</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1124</td>\n",
       "      <td>DFG9768</td>\n",
       "      <td>MI</td>\n",
       "      <td>HOND</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A01</td>\n",
       "      <td>EXPIRED METER</td>\n",
       "      <td>300 BLK OF DETROIT S</td>\n",
       "      <td>1533</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2060009977</td>\n",
       "      <td>827</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1123</td>\n",
       "      <td>B61AN</td>\n",
       "      <td>MI</td>\n",
       "      <td>LEXU</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A01</td>\n",
       "      <td>EXPIRED METER</td>\n",
       "      <td>300 BLK OF DETROIT S</td>\n",
       "      <td>1539</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2060009976</td>\n",
       "      <td>827</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1122</td>\n",
       "      <td>AVC847</td>\n",
       "      <td>MI</td>\n",
       "      <td>FORD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A01</td>\n",
       "      <td>EXPIRED METER</td>\n",
       "      <td>300 BLK OF DETROIT S</td>\n",
       "      <td>1553</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2060010001</td>\n",
       "      <td>827</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1429</td>\n",
       "      <td>DEC7315</td>\n",
       "      <td>MI</td>\n",
       "      <td>CHEV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A01</td>\n",
       "      <td>EXPIRED METER</td>\n",
       "      <td>1300 BLK OF ANN EAST</td>\n",
       "      <td>5031B</td>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2060010002</td>\n",
       "      <td>827</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1435</td>\n",
       "      <td>Y838603</td>\n",
       "      <td>IL</td>\n",
       "      <td>DODG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A01</td>\n",
       "      <td>EXPIRED METER</td>\n",
       "      <td>300 BLK OF OBSERVATO</td>\n",
       "      <td>5073A</td>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2060009972</td>\n",
       "      <td>827</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1110</td>\n",
       "      <td>AWL972</td>\n",
       "      <td>MI</td>\n",
       "      <td>ACUR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A01</td>\n",
       "      <td>EXPIRED METER</td>\n",
       "      <td>LOT 11</td>\n",
       "      <td>6A</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Ticket #  Badge Issue Date   IssueTime    Plate State  Make Model  \\\n",
       "0   2060009973    827  2016-01-02       1111    A27VT    MI  SATU   NaN   \n",
       "1   2060009996    827  2016-01-02       1304  DCZ5024    MI  HOND   NaN   \n",
       "2   2060009995    827  2016-01-02       1302  BEJ2048    MI  FORD   NaN   \n",
       "3   2060009994    827  2016-01-02       1257  DKQ9157    MI  FORD   NaN   \n",
       "4   2060009993    827  2016-01-02       1254  GJR3356    OH  CHEV   NaN   \n",
       "5   2060009992    827  2016-01-02       1250  DEW0285    MI  DODG   NaN   \n",
       "6   2060009991    827  2016-01-02       1246  BXT4125    MI  FORD   NaN   \n",
       "7   2060009990    827  2016-01-02       1242  DJP4710    MI  HOND   NaN   \n",
       "8   2060009989    827  2016-01-02       1231  DSR7530    OH   BMW   NaN   \n",
       "9   2060009988    827  2016-01-02       1230  CGX8176    MI  VOLK   NaN   \n",
       "10  2060009987    827  2016-01-02       1205  BRU7564    MI   GMC   NaN   \n",
       "11  2060009986    827  2016-01-02       1204  BNA8691    MI  FORD   NaN   \n",
       "12  2060009985    827  2016-01-02       1203   6LNR36    MI  SATU   NaN   \n",
       "13  2060009984    827  2016-01-02       1155  DGS5220    MI  HOND   NaN   \n",
       "14  2060009983    827  2016-01-02       1153  CGT0482    MI  CHEV   NaN   \n",
       "15  2060009982    827  2016-01-02       1140  CLL1347    MI  JAGU   NaN   \n",
       "16  2060009981    827  2016-01-02       1137  BGU3003    MI  PORS   NaN   \n",
       "17  2060009980    827  2016-01-02       1135   CMUDAD    MI  FORD   NaN   \n",
       "18  2060010000    827  2016-01-02       1339        0    NE   NaN   NaN   \n",
       "19  2060009978    827  2016-01-02       1124  DFG9768    MI  HOND   NaN   \n",
       "20  2060009977    827  2016-01-02       1123    B61AN    MI  LEXU   NaN   \n",
       "21  2060009976    827  2016-01-02       1122   AVC847    MI  FORD   NaN   \n",
       "22  2060010001    827  2016-01-02       1429  DEC7315    MI  CHEV   NaN   \n",
       "23  2060010002    827  2016-01-02       1435  Y838603    IL  DODG   NaN   \n",
       "24  2060009972    827  2016-01-02       1110   AWL972    MI  ACUR   NaN   \n",
       "\n",
       "   Violation      Description              Location  Meter   Fine   Penalty  \n",
       "0        A01    EXPIRED METER                LOT 11     5A      20        0  \n",
       "1        A04  NO PRKNG ANYTME  200 BLK OF STATE SOU    NaN      25        0  \n",
       "2        A01    EXPIRED METER  900 BLK OF WASHINGTO  5066B      20        0  \n",
       "3        A01    EXPIRED METER  300 BLK OF THAYER SO   5088      10        0  \n",
       "4        A01    EXPIRED METER  700 BLK OF WASHINGTO   5046      20       50  \n",
       "5        A01    EXPIRED METER  900 BLK OF WASHINGTO  5121B      20       20  \n",
       "6        A04  NO PRKNG ANYTME   300 BLK OF FLETCHER    NaN      25        0  \n",
       "7        A01    EXPIRED METER  900 BLK OF WASHINGTO   5068      10        0  \n",
       "8        A04  NO PRKNG ANYTME     800 BLK OF POTTER    NaN      35        0  \n",
       "9        A04  NO PRKNG ANYTME     800 BLK OF POTTER    NaN      25        0  \n",
       "10       A01    EXPIRED METER  200 BLK OF HOOVER EA  4057B      10        0  \n",
       "11       A01    EXPIRED METER  300 BLK OF HOOVER EA  4055B      10        0  \n",
       "12       A01    EXPIRED METER  300 BLK OF HOOVER EA  4051B      20        0  \n",
       "13       A15  15' FIRE HYDRAN           402 PAULINE    NaN      40        0  \n",
       "14       A20   BLOCKING D'WAY           312 PAULINE    NaN      25        0  \n",
       "15       A01    EXPIRED METER  300 BLK OF ASHLEY NO   1165      20        0  \n",
       "16       A01    EXPIRED METER  200 BLK OF MAIN ST N  1026A      10        0  \n",
       "17       A01    EXPIRED METER  200 BLK OF MAIN ST N  1023A      10        0  \n",
       "18       NaN              NaN                   NaN    NaN       0        0  \n",
       "19       A01    EXPIRED METER  300 BLK OF DETROIT S   1533      10        0  \n",
       "20       A01    EXPIRED METER  300 BLK OF DETROIT S   1539      20        0  \n",
       "21       A01    EXPIRED METER  300 BLK OF DETROIT S   1553      10        0  \n",
       "22       A01    EXPIRED METER  1300 BLK OF ANN EAST  5031B      20       50  \n",
       "23       A01    EXPIRED METER  300 BLK OF OBSERVATO  5073A      20       50  \n",
       "24       A01    EXPIRED METER                LOT 11     6A      10        0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_excel(\"datasets/AnnArbor-TicketViolation2016.xls\",skiprows=1)\n",
    "print(df.columns)\n",
    "df.head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>Ticket #</th>\n",
       "      <th>Badge</th>\n",
       "      <th>Issue Date</th>\n",
       "      <th>IssueTime</th>\n",
       "      <th>Plate</th>\n",
       "      <th>State</th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Violation</th>\n",
       "      <th>Description</th>\n",
       "      <th>Location</th>\n",
       "      <th>Meter</th>\n",
       "      <th>Fine</th>\n",
       "      <th>Penalty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-02 11:11:00</th>\n",
       "      <td>2060009973</td>\n",
       "      <td>827</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1111</td>\n",
       "      <td>A27VT</td>\n",
       "      <td>MI</td>\n",
       "      <td>SATU</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A01</td>\n",
       "      <td>EXPIRED METER</td>\n",
       "      <td>LOT 11</td>\n",
       "      <td>5A</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02 13:04:00</th>\n",
       "      <td>2060009996</td>\n",
       "      <td>827</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1304</td>\n",
       "      <td>DCZ5024</td>\n",
       "      <td>MI</td>\n",
       "      <td>HOND</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A04</td>\n",
       "      <td>NO PRKNG ANYTME</td>\n",
       "      <td>200 BLK OF STATE SOU</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02 13:02:00</th>\n",
       "      <td>2060009995</td>\n",
       "      <td>827</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1302</td>\n",
       "      <td>BEJ2048</td>\n",
       "      <td>MI</td>\n",
       "      <td>FORD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A01</td>\n",
       "      <td>EXPIRED METER</td>\n",
       "      <td>900 BLK OF WASHINGTO</td>\n",
       "      <td>5066B</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02 12:57:00</th>\n",
       "      <td>2060009994</td>\n",
       "      <td>827</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1257</td>\n",
       "      <td>DKQ9157</td>\n",
       "      <td>MI</td>\n",
       "      <td>FORD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A01</td>\n",
       "      <td>EXPIRED METER</td>\n",
       "      <td>300 BLK OF THAYER SO</td>\n",
       "      <td>5088</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02 12:54:00</th>\n",
       "      <td>2060009993</td>\n",
       "      <td>827</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1254</td>\n",
       "      <td>GJR3356</td>\n",
       "      <td>OH</td>\n",
       "      <td>CHEV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A01</td>\n",
       "      <td>EXPIRED METER</td>\n",
       "      <td>700 BLK OF WASHINGTO</td>\n",
       "      <td>5046</td>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Ticket #  Badge Issue Date   IssueTime    Plate State  \\\n",
       "2016-01-02 11:11:00  2060009973    827  2016-01-02       1111    A27VT    MI   \n",
       "2016-01-02 13:04:00  2060009996    827  2016-01-02       1304  DCZ5024    MI   \n",
       "2016-01-02 13:02:00  2060009995    827  2016-01-02       1302  BEJ2048    MI   \n",
       "2016-01-02 12:57:00  2060009994    827  2016-01-02       1257  DKQ9157    MI   \n",
       "2016-01-02 12:54:00  2060009993    827  2016-01-02       1254  GJR3356    OH   \n",
       "\n",
       "                     Make Model Violation      Description  \\\n",
       "2016-01-02 11:11:00  SATU   NaN       A01    EXPIRED METER   \n",
       "2016-01-02 13:04:00  HOND   NaN       A04  NO PRKNG ANYTME   \n",
       "2016-01-02 13:02:00  FORD   NaN       A01    EXPIRED METER   \n",
       "2016-01-02 12:57:00  FORD   NaN       A01    EXPIRED METER   \n",
       "2016-01-02 12:54:00  CHEV   NaN       A01    EXPIRED METER   \n",
       "\n",
       "                                 Location  Meter   Fine   Penalty  \n",
       "2016-01-02 11:11:00                LOT 11     5A      20        0  \n",
       "2016-01-02 13:04:00  200 BLK OF STATE SOU    NaN      25        0  \n",
       "2016-01-02 13:02:00  900 BLK OF WASHINGTO  5066B      20        0  \n",
       "2016-01-02 12:57:00  300 BLK OF THAYER SO   5088      10        0  \n",
       "2016-01-02 12:54:00  700 BLK OF WASHINGTO   5046      20       50  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First up, let's create a date/time index. We have an issue date column and \n",
    "# an issuetime column\n",
    "def clean_time(x):\n",
    "    issue_time=str(x[\"IssueTime\"])\n",
    "    if len(issue_time) < 4:\n",
    "        issue_time=\"0\"+issue_time\n",
    "    date_time=\"{}{}:{}\".format(\n",
    "        str(x[\"Issue Date \"])[0:11], \n",
    "        issue_time[0:2], \n",
    "        issue_time[-2:], axis=1)\n",
    "    return pd.to_datetime(date_time, format='%Y-%m-%d %H:%M')\n",
    "df=df.set_index(df[[\"Issue Date \",\"IssueTime\"]].apply(clean_time, axis=1))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd44a9082b0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# Now let's plot the fines over the year!\n",
    "import matplotlib.pyplot as plt\n",
    "df[\" Fine \"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd4498f7be0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# gah! That's meaningless. How would we find signal in that noise?\n",
    "# let's zoom in on a single month, pandas does the \"right thing\" with date/time slicing!\n",
    "df.loc[\"2016-01-01\":\"2016-02-01\", \" Fine \"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True, ..., False, False, False])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This, is, btw, much cooler than it seems at first blush, check this out\n",
    "df.index < \"2016-01-03\"\n",
    "# WOW!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>Ticket #</th>\n",
       "      <th>Badge</th>\n",
       "      <th>Issue Date</th>\n",
       "      <th>IssueTime</th>\n",
       "      <th>Plate</th>\n",
       "      <th>State</th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Violation</th>\n",
       "      <th>Description</th>\n",
       "      <th>Location</th>\n",
       "      <th>Meter</th>\n",
       "      <th>Fine</th>\n",
       "      <th>Penalty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-02 11:11:00</th>\n",
       "      <td>2060009973</td>\n",
       "      <td>827</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1111</td>\n",
       "      <td>A27VT</td>\n",
       "      <td>MI</td>\n",
       "      <td>SATU</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A01</td>\n",
       "      <td>EXPIRED METER</td>\n",
       "      <td>LOT 11</td>\n",
       "      <td>5A</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02 13:04:00</th>\n",
       "      <td>2060009996</td>\n",
       "      <td>827</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1304</td>\n",
       "      <td>DCZ5024</td>\n",
       "      <td>MI</td>\n",
       "      <td>HOND</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A04</td>\n",
       "      <td>NO PRKNG ANYTME</td>\n",
       "      <td>200 BLK OF STATE SOU</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02 13:02:00</th>\n",
       "      <td>2060009995</td>\n",
       "      <td>827</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1302</td>\n",
       "      <td>BEJ2048</td>\n",
       "      <td>MI</td>\n",
       "      <td>FORD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A01</td>\n",
       "      <td>EXPIRED METER</td>\n",
       "      <td>900 BLK OF WASHINGTO</td>\n",
       "      <td>5066B</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02 12:57:00</th>\n",
       "      <td>2060009994</td>\n",
       "      <td>827</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1257</td>\n",
       "      <td>DKQ9157</td>\n",
       "      <td>MI</td>\n",
       "      <td>FORD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A01</td>\n",
       "      <td>EXPIRED METER</td>\n",
       "      <td>300 BLK OF THAYER SO</td>\n",
       "      <td>5088</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02 12:54:00</th>\n",
       "      <td>2060009993</td>\n",
       "      <td>827</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1254</td>\n",
       "      <td>GJR3356</td>\n",
       "      <td>OH</td>\n",
       "      <td>CHEV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A01</td>\n",
       "      <td>EXPIRED METER</td>\n",
       "      <td>700 BLK OF WASHINGTO</td>\n",
       "      <td>5046</td>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Ticket #  Badge Issue Date   IssueTime    Plate State  \\\n",
       "2016-01-02 11:11:00  2060009973    827  2016-01-02       1111    A27VT    MI   \n",
       "2016-01-02 13:04:00  2060009996    827  2016-01-02       1304  DCZ5024    MI   \n",
       "2016-01-02 13:02:00  2060009995    827  2016-01-02       1302  BEJ2048    MI   \n",
       "2016-01-02 12:57:00  2060009994    827  2016-01-02       1257  DKQ9157    MI   \n",
       "2016-01-02 12:54:00  2060009993    827  2016-01-02       1254  GJR3356    OH   \n",
       "\n",
       "                     Make Model Violation      Description  \\\n",
       "2016-01-02 11:11:00  SATU   NaN       A01    EXPIRED METER   \n",
       "2016-01-02 13:04:00  HOND   NaN       A04  NO PRKNG ANYTME   \n",
       "2016-01-02 13:02:00  FORD   NaN       A01    EXPIRED METER   \n",
       "2016-01-02 12:57:00  FORD   NaN       A01    EXPIRED METER   \n",
       "2016-01-02 12:54:00  CHEV   NaN       A01    EXPIRED METER   \n",
       "\n",
       "                                 Location  Meter   Fine   Penalty  \n",
       "2016-01-02 11:11:00                LOT 11     5A      20        0  \n",
       "2016-01-02 13:04:00  200 BLK OF STATE SOU    NaN      25        0  \n",
       "2016-01-02 13:02:00  900 BLK OF WASHINGTO  5066B      20        0  \n",
       "2016-01-02 12:57:00  300 BLK OF THAYER SO   5088      10        0  \n",
       "2016-01-02 12:54:00  700 BLK OF WASHINGTO   5046      20       50  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# so this means we can use date/times as masks!\n",
    "df[df.index<\"2016-02\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd449800cf8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now let's resample this and look at daily totals\n",
    "df.loc[\"2016-01-01\":\"2016-02-01\", \" Fine \"].resample(\"1D\").apply(np.sum).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd4498f7908>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# January 10th 2016 was a sunday! Looks pretty clear that sundays very few tickets \n",
    "# are given out!\n",
    "# Also, David Bowie died on this day. :(\n",
    "# How do things change if we look at mean values?\n",
    "df.loc[\"2016-01-01\":\"2016-02-01\", \" Fine \"].resample(\"1D\").apply(np.mean).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd449800cc0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We could also look at tickets per hour in a single week\n",
    "df.loc[\"2016-01-11\":\"2016-01-18\", \" Fine \"].resample(\"1H\").apply(len).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd449546be0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# That 13th-14th has some big values, let's zoom in a bit\n",
    "df.loc[\"2016-01-13\":\"2016-01-14\", \" Fine \"].resample(\"5T\").apply(len).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd4494abd68>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XndCRHpvulvkGxg7knIRz+GDHB5gtLu3Po7ADnxX3IzVHaJJNPue5g9qpqnAPucW5pHVOc+ua1LQB0zCYDPxi/MVtcypax2fF3VhlBHwjDdJGYriWtaDEXeFqpJTkluQyKG6QW+cd230sCWEJKi3SC/BZcfelDUwtyYzLVIuqCpdzsOoglQ2Vbou327ClRa47tI695XvdOrfiRHxX3KsMBIgAuoR38bQp7SIjLoMDlQeoqK/wtCkKP8bWHMYdmTInc12f69Dr9CzascjtcyuO06a4CyFShBA/CiHyhRDbhBD3WI/HCCFWCCF2W5+jW1zziBCiQAixUwgxzhWGG0wGuoR3IVDnWxUUbJ6Uqws5Kc5ucktyCQ0MJS0qze1zx4bGNqdFVjVUuX1+hYY9nnsT8ICUcgAwApgthBgI/AVYKaXsA6y0/oz1valAOjAeeF0I4fRqQr5Q6rc10mPTEQhySlTbPYXryC3OJT023WOFvJrTIgtUWqSnaFPcpZSHpJSbra+rgHwgCZgILLCetgC4xvp6IrBYSlkvpdwHFADDnW24ocrgMxuYWhIRFEGvqF4q7q5wGQ3mBnYc2+GRkIyN9Nh0hsQP4YMdH2CRFo/ZcTbTrpi7ECIVOAfYACRKKQ+B9gcAsBWvSAIKW1xmsB5zGjWNNZTVlfmk5w7H2+5JKT1tisIP2VG2gyZLk9szZU7mxgE3UlhVqNIiPYTd4i6EiAA+Be6VUp5pf3Fr/e5OUTEhxCwhRLYQIru4uNheMwAtJAO+lyljIzMuk7K6Mg5VH/K0KQo/xJZq6+5MmZMZ22MsCaEJLMpXC6uewC5xF0Lo0YT9fSnlZ9bDR4QQXa3vdwVsDUINQEqLy5OBopPHlFLOlVJmSSmz4uPj22W0Tdx9MSwDkBGvNjMpXEdOcQ4JoQkkhid61A69Ts+UflNYU7SGfRX7PGrL2Yg92TICmA/kSylfaPHWEmCG9fUM4MsWx6cKIYKFED2BPsBG55l8vI57UqRveu59o/sSpAtS5X8VLiG3JNej8faWXN/3evQ6vaoW6QHs8dwvAKYDY4QQW6yPCcBTwGVCiN3AZdafkVJuAz4CtgPLgNlSSqcWmjCajIQFhhEdHN32yV6IXqdnQOwA5bkrnE55XTmFVYUeD8nYsKVFflnwJaYGk6fNOauwJ1vmFymlkFIOklIOsT6+kVKWSinHSin7WJ/LWlzzhJQyTUrZT0r5rbONNlQZSIpMQrup8E0y4zLJL9NaoCkUzsLmMAyK9+xiakum9Z9GTVMNX+75su2TFU7DJ3eoGky+mQbZkoy4DGqbalXnGoVTyS3JRSAYGDvQ06Y0kx6XzuD4wSzKX6TSIt2Iz4m7lBKjyeizmTI2mtvuqdCMwonklOSQ1jmNcH24p005gWn9p3Gw6iBrjGs8bcpZg8+Je1ldGbVNtT6b424jJTKFqOAotZlJ4TSklOSV5HlVSMbGZT0uIz40nvd3qGqR7sLnxN2XmmKfCSFE82YmhcIZFFYVUlFf4TWLqS3RB+iZ3G8ya4wqLdJd+Jy4+2Id99ORGZdJQXkBNY01njZF4QfY6hV5o7gDTO47mUBdIIt3LPa0KWcFPifuNs+9W0Q3D1viOJlxmVikhfyyfE+bovADcoutlSA7u78SpD3EhcYxPnU8XxR8odIi3YDPibvRZCQuNI7QwFBPm+IwzW331GYmhRPIK8ljYOxAry6DfeOAG1VapJvwOXE3VBl8PlPGRkxIDEkRSSrurnCYBnMD+WX5Hi8W1hYZcRkMihukqkW6AZ8Td1+t4346VNs9hTPYWbaTRkuj15QdOBPTBkzjQOUB1hat9bQpfo1PiXujpZFD1Yf8xnMHzZMpqi6ipLbE06YofBhvX0xtyeU9LicuNE410XYxPiXuh02HsUiLz6dBtkS13VM4g7ySPOJD40kM82wlSHvQB+iZ0ncKvxh/4UDlAU+b47f4lLg357j7UVhmQOwAAkSAaruncIjcklwy4zJ9pt7S5H5aWqSqFuk6fFPc/chzDw0MpU90HxV3V3SYivoKDlQe8Il4u4240DjGpY7ji4IvqG6s9rQ5folPibuxykigLpCEsIS2T/YhVNs9hSN4S+el9jKt/zSqG6v5skClRboCnxJ3g8lAt/BuHuvo7ioy4zKpaqjiYNVBT5ui8EFyi7VKkOmx6Z42pV0Mih9EZlymSot0ET4l7sYq368G2RrNm5lUvruiA+SW5JLWOY2IoAhPm9Juft//9+yv3M+6onWeNsXv8ClxN5gMfrWYaiMtKo3QwFC1U1XRbqSUzYupvsi41HHEhsSyaIdqou1sfEbcTQ0myuvL/dJzD9AFkB6brhZVFe3GUGWgvL68+e7P1wgKCGJKvyn8bPiZg5UqLOlMfEbcjSb/qQbZGra2e43mRk+bovAhbCm03ljD3V4m951MgAhQaZFOxmfE3R/TIFuSEZdBo6WRXcd2edoUhQ+RV5JHaGAovTv39rQpHSY+LJ7LUy/ni4IvVPlrJ9KmuAsh3hJCHBVC5LU49rgQwiiE2GJ9TGjx3iNCiAIhxE4hxDhnGWqo8r8NTC2xxUzVZiZFe8gpyWFAzACvrgRpD9MGTMPUaGLJniWeNsVvsMdzfwcY38rxF6WUQ6yPbwCEEAOBqUC69ZrXhRBOyVs0moxE6CPoFNTJGcN5HV3CuxAbEqvi7gq7aTQ3sqN0h88uprZkUNwgMmIzWLRDNdF2Fm2Ku5RyNVBm53gTgcVSynop5T6gABjugH3NGKq0TBlf2V7dXoQQZMZnqnRIhd3sPLaTBkuDT+1MPR1CCKYNmMa+in2sL1rvaXP8Akdi7v8nhMixhm2irceSgMIW5xisxxzGaDL6bbzdRmZcJvsq9lHVUOVpUxQ+gM0R8PYa7vYyLnUcMSExKi3SSXRU3N8A0oAhwCHgeevx1tzqVvfUCyFmCSGyhRDZxcXFZ5xMSonR5J8bmFpiS2fbVqoqRCraJrc4l7jQOLqEd/G0KU4hKCCIyX0ns9qwmsLKwrYvUJyRDom7lPKIlNIspbQA8zgeejEAKS1OTQaKTjPGXClllpQyKz4+/ozzldSWUG+u99vFVBuq7Z6iPeSW5JIRl+FXocop/aZoaZE7VVqko3RI3IUQXVv8OAmwrQIuAaYKIYKFED2BPsBGx0w8ngbp7557p6BOpHZKVXF3RZtU1Fewv3K/34RkbCSEJXBZj8v4fPfnKi3SQexJhfwAWAf0E0IYhBC3As8IIXKFEDnAaOA+ACnlNuAjYDuwDJgtpTQ7aqS/p0G2JDMuU1WIVLSJLavKHxZTT8aWFvnVnq88bYpPY0+2zO+llF2llHopZbKUcr6UcrqUMlNKOUhK+Tsp5aEW5z8hpUyTUvaTUn7rDCNtnnu3iG7OGM6ryYjLoKS2hCM1RzxtisKLyS3RKkFmxPpm2YEzMTh+MANjB7JoxyLl5DiAT+xQNVYZSQhLIDgg2NOmuBxbzrLKd1ecidySXHpF9fLJSpBtIYTgxgE3srdiL+sPqbTIjuIT4m4wGfw+DdJGv5h+6HV6tVNVcVqklOQW5/pssTB7GJ86XkuLzFdpkR3FJ8TdaDKeFfF20NLB+sf0V5674rQYTAaO1R/z6WJhbREUEMT1fa/nJ8NPFFaptMiO4PXi3mBu4Ej1Ec9lykgJdZVunTIjLoNtJdswWxxei1b4Ic2LqX5QduBMTOmrpUUu3rHY06b4JF4v7oeqDyGRnvHcqw7DhzfBUynwylD49i9QsBIa61w6bWZcJjVNNeyr2OfSeRS+SU5xDiEBIfSO9t1KkPaQGJ7IpT0uVWmRHcTrxd2WBulWz11K+G0h/Hc4FHwPI+6C2DTY9DYsvBae6QmLpsKv86Hc+Q0GVNs9xZnILcllQOwA9Dq9p01xOdMGTKOqsYqle5d62hSfw+vrhDY36XDXguqxA/DVPbD3R+g+En73KsRZPaSGGtj/C+z+DnYvh13WTM9uQ+GmTyEsxikm9OjUg0h9JLkluUzqM8kpYyr8g0ZzI/ml+UztP9XTpriFIfFDGBg7kHe3v8t1fa4jQOeUIrNnBT7huQfpgogPO3OJAoexmGH9/+D188HwK1z5PMz8+riwAwSFQd/L4crn4J4cmP0rjH0UijZD7sdOM0UndGTEZahFVcUp7Crf5TeVIO1BCMFtmbdxoPIAy/Yv87Q5PoX3i7vJQLeIbuiEC00t3glvXwHLHoYe58Nd6+Hc20B3hjmFgPi+MOoBSMyA3E+calJGXAa7ju2irsm18X2Fb2GrO+RvZQfOxNjuY+nduTfzcuapWu/twPvFvcpAUqQL4+2Fv8L/LoSSXTBpDtz4CXROafu6lmRcB4aNWkjHSWTGZWKWZnaU7XDamArfJ7ckl5iQGLqGd237ZD9BJ3Tcnnk7eyr28P2B7z1tjs/g/eLu6g1M2W9BYAjctQEGT9U88vaSca32vO0zp5llW1TNKVabmRTHySnOYVDcIL+qBGkP41LHkdoplbk5c1VJAjvxanGvqK+gqqHKdeLeWAc7lsKAqyEysePjRKdC8rmQ+6nTTIsPi6dLeBcVd1c0U9lQyf7K/WdNvL0lAboAbsu8jZ3HdrKqcJWnzfEJvFrcmzNlXJXjXrAC6iu1sIqjZFwHR3K1+L2TsFWIVCjg7Nm8dDom9JpAUkQSc3LmKO/dDnxC3F2W4577CYTFQc+LHR8rfRIgIM953ntmXKa21bzumNPGVPgutsXU9Lh0D1viGfQ6Pbdl3sa20m2sKVrjaXO8Hq8Wd5fWca+vgl3LIP0aCHBCun9kF0i9UBN3J3kVtri7Cs0oQFtM7RnVk05BnTxtiseYmDaRLuFdmLNVee9t4dXibjQZ6RTUicigSOcPvvNbaKqDjOudN2bm9VBaAIe2OmW49Nh0dEKnQjMKrRJkSe5ZG5KxoQ/Qc0vGLWwp3sLGww43efNrvFrcDVUG18Xbcz+BTkmQcp7zxhzwO9AFOi00E6YPo1dULyXuCoqqiyirKzur8ttPx7V9riU+NJ45OXM8bYpX49XibjQZXRNvrymDPSu1FMYzbVRqL2ExkDYW8j4Di3M2WwyKH0ReSZ66BT3LscXbz8ZMmZMJDghmZvpMfj38K5uPbPa0OV6L14q7RVpcV8c9fwlYmpyTJXMyGddBpUHb1OSM4eIyKK8vVzWtz3JySnIIDgimT3QfT5tyKr/Ohz0/uHXKyf0mExMSw9ycuW6d15fwWnE/WnOURkuja3Lc8z6FmDToOsT5Y/efoG2KclI5ghFdRxAgAnh3+7tOGU/hm+QW5zIgxgsrQW5aAF/fDx/+ASoMbps2NDCUPwz8A2uK1jTf1ShOpE1xF0K8JYQ4KoTIa3EsRgixQgix2/oc3eK9R4QQBUKInUKIcR01rDlTxtniXnUY9v2sediu2OUXHAl9x8H2L8Dc5PBwKZEp3NDvBj7e9TE7y5yXQ6/wHRotjeSX5XtfSObgevj6Aeh+PkizVk3VjeHDqf2nEhUcpbz302CP5/4OMP6kY38BVkop+wArrT8jhBgITAXSrde8LoToUI3O5hx3Z9eV2fYFILXMFleRcT1UF8P+1U4Z7q4hdxEZFMkzvz6jYu9nIbuP7abeXO9di6kVBvhwulaH6fcfwKWPa70Ptn7gNhPC9eHcNOAmVhlWqRpMrdCmuEspVwNlJx2eCCywvl4AXNPi+GIpZb2Uch9QAAzviGEGkwGBoFt4t45cfnryPoHETIjv59xxW9LncgiKdFo5gqjgKGYPmc3Gwxv54aB7Y5sKz2MLO3hNQ+zGWlh8o/Y89QMIjYZzb9c8+GV/gcpDbjNl2oBpROgjlPfeCh2NuSdKKQ8BWJ8TrMeTgJYrfwbrsXZjrDKSGJ6IPsCJMcZj+7Va7bZCX65CHwIDroL8r6Cp3ilDTu47md6de/Nc9nPUm50zpsI3yCnJISYkxnN9hFsiJSy5W9vLcd08SOivHdfpYOJ/tc/71/e7LTzTKagT0wZMY8WBFRQcK3DLnL6CsxdUWwtit/q/LISYJYTIFkJkFxcXn/K+S6pB5lmrNroiS+ZkMq6H+grtVtUJBOoCeejchzCYDLy3/T2njKnwDfJK8siMy/SOSpBrX4Xcj2DM/4N+V5z4Xi/D01AAACAASURBVGyadnznN04tw9EW0wdMJywwjLm5yntvSUfF/YgQoiuA9fmo9bgBaFkMPRkoam0AKeVcKWWWlDIrPv7ULkvGKhfkuOd9plVvjO7h3HFbo9fFEBrj1A/5+d3OZ3TKaOblzKO45tQ/iAr/o6qhin0V+7xjZ+ru7+H7x2DgNVqTmtYYcRckZcE3fwbT0dbPcTKdQzpzQ/8bWL5/Ofsr9rtlTl+go+K+BJhhfT0D+LLF8alCiGAhRE+gD9DuhO+6pjqO1h51bo578U6taqMzyw2ciQC9Vrdm57fQUO20YR/MepBGSyMvb37ZaWMqvJe8kjwk0vPiXlIAn9wCielwzeunzzTTBWjhmQYTfPOg28ybMXAGQbog5uXOc9uc3o49qZAfAOuAfkIIgxDiVuAp4DIhxG7gMuvPSCm3AR8B24FlwGwppbm9RhVVa86+Uz333E9A6DTBdRcZ10FjjSbwTqJ7p+7cNPAmvtzzpSoodhZgKz2REe/BxdS6Clj8e63A3tRFEBR+5vMT+sMlf4HtX1qz01xPbGgs1/e9nq/3fq02/FmxJ1vm91LKrlJKvZQyWUo5X0pZKqUcK6XsY30ua3H+E1LKNCllPyllh1TNluOeEtnOdnen/yW08EjqhVr1RnfRfSREdnV6/HFW5ixiQ2J5auNTKjXSz8ktySW1U6rnKkFKCZ//Ecr2wpR3oXN3+64beTd0Hax579WlrrXRys0ZNxMgApifO98t83k7XrlD1el13A9tgbI97gvJ2NDpIP1a2L0CNswF42ZoanB42IigCO4Zeg9bi7fyzb5vnGCowhuRUpJbnMugeA/mt296W1sgvfzfmnNkLwF6mPg61B7TGs+7gYSwBCb1mcSXe77kkMl96ZjeileKu6HKQHBAMHGhcc4ZMO9T0Om1dnru5txboVNX+PbPMG80PJkM8y+HZX/VFnjLD3YobWxi74kMjB3Ii5tepKaxxgWGKzzNoepDlNaVei6/vXQPLP8b9BoNw+9o//VdMmDUg5D7MexwjxNya8atALyV95Zb5vNmvFLcbdUgnZL6ZbFoItp7rFa10d3EpsE9OXDfNpj8Dgy/HRCQPR8+uRleytQeBze0a1id0PHwuQ9zpOYIb2972yWmKzxLTonWHN0jO1PNTVo4JkCvLaB2tHrqqAe0Ok7rX3eufaeha0RXJqZN5LPdn531GWVeKe5OreNeuAEqje7JbT8dQkBUstaKb9wTcOty+Esh3P4jTHhO+wItvA4Kf23XsEMTh3JF6hW8nfe2ug31Q3KLcwnSBdE3uq/7J1/zklbZ9MoXoJMDu8QDg7R8+MKNWkN6N3Br5q2Ypfmsd3q8TtyllM6t477/Z0Boxby8icAgSBqqefIzv4bwOFh4LRg2tWuY+4bdh0DwwqYXXGSowlPkleQxIHaAc3dp20PRFlj1pOYQOaMGU+qFYK4HY7bjY9lBSmQKV/a6ko93fkxprXsWc70RrxP3ivoKTI0m5+1ONWRrdWRCopwznivo1A1mLtXCRu9N0hZe7aRrRFduzriZZfuXselI+/4wKLyXRksj20u3uz+/vbEOPr8DwuO1u0pn0P18LQ1538/OGc8Obsu8jXpz/VldKtvrxN2WKeOUsIyUYNwEScMcH8vVRCXDjKUQGgXvXaN5T3Zyc8bNJIYl8vTGpzFb2r2tQOGFFBwroM5c535xX/lPKN6hbURy1hpVaGfoMgj2/+Kc8eygZ1RPxqeOZ/GOxZTXlbttXm/C68S90KRtQHBKWKb8ANSU+Ia4g1Y+dcZSCO4E706EQzl2XRYaGMr9w+4nvyyfL/d82fYFCq/HtnnJrTXc962G9f/VKjz2HuvcsVMv1Ir2uSnuDnD7oNupaaphYf5Ct83pTXiduDc36XCG5260hil8RdxBq3szcykERWgCf9i+XahX9LyCcxLO4eXNL2NqMLnYSIWryS3JJTo42jWdyFqjrgI+vxNie8Nl/3T++D0v0uLuTmo/aQ99ovtwafdLWZS/iKqGKrfN6y14nbgbTUaig6MJ17exxdkeDJu0lneJ6Y6P5U6iU2HmV6APhXd/B0e2t3mJEIKHz32YsroyVdvaD8gtziUz3o2VIL99GKoOwaS5EBTm/PG7j9Di7m4MzQDMGjSLqsYqFuUvcuu83oDXibtT0yCNm7Qt0O7ONnAGMb1gxlcQEAwLrtbaA7ZBelw61/S+hvfy3+NA5QE3GKlwBaYGE3sr9rpv89L2JVoHpYsehGQX3eWGRGk9i924qAowIHYAFydfzHv571Hd6LwCfr6A14m709IgzY1a2YGkLMfH8hSxaTDtQ23dIP8ruy65Z+g9BOmCeC7bSZkOCreTV6pVgnTp5iUp4cBarVXexzOg2zlw0Z9dNx9ocXdjNjS4d0f1HYPuoKK+gg93fujWeT2NV4m72WLmkOmQczz3o9uhqU7LJfdlug7SwjQFK+06PS40jlmDZrGqcBVri9a61jaFS7BV+3SJ595UD1sWwZyL4O0rtEXUkXfDjZ+4/g43dRSYG9wadwdtUXpkt5Es2LaA2qZat87tSbxK3I/UHKFJNjnHczdYN0wk+7DnbiNtrLYZy86iY9MHTiclMoVnf32WJkuTi41TOJuc4hxSO6USFezEvRlVR+DH/8CL6fDFnZrIXvUS3J8Pl/1D20TnarqPABHQ/rj7sr/Cj086NPUdg+6grK6MT3Z94tA4voRXibtzM2U2Q1gsdHZD1yVX03us1vyg0L76M0EBQTyQ9QAF5QV8vOtjFxuncCZSSnJLcp3rta94TBP1n56GbkNh+udw13rIutk1i6enI6QTdBvSPnGvMMKGN2D1s1ohsw4yNHEo53Y5l7fz3j5rehB7lbg7tdSvMVuLt3tD30lHSR0FukDY84Pdl4xJGcN5Xc/jtd9eO2s3cfgih6sPU1Jb4rzNS2X7tDox/cbDnzbDjR9B2hjPfS9SR2l31fbG3X9bCNKiff5/esahqe8YdAfFtcV8vvtzh8bxFbxK3AurCgkQAXQJd7ChRl2l1lbPl/Lbz0RIJ0g5D/bYF3cHLTXyoXMfwtRo4vWt7qnIp3Ac2+Ylp9Vwz/lIex73pLZA72lSR4Gl0b67UIsZfntPKzl83iytMXfxrg5PPbzLcIbED2F+3nwazY0dHsdX8CpxN5qMdAnvgl7n4MJO0W+A9B9xB0gbDYe2gsn+MqZ9o/syue9kPtr5EQXHClxonMJZ5Jbkotfp6Rfdz/HBpIScxZqgdnZSVzNH6X6eNe5uR0rknh+hohCGzYAL7oXAUK2gWQcRQnDH4Ds4XH2YJXuWdHgcX8GrxN1gMjhnR17zzlQfz5RpSZp1O/jeH9t12ewhswnTh/HMr8+olnw+QE5xDgNinFQJ0pCttccbPNXxsZxFcKT2vbQn7r7pbQiLg35Xagu+I/4I2z6DI9s6PP0F3S4gPTadN3Pf9PtkA68Sd2OVkaRIZ8TbN2mbgDzRnMNVdB2iLRDbmRJpIzokmtlDZrPu0Dp+MvzkIuMUzqDJ0kR+Wb7z6sls/UDboT3gd84Zz1mkXqh9RxvOsKmo6gjsWgZDfq+VxwY4//+0uks//qfDUwshmDVoFgaTwe9bVDok7kKI/UKIXCHEFiFEtvVYjBBihRBit/U52p6xahprKK0rdZ7n7subl1pDp9Nij3t+aHdbvin9ptArqhfP/vosDWbHe7gqXENBeQG1TbXOWUxtatC83P5Xams23kTqKLA0wcH1pz9ny/vaOUNnHD8WFgPnz4YdS62h144xOmU0faP7Mi9nnl9XUXWG5z5aSjlESmlT078AK6WUfYCV1p/bpMhUBDghU6bCqNXI8Kd4u43eY6H6KByxr5iYDb1Oz0PnPsTBqoNnZY0NX6F5MdUZO1N3f6c1px7kRSEZGynnadkvpwvNWCyw+V3ocQHE9TnxvRF3Qkhnp3jv+yv3892B7zo8jrfjirDMRGCB9fUC4Bp7LjKYnJTjbou3+8PmpZPpNVp7bmdoBuCCpAu4OPli/pfzP0pqS5xsmMIZ5Bbn0jm4s3P2eeQs1hpupI1xfCxnExyh5dufblF1/89wbB8Mm3nqeyFRcMHd2h+vwo7vdL2sx2X0iurF3Jy5WKSlw+N4M46KuwS+E0JsEkLMsh5LlFIeArA+J9gzkNNy3I3ZoNNDooc6xruSTl0hIb1dKZEteTDrQerN9bz222tONkzhDGyblxyuBFlTBruWQ+ZkCAh0jnHOpucobaNhfSvlqTe9o3nnp1srGH6HttD64xMdnl4ndNw+6HYKygv44aD9+0d8CUfF/QIp5VDgCmC2EOIiey8UQswSQmQLIbKLi4sxVBkIDQwlJsTBRVDjZuiSCfoQx8bxVnqP0WKVZ1qMOg2pUanc2P9GPtv9GdtL2y4jrHAfpgYTe8r3OCcks+1zrbzAoBscH8tVpF4I0gyFJ8Xdq0u1mPrgqaf/DgdHwIX3wd5VsH9Nh00Ynzqe7pHdmZsz1y8zyRwSdyllkfX5KPA5MBw4IoToCmB9Pnqaa+dKKbOklFnx8fEYTAaSIpIc81osZm2hxR/j7TbSxmpf3A7Wxb5j8B1Eh0Tz9Man/fID7atsL92ORDonUybnQ4jvr5W79lZSztPusE8uAbz1A+3z3XIhtTXOvRUiusAP/253goGNQF0gt2XeRn5ZPqsNqzs0hjfTYXEXQoQLISJtr4HLgTxgCWD7n5kB2NX3zSl13It3ajVYXBhvr65vYsHa/TSaPRSn636+tpmjHaUIWhIZFMmfzvkTm49uZvmB5U42TtFRckq0looOZ8qU7dV2fw66wbtLbwSFa05YSydFSti8AJLPhcSBZ75eHwqjHoCDa9u996MlV6VdRVJEEs9mP+t3a1GOeO6JwC9CiK3ARuBrKeUy4CngMiHEbuAy689tYjQZHU+DdENbve+2H+axJdv4Nq/t5hkuQR8CqRd0aFHVxqTek+gX3Y8Xsl+grsl9PS0Vpye3OJfukd0drwSZ8xEgYNAUp9jlUlIv1O60660t8A6ug5JdrS+ktsawGdApGX54osPeu16n58lRT3K05iizVszyqzpMHRZ3KeVeKeVg6yNdSvmE9XiplHKslLKP9bmsrbGaLE3UNtU6IVMmW1tNj3FdDQ1DmVYP+uPsQpfN0SZpY6F0N5Qf7NDlAboAHh7+MIeqD/HOtneca5ui3dgqQTockpESti7WFiuj3NR71RF6jtLi7rZ8900LtE1K6ZPsuz4wWOseZczWsmc6yDkJ5/DKmFc4UHGAWStmUdlQ2eGxvAmv2KHaaNGK+DieKbNJS7HSue7XMhzTxP2XghIMx06tbHekso7/fJNPbYMLN0fYOtM74L2f2+VcLu9xOW/lvcXhag/dhSgArY9BcW2x4yGZwo1aCqE35ra3RvJwa9x9tZaTv/0LyLxeC9nYyzk3ac1svn1YG6ODjOg6gpdGv8Tu8t3c+f2dftGSzyvE3bZr0qGwTEON1kjazni7qb6JTzcZzrioWFRey90f/Magx5dzpFILXxjKa0jqHArAJ5sMJ5xvsUju/2gLc1fvZfk2FwpmXF/tdrSDKZE27s+6H7PFzEubX3KSYYqO4LTNSzmLtfWYgV5WbuB0BIVp8fX9v0DOx1rntLYWUk8mQA+T5kCFAT65BcwdrxczKnkUz130HNtKtjF75Wyf79rkFeJu89y7RXTr+CCHtmq3eHbG21//sYAHPt7K5oMn/rVvMlv4ZJOBOxduYszzq1iaU0RlXRM5hgoAjMdqOad7Zy7sHcfH2QYsluN/HBas28+aglICdaJZ3C0WecocDiOElhK5d7VDH+akiCRmZszk671fs2zfMicaqGgPucXWSpAxDlSCbKqHvM9gwFVacS43sK+kmhKTg40vUi/Ueh1vnKNl93Qb0v4xuo+AK5/Tkgy+f8whc8b2GMuTo57kt6O/cfcPd/t0Yw+vEPcGcwOxIbGE6R3oCmO0ttWzQ9zrGs18sFGLV6/fe+KSwJPf7uDBj7fy28FyrhuazNd3jwJgT7EJi0ViLK8lKTqUKVkpGMtr+T7/CAB7i0089e0OxvRP4IZzU1i1s5i6RjPvbzjAta+vZdMBJwt82hiorzi+iNxBbs24lXMSzuHPq//MW3lvqfRID5BTkkP/mP4EBQR1fJDd30FduVtDMtPnb+D3c9dT1+hACDL1Qq0ZR2mB/QuprTFsJpx7O6x7DbZ80PFxgCt6XsE/R/6T9YfWc/+q+3229rtXiHujpdHxapDGTRDVHSLa3hD7xW9GjtU0Eh4UwIZ9x8V97Z4S5v+yj5tGdGfdI2N4YlImA7p2IiEymIKjJo5W1dNoliRHh3FFRhd6xIbxyg+7kVLyz6XbCQrQ8dS1mYxL70Jto5kfdhzl9VVaa7DfnO2997oEhM7h0EyYPox5l89jfOp4Xtz0Iv9e/2+/L4XqTXxR8AVbi7cyON7BnPStiyE8QftcuIGjVXUYjtWy+6iJ55bv7PhAKcMhIAj0YZBxvWNGjX9SK0r21T1gcMzpmdh7In8f8XdWG1bz8M8P++R3wivEvcHc4HgapGGTXfXbpZS8s3Y//btEcu3QZDbtL6PRbKGyrpEHP9pKz7hw/jphwAmbqdLiI9hTbMJYri2gJkeHEhigY/bo3uQZK3l8yTZW7Szm7rF9SOgUwohesUSGBPLol3kcqqhDHyDINVa069dpMltYlncIs+U0nnRotHaX4sCiqo3ggGCevuhpbs24lY92fcSffviTXywoeTP15noeX/s4f1/zd4YlDGPWoFltX3Q6PFBuIM/6eR6c0pn5a/axbk9pm9fc/m42T32748SD+lA4ZzqMvNvx6pUBepi8ACIT4cMbocqxda8p/abw0LkPseLACv72y998roKkV4h7o6XRsUwZ01GoOGjXYuqv+4+x43AVN1+Qynm9YqhuMJNnrGDOT3s4VFnHC1MGExZ04hekd0IEe46aKLSmQSZbF1QnnZNEcnQoC9YdoGdcODNGpgIQFKhjTP8ESkwNDO3emUv6JZBraJ+4f/abkT8u3MzSnKLTn5Q2Foo2a19uB9EJHfcOu5dHz3+UdUXrmLlsJkeqjzg8ruJUDFUGpn8znU93f8rtmbcz57I5RIfYVRm7dbZ9prWuG+y+cgM5hgp0AubPyKJHTBgPfryVyrrj4QspJY98lsM7a/YBcKiilhXbj7Bki/HU0N9VL8DoR5xjWHgsTP0A6irgw5ug0bF9HNMHTueeoffwzb5v+Me6f/hUkTGvEHeJJCXSgTZg7di8tH6v5mFMyOzKeT1jAVi+7QgL1h5gQmZXzul+6pcsLT6cyromthRqGxySojVx1wfouHtsH4SAv181gKDA4/+cV2Z2BeDeS/syODmKvSXVJ3z428KWR//V1kMAlFU38Pcv8qioaTFG77FavHLvKrvHbYvJfSfz2tjXOFh5kBu/uZGdZQ7ccitOYbVhNVOWTsFgMvDqmFe5e+jdBOgCHBt062JIGAhdnNR31Q5yDRX0ToggLiKYF24YwqGKWv6x5Hi9ooXrD/DBxkJeXrmb+iYzK7ZrjkJRRV1zOrHL6JIBk/4Hhl/h6/s7vMHJxm2Zt/HHwX/k84LP+c+G//jMupRXiDs4mONu3KT1ZbSjlsa2ogp6xYUTGaInPjKYtPhw5v28F1N9E38a07vVa9ISIgBYvauY2PCgEzz7KVkpbPjrWMb0TzzhmssGJvLTny/hor7xZCZ3Bo7fyrbFnmITv+4/Rmx4ED/tOkpFTSOv/1jAe+sP8N32Frea3YZCcFSHSxGcjguTLuTdK95FIpmxbAZrjWudOv7ZiNli5pXNrzB75WySI5L58KoPuSTlEscH3rtKE7GhM9xWbkBKSY6xgswk7XM9tHs0s0f35tPNBt5YtYeN+8p44pt8UmJCOVbTyMr8o3y37QiRIdr3xuZguZSBE+Gih7SmHxvmODzcXYPv4ub0m/lw54c8n/28Twi814i7Q7tTDdma52LH5oc8YyUDux2P7Z3XKxazRXJFRhf6d2k95pcWr4n73pLqZq+9JQmRp1avE0LQI1azJzNJ21J+cmimtsHMiyt2Mfb5VRSWHd8Q9XG2gQCd4JnrB9Folry/8QALNxwAODHrJiAQel3coe5MbdEvph/vT3ifpIgk7lp5F5/t/syp459NlNaWcsf3dzAvdx7X9bmO9ya859idqg0p4fvHISqlXZkmt77zKy99v+uU47PezeYNawJAS3YdqToh5fFIZT3FVfUMSj5eKuHusX0Y3jOGp5ftYMqcdQQHBvDhrPPpGhXC/F/2sX5vKdPO6050mP6UDLX2crC0hkMVdnj/lzyi9V9d/ldY/az2WPZX+PxOWHQDzL8cXs2CZ9Jg7iWwZZGWUtoKQgjuG3Yf0/pPY8H2Bby2xfvLZnuFuMeExJAQZlfZ91OxWLS4c3LbIZnymgaM5bWkdzv+oRzbP4GgAB1/GtPntNd1jQohLEi7dU5uRdzbIiY8iJSYUHJaeO6FZTWMfX4VL6/czZ7iar6z3rY2mS18utnA6H4JjOmfQI/YMJ5bvpOGJgt9EyNOTansPRYqjVo1PScLfJfwLiwYv4ARXUfw2NrHeGXzKz7hsXgTW45uYcrSKWw5uoV/jvwnj498nOCAYOcMnr9Eq81yySN2l7g+WlnHyh1HeXnl7hMWQQ3Havhu+xFeX1VAdf3xzJANe0u56pVf+L9Fm5uP5Ri08GRmC3HXB+j4cNYIlt07ir9O6M+8P2TRrXMo1w9LZtOBYzRZJOPTuzC8Zwwb9nXcc99eVMkVL6/m/xbZ0WZPp4Nr52iO3w//1h6b3tF2xFYatfIFiena3oDGWvjiTngxXevyVHXqepMQgoeHP8x1fa5jbs5c5ubM7fDv4Q68Qty7hnclUNfBVf6yPdriiR3x9u1FWs2I9Bae+9gBifz26GUnePMnI4Ro9t5tu1Pby6Ckzs1fCoB5P++lxNTAh7NG0DMunDUFWkW6n3eXUFxVz5SsZIQQ/G5wNywSJg5J4neDu7H7qInymhZ9UAdO1GKtX9wJ71wJh3I6ZN/piAiK4NWxr3Jdn+uYlzuPh39+WPVhtQMpJQu3L+TmZTcTHBDMwgkLmdTHzpop9mBugpX/0kr7DrY/t92W+hsVqj9hEXTVzmIAquqa+GyztvN6T7GJWe9tAqHtB7F9f3IMFQToBAO7nvidEULQv0snZl2UxvCeWl+GycO0O5TETsEMTu7MeT1jMRyrxXCsBotFtstZOFxRxy3v/Ep1g5nNB49RVn38c1jXaObtNfv4cedJFcaDI2HWKrh/B/ztCPytCO7fBn/8BWZ8BVMWwNUvw13rYfoXmo789Iwm8p/N0vpDtEAndPx9xN+5qtdVvPrbqyzYtgBvxSvE3SHyl2jPdjTE3taKuAOEB7f9hyUtXguxJEd3bKNVZnIUhWW1HKtuoKK2kU82Gbh6cDfO6xXLBb1jWb+3lIYmC0u2FhEVqueSftqdzJSsFIb1iOaesX0Y1kP7wpyw4zU0WvvwXvUSFO+AORdpeb7Vzitfqtfpeez8x7h36L18u+9bbv/udr+qnudsqhur+fPqP/P0r08zKnkUi69aTP+Y/s6dZOsirXjcmL9DOxZk1+8tJSI4kPkzsjhcWcezy7QF81U7j5IcHcrg5CjeXrufXUeq+MP8jegDBJ/dOZJQfQDvrNUyX7YayumbGEmIvu15u8eG8Yfze3D7qF7odIIRvbQkhhdX7GbUMz9yw5z1J4j06Wg0W7jjvWyq6hp56tpMpNTWwADWFpRw+Yur+cdX23n65FRL0MKXnbqe+e5GCEgbDdM+hD9t0urF7/ga5o3Wwjd7jpcVDtAF8K8L/sXlPS7nueznWLxjcZv2ewLfFvfCjdotVL8JkDCgzdPziiro0imE2Ij23xbbPPeOhGUAzrd+qP/x1TY++rWQmgYzN1+QCsCFveOpaTCzbm8p3207zITMLs2ZNykxYXx650hS48IZktKZQJ0ge/9JoRldAGTdDH/aDCPugt8WwitDYd1/oclBL9vcBAc3IOoruTXzVp696FlyS3KZ/u10Cis9WBnTSyk4VsDUpVNZcWAF9w27j5dHv0ynIAfzt0+msRZ+fFKry9L/ynZdumFfGVmp0QzrEcPUc1NY/OtB9pdUs6aglDH9E7j5gp7sLa7mqld/ocFs4Z2bh5ORFMWkoUl8saWIfy/dzs+7SxjVJ87uOf85MYPbRvUCoH+XSKJC9Xy62UB4cABbDOVc+/oa9peceV/Fqz8UsNVQwbOTBzMlK4XY8CB+3HkUU30Tdy3aTIBOMLZ/AruPmhzbMQsQmwZXPA3358P4p7R8+fevh93fN58SqAvkqYue4pLkS3hiwxN8vvtzx+Z0Ab4r7tUl8PFMrbTpNW/YlSmwraiSjKSOfdEGp3QmQCfok9Cxuh2DUzrz53H9+GJLEc8u38m5qdFkWBdaz+8Vi07Ak9/kU91g5upBrdfYCQ0KIL1bJ7JPV8ogtDOM/w/cuQ5SztUWkt4YCbu+a3883mLWijm9fh68dTk82wc+msH4BsmbY9/gWP0xbvzmRrYWb23fuH7M13u/Zto306hqqOLNy9/kloxbHO+H2hob50FVEVz6eLsyZEpM9RQcNTV7z3eN1rLD7nx/M7WNZi7pF8+EzK4kdQ4lNTaMz+8a2fwZvXlkKg1NFt78ZR83ZKXw53Edq4Oj0wmevDaTZ68fxLf3XMQHt59HRW0jd7y3iabTNMDZUljOf38s4NpzkpiQ2RWdTnBxv3h+2lXMgrX7Ka9p5MUbhnDdsGTMFsmuI1Udsu0UQjrBiDvhjz9rzuNH009oyq3X6XnukucY2W0kj619jK/3fu2ceZ2Eb4q7xQyf3qoJ/JR3NVFrg9oGM3uLTQzs1rFmCBf1jWfDX8fSPbbj9W/uuiSNG7JSaDBbuOWCns3Ho8L0ZCZ3Zsfhd9qxowAAFwJJREFUKuIjgznP+uVrjazUGLYWlrMs7zD//bGA4qpWVvfj+8JNn8K0jwEJiybDa1laLLFs35mNtFgg71N4/Xz47DYICIbfvaY1Rtj/M3wwlaHvTWVhWCYROj23Lr+VFQdWdPBfxD9oMDfwxPon+MvPf2FAzAA+vvpjzu1yrmsmqy2Hn5+H3pdqdVnawQZrlsp51nh4knXBM/9QJUGBOs7vFUdQoI5v7x3F13ePOiEE2ScxkrvH9ObvVw3kqesy0Qd0XDomZHZlclYKATrBsB4xPHltJjuPVDXXezqZx5ZsIzEymMcnpjcfu6RfAuU1jbz8/W4u7hvPkJTOzeFWW/jVaYREwU2fQWQXeH+yVn3WSnBAMC+NfomsLln87Ze/edV3wTfF/aentfzeCc/a3Scy/3AlFnlqvL09xHUgnNMSIQRPTMrg87tGMj6jywnvXdhbE/QrM7sSoDu9N5bVI5r6Jgt/XLiJZ5fv5No31lBwtJUO8gB9L9e8+In/hciuWrf4V4bA/HGQ/daJO1stFq2x8hsjtdKpQsDkd7SFp6HTtX/rB3bC7z+EXheTmvMZC3dspn99PQ+sup8FW/2zyXBbHDIdYuaymSzeuZiZ6TN5c9ybxIfFu27Cta9qBcLGPmrX6TUNTfzutV+YPn8Dn/9mJCwooNkbB7jrkt4EWmPhodaMsE4h+lbF+/7L+3HrhT2dfjcyLr0L5/eK5fkVu05MFgCM5bVsLSznDyNT6RSibz5+UZ84dAIazBbuHqtluqVEhxEZHMi2ovbtBj8dB0qrKThqvQuISNAWXPWh8N4kOLa/+bzQwFBeG/MaGXEZPLT6Ia/px+p74r77e80DHXIjDP2DXZc0mi288N0uAnWCc1La9vJdSWCAjnO6R5/yBRmXrsXZrx925nz/sQMSefzqgSy6/Tw+vfN8ahvMTHp9DY9+mcfPu4tbmTBIa2gwcyncmwdjH9PEYel98Hw/WHyjtsnjfxdqYS5phuvmw51rtY44LRufBOih33iY/A5ld+bxVcz9vNTUjUura3huy6v856eHfLLAUkdZa1zLlKVT2FuxlxcveZEHsh5Ar9O3fWFHqToC61/XCmydxqk5Vt3AnQs38d8fC5BS8p9v8sk1VrClsJzv848wrEf0CcKdEhPGnOnD+PuVba9ZuQohBI9ePZDK2kZeWVlwwnvfWUtnXz7wxE2CncOCuKhvPJcOSGBYD21XuU4nGNitk92ee1l1A6WnKVlc09DEDXPWM/l/647vCo/uoXnwTXXw7jVa2RMrYfow3rj0DfpF9+O+H+9jXdE6u2xwJcIbvK2srCyZnZ3d9onlB7VskE5JcOsKrdg/0NBkOWHrf/PpNQ00miXPLd/Jh9mFPHv9ICZnOWHziItoMlsIbOftbmFZDf+yLnLVNpp5eeoQJg45dbdviameuav3cvuoXsRHBMHhHK3fZu7HYDoCsb3h4och4zq7si+e/CafOav30iM2jG8mwpzv7+TtcD0Xd+7PMxMWOFa+2cuxSAtzcubwxpY36B3dmxcufoHUqFTXT/z1g7DpbZi9UVv0O4kDpdXMfPtX9pdWIyVc0DuWNQWl3HZhT+4a3Zu3ftnHqD5xZwz7eZKHPtnKF1uK+Pmh0SR20jJbfj93PSWmelbcf/Ep51ssEgkn3On+86vtfLDxIHn/GNd8/IvfjByqqGPmyNTmuxOAif9dQ1VtI8vuvegU/Xjhu5288kMBQsDMkak8dvXxkBCFG+HdiVo7z5lLTwgLV9RXcMvyWzhYeZA3Ln2DrC72NQ/qKEKITVLKVifxHc+9qV7zLC1mLc5uFfYPfz1I+mPL+NfS7VTUHq+78vlvBob8cwXnPvE9H2YXcveY3l4t7EC7hR00z2vuH7L47dHL6N8lkpdX7j6lkmRtg5nbFmQzd/VeXl65Swu5dB0M456A+7ZroZu7NmhNle0Q9lJTPe+uO8Cg5CgOltXw77w4Lr34E+4xBfPzsXxmfjyeYpN/tu4rryvnrpV38fqW17k67Wren/C+e4S9bK8m7ENntCrshWU1XP+/dRyraeCjO87nvkv7sqaglL6JETw4rh8x4UE8OK6f1wo7wP9v797joyjPBY7/nt0kkBCIECAQIAURUEAUFBAsKiigHittvZx6ObVatVXxfgpYTr0ce1qtt3qOrRUsRThKi6j1fuWIoHIT5BZu4RJIAAkhCSEhJJvd5/wxQ8hlNwTNupP1+X4+89nZ2Xknz8688+6bmXfed+LoPgRDynOfbAOc/0KW5RYxbkBG2PV9PmlwCXNAZjsqAkG2FzqXKksPB5j62loefW8j5z+xgE/c5pPrd5eyOq+EbYXlNU08j8grOsRzC7dx6WmZXDUsi9mLdxy9PAN8XnUiS4c9je7bCHOuclovudJapTFt7DQyUzO5bf5tMW1wELXCXUQuFJFNIrJFRKY0unJFCZTkNd6i4/2pTh8yE/5Uk7lLDlXxyLsb6dAmiRmfbeeCJz8hv/gQ1cEQT32YQ7+Mtjz8w4E8e80Q7h7btzm/nue0TvRzx/l92LavnLfX7qlZHgwpd/9jFavzSzitxwnMXZ5f99FtfwJk9Ad/AoFgiL9+up0xTyxotAvX5z/dzuHqIE9eeRo3jTqROct2cunsHTyWN5Xby3qSW7mfq1+5iJyvvlmf2l6zrnAdV751Jcv2LOP+Effz27N/S3LC12saS2UZbHwH3rwL/nswzLiIPW//jgPbvwx/Hnz8O6ff83MnNfjoQEWA62cupzIQZO4vRjC0ZwfuvKAP/7j5LGbdMLxJ7dG9ICs9hR8N7saLS3dQcNB5kjYYUsYP6HLsxK4B3ereVH35i3zKq4I8PGEAbVolMPGllewvq+TlFXkk+X0M69WB/5m/hUU5+7j2+aUMevB9xj71CSIw5aKTuXdsX5KT/Ex86UtW5ZUwa3Eu1z6/lH+d34Yn2tyD7lwML18PtQb0SE9OZ/q46aQnp3PLh7ewfv/6cKFGXVQuy4iIH9gMjAXygeXAVaoa9luemenXL25OhdQMgplncqjzYNr2PgsyB0OrVKdJ3qs3woiJTm3T9eAb2cxanMvbd4wiEAxx1bQlnNo9jR8P6c6keWuY/tMzGds//K9+PAqFlAufXogqvHvnKOdm05xVfLRhL7+5pD/j+mcw+vEFXDM8i4cmDKyTdsGmAh5+az1b95XTOtFH+5Qk3rvrHNKSnWvIWwrK+O3b6ymtCJC9u5Sx/TN45uohVFYHmb5wGz07tmH59iJmLcll5qgveGjPXCr8fp4cOpURA1rIgM0RqCpzN83l0eWP0im5E0+e9yQDOg44dsK6G4H9W50Rk3I+gB2fQbAKktpCr1GU7t1Bu5JsAAJtupDYbzz0Gef0HVS0HZ4bBaPubXAjddu+Mn41bw1r8kt44YZhjOzd9PbnXrS9sJzzn1hAVocUyquCJPiEz6eMafJN3EAwxIAH3ue6Ed9jykWnMPrxBXRu24p5t4xkS8FBxv9xET8a3I35G/Yy8qSO3DO2L+OfWkh1SGmfksglgzLx+4QxJ3fmnL7OjfH5G/Yy+ZW1Nf3rXHBKZ8b178JjH2zi4sNv85B/BvQ4C/pc4DxM2W0ItE6rudleXl3OjPEz6Nv+a1QwQyGnq4SKYujasNfPxi7LRKtwHwE8qKrj3ff3Aajq78Otn9Wrtz495VoySteRWZZNT3Fqnio+tNMpSPF2tMsgqq55HfyJFJZV8vHGAh58cz0/GdqD//qRM2r83OV5THplDUkJPk7qlMrbd3w/Ou2MPeytNbuZ+NKXpCUnckJKInlFh3jgBwNq+pqfPG8Nr63axYTTMmuaSOcXV/D51v30TE/hN5f0p2NqK3787Of8YFBXHrlsECt3FvPL2Svw+4SB3dJI9Pu4/5L+9OxYt6O20sMBxjy+gJ7pbXjivCLu+nwyuX7h11mXMnboPdH70grVWk0gFHAnZ746FKAqGHDn635e7c5XhQI16wZqrVMdqqYqVEUgVM3u8t0s/moJI7uM4D+HP0BaUv3mtAqVB5GKIqgoQSqKnfnDxUhFMaGyfQS3LSK5zGnqV9W+L75+4wj1Hkuox3DWFxzmmulLGdKhkpPLljK8egXnJawjKViG+hKdp5CDVVTe9iW0TqM6pKzaWcJ72Xv4+7I8khP9/P6yU7kkwvMRLc1zn2zlU7c7jgmndztmI4P6Lnv2czZ/dZDxA7swb0U+z1w9uGbfPPRmNn/7LBeAWTcM45y+nXhx6Q7yiyv45Tm9SUsJf0P84OEA0xZuQ0S48/w++H1CcXkVv5i9gpPz5nBrysd0CTjHN4RQlNwT7T6U0qxTuGnXP0lKSGbehf+I3M1KRTG+4m1I0VakaJszFW9FinOR6sOE2vei6tYVbC8s5+NNBTUPfT12xenfeuF+OXChqt7ovv83YLiqTgy3fmr3fnrqbX8mJcnP2Sd15AQtZcOKBQzUHAZLDu2knJur7qWAun2t981IZc5NZ9U8caqq3DRrBR9t2Mtfrj2jQXPD7wJV5aMNBXyQ/RWb9h7k9jF96vz3kld0iOtnLq/TOVRSgo9rhmfxs5G9am4s/fGjzfzxo5yadXp3asPM64fRo0PjN0r/vmwnU15dC0An31f07vE02Smxv2l/PESVJFWSFBJx5q8oLePGA6XHfR2zSv0cIJXVoRNZqIP5v+Bp5GvDppLd2yfz2q1nEwiGmPjSStbsLOQMyWG0fxVn+9YyOziWucHRddIkJfj48eBu3DuuH53aNlNnZHEgt7Cc37y+jkU5hXRNa83CSaNrWggdOBTgvMc/JjnRz6LJYxptdtwUldXBmpHY2moZp+gWBoQ20yewkdNlC+2ljO2JCZT4fAyubNrT4pWawE7NIFe7sF27kKtd2KZdWRLqX7NORrtW+ERY8usLvvXC/QpgfL3CfZiq3l5rnZuBmwGysrLO2LFjR51t7Nx/iPey9xDuobWUJD8je6dzUufUBjXz0sMBFm/dz7j+Gd+5Wntzqg6GeHXlLvaXV9EqwcdlQ7pHrNXUFgopr6/exVcHnH9hNVhB8Z6nqQo284Ml9fjxkeBO/ppXcZf5SXDn/fXWqZ8mAR++r5FvAv42HE5sx+GEE6hMbMfhxDQqE9II+FNAhFO6tuWsE9MpKK1k0ZZ9lFYc/XH1CfzLoK51HhrKKzrEopzCOo0EauvTOZWRJ6U3GDXMOFSVz7bsJy05sU7vlQCr3UF3Totis+gDFQE+3byPkl2byChdS5uqMM2Uawn4UziQnMWB5CzKWmegEv4+SXpqEuf27VTTmsjzl2Wa3BTSGGNMjVg0hVwO9BGRXiKSBPwEeCNKf8sYY0w9UfmfTlWrRWQi8D7gB2aoanY0/pYxxpiGonbBTlXfAd6J1vaNMcZE1nKeUDXGGNNkVrgbY0wcssLdGGPikBXuxhgTh6xwN8aYOOSJ/txF5CCwKdZxfANpQPMM/xI7Lf07dAQKYx3EN9DS97/FHxv9VDXswM5eeXZ5U6SnrFoCEZmmqjfHOo5voqV/BxH5wvJQ7Fj8sSEiER/tt8syzePNWAfQDOLhO7RkLX3/W/we45XLMi261mViz/KQ+S5qLN97peY+LdYBmBbP8pD5LoqY7z1RuKuqZ05MEZkhIgUisq7WssdEZKOIrBGR10QkbF+hkYYWFJEOIvKhiOS4r+3DpY9i/KeLyBIRWSUiX4jIMK/G/3V5LA9F2o+3u8uzReQPx5k21nnIzgEPngON5ntVtanWBJwDDAHW1Vo2Dkhw5x8FHg2Tzg9sBU4EkoDVQH/3sz8AU9z5KeHSRzn+D4CL3PmLgQVejb+lT5H2IzAa+Aho5a7X2avHwM6B+DgHolJzb8k1F1VdCBTVW/aBqh4ZXWEJEG7cr2HAFlXdpqpVwN+BCe5nE4AX3PkXgB82e+BHY20QP6BAO3c+DdgdJqkn4ofweaCpxz/W+YfI+/EW4BFVrQRQ1YLjSAsxzkN2Dny750BzaPbCXZzBsf8EXIRTY7lKRPqLyGicHTRIVQcAjzc1rfvxFGC+qvYB5rvvY+EG4F0AEckUkSM9X3YD8mqtl+8uA8hQ1T0A7mvnbynWI+4CHhORPJz9fh94M/5G8sAxj79H8k+k/dgXGCUiS0XkExEZ6sbsuWPQBHYOxHb/N0k0au4tvuYSiYhMBaqBFwFUdbeqXnzk4zBJYt8UyXELcLeq9gDuBv4Kno0/Uh5oyvH3Qv6JtB8TgPbAWcCvgLkiIh49BhHZOdByRKNwj8uai4hcB1wCXKPuhbd68oEetd535+i/fntFpKu7na5AuB+2aLoOeNWdfxmnEKzPK/FHygNhj78H80+k/ZgPvKqOZUAI56napqSF2OchOwdivP+PVzQK97iruYjIhcBk4FJVPRRhtcaGFnwDJ3Phvr4ezXjD2A2c686PAXLCrOOV+I8rD3gw/0Taj//E2feISF+cG3b1u0vwyjFowM6BmMd//Jr7Di0wAni/1vv73Ok94Lxay7cCnZqS1p3fBHR157vidFkQjfjnAHuAAM4v+c+BLTg1wlXu9Bd33UzgnVppLwY2u99taq3l6TjXeXPc1w7RiL2R+L8PrMC5+78UOMPD8UfKP8c8/l7IP5H2I05h/r/AOmAlMMbDx8DOgRjG31xTsz+hKiIJ7s45H9iF82t4NTAKyFTV+92ay3wgS2sFECmtqmaLyGPAflV9xG0F0UFVJzVr8CbmGsk/P+MYx9/yjzG1ROmXs0XXXGyK7RQh/4Q9/pZ/bLIp/OSJvmWMMcY0L090P2CMMaZ5WeFujDFxyAp34xkSpsOnWp/9u4ioiNRvG37k85kicnn0ozSmZbDC3XjJTODC+gtFpAcwFtj5bQdkTEtlhbvxDA3f4RPAU8AkmvhAkojcLyLLRWSdiEwTEXGXLxCRR0VkmYhsFpFRzRe9Md5ihbvxNBG5FNilqquPI9kzqjpUVQcCyTiPzB+RoKrDcDqSeqAZQzXGU7wyQLYxDYhICjAVpy/x4zFaRCYBKUAHIJujY2Qe6V9kBdCzGcI0xpOs5m68rDfQC1gtIrk4HTmtFJEuIvI3cUbVead2AhFpDfwZuFxVTwWmA61rrVLpvgaxyo2JY5a5jWep6lpq9d7oFvBnqmohcH2EZEcK8kIRSQUuB+ZFM05jvMhq7sYzRGQOsBjoJyL5IvLz40ieAFSqaglObX0tTk+My5s/UmO8z7ofMC2eiPhwCvGfqmp2rOMxxgus5m5aNBHJxOmMbokV7MYcZTV3Y4yJQ1ZzN8aYOGSFu/EkEekhIh+LyAYRyRaRO93lHUTkQxHJcV/bu8vT3fXLROSZettKcp9U3SwiG0Xkslh8J2O+TXZZxniSOwhxV1VdKSJtcR46+iHOiExFenREpfaqOllE2gCDgYHAQFWdWGtbDwF+Vf0P9+ZrB7c5pTFxy9q5G09S1T0442CiqgdFZAPQDZgAnOeu9gKwAJisquXApyJyUpjN3QCc7G4rRMOBqY2JO3ZZxnieiPTEqZUvBTLcgv/ID0DnyClBRE5wZx8WkZUi8rKIZEQxXGM8wQp342nuU6avAHepaunX2EQCTrcFn6nqEJyHpB5vxhCN8SQr3I1niUgiTsH+oqoe6fBrr3s9/sh1+YJjbGY/cAh4zX3/MjAkCuEa4ylWuBtPcvtg/yuwQVWfrPXRG8B17vx1wOuNbUedFgNvcvQ6/fnA+mYN1hgPstYyxpNE5PvAIpw+YkLu4l/jXHefC2ThjMx0haoWuWlygXZAElACjFPV9SLyPWA2cAKwD7heVW1UJxPXrHA3xpg4ZJdljDEmDlnhbowxccgKd2OMiUNWuBtjTByywt0YY+KQFe7GGBOHrHA3xpg4ZIW7McbEof8H1CHwIUVTB/QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can also explore multiple series of data plotted on the same chart by executing plot() on a\n",
    "# dataframe multiple times in a single cell\n",
    "df.loc[\"2016-01-13\":\"2016-01-14\", \" Fine \"].resample(\"15T\").apply(len).plot()\n",
    "df.loc[\"2016-01-13\":\"2016-01-14\", \" Fine \"].resample(\"60T\").apply(len).plot()\n",
    "df.loc[\"2016-01-13\":\"2016-01-14\", \" Fine \"].resample(\"180T\").apply(len).plot()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}