{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Timeseries Analysis with Pandas - pd.Grouper \n", "\n", "I have been doing time series analysis for some time in python. The `pd.Grouper` class used in unison with the groupy calls are extremely powerful and flexible. Understanding the framework of how to use it is easy, and once those hurdles are defined it is straight forward to use effectively.\n", "\n", "I began to look into alternatives to excel (my companies standard), when I started to max out the limitations of excel:\n", "- limit the number of entries \n", "- calculation spread out over multiple sheets/workbooks\n", "- error prone copying of data down columns\n", "\n", "Then I found pandas. Pandas was a great all around tool for analysis, especially for people familiar with excel. But what really makes pandas shine is the incorporation of datatime objects, and the tools that can be uses to group and aggregate them. As always we start importing the primary tools:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Making the Pandas Dataframe time aware\n", "The first thing that is required is to import our data, in this case it is 1-minute A-weighted sound levels from a long term noise monitor I have used in the past." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('test_data_noise.csv')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Date (MDT)LAEQ
02020-04-08 14:08:0060.9
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" ], "text/plain": [ " Date (MDT) LAEQ\n", "0 2020-04-08 14:08:00 60.9\n", "1 2020-04-08 14:09:00 59.1\n", "2 2020-04-08 14:10:00 58.7\n", "3 2020-04-08 14:11:00 94.0\n", "4 2020-04-08 14:12:00 94.0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Importing this way does a few things. The first is that a index is set for the dataset, \n", "\n", "My preference is to set the index to a [datetimeindex](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DatetimeIndex.html), or to have the index be a datetime. The new version of pandas at the time of this writing states that the datetime index is:\n", "> Immutable ndarray of datetime64 data, represented internally as int64, and which can be boxed to Timestamp objects that are subclasses of datetime and carry metadata such as frequency information.\n", "\n", "The are a couple of methods to do this: the first is defining the datetime index when importing the data, and the second is to import as normal and convert the timestamp data into a datetime index. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Converting a dataseries to a datetimeindex:\n", "This is done simply by using the pandas method for converting a column to a datetime, aptly named [pandas.to_datetime](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html). The index for the dataframe can be directly called and set to the new datetime series:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Date (MDT)LAEQ
Date (MDT)
2020-04-08 14:08:002020-04-08 14:08:0060.9
2020-04-08 14:09:002020-04-08 14:09:0059.1
2020-04-08 14:10:002020-04-08 14:10:0058.7
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" ], "text/plain": [ " Date (MDT) LAEQ\n", "Date (MDT) \n", "2020-04-08 14:08:00 2020-04-08 14:08:00 60.9\n", "2020-04-08 14:09:00 2020-04-08 14:09:00 59.1\n", "2020-04-08 14:10:00 2020-04-08 14:10:00 58.7\n", "2020-04-08 14:11:00 2020-04-08 14:11:00 94.0\n", "2020-04-08 14:12:00 2020-04-08 14:12:00 94.0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.index = pd.to_datetime(df['Date (MDT)'])\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note how the Jupyter renders the index name for the index. We now have a datetime aware index of our data. We can inspect the data type further by calling the index and looking at the results. We also need to drop the previous datetime data." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "df = df.drop('Date (MDT)', axis = 1)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['2020-04-08 14:08:00', '2020-04-08 14:09:00',\n", " '2020-04-08 14:10:00', '2020-04-08 14:11:00',\n", " '2020-04-08 14:12:00', '2020-04-08 14:13:00',\n", " '2020-04-08 14:14:00', '2020-04-08 14:14:01',\n", " '2020-04-08 14:15:00', '2020-04-08 14:16:00',\n", " ...\n", " '2020-04-11 07:32:00', '2020-04-11 07:33:00',\n", " '2020-04-11 07:34:00', '2020-04-11 07:35:00',\n", " '2020-04-11 07:36:00', '2020-04-11 07:37:00',\n", " '2020-04-11 07:38:00', '2020-04-11 07:39:00',\n", " '2020-04-11 07:40:00', '2020-04-11 07:41:00'],\n", " dtype='datetime64[ns]', name='Date (MDT)', length=4137, freq=None)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Importing data setting a datetime index: \n", "This is the method that I usually use, as it sets everything from the start. It uses some of the additional parameters passed in the `pandas.read_csv` method (or any of the other methods). Looking at the input parameters, by setting two of them when can create a \n", "\n", "- `index_col`: the column we want to become the index\n", "- `parse_dates` : the column we want to pass through a date parsing function" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('test_data_noise.csv', index_col=0, parse_dates=[0])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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LAEQ
Date (MDT)
2020-04-08 14:08:0060.9
2020-04-08 14:09:0059.1
2020-04-08 14:10:0058.7
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2020-04-08 14:12:0094.0
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" ], "text/plain": [ " LAEQ\n", "Date (MDT) \n", "2020-04-08 14:08:00 60.9\n", "2020-04-08 14:09:00 59.1\n", "2020-04-08 14:10:00 58.7\n", "2020-04-08 14:11:00 94.0\n", "2020-04-08 14:12:00 94.0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using a time aware dataframe: Indexing and Slicing\n", "\n", "The next step i typically do is to review and filter my data. In the environmental acoustic world this typically means removing calibration signals, invalid data (capturing noises in close proximity to the microphone), and marking certain times when an activity of interest is captured.\n", "\n", "I generally spit out my data to a interactive plot making the review easier. In this case I have embedded a bokeh plot. This is done through rendering to HTML, and displaying via the Ipython.display HTML method." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " Bokeh Application\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "" ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from bokeh.plotting import figure\n", "from bokeh.resources import CDN, INLINE\n", "from bokeh.embed import file_html\n", "\n", "from IPython.display import HTML\n", "# create a new plot with a title and axis labels\n", "\n", "p = figure(title=\"Interactive Review Plot\",\n", " x_axis_label='Timestamp',\n", " y_axis_label='Sound Level (dBA)',\n", " plot_width=900,\n", " plot_height=500,\n", " x_axis_type=\"datetime\")\n", "\n", "# add a line renderer with legend and line thickness\n", "p.step(df.index, df['LAEQ'], legend_label=\"LAeq\", line_width=2)\n", "HTML(file_html(p, INLINE))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the interactive plot the data can be reviewed. To keep track of what the data is I create two new columns in my data frame; `Exclude` and `Reason`. I set the entire `Exclude` column initially to `False` and the `Reason` to NaN. As I identify the times of interest or times that need exclusion, I can slice those time and set the columns.\n", "\n", "The new columns are set using:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "df['Exclude'] = False\n", "df['Reason'] = np.nan" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A quick example of doing this is excluding the calibration that occurs at the beginning of the record, which has a very distinct level of 94 dB. To mark this as an exclusion and to note a reason, the pandas `.loc` method can be used. The loc method takes two parameters, the rows and the columns you want to set. In this case I provide a a range of dates and the columns and the values to set them to." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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LAEQExcludeReason
Date (MDT)
2020-04-08 14:08:0060.9FalseNaN
2020-04-08 14:09:0059.1FalseNaN
2020-04-08 14:10:0058.7TrueCalibration
2020-04-08 14:11:0094.0TrueCalibration
2020-04-08 14:12:0094.0TrueCalibration
2020-04-08 14:13:0094.0TrueCalibration
2020-04-08 14:14:0094.0TrueCalibration
2020-04-08 14:14:0194.0TrueCalibration
2020-04-08 14:15:0094.0TrueCalibration
2020-04-08 14:16:0094.0TrueCalibration
2020-04-08 14:17:0094.0TrueCalibration
2020-04-08 14:17:0287.6TrueCalibration
2020-04-08 14:18:0067.4FalseNaN
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" ], "text/plain": [ " LAEQ Exclude Reason\n", "Date (MDT) \n", "2020-04-08 14:08:00 60.9 False NaN\n", "2020-04-08 14:09:00 59.1 False NaN\n", "2020-04-08 14:10:00 58.7 True Calibration\n", "2020-04-08 14:11:00 94.0 True Calibration\n", "2020-04-08 14:12:00 94.0 True Calibration\n", "2020-04-08 14:13:00 94.0 True Calibration\n", "2020-04-08 14:14:00 94.0 True Calibration\n", "2020-04-08 14:14:01 94.0 True Calibration\n", "2020-04-08 14:15:00 94.0 True Calibration\n", "2020-04-08 14:16:00 94.0 True Calibration\n", "2020-04-08 14:17:00 94.0 True Calibration\n", "2020-04-08 14:17:02 87.6 True Calibration\n", "2020-04-08 14:18:00 67.4 False NaN" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc['2020-04-08 14:10' : '2020-04-08 14:17', ['Exclude','Reason']] = [True, 'Calibration']\n", "df.head(13)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data is now marked as excluded and gives a reason. This can be used as a filter mask later on for analysis. Typically I would have additional metrics that could be used for tagging and exclusions, such as weather data or some other measurement. Below I show two more additional exclusions:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "df.loc['2020-04-09 06:00' : '2020-04-09 09:00', ['Exclude','Reason']] = [True, 'Precipitation']\n", "df.loc['2020-04-10 12:30' : '2020-04-10 13:45', ['Exclude','Reason']] = [True, 'Resident Acivity']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using pd.Grouper for Analysis\n", "\n", "Pandas provides a class called [pd.Grouper](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Grouper.html), which can be used with great success in time series analysis. I first started using it when it was called pd.TimeGrouper (now deprecated), which seemed a little more appropriate name for time series analysis. `pd.Grouper` has a parameter `freq` which can be used to quickly do meaningful time based analysis and reductions in data. Once data has been grouped, the groups can be passed to aggregate functions.\n", "\n", "The first step is to create a valid data dataframe by using the `Exclude` mask to remove the data points marked for exclusion. This is easily done by applying the inverse of the `Exclude` series: " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "df_valid = df[~df['Exclude']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A typical analysis is to look at data on an hourly basis. to do this the dataframe is given the `groupby` method, and the" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# group by 10 minute intervals\n", "min_10_grouper = pd.Grouper(freq='10min')\n", "\n", "# group the dataframe by \"LAEQ\" and use the 10 min intervals\n", "min_10_groups = df_valid['LAEQ'].groupby(min_10_grouper)\n", "\n", "#inspect the Groups\n", "min_10_groups" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Methods can now be passed to the groups to get meaningful data out. For example if I wanted the maximum sound level that occurred in each 10-min interval and quickly plotted. Notice the gaps in the data from the applied exclusions." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "min_10_groups.max().plot(figsize=(15,4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets say I want the hourly minimum sound level in a single line of code:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "houly_average = df_valid['LAEQ'].groupby(pd.Grouper(freq='1h')).min()\n", "houly_average.plot(figsize=(15,4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also trivial to apply a function to the groups. For sound levels, the average time weighted sound level is calculated using a log base 10 average. This can be easily defined in a function and applied to the groups to return the average:\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Eebvg4+pkdBQhmlTvDq2YNawTy+Iy+G3/caPjCCGEEBZLCrg6uqyHH3eO6MTSmHSWRh8xOo5ohmpNmui0PKICPY2OIoQh7hvZha6+bsz+Zi95pVVGxxFCCCEskhRw9fDAJSEMCfbiqe/j2ZMhc4uEeSVkFVFcUUNUYGujowhhCAc7G/57dQ8Ky6t4WlY7CCGEEGckBVw92Noo3rq2F94tHbl98Q55h1iY1faUPAD6yR04cQHr6uvGnSM688Puo6w/mGN0HCGEEMLiSAFXT54uDrx/fW9ySiq554udMiNOmE10ah7tPVvg59HC6ChCGOr24Z0I8nLhyW/3SeMoIYQQ4m+kgDsP3f09eH5CGJuScvnvr4lGxxHNgNYn97/1C5Dlk0I42tnywqRwjuSV8c6aQ0bHEUIIISyKXV2epJRKA4qBWqBGax2plPoSCDn1FA+gQGvdsy7HmiG34a7p24GdRwqYuzaZHv4ejApra3QkYcWSskvIK60iKkiWTwoBMLCTF5N7t+OjDSlM7NmO4DauRkcSQgghLEJ97sCN0Fr3PF2Aaa2vOfV1T2A58E1dj20unrk8jO7+7jz41W5SckqMjiOs2LbUk/vfpAOlEP/viXFdcXG04/EVezHJcnUhhBACMMMSSqWUAq4Gvmh4HOviZG/Le9f1xs5WMWtxHKWVNUZHElYqOjWPtm5OdPB0NjqKEBajdUtHHhsbSkxaPsvi0o2OI4QQQliEuhZwGlitlIpTSt36t+8NAY5rrc+2UeHfjrV6/q2ceWdKb5KyS3h0+R60lneJRf1ordmecoJ+gZ6cfD9ECHHaVX3a0zegFS//ksCJkkqj4wghhBCGq2sBN0hr3RsYC9yplBr6p+9N4d/vvv3bsX9QSt2qlIpVSsXm5FhX6+jBwV48OCqEH/ccY97mNKPjCCtz+EQZ2cWVsv9NiDOwsVG8OCmCkooaXvz5gNFxhBBCCMPVqYDTWh899TEbWAH0A1BK2QGTgS/re+wZnveR1jpSax3p7e1dn5/BItwxvBOjurXhpZ8PEH1qP5MQdbE99QQg+9+EOJsubVy5bVgQ3+zIZEtyrtFxhBBCCEOds4BTSrkopVxPfw6MAvad+vZIIEFrnXEexzYrSineuLoHHT2duWPJDo4XVRgdSViJ7al5tHZxoJN3S6OjCGGx7r4omA6ezvxnxT4qa2Q2nBBCiAtXXe7AtQE2KaV2A9HAT1rrlae+dy1/Wz6plPJTSv1ch2ObHTcnez64oQ9lVTXcsWQHVTUmoyMJK7A9JU/2vwlxDk72tjw/MZyU3FI+WJdidBwhhBDCMOcs4LTWKVrrHqf+C9Nav/in792ktf7gb88/qrUed65jm6subVx59YruxB3O5yXZryHOISO/jMyCcvrJ8kkhzmlYF2/Gd/dl7tokGd0ihBDiglWnQd6ifi7r4ceu9AI+3ZRKz/YeTOzVzuhIwkJF/zH/rbXBSYSwDk+N78b6xBz+8+0+lsyMkjvXwqqYTJpqk4mqGhPVtfrURxOVpz5W1578XlWtic7eLfFxczI6shDCAkkB10hmjw1lT0YBz/wQz6iwNjg7yB+1+Kfo1DzcnOwIaetqdBQhrIKPmxOPjAnhye/i+XZXJpN6+RsdSYizSsou4bWVCWxOyqWq9mTRVlduTnYsnNGPXh1aNWJCIYQ1kqqikdjb2jB7bFeueH8LX8akM31QoNGRhAXannpy/5utjdxFEKKupkZ1ZPmOTF748QAjQnzwcHYwOpIQf5FbUsn/fjvIF9HptLC3ZXJvf9xa2GFva4O9rQ2OdjZ/fO5gZ4O9rfrLYyatefr7eK77ZDufTItkYCcvo38kIYQFkQKuEfXp2Iq+Aa34ZGMq1/fviL1tXcfuiQtBdlEFqbmlTOnX3ugoQlgVWxvFS5MiuOzdTby6MoGXJ3c3OpIQAJRX1fLpphQ+WJ9CeXUt10V14J6Lg/Fq6Vjvcy27zY3rP93O9PkxvH99by4KbdMIiYUQ1kgqikZ229BOZBaU89OeY0ZHERZmu+x/E+K8dfNzY8agAL6ITic2TWZvCmPVmjTLYtMZ8cY63lh9kIGdWrP6/qE8NyH8vIo3OLlceOmtA+jSxpVbF8Xx456jZk4thLBWUsA1sotCfQj2ackH65PRuu5r30XzF52ah4uDLWF+bkZHEcIq3TeyC37uTjy+Yq+MbRGG2Xgoh0vf3sjDX++hjbsTX902gI9ujDTLbE9PFwc+vyWK3h1acc8XO/kqJt0MiYUQ1k4KuEZmY6O4dWgQCVnFbDiUa3QcYUG2p56gT4AndrK0Vojz4uJox7MTwjl4vIRPNslsONG0ErKKuHFeNDd8Gk1JZQ3vTOnFt3cMNPtYGFcnexbO6MfgYG8eWb6HeZtSzXr+xlRZU8s3OzJ48Kvd7E4vMDqOEM2G7IFrAhN6tuPN1Qf5YF0yw7p4Gx1HWIC80ioOHi9hQk8ZMSFEQ1zSrQ2jurXh7d8PMT7Cjw6tnY2OJJq540UVvLk6ka/jMmjpaMd/Lu3KDQM64mhn22jXbOFgy8c39uHeL3bx3I/7Ka2s4a6LOlvsGI3MgnKWbDvMlzHpnCitwt5W8cOeo7w8KYIr+kjnWCEaSgq4JuBgZ8PNgwN58ecD7E4voEd7D6MjCYP9//w3GeAtREM9c3kYI95YxyebUnhuQrjRcUQzlZZbysKtaSyNTqfWpJkxKJC7LurcZF1QHe1seXdqLx5Zvoc3fz1ISWUNs8eGWkwRp7VmS/IJFm1N49f9xwEY2bUNNw4IoKuvK3d/sZMHl+0m/mgRj48LldUnQjSAFHBN5Np+7Xl7zSE+2pDC3Ot6Gx1HGCw6NQ9HOxsi/N2NjiKE1fPzaMGIEB9WxWfxzGVh2MhYDmEmWms2JeWyYHMaaxKzsbNRjO/ux/0juxhyt9fO1oY3ruyBi4MdH25IoaSyhucnhBv6d764opoVOzNZtPUwSdkleLo4cNuwTlwX1QH/Vv//Z7RoRj9e/PkA8zankni8iHen9KaVi4wAEeJ8SAHXRFyd7Lm+f0c+XJ9MWm4pAV4uRkcSBtqeeoLeHVo16pIbIS4kY8LbsjI+i10ZBfSWwceigcqqavhmRyYLtqSRlF2CV0sH7r4omOujOuDj5mRoNhsbxXMTwmjpZMf765Ipq6rl9Su7N/kdraTsYhZtPczyuAxKq2rp0d6DN6/qwaXdfXGy/+e/bXa2Njx9WRhhfu48vmIvl8/dxEc3RNLVVxp5CVFfUsA1oemDAvh0Yyofb0zhxUkRRscRBimqqGb/sSLuuSjY6ChCNBsjQn2wt1Ws2pclBZw4b+l5ZXy27TBLo49QVFFDeDs33ryqB+N7+FrUG25KKR4dE0pLRzteX5VIaWUN70zt1egZtdb8uv84C7aksSX5BA62Nozv4cuNAwLoWcftIVf28aezT0tu+yyWye9t4c2rezAuwrdRcwvR3EgB14R8XJ24ok87lsVlcN/ILni7nt9sGGHdYtPy0BqigmT/mxDm4t7CnoGdvFgZn2VR+4KE5dNasy0lj/mbU/ntwHGUUowJb8uMQQH07tDKov8u3TmiMy0d7Xj6+3hmLozlwxv64OzQOL/aJeeU8OS3+9iSfIJ2Hi14ZEwI10S2p/V5zLnr2d6DH+4azKzFcdyxZAd3X9SZ+0d2keXPQtSRFHBN7JYhQSyNSWfhljQeGh1idBxhgO2pedjbKnq1l7sEQpjTmPC2PPbNXhKyimVZljinWpPm67h05m9OIyGrmFbO9swa1okbBnTE172F0fHqbNrAAJwdbHl0+R7GvbWR+y/pwvjuftiaqRiqqK5l7tokPlyfgqO9Dc9PDGdK3/YNXrLp4+bEF7f256lv43lnTRIHjhUx55qeuDrZmyW3EM2ZtABqYkHeLRndrS2LtqZRWlljdBxhgO0pefTw96CFg+UsxxGiObikWxuUgpX7soyOIqzAW78f4tHlewF47YrubH3sYh4ZE2pVxdtpV0W2Z9GMKFo42HHv0l2MfWsDK/dlobVu0HnXJmYzas4G3lmTxKXdfVnz4HBu6N/RbPvtHO1seeWKCJ6bEMa6xBwmzt1MSk6JWc4tRHMmBZwBbhsWRFFFDV9EHzE6imhipZU17MssNPugVyEEeLV0pG+AJ6vipYAT/y4xq5j31yUxsacfv9w7hKv7tj9j4w1rMjjYi5/uHsy7U3tRY9LMWhzH5e9uZl1idr0LuWOF5dy+OI7p82Ows1V8PjOKOdf0bJStH0opbhwQwOKZUeSXVTNh7mbWJmSb/Tp/l1daxTPfx/PAV7saXOgK0dTqVMAppdKUUnuVUruUUrGnHntGKZV56rFdSqlxZzl2jFIqUSmVpJSabc7w1qpXh1ZEBXry6aZUqmtNRscRTWjHkXxqTJqooNZGRxGiWRoT1paErGJSc0uNjiIsVK1J8+jyPbg62fPUZWEWvcetvmxOjTlYfd9Q3riqB/llVdw0P4arP9zKtpQT5zy+ptbEJxtTGPnmetYkZPPQqC78cu8QBnb2avTs/YNa8/1dg2jfypkZC2OYuzapUX5Hqqo5+TMOe30tC7ak8c2OTOIO55v9OkI0pvrcgRuhte6ptY7802NzTj3WU2v9898PUErZAnOBsUA3YIpSqlvDIjcPs4Z14lhhBT/sPmp0FNGEolPzsLVR9Oko+9+EaAyjw9sCyF04cVafbU1jV3oBT43vhmcznUNmZ2vDlX38WfPgcF6YGM6RvDKu/Wgb13+ynZ1Hzlys7DiSz2XvbuaFnw7QN9CTX+8fxl0XBTdp903/Vs4sv30g47v78fqqRIa+tpYP1ydTVFHd4HNrrVkdn8WoOet54acD9OnYiu/uHISrox2Ltx02Q3ohmk5jL6HsByRprVO01lXAUmBCI1/TKgwP8SakjSsfrk+RW/cXkO0peYT7udHSUfoHCdEY2nm0oLu/O7/IPjhxBpkF5by2KpGhXbyZ0NPP6DiNzsHOhuv7d2T9wyP4z6VdOXCsiEnvbWHmwhj2Hy0CoKCsise+2csV728hv7SK96/rzfyb+hoyqByghYMtb1/bk/k39SXQy4WXf0lgwEu/89wP+0nPKzuvc+4/WsTUj7dz62dx2NnasGB6XxZM70eP9h5M7t2On/dmcaKk0sw/iRCNp64FnAZWK6XilFK3/unxu5RSe5RS85RSZ7ql0A5I/9PXGaceu+AppbhtWBCJx4tZl5hjdBzRBCqqa9mVXiD734RoZKPD2rI7vYCjBeVGRxEWRGvNf1bsRWt4cWJ4s1o6eS5O9rbMHBLEhkdG8NCoLmxPzWPc2xuZuTCWi99cz1ex6dw8KJDfHhzG2Ahfw/9slFKMCPXh81v68+PdgxkVdrL527DX13Ln5zvYlV5Qp/PkFFcye/keLn1nIwlZRTw3IYyV9w5heIjPH8+5rn9HqmpNLIvLaKSfRgjzq2sBN0hr3ZuTSyHvVEoNBd4HOgE9gWPAm2c47kyvAGe83aSUulUpFauUis3JuTAKmst6+OHn7sQH65ONjtJoamSP3x92pRdQVWsiKlD2vwnRmMacWka5WpZRij/5fvdR1ibm8NDoENp7GnN3yWgujnbcdVEwmx65iLtGdGZrci4dWzvzw12D+c/4bha5OiS8nTtzrunJxkdHcMuQIDYcPNmt8qoPtrAqPota0z9/rayoruW9dUmMeGMdX8dlMGNQIOseGsGNAwL+0UGzSxtX+gV68vn2I5jOcC4hLFGdCjit9dFTH7OBFUA/rfVxrXWt1toEfMzJ5ZJ/lwG0/9PX/sAZN31prT/SWkdqrSO9vb3r8zNYLXtbG2YMDmR7at5Z16RbsyXbD9PzuV/ZkpxrdBSLEJ2ah1LQN0DuwAnRmDp5tyTYpyUrpYATp+SXVvHcD/vp4e/OTQMDjI5jOHdnex4aHcK+Z0fzzR2D6OZn+XMTfd1b8Ni4rmx97GKeHN+NowUV3PZZHBe/uY7PtqZRXlWL1pqf9hxj5H/X89rKRPoHtWb1/UN5cnw33J3PPl/uhv4dOZJXxoZDF8YNBGH9zlnAKaVclFKupz8HRgH7lFK+f3raJGDfGQ6PAYKVUoFKKQfgWuD7hsduPqb064B7C3s+XJ9idBSz+m5XJv/5dtu0iagAACAASURBVB+lVTXMXr6X8qpaoyMZbnvqCULbuv3rPyJCCPMYE96W6NQ82dciAHjhpwMUllfzyhXdzTbgujkweqnk+WjpaMfNgwNZ//Bw3p3aC3dnB578Lp4Br/zOhLmbufPzHbR0tGPJzCg+mRZJkHfLc55zdFhbvFo6snibjHcS1qEud+DaAJuUUruBaOAnrfVK4LVTowX2ACOA+wGUUn5KqZ8BtNY1wF3AKuAA8JXWOr4Rfg6r5eJoxw39O7JqfxbJzWR45ZqE4zz41W76BXgy/6a+HMkr47+/Jhody1BVNSbiDucTJfvfhGgSo8PaYtLw24HjRkcRBtt4KIflOzKYNawTXX0t/06TqBs7WxvGd/fj2zsGsmzWAPoFeFJcUcNLkyL46Z4hDKrH6AMHOxuu6evPmoTjZMreWWEFzrnYWWudAvQ4w+M3nOX5R4Fxf/r6Z+AfIwbE/5s2MICPNqbwycYUXp7c3eg4DbI95QS3L95BV183PpkWiauTPVOjOvDpplQu7e5Hz/YeRkc0xN7MQiqqTVLACdFEwvzc8G/VgpX7srimbwej4wiDlFXV8PiKvQR5uXDXRZ2NjiMagVKKvgGeDd6eMKVfB95bl8zS6CM8OCrETOmEaByNPUZA1IG3qyNX9fFneVwm2cUVRsc5b/syC5m5MBb/Vi1YOKMfrk4nlwrOHhuKj6sTj369h6qaC7OpSXRqHgB9pYATokkopRgT1pbNSSfMMkNKWKc5vx4kPa+clydH4GTfdPPMhPXxb+XMRSE+LI1Jb5QB4kKYkxRwFuKWIUFUm0zM35xmdJTzkpRdwo3zonFrYc/imVF/GY7q5mTPCxPDSTxezPvrmm/HzX+zPfUEnX1a4tXS0egoQlwwxoS3parWxNqEbKOjCAPszSjk002pTOnXgagg6f4rzu36/h3JKa5kdbwsvRaWTQo4CxHg5cLY8LYs3naYYit7tzgjv4wbPt2OjVIsnhmFr3uLfzxnZLc2XN7Dj3fXHuLg8WIDUhqn1qSJTcuX+W9CNLHeHVrh7erIKulGecGprjXx6PI9eLV0ZPbYUKPjCCsxtIs3/q1asHjbYaOjCPGvpICzILcN7URxRQ1fRFtPF6Sc4kpu+DSa0soaFs3oR6CXy1mf+/RlJ2fMPPL1njPObWmu9h8toqSyRva/CdHEbGwUo8PasDYhh4pq6YR7IflkYyr7jxXx3IRw3FtI519RN7Y2iqlRHdiacoKk7ObRWE40T1LAWZAe7T0Y1Lk1769LprDc8u/CFZZXc+O8aLIKK5g/ve8558i0bunIM5eHsSu9gAVb0pompAXYnnoCQAZ4C2GAMWG+lFfXsuGgzHe6UKTmlvK/3w4yJqztH0PdhairqyPbY2+rWLJd7sIJyyUFnIV5bGxXCsqreef3Q0ZH+VflVbXcvCCGpOxiPrihD3061u3u0uU9/Lgo1Ic3ViVy5ERZI6e0DNtT8+jY2pm27k5GRxHighMV5Il7C3sZ6n2B0Frz2Dd7cLCz4dkJYUbHEVbIq6UjY8N9WR6XITNshcWSAs7ChLdz5+o+7VmwJY0UC50LV1VjYtbiOHYcyeeta3sxrIt3nY9VSvHCxHBsbRSPr9iL1s17KaXJpIlJy6NfA9sbCyHOj72tDSO7tuG3/cels9wF4KvYdLal5PH4uK60cZM3zcT5ub5/R4oqavhh91GjowhxRlLAWaAHR3fB0c6Gl35OMDrKP9SaNPd/uYv1B3N4eXIE4yJ8630OP48WzB4byqakXJbFZTRCSstxMLuYgrJq6YAmhIHGhLelqKKGbSknjI4iGlF2cQUv/nSAfoGeXBPZ3ug4wor1DWhFlzYtWSzLKIWFkgLOAvm4OnHnRZ357cBxNiflGh3nD1prnlixl5/2HuOJcV0bNBx3ar8O9Av05IUf95NdZL2z787l9Pw3aWAihHGGBHvh7GDLyn2yjLI5e/b7/VTUmHh5cgQ2NsroOMKKKaW4vn9H9mQUsju9wOg4QvyDFHAWasagQPxbteD5H/dbRMdGrTWv/JLA0ph07hrRmVuGBjXofDY2ilcmR1BZY+Kp7+LNlNLybE/Jw8/dCf9W/xytIIRoGk72towI8WFV/HGLeD0V5jd/cyo/7T3GPRd1ppN3S6PjiGZgUq92ODvYykgBYZGkgLNQTva2PD6uKwlZxXwZk250HD5Yn8KHG1K4oX9HHhzVxSznDPJuyf2XdGFlfBa/7D1mlnNakrTcUtYfzKF/UGuUkneDhTDS6PC25JZUsvNIvtFRLEZhWTWFZZbf8fhclkYf4dkf9jM6rA2zhnUyOo5oJlyd7JnQsx0/7DnaLP4/Ec2LFHAWbGx4W/oFePLm6kSKDBzu/WXMEV5dmcDlPfx49vIwsxYjMwcHEt7OjSe/i6egrMps5zVacUU1tyyKxc5Wcd9I8xS8QojzNyLEGwdbG1lGycm9zAu3pDHo1TXMWBhjdJwG+XZnJo+t2MvwEG/entILO1v5tUaYz/X9O1BRbeLrHc17v76wPvJKZ8GUUjw5vht5ZVXMXZNkSIaV+7J47Ju9DO3izRtX9TD7vgI7Wxteu6IHBWVVvPDTAbOe2ygmk+b+L3eTklvKe1N706G1s9GRhLjguTrZMzjYi5XxWc2+++2/2X+0iCve38LT38fTwsGWuMP5VrsP+Ze9x3hw2W76B7bmg+v74Ghna3Qk0cyE+bnTq4MHS7YfvqBfN4TlkQLOwkX4u3Nlb3/mbU4lLbe0Sa+9NfkE9yzdSY/2HnxwfW8c7Brnr0s3PzdmDevE13EZzWLY7v9+O8hvB47z5KVdGdjZy+g4QohTxoS1JSO/nPijRUZHaXJlVTW8/PMBLnt3E+l5Zbx1bU8WTO8LwDorfN1dk3Cce5bupGd7Dz6ZFomTvRRvonFcH9WRlJxStiZLF1thOaSAswIPjw7B3taGl39pujtU+zILuWVRLB09nZl/U1+cHewa9Xp3XdSZTt4uPPbNXkoraxr1Wo3p573HeHtNEldH+jNtYIDRcYQQfzKyWxtsFKy6wIZ6r0vMZtScDXy4IYWr+vjz+4PDmNCzHd183Wjj5si6xGyjI9bL5qRcZi3eQWhbN+ZP74uLY+P++yQubJd298XD2V5GCgiLIgWcFfBxc+LOEZ1ZFX+cLcmNP1YgNbeUm+ZH497CnkU398PD2aHRr+lkb8urV3TnaGE5r69KbPTrNYb9R4t48Kvd9O7gwfMTw6VxiRAWxtPFgajA1hfMPrjs4gru/mInN82PwdHOhi9v7c8rV3T/4zVdKcWIEB82Hsy1miHnMWl5zFwYS2BrFxbN6Iebk73RkUQz52Rvy1V9/Fkdf9xqlxuL5kcKOCtx8+BA2nm04PkfDzRqG+zjRRXc8Ol2TBoW3dwPX/ema38fGeDJtAEBLNyaZlHz7+oir7SKWxbF4t7CXvZiCGHBxoS35VB2CUnZJUZHaTQmk+bz7UcY+eZ6Vu3L4v6RXfj53iFEBbX+x3NHhPpQXFlDbJrld+fcnV7A9Pkx+Lo7sXhmFK1cGv/NRSEApkZ1pMakWWoBXcGFAKjTugOlVBpQDNQCNVrrSKXU68BlQBWQDEzXWv9j2uGZjjVP9AuLk70tj40L5a7Pd7IsNp1r+53/EO2zKSyr5sZPo8kvreKLW/sbMkvnkTEhbDyUw31f7uKXe4fg1dKxyTPUV3WtiTuWxJFTUsmy2wbg4+ZkdCQhxFmMCmvD09/Hsyo+i84+nY2OY3aHjhfz2Dd7iT2cT/8gT16cFPGvr+WDOnthb6tYl5jNgE7/LPAsxYFjRdw4L5pWLvYsuSUKb1fL/7dBNB+BXi4MCfbii+gj3DG8k3Q7FfWWXVTB8aJKarWm1qQxnf5o0n97jD8+N/1L45z6LBwfobX+822RX4HHtNY1SqlXgceAR+t4rDgPl0b4sqBjGm+sTuTS7r64mnHpSHlVLTcvjCE1t5T50/vS3d/DbOeuD2cHO96d2psJczfz0LLdzJvW1+ydL83t+R/3sy0lj/9e3YMe7Y35cxNC1I2vewt6tvdg5b4s7hzRfAq4iupa5q5N4oP1ybg42vH6ld25so//OZdyt3S0IyqwNWsSsnlsXNcmSls/SdklXP/JdpwdbPl8Zv8mXRkixGnXRXVk1uI41iRkMyqsrdFxhBXZmnyCG+dtp7rWfCvoznvnr9Z69Z++3AZc2fA44t8opXjqsm5c/u5m5q5NZvbYULOc9/QdpLgj+cyd2ptBBndO7OrrxpOXduXJ7+KZtzmVmUOCDM3zb76IPsKirYe5ZUggk3v7Gx1HCFEHY8Lb8sovCWTkl+HfyvrHfJRW1jBtXjSxh/OZ3KsdT1zaldb1WL0wPMSbF346QHpeGe09LevP4/CJUq77ZBtKKZbMjLK4fOLCMbKrD23dnFi8/YgUcKLO0nJLuX1JHB1bu/DomFBsbcBGKWxtFLZKYWNz8vO/PsYfn3d59cznrWsBp4HVSikNfKi1/uhv358BfHmex4p66O7vwRW9/Zm3KZWp/To0eMaYyaR55Os9rE3M4aVJEYyL8DVT0oa5vn9HNiXl8urKBPoGeFrkna3YtDye+m4fQ4K9mD3WMt+5FkL80+iwkwXcqvjj3Dw40Og4DVJeVcuMBTHsTC/gnSm9uKyHX73PMSLUhxd+OsC6gznc0L9jI6Q8P5kF5Uz9eDtVNSaW3jqAIAOW9Qtxmp2tDdf2a8//fjvE4ROldGztYnQkYeEKy6u5eWEMCvh0WqRZ/87UdRHvIK11b2AscKdSaujpbyilngBqgCX1PfbPlFK3KqVilVKxOTnWN5OmKT0yJgRbG9XgsQJaa1746QArdmby0KguTI0y/76686WU4tUruuPd0pG7v9hJcUW10ZH+4mhBObMW76CdRwvendIbWwtf5imE+H+BXi6EtnVllZV3o6yoruWWRbHEpJ1cwn0+xRtAkJcLHVs7szbBcsYJZBdVcN3H2yiqqOazm6MIaetqdCQhuLZvB2xtFB9vTMHUiA3lhPWrqTVx1+c7OHyijPev72P2gr9OBZzW+uipj9nACqAfgFJqGjAeuE6fZUT92Y49w/M+0lpHaq0jvb296/tzXFDauDlx+/BO/LIvi20p5z9Y8r11yczbnMr0QQEWuRfEw9mBt6f0IrOgnCdW7OMsf8WaXEV1Lbd9FkdFdS0f3xiJu7O0sRbC2owOa0vM4TxyiiuNjnJeKmtqmbU4js3Jubx2ZQ8m9Gx33uc6PU5gS3IuFdW1Zkx5fkoqa7j+0+1kF1eyYHo/wtu5Gx1JCADaujsxsWc7Fm87wri3N7Jy3zEp5MQZvfDTATYeyuXFSeH0P0MH4IY6ZwGnlHJRSrme/hwYBexTSo3hZNOSy7XWZfU51lzhL2S3DAnCz92J53/cX++xAsUV1czblMrrqxKZ1KsdT17azWJnlkUGeHLfxcF8v/soy+IyjI6D1ppHl+9h39FC/ndNT4LbyLvCQlijsRFt0RpuWRTLvsxCo+PUS1WNiTuX7GTdqaXvV/Zp+P7b4SHeVFSb2NqANwXN5fkf9pOUXcJHN0TSp2Mro+MI8RevXdmdt67tSVWtiVmLd3DpO5tYFZ9lMW8yC+Mt2X6YBVvSuHlwINf0bZzVbXXZA9cGWHHqF3w74HOt9UqlVBLgCPx66nvbtNazlFJ+wCda63FnO7YRfo4LTgsHWx4dG8q9S3exPC6Dq/u2/8dzak2atBOlJBwrJiGriAOnPmbklwMwIsSb167sbvFdHu8Y0ZktySd4+rt4endoRWcf4/ZBfLQhhe92HeXh0SGM7NbGsBxCiIYJbevGnGt68MKPB7j83U1cF9WRh0aFWPwd9ZpaE/cu3clvB47z/IQwpphppEz/oNY42duwLiGbESE+Zjnn+Vgdn8WXsencPrwTg4ONbaglxJnY2igm9GzH+O5+fL87k7d/T+K2z+II83PjvpFdGNnVx2LfFBeNb0tSLk9/F8+IEG8eb8TOvsoS3zGIjIzUsbGxRseweFprJr+/hfS8clbcMZD0/LI/irWErGISs4qprDEBJ19wgrxcCPV1I7StK119XRnc2RsHO+uYZXK8qIKxb23Ex9WRb+8chJN90w/KXpuYzYwFMYwL9+Xdqb3kBVqIZqCwrJr//prIZ9sO4+HswOwxoVzZx98i39iqNWnu+3IXP+w+ypPju5m9AcvNC2I4lF3C+oeHG/L6lltSyeg5G/Bxc+K7OwdZzb9P4sJWU2viu11HeXvNIQ6fKCOinTv3jQzmolAp5C40qbmlTJy7GR9XR765Y6BZxn0ppeLONENbCjgrt/NIPpPe2/KXx1q7OND1VKF2umDr7NPSkKLHnNYkHGfGglimDejIsxPCm/Ta+zILuebDrXRo7cLy2wfg7HDeEziEEBYo/mghT38XT+zhfHq29+CFieEWtffKZNI89PVuvtmRyeyxocwa1sns11i87TD/+XYfvz0wrMlXOmituWVRHBsO5vDD3YOlaYmwOjW1JlbszOSdNUkcySujh787943swvAQbynkLgCF5dVMem8z+aVVfHfn4AZ3iT/tbAWc/BZq5Xp1aMXrV3anoKyaUF9XQtu64e1a9/k/1uSi0DbcPDiQTzelMrCzF6ObaA7LkRNl3DQ/BvcW9sy/qa8Ub0I0Q2F+7iybNYBvdmTy8i8HuOzdTVwX1YGHRoXg4exgaDaTSfP4ir18syOTBy7p0ijFG5zcBwewLjG7yQu4ZbEZ/HbgOP+5tKsUb8Iq2dnacFVkeyb2aseKHZm8s/YQ0xfE0KO9B/eNDGZ4FynkmqvTHSfT88pYfHOU2Yq3fyN34IRVqayp5cr3t3Ikr4xf7h2Cn0eLRr1ebkklV76/hfyyapbfPoDOPvKLhRDNXWF5NXN+PciirWm4t7Dn0TGhXB3Z3pBllVprnvouns+2Hebuizrz4KiQRr3eqDnr8XZ1ZMnM/o16nT9LzytjzP82EOHvzucz+1vk8lUh6qu61sTyuAzeWZNEZkE5Y8La8vaUXrI0uBl65vt4FmxJ49UrIszetORsd+Dkb5GwKo52trwzpdcfG/lrak2Ndq3SyhpuXhDDscIK5t0UKcWbEBcI9xb2PHN5GD/ePYTOPi2Z/c1eJr+/hb0ZTdutUmvN8z8e4LNth7ltWBAPXNKl0a85ItSH6NQ8SiprGv1acHJf3wNf7cJGKd64qocUb6LZsLe14dp+HVj70HAeHRPKyvgs7vp8B9WN+HuLaHqLt53sODmzETtOnokUcMLqBHi58OKkCGLS8nn790ONco3qWhN3LNnB3sxC3p3amz4dPRvlOkIIy9XNz42vbhvAf6/uQUZ+OZfP3cQTK/Y2yaw0rTWvrEz4Y1bn7DGhTbL8akSID9W1mk2Hchv9WgAfb0whJi2fZyeE4d+q8ZcdCdHUHOxsuH14J569PIzV+49z9+c7pYhrJrYk5fL09yc7Tj7WiB0nz0QKOGGVJvZqxxW9/XlnbRJbks37i8bpWW/rD+bw4qQILpFxAUJcsJRSTO7tz5qHhjF9YCBLth/hlV8SGv26c349yIfrU7i+fweeGt90szr7dGyFq5Md6xKzG/1a+48W8ebqRMaGt2VSr/MfRC6ENZg2MICnxndjZXwW9y6VIs7apeaWcvuSHXTyduHtKb2wbeLVA1LACav13IQwAlu7cP+Xu8grrTLbeV9blcg3OzK5f2QXs81YEkJYNzcne566rBvTBwWwYEsaGw/lNNq15m1K5e01SVzbtz3PXR7epI0P7G1tGBrszdrE7EYdTFxRXcsDX+3Cw9mBFydFSHMHcUGYMTiQ/1zalZ/3ZnHfl7sadRuIaDyFZdXcvCAGWxvFp9P6mmVcQH1JASesloujHe9M7UV+aTV3f7GDY4XlDT7n/M2pvL8umalRHbjn4s5mSCmEaE4eHRNKZ5+WPLRsNwVl5nvj6LS4w/m89PMBRnVrw0uTIgzZEzY8xJvjRZXsP1bUaNf4768HScgq5rUru+PpYmyXTyGa0swhQTwxris/7TnG/V/tliLOCj24bBfp+WV8cH0f2nsas/RbCjhh1cL83Hl+YhjRqXkMe30dz3wfT3ZxxXmd68c9R3nux/2M6taG5yc07bveQgjr4GRvy5yre3KipIonv4s367nzS6u4+/Md+Ho48bqBDT2Gh/gAsDahcZZRbks5wccbU7guqgMjTl1LiAvJLUODmD02lB92H+XBZbupNVleR3hxZsUV1fx2IJtbhgTRL9C4/ghSwAmrd03fk12eJvVsx2fbDjP0tbW8/POBei2r3JKcywNf7qZPh1aGrGUWQliPCH937hsZzA+7j/LdrkyznNN0qhtjbkkV703tg3uLpl+Sc5q3qyPd/d1Zm2j+ZaLFFdU8+NVuOno688SlTbvpXwhLMmtYJx4ZE8J3u47ysBRxVmP/0ZMrE/oGGNvcTgo40Sz4t3Lm1Su78/sDwxgb7stHG1MY8uoa3lydSGF59b8eu/9oEbctiqNja2c+mRaJk71tE6UWQlirWcM60auDB09+u88sy7c/2JDM2sQcnhzflQh/dzMkbJjhIT7sPJJPvhn3FwM8+8N+jhWW899reuLsYGfWcwthbe4Y3pmHRnXhm52ZPPL1HinirMC+UwVceDtjX6elgBPNSoCXC3Ou6cnq+4YyPMSHd9YkMfjVNbzz+6EzzjVKzyvjpvnRuDjasXBGPzycZS+GEOLc7GxtmHN1T6prNQ8t242pAb94Rafm8ebqg4zv7sv1/TuaMeX5uyjUB5OGDWZs1rJyXxZfx2Vw54jO9O7QymznFcKa3XVRMA9c0oXlOzKYvXxPg15LROOLzyykjZsj3q6OhuaQAk40S8FtXJl7XW9+vmcIUYGtefPXgwx5dQ0frk+mvOrkDKe80iqmzY+morqWRTf3w8+jhcGphRDWJMDLhSfHd2Nz0gkWbk07r3PkllRy9xc76ODpzMuTLacbY/d27rR2cTDbPrjs4goeX7GXiHbu3HNxsFnOKURzcc/Fwdx7cTDL4jJ47Ju9UsRZsL2ZhYT7Gb9KQtYviGatm58bn0yLZHd6Af/99SAv/5LAxxtTuX14J37YfZSM/HIW3xxFlzauRkcVQlihKf3a89uB47zySwJDgr3o7FP315Jak+b+L3dRUFbN/Jv6GdKK+mxsbBTDQrxZm5BNrUk3aF+w1prZy/dSWlnDnGt6YG8r7x0L8Xf3jQzGpDXvrEnCxgZenGhMF1pxdmVVNSTnlDAuwtfoKHIHTlwYerT3YOGMfnw9awDBPi15/sf97Mko4O1rexnaRUgIYd2UUrxyRQTODrbc9+Uuqmrq3hJ87tokNh7K5dnLw+jm59aIKc/PiBAf8suq2ZVe0KDzLI1JZ01CNrPHhtarwBXiQqKU4oFLunDniE58EZ3Ok9/ta9RZjKL+DhwrwqSN3/8GcgdOXGAiAzz54tb+bEs5QXWtiSHB3kZHEkJYOR9XJ16eHMGsxTt4Z80hHhwVcs5jtiTlMue3g0zq1Y5r+rZvgpT1NzTYG1sbxbrEbPp0PL89azuP5PPsD/EM6tyaaQMCzBtQiGZGKcVDo0KoNcEH65OpqjHx0uQIuWttIfZlnmxgEmEBBZz8jRAXpP5BraV4E0KYzZhwX67o7c/ctUnEHc7/1+dmF1Vwz9JddPJuyQsTLXfmpLuzPX06tGLNee6DS84pYcaCGNq4OfG/a3rJcjAh6kApxaNjQv7YE3fT/OhzdtMWTWNfZiFeLR1o42ZsAxOoYwGnlEpTSu1VSu1SSsWeesxTKfWrUurQqY9nfHtOKTVGKZWolEpSSs02Z3ghhBDCUjx9eTd83Vvw4Fe7KD1D11uAmloT9yzdSWllDe9d1xsXR8teCDM81Jv4o0UcL6qo13FZhRXc+Gk0tjaKRTP6Gd6xTQhropTi/ku68OZVPYhOzeOK97eQnldmdKwL3t7MQsL83C3iTbf63IEbobXuqbWOPPX1bOB3rXUw8Pupr/9CKWULzAXGAt2AKUqpbg3MLIQQQlgcNyd73ry6B4fzynjx5wNnfM5bvx9iW0oez08Mt4rmSSNCfABYX4+h3oXl1UybF01BWRULpvejY2uXxoonRLN2RR9/Fs2IIruogknvbWbnkX+/uy8aT0V1LYeySyxi+SQ0bAnlBGDhqc8XAhPP8Jx+QJLWOkVrXQUsPXWcEEII0ez0D2rNLUOC+Hz7EdYkHP/L99YfzOHdtUlcHenPlX38DUpYP6FtXfF1d6rzMsqK6lpuWRRLSm4JH94QaRGb/YWwZgM6teabOwbh7GDHtR9t45e9x4yOdEFKyCqm1qQJb2cZDafqWsBpYLVSKk4pdeupx9porY8BnProc4bj2gHpf/o649Rj/6CUulUpFauUis3JMd/gUCGEEKIpPTiqC6FtXXnk673klVYBcKywnPu/3EVIG1eevTzc4IR1p5RieIgPm5Jyz9lhs9akuW/prpODya/uyeBgryZKKUTz1tmnJSvuGEiYnxu3L9nBh+uTpUNlE9uXWQhAmAXMgIO6F3CDtNa9ObkU8k6l1NA6HnemRaJn/Buntf5Iax2ptY709pbmEkIIIayTo50tc67pSVF5NY99s4fqWhP3fLGTyupa5l7XmxYOtkZHrJeLQn0oqawh9nDeWZ+jteap7/axMj6Lp8Z34/Iefk2YUIjmr3VLRz6/pT/ju/vy8i8JPL5iH9W1dR9bIhom/mghHs72+LdqYXQUoI4FnNb66KmP2cAKTi6NPK6U8gU49fFM6ysygD/3R/YHjjYksBBCCGHpuvq68cCoLqyKP861H20jJi2flyZH0Mm7pdHR6m1gp9Y42Nqw9l+WUb79exJLth9h1rBOzBgc2ITphLhwONnb8va1vU7NijvCjAUxFFVIh8qmsDezkHALaWACdSjglFIuSinX058Do4B9wPfAtFNPmwZ8d4bDY4BgpVSgsGQSHAAAHWVJREFUUsoBuPbUcUIIIUSzdsuQIPoFeBJ3OJ/rojowoecZdxBYPBdHO6KCPFl7lkYmn28/wpzfDnJFb38eHXPuGXhCiPNnY6N4eHQor13Rna3JJ7jy/S1k5EuHysZUVWMiMavYovb01uUOXBtgk1JqNxAN/KS1Xgm8AlyilDoEXHLqa5RSfkqpnwG01jXAXcAq4ADwldY63vw/hhBCCGFZbG0Ub0/pxaNjQnlyvHU3YB4R4kNSdsk/Wpmvis/iP9/uZUSIN69cEWEx704L0dxd3bc9C2f041hhBRP/r717D/OrrA49/l1zy5CQBHK/AQEMlyRAohG5qIAIAoZbLRWKisUeqtbWo20t1F5sPT71HGvLOUe8UKVgsVxOBRHkWgQUBSFcQiaESxJSSCYhISkTEnKbyTp/zI6OcSYzSWZm/34z38/zzLN/+9373Xv9MnneZO1377Wv+jnzX3m97JAGrBdefYNtbZVTwAR6kMAVFSSPKX5mZOaXiva1mXlqZk4rluuK9ubMPKtD/zsz87DMPHRHX0mSBoMJIxv5xMmH0lhfXc+97eyUI9rrlD3w/K9uo3zspXX88Q1PcfSU/bjq4rdSX7s3ha0l7a4T3zKGWz5xAo31NXzw6ke4u2lV2SENSDsKmMyskAImsHevEZAkSYPAwWOGMXX00F8+B/f8qjf4/eseZ/L++3DNR9/O0IbKfiG5NFBNGz+cWz95IkdMGMEnvvcEdzf5moHe1tTcwvDGOg4aPbTsUH7JBE6SJHXrlCPG8fMla1myZgOXXPMY+zTU8t1Lj2XUsIayQ5MGtbHDh3DjZccxY9II/vb2Z3lza2vZIQ0oC1asZ8akERV1i7gJnCRJ6tYph49jS+t2zr/qZ2zc2sp1lx7LlP0r54q0NJg11tfyhbNnsLJlM998aGnZ4QwY29q2s2jleo6qoAImYAInSZJ64B2HjGKf+lo2t27n2x+ZwxETKueBfkkwZ+oozjlmEt96aImVKXvJ4tUb2Nq6vaIqUIIJnCRJ6oEhdbVceeEsrv/YO3jHIaPLDkdSJy4/8wgi4O/veq7sUAaEHQVMZlRQARMwgZMkST30vhkTOPbgUWWHIakLk/bbh0+c9BZ+9MxKfrF0bdnhVL2FzesZ1lDLIWOGlR3KrzGBkyRJkgaIy959CJNGNvK3tz9L2/YsO5yqtmBFC9MnjaCmpnIKmIAJnCRJkjRg7NNQyxVnHcmzK9dz87xXyg6narVtT55tXl9xz7+BCZwkSZI0oMw9eiJvn7o//3DP87Rs2lZ2OFVp6ZoNbNrWVlEv8N7BBE6SJEkaQCKCvzl7Buve3Mr/vf/FssOpSk3N7QVMnIGTJEmS1OdmTh7JB+ccwLU/X8aSNRvKDqfqNK1YT2N9DYeOrawCJmACJ0mSJA1If3L64exTX8uXfrSo7FCqzoIVLRw5cQR1tZWXLlVeRJIkSZL22tjhQ/jjU6fx4+dW88Dzq8sOp2psLwqYHFWBt0+CCZwkSZI0YF1ywlQOHjOML97xLNvatpcdTlVYtnYjG7a0VmQBEzCBkyRJkgashroa/mrukSxds5HvPvKfZYdTFZqa1wMwY/KIkiPpnAmcJEmSNICdcvg4TjpsLFf+xwus3bCl7HAq3sIVLTTU1nDY+OFlh9KpHidwEVEbEU9FxB3F+k0R8XTxsywinu6i37KIWFDsN6+3ApckSZLUvYjgr+YeyaatbXz1vhfKDqfiLVjRwhETh1NfgQVMYPdm4D4N/LKETWZ+MDNnZeYs4PvALbvoe0qx75w9jFOSJEnSHnrLuOF85Pip3PDYyyws3nGm35SZNK1oqcj3v+3QowQuIqYA7we+3cm2AH4HuKF3Q5MkSZLUWz596jT2H9rA393+LJlZdjgV6ZV1m1i/uXILmEDPZ+CuBD4HdFa65l3Aq5nZ1WveE7g3Ip6IiMv2IEZJkiRJe2nk0Ho+e9ph/OKlddzVtKrscCpSUzE7ObNCC5hADxK4iJgLrM7MJ7rY5SJ2Pft2Yma+FTgT+MOIeHcX57ksIuZFxLw1a9Z0F5YkSZKk3XTRsQdyxIThfOlHi9i8ra3scCpO04oW6mqCwydUZgET6NkM3InAORGxDLgReE9EXA8QEXXAbwE3ddU5M5uL5WrgVuDYLva7OjPnZOacsWPH7taXkCRJktS92prgr8+ezorXN/HPP1ladjgVZ8GKFg4bP5whdbVlh9KlbhO4zLwiM6dk5lTgQuDHmfmhYvN7gecyc3lnfSNiWEQM3/EZOB1o6pXIJUmSJO22Ew4dw5kzJ/D1B5ewqmVz2eFUjMxkYfN6jqrgAiaw9++Bu5Cdbp+MiEkRcWexOh54OCLmA48BP8rMu/fynJIkSZL2wuVnHsHm1jZuevyVskOpGCtbNrNu49aKfv4NoG53ds7MB4EHO6x/tJN9moGzis9LgWP2JkBJkiRJveug0cN4x8Gj+MHTK/jjU99Ce2H5wW3BivYCJjMG+AycJEmSpCp0/uzJvPTaRuYv971wAAtXtFBbE0yfWNkzcCZwkiRJ0iB05lETaair4QdPrSg7lIqwYEULbxm7L431lVvABEzgJEmSpEFpRGM9px05ntvnN7OtrbPXPQ8uTc3rmVHhz7+BCZwkSZI0aJ03ezJrN27lpy8O7vcwr16/mTVvbKn4CpRgAidJkiQNWicdNpb9h9Zz61PNZYdSqh0FTGaawEmSJEmqVA11Ncw9ehL3LlzFG5u3lR1OaZpWrCeCii9gAiZwkiRJ0qB23uzJbGndzt1Nq8oOpTQLVrRwyJhhDBuyW29ZK4UJnCRJkjSIvfXA/Tho9FB+8PTgrUa5sLmlKm6fBBM4SZIkaVCLCM6bNZmfL1nLqpbNZYfT717bsIWVLZurooAJmMBJkiRJg955syeTCbcNwlm4pqKAyYxJJnCSJEmSqsDBY4Yx64D9uHUQvtR7YfN6gKp4BxyYwEmSJEkCzp89medWvcGilevLDqVfLVjewtTRQxnRWF92KD1iAidJkiSJuUdPpK4mBl0xk6bmFmZUyfNvYAInSZIkCRi97xBOOmwstz3VTNv2LDucfvH6m1tZ/l+bqqaACZjASZIkSSqcN3syq9Zv5hdL15YdSr9oWtF+u+jMKilgAiZwkiRJkgqnTR/PvkPqBk0xk6bm9gqUM6ukgAmYwEmSJEkqNNbXcubMCdzVtIpNW9vKDqfPLVjRwpT992G/oQ1lh9JjPU7gIqI2Ip6KiDuK9S9ExIqIeLr4OauLfmdExPMRsTgiLu+twCVJkiT1vvNnT2bDllb+Y9GrZYfS5xauaKmq2ydh92bgPg0s2qntnzJzVvFz584dIqIWuAo4E5gOXBQR0/c4WkmSJEl96h2HjGbCiEZ+MMBvo1y/eRvL1r7JUVMGYAIXEVOA9wPf3s3jHwsszsylmbkVuBE4dzePIUmSJKmf1NYE586exEMvrGHthi1lh9NnFhYFTGZMqp7n36DnM3BXAp8Dtu/U/qmIeCYiromI/TvpNxl4pcP68qJNkiRJUoU6f/ZkWrcndzyzsuxQ+szCXxYwGWAzcBExF1idmU/stOkbwKHALGAl8NXOunfS1ulLJSLisoiYFxHz1qxZ011YkiRJkvrIERNGcMSE4QO6GuWCFS1MHNnImH2HlB3KbunJDNyJwDkRsYz2WyDfExHXZ+armdmWmduBf6b9dsmdLQcO6LA+BWju7CSZeXVmzsnMOWPHjt2tLyFJkiSpd50/ezJPv/I6L722sexQet3mbW38bPFajpmyX9mh7LZuE7jMvCIzp2TmVOBC4MeZ+aGImNhht/OBpk66Pw5Mi4iDI6Kh6P/DXohbkiRJUh86d9ZkIhiQxUy+/+RyXtuwhY8cf1DZoey2vXkP3P+KiAUR8QxwCvAZgIiYFBF3AmRmK/Ap4B7aK1jenJkL9zJmSZIkSX1swshGTjh0ND94egWZnT4FVZVa27bzrYeWcsyUkRx/6Oiyw9ltdbuzc2Y+CDxYfP5wF/s0A2d1WL8T+I1XDEiSJEmqbOfNmsyf/fszPPny67ztoM5qFlafO5tW8fK6N/mLs95GRGclOyrb3szASZIkSRrAzpg5gcb6mgFzG2Vm8o0Hl3Do2GGcPn182eHsERM4SZIkSZ0a3ljPadMncPszzWxt3fmNYtXnwefXsGjlej5+0qHU1FTf7BuYwEmSJEnahfNnT+L1N7fx0AvV/6qvbzy4hEkjGzl3VvW+mtoETpIkSVKX3jVtLKOHNVT9bZTzlq3jsWXr+G/vPoSGuupNg6o3ckmSJEl9rr62hrOPmcR9i15l/eZtZYezx77+4BJGDWvgwrcfWHYoe8UETpIkSdIunTd7Mltbt3PXgpVlh7JHFq1cz4+fW81HT5jKPg21ZYezV0zgJEmSJO3SMVNGcvCYYdxapbdRfvOhJQxrqOWS46eWHcpeM4GTJEmStEsRwXmzJvPo0nXMW7au7HB2y8tr3+T2+c1cfNxBjBxaX3Y4e80ETpIkSVK3Lj7uQKaOHsqHv/MYD7/4Wtnh9Ni3frKEupoaPvbOg8sOpVeYwEmSJEnq1ph9h3Dzx4/noNFDufTax7ln4aqyQ+rW6jc28/+eWM4H3jaZ8SMayw6nV5jASZIkSeqRccMbuemy45kxeQSf/N6T3PLk8rJD2qVrHl5Ga9t2/uDdh5YdSq8xgZMkSZLUYyOH1nP9x97BcYeM4rM3z+e7jywrO6ROtWzaxvWP/idnHTWRqWOGlR1OrzGBkyRJkrRbhg2p4zuXvJ3Tpo/nr29byFUPLCYzyw7r11z/6H+yYUsrnzh54My+gQmcJEmSpD3QWF/L1y9+K+fPnsxX7nmeL9/9XMUkcZu3tXHNwy9x0mFjmTFpZNnh9Kq6sgOQJEmSVJ3qa2v46gXHsO+QOr710FLe2NzKF8+dSW1NlBrXzfNeYe3GrXxygM2+gQmcJEmSpL1QUxP83bkzGLFPHVc9sIQNm1v56u8cQ31tOTf7bWvbzrceWsrbDtqfYw8eVUoMfanHCVxE1ALzgBWZOTcivgKcDWwFlgC/l5mvd9JvGfAG0Aa0Zuac3ghckiRJUmWICP7sfUcwvLGeL9/1HBu3tHLVxW+lsb6232O5fX4zK17fxN+eM4OIcmcC+8LupMWfBhZ1WL8PmJmZRwMvAFfsou8pmTnL5E2SJEkauD5+0qH8j/Nm8uPnV/PRf3mMDVta+/X827cn33xoCYePH857jhjXr+fuLz2agYuIKcD7gS8BnwXIzHs77PIo8Nu9Hp0kSZKkqvKh4w5ieGMdn715Phf/86Nc+3vHsv+whl9u37492bi1lTe3trFhSytvbimWW1vZsKWVjVva2LexjpMPH8uIxvrdOvf9z63mhVc3cOUHZ1FT8nN4faWnt1BeCXwOGN7F9kuBm7rYlsC9EZHAtzLz6t0LUZIkSVI1OXfWZIY11PHJf3uS0/7pIfYdUseGLW28WSRuPdFQV8PJh41l7jGTeO+R4xjasOvUJTP5+oOLmbL/Psw9emJvfI2K1G0CFxFzgdWZ+UREnNzJ9s8DrcD3ujjEiZnZHBHjgPsi4rnM/Eknx7kMuAzgwAMP3I2vIEmSJKnSvHf6eP710mO59ufLaKirYWhDHfsOqS2WdQwdUtu+bKhj2JBahu1YDqmj+fVN3D5/JXcuWMm9z75KY30Npx45nrOPnsjJh4/r9Nm6R5eu46mXX+eL586grqQCKv0huntXQ0T8PfBh2pO0RmAEcEtmfigiLgE+DpyamW92e7KILwAbMvMfdrXfnDlzct68eT37BpIkSZIGpLbtyePL1nH7/GbualrFuo1bGdZQy+kzJjD36Im8a9pYGurak7WPXPMYzza38PCfv6eU4im9LSKe6KyGSLcJ3E4HORn406IK5RnAPwInZeaaLvYfBtRk5hvF5/uAv8vMu3d1HhM4SZIkSR21tm3nkaVruWP+Su5qWsn6za2MaKzjjJkTOOaA/fj8rU187ozD+eTJbyk71F7RVQK3N++B+xowhPbbIgEezcyPR8Qk4NuZeRYwHri12F4H/Ft3yZskSZIk7ayutoZ3TRvLu6aN5YvnzeThxWu4Y/5K7lywipvnLWf4kDo+dNxBZYfZ53ZrBq6/OAMnSZIkqSc2b2vjoRfWMGpYA2+fOnBe3N0XM3CSJEmSVKrG+lreN2NC2WH0m4FbnkWSJEmSBhgTOEmSJEmqEiZwkiRJklQlTOAkSZIkqUqYwEmSJElSlTCBkyRJkqQqYQInSZIkSVXCBE6SJEmSqkRkZtkx/IaIeAN4vuw4VJoxwGtlB6FBYSTQUnYQGvAc09RfHNPUHxzT+s/hmTl858a6MiLpgeczc07ZQagcETHP37/6Q0RcnZmXlR2HBjbHNPUXxzT1B8e0/hMR8zpr9xZKSYPZ7WUHIEm9yDFNGgRM4CQNWpnpf3YkDRiOadLgUKkJ3NVlB6BS+fuXNJA4pkkaSBzT+k+nf9YVmcBlpn8xBjF//9oTEXFNRKyOiKYObV+JiOci4pmIuDUi9uui7xkR8XxELI6Iyzu0j4qI+yLixWK5f398Fw0sjmnaE45pqlSOaf2nqz/rikzgJGkPXAucsVPbfcDMzDwaeAG4YudOEVELXAWcCUwHLoqI6cXmy4H7M3MacH+xLkn94Voc0yR1wgROfa6zK4E9vQroVUT1VGb+BFi3U9u9mdlarD4KTOmk67HA4sxcmplbgRuBc4tt5wLXFZ+vA87r9cBVdRzT1B8c09RfHNOqjwmc+tQurgR2exXQq4jqZZcCdwFExKSIuLNonwy80mG/5UUbwPjMXAlQLMf1U6yqUI5pqiCOadprjmnVyQROfa2rK4E9uQroVUT1ioj4PNAKfA8gM5sz86wdmzvpkv0Vm6qOY5pK55imXuSYVoVM4NTXuroS2OlVQK8iqrdFxCXAXODizOzsPzHLgQM6rE8BmovPr0bExOI4E4HVfRmrqoJjmkrlmKZe5phWhUzg1Nd260qgVxHVmyLiDODPgXMy880udnscmBYRB0dEA3Ah8MNi2w+BS4rPlwC39WW8qgqOaSqNY5r6gGNaFTKBU1/r6kpgT64CehVRPRYRNwCPAIdHxPKI+BjwNWA4cF9EPB0R3yz2/eUVxKIgwKeAe4BFwM2ZubA47JeB0yLiReC0Yl2Dm2Oa+oVjmvqJY1oVis5n36XeERF1tJc6PhVYQfuVwd8FPgqszcwvF1WLRmXm53rSNzMXRsRXuusvSb3NMU3SQOKYVp1M4NTnIuIs4EqgFrgmM78UEaOBm4EDgZeBCzJzXURMAr69Y3q+s75Fe6f9+/mrSRqEHNMkDSSOadXHBE6SJEmSqoTPwEmSJElSlTCBkyRJkqQqYQKnPhERZ0TE8xGxuHh4lYg4JiIeiYgFEXF7RIzYRf/ZEZER8b7+i1qSOhcR10TE6oho6tA2KiLui4gXi+X+u+jvmCapYnQxpl0QEQsjYntEzOmmv2NaiUzg1Osioha4CjgTmA5cFBHTgW8Dl2fmUcCtwJ/t4jAXAQ8Xyz05vyT1pmuBM3Zquxy4PzOnAfcX611xTJNUSa7lN8e0JuC3gJ/0oL9jWolM4NQXjgUWZ+bSzNwK3AicCxzOrwaF+4APdNY5IgL4bdpL2J4eEY1F+9SIeC4irouIZyLi3yNiaLFtWUT8dUQ8DFzQl19O0uCTmT8Bdq6gdi5wXfH5OuC8zvo6pkmqNJ2NaZm5KDOf766vY1r5TODUFyYDr3RYX160NQHnFG0X8Osvf+zoROClzFwCPAic1WHb4cDVmXk0sB74ZIdtmzPznZl5415/A0nq3vjMXAlQLMd1sZ9jmqSBxDGtZCZw6gvRSVsClwJ/GBFPAMOBrV30v4j2WTuKZcfp+Vcy82fF5+uBd3bYdtMeRyxJfccxTdJA4phWsrqyA9CAtJxfn12bAjRn5nPA6QARcRjw/uLzvwCzgWbgbNpvrTwnIj5PezI4OiKGF8fa+cWFHdc39vL3kKRdeTUiJmbmyoiYCKwGxzRJA4tjWuVxBk594XFgWkQcHBENwIXADyNiHEBE1AB/CXwTIDN/LzNnZeZZwHuB+Zl5QGZOzcyDgO/zq2dLDoyI44vPOx6glaQy/BC4pPh8CXAbOKZJGlgc0yqPCZx6XWa2Ap8C7gEWATdn5kLaq1G+ADxH+1Wcf+mk+0W0V6js6PvA7xafFwGXRMQzwCjgG73/DSTp10XEDcAjwOERsTwiPgZ8GTgtIl4ETivWd+aYJqnidDamRcT5EbEcOB74UUTc00lXx7QKEJk7z3RKlSkipgJ3ZObMkkORpL3mmCZpIHFM6z/OwEmSJElSlXAGTpIkSZKqhDNwkiRJklQlTODU5yLigIh4ICIWRcTCiPh00T4qIu6LiBeL5f5F+2kR8URELCiW7+lwrLcV7Ysj4v9ERGfvnJMkSZIGJBM49YdW4E8y80jgONpf5j0duBy4PzOnAfcX6wCvAWdn5lG0l+b+1w7H+gZwGTCt+Dmjf76CJEmSVD4TOPW5zFyZmU8Wn9+gvcTsZOBc4Lpit+so3iGSmU9lZnPRvhBojIghxYtyR2TmI9n+8OZ3+dV7RyRJkqQBzwRO/aooMTsb+AUwPjNXQnuSB4zrpMsHgKcycwvtSd/yDtuWF22SJEnSoFBXdgAaPCJiX9pf9vjfM3N9d4+vRcQM4H8Cp+9o6mQ3y6hKkiRp0HAGTv0iIuppT96+l5m3FM2vFrdFUixXd9h/CnAr8JHMXFI0LwemdDjsFKAZSZIkaZAwgVOfKypFfgdYlJn/2GHTD2kvUkKxvK3Yfz/gR8AVmfmzHTsXt1m+ERHHFcf8yI4+kiRJ0mDgi7zV5yLincBPgQXA9qL5L2h/Du5m4EDgZeCCzFwXEX8JXAG82OEwp2fm6oiYA1wL7APcBfxR+pdYkiRJg4QJnCRJkiRVCW+hlCRJkqQqYQInSZIkSVXCBE6SJEmSqoQJnCRJkiRVCRM4SZIkSaoSJnCSpKoQEW0R8XRELIyI+RHx2YjY5b9jETE1In53D861T0Q8FBG1xTEyIr7YYfuYiNgWEV8r1r8QESuK+F6MiFsiYnqx7daifXFEtBSfn46IEyLixoiYtrvxSZIGLxM4SVK12JSZszJzBnAacBbwN930mQrsdgIHXArckpltxfpSYG6H7RcAC3fq809FfNOAm4AfR8TYzDw/M2cBvw/8tNhnVmb+HPgG8Lk9iE+SNEiZwEmSqk5mrgYuAz4V7aZGxE8j4sni54Ri1y8D7ypmvD5TzKh9JSIej4hnIuIPujjFxcBtHdY3AYsiYk6x/kHg5l3EdxNwL90njz8F3hsRdd3sJ0kSAP6DIUmqSpm5tLiFchywGjgtMzcXtyTeAMwBLgf+NDPnAkTEZUBLZr49IoYAP4uIezPzpR3HjYgG4JDMXLbTKW8ELoyIVUAb0AxM2kWITwJHdPMdtkfEYuAY4ImefndJ0uBlAidJqmZRLOuBr0XELNqTq8O62P904OiI+O1ifSQwDXipwz5jgNc76Xs38EXgVdpvkexpbN1ZTXsiaAInSeqWCZwkqSpFxCG0J2uraX8W7lXaZ7JqgM1ddQP+KDPv2cWhNwGNOzdm5taIeAL4E2AGcHY3Ic4G5nWzD8W5NvVgP0mSfAZOklR9ImIs8E3ga5mZtM+krczM7cCHgdpi1zeA4R263gN8IiLqi+McFhHDOh47M/8LqI2I30jigK8Cf56Za7uJ7wO0z/bd0IOvcxi/WRBFkqROOQMnSaoW+0TE07TfLtkK/Cvwj8W2rwPfj4gLgAeAjUX7M0BrRMwHrgX+N+2VKZ+MiADWAOd1cq57gXcC/9GxMTMX0nWy9ZmI+BAwDGgC3pOZa3b1hSJiPO3VNVfuaj9JknaI9guXkiRph4iYDXw2Mz/cx+f5DLA+M7/Tl+eRJA0c3kIpSdJOMvMp4IGIqO12573zOnBdH59DkjSAOAMnSZIkSVXCGThJkiRJqhImcJIkSZJUJUzgJEmSJKlKmMBJkiRJUpUwgZMkSZKkKmECJ0mSJElV4v8Dk+bpcbKPK9kAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def time_weighted_average(series):\n", " twa = 10*np.log10((10**(series/10).mean()))\n", " return twa\n", "\n", "hourly_groups = df_valid['LAEQ'].groupby(pd.Grouper(freq='1h'))\n", "hourly_groups.apply(time_weighted_average).plot(figsize=(15,4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "pd.Grouper has many additional parameters for timeseries grouping. The one i use most often is the base parameter, in which you can define when a grouping starts. A good example of this is grouping by day, but where the start of the day isn't midnight.\n", "\n", "In my province, the noise regulations for daytime and nighttime are:\n", "- daytime hours: 07:00 - 22:00\n", "- nighttime hours: 22:00 - 07:00\n", "\n", "This technically makes a day start at 7 am. Doing groupby operations while applying this convention is easy using pd.Grouper. This is done by changing the frequency to 24 Hours (IE a day) and then setting the base hour as 7 (7 am). The results can be easily plotted to show the effects:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Date (MDT)\n", "2020-04-08 07:00:00 AxesSubplot(0.125,0.2;0.775x0.68)\n", "2020-04-09 07:00:00 AxesSubplot(0.125,0.2;0.775x0.68)\n", "2020-04-10 07:00:00 AxesSubplot(0.125,0.2;0.775x0.68)\n", "2020-04-11 07:00:00 AxesSubplot(0.125,0.2;0.775x0.68)\n", "Freq: 24H, Name: LAEQ, dtype: object" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "day_grouper = pd.Grouper(freq='24H', base = 7)\n", "day_groups = df_valid['LAEQ'].groupby(day_grouper)\n", "day_groups.plot(figsize=(15,4), grid=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see now that the days at seperated into days starting at 7 am. Day and night levels can be extracted using the `between_time` function and using the timeweighted average in a custom function:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "def day_night(series):\n", " d = []\n", " day_hours = series.between_time(start_time='07:00', end_time='22:00',include_start=True, include_end=False)\n", " day_level = time_weighted_average(day_hours)\n", " night_hours = series.between_time(start_time='22:00', end_time='07:00',include_start=True, include_end=False)\n", " night_level = time_weighted_average(night_hours)\n", " d.append(day_level)\n", " d.append(night_level)\n", " return pd.DataFrame(d, index=['day', 'night'], columns=['LAEQ'])" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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LAEQ
daynight
Date (MDT)
2020-04-08 07:00:0056.58579353.413198
2020-04-09 07:00:0059.87856251.405926
2020-04-10 07:00:0054.74065546.252037
2020-04-11 07:00:0052.747619NaN
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" ], "text/plain": [ " LAEQ \n", " day night\n", "Date (MDT) \n", "2020-04-08 07:00:00 56.585793 53.413198\n", "2020-04-09 07:00:00 59.878562 51.405926\n", "2020-04-10 07:00:00 54.740655 46.252037\n", "2020-04-11 07:00:00 52.747619 NaN" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "day_groups.apply(day_night).unstack(level=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Timeseries analysis becomes much easier and less cumbersome when using Pandas. By making the index a datetimeindex, the data becomes time aware making standard and non standard tasks quiet trivial, and one can easily:\n", "- aggregate date by time groupings\n", "- apply simple data processing functions\n", "- write custom functions for more detailed analysis \n", "- slice and filter through the data using datetime calls\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 4 }