{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This block contains comments as markdown code\n", "# **Chapter 1** \n", "**ATMOS 5340: Environmental Programming and Statistics** \n", "**John Horel **\n", "\n", "- Follow these directions for doing these steps using a linux terminal window\n", "- are you in your atmos_5340/chapter1 directory?\n", "- check the directory you are in: pwd\n", "- If not in that directory, then type: cd \n", "- And then type: cd atmos_5340/chapter1\n", "- Type the following (Note the dot after the space): cp ~u0035056/atmos_5340_2022/chapter1/* . \n", "- Have you already copied the data directory? If not, you'll need to do that too. Review the instructions for that\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Using Python modules\n", "\n", "`numpy` provides routines to handle arrays and many calculations efficiently and is imported by convention as `np`. Numpy functions are very good at handling homogeneous data arrays (and similar in that respect to matlab functions).\n", "\n", "`pandas` is really good at handling tabular/array data that may have heterogeneous types (floating and text, for example). It is imported by convention as `pd`. \n", "\n", "There are a couple sets of panda library routines (`Series`, and `DataFrame`) used so frequently that we'll import those directly too.\n", "\n", "`scipy` has a bunch of statistical functions and we'll import `stats` from `scipy`\n", "\n", "\n", "`pyplot` is a _submodule_ of matplotlib. It is typically imported as the alias `plt` to handle basic plotting" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# these are python modules used in the program\n", "import numpy as np\n", "import pandas as pd\n", "from pandas import Series, DataFrame\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "from matplotlib.ticker import MaxNLocator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alta snowfall\n", "https://utahavalanchecenter.org/alta-monthly-snowfall\n", "\n", "\n", "Look in the `data` folder at the called `alta_snow.csv`\n", "\n", "Open the `alta_snow.csv` file see the column contents and the units.\n", "\n", "- The 0th column is the Year at Season End\n", "- The 1st-6th column are the total snowfall in each month from November to April (in inches)\n", "- The 7th column is the Nov-Apr total snowfall (inches)\n", "\n", "Begins in the 1946 season and ends in 2022" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1946. 1947. 1948. 1949. 1950. 1951. 1952. 1953. 1954. 1955. 1956. 1957.\n", " 1958. 1959. 1960. 1961. 1962. 1963. 1964. 1965. 1966. 1967. 1968. 1969.\n", " 1970. 1971. 1972. 1973. 1974. 1975. 1976. 1977. 1978. 1979. 1980. 1981.\n", " 1982. 1983. 1984. 1985. 1986. 1987. 1988. 1989. 1990. 1991. 1992. 1993.\n", " 1994. 1995. 1996. 1997. 1998. 1999. 2000. 2001. 2002. 2003. 2004. 2005.\n", " 2006. 2007. 2008. 2009. 2010. 2011. 2012. 2013. 2014. 2015. 2016. 2017.\n", " 2018. 2019. 2020. 2021. 2022.]\n", "[451. 374. 549. 523. 477. 349. 641. 411. 383. 472. 460. 386.\n", " 559.5 386. 395.5 326. 401.5 401. 566. 573. 433. 544. 479.5 566.1\n", " 459. 481.5 466.6 496.5 595.6 605. 439.5 314.5 524.5 488. 514. 391.\n", " 696. 637. 743.5 457. 599. 381.8 410.3 581.5 448. 580.2 395. 650.4\n", " 490.3 745.4 562. 599.1 574.9 458.4 446. 469.7 567.7 399.4 570.8 553.6\n", " 633.5 356. 654. 578. 430. 553. 329.5 382.5 357.5 267.5 393. 530.5\n", " 288. 475. 416. 374. 282.4]\n", "Min: 267.5 Max: 745.4 Mean: 483.1\n" ] } ], "source": [ "#read the year of the Alta snowfall data\n", "year = np.genfromtxt('../data/alta_snow.csv', delimiter=',',usecols=0,skip_header = 1)\n", "print(year)\n", "#read the seasonal total and convert from inches to cm\n", "snow = np.genfromtxt('../data/alta_snow.csv', delimiter=',', usecols=7, skip_header=1)\n", "#print out the data after converting it to cm\n", "print(snow)\n", "#what are the min and max values?\n", "print(\"Min: %.1f Max: %.1f Mean: %.1f\" % (np.min(snow),np.max(snow),np.mean(snow)))\n", "snow_per = 100.* (snow)/np.mean(snow)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "821.27\n" ] } ], "source": [ "val_2023 = 1.7*483.1\n", "print(val_2023)\n", "year=np.append(year,2023)\n", "snow_per=np.append(snow_per,159.)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Alta Snow (%)
194693.4
194777.4
1948113.6
1949108.3
195098.7
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" ], "text/plain": [ " Alta Snow (%)\n", "1946 93.4\n", "1947 77.4\n", "1948 113.6\n", "1949 108.3\n", "1950 98.7\n", "... ...\n", "2019 98.3\n", "2020 86.1\n", "2021 77.4\n", "2022 58.5\n", "2023 159.0\n", "\n", "[78 rows x 1 columns]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#use pandas module to organize the data in a convenient manner\n", "#by default the pandas display format shows up to 5 places to the right of the decimal point, limit it to 1\n", "#this next line is really obtuse, so don't stress over what it means\n", "pd.set_option('display.float_format', lambda x: '%.1f' % x)\n", "#define a dataframe, df, from the snow organized by year\n", "df = pd.DataFrame(snow_per, index=year.astype(int),columns=['Alta Snow (%)'])\n", "#list out the content of the dataframe\n", "df" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "\n", "below = df[df['Alta Snow (%)']<100]\n", "above = df[df['Alta Snow (%)']>=100]\n" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Alta Snow (%)
194693.4
194777.4
195098.7
195172.2
195385.1
195479.3
195597.7
195695.2
195779.9
195979.9
196081.9
196167.5
196283.1
196383.0
196689.6
196899.3
197095.0
197199.7
197296.6
197691.0
197765.1
198180.9
198594.6
198779.0
198884.9
199092.7
199281.8
199994.9
200092.3
200197.2
200382.7
200773.7
201089.0
201268.2
201379.2
201474.0
201555.4
201681.3
201859.6
201998.3
202086.1
202177.4
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" ], "text/plain": [ " Alta Snow (%)\n", "1946 93.4\n", "1947 77.4\n", "1950 98.7\n", "1951 72.2\n", "1953 85.1\n", "1954 79.3\n", "1955 97.7\n", "1956 95.2\n", "1957 79.9\n", "1959 79.9\n", "1960 81.9\n", "1961 67.5\n", "1962 83.1\n", "1963 83.0\n", "1966 89.6\n", "1968 99.3\n", "1970 95.0\n", "1971 99.7\n", "1972 96.6\n", "1976 91.0\n", "1977 65.1\n", "1981 80.9\n", "1985 94.6\n", "1987 79.0\n", "1988 84.9\n", "1990 92.7\n", "1992 81.8\n", "1999 94.9\n", "2000 92.3\n", "2001 97.2\n", "2003 82.7\n", "2007 73.7\n", "2010 89.0\n", "2012 68.2\n", "2013 79.2\n", "2014 74.0\n", "2015 55.4\n", "2016 81.3\n", "2018 59.6\n", "2019 98.3\n", "2020 86.1\n", "2021 77.4\n", "2022 58.5" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "below" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Alta Snow (%)
1948113.6
1949108.3
1952132.7
1958115.8
1964117.2
1965118.6
1967112.6
1969117.2
1973102.8
1974123.3
1975125.2
1978108.6
1979101.0
1980106.4
1982144.1
1983131.9
1984153.9
1986124.0
1989120.4
1991120.1
1993134.6
1994101.5
1995154.3
1996116.3
1997124.0
1998119.0
2002117.5
2004118.1
2005114.6
2006131.1
2008135.4
2009119.6
2011114.5
2017109.8
2023159.0
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" ], "text/plain": [ " Alta Snow (%)\n", "1948 113.6\n", "1949 108.3\n", "1952 132.7\n", "1958 115.8\n", "1964 117.2\n", "1965 118.6\n", "1967 112.6\n", "1969 117.2\n", "1973 102.8\n", "1974 123.3\n", "1975 125.2\n", "1978 108.6\n", "1979 101.0\n", "1980 106.4\n", "1982 144.1\n", "1983 131.9\n", "1984 153.9\n", "1986 124.0\n", "1989 120.4\n", "1991 120.1\n", "1993 134.6\n", "1994 101.5\n", "1995 154.3\n", "1996 116.3\n", "1997 124.0\n", "1998 119.0\n", "2002 117.5\n", "2004 118.1\n", "2005 114.6\n", "2006 131.1\n", "2008 135.4\n", "2009 119.6\n", "2011 114.5\n", "2017 109.8\n", "2023 159.0" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "above" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Colours - Choose the extreme colours of the colour map\n", "colors_low = [\"#c81d25\",\"#ffacaf\"] # Extreme colours of the low scale\n", "colors_high = [\"#bbdefb\",\"#2196f3\"] # Extreme colours of the high scale\n", "\n", "# Colormap - Build the colour maps\n", "cmap_low = mpl.colors.LinearSegmentedColormap.from_list(\"low_map\", colors_low, N=256)\n", "cmap_high = mpl.colors.LinearSegmentedColormap.from_list(\"high_map\", colors_high, N=256)\n", "norm_low = mpl.colors.Normalize(below.min(), 100.) # linearly normalizes data into the [0.0, 1.0] interval\n", "norm_high = mpl.colors.Normalize(100., above.max())\n", "norm_low" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#create a fig of Alta snowfall time series\n", "fig,(ax1) = plt.subplots(1,1,figsize=(20,4))\n", "# Plot bars and average (horizontal) line\n", "bar1 = ax1.bar(below.index, below['Alta Snow (%)'], color=cmap_low(norm_low(below['Alta Snow (%)'])), width=0.6, label='Below Average', zorder=2)\n", "bar2 = ax1.bar(above.index, above['Alta Snow (%)'], color=cmap_high(norm_high(above['Alta Snow (%)'])), width=0.6, label='Above Average', zorder=2)\n", "plt.axhline(y=100., color = 'grey', linewidth=3)\n", "\n", "decade_ticks = np.arange(1950,2030,10)\n", "\n", "ax1.set(xlim=(1945,2024),ylim=(50.,160.))\n", "ax1.set(xlabel=\"Year\",ylabel=\"Snowfall (% of Normal)\")\n", "ax1.set(xticks=decade_ticks)\n", "ax1.set(title=\"Alta Snowfall: 1946-2023\")\n", "#add grids to the plot\n", "ax1.grid(linestyle='--', color='grey', linewidth=.2)\n", "\n", "#save the figure to \n", "plt.savefig('alta_per.png')" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Creat5e bar plot time series of Alta seasonal snowfall\n", "#create a list for the times for tick marks on the x axis. This will stop at 2020 (not 2030)\n", "decade_ticks = np.arange(1950,2030,10)\n", "\n", "#create a fig of Alta snowfall time series\n", "fig,(ax1) = plt.subplots(1,1,figsize=(10,3))\n", "ax1.bar(year,snow_per,color='green')\n", "ax1.set(xlim=(1945,2024),ylim=(0.,300.))\n", "ax1.set(xlabel=\"Year\",ylabel=\"Snowfall (%)\")\n", "ax1.set(xticks=decade_ticks)\n", "ax1.set(title=\"Alta Snowfall: 1946-2023\")\n", "#add grids to the plot\n", "ax1.grid(linestyle='--', color='grey', linewidth=.2)\n", "\n", "#save the figure to \n", "plt.savefig('alta_snowfall.png')" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": 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dUtdcc43q27ev6tu3r7rmmmtUVVVVXJmPP/5YTZkyRaWlpakBAwaoW265Je7WL6WUeuedd9Sll16qTNNU2dnZqqCgoFO3giklt4PV19er+vp6p8NwFN090F2/UuKB7vqVUqqsrKzLucBQSv76JJPa2loCgQA1NTVanu8+cuQIkNiFSamO7h7orh/EA931Axw4cIDPfe5zXcoFel8dICQd3c9rgXigu34QD3TXDyl8jlvQj+ZJDHRGdw901w/ige76ITEPeuxV5YIgCIIgtEUSt5BUWk8BqyO6e6C7fhAPdNcPJKRfhsqFpNI8r6/O6O6B7vpBPNBdPyTmgRxxC4IgCEIKIYlbSCrBYDChp+L0BnT3QHf9IB7orh9ISL8MlQtJRYbIxAPd9YN4oLt+kKFyQRAEQdAGSdxCUpEhMvFAd/0gHuiuH2SoXEghTNN0OgTH0d0D3fWDeKC7fkjMA0ncQlKRJ6mJB7rrB/FAd/2QmAcyVC4kFZl4QTzQXT+IB7rrB5mARUghvF6v0yE4ju4e6K4fxAPd9UNiHkjiFpKKDJGJB7rrB/FAd/2QmAeSuIWkYtu20yE4ju4e6K4fxAPd9UNiHkjiFpKK2+12OgTH0d0D3fWDeKC7fkjMA0ncQlKRHVY80F0/iAe66wdJ3EIKIUNk4oHu+kE80F0/yFC5kELIRSnige76QTzQXT/IxWlCCuHxSJPT3QPd9YN4oLt+SMwDcU9IKuFw2OkQHEd3D3TXD+KB7vohMQ8kcQtJRSnldAiOo7sHuusH8UB3/ZCYB5K4haQiMyaJB7rrB/FAd/0gM6cJKUQkEnE6BMfR3QPd9YN4oLt+SMwDSdxCUpEdVjzQXT+IB7rrB0ncQgohQ2Tige76QTzQXT/IULmQQshFKeKB7vpBPNBdP8jFaUIKITMmiQe66wfxQHf9IDOnCSmEaZpOh+A4unugu34QD3TXD4l5IIlbSCoyRCYe6K4fxAPd9UNiHri6MQ5BOCGWZWFZltNhOIruHuiuH8QD3fUDCemXI24hqfj9fqdDcJze4kFpaSmVlZWdXq+5w2o9VJiVlcWQIUO6JbaeTm9pA11Fd/2QmAeSuAVB6DSlpaUMy8sj2NDQ6XXT09MBaGi1rj89nb0lJdokb0HoKpK4haQSDAaBf3feOtIbPKisrGxK2mvWQF5ep9b119cD0JCR8e+FJSUEZ86ksrJSi8TdG9pAIuiuH/7tQVeQxC0kFRki62Ue5OXBBRd0apXYUbrGnXavagNdQHf9kJgHcnGaIAiCIKQQkriFpBIMBhMaIuoN6O6BGQxiaqwfpA3orh9kqFxIIWTiBfEgpLl+kDagu36QCVgEQUghjjftRElJSbfWpdMtZoI+SOIWkkooFAIgo+UVxZqhuwe+o/rjriovKwOXi5kzZ3ZrXT31FjPd24Du+uHfHnQFR89xv/nmm0ybNo2cnBwMw+D555+PfWfbNj/72c8YOXIkGRkZ5OTk8P3vf59PP/00bhuWZTFv3jyysrLIyMhg+vTp7N+/P65MVVUV+fn5BAIBAoEA+fn5VFdXx5UpLS1l2rRpZGRkkJWVxfz589sY++677zJ+/HjS0tI47bTTuOuuu2Tqvk7i8XjwePT+v6i7B2GPh3Br/dXVEI023V5WVNQ9rzVrCDY0dGmSmJON7m1Ad/1AQvodda6+vp7zzjuPa6+9lv/8z/+M+66hoYG33nqL2267jfPOO4+qqioWLFjA9OnT2bVrV6zcggULePHFF1m7di0DBw5k0aJFTJ06laKiItxuNwAzZsxg//79FBYWAnDDDTeQn5/Piy++CDQ90HzKlCmccsopbN68mUOHDjFr1iyUUqxYsQKA2tparrzySiZMmMDOnTv54IMPmD17NhkZGSxatCgZdvUKmn8TndHdg+jx9Hfh9rJURPc2oLt+SNAD1UMA1Lp1645bZseOHQpQH3/8sVJKqerqauX1etXatWtjZQ4cOKBcLpcqLCxUSim1Z88eBaht27bFymzdulUB6v3331dKKbV+/XrlcrnUgQMHYmWeffZZZZqmqqmpUUop9fDDD6tAIKCCwWCszLJly1ROTo6KRqMd1llTU6OA2HZ1o7q6WlVXVzsdhqP0Bg+KiooUoCgqUijVqVeguloFqqvjl69Z0+XtHfN1NMaioqKT7kdn6Q1tIBF016+UUqWlpV3OBSk1VlFTU4NhGPTv3x+AoqIibNtm4sSJsTI5OTmMGDGCLVu2MGnSJLZu3UogEGDMmDGxMhdffDGBQIAtW7YwbNgwtm7dyogRI8jJyYmVmTRpEpZlUVRUxIQJE9i6dSvjx4+PuxJw0qRJLFmyhI8++oihQ4e2G3PryfRra2uBphEFj8dDMBjENE0Mw8CyLLxeL4ZhYNs2brcbt9uNbdsYhoHH4yEcDqOUwuv1EolEiEQieL1elFLYto1pmiilsCwrdoN/MBiMe9+sIRQK4fF42q0DaLe+cDiMz+eLaTteHe1pamhoiP3T7C2aOvs71dTUYJomHo8nZTUFg0ECgQC2ZeGqq8Nr21imCUrhtyyCR+vwB4Ox92YwSMg0MRsb8do2EZeLqNuN17aJhsNE0tPxNDZCbS1hjwd3OIxLKWyvF1ckgiccJuTzYQA+y8Jqpw5/MEjQNMEwMOvrsfv0wbIsqqure9T+FAwGiUQiRKPRpLa9nrI/1dTU4PP58Hq9vUZTZ3+n1lP+doaUuY87GAzy85//nBkzZtCvXz8AysvL8fl8ZGZmxpUdPHgw5eXlsTKDBg1qs71BgwbFlRk8eHDc95mZmfh8vuOWaf7cXKY9li1bFju3HggEyM3N7YzsXkfzDqMzunsQ8XiOP1yuAW63W+tzvLrrh8SGylPCOdu2+e53v0s0GuXhhx8+YXmlFIZhxD63fN+dZdTRC9PaW7eZJUuWsHDhwtjn2tpacnNzSU9Pj72a6c55e1terXmsOhK9orMjdbTW1Dzi0Pznq7vrc0JTZ4lGo7G6UlWT3++npqYGTBP69In7ruXV4g0tttv83h2JYPt81PXt+++VPB5oaIC0NOhA26g/QR2xOOrqME0zNkrXVbr7d2pvP0hG22uJk22v+ei2dR+YKKm0P/XqKU9t2+bqq69m3759bNy4Ma6hZ2dnEwqFqKqqilunoqIidjScnZ3NZ5991ma7Bw8ejCvT+qi5qqoK27aPW6aiogKgzZF4S0zTpF+/fnEvQRAEQegqPTpxNyft//u//+PVV19l4MCBcd+PGjUKr9fLxo0bY8vKysrYvXs348aNA2Ds2LHU1NSwY8eOWJnt27dTU1MTV2b37t2UlZXFymzYsAHTNBk1alSszJtvvhl3i9iGDRvIycnhjDPO6HbtvRW5DUQ8aPd2MM3QvQ3orh8Sux3M0cRdV1dHcXExxcXFAOzbt4/i4mJKS0sJh8N861vfYteuXTz99NNEIhHKy8spLy+PJc9AIMD111/PokWLeO2113j77beZOXMmI0eO5IorrgAgLy+PyZMnM2fOHLZt28a2bduYM2cOU6dOZdiwYQBMnDiR4cOHk5+fz9tvv81rr73G4sWLmTNnTuwIecaMGZimyezZs9m9ezfr1q1j6dKlLFy48LhD5UI84XA4NkymK7p74A6HcWusH6QN6K4fSEx/F69k7xZef/31pltAWr1mzZql9u3b1+53gHr99ddj22hsbFS33HKLGjBggEpLS1NTp05VpaWlcfUcOnRIXXPNNapv376qb9++6pprrlFVVVVxZT7++GM1ZcoUlZaWpgYMGKBuueWWuFu/lFLqnXfeUZdeeqkyTVNlZ2ergoKCTt0KppTcDia3gfQOD+R2sMToDW0gEXTXr1QK3w522WWXHXfmseN914zf72fFihWxiVLaY8CAAaxZs+a42xkyZAgvvfTSccuMHDmSN99884QxCcfG6/U6HYLj6O6Brbl+kDagu35IzAO9TzIISScSiTgdguPo7oFLc/0gbUB3/ZCYB5K4haSi+3ktcM6D0tLSbpu3O5GneHmkDWi/H+iuHxLzQBK3kFSaZx/SGSc8KC0tZVheHsEEZmvqLkLSBrTfD3TXD4l5IIlbEDSgsrKyKWmvWdP0II9EWb8ebrutS6vKPRiCkBiSuIWk0jxvu87P4XXUg+56+lYCQ+W+o/rrpQ1oux/orh+Ie4ZFZ5HELSSVRKb56y3o7oGluX6QNqC7fujlU54KgiAIgvBvJHELSSUYDBIMBp0Ow1F098AfDOLXWD9IG9BdP5CQfhkqF5KKDJGJB0HN9YO0Ad31gwyVC4IgCII2SOIWkooMkYkHMlQubUB3/SBD5UIKYZqm0yE4ju4eBDXXD9IGdNcPiXkgiVtIKvIIVPEA3fUjbUB3/ZCYBzJULiQVy7ISmnigN6C7B6ZlYWqsH6QN6K4fZAIWIYWQx/mJB/JYT2kDuusHeaynkELIEJl4ENVcP0gb0F0/JOaBJG4hqdi27XQIjqO7B17N9YO0Ad31Q2IeSOIWkorb7XY6BMfR3YOo5vpB2oDu+iExDyRxC0lFdljxIKK5fpA2oLt+kMQtpBAyRCYeeDTXD9IGdNcPMlQupBByUYp4oDTXD9IGdNcPcnGakEJ4PNLkdPcgorl+kDagu35IzANxT0gq4XDY6RAcR3cP3JrrB2kDuuuHxDyQxC0kFaWU0yE4ju4eGEnWX1JS0m3bysrKYsiQIQlvR/c2oLt+SMwDSdxCUpEZk8SDcLL0l5WBy8XMmTO7bZP+9HT2lpQknLx1bwO66weZOU1IISKRiNMhOI7uHriTpb+6GqJRWLMG8vIS315JCcGZM6msrEw4ceveBnTXD4l5IIlbSCqyw4oHrmTrz8uDCy5Ibp0nQPc2oLt+kMQtpBAyRCYeyENGpA3orh9kqFxIIeSiFPHApbl+kDagu36Qi9OEFEJmTBIP5CEj0gZ01w8yc5qQQpim6XQIjqO7B5bm+kHagO76ITEPJHELSUWGyMQDdNePtAHd9UNiHri6MQ5BOCGWZWFZltNhOIruHvgtC7/G+kHagO76gYT0yxG3kFT8fr/TITiO7h4ENdcP0gZ01w+JeSBH3IIgCIKQQkjiFpJKMBgkGAw6HYaj6O6BPxjEr7F+kDagu34gIf0yVC4kFRkiEw9kqFzagO76QYbKBUEQBEEbJHELSUWGyMQDMxjE1Fg/SBvQXT/IULmQQsjEC+JBSHP9IG1Ad/2QmAeOHnG/+eabTJs2jZycHAzD4Pnnn4/7XilFQUEBOTk5pKWlcdlll/Hee+/FlbEsi3nz5pGVlUVGRgbTp09n//79cWWqqqrIz88nEAgQCATIz8+nuro6rkxpaSnTpk0jIyODrKws5s+fTygUiivz7rvvMn78eNLS0jjttNO46667ZCIBQegk6uhLEISu4Wjirq+v57zzzmPlypXtfn/vvfeyfPlyVq5cyc6dO8nOzubKK6/kyJEjsTILFixg3bp1rF27ls2bN1NXV8fUqVPjHpk2Y8YMiouLKSwspLCwkOLiYvLz82PfRyIRpkyZQn19PZs3b2bt2rU899xzLFq0KFamtraWK6+8kpycHHbu3MmKFSu4//77Wb58+UlwpvcSCoXa/CHSDd098IVC+DTWD9IGdNcPJKZf9RAAtW7dutjnaDSqsrOz1a9//evYsmAwqAKBgHr00UeVUkpVV1crr9er1q5dGytz4MAB5XK5VGFhoVJKqT179ihAbdu2LVZm69atClDvv/++Ukqp9evXK5fLpQ4cOBAr8+yzzyrTNFVNTY1SSqmHH35YBQIBFQwGY2WWLVumcnJyVDQa7bDOmpoaBcS2qxu1tbWqtrbW6TAcxQkPioqKmg50i4oUSiX+WrOmy9vrU1ur+tTWdtv2TkaM7b6OelhUVJTgryH7ge76lVJq//79Xc4FPfYc9759+ygvL2fixImxZaZpMn78eLZs2cKNN95IUVERtm3HlcnJyWHEiBFs2bKFSZMmsXXrVgKBAGPGjImVufjiiwkEAmzZsoVhw4axdetWRowYQU5OTqzMpEmTsCyLoqIiJkyYwNatWxk/fnzceYlJkyaxZMkSPvroI4YOHdqujtZT+9XW1gLQ0NCAx+MhGAximiaGYWBZFl6vF8MwsG0bt9uN2+3Gtm0Mw8Dj8RAOh1FK4fV6iUQiRCIRvF4vSils28Y0TZRSWJYVu90gGAzGvW/WEAqF8Hg87dYBtFtfOBzG5/PFtB2vjvY0hUIhXC4X0Wi012jq7O905MgRfD4fLpcraZosyyI9PR1/fT3Bhgag6X7qoGmCYWBaFrbXS9Qw8No2UbebiNuNx7ZRhkHE48EdDmMoRdjrxR0O4woEsC0LV10dXttueniIUvgtK3bLlz8YjL03g0FCpolpWbEj7qjb3VRfOEwkPR1PYyPU1hI+Wp9LKWyvF1ckgiccJuTzYQA+y8Jqp444TbaN3acPUcvCW119Yk2RCK5IpKk+pdpqsixIT6e+vp6Gox529XeybZtoNJr0ttdT9qfa2lpM08TtdvcaTZ39nRobG+kqPfaq8vLycgAGDx4ct3zw4MGx78rLy/H5fGRmZh63zKBBg9psf9CgQXFlWteTmZmJz+c7bpnmz81l2mPZsmWxc+uBQIDc3NzjC+/l2LYd20F0JRwOa+2B17bxaKwfpA2Ew2HtH+2ZyO/fY4+4mzEMI+6zUqrNsta0LtNe+e4oo45emHa8eJYsWcLChQtjn2tra8nNzSU9PT32aqbl+0TJyMhod7st37csc7LqaK3J5Wr6r9ivX7+TUp8TmjpLNBqN1ZUsTaZp0tDQQENGBhzddkOLOho6q8njgZoaME3o0yfuq4YW8bZXh+vo9Sd1ffvGb6+hAdLSoANto/4EdQA0eL1QV9cUY//+HdN1DBoyMpq209BARkZG7Pfp6u/U3Ge03A+S0fZa4uT+1Hyw1boPTJRU6iP6tNpvOkOPPeLOzs4G2h7NVlRUxI50s7OzCYVCVFVVHbfMZ5991mb7Bw8ejCvTup6qqips2z5umYqKCqDtqEBLTNOkX79+cS+d8Xg8eDw9/v/iSUV3DyIeDxGN9YO0Ad31Awnp77GJe+jQoWRnZ7Nx48bYslAoxKZNmxg3bhwAo0aNwuv1xpUpKytj9+7dsTJjx46lpqaGHTt2xMps376dmpqauDK7d++mrKwsVmbDhg2YpsmoUaNiZd588824KwE3bNhATk4OZ5xxRvcb0EvRfYgQxANPOCxD5Zq3Ad31Q2JD5Y4m7rq6OoqLiykuLgaaLkgrLi6mtLQUwzBYsGABS5cuZd26dezevZvZs2eTnp7OjBkzAAgEAlx//fUsWrSI1157jbfffpuZM2cycuRIrrjiCgDy8vKYPHkyc+bMYdu2bWzbto05c+YwdepUhg0bBsDEiRMZPnw4+fn5vP3227z22mssXryYOXPmxI6QZ8yYgWmazJ49m927d7Nu3TqWLl3KwoULTzh0LwiCIAjdhaNjFbt27WLChAmxz83ngmfNmsXq1av56U9/SmNjI3PnzqWqqooxY8awYcMG+rY4N/bggw/i8Xi4+uqraWxs5PLLL2f16tW43e5Ymaeffpr58+fHrj6fPn163L3jbrebl19+mblz53LJJZeQlpbGjBkzuP/++2NlAoEAGzdu5Oabb2b06NFkZmaycOHCuPPXwonRfXgMOuZBaWkplZWV3VZnSUlJt20rUcLSBrTfD3TXD4l54Kh7l1122XFnHjMMg4KCAgoKCo5Zxu/3s2LFClasWHHMMgMGDGDNmjXHjWXIkCG89NJLxy0zcuRI3nzzzeOWEY6P7sNjcGIPSktLGZaXF7ttq7fhljag/X6gu37o5VeVC72L4/1R04UTeVBZWdmUtNesgby87ql0/Xq47bbu2VaCuKQNaL8f6K4fEvNAEreQVLxer9MhOE6HPcjLgwsu6J5Ke9BQuS1tQPv9QHf9kJgHkriFpNJyDnld0d0Dl+b6QdqA7vohMQ8kcQtJRc5tiQe63woG0gZ01w9yjltIIZrn+9UZ3T0Iaa4fpA3orh8S80AStyAISUVmPRCExOixM6cJvZPWT0vTEd098FkWPo31g7QB3fUDCemXI24hqTQ/Ak9ndPfA0lw/SBvQXT8k5oEccQuCIAhCCiGJW0gqwWCQYDDodBiOorsH/mAQv8b6QdqA7vqBhPTLULmQVGSITDwIaq4fpA3orh9kqFwQBEEQtEESt5BUZIhMPJChcmkDuusHGSoXUgjTNJ0OwXF09yCouX6QNqC7fkjMA0ncQlIxDJl+Q3sPdNePtAHd9UNiHshQuZBUZOIF8cC0LEyN9YO0Ad31g0zAIqQQ8jg/8UAe6yltQHf9II/1FFIIGSITD6Ka6wdpA7rrh8Q8kMQtJBXbtp0OwXF098CruX6QNqC7fkjMA0ncQlJxu91Oh+A4unsQ1Vw/SBvQXT8k5oEkbiGpyA4rHkQ01w/SBnTXD5K4hRRChsjEA4/m+kHagO76QYbKhRRCLkoRD5Tm+kHagO76QS5OE1IIj0eanO4eRDTXD9IGdNcPiXkg7glJJRwOOx2C4+jugVtz/SBtQHf9kJgHkriFpKKUcjoEx9HdA0Nz/SBtQHf9kJgHkriFpCIzJokHYc31g7QB3fWDzJwmpBCRSMTpEBxHdw/cmusHaQO664fEPJDELSQV2WHFA1eK6y8pKUl4Gw0NDQCkp6eTlZXFkCFDEt5mKqH7PgCSuIUUQobIxIOUfchIWRm4XMycOTPhTaWnpwNNCdyfns7ekhKtkrfu+wDIULmQQshFKeKBK1X1V1dDNApr1kBeXkKbcgWDTW/27SM4cyaVlZVaJW7d9wGQi9OEFEJmTBIPUv4hI3l5cMEFCW3CW1XV9Mbv74aAUg/d9wGQmdOEFMI0TadDcBzdPbA01w/ige77ACTmgSRuIanIEJl4gO76QXsPtN8HSMwDVzfGIQgnxLIsLMtyOgxH0d0Dv2Xh11g/iAe67wNAQvrliFtIKn5Nz+m1RHcPgprrB/FA930AEvNAjrgFQRAEIYWQxC0klWAwSLD5VhhN0d0DfzCIX2P9IB7ovg8ACemXoXIhqcgQmXig+zAxiAe67wPgwFD5mWeeyaFDh9osr66u5swzz+xyMIIgCIIgHJ8uJe6PPvqo3XlWLcviwIEDCQfVTDgc5pe//CVDhw4lLS2NM888k7vuuotoNBoro5SioKCAnJwc0tLSuOyyy3jvvffaxDVv3jyysrLIyMhg+vTp7N+/P65MVVUV+fn5BAIBAoEA+fn5VFdXx5UpLS1l2rRpZGRkkJWVxfz58wmFQt2mVwdkiEw8MINBTI31g3ig+z4ASRwq/8tf/hJ7/8orrxAIBGKfI5EIr732GmeccUaXg2nNPffcw6OPPsqTTz7JOeecw65du7j22msJBAL86Ec/AuDee+9l+fLlrF69mrPPPptf/epXXHnllezdu5e+ffsCsGDBAl588UXWrl3LwIEDWbRoEVOnTqWoqAi32w3AjBkz2L9/P4WFhQDccMMN5Ofn8+KLL8b0TZkyhVNOOYXNmzdz6NAhZs2ahVKKFStWdJvm3o5MvCAehDTXD+KB7vsAJOiB6gSGYSjDMJTL5Yq9b375fD519tlnqxdffLEzmzwuU6ZMUdddd13csquuukrNnDlTKaVUNBpV2dnZ6te//nXs+2AwqAKBgHr00UeVUkpVV1crr9er1q5dGytz4MAB5XK5VGFhoVJKqT179ihAbdu2LVZm69atClDvv/++Ukqp9evXK5fLpQ4cOBAr8+yzzyrTNFVNTU2HNdXU1CigU+v0Jurq6lRdXZ3TYTjKiTwoKipSgKKoSKFU97zWrOnebSawvfS6OpVeV3dy4+thmo/pwdHfuqio6JjtoTci/YBSn376aZdzQaeOuJuHqIcOHcrOnTvJysrq+j+GDvDlL3+ZRx99lA8++ICzzz6bf/zjH2zevJmHHnoIgH379lFeXs7EiRNj65imyfjx49myZQs33ngjRUVF2LYdVyYnJ4cRI0awZcsWJk2axNatWwkEAowZMyZW5uKLLyYQCLBlyxaGDRvG1q1bGTFiBDk5ObEykyZNwrIsioqKmDBhQrsaWk80UFtbCzQ9Fcjj8RAMBjFNE8MwsCwLr9eLYRjYto3b7cbtdmPbNoZh4PF4CIfDKKXwer1EIhEikQherxelFLZtY5omSiksy4pd/BAMBuPeN//TC4VCeDyedusA2q0vHA7j8/li2o5XR3uajhw5gtvtJhwO9xpNnf2dKisrj1ufZVlkZmYSsixUfT2+UIiwx0PU7cZr20QNg4jHg+eoprDHgzscxqUUtteLKxLBEw4T8vkwAJ9lYUUikJ6Ov76e4NFHSvqDQYKmCYaBaVnYXi9Rw2iqw+0m4nbjsW3U0frc4TCGUoS93qb6AgFsy8JVV4fXtpum8VQKv2XFLr7yB4Ox92YwSMg06Vddjde2ORyN/ltTOEwkPR1PYyPU1nZMUzt1xGmybew+fYhaFt7q6hNrikRwRSJN9SnVVlM7HjZrUtCp3ymtvh5XNIqvoYFQejqWZXH48OGT3vZ6yv5UUVGBz+frVZo6+ztVNc9X3wW6dFX5vn37ulxhZ/jZz35GTU0NX/ziF3G73UQiEe6++26+973vAVBeXg7A4MGD49YbPHgwH3/8cayMz+cjMzOzTZnm9cvLyxk0aFCb+gcNGhRXpnU9mZmZ+Hy+WJn2WLZsGXfeeWdnZPdqmncYnXG73Xg8+t7QEfZ4UvcJYd1ExONBafxMat33ASChfrDLzr322mu89tprVFRUxF0sBvCHP/yhywG15E9/+hNr1qzhmWee4ZxzzqG4uJgFCxaQk5PDrFmzYuUMw4hbTynVZllrWpdpr3xXyrRmyZIlLFy4MPa5traW3Nxc0tPTY69mWr5PlIyMjHa32/J9yzInq47Wmpr/tXZFa0/V1FlcLldsO+3VZ5pm079x04SMDBoS1FSfkQFuNzQ0NG2r+VnQLXQ0dFaTxwM1NU0x9ukT91XLeNurQxkG9a3r9HigoQHS0qBfv45pOk4dAA1eL9TVNcXYv39HlbVLQ0c87MTvlH70iL0hPR0aGjBNkwEDBvz7+5PU9lri5P7UPFrbug9MlFTqI1peI9ZZupS477zzTu666y5Gjx7NqaeeesIk2VV+8pOf8POf/5zvfve7AIwcOZKPP/6YZcuWMWvWLLKzs4Gmo+FTTz01tl5FRUXs6Dg7O5tQKERVVVXcUXdFRQXjxo2Llfnss8/a1H/w4MG47Wzfvj3u+6qqKmzbbnMk3hLTNOVCjBbI4/zEg5R/rGc3oLsHuu8DkJgHXbod7NFHH2X16tVs376d559/nnXr1sW9uouGhobY0Ukzbrc77lx7dnY2GzdujH0fCoXYtGlTLCmPGjUKr9cbV6asrIzdu3fHyowdO5aamhp27NgRK7N9+3ZqamriyuzevZuysrJYmQ0bNmCaJqNGjeo2zb0dwzBO2h+9VEF3D6KGQVRj/SAe6L4PQPsjuB2lS0fcoVAoltBOJtOmTePuu+9myJAhnHPOObz99tssX76c6667DmgSvmDBApYuXcpZZ53FWWedxdKlS0lPT2fGjBlA03DE9ddfz6JFixg4cCADBgxg8eLFjBw5kiuuuAKAvLw8Jk+ezJw5c1i1ahXQdDvY1KlTGTZsGAATJ05k+PDh5Ofnc99993H48GEWL17MnDlz6NeBoT2hCd3Pa4F4ENFcP4gHuu8DkJgHXVrzBz/4Ac888wy33XZblyvuCCtWrOC2225j7ty5VFRUkJOTw4033sjtt98eK/PTn/6UxsZG5s6dS1VVFWPGjGHDhg2xe7gBHnzwQTweD1dffTWNjY1cfvnlrF69Ou7igKeffpr58+fHrj6fPn06K1eujH3vdrt5+eWXmTt3LpdccglpaWnMmDGD+++//6R60NtovnJTZ3T3wKO5fhAPdN8HIDEPupS4g8Egjz32GK+++irnnnsuXq837vvly5d3OaCW9O3bl4ceeih2+1d7GIZBQUEBBQUFxyzj9/tZsWLFcSdKGTBgAGvWrDluPEOGDOGll146UdiCIAiCcNLoUuJ+5513OP/88wHYvXt33He6n7cQjo8MkYkHYc31g3ig+z4ADgyVv/76612uUNAbGSITD9ya6wfxQPd9ABwYKheErqI0n3gDxAPdJ18B8UD3fQAS86BLiXvChAnHHRL/29/+1uWAhN5N6+shdER3D2zN9YN4oPs+AIl50KXE3Xx+uxnbtikuLmb37t1xM5oJQmvaexysbujugUtz/SAe6L4PQGIedClxP/jgg+0uLygooK6ursvBCL0fObclHuh+KxSIB7rvA9CDznHPnDmTiy66SO5tFo5J81zlvY3S0lIqKys7VLb5aXHHmgq3pKSk2+LqiYR6aRvoDLp70Fv7gc6QiAfdmri3bt0ae8SZIOhCaWkpw/LyYo96PBHNDymor68/mWH1WOSGUfFASIwuJe6rrroq7rNSirKyMnbt2nXSZ1MTUpvmo81En9DTk6isrGxK2mvWQF7eCcv7jibs+mN5sH499OL9yHe0DRxTvwbEPHA4Dqfojf1AZ2n2oCt0KXG3fhyZy+Vi2LBh3HXXXbEpQwWhPXr1iExeHlxwwQmLWc1H5sd69F8vHyq3enMb6CC6e9Cr+4EOkogHXUrcTzzxRJcrFARBEASh6yR0jruoqIiSkhIMw2D48OF86Utf6q64hF5KMBgEOvag+d6K/6gHDZp6oLt+aOGBw3E4hfQD//agK3QpcVdUVPDd736XN954g/79+6OUoqamhgkTJrB27VpOOeWULgck9G5kiAyCmnugu34QD6QfSMwDV1dWmjdvHrW1tbz33nscPnyYqqoqdu/eTW1tLfPnz+9yMIIgCIIgHJ8uHXEXFhby6quvktfiCtrhw4fz29/+Vi5OE46LDJHJULHu+kGGyqUfcGCoPBqNtjvPqtfrJRqNdjkYofdzrElHdCKouQe66wfxQPqBxDzo0lD5V7/6VX70ox/x6aefxpYdOHCAH//4x1x++eVdDkbo/RiGIc9sN4yml67orh+090D6ARLS36XEvXLlSo4cOcIZZ5zB5z//eb7whS8wdOhQjhw5wooVK7ocjND7sSwroYkHegOmZWFq7IHu+kE8kH7AgQlYcnNzeeutt9i4cSPvv/8+SimGDx/OFVdc0eVABD2Qx/nJIx111w/igfQDiXnQqSPuv/3tbwwfPpza2loArrzySubNm8f8+fO58MILOeecc/j73//e5WCE3o8MkUHUMIhq7IHu+kE8kH4giUPlDz30EHPmzKFfv35tvgsEAtx4440sX768y8EIvR/btrFt2+kwHMVr23g19kB3/SAeSD9AQvo7lbj/8Y9/MHny5GN+P3HiRIqKirocjND7cbvduN1up8NwlKjbTVRjD3TXD+KB9AMkpL9T57g/++yz447LezweDh482OVghN6P7jsrQERzD3TXD+KB9AOJedCpI+7TTjuNd99995jfv/POO5x66qldDkbo/cgQGXhsG4/GHuiuH8QD6QeSOFT+H//xH9x+++3tzvjS2NjIHXfcwdSpU7scjND7kYtSQBkGSmMPdNcP4oH0A4ldnNapofJf/vKX/PnPf+bss8/mlltuYdiwYRiGQUlJCb/97W+JRCLceuutXQ5G6P14PAk9kK5XENHcA931g3gg/UBiHnRqzcGDB7NlyxZ++MMfsmTJEpRSQNM/h0mTJvHwww8zePDgLgcj9H7C4bDTITiOW3MPdNcP4oH0A4l50OmUf/rpp7N+/Xqqqqr45z//iVKKs846i8zMzC4HIehD8589nTE090B3/SAeSD+QmAddPlbPzMzkwgsv7HLFgp7IjEkQ1twD3fWDeCD9QGIeyIkGIalEIhGnQ3Act+Ye6K4fxAPpBxLzQBK3kFRkhwWX5h7orh/EA+kHJHELKYQMkckDJnTXD+KB9AMyVC6kEHJRCrg090B3/SAeSD/g0MVpgtAVdJ8tCdD64RIg+kE8kH4gMQ8kcQtJxTRNp0NwHEtzD3TXD+KB9AOJeSCJW0gqMkQG6O6B7vpBew+kH0jMg07NVS4IiWJZFpZlOR2Go/gtC7/GHuiuH8QD6QdISL8ccQtJxe/3Ox2C4wQ190B3/dDWg5KSkm7bdlZWFkOGDOm27Z0MpB9IzANJ3IIgCE5RVgYuFzNnzuy2TfrT09lbUtLjk7fQdSRxC0ml+ZGw6enpDkfiHP6jHjRo6oHu+qGFB9XVEI3CmjWQl5f4hktKCM6cSWVlZY9O3NIP0O7jsTuKJG4hqcgQmQwV664f2vEgLw8uuMCZYBxA+oHEPOjxF6cdOHCAmTNnMnDgQNLT0zn//PMpKiqKfa+UoqCggJycHNLS0rjssst477334rZhWRbz5s0jKyuLjIwMpk+fzv79++PKVFVVkZ+fTyAQIBAIkJ+fT3V1dVyZ0tJSpk2bRkZGBllZWcyfP59QKHTStAuCIAhCa3p04q6qquKSSy7B6/Xy17/+lT179vDAAw/Qv3//WJl7772X5cuXs3LlSnbu3El2djZXXnklR44ciZVZsGAB69atY+3atWzevJm6ujqmTp0aN1fsjBkzKC4uprCwkMLCQoqLi8nPz499H4lEmDJlCvX19WzevJm1a9fy3HPPsWjRoqR40VsIBoMJDRH1BsxgEFNjD3TXD+KB9AO9eKj8nnvuITc3lyeeeCK27Iwzzoi9V0rx0EMPceutt3LVVVcB8OSTTzJ48GCeeeYZbrzxRmpqavj973/PU089xRVXXAHAmjVryM3N5dVXX2XSpEmUlJRQWFjItm3bGDNmDACPP/44Y8eOZe/evQwbNowNGzawZ88ePvnkE3JycgB44IEHmD17NnfffTf9+vVLkiupjUy8ACHNPdBdP4gH0g/04glY/vKXvzBp0iS+/e1vs2nTJk477TTmzp3LnDlzANi3bx/l5eVMnDgxto5pmowfP54tW7Zw4403UlRUhG3bcWVycnIYMWIEW7ZsYdKkSWzdupVAIBBL2gAXX3wxgUCALVu2MGzYMLZu3cqIESNiSRtg0qRJWJZFUVEREyZMaFdD6/sVa2trAWhoaMDj8RAMBjFNE8MwsCwLr9eLYRjYto3b7cbtdmPbNoZh4PF4CIfDKKXwer1EIhEikQherxelFLZtY5omSiksy4qdQwkGg3HvmxtMKBTC4/G0WwfQbn3hcBifzxfTdrw62tNkWRYul4twONxrNAWDQdLT0/E0NqJqa4l4PLjDYQylCHu9uCMRXJEItteLSyn61tQQ8vlQNN3P23y+0x8MNr2PRDAzMwlZFqq+Hl8oRNjjIep247VtooZBxOPBc1RT+Gh9LqWa6ohE8ITDhHw+DMBnWViRCKSn46+vJ9jQ8O/6TBMMA9OysL1eoobRVIfbTcTtxmPbqKP1xWkKh3EFAtiWhauuDq9tN80GplT7mmg6ygyZJv6GBnyhEEY0+m9N4TCRox5SW9sxTe3UEafJtrH79CFqWXirq0+sqdXv1EZTOx42a1LQqd/JEwrhikbx2jah9HQMy8J3+PCJNZ3od7Is3H370tjYSENDQ4/dn6qqqrTq99rTVFdXR1fp0UPl//rXv3jkkUc466yzeOWVV7jpppuYP38+f/zjHwEoLy8HYPDgwXHrDR48OPZdeXk5Pp+PzMzM45YZNGhQm/oHDRoUV6Z1PZmZmfh8vliZ9li2bFnsvHkgECA3N7czFvQ6QqGQ9vMUe21b67mqfaGQ1vpB2oBt29pfH9Rr5yqPRqOMHj2apUuXAvClL32J9957j0ceeYTvf//7sXKGYcStp5Rqs6w1rcu0V74rZVqzZMkSFi5cGPtcW1tLbm4u6enpsVcz3XlrREZGRrvbbfm+ZZmTVUdrTW63G4C+ffuelPqc0OT3+2loaIC0NOjIKZOjUx02ZGTQ0KK+2O1RbjcNVVVgmtCqTFeoz8gAtxsaGpq2dbSelrdjdfrWLI8HamqaYuzTJ+6rdjW1eG9EowDUtWwDHg90wsP6E9QB0OD1Ql1dU4wtrovpCg0d8bATv1PE1XTMVOf1Nuk2TeoHDGhfR2d+J9OEI0dIS0s7YX/i5P6UlZUVW6ZDv9cegUCgyzH06CPuU089leHDh8cty8vLo7S0FIDs7GyANke8FRUVsaPj7OxsQqEQVVVVxy3z2Weftan/4MGDcWVa11NVVYVt222OxFtimib9+vWLe+lM85CRzkTdbqIae6C7fhAPpB8gIf09OnFfcskl7N27N27ZBx98wOmnnw7A0KFDyc7OZuPGjbHvQ6EQmzZtYty4cQCMGjUKr9cbV6asrIzdu3fHyowdO5aamhp27NgRK7N9+3ZqamriyuzevZuysrJYmQ0bNmCaJqNGjepm5b0X27ZlqFzzYVLd9YN4IP1ALx4q//GPf8y4ceNYunQpV199NTt27OCxxx7jscceA5qGrhcsWMDSpUs566yzOOuss1i6dCnp6enMmDEDaBqOuP7661m0aBEDBw5kwIABLF68mJEjR8auMs/Ly2Py5MnMmTOHVatWAXDDDTcwdepUhg0bBsDEiRMZPnw4+fn53HfffRw+fJjFixczZ84c7Y+iO8OJTmHoQFRzD3TXD+KB9AOJedCjE/eFF17IunXrWLJkCXfddRdDhw7loYce4pprromV+elPf0pjYyNz586lqqqKMWPGsGHDhrhzqA8++CAej4err76axsZGLr/8clavXh03VPH0008zf/782NXn06dPZ+XKlbHv3W43L7/8MnPnzuWSSy4hLS2NGTNmcP/99yfBid6Dx9Ojm1xSiGjuge76QTyQfiAxD3q8e1OnTmXq1KnH/N4wDAoKCigoKDhmGb/fz4oVK1ixYsUxywwYMIA1a9YcN5YhQ4bw0ksvnTBm4dg033KhMx7NPdBdP4gH0g8k5kGPPsctCIIgCEI8Pf6IW+hdyBBZ00QcOqO7fhAPpB/o5UPlQu9ChsjArbkHuusH8UD6gcQ8kMQtJBV1dPIRnXFp7oHu+kE8kH4gMQ8kcQtJxev1Oh2C49iae6C7fhAPpB9IzANJ3EJSafkoVV1xae6B7vpBPJB+IDEPJHELSUXObcmtQLrrB/FA+gE5xy2kEM2PxtOZkOYe6K4fxAPpBxLzQBK3ICQZ3Sd71F0/iAdCYsgELEJSsSwLy7KcDsNRfJaFT2MPdNcP4oH0AySkX464haTi9/udDsFxLM090F0/iAfSDyTmgRxxC4IgCEIKIYlbSCrBYJBgMOh0GI7iDwbxa+yB7vpBPJB+gIT0y1C5kFRkiAyCmnugu34QD6QfSMwDSdyClpSWllJZWdkt2yopKemW7QiCIHQESdxCUmkeHkpPT3cshtLSUobl5RFsaHCk/uYh0gYHPXAS3fVDCw8cjsMpekI/4DQyVC6kDKZpOh0ClZWVTUl7zRrIy0t8g+vXw223dbh4sAd44CS66wfxoCf0A06TiAeSuIWkYhg9aOqJvDy44ILEt9PZofKe5IET6K4ftPegR/UDDpGIB3JVuZBUZOIFMC0LU2MPdNcP4oH0AzIBi5BCyOP85JGOuusH8UD6AXmsp5BCyBAZRDX3QHf9IB5IP5CYB5K4haRi27bTITiOV3MPdNcP4oH0A4l5IIlbSCput9vpEBwnqrkHuusH8UD6gcQ8kMQtJBXZYSGiuQe66wfxQPoBSdxCCiFDZODR3APd9YN4IP2ADJULKYRclAJKcw901w/igfQDcnGakEJ4PNLkIpp7oLt+EA+kH0jMA3FPSCrhcNjpEBzHrbkHuusH8UD6gcQ8kMQtJBWllNMhOI6huQe66wfxQPqBxDyQxC0kFZkxCcKae6C7fhAPpB+QmdOEFCISiTgdguO4NfdAd/0gHkg/kJgHkriFpCI7LLg090B3/SAeSD8giVtIIWSITB4wobt+EA+kH5ChciGFkItSwKW5B7rrB/FA+gG5OE1IIWTGJHnAhO76QTyQfkBmThNSCNM0nQ7BcSzNPdBdP4gH0g8k5oEkbiGpyBAZoLsHuusH7T2QfiAxD1zdGIcgnBDLsrAsy+kwHMVvWfg19kB3/SAeSD9AQvrliFtIKn6/3+kQHCeouQe66wfxQPqBxDyQI25BEARBSCFSKnEvW7YMwzBYsGBBbJlSioKCAnJyckhLS+Oyyy7jvffei1vPsizmzZtHVlYWGRkZTJ8+nf3798eVqaqqIj8/n0AgQCAQID8/n+rq6rgypaWlTJs2jYyMDLKyspg/fz6hUOhkye2VBINBgsGg02E4ij8YxK+xB7rrB/FA+gES0p8yQ+U7d+7kscce49xzz41bfu+997J8+XJWr17N2Wefza9+9SuuvPJK9u7dS9++fQFYsGABL774ImvXrmXgwIEsWrSIqVOnUlRUhNvtBmDGjBns37+fwsJCAG644Qby8/N58cUXgaZZbqZMmcIpp5zC5s2bOXToELNmzUIpxYoVK5LoRGojQ2QyTKq7fjj5HpSUlHTbtrKyshgyZEi3bQ+kH4DEPEiJxF1XV8c111zD448/zq9+9avYcqUUDz30ELfeeitXXXUVAE8++SSDBw/mmWee4cYbb6Smpobf//73PPXUU1xxxRUArFmzhtzcXF599VUmTZpESUkJhYWFbNu2jTFjxgDw+OOPM3bsWPbu3cuwYcPYsGEDe/bs4ZNPPiEnJweABx54gNmzZ3P33XfTr1+/JLsiCILQirIycLmYOXNmt23Sn57O3pKSbk/eQtdJicR98803M2XKFK644oq4xL1v3z7Ky8uZOHFibJlpmowfP54tW7Zw4403UlRUhG3bcWVycnIYMWIEW7ZsYdKkSWzdupVAIBBL2gAXX3wxgUCALVu2MGzYMLZu3cqIESNiSRtg0qRJWJZFUVEREyZMaDf21ldP1tbWAtDQ0IDH4yEYDGKaJoZhYFkWXq8XwzCwbRu3243b7ca2bQzDwOPxEA6HUUrh9XqJRCJEIhG8Xi9KKWzbxjRNlFJYlhX7RxcMBuPeN98/GAqF8Hg87dYBtFtfOBzG5/PFtB2vjvY01dbWOq6psbGRQCCAbVm4jhzBEw4T8vkwAJ9lYR3drj8YjB0Z+YNBgqYJhoFpWdheL1HDwGvbRMNhIunpeBobUbW1RDwe3OEwhlKEvV7ckQiuSATb68WlFAMPHiTk84FS+C0rvg6/HyIRzMxMQpaFqq/HFwoR9niIut1N9RkGEY8Hz1FN4aP1uZRqqiMSaaspEoH0dPz19QQbGk6sye0m4nbjsW3U0friNIXDuJo9rKvDa9tN9yYfSxNgBoOETJNAdTXeUIhDWVn/1tTCQ2prO6bpRL+TbWP36UPUsvBWV59YU6vfqY2mdjxs1qSgU79Tel0drmgUn20TSk/HsCx8hw93vu211nToEO6MDIz/+i/Cw4bhVgpXNIrtdjdpikSwPE3dvj8cJtjOezMcJuTxNGnau5fwvHkcOHCAQCDQbX1EeXl5bJ/Vod9rT9OhQ4foKj0+ca9du5a33nqLnTt3tvmuvLwcgMGDB8ctHzx4MB9//HGsjM/nIzMzs02Z5vXLy8sZNGhQm+0PGjQorkzrejIzM/H5fLEy7bFs2TLuvPPOE8nUBtM0cblS6tKKbsf2+bCPdgI6EtJYezO2z4crGj15FZx1Fpx3HkQiTS+vt+necduGo39GsCxoHq4NBuPfN08OcpIeBuLz+WKJUFcS0d+jE/cnn3zCj370IzZs2HDc8wGGYcR9Vkq1Wdaa1mXaK9+VMq1ZsmQJCxcujH2ura0lNzeX9PT02KuZlu8TJSMjo93ttnzfsszJqqO1pvr6+i7X3V2a0tLSqKmpaeqcjl4HEYuvxfoNLbZ7rPcAeDzQ0ABpadCBUybRo+2lISODhvbqc7tpqKpqiq9Vma5Qn5EBbjc0NDRt62g9x9V0IjweaPawT5+4r9rV1OK9Ovo+TlcnPezQ7+T1Ql1dU4z9+3dUWbs0dMTDTvxO6Uf3gwavt0m3aVI/YED7OjrzO7ndcORIk48nKHui3yn2vq4O0zQJBALHr7sdjrXPNk8+0roPTJSe2u+1R59W+01n6NGHPkVFRVRUVDBq1Cg8Hg8ej4dNmzbxX//1X3g8ntgRcOsj3oqKith32dnZhEIhqqqqjlvms88+a1P/wYMH48q0rqeqqgrbttscibfENE369esX99KZUCik/ZX4vlAIn8Ye6K4fxAPpB0hIf49O3JdffjnvvvsuxcXFsdfo0aO55pprKC4u5swzzyQ7O5uNGzfG1gmFQmzatIlx48YBMGrUKLxeb1yZsrIydu/eHSszduxYampq2LFjR6zM9u3bqampiSuze/duysrKYmU2bNiAaZqMGjXqpPrQm2j+A6YzYY+HsMYe6K4fxAPpB0hIf492rm/fvowYMSJuWUZGBgMHDowtX7BgAUuXLuWss87irLPOYunSpaSnpzNjxgwAAoEA119/PYsWLWLgwIEMGDCAxYsXM3LkyNhV5nl5eUyePJk5c+awatUqoOl2sKlTpzJs2DAAJk6cyPDhw8nPz+e+++7j8OHDLF68mDlz5mh/FN0Zmm+/05mo5h7orh/EA+kHEvOgRyfujvDTn/6UxsZG5s6dS1VVFWPGjGHDhg2xe7gBHnzwQTweD1dffTWNjY1cfvnlrF69Os64p59+mvnz58euPp8+fTorV66Mfe92u3n55ZeZO3cul1xyCWlpacyYMYP7778/eWJ7AfI4P3mko+76QTyQfkCzx3q+8cYbcZ8Nw6CgoICCgoJjruP3+1mxYsVxJ0oZMGAAa9asOW7dQ4YM4aWXXupMuEIrTnTRoA5ENfdAd/0gHkg/kJgHKZe4hdRG9/NaABHNPdBdP4gH0g/04nPcQu+jeZIDnfFo7oHu+kE8kH4gMQ969FXlgiAIgiDEI0fcQlKRITK0vg0IRD+IB9IPyFC5kELIEBm4NfdAd/0gHkg/kJgHkriFpNI81aHOuDT3QHf9IB5IP5CYB5K4haTi9XqdDsFxbM090F0/iAfSDyTmgSRuIalETtLThlIJl+Ye6K4fxAPpBxLzQBK3kFTk3JbcCqS7fhAPpB+Qc9xCCqH7M3hBnketu34QD6Qf6MXP4xaE3ojukz3qrh/EAyExZAIWIalYloVlWU6H4Sg+y8KnsQe66wfxQPoBEtIvR9xCUvH7/Z1ep7S0lMrKym6LoaSkpNu21RWsLnjQm9BdP4gHXekHehuJeCCJW+jRlJaWMiwvj2BDg9OhCIIg9AgkcQtJJRgMApCent6h8pWVlU1Je80ayMvrniDWr4fbbuuebXUB/1EPGjroQW9Dd/3QwgOH43CKzvYDvZFmD7qCJG4hqXR5eCgvDy64oHuCcHioPKj5MKHu+kE8kKHyxDyQi9MEQRAEIYWQxC0klWAwmNAQUW/AHwzGhkp1RHf9IB5IPyBD5UIKYZqm0yE4TlBzD3TXD+KB9AOJeSCJW0gqhiFTT6C7B7rrB+09kH4gMQ9kqFxIKjLxApiWhamxB7rrB/FA+gGZgEVIIeRxfvJIR931g3gg/YA81lNIIWSIDKKae6C7fhAPpB9IzANJ3EJSsW3b6RAcx6u5B7rrB/FA+oHEPJDELSQVt9vtdAiOE9XcA931g3gg/UBiHkjiFpKK7LAQ0dwD3fWDeCD9gCRuIYWQITLwaO6B7vpBPJB+QIbKhRRCLkoBpbkHuusH8UD6Abk4TUghPB5pchHNPdBdP4gH0g8k5oG4JySVcDjsdAiO49bcA931g3gg/UBiHkjiFpKKUsrpEBzH0NwD3fWDeCD9QGIeSOIWkorMmARhzT3QXT+kngcl3fwM+z59+vC5z32uW7eZasjMaULKEIlEnA7Bcdyae6C7fkghD8rKwOVi5syZ3brZrEGD+PumTXzxi1/s1u2mEon0hZK4haQiiRtcmnugu35IIQ+qqyEahTVrIC+ve7ZZUoJ9881UVlZ2z/ZSFEncQsogQ+XygAnd9UMKepCXBxdc0G2bs21b+0lYZKhcSBnkohRwae6B7vpBPHC5XNr3BXJxmpAyyIxJ8oAJ3fWDeOD1erU/bSYzpwkpg2maTofgOJbmHuiuH8QDy7K0n4Qlkb5Qb+eEpKP78BgAunugu34QD4SE+kJXN8YhCCfEsiwsy3I6DEfxWxZ+jT3QXT+IB36/X/vZ0xLpB+WIW0gqfr/f6RAcJ6i5B7rrB/EgGAxqP1SeSF/Yo4+4ly1bxoUXXkjfvn0ZNGgQ3/jGN9i7d29cGaUUBQUF5OTkkJaWxmWXXcZ7770XV8ayLObNm0dWVhYZGRlMnz6d/fv3x5WpqqoiPz+fQCBAIBAgPz+f6urquDKlpaVMmzaNjIwMsrKymD9/PqFQ6KRoFwRBEIT26NGJe9OmTdx8881s27aNjRs3Eg6HmThxIvX19bEy9957L8uXL2flypXs3LmT7OxsrrzySo4cORIrs2DBAtatW8fatWvZvHkzdXV1TJ06Ne6qxhkzZlBcXExhYSGFhYUUFxeTn58f+z4SiTBlyhTq6+vZvHkza9eu5bnnnmPRokXJMaOXEAwGCQaDTofhKP5gEL/GHuiuH8QDGSonoX6wR49VFBYWxn1+4oknGDRoEEVFRXzlK19BKcVDDz3ErbfeylVXXQXAk08+yeDBg3nmmWe48cYbqamp4fe//z1PPfUUV1xxBQBr1qwhNzeXV199lUmTJlFSUkJhYSHbtm1jzJgxADz++OOMHTuWvXv3MmzYMDZs2MCePXv45JNPyMnJAeCBBx5g9uzZ3H333fTr169dDa3P6dbW1gLQ0NCAx+MhGAximiaGYWBZFl6vF8MwYhMUuN1ubNvGMAw8Hg/hcBilVOx2ikgkgtfrRSmFbduYpolSCsuyYkMxwWAw7n3z1YyhUAiPx9NuHUC79YXDYXw+X0zb8epoT1M4HMblclFTU9MhTfX19aSnp4Nl4T98ODbE6A8GY+/NYJCQaaIAXyhE2OMh6nbjtW2ihkHE48FzVFPY48EdDuMKBLAtC9eRI3jCYUI+HwbgsyysdurwB4METRMMA9OysL1eoobRVEc4TCQ9HU9jI6q2lsjROgylCHu9uCMRXJEItteLS6mmeH0+0uvr8VtWW02RCGZmJiHLQtXXd1yTUk11RCJtNUUikJ6Ov76eYEPDiTW53UTcbjy2jTpaX5ymlh7W1eG17aYrpZVqX1OL38mIRvGFQvQ5cuTfmlp4SG1txzSd6Heybew+fYhaFt7q6hNravU7tdHUjoddantKYUSjuKJR+ts2ofR0DMvCd/hw59tea02RCO6+fTEaGwk3NJxY04n2J9sm3OxhTU3X2l7rOo72G9FolIaGBi36vWNp6io9+oi7NTU1NQAMGDAAgH379lFeXs7EiRNjZUzTZPz48WzZsgWAoqIibNuOK5OTk8OIESNiZbZu3UogEIglbYCLL76YQCAQV2bEiBGxpA0wadIkLMuiqKjomDEvW7YsNvweCATIzc1N1AZBEARBY3r0EXdLlFIsXLiQL3/5y4wYMQKA8vJyAAYPHhxXdvDgwXz88cexMj6fj8zMzDZlmtcvLy9n0KBBbeocNGhQXJnW9WRmZuLz+WJl2mPJkiUsXLgw9rm2tpbc3FzS09Njr2Zavk+UjIyMdrfb8n3LMierjtaaDh8+DEAgEOhwHQ0NDWCaNBz9wwbQ0GK7ce87osnjgZoaME3o2zfuq/oW6x+zjta/k8cDDQ2QlgbHGHlpiffodRENGRlx8ca263bTUFXVFF+rMl2hPiMD3G5oaGja1tF6jqvpRLT0sE+fuK/a1dTive/okV5dS+876WGHfievF+rqmmLs37/D0tqjoSMeduJ3yjx8mKjLRbXX26TbNKnvSPs+0e/kdsORI00+nqDsiX4naOVhB/fZlrT7O5kmZmMjLperTR+YKD2132uPRC7OS5nEfcstt/DOO++wefPmNt8ZhhH3WSnVZllrWpdpr3xXyrTGNE2ZdKQF4gWENPdAd/0gHjQPV+tMIn1hSgyVz5s3j7/85S+8/vrrcc9wzc7OBmhzxFtRURE7Os7OziYUClFVVXXcMp999lmbeg8ePBhXpnU9VVVV2Lbd5khcEI6HOvrSFd31g3ggEzElRo/+y6OUYt68eaxbt4433niDoUOHxn0/dOhQsrOz2bhxI1/60peApn9ymzZt4p577gFg1KhReL1eNm7cyNVXXw1AWVkZu3fv5t577wVg7Nix1NTUsGPHDi666CIAtm/fTk1NDePGjYuVufvuuykrK+PUU08FYMOGDZimyahRo06+GSlEaWnpMR/Z13D0op6ODo+VlJR0W1w9BV+LoXId0V0/tPDA4TicwufzaT9XeSK3EvfoxH3zzTfzzDPP8MILL9C3b9/YEW8gECAtLQ3DMFiwYAFLly7lrLPO4qyzzmLp0qWkp6czY8aMWNnrr7+eRYsWMXDgQAYMGMDixYsZOXJk7CrzvLw8Jk+ezJw5c1i1ahUAN9xwA1OnTmXYsGEATJw4keHDh5Ofn899993H4cOHWbx4MXPmzDnmFeU6UlpayrC8vNhVt63pc/R8aF1dXTLD6lGENR8i1F0/iAfNd5foTK89x/3II48AcNlll8Utf+KJJ5g9ezYAP/3pT2lsbGTu3LlUVVUxZswYNmzYQN8WF748+OCDeDwerr76ahobG7n88stZvXp13PNgn376aebPnx+7+nz69OmsXLky9r3b7ebll19m7ty5XHLJJaSlpTFjxgzuv//+k6Q+NamsrGxK2mvWND3DtxXR5lvjOnp+Z/16uO22bozQeaKaP4dYd/0gHkSj0RNeh9TbSeR55D06cXfkPIhhGBQUFFBQUHDMMn6/nxUrVrBixYpjlhkwYABr1qw5bl1DhgzhpZdeOmFMAk1J+4IL2iz2Hr2lr8NXqPbCoXLtH+mouX4QD+SxnvJYTyGFiGr+LxvEA931g3ggR9zt36XUUSRxC0klovm5PRAPdNcP4kEkEpFz3L31HLfQ+/BoPj8xiAe66wfxwOPxEI1GnQ7DUbSZ8lQQBEEQdEeOuIWkovttMCAe6K4fxAO5HUyGyoUUwq35ECGIB7rrB/HA7XbLUHkCbUASt5BUXDLVofYe6K4fxAOXy6X9tKeJ6JfELSQV2+t1OgTH0d0D3fWDeND8jGqd8SbQBiRxC0nFpfmkCyAe6K4fxAM54iahCWgkcQtJRffbYEA80F0/iAcej4cPPvgAv9/fLdvLyspiyJAh3bKtZCHnuIWUIeTzOR2C4+juge76QXMPysoIhcPcfPPNsacFJoo/PZ29JSUplbx9CbQBSdxCUtF7ksMmdPdAd/2guQfV1RhKwcMPw8iRiW+vpITgzJlUVlamVOJOBEncQlLxHX06WL3Oz2LW3APd9UMLDxyOwyl8Ph+ceSb17TyISBes5icldgFJ3EJSsbrpnFYqo7sHuusH8cCyLNB8EppEzu/rPXWNIAiCIKQYkriFpOIPBvEHg06H4Si6e6C7fhAP/H4/fs2vrA8m8PvrPVYhJJ2g5kOEIB7orh/Eg2AwKEPlMlQuCIIgCHogiVtIKroPEYJ4oLt+EA9kqFyGyoUEKS0tpbKyslu2VVJSctzvg6bZLfWkMrp7oLt+EA9kqBzMBNqA3s4JlJaWMiwvj2A3zWB0Qgytp55oQncPdNcP4oGAkUAbkMStOZWVlU1Je80ayMtLfIPr18Nttx3za/PopAMN6emJ15Wi6O6B7vqhhQcOx+EUpmlCOKytfpAJWITuIC8PumMWoxMMlev+OEMQD3TXD+KBbdsgj/Xs8rqSuIWkEpUhQu090F0/iAfRaFT70wUyVC6kDF7bdjoEx9HdA931g3jg9XpB82eS2wm0AUncQlKJaj48BuKB7vpBPIhGo+DS+25kdwJtQBK3kFQimndYIB7orh/Eg0gk0u1D5Se6FbUzZGVlnfRHhEriFlIGj+ZDhCAe6K4fxAOPxwPRaPdsrKwMXC5mzpzZPdsD/Onp7C0pOanJW4bKhZRBaX5BCogHuusH8UAp1X0bq65u+hPQXbe0lpQQnDmTysrKk5q45eI0IWWIaD5bEogHuusH8SASiXT/Oe7uuqU1SXgSaAN6tx4h6bg1n58YxAPd9YN44Ha7u2+oPEUJJ9AGJHGnGN05rzh07wUdHcHoziGyFEV3D3TXD+JBIsPEvYVEThdI4k4hkj6v+EkgrPmMUSAe6K4fxINwOKz97WAyc5omdPu84nDCucW7G7fmky6AeKC7fhAP3G43aD7qEEmgDUjiTkW68yKMJA+VuzTvsEA80F0/iAcul0v7c9ySuIWUQfeHK4B4oLt+EA9S4SEj3X39T+tJXWSoXEgZXJoPj4F4oLt+EA9cLlfPHSo/CRO6QNtJXeTiNCFl0P3hCiAe6K4fxIMe/ZCR7p7QBdqd1EVmTktBiouL6dOnT6fWSfatWycDyzSdDsFxdPdAd/0gHliWBT19EpqTPKGLmUAb6OHO9V7Gjx/vdAjO0FOHx5KJ7h7orh/EA0GGypPNww8/zH333UdZWRnnnHMODz30EJdeemnnNrJpE3TyiDvZt26dDPyWBUBDRobDkTiH7h7orh9aeOBwHE7h9/shHNZOf8tR04qKii5vRxJ3J/nTn/7EggULePjhh7nkkktYtWoVX/va19izZ0/nJqQ//3zo169zlfeCofKg3+90CI6juwe66wfxIBgM9vyh8u6knQve0tLSurw5vaeu6QLLly/n+uuv5wc/+AF5eXk89NBD5Obm8sgjjzgdmiAIgtATaXnBW1FR0+svf+ny5jT6y5M4oVCIoqIifv7zn8ctnzhxIlu2bGl3Hcuymi7EOEpNTQ0A/tdfx8jIwB8OEzz6z9MMh7HdbqKGgTcSIepyETEMPNEoCojs2YO7Tx+M7dsJHz6MWylc0Si2241LKbyRCNbRbbXcbus6Qh4PCvBFIoT37CGaloZ3+3ailZVEXC48RydGCLtcuKNRXErF6vBEo4TcbgzAFw63rW/PHvyZmQR37oTDh9to6hsMEjEM3H5/k6ajdRjN9bXWtGcPVloa7NyJv6KiY5pcrn97aBhtNe3Zg6tfP+ydO3EdOnRiTe3UF/c77dlDJC0Nz/btqKMeHk/TwLo6Qm43Kj29/Tr27MHs35/Qjh2ow4c7pulEv9OePZCWhn/bNoKHD59YU+u211pTKw870/YCDQ14IxEO9enT1sMdO6Cysmttr3V9e/ZgZ2QQ3bkT78GDJ9Z0ov2pHQ873faOakq3rFj7DqWlYezYga+iovNt72T3EXv2EG7pYVfaXjvt+5RTTiG0axeqvr7zbe9k9xFHPXRt3459+HDX2t6x+oja2lgf0f/gQRrp4rluJXSYAwcOKED97//+b9zyu+++W5199tntrnPHHXcoQF7ykpe85CWvNq8PP/yw07lIjri7QOsn2yiljvm0myVLlrBw4cLY5+rqak4//XRKS0sJBAInNc6eSG1tLbm5uXzyySf06+w5/l6C7h7orh/EA931Q9Po65AhQxgwYECn15XE3QmysrJwu92Ul5fHLa+oqGDw4MHtrmOaZrv36wUCAW0bLEC/fv201g/ige76QTzQXT8cnUWus+uchDh6LT6fj1GjRrFx48a45Rs3bmTcuHEORSUIgiDohBxxd5KFCxeSn5/P6NGjGTt2LI899hilpaXcdNNNTocmCIIgaIAk7k7yne98h0OHDnHXXXdRVlbGiBEjWL9+PaeffnqH1jdNkzvuuCOh6e5SGd31g3igu34QD3TXD4l5YCglc+8JgiAIQqog57gFQRAEIYWQxC0IgiAIKYQkbkEQBEFIISRxC4IgCEIKIYnbQaZPn86QIUPw+/2ceuqp5Ofn8+mnnzodVlL46KOPuP766xk6dChpaWl8/vOf54477iAUCjkdWlK5++67GTduHOnp6fTv39/pcJLCww8/zNChQ/H7/YwaNYq///3vToeUNN58802mTZtGTk4OhmHw/PPPOx1SUlm2bBkXXnghffv2ZdCgQXzjG99g7969ToeVVB555BHOPffc2OQzY8eO5a9//WuntiGJ20EmTJjA//f//X/s3buX5557jg8//JBvfetbToeVFN5//32i0SirVq3ivffe48EHH+TRRx/lF7/4hdOhJZVQKMS3v/1tfvjDHzodSlJofizurbfeyttvv82ll17K1772NUpLS50OLSnU19dz3nnnsXLlSqdDcYRNmzZx8803s23bNjZu3Eg4HGbixInU19c7HVrS+NznPsevf/1rdu3axa5du/jqV7/K17/+dd57772Ob6Tzj9oQThYvvPCCMgxDhUIhp0NxhHvvvVcNHTrU6TAc4YknnlCBQMDpME46F110kbrpppviln3xi19UP//5zx2KyDkAtW7dOqfDcJSKigoFqE2bNjkdiqNkZmaq3/3udx0uL0fcPYTDhw/z9NNPM27cOLxer9PhOEJNTU2XJtwXUoPmx+JOnDgxbvnxHosr9G6aH3Os634fiURYu3Yt9fX1jB07tsPrSeJ2mJ/97GdkZGQwcOBASktLeeGFF5wOyRE+/PBDVqxYIVPH9mIqKyuJRCJtHsgzePDgNg/uEXo/SikWLlzIl7/8ZUaMGOF0OEnl3XffpU+fPpimyU033cS6desYPnx4h9eXxN3NFBQUYBjGcV+7du2Klf/JT37C22+/zYYNG3C73Xz/+9/v2oPVewid1Q/w6aefMnnyZL797W/zgx/8wKHIu4+ueKATnXksrtB7ueWWW3jnnXd49tlnnQ4l6QwbNozi4mK2bdvGD3/4Q2bNmsWePXs6vL7MVd7N3HLLLXz3u989bpkzzjgj9j4rK4usrCzOPvts8vLyyM3NZdu2bZ0aNulJdFb/p59+yoQJE2IPbOkNdNYDXejKY3GF3sm8efP4y1/+wptvvsnnPvc5p8NJOj6fjy984QsAjB49mp07d/Kb3/yGVatWdWh9SdzdTHMi7grNR9qWZXVnSEmlM/oPHDjAhAkTGDVqFE888USXnkvbE0mkDfRmWj4W95vf/GZs+caNG/n617/uYGRCslBKMW/ePNatW8cbb7zB0KFDnQ6pR6CU6lS/L4nbIXbs2MGOHTv48pe/TGZmJv/617+4/fbb+fznP5+yR9ud4dNPP+Wyyy5jyJAh3H///Rw8eDD2XXZ2toORJZfS0lIOHz5MaWkpkUiE4uJiAL7whS/Qp08fZ4M7Cej+WNy6ujr++c9/xj7v27eP4uJiBgwYwJAhQxyMLDncfPPNPPPMM7zwwgv07ds3NvoSCARIS0tzOLrk8Itf/IKvfe1r5ObmcuTIEdauXcsbb7xBYWFhxzdyUq5tF07IO++8oyZMmKAGDBigTNNUZ5xxhrrpppvU/v37nQ4tKTzxxBMKaPelE7NmzWrXg9dff93p0E4av/3tb9Xpp5+ufD6fuuCCC7S6Fej1119v9/eeNWuW06ElhWPt80888YTToSWN6667Ltb+TznlFHX55ZerDRs2dGob8lhPQRAEQUghesdJRUEQBEHQBEncgiAIgpBCSOIWBEEQhBRCErcgCIIgpBCSuAVBEAQhhZDELQiCIAgphCRuQRAEQUghJHELgiAIQgohiVsQBMcoKCjg/PPPPynbXr16Nf379z8p2xYEJ5HELQiaMHv2bAzDaHde8Llz52IYBrNnz05qTIsXL+a1116LfZ49ezbf+MY3khqDIKQakrgFQSNyc3NZu3YtjY2NsWXBYJBnn33WkYdc9OnTh4EDBya9XkFIZSRxC4JGXHDBBQwZMoQ///nPsWV//vOfyc3N5Utf+lJsWWFhIV/+8pfp378/AwcOZOrUqXz44Ydx29qyZQvnn38+fr+f0aNH8/zzz2MYRuwJZ2+88QaGYfDaa68xevRo0tPTGTduHHv37o1to+VQeUFBAU8++SQvvPAChmFgGAZvvPFGbDvV1dWx9YqLizEMg48++ii2bPXq1QwZMoT09HS++c1vcujQoTb6X3zxRUaNGoXf7+fMM8/kzjvvJBwOJ+CoICQfSdyCoBnXXnstTzzxROzzH/7wB6677rq4MvX19SxcuJCdO3fy2muv4XK5+OY3v0k0GgXgyJEjTJs2jZEjR/LWW2/x//7f/+NnP/tZu/XdeuutPPDAA+zatQuPx9OmrmYWL17M1VdfzeTJkykrK6OsrIxx48Z1SNP27du57rrrmDt3LsXFxUyYMIFf/epXcWVeeeUVZs6cyfz589mzZw+rVq1i9erV3H333R2qQxB6DCfluWWCIPQ4Zs2apb7+9a+rgwcPKtM01b59+9RHH32k/H6/OnjwoPr6179+zMdLVlRUKEC9++67SimlHnnkETVw4EDV2NgYK/P4448rQL399ttKqX8/wvLVV1+NlXn55ZcVEFvvjjvuUOedd16bGFvSvJ2qqqrYsrffflsBat++fUoppb73ve+pyZMnx633ne98RwUCgdjnSy+9VC1dujSuzFNPPaVOPfXUY1kmCD0SOeIWBM3IyspiypQpPPnkkzzxxBNMmTKFrKysuDIffvghM2bM4Mwzz6Rfv34MHToUgNLSUgD27t3Lueeei9/vj61z0UUXtVvfueeeG3t/6qmnAlBRUdGtmkpKShg7dmzcstafi4qKuOuuu+jTp0/sNWfOHMrKymhoaOjWeAThZOJxOgBBEJLPddddxy233ALAb3/72zbfT5s2jdzcXB5//HFycnKIRqOMGDGCUCgEgFIKwzDi1lFKtVuX1+uNvW9ep3nIvSO4XK4227dtu0N1tyQajXLnnXdy1VVXtfmu5R8QQejpSOIWBA2ZPHlyLAlPmjQp7rtDhw5RUlLCqlWruPTSSwHYvHlzXJkvfvGLPP3001iWhWmaAOzatSvhuHw+H5FIJG7ZKaecAkBZWRmZmZkAsQvgmhk+fDjbtm2LW9b68wUXXMDevXv5whe+kHCcguAkMlQuCBridrspKSmhpKQEt9sd911mZiYDBw7kscce45///Cd/+9vfWLhwYVyZGTNmEI1GueGGGygpKeGVV17h/vvvB2hzJN4ZzjjjDN555x327t1LZWUltm3zhS98gdzcXAoKCvjggw94+eWXeeCBB+LWmz9/PoWFhdx777188MEHrFy5ksLCwrgyt99+O3/84x8pKCjgvffeo6SkhD/96U/88pe/7HK8guAEkrgFQVP69etHv3792ix3uVysXbuWoqIiRowYwY9//GPuu+++Nuu++OKLFBcXc/7553Prrbdy++23A4kNO8+ZM4dhw4YxevRoTjnlFP73f/8Xr9fLs88+y/vvv895553HPffc0+aK8Ysvvpjf/e53rFixgvPPP58NGza0SciTJk3ipZdeYuPGjVx44YVcfPHFLF++nNNPP73L8QqCExiqIyeHBEEQTsDTTz/NtddeS01NDWlpaU6HIwi9FjnHLQhCl/jjH//ImWeeyWmnncY//vEPfvazn3H11VdL0haEk4wkbkEQukR5eTm333475eXlnHrqqXz729+WyUwEIQnIULkgCIIgpBBycZogCIIgpBCSuAVBEAQhhZDELQiCIAgphCRuQRAEQUghJHELgiAIQgohiVsQBEEQUghJ3IIgCIKQQkjiFgRBEIQU4v8H6zK75IUbSRUAAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#generate a Gaussian type empirical distribution for figure 1.3\n", "from numpy.random import normal\n", "sample = normal(loc=0, scale=1, size=1000000)\n", "# plot the histogram\n", "fig,(ax1) = plt.subplots(1,1,figsize=(5,5))\n", "ax1.hist(sample, bins=31, color='cyan',edgecolor='black',linewidth=1,align='mid')\n", "ax1.set(xlim=(-3,3),ylim=(0,150000))\n", "ax1.set(xlabel=\"Magnitude\",ylabel=\"Count\")\n", "ax1.set(title=\"Figure 1.3\")\n", "#add grids to the plot\n", "ax1.grid(linestyle='--', color='grey', linewidth=.2)\n", "#save the figure to \n", "plt.savefig('figure_1.3.png')\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.6" } }, "nbformat": 4, "nbformat_minor": 4 }