{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%html \n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## An open science approach to a recent false-positive between solar activity and the Indian monsoon## \n", "\n", "**Summary:** A litany of research has been published claiming strong Solar influences on the Earth's weather and climate. Much of this work includes documented errors and false-positives, yet is still frequently used to substantiate arguments of global warming denial. [Badruddin & Aslam (2015)](http://www.sciencedirect.com/science/article/pii/S1364682614002697), which I shall also refer to as BA15 for convenience, have recently reported a link between solar activity and extremes in the Indian Monsoon. They further speculated that the relationship they observed may apply across the entire tropical and sub-tropical belt, and be of global importance. However, their statistical analysis rested on the assumption that their data followed a Student's $t$-distribution. This notebook reproduces their results, and demonstrates why their claims are unsupported by their analysis.\n", "\n", "The main error of BA15 resulted from the assumption that their datasets distribution was Gaussian, this assumption was built into their statistical test. However, as demonstrated below, their data closely follow an ergodic chaotic distribution biased towards extreme values. From a probability density function (PDF), calculated using a Monte Carlo (MC) sampling approach, I show that far from being uncommon, the samples they obtained are actually from the most statistically likely portion of the distribution.\n", "\n", "Authour: Benjamin A. Laken\n", "\n", "\n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", " \n", " \"Profile_Image\"\n", " \n", " \n", " \n", " \"Profile_Image\"\n", " \n", " \t\"Profile_Image\"\n", " \n", " \n", " \"Profile_Image\"\n", " \n", " \n", " \"Profile_Image\"\n", " \n", " \n", " \"Profile_Image\"\n", "
" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running Python 3.4\n", "Adding xrange for backwards compatibility\n", "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/Ben/anaconda/lib/python3.4/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", " warnings.warn(self.msg_depr % (key, alt_key))\n" ] } ], "source": [ "# Code is compatible with either Python 2.7 or 3.X\n", "# NB. past module required for python 3 users (https://pypi.python.org/pypi/past)\n", "from __future__ import print_function, division, generators\n", "import sys\n", "print(\"Running Python {0}.{1}\".format( \n", " sys.version_info[:2][0],sys.version_info[:2][1]))\n", "if sys.version_info[:2] > (3, 0):\n", " print(\"Adding xrange for backwards compatibility\".format(\n", " sys.version_info[:2][0],sys.version_info[:2][1]))\n", " from past.builtins import xrange\n", "#from __future__ import print_function,division,generators\n", "%pylab inline\n", "from scipy.stats.stats import pearsonr\n", "import pandas as pd\n", "import datetime as dt\n", "from scipy.stats import kendalltau\n", "import seaborn as sns\n", "from random import randrange\n", "sns.set(style=\"darkgrid\") # Optionally change plotstyle ('whitegrid', 'ticks','darkgrid')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Read data###\n", "\n", "Fetch the required data from CSV files held on a public server. Then automatically create date objects, and remove the original date column (leaving it only as an index)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "webpth = 'http://www.files.benlaken.com/documents/'\n", "\n", "monsoon = pd.read_csv(webpth+'Monsoon_data.csv', parse_dates=['Date'])\n", "monsoon.index = monsoon.Date\n", "monsoon = monsoon.drop('Date', 1) \n", "\n", "olou = pd.read_csv(webpth+'Olou_counts.csv',parse_dates=['Date'])\n", "olou.index = olou.Date\n", "olou = olou.drop('Date', 1) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Explore the data ###\n", "\n", "Some plots to give you an idea of what we are working with." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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l3HnnnTz11FMMGTIEgDvvvJOZM2fSp08fNm3aRK9evejTpw/z5s3D5XLhdDrJ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\nBJu1YYoCGhvbnNaRW5pVMBiMkErN1obWVhX0OoP134D5QUu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uGd8d824vcPkiF4t4eHnR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\nGvHgwQMRE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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the simple time series \n", "my_ts = plt.figure()\n", "my_ts.set_size_inches(10,5) # Specify the output size\n", "ax1 = my_ts.add_subplot(211) # Add an axis frame object to the plot (i.e. a pannel)\n", "ax2 = my_ts.add_subplot(212) \n", "\n", "ax1.step(monsoon.index.date,monsoon.Precip,lw=1.0)\n", "ax1.set_title(r'Monthly Precipitation and NM counts')\n", "ax1.set_ylabel(r'Precipitation (mm)')\n", "ax1.grid(True)\n", "#ax1.set_yscale('log')\n", "\n", "ax2.plot(olou.index.date,olou.Counts/1000,'r.',ms=3.0)\n", "ax2.set_ylabel(r'Olou NM (cnt./min.$\\times10^{3}$)')\n", "ax2.set_xlabel('Date')\n", "ax2.grid(True)\n", "plt.show(my_ts)\n", "my_ts.savefig('Monthly_ts.pdf',dpi=300)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def return_stderr(data):\n", " \"\"\"Calculate uncertainty of a np array as Standard Error of the Mean\"\"\"\n", " return np.nanstd(data)/np.sqrt(np.count_nonzero(data) - 1)\n", "\n", "climo = {} # Produce a dic of monthly climatology using list comprehension\n", "climo['means'] = [np.mean(monsoon.Precip[monsoon.index.month == (mnth+1)])\n", " for mnth in xrange(12)]\n", "climo['error'] = [return_stderr(monsoon.Precip[monsoon.index.month == (mnth+1)].values) \n", " for mnth in xrange(12)]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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iUiG8pS96T5kOWZvq/X4UBYwVskFaY7HoCPP48ePk5+fX+KTq06eP1YoSojbl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\noFGraOats9t2u9rvpD7hwd6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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# -- Plot the climatology --\n", "my_climo = plt.figure()\n", "my_climo.set_size_inches(5,5)\n", "ax1 = my_climo.add_subplot(111)\n", "ax1.errorbar(x=range(12),y=climo['means'],yerr=climo['error'])\n", "ax1.set_title(r'Precipitation climatology')\n", "ax1.set_ylabel(r'Precipitation (mm)')\n", "ax1.set_xlabel(r'Month')\n", "ax1.set_xlim(0,11)\n", "ax1.set_xticklabels(labels=['Jan','Mar','May','Jul','Sep','Nov'])\n", "ax1.grid(True)\n", "plt.show(my_climo)\n", "my_climo.savefig('Monthly_climo.pdf',dpi=300)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Calculate monthly 𝛿 precip. (anomaly with respect to seasonal climatology)\n", "delta = []\n", "for date in monsoon.Precip.index:\n", " delta.append(monsoon.Precip[date] - climo['means'][date.month-1])\n", "dseries = pd.Series(delta, index=monsoon.index)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create a dictionary of May, June, July, August and September data\n", "def lookup_index(yr):\n", " return ((monsoon.index.year == yr) & (monsoon.index.month >= 5) \n", " &(monsoon.index.month <= 9))\n", "mjjas = {}\n", "mjjas['means']=[np.mean(dseries[lookup_index(yr)]) for yr in xrange(1964,2012,1)]\n", "mjjas['SEM']=[return_stderr(dseries[lookup_index(yr)])for yr in xrange(1964,2012,1)]\n", "mjjas['sum']=[np.sum(dseries[lookup_index(yr)]) for yr in xrange(1964,2012,1)]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/Ben/anaconda/lib/python3.4/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", " warnings.warn(self.msg_depr % (key, alt_key))\n" ] }, { "data": { "image/png": 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ibr3FH5UNsyIE+HlRP8iXS+m5ZOry7RihqCxJ3FycNMYXQghxvbOXi0dZCG9o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\nFJfXkHm+nAyljG7+nh23cIuLiyMuLq7Bc/PmzeNPf/oTU6dO5cSJEzz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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot precipitation anomaly\n", "sns.set(style=\"darkgrid\")\n", "yrange = xrange(1964,2012,1)\n", "my_mjjas = plt.figure()\n", "my_mjjas.set_size_inches(10,5)\n", "ax1 = my_mjjas.add_subplot(121)\n", "ax2 = my_mjjas.add_subplot(122)\n", "\n", "ax1.errorbar(x=yrange,y=mjjas['means'],yerr=mjjas['SEM'],fmt='.',ms=10)\n", "ax1.set_xlim(min(yrange)-1,max(yrange)+1)\n", "ax1.set_title(r'Mean MJJAS precipitation anomaly')\n", "ax1.set_ylabel(r'$\\delta$ precipitation (mm/month)')\n", "ax1.set_xlabel(r'Year')\n", "ax1.grid(True)\n", "\n", "sns.distplot(mjjas['means'], ax = ax2)\n", "ax2.set_title(\"Distribution of MJJAS anomalies\")\n", "ax2.set_xlabel(r\"$\\delta$ precipitation (mm/month)\")\n", "ax2.set_ylabel(\"Density\")\n", "\n", "plt.show(my_mjjas)\n", "my_mjjas.savefig('delta_precip_pop.pdf',dpi=300)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Composites of $\\delta$ precip. from $r$-values from 'Drought' and 'Flood' years ###\n", "\n", "[Badruddin & Aslam (2015)](http://www.sciencedirect.com/science/article/pii/S1364682614002697) took the largest/smallest 12 years of 𝛿 precipitation data, and composited corresponding Oulu pressure-adjusted neutron monitor count (counts per min) data during the May--September months for these 12 years. He calculated the linear regressions of these composites, obtaining a Pearson's *r*-value and *p*-value. They found that during the drought (D) composite the $r$ values were negative, while during the flood (F) composite the *r*-value was positive. The associated *p*-values appear to be statistically significant, however, these *p*-values are based on a Student's t-test (assuming a Gaussian distribution).\n", "\n", "\n", "|Drought | Flood\n", "| ------------- |:-------------:| -----:|\n", "|1965 | 1964|\n", "|1966 | 1970|\n", "|1968 | 1971|\n", "|1972 |1973|\n", "|1974 |1975|\n", "|1979 |1978|\n", "|1982 |1983|\n", "|1986 |1988|\n", "|1987 |1990|\n", "|2002 |1994|\n", "|2004 |2007|\n", "|2009 |2008|\n", "\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def make_cframe(c_years):\n", " '''\n", " Function to take a list of composite years (c_years)\n", " and create a numpy array (c_years, months) for analysis.\n", " Also returns back a month-wise set of means, and SEM values.\n", " '''\n", " c_group = np.zeros((12,12),dtype=float) \n", " for n, yr in enumerate(c_years):\n", " tmp = olou.index.year == yr\n", " for i in xrange(len(olou.Counts[tmp])):\n", " c_group[n,i] = olou.Counts[tmp][i]\n", " aaa = np.where(c_group == 0)\n", " c_group[aaa] = np.nan\n", " c_means = []\n", " c_errors = []\n", " for i in xrange(12):\n", " c_means.append(np.nanmean(c_group[:,i])) # per month, all years\n", " c_errors.append(return_stderr(c_group[:,i]))\n", " return c_group,c_means,c_errors" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "drought_years = [1965, 1966, 1968, 1972, 1974,1979,\n", " 1982, 1986, 1987, 2002, 2004, 2009]\n", "\n", "flood_years = [1964, 1970, 1971, 1973, 1975, 1978, \n", " 1983, 1988, 1990, 1994, 2007, 2008]\n", "\n", "d_group,d_means,d_errors = make_cframe(drought_years)\n", "f_group,f_means,f_errors = make_cframe(flood_years)\n", "\n", "d_means = np.array(d_means) * 0.001\n", "f_means = np.array(f_means) * 0.001\n", "d_errors = np.array(d_errors) * 0.001 # Make the values smaller for plotting\n", "f_errors = np.array(f_errors) * 0.001" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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SzGY0x46gXEly+LlcKtP1Q0mk8kEI+O03lfh4mXw9nc01YJ1OR9euXYmJicHH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\nM8vU68Ednnq+3mv1TEWFYLMRX1JKu5ZCWUm3bv5Rb1JS+I9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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a composite plot of the BA15 data. Emphasise the May to September period \n", "# without removing the rest of the data. This is useful for people to see. \n", "# Use diffrent line properties to acheive this effect.\n", "\n", "def simBA15plot(ax, dataset, derr, col_key):\n", " \"\"\"\n", " Set the plot and properties of the figure sub-pannels.\n", " \"\"\"\n", " lthick=1.0\n", " ax.plot(mrange[0:5], dataset[0:5], 'k--',lw=lthick) # Jan to May\n", " ax.plot(mrange[4:9], dataset[4:9], 'k-',lw=lthick) # May to Sep\n", " ax.plot(mrange[8:], dataset[8:], 'k--',lw=lthick) # Sep to Dec\n", " ax.fill_between(mrange[0:5],(dataset[0:5] - derr[0:5]),\n", " (dataset[0:5] + derr[0:5]), color=col_key, linewidth=0.1,alpha=0.15)\n", " ax.fill_between(mrange[4:9], (dataset[4:9] - derr[4:9]), (dataset[4:9] + derr[4:9]),\n", " color=col_key, linewidth=0.1, alpha=0.3)\n", " ax.fill_between(mrange[8:],(dataset[8:] - derr[8:]), (dataset[8:] + derr[8:]),\n", " color=col_key, linewidth=0.1, alpha=0.15)\n", " ax.set_xticklabels(xlabs)\n", " ax.set_xlim(0,11)\n", " return\n", "\n", "mrange = arange(0,12)\n", "xlabs =['Jan','Mar','May','Jul','Sep','Nov']\n", "\n", "figBA15 = plt.figure()\n", "figBA15.set_size_inches(7.48,3.54)\n", "ax1 = figBA15.add_subplot(121) \n", "ax2 = figBA15.add_subplot(122) \n", "simBA15plot(ax=ax1, dataset=d_means, derr=d_errors, col_key='r')\n", "simBA15plot(ax=ax2, dataset=f_means, derr=f_errors, col_key='b')\n", "ax1.set_ylabel(r\"Neutron counts (cnt./min.$\\times10^{3}$)\", fontsize=11)\n", "ax1.set_title('Drought sample')\n", "ax2.set_title('Flood sample')\n", "plt.show(figBA15)\n", "figBA15.savefig('Composite_samples.pdf',dpi=300)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A Pearson's r test, gives linear regressions and two-tailed p-values of:\n", "Drought sample: r-value = -0.949, p-value = 0.014\n", "Flood sample: r-value = 0.001, p-value = 0.001\n" ] } ], "source": [ "# BA15 used 5 months starting in May (position 4 in the array)\n", "# Confirmed: these are the r-values BA15 obtained\n", "rval_d,pval_d = pearsonr(xrange(5),d_means[4:9])\n", "rval_f,pval_f = pearsonr(xrange(5),f_means[4:9])\n", "print(\"A Pearson's r test, gives linear regressions and two-tailed p-values of:\")\n", "print(\"Drought sample: r-value = {0:4.3f}, p-value = {1:4.3f}\".format(rval_d, pval_d)) \n", "print(\"Flood sample: r-value = {1:4.3f}, p-value = {1:4.3f}\".format(rval_f, pval_f))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Indeed, based on the significance of the $r$-values alone it would seem that there are significant correlations between steep changes in the cosmic ray flux and extremes of the Indian Monsoon. This is the core finding reported by BA15. However, the statistical test applied assumes the data are roughly Gaussian. This is impossible for these data, as they are monthly time-scale composites of the solar cycle, which is essentially a sine wave.\n", "\n", "I will now demonstrate how this fact impacts the distribution of these data:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. Bootstrap to identify the possible $r$-values of the samples ###\n", "Assuming any value within the SEM range is equally possible, what are the r-values that may be true." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Some more functions...\n", "def bootstrap_r(mean_list, error_list, iterations=1000):\n", " \"\"\"\n", " Bootstrap info. Error and means are set up to accept data from make_cframe()\n", " which is why the offsets within the arrays have been hard-coded to the months\n", " of key interest (monsoon period).\n", " Guess a possible min and max (poss_min/max) values based on observed mean and \n", " calculate SEM values. Assume an equal chance of any value in that range occurring.\n", " For a specified number of realization (controlled by iterations) calculate the \n", " r-value and p-value, of the linear correlation. Save them to a MC array.\n", " \"\"\"\n", " bs_rvals = []\n", " bs_pvals = []\n", " for itr in xrange(iterations):\n", " poss_vals = [] # List for possible values of CR flux\n", " for n in range(5):\n", " #nb. Below factor preserves sig. figs. in change float > ints\n", " poss_min = int((mean_list[4 + n] - error_list[4 + n]) * 100000) \n", " poss_max = int((mean_list[4 + n] + error_list[4 + n]) * 100000)\n", " poss_vals.append(randrange(poss_min,poss_max)/100)\n", " #print(4+n,poss_min/100,poss_max/100,poss_vals[-1])\n", " rv, pv = pearsonr([0,1,2,3,4],poss_vals) # rval, pval\n", " bs_rvals.append(rv)\n", " bs_pvals.append(pv)\n", " bs_rvals = np.array(bs_rvals)\n", " bs_pvals = np.array(bs_pvals)\n", " return bs_rvals, bs_pvals\n", "\n", "\n", "def freedman_diaconis_bins(a):\n", " \"\"\"Calculate number of hist bins using Freedman-Diaconis rule.\"\"\"\n", " # From http://stats.stackexchange.com/questions/798/\n", " a = np.asarray(a)\n", " h = 2 * iqr(a) / (len(a) ** (1 / 3))\n", " # fall back to sqrt(a) bins if iqr is 0\n", " if h == 0:\n", " return np.sqrt(a.size)\n", " else:\n", " return np.ceil((a.max() - a.min()) / h)\n", "\n", " \n", "def iqr(data):\n", " \"\"\"Return Inter Quartile Range\"\"\"\n", " q75, q25 = np.percentile(data, [75 ,25])\n", " return q75 - q25\n", "\n", "def add_hist(data, col_key, axobj, mkstyle='o', \n", " obsval=None, mylabel=None, bin_num=None):\n", " \"\"\"\n", " Custom Plotting function for histograms.\n", " data - np.array of values, e.g. generated by bootstrap_r()\n", " col_key - code for setting color\n", " axobj - axis object to add the plot to\n", " mylabel - a label for the plotting legend\n", " obsval - the observed r-value for comparison (e.g. rval_d)\n", " mkstyle - matplotlib marker style (default set to circle 'o')\n", " \"\"\"\n", " if not bin_num:\n", " bin_num = freedman_diaconis_bins(data) #if no bins set, use FD spacing\n", " hist, bin_edges = np.histogram(data, bins=bin_num, density=False)\n", " norm_hist = hist / sum(hist) # Normalize the data to show density\n", " axobj.bar(bin_edges[0:-1], norm_hist, width = bin_edges[1] - bin_edges[0], \n", " color = col_key, edgecolor = col_key, alpha = 0.3, label=mylabel)\n", " mylabel = None\n", " if obsval:\n", " lookup = np.where(abs(obsval - bin_edges[0:-1]) == \n", " min(abs(obsval - bin_edges[0:-1])))\n", " axobj.vlines(obsval,0,norm_hist[lookup], linestyles='dashed',\n", " lw=1.0, zorder=6, label=mylabel)\n", " axobj.plot(obsval, norm_hist[lookup], color='k', marker=mkstyle, \n", " ms=5., zorder=7, label=mylabel)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate two arrays: bootstraps assuming that the values within the SEM range have an equal chance of occurring" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rbs1, pbs1 = bootstrap_r(mean_list = d_means, error_list = d_errors)\n", "rbs2, pbs2 = bootstrap_r(mean_list = f_means, error_list = f_errors)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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88cR8FixYzH/+s4KmFbRzW61W3n//Xf7znxX85z/vo2kGMjLS+emnLcTEdOHV\nV9/io4/WsHfvHg4c2A9AcvJeHnlkPu+/v4q1az8hNDSMN998j44dO7Hp4jRJGRnpDBgwiHff/Yhh\nw0bw/PNPlzqrYv36tYDGW2+9z+uvv8P//vcdu3btdM4bK4RwP2YzxqOH8fnfdxhSTmGLisYy5A/o\nUdGSHDpIKTh2TOOnn4wEBysGDbLRvLmro2p8vLoGUbiH5s3DK2xivmTnzkTi4q6lWTN7B+1bbhnP\nokUL6NWrN127divp1zdmzDjef/8djhw5zBVXNC+pORw9egwvvvhsmWOaTCZ69LiaP/85gcGD/8D4\n8ZNo3jycYcNuZN++JD7++EOOHz9GTk4ORUX2UTzR0R0JD28BQEhICL179wWgVavW5OXZ1wBt2rQp\nw4bdCMBNN/2RV199ucx5f/llO4cPHyIxcQcAhYVFHDt2mJ49e9X+DRRCuL+8PIwnjmNIS0Vv0Qpr\n7z4y6KQWsrNh3z4jfn6K/v2r7+cunMdpCaKu68yfP5+DBw/i4+PDk08+SURERJnXFBYWcuedd7Jo\n0SKio39vJszMzGT8+PG88847REVFOStE4SbszQaqzGObzYaul21O0HWFzWa92N/v99cbDBV38F68\n+N/s3ZvEzz9vYdasmcydu5DDhw/y3XebGTNmPH369OfYsaMoZT+WyeRTZn+jsfyfh7FUZ3JdV2Ue\nX9r2t7/dx7XXXgfA+fPnCQgIqPY9EEJ4Ji37PMZjR9Gys9HbR2CJu1bWRK4Fi8U+CCU9XaNLF51W\nrWTtZFdzWhPzpk2bsFgsrFy5klmzZrFkyZIyz+/Zs4epU6eSkpJSpoO/xWJh7ty5+Pv7Oyu0Rsds\nhqKi+v0xm+svvmuu6cOPP/6PCxcuAPDpp+vo3bsP3bp1Z+/ePZw9m3px+xpiY/vSsWMnzp8/z8GD\n9qbhr7/+otwxs7Ozuf32SURHd2TGjLvp27c/R44c4pdftjNmzHiGD78JgMOHD2Kz2aqMTylVkkRe\nuHCBbdt+AmDjxk8ZODCuzGt79+7Dp5+uwWq1UlBQwN///meSk/fW4d0RQrgjLT0d0/ZtmHbvQr+i\nuX35u06dJTmshTNn7EvkGQwweLBNkkM34bQaxMTERIYMGQJAz549SUpKKvO8xWJh+fLlzJ49u8z2\np556iilTpvDaa3WfsmTxp2uZM3lKnY/jyfz97dPROOvYjql6hG/Hjp1ISLiDe+/9C1arlS5dujJr\n1sP4+/vYNxl6AAAgAElEQVTzr389wsMPz8JisdC6dRseeugxTCYTCxYsYtGixzEYDHTtelW5UcQh\nISHccss47rrrT/j5NaFVq1aMGnULUVEdeeaZxaxevZJWrVozaNAQUlPP0LZtu0pHImuaVvKcyWTi\nyy83snz5i4SHh/PIIwvKXOeYMRM4deoU06dPxWazMXr0GHr1inX0jRKN3OI1q2WgijtTCkPaWYxH\nj4CmYYuKRm/ZCplvpXZKz2l4zTU2goNdHZEoTVOXqkbq2aOPPsqIESO49tprARg6dCjffPMNhstm\nVk5ISODxxx8nKiqKNWvWkJaWxj333ENCQgILFiwo0/RcmfT03PIb8/NpEdWa7BWrym7PybH/ruib\nWFSEedgIPKXTQ3h4UJlrNxrthZTNJndfznL99XFs3rylwc5X2Wd6+Wff2ISHB7k6hPqxejU5xb9/\ntiEJt5YvszykXPKm72S5a1EKQ+oZjEePoEw+2Dp2QoWHuy7AGnDHz0XX4ehRjZMnDRfnNFTV5tju\neB215Snll9NqEAMDA8nPzy95rOt6ueTwcmvWrEHTNLZu3cr+/ft56KGHWL58Oc2rGb5U4ZsdYD9X\ncPBl1Vzq4lrMl28H8NMgPMjtC+LSLr/2rKw8F0XSOLiioiAsrOLVgDylkBHCYymF4cxpe2Lo1wRr\n126oK65wdVQeLSNDIznZQLNm9tHJrpyjV1TNaQlibGws3377LSNHjmTnzp3ExMRUu8/77/8+/cml\nmsXqkkOovAYRICfnsnXmLhTZf2sVrD9XVIQ5PRcKPGOupcpqEIXzfPNNw9UeXpKVlSc1iJeR5Fg4\n1aXE8MhhVJMmWLt1R4VJYlgXRUX2QSjZ2Rpdu+qEh0tLl7tzWoI4fPhwtmzZQnx8PACLFy9mw4YN\nFBQUMHnyZGedtlHT9UtJovzheQtN09B1+TyFaChaWhrs/RVDnkUSw3qgFJw8qXH0qIF27RTdutlk\nZUEP4bQEUdM0FixYUGZbRVPWrFixosL9K9suKqeUwmQyYrV6Rg2oqJ7BYMBqtbo6DCG8nnY+C+OB\nA2hKhwGxWDWZSaOusrMhOdmIyaTo29dGYMW9ZYSb8uqJshvjWszFxVaaNDFhteo4afyRaCAGg6Hc\nXJDCu8lazC6Qn4/p0AG0Cxewdb4SvXUbe1/0RtyFo67MZjh0yD6nYUyMTuvW8n+RJ/LqBLExrsWs\nFBQV2SeTrmZMkMcLCwv06kE5VqsNyfEbF5nipgHZbBiPHsFw6iS2DlHoPXoibZ91l5KiceiQgVat\nFIMH2zB5dZbh3eSj81JKKaqZ/9kryJQ+Qoia0tLSMO3fhwoLwzJoMDKUtu4uXLAvkadp0KePjSAZ\nR+bxJEEUQgjROBQXY0rei5aXh7XH1TIApR6Ubk6+8kqdNm3kpt1bSIIohBDC6xnSzmLctxe9XXus\nV/fC6/vgOJlS9ubkw4cNtG6tiIuz4eNT/X7Cc0iCKIQQwntZrfZaw5wcrLG9UcEhro7I450/D/v3\nGzEalTQnezGvvoVa/OlaV4cghBAOW7xmtatD8CpaXi4+P29FGYxYBsZJclhHRUWwZ4+B3buNdOig\n06+fLsmhF/PqBHHp+nWuDkEIIRy2dO1/XR2C1zCcOY1px3Zs0R2xdesuI5Tr4NLayVu3GmnSBAYP\ntsnUNY2ANDELIYTwHkphPHQQQ9pZrH37oQKliqsuzp3TOHDAQGCgon9/G02bujoi0VAkQRRCCOEd\nbDZMu3eCxYql/0Dw9XV1RB4rLw8OHDBQWGhfO7l5c6kxbGwkQRRCCOH5LBZMib9CgD/WntfIKOVa\nslrhyBEDp09rREfrRETo8lY2UpIgCiGE8GxFRfj8ugO9eTi2mC6ujsZjnT5tXwWleXP7tDV+fq6O\nSLiSVyeIjXEtZiFE/dB1nfnz53Pw4EF8fHx48skniYiIKHl+w4YNvPfeexiNRq688krmz5+PUqrK\nfaojazHXQlERPju2YWvbDj26o6uj8UjZ2fZpazQNYmNtNGvm6ogar+rKna+//ppXX30VTdOYMGEC\nU6ZMcVosXp0gNsa1mIUQ9WPTpk1YLBZWrlzJrl27WLJkCcuXLwegqKiIF154gQ0bNuDn58cDDzzA\nt99+i9VqrXQfR8hazDV0KTlsH4HeIcrV0Xic4mI4eNBAZqasguIuqip3ABYvXsy6devw9/fnj3/8\nI6NHjybISXMNeXWCKISoH7oOhYU128ff37O7gSUmJjJkyBAAevbsSVJSUslzfn5+rFq1Cr+LbXBW\nqxU/Pz+2b99e6T6inhUXS3JYS7oOJ05oHDtmoF07xeDBNkySDbiFqsodAB8fHy5cuIDBYEAphaZp\nTotFvhJCiGoVFsLGjUaHB4WazTBqlGdPiZGXl0dgYGDJY6PRiK7rGAwGNE0jLCwMgBUrVlBYWEhc\nXByff/55pfuIemSx4PPrDnuzsiSHNZKRobF/v4GAAJm2xh1VVe4A3HnnnUyYMAF/f39GjBhR5rX1\nTRJEIYRDfH2hSRNXR9FwAgMDyc/PL3l8eaKn6zpPP/00J06c4KWXXnJoH1EPbDZMib+iX9Fc+hzW\nQGGhfdqa3FyNmBidFi2kOdkdVVWGnDlzhg8++IDNmzfj7+/P7Nmz+eKLL7jpppucEoskiEIIUYHY\n2Fi+/fZbRo4cyc6dO4mJiSnz/Ny5c/Hz8+Pll18uaeapbp+KBAf7V/0CPw3Cg/CEqp7wcCdPSq0U\n7NgB7cKhVy+nnsrp19JAdB2ys4M4ehSio6FjR8/t+uEtn0lVqipDiouLMRgM+Pr6YjAYCAsLIzc3\n12mxeHWCuPjTtcyZ7LwRPkII7zV8+HC2bNlCfHw8YO8cvmHDBgoKCujevTuffPIJffr04U9/+hMA\n06ZNq3Cf6uTk/N65c/Ga1eUHqhQVYU7PhQK9nq7MOcLDg0hPd95/VgDG/cloeblYe/cFJ56rIa6l\nIWRkaJw9G4jFkk/Xrjr+/pCZ6eqoasdbPhOoOtGtqtyZPHky48aNIz4+Hj8/PyIjIxk3znmDcTWl\nlMfXM1f4pcnPp0VUa7JXrCq7PSfH/js4uPw+RUWYh43wiDt18K4/mNpozNff0Neenw+bNhkdbmIu\nKoJhw5zXv8lrahJWryan+PciOCTh1vJlloeUS87+ThpOHMeYcgpLvwHg4+O084Dnly1FRfbm5Jwc\njSFDAtA0z72WSzz9MynNU8ovD61oFkII0VhoGRkYjx3Fck1vpyeHnkwp++jkn34yEhAAcXE2WrRw\ndVTCU3l1E7MQQggPV1CAac8urL2ugYAAV0fjtnJzYe9eIwaDom9fG04c3CoaCUkQhRBCuCebDZ+d\nidg6dkKFhrk6Grdks/2+dnLnzjrt2nl8rzHhJiRBFEII4ZZMSbvRmwWjR0S6OhS3dP48JCUZadZM\nMWiQrJ0s6pdXJ4iyFrMQlavJ6igFBc6NRdjJWsy/M5w8gVZQgLX/QFeH4nasVvsSeefOaXTtqtOy\npdQaivrn1QmirMUsROVqsjpKbm7jmyjbFWQtZjstJxvjkcNY+g/03En7nCQzU2PvXgOhoYq4OJuM\n2RFO47S/PF3XmTt3LvHx8SQkJHDy5MlyryksLCQ+Pp6jR48CYLFYmD17NlOnTmXSpEls3rzZWeEJ\nIfg96avux9El9oSoM4sF066dWK/qLoNSSrHZIDnZQFKSga5ddXr00CU5FE7ltARx06ZNWCwWVq5c\nyaxZs1iyZEmZ5/fs2cPUqVNJSUkpWYVg/fr1hIWF8cEHH/Dmm2+ycOFCZ4UnhBDCDZn2JaGHt0C1\nbOnqUNxGTg789JMRqxUGDbIRHi5NysL5nJYgJiYmMmTIEAB69uxJUlJSmectFgvLly8nKur3hdZv\nuukmZs6cCdhrII1Go7PCE0II4WYMKafQ8vOxxXRxdShuQSk4ckQjMdFI585SaygaltP6IObl5RFY\naiImo9FYZtHp2NjYcvsEXGxOyMvL47777uP+++93VnhCCCHcSV4exkMHsfTtL/0OsfcR3r3biNGo\nGDjQJv1/RYNz2l9hYGAg+fn5JY9LJ4dVSU1NZdq0aYwdO5Y//vGPdYph8adr67S/EEI0pMVrVrs6\nBNfQdXySdmPr1BmZ4RnOntX4+WcjLVvq9OmjS3IoXMJpNYixsbF8++23jBw5kp07dxITE1PtPhkZ\nGUyfPp158+YxYMAAh89V4bqGAQaWrl/Hkruml92uiu2/g/3L7+OnQXiQ2695WpqnrOnoLI35+ut6\n7QEB9iXJHfnP59KK7RUtYV4RPz8ID/eoPyW3sHTtfxvlSGbj0SMoX1/09hGuDsWlbDbYv9/A+fMa\nvXvbaNbM1RGJxsxpCeLw4cPZsmUL8fHxACxevJgNGzZQUFDA5MmTK9zn1VdfJTc3l5dffpmXX34Z\ngDfffBO/amb/rHAB74u1lzk5l030dqHI/lurYAK4oiLM6blQoFd5PnfhTYuX10Zjvv76uPb8fMjJ\nMVJcXP1rL1yw/744nqxaRUWQnm5z2vyJjfnGwNto2ecxnDqJZdBgV4fiUnl59iblwEDFgAE2TF49\nCZ3wBE77CmqaxoIFC8psKz0g5ZIVK1aU/PvRRx/l0UcfdVZIQggh3InNhmnPbvuUNo14GZCzZzWS\nkw2yVJ5wK3KPIoQQwiWMBw+gQkIa7ZQ2um5fESU9XaNPHxtBUjEu3IgMFRNCCNHgtKxMDGlnsXa5\nytWhuERxMezYYaSgAAYMkORQuB+vrkGUtZiFEJ6k0azFbLNh2ptkb1puhBP75eTAzp1G2rXT6dhR\nmpSFe/LqBFHWYhbCNXSdGg1Q8feXqe+g8azFbDx00N603KKFq0NpcKdPaxw8aKBbN50WLSQ5FO7L\nqxNEIYRrWCzw5ZdGh5rNzGYYNcomU+I0Elr2eQxnU7HEDXF1KA1KKXt/w3PnNPr2tcl0j8LtSYIo\nhHAKX1/H5lgUjYiuY9qbhK3rVY2qadlqhd27Ddhs9v6GjejShQeTBFEIIUSDMB49gmraFL1lK1eH\n0mAKC+G334wEByuuukp3eC5RIVxNev0IIYRwOi0vF8Opk41q1PKFC7Btm5E2bXS6dZPkUHgWr04Q\nZS1mIYQn8ea1mI1799rXWm4k/Q7OndP49VcjXbvqdOggg1GE5/HqBHHp+nWuDkEIIRy2dO1/XR2C\nUxhOnQRoNGstp6Ro7NtnIDbWRsuWkhwKz+TVCaIQQggXKy7GePgQtm7dXB1JgzhyROPYMQN9+9oI\nDnZ1NELUngxSEUII4TSmA8no7dqjAr17qRClYN8+A7m5Gv362Rrz0tLCS0gNohBCCKfQMjLQsrOx\nRXd0dShOpev2aWwKCqBPH0kOhXeQBFEIIUT903VMyXvto5aNRldH4zQ2GyQmGlAKevfWMUm7nPAS\nXv1VlrWYhRCexJvWYjYeP4oKCvLq5fQsFvj1VyNBQTLHofA+Xp0gylrMQghPUuFazJ64sHVBAYbj\nx7EMjHNtHE5kNtuTw9BQRZcuuqvDEaLeeXWCKIQQHs9iwe/LjaigZtW+VDObKR41GlcvbG06kIyt\nQ5Q9WfVCRUX25LBlS51OnWQaG+GdJEEUQgg3p3z9HJpg2h1SFS09HS0vD73nNa4OxSkKC+GXX4y0\nbasTHe0O77gQziEJohBCiPqh65j277MPTHF1M7cTFBbCjh1GIiJkdRTh/SRBFEIIUS8Mx4+hAgNR\n4eGuDqXeFRTYaw4jI3UiIyU5FN7P+27xSpG1mIUQnsSj12IuKsJ4/BjWmK6ujqTeXWpW7tBBkkPh\nOcxmc5329+oEUdZiFkJ4Eo9eizk52b7WckCAqyOpV5ealTt00ImIkORQeI4RI0awYMECdu/eXav9\nvTpBFEII4XxaTjZkZGCLinZ1KPXqUnIYGSnJofA8GzdupGfPnvz73//m5ptv5s033yQ9Pd3h/SVB\nFEIIUSfG5GTo2hVvWkakuNjerBwRIc3KwjMFBAQwduxY3n33Xe69915WrFjB8OHD+dvf/saJEyeq\n3d97/pqFEEI0OEPqGTQUtGsH6bmuDqdemM2/T2Ujo5WFpzp+/DiffvopGzZsoE2bNsyaNYvhw4ez\nbds27rrrLr766qsq95cEUQghRO3oOsZDB7H2uNrVkdQbiwV++glatpR5DoVnmz59OuPGjeOtt96i\nXbt2JduvvfZafvzxx2r39+omZlmLWQjhSTxtLWbDieOoZs1QoWGuDqVeWK32FVLCw5EVUoTH+9vf\n/sa9995bJjn84IMP0DSNRx55pNr9nVaDqOs68+fP5+DBg/j4+PDkk08SERFR5jWFhYXceeedLFq0\niOjoaIf2qQlZi1kI4UkqXIvZXZnNGI8dxdJ/oKsjqRe6Dr/9ZiAoSHHVVVCDvvxCuJV33nmHvLw8\nVq5cydmzZ0u2W61W1q9fz9SpUx06jtNqEDdt2oTFYmHlypXMmjWLJUuWlHl+z549TJ06lZSUFDRN\nc2gfIYQQ7sF45DB66zYuX/e5PigFu3YZ8PODq67SXR2OEHUSERGBUgql7LXgl/7t5+fH0qVLHT6O\n02oQExMTGTJkCAA9e/YkKSmpzPMWi4Xly5cze/Zsh/cRQgjhBgoKMKSewTL4WldHUi+SkgwoBT16\n6FysrxDCY11//fVcf/31jBo1io4dO9b6OE5LEPPy8ggMDCx5bDQa0XUdw8X1OWNjY2u8jxBCCNcz\nHTmEHhkJvr6uDqXO9u83UFCg0aePTZJD4RX+8pe/8Prrr3PXXXeVe07TNL755huHjuO0BDEwMJD8\n/PySx44kerXZByA8PKj8xgADBPtDkyZlt6ti++9g//L7+GkQHuRRTSYVXnsj0pivv67XHhAAwcHl\n/0QqcrGlguBgx45dk9f7+UF4uEf92TVqWu4FtIwMrEP+4OpQ6uzoUY2sLI2+fW0Yja6ORoj6sXDh\nQgDee++9km2XuvJdanZ2hNMSxNjYWL799ltGjhzJzp07iYmJcco+AOkVzb2Vn8+/V3zInMlTym6/\nUGT/rRWW36eoCHN6LhR4Rh+U8PCgiq+9kWjM11/Zteu6ffUHRxQUQE6OkeLi6l974YL9t6M1LDV5\nfVERpKfbKChw7NjgvTcGi9esdvuBKsaDB7BFd/T4SbFPn9ZISTHQv78NHx9XRyNE/WnZsiUATZs2\nJTk5mUGDBvHqq6+yb98+Zs6c6fBxnPYXPnz4cLZs2UJ8fDwAixcvZsOGDRQUFDB58mSH96mLpevX\nlU8QhfBihYWwcaPRoZa/3Fx7C6EjNYiiYSxd+1+3ThC181lo+fno1/R2dSh1cu6cxqFDBvr2teHn\n5+pohHCOBx54gKFDhwLw5ZdfMm3aNObNm8cHH3zg0P5OSxA1TWPBggVltkVFRZV73YoVK6rcx21U\nVTXj7w/ST1K4CUeTPkdqDhuz2kzVBTBu3LiSvtTt27dn0aJFDR67sxgPH8LWsZNHl3fZ2bB3r4HY\nWJt0axBup7pyZ/fu3SxduhSlFC1btmTp0qX4VlIjkJOTQ0JCAgsXLmTs2LGMHTu2TLNzdTy7jaAh\nFRbit3ED6rIPQjObKR41WjpQCeFlSk+7tWvXLpYsWcLy5ctLnt+zZw/z5s3j3LlzJf17ii9m3aVv\nfL2FlpWJVlSE3qatq0Optfx8+O03I9276w73pxWiIVVV7iilmDt3Li+99BLt27fn448/JiUlpeTm\n9HJKKZKSkti0aRMrVqwgOTkZm83mcCyeexvoAupS1Uypn8sTRiGE8zz11CKeeqphauQcnaqrdMvI\n/v37KSwsZMaMGUybNo1du3Y1SKwNwXj4sL320EOH+prNkJhopHNnnfBwWSVFuKeqyp1jx44REhLC\n22+/TUJCAhcuXKg0OQSYPXs2Tz31FHfeeScRERHMnz+fhx56yOFYpAaxNF2n0p7yBQWAZwxeEcIb\nPfXUIp55xj55ftOmfsyfP9+p56vNVF3+/v7MmDGDSZMmcfz4ce666y6+/PJLj5+qS8vIQDMX2yfG\n9kA2mz05bN1ap107SQ6F+6qq3Dl//jy//fYbc+fOJSIigrvvvpvu3bszYMCACo81cOBABg78faWj\nVatW1SgWr04Qa7wWs8WC35cbUUHNyj2l5eZerEGsp+CEEA4rnRwCLFiwwOkJYm2m3erQoQORkZEl\n/w4JCSE9Pb1kVGFFgktNuTUvPr7MY6Dqqbku56ypug7uhn69oEX5srE0dxxdrhT88gu0bw+9ejm+\nnzteS215y7V4y3VUpapyJyQkhIiIiJJawyFDhpCUlFRpgrhmzRqWLl1KTk5OyTZN00hOTnYoFs9P\nEPPz7T+XKyhgzi1janw45etXYQ9/JT36hXCKqiruAZ5/fhEvvNDwy27WZtqtNWvWcODAAebNm0da\nWhp5eXmEh4dXuU9Ozu+D3+7/47gyj4Gqp+a6nBOm6tKyMjGdy8bSOQiqmFbKXaed2r/fQG4u9O6t\nO7y+srteS214y7V4y3VA1YluVeVO+/btKSgo4OTJk0RERPDrr78yceLESo+1bNkyVqxYQefOnUv6\nSdeE5yeIa9fiW1S+MJQaPyE8g8UCX35pJKiSMvPoUdc0z9Zmqq6JEycyZ84cpk6dWrKPpzcvG48e\nwRYV7ZF9D0+e1MjM1OjXz+bJA69FI1JdufPkk0/ywAMPoJQiNjaWP/yh8gnrW7VqxZVXXlnrWDw/\nQfTzA8r3KZEaPyE8R1VT80ye/AgmE6xd27DTxdRmqi6TycTTTz/t9NgaipaTjVZQ4JF9D9PTNY4e\nNdCvn0yELTxHdeXOgAEDWL16tUPH6tatGzNnziQuLq5kKhxN0xg71rHud56fIAohvN748Y8AvyeJ\n8+bNc2U4jYbx6BFsHaI8bt7DvDxISjJwzTU2AgJcHY0QrpGbm0tAQAA7d+4ss10SRCGEVxk//hGs\nVoiO1p0+QEWAlpeLlpODfnUNRna4gUvT2XTpohMS4upohHCdJUvsfbezs7MJqcUfg2fdFtbQ4s8+\ndXUIQoh6dMstj7g6BKdavMaxpqOGYDh2DFtEJBiNrg7FYbpunwi7dWtF69YynY1o3JKTk7npppsY\nM2YMqampDBs2rNx8rlXx6gRx6ecbXB2CEKKeuWJEc0NZuva/rg7BrqgIQ3oaevuI6l/rRvbuNeDn\np+jcWeasFWLhwoUsW7aM0NBQWrduXePpwbw6QRRCCFFzxlMn0Vu3xZNGdxw/rpGXp9GjhySHQgAU\nFRXRqVOnksdxcXGYzWaH95cEUQghxO9sNgynTmKL7ODqSByWkaFx/LiBXr1sntQiLoRThYSElJkU\n+9NPPyW4BouQyyAVIYQQJQwpp1BhYXjK8N/8fNizx54c+juw2IwQjcW8efN48MEHOXz4ML179yYy\nMpJnnnnG4f0lQRRCCFHCePIE1u49XB2GQ6xW2LnTSKdOOqGhro5GCPcSGRnJypUrKSgoQNf1Mms8\nO6LaBPGNN95g7Nix1S4X5Y4eHDna1SEIUWe6DoWXrbIWEFDpCpNe7777HnJ1CE7z4LjKl81qCFpG\nBphMqNAwl8bhqKQkAyEhivbtZcSyEJckJCSU/FvTNJRSZR6/9957Dh2n2gSxuLiY22+/nYiICMaP\nH8+wYcPw8ZCOy3P+eIurQxCizgoLYeNGIxcnwgcgOBhycsp3tsrNrXpVEm/wf//3sKtDcJo54ye5\n9PzGk8ftU9t4gKNHNYqKNK6+2ubqUIRwK//6178AeP/99wkMDGTixIkYDAY2bNhATk6Ow8epNkH8\nxz/+wd///nd+/fVXNmzYwEsvvcSAAQOYNGkSXbt2rf0VCCEcdnnS16QJVLSapKwwKWqtoMA+MXbP\na1wdSbUyMjROnjQwYICssSzE5Xr0sHcROXToEGvWrCnZHhMTw/jx4x0+jkN/WkVFRaSkpHDq1CkM\nBgPBwcE8+eSTNersKIQQwn0ZT51Eb9vO7SfGLiy0D0rp2dPm1TXlQtSVxWLh8OHDJY/37duHzeZ4\njXu1NYgPPPAAP//8M9deey333HMPffr0AcBsNjN48GBmzZpVi7CFEEK4DZsNw5nTWPoPdHUkVdJ1\n2LXLSFSUDEoRojoPPfQQd9xxBy1atEApRWZmJs8995zD+1ebIA4cOJDHH3+cpk2blmwzm834+vqy\nYYOsVCKEEJ7OcDYVFRzs9lPbJCcb8PdXdOggg1KEqE5cXBybN2/mwIEDaJpGTExMjcaQVNvEvHr1\n6jLJoc1mY8KECQC0aNGiFiE3HFmLWQjv8/zzi1wdgtO4ai1mQ0oKtrbtXXJuR505o3H+vEa3brJS\nihCO8vX1pUePHnTv3r3GA4wrTRATEhLo0qULu3btokuXLiU/V199NVFRUXUOuiHIWsxCeB9Zi7me\n5eWhFRag3PiGPy8PDhywT4Ztktl7hWgQlf6prVixAoAnnniCRx99tMECEkII0XCMp1Psg1M0zdWh\nVOjSZNgxMTo1nOdXCFEHlSaI3377LUOHDqVbt26sW7eu3PNjx451amBCCCGcTNfdfnDKvn32ybDb\ntJF+h0LURHZ2Ns888wwnTpzghRde4Omnn+ahhx5yeD3mShPEPXv2MHToULZt24ZWwZ2lJIgOqGgJ\njNL8/ZFJvIQQrmI4l4YKDHTbwSkpKRp5eRr9+8tk2ELU1GOPPUZcXBy7du2iadOmtGjRgtmzZ/P6\n6687tH+lCeLMmTMBWLLk9/4+ubm5pKamcuWVV9Yx7EaisBC/jRtQpZfAuEgzmykeNRpKDQASQoiG\nZDidgt7OPQen5OXBwYMG+vWzufvUjEK4pZSUFOLj41m5ciV+fn7cf//93HzzzQ7vX21339WrV5OY\nmMisWbMYN24cAQEB3Hjjjdx///1V7qfrOvPnz+fgwYP4+Pjw5JNPEhERUfL85s2bWb58OSaTiQkT\nJjBp0iR0XeeRRx7h+PHjGAwGFi5cSHR0tMMXczl3WItZVbLumTSWCFE7shZzPSkqsq+c0iu24c7p\nIFur3VcAACAASURBVJvNPt+h9DsUovZMJhO5ubklj48fP46xBndb1bZvfvjhhzz44IN89tln3HDD\nDWzYsIEffvih2gNv2rQJi8XCypUrmTVrVpmaSIvFwpIlS3j77bdZsWIFq1atIjMzkx9//JHCwkI+\n+ugj/v73v/P88887fCEVkbWYhfA+shZz/TCknkFv0dItV07Zv99As2aKtm3lVlqI2rr33ntJSEgg\nNTWVe+65hylTpnDfffc5vL9DEwaEhITw/fffk5CQgMlkotiBBV8TExMZMmQIAD179iQpKankuSNH\njhAREUFQUBAAvXv3ZseOHYSFhZGbm4tSitzc3BrP2SOEEMIxxtQzWLt0dXUY5aSlaWRlaQwcKP0O\nhaiLuLg4unfvzq5du7DZbDz++OOEh4c7vH+1CWKnTp24++67OXXqFIMGDeK+++4rWQi6Knl5eQSW\nahswGo3ouo7BYCAvL68kOQRo2rQpubm5DB8+HLPZzE033UR2djavvvqqwxcihBDCMVruBbBaUWFX\nuDqUMoqK7KOWY2NlvkMh6uq6665j+PDh3HLLLfTq1avG+1f7J7ho0SJ27txJ586d8fX1Zdy4cQwe\nPLjaAwcGBpKfn1/y+FJyCBAUFFTmufz8fJo1a8Ybb7xBbGws999/P2fPnmXatGmsX78e3woGeZQW\nHOxffqO6WMt5+XOVba/tc34ahAdVPNgkwGB/fUUryle1Xw2EhwdV/yIv1hiuPyAAgoPLf42Cg8uP\nPFXq0nPVH9dZr3Xmsf38oAY3wKIShjNn0Fu3cXUYZSgFe/YYiIzUHf6eCSEqt379er766iueffZZ\n0tLSGD16NLfccguRkZEO7V9tglhQUMCBAwfYtm1bybakpCT+8Y9/VLlfbGws3377LSNHjmTnzp3E\nxMSUPBcdHc2JEyfIycnB39+fHTt2MH36dPbt21eyrF+zZs2wWCzoevXLKuXkVDCVzIUi+2+t0LHt\ntX2uqAhzei4UVBBnfj6+OYVQXEE/mqr2c1B4eBDp6bnVv9BLNZbrz8+HnBwjpXt2BAcHkJNTUO61\nFy7Yfzsy57GzXuvMYxcVQXq6jaZNvf/GwGmUwpB6Bkuffq6OpIxjxzSUgqgo6XcoRH0ICQlh8uTJ\nTJ48md27dzNv3jxeeeUV9u3b59D+1Q5Sue+++9i+fTtK1eyPdvjw4fj6+hIfH8+SJUuYM2cOGzZs\n4OOPP8bHx4eHHnqIGTNmEB8fz8SJE2nZsiUzZsxg165d3Hbbbdxxxx088MADNKmo9s1BshazEN5H\n1mKuGy0ry14d7UbDgy9cgBMnDPToobvrgi5CeJzMzEw++OADbrvtNubMmcONN97I119/7fD+1dYg\nZmZm8s4779Q4ME3TWLBgQZltpddwHjp0KEOHDi3zfLNmzXj55ZdrfK7KLP18A3NuS6i34wlRH6qb\nP/1yBeUrChu1F15YwvPPL3Z1GE6xdO1/nT6S2XA2FVvLVk49R03YbLBnj5EuXXT8K+jdI4SonTFj\nxjBy5EgefvhhunfvXuP9q00Qu3btyv79++nSpUutAhRClFVYCBs3Gqmma22J3FyoZDpNIWpGKQzn\n0rAMGOTqSEocOmQgMFDRurU0LQtRn77//vsazXt4uWoTxIMHDzJu3DiuuOKKksEimqbxzTff1Pqk\nQjR2NUn4HJhVSgiHaJmZKP8A3KWqLjNTIy1NY9AgmdJGiPoyduxY1q1bR7du3co9p2kaycnJDh2n\n2gRx2bJlJQetaT9EIYQQ7sNwNhW9lXs0L1utsHevgW7ddGTKWyHqz7p16wDYv39/uefMZrPDx6l2\nkEq7du1ITEzk448/JjQ0lF9++YV27drVIFQhhBAup+sYzqWhu0n/w/37DVxxhaJ5c6l4EMIZbr31\n1jKPbTYbEyZMcHj/amsQn376ac6ePcu+ffuYPn06n3zyCcnJycyZM6fm0TYwd1iLWQhRv2Qt5trR\nMjNRTQPdonk5Pd2+Woo0LQtR/xIS/n979x4dVXnvDfy75z65JxAQkSCIoi2rtAEUbRGDoqK0AgkQ\nkEBtFj3Lvlgv1B56liJUkViW5+CxpNXVpVReT1FEK41olIv1LUcggqCAgCIGFCUBkslc91z2fv8Y\nMmTIXPYks+ey8/2sNSuT2Xs/+9lJ5slvnv08z68GTU1NABA2f0Sv1+Pmm29WXE7cHsR//etfWLVq\nFcxmMwoLC/Hiiy/igw8+6EGVU4+5mIm0h7mYeyZTbi/7fMFsKaNGMVsKkRrWrVuHw4cPY968eTh8\n+HDocfDgQfz3f/+34nLivj0vngHj9Xp7NSuGiIhSTJaha22Bb8SV6a4JDh/WYcAAGSUl6a4JkbY9\n/PDDeO+990KZ6wKBAL7++mvcf//9io6PGyDefvvtePDBB2Gz2bB27Vq8+eabuPPOO3tXayIiShmh\n7VwwZ2Oaby+fOSOgrU3Aj3/MW8tEalu0aBE8Hg+am5sxbtw4NDU1JfcW88SJE1FRUYHi4mLs2bMH\nv/71r3Hvvff2qtJERAQse/XVlGRP0bW0QBowQPXzxOL3B28tf+97EngTikh9x48fx0svvYTJkyej\ntrYWGzZswLfffqv4+Kg9iGfPnsWvf/1rfP755xg6dCj0ej127twJj8eDMWPGoKCgICkXQETUVy1/\n7bXQc1XHH7achr98jGrlK3H0qA4lJZy1TJQq/fv3hyAIGD58OI4cOYLp06ejtbVV8fFRexB///vf\nY8yYMdixYwc2bNiADRs2YMeOHbj66qvx5JPZkQuVuZiJtEeLuZifeuM1rHx9gyq9iUKHDRAEyHn5\nSS9bqba24MzlkSOltNWBqK8ZMWIEHn/8cVx77bX461//iueeey456yAeOXIEDz30EIxdVjA1mUx4\n8MEHcfDgwd7VOkWeersh3VUgoiR75pm6dFdBFU+98RqeeuM1OD368IdogFM0QOphbBW8vTwwuZVN\ngCQBBw8Gcy1zQWyi1Fm+fDmmTJmCK6+8Evfddx9aW1vx9NNPKz4+6i1mS5Q8YDqdjrOYiYhU8u7+\ni5aicRXC69dh6oQ25FoSn9yhazkN/zXfS1LtEvfllwLy8mQMHMhby0SpsHv3bgiCAACQZRlNTU3I\nz8/HrbfeCpvNprgcrkJFRJQBKsf/HBt3roXFeFFXobEXM35dLkAUIRcV965yPeRwACdP6nD99Zy1\nTJQqzz77bMzt69atU1RO1ADxiy++wKRJkyJua2lpUVQ4ERHFVzn+56i6/hfYuHNtUsvVtbZAKh0A\nnO9NSLUDB/QYMUJClBtSRKQCpQFgPFEDxHfeeScpJyAiosiqrvsFXtv1Aqqu/4Uq5evOtEK6bIgq\nZcdz8qQAQQCGDOGtZaJ0qKmp6faaIAh46aWXFB0fNUC87LLLel6rDMFczETao6VczLOur4UsXLil\nXDn+58krPBCArr0N/tE/Sl6ZCoki8MUXOowbx1vLROmyaNGi0HO/34+tW7cmtEShpscgMhczkfZo\nLRdz197DZPYkCmfPQiooRDoSHh89qsPgwTLy8lJ+aiI677rrrgv7/sc//jGqqqrwwAMPKDpe0wEi\nEVFfpTvTCql/acrPe/Ys0+kRZYJTp06FnsuyjM8//5yzmImI+iRJCs5cBqD7+iR8PywHnM7I+1qt\ngC5uttWET//ZZzpcfTXT6RGl27x580LPBUFAcXExHnnkEcXHM0BMly4NeTcqNNxElBhJkrBs2TIc\nPXoURqMRK1asQFlZWdg+brcb99xzD5588kkMHz5c0TGq8vlgbtwM2WiE/viXENzuiLsJXi/EO6YC\nublJPf1XXwnIyZExYAAnphD1hNI25NFHH0VRUREWL14ctaxt27b1qi4MEHsrVqDncgGIkv6gsyHP\nDx8wqlbDTUSJ2bJlC3w+H9avX4/9+/ejrq4O9fX1oe2ffvopHnvsMbS0tIQWpY13TCrIJjN0Hjfk\n/qWItr6MGuGb2w189ZUO48fz1jJRTylpQ9avX4/PP/8c1157bcyyjh07hldffRUdHR1hr69cuVJR\nXTTdTZWSXMznAz3Tlne7PcyNbwOiL+qhsskcbMC7PGSTSf06E2WxVOVi3rt3LyZMmAAAGD16NA4c\nOBC23efzob6+HsOGDVN8TCSvffhCxOe9IdhskAsKk1KWUocOAUOHSsjJSelpiTQlXhuyd+9efPLJ\nJ5g9ezZkOfZHvUWLFiE/Px/jxo0LPeIFlV1pOkBMVS7mSIEegz0idaQqF7PD4UBel2m4er0eUpeE\nyOXl5bjkkksSOiaSrotjJ2WhbEmC4HZBzs/vfVkKnTkjoL0duPxy3lom6o1YbUhLSwvWrFmDpUuX\nxg0OAaCwsBCLFi3CjBkzQo/p06crrgtvMRMRRZCXlwdnlwkekiRBF2dscE+OAYDcXHPE50F+6H06\nFBZa4+dilkWgowMYUAIUxximYhaA0vykDGWRJODAAWDUKGDgwNQFpWorLeW1ZBqtXEcssdqQxsZG\ntLW1YeHChThz5gw8Hg+uuOIKTJs2LWJZ06dPx3/9139h/PjxMHRZ7mrcuHGK6sIAkYgogvLycmzf\nvh1TpkzBvn37MHLkSFWOAQCnU4z4HADg8sLj08Nmc8MvxgkQOzzQfXsGKC6GZIs8QQUA4PHA22oH\nXLF7N5X46isBoihg4MBctLbae11eJigtzee1ZBitXAcQO9CN1YbU1NSEsqO88cYb+PLLL6MGhwCw\ne/dufPrpp9i7d2/Y673OxUxE1JdNnjwZO3bsQHV1NYDgwO6Ghga4XC7MmjVL8TGpJtjtCJQNTcm5\nRBH48ksdrr2WE1OIkiGRdkeIk2P9wIEDaGxsjLtfNKoFiPGmam/btg319fUwGAyorKzEzJkzAQDP\nPfcctm/fDp/Ph3nz5iV0v5yIKFkEQcDy5cvDXus6IaVT10/jkY5JKa8XQsCPVM0U+fxzZkwhSial\n7Y6S2Oiqq67CkSNHcPXVV/eoLqoFiLGmavt8PtTV1WHjxo2wWCyYM2cOJk2ahC+++AIff/wx1q9f\nD5fLhb/85S+9qgNzMRNpj5ZyMQPh+Zd7m4tZcDogpSha6+gITk75yU/Ye0iUiU6cOIHp06ejf//+\nMBqNAIIB6NatWxUdr1qAGGuq9rFjx1BWVob887PsxowZg6amJhw6dAgjR47Er371KzgcDvz2t7/t\nVR2Yi5lIe5iLOTrBYYecl5qB/J99pseIEVI6Uj0TkQL19fXdZjsncrtZtbd2tKnaOp0ODocjFBwC\nQG5uLux2O9ra2nDq1Ck899xzOHnyJO6991688847alWRiEhTdA4H/AMHqX6e774TIEnA4MFc1oYo\nU+3evTtiQDh48GBFx6sWIMaaqp2fnx+2zel0oqCgAEVFRRg+fDgMBgOGDRsGs9mMc+fOoaSkJOa5\nCgut3V+Uz88EvHhbtNd7ui3Z5SW4/ERfmPYfSzZef04OUFgYNclFN50fAAsvWve4sLD7OLNo+yZS\nbm/3VbNssxkoLVVWhz7H5YKsNwAqr78qScDRozqMGhVAD8e+E1EK7Nq1KxQg+nw+7NmzB2PHjo05\n87kr1QLEWFO1hw8fjubmZthsNlitVjQ1NaG2thZmsxkvvfQS7rnnHpw+fRputxvFxcVxz2WLtJxD\nhyf4VXAre72n25JdXgLLT2hp2n9PZOv1O52AzaaHKMbfFwiO9QIQ9s+4sDAHNlv3FI+R9k2k3GTs\nq2bZHg/Q2hpAbm72fTBQm87eATkF4w+/+kpAQYGMOJ/biSjN6urCkwq0t7fjgQceUHy8agFivKna\nS5YsQW1tLSRJQlVVFQYMGIABAwagqakJVVVVkCQJjz32WI+nZxMR9SWC3a76BBVRDOZbvu46Tkwh\nyjY5OTn45ptvFO+vWoAYb6p2RUUFKioquh338MMPJ60OK9/ahN/NrUlaeUSUfqtXP4nVq1O/vqBa\nXvvwhdDklK7PEyLLEJwOyCp3633xRXBZmyQkYCEilXUuqt3p5MmTmDhxouLjNT3/7Km3GxggEmnM\nM8/UaSpA3LhzbSgo7Po8IS4nZLMZ0KvXpDscQEsLl7UhyhaLFi0KPdfpdCgqKsKVV16p+HhNB4hE\nRH2Bzm6HnF+g6jmOHtVh2DAJ55dTI6IMZrPZcOWVV4Ym+e7atQv9+vVLqIz4WeSJiCitJBlwiXo4\nPZEfnrNOOI1FcIoGSL1Pr9zNuXOAwyGgrIzL2hBlukOHDuGOO+4IW3/6X//6F372s5/h8OHDisth\nDyIRUYbz+XXY/PGlKLD6um0TpAAuPXkcp4ZcAdERwNTRzUj2EMGjR/W46ioJOnYpEGW8uro6/Od/\n/ieuu+660GuLFy/Gtddei7q6Oqxdu1ZROXy7ExFlAbNBgsXY/ZEv2SBYrTCbdTAZFHYfShLgcgXX\nXIrz+PabYK/hJZew95AoG3R0dIQFh50mTJiAc+fOKS5H0z2IzMVMpD3MxRzO7O6AaFW4gnknnw/m\nxs1xxy3KHi8+L56BUWO53BhRtggEAmHJSTpJkgS/36+4HE33IDIXM5H2MBdzOLPblniACEA2mYPp\nfGI8mu0lyMvlothE2WTs2LH44x//2O31+vp6jBo1SnE5mu5BJCLSMkEKwOh1wmdOfmYZf0DAly35\n+OFP/QDUTd9HRMmzePFiLFy4EJs2bcIPfvADSJKEQ4cOoaSkBH/6058Ul8MAkYgoSxlFO3ymXMg6\nfdLLPt6Sh/75HuTlcewhUTbJy8vDyy+/jF27duHQoUPQ6/WYN28exo4dm1A5DBCJiLJUcPxh8tc/\nFH06nGjNwQ2Xf530solIfTqdDtdffz2uv/76npeRxPoQEVEKmTw2eC2Jjz+M54vv8jG4nxtWE7Om\nEPVVmg4QV761Kd1VIKIkW736yXRXIale+/CFiM/jEaQATKITXktewueUJMApGiIuun3GZsJXp3Mx\nsNCl2sLbRJT5NB0gPvV2Q7qrQERJ9swzdemuQlJt3Lk24vN4jKIDPlMOZF3iI4XcPgMa9g/Fu/sv\n6fb4v//vcpw4a8X7Bwei4eMhcHu4xA1RX8QxiEREWcjs6YDX0vPxh6bzC2935RL1EH16jLjEDr0O\ngI+3mIn6Kk33IBIRaZWph+sfxnKqzYpLitzB4JCI+jQ2A0RE2UaWYBId8FqSt/6hw2OAx2tA/wIx\naWUSUfbiLWaiJJAkwO1Wtq/LpW5dSPtMogN+o7VH4w+jOXXOikHFLug45JCIoPEAkbmYKVXcbmDz\nZj1MChJO2O2AyRTMZkaJYy5mwOTpgNiL8YcX63AZ4QvoUJLnTVqZRJTdNB0gMhczpZLSoE/kHbxe\nYS5mwOTugKtgYNLqEOw9dEPoae+hJAFOZ/ARj9UK6Di6iSjTaTpAJCLSHFmGSbSj3XJlUoprdxoh\nA73rPXS7gfe2wOSJvWii4PVCvGMqkJvb83MRUUowQCQiyiJGrxOS3gRJb+x1WbIMnGrLwaXFSRgY\nazYDiJ23mVmdibIH+/mJiLKIKYn5l9ucJugEGUW5vqSUR0TawR5EIqIsYvJ0wJPXL+I2SQZcogHw\n6GOW4RINkALAtx1WDOmvYNwgEfU5mg4QV761Cb+bW5PuahBREq1e/SRWr16Z7mokzWsfvhCanNL1\neTRmsQMd/YdF3Obz67D50zIU9IvdtNvP+uAQjTAaJRRY/T2rOBFpmqZvMTMXM5H29OVczAavC7Kg\nR8BgjrqP2RCAxSjFfBj1Elo6cnBpicLFO4moz9F0DyJRb3Dxa8o0yVr/sM1lhskQQJ6FvYdEFBkD\nRKIouPg1ZRqTpwNeS+/yL0sycLojB0P72QEwbQoRRabaLWZJkrB06VJUV1ejpqYGJ06cCNu+bds2\nVFVVobq6Ghs2bAjbdvbsWUycOBHHjx9Xq3pEinQGffEeSoJIot4yJ2EG89kOMyxGP3JM7D0kouhU\nCxC3bNkCn8+H9evX4ze/+Q3q6i6MG/L5fKirq8OLL76IdevW4ZVXXsHZs2dD25YuXQqr1apW1YiI\nso7e74EACQFjz9tGSQK+bbfikgKOiSCi2FQLEPfu3YsJEyYAAEaPHo0DBw6Eth07dgxlZWXIz8+H\n0WjEmDFj0NTUBAD4wx/+gDlz5qC0tLTXdWAuZiLt6au5mC2eDoi9vL3c2mFBrtkPqynQq3KISPtU\nCxAdDgfy8vJC3+v1ekiSFNqWn58f2pabmwu73Y7XX38dJSUl+MlPfgIAkOXerbuflbmYJSk446Ez\nr+nFDyl2KisireuruZjNYge8lvyo2+ORJOC0zYJBxZy5TETxqTZJJS8vD84uidslSYLufIL2/Pz8\nsG1OpxMFBQVYt24dBEHA//7v/+Lw4cNYsmQJ6uvr0b9//5jnKiyMcMtFFs9vtCp7vafb1Cjvw+1A\nQYRxRqIITJ8else0tLTn/zC0QM3rz8kBCguVTTzp/CxTqKCDJ5F9Y+1fWJiTsnokq8693ddsBpJw\ncyErWTw2iAOu6PHxLR0W5Fn8yDEHYLMnsWIIBp9Ojx5OMc6HetEAvaTx9dWINEK1ALG8vBzbt2/H\nlClTsG/fPowcOTK0bfjw4WhubobNZoPVakVTUxNqa2tx2223hfapqanB73//+7jBIQDYbBE+EXd4\ngl8Ft7LXe7pNrfLMERpajwRvqx1wBXsRS0vz0dqa5JY+i6h9/U4nYLPpIYrx9+3oCH4VFEwKTWTf\naPsXFubAZus+jkyteiSjzsnY1+MBWlsDyM3tWx+MdAEv9AEv/KbuHwqUCEhAi82CqwZ1JHScJJ9f\nwilOshXXOQGNuy6FJMS+de11S7i1QkAf+/URZSXVAsTJkydjx44dqK6uBgCsXLkSDQ0NcLlcmDVr\nFpYsWYLa2lpIkoSqqioMGDBAraoQEWU1i2iHx1KgPEK/SIvNgnyrDxZTYkNUfH4dGreYkd8/duo+\n+xkz+guAxRinfB/HPhJlC9UCREEQsHz58rDXhg27kB6qoqICFRUVUY9ft26dWlWjPoyLX1M2Mnvt\nEHMLerRqoT8goLXDgqsuTaz3sJPJJMcdZiGaejdenIgyj6YXymYuZroYF7/Ofn0xF7NF7MDZfpeh\nJwvctHZYUGD1xe/dIyLqQtNjhZmLmSLh4tfZra/lYhakAAx+N7ymvG7b4vEHBLTYzJy5TEQJ03QP\nImlfIreMAd42puxjEu3B4FDQAUhsDN9pmwVFuV6Y2XtIRAligEhZLZFbxgBvG1P2MXs60GFOPL2e\nPyDgTIcZ11zWs7GHRNS3MUCkrJdIwKdkyRqiTGLydMBjGZ7wcd+1W1Gc54XJwN5DIkqcpscgEhFl\nM0EKwOhzw2tMbPyhzy/grN2ES4o49pCIekbTPYjMxUykPanKxSxJEpYtW4ajR4/CaDRixYoVKCsr\nC23ftm0b6uvrYTAYUFlZiZkzZwIApk+fHkozOmTIEDz55JMxzxMrF7PJY4fXlAtZF3sdwot9125F\nSZ4XJgOXnyHKJvHanYaGBrz00kvQ6/W46qqrsGzZMgg9XB81Hk0HiFmZi5mIYkpVLuYtW7bA5/Nh\n/fr12L9/P+rq6lBfXw8A8Pl8qKurw8aNG2GxWDBnzhzcfPPNyD2fBjORdVxj5WI2eWzwmhNLO+L1\n63DOY8L3LrMldBwRpV+sdsfj8eCZZ55BQ0MDzGYzFi9ejO3bt2PSpEmq1IW3mImIIti7dy8mTJgA\nABg9ejQOHDgQ2nbs2DGUlZUhPz8fRqMRY8aMwe7du3H48GG43W7U1tZiwYIF2L9/f6/qYHZ3wGtJ\nLEBssVvRP1+Ekb2HRFknVrtjNpvxyiuvwGw2AwD8fj8sKs641HQPIhFRTzkcjtCtYgDQ6/WQJAk6\nnQ4OhwP5+RcCt9zcXNjtdgwfPhy1tbWYOXMmvvrqKyxcuBCNjY3Q6RL/LC5IARi9TngLLwc8yo7x\n+nWwuc0YWeRI+HwRSRIgeuOfX/QGEzcTUa/EancEQUBJSQmA4F0Kt9uNG264QbW6MEAkIoogLy8P\nTqcz9H1nIw0A+fn5YducTicKCwtx+eWXY+jQoQCAyy+/HEVFRWhtbcXAgQOjnic31xzxdZOrHfrC\nIuTkWeHVGQCrCbm5sQPNL7wGXNbfj8KC2Os++Vyxz31hPxE4dgiF7caY+8lnfIDRELc8vexHaf88\n5JbmxtxP8ktwn1M4wcZqhTVXhx7E4DGVlibWc5vJtHItWrmOWGK1O53fr1q1Cs3NzXj22WdVrQsD\nRCKiCMrLy7F9+3ZMmTIF+/btw8iRI0Pbhg8fjubmZthsNlitVjQ1NaG2thavv/46jhw5gsceewyn\nT5+Gw+FAaWlpzPM4nZHXXtKda4UNVjhdXrhcMgAvjIIvajkerw6t7SaMvMQGpzN20+5yBcsxOmPu\ndmE/X+zeQZdPRo4x+rWE6ugOoPWMAy4h9tI7zlYX3vvDIRgtsQffC/4AnN8fgzum65EbO+ZMSGlp\nPlpb7ckrMI20ci1auQ4gdqAbq90BgKVLl8JsNmPNmjWqTU7ppOkxiCvf2pTuKhBRkq1eHXtWcLJM\nnjwZJpMJ1dXVqKurw+9+9zs0NDTg1VdfhdFoxJIlS1BbW4vq6mpUVVVhwIABqKqqgsPhwN13342H\nHnoIK1eujHt7+bUPX4j43OSxwWspVFzfU205KM13Q6/Txq1eo0WAJUcf82G2CjCZtHG9REDsdufQ\noUPYuHEjjh49ivnz56OmpgZbtmxRrS6a7kF86u0G/G5uTbqrQURJ9MwzdVi9eqXq5xEEAcuXLw97\nbdiwYaHnFRUVqKioCNtuMBiwatWqhM6zcefa0OzlzueCFIBJdMJryQM83rhluEQ9HB4DhuRy3UOi\nbBav3fnss89SVhdN9yASEWUjo2iHz5QLWafsM/ypNisGFbmTPg6PiPouTfcgEhFlI7PbBtGq7Pay\nw2OAx2vA8IEO2M+qXLFekgISXGfj93K6zrohSxKAxBYIJ6LkYYBIRJRhLK522Ppdrmjfb87mRNV0\ncQAAG7tJREFUYFCxCzp1x6snhU8Etv7xGPKLYwd+jrYAjGYB1sQyDBJREjFAJCLKIELAD4PPrWiB\n7HanEQFZQEle/HGKmcJkDk4+iUV0xZ7l3BOSBLgVDtHs1y/ppyfKOpoOEDWXi1mSAJfrwvc5OqDL\nekmwWpGpg5ASaZyBjL4USrNU5WJOlYtzMZs9tmBwKMR+A8hycObypcUuqLzahSa43cDmzXqYYi8R\nCa8XWLAgNXUiymSaDhA1l4vZ54O5cTPk/ILg94VWmGzBqEvweiHeMRVJXQwsiZQ2zkCwgb7jjkCm\nXgqlWapyMafKxbmYTWeOKxp/eM5hgl6QUZQbfW1ECmcyASpmJiPSFE0HiFokm8wXWjiLBRCDa4Bl\nw0pgbJyJ4rO429FWOiLmPpIMfNuWg8sHJCmlXha7+MZKNEr2IaILGCASEWUInd8LXcALnzn27IxW\nmwVWkx95Fn+Kapa5fD6gsVGP/DhDNu12fkglSgQDRCKiDBEcf1iAWIMK/QEBp20WXDWoI4U1y2xK\nAj8xdhZAIroIpwEQEWUIs6sNnpzimPt822ZFUa4XFlPyZ/oSEXXSdIDIXMxE2pOqXMyp0jX/ctOe\nVyDGCBBFnw7nHCYMKmJKPSJSl6YDxKfebkh3FVKnc6S209n9IbGngbTjmWfq0l2FpNq4cy0AwCg6\nsPXwewgYzFH3/eZcDgYUemA0ZMO0NCLKZhyDqBUXL4FznuDxQJx0C5CT0/2YzsAx0oKDXIiQKKXM\nrja0xtju8BjgEg24vJQzl9UkScHP1fFmPcdqPi/G5pSykWoBoiRJWLZsGY4ePQqj0YgVK1agrKws\ntH3btm2or6+HwWBAZWUlZs6cCZ/Ph//4j//AqVOn4PV6ce+992LSpElqVVFzwpbA6XxNFCMGjgAg\n2O0A5O5BZYavqUikRRZXO1qibJNl4NtzVgy/xMFAQ2U+H/CPfwCyHDvbi90e/Bpv9jTXdaVspVqA\nuGXLFvh8Pqxfvx779+9HXV0d6uvrAQA+nw91dXXYuHEjLBYL5syZg0mTJuGf//wnSkpKsGrVKths\nNkybNo0BYhJEChyBYPAIoHtQmYpKEVGIEPDD6HXibJTtbS4zBJ2cVSn1spnJFHMiOYALs6K5bA5p\nlWoB4t69ezFhwgQAwOjRo3HgwIHQtmPHjqGsrAz55z96jRkzBk1NTbj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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#---- Create plot object 1 (possible r and p-vals)\n", "possible_r = plt.figure()\n", "possible_r.set_size_inches(10,5)\n", "ax1 = possible_r.add_subplot(121)\n", "ax2 = possible_r.add_subplot(122)\n", "\n", "# --- Pannel 1 (r-values)\n", "add_hist(data=rbs1, col_key='r', axobj=ax1, obsval=rval_d, \n", " mylabel=\"Drought sample\", mkstyle='o')\n", "add_hist(data=rbs2, col_key='b', axobj=ax1, obsval=rval_f, \n", " mylabel=\"Flood sample\", mkstyle='D')\n", "ax1.legend(loc=9, prop={'size':11}, numpoints=1, markerscale=5.,\n", " frameon=True, fancybox=True)\n", "\n", "#ax1.set_xlim(-1,1)\n", "ax1.set_ylabel('Density')\n", "ax1.set_xlabel('$r$-values')\n", "ax1.set_title('Potential $r$-values from Bootstrap')\n", "\n", "# --- Pannel 2 (p-values)\n", "add_hist(data=pbs1, col_key='r', axobj=ax2, bin_num=25,\n", " obsval=pval_d, mkstyle='o')\n", "add_hist(data=pbs2, col_key='b', axobj=ax2, bin_num=25,\n", " obsval=pval_f, mkstyle='D')\n", "\n", "ax3 = ax2.twinx()\n", "sns.kdeplot(pbs1, cumulative=True, color='r', ax=ax3, \n", " lw=1, alpha=0.3, zorder = 10 )\n", "sns.kdeplot(pbs2, cumulative=True, color='b', ax=ax3, \n", " lw=1, alpha=0.3, zorder = 11)\n", "ax3.grid(False)\n", "ax3.set_ylabel(\"Cumulative density\")\n", "\n", "ax2.set_title(r'Potential $p$-values from Bootstrap')\n", "ax2.set_xlim(0,1)\n", "ax2.set_xlabel(r'$p$-value')\n", "plt.show(possible_r)\n", "possible_r.savefig('possible_rvals.pdf',dpi=300)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Bootstrap confirms that when uncertainty is taken into account, the $r$-values of the Drought and Flood composites are most likely to possess opposite, extreme, and statistically significant $r$-values (according to standard significance testing). However, the results do highlight the large uncertainty in the data: within the potential range of mean values for the neutron monitor data, given by the SEM uncertainty, trends may potentially be far weaker ($r$-values closer to 0.0), or even of opposite sign to those resulting from calculations of the simple mean.\n", "\n", "From the Bootstrap we can conclude that there is a high chance the $r$-values do show opposing trends, biased towards extreme $r$-values. At face value, this would suggest that the Drought sample is associated with strong (and significant) decreases in the cosmic ray flux, opposite to the Flood sample.\n", "\n", "To understand why this correlation does not imply causality however we must examine distributions of $r$-values under the null hypothesis, $H_{0}$, I.e. $r$-values of identical samples dimensions, but in the absence of droughts or floods." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5. Generate $r$-values randomly using a Monte Carlo permutation approach ###\n", "\n", "Groups of May--September extract from 12 random years and composited. This procedure is repeated thousands of times, building up a null distribution (I.e. a distribution of $r$-values that you can expect to observe in the absence of any forcing). If the Drought and Flood samples occur within a high-density portion of the distribution, the hypothesis that they indicate a relationship to the cosmic ray flux can be rejected." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import random \n", "def MonteCarlo(s_size,iter_num):\n", " \"\"\"\n", " Monte Carlo function, randomly draws groups of s-size from a\n", " list of integers representing years from 1964 to 2012. These\n", " are used as indexes to lookup data from the cosmic ray data\n", " using the make_cframe() function. R-values identified during \n", " key months, and placed into a mc_array. This is repeated for \n", " iter_num.\n", " Nb. the simulations represent realizations of H0, the null \n", " hypothesis.\n", " \"\"\"\n", " mc_array = []\n", " subs = xrange(1964,2012,1)\n", " for n in xrange(iter_num):\n", " print('\\rLoop {0} of {1}'.format(n,iter_num-1),end='')\n", " rnd_lookup = random.sample(subs,s_size)\n", " rnd_group,rnd_means,rnd_errors =make_cframe(rnd_lookup)\n", " rval,pval = pearsonr([0,1,2,3,4],rnd_means[4:9]) \n", " mc_array.append(rval)\n", " mc_array = np.array(mc_array)\n", " print('\\nFinished MC process')\n", " return mc_array" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#mc_array = MonteCarlo(12,100) # If you want to generate the MC again, uncomment here\n", "#np.savetxt('mc_results.txt',mc_array,fmt='%7.4f') # Uncomment if you want to save your own MC output here" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading MC data from file of earlier runs from public URL...\n" ] } ], "source": [ "# Reproduce the specific set of random results which I generated the publication plot from:\n", "# A run of of 100,000 iterations (800kb csv file)\n", "print('Reading MC data from file of earlier runs from public URL...')\n", "tmp =pd.read_csv('http://www.files.benlaken.com/documents/Monsoon_MC_saved.csv')\n", "mc_array = np.array(tmp.mc_results,dtype=float)\n", "tmp=[]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For a specified number of bins (bsize), calculate a histogram. Normalize the histogram, so the bins sum add to 1.0 (I.e. indicates density). Also make two fits to the data, one is a Gaussian (this is incorrect), the second is calculated from an $n^{th}$ order polynomial fit." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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m7NgRSJLEjz/+D1fXfuzYsQUjIyPevn3DxImj8fScm30vgJDjRFIXtNrFixf4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\n8O677+Fw3kGEDnoe0bNjEdNyNF57LQn//Oe3dnM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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bsize = 101 # Number of bins in histogram\n", "n, bins, patches = ax1.hist(mc_array, bsize, normed=False,\n", " facecolor='blue', alpha=0.75,\n", " histtype='stepfilled')\n", "density = n/sum(n)\n", "\n", "# Center the bins for plotting:\n", "step = (bins[1] - bins[0]) *0.5\n", "bcenter = [bpos + step for bpos in bins[:-1]]\n", "\n", "width = bins[1] - bins[0]\n", "xx = np.ravel(zip(bins[0:-1], bins[0:-1] + width))\n", "yy = np.ravel(zip(density, density))\n", "\n", "# Calculate a polynomial fit of a specified order\n", "order = 4 # <-- Change order of the polynomial here\n", "z = np.polyfit(bcenter, density, order)\n", "f = np.poly1d(z)\n", "x_new = np.linspace(bins[0], bins[-1], bsize)\n", "y_new = f(x_new)\n", "\n", "# Gaussian dist. based on the mean(mu) and stdev(sigma) of MC array\n", "mu, sigma = np.mean(mc_array), np.std(mc_array)\n", "ygauss = normpdf(bins, mu, sigma) # a function from matplotlib.mlab.\n", "ynormgauss = ygauss/sum(ygauss) # Normalize distribution so sum is 1.0\n", "\n", "\n", "#-----Plotting-----\n", "mc_hist = plt.figure()\n", "mc_hist.set_size_inches(5.5,5.5) # Specify the output size\n", "ax1 = mc_hist.add_subplot(111) # Add an axis frame object to the plot\n", "ax1.plot(bins,ynormgauss,'k--',linewidth=1.) # Ideal Gaussian\n", "\n", "ax1.bar(bins[0:-1], density, width=width, facecolor='g', \n", " linewidth=0.0,alpha=0.5) # Filled bars\n", "ax1.plot(x_new, y_new,'k-',lw=1) # Polynomial\n", "\n", "ax1.set_ylabel(r'Density',fontsize=11)\n", "ax1.set_xlabel('$r$-value',fontsize=11)\n", "\n", "leg1=ax1.legend(['BA14 assumed',r'$4^{th}$ order poly-fit',r'MC simulations'],\n", " loc=9,prop={'size':11}, numpoints=1, markerscale=5.,\n", " frameon=True, fancybox=True).get_frame()\n", "\n", "ax1.set_title(\"Monte Carlo realizations of Pearson's $r$ \\n\" + \n", " \"to 5-month composites of CR flux data\")\n", "ax1.set_ylabel(r'Density',fontsize=11)\n", "ax1.set_xlabel('$r$-value',fontsize=11)\n", "\n", "plt.show(mc_hist)\n", "mc_hist.savefig('MC_rvals.pdf',dpi=300)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6. Calculate density differences ###\n", "\n", "Using a polynomial fit, you can calculate the density for any given $r$-value. Note that this density changes depending on the bin-width of the histogram (a parameter set by bsize above)." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Polynomial function:\n", " 4 3 2\n", "0.01344 x - 8.354e-05 x - 0.001013 x + 0.000259 x + 0.007552\n" ] } ], "source": [ "print('Polynomial function:')\n", "print(f)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def sumarise(observed, sample_name=\"\"):\n", " \"\"\"\n", " For any given r-value (observed), print out the density predicted\n", " by a Gaussian fit and a polynomial fit, and compare the values.\n", " The name of the sample, given as a keyword can also be provided.\n", " \"\"\"\n", " norm_density = normpdf(observed, mu, sigma)/sum(ygauss)\n", " poly_density = f(observed)\n", " print(\"{0} sample (r = {4:3.2f}) \\n \\\n", " Gaussian density: {1:4.3f}, Polynomial density: {2:4.3f} \\n \\\n", " Overestimatated: x{3:3.1f}\".format(\n", " sample_name, norm_density, poly_density,\n", " poly_density/norm_density, observed))\n", " return" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Drought sample (r = -0.95) \n", " Gaussian density: 0.005, Polynomial density: 0.017 \n", " Overestimatated: x3.7\n" ] } ], "source": [ "sumarise(rval_d, sample_name=\"Drought\")" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Flood sample (r = 0.99) \n", " Gaussian density: 0.004, Polynomial density: 0.020 \n", " Overestimatated: x4.4\n" ] } ], "source": [ "sumarise(rval_f, sample_name=\"Flood\")" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def integrate_fits(start, stop):\n", " \"\"\"\n", " Integrate the Gaussian and polynomial over a specified range \n", " to estimate probability.\n", " \n", " start - smallest value on x-axis (e.g. r-value range)\n", " stop - largest position on x-axis (e.g. r-value range)\n", " \"\"\"\n", " cum_density_poly = 0\n", " cum_density_gauss = 0\n", " bstep = 2 / bsize\n", " hw = bstep/2\n", " for x in arange(start+hw, stop+hw, bstep):\n", " cum_density_poly += f(x)\n", " cum_density_gauss += normpdf(x, mu, sigma)/sum(ygauss)\n", " print(\"P of r-values from {0:3.2f} to {1:3.2f}\".format(start, stop))\n", " print('Gaussian fit: {0:3.3f}'.format(cum_density_gauss))\n", " print('Polynomial fit: {0:3.3f}'.format(cum_density_poly))\n", " print(\"\")" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "P of r-values from -1.00 to -0.92\n", "Gaussian fit: 0.024\n", "Polynomial fit: 0.087\n", "\n", "P of r-values from 0.92 to 1.00\n", "Gaussian fit: 0.023\n", "Polynomial fit: 0.094\n", "\n" ] } ], "source": [ "# examine what should be the significant tails supposing a Gaussian distribution...\n", "integrate_fits(-1, -0.92)\n", "\n", "integrate_fits(0.92, 1.0)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "P of r-values from -1.00 to -0.50\n", "Gaussian fit: 0.187\n", "Polynomial fit: 0.309\n", "\n", "P of r-values from 0.50 to 1.00\n", "Gaussian fit: 0.187\n", "Polynomial fit: 0.327\n", "\n" ] } ], "source": [ "# Also see how likely it is the population is skewed...\n", "integrate_fits(-1, -0.5)\n", "\n", "integrate_fits(0.5, 1.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, the above integrations highlight that the two-tailed 0.05 confidence interval for the Gaussian fit occurs at positive and negative r-values of around 0.92--1. However, when the same interval is integrated using a 4$^{th}$ order polynomial function, which closely follows the observed distribution, the tails accumulate to a density of 0.18 (0.087 + 0.094). \n", "\n", "This indicates there is an 18% chance that values that would appear as statistically significant ($p$<0.05) in a Gaussian distribution will occur randomly in the sample. \n", "\n", "Plotting the MC distributions has qualitatively shown that these samples are biased to extreme $r$-values. To quantify this, I have integrated the positive and negative $r$-values from 0.5 to 1. The Gaussian fit shows that these combined intervals cover a population density of 0.37 (0.187 + 0.187), while the polynomial fit covers a density of 0.65 (0.327 + 0.327). I.e. In the absence of any hypothetical forcing (e.g. cosmic ray flux), 65% of composite samples should show $r$-values beyond ±0.5. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7. Conclusion###\n", "\n", "Contrary to the claims of [Badruddin & Aslam (2015)](http://www.sciencedirect.com/science/article/pii/S1364682614002697), trends in the cosmic ray flux during extremes of the Monsoon were not unusual. The significance of linear regressions of neutron monitor data during Drought and Flood conditions over the Indian Monsoon period are considerably over-estimated by traditional statistical methods, which assumed incorrectly that the data may be accurately represented by a Student's $t$-distribution. \n", "\n", "The correlation coefficients resulting from these data are essentially drawn from ergodic chaotically oscillating data--the inevitable result of compositing monthly time-scale periods randomly over data that follow the Solar Cycle. This led BA15 to considerably over-estimate the statistical significance of their samples and interpret the high $r$-values they obtained as indicative of a physical relationship between the Monsoon and cosmic ray flux. As I have demonstrated however, far from being unusual, the high $r$-values were actually the most common values in the distribution." ] } ], "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": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }